FN Clarivate Analytics Web of Science VR 1.0 PT J AU Cheng, CX Jiang, P Lu, JH AF Cheng, Changxiu Jiang, Ping Lu, Junhua TI A Common Traceability Method for Agricultural Products Based on Data Center SO SENSOR LETTERS DT Article DE Agricultural; Traceability; Traceable Resource Unit; Supply Chain; Information Granule ID SUPPLY-SYSTEM TRACEABILITY; FOOD; CHAIN AB Agricultural traceability system plays an important role to improve our food safety. Based on the analysis of existing agricultural traceability systems, this paper analyses the weaknesses of these systems, that is, they are too dependent on a certain of production. Motivated by this consideration, this paper introduces a common agricultural traceability method based on data center. The method describes the traceability information of different agricultural products with different processes of circulation by quoting the following three concepts: traceable resource unit, information granule, supply chain, which can be applied to all agricultural traceability. Finally, this paper designs a common agricultural traceability system on this method. C1 [Cheng, Changxiu; Jiang, Ping] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China. [Lu, Junhua] China Natl Inst Standardizat, Beijing 100088, Peoples R China. C3 Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; China National Institute of Standardization RP Lu, JH (corresponding author), China Natl Inst Standardizat, Beijing 100088, Peoples R China. EM liujh@cnis.gov.cn CR Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 Lou P, 2004, INT J ADV MANUF TECH, V23, P197, DOI 10.1007/s00170-003-1626-x Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Rabade L.A., 2006, J PURCH SUPPLY MANAG, V12, p39?50, DOI DOI 10.1016/J.PURSUP.2006.02.003 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 SAHIN E, 2002, P IEEE INT C SYST MA, V3, P210 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Swinnen JFM, 2005, AGR ECON-BLACKWELL, V32, P175, DOI 10.1111/j.0169-5150.2004.00022.x NR 17 TC 2 Z9 2 U1 1 U2 34 PD JUN-JUL PY 2013 VL 11 IS 6-7 SI SI BP 1269 EP 1273 DI 10.1166/sl.2013.2847 WC Chemistry, Analytical; Electrochemistry; Instruments & Instrumentation; Physics, Applied SC Chemistry; Electrochemistry; Instruments & Instrumentation; Physics UT WOS:000328005300047 DA 2022-12-14 ER PT J AU Mgonja, JT Luning, P Van der Vorst, JGAJ AF Mgonja, John Thomas Luning, Pieternel Van der Vorst, Jack G. A. J. TI Diagnostic model for assessing traceability system performance in fish processing plants SO JOURNAL OF FOOD ENGINEERING DT Article DE Traceability system design; Fish processing plants ID FOOD QUALITY MANAGEMENT; IMPLEMENTING TRACEABILITY; SUPPLY CHAIN; SEAFOOD TRACEABILITY; SAFETY MANAGEMENT; HAZARD ANALYSIS; HACCP; INFORMATION; GRANULARITY; KNOWLEDGE AB This paper introduces a diagnostic tool that can be used by fish processing companies to evaluate their own traceability systems in a systematic manner. The paper begins with discussions on the rationale of traceability systems in food manufacturing companies, followed by a detailed analysis of the most important indicators in the designing and executing traceability systems. The diagnostic tool is presented in four grids through which fish companies can evaluate their own developed traceability system. The paper argues that if a company operates at a higher level of contextual factors, then design and execution of traceability system needs to be at a higher level as well so as to achieve a higher level of traceability system performance. The paper concludes that companies that are able to systematically assess their own developed traceability systems are able to determine food safety problems well in advance, and thereby take appropriate corrective actions. (c) 2013 Elsevier Ltd. All rights reserved. C1 [Mgonja, John Thomas] Sokoine Univ Agr, Morogoro, Tanzania. [Luning, Pieternel] Univ Wageningen & Res Ctr, Prod Design & Qual Management Grp, NL-6700 EV Wageningen, Netherlands. [Van der Vorst, Jack G. A. J.] Univ Wageningen & Res Ctr, NL-6706 KN Wageningen, Netherlands. C3 Sokoine University of Agriculture; Wageningen University & Research; Wageningen University & Research RP Mgonja, JT (corresponding author), Sokoine Univ Agr, POB 3073, Morogoro, Tanzania. EM john.thomasmg@yahoo.com; pieternel.luning@wur.nl; Jack.vanderVorst@wur.nl CR [Anonymous], FOOD SAFETY LAW EURO Azanza MPV, 2005, FOOD CONTROL, V16, P15, DOI 10.1016/j.foodcont.2003.10.009 Black SA, 1996, DECISION SCI, V27, P1, DOI 10.1111/j.1540-5915.1996.tb00841.x Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Cook M.L., 2001, INT J CHAIN NETWORK, V1, P1 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Donnelly KAM, 2010, J AQUAT FOOD PROD T, V19, P38, DOI 10.1080/10498850903430813 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 EAN, 2003, UCC TRAC IMPL TRAC 1 EAN.UCC, 2005, GS1 TRAC STAND IRT T European Food Safety Authority (EFSA), 2005, ADV SAF NUTR CONTR W Ferreira V, 2009, INNOV FOOD SCI EMERG, V10, P279, DOI 10.1016/j.ifset.2008.11.001 Fielding LM, 2005, INT J ENVIRON HEAL R, V15, P117, DOI 10.1080/09603120500061583 Food Standards Agency, 2002, TRAC FOOD CHAIN PREL Foote D. A., 2004, EMERALD MANAGEMENT, V42, P963 Frosch S, 2008, J AQUAT FOOD PROD T, V17, P387, DOI 10.1080/10498850802369179 Greenberg J., 2000, ORG BEHAV Holt G, 2000, FOOD CONTROL, V11, P319, DOI 10.1016/S0956-7135(99)00117-6 Ivancevich J. M., 1994, MANAGEMENT QUALITY C Jacxsens L, 2009, QUALITY MANAGEMENT S Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Karlsen KM, 2011, FOOD CONTROL, V22, P1209, DOI 10.1016/j.foodcont.2011.01.020 Karlsen KM, 2010, BRIT FOOD J, V112, P187, DOI 10.1108/00070701011018860 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kvenberg JE, 2000, J FOOD PROTECT, V63, P810, DOI 10.4315/0362-028X-63.6.810 Loftus R, 2005, REV SCI TECH OIE, V24, P231, DOI 10.20506/rst.24.1.1563 Luning PA, 2009, TRENDS FOOD SCI TECH, V20, P300, DOI 10.1016/j.tifs.2009.03.003 Luning PA, 2008, TRENDS FOOD SCI TECH, V19, P522, DOI 10.1016/j.tifs.2008.03.005 Luning P. A., 2009, FOOD QUALITY MANAGEM Luning P.A., 2002, FOOD QUALITY MANAGEM Luning PA, 2007, TRENDS FOOD SCI TECH, V18, P159, DOI 10.1016/j.tifs.2006.10.021 Manning L, 2006, BRIT FOOD J, V108, P605, DOI 10.1108/00070700610681987 McMeekin TA, 2006, INT J FOOD MICROBIOL, V112, P181, DOI 10.1016/j.ijfoodmicro.2006.04.048 Mgonja J.T., 2012, PAKISTAN J FOOD SCI, V22, P133 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Myhr N, 1998, INT J PHYS DISTRIBUT, V28 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Panisello PJ, 2001, FOOD CONTROL, V12, P165, DOI 10.1016/S0956-7135(00)00035-9 Petersen A., 2005, SEAFOOD TRACEBILITY Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Render B., 1993, PRODUCTION OPERATION Taylor E, 2005, FOOD CONTROL, V16, P833, DOI 10.1016/j.foodcont.2004.06.025 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x Tracefish, 2001, EUR COMM CONC ACT PR Trienekens J, 2006, SAFETY IN THE AGRI-FOOD CHAIN, P439 US FDA, 2001, FISH FISH PROD HAZ C, V3 Van der Spiegel M, 2005, NJAS-WAGEN J LIFE SC, V53, P131, DOI 10.1016/S1573-5214(05)80002-8 Van der Spiegel M, 2004, CRIT REV FOOD SCI, V44, P501, DOI 10.1080/10408690490489350 Van der Spiegel M., 2006, TOTAL QUAL MANAGE, V17, P1 Van der Vorst J.G.A.J, 2003, PERFORMANCE LEVELS F Vander Spiegel M., 2004, THESIS WAGENINGEN U Vernede R., 2003, TRACEBILITY FOOD PRO Von Holy A., 2004, FOOD REV, V31, P33 Walker E, 2003, FOOD CONTROL, V14, P339, DOI 10.1016/S0956-7135(02)00101-9 Wallace CA, 2005, BRIT FOOD J, V107, P743, DOI 10.1108/00070700510623522 Wallace CA, 2005, BRIT FOOD J, V107, P723, DOI 10.1108/00070700510623513 NR 60 TC 17 Z9 19 U1 1 U2 59 PD SEP PY 2013 VL 118 IS 2 BP 188 EP 197 DI 10.1016/j.jfoodeng.2013.04.009 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000320414700004 DA 2022-12-14 ER PT J AU Wang, IC Hsu, RJC Lu, S AF Wang, I. C. Hsu, Rachel Jui-Cheng Lu, Shin TI Rice traceability system in Taiwan SO QUALITY ASSURANCE AND SAFETY OF CROPS & FOODS DT Article DE brown rice; ICP; Japonica rice; NIR; rice traceability ID INFRARED REFLECTANCE SPECTROSCOPY; GEOGRAPHICAL ORIGIN; ELEMENT AB Introduction Taiwan utilizes a voluntary Agricultural Products Traceability Certification System. This system was implemented in 2007 under the 'Agricultural Production and Certification Act.' Since then, certain agricultural products have been certified using a label that includes product name, trace code, public information, and the Traceability Agricultural Product logo. Consumers can easily identify the traceability information through the bar code reader in the store. Being the staple food in Taiwan, rice plays an important role in the Traceability Certification System. In the east part of Taiwan, where there is a well-established rice traceability system, the rice provides higher added value in both the local market and international markets. Methods Generally speaking, the traceability system is based on the document and recording systems used on farms and in food-processing plants. In order to use scientific evidence to identify rice origins, near-infrared spectroscopy and inductively coupled plasma element analysis were used to determine rice origins from the north, middle, south, and east parts of the island. Rice varieties and planting methods were considered as well as geographic origins. Eighty-three paddy rice samples were collected island wide, including japonica cultivars of TK2, TK9, TN11, and T71. During processing, all samples were hulled and milled under the same conditions. Brown rice and milled rice were analyzed separately using both near-infrared and inductively coupled plasma methods. Results Results show that near-infrared and inductively coupled plasma methods are able to statistically significantly distinguish the rice geographic authenticity for the same rice cultivars, regardless of whether the samples were brown rice or milled rice. Conclusions When testing different cultivars of japonica rice from different areas, the near-infrared method produced more accurate results for brown rice, whereas the inductively coupled plasma method produced better results for milled rice from different areas. C1 [Wang, I. C.; Hsu, Rachel Jui-Cheng; Lu, Shin] China Grain Prod R&D Inst, Bali, Taipei County, Taiwan. RP Lu, S (corresponding author), China Grain Prod R&D Inst, Bali, Taipei County, Taiwan. EM shin.lu@cgprdi.org.tw CR CHAISERI S, 1989, J AM OIL CHEM SOC, V66, P1771, DOI 10.1007/BF02660745 CHEN TW, 2005, THESIS NATL TAIWAN U Kelly S, 2002, EUR FOOD RES TECHNOL, V214, P72, DOI 10.1007/s002170100400 Kim SS, 2003, CEREAL CHEM, V80, P346, DOI 10.1094/CCHEM.2003.80.3.346 Natsuga M, 2006, T ASABE, V49, P1069, DOI 10.13031/2013.21712 Osborne B. G., 1993, Journal of Near Infrared Spectroscopy, V1, P77 Rittiron R, 2005, J NEAR INFRARED SPEC, V13, P19, DOI 10.1255/jnirs.453 Suzuki Y, 2008, J JPN SOC FOOD SCI, V55, P250, DOI 10.3136/nskkk.55.250 Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Yasui A, 2000, BUNSEKI KAGAKU, V49, P405, DOI 10.2116/bunsekikagaku.49.405 NR 10 TC 5 Z9 6 U1 2 U2 39 PD JUN PY 2011 VL 3 IS 2 BP 74 EP 81 DI 10.1111/j.1757-837X.2011.00095.x WC Food Science & Technology SC Food Science & Technology UT WOS:000290761700005 DA 2022-12-14 ER PT J AU Li, WY Sun, CH Li, M Qian, JP Yang, XT Du, SF AF Li, W. -Y. Sun, C. -H. Li, M. Qian, J. -P. Yang, X. -T. Du, S. -f. TI AN INTEGRATED WIRELESS TERMINAL FOR DATA ACQUISITION IN AGRICULTURAL PRODUCT TRACEABILITY SYSTEM SO APPLIED ENGINEERING IN AGRICULTURE DT Article DE Agricultural products; Packaging procedure integration; QR; RFID technology; Traceability ID FOOD-SUPPLY CHAIN; QUALITY; SAFETY; CHINA; CODE; IDENTIFICATION; TECHNOLOGY; IMPACT AB The significant link between farms and supermarkets has a far-reaching influence on the circulation pattern and traceability of agricultural products and supermarket business structure. This article presents a new platform called the integrated wireless traceability terminal (WTT) solution for agricultural products traceability information collection and packaging procedure integration. It collects traceability data such as package weight, field number, and other origin information about the corresponding address of farm or company. Meanwhile, it will print a traceability label which is used by the consumer or administrators to query traceability information through the internet. A test evaluation of the WIT solution is performed during vegetable harvest time in a large organic agricultural products company. Compared to the current traditional packaging procedure, a statistical analysis shows that this solution not only decreases the packaging time (total time for packing a package) about 15%, but also reduces the total material cost by approximately 37%. In addition, the traceability content is more detailed and precise than traditional method. These results suggest that the WTT solution can be reliably used for packaging agricultural products and traceability information acquisition. C1 [Li, W. -Y.; Sun, C. -H.; Li, M.; Qian, J. -P.; Yang, X. -T.] Natl Engn Res Ctr Informat Technol Agr, Beijing 10097, Peoples R China. [Li, W. -Y.; Du, S. -f.] China Agr Univ, Coll Informat & Elect Engn, Beijing 100094, Peoples R China. C3 China Agricultural University RP Yang, XT (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Beijing 10097, Peoples R China. EM yangxt@nercita.org.cn CR [Anonymous], 2010, 24 BIT AN TO DIG CON [Anonymous], 2004, P89LPC931FDH [Anonymous], 2000, 18284 GBT [Anonymous], 2011, MFRC522 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Cunha CR, 2010, COMPUT ELECTRON AGR, V73, P154, DOI 10.1016/j.compag.2010.05.007 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Froschle HK, 2009, COMPUT ELECTRON AGR, V66, P126, DOI 10.1016/j.compag.2009.01.002 Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Jin SS, 2011, FOOD CONTROL, V22, P204, DOI 10.1016/j.foodcont.2010.06.021 Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Mc Inerney B, 2011, COMPUT ELECTRON AGR, V77, P1, DOI 10.1016/j.compag.2011.03.001 Ngai E, 2008, INT J PROD ECON, V112, P507, DOI 10.1016/j.ijpe.2007.05.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Sarac A, 2010, INT J PROD ECON, V128, P77, DOI 10.1016/j.ijpe.2010.07.039 Shi L., 2010, COMPUTER APPL SOFTWA, V27, P40 SNBC, 2009, PROD DET BT UC05611 Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 Sun CH, 2013, COMPUT ELECTRON AGR, V92, P82, DOI 10.1016/j.compag.2012.12.014 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 Wang Y. S., 2010, LOGISTICS SCI TECH, V11, P93 Zhang L., 2012, INTERDISCIPLINARY J, V3, P13 NR 26 TC 0 Z9 0 U1 3 U2 18 PD JUL PY 2014 VL 30 IS 4 BP 631 EP 639 WC Agricultural Engineering SC Agriculture UT WOS:000343529200012 DA 2022-12-14 ER PT J AU Qian, JP Dai, BY Wang, BG Zha, Y Song, Q AF Qian, Jianping Dai, Bingye Wang, Baogang Zha, Yan Song, Qian TI Traceability in food processing: problems, methods, and performance evaluations-a review SO CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION DT Review DE Artificial intelligence (AI); batch mixing; food processing; resource transformation; traceability ID SUPPLY-SYSTEM TRACEABILITY; IMPROVING TRACEABILITY; ACQUISITION-SYSTEM; CHAIN MANAGEMENT; GRADE TRACERS; SAFETY; TECHNOLOGY; QUALITY; MODEL; IDENTIFICATION AB Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing. C1 [Qian, Jianping; Zha, Yan; Song, Qian] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing AGRIRS, Beijing 100081, Peoples R China. [Dai, Bingye] Beijing Technol & Business Univ, Beijing, Peoples R China. [Wang, Baogang] Beijing Acad Forestry & Pomol Sci, Beijing, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Ministry of Agriculture & Rural Affairs; Beijing Technology & Business University RP Qian, JP (corresponding author), Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing AGRIRS, Beijing 100081, Peoples R China. EM qianjianping@caas.cn CR Abd Rahman A, 2017, FOOD CONTROL, V73, P1318, DOI 10.1016/j.foodcont.2016.10.058 [Anonymous], 2012, MEAT TRADE NEWS DAIL Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Augustin MA, 2016, TRENDS FOOD SCI TECH, V56, P115, DOI 10.1016/j.tifs.2016.08.005 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Banerjee M, 2018, DIGIT COMMUN NETW, V4, P149, DOI 10.1016/j.dcan.2017.10.006 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Bergquist B, 2012, MINER ENG, V30, P44, DOI 10.1016/j.mineng.2012.01.010 Bernard A, 2002, ENVIRON RES, V88, P1, DOI 10.1006/enrs.2001.4274 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Chen TB, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106770 Christopher M., 1998, LOGISTICS SUPPLY CHA Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Combo L, 2011, J FOOD ENG, V106, P177, DOI 10.1016/j.jfoodeng.2011.04.015 Cooper M.C., 1993, INT J LOGIST MANAG, V4, P13, DOI DOI 10.1108/09574099310804957 Corallo A, 2020, TRENDS FOOD SCI TECH, V101, P28, DOI 10.1016/j.tifs.2020.04.022 Crandall PG, 2013, MEAT SCI, V95, P137, DOI 10.1016/j.meatsci.2013.04.022 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Pierini GD, 2016, MICROCHEM J, V128, P62, DOI 10.1016/j.microc.2016.04.015 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 Euromonitor, 2016, GLOB PACK FOOD MARK European Commission, 2003, OFF J EUR UNION European Commission, 2003, OFF J EUR UNION L, VL268, P24 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Floros JD, 2010, COMPR REV FOOD SCI F, V9, P572, DOI 10.1111/j.1541-4337.2010.00127.x Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Golan E., 2004, Agricultural Economic Report - Economic Research Service, US Department of Agriculture Goransson M, 2018, FOOD CONTROL, V86, P332, DOI 10.1016/j.foodcont.2017.10.029 Haleem Abid, 2019, Information Processing in Agriculture, V6, P335, DOI 10.1016/j.inpa.2019.01.003 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Heaton MP, 2005, JAVMA-J AM VET MED A, V226, P1311, DOI 10.2460/javma.2005.226.1311 Hirai Y, 2006, APPL ENG AGRIC, V22, P747 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 IMAP, 2010, FOOD BEV IND GLOB RE International Food Information Council Foundation, 2010, INF HAND INT FOOD IN Kallel L., 2011, 2011 4th International Conference on Logistics (LOGISTIQUA), P144, DOI 10.1109/LOGISTIQUA.2011.5939417 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Kemeny Z., 2008, IFAC P, V41, DOI [10.3182/20080706-5-KR-1001.00757, DOI 10.3182/20080706-5-KR-1001.00757] Ketterhagen WR, 2007, CHEM ENG SCI, V62, P6423, DOI 10.1016/j.ces.2007.07.052 Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Knorr D, 2011, ANNU REV FOOD SCI T, V2, P203, DOI 10.1146/annurev.food.102308.124129 Kozlenkova IV, 2015, J RETAILING, V91, P586, DOI 10.1016/j.jretai.2015.03.003 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Kumar Y, 2015, COMPR REV FOOD SCI F, V14, P796, DOI 10.1111/1541-4337.12156 Kvarnstrom B, 2012, PROD PLAN CONTROL, V23, P396, DOI 10.1080/09537287.2011.561813 Kvarnstrom B, 2011, QUAL ENG, V23, P343, DOI 10.1080/08982112.2011.602278 Silva VL, 2019, J FOOD ENG, V250, P33, DOI 10.1016/j.jfoodeng.2019.01.010 Lazzarini S. G., 2001, THE J, V1, P7, DOI [10.3920/JCNS2001.x002, DOI 10.3920/JCNS2001.X002] Lee KM, 2010, J AGR FOOD CHEM, V58, P10945, DOI 10.1021/jf101370k Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Liang K, 2019, COMPUT ELECTRON AGR, V162, P709, DOI 10.1016/j.compag.2019.04.039 Liang K, 2012, BIOSYST ENG, V113, P395, DOI 10.1016/j.biosystemseng.2012.09.012 Lupien JR, 2005, CRIT REV FOOD SCI, V45, P119, DOI 10.1080/10408690490911774 Luvisi A, 2012, BIOSYST ENG, V113, P129, DOI 10.1016/j.biosystemseng.2012.06.015 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Maiyar LM, 2019, TRANSPORT RES E-LOG, V127, P220, DOI 10.1016/j.tre.2019.05.006 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Miclotte L, 2020, CRIT REV FOOD SCI, V60, P1769, DOI 10.1080/10408398.2019.1596878 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Padua I, 2019, FOOD CONTROL, V98, P389, DOI 10.1016/j.foodcont.2018.11.051 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Pollard S., 2018, ENCY FOOD CHEM, DOI [10.1016/B978-0-08-100596-5.21839-8, DOI 10.1016/B978-0-08-100596-5.21839-8] PORTER ME, 1985, HARVARD BUS REV, V63, P149 Porto SMC, 2011, BIOSYST ENG, V109, P120, DOI 10.1016/j.biosystemseng.2011.02.008 Qian JP, 2015, COMPUT ELECTRON AGR, V116, P101, DOI 10.1016/j.compag.2015.06.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Qian JP, 2018, FOOD CONTROL, V87, P192, DOI 10.1016/j.foodcont.2017.12.015 Qian JP, 2017, FOOD CONTROL, V74, P98, DOI 10.1016/j.foodcont.2016.11.034 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Reyes JF, 2012, COMPUT ELECTRON AGR, V84, P62, DOI 10.1016/j.compag.2012.02.018 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Rodriguez-Ramirez R, 2011, GENET MOL RES, V10, P2358, DOI 10.4238/2011.October.6.1 Rodriguez-Salvador B, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107142 Roth AV, 2008, J SUPPLY CHAIN MANAG, V44, P22, DOI 10.1111/j.1745-493X.2008.00043.x Sanchez-Moreno C, 2009, CRIT REV FOOD SCI, V49, P552, DOI 10.1080/10408390802145526 Sardina MT, 2015, FOOD RES INT, V74, P115, DOI 10.1016/j.foodres.2015.04.038 Scholten H, 2016, WOODHEAD PUBL FOOD S, V301, P9, DOI 10.1016/B978-0-08-100310-7.00002-8 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 Shackell GH, 2008, INT J FOOD SCI TECH, V43, P2134, DOI 10.1111/j.1365-2621.2008.01812.x Skoglund T, 2007, FOOD BIOPROD PROCESS, V85, P354, DOI 10.1205/fbp07044 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Sun SN, 2019, J CLEAN PROD, V217, P658, DOI 10.1016/j.jclepro.2019.01.296 Tama BA, 2017, 2017 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (ICECOS), P109 Tamayo S, 2009, ENG APPL ARTIF INTEL, V22, P557, DOI 10.1016/j.engappai.2009.02.007 Tamplin ML, 2018, FOOD MICROBIOL, V75, P90, DOI 10.1016/j.fm.2017.12.001 Tao Y, 2015, CRIT REV FOOD SCI, V55, P570, DOI 10.1080/10408398.2012.667849 Thakur M, 2020, COMPUT ELECTRON AGR, V174, DOI 10.1016/j.compag.2020.105478 Thakur M, 2011, COMPUT ELECTRON AGR, V75, P327, DOI 10.1016/j.compag.2010.12.010 Van der Spiegel M, 2013, TRENDS FOOD SCI TECH, V34, P137, DOI 10.1016/j.tifs.2013.10.001 Van Puyvelde DR, 2006, POWDER TECHNOL, V164, P1, DOI 10.1016/j.powtec.2005.12.017 Wales C, 2006, APPETITE, V47, P187, DOI 10.1016/j.appet.2006.05.007 Wang ShanShan, 2018, Transactions of the Chinese Society of Agricultural Engineering, V34, P263, DOI 10.11975/j.issn.1002-6819.2018.14.034 Weaver CM, 2014, AM J CLIN NUTR, V99, P1525, DOI 10.3945/ajcn.114.089284 WEBSTER FE, 1992, J MARKETING, V56, P1, DOI 10.2307/1251983 Wible B, 2014, SCIENCE, V344, P1100, DOI 10.1126/science.344.6188.1100 Wiersinga RC, 2010, TOWARDS EFFECTIVE FOOD CHAINS: MODELS AND APPLICATIONS, P113 Wu YN, 2018, FOOD CONTROL, V90, P429, DOI 10.1016/j.foodcont.2018.03.009 Xing Bin, 2015, Transactions of the Chinese Society of Agricultural Engineering, V31, P309, DOI 10.11975/j.issn.1002-6819.2015.10.042 Yan SX, 2016, BIOCHEM SYST ECOL, V69, P27, DOI 10.1016/j.bse.2016.08.008 Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhao HY, 2020, J SCI FOOD AGR, V100, P4040, DOI 10.1002/jsfa.10449 Zhao SS, 2020, FOOD CHEM, V310, DOI 10.1016/j.foodchem.2019.125826 Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 NR 127 TC 23 Z9 25 U1 33 U2 118 PD JAN 27 PY 2022 VL 62 IS 3 BP 679 EP 692 DI 10.1080/10408398.2020.1825925 EA OCT 2020 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000574977300001 DA 2022-12-14 ER PT J AU Suh, D Pomeroy, R AF Suh, David Pomeroy, Robert TI Evaluation of seafood traceability system in Korea: demand-oriented analysis SO ITALIAN JOURNAL OF FOOD SAFETY DT Article DE Traceability system; seafood; food safety; contingent valuation ID CONTINGENT; FRAMEWORK; ILLEGAL AB The Korean government tried to secure food safety by revitalization of seafood traceability system since there has been growing dissatisfaction toward of food system related to seafood in Korea. This study examines the consumers' perspective on seafood traceability system and the value of seafood traceability in Korea using contingent valuation method. The model includes preference and recognition of respondents for the seafood traceability system, and socio-demographic characteristics. The result of the model shows that respondents think positively about seafood traceability system and it is expected that approximately $44.94 million can be generated annually from the seafood traceability system. The result implies that it is necessary to promote the system in order to make this system known, the benefit of which is helpful in food safety. C1 [Suh, David] Univ Connecticut, Dept Agr & Resource Econ, Storrs, CT USA. [Pomeroy, Robert] Univ Connecticut, Dept Agr & Resource Econ, Connecticut Sea Grant, Groton, CT 06340 USA. C3 University of Connecticut; University of Connecticut RP Pomeroy, R (corresponding author), Univ Connecticut, 380 Marine Sci Bldg 1080 Shennecossett Rd, Groton, CT 06340 USA. EM robert.pomeror@uconn.edu CR Ajzen I, 1992, J CONSUM PSYCHOL, V1, P297, DOI [DOI 10.1207/S15327663JCP0104_01, DOI 10.1016/S1057-7408(08)80057-5] Borit M, 2012, MAR POLICY, V36, P96, DOI 10.1016/j.marpol.2011.03.012 Boyle KJ, 1997, AM J AGR ECON, V79, P1495, DOI 10.2307/1244370 Brown T.C., 2003, PRIMER NONMARKET VAL, DOI 10.1007/978-94-007-0826-6 Caswell JA, 1998, AUST J AGR RESOUR EC, V42, P409, DOI 10.1111/1467-8489.00060 DUFFIELD JW, 1991, LAND ECON, V67, P225, DOI 10.2307/3146413 FAO and Plan Bleu, 2018, STATE MEDITERRANEAN Fricker S, 2005, PUBLIC OPIN QUART, V69, P370, DOI 10.1093/poq/nfi027 He J, 2018, MAR POLICY, V96, P163, DOI 10.1016/j.marpol.2018.08.003 Hoyos D, 2010, PRAGUE ECON PAP, V19, P329, DOI 10.18267/j.pep.380 Leal MC, 2015, TRENDS BIOTECHNOL, V33, P331, DOI 10.1016/j.tibtech.2015.03.003 MOF, 2015, SURV AW SEAF TRAC SY MOF, 2016, STUD APPL FOR SEAF C MOF, 2018, FISH PROD FISH IND F Pramod G, 2014, MAR POLICY, V48, P102, DOI 10.1016/j.marpol.2014.03.019 Shin Yong-Min, 2018, [Journal of Maritime Business, 해양비즈니스], V40, P93 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Whitehead JC, 1998, ENVIRON RESOUR ECON, V11, P107, DOI 10.1023/A:1008231430184 Yasuda T, 2006, MAR POLLUT BULL, V53, P640, DOI 10.1016/j.marpolbul.2006.08.015 NR 19 TC 0 Z9 0 U1 5 U2 13 PY 2020 VL 9 IS 3 BP 150 EP 154 DI 10.4081/ijfs.2020.9021 WC Food Science & Technology SC Food Science & Technology UT WOS:000608109900004 DA 2022-12-14 ER PT J AU Islam, S Cullen, JM Manning, L AF Islam, Samantha Cullen, Jonathan M. Manning, Louise TI Visualising food traceability systems: A novel system architecture for mapping material and information flow SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Article DE Food traceability; Food traceability system; Information loss; Model based system engineering; Visualisation ID SUPPLY CHAIN TRACEABILITY; FRAMEWORK; DESIGN; IMPLEMENTATION; MODEL; PERSPECTIVES; TECHNOLOGIES; METHODOLOGY; INTEGRATION; PRODUCTS AB Background: Traceability of food products, ingredients and associated operations are important requirements for improving food safety and consumer confidence. Food traceability systems (FTSs) often suffer from inefficiency in either material or information flow within an enterprise or between supply chain partners. Modelling of system architecture is a visualisation approach that allows multiple parties to collaborate in a system design process, identify its inefficiencies and propose improvements. However, there is little academic research on the ability to use a standard visualisation tool that supports collaborative design and considers both material and information flow for a given food traceability system. Scope & approach: The aim of this research is to propose a new visualisation approach that allows supply chain operators to collaborate effectively in the design process of FTSs capable of maintaining streamlined information flow, minimising information loss, and improving supply chain performance. Key findings & conclusion: Food traceability systems are complex, encompassing processes, material flow, information flow, techniques, infrastructure, people and control strategies. Screening of literature demonstrates that model-based system engineering (MBSE) offers a sound way for visualisation of such complex systems. However, in the food traceability literature, an MBSE-based standardised traceability system modelling approach is absent. This study makes a strong contribution to existing literature by proposing a novel, material and information flow modelling technique (MIFMT), to visualise FTS architecture. MIFMT can support common understanding and iterative implementation of effective FTSs that contextualise food supply chains at multiple levels and provides opportunity to identify points at where inefficiencies can occur so that actions can be taken to mitigate them. C1 [Islam, Samantha; Cullen, Jonathan M.] Univ Cambridge, Dept Engn, Trumpington St, Cambridge CB2 1PZ, England. [Manning, Louise] Royal Agr Univ, Cirencester GL7 6JS, Glos, England. C3 University of Cambridge RP Islam, S (corresponding author), Univ Cambridge, Dept Engn, Trumpington St, Cambridge CB2 1PZ, England. EM si313@cam.ac.uk; jmc99@cam.ac.uk; louise.manning@rau.ac.uk CR Alfian G, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107016 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bevilacqua M, 2009, J FOOD ENG, V93, P13, DOI 10.1016/j.jfoodeng.2008.12.020 Bjorkman E., 2012, SYSTEMS ENG, V14, P305, DOI [10.1002/sys, DOI 10.1002/SYS] Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 Cheng-Leong A, 1999, INT J PROD RES, V37, P3839, DOI 10.1080/002075499189790 Chong HY, 2020, AUTOMAT CONSTR, V114, DOI 10.1016/j.autcon.2020.103158 Clarkson PJ, 2018, DES SCI, V4, DOI 10.1017/dsj.2018.13 Cullen JM, 2012, ENVIRON SCI TECHNOL, V46, P13048, DOI 10.1021/es302433p Cullen JM, 2010, ENERGY, V35, P2059, DOI 10.1016/j.energy.2010.01.024 da Silva AR, 2015, COMPUT LANG SYST STR, V43, P139, DOI 10.1016/j.cl.2015.06.001 Dai HY, 2015, INT J PROD ECON, V170, P14, DOI 10.1016/j.ijpe.2015.08.010 Duan YQ, 2017, INFORM SOC, V33, P226, DOI 10.1080/01972243.2017.1318325 Eyers DR, 2017, COMPUT IND, V92-93, P208, DOI 10.1016/j.compind.2017.08.002 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Feldmann C, 2013, INSTRUMENTATION CONT Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Gao JY, 2018, ACS SUSTAIN CHEM ENG, V6, P11734, DOI 10.1021/acssuschemeng.8b01983 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Golan E., 2004, AGR EC REPORT Goodman -Deane J, 2018, SSA TOOLKIT INTRO DE Hardt MJ, 2017, J FOOD SCI, V82, pA3, DOI 10.1111/1750-3841.13796 Hassim MH, 2010, PROCESS SAF ENVIRON, V88, P173, DOI 10.1016/j.psep.2010.01.006 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 IEEE, 1998, IEEE STAND FUNCT MOD Islam S., DEV SMART AGRI FOOD Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 ISO, 2005, 220052005 ISOTC 176S Karlsen KM, 2016, WOODHEAD PUBL FOOD S, V301, P35, DOI 10.1016/B978-0-08-100310-7.00003-X Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2011, FOOD CONTROL, V22, P1209, DOI 10.1016/j.foodcont.2011.01.020 Karlsen KM, 2010, BRIT FOOD J, V112, P187, DOI 10.1108/00070701011018860 Kikuchi Y, 2009, J IND ECOL, V13, P945, DOI 10.1111/j.1530-9290.2009.00180.x Kim CH, 2003, COMPUT IND, V50, P35, DOI 10.1016/S0166-3615(02)00145-8 Kuo TC, 2014, INT J COMPUT INTEG M, V27, P266, DOI 10.1080/0951192X.2013.814157 KUSIAK A, 1994, COMPUT IND ENG, V26, P521, DOI 10.1016/0360-8352(94)90048-5 Lee J, 2015, EXAMINATION BENEFITS Lightsey B., 2001, SYSTEMS ENG FUNDAMEN Madni AM, 2018, SYSTEMS ENG, V21, P172, DOI 10.1002/sys.21438 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Marconi M, 2017, INT J PROD RES, V55, P6638, DOI 10.1080/00207543.2017.1332437 Mattevi M, 2016, BRIT FOOD J, V118, P1107, DOI 10.1108/BFJ-07-2015-0261 Menzel C. P., 1991, IDEF3 FORMALIZATION Michalakakis C, 2019, SUSTAIN PROD CONSUMP, V19, P270, DOI 10.1016/j.spc.2019.07.004 Ministry of Agriculture of the People's Republic of China (MOA, 2009, REG ADM LIV POULTR I Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Norton T., 2014, GUIDE TRACEABILITY, V45 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Patterson D., 2019, IEEE THINGS MAGA MAR, V11, P1 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Povetkin K, 2020, J CLEAN PROD, V266, DOI 10.1016/j.jclepro.2020.122024 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Ramos AL, 2012, IEEE T SYST MAN CY C, V42, P101, DOI 10.1109/TSMCC.2011.2106495 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Rouhani BD, 2015, INFORM SOFTWARE TECH, V62, P1, DOI 10.1016/j.infsof.2015.01.012 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Shen H, 2004, COMPUT IND, V54, P307, DOI 10.1016/j.compind.2003.07.009 Shunk DL, 2003, COMPUT IND ENG, V45, P167, DOI 10.1016/S0360-8352(03)00024-X Simsekler MCE, 2018, INT J QUAL HEALTH C, V30, P227, DOI 10.1093/intqhc/mzx176 Stapel K, 2014, EXPERT SYST, V31, P234, DOI 10.1111/exsy.649 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Tian F, 2017, I C SERV SYST SERV M Tuna M, 2016, OPTIK, V127, P11786, DOI 10.1016/j.ijleo.2016.09.087 Verdouw CN, 2010, COMPUT ELECTRON AGR, V73, P174, DOI 10.1016/j.compag.2010.05.005 Waissi GR, 2015, OPER RES PERSPECT, V2, P106, DOI 10.1016/j.orp.2015.05.001 Wolfert J, 2010, COMPUT ELECTRON AGR, V70, P389, DOI 10.1016/j.compag.2009.07.015 Zhang JR, 2014, COMPR REV FOOD SCI F, V13, P1074, DOI 10.1111/1541-4337.12103 NR 81 TC 7 Z9 7 U1 8 U2 24 PD JUN PY 2021 VL 112 BP 708 EP 719 DI 10.1016/j.tifs.2021.04.020 EA APR 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000652540900005 DA 2022-12-14 ER PT J AU Hallak, JC Tacsir, A AF Carlos Hallak, Juan Tacsir, Andres TI Traceability systems as a differentiation tool in agri-food value chains: a framework for public policies in Latin America SO JOURNAL OF AGRIBUSINESS IN DEVELOPING AND EMERGING ECONOMIES DT Article DE Traceability; Agri-food value chains; Latin America ID SUPPLY CHAIN AB Purpose The goal of this paper is to develop a classification of traceability systems that will help academics and policymakers think of them as a tool for differentiation in agri-food value chains. Design/methodology/approach Based on the analysis of case studies and a literature review, the authors develop a conceptual framework to classify traceability systems based on two dimensions: their scope in the value chain (individual vs integrated) and the type of information they contain (basic vs advanced). Findings Integrated traceability systems provide a variety of benefits vis-a-vis individual systems as a tool to achieve greater product differentiation by meeting current and latent requirements from foreign countries' governments and consumers. Also they serve as a platform for including advanced (vis-a-vis basic) information into the system. Research limitations/implications A series of studies would be required to quantify the relative costs of different traceability systems and compare them on a cost-benefit basis. Nevertheless, since integrated traceability systems are subject to coordination failures, significant public focus and efforts should be placed on the potential promotion of those systems. Originality/value This paper provides a novel classification of traceability systems that distinguishes them according to scope and information content. C1 [Carlos Hallak, Juan; Tacsir, Andres] Univ Buenos Aires, CONICET, Inst Interdisciplinario Econ Polit, Buenos Aires, DF, Argentina. C3 Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); University of Buenos Aires RP Hallak, JC (corresponding author), Univ Buenos Aires, CONICET, Inst Interdisciplinario Econ Polit, Buenos Aires, DF, Argentina. EM jhallak@gmail.com CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bianchi C., 2016, 2 TANGO PUBLIC PRIVA Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Cho S, 2019, J FOOD PROD MARK, V25, P92, DOI 10.1080/10454446.2018.1522285 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 FAO, 2017, FOOD TRAC GUID FAO/WHO, 2012, FAO WHO GUID DEV IMP Garaus M, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108082 Gonzalez A., 2021, 104 REDNIE Green R., 2007, TRAZABILIDAD CARNES Gupta P., 2017, INT J LATEST TECHNOL, V6, P6 Hallak J.C., 2021, IDBTN2248 Henson S., 2009, IMPACTS PRIVATE FOOD Hou B, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17010146 Jaffee S., 2005, FOOD SAFETY AGR HLTH Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Mello MMM, 2021, J AGRIBUS DEV EMERG, V11, P538, DOI 10.1108/JADEE-04-2020-0071 Narine LK, 2015, J AGRIBUS DEV EMERG, V5, P76, DOI 10.1108/JADEE-04-2013-0015 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Organization for Economic Co-operation and Development, 2007, PRIV STAND SCHEM DEV Paolino C., 2014, SERIE ESTUDIOS PERSP Rius A., 2015, DP376 INT AM DEV BAN Routroy S, 2017, J AGRIBUS DEV EMERG, V7, P275, DOI 10.1108/JADEE-06-2016-0039 Scholten H, 2016, WOODHEAD PUBL FOOD S, V301, P9, DOI 10.1016/B978-0-08-100310-7.00002-8 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Stranieri S, 2016, BRIT FOOD J, V118, P1025, DOI 10.1108/BFJ-04-2015-0151 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] Zurbriggen C., 2015, INNOVACION TECNOLOGI NR 31 TC 1 Z9 1 U1 1 U2 1 PD AUG 2 PY 2022 VL 12 IS 4 SI SI BP 673 EP 688 DI 10.1108/JADEE-10-2021-0272 EA APR 2022 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000781312200001 DA 2022-12-14 ER PT J AU Zhang, XS Lv, SY Xu, M Mu, WS AF Zhang, Xiaoshuan Lv, Shunyi Xu, Mark Mu, Weisong TI Applying evolutionary prototyping model for eliciting system requirement of meat traceability at agribusiness level SO FOOD CONTROL DT Article DE Evolutionary prototyping model; Longitudinal case study; System requirement; Traceability system; China ID FOOD-INDUSTRY; FRAMEWORK AB Traceability has become an effective method of ensuring food safety and connecting stakeholders in the food chain. There is an increasing growth trend in developing IT-based traceability system in recent years. But implementing hastily traceability system is likely to fail to achieve its goal if the system requirement has not been well-defined according to the changing business environment. This paper adopted an evolutionary prototyping model and used longitudinal case study to elicit the traceability system requirement at the level of agribusiness. The results show that a traceability system can support not only information tracking at operational level, but also diagnostic analysis and strategic decision making at managerial level, Hence, system requirements can be categorized as fundamental, decisive and strategic levels. The evolutionary prototyping model can improve the effectiveness of requirement elicitation. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Zhang, Xiaoshuan; Lv, Shunyi; Mu, Weisong] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. [Zhang, Xiaoshuan; Lv, Shunyi; Mu, Weisong] China Agr Univ, Minist Educ, Key Lab Modern Precis Agr Integrat, Beijing 100083, Peoples R China. [Xu, Mark] Univ Portsmouth, Portsmouth Business Sch, Portsmouth PO1 3DE, Hants, England. C3 China Agricultural University; China Agricultural University; University of Portsmouth RP Mu, WS (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. EM wsmu@cau.edu.cn CR Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Becker T., 2000, British Food Journal, V102, P158, DOI 10.1108/00070700010371707 CHEN HH, 2009, COMP ANAL GOVT ORIEN Chen N.-S., 2002, INTERACTIVE ED MULTI, V4, P62 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Fu JC, 2008, LECT NOTES COMPUT SC, V5320, P43 Laudon K. C., 1999, MANAGEMENT INFORM SY MAURIZIO C, 2006, 2006 ANN M AUG 12 18 Paarlberg RL, 2002, FOOD POLICY, V27, P247, DOI 10.1016/S0306-9192(02)00014-3 PERI C, 2002, RINTRACCIABILITA FIL QIAN H, 2009, FOOD SCI TECHNOLOGY, V10, P248 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Rolland C, 2000, ANN SOFTW ENG, V10, P151, DOI 10.1023/A:1018939700514 SERRANO A, 2003, P 15 EUR SIM S EUR B Setboonsarng S., 2009, FOOD SAFETY ICT TRAC SZULECKA O, 2006, PET WORKSH CONS EXPL Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Wall B., 1994, Industrial Management + Data Systems, V94, P24, DOI 10.1108/02635579410068257 WANG F, 2009, THESIS CHINA AGR U YOKOYAMA K, 2007, INT S TRAC FOOD SAF, P154 NR 20 TC 15 Z9 17 U1 0 U2 22 PD NOV PY 2010 VL 21 IS 11 BP 1556 EP 1562 DI 10.1016/j.foodcont.2010.03.020 WC Food Science & Technology SC Food Science & Technology UT WOS:000280932100023 DA 2022-12-14 ER PT J AU Jin, SS Zhou, L AF Jin, Shaosheng Zhou, Lin TI Consumer interest in information provided by food traceability systems in Japan SO FOOD QUALITY AND PREFERENCE DT Article DE Beef; Information; Food traceability system; Fresh produce; Japan ID WILLINGNESS-TO-PAY; ORIGIN; CHINA; BEEF; PERCEPTIONS; PREFERENCES; QUALITY; SAFETY; CHOICE; MILK AB Food traceability systems are an important means to provide food safety and quality information to consumers. We studied consumers' interest in the information provided through food traceability systems by examining a national representative sample of 6243 Japanese consumers through a 2006 online survey. The ratio of respondents who have accessed information through traceability systems is low. With respect to the 11 kinds of information we focused on in our study, respondents attached most importance to harvest date, production method, and production method certification. Our results show that more educated females have a stronger desire to access more specific information related to fresh produce, whereas less educated males are more likely to trace information through fresh produce traceability systems. We have outlined the implications of these findings. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Jin, Shaosheng] Zhejiang Univ, Ctr Agr & Rural Dev, Hangzhou 310058, Zhejiang, Peoples R China. [Zhou, Lin] Minist Agr, Inst Food & Nutr Dev, Beijing 100081, Peoples R China. C3 Zhejiang University; Ministry of Agriculture & Rural Affairs RP Jin, SS (corresponding author), Zhejiang Univ, Ctr Agr & Rural Dev, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, Peoples R China. EM ssjin@zju.edu.cn CR Aihara Y, 2011, HEALTH PROMOT INT, V26, P421, DOI 10.1093/heapro/dar005 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Cicia G., 2010, International Journal on Food System Dynamics, V1, P252 Cornelisse-Vermaat JR, 2008, EUR J PUBLIC HEALTH, V18, P115, DOI 10.1093/eurpub/ckm032 Czinkota M. R., 1985, INT MARKET REV, V3, P39 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Food Standards Agency, 2002, TRAC FOOD CHAIN PRE Golan E., 2004, AGR EC RE Greene W.H., 2003, ECONOMETRIC ANAL Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 Herbig P. A., 1994, J CONSUM MARK, V11, P5, DOI [10.1108/07363769410053655, DOI 10.1108/07363769410053655] Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 LIDDELL S, 2001, INT FOOD AGRIBUS MAN, V4, P287, DOI DOI 10.1016/S1096-7508(01)00081-7 Long Sott, 1997, REGRESSION MODELS CA Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Loureiro ML, 2003, J AGR RESOUR ECON, V28, P287 Makino C., 2008, ASIA TIMES ONLINE McCluskey J. J., 2003, Agricultural and Resource Economics Review, V32, P222 Menozzi D, 2015, FOOD CONTROL, V49, P40, DOI 10.1016/j.foodcont.2013.09.006 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pieniak Z, 2007, J INT FOOD AGRIBUS M, V19, P117, DOI 10.1300/J047v19n02_07 Roitner-Schobesberger B, 2008, FOOD POLICY, V33, P112, DOI 10.1016/j.foodpol.2007.09.004 Sakagami M, 2006, NEW ZEAL J AGR RES, V49, P247, DOI 10.1080/00288233.2006.9513715 Sato M., 2008, J FOOD SYST RES, V14, P13, DOI [10.5874/jfsr.14.3_13, DOI 10.5874/JFSR.14.3_13] Setboonsarng S., 2009, 139 ADBI Shigeno R., 2012, J FOOD SYSTEM RES, V19, P37 Souza-Monteiro D.M., 2004, 20046 U MASSS Takahashi K., 2008, J FOOD SYSTEM RES, V14, P2 Tani A., 2009, J RURAL PROBLEMS, V45, P203 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Voordouw J, 2011, FOOD QUAL PREFER, V22, P384, DOI 10.1016/j.foodqual.2011.01.009 Wu LH, 2012, CAN J AGR ECON, V60, P317, DOI 10.1111/j.1744-7976.2011.01236.x Wu LH, 2011, BRIT FOOD J, V113, P519, DOI 10.1108/00070701111123998 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 44 TC 57 Z9 62 U1 6 U2 76 PD SEP PY 2014 VL 36 BP 144 EP 152 DI 10.1016/j.foodqual.2014.04.005 WC Food Science & Technology SC Food Science & Technology UT WOS:000337207600017 DA 2022-12-14 ER PT J AU Qi, L Zhang, J Mark, X Fu, ZT Chen, W Zhang, XS AF Qi Lin Zhang Jian Mark Xu Fu Zetian Chen Wei Zhang Xiaoshuan TI Developing WSN-based traceability system for recirculation aquaculture SO MATHEMATICAL AND COMPUTER MODELLING DT Article DE Food safety; Traceability system; WSN; Recirculation aquaculture ID WASTE-WATER TREATMENT; SENSOR; TECHNOLOGY; QUALITY AB Aquaculture has moved from conventional open systems to high density and highly productive land-based recirculation systems. Consumers have increased consumption of fish and fish products due to recognition of their nutritional value along with social progress and the improvement of living standards. A traceability system is considered as an effective tool to guarantee safety in fish products and improve the supply chain transparency. This paper developed a Wireless Sensor Network (WSN) based Traceability System for Recirculation Aquaculture (RATS). System tests shows that the WSN-based traceability system has comparable data accuracy and advantage of easy installment and configuration. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Qi Lin; Fu Zetian; Chen Wei; Zhang Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Zhang Jian] Beijing Informat S&T Univ, Beijing 100192, Peoples R China. [Mark Xu] Univ Portsmouth, Portsmouth Business Sch, Portsmouth PO1 3DE, Hants, England. C3 China Agricultural University; University of Portsmouth RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Aqeel-ur-Rehman, 2008, 2008 International Conference on Computer Science and Software Engineering (CSSE 2008), P641, DOI 10.1109/CSSE.2008.1528 Birgisdottir BE, 2008, FOOD CONTROL, V19, P648, DOI 10.1016/j.foodcont.2007.07.003 Cerpa A, 2001, ACM SIGCOMM COMP COM, V31, P20, DOI 10.1145/844193.844196 Cripps SJ, 2000, AQUACULT ENG, V22, P33, DOI 10.1016/S0144-8609(00)00031-5 [崔莉 Cui Li], 2005, [计算机研究与发展, Journal of Computer Research and Development], V42, P163, DOI 10.1360/crad20050121 CUI YL, 2006, MICROCOMPUTER INFORM, V22, P252 CUI YL, 2006, MICROCOMPUTER INFORM, V22, P167 FAO, 2003, REP EXP CONS INT FIS Glasgow HB, 2004, J EXP MAR BIOL ECOL, V300, P409, DOI 10.1016/j.jembe.2004.02.022 Jaradat MAK, 2008, COMPUT ELECTRON AGR, V64, P104, DOI 10.1016/j.compag.2008.04.007 Jorquera MA, 2002, AQUACULTURE, V207, P213, DOI 10.1016/S0044-8486(01)00766-9 LU WH, 2002, J YANCHENG I TECHNOL, V15, P50 LU WH, 2002, J YANCHENG I TECHNOL, V15, P61 Morais R, 2008, COMPUT ELECTRON AGR, V62, P94, DOI 10.1016/j.compag.2007.12.004 Nadimi ES, 2009, COMPUT ELECTRON AGR, V68, P9, DOI 10.1016/j.compag.2009.03.006 Pierce FJ, 2008, COMPUT ELECTRON AGR, V61, P32, DOI 10.1016/j.compag.2007.05.007 Qi ZZ, 2009, AQUACULTURE, V290, P15, DOI 10.1016/j.aquaculture.2009.02.012 Qin G, 2005, AQUACULT ENG, V32, P365, DOI 10.1016/j.aquaeng.2004.09.002 QIN Y, 2006, J AGR MECH RES, V9, P171 RABAEY JM, 2000, IEEE COMPUT, V33, P42 Ren Xi, 2009, Computer Engineering and Design, V30, P3883 Schuster C, 1998, AQUACULT ENG, V17, P167, DOI 10.1016/S0144-8609(98)00013-2 SHI MN, 2008, DEV WATER QUALITY RE Tan KK, 2009, COMPUT STAND INTER, V31, P573, DOI 10.1016/j.csi.2008.03.024 Tschmelak J, 2005, BIOSENS BIOELECTRON, V20, P1499, DOI 10.1016/j.bios.2004.07.032 Vellidis G, 2008, COMPUT ELECTRON AGR, V61, P44, DOI 10.1016/j.compag.2007.05.009 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 WANG NY, 2006, FISHERIES MODERNIZAT, V33, P9 WANG XC, 1996, J SHANGHAI FISHERIES, V5, P217 Wik TEI, 2009, AQUACULTURE, V287, P361, DOI 10.1016/j.aquaculture.2008.10.056 ZHEN H, 2009, IMPLEMENTATION SCHEM [No title captured] NR 33 TC 46 Z9 54 U1 0 U2 55 PD JUN PY 2011 VL 53 IS 11-12 BP 2162 EP 2172 DI 10.1016/j.mcm.2010.08.023 WC Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Mathematics, Applied SC Computer Science; Mathematics UT WOS:000290102500010 DA 2022-12-14 ER PT J AU Demestichas, K Peppes, N Alexakis, T Adamopoulou, E AF Demestichas, Konstantinos Peppes, Nikolaos Alexakis, Theodoros Adamopoulou, Evgenia TI Blockchain in Agriculture Traceability Systems: A Review SO APPLIED SCIENCES-BASEL DT Review DE blockchain; distributed ledger; traceability; agriculture supply chain; agri-food industry ID FOOD-SUPPLY CHAIN; ARCHITECTURE; PRODUCTS AB Featured Application The paper elaborates on the applicability of blockchain technology in traceability systems of agri-food products. Food holds a major role in human beings' lives and in human societies in general across the planet. The food and agriculture sector is considered to be a major employer at a worldwide level. The large number and heterogeneity of the stakeholders involved from different sectors, such as farmers, distributers, retailers, consumers, etc., renders the agricultural supply chain management as one of the most complex and challenging tasks. It is the same vast complexity of the agriproducts supply chain that limits the development of global and efficient transparency and traceability solutions. The present paper provides an overview of the application of blockchain technologies for enabling traceability in the agri-food domain. Initially, the paper presents definitions, levels of adoption, tools and advantages of traceability, accompanied with a brief overview of the functionality and advantages of blockchain technology. It then conducts an extensive literature review on the integration of blockchain into traceability systems. It proceeds with discussing relevant existing commercial applications, highlighting the relevant challenges and future prospects of the application of blockchain technologies in the agri-food supply chain. C1 [Demestichas, Konstantinos; Peppes, Nikolaos; Alexakis, Theodoros; Adamopoulou, Evgenia] Inst Commun & Comp Syst, Athens 15773, Greece. RP Demestichas, K (corresponding author), Inst Commun & Comp Syst, Athens 15773, Greece. EM cdemest@cn.ntua.gr; npeppes@cn.ntua.gr; talexakis@cn.ntua.gr; eadam@cn.ntua.gr CR [Anonymous], 2018, CISC VIS NETW IND GL Arsyad A.A, 2018, LECT NOTES DATA ENG, P332 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Avery D., 2018, P ARBS 2018 5 ANN C, VIII, P11 Awan S.H, 2020, INT J ADV COMPUT SCI, V11, DOI [10.14569/ijacsa.2020.0110457, DOI 10.14569/IJACSA.2020.0110457] Baralla G, 2019, 2019 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON EMERGING TRENDS IN SOFTWARE ENGINEERING FOR BLOCKCHAIN (WETSEB 2019), P40, DOI 10.1109/WETSEB.2019.00012 Baralla G, 2019, LECT NOTES COMPUT SC, V11339, P379, DOI 10.1007/978-3-030-10549-5_30 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Berg C, BLOCKCHAIN EC BEGINN Bermeo-Almeida O, 2018, COMM COM INF SC, V883, P44, DOI 10.1007/978-3-030-00940-3_4 Biswas A, 2017, 2017 8TH ANNUAL INDUSTRIAL AUTOMATION AND ELECTROMECHANICAL ENGINEERING CONFERENCE (IEMECON), P56, DOI 10.1109/IEMECON.2017.8079561 Buhler, SMART SUPPL CHAIN FA Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Burke T., 2019, FOOD TRACEABILITY BI, DOI DOI 10.1007/978-3-030-10902-8_10 Buterin V., MEANING DECENTRALIZA Cargill Inc, 2018, HONEYSUCKLE WHITE EX Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Castor A., SHORT GUIDE BLOCKCHA Chandra K.S., 2019, IND GEOT C, P10, DOI DOI 10.14569/IJACSA.2019.0101120 Cheng CX, 2013, SENSOR LETT, V11, P1269, DOI 10.1166/sl.2013.2847 Cook, 2018, COOK BLOCKCHAIN TRAN Corallo A, 2018, 2018 IEEE WORKSHOP ON ENVIRONMENTAL, ENERGY, AND STRUCTURAL MONITORING SYSTEMS (EESMS), P1 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 deRuyter de Wildt M, 2019, BLOCKCHAIN FOOD MAKI Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Ge L., 2017, 2017112 WAG U RES George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Gunasekera D, 2020, ECON PAP, V39, P152, DOI 10.1111/1759-3441.12274 Haber S., 1991, Journal of Cryptology, V3, P99, DOI 10.1007/BF00196791 Hayati Hashri, 2018, 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), P120, DOI 10.1109/ISRITI.2018.8864477 Hong WB, 2018, PROCEEDINGS OF 2018 1ST IEEE INTERNATIONAL CONFERENCE ON HOT INFORMATION-CENTRIC NETWORKING (HOTICN 2018), P254, DOI 10.1109/HOTICN.2018.8605963 Hua J, 2018, IEEE INT VEH SYM, P97 Huynh T.S, 2019, INT J INNOV TECHNOL, V8 ISO Technical Committee, 2016, 220052007 ISO TECHN Jabbari A., 2018, BLOCKCHAIN SUPPLY CH Kakkar A, 2020, BLOCKCHAIN TECHNOLOG, V39 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kim H., 2018, SUPPLY CHAIN REVOLUT Kim H.M., 2016, SSRN ELECT J, DOI [10.2139/ssrn.2828369, DOI 10.2139/SSRN.2828369] Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Koirala RC, 2019, 2019 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2019), P538, DOI 10.1109/CONFLUENCE.2019.8776900 Konstantopoulos G, UNDERSTANDING BLOCKC Kumar M., 2017, FUTURE GENER COMMUN, P125 LAMPORT L, 1982, ACM T PROGR LANG SYS, V4, P382, DOI 10.1145/357172.357176 Latino M., 2018, WORLD ACAD SCI ENG T, V12, P126 Lei H., 2020, SECURE FISH FARM PLA, V170 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Lucena Percival., 2018, P S FDN APPL BLOCKCH Malik S., 2018, P IEEE 17 INT S NETW, P1, DOI DOI 10.1109/NCA.2018.8548322 Mao DH, 2019, ARAB J SCI ENG, V44, P3439, DOI 10.1007/s13369-018-3537-z Mao DH, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093149 Massessi D., PUBLIC VS PRIVATE BL Mohan T, 2018, IMPROVE FOOD SUPPLY Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Nakamoto S., 2008, DECENTRALIZED BUS RE, P21260 Orsato R.J., 2020, IMPACT BLOCKCHAIN TE Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Penard W., 2008, CRYPTOGRAPHY CONTEXT, P1 Pezzuolo A., 2017, NANTES, P258 Raikwar M, 2019, IEEE ACCESS, V7, P148550, DOI 10.1109/ACCESS.2019.2946983 Rejeb A., 2018, ACTA TECH JAURINENSI, V11, DOI [DOI 10.14513/ACTATECHJAUR.V11.N4.467, 10.14513/actatechjaur.v11.n4.467] Saji A.C, 2020, ADV INTELLIGENT SYST, P1059 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Schmidhuber J.T.M., 2018, EMERGING OPPORTUNITI Scuderi A, 2019, QUAL-ACCESS SUCCESS, V20, P580 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Shi XJ, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19081833 Tian F, 2017, INT C SERV SYST SERV, V1, P6, DOI DOI 10.1109/ICSSSM.2017.7996119 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Umamaheswari S, 2019, INT CONF ADV COMPU, P324, DOI 10.1109/ICoAC48765.2019.246860 Unurjargal E., 2019, BLOCKCHAIN SUPPORTED Verzijl E.R. Diederik, 2015, TRACEABILITY VALUE C Visser C, 2017, BLOCKCHAIN IS STRENG Wattanajantra A., BLOCKCHAIN TRACEABIL Yuxin Liao, 2019, Journal of Physics: Conference Series, V1288, DOI 10.1088/1742-6596/1288/1/012062 Zhang Q, 2020, COMPUT ELECTRON AGR, V173, DOI 10.1016/j.compag.2020.105395 Zhihua Wang, 2019, Artificial Intelligence and Security. 5th International Conference, ICAIS 2019. Proceedings: Lecture Notes in Computer Science (LNCS 11634), P81, DOI 10.1007/978-3-030-24271-8_8 NR 87 TC 68 Z9 70 U1 38 U2 130 PD JUN PY 2020 VL 10 IS 12 AR 4113 DI 10.3390/app10124113 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000553498500001 DA 2022-12-14 ER PT J AU Alvarez-Rodriguez, JM Mendieta, R Moreno, V Sanchez-Puebla, M Llorens, J AF Maria Alvarez-Rodriguez, Jose Mendieta, Roy Moreno, Valentin Sanchez-Puebla, Miguel Llorens, Juan TI Semantic Recovery of Traceability Links between System Artifacts SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING DT Article DE Software traceability; software system artifact representation; software reuse ID KNOWLEDGE; DOMAIN; ONTOLOGY AB This paper introduces a mechanism to recover traceability links between the requirements and logical models in the context of critical systems development. Currently, lifecycle processes are covered by a good number of tools that are used to generate different types of artifacts. One of the cornerstone capabilities in the development of critical systems lies in the possibility of automatically recovery traceability links between system artifacts generated in different lifecycle stages. To do so, it is necessary to establish to what extent two or more of these work products are similar, dependent or should be explicitly linked together. However, the different types of artifacts and their internal representation depict a major challenge to unify how system artifacts are represented and, then, linked together. That is why, in this work, a concept-based representation is introduced to provide a semantic and unified description of any system artifact. Furthermore, a traceability function is defined and implemented to exploit this new semantic representation and to support the recovery of traceability links between different types of system artifacts. In order to evaluate the traceability function, a case study in the railway domain is conducted to compare the precision and recall of recovery traceability links between text-based requirements and logical model elements. As the main outcome of this work, the use of a concept-based paradigm to represent that system artifacts are demonstrated as a building block to automatically recover traceability links within the development lifecycle of critical systems. C1 [Maria Alvarez-Rodriguez, Jose; Mendieta, Roy; Moreno, Valentin; Sanchez-Puebla, Miguel; Llorens, Juan] Carlos III Univ Madrid, Dept Comp Sci & Engn, Avd Univ 30, Madrid 28911, Spain. RP Alvarez-Rodriguez, JM (corresponding author), Carlos III Univ Madrid, Dept Comp Sci & Engn, Avd Univ 30, Madrid 28911, Spain. EM josemaria.alvarez@uc3m.es; roy.mendieta@kr.inf.uc3m.es; valentin.moreno@uc3m.es; masrodri@inf.uc3m.es; llorens@inf.uc3m.es CR Aagedal J. O., 2005, OBJ OR TECHN ECOOP 2, V3344, P148 Albinet A., 2007, P 3 EUR C MOD DRIV A, P27 Alvarez-Rodriguez JM, 2013, COMM COM INF SC, V390, P315 Alvarez-Rodriguez JM, 2014, COMPUT HUM BEHAV, V30, P674, DOI 10.1016/j.chb.2013.07.046 AlvarezRodriguez J. M., 2015, INCOSE INT S, V25, P16 Araujo S., 2012, WEBDB, P25 Beckett D., 2008, RDF XML SYNTAX SPECI Bezivin J, 2006, LECT NOTES COMPUT SC, V4143, P36 Bilenko M., 2003, P ACM INTCONF KNOWLE, P39 Blanco R, 2013, LECT NOTES COMPUT SC, V8219, P33, DOI 10.1007/978-3-642-41338-4_3 Blanco R, 2013, J WEB SEMANT, V21, P14, DOI 10.1016/j.websem.2013.05.005 Bob Carpenter B. B. M. M., 2012, TEXT PROCESSING JAVA, V1 Bontcheva K, 2013, LANG RESOUR EVAL, V47, P1007, DOI 10.1007/s10579-013-9215-6 Castaneda V., 2010, GLOB J ENG RES, V10 Christ F, 2011, P ENT MOD INF SYST A, VP-190, P135 Cohen W., 2003, KDD WORKSH DAT CLEAN, V3, P73 Colomo-Palacios R, 2014, INTERACT LEARN ENVIR, V22, P221, DOI 10.1080/10494820.2012.745430 Colomo-Palacios R, 2014, COMPUT SCI INF SYST, V11, P29, DOI 10.2298/CSIS130129019C Dang T., 2014, W20255 NATL BUR EC R de la Vara JL, 2017, COMPUT STAND INTER, V50, P179, DOI 10.1016/j.csi.2016.10.002 Dick J., 2011, REQUIR ENG, V3rd Domingos P., 2004, P KDD 2004 WORKSHOP, P31 Ebert C, 2018, IEEE SOFTWARE, V35, P94, DOI 10.1109/MS.2018.3571228 Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581 Enriquez JG, 2017, EXPERT SYST APPL, V80, P14, DOI 10.1016/j.eswa.2017.03.010 Faro S, 2013, ACM COMPUT SURV, V45, DOI 10.1145/2431211.2431212 Ferrara A, 2013, J WEB SEMANT, V21, P49, DOI 10.1016/j.websem.2013.05.004 Galvez C, 2006, SCIENTOMETRICS, V69, P323, DOI 10.1007/s11192-006-0156-3 Galvez C, 2012, J DOC, V68, P5, DOI 10.1108/00220411211200301 Gangemi Aldo, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P351 Rodriguez MG, 2012, J UNIVERS COMPUT SCI, V18, P1576 Geensoft R., EFF SOL MAN REQ TRAC Gotel O., 2012, SOFTWARE SYSTEMS TRA, P343 Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), P94, DOI 10.1109/ICRE.1994.292398 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Guarino N, 1995, INT J HUM-COMPUT ST, V43, P625, DOI 10.1006/ijhc.1995.1066 Haskins C., 2011, SYSTEMS ENG HDB GUID Hogan A, 2012, J WEB SEMANT, V10, P76, DOI 10.1016/j.websem.2011.11.002 INCOSE, 2004, INCOSETP200400402 Isele R., 2010, P 1 INT WORKSH CONS Ittoo A, 2013, DATA KNOWL ENG, V88, P142, DOI 10.1016/j.datak.2013.08.004 Ittoo A, 2013, EXPERT SYST APPL, V40, P2530, DOI 10.1016/j.eswa.2012.10.067 Kopcke H, 2010, DATA KNOWL ENG, V69, P197, DOI 10.1016/j.datak.2009.10.003 Leroy E., 2011, DSP2R33M3 CESAR, V3 Li CL, 2012, SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P721, DOI 10.1145/2348283.2348380 Li JZ, 2009, IEEE T KNOWL DATA EN, V21, P1218, DOI 10.1109/TKDE.2008.202 Lindsay JD, 2009, Patent No. [7490319B2, 7490319] Llorens J, 2004, STUD FUZZ SOFT COMP, V159, P221 Llorens J., 2019, 31 INT C SOFTW ENG K, P64 Loper E, 2002, CS0205028 ARXIV Lormans, 2005, P 3 INT WORKSH TRAC Maali F., 2011, LDOW, V813 Mahmood T, 2013, COMPUT STAND INTER, V35, P6, DOI 10.1016/j.csi.2012.02.004 Manfred B., 2013, NATO SCI PEACE SECUR, VD, P1 Mason P., 2005, Proceedings. 12th Asia-Pacific Software Engineering Conference Montes-Garcia A, 2013, EXPERT SYST APPL, V40, P6735, DOI 10.1016/j.eswa.2013.06.032 Morillo F, 2013, SCIENTOMETRICS, V94, P207, DOI 10.1007/s11192-012-0733-6 Nadeau D, 2007, LINGUIST INVESTIG, V30, P3 Ngomo A.C.N., 2011, P 22 INT JOINT C ART, P2312, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-385 Niepert M., 2010, P 5 INT WORKSH ONT M, P142 Noy NF, 2000, SEVENTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-2001) / TWELFTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-2000), P450 Pandey KL, 2012, LECT NOTES COMPUT SC, V7336, P147, DOI 10.1007/978-3-642-31128-4_11 Ratinov L, 2004, IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2004), PROCEEDINGS, P485, DOI 10.1109/WI.2004.10083 Read J, 2012, MACH LEARN, V88, P243, DOI 10.1007/s10994-012-5279-6 S. N. L. P. Lecture, 2013, APACHE OPENNLP DEVEL Schar F., 2005, P 1 INT C SIGN IM TE, P267 Sheik SS, 2004, J CHEM INF COMP SCI, V44, P1251, DOI 10.1021/ci030463z Smiley D., 2011, APACHE SOLR 3 ENTERP Sopena J. G., 2005, ECMDA TRAC WORKSH P Stoermer H, 2010, LECT NOTES COMPUT SC, V6051, P180, DOI 10.1007/978-3-642-13094-6_15 Wang YM, 2009, PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON PUBLIC ECONOMICS AND MANAGEMENT ICPEM 2009, VOL 2, P18, DOI 10.3115/1667884.1667888 Warmer J., 2006, MODEL DRIVEN ARCHITE, V4066 Weis M., 2006, RELATIONSHIP BASED D Winkler S, 2010, SOFTW SYST MODEL, V9, P529, DOI 10.1007/s10270-009-0145-0 Yeates S., 1999, P 3 NZ COMP SCI RES, P117 Zojer H., 2011, DSP2R33M3 CESAR NR 76 TC 2 Z9 2 U1 0 U2 1 PD OCT PY 2020 VL 30 IS 10 BP 1415 EP 1442 DI 10.1142/S0218194020400197 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering UT WOS:000589962500003 DA 2022-12-14 ER PT J AU Li, LY Paudel, KP Guo, JY AF Li, Lianying Paudel, Krishna P. Guo, Jinyong TI Understanding Chinese farmers' participation behavior regarding vegetable traceability systems SO FOOD CONTROL DT Article DE Technology acceptance model; Structural equation modeling; Food safety; Traceability system; Participation behavior ID TECHNOLOGY ACCEPTANCE MODEL; FOOD TRACEABILITY; USER ACCEPTANCE; INFORMATION-TECHNOLOGY; IMPLEMENTATION; ADOPTION; SAFETY; CHAIN; INTENTIONS; FRAMEWORK AB Implementing vegetable traceability systems is complex. Farmers are producers and the first point of traceability. Their intention and behavior related to implementation of vegetable traceability systems directly determine the success or failure of the implementation of those systems. The objective of this study was to identify factors that affect participation behavior in vegetable traceability systems by using a structural equation model and a logistic regression model. Data collected from a 2019 in-person interview survey of vegetable farmers located in Jiangxi province, China were analyzed. Results from the structural equation model showed that perceived usefulness positively affected farmers' participation intentions and their participation intentions positively affected their participation behavior. The impacts of social influence on perceived usefulness were greater than the impacts of traceability system characteristics on perceived usefulness. Facilitating conditions such as equipment resources, knowledge, and skills that farmers have a significant positive impact on the perceived ease of use. Facilitating conditions and perceived usefulness were two key factors influencing participation intention. Results from the logistic regression model showed that gender, age, marital status, cooperative organization membership, and whether the respondents were near a city had a significant impact on participation intention. The conclusions of this study further expanded the current research scope regarding farmers' quality and safety behaviors and revealed the internal decision-making mechanism for farmers' participation behavior regarding vegetable traceability systems. This study has implications for implementing vegetable traceability systems to ensure food safety in China or elsewhere in the world. C1 [Li, Lianying; Guo, Jinyong] Jiangxi Agr Univ, Coll Econ & Management, Nanchang 330045, Jiangxi, Peoples R China. [Li, Lianying; Guo, Jinyong] Jiangxi Agr Univ, Rural Revitalizat Strategy Res Inst, Nanchang 330045, Jiangxi, Peoples R China. [Paudel, Krishna P.] Louisiana State Univ, Dept Agr Econ & Agribusiness, Baton Rouge, LA 70803 USA. [Paudel, Krishna P.] LSU Agr Ctr, Baton Rouge, LA 70803 USA. C3 Jiangxi Agricultural University; Jiangxi Agricultural University; Louisiana State University System; Louisiana State University; Louisiana State University System; Louisiana State University RP Paudel, KP (corresponding author), Louisiana State Univ, Dept Agr Econ & Agribusiness, Baton Rouge, LA 70803 USA.; Paudel, KP (corresponding author), LSU Agr Ctr, Baton Rouge, LA 70803 USA. EM kpaudel@agcenter.lsu.edu CR AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T AJZEN I, 1986, J EXP SOC PSYCHOL, V22, P453, DOI 10.1016/0022-1031(86)90045-4 Ajzen I, 2009, J APPL SOC PSYCHOL, V39, P1356, DOI 10.1111/j.1559-1816.2009.00485.x Akkermans H, 2002, EUR J INFORM SYST, V11, P35, DOI 10.1057/palgrave/ejis/3000418 Al-Emran M, 2018, COMPUT EDUC, V125, P389, DOI 10.1016/j.compedu.2018.06.008 Allata S, 2017, FOOD CONTROL, V79, P239, DOI 10.1016/j.foodcont.2017.04.002 Amegashie-Duvon E, 2014, THESIS NEWCASTLE U THESIS NEWCASTLE U Sanchez RA, 2010, COMPUT HUM BEHAV, V26, P1632, DOI 10.1016/j.chb.2010.06.011 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bailey D., 2005, CHOICE, V20, P293 Barling D, 2009, INT J AGR SUSTAIN, V7, P261, DOI 10.3763/ijas.2009.0331 Bediako IA, 2018, TECHNOL SOC, V55, P70, DOI 10.1016/j.techsoc.2018.06.005 Beza E, 2018, COMPUT ELECTRON AGR, V151, P295, DOI 10.1016/j.compag.2018.06.015 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bouzembrak Y, 2019, TRENDS FOOD SCI TECH, V94, P54, DOI 10.1016/j.tifs.2019.11.002 Cai Z, 2010, SCI TECHNOLOGY PROGR, V27, P52 Caswell J. A., 1998, Agricultural and Resource Economics Review, V27, P151 Cheek P, 2006, REV SCI TECH OIE, V25, P313, DOI 10.20506/rst.25.1.1664 Chen, 2014, CHIN MED MOD 501 ED, V12, P69 Chen L., 2016, RURAL EC, V10, P106 Chen Y., 2014, CHINESE RURAL EC, V12, P41 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982 DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 Davis FD, 1996, INT J HUM-COMPUT ST, V45, P19, DOI 10.1006/ijhc.1996.0040 Dishaw MT, 1999, INFORM MANAGE-AMSTER, V36, P9, DOI 10.1016/S0378-7206(98)00101-3 Duan YQ, 2017, INFORM SOC, V33, P226, DOI 10.1080/01972243.2017.1318325 Fang K., 2013, J AGROTECHNICAL EC, V6, P63 Fang Y., 2005, QUALITY SAFETY AGRO, V2, P37 Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 Gellynck X., 2001, Agrarwirtschaft, V50, P368 Golan E., 2004, Agricultural Economic Report - Economic Research Service, US Department of Agriculture GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689 He DeHua, 2019, Journal of Agricultural Science and Technology (Beijing), V21, P123 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs J. E, 2003, INT AGR TRADE RES CO, V3, P58 Hofmann D. W., 2002, TECH DIRECTIONS, V62, P28 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Hwang W, 2006, BEHAV INFORM TECHNOL, V25, P3, DOI 10.1080/01449290512331335636 Igbaria M, 1997, MIS QUART, V21, P279, DOI 10.2307/249498 Im I, 2011, INFORM MANAGE-AMSTER, V48, P1, DOI 10.1016/j.im.2010.09.001 International Standard Organization, 2007, 220052007 ISO Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Lee Y., 2003, COM ASS INFORM SYST, V12, P752, DOI [10.17705/1CAIS.01250, DOI 10.17705/1CAIS.01250] Leyton D, 2015, INF SYST E-BUS MANAG, V13, P211, DOI 10.1007/s10257-014-0255-2 Li H., 2012, CHINA RURAL SURVEY, V2, P66 Li Z. D., 2020, J SHANDONG TECHNOLOG, V34, P41 Lopez-Nicolas C, 2008, INFORM MANAGE-AMSTER, V45, P359, DOI 10.1016/j.im.2008.05.001 Lu Y., 2007, J IND TECHNOLOGICAL, V26, P48 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 Ming J., 2017, LIB TRIBUNE, V37, P125 Ming J., 2018, LIB TRIBUNE, V38, P93 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Mottaleb KA, 2018, TECHNOL SOC, V55, P126, DOI 10.1016/j.techsoc.2018.07.007 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sarcheshmeh EE, 2018, TECHNOL SOC, V55, P175, DOI 10.1016/j.techsoc.2018.08.001 Seol S, 2016, INT J MOB COMMUN, V14, P1, DOI 10.1504/IJMC.2016.073341 Sorebo O, 2008, COMPUT HUM BEHAV, V24, P2357, DOI 10.1016/j.chb.2008.02.011 Sumner M, 1999, J COMPUT INFORM SYST, V40, P81 TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144 Tsai HT, 2014, ANTHROPOLOGIST, V17, P845, DOI 10.1080/09720073.2014.11891499 Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926 Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Verma S, 2018, INFORM PROCESS MANAG, V54, P791, DOI 10.1016/j.ipm.2018.01.004 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Walker GS, 2017, FOOD CONTROL, V72, P168, DOI 10.1016/j.foodcont.2016.01.028 Wang F, 2009, J FOOD AGRIC ENVIRON, V7, P64 Wang H., 2011, ISSUES AGR EC, V2, P45 WHO, FOOD SAFETY Williams P., 2002, ACTIVE LEARNING HIGH, V3, P40, DOI DOI 10.1177/1469787402003001004 Wu L., 2012, FORECASTING, V5, P47 Wu M., 2010, STRUCTURAL EQUATION, V152, P482 Xiao K., 2017, SCI TECHNOLOGY MANAG, V2, P249 Xu S., 2019, J GUIZHOU U FINANCE, V198, P82 Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Zhao J, 2020, CHINA AGR ECON REV, V12, P20, DOI 10.1108/CAER-05-2018-0111 Zhao R, 2011, J CHINA AGR U, V3, P169 Zhou J., 2011, ISSUES AGR EC, V1, P32 NR 84 TC 9 Z9 9 U1 26 U2 38 PD DEC PY 2021 VL 130 AR 108325 DI 10.1016/j.foodcont.2021.108325 EA JUN 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000684816400008 DA 2022-12-14 ER PT J AU Wang, ZF Wang, L Xiao, FA Chen, QS Lu, LM Hong, JM AF Wang, Zhengfei Wang, Lai Xiao, Fu'an Chen, Qingsong Lu, Liming Hong, Jiaming TI A Traditional Chinese Medicine Traceability System Based on Lightweight Blockchain SO JOURNAL OF MEDICAL INTERNET RESEARCH DT Article DE blockchain; traditional Chinese medicine; TCM; traceability system; fake drugs; IPFS; fraud; traceability AB Background: Recently, the problem of traditional Chinese medicine (TCM) safety has attracted attention worldwide. To prevent the spread of counterfeit drugs, it is necessary to establish a drug traceability system. A traditional drug traceability system can record the whole circulation process of drugs, from planting, production, processing, and warehousing to use by hospitals and patients. Once counterfeit drugs are found, they can be traced back to the source. However, traditional drug traceability systems have some drawbacks, such as failure to prevent tampering and facilitation of sensitive disclosure. Blockchain (including Bitcoin and Ethernet Square) is an effective technology to address the problems of traditional drug traceability systems. However, some risks impact the reliability of blockchain, such as information explosion, sensitive information leakage, and poor scalability. Objective: To avoid the risks associated with the application of blockchain, we propose a lightweight block chain framework. Methods: In this framework, both horizontal and vertical segmentations are performed when designing the blocks, and effective strategies are provided for both segmentations. For horizontal segmentation operations, the header and body of the blockchain are separated and stored in the blockchain, and the body is stored in the InterPlanetary File System. For vertical segmentation operations, the blockchain is cut off according to time or size. For the addition of new blocks, miners only need to copy the latest part of the blockchain and append the tail and vertical segmentation of the block through the consensus mechanism. Results: Our framework could greatly reduce the size of the blockchain and improve the verification efficiency. Conclusions: Experimental results have shown that the efficiency improves compared with ethernet when a new block is added to the blockchain and a search is conducted. C1 [Wang, Zhengfei; Wang, Lai; Xiao, Fu'an; Hong, Jiaming] Guangzhou Univ Chinese Med, Sch Med Informat Engn, Guangzhou Higher Educ Mega Ctr, 232 Waihuandong Rd, Guangzhou 510006, Peoples R China. [Chen, Qingsong] Shenzhen Ant Network Serv Co LTD, Guangzhou, Peoples R China. [Lu, Liming] Guangzhou Univ Chinese Med, Med Coll Acu Moxi & Rehabil, Clin Res & Data Ctr, South China Res Ctr Acupuncture & Moxibust, Guangzhou, Peoples R China. C3 Guangzhou University of Chinese Medicine; Guangzhou University of Chinese Medicine RP Hong, JM (corresponding author), Guangzhou Univ Chinese Med, Sch Med Informat Engn, Guangzhou Higher Educ Mega Ctr, 232 Waihuandong Rd, Guangzhou 510006, Peoples R China. EM hjm@gzucm.edu.cn CR Aitken R, 2017, FORBES Angeles R., 2019, J INF TECHNOL MANAG, V27 [Anonymous], 2019, ANN CHIN STAT FOOD D [Anonymous], Guiding opinions of SFDA on the construction of drug information traceability system Arjona R, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18082429 Azaria A, 2016, PROCEEDINGS 2016 2ND INTERNATIONAL CONFERENCE ON OPEN AND BIG DATA - OBD 2016, P25, DOI 10.1109/OBD.2016.11 Chowdhury MJM, 2019, IEEE ACCESS, V7, P167930, DOI 10.1109/ACCESS.2019.2953729 Ding Jinxi J, 2012, Chinese Journal of Pharmaceuticals, V43, P718 Dubovitskaya Alevtina, 2017, AMIA Annu Symp Proc, V2017, P650 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fu JS, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20071898 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 GitHub, 2018, MEDIBLOC TECHN WHIT Hang L, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8040467 Ismail L, 2019, IEEE ACCESS, V7, P149935, DOI 10.1109/ACCESS.2019.2947613 Jamil F, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8050505 Krittanawong C, 2020, NAT REV CARDIOL, V17, P1, DOI 10.1038/s41569-019-0294-y Kuo TT, 2019, J AM MED INFORM ASSN, V26, P462, DOI 10.1093/jamia/ocy185 Lewandowski M, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19194060 Lwin MT, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20030698 Mamo N, 2020, EUR J HUM GENET, V28, P609, DOI 10.1038/s41431-019-0560-9 MedicoHealth, 2018, MEDICOHEALTH WHITEPA MeFy, 2018, MEFY WHITEPAPER National Medical Products Administration, 2020, ANNOUNCEMENT STATE F Nguyen DC, 2019, IEEE ACCESS, V7, P66792, DOI 10.1109/ACCESS.2019.2917555 Olson LE, 2009, ILLINOIS DIGITAL ENV Rangachari P, 2019, J HEALTHC LEADERSH, V11, P23, DOI 10.2147/JHL.S198951 San Miguel E, 2019, LECT NOTES COMPUTER, V11819 Shi MY, 2016, J CHENGDU U TRADITIO, P04 State Administration of Traditional Chinese Medicine, 2015, NOT OFF STAT ADM TRA Toyoda K, 2017, IEEE ACCESS, V5, P17465, DOI 10.1109/ACCESS.2017.2720760 Tse D, 2017, IN C IND ENG ENG MAN, P1357 US Food and Drug Administration, PIL PROJ PROGR DRUG Wu R, 2012, COLLABORATECOM, P711, DOI 10.4108/icst.collaboratecom.2012.250497 NR 34 TC 5 Z9 5 U1 15 U2 70 PD JUN 21 PY 2021 VL 23 IS 6 AR e25946 DI 10.2196/25946 WC Health Care Sciences & Services; Medical Informatics SC Health Care Sciences & Services; Medical Informatics UT WOS:000664310700007 DA 2022-12-14 ER PT J AU Felmer, R Sagredo, B Chavez, R Iraira, S Folch, C Parra, L Catrileo, A Ortiz, M AF Felmer D, Ricardo Sagredo D, Boris Chavez R, Renato Iraira H, Sergio Folch M, Carolina Parra G, Lorena Catrileo S, Adrian Ortiz L, Manuel TI IMPLEMENTATION OF A MOLECULAR SYSTEM FOR TRACEABILITY OF BEEF BASED ON MICROSATELLITE MARKERS SO CHILEAN JOURNAL OF AGRICULTURAL RESEARCH DT Article DE traceability; microsatellites; individual identification; beef ID GENETIC-MARKERS; IDENTIFICATION AB Animal products traceability has acquired considerable importance as a security measure in EEC member countries since the food crisis of the mid-nineties. This has led to reinforcing the capacity to manage cattle product quality, with traceability emerging as the main tool to prevent risks to product security and quality as demanded by consumers in developed countries. The practical application of a traceability system for beef, based on molecular markers requires the election of a panel of microsatellites, as well as the optimization of methods of sampling and DNA analysis. In this work, a traceability system for beef based on a panel of 10 microsatellites markers was implemented. Different biological samples were evaluated, such as hair, blood, tissue and meat. Hair samples were the most suitable because they are easy to obtain and to manipulate, and have a low storage cost; whereas in the food processing chain, meat samples were the most suitable due to the facility of obtaining from the maturation room. The traceability system was evaluated in a meat processing plant, confirming traceability of 150 samples of hair with their respective meat counterparts with a 100% of certainty, demonstrating the reliability of the developed method. The implemented system is an important contribution since it allows for ensuring the quality of animal products, and can be used as a tool to certify conventional traceability systems. This would allow for increasing the competitiveness of this sector and generating greater confidence among consumers. C1 [Felmer D, Ricardo; Chavez R, Renato; Parra G, Lorena; Catrileo S, Adrian] Ctr Reg Invest Carillanca, Inst Invest Agropecuarias, Temuco, Chile. [Sagredo D, Boris; Iraira H, Sergio; Folch M, Carolina] Ctr Reg Invest Remehue, Inst Invest Agropecuarias, Osorno, Chile. [Ortiz L, Manuel] Univ Austral Chile, Ctr Inseminac Artificial, Valdivia, Chile. C3 Instituto de Investigacion Agropecuaria (INIA); Instituto de Investigacion Agropecuaria (INIA); Universidad Austral de Chile RP Felmer, R (corresponding author), Ctr Reg Invest Carillanca, Inst Invest Agropecuarias, Casilla 58-D, Temuco, Chile. EM rfelmer@inia.cl CR Bedoya G, 2001, REV COLOMB CIENCIAS, V14, P109 Blott SC, 1999, HEREDITY, V82, P613, DOI 10.1046/j.1365-2540.1999.00521.x BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 CAJA G, 2000, MEAT AUTOMATION, V1, P18 CHAVEZ R, 2004, 29 REUN AN SOCHIPA V, P151 Conill C, 2002, J ANIM SCI, V80, P919 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Felmer R, 2006, ARCH MED VET, V38, P197, DOI 10.4067/S0301-732X2006000300002 FERNANDEZ R, 2002, DISTRIBUCION CON MAR, P5 FOLCH C, 2004, 29 REUN AN SOCHIPA V, P153 Lodish H., 1995, MOL CELL BIOL MASAHIKO S, 2001, SYSTEMS COMPUTERS JA, V32, P12 Mathias M, 2007, AGR TEC, V67, P3 MUJICA F, 2005, B INIA, V137 PANACCIO M, 1991, NUCLEIC ACIDS RES, V19, P1151, DOI 10.1093/nar/19.5.1151 Ribo O, 2001, REV SCI TECH OIE, V20, P426 *SAG, 2006, PROC GEN PROGR OF TR TAUTZ D, 1989, NUCLEIC ACIDS RES, V17, P6463, DOI 10.1093/nar/17.16.6463 Tessier C, 1999, THEOR APPL GENET, V98, P171, DOI 10.1007/s001220051054 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Visscher PM, 2002, J DAIRY SCI, V85, P2368, DOI 10.3168/jds.S0022-0302(02)74317-8 Zamora M., 2004, Archivos Latinoamericanos de Produccion Animal, V12, P12 NR 22 TC 8 Z9 11 U1 0 U2 10 PD DEC PY 2008 VL 68 IS 4 BP 342 EP 351 WC Agriculture, Multidisciplinary; Agronomy SC Agriculture UT WOS:000265577900004 DA 2022-12-14 ER PT J AU Corallo, A Latino, ME Menegoli, M Striani, F AF Corallo, Angelo Latino, Maria Elena Menegoli, Marta Striani, Fabrizio TI What factors impact on technological traceability systems diffusion in the agrifood industry? An Italian survey SO JOURNAL OF RURAL STUDIES DT Article DE Traceability system; Food traceability; Agrifood industry; Technology diffusion; Traceability system diffusion; Survey ID FOOD-SUPPLY CHAIN; INFORMATION ASYMMETRY; EMPIRICAL-EVIDENCE; MANAGEMENT; FRAMEWORK; BENEFITS; TRACKING; BEHAVIOR; SAFETY; IMPLEMENTATION AB Since the 1990s, governments look to traceability as a part of their safety and quality assurance strategies. This study aims to analyse the diffusion levels of the traceability systems in the Italian agrifood industry. A theoretical framework was developed considering the meaning of traceability as well as the drivers, benefits, barriers and intentions of a company that adopts a traceability system. Nineteen hypotheses were defined and tested through correlation and empirical analysis. Data was obtained through a survey conducted on a sample of Italian agrifood companies. The results showed that variables like what role, educational level and company size affect an agrifood company's propensity to adopt a traceability system. C1 [Corallo, Angelo; Latino, Maria Elena; Menegoli, Marta] Univ Salento, Dept Innovat Engn, Via Monteroni Sn, I-73100 Lecce, Italy. [Striani, Fabrizio] Univ Salento, Dept Econ & Commerce, Via Monteroni Sn, I-73100 Lecce, Italy. C3 University of Salento; University of Salento RP Latino, ME (corresponding author), Univ Salento, Dept Innovat Engn, Via Monteroni Sn, I-73100 Lecce, Italy. EM mariaelena.latino@unisalento.it CR Ackerley N., 2010, Food Protection Trends, V30, P212 Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Alfaro J. A., 2006, Journal of Purchasing and Supply Management, V12, P39, DOI 10.1016/j.pursup.2006.02.003 Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 [Anonymous], 2003, MIDWEST RES TO PRACT Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Banerjee D, 2018, J RURAL STUD, V61, P155, DOI 10.1016/j.jrurstud.2018.04.011 Banterle A., 2008, AGRIREGIONIEUROPA, V4, P15 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Barnett V., 2002, SAMPLE SURVEY PRINCI, Vthird Benatia M. A., 2018, 2018 4 INT C ADV TEC, P1, DOI DOI 10.1109/ATSIP.2018 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bougdira A, 2016, 2016 2ND INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGIES AND APPLICATIONS (CLOUDTECH), P214, DOI 10.1109/CloudTech.2016.7847701 Bougdira A, 2016, AIP CONF PROC, V1758, DOI 10.1063/1.4959406 Brandl D., 2002, REV ELECT ELECT, V8, P46, DOI 10.3845/ree.2002.087 Bremmers H.J., 2004, DYNAMICS CHAINS NETW, DOI DOI 10.3920/978-90-8686-526-0 Brunori G, 2013, J RURAL STUD, V29, P19, DOI 10.1016/j.jrurstud.2012.01.013 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chan FTS, 2013, INT J PROD RES, V51, P1196, DOI 10.1080/00207543.2012.693961 Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 Chau PYK, 2002, INFORM MANAGE-AMSTER, V39, P297, DOI 10.1016/S0378-7206(01)00098-2 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Cicchitelli G., 1997, CAMPIONAMENTO STAT Cochran W.G., 2007, SAMPLING TECHNIQUES Cochran W. G., 1963, SAMPLING TECHNIQUES Creswell J, 2017, RES DESIGN QUALITATI Dai HY, 2015, INT J PROD RES, V53, P511, DOI 10.1080/00207543.2014.955922 Dai JB, 2017, INT J PROD RES, V55, P5465, DOI 10.1080/00207543.2017.1321800 Denzin NK, 2017, RES ACT THEORETICAL, DOI DOI 10.4324/9781315134543 Dessureault S., 2006, Journal of Food Distribution Research, V37, P154 Dhouib S, 2013, 2013 INTERNATIONAL CONFERENCE ON ADVANCED LOGISTICS AND TRANSPORT (ICALT), P417 Donnelly KAM, 2012, FOOD CONTROL, V27, P228, DOI 10.1016/j.foodcont.2012.03.021 Dubey R, 2015, INT J PROD RES, V53, P371, DOI 10.1080/00207543.2014.933909 Enneking U, 2007, FOOD QUAL PREFER, V18, P133, DOI 10.1016/j.foodqual.2005.09.008 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Espineira M, 2016, WOODHEAD PUBL FOOD S, V301, P3, DOI 10.1016/B978-0-08-100310-7.00001-6 Faisal M. N., 2016, Journal of Foodservice Business Research, V19, P171, DOI 10.1080/15378020.2016.1159894 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Fortunato L., 2011, P WISS, V182, P123 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Golan E.H., 2003, TRACEABILITY FOOD SA Golan E.H., 2004, TRACEABILITY US FOOD Grimm JH, 2014, INT J PROD ECON, V152, P159, DOI 10.1016/j.ijpe.2013.12.011 Habersetzer A, 2019, J RURAL STUD, V65, P143, DOI 10.1016/j.jrurstud.2018.10.009 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs J. E., 2003, CONSUMER DEMAND TRAC Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Jacobsen JB, 2009, ENVIRON RESOUR ECON, V43, P137, DOI 10.1007/s10640-008-9226-8 Jenkins H., 2004, Journal of General Management, V29, P37 JICK TD, 1979, ADMIN SCI QUART, V24, P602, DOI 10.2307/2392366 Khabbazi MR, 2011, INT J PROD RES, V49, P731, DOI 10.1080/00207540903530810 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Khoifin K., 2018, INT J SUPPLY CHAIN M, V7, P153 KLONTZ KC, 1995, J FOOD PROTECT, V58, P927, DOI 10.4315/0362-028X-58.8.927 Lehr H., 2013, PRECIS LIVEST FARMIN Lehtinen U., 2011, Intelligent agrifood chains and networks, P151 Lewis JB, 2005, POLIT ANAL, V13, P345, DOI 10.1093/pan/mpi026 Liang RD, 2016, BRIT FOOD J, V118, P183, DOI 10.1108/BFJ-06-2015-0215 Liao D, 2012, SURV METHODOL, V38, P53 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Lindley DV, 1997, J ROY STAT SOC D-STA, V46, P129 Lokunarangodage C. K., 2015, CONSTRAINTS COMPLIAN Loomis JB, 1996, ECOL ECON, V18, P197, DOI 10.1016/0921-8009(96)00029-8 Lusk JL, 2005, J AGR RESOUR ECON, V30, P28 Mania I., 2018, Traceability in the Dairy Industry in Europe, P3, DOI 10.1007/978-3-030-00446-0_1 Manikas Ioannis, 2009, International Journal of Postharvest Technology and Innovation, V1, P430, DOI 10.1504/IJPTI.2009.030691 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 Maram B., 2019, INT J MECH ENG TECHN Marconi M, 2017, INT J PROD RES, V55, P6638, DOI 10.1080/00207543.2017.1332437 Mattevi M, 2016, BRIT FOOD J, V118, P1107, DOI 10.1108/BFJ-07-2015-0261 Mayer A, 2019, J RURAL STUD, V65, P79, DOI 10.1016/j.jrurstud.2018.11.005 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x McKitterick L, 2016, J RURAL STUD, V48, P41, DOI 10.1016/j.jrurstud.2016.09.005 Mejia C, 2010, COMPR REV FOOD SCI F, V9, P159, DOI 10.1111/j.1541-4337.2009.00098.x Mishra D.K., 2018, ARXIV181106358 Mishra D.K., 2016, PRODUCT LIFECYCLE MA, P377, DOI DOI 10.1007/978-3-319-54660-5_34 Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192 Tran N, 2013, WORLD DEV, V45, P325, DOI 10.1016/j.worlddev.2013.01.025 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Overbosch Peter, 2014, FOOD SAFETY MANAGEME, DOI [10.1016/B978-0-12-381504-0.00022-6 ., DOI 10.1016/B978-0-12-381504-0.00022-6, 10.1016/b978-0-12-381504-0.00022-6] Pachoud C, 2019, J RURAL STUD, V72, P58, DOI 10.1016/j.jrurstud.2019.10.003 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Parnell J., 2006, European Management Journal, V24, P236, DOI 10.1016/j.emj.2006.03.013 Pascucci S., 2010, International Journal on Food System Dynamics, V1, P224 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Richardson L, 2009, ECOL ECON, V68, P1535, DOI 10.1016/j.ecolecon.2008.10.016 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 ROHNER RP, 1977, BEHAV SCI RES, V12, P117, DOI 10.1177/106939717701200203 Rueda L, 2017, INFORM MANAGE-AMSTER, V54, P1059, DOI 10.1016/j.im.2017.06.002 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Salomie I, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR 2008), THETA 16TH EDITION, VOL I, PROCEEDINGS, P393, DOI 10.1109/AQTR.2008.4588774 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Schniederjans DG, 2017, INT J PROD ECON, V183, P287, DOI 10.1016/j.ijpe.2016.11.008 Shamim S., 2018, INFORM MANAGE, DOI [10.1016/j.im.2018.12.003, DOI 10.1016/J.IM.2018.12.003.S0378720618302854] Siegrist M, 2018, RISK ANAL, V38, P504, DOI 10.1111/risa.12857 Skallerud K, 2019, J RURAL STUD, V67, P79, DOI 10.1016/j.jrurstud.2019.02.020 Spearman C, 1904, AM J PSYCHOL, V15, P72, DOI 10.2307/1412159 Spur N, 2019, J RURAL STUD, V72, P23, DOI 10.1016/j.jrurstud.2019.09.011 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Szewczyk R., 2008, J AUTOMATION MOBILE, V2, P55 Tajudeen FP, 2018, INFORM MANAGE-AMSTER, V55, P308, DOI 10.1016/j.im.2017.08.004 Terzi Sergio, 2007, International Journal of Product Lifecycle Management, V2, P253, DOI 10.1504/IJPLM.2007.016292 Tiwari S, 2019, J RURAL STUD, V66, P1, DOI 10.1016/j.jrurstud.2019.01.029 Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926 Verbeke W, 1999, FOOD QUAL PREFER, V10, P437, DOI 10.1016/S0950-3293(99)00031-2 Wahlster W, 2013, SEMPROM FDN SEMANTIC, P3, DOI DOI 10.1007/978-3-642-37377-0_1 Wang X, 2009, INT J PROD RES, V47, P2865, DOI 10.1080/00207540701725075 Wang XB, 2006, PROCEEDINGS OF THE 2006 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING (13TH), VOLS 1-3, P493, DOI 10.1109/SOLI.2006.329074 Wang ZQ, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA), P100, DOI 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.43 Wei J, 2015, INFORM MANAGE-AMSTER, V52, P628, DOI 10.1016/j.im.2015.05.001 Weisberg H. F., 1989, INTRO SURVEY RES DAT WiKi T.F., 2009, RECOMMENDATIONS GOOD Wilcock A, 2004, TRENDS FOOD SCI TECH, V15, P56, DOI 10.1016/j.tifs.2003.08.004 Wilson T., 1998, SUPPLY CHAIN MANAG I, V3, P127, DOI [10.1108/13598549810230831, DOI 10.1108/13598549810230831] Wongprawmas R, 2015, BRIT FOOD J, V117, P2234, DOI 10.1108/BFJ-08-2014-0300 Wu B., 2019, J RURAL STUD, DOI [10.1016/j.jrurstud.2019.05.008, DOI 10.1016/J.JRURSTUD.2019.05.008.S0743016718303218] Xu LD, 2018, INT J PROD RES, V56, P2941, DOI 10.1080/00207543.2018.1444806 Yoo CW, 2015, INFORM MANAGE-AMSTER, V52, P692, DOI 10.1016/j.im.2015.06.003 Young L. M., 2002, Review of Agricultural Economics, V24, P428, DOI 10.1111/1467-9353.00107 Zhang J, 2008, IEEE INT CON AUTO SC, P1, DOI 10.1109/COASE.2008.4626431 Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 NR 123 TC 6 Z9 7 U1 9 U2 20 PD APR PY 2020 VL 75 BP 30 EP 47 DI 10.1016/j.jrurstud.2020.02.006 WC Geography; Regional & Urban Planning SC Geography; Public Administration UT WOS:000528045000004 DA 2022-12-14 ER PT J AU Moga, LM Dediu, L Zhang, X Nenciu, MI Novac, CU Gavrila, SP AF Moga, L. M. Dediu, L. Zhang, X. Nenciu, M. I. Novac, C. Ududec Gavrila, S. P. TI BARRIERS OF ADOPTING TRACEABILITY SYSTEMS BY ROMANIAN FISHERY AND AQUACULTURE SO JOURNAL OF ENVIRONMENTAL PROTECTION AND ECOLOGY DT Article DE safety; environment; traceability system; implementation; barriers; fish products AB The increase of the awareness about the environmental, social, and legal issues associated with seafood and fish products led to significant concerns of the shareholders, and challenges to the corporate social responsibility initiatives of companies. Many countries have begun to implement a traceability system to increase vertical coordination and guarantee safety in fish products. This paper aims to provide information about the barriers that influence the implementation of the traceability system for the fish products in Romania. The research is based on the results of an empirical study on the barriers that influence Romanian fish farms to adopt the traceability system. The questioners applied in Romanian fish farms investigate barriers like the basic characteristics of the enterprises and whether they export, the costs of adoption, and different traceability standards in the markets, in order to provide the information necessary for the development of a traceability system which will satisfy the needs of all the stakeholders and will be adopted by the companies. C1 [Moga, L. M.; Dediu, L.; Novac, C. Ududec; Gavrila, S. P.] Dunarea de Jos Univ Galati, 47 Domneasca St, Galati 800008, Romania. [Zhang, X.] China Agr Univ, 17 Qinghua Donglu St, Beijing 100083, Peoples R China. [Nenciu, M. I.] NIRDEP Natl Inst Marine Res & Dev Grigore Antipa, 300 Mamaia Blvd, Constanta 900581, Romania. C3 Dunarea De Jos University Galati; China Agricultural University; National Institute for Marine Research & Development "Grigore Antipa" RP Moga, LM (corresponding author), Dunarea de Jos Univ Galati, 47 Domneasca St, Galati 800008, Romania. EM liliana.moga@gmail.com CR ANDRE V., 2014, REV ANAL CURRENT TRA BOYLE M. D., 2012, TRACE 2 UPDATED SUMM GREENE J. L., 2010, ANIMAL IDENTIFICATIO Jolliffe I. T., 2002, SPRINGER SERIES STAT, Vsecond Kokkinakis AK, 2014, J ENVIRON PROT ECOL, V15, P630 NICOLAE C. G., 2014, SCI J MANAGE SYST, V15, P95 O'DONOGHUE J., 2015, FOOD MAGAZINE 0327 OYVIND L., 2008, IMPROVED FARMED FISH PETRU A., 2014, SEAFOOD TRACEABILITY Regan G, 2012, EUROMICRO CONF PROC, P319, DOI 10.1109/SEAA.2012.80 St Raykov V, 2014, J ENVIRON PROT ECOL, V15, P1092 Sterling B., 2014, ENHANCING SEAFOOD TR Wang F, 2009, J FOOD AGRIC ENVIRON, V7, P64 Winter E., 2006, INT ASS AGR EC C GOL Zheng XP, 2014, J ENVIRON PROT ECOL, V15, P589 NR 15 TC 1 Z9 1 U1 0 U2 9 PY 2016 VL 17 IS 1 BP 284 EP 290 WC Environmental Sciences SC Environmental Sciences & Ecology UT WOS:000375503300032 DA 2022-12-14 ER PT J AU Tagarakis, AC Benos, L Kateris, D Tsotsolas, N Bochtis, D AF Tagarakis, Aristotelis C. Benos, Lefteris Kateris, Dimitrios Tsotsolas, Nikolaos Bochtis, Dionysis TI Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System SO APPLIED SCIENCES-BASEL DT Article DE traceability; food safety; IoT; event capturing; integrated information system; information management; transaction support; data sharing; real-time communication; interoperability ID FOOD TRACEABILITY; BLOCKCHAIN; AGRICULTURE; TECHNOLOGY; INTERNET; MODEL AB Traceability, namely the ability to access information about a product and its movement across all stages of the supply chain, has been emerged as a key criterion of a product's quality and safety. Managing fresh products, such as fruits and vegetables, is a particularly complicated task, since they are perishable with short shelf lives and are vulnerable to environmental conditions. This makes traceability of fresh produce very significant. The present study provides a brief overview of the relative literature on fresh produce traceability systems. It was concluded that the commercially available traceability systems usually neither cover the entire length of the supply chain nor rely on open and transparent interoperability standards. Therefore, a user-friendly open access traceability system is proposed for the development of an integrated solution for traceability and agro-logistics of fresh products, focusing on interoperability and data sharing. Various Internet of Things technologies are incorporated and connected to the web, while an android-based platform enables the monitoring of the quality of fruits and vegetables throughout the whole agri-food supply chain, starting from the field level to the consumer and back to the field. The applicability of the system, named AgroTRACE, is further extended to waste management, which constitutes an important aspect of a circular economy. C1 [Tagarakis, Aristotelis C.; Benos, Lefteris; Kateris, Dimitrios; Bochtis, Dionysis] Ctr Res & Technol Hellas CERTH, Inst Bioecon & AgriTechnol IBO, 6th Km Charilaou Thermi Rd, Thessaloniki 57001, Greece. [Tsotsolas, Nikolaos] Green Projects SA, R&D Dept, Admitou 15, Athens 15238, Greece. C3 Centre for Research & Technology Hellas RP Kateris, D (corresponding author), Ctr Res & Technol Hellas CERTH, Inst Bioecon & AgriTechnol IBO, 6th Km Charilaou Thermi Rd, Thessaloniki 57001, Greece. EM a.tagarakis@certh.gr; e.benos@certh.gr; d.kateris@certh.gr; ntsotsolas@green-projects.gr; d.bochtis@certh.gr CR Anagnostis A, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10020469 [Anonymous], Multistate Outbreak of Listeriosis Linked to Whole Cantaloupes from Jensen Farms, Colorado | Listeria | CDC [Anonymous], ROCKMELON LISTERIA I Benos L, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21113758 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Burke T, 2019, BLOCKCHAIN FOOD TRAC, P133 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Chun-Ting P, 2020, 2020 34TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2020), P158, DOI 10.1109/ICOIN48656.2020.9016535 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Ferrag MA, 2020, IEEE ACCESS, V8, P32031, DOI 10.1109/ACCESS.2020.2973178 FoodLogiQ|Food Safety, TRAC SUST FOOD TRAC Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Gao HM, 2013, APPL MECH MATER, V321-324, P3056, DOI 10.4028/www.scientific.net/AMM.321-324.3056 Gaukler GM, 2007, SPRINGER SER ADV MAN, P29, DOI 10.1007/978-1-84628-607-0_2 Haji M, 2020, LOGISTICS-BASEL, V4, DOI 10.3390/logistics4040033 Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Huang Y., 2008 MILK SCANDAL RE IERC-European Research Cluster on the Internet of Things, INTERNET THINGS IOT Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Keshri N., 2019, REFERENCE MODULE FOO Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Li Z, 2017, IND MANAGE DATA SYST, V117, P1906, DOI 10.1108/IMDS-11-2016-0489 Lu Y, 2018, J MANAG ANAL, V5, P231, DOI 10.1080/23270012.2018.1516523 Mao DH, 2019, ARAB J SCI ENG, V44, P3439, DOI 10.1007/s13369-018-3537-z Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qian JP, 2022, CRIT REV FOOD SCI, V62, P679, DOI 10.1080/10408398.2020.1825925 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Qiao SY, 2013, INT SYM COMPUT INTEL, P317, DOI 10.1109/ISCID.2013.86 Raikwar M, 2019, IEEE ACCESS, V7, P148550, DOI 10.1109/ACCESS.2019.2946983 Rejeb A, 2020, LOGISTICS-BASEL, V4, DOI 10.3390/logistics4040027 Rose DC, 2021, LAND USE POLICY, V100, DOI 10.1016/j.landusepol.2020.104933 Rose DC, 2018, FRONT SUSTAIN FOOD S, V2, DOI 10.3389/fsufs.2018.00087 Saurabh S, 2021, J CLEAN PROD, V284, DOI 10.1016/j.jclepro.2020.124731 Tack DM, 2020, MMWR-MORBID MORTAL W, V69, P509, DOI 10.15585/mmwr.mm6917a1 Tagarakis AC, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11135858 Taylor S., HORSEMEAT SCANDAL FA Tzounis A, 2017, BIOSYST ENG, V164, P31, DOI 10.1016/j.biosystemseng.2017.09.007 Ullo SL, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20113113 Verdouw CN, 2016, J FOOD ENG, V176, P128, DOI 10.1016/j.jfoodeng.2015.11.009 Want R, 2006, IEEE PERVAS COMPUT, V5, P25, DOI 10.1109/MPRV.2006.2 Xu Z, 2015, AEBMR ADV ECON, V6, P120 Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 [于合龙 Yu Helong], 2020, [农业机械学报, Transactions of the Chinese Society for Agricultural Machinery], V51, P328 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhou X, 2020, WEB INTELL, V18, P181, DOI 10.3233/WEB-200440 NR 51 TC 5 Z9 5 U1 29 U2 70 PD AUG PY 2021 VL 11 IS 16 AR 7596 DI 10.3390/app11167596 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000688642600001 DA 2022-12-14 ER PT J AU Zhang, XS Zhang, J Liu, F Fu, ZT Mu, WS AF Zhang Xiaoshuan Zhang Jian Liu Feng Fu Zetian Mu Weisong TI Strengths and limitations on the operating mechanisms of traceability system in agro food, China SO FOOD CONTROL DT Article DE Traceability system; Agribusiness; Comparative analysis; Operating mechanism ID INFORMATION-SYSTEMS; ASSURANCE; SUCCESS AB Traceability system can effectively trace food quality and reduce safety scares. Recent researches on traceability systems in China focus on technology innovation, traceability system management, and determinants of traceability system implementation. This paper proposes four criteria to analyze strengths and limitations of the operating mechanisms. The result shows the operating mechanism of traceability system in Chinese agribusiness can be classified into three categories and none of operating mechanism of traceability system completely meets the agribusiness traceability service requirement, and each of the three operating mechanisms has its own strengths and limitations. It is suggested that an integrated mechanism is needed to implement traceability system in agribusiness. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Zhang Xiaoshuan; Zhang Jian; Liu Feng; Fu Zetian; Mu Weisong] China Agr Univ, Key Lab Modern Precis Agr Integrat, Minist Educ, Beijing 100083, Peoples R China. [Zhang Jian] Beijing Informat S&T Univ, Beijing 100192, Peoples R China. C3 China Agricultural University RP Mu, WS (corresponding author), China Agr Univ, Key Lab Modern Precis Agr Integrat, Minist Educ, Beijing 100083, Peoples R China. EM wsmu@cau.edu.cn CR BAI Y, 2005, FOOD SCI, V8, P473 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 BRIAR BL, 2003, J FOOD DISTRIBUTION, V34, P13 Caswell JA, 2006, MAR POLLUT BULL, V53, P650, DOI 10.1016/j.marpolbul.2006.08.007 DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60 Frederiksen M, 2001, FOOD AUST, V53, P117 GOLAN E, 2003, CHOICES, V18, P17 Koutsoumanis K, 2005, INT J FOOD MICROBIOL, V100, P253, DOI 10.1016/j.ijfoodmicro.2004.10.024 Laudon K. C., 1999, MANAGEMENT INFORM SY Li XP, 2006, CLONING STEM CELLS, V8, P41, DOI 10.1089/clo.2006.8.41 Matthias MS, 2010, PAIN MANAG NURS, V11, P26, DOI 10.1016/j.pmn.2008.12.003 McMeekin TA, 2006, INT J FOOD MICROBIOL, V112, P181, DOI 10.1016/j.ijfoodmicro.2006.04.048 Miraglia M, 2004, FOOD CHEM TOXICOL, V42, P1157, DOI 10.1016/j.fct.2004.02.018 MONTEIRO DMS, 2006, 2006 AM AGR EC ASS A Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 RESENDE M, 2007, 105 INT MARK INT TRA Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Sykuta M., 2005, Journal of Agricultural and Applied Economics, V37, P365, DOI 10.1017/S1074070800006842 TANIGUCHI Y, 2005, ICDE 2005 21 INT C D VANDORP CA, 2003, 4 EFITA EUR FED INF Wu JH, 2006, INFORM MANAGE-AMSTER, V43, P728, DOI 10.1016/j.im.2006.05.002 YANG L, 2005, WORLDS STANDARDIZATI, V3, P43 [No title captured] NR 23 TC 22 Z9 26 U1 3 U2 53 PD JUN PY 2010 VL 21 IS 6 BP 825 EP 829 DI 10.1016/j.foodcont.2009.10.015 WC Food Science & Technology SC Food Science & Technology UT WOS:000275225000004 DA 2022-12-14 ER PT J AU Schulz, LL Tonsor, GT AF Schulz, Lee L. Tonsor, Glynn T. TI Cow-Calf Producer Preferences for Voluntary Traceability Systems SO JOURNAL OF AGRICULTURAL ECONOMICS DT Article DE Animal traceability; cattle producer; cow-calf; National Animal Identification System; voluntary traceability ID CONTINGENT VALUATION; HETEROGENEOUS PREFERENCES; BEEF INDUSTRY; CHEAP TALK; CATTLE; IDENTIFICATION; CONSUMERS; LESSONS; DEMAND; DESIGN AB This article identifies preferences of US cow-calf producers for voluntary traceability systems to better identify the potential success of alternative voluntary traceability systems. Results suggest that notable heterogeneity exists between cow-calf producers in their preferences and the welfare effects of mandating traceability adoption. Producers are sensitive to price, managing entity and information requirements. We provide forecasts of voluntary participation rates under different price premium and discount scenarios that producers may face. This analysis has policy implications as success of voluntary traceability systems hinges critically upon cow-calf producer preferences. C1 [Schulz, Lee L.] Kansas State Univ, Dept Agr Econ, Manhattan, KS 66506 USA. [Tonsor, Glynn T.] Michigan State Univ, Dept Agr Food & Resource Econ, E Lansing, MI 48824 USA. C3 Kansas State University; Michigan State University RP Schulz, LL (corresponding author), Kansas State Univ, Dept Agr Econ, 342 Waters Hall, Manhattan, KS 66506 USA. EM leeschulz@agecon.ksu.edu CR Adamowicz W, 1998, AM J AGR ECON, V80, P64, DOI 10.2307/3180269 Alfnes F, 2004, EUR REV AGRIC ECON, V31, P19, DOI 10.1093/erae/31.1.19 BAILEY D, 2004, AAEA ANN M DENV CO 1 Bailey D, 2007, J AGR RESOUR ECON, V32, P403 BOCKSTAEL NE, 1987, LAND ECON, V63, P11, DOI 10.2307/3146652 Boxall PC, 2002, ENVIRON RESOUR ECON, V23, P421, DOI 10.1023/A:1021351721619 Boyle K., 2003, PRIMER NONMARKET VAL, P111 BUHR BL, 2003, 035 INT AGR TRAD RES *CATTL NETW, 2008, 5 MIN DR J WIEM USDA Cummings RG, 1999, AM ECON REV, V89, P649, DOI 10.1257/aer.89.3.649 Davis CG, 2007, REV AGR ECON, V29, P331, DOI 10.1111/j.1467-9353.2007.00346.x Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 DICKINSON DL, 2003, AAEA ANN M MONTR CAN Golan E., 2004, 830 ERS USDA Gracia A., 2005, Journal of Food Distribution Research, V36, P45 GREGORY A, 2008, THESIS KANSAS STATE Key N, 2005, AGR ECON-BLACKWELL, V33, P117, DOI 10.1111/j.1574-0862.2005.00339.x KRINSKY I, 1986, REV ECON STAT, V68, P715, DOI 10.2307/1924536 KUHFELD WF, 1994, J MARKETING RES, V31, P545, DOI 10.2307/3151882 Lusk JL, 2008, J AGR RESOUR ECON, V33, P270 Lusk JL, 2006, AM J AGR ECON, V88, P1015, DOI 10.1111/j.1467-8276.2006.00913.x Lusk JL, 2003, AM J AGR ECON, V85, P840, DOI 10.1111/1467-8276.00492 Lusk JL, 2003, AM J AGR ECON, V85, P16, DOI 10.1111/1467-8276.00100 Meyerhoff J, 2008, ENVIRON RESOUR ECON, V39, P433, DOI 10.1007/s10640-007-9134-3 MONTEIRO DMS, 2004, WP06 U MASS DEP RES MOREY ER, 1999, VALUING RECREATION E, P65 Murphy R. G. L., 2008, Professional Animal Scientist, V24, P277 Murphy RGL, 2009, INT FOOD AGRIBUS MAN, V12, P1 Nahuelhual L, 2004, J AGR RESOUR ECON, V29, P537 Norwood FB, 2006, J AGR RESOUR ECON, V31, P74 PARK T, 1991, LAND ECON, V67, P64, DOI 10.2307/3146486 Rigby D, 2005, EUR REV AGRIC ECON, V32, P269, DOI 10.1093/eurrag/jbi009 Scarpa R., 2004, Journal of Agricultural & Food Industrial Organization, V2, P7, DOI 10.2202/1542-0485.1080 SCHROEDER TC, 2009, BENEFIT COSTANALYSIS Schulz L. L., 2008, THESIS MICHIGAN STAT SCHULZ LL, 2008, BEEF MAGAZINE AUG, P52 SMALL KA, 1978, ECONOMETRICA, V49, P43 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Tonsor GT, 2006, J INT FOOD AGRIBUS M, V18, P103, DOI 10.1300/J047v18n03_07 Tonsor GT, 2005, J AGR RESOUR ECON, V30, P367 *USDA, 2009, PREM REG UPD *USDA, 2008, USDAS 840 AN ID SOL *USDA, 2007, NAT AN ID SYST NAIS *USDA, 2008, BUS PLAN ADV AN DIS NR 44 TC 45 Z9 48 U1 1 U2 38 PD FEB PY 2010 VL 61 IS 1 BP 138 EP 162 DI 10.1111/j.1477-9552.2009.00226.x WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000273687400008 DA 2022-12-14 ER PT J AU Zhao, J Xu, ZZ You, XY Zhao, Y He, WJ Zhao, LY Chen, AL Yang, SM AF Zhao, Jie Xu, Zhenzhen You, Xinyong Zhao, Yan He, Wenjing Zhao, Luyao Chen, Ailiang Yang, Shuming TI Genetic traceability practices in a large-size beef company in China SO FOOD CHEMISTRY DT Article DE Genetic traceability practice; SNP markers; Meat traceability; Chinese enterprise ID FOOD TRACEABILITY; MEAT TRACEABILITY; SUPPLY CHAIN; SNP; PRODUCTS; MARKERS; TECHNOLOGIES; SYSTEM; PANEL AB An effective and trustworthy traceability system is important for food safety and quality; however, traditional traceability systems that only rely on the recording method do not completely prevent food fraud. DNA-based traceability techniques facilitate seamless connectivity within the entire food supply chain. A convenient and low-cost ear tag device was invented for collecting animal blood samples as an identity control, and a panel including 12 single nucleotide polymorphic (SNP) loci was selected to distinguish individuals with a matching probability of 1.70 x 10(-5). The exact animal individual was identified by comparing the SNP genotype barcodes between the meat and blood samples derived from the recording system to further validate authenticity of the recording system. These results illustrate that a combination of the genetic traceability method and a traditional recording system can provide trustworthy traceability for consumers. C1 [Zhao, Jie; Xu, Zhenzhen; You, Xinyong; Zhao, Yan; He, Wenjing; Zhao, Luyao; Chen, Ailiang; Yang, Shuming] Minist Agr China, Key Lab Agrifood Safety & Qual, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Zhao, Jie; Xu, Zhenzhen; You, Xinyong; Zhao, Yan; He, Wenjing; Zhao, Luyao; Chen, Ailiang; Yang, Shuming] Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Zhao, Jie; You, Xinyong] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, Beijing 100097, Peoples R China. C3 Ministry of Agriculture & Rural Affairs; Chinese Academy of Agricultural Sciences; Beijing Academy of Agriculture & Forestry RP Chen, AL; Yang, SM (corresponding author), Minist Agr China, Key Lab Agrifood Safety & Qual, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China. EM ailiang.chen@gmail.com; yangshumingcaas@sina.com CR Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Canavari M, 2010, J INT FOOD AGRIBUS M, V22, P203 Cassol S. A, 1999, DIAGNOSIS DIRECT AUT Charlebois S, 2015, COMPREHENSIVE REV FO, V13, P1104 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Hollegaard MV, 2011, BMC GENET, V12, DOI 10.1186/1471-2156-12-58 Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Iudith I, 2013, INT C EC SCI RES THE, V8, P414 Kim S, 2007, ANNU REV BIOMED ENG, V9, P289, DOI 10.1146/annurev.bioeng.9.060906.152037 Lang W., 2014, OPTIMIZATION DRUG DI, P461, DOI DOI 10.1007/978-1-62703-742-6_27 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Manning L, 2016, CURR OPIN FOOD SCI, V10, P16, DOI 10.1016/j.cofs.2016.07.001 Mei JV, 2001, J NUTR, V131, p1631S, DOI 10.1093/jn/131.5.1631S Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Shikha K, 2016, INNOVATION FUTURE TR Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] Walker GS, 2017, FOOD CONTROL, V72, P168, DOI 10.1016/j.foodcont.2016.01.028 Walker M. J., 2013, Journal of the Association of Public Analysts, V41, P67 Wang L, 2007, P CHIN ASS SCI TECHN Wang YR, 2010, MOL CELL PROBE, V24, P15, DOI 10.1016/j.mcp.2009.08.001 Weir B. S., 1996, GENETIC DATA ANAL Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 Xu Y, 2012, ANAL BIOANAL CHEM, V404, P3037, DOI 10.1007/s00216-012-6440-6 Yang S, 2017, Patent, Patent No. [ZL201720033055.1, 201720033055] Zhao J, 2018, FOOD CONTROL, V87, P94, DOI 10.1016/j.foodcont.2017.11.039 NR 33 TC 4 Z9 4 U1 8 U2 189 PD MAR 30 PY 2019 VL 277 BP 222 EP 228 DI 10.1016/j.foodchem.2018.10.007 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000451430800027 DA 2022-12-14 ER PT J AU Galvao, JA Margeirsson, S Garate, C Vidarsson, JR Oetterer, M AF Galvao, Juliana Antunes Margeirsson, Sveinn Garate, Cecilia Vidarsson, Jonas Runar Oetterer, Marilia TI Traceability system in cod fishing SO FOOD CONTROL DT Article DE Traceability; Cod fish; Quality control ID YIELD AB The objective of this research was to examine how various factors in Icelandic cod fishing can influence the quality of the raw material, using traceability systems to link these factors, and how transfer that knowledge and techniques to the Brazilian seafood industry. Data were collected in 2007 and analysed, to find a functional relationship between various quality factors. The analysis, showed, that there is a correlation between the number of parasites in the fillets and location of the fishing ground. It also showed that fishing ground and volume in haul can influence gaping, and that fillet yield differs between fishing grounds. These conclusions could only be drawn because of the ability to trace the fish from catch and all the way through processing. Recommendations drawn from this research to the Brazilian Competent Authority are to revise the countries fisheries legislation in order to enable the implementation of a traceability system that could be used as a tool to improve the quality of the raw material. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Galvao, Juliana Antunes; Oetterer, Marilia] Univ Sao Paulo, Coll Agr, Sao Paulo, Brazil. [Margeirsson, Sveinn; Garate, Cecilia; Vidarsson, Jonas Runar] Matis Ohf Iceland Food Res, IS-101 Reykjavik, Iceland. C3 Universidade de Sao Paulo RP Galvao, JA (corresponding author), Univ Sao Paulo, Coll Agr, Av Padua Dias 11,CP9, Sao Paulo, Brazil. EM jagalvao@esalq.usp.br CR ALMEIDA J, 2006, TRACEABILITY QUALITY BIGIRSSON R, 1995, 111 IC FISH LAB, P6 *CEN, 2002, CEN WORKSH TRAC FISH Cibert C, 1999, AQUAT LIVING RESOUR, V12, P1, DOI 10.1016/S0990-7440(99)80009-6 DAGBJARTSSON B, 1973, 35 IC FISH LAB DIAS J, 2003, SINTESE SITUACAO PES Diegues AC, 1983, PESCADORES CAMPONESE Eyjolfsson B, 2001, 201 IC FISH LAB GADARSSON F, 2007, TRACEABILITY ICELAND GUOMUNDSSON R, 2006, COD PROCESSING FOREC *IC MIN FISH, 2007, RESP FISH ATL STOCKS *IC MIN FISH, 2005, IC FISH FIG LAVETY J, 2001, GAPING FARMED SALMON LIU J, 2005, INVESTIGATION TRACEA LOVE RM, 2001, PROCESSING COD INFLU LOVE RM, 2001, GAPING FILLETS Margeirsson S, 2007, J FOOD ENG, V80, P503, DOI 10.1016/j.jfoodeng.2006.05.032 Margeirsson Sveinn, 2006, P265 NAHMIAS S, 2000, PRODUCTIONS OPERATIO POWELS PM, 1958, J FISHERY RES BOARD, V15, P1383 *SAS I, 2002, SAS SYST REL 9 1 3 S *SEAP, 2003, DIAGN PESC EXTR BRAS *SEAP, 2006, CONV SEAP PROZEEE IB *SEAP, 2007, AQ PESC GRAND NEG BR *SEAP, 2007, PROGR NAC REST EMBR Williams J., 2000, MANUAL FISHERIES SUR, VII WOOTEN R, 2001, ROUNDS WORMS FISH WOULD JP, 2001, APPL SPECTROSC, V55, pA160 NR 28 TC 20 Z9 23 U1 1 U2 18 PD OCT PY 2010 VL 21 IS 10 BP 1360 EP 1366 DI 10.1016/j.foodcont.2010.03.010 WC Food Science & Technology SC Food Science & Technology UT WOS:000279617700010 DA 2022-12-14 ER PT J AU Son, NM Nguyen, TL Huong, PT Hien, LT AF Nguyen Minh Son Thanh-Lam Nguyen Phan Thi Huong Lam Thanh Hien TI Novel System Using Blockchain for Origin Traceability of Agricultural Products SO SENSORS AND MATERIALS DT Article DE blockchain; origin traceability; agricultural products; blockchain system AB In recent years, many countries have faced inextricable problems in how to deal with the rapid increase in the number of different diseases, which mostly result from the environment and food taken daily: thus, eating healthy and clean foods is always a concern of almost every consumer. Consumers usually make their decisions on the basis of the information provided on the packaging. However, for agricultural products, ensuring that they get exactly what is mentioned on the packaging is a really thorny problem. In practice, several standards have been set up to help agricultural businesses and farmers improve their operational efficiency and ensure their food hygiene and safety. Standards such as Global Good Agricultural Practices (GlobalGAP), Vietnamese Good Agricultural Practices (VietGAP), and Hazard Analysis and Critical Control Points (HACCP) usually cover all production processes from farming inputs such as feeding materials, seeds, and related farming activities to the finally packaged outputs. If businesses and farmers are certified to meet certain standards and they strictly follow the guidelines during the production, consumers can trust in their own decisions. However, it is normally unknown if the production closely follows what has been certified or whether there has been any bribery, cheating, or other negative issues hidden for profit purposes because related information recorded or stored in farm log-books may also be erroneous for intentional or unintentional reasons. To overcome the problem, in this paper, we propose a novel method applying blockchain and IoT technology to support the origin traceability of agricultural products at farms. By using sensors and computational codes to read parameters that affect farming processes such as temperature, humidity, solute concentration, and pH, all related information and data used in origin traceability will be recorded and saved in the blockchain system in the form of logs in a ledger through smart contracts. Specifically, the system operates automatically on the basis of installed smart contracts, i.e., recorded data are stored on a blockchain through smart contracts and are free from human intervention. The proposed system has been constructed and tested with highly satisfactory results. Even if some nodes in the system are knocked down for some reason, when the knocked-down nodes are reconstructed, their original data are automatically and fully recovered from their friend nodes. Consequently, our proposed system provides not only an objective mechanism for collecting and storing data but also a secure environment to effectively trace the origin of agricultural products. C1 [Nguyen Minh Son; Phan Thi Huong; Lam Thanh Hien] Lac Hong Univ, Fac Informat Technol, Bien Hoa 810000, Dong Nai, Vietnam. [Thanh-Lam Nguyen] Lac Hong Univ, Off Int Affairs, Bien Hoa 810000, Dong Nai, Vietnam. C3 Lac Hong University; Lac Hong University RP Nguyen, TL (corresponding author), Lac Hong Univ, Off Int Affairs, Bien Hoa 810000, Dong Nai, Vietnam. EM sonm88@gmail.com; green4rest.vn@gmail.com; phanhuong16@gmail.com; lthien@lhu.edu.vn CR Aramyan LH, 2007, SUPPLY CHAIN MANAG, V12, P304, DOI 10.1108/13598540710759826 Atmoko RA, 2017, J PHYS CONF SER, V853, DOI 10.1088/1742-6596/853/1/012003 Ayers R S, 1985, WATER QUALITY AGR Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 Foroglou G., SCI TECHNOLOGY HABER S., 1999, J CRYPTOL, V3, P99 Hounsell NB, 2009, IET INTELL TRANSP SY, V3, P419, DOI 10.1049/iet-its.2009.0046 Hummen R., 2012, 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom). Proceedings, P232, DOI 10.1109/CloudCom.2012.6427523 Tian F, 2017, I C SERV SYST SERV M NR 9 TC 3 Z9 4 U1 8 U2 39 PY 2021 VL 33 IS 2 SI SI BP 601 EP 613 DI 10.18494/SAM.2021.2490 WC Instruments & Instrumentation; Materials Science, Multidisciplinary SC Instruments & Instrumentation; Materials Science UT WOS:000620350100005 DA 2022-12-14 ER PT J AU Bai, HW Zhou, GH Hu, YN Sun, AD Xu, XL Liu, XJ Lu, CH AF Bai, Hongwu Zhou, Guanghong Hu, Yinong Sun, Aidong Xu, Xinglian Liu, Xianjin Lu, Changhua TI Traceability technologies for farm animals and their products in China SO FOOD CONTROL DT Review DE Food traceability; Meat traceability; Traceability technologies; Packaging traceability; Identification ID ELECTRONIC IDENTIFICATION; IMPLEMENTING TRACEABILITY; RUMINAL BOLUSES; EUROPEAN-UNION; SUPPLY CHAIN; FOOD; SYSTEMS; BEEF; MEAT; REQUIREMENTS AB The history of traceability reveals that nomadic herders as early as 1000 BCE marked livestock with irons and ear incisions in order to protect against thefts. Nowadays, we build traceability systems to document the origin of foods, and in order to ensure safer foods when tracking and recalling products. A holistic traceability system includes, as a minimum, identification elements, databases and an information flow. The animal identification elements refers to body marks, ear tags, Radio-Frequency Identification (RFID) tags, retina image recognition, or DNA fingerprinting. The product identification refers to barcodes (EAN UCC, PLU, and GS1), 2D barcodes (QR, VC, and DM) and RFID or Electronic Product Code (EPC). The present review describes existing and upcoming traceability technologies for farm animals and their products, to update the common methods for information collection and data inquiry, with the view to expound traceability policies and regulations between developed and developing countries. The benefits of the new technologies and their practical limitations are also discussed. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Bai, Hongwu; Sun, Aidong; Liu, Xianjin] Jiangsu Acad Agr Sci, State Key Lab Breeding Base, Key Lab Food Qual & Safety Jiangsu Prov, Inst Food Safety & Monitoring Technol, Nanjing 210014, Jiangsu, Peoples R China. [Bai, Hongwu; Zhou, Guanghong; Xu, Xinglian] Nanjing Agr Univ, EDU, Lab Meat Proc & Qual Control, Nanjing 210014, Jiangsu, Peoples R China. [Hu, Yinong; Lu, Changhua] Jiangsu Acad Agr Sci, Inst Vet Med, Nanjing 210014, Jiangsu, Peoples R China. C3 Jiangsu Academy of Agricultural Sciences; Nanjing Agricultural University; Jiangsu Academy of Agricultural Sciences RP Zhou, GH (corresponding author), Nanjing Agr Univ, EDU, Lab Meat Proc & Qual Control, Nanjing 210014, Jiangsu, Peoples R China. EM guanghong.zhou@hotmail.com CR Admin, 2012, NOT GEN OFF MIN AGR Admin, 2014, SHEEP GOATS TYP COMB Admin, 2015, DAT Ahmed M, 2002, FOOD POLICY, V27, P125, DOI 10.1016/S0306-9192(02)00007-6 Ammendrup S, 2001, REV SCI TECH OIE, V20, P437, DOI 10.20506/rst.20.2.1287 Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Bai HongWu, 2013, Jiangsu Journal of Agricultural Sciences, V29, P415 Bai HongWu, 2010, Jiangsu Journal of Agricultural Sciences, V26, P446 Bai L, 2007, FOOD CONTROL, V18, P480, DOI 10.1016/j.foodcont.2005.12.005 Barcos LO, 2001, REV SCI TECH OIE, V20, P640, DOI 10.20506/rst.20.2.1294 Basarab JA, 1997, CAN J ANIM SCI, V77, P525, DOI 10.4141/A97-047 Broughton EI, 2010, FOOD POLICY, V35, P471, DOI 10.1016/j.foodpol.2010.05.007 Caja G, 1999, EAAP PUBLIC, P406 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 CCIA, 2015, CAN CATTL ID AG ENH Conill C, 2002, J ANIM SCI, V80, P919 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Elbers ARW, 2001, REV SCI TECH OIE, V20, P614, DOI 10.20506/rst.20.2.1296 Fallon M, 2001, REV SCI TECH OIE, V20, P538, DOI 10.20506/rst.20.2.1289 Fallon RJ, 2001, REV SCI TECH OIE, V20, P480, DOI 10.20506/rst.20.2.1285 Fan HP, 2009, FOOD CONTROL, V20, P627, DOI 10.1016/j.foodcont.2008.09.013 Garin D, 2003, J ANIM SCI, V81, P879 Gillmor Dan., 2006, WE MEDIA GRASSROOTS GS1, 2015, ON DIM 1D BARC US EX GS1, 2007, GS1 TRAC STAND WHAT HAC, 2015, NAT LIV ID DAIR NLID Hastein T, 2001, REV SCI TECH OIE, V20, P564, DOI 10.20506/rst.20.2.1300 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Houston R, 2001, REV SCI TECH OIE, V20, P652, DOI 10.20506/rst.20.2.1293 IFPS, 2015, PROD IFPS PLU COD US Jia CH, 2013, FOOD CONTROL, V32, P236, DOI 10.1016/j.foodcont.2012.11.042 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2010, BRIT FOOD J, V112, P187, DOI 10.1108/00070701011018860 Kerry JP, 2006, MEAT SCI, V74, P113, DOI 10.1016/j.meatsci.2006.04.024 Kong H.-I, 2004, FOOD SCI, V6, P49 Kong Q., 2009, INN COMP INF CONTR I Landais E., 2001, Revue Scientifique et Technique Office International des Epizooties, V20, P445 Lu ChangHua, 2004, Jiangsu Journal of Agricultural Sciences, V20, P259 Luo Q, 2010, INT C COMP COMP TECH Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 McGrann J, 2001, REV SCI TECH OIE, V20, P406, DOI 10.20506/rst.20.2.1283 McMahon IC, 2000, FARM IND NEWS, V33, P26 Moderator, 2010, TRAC MEAT JAP HAS RI Mousavi A., 2002, British Food Journal, V104, P7, DOI 10.1108/00070700210418703 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 NIAA, 2015, 2015 2016 NIAA RES A NIB, 2015, BEEF TRAC SYST Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Ozawa Y, 2001, REV SCI TECH OIE, V20, P605, DOI 10.20506/rst.20.2.1297 PMA, 2015, GS1 DAT Qinglin D., 2016, MEASURES ADM LIVESTO Ribo O, 2001, REV SCI TECH OIE, V20, P426 Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 Shougang R., 2010, COMP APPL SYST MOD I Sluyter FJH, 2001, REV SCI TECH OIE, V20, P500, DOI 10.20506/rst.20.2.1292 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Tonsor G. T., 2004, AUSTR LIVESTOCK IDEN Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Wang L, 2007, P CHIN ASS SCI TECHN Webb J, 2004, ADVANCES IN PORK PRODUCTION, VOL 15, P33 Wilson DW, 2001, REV SCI TECH OIE, V20, P379, DOI 10.20506/rst.20.2.1278 WTO, 2015, WTO AGR APPL SAN PHY Xinting Y., 2008, T CHINESE SOC AGR EN, V2008 Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y NR 66 TC 32 Z9 34 U1 8 U2 146 PD SEP PY 2017 VL 79 BP 35 EP 43 DI 10.1016/j.foodcont.2017.02.040 WC Food Science & Technology SC Food Science & Technology UT WOS:000403033200005 DA 2022-12-14 ER PT J AU Purwandoko, PB Seminar, KB Sutrisno Sugiyanta AF Purwandoko, Pradeka Brilyan Seminar, Kudang Boro Sutrisno Sugiyanta TI Development of a Smart Traceability System for the Rice Agroindustry Supply Chain in Indonesia SO INFORMATION DT Article DE rice; supply chain; smart system; traceability system ID FOOD AB Rice is an essential food commodity in national and food security in Indonesia with a complex supply chain network. Various risks related to food quality and food safety occurs along the supply chain. Therefore, a tool is needed to monitor the rice production process from upstream to downstream (land-to-table) by implementing a traceability system to promote food transparency. In this system, all actors must be responsible for ensuring the quality and safety of products through various handling processes carried out from cultivation to product distribution. This paper aimed to develop a smart IT (Information Technology)-based traceability system in the rice supply chain using the System Development Life Cycle (SDLC). The actors involved in the rice supply chain consist of farmers, processing industries, distributors, bulogs, and retailers. Furthermore, this paper discussed the system architecture and the development of traceability system design using a data flow diagram (DFD). The developed prototype system shows the functional requirements of the system and can be used by stakeholders to monitor the production process and assist the decision-making process. C1 [Purwandoko, Pradeka Brilyan; Seminar, Kudang Boro; Sutrisno] IPB Univ, Fac Agr Technol, Dept Mech & Biosyst Engn, Bogor 16680, West Java, Indonesia. [Sugiyanta] IPB Univ, Fac Agr, Dept Agron & Hort, Bogor 16680, West Java, Indonesia. C3 Bogor Agricultural University; Bogor Agricultural University RP Seminar, KB (corresponding author), IPB Univ, Fac Agr Technol, Dept Mech & Biosyst Engn, Bogor 16680, West Java, Indonesia. EM pradekabrilyan@gmail.com; kseminar@apps.ipb.ac.id; kensutrisno@yahoo.com; mr_sugiyanta@yahoo.co.id CR Aminah S., 2019, P NAT SEM FOOD TECHN, P171 [Anonymous], 1999, CIMAC, V17, P1 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Dwiyitno D, 2009, B MARINE FISHERIES P, V4, P99, DOI DOI 10.15578/squalen.v4i3.155 HWANG YM, 2015, INT J CONTROL AUTOM, V8, P397, DOI DOI 10.14257/IJCA.2015.8.4.36 Ijtihadie RoyyanaMuslim., 2016, SUPPLY CHAIN FORUM I, V17, P26, DOI [10.1080/16258312.2016.1143206, DOI 10.1080/16258312.2016.1143206] Isik O, 2011, INTELL SYST ACCOUNT, V18, P161, DOI 10.1002/isaf.329 Jen L.R, 2000, WORKING GROUP IEEE R Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Kassahun A, 2014, COMPUT ELECTRON AGR, V109, P179, DOI 10.1016/j.compag.2014.10.002 Khajvand M, 2011, PROCEDIA COMPUT SCI, V3, DOI 10.1016/j.procs.2010.12.011 Kresna B.A., 2017, INT J SUPPLY CHAIN M, V6, P52 Kumar V, 2017, SYSTEMS, V5, DOI 10.3390/systems5020033 Lankhorst M., 2009, ENTERPRISE ARCHITECT, V352 Magliulo L., 2013, AGR SCI, V4, P41, DOI DOI 10.4236/as.2013.45B008 Marimin M.N., 2010, BOGOR ID IPB PR Moniruzzaman M., 2016, ARXIV16060351 ozbek A., 2015, J EC SUSTAIN DEV, V6, P114 Pinto DB, 2006, FOOD RES INT, V39, P772, DOI 10.1016/j.foodres.2006.01.015 Purwandoko P.B., 2018, IOP C SERIES EARTH E, P012044 Purwandoko PB, 2019, INFORMATION, V10, DOI 10.3390/info10060218 PUSDATIN, 2016, OUTL KOM PERT SUB SE Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qiasi R, 2012, J MATH COMPUT SCI-JM, V4, P172 Rizqya E., 2017, INT J RES SCI MANAG, V4, P69, DOI [10.5281/zenodo.1066701, DOI 10.5281/ZENODO.1066701] Satzinger J.W., 2010, SYSTEMS ANAL DESIGN Seminar K., 2016, P AFITA C KOR SUNCH Sudibyo Agus, 2012, J AGRO BASED IND, V29, P43 Suismono, 2010, JURNAL PANGAN, V19, P30 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Vanany I., 2015, IPTEK J P SERIES, P4, DOI [10.12962/j23546026.y2014i1.297, DOI 10.12962/J23546026.Y2014I1.297] Yanuarti A.R., 2016, RICE COMMODITY PROFI, P1 Yude S.A., 2016, J KESEHAT ANDALAS, V5 Zhang XM, 2012, PROCEDIA ENGINEER, V29, P775, DOI 10.1016/j.proeng.2012.01.040 [No title captured] NR 35 TC 2 Z9 2 U1 2 U2 13 PD OCT PY 2019 VL 10 IS 10 AR 288 DI 10.3390/info10100288 WC Computer Science, Information Systems SC Computer Science UT WOS:000493539300034 DA 2022-12-14 ER PT J AU Dabbene, F Gay, P AF Dabbene, Fabrizio Gay, Paolo TI Food traceability systems: Performance evaluation and optimization SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Traceability; Optimization; Supply chain management; Batch dispersion; MILP ID SUPPLY CHAIN; INDUSTRY; MANAGEMENT; INFORMATION; FRAMEWORK; PRODUCTS; RECALLS; MODEL AB The aim of a traceability system is to collect in a rigorous way all the information related to the displacement of the different products along the supply chain. This information proves essential when facing food safety crisis, and allows efficiently managing the consequent product recall action. Although a recall action could be absolutely critical for a company, both in terms of incurred costs and of media impact, at present most companies do not posses reliable methods to precisely estimate the amount of product that would be discarded in the case of recall. The skill of limiting the quantity of recalled products to the minimum can be assumed as a measure of the performance and of the efficiency of the traceability system adopted by the company. Motivated by this consideration, this paper introduces novel criteria and methodologies for measuring and optimizing the performance of a traceability system. As opposed to previous introduced methods, which optimize indirect measures, the proposed approach takes into direct account the worst-case (or the average) quantity of product that should be recalled in the case of a crisis. Numerical examples concerning the mixing of batches in a sausage production process are reported to show the effectiveness of the proposed approach. (C) 2010 Elsevier B.V. All rights reserved. C1 [Gay, Paolo] Univ Turin, DEIAFA, I-10095 Grugliasco, TO, Italy. [Dabbene, Fabrizio] Politecn Torino, IEIIT CNR, I-10129 Turin, Italy. C3 University of Turin; Consiglio Nazionale delle Ricerche (CNR); Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni (IEIIT-CNR); Polytechnic University of Turin RP Gay, P (corresponding author), Univ Turin, DEIAFA, 44 Via Leonardo da Vinci, I-10095 Grugliasco, TO, Italy. EM fabrizio.dabbene@polito.it; paolo.gay@unito.it CR ACHTERBERG T, 2007, 0737 ZIB KONR ZUS ZE Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 GAY P, 2009, P 23 CIOSTA REGG CAL, P465 Jacobs RM, 1996, MICROELECTRON RELIAB, V36, P101, DOI 10.1016/0026-2714(95)00001-I Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Kumar S, 2006, TECHNOVATION, V26, P739, DOI 10.1016/j.technovation.2005.05.006 Lofberg J., 2004, P IEEE INT S COMP AI, P284 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Rabade L.A., 2006, J PURCH SUPPLY MANAG, V12, p39?50, DOI DOI 10.1016/J.PURSUP.2006.02.003 Randrup M, 2008, FOOD CONTROL, V19, P1064, DOI 10.1016/j.foodcont.2007.11.005 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 SAHIN E, 2002, P IEEE INT C SYST MA, V3, P210 Skoglund T, 2007, FOOD BIOPROD PROCESS, V85, P354, DOI 10.1205/fbp07044 Tamayo S, 2009, ENG APPL ARTIF INTEL, V22, P557, DOI 10.1016/j.engappai.2009.02.007 Thakur M, 2010, J FOOD ENG, V101, P193, DOI 10.1016/j.jfoodeng.2010.07.001 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Wang X, 2010, INT J PROD ECON, V124, P463, DOI 10.1016/j.ijpe.2009.12.009 NR 26 TC 66 Z9 76 U1 0 U2 48 PD JAN PY 2011 VL 75 IS 1 BP 139 EP 146 DI 10.1016/j.compag.2010.10.009 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000286713700017 DA 2022-12-14 ER PT J AU Chen, RY AF Chen, Rui-Yang TI An intelligent value stream-based approach to collaboration of food traceability cyber physical system by fog computing SO FOOD CONTROL DT Article DE Food traceability system; Cyber physical system; Fog computing; Value stream mapping ID SUPPLY CHAIN TRACEABILITY; ENTERPRISE INTEGRATION; TECHNOLOGIES; ARCHITECTURE; SIMULATION; FRAMEWORK; AGRICULTURE; MANAGEMENT; STANDARDS; PATTERNS AB Good advanced food traceability systems help to minimize unsafe or poor quality products in food supply chain through value-based process. From the emerging technologies forthcoming for industry automation, future advanced food traceability system must consider not only cyber physical system (CPS) and fog computing but also value-added business in food supply chain. Accordingly, this study presents a novel intelligent value stream-based food traceability cyber physical system approach integrated with enterprise architectures, EPCglobal and value stream mapping method by fog computing network for traceability collaborative efficiency. Furthermore, the proposed intelligent approach explores distributive and central traceable stream mechanism in assessing the most critical traceable events for tracking and tracing process. Successful case study, software system design and implementation demonstrated the performance of the proposed approach. Furthermore, experiment shows the better results obtained after the simulation execution for intelligent predictive algorithm. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Chen, Rui-Yang] Aletheia Univ, Dept Business Adm, 32 Chen Li St, New Taipei 25103, Taiwan. C3 Aletheia University RP Chen, RY (corresponding author), Aletheia Univ, Dept Business Adm, 32 Chen Li St, New Taipei 25103, Taiwan. EM a168.cloudy@msa.hinet.net CR Abdulmalek FA, 2007, INT J PROD ECON, V107, P223, DOI 10.1016/j.ijpe.2006.09.009 Allais I, 2007, J FOOD ENG, V83, P156, DOI 10.1016/j.jfoodeng.2007.02.016 Atzori L, 2010, COMPUT NETW, V54, P2787, DOI 10.1016/j.comnet.2010.05.010 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Barr A., 1982, HDB ARTIFICIAL INTEL, V3 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bonomi F, 2011, VEHICULAR AD HOC NET Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bottani E, 2008, INT J PROD ECON, V112, P548, DOI 10.1016/j.ijpe.2007.05.007 Brown A, 2014, J CLEAN PROD, V85, P164, DOI 10.1016/j.jclepro.2014.05.101 Carlborg P, 2013, MANAG SERV QUAL, V23, P291, DOI 10.1108/MSQ-04-2013-0052 Chen NC, 2015, COMPUT ELECTRON AGR, V111, P78, DOI 10.1016/j.compag.2014.12.009 El-Sayed AFM, 2015, AQUACULTURE, V437, P92, DOI 10.1016/j.aquaculture.2014.11.033 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Golan E.H., 2004, TRACEABILITY US FOOD Hayes R.F., 1984, J COMPUTER, V17, P263 Hines P, 1997, INT J OPER PROD MAN, V17, P46, DOI 10.1108/01443579710157989 Hong K., 2013, P 2 ACM SIGCOMM WORK, P15 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Huang YM, 2008, EXPERT SYST APPL, V34, P446, DOI 10.1016/j.eswa.2006.09.037 Jakkhupan W, 2011, J NETW COMPUT APPL, V34, P949, DOI 10.1016/j.jnca.2010.04.003 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kang YS, 2013, COMPUT IND, V64, P609, DOI 10.1016/j.compind.2013.03.004 Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Khaswala Z. N., 2001, P LEAN MAN SOL C ST, P47 Konigs SF, 2012, ADV ENG INFORM, V26, P924, DOI 10.1016/j.aei.2012.08.002 Kruize JW, 2013, COMPUT ELECTRON AGR, V96, P75, DOI 10.1016/j.compag.2013.04.017 Lai CF, 2011, COMPUT COMMUN, V34, P184, DOI 10.1016/j.comcom.2010.03.034 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Le LS, 2013, COMPUT STAND INTER, V35, P277, DOI 10.1016/j.csi.2012.01.008 Lee E. A., 2011, INTRO EMBEDDED SYSTE Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Mader P, 2012, J SYST SOFTWARE, V85, P2205, DOI 10.1016/j.jss.2011.10.023 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Mamaghani ND, 2012, TELEMAT INFORM, V29, P219, DOI 10.1016/j.tele.2011.07.001 Manzanares-Lopez P, 2011, J NETW COMPUT APPL, V34, P925, DOI 10.1016/j.jnca.2010.04.018 Mendes JM, 2012, INT J PROD RES, V50, P1650, DOI 10.1080/00207543.2011.575892 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Monden Y, 1993, TOYOTA PRODUCTION SY Nam T, 2011, J NETW COMPUT APPL, V34, P958, DOI 10.1016/j.jnca.2010.04.021 Narman P, 2013, J STRATEGIC INF SYST, V22, P70, DOI 10.1016/j.jsis.2012.10.002 Narman P, 2012, J SYST SOFTWARE, V85, P1953, DOI 10.1016/j.jss.2012.02.035 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Panetto H, 2012, ANNU REV CONTROL, V36, P284, DOI 10.1016/j.arcontrol.2012.09.009 Panunzio M, 2014, J SYST ARCHITECT, V60, P770, DOI 10.1016/j.sysarc.2014.06.001 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Munoz-Gea JP, 2010, COMPUT IND, V61, P480, DOI 10.1016/j.compind.2010.01.006 Piramuthu S, 2013, EUR J OPER RES, V225, P253, DOI 10.1016/j.ejor.2012.09.024 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Sasa A, 2011, J SYST SOFTWARE, V84, P1480, DOI 10.1016/j.jss.2011.02.043 Shi J, 2012, COMPUT IND, V63, P574, DOI 10.1016/j.compind.2012.03.006 Skoglund T, 2007, FOOD BIOPROD PROCESS, V85, P354, DOI 10.1205/fbp07044 Suprem A, 2013, COMPUT STAND INTER, V35, P355, DOI 10.1016/j.csi.2012.09.002 Teichgraber UK, 2012, EUR J RADIOL, V81, pE47, DOI 10.1016/j.ejrad.2010.12.045 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Tyagi S, 2015, INT J PROD ECON, V160, P202, DOI 10.1016/j.ijpe.2014.11.002 Wang G, 2014, INFORM SCIENCES, V282, P1, DOI 10.1016/j.ins.2014.06.021 Wolfert J, 2010, COMPUT ELECTRON AGR, V70, P389, DOI 10.1016/j.compag.2009.07.015 Yang TH, 2015, J MANUF SYST, V34, P66, DOI 10.1016/j.jmsy.2014.11.010 Zachman J. A., 2008, ZACHMAN FRAMEWORK ZACHMAN JA, 1987, IBM SYST J, V26, P276, DOI 10.1147/sj.263.0276 NR 66 TC 76 Z9 76 U1 2 U2 101 PD JAN PY 2017 VL 71 BP 124 EP 136 DI 10.1016/j.foodcont.2016.06.042 WC Food Science & Technology SC Food Science & Technology UT WOS:000384778400018 DA 2022-12-14 ER PT J AU Hu, JY Zhang, X Moga, LM Neculita, M AF Hu, Jinyou Zhang, Xu Moga, Liliana Mihaela Neculita, Mihaela TI Modeling and implementation of the vegetable supply chain traceability system SO FOOD CONTROL DT Article DE Traceability system; Supply chain; European law; Vegetable; User requirement; Information modeling; Unified Modeling Language ID INFORMATION; QUALITY; MANAGEMENT; CHINA; FARM; FOOD AB In a traceability system, a large and dynamic group of participants must be identified. The identification of information to be recorded represents the most important requirement for developing an effective traceability system. The information identified during the transport and processing of vegetables is often lost and inaccurate. In this paper, a system approach is used in order to develop a methodology for the implementation of the vegetable supply chain traceability. First of all, the main discussed issues emerge at various abstraction levels throughout the elaboration of traceability systems. Secondly, a Unified Modeling Language model is introduced for traceability along with a set of suitable patterns. A series of Unified Modeling Language class diagrams is developed in order to conceive a method for modeling the product, process, and quality information in the vegetable supply chain. Then will be discussed the adequate technological standards for setting out, registering, as well as for enabling the business collaborations. Finally, a traceability system implementation will be shown through a case study on vegetable supply chains and a comparison with European Union's General Food Law. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Moga, Liliana Mihaela; Neculita, Mihaela] Dunarea Jos Univ Galati, Fac Econ & Business Adm, Galati 800008, Romania. [Hu, Jinyou; Zhang, Xu] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China. [Moga, Liliana Mihaela] Bucharest Acad Econ Studies, Bucharest, Romania. C3 Dunarea De Jos University Galati; China Agricultural University; Bucharest University of Economic Studies RP Moga, LM (corresponding author), Dunarea Jos Univ Galati, Fac Econ & Business Adm, 47 Domneasca St, Galati 800008, Romania. EM liliana.moga@gmail.com CR [Anonymous], 2006, GS1 TRAC Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x Dannson A, 2004, STRENGTHENING FARM A Eriksson H., 2000, UML PRIMER BUSINESS Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Food Production and Management, 2009, CHIN FOOD SAF LAW *INT ORG STAND, 2007, 220052007 ISO Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Karlsen KM, 2011, FOOD CONTROL, V22, P1209, DOI 10.1016/j.foodcont.2011.01.020 Kim CH, 2003, COMPUT IND, V50, P35, DOI 10.1016/S0166-3615(02)00145-8 Li N, 2009, EXPERT SYST APPL, V36, P6500, DOI 10.1016/j.eswa.2008.07.065 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Porto SMC, 2011, BIOSYST ENG, V109, P120, DOI 10.1016/j.biosystemseng.2011.02.008 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Ren Xi, 2009, Computer Engineering and Design, V30, P3883 Resende MA, 2008, AM J AGR ECON, V90, P1091, DOI 10.1111/j.1467-8276.2008.01150.x Roussos G, 2006, COMPUTER, V39, P25, DOI 10.1109/MC.2006.88 Ruben R, 2007, SUPPLY CHAIN MANAG, V12, P60, DOI 10.1108/13598540710724365 Sorensen CG, 2010, COMPUT ELECTRON AGR, V72, P37, DOI 10.1016/j.compag.2010.02.003 Stringer R, 2009, WORLD DEV, V37, P1773, DOI 10.1016/j.worlddev.2008.08.027 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Van Dorp C., 2003, P EFITA C DEBR HUNG, P601 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X Yang X., 2008, NONGYE GONGCHENG XUE, V24, P159 Zhang J, 2011, FOOD CONTROL, V22, P1126, DOI 10.1016/j.foodcont.2011.01.019 Zhang QF, 2004, CHINA QUART, P1050, DOI 10.1017/S0305741004000748 Zhang XS, 2011, J SCI FOOD AGR, V91, P1316, DOI 10.1002/jsfa.4320 Zhang XS, 2010, FOOD CONTROL, V21, P1556, DOI 10.1016/j.foodcont.2010.03.020 Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 NR 32 TC 76 Z9 83 U1 2 U2 125 PD MAR PY 2013 VL 30 IS 1 BP 341 EP 353 DI 10.1016/j.foodcont.2012.06.037 WC Food Science & Technology SC Food Science & Technology UT WOS:000311175500051 DA 2022-12-14 ER PT J AU Freeman, KK Valencia, V Marzaroli, J van Zanten, HHE AF Freeman, Katie Kennedy Valencia, Vivian Marzaroli, Jorge van Zanten, Hannah H. E. TI Digital traceability to enhance circular food systems and reach agriculture emissions targets SO OUTLOOK ON AGRICULTURE DT Article; Early Access DE agriculture; digital; circularity; emissions reductions; Nationally Determined Contributions (NDCs); traceability; food ID FARM AB How can digital tools increase national circularity measures in agriculture towards GHG reduction and other national goals? During the 26(th) Conference of Parties (COP) held in November 2021, it was clear that circularity will play a role in meeting important international targets but that the global community and national governments lack the tools to measure the contribution of circular actions in the agriculture sector towards meeting these commitments. In the absence of monitoring and decision-support tools, governments will not know the full impact of their actions towards meeting commitments. This perspective looks at the way that digital agricultural traceability systems can form the building blocks for government action to incentivize enhanced circularity in the agriculture sector and track progress towards international targets. Among the many countries working on digital traceability systems, Uruguay stands out an example of a country pushing towards systemic traceability in multiple aspects of the food system. We examine Uruguay's use of digital traceability systems for sustainable production and redefinition of green markets as an example of a rapidly modernizing digital food system and a beacon for other countries to follow. The case of Uruguay shows that digital tools can create transparency in productive systems and allow the government to target sustainability policies. By using digital traceability systems for livestock, dairy effluents, soil rotations, agricultural chemicals, and forests Uruguay is creating a replicable framework for circularity and long-term sustainable production in the agriculture sector, one policy at a time. This framework serves as a benchmark for other countries in Latin America to reach their traceability, circularity, and emissions reductions targets. C1 [Freeman, Katie Kennedy] World Bank, Agr & Food Global Practice, 1818 H St, Washington, DC USA. [Freeman, Katie Kennedy; Valencia, Vivian; van Zanten, Hannah H. E.] Wageningen Univ & Res, Dept Plant Sci, Farming Syst Ecol Grp, Wageningen, Netherlands. [Marzaroli, Jorge] Minist Livestock Agr & Fisheries, Project Implementat Unit, Montevideo, Uruguay. [van Zanten, Hannah H. E.] Cornell Univ, Dept Global Dev, Ithaca, NY USA. C3 The World Bank; Wageningen University & Research; Cornell University RP Freeman, KK (corresponding author), 1818 H St NW, Washington, DC 20433 USA. EM kkennedy1@worldbank.org CR [Anonymous], 2021, COP26 SEES SIGNIFICA [Anonymous], 2021, TIME MAGAZINE [Anonymous], 2021, WORLD BANK SUPPORTS [Anonymous], 2022, VISUALIZADOR AVANCES Baraldo, 2020, IMPACTO POTENCIAL CO Basso B, 2021, AGR SYST, V193, DOI 10.1016/j.agsy.2021.103244 CGIAR Climate Change Agriculture and Food Security Program, AGR MIT NAT DET CONT Freeman, 2015, KNOWING YOUR STEAKS Freeman KK, 2022, J CLEAN PROD, V372, DOI 10.1016/j.jclepro.2022.133470 Gadema Z, 2011, FOOD POLICY, V36, P815, DOI 10.1016/j.foodpol.2011.08.001 Hartikainen H, 2014, J CLEAN PROD, V73, P285, DOI 10.1016/j.jclepro.2013.09.018 Herrero M, 2020, NAT FOOD, V1, P266, DOI 10.1038/s43016-020-0074-1 Hsu A, 2020, FRONT BIG DATA, V3, DOI 10.3389/fdata.2020.00029 Inwood SEE, 2019, AGRON SUSTAIN DEV, V39, DOI 10.1007/s13593-018-0549-8 Irish Food Board, 2022, BORD BIA Kazancoglu Y, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120927 Kingra P. K., 2016, Agricultural Research Journal, V53, P295, DOI 10.5958/2395-146X.2016.00058.2 Koppelmaki K, 2021, FOOD ENERGY SECUR, V10, DOI 10.1002/fes3.279 Koppelmaki K, 2021, RESOUR CONSERV RECY, V164, DOI 10.1016/j.resconrec.2020.105218 Kumar N., 2015, J ANDAMAN SCI ASS, V20, P1 MGAP, 2022, PROYECT GAN CLIM MGAP, 2020, NORM SUEL AG MVOTMA, 2015, MED PROM MVOTMA National Livestock information system (SNIG), NATL LIVESTOCK INFOR Ranta V, 2021, RESOUR CONSERV RECY, V164, DOI 10.1016/j.resconrec.2020.105155 Rose S., 2021, LIVESTOCK MANAGEMENT Sachs JD, 2019, NAT SUSTAIN, V2, P805, DOI 10.1038/s41893-019-0352-9 Schroder K., 2021, WHATS COOKING DIGITA Sharma R, 2021, J ENTERP INF MANAG, DOI 10.1108/JEIM-02-2021-0094 Shewmake S, 2015, ECOL ECON, V119, P168, DOI 10.1016/j.ecolecon.2015.08.007 Sun LC, 2020, J CLEAN PROD, V264, DOI 10.1016/j.jclepro.2020.121664 The Intergovernmental Panel on Climate Change (IPCC), 2021, FOOD SECUR UNFCCC, 2022, NAT DET CONTR COUNTR UNFCCC Workstreams, 2021, ISS REL AGR Van Zanten HHE, 2019, GLOB FOOD SECUR-AGR, V21, P18, DOI 10.1016/j.gfs.2019.06.003 Vyas S, 2022, GLOB FOOD SECUR-AGR, V32, DOI 10.1016/j.gfs.2022.100612 Westerlund M, 2021, TECHNOL INNOV MANAG, V11, P6, DOI 10.22215/timreview/1446 Wiese L, 2021, CLIM POLICY, V21, P1005, DOI 10.1080/14693062.2021.1969883 World Bank, 2022, SUSTAINABLE MANAGEME NR 39 TC 0 Z9 0 U1 5 U2 5 DI 10.1177/00307270221133854 EA NOV 2022 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000877817500001 DA 2022-12-14 ER PT J AU Feng, JY Fu, ZT Wang, ZQ Xu, M Zhang, XS AF Feng, Jianying Fu, Zetian Wang, Zaiqiong Xu, Mark Zhang, Xiaoshuan TI Development and evaluation on a RFID-based traceability system for cattle/beef quality safety in China SO FOOD CONTROL DT Article DE Traceability system; RFID; System evaluation; Cattle/beef supply chain; PDA ID CONSUMERS; FOOD; INFORMATION; FRAMEWORK; PRODUCTS; FARM AB Beef has become a kind of important food in China because of its nutritional value perceived by consumers. With increasing consumes' awareness and governments' regulations on beef quality and safety, traceability is becoming a mandatory requirement in cattle/beef industry. This paper developed and evaluated a cattle/beef traceability system that integrated RFID technology with PDA and barcode printer. The system requirements, the business flow of the cattle/beef chain, and the key traceability information for the system were identified through a survey. Then a conceptual model was proposed to describe the process of traceability information acquisition, transformation and transmission along the supply chain. Finally, the system was evaluated and optimized in the sampled supply chain. The results show that the major benefits gained from the RFID-enabled traceability system are the real-time and accurate data acquisition and transmission, and the high efficiency of information tracking and tracing across the cattle/beef supply chain; the main barriers for implementing the system are the inapplicable method of inputting information, the inefficient sequence of data input and communication mechanism associated with RFID reader, and the high implementation cost. (c) 2012 Elsevier Ltd. All rights reserved. C1 [Feng, Jianying; Fu, Zetian] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China. [Wang, Zaiqiong; Zhang, Xiaoshuan] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. [Xu, Mark] Univ Portsmouth, Portsmouth Business Sch, Portsmouth PO1 3DE, Hants, England. C3 China Agricultural University; China Agricultural University; University of Portsmouth RP Zhang, XS (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Berg L, 2004, APPETITE, V42, P21, DOI 10.1016/S0195-6663(03)00112-0 Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Coskun E, 2005, J SYST SOFTWARE, V78, P128, DOI 10.1016/j.jss.2005.01.009 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Golan E.H., 2004, AGR EC REPORTS, P1362 Hillerton JE, 1998, J DAIRY SCI, V81, P3042, DOI 10.3168/jds.S0022-0302(98)75869-2 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Kang RuiJuan, 2010, Transactions of the Chinese Society of Agricultural Engineering, V26, P227 Li Min-bo, 2010, Computer Integrated Manufacturing Systems, V16, P202 Lu G. M., 2010, COMPUTER APPL SOFTWA, V27, P20 Lu Gong-ming, 2009, Computer Engineering and Design, V30, P3657 Maldini M, 2006, AQUACULTURE, V261, P487, DOI 10.1016/j.aquaculture.2006.07.010 MOA Ministry of Agriculture of the People's Republic of China, 2006, REG ADM LIV POULTR I National Bureau of Statistics of China (NBSC), 2006, COD ADM REG PEOPL RE NBSC, 2011, CHIN STAT YB Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shackell GH, 2008, INT J FOOD SCI TECH, V43, P2134, DOI 10.1111/j.1365-2621.2008.01812.x Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Shen GuangLei, 2007, Transactions of the Chinese Society of Agricultural Engineering, V23, P170 Shi L., 2010, COMPUTER APPL SOFTWA, V27, P40 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 UDEN L, 1995, COMPUT INTEGR MANUF, V8, P83, DOI 10.1016/0951-5240(95)00002-B Van Wezemael L, 2011, FOOD CONTROL, V22, P1776, DOI 10.1016/j.foodcont.2011.04.017 Van Wezemael L, 2010, FOOD CONTROL, V21, P835, DOI 10.1016/j.foodcont.2009.11.010 Verbeke W, 2010, APPETITE, V54, P289, DOI 10.1016/j.appet.2009.11.013 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Xiao Y, 2007, WIREL COMMUN MOB COM, V7, P457, DOI 10.1002/wcm.365 Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X Yang Y, 2005, CODE INFORM SYSTEMS, V2, P6 Zan LinSen, 2006, Scientia Agricultura Sinica, V39, P2083 Zhang XS, 2011, J SCI FOOD AGR, V91, P1316, DOI 10.1002/jsfa.4320 Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 Zhao R., 2012, WORLD AGR, V3, P1 NR 39 TC 75 Z9 78 U1 2 U2 170 PD JUN PY 2013 VL 31 IS 2 BP 314 EP 325 DI 10.1016/j.foodcont.2012.10.016 WC Food Science & Technology SC Food Science & Technology UT WOS:000316304600009 DA 2022-12-14 ER PT J AU Thakur, M Hurburgh, CR AF Thakur, Maitri Hurburgh, Charles R. TI Framework for implementing traceability system in the bulk grain supply chain SO JOURNAL OF FOOD ENGINEERING DT Article DE Supply chain traceability; Internal traceability; Bulk grain; Information exchange; Framework ID INFORMATION AB Implementation of a traceability system in the bulk grain supply chain is a complex task. Grain lots are often commingled to meet buyer specifications and the lot-identity is not maintained. In this paper, a systems approach is used to develop methods for implementing bulk grain supply chain traceability in the United States, that includes both internal and chain traceability. First, the usage requirements of a traceability system are defined for all the actors in the supply chain. Second. a model is developed for implementing internal traceability system for a grain elevator that handles specialty grain. Then, we develop a model for information exchange between the supply chain actors. The model shows what grain lot information must be recorded and then passed on to the next actor. A sequence diagram is developed to show the information exchange in the grain supply chain when a user requests additional information about a suspect product. Finally, we discuss some suitable technologies to enable this information exchange. A few sample XML documents are shown for the transfer and sharing of information in the grain supply chain. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Thakur, Maitri; Hurburgh, Charles R.] Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. [Hurburgh, Charles R.] Iowa State Univ, Dept Food Sci & Human Nutr, Ames, IA 50011 USA. C3 Iowa State University; Iowa State University RP Thakur, M (corresponding author), Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. EM maitri@iastate.edu CR ANDERSON T, 2004, INTRO XML [Anonymous], 2002, OFFICIAL J EUROPEAN Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bell D, 2004, UMLS SEQUENCE DIAGRA Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x *DEP DEF, 2001, SYST ENG FUND SUPPL Dorador JM, 2000, INT J COMP INTEG M, V13, P430, DOI 10.1080/09511920050117928 ERIKSSON H, 2000, UML PRIMER BUSINESS, P17 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Golan E., 2004, Amber Waves, V2, P14 *IDEF, 1993, INT DEF METH *INT ORG STAND, 2007, 220052007 ISO Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 LAUX CM, 2007, THESIS IOWA STATE U Lee J, 1999, IEEE SOFTWARE, V16, P92, DOI 10.1109/52.776956 Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 MILLER R, 2003, PRACTICAL UML HANDS NARDI MG, 2007, ESRI PROFESSIONAL PA Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 *REUT, 2008, N AM TOM IND REEL GR *TRACEFOOD, 2009, TRACEFOOD WIK *TRACEFOOD, 2007, TRACECORE XML STAND van Dorp K.-J., 2002, Logistics Information Management, V15, P24, DOI 10.1108/09576050210412648 2008, ELECT DATA INTERCHAN NR 26 TC 119 Z9 136 U1 2 U2 51 PD DEC PY 2009 VL 95 IS 4 BP 617 EP 626 DI 10.1016/j.jfoodeng.2009.06.028 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000270252200011 DA 2022-12-14 ER PT J AU Kane, JS Potts, PJ AF Kane, JS Potts, PJ TI Traceability in geochemical analysis SO GEOSTANDARDS NEWSLETTER-THE JOURNAL OF GEOSTANDARDS AND GEOANALYSIS DT Article DE traceability; geochemical analysis; review; standard methods; definitive methods; reference methods ID DILUTION MASS-SPECTROMETRY; CHEMISTRY AB A review is presented of "traceability" and the way this concept can be demonstrated in the analysis of geochemical samples. If is suggested that the two practical methods are (i) by the analysis of appropriate certified reference materials with an evaluation of the agreement between analysed and certified values, and (ii) by the appropriate use of definitive, reference and standard methods of analysis. The differences between these two methods of demonstrating traceability ore considered and the difficulties in applying them are discussed. C1 Robert J Kane Associates Inc, Brightwood, VA 22715 USA. Open Univ, Dept Earth Sci, Milton Keynes MK7 6AA, Bucks, England. C3 Open University - UK RP Kane, JS (corresponding author), Robert J Kane Associates Inc, HCR 4,Box 231, Brightwood, VA 22715 USA. CR Alexandrov YI, 1996, ANALYST, V121, P1137, DOI 10.1039/an9962101137 BARNES IL, 1973, ANAL CHEM, V45, P880, DOI 10.1021/ac60328a005 BEARY ES, 1993, ANAL CHEM, V65, P1602, DOI 10.1021/ac00059a020 BEARY ES, 1987, ANALYST, V112, P441, DOI 10.1039/an9871200441 EPSTEIN MS, 1991, SPECTROCHIM ACTA B, V46, P1583, DOI 10.1016/0584-8547(91)80162-V FASSETT JD, 1989, ANAL CHEM, V61, pA643, DOI 10.1021/ac00185a715 Heumann KG, 1988, INORGANIC MASS SPECT, P301 HORWITZ W, 1992, J AOAC INT, V75, P368 JOCHUM KP, 1981, FRESEN Z ANAL CHEM, V309, P285, DOI 10.1007/BF00488603 Kane JS, 1997, ANALYST, V122, P1283, DOI 10.1039/a704789d Kane JS, 1999, GEOSTANDARD NEWSLETT, V23, P209, DOI 10.1111/j.1751-908X.1999.tb00575.x King B, 1997, ANALYST, V122, P197, DOI 10.1039/a606557k Moody JR, 1996, J RES NATL INST STAN, V101, P155, DOI 10.6028/jres.101.017 Potts PJ, 1997, GEOSTANDARD NEWSLETT, V21, P157, DOI 10.1111/j.1751-908X.1997.tb00934.x Thomas M, 2000, VIB SPECTROSC, V24, P137, DOI 10.1016/S0924-2031(00)00086-2 Thompson M, 1997, ANALYST, V122, P1249, DOI 10.1039/a705095j Thompson M, 1997, ANALYST, V122, P1201, DOI 10.1039/a704906d Thompson M, 1996, ANALYST, V121, P285, DOI 10.1039/an9962100285 VETTER TW, 1995, ANALYST, V120, P2025, DOI 10.1039/an9952002025 WORSWICK RD, 1995, ANAL PROC, V32, P285, DOI 10.1039/ai9953200285 [No title captured] [No title captured] [No title captured] NR 23 TC 10 Z9 13 U1 1 U2 8 PY 2002 VL 26 IS 2 BP 171 EP 180 DI 10.1111/j.1751-908X.2002.tb00885.x WC Geosciences, Multidisciplinary SC Geology UT WOS:000178963900004 DA 2022-12-14 ER PT J AU Rincon, DL Ramirez, JEF Castro, JAO AF Rincon B, Dora Lucia Fonseca Ramirez, Johan Esteban Orjuela Castro, Javier Arturo TI Towards a Common Reference Framework for Traceability in the Food Supply Chain SO INGENIERIA DT Article DE Traceability System; Food Safety; Meat; Legislation ID PRODUCT TRACEABILITY; SYSTEM; SAFETY; INFORMATION; PERSPECTIVES; AGRICULTURE; TECHNOLOGY; MANAGEMENT; ORIGIN; PART AB Background: The absence of a common conceptual framework on traceability in the food supply chain (SCF), prevents a cohesive development of this concept. The absence has generated confusion and has made it impossible to demonstrate the social and commercial advantages of its implementation. In addition, not having a common framework in countries such as Colombia obstructs the development of public policies. Method: A systematic review of the literature was carried out in four stages: search protocol to consult articles in the databases Scopus, Science Direct and ISI Web; review and selection of relevant articles; extraction and incorporation of data into tables and formats designed for this purpose and elaboration of the conceptual framework. Results: A common conceptual framework is proposed for the design and implementation of a traceability system in the SCF covering the following aspects: definition of traceability, characteristics and properties, schemes, traceable resource unit, motivators and recording systems. The international and national legislation is evaluated and aspects for its incorporation are established. The proposed conceptual framework is exemplified by the meat supply chain to guide the implementation of traceability systems in CSA in Colombia. Conclusions: The conceptual framework for SCF traceability can be a guide for the implementation and development of food chains in the Colombian context. Implementing this in agricultural chains would allow the differentiation of origin, which can be a competitive factor for producers with good agricultural practices, as well as provide effective logistic capacities for all agents of the SCF. The effect of its implementation should be evaluated with special emphasis on the impact on brand positioning and the establishment of fair prices as an effect of tracking and tracing the traceability system. C1 [Rincon B, Dora Lucia] Univ Salamanca, Salamanca, Spain. [Fonseca Ramirez, Johan Esteban] Univ Dist Francisco Jose de Caldas, Bogota, Colombia. [Orjuela Castro, Javier Arturo] Univ Dist Francisco Jose de Caldas, Ingn Prod, Bogota, Colombia. C3 University of Salamanca; Universidad Distrital Francisco Jose de Caldas; Universidad Distrital Francisco Jose de Caldas RP Rincon, DL (corresponding author), Univ Salamanca, Salamanca, Spain. EM doralucia@usa.es; fonseca_johan@hotmail.com; jorjuela@udistrital.edu.co CR Abd Rahman A., 2016, FOOD CONTROL Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 Aguinis H, 2012, J MANAGE, V38, P932, DOI 10.1177/0149206311436079 Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Tanco JAA, 2007, UNIVERSIA BUS REV, P54 AO, 2003, FAOS STRAT FOOD CHAI Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bagshaw L. S. N., 2000, PREFACE 11 OPENING A Banco-Mundial, 2014, IND DES LOG Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bellon-Maurel V, 2014, J CLEAN PROD, V69, P60, DOI 10.1016/j.jclepro.2014.01.079 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Borit M, 2016, WOODHEAD PUBL FOOD S, V301, P225, DOI 10.1016/B978-0-08-100310-7.00012-0 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chen K., 2009, GLOBAL PERSPECTIVE C Chilton J., 2004, MEAT POULTRY DEC, P48 Consejo Nacional de politica Economica y Social de Colombia, 2005, 3376 CONPE Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Crandall PG, 2013, MEAT SCI, V95, P137, DOI 10.1016/j.meatsci.2013.04.022 Dabbene F., 2014, BIOSYST ENG, P1 De Marco A, 2012, INT J PROD ECON, V135, P333, DOI 10.1016/j.ijpe.2011.08.009 du Plessis HJ, 2012, FOOD RES INT, V47, P210, DOI 10.1016/j.foodres.2011.05.029 Engelseth P, 2009, J BUS IND MARK, V24, P421, DOI 10.1108/08858620910966291 Evizal AK, 2016, WOODHEAD PUBL FOOD S, V301, P191, DOI 10.1016/B978-0-08-100310-7.00010-7 FAO-OMS, 2014, AL COM COD Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Giacomini M. C., 2002, 17 S INT FARM SYST A Golan E., 2014, TRACEABILITY US FOOD, P163 Herrera M. M., 2014, INGENIERIA, V19 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2016, WOODHEAD PUBL FOOD S, V301, P321, DOI 10.1016/B978-0-08-100310-7.00017-X Hsiao HI, 2016, FOOD CONTROL, V64, P181, DOI 10.1016/j.foodcont.2015.12.020 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Jin S., 2014, FOOD QUALITY PREFERE Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2010, BRIT FOOD J, V112, P187, DOI 10.1108/00070701011018860 Kelepouris T., 2007, IND MANAGEMENT DATA Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 KIM HM, 1995, P 4 WORKSH EN TECHN Kitchenham B, 2004, PROCEDURES PERFORMIN, V33 Landt J., 2005, IEEE POTENTIALS Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Mamone G, 2009, J CHROMATOGR A, V1216, P7130, DOI 10.1016/j.chroma.2009.07.052 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 Marchante A. P., 2014, J FOOD ENG, P50 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 McMeekin TA, 2006, INT J FOOD MICROBIOL, V112, P181, DOI 10.1016/j.ijfoodmicro.2006.04.048 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Olsen P., 2013, TRENDS FOOD RSCIENCE, P142 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Opara L. U., 2003, SCI TECHNOLOGY, P101 Rahadian Y., 2012, PROCEDIA SOCIAL BEHA Regattieri a., 2007, J FOOD ENG Rodriguez-Ramirez R, 2011, ANAL CHIM ACTA, V685, P120, DOI 10.1016/j.aca.2010.11.021 Ruviaro CF, 2014, LAND USE POLICY, V38, P104, DOI 10.1016/j.landusepol.2013.08.019 Sabbaghi A., 2008, J THEORETICAL APPL E Sahin E., 2002, SYSTEMS MAN CYBERNET, V3, P3 Saltini R, 2013, FOOD CONTROL, V29, P167, DOI 10.1016/j.foodcont.2012.05.054 Sarac A., 2010, INT J PRODUCTION EC Sarig Y., 2003, AGR ENG INT CIGR J Scholten H, 2016, WOODHEAD PUBL FOOD S, V301, P9, DOI 10.1016/B978-0-08-100310-7.00002-8 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Smith GC, 2008, MEAT SCI, V80, P66, DOI 10.1016/j.meatsci.2008.05.024 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Suprem A, 2013, COMPUT STAND INTER, V35, P355, DOI 10.1016/j.csi.2012.09.002 Thakur M., 2010, J FOOD ENG Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Zach L, 2012, FOOD CONTROL, V27, P153, DOI 10.1016/j.foodcont.2012.03.013 Zhang X., 2011, INT J OPERATIONS PRO Zhang XS, 2010, FOOD CONTROL, V21, P1556, DOI 10.1016/j.foodcont.2010.03.020 Zhu X., 2012, J ENG TECHNOLOGY MAN NR 79 TC 4 Z9 10 U1 1 U2 23 PD MAY-AUG PY 2017 VL 22 IS 2 BP 161 EP 189 DI 10.14483/udistrital.jour.reving.2017.2.a01 WC Engineering, Multidisciplinary SC Engineering UT WOS:000448061400002 DA 2022-12-14 ER PT J AU Liu, P Li, Q Liu, W Yuan, SS Nian, YY Dai, Y Duan, M AF Liu Peng Li Qiang Liu Wen Yuan Shanshan Nian Yiying Dai Yue Duan Min TI An Analysis on E-Evaluation of Food Quality Traceability System SO INTERNATIONAL JOURNAL OF E-COLLABORATION DT Article DE Evaluation; Food Quality; Grading; Traceability System AB In recent years, China has obtained positive achievements in the construction of traceability systems for key products, such as edible agricultural products and food. However, problems such as complex situations, one-sided information, repeated system construction, and lack of qualification of information testing agencies still exist in food quality traceability. Based on the development features of the industry, this paper puts forward countermeasures and suggestions for the construction of a food quality traceability system. C1 [Liu Peng; Li Qiang; Liu Wen; Yuan Shanshan; Nian Yiying; Dai Yue; Duan Min] China Natl Inst Standardizat, Beijing, Peoples R China. C3 China National Institute of Standardization RP Liu, W (corresponding author), China Natl Inst Standardizat, Beijing, Peoples R China. CR Aday S, 2020, FOOD QUAL SAF-OXFORD, V4, P167, DOI 10.1093/fqsafe/fyaa024 Ahmad M, 2019, ADV SCI TECHNOL-RES, V13, P88, DOI 10.12913/22998624/103425 Guo W., 2017, SCI TECHNOLOGY MANAG, V37, P81, DOI [10.3969/j.issn.1000-7695.2017.10.012, DOI 10.3969/J.ISSN.1000-7695.2017.10.012] Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 Karlsen KM, 2016, WOODHEAD PUBL FOOD S, V301, P35, DOI 10.1016/B978-0-08-100310-7.00003-X Lethbridge T., 2018, TWISTED FOOD Li M., 2018, THESIS HEBEI U EC BU Li Z., 2019, FOOD SAFETY GUIDE, P179, DOI [10.16043/j.cnki.cfs.2019.18.124, DOI 10.16043/J.CNKI.CFS.2019.18.124] Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Qian JianPing, 2014, Transactions of the Chinese Society of Agricultural Engineering, V30, P98 Robson K, 2020, FOOD CONTROL, V116, DOI 10.1016/j.foodcont.2020.107310 Sun D, 2014, LOGISTICS TECHNOLOGY, V33, P342, DOI [10.3969/j.issn.1005-152X.2014.04.111, DOI 10.3969/J.ISSN.1005-152X.2014.04.111] Tao D., 2021, AGR TECHNOLOGY AGR T, V20, P166, DOI [10.19754/j.nyyjs.20211030040, DOI 10.19754/J.NYYJS.20211030040] You SB, 2014, SHS WEB CONF, V6, DOI 10.1051/shsconf/20140603013 Zhang L, 2021, ELECTRON WORLD, P162, DOI [10.19353/j.cnki.dzsj.2021.14.063, DOI 10.19353/J.CNKI.DZSJ.2021.14.063] Zhang Yong-cha, 2008, Journal of Shanghai Jiaotong University, V42, P874 Zhong D., 2021, ZHONGGUO NONGXUE TON, V2021 NR 18 TC 0 Z9 0 U1 0 U2 0 PY 2022 VL 18 IS 3 DI 10.4018/IJeC.307127 WC Computer Science, Information Systems SC Computer Science UT WOS:000886239800010 DA 2022-12-14 ER PT J AU Qian, JP Fan, BL Wu, XM Han, S Liu, SC Yang, XT AF Qian, Jianping Fan, Beilei Wu, Xiaoming Han, Shuai Liu, Shouchun Yang, Xinting TI Cbmprehensive and quantifiable granularity: A novel model to measure agro-food traceability SO FOOD CONTROL DT Article DE Traceability; Food traceability; Granularity; Model; Index system ID SUPPLY-SYSTEM TRACEABILITY; DECISION-SUPPORT-SYSTEM; FOOD TRACEABILITY; ACQUISITION-SYSTEM; CHAIN; MANAGEMENT; FRAMEWORK; PRODUCTS; PART; AHP AB Recent developments in the legal establishment and the market have motivated more agro-food companies to implement traceability systems (TS). TS play an important role not only for planning system implementation before development, but also for analyzing system performance after using the system. A novel agro-food TS model is presented here, based on comprehensive and quantifiable granularity concepts. A 2-layer index system was established; the first layer was mainly factors such as precision, breadth, and depth, and the second layer included seven indicator sub-factors: external trace units, internal flow units, IU conversion, information collection content, information update frequency, forward tracking distance, and backward tracing distance. An indicator's overall score was scaled with five contributing scores that graded the assignment method. Indicator weight was confirmed with the AHP method. The weight values of the seven indicators were 0.1985, 0.1141, 0.0872, 0.1870, 0.1248, 0.1442, and 0.1442, respectively. A weighted sum model was adopted to calculate the evaluation value. A high evaluation value indicated high granularity. The granularity model was applied in two enterprises, here identified as WPF and WFPE, which were located at different stages in wheat-flour supply chain. The survey results showed that WFPE should invest more in tracing equivalent granularity than WPF should because it involves multi-stage processing, a complicated supply chain structure, it is a large enterprise, and operates in a strict regulatory environment. Furthermore, WFPE was motivated to implement a high granularity level because of benefits in supply chain management, market and customer response, and recall and risk management. In the future, an updated granularity evaluation model that could combine enterprise characteristics and uncover hidden costs and benefits will be studied further. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Qian, Jianping; Fan, Beilei; Wu, Xiaoming; Han, Shuai; Liu, Shouchun; Yang, Xinting] Natl Engn Res Ctr Informat Technol Agr, Bldg A,11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China. [Qian, Jianping; Fan, Beilei; Wu, Xiaoming; Han, Shuai; Liu, Shouchun; Yang, Xinting] Minist Agr, Key Lab Informat Technol Agr, Beijing 100097, Peoples R China. C3 Beijing Academy of Agriculture & Forestry; Ministry of Agriculture & Rural Affairs RP Yang, XT (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Bldg A,11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China. EM xintingyang@nercita.org.cn CR Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bernard A, 2002, ENVIRON RES, V88, P1, DOI 10.1006/enrs.2001.4274 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bouzon M, 2016, RESOUR CONSERV RECY, V108, P182, DOI 10.1016/j.resconrec.2015.05.021 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Pierini GD, 2016, MICROCHEM J, V128, P62, DOI 10.1016/j.microc.2016.04.015 Deng XY, 2014, EXPERT SYST APPL, V41, P156, DOI 10.1016/j.eswa.2013.07.018 Dweiri F, 2016, EXPERT SYST APPL, V62, P273, DOI 10.1016/j.eswa.2016.06.030 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Golan E., 2004, TRACE ABILITY US FOO Golan E., 2004, AGR EC REPORT, V830, P1 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Luvisi A, 2012, BIOSYST ENG, V113, P129, DOI 10.1016/j.biosystemseng.2012.06.015 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 McEntire J. C., 2010, COMPREHENSIVE REV FO, V1, P92 Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Ozgen D, 2014, INFORM SCIENCES, V268, P185, DOI 10.1016/j.ins.2014.01.024 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Porto SMC, 2011, BIOSYST ENG, V109, P120, DOI 10.1016/j.biosystemseng.2011.02.008 Qian J.-P., 2013, J FOOD AGRIC ENVIRON, V11, P317 Qian JP, 2015, COMPUT ELECTRON AGR, V116, P101, DOI 10.1016/j.compag.2015.06.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Reyes JF, 2012, COMPUT ELECTRON AGR, V84, P62, DOI 10.1016/j.compag.2012.02.018 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Skoglund T, 2007, FOOD BIOPROD PROCESS, V85, P354, DOI 10.1205/fbp07044 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Van der Spiegel M, 2013, TRENDS FOOD SCI TECH, V34, P137, DOI 10.1016/j.tifs.2013.10.001 Wales C, 2006, APPETITE, V47, P187, DOI 10.1016/j.appet.2006.05.007 Wang D, 2016, SOFT COMPUT, V20, P2119, DOI 10.1007/s00500-015-1904-1 Wang X, 2010, INT J PROD ECON, V124, P463, DOI 10.1016/j.ijpe.2009.12.009 Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 Yu FS, 2009, APPL SOFT COMPUT, V9, P264, DOI 10.1016/j.asoc.2007.10.026 Zadeh LA, 2002, STUD FUZZ SOFT COMP, V95, P3 Zhang C., 2012, ABSTR APPL ANAL, V2012, P1, DOI DOI 10.1155/2012/360631 NR 49 TC 12 Z9 16 U1 0 U2 47 PD APR PY 2017 VL 74 BP 98 EP 106 DI 10.1016/j.foodcont.2016.11.034 WC Food Science & Technology SC Food Science & Technology UT WOS:000392791000012 DA 2022-12-14 ER PT J AU Moretti, R Criscione, A Turri, F Bordonaro, S Marletta, D Castiglioni, B Chessa, S AF Moretti, Riccardo Criscione, Andrea Turri, Federica Bordonaro, Salvatore Marletta, Donata Castiglioni, Bianca Chessa, Stefania TI A 20-SNP Panel as a Tool for Genetic Authentication and Traceability of Pig Breeds SO ANIMALS DT Article DE pig breeds; traceability; molecular markers; SNP ID SINGLE-NUCLEOTIDE POLYMORPHISMS; IDENTIFICATION; SNPS; EVOLUTIONARY; ASSIGNMENT; SELECTION; PRODUCTS; MARKERS; CATTLE AB Simple Summary Given the high economic and qualitative values of local-breed meat products, it is not uncommon that substitution or mislabeling (either fraudulent or accidental) occurs at the market level. Therefore, to protect the interests of both producers and consumers, a reliable traceability tool should be developed. Nowadays, traceability usually relies on physical labeling systems (e.g., ear tags, tattoos, or electronic transponders). These systems do not, however, have good performances when dealing with carcasses or processed meat products. Molecular markers (i.e., based on the DNA sequence) can be a solution, since DNA is easily extracted from a wide variety of animal products and parts, and is not degraded during processing, even at the high temperatures involved. The aim of this study was to identify a small number of DNA mutations for breed-traceability purposes, in particular of the Italian Nero Siciliano pig and its derived products. A small panel of 12 DNA mutations was enough to discriminate Nero Siciliano pigs from other pig breeds and from wild boars. Food authentication in local breeds has important implications from both an economic and a qualitative point of view. Meat products from autochthonous breeds are of premium value, but can easily incur fraudulent or accidental substitution or mislabeling. The aim of this study was to identify a small number of SNPs using the Illumina PorcineSNP60 BeadChip for breed traceability, in particular of the Italian Nero Siciliano pig and its derived products. A panel of 12 SNPs was sufficient to discriminate Nero Siciliano pig from cosmopolitan breeds and wild boars. After adding 8 SNPs, the final panel of 20 SNPs allowed us to discriminate all the breeds involved in the study, to correctly assign each individual to its breed, and, moreover, to discriminate Nero Siciliano from first-generation hybrids. Almost all livestock breeds are being genotyped with medium- or high-density SNP panels, providing a large amount of information for many applications. Here, we proposed a method to select a reduced SNP panel to be used for the traceability of pig breeds. C1 [Moretti, Riccardo; Chessa, Stefania] Univ Turin, Dept Vet Sci, I-10095 Turin, Italy. [Criscione, Andrea; Bordonaro, Salvatore; Marletta, Donata] Univ Catania, Dept Agr Food & Environm, I-95131 Catania, Italy. [Turri, Federica; Castiglioni, Bianca] CNR, Inst Agr Biol & Biotechnol, I-26900 Lodi, Italy. C3 University of Turin; University of Catania; Consiglio Nazionale delle Ricerche (CNR) RP Chessa, S (corresponding author), Univ Turin, Dept Vet Sci, I-10095 Turin, Italy. EM riccardo.moretti@unito.it; a.criscione@unict.it; turri@ibba.cnr.it; s.bordonaro@unict.it; donata.marletta@unict.it; castiglioni@ibba.cnr.it; stefania.chessa@unito.it CR Alves E, 2009, ANIMAL, V3, P1216, DOI 10.1017/S1751731109004819 Ammendrup S, 2001, REV SCI TECH OIE, V20, P437, DOI 10.20506/rst.20.2.1287 BOWCOCK AM, 1994, NATURE, V368, P455, DOI 10.1038/368455a0 Chessa S., 2013, ITAL J ANIM SCI, V12, P68 Chessa S., 2011, ITAL J ANIM SCI, V10, P131 Dimauro C, 2013, ANIM GENET, V44, P377, DOI 10.1111/age.12021 Fontanesi L, 2009, ITAL J ANIM SCI, V8, P9, DOI 10.4081/ijas.2009.s2.9 Friendly M., 2015, CANDISC VISUALIZING Gama LT, 2013, GENET SEL EVOL, V45, DOI 10.1186/1297-9686-45-18 Geibel J, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0245178 GenABEL Project Developers, 2013, GENOME WIDE SNP ASS Goedbloed DJ, 2013, MOL ECOL, V22, P856, DOI 10.1111/j.1365-294X.2012.05670.x Guastella AM, 2010, GENET MOL BIOL, V33, P650, DOI 10.1590/S1415-47572010005000075 Gvozdanovic K, 2020, FOOD CONTROL, V109, DOI 10.1016/j.foodcont.2019.106917 Kim S, 2007, ANNU REV BIOMED ENG, V9, P289, DOI 10.1146/annurev.bioeng.9.060906.152037 Lenstra J. A., 2003, Food authenticity and traceability, P34, DOI 10.1533/9781855737181.1.34 Munoz M, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-49830-6 Munoz M, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0207475 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Nielsen EE, 2006, MOL ECOL NOTES, V6, P971, DOI 10.1111/j.1471-8286.2006.01433.x Oh JD, 2014, ASIAN AUSTRAL J ANIM, V27, P926, DOI 10.5713/ajas.2013.13829 Ojeda A, 2011, HEREDITY, V106, P330, DOI 10.1038/hdy.2010.61 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Paschou P, 2007, PLOS GENET, V3, P1672, DOI 10.1371/journal.pgen.0030160 Pizzi F., 2013, ITAL J ANIM SCI, V12, P83 Purcell S, 2007, AM J HUM GENET, V81, P559, DOI 10.1086/519795 R Core Team, 2014, R LANG ENV STAT COMP Ramos AM, 2011, ANIM GENET, V42, P613, DOI 10.1111/j.1365-2052.2011.02198.x Ramos AM, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006524 Ripoli M.V, 2013, BAG, J. basic appl. genet., V24, P31 Schiavo G, 2020, LIVEST SCI, V236, DOI 10.1016/j.livsci.2020.104015 Shriver MD, 1997, AM J HUM GENET, V60, P957 Venables W.N., 2002, MODERN APPL STAT S, Vfourth, DOI DOI 10.1198/TECH.2003.S33 Wiggans GR, 2009, J DAIRY SCI, V92, P3431, DOI 10.3168/jds.2008-1758 Wilkinson S, 2012, BMC GENOMICS, V13, DOI 10.1186/1471-2164-13-580 Wilkinson S, 2011, BMC GENET, V12, DOI 10.1186/1471-2156-12-45 NR 36 TC 1 Z9 1 U1 1 U2 1 PD JUN PY 2022 VL 12 IS 11 AR 1335 DI 10.3390/ani12111335 WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology SC Agriculture; Veterinary Sciences; Zoology UT WOS:000808681700001 DA 2022-12-14 ER PT J AU Hao, ZH Mao, DH Zhang, B Zuo, M Zhao, ZH AF Hao, Zhihao Mao, Dianhui Zhang, Bob Zuo, Min Zhao, Zhihua TI A Novel Visual Analysis Method of Food Safety Risk Traceability Based on Blockchain SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH DT Article DE blockchain; visualization; risk; traceability; food safety ID QUALITY; SYSTEM AB Current food traceability systems have a number of problems, such as data being easily tampered with and a lack of effective methods to intuitively analyze the causes of risks. Therefore, a novel method has been proposed that combines blockchain technology with visualization technology, which uses Hyperledger to build an information storage platform. Features such as distribution and tamper-resistance can guarantee the authenticity and validity of data. A data structure model is designed to implement the data storage of the blockchain. The food safety risks of unqualified detection data can be quantitatively analyzed, and a food safety risk assessment model is established according to failure rate and qualification deviation. Risk analysis used visual techniques, such as heat maps, to show the areas where unqualified products appeared, with a migration map and a force-directed graph used to trace these products. Moreover, the food sampling data were used as the experimental data set to test the validity of the method. Instead of difficult-to-understand and highly specialized food data sets, such as elements in food, food sampling data for the entire year of 2016 was used to analyze the risks of food incidents. A case study using aquatic products as an example was explored, where the results showed the risks intuitively. Furthermore, by analyzing the reasons and traceability processes effectively, it can be proven that the proposed method provides a basis to formulate a regulatory strategy for regions with risks. C1 [Hao, Zhihao; Mao, Dianhui; Zuo, Min] Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China. [Hao, Zhihao; Zhang, Bob] Univ Macau, Dept Comp & Informat Sci, PAMI Res Grp, Taipa 999078, Macao, Peoples R China. [Hao, Zhihao; Mao, Dianhui; Zuo, Min] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China. [Zhao, Zhihua] Chinese Univ Polit Sci & Law, Sch Law, Beijing 102249, Peoples R China. C3 Beijing Technology & Business University; University of Macau; Beijing Technology & Business University RP Mao, DH (corresponding author), Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China.; Zhang, B (corresponding author), Univ Macau, Dept Comp & Informat Sci, PAMI Res Grp, Taipa 999078, Macao, Peoples R China.; Mao, DH (corresponding author), Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China. EM hao_zhihao@126.com; maodh@th.btbu.edu.cn; bobzhang@um.edu.mo; zuomin@th.btbu.edu.cn; zhaozhihua@cupl.edu.cn CR Alfian G, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107016 Androulaki E, 2018, EUROSYS '18: PROCEEDINGS OF THE THIRTEENTH EUROSYS CONFERENCE, DOI 10.1145/3190508.3190538 Aslam N, 2017, FUTURE INTERNET, V9, DOI 10.3390/fi9040054 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bannister M. J., 2012, P 20 INT C GRAPH DRA, P414 Bojko A, 2009, LECT NOTES COMPUT SC, V5610, P30, DOI 10.1007/978-3-642-02574-7_4 Chen YL, 2017, J PARALLEL DISTR COM, V103, P96, DOI 10.1016/j.jpdc.2016.11.008 Cropotova J, 2019, FOOD CHEM, V297, DOI 10.1016/j.foodchem.2019.125006 ElMasry G, 2012, J FOOD ENG, V110, P127, DOI 10.1016/j.jfoodeng.2011.11.028 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Fan G., 2018, GRAIN DISTRIB TECHNO, V98, P83 Hong WB, 2018, PROCEEDINGS OF 2018 1ST IEEE INTERNATIONAL CONFERENCE ON HOT INFORMATION-CENTRIC NETWORKING (HOTICN 2018), P254, DOI 10.1109/HOTICN.2018.8605963 Kaufmann W., 2017, GOING BOOK PROBLEM R, P119 Kranen P., 2010, Proceedings 2010 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010), P1400, DOI 10.1109/ICDMW.2010.17 Lohumi S, 2017, FOOD ADDIT CONTAM A, V34, P678, DOI 10.1080/19440049.2017.1290828 [卢剑 LU Jian], 2010, [食品科学, Food Science], V31, P319 Mao DH, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11050703 Mao DH, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8030117 Mao DH, 2019, ARAB J SCI ENG, V44, P3439, DOI 10.1007/s13369-018-3537-z Mao DH, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093149 Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Pedersen T, 2005, LECT NOTES COMPUT SC, V3406, P226 Perez-Rodriguez F, 2018, FOOD MICROBIOL FOOD, P1, DOI 10.1007/978-3-319-68177-1_1 Saura JR, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9214603 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Song DH, 2017, INFORM RES, V22 Ha TM, 2019, FOOD CONTROL, V98, P238, DOI 10.1016/j.foodcont.2018.11.031 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Verdouw C, 2019, COMPUT ELECTRON AGR, V165, DOI 10.1016/j.compag.2019.104939 Vilanova E, 2019, J THROMB HAEMOST, V17, P254, DOI 10.1111/jth.14372 Walshaw Chris, 2000, GRAPH DRAWING, P171, DOI DOI 10.1142/9789812773296_0012 Wang S., 2018, 2018 IEEE INT VEH S, P108, DOI 10.1109/IVS.2018.8500488 Wang S.-B., 2017, CHINA CONDIMENT, V37 Wang YK, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P1, DOI 10.1109/AUS.2016.7748011 Xu LD, 2014, IEEE T IND INFORM, V10, P2233, DOI 10.1109/TII.2014.2300753 Xu X.-H., 2011, HUBEI AGR SCI, V21 Yuan Y, 2018, IEEE T SYST MAN CY-S, V48, P1421, DOI 10.1109/TSMC.2018.2854904 Zeng W.-G., 2013, J DALIAN U TECHNOL S, V3 Zhang CL, 2019, INT J ONLINE BIOMED, V15, P119, DOI 10.3991/ijoe.v15i05.10128 Zhang P., 2017, MOD PREV MED, V44, P4256 NR 41 TC 19 Z9 24 U1 12 U2 43 PD APR PY 2020 VL 17 IS 7 AR 2300 DI 10.3390/ijerph17072300 WC Environmental Sciences; Public, Environmental & Occupational Health SC Environmental Sciences & Ecology; Public, Environmental & Occupational Health UT WOS:000530763300132 DA 2022-12-14 ER PT J AU Hickey, C Bhatt, T AF Hickey, Caitlin Bhatt, Tejas TI Proceedings of the November 2011 Traceability Research Summit This report is the third in a series on Traceability Summits sponsored by IFT beginning in July 2011 SO JOURNAL OF FOOD SCIENCE DT Article DE food safety; regulations; traceability AB Fifty thought leaders in the area of food traceability met for a 3rd time to discuss methodologies and finalize the principles that define their vision for traceability. Participants in the summit included representatives from industry, trade associations, government, academia, consumer groups, and more. One main focus of this summit included a discussion on the current regulations and voluntary initiatives in place regarding traceability. Overall, it was recognized that the recommendations from this summit group would be more specific and stringent in comparison to these current regulations and initiatives. The participants sought to be leaders in the traceability arena, with their recommendations leading the industry to optimal traceability systems and methods. Participants agreed on many principles for their vision of traceability, emphasizing the importance of access to traceability data. They discussed having industry be asked for "basic" tracing data prior to the need for a large-scale investigation, having standards for sharing data, and having the data in electronic form. Participants foresaw the importance of capturing data electronically in the future, although they recognized that many firms do not currently do this. The group also saw a need for a transition period to implement changes, and to provide implementation training and resource aid to small businesses. Summit participants discussed specific definitions and examples for key data elements and critical tracking events that could be used by industry to capture tracing data at specific points within the supply chain. Overall, participants refined the goals of the summit group and started to identify specific ways to achieve those goals. C1 [Hickey, Caitlin; Bhatt, Tejas] Inst Food Technologists, Washington, DC 20036 USA. RP Bhatt, T (corresponding author), Inst Food Technologists, 1025 Connecticut Ave NW,Suite 503, Washington, DC 20036 USA. EM tbhatt@ift.org NR 0 TC 1 Z9 1 U1 0 U2 9 PD DEC PY 2013 VL 78 SU 2 SI SI BP B15 EP B20 DI 10.1111/1750-3841.12042 WC Food Science & Technology SC Food Science & Technology UT WOS:000331148000004 DA 2022-12-14 ER PT J AU Bendaoud, M Lecomte, C Yannou, B AF Bendaoud, Mhamed Lecomte, Catherine Yannou, Bernard TI A Methodological Framework to Design and Assess Food Traceability Systems SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Article DE traceability; food tracing; food safety; performance system; information system ID MANUFACTURE; INFORMATION; INDUSTRY; MODEL AB A methodological framework to design, assess and manage food traceability systems (TS) is proposed. The services delivered for the multiple beneficiaries of the TS are listed and featured by a series of high-level performance criteria. We also propose a library of modular technical solutions to guide designers in choosing appropriate traceability solutions. Again, at this technical level, practical performance criteria are provided for daily traceability control. This performance system may be used in a design methodology as well as for auditing a TS. Based on this model, we develop an Information System that we apply to a poultry processing company. C1 [Yannou, Bernard] Ecole Cent Paris, Lab Genie Ind, F-92290 Chatenay Malabry, France. [Bendaoud, Mhamed] VIF, F-44244 La Chapelle Sur Erdre, France. [Lecomte, Catherine] AgroParisTech, UFR CEPAL Dept SESG, F-91744 Massy, France. [Lecomte, Catherine] Lab PESOR Fac Jean Monet, F-92331 Sceaux, France. C3 UDICE-French Research Universities; Universite Paris Saclay; AgroParisTech RP Yannou, B (corresponding author), Ecole Cent Paris, Lab Genie Ind, Grande Voie Vignes, F-92290 Chatenay Malabry, France. EM mhamed.bendaoud@graduates.centraliens.net; catherine.lecomte@agroparistech.fr; bernard.yannou@ecp.fr CR Aarnisalo K., 2007, RES NOTES VTT TECHNI Bendaoud M., 2007, 16 INT C ENG DES PAR Bendaoud M., 2008, THESIS ECOLE CENTRAL Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bytheway C.W., 2005, VALUE R L, V28, P1 Campos JG, 2006, COMPUT AIDED DESIGN, V38, P540, DOI 10.1016/j.cad.2006.01.011 Chew E., 2008, INFORM SECURITY PERF Chitode J. S., 2008, COMMUNICATION THEORY DUPUY C, 2004, THESIS I NATL SCI AP Golan E.H., 2004, TRACEABILITY US FOOD GREEN B, 1997, UNIQUE IDENTIFIERS B GS1, 2005, GSI TRAC STAND BUS P HOAGLAND JA, 1998, SECURITY POLICY SPEC Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kaufman J.J., 2003, BUILDING FAST MODELS Lecomte C., 2006, ANAL IMPROVEMENT TRA, P214 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Morris RJT, 2003, IBM SYST J, V42, P205, DOI 10.1147/sj.422.0205 Pahl G., 1996, ENG DESIGN SYSTEMATI Pinto DB, 2006, FOOD RES INT, V39, P772, DOI 10.1016/j.foodres.2006.01.015 Pipino L.L., 2002, COMMUN ACM, V45, P211, DOI DOI 10.1145/505248.506010 Prudhomme G, 2003, J ENG DESIGN, V14, P333, DOI 10.1080/0954482031000091086 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Resende-Filho M., 2007, EC TRACEABILITY MITI Ronkko M, 2007, COMPUT IND, V58, P814, DOI 10.1016/j.compind.2007.02.003 Sharp K.R., 1990, AUTOMATIC IDENTIFICA, P276 Steele D. C., 1995, Production and Inventory Management Journal, V36, P53 TELLKAMP C, 2006, THESIS U ST GALLEN Toyryla I., 1999, REALISING POTENTIAL Verdenius F, 2006, WOODHEAD PUBL FOOD S, P26, DOI 10.1533/9781845691233.1.26 Viruega J.-L., 2005, TRACEABILITY TOOLS M, P237 Wang R. Y., 1996, Journal of Management Information Systems, V12, P5 Wixon J.R., 1999, 9 INT COUNC SYST ENG Wray B., 2007, ISBT 128 INTRO BAR C Yannou B., 1998, CONCEPTION PRODUITS, P77 YIALELIS N, 1996, DOMAIN BASED SECURIT NR 36 TC 8 Z9 9 U1 1 U2 10 PY 2012 VL 15 IS 1 BP 103 EP 125 WC Agricultural Economics & Policy SC Agriculture UT WOS:000305935500007 DA 2022-12-14 ER PT J AU Dandage, K Badia-Melis, R Ruiz-Garcia, L AF Dandage, K. Badia-Melis, R. Ruiz-Garcia, L. TI Indian perspective in food traceability: A review SO FOOD CONTROL DT Review DE Food traceability; Food waste; Traceability systems; India; Food distribution ID SUPPLY CHAIN; IMPLEMENTATION; CHALLENGES; PRODUCTS; SAFETY; ENTERPRISES; MANAGEMENT; FRAMEWORK; SYSTEMS AB India is the second largest producer of fruit and vegetables in the world. Fruit production in India has increased 89% in the last decade. In the present paper It is exposed the necessity for a proper traceability in the Indian food industry, because the sector is demanding an adequate system due to the precarious nature of existing supply chain, and to reduce the numerous cases of food safety incidents and fraudulence. This work also presents the existing traceability techniques in India which include RFID, Holograms, Barcode, Nuclear techniques and other tracking media to monitor production process. Furthermore it is revealed the initiatives implementation from APEDA and its association with GS1 India in the form of Anarnet, Peanut.net, Meat.net, and Grapenet for the Indian farming products, as well as several ICTs initiatives that are actively working in many states of India. However the development of an effective food traceability system is affected by a numbers of factors like restrictive government marketing standardization, insecure policies and unstable actions for food safety, underdeveloped and unorganized infrastructure in market area and the supply chains, from the farmers to non-existent cold chain facilities and small local stores, and inadequate agricultural practices with large number of small and medium industries and famers. Therefore an effective food traceability system is not only an important tool to manage food quality and safety risks, but also to promote the development of effective supply chain management in India. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Dandage, K.; Badia-Melis, R.; Ruiz-Garcia, L.] Univ Politecn Madrid, ETSI Agron Alimentaria & Biosistemas, Dept Ingn Agroforestal, E-28040 Madrid, Spain. C3 Universidad Politecnica de Madrid RP Badia-Melis, R (corresponding author), Univ Politecn Madrid, ETSI Agron Alimentaria & Biosistemas, Dept Ingn Agroforestal, E-28040 Madrid, Spain. EM ricardobadia@live.com CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Abbott H., 1991, MANAGING PRODUCT REC Agarwal M, 2014, 2014 5TH INTERNATIONAL CONFERENCE CONFLUENCE THE NEXT GENERATION INFORMATION TECHNOLOGY SUMMIT (CONFLUENCE), P485, DOI 10.1109/CONFLUENCE.2014.6949353 Ajjappa V., 2013, S ASIAN J MARKETING, V3, P17 Anica-Popa I., 2011, MANAGEMENT MARKETING, V6, P139 [Anonymous], 2015, FOOD SAFETY NEWS APEDA, 2015, CERT SYST EXP MEAT P Arijit Das, 2010, Journal of Biological Sciences, V10, P255 Aula S., 2014, PROBLEM ENGLISH LANG Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Banerji A., 2012, MOST MILK INDIA CONT Barger M. S., 2000, DAGUERREOTYPE 19 CEN Basavaraja H., 2007, AGR EC RES REV, V20, P117 Bhosale J., 2013, EC TIMES Biederman D., 2006, J COMMERCE, V1 Biswas A. K., 2015, INDIAS FOOD SAFETY C Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Cargill, 2014, TRACK PROGR SUST PAL Cargill India, 2015, CARG LAUNCH SUR KHAD CFIA, 2012, LEG FRAM TRAC PROP E Chandra N., 2014, DAILY MAIL Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chauhan C., 2013, HINDUSTAN TIMES Dias MAC, 2012, FOOD CONTROL, V24, P199, DOI 10.1016/j.foodcont.2011.09.028 Cusato S, 2014, QUAL ASSUR SAF CROP, V6, P135, DOI 10.3920/QAS2012.0195 Cusato S, 2013, FOODBORNE PATHOG DIS, V10, P6, DOI 10.1089/fpd.2012.1286 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 DMonte L, 2015, CHITALE DAIRY TAKES Emerson, 2013, FOOD WAST COLD STOR [ERS USDA], 2014, ISS AND AN Foras E, 2015, FOOD CONTROL, V57, P65, DOI 10.1016/j.foodcont.2015.03.027 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 FSANZ, 2013, HALD FOOD INT PRIV L Furness A., 2003, Food authenticity and traceability, P473, DOI 10.1533/9781855737181.3.473 Golan E., 2004, AGR EC REPORT, V830, P183 GS1 US, 2007, IND ROADM BUILD FRES, V6, P12 GSCG, 2015, BATTL IND PROF FOOD Gulati A., 2012, 2 COMM AGR COSTS PRI Gupta P. R., 2007, DAIRY INDIA 2007 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x IAEA, 2011, ENH FOOD SAF QUAL IS IAI, 2011, 1 INT S FUT IND DAIR IBEF, 2012, FOOD PROCESSING IFPRI, 2013, GLOB HUNG IND 2013 Karipidis P, 2009, FOOD CONTROL, V20, P93, DOI 10.1016/j.foodcont.2008.02.008 Karippacheril T., 2012, MODULE 12 IMPROVING Kazmin A, 2014, FINANCIAL TIMES Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kornstein, 2012, SUSTAINABLE APPROACH Kulkarni M., 2015, BUSINESS STANDARD Kumar V. S., 2015, INCREASING EXPORTS C Larsen E., 2003, Food authenticity and traceability, P507, DOI 10.1533/9781855737181.3.507 Lashgarara F, 2011, AFR J BIOTECHNOL, V10, P11537 Li Y., 2006, USING DATA MINING IM, P163 Luo Q, 2010, INT C COMP COMP TECH, P710 Mahale D. P., 2008, INTERNET J FOOD SAFE, V10 Malviya S., 2015, EC TIMES Mathis R., 2010, MILK TASTES BETTER R Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 McDonald, 2015, FARM TO FORK POT Mehrjerdi Yahia Zare, 2010, Business Strategy Series, V11, P107, DOI 10.1108/17515631011026434 Michael K, 2005, ICMB 2005: International Conference on Mobile Business, P623, DOI 10.1109/ICMB.2005.103 Mishra A., 2010, TIMES INDIA Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 MSME, 2007, FIN ASS BARC Narain S., 2010, CONTROL YOUR FOOD IT Narayan A., 2016, BLOOMBERG NCCD, 2012, COMPR NOT CREAT MAN New Eastern Outlook, 2015, WORLDS LARGEST COUNT NZMPI, 2013, NAT AN ID TRAC PROJ OEC, 2015, OB EC COMPL IND EXP Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Pant RR, 2015, PROCD SOC BEHV, V189, P385, DOI 10.1016/j.sbspro.2015.03.235 Parwez S., 2014, AFRICAN J BUSINESS M, V8, P572 Patnaik S., 2015, WALMART INDIA REACHE Paul J., 2016, TIMES INDIA Petersen A., 2005, SEAFOOD TRACEABILITY Phukan R. S., 2014, LOST TRANSIT HAS ALL Pradhan N., 2015, FACETS NANOTECHNOLOG, V2015 Radyuhin V., 2010, HINDU Rao C. S. S., 2012, ELECT IDENTIFICATION Rao S., 2009, J COMMUNITY INFORM, V5 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riviere Jim E., 2012, ENSURING SAFE FOODS, pix Rohatgi M., 2014, TIMES INDIA Rohit T. K., 2016, HINDU Rothschild M., 2012, MORE FROZEN TUNA IND Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Saxena M., 2015, HORTICULTURAL STAT G Saxena M, 2015, INDIAN HORTICULTURE Saxena S., 2016, TIMES INDIA Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Shah A., 2011, LIST TOP FOOD PROCES Shawna, 2015, NATL FISH SEAFOOD LA Shi Y. -D., 2009, RES J APPL SCI, V4, P57 Simon K., 2015, IMPLEMENTATION NUCL Sonwalkar Prasun, 2014, HINDUSTAN TIMES Spink J, 2016, FOOD CONTROL, V69, P306, DOI 10.1016/j.foodcont.2016.03.016 Srivastava B., 2004, Business Horizons, V47, P60, DOI 10.1016/j.bushor.2004.09.009 Stefansson G., 2001, INT J SERV TECHNOL M, V2, P187, DOI [10.1504/IJSTM.2001.001599, DOI 10.1504/IJSTM.2001.001599] Sugahara K., 2008, INT C COMP COMP TECH, P2293 Swedberg C., 2010, RFID J Umali-Deininger D, 2007, AGR ECON-BLACKWELL, V37, P135, DOI 10.1111/j.1574-0862.2007.00240.x UNFAO, 2013, GLOB FOOD REP UNFAO USDA, 2014, IND RET FOODS 2014 van Dorp K.-J., 2002, Logistics Information Management, V15, P24, DOI 10.1108/09576050210412648 Veronneau S, 2009, INT J PROD ECON, V122, P692, DOI 10.1016/j.ijpe.2009.06.038 Wall B., 1994, Industrial Management + Data Systems, V94, P24, DOI 10.1108/02635579410068257 World Bank, 2008, IND TAK AGR MARK Zuurbier P., 2008, INT J PROD ECON, V113, P107 NR 112 TC 52 Z9 57 U1 11 U2 171 PD JAN PY 2017 VL 71 BP 217 EP 227 DI 10.1016/j.foodcont.2016.07.005 WC Food Science & Technology SC Food Science & Technology UT WOS:000384778400029 DA 2022-12-14 ER PT J AU Nicolae, CG Moga, LM Bahaciu, GV Marin, MP AF Nicolae, Carmen Georgeta Moga, Liliana Mihaela Bahaciu, Gratziela Victoria Marin, Monica Paula TI TRACEABILITY SYSTEM STRUCTURE DESIGN FOR FISH AND FISH PRODUCTS BASED ON SUPPLY CHAIN ACTORS NEEDS SO SCIENTIFIC PAPERS-SERIES D-ANIMAL SCIENCE DT Article DE design; fish and fishery products; legal framework; stakeholders' needs; traceability system AB This paper presents the structure design of a traceability system in fishery supply chain based on artificial intelligence and information technology for data acquisition and processing. The design activity takes into consideration the need of the Romanian fisheries to get an effective and practical quality safety monitoring tool for fish and fishery products. The traceability system development is based on the European and national legal framework, which was reviewed and on the all stakeholder's informational needs, which were identified by interviews with the stakeholders within the fish and fish products supply chain. C1 [Nicolae, Carmen Georgeta; Bahaciu, Gratziela Victoria; Marin, Monica Paula] Univ Agron Sci & Vet Med Bucharest, 59 Marasti Blvd,Dist 1, Bucharest 011464, Romania. [Moga, Liliana Mihaela] Dunarea Jos Univ Galati, 47 Domneasca St, Galati 800008, Romania. C3 University of Agronomic Science & Veterinary Medicine - Bucharest; Dunarea De Jos University Galati RP Moga, LM (corresponding author), Dunarea Jos Univ Galati, 47 Domneasca St, Galati 800008, Romania. EM liliana.moga@gmail.com CR Alfaro J. A., 2006, Journal of Purchasing and Supply Management, V12, P39, DOI 10.1016/j.pursup.2006.02.003 [Anonymous], FOOD SAFETY LAW EURO Choe YC, 2009, INFORM SYST FRONT, V11, P167, DOI 10.1007/s10796-008-9134-z Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Moga L. M, 2016, INT C RISK CONT EC 1, P506 Moga L. M., 2016, INT C NOV 2016 IAS R, P109 Moga LM, 2016, QUAL-ACCESS SUCCESS, V17, P97 Nicolae CG, 2014, QUAL-ACCESS SUCCESS, V15, P95 Nicolae CG, 2015, AGROLIFE SCI J, V4, P111 Popa M., 2010, GOOD PRACTICE GUIDE Resende-Filho M.A., 2007, 105 EAAE SEM INT MAR Romanian parliament, 2014, ROM AQ FISH LAW NR 12 TC 4 Z9 4 U1 0 U2 5 PY 2017 VL 60 BP 353 EP 358 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000413674700063 DA 2022-12-14 ER PT J AU Poniman, D Purchase, S Sneddon, J AF Poniman, Delma Purchase, Sharon Sneddon, Joanne TI Traceability systems in the Western Australia halal food supply chain SO ASIA PACIFIC JOURNAL OF MARKETING AND LOGISTICS DT Article DE Business networks; Leximancer; Facilitating organizations; Halal food supply chain; Traceability system ID AGRICULTURE; INNOVATIONS; FRAMEWORK; DYNAMICS; INDUSTRY; MODEL AB Purpose - The purpose of this paper is to explore the emergence and implementation of traceability systems in the Western Australian (WA) Halal food industry. In particular, to understand how individuals in facilitating organizations perceive the Halal idea logic and the benefits that a traceability system can provide to the Halal food processing industry. Design/methodology/approach - An empirical qualitative approach was employed to examine these issues utilizing in-depth interviews. Thematic analysis was carried out using Leximancer software. Findings - Findings suggest that individual's perception of Halal idea logic is aligned to the roles they perform. These perceptions were impacted by the specific objectives or business interests of each organization. Facilitating organizations also perceive that traceability systems are a strategic tool in the Halal food processing industry. Practical implications - The research provides insights into how to improve existing understanding of the Halal idea logic within Halal food business networks and the benefits of implementing traceability systems in Halal food production. Joint activity between firms creates a network effect, where the value created is greater than that which the firms alone can create. Originality/value - Though traceability systems have become increasingly popular in the food industry, little research has been undertaken to understand how individuals in facilitating organizations perceive these systems, particularly in the growing Halal food industry. Hence, the study contributes to the literature of traceability studies and the area of change and process adaptation in business relationships in the context of halal food production. C1 [Poniman, Delma] Univ Teknol Malaysia, Fac Management, Johor Baharu, Malaysia. [Poniman, Delma; Sneddon, Joanne] Univ Western Australia, Sch Business, Crawley, Australia. [Purchase, Sharon] Univ Western Australia, Crawley, Australia. C3 Universiti Teknologi Malaysia; University of Western Australia; University of Western Australia RP Poniman, D (corresponding author), Univ Teknol Malaysia, Fac Management, Johor Baharu, Malaysia. EM delma.poniman@research.uwa.edu.au CR Aarikka-Stenroos L, 2014, IND MARKET MANAG, V43, P365, DOI 10.1016/j.indmarman.2013.12.005 Adams IA, 2011, INTELLECT DISCOURSE, V19, P123 Al Jallad N., 2008, LANGUAGE DESIGN, V10, P77 Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Alserhan BA, 2010, J ISLAMIC MARK, V1, P101, DOI 10.1108/17590831011055842 Anderson H., 1998, SCAND J MANAG, V14, P167 Anir Norman Azah, 2008, WSEAS Transactions on Information Science and Applications, V5, P843 Aronson J., 1995, QUAL REP, V2, P1, DOI [10.46743/2160-3715/1995.2069, DOI 10.46743/2160-3715/1995.2069] Berry B., 2011, GLOBAL HALAL FOOD MA Boecker A., 2013, CANADIAN FOOD INSIGH Bonne K, 2008, AGR HUM VALUES, V25, P35, DOI 10.1007/s10460-007-9076-y Boyatzis R.E., 1998, TRANSFORMING QUALITA, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA] Chansud W, 2008, ECTI-CON 2008: PROCEEDINGS OF THE 2008 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY, VOLS 1 AND 2, P753, DOI 10.1109/ECTICON.2008.4600540 CORBIN J, 1990, Z SOZIOL, V19, P418, DOI 10.1007/BF00988593 Crabtree B, 1999, TEMPLATE APPROACH TE Creswell J.W., 2018, QUAL INQ Cretchley J, 2010, J CROSS CULT PSYCHOL, V41, P318, DOI 10.1177/0022022110366105 Crotty M, 2020, FDN SOCIAL RES MEANI Doyle S., 2012, QUALITATIVE HLTH RES Dubois A, 2002, J BUS RES, V55, P553, DOI 10.1016/S0148-2963(00)00195-8 EASTON G, 1992, IND NETWORKS REV Favaro G, 2005, J DAIRY SCI, V88, P3426, DOI 10.3168/jds.S0022-0302(05)73026-5 Fereday J., 2006, INT J QUAL METH, V5, P80, DOI [10.1177/160940690600500107, DOI 10.1177/160940690600500107] Florence D., 1993, LOGISTICS INFORM MAN, V6, P3 Gapp R., 2013, P BRIT AC MAN C LIV, P1 Golan K., 2002, AGR OUTLOOK, V288, P21 Golan K. B., 2004, 830 USDA EC RES SERV GRANOVETTER M, 1985, AM J SOCIOL, V91, P481, DOI 10.1086/228311 Grech M.R., 2002, PROC HUMAN FACTORS E, V46, P1718, DOI DOI 10.1177/154193120204601906 Guercini S, 2009, IND MARKET MANAG, V38, P883, DOI 10.1016/j.indmarman.2009.03.016 Hakansson H, 2002, J BUS RES, V55, P133, DOI 10.1016/S0148-2963(00)00148-X Hakansson H., 1987, IND TECHNOLOGICAL DE Hakansson H., 2009, BUSINESS NETWORKS Hakansson H., 1989, SCAND J MANAG, V5, P187, DOI DOI 10.1016/0956-5221(89)90026-2 Halinen A, 2013, IND MARKET MANAG, V42, P1213, DOI 10.1016/j.indmarman.2013.05.001 Hienerth C, 2011, J PROD INNOVAT MANAG, V28, P175, DOI 10.1111/j.1540-5885.2011.00869.x Hobbs J. E., 2003, CONSUMER DEMAND TRAC Holloway I, 2011, QUAL HEALTH RES, V21, P968, DOI 10.1177/1049732310395607 Holsti O.R., 1968, HDB SOCIAL PSYCHOL, V2, P596 ISO European Standard, 1995, 84021995 ISO EUR STA Johnson RB., 2014, ED RES QUANTITATIVE, V5th Kehagia O, 2007, SOCIOL RURALIS, V47, P400, DOI 10.1111/j.1467-9523.2007.00445.x Laldin M.A., 2006, ISLAMIC LAW INTRO Lam Y, 2008, LECT NOTES ARTIF INT, V5357, P259, DOI 10.1007/978-3-540-89674-6_29 Letch N., 2013, KNOWLEDGE MANAGEMENT, V4, P435 Lodhi A., 2009, UNDERSTANDING HALAL Manikas I, 2009, INT J DAIRY TECHNOL, V62, P126, DOI 10.1111/j.1471-0307.2008.00444.x Mattsson L. G, 1997, J MARKET MANAG, V13, P447, DOI DOI 10.1080/0267257X.1997.9964485 McBratney Alex, 2005, Precision Agriculture, V6, P7, DOI 10.1007/s11119-005-0681-8 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Miles MB., 2019, QUALITATIVE DATA ANA Miller B. D., 2013, USE CRITICAL TRACKIN Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Morse J., 1994, HDB QUALITATIVE RES, V2, P220, DOI DOI 10.1109/HICSS.2013.531 Munksgaard KB, 2014, IND MARKET MANAG, V43, P613, DOI 10.1016/j.indmarman.2014.02.006 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Patton MQ, 2002, QUALITATIVE RES EVAL, V3rd Perez-Aloe R., 2007, APPL RFID TAGS OVERA, P1, DOI [DOI 10.1109/RFIDEURAS1A.2007.4368136, 10.1109/RFIDEURASIA.2007.4368136] Petroff J. N., 1991, Production and Inventory Management Journal, V32, P55 Porter L., 2014, HALAL GUIDE CONSUMER Powell WW, 2005, AM J SOCIOL, V110, P1132, DOI 10.1086/421508 Power C., 2009, TIME MAGAZINE Rajaguru R, 2013, IND MARKET MANAG, V42, P620, DOI 10.1016/j.indmarman.2012.09.002 Ramesh B, 1998, COMMUN ACM, V41, P37, DOI 10.1145/290133.290147 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 SANDELOWSKI M, 1995, RES NURS HEALTH, V18, P179, DOI 10.1002/nur.4770180211 Smith AE, 2006, BEHAV RES METHODS, V38, P262, DOI 10.3758/BF03192778 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Sohal AS, 1997, TECHNOVATION, V17, P583, DOI 10.1016/S0166-4972(97)00039-4 Spoor STP, 2007, INT J EAT DISORDER, V40, P493, DOI [10.1002/eat, 10.1002/eat.20402] Suhaiza Zailani, 2010, Journal of Food Technology, V8, P74, DOI 10.3923/jftech.2010.74.81 Sungkar I., 2009, WORLD BANK 1 EAP REG Tieman M, 2012, J ISLAMIC MARK, V3, P217, DOI 10.1108/17590831211259727 Toyryla I., 1999, REALISING POTENTIAL Turnbull P., 1996, J BUSINESS IND MARKE, V11, P44 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Welch C., 2002, J BUSINESS BUSINESS, V9, P27, DOI DOI 10.1300/J033V09N03_02 Wilkinson I, 2005, IND MARKET MANAG, V34, P669, DOI 10.1016/j.indmarman.2005.06.003 Wilson JAJ, 2010, J ISLAMIC MARK, V1, P107, DOI 10.1108/17590831011055851 Yin R., 2009, CASE STUDY RES DESIG Zakaria N, 2010, J ISLAMIC MARK, V1, P51, DOI 10.1108/17590831011026222 Zurina Shafii, 2012, World Applied Sciences Journal, V17, P1 NR 83 TC 28 Z9 28 U1 0 U2 22 PY 2015 VL 27 IS 2 BP 324 EP 348 DI 10.1108/APJML-05-2014-0082 WC Business SC Business & Economics UT WOS:000355624300009 DA 2022-12-14 ER PT J AU Cohen, FPA Valenti, WC Calado, R AF Cohen, Felipe P. A. Valenti, Wagner C. Calado, Ricardo TI Traceability Issues in the Trade of Marine Ornamental Species SO REVIEWS IN FISHERIES SCIENCE DT Article DE traceability methods; marking methods; certification; sustainability; supply chain ID PASSIVE INTEGRATED TRANSPONDER; VISIBLE IMPLANT ELASTOMER; ALGA CAULERPA-TAXIFOLIA; AQUARIUM TRADE; TAG RETENTION; TUBASTRAEA-COCCINEA; ATLANTIC SALMON; EUROPEAN WATERS; PROTECTED AREA; MARK-RECAPTURE AB In the last decade, the trade of marine ornamental species has experienced a significant expansion worldwide; however, this industry still relies on a large number of unsustainable practices (e. g., cyanide fishing, overexploitation of target species) and needs to shift its operations urgently to avoid collapsing. Under this scenario, traceability and certification emerge as important management tools that may help this industry to shift toward sustainability. This industry relies on the trade of thousands of small-sized species that are traded live on a unitary basis with high market value. These features, along with a fragmented and complex supply chain, make the traceability of marine ornamental species a challenging task. This study presents the most commonly used methods to trace aquatic organisms and discusses their suitability to trace marine ornamental species. The use of bacterial fingerprints appears to be the most promising method to successfully trace marine ornamentals, but it is most likely that a combination of two or more traceability methods need to be implemented to cover all the unique features displayed by the live trade of marine ornamental species. C1 [Cohen, Felipe P. A.; Valenti, Wagner C.] Univ Estadual Paulista, Ctr Aquicultura Caunesp, BR-14884900 Sao Paulo, Brazil. [Valenti, Wagner C.] Univ Estadual Paulista, BR-14884900 Sao Paulo, Brazil. [Calado, Ricardo] Univ Aveiro, Dept Biol, P-3800 Aveiro, Portugal. [Calado, Ricardo] Univ Aveiro, CESAM, P-3800 Aveiro, Portugal. C3 Universidade Estadual Paulista; Universidade Estadual Paulista; Universidade de Aveiro; Universidade de Aveiro RP Cohen, FPA (corresponding author), Univ Estadual Paulista, Ctr Aquicultura Caunesp, Via Acesso Prof Paulo Donato Castellane, BR-14884900 Sao Paulo, Brazil. EM fcohen.bio@gmail.com CR Acolas ML, 2007, FISH RES, V86, P280, DOI 10.1016/j.fishres.2007.05.011 Alencastro L. A., 2005, SPC LIVE REEF FISH I, V15, P19 Baras E, 1999, N AM J AQUACULT, V61, P246, DOI 10.1577/1548-8454(1999)061<0246:EOIPFP>2.0.CO;2 Baras E, 2000, AQUACULTURE, V185, P159, DOI 10.1016/S0044-8486(99)00346-4 Becker BJ, 2005, LIMNOL OCEANOGR, V50, P48, DOI 10.4319/lo.2005.50.1.0048 Bell JG, 2007, J AGR FOOD CHEM, V55, P5934, DOI 10.1021/jf0704561 Bell JD, 2009, REV FISH SCI, V17, P223, DOI 10.1080/10641260802528541 Betancur-R R, 2011, J BIOGEOGR, V38, P1281, DOI 10.1111/j.1365-2699.2011.02496.x BEUKERS JS, 1995, MAR ECOL PROG SER, V125, P61, DOI 10.3354/meps125061 Blundell AG, 2005, CONSERV BIOL, V19, P2020, DOI 10.1111/j.1523-1739.2005.00253.x Bolland JD, 2009, J APPL ICHTHYOL, V25, P381, DOI 10.1111/j.1439-0426.2009.01261.x Bolton TF, 2006, BIOL INVASIONS, V8, P651, DOI 10.1007/s10530-005-2017-z BRANNAS E, 1994, T AM FISH SOC, V123, P395, DOI 10.1577/1548-8659(1994)123<0395:UOTPIT>2.3.CO;2 Brown JH, 2003, AQUAC RES, V34, P49, DOI 10.1046/j.1365-2109.2003.00793.x Bruce B. D., 1997, AQUARIUM SCI CONSERV, DOI DOI 10.1023/A:1011369015080 Bruckner AW, 2005, REV BIOL TROP, V53, P127 Bubb DH, 2006, CAN J ZOOL, V84, P1202, DOI 10.1139/Z06-100 Bubb DH, 2002, HYDROBIOLOGIA, V483, P225, DOI 10.1023/A:1021352217332 BUCKLEY RM, 1994, B MAR SCI, V55, P848 Bumgarner JD, 2009, N AM J FISH MANAGE, V29, P903, DOI 10.1577/M07-155.1 Burke L, 2011, REEFS RISK REVISITED Busetto ML, 2008, J AGR FOOD CHEM, V56, P2742, DOI 10.1021/jf0734267 Calado R., 2008, MARINE ORNAMENTAL SH Calado R, 2008, AQUAT CONSERV, V18, P335, DOI 10.1002/aqc.852 Calado R, 2006, SCI MAR, V70, P389, DOI 10.3989/scimar.2006.70n3389 Calado R, 2006, MAR POLLUT BULL, V52, P599, DOI 10.1016/j.marpolbul.2006.02.010 Campana SE, 1999, MAR ECOL PROG SER, V188, P263, DOI 10.3354/meps188263 Campana SE, 2001, CAN J FISH AQUAT SCI, V58, P30, DOI 10.1139/f00-177 Castro-Santos T, 1996, FISH RES, V28, P253, DOI 10.1016/0165-7836(96)00514-0 Coley D, 2011, BRIT FOOD J, V113, P919, DOI 10.1108/00070701111148432 Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES), 2012, ID CITES LIST COR TR Convention on International Trade in Endangered Species of Wild Flora and Fauna (CITES), 2002, SUMM REC 18 M AN COM Davis JLD, 2004, FISH RES, V67, P265, DOI 10.1016/j.fishres.2003.11.005 Dawson TE, 2001, CURR PLANT SCI BIOT, V40, P1 de Paula AF, 2004, B MAR SCI, V74, P175 Dempson JB, 2004, ECOL FRESHW FISH, V13, P176, DOI 10.1111/j.1600-0633.2004.00057.x Diaz S, 2012, ENVIRON MANAGE, V50, P89, DOI 10.1007/s00267-012-9860-3 DiBacco C, 2000, LIMNOL OCEANOGR, V45, P871, DOI 10.4319/lo.2000.45.4.0871 Doupe RG, 2003, AQUAC RES, V34, P681, DOI 10.1046/j.1365-2109.2003.00860.x Drew MM, 2012, CRUSTACEANA, V85, P89, DOI 10.1163/156854012X623656 Fenner D, 2004, CORAL REEFS, V23, P505, DOI 10.1007/s00338-004-0422-x Ferreira CEL, 2003, CORAL REEFS, V22, P498, DOI 10.1007/s00338-003-0328-z Filonzi L, 2010, FOOD RES INT, V43, P1383, DOI 10.1016/j.foodres.2010.04.016 FITZ HC, 1991, J CRUSTACEAN BIOL, V11, P229, DOI 10.2307/1548360 Fraser KPP, 1998, AQUAC RES, V29, P289 Godin DM, 1996, AQUACULTURE, V139, P243, DOI 10.1016/0044-8486(95)01174-9 Green E, 2003, MARINE ORNAMENTAL SPECIES: COLLECTION, CULTURE & CONSERVATION, P31 Green EP, 1999, CORAL REEFS, V18, P403, DOI 10.1007/s003380050218 Green SJ, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0032596 Hand DM, 2010, N AM J AQUACULT, V72, P10, DOI 10.1577/A08-065.1 Harmon T., 2012, DRUM CROAKER, V43, P3 Hastein T, 2001, REV SCI TECH OIE, V20, P564, DOI 10.20506/rst.20.2.1300 Holthus M, 1999, SPC LIVE REEF FISH I, V5, P34 Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Dinh H, 2012, AQUAC RES, V43, P1471, DOI 10.1111/j.1365-2109.2011.02949.x Isely JJ, 1998, T AM FISH SOC, V127, P658, DOI 10.1577/1548-8659(1998)127<0658:TRGASO>2.0.CO;2 Jennings MJ, 2009, N AM J AQUACULT, V71, P330, DOI 10.1577/A09-004.1 Jerry DR, 2001, AQUACULTURE, V193, P149, DOI 10.1016/S0044-8486(00)00477-4 Jones AM, 2008, J FISH BIOL, V73, P753, DOI 10.1111/j.1095-8649.2008.01969.x Jones R., 2008, ADV CORAL HUSBANDRY, P351 Josephson DC, 2008, N AM J FISH MANAGE, V28, P1758, DOI 10.1577/M08-019.1 Jousson O, 2000, NATURE, V408, P157, DOI 10.1038/35041623 Kneib RT, 2001, J EXP MAR BIOL ECOL, V266, P109, DOI 10.1016/S0022-0981(01)00347-1 Koldewey HJ, 2010, AQUACULTURE, V302, P131, DOI 10.1016/j.aquaculture.2009.11.010 Kolm N, 2003, CONSERV BIOL, V17, P910, DOI 10.1046/j.1523-1739.2003.01522.x Layzer JB, 2004, N AM J FISH MANAGE, V24, P228, DOI 10.1577/M02-168 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Lecchini D, 2006, FISHERIES SCI, V72, P40, DOI 10.1111/j.1444-2906.2006.01114.x MAC Marine Aquarium Council., 2001, COR HANDL HUSB TRANS Malone JC, 1999, J EXP MAR BIOL ECOL, V237, P243, DOI 10.1016/S0022-0981(99)00003-9 Marine Aquarium Council (MAC), 2001, COR COLL FISH HOLD I Marine Aquarium Council (MAC), 2001, COR EC FISH MAN INT Mathews Amos A., 2009, CERTIFICATION CONSER McCormick MI, 2004, CORAL REEFS, V23, P570, DOI 10.1007/s00338-004-0413-y MEINESZ A, 1991, OCEANOL ACTA, V14, P415 Meinesz Alexandre, 2001, Biological Invasions, V3, P201, DOI 10.1023/A:1014549500678 Moorhead JA, 2010, REV FISH SCI, V18, P315, DOI 10.1080/10641262.2010.516035 Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Murray JM, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0029543 Oliveira ACB, 2008, AQUACULT NUTR, V14, P10, DOI 10.1111/j.1365-2095.2007.00496.x Olivier K, 2003, MARINE ORNAMENTAL SPECIES: COLLECTION, CULTURE & CONSERVATION, P49, DOI 10.1002/9780470752722.ch4 Olivotto I, 2011, J WORLD AQUACULT SOC, V42, P135, DOI 10.1111/j.1749-7345.2011.00453.x Ombredane D, 1998, HYDROBIOLOGIA, V372, P99, DOI 10.1023/A:1017022026937 Piedra R, 2007, CHELONIAN CONSERV BI, V6, P111, DOI 10.2744/1071-8443(2007)6[111:NOTLTD]2.0.CO;2 Pomeroy R, 2004, ASIAN FISH SCI, V17, P365 Pomeroy RS, 2006, MAR POLICY, V30, P111, DOI 10.1016/j.marpol.2004.09.001 Pratt V., 1997, SULLIED SEAS STRATEG Randall JE, 2001, FAO SPECIES IDENTIFI, V6, P3653 Rhyne A, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0008413 Rhyne AL, 2012, CONSERV LETT, V5, P478, DOI 10.1111/j.1755-263X.2012.00265.x Rhyne AL, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0035808 Roheim C. A., 2008, SEAFOOD ECOLABELLING, P38, DOI DOI 10.1002/9781444301380.CH2 Rojas JMM, 2007, RAPID COMMUN MASS SP, V21, P207, DOI 10.1002/rcm.2836 Rosenlund G, 2001, AQUAC RES, V32, P323, DOI 10.1046/j.1355-557x.2001.00025.x Roussel JM, 2000, CAN J FISH AQUAT SCI, V57, P1326, DOI 10.1139/cjfas-57-7-1326 Ruamkuson D, 2011, FOOD CONTROL, V22, P1742, DOI 10.1016/j.foodcont.2011.04.008 Rubec P. J., 2001, AQUARIUM SCI CONSERV, V3, P37, DOI DOI 10.1023/A:1011370106291 Ruetz CR, 2006, T AM FISH SOC, V135, P1456, DOI 10.1577/T05-295.1 Sadovy YJ, 2002, SPC LIVE REEF FISH I, V10 Schaffelke B, 2002, MAR POLLUT BULL, V44, P204, DOI 10.1016/S0025-326X(01)00202-8 Schroder V, 2011, BIOL INVASIONS, V13, P203, DOI 10.1007/s10530-010-9802-z Semmens BX, 2004, MAR ECOL PROG SER, V266, P239, DOI 10.3354/meps266239 Sharp WC, 2000, J CRUSTACEAN BIOL, V20, P510, DOI 10.1651/0278-0372(2000)020[0510:TUOCMT]2.0.CO;2 Shuman CS, 2004, ENVIRON CONSERV, V31, P339, DOI 10.1017/S0376892904001663 Silva AG, 2011, AQUAT INVASIONS, V6, pS105, DOI 10.3391/ai.2011.6.S1.024 Silva J. M, 2011, PLOS ONE, V6 Smith CJ, 2009, CAN J FISH AQUAT SCI, V66, P713, DOI 10.1139/F09-035 Smith KF, 2008, CONSERV LETT, V1, P103, DOI 10.1111/j.1755-263X.2008.00014.x Sonnenholzner Jorge I., 2010, Pan-American Journal of Aquatic Sciences, V5, P414 Soula M, 2012, AQUACULT INT, V20, P571, DOI 10.1007/s10499-011-9486-0 Steinke D., 2009, PLOS ONE, V4 Tanner SE, 2012, MAR ECOL PROG SER, V452, P193, DOI 10.3354/meps09621 Tatara CP, 2009, N AM J FISH MANAGE, V29, P417, DOI 10.1577/M07-225.1 Tatsadjieu NL, 2010, FOOD CONTROL, V21, P673, DOI 10.1016/j.foodcont.2009.10.006 Thornhill DJ., 2012, ECOLOGICAL IMPACTS P Thorrold SR, 2001, SCIENCE, V291, P297, DOI 10.1126/science.291.5502.297 Tissot BN, 2003, CONSERV BIOL, V17, P1759, DOI 10.1111/j.1523-1739.2003.00379.x Tissot BN, 2010, MAR POLICY, V34, P1385, DOI 10.1016/j.marpol.2010.06.002 Tlusty M, 2002, AQUACULTURE, V205, P203, DOI 10.1016/S0044-8486(01)00683-4 Tlusty MF, 2006, OFI J, V51, P49 Tlusty MF, 2004, OFI J, V46, P6 Tlusty MF, 2012, FISH FISH, V13, P1, DOI 10.1111/j.1467-2979.2011.00404.x Tsounis G, 2010, OCEANOGR MAR BIOL, V48, P161, DOI 10.1201/EBK1439821169-c3 Turchini GM, 2009, J AGR FOOD CHEM, V57, P274, DOI 10.1021/jf801962h Uglem I, 1996, AQUACULT ENG, V15, P499, DOI 10.1016/S0144-8609(96)01005-9 Uglem I., 1995, Aquaculture Research, V26, P837, DOI 10.1111/j.1365-2109.1995.tb00877.x Valladares S, 2012, AQUAT ECOL, V46, P363, DOI 10.1007/s10452-012-9407-y Vaz MCM, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0035355 Veinott G, 2012, ECOL FRESHW FISH, V21, P541, DOI 10.1111/j.1600-0633.2012.00574.x Wabnitz C., 2003, OCEAN AQUARIUM GLOBA Walters L, 2011, FRONT ECOL ENVIRON, V9, P206, DOI 10.1890/11.WB.007 Ward T. J., 2008, SEAFOOD ECOLABELLING, P472, DOI [10.1002/9781444301380.ch1, DOI 10.1002/9781444301380.CH1] Wessells C.R., 2001, PRODUCT CERTIFICATIO Wessells CR, 1999, AM J AGR ECON, V81, P1084, DOI 10.2307/1244088 Whitfield PE, 2002, MAR ECOL PROG SER, V235, P289, DOI 10.3354/meps235289 Williams ID, 2009, BIOL CONSERV, V142, P1066, DOI 10.1016/j.biocon.2008.12.029 Wood E., 2001, COLLECTION CORAL REE Woodall LC, 2012, J MAR BIOL ASSOC UK, V92, P1427, DOI 10.1017/S0025315411001810 Younk JA, 2010, N AM J FISH MANAGE, V30, P281, DOI 10.1577/M08-257.1 Zacherl DC, 2003, MAR ECOL PROG SER, V248, P297, DOI 10.3354/meps248297 Zajicek P, 2009, REV FISH SCI, V17, P156, DOI 10.1080/10641260802536577 Zaroban DW, 2010, WEST N AM NATURALIST, V70, P218, DOI 10.3398/064.070.0208 NR 142 TC 41 Z9 41 U1 0 U2 60 PY 2013 VL 21 IS 2 BP 98 EP 111 DI 10.1080/10641262.2012.760522 WC Fisheries SC Fisheries UT WOS:000318979900002 DA 2022-12-14 ER PT J AU Yang, XT Qian, JP Li, J Ji, ZT Fan, BL Xing, B Li, WY AF Yang, Xin-ting Qian, Jian-ping Li, Jie Ji, Zeng-tao Fan, Bei-lei Xing, Bin Li, Wen-yong TI A real-time agro-food authentication and supervision system on a novel code for improving traceability credibility SO FOOD CONTROL DT Article DE Agriculture food; Traceability credibility; Quality safety; QR code; Traceability system ID ACQUISITION-SYSTEM; FOOD; IDENTIFICATION; CHINA AB Counterfeiting products and abusing labels lead to less credibility for traceability system in China recently. Authentication and supervision agencies driven by government departments play an important role for ensuring the quality safety in the case of lacking the willingness and credit of enterprises. A complete authentication and supervision flow framework was constructed based on an identification code (IdC) for authenticated origin base, which linked two actors of the agencies and the enterprises, and three subsystems of On-line Authentication Subsystem (OAS), Safety Production Management Client (SMC) and Mobile Supervision Application (MSA). IdC consisted of longitude and latitude of origin base as position code, production code and authentication type code. With a relative position partition method on 6 zones every 27 for China map and a coordination transformation algorithm, an absolute longitude and latitude value was converted into a relative position value and a zone mark value. IdC and packaging date code formed initial traceability code (TC). 8 digits packaging date code was reconstructed into 3 digits relative time value and 1 digit period mark according to a relative time period partition method with a period of 999d as time intervals and four periods form a cycle. Validation code was generated integrating the zone mark value, period mark value and authentication type code. Therefore, transformed 20 digits TC with the characters of shorter code length and stronger encryption was formed with IdC, relative time value and validation code. Three subsystems for different actors which provide the main function such as origin base registration, agency authentication, QR code generation, data uploading and product verification, were developed. The system has been used in Tianjin city from 2012. 213 enterprises were audited through OAS and used SMC. Through investigating 8 supervision agency staffs, 30 origin base owners, and 50 customers, it is shown that the positive effects are approved by most of the investigators and two negative effects for enhancing the costs and doubting the authentication reliability are laid by 17 enterprises and 12 customers. Furthermore, 4 typical cases for counterfeiting and abusing the labels were exampled and can be solved to a certain extent with the system. However, except for the technology itself, a management measures fitting the supervision flow and system need to draft in order to improve the system application well in the future. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Yang, Xin-ting; Qian, Jian-ping; Ji, Zeng-tao; Fan, Bei-lei; Xing, Bin; Li, Wen-yong] Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. [Li, Jie] Tianjin Rural Affairs Comm Informat Ctr, Tianjin 300061, Peoples R China. C3 Beijing Academy of Agriculture & Forestry RP Qian, JP (corresponding author), Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. EM qianjp@nercita.org.cn CR Amiama C, 2008, COMPUT ELECTRON AGR, V61, P192, DOI 10.1016/j.compag.2007.11.006 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Fan HP, 2009, FOOD CONTROL, V20, P627, DOI 10.1016/j.foodcont.2008.09.013 Furia E, 2011, J AGR FOOD CHEM, V59, P8450, DOI 10.1021/jf201556e Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Qian J.-P., 2013, J FOOD AGRIC ENVIRON, V11, P317 Qian JP, 2015, COMPUT ELECTRON AGR, V116, P101, DOI 10.1016/j.compag.2015.06.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 So-In C, 2014, COMPUT ELECTRON AGR, V109, P287, DOI 10.1016/j.compag.2014.10.004 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 Sun CH, 2013, COMPUT ELECTRON AGR, V92, P82, DOI 10.1016/j.compag.2012.12.014 Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X Yang XinTing, 2008, Transactions of the Chinese Society of Agricultural Engineering, V24, P162 Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 NR 17 TC 14 Z9 16 U1 6 U2 66 PD AUG PY 2016 VL 66 BP 17 EP 26 DI 10.1016/j.foodcont.2016.01.032 WC Food Science & Technology SC Food Science & Technology UT WOS:000375163700003 DA 2022-12-14 ER PT J AU Badia-Melis, R Mishra, P Ruiz-Garcia, L AF Badia-Melis, R. Mishra, P. Ruiz-Garcia, L. TI Food traceability: New trends and recent advances. A review SO FOOD CONTROL DT Review DE Food traceability; Food control; Traceability systems; Internet of things ID NEAR-INFRARED SPECTROSCOPY; SUPPLY-CHAIN; STABLE-ISOTOPE; GEOGRAPHICAL ORIGIN; RFID APPLICATION; SYSTEM; CHEMOMETRICS; SAFETY; TECHNOLOGIES; FRAMEWORK AB Current traceability systems are characterized by the inability to link food chains records, inaccuracy and errors in records and delays in obtaining essential data, which are fundamental in case of food outbreak disease; these systems should address the recall and withdraw of non-consumable products. The present paper provides a review of the various latest technological advancements such as innovative implementations of RFID that can make to increase the sales of wheat flour, or allowing the consumer to know the full record of the IV range products through the smartphone; knowing the food authenticity with an isotope analysis or by analysing the DNA sequences. There are also presented some conceptual advancements in the field of food traceability such as the development of a common framework towards unifying the present technical regulations, the interconnectivity between agents, environment loggers and products, all of them in the form of Internet of things system as well as the development of intelligent traceability, where it is possible to retrieve the temperature of a product or its remaining shelf-life. These new techniques and concepts provide new opportunities for enhancing the efficiency and compatibility of the present traceability systems. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Badia-Melis, R.; Ruiz-Garcia, L.] Univ Politecn Madrid, Dept Ingn Agroforestal, ETSI Agronomos, E-28040 Madrid, Spain. [Mishra, P.] Univ Politecn Madrid, Lab Propiedades Fis & Tecnol Avanzadas Agroalimen, ETSI Agronomos, E-28040 Madrid, Spain. C3 Universidad Politecnica de Madrid; Universidad Politecnica de Madrid RP Badia-Melis, R (corresponding author), Univ Politecn Madrid, Dept Ingn Agroforestal, ETSI Agronomos, E-28040 Madrid, Spain. EM ricardobadia@live.com CR AMADOR C, 2010, 17 WORLD C INT COMM Amaral LA, 2011, J NETW COMPUT APPL, V34, P972, DOI 10.1016/j.jnca.2010.04.005 Angeles R, 2005, INFORM SYST MANAGE, V22, P51, DOI 10.1201/1078/44912.22.1.20051201/85739.7 Arcuri EF, 2013, FOOD CONTROL, V30, P1, DOI 10.1016/j.foodcont.2012.07.007 Attaran M, 2007, SUPPLY CHAIN MANAG, V12, P249, DOI 10.1108/13598540710759763 Atzori L, 2010, COMPUT NETW, V54, P2787, DOI 10.1016/j.comnet.2010.05.010 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Australia GSI, 2010, AUSTR SERV UP SAF TR Ayalew G., 2011, AGR ENG INT CIGR J, V13, P1 Badia-Melis R., 2013, AM SOC AGR BIOL ENG Badia-Melis R, 2015, SENSORS-BASEL, V15, P4781, DOI 10.3390/s150304781 Badia-Melis R, 2014, COMPUT ELECTRON AGR, V103, P11, DOI 10.1016/j.compag.2014.01.014 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Becker S, 2011, MITOCHONDR DNA, V22, P3, DOI 10.3109/19401736.2010.535528 Bertacchini L, 2013, DATA HANDL SCI TECHN, V28, P371, DOI 10.1016/B978-0-444-59528-7.00010-7 Bevilacqua M, 2012, ANAL CHIM ACTA, V717, P39, DOI 10.1016/j.aca.2011.12.035 Bhatt T, 2013, J FOOD SCI, V78, pB21, DOI 10.1111/1750-3841.12278 Bhatt T, 2013, J FOOD SCI, V78, pB9, DOI 10.1111/j.1750-3841.2011.02617.x Bhatt T, 2013, J FOOD SCI, V78, pB28, DOI 10.1111/1750-3841.12299 Bhatt T, 2013, J FOOD SCI, V78, pB34, DOI 10.1111/1750-3841.12298 Blanchfield J. R., 2012, UFOST SCI INFORM B S Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bujji M., 2012, FNB NEWS INDIA Cai YS, 2011, CHINESE SCI BULL, V56, P164, DOI 10.1007/s11434-010-4302-1 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Catarinucci L., 2011, SOFTW TEL COMP NETW Chen YY, 2014, J FOOD ENG, V141, P113, DOI 10.1016/j.jfoodeng.2014.05.014 Chifu VR, 2007, INT C INTELL COMP CO, P1, DOI 10.1109/ICCP.2007.4352135 Consonni R, 2010, ADV FOOD NUTR RES, V59, P87, DOI 10.1016/S1043-4526(10)59004-1 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Cozzolino D, 2014, FOOD RES INT, V60, P262, DOI 10.1016/j.foodres.2013.08.034 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 De Mattia F, 2011, FOOD RES INT, V44, P693, DOI 10.1016/j.foodres.2010.12.032 Drummond N., 2007, PIZZA ONTOLOGY 1 5 Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 Emond JP, 2006, COOL CHAIN ASS WORKS Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Giusto D., 2010, INTERNET THINGS Glykas M, 2010, STUD FUZZ SOFT COMP, V247, P1, DOI 10.1007/978-3-642-03220-2 Golan E., 2004, Amber Waves, V2, P14 Graca MAS, 2001, FRESHWATER BIOL, V46, P947, DOI 10.1046/j.1365-2427.2001.00729.x Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Gonzalez-Martin MI, 2014, FOOD CHEM, V145, P802, DOI 10.1016/j.foodchem.2013.08.103 Jedermann R., 2014, PHILOS T ROYAL SOC A Jedermann R, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0302 Jones P, 2004, INT J RETAIL DISTRIB, DOI DOI 10.1108/09590550410524957 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kim H. M., 1995, P WET ICE LOS ALB CA Kiritsis D, 2011, COMPUT AIDED DESIGN, V43, P479, DOI 10.1016/j.cad.2010.03.002 KOSKO B, 1986, INT J MAN MACH STUD, V24, P65, DOI 10.1016/S0020-7373(86)80040-2 Koutsoumanis K, 2005, INT J FOOD MICROBIOL, V100, P253, DOI 10.1016/j.ijfoodmicro.2004.10.024 Mack M, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0308 Mainetti L., 2012, J COMMUNICATION SOFT, V8, P1 Mainetti L., 2013, INT J ANTENN PROPAG, V2013, DOI [DOI 10.1155/2013/531364, 10.1155/2013/531364] Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Mc Inerney B, 2010, COMPUT ELECTRON AGR, V73, P112, DOI 10.1016/j.compag.2010.06.004 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Miller B. D., 2014, ENCY AGR FOOD SYSTEM, P387 Molkentin J, 2007, ANAL BIOANAL CHEM, V388, P297, DOI 10.1007/s00216-007-1222-2 National Agriculture and Food Traceability System (NAFTS), 2013, CAN TRAC Newsome RL, 2013, J FOOD SCI, V78, pB1, DOI 10.1111/j.1750-3841.2011.02616.x NFC Forum, 2014, NFC FOR WHAT IS NFC Ngai EWT, 2007, DECIS SUPPORT SYST, V43, P62, DOI 10.1016/j.dss.2005.05.006 Noy, 2001, SMI20010880 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ren GX, 2013, FOOD RES INT, V53, P822, DOI 10.1016/j.foodres.2012.10.032 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Sakai Y, 2010, J AGR FOOD CHEM, V58, P8145, DOI 10.1021/jf100675c Scheer FP, 2006, WOODHEAD PUBL FOOD S, P52, DOI 10.1533/9781845691233.1.52 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 SUGAHARA K, 2009, COMPUTER COMPUTING T, V3 Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Theodorou S., 2007, THESIS U CAMBRIDGE Thottupuram R., 2011, INT J RES REV INFORM, V1 Trafton A., 2014, DETECTING GASES WIRE Twist DC, 2005, J FACIL MANAG, V3, P226, DOI 10.1108/14725960510808491 Van der Vorst G.A.J., 2007, P EUR 14 INT ANN C 1 Vandeginste B, 2013, WOODHEAD PUBL FOOD S, V245, P117, DOI 10.1533/9780857097590.2.117 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Wang Y., 2012, J CONVERGENCE INFORM, V7, P86 Xinhua News Agency, 2011, CHINA DAILY Yue J., 2005, P 2005 1 INT C SEMAN, P130 Zhang M, 2012, PHYSCS PROC, V25, P636, DOI 10.1016/j.phpro.2012.03.137 Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 Zou Z., 2014, PHILOS T R SOC A, V372 NR 98 TC 174 Z9 183 U1 12 U2 346 PD NOV PY 2015 VL 57 BP 393 EP 401 DI 10.1016/j.foodcont.2015.05.005 WC Food Science & Technology SC Food Science & Technology UT WOS:000357839100058 DA 2022-12-14 ER PT J AU Zappa, G Zoani, C AF Zappa, G. Zoani, C. TI Reference materials in support to food traceability SO AGROCHIMICA DT Article DE authenticity; food traceability; markers; metrological traceability; reference materials ID GEOGRAPHICAL ORIGIN; MULTIELEMENT AB Traceability represents the ability to trace and follow a food and any ingredient through all the stages from production, up to the distribution and the end use. Thanks to the progress of knowledge, technologies and data processing systems, it is possible to identify markers and develop systems and analytical methods for demonstrating the origin and authenticity of raw materials and products. The application of the metrological rules to chemical and biological measurements requires more and more a wide availability of Reference Materials (RMs), which constitute very often the only way for establishing metrological traceability of the measurement results and for quantifying measurement uncertainty. In this work, an examination of the current availability of RMs for traceability of food products is reported, and the ENEA activities on the development of new RMs for food traceability are described. C1 [Zappa, G.; Zoani, C.] ENEA, Italian Natl Agcy New Technol Energy & Sustainabl, Dept Sustainabil Prod & Terr Syst SSPT, Div Biotechnol & Agroind BIOAG,Casaccia Res Ctr, Via Anguillarese 301, I-00123 Rome, Italy. C3 Italian National Agency New Technical Energy & Sustainable Economics Development RP Zoani, C (corresponding author), ENEA, Italian Natl Agcy New Technol Energy & Sustainabl, Dept Sustainabil Prod & Terr Syst SSPT, Div Biotechnol & Agroind BIOAG,Casaccia Res Ctr, Via Anguillarese 301, I-00123 Rome, Italy. EM claudia.zoani@enea.it CR [Anonymous], 2015, ISO REMCO GUID 30 20 [Anonymous], 2009, ISO REMCO GUID 34 20 Coetzee PP, 2005, J AGR FOOD CHEM, V53, P5060, DOI 10.1021/jf048268n Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 EU-DG SANCO, 2007, FOOD TRAC TRAC FOOD Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 TUAH L., 2011, CLASSIFICATION GEOGR ZAPPA G., 2007, EFSA SCI S PARM IT 4 ZAPPA G., 2009, 3 NAT C MYC FOOD CHA, P104 ZAPPA G., 2009, 4 INT S REC ADV FOOD ZAPPA G., 2007, P 5 C METR QUAL TOR ZAPPA G., 2013, 16 INT C METR PAR FR, DOI DOI 10.1051/METROLOGY/201316001 ZAPPA G., 2000, 9904ENEART20007 ZOANI C., 2014, 1 INT C IMEKOFOODS M ZOANI C., 2013, IMEKO INT TC8 TC23 T NR 15 TC 0 Z9 0 U1 0 U2 1 PD OCT-DEC PY 2015 VL 59 IS 4 BP 304 EP 318 WC Chemistry, Applied; Soil Science SC Chemistry; Agriculture UT WOS:000374070500003 DA 2022-12-14 ER PT J AU He, XJ Chen, XM Li, KZ AF He, Xiaojian Chen, Ximeng Li, Kangzi TI A Decentralized and Non-reversible Traceability System for Storing Commodity Data SO KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS DT Article DE Traceability system; decentralized; non-reversible; blockchain; data storage AB In the field of traceability systems, researchers focus on applications in the agricultural food traceability and scanning commodities. The purposes of this paper, however, is to propose an efficient and reliable traceability system that can be applied to all kinds of commodities. Currently, most traceability systems store data in a central server, which is unreliable when the system is under attack or if the administrator tampers with the data for personal interests. Therefore, it is necessary to design a system that can eliminate these threats. In this paper, we propose a decentralized and non-reversible traceability system for storing commodity data. This system depends on blockchain technology, which organizes data in the form of chains without a central server. This chain-style storage mechanism can prevent malicious modifications. In addition, some strategies are adopted to reduce the storage pressure and response time when the system has stored all kinds of commodity data. C1 [He, Xiaojian; Chen, Ximeng; Li, Kangzi] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China. C3 South China University of Technology RP Li, KZ (corresponding author), South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China. EM hexj@scut.edu.cn; cxmlzl@foxmail.com; likangzi@foxmail.com CR Abeyratne S. A., 2016, INT J RES ENG TECHNO, V5, P1, DOI DOI 10.15623/IJRET.2016.0509001 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Benshoof B, 2016, IEEE SYM PARA DISTR, P1279, DOI 10.1109/IPDPSW.2016.109 Bernardi P, 2007, 21ST INTERNATIONAL CONFERENCE ON ADVANCED NETWORKING AND APPLICATIONS, PROCEEDINGS, P68, DOI 10.1109/AINA.2007.29 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Indu T.R., 2016, INT J SCI RES SCI, V2, P300, DOI [10.32628/IJSRSET162375, DOI 10.32628/IJSRSET162375] Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 Nakamoto S., 2008, DECENTRALIZED BUSINE, V21260 Neher Jon O., 2017, EVIDENCE BASED PRACT, V20, P2 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Powell Connie Davis, 2012, N C J L TECH, V14, P1 Ruey-Shun Chen, 2008, WSEAS Transactions on Information Science and Applications, V5, P1551 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Scorpecci Danny, 2009, P C TRANS IP COLL AP, V27 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Wu H. T., 2017, THESIS Zyskind G, 2015, 2015 IEEE SECURITY AND PRIVACY WORKSHOPS (SPW), P180, DOI 10.1109/SPW.2015.27 NR 20 TC 8 Z9 8 U1 2 U2 42 PD FEB 28 PY 2019 VL 13 IS 2 BP 619 EP 634 DI 10.3837/tiis.2019.02.008 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000459989500008 DA 2022-12-14 ER PT J AU Yang, SS Li, CB Wu, QY Zhu, CQ Xu, XL Zhou, GH AF Yang, Sasa Li, Chunbao Wu, Qiayu Zhu, Changqing Xu, Xinglian Zhou, Guanghong TI High-resolution melting analysis: a promising molecular method for meat traceability SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE DNA traceability; HRM; SNPs; Pork ID TOOL AB To find a promising molecular method for meat traceability, three methods of single nucleotide polymorphism (SNP) detection: RFLP-PCR analysis, high-resolution melting (HRM) analysis, and TaqMan probe analysis, have been compared in terms of accuracy, ease of use, throughput capability, and cost. We genotyped ten pork DNA samples across three SNPs. The results showed that the HRM genotyping method was the most accurate and easiest to use with the lowest cost, while TaqMan probe analysis provided similar results, but its cost was much higher. C1 [Yang, Sasa; Li, Chunbao; Wu, Qiayu; Zhu, Changqing; Xu, Xinglian; Zhou, Guanghong] Nanjing Agr Univ, Coll Food Sci & Technol, Synerget Innovat Ctr Food Safety & Nutr,MOE, Key Lab Anim Prod Proc,MOA,Key Lab Meat Proc & Qu, Nanjing 210095, Jiangsu, Peoples R China. C3 Nanjing Agricultural University RP Zhou, GH (corresponding author), Nanjing Agr Univ, Coll Food Sci & Technol, Synerget Innovat Ctr Food Safety & Nutr,MOE, Key Lab Anim Prod Proc,MOA,Key Lab Meat Proc & Qu, Weigang 1, Nanjing 210095, Jiangsu, Peoples R China. EM sasayang23@gmail.com; chunbao.li@njau.edu.cn; ghzhou@njau.edu.cn CR Capoferri R, 2005, 4 WORLD IT BEEF CATT Farrar JS, 2010, MOLECULAR DIAGNOSTICS, 2ND EDITION, P229, DOI 10.1016/B978-0-12-374537-8.00015-8 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Gundry CN, 2003, CLIN CHEM, V49, P396, DOI 10.1373/49.3.396 Herrmann MG, 2007, CLIN CHEM, V53, P1544, DOI 10.1373/clinchem.2007.088120 Herrmann MG, 2006, CLIN CHEM, V52, P494, DOI 10.1373/clinchem.2005.063438 Lichtenberg L., 2008, 12 C EUR ASS AGR EC, P1 Martino A, 2010, J BIOMOL SCREEN, V15, P623, DOI 10.1177/1087057110365900 Rohrer GA, 2007, ANIM GENET, V38, P253, DOI 10.1111/j.1365-2052.2007.01593.x Sakaridis I, 2013, MEAT SCI, V94, P84, DOI 10.1016/j.meatsci.2012.12.017 Slinger R, 2007, DIAGN MICROBIOL INFE, V57, P55 Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Vossen RHAM, 2009, HUM MUTAT, V30, P860, DOI 10.1002/humu.21019 Wittwer CT, 2009, HUM MUTAT, V30, P857, DOI 10.1002/humu.20951 Wittwer CT, 1997, BIOTECHNIQUES, V22, P130, DOI 10.2144/97221bi01 Wu SB, 2008, THEOR APPL GENET, V118, P1, DOI 10.1007/s00122-008-0870-8 Zhang RF, 2005, NUCLEIC ACIDS RES, V33, pW489, DOI 10.1093/nar/gki358 NR 17 TC 7 Z9 7 U1 0 U2 25 PD SEP PY 2014 VL 239 IS 3 BP 473 EP 480 DI 10.1007/s00217-014-2241-9 WC Food Science & Technology SC Food Science & Technology UT WOS:000340587400012 DA 2022-12-14 ER PT J AU Curto, JP Gaspar, PD AF Curto, Joao Paulo Gaspar, Pedro Dinis TI Traceability in food supply chains: SME focused traceability framework for chain-wide quality and safety-Part 2 SO AIMS AGRICULTURE AND FOOD DT Article DE traceability; framework; prototype; supply chain simulation ID IMPLEMENTING TRACEABILITY; PRODUCT QUALITY; SYSTEM; MODEL; TRANSPARENCY; SUSTAINABILITY; DETERIORATION; TECHNOLOGIES; GRANULARITY; INFORMATION AB It is relevant for traceability systems to have a common structure for information exchange. Without it, these systems lose much of their utility as they will only be usable internally and will have reduced capacity to add value to products and manage recalls. Based on extensive literature review, a non-proprietary framework for traceability was developed. This framework encompasses whole food supply chains and aims to maintain records of quality and safety while not necessitating mature IT capabilities, uncommon characteristic of SME's. As such the volume of information is divided between all stakeholders according to their necessities and funding capacities. Most of the information is stored by regulators as they have access to more funding. This improves the ease and flexibility of implementation of traceability systems by the companies. Tools were developed and simulated, and all results are presented, clearly demonstrating the capability for quality information sharing through food supply chains which in turn can increase transparency between consumers and producers as well as adjusting the quality to the desired end use. C1 [Curto, Joao Paulo; Gaspar, Pedro Dinis] Univ Beira Interior, Rua Marques Avila & Bolama, P-6201001 Covilha, Portugal. [Gaspar, Pedro Dinis] C MAST Ctr Mech & Aerosp Sci & Technol, Covilha, Portugal. C3 Universidade da Beira Interior RP Gaspar, PD (corresponding author), Univ Beira Interior, Rua Marques Avila & Bolama, P-6201001 Covilha, Portugal.; Gaspar, PD (corresponding author), C MAST Ctr Mech & Aerosp Sci & Technol, Covilha, Portugal. EM dinis@ubi.pt CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bakker M, 2012, EUR J OPER RES, V221, P275, DOI 10.1016/j.ejor.2012.03.004 Bechim A, 2005, 2005 SYMPOSIUM ON APPLICATIONS AND THE INTERNET WORKSHOPS, PROCEEDINGS, P366, DOI 10.1109/SAINTW.2005.1620050 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bendaoud M, 2012, INT FOOD AGRIBUS MAN, V15, P103 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Borit M, 2012, MAR POLICY, V36, P96, DOI 10.1016/j.marpol.2011.03.012 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Frosch S, 2008, J AQUAT FOOD PROD T, V17, P387, DOI 10.1080/10498850802369179 Gaukler G, 2017, INT J PROD ECON, V193, P617, DOI 10.1016/j.ijpe.2017.07.019 Germani M, 2015, PROC CIRP, V29, P227, DOI 10.1016/j.procir.2015.02.199 Gessner GH, 2007, INFORM SYST MANAGE, V24, P213, DOI 10.1080/10580530701404561 Grunow M, 2013, INT J PROD ECON, V146, P717, DOI 10.1016/j.ijpe.2013.08.028 Heese HS, 2007, PROD OPER MANAG, V16, P542 Hertog MLATM, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0306 Hsiao HI, 2016, FOOD CONTROL, V64, P181, DOI 10.1016/j.foodcont.2015.12.020 Hsu CI, 2007, J FOOD ENG, V80, P465, DOI 10.1016/j.jfoodeng.2006.05.029 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Huang FH, 2012, IFIP ADV INF COMM TE, V368, P371 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Jedermann Reiner, 2007, International Journal of Radio Frequency Identification Technology and Applications, V1, P247, DOI 10.1504/IJRFITA.2007.015849 Jedermann R, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0302 Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kwok SK, 2008, IFAC, V41, P5482 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Liu F, 2015, INT J SENS NETW, V17, P211, DOI 10.1504/IJSNET.2015.069582 Liu XF, 2008, ASIA PAC J MARKET LO, V20, P7, DOI 10.1108/13555850810844841 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Ndraha N, 2018, FOOD CONTROL, V89, P12, DOI 10.1016/j.foodcont.2018.01.027 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Oskarsdottir K, 2019, J FOOD ENG, V240, P153, DOI 10.1016/j.jfoodeng.2018.07.013 Pahl J, 2014, EUR J OPER RES, V238, P654, DOI 10.1016/j.ejor.2014.01.060 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Piramuthu S, 2013, EUR J OPER RES, V225, P253, DOI 10.1016/j.ejor.2012.09.024 Pizzuti Teresa, 2012, Proceedings of the 3rd 2012 International Conference on Industrial Engineering and Operations Management, P1065 Qian JP, 2017, FOOD CONTROL, V74, P98, DOI 10.1016/j.foodcont.2016.11.034 Raak N, 2017, WASTE MANAGE, V61, P461, DOI 10.1016/j.wasman.2016.12.027 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Shankar R, 2018, TRANSPORT RES E-LOG, V119, P205, DOI 10.1016/j.tre.2018.03.006 Skoglund T, 2007, FOOD BIOPROD PROCESS, V85, P354, DOI 10.1205/fbp07044 Sloof M, 1996, TRENDS FOOD SCI TECH, V7, P165, DOI 10.1016/0924-2244(96)81257-X Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Stranieri S., 2018, Wine Economics and Policy, V7, P45, DOI 10.1016/j.wep.2018.02.001 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Thakur M, 2011, COMPUT ELECTRON AGR, V75, P327, DOI 10.1016/j.compag.2010.12.010 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Tijskens LMM, 1996, AGR SYST, V51, P431, DOI 10.1016/0308-521X(95)00058-D Trebar M, 2013, INT J ANTENN PROPAG, V2013, DOI 10.1155/2013/875973 van der Vorst JGAJ, 2009, INT J PROD RES, V47, P6611, DOI 10.1080/00207540802356747 Verdouw CN, 2013, COMPUT ELECTRON AGR, V99, P160, DOI 10.1016/j.compag.2013.09.006 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Wang X, 2018, FOOD CONTROL, V88, P169, DOI 10.1016/j.foodcont.2018.01.008 Wang XJ, 2012, OMEGA-INT J MANAGE S, V40, P906, DOI 10.1016/j.omega.2012.02.001 Woo SH, 2009, COMPUT IND, V60, P149, DOI 10.1016/j.compind.2008.12.001 Zhang Hu, 2009, WSEAS Transactions on Information Science and Applications, V6, P1094 Zhou W, 2009, DECIS SUPPORT SYST, V48, P169, DOI 10.1016/j.dss.2009.07.008 NR 73 TC 3 Z9 3 U1 4 U2 13 PY 2021 VL 6 IS 2 BP 708 EP 736 DI 10.3934/agrfood.2021042 WC Agriculture, Multidisciplinary; Agronomy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000677621900016 DA 2022-12-14 ER PT J AU Voorhuijzen, MM van Dijk, JP Prins, TW Van Hoef, AMA Seyfarth, R Kok, EJ AF Voorhuijzen, Marleen M. van Dijk, Jeroen P. Prins, Theo W. Van Hoef, A. M. Angeline Seyfarth, Ralf Kok, Esther J. TI Development of a multiplex DNA-based traceability tool for crop plant materials SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article DE Authenticity; DNA; Ligation; Detection; Traceability; Multiplex ID BASMATI RICE; FRAGRANCE; GENE; ADULTERATION; BREAD; WHEAT AB The authenticity of food is of increasing importance for producers, retailers and consumers. All groups benefit from the correct labelling of the contents of food products. Producers and retailers want to guarantee the origin of their products and check for adulteration with cheaper or inferior ingredients. Consumers are also more demanding about the origin of their food for various socioeconomic reasons. In contrast to this increasing demand, correct labelling has become much more complex because of global transportation networks of raw materials and processed food products. Within the European integrated research project 'Tracing the origin of food' (TRACE), a DNA-based multiplex detection tool was developed-the padlock probe ligation and microarray detection (PPLMD) tool. In this paper, this method is extended to a 15-plex traceability tool with a focus on products of commercial importance such as the emmer wheat Farro della Garfagnana (FdG) and Basmati rice. The specificity of 14 plant-related padlock probes was determined and initially validated in mixtures comprising seven or nine plant species/varieties. One nucleotide difference in target sequence was sufficient for the distinction between the presence or absence of a specific target. At least 5% FdG or Basmati rice was detected in mixtures with cheaper bread wheat or non-fragrant rice, respectively. The results suggested that even lower levels of (un-)intentional adulteration could be detected. PPLMD has been shown to be a useful tool for the detection of fraudulent/intentional admixtures in premium foods and is ready for the monitoring of correct labelling of premium foods worldwide. C1 [Voorhuijzen, Marleen M.; van Dijk, Jeroen P.; Prins, Theo W.; Van Hoef, A. M. Angeline; Kok, Esther J.] Wageningen UR, RIKILT Inst Food Safety, NL-6700 AE Wageningen, Netherlands. [Seyfarth, Ralf] Biolytix AG, CH-4108 Witterswil, Switzerland. C3 Wageningen University & Research RP Voorhuijzen, MM (corresponding author), Wageningen UR, RIKILT Inst Food Safety, POB 230, NL-6700 AE Wageningen, Netherlands. EM marleen.voorhuijzen@wur.nl CR Asakura N, 2009, GENES GENET SYST, V84, P233, DOI 10.1266/ggs.84.233 Bradbury LMT, 2005, PLANT BIOTECHNOL J, V3, P363, DOI 10.1111/j.1467-7652.2005.00131.x BUTTERY RG, 1983, J AGR FOOD CHEM, V31, P823, DOI 10.1021/jf00118a036 Devor E, 2005, LOCKED NUCL ACIDS LN European Commission, AGR RUR DEV DOOR DAT European Commission, AGR RUR DEV QUAL POL European Commission, GEN FOOD LAW TRAC FA Fitzgerald MA, 2008, PLANT BIOTECHNOL J, V6, P416, DOI 10.1111/j.1467-7652.2008.00327.x Koshkin AA, 1998, TETRAHEDRON, V54, P3607, DOI 10.1016/S0040-4020(98)00094-5 Kovach MJ, 2009, P NATL ACAD SCI USA, V106, P14444, DOI 10.1073/pnas.0904077106 Lopez SJ, 2008, EUR FOOD RES TECHNOL, V227, P619, DOI 10.1007/s00217-007-0763-0 Magness J. R., 1971, IR B, V828 Magness JR, 1971, B AGR EXPT STA MURAMATSU M, 1963, GENETICS, V48, P469 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3312, DOI 10.1021/jf063383e Pestsova E, 2000, GENOME, V43, P689, DOI 10.1139/gen-43-4-689 Prins TW, 2010, FOOD CHEM, V118, P966, DOI 10.1016/j.foodchem.2008.10.085 Prins TW, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-584 Sakthivel K, 2009, BIOTECHNOL ADV, V27, P468, DOI 10.1016/j.biotechadv.2009.04.001 Sanchez JA, 2004, P NATL ACAD SCI USA, V101, P1933, DOI 10.1073/pnas.0305476101 Sonnante G, 2009, J AGR FOOD CHEM, V57, P10199, DOI 10.1021/jf902624z Szemes M, 2005, NUCLEIC ACIDS RES, V33, DOI 10.1093/nar/gni069 Vemireddy LR, 2007, J AGR FOOD CHEM, V55, P8112, DOI 10.1021/jf0714517 Wengel J., LOCKED NUCL ACID TEC Widjaja R, 1996, J SCI FOOD AGR, V70, P151, DOI 10.1002/(SICI)1097-0010(199602)70:2<151::AID-JSFA478>3.0.CO;2-U NR 25 TC 21 Z9 23 U1 2 U2 18 PD JAN PY 2012 VL 402 IS 2 BP 693 EP 701 DI 10.1007/s00216-011-5534-x WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000298645300011 DA 2022-12-14 ER PT J AU Foras, E Thakur, M Solem, K Svarva, R AF Foras, Eskil Thakur, Maitri Solem, Kristian Svarva, Reidun TI State of traceability in the Norwegian food sectors SO FOOD CONTROL DT Article DE Traceability; Trace-back; Tracking; Tracing; Simulated recall; eSporing; Norway ID SUPPLY-SYSTEM TRACEABILITY; FRAMEWORK; PRODUCTS; INDUSTRY; CHAINS; MARKET AB The goal of this study conducted in 2012-2013 was to illustrate the status of food traceability in the Norwegian food sector and the effectiveness of current traceability systems used by the food industry after the conclusion of the National eSporing (e-traceability) project. One of the main focus areas of the eSporing project was to facilitate and encourage traceability discussions and traceability projects within various subsectors in the food value chains, aimed at finding efficient, cost-effective ways to implement better traceability. The subsectors organized their work as independent traceability pilots, working in close collaboration with the eSporing project. This study to determine the status of traceability included five main food sectors including red meat, dairy, grains, fruits and vegetables and fish. 30 products of national and international origin were selected and trace backs were performed using a simulated recall method to determine the pathway through the supply chain from retailer back to the origin. Of these, 28 products were traceable back to the origin. A similar study was conducted in 2008 and the results show significant improvement from the previous investigation. The increase in the number of traceable food products indicates that the Norwegian food industry has established a more robust traceability system during the period between 2008 and 2013. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Foras, Eskil; Thakur, Maitri] SINTEF Fisheries & Aquaculture, N-7010 Trondheim, Norway. [Solem, Kristian; Svarva, Reidun] Ramboll, N-7493 Trondheim, Norway. C3 SINTEF RP Foras, E (corresponding author), SINTEF Fisheries & Aquaculture, Brattorkaia 17C, N-7010 Trondheim, Norway. EM eskilfs@gmail.no CR [Anonymous], 2009, 08 STAND Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bollen A. F., 2006, INT J POSTHARVEST TE, V1, P92 Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x Donnelly KAM, 2012, BRIT FOOD J, V114, P1016, DOI 10.1108/00070701211241590 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Karlsen K. M., 2009, 82009 NOF Karlsen Kine Mari, 2006, P251 Kiesel K, 2005, AM J AGR ECON, V87, P378, DOI 10.1111/j.1467-8276.2005.00729.x Matloven, 2003, LOV MATPR MATTR MV Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Pettitt RG, 2001, REV SCI TECH OIE, V20, P584, DOI 10.20506/rst.20.2.1299 Randrup M, 2008, FOOD CONTROL, V19, P1064, DOI 10.1016/j.foodcont.2007.11.005 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Schimmer B, 2008, BMC INFECT DIS, V8, DOI 10.1186/1471-2334-8-41 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 van Dorp K.-J., 2002, Logistics Information Management, V15, P24, DOI 10.1108/09576050210412648 NR 20 TC 10 Z9 10 U1 1 U2 55 PD NOV PY 2015 VL 57 BP 65 EP 69 DI 10.1016/j.foodcont.2015.03.027 WC Food Science & Technology SC Food Science & Technology UT WOS:000357839100011 DA 2022-12-14 ER PT J AU Yang, XT Li, MQ Yu, HJ Wang, MT Xu, DM Sun, CH AF Yang, Xinting-- Li, Mengqi Yu, Huajing Wang, Mingting Xu, Daming Sun, Chuanheng TI A Trusted Blockchain-Based Traceability System for Fruit and Vegetable Agricultural Products SO IEEE ACCESS DT Article DE Blockchain; Agricultural products; Supply chains; Safety; Production; Smart contracts; Memory; Blockchain; traceability; on-chain and off-chain; agricultural products AB Traditional traceability system has problems of centralized management, opaque information, untrustworthy data, and easy generation of information islands. To solve the above problems, this paper designs a traceability system based on blockchain technology for storage and query of product information in supply chain of agricultural products. Leveraging the characteristics of decentralization, tamper-proof and traceability of blockchain technology, the transparency and credibility of traceability information increased. A dual storage structure of "database + blockchain" on-chain and off-chain traceability information is constructed to reduce load pressure of the chain and realize efficient information query. Blockchain technology combined with cryptography is proposed to realize the safe sharing of private information in the blockchain network. In addition, we design a reputation-based smart contract to incentivize network nodes to upload traceability data. Furthermore, we provide performance analysis and practical application, the results show that our system improves the query efficiency and the security of private information, guarantees the authenticity and reliability of data in supply chain management, and meets actual application requirements. C1 [Yang, Xinting--; Li, Mengqi; Yu, Huajing; Wang, Mingting; Xu, Daming; Sun, Chuanheng] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. [Yang, Xinting--; Li, Mengqi; Yu, Huajing; Wang, Mingting; Xu, Daming; Sun, Chuanheng] Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. [Yang, Xinting--; Li, Mengqi; Yu, Huajing; Wang, Mingting] Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China. C3 Beijing Academy of Agriculture & Forestry; Shanghai Ocean University RP Sun, CH (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China.; Sun, CH (corresponding author), Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. EM sunch@nercita.org.cn CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Christidis K, 2016, IEEE ACCESS, V4, P2292, DOI 10.1109/ACCESS.2016.2566339 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Desai AN, 2019, INT J INFECT DIS, V84, P48, DOI 10.1016/j.ijid.2019.04.021 Dong Y., 2019, FOOD SCI, V41, P30, DOI [10.7506/ spkx1002-6630-20190418-227, DOI 10.7506/SPKX1002-6630-20190418-227] Dwivedi SK, 2020, J INF SECUR APPL, V54, DOI 10.1016/j.jisa.2020.102554 Francois G, 2020, PHYTOCHEMISTRY, V173, DOI 10.1016/j.phytochem.2020.112291 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Li MingJia, 2019, Shipin Kexue / Food Science, V40, P279 Li WW, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107359 Li XF, 2020, IEEE ACCESS, V8, P69754, DOI 10.1109/ACCESS.2020.2986220 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 [刘家稷 Liu Jiaji], 2018, [信息安全学报, Journal of Cyber Security], V3, P17 Lu Y, 2018, J MANAG ANAL, V5, P231, DOI 10.1080/23270012.2018.1516523 Luber P, 2014, WOODHEAD PUBL FOOD S, V260, P356, DOI 10.1533/9781782420279.5.356 NBSC National Bureau of Statistics, 2019, NAT DAT Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Tian F, 2017, I C SERV SYST SERV M Vacca A, 2021, J SYST SOFTWARE, V174, DOI 10.1016/j.jss.2020.110891 Yang L, 2019, J IND INF INTEGR, V15, P80, DOI 10.1016/j.jii.2019.04.002 Yang Xinting, 2019, Transactions of the Chinese Society of Agricultural Engineering, V35, P323, DOI 10.11975/j.issn.1002-6819.2019.22.038 Yang XinTing, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P212 [于合龙 Yu Helong], 2020, [农业机械学报, Transactions of the Chinese Society for Agricultural Machinery], V51, P328 [袁勇 Yuan Yong], 2018, [自动化学报, Acta Automatica Sinica], V44, P2011 Yuxin Liao, 2019, Journal of Physics: Conference Series, V1288, DOI 10.1088/1742-6596/1288/1/012062 Zhang YJ, 2019, FOOD CONTROL, V95, P283, DOI 10.1016/j.foodcont.2018.08.018 Zhao Lei, 2020, Shipin Kexue / Food Science, V41, P314, DOI 10.7506/spkx1002-6630-20181119-217 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 Zhu P, 2020, IEEE ACCESS, V8, P184256, DOI 10.1109/ACCESS.2020.3029196 NR 34 TC 27 Z9 31 U1 40 U2 145 PY 2021 VL 9 BP 36282 EP 36293 DI 10.1109/ACCESS.2021.3062845 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000626509000001 DA 2022-12-14 ER PT J AU Oger, R Krafft, A Buffet, D Debord, M AF Oger, Robert Krafft, Alain Buffet, Dominique Debord, Michel TI Geotraceability: an innovative concept to enhance conventional traceability in the agri-food chain SO BIOTECHNOLOGIE AGRONOMIE SOCIETE ET ENVIRONNEMENT DT Article DE Geotraceability; indicator; geoidentifier; food safety; quality of agricultural products; certification of agricultural products; traceability system; geographical data ID BUFFALO MOZZARELLA CHEESE; GEOGRAPHIC ORIGIN; MEAT AB With the globalization of trade, people have become enlightened and demanding consumers as regards the origin of their food and the environment in which it is produced. The concept of geotraceability described in this article responds to that requirement by combining geographical information with conventional traceability data. The inclusion of geographical information relating to the environment of the production plots is based not only on exploiting some functionalities of spatial analysis tools that exist in geographical information systems (GIS) but also on developing specific tools such as a geoidentifier and geoindicators. This article also describes the characteristics and methods of implementing a geographical information management system linked with traceability information. Lastly, the potential for using geotraceability systems in supply chains is analyzed, in particular for consumer warnings in cases of food crisis and assistance for certification of differentiated quality agricultural products. C1 [Oger, Robert; Krafft, Alain; Buffet, Dominique] Ctr Wallon Rech Agron CRA W, Unite Syst Agraires Terr & Technol Informat, B-5030 Gembloux, Belgium. [Debord, Michel] Chambre Commerce & Ind Auch & Gers, Maison Commerce & Ind, F-32004 Auch, France. RP Oger, R (corresponding author), Ctr Wallon Rech Agron CRA W, Unite Syst Agraires Terr & Technol Informat, Rue Liroux 9, B-5030 Gembloux, Belgium. EM oger@cra.wallonie.be CR [Anonymous], 1994, 8402 ISO [Anonymous], 2002, OFF J EUR COMMUNIT L, VL 31, P1 [Anonymous], 2018, STANDARD ISO 2200020 BOISVERT I, 2005, COMMUNITY SUPPORTED Bonizzi I, 2007, J APPL MICROBIOL, V102, P667, DOI 10.1111/j.1365-2672.2006.03131.x DEBORD M, 2005, GEOTRACEABILITY INNO *EN ISO, 1995, 84021995 EN ISO Franke BM, 2008, EUR FOOD RES TECHNOL, V226, P761, DOI 10.1007/s00217-007-0588-x Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Golan E., 2004, 830 USDA HOBBS J, 2004, P FRONT WORKSH QUANT *ISO, 2005, 22005 ISO Mauriello G, 2003, J DAIRY SCI, V86, P486, DOI 10.3168/jds.S0022-0302(03)73627-3 MAURIZI B, 2002, INGENIERIES, V30, P37 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 NANSEKI T, 2008, FOOD TRACEABILITY WO, V1, P46 Pillonel L, 2003, EUR FOOD RES TECHNOL, V216, P179, DOI 10.1007/s00217-002-0629-4 Prache S, 2005, SMALL RUMINANT RES, V59, P157, DOI 10.1016/j.smallrumres.2005.05.004 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 SUHAJ M, 2008, EUR FOOD RES TECHNOL, V1, P101 Van Dorp C.A., 2004, REFERENCE DATA MODEL Zunin P, 2005, J CHROMATOGR A, V1089, P243, DOI 10.1016/j.chroma.2005.07.005 2001, TRACABILITE CHAINES 2002, EUROPEENS POLITIQUE NR 25 TC 3 Z9 4 U1 0 U2 13 PY 2010 VL 14 IS 4 BP 633 EP 642 WC Agronomy; Biotechnology & Applied Microbiology; Environmental Sciences SC Agriculture; Biotechnology & Applied Microbiology; Environmental Sciences & Ecology UT WOS:000285420600004 DA 2022-12-14 ER PT J AU Feng, HH Wang, X Duan, YQ Zhang, J Zhang, XS AF Feng, Huanhuan Wang, Xiang Duan, Yanqing Zhang, Jian Zhang, Xiaoshuan TI Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challenges SO JOURNAL OF CLEANER PRODUCTION DT Review DE Traceability; Blockchain technology; Security and transparency; Food sustainability ID CHAIN MANAGEMENT; INFORMATION; FUTURE; INTEGRATION; SECURITY; INTERNET; FRAMEWORK; CONSUMERS; ROLES AB Traceability plays a vital role in food quality and safety management. Traditional Internet of Things (IoT) traceability systems provide the feasible solutions for the quality monitoring and traceability of food supply chains. However, most of the IoT solutions rely on the centralized server-client paradigm that makes it difficult for consumers to acquire all transaction information and to track the origins of products. Blockchain is a cutting-edge technology that has great potential for improving traceability performance by providing security and full transparency. However, the benefits, challenges and development methods of blockchain-based food traceability systems are not yet fully explored in the current literature. Therefore, the main aim of this paper is to review the blockchain technology characteristics and functionalities, identify blockchain-based solutions for addressing food traceability concerns, highlight the benefits and challenges of blockchain-based traceability systems implementation, and help researchers and practitioners to apply blockchain technology based food traceability systems by proposing an architecture design framework and suitability application analysis flowchart of blockchain based food traceability systems. The results of this study contribute to better understanding and knowledge on how to improve the food traceability by developing and implementing blockchain-based traceability systems. The paper provides valuable information for researchers and practitioners on the use of blockchain-based food traceability management and has a positive effect on the improvement of food sustainability. (C) 2020 Elsevier Ltd. All rights reserved. C1 [Feng, Huanhuan; Wang, Xiang; Zhang, Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Feng, Huanhuan; Wang, Xiang; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [Duan, Yanqing] Univ Bedfordshire, Bedford, England. [Zhang, Jian] Beijing Informat Sci & Technol Univ, Key Lab Big Data Decis Making Green Dev, Beijing, Peoples R China. C3 China Agricultural University; University of Bedfordshire; Beijing Information Science & Technology University RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China.; Zhang, J (corresponding author), Beijing Informat Sci & Technol Univ, Beijing 100192, Peoples R China. EM zhangjian@bistu.edu.cn; zhxshuan@cau.edu.cn CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Alharby M., 2017, 3 INT C ARTIFICIAL I, P125 Alzahrani N., 2018, P 1 WORKSH CRYPT BLO, P30, DOI DOI 10.1145/3211933.3211939 Andoni M, 2019, RENEW SUST ENERG REV, V100, P143, DOI 10.1016/j.rser.2018.10.014 [Anonymous], 2019, TRENDS FOOD SCI TECH, DOI DOI 10.2139/SSRN.2849251 [Anonymous], T FDN MASTERING CHAN [Anonymous], 2017, CHINA YOU CAN TRACK [Anonymous], 2017, AGR BLOCKCHAIN SUSTA, DOI [10.2139/SSRN.3028164., 10.2139/SSRN.3028164, DOI 10.2139/SSRN.3028164] Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bahga A., 2016, J SOFTWARE ENG APPL, P533, DOI [10.4236/jsea.2016.910036, DOI 10.4236/JSEA.2016.910036] Banerjee M, 2018, DIGIT COMMUN NETW, V4, P149, DOI 10.1016/j.dcan.2017.10.006 Bastas A, 2018, J CLEAN PROD, V181, P726, DOI 10.1016/j.jclepro.2018.01.110 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bozic N, 2016, 2016 3RD SMART CLOUD NETWORKS & SYSTEMS (SCNS) Buyukozkan G, 2018, COMPUT IND, V97, P157, DOI 10.1016/j.compind.2018.02.010 Cai HM, 2014, IEEE T IND INFORM, V10, P1558, DOI 10.1109/TII.2014.2306391 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Cartier LE, 2018, J GEMMOL, V36, P212, DOI 10.15506/JoG.2018.36.3.212 Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 Chang SE, 2019, TECHNOL FORECAST SOC, V144, P1, DOI 10.1016/j.techfore.2019.03.015 Conoscenti M, 2016, I C COMP SYST APPLIC Dorri Ali, 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), P618, DOI 10.1109/PERCOMW.2017.7917634 Feng HH, 2019, FOOD CONTROL, V98, P348, DOI 10.1016/j.foodcont.2018.11.050 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fernandez-Carames TM, 2018, IEEE ACCESS, V6, P32979, DOI 10.1109/ACCESS.2018.2842685 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Haddud A, 2017, J MANUF TECHNOL MANA, V28, P1055, DOI 10.1108/JMTM-05-2017-0094 Helo P, 2019, COMPUT IND ENG, V136, P242, DOI 10.1016/j.cie.2019.07.023 Hong JT, 2018, J CLEAN PROD, V172, P3508, DOI 10.1016/j.jclepro.2017.06.093 Imeri A, 2018, INT CONF NEW TECHNOL Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Kakavand H., 2017, BLOCKCHAIN REVOLUTIO Kang YS, 2013, COMPUT IND, V64, P609, DOI 10.1016/j.compind.2013.03.004 Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Kiayias A, 2017, PROGR CRYPTOLOGY LAT, P327 Korpela K, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P4182 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Kshetri N, 2017, TELECOMMUN POLICY, V41, P1027, DOI 10.1016/j.telpol.2017.09.003 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Li XQ, 2020, FUTURE GENER COMP SY, V107, P841, DOI 10.1016/j.future.2017.08.020 Liang GQ, 2019, IEEE T SMART GRID, V10, P3162, DOI 10.1109/TSG.2018.2819663 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Lo SK, 2017, IEEE INT C ENG COMP, P158, DOI 10.1109/ICECCS.2017.26 Lomotey RK, 2017, PERVASIVE MOB COMPUT, V40, P692, DOI 10.1016/j.pmcj.2017.06.020 Mallick P. K., 2018, PROCEDIA COMPUTER SC, V132, P1815, DOI [10.1016/j.procs.2018.05.140, DOI 10.1016/J.PROCS.2018.05.140] Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Min H, 2019, BUS HORIZONS, V62, P35, DOI 10.1016/j.bushor.2018.08.012 Mohanta BK, 2018, INT CONF COMPUT Nizamuddin N, 2019, COMPUT ELECTR ENG, V76, P183, DOI 10.1016/j.compeleceng.2019.03.014 Olnes S, 2017, GOV INFORM Q, V34, P355, DOI 10.1016/j.giq.2017.09.007 Olnes S, 2019, LECT NOTES COMPUT SC, V9820, P253, DOI 10.1007/978-3-319-44421-5_20 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Puthal D, 2018, IEEE CONSUM ELECTR M, V7, P18, DOI 10.1109/MCE.2017.2776459 Reyna A, 2018, FUTURE GENER COMP SY, V88, P173, DOI 10.1016/j.future.2018.05.046 Risius M, 2017, BUS INFORM SYST ENG+, V59, P385, DOI 10.1007/s12599-017-0506-0 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Sikorski JJ, 2017, APPL ENERG, V195, P234, DOI 10.1016/j.apenergy.2017.03.039 Sun SN, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9060999 Tama BA, 2017, 2017 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (ICECOS), P109 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Tian F, 2017, I C SERV SYST SERV M Tsang YP, 2018, IND MANAGE DATA SYST, V118, P1432, DOI 10.1108/IMDS-09-2017-0384 Ul Hassan M, 2019, FUTURE GENER COMP SY, V97, P512, DOI 10.1016/j.future.2019.02.060 Velis CA, 2013, WASTE MANAGE RES, V31, P539, DOI 10.1177/0734242X13489782 Wang S, 2019, IEEE T SYST MAN CY-S, V49, P2266, DOI 10.1109/TSMC.2019.2895123 Watanabe H, 2015, 2015 IEEE 4TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS (GCCE), P577, DOI 10.1109/GCCE.2015.7398721 Xu XW, 2019, FUTURE GENER COMP SY, V92, P399, DOI 10.1016/j.future.2018.10.010 Yang GY, 2016, J SYST SOFTWARE, V113, P130, DOI 10.1016/j.jss.2015.11.044 Yiannas F., 2018, Innovations: Technology / Governance / Globalization, V12, P46, DOI 10.1162/inov_a_00266 Yli-Huumo J, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0163477 Yong BB, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.10.009 Zhang J, 2011, FOOD CONTROL, V22, P1126, DOI 10.1016/j.foodcont.2011.01.019 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zhao GQ, 2017, IFIP ADV INF COMM TE, V506, P739, DOI 10.1007/978-3-319-65151-4_66 Zheng PL, 2018, 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING - SOFTWARE ENGINEERING IN PRACTICE TRACK (ICSE-SEIP 2018), P134, DOI 10.1145/3183519.3183546 Zheng ZB, 2018, INT J WEB GRID SERV, V14, P352, DOI 10.1504/IJWGS.2018.095647 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 Zile K, 2018, APPL COMPUT SYST, V23, P12, DOI 10.2478/acss-2018-0002 Zyskind G, 2015, 2015 IEEE SECURITY AND PRIVACY WORKSHOPS (SPW), P180, DOI 10.1109/SPW.2015.27 NR 82 TC 184 Z9 189 U1 126 U2 532 PD JUL 1 PY 2020 VL 260 AR 121031 DI 10.1016/j.jclepro.2020.121031 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000531560500002 HC Y HP N DA 2022-12-14 ER PT J AU Guo, JL Cengiz, K Tomar, R AF Guo, Jianli Cengiz, Korhan Tomar, Ravi TI AN IOT AND BLOCKCHAIN APPROACH FOR FOOD TRACEABILITY SYSTEM IN AGRICULTURE SO SCALABLE COMPUTING-PRACTICE AND EXPERIENCE DT Article DE Blockchain; Food quality; IoT; Traceability system; agricultural products; Food production; Food material ID THINGS IOT; SMART; INTERNET; TECHNOLOGY; CHALLENGES AB The food holds a major role and at the worldwide level in human lives and societies, the agriculture sector is known to be a major employer. In all the developing countries, food supply chain is the major domains of research which need a growth. Nowadays, the world wide serious topic is a food safety and the food safety issues are tackled by the trusted food traceability system. It can track and monitor the food production whole lifespan in which the processes of food raw material cultivation/breeding, processing, transporting, and selling etc. are included. In this paper, food quality problems are discussed and the food traceability system is proposed which is based on the Internet of Things (IoT) and blockchain technique for agricultural products. The presented system architecture is detailed and other existing problems are also discussed. The consortium block-chain is utilizing as the basic network and the traceability system can achieve more reliable and trustable devices. C1 [Guo, Jianli] Weifang Univ Sci & Technol, Sch Comp & Software, Wqeifang 262700, Shandong, Peoples R China. [Cengiz, Korhan] Trakya Univ, Dept Elect Elect Engn, TR-22030 Edirne, Turkey. [Tomar, Ravi] Univ Petroleun & Energy Studies, Sch Comp Sci, Dehra Dun, Uttarakhand, India. C3 Trakya University; University of Petroleum & Energy Studies (UPES) RP Guo, JL (corresponding author), Weifang Univ Sci & Technol, Sch Comp & Software, Wqeifang 262700, Shandong, Peoples R China. EM jinaliguo1@gmail.com; korhancengiz@trakya.edu.tr; ravitomar7@gmail.com CR Ahmed N, 2018, IEEE INTERNET THINGS, V5, P4890, DOI 10.1109/JIOT.2018.2879579 Akmandor AO, 2018, IEEE T MULTI-SCALE C, V4, P914, DOI 10.1109/TMSCS.2018.2864297 Atlam Hany F., 2018, International Journal of Intelligent Systems and Applications, V10, P40, DOI 10.5815/ijisa.2018.06.05 Awan SH, 2019, J MECH CONTIN MATH S, V14, P170, DOI 10.26782/jmcms.2019.10.00014 Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Colombo PE, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16173019 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Devi MS, 2019, COMM COM INF SC, V985, P7, DOI 10.1007/978-981-13-8300-7_2 Du MX, 2020, IEEE T ENG MANAGE, V67, P1045, DOI 10.1109/TEM.2020.2971858 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Garcia L, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20041042 Kamienski C, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19020276 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kokkonis G., 2017, POWER MW, V100, P25 Kumar M. V., 2019, ADV SCI TECHNOL LETT, V146, P125 Kumar M.V., 2017, ADV SCI TECHNOLOGY L, V146, P125, DOI DOI 10.14257/AST1.2017.146.22 Li J., 2018, 2018 2 IEEE ADV INFO, P2637 Li WJ, 2018, IEEE INTERNET THINGS, V5, P716, DOI 10.1109/JIOT.2017.2720635 McCready MS, 2009, AGR WATER MANAGE, V96, P1623, DOI 10.1016/j.agwat.2009.06.007 Mekala MS, 2017, 2017 INTERNATIONAL CONFERENCE ON MICROELECTRONIC DEVICES, CIRCUITS AND SYSTEMS (ICMDCS) Monrat AA, 2019, IEEE ACCESS, V7, P117134, DOI 10.1109/ACCESS.2019.2936094 Rajakumar G., 2018, ASIAN J APPL SCI TEC, V2, P474 Rathee G, 2020, MULTIMED TOOLS APPL, V79, P9711, DOI 10.1007/s11042-019-07835-3 Rathee G, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19143165 Ruan JH, 2019, IEEE COMMUN MAG, V57, P90, DOI 10.1109/MCOM.2019.1800332 Shackelford S.J., 2017, YALE J TECH, V19, P334 Sharma A, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9101609 Srilakshmi A., 2018, Pharmacognosy Journal, V10, P260, DOI 10.5530/pj.2018.2.46 Tian F, 2017, I C SERV SYST SERV M Khoa TA, 2019, J SENS ACTUAR NETW, V8, DOI 10.3390/jsan8030045 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Wood AD, 2002, COMPUTER, V35, P54, DOI 10.1109/MC.2002.1039518 Xia F, 2012, INT J COMMUN SYST, V25, P1101, DOI 10.1002/dac.2417 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 34 TC 0 Z9 0 U1 14 U2 32 PD JUN PY 2021 VL 22 IS 2 SI SI BP 127 EP 137 DI 10.12694/scpe.v22i2.1876 WC Computer Science, Software Engineering SC Computer Science UT WOS:000711163400004 DA 2022-12-14 ER PT J AU Wang, X Fu, DQ Fruk, G Chen, EX Zhang, XS AF Wang, Xiang Fu, Daqi Fruk, Goran Chen, Enxiu Zhang, Xiaoshuan TI Improving quality control and transparency in honey peach export chain by a multi-sensors-managed traceability system SO FOOD CONTROL DT Article DE Honey peach; Multi-sensors; HACCP; Traceability system; Export chain ID SOLUBLE SOLIDS CONTENT; HAZARD ANALYSIS; CHILLING INJURY; COLD CHAIN; FOOD TRACEABILITY; HACCP SYSTEM; FRUIT; IMPLEMENTATION; SPECTROSCOPY; STANDARDS AB China is the world leader in peach production and export. Peaches are prone to rapid deterioration after harvest, especially when honey peach export chain always is longer and complex. Both traceability and Hazard Analysis and Critical Control Point (HACCP) are considered as the effective tools to improve quality control and chain transparency. This paper presents an effort to develop and evaluate a multi-sensors-managed traceability system for honey peach export chain. The key traceable information and quality control point were identified based on the principles of HACCP; a traceability system, integrated multi-sensors and a HACCP based quality control plan, was developed to monitor identified traceable information in real-time and provide the quality evaluation and control decision; The system was evaluated and validated at a sampled honey peach export chain from Shandong, China to Singapore. The results show that the critical ambient parameters (CAPs) in the honey peach export chain are temperature, relative humidity (RH), O-2, CO2 and ethylene. The temperature influences the change of RH, CO2 and ethylene. The traceability system can improve the transparency and quality control by tracing the CAPs in honey peach export chain and provide the tracking service. The quality loss of honey peach export chain decreased from 25% to 30% to below 13% based two years' statistics with the system application. (C) 2018 Elsevier Ltd. All rights reserved. C1 [Wang, Xiang; Fu, Daqi; Zhang, Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Wang, Xiang; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [Fruk, Goran] Univ Zagreb, Fac Agr, HR-10000 Zagreb, Croatia. [Chen, Enxiu] Shandong Inst Commerce & Technol, Shandong 250103, Peoples R China. C3 China Agricultural University; University of Zagreb; University of Zagreb, School of Dental Medicine; Shandong Institute of Commerce & Technology RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Al-Busaidi MA, 2017, FOOD CONTROL, V73, P900, DOI 10.1016/j.foodcont.2016.09.042 Asefa DT, 2011, FOOD CONTROL, V22, P831, DOI 10.1016/j.foodcont.2010.09.014 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bonany J., 2014, Journal of Food, Agriculture & Environment, V12, P67 Carrascosa C, 2016, J DAIRY SCI, V99, P2606, DOI 10.3168/jds.2015-10301 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 Chen TJ, 2017, FOOD CONTROL, V72, P306, DOI 10.1016/j.foodcont.2015.08.034 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Fadda C., 2016, ACTA ALIMENT HUNG, P1 FAO, 2014, YOUTH AGR KEY CHALL Gang CC, 2015, J FOOD PROCESS PRES, V39, P1108, DOI 10.1111/jfpp.12325 Gao H, 2016, POSTHARVEST BIOL TEC, V118, P103, DOI 10.1016/j.postharvbio.2016.03.006 Jaffee S, 2005, FOOD POLICY, V30, P316, DOI 10.1016/j.foodpol.2005.05.009 Kafetzopoulos DP, 2014, FOOD CONTROL, V40, P1, DOI 10.1016/j.foodcont.2013.11.029 Kafetzopoulos DP, 2013, FOOD CONTROL, V33, P505, DOI 10.1016/j.foodcont.2013.03.044 Lombardo VA, 2011, PLANT PHYSIOL, V157, P1696, DOI 10.1104/pp.111.186064 Luning PA, 2015, FOOD CONTROL, V49, P11, DOI 10.1016/j.foodcont.2013.09.009 Lupin HM, 2010, FOOD CONTROL, V21, P1143, DOI 10.1016/j.foodcont.2010.01.009 Lurie S, 2005, POSTHARVEST BIOL TEC, V37, P195, DOI 10.1016/j.postharvbio.2005.04.012 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Pardo JM, 2017, ANIM CONSERV, V20, P543, DOI 10.1111/acv.12354 Nascimento PAM, 2016, POSTHARVEST BIOL TEC, V111, P345, DOI 10.1016/j.postharvbio.2015.08.006 Ortmann F. G., 2005, THESIS Panisello PJ, 2001, FOOD CONTROL, V12, P165, DOI 10.1016/S0956-7135(00)00035-9 Rizzolo A, 2013, J AGR FOOD CHEM, V61, P1671, DOI 10.1021/jf302808g Ruiz-Altisent M, 2006, J FOOD ENG, V74, P490, DOI 10.1016/j.jfoodeng.2005.01.048 Schmutzler M, 2016, FOOD CONTROL, V66, P27, DOI 10.1016/j.foodcont.2016.01.026 Shen ZJ, 2015, GENET MOL RES, V14, P101, DOI 10.4238/2015.January.15.13 Sohail M, 2015, INT FOOD RES J, V22, P2225 Soman R, 2016, FOOD CONTROL, V69, P191, DOI 10.1016/j.foodcont.2016.05.001 Song LL, 2016, J AGR FOOD CHEM, V64, P4665, DOI 10.1021/acs.jafc.6b00623 Tareen MJ, 2012, SCI HORTIC-AMSTERDAM, V142, P221, DOI 10.1016/j.scienta.2012.04.027 Taylor K. C., 2012, U GEORGIA COLL AGR E, p[1, FS100] Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Tomasevic I, 2016, MEAT SCI, V114, P54, DOI 10.1016/j.meatsci.2015.12.008 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Vilar MJ, 2012, TRENDS FOOD SCI TECH, V23, P4, DOI 10.1016/j.tifs.2011.08.002 Wan NF, 2016, ECOL ENG, V90, P427, DOI 10.1016/j.ecoleng.2016.01.045 Wang JH, 2015, POSTHARVEST BIOL TEC, V101, P1, DOI 10.1016/j.postharvbio.2014.11.004 Wang X, 2017, COMPUT ELECTRON AGR, V135, P195, DOI 10.1016/j.compag.2016.12.019 Wang X, 2017, APPL SCI-BASEL, V7, DOI 10.3390/app7020133 Wang YS, 2005, FOOD CHEM, V91, P99, DOI 10.1016/j.foodchem.2004.05.053 Xiao XQ, 2017, FOOD CONTROL, V73, P1556, DOI 10.1016/j.foodcont.2016.11.019 Xiao XQ, 2016, FOOD CONTROL, V60, P656, DOI 10.1016/j.foodcont.2015.09.012 Xie RJ, 2010, SCI HORTIC-AMSTERDAM, V125, P622, DOI 10.1016/j.scienta.2010.05.015 Zhang K, 2011, EXPERT SYST APPL, V38, P2500, DOI 10.1016/j.eswa.2010.08.039 Zhang LF, 2010, LWT-FOOD SCI TECHNOL, V43, P26, DOI 10.1016/j.lwt.2009.06.015 Zhang Shuang, 2015, Journal of Food Safety and Quality, V6, P3780 NR 50 TC 14 Z9 16 U1 6 U2 75 PD JUN PY 2018 VL 88 BP 169 EP 180 DI 10.1016/j.foodcont.2018.01.008 WC Food Science & Technology SC Food Science & Technology UT WOS:000427343400021 DA 2022-12-14 ER PT J AU Velychko, O Gordiyenko, T AF Velychko, O. Gordiyenko, T. TI Application of systems thinking to the establishment of metrological traceability chains SO UKRAINIAN METROLOGICAL JOURNAL DT Article DE metrological traceability; measurement uncertainty; measurement standard; measuring instrument; systems thinking AB International agreements in the field of metrology and accreditation of calibration laboratories are the basis for establishing global metrological traceability. Important elements of metrological traceability are calibration of measurement standards and measuring instruments, assessment of measurement uncertainty. The International Laboratory Accreditation Cooperation has a specific policy regarding on traceability of measurement results and estimation of measurement uncertainty in calibration. The partial concept diagram around metrological traceability in accordance with the International Vocabulary of Metrology is proposed. This diagram contains a total of nine metrological concepts, which have most of the associative relations. There are associative relations between the concept of metrological traceability chain and concepts of metrological traceability, measurement standard, calibration and calibration hierarchy, and through the concept of measurement standard with the concept of measurement uncertainty. Systems thinking to the analysis of state of proposed terminological system around metrological traceability was applied. For construction of generalized metrological traceability chain, all the established properties of the system elements around the terminology system of metrological traceability were taken into account. Generalized metrological traceability chain for different levels of the calibration hierarchy was proposed. The proposed chain can be used to develop appropriate chains for specific areas of measurement. To achieve this, it is necessary to determine the specific measured value, the required measurement uncertainty for different levels of the calibration hierarchy and select the necessary measurement standards. Such schemes should be used in national metrology institutes and calibration laboratories. C1 [Velychko, O.] State Enterprise Ukrmetrteststandard, Metrolohichna Str 4, UA-03143 Kiev, Ukraine. [Gordiyenko, T.] State Univ Telecommun, Solomenska Str 7, UA-03110 Kiev, Ukraine. C3 Ministry of Education & Science of Ukraine; Igor Sikorsky Kyiv Polytechnic Institute; National University of Life & Environmental Sciences of Ukraine; State University of Telecommunications RP Velychko, O (corresponding author), State Enterprise Ukrmetrteststandard, Metrolohichna Str 4, UA-03143 Kiev, Ukraine. EM velychko@ukrcsm.kiev.ua; t_gord@hotmail.com CR [Anonymous], 2013, EA0402 [Anonymous], 2020, 10072020 ILAC [Anonymous], 2020, P14092020 ILAC [Anonymous], 2015, B7102015 ILAC [Anonymous], 2009, 7042009 ISO [Anonymous], 2019, 10872019 ISO [Anonymous], 2007, 2002012 JCGM, V3rd [Anonymous], 2003, MUTUAL RECOGNITION N [Anonymous], 1002008 JCGM Barwick VJ, 2011, EURACHEM GUIDE TERMI Montuori Alfonso., 2011, ENCY CREATIVITY, P414, DOI 10.1016/B978-0-12-375038-9.00212-0 Velichko ON, 2009, MEAS TECH+, V52, P1242, DOI 10.1007/s11018-010-9428-7 Velychko O, 2021, UKR METROL J, P15, DOI 10.24027/2306-7039.2.2021.236057 Velychko O., 2019, STANDARDS METHODS SO, DOI [10.5772/intechopen.84853, DOI 10.5772/INTECHOPEN.84853] NR 14 TC 1 Z9 1 U1 1 U2 1 PY 2021 IS 4 BP 3 EP 7 WC Instruments & Instrumentation SC Instruments & Instrumentation UT WOS:000740417100001 DA 2022-12-14 ER PT J AU Kamarulzaman, NH Muhamad, NA Nawi, NM AF Kamarulzaman, Nitty Hirawaty Muhamad, Nur Aminin Nawi, Nolila Mohd TI An investigation of adoption intention of halal traceability system among food SMEs SO JOURNAL OF ISLAMIC MARKETING DT Article DE Food SMEs; Adoption; Intention; Halal; Traceability system ID TECHNOLOGY ACCEPTANCE MODEL; SUPPLY CHAIN MANAGEMENT; CLOUD COMPUTING ADOPTION; CRITICAL SUCCESS FACTORS; PERCEIVED USEFULNESS; COMMUNICATION TECHNOLOGY; ICT ADOPTION; INFORMATION; EASE; IMPLEMENTATION AB Purpose The incredulity among Muslim consumers due to fake and doubtful halal logos has led to some querying the halal compliance and halal integrity among food small and medium enterprises (SMEs). By using the traceability systems consumers may track and trace the movement of food products available in the market. The purpose of this paper is to investigate factors that influence food SMEs' intention to adopt a halal traceability system. Design/methodology/approach A structured questionnaire survey was developed and administered to a systematic random sampling of 260 food SMEs. The data were analyzed using descriptive analysis, Chi-square analysis, Pearson correlation analysis and logistic regression analysis. Findings The results revealed a strong correlation between the environmental aspect (EA) and perceived usefulness (PU) of a halal traceability system. Sales turnover, PU, perceived ease of use, technological aspect, organizational aspect and EA are the factors that influenced food SMEs' intention to adopt a halal traceability system. Research limitations/implications The context of this study is confined to the SMEs in the food industry in Peninsular Malaysia, thereby limiting the generalizability of the findings to other industries. Practical implications This study shows a halal traceability system facilitates food SMEs in enhancing their business and provides tremendous potential to further improve the halal industry in Malaysia. Originality/value The traceability system that is perceived to be easy and useful are the most influential factors toward the adoption of technology among food SMEs. Thus, this study confirms the growing importance of the halal traceability system in the food industry. C1 [Kamarulzaman, Nitty Hirawaty; Nawi, Nolila Mohd] Univ Putra Malaysia, Dept Agribusiness & Bioresource Econ, Fac Agr, Serdang, Malaysia. [Kamarulzaman, Nitty Hirawaty; Muhamad, Nur Aminin] Univ Putra Malaysia, Halal Prod Res Inst, Serdang, Malaysia. C3 Universiti Putra Malaysia; Universiti Putra Malaysia RP Kamarulzaman, NH (corresponding author), Univ Putra Malaysia, Dept Agribusiness & Bioresource Econ, Fac Agr, Serdang, Malaysia.; Kamarulzaman, NH (corresponding author), Univ Putra Malaysia, Halal Prod Res Inst, Serdang, Malaysia. EM nitty@upm.edu.my CR Ab Rashid N, 2020, J ISLAMIC MARK, V11, P117, DOI 10.1108/JIMA-01-2018-0016 Abd Rahman A, 2017, FOOD CONTROL, V73, P1318, DOI 10.1016/j.foodcont.2016.10.058 Abdul B., 2011, INT BUSINESS MANAGEM, V5, P421 Abdul M, 2017, INT J ACAD RES BUSIN, V7, P130 Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204 Aghaei I, 2020, EURASIAN BUS REV, V10, P391, DOI 10.1007/s40821-019-00137-6 Ahmad, 2018, POLYTECHNIC J, V8, P43 Ahmad Tarmizi H., 2014, FACTORS 3 PARTY LOGI, V3, P53 Ahmad Tarmizi H., 2020, FOOD RES, V4, P256, DOI [10.26656/fr.2017.4(S1).S26, DOI 10.26656/FR.2017.4(S1).S26] AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Ajzen I., 1985, UNDERSTANDING ATTITU, P11, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2] Al Buraiki A., 2018, INT J MANAG INNOV EN, V4, P2395, DOI [10.18510/ijmier.2018.421, DOI 10.18510/IJMIER.2018.421] AL-Shboul MA, 2019, BUS PROCESS MANAG J, V25, P887, DOI 10.1108/BPMJ-01-2018-0004 Ali J, 2016, INT FOOD AGRIBUS MAN, V19, P53, DOI 10.22434/IFAMR2016.0024 Ali MH, 2016, INT J PROD ECON, V181, P303, DOI 10.1016/j.ijpe.2016.06.003 Amin M, 2014, NANKAI BUS REV NT, V5, P258, DOI 10.1108/NBRI-01-2014-0005 Anderson M, 2015, INT CONF E BUS ENG, P382, DOI 10.1109/ICEBE.2015.71 Appiah MK, 2018, ECON SOCIOL, V11, P124, DOI 10.14254/2071-789X.2018/11-1/8 Arachchilage M.A, 2017, J COMPUT ENG INF TEC, V6, P1, DOI DOI 10.4172/2324-9307.1000171 Ashraf AR, 2014, J INT MARKETING, V22, P68, DOI 10.1509/jim.14.0065 Awa HO, 2017, J ENTERP INF MANAG, V30, P893, DOI 10.1108/JEIM-03-2016-0079 Azeem H.M., 2020, PAKISTAN J COMMERCE, V14, P255 Bach MP, 2016, PROCEDIA COMPUT SCI, V100, P995, DOI 10.1016/j.procs.2016.09.270 Bahron A., 2016, INT J CURRENT RES, V8, P28909 Baker J, 2010, INTEGR SER INFORM SY, V28, P231, DOI 10.1007/978-1-4419-6108-2_12 Bananuka J, 2019, ISRA INT J ISLAMIC F, V11, P166, DOI 10.1108/IJIF-04-2018-0040 Bojei, 2018, INT J ASIAN SOCIAL S, V8, P569, DOI DOI 10.18488/JOURNAL.1.2018.88.569.579 Bouchard L., 1993, P 14 INT C INF SYST, P365 Butt I., 2016, J INTERNET BANKING C, V21, P1 Cao Y, 2017, PROCEDIA COMPUT SCI, V122, P617, DOI 10.1016/j.procs.2017.11.414 Carmeli A, 2001, TECHNOVATION, V21, P661, DOI 10.1016/S0166-4972(01)00050-5 Chang CT, 2017, COMPUT EDUC, V111, P128, DOI 10.1016/j.compedu.2017.04.010 Chen CD, 2007, TRANSPORT RES C-EMER, V15, P300, DOI 10.1016/j.trc.2007.04.004 Chen HW, 2017, MOB INF SYST, V2017, DOI 10.1155/2017/5039702 Chen NH, 2019, ASIA PAC J MARKET LO, V31, P37, DOI 10.1108/APJML-02-2018-0074 Chen Y.-C., 2013, TURK ONLINE J EDUC T, V12, P111 Chin TA, 2012, PROCD SOC BEHV, V65, P614, DOI 10.1016/j.sbspro.2012.11.173 COLLINS PD, 1988, ACAD MANAGE J, V31, P512, DOI 10.2307/256458 Corbitt, 2002, 6 PAC AS C INF SYST, P343 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dachs B, 2019, J WORLD BUS, V54, DOI 10.1016/j.jwb.2019.101017 Das K.K., 2012, INT J ENG RES APPL, V2, P2493 Davis F.D., 1986, TECHNOLOGY ACCEPTANC DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 Davis FD, 1996, INT J HUM-COMPUT ST, V45, P19, DOI 10.1006/ijhc.1996.0040 Dediu L., 2016, AACL Bioflux, V9, P1323 Department of Islamic Development Malaysia (JAKIM, 2015, MAN PROC MAL HAL CER Department of Islamic Development Malaysia (JAKIM), 2018, HAL MAL OFF PORT CHE Department of Islamic Development Malaysia (JAKIM), 2011, GUID HAL ASS MAN SYS Detlor B, 2013, GOV INFORM Q, V30, P23, DOI 10.1016/j.giq.2012.08.004 Dinca VM, 2019, J BUS ECON MANAG, V20, P798, DOI 10.3846/jbem.2019.9856 Djatna, 2018, P INT C IND SYST ENG, P1 Duan YQ, 2017, INFORM SOC, V33, P226, DOI 10.1080/01972243.2017.1318325 Elbeltagi I, 2013, J GLOB INF MANAG, V21, P23, DOI 10.4018/jgim.2013040102 Forman C, 2019, RES POLICY, V48, DOI 10.1016/j.respol.2018.10.021 Giotopoulos I, 2017, J BUS RES, V81, P60, DOI 10.1016/j.jbusres.2017.08.007 Hair J., 2016, ESSENTIALS BUSINESS, V3rd edn Haleem Abid, 2019, Information Processing in Agriculture, V6, P335, DOI 10.1016/j.inpa.2019.01.003 Hameed MA, 2012, J ENG TECHNOL MANAGE, V29, P358, DOI 10.1016/j.jengtecman.2012.03.007 HAMILL J, 1997, INT MARKET REV, V14, P300, DOI DOI 10.1108/02651339710184280 Hart P. J., 1998, Journal of Management Information Systems, V14, P87 Hassan H, 2017, J INF COMMUN TECHNOL, V16, P21 Hess TJ, 2014, MIS QUART, V38, P1 Hew JJ, 2020, SUPPLY CHAIN MANAG, V25, P863, DOI 10.1108/SCM-01-2020-0044 Hussin, 2020, FOOD RES, V4, P124 Iacobucci D, 2015, J CONSUM PSYCHOL, V25, P690, DOI 10.1016/j.jcps.2015.06.014 Iacobucci D, 2015, J CONSUM PSYCHOL, V25, P652, DOI 10.1016/j.jcps.2014.12.002 Ibrahim O., 2011, P ANN INT C ENT RES International Organization for Standardization, 2007, 22005 ISO Inyang B., 2011, ANN M 40 W DEC SCI I, P88 Janahi MA, 2017, J ISLAMIC MARK, V8, P595, DOI 10.1108/JIMA-07-2015-0049 Kamarulzaman N.H., 2019, INT J SUPPLY CHAIN M, V8, P827 Khan S, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010204 Kuan KKY, 2001, INFORM MANAGE, V38, P507, DOI 10.1016/S0378-7206(01)00073-8 Kusuma H, 2020, J ASIAN FINANC ECON, V7, P969, DOI 10.13106/jafeb.2020.vol7.no10.969 Lai PC, 2017, JISTEM J.Inf.Syst. Technol. Manag., V14, P21, DOI 10.4301/s1807-17752017000100002 Leech N.L., 2015, SPSS INTERMEDIATE ST, V5th Lopez-Nunez MI, 2020, PERS INDIV DIFFER, V154, DOI 10.1016/j.paid.2019.109699 Lorente-Martinez J, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102225 Makhbul Z.M., 2013, ASIA PACIFIC IND ENG, V44, P1 Marimuthu, 2009, INT J EC MANAGEMENT, V3, P512 Martins C, 2014, INT J INFORM MANAGE, V34, P1, DOI 10.1016/j.ijinfomgt.2013.06.002 Maynard N.C., 2007, THESIS U N CAROLINA Me A., 2018, ACAD STRATEGIC MANAG, V17, P1 Mohamed Y.H., 2016, INT RES J ENG TECHNO, V3, P68 Mohammad M.F., 2019, INT J SUPPLY CHAIN M, V8, P1 Mohayidin MG, 2014, J INT FOOD AGRIBUS M, V26, P125, DOI 10.1080/08974438.2012.755720 Mohd Nawi, 2020, FOOD RES, V4, P93 Mohd Nawi N., 2018, International Food Research Journal, V25, pS157 Morana, 2016, INT ASS RES LOG SUPP Morck R, 2005, J ECON LIT, V43, P655, DOI 10.1257/002205105774431252 Morgan L., 2007, P 15 EUR C INF SYST, P973 Morosan C, 2012, J HOSP TOUR RES, V36, P52, DOI 10.1177/1096348010380601 Nair J, 2019, J ASIA BUS STUD, V13, P694, DOI 10.1108/JABS-09-2018-0254 Naqvi M, 2020, ASIA PAC J MARKET LO, V32, P232, DOI 10.1108/APJML-01-2019-0029 Neumuller C, 2006, IEEE INT CONF AUTOM, P145 Ntwoku H, 2017, INFORM TECHNOL DEV, V23, P296, DOI 10.1080/02681102.2017.1289884 OECD, 2019, GOING DIG SHAP POL I Pallant J., 2020, SPSS SURVIVAL MANUAL Paul, 1996, J CONSUMER MARKETING, V13, P27, DOI DOI 10.1108/07363769610124528 Poniman D, 2015, ASIA PAC J MARKET LO, V27, P324, DOI 10.1108/APJML-05-2014-0082 Premkumar G, 1995, DECISION SCI, V26, P303, DOI 10.1111/j.1540-5915.1995.tb01431.x Premkumar G, 1999, OMEGA-INT J MANAGE S, V27, P467, DOI 10.1016/S0305-0483(98)00071-1 Radicic D, 2019, J SMALL BUS ENTERP D, V26, P612, DOI 10.1108/JSBED-08-2018-0259 Radipere S., 2014, MEDITERRANEAN J SOCI, V5, P104 Raharja Sam'un Jaja, 2019, International Journal of Trade and Global Markets, V12, P287 Ramdani B, 2013, J SMALL BUS ENTERP D, V20, P735, DOI 10.1108/JSBED-12-2011-0035 Ramsey E, 2005, J SMALL BUS ENTERP D, V12, P528, DOI 10.1108/14626000510628207 Regan G, 2012, EUROMICRO CONF PROC, P319, DOI 10.1109/SEAA.2012.80 Roberts-Lombard M, 2020, J ISLAMIC MARK, V11, P1851, DOI 10.1108/JIMA-09-2018-0164 Pinho JCMR, 2011, J RES INTERACT MARK, V5, P116, DOI 10.1108/17505931111187767 Rouibah K, 2011, TECHNOL SOC, V33, P271, DOI 10.1016/j.techsoc.2011.10.001 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Sivo SA, 2018, AUSTRALAS J EDUC TEC, V34, P72, DOI 10.14742/ajet.2806 SME Corporation Malaysia, 2018, DIG SURV SMES 2018 Stopher Peter., 2012, COLLECTING MANAGING Suhaiza H., 2010, J AGRIBUSINESS MARKE, V2010, P91 Suhaiza Zailani, 2010, Journal of Food Technology, V8, P74, DOI 10.3923/jftech.2010.74.81 Suki NM, 2018, J ISLAMIC MARK, V9, P338, DOI 10.1108/JIMA-02-2017-0014 Taherdoost H, 2018, PROCEDIA MANUF, V22, P960, DOI 10.1016/j.promfg.2018.03.137 Talib HHA, 2014, J SMALL BUS ENTERP D, V21, P152, DOI 10.1108/JSBED-10-2013-0162 Teo TSH, 2009, OMEGA-INT J MANAGE S, V37, P972, DOI 10.1016/j.omega.2008.11.001 Thong J. Y. L., 1999, Journal of Management Information Systems, V15, P187 THONG JYL, 1995, OMEGA-INT J MANAGE S, V23, P429, DOI 10.1016/0305-0483(95)00017-I Thong JYL, 1996, INFORM SYST RES, V7, P248, DOI 10.1287/isre.7.2.248 Tornatzky L. G., 1990, PROCESSES TECHNOLOGI, DOI DOI 10.1007/BF02371446 Upadhyay AK, 2018, ASIA PAC J MARKET LO, V30, P257, DOI 10.1108/APJML-01-2017-0001 Venkatesh V, 1996, DECISION SCI, V27, P451, DOI 10.1111/j.1540-5915.1996.tb01822.x Waseem Khan, 2017, Agricultural Economics Research Review, V30, P27, DOI 10.5958/0974-0279.2017.00019.2 Nguyen XT, 2020, J ASIAN FINANC ECON, V7, P255, DOI 10.13106/jafeb.2020.vol7.no6.255 Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Yusuf N., 2019, GLOB BUS REV, DOI 10.1177%2F0972150919825514 Zhu K., 2002, 23 INT C INF SYST BA, P337 Zou T., 2017, PLOS ONE, V2, P1 Zulfakar M. H., 2014, PROCEADIA SOCIAL BEH, V121, P58, DOI DOI 10.1016/J.SBSPRO.2014.01.1108 Zurina Shafii, 2012, World Applied Sciences Journal, V17, P1 NR 136 TC 0 Z9 0 U1 4 U2 9 PD JUL 27 PY 2022 VL 13 IS 9 BP 1872 EP 1900 DI 10.1108/JIMA-11-2020-0349 EA JUN 2021 WC Business SC Business & Economics UT WOS:000663596100001 DA 2022-12-14 ER PT J AU Wang, F Fu, ZT Mu, WS Moga, LM Zhang, XS AF Wang, Feng Fu, Zetian Mu, Weisong Moga, Liliana M. Zhang, Xiaoshuan TI Adoption of traceability system in Chinese fishery process enterprises: Difficulties, incentives and performance SO JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT DT Article DE Traceability system; fishery process enterprises; food safety AB The paper presents the results of an empirical study on traceability system adoption in Chinese fishery process enterprises, which are based on a survey on 21 tilapia enterprises in Guangdong and Hainan province. The results show that the function of traceability system is recognized by most enterprises, but traceability system is adopted mainly in large enterprises. Inconsistent traceability standard, high costs, lack of necessary conditions and little government support are the main barriers of system adoption for small and medium-sized enterprises. For all the enterprises, the common incentive factors influencing traceability system adoption are improvement of product quality, need of healthy consumption and improvement of management style, also different ownership enterprises have different preference motives. The most notable adoption performance is proved in private enterprises, followed by state-owned enterprises and joint-vent enterprises. Finally, some suggestions are shown for effective adoption of traceability system in Chinese fishery process enterprises. C1 [Wang, Feng; Fu, Zetian; Mu, Weisong; Zhang, Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Moga, Liliana M.] Dunarea Jos Univ Galati, Galati 800008, Romania. C3 China Agricultural University; Dunarea De Jos University Galati RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Abdulkadri AO, 2007, J FOOD AGRIC ENVIRON, V5, P8 Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Golan E., 2004, 830 EC RES SERV JUAN Q, 2007, CHINESE J ANIMAL SCI, V43, P10 Mehmetoglu AC, 2007, J FOOD AGRIC ENVIRON, V5, P74 RESENDE M, 2007, PRINCIPAL AGENT MODE Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 SHI Y, 2006, STUDY AGR PRODUCTS T SOUZAMONTEIRO DM, 2006, TRACEABILITY ADOPTIO SWANSON D, 2004, AUDITING SYSTEMS CON SYKUTA M, 2005, J AGR APPL EC, V87 YANG Q, 2008, J SICHUAN AGR U, V1, P99 YU H, 2006, STUDY FOOD TRACEABIL PRODUCTION ACCESSIBI NR 14 TC 9 Z9 9 U1 0 U2 10 PD APR PY 2009 VL 7 IS 2 BP 64 EP 69 WC Food Science & Technology SC Food Science & Technology UT WOS:000266192600010 DA 2022-12-14 ER PT J AU Galliano, D Orozco, L AF Galliano, Danielle Orozco, Luis TI New Technologies and Firm Organization: The Case of Electronic Traceability Systems in French Agribusiness SO INDUSTRY AND INNOVATION DT Article DE traceability systems; firm organization; technological change; agribusiness ID VOLUNTARY TRACEABILITY; KNOWLEDGE; CHAIN; ECONOMICS; ADOPTION AB This paper considers the relationship between the adoption of electronic traceability systems (ETSs) and the organization of firms. More precisely, it analyzes the respective roles of a firm's organizational structure, and organizational changes, in the process of ETS adoption in agribusiness. We use data from the French Organizational Changes and Computerization survey from 2006. We test a probit model to demonstrate the organizational structure and organizational changes underlying the firm's ETS adoption choice. Results show that ETS adoption is strongly favored by organizations with heavy hierarchical structures, standardized managerial practices and contractual mechanisms with external partners. This adoption process seems to coevolve with the organization: firms that implemented an ETS during the observed period (20032006) have experienced the most important organizational changes in terms of managerial practices, information systems and contractual relations, as well as the strengthening of the intermediate levels in the hierarchy. C1 [Galliano, Danielle] INRA, UMR AGIR, F-31326 Castanet Tolosan, France. [Orozco, Luis] Univ Toulouse, LEREPS, F-31042 Toulouse, France. C3 INRAE; Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Universite Federale Toulouse Midi-Pyrenees (ComUE); Institut d'Etudes Politiques Toulouse (SciencePo Toulouse) RP Galliano, D (corresponding author), Univ Toulouse, LEREPS, UT1,21 Allee Brienne, F-31042 Toulouse, France. EM luis.orozco-noguera@ut-capitole.fr CR ACTA-ACTIA, 2007, TRAC GUID PRAT AGR I Antonelli C., 1999, MICRODYNAMICS TECHNO Astebro T, 2004, J IND ECON, V52, P381, DOI 10.1111/j.0022-1821.2004.00231.x Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bathelt H, 2004, PROG HUM GEOG, V28, P31, DOI 10.1191/0309132504ph469oa Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Benghozi P.-J., 2001, REV EC, V52, P165, DOI DOI 10.3917/RECO.527.0165 Boschma RA, 2006, J ECON GEOGR, V6, P273, DOI 10.1093/jeg/lbi022 BRESNAHAN TF, 1995, J ECONOMETRICS, V65, P83, DOI 10.1016/0304-4076(94)01598-T Brousseau E, 1998, TELECOMMUNICATIONS AND SOCIO-ECONOMIC DEVELOPMENT, P245, DOI 10.1016/B978-044482648-0/50016-0 Brynjolfsson E, 2000, J ECON PERSPECT, V14, P23, DOI 10.1257/jep.14.4.23 Burt R.S., 1992, STRUCTURAL HOLES Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 Codron Jean-Marie, 2007, International Journal of Agricultural Resources Governance and Ecology, V6, P415, DOI 10.1504/IJARGE.2007.012845 Daft R. L., 1993, ORGAN SCI, P1 Dosi G., 1994, EVOLUTIONARY CONCEPT, P157 Fares M, 2010, FOOD POLICY, V35, P412, DOI 10.1016/j.foodpol.2010.05.008 Forest F., 2000, APPRENTISSAGE INNOVA, P159 Frigant V., 2005, IND INNOV, V12, P337, DOI [10.1080/13662710500195934, DOI 10.1080/13662710500195934] Gale HF, 1998, AM J AGR ECON, V80, P347, DOI 10.2307/1244507 Galliano D., 2008, HDB INNOVATION FOOD, P267 Galliano D, 2008, ANN REGIONAL SCI, V42, P425, DOI 10.1007/s00168-007-0157-z Galliano D, 2011, AGRIBUSINESS, V27, P379, DOI 10.1002/agr.20272 Garicano L, 2000, J POLIT ECON, V108, P874, DOI 10.1086/317671 Garicano L, 2010, INT J IND ORGAN, V28, P355, DOI 10.1016/j.ijindorg.2010.03.004 Golan E.H., 2004, AGR EC REPORTS, P1362 Granovetter M., 1983, SOCIOLOGICAL THEORY, V1, P201, DOI DOI 10.2307/202051 Greenan N, 2003, CAMB J ECON, V27, P287, DOI 10.1093/cje/27.2.287 Greenan N., 2010, RESEAUX, V28, P9, DOI DOI 10.3917/RES.162.0009 Greene W.H., 2003, ECONOMETRIC ANAL Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs J. E., 1996, British Food Journal, V98, P16, DOI 10.1108/00070709610131339 Holl A, 2010, REG STUD, V44, P519, DOI 10.1080/00343400902821626 HUBER GP, 1990, ACAD MANAGE REV, V15, P47, DOI 10.2307/258105 Hwang JS, 1998, PAP REG SCI, V77, P131 Jensen MB, 2007, RES POLICY, V36, P680, DOI 10.1016/j.respol.2007.01.006 Kumar S, 2006, TECHNOVATION, V26, P739, DOI 10.1016/j.technovation.2005.05.006 Lam A, 2005, OXFORD HDB INNOVATIO, P115, DOI 10.1093/oxfordhb/9780199286805.003.0005 Lazaric N, 2005, IND CORP CHANGE, V14, P873, DOI 10.1093/icc/dth074 Lundvall B., 2004, LOC NIS POL WORKSH E Lundvall B.A, 1999, REV EC IND, V88, P67, DOI DOI 10.3406/REI.1999.1745 Malerba F, 2005, ECON INNOV NEW TECH, V14, P63, DOI 10.1080/1043859042000228688 Malmberg A, 2000, ENVIRON PLANN A, V32, P305, DOI 10.1068/a31202 Mazzanti M, 2009, IND INNOV, V16, P331, DOI 10.1080/13662710902923909 Menard C, 2005, EUR REV AGRIC ECON, V32, P421, DOI 10.1093/eurrag/jbi013 MILGROM P, 1990, AM ECON REV, V80, P511 Nelson R., 1982, EVOL THEOR Phillips PWB, 2002, INNOVATION AND ENTREPRENEURSHIP IN WESTERN CANADA: FROM FAMILY BUSINESSES TO MULTINATIONALS, P31 Rosenberg Nathan, 1982, INSIDE BLACKBOX TECH Souza-Monteiro DM, 2010, AGRIBUSINESS, V26, P122, DOI 10.1002/agr.20233 Starbird S. A., 2007, Journal of Agricultural & Food Industrial Organization, V5, P2, DOI 10.2202/1542-0485.1141 STEIN EW, 1995, INFORM SYST RES, V6, P85, DOI 10.1287/isre.6.2.85 Sterns PA, 2002, J ECON ISSUES, V36, P1 Teece D.J., 1998, DYNAMIC FIRM ROLE TE, P134 Teece D. J., 2010, HDB EC INNOVATION, VI, P679, DOI DOI 10.1016/S0169-7218(10)01016-6 Torre A, 2005, REG STUD, V39, P47, DOI 10.1080/0034340052000320842 Vos E., 2000, J CONSUMER POLICY, V23, P227, DOI [10.1023/A:1007123502914, DOI 10.1023/A:1007123502914] Williamson O., 1985, EC I CAPITALISM WILLIAMSON OE, 1967, J POLIT ECON, V75, P123, DOI 10.1086/259258 NR 59 TC 3 Z9 3 U1 3 U2 29 PD JAN 1 PY 2013 VL 20 IS 1 BP 22 EP 47 DI 10.1080/13662716.2013.761379 WC Economics; Management SC Business & Economics UT WOS:000315677100002 DA 2022-12-14 ER PT J AU Porto, SMC Arcidiacono, C Cascone, G AF Porto, Simona M. C. Arcidiacono, Claudia Cascone, Giovanni TI A method to develop the requirements analysis and specifications phase of integrated computer-based information systems for certified plant traceability SO JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT DT Article DE Traceability; requirements analysis; information system; ISO standards; plant propagating material; certification programs; plant-disease prevention; citrus-plant nursery chain; screen-houses; greenhouses ID FARM AB Worldwide certification programs aim at ensuring that healthy plants and propagating materials are produced and distributed. These productions usually require a number of sub-products deriving from various production centres, often located in different geographical sites. Consequently, computer-based information systems (CBI Ss) which integrate data from different production centres, are those suitable to obtain plant supply-chain traceability. This study proposes an iterative waterfall model to describe the life cycle of integrated CBISs for the traceability of certified plants for food, fresh fruit production and agro-processing industries. Furthermore, it puts forward a methodology to carry out the phase of requirements analysis and specifications (RAS) that is crucial for the development and implementation of integrated CBISs. In detail, the overall objective of this work was to overcome the problems deriving from the lack of standards in traceability procedures in the field of certified-plant supply chain. To this aim the proposed methodology incorporates the main principles and objectives of traceability defined within the European Standard UNI EN ISO 22005:2007, following the consideration that plant traceability systems could be relevant to improve safety against the diffusion of severe plant diseases as well as to achieve a more effective product marketing. As a consequence, a contribution towards the improvement of competitiveness, efficiency and productivity of enterprises would be given. The developed methodology was applied to the case study of the Italian citrus-plant nursery chain that shows to have common elements with other worldwide certification programs. Therefore, the results achieved in this paper could constitute guidelines for developing the RAS phase of integrated CBISs for citrus-plant nursery chain traceability in other major producing countries. C1 [Porto, Simona M. C.; Arcidiacono, Claudia; Cascone, Giovanni] Dept Agrifood & Environm Syst Management, Bldg & Land Engn Sect, I-95123 Catania, Italy. RP Arcidiacono, C (corresponding author), Dept Agrifood & Environm Syst Management, Bldg & Land Engn Sect, Via S Sofia 100, I-95123 Catania, Italy. EM siporto@unict.it; claudia.arcidiacono@unict.it; gcascone@unict.it CR Atzeni P., 2002, BASI DATI MODELLI LI Bar-Joseph M., 1989, AAB DESCRIPTIONS PLA, V353 Batini C, 2001, SISTEMI INFORM, VV Batini C, 2001, SISTEMI INFORM, VII Batini C, 2001, SISTEMI INFORM, VI Batini C, 2001, SISTEMI INFORM, VIII Catara A., 2006, Italus Hortus, V13, P49 Cherry C., 1999, Requirements Engineering, V4, P103, DOI 10.1007/s007660050017 Dunn C. L., 2005, International Journal of Accounting Information Systems, V6, P83, DOI 10.1016/j.accinf.2004.03.002 *EUR COMM, 2000, WHIT PAP FOOD SAF 71 *EUR PARL, 2002, OFFICIAL J EUROPEA L, V31 Fountas S, 2009, PRECIS AGRIC, V10, P247, DOI 10.1007/s11119-008-9098-5 *INT ORG STAND, 220052007 UNI EN ISO *IT STAND I, 2002, 110202002 UNI IT STA *IT STAND I, 2001, 109392001 UNI IT STA Lee R.F., 2000, COMPENDIUM CITRUS DI, P61 LEE RF, 2004, DIS FRUIT VEGETABLES, V1, P291 *MIN AGR FOR, 2006, DECR MAY 4 2006 DISP *MIN AGR FOR, 2003, DECR JUL 24 2003 ORG *MIN AGR FOR, 2007, DECR NOV 20 2006 NOR *MIN AGR FOR, 1993, DECR OCT 29 1993 NOR *MIN AGR FOR, 1991, DECR JUL 2 1991 REGL Moreno P, 2008, MOL PLANT PATHOL, V9, P251, DOI 10.1111/J.1364-3703.2007.00455.X NAVARRO L, 2002, 15 IOCV C 2002 SURV, P306 Niederhauser N, 2008, COMPUT ELECTRON AGR, V61, P241, DOI 10.1016/j.compag.2007.12.001 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Porto SMC, 2011, BIOSYST ENG, V109, P120, DOI 10.1016/j.biosystemseng.2011.02.008 Sorensen CG, 2010, COMPUT ELECTRON AGR, V72, P37, DOI 10.1016/j.compag.2010.02.003 VAPNEK J, 2009, LEGISLTATIVELY ESTAB Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X NR 30 TC 0 Z9 0 U1 0 U2 10 PD JUL-OCT PY 2011 VL 9 IS 3-4 BP 847 EP 853 PN 2 WC Food Science & Technology SC Food Science & Technology UT WOS:000297660700054 DA 2022-12-14 ER PT J AU Xu, XH AF Xu, Xiao-Hua TI Quality and Safety Traceability System of Aquatic Products Based on Internet of Things SO INTERNATIONAL JOURNAL OF ONLINE ENGINEERING DT Article DE Traceability system; Internet of things; marine products AB In order to better detect the quality of aquatic products, a water quality and safety traceability system based on Internet of things has been designed. By analyzing the marine product quality and safety control, a standardized, intelligent and universal marine product quality safety traceability system is proposed based on the Internet of things technology and the characteristics of the seafood industry chain are analyzed. The process about "seedling purchase (seedling propagation) - seafood cultivation - seafood fishing - seafood processing - seafood sales" is selected as the main line, and sensors are used to collect seafood information. The final information is pooled into the seafood traceability data center. Companies can view the seafood traceability data in the traceability system, and consumers can obtain product traceability data by scanning the two-dimensional code of product packaging. The system adopts B/S model and SSH framework to complete system development. The experimental results show that the new system provides powerful technical support for marine product traceability and marine product quality safety management. Based on the above finding, it is concluded that the traceability system has been technically able to effectively improve the product management of marine fishery enterprises through the information management technology. At the same time, it lays a good foundation for the overall upgrading and reform of China's marine fishery industry. C1 [Xu, Xiao-Hua] Tianjin Agr Univ, Tianjin 300384, Peoples R China. C3 Tianjin Agricultural University RP Xu, XH (corresponding author), Tianjin Agr Univ, Tianjin 300384, Peoples R China. EM xuxiaohua@tjau.edu.cn CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Bunger L, 2015, INT J THERMOPHYS, V36, P1784, DOI 10.1007/s10765-015-1915-2 Dubovikov NI, 2016, INT J THERMOPHYS, V37, DOI 10.1007/s10765-015-2014-0 Efremov S, 2015, PROCEDIA ENGINEER, V100, P1215, DOI 10.1016/j.proeng.2015.01.486 Fan KG, 2015, INT J ADV MANUF TECH, V80, P907, DOI 10.1007/s00170-015-7070-x Jakkhupan W, 2015, TELECOMMUN SYST, V58, P273, DOI 10.1007/s11235-014-9947-7 Kang YS, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16122126 Mader P, 2015, EMPIR SOFTW ENG, V20, P413, DOI 10.1007/s10664-014-9314-z Martinez Perez M., 2016, EVALUATION TRACKING, V16, P2031, DOI DOI 10.3390/S16122031 Meyer D, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16122027 Wang Y, 2016, IEEE T MOBILE COMPUT, V15, P1412, DOI 10.1109/TMC.2015.2460240 Yongsawatdigul J, 2016, J AQUAT FOOD PROD T, V25, P895, DOI 10.1080/10498850.2014.964433 Zhou J., 2015, J PHYS CONDENS MATT, V18, P205 NR 13 TC 3 Z9 3 U1 3 U2 12 PY 2017 VL 13 IS 9 BP 132 EP 139 DI 10.3991/ijoe.v13i09.7590 WC Computer Science, Interdisciplinary Applications SC Computer Science UT WOS:000416624400011 DA 2022-12-14 ER PT J AU Salah, K Nizamuddin, N Jayaraman, R Omar, M AF Salah, Khaled Nizamuddin, Nishara Jayaraman, Raja Omar, Mohammad TI Blockchain-Based Soybean Traceability in Agricultural Supply Chain SO IEEE ACCESS DT Article DE Blockchain; Ethereum; smart contracts; traceability; Soybean; agricultural supply chain; food safety ID FOOD TRACEABILITY; IMPLEMENTING TRACEABILITY; MANAGEMENT; FRAMEWORK; SYSTEM AB The globalized production and the distribution of agriculture production bring a renewed focus on the safety, quality, and the validation of several important criteria in agriculture and food supply chains. The growing number of issues related to food safety and contamination risks has established an immense need for effective traceability solution that acts as an essential quality management tool ensuring adequate safety of products in the agricultural supply chain. Blockchain is a disruptive technology that can provide an innovative solution for product traceability in agriculture and food supply chains. Today's agricultural supply chains are complex ecosystem involving several stakeholders making it cumbersome to validate several important criteria such as country of origin, stages in crop development, conformance to quality standards, and monitor yields. In this paper, we propose an approach that leverages the Ethereum blockchain and smart contracts efficiently perform business transactions for soybean tracking and traceability across the agricultural supply chain. Our proposed solution eliminates the need for a trusted centralized authority, intermediaries and provides transactions records, enhancing efficiency and safety with high integrity, reliability, and security. The proposed solution focuses on the utilization of smart contracts to govern and control all interactions and transactions among all the participants involved within the supply chain ecosystem. All transactions are recorded and stored in the blockchain's immutable ledger with links to a decentralized file system (IPFS) and thus providing to all a high level of transparency and traceability into the supply chain ecosystem in a secure, trusted, reliable, and efficient manner. C1 [Salah, Khaled; Nizamuddin, Nishara] Khalifa Univ Sci & Technol, Dept Elect & Comp Engn, Abu Dhabi, U Arab Emirates. [Jayaraman, Raja; Omar, Mohammad] Khalifa Univ Sci & Technol, Dept Ind & Syst Engn, Abu Dhabi, U Arab Emirates. C3 Khalifa University of Science & Technology; Khalifa University of Science & Technology RP Salah, K (corresponding author), Khalifa Univ Sci & Technol, Dept Elect & Comp Engn, Abu Dhabi, U Arab Emirates. EM khaled.salah@ku.ac.ae CR [Anonymous], 2018, BLOOMBERG Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bazoni CHV, 2017, J STORED PROD RES, V73, P1, DOI 10.1016/j.jspr.2017.05.003 Beck R., 2016, P EUR C INF SYST ECI Bogner Andrea, 2016, P 6 INT C INTERNET T, P177 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Chinaka M., 2016, BLOCKCHAIN TECHNOLOG Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Hasan HR, 2019, IEEE ACCESS, V7, P41596, DOI 10.1109/ACCESS.2019.2905689 Hasan HR, 2018, IEEE ACCESS, V6, P46781, DOI 10.1109/ACCESS.2018.2866512 Hobbs JE, 2006, WAG UR FRON, V15, P87, DOI 10.1007/1-4020-4693-6_7 Holmberg A., 2018, THESIS UIT ARCTIC U Kamath R., 2018, J BRIT BLOCKCHAIN AS, V1, P3712 Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Lucas Louise, 2018, FINANCIAL TIMES Lucena P., 2018, P S FDN APPL BLOCKCH MAO DH, 2018, SUSTAINABILITY-BASEL, V10, DOI DOI 10.3390/SU10093149 Mao DH, 2019, ARAB J SCI ENG, V44, P3439, DOI 10.1007/s13369-018-3537-z Mao DH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15081627 Nizamuddin N., 2018, LECT NOTES COMPUTER Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Peck ME, 2017, IEEE SPECTRUM, V54, P24, DOI 10.1109/MSPEC.2017.8048835 Salah K, 2019, IEEE ACCESS, V7, P10127, DOI 10.1109/ACCESS.2018.2890507 Schmidhuber, 2018, EMERGING OPPORTUNITI Schneider M., 2017, THESIS Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Tian F, 2019, POLYCYCL AROMAT COMP, V39, P353, DOI 10.1080/10406638.2017.1326952 Toyoda K, 2017, IEEE ACCESS, V5, P17465, DOI 10.1109/ACCESS.2017.2720760 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Wang YK, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P1, DOI 10.1109/AUS.2016.7748011 NR 36 TC 196 Z9 206 U1 70 U2 313 PY 2019 VL 7 BP 73295 EP 73305 DI 10.1109/ACCESS.2019.2918000 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000472204600001 HC Y HP N DA 2022-12-14 ER PT J AU Wang, MC Yang, CY AF Wang, Mao-Chang Yang, Chin-Ying TI Analysing the traceability system in herbal product industry by game theory SO AGRICULTURAL ECONOMICS-ZEMEDELSKA EKONOMIKA DT Article DE certification agencies; consumers; farmers; game theory; government authorities; herbal product industry; traceability system ID FOOD; QUALITY AB The agricultural traceability system provides information transparency throughout the agricultural supply chain. This paper applies game theory to analyse the traceability system used by the herbal product industry in order to elucidate the strategic choices made by government authorities, farmers (e.g. producers), certification agencies, and consumers. This paper clarifies how relevant variables affect the traceability system employed in the herbal product industry. The analysis yields strong results and indicates a superior equilibrium; the observed strategic choices comprise active traceability system promotion by authorities, development of a comprehensive traceability system by farmers, maintenance of independence by certification agencies, and purchase of herbal products by consumers. The traceability system and existing herbal product safety programs must be refined because they are crucial to consumers, farmers, and people who support agricultural communities. These results contribute to the literature in the field, serving as a reference for members of the herbal product industry, government authorities, and academics. C1 [Wang, Mao-Chang] Chinese Culture Univ, Dept Accounting, Taipei, Taiwan. [Yang, Chin-Ying] Natl Chung Hsing Univ, Dept Agron, Taichung, Taiwan. C3 Chinese Culture University; National Chung Hsing University RP Wang, MC (corresponding author), Chinese Culture Univ, Dept Accounting, Taipei, Taiwan. EM wmaochang@yahoo.com.tw CR Caswell JA, 1996, AM J AGR ECON, V78, P1248, DOI 10.2307/1243501 Chen YC, 2012, PUBLIC ADM POLICY, V55, P67 Dani S, 2010, INT J LOGIST-RES APP, V13, P395, DOI 10.1080/13675567.2010.518564 European Commission, 2007, FACTSH TRAC Fudenberg D., 1991, GAME THEORY Gibbons Robert, 1992, PRIMER GAME THEORY Gu Y. G., 2009, SCI DEV, V441, P42 Heinrich M, 2015, BRIT J CLIN PHARMACO, V80, P62, DOI 10.1111/bcp.12586 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 Pan RL, 2009, PUBLIC ADM POLICY, V48, P1 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Tirole J, 2001, ECONOMETRICA, V69, P1, DOI 10.1111/1468-0262.00177 Wang MC, 2015, J SCI FOOD AGR, V95, P1252, DOI 10.1002/jsfa.6814 Wang MC, 2013, INT J HUM RESOUR MAN, V24, P3020, DOI 10.1080/09585192.2013.767063 Wang X, 2009, INT J PROD RES, V47, P2865, DOI 10.1080/00207540701725075 Wilson WW, 2008, AGRIBUSINESS, V24, P85, DOI [10.1002/agr.20148, 10.1002/AGR.20148] Wu LH, 2012, CAN J AGR ECON, V60, P317, DOI 10.1111/j.1744-7976.2011.01236.x NR 18 TC 1 Z9 1 U1 3 U2 32 PY 2019 VL 65 IS 2 BP 74 EP 81 DI 10.17221/102/2018-AGRICECON WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000459805500004 DA 2022-12-14 ER PT J AU Tseng, Y Lee, BY Chen, CG He, W AF Tseng, Yafen Lee, Beyfen Chen, Chingi He, Wang TI Understanding Agri-Food Traceability System User Intention in Respond to COVID-19 Pandemic: The Comparisons of Three Models SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH DT Article DE COVID-19; continuance intention; traceability system; technology acceptance model; information systems success model; expectation confirmation model ID TECHNOLOGY ACCEPTANCE MODEL; REPEAT PURCHASE INTENTION; PERCEIVED VALUE; BEHAVIORAL INTENTION; MODERATING ROLE; SATISFACTION; QUALITY; SUCCESS; HABIT; ANTECEDENTS AB Scientists believed the outbreak of COVID-19 could be linked to the consumption of wild animals, so food safety and hygiene have become the top concerns of the public. An agri-food traceability system becomes very important in this context because it can help the government to trace back the entire production and delivery process in case of food safety concerns. The traceability system is a complicated digitalized system because it integrates information and logistics systems. Previous studies used the technology acceptance model (TAM), information systems (IS) success model, expectation confirmation model (ECM), or extended model to explain the continuance intention of traceability system users. Very little literature can be found integrating two different models to explain user intention, not to mention comparing three models in one research context. This study proposed the technology acceptance model (TAM), technology acceptance model-information systems (TAM-IS) success, and technology acceptance model-expectation confirmation model (TAM-ECM) integrated models to evaluate the most appropriate model to explain agri-food traceability system during the COVID-19 pandemic. A questionnaire was designed based on a literature review, and 197 agri-food traceability system users were sampled. The collected data were analyzed by partial least square (PLS) to understand the explanatory power and the differences between the three models. The results showed that: (1) the TAM model has a fair explanatory power of continuance intention (62.2%), but was recommended for its' simplicity; (2) the TAM-IS success integrated model had the best predictive power of 78.3%; and (3) the system providers should raise users' confirmation level, so their continuance intention could be reinforced through mediators, perceived value, and satisfaction. The above findings help to understand agri-food traceability system user intention, and provide theoretical and practical implications for system providers to refine their system design. C1 [Tseng, Yafen] Chung Hwa Univ Med Technol, Digital Design & Informat Management, Tainan 71703, Taiwan. [Lee, Beyfen] Chung Hwa Univ Med Technol, Dept Hospitality Management, Tainan 71703, Taiwan. [Chen, Chingi] Chung Hwa Univ Med Technol, Dept Hlth Care Adm, Tainan 71703, Taiwan. [He, Wang] Jiangxi Univ Finance & Econ, Sch Int Business, Nanchang 330013, Jiangxi, Peoples R China. C3 Chung Hua University; Chung Hua University; Chung Hua University; Jiangxi University of Finance & Economics RP Tseng, Y (corresponding author), Chung Hwa Univ Med Technol, Digital Design & Informat Management, Tainan 71703, Taiwan.; Lee, BY (corresponding author), Chung Hwa Univ Med Technol, Dept Hospitality Management, Tainan 71703, Taiwan.; Chen, CG (corresponding author), Chung Hwa Univ Med Technol, Dept Hlth Care Adm, Tainan 71703, Taiwan. EM r3890102@gmail.com; michelle@mail.hwai.edu.tw; vickyc920@hotmail.com; hewang2004@126.com CR Abd Rahman A, 2017, FOOD CONTROL, V73, P1318, DOI 10.1016/j.foodcont.2016.10.058 Abdullah D., 2021, SYSTEMATIC REV PHARM, V12, P142 Al Khasawneh Mohammad Hamdi, 2020, International Journal of Electronic Marketing and Retailing, V11, P217 Al-Emran M, 2018, COMPUT EDUC, V125, P389, DOI 10.1016/j.compedu.2018.06.008 Ali B., 2013, J KNOWL MANAG EC INF, V3, P128 Altal S, 2012, ELECTRON J APPL STAT, V5, P237, DOI 10.1285/i20705948v5n2p237 ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411 Anderson RE, 2003, PSYCHOL MARKET, V20, P123, DOI 10.1002/mar.10063 [Anonymous], 2012, INT J ACAD RES EC MA [Anonymous], 2010, MULTIVARIATE DATA AN Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Ballin NZ, 2019, TRENDS FOOD SCI TECH, V86, P537, DOI 10.1016/j.tifs.2018.09.025 Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921 Briggs N., 2006, THESIS OHIO STATE U Buaprommee N, 2016, ASIA PAC MANAG REV, V21, P161, DOI 10.1016/j.apmrv.2016.03.001 BYRNE BM, 1989, PSYCHOL BULL, V105, P456, DOI 10.1037/0033-2909.105.3.456 Chang A, 2013, BRIT FOOD J, V115, P1361, DOI 10.1108/BFJ-11-2011-0286 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Chen SC, 2019, TECHNOL FORECAST SOC, V140, P22, DOI 10.1016/j.techfore.2018.11.025 Chen X., 2018, P AGR APPL EC ASS AA Cheng YM, 2021, J ENTERP INF MANAG, V34, P1169, DOI 10.1108/JEIM-12-2019-0401 Chiu CM, 2012, DECIS SUPPORT SYST, V53, P835, DOI 10.1016/j.dss.2012.05.021 Chiu WS, 2021, INFORM TECHNOL PEOPL, V34, P978, DOI 10.1108/ITP-09-2019-0463 Dai HM, 2020, COMPUT EDUC, V150, DOI 10.1016/j.compedu.2020.103850 DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 DeLone WH, 2004, INT J ELECTRON COMM, V9, P31, DOI 10.1080/10864415.2004.11044317 DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748 DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60 Estriegana R, 2019, COMPUT EDUC, V135, P1, DOI 10.1016/j.compedu.2019.02.010 Fanelli V, 2021, FOODS, V10, DOI 10.3390/foods10071644 FMRIC (Food Marketing Research and Information Center), 2008, HDB INTR FOOD TRAC T FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Galanakis CM, 2020, FOODS, V9, DOI 10.3390/foods9040523 Gan CM, 2017, INTERNET RES, V27, P772, DOI 10.1108/IntR-06-2016-0164 Golan E.H., 2004, TRACEABILITY US FOOD Gupta A, 2021, BEHAV INFORM TECHNOL, V40, P1341, DOI 10.1080/0144929X.2020.1748715 Hailu G, 2020, CAN J AGR ECON, V68, P163, DOI 10.1111/cjag.12241 Hair J. F., 1998, PEARSON LOND, V5nd Han WL, 2015, INFORM SYST FRONT, V17, P275, DOI 10.1007/s10796-012-9372-y Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs JE, 2021, MEAT SCI, V181, DOI 10.1016/j.meatsci.2021.108459 Hsu CL, 2015, ELECTRON COMMER R A, V14, P46, DOI 10.1016/j.elerap.2014.11.003 Hsu MH, 2015, INT J INFORM MANAGE, V35, P45, DOI 10.1016/j.ijinfomgt.2014.09.002 Iftekhar A, 2021, FOODS, V10, DOI 10.3390/foods10061289 Jackson DL, 2009, PSYCHOL METHODS, V14, P6, DOI 10.1037/a0014694 Jen WL, 2011, TRANSPORTATION, V38, P321, DOI 10.1007/s11116-010-9306-9 Jin N, 2015, INT J TOUR RES, V17, P82, DOI 10.1002/jtr.1968 Jumaan IA, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101355 Kamal SA, 2020, TECHNOL SOC, V60, DOI 10.1016/j.techsoc.2019.101212 Khalifa M, 2007, EUR J INFORM SYST, V16, P780, DOI 10.1057/palgrave.ejis.3000711 Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Knorr D, 2020, FRONT NUTR, V7, DOI 10.3389/fnut.2020.598913 Li LY, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108325 Li Y, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103197 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Limayem M., 2003, P 24 INT C INF SYST, P720 Limayem M, 2008, INFORM MANAGE-AMSTER, V45, P227, DOI 10.1016/j.im.2008.02.005 Machdar N.M., 2019, BUS ENTREPREN REV, V15, P131 Maitiniyazi S, 2020, J DAIRY SCI, V103, P11257, DOI 10.3168/jds.2020-18408 Manis KT, 2019, J BUS RES, V100, P503, DOI 10.1016/j.jbusres.2018.10.021 Masudin I., 2021, GLOBAL J FLEXIBLE SY, V22, P331, DOI [10.1007/s40171-021-00281-x, DOI 10.1007/S40171-021-00281-X] McEwan K, 2020, CAN J AGR ECON, V68, P201, DOI 10.1111/cjag.12236 McIver J.P., 1981, UNIDIMENSIONAL SCALI, DOI [10.4135/978141298644, DOI 10.4135/978141298644] Meng SM, 2011, AFR J BUS MANAGE, V5, P19 Muna S.K., 2008, P KOR SOC IND SYST C, P404 Mustapha B, 2015, PROCD SOC BEHV, V172, P2, DOI 10.1016/j.sbspro.2015.01.328 Nakat Z, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107661 Nunnally J, 1994, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882 OLIVER RL, 1980, J MARKETING RES, V17, P460, DOI 10.2307/3150499 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Pelegrino BO, 2020, J DAIRY SCI, V103, P4874, DOI 10.3168/jds.2019-17997 Putri W.K., 2019, INT TECHNOL MANAG RE, V8, P10, DOI [https://doi.org/10.2991/itmr.b.190417.002, DOI 10.2991/ITMR.B.190417.002, 10.2991/itmr.b.190417.002] Rafique H, 2020, COMPUT EDUC, V145, DOI 10.1016/j.compedu.2019.103732 Sarmento A., 2013, USER PERCEPTION INFL, P303 Shah H.J., 2016, ABASYN J SOC SCI, V9, P124 Tam C, 2020, INFORM SYST FRONT, V22, P243, DOI 10.1007/s10796-018-9864-5 TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144 TRIANDIS HC, 1982, INT STUDIES MANAGEME, V12, P136, DOI DOI 10.1080/00208825.1982.11656354 Tsao WC, 2016, IND MANAGE DATA SYST, V116, P1987, DOI 10.1108/IMDS-07-2015-0293 U.S. Food and Drug Administration, 2020, FOOD SAF COR DIS 201 Wang CL, 2020, TELEMAT INFORM, V48, DOI 10.1016/j.tele.2020.101348 Wang T, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084586 Williams J, 2008, STRUCT EQU MODELING, V15, P23, DOI 10.1080/10705510701758166 Wu IL, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2020.102099 Xia Y, 2019, BEHAV RES METHODS, V51, P409, DOI 10.3758/s13428-018-1055-2 Xiao KP, 2020, NATURE, V583, P286, DOI 10.1038/s41586-020-2313-x Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 ZEITHAML VA, 1988, J MARKETING, V52, P2, DOI 10.2307/1251446 Zulkafli, 2018, TURK ONLINE J DES AR, V8, P701, DOI [10.7456/1080SSE/102, DOI 10.7456/1080SSE/102] NR 89 TC 0 Z9 0 U1 6 U2 11 PD FEB PY 2022 VL 19 IS 3 AR 1371 DI 10.3390/ijerph19031371 WC Environmental Sciences; Public, Environmental & Occupational Health SC Environmental Sciences & Ecology; Public, Environmental & Occupational Health UT WOS:000757454500001 DA 2022-12-14 ER PT J AU Martins-Lopes, P Gomes, S Pereira, L Guedes-Pinto, H AF Martins-Lopes, Paula Gomes, Sonia Pereira, Leonor Guedes-Pinto, Henrique TI Molecular Markers for Food Traceability SO FOOD TECHNOLOGY AND BIOTECHNOLOGY DT Review DE food traceability; DNA extraction; PCR-based methods; molecular markers; GMO ID POLYMERASE-CHAIN-REACTION; REAL-TIME PCR; SINGLE NUCLEOTIDE POLYMORPHISMS; ENDOGENOUS REFERENCE GENE; VIRGIN OLIVE OILS; DNA EXTRACTION METHODS; CULTIVAR IDENTIFICATION; QUANTITATIVE DETECTION; PROTECTED DESIGNATION; MITOCHONDRIAL MARKERS AB DNA analysis with molecular markers has opened a way to understand complex organism's genome. It is presently being widely applied across different fields, where food takes a preeminent position. Constant outbreaks of foodborne illnesses are increasing consumer's attention towards more detailed information related to what they are consuming. This overview reports on the areas where food traceability has been considered, and the problems that still remain to be bypassed in order to be widely applied. An outline of the most broadly used PCR-based methods for food traceability is described. Applications in the area of detection of genetically modified organisms, protected denomination of origin, allergenic and intolerance reactions are detailed in order to understand the dimension of the performed studies. C1 [Martins-Lopes, Paula; Gomes, Sonia; Pereira, Leonor; Guedes-Pinto, Henrique] Univ Tras Os Montes & Alto Douro IBB CGB UTAD, Inst Biotechnol & Bioengn, Ctr Genom & Biotechnol, P-5001801 Vila Real, Portugal. C3 University of Tras-os-Montes & Alto Douro RP Martins-Lopes, P (corresponding author), Univ Tras Os Montes & Alto Douro IBB CGB UTAD, Inst Biotechnol & Bioengn, Ctr Genom & Biotechnol, POB 1013, P-5001801 Vila Real, Portugal. EM plopes@utad.pt CR Abdullah T, 2006, FOOD CHEM, V98, P575, DOI 10.1016/j.foodchem.2005.07.035 ADAM RE, 1977, NUCLEIC ACIDS RES, V4, P1513, DOI 10.1093/nar/4.5.1513 Ahmed M. M. M., 2007, Biotechnology, V6, P426 Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Passone MA, 2010, INT J FOOD MICROBIOL, V138, P276, DOI 10.1016/j.ijfoodmicro.2010.01.003 Alves E, 2009, ANIMAL, V3, P1216, DOI 10.1017/S1751731109004819 Aslan O, 2009, J ANIM SCI, V87, P57, DOI 10.2527/jas.2008-0995 Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Bauer T, 2003, EUR FOOD RES TECHNOL, V217, P338, DOI 10.1007/s00217-003-0743-y Bracci T, 2011, PLANT CELL REP, V30, P449, DOI 10.1007/s00299-010-0991-9 BRAND B, 2008, EUR FOOD RES TECHNOL, V226, P1113 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Brezna B, 2009, EUR FOOD RES TECHNOL, V229, P397, DOI 10.1007/s00217-009-1070-8 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 CANAL MW, 2009, CURR OPIN PLANT BIOL, V12, P211 Caramante M, 2011, FOOD CONTROL, V22, P549, DOI 10.1016/j.foodcont.2010.10.002 Cercaci L, 2003, J CHROMATOGR A, V985, P211, DOI 10.1016/S0021-9673(02)01397-3 Chambers GK, 2000, COMP BIOCHEM PHYS B, V126, P455, DOI 10.1016/S0305-0491(00)00233-9 Chaouachi M, 2008, J AGR FOOD CHEM, V56, P1818, DOI 10.1021/jf073313n Cheng H, 2007, FOOD BIOPROD PROCESS, V85, P141, DOI 10.1205/fbp06056 Chisholm J, 2008, EUR FOOD RES TECHNOL, V228, P39, DOI 10.1007/s00217-008-0904-0 Chuang HY, 2011, BOT STUD, V52, P393 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Cornelisse-Vermaat JR, 2008, EUR J PUBLIC HEALTH, V18, P115, DOI 10.1093/eurpub/ckm032 Corrado G, 2011, J HORTIC SCI BIOTECH, V86, P461, DOI 10.1080/14620316.2011.11512789 Costa J, 2012, FOOD CHEM, V133, P1062, DOI 10.1016/j.foodchem.2012.01.077 Dahinden I, 2001, EUR FOOD RES TECHNOL, V212, P228, DOI 10.1007/s002170000252 Dalvit C, 2008, MEAT SCI, V80, P389, DOI 10.1016/j.meatsci.2008.01.001 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Damirchi SA, 2005, J AM OIL CHEM SOC, V82, P717, DOI 10.1007/s11746-005-1133-y de la Torre F, 2004, J FOOD AGRIC ENVIRON, V2, P84 Demeke T, 2010, ANAL BIOANAL CHEM, V396, P1977, DOI 10.1007/s00216-009-3150-9 Di Bernardo G, 2007, BIOTECHNOL PROGR, V23, P297, DOI 10.1021/bp060182m Di Pinto A, 2007, FOOD CONTROL, V18, P76, DOI 10.1016/j.foodcont.2005.08.011 Diaz-Amigo C, 2012, J AOAC INT, V95, P337, DOI 10.5740/jaoacint.SGE_Diaz-Amigo Ding JY, 2004, J AGR FOOD CHEM, V52, P3372, DOI 10.1021/jf049915d Doveri S, 2007, J AGR FOOD CHEM, V55, P4640, DOI 10.1021/jf063259v Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a Drabek J, 2008, EUR FOOD RES TECHNOL, V226, P491, DOI 10.1007/s00217-007-0561-8 Fajardo V, 2009, J SCI FOOD AGR, V89, P843, DOI 10.1002/jsfa.3522 Faria MA, 2008, EUR FOOD RES TECHNOL, V227, P845, DOI 10.1007/s00217-007-0795-5 Flores G, 2006, FOOD CHEM, V97, P336, DOI 10.1016/j.foodchem.2005.04.021 Fonseca C, 2012, J PROTEOMICS, V75, P2027, DOI 10.1016/j.jprot.2012.01.005 Ganopoulos I, 2011, FOOD CONTROL, V22, P532, DOI 10.1016/j.foodcont.2010.09.040 Garcia-Beneytez E, 2002, J AGR FOOD CHEM, V50, P6090, DOI 10.1021/jf0202077 Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Gomes S, 2008, J HORTIC SCI BIOTECH, V83, P395, DOI 10.1080/14620316.2008.11512397 Gruentzig AW, 2011, ANAL METHODS-UK, V3, P1507, DOI 10.1039/c0ay00701c Grunert KG, 2007, J PUBLIC HEALTH-HEID, V15, P385, DOI 10.1007/s10389-007-0101-9 Gryson N, 2004, J SCI FOOD AGR, V84, P1357, DOI 10.1002/jsfa.1767 Gryson N, 2010, ANAL BIOANAL CHEM, V396, P2003, DOI 10.1007/s00216-009-3343-2 Guo JC, 2009, J AGR FOOD CHEM, V57, P6502, DOI 10.1021/jf900656t Hernandez M, 2004, J CEREAL SCI, V39, P99, DOI 10.1016/S0733-5210(03)00071-7 Hernandez M, 2004, J AGR FOOD CHEM, V52, P4632, DOI 10.1021/jf049789d Hernandez M, 2005, CURR ANAL CHEM, V1, P203, DOI 10.2174/1573411054021574 Hernandez M, 2005, J AGR FOOD CHEM, V53, P7003, DOI 10.1021/jf050797j Hernandez M, 2001, J AGR FOOD CHEM, V49, P3622, DOI 10.1021/jf010173n Herrero B, 2012, FOOD ADDIT CONTAM A, V29, P12, DOI 10.1080/19440049.2011.623682 Holst-Jensen A, 2009, BIOTECHNOL ADV, V27, P1071, DOI 10.1016/j.biotechadv.2009.05.025 Hupfer C, 1998, Z LEBENSM UNTERS F A, V206, P203, DOI 10.1007/s002170050243 Hupfer C, 1999, EUR FOOD RES TECHNOL, V209, P301, DOI 10.1007/s002170050498 Iida M, 2005, J AGR FOOD CHEM, V53, P6294, DOI 10.1021/jf0505731 Intrieri MC, 2007, J HORTIC SCI BIOTECH, V82, P109, DOI 10.1080/14620316.2007.11512206 James D, 2003, J AGR FOOD CHEM, V51, P5829, DOI 10.1021/jf0341159 Jonas DA, 2001, ANN NUTR METAB, V45, P235, DOI 10.1159/000046734 Kalia RK, 2011, EUPHYTICA, V177, P309, DOI 10.1007/s10681-010-0286-9 Kenk M, 2012, EUR FOOD RES TECHNOL, V234, P351, DOI 10.1007/s00217-011-1639-x Klein J, 1998, J BIOTECHNOL, V60, P145, DOI 10.1016/S0168-1656(98)00006-6 Kluga L, 2012, EUR FOOD RES TECHNOL, V234, P109, DOI 10.1007/s00217-011-1615-5 Koppel R, 2010, EUR FOOD RES TECHNOL, V230, P367, DOI 10.1007/s00217-009-1164-3 La Paz JL, 2007, J AGR FOOD CHEM, V55, P4312, DOI 10.1021/jf063725g LINDAHL T, 1973, BIOCHEMISTRY-US, V12, P5151, DOI 10.1021/bi00749a020 Lopez-Andreo M, 2012, FOOD CHEM, V134, P518, DOI 10.1016/j.foodchem.2012.02.111 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Makhzami S, 2008, J MICROBIOL METH, V75, P485, DOI 10.1016/j.mimet.2008.07.025 Mane BG, 2009, FOOD CHEM, V116, P806, DOI 10.1016/j.foodchem.2009.03.030 Mariani C, 2006, EUR FOOD RES TECHNOL, V223, P655, DOI 10.1007/s00217-005-0249-x Martin I, 2007, MEAT SCI, V76, P721, DOI 10.1016/j.meatsci.2007.02.013 Martins-Lopes P, 2008, J AGR FOOD CHEM, V56, P11786, DOI 10.1021/jf801146z Martins-Lopes P, 2009, SCI HORTIC-AMSTERDAM, V123, P82, DOI 10.1016/j.scienta.2009.08.001 Meyer R, 1996, Z LEBENSM UNTERS FOR, V203, P339, DOI 10.1007/BF01231072 Miraglia M, 2004, FOOD CHEM TOXICOL, V42, P1157, DOI 10.1016/j.fct.2004.02.018 Monaci L, 2010, TRENDS FOOD SCI TECH, V21, P272, DOI 10.1016/j.tifs.2010.02.003 Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z Moreano F, 2005, J AGR FOOD CHEM, V53, P9971, DOI 10.1021/jf051894f Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Ohtsubo K, 2007, J AGR FOOD CHEM, V55, P1501, DOI 10.1021/jf062737z Olson KM, 2012, J DAIRY SCI, V95, P5378, DOI 10.3168/jds.2011-5006 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Pafundo S, 2010, ANAL BIOANAL CHEM, V396, P1831, DOI 10.1007/s00216-009-3419-z Pafundo S, 2011, J AGR FOOD CHEM, V59, P10414, DOI 10.1021/jf202382s Pafundo S, 2010, FOOD CHEM, V123, P787, DOI 10.1016/j.foodchem.2010.05.027 Pafundo S, 2009, FOOD CHEM, V116, P811, DOI 10.1016/j.foodchem.2009.03.040 Palmieri L, 2009, NUTRIENTS, V1, P316, DOI 10.3390/nu1020316 Pasqualone A, 1999, EUR FOOD RES TECHNOL, V210, P144, DOI 10.1007/s002170050551 Pasqualone A, 2001, EUR FOOD RES TECHNOL, V213, P240, DOI 10.1007/s002170100367 Pasqualone A, 2000, GRASAS ACEITES, V51, P177 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3312, DOI 10.1021/jf063383e Pasqualone A, 2010, EUR FOOD RES TECHNOL, V230, P723, DOI 10.1007/s00217-009-1210-1 Pereira L, 2012, FOOD ANAL METHOD, V5, P1252, DOI 10.1007/s12161-012-9369-7 Pereira L, 2011, AM J ENOL VITICULT, V62, P547, DOI 10.5344/ajev.2011.10022 Piknova L, 2008, EUR FOOD RES TECHNOL, V226, P1155, DOI 10.1007/s00217-007-0644-6 Poms RE, 2004, J AOAC INT, V87, P1391 Querci M, 2009, FOOD ANAL METHOD, V2, P325, DOI 10.1007/s12161-009-9093-0 Rafalski A, 2002, CURR OPIN PLANT BIOL, V5, P94, DOI 10.1016/S1369-5266(02)00240-6 Rasmussen RS, 2009, COMPR REV FOOD SCI F, V8, P118, DOI 10.1111/j.1541-4337.2009.00073.x Rodriguez-Plaza P, 2006, EUR FOOD RES TECHNOL, V223, P625, DOI 10.1007/s00217-005-0244-2 Rodriguez-Ramirez R, 2011, GENET MOL RES, V10, P2358, DOI 10.4238/2011.October.6.1 Rohrer GA, 2007, ANIM GENET, V38, P253, DOI 10.1111/j.1365-2052.2007.01593.x Rojas M, 2010, POULTRY SCI, V89, P1021, DOI 10.3382/ps.2009-00217 Ronning SB, 2006, J AGR FOOD CHEM, V54, P682, DOI 10.1021/jf052328n Rott ME, 2004, J AGR FOOD CHEM, V52, P5223, DOI 10.1021/jf030803g Russo V, 2007, ITAL J ANIM SCI, V6, P257 Scaravelli E, 2008, EUR FOOD RES TECHNOL, V227, P857, DOI 10.1007/s00217-007-0797-3 Selma MV, 2008, INT J FOOD MICROBIOL, V122, P126, DOI 10.1016/j.ijfoodmicro.2007.11.049 Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Smith DS, 2005, J AGR FOOD CHEM, V53, P9848, DOI 10.1021/jf051201v Taverniers I, 2005, J AGR FOOD CHEM, V53, P3041, DOI 10.1021/jf0483467 Taverniers I, 2004, ANAL BIOANAL CHEM, V378, P1198, DOI 10.1007/s00216-003-2372-5 TAYLOR SL, 1987, NUTRITIONAL TOXICOLO, V2, P173 Terry CF, 2001, EUR FOOD RES TECHNOL, V213, P425, DOI 10.1007/s002170100404 U.S. Food and Drug Administration, 2006, APPR EST THRESH MAJ Vaitilingom M, 1999, J AGR FOOD CHEM, V47, P5261, DOI 10.1021/jf981208v van Hengel AJ, 2007, ANAL BIOANAL CHEM, V389, P111, DOI 10.1007/s00216-007-1353-5 Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 Vijayakumar KR, 2009, FOOD CHEM, V117, P514, DOI 10.1016/j.foodchem.2009.04.028 Weng HB, 2005, J AOAC INT, V88, P577 Xu WT, 2006, J SCI FOOD AGR, V86, P1103, DOI 10.1002/jsfa.2464 Xu W, 2008, EUR FOOD RES TECHNOL, V228, P301, DOI 10.1007/s00217-008-0935-6 Yang LT, 2005, J AGR FOOD CHEM, V53, P183, DOI 10.1021/jf0493730 Yu JM, 2005, FOOD CHEM, V90, P199, DOI 10.1016/j.foodchem.2004.03.048 Zeltner D, 2009, EUR FOOD RES TECHNOL, V228, P321, DOI 10.1007/s00217-008-0937-4 2004, SWISS FOOD MANUAL 1985, 11985 CODEX STAN 2006, 60 CACGL NR 140 TC 24 Z9 25 U1 0 U2 57 PD APR-JUN PY 2013 VL 51 IS 2 SI SI BP 198 EP 207 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000321600800006 DA 2022-12-14 ER PT J AU Aiello, G Enea, M Muriana, C AF Aiello, Giuseppe Enea, Mario Muriana, Cinzia TI The expected value of the traceability information SO EUROPEAN JOURNAL OF OPERATIONAL RESEARCH DT Article; Proceedings Paper CT 26th EURO-INFORMS Joint International Conference CY JUL 01-04, 2013 CL Rome, ITALY DE Traceability systems; Quality control; Supply chain optimization ID SHELF-LIFE; RFID TECHNOLOGY; TEMPERATURE; SYSTEM; MODEL; FRUIT; IMPLEMENTATION; GRANULARITY; VEGETABLES AB Recent regulations on agri-food traceability prescribe traceability throughout the entire supply chain, in order to ensure consumers' safety and product quality. This has led producers and retailers to consider the opportunity to improve the firm's reputation and consumer confidence through the implementation of traceability systems designed not only to satisfy the legal requirements, but also to track the quality of the products through the supply chain for optimization purposes. However the actual implementation of such systems depends on the possibility of gathering specific information related to the product quality. Nowadays, innovative and non invasive technologies such as the Radio Frequency Identification (RFID) allow the automatic real time collection of data, thus enabling the development of effective traceability systems. In such context the expected value of traceability is a fundamental issue concerning the economic analysis of costs involved in such an investment and the optimal granularity level of implementation. This paper aims at evaluating the expected value of the implementation of traceability systems for perishable products like fruits and vegetables, and its profit. The study presents a mathematical stochastic approach for optimizing the supply chain profit and establishing the optimal granularity level (namely the Economic Traceability Lot) when a RFID solution is adopted. In particular, the supply chain profit in the presence of RFID traceability system has been calculated and compared with the expected profit in absence of such a system, and the results confirm the importance of the specific characteristics of the supply chain in determining the optimal configuration of the traceability system. (C) 2015 Elsevier B.V. All rights reserved. C1 [Aiello, Giuseppe; Enea, Mario; Muriana, Cinzia] Univ Palermo, DICGIM, I-90128 Palermo, Italy. C3 University of Palermo RP Muriana, C (corresponding author), Univ Palermo, DICGIM, Viale Sci,Bldg 8, I-90128 Palermo, Italy. EM giuseppe.aiello03@unipa.it; mario.enea@unipa.it; cinzia.muriana@unipa.it CR Aiello G, 2012, PROD PLAN CONTROL, V23, P468, DOI 10.1080/09537287.2011.564219 Angeles R, 2005, INFORM SYST MANAGE, V22, P51, DOI 10.1201/1078/44912.22.1.20051201/85739.7 Asioli D., 2011, INT J FOOD SYSTEM DY, V2, P1 Atsushi O, 2006, NEC TECH J, V1, P82 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 BRASH DW, 1995, POSTHARVEST BIOL TEC, V5, P77, DOI 10.1016/0925-5214(94)00017-M CHENG MJ, 1994, INT J OPER PROD MAN, V14, P4, DOI 10.1108/01443579410067199 Cimino MGCA, 2005, Seventh IEEE International Conference on E-Commerce Technology Workshops, P90 Corbo MR, 2004, POSTHARVEST BIOL TEC, V31, P93, DOI 10.1016/S0925-5214(03)00133-9 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 De Cindio B., 2011, MAS INT C MOD APPL S, P488 Dessureault S., 2006, Journal of Food Distribution Research, V37, P154 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Food Chain Strategy Division Food Standards Agency, 2002, TRAC FOOD CHAIN PREL Gabler R., 2005, 10 FRISCHEUND LEBENS Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Giannakourou MC, 2003, J FOOD SCI, V68, P201, DOI 10.1111/j.1365-2621.2003.tb14140.x Giannakourou MC, 2003, FOOD CHEM, V83, P33, DOI 10.1016/S0308-8146(03)00033-5 Gunasekaran A, 2004, EUR J OPER RES, V159, P269, DOI 10.1016/j.ejor.2003.08.016 Hollmann-Hespos T., 2005, EC TRACEABILITY MODE, P914 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Kim HS, 2009, EUR J OPER RES, V194, P406, DOI 10.1016/j.ejor.2007.12.015 Lakshmi B., 2011, INT J PRODUCTIVITY Q, V7, P325 Lee I, 2010, INT J PROD ECON, V125, P313, DOI 10.1016/j.ijpe.2010.02.006 Lee SJ, 2014, INNOVATIONS IN FOOD PACKAGING, 2ND EDITION, P171, DOI 10.1016/B978-0-12-394601-0.00008-4 Limbo S, 2010, MEAT SCI, V84, P129, DOI 10.1016/j.meatsci.2009.08.035 Liu L, 2004, BREEDING SCI, V54, P297, DOI 10.1270/jsbbs.54.297 Meyer GG, 2009, COMPUT IND, V60, P137, DOI 10.1016/j.compind.2008.12.005 Nguyen Q., 2004, TRACEABILITY SYSTEM Nishantha GGD, 2010, INT CONF ADV COMMUN, P1445 OPARA LU, 2004, EUR J OPER RES, V159, P269 Piazzi P., 2011, 1 INT C FOOD SUPPL C, P1 Piramuthu S, 2014, EUR J OPER RES, V233, P281, DOI 10.1016/j.ejor.2013.08.051 Piramuthu S, 2013, EUR J OPER RES, V225, P253, DOI 10.1016/j.ejor.2012.09.024 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Puligundla P, 2012, FOOD CONTROL, V25, P328, DOI 10.1016/j.foodcont.2011.10.043 Raghuram G., 2003, LOGISTICS SUPPLY CHA Reinhardt W., 2008, ENERGY INVESTMENTS C Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Sari K, 2010, EUR J OPER RES, V207, P174, DOI 10.1016/j.ejor.2010.04.003 Shah Jaymeen R., 2008, IMPACT RFID DATA SHA Shah N.H., 2008, APP MATH SCI, V2, P793 Shrestha S., 2009, P SPIE 7352 INT SENS Silva E, 2010, WHOLESALE SUCCESS FA Taoukis PS, 1999, INT J FOOD MICROBIOL, V53, P21, DOI 10.1016/S0168-1605(99)00142-7 Tripathy C. K, 2012, IMITATIONAL J IND CO, V3, P115 Venkateswarlu R., 2013, J COMPUT APPL MATH, V1, P21 Venkateswarlu R., 2011, INT J APPL MATH SCI, V5, P11 Vergara A, 2007, SENSOR ACTUAT B-CHEM, V127, P143, DOI 10.1016/j.snb.2007.07.107 Wang X, 2010, INT J PROD ECON, V124, P463, DOI 10.1016/j.ijpe.2009.12.009 Zhang Hu, 2009, WSEAS Transactions on Information Science and Applications, V6, P1094 Zheng XY, 2000, THEOR APPL GENET, V101, P613, DOI 10.1007/s001220051523 Zhu XW, 2012, J ENG TECHNOL MANAGE, V29, P152, DOI 10.1016/j.jengtecman.2011.09.011 Zhuang H, 2014, INNOVATIONS IN FOOD PACKAGING, 2ND EDITION, P445, DOI 10.1016/B978-0-12-394601-0.00018-7 NR 56 TC 66 Z9 70 U1 14 U2 136 PD JUL 1 PY 2015 VL 244 IS 1 BP 176 EP 186 DI 10.1016/j.ejor.2015.01.028 WC Management; Operations Research & Management Science SC Business & Economics; Operations Research & Management Science UT WOS:000352667900017 DA 2022-12-14 ER PT J AU Roncero-Diaz, M Panea, B Cordoba, MD Arguello, A Alcalde, MJ AF Roncero-Diaz, Mercedes Panea, Begona Cordoba, Maria de Guia Arguello, Anastasio Alcalde, Maria J. TI Retinol and alpha-Tocopherol Contents, Fat Color, and Lipid Oxidation as Traceability Tools of the Feeding System in Suckling Payoya Kids SO ANIMALS DT Article DE goat kid; fat-soluble vitamins; color; traceability; lipid oxidation; feeding systems ID VITAMIN-E SUPPLEMENTATION; ADIPOSE-TISSUE; MEAT QUALITY; REFLECTANCE SPECTROSCOPY; BETA-CAROTENE; PLASMA; PASTURE; MILK; PERFORMANCE; DIET AB Simple Summary In Spain, goat farms are mainly oriented to milk production, although kid meat contributes to their sustainability, particularly in autochthonous breeds such as Payoya. Usually, kids are fed artificial milk until slaughter, allowing the use of goat milk for the commercialization of cheese, but several studies indicate that feeding kids natural milk improve the quality of their meat. The aim of the present study was to find traceability markers to discriminate between kids that are fed natural milk (with different goat management systems) and those fed a milk replacer. For this purpose, we proposed the quantification of retinol and alpha-tocopherol contents in plasma and fat, the amount of kidney fat, lipid oxidation, and some fat color parameters as potential markers. The results showed that plasma retinol concentrations were higher in kids fed feeding systems with synthetic vitamins. The plasma alpha-tocopherol concentrations were higher in kids fed grass-based feeding systems (which contain the natural forms of these vitamins). A dilution effect was shown for the retinol concentration in fat. Collectively, the analyzed variables allowed a discriminant analysis to correctly classify kids according to their feeding system and could ensure traceability to consumers. The effects of Payoya kid feeding systems on the fat-soluble vitamin (retinol/alpha-tocopherol) contents, fat content, fat color, and the oxidation index were evaluated to determine their potential for use as feeding system traceability tools. Four groups of Payoya kids (55 animals in total) fed milk exclusively were studied: a group fed a milk replacer (MR) and three groups fed natural milk from dams reared with different management systems (mountain grazing (MG), cultivated meadow (CM) and total mixed ration (TMR)). Kids were slaughtered around one month of age and 8 kg of live weight. Kids from the MG and CM groups presented lower retinol (5.56 and 3.72 mu g/mL) and higher alpha-tocopherol plasma (11.43 and 8.85 mu g/mL) concentrations than those from the TMR and MR groups (14.98 and 22.47 mu g/mL of retinol; 2.49 and 0.52 mu g/mL of alpha-tocopherol, respectively) (p < 0.001). With respect to fat, kids with a higher intramuscular fat percentage (CM and TMR groups) had lower retinol contents (16.52 and 15.99 mu g/mL, respectively) than kids from the MG and MR groups (26.81 and 22.63 mu g/mL, respectively) (p < 0.001). A dilution effect of vitamins on fat was shown: the higher the amount of fat, the lower the vitamin concentrations, the higher the lipid oxidation index (MDA), and the lower the SUM (absolute value of the integral of the translated spectra between 450 and 510 nm). A discriminant analysis that included all studied variables showed that 94.4% of the kids were classified correctly according to their feeding system and could allow traceability to the consumer. C1 [Roncero-Diaz, Mercedes; Alcalde, Maria J.] Univ Seville, Dept Agron, Ctra Utrera Km 1, Seville 41013, Spain. [Panea, Begona] Ctr Invest & Tecnol Agroalimentaria Aragon CITA, Unidad Prod & Sanidad Anim, Avda Montanana 930, Zaragoza 50059, Spain. [Panea, Begona] Univ Zaragoza, CITA, Inst Agroalimentario Aragon IA2, C Miguel Servet, Zaragoza 50059, Spain. [Cordoba, Maria de Guia] Univ Extremadura, Dept Anim Prod & Food Sci, Ave Adolfo Suarez S-N, Badajoz 06007, Spain. [Arguello, Anastasio] Univ Las Palmas Gran Canaria, Inst Anim Hlth & Food Safety, Anim Prod & Biotechnol Grp, Arucas 35413, Spain. C3 University of Sevilla; University of Zaragoza; Universidad de Extremadura; Universidad de Las Palmas de Gran Canaria RP Alcalde, MJ (corresponding author), Univ Seville, Dept Agron, Ctra Utrera Km 1, Seville 41013, Spain. EM mroncerodiaz@gmail.com; bpanea@cita-aragon.es; mdeguia@unex.es; tacho@ulpgc.es; aldea@us.es CR Abril N., 2000, ESPECTROFOMETRIA, P1 Alcalde M.J., 2013, EAAP SCI SERIES, V133, P97 Alvarez R, 2014, J FOOD COMPOS ANAL, V36, P59, DOI 10.1016/j.jfca.2014.08.001 [Anonymous], TIEMPO GRAZALEMA DAT [Anonymous], MAPA Anuario de Estadistica Agraria [Anonymous], MAPA CATALOGO OFICIA, P2021 [Anonymous], 2010, OFFICIAL J EUROPEAN, VL276, P33 [Anonymous], FUNDACION ESPANOLA D Arnett AM, 2007, J ANIM SCI, V85, P3062, DOI 10.2527/jas.2007-0176 Asadian A, 1996, SMALL RUMINANT RES, V23, P1, DOI 10.1016/S0921-4488(96)00903-0 Ballin NZ, 2010, MEAT SCI, V86, P577, DOI 10.1016/j.meatsci.2010.06.001 Banon S, 2006, MEAT SCI, V72, P216, DOI 10.1016/j.meatsci.2005.07.004 Bastianelli D, 1998, ANIM SCI, V67, P609, DOI 10.1017/S1357729800033051 Beratto E., 2002, TEMUCO B INIA I INVE, V87, P9 Bernues A, 2011, LIVEST SCI, V139, P44, DOI 10.1016/j.livsci.2011.03.018 Blanco M, 2019, ARCH ANIM NUTR, V73, P472, DOI 10.1080/1745039X.2019.1655354 BLIGH EG, 1959, CAN J BIOCHEM PHYS, V37, P911 Canto Guerrero L., 2018, EFECTO VITAMINAS EXP Castel JM, 2011, SMALL RUMINANT RES, V96, P83, DOI 10.1016/j.smallrumres.2011.01.002 Castel JM, 2010, SMALL RUMINANT RES, V89, P207, DOI 10.1016/j.smallrumres.2009.12.045 Commission Internationale de L'Eclairage, 1986, COLORIMETRY, V2nd ed., DOI [10.1002/col.5080130115, DOI 10.1002/COL.5080130115] Cozzolino D, 2002, ANIM SCI, V74, P477, DOI 10.1017/S1357729800052632 de-la-Vega F, 2013, SPAN J AGRIC RES, V11, P759, DOI 10.5424/sjar/2013113-3808 Debier C, 2005, BRIT J NUTR, V93, P153, DOI 10.1079/BJN20041308 Delgado-Pertinez M, 2013, SMALL RUMINANT RES, V114, P167, DOI 10.1016/j.smallrumres.2013.06.001 Dian PHM, 2007, ANIMAL, V1, P1198, DOI 10.1017/S175173110700047X Dunne PG, 2009, MEAT SCI, V81, P28, DOI 10.1016/j.meatsci.2008.06.013 European Union, 2009, OFF J EUR UNION, V303, P30 Ferrer C., 2001, REV SOC ESPANOLA EST, VPastos XXI, P7, DOI [10.1017/CBO9781107415324.004, DOI 10.1017/CBO9781107415324.004] Gentili A, 2013, J AGR FOOD CHEM, V61, P1628, DOI 10.1021/jf302811a Joy M, 2012, MEAT SCI, V90, P775, DOI 10.1016/j.meatsci.2011.11.013 Karami M, 2011, MEAT SCI, V88, P102, DOI 10.1016/j.meatsci.2010.12.009 Kasapidou E, 2009, ANIMAL, V3, P516, DOI 10.1017/S1751731108003820 Lietz G, 2012, MOL NUTR FOOD RES, V56, P241, DOI 10.1002/mnfr.201100387 Liu YN, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-44509-4 Lobo GP, 2012, BBA-MOL CELL BIOL L, V1821, P78, DOI 10.1016/j.bbalip.2011.04.010 Guzman JL, 2020, ANIMALS-BASEL, V10, DOI 10.3390/ani10050766 Lyan B, 2001, J CHROMATOGR B, V751, P297, DOI 10.1016/S0378-4347(00)00488-6 Melendez-Martinez AJ, 2006, PHYTOCHEMISTRY, V67, P771, DOI 10.1016/j.phytochem.2006.02.002 Mena Y., 2009, Options Mediterraneennes. Serie A, Seminaires Mediterraneens, P253 Morales-Jerrett E, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12031181 Morrissey PA, 1998, MEAT SCI, V49, pS73, DOI 10.1016/S0309-1740(98)00017-5 Mouly PP, 1999, J CHROMATOGR A, V844, P149, DOI 10.1016/S0021-9673(99)00337-4 Noziere P, 2006, ANIM FEED SCI TECH, V131, P418, DOI 10.1016/j.anifeedsci.2006.06.018 Pena F, 2009, REV CIENT-FAC CIEN V, V19, P619 Prache S, 2005, SMALL RUMINANT RES, V59, P157, DOI 10.1016/j.smallrumres.2005.05.004 Prache S, 1999, ANIM SCI, V69, P29, DOI 10.1017/S1357729800051067 Priolo A, 2002, J ANIM SCI, V80, P886 Ripoll G, 2008, MEAT SCI, V80, P239, DOI 10.1016/j.meatsci.2007.11.025 Ripoll G., 2014, XXXIX Congreso Nacional de la Sociedad Espanola de Ovinotecnia y Caprinotecnia (SEOC), XV Congreso Internacional, Ourense, Espana, 17-19 de septiembre de 2014, P255 Ripoll G, 2015, ANIM FEED SCI TECH, V207, P20, DOI 10.1016/j.anifeedsci.2015.05.014 Ripoll G, 2012, MEAT SCI, V92, P62, DOI 10.1016/j.meatsci.2012.04.011 Ripoll G., 2008, Mediterranean livestock production: uncertainties and opportunities. Proceedings of the 2nd Seminar of the Scientific-Professional Network on Mediterranean Livestock Farming (RME), Zaragoza, Spain, 18-20 May 2006, P301 Ripoll G., 2009, 13 JORN PROD AN AIDA, VII, P589 Ripoll G, 2020, FOODS, V9, DOI 10.3390/foods9040471 Roncero-Diaz M, 2021, ANIMALS-BASEL, V11, DOI 10.3390/ani11082326 Rufino-Moya PJ, 2020, ANIMALS-BASEL, V10, DOI 10.3390/ani10101813 Sampelayo RS, 2006, ANIM SCI, V82, P337, DOI 10.1079/ASC200646 Schweigert F. J., 1998, Carotenoids, volume 3: Biosynthesis and metabolism., P249 Sorensen G, 1996, Z LEBENSM UNTERS FOR, V202, P205, DOI 10.1007/BF01263541 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Osorio MT, 2008, SMALL RUMINANT RES, V78, P1, DOI 10.1016/j.smallrumres.2008.03.015 van den Berg H, 2000, J SCI FOOD AGR, V80, P880, DOI 10.1002/(SICI)1097-0010(20000515)80:7<880::AID-JSFA646>3.0.CO;2-1 van Dijk H, 2008, APPETITE, V50, P340, DOI 10.1016/j.appet.2007.08.011 YANG A, 1993, BIOCHEM MOL BIOL INT, V30, P209 Zervas G, 2011, SMALL RUMINANT RES, V101, P140, DOI 10.1016/j.smallrumres.2011.09.034 NR 66 TC 1 Z9 1 U1 1 U2 4 PD JAN PY 2022 VL 12 IS 1 AR 104 DI 10.3390/ani12010104 WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology SC Agriculture; Veterinary Sciences; Zoology UT WOS:000743438200001 DA 2022-12-14 ER PT J AU Sharma, R Hurburgh, C Mosher, GA AF Sharma, Richa Hurburgh, Charles Mosher, Gretchen A. TI Vulnerability analysis using evidence-based traceability in the grain supply chain SO CEREAL CHEMISTRY DT Article DE critical traceability events; evidence-based method; key data elements; vulnerability ID FOOD TRACEABILITY; CLIMATE-CHANGE; ANIMAL FEED; MYCOTOXINS AB Background and Objectives This article analyses the approach of identifying critical traceability events (CTE) and corresponding key data elements (KDE). The CTE-KDE approach is evidence-based that identifies, and documents activities. It can be easily performed by (i) auditors; (ii) food manufacturers/processors; (iii) food suppliers to access gaps in process and supply chain. For example, for a grain processor storage of grain is identified as a CTE performed under grain elevator, and the necessary KDE includes the location of storage bin and supplier details. The CTE-KDE approach requires verifiable data, and the ability to assess graduated levels of success based on data. This article proposes the use of vulnerability analysis to predict levels of success in each CTE-KDE situation. Significance and Novelty A vulnerability analysis model identifies, quantifies, and prioritizes various factors responsible for reducing the efficacy of a system. Vulnerability analysis measures system attributes (data) related to: (i) frequency of occurrence; (ii) degree of impact of occurrence; and (iii) likelihood of detection. This article applies vulnerability analysis as a standard method for identifying when and how a traceability system will fail. Vulnerability analysis of an evidence-based CTE-KDE framework accounts for complex interactions among supply chain participants' critical activities. The need for standard measures of evaluating traceability systems is clear. Such analysis must restrict CTE to be measurable events and key data elements to measurable system attributes. C1 [Sharma, Richa; Hurburgh, Charles; Mosher, Gretchen A.] Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. C3 Iowa State University RP Sharma, R (corresponding author), Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. EM ricssharma09@gmail.com CR Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Diallo TML, 2016, WOODHEAD PUBL FOOD S, V301, P263, DOI 10.1016/B978-0-08-100310-7.00014-4 Eaton DL., 1994, TOXICOLOGY AFLATOXIN Farley A.J., 2009, SOCIAL WORK SOC, V7, P1 Gong YunYun, 2008, Mycotoxins: detection methods, management, public health and agricultural trade, P53, DOI 10.1079/9781845930820.0053 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Khlangwiset P, 2010, FOOD ADDIT CONTAM A, V27, P998, DOI 10.1080/19440041003677475 Laux C.M., 2010, J IND TECHNOLOGY, V26, P1 Lewis LC, 2009, BIOL CONTROL, V48, P223, DOI 10.1016/j.biocontrol.2008.10.009 Lin W., 2003, Agribusiness (New York), V19, P473, DOI 10.1002/agr.10075 Liu W., 2013, J FOOD SCI TECHNOLOG, V1, P599, DOI [10.19026/ajfst.5.3297, DOI 10.19026/AJFST.5.3297] Liu Y, 2010, ENVIRON HEALTH PERSP, V118, P818, DOI 10.1289/ehp.0901388 Manzouri M, 2013, INT J LEAN SIX SIG, V4, P389, DOI 10.1108/IJLSS-10-2012-0011 Medina A, 2017, FUNGAL BIOL REV, V31, P143, DOI 10.1016/j.fbr.2017.04.002 Mitchell NJ, 2016, FOOD ADDIT CONTAM A, V33, P540, DOI 10.1080/19440049.2016.1138545 Mora C., 2008, FOOD EC ACTA AGR S C, V5, P92, DOI [10.1080/16507540903034907, DOI 10.1080/16507540903034907] Placinta CM, 1999, ANIM FEED SCI TECH, V78, P21, DOI 10.1016/S0377-8401(98)00278-8 RUSSELL L, 1991, J ANIM SCI, V69, P5 Schmale D.G.I., 2018, HLTH MYCOTOXINS CROP Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Sundstrom F.J., 2002, IDENTITY PRESERVATIO, DOI [10.3733/ucanr.8077, DOI 10.3733/UCANR.8077] United States Department of Agriculture Economic Research Service, 2018, USDA ERS BACKGR United States. Food and Drug Administration, 2017, PART AN REC DOG FOOD USDA, 2013, GRAIN INSP HDB BOOK USDA, 2017, USDA ERS FOOD SEC US Van der Fels-Klerx HJ, 2016, WORLD MYCOTOXIN J, V9, P717, DOI 10.3920/WMJ2016.2066 Williams JH, 2004, AM J CLIN NUTR, V80, P1106, DOI 10.1093/ajcn/80.5.1106 Wu F, 2011, WORLD MYCOTOXIN J, V4, P79, DOI 10.3920/WMJ2010.1246 Zain ME, 2011, J SAUDI CHEM SOC, V15, P129, DOI 10.1016/j.jscs.2010.06.006 NR 30 TC 0 Z9 0 U1 0 U2 2 PD JUL PY 2022 VL 99 IS 4 BP 860 EP 872 DI 10.1002/cche.10545 EA MAR 2022 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000771628600001 DA 2022-12-14 ER PT J AU Purwandoko, PB Seminar, KB Sutrisno Sugiyanta AF Purwandoko, Pradeka Brilyan Seminar, Kudang Boro Sutrisno Sugiyanta TI Design Framework of a Traceability System for the Rice Agroindustry Supply Chain in West Java SO INFORMATION DT Article DE design framework; rice; supply chain; traceability system ID INFORMATION; MANAGEMENT AB Rice is a vital food commodity in Indonesia due to its role as a staple food for most Indonesian people. The rice supply chain in Indonesia varies from one region to another and it is difficult to trace movement along the chain from land to customers. This introduces non-transparency and uncertainty in the quantity and quality of rice at every node along the supply chain. The crucial issues of food safety and security, as well as consumer concern and curiosity in buying and consuming foods, increases the need for a traceability system for the rice value chain which can be easily and widely accessed. This paper describes the design framework of an IT (Information Technology)-based traceability system for the rice supply chain on web platforms. The system approach has been followed (where the system requirements are identified based on supply chain characteristics) and then the logical framework for implementing internal and external traceability was modeled using IDEF-0 (Integrated Definition Modeling). This paper further presents an explanation of the ERD (Entity Relationship Diagram) as an initial step to modeling the data requirements and a model of information exchange between stakeholders that explains the data that must be recorded and forwarded to the next stakeholder. Finally, we propose the CBIS (Computer Based Information System) concept to develop a traceability system in the rice supply chain. C1 [Purwandoko, Pradeka Brilyan; Seminar, Kudang Boro; Sutrisno] IPB Univ, Fac Agr Technol, Dept Mech & Biosyst Engn, Bogor 16680, West Java, Indonesia. [Sugiyanta] IPB Univ, Dept Agron & Hort, Fac Agr, Bogor 16680, West Java, Indonesia. C3 Bogor Agricultural University; Bogor Agricultural University RP Seminar, KB (corresponding author), IPB Univ, Fac Agr Technol, Dept Mech & Biosyst Engn, Bogor 16680, West Java, Indonesia. EM pradekabrilyan@gmail.com; seminarkudangboro@gmail.com; kensutrisno@yahoo.com; mr_sugiyanta@yahoo.co.id CR Alkahtani M, 2019, COMPUT IND ENG, V128, P1027, DOI 10.1016/j.cie.2018.04.033 Awasthi A, 2015, STUD FUZZ SOFT COMP, V319, P195, DOI 10.1007/978-3-319-12883-2_7 De A, 2019, IEEE T ENG MANAGE, V66, P35, DOI 10.1109/TEM.2017.2766443 European Parliament, 2002, OFFICIAL J EUROPEA L, V31, P1 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Golan E.H., TRACEABILITY US FOOD Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Huo BF, 2014, PROD OPER MANAG, V23, P552, DOI 10.1111/poms.12044 Ijtihadie RoyyanaMuslim., 2016, SUPPLY CHAIN FORUM I, V17, P26, DOI [10.1080/16258312.2016.1143206, DOI 10.1080/16258312.2016.1143206] Indonesia Statistics Center, 2016, DISTR IND RIC COMM T Irawan B., 2011, CONVERSION LAND RICE Klimek R, 2010, COMPUT SCI-AGH, V11, P115 Kumar V, 2017, SYSTEMS, V5, DOI 10.3390/systems5020033 Lam JKC, 2006, INT J CLOTH SCI TECH, V18, P265, DOI 10.1108/09556220610668491 Li YN, 2014, INT J OPER PROD MAN, V34, P1440, DOI 10.1108/IJOPM-03-2013-0132 Liu C, 2015, IND MANAGE DATA SYST, V115, P41, DOI 10.1108/IMDS-08-2014-0233 Lotfi Z, 2013, PROC TECH, V11, P298, DOI 10.1016/j.protcy.2013.12.194 Meindl P., 2013, SUPPLY CHAIN MANAGEM, V5th Muthayya S, 2014, ANN NY ACAD SCI, V1324, P7, DOI 10.1111/nyas.12540 Nanda MA, 2018, INT J TECHNOL, V9, P840, DOI 10.14716/ijtech.v9i4.455 Nanda MA, 2018, INFORMATION, V9, DOI 10.3390/info9010005 Porto SMC, 2011, BIOSYST ENG, V109, P120, DOI 10.1016/j.biosystemseng.2011.02.008 Purwandoko PB, 2018, IOP C SER EARTH ENV, V147, DOI 10.1088/1755-1315/147/1/012044 Pusat Data dan Sistem Informasi Pertanian, 2016, AGR COMM OUTL FOOD C Ramadhan F., 2018, KALBI SCI, V5, P43 Ramli T.A., 2012, JURNAL HUKUM PEMBANG, V42, P360 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Rosa S.L., 2014, P ELECT ENG COMPUT S, V1, P255 Roshan H., 2017, J APPL RES INDUS ENG, V4, P259 Satzinger J.W., 2010, SYSTEMS ANAL DESIGN Seminar K. B., 2016, Journal of Developments in Sustainable Agriculture, V11, P17 Seuring S., 2008, SUSTAINABILITY SUPPL Sudibyo Agus, 2012, J AGRO BASED IND, V29, P43 Suismono, 2010, JURNAL PANGAN, V19, P30 Tai YM, 2010, IND MANAGE DATA SYST, V110, P1385, DOI 10.1108/02635571011087446 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Tong J.C., 2013, COMPUTER AIDED VACCI Triyanto D., 2016, DEV TRACEABILITY SYS Van der Vorst JGAJ, 2006, WAG UR FRON, V15, P15, DOI 10.1007/1-4020-4693-6_2 Vanany I., 2015, IPTEK J P SERIES, P4, DOI [10.12962/j23546026.y2014i1.297, DOI 10.12962/J23546026.Y2014I1.297] Zhang XS, 2011, J SCI FOOD AGR, V91, P1316, DOI 10.1002/jsfa.4320 NR 41 TC 4 Z9 4 U1 3 U2 21 PD JUN PY 2019 VL 10 IS 6 AR 218 DI 10.3390/info10060218 WC Computer Science, Information Systems SC Computer Science UT WOS:000473806600036 DA 2022-12-14 ER PT J AU Houston, R AF Houston, R TI A computerised database system for bovine traceability SO REVUE SCIENTIFIQUE ET TECHNIQUE DE L OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE animal health; animal movement; cattle; computer systems; databases; identification; information systems; Northern Ireland; public health; traceability AB A computerised database system to record the details of all individual cattle, cattle holdings, cattle movements and cattle tests has been in use in Northern Ireland since 1988. This system was originally used purely to administer official tuberculosis and brucellosis eradication schemes, but subsequent developments have employed the traceability function to extend the use of the system to quality assurance, public health and marketing of beef and beef products. The database has evolved into the current, second generation system and this case study details that evolution from a manual system and describes potential future developments of the system. C1 Dept Agr & Rural Dev, Belfast BT4 3SB, Antrim, North Ireland. RP Houston, R (corresponding author), Dept Agr & Rural Dev, Dundonald House,Upper Newtownards Rd, Belfast BT4 3SB, Antrim, North Ireland. CR *COMM EUR COMM, 1999, OFF J EUR COMMUNIT L, V275, P32 *COMM EUR COMM, 2000, OFF J EUR COMMUNIT L, V204, P1 NR 2 TC 42 Z9 46 U1 0 U2 6 PD AUG PY 2001 VL 20 IS 2 BP 652 EP 661 DI 10.20506/rst.20.2.1293 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000170689800025 DA 2022-12-14 ER PT J AU Mutua, F Lindahl, J Randolph, D AF Mutua, Florence Lindahl, Johanna Randolph, Delia TI Possibilities of establishing a smallholder pig identification and traceability system in Kenya SO TROPICAL ANIMAL HEALTH AND PRODUCTION DT Article DE Smallholder pig systems; Animal identification; Traceability; Disease surveillance; Food safety; Welfare ID AFRICAN-SWINE-FEVER; ELECTRONIC IDENTIFICATION; INJECTABLE TRANSPONDERS; PORCINE CYSTICERCOSIS; KIKUYU DIVISION; PREVALENCE; FARMS; RISK; LIVESTOCK; HEALTH AB Consumers have a right to safer foods, and traceability is one approach to meeting their expectations. Kenya does not have an operational animal traceability system, and while a few initiatives have been piloted, these have only focused on the beef value chain. In this paper, we begin a discussion on traceability in the pig value chain, with an initial focus on smallholder systems of Western Kenya. First, a background to local pig production is given, and a description of animal identification and traceability options applicable to these systems is explained. Based on this, a "butcher-to-farm" traceability system, with health, production and food safety as objectives, is discussed. Requirements for establishing such a system (including actor incentives) are additionally discussed. The proposed approach can be piloted in the field and findings used to inform the design of a larger pilot and possibly pave way for implementation of a national traceability system, in line with the guidelines provided by the World Organization for Animal Health (OIE). Organized systems in the area (including commercial producer and trader groups) would offer a useful starting point. C1 [Mutua, Florence; Lindahl, Johanna; Randolph, Delia] Int Livestock Res Inst, POB 30709, Nairobi 00100, Kenya. [Lindahl, Johanna] Uppsala Univ, Zoonoses Sci Ctr, POB 70790, SE-75007 Uppsala, Sweden. [Lindahl, Johanna] Swedish Univ Agr Sci, Dept Clin Sci, POB 70790, SE-75007 Uppsala, Sweden. C3 CGIAR; International Livestock Research Institute (ILRI); Uppsala University; Swedish University of Agricultural Sciences RP Mutua, F (corresponding author), Int Livestock Res Inst, POB 30709, Nairobi 00100, Kenya. EM f.mutua@cgiar.org CR Akoko JM, 2019, PARASITE EPIDEM CONT, V4, DOI 10.1016/j.parepi.2019.e00093 Bellini S, 2016, ACTA VET SCAND, V58, DOI 10.1186/s13028-016-0264-x Beltran-Alcrudo D., 2017, FAO ANIMAL PRODUCTIO Blancou J, 2001, REV SCI TECH OIE, V20, P420 Bortolotti L, 2018, ITAL J ANIM SCI, V17, P1044, DOI 10.1080/1828051X.2018.1448725 Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Caja G., 2004, ICAR Technical Series, P21 Caja G, 2005, J ANIM SCI, V83, P2215 Carter N, 2013, TROP ANIM HEALTH PRO, V45, P1533, DOI 10.1007/s11250-013-0395-2 Chemeltorit P., 2018, FOOD TRACEABILITY DO Cook E., 2014, THESIS Cook EAJ, 2017, BMC PUBLIC HEALTH, V17, DOI 10.1186/s12889-016-3923-y Costard S, 2009, PHILOS T R SOC B, V364, P2683, DOI 10.1098/rstb.2009.0098 Delgado C.L, 2003, ANIMAL SOURCE FOODS Dorny P, 2004, INT J PARASITOL, V34, P569, DOI 10.1016/j.ijpara.2003.11.014 Eshitera EE, 2012, BMC VET RES, V8, DOI 10.1186/1746-6148-8-234 FAO, 2012, FAO AN PROD HLTH LIV, V3 FAO/OIE/WB, 2010, 169 FAOOIEWB, P74 Forsberg F, 2014, THESIS Gallardo C, 2011, J GEN VIROL, V92, P432, DOI 10.1099/vir.0.025874-0 Githigia S. M., 2005, The Kenya Veterinarian, V29, P37 Gosalvez LF, 2007, J ANIM SCI, V85, P2746, DOI 10.2527/jas.2007-0173 Guyatt HL, 2016, TROP MED INT HEALTH, V21, P1319, DOI 10.1111/tmi.12760 Haftman A.M, 2014, THESIS Hernandez-Jover M, 2008, ANIMAL, V2, P1692, DOI 10.1017/S1751731108003066 ICAR, 2014, ICAR REC GUID ICAR, 2018, PROC 4 SECT 10 ICAR Irungu P., 2015, PILOT SURVEY FARMERS Jensen H.H., 2006, FOOD REGULATION TRAD Kagira J, 2010, TANZANIA VET J, V27, P26 Kikuvi GM, 2010, J INFECT DEV COUNTR, V4, P243, DOI 10.3855/jidc.446 Kirima K., 2017, INT J ADV RES, V5, P1527, DOI [10.21474/ijar01/4566, DOI 10.21474/IJAR01/4566] LAMBOOIJ E, 1995, VET QUART, V17, P118, DOI 10.1080/01652176.1995.9694549 Leslie E, 2010, APPL ANIM BEHAV SCI, V127, P86, DOI 10.1016/j.applanim.2010.09.006 Levy M., 2013, J AGR EC DEV, V2, P371 Lichoti JK, 2017, PREV VET MED, V140, P87, DOI 10.1016/j.prevetmed.2017.03.005 Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 Magouras I, 2017, FRONT VET SCI, V4, DOI 10.3389/fvets.2017.00148 Maina A, 2015, OIE AVTA GALVMED REG Marchi Enrico, 2007, Vet Ital, V43, P97 MBN, 2017, ID LIV Mbuthia JM, 2015, TROP ANIM HEALTH PRO, V47, P369, DOI 10.1007/s11250-014-0730-2 McCathie L, 2004, ADV DISADVANTAGES BA Meisinger J. L., 2008, Professional Animal Scientist, V24, P295 Mekuriaw Y., 2014, ASSESSMENT PIG PRODU Mousavi A., 2002, British Food Journal, V104, P7, DOI 10.1108/00070700210418703 Mujibi FD, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0190080 Murungi M, 2015, ASSESSING UNDERSTAND Muthuma E.N., 2016, AM J RES COMMUN, V4, P14 Muthuramalingam T., 2011, Tamilnadu Journal of Veterinary and Animal Sciences, V7, P312 Mutua F., 2011, LIVESTOCK RES RURAL, V23 Mutua FK, 2011, AFR J AGR RES, V6, P6485, DOI 10.5897/AJAR11.822 Mutua F, 2018, TROP ANIM HEALTH PRO, V50, P299, DOI 10.1007/s11250-017-1431-4 Mutua FK, 2007, J SWINE HEALTH PROD, V15, P206 Mutua FK, 2011, J SWINE HEALTH PROD, V19, P26 Mutua FK, 2012, TROP ANIM HEALTH PRO, V44, P1157, DOI 10.1007/s11250-011-0052-6 Nabarro D, 2014, REV SCI TECH OIE, V33, P475, DOI 10.20506/rst.33.2.2292 Nantima N., 2015, LIVESTOCK RES RURAL, V27, P8 Nantima N, 2015, TROP ANIM HEALTH PRO, V47, P589, DOI 10.1007/s11250-015-0768-9 OIE, 2018, GLOSS OIE TERR AN HL OIE, 2018, INF TAEN SOL PORC CY Okoth E, 2013, PREV VET MED, V110, P198, DOI 10.1016/j.prevetmed.2012.11.012 Pavon S, 2011, ANIMAL IDENTIFICATIO Prola L, 2010, ITAL J ANIM SCI, V9, P183, DOI 10.4081/ijas.2010.e35 Santamarina C, 2007, J ANIM SCI, V85, P497, DOI 10.2527/jas.2006-317 Senk I, 2013, IFIP ADV INF COMM TE, V394, P155 Setboonsarng S., 2009, 139 ADBI Stark KDC, 1998, LIVEST PROD SCI, V53, P143, DOI 10.1016/S0301-6226(97)00154-1 Steinaa L., 2016, PIG VACCINES DIAGNOS Thomas LF, 2017, PLOS NEGLECT TROP D, V11, DOI 10.1371/journal.pntd.0005371 Thomas LF, 2016, BMC VET RES, V12, DOI 10.1186/s12917-016-0830-5 Thomas LF, 2013, BMC VET RES, V9, DOI 10.1186/1746-6148-9-46 Thomas LF, 2016, TROP ANIM HEALTH PRO, V48, P233, DOI 10.1007/s11250-015-0949-6 von Wissmann B, 2011, PLOS NEGLECT TROP D, V5, DOI 10.1371/journal.pntd.0000941 Wabacha JK, 2004, PREV VET MED, V63, P197, DOI 10.1016/j.prevetmed.2004.02.006 Wabacha JK, 2004, PREV VET MED, V63, P183, DOI 10.1016/j.prevetmed.2004.02.007 Wilson C, 2018, PREVALENCE ANTIMICRO NR 77 TC 4 Z9 4 U1 3 U2 6 PD MAR PY 2020 VL 52 IS 2 BP 859 EP 870 DI 10.1007/s11250-019-02077-9 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000519374900046 DA 2022-12-14 ER PT J AU Stranieri, S Cavaliere, A Banterle, A AF Stranieri, Stefanella Cavaliere, Alessia Banterle, Alessandro TI Voluntary traceability standards and the role of economic incentives SO BRITISH FOOD JOURNAL DT Article DE Cluster analysis; Economic incentives; Traceability systems ID FOOD SAFETY; EFFECTIVE IMPLEMENTATION; SUPPLY CHAINS; MANAGEMENT; SYSTEMS; INFORMATION; TRACKING; IMPACT; COST; MEAT AB Purpose - The proliferation of traceability standards shed light on the understanding of the mechanisms leading Agri-food firms to choose among different kind of rules and systems for their implementation. The purpose of this paper is to investigate the role of firms economic incentives on the adoption of different traceability systems. In specific, the analysis aims at segmenting food firms on the basis of economic incentives for the adoption of voluntary traceability and the levels of the system complexity implemented. Design/methodology/approach - A survey based on an ad hoc questionnaire was conducted in 2014. on a sample of firms certified ISO 22005/2008. Cluster analysis was run for the analysis and one-way ANOVA was used to confirm differences among clusters. Findings - The analysis presents three different clusters in terms of economic incentives for voluntary traceability and the level of systems complexity implemented. All the clusters reveal that supply chain incentives play a key role. Moreover, "fine traceability" Ousters firms with high level of traceability. They consider food safety as an important incentive to adopt a voluntary standard. "Medium traceability" groups firms with an average level of traceability system complexity. The interviewed consider the firm reputation as strategic driver for voluntary standard implementation. "The cluster "coarse traceability" groups firms which introduced traceability for quality differentiation of products on the market. The,se, firms implemented a low level of traceability system complexity. Research limitations/implications - The paper presents some limitations due to the sample dimension. Future research is oriented to test such results on an extended sample and to analyse the relationships between the traceability system implemented and the different kind of economic incentives for traceability standards. Originality/value - The present paper offers two main contributions. From a conceptual point of view it tries to deepen existing knowledge on the mechanisms regulating the existence of different traceability standards. From a managerial point of view, the analysis contributes in the understanding of firm strategies in relation to the adoption of different traceability systems. Such results could address firm management on the allocation of financial resources for the adoption of different traceability systems. C1 [Stranieri, Stefanella; Cavaliere, Alessia; Banterle, Alessandro] Univ Milan, Dept Econ Management & Quantitat Methods DEMM, Milan, Italy. C3 University of Milan RP Cavaliere, A (corresponding author), Univ Milan, Dept Econ Management & Quantitat Methods DEMM, Milan, Italy. EM alessia.cavaliere@unimi.it CR Banterle A., 2006, Journal on Chain and Network Science, V6, P69, DOI 10.3920/JCNS2006.x066 Banterle A, 2008, AGRIBUSINESS, V24, P320, DOI 10.1002/agr.20169 Banterle A, 2014, AGRIBUSINESS, V30, P113, DOI 10.1002/agr.21354 Banterle A, 2013, SUSTAINABILITY-BASEL, V5, P5272, DOI 10.3390/su5125272 Banterle A, 2013, BRIT FOOD J, V115, P769, DOI 10.1108/00070701311331544 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Cembalo L, 2015, AGR FOOD EC, V3, P1 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chen EC, 2015, FOOD CONTROL, V47, P569, DOI 10.1016/j.foodcont.2014.08.009 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Crandall P, 2012, J FOOD PROTECT, V75, P1660, DOI 10.4315/0362-028X.JFP-11-550 Donnelly KAM, 2013, FOOD CONTROL, V33, P25, DOI 10.1016/j.foodcont.2013.01.021 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Fotopoulos CV, 2009, INT J QUAL RELIAB MA, V26, P894, DOI 10.1108/02656710910995082 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Gawron JC, 2009, J INT FOOD AGRIBUS M, V21, P239, DOI 10.1080/08974430802589683 Golan E.H., 2004, AGR EC REPORTS, P1362 Hall D, 2010, GEOFORUM, V41, P826, DOI 10.1016/j.geoforum.2010.05.005 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Holleran E, 1999, FOOD POLICY, V24, P669, DOI 10.1016/S0306-9192(99)00071-8 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Jayasinghe-Mudalige U, 2007, FOOD CONTROL, V18, P1363, DOI 10.1016/j.foodcont.2006.08.010 Kafetzopoulos DP, 2014, FOOD CONTROL, V40, P1, DOI 10.1016/j.foodcont.2013.11.029 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Mensah LD, 2011, FOOD CONTROL, V22, P1216, DOI 10.1016/j.foodcont.2011.01.021 Olsen P., 2009, HARMONIZING METHODS, P5 Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Schulz LL, 2010, J AGR ECON, V61, P138, DOI 10.1111/j.1477-9552.2009.00226.x Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Shamsuzzoha AHM, 2013, INT J SHIP TRANS LOG, V5, P31, DOI 10.1504/IJSTL.2013.050587 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Starbird SA, 2006, J AGR RESOUR ECON, V31, P14 Wilcock A, 2011, FOOD CONTROL, V22, P27, DOI 10.1016/j.foodcont.2010.06.005 Wilson WW, 2008, AGRIBUSINESS, V24, P85, DOI [10.1002/agr.20148, 10.1002/AGR.20148] Wu SL, 2012, FOOD CONTROL, V28, P265, DOI 10.1016/j.foodcont.2012.05.038 NR 48 TC 17 Z9 17 U1 0 U2 20 PY 2016 VL 118 IS 5 BP 1025 EP 1040 DI 10.1108/BFJ-04-2015-0151 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000376118700002 DA 2022-12-14 ER PT J AU Huang, JW Zhu, YP Cheng, B Lin, C Chen, JL AF Huang, Jiwei Zhu, Yeping Cheng, Bo Lin, Chuang Chen, Junliang TI A PetriNet-Based Approach for Supporting Traceability in Cyber-Physical Manufacturing Systems SO SENSORS DT Article DE Petri net; traceability; cyber-physical manufacturing systems; product quality control AB With the growing popularity of complex dynamic activities in manufacturing processes, traceability of the entire life of every product has drawn significant attention especially for food, clinical materials, and similar items. This paper studies the traceability issue in cyber-physical manufacturing systems from a theoretical viewpoint. Petri net models are generalized for formulating dynamic manufacturing processes, based on which a detailed approach for enabling traceability analysis is presented. Models as well as algorithms are carefully designed, which can trace back the lifecycle of a possibly contaminated item. A practical prototype system for supporting traceability is designed, and a real-life case study of a quality control system for bee products is presented to validate the effectiveness of the approach. C1 [Huang, Jiwei; Cheng, Bo; Chen, Junliang] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China. [Zhu, Yeping] Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China. [Lin, Chuang] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. C3 Beijing University of Posts & Telecommunications; Chinese Academy of Agricultural Sciences; Agriculture Information Institute, CAAS; Tsinghua University RP Huang, JW (corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China. EM huangjw@bupt.edu.cn; zhuyeping@caas.cn; chengbo@bupt.edu.cn; chlin@tsinghua.edu.cn; chjl@bupt.edu.cn CR Blackburn M, 2014, PROCEDIA COMPUT SCI, V28, P883, DOI 10.1016/j.procs.2014.03.006 Bogdan P, 2011, ACM IEEE INT CONF CY, P99, DOI 10.1109/ICCPS.2011.14 Sanchez BB, 2015, SENSORS-BASEL, V15, P29478, DOI 10.3390/s151129478 Budinsky F, 2004, IBM SYST J, V43, P384, DOI 10.1147/sj.432.0384 Cao H, 2009, INT J COMPUT INTEG M, V22, P616, DOI 10.1080/09511920701522981 Cheng B, 2014, SENSORS-BASEL, V14, P22447, DOI 10.3390/s141222447 Condea C, 2012, DECIS SUPPORT SYST, V52, P839, DOI 10.1016/j.dss.2011.11.018 Dai QY, 2012, INT J COMPUT INTEG M, V25, P51, DOI 10.1080/0951192X.2011.562546 Finkel A, 2015, SOFTW SYST MODEL, V14, P719, DOI 10.1007/s10270-014-0426-0 Fishkin KP, 2005, NINTH IEEE INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS, PROCEEDINGS, P38, DOI 10.1109/ISWC.2005.25 Ghorbani M., 2013, PROC 9 IEEEACMIFIP I, P1 Hu HS, 2015, IEEE T CONTR SYST T, V23, P2026, DOI 10.1109/TCST.2015.2391014 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Khaitan SK, 2015, IEEE SYST J, V9, P350, DOI 10.1109/JSYST.2014.2322503 Kolacinski R. M., 2012, P 2012 IEEE POW EN S, P1 Lee EA, 2015, SENSORS-BASEL, V15, P4837, DOI 10.3390/s150304837 Maturana Francisco, 2014, ICINCO 2014. 11th International Conference on Informatics in Control, Automation and Robotics. Proceedings, P338 Mitchell R, 2013, IEEE T RELIAB, V62, P199, DOI 10.1109/TR.2013.2240891 Mora-Mora H, 2015, SENSORS-BASEL, V15, P13591, DOI 10.3390/s150613591 Neves P, 2014, IEEE INTL CONF IND I, P511, DOI 10.1109/INDIN.2014.6945566 Saldivar AAF., 2015, 2015 21 INT C AUT CO, P1 Sharma Abhishek B., 2014, ACM SIGMETRICS Performance Evaluation Review, V41, P74 Tan W, 2009, IEEE T AUTOM SCI ENG, V6, P94, DOI 10.1109/TASE.2008.916747 Van der Aalst WMP, 2003, DISTRIB PARALLEL DAT, V14, P5, DOI 10.1023/A:1022883727209 Vyatkin V, 2013, IEEE T IND INFORM, V9, P1234, DOI 10.1109/TII.2013.2258165 Wang Y., 2008, SIGBED REV, V5, P12 Woodall WH, 2014, J QUAL TECHNOL, V46, P78, DOI 10.1080/00224065.2014.11917955 Zhang L. J., 2007, SERVICES COMPUTING NR 28 TC 16 Z9 16 U1 3 U2 43 PD MAR PY 2016 VL 16 IS 3 DI 10.3390/s16030382 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000373713600085 DA 2022-12-14 ER PT J AU Peets, S Gasparin, CP Blackburn, DWK Godwin, RJ AF Peets, Sven Gasparin, C. P. Blackburn, D. W. K. Godwin, R. J. TI RFID tags for identifying and verifying agrochemicals in food traceability systems SO PRECISION AGRICULTURE DT Article; Proceedings Paper CT 6th European Conference on Precision Agriculture CY JUN, 2007 CL Skiathos Isl, GREECE DE RFID; Agrochemical identification; Traceability systems; Data verification; Data security AB The objective of this paper is to identify what data should be stored in an automatic recording system to trace the use of agrochemicals. RFID tags are proposed as the most appropriate storage systems. The essential information to store on an RFID tag is as follows: country of registration, chemical type, unique registration number of an agrochemical, container size, specific gravity, unit of measure, and a digital signature. Digital signatures address issues of verification of data integrity and security-a major concern of the agrochemical industry. Detailed data will be drawn from publicly available databases of approved pesticides. Encoding schemes have been designed which can record all of the essential information on commonly available cheap RFID labels. A prototype system to record and transfer data in a traceability system is developed, including hardware and software aspects. The user interface of the system is presented with a sample sequence of user screens to assist the loading of a full pack of agrochemical tagged with an RFID transponder. An experimental trial with practicing agrochemical professionals was undertaken. The user interface proved effective and acceptable. During the trial, more than 250 product identification cycles with RFID were carried out without failure. C1 [Peets, Sven; Gasparin, C. P.; Blackburn, D. W. K.; Godwin, R. J.] Cranfield Univ, Cranfield MK45 4DT, Beds, England. C3 Cranfield University RP Peets, S (corresponding author), Cranfield Univ, Cranfield MK45 4DT, Beds, England. EM s.peets.s05@cranfield.ac.uk CR *BS, 1989, 63564 BS BRIT STAND FINKENZELLER K, 2003, RFID HDB FUNDAMENTAL, P446 Gasparin C. P., 2007, Precision agriculture '07. Papers presented at the 6th European Conference on Precision Agriculture, Skiathos, Greece, 3-6 June, 2007, P793 Knospe H., 2004, INF SECUR TECH REP, V9, P39, DOI DOI 10.1016/S1363-4127(05)70039-X McBratney Alex, 2005, Precision Agriculture, V6, P7, DOI 10.1007/s11119-005-0681-8 MILLER PCH, 1999, P INT FERTILISER SOC, V439, P19 NIX J, 2006, FARM MANAGEMENT POCK, P260 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 PEETS S, 2008, P 9 INT C PREC AGR U SAHIN E, 2002, P IEEE INT C SYST MA, V3, P210 SARMA SE, 2002, 4 INT WORKSH CRYPT H, P454 Thiesse F., 2006, Sensor Review, V26, P101, DOI 10.1108/02602280610652677 Wager PA, 2005, ENVIRON IMPACT ASSES, V25, P567, DOI 10.1016/j.eiar.2005.04.009 Watts AJ, 2003, BCPC INTERNATIONAL CONGRESS CROP SCIENCE & TECHNOLOGY 2003, VOL 1 AND 2, CONGRESS PROCEEDINGS, P323 Wong KHM, 2006, COMPUT IND, V57, P342, DOI 10.1016/j.compind.2005.09.002 NR 15 TC 20 Z9 23 U1 1 U2 12 PD OCT PY 2009 VL 10 IS 5 BP 382 EP 394 DI 10.1007/s11119-009-9106-4 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000269218800003 DA 2022-12-14 ER PT J AU Martinez, JAD Higuera, AG Esteban, MR Asensio, JR Delgado, MC Berruga, I Molina, A AF Vara Martinez, Juan Angel de la Garcia Higuera, Andres Roman Esteban, Mario Romero Asensio, Jesus Carmona Delgado, Manuel Berruga, Isabel Molina, Ana TI Monitoring bulk milk quality by an integral traceability system of milk SO JOURNAL OF APPLIED ANIMAL RESEARCH DT Article DE Traceability; milk quality; RFID ID SOMATIC-CELL COUNT; PHYSICOCHEMICAL CHARACTERISTICS; MICROBIOLOGICAL QUALITY; SHEEP MILK; STORAGE-CONDITIONS; GOAT; PRESERVATION AB The Integral Traceability System for tracking and tracing the milk samples used in quality control was checked for one year while monitoring 526 milk samples from sheep's, goats' and cows'. This system includes a customized automated cooler for carrying samples with a smart sensor inside to store the data collected during the process, and a dongle to transfer the collected data to a computer to be further analysed. The technologies combined to record and trace milk samples on trips from farms to the laboratory (e.g. microcontrollers, sensors, radio frequency identification and global positioning system) were linked. This system allowed us to objectively know the duration of the sampling route and the temperature and time conditions of samples travelled in until they were analysed in an official dairy laboratory. These conditions ensured that the baseline milk quality was preserved, and was therefore adequate according to both European regulations and the price set to be paid for quality. Hardware and software prototypes worked successfully under the real study conditions, and this system may be proposed to become a reference method in the dairy sector. C1 [Vara Martinez, Juan Angel de la; Berruga, Isabel; Molina, Ana] Univ Castilla La Mancha, Dept Agroforestry Technol & Sci & Genet, ETSIAM IDR, Albacete 02071, Spain. [Garcia Higuera, Andres] Univ Castilla La Mancha, Sch Ind Engn, AutoLog Grp, Ciudad Real, Spain. [Roman Esteban, Mario] Qualiam SL, Villarrobledo, Spain. [Romero Asensio, Jesus] Interprofess Dairy Lab Castilla La Mancha LILCAM, Talavera De La Reina, Spain. [Carmona Delgado, Manuel] Univ Europea Madrid, Sch Doctoral Studies & Res, Villaviciosa De Odon, Spain. C3 Universidad de Castilla-La Mancha; Universidad de Castilla-La Mancha; European University of Madrid RP Molina, A (corresponding author), Univ Castilla La Mancha, Dept Agroforestry Technol & Sci & Genet, ETSIAM IDR, Albacete 02071, Spain. EM ana.molina@uclm.es CR Balthazar CF, 2017, COMPR REV FOOD SCI F, V16, P247, DOI 10.1111/1541-4337.12250 Barbano DM, 2006, J DAIRY SCI, V89, pE15, DOI 10.3168/jds.S0022-0302(06)72360-8 Bates D, 2015, J STAT SOFTW, V67, P1, DOI 10.18637/jss.v067.i01 Baudry C, 1997, VET RES, V28, P277 Beltran MC, 2015, SPAN J AGRIC RES, V13, DOI 10.5424/sjar/2015131-6522 Charlebois S, 2015, J DAIRY SCI, V98, P3514, DOI 10.3168/jds.2014-9247 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 de Garnica ML, 2011, J DAIRY SCI, V94, P1922, DOI 10.3168/jds.2010-3787 de las Morenas J, 2014, COMPUT ELECTRON AGR, V101, P34, DOI 10.1016/j.compag.2013.12.011 Franciosi E, 2011, WORLD J MICROB BIOT, V27, P171, DOI 10.1007/s11274-010-0443-2 Gonzalo C, 2005, J DAIRY SCI, V88, P969, DOI 10.3168/jds.S0022-0302(05)72764-8 Gonzalo C, 2010, J DAIRY SCI, V93, P1587, DOI 10.3168/jds.2009-2838 GUINOTTHOMAS P, 1995, INT DAIRY J, V5, P211, DOI 10.1016/0958-6946(95)92211-L Hothorn T, 2008, BIOMETRICAL J, V50, P346, DOI 10.1002/bimj.200810425 LeTra Q, 2017, MILK QUALITY Malacarne M, 2013, INT DAIRY J, V29, P36, DOI 10.1016/j.idairyj.2012.10.005 Martinez JR, 2003, J DAIRY SCI, V86, P2583, DOI 10.3168/jds.S0022-0302(03)73853-3 Park YW, 2007, SMALL RUMINANT RES, V68, P88, DOI 10.1016/j.smallrumres.2006.09.013 Pirisi A, 2007, SMALL RUMINANT RES, V68, P167, DOI 10.1016/j.smallrumres.2006.09.009 PMO, 2015, GRAD PAST MILK ORD Quigley L, 2013, FEMS MICROBIOL REV, V37, P664, DOI 10.1111/1574-6976.12030 R Core Team, 2020, R LANG ENV STAT COMP Raynal-Ljutovac K, 2007, SMALL RUMINANT RES, V68, P126, DOI 10.1016/j.smallrumres.2006.09.012 Sanchez A, 2005, J DAIRY SCI, V88, P3095, DOI 10.3168/jds.S0022-0302(05)72991-X Ceballos LS, 2009, J FOOD COMPOS ANAL, V22, P322, DOI 10.1016/j.jfca.2008.10.020 Sun H., 2016, INT J MULTIMEDIA, V11, P335, DOI [10.14257/ijmue.2016.11.4.33, DOI 10.14257/IJMUE.2016.11.4.33] Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Yamazi AK, 2013, SMALL RUMINANT RES, V113, P205, DOI 10.1016/j.smallrumres.2013.02.004 Zeng SS, 2007, J FOOD PROTECT, V70, P1281, DOI 10.4315/0362-028X-70.5.1281 NR 30 TC 5 Z9 5 U1 2 U2 14 PY 2018 VL 46 IS 1 BP 784 EP 790 DI 10.1080/09712119.2017.1403327 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000418936300001 DA 2022-12-14 ER PT J AU Ameri, F Wallace, E Yoder, R Riddick, F AF Ameri, Farhad Wallace, Evan Yoder, Reid Riddick, Frank TI Enabling Traceability in Agri-Food Supply Chains Using an Ontological Approach SO JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING DT Article DE ontology; supply chain; food traceability; traceable resource unit; critical tracking event; interoperability; data-driven engineering; agri-food; knowledge engineering AB Traceability of food products to their sources is critical for quick responses to food emergencies. US law now requires stakeholders in the agri-food supply chain to support traceability by tracking food materials they acquire and sell. However, having the complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. There are multiple reasons that food traceability is a challenging task, including diversity of stakeholders and their lexicons, standards, tools, and methods; unwillingness to expose information about internal operations; lack of a common understanding of the steps in a supply chain; and incompleteness of data. The objective of this work is to address the traceability challenge by developing a formal ontology that can provide a shared and common understanding of the traceability model across all stakeholders in bulk food supply chains. A formal ontology can support semantic mediation, data integration, and data exploration, thus improving the intelligence and reliability of trace and track process. The Industrial Ontologies Foundry (IOF) procedures and principles are employed in the development of the supply chain traceability ontology. Basic Formal Ontology (BFO) is selected as the top-level ontology. A bottom-up approach is also adopted in a sense that a real use case related to the bulk grain domain is selected to be used for requirements definition and ontology validation. A software tool for visualization of the traceability graph is developed to validate the developed ontology based on simulated data. The test and validation results indicate that the developed ontology has the expressivity needed to represent the semantics of traceability data models within the scope of the selected use case. Also, it was observed that the developed supply chain traceability tool can effectively facilitate the track and trace process through visualizing the Resource Description Framework (RDF) triples, thus eliminating the need to formulate complex SPARQL queries for information retrievals. C1 [Ameri, Farhad; Yoder, Reid] Texas State Univ, Engn Informat Lab, San Marcos, TX 78666 USA. [Wallace, Evan; Riddick, Frank] NIST, Syst Integrat Div, Gaithersburg, MD 20899 USA. C3 Texas State University System; Texas State University San Marcos; National Institute of Standards & Technology (NIST) - USA RP Ameri, F (corresponding author), Texas State Univ, Engn Informat Lab, San Marcos, TX 78666 USA. EM ameri@txstate.edu; evan.wallace@nist.gov; rjy15@txstate.edu; frank.riddick@nist.gov CR Ameri F., 2019, ASME 2019 INT DESIGN, DOI [10.1115/detc2019-98278, DOI 10.1115/DETC2019-98278] [Anonymous], 2019, IOF TECHNICAL PRINCI [Anonymous], EPCIS COR BUS VOC CB [Anonymous], 2019, IOF CHART Arp R, 2015, BUILDING ONTOLOGIES WITH BASIC FORMAL ONTOLOGY, P1, DOI 10.7551/mitpress/9780262527811.001.0001 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Ceusters W., CEUR, V1515, P1 Chifu VR, 2007, INT C INTELL COMP CO, P1, DOI 10.1109/ICCP.2007.4352135 Dooley DM, 2018, NPJ SCI FOOD, V2, DOI 10.1038/s41538-018-0032-6 Friedman M, 1999, SIXTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-99)/ELEVENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE (IAAI-99), P67 Hoehndorf R, 2015, BRIEF BIOINFORM, V16, P1069, DOI 10.1093/bib/bbv011 Lenat DB., 1985, AI MAG, V6, P65 Masolo C., 2002, WONDERWEB DELIVERABL Niles I., 2001, Formal Ontology in Information Systems. Collected Papers from the Second International Conference, P2, DOI 10.1145/505168.505170 Pizzuti Teresa, 2013, 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), P281, DOI 10.1109/IDAACS.2013.6662689 Pizzuti T, 2017, FOOD CONTROL, V72, P123, DOI 10.1016/j.foodcont.2016.07.038 Riddick F.H., 2018, AM SOC AGR BIOLOGICA Salampasis Michail, 2012, Journal of Systems and Information Technology, V14, P302, DOI 10.1108/13287261211279053 Shneiderman B, 1996, IEEE SYMPOSIUM ON VISUAL LANGUAGES, PROCEEDINGS, P336, DOI 10.1109/VL.1996.545307 Smith B., P JOINT ONT WORKSH J Solanki M, 2014, INT J SEMANT WEB INF, V10, P45, DOI 10.4018/IJSWIS.2014070102 Uschold M, 1996, KNOWL ENG REV, V11, P93, DOI 10.1017/S0269888900007797 Zhang JR, 2014, COMPR REV FOOD SCI F, V13, P1074, DOI 10.1111/1541-4337.12103 NR 24 TC 0 Z9 0 U1 1 U2 1 PD OCT 1 PY 2022 VL 22 IS 5 AR 051002 DI 10.1115/1.4054092 WC Computer Science, Interdisciplinary Applications; Engineering, Manufacturing SC Computer Science; Engineering UT WOS:000851558400013 DA 2022-12-14 ER PT J AU Duan, YQ Miao, MY Wang, RM Fu, ZT Xu, M AF Duan, Yanqing Miao, Meiyin Wang, Ruimei Fu, Zetian Xu, Mark TI A framework for the successful implementation of food traceability systems in China SO INFORMATION SOCIETY DT Article DE Chinese food enterprises; critical success factors; information systems success; traceability systems ID RESOURCE PLANNING IMPLEMENTATION; INFORMATION-SYSTEMS; ERP IMPLEMENTATION; GENERAL FRAMEWORK; MCLEAN MODEL; E-BUSINESS; ENTERPRISE; ADOPTION; DETERMINANTS; MANAGEMENT AB Implementation of food traceability systems in China faces many challenges due to the scale, diversity, and complexity of China's food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a prestudy helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in 83 food companies. This study identifies six dimensions for critical success factors: laws, regulations, and standards; government support; consumer knowledge and support; effective management and communication; top management, company wide, and vendor support; and information and system quality. C1 [Duan, Yanqing] Univ Bedfordshire, Sch Business, Vicarage St, Luton LU1 3JU, Beds, England. [Miao, Meiyin; Fu, Zetian] China Agr Univ, Sch Engn, Beijing, Peoples R China. [Wang, Ruimei] China Agr Univ, Sch Econ & Management, Beijing, Peoples R China. [Xu, Mark] Univ Portsmouth, Portsmouth Business Sch, Portsmouth, Hants, England. C3 University of Bedfordshire; China Agricultural University; China Agricultural University; University of Portsmouth RP Duan, YQ (corresponding author), Univ Bedfordshire, Sch Business, Vicarage St, Luton LU1 3JU, Beds, England. EM Yanqing.Duan@beds.ac.uk CR Akkermans H, 2002, EUR J INFORM SYST, V11, P35, DOI 10.1057/palgrave/ejis/3000418 Al Nahian Riyadh M., 2009, INT REV BUS RES PAP, V5, P212 Al-Mashari M, 2003, EUR J OPER RES, V146, P352, DOI 10.1016/S0377-2217(02)00554-4 Amoako-Gyampah K, 2004, INFORM MANAGE-AMSTER, V41, P731, DOI 10.1016/j.im.2003.08.010 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 BADIRU AB, 1988, IEEE T ENG MANAGE, V35, P186, DOI 10.1109/17.7439 Baker J, 2010, INTEGR SER INFORM SY, V28, P231, DOI 10.1007/978-1-4419-6108-2_12 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Caswell JA, 2006, MAR POLLUT BULL, V53, P650, DOI 10.1016/j.marpolbul.2006.08.007 CHEN HH, 2008, WORLD STANDARDIZATIO, V8, P56 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 D'Amico P, 2014, FOOD CONTROL, V35, P7, DOI 10.1016/j.foodcont.2013.06.029 DANIEL DR, 1961, HARVARD BUS REV, V39, P111 Davenport TH, 1998, HARVARD BUS REV, V76, P121 DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748 DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60 Doom C, 2010, J ENTERP INF MANAG, V23, P378, DOI 10.1108/17410391011036120 Finney S, 2007, BUS PROCESS MANAG J, V13, P329, DOI 10.1108/14637150710752272 Fortune J., 2006, International Journal of Project Management, V24, P53, DOI 10.1016/j.ijproman.2005.07.004 Fui-Hoon Nah F., 2001, BUSINESS PROCESS MAN, V7, P285, DOI DOI 10.1108/14637150110392782 Gangwar H, 2015, J ENTERP INF MANAG, V28, P107, DOI 10.1108/JEIM-08-2013-0065 Gibbs J. L., 2004, Electronic Markets, V14, P124, DOI 10.1081/10196780410001675077 Hair JFJ, 1995, MULTIVARIATE DATA AN Han Y., 2009, TECHNOL EC, V28, P37, DOI DOI 10.3969/J.ISSN.1002-980X.2009.04.006 HE Y, 2014, COMPUTER MODELLING N, V0018, P00195 Hsu PF, 2006, INT J ELECTRON COMM, V10, P9, DOI 10.2753/JEC1086-4415100401 Iacovou CL, 1995, MIS QUART, V19, P465, DOI 10.2307/249629 Jia CH, 2013, FOOD CONTROL, V32, P236, DOI 10.1016/j.foodcont.2012.11.042 Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Karlsen Kine Mari, 2006, P251 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kuang-Wei Wen, 2010, International Journal of Electronic Business, V8, P80, DOI 10.1504/IJEB.2010.030717 Lee S, 2007, COMPUT HUM BEHAV, V23, P1853, DOI 10.1016/j.chb.2005.12.001 LI X, 2006, CHINA FISHERY, V9, P21 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 LIN L, 2005, COMMERCIAL RES, V21, P41 Liu R, 2013, FOOD CONTROL, V33, P1, DOI 10.1016/j.foodcont.2013.02.008 Loh TC, 2004, INT J PROD RES, V42, P3433, DOI 10.1080/00207540410001671679 Mao B, 2015, PROCEDIA COMPUT SCI, V55, P1285, DOI 10.1016/j.procs.2015.07.139 McMeekin TA, 2006, INT J FOOD MICROBIOL, V112, P181, DOI 10.1016/j.ijfoodmicro.2006.04.048 Mensah LD, 2011, FOOD CONTROL, V22, P1216, DOI 10.1016/j.foodcont.2011.01.021 Miraglia M, 2004, FOOD CHEM TOXICOL, V42, P1157, DOI 10.1016/j.fct.2004.02.018 Nah FFH, 2006, J COMPUT INFORM SYST, V46, P99, DOI 10.1080/08874417.2006.11645928 Ngai EWT, 2004, PROD PLAN CONTROL, V15, P622, DOI 10.1080/09537280412331283928 Nunally, 1978, PSYCHOMETRIC THEORY O'Brien, 2002, MANAGEMENT INFORM SY Oliveira T., 2011, ELECT J INF SYST EVA, V14, P110, DOI DOI 10.1017/CBO9781107415324.004 Peets S, 2009, PRECIS AGRIC, V10, P382, DOI 10.1007/s11119-009-9106-4 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Remus U, 2007, BUS PROCESS MANAG J, V13, P538, DOI 10.1108/14637150710763568 ROCKART JF, 1979, HARVARD BUS REV, V57, P81 Roth AV, 2008, J SUPPLY CHAIN MANAG, V44, P22, DOI 10.1111/j.1745-493X.2008.00043.x Ruey-Shun Chen, 2008, WSEAS Transactions on Information Science and Applications, V5, P1551 Saunders M., 2007, RES METHODS BUSINESS Seddon PB, 1997, INFORM SYST RES, V8, P240, DOI 10.1287/isre.8.3.240 Sioen I., 2007, Open Food Science Journal, V1, P33, DOI 10.2174/1874256400701010033 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Sumner M., 1999, P 1999 ACM SIGCPR C Tabachnick B. G., 2000, USING MULTIVARIATE S Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Thong J. Y. L., 1999, Journal of Management Information Systems, V15, P187 Tornatzky L. G., 1990, PROCESSES TECHNOLOGI, DOI DOI 10.1007/BF02371446 Upadhyay P., 2009, INT J MANAG INNOV SY, V1, P1 Wang YM, 2010, TECHNOL FORECAST SOC, V77, P803, DOI 10.1016/j.techfore.2010.03.006 XU J, 2008, J HUNAN AGR U, V9, P24 ZEN Y, 2005, VEGETABLES, V10, P1 [翟丽 Zhai Li], 2008, [系统工程学报, Journal of Systems Engineering], V23, P352 [张兵 ZHANG Bing], 2007, [食品科学, Food Science], V28, P573 Zhang L., 2002, P 36 HAW INT C SYST Zhang Z, 2005, INT J PROD ECON, V98, P56, DOI 10.1016/j.ijpe.2004.09.004 Zhu K, 2004, J MANAGE INFORM SYST, V21, P17, DOI 10.1080/07421222.2004.11045797 Zhu K, 2003, EUR J INFORM SYST, V12, P251, DOI 10.1057/palgrave.ejis.3000475 ZHU X, 2008, VEGETABLES, V7, P34 [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] NR 92 TC 20 Z9 20 U1 1 U2 41 PY 2017 VL 33 IS 4 BP 226 EP 242 DI 10.1080/01972243.2017.1318325 WC Communication; Information Science & Library Science SC Communication; Information Science & Library Science UT WOS:000402660600005 DA 2022-12-14 ER PT J AU Naas, ID Neto, MM Vendrametto, O Canuto, SA AF Naeaes, Irenilza de A. Mollo Neto, Mario Vendrametto, Oduvaldo Canuto, Simone A. TI COMPARATIVE ANALYSIS OF DIFFERENT MEAT TRACEABILITY SYSTEMS USING MULTIPLE CRITERIA AND A SOCIAL NETWORK APPROACH SO ENGENHARIA AGRICOLA DT Article DE analytic hierarchy process; food traceability; social network analysis-SNA ID ELECTRONIC IDENTIFICATION; PIGS AB The adoption of a proper traceability system is being incorporated into meat production practices as a method of gaining consumer confidence. The various partners operating in the chain of meat production can be considered a social network, and they have the common goal of generating a communication process that can ensure each characteristic of the product, including safety. This study aimed to select the most appropriate meat traceability system "from farm to fork" that could be applied to Brazilian beef and pork production for international trade. The research was done in three steps. The first used the analytical hierarchy process (AHP) for selecting the best on-farm livestock traceability. In the second step, the actors in the meat production chain were identified to build a framework and defined each role in the network. In the third step, the selection of the traceability system was done. Results indicated that with an electronic traceability system, it is possible to acquire better connections between the links in the chain and to provide the means for managing uncertainties by creating structures that facilitate information flow more efficiently. C1 [Naeaes, Irenilza de A.; Vendrametto, Oduvaldo] Univ Paulista, Programa Pos Grad Enga Prod, Sao Paulo, SP, Brazil. [Mollo Neto, Mario] UNESP, Enga Biossistemas, Tupa, SP, Brazil. [Canuto, Simone A.] Dra Enga Prod, Sao Paulo, SP, Brazil. C3 Universidade Paulista; Universidade Estadual Paulista RP Naas, ID (corresponding author), Univ Paulista, Programa Pos Grad Enga Prod, Sao Paulo, SP, Brazil. EM irenilza@gmail.com; mariomollo@tupa.unesp.br; oduvaldov@uol.com.br; simcanuto@gmail.com CR Abebe M., 2010, STRATEGIC MANAGEMENT, V4, P30 [Anonymous], 2010, BRAZ C EC RUR SOC 32 [Anonymous], 2005, AGR ENG INT CIGR EJO Arranz N, 2007, TECHNOL FORECAST SOC, V74, P645, DOI 10.1016/j.techfore.2006.05.009 Borgatti SP, 2009, J SUPPLY CHAIN MANAG, V45, P5, DOI 10.1111/j.1745-493X.2009.03166.x BUAINAIN A.M., 2007, CADEIA PRODUTIVA CAR Euclides K, 2004, LIVEST PROD SCI, V90, P53, DOI 10.1016/j.livprodsci.2004.07.006 EXPERT CHOICE, 2009, EXP CHOIC 11 5 Ferrier P, 2007, FOOD POLICY, V32, P84, DOI 10.1016/j.foodpol.2006.01.004 Freeman L., 2002, UCINET 6 WINDOWS SOF Gosalvez LF, 2007, J ANIM SCI, V85, P2746, DOI 10.2527/jas.2007-0173 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Lambooij E, 1999, COMPUT ELECTRON AGR, V24, P81, DOI 10.1016/S0168-1699(99)00038-1 MUS M., 2006, THESIS WASHINGTON SC Petersen B, 2002, LIVEST PROD SCI, V76, P207, DOI 10.1016/S0301-6226(02)00120-3 Richardson AJ, 2009, ACCOUNT ORG SOC, V34, P571, DOI 10.1016/j.aos.2008.11.005 ROTH M., 2006, COST BENEFIT ANAL QU Saaty T.L., 1980, ANAL HIERARCHY PROCE Samaddar S, 2006, EUR J OPER RES, V170, P192, DOI 10.1016/j.ejor.2004.06.024 Schembri N, 2007, J ANIM SCI, V85, P3123, DOI 10.2527/jas.2007-0169 Schnettler B, 2009, CHIL J AGR RES, V69, P373, DOI 10.4067/S0718-58392009000300010 Vaidya OS, 2006, EUR J OPER RES, V169, P1, DOI 10.1016/j.ejor.2004.04.028 Villalobos P, 2010, CHIL J AGR RES, V70, P85, DOI 10.4067/S0718-58392010000100009 NR 23 TC 5 Z9 5 U1 0 U2 22 PD MAR-APR PY 2015 VL 35 IS 2 BP 340 EP 349 DI 10.1590/1809-4430-Eng.Agric.v35n2p340-349/2015 WC Agricultural Engineering SC Agriculture UT WOS:000356816300016 DA 2022-12-14 ER PT J AU Zheng, MM Zhang, SS Zhang, YD Hu, BZ AF Zheng, Miaomiao Zhang, Shanshan Zhang, Yidan Hu, Baozhong TI Construct Food Safety Traceability System for People's Health Under the Internet of Things and Big Data SO IEEE ACCESS DT Article DE Safety; Big Data; Internet of Things; Production; Radiofrequency identification; Python; Epidemics; Two-dimensional code technology; Internet of Things; big data; artificial intelligence; food safety traceability system ID SUPPLY CHAIN; QUALITY; BLOCKCHAIN AB In the context of epidemic prevention and control, food safety monitoring, data analysis and food safety traceability have become more important. At the same time, the most important reason for food safety issues is incomplete, opaque, and asymmetric information. The most fundamental way to solve these problems is to do a good job of traceability, and establish a reasonable and reliable food safety traceability system. The traceability system is currently an important means to ensure food quality and safety and solve the crisis of trust between consumers and the market. Research on food safety traceability systems based on big data, artificial intelligence and the Internet of Things provides ideas and methods to solve the problems of low credibility and difficult data storage in the application of traditional traceability systems. Therefore, this research takes rice as an example and proposes a food safety traceability system based on RFID two-dimensional code technology and big data storage technology in the Internet of Things. This article applies RFID technology to the entire system by analyzing the requirements of the system, designing the system database and database tables, encoding the two-dimensional code and generating the design for information entry. Using RFID radio frequency technology and the data storage function in big data to obtain information in the food production process. Finally, the whole process of food production information can be traced through the design of dynamic query platform and mobile terminal. In this research, the food safety traceability system based on big data and the Internet of Things guarantees the integrity, reliability and safety of traceability information from a technical level. This is an effective solution for enhancing the credibility of traceability information, ensuring the integrity of information, and optimizing the data storage structure. C1 [Zheng, Miaomiao; Zhang, Shanshan; Zhang, Yidan; Hu, Baozhong] Harbin Univ, Sch Food Engn, Dept Food, Harbin 150001, Peoples R China. [Zheng, Miaomiao; Hu, Baozhong] Northeast Agr Univ, Coll Life Sci, Harbin 150036, Peoples R China. C3 Harbin University; Northeast Agricultural University - China RP Zheng, MM (corresponding author), Harbin Univ, Sch Food Engn, Dept Food, Harbin 150001, Peoples R China.; Zheng, MM (corresponding author), Northeast Agr Univ, Coll Life Sci, Harbin 150036, Peoples R China. EM miaomiao_0000@hrbu.edu.cn CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Alfian G, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107016 An-Qi Jin, 2020, Zhongguo Zhong Yao Za Zhi, V45, P5304, DOI 10.19540/j.cnki.cjcmm.20200708.601 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Castro-Puyana M, 2017, TRAC-TREND ANAL CHEM, V93, P102, DOI 10.1016/j.trac.2017.05.004 Chen MC, 2020, APPL MATH LETT, V107, DOI 10.1016/j.aml.2020.106476 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 Chen RY, 2015, FOOD CONTROL, V51, P70, DOI 10.1016/j.foodcont.2014.11.004 Chen SW, 2016, J MED SYST, V40, DOI 10.1007/s10916-016-0484-7 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Deng M., 2020, J COMPUTER COMMUNICA, V08, P17, DOI [10.4236/jcc.2020.89002, DOI 10.4236/JCC.2020.89002] Dong YH, 2020, IEEE ACCESS, V8, P161261, DOI 10.1109/ACCESS.2020.3019593 Galanakis CM, 2020, FOODS, V9, DOI 10.3390/foods9040523 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Han JL, 2021, IEEE INTERNET THINGS, V8, P9683, DOI 10.1109/JIOT.2020.3037729 Huang H, 2014, SENSORS-BASEL, V14, P7248, DOI 10.3390/s140407248 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu F, 2015, INT J SENS NETW, V17, P211, DOI 10.1504/IJSNET.2015.069582 Liu W., 2013, ADV J FOOD SCI TECHN, V5, P492 Lv M, 2018, BIOSENS BIOELECTRON, V106, P122, DOI 10.1016/j.bios.2018.01.049 Pizzuti T, 2017, FOOD CONTROL, V72, P123, DOI 10.1016/j.foodcont.2016.07.038 Qi SY, 2021, IEEE INTERNET THINGS, V8, P2886, DOI 10.1109/JIOT.2020.3020979 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Thibaud M, 2018, DECIS SUPPORT SYST, V108, P79, DOI 10.1016/j.dss.2018.02.005 Tian F, 2017, I C SERV SYST SERV M Wang J, 2017, FOOD CONTROL, V79, P363, DOI 10.1016/j.foodcont.2017.04.013 Wang W, 2020, IEEE NETWORK, V34, P295, DOI 10.1109/MNET.011.2000250 Wei W, 2020, IEEE T IND INFORM, V16, P2562, DOI 10.1109/TII.2019.2958638 Xiao XQ, 2015, J SCI FOOD AGR, V95, P2693, DOI 10.1002/jsfa.7005 Zhang XS, 2010, FOOD CONTROL, V21, P1556, DOI 10.1016/j.foodcont.2010.03.020 Zhao Guo, 2015, Journal of Food Safety and Quality, V6, P747 NR 33 TC 11 Z9 11 U1 19 U2 77 PY 2021 VL 9 BP 70571 EP 70583 DI 10.1109/ACCESS.2021.3078536 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000652042300001 DA 2022-12-14 ER PT J AU Liu, ZJ Geng, N Yu, Z AF Liu, Zengjin Geng, Ning Yu, Zhuo TI Does a Traceability System Help to Regulate Pig Farm Households' Veterinary Drug Use Behavior? Evidence from Pig Farms in China SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH DT Article DE pork traceability system; safety effect; pig farm households; safety behavior ID FOOD-SUPPLY CHAIN; WILLINGNESS-TO-PAY; SAFETY; QUALITY; DETERMINANTS; PERSPECTIVES; INFORMATION; PERCEPTIONS; MANAGEMENT; ATTITUDES AB In China, there is a renewed interest in traceability systems as an efficient tool to guarantee pork safety. One of the pathways through which a traceability system can benefit consumers is by easing information asymmetry. However, past literature on the traceability system in China pays more attention to theoretical analysis and less to empirical analysis. Using a large-scale survey of pig farms in China, we investigate the effects influencing farmers' participation in the traceability system. Findings show that a traceability system can influence the safety of pork indirectly through its impacts on farmers' production behaviors. Another important finding is that unsafe pork is a result of non-standard use of veterinary drugs, and the traceability system works well for farmers by pushing them to take stricter safety measurements. C1 [Liu, Zengjin] Shanghai Acad Agr Sci, Shanghai 201403, Peoples R China. [Geng, Ning] Shandong Normal Univ, Sch Publ Adm, Jinan 250014, Peoples R China. [Yu, Zhuo] Ocean Univ China, Sch Management, Qingdao 266100, Peoples R China. C3 Shanghai Academy of Agricultural Sciences; Shandong Normal University; Ocean University of China RP Geng, N (corresponding author), Shandong Normal Univ, Sch Publ Adm, Jinan 250014, Peoples R China. EM gengning@sdnu.edu.cn CR Adam BD, 2016, INT FOOD AGRIBUS MAN, V19, P191 Adesokan HK, 2014, TROP ANIM HEALTH PRO, V46, P159, DOI 10.1007/s11250-013-0467-3 Alarcon P, 2014, PREV VET MED, V116, P223, DOI 10.1016/j.prevetmed.2013.08.004 [Anonymous], TIME Antle J.M., 1995, CHOICE EFFICIENCY FO, V1st, P25 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Boger S, 2001, EUR REV AGRIC ECON, V28, P241, DOI 10.1093/erae/28.3.241 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Caswell JA, 1996, AM J AGR ECON, V78, P1248, DOI 10.2307/1243501 Chattopadhyay MK, 2014, FRONT MICROBIOL, V5, DOI 10.3389/fmicb.2014.00334 Chen Q., 2010, ADV ECONOMETRICS STA, P329 Chen TB, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106770 Chen XJ, 2016, TROP ANIM HEALTH PRO, V48, P1395, DOI 10.1007/s11250-016-1104-8 Cook MA, 2019, J FOOD QUALITY, DOI 10.1155/2019/1048092 Corallo A, 2020, J RURAL STUD, V75, P30, DOI 10.1016/j.jrurstud.2020.02.006 Dima A, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14095111 Fallon M, 2001, REV SCI TECH OIE, V20, P538, DOI 10.20506/rst.20.2.1289 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Green W.H., 2013, ECONOMETRIC ANAL, V7th Hirschauer N, 2012, BRIT FOOD J, V114, P1212, DOI 10.1108/00070701211258781 Hisjam M., 2018, P 1 AGRIFOOD SYSTEM Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Nguyen-Viet H, 2017, INFECT DIS POVERTY, V6, DOI 10.1186/s40249-017-0249-7 Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 Ji C, 2019, FOOD POLICY, V83, P231, DOI 10.1016/j.foodpol.2019.01.007 Ji C, 2018, J INTEGR AGR, V17, P2345, DOI [10.1016/S2095-3119(18)62058-1, 10.1016/s2095-3119(18)62058-1] Jiang WJ, 2018, AGR ECON-CZECH, V64, P477, DOI [10.17221/154/2017-agricecon, 10.17221/154/2017-AGRICECON] Jiehong Z., 2012, J AGR TECH EC, V8, P29 Jin CY, 2021, J INTEGR AGR, V20, P2807, DOI 10.1016/S2095-3119(21)63624-9 Jin CY, 2021, MANAGE SCI, V67, P2985, DOI 10.1287/mnsc.2020.3839 Jin L.W., 2021, J ZHEJIANG J AGR, V33, P541 Li SW, 2021, J DAIRY SCI, V104, P8554, DOI 10.3168/jds.2020-19733 Liu Z.J., 2016, CHIN J AGR RESOUR RE, V37, P105 Lu J, 2020, SINGAP ECON REV, V65, P737, DOI 10.1142/S0217590818500108 Ngo HHT, 2021, INT J FOOD MICROBIOL, V346, DOI 10.1016/j.ijfoodmicro.2021.109163 Ning Y.L., 2011, CHINA J ANIM HUSB, V47, P10 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pedersen RE, 2009, LIVEST SCI, V121, P215, DOI 10.1016/j.livsci.2008.06.007 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Rodriguez-Salvador B, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107142 Saltini R, 2013, FOOD CONTROL, V29, P167, DOI 10.1016/j.foodcont.2012.05.054 Sinh DX, 2016, J FOOD PROTECT, V79, P1490, DOI 10.4315/0362-028X.JFP-15-402 Sun RY, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13168861 Sun SN, 2019, J CLEAN PROD, V217, P658, DOI 10.1016/j.jclepro.2019.01.296 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Visciano P, 2021, TRENDS FOOD SCI TECH, V114, P424, DOI 10.1016/j.tifs.2021.06.010 Visschers VHM, 2014, LIVEST SCI, V162, P223, DOI 10.1016/j.livsci.2014.02.002 Wang EP, 2019, FOOD POLICY, V89, DOI 10.1016/j.foodpol.2019.101791 Wang HS, 2019, AGRIBUSINESS, V35, P97, DOI 10.1002/agr.21591 Wang JH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15102185 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu YN, 2018, FOOD CONTROL, V90, P429, DOI 10.1016/j.foodcont.2018.03.009 Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X Xu LL, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16193616 Zhong YQ, 2017, FOOD CONTROL, V73, P1532, DOI 10.1016/j.foodcont.2016.11.016 Zhou JH, 2013, J INTEGR AGR, V12, P1112, DOI 10.1016/S2095-3119(13)60490-6 Ziggers GW, 1999, INT J PROD ECON, V60-1, P271, DOI 10.1016/S0925-5273(98)00138-8 NR 61 TC 0 Z9 0 U1 0 U2 0 PD OCT PY 2022 VL 19 IS 19 AR 11879 DI 10.3390/ijerph191911879 WC Environmental Sciences; Public, Environmental & Occupational Health SC Environmental Sciences & Ecology; Public, Environmental & Occupational Health UT WOS:000866715300001 DA 2022-12-14 ER PT J AU Jansen-Vullers, MH van Dorp, CA Beulens, AJM AF Jansen-Vullers, MH van Dorp, CA Beulens, AJM TI Managing traceability information in manufacture SO INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT DT Article DE product quality; tracking and tracing; traceability information management; reference models; information systems ID METHODOLOGY; SYSTEMS AB In this paper, an approach to design information systems for traceability is proposed. The paper applies gozinto graph modelling for traceability of the goods flow. A gozinto graph represents a graphical listing of raw materials, parts, intermediates and subassemblies, which a process transforms into an end product, through a sequence of operations. Next, the graphical listing has been translated into a reference data model that is the basis for designing an information system for tracking and tracing. Materials that are modelled this way represent production and/or purchase lots or batches. The composition of a certain end product is then represented through modelling all its constituent materials along with their intermediate relations. By registering all relations between sub-ordinate and super-ordinate material lots, a method of tracking the composition of the end product is obtained. When the entire sequence of operations required for manufacturing an end product adheres to this registering of relations, a multilevel bill of lots can be compiled. That bill of lots then, provides the necessary information to determine the composition of a material item out of component items. These composition data can be used to recall any items having consumed a certain component of specific interest (e.g., deficient), but also to certify product quality or to pro-actively adjust production processes to optimise the product quality in relation to its production characteristics (e.g., scarcity, costs or time). (C) 2003 Elsevier Ltd. All rights reserved. C1 Eindhoven Univ Technol, Dept Technol Management, NL-5600 MB Eindhoven, Netherlands. Wageningen Univ Agr, Appl Comp Sci Grp, NL-6703 HB Wageningen, Netherlands. Univ Limburg, Inst Knowledge & Agent Technol, Dept Comp Sci, NL-6200 MD Maastricht, Netherlands. C3 Eindhoven University of Technology; Wageningen University & Research; Hasselt University RP Jansen-Vullers, MH (corresponding author), Eindhoven Univ Technol, Dept Technol Management, Paviljoen D-13,POB 513, NL-5600 MB Eindhoven, Netherlands. CR ABBOT H, 1991, MANAGING PRODUCT REC [Anonymous], 1998, BUSINESS PROCESS ENG BEMELMANS TMA, 1998, ADM INFORMATION SYST Bertrand J.W.M., 1990, PRODUCTION CONTROL S CHENG MJ, 1994, INT J OPER PROD MAN, V14, P4, DOI 10.1108/01443579410067199 Cox J. F., 1998, ED SOC RESOURCE MANA CRAMA Y, 2001, 200142 CORE Deasy DJ, 2002, INT J DAIRY TECHNOL, V55, P1, DOI 10.1046/j.1364-727X.2001.00036.x Elmasri R., 2000, FUNDAMENTALS DATABAS, V3rd Iivari J, 1998, INFORM SYST RES, V9, P164, DOI 10.1287/isre.9.2.164 Juran J., 1988, JURANS QUALITY CONTR LOOS P, 2001, P 7 AM C INF SYST AM Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Petroff J. N., 1991, Production and Inventory Management Journal, V32, P55 RUTTEN WGM, 1998, EUROPEAN J OPERATION, V10, P626 Salaun Y, 2001, INT J INFORM MANAGE, V21, P21, DOI 10.1016/S0268-4012(00)00048-7 Salaun-Bidart A, 2002, INT J INFORM MANAGE, V22, P225, DOI 10.1016/S0268-4012(02)00007-5 Scipioni A, 2002, FOOD CONTROL, V13, P495, DOI 10.1016/S0956-7135(02)00029-4 Steele D. C., 1995, Production and Inventory Management Journal, V36, P53 Toyryla I., 1999, ACTA POLYTECH SCAND, V1999, P211 TRIENEKENS JH, 2001, INT FOOD AGR MAN ASS VANDORP CA, 2002, P 8 AM C INF SYST AM, P945 VANRIJN TMJ, 1985, THESIS EINDHOVEN U T vanStrien PJ, 1997, THEOR PSYCHOL, V7, P683, DOI 10.1177/0959354397075006 VANTWILLERT J, 1999, THESIS WAGENINGEN AG NR 25 TC 164 Z9 179 U1 3 U2 31 PD OCT PY 2003 VL 23 IS 5 BP 395 EP 413 DI 10.1016/S0268-4012(03)00066-5 WC Information Science & Library Science SC Information Science & Library Science UT WOS:000185219900003 DA 2022-12-14 ER PT J AU Gonzalez-Dominguez, R Sayago, A Akhatou, I Fernandez-Recamales, A AF Gonzalez-Dominguez, Raul Sayago, Ana Akhatou, Ikram Fernandez-Recamales, Angeles TI Multi-Chemical Profiling of Strawberry as a Traceability Tool to Investigate the Effect of Cultivar and Cultivation Conditions SO FOODS DT Article DE strawberry; traceability; sugars; organic acids; phenolic compounds; mineral elements; cultivar; cultivation system ID ANTIOXIDANT CAPACITY; QUALITY; COMBINATION; IMPACT; FRUIT; FOOD AB The chemical composition of foods is tightly regulated by multiple genotypic and agronomic factors, which can thus serve as potential descriptors for traceability and authentication purposes. In the present work, we performed a multi-chemical characterization of strawberry fruits from five varieties (Aromas, Camarosa, Diamante, Medina, and Ventana) grown in two cultivation systems (open/closed soilless systems) during two consecutive campaigns with different climatic conditions (rainfall and temperature). For this purpose, we analyzed multiple components closely related to the sensory and health characteristics of strawberry, including sugars, organic acids, phenolic compounds, and essential and non-essential mineral elements, and various complementary statistical approaches were applied for selecting chemical descriptors of cultivar and agronomic conditions. Anthocyanins, phenolic acids, sucrose, and malic acid were found to be the most discriminant variables among cultivars, while climatic conditions and the cultivation system were behind changes in polyphenol contents. These results thus demonstrate the utility of combining multi-chemical profiling approaches with advanced chemometric tools in food traceability research. C1 [Gonzalez-Dominguez, Raul; Sayago, Ana; Akhatou, Ikram; Fernandez-Recamales, Angeles] Univ Huelva, Fac Expt Sci, Dept Chem, Huelva 21007, Spain. [Gonzalez-Dominguez, Raul; Sayago, Ana; Akhatou, Ikram; Fernandez-Recamales, Angeles] Univ Huelva, Int Campus Excellence CeiA3, Huelva 21007, Spain. C3 Universidad de Huelva; Universidad de Huelva RP Gonzalez-Dominguez, R; Fernandez-Recamales, A (corresponding author), Univ Huelva, Fac Expt Sci, Dept Chem, Huelva 21007, Spain.; Gonzalez-Dominguez, R; Fernandez-Recamales, A (corresponding author), Univ Huelva, Int Campus Excellence CeiA3, Huelva 21007, Spain. EM raul.gonzalez@dqcm.uhu.es; ana.sayago@dqcm.uhu.es; ikram.akhatou@alu.uhu.es; recamale@dqcm.uhu.es CR Aaby K, 2012, FOOD CHEM, V132, P86, DOI 10.1016/j.foodchem.2011.10.037 Akhatou I, 2017, J AGR FOOD CHEM, V65, P9559, DOI 10.1021/acs.jafc.7b03701 Akhatou I, 2016, PLANT PHYSIOL BIOCH, V101, P14, DOI 10.1016/j.plaphy.2016.01.016 Akhatou I, 2014, J AGR FOOD CHEM, V62, P5749, DOI 10.1021/jf500769x Akhatou I, 2014, J SCI FOOD AGR, V94, P866, DOI 10.1002/jsfa.6313 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bagchi D., 2016, DEV NEW FUNCTIONAL F Basu A, 2014, CRIT REV FOOD SCI, V54, P790, DOI 10.1080/10408398.2011.608174 Crespo P, 2010, FOOD CHEM, V122, P16, DOI 10.1016/j.foodchem.2010.02.010 Giampieri F, 2015, FOOD FUNCT, V6, P1386, DOI [10.1039/C5FO00147A, 10.1039/c5fo00147a] Giampieri F, 2013, NAT PROD RES, V27, P448, DOI 10.1080/14786419.2012.706294 Giampieri F, 2012, NUTRITION, V28, P9, DOI 10.1016/j.nut.2011.08.009 Gonzalez-Dominguez R, 2019, FOODS, V8, DOI 10.3390/foods8080287 GONZALEZDOMINGU.R, 2019, ENG TOOLS BEVERAGE I, V3, P315, DOI DOI 10.1016/B978-0-12-815258-4.00011-1 Krueger E, 2012, J BERRY RES, V2, P143, DOI 10.3233/JBR-2012-036 Nowicka A, 2019, FOOD CHEM, V270, P32, DOI 10.1016/j.foodchem.2018.07.015 Pereira GE, 2006, J AGR FOOD CHEM, V54, P6765, DOI 10.1021/jf061013k Sayago A, 2019, LWT-FOOD SCI TECHNOL, V111, P99, DOI 10.1016/j.lwt.2019.05.009 Sayago A, 2018, FOOD CHEM, V261, P42, DOI 10.1016/j.foodchem.2018.04.019 Sturm K, 2003, FOOD CHEM, V83, P417, DOI 10.1016/S0308-8146(03)00124-9 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Wang SY, 2009, J AGR FOOD CHEM, V57, P9651, DOI 10.1021/jf9020575 Wang SY, 2001, J AGR FOOD CHEM, V49, P4977, DOI 10.1021/jf0106244 NR 23 TC 13 Z9 13 U1 3 U2 8 PD JAN PY 2020 VL 9 IS 1 AR 96 DI 10.3390/foods9010096 WC Food Science & Technology SC Food Science & Technology UT WOS:000513235300096 DA 2022-12-14 ER PT J AU Marchesi, L Mannaro, K Marchesi, M Tonelli, R AF Marchesi, Lodovica Mannaro, Katiuscia Marchesi, Michele Tonelli, Roberto TI Automatic Generation of Ethereum-Based Smart Contracts for Agri-Food Traceability System SO IEEE ACCESS DT Article DE Agri-food product traceability; blockchain; smart contract; supply chain ID BLOCKCHAIN; TECHNOLOGY AB There is a growing demand for transparency along the agri-food chain, both from customers and governments. The adoption of blockchain technology to enable secure traceability for the management of the agri-food chain, provide information such as the provenance of a food product and prevent food fraud, is emerging rapidly, due to the inherent trust and inalterability provided by this technology. However, developing the right smart contracts for these use cases is even more of a challenge than it is for those used in other fields. Several management systems for the agri-food chain based on blockchain technology and smart contract have been proposed, all however ad-hoc for a specific product or production process and difficult to generalize. In this paper, we propose a new approach to easily customize and compose general Ethereum-based smart contracts designed for the agri-food industrial domain, to be able to reuse the code and modules and automate the process to shorten development times, while keeping it safe and reliable. Starting from the definition of the real production process, we aim to automatically generate both the smart contracts to manage the system and the user interfaces to interact with them, thus producing a system that works semi-automatically. Additionally, we describe a honey production case study to show how our approach works. Future work will first extend the scope of the approach to other supply chains, furthermore, while the current platform used is Ethereum, in the future our approach will be easily extended to other blockchain platforms. C1 [Marchesi, Lodovica; Mannaro, Katiuscia; Marchesi, Michele; Tonelli, Roberto] Univ Cagliari, Dept Math & Comp Sci, I-09124 Cagliari, Italy. C3 University of Cagliari RP Marchesi, L; Mannaro, K (corresponding author), Univ Cagliari, Dept Math & Comp Sci, I-09124 Cagliari, Italy. EM lodovica.marchesi@unica.it; katiuscia.mannaro@unica.it CR Alharby M., 2017, 3 INT C ARTIFICIAL I, P125 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Baralla G, 2019, 2019 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON EMERGING TRENDS IN SOFTWARE ENGINEERING FOR BLOCKCHAIN (WETSEB 2019), P40, DOI 10.1109/WETSEB.2019.00012 Bottoni P., 2020, FRONTIERS BLOCKCHAIN, V3, P52 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Chang YL, 2020, INT J PROD RES, V58, P2082, DOI 10.1080/00207543.2019.1651946 Clack CD, 2016, ARXIV160800771 Cocco L, 2021, 2021 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING (SANER 2021), P669, DOI 10.1109/SANER50967.2021.00085 Cocco L, 2021, IEEE ACCESS, V9, P62899, DOI 10.1109/ACCESS.2021.3074874 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 de Sousa V. A., 2020, PROC C ADV INF SYST, P1 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 E. Commission, GEN FOOD LAW Farooq MS, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9020319 Frantz CK, 2016, 2016 IEEE 1ST INTERNATIONAL WORKSHOPS ON FOUNDATIONS AND APPLICATIONS OF SELF* SYSTEMS (FAS*W), P210, DOI 10.1109/FAS-W.2016.53 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Haque B., 2021, IET BLOCKCHAIN, V1, P95, DOI [10.1049/blc2.12005, DOI 10.1049/BLC2.12005] Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625 Iftekhar A, 2020, J FOOD QUALITY, V2020, DOI 10.1155/2020/5385207 Jurgelaitis M., 2019, CEUR WORKSHOP PROC, P43 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Li J., 2018, 2018 2 IEEE ADV INFO, P2637 Lu QH, 2021, SOFTWARE PRACT EXPER, V51, P1059, DOI 10.1002/spe.2931 Marchesi Lodovica, 2021, 2021 IEEE/ACM 4th International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB), P41, DOI 10.1109/WETSEB52558.2021.00013 Marchesi L, 2020, PROCEEDINGS OF THE 2020 IEEE 3RD INTERNATIONAL WORKSHOP ON BLOCKCHAIN ORIENTED SOFTWARE ENGINEERING (IWBOSE '20), P9, DOI 10.1109/IWBOSE50093.2020.9050163 Marchesi Lodovica, 2020, BLOCKCHAIN RES APPL, V1 Marchesi M, 2018, CEE-SECR'18: PROCEEDINGS OF THE 14TH CENTRAL AND EASTERN EUROPEAN SOFTWARE ENGINEERING CONFERENCE RUSSIA, DOI 10.1145/3290621.3290627 Mavridou A, 2018, LECT NOTES COMPUT SC, V10957, P523, DOI 10.1007/978-3-662-58387-6_28 Nakamoto Satoshi, 2008, BITCOIN PEER TO PEER, P1 OpenZeppelin, 2020, OPENZEPPELIN CONTRAC Pranto TH, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.407 Rocha H, 2018, 2018 IEEE/ACM 1ST INTERNATIONAL WORKSHOP ON EMERGING TRENDS IN SOFTWARE ENGINEERING FOR BLOCKCHAIN (WETSEB), P52 Tian F, 2017, I C SERV SYST SERV M Tran A. B., 2018, PROC 16 INT C BPM Tribis Youness, 2018, MATEC Web of Conferences, V200, DOI 10.1051/matecconf/201820000020 Tripoli M., 2018, EMERGING OPPORTUNITI, V3 Udokwu C., 2021, INT J INF TECHNOL, V13, P2245 Udokwu C, 2021, ARAB J SCI ENG, V46, P8397, DOI [10.1145/3456126.3456134, 10.1007/s13369-020-05245-4] Wang SP, 2019, IEEE ACCESS, V7, P115122, DOI 10.1109/ACCESS.2019.2935873 Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 Xu X, 2019, ARCHITECTURE BLOCKCH, DOI [DOI 10.1007/978-3-030-03035-3_1, 10.1007/978-3-030-03035-3, DOI 10.1007/978-3-030-03035-3] Yu B, 2020, IEEE ACCESS, V8, P12479, DOI 10.1109/ACCESS.2020.2966020 Zou WQ, 2021, IEEE T SOFTWARE ENG, V47, P2084, DOI 10.1109/TSE.2019.2942301 NR 45 TC 1 Z9 1 U1 10 U2 11 PY 2022 VL 10 BP 50363 EP 50383 DI 10.1109/ACCESS.2022.3171045 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000795112600001 DA 2022-12-14 ER PT J AU Tudora, E Tirziu, E AF Tudora, Eleonora Tirziu, Eugenia TI Traceability Technologies in the Agri-food Sector SO ROMANIAN JOURNAL OF INFORMATION TECHNOLOGY AND AUTOMATIC CONTROL-REVISTA ROMANA DE INFORMATICA SI AUTOMATICA DT Article DE Traceability; traceability technologies; RFID; agri-food sector ID SUPPLY CHAIN AB With the globalization of the food industry, traceability has become an important factor for the agri-food sector. Therefore, it is important to have a reliable identification and tracking system to guarantee the safety and quality of food that reaches consumers. However, the current tracking technology market is restrained by many factors such as high cost, skepticism about efficiency and technology responsibility. This article provides an overview of the relevant food traceability technologies available on the market and a description of their characteristics. In order to contribute to the development and implementation of appropriate technologies, the concepts and implications of traceability in the agri-food sector must be well understood by consumers. The purpose of this paper is to discuss the importance of traceability technologies in the agri-food sector. C1 [Tudora, Eleonora; Tirziu, Eugenia] Inst Natl Cercetare Dezvoltare Informat ICI Bucur, B Dul Maresal Averescu 8-10, Bucharest 011455, Romania. RP Tudora, E (corresponding author), Inst Natl Cercetare Dezvoltare Informat ICI Bucur, B Dul Maresal Averescu 8-10, Bucharest 011455, Romania. EM eleonora.tudora@ici.ro; eugenia.tirziu@ici.ro CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Cai Y., 2015, TRACEABILITY QUALITY Connolly C., 2005, PART TRACKING LABELI Corina E, 2013, EKON POLJOPR, V60, P287 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 European Commission, 2017, S3P PLATF SCOP NOT T European Parliament, 2002, OFFICIAL J EUROPEA L, V31, P1 Gandino Filippo, 2009, International Journal of Advanced Pervasive and Ubiquitous Computing, V1, P49, DOI 10.4018/japuc.2009040104 GARNER EL, 1993, J TEST EVAL, V21, P505 Gupta P., 2017, INT J LATEST TECHNOL, V6, P6 Heneghan C., 2016, INTEGRATING IOT CAN IBM, 2006, WHOL CHAIN TRAC AGR Kraisintu K., 2011, ROLE TRACEABILITY SU Kumar V., 2018, AGR RES TECHNOLOGY O, V14 Mehrjerdi Yahia Zare, 2010, Business Strategy Series, V11, P107, DOI 10.1108/17515631011026434 Munro T., 2018, BLOCKCHAIN CAN IMPRO Nambiar Arun N., 2010, Proceedings of 2010 International Symposium on Information Technology (ITSim 2010), P874, DOI 10.1109/ITSIM.2010.5561567 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Perez-Aloe R., 2010, DATA STORAGE Tudora E, 2017, PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INFORMATICS IN ECONOMY (IE 2017): EDUCATION, RESEARCH & BUSINESS TECHNOLOGIES, P78 Zhao X., 2009, US CHINA BUSINESS CO NR 22 TC 0 Z9 0 U1 2 U2 14 PY 2019 VL 29 IS 2 BP 101 EP 112 DI 10.33436/v29i2y201908 WC Computer Science, Interdisciplinary Applications SC Computer Science UT WOS:000473688600008 DA 2022-12-14 ER PT J AU Song, M Liu, LJ Wang, Z Nanseki, T AF Song, Min Liu, Li-Jun Wang, Zhigang Nanseki, Teruaki TI Consumers' Attitudes to Food Traceability System in China-Evidences from the Pork Market in Beijing SO JOURNAL OF THE FACULTY OF AGRICULTURE KYUSHU UNIVERSITY DT Article DE food traceability system; attitudes; pork market; China AB This paper briefly introduces the history, structure and operation of food traceability system in China and then uses consumer survey data to observe consumers' attitudes to traceability system in pork market. The facts show that food traceability system (FTS) already intrigued policy makers' interest..,; in China, but the development of FTS reaches just, at, initial stage in legislation and practice at, both national and local level. Meanshile the survey data shows that, most of consumers didn't know much about, FTS. Though 92.8% considered FTS was Very necessary, only 62.9% had confidence oil FTS in faith. And majority of respondents hold the View that, government, should bear more extra cost of food brought, by FTS. C1 [Song, Min; Liu, Li-Jun] Chinese Acad Agr Sci, E Asia Ctr Agr Resources & Environm Studies, Beijing 100081, Peoples R China. [Wang, Zhigang] Renmin Univ China, Sch Agr Econ & Rural Dev, Beijing 100872, Peoples R China. Kyushu Univ, Fac Agr, Dept Agr & Resource Econ,Lab Farm Management, Div Int Agr Resource Econ & Business Adm, Fukuoka 8128581, Japan. C3 Chinese Academy of Agricultural Sciences; Renmin University of China; Kyushu University RP Song, M (corresponding author), Chinese Acad Agr Sci, E Asia Ctr Agr Resources & Environm Studies, 12 Zhongguancun Nandajie, Beijing 100081, Peoples R China. EM m_song_jp@yahoo.co.jp CR Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 GIRAUD G, 2006, INT FOOD AGR MAN ASS, P1 GOLAN E, 2003, CHOICES, V18, P17 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Liu X, 2007, NEW ZEAL J AGR RES, V50, P911 *MIN COMM CHIN, 2006, REP FOOD SAF MARK *MLA, 2004, AUSTR SYST LIV ID TR NANSEKI T, 2008, FOOD TRACEABILITY WO, V1, P46 NANSEKI T, 2007, 6 BIENN C EUR FED IT *NBS CHIN, 2007, CHIN STAT YB NBS, P1 SEBASTIEN P, 2008, AM J AGR ECON, V90, P15 SMITH IG, 2008, FOOD TRACEABILITY WO, V1, P1 SOUZAMONTEIRO DM, 2004, EC TRACEABILITY BEEF, P32 *USDA, 2004, 830 USDA, P2 NR 14 TC 11 Z9 11 U1 0 U2 9 PD OCT PY 2008 VL 53 IS 2 BP 569 EP 574 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000261391100033 DA 2022-12-14 ER PT J AU Pappa, IC Iliopoulos, C Massouras, T AF Pappa, Ioanna C. Iliopoulos, Constantine Massouras, Theofilos TI What determines the acceptance and use of electronic traceability systems in agri-food supply chains? SO JOURNAL OF RURAL STUDIES DT Article DE Electronic traceability systems; Behavior analysis; Agri-food supply chain; Technology acceptance; Dairy products; Origin labelling ID PARTIAL LEAST-SQUARES; UNDERSTANDING FARMERS INTENTION; INFORMATION-TECHNOLOGY; FORMATIVE MEASUREMENT; MANAGEMENT RESEARCH; ADOPTION DECISIONS; MODEL; INDUSTRY; SAFETY; CONSERVATION AB The paper is investigating the electronic-based traceability systems (ETsystems) that are considered as a valuable tool for the assurance of food safety and quality, for guaranteeing value added to products and ultimately, for serving the transparency and sustainability of agri-food chains. The objective of this research is to investigate the factors influencing the acceptance and use of ETsystems in agri-food chains. A model that identifies the most significant factors influencing farmers' and processors' behavior regarding the installation and operation of an ETsystem is proposed. The theoretical approach is based on a combination of the Technology Acceptance Model 2 (TAM2) and the Theory of Planned Behavior (TPB). The theoretical concept and related hypotheses are tested by means of PLS-SEM analysis of data from the dairy supply chain in Greece. 'Perceived Control' and most importantly, the 'perceived costs' over the installation and operation of the ETsystem, is the most important factor with the strongest direet effect influencing the intention to install and operate such a system. This effect is stronger in the case of dairy farmers than in the case of dairy processors. Stronger for dairy farmers is also the identification mechanism thus, their need to comply with their social/business group expectations. Useful findings offered for policy makers and regulators interested in the way traceability systems could be successfully integrated within an agri-food sector to guarantee its added value. The limitation of voluntariness and the enforcement of certain mandatory requirements is one tool to exploit and, based on our study, would be more effective at the processors' level. C1 [Pappa, Ioanna C.; Iliopoulos, Constantine; Massouras, Theofilos] Agr Univ Athens, Athens, Greece. [Iliopoulos, Constantine] Agr Econ Res Inst, Athens, Greece. C3 Agricultural University of Athens RP Pappa, IC (corresponding author), Iera Odos Str 75, Athens 11855, Greece. EM ipappa@aua.gr CR Adrian AM, 2005, COMPUT ELECTRON AGR, V48, P256, DOI 10.1016/j.compag.2005.04.004 AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Ajzen I, 1980, UNDERSTANDING ATTITU [Anonymous], 2015, INT J BUS MANAGE Aubert BA, 2012, DECIS SUPPORT SYST, V54, P510, DOI 10.1016/j.dss.2012.07.002 Augustin MA, 2013, INT DAIRY J, V31, P2, DOI 10.1016/j.idairyj.2012.03.009 Bagozzi R., 2007, J ASSOC INF SYST, V8, P244, DOI DOI 10.17705/1jais.00122 Bagozzi RP, 2007, PSYCHOL METHODS, V12, P229, DOI 10.1037/1082-989X.12.2.229 Banterle A., 2006, 98 EAAE SEM MARK DYN, P1 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Barjolle D., 2002, AGR FOOD SUPPLY CHAI, P1 Barker GC, 2009, TRENDS FOOD SCI TECH, V20, P220, DOI 10.1016/j.tifs.2009.03.002 Becker JM, 2012, LONG RANGE PLANN, V45, P359, DOI 10.1016/j.lrp.2012.10.001 Becket T., 2008, 12 EAAE C PEOPL FOOD Borges JAR, 2014, LIVEST SCI, V169, P163, DOI 10.1016/j.livsci.2014.09.014 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Brofman F., 2008, 110 EAAE SEM SYST DY Burton RJF, 2004, J RURAL STUD, V20, P359, DOI 10.1016/j.jrurstud.2003.12.001 Cardenas Tamayo R. A., 2010, 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) (Formerly known as ICEEE), P412, DOI 10.1109/ICEEE.2010.5608629 Chin W.W., 2010, HDB PARTIAL LEAST SQ, DOI DOI 10.1007/978-3-540-32827-8_29 Chin WW, 1998, QUANT METH SER, P295 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Davis F.D., 1985, THESIS MIT DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x Deimel M., 2010, SPATIAL DYNAMICS AGR, P1 Diamantopoulos A, 2008, J BUS RES, V61, P1203, DOI 10.1016/j.jbusres.2008.01.009 FERBER R, 1977, J CONSUM RES, V4, P57, DOI 10.1086/208679 Fishbein M., 1975, BELIEF ATTITUDES INT Flett R, 2004, AGR SYST, V80, P199, DOI 10.1016/j.agsy.2003.08.002 Folorunso O., 2008, DATA SCI J, V7, P31, DOI [DOI 10.2481/DSJ.7.31, 10.2481/dsj.7.31] Frentrup M., 2009, EFITA C, P655 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Gaskin J., 2016, DATA SCREENING GASKI Gellynck X., 2007, Quality management in food chains, P45 Giacomini C., 2010, 116 EAAE SEM SPAT DY Golan E., 2004, AGR EC REPORT, V830, P1 Golan E., 2003, CURRENT AGR FOOD RES, V4, P27 Goldsmith P., 2004, ASS M, P1 Gong M., 2004, PACIS 2004 P Haenlein M., 2004, UNDERSTANDING STAT, V3, P283, DOI [10.1207/s15328031us0304_4, DOI 10.1207/S15328031US0304_4] Hair J.F., 2018, ADV ISSUES PARTIAL L, DOI DOI 10.1007/978-3-319-71691-6 Hair JF, 2012, J ACAD MARKET SCI, V40, P414, DOI 10.1007/s11747-011-0261-6 Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202 Hair JF, 2012, LONG RANGE PLANN, V45, P320, DOI 10.1016/j.lrp.2012.09.008 Hair JF, 2013, LONG RANGE PLANN, V46, P1, DOI 10.1016/j.lrp.2013.01.001 Hansson H, 2012, J AGR ECON, V63, P465, DOI 10.1111/j.1477-9552.2012.00344.x Henson S., 2005, TRACEABILITY CANADIA Heyder M., 2010, International Journal on Food System Dynamics, V1, P133 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs J. E, 2003, POL DISP INF CONS 9 Hobbs J. E., 2002, IATRC ANN M APR Hobbs J.E., 2006, QUANTIFYING AGRIFOOD, P85 Hofstede G. J., 2003, INFORM TECHNOLOGY BE, P17 Holleran E, 1999, FOOD POLICY, V24, P669, DOI 10.1016/S0306-9192(99)00071-8 Hollmann-Hespos T., 2005, EC TRACEABILITY MODE, P914 JACOBY J, 1971, J MARKETING RES, V8, P495, DOI 10.2307/3150242 Jarvis CB, 2003, J CONSUM RES, V30, P199, DOI 10.1086/376806 Kelman H.C., 1958, J CONFLICT RESOLUT, V2, P51, DOI [10.1177/002200275800200106, DOI 10.1177/002200275800200106] King WR, 2006, INFORM MANAGE-AMSTER, V43, P740, DOI 10.1016/j.im.2006.05.003 Lazzarini S. G., 2001, THE J, V1, P7, DOI [10.3920/JCNS2001.x002, DOI 10.3920/JCNS2001.X002] Lee Y, 2003, COMMUNICATIONS ASS I, V12, P752 Lehmann RJ, 2012, COMPUT ELECTRON AGR, V89, P158, DOI 10.1016/j.compag.2012.09.005 Lowry PB, 2014, IEEE T PROF COMMUN, V57, P123, DOI 10.1109/TPC.2014.2312452 LYNNE GD, 1995, J ECON PSYCHOL, V16, P581, DOI 10.1016/0167-4870(95)00031-6 Marcoulides GA, 2009, MIS QUART, V33, P171 MATELL MS, 1971, EDUC PSYCHOL MEAS, V31, P657, DOI 10.1177/001316447103100307 Mejia C, 2010, COMPR REV FOOD SCI F, V9, P159, DOI 10.1111/j.1541-4337.2009.00098.x Menard C, 2005, EUR REV AGRIC ECON, V32, P421, DOI 10.1093/eurrag/jbi013 Molnar A., 2011, P SYST DYN INN FOOD, P435 Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192 Pascucci S., 2010, International Journal on Food System Dynamics, V1, P224 Peng DX, 2012, J OPER MANAG, V30, P467, DOI 10.1016/j.jom.2012.06.002 Petter S, 2007, MIS QUART, V31, P623 Pierpaoli E, 2013, PROC TECH, V8, P61, DOI 10.1016/j.protcy.2013.11.010 Plumeyer C., 2007, EFITA Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Resende-Filho M., 2007, EC TRACEABILITY MITI Rezaei-Moghaddam K, 2010, AFR J AGR RES, V5, P1191 Riemenschneider CK, 2003, INFORM MANAGE-AMSTER, V40, P269, DOI 10.1016/S0378-7206(02)00010-1 Ringle C.M., 2012, MIS Q, V36, P1 Ringle CM, 2005, SMARTPLS 2 Roth M., 2007, COST BENEFIT ANAL QU Schepers J, 2007, INFORM MANAGE-AMSTER, V44, P90, DOI 10.1016/j.im.2006.10.007 Schiefer G., 2003, NEW APPROACHES FOOD Siebert R, 2006, SOCIOL RURALIS, V46, P318, DOI 10.1111/j.1467-9523.2006.00420.x Sparling D., 2006, Journal of Food Distribution Research, V37, P154 STRAUB D, 1995, MANAGE SCI, V41, P1328, DOI 10.1287/mnsc.41.8.1328 Sumner D. A., 2008, TRACEABILITY FOOD SA TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144 Theuvsen L., 2004, Journal on Chain and Network Science, V4, P125, DOI 10.3920/JCNS2004.x047 Trautman D., 2008, TRACEABILITYA LIT RE Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Turner M, 2010, INFORM SOFTWARE TECH, V52, P463, DOI 10.1016/j.infsof.2009.11.005 Urbach N., 2010, J INFORM TECHNOLOGY, V11, P5, DOI [DOI 10.1037/0021-9010.90.4.710, https://doi.org/10.2753/MTP1069-6679190202, DOI 10.2753/MTP1069-6679190202] Valeeva NI, 2004, NJAS-WAGEN J LIFE SC, V51, P369, DOI 10.1016/S1573-5214(04)80003-4 van der Vorst JGAJ, 2007, RAPID METHODS FOR FOOD AND FEED QUALITY DETERMINATION, P239 Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926 Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540 Wauters E, 2010, LAND USE POLICY, V27, P86, DOI 10.1016/j.landusepol.2009.02.009 Wetzels M, 2009, MIS QUART, V33, P177, DOI 10.2307/20650284 Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 Yang HD, 2004, DECIS SUPPORT SYST, V38, P19, DOI 10.1016/S0167-9236(03)00062-9 Yazdanpanah M, 2014, J ENVIRON MANAGE, V135, P63, DOI 10.1016/j.jenvman.2014.01.016 Young L. M., 2002, Review of Agricultural Economics, V24, P428, DOI 10.1111/1467-9353.00107 NR 109 TC 35 Z9 38 U1 8 U2 80 PD FEB PY 2018 VL 58 BP 123 EP 135 DI 10.1016/j.jrurstud.2018.01.001 WC Geography; Regional & Urban Planning SC Geography; Public Administration UT WOS:000425574500012 DA 2022-12-14 ER PT J AU Kang, YS Lee, YH AF Kang, Yong-Shin Lee, Yong-Han TI Development of generic RFID traceability services SO COMPUTERS IN INDUSTRY DT Article DE EPCglobal network; RFID; Traceability; Traceability services AB EPCglobal Architecture Framework is a de-facto standard for connecting distributed RFID systems in a supply chain. Thanks to the 'service orientated' design of this framework, the core services including ONS, DS, and EPCIS can be easily found and accessed by various information systems. Thereby it is able to achieve full traceability. However, for users to acquire traceability information of a specific product, they have to repeatedly invoke the core services. Hence, it would be beneficial for the system developers to have a set of typical shared services. Each shared service should be able to organize and combine the tedious and repetitive service inquiries in to an abstract and efficient way. We propose and develop a novel set of services called the traceability services (TS). The suggested algorithms embedded in TS allow multiple aggregations of products into containers, and it works efficiently by invoking EPCISs in parallel. The algorithms and the system have been evaluated successfully by using an EPCglobal-certified EPCIS system. (C) 2013 Elsevier B.V. All rights reserved. C1 [Kang, Yong-Shin] Dongguk Univ Seoul, U SCM Res Ctr, Seoul 100272, South Korea. [Lee, Yong-Han] Dongguk Univ Seoul, Dept Ind & Syst Engn, Seoul 100715, South Korea. C3 Dongguk University; Dongguk University RP Lee, YH (corresponding author), Dongguk Univ Seoul, Dept Ind & Syst Engn, 26,Pil Dong 3-Ga, Seoul 100715, South Korea. EM yonghan@dgu.edu CR Agrawal R, 2006, INT DATABASE ENG APP, P174 Armenio F., EPCGLOAL ARCHITECTUR Bi HH, 2009, IEEE T ENG MANAGE, V56, P129, DOI 10.1109/TEM.2008.922636 Cambridge Univ. B. Research S. Research, SER LEV INV TRACK MO Cantero JJ, 2008, IEEE INT C EMERG, P1332, DOI 10.1109/ETFA.2008.4638572 CHENG MJ, 1994, INT J OPER PROD MAN, V14, P4, DOI 10.1108/01443579410067199 Coskun V, 2013, WIRELESS PERS COMMUN, V71, P2259, DOI 10.1007/s11277-012-0935-5 Eeghem M. V., EPC ADV BUSINESS ASP EPCglobal Inc, EPC INF SERV EPCIS V EPCglobal Inc, OBJ NAM SERV ONS VER HLS Track & Trace Interest Group, BUS REQ PROC FLOWS Karygiannis T, 2007, SPECIAL PUBLICATION, V800-98 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Lee G, 2008, EUC 2008: PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING, VOL 2, WORKSHOPS, P553, DOI 10.1109/EUC.2008.69 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Muller J., 2010, P 43 HAW INT C SYST, P1 Murthy K., 2008, P INT C INF SYST LOG Munoz-Gea JP, 2010, COMPUT IND, V61, P480, DOI 10.1016/j.compind.2010.01.006 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Thiesse F, 2009, IEEE INTERNET COMPUT, V13, P36, DOI 10.1109/MIC.2009.46 van Dorp K.-J., 2002, Logistics Information Management, V15, P24, DOI 10.1108/09576050210412648 VeriSign Inc, 2004, VERISIGN DISC SERV D Yong-Shin Kang, 2011, [Industrial Engineers Interfaces, 산업공학(IE interfaces)], V24, P139 Zhang J., 2008, P 7 ACM SIGGRAPH INT, P1, DOI [10.1145/1477862.1477897, DOI 10.1145/1477862.1477897, DOI 10.1109/WICOM.2008.2124] NR 24 TC 54 Z9 56 U1 0 U2 23 PD JUN PY 2013 VL 64 IS 5 BP 609 EP 623 DI 10.1016/j.compind.2013.03.004 WC Computer Science, Interdisciplinary Applications SC Computer Science UT WOS:000319099200010 DA 2022-12-14 ER PT J AU Sardina, MT Tortorici, L Mastrangelo, S Di Gerlando, R Tolone, M Portolano, B AF Sardina, Maria Teresa Tortorici, Lina Mastrangelo, Salvatore Di Gerlando, Rosalia Tolone, Marco Portolano, Baldassare TI Application of microsatellite markers as potential tools for traceability of Girgentana goat breed dairy products SO FOOD RESEARCH INTERNATIONAL DT Article DE Molecular markers; Breed genetic traceability; Girgentana goat dairy products ID SPECIES IDENTIFICATION; GENETIC TRACEABILITY; DNA; AUTHENTICATION; VERIFICATION; POLYMORPHISM; PARENTAGE AB In livestock, breed assignment may play a key role in the certification of products linked to specific breeds. Traceability of farm animals and authentication of their products can contribute to improve breed profitability and sustainability of animal productions with significant impact on the rural economy of particular geographic areas and on breed and biodiversity conservation. With the goal of developing a breed genetic traceability system for Girgentana dairy products, the aim of this study was to identify specific microsatellite markers able to discriminate among the most important Sicilian dairy goat breeds, in order to detect possible adulteration in Girgentana dairy products. A total of 20 microsatellite markers were analyzed on 338 individual samples from Girgentana, Maltese, and Derivata di Siria goat breeds. Specific microsatellite markers useful for traceability of dairy products were identified. Eight microsatellite markers showed alleles present at the same time in Maltese and Derivata di Siria and absent in Girgentana and, therefore, they were tested on DNA pools of the three breeds. Considering the electropherograms' results, only FCB20, SRCRSP5, and TGLAl22 markers were tested on DNA samples extracted from cheeses of Girgentana goat breed. These three microsatellite markers could be applied in a breed genetic traceability system of Girgentana dairy products in order to detect adulteration due to Maltese and Derivata di Siria goat breeds. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Sardina, Maria Teresa; Tortorici, Lina; Mastrangelo, Salvatore; Di Gerlando, Rosalia; Tolone, Marco; Portolano, Baldassare] Univ Palermo, Dipartimento Sci Agr & Forestali, I-90128 Palermo, Italy. C3 University of Palermo RP Sardina, MT (corresponding author), Univ Palermo, Dipartimento Sci Agr & Forestali, I-90128 Palermo, Italy. EM mariateresa.sardina@unipa.it CR [Anonymous], 1994, 8402 ISO Arcuri EF, 2013, FOOD CONTROL, V30, P1, DOI 10.1016/j.foodcont.2012.07.007 BERGER RG, 1988, J ASSOC OFF ANA CHEM, V71, P406 BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 Bottero MT, 2011, VET J, V190, P34, DOI 10.1016/j.tvjl.2010.09.024 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Ercolini D, 2004, J APPL MICROBIOL, V96, P263, DOI 10.1046/j.1365-2672.2003.02146.x European Parliament, 2002, OFFICIAL J EUROPEA L, V31, P1 Excoffier L, 2010, MOL ECOL RESOUR, V10, P564, DOI 10.1111/j.1755-0998.2010.02847.x Fernandez ME, 2013, GENET MOL BIOL, V36, P185, DOI 10.1590/S1415-47572013000200008 Fontanesi L, 2011, J DAIRY RES, V78, P122, DOI 10.1017/S0022029910000890 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Gandini G. C., 1999, Genebanks and the conservation of farm animal genetic resources., P11 Garcia D, 2006, MEAT SCI, V72, P560, DOI 10.1016/j.meatsci.2005.09.005 Goudet J, 1995, J HERED, V86, P485, DOI 10.1093/oxfordjournals.jhered.a111627 Heaton MP, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0094851 Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 International Organisation for Standardization (ISO), 2005, 215712005 ISO International Society for Animal Genetics (ISAG)/Food and Agricultural Organization (FAO), 2004, MEAS DOM AN DIV MODA Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Marshall TC, 1998, MOL ECOL, V7, P639, DOI 10.1046/j.1365-294x.1998.00374.x Mastrangelo S, 2013, ANIM PROD SCI, V53, P403, DOI 10.1071/AN12242 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 MILLER SA, 1988, NUCLEIC ACIDS RES, V16, P1215, DOI 10.1093/nar/16.3.1215 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 PATTERSON RLS, 1990, ANALYST, V115, P501, DOI 10.1039/an9901500501 Plath A, 1997, Z LEBENSM UNTERS F A, V205, P437, DOI 10.1007/s002170050195 Rosa AJM, 2013, SMALL RUMINANT RES, V113, P62, DOI 10.1016/j.smallrumres.2013.03.021 Rousset F, 2008, MOL ECOL RESOUR, V8, P103, DOI 10.1111/j.1471-8286.2007.01931.x Russo V, 2007, ITAL J ANIM SCI, V6, P257 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 Siwek M., 2010, ANIMAL GENETICS, V4, P93 Toione M, 2012, SMALL RUMINANT RES, V102, P18, DOI 10.1016/j.smallrumres.2011.09.010 NR 36 TC 27 Z9 29 U1 1 U2 24 PD AUG PY 2015 VL 74 BP 115 EP 122 DI 10.1016/j.foodres.2015.04.038 WC Food Science & Technology SC Food Science & Technology UT WOS:000358971200012 DA 2022-12-14 ER PT J AU Qian, JP Ruiz-Garcia, L Fan, BL Villalba, JIR McCarthy, U Zhang, BH Yu, QY Wu, WB AF Qian, Jianping Ruiz-Garcia, Luis Fan, Beilei Villalba, Jose Ignacio Robla McCarthy, Ultan Zhang, Baohui Yu, Qiangyi Wu, Wenbin TI Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review DE Traceability; Traceability system; Food safety; Artificial intelligence; Blockchain; EU; China ID DATA-ACQUISITION SYSTEM; COUNTRY-OF-ORIGIN; SUPPLY CHAIN; IMPLEMENTING TRACEABILITY; PURCHASE INTENTIONS; COLD CHAIN; SAFETY; PRODUCTS; FIELD; FRAMEWORK AB Background: Food safety has garnered much worldwide attention recently for reasons that are, unfortunately, not always positive. Traceability system (TS) is designed to assure safe and good quality food, while reducing the costs of food recalls. It should encompass all stakeholders, including governments, companies, and consumers, each of whom has an important role in the implementation and guardianship of such systems. The EU and China are amongst the main players implementing TS and are constantly exploring new opportunities and monitoring challenges for TS in a time of shifting consumer demands and rapid new technology innovation. Scope and approach: This article states development stages from TS 1.0 to 3.0. and reviews TS development in a number of key countries and regions. Comparisons between the EU and China are drawn in terms of government, corporate, and consumer involvement in traceability. Key findings and conclusions: A functional TS, while providing bi-directional communication between trading partners, must meet the laws and regulations where it operates. A functional system must also consider consumer value and perception, which varies with geography. There are a variety of promising technologies available on the market today to modernize TS, including artificial intelligence (AI) and blockchain. A key finding of this research is that both the EU and China have developed significant trade links in recent years which will certainly positively impact both economies. Key to underpinning the sustainability of these trade links will be the adoption of common TS to prevent negative associations. C1 [Qian, Jianping; Fan, Beilei; Zhang, Baohui; Yu, Qiangyi; Wu, Wenbin] Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing AGRIRS, Beijing 100081, Peoples R China. [Ruiz-Garcia, Luis] Univ Politecn Madrid, Dept Agroforestry Engn, Av Complutense S-N, E-28040 Madrid, Spain. [Villalba, Jose Ignacio Robla] CSIC, CENIM, Ctr Nacl Invest Met, Ave Gregorio del Amo 8, Madrid 28040, Spain. [McCarthy, Ultan] Waterford Inst Technol, Dept Sci, Sch Sci & Comp, Waterford, Ireland. C3 Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Ministry of Agriculture & Rural Affairs; Universidad Politecnica de Madrid; Consejo Superior de Investigaciones Cientificas (CSIC); Centro Nacional de Investigaciones Metalurgicas (CENIM); South East Technological University (SETU) RP Qian, JP; Wu, WB (corresponding author), Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing AGRIRS, Beijing 100081, Peoples R China.; Ruiz-Garcia, L (corresponding author), Univ Politecn Madrid, Dept Agroforestry Engn, Av Complutense S-N, E-28040 Madrid, Spain. EM qianjianping@caas.cn; luis.ruiz@upm.es; wuwenbin@caas.cn CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Allen A, 2008, LIVEST SCI, V116, P42, DOI 10.1016/j.livsci.2007.08.018 Amador Cecilia, 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P26, DOI 10.1007/s11694-009-9072-6 Amiama C, 2008, COMPUT ELECTRON AGR, V61, P192, DOI 10.1016/j.compag.2007.11.006 Ampatzidis Y, 2009, PRECIS AGRIC, V10, P63, DOI 10.1007/s11119-008-9095-8 [Anonymous], 4 INT C LOG HAMM TUN [Anonymous], TRACEABILITY DATABAS [Anonymous], QUAL MAN SYST FUND V [Anonymous], COD PROC MAN [Anonymous], EUR ASS AGR EC EAAE [Anonymous], FOOD CONTROL [Anonymous], AQ CHIN AS Ardeshiri A, 2018, FOOD QUAL PREFER, V65, P146, DOI 10.1016/j.foodqual.2017.10.018 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2016, FOOD BIOPROCESS TECH, V9, P1089, DOI 10.1007/s11947-016-1700-7 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Bernard A, 2002, ENVIRON RES, V88, P1, DOI 10.1006/enrs.2001.4274 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Boys KA, 2019, RENEW AGR FOOD SYST, V34, P226, DOI 10.1017/S1742170518000030 Chen K, 2015, J INTEGR AGR, V14, P2203, DOI 10.1016/S2095-3119(15)61113-3 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Corkery GP, 2007, T ASABE, V50, P313, DOI 10.13031/2013.22395 D'Amico P, 2014, FOOD CONTROL, V35, P7, DOI 10.1016/j.foodcont.2013.06.029 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Darwaish SF, 2014, PROCEDIA COMPUT SCI, V35, P832, DOI 10.1016/j.procs.2014.08.250 Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Ding JP, 2015, J INTEGR AGR, V14, P2380, DOI 10.1016/S2095-3119(15)61127-3 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Dopico D.C., 2016, SPANISH J MARKETING, V20, P93, DOI [DOI 10.1016/J.SJME.2016.07.001, 10.1016/j.sjme.2016.07.001] Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Ehmke MD, 2008, AGR ECON-BLACKWELL, V38, P277, DOI 10.1111/j.1574-0862.2008.00299.x Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Fernandes MA, 2013, COMPUT ELECTRON AGR, V95, P19, DOI 10.1016/j.compag.2013.04.001 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Garcia-Sanchez AJ, 2011, COMPUT ELECTRON AGR, V75, P288, DOI 10.1016/j.compag.2010.12.005 Geng S, 2015, J INTEGR AGR, V14, P2136, DOI 10.1016/S2095-3119(15)61164-9 Gerbig S, 2017, ANAL CHEM, V89, P10717, DOI 10.1021/acs.analchem.7b01689 Ghaani M, 2016, TRENDS FOOD SCI TECH, V51, P1, DOI 10.1016/j.tifs.2016.02.008 Giusto D., 2010, INTERNET THINGS Golan E., 2004, Agricultural Economic Report - Economic Research Service, US Department of Agriculture Goransson M, 2018, FOOD CONTROL, V86, P332, DOI 10.1016/j.foodcont.2017.10.029 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x ISO, 2016, 220052007 ISO Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Jimenez-Gamero I, 2006, SMALL RUMINANT RES, V65, P266, DOI 10.1016/j.smallrumres.2005.07.019 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Koch M, 2018, CELL, V173, P531, DOI 10.1016/j.cell.2018.04.007 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Li WY, 2017, COMPUT ELECTRON AGR, V142, P622, DOI 10.1016/j.compag.2017.10.029 Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Luvisi A, 2012, BIOSYST ENG, V113, P129, DOI 10.1016/j.biosystemseng.2012.06.015 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Mc Carthy U, 2018, TRENDS FOOD SCI TECH, V77, P11, DOI 10.1016/j.tifs.2018.05.002 Mc Carthy U, 2010, PACKAG TECHNOL SCI, V23, P339, DOI 10.1002/pts.903 Mc Carthy U, 2009, COMPUT ELECTRON AGR, V69, P135, DOI 10.1016/j.compag.2009.07.018 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Menozzi D, 2015, FOOD CONTROL, V49, P40, DOI 10.1016/j.foodcont.2013.09.006 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Notarnicola B, 2012, J CLEAN PROD, V28, P1, DOI 10.1016/j.jclepro.2012.02.007 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Padua I, 2019, FOOD CONTROL, V98, P389, DOI 10.1016/j.foodcont.2018.11.051 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qian JP, 2017, FOOD CONTROL, V74, P98, DOI 10.1016/j.foodcont.2016.11.034 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Reyes JF, 2012, COMPUT ELECTRON AGR, V84, P62, DOI 10.1016/j.compag.2012.02.018 Ruiz-Garcia L, 2008, J FOOD ENG, V87, P405, DOI 10.1016/j.jfoodeng.2007.12.033 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Ruviaro CF, 2014, LAND USE POLICY, V38, P104, DOI 10.1016/j.landusepol.2013.08.019 Schuster EW, 2011, COMPUT ELECTRON AGR, V78, P150, DOI 10.1016/j.compag.2011.07.002 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 So-In C, 2014, COMPUT ELECTRON AGR, V109, P287, DOI 10.1016/j.compag.2014.10.004 Song M, 2008, J FAC AGR KYUSHU U, V53, P569 Spence M, 2018, FOOD CONTROL, V91, P138, DOI 10.1016/j.foodcont.2018.03.035 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Stranieri S, 2017, FOOD CONTROL, V80, P187, DOI 10.1016/j.foodcont.2017.04.047 Sun SN, 2019, J CLEAN PROD, V217, P658, DOI 10.1016/j.jclepro.2019.01.296 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Tharwat A., 2015, INT J IMAGE MIN, V1, P342, DOI [DOI 10.1504/IJIM.2015.073902, 10.1504/IJIM.2015.073902] Thiollet-Scholtus M, 2015, EUR J AGRON, V62, P13, DOI 10.1016/j.eja.2014.09.001 Tsakiridou E, 2011, J FOOD PROD MARK, V17, P211, DOI 10.1080/10454446.2011.548749 Tseng CL, 2006, COMPUT ELECTRON AGR, V53, P45, DOI 10.1016/j.compag.2006.03.005 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 Van der Spiegel M, 2013, TRENDS FOOD SCI TECH, V34, P137, DOI 10.1016/j.tifs.2013.10.001 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Verbeke W., 2009, Estey Centre Journal of International Law and Trade Policy, V10, P20 Verbeke W, 2002, FOOD POLICY, V27, P339, DOI 10.1016/S0306-9192(02)00037-4 Baroni MV, 2009, FOOD CHEM, V114, P727, DOI 10.1016/j.foodchem.2008.10.018 Wales C, 2006, APPETITE, V47, P187, DOI 10.1016/j.appet.2006.05.007 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 Wang ShanShan, 2018, Transactions of the Chinese Society of Agricultural Engineering, V34, P263, DOI 10.11975/j.issn.1002-6819.2018.14.034 Wu LH, 2017, AGRIBUSINESS, V33, P424, DOI 10.1002/agr.21509 Wu LH, 2011, BRIT FOOD J, V113, P519, DOI 10.1108/00070701111123998 Wu YN, 2018, FOOD CONTROL, V90, P429, DOI 10.1016/j.foodcont.2018.03.009 Xiao XQ, 2016, FOOD CONTROL, V60, P656, DOI 10.1016/j.foodcont.2015.09.012 Xing Bin, 2015, Transactions of the Chinese Society of Agricultural Engineering, V31, P309, DOI 10.11975/j.issn.1002-6819.2015.10.042 Yang XinTing, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P212 NR 123 TC 46 Z9 47 U1 31 U2 116 PD MAY PY 2020 VL 99 BP 402 EP 412 DI 10.1016/j.tifs.2020.03.025 WC Food Science & Technology SC Food Science & Technology UT WOS:000526719400032 DA 2022-12-14 ER PT J AU Parreno-Marchante, A Alvarez-Melcon, A Trebar, M Filippin, P AF Parreno-Marchante, Alfredo Alvarez-Melcon, Alejandro Trebar, Mira Filippin, Piero TI Advanced traceability system in aquaculture supply chain SO JOURNAL OF FOOD ENGINEERING DT Article DE Traceability; Aquaculture; Farmed fish; Supply chain; RFID; WSN ID RFID TECHNOLOGIES; FRAMEWORK; TRENDS; FOODS AB The paper presents a novel traceability system architecture based on web services, which are used to integrate traceability data captured through Radio Frequency Identification (RFID) systems with environmental data collected with Wireless Sensor Networks (WSN) infrastructure. The solution, suitable to be deployed in Small to Medium Enterprises (SMEs), is provided by integrating information collected along the entire food supply chain, tracking the products from the farm to the consumer. The results of the deployment of the novel system in two pilots in the aquaculture business are also presented, showcasing how business processes in the aquaculture supply chain can be improved by the architecture and flexibility of the new system, since the two companies involved in the project are of very different sizes. Additionally, we present an analysis of the benefits obtained by the introduction of the new system in the companies based on predefined objectives and the evaluation of KPIs. The evaluation of KPIs is presented as the time reduction of activities and can improve the Efficiency of the companies in 89-95%. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Parreno-Marchante, Alfredo; Alvarez-Melcon, Alejandro] Univ Politecn Cartagena, Cartagena, Spain. [Trebar, Mira] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia. [Filippin, Piero] Wolverhampton Univ, Sch Technol, Wolverhampton WV1 1DJ, W Midlands, England. C3 Universidad Politecnica de Cartagena; University of Ljubljana; University of Wolverhampton RP Parreno-Marchante, A (corresponding author), Univ Politecn Cartagena, Campus Muralla Mar Antigones, Cartagena, Spain. EM alfredo.parreno@sidcom.es; alejandro.alvarez@upct.es; mira.trebar@fri.uni-lj.si; p.filippin@wlv.ac.uk CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Alasalvar C, 2010, HDB SEAFOOD QUALITY Angeles R, 2005, INFORM SYST MANAGE, V22, P51, DOI 10.1201/1078/44912.22.1.20051201/85739.7 [Anonymous], 128752011 ISO Aramyan L, 2006, WAG UR FRON, V15, P49, DOI 10.1007/1-4020-4693-6_5 Banati D, 2011, TRENDS FOOD SCI TECH, V22, P56, DOI 10.1016/j.tifs.2010.12.007 Bevilacqua M, 2009, J FOOD ENG, V93, P13, DOI 10.1016/j.jfoodeng.2008.12.020 Cai J, 2009, DECIS SUPPORT SYST, V46, P512, DOI 10.1016/j.dss.2008.09.004 CEN, 2003, 14659 CEN CWA EUR CO, V14659 CEN, 2003, 14660 CEN CWA EUR CO, V14660 Connolly C, 2007, SENSOR REV, V27, P103, DOI 10.1108/02602280710731669 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P2190, DOI 10.1007/s11947-011-0773-6 EPCglobal, 2013, GS1 EPCGLOBAL ARCH F EPCIS Standard, 2007, EPC INF SERV EPCIS V FAO, 2012, STAT WORLD FISH AQ 2 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 Grabacki S. T., 2007, INT SMOK SEAF C P AL, P101 GS1, 2013, GS1 STAND KNOWL CTR Hsu Y.-C, 2008, QINGD P IEEE INT C A Huber N, 2007, RFID EURASIA 1 ANN, P1 Jakkhupan W, 2011, J NETW COMPUT APPL, V34, P949, DOI 10.1016/j.jnca.2010.04.003 Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Jones P, 2005, BRIT FOOD J, V107, P356, DOI 10.1108/00070700510602156 Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kumar P, 2009, J FOOD SCI, V74, pR101, DOI 10.1111/j.1750-3841.2009.01323.x Launois A, 2008, RFID TRACKING SYSTEM Luning P.A., 2002, FOOD QUALITY MANAGEM Michael K, 2005, ICMB 2005: International Conference on Mobile Business, P623, DOI 10.1109/ICMB.2005.103 Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Myhre B, 2009, P 5 EUR WORKSH RFID Palsson P.G, 2000, 3 NORD MIN COUNC, P1 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ruiz-Garcia L, 2007, SPAN J AGRIC RES, V5, P142, DOI 10.5424/sjar/2007052-234 Sandoval J., 2009, RESTFUL JAVA WEB SER Sarac A, 2010, INT J PROD ECON, V128, P77, DOI 10.1016/j.ijpe.2010.07.039 Schroder U, 2008, J VERBRAUCH LEBENSM, V3, P45, DOI 10.1007/s00003-007-0302-8 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Sioen I., 2007, Open Food Science Journal, V1, P33, DOI 10.2174/1874256400701010033 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Swedberg C., 2010, RFID J Thakur M, 2011, FOOD INT TRAC C BELF, P21 Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x Van der Vorst J. G. A. J., 2006, QUANTIFYING AGRIFOOD, P13 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 Wang R., 2011, 2010 IEEE COMP SOC C, P58 Wang TM, 2010, AFR J BIOTECHNOL, V9, P6146 Zhang L, 2006, GCC 2006: FIFTH INTERNATIONAL CONFERENCE ON GRID AND COOPERATIVE COMPUTING WORKSHOPS, PROCEEDINGS, P463 NR 51 TC 71 Z9 76 U1 4 U2 146 PD FEB PY 2014 VL 122 BP 99 EP 109 DI 10.1016/j.jfoodeng.2013.09.007 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000326561400015 DA 2022-12-14 ER PT J AU Bosona, T Gebresenbet, G AF Bosona, Techane Gebresenbet, Girma TI Food traceability as an integral part of logistics management in food and agricultural supply chain SO FOOD CONTROL DT Review DE Food traceability; Food traceability information; Food traceability technology; Food recall; Food traceability performance ID INFORMATION-TECHNOLOGY; PRODUCT TRACEABILITY; SYSTEM TRACEABILITY; DNA MARKERS; IMPLEMENTATION; STRATEGIES; FRUIT; OPTIMIZATION; FRAMEWORK; ORIGIN AB The contemporary food supply chain (FSC) should adequately provide information that consumers and other concerned bodies need to know such as variety of the food attributes, country of origin, animal welfare, and genetic engineering related issues. For this, effective food traceability system (FTS) is important. The objective of this study was to conduct a comprehensive literature review on food traceability issues. About 74 studies, mainly focusing on food traceability issues and published during 2000-2013, were reviewed. Based on the review results, the definition, driving forces, barriers in developing and implementing FTSs, benefits, traceability technologies, improvements, and performances of FTSs have been identified and discussed. Considering FTS as an integral part of logistics management, new conceptual definition of FTS has been provided. This review has pointed out that the issue of developing effective and full chain FTS is quite complex in nature as it requires a deeper understanding of real processes from different perspectives such as economic, legal, technological, and social issues. Therefore, future researches (recommended here) on traceability should focus on: integration of traceability activities with food logistics activities; technological aspects of FTSs; the linkage between traceability system and food production units; standardization of data capturing and information exchange; awareness creation strategies; continuity of information flow and effective communication of traceability information to consumers and other stakeholders; the linkage between different drivers of FTS; improvement strategies of FTS; and development of performance evaluation frameworks for FTSs. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Bosona, Techane; Gebresenbet, Girma] Swedish Univ Agr Sci SLU, Dept Energy & Technol, Uppsala, Sweden. C3 Swedish University of Agricultural Sciences RP Bosona, T (corresponding author), Swedish Univ Agr Sci SLU, Dept Energy & Technol, Lennart Hjelms Vag 9, Uppsala, Sweden. EM techane.bosona@slu.se CR Ackerley N., 2010, Food Protection Trends, V30, P212 Alfaro J. A., 2006, Journal of Purchasing and Supply Management, V12, P39, DOI 10.1016/j.pursup.2006.02.003 Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 [Anonymous], DOMPREP J Aramyan LH, 2007, SUPPLY CHAIN MANAG, V12, P304, DOI 10.1108/13598540710759826 Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Atkins P, 2008, APPETITE, V51, P18, DOI 10.1016/j.appet.2008.02.006 Azuara G, 2012, IND MANAGE DATA SYST, V112, P340, DOI 10.1108/02635571211210022 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bourlakis M, 2006, J ENTERP INF MANAG, V19, P389, DOI 10.1108/17410390610678313 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chow HKH, 2007, SUPPLY CHAIN MANAG, V12, P221, DOI 10.1108/13598540710742536 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Donnelly KAM, 2012, FOOD CONTROL, V27, P228, DOI 10.1016/j.foodcont.2012.03.021 Donnelly KAM, 2012, BRIT FOOD J, V114, P1016, DOI 10.1108/00070701211241590 Engelseth P, 2009, J BUS IND MARK, V24, P421, DOI 10.1108/08858620910966291 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Food Standards Agency, 2002, TRAC FOOD CHAIN PREL Fugate BS, 2010, J BUS LOGIST, V31, P43, DOI 10.1002/j.2158-1592.2010.tb00127.x Golan E.H., 2004, AGR EC REPORTS, P1362 Greger M, 2007, BIOSECUR BIOTERROR, V5, P301, DOI 10.1089/bsp.2007.0028 Hall D, 2010, GEOFORUM, V41, P826, DOI 10.1016/j.geoforum.2010.05.005 Hayes B, 2005, AQUACULTURE, V250, P70, DOI 10.1016/j.aquaculture.2005.03.008 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Karippacheril T. G., 2011, GLOBAL MARKETS GLOBA Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Kimura A, 2008, APPETITE, V51, P628, DOI 10.1016/j.appet.2008.05.054 Levinson D. R., 2009, OEI020600210 DEP HLT Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Liu HA, 2012, BRIT FOOD J, V114, P372, DOI 10.1108/00070701211213474 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Mangina E, 2005, J FOOD ENG, V70, P403, DOI 10.1016/j.jfoodeng.2004.02.044 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x McMeekin TA, 2006, INT J FOOD MICROBIOL, V112, P181, DOI 10.1016/j.ijfoodmicro.2006.04.048 Mohtadi H, 2008, CAN J AGR ECON, V56, P163, DOI 10.1111/j.1744-7976.2008.00123.x Mousavi A., 2002, British Food Journal, V104, P7, DOI 10.1108/00070700210418703 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Orre K, 2005, J ENVIRON RADIOACTIV, V83, P429, DOI 10.1016/j.jenvrad.2004.05.022 Pacquit A, 2006, TALANTA, V69, P515, DOI 10.1016/j.talanta.2005.10.046 Pacquit A, 2007, FOOD CHEM, V102, P466, DOI 10.1016/j.foodchem.2006.05.052 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Qu XiaoHui, 2007, Agricultural Sciences in China, V6, P724, DOI 10.1016/S1671-2927(07)60105-9 Randrup M, 2012, FOOD CONTROL, V26, P439, DOI 10.1016/j.foodcont.2012.02.003 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Salampasis M., 2012, J SYSTEMS INFORM TEC, V14 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Smith GC, 2008, MEAT SCI, V80, P66, DOI 10.1016/j.meatsci.2008.05.024 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Sonneveld K, 2000, PACKAG TECHNOL SCI, V13, P29, DOI 10.1002/(SICI)1099-1522(200001/02)13:1<29::AID-PTS489>3.0.CO;2-R Suhong Li, 2006, Sensor Review, V26, P193, DOI 10.1108/02602280610675474 Tamayo S, 2009, ENG APPL ARTIF INTEL, V22, P557, DOI 10.1016/j.engappai.2009.02.007 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 van der Vorst JGAJ, 2004, DYNAMICS IN CHAINS AND NETWORKS, P175 van Dorp KF, 2003, SUPPLY CHAIN MANAG, V8, P32, DOI 10.1108/13598540310463341 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wilson T., 1998, SUPPLY CHAIN MANAG I, V3, P127, DOI [10.1108/13598549810230831, DOI 10.1108/13598549810230831] Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 World Health Organization (WHO), 2011, FAQS JAP NUCL CONC F Yam KL, 2005, J FOOD SCI, V70, pR1, DOI 10.1111/j.1365-2621.2005.tb09052.x Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 NR 77 TC 290 Z9 316 U1 29 U2 486 PD SEP PY 2013 VL 33 IS 1 BP 32 EP 48 DI 10.1016/j.foodcont.2013.02.004 WC Food Science & Technology SC Food Science & Technology UT WOS:000319032300006 HC Y HP N DA 2022-12-14 ER PT J AU Chen, XM Shang, J Zada, M Zada, S Ji, XQ Han, H Ariza-Montes, A Ramirez-Sobrino, J AF Chen, Ximing Shang, Jie Zada, Muhammad Zada, Shagufta Ji, Xueqiang Han, Heesup Ariza-Montes, Antonio Ramirez-Sobrino, Jesus TI Health Is Wealth: Study on Consumer Preferences and the Willingness to Pay for Ecological Agricultural Product Traceability Technology: Evidence from Jiangxi Province China SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH DT Article DE ecological agricultural products; traceability technology; food safety; selective experiment method ID FOOD TRACEABILITY; SAFETY; BLOCKCHAIN; ORIGIN; IMPACT; BEEF; GOVERNANCE; SYSTEMS; MODEL; PORK AB The application of traceability technology is an important way to solve food safety problems. Different traceability technologies bring different effects to consumers. Existing studies have not explored consumers' preferences in regards to product traceability technology applications, and they have not analyzed their willingness to pay. Therefore, this study focused on organic rice, an ecological agricultural product. The study was based on a survey from Jiangxi Province, China. It used a selective experiment method in order to analyze consumer preferences and the willingness to pay for ecological agricultural product traceability technology. The results show that consumer preferences are as follows: blockchain technology application attributes, traditional traceability-technology-application attributes, high credit-supervision attributes, and international-certification attributes. In terms of willingness to pay, consumers have the highest willingness to pay for the application of blockchain technology, which they are willing to pay CNY 21.902 more per kg for this attribute. At the same time, consumers are also willing to make additional payments for traditional traceability-technology-application attributes, high credit-supervision attributes, and international-certification attributes. Their willingness to pay is CNY 20.426, CNY 17.115 yuan, and CNY 11.049, respectively. C1 [Chen, Ximing; Shang, Jie; Ji, Xueqiang] Northeast Forestry Univ, Sch Econ & Management, Harbin 150040, Heilongjiang, Peoples R China. [Zada, Muhammad] Henan Univ, Business Sch, Kaifeng 475000, Peoples R China. [Zada, Muhammad] Alhamd Islamic Univ, Dept Management Sci, Islamabad 45400, Pakistan. [Zada, Shagufta] Northeast Forestry Univ, Sch Marxism, Ideol & Polit Educ Dept, Harbin 150040, Heilongjiang, Peoples R China. [Han, Heesup] Sejong Univ, Coll Hospitality & Tourism Management, Seoul 05006, South Korea. [Ariza-Montes, Antonio] Univ Loyola Andalucia, Social Matters Res Grp, C-Escritor Castilla Aguayo 4, E-14004 Cordoba, Spain. [Ramirez-Sobrino, Jesus] Univ Loyola Andalucia, Business Growth Challenges Res Grp, C-Escritor Castilla Aguayo 4, E-14004 Cordoba, Spain. C3 Northeast Forestry University - China; Henan University; Northeast Forestry University - China; Sejong University; Universidad Loyola Andalucia; Universidad Loyola Andalucia RP Shang, J (corresponding author), Northeast Forestry Univ, Sch Econ & Management, Harbin 150040, Heilongjiang, Peoples R China.; Han, H (corresponding author), Sejong Univ, Coll Hospitality & Tourism Management, Seoul 05006, South Korea. EM chenximing0313@126.com; shangjie2005@126.com; mzada@henu.edu.cn; shaguftanefu@yahoo.com; 13576274061@163.com; heesup.han@gmail.com; ariza@uloyola.es; jramirez@uloyola.es CR Abramova S., 2016 INT C INF SYST, P1, DOI [10.17705/4icis.00001, DOI 10.17705/4ICIS.00001] Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Ali O, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102199 Beck R, 2018, J ASSOC INF SYST, V19, P1020, DOI 10.17705/1jais.00518 Carter DP, 2019, J CONSUM AFF, V53, P652, DOI 10.1111/joca.12196 Chamorro A, 2015, BRIT FOOD J, V117, P820, DOI 10.1108/BFJ-03-2014-0112 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Corallo A, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11154019 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Gracia A., 2005, Journal of Food Distribution Research, V36, P45 Gracia A, 2016, FOOD CONTROL, V61, P39, DOI 10.1016/j.foodcont.2015.09.023 Hasimu H, 2017, APPETITE, V108, P191, DOI 10.1016/j.appet.2016.09.019 Helo P, 2019, COMPUT IND ENG, V136, P242, DOI 10.1016/j.cie.2019.07.023 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Jin Y, 2020, J ENVIRON ECON MANAG, V103, DOI 10.1016/j.jeem.2020.102355 Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Kwon H, 2017, IEEE T INF FOREN SEC, V12, P544, DOI 10.1109/TIFS.2016.2623586 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Liu XL, 2015, BRIT FOOD J, V117, P1440, DOI 10.1108/BFJ-08-2014-0295 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lusk JL, 2005, AM J AGR ECON, V87, P771, DOI 10.1111/j.1467-8276.2005.00761.x McCarthy BL, 2015, J ECON SOC POLICY, V17 Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Mohanta BK, 2018, INT CONF COMPUT Naerland K., 2017, P 38 INT C INF SYST Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Penn J, 2020, INT J HOSP MANAG, V89, DOI 10.1016/j.ijhm.2020.102568 Roheim CA, 2018, FOOD POLICY, V79, P92, DOI 10.1016/j.foodpol.2018.06.002 Shi LJ, 2018, AGR ECON-BLACKWELL, V49, P353, DOI 10.1111/agec.12421 Singh S, 2020, SUSTAIN CITIES SOC, V63, DOI 10.1016/j.scs.2020.102364 Su L., 2017, RESOUR EC, V42, P255 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wongprawmas R, 2017, FOOD POLICY, V69, P25, DOI 10.1016/j.foodpol.2017.03.004 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2012, CAN J AGR ECON, V60, P317, DOI 10.1111/j.1744-7976.2011.01236.x Xu XL, 2020, IEEE T IND INFORM, V16, P4187, DOI 10.1109/TII.2019.2936869 Yang Y, 2020, FOOD POLICY, V92, DOI 10.1016/j.foodpol.2020.101846 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] Yin SJ, 2017, CHINA AGR ECON REV, V9, P141, DOI [10.1108/caer-11-2015-0147, 10.1108/CAER-11-2015-0147] Yli-Huumo J, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0163477 Zada M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su131910502 Zada M, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11102989 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhao R, 2010, AGRIC AGRIC SCI PROC, V1, P334, DOI 10.1016/j.aaspro.2010.09.042 Zhou JH, 2015, J INTEGR AGR, V14, P2189, DOI 10.1016/S2095-3119(15)61115-7 NR 51 TC 2 Z9 2 U1 33 U2 58 PD NOV PY 2021 VL 18 IS 22 AR 11761 DI 10.3390/ijerph182211761 WC Environmental Sciences; Public, Environmental & Occupational Health SC Environmental Sciences & Ecology; Public, Environmental & Occupational Health UT WOS:000724621300001 DA 2022-12-14 ER PT J AU Porto, LFD Lopes, MA Zambalde, AL AF de Abreu Porto, Luis Fernando Lopes, Marcos Aurelio Zambalde, Andre Luiz TI Development of a traceability system applied to the wine production chain SO CIENCIA E AGROTECNOLOGIA DT Article DE food safety; traceability; wine AB The present work aimed to develop a traceability system applied to the wine production chain. The JSP development technology (Java Server Pages), the MySQL data base, as well as the Tomcat for JSP server, have been used. The developed system for wine traceability is useful for the producers as well as for the consumers for being an indicator of food safety, since with it, it is possible to track the history of the bottle of a wine from the plantation of the grape to the moment of its consumption. C1 [de Abreu Porto, Luis Fernando] Univ Fed Lavras, Acad Curso Ciencia Computacao DCC, BR-37200000 Lavras, MG, Brazil. [Lopes, Marcos Aurelio] Univ Fed Lavras, Dept Vet Med, BR-37200000 Lavras, MG, Brazil. [Zambalde, Andre Luiz] Univ Fed Lavras, Dept Ciencia Computacao DCC, BR-37200000 Lavras, MG, Brazil. C3 Universidade Federal de Lavras; Universidade Federal de Lavras; Universidade Federal de Lavras RP Porto, LFD (corresponding author), Univ Fed Lavras, Acad Curso Ciencia Computacao DCC, Cx P 3037, BR-37200000 Lavras, MG, Brazil. EM luisfaporto@yahoo.com.br; malopes@ufla.br; zamba@ufla.br CR *I GEN, 2004, CERT VEG I GEN LOPES M. A., 2003, RASTREABILIDADE BOVI MARIUZZO D, 2003, C BRAS SOC BRAS INF, V4 NAAS IA, 2003, C BRAS SOC BRAS INF, V4 PALLET D, 2003, PAN RASTR PROD AGR B RASTREABILIDADE D, 2004, REV AUTOMACAO ROCHA JLP, 2002, REV BRASILEIRA AGROI, V4, P130 VINHOSONLINE, 2004, VINH PAIS CACH NR 8 TC 2 Z9 2 U1 1 U2 7 PD SEP-OCT PY 2007 VL 31 IS 5 BP 1310 EP 1319 WC Agriculture, Multidisciplinary; Agronomy SC Agriculture UT WOS:000256597200006 DA 2022-12-14 ER PT J AU Tagarakis, AC Tsotsolas, N Kateris, D Koidis, C Koutsouraki, E Bochtis, D AF Tagarakis, Aristotelis C. Tsotsolas, Nikos Kateris, Dimitrios Koidis, Christos Koutsouraki, Eleni Bochtis, Dionysis TI The concept for an integrated IoT-based traceability platform SO INTERNATIONAL JOURNAL OF SUSTAINABLE AGRICULTURAL MANAGEMENT AND INFORMATICS DT Article DE internet of things; IoT; traceability; fresh produce; supply chain ID FOOD-SUPPLY CHAINS; INFORMATION; CONSUMERS; PRODUCTS; SYSTEM AB A significant proportion of fresh products either does not reach the market due to quality deterioration or reaches the consumer in poor condition raising concerns about marketing and public health. Latest advances in sensing and communication technologies in agriculture and agri-food supply chain, facilitate traceability, monitoring the products throughout the whole supply chain, starting from in-field production and through all steps of transportation, processing, and marketing. In this work, an IoT-based web and android-based platform is proposed which will monitor and guarantee the quality of the fresh products through a traceability system. The system supports enhanced communication between sellers and traders and includes all the significant steps from the farm to the storage, processing, packaging, transportation and placement at the store's shelf and the final receiver, the consumer. Thus, this work contributes with new knowledge, providing an overview of the design of integrated system for advanced traceability of fresh produce. C1 [Tagarakis, Aristotelis C.; Kateris, Dimitrios; Bochtis, Dionysis] Ctr Res & Technol Hellas CERTH, Inst Bioecon & Agritechnol iBO, 6th Km Charilaou,Thermi Rd, Thessaloniki 57001, Greece. [Tsotsolas, Nikos; Koidis, Christos; Koutsouraki, Eleni] Green Projects SA, R&D Dept, Admitou 15, Athens 15238, Greece. C3 Centre for Research & Technology Hellas RP Tagarakis, AC (corresponding author), Ctr Res & Technol Hellas CERTH, Inst Bioecon & Agritechnol iBO, 6th Km Charilaou,Thermi Rd, Thessaloniki 57001, Greece. EM a.tagarakis@certh.gr; ntsotsolas@green-projects.gr; d.kateris@certh.gr; ck741@efb.gr; ekoutsouraki@green-projects.gr; d.bochtis@certh.gr CR Accorsi R, 2016, WOODHEAD PUBL FOOD S, V301, P337, DOI 10.1016/B978-0-08-100310-7.00018-1 [Anonymous], FOOFLOGICQ TRACK TRA [Anonymous], GLOBERANGER IOT ASSE APACHE SOFTWARE FOUNDATION, ABOUTUS ASCM, SCORMARK BENCHM Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bimbo F, 2017, APPETITE, V113, P141, DOI 10.1016/j.appet.2017.02.031 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 de Boer J, 2016, APPETITE, V103, P95, DOI 10.1016/j.appet.2016.03.028 Ferquentz, US Foras E, 2015, FOOD CONTROL, V57, P65, DOI 10.1016/j.foodcont.2015.03.027 Ghamrawy M., 2019, FOOD LOSS WASTE VALU GS1, 2013, CISC VIS NETW IND GL HesvestMark, ABOUTUS Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 ORBCOMM, US PakSense, COLD CHAIN TRACKING Verdouw CN, 2016, J FOOD ENG, V176, P128, DOI 10.1016/j.jfoodeng.2015.11.009 Yang XinTing, 2008, Transactions of the Chinese Society of Agricultural Engineering, V24, P162 NR 22 TC 0 Z9 0 U1 4 U2 4 PY 2022 VL 8 IS 1 BP 25 EP 39 DI 10.1504/IJSAMI.2022.123050 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications; Green & Sustainable Science & Technology SC Agriculture; Computer Science; Science & Technology - Other Topics UT WOS:000801013000002 DA 2022-12-14 ER PT J AU Chen, HH Tian, ZH Xu, F AF Chen, Honghua Tian, Zhihong Xu, Fen TI What are cost changes for produce implementing traceability systems in China? Evidence from enterprise A SO APPLIED ECONOMICS DT Article DE Traceability system; break-even pricing; fixed cost; variable cost; consumers' willingness to pay ID WILLINGNESS-TO-PAY; FOOD-SUPPLY CHAIN; INFORMATION; CONSUMERS; PART; MANAGEMENT; PORK AB Based on the survey data and information, this paper conducts analysis on the extra cost of traceability systems for agro-product enterprises in China. Calculation on concrete pricing of traceable products is conducted with Enterprise A as an example by using break-even pricing. The price that consumers are willing to pay for 10 traceable products in Enterprise A was measured by collecting 576 valid questionnaires in three cities in China. The benefit-cost ratio, contribution margin ration and profit growth rate are figured before and after implementing the traceability system to illustrate if Enterprise A is profitable after implementing the traceability system. The results show that extra cost incurred by the traceability system of Enterprise A is not high in China; it is not the main reason for the high price of traceable products. Enterprise A increases its operating efficiency and profit growth rate after implementing the traceability system. The pricing of traceable product should consider target consumers' willingness to pay rather than setting prices blindingly. Government support and education to consumers are important to promote the construction of traceability system at the early stage of establishment of traceability system in China. C1 [Chen, Honghua; Tian, Zhihong; Xu, Fen] China Agr Univ, Coll Econ & Management, Beijing 100083, Peoples R China. C3 China Agricultural University RP Chen, HH (corresponding author), China Agr Univ, Coll Econ & Management, Beijing 100083, Peoples R China. EM myxinge@sina.com CR Ancev T, 2005, PLOS ONE, V5 [Anonymous], 2015, AFRICAN J BUSINESS M, DOI DOI 10.5897/AJBM2014.7532 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Farooq U, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8090839 Han Y., 2009, TECHNOL EC, V28, P37, DOI DOI 10.3969/J.ISSN.1002-980X.2009.04.006 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Kangoh L., 2010, J FACULTY AGR KYUSHU, V55, P341 [刘晓琳 Liu Xiaolin], 2015, [中国人口·资源与环境, China Population Resources and Environment], V25, P170 Liu X, 2007, NEW ZEAL J AGR RES, V50, P911 Lopatovska I, 2008, INFORM PROCESS MANAG, V44, P92, DOI 10.1016/j.ipm.2007.01.020 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Sun C H, 2010, CHINESE SOC AGR ENG, V26, P208 Whitehead JC, 2007, RESOUR ENERGY ECON, V29, P247, DOI 10.1016/j.reseneeco.2006.10.001 Wu L, 2017, AGRIBUSINESS, V33, P1 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2015, CHINA AGR ECON REV, V7, P303, DOI 10.1108/CAER-11-2013-0153 Yang XinTing, 2011, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V42, P125 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 28 TC 7 Z9 10 U1 1 U2 36 PD FEB 7 PY 2019 VL 51 IS 7 BP 687 EP 697 DI 10.1080/00036846.2018.1510470 WC Economics SC Business & Economics UT WOS:000453575500003 DA 2022-12-14 ER PT J AU Francois, G Fabrice, V Didier, M AF Francois, Guyon Fabrice, Vaillant Didier, Montet TI Traceability of fruits and vegetables SO PHYTOCHEMISTRY DT Review DE Fruits and vegetables traceability; CE 178/2002; Food safety; DNA fingerprinting; Metabolomics profiling; Stable isotope analysis; Biological markers ID STABLE-ISOTOPE RATIO; NUCLEAR-MAGNETIC-RESONANCE; GEOGRAPHIC GROWING ORIGIN; MASS-SPECTROMETRY; PCR-DGGE; CITRIC-ACID; BACTERIAL COMMUNITIES; MICROBIAL ECOLOGY; BOTANICAL ORIGIN; ORGANIC-ACIDS AB Food safety and traceability are nowadays a constant concern for consumers, and indeed for all actors in the food chain, including those involved in the fruit and vegetable sector. For the EU, the principles and legal requirements of traceability are set out in Regulation 178/2002. Currently however the regulation does not describe any analytical traceability tools. Furthermore, traceability systems for fruits and vegetables face increasing competition due to market globalization. The current challenge for actors in this sector is therefore to be sufficiently competitive in terms of price, traceability, quality and safety to avoid scandal and fraud. For all these reasons, new, flexible, cheap and efficient traceability tools, as isotopic analysis, DNA fingerprinting and metabolomic profiling coupled with chemometrics are needed. C1 [Francois, Guyon] Serv Commun Labs, Lab Bordeaux Pessac, 3 Ave Dr A Schweitzer, F-33608 Pessac, France. [Fabrice, Vaillant; Didier, Montet] Univ La Reunion, Univ Avignon, Montpellier SupAgro, Qualisud,Univ Montpellier,CIRAD, Montpellier, France. [Fabrice, Vaillant] AGROSAVIA Colombian Corp Agr Res, CI La Selva, Km 7 Via Palmas, Rionegro, Antioquia, Colombia. C3 Avignon Universite; CIRAD; Institut Agro; Montpellier SupAgro; Universite de Montpellier; University of La Reunion; Corporacion Colombiana de Investigacion Agropecuaria, AGROSAVIA RP Francois, G (corresponding author), Serv Commun Labs, Lab Bordeaux Pessac, 3 Ave Dr A Schweitzer, F-33608 Pessac, France. EM guyonfra@gmail.com CR AIJN-European Fruit Juice Association, 2019, COD OF PRACT Anderson KA, 2005, J AGR FOOD CHEM, V53, P410, DOI 10.1021/jf048907u Barcaccia G, 2016, DIVERSITY-BASEL, V8, DOI 10.3390/d8010002 Bazakos C, 2016, PRODUCTS FROM OLIVE TREE, P115, DOI 10.5772/64494 Bevilacqua M, 2012, ANAL CHIM ACTA, V717, P39, DOI 10.1016/j.aca.2011.12.035 Bigot C, 2015, FOOD CONTROL, V48, P123, DOI 10.1016/j.foodcont.2014.03.035 Bohme K, 2019, J AGR FOOD CHEM, V67, P3854, DOI 10.1021/acs.jafc.8b07016 Bontempo L, 2014, J MASS SPECTROM, V49, P785, DOI 10.1002/jms.3420 Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Camin F, 2016, COMPR REV FOOD SCI F, V15, P868, DOI 10.1111/1541-4337.12219 Castro-Puyana M, 2013, TRAC-TREND ANAL CHEM, V52, P74, DOI 10.1016/j.trac.2013.05.016 Cocolin L, 2013, INT J FOOD MICROBIOL, V167, P29, DOI 10.1016/j.ijfoodmicro.2013.05.008 CRAIG H, 1961, SCIENCE, V133, P1833, DOI 10.1126/science.133.3467.1833 Cristea G, 2017, ANAL LETT, V50, P2677, DOI 10.1080/00032719.2016.1263312 Cubero-Leon E, 2014, FOOD RES INT, V60, P95, DOI 10.1016/j.foodres.2013.11.041 di Rienzo V, 2016, FOOD CONTROL, V60, P124, DOI 10.1016/j.foodcont.2015.07.015 Diaz R, 2014, FOOD CHEM, V157, P84, DOI 10.1016/j.foodchem.2014.02.009 DONER LW, 1985, J AGR FOOD CHEM, V33, P770, DOI 10.1021/jf00064a053 Dufosse L, 2013, FOOD CONTROL, V32, P644, DOI 10.1016/j.foodcont.2013.01.045 DUNBAR J, 1983, PLANT PHYSIOL, V72, P725, DOI 10.1104/pp.72.3.725 Durand N, 2013, FOOD CONTROL, V34, P466, DOI 10.1016/j.foodcont.2013.05.017 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Ercolini D, 2004, J MICROBIOL METH, V56, P297, DOI 10.1016/j.mimet.2003.11.006 Eriksson E., 1965, TELLUS, V17, P498, DOI [10.1111/j.2153-3490.1965.tb00212.x, DOI 10.1111/J.2153-3490.1965.TB00212.X] Esslinger S, 2014, FOOD RES INT, V60, P189, DOI 10.1016/j.foodres.2013.10.015 Foerstel H, 2007, ANAL BIOANAL CHEM, V388, P541, DOI 10.1007/s00216-007-1241-z Galimberti A, 2014, ADV AGR, V2014, P831875, DOI [10.1155/2014/831875, DOI 10.1155/2014/831875, 10.1155/2014/831875.] Galimberti A, 2015, FOOD RES INT, V69, P424, DOI 10.1016/j.foodres.2015.01.017 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Ganopoulos I, 2013, J SCI FOOD AGR, V93, P2281, DOI 10.1002/jsfa.6040 Ghidini S., 2006, Annali della Facolta di Medicina Veterinaria, Universita di Parma, V26, P193 Ghisoni S, 2019, FOOD RES INT, V121, P746, DOI 10.1016/j.foodres.2018.12.052 Gilbert A, 2011, PLANT CELL ENVIRON, V34, P1104, DOI 10.1111/j.1365-3040.2011.02308.x Gilbert A, 2009, ANAL CHEM, V81, P8978, DOI 10.1021/ac901441g Guillou C, 1999, ANAL CHIM ACTA, V388, P137, DOI 10.1016/S0003-2670(99)00112-9 Guyon F, 2018, TALANTA, V189, P653, DOI 10.1016/j.talanta.2018.07.022 Guyon F, 2015, ANAL BIOANAL CHEM, V407, P9053, DOI 10.1007/s00216-015-9072-9 Guyon F, 2015, ANAL METHODS-UK, V7, P3211, DOI [10.1039/c5ay00038f, 10.1039/C5AY00038F] Guyon F, 2013, J CHROMATOGR A, V1322, P62, DOI 10.1016/j.chroma.2013.10.088 Guyon F, 2014, FOOD CHEM, V146, P36, DOI 10.1016/j.foodchem.2013.09.020 Hamdouche Y, 2015, FOOD CONTROL, V48, P117, DOI 10.1016/j.foodcont.2014.05.031 HATCH MD, 1970, ANN REV PLANT PHYSIO, V21, P141, DOI 10.1146/annurev.pp.21.060170.001041 Hohmann M, 2015, J AGR FOOD CHEM, V63, P9666, DOI 10.1021/acs.jafc.5b03853 Holst-Jensen A, 2012, BIOTECHNOL ADV, V30, P1318, DOI 10.1016/j.biotechadv.2012.01.024 Houerou G, 1999, RAPID COMMUN MASS SP, V13, P1257, DOI 10.1002/(SICI)1097-0231(19990715)13:13<1257::AID-RCM561>3.0.CO;2-G Jamin E, 2005, J AGR FOOD CHEM, V53, P5130, DOI 10.1021/jf050400b Jamin E, 2003, J AGR FOOD CHEM, V51, P5202, DOI 10.1021/jf030167m Jamin E, 1998, J AGR FOOD CHEM, V46, P5118, DOI 10.1021/jf980664g Jamin E, 1998, J AOAC INT, V81, P604 Jandric Z, 2017, FOOD CONTROL, V72, P181, DOI 10.1016/j.foodcont.2015.10.044 Kendall C, 2001, HYDROL PROCESS, V15, P1363, DOI 10.1002/hyp.217 Krauss S, 2019, J AGR FOOD CHEM, V67, P4054, DOI 10.1021/acs.jafc.9b01631 Krivachy N, 2015, FOOD CONTROL, V48, P143, DOI 10.1016/j.foodcont.2014.06.002 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Longobardi F, 2015, FOOD CHEM, V188, P343, DOI 10.1016/j.foodchem.2015.05.020 Longobardi F, 2012, FOOD CHEM, V130, P177, DOI 10.1016/j.foodchem.2011.06.045 Luo DH, 2015, FOOD CHEM, V174, P197, DOI 10.1016/j.foodchem.2014.11.006 Madesis P, 2014, FOOD RES INT, V60, P163, DOI 10.1016/j.foodres.2013.10.042 Magdas DA, 2018, FOOD CHEM, V267, P231, DOI 10.1016/j.foodchem.2017.10.048 Magdas DA, 2012, SCI WORLD J, DOI 10.1100/2012/878242 Martins-Lopes P, 2013, FOOD TECHNOL BIOTECH, V51, P198 Mimmo T, 2015, RAPID COMMUN MASS SP, V29, P1984, DOI 10.1002/rcm.7306 Montemurro C, 2015, J CHEM-NY, V2015, DOI 10.1155/2015/496986 Mukome FND, 2013, CALIF AGR, V67, P210, DOI 10.3733/ca.v067n04p210 Nocker A, 2007, MICROB ECOL, V54, P276, DOI 10.1007/s00248-006-9199-5 Ogrinc N, 2003, ANAL BIOANAL CHEM, V376, P424, DOI 10.1007/s00216-003-1804-6 OLEARY MH, 1988, BIOSCIENCE, V38, P328, DOI 10.2307/1310735 Opatic AM, 2018, FOOD CONTROL, V89, P133, DOI 10.1016/j.foodcont.2017.11.013 Pacifico D, 2013, J AGR FOOD CHEM, V61, P11201, DOI 10.1021/jf402961m Palmieri L, 2009, NUTRIENTS, V1, P316, DOI 10.3390/nu1020316 Paolini M, 2015, J AGR FOOD CHEM, V63, P5841, DOI 10.1021/acs.jafc.5b00662 Pasqualone A, 2013, J AGR FOOD CHEM, V61, P3068, DOI 10.1021/jf400014g Perez AL, 2006, J AGR FOOD CHEM, V54, P4506, DOI 10.1021/jf0600455 Perini M, 2018, FOOD CHEM, V239, P48, DOI 10.1016/j.foodchem.2017.06.023 Psomiadis D, 2018, J FOOD SCI TECH MYS, V55, P2994, DOI 10.1007/s13197-018-3217-8 Rogers KM, 2008, J AGR FOOD CHEM, V56, P4078, DOI 10.1021/jf800797w Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Rummel S, 2010, FOOD CHEM, V118, P890, DOI 10.1016/j.foodchem.2008.05.115 Salzano A, 2020, MOLECULES, V25, DOI 10.3390/molecules25020304 Sardaro MLS, 2013, FOOD SCI NUTR, V1, P54, DOI 10.1002/fsn3.8 Scarano D., 2014, Diversity, V6, P579 Schlicht C., 2006, Journal fur Verbraucherschutz und Lebensmittelsicherheit, V1, P97, DOI 10.1007/s00003-006-0017-2 Senizza B, 2019, FOOD RES INT, V126, DOI 10.1016/j.foodres.2019.108584 Sieper HP, 2006, RAPID COMMUN MASS SP, V20, P2521, DOI 10.1002/rcm.2619 Sturm M, 2011, ISOT ENVIRON HEALT S, V47, P214, DOI 10.1080/10256016.2011.570865 Sun D., 2018, MODERN TECHNIQUES FO, P659, DOI DOI 10.1016/B978-0-12-814264-6.00016-5. Tatsadjieu NL, 2010, FOOD CONTROL, V21, P673, DOI 10.1016/j.foodcont.2009.10.006 Tcherkez G, 2013, NEW PHYTOL, V200, P44, DOI 10.1111/nph.12314 Thomas F, 2010, J AGR FOOD CHEM, V58, P11580, DOI 10.1021/jf102983v Turci M, 2010, FOOD CONTROL, V21, P143, DOI 10.1016/j.foodcont.2009.04.012 Uawisetwathana U, 2019, CURR OPIN FOOD SCI, V28, P58, DOI 10.1016/j.cofs.2019.08.008 Vaclavik L, 2012, METABOLOMICS, V8, P793, DOI 10.1007/s11306-011-0371-7 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Wilkes T., 2016, J ASSOC PUBLIC ANAL, V44 YUNIANTA, 1995, J AGR FOOD CHEM, V43, P2411, DOI 10.1021/jf00057a018 [钟其顶 Zhong Qiding], 2014, [质谱学报, Journal of Chinese Mass Spectrometry Society], V35, P361 NR 96 TC 15 Z9 16 U1 12 U2 61 PD MAY PY 2020 VL 173 AR 112291 DI 10.1016/j.phytochem.2020.112291 WC Biochemistry & Molecular Biology; Plant Sciences SC Biochemistry & Molecular Biology; Plant Sciences UT WOS:000528188900018 DA 2022-12-14 ER PT J AU Surasak, T Wattanavichean, N Preuksakarn, C Huang, SCH AF Surasak, Thattapon Wattanavichean, Nungnit Preuksakarn, Chakkrit Huang, Scott C. -H. TI Thai Agriculture Products Traceability System using Blockchain and Internet of Things SO INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS DT Article DE Blockchain; internet of things; supply chain management; product traceability; distributed database; data integrity; ourSQL AB In this paper, we successfully designed and developed Thai agriculture products traceability system using blockchain and Internet of Things. Blockchain, which is the distributed database, is used for our proposed traceability system to enhance the transparency and data integrity. OurSQL is added on another layer to easier query process of blockchain database, therefore the proposed system is a user-friendly system, which cannot be found in ordinary blockchain database. The website and android application have been developed to show the tracking information of the product. The blockchain database coupling with Internet of Things give a number of benefits for our traceability system because all of the collecting information is in real-time and kept in a very secured database. Our system could have a huge impact on food traceability and supply chain management become more reliable as well as rebuild public awareness in Thailand on food safety and quality control. C1 [Surasak, Thattapon; Huang, Scott C. -H.] Natl Tsing Hua Univ, Inst Commun Engn, Hsinchu, Taiwan. [Wattanavichean, Nungnit] Natl Chiao Tung Univ, Dept Appl Chem, Hsinchu, Taiwan. [Preuksakarn, Chakkrit] Kasetsart Univ, Dept Comp Engn, Nakhon Pathom, Thailand. C3 National Tsing Hua University; National Yang Ming Chiao Tung University; Kasetsart University RP Surasak, T (corresponding author), Natl Tsing Hua Univ, Inst Commun Engn, Hsinchu, Taiwan. CR Adam B., 2016, INT FOOD AGRIBUSINES Adams R, 2017, STRATEG CHANG, V26, P417, DOI 10.1002/jsc.2141 Aich S, 2019, INT CONF ADV COMMUN, P138, DOI 10.23919/ICACT.2019.8701910 Ammendrup S, 2006, REV SCI TECH OIE, V25, P763, DOI 10.20506/rst.25.2.1689 Angel G., 2011, 2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering (ICONRAEeCE), P398, DOI 10.1109/ICONRAEeCE.2011.6129739 Bhatt T, 2013, J FOOD SCI, V78, pB28, DOI 10.1111/1750-3841.12299 Sanchez BB, 2015, SENSORS-BASEL, V15, P29478, DOI 10.3390/s151129478 Campos LB, 2015, 2015 INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS), P212, DOI 10.1109/DCOSS.2015.31 Cao W., 2009, CCTA Caro MP, 2018, IOT VERT TOP SUMM AG, P1 CHOMCHALOW N, 1993, SYST APPR S, V2, P427 Hejazi H., 2018, P 2018 IEEE INT C FU, P1 Hong WB, 2018, PROCEEDINGS OF 2018 1ST IEEE INTERNATIONAL CONFERENCE ON HOT INFORMATION-CENTRIC NETWORKING (HOTICN 2018), P254, DOI 10.1109/HOTICN.2018.8605963 Karafiloski E, 2017, 17TH IEEE INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES - IEEE EUROCON 2017 CONFERENCE PROCEEDINGS, P763, DOI 10.1109/EUROCON.2017.8011213 Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Li Y, 2017, LECT NOTES COMPUT SC, V10178, P556, DOI 10.1007/978-3-319-55699-4_34 Liang WJ, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0139558 Lin J, 2017, IEEE INTERNET THINGS, V4, P1125, DOI 10.1109/JIOT.2017.2683200 Muzammal M, 2019, FUTURE GENER COMP SY, V90, P105, DOI 10.1016/j.future.2018.07.042 Ngu AH, 2017, IEEE INTERNET THINGS, V4, P1, DOI 10.1109/JIOT.2016.2615180 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Poapongsakorn N., 1998, THAILAND DEV RES I Q, V13 Rahmadika S, 2018, INT CONF DAT MIN WOR, P156, DOI 10.1109/ICDMW.2018.00029 Rasyid M. U. H. A., 2016, 2016 INT C KNOWL CRE, P105 Reyna A, 2018, FUTURE GENER COMP SY, V88, P173, DOI 10.1016/j.future.2018.05.046 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Samaniego M., 2016, P IEEE INT C INT THI, P433 Singh M, 2018, 2018 IEEE 4TH WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P51 Surasak T, 2019, INT CONF COMPUT NETW, P180, DOI 10.1109/ICCNC.2019.8685553 Swamy SA, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMMUNICATION AND ELECTRONICS SYSTEMS (ICCES 2018), P271, DOI 10.1109/CESYS.2018.8724077 Tian F, 2019, POLYCYCL AROMAT COMP, V39, P353, DOI 10.1080/10406638.2017.1326952 Vafiadis NV, 2019, 2019 IEEE 5TH WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P864, DOI 10.1109/WF-IoT.2019.8767288 Wang YK, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P1, DOI 10.1109/AUS.2016.7748011 Wongpatikaseree K., P180, DOI 10.1109/icis.2018.8466479 Yang YC, 2017, IEEE INTERNET THINGS, V4, P1250, DOI 10.1109/JIOT.2017.2694844 NR 35 TC 23 Z9 24 U1 4 U2 24 PD SEP PY 2019 VL 10 IS 9 BP 578 EP 583 WC Computer Science, Theory & Methods SC Computer Science UT WOS:000499999000077 DA 2022-12-14 ER PT J AU Curto, JP Gaspar, PD AF Curto, Joao Paulo Gaspar, Pedro Dinis TI Traceability in food supply chains: Review and SME focused analysis-Part 1 SO AIMS AGRICULTURE AND FOOD DT Review DE traceability; small and medium enterprises; advantages; main issues ID IMPLEMENTING TRACEABILITY; PRODUCT QUALITY; SYSTEM; FRAMEWORK; TRANSPARENCY; INFORMATION; SAFETY; SUSTAINABILITY; DETERIORATION; TECHNOLOGIES AB Traceability can be a tool for safety and quality assurance for food perishables as well as for process optimization and economic gain. However, it is often considered mere bureaucracy and an economic burden. Such is prevalent in small and medium-sized enterprises. As they constitute most of food sector, the adoption of traceability systems is quite slow and mostly to satisfy legal requirements. To determine the main advantages and disadvantages of traceability models, implementation and technologies, a literature review and Small and Medium Enterprises (SME) focused analysis was performed in the part I of this study. In Part II, a low cost open-source traceability focused on food safety and quality is developed. It is based on HACCP flowcharts to define gateways for quality evaluation and encompasses external verification and product history maintenance. Economic gains, more quality and safety, better efficiency and a more direct contact with consumers are some of the main advantages. High implementation costs, poorly defined benefits, lack of compatibility, consumer focused perspective and exposure of sensitive information are some of the main issues. This study serves to expose these issues and suggest solutions, aiming to encourage the adoption of traceability systems, with last-end benefits to producers, retailers, and consumers. C1 [Curto, Joao Paulo; Gaspar, Pedro Dinis] Univ Beira Interior, Rua Marques Avila & Bolama, P-6201001 Covilha, Portugal. [Gaspar, Pedro Dinis] C MAST Ctr Mech & Aerosp Sci & Technol, Covilha, Portugal. C3 Universidade da Beira Interior RP Gaspar, PD (corresponding author), Univ Beira Interior, Rua Marques Avila & Bolama, P-6201001 Covilha, Portugal.; Gaspar, PD (corresponding author), C MAST Ctr Mech & Aerosp Sci & Technol, Covilha, Portugal. EM dinis@ubi.pt CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Alonso-Roris VM, 2016, COMPUT IND, V83, P1, DOI 10.1016/j.compind.2016.08.003 [Anonymous], 2015, 90002015 ISO Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bakker M, 2012, EUR J OPER RES, V221, P275, DOI 10.1016/j.ejor.2012.03.004 Bechim A, 2005, 2005 SYMPOSIUM ON APPLICATIONS AND THE INTERNET WORKSHOPS, PROCEEDINGS, P366, DOI 10.1109/SAINTW.2005.1620050 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Borit M, 2012, MAR POLICY, V36, P96, DOI 10.1016/j.marpol.2011.03.012 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Frosch S, 2008, J AQUAT FOOD PROD T, V17, P387, DOI 10.1080/10498850802369179 Gaukler G, 2017, INT J PROD ECON, V193, P617, DOI 10.1016/j.ijpe.2017.07.019 Germani M, 2015, PROC CIRP, V29, P227, DOI 10.1016/j.procir.2015.02.199 Gessner GH, 2007, INFORM SYST MANAGE, V24, P213, DOI 10.1080/10580530701404561 Grunow M, 2013, INT J PROD ECON, V146, P717, DOI 10.1016/j.ijpe.2013.08.028 Heese HS, 2007, PROD OPER MANAG, V16, P542 Hertog MLATM, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0306 Hsiao HI, 2016, FOOD CONTROL, V64, P181, DOI 10.1016/j.foodcont.2015.12.020 Hsu CI, 2007, J FOOD ENG, V80, P465, DOI 10.1016/j.jfoodeng.2006.05.029 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Huang FH, 2012, IFIP ADV INF COMM TE, V368, P371 International Standard Organization, 2007, 220052007 ISO ISO, 1994, 112601994 ISO Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Jedermann Reiner, 2007, International Journal of Radio Frequency Identification Technology and Applications, V1, P247, DOI 10.1504/IJRFITA.2007.015849 Jedermann R, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0302 Joint FAO/WHO Codex Alimentarius Commission, 2013, COD AL CER PULS LEG COD AL CER PULS LEG, V21st Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kwok SK, 2008, IFAC, V41, P5482 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Liu F, 2015, INT J SENS NETW, V17, P211, DOI 10.1504/IJSNET.2015.069582 Liu XF, 2008, ASIA PAC J MARKET LO, V20, P7, DOI 10.1108/13555850810844841 Lupien JR, 2005, CRIT REV FOOD SCI, V45, P119, DOI 10.1080/10408690490911774 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Ndraha N, 2018, FOOD CONTROL, V89, P12, DOI 10.1016/j.foodcont.2018.01.027 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Oskarsdottir K, 2019, J FOOD ENG, V240, P153, DOI 10.1016/j.jfoodeng.2018.07.013 Pahl J, 2014, EUR J OPER RES, V238, P654, DOI 10.1016/j.ejor.2014.01.060 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Piramuthu S, 2013, EUR J OPER RES, V225, P253, DOI 10.1016/j.ejor.2012.09.024 Pizzuti Teresa, 2012, Proceedings of the 3rd 2012 International Conference on Industrial Engineering and Operations Management, P1065 Qian JP, 2017, FOOD CONTROL, V74, P98, DOI 10.1016/j.foodcont.2016.11.034 Raak N, 2017, WASTE MANAGE, V61, P461, DOI 10.1016/j.wasman.2016.12.027 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Skoglund T, 2007, FOOD BIOPROD PROCESS, V85, P354, DOI 10.1205/fbp07044 Sloof M, 1996, TRENDS FOOD SCI TECH, V7, P165, DOI 10.1016/0924-2244(96)81257-X Souali K, 2017, ADV SCI TECHNOL ENG, V2, P356 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Stranieri S., 2018, Wine Economics and Policy, V7, P45, DOI 10.1016/j.wep.2018.02.001 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Thakur M, 2011, COMPUT ELECTRON AGR, V75, P327, DOI 10.1016/j.compag.2010.12.010 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Trebar M, 2013, INT J ANTENN PROPAG, V2013, DOI 10.1155/2013/875973 van der Vorst JGAJ, 2009, INT J PROD RES, V47, P6611, DOI 10.1080/00207540802356747 Verdouw CN, 2013, COMPUT ELECTRON AGR, V99, P160, DOI 10.1016/j.compag.2013.09.006 Walker GS, 2017, FOOD CONTROL, V72, P168, DOI 10.1016/j.foodcont.2016.01.028 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Wang X, 2018, FOOD CONTROL, V88, P169, DOI 10.1016/j.foodcont.2018.01.008 Wang XJ, 2012, OMEGA-INT J MANAGE S, V40, P906, DOI 10.1016/j.omega.2012.02.001 Woo SH, 2009, COMPUT IND, V60, P149, DOI 10.1016/j.compind.2008.12.001 Zhang Hu, 2009, WSEAS Transactions on Information Science and Applications, V6, P1094 Zhou W, 2009, EUR J INFORM SYST, V18, P570, DOI 10.1057/ejis.2009.45 NR 80 TC 4 Z9 4 U1 7 U2 16 PY 2021 VL 6 IS 2 BP 679 EP 707 DI 10.3934/agrfood.2021041 WC Agriculture, Multidisciplinary; Agronomy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000677621900015 DA 2022-12-14 ER PT J AU Sun, SN Wang, XP AF Sun, Shengnan Wang, Xinping TI Promoting traceability for food supply chain with certification SO JOURNAL OF CLEANER PRODUCTION DT Article DE Food traceability; Supply chain; Certification; Information asymmetry ID WILLINGNESS-TO-PAY; VOLUNTARY TRACEABILITY; PRODUCT-SAFETY; QUALITY; INFORMATION; INCENTIVES; MODEL; INTEGRATION; MANAGEMENT; SECURITY AB Traceability systems are perceived as a powerful solution to cope with food safety issues. It is important to improve the traceability for food supply chains to address potential food safety events. This paper studies the sourcing decisions in a food supply chain, in which the buyer is exposed to potential food safety events due to supplier responsibility. It is shown that the buyer may fail to source from the supplier with high-level traceability on account of information asymmetry. To address this adverse selection problem, this paper employs the certification as a screening tool, and find that the buyer could separate the two types perfectly with a pooling contract with certification, if the probability of the high type is low; otherwise, a pooling contract only separate the two types partially. This paper further considers a setting in which traceability strengthens the food safety level. It is shown that the buyer prefers the supplier with high-level traceability even the unit cost incurred by food safety events or the probability of the supplier with high-level traceability is lower. (C) 2019 Elsevier Ltd. All rights reserved. C1 [Sun, Shengnan] Southeast Univ, Sch Econ & Management, Sipailou 2, Nanjing 210096, Jiangsu, Peoples R China. [Wang, Xinping] Nanjing Agr Univ, Coll Econ & Management, Weigang 1, Nanjing 210095, Jiangsu, Peoples R China. C3 Southeast University - China; Nanjing Agricultural University RP Sun, SN (corresponding author), Southeast Univ, Sch Econ & Management, Sipailou 2, Nanjing 210096, Jiangsu, Peoples R China. EM sun.shengnan@seu.edu.cn CR Accorsi R, 2018, J CLEAN PROD, V203, P1039, DOI 10.1016/j.jclepro.2018.08.275 Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Baiman S, 2000, MANAGE SCI, V46, P776, DOI 10.1287/mnsc.46.6.776.11939 Borit M, 2015, J CLEAN PROD, V104, P13, DOI 10.1016/j.jclepro.2015.05.003 Brat I., 2016, COMPANIES STEP EFFOR Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chen L, 2017, MANAGE SCI, V63, P2795, DOI 10.1287/mnsc.2016.2466 Chen YJ, 2013, NAV RES LOG, V60, P175, DOI 10.1002/nav.21527 Dai HY, 2015, INT J PROD RES, V53, P511, DOI 10.1080/00207543.2014.955922 Giacomarra M, 2016, J CLEAN PROD, V112, P267, DOI 10.1016/j.jclepro.2015.09.026 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hwang I, 2006, MANAGE SCI, V52, P1472, DOI 10.1287/mnsc.1060.0557 Iyer G, 2018, MANAGE SCI, V64, P695, DOI 10.1287/mnsc.2016.2625 Kim SH, 2013, MANAGE SCI, V59, P189, DOI 10.1287/mnsc.1120.1573 King T, 2017, TRENDS FOOD SCI TECH, V68, P160, DOI 10.1016/j.tifs.2017.08.014 Marucheck A, 2011, J OPER MANAG, V29, P707, DOI 10.1016/j.jom.2011.06.007 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Mogale DG, 2018, TRANSPORT RES E-LOG, V111, P40, DOI 10.1016/j.tre.2018.01.004 Mogale DG, 2018, CONTROL ENG PRACT, V70, P98, DOI 10.1016/j.conengprac.2017.09.017 Nilsson T, 2006, CAN J AGR ECON, V54, P567, DOI 10.1111/j.1744-7976.2006.00067.x Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 Resende MA, 2008, AM J AGR ECON, V90, P1091, DOI 10.1111/j.1467-8276.2008.01150.x Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Rueda X, 2017, J CLEAN PROD, V142, P2480, DOI 10.1016/j.jclepro.2016.11.026 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Smith TG, 2011, FOOD POLICY, V36, P239, DOI 10.1016/j.foodpol.2010.11.021 Souza-Monteiro DM, 2010, AGRIBUSINESS, V26, P122, DOI 10.1002/agr.20233 Spencer R., 2009, 2 SENTENCED DEATH CH Starbird SA, 2006, J AGR RESOUR ECON, V31, P14 Starbird SA, 2007, J AGR FOOD IND ORG, V5 Stranieri S, 2016, BRIT FOOD J, V118, P1025, DOI 10.1108/BFJ-04-2015-0151 UN, 2015, TRANSF OUR WORLD 203, VA/R Wang X, 2009, INT J PROD RES, V47, P2865, DOI 10.1080/00207540701725075 Wang X, 2010, INT J PROD ECON, V124, P463, DOI 10.1016/j.ijpe.2009.12.009 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Zhang L, 2016, J CLEAN PROD, V134, P269, DOI 10.1016/j.jclepro.2015.09.078 NR 44 TC 34 Z9 36 U1 13 U2 121 PD APR 20 PY 2019 VL 217 BP 658 EP 665 DI 10.1016/j.jclepro.2019.01.296 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000461410100066 DA 2022-12-14 ER PT J AU Choe, YC Park, J Chung, M Moon, J AF Choe, Young Chan Park, Joowon Chung, Miri Moon, Junghoon TI Effect of the food traceability system for building trust: Price premium and buying behavior SO INFORMATION SYSTEMS FRONTIERS DT Article; Proceedings Paper CT 2nd International Conference on Information and Communication Technologies and Development CY DEC 15-16, 2007 CL Bangalore, INDIA DE Food traceability systems; Uncertainty; Pavlou's TAM; Price premium ID CONSUMER; COMMUNICATION; CONSEQUENCES; INFORMATION; PRODUCTS; ADOPTION AB Facing a series of food-related accidents, consumers worldwide have become more concerned with the safety of the food they consume. The Food Traceability System has been introduced in many countries to reduce the uncertainties originating in the food purchasing process by providing information about the entire food process, from farm to table, in terms of quality and safety. However, this relatively new information system has not yet been explored with an academic approach. The main goal of this study was to determine whether reduced uncertainty provides benefits for producers and consumers, thereby warranting the adoption of the food traceability system. We also analyzed the factors and mechanisms that explain consumer behavior within the system. We have modified and applied the uncertainty model of Pavlou et al. (MIS Quarterly 31(1):105-136, 2007) derived from the principal-agent perspective in order to fulfill our research objectives. Through a survey conducted in Korea, we found that Korean consumers were not only willing to purchase greater quantities of food, but also willing to pay more for food managed with the traceability system. The results of this study indicate that the mitigated uncertainty given by the traceability system plays a key role in price premium and purchase intention. In addition, mitigated uncertainty has a larger impact on purchase intention than on price premium, implying that consumers are inclined to buy more rather than pay more. These results provide valuable suggestions for producers for how to deal with increased costs resulting from adoption of the system. We also found that in the context of the Food Traceability System, perceived uncertainty was mitigated as a result of a reduced fear of seller opportunism originating from increased trust and reduced information asymmetry originating from increased product diagnosticity, informativeness, and trust. Reduced fear of seller opportunism had a stronger impact than reduced information asymmetry on perceived mitigated uncertainty. C1 [Park, Joowon; Moon, Junghoon] ICU, Sch IT Business, Taejon 305732, South Korea. [Choe, Young Chan; Chung, Miri] Seoul Natl Univ, Program Reg Informat, Seoul 151742, South Korea. [Choe, Young Chan; Chung, Miri] Seoul Natl Univ, Res Inst Agr & Life Sci, Coll Agr & Life Sci, Seoul 151742, South Korea. C3 Seoul National University (SNU); Seoul National University (SNU) RP Moon, J (corresponding author), ICU, Sch IT Business, 103-6 Munji Dong, Taejon 305732, South Korea. EM aggi@snu.ac.kr; joowon@icu.ac.kr; miri0121@snu.ac.kr; jmoon@icu.ac.kr CR Aaker DA, 1996, CALIF MANAGE REV, V38, P102, DOI 10.2307/41165845 Aboulnasr K, 2006, MARK MANAG, V16, P1 AGARWAL MK, 1996, MARKET LETT, V7, P237, DOI DOI 10.1007/BF00435740 *AGR EUR, 2001, SEV ISS OCT DEC AHN DH, 2006, AGENDA 21 MONITORING AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 Ba SL, 2002, MIS QUART, V26, P243, DOI 10.2307/4132332 Bauer R., 1967, RISK TAKING INFORM H Chen QM, 1999, J ADVERTISING RES, V39, P27 Chiles TH, 1996, ACAD MANAGE REV, V21, P73, DOI 10.2307/258630 CHUN MH, 2007, J AGR EXTENSION COMM, V14, P117 Chung M., 2006, P 12 AM C INF SYST A, P418 Crespo AH, 2008, INTERACT COMPUT, V20, P212, DOI 10.1016/j.intcom.2007.11.005 de Figueiredo JM, 2000, SLOAN MANAGE REV, V41, P41 DEBI PM, 1998, J MARKETING RES, V35, P277 Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Ducoffe RH, 1996, J ADVERTISING RES, V36, P21 Dwivedi YK, 2008, INFORM SYST FRONT, V10, P385, DOI 10.1007/s10796-008-9101-8 *EU, 2002, GEN FOOD LAW Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Golan E., 2004, Amber Waves, V2, P14 Griffiths M, 2008, INFORM SYST FRONT, V10, P447, DOI 10.1007/s10796-008-9105-4 GRONHAUGE K, 1995, EUROPEAN ADV CONSUME, V2, P1 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hong SJ, 2008, INFORM SYST FRONT, V10, P431, DOI 10.1007/s10796-008-9096-1 Hunt S, 2001, RISK DECISION POLICY, V6, P91, DOI DOI 10.1017/S135753090100031X Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Jarvenpaa S. L., 2000, Information Technology & Management, V1, P45, DOI 10.1023/A:1019104520776 Jiang ZH, 2004, J MANAGE INFORM SYST, V21, P111, DOI 10.1080/07421222.2004.11045817 KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kempf DS, 1998, J MARKETING RES, V35, P325, DOI 10.2307/3152031 Luo X., 2002, J INTERACTIVE ADVERT, V2, DOI [DOI 10.1080/15252019.2002.10722060, 10.1080/15252019.2002.10722060] McEachern MG, 2005, BRIT FOOD J, V107, P572, DOI 10.1108/00070700510610986 Meyer H., 1999, J BUS STRAT, V20, P27, DOI DOI 10.1108/EB040015 Miles S, 2005, BRIT FOOD J, V107, P246, DOI 10.1108/00070700510589521 Milgrom Paul., 1992, EC ORG MANAGEMENT, V6th ed. Oberholtzer L., 2005, PRICE PREMIUMS HOLD Pavlou PA, 2006, MIS QUART, V30, P115 Pavlou PA, 2004, INFORM SYST RES, V15, P37, DOI 10.1287/isre.1040.0015 Pavlou PA, 2007, MIS QUART, V31, P105 Rao AR, 1996, J BUS, V69, P511, DOI 10.1086/209703 Rousseau DM, 1998, ACAD MANAGE REV, V23, P393, DOI 10.5465/AMR.1998.926617 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 SHAPIRO C, 1983, Q J ECON, V98, P659, DOI 10.2307/1881782 Shen LP, 2008, INFORM SYST FRONT, V10, P461, DOI 10.1007/s10796-008-9104-5 Singh J, 2000, J ACAD MARKET SCI, V28, P150, DOI 10.1177/0092070300281014 Swan J. E., 1985, J PERS SELL SALES M, V5, P39 Yang XL, 2007, J CENT SOUTH UNIV T, V14, P165, DOI 10.1007/s11771-007-0237-3 NR 50 TC 71 Z9 72 U1 9 U2 72 PD APR PY 2009 VL 11 IS 2 SI SI BP 167 EP 179 DI 10.1007/s10796-008-9134-z WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science UT WOS:000266240700007 DA 2022-12-14 ER PT J AU Dasaklis, TK Voutsinas, TG Tsoulfas, GT Casino, F AF Dasaklis, Thomas K. Voutsinas, Theodore G. Tsoulfas, Giannis T. Casino, Fran TI A Systematic Literature Review of Blockchain-Enabled Supply Chain Traceability Implementations SO SUSTAINABILITY DT Article DE supply chain; traceability; blockchain; sustainability ID QUALITATIVE CONTENT-ANALYSIS; CHALLENGES; MANAGEMENT; TECHNOLOGY; DESIGN; MODEL; TRANSPARENT; PERFORMANCE; FRAMEWORK; TRACKING AB In recent years, traceability systems have been developed as practical tools for improving supply chain (SC) transparency and visibility, especially in health and safety-sensitive sectors like food and pharmaceuticals. Blockchain-related SC traceability research has received significant attention during the last several years, and arguably blockchain is currently the most promising technology for providing traceability-related services in SC networks. This paper provides a systematic literature review of the various technical implementation aspects of blockchain-enabled SC traceability systems. We apply different drivers for classifying the selected literature, such as (a) the various domains of the available blockchain-enabled SC traceability systems and relevant methodologies applied; (b) the implementation maturity of these traceability systems along with technical implementation details; and (c) the sustainability perspective (economic, environmental, social) prevalent to these implementations. We provide key takeaways regarding the open issues and challenges of current blockchain traceability implementations and fruitful future research areas. Despite the significant volume and plethora of blockchain-enabled SC traceability systems, academia has so far focused on unstructured experimentation of blockchain-associated SC traceability solutions, and there is a clear need for developing and testing real-life traceability solutions, especially taking into account feasibility and cost-related SC aspects. C1 [Dasaklis, Thomas K.; Voutsinas, Theodore G.] Hellen Open Univ, Sch Social Sci, Patras 26335, Greece. [Tsoulfas, Giannis T.] Agr Univ Athens, Dept Agribusiness & Supply Chain Management, Thiva 32200, Greece. [Casino, Fran] Univ Rovira & Virgili, Dept Comp Engn & Math, Tarragona 43007, Spain. [Casino, Fran] Athena Res Ctr, Informat Management Syst Inst, Maroussi 15125, Greece. C3 Hellenic Open University; Universitat Rovira i Virgili RP Casino, F (corresponding author), Univ Rovira & Virgili, Dept Comp Engn & Math, Tarragona 43007, Spain.; Casino, F (corresponding author), Athena Res Ctr, Informat Management Syst Inst, Maroussi 15125, Greece. EM dasaklis@eap.gr; voutsinas.theodoros@ac.eap.gr; giannis@aua.gr; francasino@unipi.gr CR Adarsh S, 2021, IT PROF, V23, P28, DOI 10.1109/MITP.2021.3094194 Agrawal TK, 2021, COMPUT IND ENG, V154, DOI 10.1016/j.cie.2021.107130 Ahmad RW, 2021, IEEE ACCESS, V9, P44905, DOI 10.1109/ACCESS.2021.3066503 Ahmed M, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21062239 Alkhader W, 2020, IEEE ACCESS, V8, P188363, DOI 10.1109/ACCESS.2020.3031536 Alkhader W, 2021, IEEE ACCESS, V9, P137923, DOI 10.1109/ACCESS.2021.3118085 Aquilina S.J., 2021, BLOCKCHAIN RES APPL, V2 Arora A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084151 Ates MA, 2022, J SUPPLY CHAIN MANAG, V58, P3, DOI 10.1111/jscm.12264 Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Balamurugan S., 2022, International Journal of Information Technology, V14, P1087, DOI 10.1007/s41870-020-00581-y Banerjee A, 2018, ADV COMPUT, V111, P69, DOI 10.1016/bs.adcom.2018.03.007 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bryman, 2009, SAGE HDB ORG RES MET, P671, DOI DOI 10.1080/03634528709378635 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Cao SF, 2021, COMPUT ELECTRON AGR, V180, DOI 10.1016/j.compag.2020.105886 Cao Y, 2020, IEEE T IND INFORM, V16, P6004, DOI 10.1109/TII.2019.2942211 Cartier LE, 2018, J GEMMOL, V36, P212, DOI 10.15506/JoG.2018.36.3.212 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Casino F, 2020, IEEE ACCESS, V8, P4737, DOI 10.1109/ACCESS.2019.2962017 Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 Chan KY, 2019, INT J ADV COMPUT SC, V10, P149 Cizmesija A., 2021, 2021 IEEE TECHNOLOGY, P1 Cocco L, 2021, IEEE ACCESS, V9, P62899, DOI 10.1109/ACCESS.2021.3074874 Cui P, 2019, IEEE ACCESS, V7, P157113, DOI 10.1109/ACCESS.2019.2949951 Dasaklis TK, 2019, LECT NOTES BUS INF P, V362, P704, DOI 10.1007/978-3-030-37453-2_56 Dasaklis TK, 2019, INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS (COINS), P184, DOI 10.1145/3312614.3312652 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Deng WS, 2020, J CLEAN PROD, V275, DOI 10.1016/j.jclepro.2020.124061 Denyer D, 2006, MANAGE DECIS, V44, P213, DOI [DOI 10.1108/00251740610650201, 10.1108/00251740610650201] Dey S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063486 Ding QY, 2020, IEEE ACCESS, V8, P6209, DOI 10.1109/ACCESS.2019.2962274 dos Santos RB, 2019, INFORMATICS-BASEL, V6, DOI 10.3390/informatics6010011 Downe-Wamboldt B, 1992, Health Care Women Int, V13, P313 Elo S, 2008, J ADV NURS, V62, P107, DOI 10.1111/j.1365-2648.2007.04569.x Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Ferdousi T, 2020, IEEE ACCESS, V8, P154833, DOI 10.1109/ACCESS.2020.3019000 Fernandez-Carames TM, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19102394 Fernando E., 2021, ADV SCI TECHNOL ENG, V6, P765, DOI [10.25046/aj060184, DOI 10.25046/AJ060184] Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gao K, 2020, INT J COMPUT SCI ENG, V23, P185, DOI 10.1504/IJCSE.2020.110547 Graneheim UH, 2017, NURS EDUC TODAY, V56, P29, DOI 10.1016/j.nedt.2017.06.002 Grecuccio J, 2020, ENERGIES, V13, DOI 10.3390/en13153820 Hastig GM, 2020, PROD OPER MANAG, V29, P935, DOI 10.1111/poms.13147 Hewa TM, 2021, IEEE ACCESS, V9, P87643, DOI 10.1109/ACCESS.2021.3068178 Ho GTS, 2021, EXPERT SYST APPL, V179, DOI 10.1016/j.eswa.2021.115101 Hu SS, 2021, COMPUT IND ENG, V153, DOI 10.1016/j.cie.2020.107079 Islam MN, 2019, ACM T DES AUTOMAT EL, V24, DOI 10.1145/3315669 Kabadurmus O, 2020, ANN OPER RES, V292, P47, DOI 10.1007/s10479-020-03654-0 Katsikouli P, 2021, J SCI FOOD AGR, V101, P2175, DOI 10.1002/jsfa.10883 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kim HM, 2018, INTELL SYST ACCOUNT, V25, P18, DOI 10.1002/isaf.1424 Kontopanou M, 2021, SCI PAP-SER MANAG EC, V21, P331 Kuhn M, 2021, J MANUF SYST, V59, P617, DOI 10.1016/j.jmsy.2021.04.013 Kumar G, 2020, SUSTAIN CITIES SOC, V62, DOI 10.1016/j.scs.2020.102361 Laforet L, 2021, SUPPLY CHAIN FORUM, V22, P240, DOI 10.1080/16258312.2021.1953931 Lashkari B, 2021, IEEE ACCESS, V9, P43620, DOI 10.1109/ACCESS.2021.3065880 Latif RMA, 2021, CLUSTER COMPUT, V24, P1, DOI 10.1007/s10586-020-03165-4 Lee CH, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11114811 Li J, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11209744 Li XQ, 2020, FUTURE GENER COMP SY, V107, P841, DOI 10.1016/j.future.2017.08.020 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Lin WJ, 2020, IEEE ACCESS, V8, P143920, DOI 10.1109/ACCESS.2020.3014522 Liu L.J., 2020, INT J CIRCUITS SYST, V14, P526, DOI [10.46300/9106.2020.14.68, DOI 10.46300/9106.2020.14.68] Liu PD, 2021, TECHNOL ECON DEV ECO, V27, P656, DOI 10.3846/tede.2021.14421 Liu XL, 2021, COMPUT IND ENG, V161, DOI 10.1016/j.cie.2021.107669 Lou MH, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/8884478 Lu D., 2021, SAE INT J TRANSP CYB, V4, P61, DOI [10.4271/11-04-02-0004, DOI 10.4271/11-04-02-0004] Lumineau F, 2021, ORGAN SCI, V32, P500, DOI 10.1287/orsc.2020.1379 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Monteiro ES, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11178149 Mukhtar A, 2020, INT J ADV COMPUT SC, V11, P76 Musamih A, 2021, IEEE ACCESS, V9, P145397, DOI 10.1109/ACCESS.2021.3121545 Musamih A, 2021, IEEE ACCESS, V9, P9728, DOI 10.1109/ACCESS.2021.3049920 Negka LD, 2021, IEEE ACCESS, V9, P160277, DOI 10.1109/ACCESS.2021.3131419 Nguyen NBT, 2021, PROCESSES, V9, DOI 10.3390/pr9081274 Omar IA, 2020, IEEE ACCESS, V8, P182704, DOI 10.1109/ACCESS.2020.3028031 Orjuela KG, 2021, ACTA AGR SCAND B-S P, V71, P1, DOI 10.1080/09064710.2020.1840618 Oropallo E., 2021, INFORM MANAGE-AMSTER, DOI [10.1016/j.im.2021.103508, DOI 10.1016/J.IM.2021.103508] Panda S, 2021, P 8 INT C, P1 Papathanasiou A, 2020, EUR MANAG J, V38, P927, DOI 10.1016/j.emj.2020.04.007 Patel H., 2021, ICT EXPRESS, DOI [10.1016/J.ICTE.2021.07.003, DOI 10.1016/J.ICTE.2021.07.003] Patel N, 2021, T EMERG TELECOMMUN T, DOI 10.1002/ett.4286 Politou E, 2020, FUTURE GENER COMP SY, V112, P956, DOI 10.1016/j.future.2020.06.037 Politou E, 2021, IEEE T EMERG TOP COM, V9, P1972, DOI 10.1109/TETC.2019.2949510 Powell W., 2021, J IND INF INTEGR, DOI [10.1016/j.jii.2021.100261, DOI 10.1016/J.JII.2021.100261] Pranto TH, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.407 Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497 Reklitis P, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13168734 Sahoo S, 2021, INT J WEB INF SYST, V17, P449, DOI 10.1108/IJWIS-03-2021-0024 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Omar AS, 2020, CAN J ELECT COMPUT E, V43, P174, DOI 10.1109/CJECE.2020.2970737 Shahbazi Z, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10010041 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Suhail S, 2020, COMPUT IND, V123, DOI 10.1016/j.compind.2020.103334 Sund T, 2020, ROBOT CIM-INT MANUF, V65, DOI 10.1016/j.rcim.2020.101971 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Surasak T, 2019, INT J ADV COMPUT SC, V10, P578 Tharatipyakul A, 2021, IEEE ACCESS, V9, P82909, DOI 10.1109/ACCESS.2021.3085982 Tonnissen S, 2018, LECT NOTES BUS INF P, V320, P253, DOI 10.1007/978-3-319-93931-5_18 Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Uddin M, 2021, INT J PHARMACEUT, V597, DOI 10.1016/j.ijpharm.2021.120235 van Pelt R, 2021, INFORM SYST MANAGE, V38, P21, DOI 10.1080/10580530.2020.1720046 Wang H, 2020, EUR J OPER RES, V285, P393, DOI 10.1016/j.ejor.2020.01.057 Wang L, 2021, IEEE ACCESS, V9, P9296, DOI 10.1109/ACCESS.2021.3050112 Wang SP, 2019, IEEE ACCESS, V7, P115122, DOI 10.1109/ACCESS.2019.2935873 Wang ZJ, 2020, AUTOMAT CONSTR, V111, DOI 10.1016/j.autcon.2019.103063 Westerkamp M, 2020, DIGIT COMMUN NETW, V6, P167, DOI 10.1016/j.dcan.2019.01.007 Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 Yiu NCK, 2021, FUTURE INTERNET, V13, DOI 10.3390/fi13040084 Yong BB, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.10.009 Yu H, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13158331 Zachariadis M, 2019, INFORM ORGAN-UK, V29, P105, DOI 10.1016/j.infoandorg.2019.03.001 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhang YJ, 2021, J FOOD PROCESS ENG, V44, DOI 10.1111/jfpe.13669 Zheng ZB, 2020, FUTURE GENER COMP SY, V105, P475, DOI 10.1016/j.future.2019.12.019 Zhu P, 2020, IEEE ACCESS, V8, P184256, DOI 10.1109/ACCESS.2020.3029196 NR 119 TC 13 Z9 13 U1 55 U2 91 PD FEB PY 2022 VL 14 IS 4 AR 2439 DI 10.3390/su14042439 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000764333200001 HC Y HP N DA 2022-12-14 ER PT J AU Zhao, J Li, A Jin, XX Pan, LG AF Zhao, Jie Li, An Jin, Xinxin Pan, Ligang TI Technologies in individual animal identification and meat products traceability SO BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT DT Review DE Food safety; food quality; traceability technology; individual animal identification; meat products traceability ID ELEMENT SIGNATURE ANALYSIS; STABLE-ISOTOPE ANALYSIS; FOOD TRACEABILITY; SUPPLY CHAIN; GEOGRAPHICAL ORIGIN; SNP MARKERS; ELECTRONIC IDENTIFICATION; GENETIC TRACEABILITY; CATTLE BREEDS; DRIED BEEF AB An effective and trustworthy traceability system contributes to improving food quality and safety and responds to consumers' demand for food provenance information. Safe meat and its products are crucial to consumers and society. Livestock feeding regime and geographical origin are closely related to the properties and the safety of animal origin food, but the information is often invisible to consumers, which makes is easier to use fraudulent practices throughout the whole supply chain. Technologies and their proper use in traceability systems are important for the safety of animal origin foods. An essential component in an integrated traceability chain includes individual animal identification and trace back of related meat products. In this review, we examine the technologies for individual animal identification, including the radio frequency identification system and DNA fingerprinting. For meat products, traceability technologies focus on the chemical components fingerprinting, including measurement of stable isotope ratios, mineral element tracing and organic component fingerprinting. Also, future trends in food traceability systems need to be improved to promote the establishment of more efficient and trustworthy traceability systems to ensure food safety and quality up to standard. C1 [Zhao, Jie; Li, An; Jin, Xinxin; Pan, Ligang] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, Dept Agrifood Safety, Beijing, Peoples R China. [Zhao, Jie; Li, An; Jin, Xinxin; Pan, Ligang] Minist Agr, Dept Agrifood Safety, Risk Assessment Lab Agroprod Beijing, Beijing, Peoples R China. C3 Beijing Academy of Agriculture & Forestry; Ministry of Agriculture & Rural Affairs RP Pan, LG (corresponding author), Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, Beijing 100097, Peoples R China. EM panlg@brcast.org.cn CR Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Allen AR, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-5 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Barge P, 2013, CAN J ANIM SCI, V93, P23, DOI [10.4141/CJAS2012-029, 10.4141/cjas2012-029] Bernard A, 2002, ENVIRON RES, V88, P1, DOI 10.1006/enrs.2001.4274 Bong YS, 2012, FOOD SCI BIOTECHNOL, V21, P233, DOI 10.1007/s10068-012-0030-4 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 [程碧君 Cheng Bijun], 2012, [核农学报, Acta Agriculturae Nucleatae Sinica], V26, P517 Cheong HS, 2013, MEAT SCI, V94, P355, DOI 10.1016/j.meatsci.2013.03.014 CHESSA G, 2000, TRACE ELEMENTS THEIR, P479 Crandall PG, 2013, MEAT SCI, V95, P137, DOI 10.1016/j.meatsci.2013.04.022 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 De Montera B, 2004, CLONING STEM CELLS, V6, P133, DOI 10.1089/1536230041372382 Dimauro C, 2015, SMALL RUMINANT RES, V128, P27, DOI 10.1016/j.smallrumres.2015.05.001 Duroc Y, 2018, CR PHYS, V19, P64, DOI 10.1016/j.crhy.2018.01.003 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Fisher AV, 2000, MEAT SCI, V55, P141, DOI 10.1016/S0309-1740(99)00136-9 Franke BM, 2008, EUR FOOD RES TECHNOL, V227, P701, DOI 10.1007/s00217-007-0776-8 Franke BM, 2007, EUR FOOD RES TECHNOL, V225, P501, DOI 10.1007/s00217-006-0446-2 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Garin D, 2003, J ANIM SCI, V81, P879 Gioacchini AM, 2008, RAPID COMMUN MASS SP, V22, P3147, DOI 10.1002/rcm.3714 Goran GV, 2016, MEAT SCI, V118, P117, DOI 10.1016/j.meatsci.2016.03.031 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Iudith I, 2014, PROC ECON FINANC, V8, P414, DOI 10.1016/S2212-5671(14)00108-7 Johnson R, 2014, C RES SERVICE REPORT Cuevas FJ, 2017, FOOD CHEM, V221, P1930, DOI 10.1016/j.foodchem.2016.11.156 Kang RuiJuan, 2010, Transactions of the Chinese Society of Agricultural Engineering, V26, P227 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 LENSTRA JA, 2003, FOOD AUTHENTICITY TR Li C, 2013, QUAL SAF AGROPROD, V5, P53 Li J., 2014, THESIS Liu RD, 2014, FOOD CONTROL, V46, P291, DOI 10.1016/j.foodcont.2014.05.033 Liu R, 2013, FOOD CONTROL, V33, P1, DOI 10.1016/j.foodcont.2013.02.008 Lo YT, 2018, FOOD CHEM, V240, P767, DOI 10.1016/j.foodchem.2017.08.022 Loftus R, 2005, REV SCI TECH OIE, V24, P231, DOI 10.20506/rst.24.1.1563 Lu G. M., 2010, COMPUTER APPL SOFTWA, V27, P20 Lv J., 2015, QUAL SAF AGRO PROD, V3, P32, DOI [10.3969/j.issn.1674-8255.2015.03.011, DOI 10.3969/J.ISSN.1674-8255.2015.03.011] Ma N., 2016, MOD AGR SCI TECHNOL, V9, P296, DOI [10.3969/j.issn.1007-5739.2016.09.176, DOI 10.3969/J.ISSN.1007-5739.2016.09.176] [马奕颜 Ma Yiyan], 2013, [中国农业科学, Scientia Agricultura Sinica], V46, P3864 Manning L, 2016, CURR OPIN FOOD SCI, V10, P16, DOI 10.1016/j.cofs.2016.07.001 Mateus JC, 2015, FOOD CONTROL, V47, P487, DOI 10.1016/j.foodcont.2014.07.038 Mezes M., 2013, Animal Welfare, Ethology and Housing Systems, V9, P239 Mohammed A, 2017, INT J FOOD PROP, V20, P1145, DOI 10.1080/10942912.2016.1203933 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3295, DOI 10.1021/jf1040959 Pavon S, 2013, ICAR TECHNICAL, P1 Pendell DL, 2010, AM J AGR ECON, V92, P927, DOI 10.1093/ajae/aaq037 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Qian JP, 2017, FOOD CONTROL, V74, P98, DOI 10.1016/j.foodcont.2016.11.034 Rees G, 2016, FOOD CONTROL, V67, P144, DOI 10.1016/j.foodcont.2016.02.018 Ribo O, 2001, REV SCI TECH OIE, V20, P426 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Rodrigues CI, 2009, J FOOD COMPOS ANAL, V22, P463, DOI 10.1016/j.jfca.2008.06.010 Rogberg-Munoz A, 2014, MEAT SCI, V98, P822, DOI 10.1016/j.meatsci.2014.07.028 Shan X, 2005, LETT BIOTECHNOL, V4, P463 Shi L., 2010, COMPUTER APPL SOFTWA, V27, P40 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Soon JM, 2019, FOOD CONTROL, V101, P225, DOI 10.1016/j.foodcont.2019.03.002 Spink J, 2016, CHIMIA, V70, P320, DOI 10.2533/chimia.2016.320 Spink J, 2011, J FOOD SCI, V76, pR157, DOI 10.1111/j.1750-3841.2011.02417.x Sun FM, 2012, J NUCL AGR SCI, V26, P1148 Sun ShuMin, 2011, Scientia Agricultura Sinica, V44, P5050 Sun SM, 2011, FOOD CHEM, V124, P1151, DOI 10.1016/j.foodchem.2010.07.027 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Tedeschi P, 2011, J FOOD COMPOS ANAL, V24, P131, DOI 10.1016/j.jfca.2010.06.008 Todea A, 2010, B UASVM, V66, P463 Tomes J., 2009, Agricultura Tropica et Subtropica, V42, P98 TRAUTMAN D, 2008, 0802 U ALB DEP RUR E Van Wezemael L, 2010, FOOD CONTROL, V21, P835, DOI 10.1016/j.foodcont.2009.11.010 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Wales C, 2006, APPETITE, V47, P187, DOI 10.1016/j.appet.2006.05.007 Wang HY, 2010, BIOMATERIALS, V31, P413, DOI 10.1016/j.biomaterials.2009.09.066 [魏益民 Wei Yimin], 2012, [中国农业科学, Scientia Agricultura Sinica], V45, P5073 Wilson DW, 2001, REV SCI TECH OIE, V20, P379, DOI 10.20506/rst.20.2.1278 Wu QY, 2017, J CONSUM PROT FOOD S, V12, P125, DOI 10.1007/s00003-017-1092-2 [项洋 Xiang Yang], 2015, [食品工业科技, Science & Technology of Food Industry], V36, P371 Xinhua News Agency, 2011, CHINA DAILY Yordanov D., 2006, BIOTECHNOL BIOTEC EQ, V20, P3, DOI [10.1080/13102818.2006.10817295, DOI 10.1080/13102818.2006.10817295] [张晓焱 ZHANG Xiao-yan], 2010, [食品科学, Food Science], V31, P271 [张小波 Zhang Xiaobo], 2011, [中国农业科技导报, Journal of Agricultural Science and Technology], V13, P85 Zhao J, 2018, FOOD CONTROL, V87, P94, DOI 10.1016/j.foodcont.2017.11.039 Zhao J, 2017, FOOD CONTROL, V78, P469, DOI 10.1016/j.foodcont.2017.03.017 Zhao Y, 2016, MEAT SCI, V118, P103, DOI 10.1016/j.meatsci.2016.03.030 Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y [钟聪儿 Zhong Conger], 2016, [福建农林大学学报. 自然科学版, Journal of Fujian Agriculture and Forestry University. Natural Science Edition], V45, P471 NR 93 TC 17 Z9 17 U1 14 U2 56 PD JAN 1 PY 2020 VL 34 IS 1 BP 48 EP 57 DI 10.1080/13102818.2019.1711185 WC Biotechnology & Applied Microbiology SC Biotechnology & Applied Microbiology UT WOS:000508863300001 DA 2022-12-14 ER PT J AU Sun, CH Li, WY Zhou, C Li, M Ji, ZT Yang, XT AF Sun, Chuan-Heng Li, Wen-Yong Zhou, Chao Li, Ming Ji, Zeng-Tao Yang, Xin-Ting TI Anti-counterfeit code for aquatic product identification for traceability and supervision in China SO FOOD CONTROL DT Article DE Aquatic products; Anti-counterfeit code; Advanced encryption standard; Traceability and supervision; Identification ID FOOD SAFETY; IMPLEMENTING TRACEABILITY; SYSTEM; FRAMEWORK; GRANULARITY AB There are not or weak anti-counterfeit functions in the current traceability system. As a result, the counterfeiters could imitate this system easily. This phenomenon had a large impact on the traceability system construction and on consumer trust in the traceability information. The aim of our research was to construct an anti-counterfeit code for aquatic product identification, for traceability and supervision of aquatic enterprises in the domestic market. The aquatic products batch code (APBC) was in the form of a segmented combination encoding an enterprise identification code, a product code and a check code, which implements a combination of traceability and supervision. An encryption algorithm based on the Advanced Encryption Standard (AES) was designed for decimal anti-counterfeit code based on the unique identification of the aquatic trace units. Simulation tests indicated that a diffusion rate of greater than 90% was achieved when the encryption was run four or more times, thereby leading to the implementation of an anti-counterfeiting technique for aquatic traceability, known as "one time, one code". The anti-counterfeit code combined with GS1 was used in a product label, and the method has a high level of security and is used for supervision and tracing of aquatic products in China. (C) 2013 Published by Elsevier Ltd. C1 [Sun, Chuan-Heng; Li, Wen-Yong; Zhou, Chao; Li, Ming; Ji, Zeng-Tao; Yang, Xin-Ting] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. [Sun, Chuan-Heng; Li, Wen-Yong; Zhou, Chao; Li, Ming; Ji, Zeng-Tao; Yang, Xin-Ting] Minist Agr, Key Lab Informat Technol Agr, Beijing 10097, Peoples R China. C3 Beijing Academy of Agriculture & Forestry; Ministry of Agriculture & Rural Affairs RP Yang, XT (corresponding author), Room 307,Bldg A,11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China. EM sunch@nercita.org.cn; liwy@nercita.org.cn; zhouc@nercita.org.cn; lim@nercita.org.cn; jizt@nercita.org.cn; xintingyang@nercita.org.cn CR Ahmed M, 2002, FOOD POLICY, V27, P125, DOI 10.1016/S0306-9192(02)00007-6 Bai L, 2007, FOOD CONTROL, V18, P480, DOI 10.1016/j.foodcont.2005.12.005 Broughton EI, 2010, FOOD POLICY, V35, P471, DOI 10.1016/j.foodpol.2010.05.007 Chen H., 2008, CHINA STANDARDS REV, P12 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Fan HP, 2009, FOOD CONTROL, V20, P627, DOI 10.1016/j.foodcont.2008.09.013 FAO, 2007, WORLD AQ PROD CULT E Furia E, 2011, J AGR FOOD CHEM, V59, P8450, DOI 10.1021/jf201556e GS1, 2012, GS1 ARCH PRINC GS1, 2007, GS1 TRACEABILITY STA Heron Simon, 2009, Network Security, V2009, P8, DOI 10.1016/S1353-4858(10)70006-4 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Jia CH, 2013, FOOD CONTROL, V32, P236, DOI 10.1016/j.foodcont.2012.11.042 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Karlsen KM, 2010, BRIT FOOD J, V112, P187, DOI 10.1108/00070701011018860 Li YH, 2010, AGRIC AGRIC SCI PROC, V1, P288, DOI 10.1016/j.aaspro.2010.09.036 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Patidar V, 2010, COMMUN NONLINEAR SCI, V15, P2755, DOI 10.1016/j.cnsns.2009.11.010 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Shen GuangRong, 2005, Journal of Shanghai Jiaotong University - Agricultural Science, V23, P77 Storoy J, 2008, WOODHEAD PUBL FOOD S, P516, DOI 10.1533/9781845694586.5.516 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Sun CH, 2013, COMPUT ELECTRON AGR, V92, P82, DOI 10.1016/j.compag.2012.12.014 Teng L, 2012, OPT COMMUN, V285, P4048, DOI 10.1016/j.optcom.2012.06.004 Tracefood, 2012, TRAC WIK Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X Yang X.T., 2009, T CHINESE SOC AGR EN, V25, P131 Yang XinTing, 2008, Transactions of the Chinese Society of Agricultural Engineering, V24, P162 Ye RS, 2011, OPT COMMUN, V284, P5290, DOI 10.1016/j.optcom.2011.07.070 Zhang J, 2011, FOOD CONTROL, V22, P1126, DOI 10.1016/j.foodcont.2011.01.019 Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 Zhou S., 2007, STUD BUILD TRAC METH NR 36 TC 17 Z9 18 U1 4 U2 80 PD MAR PY 2014 VL 37 BP 126 EP 134 DI 10.1016/j.foodcont.2013.08.013 WC Food Science & Technology SC Food Science & Technology UT WOS:000328518200021 DA 2022-12-14 ER PT J AU Yao, Q Zhang, HJ AF Yao, Qi Zhang, Huajun TI Improving Agricultural Product Traceability Using Blockchain SO SENSORS DT Article DE blockchain; traceability; agricultural products; Ethereum; IPFS; smart contract ID FOOD-SUPPLY CHAINS; SMART CONTRACT; IOT SYSTEMS; QR CODE; CHALLENGES; MANAGEMENT; IMPLEMENTATION; TECHNOLOGY; ISSUES AB Most traditional agricultural traceability systems are centralized, which could result in the low reliability of traceability results, enterprise privacy data leakage vulnerabilities, and the generation of information islands. To solve the above problems, we propose a trusted agricultural product traceability system based on the Ethereum blockchain in this paper. We designed a dual storage model of "Blockchain+IPFS (InterPlanetary File System)" to reduce the storage pressure of the blockchain and realize efficient information queries. Additionally, we propose a data privacy protection solution based on some cryptographic primitives and the Merkle Tree that can avoid enterprise privacy and sensitive data leakage. Furthermore, we implemented the proposed system using the Ethereum blockchain platform and provided the cost, performance, and security analysis, as well as compared it with the existing solutions. The results showed that the proposed system is both efficient and feasible and can meet the practical application requirements. C1 [Yao, Qi; Zhang, Huajun] Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou 213164, Jiangsu, Peoples R China. C3 Changzhou University RP Zhang, HJ (corresponding author), Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou 213164, Jiangsu, Peoples R China. EM 17315383917@163.com; zhang.huajun@cczu.edu.cn CR Ada N, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126812 Ahn B, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14052917 Alammary A, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9122400 Albert Elvira, 2020, Tools and Algorithms for the Construction and Analysis of Systems. 26th International Conference, TACAS 2020. Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12079), P118, DOI 10.1007/978-3-030-45237-7_7 Amjad S, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22051972 Antonopoulos A. M., 2018, MASTERING ETHEREUM B Asif M, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22072604 Baralla G, 2019, 2019 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON EMERGING TRENDS IN SOFTWARE ENGINEERING FOR BLOCKCHAIN (WETSEB 2019), P40, DOI 10.1109/WETSEB.2019.00012 Chen CL, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22031146 Chen Fei, 2021, Computer Engineering and Applications, V57, P60, DOI 10.3778/j.issn.1002-8331.2007-0324 Gonzalez CD, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12020531 Dey S, 2022, SMART CITIES-BASEL, V5, P162, DOI 10.3390/smartcities5010011 Dey S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063486 Ethgasstation.info, ETH GAS STAT Francois G, 2020, PHYTOCHEMISTRY, V173, DOI 10.1016/j.phytochem.2020.112291 Friedman N, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121403 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Han D, 2020, ENERGY, V199, DOI 10.1016/j.energy.2020.117417 Ho GTS, 2021, EXPERT SYST APPL, V179, DOI 10.1016/j.eswa.2021.115101 Huang J, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22051829 Jagtap S, 2021, LOGISTICS-BASEL, V5, DOI 10.3390/logistics5010002 Kirwan J, 2017, J RURAL STUD, V52, P21, DOI 10.1016/j.jrurstud.2017.03.008 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Kumarathunga M, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14052916 Kuo TT, 2017, J AM MED INFORM ASSN, V24, P1211, DOI 10.1093/jamia/ocx068 Kwak S, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22062410 Li MingJia, 2019, Shipin Kexue / Food Science, V40, P279 Li XF, 2020, IEEE ACCESS, V8, P69754, DOI 10.1109/ACCESS.2020.2986220 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu Y., 2018, COMPUTER SCI, V45, P367 Lu Y, 2018, J MANAG ANAL, V5, P231, DOI 10.1080/23270012.2018.1516523 Muralikumar MD, 2018, PROCEEDINGS OF THE 2018 WORKSHOP ON COMPUTING WITHIN LIMITS (LIMITS '18), DOI 10.1145/3232617.3232620 Nakamoto S., 2008, DECENTRALIZED BUS RE, P21260 Nizamuddin N, 2019, COMPUT ELECTR ENG, V76, P183, DOI 10.1016/j.compeleceng.2019.03.014 Nofer M, 2017, BUS INFORM SYST ENG+, V59, P183, DOI 10.1007/s12599-017-0467-3 Omar IA, 2022, IEEE ACCESS, V10, P2345, DOI 10.1109/ACCESS.2021.3139829 Omar IA, 2021, IEEE ACCESS, V9, P37397, DOI 10.1109/ACCESS.2021.3062471 [欧阳丽炜 Ouyang Liwei], 2019, [自动化学报, Acta Automatica Sinica], V45, P445 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Pelekoudas-Oikonomou F, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22072449 Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497 Qi SY, 2021, IEEE T IND INFORM, V17, P2376, DOI 10.1109/TII.2020.2998166 Szabo N., 1997, First Monday, V2 Tian F, 2017, I C SERV SYST SERV M Tsaur WJ, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22020530 Ul Hassan M, 2019, FUTURE GENER COMP SY, V97, P512, DOI 10.1016/j.future.2019.02.060 Underwood S, 2016, COMMUN ACM, V59, P15, DOI 10.1145/2994581 Wang QY, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app112411693 Yang L, 2019, J IND INF INTEGR, V15, P80, DOI 10.1016/j.jii.2019.04.002 [于合龙 Yu Helong], 2020, [农业机械学报, Transactions of the Chinese Society for Agricultural Machinery], V51, P328 Zarir AA, 2021, ACM T SOFTW ENG METH, V30, DOI 10.1145/3431726 Zhang HJ, 2019, PEER PEER NETW APPL, V12, P1346, DOI 10.1007/s12083-018-0694-5 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 53 TC 4 Z9 4 U1 33 U2 39 PD MAY PY 2022 VL 22 IS 9 AR 3388 DI 10.3390/s22093388 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000795350900001 DA 2022-12-14 ER PT J AU Rodriguez-Ramirez, R Arana, A Alfonso, L Gonzalez-Cordova, AF Torrescano, G Legarreta, IG Vallejo-Cordoba, B AF Rodriguez-Ramirez, R. Arana, A. Alfonso, L. Gonzalez-Cordova, A. F. Torrescano, G. Guerrero Legarreta, I. Vallejo-Cordoba, B. TI Molecular traceability of beef from synthetic Mexican bovine breeds SO GENETICS AND MOLECULAR RESEARCH DT Article DE Meat traceability; Microsatellite markers; Synthetic breeds ID MARKERS; MICROSATELLITES; ORIGIN AB Traceability ensures a link between carcass, quarters or cuts of beef and the individual animal or the group of animals from which they are derived. Meat traceability is an essential tool for successful identification and recall of contaminated products from the market during a food crisis. Meat traceability is also extremely important for protection and value enhancement of good-quality brands. Molecular meat traceability would allow verification of conventional methods used for beef tracing in synthetic Mexican bovine breeds. We evaluated a set of 11 microsatellites for their ability to identify animals belonging to these synthetic breeds, Brangus and Charolais/Brahman (78 animals). Seven microsatellite markers allowed sample discrimination with a match probability, defined as the probability of finding two individuals sharing by chance the same genotypic profile, of 10(-8). The practical application of the marker set was evaluated by testing eight samples from carcasses and pieces of meat at the slaughterhouse and at the point of sale. The DNA profiles of the two samples obtained at these two different points in the production-commercialization chain always proved that they came from the same animal. C1 [Rodriguez-Ramirez, R.; Gonzalez-Cordova, A. F.; Vallejo-Cordoba, B.] Ctr Invest Alimentac & Desarrollo AC, Lab Calidad Autenticidad & Trazabilidad Alimentos, Hermosillo, Sonora, Mexico. [Arana, A.; Alfonso, L.] Univ Publ Navarra, Dept Agr Prod, Pamplona, Spain. [Torrescano, G.] Ctr Invest Alimentac & Desarrollo AC, Lab Ciencia Carne, Hermosillo, Sonora, Mexico. [Guerrero Legarreta, I.] Univ Autonoma Metropolitana, Unidad Iztapalapa, Dept Biotecnol, Vicentina, Mexico. C3 CIAD - Centro de Investigacion en Alimentacion y Desarrollo; Universidad Publica de Navarra; CIAD - Centro de Investigacion en Alimentacion y Desarrollo; Universidad Autonoma Metropolitana - Mexico RP Vallejo-Cordoba, B (corresponding author), Ctr Invest Alimentac & Desarrollo AC, Lab Calidad Autenticidad & Trazabilidad Alimentos, Hermosillo, Sonora, Mexico. EM vallejo@ciad.mx CR Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Bicalho HMS, 2006, GENET MOL RES, V5, P432 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Dalvit C, 2008, MEAT SCI, V80, P389, DOI 10.1016/j.meatsci.2008.01.001 Dalvit C, 2008, J ANIM BREED GENET, V125, P137, DOI 10.1111/j.1439-0388.2007.00707.x Gutierrez JP, 2005, J HERED, V96, P718, DOI 10.1093/jhered/esi118 Mendez RD, 2009, J ANIM SCI, V87, P3782, DOI 10.2527/jas.2009-1889 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Rodriguez-Ramirez R, 2011, ANAL CHIM ACTA, V685, P120, DOI 10.1016/j.aca.2010.11.021 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Vetharaniam I, 2009, J FOOD PROTECT, V72, P1948, DOI 10.4315/0362-028X-72.9.1948 Weir BS, 1996, SINAUER ASS SUNDERL NR 13 TC 8 Z9 11 U1 0 U2 12 PY 2011 VL 10 IS 4 BP 2358 EP 2365 DI 10.4238/2011.October.6.1 WC Biochemistry & Molecular Biology; Genetics & Heredity SC Biochemistry & Molecular Biology; Genetics & Heredity UT WOS:000300617600008 DA 2022-12-14 ER PT J AU Xu, LL Shan, LJ Wu, LH AF Xu, Lingling Shan, Lijie Wu, Linhai TI Farmers' adoption willingness of food traceability system: An empirical analysis of the Chinese apple industry SO JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT DT Article DE Farmers; Chinese apple industry; traceability; penalized likelihood estimation AB Using 263 apple farmer households in Fengxian County in southeast China as an example, this paper conducts an empirical study on farmers' adoption willingness of food traceability system and its main determinants on the base of penalized likelihood estimation. As the result shows, 38% of the investigated farmers are willing to adopt the food traceability system and the adoption is strongly influenced by determinants including the apple farmers' age and degree, household income contributed by apple planting, operated apple farmland size, information records keeping, awareness, expected price and preferential policy. Based on this conclusion, this paper gives advises on how to stimulate the adoption of food traceability system so as to promote the construction of China's food traceability system and provide a useful reference for other developing countries. C1 [Xu, Lingling; Shan, Lijie] Jiangnan Univ, Sch Business, Food Safety Res Base Jiangsu Prov, Wuxi, Jiangsu, Peoples R China. C3 Jiangnan University RP Xu, LL (corresponding author), Jiangnan Univ, Sch Business, Food Safety Res Base Jiangsu Prov, Wuxi, Jiangsu, Peoples R China. EM hualongxifeng6130@sina.com; wlh6799@vip.163.com CR Banterle A., 2006, 99 EUR SEM EAAE TRUS, DOI [10.22004/ag.econ.7722, DOI 10.22004/AG.ECON.7722] BARTLETT MS, 1953, BIOMETRIKA, V40, P306 FIRTH D, 1993, BIOMETRIKA, V80, P27, DOI 10.2307/2336755 Golan E.H., 2004, AGR EC REPORTS, P1362 Heyder M., 2009, 3 INT EUR FOR SYST D Hobbs J. E., 1996, British Food Journal, V98, P16, DOI 10.1108/00070709610131339 Hobbs J. E., 2003, 031 IATRC Kotsiri S., 2011, SO AGR EC ASS ANN M Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Mora C., 2005, Agribusiness (New York), V21, P213, DOI 10.1002/agr.20044 Schulz LL, 2010, J AGR ECON, V61, P138, DOI 10.1111/j.1477-9552.2009.00226.x Sodano V., 2003, 52003 U STUD NAP FED Wang F, 2009, J FOOD AGRIC ENVIRON, V7, P64 NR 15 TC 2 Z9 2 U1 3 U2 23 PY 2012 VL 10 IS 3-4 BP 1581 EP 1584 WC Food Science & Technology SC Food Science & Technology UT WOS:000312100100001 DA 2022-12-14 ER PT J AU Wang, MC Yang, CY AF Wang, Mao-Chang Yang, Chin-Ying TI Analyzing organic tea certification and traceability system within the Taiwanese tea industry SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE organic tea certification; traceability system; tea industry; managerial accounting; game theory ID BREW AB BACKGROUNDWe applied game theory to the organic tea certification process and traceability system used by the Taiwanese tea industry to elucidate the strategic choices made by tea farmers and organic tea certification agencies. Thus, this paper clarifies how relevant variables affect the organic certification process and traceability system used within the tea industry. RESULTSThe findings indicate that farmers who generate high revenues experience failures regarding tea deliveries, cash outflow, damage compensation, and quasi-rent. An additional problem included the high costs yielded when tea farmers colluded with or switched organic tea certification agencies. Furthermore, there could be decreasing levels of personal interest in planting non-organic tea and lowering the costs of planting organic tea and the managerial accounting costs of building comprehensive traceability systems; thus, the analysis yielded strong results and a superior equilibrium. CONCLUSIONThis research is unprecedented, using an innovative model and providing a novel analysis structure for use in the tea industry. These results contribute to the field of literature and should serve as a valuable reference for members of the tea industry, government, and academia. (c) 2014 Society of Chemical Industry C1 [Wang, Mao-Chang] Chinese Culture Univ, Dept Accounting, Taipei 11114, Taiwan. [Yang, Chin-Ying] Natl Chung Hsing Univ, Dept Agron, Taichung 40227, Taiwan. C3 Chinese Culture University; National Chung Hsing University RP Wang, MC (corresponding author), Chinese Culture Univ, Dept Accounting, 55 Hwa Kang Rd, Taipei 11114, Taiwan. EM wmaochang@yahoo.com.tw CR Acs S, 2009, AUST J AGR RESOUR EC, V53, P393, DOI 10.1111/j.1467-8489.2009.00458.x Chang CH, 2012, THESIS NAT TAIW U DE Chen YC, 2012, PUBLIC ADM POLICY, V55, P67 Fudenberg D., 1991, GAME THEORY Gibbons Robert, 1992, PRIMER GAME THEORY Hilton WR, 2011, MANAGERIAL ACCOUNTIN Jaggi S, 2001, J AGR FOOD CHEM, V49, P5479, DOI 10.1021/jf010436d Jason SP, 2012, AGR HUM VALUES, V29, P303 Jhuang YH, 2009, TEA NEWS FLASH, V69, P1 Ku HC, 2011, THESIS TAMK U DEP HI Kumar V, 2004, FOOD CHEM TOXICOL, V42, P423, DOI 10.1016/j.fct.2003.10.004 Lin TH, 2009, THESIS NAT DONG HWA Shen PH, 2012, THESIS TUNGN U DEP I Su TC, 2008, AGR POLICY AGR SITUA, V192, P47 Su TC, 2009, AGR POLICY AGR SITUA, V201, P68 Tirole J, 2001, ECONOMETRICA, V69, P1, DOI 10.1111/1468-0262.00177 Wang MC, 2013, INT J HUM RESOUR MAN, V24, P3020, DOI 10.1080/09585192.2013.767063 NR 17 TC 2 Z9 2 U1 1 U2 40 PD APR PY 2015 VL 95 IS 6 BP 1252 EP 1259 DI 10.1002/jsfa.6814 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000351394200014 DA 2022-12-14 ER PT J AU Hirai, Y Schrock, MD Oard, DL Herrman, TJ AF Hirai, Y. Schrock, M. D. Oard, D. L. Herrman, T. J. TI Delivery system of tracing caplets for wheat grain traceability SO APPLIED ENGINEERING IN AGRICULTURE DT Article DE traceability; identity preservation; tracing caplet; delivery system; combine; wheat grain AB The system for delivering tracing caplets into grain on a combine was developed as a part of a grain traceability system. A commercially available seed dispenser was utilized to meter tracing caplets into wheat in the combine bin tank during the unloading process. The caplets were delivered into the grain stream close to the unloading auger to attain uniform distribution. The distribution uniformity of caplets was sampled on five consecutive bin loads, with six 1-L grain samples from each load. The number of caplets in the samples was reasonably consistent at unloading time of 20 and 30 s when the grain unloading rate was stable. However, caplet concentration increased as grain flow was reduced at the beginning and end of each unloading event and caplets that remained in the grain tank sump after each test was discharged in high concentrations at the beginning of the subsequent unloading process. C1 Kyushu Univ, Lab Bioprod & Environm Informat Sci, Higashi Ku, Fukuoka 8128581, Japan. Kansas State Univ, Dept Biol & Agr Engn, Manhattan, KS 66506 USA. Texas A&M Univ, Off Texas State Chemist, College Stn, TX USA. C3 Kyushu University; Kansas State University; Texas A&M University System; Texas A&M University College Station RP Hirai, Y (corresponding author), Kyushu Univ, Lab Bioprod & Environm Informat Sci, Higashi Ku, 6-10-1 Hakozaki, Fukuoka 8128581, Japan. EM hirai@bpes.kyushu-u.ac.jp CR CLEMENS R, 2003, MEAT TRACEABILITY JA Golan E.H., 2004, AGR EC REPORTS, P1362 Herrman T., 2002, WHITE PAPER TRACEABI Kondo N., 2005, 2005 ASABE ANN INT M *PAMI, 1990, 631 PAMI Qiao J, 2005, BIOSYST ENG, V90, P135, DOI 10.1016/j.biosystemseng.2004.10.002 NR 6 TC 9 Z9 9 U1 0 U2 0 PD SEP PY 2006 VL 22 IS 5 BP 747 EP 750 WC Agricultural Engineering SC Agriculture UT WOS:000241313300016 DA 2022-12-14 ER PT J AU Bosona, T Gebresenbet, G Olsson, SO AF Bosona, Techane Gebresenbet, Girma Olsson, Sven-Olof TI Traceability System for Improved Utilization of Solid Biofuel from Agricultural Prunings SO SUSTAINABILITY DT Article DE pruning biomass; solid biofuel; traceability system; smart logistics system; information flow ID OF-THE-ART; FOOD; MANAGEMENT; ENERGY; BIOMASS AB Biomass production and supply for renewable energy generation should be managed well and carried out in a sustainable manner. An effective traceability system (TS) is required to provide sufficient information and assure the quality of the biomass. The objective of this study is to define and develop a TS to assure the pruning biomass quality for the production of solid biofuels and to provide guarantee to the final user that the biomass is in good condition according to recommended quality criteria. It is designed for an agricultural pruning supply chain in which farmers, biomass traders, transporters, and end users are major actors. It is based on the biofuel quality requirements required by final users and other standards such as the new European standards EN 14961-1, EN15234:1-2011, and EN14961-1:2010, which describe solid fuel quality parameters. Traceable quality parameters include origin and source of product, traded form, bale dimension, chips size distribution, moisture content, ash content, and density of biomass. In this TS, a unique product label is introduced and integrated into a smart logistics system (SLS). The TS uses information captured at different stages of the product supply chain. It enables the management of the whole pruning biomass supply chain with the support of a centralized web-based information platform, an integral part of the SLS. C1 [Bosona, Techane; Gebresenbet, Girma] Swedish Univ Agr Sci, Dept Energy & Technol, POB 75671, Uppsala, Sweden. [Olsson, Sven-Olof] Mobitron AB, POB 56146, Huskvarna, Sweden. C3 Swedish University of Agricultural Sciences RP Bosona, T (corresponding author), Swedish Univ Agr Sci, Dept Energy & Technol, POB 75671, Uppsala, Sweden. EM techane.bosona@slu.se; girma.gebresenbet@slu.se; soo@mobitron.se CR [Anonymous], 1523412011 EN [Anonymous], 1496112010 EN [Anonymous], 2016, EUROPRUNING PROJ REP Azuara G, 2012, IND MANAGE DATA SYST, V112, P340, DOI 10.1108/02635571211210022 Bioenergy 2020, 2013, PROP PROD LAB SMALL Biomasud, 2012, HDB QUAL LAB DOM SOL Blennow K, 2014, BIOMASS BIOENERG, V67, P223, DOI 10.1016/j.biombioe.2014.05.002 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Caja G, 2014, SMALL RUMINANT RES, V121, P42, DOI 10.1016/j.smallrumres.2014.05.012 Cavicchi B, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9030406 CIRCE and SLU, 2013, PROJECT REPORT Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 DENSO ADC, 2011, QR COD ESS EN, 149611 EN EUROSTAT, REN EN STAT Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Gebresenbet G., 2018, SMART SYSTEM O UNPUB Iakovou E, 2010, WASTE MANAGE, V30, P1860, DOI 10.1016/j.wasman.2010.02.030 Intelligent Energy Europe, 2011, EIE11218 INT EN EUR ISCC, 2011, 201 ISCC SYST Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Ruiz-Garcia L, 2009, SENSORS-BASEL, V9, P4728, DOI 10.3390/s90604728 Spugnoli P, 2012, BIOSYST ENG, V112, P49, DOI 10.1016/j.biosystemseng.2012.02.004 Sustainability Criteria and Certification Systems for Biomass Production. Biomass Technology Group (BTG), 2008, 1386 BTG Torquati B, 2016, BIOMASS BIOENERG, V95, P124, DOI 10.1016/j.biombioe.2016.09.017 van Dam J, 2008, BIOMASS BIOENERG, V32, P749, DOI 10.1016/j.biombioe.2008.01.018 NR 26 TC 6 Z9 6 U1 0 U2 12 PD FEB PY 2018 VL 10 IS 2 AR 258 DI 10.3390/su10020258 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000425943100007 DA 2022-12-14 ER PT J AU Bezerra, AC Pandorfi, H Gama, RM De Carvalho, FFR Guiselini, C AF Bezerra, Alan C. Pandorfi, Heliton Gama, Rafael M. De Carvalho, Francisco F. R. Guiselini, Cristiane TI DEVELOPMENT OF A TRACEABILITY MODEL APPLIED TO GOAT AND SHEEP MEAT PRODUCTION SO ENGENHARIA AGRICOLA DT Article DE goat and sheep meat production; traceability; food safety; management system ID SUPPLY CHAIN; SYSTEM AB The goat and sheep meat producer chain has developed in last years, thus, it is imperative to organize and structure the supply chain and to adopt reliable policies for products' traceability, as a tool to achieve these requirements. The study aimed to make a management program with a traceability model for goat and sheep meat production, with emphasis on ensuring product origin and management practices' transparency at the animal production unit. For this purpose, it was made a reference model in order to emit an origin certificate which, in turn, provides specific information concerning the final product from each unit. Secondly, the program was developed using Hipertext Preprocessor (PHP) technology and as for the Database Management System, it was used MySQL. The schematic model proposed meets the requirements of a traceability system for goat and sheep meat. Furthermore, the program can work as a tool for farm management, by reports and real-time remote access to information. C1 [Bezerra, Alan C.] Univ Fed Rural Pernambuco, Unidade Acad Serra Talhada, Serra Talhada, PE, Brazil. [Pandorfi, Heliton; Gama, Rafael M.; De Carvalho, Francisco F. R.; Guiselini, Cristiane] Univ Fed Rural Pernambuco, Recife, PE, Brazil. C3 Universidade Federal Rural de Pernambuco (UFRPE); Universidade Federal Rural de Pernambuco (UFRPE) RP Bezerra, AC (corresponding author), Univ Fed Rural Pernambuco, Unidade Acad Serra Talhada, Serra Talhada, PE, Brazil. EM cezaralan.a@gmail.com CR BARRETO NETO A. D., 2010, TECNOLOGIA CIENCIA A, V4, P81 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Sobrinho OG, 2010, ENG AGRIC, V30, P100, DOI 10.1590/S0100-69162010000100011 Holanda Junior EV, 2013, REV CIENTIFICA PRODU, V15, P77 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Nicoloso C, 2013, REV AGRONEGOCIOS MEI, V6, P79 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Pizzani L, 2012, REV DIGITAL BIBLIOTE, V10, P53, DOI DOI 10.20396/RDBCI.V10I1.1896 Porto LFA, 2009, CIENC AGROTEC, V31, P1310 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 SANDOVAL Jr P., 2011, MANUAL CRIACAO CAPRI Santos TC, 2011, CIENCIA ANIMAL BRASI, V12, P206, DOI DOI 10.5216/CAB.V12I2.4420 Teixeira Izabelle Auxiliadora Molina, 2013, Rev. bras. saúde prod. anim., V14, P104 Thakur M, 2011, COMPUT ELECTRON AGR, V75, P327, DOI 10.1016/j.compag.2010.12.010 Zanette P. M., 2012, AMBIENCIA, V8, P415, DOI [10.5777/ambiencia.2012.02.03rb, DOI 10.5777/AMBIENCIA.2012.02.03RB] NR 15 TC 1 Z9 1 U1 1 U2 20 PD SEP-OCT PY 2017 VL 37 IS 5 BP 1062 EP 1072 DI 10.1590/1809-4430-eng.agric.v37n5p1062-1072/2017 WC Agricultural Engineering SC Agriculture UT WOS:000411815600021 DA 2022-12-14 ER PT J AU Cui, Y Feng, HH Ablikim, B Gu, Z Zhang, XS Li, J AF Cui Yan Feng Huanhuan Ablikim, Batur Gu Zheng Zhang Xiaoshuan Li Jun TI Traceability information modeling and system implementation in Chinese domestic sheep meat supply chains SO JOURNAL OF FOOD PROCESS ENGINEERING DT Article ID WILLINGNESS-TO-PAY; GENERAL FRAMEWORK; SAFETY ATTRIBUTES; FOOD TRACEABILITY; PORK; QUALITY; DESIGN; PRINCIPLES; MANAGEMENT; HACCP AB Traceability has become a prerequisite for ensuring meat quality and safety. It is the most critical requirement for developing a well-structured traceability system to identify efficiently and model the traceability information need to be captured. This article identified Chinese domestic sheep meat supply chain and proposed the Petri nets and UML based modeling approach for identified traceability information and process. This approach followed the definition of state and transition in sheep meat production process. An in-depth analysis was conducted for the sheep stakeholders and production flow of the sheep meat chain. The state-transition for the production process and specific traceability information need to be captured were identified including the product, process, quality, and transformation information. Furthermore, traceability functionality requirement and system architecture were presented. System evaluation results illustrate the model can help to provide guidance for standardizing meat product flow and supporting quality traceability management in meat industry. The system can provide making-decision for the meat product quality and safety control. Practical applications The proposed method could be applied widely in development of traceability system and provided making-decision for the control of meat product quality and safety. C1 [Cui Yan; Feng Huanhuan; Gu Zheng; Zhang Xiaoshuan; Li Jun] China Agr Univ, Beijing 100083, Peoples R China. [Cui Yan; Feng Huanhuan; Gu Zheng; Zhang Xiaoshuan; Li Jun] China Agr Univ, Beijing Lab Food Qual & Safety, Beijing, Peoples R China. [Ablikim, Batur] Xinjiang Agr Univ, Xinjiang, Peoples R China. C3 China Agricultural University; China Agricultural University; Xinjiang Agricultural University RP Zhang, XS; Li, J (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn; sirlijun@126.com CR Allata S, 2017, FOOD CONTROL, V79, P239, DOI 10.1016/j.foodcont.2017.04.002 Aris A. I. M., 2014, J FOOD PROCESSING, V7, P1 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Brace W, 2012, J ENG DESIGN, V23, P873, DOI 10.1080/09544828.2011.636735 Chen RY, 2015, FOOD CONTROL, V51, P70, DOI 10.1016/j.foodcont.2014.11.004 Coskun E, 2005, J SYST SOFTWARE, V78, P128, DOI 10.1016/j.jss.2005.01.009 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Endo V, 2015, CIENC RURAL, V45, P304, DOI 10.1590/0103-8478cr20131679 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Food Production and Management, 2009, CHIN FOOD SAF LAW Hsu SY, 2016, BRIT FOOD J, V118, P200, DOI 10.1108/BFJ-11-2014-0376 Huang JW, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16030382 Kaur K, 2016, IEEE T POWER SYST, V31, P4638, DOI 10.1109/TPWRS.2016.2518743 Lam Jasmine Siu Lee, 2012, Computational Logistics. Proceedings of the Third International Conference, ICCL 2012, P72, DOI 10.1007/978-3-642-33587-7_5 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Liang WJ, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0139558 Lucke FK, 2014, FOOD CONTROL, V43, P217, DOI 10.1016/j.foodcont.2014.03.014 Marchi M. D., 2013, MEAT SCI, V93, P329 Mishra N, 2014, INT J INTELL ENG INF, V2, P195, DOI 10.1504/IJIEI.2014.066213 Mol APJ, 2014, FOOD CONTROL, V43, P49, DOI 10.1016/j.foodcont.2014.02.034 Morkbak MR, 2011, FOOD CONTROL, V22, P445, DOI 10.1016/j.foodcont.2010.09.024 MURATA T, 1989, P IEEE, V77, P541, DOI 10.1109/5.24143 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 PETERSON JL, 1982, PETRI NET THEORY MOD Pizzuti T, 2017, FOOD CONTROL, V72, P123, DOI 10.1016/j.foodcont.2016.07.038 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Soman R, 2016, FOOD CONTROL, V69, P191, DOI 10.1016/j.foodcont.2016.05.001 Song M, 2008, J FAC AGR KYUSHU U, V53, P569 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Tao FF, 2014, J FOOD ENG, V126, P98, DOI 10.1016/j.jfoodeng.2013.11.006 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 UDEN L, 1995, COMPUT INTEGR MANUF, V8, P83, DOI 10.1016/0951-5240(95)00002-B Vol N., 2004, AMBER WAVES, V174, P59 Wang DongTing, 2013, Transactions of the Chinese Society of Agricultural Engineering, V29, P228 Winn K. J., 2015, MEAT SCI, V101, P142 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Xiao XQ, 2017, J FOOD PROCESS ENG, V40, DOI 10.1111/jfpe.12303 Xu DX, 2015, IEEE T COMPUT, V64, P2490, DOI 10.1109/TC.2014.2375189 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhang XS, 2010, FOOD CONTROL, V21, P1556, DOI 10.1016/j.foodcont.2010.03.020 Zhang YJ, 2017, J FOOD PROCESS ENG, V40, DOI 10.1111/jfpe.12495 Zhao N, 2018, CLUSTER COMPUT, P1 NR 47 TC 8 Z9 8 U1 2 U2 62 PD NOV PY 2018 VL 41 IS 7 AR e12864 DI 10.1111/jfpe.12864 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000449818300024 DA 2022-12-14 ER PT J AU Chryssochoidis, G Karagiannaki, A Pramatari, K Kehagia, O AF Chryssochoidis, George Karagiannaki, Angeliki Pramatari, Katerina Kehagia, Olga TI A cost-benefit evaluation framework of an electronic-based traceability system SO BRITISH FOOD JOURNAL DT Article DE Cost benefit analysis; Tracer methods; Food safety; Product management; Quality assurance AB Purpose - The concept of "traceability as a strategy and mandatory initiative" has replaced that of "traceability as a cost of a business or as a voluntary responsibility". This implies that the introduction of a traceability system should be perceived and positioned as a catalyst for better business practices. However, despite these benefits, a traceability system is also investment-worthy. Hence, the value of investment in a traceability system constitutes a matter of considerable concern and debate for both practitioners and academics alike. This paper seeks to present a cost-benefit evaluation applied in a natural mineral water company regarding the pilot deployment of an electronic-based traceability system. Design/methodology/approach - This is a case based study. Findings - Based on the experience described previously, a high-level framework is generated that any organisation can refer to as a proper guideline in order to demonstrate how the costs and benefits can be compared for overall evaluation of the deployment of any traceability system. The details of the framework are described by applying it to a specific case. Practical implications - The present framework has theoretical interest for replicability in a different number of food sectors. Originality/value - The paper contributes to closing the existing gap regarding the theoretical approach that food traceability systems can adopt when their costs and benefits are investigated. C1 [Chryssochoidis, George; Karagiannaki, Angeliki; Kehagia, Olga] Agr Univ Athens, Athens, Greece. [Karagiannaki, Angeliki; Pramatari, Katerina] Athens Univ Econ & Business, Athens, Greece. C3 Agricultural University of Athens; Athens University of Economics & Business RP Chryssochoidis, G (corresponding author), Agr Univ Athens, Athens, Greece. EM gc@agribusiness.aua.gr CR DeLone WH, 2004, INT J ELECTRON COMM, V9, P31, DOI 10.1080/10864415.2004.11044317 *ECR, 2004, US TRAC SUPPL CHAIN Golan E., 2003, CURRENT AGR FOOD RES, V4, P27 GOLAN E, 2003, 830 USDA Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), P94, DOI 10.1109/ICRE.1994.292398 *ISO, 1995, 8492 EN ISO EUR COMM LITTLEFIELD M, 2006, COMPLIANCE TRACEABIL MARSHALL P, 2004, AQUATT STUD WORKSH T NR 8 TC 26 Z9 28 U1 0 U2 15 PY 2009 VL 111 IS 6-7 BP 565 EP 582 DI 10.1108/00070700910966023 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000268613200005 DA 2022-12-14 ER PT J AU Zhang, J Liu, L Mu, WS Moga, LM Zhang, XS AF Zhang, Jian Liu, Lu Mu, Weisong Moga, Liliana M. Zhang, Xiaoshuan TI Development of temperature-managed traceability system for frozen and chilled food during storage and transportation SO JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT DT Article DE Traceability system; RFID; TTT (temperature-time tolerance) theory; food safety ID INDICATOR; ABUSE AB Temperature is one of the most important parameters of the quality control for frozen and chilled food, and then freshness is almost exclusively a function of time and temperature. As the temperature management is a very important function for fresh foods, it is desirable that the quality conditions during storage and transport are clearly understood and traceable. This paper describes the development of a temperature-managed traceability system based on RFID tag, GPS, mobile communication based on TTT theory. The system has been tested with the Haikou-Beijing transport scenario for chilled tilapia. The results show that the system is helpful in quality control, traceability and efficiency of frozen and chilled food during storage and transportation chain. In addition, the system also has significant impact on stakeholders across the supply chain via improved quality and reduced cost. C1 [Zhang, Jian; Liu, Lu; Mu, Weisong; Zhang, Xiaoshuan] China Agr Univ, Minist Educ, Key Lab Modern Precis Agr Integrat, Beijing 100083, Peoples R China. [Zhang, Jian] Beijing Informat S&T Univ, Beijing 100192, Peoples R China. [Moga, Liliana M.] Dunarea Jos Univ Galati, Galati 800008, Romania. C3 China Agricultural University; Dunarea De Jos University Galati RP Zhang, XS (corresponding author), China Agr Univ, Minist Educ, Key Lab Modern Precis Agr Integrat, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Atsushi O, 2006, NEC TECH J, V1, P82 BASAT S, 2006, ANT TECHN SMALL ANT, P160 Bobelyn E, 2006, POSTHARVEST BIOL TEC, V42, P104, DOI 10.1016/j.postharvbio.2006.05.011 Bogataj M, 2005, INT J PROD ECON, V93-4, P345, DOI 10.1016/j.ijpe.2004.06.032 Cyprian OO, 2008, J AQUAT FOOD PROD T, V17, P303, DOI 10.1080/10498850802195038 GRISIUS R, 1987, J FOOD PROCESS PRES, V11, P309, DOI 10.1111/j.1745-4549.1987.tb00057.x LABUZA TP, 1995, J FOOD SAFETY, V15, P201, DOI 10.1111/j.1745-4565.1995.tb00134.x Likar K, 2006, FOOD CONTROL, V17, P108, DOI 10.1016/j.foodcont.2004.09.009 SHELLHAMMER TH, 1991, J FOOD SCI, V56, P402, DOI 10.1111/j.1365-2621.1991.tb05290.x SINGH RP, 1987, INT J REFRIG, V10, P296, DOI 10.1016/0140-7007(87)90074-0 TAOUKIS PS, 1989, J FOOD SCI, V54, P789, DOI 10.1111/j.1365-2621.1989.tb07883.x Tinker J. H., 1985, EVALUATION AUTOMATED, P286 URSA Y, 2006, COLD TRACE MOBILE BA NR 13 TC 31 Z9 31 U1 0 U2 24 PD JUL-OCT PY 2009 VL 7 IS 3-4 BP 28 EP 31 PN 1 WC Food Science & Technology SC Food Science & Technology UT WOS:000272054400005 DA 2022-12-14 ER PT J AU Giagnocavo, C Bienvenido, F Ming, L Zhao, YR Sanchez-Molina, JA Yang, XT AF Giagnocavo, Cynthia Bienvenido, Fernando Ming, Li Zhao Yurong Sanchez-Molina, Jorge Antonio Yang Xinting TI Agricultural cooperatives and the role of organisational models in new intelligent traceability systems and big data analysis SO INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING DT Article DE food traceability system; big data; internet of things; organisational structure; business model; agricultural cooperatives; net-chains; food supply chain ID MANAGEMENT AB Traceability systems are key to assuring food safety, creating a benefit for food supply chain components. Currently, the appearance of new technologies such as IoT and Big Data Analysis leads to a new generation of more functional, but complex, traceability systems. Organisational models based on cooperation of multiple small/medium size agents, for example of small/family farming cooperatives, play an important role in high standard agricultural production and commercialization processes. These function as both social and economic networks, with high social and economic impact in the rural areas. The case of Almeria as an example of this model was used to analyze its cooperative model. The actual traceability systems in the Almeria model were studied, taking account of the different networked agents and their interrelation. This study includes two main parts: a) analysis of the net-chains that constitute the food supply chains and their different relationships, and b) actual traceability. The next step studied how the net-chain model, including many diverse agents, may be applied to develop a new generation of traceability systems based of IoT and Big Data. This implies extending the special and functional scope of the actual systems and defining clear rules of exchange of the results of the Big Data Analysis, taking account of the adequate Privacy Rules. This work analyses the current organisation as a base for a new generation of traceability systems considered in the European project IoF2020 (Internet of the Food and Farm 2020). Some parallels between Almeria's model and certain areas in China, mainly in the areas of Beijing-Tianjin-Hebei and Shandong were detected. Another of the objectives of this work is to deepen the understanding of these similarities and analyze the possible adaptation of the results from Almeria to China. C1 [Giagnocavo, Cynthia] Univ Almeria, Catedra Chair Coexphal UAL Hort Cooperat Studies, Almeria, Spain. [Bienvenido, Fernando] Univ Almeria, Dept Informat, Vegetable Traceabil & Modeling Agrosyst, Almeria, Spain. [Sanchez-Molina, Jorge Antonio] Univ Almeria, Dept Informat, Greenhouse Climate & Irrigat Modelling & Automat, Almeria, Spain. [Ming, Li] Minist Agr, Beijing Res Ctr Informat Technol Agr, Natl Engn Res Ctr Informat Technol Agr,Warning Sy, Natl Engn Lab Agriprod Qual Traceabil,Key Lab Inf, Beijing, Peoples R China. [Yang Xinting] Minist Agr, Beijing Res Ctr Informat Technol Agr, Natl Engn Res Ctr Informat Technol Agr,IOT Agripr, Natl Engn Lab Agriprod Qual Traceabil,Key Lab Inf, Beijing, Peoples R China. [Zhao Yurong] Shandong Agr Univ, Coll Econ & Management, Theory & Practice Rural Finance, Tai An, Shandong, Peoples R China. C3 Universidad de Almeria; Universidad de Almeria; Universidad de Almeria; Beijing Academy of Agriculture & Forestry; Ministry of Agriculture & Rural Affairs; Beijing Academy of Agriculture & Forestry; Ministry of Agriculture & Rural Affairs; Shandong Agricultural University RP Giagnocavo, C (corresponding author), Univ Almeria, Catedra Chair Coexphal UAL Hort Cooperat Studies, Agr Cooperat Innovat ICT Org & Business Models &, Almeria, Spain. EM cgiagnocavo@ual.es; fbienve@ual.es; lim@nercita.org.cn; zhaoyr9083@163.com; jorgesanchez@ual.es; yangxt@nercita.org.cn CR [Anonymous], 2012, HDB ORG EC [Anonymous], 2016, INF 2016 [Anonymous], 2005, MEAS SCI TECHN ACT O Banhazi T. M., 2012, International Journal of Agricultural and Biological Engineering, V5, P1, DOI 10.3965/j.ijabe.20120503.001 Bijman J., 2012, SUPPORT FARMERS COOP Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chaddad F., 2009, INT WORKSH RUR COOP Chen M, 2014, MOBILE NETW APPL, V19, P171, DOI 10.1007/s11036-013-0489-0 Coase RH, 1937, ECONOMICA-NEW SER, V4, P386, DOI 10.1111/j.1468-0335.1937.tb00002.x deAlmeria LaVoz, 2017, ALM TIEN 398 EMPR EX Galdeano-Gomez E, 2013, BRIT FOOD J, V117, P596 Galeotti A, 2010, REV ECON STUD, V77, P218, DOI 10.1111/j.1467-937X.2009.00570.x Giagnocavo C., 2012, ROLE COOPERATIVES PO GRANDORI A, 1995, ORGAN STUD, V16, P183, DOI 10.1177/017084069501600201 Iliopoulos C., 2016, Journal on Chain and Network Science, V16, P1, DOI 10.3920/JCNS2016.x003 Karantininis K., 2007, NETWORK FORM COOPERA, P19, DOI DOI 10.1007/1-4020-5543-0_2 Lam A., 2006, OXFORD HDB INNOVATIO Lazzarini S. G., 2001, THE J, V1, P7, DOI [10.3920/JCNS2001.x002, DOI 10.3920/JCNS2001.X002] [李秀峰 Li Xiufeng], 2014, [中国农业科技导报, Journal of Agricultural Science and Technology], V16, P10 Liu DongHong, 2012, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V43, P146 Menard C., 2007, VERTICAL MARKETS COO, P1, DOI DOI 10.1007/1-4020-5543-0 OSCAE, COOP AGR MACR COOP A Osterwalder A., 2010, BUSINESS MODEL GENER Podolny JM, 1998, ANNU REV SOCIOL, V24, P57, DOI 10.1146/annurev.soc.24.1.57 Poppe K., 2015, FARM POL J, V12, P11 Provan KG, 2008, J PUBL ADM RES THEOR, V18, P229, DOI 10.1093/jopart/mum015 RDI (Rural Development Institute), 2005, ROL COOP COMM EC DEV Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sanjeev G, 2007, CONNECTIONS INTRO EC Sonka S, 2014, INT FOOD AGRIBUS MAN, V17, P1 The Permanent Mission of the People's Republic of China to the UN, 2012, CHIN NAT REP SUST WILLIAMSON OE, 1991, ADMIN SCI QUART, V36, P269, DOI 10.2307/2393356 Wolfert S, 2014, AGR SYST, V153, P69 Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 Yu Y, 2014, INT J AGR BIOL ENG, V4, P59 NR 35 TC 27 Z9 29 U1 4 U2 88 PD SEP PY 2017 VL 10 IS 5 BP 115 EP 125 DI 10.25165/j.ijabe.20171005.3089 WC Agricultural Engineering SC Agriculture UT WOS:000412990100011 DA 2022-12-14 ER PT J AU Di Stasio, L Piatti, P Fontanella, E Costa, S Bigi, D Lasagna, E Pauciullo, A AF Di Stasio, Liliana Piatti, Piergiovanni Fontanella, Edoardo Costa, Stefano Bigi, Daniele Lasagna, Emiliano Pauciullo, Alfredo TI Lamb meat traceability: The case of Sambucana sheep SO SMALL RUMINANT RESEARCH DT Article DE Genetic traceability; Meat; Sheep; Sambucana breed ID MICROSATELLITE MARKERS; GENETIC TRACEABILITY; POPULATION-STRUCTURE; PRODUCTS; PROGRAM; FRAUD AB Genetic traceability has a key role in the product certification, but it is rarely implemented in sheep so far, especially in the fresh meat sector. In this study, the case of the Sambucana sheep is analysed with the aim of developing a genetic system able to certify the origin of its traditional product, the Sambucano lamb, protected by a registered trademark. A set of 14 microsatellite markers was identified as an efficient tool to genetically discriminate the Sambucana sheep from other breeds potentially involved in mislabelling and to allow for an effective allocation test of meat cuts labelled as 'Guaranteed Sambucano lamb'. The paternity test proved to be an additional means to improve the reliability of the control. The traceability system here described is easy to implement in local minor sheep breeds and is recommended in the framework of meat certification. (C) 2017 Elsevier B.V. All rights reserved. C1 [Di Stasio, Liliana; Pauciullo, Alfredo] Univ Torino, Dipartimento Sci Agrarie Forestali & Alimentari, Largo Braccini 2, I-10095 Grugliasco, Italy. [Piatti, Piergiovanni; Fontanella, Edoardo; Costa, Stefano] Lab Chim Camera Commercio Torino, Turin, Italy. [Bigi, Daniele] Univ Bologna, DISTAL, Dipartimento Sci Tecnol Agroalimentari, Reggio Emilia, Italy. [Lasagna, Emiliano] Univ Perugia, Dipartimento Sci Agrarie Alimentari & Ambientali, Perugia, Italy. C3 University of Turin; University of Bologna; University of Perugia RP Di Stasio, L (corresponding author), Univ Torino, Dipartimento Sci Agrarie Forestali & Alimentari, Largo Braccini 2, I-10095 Grugliasco, Italy. EM liliana.distasio@unito.it CR Sentandreu MA, 2014, FOOD RES INT, V60, P19, DOI 10.1016/j.foodres.2014.03.030 Balloux F, 2002, MOL ECOL, V11, P155, DOI 10.1046/j.0962-1083.2001.01436.x Bigi D., 2008, ATLANTE RAZZE AUTOCT, P174 BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 Boyle T., 1999, POPULATION GENETIC A Brooke C. H., 1978, DECLINING BREEDS MED, P8 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 FAO, 2013, VIV CONS AN GEN RES FAO (Food and Agriculture Organization), 2007, GLOB PLAN ACT AN GEN FAO/ISAG, 2004, SEC GUID DEV NAT FAR Fernandez ME, 2013, GENET MOL BIOL, V36, P185, DOI 10.1590/S1415-47572013000200008 Font I Furnols M., 2011, FOOD QUALITY AND PRE, V22, P443, DOI DOI 10.1016/J.F00DQUAL.2011.02.007 Garcia D, 2006, MEAT SCI, V72, P560, DOI 10.1016/j.meatsci.2005.09.005 Goudet J, 2002, FSTAT 2 9 3 2 PROGRA Hersleth M, 2012, MEAT SCI, V90, P899, DOI 10.1016/j.meatsci.2011.11.030 Kalinowski ST, 2007, MOL ECOL, V16, P1099, DOI 10.1111/j.1365-294X.2007.03089.x Lasagna E, 2011, SMALL RUMINANT RES, V96, P111, DOI 10.1016/j.smallrumres.2010.11.014 Montossi F, 2013, MEAT SCI, V95, P772, DOI 10.1016/j.meatsci.2013.04.048 Nagy S, 2012, BIOCHEM GENET, V50, P670, DOI 10.1007/s10528-012-9509-1 Oh JD, 2014, ASIAN AUSTRAL J ANIM, V27, P926, DOI 10.5713/ajas.2013.13829 Peter C, 2007, ANIM GENET, V38, P37, DOI 10.1111/j.1365-2052.2007.01561.x Pritchard JK, 2000, GENETICS, V155, P945 RICE WR, 1989, EVOLUTION, V43, P223, DOI 10.1111/j.1558-5646.1989.tb04220.x Rogberg-Munoz A, 2016, MEAT SCI, V111, P47, DOI 10.1016/j.meatsci.2015.08.014 Rosenberg NA, 2004, MOL ECOL NOTES, V4, P137, DOI 10.1046/j.1471-8286.2003.00566.x Scarano D., 2014, Diversity, V6, P579 Slow Food Foundation, 2016, SMB LAMB NR 28 TC 8 Z9 8 U1 2 U2 14 PD APR PY 2017 VL 149 BP 85 EP 90 DI 10.1016/j.smallrumres.2017.01.013 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000402348100014 DA 2022-12-14 ER PT J AU Wang, WS Xu, JC Zhang, WF Glamuzina, B Zhang, XS AF Wang, Wensheng Xu, Jinchao Zhang, Wenfeng Glamuzina, Branko Zhang, Xiaoshuan TI Optimization and validation of the knowledge-based traceability system for quality control in fish waterless live transportation SO FOOD CONTROL DT Article DE Optimization; Traceability system; Waterless transport; Knowledge-based HACCP quality plan; Non-destructive forecasting ID STRESS; HACCP; RESPONSES; TEMPERATURE AB Waterless live transportation is an effective strategy to realize a larger volume of live fish, and it is pollution-free by contrast with traditional live transport with water. Based on previous study results, this paper aims to propose the knowledge-based HACCP quality control plan to realize real-time feedback and adjustment for fish waterless live transportation. Meantime, the improved traceability system was established to realize the CCPs determination and non-destructive dynamic monitoring of fish stress level in waterless live transportation. Furthermore, to verify the accuracy and applicability of the established traceability system, the relevant manager, workers, and experts are invited to participate in the efficiency and performance analysis. The final results show that the accuracy of the forecasting model is 96.7%, and all the regression coefficients are close to 1.00. Besides, the improved traceability system would offer a better solution of effective quality control for the fish waterless live transportation, and help to improve the transport management efficiency. C1 [Wang, Wensheng; Xu, Jinchao; Zhang, Xiaoshuan] China Agr Univ, Beijing Lab Food Qual & Safety, Coll Engn, Beijing, Peoples R China. [Zhang, Wenfeng] Zhongkai Univ Agr & Engn, Guangzhou, Guangdong, Peoples R China. [Glamuzina, Branko] Univ Dubrovnik, Dubrovnik, Croatia. C3 China Agricultural University; Zhongkai University of Agriculture & Engineering; University of Dubrovnik RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM victorwong@cau.edu.cn; xuvic@cau.edu.cn; zhwf999@163.com; branko.glamuzina@unidu.hr; zhxshuan@cau.edu.cn CR Allata S, 2017, FOOD CONTROL, V79, P239, DOI 10.1016/j.foodcont.2017.04.002 Barko G, 1997, TALANTA, V44, P2237, DOI 10.1016/S0039-9140(97)00168-9 Cheng CH, 2017, FISH SHELLFISH IMMUN, V64, P137, DOI 10.1016/j.fsi.2017.03.003 Fan XP, 2019, AQUACULTURE, V508, P10, DOI 10.1016/j.aquaculture.2019.04.054 Feng HH, 2019, FOOD CONTROL, V98, P348, DOI 10.1016/j.foodcont.2018.11.050 Han JW, 2019, APPL SOFT COMPUT, V84, DOI 10.1016/j.asoc.2019.105733 Harmon TS, 2009, REV AQUACULT, V1, P58, DOI 10.1111/j.1753-5131.2008.01003.x Hur JW, 2019, AQUACULTURE, V502, P142, DOI 10.1016/j.aquaculture.2018.12.038 Li SS, 2019, INTERACT COMPUT, V31, P1, DOI 10.1093/iwc/iwz001 Long LN, 2019, COMP BIOCHEM PHYS C, V219, P25, DOI 10.1016/j.cbpc.2019.02.002 Martins EA, 2008, FOOD CONTROL, V19, P764, DOI 10.1016/j.foodcont.2007.08.001 Muangprathub J, 2019, COMPUT ELECTRON AGR, V156, P467, DOI 10.1016/j.compag.2018.12.011 Mukherjee J, 2017, ECOL INDIC, V78, P381, DOI 10.1016/j.ecolind.2017.03.038 Nie XB, 2019, AQUACULTURE, V508, P30, DOI 10.1016/j.aquaculture.2019.04.060 Rawat S, 2016, COMPUT IND, V75, P27, DOI 10.1016/j.compind.2015.10.012 Wallace C. A., 2014, ENCY FOOD SAFETY, DOI [10.1016/B978-0-12-378612-8.00358-9, DOI 10.1016/B978-0-12-378612-8.00358-9] Wang CH, 2020, J ENVIRON MANAGE, V268, DOI 10.1016/j.jenvman.2020.110709 Wang WS, 2020, AQUACULTURE, V518, DOI 10.1016/j.aquaculture.2019.734834 Wu HY, 2019, ANAL METHODS-UK, V11, P5623, DOI [10.1039/c9ay01752f, 10.1039/C9AY01752F] Xu ZH, 2019, DEV COMP IMMUNOL, V100, DOI 10.1016/j.dci.2019.103413 Xu ZH, 2018, FISH SHELLFISH IMMUN, V72, P564, DOI 10.1016/j.fsi.2017.11.016 Yang YH, 2019, FOOD CONTROL, V96, P291, DOI 10.1016/j.foodcont.2018.09.013 Zeng P, 2014, FISH PHYSIOL BIOCHEM, V40, P973, DOI 10.1007/s10695-013-9898-z Zhang YJ, 2020, IEEE ACCESS, V8, P40955, DOI 10.1109/ACCESS.2020.2976509 Zhang YJ, 2019, FOOD CONTROL, V95, P283, DOI 10.1016/j.foodcont.2018.08.018 Zhang YJ, 2018, COMPUT ELECTRON AGR, V145, P43, DOI 10.1016/j.compag.2017.12.017 NR 26 TC 4 Z9 5 U1 3 U2 26 PD APR PY 2021 VL 122 AR 107809 DI 10.1016/j.foodcont.2020.107809 WC Food Science & Technology SC Food Science & Technology UT WOS:000609081100011 DA 2022-12-14 ER PT J AU Lafquih, H Elhaq, SL Krimi, I Berquedich, M AF Lafquih, Hind Elhaq, Saad Lissane Krimi, Issam Berquedich, Mouna TI Modeling and analysis of a quality traceability framework for phosphate extraction process: evidence from Morocco SO INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT DT Article DE Digital quality; Traceability; Open-pit mine; System modeling; Business process modeling ID FOOD-SUPPLY CHAIN; SYSTEM AB Purpose According to United Nations reports, the worldwide population is expected to reach around 9.6 billion by 2050. This forecasting emphasizes the role of phosphate-based fertilizers for developing sustainable agriculture and ensures the demand all over the planet. From this perspective, phosphate companies are racing to improve their industrial performance and guarantee the quality, reliability and integrity of information efficiently. The purpose of this paper is to propose a traceability system framework that ensures product quality tracing and real-time operations monitoring for open-pit mines. Design/methodology/approach The authors develop a hybrid approach that integrates Business Process Model and Notation techniques with System Modeling Language to formalize several use cases and scenarios to model quality traceability processes related to open-pit mines. This framework also embeds an optimization module based on mathematical modeling approaches to optimize stockpiles' movement and respect the distinction between different qualities. Findings This paper explains a successful implementation of a quality traceability tool for an African mining company. The research team was able to understand and scale down the problem faced by the managers. Further, the study is focused on improving quality tracing over time and automatizing the current compliance processes related to the mine extraction activities. The proposed tool is proved highly effective in reducing the time of tracing quality claims by 46% compared with the manual procedure. Second, the implementation of this tool reduced fuel costs by 34% and CO2 emissions by 10%. Originality/value The originality of the contributions lies in four aspects: (1) adapting quality traceability concept for the mining industry; (2) assessing the current trends of traceability systems considering the mining industry context; (3) hybridizing business processes re-engineering, quality system and optimization modeling; and (4) using a real case study of a phosphate company to evaluate the framework. C1 [Lafquih, Hind; Elhaq, Saad Lissane; Berquedich, Mouna] Mohammed VI Polytech Univ, Innovat Lab Operat, Ben Guerir, Morocco. [Lafquih, Hind; Elhaq, Saad Lissane] Univ Hassan II Casablanca, High Natl Sch Elect & Mech, Lab Res Engn LRI, Optimizat Prod Syst & Energy Team, Casablanca, Morocco. [Krimi, Issam] Int Univ Rabat, Rabat Business Sch, Rabat, Morocco. C3 Mohammed VI Polytechnic University; Hassan II University of Casablanca; Universite Internationale de Rabat RP Krimi, I (corresponding author), Int Univ Rabat, Rabat Business Sch, Rabat, Morocco. EM issam.krimi@uir.ac.ma CR Alfaro J, 2007, PROD PLAN CONTROL, V18, P641, DOI 10.1080/09537280701599772 Asioli D, 2014, INT J FOOD SCI TECH, V49, P1565, DOI 10.1111/ijfs.12485 Bergquist B, 2012, MINER ENG, V30, P44, DOI 10.1016/j.mineng.2012.01.010 Bernhard, 2011, STUDY USE BUSINESS P Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 CHENG MJ, 1994, INT J OPER PROD MAN, V14, P4, DOI 10.1108/01443579410067199 de Cesare, 2009, UK ACAD INFORM SYSTE, P1 Ding JH, 2011, PROD PLAN CONTROL, V22, P282, DOI 10.1080/09537287.2010.498606 Dori D, 2008, INCOSE INT S, V18, P1023 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Flapper SDP, 2002, PROD PLAN CONTROL, V13, P26, DOI 10.1080/09537280110061548 Golan E.H., 2004, TRACEABILITY US FOOD Jansen-Vullers MH, 2004, PROD PLAN CONTROL, V15, P303, DOI 10.1080/09537280410001697738 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kalenkova AA, 2017, SOFTW SYST MODEL, V16, P1019, DOI 10.1007/s10270-015-0502-0 Kvarnstrom B, 2008, MINER ENG, V21, P720, DOI 10.1016/j.mineng.2008.02.002 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 McGinnis L.F, 2007, OMG SE DSIG Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mousavi A., 2002, British Food Journal, V104, P7, DOI 10.1108/00070700210418703 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Petrasch R, 2016, INT JOINT CONF COMP, P275 Pieters R., 1995, INT J RES MARK, V12, P227, DOI [DOI 10.1016/0167-8116(95)00023-U, 10.1016/0167-8116(95)00023-U] Pimentel B., 2010, INTELL SYST OPER MET, P133, DOI [10.4018/978-1-61520-605-6.ch008, DOI 10.4018/978-1-61520-605-6.CH008] Radziwill NM., 2018, QUAL PROG Ralea C, 2020, 13 INT MAN C MAN STR Reinhartz-Berger I., 2005, BUSINESS SYSTEMS ANA, P130, DOI DOI 10.4018/978-1-59140-339-5.CH006 Reynolds T.J., 2001, UNDERSTANDING CONSUM, P40, DOI [10.4324/9781410600844, DOI 10.4324/9781410600844] Schleipen M, 2009, 2009 IEEE C EM TECHN, P1, DOI DOI 10.1109/ETFA.2009.5347260 Somekh Yudit, 2007, Proceedings of the International Conference on Systems Engineering and Modeling, ICSEM'07, P60, DOI 10.1109/ICSEM.2007.373333 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Vincent J, 2009, THESIS INRIA Wang KS, 2014, ADV MANUF, V2, P106, DOI 10.1007/s40436-014-0053-6 Wang YC, 2018, TECHNOL FORECAST SOC, V126, P3, DOI 10.1016/j.techfore.2015.12.019 Wolny S, 2020, SOFTW SYST MODEL, V19, P111, DOI 10.1007/s10270-019-00735-y Zuniga R, 2015, PROD PLAN CONTROL, V26, P81, DOI 10.1080/09537287.2013.855335 NR 38 TC 0 Z9 0 U1 0 U2 1 PD MAY 19 PY 2022 VL 39 IS 6 SI SI BP 1412 EP 1428 DI 10.1108/IJQRM-05-2021-0132 EA SEP 2021 WC Management SC Business & Economics UT WOS:000695611800001 DA 2022-12-14 ER PT J AU Gvozdanovic, K Djurkin Kusec, I Margeta, P Salajpal, K Dzijan, S Bosnjak, Z Kusec, G AF Gvozdanovic, Kristina Djurkin Kusec, Ivona Margeta, Polonca Salajpal, Kresimir Dzijan, Snjezana Bosnjak, Zinka Kusec, Goran TI Multiallelic marker system for traceability of Black Slavonian pig meat SO FOOD CONTROL DT Article DE Pig breed; Pork; Traceability; Molecular markers; Match probability ID GENETIC DIVERSITY; MICROSATELLITE; BREEDS; SOFTWARE AB Meat from Black Slavonian pig (Crna slavonska, CS), an autochthonous Croatian breed, is used for the production of high-quality products, which makes it an interesting target for meat fraud. The aim of the study was to develop a set of microsatellite markers suitable for precise genetic traceability of the meat originating from this breed. Initially, 23 microsatellite markers were selected and grouped into three multiplex reactions for the genetic characterization of the seven breeds included in the study; subsequently, a set of eight microsatellite markers for traceability of CS pig was created based on the match probability value. The results indicated that when eight highly polymorphic loci were combined, the chance of finding identical genotype in two random individuals was about three in ten million (10(-7)). This provided the basis for establishing a reliable genetic traceability system for meat originating from CS pigs. C1 [Gvozdanovic, Kristina; Djurkin Kusec, Ivona; Margeta, Polonca; Kusec, Goran] Josip Juraj Strossmayer Univ Osijek, Fac Agrobiotech Sci Osijek, Dept Anim Prod & Biotechnol, Osijek 31000, Croatia. [Dzijan, Snjezana] Josip Juraj Strossmayer Univ Osijek, Fac Dent Med & Hlth, Dept Pharmacol & Biochem, Osijek 31000, Croatia. [Salajpal, Kresimir] Univ Zagreb, Fac Agron, Dept Anim Sci, Zagreb 10000, Croatia. [Bosnjak, Zinka] Inst Publ Hlth Osijek Baranja Cty, Microbiol Dept, Osijek 31000, Croatia. C3 University of JJ Strossmayer Osijek; University of JJ Strossmayer Osijek; University of Zagreb; University of Zagreb, School of Dental Medicine RP Djurkin Kusec, I (corresponding author), Josip Juraj Strossmayer Univ Osijek, Fac Agrobiotech Sci Osijek, Dept Anim Prod & Biotechnol, Osijek 31000, Croatia. EM idurkin@fazos.hr CR [Anonymous], ANN REP PIG BREED RE [Anonymous], MOL GEN CHAR AN GEN [Anonymous], 2018, LANG ENV STAT COMP [Anonymous], EUROPEAN LOCAL PIG B Belkhir K, 2004, 5000 CNRS UMR BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 Charoensook R, 2019, ASIAN AUSTRAL J ANIM, V32, P1491, DOI 10.5713/ajas.18.0832 Cortes O, 2016, HEREDITY, V117, P14, DOI 10.1038/hdy.2016.21 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Druml T, 2012, GENET SEL EVOL, V44, DOI 10.1186/1297-9686-44-5 Evanno G, 2005, MOL ECOL, V14, P2611, DOI 10.1111/j.1365-294X.2005.02553.x Gvozdanovic K, 2019, ANIM BIOTECHNOL, V30, P242, DOI 10.1080/10495398.2018.1478847 Jombart T, 2008, BIOINFORMATICS, V24, P1403, DOI 10.1093/bioinformatics/btn129 Jombart T, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-94 Kalinowski ST, 2007, MOL ECOL, V16, P1099, DOI 10.1111/j.1365-294X.2007.03089.x Kannur BH, 2017, J FOOD SCI TECH MYS, V54, P558, DOI 10.1007/s13197-017-2500-4 Kopelman NM, 2015, MOL ECOL RESOUR, V15, P1179, DOI 10.1111/1755-0998.12387 Margeta P., 2016, Acta Agriculturae Slovenica. Supplement, P66 Margeta P, 2018, J CENT EUR AGRIC, V19, P865, DOI 10.5513/JCEA01/19.4.2331 Michailidou S, 2014, GENET MOL RES, V13, P2752, DOI 10.4238/2014.April.14.4 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Peakall R, 2006, MOL ECOL NOTES, V6, P288, DOI 10.1111/j.1471-8286.2005.01155.x Pritchard JK, 2000, GENETICS, V155, P945 Rebala K, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0166563 Reiner G, 2019, EUR J WILDLIFE RES, V65, DOI 10.1007/s10344-019-1262-x Rohman A, 2019, J ADV VET ANIM RES, V6, P9, DOI 10.5455/javar.2019.f306 Rousset F, 2008, MOL ECOL RESOUR, V8, P103, DOI 10.1111/j.1471-8286.2007.01931.x SanCristobal M, 2006, ANIM GENET, V37, P189, DOI 10.1111/j.1365-2052.2005.01385.x Sardina MT, 2015, FOOD RES INT, V74, P115, DOI 10.1016/j.foodres.2015.04.038 Sollero BP, 2009, LIVEST SCI, V123, P8, DOI 10.1016/j.livsci.2008.09.025 Sprem N, 2014, LIVEST SCI, V162, P1, DOI 10.1016/j.livsci.2013.12.010 Szpiech ZA, 2011, THEOR POPUL BIOL, V80, P100, DOI 10.1016/j.tpb.2011.03.006 Vrtkova I, 2015, ARCH TIERZUCHT, V58, DOI 10.5194/aab-58-115-2015 WEIR BS, 1984, EVOLUTION, V38, P1358, DOI [10.2307/2408641, 10.1111/j.1558-5646.1984.tb05657.x] Yu GC, 2015, GENET MOL RES, V14, P1362, DOI 10.4238/2015.February.13.15 Zhao J, 2018, FOOD SCI TECHNOL INT, V24, P292, DOI 10.1177/1082013217748457 NR 36 TC 5 Z9 5 U1 4 U2 18 PD MAR PY 2020 VL 109 AR 106917 DI 10.1016/j.foodcont.2019.106917 WC Food Science & Technology SC Food Science & Technology UT WOS:000500052400029 DA 2022-12-14 ER PT J AU Shi, GZ Chen, M Zhang, CY AF Shi Guo-zhong Chen Ming Zhang Chong-yang TI Accurate traceability system of crab based on improved deep residual network SO CHINESE JOURNAL OF LIQUID CRYSTALS AND DISPLAYS DT Article DE feature extraction; deep residual network; crab recognition; traceability of crab AB Aiming at the problems of current commercial river crab quality safety, hard to distinguish between real and false Yangcheng Lake hairy crabs, and too large feature dimension of deep residual network extraction, a precise source tracing system of river crab based on improved deep residual network is proposed. The system consists of four parts: breed, detection, sale and traceability. In breed, detection and sale parts, the data of crab breed, detection and sale are stored in traceability database. The traceability part identifies whether there is traceable river in traceability database by identifying crab based on improved deep residual network. According to the result of identification, the data of crab breeding, testing and marketing are output. Finally, the precise traceability of each commercial crab from consumer to farm is realized. The improved crab identification technology of the deep residual network can reduced the extracted crab shell feature vector from 2 048 to 156 dimensions, the recognition time is reduced by 92%, and the recognition accuracy is 92.1%. C1 [Shi Guo-zhong; Chen Ming; Zhang Chong-yang] Shanghai Ocean Univ, Sch Informat, Shanghai 201306, Peoples R China. [Shi Guo-zhong; Chen Ming; Zhang Chong-yang] Minist Agr, Key Lab Fisheries Informat, Shanghai 201306, Peoples R China. C3 Shanghai Ocean University; Ministry of Agriculture & Rural Affairs RP Shi, GZ (corresponding author), Shanghai Ocean Univ, Sch Informat, Shanghai 201306, Peoples R China.; Shi, GZ (corresponding author), Minist Agr, Key Lab Fisheries Informat, Shanghai 201306, Peoples R China. EM shiguozhong0729@yeah.net CR CALDWELL T, 2015, BOMETRIC TECHNOLOGY, V2015, P2 CAO H Y, 2015, RES FACE RECOGNITION FU Y S, 2014, PRINTING QUALITY STA, P28 [何云 He Yun], 2018, [计算机应用研究, Application Research of Computers], V35, P292 HUANG Y W, 2007, CHINA BRAND ANTICOUN, P60 Kaiming He, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P770, DOI 10.1109/CVPR.2016.90 [栗科峰 Li Kefeng], 2018, [计算机工程与应用, Computer Engineering and Application], V54, P206 LIU J, 2014, HEBEI FISHERIES, P67, DOI DOI 10.1007/978-3-642-40549-5_5 Lu Jun, 2018, Marine Fisheries, V40, P89 Naseem I, 2010, IEEE T PATTERN ANAL, V32, P2106, DOI 10.1109/TPAMI.2010.128 [彭姣 Peng Jiao], 2019, [水生态学杂志, Journal of Hydroecology], V40, P91 Roman-Rangel E, 2019, ENG APPL ARTIF INTEL, V81, P336, DOI 10.1016/j.engappai.2019.01.015 Srivastava N, 2014, J MACH LEARN RES, V15, P1929 Tan HL, 2014, IET COMPUT VIS, V8, P224, DOI 10.1049/iet-cvi.2012.0302 TANL LI B S., 2014, ACAD PERIODICAL FARM, P81 Wang Cheng-hui, 2002, Journal of Fishery Sciences of China, V9, P82 WANG W, 2013, RIVER CRAB ECOLOGICA Wen YD, 2016, LECT NOTES COMPUT SC, V9911, P499, DOI 10.1007/978-3-319-46478-7_31 YU M H, 2017, PEOPLES RULE LAW, P80 ZHANG B L, 2014, SCI FISH FARMING, P77 ZHANG F, 2019, COMPUTER ENG DESIGN, V40, P1689 ZHOU X G, 2016, AGR NETWORK INFORM, P48 NR 22 TC 1 Z9 2 U1 0 U2 3 PD DEC 5 PY 2019 VL 34 IS 12 BP 1202 EP 1209 DI 10.3788/YJYXS20193412.1202 WC Crystallography SC Crystallography UT WOS:000504688000011 DA 2022-12-14 ER PT J AU Thakur, M Donnelly, KAM AF Thakur, Maitri Donnelly, Kathryn A. -M. TI Modeling traceability information in soybean value chains SO JOURNAL OF FOOD ENGINEERING DT Article DE Soybean value chain; Traceability; Information modeling; Information exchange; Soybean oil; Elevator; Processor ID SUPPLY CHAIN; QUALITY; MANUFACTURE; MANAGEMENT; FRAMEWORK AB Identification of the information to be recorded is the most important requirement for developing an effective traceability system. In this paper, we present a soybean value chain and model the information capture by three links in the chain including the farming, bulk handling and processing sectors. Internal information capture points were identified for each sector and the corresponding traceability information to be recorded was determined. In-depth analyses were conducted for a soybean elevator and an oil and meal processor to determine the importance of traceability information from their perspective. A lot of information is available at different links in the soybean value chain. The method presented here can be used to create a standardized list of data elements that need to be recorded internally or exchanged with other links in the chain. A UML class diagram is developed to represent a method for modeling the product, process, quality and transformation information at any link in the chain. Finally, some suitable technologies for electronic information exchange within the food supply chains are presented. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Thakur, Maitri] Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. [Thakur, Maitri] Iowa State Univ, Dept Ind & Mfg Syst Engn, Ames, IA 50011 USA. [Donnelly, Kathryn A. -M.] Norwegian Inst Food Fisheries & Aquaculture Res N, N-9291 Tromso, Norway. C3 Iowa State University; Iowa State University; Nofima RP Thakur, M (corresponding author), Iowa State Univ, Dept Agr & Biosyst Engn, 1553 Food Sci Bldg, Ames, IA 50011 USA. EM maitri@iastate.edu CR AMBLER SW, 2008, OBJECT PRIMER, P351 [Anonymous], 2009, AGR BUSINESS WEEK [Anonymous], 2008, ANN 1 TRACE TRAC FOO Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 *CAN TRAC, 2003, AGR AGR FOOD CAN Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x *CEN, 2003, 14659 CEN EUR COMM S *CEN, 2003, 14660 CEN EUR COMM S DEBRUYNE I, 2004, IUPAC AOCS WORKSH FA Denton W, 2003, QUALITY OF FISH FROM CATCH TO CONSUMER: LABELLING, MONITORING AND TRACEABILITY, P75 Donnelly Kathryn Anne-Marie, 2008, International Journal of Metadata, Semantics and Ontologies, V3, P283, DOI 10.1504/IJMSO.2008.023575 Donnelly KAM, 2009, COMM COM INF SC, V46, P312 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Dreyer C., 2004, P 16 ANN C NORD RES, P155 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 FSA, 2002, TRAC FOOD CHAIN PREL Haverkort A. J., 2007, Potato Research, V50, P357, DOI 10.1007/s11540-008-9054-9 Haverkort AJ, 2005, POTATO RES, V49, P177 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 KARLSEN KM, 2008, NOFIMA REPORTS Kim HM, 1999, BT TECHNOL J, V17, P131, DOI 10.1023/A:1009611528866 Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 *NAT SOYB RES LAB, 2009, SOY Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 STUCKENSCHMIDT H, 2003, 12 IEEE INT WORKSH E Thakur M, 2007, J AM OIL CHEM SOC, V84, P835, DOI 10.1007/s11746-007-1107-8 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 *TRACEFOOD, 2007, TRACECORE XML STAND NR 32 TC 56 Z9 58 U1 2 U2 36 PD JUL PY 2010 VL 99 IS 1 BP 98 EP 105 DI 10.1016/j.jfoodeng.2010.02.004 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000277646500014 DA 2022-12-14 ER PT J AU Chen, JB Huang, Y Xia, PX Zhang, YY Zhong, Y AF Chen, Jinbo Huang, Ye Xia, Pengxiao Zhang, Yuying Zhong, Yu TI Design and implementation of real-time traceability monitoring system for agricultural products supply chain under Internet of Things architecture SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE DT Article DE agricultural products supply chain; Internet of Things architecture; real-time traceability monitoring system AB Agricultural products are an important guarantee for human health. The supply chain of agricultural products as its carrier has always been a research hotspot in academia and business circles. Applying the Internet of Things and NFC technology to the agricultural product supply chain area will play an important role in the intelligent management and traceability of agricultural product supply chains. The intelligent management and traceability of agricultural products supply chain are the management of data information. How to collect, store, process, analyze, and visualize the data information during the flow of agricultural products from the source to the table is an important issue which needs urgent research. This paper proposes and validates the NFC technology assumption of the application value of agricultural product supply chain. The communication interface between the agricultural product supply chain data acquisition terminal and the collection terminal module and peripheral equipment based on BD, GSM, and NFC module is designed. The physical storage structure of the NFC tag is then planned, and the DES and RSA encryption algorithms of the NFC tag are implemented. According to the theory of Internet of Things, the traceability system of agricultural product supply chain is divided into four levels: physical layer, service layer, data layer, and application layer. The three major areas of enterprise management, user query, and government supervision are designed to trace the information flow and system. Finally, this paper analyzes the hardware resources needed for the traceability system of agricultural product supply chain and uses the resources of the cooperative supply chain enterprise to implement the system application and deployment. The experiment verifies that the real-time traceability monitoring system for agricultural product supply chain proposed in this paper is effective in practical application. C1 [Chen, Jinbo; Huang, Ye; Xia, Pengxiao; Zhang, Yuying] Hubei Univ Econ, Sch Business Adm, Wuhan, Hubei, Peoples R China. [Zhong, Yu] Chinese Acad Agr Sci, Inst Agr Econ & Dev, Beijing, Peoples R China. C3 Hubei University of Economics; Chinese Academy of Agricultural Sciences; Institute of Agricultural Economics & Development, CAAS RP Zhong, Y (corresponding author), Chinese Acad Agr Sci, Inst Agr Econ & Dev, Beijing, Peoples R China. EM zhongyu790925@126.com CR Introini SC, 2018, DIR ORGAN, V64, P50 Kshetri N, 2017, THIRD WORLD Q, V38, P311, DOI 10.1080/01436597.2016.1191942 Kshetri N, 2017, TELECOMMUN POLICY, V41, P49, DOI 10.1016/j.telpol.2016.11.002 Li Z, 2017, IND MANAGE DATA SYST, V117, P1906, DOI 10.1108/IMDS-11-2016-0489 Liu YS, 2015, MOL SYST BIOL, V11, DOI 10.15252/msb.20145728 Mishra D, 2016, IND MANAGE DATA SYST, V116, P1331, DOI 10.1108/IMDS-11-2015-0478 Pang ZB, 2015, INFORM SYST FRONT, V17, P289, DOI 10.1007/s10796-012-9374-9 Shaofei W., 2015, INT J BIFURCAT CHAOS, V25 Tu MR, 2018, IND MANAGE DATA SYST, V118, P65, DOI 10.1108/IMDS-11-2016-0503 Wang XiaoHui, 2014, Journal of Chemical and Pharmaceutical Research, V6, P2304 Xu LD, 2014, IEEE T IND INFORM, V10, P2233, DOI 10.1109/TII.2014.2300753 Yan B, 2017, AGREKON, V56, P1, DOI 10.1080/03031853.2017.1284680 Zhang YF, 2017, IND MANAGE DATA SYST, V117, P1890, DOI 10.1108/IMDS-10-2016-0456 Zhang YF, 2015, INT J COMPUT INTEG M, V28, P811, DOI 10.1080/0951192X.2014.900874 Zhou MC, 2016, IEEE T AUTOM SCI ENG, V13, P1225, DOI 10.1109/TASE.2016.2579538 NR 15 TC 8 Z9 8 U1 8 U2 63 PD MAY 25 PY 2019 VL 31 IS 10 SI SI AR e4766 DI 10.1002/cpe.4766 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science UT WOS:000468594300031 DA 2022-12-14 ER PT J AU Dou, XJ Zhang, LX Yang, RN Wang, X Yu, L Yue, XF Ma, F Mao, J Wang, XP Zhang, W Li, PW AF Dou, Xinjing Zhang, Liangxiao Yang, Ruinan Wang, Xiao Yu, Li Yue, Xiaofeng Ma, Fei Mao, Jin Wang, Xiupin Zhang, Wen Li, Peiwu TI Mass spectrometry in food authentication and origin traceability SO MASS SPECTROMETRY REVIEWS DT Review; Early Access DE adulteration detection; analytical technique; fingerprint; food authentication; markers; mass spectrometry; origin traceability ID PRESSURE CHEMICAL-IONIZATION; OLIVE OIL ADULTERATION; PERFORMANCE LIQUID-CHROMATOGRAPHY; ISOTOPE-RATIO ANALYSIS; FATTY-ACID-COMPOSITION; RARE-EARTH-ELEMENTS; GEOGRAPHICAL ORIGIN; GAS-CHROMATOGRAPHY; QUADRUPOLE-TIME; VEGETABLE-OILS AB Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS. C1 [Dou, Xinjing; Zhang, Liangxiao; Yang, Ruinan; Wang, Xiao; Yu, Li; Yue, Xiaofeng; Ma, Fei; Mao, Jin; Wang, Xiupin; Zhang, Wen; Li, Peiwu] Chinese Acad Agr Sci, Oil Crops Res Inst, Wuhan 430062, Peoples R China. [Zhang, Liangxiao; Yue, Xiaofeng; Mao, Jin; Li, Peiwu] Hubei Hongshan Lab, Wuhan, Peoples R China. [Zhang, Liangxiao; Li, Peiwu] Minist Agr & Rural Affairs, Key Lab Biol & Genet Improvement Oil Crops, Wuhan, Peoples R China. [Zhang, Liangxiao; Mao, Jin; Li, Peiwu] Minist Agr & Rural Affairs, Lab Qual & Safety Risk Assessment Oilseed Prod Wu, Wuhan, Peoples R China. [Yu, Li; Ma, Fei; Wang, Xiupin; Zhang, Wen; Li, Peiwu] Minist Agr & Rural Affairs, Qual Inspect & Test Ctr Oilseeds Prod, Wuhan, Peoples R China. [Ma, Fei; Zhang, Wen] Nanjing Univ Finance & Econ, Collaborat Innovat Ctr Modern Grain Circulat & Sa, Nanjing, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Oil Crops Research Institute, CAAS; Ministry of Agriculture & Rural Affairs; Ministry of Agriculture & Rural Affairs; Ministry of Agriculture & Rural Affairs; Nanjing University of Finance & Economics RP Zhang, LX (corresponding author), Chinese Acad Agr Sci, Oil Crops Res Inst, Wuhan 430062, Peoples R China. EM zhanglx@caas.cn CR Aebersold R, 2016, PROTEOMICS, V16, P2065, DOI 10.1002/pmic.201600203 Aliferis KA, 2010, FOOD CHEM, V121, P856, DOI 10.1016/j.foodchem.2009.12.098 Araguas-Araguas L, 2000, HYDROL PROCESS, V14, P1341, DOI [10.1002/1099-1085(20000615)14:8<1341::AID-HYP983>3.0.CO;2-Z, 10.1002/1099-1085(20000615)14:8<1341::AID-HYP983>3.0.CO;2-Z] Arbulu M, 2015, ANAL CHIM ACTA, V858, P32, DOI 10.1016/j.aca.2014.12.028 Arntzen MO, 2011, J PROTEOME RES, V10, P913, DOI 10.1021/pr1009977 Ballin NZ, 2019, TRENDS FOOD SCI TECH, V86, P537, DOI 10.1016/j.tifs.2018.09.025 Barik SK, 2013, APPL BIOCHEM BIOTECH, V171, P1011, DOI 10.1007/s12010-013-0384-y Batista BL, 2012, FOOD RES INT, V49, P209, DOI 10.1016/j.foodres.2012.07.015 Black C, 2016, TRAC-TREND ANAL CHEM, V82, P268, DOI 10.1016/j.trac.2016.06.005 Bonick J, 2017, J FOOD COMPOS ANAL, V58, P82, DOI 10.1016/j.jfca.2017.01.019 Bontempo L, 2019, FOOD CHEM, V276, P782, DOI 10.1016/j.foodchem.2018.10.077 Cajka T, 2016, ANAL CHEM, V88, P524, DOI 10.1021/acs.analchem.5b04491 Caligiani A, 2016, J AGR FOOD CHEM, V64, P4158, DOI 10.1021/acs.jafc.6b00913 Calvano CD, 2012, FOOD CHEM, V134, P1192, DOI 10.1016/j.foodchem.2012.02.154 Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Castro-Puyana M, 2013, TRAC-TREND ANAL CHEM, V52, P74, DOI 10.1016/j.trac.2013.05.016 Cavanna D, 2018, TRENDS FOOD SCI TECH, V80, P223, DOI 10.1016/j.tifs.2018.08.007 Cavanna D, 2019, FOOD CHEM, V271, P691, DOI 10.1016/j.foodchem.2018.07.204 Cecchi L, 2017, FOOD CHEM, V219, P148, DOI 10.1016/j.foodchem.2016.09.132 Centonze V, 2019, FOOD CHEM, V277, P25, DOI 10.1016/j.foodchem.2018.10.105 Cheajesadagul P, 2013, FOOD CHEM, V141, P3504, DOI 10.1016/j.foodchem.2013.06.060 Chiesa L, 2016, FOOD CHEM, V212, P296, DOI 10.1016/j.foodchem.2016.05.180 Chung IM, 2019, J AGR FOOD CHEM, V67, P711, DOI 10.1021/acs.jafc.8b05063 Chung IM, 2018, FOOD CHEM, V240, P840, DOI 10.1016/j.foodchem.2017.08.023 Chung IM, 2017, FOOD CHEM, V234, P425, DOI 10.1016/j.foodchem.2017.05.014 Coetzee PP, 2014, FOOD CHEM, V164, P485, DOI 10.1016/j.foodchem.2014.05.027 Crews C, 2014, FOOD RES INT, V60, P117, DOI 10.1016/j.foodres.2013.11.023 Creydt M, 2018, J AGR FOOD CHEM, V66, P13328, DOI 10.1021/acs.jafc.8b05791 Danezis GP, 2017, ANAL CHIM ACTA, V991, P46, DOI 10.1016/j.aca.2017.09.013 Danezis GP, 2022, CURR OPIN FOOD SCI, V44, DOI 10.1016/j.cofs.2022.100812 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 de Gouw J, 2007, MASS SPECTROM REV, V26, P223, DOI 10.1002/mas.20119 Dettmer K, 2007, MASS SPECTROM REV, V26, P51, DOI 10.1002/mas.20108 Dias C, 2018, FOOD RES INT, V103, P492, DOI 10.1016/j.foodres.2017.09.059 Diaz R, 2014, FOOD CHEM, V157, P84, DOI 10.1016/j.foodchem.2014.02.009 Dou XJ, 2020, METABOLITES, V10, DOI 10.3390/metabo10030085 Dou XJ, 2018, MOLECULES, V23, DOI 10.3390/molecules23020241 Drivelos SA, 2014, FOOD CHEM, V165, P316, DOI 10.1016/j.foodchem.2014.03.083 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Erasmus SW, 2016, FOOD CHEM, V192, P997, DOI 10.1016/j.foodchem.2015.07.121 Escriche I, 2011, FOOD RES INT, V44, P1504, DOI 10.1016/j.foodres.2011.03.049 Esslinger S, 2014, FOOD RES INT, V60, P189, DOI 10.1016/j.foodres.2013.10.015 European Commission website, 2022, SCI IMMUNOL Evans JA, 2015, SCI TOTAL ENVIRON, V537, P447, DOI 10.1016/j.scitotenv.2015.07.133 Everstine K, 2013, J FOOD PROTECT, V76, P723, DOI 10.4315/0362-028X.JFP-12-399 Fang GH, 2013, FOOD CHEM, V138, P1461, DOI 10.1016/j.foodchem.2012.09.136 Farag MA, 2014, FOOD RES INT, V64, P218, DOI 10.1016/j.foodres.2014.06.021 Fasciotti M, 2010, TALANTA, V81, P1116, DOI 10.1016/j.talanta.2010.02.006 Fiehn O, 2002, PLANT MOL BIOL, V48, P155, DOI 10.1023/A:1013713905833 Fiorino GM, 2018, J MASS SPECTROM, V53, P781, DOI 10.1002/jms.4260 Fornal E, 2019, FOOD CHEM, V283, P489, DOI 10.1016/j.foodchem.2019.01.074 Fragni R, 2018, FOOD CONTROL, V93, P211, DOI 10.1016/j.foodcont.2018.06.002 Funasaki M, 2012, J AGR FOOD CHEM, V60, P11263, DOI 10.1021/jf303877t Gallart-Ayala H, 2015, BIOANALYSIS, V7, P133, DOI 10.4155/bio.14.267 Galle SA, 2011, J AGR FOOD CHEM, V59, P2554, DOI 10.1021/jf104170r Gan HH, 2014, FOOD CHEM, V146, P149, DOI 10.1016/j.foodchem.2013.09.024 Georgiou CA, 2017, FOOD AUTHENTICATION: MANAGEMENT, ANALYSIS AND REGULATION, P1, DOI 10.1002/9781118810224 Ghasemi N, 2017, FOOD CHEM, V221, P2005, DOI 10.1016/j.foodchem.2016.11.079 Gil-Solsona R, 2016, FOOD CONTROL, V70, P350, DOI 10.1016/j.foodcont.2016.06.008 Godelmann R, 2013, J AGR FOOD CHEM, V61, P5610, DOI 10.1021/jf400800d Gonzalez Vaque L., 2018, European Food and Feed Law Review, P532 Gunning Y, 2019, FOOD CONTROL, V101, P189, DOI 10.1016/j.foodcont.2019.02.029 Guyon F, 2013, J CHROMATOGR A, V1322, P62, DOI 10.1016/j.chroma.2013.10.088 Hill CB., 2014, ADV LC MS APPLICAFIO Hrbek V, 2014, FOOD CONTROL, V36, P138, DOI 10.1016/j.foodcont.2013.08.003 Hu LP, 2018, J AGR FOOD CHEM, V66, P10567, DOI 10.1021/acs.jafc.8b04375 Hu N, 2014, J CHROMATOGR B, V972, P65, DOI 10.1016/j.jchromb.2014.09.039 Hu Q, 2022, FOOD CHEM, V373, DOI 10.1016/j.foodchem.2021.131534 Hu W, 2014, TALANTA, V129, P629, DOI 10.1016/j.talanta.2014.06.010 Oliveras-Lopez MJ, 2007, TALANTA, V73, P726, DOI 10.1016/j.talanta.2007.04.045 Johnson AE, 2019, FOOD CONTROL, V100, P165, DOI 10.1016/j.foodcont.2019.01.023 Kang TS, 2019, TRENDS FOOD SCI TECH, V91, P574, DOI 10.1016/j.tifs.2019.07.037 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kenar A., 2019, FOOD ANAL METHOD, P1 Kim HJ, 2011, FOOD CHEM, V129, P1305, DOI 10.1016/j.foodchem.2011.05.083 Kim JS, 2017, MEAT SCI, V123, P13, DOI 10.1016/j.meatsci.2016.08.011 Lang HH, 2019, LWT-FOOD SCI TECHNOL, V107, P221, DOI 10.1016/j.lwt.2019.03.018 Laursen KH, 2016, WOODHEAD PUBL FOOD S, P227, DOI 10.1016/B978-0-08-100220-9.00009-6 Li BN, 2015, FOOD CHEM, V181, P25, DOI 10.1016/j.foodchem.2015.02.079 Li GL, 2011, FOOD CHEM, V125, P1365, DOI 10.1016/j.foodchem.2010.10.007 Li L, 2016, FOOD CHEM, V194, P1238, DOI 10.1016/j.foodchem.2015.08.123 Li Y, 2017, J CHROMATOGR A, V1499, P78, DOI 10.1016/j.chroma.2017.03.071 Li YY, 2018, FOOD CHEM, V245, P125, DOI 10.1016/j.foodchem.2017.09.066 Liu GY, 2016, INT J FOOD SCI TECH, V51, P2591, DOI 10.1111/ijfs.13244 Liu HY, 2016, FOOD CHEM, V212, P367, DOI 10.1016/j.foodchem.2016.06.002 Liu M, 2018, FOOD CHEM, V242, P338, DOI 10.1016/j.foodchem.2017.09.069 Lu WY, 2017, J DAIRY SCI, V100, P6980, DOI 10.3168/jds.2017-12574 Lu WY, 2014, J AGR FOOD CHEM, V62, P9073, DOI 10.1021/jf502156n Lv SD, 2015, FOOD ANAL METHOD, V8, P321, DOI 10.1007/s12161-014-9900-0 Magagna F, 2016, ANAL CHIM ACTA, V936, P245, DOI 10.1016/j.aca.2016.07.005 Malheiro R, 2013, FOOD RES INT, V54, P186, DOI 10.1016/j.foodres.2013.06.010 Mannina L, 2001, J AGR FOOD CHEM, V49, P2687, DOI 10.1021/jf001408i Gallardo JM, 2013, TRAC-TREND ANAL CHEM, V52, P135, DOI 10.1016/j.trac.2013.05.019 Mattarucchi E, 2010, J AGR FOOD CHEM, V58, P12089, DOI 10.1021/jf102632g Matthaus B, 2016, EUR FOOD RES TECHNOL, V242, P221, DOI 10.1007/s00217-015-2533-8 Mazzeo MF, 2008, J AGR FOOD CHEM, V56, P11071, DOI 10.1021/jf8021783 Mendes TO, 2015, FOOD ANAL METHOD, V8, P2339, DOI 10.1007/s12161-015-0121-y Mi S, 2019, MICROCHEM J, V144, P26, DOI 10.1016/j.microc.2018.08.027 Mi S, 2018, FOOD RES INT, V109, P187, DOI 10.1016/j.foodres.2018.04.038 Molkentin J, 2013, FOOD CHEM, V137, P25, DOI 10.1016/j.foodchem.2012.09.093 Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Monteiro PI, 2018, FOOD CONTROL, V91, P276, DOI 10.1016/j.foodcont.2018.04.009 Montowska M, 2019, FOOD CHEM, V274, P857, DOI 10.1016/j.foodchem.2018.08.131 Montowska M, 2017, FOOD CHEM, V237, P1092, DOI 10.1016/j.foodchem.2017.06.059 Montowska M, 2014, ANAL CHEM, V86, P10257, DOI 10.1021/ac502449w Montowska M, 2013, FOOD CHEM, V136, P1461, DOI 10.1016/j.foodchem.2012.09.072 Nalazek-Rudnicka K, 2019, FOOD CHEM, V283, P367, DOI 10.1016/j.foodchem.2019.01.007 Ng TT, 2015, ANAL CHIM ACTA, V884, P70, DOI 10.1016/j.aca.2015.05.013 Novotna H, 2012, FOOD ADDIT CONTAM A, V29, P1335, DOI 10.1080/19440049.2012.690348 Oddone M, 2009, J AGR FOOD CHEM, V57, P3404, DOI 10.1021/jf900312p Ogrinc N, 2003, ANAL BIOANAL CHEM, V376, P424, DOI 10.1007/s00216-003-1804-6 Ollivier D, 2003, J AGR FOOD CHEM, V51, P5723, DOI 10.1021/jf034365p Orduna AR, 2015, FOOD ADDIT CONTAM A, V32, P1709, DOI 10.1080/19440049.2015.1064173 Ortea I, 2015, FOOD CHEM, V170, P145, DOI 10.1016/j.foodchem.2014.08.049 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3295, DOI 10.1021/jf1040959 Paglia G, 2022, MASS SPECTROM REV, V41, P722, DOI 10.1002/mas.21686 Pavlidis DE, 2019, MEAT SCI, V151, P43, DOI 10.1016/j.meatsci.2019.01.003 Pavon JLP, 2009, ANAL BIOANAL CHEM, V394, P1463, DOI 10.1007/s00216-009-2795-8 Perez-Alvarez EP, 2019, FOOD CHEM, V270, P273, DOI 10.1016/j.foodchem.2018.07.087 Peris M, 2016, TRENDS FOOD SCI TECH, V58, P40, DOI 10.1016/j.tifs.2016.10.014 Persuric Z, 2018, LWT-FOOD SCI TECHNOL, V95, P326, DOI 10.1016/j.lwt.2018.04.072 Portarena S, 2014, FOOD CHEM, V164, P12, DOI 10.1016/j.foodchem.2014.04.115 Psomiadis D, 2018, J FOOD SCI TECH MYS, V55, P2994, DOI 10.1007/s13197-018-3217-8 Pu F, 2020, ACS MED CHEM LETT, V11, P2108, DOI 10.1021/acsmedchemlett.0c00314 Qu QS, 2012, ANAL CHIM ACTA, V757, P83, DOI 10.1016/j.aca.2012.10.032 Rodrigues SM, 2011, J FOOD COMPOS ANAL, V24, P548, DOI 10.1016/j.jfca.2010.12.003 Robledo VR, 2014, ELECTROPHORESIS, V35, P2292, DOI 10.1002/elps.201300561 Rodriguez-Bermudez R, 2018, FOOD CHEM, V240, P686, DOI 10.1016/j.foodchem.2017.08.011 Rodriguez-Bermudez R, 2018, FOOD CHEM, V264, P210, DOI 10.1016/j.foodchem.2018.05.044 Rubert J, 2015, FOOD ADDIT CONTAM A, V32, P1685, DOI 10.1080/19440049.2015.1084539 Rubert J, 2014, ANAL BIOANAL CHEM, V406, P6791, DOI 10.1007/s00216-014-7864-y Ruiz-Samblas C, 2013, TALANTA, V116, P788, DOI 10.1016/j.talanta.2013.07.054 Ruiz-Samblas C, 2011, ANAL BIOANAL CHEM, V399, P2093, DOI 10.1007/s00216-010-4423-z Saba A, 2005, J AGR FOOD CHEM, V53, P4867, DOI 10.1021/jf050274b Sales C, 2019, FOOD CHEM, V271, P488, DOI 10.1016/j.foodchem.2018.07.200 Sales C, 2017, FOOD CHEM, V216, P365, DOI 10.1016/j.foodchem.2016.08.033 Sanchez-Hernandez L, 2012, J AGR FOOD CHEM, V60, P896, DOI 10.1021/jf202857y Sanchez-Hernandez L, 2011, ELECTROPHORESIS, V32, P1394, DOI 10.1002/elps.201100005 Sarah SA, 2016, FOOD CHEM, V199, P157, DOI 10.1016/j.foodchem.2015.11.121 Saraiva SA, 2009, J AGR FOOD CHEM, V57, P4030, DOI 10.1021/jf900043u Sciarrone D, 2018, ANAL CHEM, V90, P6610, DOI 10.1021/acs.analchem.8b00386 Sedo O, 2012, FOOD CHEM, V135, P473, DOI 10.1016/j.foodchem.2012.05.021 Spink J, 2015, FOOD CHEM, V189, P102, DOI 10.1016/j.foodchem.2014.09.106 Spink J, 2011, J FOOD SCI, V76, pR157, DOI 10.1111/j.1750-3841.2011.02417.x Springer AE, 2014, J AGR FOOD CHEM, V62, P6844, DOI 10.1021/jf502042c Springer AE, 2019, EUR FOOD RES TECHNOL, V245, P179, DOI 10.1007/s00217-018-3151-z Strojnik L, 2019, FOOD CHEM, V277, P766, DOI 10.1016/j.foodchem.2018.10.140 Sun XJ, 2010, J SEP SCI, V33, P3159, DOI 10.1002/jssc.201000030 Sun XM, 2015, LWT-FOOD SCI TECHNOL, V63, P430, DOI 10.1016/j.lwt.2015.02.023 Tette PAS, 2017, FOOD CHEM, V229, P527, DOI 10.1016/j.foodchem.2017.02.108 Tistaert C, 2011, ANAL CHIM ACTA, V690, P148, DOI 10.1016/j.aca.2011.02.023 Tranchida PQ, 2013, TRAC-TREND ANAL CHEM, V52, P186, DOI 10.1016/j.trac.2013.07.008 Tsukui A, 2019, LWT-FOOD SCI TECHNOL, V103, P205, DOI 10.1016/j.lwt.2018.12.078 Tu AQ, 2017, FOOD CHEM, V221, P555, DOI 10.1016/j.foodchem.2016.11.139 Tuberoso CIG, 2011, J AGR FOOD CHEM, V59, P364, DOI 10.1021/jf1039074 Vaclavik L, 2011, J AGR FOOD CHEM, V59, P5919, DOI 10.1021/jf200734x Vetter W, 2012, J AGR FOOD CHEM, V60, P6103, DOI 10.1021/jf301373k Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Voerkelius S, 2010, FOOD CHEM, V118, P933, DOI 10.1016/j.foodchem.2009.04.125 von Bargen C, 2014, J AGR FOOD CHEM, V62, P9428, DOI 10.1021/jf503468t Wang DD, 2018, MOLECULES, V23, DOI 10.3390/molecules23051180 Wang GJ, 2018, J FOOD COMPOS ANAL, V73, P47, DOI 10.1016/j.jfca.2018.07.004 Wang J, 2009, J AGR FOOD CHEM, V57, P10081, DOI 10.1021/jf902286p Wang YMV, 2018, FOOD CHEM, V256, P380, DOI 10.1016/j.foodchem.2018.02.095 Watson AD, 2015, ANAL CHEM, V87, P10315, DOI 10.1021/acs.analchem.5b02318 Wei F, 2015, J CHROMATOGR A, V1404, P60, DOI 10.1016/j.chroma.2015.05.058 Wei F, 2013, J CHROMATOGR A, V1312, P69, DOI 10.1016/j.chroma.2013.09.005 Wishart DS, 2008, TRENDS FOOD SCI TECH, V19, P482, DOI 10.1016/j.tifs.2008.03.003 Wishart DS, 2008, TRAC-TREND ANAL CHEM, V27, P228, DOI 10.1016/j.trac.2007.12.001 Wu R, 2016, FOOD CHEM, V204, P334, DOI 10.1016/j.foodchem.2016.02.086 Xiao R, 2018, J CEREAL SCI, V82, P73, DOI 10.1016/j.jcs.2018.05.012 Xin ZQ, 2018, FOOD CHEM, V266, P534, DOI 10.1016/j.foodchem.2018.06.056 Xu BC, 2018, FOOD CHEM, V245, P415, DOI 10.1016/j.foodchem.2017.10.114 Xu BC, 2015, FOOD CHEM, V178, P128, DOI 10.1016/j.foodchem.2015.01.035 Xu BC, 2014, ANAL METHODS-UK, V6, P6860, DOI 10.1039/c4ay01194e Xu L, 2019, FOOD CHEM, V286, P106, DOI 10.1016/j.foodchem.2019.01.154 Xu SL, 2018, ELECTROPHORESIS, V39, P1558, DOI 10.1002/elps.201700481 Xue XF, 2013, J AGR FOOD CHEM, V61, P7488, DOI 10.1021/jf401912u Yang Y, 2013, J AGR FOOD CHEM, V61, P3693, DOI 10.1021/jf4000538 Yuan YW, 2016, J AGR FOOD CHEM, V64, P5633, DOI 10.1021/acs.jafc.6b00453 Yuan Z, 2020, LWT-FOOD SCI TECHNOL, V125, DOI 10.1016/j.lwt.2020.109247 Yun YH, 2019, TRAC-TREND ANAL CHEM, V113, P102, DOI 10.1016/j.trac.2019.01.018 Zeng J, 2019, CHEM-EUR J, V25, P5427, DOI 10.1002/chem.201900539 Zhang CY, 2019, FOOD CHEM, V271, P211, DOI 10.1016/j.foodchem.2018.07.169 Zhang HW, 2019, FOOD CHEM, V274, P592, DOI 10.1016/j.foodchem.2018.08.082 Zhang LX, 2017, CHEMOMETR INTELL LAB, V169, P94, DOI 10.1016/j.chemolab.2017.09.002 Zhang LX, 2015, RSC ADV, V5, P85046, DOI 10.1039/c5ra07329d Zhang LX, 2016, FOOD CHEM, V192, P60, DOI 10.1016/j.foodchem.2015.06.096 Zhang LX, 2014, J AGR FOOD CHEM, V62, P8745, DOI 10.1021/jf501097c Zhang LX, 2014, ANAL CHIM ACTA, V839, P44, DOI 10.1016/j.aca.2014.06.040 Zhang SS, 2017, INT J FOOD SCI TECH, V52, P457, DOI 10.1111/ijfs.13301 Zhang YL, 2018, INT J MASS SPECTROM, V431, P56, DOI 10.1016/j.ijms.2018.06.005 Zhang Y, 2016, J CHROMATOGR B, V1026, P67, DOI 10.1016/j.jchromb.2015.11.015 Zhao FF, 2013, EUR J LIPID SCI TECH, V115, P337, DOI 10.1002/ejlt.201200133 Zhao X, 2015, FOOD CHEM, V176, P465, DOI 10.1016/j.foodchem.2014.12.082 Zhao XD, 2018, FOOD CONTROL, V91, P128, DOI 10.1016/j.foodcont.2018.03.041 Zhou P, 2019, FOOD CHEM, V283, P73, DOI 10.1016/j.foodchem.2019.01.050 NR 197 TC 3 Z9 3 U1 51 U2 60 AR e21779 DI 10.1002/mas.21779 EA MAY 2022 WC Spectroscopy SC Spectroscopy UT WOS:000792375400001 DA 2022-12-14 ER PT J AU Tarjan, L Senk, I Tegeltija, S Stankovski, S Ostojic, G AF Tarjan, Laslo Senk, Ivana Tegeltija, Srdjan Stankovski, Stevan Ostojic, Gordana TI A readability analysis for QR code application in a traceability system SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Traceability; QR code; Barcode; Mobile phone; Readability ID FARM-ANIMAL WELFARE; PRELIMINARY IN-VIVO; POTENTIAL APPLICATION; FOOD TRACEABILITY; SUPPLY CHAIN; 2D BARCODE; E-TRACKING; IMPACT AB Traceability of data through transformation stages of each individual food product, starting from raw products and to the final product, as well as printing the key data on the product package, adds to the consumers' trust in product quality. For each food product, it is necessary to track data starting from the stage of raw products farming, through food processing, transport, warehousing, to retailing and reaching the end consumer. In order to allow insight to the key data to the user (mostly end consumer), this paper suggests recording the data on the product package in the form of a quick response two-dimensional barcode (QR code) in key points of the product's life cycle. For efficient functioning of the proposed system, it is essential to ensure fast and reliable operation through proper placement of the QR code on the package during production, and fast and easy data reading by the product consumer. This paper presents the results of a readability analysis of QR code of variable contents, size and data error correction level, which are read by smartphones running an Android platform. The experiments were performed with various types of base material on which the code was printed. Furthermore, QR code readability analysis was conducted in the case when there is a geometric deformation of the code. Based on the detailed analysis of the collected data, it can be concluded that QR code readability is not directly influenced by the number of coded characters, or by the error correction level, but only by the size of modules that constitute the code. Furthermore, the results show that the change of the base material does not influence the read time, but influences the code readability. The paper further presents an example of the proposed traceability system, where the QR codes are used for data tracking and tracing for fruit yogurts, based on the recommendations gained through the readability analysis. This traceability system concept is universal and can be used for various products with slight modifications. (C) 2014 Elsevier B.V. All rights reserved. C1 [Tarjan, Laslo; Senk, Ivana; Tegeltija, Srdjan; Stankovski, Stevan; Ostojic, Gordana] Univ Novi Sad, Fac Tech Sci, Novi Sad 21000, Serbia. C3 University of Novi Sad RP Ostojic, G (corresponding author), Univ Novi Sad, Fac Tech Sci, Trg Dositeja Obradovica 6, Novi Sad 21000, Serbia. EM laci@uns.ac.rs; ivanas@uns.ac.rs; srkit@uns.ac.rs; stevan@uns.ac.rs; goca@uns.ac.rs CR Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chen CS, 2013, PATTERN RECOGN, V46, P2588, DOI 10.1016/j.patcog.2013.01.031 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Cunha CR, 2010, COMPUT ELECTRON AGR, V73, P154, DOI 10.1016/j.compag.2010.05.007 Frewer LJ, 2005, J AGR ENVIRON ETHIC, V18, P345, DOI 10.1007/s10806-005-1489-2 Froschle HK, 2009, COMPUT ELECTRON AGR, V66, P126, DOI 10.1016/j.compag.2009.01.002 Harper G. C., 2002, British Food Journal, V104, P287, DOI 10.1108/00070700210425723 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 International Standard Organization, 2007, 220052007 ISO LabbGroup, 2013, PLAST PACK FRUIT VEG Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Maria GA, 2006, LIVEST SCI, V103, P250, DOI 10.1016/j.livsci.2006.05.011 Mc Inerney B, 2011, COMPUT ELECTRON AGR, V79, P51, DOI 10.1016/j.compag.2011.08.004 Mc Inerney B, 2011, COMPUT ELECTRON AGR, V77, P1, DOI 10.1016/j.compag.2011.03.001 Osman KA, 2000, ASSEMBLY AUTOM, V20, P52, DOI 10.1108/01445150010311707 Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 Phuong N.T., 2013, J AGR EC DEV, V2, P44 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Saltini R, 2013, FOOD CONTROL, V29, P167, DOI 10.1016/j.foodcont.2012.05.054 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Senk I, 2013, IFIP ADV INF COMM TE, V394, P155 Standardization, 2006, ISOIEC180042006 Tarjan L., 2011, INT J IND ENG MANAG, V2, P151 Vanhonacker F, 2008, LIVEST SCI, V116, P126, DOI 10.1016/j.livsci.2007.09.017 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Zamberletti A, 2011, COMM COM INF SC, V229, P45 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhang XS, 2010, FOOD CONTROL, V21, P1556, DOI 10.1016/j.foodcont.2010.03.020 ZXing T., 2013, BARCODE SCANNER ZXing T., 2013, MULTIFORMAT 1D 2D BA NR 33 TC 63 Z9 67 U1 9 U2 120 PD NOV PY 2014 VL 109 BP 1 EP 11 DI 10.1016/j.compag.2014.08.015 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000346215800001 DA 2022-12-14 ER PT J AU Zhang, XS Feng, JY Xu, M Hu, JY AF Zhang, Xiaoshuan Feng, Jianying Xu, Mark Hu, Jinyou TI Modeling traceability information and functionality requirement in export-oriented tilapia chain SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE export-oriented tilapia chain; traceability system; functionality requirement; China ID FRAMEWORK; FOOD; AGRIBUSINESS; QUALITY; SAFETY; SYSTEM AB BACKGROUND: Tilapia has been named as the 'food fish of the 21st century' and has become the most important farmed fish. China is the world leader in tilapia production and export. Identifying information and functional requirements is critical in developing an efficient traceability system because traceability has become a fundamental prerequisite for exporting aquaculture products. RESULTS: This paper examines the export-oriented tilapia chains and information flow in the chains, and identifies the key actors, information requirements and information-capturing points. Unified Modeling Language (UML) technology is adopted to describe the information and functionality requirement for chain traceability. The barriers of traceability system adoption are also identified. CONCLUSION: The results show that the traceability data consist of four categories that must be recorded by each link in the chain. The functionality requirement is classified into four categories from the fundamental information record to decisive quality control; the top three barriers to the traceability system adoption are: high costs of implementing the system, lack of experienced and professional staff; and low level of government involvement and support. (C) 2011 Society of Chemical Industry C1 [Feng, Jianying; Hu, Jinyou] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China. [Zhang, Xiaoshuan] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. [Xu, Mark] Univ Portsmouth, Portsmouth Business Sch, Portsmouth PO1 3DE, Hants, England. C3 China Agricultural University; China Agricultural University; University of Portsmouth RP Hu, JY (corresponding author), China Agr Univ, Coll Engn, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Acosta BO, 2010, SUCCESS STORIES IN ASIAN AQUACULTURE, P149, DOI 10.1007/978-90-481-3087-0_8 ASIOLI D, 2009, THESIS U BOLOGNA ITA Belkadi F, 2006, LECT NOTES COMPUT SC, V3865, P355 Bruque S, 2007, TECHNOVATION, V27, P241, DOI 10.1016/j.technovation.2006.12.003 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Dannson A, 2004, STRENGTHENING FARM A El-Sayed AFM, 1999, AQUACULTURE, V179, P149, DOI 10.1016/S0044-8486(99)00159-3 FITZSIMMONS K, 2004, 6 INT S TIL AQ MAN P Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Frederiksen M, 2001, FOOD AUST, V53, P117 HARTLEYALCOCER AG, 2007, THESIS U STIRLING UK HE Y, 2009, CHIN FISH EC, P85 Kim CH, 2003, COMPUT IND, V50, P35, DOI 10.1016/S0166-3615(02)00145-8 Kirsten J., 2002, FARM AGRIBUSINESS LI Laudon K. C., 1999, MANAGEMENT INFORM SY Li S.F, 2000, CHINA FISH, P15 Li SM, 2010, SCAND ACTUAR J, P136, DOI 10.1080/03461230902850162 LUO L, 2006, FISH EC RES, P27 Naylor RL, 2000, NATURE, V405, P1017, DOI 10.1038/35016500 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x REN X, 2009, NONGYE GONGCHENG XUE, V25, P163 RESENDE M, 2007, 3821 MPRA SIRKKA A, 2008, IEEE 24 INT C DAT EN Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 [王慧芝 Wang Huizhi], 2010, [山西农业科学, Journal of Shanxi Agricultural Sciences], V38, P81 Wu Chunming, 2008, Tsinghua Science and Technology, V13, P318, DOI 10.1016/S1007-0214(08)70051-8 Yang X., 2008, NONGYE GONGCHENG XUE, V24, P159 YOKOYAMA K, 2007, INT S TRAC FOOD SAF, P154 Zhang XS, 2010, FOOD CONTROL, V21, P1556, DOI 10.1016/j.foodcont.2010.03.020 Zheng XP, 2010, J FOOD AGRIC ENVIRON, V8, P1144 Ziggers GW, 1999, INT J PROD ECON, V60-1, P271, DOI 10.1016/S0925-5273(98)00138-8 FRESH TILAPIA FACTS NR 35 TC 12 Z9 13 U1 0 U2 24 PD MAY PY 2011 VL 91 IS 7 BP 1316 EP 1325 DI 10.1002/jsfa.4320 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000289527800023 DA 2022-12-14 ER PT J AU Fanelli, V Mascio, I Miazzi, MM Savoia, MA De Giovanni, C Montemurro, C AF Fanelli, Valentina Mascio, Isabella Miazzi, Monica Marilena Savoia, Michele Antonio De Giovanni, Claudio Montemurro, Cinzia TI Molecular Approaches to Agri-Food Traceability and Authentication: An Updated Review SO FOODS DT Review DE molecular traceability; authentication; agri-food; molecular markers; DNA barcoding; isothermal amplification; sequencing ID CHLOROPLAST GENOME SEQUENCES; GRAPE DNA ADULTERATION; MELTING SSR-HRM; GEOGRAPHICAL ORIGIN; MASS-SPECTROMETRY; DIGITAL PCR; FLUORESCENCE SPECTROSCOPY; RECOMBINASE POLYMERASE; GAS-CHROMATOGRAPHY; PROCESSED FOOD AB In the last decades, the demand for molecular tools for authenticating and tracing agri-food products has significantly increased. Food safety and quality have gained an increased interest for consumers, producers, and retailers, therefore, the availability of analytical methods for the determination of food authenticity and the detection of major adulterations takes on a fundamental role. Among the different molecular approaches, some techniques such as the molecular markers-based methods are well established, while some innovative approaches such as isothermal amplification-based methods and DNA metabarcoding have only recently found application in the agri-food sector. In this review, we provide an overview of the most widely used molecular techniques for fresh and processed agri-food authentication and traceability, showing their recent advances and applications and discussing their main advantages and limitations. The application of these techniques to agri-food traceability and authentication can contribute a great deal to the reassurance of consumers in terms of transparency and food safety and may allow producers and retailers to adequately promote their products. C1 [Fanelli, Valentina; Mascio, Isabella; Miazzi, Monica Marilena; Savoia, Michele Antonio; De Giovanni, Claudio; Montemurro, Cinzia] Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, Via Amendola 165-A, I-70126 Bari, Italy. [Montemurro, Cinzia] Univ Bari Aldo Moro, Spin Off Sinagri Srl, Via Amendola 165-A, I-70126 Bari, Italy. [Montemurro, Cinzia] Natl Res Council Italy CNR, Inst Sustainable Plant Protect, Support Unit Bari, Via Amendola 122-D, I-70126 Bari, Italy. C3 Universita degli Studi di Bari Aldo Moro; Universita degli Studi di Bari Aldo Moro; Consiglio Nazionale delle Ricerche (CNR); Istituto per la Protezione Sostenibile delle Piante (IPSP-CNR) RP Fanelli, V (corresponding author), Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, Via Amendola 165-A, I-70126 Bari, Italy. EM valentina.fanelli@uniba.it; mascioisa@gmail.com; monicamarilena.miazzi@uniba.it; michele.savoia@uniba.it; claudio.degiovanni@uniba.it; cinzia.montemurro@uniba.it CR Abbas O, 2018, FOOD CHEM, V246, P6, DOI 10.1016/j.foodchem.2017.11.007 Ahmad MH, 2017, ADV BIOCHEM ENG BIOT, V161, P121, DOI 10.1007/10_2017_11 Akpertey A, 2021, FRONT PLANT SCI, V11, DOI 10.3389/fpls.2020.612593 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Ballin NZ, 2019, FOOD CONTROL, V105, P141, DOI 10.1016/j.foodcont.2019.05.022 Ballin NZ, 2019, TRENDS FOOD SCI TECH, V86, P537, DOI 10.1016/j.tifs.2018.09.025 Banchi E, 2020, DATABASE-OXFORD, DOI 10.1093/database/baz155 Barrias S, 2019, FOOD CHEM, V270, P299, DOI 10.1016/j.foodchem.2018.07.058 Beck KL, 2021, NPJ SCI FOOD, V5, DOI 10.1038/s41538-020-00083-y Ben Ayed R, 2019, BIOMED RES INT, V2019, DOI 10.1155/2019/8291341 Besse Pascale, 2021, Methods Mol Biol, V2222, P131, DOI 10.1007/978-1-0716-0997-2_8 Boccacci P, 2020, FOOD CHEM, V312, DOI 10.1016/j.foodchem.2019.126100 Bojang KP, 2021, J FOOD SCI TECH MYS, V58, P3561, DOI 10.1007/s13197-021-05079-4 Bosmali I, 2017, FOOD RES INT, V100, P899, DOI 10.1016/j.foodres.2017.08.001 Bosmali I, 2012, FOOD RES INT, V46, P141, DOI 10.1016/j.foodres.2011.12.013 Bosmali I, 2021, LWT-FOOD SCI TECHNOL, V137, DOI 10.1016/j.lwt.2020.110336 Bruno A, 2019, GENES-BASEL, V10, DOI 10.3390/genes10030248 Castro-Puyana M, 2013, TRAC-TREND ANAL CHEM, V52, P74, DOI 10.1016/j.trac.2013.05.016 Chedid E, 2020, FOOD CHEM X, V6, DOI 10.1016/j.fochx.2020.100082 Cibecchini G, 2020, FOODS, V9, DOI 10.3390/foods9111691 Consonni R, 2019, MAGN RESON CHEM, V57, P558, DOI 10.1002/mrc.4807 Corrado G, 2016, TRENDS FOOD SCI TECH, V52, P80, DOI 10.1016/j.tifs.2016.04.003 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Demeke T, 2018, ANAL BIOANAL CHEM, V410, P4039, DOI 10.1007/s00216-018-1010-1 di Rienzo V, 2017, ACTA HORTIC, V1188, P365, DOI 10.17660/ActaHortic.2017.1188.49 di Rienzo V, 2016, FOOD CONTROL, V60, P124, DOI 10.1016/j.foodcont.2015.07.015 Ding YF, 2020, J SCI FOOD AGR, V100, P2372, DOI 10.1002/jsfa.10241 Dong XW, 2020, ANAL BIOANAL CHEM, V412, P1701, DOI 10.1007/s00216-020-02410-4 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Dymerski T, 2018, CRIT REV ANAL CHEM, V48, P252, DOI 10.1080/10408347.2017.1411248 Esteki M, 2019, FOOD RES INT, V122, P303, DOI 10.1016/j.foodres.2019.04.025 Esteki M, 2018, FOOD CONTROL, V91, P100, DOI 10.1016/j.foodcont.2018.03.031 Fang WP, 2014, HORTIC RES-ENGLAND, V1, DOI 10.1038/hortres.2014.35 Fang WP, 2014, J AGR FOOD CHEM, V62, P481, DOI 10.1021/jf404402v Frigerio J, 2019, J APPL BOT FOOD QUAL, V92, P33, DOI 10.5073/JABFQ.2019.092.005 Galimberti A, 2019, CURR OPIN FOOD SCI, V28, P41, DOI 10.1016/j.cofs.2019.07.008 Galimberti A, 2015, FOOD RES INT, V69, P424, DOI 10.1016/j.foodres.2015.01.017 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Ganopoulos I, 2011, FOOD CONTROL, V22, P532, DOI 10.1016/j.foodcont.2010.09.040 Gomes S, 2018, J FOOD SCI, V83, P2415, DOI 10.1111/1750-3841.14333 Gong L, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0201240 Gostel MR, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-64919-z Haiminen N, 2019, NPJ SCI FOOD, V3, DOI 10.1038/s41538-019-0056-6 Haynes E, 2019, FOOD CONTROL, V101, P134, DOI 10.1016/j.foodcont.2019.02.010 He L, 2020, FOOD MICROBIOL, V90, DOI 10.1016/j.fm.2020.103466 Hebert PDN, 2003, P ROY SOC B-BIOL SCI, V270, P313, DOI [10.1098/rspb.2002.2218, 10.1098/rsbl.2003.0025] Hongsibsong S, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17134723 Hu YX, 2020, ACS SENSORS, V5, P2168, DOI 10.1021/acssensors.0c00786 Huo YQ, 2017, J CEREAL SCI, V76, P243, DOI 10.1016/j.jcs.2017.07.002 Jagadeesan B, 2019, FOOD MICROBIOL, V79, P96, DOI 10.1016/j.fm.2018.11.005 Kane N, 2012, AM J BOT, V99, P320, DOI 10.3732/ajb.1100570 Kane NC, 2008, MOL ECOL, V17, P5175, DOI 10.1111/j.1365-294X.2008.03972.x Khansaritoreh E, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e05596 Kim Y, 2020, MITOCHONDRIAL DNA B, V5, P1843, DOI 10.1080/23802359.2020.1750318 Kumar KR, 2019, SEMIN THROMB HEMOST, V45, P661, DOI 10.1055/s-0039-1688446 Kumar P, 2009, PLANT OMICS, V2, P141 Lagiotis G, 2020, J FOOD SCI, V85, P1629, DOI 10.1111/1750-3841.15138 Lee HJ, 2017, J AGR FOOD CHEM, V65, P10350, DOI 10.1021/acs.jafc.7b04167 Leonardo S, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21020602 Liu Y, 2016, FRONT PLANT SCI, V7, DOI 10.3389/fpls.2016.00319 Liu Y, 2018, FOOD CHEM, V242, P62, DOI 10.1016/j.foodchem.2017.09.040 Lo YT, 2019, BIOTECHNOL ADV, V37, DOI 10.1016/j.biotechadv.2019.107450 Lo YT, 2018, FOOD CHEM, V240, P767, DOI 10.1016/j.foodchem.2017.08.022 Lohumi S, 2015, TRENDS FOOD SCI TECH, V46, P85, DOI 10.1016/j.tifs.2015.08.003 Longobardi F, 2017, FOOD CHEM, V237, P743, DOI 10.1016/j.foodchem.2017.05.159 Lopez-Calleja IM, 2015, FOOD ADDIT CONTAM A, V32, P1772, DOI 10.1080/19440049.2015.1079650 Low HY, 2017, ANAL BIOANAL CHEM, V409, P1869, DOI 10.1007/s00216-016-0131-7 Maitiniyazi S, 2020, J DAIRY SCI, V103, P11257, DOI 10.3168/jds.2020-18408 Marrano A, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0170655 Martins-Lopes P, 2013, FOOD TECHNOL BIOTECH, V51, P198 Matsuoka Y, 2012, FOOD HYG SAFE SCI, V53, P195, DOI 10.3358/shokueishi.53.195 Medina S, 2019, TRENDS FOOD SCI TECH, V85, P163, DOI 10.1016/j.tifs.2019.01.017 Morcia C, 2020, FOODS, V9, DOI 10.3390/foods9070911 Morisset D, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0062583 Nielsen R, 2011, NAT REV GENET, V12, P443, DOI 10.1038/nrg2986 Nolvachai Y, 2017, TRAC-TREND ANAL CHEM, V96, P124, DOI 10.1016/j.trac.2017.05.001 Opatic AM, 2018, FOOD CONTROL, V89, P133, DOI 10.1016/j.foodcont.2017.11.013 Papapetros S, 2018, J FOOD COMPOS ANAL, V72, P48, DOI 10.1016/j.jfca.2018.06.006 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3312, DOI 10.1021/jf063383e Pavan S, 2019, FRONT GENET, V10, DOI 10.3389/fgene.2019.00872 Pelegrino BO, 2020, J DAIRY SCI, V103, P4874, DOI 10.3168/jds.2019-17997 Peletto, 2021, FOOD CONTROL, V128 Pereira L, 2018, FOOD RES INT, V103, P170, DOI 10.1016/j.foodres.2017.10.026 Pereira L, 2011, AM J ENOL VITICULT, V62, P547, DOI 10.5344/ajev.2011.10022 Piarulli L, 2019, FOODS, V8, DOI 10.3390/foods8100462 Pierboni E, 2018, FOOD CONTROL, V92, P128, DOI 10.1016/j.foodcont.2018.04.039 Pinczinger D, 2020, EUR J HORTIC SCI, V85, P79, DOI 10.17660/eJHS.2020/85.2.1 Powell W, 1996, TRENDS PLANT SCI, V1, P215, DOI 10.1016/1360-1385(96)86898-1 Raime K, 2020, FRONT PLANT SCI, V11, DOI 10.3389/fpls.2020.00646 Ratnasingham S, 2007, MOL ECOL NOTES, V7, P355, DOI 10.1111/j.1471-8286.2007.01678.x Ripp F, 2014, BMC GENOMICS, V15, DOI 10.1186/1471-2164-15-639 Rubert J, 2015, FOOD ADDIT CONTAM A, V32, P1685, DOI 10.1080/19440049.2015.1084539 Sabetta W, 2017, RIV ITAL SOSTANZE GR, V94, P37 Salazar MO, 2018, MICROCHEM J, V141, P264, DOI 10.1016/j.microc.2018.05.037 Santiago-Felipe S, 2014, ANAL CHIM ACTA, V811, P81, DOI 10.1016/j.aca.2013.12.017 Scarano D., 2014, Diversity, V6, P579 Shi J., 2015, OPEN ACCESS LIB J, V2, pE1264, DOI DOI 10.4236/0ALIB.1101264 Silletti S, 2019, FOOD CHEM, V271, P410, DOI 10.1016/j.foodchem.2018.07.178 Singh R, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-41204-2 Soares S, 2015, FOOD CONTROL, V48, P130, DOI 10.1016/j.foodcont.2014.02.035 Sonnante G, 2009, J AGR FOOD CHEM, V57, P10199, DOI 10.1021/jf902624z Spaniolas S, 2014, J AOAC INT, V97, P1114, DOI 10.5740/jaoacint.13-237 Speranskaya AS, 2018, FOOD CONTROL, V93, P315, DOI 10.1016/j.foodcont.2018.04.040 Stagnati L, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107392 Swetha VP, 2017, FOOD CONTROL, V73, P1010, DOI 10.1016/j.foodcont.2016.10.004 Tan J, 2017, FOOD CHEM, V217, P274, DOI 10.1016/j.foodchem.2016.08.053 Taranto F, 2018, ACTA HORTIC, V1199, P27, DOI [10.17660/ActaHortic.2018.1199.5, 10.17660/actahortic.2018.1199.5] Uncu AT, 2018, FOOD CONTROL, V91, P32, DOI 10.1016/j.foodcont.2018.03.029 Uncu AO, 2020, CYTA-J FOOD, V18, P187, DOI 10.1080/19476337.2020.1727961 Utzeri VJ, 2018, FOOD CONTROL, V86, P342, DOI 10.1016/j.foodcont.2017.11.033 Valentini P, 2017, ANGEW CHEM INT EDIT, V56, P8094, DOI 10.1002/anie.201702120 Verdone M, 2018, FOOD CONTROL, V84, P197, DOI 10.1016/j.foodcont.2017.07.039 Voorhuijzen MM, 2012, ANAL BIOANAL CHEM, V402, P693, DOI 10.1007/s00216-011-5534-x Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Watanabe E, 2018, J ENVIRON SCI HEAL B, V53, P707, DOI 10.1080/03601234.2018.1480154 Wilkinson MJ, 2017, SCI REP-UK, V7, DOI 10.1038/srep46040 Wong YP, 2018, J APPL MICROBIOL, V124, P626, DOI 10.1111/jam.13647 Wu L, 2019, TRAC-TREND ANAL CHEM, V113, P140, DOI 10.1016/j.trac.2019.02.002 Wu YJ, 2018, J FOOD SCI, V83, P1494, DOI 10.1111/1750-3841.14177 Xia YM, 2021, FOOD ANAL METHOD, V14, P196, DOI 10.1007/s12161-020-01867-4 Xu Y, 2020, TRAC-TREND ANAL CHEM, V131, DOI 10.1016/j.trac.2020.116017 Zambianchi S, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107929 Zhang DP, 2020, J AOAC INT, V103, P315, DOI 10.1093/jaocint/qsz002 Zhang N, 2017, PLANTA MED, V83, P1420, DOI 10.1055/s-0043-113449 Zhang XZ, 2014, BIOSENS BIOELECTRON, V61, P491, DOI 10.1016/j.bios.2014.05.039 Zhao J, 2020, BIOTECHNOL BIOTEC EQ, V34, P48, DOI 10.1080/13102818.2019.1711185 Zhao MM, 2019, FOOD CONTROL, V100, P117, DOI 10.1016/j.foodcont.2019.01.011 Zhao MM, 2016, SCI REP-UK, V6, DOI 10.1038/srep25370 Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 NR 129 TC 16 Z9 16 U1 8 U2 35 PD JUL PY 2021 VL 10 IS 7 AR 1644 DI 10.3390/foods10071644 WC Food Science & Technology SC Food Science & Technology UT WOS:000676309100001 DA 2022-12-14 ER PT J AU Hou, B Wu, LH Chen, XJ Zhu, D Ying, RY Tsai, FS AF Hou, Bo Wu, Linhai Chen, Xiujuan Zhu, Dian Ying, Ruiyao Tsai, Fu-Sheng TI Consumers' Willingness to Pay for Foods with Traceability Information: Ex-Ante Quality Assurance or Ex-Post Traceability? SO SUSTAINABILITY DT Article DE willingness to pay; traceability in pork information; ex-ante quality assurance; ex-post traceability; Becker-DeGroot-Marschak experimental auction; multiple price list ID COUNTRY-OF-ORIGIN; BEEF; SAFETY; PREFERENCES; VALUATION; AUCTION; MILK; ATTRIBUTES; PRICE; MEAT AB In this study, traceability in pork profile information with ex-ante quality assurance and ex-post traceability are constructed. Consumers' willingness to pay (WTP) for traceability information is investigated in Wuxi, China, by combining the Multiple Price Lists method and the Becker-DeGroot-Marschak (BDM) experimental auction. The main factors affecting consumers' WTP are also analyzed using a Tobit model. The results demonstrate that consumers have higher WTP for ex-ante quality assurance than for ex-post traceability. The highest WTP is for the ex-ante quality assurance attribute of pork quality inspection. Consumers' WTP for traceability information is influenced by their individual characteristics, including age, education and income, as well as their concern and satisfaction about food safety and confidence in food safety labeling. The contribution of this paper is that it improves the meaning of traceable food information attributes and measures the significance of attributes to consumers. Furthermore, this paper introduces a Becker-DeGroot-Marschak experimental auction method which amends the measurement deviation of hypothetical experiments. C1 [Hou, Bo] Jiangsu Normal Univ, Sch Philosophy & Publ Adm, Xuzhou 221116, Jiangsu, Peoples R China. [Wu, Linhai; Chen, Xiujuan] Jiangnan Univ, Synerget Innovat Ctr Food Safety & Nutr, Food Safety Res Base Jiangsu Prov Sch Business, Wuxi 214122, Peoples R China. [Zhu, Dian] Soochow Univ, Sch Dongwu Business, Suzhou 215021, Peoples R China. [Ying, Ruiyao] Nanjing Agr Univ, Coll Econ & Management, Nanjing 210095, Jiangsu, Peoples R China. [Tsai, Fu-Sheng] Cheng Shiu Univ, Dept Business Adm, Kaohsiung 83347, Taiwan. [Tsai, Fu-Sheng] Cheng Shiu Univ, Ctr Environm Toxin & Emerging Contaminant Res, Kaohsiung 83347, Taiwan. [Tsai, Fu-Sheng] Cheng Shiu Univ, Super Micro Mass Res & Technol Ctr, Kaohsiung 83347, Taiwan. C3 Jiangsu Normal University; Jiangnan University; Soochow University - China; Nanjing Agricultural University; Cheng Shiu University; Cheng Shiu University; Cheng Shiu University RP Wu, LH (corresponding author), Jiangnan Univ, Synerget Innovat Ctr Food Safety & Nutr, Food Safety Res Base Jiangsu Prov Sch Business, Wuxi 214122, Peoples R China. EM houbo0451@126.com; wlh6799@jiangnan.edu.cn; xjchen@jiangnan.edu.cn; 15150157673@139.com; yry@njau.edu.cn; tsaifs@gcloud.csu.edu.tw CR Akaichi F, 2012, CAN J AGR ECON, V60, P469, DOI 10.1111/j.1744-7976.2012.01254.x Akaichi F, 2012, BRIT FOOD J, V114, P19, DOI 10.1108/00070701211197347 Alphonce R, 2017, J AGR ECON, V68, P123, DOI 10.1111/1477-9552.12170 Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 BECKER GM, 1964, BEHAV SCI, V9, P226, DOI 10.1002/bs.3830090304 Csermely T, 2016, J RISK UNCERTAINTY, V53, P107, DOI 10.1007/s11166-016-9247-6 de-Magistris T, 2013, AM J AGR ECON, V95, P1136, DOI 10.1093/ajae/aat052 Drichoutis AC, 2016, J RISK UNCERTAINTY, V53, P89, DOI 10.1007/s11166-016-9248-5 Gao Z., 2011, THESIS Ginon E, 2014, FOOD QUAL PREFER, V33, P54, DOI 10.1016/j.foodqual.2013.11.003 Ginon E, 2014, FOOD QUAL PREFER, V31, P173, DOI 10.1016/j.foodqual.2011.08.007 Haghiri M, 2014, BRIT FOOD J, V116, P1092, DOI 10.1108/BFJ-11-2012-0289 Hamukwala P, 2019, J AGR ECON, V70, P81, DOI 10.1111/1477-9552.12273 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2006, CAN J AGR ECON, V54, P269, DOI 10.1111/j.1744-7976.2006.00049.x Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131 Liljenstolpe C, 2008, AGRIBUSINESS, V24, P67, DOI [10.1002/agr.20147, 10.1002/AGR.20147] Lim KH, 2013, CAN J AGR ECON, V61, P93, DOI 10.1111/j.1744-7976.2012.01260.x Liu XL, 2015, BRIT FOOD J, V117, P1440, DOI 10.1108/BFJ-08-2014-0295 Lombardi A, 2019, FOOD QUAL PREFER, V72, P177, DOI 10.1016/j.foodqual.2018.10.001 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Lusk JL, 2004, AM J AGR ECON, V86, P389, DOI 10.1111/j.0092-5853.2004.00586.x NELSON P, 1974, J POLIT ECON, V82, P729, DOI 10.1086/260231 Ortega DL, 2014, J INTEGR AGR, V13, P1404, DOI 10.1016/S2095-3119(13)60676-0 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Reid LM, 2006, TRENDS FOOD SCI TECH, V17, P344, DOI 10.1016/j.tifs.2006.01.006 Sanchez M, 2012, FOOD QUAL PREFER, V24, P30, DOI 10.1016/j.foodqual.2011.08.008 Schnettler B, 2009, FOOD QUAL PREFER, V20, P156, DOI 10.1016/j.foodqual.2008.07.006 Sterling B, 2015, COMPR REV FOOD SCI F, V14, P205, DOI 10.1111/1541-4337.12130 Ubilava D, 2010, TECHNOL FORECAST SOC, V77, P587, DOI 10.1016/j.techfore.2009.02.002 Unnevehr L, 2010, AM J AGR ECON, V92, P506, DOI 10.1093/ajae/aaq007 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wang LJ, 2016, FOOD CONTROL, V64, P240, DOI 10.1016/j.foodcont.2016.01.005 Wu L.H., 2016, CHIN RURAL EC, V6, P47 Wu L.H., 2013, J PUBLIC MANAG, V3, P119 Wu LT, 2014, BIOMEDICINE-TAIWAN, V4, P35, DOI 10.7603/s40681-014-0021-2 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Wu LH, 2015, CHINA AGR ECON REV, V7, P303, DOI 10.1108/CAER-11-2013-0153 Wu LH, 2012, CAN J AGR ECON, V60, P317, DOI 10.1111/j.1744-7976.2011.01236.x Xue H, 2010, FOOD QUAL PREFER, V21, P857, DOI 10.1016/j.foodqual.2010.05.004 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 47 TC 11 Z9 11 U1 7 U2 36 PD MAR 1 PY 2019 VL 11 IS 5 AR 1464 DI 10.3390/su11051464 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000462661000248 DA 2022-12-14 ER PT J AU Isanta-Munoz, F De Tena-Fernandez, AG Moyano-Salvago, R Villarroel-Molina, O Barba-Capote, C AF Isanta-Munoz, F. De Tena-Fernandez, A. Garcia Moyano-Salvago, R. Villarroel-Molina, O. Barba-Capote, C. TI Process management in the traceability system LeTrA Q of goat and sheep milk in Andalusia, Spain SO ESIC MARKET DT Article DE Goat milk; sheep milk; LeTrA Q traceability system AB Objective: This paper aims to analyse the process management in the traceability system LeTtrA Q of goat and sheep milk, and thus contribute to the knowledge of the business value chain in Andalusia. Methodology: Five process management variables were studied: sample status (EM), sample results (RM), time from the sample collection to the reception in the laboratory (TT-R), time from sample reception to its analysis (TR-A), and the total time from the sample collection to its analysis (TT-A). A sample of 84,484 goat milk (628 farms) and 7,507 sheep milk (53 farms) was analyzed. A non-parametric analysis of variance was applied to the qualitative variables, while an univariate analysis was performed to the quantitative variables. Results: The comparative analysis has shown significant differences in the process management variables for the factor year and type of laboratory. Limitations: This study focuses on a single case study resulting in a limited generalization of its findings. Practical implications: The study of process management in the agri-food system contributes to the improvement of food security and it is a useful tool to enhance the technical-economic management of the farms. C1 [Isanta-Munoz, F.; De Tena-Fernandez, A. Garcia] Consejeria Agr Ganaderia Pesca & Desarrollo Soste, Seville, Spain. [Moyano-Salvago, R.; Villarroel-Molina, O.; Barba-Capote, C.] Univ Cordoba, Cordoba, Spain. C3 Universidad de Cordoba RP Isanta-Munoz, F (corresponding author), Consejeria Agr Ganaderia Pesca & Desarrollo Soste, Seville, Spain. EM fernando.isanta@juntadeandalucia.es; agustin.garciatena@juntadeandalucia.es; ft1mosam@uco.es; economistavillarroel@gmail.com; cjbarba@uco.es CR Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Brown B. E. P., 2009, THESIS CAGPDS [Consejeria de Agricultura Ganaderia Pesca y Desarrollo Sostenible], 2019, CAR SECT OV CAPR AND De Pablos-Heredero C., 2013, CIEN ERRORES EMPREND FEGA [ Fondo Espanol de Garantia Agraria], 2018, DECL OBL SECT OV CAP Heredero C., 2012, ORG TRANSFORMACION S Isanta F, 2018, REV CIENT-FAC CIEN V, V28, P360 MAPA [Ministerio de Agricultura Pesca y Alimentacion], 2011, LETR Q MAPA [Ministerio de Agricultura Pesca y Alimentacion], 2019, INF SECT OV CAPR MAPA [Ministerio de Agricultura Pesca y Alimentacion], 2018, LETR Q StatSoft Inc, 2011, STATISTICA DAT AN SO Martinez JAD, 2018, J APPL ANIM RES, V46, P784, DOI 10.1080/09712119.2017.1403327 Vilaboa-Arroniz J., 2014, Agroproductividad, V7, P3 Villarroel-Molina O, 2019, ESIC MARK, V50, P233, DOI 10.7200/esicm.163.0502.1 NR 14 TC 0 Z9 0 U1 0 U2 2 PD MAY-AUG PY 2020 VL 51 IS 2 SI SI BP 341 EP 359 DI 10.7200/esicm.166.0512.3 WC Business SC Business & Economics UT WOS:000583177200004 DA 2022-12-14 ER PT J AU Mitchell, J Tonsor, GT Schulz, L AF Mitchell, James Tonsor, Glynn T. Schulz, Lee TI The market for traceability with applications to US feeder cattle SO EUROPEAN REVIEW OF AGRICULTURAL ECONOMICS DT Article DE identification; cattle markets; cost-share policy; traceability ID VOLUNTARY TRACEABILITY; ANIMAL IDENTIFICATION; PRODUCER PREFERENCES; FOOD SAFETY; INFORMATION; INCENTIVES; SYSTEM AB For voluntary traceability programs, a key interest for program designers and policy-makers is how to encourage participation. We contend that participating in voluntary traceability can be viewed as a product characteristic, and thus serves as a source of product differentiation. We study the implicit market for traceability systems for the first known time. In our empirical example, we use stated choice experiments to link feeder cattle sellers and buyers through premiums and discounts for cattle traceability systems. Using results from discrete choice models, we simulate changes in traceability supply and demand in response to prices and policies. We find that cost-share policies might be an effective way of encouraging participation for feeder cattle sellers and could serve as an alternative to mandating traceability. C1 [Mitchell, James; Tonsor, Glynn T.] Univ Arkansas, Dept Agr Econ & Agribusiness, Fayetteville, AR 72701 USA. [Schulz, Lee] Iowa State Univ Ames, Econ, Ames, IA USA. C3 University of Arkansas System; University of Arkansas Fayetteville; Iowa State University RP Mitchell, J (corresponding author), Univ Arkansas, Dept Agr Econ & Agribusiness, Fayetteville, AR 72701 USA. EM jlmitche@uark.edu CR Adamowicz W, 1998, AM J AGR ECON, V80, P64, DOI 10.2307/3180269 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bayer P., 2004, WORKING PAPER SERIES, V10865 Brester G., 2011, EC ASSESSMENT UNPUB Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 European Commission Health and Consumer Protection Directorate-General, 2007, FOOD TRAC Golan E., 2004, Agricultural Economic Report - Economic Research Service, US Department of Agriculture Hensher D. A., 2005, APPL CHOICE ANAL PRI Hobbs J., 2003, 9 AGR FOOD POL INF W Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x McKendree M. G. S., 2017, THESIS KANSAS STATE Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Murphy RGL, 2009, INT FOOD AGRIBUS MAN, V12, P1 National Cattlemen's Beef Association, 2018, BEEF IND LONG RANG P Norwood FB, 2006, J AGR RESOUR ECON, V31, P74 Pendell DL, 2013, FOOD POLICY, V43, P332, DOI 10.1016/j.foodpol.2013.05.013 Pendell DL, 2010, AM J AGR ECON, V92, P927, DOI 10.1093/ajae/aaq037 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Resende-Filho M. A., 2007, TRACEABILITY MITIGAT Resende MA, 2008, AM J AGR ECON, V90, P1091, DOI 10.1111/j.1467-8276.2008.01150.x Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Revelt D, 1998, REV ECON STAT, V80, P647, DOI 10.1162/003465398557735 Roe B, 2004, AM J AGR ECON, V86, P115, DOI 10.1111/j.0092-5853.2004.00566.x ROSEN S, 1974, J POLIT ECON, V82, P34, DOI 10.1086/260169 Schulz L. L., 2010, Journal of Agricultural and Applied Economics, V42, P659 Schulz L. L., 2008, THESIS MICHIGAN STAT Schulz LL, 2010, J AGR ECON, V61, P138, DOI 10.1111/j.1477-9552.2009.00226.x Smyth S., 2006, MANAGING LIABILITIES, P81 Souza-Monteiro DM, 2010, AGRIBUSINESS, V26, P122, DOI 10.1002/agr.20233 SUMNER DA, 2005, INT FOOD AGRIBUS MAN, V8, P78 Tonsor GT, 2018, AM J AGR ECON, V100, P1120, DOI 10.1093/ajae/aay034 Tonsor GT, 2009, CAN J AGR ECON, V57, P395, DOI 10.1111/j.1744-7976.2009.01158.x Tonsor GT, 2005, J AGR RESOUR ECON, V30, P367 Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1, DOI 10.1017/CBO9780511805271 U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS), 2006, NAT AN ID SYST NAIS USDA APHIS, 2020, SHEEP GOAT ID USDA APHIS, 2018, AN DIS TRAC SUMM PRO USDA APHIS, 2014, AN DIS TRAC ADT OV USDA APHIS National Animal Health Monitoring System, 2013, FEEDL 2011 CATTL ID USDA Economic Research Service (ERS), 2019, SECT GLANC US CATTL USDA National Agricultural Statistics Service (NASS), 2020, JAN CATTL INV Vestal MK, 2013, AGR ECON-BLACKWELL, V44, P337, DOI 10.1111/agec.12016 World Organisation for Animal Health (OIE), 2011, TERR AN HLTH COD GEN World Perspectives Inc, 2018, US BEEF CATTL ID TRA NR 45 TC 0 Z9 0 U1 1 U2 9 PD JUL PY 2021 VL 48 IS 3 BP 447 EP 476 DI 10.1093/erae/jbaa027 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000693242800001 DA 2022-12-14 ER PT J AU Yang, HG Li, SW Tu, LJ Ma, RR Chen, Y AF Yang, Honggang Li, Shaowen Tu, Lijing Ma, Rongrong Chen, Yin TI Unsupervised Outlier Detection Mechanism for Tea Traceability Data SO IEEE ACCESS DT Article DE Anomaly detection; Machine learning; Tuning; Safety; Feature extraction; Data models; Time series analysis; Feature combination; LOKI algorithm; machine learning; outlier detection mechanism; parameter tuning method; tea traceability ID INFORMATION; SYSTEMS AB The presence of outliers in tea traceability data can mislead customers and have a significant impact on the reputation and profits of tea companies. To solve this problem, an unsupervised outlier detection mechanism for tea traceability data is proposed. Firstly, tea traceability data is uploaded to the MySQL database, and then the data is preprocessed to aggregate features based on relevance, which makes it easier to identify abnormal features. Secondly, the LOKI algorithm based on Local Outlier Factor (LOF), Isolation Forest (IForest), and K-Nearest Neighbors (KNN) algorithms is used to achieve unsupervised outlier detection of tea traceability data. In addition, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN-based) tuning method for unsupervised outlier detection algorithms is also provided. Finally, the types of anomalies among the identified outliers are identified to investigate the causes of the anomalies in order to develop remedial procedures to eliminate the anomalies, and the analysis results are fed back to the tea companies. Experiments on real datasets show that the DBSCAN-based tuning method can effectively help the unsupervised outlier detection algorithm optimize the parameters, and that the LOF-KNN-IForest (LOKI) algorithm can effectively identify the outliers in tea traceability data. This proves that the unsupervised outlier detection mechanism for tea traceability data can effectively guarantee the quality of tea traceability data. C1 [Yang, Honggang; Tu, Lijing; Ma, Rongrong; Chen, Yin] Anhui Agr Univ, Sch Informat & Comp Sci, Hefei 230036, Anhui, Peoples R China. [Li, Shaowen] Anhui Prov Key Lab Smart Agr Technol & Equipment, Hefei 230036, Anhui, Peoples R China. C3 Anhui Agricultural University RP Li, SW (corresponding author), Anhui Prov Key Lab Smart Agr Technol & Equipment, Hefei 230036, Anhui, Peoples R China. EM shwli123@163.com CR Anderlini E., 2021, OCEAN ENG, V236, P912 Andriyanov NA, 2022, COMPUT OPT, V46, P139, DOI 10.18287/2412-6179-CO-922 Bellon-Maurel V, 2014, J CLEAN PROD, V69, P60, DOI 10.1016/j.jclepro.2014.01.079 Blazquez-Garcia A, 2021, INFORM SCIENCES, V574, P528, DOI 10.1016/j.ins.2021.06.015 Breunig MM, 2000, SIGMOD REC, V29, P93, DOI 10.1145/335191.335388 Burgan HI, 2017, WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS 2017: HYDRAULICS AND WATERWAYS AND WATER DISTRIBUTION SYSTEMS ANALYSIS, P327 Chebana F, 2021, J HYDROL, V593, DOI 10.1016/j.jhydrol.2020.125907 Dang TT, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP), P507, DOI 10.1109/ICDSP.2015.7251924 Du X., 2022, INFORM SCIENCES, V608, P532 Faura AG, 2021, FORESTS, V12, DOI 10.3390/f12020194 Fu S, 2021, ENG APPL ARTIF INTEL, V101, DOI 10.1016/j.engappai.2021.104199 He ZY, 2003, PATTERN RECOGN LETT, V24, P1641, DOI 10.1016/S0167-8655(03)00003-5 Hendrickx K, 2020, MECH SYST SIGNAL PR, V139, DOI 10.1016/j.ymssp.2019.106585 Iftekhar A, 2021, FOODS, V10, DOI 10.3390/foods10061289 Ijaz MF, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102809 Islam S, 2021, TRENDS FOOD SCI TECH, V112, P708, DOI 10.1016/j.tifs.2021.04.020 Khuat TT., 2020, SOCIAL NETW COMPUT S, V1, P1 Kriegel H.-P., 2008, PROCEEDING KDD, P444, DOI [10.1145/1401890.1401946, DOI 10.1145/1401890.1401946] Lee H, 2020, ADV ENG INFORM, V44, DOI 10.1016/j.aei.2020.101071 [李晨 Li Chen], 2021, [小型微型计算机系统, Journal of Chinese Computer Systems], V42, P700 Li LY, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108325 Li Z., 2022, IEEE T KNOWL DATA EN, DOI [10.1109/TKDE.2022.3159580, DOI 10.1109/TKDE.2022.3159580] Liu C, 2020, EURASIP J WIREL COMM, V2020, DOI 10.1186/s13638-019-1591-1 Liu FT, 2012, ACM T KNOWL DISCOV D, V6, DOI 10.1145/2133360.2133363 Liu LMW, 2020, INFORMATION, V11, DOI 10.3390/info11020105 Ma W, 2008, ISI 2008: 2008 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, P245, DOI 10.1109/ISI.2008.4565069 Maleki S, 2021, APPL SOFT COMPUT, V108, DOI 10.1016/j.asoc.2021.107443 Meng XY, 2021, INFORM SCIENCES, V571, P527, DOI 10.1016/j.ins.2021.04.056 Mikhailova Aleksandra, 2019, Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, V172, P135, DOI 10.1680/jsmic.19.00022 Mingtian Shao, 2021, Journal of Physics: Conference Series, V1944, DOI 10.1088/1742-6596/1944/1/012017 Morris T, 2011, INT J CRIT INFR PROT, V4, P88, DOI 10.1016/j.ijcip.2011.06.005 Moxian W., 2020, J FRONTIERS COMPUT S, V15, P1, DOI [10.3778/J, DOI 10.3778/J] Park S, 2019, MULTIMED TOOLS APPL, V78, P4417, DOI 10.1007/s11042-018-5845-4 Pearson K, 1901, PHILOS MAG, V2, P559, DOI 10.1080/14786440109462720 Piciarelli C, 2021, INT J PATTERN RECOGN, V35, DOI 10.1142/S0218001421520108 Vargaftik S, 2021, MACH LEARN, V110, P2835, DOI 10.1007/s10994-021-06047-x Wang J., 2018, PROC 4 ANN INT C NET Wang ZF, 2021, J MED INTERNET RES, V23, DOI 10.2196/25946 Wu P, 2021, ENG APPL ARTIF INTEL, V104, DOI 10.1016/j.engappai.2021.104379 [向玲 Xiang Ling], 2021, [振动与冲击, Journal of Vibration and Shock], V40, P11 Yahaya SW, 2021, PATTERN RECOGN LETT, V145, P200, DOI 10.1016/j.patrec.2021.02.006 Yang Y., 2021, PROC E3SWEB C, V235, P1 [张琪 Zhang Qi], 2018, [计算机研究与发展, Journal of Computer Research and Development], V55, P524 Zhang X, 2021, MEAS SCI TECHNOL, V32, DOI 10.1088/1361-6501/ac03e5 Zhou Dong, 2009, Computer Engineering and Applications, V45, P137, DOI 10.3778/j.issn.1002-8331.2009.11.042 Zhou YJ, 2021, KNOWL-BASED SYST, V228, DOI 10.1016/j.knosys.2021.107153 NR 46 TC 0 Z9 0 U1 4 U2 4 PY 2022 VL 10 BP 94818 EP 94831 DI 10.1109/ACCESS.2022.3204760 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000854619400001 DA 2022-12-14 ER PT J AU Sun, YK Du, GB Cao, Y Lin, QZ Zhong, LH Qiu, J AF Sun, Yongke Du, Guanben Cao, Yong Lin, Qizhao Zhong, Lihui Qiu, Jian TI Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System SO IEEE ACCESS DT Article DE Wood identification; AKAZE; trust label; blockchain ID LOG TRACEABILITY; IDENTIFICATION; CLASSIFICATION; FINGERPRINT AB Tracking of the wood product is an important technology in the trade activity of rare plants. Normally, the factories use Quick Response (QR) and Radio-Frequency Identification (RFID) to identify the individual wood product, but these technologies are not safe enough because they can be easily falsified. It can be seen that traditional methods are hard to catch the detail of the slim wood texture from the wood product. In this study, a novel method is employed to resolve these problems using a biometric feature on the surface of the real wood product to distinguish the individual wood product. AKAZE is used to extract the key-point of wood texture. A sub-area detection technique along with a serialization method is then developed to improve the rate of identification. The sub-area detection technique deals with picking out a sub-region in which there are enough AKAZE points as small as possible. The serialization method is also utilized to reduce the redundant process of feature extraction. The experimental results demonstrate that the values of accuracy, recall, and F1 reach 0.98, 0.96, and 0.96, respectively. The match time that uses serialized function is reduced to 1/3 of which has no application in the original image. The validated results also reveal that our proposed methodology improves the robustness of the wood product identification, and it can be used in Wood Traceability System (WTS) with the blockchain to resolve the digital trust problem and the fast distinction issues of the real wood product. C1 [Sun, Yongke; Du, Guanben] Southwest Forestry Univ, Yunnan Prov Key Lab Wood Adhes & Glued Prod, Kunming 650224, Yunnan, Peoples R China. [Cao, Yong; Lin, Qizhao] Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China. [Zhong, Lihui; Qiu, Jian] Southwest Forestry Univ, Coll Mat Sci & Engn, Kunming 650224, Yunnan, Peoples R China. C3 Southwest Forestry University - China; Southwest Forestry University - China; Southwest Forestry University - China RP Du, GB (corresponding author), Southwest Forestry Univ, Yunnan Prov Key Lab Wood Adhes & Glued Prod, Kunming 650224, Yunnan, Peoples R China.; Cao, Y (corresponding author), Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China.; Qiu, J (corresponding author), Southwest Forestry Univ, Coll Mat Sci & Engn, Kunming 650224, Yunnan, Peoples R China. EM gongben9@hotmail.com; cn_caoyong@126.com; qiujian@swfu.edu.cn CR Alcantarilla PF, 2012, LECT NOTES COMPUT SC, V7577, P214, DOI 10.1007/978-3-642-33783-3_16 Appelhanz S, 2016, J CLEAN PROD, V110, P132, DOI 10.1016/j.jclepro.2015.02.034 Baas P, 2020, IAWA J, V41, P259 Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014 Dormontt EE, 2015, BIOL CONSERV, V191, P790, DOI 10.1016/j.biocon.2015.06.038 Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 FISCHLER MA, 1981, COMMUN ACM, V24, P381, DOI 10.1145/358669.358692 Geng C, 2009, IEEE IMAGE PROC, P3313, DOI 10.1109/ICIP.2009.5413956 Godbout J, 2018, FOREST CHRON, V94, P75, DOI 10.5558/tfc2018-010 He T, 2019, HOLZFORSCHUNG, V73, P277, DOI 10.1515/hf-2018-0076 He W, 2009, IEEE I CONF COMP VIS, P1586, DOI 10.1109/ICCV.2009.5459360 Hwang SW, 2018, J WOOD SCI, V64, P69, DOI 10.1007/s10086-017-1680-x Jo M, 2020, IEEE NETWORK, V34, P76, DOI 10.1109/MNET.2020.9199796 Kannangara S, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-61415-2 Kaur A, 2020, CRYPTOCUR BLOCK TECH, P25 Kharbach M, 2018, FOOD CHEM, V263, P8, DOI 10.1016/j.foodchem.2018.04.059 Lever J, 2016, NAT METHODS, V13, P603, DOI 10.1038/nmeth.3945 Lowe AJ, 2011, IAWA J, V32, P251, DOI 10.1163/22941932-90000055 Ma CQ, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20040975 Magdy S, 2020, IET IMAGE PROCESS, V14, P874, DOI 10.1049/iet-ipr.2019.0575 Matuska S, 2014, AASRI PROC, V9, P25, DOI 10.1016/j.aasri.2014.09.006 Meixi C., 2013, P 3 INT C MULT TECHN P 3 INT C MULT TECHN, P1250 Nabiyev VV, 2017, FORENSIC SCI INT, V278, P280, DOI 10.1016/j.forsciint.2017.07.014 Oyallon E, 2015, IMAGE PROCESS ON LIN, V5, P176, DOI 10.5201/ipol.2015.69 Pahlberg T, 2015, COMPUT ELECTRON AGR, V111, P164, DOI 10.1016/j.compag.2014.12.014 Powers D. M. W., 2011, J MACH LEARN TECHNOL, V2, P37 Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497 Puri A, 2019, 2019 INT C ISS CHALL, V1, P1 Rajagopal H, 2019, WOOD SCI TECHNOL, V53, P967, DOI 10.1007/s00226-019-01110-2 Redei P. G, 2008, ENCY GENETICS GENOMI, P638 Rublee E, 2011, IEEE I CONF COMP VIS, P2564, DOI 10.1109/ICCV.2011.6126544 Schraml R, 2015, COMPUT ELECTRON AGR, V119, P112, DOI 10.1016/j.compag.2015.10.003 Schraml R, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8071071 Schraml R, 2016, MACH VISION APPL, V27, P1289, DOI 10.1007/s00138-016-0814-2 Sermanet P, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P2809, DOI 10.1109/IJCNN.2011.6033589 Sharma SK, 2020, J INDIAN SOC REMOTE, V48, P1389, DOI 10.1007/s12524-020-01163-y Sharma V, 2020, VIB SPECTROSC, V110, DOI 10.1016/j.vibspec.2020.103097 Simonovic J, 2011, CELLULOSE, V18, P1433, DOI 10.1007/s10570-011-9584-1 Tareen S. A. K., 2018, P INT C COMP MATH EN, P1 Tnah LH, 2012, WOOD SCI TECHNOL, V46, P813, DOI 10.1007/s00226-011-0447-6 Trundle E, 2012, PRAIRIE SCHOONER, V86, P85 Weik H. M, 2000, COMPUTER SCI COMMUNI, P706 Yang D, 2021, J NETW COMPUT APPL, V173, DOI 10.1016/j.jnca.2020.102817 Zhang HJ, 2011, 2011 INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND CONTROL (ICECC), P1460, DOI 10.1109/ICECC.2011.6066546 Zhang L, 2013, PROC CVPR IEEE, P1586, DOI 10.1109/CVPR.2013.208 Zhang L, 2015, SENSORS-BASEL, V15, P19937, DOI 10.3390/s150819937 Zhang SC, 2018, IEEE T NEUR NET LEAR, V29, P1774, DOI 10.1109/TNNLS.2017.2673241 Zhang ZH, 2016, ANN TRANSL MED, V4, DOI 10.21037/atm.2016.03.37 Zhao L, 2020, J COASTAL RES, P570, DOI 10.2112/SI103-116.1 Zhuo L, 2016, NEUROCOMPUTING, V173, P511, DOI 10.1016/j.neucom.2015.06.055 NR 50 TC 1 Z9 1 U1 6 U2 15 PY 2021 VL 9 BP 88552 EP 88563 DI 10.1109/ACCESS.2021.3088236 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000673569000001 DA 2022-12-14 ER PT J AU Ma, TT Wang, HL Wei, MY Lan, T Wang, JQ Bao, SH Ge, Q Fang, YL Sun, XY AF Ma, Tingting Wang, Haoli Wei, Mengyuan Lan, Tian Wang, Jiaqi Bao, Shihan Ge, Qian Fang, Yulin Sun, Xiangyu TI Application of smart-phone use in rapid food detection, food traceability systems, and personalized diet guidance, making our diet more health SO FOOD RESEARCH INTERNATIONAL DT Review DE Mobile phone; Rapid food detection; Food traceability systems; Personalized diet guidance ID ASCORBIC-ACID; QUANTITATIVE DETECTION; VOLTAMMETRY SYSTEM; MICROEXTRACTION; ASSAY; MILK; GRAPHENE; SENSOR; GREEN AB As the concept of dietary health is gradually recognized by the public, on-the-spot monitoring of food safety and nutrition, tracing the source of food and individualized guidance of nutritional and healthy eating habits are becoming more and more important. The promotion and use of smartphones and their powerful functions have greatly changed our lives and are also expected to aid applications in food field. There are three types of applications of smartphones in terms of food: rapid food detection, food traceability systems, and personalized diet guidance. Rapid food testing is classified according to the types of test objects, including food quality and freshness, nutritional and functional ingredients, adulterated ingredients, food additives, enzyme activities, and harmful substances. The performance of detection methods and instruments is analyzed and their advantages and disadvantages are compared, determining the feasibility of a practical application. In addition, the process and principle of food traceability system in the field of food safety and individualized dietary guidance for different groups were analyzed based on practical examples. Finally, it analyzes the latest development of the application of smart phones in food and prospects the feasibility of the practical application in the future. It is expected to lay a theoretical foundation for the development of food-related fields such as rapid detection of food, tracing the source of food, and personal nutritional diet. C1 [Ma, Tingting; Wang, Haoli; Wei, Mengyuan; Lan, Tian; Wang, Jiaqi; Bao, Shihan; Ge, Qian; Fang, Yulin; Sun, Xiangyu] Northwest A&F Univ, Shaanxi Engn Res Ctr Viti Viniculture, Coll Food Sci & Engn, Coll Enol,Vitiviniculture Engn Technol Ctr State, Yangling 712100, Shaanxi, Peoples R China. [Ge, Qian] Qual Stand & Testing Inst Agr Technol, Yinchuan 750002, Ningxia, Peoples R China. C3 Northwest A&F University - China RP Fang, YL; Sun, XY (corresponding author), Northwest A&F Univ, Shaanxi Engn Res Ctr Viti Viniculture, Coll Food Sci & Engn, Coll Enol,Vitiviniculture Engn Technol Ctr State, Yangling 712100, Shaanxi, Peoples R China. EM fangyulin@nwsuaf.edu.cn; sunxiangyu@nwafu.edu.cn CR Acevedo MSMSF, 2018, MICROCHEM J, V143, P259, DOI 10.1016/j.microc.2018.08.002 Aguirre MA, 2019, FOOD CHEM, V272, P141, DOI 10.1016/j.foodchem.2018.08.002 Barabasi AL, 2020, NAT FOOD, V1, P33, DOI 10.1038/s43016-019-0005-1 Chapman J, 2022, CRIT REV FOOD SCI, V62, P2845, DOI 10.1080/10408398.2020.1863328 Chen JL, 2020, BIOSENS BIOELECTRON, V170, DOI 10.1016/j.bios.2020.112668 Chen J, 2018, PATIENT EDUC COUNS, V101, P750, DOI 10.1016/j.pec.2017.11.011 Chen Y, 2017, FOOD CONTROL, V82, P227, DOI 10.1016/j.foodcont.2017.07.003 Cheng N, 2019, BIOSENS BIOELECTRON, V142, DOI 10.1016/j.bios.2019.111498 Cheng N, 2017, FOOD CHEM, V214, P169, DOI [10.1016/j.foodchem.2016.07.058, 10.1016/j.foodchem.201] Clever GH, 2007, ANGEW CHEM INT EDIT, V46, P6226, DOI 10.1002/anie.200701185 dos Santos VB, 2019, FOOD CHEM, V285, P340, DOI 10.1016/j.foodchem.2019.01.167 Giungato P, 2017, FOOD ANAL METHOD, V10, P3424, DOI 10.1007/s12161-017-0909-z Guo LL, 2020, ADV MATER, V32, DOI 10.1002/adma.202004805 Hosseinpour S, 2019, J FOOD ENG, V248, P9, DOI 10.1016/j.jfoodeng.2018.12.009 Hu XT, 2019, FOOD CHEM, V272, P58, DOI 10.1016/j.foodchem.2018.08.021 Ipjian ML, 2017, NUTRITION, V33, P343, DOI 10.1016/j.nut.2016.08.003 Ji DZ, 2018, BIOSENS BIOELECTRON, V119, P55, DOI 10.1016/j.bios.2018.07.074 Ji DZ, 2017, BIOSENS BIOELECTRON, V98, P449, DOI 10.1016/j.bios.2017.07.027 Jiang Z., 2021, PACKAGING ENG, V42, P247 Kanchi S, 2018, BIOSENS BIOELECTRON, V102, P136, DOI 10.1016/j.bios.2017.11.021 Leon-Roque N, 2019, TALANTA, V204, P576, DOI 10.1016/j.talanta.2019.06.014 Li G. H, 2014, IND Li XM, 2019, SENSOR ACTUAT B-CHEM, V290, P170, DOI 10.1016/j.snb.2019.03.108 Lillehoj PB, 2013, LAB CHIP, V13, P2950, DOI 10.1039/c3lc50306b Lu YL, 2019, CURR OPIN FOOD SCI, V28, P74, DOI 10.1016/j.cofs.2019.09.003 Mcknight W., 2014, INFORM MANAGE, P97 Peng B, 2019, MICROCHEM J, V149, DOI 10.1016/j.microc.2019.104072 Robinson E, 2013, BMC PUBLIC HEALTH, V13, DOI 10.1186/1471-2458-13-639 Roda A, 2016, TRAC-TREND ANAL CHEM, V79, P317, DOI 10.1016/j.trac.2015.10.019 Ross GMS, 2018, ANAL BIOANAL CHEM, V410, P5353, DOI 10.1007/s00216-018-0989-7 Schafer M, 2018, SENSOR ACTUAT B-CHEM, V255, P1902, DOI 10.1016/j.snb.2017.08.207 Sergeyev T, 2019, TALANTA, V201, P204, DOI 10.1016/j.talanta.2019.04.016 Shahvar A, 2019, TALANTA, V197, P578, DOI 10.1016/j.talanta.2019.01.071 Shahvar A, 2018, SENSOR ACTUAT B-CHEM, V273, P1474, DOI 10.1016/j.snb.2018.07.071 Song WR, 2020, SENSOR ACTUAT B-CHEM, V304, DOI 10.1016/j.snb.2019.127247 Thongprajukaew K, 2014, FOOD CHEM, V163, P87, DOI 10.1016/j.foodchem.2014.04.080 Vasseur P, 2021, INFLAMM BOWEL DIS, V27, P65, DOI 10.1093/ibd/izaa018 Waller AW, 2019, NUTRIENTS, V11, DOI 10.3390/nu11071673 Wang HH, 2020, CHEMISTRYSELECT, V5, P9952, DOI 10.1002/slct.202002406 Wang Y, 2016, TALANTA, V160, P194, DOI 10.1016/j.talanta.2016.07.012 Wei ZB, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17112500 Yu L, 2015, BIOSENS BIOELECTRON, V69, P307, DOI 10.1016/j.bios.2015.02.035 Zeinhom MMA, 2018, SENSOR ACTUAT B-CHEM, V261, P75, DOI 10.1016/j.snb.2017.11.093 Zhang DM, 2016, BIOSENS BIOELECTRON, V75, P273, DOI 10.1016/j.bios.2015.08.037 Zhang HH, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20041065 Zhang W, 2020, FOOD CHEM, V303, DOI 10.1016/j.foodchem.2019.125378 Zhang X., 2017, FOOD INDUSTRY, V38, P252 Zhang XX, 2018, ANAL BIOANAL CHEM, V410, P2665, DOI 10.1007/s00216-018-0965-2 Zia J, 2016, CLIN TRANSL GASTROEN, V7, DOI 10.1038/ctg.2016.9 NR 49 TC 4 Z9 4 U1 26 U2 51 PD FEB PY 2022 VL 152 AR 110918 DI 10.1016/j.foodres.2021.110918 EA DEC 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000741827800008 DA 2022-12-14 ER PT J AU Blackmore, S Hamilton, M Lee, A Worwood, M Brierley, M Heath, A Thorpe, SJ AF Blackmore, Sheena Hamilton, Malcolm Lee, Anne Worwood, Mark Brierley, Matthew Heath, Alan Thorpe, Susan J. TI Automated immunoassay methods for ferritin: recovery studies to assess traceability to an international standard SO CLINICAL CHEMISTRY AND LABORATORY MEDICINE DT Article DE commutability; comparability; serum ferritin; standardisation of immunoassays; traceability ID SERUM; EVALUATE; FOLATE AB Background: Ferritin standardisation is problematical due to the heterogeneity of ferritin isoforms and the antibodies used in its immunoassay, and the lack of a reference measurement procedure. We investigated the performance of the 1st (liver), 2nd (spleen) and 3rd (recombinant) International Standards (ISs) for ferritin in major assays. Methods: The ferritin in a serum pool 'spiked' with either the 2nd or 3rd IS for ferritin was measured by 52 laboratories using five automated methods and the recovery of the target values calculated. A smaller serum pool was 'spiked' with the 1st IS for a limited recovery exercise. The ferritin values of five serum samples were also measured and recalculated relative to the ISs. Results: Recoveries of each of the 2nd and 3rd ISs were 90%-110% for four of five methods; recoveries of the 1st IS were 104% and 111% for two of three methods claiming traceability to this IS. One method significantly over-recovered each of the IS (124%-155%). Recalculating the ferritin values of the serum samples relative to the IS reduced the overall inter-method agreement, largely because of the anomalous over-recovery of the IS by one method. Conclusions: The use of the 3rd IS to standardise assays will minimise assay drift due to manufacturers adopting a 'harmonisation' approach in which the calibration is adjusted to conform to overall mean values. Standardisation against the current IS also ensures compliance with the European Union In-Vitro Diagnostic Directive which requires traceability of assay calibrators to reference materials of a higher order. Assay drift may result in poor sensitivity and specificity in the diagnosis of iron status, and would require laboratories to continually re-evaluate reference intervals. C1 [Thorpe, Susan J.] NIBSC, Biotherapeut Grp, Potters Bar EN6 3QG, Herts, England. [Blackmore, Sheena; Hamilton, Malcolm; Lee, Anne] Good Hope Hosp, UK NEQAS Scheme Haematin, Heart England Fdn Trust, Sutton Coldfield, West Midlands, England. [Worwood, Mark] Cardiff Univ, Sch Med, Cardiff, S Glam, Wales. C3 National Institute for Biological Standards & Control; Heart of England NHS Foundation Trust; Good Hope Hospital; Cardiff University RP Thorpe, SJ (corresponding author), NIBSC, Biotherapeut Grp, Blanche Lane,S Mimms, Potters Bar EN6 3QG, Herts, England. EM sthorpe@nibsc.ac.uk CR [Anonymous], 1985, BRIT J HAEMATOL, V61, P61 HAMILTON MS, 2003, BR J HAEMATOL S1, V125 HAZARD JT, 1977, BLOOD, V49, P139 *ISO DIS, 1999, 15194 ISO DIS *ISO PREN, 1999, 17511 ISOPREN SANTAMBROGIO P, 1993, J BIOL CHEM, V268, P12744 Satterfield MB, 2006, ANAL BIOANAL CHEM, V385, P612, DOI 10.1007/s00216-006-0434-1 Thorpe SJ, 1997, CLIN CHEM, V43, P1582 Thorpe SJ, 2007, CLIN CHEM LAB MED, V45, P380, DOI 10.1515/CCLM.2007.072 *WHO, 1998, WHO TECH REP SER, V878, P12 *WHO, 1985, WHO TECH REP SER, V725, P25 *WHO, 1994, WHO TECH REP SER, V840, P7 Wilson DH, 2005, CLIN CHEM, V51, P684, DOI 10.1373/clinchem.2004.042358 WORWOOD M, 1986, CLIN SCI, V70, P215, DOI 10.1042/cs0700215 WORWOOD M, 1984, CLIN LAB HAEMATOL, V6, P177 NR 15 TC 25 Z9 25 U1 0 U2 10 PD OCT PY 2008 VL 46 IS 10 BP 1450 EP 1457 DI 10.1515/CCLM.2008.304 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000259929400018 DA 2022-12-14 ER PT J AU Tibola, CS Fachinellol, JC Rombaldi, CV Nora, L Rufato, AD Rufato, L AF Tibola, Casiane Salete Fachinellol, Jose Carlos Rombaldi, Cesar Valmor Nora, Leonardo Rufato, Andrea de Rossi Rufato, Leo TI Traceability of peaches from integrated production in South Brazil SO SCIENTIA AGRICOLA DT Article DE traceability; quality; food safety ID FOOD-PRODUCTS AB Traceability is becoming the most effective method to provide a safer food chain and connection producers to consumers. This paper report the application and validation of a traceability system on the production chain of peaches, according the rules for Integrated Production of Peach (IP) and a Hazard Analysis and Critical Control Point (HACCP) systems. The harvesting plots were discriminated using a Global Positioning System (GPS) device. The horticultural practices were registered in a field book according to the Brazilian IP rules. Boxes to transport the fruit, from the orchard on, were barcode labelled to identify the fruits in terms of origin (orchard and harvesting plot), cultivar, quality, picking date and time. Arriving in the factory, by an optical barcode reading device, the fruits in the boxes were assigned to homogeneous batches. Peach cans were labelled according to their corresponding batch number and monitored based on physical and chemical analysis as preconized by the IP rules and HACCP system. An electronic data base was set up and placed over the Internet. Using the batch number, the history of each peaches can could be traceable back to their harvesting plot. Therefore, manufacturers can monitor the product at any time and take any necessary action, such as product recall and/or product reprocessing. C1 [Tibola, Casiane Salete] Embrapa Trigo, BR-99001970 Passo Fundo, RS, Brazil. [Fachinellol, Jose Carlos; Rombaldi, Cesar Valmor; Nora, Leonardo; Rufato, Andrea de Rossi] Univ Fed Pelotas, FAEM, BR-96010900 Pelotas, RS, Brazil. [Rufato, Leo] UDESC, CAV, BR-88520000 Lages, SC, Brazil. C3 Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA); Universidade Federal de Pelotas RP Tibola, CS (corresponding author), Embrapa Trigo, Rod BR 285 Km 294,CP 451, BR-99001970 Passo Fundo, RS, Brazil. EM casiane@cnpt.embrapa.br CR ANDRIGUETO JR, 2004, TREINAMENTO TECHNICO, P15 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Fachinello J.C., 2003, NORMAS TECNICAS DOCU FACHINELLO JC, 2004, C INT SOBRE RASTREAB, P141 *GSI BELG LUX EAN, 2003, UCC SPEC ID TRAC FRU, P1 IBA SK, 2003, PANORAMA RASTREABILI Kuiper HA, 2004, FOOD CHEM TOXICOL, V42, P1195, DOI 10.1016/j.fct.2004.02.004 Nilsson H, 2004, J CLEAN PROD, V12, P517, DOI 10.1016/S0959-6526(03)00114-8 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sarig Y., 2006, Stewart Postharvest Review, V2, P1, DOI 10.2212/spr.2006.2.2 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 NR 11 TC 5 Z9 7 U1 1 U2 6 PD JAN-FEB PY 2008 VL 65 IS 1 BP 10 EP 15 DI 10.1590/S0103-90162008000100002 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000252892000002 DA 2022-12-14 ER PT J AU Li, XW Yang, L Duan, YQ Wu, ZG Zhang, XS AF Li, Xinwu Yang, Lin Duan, Yanqing Wu, Zhigang Zhang, Xiaoshuan TI Developing a Real-time Monitoring Traceability System for Cold Chain of Tricholoma matsutake SO ELECTRONICS DT Article DE T; matsutake; logistics environment parameters; safety; real-time; wireless monitoring; traceability system ID MANAGEMENT; INTERNET; THINGS AB Tricholoma matsutake (T. matsutake) is a special type of fungus known as "the king of bacteria", and has the very high economic value. However, it is also very difficult to transport due to its corruptibility. Therefore, tracing and tracking the quality and safety of T. matsutake in the cold chain is very important and necessary. Based on changes in the cold chain environmental parameters determine the safety of T. matsutake is a viable option. This paper developed and tested a real-time monitoring traceability system (RM-TM) using emerging Internet of Things (IoT) technologies for monitoring the cold chain logistics environmental parameters of T. matsutake. Finally, system testing and evaluation have shown that RM-TM can track and monitor temperature, humidity, oxygen and carbon dioxide fluctuations in the cold chain in real-time. In addition, the collected data can be used to increase the transparency of cold chain logistics and to more effectively control quality, safety, and traceability. In general, the system evaluation results show that it is reliable and meets the requirements of users. C1 [Li, Xinwu; Zhang, Xiaoshuan] China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [Yang, Lin] Tibet Agr & Anim Husb Coll, Coll Food Sci, Linzhi 860000, Peoples R China. [Duan, Yanqing] Univ Bedforshire, Business & Informat Syst Res Ctr BISC, Business Sch, Luton LU1 3JU, Beds, England. [Wu, Zhigang] China Agr Univ, Coll Continuing Studies, Beijing 100083, Peoples R China. C3 China Agricultural University; Tibet Agriculture & Animal Husbandry University; University of Bedfordshire; China Agricultural University RP Zhang, XS (corresponding author), China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. EM xinwuli@cau.edu.cn; yanglin8989239@163.com; yanqing.duan@beds.ac.uk; wuzhigang@cau.edu.cn; zhxshuan@cau.edu.cn CR Akyildiz IF, 2015, IEEE COMMUN MAG, V53, P32, DOI 10.1109/MCOM.2015.7060516 Alayev Y, 2014, IEEE T WIREL COMMUN, V13, P4066, DOI 10.1109/TWC.2014.2315196 Augustin A, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16091466 Aung MM, 2014, FOOD CONTROL, V40, P198, DOI 10.1016/j.foodcont.2013.11.016 Bello O, 2016, IEEE SYST J, V10, P1172, DOI 10.1109/JSYST.2014.2298837 Bergius N, 2000, SCAND J FOREST RES, V15, P318, DOI 10.1080/028275800447940 Bhanarkar MK, 2016, COGENT ENG, V3, DOI 10.1080/23311916.2016.1164021 Bhargava K., 2014, P NAT C COMM KANP IN, P1 Chandra A.A., 2014, INT J MULTIMEDIA UBI, P145, DOI 10.14257/ijmue.2014.9.10.15 Coates RW, 2013, COMPUT ELECTRON AGR, V96, P13, DOI 10.1016/j.compag.2013.04.013 Gogou E, 2015, INT J REFRIG, V52, P109, DOI 10.1016/j.ijrefrig.2015.01.019 La Scalia G, 2017, J FOOD PROCESS ENG, V40, DOI 10.1111/jfpe.12311 Laith F, 2017, SENS TECHN ICST 2017, P1 Li C.J., 2012, J CARDIOVASC DIABETO, V11, P1 Liu C., 2014, COMPUTER COMPUTING T, VVII, P255 Liu HG, 2012, BIOL TRACE ELEM RES, V147, P341, DOI 10.1007/s12011-012-9321-0 Mau JL, 2002, J AGR FOOD CHEM, V50, P6072, DOI 10.1021/jf0201273 Palattella MR, 2016, IEEE J SEL AREA COMM, V34, P510, DOI 10.1109/JSAC.2016.2525418 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Saad SM, 2014, 2014 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC DESIGN (ICED), P521, DOI 10.1109/ICED.2014.7015862 Sciortino R, 2016, COMPUT ELECTRON AGR, V127, P451, DOI 10.1016/j.compag.2016.07.004 Suryadevara NK, 2015, IEEE-ASME T MECH, V20, P564, DOI 10.1109/TMECH.2014.2301716 Tsai CW, 2014, WIREL NETW, V20, P2201, DOI 10.1007/s11276-014-0731-0 Xiao XQ, 2016, FOOD CONTROL, V60, P656, DOI 10.1016/j.foodcont.2015.09.012 Xue ZH, 2017, J FOOD PROCESS ENG, V40, DOI 10.1111/jfpe.12437 Yang XF, 2008, ECON BOT, V62, P269, DOI 10.1007/s12231-008-9019-6 Yun W, 1997, ECON BOT, V51, P311, DOI 10.1007/BF02862101 Zhao Zi-kai, 2008, Instrument Techniques and Sensor, P25 NR 29 TC 5 Z9 5 U1 11 U2 63 PD APR PY 2019 VL 8 IS 4 AR 423 DI 10.3390/electronics8040423 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied SC Computer Science; Engineering; Physics UT WOS:000467751100054 DA 2022-12-14 ER PT J AU Li, M Qian, JP Yang, XT Sun, CH Ji, ZT AF Li, Ming Qian, Jian-Ping Yang, Xin-Ting Sun, Chuan-Heng Ji, Zneg-Tao TI A PDA-based record-keeping and decision-support system for traceability in cucumber production SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE PDA; Cucumber; Record-keeping; Decision-support system; Traceability; Early warning ID TECHNOLOGY; MANAGEMENT; DIAGNOSIS; MODEL; PC AB For the small-scale and scattered fresh cucumber production in China, the result that production record-keeping and its transfer are inefficient have prevented the wide application of traceability systems in China. With the mobility and computability, Personal Digital Assistant (PDA) provides a new way for agricultural information collection to solve the above problems. Thus a PDA-based Record-keeping and Decision-support System (PRDS) for traceability in cucumber production was developed on Windows Mobile platform invoking a Geographic Information System (GIS) control. For improving the decision making feasibility of PRDS, the fertilization recommendation model and pesticide usage early warning model were developed by using the Technical Specification of Balanced Fertilization by Soil Testing and the Guideline for Safety Application of Pesticides in China. The architecture of PRDS was provided. With Unified Modeling Language (UML), a requirement model including two types of users and 17 use cases was described, and a static class model was also designed, which consisted of table class, table operation class, algorithm class and interface class. Based on these models, the functions of system setup, map management, data management, production record-keeping and decision-support and query, etc., were implemented by adopting Hosting MapInfo MapX Mobile Controls on the .NET Compact Framework 2.0, and the data synchronization was realized by Remote Data Access (RDA). Two agricultural production enterprises were chosen as case study to evaluate the system by questionnaires. The results show that the efficiency of production record-keeping and decision-support is improved by the simple and friendly system. (C) 2009 Elsevier B.V. All rights reserved. C1 [Li, Ming; Qian, Jian-Ping; Yang, Xin-Ting; Sun, Chuan-Heng; Ji, Zneg-Tao] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. [Li, Ming] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. C3 Beijing Academy of Agriculture & Forestry; China Agricultural University RP Yang, XT (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. EM yangxt@nercita.org.cn CR Amiama C, 2008, COMPUT ELECTRON AGR, V61, P192, DOI 10.1016/j.compag.2007.11.006 Bange MP, 2004, COMPUT ELECTRON AGR, V43, P131, DOI 10.1016/j.compag.2003.12.003 Blancou J., 2001, Revue Scientifique et Technique Office International des Epizooties, V20, P413 BOOCH G, 1999, UNIFIED MODELING LAN CAI G, 2005, GEOGRAPHIC INFORM SC, V11, P4, DOI DOI 10.1080/10824000509480595 Casademont J, 2004, COMPUT GEOSCI-UK, V30, P671, DOI 10.1016/j.cageo.2004.02.004 Chen XinPing, 1996, Journal of China Agricultural University, V1, P63 Clegg P, 2006, COMPUT GEOSCI-UK, V32, P1682, DOI 10.1016/j.cageo.2006.03.007 Conallen J, 1999, COMMUN ACM, V42, P63, DOI 10.1145/317665.317677 Deng X, 2008, T CHINESE SOC AGR EN, V24, P172 DONG F, 2004, 04MWP8 MATRIC ERICK DD, 2007, ANNU REV PHYTOPATHOL, V45, P203 Fang H, 2008, COMPUT ELECTRON AGR, V61, P254, DOI 10.1016/j.compag.2007.11.005 FEINMAN A, 2006, HOSTING ACTIVEX CONT *GB T, 2006, 83215 GBT *GB T, 2006, 83214 GBT *GB T, 2005, 2001412001411 GBT Geypens M, 1996, PLANT SOIL, V181, P31, DOI 10.1007/BF00011289 *GLOBALGAP, 2007, GLOBALGAP GEN REG 1 GOHLER A, 1989, ACTA HORTIC, V248, P453 HOU YL, 2004, CHINESE J SOIL SCI, V35, P493 LEE WM, 2003, USING REMOTE DATA AC LINUS UO, 2003, FOOD AGR ENV, V11, P101 LV YH, 2002, SOIL TEST FERTILIZAT *MAPINFO CORP, 2002, MAPINFO MAPX MOB PRE Massimo B., 2004, FOOD CONTROL, V17, P1 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mousavi A., 2002, British Food Journal, V104, P7, DOI 10.1108/00070700210418703 Nemenyi M, 2003, COMPUT ELECTRON AGR, V40, P45, DOI 10.1016/S0168-1699(03)00010-3 *NY T, 2006, 1118 NYT Otuka A., 2003, Agricultural Information Research, V12, P95 Otuka A., 2003, Agricultural Information Research, V12, P113 Pan DaFeng, 2000, Transactions of the Chinese Society of Agricultural Engineering, V16, P109 Papadopoulos AP, 2001, ACTA HORTIC, P115, DOI 10.17660/ActaHortic.2001.548.11 PENUGONDA R, 2003, MICS MIDW INSTR COMP Pinto DB, 2006, FOOD RES INT, V39, P772, DOI 10.1016/j.foodres.2006.01.015 Prandl-Zika V, 2008, J ENVIRON MANAGE, V87, P236, DOI 10.1016/j.jenvman.2006.10.028 Qian JP, 2008, INT FED INFO PROC, V259, P1205 Qu XiaoHui, 2007, Agricultural Sciences in China, V6, P724, DOI 10.1016/S1671-2927(07)60105-9 Ramakrishna A, 2009, PLANT SOIL, V316, P107, DOI 10.1007/s11104-008-9763-5 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shtienberg D, 1997, PHYTOPATHOLOGY, V87, P332, DOI 10.1094/PHYTO.1997.87.3.332 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Sun Bo, 2006, Transactions of the Chinese Society of Agricultural Engineering, V22, P75 Sun CH, 2007, NEW ZEAL J AGR RES, V50, P1269, DOI 10.1080/00288230709510412 Tseng CL, 2006, COMPUT ELECTRON AGR, V53, P45, DOI 10.1016/j.compag.2006.03.005 Wang LiFang, 2005, Transactions of the Chinese Society of Agricultural Engineering, V21, P168 Wu X, 2006, J INTELL INF SYST, V27, P5, DOI 10.1007/s10844-006-0731-3 XIAO CL, 1988, THESIS BEIJING AGR U Yang XT, 2007, NEW ZEAL J AGR RES, V50, P1261, DOI 10.1080/00288230709510411 Yao AWL, 2005, INT J ADV MANUF TECH, V25, P723, DOI 10.1007/s00170-003-1914-5 [周星 ZHOU Xing], 2007, [农业系统科学与综合研究, System Sciences and Comprehensive Studies in Agriculture], V23, P389 NR 53 TC 42 Z9 49 U1 1 U2 33 PD JAN PY 2010 VL 70 IS 1 BP 69 EP 77 DI 10.1016/j.compag.2009.09.009 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000273933600008 DA 2022-12-14 ER PT J AU McVey, C Elliott, CT Cannavan, A Kelly, SD Petchkongkaew, A Haughey, SA AF McVey, Claire Elliott, Christopher T. Cannavan, Andrew Kelly, Simon D. Petchkongkaew, Awanwee Haughey, Simon A. TI Portable spectroscopy for high throughput food authenticity screening: Advancements in technology and integration into digital traceability systems SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Article DE Portable spectroscopy; Handheld spectroscopy; Food traceability; Food authenticity; Digitalisation ID HAND-HELD; INFRARED-SPECTROSCOPY; GEOGRAPHICAL ORIGIN; COCOA BEANS; FT-NIR; SPECTROMETER; ADULTERATION; BLOCKCHAIN; DEVICE; SAFETY AB Background: Increasing consumer demands for more information about the food we eat, in combination with a greater incidence of sophisticated food fraud cases, has resulted in the development and application of rapid, non-targeted and cost effective analytical methods. Vibrational spectroscopy equipment has evolved from bulky benchtop instruments to miniaturised sensors that have the capability of sensitive, real-time and on-site monitoring of food constituents. This has been driven by advances in semi-conductor and photonic technologies, and resulted in a plethora of commercialised, portable and handheld devices that can be used at various control points in food supply chains. Scope and approach: In this review, the recent technological advances in spectroscopic devices are explored and the development of commercially available devices for food authenticity analysis is discussed. Finally, the key challenges and potential of the integration of rapid analysis into digital traceability systems for food chain actors are outlined. Key findings and conclusions: Within the last five years there has been an increase in the development of methods utilising handheld and portable spectroscopy devices, in combination with chemometric analysis, for food authenticity and traceability verification. Full connectivity of food chains between growers, processors and retailers, validated by rapid and frequent analysis, has the potential to ensure food traceability in a systematic and cost-effective way. However, more focus is needed in this area to overcome current challenges. C1 [McVey, Claire; Elliott, Christopher T.; Petchkongkaew, Awanwee; Haughey, Simon A.] Queens Univ Belfast, Sch Biol Sci, Inst Global Food Secur, Belfast BT9 5DL, Antrim, North Ireland. [Cannavan, Andrew; Kelly, Simon D.] IAEA, Joint FAO IAEA Ctr Nucl Tech Food & Agr, Dept Nucl Sci & Applicat, Food & Environm Protect Lab,Vienna Int Ctr, POB 100, A-1400 Vienna, Austria. [Petchkongkaew, Awanwee] Thammasat Univ, Sch Food Sci & Technol, Fac Sci & Technol, Rangsit Campus, Bangkok, Thailand. C3 Queens University Belfast; International Atomic Energy Agency; Thammasat University RP Haughey, SA (corresponding author), Queens Univ Belfast, Sch Biol Sci, Inst Global Food Secur, Belfast BT9 5DL, Antrim, North Ireland. EM s.a.haughey@qub.ac.uk CR Abu-Khalaf N, 2020, COMPUT ELECTRON AGR, V173, DOI 10.1016/j.compag.2020.105445 Antila J, 2010, PROC SPIE, V7680, DOI 10.1117/12.850164 Anyidoho EK, 2020, ANAL METHODS-UK, V12, P4150, DOI 10.1039/d0ay00901f Aykas DP, 2020, FOOD CONTROL, V117, DOI 10.1016/j.foodcont.2020.107346 Aykas DP, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107670 Basri KN, 2018, ANAL METHODS-UK, V10, P4143, DOI [10.1039/c8ay01239c, 10.1039/C8AY01239C] Bec KB, 2021, CHEM-EUR J, V27, P1514, DOI 10.1002/chem.202002838 Beer T, 2018, IMPACT EXTREME WEATH, DOI [10.1007/978-3-319-56469-2_8, DOI 10.1007/978-3-319-56469-2_8] Black C, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-42796-5 Black C, 2016, FOOD CHEM, V210, P551, DOI 10.1016/j.foodchem.2016.05.004 Borghi FT, 2020, MICROCHEM J, V159, DOI 10.1016/j.microc.2020.105544 Buratti S, 2018, TALANTA, V182, P131, DOI 10.1016/j.talanta.2018.01.096 Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Camin F, 2016, COMPR REV FOOD SCI F, V15, P868, DOI 10.1111/1541-4337.12219 Correia RM, 2018, TALANTA, V176, P59, DOI 10.1016/j.talanta.2017.08.009 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Crocombe RA, 2018, APPL SPECTROSC, V72, P1701, DOI 10.1177/0003702818809719 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Du ZH, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9020338 Dumalisile P, 2020, FOOD ANAL METHOD, V13, P1220, DOI 10.1007/s12161-020-01739-x Dumalisile P, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.106981 Edwards K, 2020, MOLECULES, V25, DOI 10.3390/molecules25081845 Ellis DI, 2019, ANALYST, V144, P324, DOI [10.1039/C8AN01702F, 10.1039/c8an01702f] Ellis DI, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-12263-0 Esteki M, 2018, FOOD CONTROL, V93, P165, DOI 10.1016/j.foodcont.2018.06.015 EU Rapid Alert System for Food and Feed, 2020, ONL FOOD SAF REG European Commission, 2013, HORS 2013 14 *FAO, 2008, MEL MILK CRIS Fardin-Kia A.R., 2017, NIR NEWS, V28, P9, DOI [10.1177/0960336016687521, DOI 10.1177/0960336016687521] Fu XP, 2017, BIOSYST ENG, V163, P87, DOI 10.1016/j.biosystemseng.2017.08.022 Galvin-King P, 2018, FOOD CONTROL, V88, P85, DOI 10.1016/j.foodcont.2017.12.031 Gan ZL, 2016, J FOOD ENG, V178, P151, DOI 10.1016/j.jfoodeng.2016.01.016 Gao BY, 2019, J AGR FOOD CHEM, V67, P8425, DOI 10.1021/acs.jafc.9b03085 Gnyba M, 2011, B POL ACAD SCI-TECH, V59, P325, DOI 10.2478/v10175-011-0040-z Grassi S, 2018, FOOD CHEM, V243, P382, DOI 10.1016/j.foodchem.2017.09.145 Guelpa A, 2017, FOOD CONTROL, V73, P1388, DOI 10.1016/j.foodcont.2016.11.002 Hamamatsu Photonics, 2011, CHAR US INF DET Jagadeesan B, 2019, FOOD MICROBIOL, V79, P96, DOI 10.1016/j.fm.2018.11.005 Jentzsch PV, 2016, FOOD CHEM, V211, P274, DOI 10.1016/j.foodchem.2016.05.017 Jin CY, 2020, CURR OPIN FOOD SCI, V36, P24, DOI 10.1016/j.cofs.2020.11.006 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Karunathilaka SR, 2018, FOOD CONTROL, V92, P137, DOI 10.1016/j.foodcont.2018.04.046 Kaufman K, 2010, SPECTROSCOPY SANTA M, V25 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kiani S, 2019, LWT-FOOD SCI TECHNOL, V104, P61, DOI 10.1016/j.lwt.2019.01.045 Korinth F, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-67897-4 Laborde D, 2020, SCIENCE, V369, P500, DOI 10.1126/science.abc4765 Laganovska K, 2020, HARDWAREX, V7, DOI 10.1016/j.ohx.2020.e00108 Li Vigni M, 2020, FOODS, V9, DOI 10.3390/foods9111563 Limm W, 2018, INT DAIRY J, V85, P177, DOI 10.1016/j.idairyj.2018.06.005 Liu NJ, 2018, TALANTA, V184, P128, DOI 10.1016/j.talanta.2018.02.097 Logan BG, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107652 Lohumi S, 2015, TRENDS FOOD SCI TECH, V46, P85, DOI 10.1016/j.tifs.2015.08.003 MacArthur RL, 2020, VIB SPECTROSC, V110, DOI 10.1016/j.vibspec.2020.103129 Manfredi M, 2018, SPECTROCHIM ACTA A, V189, P427, DOI 10.1016/j.saa.2017.08.050 McGonigle AJS, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18010223 McGrath TF, 2021, J AOAC INT, V104, P16, DOI 10.1093/jaoacint/qsaa109 McVey C, 2021, FOODS, V10, DOI 10.3390/foods10050956 McVey C, 2021, TALANTA, V222, DOI 10.1016/j.talanta.2020.121533 Menevseoglu A, 2021, J FOOD MEAS CHARACT, V15, P1075, DOI 10.1007/s11694-020-00710-y Mishra P, 2021, TALANTA, V223, DOI 10.1016/j.talanta.2020.121733 Misra NN, 2022, IEEE INTERNET THINGS, V9, P6305, DOI 10.1109/JIOT.2020.2998584 Perez IMN, 2018, APPL SPECTROSC, V72, P1774, DOI 10.1177/0003702818788878 Parastar H, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107149 Perez-Marin D, 2021, TALANTA, V222, DOI 10.1016/j.talanta.2020.121511 Pujolras MP, 2015, J AM OIL CHEM SOC, V92, P175, DOI 10.1007/s11746-015-2591-x Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qian JP, 2022, CRIT REV FOOD SCI, V62, P679, DOI 10.1080/10408398.2020.1825925 Robson K, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107516 Rodriguez-Saona L, 2020, CURR OPIN FOOD SCI, V31, P136, DOI 10.1016/j.cofs.2020.04.008 Rukundo IR, 2020, J NEAR INFRARED SPEC, V28, P81, DOI 10.1177/0967033519898889 Salvador L, 2019, FOODS, V8, DOI 10.3390/foods8030105 Santos PM, 2013, J AGR FOOD CHEM, V61, P1205, DOI 10.1021/jf303814g Schmutzler M, 2015, FOOD CONTROL, V57, P258, DOI 10.1016/j.foodcont.2015.04.019 Shotts ML, 2018, J CEREAL SCI, V82, P65, DOI 10.1016/j.jcs.2018.04.005 Silva LCR, 2020, VIB SPECTROSC, V111, DOI 10.1016/j.vibspec.2020.103158 Spink J, 2011, J FOOD SCI, V76, pR157, DOI 10.1111/j.1750-3841.2011.02417.x Taylan O, 2021, J SCI FOOD AGR, V101, P1699, DOI 10.1002/jsfa.10845 Taylan O, 2020, FOOD CHEM, V332, DOI 10.1016/j.foodchem.2020.127344 Teye E, 2019, FOOD ADDIT CONTAM A, V36, P1589, DOI 10.1080/19440049.2019.1658905 Teye E, 2019, SPECTROCHIM ACTA A, V217, P147, DOI 10.1016/j.saa.2019.03.085 Trendov N., 2019, DIGITAL TECHNOLOGIES van Ruth Saskia, 2019, NIR News, V30, P18, DOI 10.1177/0960336018823490 Vincent J, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18061708 Wang YJ, 2020, J PHYS CONF SER, V1634, DOI 10.1088/1742-6596/1634/1/012025 Wang ZJ, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20205793 Weagant S, 2015, PROC SPIE, V9482, DOI 10.1117/12.2178359 WHO, 2012, GLOBAL TUBERCULOSIS REPORT 2012, P1 Wiedemair V, 2018, CURR ANAL CHEM, V14, P58, DOI 10.2174/1573411013666170207121113 Wielogorska E, 2018, FOOD CHEM, V239, P32, DOI 10.1016/j.foodchem.2017.06.083 Yan J, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201900031 Yoon HW, 2006, PROC SPIE, V6297, DOI 10.1117/12.684614 Zampetti A, 2019, ADV FUNCT MATER, V29, DOI 10.1002/adfm.201807623 Zhao XD, 2011, SENSOR LETT, V9, P1055, DOI 10.1166/sl.2011.1375 NR 94 TC 12 Z9 12 U1 13 U2 17 PD DEC PY 2021 VL 118 BP 777 EP 790 DI 10.1016/j.tifs.2021.11.003 EA NOV 2021 PN B WC Food Science & Technology SC Food Science & Technology UT WOS:000746594700002 DA 2022-12-14 ER PT J AU Liao, PA Chang, HH Chang, CY AF Liao, Pei-An Chang, Hung-Hao Chang, Chun-Yen TI Why is the food traceability system unsuccessful in Taiwan? Empirical evidence from a national survey of fruit and vegetable farmers SO FOOD POLICY DT Article DE Food traceability system; Awareness; Pesticide residue testing; Taiwan ID TECHNOLOGY ADOPTION; PREFERENCES; CHOICE; WILLINGNESS; PERCEPTIONS; MANAGEMENT; DIFFUSION; COUNTRIES; TANZANIA; SAFETY AB Food traceability systems allow the consumers or administrative authorities to trace the origins or ingredients of food products. Given the expressed concerns for food safety, the promotion of food traceability systems has occurred in many countries. Although a considerable body of literature has examined the consumer responses in regard to food traceability, relatively little is known about the producers' adoption behaviors. To fill this knowledge gap, this paper investigates Taiwanese farmers' participation decision in relation to the Taiwan Agriculture and Food Traceability (TAFT) program; special attention is paid to understanding the roles of the farmers' program awareness and pesticide residue testing adoption in regard to TAFT participation. Using a national representative sample of the fruit and vegetable farmers in Taiwan, the results indicate that program awareness and pesticide residue testing adoption are the significant determinants of TAFT participation. An awareness of the government's promotion of the TAFT program and adoption of pesticide residue testing has significantly reduced TAFT non-participation by 28.2% and 21.9% points, respectively. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Chang, Hung-Hao] Natl Taiwan Univ, Dept Agr Econ, Taipei 10617, Taiwan. [Liao, Pei-An] Shih Hsin Univ, Dept Econ, Taipei 11645, Taiwan. [Chang, Chun-Yen] Council Agr, Taipei 10014, Taiwan. C3 National Taiwan University; Shih Hsin University RP Chang, HH (corresponding author), Natl Taiwan Univ, Dept Agr Econ, 1 Roosevelt Rd,Sec 4, Taipei 10617, Taiwan. EM paliao@cc.shu.edu.tw; hunghaochang@ntu.edu.tw; er6324@mail.coa.gov.tw CR Abdulai A, 2005, AM J AGR ECON, V87, P645, DOI 10.1111/j.1467-8276.2005.00753.x Alene AD, 2006, AGR ECON, V35, P203, DOI 10.1111/j.1574-0862.2006.00153.x Allen DW, 1999, J LAW ECON ORGAN, V15, P704, DOI 10.1093/jleo/15.3.704 [Anonymous], 2010, TAIPEI TIMES Banterle A., 2006, 99 EUR SEM EAAE TRUS, DOI [10.22004/ag.econ.7722, DOI 10.22004/AG.ECON.7722] Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Bukenya JO, 2007, REV AGR ECON, V29, P306, DOI 10.1111/j.1467-9353.2007.00344.x Burton M, 2003, AUST J AGR RESOUR EC, V47, P29, DOI 10.1111/1467-8489.00202 Chib S, 1998, BIOMETRIKA, V85, P347, DOI 10.1093/biomet/85.2.347 *COUNC AGR, 2004, AGR STAT YB *COUNC AGR, 2005, ENV SUST IND Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Dimara E, 2003, AGR ECON-BLACKWELL, V28, P187, DOI [10.1016/S0169-5150(03)00003-3, 10.1111/j.1574-0862.2003.tb00137.x] Dong DS, 1998, APPL ECON, V30, P893, DOI 10.1080/000368498325327 FMRIC-Food Marketing Research and Information Center, 2008, HDB INTR FOOD TRAC S Fonsah EG, 2006, CHOICES, V21, P243 Fulponi L, 2006, FOOD POLICY, V31, P1, DOI 10.1016/j.foodpol.2005.06.006 Genius M, 2006, J AGR RESOUR ECON, V31, P93 Golan E.H., 2004, AGR EC REPORTS, P1362 Greene W. H., 1993, ECONOMETRIC ANAL Hajivassiliou V, 1996, J ECONOMETRICS, V72, P85, DOI 10.1016/0304-4076(94)01716-6 HAUSMAN JA, 1978, ECONOMETRICA, V46, P403, DOI 10.2307/1913909 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Honlonkou AN, 2004, ENVIRON DEV ECON, V9, P289, DOI 10.1017/S1355770X03001128 Houghton JR, 2008, FOOD POLICY, V33, P13, DOI 10.1016/j.foodpol.2007.05.001 Knowler D, 2007, FOOD POLICY, V32, P25, DOI 10.1016/j.foodpol.2006.01.003 Lee DR, 2005, AM J AGR ECON, V87, P1325, DOI 10.1111/j.1467-8276.2005.00826.x Lin Y.F., 1991, AM J AGR ECON, V73, P713 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lur HS, 2005, INT WORKSH TECHN GAP MADDALA GS, 1983, DEPENDENT QUANTITATI Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Nkonya E, 1997, J AGR ECON, V48, P1, DOI 10.1111/j.1477-9552.1997.tb01126.x POGHOSYAN A, 2004, INT FOOD AGRIBUS MAN, V7, P118 Schulz LL, 2010, J AGR ECON, V61, P138, DOI 10.1111/j.1477-9552.2009.00226.x SMITH IG, 2008, FOOD TRACEABILITY WO Soule MJ, 2000, AM J AGR ECON, V82, P993, DOI 10.1111/0002-9092.00097 Staiger D, 1997, ECONOMETRICA, V65, P557, DOI 10.2307/2171753 Suri T, 2011, ECONOMETRICA, V79, P159, DOI 10.3982/ECTA7749 U.S. Department of Agriculture (USDA), 2005, DIET GUID AM 2005 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 VANGOOR AR, 1996, PHYS DISTRIBUTION TH Vernede R., 2003, TRACEABILITY FOOD PR Wynn G, 2001, J AGR ECON, V52, P65, DOI 10.1111/j.1477-9552.2001.tb00910.x Xu LL, 2010, J SCI FOOD AGR, V90, P1368, DOI 10.1002/jsfa.3985 NR 45 TC 49 Z9 52 U1 3 U2 72 PD OCT PY 2011 VL 36 IS 5 BP 686 EP 693 DI 10.1016/j.foodpol.2011.06.010 WC Agricultural Economics & Policy; Economics; Food Science & Technology; Nutrition & Dietetics SC Agriculture; Business & Economics; Food Science & Technology; Nutrition & Dietetics UT WOS:000295656600015 DA 2022-12-14 ER PT J AU Sun, YK Du, GB Lin, QZ Zhong, LH Zhao, YJ Qiu, J Cao, Y AF Sun, Yongke Du, Guanben Lin, Qizhao Zhong, Lihui Zhao, Youjie Qiu, Jian Cao, Yong TI Individual wood board tracing method using oriented fast and rotated brief method in the wood traceability system SO WOOD SCIENCE AND TECHNOLOGY DT Article ID RECOGNITION; TRACKING AB Due to the economic, social, and legal requirements, individual wood identity recognition is a crucial technology required in traceability systems, such as the wood block chain system. Traditionally, labeling is a widely used technology which sticks a code label on the wood entity. It can be used to find out the information related to the entity from the traceability system by scanning the code labels. However, many kinds of labels are easily switched and falsified in illegal activities. This study proposed a new security label method that extracts the innate feature as wood fingerprint to prevent the label falsify problem. This method acquired the cross-section image of log or wood board and extracted the oriented fast and rotated brief (ORB) features to identify the individual entities. It is safe because the feature of the wood texture is innate and cannot be transferred from one to another. Furthermore, this features is unique because of the anisotropy of wood. An image acquisition method was presented. A position was marked as the fingerprint area at the cross section of the wood board, and the image of the fingerprint area was acquired by using a camera with a 20x magnifying glass. The individual wood board tracking method based on the ORB was employed to identify the wood entity. The key point set was described by a invariant rotation descriptor, and it can be used as a fingerprint to recognize the wood board by comparing the marked area images in the wood traceability system. In the experiments, 80 woodblocks were chosen as the tracer objects, and 10 places were marked at each cross section of the board. The results showed that this method has high recognition accuracy and robustness. The recognition accuracy reached 100%, and this method still retained high accuracy even when different cameras were used in different environments to acquire the images. This method can be used in the timber trade to trace wood boards or logs and to prevent illegal trading. C1 [Sun, Yongke] Southwest Forestry Univ, Yunnan Prov Key Lab Wood Adhes & Glued Prod, Kunming 650224, Yunnan, Peoples R China. [Du, Guanben; Lin, Qizhao; Zhong, Lihui; Zhao, Youjie; Cao, Yong] Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China. [Qiu, Jian] Southwest Forestry Univ, Coll Mat Sci & Engn, Kunming 650224, Yunnan, Peoples R China. C3 Southwest Forestry University - China; Southwest Forestry University - China; Southwest Forestry University - China RP Cao, Y (corresponding author), Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China.; Qiu, J (corresponding author), Southwest Forestry Univ, Coll Mat Sci & Engn, Kunming 650224, Yunnan, Peoples R China. EM sunyongke@swfu.edu.cn; qiujian@swfu.edu.cn; cn_caoyong@126.com CR Appelhanz S, 2016, J CLEAN PROD, V110, P132, DOI 10.1016/j.jclepro.2015.02.034 Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014 Bjork A, 2011, COMPUT IND, V62, P830, DOI 10.1016/j.compind.2011.08.001 Chater A, 2020, TELKOMNIKA, V18, P695, DOI [10.12928/TELKOMNIKA.v18i2.13726, DOI 10.12928/TELKOMNIKA.V18I2.13726] Chen M., 2018, J INSP QUAR, V28, P47 Chuan Luo, 2019, Journal of Physics: Conference Series, V1237, DOI 10.1088/1742-6596/1237/3/032020 Dykstra DP, 2010, TECHNOLOGIES WOOD TR Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 Godbout J, 2018, FOREST CHRON, V94, P75, DOI 10.5558/tfc2018-010 Harris C., 1988, P 4 ALV VIS C, P147 Johansson E, 2015, COMPUT ELECTRON AGR, V118, P85, DOI 10.1016/j.compag.2015.08.026 Kannangara S, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-61415-2 Knowles C, 2017, INT FOREST REV, V19, P294, DOI 10.1505/146554817821865036 Lindeberg T., 2012, SCHOLARPEDIA, V7, P10491, DOI [DOI 10.4249/SCHOLARPEDIA.10491, 10.4249/scholarpedia.10491] Lipton Zachary C, 2014, Mach Learn Knowl Discov Databases, V8725, P225, DOI 10.1007/978-3-662-44851-9_15 Lowe AJ, 2011, IAWA J, V32, P251, DOI 10.1163/22941932-90000055 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Ma CQ, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20040975 Pahlberg T, 2015, COMPUT ELECTRON AGR, V111, P164, DOI 10.1016/j.compag.2014.12.014 Paula PL, 2014, MACH VISION APPL, V25, P1019, DOI 10.1007/s00138-014-0592-7 Qin YY, 2014, PROCEEDINGS OF 2014 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATICS AND COMPUTING (PIC), P204, DOI 10.1109/PIC.2014.6972325 Rublee E, 2011, IEEE I CONF COMP VIS, P2564, DOI 10.1109/ICCV.2011.6126544 Schmid JF, 2020, ARXIV200211948 Schraml R, 2015, COMPUT ELECTRON AGR, V119, P112, DOI 10.1016/j.compag.2015.10.003 Schraml R, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8071071 Schraml R, 2015, IEEE IMAGE PROC, P3665, DOI 10.1109/ICIP.2015.7351488 Schraml R, 2015, LECT NOTES COMPUT SC, V9256, P752, DOI 10.1007/978-3-319-23192-1_63 Schweingruber Fritz Hans, 2007, P1 Shin D, 2020, ONLINE INFORM REV, V44, P913, DOI 10.1108/OIR-01-2019-0013 Sun Q., 2019, MODERN ELECT TECH, V42, P169, DOI [10.16652/j.issn.1004-373x.2019.20.040, DOI 10.16652/J.ISSN.1004-373X.2019.20.040] Tnah LH, 2012, WOOD SCI TECHNOL, V46, P813, DOI 10.1007/s00226-011-0447-6 Tzoulis I. K., 2014, Journal of Agricultural Informatics, V5, P9 Vieira LC, 2020, IET OPTOELECTRON, V14, P149, DOI 10.1049/iet-opt.2018.5138 Wang J., 2013, ENCY SYSTEMBIOL, DOI [10.1007/978-1-4419-9863-7_372, DOI 10.1007/978-1-4419-9863-7_372] Wu MY, 2018, EURASIP J IMAGE VIDE, DOI 10.1186/s13640-018-0354-y Zhang SC, 2018, IEEE T NEUR NET LEAR, V29, P1774, DOI 10.1109/TNNLS.2017.2673241 Zj, 2020, ECOL ECON, V16, P26 NR 37 TC 0 Z9 0 U1 14 U2 15 PD MAY PY 2022 VL 56 IS 3 BP 947 EP 968 DI 10.1007/s00226-022-01379-w EA MAY 2022 WC Forestry; Materials Science, Paper & Wood SC Forestry; Materials Science UT WOS:000797278800001 DA 2022-12-14 ER PT J AU Bortolotti, L Rizzo, S Favero, L Bonfanti, L Comin, A Marangon, S AF Bortolotti, Laura Rizzo, Simone Favero, Laura Bonfanti, Lebana Comin, Arianna Marangon, Stefano TI Implementation of an Information System for the Traceability of Live Decoy Birds SO AVIAN DISEASES DT Article; Proceedings Paper CT 8th International Symposium on Avian Influenza CY APR 01-04, 2012 CL Univ London, Royal Holloway, London, ENGLAND HO Univ London, Royal Holloway DE avian influenza; control system; live decoy birds; identification and registration system; traceability ID PATHOGENICITY; EPIDEMIOLOGY AB In the Veneto region (northern Italy), some geographic areas in the Po Valley have a large concentration of industrial poultry farms and are located close to wet areas with high populations of wild waterfowl. Live decoy birds belonging to the orders of Anseriformes and Charadriiformes can constitute a "bridge" for avian influenza (AI) viruses between the wild reservoir and the rural holdings where live decoy birds are usually kept, sometimes together with poultry. Thus, the use of live decoy birds during bird hunting could increase the risk of exposure of poultry farms to AI viruses. Since 2008, this kind of hunting has been strictly regulated with regard to the detection and use of live decoy birds. In order to guarantee the application of appropriate AI risk-modulating and monitoring measures in the management of the live decoys according to the European Union (EU) provisions, a solid and well-structured information system has been created. The Regional Data Bank (RDB) of farms and livestock, which has been operating since 1997, also contains data on farms and poultry movements. Therefore, the RDB management software was updated to collect data from the hunters who keep live decoy birds, and specific functions were integrated to ensure the traceability of these birds. Each live decoy bird has been identified by an irremovable ring. The individual code of each ring is recorded in the RDB and linked to both the holder's code and the hunting area. Transfers and death/slaughtering of the registered birds are recorded, too. The activation of a computerized data collection system has proven to be a prerequisite for the implementation of a control system for live decoy birds and provides an essential tool for the management of AI emergencies. C1 [Bortolotti, Laura; Rizzo, Simone; Bonfanti, Lebana; Comin, Arianna; Marangon, Stefano] Ist Zooprofilatt Sperimentale Venezie, I-35020 Legnaro, Italy. [Favero, Laura] Reg Veneto Unita Progetto Vet, I-31125 Venice, Italy. C3 IZS delle Venezie RP Bortolotti, L (corresponding author), Ist Zooprofilatt Sperimentale Venezie, Viale Univ 10, I-35020 Legnaro, Italy. EM crev.lbortolotti@izsvenezie.it CR Alexander DJ, 2007, VACCINE, V25, P5637, DOI 10.1016/j.vaccine.2006.10.051 [Anonymous], 2011, TERRESTRIAL ANIMAL C, V1 [Anonymous], 2008, GAZ UFF REP ITAL, V190, P15 [Anonymous], 2008, B UFF REG VENETO, V79, P237 [Anonymous], 2009, AVIAN INFLUENZA MANU, V1 [Anonymous], 2010, GAZ UFF REP ITAL, V196, P49 Bonfanti L., 2005, AVICOLTURA, V3, P24 CEC, 2005, OFF J EUR COMM L, VL274, P104 CEC, 2006, OFF J EUR COMM L, VL228, P24 CEC, 2005, OFF J EUR COMM L, V164, P52 Cecchinato M, 2011, AVIAN DIS, V55, P13, DOI 10.1637/9500-081310-Reg.1 Commission of the European Communities, 2005, OFFICIAL J EUROPEA L, V273, P21 Morris R S, 1991, Rev Sci Tech, V10, P13 Patumi I, 2011, IMCIC'11: THE 2ND INTERNATIONAL MULTI-CONFERENCE ON COMPLEXITY, INFORMATICS AND CYBERNETICS, VOL II, P184 Spickler AR, 2008, AVIAN PATHOL, V37, P555, DOI 10.1080/03079450802499118 NR 15 TC 0 Z9 0 U1 0 U2 15 PD DEC PY 2012 VL 56 IS 4 SU S BP 1021 EP 1024 DI 10.1637/10161-040912-Reg.1 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000313136200034 DA 2022-12-14 ER PT J AU Canalias, F Garcia, E Sanchuz, M AF Canalias, Francesca Garcia, Ester Sanchuz, Maribel TI Metrological traceability of values for alpha-amylase catalytic concentration assigned to a commutable calibrator materials SO CLINICA CHIMICA ACTA DT Article DE Reference system; Traceability; alpha-Amylase; Commutability ID LACTATE-DEHYDROGENASE; ENZYMES; 37-DEGREES-C; SERUM; AMINOTRANSFERASE; PROJECT AB Background: The International Standard ISO 18153 describes how to assure the metrological traceability of catalytic concentration values assigned to commercial calibration materials following the reference measurement system. We applied this approach to the standardization of alpha-amylase measurements. Methods: Traceable values of catalytic activity with measurement uncertainty were assigned to three commercial calibrator materials using alpha-amylase primary reference measurement procedure described by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). The metrological traceable to the SI units calibration was validated by measuring human serum and plasma samples using the primary reference procedure and two routine commercial measurement procedures using different substrates (4,6-ethylidene(G1)-4-nitrophenyl(G7)-alpha-(1 -> 4)-D-maltoheptaoside (EPS) and 2-chloro-4-nitrophenyl-alpha-D-maltotrioside (CNP)) and calibrated with the traceable materials. Previously the commutability of the commercial calibration materials was verified. Results: Procedures comparison showed that the primary reference procedure and routine procedures have the same analytical specificity. The commercial calibrators tested showed to be commutable and were used to recalibrate the routine measurement procedures. The agreement of procedures improves after recalibration with commutable calibrator materials. Conclusions: The implementation of a reference system for alpha-amylase measurements was demonstrated that assures the traceability of patients' results to SI units and to harmonize the results obtained by two different routine procedures. (C) 2009 Elsevier B.V. All rights reserved. C1 [Canalias, Francesca; Garcia, Ester] Univ Autonoma Barcelona, Lab Referencia Enzimol Clin, Dept Bioquim & Biol Mol, Unitat Bioquim Med, Bellaterra 08193, Spain. [Sanchuz, Maribel] BioSystems SA, Dept Invest & Desarrollo, Div React, Barcelona, Spain. C3 Autonomous University of Barcelona RP Canalias, F (corresponding author), Univ Autonoma Barcelona, Lab Referencia Enzimol Clin, Dept Bioquim & Biol Mol, Unitat Bioquim Med, Campus Bellaterra,Edifici M, Bellaterra 08193, Spain. EM francesca.canalias@uab.cat CR Baadenhuijsen H, 2005, CLIN CHEM LAB MED, V43, P304, DOI 10.1515/CCLM.2005.052 BLAND JM, 1986, LANCET, V1, P307, DOI 10.1016/s0140-6736(86)90837-8 Brion E, 2002, CLIN CHEM LAB MED, V40, P625, DOI 10.1515/CCLM.2002.108 Canalias F, 2006, CLIN CHEM LAB MED, V44, P333, DOI 10.1515/CCLM.2006.058 Cattozzo G, 2008, CLIN CHEM, V54, P1349, DOI 10.1373/clinchem.2007.100081 Christenson RH, 2006, CLIN CHEM, V52, P1685, DOI 10.1373/clinchem.2006.068437 Franzini C, 1993, J Int Fed Clin Chem, V5, P169 Gella FJ, 1997, CLIN CHIM ACTA, V259, P147, DOI 10.1016/S0009-8981(96)06481-9 GUBERN G, 1995, CLIN CHEM, V41, P435 International Organization for Standardization, 2003, 18153 ISO ISO, 1995, GUID EXPR UNC MEAS Jansen R, 2006, CLIN CHIM ACTA, V368, P160, DOI 10.1016/j.cca.2005.12.033 Panteghini M, 2001, CLIN CHEM LAB MED, V39, P795, DOI 10.1515/CCLM.2001.131 PASSING H, 1983, J CLIN CHEM CLIN BIO, V21, P709 Ricos C, 1999, SCAND J CLIN LAB INV, V59, P491 Schumann G, 2002, CLIN CHEM LAB MED, V40, P734, DOI 10.1515/CCLM.2002.126 Schumann G, 2002, CLIN CHEM LAB MED, V40, P725, DOI 10.1515/CCLM.2002.125 Schumann G, 2002, CLIN CHEM LAB MED, V40, P718, DOI 10.1515/CCLM.2002.124 Schumann G, 2002, CLIN CHEM LAB MED, V40, P635, DOI 10.1515/CCLM.2002.110 Schumann G, 2002, CLIN CHEM LAB MED, V40, P643, DOI 10.1515/CCLM.2002.111 Schumann G, 2006, CLIN CHEM LAB MED, V44, P1146, DOI 10.1515/CCLM.2006.212 Siekmann L, 2002, CLIN CHEM LAB MED, V40, P631, DOI 10.1515/CCLM.2002.109 van der Heiden C, 1999, CLIN CHIM ACTA, V281, pS5 NR 23 TC 6 Z9 8 U1 0 U2 7 PD JAN 4 PY 2010 VL 411 IS 1-2 BP 7 EP 12 DI 10.1016/j.cca.2009.09.029 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000272855600002 DA 2022-12-14 ER PT J AU Suhandoko, AA Chen, DCB Yang, SH AF Suhandoko, Ardiansyah Azhary Chen, Dennis Chia-Bin Yang, Shang-Ho TI Meat Traceability: Traditional Market Shoppers' Preferences and Willingness-to-Pay for Additional Information in Taiwan SO FOODS DT Article DE traditional market; traceability; willingness-to-pay; pork; Taiwan ID FOOD SAFETY; CHOICE EXPERIMENTS; SUPPLY CHAIN; EMPIRICAL-EVIDENCE; CONSUMER TRUST; CHEAP TALK; QUALITY; BEEF; SYSTEM; PERCEPTIONS AB Due to food scandals that shocked the retailer markets, traceability systems were advocated to regain consumers' confidence and trust. However, while traceability systems can be more easily explored in modern markets, almost no traceability system can be found in traditional markets in Taiwan, especially when buying meat products. This study explored the preference and the willingness-to-pay (WTP) for traceability information of pork products in traditional markets in Taiwan. The random utility theory (RUT) with the contingent valuation method (CVM) was adopted to examine the total of 1420 valid responses in Taiwan. Results show that 80% of traditional market consumers are willing to pay more for traceability information of pork products. Specifically, when consumers (1) know the market price of pork, (2) do not often buy food in the traditional market, (3) live in south or north regions of Taiwan, (4) have a flexible buying schedule, (5) are aware of food safety due to frequently accessing health-related content through media, or (6) think pork grading is very important, they would tend to choose meat products with traceability information. The implication of this study suggests that there is an urgent desire for food safety labeling and traceability information system in traditional markets in Taiwan. Especially, those who usually shop in the higher-price markets are willing to pay the most for this traceability information. C1 [Suhandoko, Ardiansyah Azhary] Natl Chung Hsing Univ, Int Master Program Agr, 145 Xingda Rd, Taichung 40227, Taiwan. [Chen, Dennis Chia-Bin] Belmont Univ, Massey Coll Business, 1900 Belmont Blvd, Nashville, TN 37212 USA. [Yang, Shang-Ho] Natl Chung Hsing Univ, Grad Inst Bioind Management, 145 Xingda Rd, Taichung 40227, Taiwan. C3 National Chung Hsing University; Belmont University; National Chung Hsing University RP Yang, SH (corresponding author), Natl Chung Hsing Univ, Grad Inst Bioind Management, 145 Xingda Rd, Taichung 40227, Taiwan. EM ianazhary@smail.nchu.edu.tw; dennis.chen@belmont.edu; bruce.yang@nchu.edu.tw CR Ackerman D, 2001, J RETAILING, V77, P57, DOI 10.1016/S0022-4359(00)00046-4 Ambali AR, 2014, PROCEDIA SOCIAL BEHA, V121, P3, DOI 10.1016/j.sbspro.2014.01.1104 Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 [Anonymous], 2006, FRUIT GROWERS NEWS Arrow K.J., 1993, REPORT NOAA PANEL CO, V58 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bader F, 2020, ADV UBIQUIT SENS APP, V7, P229, DOI 10.1016/B978-0-12-815369-7.00010-0 Banati D, 2011, TRENDS FOOD SCI TECH, V22, P56, DOI 10.1016/j.tifs.2010.12.007 Banovic M, 2016, FOOD QUAL PREFER, V52, P42, DOI 10.1016/j.foodqual.2016.03.017 Carlsson F, 2001, J ENVIRON ECON MANAG, V41, P179, DOI 10.1006/jeem.2000.1138 Carmona-Torres C., 2006, BID DESIGN ITS INFLU CASCETTA E, 2001, TRANSPORTATION SYSTE Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 Chen MF, 2008, RISK ANAL, V28, P1553, DOI 10.1111/j.1539-6924.2008.01115.x Chiu J.-Z., 2018, INT J MANAG EC SOC S, V7, P272, DOI [10.32327/IJMESS.7.3.2018.17, DOI 10.32327/IJMESS.7.3.2018.17] Choe YC, 2009, INFORM SYST FRONT, V11, P167, DOI 10.1007/s10796-008-9134-z Cicia G., 2010, International Journal on Food System Dynamics, V1, P252 Council of Agriculture, 2012, AGR STAT YB 2012 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Department of Household Registration Affairs, 2018, REP CHIN TAIW HISTOR Dickinson D., 2003, COMP US CANADIAN CON Dickinson D., 2003, WILLINGNESS TO PAY I Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Doherty E, 2014, BRIT FOOD J, V116, P676, DOI 10.1108/BFJ-10-2011-0266 Enneking U, 2004, EUR REV AGRIC ECON, V31, P205, DOI 10.1093/erae/31.2.205 Faostat, 2018, MEAT CONS PER CAP TA Ford M., 2014, TAIWAN RETAIL FOODS Frederick C., 2019, TAIWAN RETAIL FOODS Gellynck X, 2006, MEAT SCI, V74, P161, DOI 10.1016/j.meatsci.2006.04.013 Golan E., 2004, Agricultural Economic Report - Economic Research Service, US Department of Agriculture Goldman A, 2002, J RETAILING, V78, P281, DOI 10.1016/S0022-4359(02)00098-2 Golnaz R., 2010, INT FOOD RES J, V17, P667 Gorton M, 2011, WORLD DEV, V39, P1624, DOI 10.1016/j.worlddev.2011.02.005 Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 GS1 Canada, 2004, GS1 CAN CAN TRAC HANEMANN M, 1991, AM J AGR ECON, V73, P1255, DOI 10.2307/1242453 Hanemann W, 1999, VALUING ENV PREFEREN, P302 HANEMANN WM, 1984, AM J AGR ECON, V66, P332, DOI 10.2307/1240800 HANEMANN WM, 1994, J ECON PERSPECT, V8, P19, DOI 10.1257/jep.8.4.19 Hansen JD, 2007, HORTTECHNOLOGY, V17, P195, DOI 10.21273/HORTTECH.17.2.195 Houghton JR, 2008, FOOD POLICY, V33, P13, DOI 10.1016/j.foodpol.2007.05.001 Hsu J.L., 2002, INT REV RETAIL DISTR, V12, P423, DOI DOI 10.1080/09593960210151180 Hsueh A., 2000, TAIWAN RETAIL FOOD S, P1 Huang CC, 2000, J VET MED SCI, V62, P677, DOI 10.1292/jvms.62.677 Huang CL, 1999, J CONSUM AFF, V33, P76, DOI 10.1111/j.1745-6606.1999.tb00761.x Islam R, 2018, J ISLAMIC MARK, V9, P240, DOI 10.1108/JIMA-03-2016-0027 Jang YJ, 2011, INT J HOSP MANAG, V30, P803, DOI 10.1016/j.ijhm.2010.12.012 Jones MA, 2007, J SERV RES-US, V9, P335, DOI 10.1177/1094670507299382 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kniazeva M, 2010, J BUS RES, V63, P748, DOI 10.1016/j.jbusres.2009.05.012 Kondo N, 2010, TRENDS FOOD SCI TECH, V21, P145, DOI 10.1016/j.tifs.2009.09.002 Kueh K., 2007, MANAG SERV QUAL, V17, P656, DOI [10.1108/09604520710834993, DOI 10.1108/09604520710834993] LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131 Latest Global Cancer Data, 2018, INT AGENCY RES CANC Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Liang WS, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0236581 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Lilavanichakul A, 2013, INT FOOD AGRIBUS MAN, V16, P25 Lin TY, 2003, EMERG INFECT DIS, V9, P291, DOI 10.3201/eid0903.020285 Liu Y., 2002, CHIN J AGRIBUS MANAG, V8, P223 Loureiro M. L., 2005, Journal of Agricultural and Applied Economics, V37, P49, DOI 10.1017/S1074070800007094 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Louviere J. J., 2000, STATED CHOICE METHOD, P27251, DOI [10.1017/CBO9780511753831.008, DOI 10.1017/CBO9780511753831.008] Lusk JL, 2004, AM J AGR ECON, V86, P467, DOI 10.1111/j.0092-5853.2004.00592.x Lusk JL, 2003, AM J AGR ECON, V85, P840, DOI 10.1111/1467-8276.00492 Maddala G. S., 1983, LTD DEPENDENT QUALIT Makweya FL, 2019, ITAL J FOOD SAF, V8, P46, DOI 10.4081/ijfs.2019.7654 Malhotra NK, 2005, INT MARKET REV, V22, P256, DOI 10.1108/02651330510602204 Maruyama M, 2014, J RETAIL CONSUM SERV, V21, P383, DOI 10.1016/j.jretconser.2013.11.002 McCluskey JJ, 2005, AUST J AGR RESOUR EC, V49, P197, DOI 10.1111/j.1467-8489.2005.00282.x McFadden D., 1974, STRUCTURAL ANAL DISC, P198 Meuwissen MPM, 2007, NJAS-WAGEN J LIFE SC, V54, P293, DOI 10.1016/S1573-5214(07)80021-2 Miller S, 2017, J CONSUM BEHAV, V16, pE13, DOI 10.1002/cb.1650 Norshamliza Chamhuri, 2009, Stewart Postharvest Review, V5, P1, DOI 10.2212/spr.2009.3.1 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Owusu V, 2013, INT FOOD AGRIBUS MAN, V16, P67 Paswan A, 2010, J BUS RES, V63, P667, DOI 10.1016/j.jbusres.2009.02.020 Perman R., 2003, NATURAL RESOURCE ENV Prathiraja PHK, 2003, SRI LANKAN J AGR EC, V5, P35 Putri W.R., 2017, JURNAL DINAMIKA MANA, V8, P122, DOI [10.15294/jdm.v8i1.10416, DOI 10.15294/JDM.V8I1.10416] Rahman M. M., 2014, HLTH SAFETY ENV, V2, P132 Rezai G, 2012, J ISLAMIC MARK, V3, P35, DOI 10.1108/17590831211206572 Saunders M., 2016, AUST NZ J EUR STUD, V8, P53, DOI [10.30722/anzjes.vol8.iss1.15159, DOI 10.30722/ANZJES.VOL8.ISS1.15159] Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Song M, 2008, J FAC AGR KYUSHU U, V53, P569 Spencer-Cotton A, 2016, AUST J AGR RESOUR EC, V60, pE17, DOI 10.1111/1467-8489.12167 Syafiq A., 2019, J HALAL PROD RES, V2, P51, DOI [10.20473/jhpr.vol.2-issue.2.51-59, DOI 10.20473/JHPR.VOL.2-ISSUE.2.51-59] TAFTS, 2019, STAT QUO PROSP TAIW Tait P, 2016, J CLEAN PROD, V124, P65, DOI 10.1016/j.jclepro.2016.02.088 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Tonsor GT, 2011, AM J AGR ECON, V93, P1015, DOI 10.1093/ajae/aar036 Trappey C.V., 1997, TAIWAN REV TAIW 0301 Tsai HT, 2014, ANTHROPOLOGIST, V17, P845, DOI 10.1080/09720073.2014.11891499 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Wezemael L, 2010, FOOD CONTROL, V21, P835, DOI 10.1016/j.foodcont.2009.11.010 Varela O., 2009, AGR EC MARKET J, V2, P19 Verbeke W., 2009, Estey Centre Journal of International Law and Trade Policy, V10, P20 Verbeke W, 2001, OUTLOOK AGR, V30, P249, DOI 10.5367/000000001101293733 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Verbeke W, 2013, MEAT SCI, V95, P790, DOI 10.1016/j.meatsci.2013.04.042 Vernede R., 2003, TRACEABILITY FOOD PR Viegas I, 2014, J AGR ECON, V65, P600, DOI 10.1111/1477-9552.12067 Wang MingQiang, 2015, Journal of Food Safety and Quality, V6, P2581 Wei YP, 2017, COGENT BUS MANAG, V4, DOI [10.1080/23311975.2017.1290891, 10.1186/s40649-017-0037-3] Wei-han C., 2016, TAIPEI TIMES 0122 Wu D., 2017, PROC IEEE TRANSP ELE, P1, DOI [10.1109/ITEC-AP.2017.8080769, DOI 10.1109/ITEC-AP.2017.8080769] Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Yam-Kwang Chen, 2012, Information Technology Journal, V11, P1154, DOI 10.3923/itj.2012.1154.1165 Zach L, 2016, WOODHEAD PUBL FOOD S, V301, P237, DOI 10.1016/B978-0-08-100310-7.00013-2 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhao J, 2018, FOOD CONTROL, V87, P94, DOI 10.1016/j.foodcont.2017.11.039 NR 114 TC 6 Z9 6 U1 10 U2 22 PD AUG PY 2021 VL 10 IS 8 AR 1819 DI 10.3390/foods10081819 WC Food Science & Technology SC Food Science & Technology UT WOS:000689324200001 DA 2022-12-14 ER PT J AU Ahmed, M Taconet, C Ould, M Chabridon, S Bouzeghoub, A AF Ahmed, Mohamed Taconet, Chantal Ould, Mohamed Chabridon, Sophie Bouzeghoub, Amel TI IoT Data Qualification for a Logistic Chain Traceability Smart Contract SO SENSORS DT Article DE IoT; data quality; smart contract; traceability; logistic; sensor; blockchain; supply chain ID DATA QUALITY; INTERNET AB In the logistic chain domain, the traceability of shipments in their entire delivery process from the shipper to the consignee involves many stakeholders. From the traceability data, contractual decisions may be taken such as incident detection, validation of the delivery or billing. The stakeholders require transparency in the whole process. The combination of the Internet of Things (IoT) and the blockchain paradigms helps in the development of automated and trusted systems. In this context, ensuring the quality of the IoT data is an absolute requirement for the adoption of those technologies. In this article, we propose an approach to assess the data quality (DQ) of IoT data sources using a logistic traceability smart contract developed on top of a blockchain. We select the quality dimensions relevant to our context, namely accuracy, completeness, consistency and currentness, with a proposition of their corresponding measurement methods. We also propose a data quality model specific to the logistic chain domain and a distributed traceability architecture. The evaluation of the proposal shows the capacity of the proposed method to assess the IoT data quality and ensure the user agreement on the data qualification rules. The proposed solution opens new opportunities in the development of automated logistic traceability systems. C1 [Ahmed, Mohamed; Ould, Mohamed] ALIS Int, 4 Rue Meunier, F-95724 Roissy En France, France. [Ahmed, Mohamed; Taconet, Chantal; Chabridon, Sophie; Bouzeghoub, Amel] Telecom SudParis, Inst Polytech Paris, Samovar, 9 Rue Charles Fourier, F-91011 Evry, France. C3 IMT - Institut Mines-Telecom; Institut Polytechnique de Paris RP Ahmed, M (corresponding author), ALIS Int, 4 Rue Meunier, F-95724 Roissy En France, France.; Ahmed, M; Taconet, C (corresponding author), Telecom SudParis, Inst Polytech Paris, Samovar, 9 Rue Charles Fourier, F-91011 Evry, France. EM Mohamed.Ahmed@alis-intl.com; Chantal.Taconet@telecom-sudparis.eu; Mohamed.Ould@alis-intl.com; Sophie.Chabridon@telecom-sudparis.eu; Amel.Bouzeghoub@telecom-sudparis.eu CR Ahmed M., 2020, P HAMB INT C LOG HIC, V29, P559, DOI [10.15480/882.3110, DOI 10.15480/882.3110] Androulaki E, 2018, EUROSYS '18: PROCEEDINGS OF THE THIRTEENTH EUROSYS CONFERENCE, DOI 10.1145/3190508.3190538 Berman Z, 2014, IEEE POSITION LOCAT, P1008, DOI 10.1109/PLANS.2014.6851466 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Byabazaire J, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9122083 Casado-Vara R, 2018, BLOCKSYS'18: PROCEEDINGS OF THE 1ST BLOCKCHAIN-ENABLED NETWORKED SENSOR SYSTEMS, P19, DOI 10.1145/3282278.3282282 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Cheng J., 2020, P 6 INT C ART INT SE, P695, DOI [10.1007/978-981-15-8086-4_65, DOI 10.1007/978-981-15-8086-4_65] Chin JS, 2020, IMAGING TECHNOLOGIES AND TRANSDERMAL DELIVERY IN SKIN DISORDERS, P15 Erazo-Garzon Lenin, 2020, Information and Communication Technologies of Ecuador (TIC.EC). Advances in Intelligent Systems and Computing (AISC 1099), P137, DOI 10.1007/978-3-030-35740-5_10 Ester M., 1996, P 2 INT C KNOWL DISC, P226, DOI DOI 10.5555/3001460.3001507 Fagundez S, 2015, J INTELL SYST, V24, P361, DOI 10.1515/jisys-2014-0166 Gu X, 2019, PROC IEEE INT SYMP, P1859, DOI 10.1109/ISIE.2019.8781332 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hasan H, 2019, COMPUT IND ENG, V136, P149, DOI 10.1016/j.cie.2019.07.022 Huang JQ, 2020, IEEE T IND INFORM, V16, P6553, DOI 10.1109/TII.2019.2963728 Hui Z., 2018, P 15 C INT SOC IND A Issaoui Y, 2019, PROCEDIA COMPUT SCI, V160, P266, DOI 10.1016/j.procs.2019.09.467 Javaid A, 2020, LECT NOTE NETW SYST, V97, P173, DOI 10.1007/978-3-030-33506-9_16 Kalman R.E., 1960, J BASIC ENG, V82, P35, DOI [10.1115/1.3662552, DOI 10.1115/1.3662552] Kara M, 2017, PROCEDIA COMPUT SCI, V113, P392, DOI 10.1016/j.procs.2017.08.354 Karkouch A, 2016, 2016 2ND INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGIES AND APPLICATIONS (CLOUDTECH), P252, DOI 10.1109/CloudTech.2016.7847707 Karkouch A, 2016, J NETW COMPUT APPL, V73, P57, DOI 10.1016/j.jnca.2016.08.002 Kolomvatsos K, 2019, COMPUTING, V101, P1687, DOI 10.1007/s00607-018-0683-9 Kuemper D, 2018, PROCEEDINGS OF THE 9TH ACM MULTIMEDIA SYSTEMS CONFERENCE (MMSYS'18), P294, DOI 10.1145/3204949.3204972 Leal F, 2021, BIG DATA RES, V24, DOI 10.1016/j.bdr.2020.100172 Lee YW, 2002, INFORM MANAGE-AMSTER, V40, P133, DOI 10.1016/S0378-7206(02)00043-5 Li F, 2012, IEEE INT C COMPUT, P602, DOI 10.1109/ICCSE.2012.88 Liu CH, 2020, COMPUTING, V102, P573, DOI 10.1007/s00607-019-00746-z Mary IPS, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN ADVANCED COMPUTING (ICCTAC) Nguyen Duy-Thanh, 2019, PROC IEEE INT S CIRC, P1 Pournader M, 2020, INT J PROD RES, V58, P2063, DOI 10.1080/00207543.2019.1650976 Sicari S, 2016, INFORM SYST, V58, P43, DOI 10.1016/j.is.2016.02.003 Suciu George, 2018, 2018 Global Wireless Summit (GWS), P370, DOI 10.1109/GWS.2018.8686563 Wang R. Y., 1996, Journal of Management Information Systems, V12, P5 Wei LJ, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9020215 Wen QS, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER PHYSICAL SYSTEMS (ICPS 2019), P695, DOI 10.1109/ICPHYS.2019.8780161 Zou SH, 2020, IEEE T IND INFORM, V16, P4206, DOI 10.1109/TII.2019.2957791 NR 38 TC 4 Z9 4 U1 9 U2 26 PD MAR PY 2021 VL 21 IS 6 AR 2239 DI 10.3390/s21062239 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000652707600001 DA 2022-12-14 ER PT J AU Karniol, B Shirak, A Baruch, E Singrun, C Tal, A Cahana, A Kam, M Skalski, Y Brem, G Weller, JI Ron, M Seroussi, E AF Karniol, B. Shirak, A. Baruch, E. Singruen, C. Tal, A. Cahana, A. Kam, M. Skalski, Y. Brem, G. Weller, J. I. Ron, M. Seroussi, E. TI Development of a 25-plex SNP assay for traceability in cattle SO ANIMAL GENETICS DT Article DE ID power; iplex; multiplex; parent exclusion; traceability ID GENETIC TRACEABILITY; ANIMAL IDENTIFICATION; GENOTYPING SYSTEM; MARKERS AB Single nucleotide polymorphisms (SNPs) are amenable to automation and therefore have become the marker of choice for DNA profiling. SNaPshot, a primer extension-based method, was used to multiplex 25 SNPs that have been previously validated as useful for identity control. Detection of extended products was based on four different fluorochromes and extension primers with oligonucleotide tails of differing lengths, thus controlling the concise length of the entire chromatogram to 81 bases. Allele frequencies for Holstein, Simmental, Limousin, Angus, Charolais and Tux Cattle were estimated and significant positive Pearson-correlation coefficients were obtained among the analysed breeds. The probability that two randomly unrelated individuals would share identical genotypes for all 25 loci varied from 10(-8) to 10(-10) for these breeds. For parentage control, the exclusion power was found to be 99.9% when the genotypes of both putative parents are known. A traceability test of duplicated samples indicated a high genotyping precision of > 0.998. This was further corroborated by analysis of 60 cases of parent-sib pairs and trio families. The 25-plex SNaPshot assay is adapted for low- and high-throughput capacity and thus presents an alternative for DNA-based traceability in the major commercial cattle breeds. C1 [Karniol, B.; Shirak, A.; Baruch, E.; Weller, J. I.; Ron, M.; Seroussi, E.] Agr Res Org, Inst Anim Sci, IL-50250 Bet Dagan, Israel. [Singruen, C.; Brem, G.] Agrobiogen GmbH, D-86567 Hilgertshausen Tandern, Germany. [Tal, A.; Cahana, A.; Kam, M.; Skalski, Y.] Bactochem Ltd, IL-74031 Ness Ziona, Israel. C3 VOLCANI INSTITUTE OF AGRICULTURAL RESEARCH RP Seroussi, E (corresponding author), Agr Res Org, Inst Anim Sci, IL-50250 Bet Dagan, Israel. EM seroussi@agri.huji.ac.il CR Ballester M, 2007, ANIM GENET, V38, P663, DOI 10.1111/j.1365-2052.2007.01654.x Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 Jamieson A, 1997, ANIM GENET, V28, P397, DOI 10.1111/j.1365-2052.1997.00186.x Latham K, 2006, INT J IMMUNOGENET, V33, P321 Ragoussis J, 2006, PLOS GENET, V2, P920, DOI 10.1371/journal.pgen.0020100 Sanchez JJ, 2006, ELECTROPHORESIS, V27, P1713, DOI 10.1002/elps.200500671 Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] Weller JI, 2006, ANIM GENET, V37, P387, DOI 10.1111/j.1365-2052.2006.01455.x Werner FAO, 2004, ANIM GENET, V35, P44, DOI 10.1046/j.1365-2052.2003.01071.x NR 11 TC 22 Z9 24 U1 0 U2 6 PD JUN PY 2009 VL 40 IS 3 BP 353 EP 356 DI 10.1111/j.1365-2052.2008.01846.x WC Agriculture, Dairy & Animal Science; Genetics & Heredity SC Agriculture; Genetics & Heredity UT WOS:000266014500013 DA 2022-12-14 ER PT J AU Xiao, XQ Fu, ZT Qi, L Mira, T Zhang, XS AF Xiao, Xinqing Fu, Zetian Qi, Lin Mira, Trebar Zhang, Xiaoshuan TI Development and evaluation of an intelligent traceability system for frozen tilapia fillet processing SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE tilapia fillet; statistical process control (SPC); fault tree analysis (FTA); intelligent traceability system ID SUPPLY CHAIN; AQUACULTURE; PERFORMANCE; MODEL; FOOD AB BACKGROUNDThe main export varieties in China are brand-name, high-quality bred aquatic products. Among them, tilapia has become the most important and fast-growing species since extensive consumer markets in North America and Europe have evolved as a result of commodity prices, year-round availability and quality of fresh and frozen products. As the largest tilapia farming country, China has over one-third of its tilapia production devoted to further processing and meeting foreign market demand. RESULTSUsing by tilapia fillet processing, this paper introduces the efforts for developing and evaluating ITS-TF: an intelligent traceability system integrated with statistical process control (SPC) and fault tree analysis (FTA). Observations, literature review and expert questionnaires were used for system requirement and knowledge acquisition; scenario simulation was applied to evaluate and validate ITS-TF performance. CONCLUSIONThe results show that traceability requirement is evolved from a firefighting model to a proactive model for enhancing process management capacity for food safety; ITS-TF transforms itself as an intelligent system to provide functions on early warnings and process management by integrated SPC and FTA. The valuable suggestion that automatic data acquisition and communication technology should be integrated into ITS-TF was achieved for further system optimization, perfection and performance improvement. (c) 2014 Society of Chemical Industry C1 [Xiao, Xinqing; Fu, Zetian; Zhang, Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Xiao, Xinqing; Fu, Zetian; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [Qi, Lin] Beijing Informat S&T Univ, Beijing 100083, Peoples R China. [Mira, Trebar] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana 1000, Slovenia. C3 China Agricultural University; University of Ljubljana RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR [Anonymous], 2013, CONTR CHARTS 2 [Anonymous], 2009, TERMS SYMB FAULT TRE [Anonymous], 2004, INF TECHN PROC ASS Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 China Catfish Institute, 2014, CHIN AQ IND REP 2011 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Ericsson CA, 2005, FAULT TREE ANAL HAZA, P183 FAO, 2014, HANDL FISH FISH PROD Galvao JA, 2010, FOOD CONTROL, V21, P1360, DOI 10.1016/j.foodcont.2010.03.010 Mataragas M, 2012, FOOD CONTROL, V28, P205, DOI 10.1016/j.foodcont.2012.05.032 Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Qi Lin, 2012, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V43, P134 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 USDA, 2009, MAND COUNTR OR LAB B Wang F, 2009, J FOOD AGRIC ENVIRON, V7, P64 Xing S, 2010, T CHINESE SOC AGR EN, V26, P384 Xu Zhong, 2005, Weishengwu Xuebao, V45, P798 Zhang J, 2009, J FOOD AGRIC ENVIRON, V7, P28 Zhang XS, 2011, J SCI FOOD AGR, V91, P1316, DOI 10.1002/jsfa.4320 Zhang XS, 2010, FOOD CONTROL, V21, P1556, DOI 10.1016/j.foodcont.2010.03.020 Zheng-cui C, 2010, MARINE FISHERIES, V4, P454 NR 22 TC 14 Z9 15 U1 0 U2 49 PD OCT PY 2015 VL 95 IS 13 BP 2693 EP 2703 DI 10.1002/jsfa.7005 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000360634900018 DA 2022-12-14 ER PT J AU Bechini, A Cimino, MGCA Marcelloni, F Tomasi, A AF Bechini, Alessio Cimino, Mario G. C. A. Marcelloni, Francesco Tomasi, Andrea TI Patterns and technologies for enabling supply chain traceability through collaborative e-business SO INFORMATION AND SOFTWARE TECHNOLOGY DT Article DE traceability; inter-organizational systems; e-business; information systems; service oriented architecture; ebXML; Web Services ID INFORMATION; SYSTEMS AB Industrial traceability systems are designed to operate over complex supply chains, with a large and dynamic group of participants. These systems need to agree on processing and marketing of goods, information management, responsibility, and identification. In addition, they should guarantee context independence, scalability, and interoperability. In this paper, we first discuss the main issues emerging at different abstraction levels in developing traceability systems. Second, we introduce a data model for traceability and a set of suitable patterns to encode generic traceability semantics. Then, we discuss suitable technological standards to define, register, and enable business collaborations. Finally, we show a practical implementation of a traceability system through a real world experience on food supply chains. (c) 2007 Elsevier B.V. All rights reserved. C1 [Bechini, Alessio; Cimino, Mario G. C. A.; Marcelloni, Francesco; Tomasi, Andrea] Univ Pisa, Dipartimento Ingegneria Informazione Elettron I, I-56122 Pisa, Italy. C3 University of Pisa RP Bechini, A (corresponding author), Univ Pisa, Dipartimento Ingegneria Informazione Elettron I, Via Diotisalvi 2, I-56122 Pisa, Italy. EM a.bechini@iet.unipi.it; m.cimino@iet.unipi.it; f.marcelloni@iet.unipi.it; a.tomasi@iet.unipi.it CR BINSTOCK C, 2003, XML SCHEMA COMPLETE Choudhury V, 1997, INFORM SYST RES, V8, P1, DOI 10.1287/isre.8.1.1 DECASTRO M, 2003, P EFITA C DEBR HUNG, P607 DEMELLO M, 2000, EFFECTS TRACEABILITY *EBXML, 2006, BUS PROC SPEC SCHEM Food Standards Agency UK, 2002, TRAC FOOD CHAIN PREL Gibbs B., 2002, EBXML CONCEPTS APPL Green PF, 2005, IEEE T KNOWL DATA EN, V17, P713, DOI 10.1109/TKDE.2005.79 Gunasekaran A, 2004, EUR J OPER RES, V159, P269, DOI 10.1016/j.ejor.2003.08.016 Hagel J., 1997, NET GAIN EXPANDING M Hagel J, 2005, ONLY SUSTAINABLE EDG Halevy AY, 2005, VLDB J, V14, P68, DOI 10.1007/s00778-003-0116-y HECKEL R, 2003, P WORKSH MOD DRIV AR, P115 *HERM, 2006, MESS SERV HANDL Hofreiter B, 2002, PR GR LAK SYMP VLSI, P7, DOI 10.1109/RIDE.2002.995093 HOGG K, 2004, P 27 C AUSTR COMP SC, V26, P331 HORNBY RM, 1999, P IEE C RFID TECHN L *ISO, 2006, 8601 ISO *ISO, 2006, OFF WEBS Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 JENZ DE, 2002, EBXML WEB SERVICES F Jones S, 2005, IEEE SOFTWARE, V22, P87, DOI 10.1109/MS.2005.80 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 KOHLHOFF C, 2003, P WORLD WID WEB C WW MALONE TW, 1987, COMMUN ACM, V30, P484, DOI 10.1145/214762.214766 Meng B, 2004, INT C COMP SUPP COOP, P597, DOI 10.1109/CACWD.2004.1349094 Ministry of Agriculture Forestry and Fisheries of Japan, 2003, GUID INTR FOOD TRAC *OASIS, 2006, UN DESCR DISC INT *OASIS, 2006, ORG ADV STRUCT INF S OPARA LU, 2004, EUR J OPER RES, V159, P269 *ORACLE9I APPL SER, 2001, WEB SERV STRAT PAPE WR, 2004, DATA COLLECTION 1 MI, P14 PAPE WR, 2003, SELECTING MOST APPRO, P21 PAPE WR, 2004, LETS GET ANIMAL TRAC, P14 Pham TT, 2003, IEEE INTERNATIONAL CONFERENCE ON E-COMMERCE, P136, DOI 10.1109/COEC.2003.1210243 Roberts B, 2005, INT FED INFO PROC, V186, P17 Roussos G, 2006, COMPUTER, V39, P25, DOI 10.1109/MC.2006.88 Rumbaugh J, 2004, UNIFIED MODELING LAN Sahai A, 2005, NETW SYST MANAG, P1, DOI 10.1007/0-387-27597-5 SILVA JP, 2003, BUSINESS EXCELLENCE, P706 *SUN SERV REG, 2006, SERV OR ARCH *UBL, 2006, UBL SPEC V 1 0 *UN CEFACT, 2006, UN CTR TRAD FAC EL B *UN EDIFACT, 2006, UN DIR EL DAT INT A Van Dorp C., 2003, P EFITA C DEBR HUNG, P601 *XML, 2006, EXT MARK LANG [No title captured] NR 47 TC 126 Z9 130 U1 0 U2 37 PD MAR PY 2008 VL 50 IS 4 BP 342 EP 359 DI 10.1016/j.infsof.2007.02.017 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science UT WOS:000253105400006 DA 2022-12-14 ER PT J AU Xie, J Wan, CX Becerra, AT Li, M AF Xie, Jing Wan, Chunxu Becerra, Alfredo Tolon Li, Ming TI Streamlining Traceability Data Generation in Apple Production Using Integral Management with Machine-to-Machine Connections SO AGRONOMY-BASEL DT Article DE single tree traceability; RFID; GPS ID FOOD TRACEABILITY; BLOCKCHAIN; SAFETY; PERSPECTIVES; SYSTEMS; CHINA AB Legal requirements and consumer demands have motivated the development and application of traceability technology. Farming practices are the starting point of the agri-food supply chain and the destination of the agri-food traceability system (AFTS). The amount of resource information and the complexity of the production process of agri-food become the main obstacles to the wide application of AFTS. This study introduces an integrated machine-to-machine system that allows collecting field operation information automatically. This system includes an IoT-based integrated hardware system, a smart farm cloud (SFC) platform, and a mobile application, which accomplished the collection, upload, and storage of operation information. This system had been used in "BSD" organic apple orchard in Qixia, Shandong Province, China for about one year. The effectiveness of the system was evaluated by managing 270 apple trees in one plot of the orchard. Finally, a label with a QR code was successfully generated to provide consumers to query traceability information from a single tree to a fruit tray. This work was a background of a blockchain traceability system. Moreover, the future extendibility of the system was also discussed and prospected. C1 [Xie, Jing; Li, Ming] Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Informat Technol Res Ctr, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. [Xie, Jing; Becerra, Alfredo Tolon] Univ Almeria, Dept Engn, Almeria 04120, Spain. [Xie, Jing; Wan, Chunxu] Beijing Vocat Coll Agr, Dept Informat Technol, Beijing 102442, Peoples R China. C3 Beijing Academy of Agriculture & Forestry; Universidad de Almeria RP Li, M (corresponding author), Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Informat Technol Res Ctr, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. EM xj_panda@126.com; 73047@bvca.edu.cn; atolon@ual.es; lim@nercita.org.cn CR Alfian G, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107016 Amalia F., 2020, J INFORM TECHNOLOGY, V5, P247 [Anonymous], 2021, FAOSTAT DATABASE CRO Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Byun J, 2020, EXPERT SYST APPL, V150, DOI 10.1016/j.eswa.2020.113287 China National Bureau of Statistics, 2021, CHIN STAT YB Corallo A, 2020, TRENDS FOOD SCI TECH, V101, P28, DOI 10.1016/j.tifs.2020.04.022 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Deng ML, 2021, J SENSORS, V2021, DOI 10.1155/2021/8860487 Dey S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063486 FAO, 2017, COPTIC MARTYRDOM AGE Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fountas S, 2015, COMPUT ELECTRON AGR, V115, P40, DOI 10.1016/j.compag.2015.05.011 Francois G, 2020, PHYTOCHEMISTRY, V173, DOI 10.1016/j.phytochem.2020.112291 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gao GD, 2019, COMPUT ELECTRON AGR, V166, DOI 10.1016/j.compag.2019.105013 Gautam R, 2017, COMPUT IND ENG, V103, P46, DOI 10.1016/j.cie.2016.09.007 Giannetti V, 2017, FOOD CONTROL, V78, P215, DOI 10.1016/j.foodcont.2017.02.036 Jing Z., 2020, THESIS SHANDONG AGR Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Liu RF, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107157 Orgeira-Crespo P, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9101680 Qian J., 2013, THESIS BEIJING FORES Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Qian JP, 2018, FOOD CONTROL, V87, P192, DOI 10.1016/j.foodcont.2017.12.015 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shanshan W., 2019, THESIS BEIJING FORES Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Thakur M, 2020, COMPUT ELECTRON AGR, V174, DOI 10.1016/j.compag.2020.105478 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 United States Department of Agriculture, 2020, PROGN 2020 E42020 00 Verdouw C, 2019, COMPUT ELECTRON AGR, V165, DOI 10.1016/j.compag.2019.104939 Villalobos JR, 2019, COMPUT ELECTRON AGR, V167, DOI 10.1016/j.compag.2019.105092 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Vo SA, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107419 Walker GS, 2017, FOOD CONTROL, V72, P168, DOI 10.1016/j.foodcont.2016.01.028 Xiaoyan Z., 2019, SHANDONG AGR SCI, V51, P148 NR 40 TC 0 Z9 0 U1 15 U2 15 PD APR PY 2022 VL 12 IS 4 AR 921 DI 10.3390/agronomy12040921 WC Agronomy; Plant Sciences SC Agriculture; Plant Sciences UT WOS:000787951500001 DA 2022-12-14 ER PT J AU Thorpe, A Hermansen, O Pollard, I Isaksen, J Failler, P Touron-Gardic, G AF Thorpe, Andy Hermansen, Oystein Pollard, Iain Isaksen, John Failler, Pierre Touron-Gardic, Gregoire TI Unpacking the tuna traceability mosaic-EU SFPAs and the tuna value chain SO MARINE POLICY DT Article DE Tuna fishing; Tuna markets; Tuna trade; Tuna value chain; Traceability; Transparency; Catch certificates; Sustainable fisheries partnership agreements (SFPA); Sustainable fishing; Global seafood market; Electronic monitoring; IUU fishing ID SEAFOOD TRACEABILITY; AGREEMENTS; AFRICA; SAFETY; KEY AB Traceability has been gaining in importance recently and has seen its potential uses within fisheries expanding from primarily food safety to combat illegal fishing and promote sustainability. In the tuna value chain, key processing actors have introduced comprehensive systems allowing consumers to trace products right back to the vessel that caught the tuna and the catch date. Traceability is also an important component of EU SFPAs (Sus-tainable Fisheries Partnership Agreements). This paper explains the rationale for the EU entering into SFPAs and shows how the current portfolio of SFPAs exhibits an increasing dependence on access to tuna stocks. Utilizing a unique dataset, we present information on area, method of capture and landing site for EU SFPA vessels. We show that there are economic incentives for vessels to misreport, and clear traceability challenges as vessels fish several species and across several areas (both coastal and in areas beyond national jurisdiction -ABNJ). The tuna value chains in Cabo Verde and the Seychelles are then examined from a traceability perspective. As we report, while an EU catch certificate scheme (CCS) operates to cover all tuna products imported into the EU market, there are flaws in the current system which need remedying. C1 [Thorpe, Andy; Failler, Pierre; Touron-Gardic, Gregoire] Univ Portsmouth, Fac Business & Law, Econ & Finance Subject Grp, Portland St, Portsmouth PO1 3DE, Hants, England. [Hermansen, Oystein; Isaksen, John] Nofima, Muninbakken 9-13, N-9291 Breivika, Tromsoe, Norway. [Pollard, Iain] Key Traceabil, Innovat Space, Halpern House, Portsmouth PO1 2QF, Hants, England. C3 University of Portsmouth; Nofima RP Thorpe, A (corresponding author), Univ Portsmouth, Fac Business & Law, Econ & Finance Subject Grp, Portland St, Portsmouth PO1 3DE, Hants, England. EM andy.thorpe@port.ac.uk CR Amador T., 2018, EXPOST EXANTE EVALUA Antonova AS, 2016, MAR POLICY, V70, P77, DOI 10.1016/j.marpol.2016.04.008 Auethavornpipat R, 2021, THIRD WORLD Q, V42, P2593, DOI 10.1080/01436597.2021.1958673 Bailey M, 2016, CURR OPIN ENV SUST, V18, P25, DOI 10.1016/j.cosust.2015.06.004 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Beyens Y., 2017, FOOD REV INT, V33, P1 Blaha F., 2020, 1207 FAO, DOI 10.4060/ca8751en Blakistone B., 2019, FOOD TRACEABILITY BI Campling L., 2015, FREE TRADE ALTERNATI Carneiro G, 2011, MAR FISH REV, V73, P1 CBI (Centre for Promotion of Imports from Developing Countries), 2019, EUR MARK POT TUN LOI Clarke S., 2013, TRACEABILITY LEGAL P COFREPECHE NFDS POSEIDON and MRAG, 2013, CONTRACT SPECIFIQUE Coughlin 2021, MOM GROWS EL MON INT Daly N., 2021, SEAFOOD SOURCE 0322 Defaux V., NETTING BILLIONS GLO Drakeford BM, 2020, MAR POLICY, V119, DOI 10.1016/j.marpol.2020.104060 El Sheikha AF, 2017, REV FISH SCI AQUAC, V25, P158, DOI 10.1080/23308249.2016.1254158 EU, 2008, COUNC REG 1005 2008 EU,, 2018, EXT DIM CFP INCL FIS EU, 2017, EU SUST FISH PARTN A, DOI [10.2771/917103, DOI 10.2771/917103] EUMOFA (European Market Observatory for Fisheries and Aquaculture Prices), 2019, EU FISH MARK European Commission, 2020, EU FISH MARK, DOI 10.2771/664425 Failler P., 2020, ENVIRON DEV, V36, P1 Failler P., 2011, OCEANS NEW FRONTIER, P166 Failler P., 2016, REV ACCORDS PECHE PA, P75 Failler P., REPORT POTENTIAL RET Failler P., 2013, REV PECHES THONIERES FAO, 2020, TRANSH SUMM FIND IND FAO FishStatJ, 2021, SOFTWARE FISHERY AQU Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Fonteneau A., 2014, 2014WPTT1636 IOTC FSA (Food Standards Authority), 2020, JOHN W REC SARD TOM Garcia-Del-Hoyo JJ, 2021, MAR POLICY, V132, DOI 10.1016/j.marpol.2021.104701 Goulding I., 2019, EXPOST EXANTE EVALUA, DOI [10.2771/47637, DOI 10.2771/47637] Goulding I., 2016, RES PECH COMMITTEE I Greenpeace,, 2020, SUST JUST HIGH SEAS GTA, 2020, 2025 PLEDG SUST TUN GTA, 2020, TUN 2020 TRAC DECL P Hamilton A, 2011, MARKET IND DYNAMICS Hammarlund C, 2019, WORLD DEV, V113, P172, DOI 10.1016/j.worlddev.2018.09.010 Havice E., 2018, CORPORATE DYNAMICS S He J, 2018, MAR POLICY, V96, P163, DOI 10.1016/j.marpol.2018.08.003 Helyar SJ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098691 Hosch G., 2016, FAO FISH AQUAC TECH, VVolume 596, P1 ICCAT (International Commission for the Conservation of Atlantic Tunas), 2018, REC ICCAT PORT STATE Iheduru OC, 1995, J DEV AREAS, V30, P63 INE (Instituto Nacional de Estatistica Cabo Verde), 2019, REEXPORTAAO MERCADOR IOTC, 2016, RESOLUTION 1611 PORT IOTC, 2019, IOTC2019WPTT21R, P43 IOTC (Indian Ocean Tuna Commission),, 2020, PREL IND OC SKIPJ TU Isaksen J.R., 2019, FARFISH DELIV, V4, P162, DOI [10.5281/zenodo.3074057, DOI 10.5281/ZENODO.3074057] ISSF (International Seafood Sustainability Foundation), 2021, AT FIN COMPL REP ACT IUUWatch, 2020, LIB NAT FISH ACQ AUT IUUWatch,, 2016, EU IUU REG BUILD SUC Jonsson JH, 2019, J COMMUNITY PRACT, V27, P213, DOI 10.1080/10705422.2019.1660290 Joseph Catanzano, 1999, THE FULL REPORT Kaczynski VM, 2002, MAR POLICY, V26, P75, DOI 10.1016/S0308-597X(01)00039-2 Kadfak A, 2021, MAR POLICY, V132, DOI 10.1016/j.marpol.2021.104656 Kvalvik I., 2021, PAY FISH GO SUSTAINA Le Manach F, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0079899 Lecomte M., 2017, INDIAN OCEAN TUNA FI Lewis SG, 2017, J FOOD SCI, V82, pA13, DOI 10.1111/1750-3841.13743 Love DC, 2021, GLOB FOOD SECUR-AGR, V28, DOI 10.1016/j.gfs.2020.100476 Macfadyen G., 2020, NETTING BILLIONS GLO Maufroy A, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0128023 MRAG, 2020, ICCAT TRANSSH BUS EC Mulazzani L, 2015, MAR POLICY, V52, P1, DOI 10.1016/j.marpol.2014.10.018 NGO, 2019, JOINT NGO PRIOR REV Oceania, 2016, FISH STOR SUCC VAL S Okafor-Yarwood I, 2020, OCEAN COAST MANAGE, V184, DOI 10.1016/j.ocecoaman.2019.104953 Rattle, 2019, CASE STUDY MANAGEMEN Rattle J., 2020, EU TUNA FLEET FISHES Rodriguez-Salvador B, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107142 Roheim CA, 2018, NAT SUSTAIN, V1, P392, DOI 10.1038/s41893-018-0115-z Seychelles News Agency, 2021, SEYCH FISH AUTH M BO Sobrino Heredia J. M., 2015, SPANISH YB INT LAW, V19, P61, DOI [10.17103/sybil.19.04, DOI 10.17103/SYBIL.19.04] Sourcing Transparency Platform, 2021, SENEG POL LINE SKIPJ State House (Office of the President of the Republic of Seychelles), 2018, SEYCH PRES REC DEL T Stemle A, 2016, FISH RES, V182, P116, DOI 10.1016/j.fishres.2015.07.022 Sterling B, 2015, COMPR REV FOOD SCI F, V14, P205, DOI 10.1111/1541-4337.12130 Stop Illegal Fishing, 2020, CHOPP WAT FORC LAB I Tickler D, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aar3279 TNI (Transnational Institute), 2017, EU FISH AGR CHEAP FI TU (Thai Union), SOURC TRANSP WILD CA Vatsov M., 2019, EUR WORLD LAW REV, V3, P1 WEF (World Economic Forum), 2020, TUN 2020 TRAC DECL Widjaja S., 2020, ILLEGAL UNREPORTED U WWF (World Wildlife Fund), 2019, STAT FUT SUST FISH P NR 89 TC 1 Z9 1 U1 2 U2 2 PD MAY PY 2022 VL 139 AR 105037 DI 10.1016/j.marpol.2022.105037 WC Environmental Studies; International Relations SC Environmental Sciences & Ecology; International Relations UT WOS:000793748300004 DA 2022-12-14 ER PT J AU Pincheira, M Vecchio, M Giaffreda, R AF Pincheira, Miguel Vecchio, Massimo Giaffreda, Raffaele TI Characterization and Costs of Integrating Blockchain and IoT for Agri-Food Traceability Systems SO SYSTEMS DT Article DE blockchain; Internet of Things; smart contracts; traceability; supply chain; costs ID SUPPLY CHAINS; INTERNET; THINGS AB An increasing amount of research focuses on integrating the Internet of Things and blockchain technology to address the requirements of traceability applications for Industry 4.0. However, there has been little quantitative analysis of several aspects of these new blockchain-based traceability systems. For instance, very few works have studied blockchain's impact on the resources of constrained IoT sensors. Similarly, the infrastructure costs of these blockchain-based systems are not widely understood. This paper characterizes the resources of low-cost IoT sensors and provides a monetary cost model for blockchain infrastructure to support blockchain-based traceability systems. First, we describe and implement a farm-to-fork case study using public and private blockchain networks. Then, we analyze the impact of blockchain in six different resource-limited IoT devices in terms of disk and memory footprint, processing time, and energy consumption. Next, we present an infrastructure cost model and use it to identify the costs for the public and private networks. Finally, we evaluate the traceability of a product in different scenarios. Our results showed that low-cost sensors could directly interact with both types of blockchains with minimal energy overhead. Furthermore, our cost model showed that setting a private blockchain infrastructure costs approximately the same as that managing 50 products on a public blockchain network. C1 [Pincheira, Miguel; Vecchio, Massimo; Giaffreda, Raffaele] Fdn Bruno Kessler, I-38123 Trento, Italy. C3 Fondazione Bruno Kessler RP Vecchio, M (corresponding author), Fdn Bruno Kessler, I-38123 Trento, Italy. EM mpincheiracaro@fbk.eu; mvecchio@fbk.eu; rgiaffreda@fbk.eu CR Accorsi R, 2017, PROCEDIA MANUF, V11, P889, DOI 10.1016/j.promfg.2017.07.192 Ali MS, 2019, IEEE COMMUN SURV TUT, V21, P1676, DOI 10.1109/COMST.2018.2886932 Ampel B, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P59, DOI 10.1109/ISI.2019.8823238 [Anonymous], Qoitech Power Analyzer Qoitech 2022 Antonopoulos A. M., 2018, MASTERING ETHEREUM B Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bibi S, 2012, IEEE SOFTWARE, V29, P86, DOI 10.1109/MS.2011.119 Bouguera T, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18072104 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Conoscenti M, 2016, I C COMP SYST APPLIC Corallo A, 2020, J RURAL STUD, V75, P30, DOI 10.1016/j.jrurstud.2020.02.006 Croman K, 2016, LECT NOTES COMPUT SC, V9604, P106, DOI 10.1007/978-3-662-53357-4_8 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Demir M, 2019, 2019 IEEE 10TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P715, DOI 10.1109/IEMCON.2019.8936297 Destefanis G, 2018, 2018 IEEE 1ST INTERNATIONAL WORKSHOP ON BLOCKCHAIN ORIENTED SOFTWARE ENGINEERING (IWBOSE), P19 etherescan.io, ETH DAIL PRIC USD CH Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Georgiou K, 2018, IEEE EMBED SYST LETT, V10, P53, DOI 10.1109/LES.2017.2741419 Heiss J, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2019), P496, DOI 10.1109/Blockchain.2019.00075 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kayikci Y, 2022, PROD PLAN CONTROL, V33, P301, DOI 10.1080/09537287.2020.1810757 Khutsoane O, 2017, IEEE IND ELEC, P6107 Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Kumar M.V., 2017, ADV SCI TECHNOLOGY L, V146, P125, DOI DOI 10.14257/AST1.2017.146.22 Leal F., 2020, SN COMPUTER SCI, V1, P1, DOI 10.1007/s42979-020- 00289-7 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Mistry I, 2020, MECH SYST SIGNAL PR, V135, DOI 10.1016/j.ymssp.2019.106382 Moschou K, 2020, 2020 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2020), P476, DOI 10.1109/Blockchain50366.2020.00069 Pincheira M., 2021, INT C BLOCKCHAIN APP, V320, P212, DOI [10.1007/978-3-030-86162-9_21, DOI 10.1007/978-3-030-86162-9_21] Pincheira M, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22030899 Pincheira M, 2020, 2020 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN AND CRYPTOCURRENCY (IEEE ICBC) Pincheira M, 2020, IEEE INT CONF COMM Pincheira M, 2021, COMPUT ELECTRON AGR, V180, DOI 10.1016/j.compag.2020.105889 Pincheira M, 2020, 2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS), P652, DOI 10.1109/LAGIRS48042.2020.9165589 Rimba P, 2020, INFORM SYST FRONT, V22, P489, DOI 10.1007/s10796-018-9876-1 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Schaffer M, 2019, LECT NOTES BUS INF P, V361, P103, DOI 10.1007/978-3-030-30429-4_8 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Tian F, 2017, I C SERV SYST SERV M Umamaheswari S, 2019, INT CONF ADV COMPU, P324, DOI 10.1109/ICoAC48765.2019.246860 Vacca A, 2021, J SYST SOFTWARE, V174, DOI 10.1016/j.jss.2020.110891 Verdouw CN, 2016, J FOOD ENG, V176, P128, DOI 10.1016/j.jfoodeng.2015.11.009 Wu KD, 2021, SOFTWARE PRACT EXPER, V51, P2033, DOI 10.1002/spe.2751 Zanoni B, 2014, EXTRA-VIRGIN OLIVE OIL HANDBOOK, P245 Zhang K, 2019, IEEE NETWORK, V33, P12, DOI 10.1109/MNET.001.1800526 NR 46 TC 1 Z9 1 U1 18 U2 18 PD JUN PY 2022 VL 10 IS 3 AR 57 DI 10.3390/systems10030057 WC Social Sciences, Interdisciplinary SC Social Sciences - Other Topics UT WOS:000816579300001 DA 2022-12-14 ER PT J AU Thume, M Lange, J Unkel, M Prange, A Schurmeyer , M AF Thume, Martina Lange, Julia Unkel, Martin Prange, Alexander Schuermeyer, Maik TI Blockchain-based traceability in food supply chains: requirements and challenges SO INTERNATIONAL JOURNAL OF SUSTAINABLE AGRICULTURAL MANAGEMENT AND INFORMATICS DT Article DE food supply chain; traceability; information system; blockchain technology; requirement AB While traceability has become an important aspect in managing safety-sensitive supply chains, blockchain arose as an innovative technology with great application potentials in this area. This research determines the requirements of the main stakeholders along the supply chain from agriculture to retail with regard to a blockchain-based traceability system. The resulting specification framework includes usage requirements formulated as data classes, technical requirements regarding data access, storage and processing, and interoperability requirements necessary to assure digital communication and permanent operability. General application guidelines for digital information systems are derived, and the blockchain technology is evaluated with regard to applicability. A distinction between public and sensitive data appears to be fundamental, while a two-part architecture involving a public permissioned blockchain network is proposed as a basis for a digital traceability system in the food industry. C1 [Thume, Martina; Prange, Alexander; Schuermeyer, Maik] Hsch Niederrhein Univ Appl Sci, Fac Food & Nutr Sci, D-41065 Monchengladbach, Germany. [Lange, Julia] Univ Kaiserslautern, Fac Business Studies & Econ, D-67663 Kaiserslautern, Germany. [Unkel, Martin] Fraunhofer Inst Appl Informat Technol, Cooperat Syst Dept, D-53757 St Augustin, Germany. [Prange, Alexander] Univ Witten Herdecke, Inst Virol & Microbiol, D-58448 Witten, Germany. C3 University of Kaiserslautern; Fraunhofer Gesellschaft; Witten Herdecke University RP Schurmeyer , M (corresponding author), Hsch Niederrhein Univ Appl Sci, Fac Food & Nutr Sci, D-41065 Monchengladbach, Germany. EM martina.thume@hs-niederrhein.de; julia.lange@wiwi.uni-kl.de; martin.unkel@fit.fraunhofer.de; alexander.prange@hs-niederrhein.de; maik.schuermeyer@hs-niederrhein.de CR Ali MM, 2017, EUR J OPER RES, V260, P984, DOI 10.1016/j.ejor.2016.11.046 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bhatt T, 2013, J FOOD SCI, V78, pB21, DOI 10.1111/1750-3841.12278 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Castro M, 2002, ACM T COMPUT SYST, V20, P398, DOI 10.1145/571637.571640 Clausen U, 2010, WEGE INNOVATIVEN FAB Croxson A., 2019, AUSTRALAS C INF SYST, P97 DIN Deutsches Institut fur Normung e.V, 2015, 9000 DIN EN ISO Drescher D., 2017, BLOCKCHAIN BASICS Dyckhoff H., 2010, PRODUKTIONSWIRTSCHAF, V3 Hackett R, 2017, FORTUNE Hackius N., 2017, DIGITALIZATION SUPPL, DOI [10.15480/882.1444, DOI 10.15480/882.1444] Hunkirchen P., 2015, USABILITY MODIFIKATI Kalenborn A., 2014, ANGEBOTSERSTELLUNG P Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Lechner U., 2020, NUTRISAFE MONITOR, V1 Maciaszek L., 2007, REQUIREMENTS ANAL SY, V3rd Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Morabito V., 2017, BUSINESS INNOVATION Nakamoto S., 2008, BITCOIN PEER PEER EL Ongaro D., 2014, USENIX ANN TECHNICAL, P305, DOI DOI 10.5555/2643634.2643666 Parmelee M, 2021, FORBES Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Perez Ferreira Chaves D., 2018, EINSATZ RUCKVERFOLGB, DOI [10.19211/KUP9783737650519, DOI 10.19211/KUP9783737650519] Popejoy S., 2019, COINTELEGRAPH Porter, 1985, COMPETITIVE ADVANTAG Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Saleh F, 2021, REV FINANC STUD, V34, P1156, DOI 10.1093/rfs/hhaa075 Schutte J., 2017, BLOCKCHAIN TECHNOLOG Sommer P, 2007, BEISPIEL LEBENSMITTE, V1 Thume M., 2021, BLOCKCHAIN BASED TRA, DOI [10.31219/osf.io/uyb64, DOI 10.31219/OSF.IO/UYB64] Tian F, 2017, I C SERV SYST SERV M Vadgama N, 2021, FRONT BLOCKCHAIN, V4, DOI 10.3389/fbloc.2021.610476 Waskow F., 2020, KENNZEICHNUNG GLAUBW Welzel C., 2017, MYTHOS BLOCKCHAIN HE Wust K, 2018, 2018 CRYPTO VALLEY CONFERENCE ON BLOCKCHAIN TECHNOLOGY (CVCBT), P45, DOI 10.1109/CVCBT.2018.00011 Zhang AR, 2020, J CONSUM PROT FOOD S, V15, P99, DOI 10.1007/s00003-020-01277-y Zheng ZB, 2018, INT J WEB GRID SERV, V14, P352, DOI 10.1504/IJWGS.2018.095647 NR 42 TC 0 Z9 0 U1 2 U2 2 PY 2022 VL 8 IS 3 BP 219 EP 241 DI 10.1504/IJSAMI.2022.125758 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications; Green & Sustainable Science & Technology SC Agriculture; Computer Science; Science & Technology - Other Topics UT WOS:000860834300001 DA 2022-12-14 ER PT J AU Qian, JP Yang, XT Wu, XM Zhao, L Fan, BL Xing, B AF Qian, Jian-Ping Yang, Xin-Ting Wu, Xiao-Ming Zhao, Li Fan, Bei-Lei Xing, Bin TI A traceability system incorporating 2D barcode and RFID technology for wheat flour mills SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Traceability; 2D barcode; RFID; Wheat flour; Supply chain ID SUPPLY CHAIN; AGRICULTURE; FRAMEWORK AB Wheat flour undergoes several processing steps in its transformation from raw wheat in the mill, which differentiates wheat flour from other farm products. At each step, various wheat sources are combined into one batch of wheat flour. This study primarily aimed to develop a Wheat Flour Milling Traceability System (WFMTS), incorporating 2D barcode and radio frequency identification (RFID) technology, and to validate the system in a wheat flour mill in China. We designed the encoding rules for the raw material, processing and traceability batches. Labels with a Quick Response Code (QR Code) were attached to small packages of wheat flour to link them to their processing information, and RFID tags were affixed to the storage bins to record logistics information. A traceability system was developed based on batch identification and record keeping. The system was applied and supported in a wheat flour mill for one year. The WFMTS management and traceability capacity was evaluated using a contrast experiment. The experiment was divided into five parts, including raw material data recording, processing data recording, package data recording, logistics data recording and traceability query. The results show that although time consumption using WFMTS in package data recording was more than that with paper recording. WFMTS was dominant in its total time consumption: five parts were reduced by 113%, and the mean accuracy of the five parts increased by 8%. The QR Code and RFID recognition accuracy was evaluated using experiments with different reading distances. The cost and income variations in application WFMTS were analyzed based on the survey. The results show that the total cost increased by 17.2% to apply the system. Compared to the cost, the sales income increase was obvious, and it reached 32.5%. Considering the good evaluation results, the system has good application potential in medium or large wheat mill enterprises. (C) 2012 Elsevier B.V. All rights reserved. C1 [Yang, Xin-Ting] NERCITA, Beijing 100097, Peoples R China. Minist Agr, Key Lab Informat Technol Agr, Beijing 100097, Peoples R China. C3 Ministry of Agriculture & Rural Affairs RP Yang, XT (corresponding author), NERCITA, Bldg A,11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China. EM xintingyang@nercita.org.cn CR Ampatzidis Y, 2009, PRECIS AGRIC, V10, P63, DOI 10.1007/s11119-008-9095-8 [Anonymous], 2007, 22005 ISO Articlesadv, 2010, CURR SIT PROSP CHIN Bevilacqua M, 2009, J FOOD ENG, V93, P13, DOI 10.1016/j.jfoodeng.2008.12.020 Ceruti F. C., 2006, Proceedings of the 9th International Working Conference on Stored-Product Protection, ABRAPOS, Passo Fundo, RS, Brazil, 15-18 October 2006, P1198 EPCGlobal, 2008, EPC RAD FREQ ID PROT FAO, 2009, FOOD OUTL WHEAT Finkenzeller K., 2004, RFID HDB RADIO FREQU Froschle HK, 2009, COMPUT ELECTRON AGR, V66, P126, DOI 10.1016/j.compag.2009.01.002 Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Inlogic, 2011, RFID VS BARC COMP Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 *REUT, 2008, N AM TOM IND REEL GR Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Thakur M, 2010, J FOOD ENG, V101, P193, DOI 10.1016/j.jfoodeng.2010.07.001 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 White Gareth R. T., 2007, Journal of Information, Information Technology and Organizations, V2, P119 NR 21 TC 59 Z9 75 U1 1 U2 129 PD NOV PY 2012 VL 89 BP 76 EP 85 DI 10.1016/j.compag.2012.08.004 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000311245300009 DA 2022-12-14 ER PT J AU Creydt, M Fischer, M AF Creydt, M. Fischer, M. TI Blockchain and more - Algorithm driven food traceability SO FOOD CONTROL DT Review DE Blockchain; Traceability; Food fraud; Authenticity; Food chain ID CHALLENGES; SYSTEM; FRAUD; CHAIN; REAL AB Food safety and quality assurance has become increasingly difficult in times of growing global flows of goods. In particular, the traceability of food turns out to be very challenging for retailers, resellers and state surveillance authorities. The reasons for this range from the proof of simple, but harmless modifications to the detection of health-endangering substances, bacteria or viruses. In addition, it concerns the verification of food authenticity, for example the correct declaration of the geographical origin, variety or cultivation. Such quality parameters justify higher prices and therefore, they are often in the focus of food fraudsters. Some of those qualities can be monitored by objective analytical methods, but not all of them. For ensuring the traceability of food trade networks blockchain algorithms incorporate a high potential, as data can be stored in an unmodifiable way and enabling quick tracking across all process steps, so that stakeholders as well as commodities or semi-finished items can be identified much faster. Areas of applications on one hand and limitations on the other hand are discussed in this review article and reflected with alternative strategies. C1 [Creydt, M.; Fischer, M.] Univ Hamburg, Inst Food Chem, Hamburg Sch Food Sci, Grindelallee 117, D-20146 Hamburg, Germany. C3 University of Hamburg RP Fischer, M (corresponding author), Univ Hamburg, Inst Food Chem, Hamburg Sch Food Sci, Grindelallee 117, D-20146 Hamburg, Germany. EM Markus.Fischer@uni-hamburg.de CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Anderson R, 1996, P PRAGOCRYPT, V96, P242 Bachmann R, 2018, J AGR FOOD CHEM, V66, P11873, DOI 10.1021/acs.jafc.8b03724 Beckett S.T., 2009, IND CHOCOLATE MANUFA, DOI [10.1002/9781444301588, DOI 10.1002/9781444301588] Bhardwaj S, 2018, BLOCKCHAIN TECHNOLOG Bohme R, 2015, J ECON PERSPECT, V29, P213, DOI 10.1257/jep.29.2.213 Burkhardt D, 2018, DISTRIBUTED LEDGER S, DOI [10.1109/ICE.2018.8436299, DOI 10.1109/ICE.2018.8436299] Casey M., 2017, GLOBAL SUPPLY CHAINS Creydt M, 2018, J AGR FOOD CHEM, V66, P13328, DOI 10.1021/acs.jafc.8b05791 Creydt M, 2018, ELECTROPHORESIS, V39, P1569, DOI 10.1002/elps.201800004 Esteki M, 2019, COMPR REV FOOD SCI F, V18, P425, DOI 10.1111/1541-4337.12419 Federal Institute for Risk Assessment, 2012, EHEC OUTBR 2011 INV Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fernandez-Carames TM, 2018, IEEE ACCESS, V6, P32979, DOI 10.1109/ACCESS.2018.2842685 Floarea AD, 2016, INT C ELECT COMPUT Fuertes G, 2016, J SENSORS, V2016, DOI 10.1155/2016/4046061 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Ge L., 2017, BLOCKCHAIN AGR FOOD Haber S., 1991, Journal of Cryptology, V3, P99, DOI 10.1007/BF00196791 Herrmann L, 2015, J AGR FOOD CHEM, V63, P4539, DOI 10.1021/acs.jafc.5b01462 John RM, 2014, CARDIAC PACING AND ICDS, 6TH EDITION, P1 Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Kim H., 2018, SUPPLY CHAIN REVOLUT Konst S, 2000, SECURE LOG FILES BAS Krause MJ, 2018, NAT SUSTAIN, V1, P711, DOI 10.1038/s41893-018-0152-7 Kuswandi Bambang, 2011, Sensing and Instrumentation for Food Quality and Safety, V5, P137, DOI 10.1007/s11694-011-9120-x Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Mao DH, 2019, ARAB J SCI ENG, V44, P3439, DOI 10.1007/s13369-018-3537-z Montecchi M, 2019, BUS HORIZONS, V62, P283, DOI 10.1016/j.bushor.2019.01.008 Nakamoto, 2008, BITCOIN PEER TO PEER Nofer M, 2017, BUS INFORM SYST ENG+, V59, P183, DOI 10.1007/s12599-017-0467-3 Pigini D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101910 Popov S., 2016, THE TANGLE Puddu M, 2014, ACS NANO, V8, P2677, DOI 10.1021/nn4063853 Raskin Max, 2017, GEO L TECH REV, V1, P305, DOI [DOI 10.2139/SSRN.2842258, 10.2139/ssrn.2842258] Richter B, 2019, FOOD CHEM, V286, P475, DOI 10.1016/j.foodchem.2019.01.105 Schelm S, 2017, J AGR FOOD CHEM, V65, P516, DOI 10.1021/acs.jafc.6b04457 Schmidhuber, 2018, EMERGING OPPORTUNITI Schneier B, 1998, PROCEEDINGS OF THE SEVENTH USENIX SECURITY SYMPOSIUM, P53 Schutte, 2017, BLOCKCHAIN TECHNOLOG Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Szabo N., 1997, First Monday, V2 Tian F, 2017, I C SERV SYST SERV M Tikhomirov S, 2018, LECT NOTES COMPUT SC, V10723, P206, DOI 10.1007/978-3-319-75650-9_14 Verhaelen K, 2018, FOOD CONTROL, V94, P93, DOI 10.1016/j.foodcont.2018.06.029 WHO, 2015, MOR 23 MILL PEOPL WH World Health Organization, 2015, WHO ESTIMATES GLOBAL, DOI DOI 10.1007/S13213-015-1147-5 Xu XW, 2016, 2016 13TH WORKING IEEE/IFIP CONFERENCE ON SOFTWARE ARCHITECTURE (WICSA), P182, DOI 10.1109/WICSA.2016.21 Yiannas F., 2018, Innovations: Technology / Governance / Globalization, V12, P46, DOI 10.1162/inov_a_00266 Zheng ZB, 2018, INT J WEB GRID SERV, V14, P352, DOI 10.1504/IJWGS.2018.095647 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 NR 51 TC 72 Z9 76 U1 12 U2 223 PD NOV PY 2019 VL 105 BP 45 EP 51 DI 10.1016/j.foodcont.2019.05.019 WC Food Science & Technology SC Food Science & Technology UT WOS:000477691300007 DA 2022-12-14 ER PT J AU van der Merwe, M Kirsten, JF AF van der Merwe, M. Kirsten, J. F. TI TRACEABILITY SYSTEMS AND ORIGIN BASED MEAT PRODUCTS IN THE SOUTH AFRICAN SHEEP MEAT INDUSTRY SO AGREKON DT Article DE Traceability; South African abattoirs; Karoo Lamb; Fisher's exact test AB In a consumer-driven world, consumers want to experience a connection between the product that they are consuming and the product's origin. To guarantee the validity of this connection and therefore the origin attribute of the product, traceability systems are required. The main purpose of this paper is to assess current traceability systems implemented in South African sheep abattoirs, thereby establishing their ability to guarantee the origin of a carcass. Research indicated that the South African sheep abattoirs have traceability systems in place and they can guarantee the origin of a meat product. The descriptive analysis and hypothesis tests identified the tipping factor for the implementation of a traceability system, as the requirement from retail markets to which these abattoirs deliver their product. C1 [van der Merwe, M.; Kirsten, J. F.] Univ Pretoria, Dept Agr Econ Extens & Rural Dev, ZA-0002 Pretoria, South Africa. C3 University of Pretoria RP van der Merwe, M (corresponding author), Univ Pretoria, Dept Agr Econ Extens & Rural Dev, ZA-0002 Pretoria, South Africa. EM melissa.verdermerwe@up.ac.za NR 0 TC 2 Z9 2 U1 0 U2 8 PY 2015 VL 54 IS 1 BP 53 EP 69 DI 10.1080/03031853.2015.1019524 WC Agricultural Economics & Policy SC Agriculture UT WOS:000356808400003 DA 2022-12-14 ER PT J AU Yu, RZ Yang, W Yang, CY AF Yu, Runzhong Yang, Wu Yang, Chengyi TI Differentially Private XGBoost Algorithm for Traceability of Rice Varieties SO APPLIED SCIENCES-BASEL DT Article DE differential privacy; variety traceability; rice data safety; machine learning ID DISCRIMINATION AB Privacy protection in agricultural traceability has received more and more attention. Most of the existing methods only protect the original data information from the perspective of cryptography and ignore the availability of the protected information. In fact, after data is processed by cryptography, blockchain, and other technologies, it cannot be directly used for machine learning model training. Therefore, differential privacy has great potential value for privacy protection in agricultural traceability, which can enable data to participate in classification tasks under privacy protection. In this paper, we propose an integrated algorithm for agricultural traceability called Differentially Private XGBoost (DP-XGB), which can protect the privacy of the original data during the training process and obtain high model utility under the condition of a small sample size. We inject Gaussian noise into the gradient operator and Hesse operator of the original XGBoost and give the calculation method of the resulting privacy budget. Experiments show that our method can effectively obtain differential privacy guarantees and achieves very high classification accuracy when the noise is small. C1 [Yu, Runzhong; Yang, Wu] Harbin Engn Univ, Informat Secur Res Ctr, Harbin 165001, Peoples R China. [Yu, Runzhong] Heilongjiang Bayi Agr Univ, Coll Informat & Elect Engn, Daqing 163319, Peoples R China. [Yang, Chengyi] East China Normal Univ, Inst AI Educ, Shanghai 200062, Peoples R China. C3 Harbin Engineering University; Heilongjiang Bayi Agricultural University; East China Normal University RP Yang, W (corresponding author), Harbin Engn Univ, Informat Secur Res Ctr, Harbin 165001, Peoples R China. EM yangwu@hrbeu.edu.cn CR Abadi M, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P308, DOI 10.1145/2976749.2978318 Bai Y, 2021, J ANHUI AGR SCI, V49, P22 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785 Chukkapalli SSL, 2021, 2021 THIRD IEEE INTERNATIONAL CONFERENCE ON TRUST, PRIVACY AND SECURITY IN INTELLIGENT SYSTEMS AND APPLICATIONS (TPS-ISA 2021), P340, DOI 10.1109/TPSISA52974.2021.00037 Cui C, 2022, CEREALS OILS, V6, P36 Dwork C, 2006, LECT NOTES COMPUT SC, V4052, P1 Dwork C, 2013, FOUND TRENDS THEOR C, V9, P211, DOI 10.1561/0400000042 Dwork C, 2010, ANN IEEE SYMP FOUND, P51, DOI 10.1109/FOCS.2010.12 Dwork C, 2009, ACM S THEORY COMPUT, P371 Fan M.S, 2021, THESIS HARBIN I TECH Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng YC, 2019, J CHEM-NY, V2019, DOI 10.1155/2019/1614504 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Ho GTS, 2021, EXPERT SYST APPL, V179, DOI 10.1016/j.eswa.2021.115101 Huning L., 2017, P 1 ACM WORKSHOP MOB, P62 Hwang J, 2012, TALANTA, V101, P488, DOI 10.1016/j.talanta.2012.10.001 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Li F, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15020312 Libo Wang, 2021, Journal of Physics: Conference Series, V1744, DOI 10.1088/1742-6596/1744/3/032149 Liu F, 2022, CHINESE J ELECTRON, V32, P140, DOI [10.1049/cje.2022.00.096, DOI 10.1049/CJE.2022.00.096] Liu X.H, 2021, FOOD SCI TECH-BRAZIL, V46, P244, DOI [10.1016/j.tifs.2021.07.025, DOI 10.1016/J.TIFS.2021.07.025] Neunhoeffer M, 2020, Arxiv, DOI arXiv:2007.11934 Maruseac M., 2014, P 4 ACM C DATA APPL, P159 Masudin I., 2021, CLEAN ENG TECHNOL, V4, P100238, DOI [https://doi.org/10.1016/j.clet.2021.100238, DOI 10.1016/J.CLET.2021.100238] McSherry F, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P19 Mumtaz M, 2019, J DISCRET MATH SCI C, V22, P9, DOI 10.1080/09720529.2018.1564201 Patil A, 2014, 2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), P2623, DOI 10.1109/ICACCI.2014.6968348 Qiu Y.C, 2018, FARM PROD PROCESS, V1, P40 [曲明哲 Qu Mingzhe], 2012, [东北农业大学学报, Journal of Northeast Agricultural University], V43, P92 Rao E.S., 2022, MEAS FOOD, V5, DOI [10.1016/j.meafoo.2021.100019, DOI 10.1016/J.MEAFOO.2021.100019] Son S, 2022, FOOD CHEM X, V49, P22 Suzuki Y., 2022, JARQ, Japan Agricultural Research Quarterly, V56, P95 Uawisetwathana U, 2019, CURR OPIN FOOD SCI, V28, P58, DOI 10.1016/j.cofs.2019.08.008 Waheed N, 2021, ACM COMPUT SURV, V53, DOI 10.1145/3417987 Wang S, 2021, IEEE T INF FOREN SEC, V16, P4451, DOI 10.1109/TIFS.2021.3097822 Xu W.Y, 2022, J INSTRUM, V17, P22 Yakubu BM, 2022, PEERJ COMPUT SCI, V8, DOI 10.7717/peerj-cs.801 Yan CH, 2022, J INSTRUM, V17, DOI 10.1088/1748-0221/17/08/P08016 Zhang L, 2015, RICE SCI, V22, P245, DOI 10.1016/j.rsci.2015.09.004 Zhao H.T, 2017, ELECT DES ENG, V25, P49 NR 41 TC 0 Z9 0 U1 3 U2 3 PD NOV PY 2022 VL 12 IS 21 AR 11037 DI 10.3390/app122111037 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000880944300001 DA 2022-12-14 ER PT J AU Liu, P Cui, XY Li, Y AF Liu, Pan Cui, Xiaoyan Li, Ye TI Subsidy policies of a fresh supply chain considering the inputs of blockchain traceability service system SO SCIENCE AND PUBLIC POLICY DT Article; Early Access DE subsidy policies; blockchain; traceability service; fresh supply chain ID FOOD TRACEABILITY; CROP INSURANCE; MANAGEMENT; WILLINGNESS; CHALLENGES; CONSUMERS; SECURITY; PAY AB To stimulate the development and application of blockchain technology, Chinese government put forward subsidy strategy. To explore the subsidy policies under the new background, we chose a fresh supply chain with one producer, one blockchain-based traceability service provider, and one retailer as the research object, and government subsidy strategies were divided into a fixed strategy and a varying strategy. Afterward, considering the trust level of blockchain-based traceability information and consumers' preference to the blockchain-based traceability information, we revised the demand function, and three subsidy models were proposed and analyzed. Findings: (1) the varying subsidy will help the retailer, the producer, and the traceability service provider set lower prices. (2) Meanwhile, the varying subsidies offered to the blockchain-based traceability service provider and the producer will help the whole supply chain members obtain more revenues. C1 [Liu, Pan; Cui, Xiaoyan; Li, Ye] Henan Agr Univ, Informat & Management Coll, NongYe Rd 63, Zhengzhou 450002, Henan, Peoples R China. C3 Henan Agricultural University RP Liu, P (corresponding author), Henan Agr Univ, Informat & Management Coll, NongYe Rd 63, Zhengzhou 450002, Henan, Peoples R China. EM hnycliupan@163.com CR Administration F. A. D., 2018, GUIDANCE REGULATION Alauddin M, 2008, ECOL ECON, V65, P111, DOI 10.1016/j.ecolecon.2007.06.004 Alizamir S, 2019, MANAGE SCI, V65, P32, DOI 10.1287/mnsc.2017.2927 [Anonymous], 2019, NEWS S APHIS U, 2018, SUMMARY PROGRAM REV APHIS U., 2020, SHEEP GOAT IDENTIFIC Bajgiran AH, 2019, INT J ENERG RES, V43, P1848, DOI 10.1002/er.4422 Biswas K., 2017, PROC FUTURE TECHNOL, P1, DOI DOI 10.1007/978-3-319-54460-1_1 Bumbudsanpharoke N, 2015, J FOOD SCI, V80, pR910, DOI 10.1111/1750-3841.12861 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Chen HH, 2019, APPL ECON, V51, P687, DOI 10.1080/00036846.2018.1510470 Chen HL, 2021, IEEE ACCESS, V9, P36008, DOI 10.1109/ACCESS.2021.3062410 Dai HY, 2015, INT J PROD RES, V53, P511, DOI 10.1080/00207543.2014.955922 Fan ZP, 2022, ANN OPER RES, V309, P837, DOI 10.1007/s10479-020-03729-y Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 Fu N., 2019, IOP C SERIES MAT SCI, V35, P55040 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Hayrutdinov S, 2020, J ADV TRANSPORT, V2020, DOI 10.1155/2020/5635404 He XJ, 2019, KSII T INTERNET INF, V13, P619, DOI 10.3837/tiis.2019.02.008 Hirbli T., 2018, PALM OIL TRACEABILIT Hu JL, 2020, SCAND J GASTROENTERO, V55, P865, DOI 10.1080/00365521.2020.1778077 Imeri A, 2018, INT CONF NEW TECHNOL Jiang YP, 2022, SUSTAIN PROD CONSUMP, V29, P432, DOI 10.1016/j.spc.2021.10.025 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kamble SS, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120465 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Kundian Economy, 2018, MAX SUBS KUNM BLOCKC [李文立 Li Wenli], 2019, [运筹与管理, Operations Research and Management Science], V28, P98 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Liu P, 2020, J CLEAN PROD, V277, DOI 10.1016/j.jclepro.2020.123646 Liu Q, 2013, PROD OPER MANAG, V22, P1269, DOI 10.1111/poms.12018 Ministry Of Industry And Technology, 2021, GUID 2 DEP ACC APPL Ministry of Internal Affairs Communications, 2020, WHIT PAP INF COMM JA Mohanta BK, 2018, INT CONF COMPUT Office of Quanzhou Municipal Governmentm, 2020, QUANZH MUN PEOPL GOV Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Park Chain Research and Institute, 2019, DET RUL MEAS IMPL PR Pedersen AB, 2019, MIS Q EXEC, V18, P99, DOI 10.17705/2msqe.00010 Peng HJ, 2019, INT J PROD ECON, V216, P274, DOI 10.1016/j.ijpe.2019.06.011 People'S Government Of Yuzhong District C, 2020, PARK DEV PROM MEAS C Pingali PL, 2012, P NATL ACAD SCI USA, V109, P12302, DOI 10.1073/pnas.0912953109 Rejeb A., 2018, ACTA TECH JAURINENSI, V11, DOI [DOI 10.14513/ACTATECHJAUR.V11.N4.467, 10.14513/actatechjaur.v11.n4.467] Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sambrekar K, 2019, INT J CLOUD APPL COM, V9, P33, DOI 10.4018/IJCAC.2019010103 Saurabh S, 2021, J CLEAN PROD, V284, DOI 10.1016/j.jclepro.2020.124731 Schmidhuber, 2018, EMERGING OPPORTUNITI Sohu, 2018, MAERSK WORLDS LARG S Solaymani S, 2019, REV SOC ECON, V77, P271, DOI 10.1080/00346764.2019.1596298 Tayal A, 2021, INT J COMMUN SYST, V34, DOI 10.1002/dac.4696 Technology S. B. O. I., 2020, IMPL OP ACC DEV BLOC Technology W. B. O. E, 2021, OP MUN PEOPL GOV ACC Tengxun, 2020, SUMM SPEC SUBS POL B Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Ul Hassan M, 2019, FUTURE GENER COMP SY, V97, P512, DOI 10.1016/j.future.2019.02.060 Wenli LI, 2019, OPERATIONS RES MANAG, V28, P99 Wilderness Technology, 2018, SHENZH HAS ISS SUPP Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Wu XY, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.1894497 Xu JF, 2014, J INTEGR AGR, V13, P2537, DOI 10.1016/S2095-3119(13)60674-7 Yanhong P, 2020, DETAILED RULES MEASU Yu JS, 2018, AGR ECON-BLACKWELL, V49, P533, DOI 10.1111/agec.12434 Yu YN, 2021, J FOOD QUALITY, V2021, DOI 10.1155/2021/6616096 Zhang F., 2017, 2017 IEEE BIOM CIRC Zhang RR, 2021, J CLEAN PROD, V285, DOI 10.1016/j.jclepro.2020.124806 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 Zi WC, 2021, J IND MANAG OPTIM, V17, P2505, DOI 10.3934/jimo.2020079 NR 71 TC 0 Z9 0 U1 14 U2 14 DI 10.1093/scipol/scac044 EA SEP 2022 WC Environmental Studies; Management; Public Administration SC Environmental Sciences & Ecology; Business & Economics; Public Administration UT WOS:000854230500001 DA 2022-12-14 ER PT J AU Prinsloo, T de Villiers, C AF Prinsloo, Tania de Villiers, Carina TI A FRAMEWORK TO DEFINE THE IMPACT OF SUSTAINABLE ICT FOR AGRICULTURE PROJECTS: THE NAMIBIAN LIVESTOCK TRACEABILITY SYSTEM SO ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES DT Article DE Livestock Traceability; Namibia; Sustainability; ICT for Agriculture; Impact Assessment Framework ID FARM; INFORMATION; MEAT AB Namibia expanded its livestock traceability system to include data of the Northern communal farmers, with ear-tagging starting in 2010, and full functionality added in 2014. The new technology enables them to export safe meat products to previously-excluded overseas markets. In this article, the complexities of a livestock traceability system are explained to provide one with a sense of the lengths countries like Namibia went through to successfully implement such a system. Next, a new framework is proposed to apply to agricultural development projects, called the Impact-for-sustainable agriculture framework, with all the facets of the framework explained. Finally, the framework is applied to the Namibian Livestock Identification and Traceability System (NamLITS), with the focus on the Northern Communal Areas (NCAs). NamLITS is an example of a successful agricultural development project, and it is hoped that this new framework can be applied to other agricultural initiatives. C1 [Prinsloo, Tania; de Villiers, Carina] Univ Pretoria, Pretoria, South Africa. C3 University of Pretoria RP Prinsloo, T (corresponding author), Univ Pretoria, Pretoria, South Africa. EM tania.prinsloo@up.ac.za; carina.devilliers@up.ac.za CR Adato M., 2002, ASSESSING IMPACT AGR Andree S, 2010, MEAT SCI, V86, P38, DOI 10.1016/j.meatsci.2010.04.020 [Anonymous], [No title captured] [Anonymous], 2003, TIMES HIGHER ED Bajardi P, 2012, J R SOC INTERFACE, V9, P2814, DOI 10.1098/rsif.2012.0289 Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Boy R.L., 2013, EUR J LOGIST PURCH S, V1, P1 Briner S, 2012, AGR ECOSYST ENVIRON, V149, P50, DOI 10.1016/j.agee.2011.12.011 Brown L, 2007, BRIT J NUTR, V97, P1027, DOI 10.1017/S0007114507691983 Burton I, 2002, CLIM POLICY, V2, P145, DOI 10.1016/S1469-3062(02)00038-4 Caja G., 2004, ICAR Technical Series, P21 Carney D., 2003, SUSTAINABLE LIVELIHO Chambers R., 1992, SUSTAINABLE RURAL LI Colombo MG, 2015, ENTREP THEORY PRACT, V39, P75, DOI 10.1111/etap.12118 Davies G, 2002, RES VET SCI, V73, P195, DOI 10.1016/S0034-5288(02)00105-4 de Visser LE, 2013, SMALL WAR INSUR, V24, P712, DOI 10.1080/09592318.2013.857942 Deloitte, 2012, ETRANSFORM AFR AGR S Donnelly CA, 2002, P ROY SOC B-BIOL SCI, V269, P2179, DOI 10.1098/rspb.2002.2156 Duffy G, 2008, MEAT SCI, V78, P34, DOI 10.1016/j.meatsci.2007.05.023 Ellison NB, 2007, J COMPUT-MEDIAT COMM, V12, P1143, DOI 10.1111/j.1083-6101.2007.00367.x Engelbrecht J., 2012, ANIMAL IDENTIFICATIO Fourie M., 2013, COMMUNICATION GS1, 2010, TRAC FRESH FRUIT VEG Harris RW, 2016, INFORM TECHNOL DEV, V22, P177, DOI 10.1080/02681102.2015.1018115 Heeks R., 2009, IMPACT ASSESSMENT IC Hewitt-Dundas N, 2016, INT STUD ENTREP, V32, P71, DOI 10.1007/978-3-319-17713-7_4 Hobbs J. E, 2003, POL DISP INF CONS 9 Hobbs J. E., 2014, INT AGR TRAD RES CON Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hoque MR, 2015, ELECTR J INF SYS DEV, V71 Kahn M., 1995, P INT SUST DEV RES C Karesh WB, 2005, FOREIGN AFF, V84, P38, DOI 10.2307/20034419 Kohler J, 2014, J ENVIRON DEV, V23, P66, DOI 10.1177/1070496513507260 Kossler R, 2015, ACTA ACAD, V47, P138 Kumba F.F., 2003, J INT AGR EXTENSION, V10, P47, DOI DOI 10.5191/JIAEE.2003.10306 Marais M. A, 2015, INT ENCY DIGITAL COM, P1, DOI [10.1002/9781118767771.wbiedcs038, DOI 10.1002/9781118767771.WBIEDCS038] Masiero S, 2016, INFORM TECHNOL DEV, V22, P487, DOI 10.1080/02681102.2016.1143346 Mdluli S., 2012, COMMUNICATION Melber H, 2003, RE EXAMINING LIBERAT Moreki J. C., 2012, J ANIM SCI ADV, V2, P925 Opara L. U., 2002, AGR ENG INT CIGR J Opp SM, 2013, URBAN AFF REV, V49, P678, DOI 10.1177/1078087412469344 Pade C, 2009, INFORMATION SYSTEMS DEVELOPMENT: CHALLENGES IN PRACTICE, THEORY AND EDUCATION, VOLS 1AND 2, P339, DOI 10.1007/978-0-387-68772-8_26 Prinsloo T., 2017, THESIS Putnam R., 2001, CAN J POL RES, V2, P41 Qureshi S, 2015, INFORM TECHNOL DEV, V21, P511, DOI 10.1080/02681102.2015.1080428 Ranjan Roy, 2013, American-Eurasian Journal of Agricultural & Environmental Sciences, V13, P449 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Renken J, 2013, PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATIONS TECHNOLOGIES AND DEVELOPMENT, VOL 2, P128, DOI 10.1145/2517899.2517928 Roosen J., 2003, Agribusiness (New York), V19, P77, DOI 10.1002/agr.10041 Sadoulet E., 2009, AGR DEV LESSONS WORL SAFA, 2003, S AFR FEEDL IN UNPUB Schultz W., 2013, COMMUNICATION Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shortall R, 2015, RENEW SUST ENERG REV, V44, P391, DOI 10.1016/j.rser.2014.12.020 Siena A, 2008, LECT NOTES COMPUT SC, V5074, P182 Smith GC, 2008, MEAT SCI, V80, P66, DOI 10.1016/j.meatsci.2008.05.024 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Tacastacas R. C., 2011, THESIS Thomson GR, 2013, TRANSBOUND EMERG DIS, V60, P492, DOI 10.1111/tbed.12175 Toldra F, 2006, TRENDS FOOD SCI TECH, V17, P482, DOI 10.1016/j.tifs.2006.02.002 UNESCO, 2015, UNESCO SUST DEV GOAL Verbeke W, 2001, OUTLOOK AGR, V30, P249, DOI 10.5367/000000001101293733 VOSLOO W, 1992, EPIDEMIOL INFECT, V109, P547, DOI 10.1017/S0950268800050536 Weiss B, 2012, AM ETHNOL, V39, P614, DOI 10.1111/j.1548-1425.2012.01384.x World Bank, 2007, WORLD DEV REP 2008 A NR 66 TC 4 Z9 4 U1 3 U2 11 PD SEP PY 2017 VL 82 IS 1 AR 6 WC Social Sciences, Interdisciplinary SC Social Sciences - Other Topics UT WOS:000447058100006 DA 2022-12-14 ER PT J AU Schwagele, F AF Schwagele, F TI Traceability from a European perspective SO MEAT SCIENCE DT Article; Proceedings Paper CT 51st International Congress of Meat Science and Technology (51st IcoMST) CY AUG 07-12, 2005 CL Baltimore, MD DE traceability; tracking; meat; meat products; food; feed ID HEAT-TREATED BEEF; SPECIES IDENTIFICATION; DNA-HYBRIDIZATION; MUSCLE MEAT; PCR; BSE; IMMUNOBIOSENSOR; AMPLIFICATION; TEMPERATURES; VALIDATION AB At pan-European level there is a need for traceability systems giving information on origin, processing, retailing and final destination of foodstuffs. Such systems shall enhance consumer confidence in food; enable the regulatory authorities to identify and to withdraw health hazardous and non-consumable foodstuffs from the market. Animal feeds are an element in this "food-to-farm" approach to public health. Such feedstuffs are preliminary elements of some foods for human consumption, and hence are an inherent element of the food chain. A harmonised pan-European food traceability protocol would greatly assist authorities in detecting fraud as well as dangerous substances. The food chain comprises a range of sequential and parallel stages bridging the full spectrum from agricultural production to the consumable foodstuffs by consumers. EU legislation on traceability and the technologies needed to implement this system for meat and meat products are the focus of this paper. (c) 2005 Elsevier Ltd. All rights reserved. C1 Fed Res Ctr Nutr & Food Locat Kulmbach, Inst Chem & Phys, D-95326 Kulmbach, Germany. RP Schwagele, F (corresponding author), Fed Res Ctr Nutr & Food Locat Kulmbach, Inst Chem & Phys, E-C Baumann Str 20, D-95326 Kulmbach, Germany. EM c-schwaegele@baff-kulmbach.de CR Al-Jowder O, 2002, J AGR FOOD CHEM, V50, P1325, DOI 10.1021/jf0108967 Altmann K, 2004, FLEISCHWIRTSCHAFT, V84, P115 Anderson KA, 1999, J AGR FOOD CHEM, V47, P1568, DOI 10.1021/jf980677u Anderson RM, 1996, NATURE, V382, P779, DOI 10.1038/382779a0 BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 Baxter GA, 1999, ANALYST, V124, P1315, DOI 10.1039/a904122b BEHRENS M, 1999, FLEISCHWIRTSCHAFT IN, V6, P16 Binke R., 2004, Mitteilungsblatt der Fleischforschung Kulmbach, V43, P155 BINKE R, 2003, INNOVATIONS FOOD TEC, V21, P130 CHIKUNI K, 1990, MEAT SCI, V27, P119, DOI 10.1016/0309-1740(90)90060-J COWIE WP, 1968, J SCI FOOD AGR, V19, P226, DOI 10.1002/jsfa.2740190411 Cozzolino D, 2002, ANIM SCI, V74, P477, DOI 10.1017/S1357729800052632 ELLEKJAER MR, 1992, J SCI FOOD AGR, V59, P335, DOI 10.1002/jsfa.2740590310 *ENOSEFOODMICRODET, 2003, RAP DET MICR CONT FO *ENTRANSFOOD, 2003, EUR NETW SAF ASS GEN Ferguson NM, 1997, P ROY SOC B-BIOL SCI, V264, P1445, DOI 10.1098/rspb.1997.0201 Gajendragad MR, 2001, VET MICROBIOL, V78, P319, DOI 10.1016/S0378-1135(00)00307-2 Garnsworthy PC, 2000, J AGR SCI, V135, P409, DOI 10.1017/S0021859699008382 Gonzalez-Martin I, 2002, ANAL CHIM ACTA, V468, P293, DOI 10.1016/S0003-2670(02)00657-8 HOFMANN K, 1986, FLEISCHWIRTSCHAFT, V66, P916 HOFMANN K, 1986, FLEISCHWIRTSCHAFT, V66, P91 Honikel K. O., 2002, Mitteilungsblatt der Bundesanstalt fuer Fleischforschung, Kulmbach, V41, P125 Isaksson T, 1996, MEAT SCI, V43, P245, DOI 10.1016/S0309-1740(96)00016-2 ISAKSSON T, 1989, J SCI FOOD AGR, V49, P385, DOI 10.1002/jsfa.2740490314 JEMMI T, 1993, FLEISCHWIRTSCHAFT, V73, P600 Jordan D, 1999, PREV VET MED, V41, P55, DOI 10.1016/S0167-5877(99)00032-X KAEMMER D, 1992, BIO-TECHNOL, V10, P1030, DOI 10.1038/nbt0992-1030 KHOMUTOV SM, 1994, ANAL LETT, V27, P2983, DOI 10.1080/00032719408000306 Kingombe CIB, 2001, MEAT SCI, V57, P35, DOI 10.1016/S0309-1740(00)00074-7 Mackie I., 1980, In 'Advances in fish science and technology ' [see FSTA (1981) 13 6R300]., P444 Marquette CA, 1999, ANAL CHIM ACTA, V398, P173, DOI 10.1016/S0003-2670(99)00456-0 MARTIN GJ, 1981, TETRAHEDRON LETT, V22, P3525, DOI 10.1016/S0040-4039(01)81948-1 McElhinney J, 1999, J FOOD SCI, V64, P587, DOI 10.1111/j.1365-2621.1999.tb15090.x MEKETOWA P, 2003, J ANAL APPL PYROL, V211, P213 Meyer R, 1995, J AOAC INT, V78, P1542 *MOLSPECID, 2004, DEV QUANT QUAL MOL B OLEARY MH, 1981, PHYTOCHEMISTRY, V20, P553, DOI 10.1016/0031-9422(81)85134-5 Pires FF, 2001, J APPL POULTRY RES, V10, P412, DOI 10.1093/japr/10.4.412 POLYCHRONIADOU A, 1985, J DAIRY SCI, V68, P147, DOI 10.3168/jds.S0022-0302(85)80808-0 Popping B, 2001, J CHEM EDUC, V78, P752 Prusiner SB, 1997, SCIENCE, V278, P245, DOI 10.1126/science.278.5336.245 *QUALITYLOWINPUTFO, 2005, IMPR QUAL SAF RED CO Rehbein H, 1999, FOOD CHEM, V64, P263, DOI 10.1016/S0308-8146(98)00125-3 SAIKI RK, 1988, SCIENCE, V239, P487, DOI 10.1126/science.2448875 Samsonova JV, 2002, BIOSENS BIOELECTRON, V17, P523, DOI 10.1016/S0956-5663(02)00016-7 Schipper EF, 1998, ANAL CHEM, V70, P1192, DOI 10.1021/ac970985b Schwagele F, 2003, FLEISCHWIRTSCHAFT, V83, P78 Schwagele F, 2001, FLEISCHWIRTSCHAFT, V81, P78 Stark KDC, 2002, FOOD CONTROL, V13, P1, DOI 10.1016/S0956-7135(01)00022-6 Thyholt K, 1998, MEAT SCI, V48, P49, DOI 10.1016/S0309-1740(97)00075-2 Vaughan RD, 2003, INT J ENVIRON AN CH, V83, P555, DOI 10.1080/0306731021000050714 Verkaar ELC, 2002, MEAT SCI, V60, P365, DOI 10.1016/S0309-1740(01)00144-9 Watts AJ, 2003, BCPC INTERNATIONAL CONGRESS CROP SCIENCE & TECHNOLOGY 2003, VOL 1 AND 2, CONGRESS PROCEEDINGS, P323 Weder JKP, 2002, J AGR FOOD CHEM, V50, P4456, DOI 10.1021/jf020216f WINTERO AK, 1990, MEAT SCI, V27, P75, DOI 10.1016/0309-1740(90)90030-A Wurz A, 1999, FOOD CONTROL, V10, P385, DOI 10.1016/S0956-7135(99)00080-8 ZIEGLER H, 1976, PLANTA, V128, P85, DOI 10.1007/BF00397183 NR 57 TC 152 Z9 188 U1 3 U2 65 PD SEP PY 2005 VL 71 IS 1 BP 164 EP 173 DI 10.1016/j.meatsci.2005.03.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000230976900016 DA 2022-12-14 ER PT J AU Laliotis, GP Koutsouli, P Bizelis, IA AF Laliotis, George P. Koutsouli, Panagiota Bizelis, Iosif A. TI Implementation of contemporary DNA based techniques on traceability process of small ruminant species and products SO JOURNAL OF ADVANCED VETERINARY AND ANIMAL RESEARCH DT Review DE DNA; Small ruminants; SNPs; STRs; Traceability ID SINGLE-NUCLEOTIDE POLYMORPHISMS; REAL-TIME PCR; GENETIC DIVERSITY; POPULATION-STRUCTURE; MEAT TRACEABILITY; SHEEP BREEDS; PARENTAGE VERIFICATION; FOOD-PRODUCTS; SNP MARKERS; IDENTIFICATION AB Traceability methods in livestock sector through the tracking of animal species, breed or even individuals, has become of utmost importance as a "vehicle" for ensuring consumers' food safety. The advent of new technology at DNA level has facilitated the convenience and the accuracy of the implementation of traceability methods. The scope of this review is to highlight the most up to date progress on DNA based approaches concerning the traceability procedures for small ruminant species and/or their products, giving emphasis on short tandem repeats (microsatellites) and single nucleotide polymorphisms. The conclusions of this review may be used either from the farmer or the State and other Organisations in order not only to certify traceability throughout the whole food process chain but to ensure also consumers' food safety. C1 [Laliotis, George P.] Hellen Agr Org, Res Inst Anim Sci, DEMETER, Pella 58100, Greece. [Koutsouli, Panagiota; Bizelis, Iosif A.] Agr Univ Athens, Dept Anim Sci & Aquaculture, Lab Anim Husb, 75 Iera Odos, Athens 11855, Greece. C3 Agricultural University of Athens RP Laliotis, GP (corresponding author), Hellen Agr Org, Res Inst Anim Sci, DEMETER, Pella 58100, Greece. EM glaliotis@rias.gr CR Awad AI, 2016, COMPUT ELECTRON AGR, V123, P423, DOI 10.1016/j.compag.2016.03.014 Barron LJR, 2018, TRACEABILITY AUTHENT, P100 Bertolini F, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0121701 Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Bramante A, 2011, ITAL J FOOD SAF, V1, P41, DOI 10.4081/ijfs.2011.1.41 Cao JH, 2017, SMALL RUMINANT RES, V148, P80, DOI 10.1016/j.smallrumres.2016.12.033 Carvalho DC, 2017, FOOD CONTROL, V80, P183, DOI 10.1016/j.foodcont.2017.04.049 Ceccobelli S, 2015, SMALL RUMINANT RES, V123, P62, DOI 10.1016/j.smallrumres.2014.09.009 Chen WM, 2007, AM J HUM GENET, V81, P913, DOI 10.1086/521580 Choi JW, 2015, DNA RES, V22, P259, DOI 10.1093/dnares/dsv011 Ciani E, 2013, SMALL RUMINANT RES, V112, P21, DOI 10.1016/j.smallrumres.2012.12.013 Clarke SM, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0093392 Crepaldi P, 2007, ITAL J ANIM SCI, V6, P91, DOI 10.4081/ijas.2007.1s.91 Cunha JT, 2017, METHODS MOL BIOL, V1620, P183, DOI 10.1007/978-1-4939-7060-5_13 Cunha JT, 2016, FOOD CHEM, V211, P631, DOI 10.1016/j.foodchem.2016.05.109 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 De Marchi Massimo, 2003, Agriculturae Conspectus Scientificus, V68, P255 Di Pinto A, 2017, FOOD CHEM, V229, P93, DOI 10.1016/j.foodchem.2017.02.067 Di Stasio L, 2017, SMALL RUMINANT RES, V149, P85, DOI 10.1016/j.smallrumres.2017.01.013 Edea Z, 2017, FRONT GENET, V8, DOI 10.3389/fgene.2017.00218 Fernandez ME, 2013, GENET MOL BIOL, V36, P185, DOI 10.1590/S1415-47572013000200008 Floren C, 2015, FOOD CHEM, V173, P1054, DOI 10.1016/j.foodchem.2014.10.138 Fontanesi L, 2009, ITAL J ANIM SCI, V8, P9, DOI 10.4081/ijas.2009.s2.9 Gettings KB, 2015, FORENSIC SCI INT-GEN, V18, P118, DOI 10.1016/j.fsigen.2015.06.005 Giraud F, 2010, INT J FOOD MICROBIOL, V137, P204, DOI 10.1016/j.ijfoodmicro.2009.11.014 Giusti A, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0185586 Glover KA, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-2 Golinelli LP, 2014, J DAIRY SCI, V97, P6693, DOI 10.3168/jds.2014-7990 Grasso AN, 2014, GENET MOL BIOL, V37, P389, DOI 10.1590/S1415-47572014000300011 Guan F, 2018, J ANAL METHODS CHEM, V2018, DOI 10.1155/2018/5890140 Gurgul A, 2014, J APPL GENET, V55, P197, DOI 10.1007/s13353-014-0202-4 Heaton MP, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0094851 Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 ISO, 2007, 2205 ISO ITC (International Trade Centre), 2015, TRAC FOOD AGR PROD Jahura F. T., 2016, Bangladesh Journal of Animal Science, V45, P41, DOI 10.3329/bjas.v45i2.29809 Jawasreh KI, 2018, VET WORLD, V11, P778, DOI 10.14202/vetworld.2018.778-781 Kannur BH, 2017, J FOOD SCI TECH MYS, V54, P558, DOI 10.1007/s13197-017-2500-4 Karniol B, 2009, ANIM GENET, V40, P353, DOI 10.1111/j.1365-2052.2008.01846.x Kawecka A, 2016, ANN ANIM SCI, V16, P975, DOI 10.1515/aoas-2016-0017 Koopaee HK, 2014, BRAZ ARCH BIOL TECHN, V57, P87, DOI 10.1590/S1516-89132014000100013 Koutsouli P., 2007, Epitheorese Zootehnikes Epistemes, P71 Kwon T, 2017, ASIAN AUSTRAL J ANIM, V30, P1540, DOI 10.5713/ajas.17.0561 Lasagna E., 2015, Italian Journal of Animal Science, V14, P86 Leache AD, 2017, ANNU REV ECOL EVOL S, V48, P69, DOI 10.1146/annurev-ecolsys-110316-022645 Lee HJ, 2017, INT J LEGAL MED, V131, P1203, DOI 10.1007/s00414-017-1564-z Marmiroli N., 2003, Food authenticity and traceability, P3, DOI 10.1533/9781855737181.1.3 Mateus JC, 2015, FOOD CONTROL, V47, P487, DOI 10.1016/j.foodcont.2014.07.038 McClure MC, 2018, FRONT GENET, V9, DOI 10.3389/fgene.2018.00084 Munira S., 2016, ASIAN J MED BIOL RES, V2, P177, DOI [10.3329/ajmbr.v2i2.29008, DOI 10.3329/AJMBR.V2I2.29008] Murphy R. G. L., 2008, Professional Animal Scientist, V24, P277 Murugappan S., 2017, INT J SCI ENG RES, V8, P1 Negrini R, 2009, ANIM GENET, V40, P18, DOI 10.1111/j.1365-2052.2008.01800.x Nguluma AS, 2018, S AFR J ANIM SCI, V48, P117, DOI 10.4314/sajas.v48i1.14 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Ozbay S., 2016, J FOOD HLTH SCI, V2, P140, DOI 10.3153/JFHS16015 Panwar N, 2015, INDIAN J ANIM RES, V49, P537, DOI 10.5958/0976-0555.2015.00077.1 Pariset L, 2006, J HERED, V97, P531, DOI 10.1093/jhered/esl020 Park YH, 2013, KOREAN J FOOD SCI AN, V33, P696, DOI 10.5851/kosfa.2013.33.6.696 Pillai S, 2017, CRIT REV ONCOL HEMAT, V116, P58, DOI 10.1016/j.critrevonc.2017.05.005 Prusakova OV, 2018, MEAT SCI, V137, P34, DOI 10.1016/j.meatsci.2017.10.017 Ramella Micheline S., 2005, Food Sci. Technol (Campinas), V25, P733, DOI 10.1590/S0101-20612005000400017 Rebala K, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0166563 Rodriguez MA, 2004, J FOOD PROTECT, V67, P172, DOI 10.4315/0362-028X-67.1.172 Rogberg-Munoz A, 2016, MEAT SCI, V111, P47, DOI 10.1016/j.meatsci.2015.08.014 Rosa AJM, 2013, SMALL RUMINANT RES, V113, P62, DOI 10.1016/j.smallrumres.2013.03.021 Rychlik M, 2018, FRONT CHEM, V6, DOI 10.3389/fchem.2018.00049 Sardina MT, 2015, FOOD RES INT, V74, P115, DOI 10.1016/j.foodres.2015.04.038 Scarano D., 2014, Diversity, V6, P579 Schopen GCB, 2008, ANIM GENET, V39, P451, DOI 10.1111/j.1365-2052.2008.01736.x Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Seckin AK, 2017, LWT-FOOD SCI TECHNOL, V77, P332, DOI 10.1016/j.lwt.2016.11.065 Silva P, 2017, SMALL RUMINANT RES, V148, P51, DOI 10.1016/j.smallrumres.2016.12.030 Tortereau F, 2017, BMC GENET, V18, DOI 10.1186/s12863-017-0518-2 Tortorici L, 2016, LIVEST SCI, V193, P39, DOI 10.1016/j.livsci.2016.09.015 Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] Wollstein A, 2007, NUCLEIC ACIDS RES, V35, DOI 10.1093/nar/gkm621 World Organization for Animal Health (OIE), 2017, TERR AN HLTH COD Wu QY, 2017, J CONSUM PROT FOOD S, V12, P125, DOI 10.1007/s00003-017-1092-2 Xu RS, 2018, MEAT SCI, V137, P41, DOI 10.1016/j.meatsci.2017.11.003 Zhao J, 2018, FOOD CONTROL, V91, P421, DOI 10.1016/j.foodcont.2018.04.022 2009, HEREDITAS, V146, P183, DOI DOI 10.1111/J.1601-5223.2009.02106.X NR 82 TC 2 Z9 2 U1 3 U2 5 PD SEP PY 2018 VL 5 IS 3 BP 255 EP 264 DI 10.5455/javar.2018.e274 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000468282100001 DA 2022-12-14 ER PT J AU Dong, YH Fu, ZT Stankovski, S Wang, SY Li, XX AF Dong, Yuhong Fu, Zetian Stankovski, Stevan Wang, Siyu Li, Xinxing TI Nutritional Quality and Safety Traceability System for China's Leafy Vegetable Supply Chain Based on Fault Tree Analysis and QR Code SO IEEE ACCESS DT Article DE Fault trees; Hazards; Indexes; Supply chains; Process control; Leafy vegetables; traceability; nutritional quality; quick response (QR) code ID HEALTH AB Leafy vegetables are consumed in most daily xdiets worldwide. As living standards improve, food quality, safety requirements, and nutrition are becoming increasingly important to consumers when purchasing leafy vegetables. This study proposes an evaluation and traceability method that can be used to track the nutritional quality of leafy vegetables. Employing the principles of the Hazard Analysis and Critical Control Point (HACCP) system combined with fault tree analysis (FTA), a traceability model for the entire production and sale process of leafy vegetables is constructed. Four common leafy vegetables, spinach, rape, lettuce, and celery are examined in this research to establish a nutritional quality index system using fuzzy mathematics subordinate function method to evaluate nutritional quality. A nutritional quality and safety traceability system based on browser/server architecture and quick response (QR) code is then designed and developed for full traceability of leafy vegetable quality. This method can ensure food safety and hygiene through the control of key factors affecting food safety throughout the entire supply chain process. C1 [Dong, Yuhong; Fu, Zetian; Wang, Siyu] China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [Stankovski, Stevan] Univ Novi Sad, Fac Tech Sci, Novi Sad 21000, Serbia. [Li, Xinxing] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. C3 China Agricultural University; University of Novi Sad; China Agricultural University RP Fu, ZT (corresponding author), China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China.; Li, XX (corresponding author), China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. EM fzt@cau.edu.cn; lxxcau@cau.edu.cn CR Bai L., 2017, FOOD NUTR CHINA, V23, P70 Bainy RG, 2020, IEEE T POWER DELIVER, V35, P1769, DOI 10.1109/TPWRD.2019.2953594 Chen M, 2021, IEEE T RELIAB, V70, P862, DOI 10.1109/TR.2019.2940651 Dulf F., 2019, ProEnvironment, V12, P5 Elderhalli Y, 2018, LECT NOTES COMPUT SC, V10811, P139, DOI 10.1007/978-3-319-77935-5_10 Gholami MA, 2018, VET RES FORUM, V9, P43 Huang JY, 2018, IEEE ACCESS, V6, P36995, DOI 10.1109/ACCESS.2018.2849700 Jackowska-Tracz A, 2018, MED WETER, V74, P219, DOI 10.21521/mw.6089 Jakubczyk M, 2018, STUD FUZZ SOFT COMP, V361, P537, DOI 10.1007/978-3-319-75408-6_41 Jayasinghe M., 2019, VIDYODAYA J SCI, V22, P816, DOI 10.31357/vjs.v22i2.4384 Jiang H., 2018, AGR ENG, V8, P47, DOI [10.3969/j.issn.2095-1795.2018.06.009.161273, DOI 10.3969/J.ISSN.2095-1795.2018.06.009.161273] Jin C., 2019, FOOD SAFETY ADOPTION Kumar A, 2018, FOOD RES INT, V108, P571, DOI 10.1016/j.foodres.2018.04.005 Li Q, 2018, FOOD MICROBIOL, V73, P237, DOI 10.1016/j.fm.2018.01.011 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Ngugi CC, 2017, AQUACULT REP, V5, P62, DOI 10.1016/j.aqrep.2017.01.003 Offringa LC, 2019, AM J LIFESTYLE MED, V13, P224, DOI 10.1177/1559827618769605 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Peeters JFW, 2018, RELIAB ENG SYST SAFE, V172, P36, DOI 10.1016/j.ress.2017.11.024 Peng YQ, 2018, J FOOD PROCESS ENG, V41, DOI 10.1111/jfpe.12685 Perveen S., 2019, LIFE CYCLE RELIABILI, V8, P129 Pitts SBJ, 2018, PUBLIC HEALTH NUTR, V21, P1664, DOI 10.1017/S136898001700430X Ramya V., 2019, INT J CHEM STUDIES, V7, P82 Ruijters E, 2017, P REL MAINT S Runfola A, 2017, EUR MANAG J, V35, P116, DOI 10.1016/j.emj.2016.04.001 Sujun Z., 2018, NO HORTICULTURE, V11, P8 Wallace C. A., 2018, FOOD SAFETY 21 CENTU Wang X, 2018, FOOD CONTROL, V88, P169, DOI 10.1016/j.foodcont.2018.01.008 Xie XL, 2018, GEODERMA, V325, P90, DOI 10.1016/j.geoderma.2018.03.029 Yahia EM, 2019, POSTHARVEST PHYSIOLOGY AND BIOCHEMISTRY OF FRUITS AND VEGETABLES, P1, DOI 10.1016/B978-0-12-813278-4.00001-4 NR 32 TC 11 Z9 11 U1 8 U2 43 PY 2020 VL 8 BP 161261 EP 161275 DI 10.1109/ACCESS.2020.3019593 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000571118100001 DA 2022-12-14 ER PT J AU Yu, ZL Jung, DY Park, S Hu, YX Huang, K Rasco, BA Wang, S Ronholm, J Lu, XN Chen, JH AF Yu, Zhilong Jung, Dongyun Park, Soyoun Hu, Yaxi Huang, Kang Rasco, Barbara A. Wang, Shuo Ronholm, Jennifer Lu, Xiaonan Chen, Juhong TI Smart traceability for food safety SO CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION DT Review DE Food traceability; food supply chain; sensor and indicator; internet-of-things (IoTs); food safety ID TIME-TEMPERATURE INDICATORS; CARBON-DIOXIDE INDICATOR; PROOF-OF-CONCEPT; PATHOGENIC BACTERIA; QUALITY-CONTROL; INTELLIGENT; ADULTERATION; WIRELESS; SENSOR; IDENTIFICATION AB Current food production faces a tremendous challenge due to the growing human population. The global population is estimated to reach 9 billion by 2050 with 70% more food being required. Safe food is an important dimension of food security, and food traceability across the supply chain is a key component of this. However, current food traceability systems are challenged by frequent occurrences of food safety incidents and food recalls that have damaged consumer confidence, caused huge economic loss, and put pressure on food safety agencies. This review focuses on smart food traceability that has the potential to significantly improve food safety in global food supply chains. The basic concepts and critical perspectives for various detection strategies for food safety are summarized, including portable detection devices, smart indicators and sensors integrated on food packages, and data-assisted whole-genome sequencing. In addition, new digital technologies, such as Internet-of-things (IoTs) and cloud computing, are discussed with the aim of providing readers with an overview of the exciting opportunities in smart food traceability systems. C1 [Yu, Zhilong; Hu, Yaxi; Lu, Xiaonan] Univ British Columbia, Fac Land & Food Syst, Food Nutr & Hlth Program, Vancouver, BC, Canada. [Yu, Zhilong; Jung, Dongyun; Park, Soyoun; Ronholm, Jennifer; Lu, Xiaonan] McGill Univ, Fac Agr & Environm Sci, Dept Food Sci & Agr Chem, Quebec City, PQ, Canada. [Huang, Kang] Univ Auckland, Sch Chem Sci, Auckland, New Zealand. [Rasco, Barbara A.] Univ Wyoming, Coll Agr & Nat Resources, Laramie, WY 82071 USA. [Wang, Shuo] Nankai Univ, Tianjin Key Lab Food Sci & Hlth, Sch Med, Tianjin, Peoples R China. [Ronholm, Jennifer] McGill Univ, Fac Agr & Environm Sci, Dept Anim Sci, Quebec City, PQ, Canada. [Chen, Juhong] Virginia Tech, Dept Biol Syst Engn, Blacksburg, VA 24061 USA. C3 University of British Columbia; McGill University; University of Auckland; University of Wyoming; Nankai University; McGill University; Virginia Polytechnic Institute & State University RP Lu, XN (corresponding author), Univ British Columbia, Fac Land & Food Syst, Food Nutr & Hlth Program, Vancouver, BC, Canada.; Ronholm, J; Lu, XN (corresponding author), McGill Univ, Fac Agr & Environm Sci, Dept Food Sci & Agr Chem, Quebec City, PQ, Canada.; Chen, JH (corresponding author), Virginia Tech, Dept Biol Syst Engn, Blacksburg, VA 24061 USA. EM jennifer.ronholm@mcgill.ca; xiaonan.lu@ubc.ca; jhchen@vt.edu CR Allard MW, 2016, J CLIN MICROBIOL, V54, P1975, DOI 10.1128/JCM.00081-16 [Anonymous], 2009, TO GENKYO TOKYO BAD Astray G, 2019, MOLECULES, V24, DOI 10.3390/molecules24050826 Bandyopadhyay D, 2011, WIRELESS PERS COMMUN, V58, P49, DOI 10.1007/s11277-011-0288-5 Bouzembrak Y, 2019, TRENDS FOOD SCI TECH, V94, P54, DOI 10.1016/j.tifs.2019.11.002 Burke T., 2019, FOOD TRACEABILITY BI, DOI DOI 10.1007/978-3-030-10902-8_10 Vu CHT, 2014, J AGR FOOD CHEM, V62, P7263, DOI 10.1021/jf5014764 CHEN Y, 2014, J FOOD ENG, V141 Choi I, 2017, FOOD CHEM, V218, P122, DOI 10.1016/j.foodchem.2016.09.050 Correia RM, 2018, TALANTA, V176, P59, DOI 10.1016/j.talanta.2017.08.009 Cruz MGN, 2018, SENSOR ACTUAT B-CHEM, V263, P550, DOI 10.1016/j.snb.2018.02.158 EC-European Commission, 2002, J EUROP COMM, VL031, P1 Escarpa A, 2014, LAB CHIP, V14, P3213, DOI 10.1039/c4lc00172a FAO, 2019, STAT FOOD AGR MOV FO, DOI DOI 10.4324/9781315764788 FAO, 1996, WORLD FOOD SUMM Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Ghaani M, 2016, TRENDS FOOD SCI TECH, V51, P1, DOI 10.1016/j.tifs.2016.02.008 GIZAW Z, 2019, ENVIRON HEALTH PREV, V24 GONZALEZFERNAND.I, 2019, CRIT REV FOOD SCI, V59 Gossner CME, 2009, ENVIRON HEALTH PERSP, V117, P1803, DOI 10.1289/ehp.0900949 Gu?nther H., 2013, NMR SPECTROSCOPY BAS Hameed S, 2018, TRENDS FOOD SCI TECH, V81, P61, DOI 10.1016/j.tifs.2018.05.020 Han JH, 2005, INNOVATIONS IN FOOD PACKAGING, P3, DOI 10.1016/B978-0-12-394601-0.00001-1 Hashimoto K, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-12442-9 Health Canada P. H. A. o C, 2011, WEIGHT EV FACT CONS Hoffmann M, 2016, J INFECT DIS, V213, P502, DOI 10.1093/infdis/jiv297 Hoffmann S, 2012, J FOOD PROTECT, V75, P1292, DOI 10.4315/0362-028X.JFP-11-417 Hwang K., 2013, DISTRIBUTED CLOUD CO Jung J, 2012, FOOD CHEM, V135, P2170, DOI 10.1016/j.foodchem.2012.07.090 Kaushik S, 2013, INT J MULTIDISCIPLIN, V2, P7 Kim YH, 2018, FOOD CHEM, V267, P149, DOI 10.1016/j.foodchem.2018.02.110 Kitchin R, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951716631130 Lee K, 2014, FOOD SCI BIOTECHNOL, V23, P115, DOI 10.1007/s10068-014-0015-6 Li LB, 2017, SENSOR ACTUAT B-CHEM, V252, P17, DOI 10.1016/j.snb.2017.05.155 Li Y, 2019, INT J BIOL MACROMOL, V127, P376, DOI 10.1016/j.ijbiomac.2019.01.060 Lopez-Ruiz N, 2014, ANAL CHEM, V86, P9554, DOI 10.1021/ac5019205 Lu LX, 2013, PACKAG TECHNOL SCI, V26, P80, DOI 10.1002/pts.2009 Mahmood Z., 2016, DATA SCI BIG DATA CO Martinez-Castillo C, 2020, EFOOD, V1, P69, DOI [DOI 10.2991/EFOOD.K.191004.001, 10.2991/efood.k.191004.001] Mavromoustakis CX, 2016, MODEL OPTIM SCI TECH, V8, P1, DOI 10.1007/978-3-319-30913-2 Mills A, 2005, CHEM SOC REV, V34, P1003, DOI 10.1039/b503997p Minhas A.S, 2016, FOOD SAFETY 21 CENTU Mishra RK, 2017, ACS SENSORS, V2, P553, DOI 10.1021/acssensors.7b00051 Moldes OA, 2017, CRIT REV FOOD SCI, V57, P2896, DOI 10.1080/10408398.2015.1078277 Nizar NNA, 2018, WOODHEAD PUBL FOOD S, P409, DOI 10.1016/B978-0-08-101892-7.00022-5 Ong KG, 2001, BIOSENS BIOELECTRON, V16, P305, DOI 10.1016/S0956-5663(01)00131-2 Patel HK, 2014, BIOL MED PHYS BIOMED, P1, DOI 10.1007/978-81-322-1548-6 Pereira VA, 2015, FOOD HYDROCOLLOID, V43, P180, DOI 10.1016/j.foodhyd.2014.05.014 Plummer D. C., 2008, CLOUD COMPUTING DEFI Popa A, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11030374 Potyrailo RA, 2008, TALANTA, V75, P624, DOI 10.1016/j.talanta.2007.06.023 Potyrailo RA, 2009, WIREL COMMUN MOB COM, V9, P1318, DOI 10.1002/wcm.711 Potyrallo RA, 2007, ANAL CHEM, V79, P45, DOI 10.1021/ac061748o Quainoo S, 2017, CLIN MICROBIOL REV, V30, P1015, DOI 10.1128/CMR.00016-17 Ray PC, 2012, CHEM SOC REV, V41, P3193, DOI 10.1039/c2cs15340h Schaefer D, 2018, PROC CIRP, V72, P1022, DOI 10.1016/j.procir.2018.03.240 Scholten H, 2016, WOODHEAD PUBL FOOD S, V301, P9, DOI 10.1016/B978-0-08-100310-7.00002-8 Shakhovska N., 2017, ADV INTELLIGENT SYST Shih CW, 2016, COMPUT STAND INTER, V45, P62, DOI 10.1016/j.csi.2015.12.004 Smiljkovikj K, 2014, WIRELESS PERS COMMUN, V78, P1777, DOI 10.1007/s11277-014-1905-x Solomon EB, 2002, APPL ENVIRON MICROB, V68, P397, DOI 10.1128/AEM.68.1.397-400.2002 Tan EL, 2007, SENSORS-BASEL, V7, P1747, DOI 10.3390/s7091747 Tao H, 2012, ADV MATER, V24, P1067, DOI 10.1002/adma.201103814 Thompson CC, 2013, BMC GENOMICS, V14, DOI 10.1186/1471-2164-14-913 Tian XJ, 2013, J FOOD ENG, V119, P744, DOI 10.1016/j.jfoodeng.2013.07.004 Tichoniuk M., 2017, STUDIA OECONOMICA PO, V5, P19, DOI [10.18559/soep.2017.7.2, DOI 10.18559/SOEP.2017.7.2] Timme RE, 2017, PEERJ, V5, DOI 10.7717/peerj.3893 Tolar B, 2019, FOODBORNE PATHOG DIS, V16, P457, DOI 10.1089/fpd.2019.2637 Hung TQ, 2017, BIOSENS BIOELECTRON, V90, P217, DOI 10.1016/j.bios.2016.11.028 Wang SY, 2019, BIOSENS BIOELECTRON, V140, P69, DOI 10.1016/j.bios.2019.111333 Wang XD, 2014, CHEM SOC REV, V43, P3666, DOI 10.1039/c4cs00039k White T., 2012, HADOOP DEFINITIVE GU Wu MYC, 2017, TRENDS BIOTECHNOL, V35, P288, DOI 10.1016/j.tibtech.2016.12.005 WU SY, 2015, MICROSYST NANOENG, V1 Xu M, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-03248-0 Xu Z, 2014, SENSORS-BASEL, V14, P2028, DOI 10.3390/s140202028 Yam KL, 2005, J FOOD SCI, V70, pR1, DOI 10.1111/j.1365-2621.2005.tb09052.x Zhang C, 2013, ACS NANO, V7, P4561, DOI 10.1021/nn401266u Zhang YL, 2015, BIOSENS BIOELECTRON, V68, P14, DOI 10.1016/j.bios.2014.12.042 Zhao Y, 2009, J AGR FOOD CHEM, V57, P517, DOI 10.1021/jf802817y NR 80 TC 38 Z9 39 U1 30 U2 131 PD FEB 21 PY 2022 VL 62 IS 4 BP 905 EP 916 DI 10.1080/10408398.2020.1830262 EA OCT 2020 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000576227200001 DA 2022-12-14 ER PT J AU Thakur, M Sorensen, CF Bjornson, FO Foras, E Hurburgh, CR AF Thakur, Maitri Sorensen, Carl-Fredrik Bjornson, Finn Olav Foras, Eskil Hurburgh, Charles R. TI Managing food traceability information using EPCIS framework SO JOURNAL OF FOOD ENGINEERING DT Article DE Food traceability; States and transitions in food production; UML statecharts; EPCIS events; Mackerel production; Corn wet milling ID SUPPLY CHAIN; SYSTEM; MANUFACTURE AB This paper introduces a new methodology for modeling traceability information using the EPCIS framework and UML statecharts. The method follows the approach of defining states and transitions in food production. A generic model is presented and evaluated based on its practical application by providing illustrations from two supply chains; frozen mackerel production and corn wet milling processes. All states and transitions for these processes as well as the information that needs to be captured for each state are indentified. This includes the product, process and quality information. The model presented in this paper is not just another process modeling tool but is used for mapping of food production processes to provide improved description and integration of traceability information. Information exchange technologies such as EPCIS are used for monitoring events based on logistic processes. Application of current EPCIS framework for managing food traceability information is presented by mapping the transitions identified in two product chains to the EPCIS events. The corresponding quality parameters to be linked to these EPCIS events are also identified. It was practical to map food production transitions for frozen mackerel to two EPCIS events; ObjectEvent and AggregationEvent. Because, EPCIS is based on discrete recording of events and event locations and corn wet milling is a continuous process, it was not possible to map transitions to AggregationEvent. Thus, quality parameters for transformation events for corn wet milling were linked to the subsequent ObjectEvent to provide certain extent of discretization. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Thakur, Maitri; Sorensen, Carl-Fredrik; Bjornson, Finn Olav; Foras, Eskil] SINTEF Fisheries & Aquaculture, N-7010 Trondheim, Norway. [Hurburgh, Charles R.] Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. [Hurburgh, Charles R.] Iowa State Univ, Dept Food Sci & Human Nutr, Ames, IA 50011 USA. C3 SINTEF; Iowa State University; Iowa State University RP Thakur, M (corresponding author), SINTEF Fisheries & Aquaculture, Brattorkaia 17C, N-7010 Trondheim, Norway. EM maitri.thakur@sintef.no CR AMBLER SW, 2004, OBJECT PRIMER AGILE, V2 [Anonymous], 2002, OFFICIAL J EUROPEAN Armenio F, 2007, EPCGLOBAL ARCHITECTU Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bottani E, 2008, INT J PROD ECON, V112, P548, DOI 10.1016/j.ijpe.2007.05.007 Corn Refiners Association, 2006, CORN WET MILL FEED P Donnelly Kathryn Anne-Marie, 2008, International Journal of Metadata, Semantics and Ontologies, V3, P283, DOI 10.1504/IJMSO.2008.023575 Donnelly KAM, 2009, COMM COM INF SC, V46, P312 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Dreyer C., 2004, P 16 ANN C NORD RES, P155 *EPCIS STAND, 2007, EPC INF SERV VERS 1 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Frances CRL, 2005, SIMUL MODEL PRACT TH, V13, P584, DOI 10.1016/j.simpat.2005.02.001 FSA, 2002, TRAC FOOD CHAIN PREL *GS1 GLOB TRAC STA, 2007, BUS PROC SYST REQ FU GUOJON Z, 2007, FRONTIERS MECH ENG C, V2, P453 *INT ORG STAND, 2007, 220052007 ISO Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kohler H. J., 2000, Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium, P241, DOI 10.1109/ICSE.2000.870415 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 MYHRE B, 2009, 5 EUR WORKSH RFID SY Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Senneset G, 2010, BRIT FOOD J, V112, P592, DOI 10.1108/00070701011052682 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 SORENSEN C, 2010, COMPUTERS IND UNPUB Storoy J., 2007, TOK INT FOR OCT 2007 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 *TRACE 2, 2008, FP62003FOOD2A TRACE VIJAYKUMAR NL, 2002, INT T OPERATIONS RES, V9, P312 NR 29 TC 52 Z9 57 U1 1 U2 38 PD APR PY 2011 VL 103 IS 4 BP 417 EP 433 DI 10.1016/j.jfoodeng.2010.11.012 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000287074500009 DA 2022-12-14 ER PT J AU He, J AF He, Juan TI IMPORTED SEAFOOD TRACEABILITY REGULATIONS: A MISHAP FOR THE WTO'S DISREGARD FOR NON-PRODUCT RELATED PROCESSES AND PRODUCTION METHODS? SO ASIAN JOURNAL OF WTO & INTERNATIONAL HEALTH LAW AND POLICY DT Article DE seafood traceability; fisheries management; NPR PPMs; TBT; technical regulation ID SUSTAINABILITY; ILLEGAL; TRADE; LAW; PREFERENCES; LABELS; DEAL; FOOD; EU AB Sustainable management of capture fisheries is complicated by distant and opaque supply chain relationships between global producers and global consumers. This could be less of a problem, given the global market is increasingly protected by government-led traceability regulations to generate a comprehensive profile of the seafood we consume. Traceability regulations extended to when, where, what, who and how produced standards inevitably increase the conflict between local market access and extraterritorial fisheries management. This paper revisits the decades-long debate on non-product related processes and production methods through a fresh investigation of two leading regulatory paradigms of seafood traceability. It proposes the adjudicative locus under the more specific Agreement on Technical Barriers to Trade to accommodate, monitor and discipline novel "technical regulations". It also maps out feasible pathways to nurture cross-regime synergies between trade and other credible co-regulators of global fishery resources. C1 [He, Juan] Univ Western Australia, Law Sch M253, 35 Stirling Highway, Perth, WA 6009, Australia. C3 University of Western Australia RP He, J (corresponding author), Univ Western Australia, Law Sch M253, 35 Stirling Highway, Perth, WA 6009, Australia. EM juan.he@uwa.edu.au CR [Anonymous], 2000, 14020 ISO [Anonymous], 2017, GTBT1REV13 COMM TECH Bosselmann Klaus, 2016, PRINCIPLE SUSTAINABI, V2nd Charnovitz Steve, 2002, YALE J INT LAW, V27, P59 Christopher Peterson H., 2007, GLOBALIZATION EFFECT, P446 Codex Alimentarius Commission, 1985, 11985 CODEXSTAN Codex Alimentarius Commission, 2013, 941981 CODEX STAN Conrad Christiane R, 2011, WTO LAW INTERFACING Dobson Tracy, 2007, P499 Du M, 2015, EUR J RISK REGUL, V6, P396, DOI 10.1017/S1867299XO0004840 Du Ming Michael, 2007, CHIN J INT LAW, V6, P269 Duran GM, 2015, EUR YEARB INT ECON L, P87, DOI 10.1007/978-3-662-46748-0_5 Emch A, 2005, LEG ISS ECON INTEGR, V32, P369 Fontanelli F, 2011, INT COMP LAW Q, V60, P895, DOI 10.1017/S0020589311000431 Grunert KG, 2014, FOOD POLICY, V44, P177, DOI 10.1016/j.foodpol.2013.12.001 Gulbrandsen L. H., 2006, International Journal of Consumer Studies, V30, P477, DOI 10.1111/j.1470-6431.2006.00534.x HARIS Chris, 2015, FRENCH FISHERIES ECO He J, 2019, J WORLD TRADE, V53, P759 He J, 2016, ASIA PAC J ENVIRON, V19, P4, DOI 10.4337/apjel.2016.01.01 He J, 2018, MAR POLICY, V96, P163, DOI 10.1016/j.marpol.2018.08.003 He Juan, 2017, Journal of International Wildlife Law & Policy, V20, P168, DOI 10.1080/13880292.2017.1346351 He J, 2015, ASIAN J WTO INT HEAL, V10, P223 Horne RE, 2009, INT J CONSUM STUD, V33, P175, DOI 10.1111/j.1470-6431.2009.00752.x Howse Robert, 2000, EUROPEAN J INT LAW, V11, P249, DOI DOI 10.1093/ejil/11.2.249 International Organization for Standardization, 2018, 14020 ISO Joshi Manoj, 2004, J WORLD TRADE, V38, P69 Kittinger JN, 2017, SCIENCE, V356, P912, DOI 10.1126/science.aam9969 Kolb Tracy L., 2007, P527 Kysar DA, 2004, HARVARD LAW REV, V118, P525, DOI 10.2307/4093392 Lydgate EB, 2011, WORLD TRADE REV, V10, P165, DOI 10.1017/S1474745610000492 Marceau G, 2002, J WORLD TRADE, V36, P811, DOI 10.1023/A:1021226926215 Negotiating Group on Rules, 2018, TNRLW274REV5 WTO Nygard B, 1998, SOCIOL RURALIS, V38, P35, DOI 10.1111/1467-9523.00062 OECD (Organisation for Economic Co-operation and Development), 1997, OECDGD97137 Perisin T, 2013, INT COMP LAW Q, V62, P373, DOI 10.1017/S0020589313000079 Pramod G, 2014, MAR POLICY, V48, P102, DOI 10.1016/j.marpol.2014.03.019 Qureshi A. H., 1999, INT COMP LAW Q, V48, P199 Rubik F, 2007, INT J INNOV SUSTAIN, V2, P175, DOI 10.1504/IJISD.2007.016932 Seyfang G, 2005, ENVIRON POLIT, V14, P290, DOI 10.1080/09644010500055209 TIETJE C, 1995, J WORLD TRADE, V29, P123 Trachtman Joel P., 1998, EUR J INT LAW, V9, P32 VRANES ERICH, 2009, TRADE ENV FUNDAMENTA Washington S., 2011, PRIVATE STANDARDS CE NR 43 TC 2 Z9 2 U1 0 U2 2 PD MAR PY 2020 VL 15 IS 1 BP 169 EP 207 WC Health Policy & Services; International Relations; Law SC Health Care Sciences & Services; International Relations; Government & Law UT WOS:000527403600005 DA 2022-12-14 ER PT J AU Zhang, Y Krogmeier, JV Ault, A Buckmaster, D AF Zhang, Y. Krogmeier, J., V Ault, A. Buckmaster, D. TI APT3: AUTOMATED PRODUCT TRACEABILITY TREES GENERATED FROM GPS TRACKS SO TRANSACTIONS OF THE ASABE DT Article DE GPS; Harvesting; Precision agriculture; Product fraceability; Traceability frees AB With increasing concerns about food safety in many countries, product traceability has become an important risk-management tool. It enables the identification of possible sources of defective goods and facilitates the withdrawal and recall of affected products to protect consumers from foodborne diseases. However, it is troublesome for farmers to maintain the records required by high precision product fraceability during harvesting because traditional traceability systems usually involve human labor in paperwork or expenses for equipment purchase and installation; in either case, the resulting records are tedious. In this article, a fully automatic algorithm is proposed for efficiently generating product fraceability trees to visualize and store the full transport record of wheat from fields to elevators. Extending previous work on harvesting activity recognition via GPS tracks, this algorithm powers our fully automatic prototype product traceability system, APT3, and demonstrates great potential for tracing products solely with the GPS logs of the vehicles involved in harvest and transport. From the output traceability trees, the product yielded at any point in the field can be tracked all the way to the elevator where it was sold or stored by starting from the corresponding leaf node in the fraceability free and walking to the root of the free. Furthermore, each truckload of product unloaded at any destination elevator can be traced back to where the product was harvested by following the tree in the opposite direction. In this way, the fraceability records can be clearly visualized for farmers and easily used by other algorithms. C1 [Zhang, Y.; Krogmeier, J., V; Ault, A.] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA. [Buckmaster, D.] Purdue Univ, Coll Agr, W Lafayette, IN 47907 USA. C3 Purdue University System; Purdue University; Purdue University West Lafayette Campus; Purdue University System; Purdue University; Purdue University West Lafayette Campus RP Zhang, Y (corresponding author), 610 Purdue Mall, W Lafayette, IN 47907 USA. EM ygzhang@purdue.edu CR Bihn E. A., 2014, FARM DECISION TREE P Hornbaker R., 2004, P 7 INT C PREC AGR O International Standard Organization, 2007, 220052007 ISO ITC, 2015, BULLETIN, V91/2015 Ko D, 2014, INT J DISTRIB SENS N, DOI 10.1155/2014/832510 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Wang Y., 2017, 1701022 ASABE, DOI [10.13031/aim.201701022, DOI 10.13031/AIM.201701022] Zhang Y., 2019, COMBINE KART TRUCK Zhang Y., 2017, 1700813 ASABE, DOI [10.13031/aim.201700813, DOI 10.13031/AIM.201700813] Zhang Y., 2019, GPS DATA VISUALIZATI Zhang Y., 2017, 1700809 ASABE, DOI [10.13031/aim.201700809, DOI 10.13031/AIM.201700809] Zhang YG, 2015, IEEE INT C INTELL TR, P1779, DOI 10.1109/ITSC.2015.289 NR 14 TC 1 Z9 1 U1 0 U2 3 PY 2020 VL 63 IS 3 BP 571 EP 582 DI 10.13031/trans.13384 WC Agricultural Engineering SC Agriculture UT WOS:000552257900003 DA 2022-12-14 ER PT J AU Mai, ZH Lai, B Sun, MW Shao, JL Guo, LX AF Mai, Zhanhua Lai, Bei Sun, Mingwei Shao, Junli Guo, Lianxian TI Food adulteration and traceability tests using stable carbon isotope technologies SO TROPICAL JOURNAL OF PHARMACEUTICAL RESEARCH DT Review DE Food adulteration; Traceability; Stable isotopes; Stable carbon isotope ratio analysis; Isotope dilution mass spectrometry ID RATIO MASS-SPECTROMETRY; CATTLE TAIL HAIR; LIQUID-CHROMATOGRAPHY; GEOGRAPHICAL ORIGIN; HONEY ADULTERATION; ORGANIC-ACIDS; ACCURATE DETERMINATION; ELEMENTAL ANALYZER; POTENTIAL TOOL; MS-MS AB Due to the fractionation of stable carbon isotopes in plant photosynthesis, bio-decomposition processes, environmental factors, plant physiology, geographical factors, climatic conditions and agricultural practices, different foods exhibit significant differences in stable carbon isotope ratios. Therefore, stable carbon isotope ratio analysis (SCIRA) presents an effective tool for detecting food adulteration and food traceability control. In addition, stable carbon isotopes can frequently be used as markers to identify veterinary drug residues, pesticide residues and toxic substances remaining in foods by isotope dilution mass spectrometry (IDMS). The emphasis of this review, which will help readers to modify stable carbon isotope technologies more easily and extend their application in adulteration and traceability for foods, is on the characteristics of various instruments and the data processing methods in SCIRA and IDMS technologies. The latest research is also reviewed and highlighted. This paper reviews potential applications of these technologies to improve current food detection and protect consumers' rights. C1 [Mai, Zhanhua; Lai, Bei; Sun, Mingwei; Shao, Junli; Guo, Lianxian] Guangdong Med Univ, Sch Publ Hlth, Dongguan Key Lab Environm Med, Dongguan 523808, Guangdong, Peoples R China. C3 Guangdong Medical University RP Guo, LX (corresponding author), Guangdong Med Univ, Sch Publ Hlth, Dongguan Key Lab Environm Med, Dongguan 523808, Guangdong, Peoples R China. EM glx525@gdmu.edu.cn CR Ahn S, 2016, FOOD CHEM, V190, P368, DOI 10.1016/j.foodchem.2015.05.114 Akamatsu F, 2017, FOOD CHEM, V228, P297, DOI 10.1016/j.foodchem.2017.01.136 Angerosa F, 1997, J AGR FOOD CHEM, V45, P3044, DOI 10.1021/jf960993d Ansari P, 2016, FOOD CHEM, V211, P978, DOI 10.1016/j.foodchem.2016.05.063 Bat KB, 2016, FOOD CHEM, V203, P86, DOI 10.1016/j.foodchem.2016.02.039 Bat KB, 2012, FOOD TECHNOL BIOTECH, V50, P107 Behkami S, 2017, FOOD CHEM, V217, P438, DOI 10.1016/j.foodchem.2016.08.130 Bensaid FF, 2002, J AGR FOOD CHEM, V50, P6271, DOI 10.1021/jf020316l Bilke S, 2002, RAPID COMMUN MASS SP, V16, P468, DOI 10.1002/rcm.599 Bontempo L, 2011, INT DAIRY J, V21, P441, DOI 10.1016/j.idairyj.2011.01.009 Cabanero AI, 2006, J AGR FOOD CHEM, V54, P9719, DOI 10.1021/jf062067x Chan S, 2007, J AGR FOOD CHEM, V55, P3339, DOI 10.1021/jf0637168 Chen H, 2013, ACCREDIT QUAL ASSUR, V18, P351, DOI 10.1007/s00769-013-0990-y Chung IM, 2018, FOOD CHEM, V261, P112, DOI 10.1016/j.foodchem.2018.04.017 Cordella C, 2002, J AGR FOOD CHEM, V50, P1751, DOI 10.1021/jf011096z Di Donna L, 2017, FOOD CHEM, V229, P354, DOI 10.1016/j.foodchem.2017.02.098 DONER LW, 1980, J AGR FOOD CHEM, V28, P362, DOI 10.1021/jf60228a051 Doner LW, 1981, J AOAC, P618 Dong H, 2018, FOOD CHEM, V240, P717, DOI 10.1016/j.foodchem.2017.08.008 Dutra SV, 2013, FOOD CHEM, V141, P2148, DOI 10.1016/j.foodchem.2013.04.106 Effkemann S, 2004, ANAL BIOANAL CHEM, V378, P842, DOI 10.1007/s00216-003-2270-x Elflein L, 2008, APIDOLOGIE, V39, P574, DOI 10.1051/apido:2008042 Erasmus SW, 2018, FOOD CHEM, V239, P926, DOI 10.1016/j.foodchem.2017.07.026 Erasmus SW, 2016, FOOD CHEM, V192, P997, DOI 10.1016/j.foodchem.2015.07.121 Feng J, 2012, ANAL METHODS-UK, V4, P4198, DOI 10.1039/c2ay25749a Feng TT, 2018, FOOD CHEM, V245, P687, DOI 10.1016/j.foodchem.2017.10.145 Geissler K, 2017, FLAVOUR FRAG J, V32, P228, DOI 10.1002/ffj.3379 Godin JP, 2011, RAPID COMMUN MASS SP, V25, P3019, DOI 10.1002/rcm.5167 Grassineau NV, 2006, APPL GEOCHEM, V21, P756, DOI 10.1016/j.apgeochem.2006.02.015 Guo LX, 2016, MODERN FOOD SCI TECH, V3, P281 Guyon F, 2014, FOOD CHEM, V146, P36, DOI 10.1016/j.foodchem.2013.09.020 Guyon F, 2011, ANAL BIOANAL CHEM, V401, P1551, DOI 10.1007/s00216-011-5012-5 Hattori R, 2011, J AGR FOOD CHEM, V59, P9049, DOI 10.1021/jf200227e Jamin E, 1997, J AGR FOOD CHEM, V45, P3961, DOI 10.1021/jf9701087 Kim D, 2015, J FOOD COMPOS ANAL, V40, P24, DOI 10.1016/j.jfca.2014.12.005 Kim H, 2015, FOOD CHEM, V172, P523, DOI 10.1016/j.foodchem.2014.09.058 Kim SH, 2012, FOOD SCI BIOTECHNOL, V21, P295, DOI 10.1007/s10068-012-0040-2 Kong WJ, 2016, TRAC-TREND ANAL CHEM, V78, P36, DOI 10.1016/j.trac.2015.07.013 Kornexl BE, 1996, Z LEBENSM UNTERS FOR, V202, P55, DOI 10.1007/BF01229685 Kropf U, 2010, J AGR FOOD CHEM, V58, P12794, DOI 10.1021/jf102940s LaPorte DF, 2009, J MASS SPECTROM, V44, P879, DOI 10.1002/jms.1549 Li L, 2016, FOOD CHEM, V194, P1238, DOI 10.1016/j.foodchem.2015.08.123 Litwinczuk Z, 2018, MED WETER, V74, P309, DOI 10.21521/mw.6022 Liu WG, 2012, J AGR FOOD CHEM, V60, P8069, DOI 10.1021/jf302410b Liu XL, 2013, FOOD CHEM, V140, P135, DOI 10.1016/j.foodchem.2013.02.020 Luo D, 2015, FOOD ANAL METHOD, V9, P1 Luo DH, 2016, FOOD ANAL METHOD, V9, P437, DOI 10.1007/s12161-015-0204-9 Maggi L, 2011, FOOD CHEM, V128, P543, DOI 10.1016/j.foodchem.2011.03.063 Martin GG, 1996, J AOAC INT, V79, P917 Masia A, 2016, ANAL CHIM ACTA, V936, P40, DOI 10.1016/j.aca.2016.07.023 Molkentin J, 2009, J AGR FOOD CHEM, V57, P785, DOI 10.1021/jf8022029 Muaksang K, 2009, DEV REFERENCE METHOD, P75 Oganesyants LA, 2016, FOOD RAW MATER, V4, P141, DOI 10.21179/2308-4057-2016-1-141-147 Oulhote Y, 2011, TRAC-TREND ANAL CHEM, V30, P302, DOI 10.1016/j.trac.2010.10.015 Perini M, 2013, FOOD CHEM, V136, P1543, DOI 10.1016/j.foodchem.2012.06.084 Portarena S, 2014, FOOD CHEM, V164, P12, DOI 10.1016/j.foodchem.2014.04.115 Scheidegger Y, 2000, OECOLOGIA, V125, P350, DOI 10.1007/s004420000466 Sighinolfi S, 2018, J FOOD COMPOS ANAL, V69, P33, DOI 10.1016/j.jfca.2018.02.002 SMITH BN, 1971, PLANT PHYSIOL, V47, P380, DOI 10.1104/pp.47.3.380 Tamosiunas V, 2008, CHROMATOGRAPHIA, V67, P783, DOI 10.1365/s10337-008-0579-5 Tenailleau EJ, 2004, J AGR FOOD CHEM, V52, P7782, DOI 10.1021/jf048847s van Leeuwen KA, 2014, COMPR REV FOOD SCI F, V13, P814, DOI 10.1111/1541-4337.12096 Vinci G, 2013, J SCI FOOD AGR, V93, P439, DOI 10.1002/jsfa.5970 Weber D, 1997, Z LEBENSM UNTERS F A, V205, P158, DOI 10.1007/s002170050145 White J, 1992, J AOAC INT US Woodbury SE, 1998, J AM OIL CHEM SOC, V75, P371, DOI 10.1007/s11746-998-0055-2 Zhang XF, 2017, FOOD CHEM, V218, P269, DOI 10.1016/j.foodchem.2016.08.083 Zhang YJ, 2009, J AGR FOOD CHEM, V57, P2550, DOI 10.1021/jf803172e Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y Zyakun AM, 2013, J ANAL CHEM+, V68, P1136, DOI 10.1134/S106193481313011X NR 70 TC 7 Z9 9 U1 9 U2 84 PD AUG PY 2019 VL 18 IS 8 BP 1771 EP 1784 DI 10.4314/tjpr.v18i8.29 WC Pharmacology & Pharmacy SC Pharmacology & Pharmacy UT WOS:000483951900029 DA 2022-12-14 ER PT J AU Huang, C Shen, L Liu, QY Zhang, YT Wang, HS AF Huang, Chao Shen, Lin Liu, Qiuyan Zhang, Yutong Wang, Hongshu TI Study on the Influence of the Construction of Traceability System in Yichun City on the Forest Ecological Food Industry SO EKOLOJI DT Article DE traceability system; forest food industry; edible fungus; forest pig AB Traceable agricultural products are receiving more and more attention. Yichun is a typical forest resource city. As the timber is completely stopped, Yichun City has transformed its economic development mode, and the forest ecological food industry has become one of the five pillar industries. This paper summarizes the development status of forest ecological food industry in Yichun City, the types and scale of special forest food products, and analyzes the opportunities faced by Yichun City's forest ecological food industry. And this paper also summarizes the development process of forest ecological food traceability system in Yichun City. Then, this paper points out the impact of traceability system on Yichun forest ecological food industry, and proposes countermeasures for the development of forest ecological food industry in Yichun City. C1 [Huang, Chao; Shen, Lin; Liu, Qiuyan; Zhang, Yutong; Wang, Hongshu] Northeast Forestry Univ, Coll Econ & Management, Harbin, Heilongjiang, Peoples R China. C3 Northeast Forestry University - China RP Wang, HS (corresponding author), Northeast Forestry Univ, Coll Econ & Management, Harbin, Heilongjiang, Peoples R China. EM lwanghongshu@163.com CR Cao Y, 2016, FORESTRY EC, V38, P3 Geng Y.D., 2013, FORESTRY SCI, V49, P150 Hua J, 2008, ACAD EXCHANGE, P171 Jiang Y, 2016, FORESTRY EC, V38, P20 Li E, 2011, FOREST RESOURCES MAN Ma M, 2017, FORESTRY EC, V37 Shen P, 2017, GUIZHOU ETHNIC RES, V38, P203 Tu C, 2011, J AGR MECH RES, V33, P16 Wang Y, 2013, FORESTRY EC, P85 Wang Y, 2017, FORESTRY EC, V37 Yan R, 2018, FORESTRY EC, V38 NR 11 TC 0 Z9 0 U1 6 U2 11 PY 2019 VL 28 IS 107 BP 1141 EP 1146 AR UNSP e107134 WC Ecology SC Environmental Sciences & Ecology UT WOS:000461678300133 DA 2022-12-14 ER PT J AU Kaarls, R AF Kaarls, R TI The CCQM and comparability and traceability in food analysis SO ACCREDITATION AND QUALITY ASSURANCE DT Article; Proceedings Paper CT CCQM Workshop on Comparability and Traceability in Food Analysis at BIPM CY NOV 18-19, 2003 CL Paris Sevres, FRANCE DE comparability; traceability; food testing; mutual recognition arrangement; CIPM-CCQM AB Comparability of the results of food analysis is essential for reliable and uniform interpretation of legal requirements on food safety, for avoiding false tests, fostering fair trade, an equal economic playing field and taking away non-tariff barriers to trade. Global comparability can only be achieved through traceability to internationally agreed common and long-term stable references, being the International System of Units (SI). The Comite International des Poids et Mesures' Mutual Recognition Arrangement (CIPM MRA), signed and operating under the intergovernmental treaty of the Metre Convention, is the basis for international recognition and acceptance. The Comite Consultatif pour la Quantite de Matiere metrologie en chimie (CCQM) is the committee under the CIPM organizing the technical scientific work establishing worldwide traceability and comparability underpinning the CIPM MRA. The CCQM cooperates with all intergovernmental and international organizations concerned. C1 CIPM, NL-2381 VX Zoeterwoude, Netherlands. RP Kaarls, R (corresponding author), CIPM, Klaverwydenstr 13, NL-2381 VX Zoeterwoude, Netherlands. EM rkaarls@euronet.nl NR 0 TC 4 Z9 4 U1 0 U2 3 PD AUG PY 2004 VL 9 IS 9 BP 530 EP 532 DI 10.1007/s00769-004-0789-y WC Chemistry, Analytical; Instruments & Instrumentation SC Chemistry; Instruments & Instrumentation UT WOS:000223271700002 DA 2022-12-14 ER PT J AU Zhao, G Guo, YM Sun, X Wang, XY AF Zhao, Guo Guo, Yemin Sun, Xia Wang, Xiangyou TI A System for Pesticide Residues Detection and Agricultural Products Traceability Based on Acetylcholinesterase Biosensor and Internet of Things SO INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE DT Article DE acetylcholinesterase biosensor; pesticide residues rapid detection; QR barcode; internet of things; agricultural products traceability ID ORGANOPHOSPHORUS; CHOLINESTERASE; FRUIT AB This study presents a system based on acetylcholinesterase (AChE) biosensor and internet of things (IoT) for pesticide residues detection and agricultural products traceability. The system we presented based on AChE biosensor and IoT aims to extend the benefits of the pesticide residues detection date-remote control ability, data processing and sharing, agricultural products traceability and so on-to detection devices (hypogynous computer) in detection locations. These detection data got from detection devices were further aggregated, processed and analyzed by Epigynous computer in order to extract useful information(detection time, production places, detection samples, pesticide residues values, detection inspector) which were effective in protecting the quality and safety of agricultural products. In this study, the code of useful information was used in form of QR code, tracing and retracing the safety of agricultural products was achieved efficiently and reliably. In view of above, we design and implement a system based on AChE biosensor and IoT for pesticide residues detection and agricultural products traceability. It provides safe fruit and vegetable information for consumer, and lay foundation for the traceability of agricultural product. C1 [Zhao, Guo; Guo, Yemin; Sun, Xia; Wang, Xiangyou] Shandong Univ Technol, Sch Agr & Food Engn, Zibo 255049, Shandong Prov, Peoples R China. C3 Shandong University of Technology RP Zhao, G (corresponding author), Shandong Univ Technol, Sch Agr & Food Engn, 12 Zhangzhou Rd, Zibo 255049, Shandong Prov, Peoples R China. EM sunxia2151@sina.com CR AGUERA A, 1993, J CHROMATOGR A, V655, P293, DOI 10.1016/0021-9673(93)83235-K Albareda-Sirvent M, 2001, ANAL CHIM ACTA, V442, P35, DOI 10.1016/S0003-2670(01)01017-0 Andreou VG, 2002, BIOSENS BIOELECTRON, V17, P61, DOI 10.1016/S0956-5663(01)00261-5 Atzori L, 2010, COMPUT NETW, V54, P2787, DOI 10.1016/j.comnet.2010.05.010 Bai L, 2014, PROCD SOC BEHV, V138, P350, DOI 10.1016/j.sbspro.2014.07.213 Bakirci GT, 2012, FOOD CHEM, V135, P1901, DOI 10.1016/j.foodchem.2012.06.051 Boobis AR, 2008, TOXICOL LETT, V180, P137, DOI 10.1016/j.toxlet.2008.06.004 Chen C, 2011, FOOD CONTROL, V22, P1114, DOI 10.1016/j.foodcont.2011.01.007 Cserhati T, 2004, BIOMED CHROMATOGR, V18, P350, DOI 10.1002/bmc.378 Gonzalez G. C., 2014, J COMPUT NETW, V64, P143 Guan HX, 2010, J CHROMATOGR A, V1217, P1867, DOI 10.1016/j.chroma.2010.01.047 Guler GO, 2010, FOOD CHEM TOXICOL, V48, P1218, DOI 10.1016/j.fct.2010.02.013 Hildebrandt A, 2008, SENSOR ACTUAT B-CHEM, V133, P195, DOI 10.1016/j.snb.2008.02.017 Ivanov AN, 2002, BIOELECTROCHEMISTRY, V55, P75, DOI 10.1016/S1567-5394(01)00163-3 Li YS, 2011, AGR SCI CHINA, V10, P805, DOI 10.1016/S1671-2927(11)60065-5 Lorenzi D, 2014, GOV INFORM Q, V31, P6, DOI 10.1016/j.giq.2013.05.025 Marco MP, 1996, MEAS SCI TECHNOL, V7, P1547, DOI 10.1088/0957-0233/7/11/002 Qin Y, 2014, OPT COMMUN, V310, P69, DOI 10.1016/j.optcom.2013.07.062 Roman R, 2013, COMPUT NETW, V57, P2266, DOI 10.1016/j.comnet.2012.12.018 Shin D, 2014, TELEMAT INFORM, V31, P519, DOI 10.1016/j.tele.2014.02.003 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Yang GM, 2008, SENSOR ACTUAT B-CHEM, V133, P105, DOI 10.1016/j.snb.2008.02.004 Ye J, 2009, TRAC-TREND ANAL CHEM, V28, P1148, DOI 10.1016/j.trac.2009.07.008 Yu Y, 2014, OPTIK, V125, P2256, DOI 10.1016/j.ijleo.2013.10.079 Zhao LZ, 2010, DATA KNOWL ENG, V69, P737, DOI 10.1016/j.datak.2010.02.009 NR 25 TC 22 Z9 22 U1 3 U2 98 PD APR PY 2015 VL 10 IS 4 BP 3387 EP 3399 WC Electrochemistry SC Electrochemistry UT WOS:000352222500050 DA 2022-12-14 ER PT J AU Abt, V Havet, A Gilain-Galliot, C Gautier, JM Joly, N AF Abt, Vincent Havet, Alain Gilain-Galliot, Caroline Gautier, Jean-Marc Joly, Nathalie TI Management and integration of traceability documents on livestock farms: realities and possible ameliorations SO CAHIERS AGRICULTURES DT Article DE farm management; information processing; information systems; traceability AB The agricultural context is more and more demanding as concerns the traceability of products and practices. In consequence the number of documents farmers have to manage has been increasing for several years. In the past, the farm documentation system was mainly based on "in-farm documents" allowing the farmer to manage his activity at short and middle terms. The issue lies from now on in a joined and reasoned management of traceability and in-farm documents. Using livestock farm surveys, we investigate the modalities of the document articulation regarding the functions performed by these documents in the farm information system and discuss potential ameliorations. C1 [Abt, Vincent] Cemagref, F-03150 Montoldre, France. [Havet, Alain] INRA, UMR Inra AgroParis Tech SAD APT 1048, F-78850 Thiverval Grignon, France. [Gilain-Galliot, Caroline] Inst Elevage Monvoisin, F-35652 Le Rheu, France. [Gautier, Jean-Marc] Inst Elevage, F-31312 Castanet Tolosan, France. [Joly, Nathalie] Enesad, SAD LISTO, UR 718, F-21079 Dijon, France. C3 INRAE; AgroParisTech; INRAE; UDICE-French Research Universities; Universite Paris Saclay RP Abt, V (corresponding author), Cemagref, Domaine Palaquins, F-03150 Montoldre, France. EM vincent.abt@cemagref.fr; havet@grignon.inra.fr; jean-marc.gautier@inst-elevage.asso.fr; nathalie.joly@educagri.fr CR ABT V, 2007, 7 C INT GEN IND TROI ABT V, 2007, INGENIERIES EAT, V52, P49 [Anonymous], SYSTEMES DINFORMATIO Bernard C, 2006, CAH AGRIC, V15, P523, DOI 10.1684/agr.2006.0025 FERRIER O., 2002, TRES PETITES ENTREPR Gras R., 1989, FAIT TECHNIQUE AGRON Havet A., 2005, Rencontres Autour des Recherches sur les Ruminants, V12, P327 Joly N, 2004, SOCIOL TRAV, V46, P511, DOI 10.1016/j.soctra.2004.09.005 Joly N., 2006, Rencontres Autour des Recherches sur les Ruminants, V13, P187 Laudon K. C., 2006, MANAGEMENT INFORM SY Maze A., 2004, NATURES SCI SOC, V12, P18 Moisdon J.-C., 1997, MODE EXISTENCE OUTIL NR 12 TC 0 Z9 0 U1 0 U2 4 PD JAN-FEB PY 2010 VL 19 IS 1 BP 7 EP 13 DI 10.1684/agr.2009.0365 WC Agriculture, Multidisciplinary; Agronomy SC Agriculture UT WOS:000274050200002 DA 2022-12-14 ER PT J AU Larrain, MA Diaz, NF Lamas, C Uribe, C Araneda, C AF Angelica Larrain, Maria Diaz, Nelson F. Lamas, Carmen Uribe, Carla Araneda, Cristian TI Traceability of mussel (Mytilus chilensis) in southern Chile using microsatellite molecular markers and assignment algorithms. Exploratory survey SO FOOD RESEARCH INTERNATIONAL DT Article DE Mytilus; Mussels; Traceability; Microsatellites; Assignment test ID POPULATION-STRUCTURE; GENETIC DIFFERENTIATION; GALLOPROVINCIALIS LMK.; MEDITERRANEAN MUSSEL; SEASCAPE GENETICS; NULL ALLELES; IDENTIFICATION; WILD; PRODUCTS; ORIGIN AB The international seafood trade has adopted the food chain or "from farm to fork" concept in terms of standards and regulations regarding food quality, safety and authenticity, from primary production to the consumer. This has led to an increasing need for traceability, but administrative traceability systems (physical labeling, information recording and automatic data treatment) are not flawless and require validation through analytical procedures. Currently, DNA-based methods used for species identification and population genetics, coupled with allocation algorithms can be used to verify administrative traceability systems. We evaluated the potential of a panel of nine microsatellite markers combined with allocation algorithms for their ability to assign Mytilus individuals from southern Chile to their geographical origin, evaluating the performance of four assignment methods: genetic distance and frequency-based criteria and a Bayesian based method using prior information or not. The reallocation test showed that the Bayesian method with prior information performed best. When tested with a real traceability verification case, the frequency-based algorithm showed the best results, re-allocating individuals to their original population at least 6 times more often than individuals from other locations in a challenging scenario with low genetic differentiation among locations. In order to apply this allocation method for traceability purposes, it would be necessary to strengthen this SSR panel with more informative loci and complement it with SNP markers. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Angelica Larrain, Maria; Uribe, Carla] Univ Chile, Fac Ciencias Quim & Farmaceut, Dept Ciencia Alimentos & Tecnol Quim, Santiago, Chile. [Diaz, Nelson F.; Lamas, Carmen; Araneda, Cristian] Univ Chile, Fac Ciencias Agron, Dept Anim Prod, Santiago, Chile. C3 Universidad de Chile; Universidad de Chile RP Larrain, MA (corresponding author), Sergio Livingstone 1007, Santiago 8380492, Chile. EM mlarrain@uchile.cl CR ALTMAN DG, 1994, BRIT MED J, V308, P1552, DOI 10.1136/bmj.308.6943.1552 Larrain MA, 2012, LAT AM J AQUAT RES, V40, P1077, DOI 10.3856/vol40-issue4-fulltext-23 Balding DJ, 2006, NAT REV GENET, V7, P781, DOI 10.1038/nrg1916 Baudouin L, 2004, J HERED, V95, P217, DOI 10.1093/jhered/esh035 Beaumont MA, 2004, NAT REV GENET, V5, P251, DOI 10.1038/nrg1318 Blohm D., 2007, GENIMPACT EVALUATION, P176 Bossier P, 1999, J FOOD SCI, V64, P189, DOI 10.1111/j.1365-2621.1999.tb15862.x Caratti S, 2010, FORENSIC SCI INT-GEN, V4, P339, DOI 10.1016/j.fsigen.2010.07.001 Carlsson J, 2008, J HERED, V99, P616, DOI 10.1093/jhered/esn048 Chapuis MP, 2007, MOL BIOL EVOL, V24, P621, DOI 10.1093/molbev/msl191 Collard BCY, 2005, EUPHYTICA, V142, P169, DOI 10.1007/s10681-005-1681-5 Cornuet JM, 1999, GENETICS, V153, P1989 Costas-Rodriguez M, 2010, ANAL CHIM ACTA, V664, P121, DOI 10.1016/j.aca.2010.03.003 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 De Innocentiis S, 2005, AQUACULTURE, V247, P227, DOI 10.1016/j.aquaculture.2005.02.022 Deeks J. J., 2004, BMJ, V329 Dias PJ, 2009, AQUAC RES, V40, P1715, DOI 10.1111/j.1365-2109.2009.02274.x Diz AP, 2008, MAR BIOL, V154, P277, DOI 10.1007/s00227-008-0921-3 Diz AP, 2009, AQUACULTURE, V287, P278, DOI 10.1016/j.aquaculture.2008.10.029 Falush D, 2007, MOL ECOL NOTES, V7, P574, DOI 10.1111/j.1471-8286.2007.01758.x FAO, 2012, FISHSTATJ VERS 2 0 0 Fernandez-Tajes J, 2011, EUR FOOD RES TECHNOL, V233, P791, DOI 10.1007/s00217-011-1574-x Flury C, 2007, GENET SEL EVOL, V39, P159, DOI 10.1051/gse:2006040 Fuentes A, 2009, FOOD CHEM, V112, P295, DOI 10.1016/j.foodchem.2008.05.064 Gardestrom J, 2008, CONSERV GENET, V9, P1003, DOI 10.1007/s10592-007-9432-x Gardner JPA, 2012, J MOLLUS STUD, V78, P66, DOI 10.1093/mollus/eyr037 Gerard K, 2008, MOL PHYLOGENET EVOL, V49, P84, DOI 10.1016/j.ympev.2008.07.006 Ghabooli S., 2013, PLOS ONE, V8 Glover KA, 2008, ICES J MAR SCI, V65, P912, DOI 10.1093/icesjms/fsn056 Glover KA, 2010, ANIM GENET, V41, P515, DOI 10.1111/j.1365-2052.2010.02025.x Glover KA, 2009, AQUACULTURE, V290, P37, DOI 10.1016/j.aquaculture.2009.01.034 Glover KA, 2008, BMC GENET, V9, DOI 10.1186/1471-2156-9-87 Groenenberg DSJ, 2011, CONTRIB ZOOL, V80, P95, DOI 10.1163/18759866-08002001 Hauser L, 2006, MOL ECOL, V15, P3157, DOI 10.1111/j.1365-294X.2006.03017.x Hayes B, 2005, AQUACULTURE, V250, P70, DOI 10.1016/j.aquaculture.2005.03.008 Holsinger KE, 2009, NAT REV GENET, V10, P639, DOI 10.1038/nrg2611 Inoue K, 1995, BIOL BULL, V189, P370, DOI 10.2307/1542155 Jones OR, 2012, ECOL EVOL, V2, P1048, DOI 10.1002/ece3.237 Kijewski T, 2009, AQUACULTURE, V287, P292, DOI 10.1016/j.aquaculture.2008.10.048 KOEHN RK, 1976, EVOLUTION, V30, P2, DOI 10.1111/j.1558-5646.1976.tb00878.x Lallias D, 2009, J SHELLFISH RES, V28, P547, DOI 10.2983/035.028.0317 Li JL, 2009, AQUACULTURE, V287, P286, DOI 10.1016/j.aquaculture.2008.10.032 Lo Presti R, 2010, ITALIAN J ANIMAL SCI, V9 Loong TW, 2003, BMJ-BRIT MED J, V327, P716, DOI 10.1136/bmj.327.7417.716 Manel S, 2005, TRENDS ECOL EVOL, V20, P136, DOI 10.1016/j.tree.2004.12.004 Martinsohn JT, 2009, FORENS SCI INT-GEN S, V2, P294, DOI 10.1016/j.fsigss.2009.08.108 Meirmans PG, 2011, MOL ECOL RESOUR, V11, P5, DOI 10.1111/j.1755-0998.2010.02927.x Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Negrini R, 2007, ITAL J ANIM SCI, V6, P174, DOI 10.4081/ijas.2007.1s.174 NEI M, 1983, J MOL EVOL, V19, P153, DOI 10.1007/BF02300753 Nielsen EE, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1845 Ogden R, 2008, FISH FISH, V9, P462, DOI 10.1111/j.1467-2979.2008.00305.x PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Presa P, 2002, CONSERV GENET, V3, P441, DOI 10.1023/A:1020571202907 Pritchard JK, 2000, GENETICS, V155, P945 Ramos AM, 2011, ANIM GENET, V42, P613, DOI 10.1111/j.1365-2052.2011.02198.x RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Rego I, 2002, J AGR FOOD CHEM, V50, P1780, DOI 10.1021/jf0110957 Rousset F, 2008, MOL ECOL RESOUR, V8, P103, DOI 10.1111/j.1471-8286.2007.01931.x Santaclara FJ, 2006, J AGR FOOD CHEM, V54, P8461, DOI 10.1021/jf061400u Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Selkoe KA, 2008, FISH FISH, V9, P363, DOI 10.1111/j.1467-2979.2008.00300.x Shields JL, 2010, MAR ECOL PROG SER, V399, P211, DOI 10.3354/meps08338 SKIBINSKI DOF, 1983, BIOL J LINN SOC, V19, P137, DOI 10.1111/j.1095-8312.1983.tb00782.x Sorenson L, 2013, CONSERV GENET RESOUR, V5, P293, DOI 10.1007/s12686-012-9747-x Stewart KR, 2013, J ANIM ECOL, V82, P791, DOI 10.1111/1365-2656.12056 Toro MA, 2009, LIVEST SCI, V120, P174, DOI 10.1016/j.livsci.2008.07.003 Van Oosterhout C, 2004, MOL ECOL NOTES, V4, P535, DOI 10.1111/j.1471-8286.2004.00684.x Varela MA, 2007, BIOCHEM GENET, V45, P565, DOI 10.1007/s10528-007-9097-7 Vera M, 2010, AQUAC RES, V41, pe568, DOI 10.1111/j.1365-2109.2010.02550.x Wei KJ, 2013, MAR BIOL, V160, P931, DOI 10.1007/s00227-012-2145-9 Wei KJ, 2013, MAR ECOL PROG SER, V477, P107, DOI 10.3354/meps10158 WEIR BS, 1984, EVOLUTION, V38, P1358, DOI [10.2307/2408641, 10.1111/j.1558-5646.1984.tb05657.x] Westfall KM, 2013, BIOL INVASIONS, V15, P1493, DOI 10.1007/s10530-012-0385-8 Wonham MJ, 2004, J SHELLFISH RES, V23, P535 Woolaver LG, 2013, CONSERV GENET, V14, P559, DOI 10.1007/s10592-013-0444-4 Yue G. H., 2012, PLOS ONE, V7 Zbawicka M, 2012, MAR BIOL, V159, P1347, DOI 10.1007/s00227-012-1915-8 NR 80 TC 31 Z9 31 U1 0 U2 51 PD AUG PY 2014 VL 62 BP 104 EP 110 DI 10.1016/j.foodres.2014.02.016 WC Food Science & Technology SC Food Science & Technology UT WOS:000340015100014 DA 2022-12-14 ER PT J AU Kravenkit, S Arch-int, S AF Kravenkit, Satit Arch-int, Somjit TI RESTful economic-ADS model for cost-effective chain-wide traceability system-based cloud computing SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Chain-wide traceability system; EPCIS (Electronic Product Code Information Services); EPCDS (Electronic Product Code Discovery Service); Cloud computing technology; RESTful web services ID SUPPLY CHAIN; EPCIS; SERVICE AB Chain-wide traceability systems conforming to the Electronic Product Code Information Services (EPCIS) standard which is a well-recognized global standard allow supply chain operators to digitally capture and directly access the traceability data within and across enterprises. Although recent studies proposed the cloud-based deployment of EPCIS-based traceability systems by which supply chain operators are likely to save their investment cost in IT infrastructure, resource utilization remains a major concern in cloud based deployment and those systems are required to deliver, optimal performance. In this research, we propose the RESTful Economic-Aggregating Discovery Service (RECO-ADS) model to improve the cost effectiveness of resource use of the EPCIS system, which runs in the cloud. In the proposed model, we select the ADS model as the query traceability data approach. We design the internal and centralized traceability systems to comply with the EPCIS specification and Electronic Product Code Discovery Service (EPCDS) deployed to the Infrastructure as a Service (IaaS) cloud business model. Moreover, we integrate the RESTful web service with the Interfaces of EPCIS and EPCDS traceability systems. The evaluation criteria to measure resource efficiency, including computing resources, data transfers, and response time, are provided. The RECO-ADS is compared with the conventional traceability models, Directory Service (DS), Query Relay (QR) and Aggregating Discovery Service (ADS), using the evaluation criteria. The comparison shows that the computing resources, which consist of the CPU and Memory percentages of EPCIS and EPCDS systems, are rather similar, whereas the total data transfer (kB/s) of RECO-ADS (661.97) was less than that of the DS (807.73), QR (794.80) and ADS (796.66) models by approximately 18.05%, 16.71%, and 16.91%, respectively. The response time of the RECO-ADS model was the shortest. Moreover, the design of the cache in the EPCDS repository in the RECO-ADS model yielded a reduced operational cost based on the performance evaluation. These findings improve on the cost-effective resource consumption in the IaaS cloud of not only the EPCIS system but also the EPCDS system. (C) 2017 Elsevier B.V. All rights reserved. C1 [Kravenkit, Satit; Arch-int, Somjit] Khon Kaen Univ, Fac Sci, Dept Comp Sci, Logist & Supply Chain LAB, Khon Kaen 40002, Thailand. C3 Khon Kaen University RP Arch-int, S (corresponding author), Khon Kaen Univ, Fac Sci, Dept Comp Sci, Logist & Supply Chain LAB, Khon Kaen 40002, Thailand. EM satit41617590@gmail.com; somjit@kku.ac.th CR [Anonymous], 2016, AMAZON EC2 PRICING [Anonymous], 2015, J WIREL MOB NETW UBI Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Byun J, 2015, IEEE INT CONF RFID, P70, DOI 10.1109/RFID.2015.7113075 Cao RZ., 2011, INT C SERV OP LOG IN, P325 de Alfonso C, 2013, FUTURE GENER COMP SY, V29, P704, DOI 10.1016/j.future.2012.08.014 Exposito I, 2013, IEEE ANTENN PROPAG M, V55, P255, DOI 10.1109/MAP.2013.6529365 Fabian B, 2014, COMPUT IND, V65, P1147, DOI 10.1016/j.compind.2014.07.001 Fielding R, 2000, THESIS GS1, 2015, EPCGLOBAL ARCH FRAM GS1, 2015, SYST ARCH DOC REL 4 GS1, 2014, EPCGLOBAL EPC INF SE GS1, 2009, APPL LEV EV ALE SPEC Guanhua Wang, 2011, Proceedings of the 2011 Third International Conference on Communications and Mobile Computing (CMC 2011), P182, DOI 10.1109/CMC.2011.25 Guinard D., 2011, P 2 INT WORKSH WEB T, P9 Guinard D., 2010, GIVING RFID REST BUI, P1 Han SN, 2016, COMPUT STAND INTER, V43, P79, DOI 10.1016/j.csi.2015.08.006 Hartley G., 2013, USE EPC REID STANDAR Jakkhupan W, 2011, J NETW COMPUT APPL, V34, P949, DOI 10.1016/j.jnca.2010.04.003 Kassahun A, 2014, COMPUT ELECTRON AGR, V109, P179, DOI 10.1016/j.compag.2014.10.002 Klems M, 2009, LECT NOTES BUS INF, V22, P110 Kurschner C, 2008, LECT NOTES COMPUT SC, V4952, P19 Kywe S. M., 2012, ADV INTERNET THINGS, V02, P37 Laya A, 2012, 2012 IEEE 23RD INTERNATIONAL SYMPOSIUM ON PERSONAL INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC), P1, DOI 10.1109/PIMRC.2012.6362682 Lorenz M., 2011, DISCOVERY SERVICES E Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Mallet M., 2009, THESIS Mell P., 2011, NIST SPECIAL PUBLICA, V800-145 Muller J., 2010, P 43 HAW INT C SYST, P1 Nam T., 2013, DYNAMICS LOGISTICS, P277 Ozcan R, 2011, ACM T WEB, V5, DOI 10.1145/1961659.1961663 Ozcan R, 2012, INFORM PROCESS MANAG, V48, P828, DOI 10.1016/j.ipm.2010.12.007 Paganelli F., 2013, J COMMUN SOFTWARE SY, V9 Pardal M. L., 2011, 2011 IEEE International Conference on RFID-Technologies and Applications (RFID-TA), P486, DOI 10.1109/RFID-TA.2011.6068683 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Pautasso C., 2008, P INT C WWW BEIJ CHI, V17, P805, DOI DOI 10.1145/1367497.1367606 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qureshi, 2013, ADV INTERNET THINGS, V03, P79 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Singer G., 2010, P 2 ICST INT C CLOUD Singh A, 2015, INT J PROD ECON, V164, P462, DOI 10.1016/j.ijpe.2014.09.019 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Turchi S, 2012, 20 INT C SOFTW TEL C, P1 Velthuis AGJ, 2006, International Agri-Food Chains and Networks: Management and Organization, P259 Wu YB, 2011, DISTRIB PARALLEL DAT, V29, P397, DOI 10.1007/s10619-011-7084-9 NR 46 TC 4 Z9 4 U1 0 U2 21 PD JUN 15 PY 2017 VL 139 BP 164 EP 179 DI 10.1016/j.compag.2017.05.015 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000404320100015 DA 2022-12-14 ER PT J AU Zhao, SS Liu, HJ Qie, MJ Zhang, JK Tan, LQ Zhao, Y AF Zhao, Shanshan Liu, Haijin Qie, Mengjie Zhang, Jiukai Tan, Liqin Zhao, Yan TI Stable Isotope Analysis for Authenticity and Traceability in Food of Animal Origin SO FOOD REVIEWS INTERNATIONAL DT Review; Early Access DE Stable isotope; animal product; authenticity; traceability ID POULTRY OFFAL MEAL; BREAM SPARUS-AURATA; CATTLE TAIL HAIR; GEOGRAPHICAL ORIGIN; POTENTIAL TOOL; RATIO ANALYSIS; FATTY-ACID; LAMB MEAT; SEA BASS; NITROGEN ISOTOPES AB As "natural fingerprint" of food growing environment, stable isotope abundance has attracted great attention in tracing and adulterating analysis of animal products. In recent years, the number of research papers on tracing animal products by stable isotope ratio analysis has increased. And the combination of stable isotope ratio analysis and other methods has gradually became a promising method for the authenticity and traceability of animal products. Our results summarized the latest progress of stable isotopes technology in animal products, including beef, lamb, poultry, pork, cheese, milk, and seafood, aiming to provide a reference for the application of stable isotope technology in animal products traceability. C1 [Zhao, Shanshan; Qie, Mengjie; Zhao, Yan] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Zhao, Shanshan; Qie, Mengjie; Zhao, Yan] Minist Agr, Key Lab Agroprod Qual & Safety, Beijing, Peoples R China. [Liu, Haijin] Tibet Autonomous Reg Agr & Livestock Prod Qual &, Lhasa, Peoples R China. [Zhang, Jiukai] Chinese Acad Inspect & Quarantine, Agroprod Safety Res Ctr, Beijing, Peoples R China. [Tan, Liqin] Changgao Agr Technol Extens Stn, Beipiao, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs; Chinese Academy of Inspection & Quarantine RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR Anastasiadis F, 2021, FOODS, V10, DOI 10.3390/foods10030543 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Aursand M, 2000, J AM OIL CHEM SOC, V77, P659, DOI 10.1007/s11746-000-0106-5 Bahar B, 2008, FOOD CHEM, V106, P1299, DOI 10.1016/j.foodchem.2007.07.053 Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Behkami S, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106780 Behkami S, 2017, FOOD CHEM, V217, P438, DOI 10.1016/j.foodchem.2016.08.130 Bell JG, 2007, J AGR FOOD CHEM, V55, P5934, DOI 10.1021/jf0704561 Bentivoglio D, 2019, J MT SCI-ENGL, V16, P428, DOI 10.1007/s11629-018-4962-x Biondi L, 2013, ANIMAL, V7, P1559, DOI 10.1017/S1751731113000645 Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Bong YS, 2010, RAPID COMMUN MASS SP, V24, P155, DOI 10.1002/rcm.4366 Bontempo L, 2016, RAPID COMMUN MASS SP, V30, P170, DOI 10.1002/rcm.7428 Bowen GJ, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005186 Busetto ML, 2008, J AGR FOOD CHEM, V56, P2742, DOI 10.1021/jf0734267 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Camin F, 2004, J AGR FOOD CHEM, V52, P6592, DOI 10.1021/jf040062z Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Camin F, 2012, ANAL CHIM ACTA, V711, P54, DOI 10.1016/j.aca.2011.10.047 Carrijo AS, 2006, Rev. Bras. Cienc. Avic., V8, P63, DOI 10.1590/S1516-635X2006000100010 Carter JF, 2015, FOOD CHEM, V170, P241, DOI 10.1016/j.foodchem.2014.08.037 Cavanna D, 2018, TRENDS FOOD SCI TECH, V80, P223, DOI 10.1016/j.tifs.2018.08.007 Chung IM, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106755 Coletta LD, 2012, FOOD CHEM, V131, P155, DOI 10.1016/j.foodchem.2011.08.051 Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 Cruz VC, 2012, POULTRY SCI, V91, P478, DOI 10.3382/ps.2011-01512 Dempson JB, 2004, ECOL FRESHW FISH, V13, P176, DOI 10.1111/j.1600-0633.2004.00057.x Denholm SJ, 2019, J DAIRY SCI, V102, P11180, DOI 10.3168/jds.2019-16960 Devincenzi T, 2014, FOOD CHEM, V152, P456, DOI 10.1016/j.foodchem.2013.11.164 Ducatti R, 2016, MEAT SCI, V122, P97, DOI 10.1016/j.meatsci.2016.07.012 EHLERINGER JR, 1986, TRENDS ECOL EVOL, V1, P42, DOI 10.1016/0169-5347(86)90072-8 Erasmus SW, 2018, FOOD CHEM, V239, P926, DOI 10.1016/j.foodchem.2017.07.026 Erasmus SW, 2016, FOOD CHEM, V192, P997, DOI 10.1016/j.foodchem.2015.07.121 Erich S, 2015, FOOD CHEM, V188, P1, DOI 10.1016/j.foodchem.2015.04.118 Esteki M, 2018, FOOD CONTROL, V93, P165, DOI 10.1016/j.foodcont.2018.06.015 Fasolato L, 2010, J AGR FOOD CHEM, V58, P10979, DOI 10.1021/jf1015126 Fortunato G, 2004, J ANAL ATOM SPECTROM, V19, P227, DOI 10.1039/b307068a Franke BM, 2008, MEAT SCI, V80, P944, DOI 10.1016/j.meatsci.2008.03.018 Franke BM, 2008, EUR FOOD RES TECHNOL, V226, P761, DOI 10.1007/s00217-007-0588-x Garbaras A, 2018, LITH J PHYS, V58, P277 Garrido-Varo A., 2007, Proceedings of the 6th International Symposium on the Mediterranean Pig, Messina, Capo d'Orlando, Italy, 11-13 October, 2007, P349 Giaccio M., 2003, Journal of Commodity Science, V42, P193 Gonzalez-Martin I, 1999, MEAT SCI, V52, P437, DOI 10.1016/S0309-1740(99)00027-3 Gonzalez-Martin I, 2001, MEAT SCI, V58, P25, DOI 10.1016/S0309-1740(00)00126-1 Gopi K, 2018, J ENVIRON BIOL, V39, P741, DOI 10.22438/jeb/39/5(SI)/24 Gregorcic SH, 2021, FOODS, V10, DOI 10.3390/foods10081729 Griboff J, 2019, FOOD CHEM, V283, P549, DOI 10.1016/j.foodchem.2019.01.067 Hammes V, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0188926 Han C, 2021, FOOD CHEM, V364, DOI 10.1016/j.foodchem.2021.130364 Harrison SM, 2011, FOOD CHEM, V124, P291, DOI 10.1016/j.foodchem.2010.06.035 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 HESSLEIN RH, 1991, CAN J FISH AQUAT SCI, V48, P2258, DOI 10.1139/f91-265 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Tran HQ, 2021, AQUACULTURE, V545, DOI 10.1016/j.aquaculture.2021.737265 Jahreis G, 1996, FETT-LIPID, V98, P356, DOI 10.1002/lipi.19960981103 Jakes W, 2015, FOOD CHEM, V175, P1, DOI 10.1016/j.foodchem.2014.11.110 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Kim KS, 2013, KOREAN J FOOD SCI AN, V33, P39, DOI 10.5851/kosfa.2013.33.1.39 Kornexl BE, 1997, Z LEBENSM UNTERS F A, V205, P19, DOI 10.1007/s002170050117 Kusche H, 2018, ISOT ENVIRON HEALT S, V54, P28, DOI 10.1080/10256016.2017.1361419 Liu HY, 2021, INT J FOOD SCI TECH, V56, P2604, DOI 10.1111/ijfs.14900 Liu HY, 2019, FOOD CHEM, V277, P448, DOI 10.1016/j.foodchem.2018.10.144 Liu XL, 2013, FOOD CHEM, V140, P135, DOI 10.1016/j.foodchem.2013.02.020 Luo DH, 2016, FOOD ANAL METHOD, V9, P437, DOI 10.1007/s12161-015-0204-9 Luo RJ, 2019, FOOD CHEM, V274, P1, DOI 10.1016/j.foodchem.2018.08.104 Lv J, 2017, FOOD ANAL METHOD, V10, P347, DOI 10.1007/s12161-016-0588-1 Magdas DA, 2016, INT DAIRY J, V61, P135, DOI 10.1016/j.idairyj.2016.06.003 Manca G, 2006, J DAIRY SCI, V89, P831, DOI 10.3168/jds.S0022-0302(06)72146-4 Manca G, 2001, J AGR FOOD CHEM, V49, P1404, DOI 10.1021/jf000706c Mekki I, 2016, J FOOD COMPOS ANAL, V53, P40, DOI 10.1016/j.jfca.2016.09.002 Molkentin J, 2007, ANAL BIOANAL CHEM, V388, P297, DOI 10.1007/s00216-007-1222-2 Molkentin J, 2009, J AGR FOOD CHEM, V57, P785, DOI 10.1021/jf8022029 Monahan FJ, 2012, TRENDS FOOD SCI TECH, V28, P69, DOI 10.1016/j.tifs.2012.05.005 Moreno-Rojas JM, 2008, RAPID COMMUN MASS SP, V22, P3701, DOI 10.1002/rcm.3773 Morrison DJ, 2007, LIPIDS, V42, P537, DOI 10.1007/s11745-007-3055-3 Nakashita R, 2008, J JPN SOC FOOD SCI, V55, P191, DOI 10.3136/nskkk.55.191 Nakashita R, 2008, ANAL CHIM ACTA, V617, P148, DOI 10.1016/j.aca.2008.03.048 Nakashita R, 2009, BUNSEKI KAGAKU, V58, P1023, DOI 10.2116/bunsekikagaku.58.1023 Necemer M, 2016, J FOOD COMPOS ANAL, V52, P16, DOI 10.1016/j.jfca.2016.07.002 Oliveira RP, 2010, BRAZ J POULTRY SCI, V12, P13, DOI 10.1590/S1516-635X2010000100002 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3295, DOI 10.1021/jf1040959 Park YM, 2018, MEAT SCI, V143, P93, DOI 10.1016/j.meatsci.2018.04.012 Perini M, 2021, MOLECULES, V26, DOI 10.3390/molecules26082155 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Pianezze S, 2020, J MASS SPECTROM, V55, DOI 10.1002/jms.4451 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Pillonel L., 2004, Mitteilungen aus Lebensmitteluntersuchung und Hygiene, V95, P489 Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Qie MJ, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107549 Rhodes CN, 2010, FOOD CHEM, V118, P927, DOI 10.1016/j.foodchem.2008.05.113 Rossmann A., 1998, Rivista di Scienza dell'Alimentazione, V27, P9 Sakamoto N, 2002, J NUCL SCI TECHNOL, V39, P323, DOI 10.3327/jnst.39.323 Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 Sernagiotto ER, 2013, BRAZ J POULTRY SCI, V15, P65, DOI 10.1590/S1516-635X2013000100011 Serrano R, 2007, CHEMOSPHERE, V69, P1075, DOI 10.1016/j.chemosphere.2007.04.034 Shin WJ, 2018, RAPID COMMUN MASS SP, V32, P1843, DOI 10.1002/rcm.8251 Sponheimer M, 2003, INT J OSTEOARCHAEOL, V13, P80, DOI 10.1002/oa.655 [孙丰梅 Sun Fengmei], 2007, [食品与发酵工业, Food and Fermentation Industries], V33, P136 Sun SM, 2016, FOOD CHEM, V213, P675, DOI 10.1016/j.foodchem.2016.07.013 Valdes A, 2018, TRENDS FOOD SCI TECH, V77, P120, DOI 10.1016/j.tifs.2018.05.014 Valenti B, 2017, RAPID COMMUN MASS SP, V31, P737, DOI 10.1002/rcm.7840 Oliveira EJVM, 2011, EUR FOOD RES TECHNOL, V232, P97, DOI 10.1007/s00217-010-1367-7 Xu SY, 2021, ANAL METHODS-UK, V13, P2537, DOI [10.1039/D1AY00339A, 10.1039/d1ay00339a] [张宏博 Zhang Hongbo], 2017, [食品工业科技, Science & Technology of Food Industry], V38, P347 Zhang P, 2018, BRIEF BIOINFORM, V19, P524, DOI 10.1093/bib/bbw131 Zhang XF, 2019, FOOD CHEM, V299, DOI 10.1016/j.foodchem.2019.125107 Zhao Y, 2016, J SCI FOOD AGR, V96, P3950, DOI 10.1002/jsfa.7567 Zhao Y, 2016, CYTA-J FOOD, V14, P163, DOI 10.1080/19476337.2015.1057235 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y Zhaxi C, 2021, FOOD CHEM, V358, DOI 10.1016/j.foodchem.2021.129893 Zhou JQ, 2015, FOOD CHEM, V182, P23, DOI 10.1016/j.foodchem.2015.02.116 NR 111 TC 4 Z9 4 U1 23 U2 54 DI 10.1080/87559129.2021.2005087 EA NOV 2021 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000721158800001 DA 2022-12-14 ER PT J AU Wu, YT Jin, X Yang, HG Tu, LJ Ye, Y Li, SW AF Wu, Yuting Jin, Xiu Yang, Honggang Tu, Lijing Ye, Yong Li, Shaowen TI Blockchain-Based Internet of Things: Machine Learning Tea Sensing Trusted Traceability System SO JOURNAL OF SENSORS DT Article ID SUPPLY CHAIN; TECHNOLOGY; CHALLENGES; CLASSIFICATION AB A framework combining the Internet of Things (IoT) and blockchain can help achieve system automation and credibility, and the corresponding technologies have been applied in many industries, especially in the area of agricultural product traceability. In particular, IoT devices (radio frequency identification (RFID), geographic information system (GIS), global positioning system (GPS), etc.) can automate the collection of information pertaining to the key aspects of traceability. The data are collected and input to the blockchain system for processing, storage, and query. A distributed, decentralized, and nontamperable blockchain can ensure the security of the data entering the system. However, IoT devices may generate abnormal data in the process of data collection. In this context, it is necessary to ensure the accuracy of the source data of the traceability system. Considering the whole-process traceability chain of agricultural products, this paper analyzes the whole-process information of a tea supply chain from planting to sales, constructs the system architecture and each function, and designs and implements a machine learning- (ML-) blockchain-IoT-based tea credible traceability system (MBITTS). Based on IoT technologies such as radio frequency identification (RFID) sensors, this article proposes a new method that combines blockchain and ML to enhance the accuracy of blockchain source data. In addition, system data storage and indexing methods and scanning and recovery mechanisms are proposed. Compared with the existing agricultural product (tea) traceability system based on blockchain, the introduction of the ML data verification mechanism can ensure the accuracy (up to 99%) of information on the chain. The proposed solution provides a basis to ensure the safety, reliability, and efficiency of agricultural traceability systems. C1 [Wu, Yuting; Jin, Xiu; Yang, Honggang; Tu, Lijing; Li, Shaowen] Anhui Agr Univ, Anhui Prov Key Lab Smart Agr Technol & Equipment, Hefei, Peoples R China. [Ye, Yong; Li, Shaowen] Anhui Agr Univ, Sch Informat & Comp Sci, Hefei, Peoples R China. C3 Anhui Agricultural University; Anhui Agricultural University RP Li, SW (corresponding author), Anhui Agr Univ, Anhui Prov Key Lab Smart Agr Technol & Equipment, Hefei, Peoples R China.; Li, SW (corresponding author), Anhui Agr Univ, Sch Informat & Comp Sci, Hefei, Peoples R China. EM wwyt@ahau.edu.cn; jinxiu123@ahau.edu.cn; yhg@ahau.edu.cn; tlj@ahau.edu.cn; scorpio_sunshine@126.com; shaowli@ahau.edu.cn CR Alfian G, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107016 Andoni M, 2019, RENEW SUST ENERG REV, V100, P143, DOI 10.1016/j.rser.2018.10.014 [Anonymous], 2009, STAT METHODS DIAGNOS Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Blaszczynski J, 2015, NEUROCOMPUTING, V150, P529, DOI 10.1016/j.neucom.2014.07.064 Boutaib S, 2021, EXPERT SYST APPL, V166, DOI 10.1016/j.eswa.2020.114076 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Cao Y, 2020, IEEE T IND INFORM, V16, P6004, DOI 10.1109/TII.2019.2942211 Chen PH, 2018, BMC BIOINFORMATICS, V19, DOI 10.1186/s12859-018-2090-9 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Ding QY, 2020, IEEE ACCESS, V8, P6209, DOI 10.1109/ACCESS.2019.2962274 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Freund Y., 1996, Machine Learning. Proceedings of the Thirteenth International Conference (ICML '96), P148 Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504 Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Jie Song, 2009, Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2009), P109, DOI 10.1109/FSKD.2009.608 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kim D, 2021, INT CONF UBIQUIT INF, DOI 10.1109/IMCOM51814.2021.9377411 Liao Y., 2019, TRACEABILITY SYSTEM Lin J, 2017, PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING ICCSE 2017, P38, DOI 10.1145/3126973.3126980 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu XL, 2015, BRIT FOOD J, V117, P1440, DOI 10.1108/BFJ-08-2014-0295 Liu Y, 2017, J BIOMED INFORM, V75, pS105, DOI 10.1016/j.jbi.2017.05.015 Lomotey RK, 2018, WORLD WIDE WEB, V21, P7, DOI 10.1007/s11280-017-0461-1 Ma JH, 2021, BMC MED INFORM DECIS, V21, DOI 10.1186/s12911-021-01486-x Miraz M.H., 2018, APPL BLOCKCHAIN TECH Nardi M., 2021, 3 INT WORKSHOP LEARN, P7 Olnes S, 2017, GOV INFORM Q, V34, P355, DOI 10.1016/j.giq.2017.09.007 Petersen M, 2018, IT-INF TECHNOL, V60, P263, DOI 10.1515/itit-2017-0031 Pigini D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101910 Polikar R, 2012, ENSEMBLE MACHINE LEARNING: METHODS AND APPLICATIONS, P1, DOI 10.1007/978-1-4419-9326-7_1 Provost F, 2001, MACH LEARN, V42, P203, DOI 10.1023/A:1007601015854 Ribeiro FD, 2018, IEEE CONF EVOL ADAPT Rokach L, 2010, ARTIF INTELL REV, V33, P1, DOI 10.1007/s10462-009-9124-7 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sen A, 2016, IEEE T CYBERNETICS, V46, P1078, DOI 10.1109/TCYB.2015.2423295 Shahbazi Z, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10010041 Sikorski JJ, 2017, APPL ENERG, V195, P234, DOI 10.1016/j.apenergy.2017.03.039 Singh A, 2021, J PHARM RES INT, V33, P1, DOI [10.9734/JPRI/2021/v33i36A31921, 10.1080/0952813X.2021.1907795] Singh A, 2020, COMPUT SECUR, V88, DOI 10.1016/j.cose.2019.101654 Sun Y, 2021, APPL ARTIF INTELL, V35, P290, DOI 10.1080/08839514.2021.1877481 Tanha J, 2020, J BIG DATA-GER, V7, DOI 10.1186/s40537-020-00349-y Wang MC, 2015, J SCI FOOD AGR, V95, P1252, DOI 10.1002/jsfa.6814 Wang WG, 2019, IEEE ACCESS, V7, P80519, DOI 10.1109/ACCESS.2019.2922995 Yong BB, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.10.009 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 49 TC 1 Z9 1 U1 14 U2 18 PD FEB 21 PY 2022 VL 2022 AR 8618230 DI 10.1155/2022/8618230 WC Engineering, Electrical & Electronic; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000772991100002 DA 2022-12-14 ER PT J AU Snirc, M Belej, L Golian, J Fekete, T Zidek, R AF Snirc, Marek Belej, L'ubomir Golian, Jozef Fekete, Tomas Zidek, Radoslav TI Molecular traceability of red deer meat products using microsatellite markers SO JOURNAL OF FOOD AND NUTRITION RESEARCH DT Article DE genetic traceability; red deer; individual identification; microsatellite ID POLYMERASE-CHAIN-REACTION; CERVUS-ELAPHUS; GENETIC DIVERSITY; DNA; AUTHENTICATION; POPULATIONS; PRIMERS; BOVIDAE AB Traceability systems help guarantee that products bought from stores exactly correspond to the description on the label. Traceability maps the journey of the food, from the store, throughout processing, right back to the original livestock. DNA can be used as a natural barcode, as it carries unique genetic information and can be used to identify individual animals. Genetic variability of markers was analysed with the aim of confirmation of the available set of 13 microsatellites. With the addition of five polymorphic markers, the probability of finding two animals sharing the same profile was, on average, four in ten million for the entire dataset. In Slovakia in 2015, 65 126 red deers were recorded. Using only the five extra polymorphic markers, it was possible to obtain a reliable individual genetic traceability system. C1 [Snirc, Marek] Slovak Univ Agr, Fac Biotechnol & Food Sci, Dept Chem, Tr A Hlinku 2, Nitra 94976, Slovakia. [Belej, L'ubomir; Golian, Jozef; Fekete, Tomas; Zidek, Radoslav] Slovak Univ Agr, Fac Biotechnol & Food Sci, Dept Hyg & Food Safety, Tr A Hlinku 2, Nitra 94976, Slovakia. C3 Slovak University of Agriculture Nitra; Slovak University of Agriculture Nitra RP Snirc, M (corresponding author), Slovak Univ Agr, Fac Biotechnol & Food Sci, Dept Chem, Tr A Hlinku 2, Nitra 94976, Slovakia. EM marek.snirc@uniag.sk CR [Anonymous], 2002, OFFICIAL J EUROPEAN, V45, P463 Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Ballin NZ, 2009, MEAT SCI, V83, P165, DOI 10.1016/j.meatsci.2009.06.003 BARENDSE W, 1994, NAT GENET, V6, P227, DOI 10.1038/ng0394-227 Beja-Pereira A, 2004, MOL ECOL NOTES, V4, P452, DOI 10.1111/j.1471-8286.2004.00678.x BUCHANAN FC, 1994, GENOMICS, V22, P397, DOI 10.1006/geno.1994.1401 Caldwell JM, 2017, ANNU REV FOOD SCI T, V8, P57, DOI 10.1146/annurev-food-030216-030216 Vieira MLC, 2016, GENET MOL BIOL, V39, P312, DOI 10.1590/1678-4685-GMB-2016-0027 Cosse M, 2007, GENET MOL RES, V6, P1118 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Ernst M, 2008, BILI JELENI VYUZITI Fajardo V, 2010, TRENDS FOOD SCI TECH, V21, P408, DOI 10.1016/j.tifs.2010.06.002 Fickel J, 2000, MOL ECOL, V9, P994, DOI 10.1046/j.1365-294x.2000.00939-2.x Jones KC, 2002, MOL ECOL NOTES, V2, P425, DOI 10.1046/j.1471-8286.2002.00264.x Kalinowski ST, 2007, MOL ECOL, V16, P1099, DOI 10.1111/j.1365-294X.2007.03089.x Kuehn R, 2003, CONSERV GENET, V4, P157, DOI 10.1023/A:1023394707884 La Neve F, 2008, MEAT SCI, V80, P216, DOI 10.1016/j.meatsci.2007.11.027 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Marsalkova L., 2014, Potravinarstvo: Scientific Journal for Food Industry, V8, P15 Marsalkova L., 2010, Acta Fytotechnica et Zootechnica, V13, P24 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Peakall R, 2012, BIOINFORMATICS, V28, P2537, DOI 10.1093/bioinformatics/bts460 Piknova L, 2016, J FOOD NUTR RES-SLOV, V55, P325 Radko A, 2014, ACTA BIOL HUNG, V65, P414, DOI 10.1556/ABiol.65.2014.4.6 Roed KH, 1998, HEREDITAS, V129, P19, DOI 10.1111/j.1601-5223.1998.00019.x Rosa AJM, 2013, SMALL RUMINANT RES, V113, P62, DOI 10.1016/j.smallrumres.2013.03.021 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Smid J, 2015, J FOOD NUTR RES, V54, P165 Spink J, 2016, FOOD CONTROL, V69, P306, DOI 10.1016/j.foodcont.2016.03.016 Talbot J, 2009, ANIMAL GENETICS, V27, P117, DOI [10.1111/j.1365-2052.1996.tb00480.x, DOI 10.1111/J.1365-2052.1996.TB00480.X] Toione M, 2012, SMALL RUMINANT RES, V102, P18, DOI 10.1016/j.smallrumres.2011.09.010 Weir B. S., 1996, GENETIC DATA ANAL Wilson GA, 1997, MOL ECOL, V6, P697, DOI 10.1046/j.1365-294X.1997.00237.x NR 34 TC 0 Z9 0 U1 0 U2 4 PY 2017 VL 56 IS 3 BP 292 EP 298 WC Food Science & Technology SC Food Science & Technology UT WOS:000415125900009 DA 2022-12-14 ER PT J AU Montealegre, C Alrgre, MLM Garcia-Ruiz, C AF Montealegre, Cristina Marina Alrgre, Maria Luisa Garcia-Ruiz, Carmen TI Traceability Markers to the Botanical Origin in Olive Oils SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Review DE Traceability; olive oil; botanical origin; marker ID OLEA-EUROPAEA L.; FATTY-ACID; MULTIVARIATE-ANALYSIS; DIFFERENT CULTIVARS; VOLATILE COMPOUNDS; MASS SPECTROMETRY; DNA MARKERS; CLASSIFICATION; VARIETIES; PROFILES AB This review provides an overview of traceability studies performed to date (April 2009) for olive oils. Special emphasis has been made on the botanical origin because high-quality monovarietal olive oils have been recently introduced on the markets and their quality control requires the development of new and powerful analytical tools as well as new regulations to avoid fraud to consumers. Several parameters with discriminant power have been used for olive oil traceability according to the olive variety used in the production of the oil. They have been considered as traceability markers to the botanical origin and classified, in this work, as compositional and genetical markers. C1 [Montealegre, Cristina; Marina Alrgre, Maria Luisa; Garcia-Ruiz, Carmen] Univ Alcala, Dept Analyt Chem, Fac Chem, Madrid 28871, Spain. C3 Universidad de Alcala RP Garcia-Ruiz, C (corresponding author), Univ Alcala, Dept Analyt Chem, Fac Chem, Ctra Madrid Barcelona Km 33-600, Madrid 28871, Spain. EM carmen.gruiz@uah.es CR Alves MR, 2005, ANAL CHIM ACTA, V549, P166, DOI 10.1016/j.aca.2005.06.033 Angerosa F, 1999, J AGR FOOD CHEM, V47, P836, DOI 10.1021/jf980911g Angiuli M, 2006, J THERM ANAL CALORIM, V84, P105, DOI 10.1007/s10973-005-7184-8 Araghipour N, 2008, FOOD CHEM, V108, P374, DOI 10.1016/j.foodchem.2007.10.056 Aramendia MA, 2007, RAPID COMMUN MASS SP, V21, P487, DOI 10.1002/rcm.2862 Aranda F, 2004, FOOD CHEM, V86, P485, DOI 10.1016/j.foodchem.2003.09.021 Arvanitoyannis IS, 2007, CRIT REV FOOD SCI, V47, P441, DOI 10.1080/10408390600846325 Baccouri F, 2007, EUR J LIPID SCI TECH, V109, P1208, DOI 10.1002/ejlt.200700132 Bandelj D, 2002, FOOD TECHNOL BIOTECH, V40, P185 Bendini A, 2007, MOLECULES, V12, P1679, DOI 10.3390/12081679 Brescia MA, 2003, J AM OIL CHEM SOC, V80, P945, DOI 10.1007/s11746-003-0801-2 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Bucci R, 2002, J AGR FOOD CHEM, V50, P413, DOI 10.1021/jf010696v Bueno EO, 2005, J AM OIL CHEM SOC, V82, P1, DOI 10.1007/s11746-005-1034-0 Casas JJS, 2003, GRASAS ACEITES, V54, P371 Cerretani L, 2006, EUR FOOD RES TECHNOL, V222, P354, DOI 10.1007/s00217-005-0088-9 Cerretani L, 2008, EUR FOOD RES TECHNOL, V226, P1251, DOI 10.1007/s00217-007-0651-7 Cichelli A, 2004, J CHROMATOGR A, V1046, P141, DOI 10.1016/j.chroma.2004.06.093 Claros MG, 2000, EUPHYTICA, V116, P131, DOI 10.1023/A:1004011829274 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Criado MN, 2007, FOOD CHEM, V100, P748, DOI 10.1016/j.foodchem.2005.10.035 D'Imperio M, 2007, FOOD CHEM, V102, P956, DOI 10.1016/j.foodchem.2006.03.003 Dellaporta SL., 1983, PLANT MOL BIOL REP, V1, P19, DOI [DOI 10.1007/BF02712670, 10.1007/BF02712670] Di Bella G, 2007, J AGR FOOD CHEM, V55, P6568, DOI 10.1021/jf070523r Diaz TG, 2005, FOOD CONTROL, V16, P339, DOI 10.1016/j.foodcont.2004.03.014 Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 Garcia A, 2003, EUR FOOD RES TECHNOL, V216, P520, DOI 10.1007/s00217-003-0706-3 Gemas VJV, 2004, GENET RESOUR CROP EV, V51, P501, DOI 10.1023/B:GRES.0000024152.16021.40 Giuffrida D, 2007, FOOD CHEM, V101, P833, DOI 10.1016/j.foodchem.2005.12.030 Gomez-Alonso S, 2002, J AGR FOOD CHEM, V50, P6812, DOI 10.1021/jf0205211 Gurdeniz G, 2007, EUR J LIPID SCI TECH, V109, P1194, DOI 10.1002/ejlt.200700087 Haddada FM, 2007, FOOD CHEM, V103, P467, DOI 10.1016/j.foodchem.2006.08.023 Issaoui M, 2007, J FOOD AGRIC ENVIRON, V5, P17 Japon-Lujan R, 2006, J AGR FOOD CHEM, V54, P9706, DOI 10.1021/jf062546w Koprivnjak O, 2005, FOOD CHEM, V90, P603, DOI 10.1016/j.foodchem.2004.04.019 Kotti F, 2009, EUR FOOD RES TECHNOL, V228, P735, DOI 10.1007/s00217-008-0984-x Lerma-Garcia MJ, 2008, FOOD CHEM, V108, P1142, DOI 10.1016/j.foodchem.2007.11.065 Lorenzo IM, 2002, ANAL BIOANAL CHEM, V374, P1205, DOI 10.1007/s00216-002-1607-1 Luna G, 2006, FOOD CHEM, V98, P243, DOI 10.1016/j.foodchem.2005.05.069 Manai H, 2008, SCI HORTIC-AMSTERDAM, V115, P252, DOI 10.1016/j.scienta.2007.10.011 Mannina L, 2003, J AGR FOOD CHEM, V51, P120, DOI 10.1021/jf025656l Marini F, 2006, CHEMOMETR INTELL LAB, V80, P140, DOI 10.1016/j.chemolab.2005.05.002 Martins-Lopes P, 2008, J AGR FOOD CHEM, V56, P11786, DOI 10.1021/jf801146z Matos LC, 2007, FOOD CHEM, V102, P406, DOI 10.1016/j.foodchem.2005.12.031 Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z MURRAY MG, 1980, NUCLEIC ACIDS RES, V8, P4321, DOI 10.1093/nar/8.19.4321 Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Nagy K, 2005, J CHROMATOGR A, V1078, P90, DOI 10.1016/j.chroma.2005.05.008 Ocakoglu D, 2009, FOOD CHEM, V113, P401, DOI 10.1016/j.foodchem.2008.07.057 Ollivier D, 2003, J AGR FOOD CHEM, V51, P5723, DOI 10.1021/jf034365p Osorio Bueno Emilio, 2003, Grasas y Aceites, V54, P1 Oueslati I, 2008, J AGR FOOD CHEM, V56, P7992, DOI 10.1021/jf801022c Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 PALMICRI L, 2003, THESIS U PARMA CAMBR Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Pasqualone C, 2008, RIV ITAL SOSTANZE GR, V85, P83 Poljuha D, 2008, SCI HORTIC-AMSTERDAM, V115, P223, DOI 10.1016/j.scienta.2007.08.018 Roca M, 2003, J AM OIL CHEM SOC, V80, P1237, DOI 10.1007/s11746-003-0848-0 Sacco A, 2000, J AM OIL CHEM SOC, V77, P619, DOI 10.1007/s11746-000-0100-y Sambroock J., 1989, MOL CLONING LAB MANU Sanz-Cortes F, 2003, PLANT BREEDING, V122, P173, DOI 10.1046/j.1439-0523.2003.00808.x Stefanoudaki E, 1997, FOOD CHEM, V60, P425, DOI 10.1016/S0308-8146(96)00045-3 Stefanoudaki E, 1999, J AM OIL CHEM SOC, V76, P623, DOI 10.1007/s11746-999-0013-7 Stefanoudaki E, 2000, J SCI FOOD AGR, V80, P381, DOI [10.1002/1097-0010(200002)80:3<381::AID-JSFA535>3.0.CO;2-4, 10.1002/(SICI)1097-0010(200002)80:3<381::AID-JSFA535>3.3.CO;2-W] Tapp HS, 2003, J AGR FOOD CHEM, V51, P6110, DOI 10.1021/jf030232s Tena N, 2007, J AGR FOOD CHEM, V55, P7852, DOI 10.1021/jf071030p Tura D, 2008, SCI HORTIC-AMSTERDAM, V118, P139, DOI 10.1016/j.scienta.2008.05.030 Vaz-Freire LT, 2009, ANAL CHIM ACTA, V633, P263, DOI 10.1016/j.aca.2008.11.057 Vinha AF, 2005, FOOD CHEM, V89, P561, DOI 10.1016/j.foodchem.2004.03.012 WAIBLINGER HU, 1999, LEBENSMITTELCHEMIE, V53, P11 NR 72 TC 84 Z9 104 U1 6 U2 45 PD JAN 13 PY 2010 VL 58 IS 1 BP 28 EP 38 DI 10.1021/jf902619z WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000273268100002 DA 2022-12-14 ER PT J AU Pop, RE Bratulescu, A AF Pop, Ruxandra-Eugenia Bratulescu, Alexandra TI E-BUSINESS APLICATION TO IMPROVE TRACEABILITY AND SUPPLY CHAIN FOR FRESH FOOD SO SCIENTIFIC PAPERS-SERIES MANAGEMENT ECONOMIC ENGINEERING IN AGRICULTURE AND RURAL DEVELOPMENT DT Article DE fresh food; supply chain; traceability; e-business tools AB The agri-food sector in Romania is of particular importance for the market economy, referring to agriculture, food industry, commerce, and not ultimately the final consumer. Due to changes in consumer behavior of agri-food products, it is increasingly desirable to know the food traceability, the concept from farm to fork being constantly developing. In the context of ensuring food security, it is important to have an efficient monitoring of the food route in order to keep it fresh. An important factor in this process is the new communication and information technologies, which are in continuous development especially from the applicative point of view. This paper aims to support the producers and suppliers of fresh agri-food products in order to improve management at the short supply chain by exemplifying some types of computer tools that can be used in their work. The conclusion was that GIS maps and GPS location enable the use to know where production is located and the storage conditions. C1 [Pop, Ruxandra-Eugenia; Bratulescu, Alexandra] Res Inst Econ Agr & Dev, 61 Marasti Blvd,Dist 1, Bucharest 011464, Romania. RP Pop, RE (corresponding author), Res Inst Econ Agr & Dev, 61 Marasti Blvd,Dist 1, Bucharest 011464, Romania. EM pop.ruxandra@iceadr.ro CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Ancuta Marin, 2016, PROIECTAREA SI EXPT, p[50, 54] European Comission, 2007, HLTH CONS PROT Flott L.W, 2002, MET FINISH, V100, P42 Kraisintu K, 2011, THESIS, p[15, 29] Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Qu XiaoHui, 2007, Agricultural Sciences in China, V6, P724, DOI 10.1016/S1671-2927(07)60105-9 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Srivastava B., 2004, Business Horizons, V47, P60, DOI 10.1016/j.bushor.2004.09.009 Webster's Dictionary, 2011, WEBSTERS DICT NR 10 TC 0 Z9 0 U1 2 U2 23 PY 2018 VL 18 IS 2 BP 329 EP 332 WC Agricultural Economics & Policy SC Agriculture UT WOS:000438506000043 DA 2022-12-14 ER PT J AU Corallo, A Latino, ME Menegoli, M Pizzi, R AF Corallo, Angelo Latino, Maria Elena Menegoli, Marta Pizzi, Roberta TI Assuring Effectiveness in Consumer-Oriented Traceability; Suggestions for Food Label Design SO AGRONOMY-BASEL DT Review DE traceability information; food labeling; food label design; food label theory; package; food choice ID WILLINGNESS-TO-PAY; OF-THE-LITERATURE; NUTRITION CLAIMS; EYE-TRACKING; PRODUCTS; INFORMATION; PERCEPTIONS; HEALTH; CHOICES; KNOWLEDGE AB Traceability is an important tool used by food companies and regulators in assuring food safety and quality, especially when consumers' needs for food information transparency are the driver. Consumers consult the label to find out more details about a food product and, although many factors influence their perceptions and purchasing acts, the label remains the primary means of communicating food information affecting consumer choice. Therefore, it represents the final step in a consumer-oriented traceability path. It follows that a suitable label design can improve the food traceability process and reduce the information asymmetry between producer and consumer. According to this view, this paper aimed to identify suggestions about food label design, in order to create a support framework for food companies in food information communication increasing label readability, customer satisfaction, and the effectiveness of traceability. A systematic literature review method with content analysis was chosen to conduct the study. Eleven specific suggestions from food labeling design theories were recognized. The novelty of the present study consisted in mapping the food label design field, synthesizing the current knowledge, and providing a support framework for food companies that would increase the readability of food labeling and enhance customer satisfaction through a well-proposed food information communication in line with the "farm to fork" strategy. C1 [Corallo, Angelo; Latino, Maria Elena; Menegoli, Marta; Pizzi, Roberta] Univ Salento, Innovat Engn Dept, I-73100 Lecce, Italy. C3 University of Salento RP Latino, ME (corresponding author), Univ Salento, Innovat Engn Dept, I-73100 Lecce, Italy. EM angelo.corallo@unisalento.it; mariaelena.latino@unisalento.it; marta.menegoli@unisalento.it; roberta.pizzi@unisalento.it CR Ahuja JKC, 2019, J ACAD NUTR DIET, V119, P293, DOI 10.1016/j.jand.2018.08.155 Al-Hamdani M, 2017, INT J FOOD SCI NUTR, V68, P131, DOI 10.1080/09637486.2016.1226274 Amodio ML, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10010007 Anagnostou A, 2015, J CONSUM MARK, V32, P422, DOI 10.1108/JCM-11-2014-1213 Antunez L, 2013, INT J FOOD SCI NUTR, V64, P515, DOI 10.3109/09637486.2012.759187 Apostolidis C, 2019, FOOD QUAL PREFER, V77, P109, DOI 10.1016/j.foodqual.2019.04.008 Ares G, 2013, J SENS STUD, V28, P138, DOI 10.1111/joss.12031 Ares G, 2012, INT J FOOD SCI NUTR, V63, P679, DOI 10.3109/09637486.2011.652598 Aslam M. M., 2006, J MARK COMMUN, V12, P15, DOI DOI 10.1080/13527260500247827 Ayaz A, 2021, NUTR FOOD SCI, V51, P517, DOI 10.1108/NFS-05-2020-0174 BAGOZZI RP, 1990, J CONSUM RES, V17, P127, DOI 10.1086/208543 Bailey R, 2019, HEALTH COMMUN, V34, P735, DOI 10.1080/10410236.2018.1434734 Bauer JM, 2019, J CONSUM POLICY, V42, P3, DOI 10.1007/s10603-018-9387-y Berhaupt-Glickstein A, 2019, NUTRIENTS, V11, DOI 10.3390/nu11040921 Bimbo F, 2017, APPETITE, V113, P141, DOI 10.1016/j.appet.2017.02.031 Bissinger K, 2017, BRIT FOOD J, V119, P1801, DOI 10.1108/BFJ-10-2016-0515 Blisard N., 2000, FoodReview, V23, P18 Bosman MJC, 2014, BRIT FOOD J, V116, P30, DOI 10.1108/BFJ-12-2011-0298 Brandt M, 2009, J FOOD COMPOS ANAL, V22, pS74, DOI 10.1016/j.jfca.2009.01.004 Bray J, 2019, BRIT FOOD J, V121, P1744, DOI 10.1108/BFJ-09-2018-0605 Brewer JM, 2013, CHEM SENSES, V38, P305, DOI 10.1093/chemse/bjs142 Brierley M, 2017, VISUAL COMMUN-US, V16, P57, DOI 10.1177/1470357216668693 Bublitz MG, 2013, J BUS RES, V66, P1211, DOI 10.1016/j.jbusres.2012.08.014 Bui M, 2013, J PROD BRAND MANAG, V22, P352, DOI 10.1108/JPBM-05-2013-0298 Butler LT, 2001, APPL COGNITIVE PSYCH, V15, P587, DOI 10.1002/acp.730 Carey R, 2017, J RURAL STUD, V54, P266, DOI 10.1016/j.jrurstud.2017.06.014 CASWELL JA, 1992, AM J AGR ECON, V74, P460, DOI 10.2307/1242500 Chambers L, 2015, TRENDS FOOD SCI TECH, V41, P149, DOI 10.1016/j.tifs.2014.10.007 Chan K, 2016, YOUNG CONSUM, V17, P32, DOI 10.1108/YC-03-2015-00520 Charlebois S, 2016, TRENDS FOOD SCI TECH, V50, P211, DOI 10.1016/j.tifs.2016.02.003 Choi J, 2016, BRIT FOOD J, V118, P2842, DOI 10.1108/BFJ-04-2016-0163 Christofi Michael, 2015, Global Business and Economics Review, V17, P93, DOI 10.1504/GBER.2015.066533 Chrysochou P, 2019, J CONSUM MARK, V36, P441, DOI 10.1108/JCM-06-2018-2720 Colby SE, 2010, J NUTR EDUC BEHAV, V42, P92, DOI 10.1016/j.jneb.2008.11.002 Connolly C, 2007, SENSOR REV, V27, P207, DOI 10.1108/02602280710758147 Crossan MM, 2010, J MANAGE STUD, V47, P1154, DOI [10.1111/J.1467-6486.2009.00880.X, 10.1111/j.1467-6486.2009.00880.x] Cucchiara C, 2015, BRIT FOOD J, V117, P1547, DOI 10.1108/BFJ-07-2014-0261 Cummings CL, 2018, SCI TECHNOL HUM VAL, V43, P888, DOI 10.1177/0162243917753991 Curl A, 2011, RES TRANSP BUS MANAG, V2, P3, DOI 10.1016/j.rtbm.2011.07.001 de Menezes LM, 2011, INT J MANAG REV, V13, P452, DOI 10.1111/j.1468-2370.2011.00301.x de-Magistris T, 2017, BRIT FOOD J, V119, P2698, DOI 10.1108/BFJ-11-2016-0562 De-Magistris T, 2016, BRIT FOOD J, V118, P560, DOI 10.1108/BFJ-09-2015-0322 Deliza R, 2020, FOOD QUAL PREFER, V80, DOI 10.1016/j.foodqual.2019.103821 Dickinson Angela, 2014, Br J Community Nurs, V19, P228 Dixon G, 2016, J RISK RES, V19, P1158, DOI 10.1080/13669877.2015.1118149 Kraemer MVD, 2015, BRIT FOOD J, V117, P719, DOI 10.1108/BFJ-11-2013-0339 Draper AK, 2013, EUR J PUBLIC HEALTH, V23, P517, DOI 10.1093/eurpub/ckr144 Egnell M, 2018, NUTRIENTS, V10, DOI 10.3390/nu10101542 Ellen PS, 2008, J PUBLIC POLICY MARK, V27, P69, DOI 10.1509/jppm.27.1.69 Elliott C, 2008, CAN PUBLIC POL, V34, P259, DOI 10.3138/cpp.34.2.259 Escribano M, 2020, J RURAL STUD, V75, P206, DOI 10.1016/j.jrurstud.2020.02.002 European Commission, FARM FORK STRAT Fernan C, 2018, HEALTH COMMUN, V33, P1425, DOI 10.1080/10410236.2017.1358240 Ferrari L, 2021, BRIT FOOD J, V123, P1268, DOI 10.1108/BFJ-09-2020-0820 Fink, 2020, CONDUCTING RES LIT R Fitzgerald MP, 2019, J CONSUM MARK, V36, P306, DOI 10.1108/JCM-08-2017-2307 Furst T, 1996, APPETITE, V26, P247, DOI 10.1006/appe.1996.0019 Gaillard A, 2011, PSYCHOL REP, V109, P187, DOI 10.2466/10.13.22.28.PR0.109.4.187-207 Golan E., 2001, J CONSUM POL, V24, P117, DOI DOI 10.1023/A:1012272504846 Goodman S, 2018, NUTRIENTS, V10, DOI 10.3390/nu10111624 Grabenhorst F, 2013, NEUROIMAGE, V74, P152, DOI 10.1016/j.neuroimage.2013.02.012 Graham DJ, 2014, HEALTH PSYCHOL, V33, P1579, DOI 10.1037/hea0000080 Graham DJ, 2012, FOOD POLICY, V37, P378, DOI 10.1016/j.foodpol.2012.03.004 Hajdu N, 2018, AMFITEATRU ECON, V20, P62 Harris JL, 2009, ANNU REV PUBL HEALTH, V30, P211, DOI 10.1146/annurev.publhealth.031308.100304 Hellier E, 2012, J RISK RES, V15, P533, DOI 10.1080/13669877.2011.646288 Hess JM, 2017, J FOOD SCI, V82, P2213, DOI 10.1111/1750-3841.13819 Hu B., 2020, INFLUENCE SUGAR FREE, VVolume 1190, P626 Huang CH, 2014, CHINA AGR ECON REV, V6, P198, DOI 10.1108/CAER-04-2012-0033 Huang JP, 2021, FOOD QUAL PREFER, V88, DOI 10.1016/j.foodqual.2020.104078 Institute J.B, 2011, COMPREHENSIVE SYSTEM Kajale DB, 2013, BRIT FOOD J, V115, P1597, DOI [10.1108/BFJ-12-2011-0302, 10.1108/BFJ-12-20110302] Kapsak WR, 2008, CRIT REV FOOD SCI, V48, P248, DOI 10.1080/10408390701286058 Khandpur N, 2020, J ACAD NUTR DIET, V120, P197, DOI 10.1016/j.jand.2019.10.008 Khandpur N, 2019, FOOD RES INT, V121, P854, DOI 10.1016/j.foodres.2019.01.008 Kimura A, 2011, J FOOD SCI, V76, pS217, DOI 10.1111/j.1750-3841.2011.02047.x Klintman M., 2006, International Journal of Consumer Studies, V30, P427, DOI 10.1111/j.1470-6431.2006.00540.x Klintman M, 2009, J CONSUM POLICY, V32, P43, DOI 10.1007/s10603-009-9094-9 Koos S, 2011, J CONSUM POLICY, V34, P127, DOI 10.1007/s10603-010-9153-2 Kumar N, 2017, BRIT FOOD J, V119, P218, DOI 10.1108/BFJ-06-2016-0249 Lang T, 1995, INF DES J, V8, P3, DOI [10.1075/idj.8.1.01lan, DOI 10.1075/IDJ.8.1.01LAN] Lapan H, 2007, AM J AGR ECON, V89, P769, DOI 10.1111/j.1467-8276.2007.01002.x Larceneux F, 2012, J CONSUM POLICY, V35, P85, DOI 10.1007/s10603-011-9186-1 Latiff ZAB, 2016, J FOOD PROD MARK, V22, P137, DOI 10.1080/10454446.2013.856053 Lee TH, 2019, BRIT FOOD J, V122, P414, DOI 10.1108/BFJ-03-2019-0195 Legendre S, 2018, J PROD BRAND MANAG, V27, P634, DOI 10.1108/JPBM-12-2016-1381 Leonidou E, 2020, J BUS RES, V119, P245, DOI 10.1016/j.jbusres.2018.11.054 Leufkens D, 2018, BRIT FOOD J, V120, P2843, DOI 10.1108/BFJ-12-2017-0710 Liang ARD, 2021, ASIA PAC J MARKET LO, V33, P396, DOI 10.1108/APJML-03-2019-0171 Liang RD, 2016, BRIT FOOD J, V118, P183, DOI 10.1108/BFJ-06-2015-0215 Limbu YB, 2019, BRIT FOOD J, V121, P1480, DOI 10.1108/BFJ-09-2018-0568 Lin CTJ, 2004, SOC SCI MED, V59, P1955, DOI 10.1016/j.socscimed.2004.02.030 Littell J.H., 2006, EVID POLICY J RES DE, V2, P535, DOI [10.1332/174426406778881728, DOI 10.1332/174426406778881728] Lwin MO, 2015, J CONSUM MARK, V32, P530, DOI 10.1108/JCM-10-2014-1191 Machado PP, 2016, BRIT FOOD J, V118, P1579, DOI 10.1108/BFJ-10-2015-0353 Marino CJ, 2005, HUM FACTORS, V47, P121, DOI 10.1518/0018720053653758 Mazocco L, 2018, NUTRIENTS, V10, DOI 10.3390/nu10091303 McCrory C, 2017, J NUTR EDUC BEHAV, V49, P304, DOI 10.1016/j.jneb.2016.11.013 McGinnis, 2006, FOOD MARKETING CHILD Medina-Molina C, 2021, BRIT FOOD J, V123, P754, DOI 10.1108/BFJ-04-2020-0353 Menegoli M., P 2019 6 INT C FRONT, P89 Merle A, 2016, RECH APPL MARKET-ENG, V31, P26, DOI 10.1177/2051570715626367 Miller AC, 2020, J ACAD NUTR DIET, V120, P45, DOI 10.1016/j.jand.2019.05.018 Miller CK, 2002, J AM DIET ASSOC, V102, P1069, DOI 10.1016/S0002-8223(02)90242-7 Miller LMS, 2014, BRIT FOOD J, V116, P1205, DOI 10.1108/BFJ-02-2013-0042 Mishra D.K., 2016, PRODUCT LIFECYCLE MA, P377, DOI DOI 10.1007/978-3-319-54660-5_34 Mohajeri M, 2019, NUTR FOOD SCI, V50, P641, DOI 10.1108/NFS-04-2019-0117 Mohamed Z, 2014, J FOOD PROD MARK, V20, P63, DOI 10.1080/10454446.2014.921876 Moher D, 2009, BMJ-BRIT MED J, V339, DOI [10.1136/bmj.i4086, 10.1136/bmj.b2535, 10.1186/2046-4053-4-1] Morrow Adam., 2020, MARITIME DOMAIN AWAR Moser AK, 2016, J CONSUM MARK, V33, P552, DOI 10.1108/JCM-04-2016-1790 Muller L, 2020, NUTRIENTS, V12, DOI 10.3390/nu12092870 Nakat Z, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107661 NEWELL R, 2006, VITAL NOTES NURSES R Newman CL, 2014, J MACROMARKETING, V34, P505, DOI 10.1177/0276146714529306 Nishida W, 2016, BRIT FOOD J, V118, P1594, DOI 10.1108/BFJ-09-2015-0325 Nobrega L, 2020, FOOD QUAL PREFER, V79, DOI 10.1016/j.foodqual.2019.103749 Nyilasy G, 2016, PUBLIC HEALTH NUTR, V19, P2122, DOI 10.1017/S1368980016000483 Odaman TA, 2020, NUTR FOOD SCI, V50, P1021, DOI 10.1108/NFS-09-2019-0301 Oliveira D, 2016, LWT-FOOD SCI TECHNOL, V68, P160, DOI 10.1016/j.lwt.2015.11.066 Patra D, 2020, FOOD CONTROL, V115, DOI 10.1016/j.foodcont.2020.107307 Paul J, 2020, INT BUS REV, V29, DOI 10.1016/j.ibusrev.2020.101717 Pereira RC, 2019, BRIT FOOD J, V121, P1550, DOI 10.1108/BFJ-08-2018-0516 Plimmer J, 2013, WOODHEAD PUBL FOOD S, V244, P35, DOI 10.1533/9780857098979.35 Pomeranz JL, 2019, FOOD POLICY, V86, DOI 10.1016/j.foodpol.2019.05.005 Przyrembel H, 2004, TRENDS FOOD SCI TECH, V15, P360, DOI 10.1016/j.tifs.2003.12.006 Rayner M, 1995, INF DES J, V8, P25, DOI [10.1075/idj.8.1.03ray, DOI 10.1075/IDJ.8.1.03RAY] Roberto CA, 2014, J CONSUM PSYCHOL, V24, P438, DOI 10.1016/j.jcps.2014.03.001 Rousu M. C., 2008, Journal of Agricultural & Food Industrial Organization, V6, P3, DOI 10.2202/1542-0485.1212 Salnikova E, 2014, BRIT FOOD J, V116, P337, DOI 10.1108/BFJ-05-2012-0115 Samotyja U, 2015, BRIT FOOD J, V117, P222, DOI 10.1108/BFJ-09-2013-0257 Sanjari SS, 2017, NUTR REV, V75, P871, DOI 10.1093/nutrit/nux043 Sapci O, 2020, EAST ECON J, V46, P673, DOI 10.1057/s41302-020-00175-3 Savchenko OM, 2018, FOOD POLICY, V80, P103, DOI 10.1016/j.foodpol.2018.09.005 Schnettler B, 2019, MEAT SCI, V152, P104, DOI 10.1016/j.meatsci.2019.02.007 Scott I., 2007, ESSENTIAL STUDY SKIL Sen M. K. C., 2014, Journal of Animal and Veterinary Advances, V13, P350 Shin S, 2020, NUTRIENTS, V12, DOI 10.3390/nu12072158 Singla M, 2010, BRIT FOOD J, V112, P83, DOI 10.1108/00070701011011227 Smith V, 2011, FACHSPRACHE, V33, P84 Smith V, 2014, COGN LINGUIST, V25, P99, DOI 10.1515/cog-2013-0032 Spaulding CJ, 2015, J PREV INTERV COMMUN, V43, P123, DOI 10.1080/10852352.2014.973272 Spendrup S, 2016, APPETITE, V100, P133, DOI 10.1016/j.appet.2016.02.007 Steptoe A, 2002, PREV MED, V35, P97, DOI 10.1006/pmed.2002.1048 Stuart SA, 2010, BRIT FOOD J, V112, P21, DOI 10.1108/00070701011011173 Szlachciuk J, 2017, BRIT FOOD J, V119, P442, DOI 10.1108/BFJ-03-2016-0107 Tanner SA, 2019, INT J RETAIL DISTRIB, V47, P1336, DOI 10.1108/IJRDM-08-2018-0175 Teng CC, 2015, BRIT FOOD J, V117, P1066, DOI 10.1108/BFJ-12-2013-0361 Tonkin E, 2015, BRIT FOOD J, V117, P318, DOI 10.1108/BFJ-07-2014-0244 Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375 Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 Turra C, 2014, J AGR ENVIRON ETHIC, V27, P663, DOI 10.1007/s10806-013-9484-5 Vanclay JK, 2011, J CONSUM POLICY, V34, P153, DOI 10.1007/s10603-010-9140-7 Wang CL, 2014, INT J MANAG REV, V16, P24, DOI 10.1111/ijmr.12007 Wansink B, 2006, J MARKETING RES, V43, P605, DOI 10.1509/jmkr.43.4.605 Weirich P., 2007, LABELING GENETICALLY Williams W, 2014, J HIST RES MARKETING, V6, P56, DOI 10.1108/JHRM-06-2013-0032 Wood A, 2018, J COMP POLICY ANAL, V20, P404, DOI 10.1080/13876988.2017.1391462 Yong-Hak Jo, 2019, DEFINITIVE RESOURCE Zerbini C, 2019, FOOD RES INT, V125, DOI 10.1016/j.foodres.2019.108547 Zhan JT, 2021, BRIT FOOD J, V123, P405, DOI 10.1108/BFJ-08-2019-0637 Zlatev J, 2010, J PRAGMATICS, V42, P2799, DOI 10.1016/j.pragma.2010.03.011 Zlatevska N, 2019, MARKET LETT, V30, P321, DOI 10.1007/s11002-019-09504-9 NR 163 TC 4 Z9 4 U1 7 U2 26 PD APR PY 2021 VL 11 IS 4 AR 613 DI 10.3390/agronomy11040613 WC Agronomy; Plant Sciences SC Agriculture; Plant Sciences UT WOS:000642659800001 DA 2022-12-14 ER PT J AU Zhang, YJ Fu, ZT Xiao, XQ Zhang, XS Li, DL AF Zhang, Yongjun Fu, Zetian Xiao, Xinqing Zhang, Xiaoshuan Li, Daoliang TI MW-MTM: A mobile wireless monitoring and traceability management system for water-free live transport of aquatic products SO JOURNAL OF FOOD PROCESS ENGINEERING DT Article DE Aquatic products; mobile wireless monitoring; traceability management; water-free live transport ID FISH TRANSPORT; STRESS; PARAMETERS; QUALITY AB Low temperature water-free keep live transport is a new method for transportation of aquatic products. The fish or other aquatic products will be suspended animation of the dormant state, by the appropriate gradient cooling of in their corresponding ecological ice temperature zone, to reduce respiration and metabolism to improve the survival rates. The article developed a mobile wireless monitoring and traceability management system to acquire micro-ambient gas, humidity, and temperature intelligently in logistics and control them automatically to release transport stress, guarantee the keep alive rates and meat quality of aquatic products. Finally, through test of live Chinese sturgeon water-free keep live transport under the management of MW-MTM system, the survival rate was increased by 5% to reach 99.5% after using this system, reduced the labor force 10% and visualized the traceability of transportation based on Baidu map system. Practical applicationsAquatic products water-free live transport in ecological ice temperature zone by the appropriate gradient cooling, to make aquatic products in suspended animation in the dormant state, reduce respiration, and metabolism to improve the survival rate. Through the implementation of the monitoring online metabolic gas and controlling of temperature, humidity of cold chain so as to control and trace the quality of the live transport. Those applications of live fish transport provided the theoretical basis for the tracing and optimization of cold chain logistics for live aquatic products. C1 [Zhang, Yongjun; Xiao, Xinqing; Li, Daoliang] China Agr Univ, Coll Informat & Elect, Beijing 656, Peoples R China. [Zhang, Yongjun] Shandong Inst Commerce & Technol, Coll Elect Informat, Beijing 202, Peoples R China. [Fu, Zetian; Xiao, Xinqing; Zhang, Xiaoshuan] China Agr Univ, Beijing Food Qual & Safety Lab, Beijing 656, Peoples R China. C3 China Agricultural University; Shandong Institute of Commerce & Technology; China Agricultural University RP Li, DL (corresponding author), China Agr Univ, Coll Informat & Elect, Beijing 656, Peoples R China.; Zhang, XS (corresponding author), China Agr Univ, Beijing Food Qual & Safety Lab, Beijing 656, Peoples R China. EM zhxshuan@cau.edu.cn; dliangl@cau.edu.cn CR Cimino EJ, 2002, COMP BIOCHEM PHYS A, V132, P591, DOI 10.1016/S1095-6433(02)00101-0 Coyle S. D., 2005, Journal of Applied Aquaculture, V17, P61, DOI 10.1300/J028v17n04_04 Dhanasiri AKS, 2011, J APPL MICROBIOL, V111, P278, DOI 10.1111/j.1365-2672.2011.05050.x Dove ADM, 2005, J SHELLFISH RES, V24, P761 Fotedar S, 2011, J INVERTEBR PATHOL, V106, P143, DOI 10.1016/j.jip.2010.09.011 Harmon TS, 2009, REV AQUACULT, V1, P58, DOI 10.1111/j.1753-5131.2008.01003.x Hegyi A, 2010, ACTA BIOL HUNG, V61, P24, DOI 10.1556/ABiol.61.2010.1.3 Iwama G. W., 2011, FISH STRESS HLTH AQU, V2011, P35 King HR, 2009, J VET BEHAV, V4, P163, DOI 10.1016/j.jveb.2008.09.034 Lin LanFen, 2015, Transactions of the Chinese Society of Agricultural Engineering, V31, P228 Lorenzon S., 2005, ISJ, V2, P132 Mi HB, 2012, FISH PHYSIOL BIOCHEM, V38, P1721, DOI 10.1007/s10695-012-9669-2 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 [齐林 Qi Lin], 2012, [农业机械学报, Transactions of the Chinese Society of Agricultural Machinery], V43, P134 Skudlarek JG, 2011, J WORLD AQUACULT SOC, V42, P603, DOI 10.1111/j.1749-7345.2011.00508.x Taylor PW, 1999, N AM J AQUACULT, V61, P150, DOI 10.1577/1548-8454(1999)061<0150:COAAAF>2.0.CO;2 Trebilcock M, 2011, INTERNATIONAL PATENT LAW: COOPERATION, HARMONIZATION AND AN INSTITUTIONAL ANALYSIS OF WIPO AND THE WTO, P1 Zeng P., 2013, FISH PHYSIOL BIOCHEM, V12, P1 NR 18 TC 7 Z9 8 U1 3 U2 56 PD JUN PY 2017 VL 40 IS 3 AR e12495 DI 10.1111/jfpe.12495 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000400153500067 DA 2022-12-14 ER PT J AU Zhao, Y Zhang, B Chen, G Chen, AL Yang, SM Ye, ZH AF Zhao, Yan Zhang, Bin Chen, Gang Chen, Ailiang Yang, Shuming Ye, Zhihua TI Recent developments in application of stable isotope analysis on agro-product authenticity and traceability SO FOOD CHEMISTRY DT Article DE Stable isotope; Agro-product; Authenticity; Traceability ID DATA-BANK WINES; GEOGRAPHICAL ORIGIN; RATIO-ANALYSIS; REGIONAL ORIGIN; FATTY-ACID; LAMB MEAT; SNIF-NMR; CARBON; MULTIELEMENT; DIFFERENTIATION AB With the globalisation of agro-product markets and convenient transportation of food across countries and continents, the potential for distribution of mis-labelled products increases accordingly, highlighting the need for measures to identify the origin of food. High quality food with identified geographic origin is a concern not only for consumers, but also for agriculture farmers, retailers and administrative authorities. Currently, stable isotope ratio analysis in combination with other chemical methods gradually becomes a promising approach for agro-product authenticity and traceability. In the last five years, a growing number of research papers have been published on tracing agro-products by stable isotope ratio analysis and techniques combining with other instruments. In these reports, the global variety of stable isotope compositions has been investigated, including light elements such as C, N, H, O and S, and heavy isotopes variation such as Sr and B. Several factors also have been considered, including the latitude, altitude, evaporation and climate conditions. In the present paper, an overview is provided on the authenticity and traceability of the agro-products from both animal and plant sources by stable isotope ratio analysis. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Zhao, Yan; Chen, Gang; Chen, Ailiang; Yang, Shuming; Ye, Zhihua] Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Zhao, Yan; Chen, Gang; Chen, Ailiang; Yang, Shuming; Ye, Zhihua] Minist Agr, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Zhang, Bin] Henan Univ Sci & Technol, Coll Food & Bioengn, Luoyang 471023, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs; Henan University of Science & Technology RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China. EM yzhao2010@yahoo.cn CR Angerosa F, 1999, J AGR FOOD CHEM, V47, P1013, DOI 10.1021/jf9809129 Bahar B, 2008, FOOD CHEM, V106, P1299, DOI 10.1016/j.foodchem.2007.07.053 Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Bahar B, 2009, J ANIM SCI, V87, P905, DOI 10.2527/jas.2008-1360 Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Brescia MA, 2002, RAPID COMMUN MASS SP, V16, P2286, DOI 10.1002/rcm.860 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Camin F, 2010, RAPID COMMUN MASS SP, V24, P1810, DOI 10.1002/rcm.4581 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 Day M., 1995, Journal International des Sciences de la Vigne et du Vin, V29, P75 DAY MP, 1995, J SCI FOOD AGR, V67, P113, DOI 10.1002/jsfa.2740670118 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 Gimenez-Miralles JE, 1999, J AGR FOOD CHEM, V47, P2645, DOI 10.1021/jf9811727 Gonzalez-Martin I, 1999, MEAT SCI, V52, P437, DOI 10.1016/S0309-1740(99)00027-3 Gonzalez-Martin I, 2001, MEAT SCI, V58, P25, DOI 10.1016/S0309-1740(00)00126-1 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Hegerding L, 2002, FLEISCHWIRTSCHAFT, V82, P95 HOLBACH B, 1994, Z LEBENSM UNTERS FOR, V198, P223, DOI 10.1007/BF01192599 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Horacek M, 2010, FOOD CHEM, V118, P910, DOI 10.1016/j.foodchem.2009.03.090 Hrastar R, 2009, J AGR FOOD CHEM, V57, P579, DOI 10.1021/jf8028144 Kawasaki A, 2002, SOIL SCI PLANT NUTR, V48, P635, DOI 10.1080/00380768.2002.10409251 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kelly S, 2002, EUR FOOD RES TECHNOL, V214, P72, DOI 10.1007/s002170100400 Kim SH, 2012, FOOD SCI BIOTECHNOL, V21, P295, DOI 10.1007/s10068-012-0040-2 Kornexl BE, 1997, Z LEBENSM UNTERS F A, V205, P19, DOI 10.1007/s002170050117 KREITLER C W, 1975, Ground Water, V13, P53, DOI 10.1111/j.1745-6584.1975.tb03065.x Martin GJ, 1999, AM J ENOL VITICULT, V50, P409 Molkentin J, 2007, ANAL BIOANAL CHEM, V388, P297, DOI 10.1007/s00216-007-1222-2 Moreno-Rojas JM, 2008, RAPID COMMUN MASS SP, V22, P3701, DOI 10.1002/rcm.3773 Nakashita R, 2008, ANAL CHIM ACTA, V617, P148, DOI 10.1016/j.aca.2008.03.048 Oda H., 2002, P 17 WORLD C SOIL SC, V59 Ogrinc N, 2001, J AGR FOOD CHEM, V49, P1432, DOI 10.1021/jf000911s Osorio MT, 2011, J AGR FOOD CHEM, V59, P3285, DOI 10.1021/jf1040433 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Remaud GS, 1997, J AGR FOOD CHEM, V45, P1844, DOI 10.1021/jf960448c Rossmann A, 1996, Z LEBENSM UNTERS FOR, V203, P293, DOI 10.1007/BF01192881 Rossmann A., 1998, Rivista di Scienza dell'Alimentazione, V27, P9 Rossmann A, 1999, Z LEBENSM UNTERS F A, V208, P400, DOI 10.1007/s002170050437 Rossmann A, 1998, Z LEBENSM UNTERS F A, V207, P237, DOI 10.1007/s002170050325 Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 Spangenberg JE, 1998, J AGR FOOD CHEM, V46, P4179 Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Thiem I, 2004, ISOT ENVIRON HEALT S, V40, P191, DOI 10.1080/10256010410001689899 NR 48 TC 79 Z9 95 U1 9 U2 269 PD FEB 15 PY 2014 VL 145 BP 300 EP 305 DI 10.1016/j.foodchem.2013.08.062 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000327685200041 DA 2022-12-14 ER PT J AU Gonzalez-Amarillo, CA Corrales-Munoz, JC Mendoza-Moreno, MA Amarillo, AMG Hussein, AF Arunkumar, N Ramirez-Gonzalez, G AF Andres Gonzalez-Amarillo, Carlos Carlos Corrales-Munoz, Juan Angel Mendoza-Moreno, Miguel Gonzalez Amarillo, Angela Maria Hussein, Ahmed Faeq Arunkumar, N. Ramirez-Gonzalez, Gustavo TI An IoT-Based Traceability System for Greenhouse seedling crops SO IEEE ACCESS DT Article DE IoT; greenhouse; embedded system; PID controller; sensor AB This paper presents the design of the Internet of Things (IoT)-based greenhouse traceability model for the tracking and recordkeeping of seedlings and other agricultural products in the germination and growth stages. Traced products are generally of high quality and high commercial value, making the voluntary adoption of traceability processes for the market in processed products and trade in fresh products more common today. The transmission of diseases to humans, along with the cases of chemical poisoning, provided the motive for changes in trade relations between the countries and in the manner of assessing consumer safety. The model allows the tracking of variables, such as luminosity, humidity, temperature, and water consumption, thereby revealing overall water use, growth patterns of the plants, and the timeline for harvest of produce. The system enables automated control of the indoor environment of the greenhouse using an irrigation system or temperature control and presents the main outline of internal traceability of agricultural products from seed to final produce. By means of an IoT platform, this greenhouse design finally facilitates a novel analysis of the behavior of a number of species that comprise the local agriculture in one region of Colombia. C1 [Andres Gonzalez-Amarillo, Carlos; Carlos Corrales-Munoz, Juan; Ramirez-Gonzalez, Gustavo] Univ Cauca, Telemat Dept, Popayan 190002, Colombia. [Angel Mendoza-Moreno, Miguel] Pedag & Technol Univ Colombia, Syst & Comp Engineer Sch, Tunja 150003, Colombia. [Gonzalez Amarillo, Angela Maria] Natl Open & Distance Univ, Sch Basic Sci Technol & Engn, Tunja, Colombia. [Hussein, Ahmed Faeq] AL Nahrain Univ, Fac Engn, Biomed Engn Dept, Baghdad 10072, Iraq. [Arunkumar, N.] SASTRA Univ, Dept Elect & Instrumentat, Thanjavur 613401, India. C3 Universidad del Cauca; Universidad Pedagogica y Tecnologica de Colombia (UPTC); Al-Nahrain University; Shanmugha Arts, Science, Technology & Research Academy (SASTRA) RP Ramirez-Gonzalez, G (corresponding author), Univ Cauca, Telemat Dept, Popayan 190002, Colombia. EM gramirez@unicauca.edu.co CR Anire R. B., 2017, P IEEE 9 INT C HUM N, P1 Asolkar PS, 2015, 1ST INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION ICCUBEA 2015, P214, DOI 10.1109/ICCUBEA.2015.47 Banerjee R., 2015, INT TRADE CTR B González-Amarillo Carlos, 2018, Dyna rev.fac.nac.minas, V85, P311, DOI 10.15446/dyna.v85n204.68264 Gouadria Faten, 2017, 2017 INT C GREEN EN, P1 Gunawan I, 2017, IN C IND ENG ENG MAN, P1688 Jiang J. A., 2017, 2017 11 INT C SENSIN, P1 Khamis MN, 2017, 2017 IEEE ASIA PACIFIC CONFERENCE ON POSTGRADUATE RESEARCH IN MICROELECTRONICS AND ELECTRONICS (PRIMEASIA), P81 Kodali R.K., 2016, PHOTOENERGY, P1 Labidi A, 2017, I C SCI TECH AUTO CO, P109 Li D, 2016, INT SYM COMPUT INTEL, P165, DOI [10.1109/ISCID.2016.152, 10.1109/ISCID.2016.2047] Li L., 2017, J BIOMATER TISSUE EN, V7, P1 Liang MH, 2017, CHIN AUTOM CONGR, P604 Lu CL, 2017, 2017 32ND YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC), P541, DOI 10.1109/YAC.2017.7967469 Medela A., 2013, P 2013 FUT NETW MOB, P1 Ruiz-Rosero J, 2017, SYMMETRY-BASEL, V9, DOI 10.3390/sym9120301 Seo Y.-D., 2018, PROC 15 IEEE INT C A, P1 TAVARES P. L., 2017, P BRAZ POW EL C COBE, P1 Treder J., 2016, P IEEE LIGHT C VIS C, P1 NR 19 TC 31 Z9 32 U1 2 U2 12 PY 2018 VL 6 BP 67528 EP 67535 DI 10.1109/ACCESS.2018.2877293 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000452453700001 DA 2022-12-14 ER PT J AU Xing, GD Hu, YN Ding, Q Wang, XX Xing, F Wang, HL Huan, HL Xu, YX AF Xing, G. D. Hu, Y. N. Ding, Q. Wang, X. X. Xing, F. Wang, H. L. Huan, H. L. Xu, Y. X. TI Pig identification and meat traceability by multiallelic amplification fragments with multiple single nucleotide polymorphisms SO ANIMAL DT Article DE pig identification; meat traceability; amplification fragment; single nucleotide polymorphism; microsatellite ID ANIMAL IDENTIFICATION; GENETIC TRACEABILITY; PRODUCTS; PATTERNS; MARKERS; ORIGIN; SNPS; FOOD AB Compared with conventional identification methods, DNA-based genetic approaches such as single nucleotide polymorphisms (SNPs) and satellites are much more reliable for pig identification and meat traceability. In this study, multiallelic amplification fragments with multiple SNPs, incorporating the advantages of both SNPs and microsatellites, were explored for the first time for pig identification and meat traceability. Primer pairs for multiallelic fragments and their optimal SNPs were successfully selected and used for identification of individuals from Suzhong and Duroc populations. Meanwhile, the combined panel of the above mentioned primer pairs together with their optimal SNPs for Suzhong and/or Duroc pigs were validated for identification of the hybrids (SuzhongxDuroc). Therefore, we have successfully selected multiallelic amplification fragments with multiple SNPs to identify pigs and their meat samples from Suzhong, Duroc or their hybrids. Our study demonstrates that our method is more powerful for pig identification or meat traceability than SNPs or microsatellites. C1 [Xing, G. D.; Hu, Y. N.; Ding, Q.; Wang, H. L.; Huan, H. L.] Jiangsu Acad Agr Sci, Inst Anim Sci, Nanjing 210014, Jiangsu, Peoples R China. [Ding, Q.; Xu, Y. X.] Nanjing Agr Univ, Coll Anim Sci & Technol, Nanjing 210095, Jiangsu, Peoples R China. [Wang, X. X.] Nanjing Univ, Model Anim Res Ctr, Nanjing 210061, Jiangsu, Peoples R China. [Xing, F.] Yangzhou Univ, Coll Anim Sci & Technol, Yangzhou 225009, Jiangsu, Peoples R China. [Wang, X. X.] Chinese Acad Sci, Inst Zool, Beijing 100101, Peoples R China. C3 Jiangsu Academy of Agricultural Sciences; Nanjing Agricultural University; Nanjing University; Yangzhou University; Chinese Academy of Sciences; Institute of Zoology, CAS RP Xing, GD (corresponding author), Jiangsu Acad Agr Sci, Inst Anim Sci, Nanjing 210014, Jiangsu, Peoples R China. EM xing_gd@jaas.ac.cn CR Britt AG, 2013, REV SCI TECH OIE, V32, P571 Brookes AJ, 1999, GENE, V234, P177, DOI 10.1016/S0378-1119(99)00219-X Chmielewski R, 2011, ANNU REV FOOD SCI T, V2, P37, DOI 10.1146/annurev-food-022510-133710 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Disney WT, 2001, REV SCI TECH OIE, V20, P385, DOI 10.20506/rst.20.2.1277 Dorne JL, 2013, TOXICOL APPL PHARM, V270, P218, DOI 10.1016/j.taap.2012.01.012 Fries R, 2001, NAT BIOTECHNOL, V19, P508, DOI 10.1038/89213 Giuffra E, 2000, GENETICS, V154, P1785 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Hueston WD, 2013, PREV VET MED, V109, P179, DOI 10.1016/j.prevetmed.2012.11.023 Jacob CJ, 2011, PUBLIC UNDERST SCI, V20, P261, DOI 10.1177/0963662509355737 Ke XY, 2004, HUM MOL GENET, V13, P577, DOI 10.1093/hmg/ddh060 Lindblad-Toh K, 2000, NAT GENET, V24, P381, DOI 10.1038/74215 Mazzanti G, 2003, TOXICOLOGY, V187, P91, DOI 10.1016/S0300-483X(03)00059-3 Nicoloso Letizia, 2013, Recent Pat Food Nutr Agric, V5, P9 Peelman LJ, 1998, ANIM GENET, V29, P161, DOI 10.1111/j.1365-2052.1998.00280.x Ramos AM, 2011, ANIM GENET, V42, P613, DOI 10.1111/j.1365-2052.2011.02198.x Rodriguez-Ramirez R, 2011, ANAL CHIM ACTA, V685, P120, DOI 10.1016/j.aca.2010.11.021 Sun YV, 2006, GENET EPIDEMIOL, V30, P627, DOI 10.1002/gepi.20173 Weller JI, 2006, ANIM GENET, V37, P387, DOI 10.1111/j.1365-2052.2006.01455.x NR 20 TC 2 Z9 2 U1 0 U2 14 PD SEP PY 2018 VL 12 IS 9 BP 1785 EP 1791 DI 10.1017/S1751731117003482 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000442226000002 DA 2022-12-14 ER PT J AU Mattos, LM Moretti, CL de Moura, MA Maldonade, IR da Silva, EYY AF Mattos, Leonora M. Moretti, Celso Luiz de Moura, Marcelo A. Maldonade, Iriani R. Yosino da Silva, Ester Yoshie TI Safe production and traceability of vegetables SO HORTICULTURA BRASILEIRA DT Article DE food safety; good agricultural pratices; traceability AB Consumers all over the world are aware about the strict relation among health and the necessity of a balanced diet, based on safe products. Nevertheless, interest in food functional properties and, especially, in its antioxidant activity has been increasing. However, the consumption of in natura food may present some risks, which are mainly related to chemical and microbiological contamination during the crop growing season. Considering the high competitiveness of the different productive chains, growers are working to offer products with superior value and quality, with emphasis in food safety and traceability. The hazards in food chain can be managed by monitoring the whole process, from production to distribution, so, in order to minimize the possibility of contamination,, the implementation of traceability systems and procedures of quality assurance, such as HACCP (Hazard Analysis and Critical Control Points) is necessary,. The application of Good Agricultural Practices (GAP) to the fruit and vegetable production is a prerequisite for the success of HACCP and, in this context, the Integrated Production Program main objective is to increase quality and competitiveness of Brazilian agribusiness in order to reach the levels required by international market. Its main focus are the preservation of vegetable functional properties and the utilization of techniques that ensure food safety and traceability, ensuring the previously guaranteed quality levels. The present work focuses on technologies that allow consumers to have a safe and traceable product, to prevent nutritional content wastes and to ensure maximum food sensory quality. C1 [Mattos, Leonora M.; Moretti, Celso Luiz; Maldonade, Iriani R.] Embrapa Hortalicas, BR-70359970 Brasilia, DF, Brazil. [de Moura, Marcelo A.] UFV Depto Fitotecnia, BR-36570000 Vicosa, MG, Brazil. [Yosino da Silva, Ester Yoshie] Univ Brasilia, Fac Ciencias Saude, BR-70910900 Brasilia, DF, Brazil. C3 Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA); Universidade de Brasilia RP Mattos, LM (corresponding author), Embrapa Hortalicas, C Postal 218, BR-70359970 Brasilia, DF, Brazil. EM leonora@cnph.embrapa.br CR ALMEIDA CR, 2001, SISTEMA HACCP COMO I Andrady A, 2006, PHOTOCH PHOTOBIO SCI, V5, P13, DOI 10.1039/b515670j Andrigueto J. R., 2009, PRODUCAO INTEGRADA B, P31 ANDRIGUETO JR, 2007, PRODUCAO INTEGRADA M, P17 *ASS BRAS PROD MAC, 2003, PROD INT MAC PIM MEM BARENDZ AW, 1998, FOOD CONTROL, V9, P2 BAUMAN H, 1990, FOOD TECHNOL-CHICAGO, V44, P156 Benevides S. D., 2007, Revista Brasileira de Agrociencia, V13, P19 Cuz A.B., 2006, CIENC TEC ALIMENT, V26, P104 Porto LFD, 2007, CIENC AGROTEC, V31, P1310, DOI 10.1590/S1413-70542007000500006 Guentzel JL, 2008, FOOD MICROBIOL, V25, P36, DOI 10.1016/j.fm.2007.08.003 LEITAO MFF, 2004, SERIE QUALIDADE SEGU *MAPA, 2001, INSTR NORM INSTR NOR MARSHALL MA, 2000, C MUNDIAL CARNE, V13 MORETTI CL, 2004, ELEMENTOS APOIO BOAS, P165 MORETTI CL, 2007, S BRASILEIRO POS COL, V2, P63 MORETTI CL, 2007, VISAO AGRICOLA, V4, P75 *NACOES UNIDAS FAO, 1996, DECL ROM SEG AL MUND Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Park H, 2004, INT J FOOD MICROBIOL, V91, P13, DOI 10.1016/S0168-1605(03)00334-9 SANDERS DC, 2003, GOOD AGR PRACTICES P *SENAI DN, 2000, EL AP SIST APPCC SILVA RW, 1996, C INICIACAO CIENTIFI, V4 Soriano JM, 2002, FOOD CONTROL, V13, P253, DOI 10.1016/S0956-7135(02)00023-3 Sperber WH, 1998, DAIRY FOOD ENV SANIT, V18, P418 Stringer M. F., 1994, Dairy, Food and Environmental Sanitation, V14, P478 TITI A, 1995, IOBC WPRS B, V18 VINHOLIS MB, 2000, WORLD C RURAL SOCIOL, V10, P1 Wallace C, 2001, FOOD CONTROL, V12, P235, DOI 10.1016/S0956-7135(00)00042-6 Wang H, 1996, J AGR FOOD CHEM, V44, P701, DOI 10.1021/jf950579y NR 30 TC 5 Z9 7 U1 3 U2 46 PD OCT-DEC PY 2009 VL 27 IS 4 BP 408 EP 413 DI 10.1590/S0102-05362009000400002 WC Horticulture SC Agriculture UT WOS:000278081900001 DA 2022-12-14 ER PT J AU Zhou, X Zheng, F Zhou, XJ Chan, KC Gururajan, R Wu, ZG Zhou, EX AF Zhou, Xiong Zheng, Fang Zhou, Xujuan Chan, Ka Ching Gururajan, Raj Wu, Zhangguang Zhou, Enxing TI From traceability to provenance of agricultural products through blockchain SO WEB INTELLIGENCE DT Article DE Internet plus; agricultural products supply chain; traceability; blockchain; provenance; food safety ID SUPPLY CHAIN MANAGEMENT; SYSTEM; DESIGN; SAFETY; ISSUES AB As China's agricultural output has improved, the national and local monitoring system of agricultural product safety has become much better, and monitoring standards have become increasingly strict. Despite this, there are agricultural product safety incidents which have caused consumer panic. One way to address this is by properly establishing tracking systems so that agricultural product logistics in China can be tracked and monitored. We explored this research objective with agricultural traceability and security in mind. One option that could be considered is the blockchain technology. Blockchain could also be used to ascertain the provenance of agricultural products to increase the quality and safety of the Chinese agricultural supply chain. In this context, this research converged on big data and technology, platforms and other means for product quality and safety of agricultural products traceability. In order to verify the accuracy of these three convergence, regression analysis were used to construct five models for verification of three hypothesis. The results show that based on "Internet+", using big data, big technology and big platform can significantly increase the accuracy of agricultural products traceability system hence improve consumer acceptance of the safety of agricultural products. C1 [Zhou, Xiong; Zheng, Fang] Fujian Vocat Coll Agr, Fuzhou, Fujian, Peoples R China. [Zhou, Xujuan; Chan, Ka Ching; Gururajan, Raj] Univ Southern Queensland, Sch Management & Enterprise, Toowoomba, Qld, Australia. [Wu, Zhangguang] Fujian Chuanzheng Commun Coll, Fuzhou, Fujian, Peoples R China. [Zhou, Enxing] Beijing Normal Univ, Sch Life Sci, Beijing, Peoples R China. C3 University of Southern Queensland; Fujian Chuanzheng Communications College; Beijing Normal University RP Zhou, XJ (corresponding author), Univ Southern Queensland, Sch Management & Enterprise, Toowoomba, Qld, Australia. EM 328475505@qq.com; 434335195@qq.com; xujuan.zhou@usq.edu.au; kc.chan@usq.edu.au; Raj.Gururajan@usq.edu.au; 122365690@qq.com; znx9821@163.com CR [Anonymous], 2018, LSE BUSINESS REV Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Chan K.C., 2019, P 2019 INT C MECH RO, P4 Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 Christidis K, 2016, IEEE ACCESS, V4, P2292, DOI 10.1109/ACCESS.2016.2566339 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dong YuDe, 2016, Transactions of the Chinese Society of Agricultural Engineering, V32, P280, DOI 10.11975/j.issn.1002-6819.2016.01.039 FAO/WHO, 1997, JOINT FAO WHO FOOD S Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 ISO, 1994, 176SC184021994 ISOTC Kasireddy P., ELI5 WHAT DO WE MEAN Kim HM, 2018, INTELL SYST ACCOUNT, V25, P18, DOI 10.1002/isaf.1424 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 LAPLANTE PA, 2018, IT PROF, V20, P15 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Liu J., 2018, ROLE BLOCKCHAIN TECH Marucheck A, 2011, J OPER MANAG, V29, P707, DOI 10.1016/j.jom.2011.06.007 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Nguyen T., 2017, COMPUTERS OPERATIONS Qin C.A., 2015, CHINESE CONCEPT INTE Sonesson UG, 2016, INT J LIFE CYCLE ASS, V21, P664, DOI 10.1007/s11367-015-0969-5 Thankappan S., 2016, ETHICS LAW SOC, P81 Tiwari S, 2018, COMPUT IND ENG, V115, P319, DOI 10.1016/j.cie.2017.11.017 Tseng JH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15061055 Xi YN, 2015, CHIN AUTOM CONGR, P155, DOI 10.1109/CAC.2015.7382487 Yang Q., 2009, J AGROTECHNICAL EC, V2, P69 NR 27 TC 3 Z9 3 U1 4 U2 18 PY 2020 VL 18 IS 3 BP 181 EP 189 DI 10.3233/WEB-200440 WC Computer Science, Artificial Intelligence SC Computer Science UT WOS:000579090600002 DA 2022-12-14 ER PT J AU Wang, X He, QL Matetic, M Jemric, T Zhang, XS AF Wang Xiang He Qile Matetic, Maja Jemric, Tomislav Zhang Xiaoshuan TI Development and evaluation on a wireless multi-gas-sensors system for improving traceability and transparency of table grape cold chain SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Table grapes; Gas microenvironment monitoring; Shelf life prediction; Cold chain; Traceability and transparency ID SULFUR-DIOXIDE FUMIGATION; MONITORING-SYSTEM; FRESH-PRODUCE; SHELF-LIFE; TEMPERATURE; QUALITY; FOOD; WSN; PERFORMANCE; MANAGEMENT AB There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O-2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS(2)) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433 MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS(2) in a cold chain scenario and analyses the monitoring data. The results show that the WGS(2) is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain. (C) 2017 Elsevier B.V. All rights reserved. C1 [Wang Xiang; Zhang Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Wang Xiang; Zhang Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [He Qile] Coventry Univ, Coventry CV1 5FB, W Midlands, England. [Matetic, Maja] Univ Rijeka, Rijeka 51000, Croatia. [Jemric, Tomislav] Univ Zagreb, Fac Agr, HR-10000 Zagreb, Croatia. C3 China Agricultural University; Coventry University; University of Rijeka; University of Zagreb; University of Zagreb, School of Dental Medicine RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Aiello G, 2012, PROD PLAN CONTROL, V23, P468, DOI 10.1080/09537287.2011.564219 Aung MM, 2014, FOOD CONTROL, V40, P198, DOI 10.1016/j.foodcont.2013.11.016 Zubeldia BB, 2016, FOOD CONTROL, V59, P614, DOI 10.1016/j.foodcont.2015.06.046 Bobelyn E, 2006, POSTHARVEST BIOL TEC, V42, P104, DOI 10.1016/j.postharvbio.2006.05.011 Carter MQ, 2015, FOOD MICROBIOL, V49, P189, DOI 10.1016/j.fm.2015.02.002 Champa WAH, 2015, LWT-FOOD SCI TECHNOL, V60, P412, DOI 10.1016/j.lwt.2014.08.044 Chen X., 2016, J FOOD NUTR RES, V55 Coates RW, 2013, COMPUT ELECTRON AGR, V96, P13, DOI 10.1016/j.compag.2013.04.013 Costa C, 2011, J FOOD ENG, V102, P115, DOI 10.1016/j.jfoodeng.2010.08.001 Defraeye T, 2016, POSTHARVEST BIOL TEC, V112, P1, DOI 10.1016/j.postharvbio.2015.09.033 Dehghannya J, 2010, FOOD ENG REV, V2, P227, DOI 10.1007/s12393-010-9027-z Deng Y, 2005, EUR FOOD RES TECHNOL, V221, P392, DOI 10.1007/s00217-005-1186-4 Di Gennaro SF, 2013, AUST J GRAPE WINE R, V19, P20, DOI 10.1111/ajgw.12006 Feng L, 2010, J AM CHEM SOC, V132, P4046, DOI 10.1021/ja910366p Ferrer G, 2010, INT J PROD ECON, V124, P414, DOI 10.1016/j.ijpe.2009.12.004 Garcia-Sanchez AJ, 2011, COMPUT ELECTRON AGR, V75, P288, DOI 10.1016/j.compag.2010.12.005 Gnanavel G., 2016, WIRELESS SENSOR NETW Gogou E, 2015, INT J REFRIG, V52, P109, DOI 10.1016/j.ijrefrig.2015.01.019 Guo Bin, 2011, Transactions of the Chinese Society of Agricultural Engineering, V27, P208 Han Xu, 2012, Transactions of the Chinese Society of Agricultural Engineering, V28, P275 HARVEY JM, 1988, AM J ENOL VITICULT, V39, P132 Jedermann R., 2006, P 2 INT WORKSH COLD, P3 Jiang J.-R, 2016, PRECIS AGRIC, P1, DOI [DOI 10.1109/INFOCOM.2016.7524415, 10.1007/s10664-016-9436-6, DOI 10.1007/S10664-016-9436-6] Koutsimanis G, 2015, FOOD BIOPROCESS TECH, V8, P655, DOI 10.1007/s11947-014-1437-0 Medina-Rodriguez S, 2015, SENSOR ACTUAT B-CHEM, V212, P278, DOI 10.1016/j.snb.2015.02.022 Narsimhalu U, 2015, PROCD SOC BEHV, V189, P17, DOI 10.1016/j.sbspro.2015.03.188 Palou L., 2010, FRESH PRODUCE, V4, P103 Ping-Ho Ting, 2013, International Journal of Information Engineering and Electronic Business, V5, P1, DOI 10.5815/ijieeb.2013.06.01 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Romanak KD, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL052426 Ruiz-Garcia L, 2008, J FOOD ENG, V87, P405, DOI 10.1016/j.jfoodeng.2007.12.033 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Trebar M, 2015, J FOOD ENG, V159, P66, DOI 10.1016/j.jfoodeng.2015.03.007 Ustun D, 2012, POSTHARVEST BIOL TEC, V68, P8, DOI 10.1016/j.postharvbio.2012.01.006 Villa-Rojas R, 2011, J SCI FOOD AGR, V91, P2265, DOI 10.1002/jsfa.4449 Wang JY, 2015, COMPUT ELECTRON AGR, V110, P196, DOI 10.1016/j.compag.2014.11.009 Wang X., 2016, EURASIP J WIREL COMM, V2016, P1, DOI DOI 10.13671/J.HJKXXB.2016.0295 Xiao XQ, 2015, APPL SCI-BASEL, V5, P747, DOI 10.3390/app5040747 Xiao XQ, 2016, FOOD CONTROL, V60, P656, DOI 10.1016/j.foodcont.2015.09.012 Xiao XQ, 2014, SENSORS-BASEL, V14, P19877, DOI 10.3390/s141019877 Xiao XinQing, 2013, Transactions of the Chinese Society of Agricultural Engineering, V29, P259 Youssef K, 2015, SCI HORTIC-AMSTERDAM, V193, P316, DOI 10.1016/j.scienta.2015.07.026 Zhang WQ, 2015, SENSORS-BASEL, V15, P19495, DOI 10.3390/s150819495 Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 Zhibo Pang, 2010, 2010 IEEE Sensors Applications Symposium (SAS), P6, DOI 10.1109/SAS.2010.5439425 NR 46 TC 27 Z9 31 U1 4 U2 60 PD APR 1 PY 2017 VL 135 BP 195 EP 207 DI 10.1016/j.compag.2016.12.019 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000398759500020 DA 2022-12-14 ER PT J AU Xiong, BH Luo, QY Liang, Y Fu, RT Lin, ZH Pan, JR AF Xiong Ben Hai Luo Qing-Yao Liang, Yang Fu Run-Ting Lin Zhao-Hui Pan Jia-Rong TI A practical web-based tracking and traceability information system for the pork products supply chain SO NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH DT Article DE bar code; food safety; information system; pork; traceability ID FOOD SAFETY; MANAGEMENT; LESSONS AB Pork is the major dietary animal protein source of Chinese. The concerns on pork quality and safety have urged the China Administration to establish a tracking and traceability system for animal product safety through legislation. Based on analysis of the factors that affect pork products quality, including inputs (vaccines, fodder, additives and veterinary drugs) and key control points durin swine feeding, slaughtering and retail process, this study has developed a practical application platform consisting of a bar-code based data identification system.. a data-record keeping system, correlated databases, and a data query interface. This application platforrn can meet the government's regulation and consumer demands by enabling the pork quality data to be collected and uploaded from different processes, and through inquiries interface terminals and a final splitting product tracing code, the safety informnation of the final fartri and corresponding individual pig can be retrieved. The designed systems have already been deployed in Tianjin's two farms, with their associated abattoirs and retail supermarkets, indicating the methodology and its technical solution are applicable, and can be used to monitor the pork products supply chain from the farrn to the dinner table. C1 [Xiong Ben Hai; Luo Qing-Yao; Liang, Yang] Chinese Acad Agr Sci, Inst Anim Sci, State Key Lab Anim Nutr, Beijing, Peoples R China. [Fu Run-Ting; Lin Zhao-Hui] Anim Husb & Vet Bur, Tianjin 300210, Peoples R China. [Pan Jia-Rong] Chinese Acad Agr Sci, Inst Manufacture Agr Food, Beijing, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Animal Science, CAAS; Chinese Academy of Agricultural Sciences RP Xiong, BH (corresponding author), Chinese Acad Agr Sci, Inst Anim Sci, State Key Lab Anim Nutr, POB Beijing 100094,2 Yuanmingyuan W Rd, Beijing, Peoples R China. EM bhxiong@iascaas.net.cn CR Bailey D, 2002, AM J AGR ECON, V84, P1337, DOI 10.1111/1467-8276.00399 Bledsoe GE, 2002, FOOD TECHNOL-CHICAGO, V56, P43 BROFMANEPELBAUM F, 2007, 17 ANN FOR S IAMA C *CAC, 2004, 27 SESS JOINT FAO WH CAJA G, 2004, ELECT IDENTIFICATION Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Gledhill J., 2002, Food Processing, USA, V63, P56 GOLLAM E, 2004, 830 US DEP AGR Latouche K, 1998, FOOD POLICY, V23, P347, DOI 10.1016/S0306-9192(98)00048-7 *MAC, 2006, MAN METH LIV POULTR Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 *PDO, 2006, PORK PROD POIS 300 S Thomas AG, 2005, TOP CAN WEED SCI, V1, P1 Unnevehr LJ, 1999, AM J AGR ECON, V81, P1096, DOI 10.2307/1244090 XIE JF, 2004, WORLDS ENG CONV 2 6 *XNA, 2006, MIN AGR INF INV RES YANG ZH, 2003, SAFETY USAGE CRITERI YOU ZQ, 2007, RADIO FREQUENCY IDEN 2006, PEOPLE DAILY ONLINE NR 19 TC 10 Z9 17 U1 1 U2 21 PD DEC PY 2007 VL 50 IS 5 BP 725 EP 733 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000258308200024 DA 2022-12-14 ER PT J AU Kadfak, A Widengard, M AF Kadfak, Alin Widengard, Marie TI From fish to fishworker traceability in Thai fisheries reform SO ENVIRONMENT AND PLANNING E-NATURE AND SPACE DT Article; Early Access DE Labour; Thailand; migrant worker; fisheries; traceability ID LABOR MIGRATION; FLUID SPACES; GOVERNANCE; WORK AB This paper explores the question of what traceability systems mean for the labour situation of fishworkers; for whom and in what respects is traceability effective, and what impact do these systems have? The limited social criteria in fishery governance is a core reason for recurrent problems of extreme abuse of fishworkers around the world, including trafficking, forced labour and so called modern slavery. New traceability systems, thus, now include social criteria to advance sustainable fisheries globally. Drawing from a Thai fisheries reform case study, we analyse how the new labour traceability system emerges and is perceived by migrant fishworkers. We base our analysis on interviews, documents and two periods of fieldwork in Thailand. We argue that labour traceability is a double-edged sword. While fishworkers have seen major improvement in limiting extreme abuse, labour traceability has a downsides of state surveillance and costs passed onto workers. Moreover, traceability does not solve underlying problems regarding the complex formalization of migrant workers, working conditions on fishing boats, freedom to change employer or the everyday vulnerability of being a migrant worker. Thus, while labour traceability has promising policy relevance for the integration of labour rights into fisheries governance, it requires contextual underpinning in migrant circumstances. C1 [Kadfak, Alin] Swedish Univ Agr Sci, Uppsala, Sweden. [Widengard, Marie] Gothenburg Univ, Gothenburg, Sweden. C3 Swedish University of Agricultural Sciences; University of Gothenburg RP Kadfak, A (corresponding author), Swedish Univ Agr Sci, Dept Urban & Rural Dev, Uppsala, Sweden. EM alin.kadfak@slu.se CR Andriamahefazafy M, 2019, J POLIT ECOL, V26, P403 [Anonymous], 2018, HIDD CHAINS RIGHTS A Arnold D, 2013, AM BEHAV SCI, V57, P289, DOI 10.1177/0002764212466239 Auethavornpipat R., 2022, GLOBAL CONSTITUTIONA, P1 Bailey M, 2016, CURR OPIN ENV SUST, V18, P25, DOI 10.1016/j.cosust.2015.06.004 Ball J., 2015, GLOBAL STUD CHILDHOO, V5, P425, DOI [10.1177/2043610615613883, DOI 10.1177/2043610615613883] Bear C, 2008, SOC CULT GEOGR, V9, P487, DOI 10.1080/14649360802224358 Bear C, 2013, CULT GEOGR, V20, P21, DOI 10.1177/1474474012463665 Belton B, 2019, J RURAL STUD, V69, P204, DOI 10.1016/j.jrurstud.2019.05.007 Bostrom M, 2015, J CLEAN PROD, V107, P1, DOI 10.1016/j.jclepro.2014.11.050 Bylander M., 2021, CONTEXTS, V20, P21 Bylander Maryann., 2019, J MIGRATION HUMAN SE, V7, P1, DOI [10.1177/2331502418821855, DOI 10.1177/2331502418821855] Campbell Stephen., 2018, BORDER CAPITALISM DI Carswell G, 2013, GEOFORUM, V44, P62, DOI 10.1016/j.geoforum.2012.06.008 Chantavanich S, 2016, MAR POLICY, V68, P1, DOI 10.1016/j.marpol.2015.12.015 Clark TP, 2022, J PEASANT STUD, V49, P652, DOI 10.1080/03066150.2021.1890041 Coalition, 2020, FALLING NETII SURVEY Coalition TC., 2018, FALLING NET SURVEY B Derks A, 2010, ASIAN J SOC SCI, V38, P915, DOI 10.1163/156853110X530804 Derrick B, 2017, FRONT MAR SCI, V4, DOI 10.3389/fmars.2017.00402 Djelantik AAASK, 2020, GEOFORUM, V116, P172, DOI 10.1016/j.geoforum.2020.07.017 Eden S, 2009, GEOFORUM, V40, P383, DOI 10.1016/j.geoforum.2008.01.001 EJF, 2019, THAIL PROGR COMB IUU, V7 EJF, 2018, THAIL PROGR COMB IUU, V6 European Commission, 2009, HDB PRACT APPL COUNC Ewell C, 2017, MAR POLICY, V81, P293, DOI 10.1016/j.marpol.2017.04.004 Franck AK, 2016, URBAN STUD, V53, P3206, DOI 10.1177/0042098015613003 ILO, 2017, OBS CEACR AD 2017 PU ILO, 2019, WORK PAP IND FISH HU ILO, 2021, ILO GLOBAL ESTIMATES ILO (International Labor Organization), 2013, CAUGHT SEA FORC LAB ILRF, 2018, TAK STOCK LAB EXPL I International Labour Organization (ILO), 2020, ENDL RES FIND FISH S IOM, 2016, MIGR INF NOT ISS 29 Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 Kadfak A, 2021, MAR POLICY, V132, DOI 10.1016/j.marpol.2021.104656 Kadfak A, 2021, MAR POLICY, V127, DOI 10.1016/j.marpol.2021.104445 Kaur A, 2010, J ASIA PAC ECON, V15, P6, DOI 10.1080/13547860903488195 Kumar V., 2017, TEXTILES CLOTHING SU, V3, P5 Leposa N., 2022, SOC NATURAL RESOURCE, P1 Lewis SG, 2017, J FOOD SCI, V82, pA13, DOI 10.1111/1750-3841.13743 Marschke M, 2021, MARIT STUD, V20, P87, DOI 10.1007/s40152-020-00205-y Marschke M, 2016, MAR POLICY, V68, P39, DOI 10.1016/j.marpol.2016.02.009 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 McDowell R., 2015, AP EXPLORE SEAFOOD S Mol APJ, 2015, J CLEAN PROD, V107, P154, DOI 10.1016/j.jclepro.2013.11.012 Molland S., 2022, SAFE MIGRATION POLIT Molland S, 2019, SOJOURN, V34, P397, DOI 10.1355/sj34-2f Mon M, 2010, J ASIA PAC ECON, V15, P33, DOI 10.1080/13547860903488211 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Peters K, 2014, MOBILITIES-UK, V9, P414, DOI 10.1080/17450101.2014.946775 Riisgaard L, 2009, WORLD DEV, V37, P326, DOI 10.1016/j.worlddev.2008.03.003 Satizabal P, 2019, ANN AM ASSOC GEOGR, V109, P1865, DOI 10.1080/24694452.2019.1587282 Serrano A, 2019, ENVIRON PLAN E-NAT, V2, P348, DOI 10.1177/2514848619838195 Sparks JLD, 2019, J HUM RIGHTS, V18, P230, DOI 10.1080/14754835.2019.1602824 Steinberg P, 2015, ENVIRON PLANN D, V33, P247, DOI 10.1068/d14148p Sterling B, 2015, COMPR REV FOOD SCI F, V14, P205, DOI 10.1111/1541-4337.12130 Stringer C, 2016, GLOBAL NETW, V16, P3, DOI 10.1111/glob.12077 Tickler D, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-07118-9 Toonen HM, 2020, J ENVIRON POL PLAN, V22, P125, DOI 10.1080/1523908X.2018.1461084 USAID Oceans, 2018, THAI UN ECDT CREW CO Vandergeest P., 2019, INT SOC SCI J, V68, P325 Vandergeest P, 2021, MAR POLICY, V132, DOI 10.1016/j.marpol.2021.104685 Vandergeest P, 2015, ENVIRON PLANN A, V47, P1907, DOI 10.1177/0308518X15599297 Vandergeest P, 2020, ANTIPODE, V52, P291, DOI 10.1111/anti.12575 Wilhelm M, 2020, MAR POLICY, V115, DOI 10.1016/j.marpol.2020.103833 Yea S, 2017, GEOFORUM, V78, P179, DOI 10.1016/j.geoforum.2016.05.003 NR 67 TC 0 Z9 0 U1 0 U2 0 DI 10.1177/25148486221104992 EA JUN 2022 WC Environmental Studies; Geography SC Environmental Sciences & Ecology; Geography UT WOS:000849059300001 DA 2022-12-14 ER PT J AU Oliveira, J Lima, JE da Silva, D Kuprych, V Faria, PM Teixeira, C Cruz, EF da Cruz, AMR AF Oliveira, Jose Lima, Jose Evaristo da Silva, Dimitri Kuprych, Volodymyr Faria, Pedro Miguel Teixeira, Claudio Cruz, Estrela Ferreira Rosado da Cruz, Antonio Miguel TI Traceability system for quality monitoring in the fishery and aquaculture value chain SO JOURNAL OF AGRICULTURE AND FOOD RESEARCH DT Article DE Food traceability; Software platform; Fish; Fishery; Aquaculture; Value chain ID SUS AB Nowadays it is important to know the origins of most products, not only for public health reasons, with regard to food products, but also because of the increasing people's awareness of environmental and social aspects, posing new challenges in the quality of products and their environmental impact. Food traceability information must be collected throughout the supply chain, and be registered on a platform external to all operators in that chain. This information must be available, not only to value chain operators and public health authorities, but also to end customers. One of the best ways to provide customers with this traceability information, in the places where they need to make a decision about which products to buy, is to add kiosks at the points of sale for those products. This article describes the design and architecture of a traceability platform for fish and fishery products, within the scope of the ValorMar R&D project, as well as the applications that supply the platform with traceability information, and a kiosk application for points of sale and a mobile app for final consumers, which will allow them to consult the traceability of fish and fishery products' lots. C1 [Oliveira, Jose; Lima, Jose Evaristo; Faria, Pedro Miguel; Cruz, Estrela Ferreira; Rosado da Cruz, Antonio Miguel] Polytech Inst Viana do Castelo, Viana Do Castelo, Portugal. [da Silva, Dimitri; Kuprych, Volodymyr; Teixeira, Claudio] Univ Aveiro, Aveiro, Portugal. [Cruz, Estrela Ferreira; Rosado da Cruz, Antonio Miguel] Univ Minho, Algoritmi Res Ctr, Guimaraes, Portugal. C3 Polytechnic Institute of Viana do Castelo; Universidade de Aveiro; Universidade do Minho RP da Cruz, AMR (corresponding author), Polytech Inst Viana do Castelo, Viana Do Castelo, Portugal. EM miguel.cruz@estg.ipvc.pt CR Alves L, 2021, ICEIS: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 2, P368, DOI 10.5220/0010482503680376 Bangor A, 2009, J USABILITY STUD, V4, P114 Baralla G, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5857 Bevilacqua M, 2009, J FOOD ENG, V93, P13, DOI 10.1016/j.jfoodeng.2008.12.020 Biswas K., 2017, PROC FUTURE TECHNOL, P1, DOI DOI 10.1007/978-3-319-54460-1_1 Brooke J, 2013, J USABILITY STUD, V8, P29 Cheng C., 2013, SENSOR LETT, V11 Cruz Estrela F., 2015, 17th International Conference on Enterprise Information Systems (ICEIS 2015). Proceedings, P49 Cruz E. F., 2019, 14 IB C INF SYST TEC Cruz EF, 2020, IBER CONF INF SYST Cruz EF, 2020, ICSOFT: PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGIES, P501, DOI 10.5220/0009889705010508 Cruz EF, 2019, PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS 2019), VOL 2, P285, DOI 10.5220/0007730502850294 Da Cruz AMR., 2019, 14 IB C INF SYST TEC, P1, DOI DOI 10.23919/CISTI.2019.8760891 da Silva Lubanco Daniel Louback, 2020, 2020 6th International Conference on Mechatronics and Robotics Engineering (ICMRE), P1, DOI 10.1109/ICMRE49073.2020.9064866 Devy NPIR, 2017, 2017 1ST INTERNATIONAL CONFERENCE ON INFORMATICS AND COMPUTATIONAL SCIENCES (ICICOS), P101 F. Food A. Organization, 2020, STAT WORLD FISH AQ Folinas D., 2003, EFITA C Gardner C.J., 2017, VALUE CHAIN CHALLENG Hevner A, 2010, INTEGR SER INFORM SY, V22, P1, DOI 10.1007/978-1-4419-5653-8 Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625 Indrasiri K., 2018, CASE MICROSERVICES, P1, DOI [10.1007/978-1-4842-3858-5_1, DOI 10.1007/978-1-4842-3858-5_1] Indrasiri K., 2018, APIS EVENTS STREAMS, P293, DOI [10.1007/978-1- 4842-3858-5_10, DOI 10.1007/978-1-4842-3858-5_10] Indrasiri K., 2018, INTERSERVICE COMMUNI, P63, DOI [10.1007/ 978-1-4842-3858-5_3, DOI 10.1007/978-1-4842-3858-5_3] Indrasiri K., 2018, DESIGNING MICROSERVI, P19, DOI [10.1007/978-1-4842- 3858-5_2, DOI 10.1007/978-1-4842-3858-5_2] Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Miatton F., 2020, 2020 INT C TECHNOLOG, P1 Moga L.M., 2017, P33 Mortimore C, 2014, OPENID CONNECT CORE Narrod C, 2009, FOOD POLICY, V34, P8, DOI 10.1016/j.foodpol.2008.10.005 Nicolae CG, 2017, SCI PAP-SER D-ANIM S, V60, P353 Oliveira Jose, 2020, HCI International 2020 - Late Breaking Papers. User Experience Design and Case Studies. 22nd HCI International Conference, HCII 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12423), P740, DOI 10.1007/978-3-030-60114-0_48 Oliveira J, 2020, IBER CONF INF SYST Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sharfina Z, 2016, INT C ADV COMP SCI I, P145, DOI 10.1109/ICACSIS.2016.7872776 Tian F, 2017, I C SERV SYST SERV M NR 36 TC 7 Z9 7 U1 0 U2 6 PD SEP PY 2021 VL 5 AR 100169 DI 10.1016/j.jafr.2021.100169 EA JUN 2021 WC Agriculture, Multidisciplinary; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000696073300016 DA 2022-12-14 ER PT J AU Zhao, J Chen, AL You, XY Xu, ZZ Zhao, Y He, WJ Zhao, LY Yang, SM AF Zhao, Jie Chen, Ailiang You, Xinyong Xu, Zhenzhen Zhao, Yan He, Wenjing Zhao, Luyao Yang, Shuming TI A panel of SNP markers for meat traceability of Halal beef in the Chinese market SO FOOD CONTROL DT Article DE SNPs markers; Meat traceability; Halal beef; Chinese market ID SINGLE NUCLEOTIDE POLYMORPHISMS; GENETIC TRACEABILITY; PRODUCTS; IDENTIFICATION; INFORMATION; POPULATION AB Genetic traceability in the whole food supply chain is a reliable method to protect the integrity of Halal beef, but there is no available SNPs panel for the meat traceability of cattle breeds in China. This study aimed at developing a useful SNP panel for Halal beef traceability in the Chinese market. Fifty-nine SNPs markers belonging to 29 autosomes of bovine genome were tested in seven cattle breeds which were the Chinese main breed source of Halal beef. SNPs with minor allele frequencies less than 30% were excluded and a thirty-six highly informative SNPs panel had been selected. With the SNPs panel, the probability that one individual is incorrectly assigned ranges from 1.12 out of 10 (x15) to 3.38 out of 10 (x12), depending on the breed. In addition, twelve most polymorphic SNPs markers in the pooled animals were successfully used for meat traceability of Halal beef by meat-blood pairs. The selected thirty-six SNPs of panel could be employed to further guarantee the safety of Halal beef in the Chinese market. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Zhao, Jie; Chen, Ailiang; You, Xinyong; Xu, Zhenzhen; Zhao, Yan; He, Wenjing; Zhao, Luyao; Yang, Shuming] Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Zhao, Jie; Chen, Ailiang; You, Xinyong; Xu, Zhenzhen; Zhao, Yan; He, Wenjing; Zhao, Luyao; Yang, Shuming] Minist Agr, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs RP Yang, SM (corresponding author), Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China. EM yangshumingcaas@sina.com CR Allen AR, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-5 [Anonymous], 2014, PROCEDIA SOC BEHAV S, DOI [DOI 10.1016/J.SBSPRO.2014.01.1127, DOI 10.1016/J.SBSPR0.2014.01.1127] Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Barcos LO, 2001, REV SCI TECH OIE, V20, P640, DOI 10.20506/rst.20.2.1294 Boonyarit H, 2014, FORENSIC SCI INT-GEN, V11, P166, DOI 10.1016/j.fsigen.2014.03.010 Bray MS, 2001, HUM MUTAT, V17, P296, DOI 10.1002/humu.27 Chen K, 2016, ANAL BIOANAL CHEM, V408, P4371, DOI 10.1007/s00216-016-9536-6 Cho RJ, 1999, NAT GENET, V23, P203, DOI 10.1038/13833 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Dearlove Andrew M., 2002, Briefings in Functional Genomics & Proteomics, V1, P139, DOI 10.1093/bfgp/1.2.139 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Hara K, 2010, ANIM SCI J, V81, P152, DOI 10.1111/j.1740-0929.2009.00720.x Khatkar MS, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-187 Kim ES, 2009, ANIM GENET, V40, P279, DOI 10.1111/j.1365-2052.2008.01831.x Kitts A, 2003, SINGLE NUCLEOTIDE PO Krawczak M, 1999, ELECTROPHORESIS, V20, P1676, DOI 10.1002/(SICI)1522-2683(19990101)20:8<1676::AID-ELPS1676>3.3.CO;2-4 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Liu J. G., 2013, J BEIFANG U NATL, V6, P56 Mohammed A, 2017, INT J FOOD PROP, V20, P1145, DOI 10.1080/10942912.2016.1203933 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Rogberg-Munoz A, 2016, MEAT SCI, V111, P47, DOI 10.1016/j.meatsci.2015.08.014 Verbeke W, 2001, OUTLOOK AGR, V30, P249, DOI 10.5367/000000001101293733 Wang MingQiang, 2015, Journal of Food Safety and Quality, V6, P2581 Weir B. S., 1996, GENETIC DATA ANAL Wu QY, 2017, J CONSUM PROT FOOD S, V12, P125, DOI 10.1007/s00003-017-1092-2 NR 26 TC 10 Z9 10 U1 5 U2 46 PD MAY PY 2018 VL 87 BP 94 EP 99 DI 10.1016/j.foodcont.2017.11.039 WC Food Science & Technology SC Food Science & Technology UT WOS:000428606500013 DA 2022-12-14 ER PT J AU Yang, ZL Li, X He, P AF Yang, Zaoli Li, Xin He, Ping TI A decision algorithm for selecting the design scheme for blockchain-based agricultural product traceability system in q-rung orthopair fuzzy environment SO JOURNAL OF CLEANER PRODUCTION DT Article DE Blockchain-based agricultural product; traceability system; Q-rung orthopair fuzzy set; Muirhead mean operator; Multi-criteria decision making AB The safety of agricultural products has always been a matter of great concern to people. To address this concern, many blockchain-based agricultural product traceability systems (BAPTSs) have been constructed. The implementation of such systems necessitates the selection of an appropriate design scheme. However, due to the diversity of agricultural products and the uncertainty of the environment, selecting an adequate design scheme for BAPTS is a difficult task for decision-makers. Because the q-rung orthopair fuzzy (q-ROF) set can dynamically delineate the space of uncertain information, this paper proposes a decision algorithm for selecting a design scheme for BAPTS using the q-ROF set. Herein, we first combine the Muirhead mean operator and power operator to develop the q-ROF power Muirhead Mean (q-ROFPMM) operator and the q-ROF weighted power Muirhead Mean (q-ROFWPMM) operator. Then, we investigate several properties of the proposed operators. Further, we construct a novel multi criteria decision-making framework for evaluating and selecting the design scheme for BAPTS based on the q-ROFWPMM operator. Next, a case study on BAPTS selection is presented to validate our method. Finally, the results of sensitivity and comparative analyses are provided to verify the efficiency and accuracy of our method. The results show that our method can effectively address the issues of BAPTS evaluation and selection, capture the relationships between any number of criteria, and eliminate the negative effects of abnormal expert evaluation values on decision-making results. (c) 2020 Elsevier Ltd. All rights reserved. C1 [Yang, Zaoli; Li, Xin] Beijing Univ Technol, Coll Econ & Management, Beijing, Peoples R China. [He, Ping] Zhaoqing Univ, Coll Tourism & Hist, Zhaoqing, Guangdong, Peoples R China. C3 Beijing University of Technology; Zhaoqing University RP Yang, ZL (corresponding author), Beijing Univ Technol, Coll Econ & Management, Beijing, Peoples R China.; He, P (corresponding author), Zhaoqing Univ, Coll Tourism & Hist, Zhaoqing, Guangdong, Peoples R China. EM yangzaoli@bjut.edu.cn; thisisheping@gmail.com CR [Anonymous], 1977, IEEE T SYST MAN CYB, V7, P495, DOI [DOI 10.1109/TSMC.1977.4309751, 10.1109/TSMC.1977.4309751] Arena A, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2019), P173, DOI 10.1109/SMARTCOMP.2019.00049 Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 ATANASSOV KT, 1986, FUZZY SET SYST, V20, P87, DOI 10.1016/S0165-0114(86)80034-3 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bonferroni C., 1950, BOLLETTINO DELLUNION, V5, P267 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Burillo P, 1996, FUZZY SET SYST, V78, P305, DOI 10.1016/0165-0114(96)84611-2 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 CATIGLIONE A, 2015, PERVASIVE MOB COMPUT, V24, P117, DOI DOI 10.1016/j.pmcj.2015.04.001 Chamarajnagar R., 2019, 2019 16 ANN IEEE INT, P1 Chen SM, 2015, INFORM SCIENCES, V291, P96, DOI 10.1016/j.ins.2014.07.033 Chen YY, 2020, J CLEAN PROD, V268, DOI 10.1016/j.jclepro.2020.122071 De SK, 2000, FUZZY SET SYST, V114, P477, DOI 10.1016/S0165-0114(98)00191-2 DELUCA A, 1972, INFORM CONTROL, V20, P301, DOI 10.1016/S0019-9958(72)90199-4 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Du WS, 2018, INT J INTELL SYST, V33, P802, DOI 10.1002/int.21968 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Ferrag MA, 2020, IEEE ACCESS, V8, P32031, DOI 10.1109/ACCESS.2020.2973178 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gao J, 2019, IEEE T FUZZY SYST, V27, P1687, DOI 10.1109/TFUZZ.2018.2887187 Garg H, 2020, INFORM SCIENCES, V517, P427, DOI 10.1016/j.ins.2019.11.035 Garg H, 2016, INT J INTELL SYST, V31, P886, DOI 10.1002/int.21809 Ge L., 2017, BLOCKCHAIN AGR FOOD George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Grzegorzewski P, 2004, FUZZY SET SYST, V148, P319, DOI 10.1016/j.fss.2003.08.005 Haoxuan Li, 2019, International Journal of High Performance Computing and Networking, V14, P385 Hua J, 2018, IEEE INT VEH SYM, P97 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 KARSLEN KM, 2013, FOOD CONTROL, V32, P409, DOI DOI 10.1016/j.foodcont.2012.12.011 Kim M., 2019, P 9 IEEE ANN INF TEC, P335 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Li XH, 2020, J CLEAN PROD, V271, DOI 10.1016/j.jclepro.2020.122503 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu P.D., 2018, 2018 10 INT C ADV CO, DOI [10.1109/ICACI.2018.8377599, DOI 10.1109/ICACI.2018.8377599] Liu PD, 2020, IEEE T SYST MAN CY-S, V50, P3741, DOI 10.1109/TSMC.2018.2852948 Liu PD, 2019, IEEE T FUZZY SYST, V27, P834, DOI 10.1109/TFUZZ.2018.2826452 Liu PD, 2018, INT J INTELL SYST, V33, P259, DOI 10.1002/int.21927 Liu PD, 2018, INT J INTELL SYST, V33, P315, DOI 10.1002/int.21933 Liu PD, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0168767 Lucena P., 2018, P S FDN APPL BLOCKCH, P1 Maclaurin C., 1997, PHILOS T, V1729, P59, DOI DOI 10.1098/RSTL.1729.0011 MAO DH, 2018, IEEE T SYST MAN CY-S, V10, P3149, DOI DOI 10.3390/su10093149 Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Muirhead R.F., 1903, P EDINBURGH MATH SOC, V21, P144, DOI [10.1017/S001309150003460X, DOI 10.1017/S001309150003460X] Peng XD, 2018, INT J INTELL SYST, V33, P2255, DOI 10.1002/int.22028 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 REGATTIERI A, 2007, TRENDS FOOD SCI TECH, V81, P347, DOI DOI 10.1016/j.jfoodeng.2006.10.032 REN PJ, 2016, J FOOD ENG, V42, P246, DOI DOI 10.1016/j.asoc.2015.12.020 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sambrekar K, 2019, INT J CLOUD APPL COM, V9, P33, DOI 10.4018/IJCAC.2019010103 SHADI AZ, 2019, INT J CLOUD APPL COM, V78, P29581, DOI DOI 10.1007/s11042-019-7367-0 SUMATHI M, 2020, MULTIMED TOOLS APPL, V10, P77, DOI DOI 10.4018/IJCAC.2020040105 SUN SN, 2019, INT J CLOUD APPL COM, V217, P658, DOI DOI 10.1016/j.jclepro.2019.01.296 Sykora S., 2009, STANS LIB CASTANO 1, V3, DOI [10.3247/SL3Math09.002, DOI 10.3247/SL3MATH09.002] Tian F, 2016, 2016 13 INT C SERV S, P1, DOI DOI 10.1109/ICSSSM.2016.7538424 WANG J, 2020, J CLEAN PROD, V16, P1073, DOI DOI 10.1016/j.dt.2019.11.007 WANG J, 2019, DEF TECHNOL, V36, P1599, DOI DOI 10.3233/JIFS-18607 Wang P, 2019, MATHEMATICS-BASEL, V7, DOI 10.3390/math7040340 Wei GW, 2018, INT J INTELL SYST, V33, P1426, DOI 10.1002/int.21985 Wei GW, 2017, J INTELL FUZZY SYST, V33, P2119, DOI 10.3233/JIFS-162030 Wen XW, 2019, J CLEAN PROD, V208, P999, DOI 10.1016/j.jclepro.2018.10.089 World Health Organization, 2020, FOOD SAF Xu ZS, 2007, IEEE T FUZZY SYST, V15, P1179, DOI 10.1109/TFUZZ.2006.890678 Xu ZS, 2011, IEEE T SYST MAN CY B, V41, P568, DOI 10.1109/TSMCB.2010.2072918 Xue, 2016, P 2016 2 INT C EL NE, P230, DOI [10.2991/icence-16.2016.46, DOI 10.2991/ICENCE-16.2016.46] Yager RR, 2017, IEEE T FUZZY SYST, V25, P1222, DOI 10.1109/TFUZZ.2016.2604005 Yager RR, 2014, IEEE T FUZZY SYST, V22, P958, DOI 10.1109/TFUZZ.2013.2278989 Yager RR, 2001, IEEE T SYST MAN CY A, V31, P724, DOI 10.1109/3468.983429 Yang W, 2019, INT J INTELL SYST, V34, P439, DOI 10.1002/int.22060 Yang ZL, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17103407 Yang ZL, 2020, INT J INTELL SYST, V35, P783, DOI 10.1002/int.22225 Yang ZQ, 2021, NAT PROD RES, V35, P4394, DOI 10.1080/14786419.2020.1716350 Yuxin Liao, 2019, Journal of Physics: Conference Series, V1288, DOI 10.1088/1742-6596/1288/1/012062 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X Zhang XL, 2016, INFORM SCIENCES, V330, P104, DOI 10.1016/j.ins.2015.10.012 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 81 TC 16 Z9 17 U1 1 U2 45 PD MAR 25 PY 2021 VL 290 AR 125191 DI 10.1016/j.jclepro.2020.125191 EA FEB 2021 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000620276600007 DA 2022-12-14 ER PT J AU Allata, S Valero, A Benhadja, L AF Allata, S. Valero, A. Benhadja, L. TI Implementation of traceability and food safety systems (HACCP) under the ISO 22000:2005 standard in North Africa: The case study of an ice cream company in Algeria SO FOOD CONTROL DT Article DE Ice cream; ISO 22000:2005; Operational prerequisite program; HACCP plan; Traceability system ID IN-PLACE CIP; LISTERIA-MONOCYTOGENES; MICROBIOLOGICAL QUALITY; HAZARD ANALYSIS; UNITED-STATES; MANAGEMENT-SYSTEM; SUPPLY CHAIN; STAPHYLOCOCCUS-AUREUS; INACTIVATION KINETICS; THERMAL INACTIVATION AB Our study aims at establishing and implementing the HACCP and traceability system, in an integrated approach, of ice cream processing in order to control food borne safety hazards, to minimize the production and distribution of unsafe or poor quality products, thereby the potential. food safety risks and associated food recalls. Internal information capture points were identified in ice cream process and the corresponding traceability information to be recorded were determined. Biological, physical, chemical and allergens hazards that could emerge at each stage of the production were identified. After hazards identification, the critical control points (CCPs) and operational prerequisites programs (oPRPs) were selected using a decision tree. Results showed that cleaning-in-place, filtration, pasteurization, cooling storage and transport stages were the critical control points identified. Critical limits, monitoring methods and frequency, responsibilities and corrective actions of the processes are also presented. Finally, the impact of implementation of food safety system (HACCP) on aerobic plate count (APC) and coliforms in vanilla, strawberry and chocolate flavoured ice cream was investigated. The results of HACCP adoption showed the reduction of APC in all flavours of the ice cream samples tested, being higher for the strawberry from 4.18 +/- 3.03 till 3.80 +/- 2.71 log CFU/g. Besides, a significant decrease of coliforms from 2.39 +/- 1.76 till 2.11 +/- 1.42 and 2.54 +/- 1.62 till 2.02 +/- 1.15 log CFU/g was observed in ice cream samples with a chocolate and strawberry flavour, respectively. In conclusion, the implementation of traceability and HACCP system, under the ISO 22000 standard has allowed tracking and tracing of ice cream products improving the microbiological quality of the ice creams. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Allata, S.; Benhadja, L.] Saad Dahlab Univ, Dept Agron Sci, Lab Vegetal Biotechnol, Algiers, Algeria. [Valero, A.] Univ Cordoba, Dept Food Sci & Technol, Campus Rabanales,Edificio Darwin, Cordoba 14014, Spain. C3 Universite Saad Dahlab de Blida; Universidad de Cordoba RP Valero, A (corresponding author), Univ Cordoba, Dept Food Sci & Technol, Campus Rabanales,Edificio Darwin, Cordoba 14014, Spain. EM avalero@uco.es CR Abd Rahman A, 2017, FOOD CONTROL, V73, P1318, DOI 10.1016/j.foodcont.2016.10.058 Alamprese C, 2002, INT DAIRY J, V12, P201, DOI 10.1016/S0958-6946(01)00159-5 Am S., 2013, INT J BIOL PHARM ALL, V2, P2192 [Anonymous], 2014, 22004 ISOFDIS [Anonymous], 2002, 6579 ISO [Anonymous], 2008, 9001 ISO [Anonymous], 2007, 22005 ISO [Anonymous], 2006, INTERNET J FOOD SAFE [Anonymous], 2018, STANDARD ISO 2200020 [Anonymous], 2006, 4832 ISO [Anonymous], 2015, AGROLIGNE [Anonymous], 2009, V08060 NF Arvanitoyannis I. S., 2009, HACCP ISO 22000 APPL, P91 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Balthazar CF, 2017, FOOD RES INT, V91, P38, DOI 10.1016/j.foodres.2016.11.008 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bremer PJ, 2006, INT J FOOD MICROBIOL, V106, P254, DOI 10.1016/j.ijfoodmicro.2005.07.004 Bremer PJ, 2002, LETT APPL MICROBIOL, V35, P321, DOI 10.1046/j.1472-765X.2002.01198.x Buvens G, 2011, FOODBORNE PATHOG DIS, V8, P421, DOI 10.1089/fpd.2010.0693 CAC/MRL (Codex Alimentarius Commission/Maximum Residue Limit), 2015, LIM MAX RES LMR REC, P42 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Capunzo M, 2005, FOOD CONTROL, V16, P183, DOI 10.1016/j.foodcont.2004.01.010 CDC, 2015, MULT OUTBR LIST LINK Cerf O, 2011, FOOD CONTROL, V22, P1839, DOI 10.1016/j.foodcont.2011.04.023 Chen EC, 2015, FOOD CONTROL, V47, P569, DOI 10.1016/j.foodcont.2014.08.009 Choi JH, 2013, FOOD CONTROL, V31, P474, DOI 10.1016/j.foodcont.2012.10.023 Claeys WL, 2013, FOOD CONTROL, V31, P251, DOI 10.1016/j.foodcont.2012.09.035 Clark JP, 2009, FOOD ENG SER, P1, DOI 10.1007/978-1-4419-0420-1_1 Codex Alimentations Commission (CAC), 2004, 1921995 CAC CACSTAN Dias MAC, 2012, FOOD CONTROL, V24, P199, DOI 10.1016/j.foodcont.2011.09.028 Cruz AG, 2009, FOOD RES INT, V42, P1233, DOI 10.1016/j.foodres.2009.03.020 Cusato S, 2013, FOODBORNE PATHOG DIS, V10, P6, DOI 10.1089/fpd.2012.1286 da Cruz AG, 2006, TRENDS FOOD SCI TECH, V17, P406, DOI 10.1016/j.tifs.2006.03.003 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Daniels NA, 2002, PEDIATR INFECT DIS J, V21, P623, DOI 10.1097/00006454-200207000-00004 De Farias FF, 2006, INT J DAIRY TECHNOL, V59, P261, DOI 10.1111/j.1471-0307.2006.00278.x De Schrijver K, 2008, EURO SURVEILL, V13 Deosarkar S. S., 2016, ICE CREAM COMPOSITIO Domenech E, 2011, FOOD CONTROL, V22, P1419, DOI 10.1016/j.foodcont.2011.03.001 Dzwolak W., 2003, HACCP DOCUMENTATION, P108 Dzwolak W, 2017, FOOD CONTROL, V73, P323, DOI 10.1016/j.foodcont.2016.08.019 El-Hofi M, 2010, ACTA SCI POLON-TECHN, V9, P331 El-Sharef N, 2006, FOOD CONTROL, V17, P637, DOI 10.1016/j.foodcont.2005.04.001 EU, 1998, OFFICIAL J L, V330, P32 FAO, 1997, HAZ AN CRIT CONTR PO Fernandez-Segovia I, 2014, FOOD CONTROL, V43, P28, DOI 10.1016/j.foodcont.2014.02.042 Fetsch A, 2014, INT J FOOD MICROBIOL, V187, P1, DOI 10.1016/j.ijfoodmicro.2014.06.017 Fine F, 2005, POWDER TECHNOL, V157, P108, DOI 10.1016/j.powtec.2005.05.016 Food Product Association (FPA), 2006, HACCP SYST APPR FOOD Foras E, 2015, FOOD CONTROL, V57, P65, DOI 10.1016/j.foodcont.2015.03.027 FSA, 2002, TRAC FOOD CHAIN PREL Gaaloul I, 2011, FOOD CONTROL, V22, P59, DOI 10.1016/j.foodcont.2010.05.008 Garcell HG, 2016, J INFECT PUBLIC HEAL, V9, P523, DOI 10.1016/j.jiph.2015.12.006 Goff H.D., 2011, ENCY DAIRY SCI, V2, P893 Gojkovic V., 2015, Journal of Hygienic Engineering and Design, V12, P76 Gombas K, 2006, PREREQUISITE PROGRAM Gougouli M, 2008, J DAIRY SCI, V91, P523, DOI 10.3168/jds.2007-0255 Gould LH, 2014, FOODBORNE PATHOG DIS, V11, P545, DOI 10.1089/fpd.2013.1650 Griffiths MW, 2010, WOODHEAD PUBL FOOD S, P451 Heuvelink AE, 2009, INT J FOOD MICROBIOL, V134, P70, DOI 10.1016/j.ijfoodmicro.2008.12.026 Honish L, 2005, CAN J PUBLIC HEALTH, V96, P182, DOI 10.1007/BF03403686 Horchner PM, 2006, FOOD CONTROL, V17, P497, DOI 10.1016/j.foodcont.2005.02.012 Ossa DEH, 2015, FOOD CONTROL, V56, P34, DOI 10.1016/j.foodcont.2015.03.011 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Hubackova M, 2007, ACTA VET BRNO, V76, P301, DOI 10.2754/avb200776020301 Hung YT, 2015, J FOOD DRUG ANAL, V23, P509, DOI 10.1016/j.jfda.2015.02.005 IDFA, 2002, IDFAS HACCP PLANT MA Lee SHI, 2017, FOOD RES INT, V91, P88, DOI 10.1016/j.foodres.2016.11.039 International Life Science Institute (ILSI), 1999, VAL VER HACCP ISO, 2004, 68881A12004 ISO ISO, 2003, ISO4833 ISO, 2001, 10013 ISO ISOTR Jelicic I, 2009, MLJEKARSTVO, V59, P155 Johler S, 2015, J DAIRY SCI, V98, P2944, DOI 10.3168/jds.2014-9123 Journal Officiel de la Republique Algerienne (JORA), 2012, J OFFICIEL REPUBLIQU, V30, P16 Journal Officiel de la Republique Algerienne (JORA), 2002, J OFFICIEL REPUBLIQU, V31, P7 Journal Officiel de la Republique Algerienne (JORA), 1998, J OFFICIEL REPUBLIQU, V35, P7 Journal Officiel de la Republique Algerienne (JORA), 2010, J OFFICIEL REPUBLIQU, V17, P8 Kafetzopoulos DP, 2013, FOOD CONTROL, V33, P505, DOI 10.1016/j.foodcont.2013.03.044 Kambamanoli-Dimou A., 2014, ENCY FOOD MICROBIOLO, V1, P235 Kanbakan U, 2004, FOOD CONTROL, V15, P463, DOI 10.1016/S0956-7135(03)00131-2 Karaman S, 2014, J DAIRY SCI, V97, P97, DOI 10.3168/jds.2013-7111 Kassem M., 2002, REV SANTE MEDITERRAN, V8, P114 Khalid SMN, 2016, FOOD CONTROL, V68, P192, DOI 10.1016/j.foodcont.2016.03.022 Kokkinakis E, 2011, PROC FOOD SCI, V1, P1079, DOI 10.1016/j.profoo.2011.09.161 Kokkinakis EN, 2008, CZECH J FOOD SCI, V26, P383, DOI 10.17221/1126-CJFS Kourtis LK, 2001, FOOD REV INT, V17, P1, DOI 10.1081/FRI-100000514 Kumari S, 2014, FOOD CONTROL, V36, P153, DOI 10.1016/j.foodcont.2013.08.014 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Lee SHI, 2016, J DAIRY SCI, V99, P2384, DOI 10.3168/jds.2015-10007 Lindstrom M, 2010, CRIT REV FOOD SCI, V50, P281, DOI 10.1080/10408390802544405 Lu JC, 2014, J FOOD DRUG ANAL, V22, P391, DOI 10.1016/j.jfda.2013.09.049 M-E-Elahi A. T. M., 2002, Pakistan Journal of Nutrition, V1, P93 MacSwane D., 2000, ESSENTIALS FOOD SAFE, P1 Martinez-Gonzales NE, 2003, J FOOD PROTECT, V66, P1490, DOI 10.4315/0362-028X-66.8.1490 Martinez-Rodriguez AJ, 2009, FOOD CONTROL, V20, P469, DOI 10.1016/j.foodcont.2008.07.015 McSwane D. Z., 2003, ESSENTIALS FOOD SAFE Melo J, 2015, FOOD RES INT, V67, P75, DOI 10.1016/j.foodres.2014.10.031 Meng L, 2011, J ANHUI AGR SCI, V39, P6531 Moerman F., 2014, CLEANING PLACE CIP F Mortimore S, 2001, FOOD CONTROL, V12, P209, DOI 10.1016/S0956-7135(01)00017-2 Motarjemi Y., 2014, FOOD SAFETY MANAGEME, P83, DOI [10.1016/B978-0-12-381504-0.00005-6, DOI 10.1016/B978-0-12-381504-0.00005-6] Nada S, 2012, FOOD CONTROL, V25, P728, DOI 10.1016/j.foodcont.2011.12.022 Nassib TA, 2003, INT J DAIRY TECHNOL, V56, P30, DOI 10.1046/j.1471-0307.2003.00072.x Oliver SP, 2005, FOODBORNE PATHOG DIS, V2, P115, DOI 10.1089/fpd.2005.2.115 Papademas P., 2002, MICROBIOLOGY ICE CRE Papademas P, 2010, INT J DAIRY TECHNOL, V63, P489, DOI 10.1111/j.1471-0307.2010.00620.x Parkar SG, 2004, J APPL MICROBIOL, V96, P110, DOI 10.1046/j.1365-2672.2003.02136.x Peri C., 2004, ANN REPORT 2009 RAPI Pettigrew L, 2015, J CLEAN PROD, V87, P583, DOI 10.1016/j.jclepro.2014.10.072 Pouillot R, 2016, EMERG INFECT DIS, V22, P2113, DOI 10.3201/eid2212.160165 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sharma M, 2002, FOOD CONTROL, V13, P469, DOI 10.1016/S0956-7135(01)00068-8 Smith K. W., 2015, SPECIALTY OILS FATS Soriano JM, 2002, FOOD CONTROL, V13, P253, DOI 10.1016/S0956-7135(02)00023-3 Sospedra I, 2013, FOOD CONTROL, V30, P418, DOI 10.1016/j.foodcont.2012.08.004 Srey S, 2013, FOOD CONTROL, V31, P572, DOI 10.1016/j.foodcont.2012.12.001 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x Trevisani M, 2014, J DAIRY SCI, V97, P642, DOI 10.3168/jds.2013-7150 Valero A, 2014, FOOD CONTROL, V43, P175, DOI 10.1016/j.foodcont.2014.03.009 van Lieverloo JHM, 2013, FOOD CONTROL, V29, P394, DOI 10.1016/j.foodcont.2012.05.078 Waisarayutt C, 2014, FOOD CONTROL, V46, P182, DOI 10.1016/j.foodcont.2014.05.008 Wang D, 2010, FOOD CONTROL, V21, P584, DOI 10.1016/j.foodcont.2009.08.009 Warke R, 2000, FOOD CONTROL, V11, P77, DOI 10.1016/S0956-7135(99)00027-4 Zhong Z, 2012, FOOD ENG, V1, P56 NR 126 TC 23 Z9 24 U1 6 U2 105 PD SEP PY 2017 VL 79 BP 239 EP 253 DI 10.1016/j.foodcont.2017.04.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000403033200032 DA 2022-12-14 ER PT J AU Skilton, PF Robinson, JL AF Skilton, Paul F. Robinson, Jessica L. TI TRACEABILITY AND NORMAL ACCIDENT THEORY: HOW DOES SUPPLY NETWORK COMPLEXITY INFLUENCE THE TRACEABILITY OF ADVERSE EVENTS? SO JOURNAL OF SUPPLY CHAIN MANAGEMENT DT Article DE supply network complexity; traceability; food supply; supply chain strategy AB In this paper, we develop theory about the relationship between supply network complexity and the traceability of adverse events. Because adverse events in complex supply networks are frequent and sometimes catastrophic, understanding how they happen is critical for the management of quality in complex supply networks. Drawing on literatures that deal with normal accidents, traceability, transparency and network complexity, we develop propositions that help explain how traceable adverse events will be in different types of supply networks. Drawing on examples from food supply networks, we illustrate the barriers to traceability associated with different types of complex network structure. We end by discussing managerial and academic implications for the design of traceability systems and supply networks. C1 [Skilton, Paul F.] Arizona State Univ Polytech, Morrison Sch Management & Agribusiness, Mesa, AZ USA. C3 Arizona State University RP Skilton, PF (corresponding author), Arizona State Univ Polytech, Morrison Sch Management & Agribusiness, Mesa, AZ USA. CR Agrawal R., 2009, TRACEABILITY SOVEREI ALCHIAN AA, 1972, AM ECON REV, V62, P777 Bowker G. C., 1999, SORTING THINGS OUT California Food Emergency Response Team, 2008, INV ESCH COL O157 H7 Chao L., 2008, WALL STREET J 1007, pA19 Charlier C., 2007, EUROPEAN J LAW EC, V25, P1 CHENG MJ, 1994, INT J OPER PROD MAN, V14, P4, DOI 10.1108/01443579410067199 Choi TY, 2006, J OPER MANAG, V24, P637, DOI 10.1016/j.jom.2005.07.002 Choi TY, 2009, J SUPPLY CHAIN MANAG, V45, P8, DOI 10.1111/j.1745-493X.2009.03151.x Choi TY, 2001, J OPER MANAG, V19, P351, DOI 10.1016/S0272-6963(00)00068-1 Contractor NS, 2006, ACAD MANAGE REV, V31, P681, DOI [10.5465/AMR.2006.21318925, 10.2307/20159236] Dyer JH, 2000, STRATEGIC MANAGE J, V21, P345, DOI 10.1002/(SICI)1097-0266(200003)21:3<345::AID-SMJ96>3.0.CO;2-N Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Food Safety and Inspection Service, 2008, SMALL VER SMALL PLAN Ford D, 2006, EUR J MARKETING, V40, P248, DOI 10.1108/03090560610648039 Golan E., 2008, TRACEABILITY US FOOD GRIFFIN A, 1992, J PROD INNOVAT MANAG, V9, P171, DOI 10.1111/1540-5885.930171 Hanneman R., 2009, INTRO SOCIAL NETWORK Harland CM., 2001, INT J PROD OPER MANA, V37, P21, DOI DOI 10.1111/J.1745-493X.20011B00109.X Havila V, 2004, INT MARKET REV, V21, P172, DOI 10.1108/02651330410531385 Hick S., 2005, BROKERAGE CLOSURE IN, V2nd Juran J.M., 1979, QUALITY CONTROL HDB KAUFMAN M, 2007, WASH POST, pA7 Lamming R, 2004, BRIT J MANAGE, V15, P291, DOI 10.1111/j.1467-8551.2004.00420.x Lamming R.C., 2001, J SUPPLY CHAIN MANAG, V37, P4, DOI DOI 10.1111/J.1745-493X.2001.TB00107.X Langfred CW, 2007, ACAD MANAGE J, V50, P885, DOI 10.5465/AMJ.2007.26279196 Langfred CW, 2004, ACAD MANAGE J, V47, P385, DOI 10.2307/20159588 Lee HL, 2002, CALIF MANAGE REV, V44, P105, DOI 10.2307/41166135 Linderman K, 2003, J OPER MANAG, V21, P193, DOI 10.1016/S0272-6963(02)00087-6 LORENZONI G, 1995, CALIF MANAGE REV, V37, P146, DOI 10.2307/41165803 Madhavan R, 2004, ACAD MANAGE J, V47, P918, DOI 10.2307/20159631 Martin A, 2009, NY TIMES, pA1 Pathak SD, 2007, DECISION SCI, V38, P547, DOI 10.1111/j.1540-5915.2007.00170.x Paxson H, 2008, CULT ANTHROPOL, V23, P15, DOI 10.1111/j.1548-1360.2008.00002.x Perrow C., 1999, NORMAL ACCIDENTS LIV Porter M., 1980, COMPETITIVE STRATEGY Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Ramesh B, 1998, COMMUN ACM, V41, P37, DOI 10.1145/290133.290147 ROBERTS KH, 1989, IEEE T ENG MANAGE, V36, P132, DOI 10.1109/17.18830 ROSENBLOOM S, 2008, NY TIMES 1022, pB1 Roth AV, 2008, J SUPPLY CHAIN MANAG, V44, P22, DOI 10.1111/j.1745-493X.2008.00043.x Sagan SD., 1993, LIMITS SAFETY ORG AC Sanchez R, 1996, STRATEGIC MANAGE J, V17, P63, DOI 10.1002/smj.4250171107 Stone Bras, 2009, NY TIMES, pB1 Turnbull P., 1996, J BUSINESS IND MARKE, V11, P44 U.S. Food and Drug Administration (USFDA), 2008, PENNS FIRM REC PAST U.S. Food and Drug Administration (USFDA), 2008, SALM SAINTP OUTBR U.S. Food and Drug Administration (USFDA), 2008, FDA FIN REP 2006 SPI USFDA (U. S. Food and Drug Administration), 2008, GUID IND GUID MIN MI Uzzi B, 1997, ADMIN SCI QUART, V42, P35, DOI 10.2307/2393808 Weick K. E., 1969, SOCIAL PSYCHOL ORG WEICK KE, 1993, ADMIN SCI QUART, V38, P357, DOI 10.2307/2393372 Weick KE, 2005, UNDERST COMPLEX SYST, P51, DOI 10.1007/10948637_5 White M., 2008, MORE CHEESES RECALLE Wu ZH, 2005, J OPER MANAG, V24, P27, DOI 10.1016/j.jom.2005.02.001 [No title captured], DOI DOI 10.2307/259249 NR 56 TC 92 Z9 92 U1 1 U2 59 PY 2009 VL 45 IS 3 BP 40 EP 53 DI 10.1111/j.1745-493X.2009.03170.x WC Management SC Business & Economics UT WOS:000207982600004 DA 2022-12-14 ER PT J AU Hardie, K Santana, AC Serio, AW Gonzalez-Herrera, JC Fordham, B Walls, B Selvy, B Schneller, D AF Hardie, Kayla Santana, Amanda C. Serio, Andrew W. Gonzalez-Herrera, Juan C. Fordham, Bart Walls, Brian Selvy, Brian Schneller, Dominik TI Systems engineering processes and tools for requirements management at the Extremely Large Telescopes SO JOURNAL OF ASTRONOMICAL TELESCOPES INSTRUMENTS AND SYSTEMS DT Article DE systems engineering; requirements management; process; traceability; tools; verification AB The new class of Extremely Large Telescopes (ELTs) has implemented more rigorous systems engineering processes and tools for requirements management than has been used in past observatory projects. The similarities and differences between these activities at the ESO-ELT, GMT, TMT, and NOIRLab US-ELTP projects are summarized. We show that, while the key steps of the requirements management process are common among the ELTs, each project has implemented its own variation of the processes and tools tailored to its needs. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE). C1 [Hardie, Kayla; Fordham, Bart] Thirty Meter Telescope, Pasadena, CA 91124 USA. [Santana, Amanda C.; Walls, Brian] GMTO Corp, Pasadena, CA USA. [Serio, Andrew W.] NOIRLab, Tucson, AZ USA. [Gonzalez-Herrera, Juan C.; Schneller, Dominik] European Southern Observ, Munich, Germany. [Selvy, Brian] Vitech Corp, Blacksburg, VA USA. C3 European Southern Observatory RP Hardie, K (corresponding author), Thirty Meter Telescope, Pasadena, CA 91124 USA. EM khardie@tmt.org CR Angeli GZ, 2018, PROC SPIE, V10705, DOI 10.1117/12.2314392 [Anonymous], ATLASSIAN JIRA SOFTW [Anonymous], SYSTEMS ENG TOOLS DA [Anonymous], 2011, E ELT CONSTR PROP [Anonymous], IBM ENG REQUIREMENTS [Anonymous], R4J REQUIREMENTS MAN Fanson J, 2020, PROC SPIE, V11445, DOI 10.1117/12.2561852 Hardie K., 2016, INCOSE INT S, V26, P848 Liu F., 2018, PROC SPIE, V700 McCarthy P, 2021, US ELTP SESSION AAS National Academies of Sciences Engineering and Medicine, 2021, PATHW DISC ASTR ASTR Oschmann JM, 2004, P SOC PHOTO-OPT INS, V5497, P1, DOI 10.1117/12.552156 NR 12 TC 0 Z9 0 U1 2 U2 2 PD APR 1 PY 2022 VL 8 IS 2 AR 021507 DI 10.1117/1.JATIS.8.2.021507 WC Engineering, Aerospace; Instruments & Instrumentation; Optics SC Engineering; Instruments & Instrumentation; Optics UT WOS:000834667000007 DA 2022-12-14 ER PT J AU Fallon, M AF Fallon, M TI Traceability of poultry and poultry products SO REVUE SCIENTIFIQUE ET TECHNIQUE DE L OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE animal heath; eggs; food safety; integration; poultry; poultry meat; public heath; traceability AB Traceability of animals and animal products is becoming an essential marketing requirement necessary to meet heightened consumer expectations, particularly with respect to food safety. Traceability is a fundamental part of the management and audit systems that have been developed to provide assurances to the consumer. Most poultry products are produced by large companies who control all facets of production to an extraordinary level. The primary breeding, commercial breeding and production stages of poultry production have had to develop comprehensive recording and traceability systems, for productivity rather than public health reasons. These systems are principally documentary trails on an individual flock basis. Each flock comprises a unit having the same or similar status. Individual identification of poultry is not generally practised commercially, except in elite breeding stock. Traceability systems are being expanded in the production, processing and distribution areas to accommodate consumer concerns regarding public health and other issues. The nature of the industry and the level of controls applied by the poultry sector can provide sound and sustainable guarantees to the consumer. Future development lies in the wider application of sophisticated computerised systems at primary and further processing levels, to ensure that traceability can be maintained. C1 Dept Agr Food & Rural Dev, Dublin 2, Ireland. RP Fallon, M (corresponding author), Dept Agr Food & Rural Dev, Agr House,Kildare St, Dublin 2, Ireland. NR 0 TC 21 Z9 24 U1 0 U2 10 PD AUG PY 2001 VL 20 IS 2 BP 538 EP 546 DI 10.20506/rst.20.2.1289 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000170689800016 DA 2022-12-14 ER PT J AU Yao, S Li, T Liu, HG Li, JQ Wang, YZ AF Yao, Sen Li, Tao Liu, HongGao Li, JieQing Wang, YuanZhong TI Traceability of Boletaceae mushrooms using data fusion of UV-visible and FTIR combined with chemometrics methods SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE data fusion; traceability; Boletaceae mushrooms; ultraviolet-visible (UV-visible) spectroscopy; Fourier transform infrared (FTIR) spectroscopy ID BOLETE BOLETUS-EDULIS; VIRGIN OLIVE OILS; INFRARED-SPECTROSCOPY; CLASSIFICATION; FOOD; QUALITY; SAMPLES; ADULTERATION; PARAMETERS; SELECTION AB BACKGROUNDBoletaceae mushrooms are wild-grown edible mushrooms that have high nutrition, delicious flavor and large economic value distributing in Yunnan Province, China. Traceability is important for the authentication and quality assessment of Boletaceae mushrooms. In this study, UV-visible and Fourier transform infrared (FTIR) spectroscopies were applied for traceability of 247 Boletaceae mushroom samples in combination with chemometrics. RESULTSCompared with a single spectroscopy technique, data fusion strategy can obviously improve the classification performance in partial least square discriminant analysis (PLS-DA) and grid-search support vector machine (GS-SVM) models, for both species and geographical origin traceability. In addition, PLS-DA and GS-SVM models can provide 100.00% accuracy for species traceability and have reliable evaluation parameters. For geographical origin traceability, the accuracy of prediction in the PLS-DA model by data fusion was just 64.63%, but the GS-SVM model based on data fusion was 100.00%. CONCLUSIONThe results demonstrated that the data fusion strategy of UV-visible and FTIR combined with GS-SVM could provide a higher synergic effect for traceability of Boletaceae mushrooms and have a good generalization ability for the comprehensive quality control and evaluation of similar foods. (c) 2017 Society of Chemical Industry C1 [Yao, Sen; Liu, HongGao; Li, JieQing; Wang, YuanZhong] Yunnan Agr Univ, Coll Agron & Biotechnol, 452 Fengyuan Rd, Kunming 650201, Yunnan, Peoples R China. [Yao, Sen; Wang, YuanZhong] Yunnan Acad Agr Sci, Inst Med Plants, 2238 Beijing Rd, Kunming 650200, Yunnan, Peoples R China. [Li, Tao] Yuxi Normal Univ, Coll Resources & Environm, Yuxi, Peoples R China. C3 Yunnan Agricultural University; Yunnan Academy of Agricultural Sciences; Yuxi Normal University RP Li, JQ; Wang, YZ (corresponding author), Yunnan Agr Univ, Coll Agron & Biotechnol, 452 Fengyuan Rd, Kunming 650201, Yunnan, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, 2238 Beijing Rd, Kunming 650200, Yunnan, Peoples R China. EM lijieqing2008@126.com; boletus@126.com CR Anghileri A, 2007, J BIOTECHNOL, V127, P508, DOI 10.1016/j.jbiotec.2006.07.021 Bassbasi M, 2014, FOOD CHEM, V146, P250, DOI 10.1016/j.foodchem.2013.09.044 Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Bertrand B, 2012, FOOD CHEM, V135, P2575, DOI 10.1016/j.foodchem.2012.06.060 Biancolillo A, 2014, ANAL CHIM ACTA, V820, P23, DOI 10.1016/j.aca.2014.02.024 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Buckley K, 2017, APPL SPECTROSC, V71, P1085, DOI 10.1177/0003702817703270 Castro-Puyana M, 2017, TRAC-TREND ANAL CHEM, V93, P102, DOI 10.1016/j.trac.2017.05.004 Choong YK, 2014, J MOL STRUCT, V1069, P188, DOI 10.1016/j.molstruc.2014.04.001 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Dankowska A, 2017, EUR J LIPID SCI TECH, V119, DOI 10.1002/ejlt.201600268 Devos O, 2014, FOOD CHEM, V148, P124, DOI 10.1016/j.foodchem.2013.10.020 Doeswijk TG, 2011, ANAL CHIM ACTA, V705, P41, DOI 10.1016/j.aca.2011.03.025 Elzey B, 2016, FOOD CONTROL, V68, P303, DOI 10.1016/j.foodcont.2016.04.008 Esteki M, 2017, FOOD CONTROL, V82, P31, DOI 10.1016/j.foodcont.2017.06.014 Everard CD, 2016, APPL SCI-BASEL, V6, DOI 10.3390/app6090246 Falandysz J, 2008, J ENVIRON SCI HEAL A, V43, P1692, DOI 10.1080/10934520802330206 Falandysz J, 2017, ECOTOX ENVIRON SAFE, V137, P265, DOI 10.1016/j.ecoenv.2016.12.014 Frankowska A, 2010, FOOD ADDIT CONTAM B, V3, P1, DOI 10.1080/19440040903505232 Gonzaga MLC, 2005, CARBOHYD POLYM, V60, P43, DOI 10.1016/j.carbpol.2004.11.022 Hirri A, 2016, FOOD ANAL METHOD, V9, P974, DOI 10.1007/s12161-015-0255-y Hsu C. W., 2003, PRACTICAL GUIDE SUPP Huang CL, 2006, EXPERT SYST APPL, V31, P231, DOI 10.1016/j.eswa.2005.09.024 Lau BF, 2017, TRENDS FOOD SCI TECH, V61, P116, DOI 10.1016/j.tifs.2016.11.017 Li Y, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0168998 Li Y, 2016, J SPECTROSC, V2016, DOI 10.1155/2016/7813405 Li Y, 2014, J ANAL METHODS CHEM, V2014, DOI 10.1155/2014/519424 Li Y, 2017, SPECTROCHIM ACTA A, V177, P20, DOI 10.1016/j.saa.2017.01.029 Liu Zemin, 2013, Journal of Multimedia, V8, P496, DOI 10.4304/jmm.8.5.496-502 Lopez-Garcia M, 2016, FOOD CHEM, V199, P597, DOI 10.1016/j.foodchem.2015.12.016 Lu J, 2012, FRONT PHARMACOL, V3, DOI 10.3389/fphar.2012.00057 Mickiewicz B, 2015, J ORTHOP RES, V33, P71, DOI 10.1002/jor.22743 Moore JC, 2012, J FOOD SCI, V77, pR118, DOI 10.1111/j.1750-3841.2012.02657.x O'Gorman A, 2010, J AGR FOOD CHEM, V58, P7770, DOI 10.1021/jf101123a Park HE, 2013, J AOAC INT, V96, P1266, DOI 10.5740/jaoacint.12-195 Pizarro C, 2013, FOOD CHEM, V138, P915, DOI 10.1016/j.foodchem.2012.11.087 Pouladzadeh P, 2015, MULTIMED TOOLS APPL, V74, P5243, DOI 10.1007/s11042-014-2116-x Qi L. M., 2017, INT J FOOD PROP, P1, DOI [10.1080/10942912.2017.1289387, DOI 10.1080/10942912.2017.1289387] Rodrigues PH, 2016, FOOD CHEM, V196, P584, DOI 10.1016/j.foodchem.2015.09.055 Roy IG, 2015, J COMPUT PHYS, V295, P307, DOI 10.1016/j.jcp.2015.04.015 Sanmee R, 2003, FOOD CHEM, V82, P527, DOI 10.1016/S0308-8146(02)00595-2 Saptoro A, 2012, CHEM PROD PROCESS MO, V7, DOI 10.1515/1934-2659.1645 Sun LP, 2017, MOLECULES, V22, DOI 10.3390/molecules22030350 Vera L, 2010, ANAL BIOANAL CHEM, V397, P3043, DOI 10.1007/s00216-010-3852-z Wang XM, 2014, FOOD CHEM, V151, P279, DOI 10.1016/j.foodchem.2013.11.062 Wang ZF, 2016, FOOD ADDIT CONTAM A, V33, P560, DOI 10.1080/19440049.2016.1138547 Wisniewska P, 2017, SPECTROCHIM ACTA A, V173, P849, DOI 10.1016/j.saa.2016.10.042 Yang TW, 2016, SPECTROSC SPECT ANAL, V36, P3510, DOI 10.3964/j.issn.1000-0593(2016)11-3510-07 Yao S, 2017, ANAL LETT, V50, P2257, DOI 10.1080/00032719.2017.1279172 NR 49 TC 27 Z9 31 U1 5 U2 35 PD APR PY 2018 VL 98 IS 6 BP 2215 EP 2222 DI 10.1002/jsfa.8707 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000428443900020 DA 2022-12-14 ER PT J AU Borit, M Santos, J AF Borit, Melania Santos, Jorge TI Getting traceability right, from fish to advanced bio-technological products: a review of legislation SO JOURNAL OF CLEANER PRODUCTION DT Review DE Environmental sustainability; EU law consistency principle; Life cycle analysis; Product traceability; Documentation of sustainability; Traceability drivers ID SUPPLY CHAIN; FOOD; AGRICULTURE; INFORMATION AB Traceability is a tool used by regulators to manage risk in multiple supply chains, including supply chains of goods derived from genetically modified organisms, human blood, seafood products, toys and hazardous waste. This tool may help support a variety of claims that range from concern for nature to consumer satisfaction and health. This paper examines the consistency of European Union legislation with the declared objective of the law, i.e., to implement a traceability system through the entire supply chain. This analysis is undertaken by benchmarking 30 European Union laws that introduced traceability in the supply chain of 16 groups of products. The conclusion is that one-half of these norms lack basic effective principles of traceability. The approaches implemented were strongly correlated with the original driver for risk management (for example, concern for environmental sustainability), moderately correlated with the type of goods involved and uncorrelated with their trade value. The paper forecasts traceability approaches for new products, and indicates how traceability systems can become operative, regardless of product and driver. In addition, the importance that the legal provisions are consistent with the declared objective of the law is discussed. This integrated view is useful for regulators, industry and consumers in general and provides legislators and businesses with guidelines for consistent application of traceability, which facilitates other processes, such as life cycle analysis. Concurrently, it provides the public with an understanding of what lies behind the (often) inaccessible wording of legal norms. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Borit, Melania; Santos, Jorge] UiT Arctic Univ Norway, Fac Biosci Fisheries & Econ, N-9037 Tromso, Norway. C3 UiT The Arctic University of Tromso RP Borit, M (corresponding author), UiT Arctic Univ Norway, Fac Biosci Fisheries & Econ, N-9037 Tromso, Norway. EM melania.borit@uit.no; jorge.santos@uit.no CR [Anonymous], 2013, THE LAWYER Arnaud-Haond S, 2011, SCIENCE, V331, P1521, DOI 10.1126/science.1200783 Arrieta JM, 2010, P NATL ACAD SCI USA, V107, P18318, DOI 10.1073/pnas.0911897107 Barnard C., 2013, CAMBRIDGE YB EUROPEA, V15 Bellon-Maurel V, 2014, J CLEAN PROD, V69, P60, DOI 10.1016/j.jclepro.2014.01.079 Blackburn D, 2010, FOREST PROD J, V60, P688 Borit M., 2014, USING CULTURALLY TAI Borit M, 2012, MAR POLICY, V36, P96, DOI 10.1016/j.marpol.2011.03.012 BREUER MA, 1972, IEEE T COMPUT, VC 21, P399, DOI 10.1109/TC.1972.5008985 COWI, 2010, STUD IMPL MEAS TRAD Darnovsky M, 2007, SCIENCE, V316, P368 Egels-Zanden N., 2014, J CLEAN PROD Elkington J., 1999, ALTERN J, V25, P42 European Commission, 2012, TRAD European Commission, 2013, PROD ENV FOOTPR PEF European Commission, 2012, CONS VERS TREAT FUNC EUROSTAT, 2013, STAT EXPL Flapper SDP, 2002, PROD PLAN CONTROL, V13, P26, DOI 10.1080/09537280110061548 Gebbers R, 2010, SCIENCE, V327, P828, DOI 10.1126/science.1183899 GOODLAND R, 1995, ANNU REV ECOL SYST, V26, P1, DOI 10.1146/annurev.es.26.110195.000245 Green KC, 2007, INT J FORECASTING, V23, P365, DOI 10.1016/j.ijforecast.2007.05.005 GUENGL, 2013, HORS SCAND NO ROOM E Hannes R, 2012, MAX PLANCK ENCY EURO, P979 Helyar SJ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098691 Hofstede G. J., 2004, HIDE CONFIDE DILEMMA, P201 Hora M, 2011, J OPER MANAG, V29, P766, DOI 10.1016/j.jom.2011.06.006 International EPD® System, 2013, WHAT IS AN EPD R ISO, 1994, 176SC18402 ISOTC Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kitchenham B., 2004, TRSE0401 KEEL U Lehuger S, 2009, J CLEAN PROD, V17, P616, DOI 10.1016/j.jclepro.2008.10.005 Lye G., 2011, GUARD Machi L. A., 2009, LIT REV 6 STEPS SUCC McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Nachay K, 2011, FOOD TECHNOL-CHICAGO, V65, P44 Naylor RL, 2009, P NATL ACAD SCI USA, V106, P15103, DOI 10.1073/pnas.0905235106 Nilsson H, 2004, J CLEAN PROD, V12, P517, DOI 10.1016/S0959-6526(03)00114-8 NTB, 2013, MATT PROBL ER USP KJ OECD Directorate for Science Technology and Industry, 2011, ISIC REV 3 TECHN INT Olsen P., 2009, P HAG ROUND TABL EC Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Organic Europe, 2013, ORG FARM EUR 2007 Pira Smithers, 2014, HORSEMEAT SCANDAL 20 Poli S, 2004, EUR LAW J, V10, P613 Porter JK, 2011, INT FOOD AGRIBUS MAN, V14, P45 Priscille L., 2013, FRANCE24 Salpin Charlotte, 2007, REV EUROPEAN COMMUNI, V16, P12, DOI DOI 10.1111/J.1467-9388.2007.00538.X Schiermeier Q, 2004, NATURE, V427, P474, DOI 10.1038/427474b Seafood Choices Alliance, 2010, THE SEAF SUMM Smith MD, 2010, SCIENCE, V327, P784, DOI 10.1126/science.1185345 Stilgoe J, 2014, PUBLIC UNDERST SCI, V23, P4, DOI 10.1177/0963662513518154 Sutton, 2012, SYSTEMATIC APPROACHE The Poultry Site, 2011, GLOB POULTR TRENDS E TheMeatSite News Desk, 2014, THEMEATSITE NEWS DES USITC, 2008, GLOB BEEF TRAD EFF A, P4033 VALIANT LG, 1984, J ALGORITHM, V5, P363, DOI 10.1016/0196-6774(84)90016-6 van der Vorst J. G. A. J., 2004, HIDE CONFIDE DILEMMA, P73 Visiongain, 2013, FOOD TRAC TECHN MARK, P144 WEEE TRACE Project, 2012, GRADL TO GRAV TRAC W Weil D, 2013, SCIENCE, V340, P1410, DOI 10.1126/science.1233480 NR 63 TC 11 Z9 11 U1 1 U2 63 PD OCT 1 PY 2015 VL 104 BP 13 EP 22 DI 10.1016/j.jclepro.2015.05.003 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000357552900002 DA 2022-12-14 ER PT J AU Wu, QY Zhou, GH Yang, SS Abulikemu, BT Luo, RM Zhang, YL Li, X Xu, XL Li, CB AF Wu, Qiayu Zhou, Guanghong Yang, Sasa Abulikemu, Ba Tur Luo, Ruiming Zhang, Yanli Li, Xiao Xu, Xinglian Li, Chunbao TI SNP genotyping in sheep from northwest and east China for meat traceability SO JOURNAL OF CONSUMER PROTECTION AND FOOD SAFETY DT Article DE DNA traceability; SNPs; HRM; Sheep ID SINGLE-NUCLEOTIDE POLYMORPHISMS; IDENTIFICATION; MARKERS; PANEL AB Meat traceability is essential for meat safety and to solve adulteration issues. Single nucleotide polymorphisms (SNPs) are commonly used as a DNA marker and provide a way to trace meat and meat products. The high resolution melting method is a relatively cheap and fast method for detecting specific SNPs. In the present study, we selected 8 SNPs from the NCBI dbSNP database and tested their feasibility in blood or meat samples from 135 sheep reared in northwestern and eastern China. The results indicated that seven SNPs were suitable for meat traceability and the probability of 2 individuals having the same genotype was 1.85 out of 1000. C1 [Wu, Qiayu; Zhou, Guanghong; Yang, Sasa; Li, Xiao; Xu, Xinglian; Li, Chunbao] MOE, Key Lab Meat Proc & Qual Control, Nanjing, Jiangsu, Peoples R China. [Wu, Qiayu; Zhou, Guanghong; Yang, Sasa; Li, Xiao; Xu, Xinglian; Li, Chunbao] MOA, Key Lab Anim Prod Proc, Nanjing, Jiangsu, Peoples R China. [Wu, Qiayu; Zhou, Guanghong; Yang, Sasa; Li, Xiao; Xu, Xinglian; Li, Chunbao] Jiangsu Synerget Innovat Ctr Meat Prod & Proc, Nanjing, Jiangsu, Peoples R China. [Wu, Qiayu; Zhou, Guanghong; Yang, Sasa; Zhang, Yanli; Li, Xiao; Xu, Xinglian; Li, Chunbao] Nanjing Agr Univ, Coll Anim Sci & Technol, Nanjing 210095, Jiangsu, Peoples R China. [Abulikemu, Ba Tur] Xinjiang Agr Univ, Xinjiang 830052, Peoples R China. [Luo, Ruiming] Ningxia Univ, Ningxia 750021, Peoples R China. C3 Nanjing Agricultural University; Xinjiang Agricultural University; Ningxia University RP Li, CB (corresponding author), MOE, Key Lab Meat Proc & Qual Control, Nanjing, Jiangsu, Peoples R China.; Li, CB (corresponding author), MOA, Key Lab Anim Prod Proc, Nanjing, Jiangsu, Peoples R China.; Li, CB (corresponding author), Jiangsu Synerget Innovat Ctr Meat Prod & Proc, Nanjing, Jiangsu, Peoples R China.; Li, CB (corresponding author), Nanjing Agr Univ, Coll Anim Sci & Technol, Nanjing 210095, Jiangsu, Peoples R China. EM chunbao.li@njau.edu.cn CR Cho RJ, 1999, NAT GENET, V23, P203, DOI 10.1038/13833 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Graham R, 2005, CLIN CHEM, V51, P1295, DOI 10.1373/clinchem.2005.051516 Karniol B, 2009, ANIM GENET, V40, P353, DOI 10.1111/j.1365-2052.2008.01846.x Kidd KK, 2006, FORENSIC SCI INT, V164, P20, DOI 10.1016/j.forsciint.2005.11.017 Lee HY, 2005, FORENSIC SCI INT, V148, P107, DOI 10.1016/j.forsciint.2004.04.073 Li J, 2009, HUM MUTAT, V30, P1583, DOI 10.1002/humu.21112 Liew M, 2004, CLIN CHEM, V50, P1156, DOI 10.1373/clinchem.2004.032136 Liu SX, 2016, AQUACULTURE, V452, P178, DOI 10.1016/j.aquaculture.2015.11.001 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Rajatileka S, 2013, BMC GENET, V14, DOI 10.1186/1471-2156-14-105 Reed GH, 2004, CLIN CHEM, V50, P1748, DOI 10.1373/clinchem.2003.029751 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Shiro S, 2013, APPL ENVIRON MICROB, V79, P3610, DOI 10.1128/AEM.00236-13 Touati A, 2015, J CLIN MICROBIOL, V53, P3182, DOI 10.1128/JCM.01156-15 Weller JI, 2006, ANIM GENET, V37, P387, DOI 10.1111/j.1365-2052.2006.01455.x Werner FAO, 2004, ANIM GENET, V35, P44, DOI 10.1046/j.1365-2052.2003.01071.x Wigginton JE, 2005, AM J HUM GENET, V76, P887, DOI 10.1086/429864 Yang SS, 2014, EUR FOOD RES TECHNOL, V239, P473, DOI 10.1007/s00217-014-2241-9 NR 19 TC 3 Z9 4 U1 0 U2 18 PD JUN PY 2017 VL 12 IS 2 BP 125 EP 130 DI 10.1007/s00003-017-1092-2 WC Food Science & Technology SC Food Science & Technology UT WOS:000402396900004 DA 2022-12-14 ER PT J AU Alfian, G Rhee, J Ahn, H Lee, J Farooq, U Ijaz, MF Syaekhoni, MA AF Alfian, Ganjar Rhee, Jongtae Ahn, Hyejung Lee, Jaeho Farooq, Umar Ijaz, Muhammad Fazal Syaekhoni, M. Alex TI Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system SO JOURNAL OF FOOD ENGINEERING DT Article DE e-pedigree; Traceability; RFID; WSN; Data mining ID QUALITY; SAFETY; IDENTIFICATION; PRODUCTS; CHAIN AB Due to the growing customer health awareness, food quality and safety has gained considerable attention. Therefore, consumer demand for complete visibility of food quality and history along the supply chain has significantly increased. This study proposes an e-pedigree food traceability system, utilizing radio frequency identification technology to track and trace product location and wireless sensor network to collect temperature and humidity during storage and transportation. Missing sensor data may occur in real cases, as sensor data are lost or corrupted due to many reasons. The proposed system utilizes data mining techniques to predict missing sensor data. The proposed system was tested for kimchi supply chain in Korea, and showed significant benefit to managers as well as customers by providing real-time location as well as complete temperature and humidity history. The multilayer perceptron model provided the best prediction accuracy for missing sensor data compared to other models. The proposed e-pedigree food traceability system will help managers optimize food distribution while also increasing customer satisfaction, as it can monitor product freshness. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Alfian, Ganjar; Lee, Jaeho] Dongguk Univ Seoul, Nano Informat Technol Acad, U SCM Res Ctr, Seoul 100715, South Korea. [Rhee, Jongtae; Ahn, Hyejung; Farooq, Umar; Ijaz, Muhammad Fazal; Syaekhoni, M. Alex] Dongguk Univ Seoul, Dept Ind & Syst Engn, Seoul 100715, South Korea. C3 Dongguk University; Dongguk University RP Alfian, G (corresponding author), Dongguk Univ Seoul, Nano Informat Technol Acad, U SCM Res Ctr, Seoul 100715, South Korea. EM ganjar@dongguk.edu CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Alazzawi L., 2008, J COMPUT SYST NETW C Aung MM, 2014, FOOD CONTROL, V40, P198, DOI 10.1016/j.foodcont.2013.11.016 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Banaee H, 2013, SENSORS-BASEL, V13, P17472, DOI 10.3390/s131217472 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 EPCglobal, 2007, PED RAT STAND VERS 1 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Gruenwald L., 2007, PROC 7 IEEE INT C DA, P207, DOI [10.1109/ICDMW.2007.103, DOI 10.1109/ICDMW.2007.103] Han J, 2012, MOR KAUF D, P1 Hou YQ, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16070945 Moron MJ, 2014, SENSORS-BASEL, V14, P575, DOI 10.3390/s140100575 Kang YS, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16122126 Kim S, 2010, IND ENG MANAG SYST, V9, P285, DOI 10.7232/iems.2010.9.3.285 Lee E.-K., 2009, P 6 ACM INT WORKSH V Lee GI, 2012, FOOD CONTROL, V24, P1, DOI 10.1016/j.foodcont.2011.09.014 Liu B, 2013, COMPUT ELECTRON AGR, V98, P117, DOI 10.1016/j.compag.2013.08.002 Mainetti L., 2013, INT J ANTENN PROPAG, V2013, P15 Park KY, 2014, J MED FOOD, V17, P6, DOI 10.1089/jmf.2013.3083 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ruzzelli A.G., 2007, P 1 ACM WORKSH CONV Sharma S, 2013, PROCEEDINGS OF THE THIRD 2013 IEEE GLOBAL HUMANITARIAN TECHNOLOGY CONFERENCE (GHTC 2013), P337, DOI 10.1109/GHTC.2013.6713707 Silva F., 2013, NAT ARTIF COMPUT ENG, V7931, P200, DOI DOI 10.1007/978-3-642-38622-0_ Sun F., 2012, MOBILE COMPUTING APP, V76, P1, DOI DOI 10.1007/978-3-642-29336-8_12 Sungwon Yang, 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops 2011). PerCom-Workshops 2011: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops 2011), P636, DOI 10.1109/PERCOMW.2011.5766966 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Zhang L, 2006, GCC 2006: FIFTH INTERNATIONAL CONFERENCE ON GRID AND COOPERATIVE COMPUTING WORKSHOPS, PROCEEDINGS, P463 NR 34 TC 73 Z9 78 U1 2 U2 131 PD NOV PY 2017 VL 212 BP 65 EP 75 DI 10.1016/j.jfoodeng.2017.05.008 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000407410100008 DA 2022-12-14 ER PT J AU Hall, D AF Hall, Derek TI Food with a visible face: Traceability and the public promotion of private governance in the Japanese food system SO GEOFORUM DT Article DE Food safety; Japan; Policy; Private governance; Trade; Traceability ID GLOBAL AGRIFOOD SYSTEM; 3RD-PARTY CERTIFICATION; STANDARDS; SAFETY; CHAIN; GOVERNMENT; POLITICS; QUALITY; MARKET; SPACE AB The years since 2000 have seen a constant stream of high-profile scandals relating to food safety and food labeling in Japan. One response of Japan's Ministry of Agriculture, Forestry and Fisheries (MAFF) has been to promote food traceability as a mechanism to improve food safety and to provide reliable information to consumers about their food. MAFF's main approach to traceability promotion, however, has not involved making traceability mandatory (as has been done in the EU) but encouraging private companies to adopt it. These actions constitute, I argue, an example of the public promotion of private governance. When viewed against the literature on the private governance of food safety and quality, MAFF's traceability activities are surprising for three reasons: their forms and extent, the relative lack of interest the Ministry has shown in third-party certification, and the way MAFF has responded to concerns about the safety of imported food by focusing almost entirely on domestic traceability. I also argue, however, that looking at the literature from a Japanese perspective suggests that public encouragement is more central to the rise of private governance in the global agri-food system than is usually appreciated. (C) 2010 Elsevier Ltd. All rights reserved. C1 Wilfrid Laurier Univ, Dept Polit Sci, Waterloo, ON N2L 3C5, Canada. C3 Wilfrid Laurier University RP Hall, D (corresponding author), Wilfrid Laurier Univ, Dept Polit Sci, 75 Univ Ave W, Waterloo, ON N2L 3C5, Canada. EM dehall@wlu.ca CR Albersmeier F, 2009, FOOD CONTROL, V20, P927, DOI 10.1016/j.foodcont.2009.01.010 Alvarez RR, 2006, HUM ORGAN, V65, P35, DOI 10.17730/humo.65.1.98qmgg2khpywgdqj [Anonymous], 2001, INT FOOD AGRIBUS MAN Ansell C., 2006, WHATS BEEF CONTESTED ARAI Y, 2008, SHOKUHIN GISO OKOSAN Arienzo A, 2008, INT LIBR ENVIRON AGR, V15, P23 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bernstein S, 2008, J INT ECON LAW, V11, P575, DOI 10.1093/jiel/jgn022 Busch L, 2004, RURAL SOCIOL, V69, P321, DOI 10.1526/0036011041730527 Cashore B, 2007, GLOBAL ENVIRON POLIT, V7, P1, DOI 10.1162/glep.2007.7.1.1 *COD AL, 2006, 602006 CACGL COD AL Codron JM, 2005, FOOD POLICY, V30, P270, DOI 10.1016/j.foodpol.2005.05.004 DEMURA M, 2005, NORIN KINYU DEC, P52 Eden S, 2009, GEOFORUM, V40, P383, DOI 10.1016/j.geoforum.2008.01.001 FAO, 2007, FAO STAT YB 2005 200 *FMRIC, 2008, NYUS RIR SHOK TOR SH Food Marketing Research and Information Center, 2007, HDB INTR FOOD TRAC S Freidberg S, 2007, CULT GEOGR, V14, P321, DOI 10.1177/1474474007078203 Fulponi L, 2007, GLOBAL SUPPLY CHAINS, STANDARDS AND THE POOR: HOW THE GLOBALIZATION OF FOOD SYSTEMS AND STANDARDS AFFECTS RURAL DEVELOPMENT AND POVERTY, P5, DOI 10.1079/9781845931858.0005 Martinez MG, 2007, FOOD POLICY, V32, P299, DOI 10.1016/j.foodpol.2006.07.005 Golan E., 2004, Amber Waves, V2, P14 HAGIWARA H, 2006, SHOHISHA SHINRAI SHO Hashimoto T., 2005, INFOFISH International, P27 Hatanaka M, 2005, FOOD POLICY, V30, P354, DOI 10.1016/j.foodpol.2005.05.006 Hatanaka M, 2008, SOCIOL RURALIS, V48, P73, DOI 10.1111/j.1467-9523.2008.00453.x Henson S, 2005, FOOD POLICY, V30, P241, DOI 10.1016/j.foodpol.2005.05.002 Higgins V, 2008, GEOFORUM, V39, P1776, DOI 10.1016/j.geoforum.2008.05.004 *INT ORG STAND, 2007, HELP SMALL BUS IMPL ISHIKAWA K, 2002, SAPIO 0821, P111 ISHIKURO M, 2007, SHUKAN ASAHI 0817, P140 *IT, KAO GA MIER SHOK *IT, MEETS HAR TAIS KOT Jacquet JL, 2008, MAR POLICY, V32, P309, DOI 10.1016/j.marpol.2007.06.007 *JTRA, 2008, TOR DOK NINSH SEID *KAIR TOR GAID SAK, 2005, KAIR KAK HOT TOR SHI Kim CM, 2008, POLYM-KOREA, V32, P49 KIMURA A, 2003, NOSEI KEIZAI KENKYTI, V25, P37 Lezaun J, 2006, SOC STUD SCI, V36, P499, DOI 10.1177/0306312706059461 *MAFF, NOR TOR KANK *MAFF, 2005, YUB SHOK ANZ ANSH SH *MAFF, 2007, TOR WA MO JOSH *MAFF, 2008, MIN DAICH MOR UM MEG *MAFF, 2004, SHITT OK SHOK TOR *MAFF, 2009, MEAS AIM RES TAINT R *MAFF, 2009, SHOK ANT KYOK KAK KA *MHLW, YUNY SHOK ANZ MAM TA Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Mulgan AG, 2005, J JPN STUD, V31, P261, DOI 10.1353/jjs.2005.0053 MULGAN AG, 2005, JAPAN INTERVENTONIST Mulgan AG, 2008, AUST J INT AFF, V62, P164, DOI 10.1080/10357710802060535 MURAI Y, 2007, EBI NIHONJIN, V2 Mutersbaugh T, 2005, J RURAL STUD, V21, P389, DOI 10.1016/j.jrurstud.2005.08.003 NANSEKI T, 2008, FOOD TRACEABILITY WO, P46 NEILSON J, 2006, TRAC SUPPL CHAINS SM *NIH KANJ NOR KEN, 2007, 2007 NEN KOT KANJ NIIYAMA Y, 2004, KOKO GEPPO DEC, P2 NISHIMURA M, 2008, NIHON SUISAN GAKKAIS, V74, P308 *NOKY RYUTS KENK, 2004, SEIK TOR DONY GAID *NOR SUIS SHOH, 2006, HEIS 18 NEND SHOK SH *NOR SUISH SHOH AN, 2007, NEND SHOK SHOH MON D OTSUKA S, 2005, AJIA MEZASU HOSHOKU Plunkett D, 2008, FOOD DRUG LAW J, V63, P657 Porter Tony, 2007, GLOBAL LIBERALISM PO, P109 REIMANN K, 2003, STATE CIVIL SOC JAPA, P298 Sakagami M, 2006, NEW ZEAL J AGR RES, V49, P247, DOI 10.1080/00288233.2006.9513715 SCHAEDE U, 2001, COOPERATIVE CAPITALI Sending OJ, 2006, INT STUD QUART, V50, P651, DOI 10.1111/j.1468-2478.2006.00418.x SHINBUN SK, 2007, SUISAN KEIZAI S 0322, P3 SHINBUN SK, 2008, SUISAN KEIZAI S 0624, P1 *SHOK TOR SHIS DAI, 2008, SHOK TOR SHIS YOK KA Skogstad Grace, 2006, WHATS BEEF CONTESTED, P213, DOI DOI 10.1080/1350176032000085333 STEINER B, 2006, WHATS BEEF CONTESTED, P181 SUISAN K, 2008, SUISAN KEIZAI S 0602, P1 TAKAHASHI T, 2006, SHOKUHIN ANZEN HINSH Tallontire A, 2007, THIRD WORLD Q, V28, P775, DOI 10.1080/01436590701336648 Tanaka K, 2008, AGR HUM VALUES, V25, P567, DOI 10.1007/s10460-008-9152-y TAYA K, 2004, KOKO GEPPO OCT, P2 TOKUMARU I, 2007, SANDE MAINICHI 0729, P147 Vandergeest P, 2007, WORLD DEV, V35, P1152, DOI 10.1016/j.worlddev.2006.12.002 Vogel D, 2006, WHATS BEEF CONTESTED, P125 Vogel D, 2008, ANNU REV POLIT SCI, V11, P261, DOI 10.1146/annurev.polisci.11.053106.141706 Vogel Steven K., 1999, SOCIAL SCI JAPAN J, V2, P3, DOI DOI 10.1093/SSJJ/2.1.3 WAKABAYASHI T, 2008, GEKKAN HACCP JUN, P227 YAMAUCHI K, 2008, KAIYO SUISAN ENJINIA, P71 YANO I, 2003, NOGYO SHIJO KENKYU, V12, P3 *YOSH TOR SHIS GAI, 2006, YOSH TOR SHIS GAID 2006, AKUANETTO DEC, P18 2008, ASAHI SHINBUN 1024 NR 88 TC 44 Z9 46 U1 1 U2 31 PD SEP PY 2010 VL 41 IS 5 BP 826 EP 835 DI 10.1016/j.geoforum.2010.05.005 WC Geography SC Geography UT WOS:000282862700018 DA 2022-12-14 ER PT J AU Banterle, A Stranieri, S AF Banterle, Alessandro Stranieri, Stefanella TI The consequences of voluntary traceability system for supply chain relationships. An application of transaction cost economics SO FOOD POLICY DT Article DE Traceability; Private standard; Vertical coordination; Transaction costs; Food supply chain ID FOOD SAFETY; QUALITY AB This paper analyses the effects Of Voluntary traceability on vertical relationships within food supply chains using a transaction cost perspective. The analysis makes reference to the Italian Situation where the national standard organization has introduced a private standard for traceability that provides a higher degree of information associated with the individual product than the European mandatory traceability system. A Survey was conducted by questionnaire to assess changes in transaction characteristics, costs and governance after the introduction Of Voluntary traceability. The sample represents all Italian firms applying this standard. Factorial and cluster analyses were applied to find the different reorganizations of transactions. The results highlight an increase ill asset specificity and a decrease in the uncertainty level throughout the Supply Chains. The introduction Of Voluntary traceability shows increased vertical coordination for firms that previously used oral agreements and variation ill transactions conditions for firms using contracts. Instead vertically integrated firms do not reveal any variation in the governance of transactions as they are already internally safeguarded. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Banterle, Alessandro; Stranieri, Stefanella] Univ Milan, Dept Agr Food & Environm Econ, I-20133 Milan, Italy. C3 University of Milan RP Banterle, A (corresponding author), Univ Milan, Dept Agr Food & Environm Econ, Via Celoria 2, I-20133 Milan, Italy. EM alessandro.banterle@unimi.it CR Banterle A., 2006, Journal on Chain and Network Science, V6, P69, DOI 10.3920/JCNS2006.x066 BANTERLE A, 2006, TRUST RISK BUSINESS Boger S, 2001, EUR REV AGRIC ECON, V28, P241, DOI 10.1093/erae/28.3.241 Caswell JA, 1998, AUST J AGR RESOUR EC, V42, P409, DOI 10.1111/1467-8489.00060 CHARLIER C, 2006, TRUST RISK BUSINESS Coase RH, 1937, ECONOMICA-NEW SER, V4, P386, DOI 10.1111/j.1468-0335.1937.tb00002.x Coase R. H., 1984, GESAMTE STAATSWISSJ, V140, P229 COASE RH, 1992, AM ECON REV, V82, P713 Cook M, 2004, AM J AGR ECON, V86, P740, DOI 10.1111/j.0002-9092.2004.00617.x European Commission, 2000, COM1999719 EUR COMM, P719 Golan E., 2004, 830 ERS USDA Gorsuch R.L., 1983, FACTOR ANAL Han J., 2006, INT AGRIFOOD CHAINS Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs J. E., 1996, SUPPLY CHAIN MANAGEM, V1, P15, DOI DOI 10.1108/13598549610155260 Hobbs JE, 2004, ROLE OF INSTITUTIONS IN RURAL POLICIES AND AGRICULTURAL MARKETS, P199 Holleran E, 1999, FOOD POLICY, V24, P669, DOI 10.1016/S0306-9192(99)00071-8 *ISO, 2005, 220052005 ISO Kalton G., 1983, INTRO SURVEY SAMPLIN, DOI DOI 10.4135/9781412984683 KLEIN PG, 1999, ENCY LAW EC, P456 KREPS DM, 1990, PERSPECTIVES POLITIC Menard C, 2004, AM J AGR ECON, V86, P750, DOI 10.1111/j.0002-9092.2004.00619.x Menard C, 2005, EUR REV AGRIC ECON, V32, P421, DOI 10.1093/eurrag/jbi013 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 NORTH DC, 1991, J ECON PERSPECT, V5, P97, DOI 10.1257/jep.5.1.97 NORTH DC, 1994, AM ECON REV, V84, P359 PERI C, 2004, IMPORTANZA TRACCIABI SODANO V, 2004, STRUMENTI ANAL EC ME SOUZAMONTEIRO DM, 2004, 20046 U MASS DEP RES TRIENEKENS J, 2001, IAMA S 2001 DEP MAN VELTHUIS AGJ, 2003, NEW APPROACHES FOOD Williamson O.E., 2004, ROLE I RURAL POLICIE WILLIAMSON OE, 1991, ADMIN SCI QUART, V36, P269, DOI 10.2307/2393356 WILLIAMSON OE, 1971, AM ECON REV, V61, P112 WILLIAMSON OE, 1979, J LAW ECON, V22, P233, DOI 10.1086/466942 Williamson OE, 1996, MECH GOVERNANCE Williamson Oliver E., 1985, EC I CAPITALISM FIRM Williamson QE, 2000, J ECON LIT, V38, P595 NR 38 TC 81 Z9 86 U1 1 U2 69 PD DEC PY 2008 VL 33 IS 6 BP 560 EP 569 DI 10.1016/j.foodpol.2008.06.002 WC Agricultural Economics & Policy; Economics; Food Science & Technology; Nutrition & Dietetics SC Agriculture; Business & Economics; Food Science & Technology; Nutrition & Dietetics UT WOS:000261566900012 DA 2022-12-14 ER PT J AU Wang, L Xu, LQ Zheng, ZY Liu, SY Li, XT Cao, L Li, JB Sun, CH AF Wang, Lu Xu, Longqin Zheng, Zhiying Liu, Shuangyin Li, Xiangtong Cao, Liang Li, Jingbin Sun, Chuanheng TI Smart Contract-Based Agricultural Food Supply Chain Traceability SO IEEE ACCESS DT Article DE Blockchain; smart contract; agricultural food supply chain; traceability; food safety AB The complexity of a supply chain makes product safety or quality issues extremely difficult to track, especially for the basic agricultural food supply chains of people's daily diets. The existing agricultural food supply chains present several major problems, such as numerous participants, inconvenient communication caused by long supply chain cycles, data distrust between participants and the centralized system. The emergence of blockchain technology effectively solves the pain-point problem existing in the traceability system of agricultural food supply chains. This paper proposes a framework based on the consortium and smart contracts to track and trace the workflow of agricultural food supply chains, implement traceability and shareability of supply chains, and break down the information islands between enterprises as much as possible to eliminate the need for the central institutions and agencies and improve the integrity of the transaction records, reliability and security. At the same time, farmers record details of the environment and crop growth data in the InterPlanetary File System (IPFS) and store file IPFS hashes in smart contracts, which not only increases data security but also alleviates the blockchain storage explosion problem. This framework has been applied in Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. Although there are still many defects, the framework has successfully realized functions such as disintermediation and tracing of agricultural product information through QR codes. Thus, the framework proposed in this paper is of great significance and reference value for enterprises to ensure product quality and safety traceability. C1 [Wang, Lu; Xu, Longqin; Zheng, Zhiying; Liu, Shuangyin; Li, Xiangtong; Cao, Liang] Zhongkai Univ Agr & Engn, Coll Informat Sci & Technol, Guangzhou 510225, Peoples R China. [Wang, Lu; Xu, Longqin; Zheng, Zhiying; Liu, Shuangyin; Li, Xiangtong; Cao, Liang] Zhongkai Univ Agr & Engn, Smart Agr Engn Technol Res Ctr, Guangdong Higher Educ Inst, Guangzhou 510225, Peoples R China. [Wang, Lu; Xu, Longqin; Liu, Shuangyin; Cao, Liang] Zhongkai Univ Agr & Engn, Guangzhou Key Lab Agr Prod Qual & Safety Traceabi, Guangzhou 510225, Peoples R China. [Xu, Longqin; Zheng, Zhiying; Liu, Shuangyin; Cao, Liang] Zhongkai Univ Agr & Engn, Acad Smart Agr Engn Innovat, Guangzhou 510225, Peoples R China. [Xu, Longqin; Liu, Shuangyin; Li, Xiangtong; Cao, Liang] Guangdong Prov Key Lab Waterfowl Hlth Breeding, Guangzhou 510225, Peoples R China. [Liu, Shuangyin; Li, Jingbin] Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832000, Peoples R China. [Sun, Chuanheng] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. C3 Zhongkai University of Agriculture & Engineering; Zhongkai University of Agriculture & Engineering; Zhongkai University of Agriculture & Engineering; Zhongkai University of Agriculture & Engineering; Shihezi University; Beijing Academy of Agriculture & Forestry RP Liu, SY (corresponding author), Zhongkai Univ Agr & Engn, Coll Informat Sci & Technol, Guangzhou 510225, Peoples R China.; Liu, SY (corresponding author), Zhongkai Univ Agr & Engn, Smart Agr Engn Technol Res Ctr, Guangdong Higher Educ Inst, Guangzhou 510225, Peoples R China.; Liu, SY (corresponding author), Zhongkai Univ Agr & Engn, Guangzhou Key Lab Agr Prod Qual & Safety Traceabi, Guangzhou 510225, Peoples R China.; Liu, SY (corresponding author), Zhongkai Univ Agr & Engn, Acad Smart Agr Engn Innovat, Guangzhou 510225, Peoples R China.; Liu, SY (corresponding author), Guangdong Prov Key Lab Waterfowl Hlth Breeding, Guangzhou 510225, Peoples R China.; Liu, SY (corresponding author), Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832000, Peoples R China. EM hdlsyxlq@126.com CR Al-Jaroodi J, 2019, IEEE ACCESS, V7, P36500, DOI 10.1109/ACCESS.2019.2903554 Baralla G, 2019, 2019 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON EMERGING TRENDS IN SOFTWARE ENGINEERING FOR BLOCKCHAIN (WETSEB 2019), P40, DOI 10.1109/WETSEB.2019.00012 Bore N., 2020, P IEEE INT C BLOCKCH, P1, DOI [10.1109/ICBC48266.2020.9169450, DOI 10.1109/ICBC48266.2020.9169450] Chen CL, 2014, INT J PROD ECON, V152, P188, DOI 10.1016/j.ijpe.2013.12.016 Chen J, 2019, FUTURE GENER COMP SY, V101, P1122, DOI 10.1016/j.future.2019.07.037 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Hao J.T., 2018, J COMPUT, V29, P158, DOI [10.3966/199115992018122906015, DOI 10.3966/199115992018122906015] Hu F., 2010, P INT C MAN SERV SCI, P1 Jin Z., 2019, IEEE ACCESS, V7 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Li H, 2016, IBM J RES DEV, V60, DOI 10.1147/JRD.2016.2598610 Li SC, 2019, IEEE T COMPUT SOC SY, V6, P1433, DOI 10.1109/TCSS.2019.2927431 Li XF, 2020, IEEE ACCESS, V8, P69754, DOI 10.1109/ACCESS.2020.2986220 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu ZY, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102059 Lu Wang, 2019, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), P104, DOI 10.1109/IAEAC47372.2019.8997818 Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Omar IA, 2020, IEEE ACCESS, V8, P182704, DOI 10.1109/ACCESS.2020.3028031 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 She W, 2019, IEEE ACCESS, V7, P62058, DOI 10.1109/ACCESS.2019.2916345 Tian F, 2017, I C SERV SYST SERV M Tripathi G, 2020, HEALTHCARE-J DEL SCI, V8, DOI 10.1016/j.hjdsi.2019.100391 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Tso R, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8040422 Vangala A, 2021, IEEE SENS J, V21, P17591, DOI 10.1109/JSEN.2020.3012294 Wang SP, 2019, IEEE ACCESS, V7, P115122, DOI 10.1109/ACCESS.2019.2935873 Wang SP, 2018, IEEE ACCESS, V6, P38437, DOI 10.1109/ACCESS.2018.2851611 Xu H, 2020, IEEE ACCESS, V8, P87552, DOI 10.1109/ACCESS.2020.2992649 Xuan SC, 2020, COMPUT ELECTR ENG, V83, DOI 10.1016/j.compeleceng.2020.106587 Yang MM, 2019, FUTURE GENER COMP SY, V94, P408, DOI 10.1016/j.future.2018.11.046 Yanjun Jiang, 2018, 2018 IEEE 4th International Conference on Computer and Communications (ICCC). Proceedings, P2379, DOI 10.1109/CompComm.2018.8781067 Yong BB, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.10.009 Yu WJ, 2018, INT SYM COMPUT INTEL, P339, DOI 10.1109/ISCID.2018.00083 Yu Z, 2020, IEEE ACCESS, V8, P13640, DOI 10.1109/ACCESS.2020.2966428 Zeng XY, 2019, CHINA COMMUN, V16, P38, DOI 10.23919/JCC.2019.08.004 Zhang HJ, 2019, PEER PEER NETW APPL, V12, P1346, DOI 10.1007/s12083-018-0694-5 Zhang S., IEEE ACCESS, V7 Zhang XH, 2019, IEEE ACCESS, V7, P97281, DOI 10.1109/ACCESS.2019.2929259 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhang YY, 2019, PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019), P2172, DOI 10.1109/ITNEC.2019.8729408 Zhao DA, 2009, ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL III, PROCEEDINGS, P562, DOI 10.1109/ICICTA.2009.601 NR 42 TC 21 Z9 21 U1 26 U2 92 PY 2021 VL 9 BP 9296 EP 9307 DI 10.1109/ACCESS.2021.3050112 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000608608800001 DA 2022-12-14 ER PT J AU Baffi, C Trincherini, PR AF Baffi, C. Trincherini, P. R. TI Food traceability using the Sr-87/Sr-86 isotopic ratio mass spectrometry SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Review DE Sr-87/Sr-86 isotope ratio; Food traceability; Mass spectrometry; MC-ICP-MS; TIMS ID HILSA TENUALOSA-ILISHA; MC-ICP-MS; GEOGRAPHIC ORIGIN; STABLE-ISOTOPE; STRONTIUM ISOTOPES; SR ISOTOPES; MULTIELEMENT ANALYSIS; PRECISE DETERMINATION; OTOLITH CHEMISTRY; ABUNDANCE RATIOS AB Today, food traceability needs to develop suitable "robust" analytical methods, in terms of the precision and of the reliability of results, which can support modern legislative tools, aimed at guaranteeing food authenticity and origin and trying to avoid possible frauds. This review paper highlights the most recent results obtained with the use of the Sr-87/Sr-86 isotopic ratio technique, when applied to the traceability of the origin of different foods for human consumption, such as vegetables, beverages, dairy products, and meat and fish products. The instrumental techniques, with the relative methodologies and the quality of the final results, will be examined and commented. C1 [Baffi, C.] Univ Cattolica Sacro Cuore, Inst Agr & Environm Chem, Fac Agr Food & Environm Sci, Via Emilia Parmense 84, I-29122 Piacenza, Italy. [Trincherini, P. R.] Ist Nazl Fis Nucl, LIMS, Lab Nazl Gran Sasso, Via G Acitelli 22, I-67100 Assergi, Italy. C3 Catholic University of the Sacred Heart; Gran Sasso Science Institute (GSSI); Istituto Nazionale di Fisica Nucleare (INFN) RP Baffi, C (corresponding author), Univ Cattolica Sacro Cuore, Inst Agr & Environm Chem, Fac Agr Food & Environm Sci, Via Emilia Parmense 84, I-29122 Piacenza, Italy. EM claudio.baffi@unicatt.it CR ABERG G, 1989, J HYDROL, V109, P65, DOI 10.1016/0022-1694(89)90007-3 ABERG G, 1995, WATER AIR SOIL POLL, V79, P309, DOI 10.1007/BF01100444 Alam A, 2002, INT TER C P SAR DEV Albarede F, 2004, GEOCHIM COSMOCHIM AC, V68, P2725, DOI 10.1016/j.gca.2003.11.024 Alonso-Salces RM, 2004, ANAL BIOANAL CHEM, V379, P464, DOI 10.1007/s00216-004-2625-y Ammann M, 1999, OECOLOGIA, V118, P124, DOI 10.1007/s004420050710 [Anonymous], 1998, CEN TC 174 FRUIT VEG Ariyama K, 2007, J AGR FOOD CHEM, V55, P347, DOI 10.1021/jf062613m Ariyama K, 2006, J AGR FOOD CHEM, V54, P3341, DOI 10.1021/jf0525481 Ariyama K, 2012, J AGR FOOD CHEM, V60, P1628, DOI 10.1021/jf204296p Babaluk JA, 1998, LINKING PROTECTED AR Barbaste M, 2002, J ANAL ATOM SPECTROM, V17, P135, DOI 10.1039/b109559p Barnett-Johnson R, 2005, CAN J FISH AQUAT SCI, V62, P2425, DOI 10.1139/F05-194 Barnett-Johnson R, 2004, 010 COAST ENV QUAL I Barnett-Johnson R, 2008, LIMNOL OCEANOGR, V53, P1633, DOI 10.4319/lo.2008.53.4.1633 Barnett-Johnson R, 2010, ENVIRON BIOL FISH, V89, P533, DOI 10.1007/s10641-010-9662-5 Beard BL, 2000, J FORENSIC SCI, V45, P1049 Bong YS, 2012, FOOD CHEM, V135, P2666, DOI 10.1016/j.foodchem.2012.07.045 Bontempo L, 2011, INT DAIRY J, V21, P441, DOI 10.1016/j.idairyj.2011.01.009 Brereton P, 2013, WOODHEAD PUBL FOOD S, V245, P3, DOI 10.1533/9780857097590.1.3 Brunner M, 2010, EUR FOOD RES TECHNOL, V231, P623, DOI 10.1007/s00217-010-1314-7 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 CAMPANA SE, 1994, CAN J FISH AQUAT SCI, V51, P1942, DOI 10.1139/f94-196 CAMPANA SE, 1985, CAN J FISH AQUAT SCI, V42, P1014, DOI 10.1139/f85-127 Capo RC, 1998, GEODERMA, V82, P197, DOI 10.1016/S0016-7061(97)00102-X Carcea M, 2009, QUAL ASSUR SAF CROP, V1, P93, DOI 10.1111/j.1757-837X.2009.00011.x Castorina F, 2011, EQA-INT J ENVIRON QU, V7, P41, DOI 10.6092/issn.2281-4485/3832 CHAUDHURI S, 1978, GEOCHIM COSMOCHIM AC, V42, P329, DOI 10.1016/0016-7037(78)90186-2 Choi SM, 2008, FOOD CHEM, V108, P1149, DOI 10.1016/j.foodchem.2007.11.079 Coetzee PP, 2005, J AGR FOOD CHEM, V53, P5060, DOI 10.1021/jf048268n CRAIG H, 1961, SCIENCE, V133, P1702, DOI 10.1126/science.133.3465.1702 Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 Dambrine E, 1997, PLANT SOIL, V192, P129, DOI 10.1023/A:1004294820733 Darbyshire DPF, 1997, CHEM GEOL, V143, P81, DOI 10.1016/S0009-2541(97)00101-0 Dehelean A, 2012, ROM J PHYS, V57, P1194 Di Giacomo F, 2007, J AGR FOOD CHEM, V55, P860, DOI 10.1021/jf062690h Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Durante C, 2015, FOOD CHEM, V173, P557, DOI 10.1016/j.foodchem.2014.10.086 Elsdon TS, 2008, OCEANOGR MAR BIOL, V46, P297, DOI 10.1201/9781420065756.ch7 EPSTEIN S, 1953, GEOCHIM COSMOCHIM AC, V4, P213, DOI 10.1016/0016-7037(53)90051-9 Fietzke J, 2006, GEOCHEM GEOPHY GEOSY, V7, DOI 10.1029/2006GC001243 FISHER RS, 1976, WATER RESOUR RES, V12, P1061, DOI 10.1029/WR012i005p01061 Fortunato G, 2004, J ANAL ATOM SPECTROM, V19, P227, DOI 10.1039/b307068a Franke BM, 2008, EUR FOOD RES TECHNOL, V226, P761, DOI 10.1007/s00217-007-0588-x Franke BM, 2007, EUR FOOD RES TECHNOL, V225, P501, DOI 10.1007/s00217-006-0446-2 Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Garcia-Ruiz S, 2007, J CHROMATOGR A, V1149, P274, DOI 10.1016/j.chroma.2007.03.048 Garcia-Ruiz S, 2007, ANAL CHIM ACTA, V590, P55, DOI 10.1016/j.aca.2007.03.016 Geoscience Australia, 2005, SCANN GEOL MAPS Gomez MDM, 2004, J AGR FOOD CHEM, V52, P2962, DOI 10.1021/jf035120f Gremaud G, 2004, EUR FOOD RES TECHNOL, V219, P97, DOI 10.1007/s00217-004-0919-0 Guillou C, 2010, 31397 JRC CT Gunther-Leopold I, 2004, ANAL BIOANAL CHEM, V378, P241, DOI 10.1007/s00216-003-2226-1 Halicz L, 2008, EARTH PLANET SC LETT, V272, P406, DOI 10.1016/j.epsl.2008.05.005 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Holmberg L., 2010, INT J WINE RES, V2, P105, DOI [10.2147/IJWR.S14102, DOI 10.2147/IJWR.S14102, 10.2147/ijwr.s14102] Holting B, 1989, HYDROGEOLOGIE, V3, P441 HORN P, 1993, Z LEBENSM UNTERS FOR, V196, P407, DOI 10.1007/BF01190802 HORWITZ EP, 1991, ANAL CHEM, V63, P522, DOI 10.1021/ac00005a027 Ingram BL, 1999, GEOLOGY, V27, P851 InterregIIIA-Projektbericht, 2008, GRENZ BEW GRUN UNPUB Kawasaki A, 2002, SOIL SCI PLANT NUTR, V48, P635, DOI 10.1080/00380768.2002.10409251 Kawasaki A, 1999, PLASMA SOURCE MASS S Kennedy BP, 2005, CAN J FISH AQUAT SCI, V62, P48, DOI 10.1139/F04-184 Kennedy BP, 2002, CAN J FISH AQUAT SCI, V59, P925, DOI [10.1139/f02-070, 10.1139/F02-070] Kennedy BP, 1997, NATURE, V387, P766, DOI 10.1038/42835 Kennedy BP, 2000, CAN J FISH AQUAT SCI, V57, P2280, DOI 10.1139/cjfas-57-11-2280 Kim KT, 2013, J NANOMATER, V2013, DOI 10.1155/2013/821657 Kornexl BE, 1997, Z LEBENSM UNTERS F A, V205, P19, DOI 10.1007/s002170050117 Kosir IJ, 2001, ANAL CHIM ACTA, V429, P195, DOI 10.1016/S0003-2670(00)01301-5 Lancelot J, 1999, P 5 EUR S FOOD AUTH LEE DC, 1995, INT J MASS SPECTROM, V146, P35, DOI 10.1016/0168-1176(95)04201-U Marchionni S, 2013, J AGR FOOD CHEM, V61, P6822, DOI 10.1021/jf4012592 Martin GG, 1996, J AGR FOOD CHEM, V44, P3206, DOI 10.1021/jf950658+ Martin J, 2013, CAN J FISH AQUAT SCI, V70, P182, DOI 10.1139/cjfas-2012-0284 Martins P, 2014, J INT SCI VIGNE VIN, V48, P21 Martinsohn J, 2013, WOODHEAD PUBL FOOD S, V245, P189, DOI 10.1533/9780857097590.3.189 McArthur JM, 2006, PALAEOGEOGR PALAEOCL, V242, P126, DOI 10.1016/j.palaeo.2006.06.004 METGES C, 1990, BRIT J NUTR, V63, P187, DOI 10.1079/BJN19900106 Milton DA, 2003, CAN J FISH AQUAT SCI, V60, P1376, DOI 10.1139/F03-133 Milton DA, 2001, MAR ECOL PROG SER, V222, P239, DOI 10.3354/meps222239 Molkentin J, 2007, ANAL BIOANAL CHEM, V388, P297, DOI 10.1007/s00216-007-1222-2 Muhlfeld CC, 2012, CAN J FISH AQUAT SCI, V69, P906, DOI 10.1139/F2012-033 Oda H., 2001, ANAL SCI, V17, pi1627 Outridge PM, 2002, ENVIRON GEOL, V42, P891, DOI 10.1007/s00254-002-0596-x Pawlewicz MJ, 2003, 974701 US DEP INT US Petrini R, 2015, FOOD CHEM, V170, P138, DOI 10.1016/j.foodchem.2014.08.051 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Pillonel L, 2002, EUR FOOD RES TECHNOL, V215, P260, DOI 10.1007/s00217-002-0548-4 Pouilly M, 2014, ENVIRON SCI TECHNOL, V48, P8980, DOI 10.1021/es500071w Prohaska T, 2002, J ANAL ATOM SPECTROM, V17, P887, DOI 10.1039/b203314c Rodushkin I, 2007, ANAL CHIM ACTA, V583, P310, DOI 10.1016/j.aca.2006.10.038 Rossmann A, 1996, Z LEBENSM UNTERS FOR, V203, P277, DOI 10.1007/BF01192878 Rossmann A., 1998, Rivista di Scienza dell'Alimentazione, V27, P9 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Rossmann A, 2000, EUR FOOD RES TECHNOL, V211, P32, DOI 10.1007/s002170050585 Rummel S, 2012, ANAL BIOANAL CHEM, V402, P2837, DOI 10.1007/s00216-012-5759-3 Rummel S, 2010, FOOD CHEM, V118, P890, DOI 10.1016/j.foodchem.2008.05.115 SCHMIDT HL, 1986, FRESEN Z ANAL CHEM, V324, P760, DOI 10.1007/BF00468387 Smet I, 2010, J ANAL ATOM SPECTROM, V25, P1025, DOI 10.1039/b926335g STEIGER RH, 1977, EARTH PLANET SC LETT, V36, P359, DOI 10.1016/0012-821X(77)90060-7 Sturm M, 2008, THESIS Swoboda S, 2008, ANAL BIOANAL CHEM, V390, P487, DOI 10.1007/s00216-007-1582-7 Taylor VF, 2003, J AGR FOOD CHEM, V51, P856, DOI 10.1021/jf025761v Thiel G, 2004, ANAL BIOANAL CHEM, V378, P1630, DOI 10.1007/s00216-003-2444-6 Trincherini PR, 2014, FOOD CHEM, V145, P349, DOI 10.1016/j.foodchem.2013.08.030 Vance D, 2002, CHEM GEOL, V185, P227, DOI 10.1016/S0009-2541(01)00402-8 Victor V, 2015, PROCED EARTH PLAN SC, V13, P252, DOI 10.1016/j.proeps.2015.07.059 Vorster C, 2010, S AFR J CHEM-S-AFR T, V63, P207 Waight T, 2002, INT J MASS SPECTROM, V221, P229, DOI 10.1016/S1387-3806(02)01016-3 Walther BD, 2006, MAR ECOL PROG SER, V311, P125, DOI 10.3354/meps311125 Wolff BA, 2013, CAN J FISH AQUAT SCI, V70, P1775, DOI 10.1139/cjfas-2013-0116 Wolff BA, 2012, CAN J FISH AQUAT SCI, V69, P724, DOI [10.1139/F2012-009, 10.1139/f2012-009] Wotte T, 2007, PALAEOGEOGR PALAEOCL, V256, P47, DOI 10.1016/j.palaeo.2007.09.002 Yang L, 2008, J ANAL ATOM SPECTROM, V23, P1269, DOI 10.1039/b803143f Yang L, 2009, MASS SPECTROM REV, V28, P990, DOI 10.1002/mas.20251 Yang YH, 2012, J ANAL ATOM SPECTROM, V27, P516, DOI 10.1039/c2ja10333h Yang YH, 2011, SPECTROCHIM ACTA B, V66, P656, DOI 10.1016/j.sab.2011.07.004 Yang YH, 2011, J ANAL ATOM SPECTROM, V26, P1237, DOI 10.1039/c1ja00001b Yasui A, 2000, BUNSEKI KAGAKU, V49, P405, DOI 10.2116/bunsekikagaku.49.405 Yurtsever Y, 1981, TECHNICAL REPORT SER, V210 Yurtsever Y., 1993, IAHS PUBL, P3 NR 123 TC 13 Z9 14 U1 2 U2 58 PD SEP PY 2016 VL 242 IS 9 BP 1411 EP 1439 DI 10.1007/s00217-016-2712-2 WC Food Science & Technology SC Food Science & Technology UT WOS:000380677400001 DA 2022-12-14 ER PT J AU Lee, JY Han, DB Nayga, RM Lim, SS AF Lee, Ji Yong Han, Doo Bong Nayga, Rodolfo M., Jr. Lim, Song Soo TI Valuing traceability of imported beef in Korea: an experimental auction approach SO AUSTRALIAN JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS DT Article DE beef; experimental auction; information effect; traceability; willingness to pay ID WILLINGNESS-TO-PAY; INFORMATION; FOOD AB The major objective of this study is to estimate Korean food shoppers' willingness to pay (WTP) for imported beef with traceability. We use an experimental elicitation method, the random nth price auction, to identify consumers' valuation for traceable imported beef. We also analyse the effect of different types of information on these valuations. Results indicate that consumers are generally willing to pay a 39 per cent premium for the traceable imported beef over similar beef without traceability. Results also suggest that in contrast to the insignificant effect of positive information, negative and two-sided information about traceability significantly reduces WTP. C1 [Lee, Ji Yong] Korea Rural Econ Inst, Seoul, South Korea. [Han, Doo Bong; Lim, Song Soo] Korea Univ, Dept Food & Resource Econ, Seoul, South Korea. [Nayga, Rodolfo M., Jr.] Univ Arkansas, Dept Agr Econ & Agribusiness, Fayetteville, AR 72701 USA. C3 Korea University; University of Arkansas System; University of Arkansas Fayetteville RP Lee, JY (corresponding author), Korea Rural Econ Inst, Seoul, South Korea. EM han@korea.ac.kr CR BECKER GM, 1964, BEHAV SCI, V9, P226, DOI 10.1002/bs.3830090304 Corrigan JR, 2006, AM J AGR ECON, V88, P1078, DOI 10.1111/j.1467-8276.2006.00917.x Corrigan JR, 2009, AM J AGR ECON, V91, P837, DOI 10.1111/j.1467-8276.2009.01267.x FOX J, 1997, EXPT AUCTIONS MEASUR, P115 Fox JA, 2002, J RISK UNCERTAINTY, V24, P75, DOI 10.1023/A:1013229427237 Jeong M. K., 2005, ANAL IMPACT US BEEF JEONG MK, 2002, ANAL BEEF MARKETING Lee Jinhong, 2006, [Korean Journal of Food Marketing Economics, 식품유통연구], V23, P51 List JA, 1999, AM J AGR ECON, V81, P942, DOI 10.2307/1244336 List JA, 2003, J REGUL ECON, V23, P193, DOI 10.1023/A:1022259014448 LUSK J, 2004, DESIGNING EXPT AUCTI Lusk J. L., 2006, USING EXP METHODS EN, P20 Lusk JL, 2004, EUR REV AGRIC ECON, V31, P179, DOI 10.1093/erae/31.2.179 Nayga RM, 2006, CAN J AGR ECON, V54, P461, DOI 10.1111/j.1744-7976.2006.00061.x Noussair C, 2004, J ECON PSYCHOL, V25, P725, DOI 10.1016/j.joep.2003.06.004 Parkhurst GM, 2004, AM J AGR ECON, V86, P222, DOI 10.1111/j.0092-5853.2004.00574.x Rousu MC, 2004, LAND ECON, V80, P125, DOI 10.2307/3147148 SANDLER T, 1980, J ECON LIT, V18, P1481 Shogren JF, 2001, J ECON BEHAV ORGAN, V46, P409, DOI 10.1016/S0167-2681(01)00165-2 TEGENE A, 2003, ERS RES BRIEFS TECHN, V1903 VICKREY W, 1961, J FINANC, V16, P8, DOI 10.2307/2977633 NR 21 TC 48 Z9 52 U1 4 U2 22 PD JUL PY 2011 VL 55 IS 3 BP 360 EP 373 DI 10.1111/j.1467-8489.2011.00553.x WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000292307700004 DA 2022-12-14 ER PT J AU Lee, KO Bae, Y Nakaji, K AF Lee, Kang Oh Bae, Yeonghwan Nakaji, Kei TI Construction and Management Status of Agricultural Traceability Information System of Korea SO JOURNAL OF THE FACULTY OF AGRICULTURE KYUSHU UNIVERSITY DT Article AB In Korea, there are separate traceability management systems for agricultural, livestock, and fishery products. In particular, the traceability management system for agricultural products (Farm2table) was developed and operated by the Korea Information Center for Agriculture, Forestry and Fisheries (KICAFF). This system is a representative information management system for agricultural products. The system has supported linked entries and inquiry services for related traceability information by developing open API linked services. The user satisfaction in the traceability management system for agricultural products is showed that 69.1% of the users were generally satisfied. Moreover, complex work processes (32.7%), the manpower shortage and cost incurrence (33.7%), etc. were appeared as major difficulties in recording and managing the traceability information. In order to promote the usability of the traceability management system in the future, simplification of traceability information on the home page of the "Farm2table" and the provision of user incentives are required. In addition, there is a need to unify each traceability management system that is operated independently. The integrated system can help users easily access all information provided in the systems by viewing single web-site. C1 [Bae, Yeonghwan] Sunchon Natl Univ, Dept Ind Machinery Engn, Coll Bioind Sci, Sunchon, South Korea. [Nakaji, Kei] Kyushu Univ, Lab Agr Ecol, Div Agron & Environm Sci, Dept Agroenvironm Sci,Fac Agr, Fukuoka 8112307, Japan. C3 Sunchon National University; Kyushu University EM leeko2@affis.net CR CHOI IS, 2009, FOOD SCI IND, V42 *CRIC, 2009, NAT FOOD SAF SURV RE *KICAFF, 2009, AGR TRAC MAN SYST RE *MAF, 2005, AGR LIV FISH PROD SA NR 4 TC 1 Z9 4 U1 1 U2 10 PD OCT PY 2010 VL 55 IS 2 BP 349 EP 355 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000284183400023 DA 2022-12-14 ER PT J AU Afrianto, I Djatna, T Arkeman, Y Hermadi, I Sitanggang, IS AF Afrianto, Irawan Djatna, Taufik Arkeman, Yandra Hermadi, Irman Sitanggang, Imas S. TI BLOCK CHAIN TECHNOLOGY ARCHITECTURE FOR SUPPLY CHAIN TRACEABILITY OF FISHERIES PRODUCTS IN INDONESIA: FUTURE CHALLENGE SO JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY DT Article; Proceedings Paper CT 3rd International Conference on Informatics, Engineering, Science, and Technology (INCITEST) CY JUN 11, 2020 CL Bandung, INDONESIA DE Architecture; Block chain; Capture fisheries; Indonesia; Traceability; Smart contract; Supply chain ID BLOCKCHAINS AB The purpose of this study is to design a block chain technology architecture model with a product traceability mechanism to maintain the safety, quality, and consumer trust in the supply chain of capture fisheries products. The methods used in the research include data collection and analysis, identification of the current supply chain system and traceability mechanisms, developing a block chain-based fisheries supply chain traceability system architecture, and designing the smart contract mechanism in the developed system architecture. The results obtained from this study were a model of supply chain architecture for fisheries in Indonesia with traceability mechanisms and the use of smart contracts. The development of the results is expected to provide a comprehensive design of the application of block chain technology and its advantages. Furthermore, it also expected to increase product traceability, transparency, and trust in Indonesian fishery products. This is intended to maintain the quality and safety of perishable food products, especially capture fisheries products in Indonesia. C1 [Afrianto, Irawan] Univ Komputer Indonesia, Dept Informat Engn, Bandung 40123, Indonesia. [Afrianto, Irawan; Hermadi, Irman; Sitanggang, Imas S.] IPB Univ, Dept Comp Sci, Bogor 16680, Indonesia. [Djatna, Taufik; Arkeman, Yandra] IPB Univ, Dept Agroind Technol, Bogor 16680, Indonesia. C3 Bogor Agricultural University; Bogor Agricultural University RP Afrianto, I (corresponding author), Univ Komputer Indonesia, Dept Informat Engn, Bandung 40123, Indonesia.; Afrianto, I (corresponding author), IPB Univ, Dept Comp Sci, Bogor 16680, Indonesia. EM irawan.afrianto@email.unikom.ac.id CR ALPHAND O, 2018, IEEE WCNC Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Chang SE, 2019, TECHNOL FORECAST SOC, V144, P1, DOI 10.1016/j.techfore.2019.03.015 Christidis K, 2016, IEEE ACCESS, V4, P2292, DOI 10.1109/ACCESS.2016.2566339 Dwiyitno D., 2009, SQUALEN B MARINE FIS, V4, P99 Eyal I, 2014, LECT NOTES COMPUT SC, V8437, P436, DOI 10.1007/978-3-662-45472-5_28 Feng Q, 2019, J NETW COMPUT APPL, V126, P45, DOI 10.1016/j.jnca.2018.10.020 FernandezCaramas T. M, 2018, MULTIDISCIPLINARY DI, V4, P26 Hamida E.B.W., 2017, INT C WIR MOB COMM I Helo P, 2019, COMPUT IND ENG, V136, P242, DOI 10.1016/j.cie.2019.07.023 Litke A, 2019, LOGISTICS-BASEL, V3, DOI 10.3390/logistics3010005 Perdana Y.R., 2017, JURNAL TRANSPORTASI, V13, P31 Pieroni A., 2018, INT J ADV SCI ENG IN, V8, P298, DOI DOI 10.18517/IJASEIT.8.1.4954 Purwandoko PB, 2019, INFORMATION, V10, DOI 10.3390/info10060218 Reijers W., 2016, LEDGER-PITTSBURGH, V1, P134, DOI [10.5915/LEDGER2016.62, DOI 10.5915/LEDGER2016.62] Saghiri AM, 2018, 2018 4TH INTERNATIONAL CONFERENCE ON WEB RESEARCH (ICWR), P138, DOI 10.1109/ICWR.2018.8387250 Tschorsch F, 2016, IEEE COMMUN SURV TUT, V18, P2084, DOI 10.1109/COMST.2016.2535718 Wang YK, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P1, DOI 10.1109/AUS.2016.7748011 Widodo K.H., 2011, JURNAL TEKNOLOGI PER, V12, P122 Xu XW, 2016, 2016 13TH WORKING IEEE/IFIP CONFERENCE ON SOFTWARE ARCHITECTURE (WICSA), P182, DOI 10.1109/WICSA.2016.21 Yang L, 2019, J IND INF INTEGR, V15, P80, DOI 10.1016/j.jii.2019.04.002 Yuan Y, 2018, IEEE T SYST MAN CY-S, V48, P1421, DOI 10.1109/TSMC.2018.2854904 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 NR 23 TC 3 Z9 3 U1 5 U2 13 PD OCT PY 2020 VL 15 IS 5 SI SI BP 41 EP 49 WC Engineering, Multidisciplinary SC Engineering UT WOS:000613810400006 DA 2022-12-14 ER PT J AU Liu, Z Zhang, WX Zhang, YZ Chen, TJ Shao, SZ Zhou, L Yuan, YW Xie, TZ Rogers, KM AF Liu, Zhi Zhang, Weixing Zhang, Yongzhi Chen, Tianjin Shao, Shengzhi Zhou, Li Yuan, Yuwei Xie, Tongzhou Rogers, Karyne M. TI Assuring food safety and traceability of polished rice from different production regions in China and Southeast Asia using chemometric models SO FOOD CONTROL DT Article DE Polished rice; Stable isotopes; Multielement; Origin; Traceability; Chemometrics ID GEOGRAPHICAL ORIGIN; ISOTOPE COMPOSITION; MULTIELEMENT; STRONTIUM; ELEMENT; SYSTEMS AB A novel strategy combining elemental analysis-isotopic ratio mass spectrometry (EA-IRMS) and inductively plasma coupled mass spectrometry (ICP-MS) analysis with chemometric data-processing was applied to differentiate polished rice from different growing regions in China with rice imported from Southeast Asia (Thailand and Malaysia). Seven stable isotope ratios (i.e. delta C-13, delta N-15, delta H-2, delta O-18, (87/86)sr, (207/206)pb and Pb-208/207) and 25 multielemental concentrations (Na, Ca, Fe, Zn, Rb, Ag, and Cd, etc.) were assayed and used to establish principal component analysis (PCA) and step-wise linear discriminant analysis (LDA) modeling to determine the rice's geographical origin. The prediction accuracies were cross-validated, and "blind sample" tests were all higher than 90.0% for rice samples from different production areas in China and 85.0% for rice imported from Southeast Asia, respectively. The high level of geographical traceability when combining stable isotope and multielement fingerprints with chemometric data-processing provides a promising tool to assure the origin of Chinese polished rice. This authentication method protects premium rice brands, combats commercial fraud, and quickly locates the origin of contaminated Cd rice to add verifiable food safety measures for consumers. C1 [Liu, Zhi; Zhang, Yongzhi; Shao, Shengzhi; Zhou, Li; Yuan, Yuwei] Zhejiang Acad Agr Sci, State Key Lab Breeding Base Zhejiang Sustainable, Hangzhou 310021, Zhejiang, Peoples R China. [Zhang, Weixing] China Natl Rice Res Inst, Hangzhou 310006, Zhejiang, Peoples R China. [Liu, Zhi; Zhang, Yongzhi; Shao, Shengzhi; Zhou, Li; Yuan, Yuwei] Minist Agr, Key Lab Informat Traceabil Agr Prod, Hangzhou 310021, Zhejiang, Peoples R China. [Chen, Tianjin] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Xie, Tongzhou] Jiaxian Rice Prod Ltd Co, Danyang 212341, Jiangsu, Peoples R China. [Rogers, Karyne M.] GNS Sci, Natl Isotope Ctr, 30 Gracefield Rd, Lower Hutt 5040, New Zealand. C3 Zhejiang Academy of Agricultural Sciences; Chinese Academy of Agricultural Sciences; China National Rice Research Institute, CAAS; Ministry of Agriculture & Rural Affairs; Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; GNS Science - New Zealand RP Yuan, YW (corresponding author), Zhejiang Acad Agr Sci, State Key Lab Breeding Base Zhejiang Sustainable, Hangzhou 310021, Zhejiang, Peoples R China. EM ywytea@163.com CR Araguas-Araguas L, 1998, J GEOPHYS RES-ATMOS, V103, P28721, DOI 10.1029/98JD02582 Ariyama K, 2012, J AGR FOOD CHEM, V60, P1628, DOI 10.1021/jf204296p ARMSTRONG RL, 1968, REV GEOPHYS, V6, P175, DOI 10.1029/RG006i002p00175 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Ballabio D, 2013, ANAL METHODS-UK, V5, P3790, DOI 10.1039/c3ay40582f Bertacchini L, 2013, DATA HANDL SCI TECHN, V28, P371, DOI 10.1016/B978-0-444-59528-7.00010-7 Capo RC, 1998, GEODERMA, V82, P197, DOI 10.1016/S0016-7061(97)00102-X Cheajesadagul P, 2013, FOOD CHEM, V141, P3504, DOI 10.1016/j.foodchem.2013.06.060 Chikaraishi Y, 2004, PHYTOCHEMISTRY, V65, P1369, DOI 10.1016/j.phytochem.2004.03.036 Egli M, 1999, ENVIRON POLLUT, V105, P367, DOI 10.1016/S0269-7491(99)00040-8 ELDERFIELD H, 1986, PALAEOGEOGR PALAEOCL, V57, P71, DOI 10.1016/0031-0182(86)90007-6 Faure G., 1986, PRINCIPLES ISOTOPE G Food N., 2008, SOURCE OECD AGR FOOD, P112 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 Hansen J., 2002, RICE SITUATION OUTLO, P32 Hu YN, 2016, ENVIRON INT, V92-93, P515, DOI 10.1016/j.envint.2016.04.042 Huang J., 2002, DEV ASIAN RICE EC, P1 Huang SW, 2014, AGRON SUSTAIN DEV, V34, P275, DOI 10.1007/s13593-013-0199-9 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Johnson J. E., 2012, AGU FALL M, V1, P45 JOUSSAUME S, 1984, NATURE, V311, P680, DOI 10.1038/311680a0 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kelly S, 2002, EUR FOOD RES TECHNOL, V214, P72, DOI 10.1007/s002170100400 Klevenhusen F, 2008, AUST J EXP AGR, V48, P119, DOI 10.1071/EA07240 Liu ZP, 2016, INT J ENV RES PUB HE, V13, DOI 10.3390/ijerph13010063 Long Xiao-lin, 2014, Zhongguo Shuidao Kexue, V28, P177, DOI 10.3969/j.issn.1001-7216.2014.02.009 Ma S., 2016, AREAL RES DEV, V2, P14 Martinez AM, 2001, IEEE T PATTERN ANAL, V23, P228, DOI 10.1109/34.908974 MOORE BC, 1981, IEEE T AUTOMAT CONTR, V26, P17, DOI 10.1109/TAC.1981.1102568 RALSTON J, 1980, INT J MINER PROCESS, V7, P175, DOI 10.1016/0301-7516(80)90016-2 Shao S., 2015, J NUCL AGR SCI, V1, P125 Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Talbot MR, 2002, DEV PALEOENVIRON RES, V2, P401 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Veeck G., 2017, MACR C, V1, P14 Vogel J. C., 1993, Stable isotopes and plant carbon-water relations., P29 Wold S., 1977, SIMCA METHOD ANAL CH, V3, P243 WONG J, 1979, J SOUTHE ASIAN STUD, V10, P451, DOI 10.1017/S002246340001434X Yuan YW, 2018, J AGR FOOD CHEM, V66, P2607, DOI 10.1021/acs.jafc.7b05422 Yuan YW, 2012, J AGR FOOD CHEM, V60, P1456, DOI 10.1021/jf203105t Yurtsever Y., 1981, STABLE ISOTOPE HYDRO, V210, P103 Zhang HF, 2002, CONTRIB MINERAL PETR, V144, P241, DOI 10.1007/s00410-002-0395-0 Zhao HY, 2012, J AGR FOOD CHEM, V60, P10957, DOI 10.1021/jf3021283 [甄燕红 ZHEN Yanhong], 2008, [安全与环境学报, Journal of Safety and Environment], V8, P119 [周歆 Zhou Xin], 2013, [中国农学通报, Chinese Agricultural Science Bulletin], V29, P145 NR 45 TC 40 Z9 45 U1 10 U2 182 PD MAY PY 2019 VL 99 BP 1 EP 10 DI 10.1016/j.foodcont.2018.12.011 WC Food Science & Technology SC Food Science & Technology UT WOS:000458708800001 DA 2022-12-14 ER PT J AU Morcia, C Stanca, AM Tumino, G Terzi, V AF Morcia, C. Stanca, A. M. Tumino, G. Terzi, V. TI Genetic traceability as a tool in managing safety and improved quality in feed and food chains SO AGROCHIMICA DT Article DE Fingerprinting; molecular markers; traceability; qPCR ID REAL-TIME PCR; FISH SPECIES IDENTIFICATION; MITOCHONDRIAL D-LOOP; VIRGIN OLIVE OILS; BUFFALO MILK; QUANTITATIVE DETECTION; MOLECULAR MARKERS; CAPILLARY-ELECTROPHORESIS; BACTERIAL COMMUNITIES; LENGTH POLYMORPHISM AB The identification of plant and animal species in food and feed can have an important role for the safety and quality of the products, for producers, in consumer protection and for regulatory enforcement. Increasing demands for traceability can be satisfied by DNA-based approaches and genetic fingerprinting and these also have useful applications in the identification of plants, animals and microorganisms involved in the food (and feed) chains, the management of these chains and in quality assurance. C1 [Morcia, C.; Terzi, V.] CRA GPG, Genom Res Ctr, I-29017 Fiorenzuola Darda, PC, Italy. [Stanca, A. M.; Tumino, G.] Univ Modena & Reggio Emilia, Dept Agr Sci, I-42100 Reggio Emilia, Italy. C3 Universita di Modena e Reggio Emilia RP Terzi, V (corresponding author), CRA GPG, Genom Res Ctr, Via San Protaso 302, I-29017 Fiorenzuola Darda, PC, Italy. EM valeria.terzi@entecra.it CR Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 Alary R, 2002, CEREAL CHEM, V79, P553, DOI 10.1094/CCHEM.2002.79.4.553 Aranceta-Garza F, 2011, FOOD CONTROL, V22, P1015, DOI 10.1016/j.foodcont.2010.11.025 Asensio L, 2008, FOOD CONTROL, V19, P1096, DOI 10.1016/j.foodcont.2007.11.002 Asensio L, 2009, FOOD CONTROL, V20, P618, DOI 10.1016/j.foodcont.2008.09.006 Ausubel JH, 2009, P NATL ACAD SCI USA, V106, P12569, DOI 10.1073/pnas.0906757106 Balitzki-Korte B, 2005, INT J LEGAL MED, V119, P291, DOI 10.1007/s00414-005-0537-9 Bania J, 2001, J DAIRY RES, V68, P333, DOI 10.1017/S0022029901004708 Barcaccia G., 2007, P 51 SIGA C RIV DEL BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 Bottero MT, 2011, VET J, V190, P34, DOI 10.1016/j.tvjl.2010.09.024 Bottero MT, 2002, J FOOD PROTECT, V65, P362, DOI 10.4315/0362-028X-65.2.362 Bottero MT, 2003, INT DAIRY J, V13, P277, DOI 10.1016/S0958-6946(02)00170-X Branciari R, 2000, J FOOD PROTECT, V63, P408, DOI 10.4315/0362-028X-63.3.408 Breviario D, 2007, MOL BREEDING, V20, P249, DOI 10.1007/s11032-007-9087-9 Budowle B, 2005, INT J LEGAL MED, V119, P295, DOI 10.1007/s00414-005-0545-9 Casazza AP, 2011, FOOD CHEM, V124, P685, DOI 10.1016/j.foodchem.2010.06.073 Catanese G, 2010, FOOD CHEM, V122, P319, DOI 10.1016/j.foodchem.2010.02.036 Chapela MJ, 2002, J FOOD SCI, V67, P1672, DOI 10.1111/j.1365-2621.2002.tb08703.x Chavez NA, 2008, FOOD AGR IMMUNOL, V19, P265, DOI 10.1080/09540100802381042 Chen TY, 2003, FOOD CHEM, V83, P475, DOI 10.1016/S0308-8146(03)00253-X Comi G, 2005, FOOD CONTROL, V16, P37, DOI 10.1016/j.foodcont.2003.11.003 Corrado G, 2011, J HORTIC SCI BIOTECH, V86, P461, DOI 10.1080/14620316.2011.11512789 D'Andrea M, 2011, FOOD CHEM, V124, P1164, DOI 10.1016/j.foodchem.2010.07.029 Dalmasso A, 2007, VET RES COMMUN, V31, P355, DOI 10.1007/s11259-007-0036-1 Dalmasso A, 2011, FOOD CHEM, V124, P362, DOI 10.1016/j.foodchem.2010.06.017 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 De S, 2011, FOOD CONTROL, V22, P690, DOI 10.1016/j.foodcont.2010.09.026 Demmel A, 2011, FOOD CONTROL, V22, P215, DOI 10.1016/j.foodcont.2010.07.001 Di Finizio A, 2007, EUR FOOD RES TECHNOL, V225, P337, DOI 10.1007/s00217-006-0420-z Di Vaio C, 2010, ACTA HORTIC, V862, P55, DOI 10.17660/ActaHortic.2010.862.6 Drabek J, 2008, EUR FOOD RES TECHNOL, V226, P491, DOI 10.1007/s00217-007-0561-8 Dubois PCA, 2010, NAT GENET, V42, P295, DOI 10.1038/ng.543 EL SHEIKHA A F, 2010, J LIFE SCI, V4, P9 El Sheikha AF, 2011, FOOD BIOTECHNOL, V25, P115, DOI 10.1080/08905436.2011.576556 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Engel KH, 2006, TRENDS FOOD SCI TECH, V17, P490, DOI 10.1016/j.tifs.2006.04.008 Espineira M, 2010, FOOD CHEM, V121, P527, DOI 10.1016/j.foodchem.2009.12.042 Espineira M, 2009, FOOD CHEM, V117, P698, DOI 10.1016/j.foodchem.2009.04.087 Etienne M, 2000, J AGR FOOD CHEM, V48, P2653, DOI 10.1021/jf990907k Fajardo V, 2008, MEAT SCI, V79, P289, DOI 10.1016/j.meatsci.2007.09.013 Fajardo V, 2008, MEAT SCI, V78, P314, DOI 10.1016/j.meatsci.2007.06.018 Fajardo V, 2010, TRENDS FOOD SCI TECH, V21, P408, DOI 10.1016/j.tifs.2010.06.002 Fajardo V, 2009, INT J FOOD SCI TECH, V44, P1997, DOI 10.1111/j.1365-2621.2009.02020.x Faria MA, 2000, J AGR FOOD CHEM, V48, P1096, DOI 10.1021/jf990837h Fazekas AJ, 2009, MOL ECOL RESOUR, V9, P130, DOI 10.1111/j.1755-0998.2009.02652.x Filonzi L, 2010, FOOD RES INT, V43, P1383, DOI 10.1016/j.foodres.2010.04.016 Fuad T, 2010, CRIT REV FOOD SCI, V50, P787, DOI 10.1080/10408390903001693 Fuchs M, 2010, J AGR FOOD CHEM, V58, P11193, DOI 10.1021/jf102452a Galasso I, 2011, MOL BREEDING, V28, P635, DOI 10.1007/s11032-010-9515-0 Gale KR, 2005, J CEREAL SCI, V41, P181, DOI 10.1016/j.jcs.2004.09.002 Ganopoulos I, 2011, FOOD CONTROL, V22, P532, DOI 10.1016/j.foodcont.2010.09.040 Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Galan AMG, 2011, FOOD CHEM, V127, P834, DOI 10.1016/j.foodchem.2011.01.019 Harrer A, 2010, MOL NUTR FOOD RES, V54, P93, DOI 10.1002/mnfr.200900096 Hebert PDN, 2005, SYST BIOL, V54, P852, DOI 10.1080/10635150500354886 Herrero B, 2011, FOOD CHEM, V127, P1268, DOI 10.1016/j.foodchem.2011.01.070 Hubalkova Z, 2007, VET MED-CZECH, V52, P273, DOI 10.17221/2044-VETMED Hurley IP, 2004, J DAIRY SCI, V87, P543, DOI 10.3168/jds.S0022-0302(04)73195-1 Infante C, 2006, FOOD RES INT, V39, P1023, DOI 10.1016/j.foodres.2006.02.006 Intrieri MC, 2007, J HORTIC SCI BIOTECH, V82, P109, DOI 10.1080/14620316.2007.11512206 Jaillon O, 2007, NATURE, V449, P463, DOI 10.1038/nature06148 Klotz A, 2001, MILCHWISSENSCHAFT, V56, P67 Knuutinen J, 1998, J CHROMATOGR B, V705, P11, DOI 10.1016/S0378-4347(97)00505-7 KOCHER TD, 1989, P NATL ACAD SCI USA, V86, P6196, DOI 10.1073/pnas.86.16.6196 Koppel R, 2010, EUR FOOD RES TECHNOL, V230, P367, DOI 10.1007/s00217-009-1164-3 Koutsogiannouli EA, 2010, MAMM BIOL, V75, P69, DOI 10.1016/j.mambio.2008.08.001 Kuchel H, 2007, MOL BREEDING, V20, P295, DOI 10.1007/s11032-007-9092-z La Neve F, 2008, MEAT SCI, V80, P216, DOI 10.1016/j.meatsci.2007.11.027 Lago FC, 2011, EUR FOOD RES TECHNOL, V232, P1077, DOI 10.1007/s00217-011-1481-1 Laube I, 2010, FOOD CHEM, V118, P979, DOI 10.1016/j.foodchem.2008.09.063 Le Nguyen DD, 2008, FRUITS, V63, P75, DOI 10.1051/fruits:2007049 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Llaca V., 2011, Journal of Botany, V2011, P104172, DOI 10.1155/2011/104172 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 Lopparelli RM, 2007, J AGR FOOD CHEM, V55, P3429, DOI 10.1021/jf0637271 Mafra I, 2007, INT DAIRY J, V17, P1132, DOI 10.1016/j.idairyj.2007.01.009 Maldini M, 2006, AQUACULTURE, V261, P487, DOI 10.1016/j.aquaculture.2006.07.010 Marieschi M, 2011, FOOD CONTROL, V22, P542, DOI 10.1016/j.foodcont.2010.10.001 Marieschi M, 2010, FOOD CONTROL, V21, P998, DOI 10.1016/j.foodcont.2009.12.018 Marmiroli N, 2008, ANAL BIOANAL CHEM, V392, P369, DOI 10.1007/s00216-008-2303-6 Marroni F, 2011, PLANT J, V67, P736, DOI 10.1111/j.1365-313X.2011.04627.x Martin I, 2008, J FOOD PROTECT, V71, P564, DOI 10.4315/0362-028X-71.3.564 Martin I, 2007, MEAT SCI, V75, P120, DOI 10.1016/j.meatsci.2006.06.019 Martin I, 2009, J SCI FOOD AGR, V89, P1202, DOI 10.1002/jsfa.3576 Mayer HK, 2005, INT DAIRY J, V15, P595, DOI 10.1016/j.idairyj.2004.10.012 Mazzeo MF, 2008, J AGR FOOD CHEM, V56, P11071, DOI 10.1021/jf8021783 Mellmann A, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0022751 Mininni AN, 2009, INT DAIRY J, V19, P617, DOI 10.1016/j.idairyj.2009.04.003 Molina E, 1999, INT DAIRY J, V9, P99, DOI 10.1016/S0958-6946(99)00028-X Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z Morcia C., 2010, INT S AUTH QUAL BEE Morcia C., 2011, P AGI SIBV SIGA C CI Morgante M, 2007, CURR OPIN PLANT BIOL, V10, P149, DOI 10.1016/j.pbi.2007.02.001 Mujico JR, 2011, FOOD CHEM, V128, P795, DOI 10.1016/j.foodchem.2011.03.061 Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Muzzalupo I, 2009, HORTSCIENCE, V44, P582, DOI 10.21273/HORTSCI.44.3.582 Nowak-Wegrzyn A, 2011, J ALLERGY CLIN IMMUN, V127, P558, DOI 10.1016/j.jaci.2010.12.1098 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Pafundo S, 2010, ANAL BIOANAL CHEM, V396, P1831, DOI 10.1007/s00216-009-3419-z Palmieri L, 2009, ACTA HORTIC, V810, P167, DOI 10.17660/ActaHortic.2009.810.21 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3312, DOI 10.1021/jf063383e Pasqualone A, 2010, EUR FOOD RES TECHNOL, V230, P723, DOI 10.1007/s00217-009-1210-1 Pepe T, 2007, J AGR FOOD CHEM, V55, P3681, DOI 10.1021/jf063321o Pereira L, 2011, AM J ENOL VITICULT, V62, P547, DOI 10.5344/ajev.2011.10022 Plath A, 1997, Z LEBENSM UNTERS F A, V205, P437, DOI 10.1007/s002170050195 Poms RE, 2004, FOOD ADDIT CONTAM A, V21, P1, DOI 10.1080/02652030310001620423 Ponzoni E, 2009, J DAIRY SCI, V92, P5583, DOI 10.3168/jds.2009-2239 Rao R, 2010, J HORTIC SCI BIOTECH, V85, P42, DOI 10.1080/14620316.2010.11512628 Rea S, 2001, J DAIRY RES, V68, P689, DOI 10.1017/S0022029901005106 Reale S, 2008, J DAIRY RES, V75, P107, DOI 10.1017/S0022029907003020 Recio I, 2001, ELECTROPHORESIS, V22, P1489, DOI 10.1002/1522-2683(200105)22:8<1489::AID-ELPS1489>3.0.CO;2-G Renon P, 2005, FOOD CONTROL, V16, P473, DOI 10.1016/j.foodcont.2004.05.009 Rodriguez-Lazaro D, 2007, TRENDS FOOD SCI TECH, V18, P306, DOI 10.1016/j.tifs.2007.01.009 Roder M, 2011, ANAL CHIM ACTA, V685, P74, DOI 10.1016/j.aca.2010.11.019 Rojas M, 2011, POULTRY SCI, V90, P211, DOI 10.3382/ps.2010-00895 Rojas M, 2009, POULTRY SCI, V88, P669, DOI 10.3382/ps.2008-00261 Rojas M, 2010, FOOD ADDIT CONTAM A, V27, P749, DOI 10.1080/19440040903503070 Rossi S, 2006, EUR FOOD RES TECHNOL, V223, P1, DOI 10.1007/s00217-005-0034-x Ruttink T, 2010, ANAL BIOANAL CHEM, V396, P2073, DOI 10.1007/s00216-009-3287-6 Saini M, 2007, BRIT POULTRY SCI, V48, P162, DOI 10.1080/00071660701285897 Savazzini F, 2006, ANAL CHIM ACTA, V563, P274, DOI 10.1016/j.aca.2005.10.078 Sforza S, 2011, CHEM SOC REV, V40, P221, DOI 10.1039/b907695f Singer GAC, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S6-S14 Sonnante G, 2009, J AGR FOOD CHEM, V57, P10199, DOI 10.1021/jf902624z Takashima Y, 2006, FISHERIES SCI, V72, P1054, DOI 10.1111/j.1444-2906.2006.01256.x Tatsadjieu NL, 2010, FOOD CONTROL, V21, P673, DOI 10.1016/j.foodcont.2009.10.006 Tavoletti S, 2009, EUR FOOD RES TECHNOL, V229, P475, DOI 10.1007/s00217-009-1077-1 Tedeschi P, 2011, J FOOD COMPOS ANAL, V24, P131, DOI 10.1016/j.jfca.2010.06.008 Tedeschi T, 2011, MOL BIOSYST, V7, P1902, DOI 10.1039/c1mb05048f Teletchea F, 2009, REV FISH BIOL FISHER, V19, P265, DOI 10.1007/s11160-009-9107-4 Terol J, 2002, J AGR FOOD CHEM, V50, P963, DOI 10.1021/jf011032o Terzi V, 2004, EUR FOOD RES TECHNOL, V219, P428, DOI 10.1007/s00217-004-0965-7 Terzi V, 2003, J CEREAL SCI, V38, P87, DOI 10.1016/S0733-5210(02)00138-8 Terzi V., 2008, FOOD SCI TECHNOLOGY, P211 This P, 2004, THEOR APPL GENET, V109, P1448, DOI 10.1007/s00122-004-1760-3 Velasco R, 2007, PLOS ONE, V2, DOI 10.1371/journal.pone.0001326 Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 Wen J, 2010, FOOD CONTROL, V21, P403, DOI 10.1016/j.foodcont.2009.06.014 Yancy HF, 2008, J FOOD PROTECT, V71, P210, DOI 10.4315/0362-028X-71.1.210 NR 143 TC 0 Z9 0 U1 1 U2 9 PD JAN-FEB PY 2012 VL 56 IS 1 BP 1 EP 27 WC Chemistry, Applied; Soil Science SC Chemistry; Agriculture UT WOS:000326480100001 DA 2022-12-14 ER PT J AU Haddad, MA Omar, SS Parisi, S AF Haddad, Moawiya A. Omar, Sharaf S. Parisi, Salvatore TI Vegan cheeses vs processed cheeses - traceability issues and monitoring countermeasures SO BRITISH FOOD JOURNAL DT Article DE Food traceability; Risk assessment; Supply chain analysis; Vegan cheeses; Processed cheeses AB Purpose The purpose of this study comes from the need of defining improved durability values and the realization of a good traceability management for selected vegan cheeses has suggested the comparison between a processed cheese and its analogous version without animal-origin raw materials. The durability should be studied at a well-defined temperature, probably agreed among the food producer and the food processor. In addition, the traceability system should consider many components and related suppliers. Design/methodology/approach A supply chain risk assessment analysis has been carried out with relation to two different products: an analogue cheese and a vegan cheese-like preparation. Raw materials and ingredients have been evaluated (production method and origin; geographical identification), with the aim of identifying simplified food. Findings An assessment of food supply networks has been carried out. In the first situation (analogue cheeses), the ingredient "cheeses" shows an important complexity: five suppliers with a related six-interconnection hub. On the other side, vegan cheeses are obtained from 11 ingredients (a challenging hub); four of them may be produced from 2-5 components of different origin (five total hubs). Tested processed cheeses are represented by means of a linear food supply network with two hubs (cheeses and "arrival" show degrees 6 and 9, respectively). Networks concerning vegan cheeses include five different hubs: four complex raw materials (degree: 2, 3, 4 and 5) and the "arrival" step (degree: 12). Originality/value The information load of vegan cheeses (two hubs, degrees >> average degree) appears high if compared with processed cheeses (two hubs), although the complexity of networks appears similar. Vegan cheeses may seem technologically simpler than processed cheeses and be sometimes questioned because of important traceability issues. Adequate traceability countermeasures in terms of preventive monitoring actions should be recommended when speaking of vegan cheeses. Anyway, a centralized manager would be always required. C1 [Haddad, Moawiya A.; Omar, Sharaf S.; Parisi, Salvatore] Al Balqa Appl Univ, Dept Nutr & Food Proc, Fac Agr Technol, Al Salt, Jordan. C3 Al-Balqa Applied University RP Haddad, MA (corresponding author), Al Balqa Appl Univ, Dept Nutr & Food Proc, Fac Agr Technol, Al Salt, Jordan. EM haddad@bau.edu.jo; sharaf@bau.edu.jo; drparisi@inwind.it CR [Anonymous], 1988, ANN OPER RES, DOI DOI 10.1007/BF02288320 Bai B.G., 2012, SECURITIES FUTURES C, V5, P88 Barbieri G., 2014, INFLUENCE CHEM NEW F, DOI [10.1007/978-3-319-11358-6, DOI 10.1007/978-3-319-11358-6] Biggs N., 1986, GRAPH THEORY 1736 19, P1736 Calvin L., 2004, EC THEORY IND STUDIE, V3, P830 Chartrand G, 1985, INTRO GRAPH THEORY Codex Alimentarius Commission, 1999, 2081999 COD AL COMM FENG W, 2010, FOOD CONTROL, V20, P918 Food Standards Agency, 2007, CHEESE RECOVERY GUID Haddad M. A., 2017, Journal of Food Research, V6, P75, DOI 10.5539/jfr.v6n2p75 He, 2012, AGR EC MANAGEMENT, V1, P71 Houghton JR, 2008, FOOD POLICY, V33, P13, DOI 10.1016/j.foodpol.2007.05.001 Jiang J., 2018, OMEGA-J DEATH DYING, V87, P191 Li, 2006, C PAP IEEE XPL JUL, DOI [10.1109/SOLI.2006.329074, DOI 10.1109/SOLI.2006.329074] [李秀婷 LI Xiu-ting], 2009, [食品科学, Food Science], V30, P346 McSweeney P. L. H., 2007, CHEESE PROBLEMS SOLV MCSWEENEY PLH, 2004, CHEESE CHEM PHYS MIC, V2, P1 Meng S.D., 2009, EC PROBLEMS, V5, P50 Obeidat M., 2017, RES J MICROBIOLOGY, V12, P218, DOI [10.3923/jm.2017.218.228, DOI 10.3923/JM.2017.218.228] Parisi S., 2002, Industrie Alimentari, V41, P295 Parisi S., 2003, Industrie Alimentari, V42, P249 Pellerito A, 2019, SPRBRIEF MOLEC SCI, P1, DOI 10.1007/978-3-030-27664-5 Qiu, 2011, J ZHE JIANG GONG SHA, V6, P66 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Wu, 2010, CHINESE AGR SCI B, V26, P323 Xu, 2010, SCIENCE, V12, P344 Ye F., 2011, LEGAL SYSTEM SOC, V17, P27 Zhang X.Y., 2012, REFORM OPENING, V14, P91 Zhang ZhiYong, 2009, China Dairy Industry, V37, P55 NR 30 TC 0 Z9 0 U1 17 U2 47 PD JUN 28 PY 2021 VL 123 IS 6 BP 2003 EP 2015 DI 10.1108/BFJ-10-2020-0934 EA JAN 2021 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000611017700001 DA 2022-12-14 ER PT J AU Xie, H Zhang, KX AF Xie, Hui Zhang, Kexin TI Construction on Evaluation Index System of Traceability System of Agricultural Internet of Things Based on Network Analysis SO MATHEMATICAL PROBLEMS IN ENGINEERING DT Article AB In order to ensure the transparency and openness of each link of agricultural products "from farmland to table" and to solve the problem of consumers' low trust degree in the safety of agricultural products and food, the application and evaluation of agricultural Internet of Things traceability system are the top priority. However, the traceability system evaluation indicators are rarely discussed in the existing studies. Consequently, this paper attempts to construct the evaluation index system of the Internet of Things traceability system of agricultural products based on an overall perspective of ecological civilization and to determine the weight of each evaluation index by using analytic hierarchy process (AHP). Then, on the basis of the relevant data of the agricultural product traceability system in recent years, the determined weight is modified by using entropy method. Finally, it points out the important practical value for management departments, production subjects, sales subjects, and consumers, providing reference for improving the quality and safety of agricultural products and standardizing the market supervision of agricultural product market. C1 [Xie, Hui; Zhang, Kexin] Kunming Univ Sci & Technol, Fac Management & Econ, Kunming, Peoples R China. C3 Kunming University of Science & Technology RP Xie, H (corresponding author), Kunming Univ Sci & Technol, Fac Management & Econ, Kunming, Peoples R China. EM 39120171@qq.com; 1271128830@qq.com CR Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Bai HongWu, 2013, Jiangsu Journal of Agricultural Sciences, V29, P415 Begona P, 2008, FOOD TEACEAILITY WOR, P89 Chen L, 2017, RES CONSTRUCTION SAF Chen L.L, 2014, QUALITY SAFETY AGRO, V4, P62 Chen S., 2013, QUALITY SAFETY AGRO, V6, P5 Chiang YM, 2016, COMPUT IND ENG, V98, P237, DOI 10.1016/j.cie.2016.06.005 Gautam R, 2017, COMPUT IND ENG, V103, P46, DOI 10.1016/j.cie.2016.09.007 Guo W., 2017, SCI TECHNOLOGY MANAG, V37, P81, DOI [10.3969/j.issn.1000-7695.2017.10.012, DOI 10.3969/J.ISSN.1000-7695.2017.10.012] He J, 2018, FOOD INDUSTRY, V12, P260 Huang R.H, 2006, PRODUCT TRACING SYST Huang Y.J, 2016, THINGS TECHNOLOGY, V16, P117 Jia T, 2018, GUANGDONG SILKWORM I, V52 Jiang K.Y., 2014, J ZHEJIANG AGR SCI, V8, P1296 Li H.L, 2011, SCI TECHNOLOGY MANAG, V1, P209 Li J., 2021, CHINESE FRUIT VEGETA, V41 Li L.S, 2019, CHINESE EDIBLE FUNGU, V38, P69 Li Youshui., 2016, INFORM AGR SCI TECHN, P35 Liu Y.P, 2014, MODERN MANAGEMENT SC, V6, P78 Liu YongSheng, 2018, Jiangsu Agricultural Sciences, V46, P334 Ma T., 2019, CHINA AGR SCI TECHNO, V21, P58 Ou Y.C., 2019, BUSINESS EC RES, V24, P131 Qian JianPing, 2014, Transactions of the Chinese Society of Agricultural Engineering, V30, P98 Shi Y.L, 2019, MODERN MARKETING, V6, P70 Song L.L, 2018, JIANGXINONGYE, V18, P67 Wang Y, 2022, DIGITAL TECHNOLOGY A, V40, P184 Wu H.Y., 2022, LOGISTICS TECHNOLOGY, V45 Wu Z.C., 2010, CHINA VENTURE CAPITA, V10, P41 Xu N, 2021, SCI J EC MANAGEMENT, V3 Xu S.B., 1988, PRACTICAL DECISION M Yang XinTing, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P212 Yuan Y., 2020, TECHNOLOGY INNOVATIO, V23, P106 Yue Y.J., 2019, CHINA AGR SCI TECHNO, V21, P79 Zhang G.B., 2020, MARKETING WEEK, V4, P53 Zhang L., 2010, CHINESE SCI, V40, P216 Zhou J.H., 2013, ISSUES AGR EC, V34, P90 NR 36 TC 0 Z9 0 U1 8 U2 8 PD AUG 31 PY 2022 VL 2022 DI 10.1155/2022/1628660 WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications SC Engineering; Mathematics UT WOS:000863223000025 DA 2022-12-14 ER PT J AU Mutua, F Kihara, A Rogena, J Ngwili, N Aboge, G Wabacha, J Bett, B AF Mutua, Florence Kihara, Absolomon Rogena, Jason Ngwili, Nicholas Aboge, Gabriel Wabacha, James Bett, Bernard TI Piloting a livestock identification and traceability system in the northern Tanzania-Narok-Nairobi trade route SO TROPICAL ANIMAL HEALTH AND PRODUCTION DT Article DE Livestock identification and traceability; Animal health and food safety surveillance AB We designed and piloted a livestock identification and traceability system (LITS) along the Northern Tanzania-Narok-Nairobi beef value chain. Animals were randomly selected and identified at the primary markets using uniquely coded ear tags. Data on identification, ownership, source (village), and the site of recruitment (primary market) were collected and posted to an online database. Similar data were collected in all the markets where tagged animals passed through until they got to defined slaughterhouses. Meat samples were collected during slaughter and later analyzed for tetracycline and diminazene residues using high-performance liquid chromatography (HPLC). Follow up surveys were done to assess the pilot system. The database captured a total of 4260 records from 741 cattle. Cattle recruited in the primary markets in Narok (n = 1698) either came from farms (43.8%), local markets (37.7%), or from markets in Tanzania (18.5%). Soit Sambu market was the main source of animals entering the market from Tanzania (54%; n = 370). Most tagged cattle (72%, n = 197) were slaughtered at the Ewaso Ng'iro slaughterhouse in Narok. Lesions observed (5%; n = 192) were related to either hydatidosis or fascioliasis. The mean diminazene aceturate residue level was 320.78 +/- 193.48 ppb. We used the traceability system to identify sources of animals with observable high drug residue levels in tissues. Based on the findings from this study, we discuss opportunities for LITS-as a tool for surveillance for both animal health and food safety, and outline challenges of its deployment in a local beef value chain-such as limited incentives for uptake. C1 [Mutua, Florence; Kihara, Absolomon; Rogena, Jason; Ngwili, Nicholas; Bett, Bernard] Int Livestock Res Inst, Nairobi, Kenya. [Mutua, Florence; Aboge, Gabriel] Univ Nairobi, Fac Vet Med, Dept Publ Hlth Pharmacol & Toxicol, Nairobi, Kenya. [Wabacha, James] African Union Interafrican Bur Anim Resources, Nairobi, Kenya. C3 CGIAR; International Livestock Research Institute (ILRI); University of Nairobi; African Union (AU) RP Mutua, F (corresponding author), Int Livestock Res Inst, Nairobi, Kenya.; Mutua, F (corresponding author), Univ Nairobi, Fac Vet Med, Dept Publ Hlth Pharmacol & Toxicol, Nairobi, Kenya. EM flmutua@yahoo.com CR Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Ekuam D., 2008, LIVESTOCK IDENTIFICA ICPALD, 2015, REG GUID LIV ID TRAC Landais E, 2001, REV SCI TECH OIE, V20, P463 Marumo D., 2009, EFFECTS EUROPE UNPUB Moreki J. C., 2012, J ANIM SCI ADV, V2, P925 Paskin R., 2004, P ICA FAO SEM TUN N, P84 Roderick S, 2000, TROP ANIM HEALTH PRO, V32, P361, DOI 10.1023/A:1005277518352 World Organization for Animal Health (OIE), 2017, TERR AN HLTH COD NR 10 TC 6 Z9 6 U1 0 U2 4 PD FEB PY 2018 VL 50 IS 2 BP 299 EP 308 DI 10.1007/s11250-017-1431-4 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000423213200009 DA 2022-12-14 ER PT J AU Numata, M Kitamaki, Y Shimizu, Y Yamazaki, T Saito, N Kuroe, M Hanari, N Ishikawa, K Saito, T Ihara, T AF Numata, Masahiko Kitamaki, Yuko Shimizu, Yoshitaka Yamazaki, Taichi Saito, Naoki Kuroe, Miho Hanari, Nobuyasu Ishikawa, Keiichiro Saito, Takeshi Ihara, Toshihide TI Conventional and new traceability schemes of organic standards for safe water supply in Japan SO METROLOGIA DT Article DE traceability; organic analysis; purity assessment; standard solution; water supply ID CERTIFIED REFERENCE MATERIALS; QUANTITATIVE NMR-SPECTROSCOPY; PERFORMANCE LIQUID-CHROMATOGRAPHY; NATIONAL-METROLOGY-INSTITUTE; DILUTION MASS-SPECTROMETRY; UNCERTAINTY EVALUATION; PURITY DETERMINATION; AMINO-ACIDS; INTERNATIONAL SYSTEM; PROCESSED FOODS AB Metrological traceability to the International System of Units (SI) is required to ensure reliability of analytical results. For this purpose, SI-traceable standard solutions of respective analytes are essential for common analytical methods such as chromatography. However, it is not so easy to fulfill demands due to the existence of a vast number of targets, especially in the case of organic analysis. Because high-purity organic materials which are raw materials of standard solutions must be characterized by compound-independent methods, the mass balance approach (subtraction method) and the primary methods of measurement (e.g. the freezing point depression method and titrimetry) are used. Recently, quantitative NMR (qNMR) has been popularized. The National Metrology Institute of Japan, the National Institute of Advanced Industry, Science and Technology (NMIJ/AIST) has been supplying many types of reference materials including high-purity organic materials and organic standard solutions. In 2015, the demand of traceable reference materials for drinking water analyses rose by an amendment of a notification under the Waterworks Act of Japan. In this manuscript, NMIJ's efforts such as supplying certified reference materials and providing calibration services in this field are outlined. Among them, direct characterization of multi-components standard solutions by qNMR/chromatography or post-column reaction gas chromatography is an efficient approach to realize new traceability schemes without the step of a purity assessment of each reference material. C1 [Numata, Masahiko; Kitamaki, Yuko; Shimizu, Yoshitaka; Yamazaki, Taichi; Saito, Naoki; Kuroe, Miho; Hanari, Nobuyasu; Ishikawa, Keiichiro; Saito, Takeshi; Ihara, Toshihide] Natl Inst Adv Ind Sci & Technol, Natl Metrol Inst Japan, 1-1-1 Umezono, Tsukuba, Ibaraki 3058563, Japan. C3 National Institute of Advanced Industrial Science & Technology (AIST); National Metrology Institute of Japan RP Numata, M (corresponding author), Natl Inst Adv Ind Sci & Technol, Natl Metrol Inst Japan, 1-1-1 Umezono, Tsukuba, Ibaraki 3058563, Japan. EM mas-numata@aist.go.jp CR [Anonymous], 2016, 170342016 ISO [Anonymous], 2017, 35 ISO [Anonymous], 2009, 34 ISO [Anonymous], 2017, 17025 ISOIEC Baldan A, 2009, INT J THERMOPHYS, V30, P325, DOI 10.1007/s10765-008-0476-z Beach DG, 2016, J AOAC INT, V99, P1151, DOI 10.5740/jaoacint.16-0151 Bunk David M, 2007, Clin Biochem Rev, V28, P131 Diaz SC, 2013, ANAL CHEM, V85, P1873, DOI 10.1021/ac3032542 Dube G, 1997, METROLOGIA, V34, P83, DOI 10.1088/0026-1394/34/1/12 ELLISON SLR, 2003, TRACEABILITY CHEM ME Exarchou V, 2005, MAGN RESON CHEM, V43, P681, DOI 10.1002/mrc.1632 Foley DA, 2013, ANAL CHEM, V85, P8928, DOI 10.1021/ac402382d Hanari N, 2014, ACCREDIT QUAL ASSUR, V19, P391, DOI 10.1007/s00769-014-1077-0 Hanari N, 2013, INT J ENVIRON AN CH, V93, P692, DOI 10.1080/03067319.2012.684048 Hanari N, 2011, BUNSEKI KAGAKU, V60, P877 Huang T, 2016, ANAL METHODS-UK, V8, P4482, DOI [10.1039/c6ay00570e, 10.1039/C6AY00570E] Ihara Toshihide, 2009, Synthesiology (English Edition), V2, P13, DOI 10.5571/syntheng.2.13 Ihara T, 2008, BUNSEKI KAGAKU, V57, P493, DOI 10.2116/bunsekikagaku.57.493 International Accreditation Japan National Institute of Technology and Evaluation, 2018, JAP CAL SERV SYST JC Iqbal MY, 2011, J PHARMACEUT BIOMED, V56, P484, DOI 10.1016/j.jpba.2011.06.003 Ishikawa K, 2008, ACCREDIT QUAL ASSUR, V13, P397, DOI 10.1007/s00769-008-0414-6 Ishikawa K, 2011, ACCREDIT QUAL ASSUR, V16, P311, DOI 10.1007/s00769-011-0767-0 Kato H, 2017, BUNSEKI KAGAKU, V66, P375 Kato M, 2015, ANAL SCI, V31, P805, DOI 10.2116/analsci.31.805 Kitamaki Y, 2008, ANAL BIOANAL CHEM, V391, P2089, DOI 10.1007/s00216-008-2118-5 Kitamaki Y, 2018, ACCREDIT QUAL ASSUR, V23, P297, DOI 10.1007/s00769-018-1338-4 Kitamaki Y, 2017, ANAL CHEM, V89, P6963, DOI 10.1021/acs.analchem.6b05074 Kubota M, 1997, ACCREDIT QUAL ASSUR, V2, P130, DOI 10.1007/s007690050117 Kuroe M, 2018, BUNSEKI KAGAKU, V67, P541, DOI 10.2116/bunsekikagaku.67.541 Lewis RJ, 2005, MAGN RESON CHEM, V43, P783, DOI 10.1002/mrc.1614 Lodi A, 2018, ACCREDIT QUAL ASSUR, V23, P211, DOI 10.1007/s00769-018-1327-7 Ma K, 2009, ANAL CHIM ACTA, V650, P227, DOI 10.1016/j.aca.2009.07.046 Maduskar S, 2015, LAB CHIP, V15, P440, DOI 10.1039/c4lc01180e Milton MJT, 2001, METROLOGIA, V38, P289, DOI 10.1088/0026-1394/38/4/1 Ministry of Ecnomy Trade and Industry Japan, 2015, JAP 2015 JCSS STAND Ministry of Health Labour and Welfare Japan, 2018, JAP 2018 WAT SUPPL J Ministry of Justice Japan, 2018, JAP 2018 MEAS ACT Nagayama T, 2005, J PESTIC SCI, V30, P418, DOI 10.1584/jpestics.30.418 Nelson MA, 2015, ANAL BIOANAL CHEM, V407, P8557, DOI 10.1007/s00216-015-9013-7 Nishizaki Y, 2018, FOOD HYG SAFE SCI, V59, P1, DOI 10.3358/shokueishi.59.1 Nogueira R, 2012, ACCREDIT QUAL ASSUR, V17, P497, DOI 10.1007/s00769-012-0918-y Ohtsuki T, 2015, TALANTA, V131, P712, DOI 10.1016/j.talanta.2014.08.002 PORTER K, 1962, ANAL CHEM, V34, P748, DOI 10.1021/ac60187a009 Quinn TJ, 1997, METROLOGIA, V34, P61, DOI 10.1088/0026-1394/34/1/9 Richter W, 1997, METROLOGIA, V34, P13, DOI 10.1088/0026-1394/34/1/3 Rigger R, 2017, J AOAC INT, V100, P1365, DOI 10.5740/jaoacint.17-0093 Saito N, 2018, TALANTA, V184, P484, DOI 10.1016/j.talanta.2018.03.003 Saito N, 2014, BUNSEKI KAGAKU, V63, P909, DOI 10.2116/bunsekikagaku.63.909 Saito T, 2004, METROLOGIA, V41, P213, DOI 10.1088/0026-1394/41/3/015 Saito T., 2003, CHROMATOGRAPHY, V24, P117 Saito T, 2009, ACCREDIT QUAL ASSUR, V14, P79, DOI 10.1007/s00769-008-0461-z Saito T, 2011, ACCREDIT QUAL ASSUR, V16, P421, DOI 10.1007/s00769-011-0798-6 Shimizu Y, 2008, ACCREDIT QUAL ASSUR, V13, P389, DOI 10.1007/s00769-008-0389-3 Tahara M, 2012, J ENV CHEM, V22, P33 Takahashi M, 2018, J CHROMATOGR A, V1555, P45, DOI 10.1016/j.chroma.2018.04.029 Takatsu A, 2008, ACCREDIT QUAL ASSUR, V13, P409, DOI 10.1007/s00769-008-0397-3 TUNNICLIFF DD, 1955, ANAL CHEM, V27, P73, DOI 10.1021/ac60097a022 Ueno H, 2010, BUNSEKI KAGAKU, V59, P117, DOI 10.2116/bunsekikagaku.59.117 Watanabe R, 2016, TOXINS, V8, DOI 10.3390/toxins8100294 Watanabe T, 2008, ANAL CHIM ACTA, V619, P26, DOI 10.1016/j.aca.2008.03.059 Watanabe T, 2007, TALANTA, V72, P1655, DOI 10.1016/j.talanta.2007.03.032 Watanabe T, 2013, BUNSEKI KAGAKU, V62, P183, DOI 10.2116/bunsekikagaku.62.183 Webster GK, 2009, J PHARMACEUT BIOMED, V49, P1261, DOI 10.1016/j.jpba.2009.02.027 WESTWOOD S, 2012, METROLOGIA, V49 Westwood S, 2013, ANAL CHEM, V85, P3118, DOI 10.1021/ac303329k WHO, 2017, GUID DRINK WAT QUAL Yamazaki T, 2018, BUNSEKI KAGAKU, V67, P397, DOI 10.2116/bunsekikagaku.67.397 Yamazaki T, 2017, METROLOGIA, V54, DOI 10.1088/1681-7575/aa5a15 Yamazaki T, 2015, ANAL SCI, V31, P463, DOI 10.2116/analsci.31.463 Yamazaki T, 2014, ACCREDIT QUAL ASSUR, V19, P275, DOI 10.1007/s00769-014-1067-2 Zhang W, 2017, TALANTA, V172, P78, DOI 10.1016/j.talanta.2017.04.080 NR 71 TC 1 Z9 1 U1 0 U2 23 PD JUN PY 2019 VL 56 IS 3 AR 034002 DI 10.1088/1681-7575/ab04c6 WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000464040400001 DA 2022-12-14 ER PT J AU Van Rijswijk, W Frewer, LJ AF Van Rijswijk, Wendy Frewer, Lynn J. TI Consumer needs and requirements for food and ingredient traceability information SO INTERNATIONAL JOURNAL OF CONSUMER STUDIES DT Article DE Consumer; Europe; food traceability; fraud; perception ID ALLERGIC CONSUMERS; LABELING PREFERENCES; EUROPEAN CONSUMERS; RISK-MANAGEMENT; QUALITY; SAFETY; PERSPECTIVES; PERCEPTIONS; CONFIDENCE; POLICIES AB The introduction of improved food traceability systems has aimed to restore consumer confidence in food safety and quality, in part by being able to provide consumers with more information about the origins of foods and food ingredients. However, little is known about consumers' opinions and beliefs associated with traceability, nor their preferences for information provision. In the current paper, consumer information needs and requirements regarding traceability are investigated. Semi-structured interviews with consumers in four European countries focused on the need for traceability, the preferred means of communication, labelling and bodies held responsible for traceability and dealing with fraud. Results show that there is a clear consumer need for varied information about food and the production processes involved. Rigorous and accountable traceability systems may assist in making such information available to consumers. C1 [Frewer, Lynn J.] Newcastle Univ Agr Bldg, Ctr Rural Econ, Sch Agr Food & Rural Dev, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England. [Van Rijswijk, Wendy; Frewer, Lynn J.] Wageningen Univ, MCB Grp, Wageningen, Netherlands. C3 Newcastle University - UK; Wageningen University & Research RP Frewer, LJ (corresponding author), Newcastle Univ Agr Bldg, Ctr Rural Econ, Sch Agr Food & Rural Dev, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England. EM lynn.frewer@newcastle.ac.uk CR Baksi S, 2007, ENVIRON RESOUR ECON, V37, P411, DOI 10.1007/s10640-006-9032-0 Beekman V, 2006, Ethics and the Politics of Food, P51 Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Codron JM, 2006, AGR HUM VALUES, V23, P283, DOI 10.1007/s10460-006-9000-x Coff C, 2006, ETHICS AND THE POLITICS OF FOOD, P56 Cornelisse-Vermaat JR, 2008, EUR J PUBLIC HEALTH, V18, P115, DOI 10.1093/eurpub/ckm032 Cornelisse-Vermaat JR, 2008, TRENDS FOOD SCI TECH, V19, P669, DOI 10.1016/j.tifs.2008.08.003 Davis O. H., 2008, Food Science & Technology, V22, P41 de Blok BMJ, 2007, ALLERGY, V62, P733, DOI 10.1111/j.1398-9995.2006.01303.x de Jonge J, 2008, APPETITE, V51, P311, DOI 10.1016/j.appet.2008.03.008 de Jonge J, 2008, FOOD QUAL PREFER, V19, P439, DOI 10.1016/j.foodqual.2008.01.002 De Pelsmacker P, 2005, INT MARKET REV, V22, P512, DOI 10.1108/02651330510624363 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Frewer L, 2004, FOOD CHEM TOXICOL, V42, P1181, DOI 10.1016/j.fct.2004.02.002 Gellynck X., 2001, Agrarwirtschaft, V50, P368 Giraud G, 2003, SCI ALIMENT, V23, P40, DOI 10.3166/sda.23.40-46 Golan E., 2002, Agricultural Outlook, P21 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Houghton JR, 2008, FOOD POLICY, V33, P13, DOI 10.1016/j.foodpol.2007.05.001 Jonge J. de, 2004, British Food Journal, V106, P837, DOI 10.1108/00070700410561423 Kehagia O, 2007, SOCIOL RURALIS, V47, P400, DOI 10.1111/j.1467-9523.2007.00445.x Kher S., INT J CONSU IN PRESS Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Korthals M, 2008, J AGR ENVIRON ETHIC, V21, P249, DOI 10.1007/s10806-007-9078-1 Lusk JL, 2006, REV AGR ECON, V28, P284, DOI 10.1111/j.1467-9353.2006.00288.x Meuwissen M. P. M., 2002, NEW APPROACHES FOOD, P41 Miles S, 2005, BRIT FOOD J, V107, P246, DOI 10.1108/00070700510589521 Mills ENC, 2004, ALLERGY, V59, P1262, DOI 10.1111/j.1398-9995.2004.00720.x Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Nayga RJ, 1999, INT FOOD AGR MANAGE, V2, P29, DOI [10.1016/S1096-7508(99)00011-7, DOI 10.1016/S1096-7508(99)00011-7] Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Przyrembel H, 2004, TRENDS FOOD SCI TECH, V15, P360, DOI 10.1016/j.tifs.2003.12.006 Resende-Filho M., 2007, EC TRACEABILITY MITI Shewfelt RL, 2006, ACTA HORTIC, P31, DOI 10.17660/ActaHortic.2006.712.1 van Amstel M, 2008, J CLEAN PROD, V16, P263, DOI 10.1016/j.jclepro.2006.07.039 van der Vorst JGAJ, 2006, ACCREDIT QUAL ASSUR, V11, P33, DOI 10.1007/s00769-005-0028-1 van Dorp KF, 2003, SUPPLY CHAIN MANAG, V8, P32, DOI 10.1108/13598540310463341 Van Kleef E, 2007, RISK ANAL, V27, P1565, DOI 10.1111/j.1539-6924.2007.00989.x van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Viaene J, 2000, ACTA HORTIC, P89, DOI 10.17660/ActaHortic.2000.524.10 NR 43 TC 72 Z9 78 U1 6 U2 100 PD MAY PY 2012 VL 36 IS 3 BP 282 EP 290 DI 10.1111/j.1470-6431.2011.01001.x WC Business SC Business & Economics UT WOS:000302398200004 DA 2022-12-14 ER PT J AU El Sheikha, AF Xu, JP AF El Sheikha, Aly Farag Xu, Jianping (JP) TI Traceability as a Key of Seafood Safety: Reassessment and Possible Applications SO REVIEWS IN FISHERIES SCIENCE & AQUACULTURE DT Article DE Fish and fishery products; seafood safety regulation; analytical approaches; PCR-DGGE; traceability benefits ID GRADIENT GEL-ELECTROPHORESIS; PCR-DGGE; BACTERIAL COMMUNITIES; GENETIC DIVERSITY; RAPID DETECTION; FISH; FOOD; ORIGIN; AQUACULTURE; WATER AB The aquaculture sector is among the most significant and fastest-growing sectors of the global agrifood system. The production of aquaculture is not only increasingly important as a primary protein source to feed the world's population, but also of enormous importance as a trade good in global commerce. However, the industry faces many challenges to meet global demands including the over-exploited natural stocks, global warming, pollution, perishability, widespread food frauds, and food-borne diseases. A major requirement to meet the increasing demand for seafood is seafood security and safety. To ensure the security and safety of seafood, a variety of regulations and techniques have been developed. However, the current techniques have several limitations, such as not being able to provide reliable information with regard to the origin of farmed specimens, the instability of the methods, and the lack of any historical records for the food. Therefore, there is an urgent need to develop a traceability technique that could record the history of seafood from sea to table. This article provides an overview of the current regulations and traceability techniques to ensure the safety and security of seafoods. We suggest that the PCR-DGGE technique is a viable method to meet both traceability and safety requirements for seafoods at the same time. C1 [El Sheikha, Aly Farag; Xu, Jianping (JP)] McMaster Univ, Dept Biol, 1280 Main St West, Hamilton, ON L8S 4K1, Canada. [El Sheikha, Aly Farag] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm, Minufiya Govern, Egypt. C3 McMaster University; Egyptian Knowledge Bank (EKB); Menofia University RP El Sheikha, AF (corresponding author), McMaster Univ, Dept Biol, 1280 Main St West, Hamilton, ON L8S 4K1, Canada. EM elsheikha_aly@yahoo.com CR Ababouch L., 2005, 473 FAO UN Abu-Hassan O., 2011, WORLDS VET J, V1, P10 Anihouvi VB, 2009, J AQUAT FOOD PROD T, V18, P370, DOI 10.1080/10498850903224919 AquaTT, 2004, TRAC AQ Aursand M., 2004, 34 WEFTA C SEPT 12 1 Austin B, 2005, ENVIRON MICROBIOL, V7, P1488, DOI 10.1111/j.1462-2920.2005.00847.x Bernard L, 2001, CYTOMETRY, V43, P314, DOI 10.1002/1097-0320(20010401)43:4<314::AID-CYTO1064>3.0.CO;2-H Bernbom N, 2009, INT J FOOD MICROBIOL, V134, P223, DOI 10.1016/j.ijfoodmicro.2009.06.012 Boon N, 2002, FEMS MICROBIOL ECOL, V39, P101, DOI 10.1111/j.1574-6941.2002.tb00911.x Borit M., 2016, FAO Fisheries and Aquaculture Circular Bueno P., 1998, CATCH CULT, V3, P6 CAIA, 2016, OPP EXP Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chaguri MP, 2015, LWT-FOOD SCI TECHNOL, V61, P194, DOI 10.1016/j.lwt.2014.11.006 Chitmanat C., 2015, Journal of Agricultural Science (Toronto), V7, P254 Diana JS, 2013, BIOSCIENCE, V63, P255, DOI 10.1525/bio.2013.63.4.5 Dolinsky LCB, 2002, NEUROMUSCULAR DISORD, V12, P845, DOI 10.1016/S0960-8966(02)00069-X dos Santos CA, 2002, PUBLIC, ANIMAL AND ENVIRONMENTAL AQUACULTURE HEALTH ISSUES, P103 Dzwolak W, 2016, J FOOD SAFETY, V36, P203, DOI 10.1111/jfs.12232 Dzwolak Waldemar, 2009, Medycyna Weterynaryjna, V65, P245 El Sheikha A. F, 2014, 2 AFSA C FOOD SAF SE El Sheikha A. F., 2015, ADV FOOD TECHNOLOGY, V1, P1, DOI DOI 10.17140/AFTNSOJ-SE-1-101 El Sheikha A. F., 2010, THESIS El Sheikha A.F., 2015, NUTR FOOD TECHNOLOGY, V1, P1, DOI [10.16966/nftoa.103, DOI 10.16966/2470-6086.103] El Sheikha AF, 2015, FOOD BIOL SER, P188 El Sheikha AF, 2016, CRIT REV FOOD SCI, V56, P306, DOI 10.1080/10408398.2012.745478 El Sheikha AF, 2014, BIOLOGICAL CONTROLS FOR PREVENTING FOOD DETERIORATION: STRATEGIES FOR PRE- AND POSTHARVEST MANAGEMENT, P409 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 FAO, 2016, CONTR FOOD SEC NUTR, DOI DOI 10.5860/CHOICE.50-5350 FAO, 2014, COFIFTXIV20147 FAO FAO, 2002, WORLD AGR 2015 2030 FAO, 2004, FOOD NUTR HDB NAM VO FAO, 2008, STAT WORLD FISH AQ 1 FDA, 1995, PROC SAF SAN PROC IM, V60 Fisheries and Oceans Canada, 2003, ACH VIS REP OFF COMM Grahl-Nielsen O, 2010, BIOCHEM SYST ECOL, V38, P478, DOI 10.1016/j.bse.2010.04.010 Grisez L, 1997, AQUACULTURE, V155, P387, DOI 10.1016/S0044-8486(97)00113-0 Hastein T, 2006, REV SCI TECH OIE, V25, P607, DOI 10.20506/rst.25.2.1678 Hixson S. M., 2014, Journal of Aquaculture Research and Development, V5, P234 Hoelzel AR, 1992, MOL GENETIC ANAL POP Islam M., 2012, INT J ANIM FISH SCI, V5, P449 Ji NN, 2006, J APPL MICROBIOL, V100, P795, DOI 10.1111/j.1365-2672.2006.02836.x Ji NN, 2004, J MICROBIOL METH, V57, P409, DOI 10.1016/j.mimet.2004.02.010 Jonker T. H., 2005, FOOD SAFETY QUALITY, P50 Karunasagar I., 2008, FAO Fisheries and Aquaculture Technical Paper, P9 Kawai M, 2002, APPL ENVIRON MICROB, V68, P699, DOI 10.1128/AEM.68.2.699-704.2002 Koo I., 2016, INFECT DIS ASS EATIN Koonse B, 2016, FOODS, V5, DOI 10.3390/foods5020031 Le Nguyen D. D., 2008, THESIS Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Leesing R., 2005, THESIS U MONTPELLIER Leesing R., 2011, DYNAMIC BIOCH PROCES, V5, P83 Li L, 2016, FOOD CHEM, V194, P1238, DOI 10.1016/j.foodchem.2015.08.123 Liu J, 2015, WORLD J MICROB BIOT, V31, P1387, DOI 10.1007/s11274-015-1890-6 Liu XF, 2012, FOOD CONTROL, V23, P522, DOI 10.1016/j.foodcont.2011.08.025 Ly F. D., 2007, SCI CREATIVE Q Mansfield B, 2011, ANTIPODE, V43, P413, DOI 10.1111/j.1467-8330.2010.00743.x Martinez I, 1999, ICES J MAR SCI, V56, P640, DOI 10.1006/jmsc.1999.0511 Martinez I., 2005, 455 FAO Martins P, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0080847 Marzorati M, 2008, ENVIRON MICROBIOL, V10, P1571, DOI 10.1111/j.1462-2920.2008.01572.x Montet D., 2008, Aspects of Applied Biology, P11 Montet D, 2012, AQUACULTURE, P93 Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Muyzer G, 1999, CURR OPIN MICROBIOL, V2, P317, DOI 10.1016/S1369-5274(99)80055-1 Nganou N. D., 2014, British Microbiology Research Journal, V4, P1 Oceania, 2016, FISH STOR SUCC VAL S Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Olsson B, 2004, HYDROBIOLOGIA, V514, P15, DOI 10.1023/B:hydr.0000018203.90350.8e Power M, 2002, ENVIRON BIOL FISH, V64, P75, DOI 10.1023/A:1016035509246 Quinn RA, 2012, CAN J MICROBIOL, V58, P563, DOI [10.1139/w2012-024, 10.1139/W2012-024] SEAFOOD plus, 2005, SEAFOOD PLUS EU INT Spanggaard B, 2000, AQUACULTURE, V182, P1, DOI 10.1016/S0044-8486(99)00250-1 Standal I. B., 2009, THESIS Tacon AGJ, 2013, REV FISH SCI, V21, P22, DOI 10.1080/10641262.2012.753405 Tanaka H, 2010, FISH RES, V102, P217, DOI 10.1016/j.fishres.2009.11.002 Teklemariam AD., 2015, INT J FISHERIES AQUA, V3, P111 UNEP, 2000, ROL MICR INT SOURC B Waples RS, 1998, J HERED, V89, P438, DOI 10.1093/jhered/89.5.438 WARD RD, 1994, J FISH BIOL, V44, P213, DOI 10.1111/j.1095-8649.1994.tb01200.x World Bank, 2016, TOP 10 COUNTR LARG F Xu J.-P., 2014, NEXT GENERATION SEQU Xu Q, 2015, J SHELLFISH RES, V34, P743, DOI 10.2983/035.034.0303 Zeng XF, 2013, FOOD CONTROL, V33, P344, DOI 10.1016/j.foodcont.2013.03.001 Zeng XF, 2013, FOOD CONTROL, V30, P590, DOI 10.1016/j.foodcont.2012.07.037 Zhou JZ, 2015, MBIO, V6, DOI 10.1128/mBio.02288-14 NR 86 TC 38 Z9 38 U1 2 U2 60 PY 2017 VL 25 IS 2 BP 158 EP 170 DI 10.1080/23308249.2016.1254158 WC Fisheries SC Fisheries UT WOS:000390576100005 DA 2022-12-14 ER PT J AU Zhang, XH Sun, YJ Sun, YX AF Zhang, Xinghua Sun, Yongjie Sun, Yongxin TI Research on Cold Chain Logistics Traceability System of Fresh Agricultural Products Based on Blockchain SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE DT Article ID FOOD TRACEABILITY AB Traditional cold chain logistics has problems such as centralized data storage, low data reliability, easy data tampering, and difficulty in locating responsible persons, which leads to the inability to guarantee consumer rights. To solve these problems, a cold chain logistics traceability system is proposed for fresh agricultural products based on blockchain. Both alliance chain and private chain are used in the paper in order to ensure that the product traceability system not only has certain openness but also must contain enough privacy and security. Alliance chain is mainly used to query and share product traceability information. The private chain will be used to collect and store the product traceability information of each enterprise and then connected to the alliance chain via hash pointers. The proposed system is beneficial for reducing the burden of network transmission of alliance chain and improving the efficiency of consumer product data query. At the same time, the private chain ensures the security and privacy of enterprise product data, which not only has high data storage efficiency but also can meet the requirements of all participants for the traceability system. In the experimental part, the feasibility of this system is verified through simulation experiments, which provides a reference for the combination of blockchain technology and cold chain logistics traceability system. C1 [Zhang, Xinghua] Changchun Sci Tech Univ, Changchun 130600, Jilin, Peoples R China. [Sun, Yongjie] Changchun Sci Tech Univ, Coll Life Sci, Changchun 130600, Jilin, Peoples R China. [Sun, Yongxin] Baicheng Normal Univ, Coll Phys & Elect Informat, Baicheng 13700, Jilin, Peoples R China. C3 Baicheng Normal University RP Sun, YX (corresponding author), Baicheng Normal Univ, Coll Phys & Elect Informat, Baicheng 13700, Jilin, Peoples R China. EM 576659967@qq.com; 513331898@qq.com; sunyongxin@bcnu.edu.cn CR Ajao L.A., 2019, MULTIDISCIPLINARY SC, V2, P300, DOI [10.3390/j2030021, DOI 10.3390/J2030021] Alfandi O, 2020, IEEE IFIP NETW OPER Chen CL, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11114939 Introini SC, 2018, DIR ORGAN, V64, P50 Dasaklis TK, 2019, INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS (COINS), P184, DOI 10.1145/3312614.3312652 Fernando E, 2019, PROCEEDINGS OF 2019 4TH INTERNATIONAL CONFERENCE ON SUSTAINABLE INFORMATION ENGINEERING AND TECHNOLOGY (SIET 2019), P353, DOI 10.1109/SIET48054.2019.8986122 Guo BF, 2019, CLIN TOXICOL, V57, P331, DOI 10.1080/15563650.2018.1529318 [何蒲 He Pu], 2017, [计算机科学, Computer Science], V44, P1 Hua J, 2018, IEEE INT VEH SYM, P97 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Li J., 2018, 2018 2 IEEE ADV INFO, P2637 Li L, 2017, FOOD CHEM, V229, P403, DOI 10.1016/j.foodchem.2017.02.090 Longo F., 2020, INT J FOOD ENG, V16 Mohanta BK, 2018, INT CONF COMPUT Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Seok B, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9183740 [邵奇峰 Shao Qifeng], 2018, [计算机学报, Chinese Journal of Computers], V41, P969 Tian F, 2017, I C SERV SYST SERV M Vergne T, 2017, VET REC, V181, DOI 10.1136/vr.103950 Wen YB, 2020, ENVIRON POLLUT, V258, DOI 10.1016/j.envpol.2019.113645 Wongpatikaseree K., P180, DOI 10.1109/icis.2018.8466479 Xiaoling Xia, 2019, Journal of Physics: Conference Series, V1314, DOI 10.1088/1742-6596/1314/1/012067 Xie WP, 2019, IEEE INT SYMP PARAL, P1266, DOI 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00181 Yaji S, 2018, 2018 IEEE 25TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING WORKSHOPS (HIPCW), P81, DOI [10.1109/HiPCW.2018.8634280, 10.1109/HiPCW.2018.00021] Yang L, 2019, J IND INF INTEGR, V15, P80, DOI 10.1016/j.jii.2019.04.002 Yuxin Liao, 2019, Journal of Physics: Conference Series, V1288, DOI 10.1088/1742-6596/1288/1/012062 Zhang Y, 2020, IEEE T SERV COMPUT, V13, P216, DOI 10.1109/TSC.2019.2947476 Zhu HG, 2021, J INF SECUR APPL, V61, DOI 10.1016/j.jisa.2021.102952 NR 30 TC 2 Z9 2 U1 38 U2 72 PD FEB 1 PY 2022 VL 2022 AR 1957957 DI 10.1155/2022/1957957 WC Mathematical & Computational Biology; Neurosciences SC Mathematical & Computational Biology; Neurosciences & Neurology UT WOS:000769418000002 DA 2022-12-14 ER PT J AU Chen, MY Zhang, WG Zheng, Y Lu, J AF Chen, Meiying Zhang, Wenguang Zheng, Yi Lu, Ji TI Food traceability system awareness and agricultural operation: A Study of tea farms in Fujian, China SO FUTURE OF FOOD-JOURNAL ON FOOD AGRICULTURE AND SOCIETY DT Article DE Food traceability; food safety; Chinese farm; pesticide control ID SUPPLY CHAIN; GENERAL FRAMEWORK; SAFETY; SMALLHOLDERS; PERSPECTIVES; COOPERATIVES; PESTICIDES; IMPACTS; POLICY AB China is establishing a Food Traceability System (FTS), but the policy implementation is behind most developed countries. The lack of FTS awareness may be a factor contributing to farming practices that are not consistent with FTS policies. Furthermore, the structure of an agri-food supply chain is a factor influencing farms' compliance with FTS. The present study focuses on pesticide residue control and traceability issues in one of the largest tea production areas in China. It aims to examine the effect of FTS awareness and related policies on tea farms' operations as well as the influences of supply chain structure on the effects of policy awareness. In this study the data were collected from Fujian province, which is a traditional, major tea-growing region in China with 18% of national production. Farms were recruited through a Stratified Sampling procedure that included 428 participating farms from the four largest tea-producing counties in Fujian. The participating farms answered questions regarding their awareness of FTS and related policies as well as the supply chain structure. The participants also reported their agricultural record-keeping practices related to pesticide residue control, including pesticide use, pesticide residue test, and sales record. The results reveal that farm owners' or operators' FTS awareness has a positive effect on pesticide use and sales record-keeping practice, and the supply chain structure importantly moderates the effects of policy awareness on operations related to pesticide residue control. Compared to independent growers, tea farms within an integrated supply chain were more likely to take pesticide residue tests or keep sales records. The results suggest that increasing FTS awareness among tea growers would be crucial to establish a safe and traceable system. Furthermore, governments need to take the supply chain structure into account. C1 [Chen, Meiying; Zheng, Yi] FUJIAN Agr & Forestry Univ, Fuzhou, Peoples R China. [Zhang, Wenguang] Beijing Normal Univ, Beijing, Peoples R China. [Lu, Ji] Dalhousie Univ, Halifax, NS, Canada. C3 Fujian Agriculture & Forestry University; Beijing Normal University; Dalhousie University RP Lu, J (corresponding author), Dalhousie Univ, Halifax, NS, Canada. EM ji.lu@dal.ca CR Atari DOA, 2009, J ENVIRON MANAGE, V90, P1269, DOI 10.1016/j.jenvman.2008.07.006 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Carvalho FP, 2006, ENVIRON SCI POLICY, V9, P685, DOI 10.1016/j.envsci.2006.08.002 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Cooper J, 2007, CROP PROT, V26, P1337, DOI 10.1016/j.cropro.2007.03.022 FAO, 2015, WORLD TEA PRODUCTION Fischer E, 2012, WORLD DEV, V40, P1255, DOI 10.1016/j.worlddev.2011.11.018 Francesconi GN, 2011, J AFR ECON, V20, P153, DOI 10.1093/jae/ejq036 Fujian People's Congress, 2012, FUJIAN PEOPLES C Gale HF, 2012, AM J AGR ECON, V94, P483, DOI 10.1093/ajae/aar069 Golan E., 2004, Amber Waves, V2, P14 Guo HD, 2007, COMP ECON STUD, V49, P285, DOI 10.1057/palgrave.ces.8100202 Heilmann S, 2008, CHINA J, V59, P1 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Ito J, 2012, FOOD POLICY, V37, P700, DOI 10.1016/j.foodpol.2012.07.009 Jia CH, 2013, FOOD CONTROL, V32, P236, DOI 10.1016/j.foodcont.2012.11.042 Lam HM, 2013, LANCET, V381, P2044, DOI 10.1016/S0140-6736(13)60776-X Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Liu P, 2019, IOP C SER EARTH ENV, V227, DOI 10.1088/1755-1315/227/5/052027 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Peets S, 2009, PRECIS AGRIC, V10, P382, DOI 10.1007/s11119-009-9106-4 People's Government of Zhejiang Province, 2001, SEV POL OP PEOPL GOV Qiao YH, 2016, RENEW AGR FOOD SYST, V31, P246, DOI 10.1017/S1742170515000162 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Roth AV, 2008, J SUPPLY CHAIN MANAG, V44, P22, DOI 10.1111/j.1745-493X.2008.00043.x Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Skevas T, 2013, NJAS-WAGEN J LIFE SC, V64-65, P95, DOI 10.1016/j.njas.2012.09.001 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Verhofstadt E, 2015, APPL ECON PERSPECT P, V37, P86, DOI 10.1093/aepp/ppu021 Wang JH, 2017, FOOD CONTROL, V80, P143, DOI 10.1016/j.foodcont.2017.05.003 Wei GX, 2012, CHINA ECON REV, V23, P253, DOI 10.1016/j.chieco.2011.11.002 Winter SC, 2001, J POLICY ANAL MANAG, V20, P675, DOI 10.1002/pam.1023 Wu YN, 2018, FOOD CONTROL, V90, P212, DOI 10.1016/j.foodcont.2018.02.049 Zhang QF, 2012, J AGRAR CHANGE, V12, P460, DOI 10.1111/j.1471-0366.2012.00352.x Zhu XF, 2021, POLICY STUD J, V49, P13, DOI 10.1111/psj.12254 NR 40 TC 0 Z9 0 U1 5 U2 11 PD SUM PY 2020 VL 8 IS 3 BP 40 EP 51 DI 10.17170/kobra-202010131941 WC Food Science & Technology SC Food Science & Technology UT WOS:000661659600005 DA 2022-12-14 ER PT J AU Zhang, AR Mankad, A Ariyawardana, A AF Zhang, Airong Mankad, Aditi Ariyawardana, Anoma TI Establishing confidence in food safety: is traceability a solution in consumers' eyes? SO JOURNAL OF CONSUMER PROTECTION AND FOOD SAFETY DT Article DE Food safety; Food origin; Consumer trust; Traceability; Willingness to pay ID WILLINGNESS-TO-PAY; TECHNOLOGY ACCEPTANCE MODEL; SYSTEMS; IMPACT; RESPONSES; QUALITY; PERSPECTIVES; PREFERENCES; MANAGEMENT; CHINA AB Consumers have become increasingly concerned about food safety due to numerous food scandals and incidents over the past two decades. Consequently, they demand to be informed of the processes involved along the food supply chain. Employing a traceability system, tracing food from 'farm to fork', has been embraced by the food industries and governments as an important tool to restore and increase consumers' confidence in food safety. However, there is limited research examining consumers' perceptions of, and confidence in, the food traceability system to fulfil the role of ensuring food safety. To bridge the knowledge gap, we conducted an online survey of 489 consumers from three major Australian cities. The results suggested that although participants had a great desire to know how their food was produced and handled, it was their understanding of, and confidence in, food traceability systems that strongly predicted their willingness to pay (WTP) for having their food traced. Participants also indicated that, in comparison to locally produced food products, it was more important to have imported food products traced. However, paradoxically, the information provided by the traceability system of imported food products was less trusted. The results highlight that, in order to use the food traceability system to gain consumer trust in food safety, it is critical to inform consumers how the system works to build their confidence in the system. C1 [Zhang, Airong] CSIRO, Hlth & Biosecur, Brisbane, Qld, Australia. [Mankad, Aditi] CSIRO, Land & Water, Brisbane, Qld, Australia. [Ariyawardana, Anoma] Univ Queensland, Sch Agr & Food Sci, Brisbane, Qld, Australia. C3 Commonwealth Scientific & Industrial Research Organisation (CSIRO); Commonwealth Scientific & Industrial Research Organisation (CSIRO); University of Queensland RP Zhang, AR (corresponding author), CSIRO, Hlth & Biosecur, Brisbane, Qld, Australia. EM airong.zhang@csiro.au CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Ajzen I, 2010, FRONT SOC PSYCHOL, P289 Amrhein V, 2017, PEERJ, V5, DOI 10.7717/peerj.3544 Ariyawardana A, 2017, FOOD CONTROL, V73, P193, DOI 10.1016/j.foodcont.2016.08.006 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Australian Pork Limited, 2019, PROD INT TRAC Barbarossa C, 2016, FOOD QUAL PREFER, V53, P71, DOI 10.1016/j.foodqual.2016.05.015 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Carter R, 2015, ARE SUPERMARKETS SAF Carter R, 2015, BERRY BAD FROZEN BER Charlebois S, 2015, J DAIRY SCI, V98, P3514, DOI 10.3168/jds.2014-9247 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chen HH, 2019, APPL ECON, V51, P687, DOI 10.1080/00036846.2018.1510470 Chen S. C., 2011, AUSTR J BUSINESS MAN, V1, P124, DOI DOI 10.1016/J.JHYDR0L.2010.12.018 Cheung CY, 2016, J VERBRAUCH LEBENSM, V11, P241, DOI 10.1007/s00003-016-1028-2 DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982 Giraud G., 2006, 98 SEM EUR ASS AGR E Hall D, 2010, GEOFORUM, V41, P826, DOI 10.1016/j.geoforum.2010.05.005 Hoorfar J, 2011, WOODHEAD PUBL FOOD S, P1, DOI 10.1533/9780857092519 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Jouanjean MA, 2019, J CONSUM PROT FOOD S, V14, P103, DOI 10.1007/s00003-019-01224-6 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Kimball AM, 2004, REV SCI TECH OIE, V23, P753, DOI 10.20506/rst.23.3.1516 Kirezieva K, 2013, FOOD RES INT, V52, P230, DOI 10.1016/j.foodres.2013.03.023 Lim KH, 2013, CAN J AGR ECON, V61, P93, DOI 10.1111/j.1744-7976.2012.01260.x Liu C, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0206793 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Maassen GH, 2001, SOCIOL METHOD RES, V30, P241, DOI 10.1177/0049124101030002004 Marszalek J, 2015, THE COURIER MAIL Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Meat & Livestock Australia, 2019, MEAT SAF TRAC Myae AC, 2012, INT J CONSUM STUD, V36, P192, DOI 10.1111/j.1470-6431.2011.01084.x Ortega DL, 2015, AUST J AGR RESOUR EC, V59, P433, DOI 10.1111/1467-8489.12097 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pieniak Z, 2007, FOOD QUAL PREFER, V18, P1050, DOI 10.1016/j.foodqual.2007.05.001 Riccioli F, 2020, FOOD CONTROL, V108, DOI 10.1016/j.foodcont.2019.106831 Rodiger M, 2015, FOOD QUAL PREFER, V43, P10, DOI 10.1016/j.foodqual.2015.02.002 Rong AY, 2011, INT J PROD ECON, V131, P421, DOI 10.1016/j.ijpe.2009.11.026 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 van der Meulen B, 2015, J VERBRAUCH LEBENSM, V10, pS19, DOI 10.1007/s00003-015-0992-2 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x WHO, 2015, ESTIMATES GLOBAL BUR Wu B, 2017, COMPUT HUM BEHAV, V67, P221, DOI 10.1016/j.chb.2016.10.028 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 50 TC 19 Z9 20 U1 4 U2 37 PD JUN PY 2020 VL 15 IS 2 BP 99 EP 107 DI 10.1007/s00003-020-01277-y EA APR 2020 WC Food Science & Technology SC Food Science & Technology UT WOS:000524403100001 DA 2022-12-14 ER PT J AU Freitas, J Vaz-Pires, P Camara, JS AF Freitas, Jorge Vaz-Pires, Paulo Camara, Jose S. TI From aquaculture production to consumption: Freshness, safety, traceability and authentication, the four pillars of quality SO AQUACULTURE DT Review DE Seafood; Freshness; Safety; Traceability; Authenticity; Aquaculture ID BASS DICENTRARCHUS-LABRAX; BREAM SPARUS-AURATA; FISH-OIL SUBSTITUTION; INDEX METHOD QIM; ANTIMICROBIAL RESISTANCE; OCCUPATIONAL-HEALTH; GEOGRAPHICAL ORIGIN; INFECTIOUS-DISEASES; FOOD TRACEABILITY; ICE STORAGE AB Farmed aquatic products are among the most widely traded commodities and one of the sectors with the fastest growth in the last years. However, aquaculture is still affected by negative connotations in comparison with other agroindustry sectors. Markets, consumer preferences and concerns about food safety and sustainability are influencing the growth of the sector and are forcing the implementation of quality management systems. Modern management systems help to minimize the environmental impacts and the distribution of unsafe or poor-quality products, thereby reducing the potential for bad image, liability and recalls. This article presents a comprehensive overview of the status, relevance, and impact of the quality management systems in the development of marine aquaculture, with the focus on four of the most important criteria associated with these systems: freshness, safety, traceability, and authenticity. C1 [Freitas, Jorge; Camara, Jose S.] Univ Madeira, CQM, Campus Univ Penteada, P-9000039 Funchal, Portugal. [Vaz-Pires, Paulo] Univ Porto, ICBAS Abel Salazar Inst Biomed Sci, R Jorge Viterbo Ferreira 228, P-228405031 Porto, Portugal. [Vaz-Pires, Paulo] CIIMAR Interdisciplinary Ctr Marine & Environm Re, Terminal Cruzeiros Leixoes, Av Gen Norton Matos S-N, P-4450208 Matosinhos, Portugal. [Camara, Jose S.] Univ Madeira, Fac Ciencias Exatas & Engn, Funchal, Portugal. C3 Universidade da Madeira; Universidade do Porto; Universidade do Porto; Universidade da Madeira RP Camara, JS (corresponding author), Univ Madeira, CQM, Campus Univ Penteada, P-9000039 Funchal, Portugal. EM jsc@staff.uma.pt CR Adams A, 2011, AQUAC RES, V42, P93, DOI 10.1111/j.1365-2109.2010.02663.x Akkerman R, 2010, OR SPECTRUM, V32, P863, DOI 10.1007/s00291-010-0223-2 Alasalvar C., 2011, HDB SEAFOOD QUALITY, P518 Alexi N, 2017, AQUAC RES, V48, P3817, DOI 10.1111/are.13208 Altinok I., 2004, TURKISH J FISH AQUAT, V138, P131 Amagliani G, 2012, FOOD RES INT, V45, P780, DOI 10.1016/j.foodres.2011.06.022 Pardo MA, 2018, FOOD CONTROL, V92, P7, DOI 10.1016/j.foodcont.2018.04.044 Appelhanz S, 2016, J CLEAN PROD, V110, P132, DOI 10.1016/j.jclepro.2015.02.034 Asche F, 2017, AQUACULT ECON MANAG, V21, P1, DOI 10.1080/13657305.2016.1272649 Ashie I.N.A., 1996, CRIT REV FOOD SCI NU, DOI [10.1080/J.267857168., DOI 10.1080/J.267857168] Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Austin B, 2010, VET MICROBIOL, V140, P310, DOI 10.1016/j.vetmic.2009.03.015 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Barbosa A, 2004, FOOD CONTROL, V15, P161, DOI 10.1016/S0956-7135(03)00027-6 Barbosa A., 2002, SAFETY QUALITY ISSUE, DOI 10.1533/9781855736788.2.173. Bayliss SC, 2017, FRONT MICROBIOL, V8, DOI 10.3389/fmicb.2017.00121 Bergleiter S, 2015, J AGR ENVIRON ETHIC, V28, P553, DOI 10.1007/s10806-015-9531-5 Beveridge M.C., 2004, CAGE AQUACULTURE, DOI [10.2134/jeq2005.0025br., DOI 10.2134/JEQ2005.0025BR] Biomar, 2018, BIOMAR GROUP SUST RE Borit M, 2012, MAR POLICY, V36, P96, DOI 10.1016/j.marpol.2011.03.012 Bosco J., 2010, FISHERIES SCI, V76, P1 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Boyd C. E., 2017, Fish diseases: prevention and control strategies, P147 Bremner H.A., 2000, J AQUAT FOOD PROD T, V9, P5, DOI DOI 10.1300/J030V09N03_02 Bremner HA, 2000, CRIT REV FOOD SCI, V40, P83, DOI 10.1080/10408690091189284 Bricknell I., 2017, Fish diseases: prevention and control strategies, P53 Brugere C, 2017, AQUACULTURE, V467, P158, DOI 10.1016/j.aquaculture.2016.04.012 Buller N.B., 2014, BACTERIA FUNGI FISH, DOI [10.1079/9781845938055, DOI 10.1079/9781845938055] Bush SR, 2019, AQUACULTURE, V498, P428, DOI 10.1016/j.aquaculture.2018.08.077 Butt AA, 2004, LANCET INFECT DIS, V4, P294, DOI 10.1016/S1473-3099(04)01005-9 Butt AA, 2004, LANCET INFECT DIS, V4, P201, DOI 10.1016/S1473-3099(04)00969-7 Cakli S, 2007, FOOD CONTROL, V18, P391, DOI 10.1016/j.foodcont.2005.11.005 Cameron AR, 2012, PREV VET MED, V105, P280, DOI 10.1016/j.prevetmed.2012.01.009 Cataudella S., 2005, GFCM STUDIES REV Chai JY, 2005, INT J PARASITOL, V35, P1233, DOI 10.1016/j.ijpara.2005.07.013 Chen BW, 2019, WOODHEAD PUBL FOOD S, P619, DOI 10.1016/B978-0-12-814217-2.00024-X Cheng JH, 2015, CRIT REV FOOD SCI, V55, P1012, DOI 10.1080/10408398.2013.769934 Claret A, 2014, APPETITE, V79, P25, DOI 10.1016/j.appet.2014.03.031 Claussen IC, 2011, PROC FOOD SCI, V1, P1907, DOI 10.1016/j.profoo.2011.09.280 Cole DW, 2009, INT J HYG ENVIR HEAL, V212, P369, DOI 10.1016/j.ijheh.2008.08.003 Conte Francesca, 2014, Ital J Food Saf, V3, P1983 Cook B., 2018, BLOCKCHAIN TRANSFORM Corina E, 2013, EKON POLJOPR, V60, P287 Crane M, 2011, VIRUSES-BASEL, V3, P2025, DOI 10.3390/v3112025 Cutarelli A, 2014, FOOD CONTROL, V37, P46, DOI 10.1016/j.foodcont.2013.08.009 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Daszak P, 2001, ACTA TROP, V78, P103, DOI 10.1016/S0001-706X(00)00179-0 DEANGELO MCB, 2008, DYN BIOCH PROCESS BI, V27, P33, DOI DOI 10.4000/APLIUT.1301 Edwards P, 2015, AQUACULTURE, V447, P2, DOI 10.1016/j.aquaculture.2015.02.001 El Sheikha AF, 2017, REV FISH SCI AQUAC, V25, P158, DOI 10.1080/23308249.2016.1254158 El Sheikha AF, 2016, CRIT REV FOOD SCI, V56, P306, DOI 10.1080/10408398.2012.745478 Elbashir S, 2018, FOOD MICROBIOL, V70, P85, DOI 10.1016/j.fm.2017.09.011 European Commission, 2021, EU CONS HAB REG FISH, DOI DOI 10.2771/734664 FAO, 2018, BRIEF WORLD FISH AQ FAO, 2016, STAT WORLD FISH AQ 2 Fevre EM, 2006, TRENDS MICROBIOL, V14, P125, DOI 10.1016/j.tim.2006.01.004 Fletcher G.C., 2012, ADV MEAT POULTRY SEA, DOI [10.1533/9780857095718.2.261., DOI 10.1533/9780857095718.2.261] Fontanesi L, 2009, ITAL J ANIM SCI, V8, P9, DOI 10.4081/ijas.2009.s2.9 Fountoulaki E, 2009, AQUACULTURE, V289, P317, DOI 10.1016/j.aquaculture.2009.01.023 Frans I, 2008, ISR J AQUACULT-BAMID, V60, P213 Freitas J, 2019, MOLECULES, V24, DOI 10.3390/molecules24193530 Gauthier DT, 2015, VET J, V203, P27, DOI 10.1016/j.tvjl.2014.10.028 Ghaly A. E., 2010, American Journal of Applied Sciences, V7, P859, DOI 10.3844/ajassp.2010.859.877 Ghisi Nedia de Castilhos, 2016, Acta Scientiarum Biological Sciences, V38, P253, DOI 10.4025/actascibiolsci.v38i3.31785 Gil LA, 2007, TRENDS FOOD SCI TECH, V18, P558, DOI 10.1016/j.tifs.2007.04.016 Gill P., 2008, NUCLEOS NUCLEOT, DOI [10.1080/J.4575, DOI 10.1080/J.4575] Giuffrida A, 2013, INT J FOOD SCI TECH, V48, P1235, DOI 10.1111/ijfs.12082 Gram L, 2002, CURR OPIN BIOTECH, V13, P262, DOI 10.1016/S0958-1669(02)00309-9 Grattan LM, 2016, HARMFUL ALGAE, V57, P2, DOI 10.1016/j.hal.2016.05.003 Griffiths AM, 2014, FOOD CONTROL, V45, P95, DOI 10.1016/j.foodcont.2014.04.020 Grigorakis K, 2011, CHEMOSPHERE, V85, P899, DOI 10.1016/j.chemosphere.2011.07.015 Guertler C, 2016, AQUACULT ENG, V70, P63, DOI 10.1016/j.aquaeng.2015.11.002 Gurr Sarah, 2011, Fungal Biology Reviews, V25, P181, DOI 10.1016/j.fbr.2011.10.004 Halide H, 2009, ENVIRON MODELL SOFTW, V24, P694, DOI 10.1016/j.envsoft.2008.10.013 Hansen AA, 2012, WOODHEAD PUBL FOOD S, P248 Hassoun A, 2017, CRIT REV FOOD SCI, V57, P1976, DOI 10.1080/10408398.2015.1047926 He J, 2018, MAR POLICY, V96, P163, DOI 10.1016/j.marpol.2018.08.003 Holen SM, 2018, MAR POLICY, V96, P184, DOI 10.1016/j.marpol.2017.08.009 Huidobro A, 2001, EUR FOOD RES TECHNOL, V212, P408, DOI 10.1007/s002170000243 Huidobro A, 2001, EUR FOOD RES TECHNOL, V213, P267, DOI 10.1007/s002170100378 Huss H. H., 1995, QUALITY QUALITY CHAN Iles A, 2007, J CLEAN PROD, V15, P577, DOI 10.1016/j.jclepro.2006.06.001 Jacquet JL, 2008, MAR POLICY, V32, P309, DOI 10.1016/j.marpol.2007.06.007 James C, 2019, ROUTL UACES CONTEMP, P181 Janes ME, 2012, WOODHEAD PUBL FOOD S, P504 Jensen GL, 1997, REV SCI TECH OIE, V16, P641, DOI 10.20506/rst.16.2.1047 Kerry JP, 2012, WOODHEAD PUBL FOOD S, P522 Klinger D, 2012, ANNU REV ENV RESOUR, V37, P247, DOI 10.1146/annurev-environ-021111-161531 Kole APW, 2009, FOOD QUAL PREFER, V20, P187, DOI 10.1016/j.foodqual.2008.09.003 Lago FC, 2014, SEAFOOD PROCESSING: TECHNOLOGY, QUALITY AND SAFETY, P419 Leal MC, 2015, TRENDS BIOTECHNOL, V33, P331, DOI 10.1016/j.tibtech.2015.03.003 dos Santos CAML, 2011, AQUACULTURE, V318, P253, DOI 10.1016/j.aquaculture.2011.05.046 Little DC, 2018, AQUACULTURE, V493, P338, DOI 10.1016/j.aquaculture.2017.12.033 Lo YT, 2018, FOOD CHEM, V240, P767, DOI 10.1016/j.foodchem.2017.08.022 Long R, 2016, NEW HOR ENV ENERG, P130 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Mateus A.P., 2017, FISH DIS PREVENTION, P187 Matos E, 2017, REV AQUACULT, V9, P388, DOI 10.1111/raq.12144 McCoy E, 2011, J FOOD PROTECT, V74, P500, DOI 10.4315/0362-028X.JFP-10-341 McNevin A. A., 2017, Fish diseases: prevention and control strategies, P249 Mialhe F, 2018, AQUACULTURE, V493, P365, DOI 10.1016/j.aquaculture.2017.09.043 Mizan MFR, 2015, FOOD MICROBIOL, V49, P41, DOI 10.1016/j.fm.2015.01.009 Mokrani D, 2018, AQUACULTURE, V490, P120, DOI 10.1016/j.aquaculture.2018.02.032 Moreau DTR, 2009, MAR POLICY, V33, P401, DOI 10.1016/j.marpol.2008.09.001 Musa A, 2014, EXPERT SYST APPL, V41, P176, DOI 10.1016/j.eswa.2013.07.020 Nasopoulou C, 2012, LWT-FOOD SCI TECHNOL, V47, P217, DOI 10.1016/j.lwt.2012.01.018 Nollet L.M.T., 2012, HDB MEAT POULTRY SEA Novoslavskij A, 2016, ANN MICROBIOL, V66, P1, DOI 10.1007/s13213-015-1102-5 Oehlenschlager J, 1997, METHODS TO DETERMINE THE FRESHNESS OF FISH IN RESEARCH AND INDUSTRY, P30 Oidtmann BC, 2011, PREV VET MED, V102, P329, DOI 10.1016/j.prevetmed.2011.07.016 Oidtmann BC, 2011, AQUACULTURE, V320, P22, DOI 10.1016/j.aquaculture.2011.07.032 Oidtmann B, 2013, PREV VET MED, V112, P13, DOI 10.1016/j.prevetmed.2013.07.008 Olafsdottir G, 1997, TRENDS FOOD SCI TECH, V8, P258, DOI 10.1016/S0924-2244(97)01049-2 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Ortea I, 2016, J PROTEOMICS, V147, P212, DOI 10.1016/j.jprot.2016.06.033 Ottinger M, 2016, OCEAN COAST MANAGE, V119, P244, DOI 10.1016/j.ocecoaman.2015.10.015 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Pavlidis M.A., 2011, SPARIDAE BIOL AQUACU Potts J., 2016, STATE SUSTAINABILITY Rasmussen RS, 2008, COMPR REV FOOD SCI F, V7, P280, DOI 10.1111/j.1541-4337.2008.00046.x Rehbein H., 2009, FISHERY PRODUCTS QUA Reilly A, 1999, J APPL MICROBIOL, V85, p249S, DOI 10.1111/j.1365-2672.1998.tb05305.x Rigos G, 2010, REV FISH BIOL FISHER, V20, P47, DOI 10.1007/s11160-009-9120-7 Sampels S, 2015, TRENDS FOOD SCI TECH, V44, P131, DOI 10.1016/j.tifs.2015.04.003 Sapkota A, 2008, ENVIRON INT, V34, P1215, DOI 10.1016/j.envint.2008.04.009 Scarano D., 2014, Diversity, V6, P579 Scholthof KBG, 2007, NAT REV MICROBIOL, V5, P152, DOI 10.1038/nrmicro1596 Semenza JC, 2009, LANCET INFECT DIS, V9, P365, DOI 10.1016/S1473-3099(09)70104-5 Sitja-Bobadilla A., 2017, Fish diseases: prevention and control strategies, P119 Skara T, 2012, WOODHEAD PUBL FOOD S, P96 Stanger-Ross J, 2017, MCG QUEENS STUD ETHN, V44, pXIII, DOI 10.1016/S0065-308X(17)30009-X Stevens JR, 2018, MAR POLICY, V90, P115, DOI 10.1016/j.marpol.2017.12.027 Svobodova Z., 2017, Fish diseases: prevention and control strategies, P167 Tacon AGJ, 2010, REV FISH SCI, V18, P94, DOI 10.1080/10641260903325680 Tejada M, 2002, EUR FOOD RES TECHNOL, V215, P1, DOI 10.1007/s00217-002-0494-1 Teletchea F, 2009, REV FISH BIOL FISHER, V19, P265, DOI 10.1007/s11160-009-9107-4 Thompson K. D., 2017, Fish diseases: prevention and control strategies, P1 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Vanhonacker F, 2011, BRIT FOOD J, V113, P535, DOI 10.1108/00070701111124005 Vasickova P, 2005, VET MED-CZECH, V50, P89, DOI 10.17221/5601-VETMED Vendramin N, 2016, B EUR ASSOC FISH PAT, V36, P38 Venugopal V, 2002, BIOSENS BIOELECTRON, V17, P147, DOI 10.1016/S0956-5663(01)00180-4 Vidacek S., 2014, SEAFOOD FOOD SAF MAN, P189, DOI [10.1016/B978-0-12-381504-0.00008-1, DOI 10.1016/B978-0-12-381504-0.00008-1] Wang JY, 2018, CRIT REV FOOD SCI, V58, P2689, DOI 10.1080/10408398.2017.1323722 Weir M, 2012, CAN VET J, V53, P619 Westerkamp M, 2020, DIGIT COMMUN NETW, V6, P167, DOI 10.1016/j.dcan.2019.01.007 Woo P., 2014, Diseases and disorders of finfish in cage culture, DOI 10.1079/9781780642079.0000 Yazdi S. K., 2010, International Journal of Environmental Science and Development, V1, P378 Zampacavallo G, 2015, J FOOD SCI TECH MYS, V52, P2585, DOI 10.1007/s13197-014-1324-8 NR 151 TC 39 Z9 39 U1 13 U2 55 PD MAR 15 PY 2020 VL 518 AR 734857 DI 10.1016/j.aquaculture.2019.734857 WC Fisheries; Marine & Freshwater Biology SC Fisheries; Marine & Freshwater Biology UT WOS:000510424900066 DA 2022-12-14 ER PT J AU Lopez-Pimentel, JC Alcaraz-Rivera, M Granillo-Macias, R Olivares-Benitez, E AF Carlos Lopez-Pimentel, Juan Alcaraz-Rivera, Miguel Granillo-Macias, Rafael Olivares-Benitez, Elias TI Traceability of Mexican Avocado Supply Chain: A Microservice and Blockchain Technological Solution SO SUSTAINABILITY DT Article DE supply chain; blockchain; microservices; traceability; avocado; Mexico ID FOOD TRACEABILITY; MANAGEMENT; AGRICULTURE; ARCHITECTURE; INTERNET; THINGS AB Currently, the Mexican avocado supply chain has some social limitations that make the traceability process a difficult task and severely limits the regions that can add their harvest to the international market. We hypothesize that modernizing the traceability process and improving the trust of the final user could help in opening the market to other regions. This paper describes the Mexican avocado supply chain characteristics, identifies the actors involved in the supply chain, and emphasizes the problems that the current actors have when exporting them to the US market. On this basis, we propose a technological solution system to automate the traceability process. The system was designed to comply with the authority and consumer requirements. It proposes a combination of the benefits of traditional data traceability using Microservices architecture with a new layer of Blockchain auditing that will add value to current and new actors in every step of the supply chain. We contribute by proposing a model that adds value to the avocado supply chain with the following characteristics: Integrity, auditing service, dual traceability, transparency, and a front-end application with trust user-oriented. Our proofs demonstrate that the blockchain layer does not represent a considered high extra transaction cost; it could be regarded as despicable for the economy of the consumer considering costs and benefits. C1 [Carlos Lopez-Pimentel, Juan; Alcaraz-Rivera, Miguel; Olivares-Benitez, Elias] Univ Panamer, Fac Ingn, Alvaro Portillo 49, Zapopan 45010, Jalisco, Mexico. [Granillo-Macias, Rafael] Autonomous Univ State Hidalgo, Higher Educ Sch Ciudad Sahagun, Pachuca 43990, Hidalgo, Mexico. C3 Universidad Panamericana - Ciudad de Mexico; Universidad Panamericana - Guadalajara; Universidad Autonoma del Estado de Hidalgo RP Lopez-Pimentel, JC; Olivares-Benitez, E (corresponding author), Univ Panamer, Fac Ingn, Alvaro Portillo 49, Zapopan 45010, Jalisco, Mexico. EM clopezp@up.edu.mx; eolivaresb@up.edu.mx CR Accorsi R, 2017, PROCEDIA MANUF, V11, P889, DOI 10.1016/j.promfg.2017.07.192 Alecio P. D. Binotto, 2018, Arxiv, DOI arXiv:1803.07877 Amentae TK, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132112181 [Anonymous], 2003, N AGR AVOCADO GROWIN [Anonymous], 2021, GET INSIGHT AVOCADO [Anonymous], 2020, WORK PLAN EXPORTATIO [Anonymous], 2022, TRANSPARENCY INT [Anonymous], CERTIFICATION ALLOWS Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Bager S.L., 2022, CURR RES ENV SUSTAIN, V4, DOI [10.1016/j.crsust.2022.100163, DOI 10.1016/J.CRSUST.2022.100163] Baralla G, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5857 Berasategi I, 2012, FOOD CHEM, V132, P439, DOI 10.1016/j.foodchem.2011.11.018 Bill M, 2014, FOOD REV INT, V30, P169, DOI 10.1080/87559129.2014.907304 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bustos CA, 2018, J CLEAN PROD, V199, P1020, DOI 10.1016/j.jclepro.2018.06.187 Lopez-Pimentel JC, 2020, 2020 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2020), P126, DOI 10.1109/Blockchain50366.2020.00023 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Chong M., 2020, HDB RES EMERGING TEC, P323 Coronado J. J. A., 2010, Journal on Chain and Network Science, V10, P1 Crosby M., 2016, APPL INNOVATION, V2, P6, DOI DOI 10.21626/innova/2016.1/01 Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067 El Bilali Hamid, 2018, Information Processing in Agriculture, V5, P456, DOI 10.1016/j.inpa.2018.06.006 Faostat: Food and Agriculture Organization of the United Nations, 2017, US Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Galen D., 2019, BLOCKCHAIN SOCIAL IM Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Geethanjali B., 2021, ICT Analysis and Applications. Proceedings of ICT4SD 2020. Lecture Notes in Networks and Systems (LNNS 154), P615, DOI 10.1007/978-981-15-8354-4_61 Hass Avocado Board, 2018, AV TRACK STUD Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kumar S, 2022, RAIRO-OPER RES, V56, P831, DOI 10.1051/ro/2021189 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Lin WJ, 2020, IEEE ACCESS, V8, P143920, DOI 10.1109/ACCESS.2020.3014522 Lopez-Pimentel J.C., 2020, P 8 INT C INFORM SYS, P292 Lopez-Pimentel JC, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12052754 Miloudi Lyna, 2020, Advanced Intelligent Systems for Sustainable Development (AI2SD-2019). Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health. Advances in Intelligent Systems and Computing (AISC 1103), P340, DOI 10.1007/978-3-030-36664-3_38 Mittal M, 2023, MATH COMPUT SIMULAT, V205, P232, DOI 10.1016/j.matcom.2022.09.007 Mondal AK, 2022, RAIRO-OPER RES, V56, P3017, DOI 10.1051/ro/2022058 Moreno-Collado J., 1995, NOM007FITO1995 NORMA Vu N, 2021, PROD PLAN CONTROL, DOI 10.1080/09537287.2021.1939902 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Paliwal V, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12187638 SAGARPA, 2016, PLAN AGR NAC 2017 20 Sahoo S, 2022, ELECTRON COMMER RES, DOI 10.1007/s10660-022-09569-1 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Sarkar B., 2022, COMPUT IND ENG, DOI [10.1016/j.cie.2022.108727, DOI 10.1016/J.CIE.2022.108727] Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Sivalakshmi P., 2021, MATER TODAY-PROC, DOI [10.1016/j.matpr.2021.05.634, DOI 10.1016/J.MATPR.2021.05.634] Statista, 2020, GLOB PROD AV COUNTR Tapscott Don, 2016, BLOCKCHAIN REVOLUTIO Torky M, 2020, COMPUT ELECTRON AGR, V178, DOI 10.1016/j.compag.2020.105476 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Tzounis A, 2017, BIOSYST ENG, V164, P31, DOI 10.1016/j.biosystemseng.2017.09.007 Wilson T., 1998, SUPPLY CHAIN MANAG I, V3, P127, DOI [10.1108/13598549810230831, DOI 10.1108/13598549810230831] Woolf A., 2009, Gourmet and health-promoting speciality oils., P73 World Economic Forum, 2020, AV GREEN GOLD CAUS E Yiannas F., 2018, Innovations: Technology / Governance / Globalization, V12, P46, DOI 10.1162/inov_a_00266 Zamora-Cuevas L., 2005, ACTUALIZACION IND AG, V87, P45 NR 59 TC 0 Z9 0 U1 0 U2 0 PD NOV PY 2022 VL 14 IS 21 AR 14633 DI 10.3390/su142114633 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000884082400001 DA 2022-12-14 ER PT J AU Riden, CP Bollen, AF AF Riden, C. P. Bollen, A. F. TI Agricultural supply system traceability, Part II: Implications of packhouse processing transformations SO BIOSYSTEMS ENGINEERING DT Article AB Traceability is becoming an integral requirement of modern supply chains. Until recently there has been little need for accepted measures to offer of less than absolute traceability. This paper describes four such concepts for horticultural packing operations: precision of traceability; packs per bin for tracking; bins per pack for tracing; and purity to describe the likely sampling accuracy of packs in an audit or monitoring system. Three major factors influence these measures: splitting the cupstream into a number of outputs during packing; any mixing that occurs before splitting; and any mixing that occurs after splitting. At low percentage cupstream, pack size, and packing lane mixing have marked influence over a broad range of traceability precision. Infeed mixing has a relatively minor effect. At high percentage cupstream, pack size, and packing lane mixing have minor effects and only a narrow range of traceability precision is dependent on these. Infeed mixing is the major determinate of traceability precision. Improvements in the precision of tracing gained from a change in granularity will always have an associated counter effect on the precision of tracking. Improvements to the precision of traceability achieved through reductions in mixing or output splitting improve the precision of both tracing and tracking. The traceability metrics investigated in the paper have broad applications in many agricultural production and supply chain systems. There is potential to implement high precision and fine granularity traceability in agricultural supply systems, which can also meet a number of other purposes such as improved feedback to producers and benefits to supply system efficiency, as well as being acceptable for compliance purposes. (c) 2007 Published by Elsevier Ltd. C1 [Riden, C. P.; Bollen, A. F.] Lincoln Ventures Ltd, Supply Chain Syst Grp, Hamilton, New Zealand. RP Bollen, AF (corresponding author), Lincoln Ventures Ltd, Supply Chain Syst Grp, Private Bag 3062, Hamilton, New Zealand. EM bollen@lvl.co.nz CR Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 BOLLEN AF, 2004, ACTA HORTIC, V687, P279 *CLUB BOL, 2002, CIGR EJOURNAL, V4, P1 SARIG Y, 2003, CIGR EJOURNAL, V4, P1 NR 4 TC 34 Z9 36 U1 0 U2 6 PD DEC PY 2007 VL 98 IS 4 BP 401 EP 410 DI 10.1016/j.biosystemseng.2007.07.004 WC Agricultural Engineering; Agriculture, Multidisciplinary SC Agriculture UT WOS:000252270200004 DA 2022-12-14 ER PT J AU Wu, LH Wang, HS Zhu, D Hu, WY Wang, SX AF Wu, Linhai Wang, Hongsha Zhu, Dian Hu, Wuyang Wang, Shuxian TI Chinese consumers' willingness to pay for pork traceability information-the case of Wuxi SO AGRICULTURAL ECONOMICS DT Article DE Random nth price auction; Real choice experiment; Traceability information; Willingness to pay ID CHOICE EXPERIMENT; BEEF; AUCTION; MILK AB In this study, consumers' willingness to pay for farming, slaughter and processing, distribution and marketing, and government certification information was investigated in Wuxi, Jiangsu Province, China using an experimental auction and real choice experiment based on incentive compatibility of the nonhypothetical elicitation method. No significant differences in consumers' willingness to pay were revealed by the experimental auction and real choice experiment on the whole, and results from both nonhypothetical experimental methods demonstrated that consumers were willing to pay certain premiums for all four types of traceability information and had the highest willingness to pay for government certification information. These results indicate that consumers trust in pork safety protection under governmental supervision. Moreover, it was found that government certification information played a significant role in improving consumer utility, demonstrating that consumers paid close attention to safety risks in farming. Therefore, this study predicts that the introduction of governmental supervision into the pork traceability system and establishment of a system for collecting traceability information starting from the stage of pig farming will play important roles in meeting consumer demands for pork quality and safety, as well as promoting the development of traceable food policies. C1 [Wu, Linhai; Wang, Hongsha; Wang, Shuxian] Jiangnan Univ, Food Safety Res Base Jiangsu Prov, Wuxi 214122, Jiangsu, Peoples R China. [Wu, Linhai] Synerget Innovat Ctr Food Safety & Nutr, Wuxi 214122, Jiangsu, Peoples R China. [Zhu, Dian] Soochow Univ, Sch Dongwu Business, Dept Econ, Suzhou 215021, Jiangsu, Peoples R China. [Zhu, Dian] Jiangnan Univ, Sch Food Sci & Technol, Wuxi 214122, Jiangsu, Peoples R China. [Hu, Wuyang] Univ Kentucky, Dept Agr Econ, Lexington, KY 40546 USA. C3 Jiangnan University; Soochow University - China; Jiangnan University; University of Kentucky RP Wu, LH (corresponding author), Jiangnan Univ, Food Safety Res Base Jiangsu Prov, 1800 Lihu Rd, Wuxi 214122, Jiangsu, Peoples R China. EM wlh6799@126.com CR ADAMOWICZ W, 1994, J ENVIRON ECON MANAG, V26, P271, DOI 10.1006/jeem.1994.1017 Allenby GM, 1999, J ECONOMETRICS, V89, P57 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Bech M, 2005, HEALTH ECON, V14, P1079, DOI 10.1002/hec.984 Chen Q, 2013, FOOD QUAL PREFER, V28, P419, DOI 10.1016/j.foodqual.2012.10.008 Gracia A, 2011, AM J AGR ECON, V93, P1358, DOI 10.1093/ajae/aar054 Hanemann W, 1999, VALUING ENV PREFEREN, P302 Hensher D. A., 2005, APPL CHOICE ANAL PRI Kong D. W., 2012, LOGIST SCI TECH, V7, P94 KRINSKY I, 1990, REV ECON STAT, V72, P189, DOI 10.2307/2109761 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Louviere J.J., 2000, STATED CHOICE METHOD, DOI DOI 10.1017/CBO9780511753831 Lusk JL, 2003, AM J AGR ECON, V85, P16, DOI 10.1111/1467-8276.00100 Morkbak MR, 2010, ENVIRON RESOUR ECON, V45, P537, DOI 10.1007/s10640-009-9327-z Murphy JJ, 2005, ENVIRON RESOUR ECON, V30, P313, DOI 10.1007/s10640-004-3332-z Olesen I, 2010, LIVEST SCI, V127, P218, DOI 10.1016/j.livsci.2009.10.001 Ortega D. L., 2009, Journal of Food Distribution Research, V40, P52 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Poe GL, 2005, AM J AGR ECON, V87, P353, DOI 10.1111/j.1467-8276.2005.00727.x Takahashi K., 2008, J FOOD SYSTEM RES, V14, P2 Uchida H, 2014, AUST J AGR RESOUR EC, V58, P263, DOI 10.1111/1467-8489.12036 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Wen L. L., 2010, CHN J ADM HUSB VET M, V7, P34 Wu L. H., 2013, 2012 CHINA DEV REPOR Yin S., 2013, J PUBLIC ADM, V3, P110 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhang Y. H., 2012, CHINA RURAL EC, V7, p72?83 Zhang Z., 2013, J AGROTECHNICAL EC, V05, P95 NR 29 TC 60 Z9 61 U1 5 U2 76 PD JAN PY 2016 VL 47 IS 1 BP 71 EP 79 DI 10.1111/agec.12210 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000367662600006 DA 2022-12-14 ER PT J AU Exposito, I Gay-Fernandez, JA Cuinas, I AF Exposito, I. Gay-Fernandez, J. A. Cuinas, I. TI A Complete Traceability System for a Wine Supply Chain Using Radio-Frequency Identification and Wireless Sensor Networks SO IEEE ANTENNAS AND PROPAGATION MAGAZINE DT Article DE Radio-frequency identification; RFID; wireless sensor network; traceability; supply chain management; wine industry; information management; food technology AB Radio-frequency identification (RFID) technology provides an effective tool for managing traceability along food supply chains. This is because it allows automatic digital registration of data, and therefore reduces errors and enables the availability of information on demand. A complete traceability system can be developed in the wine production sector by joining this technology with the use of wireless sensor networks for monitoring at the vineyards. A proposal of such a merged solution for a winery in Spain has been designed, deployed in an actual environment, and evaluated. It was shown that the system could provide a competitive advantage to the company by improving visibility of the processes performed and the associated control over product quality. Much emphasis has been placed on minimizing the impact of the new system in the current activities. C1 [Exposito, I.; Gay-Fernandez, J. A.; Cuinas, I.] Univ Vigo, Dept Teoria Sinal & Comunicac, Vigo 36310, Spain. C3 Universidade de Vigo RP Exposito, I (corresponding author), Univ Vigo, Dept Teoria Sinal & Comunicac, Rua Maxwell S-N,Campus Lagoas Marcosende, Vigo 36310, Spain. EM iexpositop@uvigo.es; jagfernandez@uvigo.es; inhigo@uvigo.es CR Asimakopoulos G., 2007, PROCEEDINGS OF THE 3 ATID, 2006, AT570 REFERENCE GUID Cimino Mario G. C. A., 2012, Methodologies and Technologies for Networked Enterprises. ArtDeco: Adaptive Infrastructures for Decentralised Organisations: LNCS 7200, P397, DOI 10.1007/978-3-642-31739-2_20 Confidex, 2010, CONFIDEX HALO PRODUC Crossbow Technology, 2009, EKO PRO SERIES USERS Cuinas I., 2011, PROGRESS IN ELECTROM Fenu G., 2009, 35TH ANNUAL CONFEREN Gay-Fernandez J. A., 2011, INTERNATIONAL CONFER GS1, 2008, EPC RADIO FREQUENCY GS1, 2007, EPC INFORMATION SERV GS1, 2011, GS1 EPC TAG DATA STA Hsu Y., 2008, IEEE INTERNATIONAL C Jeon S., 2010, SEVENTH INTERNATIONA Polycarpou AC, 2012, IEEE ANTENN PROPAG M, V54, P255, DOI 10.1109/MAP.2012.6309198 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Sharma M., 2010, 2ND IEEE INTERNATION Suijing H. Han, 2010, 2ND INTERNATIONAL CO Tan Hui, 2008, INTERNATIONAL SYMPOS Trebar M., 2011, 19TH INTERNATIONAL C van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 NR 20 TC 23 Z9 24 U1 2 U2 44 PD APR PY 2013 VL 55 IS 2 BP 255 EP 267 DI 10.1109/MAP.2013.6529365 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications UT WOS:000324264100026 DA 2022-12-14 ER PT J AU Liang, K Thomasson, JA Shen, MX Armstrong, PR Ge, Y Lee, KM Herrman, TJ AF Liang, K. Thomasson, J. A. Shen, M. X. Armstrong, P. R. Ge, Y. Lee, K. M. Herrman, T. J. TI Ruggedness of 2D code printed on grain tracers for implementing a prospective grain traceability system to the bulk grain delivery system SO FOOD CONTROL DT Article DE Data matrix code; Grain traceability; Readability; Ruggedness; Traceability; Tracer ID MICROCRYSTALLINE CELLULOSE AB Two-dimensional Data Matrix (DM) barcode printed on a food-grade tracer to carry simulated identifying information about grain in a prospective grain traceability system was evaluated for its ruggedness on different formulations, coating materials, and order of coating and printing. The key factor in evaluating the tracers was their ability to be read with a code scanner after being removed from a batch of grain at any point in the grain supply chain. After printing, the tracers were measured for initial readability, subjected to ruggedness tests involving abrasion and impact and the effect of moisture conditioning, and measured for final readability. Fourteen treatments involving two tracer types (sugar-based and cellulose-based), two coating materials (edible shellac and hydroxypropyl methylcellulose, or HPMC), and four coat print procedures were considered. One particular treatment performed very well, whereas most others did not, having either low initial readability rates or low final readability rates after they were subjected to ruggedness testing. The treatment of interest consisted of cellulose-based tracers, printed in DM code with food-grade ink, and coated with HPMC after printing. Initial readability of this treatment averaged 98%, and final readability after ruggedness testing ranged from 89 to 99%, depending on the ruggedness test applied. These readability rates are considered acceptable for intended application. Since barcode printing on information-carrying tracers was an essential part of requirements for implementing the proposed grain traceability system, most major tracing system and technological components are now available in prototype form for the next phase of research and development for practical application of the system in the grain supply chain. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Liang, K.; Shen, M. X.] Nanjing Agr Univ, Dept Agr Electrificat & Automat, Nanjing, Jiangsu, Peoples R China. [Thomasson, J. A.; Ge, Y.] Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX 77843 USA. [Armstrong, P. R.] USDA ARS, Engn & Wind Eros Res Unit, Manhattan, KS USA. [Lee, K. M.; Herrman, T. J.] Texas A&M Univ, Texas A&M AgriLife Res, Off Texas State Chemist, College Stn, TX USA. C3 Nanjing Agricultural University; Texas A&M University System; Texas A&M University College Station; United States Department of Agriculture (USDA); Texas A&M University System; Texas A&M University College Station; Texas A&M AgriLife Research RP Thomasson, JA (corresponding author), Texas A&M Univ, Dept Biol & Agr Engn, 2117 TAMU, College Stn, TX 77843 USA. EM thomasson@tamu.edu; tjh@otsc.tamu.edu CR Aarnisalo K., 2007, VTT TIEDOTTEITA RES, V2395, P1 [Anonymous], 2002, OFFICIAL J EUROPEAN [Anonymous], 2007, 22005 ISO Beplate-Haarstrich L., 2007, THESIS GEORG AUGUST Buckton G, 1999, INT J PHARM, V181, P41, DOI 10.1016/S0378-5173(98)00413-X Bulletin AgBiotech, 2003, BULLETIN, V11 Can-trace, 2003, CAN TRAC DEV TRAC ST Computing & Control Engineering, 2005, 2D DAT MATR BARC, V16 ERS (Economic Research Service of the U.S. Department of Agriculture), 2001, KNOW ITS GOING BRIN Froschle HK, 2009, COMPUT ELECTRON AGR, V66, P126, DOI 10.1016/j.compag.2009.01.002 Golan E.H., 2004, AGR EC REPORTS, P1362 Herrman T., 2002, WHITE PAPER TRACEABI Hirai Y, 2006, APPL ENG AGRIC, V22, P747 Hornbaker R. H., 2007, U.S. Patent, Patent No. [7,162,328 B2, 7162328] Lee KM, 2011, FOOD CONTROL, V22, P1085, DOI 10.1016/j.foodcont.2010.12.016 Lee KM, 2010, J AGR FOOD CHEM, V58, P10945, DOI 10.1021/jf101370k Mali S, 2002, CARBOHYD POLYM, V50, P379, DOI 10.1016/S0144-8617(02)00058-9 Mc Inerney B, 2011, COMPUT ELECTRON AGR, V79, P51, DOI 10.1016/j.compag.2011.08.004 Mc Inerney B, 2010, COMPUT ELECTRON AGR, V73, P112, DOI 10.1016/j.compag.2010.06.004 Meyer S., 2004, GRAIN TRANSPORTATION Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Nightingale SD, 2005, FOOD TECHNOL-CHICAGO, V59, P36 Oas R., 2005, TABLET CAPSULES OCT Palviainen P, 2001, PHARM DEV TECHNOL, V6, P353, DOI 10.1081/PDT-100002617 Ravenelle F, 2002, CARBOHYD POLYM, V47, P259, DOI 10.1016/S0144-8617(01)00169-2 Ridgway CJ, 2004, COLLOID SURFACE A, V236, P91, DOI 10.1016/j.colsurfa.2003.12.030 Robinson M. C., 2001, ROBINSON MC, Patent No. US 2001/ 0029996 A1 Rodarte A., 2011, INT FOOD AGR MAN ASS Sui R., 2007, 076032 ASABE Taylor R. D., 2002, U.S. Patent, Patent No. [6,406,725 B1, 6406725] Telford D, 2000, ASSEMBLY AUTOM, V20, P18, DOI 10.1108/01445150010311635 Thakur M., 2009, Resource, Engineering & Technology for a Sustainable World, V16, P20 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Thomasson J. A., 2008, P 9 INT C PREC AGR Tobyn MJ, 1998, INT J PHARMACEUT, V169, P183, DOI 10.1016/S0378-5173(98)00127-6 U.S. Congress, 2002, PUBL HLTH SEC BIOT P USDA (U.S. Department of Agriculture), 2005, GLOB TRAC LAB REQ AG Vachal K., 2001, REGIONAL ELEVATOR SU NR 38 TC 20 Z9 20 U1 0 U2 38 PD OCT PY 2013 VL 33 IS 2 BP 359 EP 365 DI 10.1016/j.foodcont.2013.03.029 WC Food Science & Technology SC Food Science & Technology UT WOS:000320286900008 DA 2022-12-14 ER PT J AU Limkhunthammo, S Phoolsawat, S Kaewchur, P Mudpetch, N Thongaram, J Sansook, J AF Limkhunthammo, Supakorn Phoolsawat, Sasiwan Kaewchur, Pornthep Mudpetch, Natthawat Thongaram, Jarunee Sansook, Jantana TI Traceability System for Upgrading Quality of Agricultural Products in Phra Nakhon Si Ayutthaya SO INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION DT Article DE Traceability; QR Trace on Cloud; agricultural products; Phra Nakhon Si Ayutthaya AB The purposes of the research article were to develop a traceability system for agricultural products in Phra Nakhon Si Ayutthaya Province. This research methodology is a documentary and quantitative research. Researchers have reviewed various sources and found that the QR Trace on Cloud system for small and medium entrepreneurs developed by the National Bureau of Agricultural Commodity and Food Standards was suitable to be used to raise the quality of agricultural products in Phra Nakhon Si Ayutthaya Province. Therefore, researchers organized a workshop and try to evaluate the system by inviting 37 farmers with products certified or currently in the process of certification to assess the system. Most of the assessors are male, aged 36-45 years or over 56 years old, had secondary education or equivalent, and produced products in the vegetable /fruit group. The results of the system evaluation showed that, overall, the assessors were satisfied with the system properties at the highest level ((X) over bar equal to 4.57 and S: 0.431) and were satisfied with the workshop at the highest level too ((X) over bar was 4.49 and S was 0.507). Finally, there was a significant evidence that the training improved the skill (p < 0.0001). C1 [Limkhunthammo, Supakorn; Phoolsawat, Sasiwan; Kaewchur, Pornthep; Mudpetch, Natthawat; Thongaram, Jarunee; Sansook, Jantana] Rajamangala Univ Technol Suvarnabhumi, Fac Business Adm & Informat Technol, Nonthaburi, Thailand. C3 Rajamangala University of Technology Suvarnabhumi RP Limkhunthammo, S (corresponding author), Rajamangala Univ Technol Suvarnabhumi, Fac Business Adm & Informat Technol, Nonthaburi, Thailand. EM supakorn.l@rmutsb.ac.th; Sasiwan.Wasukri@gmail.com; ouijishiro@yahoo.com; natthawat.m@rmutsb.ac.th; jarunee.t@rmutsb.ac.th; jantana_tuk@hotmail.com CR GS1, GS1 THAIL GS1, GS1 GLOB TRAC STAND National Bureau of Agricultural Commodity and Food Standards, QR TRAC CLOUD Wikipedia, GS1 NR 4 TC 0 Z9 0 U1 1 U2 1 PD JUN PY 2022 VL 14 IS 1 BP 2165 EP 2172 DI 10.9756/INT-JECSE/V14I1.252 WC Education, Special SC Education & Educational Research UT WOS:000788004200048 DA 2022-12-14 ER PT J AU Gandino, F Montrucchio, B Rebaudengo, M Sanchez, ER AF Gandino, Filippo Montrucchio, Bartolomeo Rebaudengo, Maurizio Sanchez, Erwing R. TI On Improving Automation by Integrating RFID in the Traceability Management of the Agri-Food Sector SO IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS DT Article DE Agri-food sector; radio-frequency identification (RFID); traceability management AB Traceability is a key factor for the agri-food sector. Radio-frequency identification (RFID) technology, which is widely adopted for supply chain management, can be used effectively for the traceability management. In this paper, a framework for the evaluation of a traceability system for the agri-food industry is presented, and the automation level in an RFID-based traceability system is analyzed and compared with respect to traditional ones. Internal and external traceabilities are both considered and formalized, in order to classify different environments, according to their automation level. Traceability systems used in a sample sector are experimentally analyzed, showing that, by using RFID technology, agri-food enterprises increase their automation level and also their efficiency, in a sustainable way. C1 [Gandino, Filippo; Montrucchio, Bartolomeo; Rebaudengo, Maurizio; Sanchez, Erwing R.] Politecn Torino, Dipartimento Automat & Informat, I-10129 Turin, Italy. C3 Polytechnic University of Turin RP Gandino, F (corresponding author), Politecn Torino, Dipartimento Automat & Informat, I-10129 Turin, Italy. EM filippo.gandino@polito.it; bartolomeo.montrucchio@polito.it; maurizio.rebaudengo@polito.it; erwing.sanchez@polito.it CR [Anonymous], 2000, 90012000 ISO Boehlje M., 2000, Journal of Agribusiness, V18, P53 Chalasani S, 2007, IEEE T IND INFORM, V3, P246, DOI 10.1109/TII.2007.904147 Cimino MGCA, 2005, Seventh IEEE International Conference on E-Commerce Technology Workshops, P90 Finkenzeller K., 2003, RFID HDB FUNDAMENTAL Gandino F, 2007, PROCEEDINGS OF THE 1ST RFID EURASIA CONFERENCE, P143 Hossain MM, 2008, IEEE T ENG MANAGE, V55, P316, DOI 10.1109/TEM.2008.919728 Juels A, 2006, IEEE J SEL AREA COMM, V24, P381, DOI 10.1109/JSAC.2005.861395 KRKKINEN M, 2003, INT J RETAIL DISTRIB, V31, P529 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Parasuraman R, 1997, HUM FACTORS, V39, P230, DOI 10.1518/001872097778543886 Reid JF, 2000, COMPUT ELECTRON AGR, V25, P155, DOI 10.1016/S0168-1699(99)00061-7 Sahin E., 2002, P IEEE INT C SYST MA, V3, P647 SANCHEZ E, 2009, SECURITY RFID SENSOR Tseng SY, 2008, IEEE T IND ELECTRON, V55, P741, DOI 10.1109/TIE.2007.910517 Xu WL, 2008, IEEE T IND ELECTRON, V55, P2121, DOI 10.1109/TIE.2008.918641 NR 16 TC 64 Z9 66 U1 5 U2 36 PD JUL PY 2009 VL 56 IS 7 BP 2357 EP 2365 DI 10.1109/TIE.2009.2019569 WC Automation & Control Systems; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Automation & Control Systems; Engineering; Instruments & Instrumentation UT WOS:000267697500008 DA 2022-12-14 ER PT J AU Wilmot, H Glorieux, G Hubin, X Gengler, N AF Wilmot, H. Glorieux, G. Hubin, X. Gengler, N. TI A genomic breed assignment test for traceability of meat of Dual-Purpose Blue SO LIVESTOCK SCIENCE DT Article DE Local breed; Breed assignment; Traceability; SNP ID GENETIC TRACEABILITY AB Assigning meat to its breed of origin for traceability purposes is not always straightforward if the breed from which products are derived is closely related to another one. The objective of this study was to determine if a genomic breed assignment test could distinguish meat of Dual-Purpose Blue, a local endangered breed, from meat of Beef Belgian Blue, a heavily used breed in the Belgian meat industry which is related to Dual-Purpose Blue. For this purpose, a genomic breed assignment test based on a panel of 2,005 SNPs and the nearest shrunken centroids method was used to classify 32 meat samples from Dual-Purpose Blue (n = 16), Beef Belgian Blue (n = 8) and Holstein (n = 8) into their breed of origin. From this SNP panel, 167 SNPs allowed to detect meat of Dual-Purpose Blue and 173 SNPs allowed to detect meat of Beef Belgian Blue. The genomic breed assignment test correctly allocated all the meat samples to their breed of origin with a probability of one. Therefore, the use of the genomic breed assignment test in routine as one step of the certification process of DualPurpose Blue meat seemed possible. C1 [Wilmot, H.] Natl Fund Sci Res FRS FNRS, Rue Egmont 5, B-1000 Brussels, Belgium. [Wilmot, H.; Gengler, N.] Univ Liege, TERRA Teaching & Res Ctr, Gembloux Agrobio Tech, Passage Deportes 2, B-5030 Gembloux, Belgium. [Glorieux, G.; Hubin, X.] Walloon Breeders Assoc Eleveo, Rue Champs Elysees 4, B-5590 Ciney, Belgium. C3 Fonds de la Recherche Scientifique - FNRS; University of Liege RP Wilmot, H (corresponding author), Natl Fund Sci Res FRS FNRS, Rue Egmont 5, B-1000 Brussels, Belgium. EM helene.wilmot@uliege.be CR Alexander DH, 2009, GENOME RES, V19, P1655, DOI 10.1101/gr.094052.109 Baumung R, 2006, J ANIM BREED GENET, V123, P265, DOI 10.1111/j.1439-0388.2006.00583.x Bertolini F, 2015, J ANIM BREED GENET, V132, P346, DOI 10.1111/jbg.12155 BlueSter, 2021, BLUESTER CHARLIER C, 1995, MAMM GENOME, V6, P788, DOI 10.1007/BF00539005 Colinet F., 2010, DUAL PURPOSE BELGIAN Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Funkhouser SA, 2017, TRANSL ANIM SCI, V1, P36, DOI 10.2527/tas2016.0003 He J, 2018, BMC GENET, V19, DOI 10.1186/s12863-018-0654-3 Hulsegge B, 2013, J ANIM SCI, V91, P5128, DOI 10.2527/jas.2013-6678 Hwang Hee Sook, 2014, ANIM BEHAV, V15, P3577, DOI [10.1021/bm500843r, DOI 10.1016/J.ANBEHAV.2014.11.002, 10.1056/NEJMoa1701719, 10.1371/journal.pmed.1002841, DOI 10.1371/JOURNAL.PMED.1002841] Iquebal MA, 2014, ANIM GENET, V45, P898, DOI 10.1111/age.12208 Josse J, 2012, J SFDS, V153, P79 Judge MM, 2017, ANIMAL, V11, P938, DOI 10.1017/S1751731116002457 Kuehn LA, 2011, J ANIM SCI, V89, P1742, DOI 10.2527/jas.2010-3530 Marquez GC, 2010, J ANIM SCI, V88, P59, DOI 10.2527/jas.2008-1292 Marquez G. C., J ANIM SCI, V88, P59, DOI [10.2527/jas.2008-1292, DOI 10.2527/JAS.2008-1292] Maudet C, 2002, J ANIM SCI, V80, P942 Mota RR, 2017, J ANIM SCI, V95, P4288, DOI 10.2527/jas2017.1748 Putnova L, 2019, J APPL GENET, V60, P187, DOI 10.1007/s13353-019-00495-x R Core Team, 2021, R AL ENV STAT COMP 3 R Studio Team, 2021, RSTUDIO INT DEV R Schiavo G, 2020, ANIMAL, V14, P223, DOI 10.1017/S1751731119002167 Tibshirani R, 2002, P NATL ACAD SCI USA, V99, P6567, DOI 10.1073/pnas.082099299 Wilkinson S, 2011, BMC GENET, V12, DOI 10.1186/1471-2156-12-45 Wilmot H, 2022, J ANIM BREED GENET, V139, P40, DOI 10.1111/jbg.12643 NR 27 TC 0 Z9 0 U1 0 U2 0 PD SEP PY 2022 VL 263 AR 104996 DI 10.1016/j.livsci.2022.104996 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000879441100004 DA 2022-12-14 ER PT J AU Taverniers, I Van Bockstaele, E De Loose, M AF Taverniers, I Van Bockstaele, E De Loose, M TI Trends in quality in the analytical laboratory. I. Traceability and measurement uncertainty of analytical results SO TRAC-TRENDS IN ANALYTICAL CHEMISTRY DT Article DE analytical method validation; measurement uncertainty; quality assurance; reliability of results; traceability ID HARMONIZED GUIDELINES; CHEMICAL MEASUREMENTS; VALIDATION AB Credibility of analytical data has never caught the public's eye more than today. The key principle for quality and reliability of results is comparability between laboratories and on a wider, international basis. In order to be comparable, analytical results must be reported with a statement oil measurement uncertainty (MU) and they must be traceable to common primary references. This work focuses on traceability and uncertainty of results. We discuss different approaches to establishing traceability and evaluating MU. We place both concepts in the broader context of analytical method validation and quality assurance. We give up-to-date information in the framework of new, more exacting European and international standards, such as those from Eurachem/CITAC, IUPAC and ISO. (C) 2004 Published by Elsevier B.V. C1 Minist Femish Community, Ctr Agr Res, Dept Plant Genet & Breeding, B-9090 Melle, Belgium. Univ Ghent, Dept Plant Prod, B-9000 Ghent, Belgium. C3 Ghent University RP Taverniers, I (corresponding author), Minist Femish Community, Ctr Agr Res, Dept Plant Genet & Breeding, Caritasstraat 21, B-9090 Melle, Belgium. EM i.taverniers@clo.fgov.be CR [Anonymous], 2000, EURACHEM CITAC GUIDE Armishaw P, 2003, ACCREDIT QUAL ASSUR, V8, P218, DOI 10.1007/s00769-003-0610-3 Charlet P, 2004, TRAC-TREND ANAL CHEM, V23, P178, DOI 10.1016/S0165-9936(04)00308-5 [CITAC Cooperation on international trace ability in analytical chemistry.], 2002, GUID QUAL AN CHEM AI Dehouck P, 2003, ANAL CHIM ACTA, V481, P261, DOI 10.1016/S0003-2670(03)00079-5 Drolc A, 2004, ANAL BIOANAL CHEM, V378, P1243, DOI 10.1007/s00216-003-2247-9 Ellison SLR, 1998, ANALYST, V123, P1387, DOI 10.1039/a706946d Eurachem, 1998, FITN PURP AN METH LA Fleming J, 1996, ACCREDIT QUAL ASSUR, V1, P87 Forstner U, 2004, TRAC-TREND ANAL CHEM, V23, P217, DOI 10.1016/S0165-9936(04)00312-7 HORWITZ W, 1982, ANAL CHEM, V54, pA67, DOI 10.1021/ac00238a002 Hund E, 2003, ANAL CHIM ACTA, V480, P39, DOI 10.1016/S0003-2670(02)01591-X Hund E, 2001, TRAC-TREND ANAL CHEM, V20, P394, DOI 10.1016/S0165-9936(01)00089-9 ISO GUM, 1995, GUID EXPR UNC MEAS, Vfirst King B, 2001, FRESEN J ANAL CHEM, V371, P714, DOI 10.1007/s002160100995 Kuppers S, 1998, ACCREDIT QUAL ASSUR, V3, P412, DOI 10.1007/s007690050275 Maroto A, 1999, ANAL CHIM ACTA, V391, P173, DOI 10.1016/S0003-2670(99)00111-7 Maroto A, 1999, TRAC-TREND ANAL CHEM, V18, P577, DOI 10.1016/S0165-9936(99)00151-X Maroto A, 2001, ANAL CHIM ACTA, V446, P133, DOI 10.1016/S0003-2670(01)00842-X MESLEY RJ, 1991, ANALYST, V116, P975, DOI 10.1039/an9911600975 Moser J, 2001, FRESEN J ANAL CHEM, V370, P679, DOI 10.1007/s002160100836 Mueller N, 2002, ACCREDIT QUAL ASSUR, V7, P79, DOI 10.1007/s00769-001-0423-1 Quevauviller P, 2004, TRAC-TREND ANAL CHEM, V23, P171, DOI 10.1016/S0165-9936(04)00314-0 Quevauviller P, 2001, TRAC-TREND ANAL CHEM, V20, P600, DOI 10.1016/S0165-9936(01)00116-9 Rosslein M, 2000, ACCREDIT QUAL ASSUR, V5, P88, DOI 10.1007/s007690050019 Roy S, 2004, TRAC-TREND ANAL CHEM, V23, P185, DOI 10.1016/S0165-9936(04)00309-7 Sabe R, 2004, TRAC-TREND ANAL CHEM, V23, P273, DOI 10.1016/S0165-9936(04)00311-5 Segura M, 2004, TRAC-TREND ANAL CHEM, V23, P194, DOI 10.1016/S0165-9936(04)00502-3 Theocharopoulos SP, 2004, TRAC-TREND ANAL CHEM, V23, P237, DOI 10.1016/S0165-9936(04)00317-6 THOMPSON M, 1995, PURE APPL CHEM, V67, P649, DOI 10.1351/pac199567040649 Thompson M, 2002, PURE APPL CHEM, V74, P835, DOI 10.1351/pac200274050835 Thompson M, 1997, J AOAC INT, V80, P676 Thompson M, 1998, ACCREDIT QUAL ASSUR, V3, P117, DOI 10.1007/s007690050202 Valcarcel M, 1999, TRAC-TREND ANAL CHEM, V18, P570, DOI 10.1016/S0165-9936(99)00157-0 Valcarcel M, 1997, FRESEN J ANAL CHEM, V359, P473, DOI 10.1007/s002160050615 van Zoonen P, 1999, TRAC-TREND ANAL CHEM, V18, P584, DOI 10.1016/S0165-9936(99)00159-4 Walsh MC, 1999, TRAC-TREND ANAL CHEM, V18, P616, DOI 10.1016/S0165-9936(99)00152-1 [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] NR 54 TC 88 Z9 95 U1 1 U2 44 PD JUL-AUG PY 2004 VL 23 IS 7 BP 480 EP 490 DI 10.1016/s0165-9936(04)00733-2 WC Chemistry, Analytical SC Chemistry UT WOS:000224399300013 DA 2022-12-14 ER PT J AU Hsu, SC Huang, YF Mahmudiono, T Chen, HL AF Hsu, Shu-Chen Huang, Yu-Fu Mahmudiono, Trias Chen, Hsiu-Ling TI Food Traceability Systems, Consumers' Risk Perception, and Purchase Intention: Evidence from the "4-label-1-Q" Approach in Taiwan SO JOURNAL OF FOOD PROTECTION DT Article DE Food label; Food safety; Food traceability system; Purchase intention; Risk perception ID MEAT-PRODUCTS; SAFETY; KNOWLEDGE; QUALITY; WILLINGNESS; INVOLVEMENT; ATTITUDES; COOKING; HAZARDS; IMPACT AB Many food safety issues have arisen in Taiwan during the past decade. Therefore, in 2016, the Taiwan government proposed the "five rings of food safety" policy to comprehensively protect consumer food supply. Among these policies, the "4labels-1-Q" approach was adopted to ensure the selection of foods with traceable labels for retrospective study. Hence, this study investigated the association between the degree of familiarity with the 4-labels-1-Q food traceability system and risk perceptions and also investigated whether a consumer's purchase intention toward fresh foods with food labels changed after viewing an educational film on food labels. This study defined subjects as the main food purchasers for their families; 290 valid questionnaire interviews were administered and educational films shown in Tainan markets and stores. Results showed that knowledge about labels significantly affected risk perception for labeling. Age, educational level, and degree of risk perception influenced purchase intention. Results also showed that after viewing the video, subjects' label knowledge and purchase intention increased significantly. However, after adjustment for age, educational level, income, and purchase places, the effect of film education on risk perception was insignificant, especially for those who had lower educational levels, including those older than 65 years. Public trust can be boosted through label education among age groups using different channels and methods, and encouraging the sale of labeled foods in traditional markets would be a useful strategy. Age, educational level, income, and risk perception of participants significantly affected purchase intention. This study can be a reference for designing risk communication strategies and promoting traceable agricultural products. C1 [Hsu, Shu-Chen] Chang Jung Christian Univ, Bachelors Degree Program Environm & Food Safety L, Tainan 711, Taiwan. [Huang, Yu-Fu; Chen, Hsiu-Ling] Natl Cheng Kung Univ, Dept Food Safety Hyg & Risk Management, Tainan 701, Taiwan. [Mahmudiono, Trias] Univ Airlangga, Fac Publ Hlth, Dept Nutr, Surabaya 60115, Indonesia. C3 Chang Jung Christian University; National Cheng Kung University; Airlangga University RP Chen, HL (corresponding author), Natl Cheng Kung Univ, Dept Food Safety Hyg & Risk Management, Tainan 701, Taiwan. EM hsiulinchen@mail.ncku.edu.tw CR Ares G, 2010, APPETITE, V55, P298, DOI 10.1016/j.appet.2010.06.016 Ateke B., 2018, J BUSINESS LAW RES, V6, P1 Balzan S, 2017, ITAL J FOOD SAF, V6, P40, DOI 10.4081/ijfs.2017.6183 Britwum K, 2018, FOOD POLICY, V74, P1, DOI 10.1016/j.foodpol.2017.10.006 Chang FC, 2018, ENVIRON SCI POLLUT R, V25, P5223, DOI 10.1007/s11356-017-9119-x Chang TZ, 1994, J ACAD MARKET SCI, V22, P16, DOI [10.1177/0092070394221002, DOI 10.1177/0092070394221002] Covin J., 1999, ENTREP THEORY PRACT, V23, P47, DOI DOI 10.1177/104225879902300304 Ergonul B, 2013, FOOD CONTROL, V32, P461, DOI 10.1016/j.foodcont.2013.01.018 Escandon-Barbosa D, 2019, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.02761 Feng YH, 2019, J FOOD PROTECT, V82, P128, DOI [10.4315/0362-028X.JFP-18-245, 10.4315/0362-028x.jfp-18-245] Findling MTG, 2018, PREV MED, V106, P114, DOI 10.1016/j.ypmed.2017.10.022 Font-I-Furnols M, 2014, MEAT SCI, V98, P361, DOI 10.1016/j.meatsci.2014.06.025 FREWER LJ, 1994, J FOOD SAFETY, V14, P19, DOI 10.1111/j.1745-4565.1994.tb00581.x Ghosh P, 1990, RETAIL MANAGEMENT, V2nd Gittelsohn J, 2010, HEALTH EDUC BEHAV, V37, P390, DOI 10.1177/1090198109343886 Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 Hansen J, 2003, APPETITE, V41, P111, DOI 10.1016/S0195-6663(03)00079-5 Henderson J, 2011, AUST NZ J PUBL HEAL, V35, P319, DOI 10.1111/j.1753-6405.2011.00725.x Hung Y, 2016, MEAT SCI, V121, P119, DOI 10.1016/j.meatsci.2016.06.002 Khandpur N, 2018, NUTRIENTS, V10, DOI 10.3390/nu10060688 Lange C, 2015, FOOD RES INT, V76, P317, DOI 10.1016/j.foodres.2015.06.017 Liu H. L, 2014, THESIS TAIWAN Liu R, 2017, J FOOD SCI, V82, P825, DOI 10.1111/1750-3841.13639 Lundeberg PJ, 2018, APPETITE, V125, P548, DOI 10.1016/j.appet.2018.02.027 McEvoy JDG, 2016, DRUG TEST ANAL, V8, P511, DOI 10.1002/dta.2015 McLean KG, 2017, J FOOD SCI, V82, P2659, DOI 10.1111/1750-3841.13934 Mitchell V. W., 1990, British Food Journal, V92, P16, DOI 10.1108/00070709010138987 Mitra K., 1999, J SERV MARK, V13, P208, DOI DOI 10.1108/08876049910273763 O'Connor RJ, 2017, HEALTH EDUC BEHAV, V44, P222, DOI 10.1177/1090198116653935 Onyango B, 2016, AGRIC RESOUR ECON RE, V35, P299 Pang SM, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13095218 Pao C. F. L, 2014, THESIS TAIWAN Roberto CA, 2012, APPETITE, V58, P651, DOI 10.1016/j.appet.2012.01.003 Rosati S, 2004, INT J FOOD SCI TECH, V39, P491, DOI 10.1111/j.1365-2621.2004.00808.x Sake F. T, 2018, RES SOCIAL ADM PHARM, V15, P1317 Schermelleh-Engel K., 2003, METHOD PSYCHOL RES O, V8, P23, DOI DOI 10.1002/0470010940 Schiffman LG., 2000, CONSUMER BEHAV Shepherd R., 1996, Food choice, acceptance and consumption., P346 Shih Y.-J, 2016, THESIS TAIWAN Thomas MS, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108279 Verbeke W, 2004, MEAT SCI, V67, P159, DOI 10.1016/j.meatsci.2003.09.017 Vijaykumar S, 2013, J NUTR EDUC BEHAV, V45, P204, DOI 10.1016/j.jneb.2012.09.001 Viola GCV, 2016, J PUBLIC HEALTH RES, V5, P139, DOI 10.4081/jphr.2016.768 Walters A, 2012, J NUTR EDUC BEHAV, V44, P350, DOI 10.1016/j.jneb.2011.08.008 Wu LH, 2013, APPETITE, V70, P90, DOI 10.1016/j.appet.2013.06.091 Wu MT, 2012, ENVIRON INT, V44, P75, DOI 10.1016/j.envint.2012.01.014 Wu WY, 2011, SCAND J PSYCHOL, V52, P290, DOI 10.1111/j.1467-9450.2011.00875.x Yen TH, 2011, J FORMOS MED ASSOC, V110, P671, DOI 10.1016/j.jfma.2011.09.002 Young I, 2010, J FOOD PROTECT, V73, P1278, DOI 10.4315/0362-028X-73.7.1278 NR 49 TC 2 Z9 2 U1 10 U2 19 PD JAN PY 2022 VL 85 IS 1 BP 155 EP 163 DI 10.4315/JFP-21-160 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000733930400019 DA 2022-12-14 ER PT J AU Ferrero, R Gandino, F Montrucchio, B Rebaudengo, M AF Ferrero, Renato Gandino, Filippo Montrucchio, Bartolomeo Rebaudengo, Maurizio TI A cost-effective proposal for an RFID-based system for agri-food traceability SO INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING DT Article DE agri-food; fruit; traceability; tracking; RFID; radio frequency identification; supply chain automation; information management; warehouse AB Agri-food companies, operating in the packaging, storage and distribution of fruit and vegetables, need to be provided with information systems able to meet the requirements imposed by the current European regulations in terms of traceability. This paper evaluates the benefits and drawbacks of a semi-automated information management tracking system for a warehouse specialised in the fruit market. It is targeted to small and medium-sized companies, with limited financial means for investments and without technical support in their premises. These requirements are met by using a personal digital assistant (PDA) equipped with an radio frequency identification (RFID) reader: the information collected throughout the production process are locally stored in the PDA and occasionally sent to a server. In this way the proposed system does not rely neither on a widespread wireless network, nor on fixed RFID readers, which can increase automation, but need more investment and assistance. C1 [Ferrero, Renato; Gandino, Filippo; Montrucchio, Bartolomeo; Rebaudengo, Maurizio] Politecn Torino, Dipartimento Automat & Informat, Corso Duca Abruzzi 24, I-10129 Turin, Italy. C3 Polytechnic University of Turin RP Ferrero, R (corresponding author), Politecn Torino, Dipartimento Automat & Informat, Corso Duca Abruzzi 24, I-10129 Turin, Italy. EM renato.ferrero@polito.it; filippo.gandino@polito.it; bartolomeo.montrucchio@polito.it; maurizio.rebaudengo@polito.it CR Amador Cecilia, 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P26, DOI 10.1007/s11694-009-9072-6 Ampatzidis YG, 2009, COMPUT ELECTRON AGR, V66, P166, DOI 10.1016/j.compag.2009.01.008 Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Collotta M, 2013, INT SYM IND EMBED, P69, DOI 10.1109/SIES.2013.6601472 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Junkkari M, 2011, INT J AD HOC UBIQ CO, V8, P220, DOI 10.1504/IJAHUC.2011.043582 Lefebvre L. A., 2011, RFID EMERGING PARADI, P109 Manzanares-Lopez P, 2011, J NETW COMPUT APPL, V34, P925, DOI 10.1016/j.jnca.2010.04.018 Nambiar Arun N., 2010, Proceedings of 2010 International Symposium on Information Technology (ITSim 2010), P874, DOI 10.1109/ITSIM.2010.5561567 Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 Park N, 2011, INT J AD HOC UBIQ CO, V8, P205, DOI 10.1504/IJAHUC.2011.043583 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Ranasinghe DC, 2011, J NETW COMPUT APPL, V34, P1015, DOI 10.1016/j.jnca.2010.04.019 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Tibola CS, 2008, SCI AGR, V65, P10, DOI 10.1590/S0103-90162008000100002 Trebar M., 2011, P 19 INT C SOFTW TEL, P1 Valenta J, 2010, REPTILES-CLASSIF EVO, P27 White Gareth R. T., 2007, Journal of Information, Information Technology and Organizations, V2, P119 Yabuki N., 2002, CIW W78 C P ARH DENM Yu-Chuan Liu H. G., 2012, 8 AS C INF TECHN AGR NR 23 TC 4 Z9 4 U1 2 U2 40 PY 2018 VL 27 IS 4 BP 270 EP 280 DI 10.1504/IJAHUC.2018.090598 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000428232900003 DA 2022-12-14 ER PT J AU Lei, MYX Xu, LQ Liu, TL Liu, SY Sun, CH AF Lei, Moyixi Xu, Longqin Liu, Tonglai Liu, Shuangyin Sun, Chuanheng TI Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges SO FOODS DT Review DE food safety; blockchain traceability; Internet of Things; artificial intelligence; privacy protection ID SUPPLY CHAIN; ARTIFICIAL-INTELLIGENCE; MANAGEMENT; AGRICULTURE; INTERNET; CONTRACT; SYSTEM; TRENDS; MODEL; IOT AB Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food. Therefore, food traceability is one of the most effective methods available. Collecting and analyzing key information on food traceability, as well as related technology needs, can improve the efficiency of the traceability chain and provide important insights for managers. Technology solutions, such as the Internet of Things (IoT), Artificial Intelligence (AI), Privacy Preservation (PP), and Blockchain (BC), are proposed for food monitoring, traceability, and analysis of collected data, as well as intelligent decision-making, to support the selection of the best solution. However, research on the integration of these technologies is still lacking, especially in the integration of PP with food traceability. To this end, the study provides a systematic review of the use of PP technology in food traceability and identifies the security needs at each stage of food traceability in terms of data flow and technology. Then, the work related to food safety traceability is fully discussed, particularly with regard to the benefits of PP integration. Finally, current developments in the limitations of food traceability are discussed, and some possible suggestions for the adoption of integrated technologies are made. C1 [Lei, Moyixi; Xu, Longqin; Liu, Tonglai; Liu, Shuangyin] Zhongkai Univ Agr & Engn, Coll Informat Sci & Technol, Guangzhou 510225, Peoples R China. [Lei, Moyixi; Sun, Chuanheng] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. C3 Zhongkai University of Agriculture & Engineering; Beijing Academy of Agriculture & Forestry RP Liu, SY (corresponding author), Zhongkai Univ Agr & Engn, Coll Informat Sci & Technol, Guangzhou 510225, Peoples R China.; Sun, CH (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. EM leimoyixi@163.com; xulongqin@126.com; liutonglai@163.com; liushuangyin@163.com; sunch@nercita.org.cn CR Ahmed W.A.H., 2022, HDB DIGITAL BUSINESS, V1, P367 Ajagbe S.A., 2022, INTELL HEALTHC, P299, DOI [10.1007/978-981-16-8150-9_14, DOI 10.1007/978-981-16-8150-9_14] Akbar NA, 2021, FUTURE INTERNET, V13, DOI 10.3390/fi13110285 Akram M, 2022, 2022 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATIONS (DASA), P1295, DOI 10.1109/DASA54658.2022.9765121 Androulaki E, 2018, EUROSYS '18: PROCEEDINGS OF THE THIRTEENTH EUROSYS CONFERENCE, DOI 10.1145/3190508.3190538 Anwar M.R., 2021, APTISI T TECHNOPRENE, V3, P181, DOI [10.34306/att.v3i2.212, DOI 10.34306/ATT.V3I2.212] Ariza JA, 2022, COMPUT APPL ENG EDUC, V30, P304, DOI 10.1002/cae.22439 Aronson JK, 2022, DRUG SAFETY, V45, P407, DOI 10.1007/s40264-022-01156-5 Ashton K., 2009, RFID J, V22, P97, DOI DOI 10.1145/2967977 Atzei N, 2017, LECT NOTES COMPUT SC, V10204, P164, DOI 10.1007/978-3-662-54455-6_8 Balamurugan S., 2022, International Journal of Information Technology, V14, P1087, DOI 10.1007/s41870-020-00581-y Balasubramanian S, 2022, J INTELL FUZZY SYST, V43, P5387, DOI 10.3233/JIFS-211265 Baralla G, 2019, 2019 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON EMERGING TRENDS IN SOFTWARE ENGINEERING FOR BLOCKCHAIN (WETSEB 2019), P40, DOI 10.1109/WETSEB.2019.00012 Baralla G, 2019, LECT NOTES COMPUT SC, V11339, P379, DOI 10.1007/978-3-030-10549-5_30 Benyam A, 2021, J CLEAN PROD, V323, DOI 10.1016/j.jclepro.2021.129099 Bieganska M., 2022, CURRENT TRENDS QUALI, P226 Blanco C, 2022, MOL PSYCHIATR, V27, P787, DOI 10.1038/s41380-021-01356-y Botcha Krishna Mohan, 2019, 2019 IEEE International Conference on Intelligent Systems and Green Technology (ICISGT). Proceedings, P45, DOI 10.1109/ICISGT44072.2019.00025 Bouraga S, 2021, EXPERT SYST APPL, V168, DOI 10.1016/j.eswa.2020.114384 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Cai L, 2021, COMMUN ACM, V64, P88, DOI 10.1145/3481627 Hallak JC, 2022, J AGRIBUS DEV EMERG, V12, P673, DOI 10.1108/JADEE-10-2021-0272 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Carter CA, 2012, APPL ECON PERSPECT P, V34, P1, DOI 10.1093/aepp/ppr047 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Chan KY, 2019, INT J ADV COMPUT SC, V10, P149 Chanson M, 2019, J ASSOC INF SYST, V20, P1274, DOI 10.17705/1jais.00567 Chao S, 2022, SECUR COMMUN NETW, V2022, DOI 10.1155/2022/4055698 Chen WJ, 2022, BIOMED J, V45, P432, DOI 10.1016/j.bj.2022.03.002 Zhao CH, 2022, Arxiv, DOI arXiv:2204.08769 Coad A., 2022, QUEST ENTREP STATE, V53, P273 Cozzolino D, 2012, APPL SPECTROSC REV, V47, P518, DOI 10.1080/05704928.2012.667858 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Dabholkar A., 2019, PROC INT C APPL TECH, P300 Daulatrao B.C., 2022, INT J MOD DEV ENG SC, V1, P11 Davda Y., 2022, DESIGN HASH ALGORITH, P1 Debats SR, 2016, REMOTE SENS ENVIRON, V179, P210, DOI 10.1016/j.rse.2016.03.010 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Devika K. N., 2019, 2019 International Conference on Communication and Signal Processing (ICCSP), P0370, DOI 10.1109/ICCSP.2019.8698069 Dewi T, 2018, 2018 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (ICECOS), P57, DOI 10.1109/ICECOS.2018.8605209 Chen DF, 2020, Arxiv, DOI arXiv:2006.08265 El Matbouly H., 2022, BIOSENSING MICRONANO, P219 Fan YK, 2022, SOFTWARE PRACT EXPER, V52, P115, DOI 10.1002/spe.2753 Feng LB, 2018, INT C COMP SUPP COOP, P75 Ferlay J, 2019, INT J CANCER, V144, P1941, DOI 10.1002/ijc.31937 Funk E, 2018, ACAD MED, V93, P1791, DOI 10.1097/ACM.0000000000002326 Gallo P., 2022, P 2022 4 DISTRIBUTED, P1 Garcia-Torres S, 2019, SUPPLY CHAIN MANAG, V24, P85, DOI 10.1108/SCM-04-2018-0152 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Gerrits Luc, 2021, SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, P492, DOI 10.1145/3485730.3493374 Gervais A., 2016, P 2016 ACM SIGSAC C, P3 Goldfeder S., 2017, ARXIV, DOI [10.1515/popets-2018-0038, DOI 10.1515/POPETS-2018-0038] Gomiero T, 2011, CRIT REV PLANT SCI, V30, P6, DOI 10.1080/07352689.2011.553515 Gopalakrishnan PK, 2021, WASTE MANAGE, V120, P594, DOI 10.1016/j.wasman.2020.10.027 Gu B., 2022, ARTIFICIAL INTELLIGE, V6, P10, DOI [DOI 10.1016/J.AIIA.2022.01.001, 10.1016/j.aiia.2022.01.001] Gunders D., 2017, NRDC ISSUE PAPER, P1 Guo JJ, 2022, J SUPERCOMPUT, V78, P18225, DOI 10.1007/s11227-022-04583-4 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hassan MU, 2020, IEEE COMMUN SURV TUT, V22, P746, DOI 10.1109/COMST.2019.2944748 Hayati Hashri, 2018, 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), P120, DOI 10.1109/ISRITI.2018.8864477 Hellwig D.P., 2022, INNOVATIVE TECHNOLOG, P31 Himthani Puneet, 2020, 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), P707, DOI 10.1109/I-SMAC49090.2020.9243527 Hua J, 2018, IEEE INT VEH SYM, P97 Huang SH, 2020, J MANUF SYST, V54, P361, DOI 10.1016/j.jmsy.2020.01.009 hyperledger, HYPERLEDGER FABRIC D INAN A., 2010, P 13 INT C EXT DAT T, P123 Ingram J, 2022, LAND USE POLICY, V114, DOI 10.1016/j.landusepol.2021.105962 Islam M., 2021, ARXIV Juels A., 2003, P 10 ACM C COMPUTER, P103, DOI DOI 10.1145/948109.948126 Kadadha M., 2022, FUTURE GENER COMP SY, V136, P170 Kalodner H, 2018, PROCEEDINGS OF THE 27TH USENIX SECURITY SYMPOSIUM, P1353 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kang JW, 2019, IEEE T VEH TECHNOL, V68, P2906, DOI 10.1109/TVT.2019.2894944 Katsikouli P, 2021, J SCI FOOD AGR, V101, P2175, DOI 10.1002/jsfa.10883 Kaur P, 2022, ARCH COMPUT METHOD E, V29, P2417, DOI 10.1007/s11831-021-09659-7 Kevorchian C., 2020, AGR EC RURAL DEV NEW, V1, P29 Kim H., 2018, SUPPLY CHAIN REVOLUT Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Koval M., 2022, THESIS U TWENTE ENSC Kshetri N, 2021, COMPUTER, V54, P98, DOI 10.1109/MC.2021.3082835 Kuhn M, 2021, J MANUF SYST, V59, P617, DOI 10.1016/j.jmsy.2021.04.013 Kuznetsov A., 2022, INFORM SECURITY TECH, P33 Lan S, 2021, WIREL COMMUN MOB COM, V2021, DOI 10.1155/2021/8693978 Lavelli V, 2022, TRENDS FOOD SCI TECH, V126, P86, DOI 10.1016/j.tifs.2022.06.013 Li HZ, 2022, IEEE INTERNET THINGS, V9, P4704, DOI 10.1109/JIOT.2021.3107846 Liang WB, 2022, CLUSTER COMPUT, V25, P2203, DOI 10.1007/s10586-021-03260-0 Lin C, 2021, IEEE SYST J, V15, P4367, DOI 10.1109/JSYST.2020.3019923 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu B, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3436755 Liu CY, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14105930 Liu S., 2022, P 2022 4 INT C BLOCK, P59 Liu WH, 2022, J CLEAN PROD, V352, DOI 10.1016/j.jclepro.2022.131622 Lu Y, 2022, PROCEDIA COMPUT SCI, V199, P629, DOI 10.1016/j.procs.2022.01.077 Luongo M., 2018, KEEP NETWORK PRIVACY Lyu D, 2022, WIREL NETW, V28, P2337, DOI 10.1007/s11276-022-02904-2 Majdalawieh M, 2021, PEER PEER NETW APPL, V14, P3831, DOI 10.1007/s12083-021-01196-1 Manning L, 2022, TRENDS FOOD SCI TECH, V125, P33, DOI 10.1016/j.tifs.2022.04.025 Marchesi L, 2022, IEEE ACCESS, V10, P50363, DOI 10.1109/ACCESS.2022.3171045 Marsden T, 2000, SOCIOL RURALIS, V40, P424, DOI 10.1111/1467-9523.00158 Masudin I., 2021, GLOBAL J FLEXIBLE SY, V22, P331, DOI [10.1007/s40171-021-00281-x, DOI 10.1007/S40171-021-00281-X] Pandey M, 2021, Arxiv, DOI arXiv:2112.11024 Meeradevi P.S., 2020, P 2020 IEEE INT C DI, P127, DOI [10.1109/DISCOVER50404.2020.9278029, DOI 10.1109/DISCOVER50404.2020.9278029] Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Mohan T, 2018, IMPROVE FOOD SUPPLY Mohit M, 2022, CLUSTER COMPUT, V25, P2223, DOI 10.1007/s10586-021-03425-x Moldovan MG, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14127123 Marin MP, 2021, Arxiv, DOI arXiv:2101.02026 Mothukuri V, 2021, FUTURE GENER COMP SY, V115, P619, DOI 10.1016/j.future.2020.10.007 Mu CG, 2022, IEEE T SMART GRID, V13, P4759, DOI 10.1109/TSG.2022.3176624 Naik G, 2018, IIMB MANAG REV, V30, P270, DOI 10.1016/j.iimb.2018.04.001 Nair P.R., 2021, P 3 RD INT C INTELLI, P279 Nandakumar K., 2022, J POSIT SCH PSYCHOL, V6, P5414 Nasir MH, 2022, FUTURE GENER COMP SY, V126, P136, DOI 10.1016/j.future.2021.07.035 Nordhagen S, 2022, FOOD CONTROL, V134, DOI 10.1016/j.foodcont.2021.108693 Omarov R, 2017, ENG RUR DEVELOP, P960, DOI 10.22616/ERDev2017.16.N195 Onal AC, 2017, IEEE INT CONF BIG DA, P2037 Pang GS, 2022, IEEE INTELL SYST, V37, P111, DOI 10.1109/MIS.2022.3165668 Pateriya R. K., 2011, 2011 International Conference on Communication Systems and Network Technologies (CSNT), P115, DOI 10.1109/CSNT.2011.31 Peng S, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/3406228 Peng XZ, 2022, AGRICULTURE-BASEL, V12, DOI 10.3390/agriculture12050689 Peng XZ, 2022, FOODS, V11, DOI 10.3390/foods11091269 Pincheira M., 2021, INT C BLOCKCHAIN APP, V320, P212, DOI [10.1007/978-3-030-86162-9_21, DOI 10.1007/978-3-030-86162-9_21] Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497 Qian JP, 2022, CRIT REV FOOD SCI, V62, P679, DOI 10.1080/10408398.2020.1825925 Rahman MA, 2020, IEEE ACCESS, V8, P205071, DOI 10.1109/ACCESS.2020.3037474 Ravi D., 2022, BLOCKCHAIN RES APPL, V3, P100072, DOI [10.1016/j.bcra.2022.100072, DOI 10.1016/J.BCRA.2022.100072] Ryffel T., 2018, ARXIV Sahai S, 2020, 2020 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2020), P134, DOI 10.1109/Blockchain50366.2020.00024 Salim AM, 2022, INT J ENERGY SECT MA, DOI 10.1108/IJESM-11-2021-0004 Sanchez D.C., 2018, ARXIV Sankar LS, 2017, INT CONF ADVAN COMPU Sezer BB, 2022, J INF SECUR APPL, V66, DOI 10.1016/j.jisa.2022.103116 Shaik C., 2021, COMPUT SCI ENG INT J, V11, P1, DOI [10.5121/cseij.2021.11101, DOI 10.5121/CSEIJ.2021.11101] Sharmila S., 2022, ARTIF INTELL, P219 Shen M, 2019, IEEE INTERNET THINGS, V6, P7702, DOI 10.1109/JIOT.2019.2901840 Showail A, 2022, COMPUT SECUR, V120, DOI 10.1016/j.cose.2022.102776 Shrestha R, 2019, ADV COMPUT, V115, P293, DOI 10.1016/bs.adcom.2019.06.002 Singh M., 2022, AUTHOREA PREPRINTS, P1 Singh S, 2020, SUSTAIN CITIES SOC, V63, DOI 10.1016/j.scs.2020.102364 Sun Wenchuan, 2022, 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)., P205, DOI 10.1109/ICCCBDA55098.2022.9778290 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Syed NF, 2022, COMPUT SECUR, V112, DOI 10.1016/j.cose.2021.102536 Takatsuji T, 2019, PRECIS ENG, V58, P1, DOI 10.1016/j.precisioneng.2019.04.016 Tan A, 2022, INT J LOGIST-RES APP, V25, P947, DOI 10.1080/13675567.2020.1825653 Tan WC., 2022, OPER RES PERSPECT, V9, P100229 Tanya R., 2022, WIRELESS CONNECTED W, P105 Tian HongKun, 2020, Information Processing in Agriculture, V7, P1, DOI 10.1016/j.inpa.2019.09.006 Tian HL, 2022, SUSTAIN ENERGY GRIDS, V32, DOI 10.1016/j.segan.2022.100807 Tran NK, 2022, CLIN CHEM, V68, P125, DOI 10.1093/clinchem/hvab239 Triastcyn A, 2020, IEEE INTELL SYST, V35, P50, DOI 10.1109/MIS.2020.2993966 Trollman H, 2022, LOGISTICS-BASEL, V6, DOI 10.3390/logistics6030043 Truex S., 2019, P 12 ACM WORKSH ART, P1, DOI DOI 10.1145/3338501.3357370 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Ul Hassan M, 2019, FUTURE GENER COMP SY, V97, P512, DOI 10.1016/j.future.2019.02.060 Urdampilleta M, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-26076-2 Vasconez JP, 2019, BIOSYST ENG, V179, P35, DOI 10.1016/j.biosystemseng.2018.12.005 Wan JF, 2019, IEEE T IND INFORM, V15, P3652, DOI 10.1109/TII.2019.2894573 Wan LM, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2019), P194, DOI 10.1109/Blockchain.2019.00033 Wang J., 2021, NATL SCI REV, V8, pnwab069, DOI [10.1093/nsr/nwab069, DOI 10.1093/NSR/NWAB069] Wang LX, 2022, FOODS, V11, DOI 10.3390/foods11050744 Wang XK, 2022, LAND-BASEL, V11, DOI 10.3390/land11040484 Wen QS, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER PHYSICAL SYSTEMS (ICPS 2019), P695, DOI 10.1109/ICPHYS.2019.8780161 Wen XJ, 2022, OPT LASER TECHNOL, V147, DOI 10.1016/j.optlastec.2021.107693 Wenhao Ou, 2010, 2010 Second International Conference on Computational Intelligence and Natural Computing (CINC 2010), P197, DOI 10.1109/CINC.2010.5643755 Wisessing K., 2022, P 2022 7 INT C BUSIN, P359 Wright C.S, 2019, SSRN ELECT J Wu GJ, 2022, IEEE J BIOMED HEALTH, V26, P1917, DOI 10.1109/JBHI.2021.3123643 Wu X., 2022, P 2022 IEEE 2 INT C Wu YL, 2022, COMPUT STAND INTER, V79, DOI 10.1016/j.csi.2021.103552 Xia Q, 2017, INFORMATION, V8, DOI 10.3390/info8020044 Yang K., CRYPTOLOGY EPRINT AR, P55 Yasir A.I., 2021, ADV SCI TECHNOL ENG, V6, P757, DOI [10.25046/aj060183, DOI 10.25046/AJ060183] Ye H., 2022, P DESIGNING INTERACT, P1626 Yin HY, 2021, ADV MATER, V33, DOI 10.1002/adma.202007764 Yu ZL, 2022, CRIT REV FOOD SCI, V62, P905, DOI [10.1080/10408398.2020.1830262, 10.1007/978-3-030-58529-7_1] Yuan R, 2018, J COMPUT SCI TECH-CH, V33, P542, DOI 10.1007/s11390-018-1839-y Yuvaraj N., 2020, INTERNET THINGS, P165 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhang YY, 2022, FOOD CHEM, V394, DOI 10.1016/j.foodchem.2022.133526 Zhao HL, 2022, IEEE T MOBILE COMPUT, V21, P4296, DOI 10.1109/TMC.2021.3074934 Zheng MM, 2021, IEEE ACCESS, V9, P70571, DOI 10.1109/ACCESS.2021.3078536 Zheng WL, 2019, IEEE ACCESS, V7, P134422, DOI 10.1109/ACCESS.2019.2941905 Zhou P, 2021, IEEE T KNOWL DATA EN, V33, P824, DOI 10.1109/TKDE.2019.2936565 Zhou PY, 2022, IEEE T INTELL TRANSP, V23, P22505, DOI 10.1109/TITS.2022.3155175 Zyskind G, 2015, 2015 IEEE SECURITY AND PRIVACY WORKSHOPS (SPW), P180, DOI 10.1109/SPW.2015.27 NR 187 TC 0 Z9 0 U1 15 U2 15 PD AUG PY 2022 VL 11 IS 15 AR 2262 DI 10.3390/foods11152262 WC Food Science & Technology SC Food Science & Technology UT WOS:000839808100001 DA 2022-12-14 ER PT J AU Lopes, LO Silva, R Guimaraes, JT Coutinho, NM Pimentel, TC Duarte, MCKH Freitas, MQ Silva, MC Esmerino, EA Azeredo, DRP Cruz, AG AF Lopes, Leo O. Silva, Ramon Guimaraes, Jonas T. Coutinho, Nathalia M. Pimentel, Tatiana C. Duarte, Maria Carmela K. H. Freitas, Monica Q. Silva, Marcia C. Esmerino, Erick A. Azeredo, Denise R. P. Cruz, Adriano G. TI Traceability: Perceptions and attitudes of Brazilian non-bovine dairy processors SO FOOD CONTROL DT Article DE Traceability; Non-bovine dairy processors; Perceptions; Attitudes ID EXPLORATORY FACTOR-ANALYSIS; MILK-PRODUCTS; FOOD; CONSUMPTION AB The perception of Brazilian non-bovine dairy processors (goat, sheep and buffalo, n = 32) located in eight states of Brazil (Rio de Janeiro, Sao Paulo, Minas Gerais, Parana, Goias, Bahia, Rio Grande do Sul, and Ceara), regarding the implementation of traceability was investigated. A questionnaire consisting of 16 statements using a 5-point Likert Scale was applied and the data were evaluated by descriptive statistics and factor analysis. It was agreed that the implementation of a traceability system allows a quick recall of products, and reduces the negative impact, the number of consumer complaints and the loss of products. In addition, it can result in increased supplier control and process safety, protecting consumer's health and increasing their confidence. However, the relationship between the traceability system and the cost savings in the process and/or price of the final product was not a consensus. Although dairy companies agreed that traceability is highly relevant to the company, many are unwilling to invest in implementation of the system. Therefore, non-bovine milk-producing units in Brazil are aware of the impact of implementing traceability, but still have doubts about the costs involved, which restrict investments in the system. C1 [Lopes, Leo O.; Silva, Ramon; Guimaraes, Jonas T.; Coutinho, Nathalia M.; Duarte, Maria Carmela K. H.; Freitas, Monica Q.; Esmerino, Erick A.] Univ Fed Fluminense, Fac Med Vet, BR-24230340 Niteroi, RJ, Brazil. [Silva, Ramon; Silva, Marcia C.; Azeredo, Denise R. P.; Cruz, Adriano G.] Inst Fed Educ Ciencia & Tecnol Rio de Janeiro IFR, Dept Alimentos, BR-20270021 Rio De Janeiro, Brazil. [Pimentel, Tatiana C.] Inst Fed Parana IFPR, BR-87703536 Paranavai, Parana, Brazil. C3 Universidade Federal Fluminense; Instituto Federal do Parana RP Cruz, AG (corresponding author), Inst Fed Educ Ciencia & Tecnol Rio de Janeiro IFR, Dept Alimentos, BR-20270021 Rio De Janeiro, Brazil. EM adriano.cruz@ifrj.edu.br CR [Anonymous], 2017, TENDENCIAS MERCADO P Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Balthazar CF, 2017, COMPR REV FOOD SCI F, V16, P247, DOI 10.1111/1541-4337.12250 Crovato S, 2019, FOOD CONTROL, V96, P410, DOI 10.1016/j.foodcont.2018.09.040 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Espifieira M., 2016, ADV FOOD TRACEABILIT, P402 FAO, 2018, MILK PRODUCTION Foras E, 2015, FOOD CONTROL, V57, P65, DOI 10.1016/j.foodcont.2015.03.027 Golan E.H., 2004, TRACEABILITY US FOOD Maciel ED, 2013, FOOD SCI TECH-BRAZIL, V33, P99, DOI 10.1590/S0101-20612013005000016 Maldonado-Siman E, 2013, J FOOD PROCESS PRES, V37, P399, DOI 10.1111/j.1745-4549.2011.00663.x Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Pacheco MHS, 2018, J SENS STUD, V33, DOI 10.1111/joss.12434 Rahnama H, 2017, J SENS STUD, V32, DOI 10.1111/joss.12248 Ranadheera CS, 2019, COMPR REV FOOD SCI F, V18, P867, DOI 10.1111/1541-4337.12447 Ranadheera CS, 2018, CURR OPIN FOOD SCI, V22, P109, DOI 10.1016/j.cofs.2018.02.010 Santos LS, 2017, INT J DAIRY TECHNOL, V70, P492, DOI 10.1111/1471-0307.12370 Sobhanifard Y, 2018, BRIT FOOD J, V120, P44, DOI 10.1108/BFJ-12-2016-0604 Walker GS, 2017, FOOD CONTROL, V72, P168, DOI 10.1016/j.foodcont.2016.01.028 NR 20 TC 4 Z9 4 U1 3 U2 9 PD MAY PY 2020 VL 111 AR 107060 DI 10.1016/j.foodcont.2019.107060 WC Food Science & Technology SC Food Science & Technology UT WOS:000517659100008 DA 2022-12-14 ER PT J AU Oskarsdottir, K Oddsson, GV AF Oskarsdottir, Kristin Oddsson, Gudmundur Valur TI Towards a decision support framework for technologies used in cold supply chain traceability SO JOURNAL OF FOOD ENGINEERING DT Article DE Food traceability; Decision support framework; Cold supply chain; Quality; Traceability technologies; Supply chain management ID FOOD TRACEABILITY; SYSTEM; QUALITY; FROZEN; AGRICULTURE; INDUSTRY AB In recent years, a need for product traceability in the cold supply chain has emerged. The purpose of this study was to identify and map out different kinds of identification technologies and techniques used for cold supply chain traceability. This was done by looking into what traceability solutions are available right now through literature review. The results from this review were then further analyzed to obtain a basis for the current state of knowledge, technical solutions and to identify possible traceability structures in the cold chain. A Decision Support Framework (DSF) was constructed for choosing a suitable technical solution. It consists of a table listing different functions and attributes of technologies and a decision-tree. The DSF created from this work will help the user to identify what kind of traceability technology and structure best suits his products. This is important, as it can often be difficult for the user to decide which technology is most beneficial for his company. That is why this decision support framework will enable him to decide what is technologically feasible, practical, economical, can sustain reputation, quality and safety of the products. C1 [Oskarsdottir, Kristin; Oddsson, Gudmundur Valur] Univ Iceland, Fac Ind Engn Mech Engn & Comp Sci, Hjardarhagi 6, IS-107 Reykjavik, Iceland. C3 University of Iceland RP Oddsson, GV (corresponding author), Univ Iceland, Fac Ind Engn Mech Engn & Comp Sci, Hjardarhagi 6, IS-107 Reykjavik, Iceland. EM kro19@hi.is; gvo@hi.is CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bhatt T, 2013, J FOOD SCI, V78, pB21, DOI 10.1111/1750-3841.12278 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chen YY, 2014, J FOOD ENG, V141, P113, DOI 10.1016/j.jfoodeng.2014.05.014 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Cuinas I, 2014, IEEE ANTENN PROPAG M, V56, P196, DOI 10.1109/MAP.2014.6837090 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Denso A.D.C., 2011, QR CODE ESSENTIALS FUERTES G, 2016, J SENSORS, V2016, P1, DOI DOI 10.1155/2016/4046061 Gessner GH, 2007, INFORM SYST MANAGE, V24, P213, DOI 10.1080/10580530701404561 Giannoglou M, 2014, INNOV FOOD SCI EMERG, V26, P294, DOI 10.1016/j.ifset.2014.10.008 Haflioason T, 2012, INT J PHYS DISTR LOG, V42, P355, DOI 10.1108/09600031211231335 Heising JK, 2014, CRIT REV FOOD SCI, V54, P645, DOI 10.1080/10408398.2011.600477 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kumar P, 2009, J FOOD SCI, V74, pR101, DOI 10.1111/j.1750-3841.2009.01323.x Kumari L, 2015, TRENDS FOOD SCI TECH, V43, P144, DOI 10.1016/j.tifs.2015.02.005 Laniel M, 2011, TRANSPORT RES C-EMER, V19, P1071, DOI 10.1016/j.trc.2011.06.008 Luo H, 2016, INTERNET RES, V26, P435, DOI 10.1108/IntR-11-2014-0294 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Mohebi E, 2015, J FOOD SCI TECH MYS, V52, P3947, DOI 10.1007/s13197-014-1588-z Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Phase IV Engineering INC, 2016, TEMP SENS CRED CARD RFMicron, 2017, RFM3200 WIR FLEX TEM Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Ruiz-Garcia L, 2010, SENSORS-BASEL, V10, P4968, DOI 10.3390/s100504968 Ruiz-Garcia L, 2009, SENSORS-BASEL, V9, P4728, DOI 10.3390/s90604728 Sharma D., 2014, INT J RES, V1, P5 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Trebar M, 2013, INT J ANTENN PROPAG, V2013, DOI 10.1155/2013/875973 Wang JY, 2015, COMPUT ELECTRON AGR, V110, P196, DOI 10.1016/j.compag.2014.11.009 Xiao XQ, 2016, FOOD CONTROL, V60, P656, DOI 10.1016/j.foodcont.2015.09.012 Zou Z, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0313 NR 38 TC 31 Z9 32 U1 8 U2 157 PD JAN PY 2019 VL 240 BP 153 EP 159 DI 10.1016/j.jfoodeng.2018.07.013 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000447479700017 DA 2022-12-14 ER PT J AU Charlebois, S Haratifar, S AF Charlebois, Sylvain Haratifar, Sanaz TI The perceived value of dairy product traceability in modern society: An exploratory study SO JOURNAL OF DAIRY SCIENCE DT Article DE traceability; dairy foods; value ID FOOD-SUPPLY CHAIN; MILK CONSUMPTION; UNITED-STATES; RAW-MILK; CONSUMERS; SAFETY; QUALITY; PERCEPTION; INDICATORS; MANAGEMENT AB The current study assessed the perceived value of food traceability in modern society by young consumers. After experiencing numerous recalls and food safety-related incidences, consumers are increasingly aware of the tools available to mitigate risks. Food traceability has been associated with food safety procedures for many years, but recent high-profile cases of food fraud around the world have given traceability a different strategic purpose. Focusing solely on dairy products, our survey results offer a glimpse of consumer perceptions of traceability as a means to preserve food integrity and authenticity. This study explored the various influences that market-oriented traceability has had on dairy consumers. For example, results show that if the dairy sector could guarantee that their product is in fact organic, 53.8% of respondents who often purchase organic milk would consider always purchasing traceable organic milk. This research produced a quantitative set of information related to the perceived value of food traceability, which could be useful for the creation and development of improved guidelines and better education for consumers. We discuss limitations and suggest areas for new research. C1 [Charlebois, Sylvain] Univ Guelph, Coll Business & Econ, Guelph, ON N1G 2W1, Canada. [Haratifar, Sanaz] Univ Guelph, Ontario Agr Coll, Guelph, ON N1G 2W1, Canada. C3 University of Guelph; University of Guelph RP Charlebois, S (corresponding author), Univ Guelph, Coll Business & Econ, Guelph, ON N1G 2W1, Canada. EM sylvain.charlebois@uoguelph.ca CR Aarnisalo K., 2007, RES NOTES [Anonymous], 2003, AGR TRAD RES CONSORT [Anonymous], J NUTR ECOL FOOD RES, DOI DOI 10.1166/JNEF.2013.1027 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Australia Food Standards Code, 2012, FOOD STAND COD DAIR Batz MB, 2012, J FOOD PROTECT, V75, P1278, DOI 10.4315/0362-028X.JFP-11-418 Berentsen PBM, 2012, J DAIRY SCI, V95, P3803, DOI 10.3168/jds.2011-5200 Bernard A, 2002, ENVIRON RES, V88, P1, DOI 10.1006/enrs.2001.4274 Canadian Consumer Raw Milk Advocacy Group, 2014, OPEN DIALOGUE IS GIA Canadian Dairy Information Centre, 2013, ORG DAIR IND CAN Casemore D., 2004, PUBLIC HLTH ISSUES R CDC (Centers for Disease Control and Prevention), 2013, FOOD SAF RAW MILK QU CFIA, 2014, LIST ALL REC ALL ALL CFIA, 2014, NEW RUL PIG IND STRE CFIA, 2013, MEAT POULTR PROD MAN CFIA, 2014, TAGS APPR NAT LIV ID Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Charlebois S, 2007, J ENTERP COMMUNITIES, V1, P252, DOI 10.1108/17506200710779558 Claeys WL, 2013, FOOD CONTROL, V31, P251, DOI 10.1016/j.foodcont.2012.09.035 Cocucci M., 2002, Italian Food Technology, P15 Codex Alimentarius, 2006, CAC GL 60 2006 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 de las Morenas J, 2014, COMPUT ELECTRON AGR, V101, P34, DOI 10.1016/j.compag.2013.12.011 Dennis D, 2000, MANAGE SCI, V46, P1085, DOI 10.1287/mnsc.46.8.1085.12031 Dimitri C., 2007, LDPM5501 Down S, 2013, PROTEOMICS GENOMICS Europa, 2011, EUR SUMM EU LEG ID L FAO, 2013, MILK DAIR PROD HUM N FAO/WHO, 1997, CODEX AL Francesconi GN, 2010, FOOD POLICY, V35, P60, DOI 10.1016/j.foodpol.2009.06.003 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Gelici-Zeko MM, 2013, PACKAG TECHNOL SCI, V26, P215, DOI 10.1002/pts.1977 Germain C., 2003, TRACEABILITY IMPLEME Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 Hegarty H, 2002, Commun Dis Public Health, V5, P151 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Hoorfar J, 2011, WOODHEAD PUBL FOOD S, P303 IFT (Institute for Food Technologists), 2009, COMPR REV FOOD SCI F, V9, P92 International Standard Organization, 2007, 220052007 ISO ISO, 1994, 112601994 ISO ISO (International Organization for Standardization), 2000, ISO 9000 2000 QUAL M Karippacheril T. G., 2011, ICT AGR WORLD BANKS, V64605, P285 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Katafiasz A. R., 2012, Food Protection Trends, V32, P124 Kumar A., 2014, INT MONTHLY REFEREED, V3, P65 Kumar S, 2006, TECHNOVATION, V26, P739, DOI 10.1016/j.technovation.2005.05.006 Langer AJ, 2012, EMERG INFECT DIS, V18, P385, DOI 10.3201/eid1803.111370 Le Y., 2010, CHINA SEIZES MORE ME Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Liu Z, 2013, APPL ECON, V45, P3110, DOI 10.1080/00036846.2012.699189 Long F., 2013, STUDY IMPACT NUTR LA Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Magliulo L., 2013, AGR SCI, V4, P41, DOI DOI 10.4236/as.2013.45B008 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Molkentin J, 2013, FOOD CHEM, V137, P25, DOI 10.1016/j.foodchem.2012.09.093 Nolan-Clark DJ, 2011, BMC PUBLIC HEALTH, V11, DOI 10.1186/1471-2458-11-843 Olsen P., 2009, HARMONIZING METHODS, P5 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Petersen A., 2004, STATUS FOOD TRACEBIL Pickrell L., 2014, RAW MILK IMPLICATION Primrose S, 2010, TRENDS FOOD SCI TECH, V21, P582, DOI 10.1016/j.tifs.2010.09.006 Qiao GH, 2010, APPETITE, V55, P190, DOI 10.1016/j.appet.2010.05.047 Resmini P, 2003, ITAL J FOOD SCI, V15, P473 Robb C. A., 2007, International Journal of Consumer Studies, V31, P90, DOI 10.1111/j.1470-6431.2006.00492.x Robinson TJ, 2014, EMERG INFECT DIS, V20, P38, DOI 10.3201/eid2001.120920 Skoglund T, 2007, FOOD BIOPROD PROCESS, V85, P354, DOI 10.1205/fbp07044 Statistics Canada, 2014, CAN DAIR IND Statistics Canada, 2006, CAN BEEF IND BSE Tremonte P, 2014, J DAIRY SCI, V97, P3314, DOI 10.3168/jds.2013-7744 Trichterborn J, 2011, EUR J CLIN NUTR, V65, P1032, DOI 10.1038/ejcn.2011.52 UK Foods Standards, 2013, WHOLEBAKE LTD REC BA Underdahl B., 2014, FOOD TRAC DUMM US Food and Drug Administration, 2014, LEH VALL DAIR COND V Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Xiu CB, 2010, FOOD POLICY, V35, P463, DOI 10.1016/j.foodpol.2010.05.001 Young I, 2010, PREV VET MED, V94, P65, DOI 10.1016/j.prevetmed.2009.11.010 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhao XD, 2013, INT J PROD ECON, V142, P115, DOI 10.1016/j.ijpe.2012.10.018 NR 83 TC 25 Z9 26 U1 6 U2 77 PD MAY PY 2015 VL 98 IS 5 BP 3514 EP 3525 DI 10.3168/jds.2014-9247 WC Agriculture, Dairy & Animal Science; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000353267900067 DA 2022-12-14 ER PT J AU Wilmot, H Glorieux, G Hubin, X Gengler, N AF Wilmot, H. Glorieux, G. Hubin, X. Gengler, N. TI A genomic breed assignment test for traceability of meat of Dual-Purpose Blue SO LIVESTOCK SCIENCE DT Article DE Local breed; Breed assignment; Traceability; SNP ID GENETIC TRACEABILITY AB Assigning meat to its breed of origin for traceability purposes is not always straightforward if the breed from which products are derived is closely related to another one. The objective of this study was to determine if a genomic breed assignment test could distinguish meat of Dual-Purpose Blue, a local endangered breed, from meat of Beef Belgian Blue, a heavily used breed in the Belgian meat industry which is related to Dual-Purpose Blue. For this purpose, a genomic breed assignment test based on a panel of 2,005 SNPs and the nearest shrunken centroids method was used to classify 32 meat samples from Dual-Purpose Blue (n = 16), Beef Belgian Blue (n = 8) and Holstein (n = 8) into their breed of origin. From this SNP panel, 167 SNPs allowed to detect meat of Dual-Purpose Blue and 173 SNPs allowed to detect meat of Beef Belgian Blue. The genomic breed assignment test correctly allocated all the meat samples to their breed of origin with a probability of one. Therefore, the use of the genomic breed assignment test in routine as one step of the certification process of Dual Purpose Blue meat seemed possible. C1 [Wilmot, H.] Natl Fund Sci Res FRS FNRS, Rue Egmont, 5, B-1000 Brussels, Belgium. [Wilmot, H.; Gengler, N.] Gembloux Agrobio Tech Univ Liege, TERRA Teaching & Res Ctr, Passage Deportes 2, B-5030 Gembloux, Belgium. [Glorieux, G.; Hubin, X.] Walloon Breeders Assoc Eleveo, Rue Champs Elysees 4, B-5590 Ciney, Belgium. [Wilmot, H.] Natl Fund Sci Res FRS FNRS, Rue Egmont, 5, B-1000 Brussels, Belgium. C3 Fonds de la Recherche Scientifique - FNRS; Fonds de la Recherche Scientifique - FNRS RP Wilmot, H (corresponding author), Natl Fund Sci Res FRS FNRS, Rue Egmont, 5, B-1000 Brussels, Belgium. EM helene.wilmot@uliege.be CR Alexander DH, 2009, GENOME RES, V19, P1655, DOI 10.1101/gr.094052.109 Baumung R, 2006, J ANIM BREED GENET, V123, P265, DOI 10.1111/j.1439-0388.2006.00583.x Bertolini F, 2015, J ANIM BREED GENET, V132, P346, DOI 10.1111/jbg.12155 BlueSter, 2021, US CHARLIER C, 1995, MAMM GENOME, V6, P788, DOI 10.1007/BF00539005 Colinet F., 2010, DUAL PURPOSE BELGIAN Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Funkhouser SA, 2017, TRANSL ANIM SCI, V1, P36, DOI 10.2527/tas2016.0003 He J, 2018, BMC GENET, V19, DOI 10.1186/s12863-018-0654-3 Hulsegge B, 2013, J ANIM SCI, V91, P5128, DOI 10.2527/jas.2013-6678 Iquebal MA, 2014, ANIM GENET, V45, P898, DOI 10.1111/age.12208 Josse J, 2012, J SFDS, V153, P79 Judge MM, 2017, ANIMAL, V11, P938, DOI 10.1017/S1751731116002457 Kuehn LA, 2011, J ANIM SCI, V89, P1742, DOI 10.2527/jas.2010-3530 Marquez GC, 2010, J ANIM SCI, V88, P59, DOI 10.2527/jas.2008-1292 Maudet C, 2002, J ANIM SCI, V80, P942 Mota RR, 2017, J ANIM SCI, V95, P4288, DOI 10.2527/jas2017.1748 Putnova L, 2019, J APPL GENET, V60, P187, DOI 10.1007/s13353-019-00495-x R Core Team, 2021, R AL ENV STAT COMP R Studio Team, 2021, RSTUDIO INT DEV R Schiavo G, 2020, ANIMAL, V14, P223, DOI 10.1017/S1751731119002167 Tibshirani R, 2002, P NATL ACAD SCI USA, V99, P6567, DOI 10.1073/pnas.082099299 Wilkinson S, 2011, BMC GENET, V12, DOI 10.1186/1471-2156-12-45 Wilmot H, 2022, J ANIM BREED GENET, V139, P40, DOI 10.1111/jbg.12643 NR 25 TC 0 Z9 0 U1 0 U2 0 PD SEP PY 2022 VL 263 AR 104996 DI 10.1016/j.livsci.2022.104996 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000886255900005 DA 2022-12-14 ER PT J AU Corallo, A Latino, ME Menegoli, M Striani, F AF Corallo, Angelo Latino, Maria Elena Menegoli, Marta Striani, Fabrizio TI The awareness assessment of the Italian agri-food industry regarding food traceability systems SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review DE Food traceability; Traceability system; Traceability driver; Traceability benefit; Traceability barrier; Traceability technology ID SUPPLY CHAIN; BLOCKCHAIN TECHNOLOGY; MANAGEMENT; AGRICULTURE; BENEFITS C1 [Corallo, Angelo; Latino, Maria Elena; Menegoli, Marta] Univ Salento, Dept Innovat Engn, Via Monteroni Sn, I-73100 Lecce, Italy. [Striani, Fabrizio] Univ Salento, Dept Econ & Commerce, Via Monteroni Sn, I-73100 Lecce, Italy. C3 University of Salento; University of Salento RP Latino, ME (corresponding author), Univ Salento, Dept Innovat Engn, Via Monteroni Sn, I-73100 Lecce, Italy. EM mariaelena.latino@unisalento.it CR Aichner T, 2017, INT REV RETAIL DISTR, V27, P43, DOI 10.1080/09593969.2016.1211028 Alonso-Roris VM, 2016, COMPUT IND, V83, P1, DOI 10.1016/j.compind.2016.08.003 Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Aung MM, 2014, FOOD CONTROL, V40, P198, DOI 10.1016/j.foodcont.2013.11.016 Barnett V., 2002, SAMPLE SURVEY PRINCI, Vthird Benatia M. A., 2018, 2018 4 INT C ADV TEC, P1, DOI DOI 10.1109/ATSIP.2018 Bevilacqua M, 2009, J FOOD ENG, V93, P13, DOI 10.1016/j.jfoodeng.2008.12.020 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Brandl D., 2002, REV ELECT ELECT, V8, P46, DOI 10.3845/ree.2002.087 Carfora V, 2019, FOOD QUAL PREFER, V76, P1, DOI 10.1016/j.foodqual.2019.03.006 Caselli G. C., 2015, AGROMAFIE 3 RAPPORTO Cicchitelli G., 1997, CAMPIONAMENTO STAT N Cochran W.G., 2007, SAMPLING TECHNIQUES Cochran W. G., 1963, SAMPLING TECHNIQUES Creswell J, 2017, RES DESIGN QUALITATI De Devitiis B, 2018, J AQUAT FOOD PROD T, V27, P430, DOI 10.1080/10498850.2018.1447059 EUFIC, 2014, FOOD TRAC CORN EU FO FoodDrink Europe, 2018, DAT TRENDS EU FOOD D Gilg A, 2005, FUTURES, V37, P481, DOI 10.1016/j.futures.2004.10.016 Gliem J., 2003, MIDWEST RES TO PRACT Gurdur D, 2019, COMPUT IND, V105, P153, DOI 10.1016/j.compind.2018.12.011 Hartmann C, 2018, FOOD CHEM TOXICOL, V116, P100, DOI 10.1016/j.fct.2018.04.006 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Jain S.K., 2003, DEV WATER SCI, P207, DOI [10.1016/S0167-5648(03)80058-8, DOI 10.1016/S0167-5648(03)80058-8] Junkkari M., 2014, P 3 INT C DAT MAN TE, P116, DOI [10.5220/0005001001160124., DOI 10.5220/0005001001160124] Kalpana S, 2019, TRENDS FOOD SCI TECH, V93, P145, DOI 10.1016/j.tifs.2019.09.008 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kumari L, 2015, TRENDS FOOD SCI TECH, V43, P144, DOI 10.1016/j.tifs.2015.02.005 Lehr H., 2013, PRECIS LIVEST FARMIN Lehtinen U., 2011, Intelligent agrifood chains and networks, P151 Lindley DV, 1997, J ROY STAT SOC D-STA, V46, P129 Lokunarangodage C. K., 2015, CONSTRAINTS COMPLIAN Mattevi M, 2016, BRIT FOOD J, V118, P1107, DOI 10.1108/BFJ-07-2015-0261 Meuwissen M.P., 2003, J AGRIBUSINESS, V21, P1, DOI [https://doi.org/10.22004/ag.econ.14666, DOI 10.22004/AG.ECON.14666] Milfont TL, 2016, CURR OPIN PSYCHOL, V10, P112, DOI 10.1016/j.copsyc.2015.12.016 Mishra D.K., 2016, PRODUCT LIFECYCLE MA, P377, DOI DOI 10.1007/978-3-319-54660-5_34 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Overbosch Peter, 2014, FOOD SAFETY MANAGEME, DOI [10.1016/B978-0-12-381504-0.00022-6 ., DOI 10.1016/B978-0-12-381504-0.00022-6, 10.1016/b978-0-12-381504-0.00022-6] Parlato A, 2014, TRENDS FOOD SCI TECH, V38, P60, DOI 10.1016/j.tifs.2014.04.001 Pinna C, 2018, COMPUT IND, V100, P184, DOI 10.1016/j.compind.2018.03.036 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ringsberg HA, 2015, BRIT FOOD J, V117, P1826, DOI 10.1108/BFJ-10-2014-0353 Salomie I, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR 2008), THETA 16TH EDITION, VOL I, PROCEEDINGS, P393, DOI 10.1109/AQTR.2008.4588774 Terzi Sergio, 2007, International Journal of Product Lifecycle Management, V2, P253, DOI 10.1504/IJPLM.2007.016292 Toyryla I., 1999, REALISING POTENTIAL van der Vorst JGAJ, 2004, DYNAMICS IN CHAINS AND NETWORKS, P175 Weisberg H. F., 1989, INTRO SURVEY RES DAT Wynn MT, 2011, COMPUT IND, V62, P776, DOI 10.1016/j.compind.2011.05.003 Zhang J, 2008, IEEE INT CON AUTO SC, P1, DOI 10.1109/COASE.2008.4626431 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 53 TC 7 Z9 9 U1 32 U2 88 PD JUL PY 2020 VL 101 BP 28 EP 37 DI 10.1016/j.tifs.2020.04.022 WC Food Science & Technology SC Food Science & Technology UT WOS:000541893100003 DA 2022-12-14 ER PT J AU Porter, JK Baker, GA Agrawal, N AF Porter, Justin K. Baker, Gregory A. Agrawal, Narendra TI The US Produce Traceability Initiative: Analysis, Evaluation, and Recommendations SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Article DE Produce Traceability Initiative (PTI); fresh produce industry; food safety AB The Produce Traceability Initiative (PTI) was created by a coalition of fresh produce industry leaders as a voluntary industry effort to improve supply chain traceability, expedite tracebacks and recalls, and isolate food safety problems when they occur. In this paper we assess the ability of the PTI to meet broad traceability goals and make recommendations for guidelines that should be followed for an effective traceback system. Our recommendations include principles for system-wide uniformity, standardization of product reference numbers, creation of a reporting mechanism, and open and transparent communication. C1 [Baker, Gregory A.] Santa Clara Univ, Leavey Sch Business, Food & Agribusiness Inst, Santa Clara, CA 95053 USA. [Porter, Justin K.] Westside Produce, Technol, Firebaugh, CA 93622 USA. C3 Santa Clara University RP Baker, GA (corresponding author), Santa Clara Univ, Leavey Sch Business, Food & Agribusiness Inst, 500 El Camino Real, Santa Clara, CA 95053 USA. EM Justin@westsideproduce.com; gbaker@scu.edu; nagrawal@scu.edu CR Alliance for Food and Farming, 2010, AN PROD REL FOODB IL *CDC, 2001, HLTH AL NETW *CDC, 2009, MORBIDITY MORTA 0206 *FDA, 2010, REP FOOD REG IND *GS1, 2009, GS1 ID KEY SER GTIN McEntire J.C., 2010, COMPREHENSIVE REV FO, V9, P93 Mead PS, 1999, EMERG INFECT DIS, V5, P607, DOI 10.3201/eid0505.990502 *NAT CTR INF DIS, 2009, WHAT IS PULSENET *PROD MARK ASS, 2009, PTI *PROD MARK ASS, 2009, PTI ACT PLAN *PROD TRAC IN, 2009, ACT PLAN *PTI STEER COMM, 2009, BEST PRACT CROSS DOC *PTI STEER COMM, 2009, TRAC IT LEV *PTI STEER COMM, 2009, PTI TOOLK INTR *PTI STEER COMM, 2009, BEST PRACT PRIV LAB *PTI STEER COMM, 2009, BEST PRACT EXT PROD *USDA, 2009, FOODB ILLN COST CALC *USDA, 2009, TRANSCR IMPR PROD TR NR 18 TC 5 Z9 5 U1 1 U2 6 PY 2011 VL 14 IS 3 BP 45 EP 66 WC Agricultural Economics & Policy SC Agriculture UT WOS:000298319900003 DA 2022-12-14 ER PT J AU Lopes, MA dos Santos, G Amado, GB AF Lopes, Marcos Aurelio dos Santos, Glauber Amado, Guilherme Beil TI The financial impact of the traceability in production systems of bovines in the State of Minas Gerais SO CIENCIA E AGROTECNOLOGIA DT Article DE beef cattle; cost of production; traceability; safety feed; simulation AB This work aimed to analyse the financial impact of the implantation of a bovine traceability system using double plastic earrings, in the rural properties of Minas Gerais State. Data of the Empresa de Assistencia Tecnica e Extensao Rural were used, being identified on March, 2004, a total of 309,551 rural properties, with an average of 67 bovines per property. The cost for the implantation of a traceability system in the rural properties of the state, with 67 bovines, varied from R$6,39/animal to R$6,43/animal. Considering that the additional income per traced animal varies from R$15,00 to R$30,00, since the majority of slaughter houses have paid the ranchers from R$0,03 to R$0,07 per per weight unit of meat (pounds), it was concluded that the implantantion of the traceability system has an economical viability, as the additional incomes exceed the costs of implantation. C1 [Lopes, Marcos Aurelio] Univ Fed Lavras, DMV, BR-37200000 Lavras, MG, Brazil. [dos Santos, Glauber; Amado, Guilherme Beil] Univ Fed Lavras, DZO, BR-37200000 Lavras, MG, Brazil. C3 Universidade Federal de Lavras; Universidade Federal de Lavras RP Lopes, MA (corresponding author), Univ Fed Lavras, DMV, Cx P 3037, BR-37200000 Lavras, MG, Brazil. EM malopes@ufla.br; glauber_zoo@yahoo.com.br; guibeil@hotmail.com CR BRASIL, 2002, DIARIO OFICIAL UNIAO, P1 DEREZENDE HMG, 2004, MONOGRAFIA POSGRADUA *FUND JOAO PINH, 2005, PROD INT BRUT MIN GE *I BRAS GEOGR EST, PROD PEC MUN Lopes MA, 2004, CIENC AGROTEC, V28, P883, DOI 10.1590/S1413-70542004000400022 LOPES ML, 2000, THESIS U ESTADUAL PA Machado JGCF, 2000, REV BRASILEIRA AGROI, V3, P41 MARTINS FM, 2003, B AGROPECUARIO, V55 MARTINS FM, 2002, MONOGRAFIA GRADUACAO MENDES RE, 2004, MONOGRAFIA POSGRADUA PIRES PP, 2003, C BRAS SOC BRAS INF, V3, P697 RESENDE EHS, 2004, B AGROPECUARIO, V58 ROCHA JLP, 2002, REV BRASILEIRA AGROI, V4, P130 Rolim FJ, 2005, CIENC AGROTEC, V29, P1052, DOI 10.1590/S1413-70542005000500021 NR 14 TC 7 Z9 11 U1 0 U2 1 PD JAN-FEB PY 2008 VL 32 IS 1 BP 288 EP 294 DI 10.1590/S1413-70542008000100041 WC Agriculture, Multidisciplinary; Agronomy SC Agriculture UT WOS:000257177100041 DA 2022-12-14 ER PT J AU Fan, BL Qian, JP Wu, XM Du, XW Li, WY Ji, ZT Xin, XP AF Fan, Beilei Qian, Jianping Wu, Xiaoming Du, Xiaowei Li, Wenyong Ji, Zengtao Xin, Xiaoping TI Improving continuous traceability of food stuff by using barcode-RFID bidirectional transformation equipment: Two field experiments SO FOOD CONTROL DT Article DE Traceability; QR code; RFID; Experiment analysis; Label transformation ID SYSTEM; PRODUCTS; TECHNOLOGIES; SAFETY; PART AB Radio frequency identification (RFID) and quick response (QR) codes are an effective way to identify traceable resource units (TRUs) and are widely applied in traceability systems. TRU transformation is common for things such as beef segmentation and wheat-flour packaging. The present study describes the development and testing of equipment for barcode-RFID bidirectional transformation to improve traceability by conserving identification association and information correspondence while transforming barcodes to RFID and vice versa. The framework of traceable bidirectional identification labels is based on comparing the features of RFID and barcodes and on analyzing TRU transformation. The proposed equipment includes a tactile industrial controller, a RFID-reader module, an embedded printing module, and a barcode scanning module. Furthermore, we develop the main functions of RFID-barcode conversion, bar-code-RFID conversion, and condition monitoring. The system is tested in field experiments based on two typical scenarios: beef segmentation and wheat-flour packaging. The average conversion success rate in RFID-barcode processing is 97%, and in barcode-RFID processing is 93.48%. The proposed equipment is more rapid than the reference method: 3.2 versus 8.7 s for RFID-barcode transformation and 10.5 versus 13.3 s for barcode-RFID transformation. In addition, the proposed equipment is less expensive than the conventional equipment. Test results indicate that continuous traceability is improved because identification association and information correspondence are conserved after TRU transformation. C1 [Fan, Beilei; Xin, Xiaoping] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. [Fan, Beilei; Qian, Jianping; Wu, Xiaoming; Du, Xiaowei; Li, Wenyong; Ji, Zengtao] Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. [Qian, Jianping; Wu, Xiaoming; Du, Xiaowei; Li, Wenyong; Ji, Zengtao] Beijing Acad Agr & Forestry Sci, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Beijing Academy of Agriculture & Forestry; Beijing Academy of Agriculture & Forestry RP Qian, JP (corresponding author), Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. EM qianjp@nercita.org.cn CR Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Cunha CR, 2010, COMPUT ELECTRON AGR, V73, P154, DOI 10.1016/j.compag.2010.05.007 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Ghaani M, 2016, TRENDS FOOD SCI TECH, V51, P1, DOI 10.1016/j.tifs.2016.02.008 International Organization for Standardization, 2006, ISOIEC180042006 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Luvisi A, 2012, BIOSYST ENG, V113, P129, DOI 10.1016/j.biosystemseng.2012.06.015 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qian JP, 2017, COMPUT ELECTRON AGR, V139, P56, DOI 10.1016/j.compag.2017.05.009 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Stranieri S, 2017, FOOD CONTROL, V80, P187, DOI 10.1016/j.foodcont.2017.04.047 Sun CH, 2007, NEW ZEAL J AGR RES, V50, P1269, DOI 10.1080/00288230709510412 NR 21 TC 25 Z9 28 U1 5 U2 71 PD APR PY 2019 VL 98 BP 449 EP 456 DI 10.1016/j.foodcont.2018.12.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000456754500056 DA 2022-12-14 ER PT J AU Parenreng, SM Pujawan, N Karningsih, PD Engelseth, P AF Parenreng, Syarifuddin Mabe Pujawan, Nyoman Karningsih, Putu Dana Engelseth, Per TI Mitigating Risk in the Tuna Supply through Traceability System Development SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Article DE risk management; traceability; complete supply chains; transvection; food; tuna ID OPERATIONS MANAGEMENT; FOOD; SAFETY; CHAINS; CHALLENGES; PERCEPTIONS; SECURITY; INDUSTRY; QUALITY; DEMAND AB This study concerns the mitigation of risk based on advances in food product traceability technology. A case study of the supply, processing and distribution of wild catch tuna on the island of Sulawesi in Indonesia provides the backdrop for describing and analyzing risk agents and how they are interrelated in the supply chain. The purpose of this study is to develop an inductive, empirically based model concerning risk mitigation in seafood supply networks. It builds upon the seminal works of Forrester's understanding of information distortion, Alderson's transvection model and Thompson's interdependency theory. C1 [Parenreng, Syarifuddin Mabe; Pujawan, Nyoman; Karningsih, Putu Dana] Sepuluh Nopember Inst Technol, Fac Ind Technol, Dept Ind Engn, Surabaya 60111, Indonesia. [Parenreng, Syarifuddin Mabe] Hasanuddin Univ, Fac Engn, Dept Ind Engn, South Sulawesimakassar 90245, Indonesia. [Engelseth, Per] Molde Univ Coll, Fac Logist, Postboks 2110, N-6402 Molde, Norway. C3 Institut Teknologi Sepuluh Nopember; Universitas Hasanuddin; Molde University College RP Engelseth, P (corresponding author), Molde Univ Coll, Fac Logist, Postboks 2110, N-6402 Molde, Norway. EM syarifmp@unhas.ac.id; pujawan@ie.its.ac.id; dana@ie.its.ac.id; peen@himolde.no CR Adebanjo D, 2009, SUPPLY CHAIN MANAG, V14, P224, DOI 10.1108/13598540910954566 Akkerman R, 2010, OR SPECTRUM, V32, P863, DOI 10.1007/s00291-010-0223-2 Alderson W., 1965, DYNAMIC MARKETING BE Angkiriwang R, 2014, PROD MANUF RES, V2, P50, DOI 10.1080/21693277.2014.882804 [Anonymous], RISK ENG BRIDGING RI Becker T., 2000, British Food Journal, V102, P158, DOI 10.1108/00070700010371707 Bijman J., 2006, INT AGRIFOOD CHAINS Bourlakis M., 2004, INT J COOPERATIVE MA, V1, P9 Choi TY, 2006, J OPER MANAG, V24, P637, DOI 10.1016/j.jom.2005.07.002 Christopher M., 2011, LOGISTICS SUPPLY CHA, V4th ed. Christopher M., 2004, INT J LOGIST MANAG, V15, P1, DOI [10.1108/09574090410700275, DOI 10.1108/09574090410700275] Cooper MJ, 2006, J BUS RES, V59, P653, DOI 10.1016/j.jbusres.2005.11.002 Corsaro D, 2012, IND MARKET MANAG, V41, P270, DOI 10.1016/j.indmarman.2012.01.002 Crevel RWR, 2014, FOOD CHEM TOXICOL, V67, P262, DOI 10.1016/j.fct.2014.01.032 Dani S, 2010, INT J LOGIST-RES APP, V13, P395, DOI 10.1080/13675567.2010.518564 Diabat A., 2012, INT J PROD RES, V50, P37 Engelsedi P., 2012, MODELLING VALUE, P373 Engelseth P, 2007, THESIS BI NORWEGIAN Engelseth P, 2016, INT J PROD ECON, V173, P99, DOI 10.1016/j.ijpe.2015.12.012 Engelseth P, 2013, INT FOOD AGRIBUS MAN, V16, P75 Engelseth P, 2012, J BUS IND MARK, V27, P673, DOI 10.1108/08858621211273619 Engelseth P, 2009, J BUS IND MARK, V24, P421, DOI 10.1108/08858620910966291 Eriksson P., 2008, QUALITATIVE METHODS, DOI DOI 10.4135/9780857028044 FORRESTER JW, 1958, HARVARD BUS REV, V36, P37 Giddens A., 1991, CONSEQUENCES MODERNI Gonzales-Barron U, 2011, FOOD MICROBIOL, V28, P823, DOI 10.1016/j.fm.2010.04.007 Jacxsens L, 2010, FOOD RES INT, V43, P1925, DOI 10.1016/j.foodres.2009.07.009 Juttner U., 2003, INT J LOGISTICS RES, V6, P197, DOI [10.1080/13675560310001627016, DOI 10.1080/13675560310001627016] Kull T, 2008, EUR J OPER RES, V186, P1158, DOI 10.1016/j.ejor.2007.02.028 Lagerkvist CJ, 2013, FOOD POLICY, V38, P92, DOI 10.1016/j.foodpol.2012.11.001 Lincoln YS., 1985, NAT, DOI 10.1016/0147-1767(85)90062-8 MARCH JG, 1987, MANAGE SCI, V33, P1404, DOI 10.1287/mnsc.33.11.1404 Marucheck A, 2011, J OPER MANAG, V29, P707, DOI 10.1016/j.jom.2011.06.007 Neiger D, 2009, J OPER MANAG, V27, P154, DOI 10.1016/j.jom.2007.11.003 Oliver R.K., 1982, LOGISTICS STRATEGIC, V5, P42 Pujawan I. N., 2004, International Journal of Integrated Supply Management, V1, P79, DOI 10.1504/IJISM.2004.004599 Pujawan IN, 2009, BUS PROCESS MANAG J, V15, P953, DOI 10.1108/14637150911003801 Rao S, 2009, INT J LOGIST MANAG, V20, P97, DOI 10.1108/09574090910954864 RICHARDSON GB, 1972, ECON J, V82, P883, DOI 10.2307/2230256 Rosenbloom B., 1995, COMPANION ENCY MARKE Slovic P. E., 2000, PERCEPTION RISK STONE ER, 1994, ORGAN BEHAV HUM DEC, V60, P387, DOI 10.1006/obhd.1994.1091 Tang O, 2011, INT J PROD ECON, V133, P25, DOI 10.1016/j.ijpe.2010.06.013 Taylor DH, 2006, SUPPLY CHAIN MANAG, V11, P379, DOI 10.1108/13598540610682381 Thompson JD., 1967, ORG ACTION SOCIAL SC Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 van der Vorst J.G.A.J., 2002, INT JNL PHYS 501 LOG, V32, P409, DOI [10.1108/09600030210437951, DOI 10.1108/09600030210437951] van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 Vanany I., SUPPLY CHAI IN PRESS Voss C, 2002, INT J OPER PROD MAN, V22, P195, DOI 10.1108/01443570210414329 Whipple JM, 2009, INT J PHYS DISTR LOG, V39, P574, DOI 10.1108/09600030910996260 Yeung R. M. W., 2003, Nutrition & Food Science, V33, P219, DOI 10.1108/00346650310499749 Zsidisin G.A., 2003, J PURCH SUPPLY MANAG, V9, P217, DOI [10.1016/j.purs, DOI 10.1016/J.PURSUP.2003.07.002] Zsidisin G.A., 2000, SUPPLY CHAIN MANAGEM, V5, P187, DOI [10.1108/13598540010347307, DOI 10.1108/13598540010347307] NR 54 TC 7 Z9 7 U1 0 U2 18 PY 2016 VL 19 IS 1 BP 59 EP 82 WC Agricultural Economics & Policy SC Agriculture UT WOS:000375405500005 DA 2022-12-14 ER PT J AU Ye, HP Yang, J Xiao, GS Zhao, Y Li, ZM Bai, WD Zeng, XF Dong, H AF Ye, Huiping Yang, Juan Xiao, Gengsheng Zhao, Yan Li, Zhanming Bai, Weidong Zeng, Xiaofang Dong, Hao TI A comprehensive overview of emerging techniques and chemometrics for authenticity and traceability of animal-derived food SO FOOD CHEMISTRY DT Article DE Food authenticity; Geographical origin traceability; Meat products; Dairy products; Aquatic products; Honey adulteration; Isotope ratio mass spectrometry ID STABLE-ISOTOPE RATIOS; CUCUMBER APOSTICHOPUS-JAPONICUS; GEOGRAPHICAL ORIGIN; LIQUID-CHROMATOGRAPHY; MULTIVARIATE-ANALYSIS; ELEMENTAL ANALYZER; MILK AUTHENTICITY; ANALYTICAL TOOL; DISCRIMINATION; IDENTIFICATION AB Authenticity and origin tracing of animal-derived food are particularly necessary due to various kinds of food fraud such as adulteration, counterfeiting, substitution and intentional mislabeling. This review focuses on the current research status of animal-derived food from the aspects of geographical origin, feeding ingredients and systems, adulteration of substitutes, and physical and chemical properties. The methods and statistical models involved in the research and their advantages and disadvantages are summarized. Stable isotope ratio analysis and element analysis are the most extensive used geographical traceability techniques. Spectroscopic techniques have the advantages of quick response, low cost and non-destructiveness. Instrument technology combined with chemometrics is the key method for origin traceability and authenticity of animal-derived food. In addition, there is a new trend of origin traceability by analyzing inedible parts of animal-derived food. This review intends to give a broad but comprehensive understanding in authenticity and geographical origin traceability of animal -derived food. C1 [Ye, Huiping; Yang, Juan; Xiao, Gengsheng; Bai, Weidong; Zeng, Xiaofang; Dong, Hao] Zhongkai Univ Agr & Engn, Acad Contemporary Agr Engn Innovat, Coll Light Ind & Food Sci, Guangdong Prov Key Lab Lingnan Specialty Food Sci, Guangzhou 510225, Peoples R China. [Yang, Juan; Xiao, Gengsheng; Bai, Weidong; Zeng, Xiaofang; Dong, Hao] Minist Agr, Key Lab Green Proc & Intelligent Mfg Lingnan Speci, Guangzhou 510225, Peoples R China. [Zhao, Yan] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Li, Zhanming] Jiangsu Univ Sci & Technol, Sch Grain Sci & Technol, Zhenjiang 212100, Peoples R China. [Zeng, Xiaofang; Dong, Hao] 24,Dongsha St,Fangzhi Rd, Guangzhou, Peoples R China. C3 Zhongkai University of Agriculture & Engineering; Ministry of Agriculture & Rural Affairs; Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Jiangsu University of Science & Technology RP Zeng, XF; Dong, H (corresponding author), 24,Dongsha St,Fangzhi Rd, Guangzhou, Peoples R China. EM xiaofang_zeng2015@163.com; donghao@zhku.edu.cn CR Abbas O, 2018, FOOD CHEM, V246, P6, DOI 10.1016/j.foodchem.2017.11.007 Akanbi TO, 2018, FOOD ANAL METHOD, V11, P178, DOI 10.1007/s12161-017-0988-x Amaral JS, 2015, FOOD CONTROL, V47, P190, DOI 10.1016/j.foodcont.2014.07.009 Antony B, 2018, INT J DAIRY TECHNOL, V71, P484, DOI 10.1111/1471-0307.12450 Bai Y, 2021, FOOD REV INT, DOI 10.1080/87559129.2021.1936000 Balthazar CF, 2021, TRENDS FOOD SCI TECH, V108, P84, DOI 10.1016/j.tifs.2020.12.011 Bandoniene D, 2018, J AGR FOOD CHEM, V66, P11729, DOI 10.1021/acs.jafc.8b03828 Behkami S, 2017, FOOD CHEM, V217, P438, DOI 10.1016/j.foodchem.2016.08.130 Bennion M, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107515 Bergamaschi M, 2020, FOOD CHEM, V305, DOI 10.1016/j.foodchem.2019.125480 Boito M, 2021, RAPID COMMUN MASS SP, V35, DOI 10.1002/rcm.9160 Bougadi ET, 2020, FOOD CHEM, V322, DOI 10.1016/j.foodchem.2020.126758 Camin F, 2018, FOOD CHEM, V267, P288, DOI 10.1016/j.foodchem.2017.06.017 Camin F, 2016, COMPR REV FOOD SCI F, V15, P868, DOI 10.1111/1541-4337.12219 Cavanna D, 2018, J MASS SPECTROM, V53, P849, DOI 10.1002/jms.4256 Chatterjee NS, 2020, J AOAC INT, V103, P78, DOI 10.5740/jaoacint.19-0208 Chung IM, 2018, FOOD CHEM, V261, P112, DOI 10.1016/j.foodchem.2018.04.017 Teixeira JLD, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107105 Di Pinto A, 2017, FOOD CHEM, V229, P93, DOI 10.1016/j.foodchem.2017.02.067 Dong H, 2020, COMPR REV FOOD SCI F, V19, P365, DOI [10.1111/1541-4337.12527, 10.1145/3334480.3383088] Dong H, 2018, FOOD CHEM, V240, P717, DOI 10.1016/j.foodchem.2017.08.008 Dong H, 2017, FOOD ANAL METHOD, V10, P2755, DOI 10.1007/s12161-017-0842-1 Dong H, 2016, J AGR FOOD CHEM, V64, P3258, DOI 10.1021/acs.jafc.6b00691 Drivelos SA, 2021, FOOD CHEM, V338, DOI 10.1016/j.foodchem.2020.127936 Edwards K, 2021, FOODS, V10, DOI 10.3390/foods10020448 Ejeahalaka KK, 2021, FOOD CHEM, V339, DOI 10.1016/j.foodchem.2020.127844 Erasmus SW, 2016, FOOD CHEM, V192, P997, DOI 10.1016/j.foodchem.2015.07.121 FAO, 2020, STATE WORLD FISHERIE, DOI [10.4060/ca9229en, 10.4060/ca9229-n, DOI 10.4060/CA9229-N, DOI 10.4060/CA9229EN] Fernandes TJR, 2017, FOOD CONTROL, V82, P8, DOI 10.1016/j.foodcont.2017.06.016 Fiamegos Y, 2020, ANAL BIOANAL CHEM, V412, P463, DOI 10.1007/s00216-019-02255-6 Molina OF, 2020, INT J FOOD PROP, V23, P2242, DOI 10.1080/10942912.2020.1850775 Geana EI, 2020, FOOD CONTROL, V109, DOI 10.1016/j.foodcont.2019.106919 Geana EI, 2020, FOOD CHEM, V306, DOI 10.1016/j.foodchem.2019.125595 Ghidini S, 2019, MOLECULES, V24, DOI 10.3390/molecules24091812 Ghidini S, 2019, FOOD CHEM, V280, P321, DOI 10.1016/j.foodchem.2018.12.075 Giese E, 2019, ANAL BIOANAL CHEM, V411, P6931, DOI 10.1007/s00216-019-02063-y Grassi S, 2018, LWT-FOOD SCI TECHNOL, V96, P469, DOI 10.1016/j.lwt.2018.05.065 Guelpa A, 2017, FOOD CONTROL, V73, P1388, DOI 10.1016/j.foodcont.2016.11.002 Guyader S, 2018, FOOD CONTROL, V91, P216, DOI 10.1016/j.foodcont.2018.03.046 Guzelmeric E, 2020, LWT-FOOD SCI TECHNOL, V132, DOI 10.1016/j.lwt.2020.109921 Hajjar G, 2021, FOOD CHEM, V360, DOI 10.1016/j.foodchem.2021.130056 Han C, 2021, FOOD CHEM, V364, DOI 10.1016/j.foodchem.2021.130364 Heidari M, 2020, J FOOD SCI TECH MYS, V57, P3415, DOI 10.1007/s13197-020-04375-9 Horcada A, 2017, SPAN J AGRIC RES, V15, DOI 10.5424/sjar/2017154-11032 Hungerford NL, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17176304 Jandric Z, 2017, FOOD CONTROL, V72, P189, DOI 10.1016/j.foodcont.2015.10.010 Jaouen K, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-23249-x Jiang HZ, 2021, FOODS, V10, DOI 10.3390/foods10092127 Kaltenbrunner M, 2018, FOOD CHEM, V269, P486, DOI 10.1016/j.foodchem.2018.07.023 Kang XM, 2020, FOOD CONTROL, V111, DOI 10.1016/j.foodcont.2019.107036 Karacaglar NNY, 2019, J FOOD DRUG ANAL, V27, P101, DOI 10.1016/j.jfda.2018.06.008 Kim MJ, 2019, LWT-FOOD SCI TECHNOL, V114, DOI 10.1016/j.lwt.2019.108390 Koulis GA, 2021, MOLECULES, V26, DOI 10.3390/molecules26092769 Kounelli ML, 2017, EUR FOOD RES TECHNOL, V243, P1773, DOI 10.1007/s00217-017-2882-6 Li QQ, 2017, J SCI FOOD AGR, V97, P2875, DOI 10.1002/jsfa.8118 Liu Z, 2020, FOOD CHEM, V328, DOI 10.1016/j.foodchem.2020.127115 Logan BG, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107652 Logan BG, 2020, J RAMAN SPECTROSC, V51, P2338, DOI 10.1002/jrs.5983 Logan BG, 2020, MEAT SCI, V160, DOI 10.1016/j.meatsci.2019.107970 Luo DH, 2016, FOOD ANAL METHOD, V9, P437, DOI 10.1007/s12161-015-0204-9 Luo RJ, 2019, FOOD CHEM, V274, P1, DOI 10.1016/j.foodchem.2018.08.104 McDonald CM, 2018, NPJ SCI FOOD, V2, DOI 10.1038/s41538-018-0016-6 Moloney AP, 2018, IRISH J AGR FOOD RES, V57, P84, DOI 10.1515/ijafr-2018-0009 Monakhova YB, 2018, ANAL LETT, V51, P2549, DOI 10.1080/00032719.2018.1440402 Munir F, 2019, TURK J CHEM, V43, P1098, DOI 10.3906/kim-1902-17 Nie J, 2020, MEAT SCI, V165, DOI 10.1016/j.meatsci.2020.108113 Padovan GJ, 2007, EURASIAN J ANAL CHEM, V2, P134, DOI 10.12973/ejac.2007.00017a Parastar H, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107149 Park YM, 2018, MEAT SCI, V143, P93, DOI 10.1016/j.meatsci.2018.04.012 Pavlidis DE, 2019, MEAT SCI, V151, P43, DOI 10.1016/j.meatsci.2019.01.003 Pianezze S, 2020, J MASS SPECTROM, V55, DOI 10.1002/jms.4451 Pimentel T, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-11552-y Prandi B, 2019, FOOD CONTROL, V97, P15, DOI 10.1016/j.foodcont.2018.10.016 Qie MJ, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107549 Rebechi SR, 2016, FOOD CHEM, V192, P1025, DOI 10.1016/j.foodchem.2015.07.107 Rees G, 2016, FOOD CONTROL, V67, P144, DOI 10.1016/j.foodcont.2016.02.018 Riuzzi G, 2021, INT DAIRY J, V112, DOI 10.1016/j.idairyj.2020.104859 Rohman A, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107864 Ross A, 2021, MEAT SCI, V181, DOI 10.1016/j.meatsci.2020.108333 Schievano E, 2020, FOOD CHEM, V309, DOI 10.1016/j.foodchem.2019.125788 Silva LKR, 2021, INT J DAIRY TECHNOL, V74, P393, DOI 10.1111/1471-0307.12767 Sun FM, 2016, J AOAC INT, V99, P1032, DOI 10.5740/jaoacint.16-0071 Sun SM, 2016, FOOD CHEM, V213, P675, DOI 10.1016/j.foodchem.2016.07.013 Suzuki Y, 2021, ANAL SCI, V37, P189, DOI 10.2116/analsci.20SAR14 Tomaszewska-Gras J, 2016, J THERM ANAL CALORIM, V126, P61, DOI 10.1007/s10973-016-5346-5 Uncu AO, 2020, FOOD CHEM, V326, DOI 10.1016/j.foodchem.2020.126986 Uysal RS, 2020, J SCI FOOD AGR, V100, P855, DOI 10.1002/jsfa.10097 Valdes A, 2018, TRENDS FOOD SCI TECH, V77, P120, DOI 10.1016/j.tifs.2018.05.014 Voica C, 2020, J MASS SPECTROM, V55, DOI 10.1002/jms.4512 Wang KW, 2021, LWT-FOOD SCI TECHNOL, V149, DOI 10.1016/j.lwt.2021.111838 Wang Q, 2021, MEAT SCI, V174, DOI 10.1016/j.meatsci.2020.108415 Wang XR, 2019, J AGR FOOD CHEM, V67, P12144, DOI 10.1021/acs.jafc.9b04438 Wang YMV, 2018, FOOD CHEM, V256, P380, DOI 10.1016/j.foodchem.2018.02.095 Wijenayake K, 2020, MOLECULES, V25, DOI 10.3390/molecules25163658 Wirta H, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-84174-0 Xie LN, 2020, FOOD CHEM, V316, DOI 10.1016/j.foodchem.2020.126332 Xu SY, 2021, ANAL METHODS-UK, V13, P2537, DOI [10.1039/D1AY00339A, 10.1039/d1ay00339a] Xu Y, 2021, FOOD CHEM, V361, DOI 10.1016/j.foodchem.2021.130147 Yayinie M, 2021, ARAB J CHEM, V14, DOI 10.1016/j.arabjc.2021.102987 You ZT, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108234 Yun ZY, 2017, FOOD SCI BIOTECHNOL, V26, P357, DOI 10.1007/s10068-017-0048-8 Zhang JL, 2020, FOOD RES INT, V137, DOI 10.1016/j.foodres.2020.109714 Zhang XY, 2017, INT J FOOD PROP, V20, P2932, DOI 10.1080/10942912.2016.1261153 Zhang XF, 2019, FOOD CHEM, V299, DOI 10.1016/j.foodchem.2019.125107 Zhang XF, 2017, FOOD CHEM, V218, P269, DOI 10.1016/j.foodchem.2016.08.083 Zhao HY, 2016, FOOD CONTROL, V66, P62, DOI 10.1016/j.foodcont.2016.01.045 Zhao SS, 2020, RAPID COMMUN MASS SP, V34, DOI 10.1002/rcm.8795 Zhao SS, 2020, FOOD CHEM, V310, DOI 10.1016/j.foodchem.2019.125826 Zhao XD, 2018, FOOD CONTROL, V91, P128, DOI 10.1016/j.foodcont.2018.03.041 Zhao Y, 2020, MEAT SCI, V165, DOI 10.1016/j.meatsci.2020.108129 Zhao Y, 2016, MEAT SCI, V118, P103, DOI 10.1016/j.meatsci.2016.03.030 Zhaxi C, 2021, FOOD CHEM, V358, DOI 10.1016/j.foodchem.2021.129893 Zhou JQ, 2015, FOOD CHEM, V182, P23, DOI 10.1016/j.foodchem.2015.02.116 Zhou XT, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-32764-w Zhou XW, 2021, FOOD CONTROL, V128, DOI 10.1016/j.foodcont.2021.108165 NR 115 TC 0 Z9 0 U1 19 U2 19 PD FEB 15 PY 2023 VL 402 AR 134216 DI 10.1016/j.foodchem.2022.134216 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000874149500003 DA 2022-12-14 ER PT J AU Basalekou, M Pappas, C Tarantilis, PA Kallithraka, S AF Basalekou, Marianthi Pappas, Christos Tarantilis, Petros A. Kallithraka, Stamatina TI Wine Authenticity and Traceability with the Use of FT-IR SO BEVERAGES DT Review DE FT-IR; wine; authentication; traceability; origin; typicity; aging AB Fourier transform infrared spectroscopy (FT-IR) has gained popularity in the wine sector due to its simplicity and ability to provide a wine's fingerprint. For this reason, it is often used for authentication and traceability purposes with more than satisfactory results. In this review, an outline of the reasons why authenticity and traceability are important to the wine sector is given, along with a brief overview of the analytical methods used for their attainment; statistical issues and compounds, on which authentication usually is based, are discussed. Moreover, insight on the mode of action of FT-IR is given, along with successful examples from its use in different areas of interest for classification. Finally, prospects and challenges for suggested future research are given. For more accurate and effective analyses, the construction of a large database consisting of wines from different regions, varieties and winemaking protocols is suggested. C1 [Basalekou, Marianthi; Kallithraka, Stamatina] Agr Univ Athens, Dept Food Sci & Human Nutr, Lab Oenol, 75 Iera Odos, Athens 11855, Greece. [Pappas, Christos; Tarantilis, Petros A.] Agr Univ Athens, Dept Food Sci & Human Nutr, Lab Gen Chem, 75 Iera Odos, Athens 11855, Greece. C3 Agricultural University of Athens; Agricultural University of Athens RP Basalekou, M (corresponding author), Agr Univ Athens, Dept Food Sci & Human Nutr, Lab Oenol, 75 Iera Odos, Athens 11855, Greece. EM marianthi@aua.gr; chrispap@aua.gr; ptara@aua.gr; stamatina@aua.gr CR Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 [Anonymous], 2003, GENERATIONS COLLIDE Arfelli G, 2007, FOOD SCI TECHNOL INT, V13, P293, DOI 10.1177/1082013207082388 Arno J, 2009, SPAN J AGRIC RES, V7, P779, DOI 10.5424/sjar/2009074-1092 Aytac B, 2016, RES INT BUS FINANC, V36, P591, DOI 10.1016/j.ribaf.2015.03.001 Azcarate SM, 2015, FOOD CONTROL, V57, P268, DOI 10.1016/j.foodcont.2015.04.025 Basalekou M, 2017, J ANAL METHODS CHEM, V2017, DOI 10.1155/2017/5767613 Basalekou M, 2019, LWT-FOOD SCI TECHNOL, V101, P48, DOI 10.1016/j.lwt.2018.11.017 Basalekou M, 2016, CURR RES NUTR FOOD S, V4, P54, DOI 10.12944/CRNFSJ.4.Special-Issue-October.08 Basalekou M, 2017, INT J FOOD SCI TECH, V52, P1307, DOI 10.1111/ijfs.13424 Baxter MJ, 1997, FOOD CHEM, V60, P443, DOI 10.1016/S0308-8146(96)00365-2 Bellomarino SA, 2009, TALANTA, V80, P833, DOI 10.1016/j.talanta.2009.08.001 Beverland M, 2006, J BUS RES, V59, P251, DOI 10.1016/j.jbusres.2005.04.007 Bevin CJ, 2006, J AGR FOOD CHEM, V54, P9713, DOI 10.1021/jf062265o Bonanno A, 2019, EUR REV AGRIC ECON, V46, P163, DOI 10.1093/erae/jby024 Bureau S, 2019, POSTHARVEST BIOL TEC, V148, P1, DOI 10.1016/j.postharvbio.2018.10.003 Cabrita MJ, 2018, FOOD CONTROL, V92, P80, DOI 10.1016/j.foodcont.2018.04.041 Castillo-Munoz N, 2007, J AGR FOOD CHEM, V55, P992, DOI 10.1021/jf062800k Catarino S, 2008, J AGR FOOD CHEM, V56, P158, DOI 10.1021/jf0720180 Cozzolino D, 2011, FOOD CHEM, V126, P673, DOI 10.1016/j.foodchem.2010.11.005 Cozzolino D, 2012, FOOD ANAL METHOD, V5, P381, DOI 10.1007/s12161-011-9249-6 Cozzolino D, 2009, FOOD CHEM, V116, P761, DOI 10.1016/j.foodchem.2009.03.022 Cuadrado MU, 2005, ANAL BIOANAL CHEM, V381, P953, DOI 10.1007/s00216-004-2954-x da Costa NL, 2016, APPL ARTIF INTELL, V30, P679, DOI 10.1080/08839514.2016.1214416 Dinca OR, 2016, FOOD ANAL METHOD, V9, P2406, DOI 10.1007/s12161-016-0404-y Dong D, 2014, FOOD CHEM, V155, P45, DOI 10.1016/j.foodchem.2014.01.025 El-Ahmady SH, 2016, WOODHEAD PUBL FOOD S, P667, DOI 10.1016/B978-0-08-100220-9.00024-2 Fabani MP, 2013, FOOD CHEM, V141, P1055, DOI 10.1016/j.foodchem.2013.04.046 Ferreiro-Gonzalez M, 2019, FOOD CHEM, V277, P6, DOI 10.1016/j.foodchem.2018.10.087 Fudge AL, 2012, J AGR FOOD CHEM, V60, P52, DOI 10.1021/jf203849h Galgano F, 2008, LWT-FOOD SCI TECHNOL, V41, P1808, DOI 10.1016/j.lwt.2008.01.015 Gambetta JM, 2019, FOOD ANAL METHOD, V12, P239, DOI 10.1007/s12161-018-1355-2 Garcia-Beneytez E, 2002, EUR FOOD RES TECHNOL, V215, P32, DOI 10.1007/s00217-002-0526-x Geana EI, 2019, MOLECULES, V24, DOI 10.3390/molecules24224166 Genisheva Z, 2018, FOOD CHEM, V246, P172, DOI 10.1016/j.foodchem.2017.11.015 Godelmann R, 2013, J AGR FOOD CHEM, V61, P5610, DOI 10.1021/jf400800d Gonzalez-Neves G, 2007, EUR FOOD RES TECHNOL, V225, P111, DOI 10.1007/s00217-006-0388-8 Gonzalvez A, 2009, FOOD CHEM, V112, P26, DOI 10.1016/j.foodchem.2008.05.043 Grijalba N, 2020, MICROCHEM J, V154, DOI 10.1016/j.microc.2019.104564 Herrero-Latorre C, 2019, FOOD CHEM X, V3, DOI 10.1016/j.fochx.2019.100046 Hu LQ, 2018, SPECTROCHIM ACTA A, V205, P574, DOI 10.1016/j.saa.2018.07.054 Hu XZ, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-44521-8 Jakubowski N, 1999, FRESEN J ANAL CHEM, V364, P424, DOI 10.1007/s002160051361 Jimenez-Carvelo AM, 2019, FOOD RES INT, V122, P25, DOI 10.1016/j.foodres.2019.03.063 Kruzlicova D, 2009, FOOD CHEM, V112, P1046, DOI 10.1016/j.foodchem.2008.06.047 Kyraleou M, 2015, J FOOD SCI, V80, pC2701, DOI 10.1111/1750-3841.13125 Langlois J, 2011, FOOD QUAL PREFER, V22, P491, DOI 10.1016/j.foodqual.2010.10.008 Louw L, 2009, J AGR FOOD CHEM, V57, P2623, DOI 10.1021/jf8037456 Makris DP, 2006, TALANTA, V70, P1143, DOI 10.1016/j.talanta.2006.03.024 Markham D., 1997, 1855 HIST BORDEAUX C Monakhova YB, 2015, J ANAL CHEM+, V70, P1055, DOI 10.1134/S1061934815090117 Murru C, 2019, COMPUT ELECTRON AGR, V164, DOI 10.1016/j.compag.2019.104922 Nicolini G, 2004, VITIS, V43, P41 Oliver SG, 1998, TRENDS BIOTECHNOL, V16, P373, DOI 10.1016/S0167-7799(98)01214-1 Palade M., 2014, Scientific Bulletin. Series F. Biotechnologies, V18, P226 Pavlousek P, 2013, CZECH J FOOD SCI, V31, P474, DOI 10.17221/40/2013-CJFS Perestrelo R, 2011, J AGR FOOD CHEM, V59, P3186, DOI 10.1021/jf104219t Peris M, 2016, TRENDS FOOD SCI TECH, V58, P40, DOI 10.1016/j.tifs.2016.10.014 Riovanto R, 2011, J AGR FOOD CHEM, V59, P10356, DOI 10.1021/jf202578f Rodrigues H, 2019, FOOD RES INT, V115, P251, DOI 10.1016/j.foodres.2018.09.008 Sagratini G, 2012, FOOD CHEM, V132, P1592, DOI 10.1016/j.foodchem.2011.11.108 SEEBER R, 1991, J AGR FOOD CHEM, V39, P1764, DOI 10.1021/jf00010a014 SOMERS TC, 1977, J SCI FOOD AGR, V28, P279, DOI 10.1002/jsfa.2740280311 Sung B, 2020, J RETAIL CONSUM SERV, V54, DOI 10.1016/j.jretconser.2020.102034 Tarantilis PA, 2008, FOOD CHEM, V111, P192, DOI 10.1016/j.foodchem.2008.03.020 Urickova V, 2015, SPECTROCHIM ACTA A, V148, P131, DOI 10.1016/j.saa.2015.03.111 Valentin L, 2020, FOOD CHEM, V302, DOI 10.1016/j.foodchem.2019.125340 Van Leeuwen Cornelis, 2006, Journal of Wine Research, V17, P1, DOI 10.1080/09571260600633135 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Vestia J, 2019, FOOD CHEM, V276, P71, DOI 10.1016/j.foodchem.2018.09.116 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Wartewig S., 2003, IR RAMAN SPECTROSCOP Yue C., 2016, NONTARIFF MEAS INT T, P339 Zou JF, 2012, AM J ENOL VITICULT, V63, P232, DOI 10.5344/ajev.2012.11087 NR 74 TC 11 Z9 12 U1 1 U2 10 PD JUN PY 2020 VL 6 IS 2 AR 30 DI 10.3390/beverages6020030 WC Food Science & Technology SC Food Science & Technology UT WOS:000616117800011 DA 2022-12-14 ER PT J AU Lavoie, G Forest, JF AF Lavoie, Gilbert Forest, Jean-Francois TI Implementation of a Traceability System From Constraints to Opportunities for the Industry: A Case Study of Quebec, Canada SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Article DE traceability; strategy; strategic alliance between industry and government; epidemiological crisis; information technology AB Increasing frequency of epidemiological crisis and their disastrous consequences are motivating nations, around the world, to introduce traceability systems. Traceability systems enable identification, prevention of propagation, and control of diseases and health problems in the shortest possible delay. However, while this effort is praise worthy and indeed necessary, the implementation of a traceability system is complicated primarily because it involves additional constraints and costs to the industry. This article describes the introduction and success of a compulsory traceability system in the Quebec province of Canada by presenting the approach and the strategies that were adopted to minimize constraints and generate opportunities for the industry. C1 [Lavoie, Gilbert; Forest, Jean-Francois] Forest Lavoie Consultants Inc, Forest Lavoie Conseil Inc, Quebec City, PQ J4P 2V8, Canada. RP Forest, JF (corresponding author), Forest Lavoie Consultants Inc, Forest Lavoie Conseil Inc, 534 Ave Curzon, Quebec City, PQ J4P 2V8, Canada. EM glavoie@forestlavoieconseil.com; jfforest@forestlavoieconseil.com NR 0 TC 3 Z9 4 U1 1 U2 4 PY 2009 VL 12 IS 2 BP 71 EP 79 WC Agricultural Economics & Policy SC Agriculture UT WOS:000208146100005 DA 2022-12-14 ER PT J AU Pouliot, S Sumner, DA AF Pouliot, Sebastien Sumner, Daniel A. TI Traceability, recalls, industry reputation and product safety SO EUROPEAN REVIEW OF AGRICULTURAL ECONOMICS DT Article DE food safety; product recall; reputation; traceability; D21; Q10; Q18 ID FOOD QUALITY; MARKET; SYSTEMS; CHOICE; WEALTH; IMPACT; CHAIN AB Sometimes, authorities are unable to rapidly identify the origin of a tainted product. In such cases, recalls or warnings often apply to all suppliers, even to those that had not contributed to the contamination. Traceability enables more targeted recalls by identifying the products origin more specifically. In this article, we show how increased traceability protects the reputation of industries by limiting the size of recalls. We show the relationships between traceability and the level of food safety with many identical small farms in a competitive industry and for an industry using collective action to set rules and standards. C1 [Pouliot, Sebastien] Iowa State Univ, Dept Econ, Ames, IA 50011 USA. [Sumner, Daniel A.] Univ Calif Davis, Davis, CA 95616 USA. C3 Iowa State University; University of California System; University of California Davis RP Pouliot, S (corresponding author), Iowa State Univ, Dept Econ, Ames, IA 50011 USA. EM pouliot@iastate.edu CR Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bocker A, 2000, J ECON BEHAV ORGAN, V43, P471, DOI 10.1016/S0167-2681(00)00131-1 Calvin L., 2007, Amber Waves, V5, P24 Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 EC-European Commission, 2002, J EUROP COMM, VL031, P1 Fischer C, 2009, EUR REV AGRIC ECON, V36, P541, DOI 10.1093/erae/jbp041 Golan E., 2004, 830 USDAERS Gray R. S., 2005, MONOGRAPH U CALIFORN, V46 Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 HARTMAN RS, 1987, REV ECON STAT, V69, P367, DOI 10.2307/1927247 HOFFER GE, 1988, J POLIT ECON, V96, P663, DOI 10.1086/261556 JARRELL G, 1985, J POLIT ECON, V93, P512, DOI 10.1086/261313 King RP, 2007, EUR REV AGRIC ECON, V34, P81, DOI 10.1093/erae/jbl030 KLEIN B, 1981, J POLIT ECON, V89, P615, DOI 10.1086/260996 KREPS DM, 1982, J ECON THEORY, V27, P253, DOI 10.1016/0022-0531(82)90030-8 Marino AM, 1997, J REGUL ECON, V12, P245, DOI 10.1023/A:1007901928782 McQuade T., 2010, 0931 RFF Merel PR, 2009, EUR REV AGRIC ECON, V36, P31, DOI 10.1093/erae/jbp004 MILGROM P, 1982, J ECON THEORY, V27, P280, DOI 10.1016/0022-0531(82)90031-X Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x RUCKER RR, 1995, J POLIT ECON, V103, P142, DOI 10.1086/261979 Tirole J, 1996, REV ECON STUD, V63, P1, DOI 10.2307/2298112 Verbeke W, 2001, FOOD QUAL PREFER, V12, P489, DOI 10.1016/S0950-3293(01)00042-8 NR 25 TC 27 Z9 32 U1 3 U2 73 PD FEB PY 2013 VL 40 IS 1 BP 121 EP 142 DI 10.1093/erae/jbs006 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000312884100006 DA 2022-12-14 ER PT J AU Latino, ME Menegoli, M Lazoi, M Corallo, A AF Latino, Maria Elena Menegoli, Marta Lazoi, Mariangela Corallo, Angelo TI Voluntary traceability in food supply chain: a framework leading its implementation in Agriculture 4.0 SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE DT Article DE Agriculture 4.0; Industry 4.0; Food traceability; Internet of Things; Analytics; Business process ID INFORMATION-TECHNOLOGY; LOGISTICS MANAGEMENT; PRODUCT RECALLS; SYSTEM; PERSPECTIVES; QUALITY; SUSTAINABILITY; INDUSTRY; DESIGN; TRANSPARENCY AB Today, the world faces a big challenge about food quality and safety. Consumers are becoming more careful during their food choices, focusing on food information. Drawing on the Industry 4.0 technologies and methodologies, food companies could adopt traceability systems that are able to collect various types of information along the food supply chain, to satisfy regulation but also to make their work transparent. With the aim of overcoming the current shortcomings in the food traceability framework (e.g., the absence of data visualisation solutions or tools for supply chain actors and consumers, adopting standards for external traceability and data identification, and fulfilling the four Industry 4.0 development dimensions), and considering the sustainability challenges, the aim of this study is to suggest a Framework for Voluntary Food Traceability based on digital technologies and explore its application by means of a case study in an organic olive oil company. The Framework, composed of three building blocks, allows the company to: map supply chain processes, identifying tasks and processes where data was generated, its format and the supply chain owner; select and use technologies to collect and analyse traceability data; and communicate to the end consumer comprehensible and useful food information. C1 [Latino, Maria Elena; Menegoli, Marta; Lazoi, Mariangela; Corallo, Angelo] Univ Salento, Dept Innovat Engn, Campus Ecotekne,Edificio Aldo Romano, I-73100 Lecce, Italy. C3 University of Salento RP Latino, ME (corresponding author), Univ Salento, Dept Innovat Engn, Campus Ecotekne,Edificio Aldo Romano, I-73100 Lecce, Italy. EM mariaelena.latino@unisalento.it; marta.menegoli@unisalento.it; mariangela.lazoi@unisalento.it; angelo.corallo@unisalento.it CR Accorsi R, 2018, J CLEAN PROD, V203, P1039, DOI 10.1016/j.jclepro.2018.08.275 Allweyer T., 2016, BPMN 2 0 INTRO STAND Andreopoulou Z., 2017, INT THINGS FOOD CIRC, V2, P43, DOI [10.3280/RISS2017-002004, DOI 10.3280/RISS2017-002004] Appelhanz S, 2016, J CLEAN PROD, V110, P132, DOI 10.1016/j.jclepro.2015.02.034 Aprile MC, 2012, INT J CONSUM STUD, V36, P158, DOI 10.1111/j.1470-6431.2011.01092.x Araujo SO, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11040667 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Azuara G, 2012, IND MANAGE DATA SYST, V112, P340, DOI 10.1108/02635571211210022 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Balamurugan S., 2019, INT J ENG ADV TECHNO, V9, P2995, DOI [10.35940/ijeat.A1379.109119, DOI 10.35940/IJEAT.A1379.109119] Baldi L, 2021, FOOD QUAL PREFER, V90, DOI 10.1016/j.foodqual.2021.104175 Banerjee S., 2020, 2020 INT C ART INT S, P1, DOI [DOI 10.1109/AISP48273.2020.9073248, 10.1109/ AISP48273.2020.9073248] Banterle A., 2008, AGRIREGIONIEUROPA, V4 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bellarby J., 2008, COOL FARMING CLIMATE Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bougdira A, 2019, J MODEL MANAG, V15, P509, DOI 10.1108/JM2-12-2018-0207 Bourlakis M, 2006, J ENTERP INF MANAG, V19, P389, DOI 10.1108/17410390610678313 Brun J, 2021, AGR SYST, V191, DOI 10.1016/j.agsy.2021.103143 Bryman A., 2011, BUSINESS RES METHODS, V3rd Bryngeisson D, 2016, FOOD POLICY, V59, P152, DOI 10.1016/j.foodpol.2015.12.012 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Carfora V, 2019, FOOD QUAL PREFER, V76, P1, DOI 10.1016/j.foodqual.2019.03.006 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Chan FTS, 2013, INT J PROD RES, V51, P1196, DOI 10.1080/00207543.2012.693961 Chang YL, 2020, INT J PROD RES, V58, P2082, DOI 10.1080/00207543.2019.1651946 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chen HL, 2021, IEEE ACCESS, V9, P36008, DOI 10.1109/ACCESS.2021.3062410 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 Chen YY, 2020, J CLEAN PROD, V268, DOI 10.1016/j.jclepro.2020.122071 Chiffoleau Y, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12239831 Chirumalla K, 2021, TECHNOVATION, V105, DOI 10.1016/j.technovation.2021.102256 Cicatiello C., 2012, SOSTENIBILTA FILIERA Clark T, 2000, TECHNOVATION, V20, P247, DOI 10.1016/S0166-4972(99)00131-5 Cui Y, 2018, J FOOD PROCESS ENG, V41, DOI 10.1111/jfpe.12864 Cui Y, 2020, IEEE POW ENER SOC GE D'Amico M., 2009, ATT 44 CONV STUD TAO Dabbene F, 2016, WOODHEAD PUBL FOOD S, V301, P67, DOI 10.1016/B978-0-08-100310-7.00005-3 Dai HY, 2015, INT J PROD RES, V53, P511, DOI 10.1080/00207543.2014.955922 De Clercq M., 2018, P WORLD GOVT SUMMIT, P11 Dey S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063486 Donnelly KAM, 2012, FOOD CONTROL, V27, P228, DOI 10.1016/j.foodcont.2012.03.021 Donnelly KAM, 2012, BRIT FOOD J, V114, P1016, DOI 10.1108/00070701211241590 Espinoza-Mejia Mauricio, 2021, Artificial Intelligence, Computer and Software Engineering Advances. Proceedings of the CIT 2020. Advances in Intelligent Systems and Computing (AISC 1326), P135, DOI 10.1007/978-3-030-68080-0_10 Faccilongo N., 2016, International Journal of Sustainable Agricultural Management and Informatics, V2, P206 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Firbank L.G., 2019, FRONT SUSTAIN FOOD S, V2, DOI [10.3389/fsufs.2018.00007, DOI 10.3389/FSUFS.2018.00007] Foley JA, 2011, NATURE, V478, P337, DOI 10.1038/nature10452 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Framling K, 2006, COMPUT IND, V57, P72, DOI 10.1016/j.compind.2005.04.004 Galstyan AG, 2019, HER RUSS ACAD SCI+, V89, P211, DOI 10.1134/S1019331619020059 Garbero A, 2021, TECHNOL FORECAST SOC, V172, DOI 10.1016/j.techfore.2021.121012 Garnett T, 2013, P NUTR SOC, V72, P29, DOI 10.1017/S0029665112002947 Campos JG, 2011, COMPUT IND, V62, P311, DOI 10.1016/j.compind.2010.09.003 Garrone P, 2014, FOOD POLICY, V46, P129, DOI 10.1016/j.foodpol.2014.03.014 Gosling J, 2016, J CLEAN PROD, V137, P1458, DOI 10.1016/j.jclepro.2014.10.029 Grant RM, 1996, STRATEGIC MANAGE J, V17, P109, DOI 10.1002/smj.4250171110 Groger C, 2018, DATENBANK SPEKTRUM, V18, DOI [10.1007/s13222-018-0273-1, DOI 10.1007/S13222-018-0273-1] Guido R, 2020, INT J IND ENG MANAGE, V11, P50, DOI 10.24867/IJIEM-2020-1-252 Heller MC, 2018, ENVIRON RES LETT, V13, DOI 10.1088/1748-9326/aab0ac Hernandez San Juan I., 2020, European Food and Feed Law Review, P563 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Howard P. H., 2006, International Journal of Consumer Studies, V30, P439, DOI 10.1111/j.1470-6431.2006.00536.x Huang ZS, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9050683 Iakovou E, 2010, WASTE MANAGE, V30, P1860, DOI 10.1016/j.wasman.2010.02.030 International Olive Oil Council, 2021, CONS OL OIL Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 ISMEA, 2020, SCHED SETT OL OL 202 ISTAT, 2021, CENS ISTAT COLT UV V Jedermann R, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0302 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Ketelsen M, 2020, J CLEAN PROD, V254, DOI 10.1016/j.jclepro.2020.120123 Khan M.E., 2019, P INT C IND ENG OP M, P143 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 Klassen RD, 2012, INT J PROD ECON, V140, P103, DOI 10.1016/j.ijpe.2012.01.021 Kumar G, 2020, SUSTAIN CITIES SOC, V62, DOI 10.1016/j.scs.2020.102361 Kumar R, 2020, J CLEAN PROD, V275, DOI 10.1016/j.jclepro.2020.124063 Kumar S, 2006, TECHNOVATION, V26, P739, DOI 10.1016/j.technovation.2005.05.006 Lawo D, 2021, SUSTAIN PROD CONSUMP, V27, P282, DOI 10.1016/j.spc.2020.11.007 Lehtinen U., 2011, Intelligent agrifood chains and networks, P151 Li Y., 2020, ARTIF INTELL, P347, DOI [10.1007/978-981-15-8083-3_31, DOI 10.1007/978-981-15-8083-3_31] Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Liu XC, 2020, LECT NOTE DATA ENG, V41, P421, DOI 10.1007/978-3-030-34986-8_30 Liu Y, 2021, IEEE T IND INFORM, V17, P4322, DOI 10.1109/TII.2020.3003910 Lo SK, 2019, LECT NOTES COMPUT SC, V11521, P65, DOI 10.1007/978-3-030-23404-1_5 Lombardo L, 2021, FOODS, V10, DOI 10.3390/foods10030501 Lovarelli D, 2020, J CLEAN PROD, V262, DOI 10.1016/j.jclepro.2020.121409 Lutton E., 2016, FOODSIM Luzzani G, 2021, SCI TOTAL ENVIRON, V759, DOI 10.1016/j.scitotenv.2020.143462 Madumidha S., 2019, 2019 TEQIP III Sponsored International Conference on Microwave Integrated Circuits, Photonics and Wireless Networks (IMICPW), P174, DOI 10.1109/IMICPW.2019.8933270 Mahroof K., 2020, J CLEAN PROD Mangina E, 2005, J FOOD ENG, V70, P403, DOI 10.1016/j.jfoodeng.2004.02.044 Mania I., 2018, TRACEABILITY DAIRY I, P129, DOI [10.1007/978-3-030-00446-0_8, DOI 10.1007/978-3-030-00446-0_8, 10.1007/978-3-030- 00446-0_8] Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 Margherita EG, 2021, TECHNOL FORECAST SOC, V172, DOI 10.1016/j.techfore.2021.121048 Mattevi M, 2016, BRIT FOOD J, V118, P1107, DOI 10.1108/BFJ-07-2015-0261 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Meuwissen M.P., 2003, J AGRIBUSINESS, V21, P1, DOI [https://doi.org/10.22004/ag.econ.14666, DOI 10.22004/AG.ECON.14666] Miranda BV, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120878 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mol APJ, 2015, J CLEAN PROD, V107, P154, DOI 10.1016/j.jclepro.2013.11.012 Mota B, 2015, J CLEAN PROD, V105, P14, DOI 10.1016/j.jclepro.2014.07.052 Negi A, 2021, FOOD CONTROL, V127, DOI 10.1016/j.foodcont.2021.108113 Ngai EWT, 2010, INT J PROD RES, V48, P2583, DOI 10.1080/00207540903564942 Norton T., 2014, GUIDE TRACEABILITY P Notarnicola B, 2017, J CLEAN PROD, V140, P399, DOI 10.1016/j.jclepro.2016.06.071 Nylund P.A., 2021, TECHNOVATION, DOI [10.1016/j.technovation.2021.102271, DOI 10.1016/J.TECHNOVATION.2021.102271] Olsen P., 2018, FOOD TRACEABILITY TH Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Ortega DL, 2015, CHINA ECON REV, V36, P359, DOI 10.1016/j.chieco.2015.04.005 Ozdogan B., 2017, J EC FINANC ACCOUNT, V4, P186, DOI DOI 10.17261/PRESSACADEMIA.2017.448 Peano C, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11215955 Peano C, 2014, ECOL SOC, V19, DOI 10.5751/ES-06972-190424 Polanyi M., 2013, SCI FAITH SOC Pournader M, 2020, INT J PROD RES, V58, P2063, DOI 10.1080/00207543.2019.1650976 PWC Universita di Bari, 2019, FORZ DELL PUGL TOP 2 Qian J., NONGYE GONGCHENG XUE, V36, P182, DOI [10.11975/ j.issn.1002-6819.2020.05.021, DOI 10.11975/J.ISSN.1002-6819.2020.05.021] Qian JP, 2020, FOOD ENERGY SECUR, V9, DOI 10.1002/fes3.249 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Qin J, 2016, PROC CIRP, V52, P173, DOI 10.1016/j.procir.2016.08.005 Queiroz MM, 2021, INT J PROD RES, V59, P6087, DOI 10.1080/00207543.2020.1803511 Randrup M, 2012, FOOD CONTROL, V26, P439, DOI 10.1016/j.foodcont.2012.02.003 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Rhee M.R., 2020, 2020 INT C DEC AID S, DOI [10.1109/DASA51403.2020.9317011, DOI 10.1109/DASA51403.2020.9317011] Rodriguez-Salvador B, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107142 Ruβmann M., 2015, BOSTON CONSULTING GR, V9, P54 Rusconi G, 2017, INT FOOD LAW POLICY, DOI [10.1007/978-3-319- 07542-6_20, DOI 10.1007/978-3-319-07542-6_20] Sala S, 2017, J CLEAN PROD, V140, P387, DOI 10.1016/j.jclepro.2016.09.054 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Schumacher D.M., 2016, IND 4 0 NEW INTERACT Senk I, 2013, IFIP ADV INF COMM TE, V394, P155 Serra-Majem L, 2017, NUTR FOOD SYSTEMS Settembre-Blundo D., 2018, SOCIAL SCI, V7, DOI [10.3390/socsci7120255, DOI 10.3390/SOCSCI7120255] Sharma R, 2021, CEREAL CHEM, V98, P52, DOI 10.1002/cche.10388 Silayoi P, 2007, EUR J MARKETING, V41, P1495, DOI 10.1108/03090560710821279 Singh RK, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20071827 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Spender JC, 1996, STRATEGIC MANAGE J, V17, P45, DOI 10.1002/smj.4250171106 SRDS, 2019, SRDS CONS MED ADV SO Stake R.E, 2005, QUALITATIVE CASE STU Stranieri S, 2017, FOOD CONTROL, V80, P187, DOI 10.1016/j.foodcont.2017.04.047 Sufiyan M, 2019, LECT N MECH ENG, P515, DOI 10.1007/978-981-13-6412-9_50 Sumintir Pharmasetiawan B., 2019, 2019 INT C ICT SMART, P1, DOI [10.1109/ICISS48059.2019.8969822, DOI 10.1109/ICISS48059.2019.8969822] Sun SN, 2019, J CLEAN PROD, V217, P658, DOI 10.1016/j.jclepro.2019.01.296 Tan A, 2022, INT J LOGIST-RES APP, V25, P947, DOI 10.1080/13675567.2020.1825653 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Thomas DR, 2006, AM J EVAL, V27, P237, DOI 10.1177/1098214005283748 Tian F, 2017, I C SERV SYST SERV M Tilman D, 2014, NATURE, V515, P518, DOI 10.1038/nature13959 Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 Tsolakis N, 2021, J BUS RES, V131, P495, DOI 10.1016/j.jbusres.2020.08.003 Tsolakis NK, 2014, BIOSYST ENG, V120, P47, DOI 10.1016/j.biosystemseng.2013.10.014 Turra C, 2014, J AGR ENVIRON ETHIC, V27, P663, DOI 10.1007/s10806-013-9484-5 Valdeza A.C., 2015, P 19 TRIENNIAL C IEA, V9, P14 van der Burg S, 2019, NJAS-WAGEN J LIFE SC, V90-91, DOI 10.1016/j.njas.2019.01.001 Van der Vorst JGAJ, 2006, WAG UR FRON, V15, P15, DOI 10.1007/1-4020-4693-6_2 Vanderroost M, 2017, COMPUT IND, V87, P15, DOI 10.1016/j.compind.2017.01.004 VELTHUIS AGJ, 2003, NEW APPROACHES FOOD Violino S, 2019, EUR FOOD RES TECHNOL, V245, P2089, DOI 10.1007/s00217-019-03321-0 Wang L, 2021, IEEE ACCESS, V9, P9296, DOI 10.1109/ACCESS.2021.3050112 Wilson WW, 2008, AGRIBUSINESS, V24, P85, DOI [10.1002/agr.20148, 10.1002/AGR.20148] Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 Yin R. K., 2003, CASE STUDY RES DESIG, V3rd Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Yuxin Liao, 2019, Journal of Physics: Conference Series, V1288, DOI 10.1088/1742-6596/1288/1/012062 Zhang JR, 2014, COMPR REV FOOD SCI F, V13, P1074, DOI 10.1111/1541-4337.12103 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 173 TC 3 Z9 3 U1 28 U2 34 PD MAY PY 2022 VL 178 AR 121564 DI 10.1016/j.techfore.2022.121564 WC Business; Regional & Urban Planning SC Business & Economics; Public Administration UT WOS:000778411300008 DA 2022-12-14 ER PT J AU Opara, LU AF Opara, Linus U. TI Traceability in agriculture and food supply chain: A review of basic concepts, technological implications, and future prospects SO JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT DT Review DE Traceability; quality; SCM; ICT; identity preservation; labeling AB In recent times, the accurate and timely traceability of products and activities in the supply chain has become a new factor in food and agribusiness. Increasingly, consumers in many parts of the world demand for verifiable evidence of traceability as an important criterion of food product quality/safety. This trend has been underpinned by several market-pull factors including increasing global demand for food products originating from diverse sources, high incidence of food-related health hazards and increasing concern over the impacts of genetically modified organisms (GMOs) on the human food chain and the environment. In order to meet consumer demands for consistent supply of top quality, safe and nutritious foods, as well as rebuild public confidence in the food chain, the design and implementation of full backward and forward traceable supply chains from farm to end-user has become an important part of the overall food quality assurance system. Farmers, postharvest handling operators, marketers, research practitioners and policy makers need good understanding of the concepts and implications of supply chain traceability to assist in developing and implementing appropriate technological interventions to meet consumer demands for traceable agricultural supply chains. The objectives of this article are to: (a) review the concepts of supply chain management and traceability in agriculture, and (b) highlight the technological challenges in implementing traceable agricultural supply chains. Development of appropriate measurement tools for food product labeling and identification, activity/process characterization, information systems for data capture, analysis, storage and communication, and the integration of the overall traceable supply chain are essential for success. C1 Sultan Qaboos Univ, Coll Agr & Marine Sci, Dept Bioresource & Agr Engn, Al Khoud 123, Oman. C3 Sultan Qaboos University RP Opara, LU (corresponding author), Sultan Qaboos Univ, Coll Agr & Marine Sci, Dept Bioresource & Agr Engn, POB 34, Al Khoud 123, Oman. EM linus@squ.edu.om CR [Anonymous], [No title captured] Antoniol G, 2001, SOFTWARE PRACT EXPER, V31, P331, DOI 10.1002/spe.374 Blank S.C., 1998, END AGR AM PORTFOLIO Boehlje M., 1994, J AGR LENDING, V7, P16 Calder R., 1998, SUPPLY CHAIN MANAG, V3, P123 Cameron J.M., 1975, J QUAL TECHNOL, V7, P193 Gardner E.L., 1993, J TEST EVAL, V21, P505 Giese J.H., 2001, FOOD TECHNOL, V55, P100 Giese J.H., 2001, FOOD TECHNOL, V55, p[100, 102] Grimsdell K., 1996, SUPPLY CHAIN MANAG, V1, P11, DOI DOI 10.1108/13598549610799031 Hobbs J. E., 1996, SUPPLY CHAIN MANAGEM, V1, P15, DOI DOI 10.1108/13598549610155260 Horvath L., 2001, SUPPLY CHAIN MANAG, V6, P205, DOI [DOI 10.1108/EUM0000000006039, 10.1108/EUM0000000006039/FULL/HTML] KEITH LH, 1994, ENVIRON SCI TECHNOL, V28, pA590, DOI 10.1021/es00062a720 Kennett J., 1998, SUPPLY CHAIN MANAG, V3, P157, DOI DOI 10.1108/13598549810230912 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Reed W., 2001, FOOD TRAK Rizos C., 2001, MANUAL GEOSPATIAL SC Smith L., 2001, TRACEABILITY CRISES Viaene J., 1998, SUPPLY CHAIN MANAG I, V3, P139 WILLIAMSON OE, 1979, J LAW ECON, V22, P233, DOI 10.1086/466942 Woods E., 1999, ACIAR POSTH TECHN IN NR 21 TC 207 Z9 216 U1 2 U2 118 PD JAN PY 2003 VL 1 IS 1 BP 101 EP 106 WC Food Science & Technology SC Food Science & Technology UT WOS:000208574700016 DA 2022-12-14 ER PT J AU Buchmann, RA Karagiannis, D AF Buchmann, Robert Andrei Karagiannis, Dimitris TI Modelling mobile app requirements for semantic traceability SO REQUIREMENTS ENGINEERING DT Article DE Mobile app requirements; Mobile interaction; Linked Data; Requirements modelling; Semantic traceability ID SOFTWARE; FRAMEWORK AB The paper presents a modelling method aimed to support the definition and elicitation of requirements for mobile apps through an approach that enables semantic traceability for the requirements representation. Business process-centricity is employed in order to capture requirements in a knowledge structure that retains procedural knowledge from stakeholders and can be traversed by semantic queries in order to trace domain-specific contextual information for the modelled requirements. Consequently, instead of having requirements represented as natural language items that are documented by diagrammatic models, the communication channels are switched: semantically interlinked conceptual models become the requirements representation, while free text can be used for requirements annotations/metadata. Thus, the method establishes a knowledge externalization channel between business stakeholders and app developers, also tackling the Twin Peaks bridging challenge (between requirements and early designs). The method is presented using its modelling procedure as a guiding thread, with each step illustrated by case-based samples of the modelling language and auxiliary functionality. The design work is encompassed by an existing metamodelling framework and introduces a taxonomy for modelling relations, since the metamodel is the key enabler for the goal of semantic traceability. The research was driven by the ComVantage EU research project, concerned with mobile app support for collaborative business process execution. Therefore, the project provides context for the illustrating examples; however, generalization possibilities beyond the project scope will also be discussed, with respect to both motivation and outcome. C1 [Buchmann, Robert Andrei] Univ Babes Bolyai Cluj Napoca, Fac Econ Sci & Business Adm, Str Teodor Mihali 58-60, Cluj Napoca 400591, Romania. [Buchmann, Robert Andrei; Karagiannis, Dimitris] Univ Vienna, Fac Comp Sci, Knowledge Engn Res Grp, Waehringerstr 29, A-1090 Vienna, Austria. C3 Babes Bolyai University from Cluj; University of Vienna RP Buchmann, RA (corresponding author), Univ Babes Bolyai Cluj Napoca, Fac Econ Sci & Business Adm, Str Teodor Mihali 58-60, Cluj Napoca 400591, Romania.; Buchmann, RA (corresponding author), Univ Vienna, Fac Comp Sci, Knowledge Engn Res Grp, Waehringerstr 29, A-1090 Vienna, Austria. EM rbuchmann@dke.univie.ac.at; dk@dke.univie.ac.at CR Ali R, 2010, REQUIR ENG, V15, P439, DOI 10.1007/s00766-010-0110-z [Anonymous], SUPPL CHAIN OP REF S Apel S, 2009, J OBJECT TECHNOL, V8, P49, DOI 10.5381/jot.2009.8.5.c5 Aquino N, 2011, HANDBOOK OF CONCEPTUAL MODELING: THEORY, PRACTICE AND RESEARCH CHALLENGES, P335 Beatty J., 2011, SEILEVELS EVALUATION Beatty J., 2012, VISUAL MODELS SOFTWA Berenbach B., 2012, 2012 IEEE 20th International Requirements Engineering Conference (RE 2012), P285, DOI 10.1109/RE.2012.6345816 Bizer C, 2013, TRIG SYNTAX SPECIFIC BOC-Group, 2013, ADONIS COMM ED TOOL BOC-Group, 2013, ADOXX TOOL Bogdan C., 2008, 41 HAW INT C SYST SC, P36 Buchmann RA, 2013, LECT NOTES BUS INF P, V158, P19 Buchmann RA, 2014, P ANN HICSS, P3390, DOI 10.1109/HICSS.2014.421 Calefato F, 2012, EMPIR SOFTW ENG, V17, P640, DOI 10.1007/s10664-011-9179-3 Calvary G, 2003, INTERACT COMPUT, V15, P289, DOI 10.1016/S0953-5438(03)00010-9 Carroll J, 2013, TRIX SYNTAX SPECIFIC Cohn Mike, 2004, USER STORIES APPL AG ComVantage Research Project Consortium, 2013, PROJ PUBL DEL da Silva PP, 2003, IEEE SOFTWARE, V20, P62, DOI 10.1109/MS.2003.1207457 DARDENNE A, 1993, SCI COMPUT PROGRAM, V20, P3, DOI 10.1016/0167-6423(93)90021-G Dijkman R, 2011, INFORM SYST, V36, P498, DOI 10.1016/j.is.2010.09.006 Fuentes-Fernandez R, 2010, REQUIR ENG, V15, P267, DOI 10.1007/s00766-009-0087-7 Future Internet Enterprise Systsems cluster, 2013, FINES RES ROADM 2025 Gordijn J, 2001, IEEE INTELL SYST, V16, P11, DOI 10.1109/5254.941353 Greenspan SJ, 1982, P INT C SOFTW ENG TO Jackson M, 2014, REQUIR ENG, V19, P107, DOI 10.1007/s00766-013-0179-2 Kaindl H, 2010, REQUIR ENG, V15, P307, DOI 10.1007/s00766-009-0095-7 Kang K., 1990, CMUSEI90TR021 Karagiannis D., 2002, LNCS, V2455, P451 Krogstie J., 2004, International Journal of Mobile Communications, V2, P220 Loucks J., 2013, FINANCIAL IMPACT BYO Monteiro E, 2013, 8 INT C QUAL INF COM, P75 Moody DL, 2009, IEEE T SOFTWARE ENG, V35, P756, DOI 10.1109/TSE.2009.67 Morgan J, 2012, GUIDELINES CHAINING Mylopoulos J., 1992, CONCEPTUAL MODELLING, P49 Nunes NJ, 2000, IEEE SOFTWARE, V17, P113, DOI 10.1109/52.877877 Nuseibeh B, 2001, COMPUTER, V34, P115, DOI 10.1109/2.910904 Object Management Group, 2011, XMI SPEC Object Management Group, 2011, REQIF DOC Object Management Group, 2012, SYSML SPEC Open Model Initiative Laboratory, 2013, COMVANTAGE MOD PROT Ralph P, 2013, REQUIR ENG, V18, P293, DOI 10.1007/s00766-012-0161-4 Seilevel, 2013, REQ MOD LANG TEMPL Sequeda J, 2010, I BELIEVE LINKED DAT Shaker P., 2012, 2012 IEEE 20th International Requirements Engineering Conference (RE 2012), P151, DOI 10.1109/RE.2012.6345799 Simon K., 2010, SIPOC DIAGRAM Sundaram SK, 2010, REQUIR ENG, V15, P313, DOI 10.1007/s00766-009-0096-6 Vidgen R, 2003, COMPUT CONTROL ENG J, V14, P12, DOI 10.1049/cce:20030202 W3C, 2004, RDF STAND RES SPEC W3C, 2013, LINK DAT PLATF US CA W3C, 2013, SPARQL 1 1 FED QUER W3C, 2013, SPARQL QUER LANG SPE Wanderley F, 2013, P 16 INT SOFTW PROD, V2, P18 Ziegler J., 2012, P 17 IEEE INT C EM T, P1 NR 54 TC 18 Z9 18 U1 0 U2 6 PD MAR PY 2017 VL 22 IS 1 BP 41 EP 75 DI 10.1007/s00766-015-0235-1 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science UT WOS:000394464600003 DA 2022-12-14 ER PT J AU Sun, CH Ji, ZT Yang, XT Han, X Wang, ZL AF Sun, Chuanheng Ji, Zengtao Yang, Xinting Han, Xiao Wang, Zhiling TI A traceability system for beef products based on radio frequency identification technology in China SO NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH DT Article DE AIDC; beef; radio frequency identification (RFID); traceability ID TRANSPONDERS; QUALITY; SAFETY; ORIGIN AB Radio frequency identification (RFID) technology is an autornatic identification and data capture (AIDQ technique and it is the representative technology for handling beef traceability. In this paper we offer a complete solution including: frequency, identifier information system, information system, data organisation, tag reclaimation and control technology, from three segments of the beef production process in China. From the farm to the slaughterhouse, electronic identification ear tag technology was used to identify the individual animal. From the slaughterhouse to the processing plant, gambrel identification was used to transfer the carcass inforinatio'n from the "old ear tag" to a;1 new ear tag". Last, gambrel RFID was converted toUCC/EAN-128 barcode labellingwith a wireless electronic scale. The whole solution was evaluated successfully in Beijing. C1 [Sun, Chuanheng; Ji, Zengtao; Yang, Xinting; Han, Xiao] Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. [Wang, Zhiling] Beijing Acad Agr & Foresty Sci, Beijing 100097, Peoples R China. C3 Beijing Academy of Agriculture & Forestry; Beijing Academy of Agriculture & Forestry RP Yang, XT (corresponding author), Natl Engn Res Ctr Informat Technol Agr, Room 307 Beijing Agr Sci Mansion Bldg A,Banjing, Beijing 100097, Peoples R China. EM yangxt@nercita.org.cn CR AARTS HLM, 1992, P 12 INT PIG VET SOC Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 BECHINI A, 2007, INFORM SOFTWARE TECH, V25, P1 Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Conill C, 2002, J ANIM SCI, V80, P919 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Eggen A, 2004, MEAT SCI, V66, P1, DOI 10.1016/S0309-1740(03)00020-2 Fallon M, 2001, REV SCI TECH OIE, V20, P538, DOI 10.20506/rst.20.2.1289 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 Furness A., 2003, Food authenticity and traceability, P473, DOI 10.1533/9781855737181.3.473 GERDEMAN JD, 1995, RADIO FREQUENCY IDEN Holm S, 2007, AQUACULT ENG, V36, P122, DOI 10.1016/j.aquaeng.2006.09.003 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 McMahon IC, 2000, FARM IND NEWS, V33, P26 Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 Sluyter FJH, 2001, REV SCI TECH OIE, V20, P500, DOI 10.20506/rst.20.2.1292 Verbeke W, 2002, FOOD POLICY, V27, P339, DOI 10.1016/S0306-9192(02)00037-4 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wang LiFang, 2005, Transactions of the Chinese Society of Agricultural Engineering, V21, P168 NR 19 TC 5 Z9 5 U1 1 U2 21 PD DEC PY 2007 VL 50 IS 5 BP 1269 EP 1275 DI 10.1080/00288230709510412 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000258308200092 DA 2022-12-14 ER PT J AU Folinas, D Manikas, I Manos, B AF Folinas, Dimitris Manikas, Ioannis Manos, Basil TI Traceability data management for food chains SO BRITISH FOOD JOURNAL DT Article DE supply chain management; food products; tracer methods; extensible markup language ID MANUFACTURE AB Purpose - The main objectives of the paper are to identify the needs in data that are considered as fundamental for the efficient food traceability and to introduce a generic framework (architecture) of traceability data management that will act as guideline for all entities/food business operators involved. Design/methodology/approach - The traceability system introduced is based on the implementation of XML (eXtensible Markup Language) technology. In the first stage, the necessary traceability data are identified and categorized. In the second stage, the selected data are transformed and inserted into a five-element generic framework/model, using PML (Physical Markup Language), which is a standard technology of XML. Findings - The assessment of information communication and diffusion underlines that the particular model is simple in use and user-friendly, by enabling information flow through conventional technologies. Practical implications - The main feature of this framework is the simplicity in use and the ability of communicating information through commonly accessible means such as the internet, e-mail, and cell phones. This makes it particularly easy to use, even when it comes to the base of the supply chains (farmers, fishermen, cattle breeders, etc). Originality/value - An integrated traceability system must be able to file and communicate information regarding product quality and origin, and consumer safety. The main features of such a system include adequate "filtering" of information, information extracting, from already existed databases, harmonization with international codification standards, internet standards and up to date technologies. The framework presented in this paper fulfills all the above features. C1 Univ Macedonia, Dept Appl Informat, Thessaloniki, Greece. Aristotle Univ Thessaloniki, Sch Agr Sci, GR-54006 Thessaloniki, Greece. C3 University of Macedonia; Aristotle University of Thessaloniki RP Folinas, D (corresponding author), Univ Macedonia, Dept Appl Informat, Thessaloniki, Greece. EM folinas@uom.gr CR Brock L., 2001, WH003 BROCK L, 2001, WH002 FOLINAS D, 2003, P EFITA 2003 C HUNG, P143 FOLINAS D, 2003, P 10 INT C HUM COMP, V3, P699 Food Standards Agency, 2002, TRAC FOOD CHAIN PREL Forcinio H., 2004, Managing Automation, V19, P23 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Karkkainen M., 2003, INT J RETAIL DISTRIB, V31, P529, DOI DOI 10.1108/09590550310497058 Kim H. M., 1995, P WET ICE LOS ALB CA LAURENT S, 1999, XML PRIMER Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Moukas A.R., 1998, P INT C EL COMM SEOU, P9 NIEMEYER A, 2003, MCKINSEY Q NOY N, 2000, ONTOLOGY DEV, V101 Prater E, 2005, SUPPLY CHAIN MANAG, V10, P134, DOI 10.1108/13598540510589205 *RFID, 2004, RFID J JAN Salin V., 1998, INT FOOD AGRIBUS MAN, V1, P329, DOI [10.1016/S1096-7508(99)80003-2, DOI 10.1016/S1096-7508(99)80003-2] TERPSIDIS I, 1997, ADV INFORM TECHNOLOG Van Dorp C. A, 2003, P EFITA 2003 C DEBR Wilson T. P., 1998, SUPPLY CHAIN MANAG, V3, P127 NR 20 TC 136 Z9 139 U1 1 U2 50 PY 2006 VL 108 IS 8 BP 622 EP 633 DI 10.1108/00070700610682319 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000241065400002 DA 2022-12-14 ER PT J AU Zhou, H Nanseki, T Hotta, K Shinkai, S Xu, Y AF Zhou, Hui Nanseki, Teruaki Hotta, Kazuhiko Shinkai, Shoji Xu, Yi TI Analysis of Consumers' Attitudes toward Traceability System on Dairy Products in China SO JOURNAL OF THE FACULTY OF AGRICULTURE KYUSHU UNIVERSITY DT Article AB Dairy industry has a large potential in China. However, recent food safety problems occurred in the livestock sector in China has somehow negatively affected consumer's confidence in food purchase. A traceability system can be one of the ways to provide consumers information about the food they purchase. In this research, consumers' attitudes toward traceability system are examined. A choice modeling technique is the tool to examine what kinds of information are significant determinants on the value people place on non-market goods i.e. traceability label on dairy products. A conditional logit model is used to analyze the data. In conclusion, a traceability system is not familiar with many consumers in China. However, most of the consumers would like to accept traceability system and were willing to pay extra money for milk with a traceability system. The consumers are concerned on the information of animal medicine usage record especially on antibiotic and willing to pay more for receiving the information. Providing this information may increase consumers' confidence on the food they consume. A traceability system could be a way to provide information of production and avoid information asymmetry, and help consumers to rebuild the confidence. C1 [Zhou, Hui; Nanseki, Teruaki; Hotta, Kazuhiko; Shinkai, Shoji; Xu, Yi] Kyushu Univ, Fac Agr, Lab Farm Management, Div Int Agr Resource Econ & Business Adm,Dept Agr, Fukuoka 8128581, Japan. C3 Kyushu University RP Nanseki, T (corresponding author), Kyushu Univ, Fac Agr, Lab Farm Management, Div Int Agr Resource Econ & Business Adm,Dept Agr, Fukuoka 8128581, Japan. CR ADAMOWICZ W, 1994, J ENVIRON ECON MANAG, V26, P271, DOI 10.1006/jeem.1994.1017 Adamowicz W., 1998, INTRO ATTRIBUTE BASE Bateman IJ., 2002, EC VALUATION STATED, P458 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 GREENE WH, 2002, LIMDEP VERSION 9 0 E, V2 HENSHER DA, 1983, J TRANSP ECON POLICY, V17, P225 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Louviere J.J., 2000, STATED CHOICE METHOD, DOI DOI 10.1017/CBO9780511753831 LOUVIERE JJ, 1983, J MARKETING RES, V20, P350, DOI 10.2307/3151440 NANSEKI T, 2008, P 2008 RES M FARM MA, P222 NANSEKI T, 2008, FOOD TRACEABILITY WO, V1, P46 Song M, 2008, J FAC AGR KYUSHU U, V53, P569 XU YT, 2010, J FACULTY A IN PRESS, V55, P167 ZHOU H, 2009, P 2009 RES M FARM MA, P218 2009, FOOD SAFETY TRACE TR NR 15 TC 5 Z9 5 U1 0 U2 17 PD FEB PY 2010 VL 55 IS 1 BP 167 EP 172 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000275795200029 DA 2022-12-14 ER PT J AU Aceto, M Bonello, F Musso, D Tsolakis, C Cassino, C Osella, D AF Aceto, Maurizio Bonello, Federica Musso, Davide Tsolakis, Christos Cassino, Claudio Osella, Domenico TI Wine Traceability with Rare Earth Elements SO BEVERAGES DT Article DE ICP-MS; rare earth elements; wine; traceability ID MASS-SPECTROMETRY; MULTIELEMENT; AUTHENTICATION; ORIGIN; CLASSIFICATION; PIEDMONT; TABLE; SOIL; TOOL AB The traceability of foodstuffs is now a relevant aspect of the food market. Scientific research has been devoted to addressing this issue by developing analytical protocols in order to find the link between soil and food items. In this view, chemical parameters that can act as soil markers are being sought. In this work, the role of rare earth elements (REEs) as geochemical markers in the traceability of red wine is discussed. The REE distribution in samples from each step of the wine making process of Primitivo wine (produced in Southern Italy) was determined using the highly sensitive inductively coupled plasma-mass spectrometry (ICP-MS) technique. Samples analyzed include grapes, must, and wine samples after every step in the vinification process. The resulting data were compared to the REE distribution in the soil, revealing that the soil fingerprint is maintained in the intermediate products up to and including grape must. Fractionation occurs thereafter as a consequence of further external interventions, which tends to modify the REE profile. C1 [Aceto, Maurizio; Musso, Davide; Tsolakis, Christos; Cassino, Claudio; Osella, Domenico] Univ Piemonte Orientale, Dipartimento Sci & Innovaz Tecnol, Viale T Michel 11, I-15121 Alessandria, Italy. [Bonello, Federica; Tsolakis, Christos] CREA Consiglio Ric Agr & Anal Econ Agr, Ctr Ric Enol, Via Pietro Micca 35, I-14100 Asti, Italy. C3 University of Eastern Piedmont Amedeo Avogadro; Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Aceto, M (corresponding author), Univ Piemonte Orientale, Dipartimento Sci & Innovaz Tecnol, Viale T Michel 11, I-15121 Alessandria, Italy. EM maurizio.aceto@uniupo.it; federica.bonello@entecra.it; davide.musso@uniupo.it; chr.tsolakis@gmail.com; claudio.cassino@uniupo.it; domenico.osella@uniupo.it CR Aceto M, 2016, WOODHEAD PUBL FOOD S, V301, P137, DOI 10.1016/B978-0-08-100310-7.00008-9 Aceto M, 2009, RED WINE HLTH, P429 Aceto M, 2017, J AGR FOOD CHEM, V65, P4200, DOI [10.1021/acs.jafc.7b00916, 10.1021/acs.jafc.7b009] Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 Benabdelkamel H, 2012, J AGR FOOD CHEM, V60, P3717, DOI 10.1021/jf2050075 Brown P.H., 1990, HDB PHYS CHEM RARE E, V13, ed., P423, DOI DOI 10.1016/S0168-1273(05)80135-7 Capone S, 2013, SENSOR ACTUAT B-CHEM, V179, P259, DOI 10.1016/j.snb.2012.10.142 Censi P, 2014, SCI TOTAL ENVIRON, V473, P597, DOI 10.1016/j.scitotenv.2013.12.073 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 di Rienzo V, 2016, FOOD CONTROL, V60, P124, DOI 10.1016/j.foodcont.2015.07.015 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Furia E, 2011, J AGR FOOD CHEM, V59, P8450, DOI 10.1021/jf201556e Gonzalvez A, 2013, COMP ANAL C, V60, P51, DOI 10.1016/B978-0-444-59562-1.00003-7 Hopfer H, 2015, FOOD CHEM, V172, P486, DOI 10.1016/j.foodchem.2014.09.113 Jaitz L, 2010, FOOD CHEM, V122, P366, DOI 10.1016/j.foodchem.2010.02.053 Liang T, 2008, J RARE EARTH, V26, P7, DOI 10.1016/S1002-0721(08)60027-7 Marchionni S, 2013, J AGR FOOD CHEM, V61, P6822, DOI 10.1021/jf4012592 Marengo E, 2003, FOOD CHEM, V81, P621, DOI 10.1016/S0308-8146(02)00564-2 Marengo E, 2002, J CHROMATOGR A, V943, P123, DOI 10.1016/S0021-9673(01)01421-2 Marisa C, 2004, FOOD CHEM, V85, P7, DOI 10.1016/j.foodchem.2003.05.003 May TW, 1998, ATOM SPECTROSC, V19, P150 MCDONOUGH WF, 1995, CHEM GEOL, V120, P223, DOI 10.1016/0009-2541(94)00140-4 Oddone M, 2009, J AGR FOOD CHEM, V57, P3404, DOI 10.1021/jf900312p Pasqualone A, 2010, EUR FOOD RES TECHNOL, V230, P723, DOI 10.1007/s00217-009-1210-1 Pepi S, 2016, ENVIRON MONIT ASSESS, V188, DOI [10.1007/s10661-016-5490-1, 10.1007/s10661-016-] Pisciotta A, 2017, FOOD CHEM, V221, P1214, DOI 10.1016/j.foodchem.2016.11.037 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Taylor VF, 2003, J AGR FOOD CHEM, V51, P856, DOI 10.1021/jf025761v Tyler G, 2004, PLANT SOIL, V267, P191, DOI 10.1007/s11104-005-4888-2 Vaclavik L, 2011, ANAL CHIM ACTA, V685, P45, DOI 10.1016/j.aca.2010.11.018 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 NR 31 TC 19 Z9 20 U1 0 U2 19 PD MAR PY 2018 VL 4 IS 1 AR 23 DI 10.3390/beverages4010023 WC Food Science & Technology SC Food Science & Technology UT WOS:000455141100022 DA 2022-12-14 ER PT J AU Shackell, GH AF Shackell, Grant H. . TI Traceability in the meat industry - the farm to plate continuum SO INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY DT Article DE Adding value; meat; provenance; risk; traceability ID POLYMERASE-CHAIN-REACTION; GROUND-BEEF; FOOD SAFETY; IDENTIFICATION; CONSUMERS; PORK; PCR; PRODUCTS; QUALITY; MARKERS AB Traceability of meat and meat products is a major issue in the meat industry with the two main drivers being food safety/risk management and authentication. Increasingly, the world marketplace is indicating that traceability systems for food products derived from individual animals (e.g. steak, chops etc.) is required now or will be required in the near future. Traceability requirements for compound products, such as ground beef, are usually less strict and are frequently limited to date and place of manufacture. As global competition increases it is imperative that technologies are available that protect against, and deter, fraudulent labelling of inferior product. Traceability offers more than marketing advantages. It can be applied at every stage of the meat production continuum and can be just as valuable to farmers and processors as it is to marketers and consumers. C1 AgRes Invermay, Mosgiel, New Zealand. C3 AgResearch - New Zealand RP Shackell, GH (corresponding author), AgRes Invermay, Private Bag 50 034, Mosgiel, New Zealand. EM grant.shackell@agresearch.co.nz CR Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Aumaitre A, 1999, LIVEST PROD SCI, V59, P113, DOI 10.1016/S0301-6226(99)00020-2 BELL RG, 1994, MEAT SCI, V36, P381, DOI 10.1016/0309-1740(94)90134-1 Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Blott SC, 1999, HEREDITY, V82, P613, DOI 10.1046/j.1365-2540.1999.00521.x Buncic S, 2002, FOOD CONTROL, V13, P425, DOI 10.1016/S0956-7135(01)00054-8 CAGNEY C, 2007, FOOD MICROBIOL, V21, P203 Calvo JH, 2002, J AGR FOOD CHEM, V50, P5265, DOI 10.1021/jf0201576 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Cavani C., 2004, PROC 8 WORLD RABBIT, P1318 Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 CLEMENS R, 2003, IOWA AG REV, V9, P4 DERBYSHIRE D, 2005, TELEGRAPH 0226 Di Pinto A, 2005, FOOD CONTROL, V16, P391, DOI 10.1016/j.foodcont.2004.04.004 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Dodds KG, 1996, THEOR APPL GENET, V92, P966, DOI 10.1007/BF00224036 ELAMIN A, 2006, BELGIUM NETHERLANDS Evans MR, 1998, EPIDEMIOL INFECT, V121, P275, DOI 10.1017/S0950268898001204 Garcia D, 2006, MEAT SCI, V72, P560, DOI 10.1016/j.meatsci.2005.09.005 Heaton MP, 2005, JAVMA-J AM VET MED A, V226, P1311, DOI 10.2460/javma.2005.226.1311 Hoban TJ, 1997, NAT BIOTECHNOL, V15, P232, DOI 10.1038/nbt0397-232 Hobbs AL, 2002, FOOD POLICY, V27, P437, DOI 10.1016/S0306-9192(02)00048-9 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Janssen FW, 1998, J IND MICROBIOL BIOT, V21, P115, DOI 10.1038/sj.jim.2900541 KANGETHE EK, 1982, MEAT SCI, V7, P229, DOI 10.1016/0309-1740(82)90088-2 Latouche K, 1998, FOOD POLICY, V23, P347, DOI 10.1016/S0306-9192(98)00048-7 Macedo-Silva A, 2000, MEAT SCI, V56, P189, DOI 10.1016/S0309-1740(00)00039-5 Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 Man YBC, 2007, FOOD CONTROL, V18, P885, DOI 10.1016/j.foodcont.2006.05.004 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Miraglia M, 2004, FOOD CHEM TOXICOL, V42, P1157, DOI 10.1016/j.fct.2004.02.018 Nortje G. L., 2005, Proceedings of the New Zealand Society of Animal Production, V65, P107 Ozawa T., 2005, Proceedings of the New Zealand Society of Animal Production, V65, P80 Shackell G. H., 2005, Proceedings of the New Zealand Society of Animal Production, V65, P97 Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 SHACKELL GH, 2001, P ASS ADVMT ANIM BRE, V14, P533 SHACKELL GH, 2005, P AHAT BSAS INT C KH, V2, pT74 Smith PG, 2004, CURR TOP MICROBIOL, V284, P161 Tomlinson JJ, 2006, J AUTOM METHOD MANAG, DOI 10.1155/JAMMC/2006/74907 Verbeke W, 2001, FOOD QUAL PREFER, V12, P489, DOI 10.1016/S0950-3293(01)00042-8 Verbeke W, 2007, ANAL CHIM ACTA, V586, P2, DOI 10.1016/j.aca.2006.07.065 Vetharaniam I., 2005, Proceedings of the New Zealand Society of Animal Production, V65, P102 2002, SOURCE DEADLY US LIS NR 43 TC 17 Z9 17 U1 2 U2 23 PD DEC PY 2008 VL 43 IS 12 BP 2134 EP 2142 DI 10.1111/j.1365-2621.2008.01812.x WC Food Science & Technology SC Food Science & Technology UT WOS:000261062200006 DA 2022-12-14 ER PT J AU Bellon-Maurel, V Short, MD Roux, P Schulz, M Peters, GM AF Bellon-Maurel, Veronique Short, Michael D. Roux, Philippe Schulz, Matthias Peters, Gregory M. TI Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies - part I: concepts and technical basis SO JOURNAL OF CLEANER PRODUCTION DT Review DE Agricultural LCA; Information and communication technology; Streamlined life cycle inventory; Traceability ID DECISION-SUPPORT-SYSTEM AB Quantitative environmental assessment methodologies such as life cycle assessment demand significant time and resource inputs during the data acquisition and life cycle inventory (LCI) phase. Approaches to streamline the LCI data collection process without degrading data quality are therefore required. This requirement is especially true for agricultural products, as agricultural systems are inherently 'open' and complex. We present a two-part paper on this topic. In this first part, we examine streamlined methods for LCI data collection in agriculture by using today's voluntary or compulsory farm traceability information systems and related information and communication technologies (ICTs), with the aim of later converting them into LCI data. The second part is to examine the application of these technologies in a case study. Our hypothesis is that both traceability data and ICTs could be major drivers for generating accurate, relevant and low-cost LCI data for use in quantitative environmental assessments of agricultural product performance. To that end, we identified the types of data being collected in agriculture as a part of current business practice, especially those with relevance to LCA studies. We also examined the status and current trends in ICTs in use in agriculture to identify the potential for automating LCI data generation. The review identified considerable potential to piggy-back current trends in ICTs in agriculture with the goal of simplifying LCI data collection. This study concludes that given the increasing need to collect traceability data in modern agriculture and the parallel growing adoption of information and communication technologies, it is likely that ICTs and associated information systems will represent an important potential route for the acquisition of future LCI data. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Bellon-Maurel, Veronique; Roux, Philippe] Irstea UMR ITAP, ELSA Grp, F-34033 Montpellier 1, France. [Short, Michael D.; Schulz, Matthias; Peters, Gregory M.] Univ New S Wales, Sch Civil & Environm Engn, UNSW Water Res Ctr, Sydney, NSW 2052, Australia. [Short, Michael D.] Univ S Australia, Sch Nat & Built Environm, SA Water Ctr Water Management & Reuse, Adelaide, SA 5095, Australia. [Schulz, Matthias] DEKRA Consulting GmbH, D-70555 Stuttgart, Germany. [Peters, Gregory M.] Chalmers Univ Technol, S-41296 Gothenburg, Sweden. C3 INRAE; University of New South Wales Sydney; University of South Australia; Chalmers University of Technology RP Bellon-Maurel, V (corresponding author), Irstea UMR ITAP, ELSA Grp, F-34033 Montpellier 1, France. EM veronique.bellon@irstea.fr; m.short@unsw.edu.au; philippe.roux@irstea.fr; Matthias.Schulz@dekra.com; petersg@chalmers.se CR Abt V., 2007, MES DOCUMENTS EXPLOI, P389 [Anonymous], 2006, 14044 ISO, V1, P46, DOI DOI 10.1136/BMJ.332.7550.1107 [Anonymous], 2012, FLOATS REPORT SHOWS Ansems A., 2005, MAKING LIFE CYCLE IN, P353 Antonopoulou E, 2010, COMPUT ELECTRON AGR, V70, P292, DOI 10.1016/j.compag.2009.07.024 Bange MP, 2004, COMPUT ELECTRON AGR, V43, P131, DOI 10.1016/j.compag.2003.12.003 Bjork A, 2011, COMPUT IND, V62, P830, DOI 10.1016/j.compind.2011.08.001 Boffety D, 2012, SCI EAUX TERRIT, V7, P16 Dalgaard R, 2006, AGR ECOSYST ENVIRON, V117, P223, DOI 10.1016/j.agee.2006.04.002 Dux D. L., 1997, Paper - American Society of Agricultural Engineers E.C, 2002, OJ L, V31, P24 E.C, 2004, OJ L, V139, P54 Eide MH, 1998, INT J LIFE CYCLE ASS, V3, P209 Fang H, 2008, COMPUT ELECTRON AGR, V61, P254, DOI 10.1016/j.compag.2007.11.005 Fernandez CJ, 2007, AGRON J, V99, P730, DOI 10.2134/agronj2005.0196n Finnveden G, 2009, J ENVIRON MANAGE, V91, P1, DOI 10.1016/j.jenvman.2009.06.018 Fukatsu T, 2009, ACTA HORTIC, V824, P183 Gelb E., 2009, EFITA JIAC 09 JUL 06, P11 Gentilleau C., 2011, AGRINAUTES ETES VOUS Godwin RJ, 2003, BIOSYST ENG, V84, P393, DOI 10.1016/S1537-5110(02)00283-0 Golan E., 2004, TRACEABILITY US FOOD, P56 Gold J.E., 2005, HANDHELDS SEARCH ENT, P10 Gonzalez B, 2002, RESOUR CONSERV RECY, V37, P61, DOI 10.1016/S0921-3449(02)00069-1 Gonzalez R., 1998, VOICE DATA RECORDERS, P27 Graham E., 2011, AGR ANSWERING CALL S, P1 Halberg N., 2004, DIAS Report, Animal Husbandry, P168 Hori M, 2010, FUJITSU SCI TECH J, V46, P446 ICRISAT, 2009, INT CONS AGR RES DEV, P20 ISO, 2005, FOOD SAF MAN SYST RE, P34 ISO, 2007, TRACT MACH AGR FOR 1, P93 Kotelnikov V., 2009, SMALL MEDIUM ENTERPR, P12 Kramer K. J., 2004, DIAS Report, Animal Husbandry, P182 Langevin B., 2010, PRISE COMPTE VARIABI, P174 Langevin B, 2010, J CLEAN PROD, V18, P747, DOI 10.1016/j.jclepro.2009.12.015 LERMAN L, 1980, IEEE T COMPON HYBR, V3, P360, DOI 10.1109/TCHMT.1980.1135619 Lewis KA, 1999, J ENVIRON MANAGE, V55, P183, DOI 10.1006/jema.1998.0253 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 Maru A., 2009, ASABE 7 WORLD C COMP, P260 Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Mourad AL, 2007, INT J LIFE CYCLE ASS, V12, P408, DOI 10.1065/lca2006.09.272 Nemeth K., 2008, USING AVATAR RFID SP, P1949 Nikkila R, 2010, COMPUT ELECTRON AGR, V70, P328, DOI 10.1016/j.compag.2009.08.013 Ninomiya S., 2004, Extension Bulletin - Food & Fertilizer Technology Center OECD, 2010, OECD INF TECHN OUTL, P325 Peets S, 2009, PRECIS AGRIC, V10, P382, DOI 10.1007/s11119-009-9106-4 Poppe K.J., 2000, AGR DATA LIFE CYCLE, P122 Reichardt M, 2009, PRECIS AGRIC, V10, P73, DOI 10.1007/s11119-008-9101-1 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Rumble M.A., 2009, DIGITAL VOICE RECORD, P3 Sallabi F, 2011, COMPUT ELECTRON AGR, V76, P28, DOI 10.1016/j.compag.2010.12.016 Schmitt A, 2008, INT J SPEECH TECHNOL, V11, P63, DOI 10.1007/s10772-009-9036-6 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Silva CB, 2011, PRECIS AGRIC, V12, P67, DOI 10.1007/s11119-009-9155-8 Spugnoli P., 2008, INNOVATION TECHNOLOG, P5 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Sugahara K, 2009, COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE II, VOLUME 3, P2293 Tan ZH, 2010, LECT NOTES COMPUT SC, V5960, P221 Tilman D, 2002, NATURE, V418, P671, DOI 10.1038/nature01014 Todd J., 1999, STREAMLINED LIFE CYC, P31 van Lierde D., 2000, AGR DATA LIFE CYCLE, P137 Veeraraghavan R., 2009, WARANA UNWIRED REPLA Weidema B, 2000, AGR DATA LIFE CYCLE World bank, 2012, INF COMM DEV MAX MOB, P221 WRI WBCSD, 2010, PROD ACC REP STAND D, P18 Yang Y, 2011, IFIP ADV INF COMM TE, V346, P375 Zackrisson M, 2008, J CLEAN PROD, V16, P1872, DOI 10.1016/j.jclepro.2008.01.001 Zhao Li, 2011, International Agricultural Engineering Journal, V20, P62 NR 68 TC 25 Z9 26 U1 2 U2 52 PD APR 15 PY 2014 VL 69 BP 60 EP 66 DI 10.1016/j.jclepro.2014.01.079 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000335102900007 DA 2022-12-14 ER PT J AU Zhao, J Zhu, C Xu, ZZ Jiang, XL Yang, SM Chen, AL AF Zhao, Jie Zhu, Chao Xu, Zhenzhen Jiang, Xiaoling Yang, Shuming Chen, Ailiang TI Microsatellite markers for animal identification and meat traceability of six beef cattle breeds in the Chinese market SO FOOD CONTROL DT Article DE Microsatellite markers; Animal identification; Meat traceability; Beef cattle; Chinese market ID INDIVIDUAL IDENTIFICATION; MOLECULAR TRACEABILITY; GENETIC TRACEABILITY; PRODUCTS; POPULATION; PANEL AB Microsatellite markers have been shown to be a useful tool in individual identification and meat traceability. Aiming at developing a genetic tracing system for beef cattle breeds in the Chinese market, this study identified a set of 16 specific microsatellite markers within six breeds, including Japanese Black, Anduo yak, Limousin, Jiaxian Red, Nanyang Yellow and Luxi Yellow. A total of 180 alleles have been detected with an average number of 11.2 per locus, and the average polymorphism information content (PIC) is 0.7696 for all loci. The 16-loci set could successfully distinguish all the individuals of the six breeds. When the six most polymorphic markers were chosen for each breed, the matching probability (MP) value was found to be about seven in one million, excluding the extremely high value in Limousin. As the number of markers increased, the MP value was gradually lowered, and the accuracy was also enhanced. Meanwhile, the traceability validation test was conducted with the seven most polymorphic markers (ETH10, ETH225, ILSTS006, INRA032, INRA035, INRA037 and TGLA122), the conforming probabilities of genotypes for 28 blood and corresponding tissue samples were 100%. The results of this study could partly prevent the food fraud incidence in the Chinese market, and they also showed further evidence in the applications of genetic markers to meat traceability based on animal identification to ensure food safety. (C) 2017 Published by Elsevier Ltd. C1 [Zhao, Jie; Zhu, Chao; Xu, Zhenzhen; Jiang, Xiaoling; Yang, Shuming; Chen, Ailiang] Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Zhao, Jie; Zhu, Chao; Xu, Zhenzhen; Jiang, Xiaoling; Yang, Shuming; Chen, Ailiang] Minist Agr, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs RP Yang, SM; Chen, AL (corresponding author), Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. EM yangshumingcaas@sina.com; ailiang.chen@gmail.com CR Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Baldo A, 2010, MEAT SCI, V85, P671, DOI 10.1016/j.meatsci.2010.03.023 Barcos LO, 2001, REV SCI TECH OIE, V20, P640, DOI 10.20506/rst.20.2.1294 Bi W. W., 2011, CHINESE J ANIMALAND, V42, P481 Botstein D., 1980, AM J HUM GENET, P324 Cao T., 2010, COMPUTER KNOWLEDGE T, V6, P2398 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Gamarra D, 2015, FORENS SCI INT-GEN S, V5, pE253, DOI 10.1016/j.fsigss.2015.09.101 Herraez DL, 2005, Z NATURFORSCH C, V60, P637 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Li D., 2015, MEAT RES, V29, P34 Li Qian-Qian, 2013, Chinese Journal of Zoology, V48, P371 Li Rong-Ling, 2007, Yichuan, V29, P1463, DOI 10.1360/yc-007-1463 Ling L, 2013, J ANHUI AGR SCI, V41, P8711 Luo Y. F., 2006, BIODIVERSITY SCI, V14, P498 Mateus JC, 2015, FOOD CONTROL, V47, P487, DOI 10.1016/j.foodcont.2014.07.038 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Peelman LJ, 1998, ANIM GENET, V29, P161, DOI 10.1111/j.1365-2052.1998.00280.x Rakoczy-Trojanowska M, 2004, CELL MOL BIOL LETT, V9, P221 RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Rodriguez-Ramirez R, 2011, GENET MOL RES, V10, P2358, DOI 10.4238/2011.October.6.1 Rogberg-Munoz A, 2016, MEAT SCI, V111, P47, DOI 10.1016/j.meatsci.2015.08.014 Rogberg-Munoz A, 2014, MEAT SCI, V98, P822, DOI 10.1016/j.meatsci.2014.07.028 Sardina MT, 2015, FOOD RES INT, V74, P115, DOI 10.1016/j.foodres.2015.04.038 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Walker M. J., 2013, Journal of the Association of Public Analysts, V41, P67 Wang Jing, 2009, Yichuan, V31, P285, DOI 10.3724/SP.J.1005.2009.00285 Weir BS, 1996, METHODS DISCRETE POP [吴潇 Wu Xiao], 2014, [食品与生物技术学报, Journal of Food Science and Biotechnology], V33, P624 Xia Jun-Hong, 2005, Acta Zoologica Sinica, V51, P142 Yan SX, 2016, BIOCHEM SYST ECOL, V69, P27, DOI 10.1016/j.bse.2016.08.008 [杨晓冰 YANG Xiaobing], 2007, [西北农业学报, Acat Agriculturae Boreali-Occidentalis Sinica], V16, P55 Zhong JinCheng, 2006, Scientia Agricultura Sinica, V39, P389 赵方, 2003, [中国法医学杂志, Chinese Journal of Forensic Medicine], V18, P297 NR 39 TC 16 Z9 17 U1 0 U2 46 PD AUG PY 2017 VL 78 BP 469 EP 475 DI 10.1016/j.foodcont.2017.03.017 WC Food Science & Technology SC Food Science & Technology UT WOS:000401878400059 DA 2022-12-14 ER PT J AU Landinez, SPC Rodriguez, PEC Gomez, DFS AF Castillo Landinez, Sandra Patricia Caicedo Rodriguez, Pablo Eduardo Sanchez Gomez, Diego Felipe TI Design and implementation of a software for the traceability of coffee processing SO REVISTA CORPOICA-CIENCIA Y TECNOLOGIA AGROPECUARIA DT Article DE coffee industry; computer programming; food safety; food traceability; quality controls ID FOOD; QUALITY; SAFETY AB This article describes the implementation of a software which allows supporting coffee producers in the process of obtaining a certification of origin for their product according to the regulations established for trade in agricultural products, especially monitored in the European Union since January 2005. This regulation set down the requirements for a follow-up that guarantees the authenticity and traceability of the food, which satisfies the final consumer. The project was carried out using the Scrum framework and the eXtreme Programming (XP) software development methodology. The results showed that the integration of the framework and the method allowed organizing the work in phases and achieve incremental results. This application represents the first step to generate added value in a coffee farm through the traceability registration of their products. C1 [Castillo Landinez, Sandra Patricia; Caicedo Rodriguez, Pablo Eduardo] Corp Univ Autonoma Cauca, Fac Ingn, Popayan, Colombia. [Sanchez Gomez, Diego Felipe] VigiVox BeeTiC, Popayan, Colombia. RP Landinez, SPC (corresponding author), Corp Univ Autonoma Cauca, Calle 5 3-85, Popayan, Colombia. EM sandra.castillo.l@uniautonoma.edu.co; pablo.caicedo.r@uniautonoma.edu.co; difesanchez@gmail.com CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Beck K., 2004, XP SER Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Colom A., 2004, CIRIEC, V49, P77 Correa-Hernando E. C., 2016, FORUMCAFE FORUM CULT, V64, P26 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Espinal C. F., 2005, CADENA CAFE COLOMBIA Evangelista SR, 2014, FOOD RES INT, V61, P183, DOI 10.1016/j.foodres.2013.11.033 Federacion Nacional de Cafeteros de Colombia, 2007, SISTEMAS PRODUCCION Federacion Nacional de Cafeteros de Colombia, 2017, COMP IND CAF COL 201 Ha OK, 2014, PERS UBIQUIT COMPUT, V18, P553, DOI 10.1007/s00779-013-0675-x Lopez C D., 2012, SCI TECHNICA, V17 Maurer F., 2011, EXTREME PROGRAMMING Melo J, 2015, FOOD RES INT, V67, P75, DOI 10.1016/j.foodres.2014.10.031 Neto M. D. C., 2003, EFITA 2003 C 5 9 JUL, P607 Ocampo-López Olga Lucía, 2017, Ing. invest. y tecnol., V18, P127 Puerta Q G., 2013, AVANCES TECNICOS CEN, V355, P1 Schwaber K., 2017, GUIA SCRUM TM Stranieri S., 2018, Wine Economics and Policy, V7, P45, DOI 10.1016/j.wep.2018.02.001 Suhaiza Zailani, 2010, Journal of Food Technology, V8, P74, DOI 10.3923/jftech.2010.74.81 Vazquez A., 2016, 8 C ARG AGROINFORMAT Wang J, 2017, FOOD CONTROL, V79, P363, DOI 10.1016/j.foodcont.2017.04.013 Xinting Y., 2008, T CHINESE SOC AGR EN, V2008, DOI [10.3969/J.ISSN.1002-6819.2008.3.032, DOI 10.3969/J.ISSN.1002-6819.2008.3.032] Zhang HL, 2011, IFIP ADV INF COMM TE, V345, P1 NR 25 TC 0 Z9 0 U1 2 U2 6 PD SEP-DEC PY 2019 VL 20 IS 3 BP 537 EP 550 DI 10.21930/rcta.vol20_num3_art:1588 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000486574400003 DA 2022-12-14 ER PT J AU Oddone, M Aceto, M Baldizzone, M Musso, D Osella, D AF Oddone, Matteo Aceto, Maurizio Baldizzone, Massimo Musso, Davide Osella, Domenico TI Authentication and Traceability Study of Hazelnuts from Piedmont, Italy SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE Hazelnut; authentication; traceability; lanthanides; chemometrics ID AVELLANA L. VARIETIES; RARE-EARTH-ELEMENTS; PLANTS AB Hazelnut is one of the most important items in high-quality food products from Piedmont, Italy. The 'Tonda Gentile delle Langhe' (TGL) variety is acknowledged all over the world as the best one, and it is particularly appreciated when used to provide flavor in chocolate products. Authentication and/or traceability studies must therefore be developed to safeguard this variety against fraud, which can occur when the product is partially or totally substituted with hazelnuts of lower quality. In this work, a classification of hazelnuts from different countries is presented, showing the possibility to discriminate the TGL from other productions on the basis of the distribution of trace elements as determined by means of inductively coupled plasma-mass spectrometry (ICP-MS), with particular reference to lanthanides. Accuracy of the sample treatment procedure was tested by analysis of biological certified materials. Data from elemental analysis were chemometrically treated with an unsupervised method, such as principal component analysis (PCA), allowing for a good discrimination among groups. C1 [Oddone, Matteo; Aceto, Maurizio; Baldizzone, Massimo; Musso, Davide; Osella, Domenico] Univ Piemonte Orientale, Dept Environm & Life Sci, I-15100 Alessandria, Italy. C3 University of Eastern Piedmont Amedeo Avogadro RP Aceto, M (corresponding author), Univ Piemonte Orientale, Dept Environm & Life Sci, Via Teresa Michel 11, I-15100 Alessandria, Italy. EM maurizio.aceto@unipmn.it CR Alasalvar C, 2004, J FOOD SCI, V69, pS99, DOI 10.1111/j.1365-2621.2004.tb13382.x [Anonymous], 1996, OFF J EUR COMMUNIT L, V148, P1 Bettinelli M, 2005, ATOM SPECTROSC, V26, P41 Brown P.H., 1990, HDB PHYS CHEM RARE E, V13, ed., P423, DOI DOI 10.1016/S0168-1273(05)80135-7 Capote FP, 2007, ANAL BIOANAL CHEM, V388, P1859, DOI 10.1007/s00216-007-1422-9 Cindric IJ, 2007, MICROCHEM J, V85, P136, DOI 10.1016/j.microc.2006.04.011 CORYELL CD, 1963, J GEOPHYS RES, V68, P559, DOI 10.1029/JZ068i002p00559 Dundar MS, 2002, ACTA CHIM SLOV, V49, P537 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Koksal AH, 2006, FOOD CHEM, V99, P509, DOI 10.1016/j.foodchem.2005.08.013 Liang T, 2008, J RARE EARTH, V26, P7, DOI 10.1016/S1002-0721(08)60027-7 Masuda A., 1962, J EARTH SCI-NAGOYA U, V10, P173 Rodushkin I, 2008, SCI TOTAL ENVIRON, V392, P290, DOI 10.1016/j.scitotenv.2007.11.024 Tyler G, 2004, PLANT SOIL, V267, P191, DOI 10.1007/s11104-005-4888-2 NR 14 TC 64 Z9 65 U1 0 U2 40 PD MAY 13 PY 2009 VL 57 IS 9 BP 3404 EP 3408 DI 10.1021/jf900312p WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000265896600002 DA 2022-12-14 ER PT J AU de las Morenas, J Garcia, A Blanco, J AF de las Morenas, Javier Garcia, Andres Blanco, Jesus TI Prototype traceability system for the dairy industry SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Milk chain traceability; Temperature tracking; RFID; Smart sensors; Embedded systems AB At the beginning of the milk manufacturing process, a refrigerated bulk tank lorry is in charge of collecting milk from dairy farms in the area within a few hours. In this process, a milk sample is also collected at every farm. At the end of the collection the milk contained in the tank is analyzed. The problems appear when the analysis reveals the presence of forbidden substances at levels above stated thresholds in the milk tank but not in any of the samples; this is mainly due to the extremely low levels that are being considered for some substances and the way in which they rapidly deteriorate at high temperatures. Therefore, all samples must be kept in optimum - temperature controlled - conditions during transportation to ensure reliable results in the laboratory. In this paper, a novelty solution for the tracking and tracing of milk samples is presented. This solution includes a customized and automated cooler for carrying samples, a smart sensor inside the cooler saving the data collected during the process, and an USB sticker to transfer the collected data to a computer for further analysis. Several technologies have been combined to register and trace milk samples on their trip from farm to laboratory: microcontrollers, sensors, Radio Frequency Identification (RFID), and Global Positioning System (GPS). Hardware and software prototypes have been successfully developed and tested in real vehicle case studies. (C) 2013 Elsevier B.V. All rights reserved. C1 [de las Morenas, Javier; Garcia, Andres; Blanco, Jesus] Univ Castilla La Mancha, Sch Ind Engn, AutoLog Grp, E-13071 Ciudad Real, Spain. C3 Universidad de Castilla-La Mancha RP de las Morenas, J (corresponding author), Univ Castilla La Mancha, Sch Ind Engn, AutoLog Grp, E-13071 Ciudad Real, Spain. EM javier.delasmorenas@uclm.es CR Abarca A., 2009, RFID 3 LEVEL BASED I, P1 Amador Cecilia, 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P26, DOI 10.1007/s11694-009-9072-6 Atmel, 2011, ATMEGA2560 MICR DAT Bertrand JA, 1996, J DAIRY SCI, V79, P145, DOI 10.3168/jds.S0022-0302(96)76346-4 Borras M., 2008, J SATEL SEOC 2008 AL Catarinucci L, 2011, SOFTCOM 2011 19 INT, P1 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 F2F, 2012, RFID FARM FORK F2F P Finkenzeller Klaus, 2010, RFID HDB FUNDAMENTAL, V3rd Ga-Escribano J, 2012, INT J RF TECHNOL-RES, V3, P181, DOI 10.3233/RFT-2012-020 ISO 2000, 2000, 90002000 EN ISO COMM Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Microchip, 2011, PIC16F1827 DAT Nordic, 2008, NRF905 DAT NXP, 2013, NXP Raab V, 2011, BRIT FOOD J, V113, P1267, DOI 10.1108/00070701111177683 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Samad A, 2010, COMPUT ELECTRON AGR, V73, P213, DOI 10.1016/j.compag.2010.05.001 Santa J, 2012, COMPUT ELECTRON AGR, V80, P31, DOI 10.1016/j.compag.2011.10.010 Thompson C, 2008, ATHLET THER TODAY, V13, P1 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 NR 22 TC 10 Z9 10 U1 3 U2 41 PD FEB PY 2014 VL 101 BP 34 EP 41 DI 10.1016/j.compag.2013.12.011 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000331686000005 DA 2022-12-14 ER PT J AU Gambino, G Ferrero, L Scalzini, G De Paolis, C Paissoni, MA Segade, SR Giacosa, S Boccacci, P Rolle, L AF Gambino, Giorgio Ferrero, Lorenzo Scalzini, Giulia De Paolis, Camilla Paissoni, Maria Alessandra Segade, Susana Rio Giacosa, Simone Boccacci, Paolo Rolle, Luca TI Impact of oenological processing aids and additives on the genetic traceability of 'Nebbiolo' wine produced with withered grapes SO FOOD RESEARCH INTERNATIONAL DT Article DE Grapevine; Wines; Oenological additives; Sfursat; Genetic traceability; SNPs ID DNA; MUSTS; AUTHENTICATION; NUCLEAR; IDENTIFICATION; EXTRACTION; VARIETIES AB 'Nebbiolo' is a well-known grapevine variety used to produce prestigious monovarietal Italian red wines. Genetic traceability is an important tool used to protect the authenticity of high-quality wines. SNP-based assays are an effective method to reach this aim in wines, but several issues have been reported for the authentication of commercial wines. In this study, the impact of the most common commercial additives and processing aids used in winemaking was analysed in `Nebbiolo' wine using SNP-based traceability. Gelatine and bentonite had the strongest impact on the turbidity, colour and phenolic composition of wines and on residual grapevine DNA. The DNA reduction associated with the use of bentonite and gelatine (>99% compared to the untreated control) caused issues in the SNP-based assay, especially when the DNA concentration was below 0.5 pg/mL of wine. This study contributed to explaining the causes of the reduced varietal identification efficiency in commercial wines. C1 [Gambino, Giorgio; Ferrero, Lorenzo; Boccacci, Paolo] CNR, IPSP, Natl Res Council Italy, Inst Sustainable Plant Protect, Str Cacce 73, I-10135 Turin, Italy. [Ferrero, Lorenzo; Scalzini, Giulia; De Paolis, Camilla; Paissoni, Maria Alessandra; Segade, Susana Rio; Giacosa, Simone; Rolle, Luca] Univ Torino, Dept Agr Forest & Food Sci, Largo Braccini 2, I-10095 Grugliasco, TO, Italy. C3 Consiglio Nazionale delle Ricerche (CNR); Istituto per la Protezione Sostenibile delle Piante (IPSP-CNR); University of Turin RP Gambino, G (corresponding author), CNR, IPSP, Natl Res Council Italy, Inst Sustainable Plant Protect, Str Cacce 73, I-10135 Turin, Italy. EM giorgio.gambino@ipsp.cnr.it CR Agrimonti C, 2018, EUR FOOD RES TECHNOL, V244, P2127, DOI 10.1007/s00217-018-3121-5 Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Barrias S, 2019, FOOD CHEM, V270, P299, DOI 10.1016/j.foodchem.2018.07.058 Boccacci P, 2020, FOOD CHEM, V312, DOI 10.1016/j.foodchem.2019.126100 Boccacci P, 2012, EUR FOOD RES TECHNOL, V235, P439, DOI 10.1007/s00217-012-1770-3 Bosso A, 2020, J FOOD SCI, V85, P2406, DOI 10.1111/1750-3841.15342 Cabezas JA, 2011, BMC PLANT BIOL, V11, DOI 10.1186/1471-2229-11-153 Marin AC, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10196877 Catalano V, 2016, J AGR FOOD CHEM, V64, P6969, DOI 10.1021/acs.jafc.6b02560 Cosme F, 2007, ITAL J FOOD SCI, V19, P39 Cosme F, 2012, LWT-FOOD SCI TECHNOL, V46, P382, DOI 10.1016/j.lwt.2011.12.016 Costa V., 2019, CIBIA 12 IB C FOOD E, P159 Fanelli V, 2021, FOODS, V10, DOI 10.3390/foods10071644 Faria MA, 2008, EUR FOOD RES TECHNOL, V227, P845, DOI 10.1007/s00217-007-0795-5 Ficagna E, 2020, FOOD SCI TECH-BRAZIL, V40, P729, DOI 10.1590/fst.18719 Gambino G, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-17405-y Gonzalez-Neves G, 2014, FOOD CHEM, V157, P385, DOI 10.1016/j.foodchem.2014.02.062 Holmberg L., 2010, INT J WINE RES, V2, P105, DOI [10.2147/IJWR.S14102, DOI 10.2147/IJWR.S14102, 10.2147/ijwr.s14102] Jaillon O, 2007, NATURE, V449, P463, DOI 10.1038/nature06148 Miglietta PP, 2018, NEW MEDIT, V17, P73, DOI 10.30682/nm1801g Myles S, 2011, P NATL ACAD SCI USA, V108, P3530, DOI 10.1073/pnas.1009363108 OIV, 2016, COMP INT METH AN WIN OIV, 2016, RES OIV OENO 567A 20 Pereira L, 2017, FOOD CHEM, V216, P80, DOI 10.1016/j.foodchem.2016.07.185 Pereira L, 2012, FOOD ANAL METHOD, V5, P1252, DOI 10.1007/s12161-012-9369-7 Perez-Magarino S, 2003, FOOD CHEM, V81, P301, DOI 10.1016/S0308-8146(02)00509-5 Petrozziello M, 2018, FRONT CHEM, V6, DOI 10.3389/fchem.2018.00137 Raimondi S, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-72799-6 Recupero M, 2013, FOOD ANAL METHOD, V6, P952, DOI 10.1007/s12161-012-9506-3 Rinaldi A, 2019, LWT-FOOD SCI TECHNOL, V105, P233, DOI 10.1016/j.lwt.2019.02.034 Savazzini F, 2006, ANAL CHIM ACTA, V563, P274, DOI 10.1016/j.aca.2005.10.078 Scalzini G, 2020, FOODS, V9, DOI 10.3390/foods9121747 SCHNEIDER A, 1987, AM J ENOL VITICULT, V38, P151 Segade SR, 2020, MOLECULES, V25, DOI 10.3390/molecules25010120 Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Siret R, 2002, J AGR FOOD CHEM, V50, P3822, DOI 10.1021/jf011462e Solovyev PA, 2021, COMPR REV FOOD SCI F, V20, P2040, DOI 10.1111/1541-4337.12700 This P, 2004, THEOR APPL GENET, V109, P1448, DOI 10.1007/s00122-004-1760-3 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Vignani R, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0211962 Villano C, 2017, FOOD CONTROL, V80, P1, DOI 10.1016/j.foodcont.2017.04.020 Zambianchi S, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107929 Zhang J, 2010, ANAL CHIM ACTA, V662, P137, DOI 10.1016/j.aca.2009.12.043 NR 43 TC 0 Z9 0 U1 2 U2 6 PD JAN PY 2022 VL 151 AR 110874 DI 10.1016/j.foodres.2021.110874 EA DEC 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000741823300002 DA 2022-12-14 ER PT J AU Morris, HA AF Morris, H. A. TI Traceability and standardization of immunoassays: A major challenge SO CLINICAL BIOCHEMISTRY DT Article; Proceedings Paper CT IEEE International Test Conference CY OCT 21-26, 2007 CL Santa Clara, CA DE Traceability; Standardization; Immunoassay; Hormone ID PROPOSAL; FSH; LH AB The clinical utility of immunoassay results is dependent on precision and trueness for the application of internationally agreed clinical protocols and common reference intervals and decision limits. The application of metrological principles to achieve traceability and standardization for these assays is being Pursued to this end. Comprehensive measurement systems are available for a number of the total serum hapten assays routinely measured by immunoassays while current research is investigating the appropriateness of developing defined systems for the measurement of "free" hormone levels. The standardization or harmonization of assays for heterogeneous polypeptide hormones is also another area of research for these assays. (c) 2008 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. C1 Univ Adelaide, Sch Med, Hanson Inst, Inst Med & Vet Sci, Adelaide, SA 5000, Australia. C3 Hanson Institute; Institute Medical & Veterinary Science Australia; University of Adelaide RP Morris, HA (corresponding author), Univ Adelaide, Sch Med, Hanson Inst, Inst Med & Vet Sci, POB 14 Rundle Mall, Adelaide, SA 5000, Australia. EM howard.morris@imvs.sa.gov.au CR Cole LA, 1997, CLIN CHEM, V43, P2233 Goodall Ian, 2005, Clin Biochem Rev, V26, P5 Jeppsson JO, 2007, CLIN CHEM LAB MED, V45, P558, DOI 10.1515/CCLM.2007.107 LANTTO O, 1980, CLIN CHEM, V26, P1899 Schumann G, 2006, CLIN CHEM LAB MED, V44, P1146, DOI 10.1515/CCLM.2006.212 Sikaris K, 2005, J CLIN ENDOCR METAB, V90, P5928, DOI 10.1210/jc.2005-0962 Stamey TA, 1998, CLIN BIOCHEM, V31, P475, DOI 10.1016/S0009-9120(98)00055-1 Stenman UH, 2006, HUM REPROD UPDATE, V12, P769, DOI 10.1093/humupd/dml029 Stenman UM, 2004, CLIN CHEM, V50, P798, DOI 10.1373/clinchem.2003.031013 STORRING PL, 1976, ACTA ENDOCRINOL-COP, V83, P700, DOI 10.1530/acta.0.0830700 Sturgeon CM, 2007, MOL CELL ENDOCRINOL, V260, P301, DOI 10.1016/j.mce.2006.09.004 Sturgeon CM, 2007, CLIN CHIM ACTA, V381, P85, DOI 10.1016/j.cca.2007.02.015 Thienpont LM, 2007, CLIN CHEM LAB MED, V45, P934, DOI 10.1515/CCLM.2007.155 Thienpont LM, 1998, CLIN BIOCHEM, V31, P483, DOI 10.1016/S0009-9120(98)00037-X Thienpont LM, 2002, CLIN CHIM ACTA, V323, P73, DOI 10.1016/S0009-8981(02)00188-2 Weykamp C, 2008, CLIN CHEM, V54, P240, DOI 10.1373/clinchem.2007.097402 Wild D., 2005, IMMUNOASSAY HDB, V3rd ed. NR 17 TC 8 Z9 8 U1 0 U2 10 PD MAR PY 2009 VL 42 IS 4-5 BP 241 EP 245 DI 10.1016/j.clinbiochem.2008.09.005 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000264309400005 DA 2022-12-14 ER PT J AU Alfian, G Syafrudin, M Farooq, U Ma'arif, MR Syaekhoni, MA Fitriyani, NL Lee, J Rhee, J AF Alfian, Ganjar Syafrudin, Muhammad Farooq, Umar Ma'arif, Muhammad Rifqi Syaekhoni, M. Alex Fitriyani, Norma Latif Lee, Jaeho Rhee, Jongtae TI Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model SO FOOD CONTROL DT Article DE Traceability; RFID; IoT; Machine learning; Classification; Tag direction; Perishable food supply chain ID RADIO-FREQUENCY IDENTIFICATION; QUALITY; MANAGEMENT; PRODUCTS; SAFETY AB Radio Frequency Identification (RFID) technology has significantly improved in the past few years and is presently sought for implementation in the identification and traceability of perishable food in the food sector to safeguard food safety and quality. It is currently considered a worthy successor to the barcode system and has significant advantages for monitoring products in the perishable food supply chain (PFSC). The present study proposes a traceability system that utilizes RFID and Internet of Things (IoT) sensors. RFID technology can be used to track and trace perishable food while IoT sensors can be used to measure temperature and humidity during storage and transportation. Furthermore, it is important that RFID gates can identify the direction of tags and whether products are being received or shipped through the gate. In this study, machine-learning models are utilized to detect the direction of passive RFID tags. The input features are derived from receive signal strength (RSS) and the timestamp of tags. The proposed system has been tested in the perishable food supply chain and has revealed significant benefits to managers and customers by providing real-time product information and complete temperature and humidity history. In addition, by integrating a machine-learning model into the RFID gate, tagged products that move in or out through a gate can be correctly identified and thus improve the efficiency of the traceability system. C1 [Alfian, Ganjar; Lee, Jaeho] Dongguk Univ, Nano Informat Technol Acad, Seoul 04626, South Korea. [Syafrudin, Muhammad; Syaekhoni, M. Alex; Fitriyani, Norma Latif; Rhee, Jongtae] Dongguk Univ, Dept Ind & Syst Engn, Seoul 04620, South Korea. [Farooq, Umar] Ghulam Ishaq Khan Inst Engn Sci & Technol, Dept Management Sci & Humanities, Swabi, Pakistan. [Ma'arif, Muhammad Rifqi] Univ Jenderal Achmad Yani, Dept Ind Engn, Yogyakarta, Indonesia. C3 Dongguk University; Dongguk University; GIK Institute Engineering Science & Technology; Universitas Jenderal Achmad Yani RP Rhee, J (corresponding author), Dongguk Univ, Dept Ind & Syst Engn, Seoul 04620, South Korea. EM ganjar@dongguk.edu; udin@dongguk.edu; umar@giki.edu.pk; rifqi@unjaya.ac.id; alexs@dongguk.edu; norma@dongguk.edu; rapidme@dongguk.edu; jtrhee.uscm@gmail.com CR Alfian G, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9061154 Alfian G, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18072183 Alfian G, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9112073 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Alien Technology, 2019, ALR 9900 ENT RFID RE Lopez YA, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18082663 Lopez YA, 2017, SENSOR ACTUAT A-PHYS, V255, P118, DOI 10.1016/j.sna.2017.01.007 Aung MM, 2014, FOOD CONTROL, V40, P198, DOI 10.1016/j.foodcont.2013.11.016 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Bunker R, 2017, ELECTRONICS-SWITZ, V6, DOI 10.3390/electronics6010009 Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785 Choi SH, 2015, COMPUT IND, V71, P10, DOI 10.1016/j.compind.2015.03.003 Chow HKH, 2006, EXPERT SYST APPL, V30, P561, DOI 10.1016/j.eswa.2005.07.023 Clarke RH, 2006, PACKAG TECHNOL SCI, V19, P45, DOI 10.1002/pts.714 Ding K, 2018, ROBOT CIM-INT MANUF, V49, P120, DOI 10.1016/j.rcim.2017.06.009 Duroc Y, 2018, CR PHYS, V19, P64, DOI 10.1016/j.crhy.2018.01.003 Farooq U, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8090839 Keller T., 2014, ACM T MANAGEMENT INF, V5, P1, DOI DOI 10.1145/2629449 Keller T., 2010, 2010 INTERNET THINGS, P1, DOI [10.1109/IOT.2010.5678439, 10.1109/I0T.2010.5678439, DOI 10.1109/IOT.2010.5678439] Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Kim J, 2012, FOOD MICROBIOL, V32, P20, DOI 10.1016/j.fm.2012.05.007 Lee CKH, 2013, EXPERT SYST APPL, V40, P784, DOI 10.1016/j.eswa.2012.08.033 Lee GI, 2012, FOOD CONTROL, V24, P1, DOI 10.1016/j.foodcont.2011.09.014 Ma HS, 2018, EXPERT SYST APPL, V91, P442, DOI 10.1016/j.eswa.2017.09.021 Ma'arif MR, 2018, I C INF COMM TECH CO, P52 Maksimovic M., 2015, International Journal of Sustainable Agricultural Management and Informatics, V1, P333, DOI 10.1504/IJSAMI.2015.075053 Perez MM, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18051627 Perez MM, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17102247 OIKAWA S, 2011, J JPN DIABETES SOC, V54, P1 Pedregosa F, 2011, J MACH LEARN RES, V12, P2825 Popa A, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11030374 Raspberry Pi Foundation, 2017, SENS HAT Raspberry Pi Foundation, 2016, RASPB PI 3 MOD B Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 RFID Systems, 2019, AR POP SMART RFID DO Rijpkema WA, 2014, INT J PHYS DISTR LOG, V44, P494, DOI 10.1108/IJPDLM-01-2013-0013 Scharff RL, 2012, J FOOD PROTECT, V75, P123, DOI 10.4315/0362-028X.JFP-11-058 Seol S, 2017, FUTURE GENER COMP SY, V76, P443, DOI 10.1016/j.future.2016.08.005 Shukla M, 2013, INT J OPER PROD MAN, V33, P114, DOI 10.1108/01443571311295608 Sokolova M, 2009, INFORM PROCESS MANAG, V45, P427, DOI 10.1016/j.ipm.2009.03.002 Stellingwerf HM, 2018, TRANSPORT RES D-TR E, V65, P178, DOI 10.1016/j.trd.2018.08.010 Syaekhoni MA, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9112008 Syafrudin M, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9010084 Syafrudin M, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18092946 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Tsang YP, 2018, FOOD CONTROL, V90, P81, DOI 10.1016/j.foodcont.2018.02.030 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Wu J, 2018, INT C COMP SUPP COOP, P588 Wu J, 2018, INT J ELECTRON, V105, P882, DOI 10.1080/00207217.2017.1409812 Zhong RY, 2013, ROBOT CIM-INT MANUF, V29, P283, DOI 10.1016/j.rcim.2012.08.001 NR 51 TC 58 Z9 62 U1 15 U2 105 PD APR PY 2020 VL 110 AR 107016 DI 10.1016/j.foodcont.2019.107016 WC Food Science & Technology SC Food Science & Technology UT WOS:000510072500048 DA 2022-12-14 ER PT J AU Huang, XB Deng, ZP Long, L Chen, JJ Tan, DM Zhu, LY Fan, XY Shen, T Lu, FM AF Huang, Xiangbo Deng, Zhongping Long, Lu Chen, Jinjun Tan, Deming Zhu, Liyan Fan, Xueying Shen, Tao Lu, Fengmin TI Traceability, reproducibility and clinical evaluation of Sansure Realtime HCV RNA assay SO BMC INFECTIOUS DISEASES DT Article DE Hepatitis C virus; RNA; Traceability; Reproducibility; Correlation ID HEPATITIS-B; VIRUS; EPIDEMIOLOGY; BOCEPREVIR; TELAPREVIR AB Background: Accurate quantitative detection of hepatitis C virus (HCV) RNA is critical for diagnosis of acute or chronic HCV infection, and for follow-up of virologic response during HCV targeted therapy. In the present study, traceability and reproducibility of a novel China-certified domestic Sansure HCV RNA diagnostic assay (Sansure, Changsha, Hunan, China) was evaluated and the clinical performance of this assay was also analyzed. Methods: Traceability of the Sansure HCV RNA assay to the WHO international standard for HCV (genotype 1a) was detected across multiple centers. Reproducibility, accuracy (the differences of observed average concentrations and expected concentrations) and precision were assessed using series dilutions of World HCV RNA performance panel WWHV303-02 (HCV-1b), WWHV303-04(HCV-2a), WWHV303-11(HCV-3a) and WWHV303-19 (HCV-6a). In addition, both Sansure HCV RNA and CAP/CTM HCV (Roche, Branchburg, NJ, USA) assays were used to detect HCV RNA in 346 EDTA anti-coagulated plasma samples from previous HCV-infected patients, during and after antiviral therapy. Results: The Sansure assay showed good traceability by agreeing with the HCV-1a WHO standard across all five concentrations tested (25, 50, 100, 1000, 10000 IU/ml). The differences between observed average concentrations and expected concentrations were all within 0.2 log(10) IU/ml. HCV WWHV303 standards across 4 HCV genotypes (1b, 2a, 3a and 6a) were used for evaluation of reproducibility and the accuracy of the test were all within 0.2 log10 IU/ml. The inter-assay variations across the above 4 HCV genotypes were all less than 0.03 on each evaluated concentration, indicating good precision of Sansure HCV RNA assay. In clinical practice, concordant results were determined in 99.42 % (344/346) samples (215 positive and 129 negative samples). Two specimens with negative HCV RNA results by Sansure assay were detected positive by CAP/CTM HCV test. Correlation analysis indicated a significantly positive correlation in detected HCV RNA concentrations (r = 0.9439, P < 0.0001). HCV RNA levels in 95.35 % (205/215) specimens were within mean difference +/- 1.96 SD as tested by both assays. Conclusions: With the advantages of traceability, reproducibility and lower price, Sansure HCV RNA assay represented an alternative option for HCV RNA detection in hospital and medical institution in China. C1 [Huang, Xiangbo; Long, Lu; Zhu, Liyan; Fan, Xueying; Shen, Tao; Lu, Fengmin] Peking Univ, Sch Basic Med Sci, Dept Microbiol, 38 Xueyuan Rd, Beijing 100191, Peoples R China. [Huang, Xiangbo; Long, Lu; Zhu, Liyan; Fan, Xueying; Shen, Tao; Lu, Fengmin] Peking Univ, Sch Basic Med Sci, Ctr Infect Dis, 38 Xueyuan Rd, Beijing 100191, Peoples R China. [Deng, Zhongping] Peking Univ, Acad Adv Interdisciplinary Studies, Beijing 100871, Peoples R China. [Chen, Jinjun] Southern Med Univ, Dept Infect Dis, Nanfang Hosp, Guangzhou 510515, Guangdong, Peoples R China. [Tan, Deming] Cent S Univ, Dept Infect Dis, Xiangya Hosp, Changsha 410008, Hunan, Peoples R China. C3 Peking University; Peking University; Peking University; Southern Medical University - China; Central South University RP Shen, T (corresponding author), Peking Univ, Sch Basic Med Sci, Dept Microbiol, 38 Xueyuan Rd, Beijing 100191, Peoples R China.; Shen, T (corresponding author), Peking Univ, Sch Basic Med Sci, Ctr Infect Dis, 38 Xueyuan Rd, Beijing 100191, Peoples R China. EM taoshen@hsc.pku.edu.cn CR Bacon BR, 2011, NEW ENGL J MED, V364, P1207, DOI 10.1056/NEJMoa1009482 Beld M, 1999, BLOOD, V94, P1183, DOI 10.1182/blood.V94.4.1183.416k15_1183_1191 Chen Yuan-sheng, 2011, Zhonghua Liu Xing Bing Xue Za Zhi, V32, P888 CLSI, 2010, EP9A2IR CLSI, V30 Cui Y, 2013, J GASTROEN HEPATOL, V28, P7, DOI 10.1111/jgh.12220 Fryer J, 2011, WHOBS20112173 ECBS Hajarizadeh B, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0122232 Hajarizadeh B, 2013, NAT REV GASTRO HEPAT, V10, P553, DOI 10.1038/nrgastro.2013.107 Jacobson IM, 2011, NEW ENGL J MED, V364, P2405, DOI 10.1056/NEJMoa1012912 Kessler HH, 2013, J CLIN VIROL, V58, P522, DOI 10.1016/j.jcv.2013.09.005 Lawitz E, 2013, NEW ENGL J MED, V368, P1878, DOI 10.1056/NEJMoa1214853 Page-Shafer K, 2008, J CLIN MICROBIOL, V46, P499, DOI 10.1128/JCM.01229-07 Pawlotsky JM, 2014, J HEPATOL, V61, P373, DOI 10.1016/j.jhep.2014.05.001 Perz JF, 2006, J HEPATOL, V45, P529, DOI 10.1016/j.jhep.2006.05.013 Poordad F, 2011, NEW ENGL J MED, V364, P1195, DOI 10.1056/NEJMoa1010494 Rao HY, 2014, J GASTROEN HEPATOL, V29, P545, DOI 10.1111/jgh.12398 Zeuzem S, 2011, NEW ENGL J MED, V364, P2417, DOI 10.1056/NEJMoa1013086 Zitzer H, 2013, J CLIN MICROBIOL, V51, P571, DOI 10.1128/JCM.01784-12 NR 18 TC 1 Z9 1 U1 0 U2 2 PD FEB 1 PY 2016 VL 16 AR 47 DI 10.1186/s12879-016-1390-9 WC Infectious Diseases SC Infectious Diseases UT WOS:000369364500001 DA 2022-12-14 ER PT J AU Zhou, YP Zhao, XJ Sun, L AF Zhou, Yan-Ping Zhao, Xiao-Jie Sun, Lu TI Research on traceability strategy of food supply chain considering delay effect SO JOURNAL OF FOOD SCIENCE DT Article DE delay effect; differential game; food safety; traceability ID PURCHASE INTENTIONS; MANAGEMENT; BLOCKCHAIN; SAFETY; AGRICULTURE; INFORMATION; TECHNOLOGY; FRAMEWORK; CONSUMERS; RECALLS AB The traceability system has significantly contributed to ensure food safety and quality. However, the biggest difficulty in food traceability is the numerous links from field to table, and there is no stable strategic partnership between supply chain members and the lack of social responsibility of some practitioners. Thus, this study aims to seek the best traceability strategy for companies in centralized model and decentralized model, respectively. Therefore, we have constructed a differential game model based on the delay effect to determine the optimal traceability level and traceable goodwill and compare the profits of the food supply chain (FSC). The results show that the delay time is positively related to the level of traceability effort and has a high impact on the traceable goodwill. Companies in the FSC can formulate optimal traceability strategies based on delay time and foster improvement in food safety and quality. C1 [Zhou, Yan-Ping; Zhao, Xiao-Jie] Shandong Univ Sci & Technol, Coll Econ & Management, Qingdao 266590, Peoples R China. [Sun, Lu] Qingdao Univ Technol, Business Sch, Qingdao, Peoples R China. C3 Shandong University of Science & Technology; Qingdao University of Technology RP Zhao, XJ (corresponding author), Shandong Univ Sci & Technol, Coll Econ & Management, Qingdao 266590, Peoples R China. EM zhaoxiaojie1886@163.com CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Blagojevic B, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107870 Borda D, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107544 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Chitrakar B, 2021, FOOD CONTROL, V125, DOI 10.1016/j.foodcont.2021.108010 Coluccia B, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107839 Djekic I, 2021, FOOD CONTROL, V122, DOI 10.1016/j.foodcont.2020.107800 El Ouardighi F, 2006, EUR J OPER RES, V175, P1021, DOI 10.1016/j.ejor.2005.06.020 El Sheikha AF, 2017, REV FISH SCI AQUAC, V25, P158, DOI 10.1080/23308249.2016.1254158 Gallo A, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107866 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Garaus M, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108082 Hardt MJ, 2017, J FOOD SCI, V82, pA3, DOI 10.1111/1750-3841.13796 Hong J.-t., 2016, CHIN J MANAG SCI, V24, P100, DOI DOI 10.16381/J.CNKI.ISSN1003-207X.2016.02.013 Hou GS, 2019, J CLEAN PROD, V228, P1143, DOI 10.1016/j.jclepro.2019.04.096 Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kong DM, 2019, FOOD POLICY, V83, P60, DOI 10.1016/j.foodpol.2018.11.005 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Lopes LO, 2020, FOOD CONTROL, V111, DOI 10.1016/j.foodcont.2019.107060 Madani SR, 2017, COMPUT IND ENG, V105, P287, DOI 10.1016/j.cie.2017.01.017 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 Nakat Z, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107661 NERLOVE M, 1962, ECONOMICA, V29, P129, DOI 10.2307/2551549 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Rizou M, 2020, TRENDS FOOD SCI TECH, V102, P293, DOI 10.1016/j.tifs.2020.06.008 Salomie I., 2008, P IIWAS2008 LINZ AUS, P339 Spence M, 2018, FOOD CONTROL, V91, P138, DOI 10.1016/j.foodcont.2018.03.035 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Stranieri S, 2021, FOOD CONTROL, V119, DOI 10.1016/j.foodcont.2020.107495 Stranieri S, 2016, BRIT FOOD J, V118, P1025, DOI 10.1108/BFJ-04-2015-0151 Sun SN, 2019, J CLEAN PROD, V217, P658, DOI 10.1016/j.jclepro.2019.01.296 Taylor M, 2016, FOOD POLICY, V62, P56, DOI 10.1016/j.foodpol.2016.04.005 Walker GS, 2017, FOOD CONTROL, V72, P168, DOI 10.1016/j.foodcont.2016.01.028 Wang EST, 2019, FOOD QUAL PREFER, V78, DOI 10.1016/j.foodqual.2019.103723 Wang HY, 2014, FOOD POLICY, V47, P13, DOI 10.1016/j.foodpol.2014.04.001 Yekta R, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107754 Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Yue L.-q., 2016, SOFT SCI, V30, P123 Zhou YJ, 2018, J CLEAN PROD, V190, P592, DOI 10.1016/j.jclepro.2018.04.133 Zhu G.J., 2020, SYSTEMS ENG, V38, P55 NR 47 TC 0 Z9 0 U1 11 U2 11 PD NOV PY 2022 VL 87 IS 11 BP 4831 EP 4838 DI 10.1111/1750-3841.16278 EA OCT 2022 WC Food Science & Technology SC Food Science & Technology UT WOS:000865423900001 DA 2022-12-14 ER PT J AU Remaud, GS Silvestre, V Akoka, S AF Remaud, GS Silvestre, V Akoka, S TI Traceability in quantitative NMR using an electronic signal as working standard SO ACCREDITATION AND QUALITY ASSURANCE DT Article DE quantitative NMR; ERETIC; SNIF-NMR; traceability; reference materials ID HYDROGEN-ISOTOPE; ERETIC METHOD; SPECTROSCOPY; PURITY; AUTHENTICATION; AGROCHEMICALS; VALIDATION; GLYPHOSATE; VANILLIN; RATIOS AB The choice of the reference, either as internal or external is not straightforward in quantitative NMR. In this context ERETIC(TM) methodology appears as an universal referencing technique. An electronic signal, generated by the NMR spectrometer during the acquisition time, operates as a virtual working standard. The processes for ensuring a traceability to primary standards is illustrated on the official method devoted to (D[H)(i) ratios measurement on ethanol, using quantitative H-2-NMR. The ERETIC approach is shown to be equivalent to its official homologue, in terms of accuracy and precision. Finally, its performance could be beneficial to other analytes, matrices and nuclei. C1 Univ Nantes, LAIEM, CNRS, UMR 6006, F-44322 Nantes 3, France. C3 Centre National de la Recherche Scientifique (CNRS); Nantes Universite RP Remaud, GS (corresponding author), Univ Nantes, LAIEM, CNRS, UMR 6006, 2 Rue Houssiniere,BP 92208, F-44322 Nantes 3, France. EM Gerald.remaud@univ-nantes.fr CR Akoka S, 1999, ANAL CHEM, V71, P2554, DOI 10.1021/ac981422i Akoka S, 2002, INSTRUM SCI TECHNOL, V30, P21, DOI 10.1081/CI-100108768 Al Deen TS, 2002, ANAL CHIM ACTA, V474, P125, DOI 10.1016/S0003-2670(02)01017-6 Al-Deen TS, 2004, ACCREDIT QUAL ASSUR, V9, P55, DOI 10.1007/s00769-003-0737-2 Barantin L, 1997, MAGN RESON MED, V38, P179, DOI 10.1002/mrm.1910380203 BauerChristoph C, 1997, Z LEBENSM UNTERS F A, V204, P445, DOI 10.1007/s002170050111 Billault I, 2000, INSTRUM SCI TECHNOL, V28, P233, DOI 10.1081/CI-100100974 Billault I, 2002, ANAL CHEM, V74, P5902, DOI 10.1021/ac025867p Burton IW, 2005, ANAL CHEM, V77, P3123, DOI 10.1021/ac048385h Gonzalez J, 1998, J AGR FOOD CHEM, V46, P2200, DOI 10.1021/jf971066p GUILLOU C, 1988, MAGN RESON CHEM, V26, P491, DOI 10.1002/mrc.1260260611 GUILLOU C, 2001, BCR656 HAGEMANN R, 1970, TELLUS, V22, P712, DOI 10.1111/j.2153-3490.1970.tb00540.x Henderson TJ, 2002, ANAL CHEM, V74, P191, DOI 10.1021/ac010809+ Jancke H, 1998, CCQM REP, V98, P1 Jones C, 2002, J PHARMACEUT BIOMED, V30, P1233, DOI 10.1016/S0731-7085(02)00462-4 *JRC, FP6WP2005 JRC Le Grand F, 2005, J MAGN RESON, V174, P171, DOI 10.1016/j.jmr.2005.01.010 Le Grand F, 2004, CR CHIM, V7, P233, DOI 10.1016/j.crci.2003.11.005 Maniara G, 1998, ANAL CHEM, V70, P4921, DOI 10.1021/ac980573i Martin G. J., 1995, ANNU REP NMR SPECTRO, V31, P81 MARTIN GJ, 1994, 123 CRM BCR MARTIN YL, 1994, J MAGN RESON SER A, V111, P1, DOI 10.1006/jmra.1994.1218 Remaud G, 1997, J AGR FOOD CHEM, V45, P4042, DOI 10.1021/jf970143d Remaud GS, 1997, J AGR FOOD CHEM, V45, P859, DOI 10.1021/jf960518f Robins Richard J., 2003, Phytochemistry Reviews, V2, P87, DOI 10.1023/B:PHYT.0000004301.52646.a8 Silvestre V, 2001, ANAL CHEM, V73, P1862, DOI 10.1021/ac0013204 Tenailleau EJ, 2004, J AGR FOOD CHEM, V52, P7782, DOI 10.1021/jf048847s *TMU IRMM, STA003 TMU IRMM Wells RJ, 2002, J AGR FOOD CHEM, V50, P3366, DOI 10.1021/jf0114379 Wells RJ, 2004, ACCREDIT QUAL ASSUR, V9, P450, DOI 10.1007/s00769-004-0779-0 Wokaun A, 1987, PRINCIPLES NUCL MAGN Zhang BL, 2002, J AGR FOOD CHEM, V50, P1574, DOI 10.1021/jf010776z Zhang BL, 2000, BIOORG CHEM, V28, P1, DOI 10.1006/bioo.1999.1145 NR 34 TC 19 Z9 19 U1 0 U2 15 PD DEC PY 2005 VL 10 IS 8 BP 415 EP 420 DI 10.1007/s00769-005-0044-1 WC Chemistry, Analytical; Instruments & Instrumentation SC Chemistry; Instruments & Instrumentation UT WOS:000234480100004 DA 2022-12-14 ER PT J AU Thakur, M Martens, BJ Hurburgh, CR AF Thakur, Maitri Martens, Bobby J. Hurburgh, Charles R. TI Data modeling to facilitate internal traceability at a grain elevator SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Internal traceability; Bulk grain handling; Elevator; Data modeling; ER model ID CHAIN; MANUFACTURE; INFORMATION; MANAGEMENT AB Data management in food supply chains to facilitate product traceability has gained importance in recent years. This paper presents a relational database model to facilitate internal traceability at a grain elevator, which is one of the first nodes in a food supply chain. This approach for modeling traceability information in bulk food supply chains has not been studied in past. At an elevator, grain lots (inbound deliveries) are blended to meet buyer specifications, and individual lot identity is not maintained. As a result, an outbound shipment to a customer likely contains grain from many different sources. In a food safety related emergency, tracing the source of a problem or tracking other affected shipments would be nearly impossible. An efficient internal data management system could mitigate these problems by recording all grain lot transformations/activities, including movement, aggregation, segregation, and destruction as well as supplier and customer information. In this paper, a relational database management system is proposed that stores all necessary information, including product and quality information, related to the grain lots in order to enable product traceability. The system can be queried to retrieve information related to incoming, internal and outgoing lots and to retrieve information that connects the individual incoming grain lots to an outgoing shipment. Furthermore, this system can be used both to trace back to the source of a given lot and to track information about previously shipped lots forward. In addition to traceability application, the information stored in this database provides a comprehensive dataset for many applications including mass flow optimization, resource optimization and improved operational efficiency of the grain elevator. (C) 2010 Elsevier B.V. All rights reserved. C1 [Thakur, Maitri; Hurburgh, Charles R.] Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. [Thakur, Maitri] Iowa State Univ, Dept Ind & Mfg Syst Engn, Ames, IA 50011 USA. [Martens, Bobby J.] Iowa State Univ, Dept Logist Operat & Management Informat Sci, Ames, IA 50011 USA. [Hurburgh, Charles R.] Iowa State Univ, Dept Food Sci & Human Nutr, Ames, IA 50011 USA. C3 Iowa State University; Iowa State University; Iowa State University; Iowa State University RP Thakur, M (corresponding author), SINTEF Fisheries & Aquaculture, Aquaculture Technol, Brattorkaia 17C, N-7010 Trondheim, Norway. EM maitri.thakur@sintef.no CR [Anonymous], 2002, OFFICIAL J EUROPEAN Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x CODD EF, 1970, COMMUN ACM, V13, P377, DOI 10.1145/357980.358007 DAYAL U, 1988, ACM SIGMOD RECORD, V17, P51 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Elmasri R., 2000, FUNDAMENTALS DATABAS, V3rd Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Food Standards Agency, 2002, TRAC FOOD CHAIN PREL Golan E., 2004, Amber Waves, V2, P14 *GS1, 2007, BUS PROC SYST REQ FU Hanson E. C., 1989, SIGMOD Record, V18, P12, DOI 10.1145/71031.71033 Hoffer Jeffrey A., 2006, MODERN DATABASE MANA HURBURGH CR, 2004, INT QUAL GRAINS C GL International Organization for Standardization, 2007, 22005 ISO Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 KOTZ AM, 1988, LECT NOTES COMPUT SC, V303, P76 Laux C.M., 2010, J IND TECHNOLOGY, V26, P1 Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 NATSUI T, 2004, TRACEABILITY SYSTEM Niederhauser N, 2008, COMPUT ELECTRON AGR, V61, P241, DOI 10.1016/j.compag.2007.12.001 Patig S, 2006, DATA KNOWL ENG, V56, P122, DOI 10.1016/j.datak.2005.03.010 Schulze C, 2007, COMPUT ELECTRON AGR, V59, P39, DOI 10.1016/j.compag.2007.05.001 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 WIDOM J, 1990, SIGMOD REC, V19, P259, DOI 10.1145/93605.98735 NR 28 TC 19 Z9 19 U1 3 U2 24 PD FEB PY 2011 VL 75 IS 2 BP 327 EP 336 DI 10.1016/j.compag.2010.12.010 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000287952500013 DA 2022-12-14 ER PT J AU Song, YJ Lv, CC Liu, JX AF Song Yingjie Lv Cuicui Liu Junxian TI Quality and safety traceability system of agricultural products based on Multi-agent SO JOURNAL OF INTELLIGENT & FUZZY SYSTEMS DT Article DE Multi-Agent; agricultural products; quality safety; artificial intelligence AB As essential consumer goods for daily life, agricultural products tend to go bad from the farm to the table. Therefore, people put forward higher requirements for the quality of agricultural products. How to manage and control the quality of agricultural products and to control the quality of agricultural products by chain type are the cores of scientific research. The purpose of combining Agent technology with agricultural product quality safety traceability system was to establish the quality and safety traceability system of agricultural products based on Multi-Agent in the study. Artificial intelligence, intelligent control and intelligent detection technologies were used effectively in the system. The framework of quality and safety traceability system of agricultural products proposed in this study can be used to analyze modules in the quality of agricultural products. Through a number of Agent refining divisions of labor, the quality safety early warning and supervision control for the supply chain of agricultural products was carried out. The performance evaluation system of agricultural product quality safety traceability system based on Multi-Agent was established, so as to provide a systematic evaluation basis for the quality supply of agricultural products. C1 [Song Yingjie; Liu Junxian] Shandong Technol & Business Univ, Sch Finance, Yantai, Peoples R China. [Song Yingjie] Shanghai Univ Finance & Econ, Sch Publ Econ & Adm, Shanghai, Peoples R China. [Lv Cuicui] Shandong Technol & Business Univ, Sch Foreign Studies, Yantai, Peoples R China. C3 Shandong Technology & Business University; Shanghai University of Finance & Economics; Shandong Technology & Business University RP Song, YJ (corresponding author), Shandong Technol & Business Univ, Sch Finance, Yantai, Peoples R China.; Song, YJ (corresponding author), Shanghai Univ Finance & Econ, Sch Publ Econ & Adm, Shanghai, Peoples R China. EM ebyltr@163.com CR Bai ShiZhen, 2014, Asian Agricultural Research, V6, P44 Davahli A., 2014, INT J COMPUTER SCI A, V4, P8 Haishui Jin, 2013, Journal of Convergence Information Technology, V8, P583, DOI 10.4156/jcit.vol8.issue10.72 Hernandez JE, 2014, PROD PLAN CONTROL, V25, P662, DOI 10.1080/09537287.2013.798086 Huang XL, 2014, APPL MECH MATER, V457-458, P1415, DOI 10.4028/www.scientific.net/AMM.457-458.1415 Kong Yaguang, 2013, Applied Mechanics and Materials, V302, P694, DOI 10.4028/www.scientific.net/AMM.302.694 Li YongBin, 2014, Journal of Agricultural Science and Technology (Beijing), V16, P91 Ruey-Shun Chen, 2008, WSEAS Transactions on Information Science and Applications, V5, P1551 Seco A, 2014, PROC TECH, V16, P163, DOI 10.1016/j.protcy.2014.10.079 Tinghong Zhao, 2014, IAES TELKOMNIKA Indonesian Journal of Electrical Engineering, V12, P3537 [王力坚 Wang Lijian], 2015, [食品科学, Food Science], V36, P267 Wang Y., 2013, T CHINESE SOC AGR EN, V31, P264 Xing Bin, 2013, Journal of Food Safety and Quality, V4, P1705 Yan Bo, 2013, Transactions of the Chinese Society of Agricultural Engineering, V29, P172 Yang SS, 2014, J INTEGR ENVIRON SCI, V11, P17, DOI 10.1080/1943815X.2014.883413 Zhang HY, 2014, APPL MECH MATER, V469, P481, DOI 10.4028/www.scientific.net/AMM.469.481 Zhao G, 2015, INT J ELECTROCHEM SC, V10, P3387 Zhao TH, 2013, APPL MECH MATER, V433-435, P1853, DOI 10.4028/www.scientific.net/AMM.433-435.1853 Zhong XiaoJun, 2014, Asian Agricultural Research, V6, P76 Zhu Fengna, 2014, Applied Mechanics and Materials, V536-537, P4460, DOI 10.4028/www.scientific.net/AMM.543-547.4460 NR 20 TC 2 Z9 2 U1 6 U2 62 PY 2018 VL 35 IS 3 BP 2731 EP 2740 DI 10.3233/JIFS-169625 WC Computer Science, Artificial Intelligence SC Computer Science UT WOS:000446239400012 DA 2022-12-14 ER PT J AU Bhatt, T Buckley, G McEntire, JC Lothian, P Sterling, B Hickey, C AF Bhatt, Tejas Buckley, Greg McEntire, Jennifer C. Lothian, Paul Sterling, Brian Hickey, Caitlin TI Making Traceability Work across the Entire Food Supply Chain SO JOURNAL OF FOOD SCIENCE DT Article DE costs-benefit analysis; food traceability; public policy and regulations; tracing technology AB The Institute of Food Technologists held Traceability Research Summits on July 14, August 22, and November 1, 2011, to address how to meet the growing requirement for agriculture and food traceability. Each meeting had a group of about 50 individuals who came from food companies, trade associations, local, state, and federal governments, 3rd-party traceability solution providers, not-for-profit corporations, consultants, and consumer groups. They discussed and deliberated the objectives of traceability and the means to develop product tracing in the food system. A total of 70 people participated in the 3 summits. These individuals were invited to participate in a small workgroup responsible for considering the details related to product tracing and presenting draft concepts to the larger group on November 1, 2011, in Chicago. During this meeting, the larger assembly further refined the concepts and came to an agreement on the basic principles and overall design of the desired approach to traceability. C1 [Bhatt, Tejas] Inst Food Technologists, Washington, DC 20036 USA. [Buckley, Greg] PepsiCo Inc, Purchase, NY 10577 USA. [McEntire, Jennifer C.] Leavitt Partners, Salt Lake City, UT 84111 USA. [Lothian, Paul] Tyson Foods Inc, Springdale, AR 72762 USA. [Sterling, Brian] OnTrace Agri Food Traceabil, Oakville, ON L6J 6H6, Canada. C3 PepsiCo RP Bhatt, T (corresponding author), Inst Food Technologists, 1025 Connecticut Ave NW,Suite 503, Washington, DC 20036 USA. EM tbhatt@ift.org CR Bhatt T, 2013, J FOOD SCI, V78, pB9, DOI 10.1111/j.1750-3841.2011.02617.x Can-Trace, 2004, CAN TRAC DEC SUPP SY Can-Trace, 2004, CAN TRAC PROD PIL PR Codex Alimentarius, 2006, PRINC TRAC PROD TRAC Hickey C, 2013, J FOOD SCI, V78, pB15, DOI 10.1111/1750-3841.12042 [ISO] Intl. Organization for Standardization, 2010, 220052007 ISO NR 6 TC 13 Z9 13 U1 0 U2 34 PD DEC PY 2013 VL 78 SU 2 SI SI BP B21 EP B27 DI 10.1111/1750-3841.12278 WC Food Science & Technology SC Food Science & Technology UT WOS:000331148000005 DA 2022-12-14 ER PT J AU Cozzolino, D AF Cozzolino, D. TI An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals SO FOOD RESEARCH INTERNATIONAL DT Article DE Cereals; Grain; NIR; MIR; Traceability; Classification ID HARD RED WHEAT; REFLECTANCE SPECTROSCOPY; CLASSIFICATION; FOOD; IDENTIFICATION; TRANSMITTANCE; FLOURS; DISCRIMINATION; VARIETIES; STARCH AB Although both near infrared (NIR) spectroscopy and mid infrared (MIR) spectroscopy combined with multivariate data analysis (MVA) have been extensively used to measure chemical composition (e.g. protein, moisture, oil) in a wide number of grains few reports can be found on the use of this methods for varietal discrimination and traceability of cereals. In this overview applications of NIR spectroscopy and MIR spectroscopy combined with multivariate data methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA) to aid on the authentication and traceability of cereals are discussed. (C) 2013 Elsevier Ltd. All rights reserved. C1 Univ Adelaide, Fac Sci, Sch Agr Food & Wine, Glen Osmond, SA 5064, Australia. C3 University of Adelaide RP Cozzolino, D (corresponding author), Univ Adelaide, Fac Sci, Sch Agr Food & Wine, Waite Campus,PMB 1, Glen Osmond, SA 5064, Australia. EM d.cozzolino@adelaide.edu.au CR Alishahi A, 2010, SPECTROCHIM ACTA A, V75, P1, DOI 10.1016/j.saa.2009.10.001 Berman M, 2007, J NEAR INFRARED SPEC, V15, P351, DOI 10.1255/jnirs.754 BERTRAND D, 1985, J SCI FOOD AGR, V36, P1120, DOI 10.1002/jsfa.2740361114 Brereton RG., 2003, APPL CHEMOMETRICS SC, DOI [10.1002/0470863242, DOI 10.1002/9780470057780.CH1] Campbell MR, 2000, CEREAL CHEM, V77, P774, DOI 10.1094/CCHEM.2000.77.6.774 CHEN YR, 1995, CEREAL CHEM, V72, P317 Choudhary R, 2009, BIOSYST ENG, V102, P115, DOI 10.1016/j.biosystemseng.2008.09.028 Christy, 2007, NEAR INFRARED SPECTR, P145 Cocchi M, 2005, ANAL CHIM ACTA, V544, P100, DOI 10.1016/j.aca.2005.02.075 Cocchi M, 2004, J AGR FOOD CHEM, V52, P1062, DOI 10.1021/jf034441o Cozzolino D, 2012, APPL SPECTROSC REV, V47, P518, DOI 10.1080/05704928.2012.667858 Cozzolino D, 2012, FOOD ANAL METHOD, V5, P381, DOI 10.1007/s12161-011-9249-6 Cozzolino D, 2011, COMB CHEM HIGH T SCR, V14, P125, DOI 10.2174/138620711794474105 Cozzolino D, 2009, PLANTA MED, V75, P746, DOI 10.1055/s-0028-1112220 DELWICHE SR, 1995, CEREAL CHEM, V72, P11 DELWICHE SR, 1993, CEREAL CHEM, V70, P29 Delwiche SR, 1996, CEREAL CHEM, V73, P399 Delwiche SR, 2011, J AGR FOOD CHEM, V59, P4002, DOI 10.1021/jf104528x Dowell FE, 2000, CEREAL CHEM, V77, P155, DOI 10.1094/CCHEM.2000.77.2.155 Foca G, 2009, CHEMOMETR INTELL LAB, V99, P91, DOI 10.1016/j.chemolab.2009.07.013 Hurburgh C.R., 2000, P PITTCON 2000 SCI 2, P1431 Kaddour A. A., 2011, American Journal of Food Technology, V6, P186 Karoui R, 2010, CHEM REV, V110, P6144, DOI 10.1021/cr100090k Kim SS, 2003, CEREAL CHEM, V80, P346, DOI 10.1094/CCHEM.2003.80.3.346 Kuhnen S, 2010, INT J FOOD SCI TECH, V45, P1673, DOI 10.1111/j.1365-2621.2010.02313.x McClure WF, 2003, J NEAR INFRARED SPEC, V11, P487, DOI 10.1255/jnirs.399 Miralbes C, 2008, FOOD CHEM, V106, P386, DOI 10.1016/j.foodchem.2007.05.090 Munck L, 2004, J CEREAL SCI, V40, P213, DOI 10.1016/j.jcs.2004.07.006 Munck L, 2001, ANAL CHIM ACTA, V446, P171, DOI 10.1016/S0003-2670(01)01056-X Pojic M., 2008, FOOD PROCESSING QUAL, V35, P11 Pojic MM, 2013, FOOD BIOPROCESS TECH, V6, P330, DOI 10.1007/s11947-012-0917-3 Roussel SA, 2001, APPL SPECTROSC, V55, P1425, DOI 10.1366/0003702011953586 Rui YK, 2005, SPECTROSC SPECT ANAL, V25, P1581 Saleh B., 2012, Journal of Plant Biology Research, V1, P1 Schwartz D, 2009, FOOD SCI TECHNOL-INT, P1, DOI 10.1016/B978-0-12-746275-2.00001-X Shannon JC, 2009, FOOD SCI TECH-INT SE, P23, DOI 10.1016/B978-0-12-746275-2.00003-3 Smyth H.E., 2011, CURR BIOACT COMPD, V7, P66, DOI [10.2174/157340711796011160, DOI 10.2174/157340711796011160] Smyth H, 2013, CHEM REV, V113, P1429, DOI 10.1021/cr300076c SONG HP, 1995, OPT ENG, V34, P2927, DOI 10.1117/12.210745 Subramanian A, 2009, INFRARED SPECTROSCOPY FOR FOOD QUALITY ANALYSIS AND CONTROL, P145, DOI 10.1016/B978-0-12-374136-3.00007-9 Wang D, 2002, CEREAL CHEM, V79, P418, DOI 10.1094/CCHEM.2002.79.3.418 Weeranantanaphan J, 2011, J NEAR INFRARED SPEC, V19, P61, DOI 10.1255/jnirs.924 Wiley PR, 2009, J AGR FOOD CHEM, V57, P4042, DOI 10.1021/jf9001523 Williams P., 2010, APPL VIBRATIONAL SPE Xie SX, 2005, FOOD CARBOHYDRATES: CHEMISTY, PHYSICAL PROPERTIES, AND APPLICATIONS, P357 Zhao HY, 2013, FOOD CHEM, V138, P1902, DOI 10.1016/j.foodchem.2012.11.037 NR 46 TC 82 Z9 86 U1 2 U2 107 PD JUN PY 2014 VL 60 SI SI BP 262 EP 265 DI 10.1016/j.foodres.2013.08.034 WC Food Science & Technology SC Food Science & Technology UT WOS:000336952900030 DA 2022-12-14 ER PT J AU Hinkes, C Peter, G AF Hinkes, Cordula Peter, Guenter TI Traceability matters A conceptual framework for deforestation-free supply chains applied to soy certification SO SUSTAINABILITY ACCOUNTING MANAGEMENT AND POLICY JOURNAL DT Article DE Chain-of-custody certification; Deforestation; Supply chain; Sustainability; Traceability ID CONSUMER PREFERENCES; FOOD TRACEABILITY; GOVERNANCE; STANDARDS; POLICIES; SUSTAINABILITY; ECONOMICS; IMPACTS; AMAZON; INITIATIVES AB Purpose Sustainability certification of agricultural commodities might be one measure to ensure deforestation-free supply chains. The purpose of this paper is to add to previous assessments of soy certification systems with respect to "zero deforestation" criteria by focusing on the aspect of traceability. Design/methodology/approach A conceptual framework for assessing certification systems is proposed based on a literature review. This concept is applied to 16 soy certification systems, considering previous studies and available chain-of-custody certification options. Findings Among the sample, five certification systems may contribute to ensuring deforestation-free soy supply chains, as they have relatively high "zero deforestation" and assurance requirements and support at least segregation. Other chain-of-custody systems are insufficient in terms of traceability, but still dominate the market. Social implications The implementation of deforestation-free supply chains should contribute to achieving sustainable development goals. Potential adverse social effects need to be considered. Originality/value This study focuses on the so far rather neglected but essential aspect of traceability, which is required for ensuring deforestation-free sourcing along the whole supply chain. C1 [Hinkes, Cordula; Peter, Guenter] Johann Heinrich von Thuenen Inst, Fed Res Inst Rural Areas Forestry & Fisheries, Thuenen Inst Market Anal, Braunschweig, Germany. C3 Johann Heinrich von Thunen Institute RP Hinkes, C (corresponding author), Johann Heinrich von Thuenen Inst, Fed Res Inst Rural Areas Forestry & Fisheries, Thuenen Inst Market Anal, Braunschweig, Germany. EM cordula.hinkes@thuenen.de CR Aapresid, 2017, PROT SIST GEST CAL P Adenle A. A, 2015, ROLE MODERN BIOTECHN ADM, 2018, ADM RESP SOYB STAND AIC, 2019, FEMAS STAND 2019 AIC, 2015, FEMAS MOD RESP SOURC Amaggi, 2016, AMAGGI RESP STAND CE AMI, 2017, AMI MARKT BILANZ GET Angelsen A, 2013, REV ENV ECON POLICY, V7, P91, DOI 10.1093/reep/res022 [Anonymous], 2018, OIL WORLD ANN 2018 [Anonymous], 2018, SOCIAL IMPACT STRATE [Anonymous], 2015, PROGR NEW YORK DECL Arima EY, 2011, ENVIRON RES LETT, V6, DOI 10.1088/1748-9326/6/2/024010 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bendell J, 2011, SUSTAIN ACCOUNT MANA, V2, P263, DOI 10.1108/20408021111185411 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bowen GA, 2009, QUAL RES J, V9, P27, DOI 10.3316/QRJ0902027 Brack D., 2013, ENDING GLOBAL DEFORE Brown S, 2013, SCIENCE, V342, P805, DOI 10.1126/science.1241277 Bullock DS, 2002, FOOD POLICY, V27, P81, DOI 10.1016/S0306-9192(02)00004-0 Bunge, 2018, NOND POL GRAINS OILS Busch J, 2017, REV ENV ECON POLICY, V11, P3, DOI 10.1093/reep/rew013 CDP, 2018, AN EUR PUBL PRIV ACT Cefetra, 2018, CERT RESP SOY CRS NO Choi SH, 2015, COMPUT IND, V68, P148, DOI 10.1016/j.compind.2015.01.004 Cornelius R., 2018, SUSTAINABLE FASHION, P129 Costa-Font M, 2008, FOOD POLICY, V33, P99, DOI 10.1016/j.foodpol.2007.07.002 D'Hollander D, 2014, SUSTAIN ACCOUNT MANA, V5, P2, DOI 10.1108/SAMPJ-04-2013-0016 da Silva Junior A.G., 2016, P SYST DYNM INN FOOD, P349 DARBY MR, 1973, J LAW ECON, V16, P67, DOI 10.1086/466756 de Oliveira ALR, 2017, INT FOOD AGRIBUS MAN, V20, P45, DOI 10.22434/IFAMR2016.0084 de Waroux YL, 2019, WORLD DEV, V121, P188, DOI 10.1016/j.worlddev.2017.05.034 DeFries RS, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa625e Delacote P, 2016, RESOUR ENERGY ECON, V45, P192, DOI 10.1016/j.reseneeco.2016.06.006 Donau Soja Association, 2019, EUR SOYA GUID Donau Soja Association, 2019, DON SOJ GUID Donofrio S., 2017, SUPPLY CHANGE TRACKI EU FLEGT Facility, 2017, ACH ZER DEF COMM LES European Commission, 2013, 2013063 EUR COMM European Parliament, 2018, REP EUR STRAT PROM P FAO, 2015, GLOBAL FOREST RESOUR, DOI [10.1002/2014GB005021., DOI 10.1002/2014GB005021] FAO, 2012, STAT WORLDS FOR 2012 FAO, 2018, STAT WORLDS FOR FOR FEFAC, 2016, FEFAC SOY SOURC GUID Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 Fuchs R, 2019, NATURE, V567, P451, DOI 10.1038/d41586-019-00896-2 Garcia-Torres S, 2019, SUPPLY CHAIN MANAG, V24, P85, DOI 10.1108/SCM-04-2018-0152 Gardner TA, 2019, WORLD DEV, V121, P163, DOI 10.1016/j.worlddev.2018.05.025 Garrett RD, 2019, GLOBAL ENVIRON CHANG, V54, P135, DOI 10.1016/j.gloenvcha.2018.11.003 Garrett RD, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/4/045003 Garrett RD, 2013, ENVIRON RES LETT, V8, DOI 10.1088/1748-9326/8/4/044055 Gassler B, 2018, J CLEAN PROD, V195, P21, DOI 10.1016/j.jclepro.2018.05.039 Geist HJ, 2002, BIOSCIENCE, V52, P143, DOI 10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2 Gereffi G, 2005, REV INT POLIT ECON, V12, P78, DOI 10.1080/09692290500049805 Ghazoul J, 2009, FOREST ECOL MANAG, V258, P1889, DOI 10.1016/j.foreco.2009.01.038 Ghozzi H, 2018, FOOD POLICY, V78, P68, DOI 10.1016/j.foodpol.2018.02.011 Ghozzi H, 2016, SUPPLY CHAIN MANAG, V21, P743, DOI 10.1108/SCM-03-2016-0089 Gibbs HK, 2015, SCIENCE, V347, P377, DOI 10.1126/science.aaa0181 Gibbs HK, 2016, CONSERV LETT, V9, P32, DOI 10.1111/conl.12175 Godar J, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/3/035015 Halalisan AF, 2019, INT FOREST REV, V21, P195, DOI 10.1505/146554819826606595 Hargita Y., 2018, 98 THUN Henders S, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/12/125012 Henson S, 2010, J DEV STUD, V46, P1628, DOI 10.1080/00220381003706494 Heron T, 2018, GLOB POLICY, V9, P29, DOI 10.1111/1758-5899.12611 Hilbeck A, 2015, ENVIRON SCI EUR, V27, DOI 10.1186/s12302-014-0034-1 IDH, 2019, EUR SOY MON INS EUR ISCC, 2018, TRAC CHAIN CUST VERS ISEAL Alliance, 2016, CHAIN CUST MOT DEF R ITC, 2019, ITC SUST MAP ITC, 2019, FEFAC SOURC GUID TOO Jopke P., 2018, 181 CIFOR Kalaitzandonakes N, 2018, FOOD POLICY, V78, P38, DOI 10.1016/j.foodpol.2018.02.005 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Kastens JH, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0176168 Kim MK, 2017, J AGRIC APPL ECON, V49, P438, DOI 10.1017/aae.2017.7 Klumper W, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0111629 Komives K., 2014, VOLUNTARY STANDARD S, P3 Kuckartz U., 2016, QUALITATIVE INHALTSA Lallemand P, 2016, FISH RES, V182, P98, DOI 10.1016/j.fishres.2016.02.003 Lambin EF, 2018, NAT CLIM CHANGE, V8, P109, DOI 10.1038/s41558-017-0061-1 Lernoud J., 2018, STATE SUSTAINABLE MA Lippert C., 2009, Agrarwirtschaft, V58, P144 Macedo MN, 2012, P NATL ACAD SCI USA, V109, P1341, DOI 10.1073/pnas.1111374109 Mariojouls C, 2019, OCEANOGRAPHY CHALLEN, P387 McEntire J., 2019, Food Traceability, P1, DOI 10.1007/978-3-030-10902-8_1 Meyer C, 2015, J SUSTAIN FOREST, V34, P559, DOI 10.1080/10549811.2015.1036886 Mielke T., 2018, BIOKEROSENE, P147, DOI DOI 10.1007/978-3-662-53065-8_8 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mol APJ, 2015, SUSTAINABILITY-BASEL, V7, P12258, DOI 10.3390/su70912258 Neeff T., 2017, ZERO DEFORESTATION I Nepstad D, 2014, SCIENCE, V344, P1118, DOI 10.1126/science.1248525 Nepstad DC, 2006, CONSERV BIOL, V20, P1595, DOI 10.1111/j.1523-1739.2006.00510.x Nielsen CP, 2003, J POLICY MODEL, V25, P777, DOI 10.1016/j.jpolmod.2003.07.001 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Peter G., 2016, VERFUGBARKEIT NICHT Pfaff A, 2013, REV ENV ECON POLICY, V7, P114, DOI 10.1093/reep/res023 Potts J., 2014, STATE SUSTAINABILITY ProAgros, 2017, SUST FARM ASS PROGR ProAgros, 2018, SUST FARM ASS PROGR ProTerra, 2018, PROT STAND SOC RESP Qaim M, 2009, ANNU REV RESOUR ECON, V1, P665, DOI 10.1146/annurev.resource.050708.144203 Rausch LL, 2016, LAND-BASEL, V5, DOI 10.3390/land5020007 Reuters, 2018, REUTERS Richards PD, 2014, GLOBAL ENVIRON CHANG, V29, P1, DOI 10.1016/j.gloenvcha.2014.06.011 Ringsberg HA, 2015, BRIT FOOD J, V117, P1826, DOI 10.1108/BFJ-10-2014-0353 RTRS, 2018, RTRS CHAIN CUST STAN RTRS, 2017, RTRS MAN REP 2016 Rueda X, 2017, J CLEAN PROD, V142, P2480, DOI 10.1016/j.jclepro.2016.11.026 Sax JK, 2016, J LAW MED ETHICS, V44, P630, DOI 10.1177/1073110516684805 SFS, 2017, SUST FEED STAND BOOK Sterling B, 2015, COMPR REV FOOD SCI F, V14, P205, DOI 10.1111/1541-4337.12130 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Stranieri S, 2017, SUPPLY CHAIN MANAG, V22, P145, DOI [10.1108/SCM-07-2016-0268, 10.] Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Tillie P., 2015, MARKETS NONGENETICAL TNC, 2019, AGR TERR INT Torraco R.J., 2005, HUMAN RESOURCE DEV R, V4, P356, DOI [10.1177/1534484305278283, https://doi.org/10.1177/1534484305278283, DOI 10.1177/1534484305278283] Trase, 2019, TRANSP SUPPL CHAIN S Tscharntke T, 2015, CONSERV LETT, V8, P14, DOI 10.1111/conl.12110 USSEC, 2018, US SOY SUST ASS PROT van der Ven H, 2018, GLOBAL ENVIRON CHANG, V52, P141, DOI 10.1016/j.gloenvcha.2018.07.002 van Gelder, 2019, SETTING BAR DEFOREST van Noordwijk M., 2017, ETFRN NEWS, P11 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Wesseler J, 2014, BIO-BASED APPL ECON, V3, P187, DOI 10.13128/BAE-15017 WILLIAMSON OE, 1979, J LAW ECON, V22, P233, DOI 10.1086/466942 Wingate KG, 2005, INT FOREST REV, V7, P342, DOI 10.1505/ifor.2005.7.4.342 Wohlin C, 2014, P 18 INT C EV ASS SO, P1, DOI [10.1145/2601248.2601268, DOI 10.1145/2601248.2601268] Wunderlich S, 2015, ADV NUTR, V6, P842, DOI 10.3945/an.115.008870 WWF, 2018, 61 MEAT SOYB PURCH C Zilberman D, 2018, FOOD POLICY, V78, P6, DOI 10.1016/j.foodpol.2018.02.008 Zilberman D, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10051514 NR 133 TC 3 Z9 3 U1 4 U2 21 PD NOV 2 PY 2020 VL 11 IS 7 BP 1159 EP 1187 DI 10.1108/SAMPJ-04-2019-0145 EA FEB 2020 WC Business, Finance; Green & Sustainable Science & Technology; Environmental Studies; Management SC Business & Economics; Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000512310500001 DA 2022-12-14 ER PT J AU Loftus, R AF Loftus, R TI Traceability of biotech-derived animals: application of DNA technology SO REVUE SCIENTIFIQUE ET TECHNIQUE-OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE animal identification; animal tracing; DNA identification; post-market surveillance; product identification; risk management; traceability ID SNP AB Traceability is increasingly becoming standard across the agri-food industry, largely driven by recent food crises and the consequent demands for transparency within the food chain. This is leading to the development of a range of traceability concepts and technologies adapted to different industry needs. Experience with genetically modified plants has shown that traceability can play a role in increasing public confidence in biotechnology, and might similarly help allay concerns relating to the development of animal biotechnology. Traceability also forms an essential component of any risk management strategy and is a key requirement for post-marketing surveillance. Given the diversity of traceability concepts and technologies available, consideration needs to be given to the scope and precision of traceability systems for animal biotechnology. Experience to date has shown that conventional tagging and labelling systems can incorporate levels of error and may not have sufficient precision for biotech-derived animals. Deoxyribonucleic acid (DNA) technology can overcome these difficulties by tracing animals and animal by-products through their DNA code rather than an associated label. This offers the possibility of tracing some by-products of animal biotechnology through the supply chain back to source animals, offering unprecedented levels of traceability. Developments in both DNA sampling and analysis technology are making large-scale applications of DNA traceability increasingly cost effective and feasible, and are likely to lead to a broader uptake of DNA traceability concepts. C1 IdentiGEN Ltd, Unit 9, Trin Enterprise Ctr, Dublin 02, Ireland. RP Loftus, R (corresponding author), IdentiGEN Ltd, Unit 9, Trin Enterprise Ctr, Pearse St, Dublin 02, Ireland. CR *AGR ENV BIOT COMM, 2002, AN BIOT [Anonymous], 1994, 8402 ISO [Anonymous], 2002, ASAHI SHIMBUN 0219 BONFINI L, 2004, REV GMO DETECTION QU Brem G, 2004, DEUT TIERARZTL WOCH, V111, P273 *BRIT RET CONS, 2002, TECHN STAND SUPPL ID CEC (Commission for Environmental Cooperation), 2004, MAIZ BIOD EFF TRANSG CIES, 2004, IMPL TRAC FOOD SUPPL CLARKE P, 2002, FOOT MOUTH DIS CRISI *COUNC EC, 2003, OFF J EUR UNION L, V268, P24 *COUNC EUR COMM, 2000, OFF J EUR COMMUNIT L, V204, P1 De Montera B, 2004, CLONING STEM CELLS, V6, P133, DOI 10.1089/1536230041372382 DEHAVEN R, 2004, TESTIMONY R DEHAVEN *ECR, 2004, US TRAC SUPPL CHAIN *EUR COMM, 2003, WALLSTR BYRN WELC EP *EUR COMM, 2003, 95052003 SANCO EUR C *FDA, 2003, FDA INV IMPR DISP BI FLETCHER A, 2004, COKE RECALL HIGHLIGH *FOOD STAND AG, 2003, CONS VIEWS GM FOOD *GENC ENA FRANC, 2001, TRAC SUPPL CHAIN STR Golan E.H., 2004, AER830 EC RES SERV U Gut IG, 2001, HUM MUTAT, V17, P475 HISEY P, 2004, JAPANESE CATTLE BREE Hlywka JJ, 2003, FOOD CHEM TOXICOL, V41, P1273, DOI 10.1016/S0278-6915(03)00116-9 Houdebine LM, 2000, TRANSGENIC RES, V9, P305, DOI 10.1023/A:1008934912555 JEFFREYS AJ, 1985, NATURE, V314, P67, DOI 10.1038/314067a0 Jenkins S, 2002, COMP FUNCT GENOM, V3, P57, DOI 10.1002/cfg.130 KOCHHAR HS, 2004, NOTIFICATION GUIDELI KOK EJ, 2003, FOOD SAFETY RISK ASS Kwok PY, 2001, ANNU REV GENOM HUM G, V2, P235, DOI 10.1146/annurev.genom.2.1.235 Lawrence J.D., 2002, QUALITY ASSURANCE MA MCCAIN M, 2004, WORLD MEAT C WINN 16 MCDOWELL B, 2004, MCDONALDS DEBUTS BEE MORRIS SH, 2003, FARM GAT DINN PLAT C *NAT RES COUNC, 2003, AN BIOT SCI BAS CONC National Research Council, 2004, SAF GEN ENG FOODS AP *NIAA, 2004, NIAA AM MEAT I ANN C OHANLUAIN D, 2001, WIRED NEWS 1106 *PEW IN FOOD BIOT, 2004, OV FIND 2004 FOC GRO *PEW IN FOOD BIOT, 2002, P WORKSH SPONS PEW I Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] WESTPHAL SP, 2001, NEW SCI 0725 2002, TORONTO GLOBE M 0219 2004, ASAHI SHIMBUN 0830 2004, TRACEABILITY STANDAR NR 45 TC 20 Z9 21 U1 2 U2 8 PD APR PY 2005 VL 24 IS 1 BP 231 EP 242 DI 10.20506/rst.24.1.1563 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000230857000020 DA 2022-12-14 ER PT J AU Zambianchi, S Soffritti, G Stagnati, L Patrone, V Morelli, L Vercesi, A Busconi, M AF Zambianchi, Sara Soffritti, Giovanna Stagnati, Lorenzo Patrone, Vania Morelli, Lorenzo Vercesi, Alberto Busconi, Matteo TI Applicability of DNA traceability along the entire wine production chain in the real case of a large Italian cooperative winery SO FOOD CONTROL DT Article DE DNA traceability; SSR; Wine; Production chain; Variety AB Wine is frequently reported as one of the most adulterated agro-food products worldwide. Among the traceability methods available, DNA is of particular interest providing the possibility to recognize uniquely the wine production cultivar/cultivars. Several studies carried out in controlled conditions (laboratory level or small production wineries) support the use of DNA in wine traceability, but the situation can change completely when moving from controlled to uncontrolled realities. In the present study, the entire production chain, in a large cooperative Italian winery, was followed, for a monovarietal (Pinot noir PDO) and a polyvarietal (Rosso Oltrep`o TGI) production. Results support the feasibility of DNA traceability from grape delivering to the whole fermentation process and through the most common oenological operations as racking and filtration. The application of most aggressive methods (as the thermovinification process) can increase DNA degradation reducing but not hampering the possibility to apply DNA for traceability purposes. A different situation concerns the storage of wine in tanks, despite the controlled temperature and light conditions, or in bottles where DNA degradation continues strongly influencing the possibility to apply traceability. C1 [Zambianchi, Sara; Soffritti, Giovanna; Stagnati, Lorenzo; Vercesi, Alberto; Busconi, Matteo] Univ Cattolica Sacro Cuore, Dept Sustainable Crop Prod, Via Emilia Parmense 84, I-29122 Piacenza, Italy. [Patrone, Vania; Morelli, Lorenzo] Univ Cattolica Sacro Cuore, Dept Sustainable Food Proc, Via Emilia Parmense 84, I-29122 Piacenza, Italy. C3 Catholic University of the Sacred Heart; Catholic University of the Sacred Heart RP Busconi, M (corresponding author), Univ Cattolica Sacro Cuore, Dept Sustainable Crop Prod, Via Emilia Parmense 84, I-29122 Piacenza, Italy. EM sara.zambianchi1@unicatt.it; giovanna.soffritti@unicatt.it; lorenzo.stagnati@unicatt.it; vania.patron@unicatt.it; lorenzo.morelli@unicatt.it; alberto.vercesi@unicatt.it; matteo.busconi@unicatt.it CR Agrimonti C, 2018, EUR FOOD RES TECHNOL, V244, P2127, DOI 10.1007/s00217-018-3121-5 [Anonymous], Italian Vitis Database. Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Barrias S, 2019, FOOD CHEM, V270, P299, DOI 10.1016/j.foodchem.2018.07.058 Bigliazzi J, 2012, AM J ENOL VITICULT, V63, P568, DOI 10.5344/ajev.2012.12014 Boccacci P, 2020, FOOD CHEM, V312, DOI 10.1016/j.foodchem.2019.126100 Boccacci P, 2012, EUR FOOD RES TECHNOL, V235, P439, DOI 10.1007/s00217-012-1770-3 Bottero MT, 2011, VET J, V190, P34, DOI 10.1016/j.tvjl.2010.09.024 Bowers JE, 1996, GENOME, V39, P628, DOI 10.1139/g96-080 Cabanis J. C., 1999, OENOLOGIE FONDEMENTS, P315 Caramante M, 2011, FOOD CONTROL, V22, P549, DOI 10.1016/j.foodcont.2010.10.002 Catalano V, 2016, J AGR FOOD CHEM, V64, P6969, DOI 10.1021/acs.jafc.6b02560 Cheng T, 2016, MOL ECOL RESOUR, V16, P138, DOI 10.1111/1755-0998.12438 Corrado G, 2011, J HORTIC SCI BIOTECH, V86, P461, DOI 10.1080/14620316.2011.11512789 Corsi A., 2019, PALGRAVE HDB WINE IN, P47 DEMEKE T, 1992, BIOTECHNIQUES, V12, P332 di Rienzo V, 2017, ACTA HORTIC, V1188, P365, DOI 10.17660/ActaHortic.2017.1188.49 DO N, 1991, BIOTECHNIQUES, V10, P162 Faria MA, 2000, J AGR FOOD CHEM, V48, P1096, DOI 10.1021/jf990837h Fazekas Aron J, 2012, Methods Mol Biol, V858, P223, DOI 10.1007/978-1-61779-591-6_11 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Garcia-Beneytez E, 2002, J AGR FOOD CHEM, V50, P6090, DOI 10.1021/jf0202077 Isci B, 2014, J I BREWING, V120, P238, DOI 10.1002/jib.129 Isci B, 2009, J I BREWING, V115, P259 Kumar P, 2009, PLANT OMICS, V2, P141 Maul E, 2021, VITIS INT VARIETY CA Pereira L, 2011, AM J ENOL VITICULT, V62, P547, DOI 10.5344/ajev.2011.10022 Piskata Z, 2019, MOLECULES, V24, DOI 10.3390/molecules24061188 Recupero M, 2013, FOOD ANAL METHOD, V6, P952, DOI 10.1007/s12161-012-9506-3 Savazzini F, 2006, ANAL CHIM ACTA, V563, P274, DOI 10.1016/j.aca.2005.10.078 Scarano D., 2011, MINERVA BIOTECHNOLOG, V23, P42 Sefc KM, 1999, GENOME, V42, P367, DOI 10.1139/gen-42-3-367 Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Siret R., 2002, J AGR FOOD CHEM, V48, P5369, DOI [10.1021/jf011462-jf011462e, DOI 10.1021/JF011462-JF011462E] Soffritti G, 2016, MOLECULES, V21, DOI 10.3390/molecules21030343 Stagnati L, 2017, APPL MICROBIOL BIOT, V101, P3907, DOI 10.1007/s00253-017-8152-5 Stagnati L, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107392 This P, 2004, THEOR APPL GENET, V109, P1448, DOI 10.1007/s00122-004-1760-3 THOMAS MR, 1993, THEOR APPL GENET, V86, P985, DOI 10.1007/BF00211051 NR 39 TC 6 Z9 6 U1 2 U2 13 PD JUN PY 2021 VL 124 AR 107929 DI 10.1016/j.foodcont.2021.107929 EA FEB 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000632541300001 DA 2022-12-14 ER PT J AU Djatna, T Ginantaka, A AF Djatna, Taufik Ginantaka, Aditia TI Traceability of Information Routing Based on Fuzzy Associative Memory Modelling in Fisheries Supply Chain SO INTERNATIONAL JOURNAL OF FUZZY SYSTEMS DT Article DE Traceability; Fuzzy associative memory; Handling time ID NEURAL-NETWORK; FOOD; DESIGN AB Traceability is the ability to verify the history and location of a food product, thus providing information on each supply-chain actor, who the immediate supplier is, and to whom the product was sent. The information system approach has been used to manage and integrate all such information by collecting, storing, then retrieving data and information about the product from earlier stages of the production process. Besides documentation and information sharing, such traceability information systems can also support timely resolution of customer complaints. This paper presents modelling of routing and handling time prediction using a fuzzy associative memory (FAM) method. As a first response to customers, information about the time required to resolve an issue can be provided after the source of a product defect has been traced. With regards to handling, traceability can assist with several issues, e.g., product replacement, product recalls based on retrieval of the contact numbers of affected customers on a recall list , and inspections at each production unit to ensure food safety standards. Based on such activities, it is assumed that the handling time will be affected by the size of the product inventory that can be used to replace a defective product, the amount of product that must be recalled from the market, and the time required internally for the inspection process, which is set as the FAM input variable. A FAM is a set of fuzzy-set pairs (A, B) that maps an input vector fuzzy set A to an output vector fuzzy set B. Our experiments show that, from such a FAM formulation, one can obtain 27 rules. The FAM will encode a fuzzy-set pair (A, B) to obtain matrix memories, denoted by M. As the prediction result, the matrix B can be obtained from the computational matrix A and the matrix M; For instance, in case of a contamination incident in a fish product with inventory conditions of as much as 4 tonnes, the product recall amounts to 21 tonnes and the inspection will take 25 h, while the results of the computational experiment show that the total handling time for this case will be 66 h with low error rates. C1 [Djatna, Taufik] IPB Univ, Fac Agr Technol & Engn, Dept Agroind Technol, Bogor, Indonesia. [Ginantaka, Aditia] Djuanda Univ, Fac Halal Food Sci, Agroind Technol Study Program, Bogor, Indonesia. C3 Bogor Agricultural University; Djuanda University RP Ginantaka, A (corresponding author), Djuanda Univ, Fac Halal Food Sci, Agroind Technol Study Program, Bogor, Indonesia. EM aditia.ginantaka@unida.ac.id CR Awalinah AA, 2017, TEKNOSI, V03, P89, DOI [10.25077/TEKNOSI.v3i1.2017.89-100, DOI 10.25077/TEKNOSI.V3I1.2017.89-100] Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Choudhury JP, 2002, NEUROCOMPUTING, V47, P241, DOI 10.1016/S0925-2312(01)00590-2 Davidow M., 2000, J HOSP TOUR MANAG, V24, P473, DOI DOI 10.1177/109634800002400404 Djatna T, 2015, PROCEDIA MANUF, V4, P163, DOI 10.1016/j.promfg.2015.11.027 Einwiller SA, 2015, PUBLIC RELAT REV, V41, P195, DOI 10.1016/j.pubrev.2014.11.012 Esmi E, 2016, FUZZY SET SYST, V292, P242, DOI 10.1016/j.fss.2015.04.004 Esmi E, 2015, IEEE T FUZZY SYST, V23, P313, DOI 10.1109/TFUZZ.2014.2312131 Fao, 2018, STATE WORLD FISHERIE Heger AS, 1996, ANN NUCL ENERGY, V23, P739, DOI 10.1016/0306-4549(95)00050-X Huang MCJ, 2014, INT J HOSP MANAG, V37, P180, DOI 10.1016/j.ijhm.2013.10.008 Kosko B, 1990, NEURAL NETWORK FUZZY Kumar N, 2016, GLOB J RES ANAL, V4, P111 Kusumadewi S, 2010, APLIKASI LOGIKA FUZZ Li YT, 2016, IFAC PAPERSONLINE, V49, P407, DOI 10.1016/j.ifacol.2016.07.068 Lin YC, 2007, INT J IND ERGONOM, V37, P531, DOI 10.1016/j.ergon.2007.03.003 Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Bui TD, 2015, NEUROCOMPUTING, V149, P59, DOI 10.1016/j.neucom.2014.01.063 Ton-That AH, 2019, J INTELL FUZZY SYST, V36, P1587, DOI 10.3233/JIFS-18594 Ton-That AH, 2014, J INTELL FUZZY SYST, V27, P273, DOI 10.3233/IFS-130995 Tripathy PP, 2009, INT J THERM SCI, V48, P1452, DOI 10.1016/j.ijthermalsci.2008.11.014 Velthuis AGJ, 2010, J DAIRY SCI, V93, P2779, DOI 10.3168/jds.2009-2654 Wilson M.H, 2015, DEFNITIVE GUIDE SOCI Yu J., 2017, US MEAT POULTRY RECA Zhong CF, 2016, NEUROCOMPUTING, V173, P1154, DOI 10.1016/j.neucom.2015.08.072 Zul MI, 2016, J KOMPUTER TERAPAN, V2, P27 NR 26 TC 3 Z9 3 U1 0 U2 10 PD MAR PY 2020 VL 22 IS 2 SI SI BP 724 EP 734 DI 10.1007/s40815-019-00754-3 EA NOV 2019 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Automation & Control Systems; Computer Science UT WOS:000499568800001 DA 2022-12-14 ER PT J AU Miller, WG Greenberg, N Budd, J Delatour, V AF Miller, W. Greg Greenberg, Neil Budd, Jeffry Delatour, Vincent CA IFCC Working Grp Commutability Met TI The evolving role of commutability in metrological traceability SO CLINICA CHIMICA ACTA DT Article DE Calibration hierarchy; Commutability; Metrological traceability; Reference materials ID WORKING GROUP RECOMMENDATIONS; NATIONAL REFERENCE SYSTEM; ACCURACY; CHOLESTEROL; STANDARDIZATION; STATE; PERFORMANCE; IMPACT; BIAS AB Commutability is a property of a reference material (RM) which denotes that the analytical response in measurement procedures (MPs) observed for the measurand is the same for the RM as for clinical samples that contain the same amount of the measurand. Matrix-based secondary calibrators are required to be commutable with clinical samples to achieve metrological traceability of results from a clinical laboratory MP to higher order references. Results for clinical samples may not agree among different end-user MPs if a noncommutable RM is used in the calibration hierarchy for one or more of the MPs. Consequently, a useful RM is one that is commutable with clinical samples for all or most MPs in common use. If a matrix-based RM is noncommutable for one or a few MPs, a correction for the noncommutability bias may be added in the calibration hierarchy to enable the results for clinical samples to be metrologically traceable to the RM. Producing a large batch of matrix-based RM requires pooling single donations and making various modifications of the matrix such as spiking with exogenous substances, freezing or lyophilization. These modifications could potentially affect commutability of the RM and compromise its suitability. Documentation of commutability of matrix-based RMs used as calibrators is required by the International Organization for Standardization and the Joint Committee for Traceability in Laboratory Medicine. We describe how commutability was recognized as a critical requirement for metrological traceability and we present recommendations from the IFCC Working Groups on Commutability and on Commutability in Metrological Traceability. C1 [Miller, W. Greg] Virginia Commonwealth Univ, Richmond, VA 23284 USA. [Greenberg, Neil] Neil Greenberg Consulting LLC, Rochester, NY USA. [Budd, Jeffry] Jeff Budd Consulting, St Paul, MN USA. [Delatour, Vincent] Lab Natl Metrol & Essais LNE, Paris, France. C3 Virginia Commonwealth University; Laboratoire National de Metrologie et d'Essais (LNE) RP Miller, WG (corresponding author), POB 980286, Richmond, VA 23298 USA. EM greg.miller@vcuhealth.org CR [Anonymous], 1988, CLIN CHEM, V34, P193 [Anonymous], 1994, GAOPEMD958 [Anonymous], 2011, ERMDA474IFCC [Anonymous], 2009, 151942009 ISO [Anonymous], 2020, 175112020 ISO [Anonymous], 2019, DBWGP02AI01 JCTLM Beary ES, 2001, P WORKSH MEAS TRAC C BERNERT JT, 1991, CLIN CHEM, V37, P2053 Budd JR, 2018, CLIN CHEM, V64, P465, DOI 10.1373/clinchem.2017.277558 Delatour V, 2019, CLIN CHEM LAB MED, V57, P1623, DOI 10.1515/cclm-2019-0219 Delatour V, 2016, CLIN CHEM, V62, P1670, DOI 10.1373/clinchem.2016.261008 FASCE CF, 1973, CLIN CHEM, V19, P5 Franzini C, 1998, CLIN BIOCHEM, V31, P449, DOI 10.1016/S0009-9120(98)00054-X Hoelzel W, 2004, CLIN CHEM, V50, P166, DOI 10.1373/clinchem.2003.024802 Killeen AA, 2013, ARCH PATHOL LAB MED, V137, P496, DOI 10.5858/arpa.2012-0134-CP KOCH DD, 1988, JAMA-J AM MED ASSOC, V260, P2552, DOI 10.1001/jama.260.17.2552 Little RR, 2019, CLIN CHEM, V65, P839, DOI 10.1373/clinchem.2018.296962 Miller W. G., 2018, J LAB PRECIS MED, V3, P87, DOI DOI 10.21037/JLPM.2018.09.14 Miller WG, 2021, J APPL LAB MED, V6, P510, DOI 10.1093/jalm/jfaa189 Miller WG, 2020, CLIN CHEM, V66, P769, DOI 10.1093/clinchem/hvaa048 Miller WG, 2018, CLIN CHEM, V64, P447, DOI 10.1373/clinchem.2017.277525 Miller WG, 2013, CLIN CHEM, V59, P1291, DOI 10.1373/clinchem.2013.208785 MILLER WG, 1982, CLIN CHEM, V28, P2195 Miller WG, 2006, CLIN CHEM, V52, P553, DOI 10.1373/clinchem.2005.063511 MILLER WG, 1993, ARCH PATHOL LAB MED, V117, P343 Miller WG, 2005, ARCH PATHOL LAB MED, V129, P297 Myers GL, 2000, CLIN CHEM, V46, P1762 NAITO HK, 1993, ARCH PATHOL LAB MED, V117, P345 Nilsson G, 2018, CLIN CHEM, V64, P455, DOI 10.1373/clinchem.2017.277541 ROSS JW, 1993, ARCH PATHOL LAB MED, V117, P393 Rzychon M, 2010, CLIN CHEM, V56, P1475, DOI 10.1373/clinchem.2010.147124 Thienpont LM, 1996, CLIN CHEM, V42, P531 VANDERLINDE RE, 1989, CLIN LAB MED, V9, P89, DOI 10.1016/S0272-2712(18)30644-9 WAYMACK PP, 1993, CLIN CHEM, V39, P2058 Zegers I, 2013, CLIN CHEM, V59, P1322, DOI 10.1373/clinchem.2012.201954 NR 35 TC 6 Z9 7 U1 1 U2 3 PD MAR PY 2021 VL 514 BP 84 EP 89 DI 10.1016/j.cca.2020.12.021 EA JAN 2021 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000612087200014 DA 2022-12-14 ER PT J AU Kampan, K Tsusaka, TW Anal, AK AF Kampan, Khwanchol Tsusaka, Takuji W. Anal, Anil Kumar TI Adoption of Blockchain Technology for Enhanced Traceability of Livestock-Based Products SO SUSTAINABILITY DT Review DE blockchain; capacity building; food safety; food supply chain; traceability; ASEAN; Thailand ID FOOD-SUPPLY CHAIN; AGRICULTURE; INTERNET; ANIMALS; SAFETY; SYSTEM; FIELD AB Blockchain has become a modern technology that can enhance the traceability of products and services, which is particularly relevant to agri-food supply chains. This paper reviews studies on blockchain technology applications to the agri-food supply chain system and food industry, and discusses potential adaptation of blockchain technology for livestock-based products with a focus on the ASEAN Region and Thailand. A comprehensive method for reviewing the literature was adopted, and this paper encompasses stakeholders along the supply chain of livestock-based products to understand the prospect of applying blockchain technology to the sector. It was found that while blockchain technology is potentially sustainable and worthy of applications, there remain various limitations and complications toward adoption, such as the low awareness among stakeholders, the weak sector-wide coordination, and the lack of capacity in primary suppliers. Potential benefits and implications of blockchain technology for the food industry have yet to be widely understood, especially in the ASEAN. These findings would call for coordinated support from both the governments and the private sector, especially to raise awareness of the technology, reinforce sector-wide coordination, and develop skills required for adoption. C1 [Kampan, Khwanchol; Anal, Anil Kumar] Asian Inst Technol, Dept Food Agr & Bioresources, Pathum Thani 12120, Thailand. [Tsusaka, Takuji W.] Asian Inst Technol, Dept Dev & Sustainabil, Pathum Thani 12120, Thailand. C3 Asian Institute of Technology; Asian Institute of Technology RP Anal, AK (corresponding author), Asian Inst Technol, Dept Food Agr & Bioresources, Pathum Thani 12120, Thailand. EM anilkumar@ait.ac.th CR Aday S, 2020, FOOD QUAL SAF-OXFORD, V4, P167, DOI 10.1093/fqsafe/fyaa024 AgriDigital, 2017, BLOCKCHAIN Ahmad D., 2021, JURNAL ADMINISTRASI, V11, P32, DOI 10.31289/jap.v11i1.4310 Ahsan Mansoor, 2019, Arxiv, DOI arXiv:1810.04699 [Anonymous], CONTENT BASED LIT RE [Anonymous], 2002, E5 REGULATION EC 178 [Anonymous], ASEAN STRATEGIC PLAN [Anonymous], 2020, RES DRIVE SURGING DE [Anonymous], GLOBAL SUPPLY CHAINS [Anonymous], WAGENINGEN WORKSHOP [Anonymous], BLOCKCHAIN TECHNOLOG Attaran M., 2017, J INT TECHNOL INF MA, V26, P112, DOI [10.1080/08276331.2018.1466850, DOI 10.1080/08276331.2018.1466850] Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Azaria A, 2016, PROCEEDINGS 2016 2ND INTERNATIONAL CONFERENCE ON OPEN AND BIG DATA - OBD 2016, P25, DOI 10.1109/OBD.2016.11 Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Banerjee A., 2020, HDB DATA SCI APPROAC, V1st ed., P121, DOI DOI 10.1016/B978-0-12-818318-2.00005-2 Berman A, 2018, EXPLORES BLOCKCHAIN Bosch A, 2018, INT J FOOD MICROBIOL, V285, P110, DOI 10.1016/j.ijfoodmicro.2018.06.001 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Choi SB, 2022, J IND MANAG OPTIM, DOI 10.3934/jimo.2022150 Choi SB, 2022, RAIRO-OPER RES, V56, P1623, DOI 10.1051/ro/2022026 Christidis K, 2016, IEEE ACCESS, V4, P2292, DOI 10.1109/ACCESS.2016.2566339 Condliffe J., 2017, MIT TECHNOL REV Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Department of Livestock Regulation of the Department of Livestock Development, 2003, REG TRAC SYST LIV PR FAO, WHO FAO WHO GUID DEV Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 GS1 Thailand, 2019, BLOCKCH APPL AGR FOO Haselsteiner E., 2006, SEMICONDUCTORS+, V11, P1, DOI [10.1145/358438.349303, DOI 10.1145/358438.349303] Hoffman A., 2018, DREYFUS TEAMS BANKS IBM, 2017, FAST FORW RETH ENT E ICT4Ag, 2017, PERSP ICT AGR ACP CO Iftekhar A, 2021, FOODS, V10, DOI 10.3390/foods10061289 Ioris AAR, 2016, LAND USE POLICY, V59, P456, DOI 10.1016/j.landusepol.2016.09.019 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Khan I, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9060638 Kumpersak S., 2019, QUAL PROD IMPROV QPI, V1, P567, DOI [10.2478/cqpi-2019-0076, DOI 10.2478/CQPI-2019-0076] Lee H.L., 2017, CISC VIS NETW IND GL Lewis SG, 2017, J FOOD SCI, V82, pA13, DOI 10.1111/1750-3841.13743 Mougayar W., 2016, BUSINESS BLOCKCHAIN Nayyar A., 2015, J WIRELESS NETWORKIN, V5, P19 Neethirajan S, 2021, SENS BIO-SENS RES, V32, DOI 10.1016/j.sbsr.2021.100408 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Ortiz S, 2006, COMPUTER, V39, P18, DOI 10.1109/MC.2006.93 Pal B, 2023, EXPERT SYST APPL, V211, DOI 10.1016/j.eswa.2022.118315 Patelli N, 2020, J FOOD SCI, V85, P3670, DOI 10.1111/1750-3841.15477 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Perboli G, 2018, IEEE ACCESS, V6, P62018, DOI 10.1109/ACCESS.2018.2875782 Rejeb A., 2018, ACTA TECH JAURINENSI, V11, P104, DOI [DOI 10.14513/ACTATECHJAUR.V11.N2.462, 10.14513/actatechjaur.v11.n2.462] Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Sarkar B, 2022, COMPUT IND ENG, V172, DOI 10.1016/j.cie.2022.108635 Shankar R, 2018, TRANSPORT RES E-LOG, V119, P205, DOI 10.1016/j.tre.2018.03.006 Singh A, 2015, INT J PROD ECON, V164, P462, DOI 10.1016/j.ijpe.2014.09.019 Sloane B., 2021, BLOCKCHAIN BASICS IN Soumya M., 2021, SSRN ELECT J, DOI [10.2139/ssrn.3814912, DOI 10.2139/SSRN.3814912] Surasak T, 2019, INT J ADV COMPUT SC, V10, P578 Sylvester G, 2019, E AGR ACTION BLOCKCH Thu H., 2021, VIETNAM ADOPTS BLOCK Tinacci L, 2018, ITAL J FOOD SAF, V7, P83, DOI 10.4081/ijfs.2018.6894 Townsend M., 2018, THAILAND WILL EXPLOR Traceability in the Food Chain, PREL STUD United Nations Global Compact, 2016, GLOB OPP REP, P1 Vaintrub MO, 2021, ANIMAL, V15, DOI 10.1016/j.animal.2020.100143 Vu T.T., 2021, SCI TECHNOL DEV J EC, V5, P1278, DOI DOI 10.32508/STDJELM.V5I1.675 Wu D, 2020, INT J ACCOUNT INF MA, V28, P184, DOI [10.1016/j.ijid.2020.03.004, 10.1108/IJAIM-12-2018-0148] Xu XW, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE (ICSA 2017), P243, DOI 10.1109/ICSA.2017.33 Yong O.K., FUTURE AGR IMPLICATI Yuan HQ, 2020, INF SYST E-BUS MANAG, V18, P681, DOI 10.1007/s10257-018-0391-1 Zhang Hu, 2009, WSEAS Transactions on Information Science and Applications, V6, P1094 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zheng MM, 2021, IEEE ACCESS, V9, P70571, DOI 10.1109/ACCESS.2021.3078536 NR 76 TC 0 Z9 0 U1 9 U2 9 PD OCT PY 2022 VL 14 IS 20 AR 13148 DI 10.3390/su142013148 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000875950700001 DA 2022-12-14 ER PT J AU Tripoli, M Schmidhuber, J AF Tripoli, M. Schmidhuber, J. TI Optimising traceability in trade for live animals and animal products with digital technologies SO REVUE SCIENTIFIQUE ET TECHNIQUE-OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE Animal health; Big data; Blockchain; Digital technology; Food safety; Identification; Predictive analytics; Remote sensing; Traceability; Trade facilitation AB Sound animal traceability systems and supply chain management rely on data and information to respond to outcomes that will both protect animal and human health and facilitate trade. Digital technologies present opportunities and new methods for identifying and tracking animals, collecting more data, integrating communication flows, sharing data securely in supply chains, and analysing data to inform decisions and predict outcomes. Together, these technologies drive more efficient, productive and traceable supply chains, which can help to build more effective animal traceability systems. In addition, they can improve monitoring of, and response to, animal disease, food safety risks and food fraud risks; ensure compliance with animal health and food safety standards; simplify border procedures; facilitate trade with less friction; and raise consumer awareness. As the cost of these technologies decline and they become more accessible, the implementation of a digitally enabled animal traceability system will require an increase in supply chain capacity, improvements in digital infrastructure, and the development of a regulatory framework of standards and policies. Ensuring that these requirements are met will require strong commitment from governments, intergovernmental organisations and the wider animal health community. C1 [Tripoli, M.; Schmidhuber, J.] Food & Agr Org United Nat, Trade & Markets Div, Viale Terme Caracalla, I-00153 Rome, Italy. C3 Food & Agriculture Organization of the United Nations (FAO) RP Tripoli, M (corresponding author), Food & Agr Org United Nat, Trade & Markets Div, Viale Terme Caracalla, I-00153 Rome, Italy. EM mischa.tripoli@fao.org CR AgVoice, 2019, AGVOICE VOIC DAT SER Beckham T. R., 2015, 83 GEN SESS WORLD AS Britt AG, 2013, REV SCI TECH OIE, V32, P571 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Common. Market for Eastern and Southern Africa (COMESA), 2009, IMPR TRAD LIV COMM C, V6 Di Rosa AR, 2017, J FOOD ENG, V210, P62, DOI 10.1016/j.jfoodeng.2017.04.024 Edwards J.E., 2004, WORLD ASS DEL OIE RE Holmstrom LK, 2017, REV SCI TECH OIE, V36, P525, DOI 10.20506/rst.36.2.2671 IDA, 2019, IDA INT DAIR FARM AS Leese S, 2013, SCI LIVE Nakashima R, 2018, GLOBE MAIL Neethirajan Suresh, 2017, Sensing and Bio-Sensing Research, V12, P15, DOI 10.1016/j.sbsr.2016.11.004 OIE, TERR AN HLTH COD R3 Global Trade Strategy Forum, 2019, CAN BLOCKH FUT PROOF Ruch P, 2019, HYPERTASTE AI ASSIST Ruch P.W., 2019, 18 INT S OLF NOS ISO Schmidhuber, 2018, EMERGING OPPORTUNITI Schouten R., 2019, FOOD BUSINESS NEWS TE-FOOD, 2019, MEDIUM Tripoli M., 2019, CAN BLOCKCHAINS GEN United States Department of Agriculture (USDA), 2018, AN DIS TRAC SUMM PRO Wong ZSY, 2019, INFECT DIS HEALTH, V24, P44, DOI 10.1016/j.idh.2018.10.002 World Organisation for Animal Health (OIE), 2008, AN ID PROD TRAC FARM NR 23 TC 2 Z9 2 U1 3 U2 7 PD APR PY 2020 VL 39 IS 1 BP 235 EP 244 DI 10.20506/rst.39.1.3076 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000604412200020 DA 2022-12-14 ER PT J AU Guerreiro, LRJ Streit, DP Rotta, MA AF Jayme Guerreiro, Luis Ricardo Streit, Danilo Pedro, Jr. Rotta, Marco Aurelio TI Traceability in management broodstocks of reophilic fish: case study SO CUSTOS E AGRONEGOCIO ON LINE DT Article DE Costs in decision making; Electromagnetic Transponders; Traceability and induced spawning ID GENETIC-DIVERSITY AB The objective of this study was to analyze the efficiency of the traceability of fish breeding reophilic performed through electromagnetic transponders as a management tool reproductive monitoring and management costs for the process of decision making unit producing fingerlings. The study was conducted in a broodstock of 280 breeding tambaqui (Colossoma macropomum), electromagnetic transponders individually marked with a production unit fingerlings the state of Rondonia. Data collection was performed by monitoring the flow of activities related to maintenance and induction the reproduction for a year. It was determined also the operating and total costs of maintenance and induction broodstock. It was found that the broodstock has provided mean results in fertilization rate of 64.37% +- 12.23 e hatching 60.91% +- 13.56. The items cost more representation in the induction and maintenance of the broodstock are the labor, hormones and ration, respectively, demonstrating the importance of optimization of the labor both in feeding and in the selection and induction of broodstock. The traceability of broodstock by electromagnetic transponders is an effective tool to management and costs analysis for broodstock, besides allow monitoring of the reproductive efficiency of fishes, it assists in the economic management of the enterprise. C1 [Jayme Guerreiro, Luis Ricardo; Streit, Danilo Pedro, Jr.] Univ Fed Rio Grande do Sul, BR-91540000 Porto Alegre, RS, Brazil. [Rotta, Marco Aurelio] Univ Fed Rio Grande do Sul, BR-79005000 Campo Grande, MS, Brazil. C3 Universidade Federal do Rio Grande do Sul; Universidade Federal do Rio Grande do Sul RP Guerreiro, LRJ (corresponding author), Univ Fed Rio Grande do Sul, Ave Bento Goncalves 7712, BR-91540000 Porto Alegre, RS, Brazil. EM luisbandeirantes@hotmail.com; danilo.streit@ufrgs.br; rottaaquicultura@terra.com.br CR ArauCjo-Lima C.A.R.M., 2005, ESPECIES NATIVAS PIS, P175 BARROS A. F., 2005, THESIS U ESTADUAL PA BRASIL. Ministerio da Pesca e Aquicultura, 2010, B ESTATISTICO 2010 Das S.K., 2004, ASIAN FISH SCI, V17, P313 de Resende EK, 2009, REV BRAS ZOOTECN, V38, P52, DOI 10.1590/S1516-35982009001300006 Gjedrem T, 2009, REV-METHODS TECHNOL, V10, P1, DOI 10.1007/978-90-481-2773-3_1 Godinho H. P., 2007, Revista Brasileira de Reproducao Animal, V31, P351 Lopes TS, 2009, ARQ BRAS MED VET ZOO, V61, P728, DOI 10.1590/S0102-09352009000300029 Martin N. B., 1995, Informacoes Economicas - Instituto de Economia Agricola, V25, P9 OLIVEIRA A. M. B., 2004, TOPICOS ESPECIAIS PI, P75 OSTRENSKY A., 2002, AQUICULTURA BRASIL, P471 Reynalte-Tataje David A., 2002, Boletim do Instituto de Pesca (Sao Paulo), V28, P11 Sekino M, 2004, AQUACULTURE, V233, P163, DOI 10.1016/j.aquaculture.2003.11.008 Vazzoler A.E.A. de M., 1996, BIOL REPROD PEIXES T Woynarovich E., 1983, PROPAGACAO ARTIFICIA Yokota M, 2003, FISHERIES SCI, V69, P101, DOI 10.1046/j.1444-2906.2003.00593.x Zaniboni Filho E., 2007, Revista Brasileira de Reproducao Animal, V31, P367 Zaniboni-Filho E, 2004, TOPICOS ESPECIAIS PI, P45 NR 18 TC 1 Z9 1 U1 1 U2 7 PD JAN-MAR PY 2015 VL 11 IS 1 BP 35 EP 52 WC Agricultural Economics & Policy; Business; Economics SC Agriculture; Business & Economics UT WOS:000361011000004 DA 2022-12-14 ER PT J AU Reynolds, M Warwick, RM Poniatowski, S Trias, E AF Reynolds, Melvin Warwick, Ruth M. Poniatowski, Stefan Trias, Esteve TI European coding system for tissues and cells: a challenge unmet? SO CELL AND TISSUE BANKING DT Article DE Cells; Coding system; Tissue; Traceability AB The Comite Europeen de Normalisation (European Committee for Standardization, CEN) Workshop on Coding of Information and Traceability of Human Tissues and Cells was established by the Expert Working Group of the Directorate General for Health and Consumer Affairs of the European Commission (DG SANCO) to identify requirements concerning the coding of information and the traceability of human tissues and cells, and propose guidelines and recommendations to permit the implementation of the European Coding system required by the European Tissues and Cells Directive 2004/23/EC (ED). The Workshop included over 70 voluntary participants from tissue, blood and eye banks, national ministries for healthcare, transplant organisations, universities and coding organisations; mainly from Europe with a small number of representatives from professionals in Canada, Australia, USA and Japan. The Workshop commenced in April 2007 and held its final meeting in February 2008. The draft Workshop Agreement went through a public comment phase from 15 December 2007 until 15 January 2008 and the endorsement period ran from 9 April 2008 until 2 May 2008. The endorsed CEN Workshop Agreement (CWA) set out the issues regarding a common coding system, qualitatively assessed what the industry felt was required of a coding system, reviewed coding systems that were put forward as potential European coding systems and established a basic specification for a proposed European coding system for human tissues and cells, based on ISBT 128, and which is compatible with existing systems of donation identification, traceability and nomenclatures, indicating how implementation of that system could be approached. The CWA, and the associated Workshop proposals with recommendations, were finally submitted to the European Commission and to the Committee of Member States that assists its management process under article 29 of the Directive 2004/23/EC on May 25 2008. In 2009 the European Commission initiated an impact assessment on the Workshop proposals and recommendations. In the absence of an agreed pan-European direction various initiatives have continued work using, adopting or adapting their preferred, or existing, methods. C1 [Reynolds, Melvin] AMS Consulting, Ross On Wye, England. [Warwick, Ruth M.] NHS Blood & Transplant, Edgware, Middx, England. [Poniatowski, Stefan] Donor Tissue Bank Victoria, Victorian Inst Forens Med, Melbourne, Vic, Australia. [Trias, Esteve] Hosp Clin Barcelona, Transplant Serv Fdn, Barcelona, Spain. C3 University of Barcelona; Hospital Clinic de Barcelona RP Reynolds, M (corresponding author), AMS Consulting, Ross On Wye, England. EM melvinr@ams-consulting.co.uk; ruth.warwick@nhsbt.nhs.uk; stefanp@vifm.org; etrias@clinic.ub.es CR *CWA, 2008, CEN WORKSH AGR COD T *DG SANCO, 2004, EXP M EU CODING SYST *DIRECTIVE, 2004, 200423EC DIRECTIVE *DOPKI, DOPKI PROJ *EUR, EUR CONS *ICCBBA, 2007, CELL THER COD LAB AD *ICCBBA, 2010, STAND TERM BLOOD CEL *JACIE, JOINT ACCR COMM EUR *WHO, 2006, AID MEM KEY SAF REQ NR 9 TC 2 Z9 2 U1 0 U2 2 PD NOV PY 2010 VL 11 IS 4 SI SI BP 353 EP 364 DI 10.1007/s10561-010-9193-5 WC Cell Biology; Engineering, Biomedical SC Cell Biology; Engineering UT WOS:000288436100006 DA 2022-12-14 ER PT J AU Lin, W Ortega, DL Ufer, D Caputo, V Awokuse, T AF Lin, Wen Ortega, David L. Ufer, Danielle Caputo, Vincenzina Awokuse, Titus TI Blockchain-based traceability and demand for US beef in China SO APPLIED ECONOMIC PERSPECTIVES AND POLICY DT Article DE blockchain; Chinese consumers; country of origin; traceability; U; S; beef demand ID WILLINGNESS-TO-PAY; FOOD SAFETY ATTRIBUTES; TECHNOLOGY READINESS; CHOICE EXPERIMENTS; HYPOTHETICAL BIAS; MORAL HAZARD; QUALITY; PREFERENCES; POVERTY; POLICY AB Amid stringent traceability requirements and a nearly 14-year ban, the U.S. beef industry is rebuilding its market presence in China. Blockchain technology offers a responsive means of meeting Chinese import traceability requirements while also addressing consumers' food safety concerns. We evaluate Chinese demand for U.S. beef and blockchain-based traceability and find that a sizeable segment of the market (37%) is willing to pay a premium for U.S. beef that is traceable using blockchains. Results indicate that investments in traceability systems that utilize blockchain technology may be an effective way for producers to capture a significant market share in China. C1 [Lin, Wen; Ortega, David L.] Zhejiang Univ, China Acad Rural Dev CARD, Sch Publ Affairs, Hangzhou, Peoples R China. [Ortega, David L.; Ufer, Danielle; Caputo, Vincenzina; Awokuse, Titus] Michigan State Univ, Dept Agr Food & Resource Econ, E Lansing, MI 48824 USA. C3 Zhejiang University; Michigan State University RP Ortega, DL (corresponding author), Michigan State Univ, Dept Agr Food & Resource Econ, E Lansing, MI 48824 USA. EM dlortega@msu.edu CR ADAMOWICZ W, 1994, J ENVIRON ECON MANAG, V26, P271, DOI 10.1006/jeem.1994.1017 AKERLOF GA, 1970, Q J ECON, V84, P488, DOI 10.2307/1879431 Anderson R.E., 2010, MULTIVARIATE DATA AN AQSIQ, 2020, MEAT REG APPL Balcombe K, 2009, J ENVIRON ECON MANAG, V57, P226, DOI 10.1016/j.jeem.2008.06.001 Bandoim L., 2019, FORBES Banovic M, 2009, FOOD QUAL PREFER, V20, P335, DOI 10.1016/j.foodqual.2009.02.009 Bodkhe U, 2020, IEEE ACCESS, V8, P79764, DOI 10.1109/ACCESS.2020.2988579 Caparros A, 2008, AM J AGR ECON, V90, P843, DOI 10.1111/j.1467-8276.2008.01137.x Collart AJ, 2022, APPL ECON PERSPECT P, V44, P219, DOI 10.1002/aepp.13134 Fifer S, 2014, TRANSPORT RES A-POL, V61, P164, DOI 10.1016/j.tra.2013.12.010 Foth Marcus, 2019, CONVERSATION, P1 Gao ZF, 2009, AM J AGR ECON, V91, P795, DOI 10.1111/j.1467-8276.2009.01259.x Greene Joel L., 2017, IN10724 US FOR AGR S Gregg David, 2018, COMPREHENSIVE FEASIB Griffin TW, 2022, APPL ECON PERSPECT P, V44, P237, DOI 10.1002/aepp.13142 Hanley N, 2001, J ECON SURV, V15, P435, DOI 10.1111/1467-6419.00145 Hess S, 2017, J CHOICE MODEL, V23, P1, DOI 10.1016/j.jocm.2017.03.001 Hmielowski JD, 2019, ENERGY RES SOC SCI, V55, P189, DOI 10.1016/j.erss.2019.05.003 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 HOLMSTROM B, 1979, BELL J ECON, V10, P74, DOI 10.2307/3003320 Jain AK, 2010, PATTERN RECOGN LETT, V31, P651, DOI 10.1016/j.patrec.2009.09.011 Jansen HGP, 2006, AGR ECON-BLACKWELL, V34, P141, DOI 10.1111/j.1574-0864.2006.00114.x LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131 Li XZ, 2018, ASIAN AUSTRAL J ANIM, V31, P984, DOI 10.5713/ajas.18.0212 Lim KH, 2013, CAN J AGR ECON, V61, P93, DOI 10.1111/j.1744-7976.2012.01260.x Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Lin W, 2019, J CONSUM AFF, V53, P520, DOI 10.1111/joca.12202 List JA, 2001, AM ECON REV, V91, P1498, DOI 10.1257/aer.91.5.1498 Liu HB, 2009, AUST J AGR RESOUR EC, V53, P485, DOI 10.1111/j.1467-8489.2009.00463.x Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Louviere JJ, 2010, J CHOICE MODEL, V3, P57 Lusk JL, 2004, AM J AGR ECON, V86, P467, DOI 10.1111/j.0092-5853.2004.00592.x Lusk JL, 2001, J AGR RESOUR ECON, V26, P91 Mao Y., 2016, AM J FOOD NUTR, V4, P30 Mao Yanwei, 2008, THESIS McFadden D., 1974, FRONTIERS ECONOMETRI, P105, DOI DOI 10.1108/EB028592 Meng J, 2009, J INT CONSUM MARK, V22, P19, DOI 10.1080/08961530902844915 Merino-Castello Anna., 2003, 705 U POMP FABR EC B MLA (Meat & Livestock Australia), 2020, AUSTR BEEF TRAD CHIN Mummalaneni V, 2016, J INTERNET COMMER, V15, P311, DOI 10.1080/15332861.2016.1237232 OECD, 2019, MEAT CONS IND, DOI 10.1787/fa290fd0 Ollinger M, 2020, AM J AGR ECON, V102, P186, DOI 10.1093/ajae/aaz031 Ortega DL, 2016, MEAT SCI, V121, P317, DOI 10.1016/j.meatsci.2016.06.032 Ortega DL, 2012, AM J AGR ECON, V94, P489, DOI 10.1093/ajae/aar074 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pantelides CP, 2017, J STRUCT INTEGR MAIN, V2, P1, DOI 10.1080/24705314.2017.1280589 Parasuraman A, 2015, J SERV RES-US, V18, P59, DOI 10.1177/1094670514539730 Parasuraman A., 2000, J SERV RES-US, V2, P307, DOI DOI 10.1177/109467050024001 Patton Dominique., 2017, REUTERS BEIJING 1024 Petrovici DA, 2005, FOOD POLICY, V30, P205, DOI 10.1016/j.foodpol.2005.02.002 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Sanjuan-Lopez AI, 2020, J AGR ECON, V71, P778, DOI 10.1111/1477-9552.12376 Scarpa R, 2008, AM J AGR ECON, V90, P994, DOI 10.1111/j.1467-8276.2008.01155.x Scarpa R, 2010, ENERG ECON, V32, P129, DOI 10.1016/j.eneco.2009.06.004 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Schulz LL, 2010, J AGR ECON, V61, P138, DOI 10.1111/j.1477-9552.2009.00226.x Smith George., 2017, NEW FOOD Starbird SA, 2005, AM J AGR ECON, V87, P15, DOI 10.1111/j.0002-9092.2005.00698.x Street D.J., 2007, CONSTRUCTION OPTIMAL Swait J.D., 2000, STATED CHOICE METHOD, DOI 10.1017/CBO9780511753831 Tian F, 2017, I C SERV SYST SERV M United Nations (UN) Statistics Division, 2018, HS CODES 0201 0202 0 USDA FAS (United States Dept of Agriculture Foreign Agricultural Service), 2019, CHIN PEOPL REP LIV P USDA FSIS (United States Dept of Agriculture Food Safety and Inspection Services), 2018, EXP REQ PEOPL REP CH USMEF, 2012, AUSTR CATTL FEED SHA Van den Broeck G, 2017, FOOD POLICY, V66, P97, DOI 10.1016/j.foodpol.2016.12.003 Wang Y, 2017, J TRAVEL RES, V56, P563, DOI 10.1177/0047287516657891 Wang ZG, 2008, FOOD POLICY, V33, P27, DOI 10.1016/j.foodpol.2007.05.006 Wiegard RB, 2019, ELECTRON MARK, V29, P107, DOI 10.1007/s12525-017-0274-1 Yiannas F., 2018, Innovations: Technology / Governance / Globalization, V12, P46, DOI 10.1162/inov_a_00266 Zulkifly M. Mohd Faizal, 2016, THESCIENTIFICWORLDJO, V2016, P3456943 NR 73 TC 8 Z9 8 U1 18 U2 66 PD MAR PY 2022 VL 44 IS 1 SI SI BP 253 EP 272 DI 10.1002/aepp.13135 EA DEC 2020 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000602666300001 DA 2022-12-14 ER PT J AU Zhao, SS Qiu, C Zhang, TW Hu, XY Zhao, Y Cheng, XY Ma, YX Qie, MJ Chen, C AF Zhao, Shanshan Qiu, Cheng Zhang, Tangwei Hu, Xiangyu Zhao, Yan Cheng, Xiyu Ma, Yuxuan Qie, Mengjie Chen, Chang TI Effects of Fertilizer on the Quality and Traceability of Tibet highland Barley (Hordeum vulgare L.): A Diagnosis Using Nutrients and Mineral Elements SO FOODS DT Article DE highland barley; traceability; nutrients; mineral elements; chemometrics; fertilizer ID HULL-LESS BARLEY; ANTIOXIDANT ACTIVITY; ATMOSPHERIC CO2; GRAIN QUALITY; RICE; ANTHOCYANINS; PURPLE AB Production areas influence the quality of highland barley (Hordeum vulgare L.), and fertilization levels may be associated with the origin traceability of highland barley. As the main object of the study, a collection of highland barley was planted in different areas in Tibet, China, to explore the effect of fertilizer on the quality and traceability of highland barley. We carried out field experiments with and without fertilizer treatment (using urea and diamine phosphate). Highland barley was distinguished by nutrient and mineral element contents in combination with chemometric methods. The results indicated that fertilizer treatment significantly affected some mineral element contents in highland barley and improved the accuracy of highland barley traceability. The combination of nutrients and mineral elements could distinguish highland barley from those raised in other areas due to influence of growing environment. P, K, Fe, and Cu provided a great contribution to the classification of highland barley. Thus, the combination of nutrients and mineral elements can be used as a powerful tool to track highland barley, indicating that fertilization treatment should be considered when tracing highland barley. C1 [Zhao, Shanshan; Zhao, Yan; Qie, Mengjie] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Qiu, Cheng; Zhang, Tangwei] Tibet Acad Agr & Anim Husb Sci, Inst Agr Prod Qual Stand & Testing Res, Lhasa 850032, Peoples R China. [Hu, Xiangyu] Shandong Agr Univ, Coll Food Sci & Engn, Tai An 271018, Shandong, Peoples R China. [Cheng, Xiyu; Chen, Chang] Beijing Jiaotong Univ, Coll Life Sci & Bioengn, Sch Sci, Beijing 100044, Peoples R China. [Ma, Yuxuan] Inner Mongolia Agr Univ, Coll Food Sci & Engn, Hohhot 010018, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Shandong Agricultural University; Beijing Jiaotong University; Inner Mongolia Agricultural University RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR [白艳波 Bai Yanbo], 2013, [生物技术通报, Biotechnology Bulletin], P15 Blotevogel S, 2019, FOOD CHEM, V298, DOI 10.1016/j.foodchem.2019.125033 Bonoli M, 2004, J AGR FOOD CHEM, V52, P5195, DOI 10.1021/jf040075c Du MJ, 2018, INT J FOOD SCI TECH, V53, P2088, DOI 10.1111/ijfs.13795 Ehlken S, 2002, J ENVIRON RADIOACTIV, V58, P97, DOI 10.1016/S0265-931X(01)00060-1 Gao Jia-jia, 2019, Journal of Ecology and Rural Environment, V35, P1484, DOI 10.19741/j.issn.1673-4831.2018.0731 Gong LX, 2012, CEREAL CHEM, V89, P290, DOI 10.1094/CCHEM-03-12-0029-R Gonzalvez A, 2011, FOOD CHEM, V126, P1254, DOI 10.1016/j.foodchem.2010.11.032 Hao Hu-lin, 2007, Zhongguo Shuidao Kexue, V21, P411 Hogy P, 2008, J CEREAL SCI, V48, P580, DOI 10.1016/j.jcs.2008.01.006 [姜丽娜 JIANG Lina], 2009, [西北农业学报, Acat Agriculturae Boreali-Occidentalis Sinica], V18, P97 Kabata-Pendias A., 1984, FOOD CHAINS HUMAN NU, Vfourth Khan ZI, 2019, EGYPT J BOT, V59, P753, DOI 10.21608/ejbo.2019.9969.1296 Kim MJ, 2007, J AGR FOOD CHEM, V55, P4802, DOI 10.1021/jf0701943 Kohyama N, 2008, J AGR FOOD CHEM, V56, P5770, DOI 10.1021/jf800626b Lange CN, 2019, FOOD CHEM, V300, DOI 10.1016/j.foodchem.2019.125145 Lausanne D., 2001, TIBET SCI TECHNOL, V100, P55 Li G, 2013, J ENVIRON SCI, V25, P144, DOI [10.1016/S1001-0742(12)60007-2, 10.1016/S1001-0742(14)60636-7] Liu BinQiu, 2015, Journal of Tea Science, V35, P179 Loladze I, 2002, TRENDS ECOL EVOL, V17, P457, DOI 10.1016/S0169-5347(02)02587-9 LYNCH DV, 1987, PLANT PHYSIOL, V83, P761, DOI 10.1104/pp.83.4.761 Martinez-Ballesta M. Carmen, 2008, Phytochemistry Reviews, V7, P251, DOI 10.1007/s11101-007-9071-3 Nilsen E. T., 1996, Physiology of plants under stress. Abiotic factors. Patras A, 2011, J FOOD COMPOS ANAL, V24, P250, DOI 10.1016/j.jfca.2010.09.012 Qian LL, 2019, J FOOD COMPOS ANAL, V83, DOI 10.1016/j.jfca.2019.103276 Shah A, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10081209 Shen YB, 2016, FOOD CHEM, V194, P1003, DOI 10.1016/j.foodchem.2015.08.083 Siebenhandl S, 2007, J AGR FOOD CHEM, V55, P8541, DOI 10.1021/jf072021j Stefanelli D, 2010, FOOD RES INT, V43, P1833, DOI 10.1016/j.foodres.2010.04.022 Wajid K, 2020, B ENVIRON CONTAM TOX, V104, P649, DOI 10.1007/s00128-020-02841-w Wang Liping, 2012, Journal of Chinese Institute of Food Science and Technology, V12, P141 Wu ShuYing, 2021, Food Research and Development, V42, P201, DOI 10.12161/j.issn.1005-6521.2021.21.030 [吴雪莲 Wu Xuelian], 2017, [麦类作物学报, Journal of Triticeae Crops], V37, P1246 Wu ZhenYing, 2014, Acta Ecologica Sinica - International Journal, V34, P160, DOI 10.1016/j.chnaes.2014.03.005 [徐菲 Xu Fei], 2016, [麦类作物学报, Journal of Triticeae Crops], V36, P1249 Zhang TW, 2021, FOOD CHEM, V346, DOI 10.1016/j.foodchem.2020.128928 [郑青松 ZHENG Qing-song], 2010, [中国油料作物学报, Chinese Journal of Oil Crop Sciences], V32, P65 Zhong ZM, 2016, SPRINGERPLUS, V5, DOI 10.1186/s40064-016-1761-0 NR 38 TC 0 Z9 0 U1 5 U2 5 PD NOV PY 2022 VL 11 IS 21 AR 3397 DI 10.3390/foods11213397 WC Food Science & Technology SC Food Science & Technology UT WOS:000885821400001 DA 2022-12-14 ER PT J AU Zhou, XY Xu, ZD AF Zhou, Xiongyong Xu, Zhiduan TI Traceability in food supply chains: a systematic literature review and future research directions SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Review DE food supply chain; traceability; tracking; systematic literature review ID WILLINGNESS-TO-PAY; VOLUNTARY TRACEABILITY; PRODUCT TRACEABILITY; INFORMATION; SAFETY; MODEL; IMPLEMENTATION; INCENTIVES; MANAGEMENT; FRAMEWORK AB The food traceability system (TS) provides visual services for consumers by recording every operation procedure of food supply, processing, marketing as well as distribution throughout the supply chain. This study aims to review the academic papers which are related to food supply chain traceability (FSCT) and proposes a framework for future research. To do this, we carry out a systematic literature review of 278 peer-reviewed scientific literature published between 1994 and September 2019. By classifying the FSCT into pre-study and post-study of TS implementation, this study reveals a number of future research directions of FSCT based on thematic findings and points out that the focus on such issues has shifted to the post-adoption study of TSs. This study further categorizes nine specific research topics from past literature and identifies specific opportunities of each theme for future research. C1 [Zhou, Xiongyong] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, 1954 Huashan Rd, Shanghai 200030, Peoples R China. [Zhou, Xiongyong] Cardiff Univ, Cardiff Business Sch, Logist Operat Management, Cardiff, Wales. [Xu, Zhiduan] Xiamen Univ, Sch Management, 422 Siming South Rd, Xiamen 361000, Fujian, Peoples R China. C3 Shanghai Jiao Tong University; Cardiff University; Xiamen University RP Xu, ZD (corresponding author), Xiamen Univ, Sch Management, 422 Siming South Rd, Xiamen 361000, Fujian, Peoples R China. EM zhiduanx@xmu.edu.cn CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Alfaro J. A., 2006, Journal of Purchasing and Supply Management, V12, P39, DOI 10.1016/j.pursup.2006.02.003 Alfnes F, 2018, AQUACULT ECON MANAG, V22, P1, DOI 10.1080/13657305.2017.1356398 Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Banterle A, 2008, AGRIBUSINESS, V24, P320, DOI 10.1002/agr.20169 Basole RC, 2018, TRANSPORT RES E-LOG, V114, P350, DOI 10.1016/j.tre.2016.08.003 Borit M, 2015, J CLEAN PROD, V104, P13, DOI 10.1016/j.jclepro.2015.05.003 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 Chen HH, 2019, APPL ECON, V51, P687, DOI 10.1080/00036846.2018.1510470 Chen LJ, 2021, INT J PROD ECON, V235, DOI 10.1016/j.ijpe.2021.108082 CHENG MJ, 1994, INT J OPER PROD MAN, V14, P4, DOI 10.1108/01443579410067199 Choe YC, 2009, INFORM SYST FRONT, V11, P167, DOI 10.1007/s10796-008-9134-z Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dai HY, 2015, INT J PROD ECON, V170, P14, DOI 10.1016/j.ijpe.2015.08.010 Dai HY, 2015, INT J PROD RES, V53, P511, DOI 10.1080/00207543.2014.955922 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Donnelly KAM, 2012, BRIT FOOD J, V114, P1016, DOI 10.1108/00070701211241590 Dzwolak W, 2016, J FOOD SAFETY, V36, P203, DOI 10.1111/jfs.12232 Engelseth P, 2014, GLOB BUS REV, V15, p87S, DOI 10.1177/0972150914550549 Engelseth P, 2009, J BUS IND MARK, V24, P421, DOI 10.1108/08858620910966291 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Foras E, 2015, FOOD CONTROL, V57, P65, DOI 10.1016/j.foodcont.2015.03.027 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Galliano D, 2013, IND INNOV, V20, P22, DOI 10.1080/13662716.2013.761379 Garcia-Torres S, 2019, SUPPLY CHAIN MANAG, V24, P85, DOI 10.1108/SCM-04-2018-0152 Golan E.H., 2004, AER830 EC RES SERV U Gunawan I, 2019, SUPPLY CHAIN FORUM, V20, P145, DOI 10.1080/16258312.2019.1570671 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2012, J FOOD ENG, V112, P78, DOI 10.1016/j.jfoodeng.2012.03.025 Lavoie G, 2009, INT FOOD AGRIBUS MAN, V12, P71 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 Matta V, 2012, INT J INFORM MANAGE, V32, P164, DOI 10.1016/j.ijinfomgt.2011.10.002 Mattevi M, 2016, BRIT FOOD J, V118, P1107, DOI 10.1108/BFJ-07-2015-0261 Mayring P., 2003, QUALITATIVE CONTENT Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mol APJ, 2015, SUSTAINABILITY-BASEL, V7, P12258, DOI 10.3390/su70912258 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Sen M. K. C., 2014, Journal of Animal and Veterinary Advances, V13, P350 Souza-Monteiro DM, 2010, AGRIBUSINESS, V26, P122, DOI 10.1002/agr.20233 Starbird SA, 2006, J AGR RESOUR ECON, V31, P14 Stranieri S, 2017, SUPPLY CHAIN MANAG, V22, P145, DOI [10.1108/SCM-07-2016-0268, 10.] Stranieri S, 2017, FOOD CONTROL, V80, P187, DOI 10.1016/j.foodcont.2017.04.047 Stranieri S, 2016, BRIT FOOD J, V118, P1025, DOI 10.1108/BFJ-04-2015-0151 United Nations, 2019, WORLD EC SITUATION P van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 Verbeke W., 2002, Agrekon, V41, P97 Viaene J., 1998, SUPPLY CHAIN MANAG I, V3, P139 Vo V.D., 2016, SUPPLY CHAIN UM I, V17, P125, DOI [10.1080/16258312.2016.1188588, DOI 10.1080/16258312.2016.1188588] Wang X, 2009, INT J PROD RES, V47, P2865, DOI 10.1080/00207540701725075 Wang X, 2010, INT J PROD ECON, V124, P463, DOI 10.1016/j.ijpe.2009.12.009 Wang XJ, 2012, OMEGA-INT J MANAGE S, V40, P906, DOI 10.1016/j.omega.2012.02.001 Wowak KD, 2016, J BUS LOGIST, V37, P132, DOI 10.1111/jbl.12125 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Yang Y, 2019, SUPPLY CHAIN MANAG, V24, P189, DOI 10.1108/SCM-11-2017-0359 Zeng YW, 2017, INT FOOD AGRIBUS MAN, V20, P439, DOI 10.22434/IFAMR2016.0156 Zhang JR, 2014, COMPR REV FOOD SCI F, V13, P1074, DOI 10.1111/1541-4337.12103 Zhou XY, 2022, OPER MANAGE RES, V15, P93, DOI 10.1007/s12063-021-00189-w NR 74 TC 2 Z9 2 U1 19 U2 22 PY 2022 VL 25 IS 2 BP 173 EP 196 DI 10.22434/IFAMR2020.0065 WC Agricultural Economics & Policy SC Agriculture UT WOS:000762945800001 DA 2022-12-14 ER PT J AU Vinholis, MDB Carrer, MJ de Souza, HM AF Brandao Vinholis, Marcela de Mello Carrer, Marcelo Jose de Souza Filho, Hildo Meirelles TI Adoption of beef cattle traceability at farm level in Sao Paulo State, Brazil SO CIENCIA RURAL DT Article DE traceability; certification; technology adoption; agriculture; beef cattle ID EMPIRICAL-ANALYSIS; STRATEGY; FRUIT AB This paper aimed to identify the determinants of beef traceability adoption at farm level in Sao Paulo State, Brazil. A sample survey of 32 farmers who adopted the European Union certified traceability and 52 other farmers who did not adopt traceability provided data to test hypotheses on determinant factors. Three binomial logit models were used in the analysis. Results suggested that capital-intensive livestock production system, high scale production, access to specialized information and high level of human and social capital play significant role in the adoption decision. C1 [Brandao Vinholis, Marcela de Mello] Empresa Brasileira Pesquisa Agr EMBRAPA, BR-13560970 Sao Carlos, SP, Brazil. [Carrer, Marcelo Jose] Inst Fed Educ Ciencia & Tecnol Sao Paulo IFSP, Sao Carlos, SP, Brazil. [de Souza Filho, Hildo Meirelles] Univ Fed Sao Carlos UFSCAR, Sao Carlos, SP, Brazil. C3 Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA); Instituto Federal de Sao Paulo (IFSP); Universidade Federal de Sao Carlos RP Vinholis, MDB (corresponding author), Empresa Brasileira Pesquisa Agr EMBRAPA, BR-13560970 Sao Carlos, SP, Brazil. EM marcela.vinholis@embrapa.br CR Adan A, 2009, PERS INDIV DIFFER, V46, P687, DOI 10.1016/j.paid.2009.01.023 Bocquet R, 2007, RES POLICY, V36, P367, DOI 10.1016/j.respol.2006.12.005 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Dill MD, 2015, J RURAL STUD, V42, P21, DOI 10.1016/j.jrurstud.2015.09.004 Ekelund J, 2005, LABOUR ECON, V12, P649, DOI 10.1016/j.labeco.2004.02.009 FEDER G, 1985, ECON DEV CULT CHANGE, V33, P255, DOI 10.1086/451461 Galliano D, 2011, AGRIBUSINESS, V27, P379, DOI 10.1002/agr.20272 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 MILGROM P, 1990, AM ECON REV, V80, P511 MINISTERIO DA AGRICULTURA PECUARIA E ABASTECIMENTO (MAPA), SIST BRAS ID CERT BO Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Souza Filho H.M.D., 2011, CADERNOS 101 NCIA TE, V28, P223, DOI DOI 10.35977/0104-1096.CCT2011.V28.12041 Sunding D, 2001, HANDB ECON, V18, P207 VICENTE J. R., 2002, PESQUISA ADOCAO TECN Vinholis Marcela de Mello Brandão, 2016, Prod., V26, P540, DOI 10.1590/0103-6513.193615 NR 15 TC 0 Z9 3 U1 1 U2 6 PY 2017 VL 47 IS 9 AR e20160759 DI 10.1590/0103-8478cr20160759 WC Agronomy SC Agriculture UT WOS:000429430900001 DA 2022-12-14 ER PT J AU Luvisi, A Panattoni, A Bandinelli, R Rinaldelli, E Pagano, M Triolo, E AF Luvisi, Andrea Panattoni, Alessandra Bandinelli, Roberto Rinaldelli, Enrico Pagano, Mario Triolo, Enrico TI Ultra-High Frequency transponders in grapevine: A tool for traceability of plants and treatments in viticulture SO BIOSYSTEMS ENGINEERING DT Article ID IDENTIFICATION; AGRICULTURE; MICROCHIPS; SYSTEM AB The storage of agrochemical data in a field log is an essential step in the plant production process in order to guarantee a safer traceability system, and grapevine health observations can help to monitor diseases. Traceability systems can be implemented by means of integrated computer-based information systems. The availability of Ultra-High Frequency (UHF) transponder implantation methods for grapevines offers the possibility, thanks to greater reading distances, to link data, such as plant treatments, to create a system in which geographically positioned plants can be registered. Two UHF transponders were designed to be implanted in grapevines and the effects on trunk histology or growth parameters were evaluated. The results indicate that the transponder and pith diameters are important factors for producing UHF-tagged plants without detrimental effects. Implantation of a UHF transponder of 2.4 +/- 0.1 mm diameter in 'Kober 5BB' does not increase loss of viability or detrimental growth compared to control. Conversely, implantation in rootstocks with a smaller pith diameter, such as '1103 Paulsen', causes reduction of viability and growth when using this kind of transponder, while viability and growth suffer after implanting UHF transponders of 3.5 +/- 0.1 mm diameter in both rootstocks. Three software programs are described which are able to integrate a digital field log with an online database for "basic" and "certified" material belonging to nurseries or farms. Post-process tests, usability and portability of the system were evaluated. Treatment registration showed higher successful read rates for UHF transponders compared to LF transponders under test conditions, suggesting an easier management of UHF-tagged grapevines. (C) 2012 IAgrE. Published by Elsevier Ltd. All rights reserved. C1 [Luvisi, Andrea; Panattoni, Alessandra; Triolo, Enrico] Univ Pisa, Dept Tree Sci Entomol & Plant Pathol G Scaramuzzi, I-56124 Pisa, Italy. [Bandinelli, Roberto] Assoc Toscana Costitutori Viticoli TOS CO VIT, I-56010 San Piero A Grado, PI, Italy. [Rinaldelli, Enrico; Pagano, Mario] Univ Florence, Dept Crop Soil & Environm Sci, I-50019 Sesto Fiorentino, FI, Italy. C3 University of Pisa; University of Florence RP Luvisi, A (corresponding author), Univ Pisa, Dept Tree Sci Entomol & Plant Pathol G Scaramuzzi, Via Borghetto 80, I-56124 Pisa, Italy. EM aluvisi@agr.unipi.it; roberto.bandinelli@unifi.it; enrico.rinaldelli@unif.it CR Ampatzidis YG, 2009, COMPUT ELECTRON AGR, V66, P166, DOI 10.1016/j.compag.2009.01.008 Ampatzidis Y, 2009, PRECIS AGRIC, V10, P63, DOI 10.1007/s11119-008-9095-8 Backhouse G., 2006, RFID FREQUENCY STAND Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Bowman KD, 2005, HORTTECHNOLOGY, V15, P352, DOI 10.21273/HORTTECH.15.2.0352 Bowman KD, 2010, HORTSCIENCE, V45, P451, DOI 10.21273/HORTSCI.45.3.451 CAMUSSI A, 1995, METODI STAT SPERIMEN Castellucci F., 2011, OIV REPORT STATE VIT Cerri S., 1993, B425 CONS NAZ RIC Finkenzeller Klaus, 2010, RFID HDB FUNDAMENTAL, V3rd KOLB TE, 1990, FOREST SCI, V36, P293 Kuhlmann F, 2001, COMPUT ELECTRON AGR, V30, P71, DOI 10.1016/S0168-1699(00)00157-5 Kumagai MH, 2006, PLANT MOL BIOL, V61, P515, DOI 10.1007/s11103-006-0025-8 Lodewijks G., 2006, P IEEE INT C SERV OP Luvisi A, 2010, SCI HORTIC-AMSTERDAM, V124, P349, DOI 10.1016/j.scienta.2010.01.015 Luvisi A, 2011, BIOSYST ENG, V109, P167, DOI 10.1016/j.biosystemseng.2011.03.001 Luvisi A, 2010, HORTTECHNOLOGY, V20, P1037, DOI 10.21273/HORTSCI.20.6.1037 Luvisi A, 2010, COMPUT ELECTRON AGR, V70, P256, DOI 10.1016/j.compag.2009.08.007 MCCARTHY JJ, 1990, ISA T, V29, P53, DOI 10.1016/0019-0578(90)90032-G Nielsen J., 1994, USABILITY ENG P.E.I. Department of Agriculture and Forestry, 2003, FIELD REC PEST APPL Peets S., 2008, P 9 INT C PREC AGR Peets S, 2009, PRECIS AGRIC, V10, P382, DOI 10.1007/s11119-009-9106-4 Porto SMC, 2011, BIOSYST ENG, V109, P120, DOI 10.1016/j.biosystemseng.2011.02.008 Richards P.K., 1977, FACTORS SOFTWARE QUA Riezebos J, 2009, COMPUT IND, V60, P237, DOI 10.1016/j.compind.2009.01.004 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 VIRZI RA, 1992, HUM FACTORS, V34, P457, DOI 10.1177/001872089203400407 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 NR 29 TC 13 Z9 14 U1 0 U2 4 PD OCT PY 2012 VL 113 IS 2 BP 129 EP 139 DI 10.1016/j.biosystemseng.2012.06.015 WC Agricultural Engineering; Agriculture, Multidisciplinary SC Agriculture UT WOS:000309433800004 DA 2022-12-14 ER PT J AU Sanz, A Ordovas, L Zaragoza, P Sanz, A de Blas, I Rodellar, C AF Sanz, Arianne Ordovas, Laura Zaragoza, Pilar Sanz, Albina de Blas, Ignacio Rodellar, Clementina TI A false single nucleotide polymorphism generated by gene duplication compromises meat traceability SO MEAT SCIENCE DT Article DE SNP; Meat traceability; Genetic identification; False SNP; Duplication gene; Excess of heterozygote ID PROSTATE-CANCER RISK; MARKERS; BEEF; SNP; DNA; REFINEMENT; CATTLE; PIG AB Controlling meat traceability using SNPs is an effective method of ensuring food safety. We have analyzed several SNPs to create a panel for bovine genetic identification and traceability studies. One of these was the transversion g.329C > T (Genbank accession no. AJ496781) on the cytochrome P450 17A1 gene, which has been included in previously published panels. Using minisequencing reactions, we have tested 701 samples belonging to eight Spanish cattle breeds. Surprisingly, an excess of heterozygotes was detected, implying an extreme departure from Hardy-Weinberg equilibrium (P<0.001). By alignment analysis and sequencing, we detected that the g.329C > T SNP is a false positive polymorphism, which allows us to explain the inflated heterozygotic value. We recommend that this ambiguous SNP, as well as other polymorphisms located in this region, should not be used in identification, traceability or disease association studies. Annotation of these false SNPs should improve association studies and avoid misinterpretations. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Sanz, Arianne; Ordovas, Laura; Zaragoza, Pilar; Rodellar, Clementina] Univ Zaragoza, Lab Genet Bioquim LAGENBIO, Fac Vet, E-50009 Zaragoza, Spain. [Sanz, Albina] Ctr Invest & Tecnol Agroalimentaria Aragon, Area Prod, Zaragoza, Spain. [de Blas, Ignacio] Univ Zaragoza, Unidad Patol Infecciosa & Epidemiol, Fac Vet, E-50009 Zaragoza, Spain. C3 University of Zaragoza; University of Zaragoza RP Sanz, A (corresponding author), Fac Vet, Lab Genet Bioquim, Miguel Servet 177, Zaragoza 50013, Spain. EM arianne@unizar.es; arianne@unizar.es CR BHASKER CR, 1989, ARCH BIOCHEM BIOPHYS, V271, P479, DOI 10.1016/0003-9861(89)90298-1 Capoferri R, 2006, J FOOD PROTECT, V69, P1971, DOI 10.4315/0362-028X-69.8.1971 Fan B, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0014726 Gautier M, 2001, CHROMOSOME RES, V9, P617, DOI 10.1023/A:1012996005762 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Heaton MP, 2005, JAVMA-J AM VET MED A, V226, P1311, DOI 10.2460/javma.2005.226.1311 Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 Ho MR, 2011, NUCLEIC ACIDS RES, V39, pD920, DOI 10.1093/nar/gkq1197 Jin H. J., 2004, 034 N DAK STAT U DEP Karniol B, 2009, ANIM GENET, V40, P353, DOI 10.1111/j.1365-2052.2008.01846.x Kaufman B, 2011, BREAST CANCER RES TR, V126, P521, DOI 10.1007/s10549-010-1123-5 Kennedy GC, 2003, NAT BIOTECHNOL, V21, P1233, DOI 10.1038/nbt869 Lundqvist E, 1999, GENE, V226, P327, DOI 10.1016/S0378-1119(98)00567-8 Partipilo G, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-639 Pastinen T, 1996, CLIN CHEM, V42, P1391 Peden JF, 2011, NAT GENET, V43, P339, DOI 10.1038/ng.782 RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Rousset F, 2008, MOL ECOL RESOUR, V8, P103, DOI 10.1111/j.1471-8286.2007.01931.x Sanz A, 2010, GENET MOL RES, V9, P843, DOI 10.4238/vol9-2gmr784 Sarma AV, 2008, PROSTATE, V68, P296, DOI 10.1002/pros.20696 Severi G, 2008, BJU INT, V101, P492, DOI 10.1111/j.1464-410X.2007.07272.x Souza-Monteiro D.M., 2004, EC IMPLEMENTING TRAC Storz JF, 2009, HEREDITY, V102, P99, DOI 10.1038/hdy.2008.114 Thomas JH, 2007, PLOS GENET, V3, P720, DOI 10.1371/journal.pgen.0030067 Van Laere AS, 2003, NATURE, V425, P832, DOI 10.1038/nature02064 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] Werner FAO, 2004, ANIM GENET, V35, P44, DOI 10.1046/j.1365-2052.2003.01071.x ZUBER MX, 1986, SCIENCE, V234, P1258, DOI 10.1126/science.3535074 NR 29 TC 0 Z9 0 U1 0 U2 12 PD JUL PY 2012 VL 91 IS 3 BP 347 EP 351 DI 10.1016/j.meatsci.2012.02.016 WC Food Science & Technology SC Food Science & Technology UT WOS:000303642800021 DA 2022-12-14 ER PT J AU Magalhaes, DR Campo, MD Maza, MT AF Rodrigues Magalhaes, Danielle del Mar Campo, Maria Teresa Maza, Maria TI Knowledge, Utility, and Preferences for Beef Label Traceability Information: A Cross-Cultural Market Analysis Comparing Spain and Brazil SO FOODS DT Article DE beef; traceability system; marketing; consumer; safety food; cross cultural study; questionnaire ID COUNTRY-OF-ORIGIN; FOOD-SUPPLY CHAIN; CONSUMER PREFERENCES; MEAT CONSUMPTION; RED MEAT; QUALITY; SAFETY; PERCEPTIONS; ATTRIBUTES; SUSTAINABILITY AB The consumer environment determines consumers' buying behavior and product preferences, and understanding these factors allows businesses in the industry to identify market demands. In view of the different contexts, Spain and Brazil, there are differences in the consumption of beef, in the production and the regulatory process concerning beef, and in particular the traceability system. The traceability system is mandatory in Spain and voluntary in Brazil. From these prerogatives, this cross-cultural study carried out through a self-administered questionnaire with 2132 Spanish and Brazilian beef buyers/consumers, aimed at comparing and understanding the familiarity with the bovine traceability system and traceability information of the label as a food security indicator. It is concluded that traceability information is well received by consumers as an attribute of credibility, and consumers are interested in ensuring that the item they buy is of known and reliable origin. But more incentives may help clarify the advantages of purchasing food with certified traceability, making it more effective for consumers to use this knowledge. C1 [Rodrigues Magalhaes, Danielle; del Mar Campo, Maria] Univ Zaragoza CITA, Inst Agroalimentario IA2, Dept Anim Husb & Food Sci, Miguel Servet 177, Zaragoza 50013, Spain. [Teresa Maza, Maria] Univ Zaragoza CITA, Inst Agroalimentario IA2, Dept Agr Sci & Nat Environm, Miguel Servet 177, Zaragoza 50013, Spain. RP Magalhaes, DR (corresponding author), Univ Zaragoza CITA, Inst Agroalimentario IA2, Dept Anim Husb & Food Sci, Miguel Servet 177, Zaragoza 50013, Spain. EM d.magalhaes@yahoo.com.br; marimar@unizar.es; mazama@unizar.es CR Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 [Anonymous], REGLAMENTO CE N 2629 [Anonymous], OECD FAO AGR OUTLOOK [Anonymous], REAL DECRETO 752009 Ardeshiri A, 2018, FOOD QUAL PREFER, V65, P146, DOI 10.1016/j.foodqual.2017.10.018 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Awada L, 2012, FOOD POLICY, V37, P21, DOI 10.1016/j.foodpol.2011.10.004 Balcombe K, 2016, FOOD POLICY, V64, P49, DOI 10.1016/j.foodpol.2016.09.008 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Becker T., 2000, British Food Journal, V102, P158, DOI 10.1108/00070700010371707 Bernues A, 2003, FOOD QUAL PREFER, V14, P265, DOI 10.1016/S0950-3293(02)00085-X Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Britt AG, 2013, REV SCI TECH OIE, V32, P571 Britwum K, 2019, FOOD POLICY, V86, DOI 10.1016/j.foodpol.2019.05.009 Buitrago-Vera J, 2016, WORLD RABBIT SCI, V24, P169, DOI 10.4995/wrs.2016.4229 Burnier PC, 2021, FOOD QUAL PREFER, V88, DOI 10.1016/j.foodqual.2020.104075 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Conchon F.L., 2012, B TEC, V91, P1 Coutinho C., 2014, METODOLOGIA INVESTIG da Cunha CF, 2011, RAE-REV ADMIN EMPRES, V51, P542, DOI 10.1590/S0034-75902011000600004 de Boer J, 2014, APPETITE, V76, P120, DOI 10.1016/j.appet.2014.02.002 Decreto N 5.741 de 30 de marco de, DECRETO N 5 741MARCO Ehmke M.D., 2006, P AM AGR EC ASS ANN Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Furnols MF, 2011, FOOD QUAL PREFER, V22, P443, DOI 10.1016/j.foodqual.2011.02.007 Galvao JA, 2010, FOOD CONTROL, V21, P1360, DOI 10.1016/j.foodcont.2010.03.010 Gellynck X, 2006, MEAT SCI, V74, P161, DOI 10.1016/j.meatsci.2006.04.013 Giraud G, 2003, SCI ALIMENT, V23, P40, DOI 10.3166/sda.23.40-46 Grande Esteban I., 2014, FUNDAMENTOS TECNICAS, V12th Grunert KG, 2018, MEAT SCI, V137, P123, DOI 10.1016/j.meatsci.2017.11.022 Henchion M, 2014, MEAT SCI, V98, P561, DOI 10.1016/j.meatsci.2014.06.007 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 IBGE Instituto Brasileiro de Geografia e Estatistica. Indicadores- Estatistica da Producao Pecuaria Producao Animal, IND EST PROD PEC PRO Instrucao Normativa n 51 de 1 de outubro de, IN EST AGR REC AB DI Barcellos JOJ, 2012, REV BRAS ZOOTECN, V41, P771, DOI 10.1590/S1516-35982012000300041 Joseph S, 2014, FOOD POLICY, V44, P14, DOI 10.1016/j.foodpol.2013.10.008 Kang J, 2015, INT J HOSP MANAG, V48, P12, DOI 10.1016/j.ijhm.2015.04.005 Klain TJ, 2014, AGR ECON-BLACKWELL, V45, P635, DOI 10.1111/agec.12112 Lagerkvist CJ, 2014, FOOD QUAL PREFER, V34, P50, DOI 10.1016/j.foodqual.2013.12.009 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lusk JL, 2014, EUR REV AGRIC ECON, V41, P627, DOI 10.1093/erae/jbt035 Macready AL, 2020, FOOD POLICY, V92, DOI 10.1016/j.foodpol.2020.101880 Magalhães Danielle Rodrigues, 2016, Arq. Inst. Biol., V83, pe1182013, DOI 10.1590/1808-1657001182013 MAPA Ministerio de Agricultura y Pesca Alimentacion y Medio Ambiente, INF CONS AL ESP MAPAMA Ministerio de Agricultura y Pesca Alimentacion y Medio Ambiente, SECTOR CARNE VACUNO Maza M. T., 2006, ITEA, V102, P360 Meyerding SGH, 2018, APPETITE, V127, P324, DOI 10.1016/j.appet.2018.05.008 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pethick DW, 2011, ANIM PROD SCI, V51, P13, DOI 10.1071/AN10041 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Roe BE, 2014, ANNU REV RESOUR ECON, V6, P407, DOI 10.1146/annurev-resource-100913-012439 Sans P, 2016, MEAT SCI, V114, P154, DOI 10.1016/j.meatsci.2015.12.003 Scozzafava G, 2016, APPETITE, V96, P70, DOI 10.1016/j.appet.2015.09.004 Sepulveda W, 2008, MEAT SCI, V80, P1282, DOI 10.1016/j.meatsci.2008.06.012 Sepulveda WS, 2010, MEAT SCI, V85, P167, DOI 10.1016/j.meatsci.2009.12.021 Stranieri S, 2015, INT FOOD AGRIBUS MAN, V18, P21 Taylor M, 2016, FOOD POLICY, V62, P56, DOI 10.1016/j.foodpol.2016.04.005 Tonsor GT, 2009, J AGR ECON, V60, P625, DOI 10.1111/j.1477-9552.2009.00209.x USDA United States Department of Agriculture Economic Research Service, ANN CUM YEAR TO DAT van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Van Wezemael L, 2010, FOOD CONTROL, V21, P835, DOI 10.1016/j.foodcont.2009.11.010 Verbeke W., 2009, Estey Centre Journal of International Law and Trade Policy, V10, P20 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Zakowska-Biemans S, 2017, MEAT SCI, V124, P105, DOI 10.1016/j.meatsci.2016.11.001 NR 68 TC 4 Z9 4 U1 9 U2 22 PD FEB PY 2021 VL 10 IS 2 AR 232 DI 10.3390/foods10020232 WC Food Science & Technology SC Food Science & Technology UT WOS:000622547800001 DA 2022-12-14 ER PT J AU Yang, MR Wang, M Zhou, J Wang, TT Liu, F Li, P Li, S Zhang, LY Liu, QH AF Yang, Mengrui Wang, Min Zhou, Jian Wang, Tongtong Liu, Fang Li, Peng Li, Shan Zhang, Liyuan Liu, Quanhui TI Establishment of metrological traceability for fluoroquinolones measurement in monitoring plan of quality and safety for agro-product in China SO MICROCHEMICAL JOURNAL DT Article DE Certified reference material; Fluoroquinolone; Characterization; Metrological traceability ID CERTIFIED REFERENCE MATERIAL; MASS-BALANCE METHOD; UNCERTAINTY EVALUATION; PURITY DETERMINATION; CALIBRATION; COMBINATION; ASSIGNMENT; QNMR; ACID AB Purity certified reference materials (CRMs) as the basis for traceability chains are play an important part in chemical metrology. In the context of supporting the implementation of monitoring plan of national quality and safety for agro-products, a project for development of fluoroquinolones purity CRMs was launched. These CRMs aimed to use as benchmark to assure the comparable and traceable measurement results of fluoroquinolone antibiotic residues in nationwide monitoring laboratories. In characterization, certified values were assigned by using mass balance (MB) and quantitative nuclear magnetic resonance (qNMR). Homogeneity, stability and uncertainty evaluation were sufficiently studied. These fluoroquinolones purity CRMs including enrofloxacin (GBW(E)090817), ciprofloxacin (GBW(E)090818), norfloxacin (GBW09251), fleroxacin (GBW09249), lomefloxacin hydrochloride (GBW09250), ofloxacin (GBW(E)090822), danofloxacin mesylate (GBW(E)100475), sarafloxacin hydrochloride (GBW(E)0900960), pefloxacin mesylate (GBW09256) can be used to ensure the accurate and reliable measurement results acquired in the mandatory routine monitoring by providing a metrological traceability source for establishing comparability. C1 [Yang, Mengrui; Wang, Min; Zhou, Jian; Wang, Tongtong; Liu, Fang; Li, Peng; Li, Shan; Zhang, Liyuan; Liu, Quanhui] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS RP Wang, M (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. EM wangmin@caas.cn CR Bonassa KPD, 2017, FOOD CHEM TOXICOL, V105, P8, DOI 10.1016/j.fct.2017.03.033 Davies SR, 2015, ANAL BIOANAL CHEM, V407, P7983, DOI 10.1007/s00216-015-8971-0 Davies SR, 2015, ANAL BIOANAL CHEM, V407, P3103, DOI 10.1007/s00216-014-7893-6 Ghaly H, 2014, EUR J PHARM SCI, V52, P206, DOI 10.1016/j.ejps.2013.11.011 Gong H, 2012, TALANTA, V101, P96, DOI 10.1016/j.talanta.2012.09.012 Gong NB, 2014, J PHARMACEUT BIOMED, V89, P106, DOI 10.1016/j.jpba.2013.10.015 Huang T, 2014, TALANTA, V125, P94, DOI 10.1016/j.talanta.2014.02.059 Kim SH, 2013, B KOREAN CHEM SOC, V34, P531, DOI 10.5012/bkcs.2013.34.2.531 Le Gresley A, 2015, CRIT REV ANAL CHEM, V45, P300, DOI 10.1080/10408347.2014.944971 Ma K, 2009, ANAL CHIM ACTA, V650, P227, DOI 10.1016/j.aca.2009.07.046 Mathkar S, 2009, J PHARMACEUT BIOMED, V49, P627, DOI 10.1016/j.jpba.2008.12.030 Nogueira R, 2013, EUR J PHARM SCI, V48, P502, DOI 10.1016/j.ejps.2012.11.005 Noman AT, 2019, INT J CARDIOL, V274, P299, DOI 10.1016/j.ijcard.2018.09.067 Odin C, 2017, SOLID STATE NUCL MAG, V85-86, P25, DOI 10.1016/j.ssnmr.2017.04.004 Pauli GF, 2014, J MED CHEM, V57, P9220, DOI 10.1021/jm500734a Pereira AMPT, 2018, FOOD CHEM TOXICOL, V118, P340, DOI 10.1016/j.fct.2018.05.035 Randall L, 2016, RES VET SCI, V108, P47, DOI 10.1016/j.rvsc.2016.07.010 Schoenberger T, 2012, ANAL BIOANAL CHEM, V403, P247, DOI 10.1007/s00216-012-5777-1 Shimizu Y, 2013, THERMOCHIM ACTA, V568, P61, DOI 10.1016/j.tca.2013.05.039 Westwood S, 2013, ANAL CHEM, V85, P3118, DOI 10.1021/ac303329k Yang MR, 2016, ACCREDIT QUAL ASSUR, V21, P341, DOI 10.1007/s00769-016-1221-0 NR 21 TC 0 Z9 0 U1 4 U2 4 PD JUN PY 2022 VL 177 AR 107315 DI 10.1016/j.microc.2022.107315 WC Chemistry, Analytical SC Chemistry UT WOS:000793739400002 DA 2022-12-14 ER PT J AU Li, Q Li, Y Wang, LL AF Li, Qian Li, Ya Wang, LanLan TI Research on application of internet of things technology in quality traceability of fruit and vegetable agricultural products SO JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING DT Article; Early Access DE Hanxin code; HBuilder; Internet of things; Quality traceability ID X-ARCHITECTURE; NEURAL-NETWORK; SYSTEMS; OPTIMIZATION; ALGORITHM AB The quality and safety of agricultural products are related to public health, social stability, and national development. The Internet of Things technology plays a vital role in the traceability of agricultural product quality. The Internet of Things coding and marking technology enables complete traceability of agricultural product supply chain process data. This article first proposes a low-cost, high-performance Internet of Things quality traceability identification code for fruits, vegetables, and agricultural products based on Internet of Things technology-Han Xin code, which has low production cost, large information capacity, strong anti-fouling and distortion ability, and reliability. High technical characteristics. Then, a miniaturization-oriented, IPv6-based Linux server and HBuilder development platform were used to construct a quality traceability system for fruit, vegetable and agricultural products in Hanxin Code. The results show that the Hanxin code coding technology designs a low-cost quality traceability implementation plan for fruit and vegetable agricultural products, and realizes the whole process monitoring of fruit and vegetable agricultural products from planting, picking, storage to sales, and has good practical value. C1 [Li, Qian; Wang, LanLan] Zhoukou Normal Univ, Sch Comp Sci & Technol, Zhoukou, Peoples R China. [Li, Ya] Zhoukou Normal Univ, Sch Math & Stat, Zhoukou, Peoples R China. C3 Zhoukou Normal University; Zhoukou Normal University RP Li, Y (corresponding author), Zhoukou Normal Univ, Sch Math & Stat, Zhoukou, Peoples R China. EM 547187293@qq.com; 37227144@qq.com CR Aihua W, CORE TECHNOLOGY INTE [Anonymous], [No title captured] Chang V, MULTIMED TOOLS APPL Chunwei F., 2017, HUNAN AGR SCI, V56, P3338 Dai HY, 2015, INT J PROD ECON, V170, P14, DOI 10.1016/j.ijpe.2015.08.010 Dong YuDe, 2016, Transactions of the Chinese Society of Agricultural Engineering, V32, P280, DOI 10.11975/j.issn.1002-6819.2016.01.039 Fuchao O, RES QR CODE IMAGE PR Giancaspro A, 2016, FOOD CONTROL, V59, P809, DOI 10.1016/j.foodcont.2015.07.006 Guo WZ, 2014, FRONT COMPUT SCI-CHI, V8, P203, DOI 10.1007/s11704-014-3008-y Hai Z., 2015, DESIGN IMPLEMENTATIO Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Jiwu S, IEEE T PARALLEL DIST, P1 Junqin S., 2017, RES HANXIN CODE RECO Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Li JM, 2016, J MANUF PROCESS, V21, P141, DOI 10.1016/j.jmapro.2015.12.007 Liu GG, 2020, SOFT COMPUT, V24, P3943, DOI 10.1007/s00500-019-04165-2 Liu GG, 2015, FRONT COMPUT SCI-CHI, V9, P576, DOI 10.1007/s11704-015-4017-1 Liu GG, 2015, IEEE T CYBERNETICS, V45, P989, DOI 10.1109/TCYB.2014.2342713 Liu GG, 2015, SOFT COMPUT, V19, P1153, DOI 10.1007/s00500-014-1329-2 Muhua Li., 2013, COMPUT MODERNIZATION, V10, P222 Niu YZ, 2018, IET COMPUT VIS, V12, P365, DOI 10.1049/iet-cvi.2017.0512 Qian JianPing, 2015, Transactions of the Chinese Society of Agricultural Engineering, V31, P306, DOI 10.3969/j.issn.1002-6819.2015.04.043 Qiang L., 2015, AUTOMAT INSTRUM, V10, P151 Sun H, RECOGNITION REALIZAT Sun XingQuan, 2015, Journal of Food Safety and Quality, V6, P10 Wang J, 2018, INFORM SCIENCES, V447, P216, DOI 10.1016/j.ins.2018.03.003 Weina W., 2017, RES APPL HANXIN CODE Xia YS, 2016, IEEE T NEUR NET LEAR, V27, P214, DOI 10.1109/TNNLS.2015.2500618 Xia YS, 2015, NEURAL NETWORKS, V67, P131, DOI 10.1016/j.neunet.2015.03.008 Xia YS, 2014, INFORM SCIENCES, V277, P808, DOI 10.1016/j.ins.2014.03.015 Xia YS, 2014, NEURAL COMPUT, V26, P449, DOI 10.1162/NECO_a_00549 Xiaoke L., 2016, SOFTWARE, V37, P93 Xiaoxiao N., 2015, RES IMPLEMENTATION T Xing H, 2016, ACM T DES AUTOMAT EL, V21, P30, DOI DOI 10.1145/2856033 Xu P., 2017, CHINA AGR INF, V6, P96 Yang DD, 2017, SCI CHINA INFORM SCI, V60, DOI 10.1007/s11432-016-9080-3 Yang LH, 2016, INFORM SCIENCES, V370, P159, DOI 10.1016/j.ins.2016.07.067 Yang Y, 2017, CONCURR COMP-PRACT E, V29, DOI 10.1002/cpe.4211 Yang Y, 2017, J NETW COMPUT APPL, V89, P26, DOI 10.1016/j.jnca.2016.11.017 Yang Y, 2016, IEEE T INF FOREN SEC, V11, P746, DOI 10.1109/TIFS.2015.2509912 Ye DY, 2015, SOFT COMPUT, V19, P1893, DOI 10.1007/s00500-014-1371-0 Yi W., 2015, BARCODE INF SYST, V5, P19 Yipeng X, 2015, STAND QUAL, V29, P29 Yongxian Pu, 2014, JIANGXI J AGR SCI, V26, P21 Youbin T., 2014, COMPUTER 400 ROM SOF, V12, P266 Yu ZY, 2016, MOBILE NETW APPL, V21, P367, DOI 10.1007/s11036-015-0668-2 Zhang SC, 2015, IEEE T NEUR NET LEAR, V26, P3227, DOI 10.1109/TNNLS.2015.2441697 NR 47 TC 2 Z9 2 U1 17 U2 71 DI 10.1007/s12652-021-03006-1 EA MAR 2021 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000632340400004 DA 2022-12-14 ER PT J AU Crawford, LM Carrasquilla-Garcia, N Cook, D Wang, SC AF Crawford, Lauren M. Carrasquilla-Garcia, Noelia Cook, Doug Wang, Selina C. TI Analysis of Microsatellites (SSRs) in Processed Olives as a Means of Cultivar Traceability and Authentication SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE California-style olive; cultivar; traceability; authentication; SSR; microsatellite ID OLEA-EUROPAEA L.; PROTECTED DESIGNATION; TABLE OLIVES; PHENOLIC-COMPOUNDS; GENOMIC DNA; IDENTIFICATION; MARKERS; ORIGIN; OIL; FRUIT AB Select cultivars of table olives have more desirable traits and a higher economic value. There are suspected issues with cultivar mislabeling and traceability in the supply chain. Here, we describe a method to identify cultivars by genotyping of processed olives. DNA was extracted from leaves and California-style olives of seven commonly packed cultivars. Processed olive fruits yielded relatively low DNA concentrations (0.04-0.86 mu g/g), and extracts had more impurities compared with leaves. From 15 candidate SSRs, five markers showing the highest number of unique allele combinations and discriminatory power were selected. These SSRs were successfully amplified and analyzed in all cultivars of olives except one. When directly comparing any two cultivars, different allele combinations were typically present for at least four of the five SSRs. Microsatellite analysis shows potential as a simple yet robust diagnostic tool. The method can be expanded to include other cultivars, styles of table olives, and potentially other processed plant-based foods. C1 [Crawford, Lauren M.; Carrasquilla-Garcia, Noelia; Cook, Doug; Wang, Selina C.] Univ Calif Davis, Davis, CA 95616 USA. C3 University of California System; University of California Davis RP Wang, SC (corresponding author), Univ Calif Davis, Davis, CA 95616 USA. EM scwangg@ucdavis.edu CR Angeles J. G. C., 2005, Plant Molecular Biology Reporter, V23, P297, DOI 10.1007/BF02772760 Baldoni L, 2009, MOL BREEDING, V24, P213, DOI 10.1007/s11032-009-9285-8 Bauer T, 2003, EUR FOOD RES TECHNOL, V217, P338, DOI 10.1007/s00217-003-0743-y Belaj A, 2004, HORTSCIENCE, V39, P1557, DOI 10.21273/HORTSCI.39.7.1557 Belaj A, 2012, TREE GENET GENOMES, V8, P365, DOI 10.1007/s11295-011-0447-6 Ben-Ayed R, 2013, COMPR REV FOOD SCI F, V12, P218, DOI 10.1111/1541-4337.12003 Bracci T, 2011, PLANT CELL REP, V30, P449, DOI 10.1007/s00299-010-0991-9 Carriero F, 2002, THEOR APPL GENET, V104, P301, DOI 10.1007/s001220100691 Charoenprasert S, 2012, J AGR FOOD CHEM, V60, P7081, DOI 10.1021/jf3017699 Chiappetta A, 2017, SCI HORTIC-AMSTERDAM, V226, P42, DOI 10.1016/j.scienta.2017.08.022 Cipriani G, 2002, THEOR APPL GENET, V104, P223, DOI 10.1007/s001220100685 Concepcion R, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201800136 De La Rosa R, 2002, MOL ECOL NOTES, V2, P265, DOI 10.1046/j.1471-8286.2002.00217.x Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 Faria MA, 2013, FOOD CONTROL, V33, P136, DOI 10.1016/j.foodcont.2013.02.020 Ganopoulos I, 2011, FOOD CONTROL, V22, P532, DOI 10.1016/j.foodcont.2010.09.040 GILLASPY G, 1993, PLANT CELL, V5, P1439, DOI 10.1105/tpc.5.10.1439 Gryson N, 2010, ANAL BIOANAL CHEM, V396, P2003, DOI 10.1007/s00216-009-3343-2 HUNTER PR, 1990, J CLIN MICROBIOL, V28, P1903, DOI 10.1128/JCM.28.9.1903-1905.1990 Japelaghi RH, 2011, MOL BIOTECHNOL, V49, P129, DOI 10.1007/s12033-011-9384-8 Kiritsakis AK, 1998, J AM OIL CHEM SOC, V75, P673, DOI 10.1007/s11746-998-0205-6 Koehmstedt AM, 2011, GENET RESOUR CROP EV, V58, P519, DOI 10.1007/s10722-010-9595-z Lo YT, 2018, FOOD CHEM, V240, P767, DOI 10.1016/j.foodchem.2017.08.022 Madesis P, 2014, FOOD RES INT, V60, P163, DOI 10.1016/j.foodres.2013.10.042 Marsilio V, 2001, FOOD CHEM, V74, P55, DOI 10.1016/S0308-8146(00)00338-1 Mousavi S, 2017, FRONT PLANT SCI, V8, DOI 10.3389/fpls.2017.01283 Nakamura S, 2007, J AGR FOOD CHEM, V55, P10388, DOI 10.1021/jf072407u Pafundo S, 2010, FOOD CHEM, V123, P787, DOI 10.1016/j.foodchem.2010.05.027 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Pasqualone A, 2016, J SCI FOOD AGR, V96, P3642, DOI 10.1002/jsfa.7711 Pasqualone A, 2013, J AGR FOOD CHEM, V61, P3068, DOI 10.1021/jf400014g Pinheiro PBM, 2005, ANAL CHIM ACTA, V544, P229, DOI 10.1016/j.aca.2005.01.014 Sangwan RS, 2000, PLANT MOL BIOL REP, V18, P265, DOI 10.1007/BF02823997 Sarri V, 2006, GENOME, V49, P1606, DOI 10.1139/G06-126 Schrader C, 2012, J APPL MICROBIOL, V113, P1014, DOI 10.1111/j.1365-2672.2012.05384.x Sefc KM, 2000, MOL ECOL, V9, P1171, DOI 10.1046/j.1365-294x.2000.00954.x Trujillo I, 2014, TREE GENET GENOMES, V10, P141, DOI 10.1007/s11295-013-0671-3 Turci M, 2010, FOOD CONTROL, V21, P143, DOI 10.1016/j.foodcont.2009.04.012 Varma Astha, 2007, Biotechnology Journal, V2, P386, DOI 10.1002/biot.200600195 NR 40 TC 3 Z9 3 U1 1 U2 13 PD JAN 29 PY 2020 VL 68 IS 4 BP 1110 EP 1117 DI 10.1021/acs.jafc.9b06890 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000510529800019 DA 2022-12-14 ER PT J AU Catarino, S Madeira, M Monteiro, F Caldeira, I de Sousa, RB Curvelo-Garcia, A AF Catarino, Sofia Madeira, Manuel Monteiro, Fernando Caldeira, Ilda de Sousa, Raul Bruno Curvelo-Garcia, Antonio TI Mineral Composition through Soil-Wine System of Portuguese Vineyards and Its Potential for Wine Traceability SO BEVERAGES DT Article DE geographic origin; geological material; multi-element composition; rare earth elements; vinification ID RARE-EARTH-ELEMENTS; PLASMA-MASS SPECTROMETRY; GERMAN WHITE WINES; ICP-MS; CONTAMINANT ELEMENTS; TRACE-ELEMENTS; ROMANIAN WINES; CLASSIFICATION; ORIGIN; PATTERN AB The control of geographic origin is one of a highest priority issue regarding traceability and wine authenticity. The current study aimed to examine whether elemental composition can be used for the discrimination of wines according to geographical origin, taking into account the effects of soil, winemaking process, and year of production. The elemental composition of soils, grapes, musts, and wines from three DO (Designations of Origin) and for two vintage years was determined by using the ICP-MS semi-quantitative method, followed by multivariate statistical analysis. The elemental composition of soils varied according to geological formations, and for some elements, the variation due to soil provenance was also observed in musts and wines. Li, Mn, Sr and rare-earth elements (REE) allowed wine discrimination according to vineyard. Results evidenced the influence of winemaking processes and of vintage year on the wine's elemental composition. The mineral composition pattern is transferred through the soil-wine system, and differences observed for soils are reflected in grape musts and wines, but not for all elements. Results suggest that winemaking processes and vintage year should be taken into account for the use of elemental composition as a tool for wine traceability. Therefore, understanding the evolution of mineral pattern composition from soil to wine, and how it is influenced by the climatic year, is indispensable for traceability purposes. C1 [Catarino, Sofia; de Sousa, Raul Bruno] Univ Lisbon, Inst Super Agron, LEAF Linking Landscape Environm Agr & Food, P-1349017 Lisbon, Portugal. [Catarino, Sofia; Caldeira, Ilda; Curvelo-Garcia, Antonio] INIAV, P-2565191 Dois Portos, Portugal. [Catarino, Sofia] Univ Lisbon, Inst Super Tecn, CEFEMA Ctr Phys & Engn Adv Mat, Av Rovisco Pais 1, P-1049001 Lisbon, Portugal. [Madeira, Manuel; Monteiro, Fernando] Univ Lisbon, Inst Super Agron, CEF Forest Res Ctr, P-1349017 Lisbon, Portugal. [Caldeira, Ilda] Univ Evora, ICAAM, Ap 94, P-7002554 Evora, Portugal. C3 Universidade de Lisboa; Instituto Nacional de Investigacao Agraria e Veterinaria, IP (INIAV); Universidade de Lisboa; Instituto Superior Tecnico; Universidade de Lisboa; Forest Research Centre; University of Evora RP Catarino, S (corresponding author), Univ Lisbon, Inst Super Agron, LEAF Linking Landscape Environm Agr & Food, P-1349017 Lisbon, Portugal.; Catarino, S (corresponding author), INIAV, P-2565191 Dois Portos, Portugal.; Catarino, S (corresponding author), Univ Lisbon, Inst Super Tecn, CEFEMA Ctr Phys & Engn Adv Mat, Av Rovisco Pais 1, P-1049001 Lisbon, Portugal. EM sofiacatarino@isa.ulisboa.pt; mavmadeira@isa.ulisboa.pt; fgmonteiro@isa.ulisboa.pt; ilda.caldeira@iniav.pt; brunosousa@isa.ulisboa.pt; ascurvelogarcia@gmail.com CR Almeida CMR, 2003, J AGR FOOD CHEM, V51, P4788, DOI 10.1021/jf034145b Augagneur S, 1996, J ANAL ATOM SPECTROM, V11, P713, DOI 10.1039/ja9961100713 Bertoldi D, 2011, J AGR FOOD CHEM, V59, P7224, DOI 10.1021/jf2006003 Cao XD, 2000, INT J ENVIRON AN CH, V76, P295, DOI 10.1080/03067310008034137 Catarino S, 2008, CIENC TEC VITIVINIC, V23, P3 Catarino S, 2010, CIENC TEC VITIVINIC, V25, P87 Catarino S., 2011, B IOIV, V84, P233 Catarino S, 2008, J AGR FOOD CHEM, V56, P158, DOI 10.1021/jf0720180 Catarino S, 2006, TALANTA, V70, P1073, DOI 10.1016/j.talanta.2006.02.022 Catarino S, 2006, J INT SCI VIGNE VIN, V40, P91 CMCE, 2007, CAN SOIL QUAL GUID P, P1 Coetzee PP, 2014, FOOD CHEM, V164, P485, DOI 10.1016/j.foodchem.2014.05.027 Coetzee PP, 2005, J AGR FOOD CHEM, V53, P5060, DOI 10.1021/jf048268n D'Antone C, 2017, ENVIRON MONIT ASSESS, V189, DOI 10.1007/s10661-017-5878-6 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Dinca OR, 2016, FOOD ANAL METHOD, V9, P2406, DOI 10.1007/s12161-016-0404-y Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 Fabani MP, 2009, J AGR FOOD CHEM, V57, P7409, DOI 10.1021/jf901572k Galgano F, 2008, LWT-FOOD SCI TECHNOL, V41, P1808, DOI 10.1016/j.lwt.2008.01.015 Geana I, 2013, FOOD CHEM, V138, P1125, DOI 10.1016/j.foodchem.2012.11.104 Gomez MDM, 2004, J AGR FOOD CHEM, V52, P2953, DOI 10.1021/jf035119g Gomez MDM, 2004, J AGR FOOD CHEM, V52, P2962, DOI 10.1021/jf035120f Gonzalvez A, 2009, FOOD CHEM, V112, P26, DOI 10.1016/j.foodchem.2008.05.043 Greenough J. D., 1997, Australian Journal of Grape and Wine Research, V3, P75, DOI 10.1111/j.1755-0238.1997.tb00118.x Greenough JD, 2005, GEOSCI CAN, V32, P129 Jakubowski N, 1999, FRESEN J ANAL CHEM, V364, P424, DOI 10.1007/s002160051361 Kaya AD, 2017, J AGR FOOD CHEM, V65, P4766, DOI 10.1021/acs.jafc.7b01510 Martin AE, 2012, FOOD CHEM, V133, P1081, DOI 10.1016/j.foodchem.2012.02.013 Martins P, 2014, J INT SCI VIGNE VIN, V48, P21 Medina B, 2000, FOOD ADDIT CONTAM, V17, P435, DOI 10.1080/02652030050034019 Medina B. J, 1990, J INT SCI VIGNE VIN, V24, P147 Minnaar P. P., 2005, South African Journal of Enology and Viticulture, V26, P95 Monteiro F. M. G, 2004, THESIS Moreira C, 2017, S AFR J ENOL VITIC, V38, P82 Nicolini G, 2004, VITIS, V43, P41 OIV, 2017, COMP INT METH WIN M OIV World Vitiviniculture Situation, 2016, OIV STAT REP WIN VIT Ribereau-Gayon P., 2006, HDB ENOLOGY CHEM WIN Rodrigues SM, 2011, J FOOD COMPOS ANAL, V24, P548, DOI 10.1016/j.jfca.2010.12.003 Rohlf F. J, 2000, NTSYS PC NUMERICAL T, P18 Volpe MG, 2009, FOOD CHEM, V117, P553, DOI 10.1016/j.foodchem.2009.04.033 Wilkes E, 2016, VITIC J, V31, P36 NR 43 TC 14 Z9 14 U1 4 U2 16 PD DEC PY 2018 VL 4 IS 4 AR 85 DI 10.3390/beverages4040085 WC Food Science & Technology SC Food Science & Technology UT WOS:000455154000015 DA 2022-12-14 ER PT J AU Hu, LQ Ma, S Yin, CL Liu, ZM AF Hu, Leqian Ma, Shuai Yin, Chunling Liu, Zhimin TI Quality evaluation and traceability of Bletilla striata by fluorescence fingerprint coupled with multiway chemometrics analysis SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE quality evaluation and traceability; B. striata; fluorescence fingerprint; multiway chemometrics ID CONSISTENCY EVALUATION; QUANTITATIVE-ANALYSIS; CLASSIFICATION; SPECTROSCOPY; PARAFAC; TOOL; IDENTIFICATION; COMBINATION; MATRICES; HERBS AB BACKGROUND Traditional methods of evaluating herbs were mainly based on chromatographic techniques. They usually included tedious sample preparation procedures, taking tens of minutes to hours, and consume solvents as well as standards for external calibration. In this paper, the feasibility of employing a fluorescence fingerprint coupled with multi-way chemometrics analysis for quality evaluation and traceability of Bletilla striata were investigated. RESULTS Relative concentrations of four markers presented in B. striata were determined by using a four-component self-weighted alternating trilinear decomposition (SWATLD) model. These markers could be applied to accurate classification and quality control of B. striata samples from different regions. Furthermore, multiway principal component analysis, multilinear partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA, and SWATLD-PLS-DA models were applied to classify the B. striata samples according to their geographic origins. Consistent results were obtained showing that B. striata samples could be successfully grouped based on their geographical origins and quality. CONCLUSION Our results revealed that the method developed can be used for quality evaluation and traceability of B. striata. Compared with the chromatographic methods, the method employed in this study was more convenient, simpler, and more sensitive. (c) 2018 Society of Chemical Industry C1 [Hu, Leqian; Ma, Shuai; Yin, Chunling; Liu, Zhimin] Henan Univ Technol, Coll Chem Chem & Environm Engn, Zhengzhou 450001, Henan, Peoples R China. C3 Henan University of Technology RP Hu, LQ (corresponding author), Henan Univ Technol, Coll Chem Chem & Environm Engn, Zhengzhou 450001, Henan, Peoples R China. EM leqianhu@163.com CR Airado-Rodriguez D, 2009, J AGR FOOD CHEM, V57, P1711, DOI 10.1021/jf8033623 Almalki AJ, 2016, PLANTA MED, V82, P1208, DOI 10.1055/s-0042-106170 Arceusz A, 2013, J PHARMACEUT BIOMED, V83, P215, DOI 10.1016/j.jpba.2013.05.020 Azcarate SM, 2015, FOOD CHEM, V184, P214, DOI 10.1016/j.foodchem.2015.03.081 Bro R, 2003, J CHEMOMETR, V17, P274, DOI 10.1002/cem.801 Chen XL, 2016, J PHARMACEUT BIOMED, V124, P281, DOI 10.1016/j.jpba.2016.02.016 Chen ZP, 2000, CHEMOMETR INTELL LAB, V52, P75, DOI 10.1016/S0169-7439(00)00081-2 Chinese Pharmacopoeia Commission, 2010, PHARMACOPOEIA PEOPLE, V2010 da Silva AC, 2016, ANAL CHIM ACTA, V938, P53, DOI 10.1016/j.aca.2016.08.009 Durante C, 2011, CHEMOMETR INTELL LAB, V106, P73, DOI 10.1016/j.chemolab.2010.09.004 El-Shazly M, 2016, J FOOD DRUG ANAL, V24, P29, DOI 10.1016/j.jfda.2015.09.001 Fan CL, 2013, J PHARMACEUT BIOMED, V84, P20, DOI 10.1016/j.jpba.2013.05.039 Guo DA, 2015, SCIENCE, V347, pS29 Hall GJ, 2005, ENVIRON SCI TECHNOL, V39, P7560, DOI 10.1021/es0503074 Hanafi M, 2015, COMPUT STAT DATA AN, V83, P129, DOI 10.1016/j.csda.2014.10.003 He XR, 2017, J ETHNOPHARMACOL, V195, P20, DOI 10.1016/j.jep.2016.11.026 Hu LQ, 2006, ANAL BIOANAL CHEM, V384, P1493, DOI 10.1007/s00216-006-0307-7 Kumar K, 2013, TALANTA, V117, P209, DOI 10.1016/j.talanta.2013.09.002 Lenhardt L, 2015, FOOD CHEM, V175, P284, DOI [10.1016/j.foodchem.2014.11.162, 10.1016/j.fo] Li T, 2018, SPECTROCHIM ACTA A, V204, P131, DOI 10.1016/j.saa.2018.06.004 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Lin CW, 2016, J NAT PROD, V79, P1911, DOI 10.1021/acs.jnatprod.6b00118 Markechova D, 2014, FOOD CHEM, V159, P193, DOI 10.1016/j.foodchem.2014.02.085 Morais CLM, 2017, CHEMOMETR INTELL LAB, V170, P1, DOI 10.1016/j.chemolab.2017.09.001 Morita H, 2005, BIOORG MED CHEM LETT, V15, P1051, DOI 10.1016/j.bmcl.2004.12.026 Okunji CO, 2002, PLANTA MED, V68, P440, DOI 10.1055/s-2002-32091 Pardo R, 2013, J HAZARD MATER, V262, P71, DOI 10.1016/j.jhazmat.2013.08.031 Rodriguez-Perez R, 2018, ANAL BIOANAL CHEM, V410, P5981, DOI 10.1007/s00216-018-1217-1 Salvatore E, 2013, COMP ANAL C, V60, P339, DOI 10.1016/B978-0-444-59562-1.00014-1 Sgorbini B, 2015, J CHROMATOGR A, V1376, P9, DOI 10.1016/j.chroma.2014.12.007 Sharma DK, 2016, J CHROMATOGR SCI, V54, P536, DOI 10.1093/chromsci/bmv182 Shieh AD, 2009, STAT APPL GENET MOL, V8, DOI 10.2202/1544-6115.1426 Tims M, 2016, J ALTERN COMPLEM MED, V22, P588, DOI 10.1089/acm.2016.29007.mjt Trivittayasil V, 2017, FOOD CHEM, V232, P523, DOI 10.1016/j.foodchem.2017.04.011 Xu QS, 2004, J CHEMOMETR, V18, P112, DOI 10.1002/cem.858 Zhang C, 2016, J CHROMATOGR SCI, V54, P1171, DOI 10.1093/chromsci/bmw046 Zhang L, 2012, J ETHNOPHARMACOL, V140, P519, DOI 10.1016/j.jep.2012.01.058 NR 37 TC 9 Z9 9 U1 6 U2 77 PD FEB PY 2019 VL 99 IS 3 BP 1413 EP 1424 DI 10.1002/jsfa.9344 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000456275900049 DA 2022-12-14 ER PT J AU Ramli, US Tahir, NI Rozali, NL Othman, A Muhammad, NH Muhammad, SA Tarmizi, AHA Hashim, N Sambanthamurthi, R Singh, R Abd Manaf, MA Parveez, GKA AF Ramli, Umi Salamah Tahir, Noor Idayu Rozali, Nurul Liyana Othman, Abrizah Muhammad, Nor Hayati Muhammad, Syahidah Akmal Tarmizi, Azmil Haizam Ahmad Hashim, Norfadilah Sambanthamurthi, Ravigadevi Singh, Rajinder Abd Manaf, Mohamad Arif Parveez, Ghulam Kadir Ahmad TI Sustainable Palm Oil-The Role of Screening and Advanced Analytical Techniques for Geographical Traceability and Authenticity Verification SO MOLECULES DT Review DE palm oil; sustainability; geographical traceability; adulteration; analytical techniques; chemical fingerprint; DNA fingerprint ID VIRGIN OLIVE OIL; FATTY-ACID-COMPOSITION; ADULTERATION DETECTION; GAS-CHROMATOGRAPHY; VOLATILE COMPOUNDS; ORIGIN; CLASSIFICATION; TRIACYLGLYCEROL; QUALITY; CRUDE AB Palm oil production from oil palm (Elaeis guineensisJacq.) is vital for the economy of Malaysia. As of late, sustainable production of palm oil has been a key focus due to demand by consumer groups, and important progress has been made in establishing standards that promote good agricultural practices that minimize impact on the environment. In line with the industrial goal to build a traceable supply chain, several measures have been implemented to ensure that traceability can be monitored. Although the palm oil supply chain can be highly complex, and achieving full traceability is not an easy task, the industry has to be proactive in developing improved systems that support the existing methods, which rely on recorded information in the supply chain. The Malaysian Palm Oil Board (MPOB) as the custodian of the palm oil industry in Malaysia has taken the initiative to assess and develop technologies that can ensure authenticity and traceability of palm oil in the major supply chains from the point of harvesting all the way to key downstream applications. This review describes the underlying framework related to palm oil geographical traceability using various state-of-the-art analytical techniques, which are also being explored to address adulteration in the global palm oil supply chain. C1 [Ramli, Umi Salamah; Tahir, Noor Idayu; Rozali, Nurul Liyana; Othman, Abrizah; Muhammad, Nor Hayati; Tarmizi, Azmil Haizam Ahmad; Hashim, Norfadilah; Sambanthamurthi, Ravigadevi; Singh, Rajinder; Abd Manaf, Mohamad Arif; Parveez, Ghulam Kadir Ahmad] 6 Persiaran Inst, Malaysian Palm Oil Board, Kajang 43000, Selangor, Malaysia. [Muhammad, Syahidah Akmal] Univ Sains Malaysia, Sch Ind Technol, USM, Analyt Biochem Res Ctr, George Town 11800, Penang, Malaysia. C3 Malaysian Palm Oil Board; Universiti Sains Malaysia RP Ramli, US (corresponding author), 6 Persiaran Inst, Malaysian Palm Oil Board, Kajang 43000, Selangor, Malaysia. EM umi@mpob.gov.my; idayu@mpob.gov.my; liyana@mpob.gov.my; abi@mpob.gov.my; ati@mpob.gov.my; syahidah.muhammad@usm.my; azmil_haizam@mpob.gov.my; dila@mpob.gov.my; ravigadevi@gmail.com; rajinder@mpob.gov.my; arifma@mpob.gov.my; parveez@mpob.gov.my CR Abd Majid R, 2012, J OIL PALM RES, V24, P1310 Abdullah R., 2010, WORLD PALM OIL SUPPL Alexandratos N, 2012, WORLD AGR 2030 2050, P12, DOI DOI 10.22004/AG.ECON.288998 [Anonymous], 1995, REP 14 SESS COD COMM [Anonymous], 2017, PALM OIL DEFORESTATI Aparicio R, 2013, FOOD RES INT, V54, P2025, DOI 10.1016/j.foodres.2013.07.039 Araghipour N, 2008, FOOD CHEM, V108, P374, DOI 10.1016/j.foodchem.2007.10.056 Aranda F, 2004, FOOD CHEM, V86, P485, DOI 10.1016/j.foodchem.2003.09.021 Ayat K.A.R., 2009, OIL PALM IND EC J, V9, P20 Azhar B, 2017, J ENVIRON MANAGE, V203, P457, DOI 10.1016/j.jenvman.2017.08.021 Azlan A, 2010, J FOOD COMPOS ANAL, V23, P772, DOI 10.1016/j.jfca.2010.03.026 Bat KB, 2016, FOOD CHEM, V203, P86, DOI 10.1016/j.foodchem.2016.02.039 Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 Ben Temime S, 2006, FOOD CHEM, V99, P315, DOI 10.1016/j.foodchem.2005.07.046 Benincasa C., 2012, 7 INT S OL GROW SAN Berger K.G., 2006, GLOBAL OIL FAT BUS M, P50 Bikrani S, 2019, FOODS, V8, DOI 10.3390/foods8110588 Breas O, 1998, RAPID COMMUN MASS SP, V12, P188, DOI 10.1002/(SICI)1097-0231(19980227)12:4<188::AID-RCM137>3.0.CO;2-7 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Canabate-Diaz B, 2007, FOOD CHEM, V102, P593, DOI 10.1016/j.foodchem.2006.05.038 Cavaliere B, 2007, J AGR FOOD CHEM, V55, P1454, DOI 10.1021/jf062929u Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chen T, 2018, INT J ANAL CHEM, V2018, DOI 10.1155/2018/3160265 Cheng XX, 2018, ARAB J CHEM, V11, P815, DOI 10.1016/j.arabjc.2017.12.025 Corley RHV, 2009, ENVIRON SCI POLICY, V12, P134, DOI 10.1016/j.envsci.2008.10.011 Crawford LM, 2020, FOOD CONTROL, V114, DOI 10.1016/j.foodcont.2020.107264 Da Ros A, 2019, MOLECULES, V24, DOI 10.3390/molecules24162896 de Rijke E, 2016, FOOD CHEM, V204, P122, DOI 10.1016/j.foodchem.2016.01.134 Dowell L., PALM OIL MILL DATA S Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Duijn G. van, 2013, Lipid Technology, V25, P15, DOI 10.1002/lite.201300251 Farah Khuwailah Ahmad Bustamam, 2019, Palm Oil Developments, P18 FLOR R V, 1989, Journal of the American Oil Chemists' Society, V66, P431 Francois G, 2020, PHYTOCHEMISTRY, V173, DOI 10.1016/j.phytochem.2020.112291 Garrido-Delgado R, 2011, ANAL CHIM ACTA, V696, P108, DOI 10.1016/j.aca.2011.03.007 Gee PT, 2007, EUR J LIPID SCI TECH, V109, P373, DOI 10.1002/ejlt.200600264 Ghazali H.H., 2019, P INT C ISL CIV TECH Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Goggin KA, 2018, OCL OILS FAT CROP LI, V25, DOI 10.1051/ocl/2018059 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 GORDON MH, 1992, FOOD CHEM, V43, P71, DOI 10.1016/0308-8146(92)90244-V Haddada FM, 2007, FOOD CHEM, V103, P467, DOI 10.1016/j.foodchem.2006.08.023 Hamdan K, 2015, T ASABE, V58, P227 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Inthiram Anand Kumar, 2015, Pertanika Journal of Tropical Agricultural Science, V38, P389 Jafari MT, 2007, ANAL CHIM ACTA, V581, P147, DOI 10.1016/j.aca.2006.08.005 Janin M, 2014, EUR FOOD RES TECHNOL, V239, P745, DOI 10.1007/s00217-014-2279-8 Jolayemi OS, 2018, FOOD SCI NUTR, V6, P773, DOI 10.1002/fsn3.614 Karabagias IK, 2014, FOOD CHEM, V146, P548, DOI 10.1016/j.foodchem.2013.09.105 Kumar PKP, 2014, GRASAS ACEITES, V65, DOI 10.3989/gya.097413 Kuntom A., 2003, OIL PALM B, V71, P15 Kushairi A, 2019, J OIL PALM RES, V31, P165, DOI 10.21894/jopr.2019.0026 Kushairi A, 2018, J OIL PALM RES, V30, P163, DOI 10.21894/jopr.2018.0030 Lazzez A, 2008, J AGR FOOD CHEM, V56, P982, DOI 10.1021/jf0722147 Lerma-Garcia MJ, 2008, FOOD CHEM, V107, P1307, DOI 10.1016/j.foodchem.2007.10.020 Li YunJing, 2018, Oil Crop Science, V3, P122 Liedtke S, 2018, FOOD CHEM, V255, P323, DOI 10.1016/j.foodchem.2018.01.193 Lim SY, 2018, INT J FOOD PROP, V21, P2428, DOI 10.1080/10942912.2018.1522332 Lucchetti S, 2018, FOOD CHEM, V245, P812, DOI 10.1016/j.foodchem.2017.11.107 Majchrzak T, 2018, FOOD CHEM, V246, P192, DOI 10.1016/j.foodchem.2017.11.013 Malaysian Palm Oil Board, 2005, MALAYSIAN PALM OIL B Malaysian Palm Oil Board [MPOB], 2020, MAL OIL PALM STAT 20 Man YBC, 2014, INT J FOOD PROP, V17, P354, DOI 10.1080/10942912.2011.631254 Man YBC, 2005, FOOD CHEM, V90, P829, DOI 10.1016/j.foodchem.2004.05.062 Marikkar JMN, 2002, FOOD CHEM, V76, P249, DOI 10.1016/S0308-8146(01)00257-6 Matos LC, 2007, FOOD CHEM, V102, P406, DOI 10.1016/j.foodchem.2005.12.031 May CY, 2014, EUR J LIPID SCI TECH, V116, P1301, DOI 10.1002/ejlt.201400076 Mba OI, 2015, FOOD BIOSCI, V10, P26, DOI 10.1016/j.fbio.2015.01.003 Meijaard E., 2018, OIL PALM BIODIVERSIT Menon NAINR., 2017, PALM OIL ENG B, V124, P11 Muhammad SA, 2018, SCI JUSTICE, V58, P59, DOI 10.1016/j.scijus.2017.05.008 Mukherjee I, 2014, RENEW SUST ENERG REV, V37, P1, DOI 10.1016/j.rser.2014.05.001 Nambiappan B, 2018, J OIL PALM RES, V30, P13, DOI 10.21894/jopr.2018.0014 Navia EA, 2014, EUR J PLANT PATHOL, V140, P711, DOI 10.1007/s10658-014-0491-9 Obisesan KA, 2017, TALANTA, V170, P413, DOI 10.1016/j.talanta.2017.04.035 Ollivier D, 2006, FOOD CHEM, V97, P382, DOI 10.1016/j.foodchem.2005.04.024 Ooi L C L, 2006, DETECTION DNA CRUDE Ortiz CML, 2006, J FOOD COMPOS ANAL, V19, P141, DOI 10.1016/j.jfca.2005.06.001 Othman A, 2019, BMC RES NOTES, V12, DOI 10.1186/s13104-019-4263-7 Park YW, 2010, FOOD CHEM, V123, P377, DOI 10.1016/j.foodchem.2010.04.049 Parveez GKA, 2020, J OIL PALM RES, V32, P159, DOI 10.21894/jopr.2020.0032 Perez-Castano E, 2015, ANAL METHODS-UK, V7, P4192, DOI [10.1039/C5AY00168D, 10.1039/c5ay00168d] Perez-Jimenez M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070507 Perri E, 2012, OLIVE GERMPLASM - THE OLIVE CULTIVATION, TABLE OLIVE AND OLIVE OIL INDUSTRY IN ITALY, P265, DOI 10.5772/51796 Piarulli L, 2019, FOODS, V8, DOI 10.3390/foods8100462 Portarena S, 2014, FOOD CHEM, V164, P12, DOI 10.1016/j.foodchem.2014.04.115 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Rabiei Z, 2012, OLIVE OIL - CONSTITUENTS, QUALITY, HEALTH PROPERTIES AND BIOCONVERSIONS, P163 Rajanaidu N., 1983, PORIM Bulletin, P9 Ramli U.S, 2019, MPOB INT PALM OIL C, P116 Rival A, 2016, OCL OILS FAT CROP LI, V23, DOI 10.1051/ocl/2016042 Ruiz-Samblas C, 2013, TALANTA, V116, P788, DOI 10.1016/j.talanta.2013.07.054 Sambanthamurthi R, 2000, PROG LIPID RES, V39, P507, DOI 10.1016/S0163-7827(00)00015-1 Schwolow S, 2019, ANAL BIOANAL CHEM, V411, P6005, DOI 10.1007/s00216-019-01978-w Sheng NgJing, 2019, Malaysian Journal of Analytical Sciences, V23, P870, DOI 10.17576/mjas-2019-2305-12 Tan Y.A, 1994, SELECTED READINGS PA, P45 Tres A, 2013, FOOD CHEM, V137, P142, DOI 10.1016/j.foodchem.2012.09.094 Tres A., 2011, OIL PALM CULTIVATION, P1, DOI DOI 10.3213/1612-1651-10072 Uncu AT, 2017, FOOD CHEM, V221, P1026, DOI 10.1016/j.foodchem.2016.11.059 Vichi S., 2010, OLIVES OLIVE OIL HLT von Geibler J, 2013, J CLEAN PROD, V56, P39, DOI 10.1016/j.jclepro.2012.08.027 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 WOODBURY SE, 1995, ANAL CHEM, V67, P2685, DOI 10.1021/ac00111a029 Yadav S., 2018, INT J CHEM STUD, V6, P1393 Zhang L, 2009, J AGR FOOD CHEM, V57, P7227, DOI 10.1021/jf901172d Zhang LX, 2014, J AGR FOOD CHEM, V62, P8745, DOI 10.1021/jf501097c Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 NR 107 TC 3 Z9 3 U1 3 U2 11 PD JUN PY 2020 VL 25 IS 12 AR 2927 DI 10.3390/molecules25122927 WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary SC Biochemistry & Molecular Biology; Chemistry UT WOS:000554645900001 DA 2022-12-14 ER PT J AU Lin, KY Chavalarias, D Panahi, M Yeh, T Takimoto, K Mizoguchi, M AF Lin, Kaiyuan Chavalarias, David Panahi, Maziyar Yeh, Tsaiching Takimoto, Kazuhiro Mizoguchi, Masaru TI Mobile-based traceability system for sustainable food supply networks SO NATURE FOOD DT Review ID ANIMAL IDENTIFICATION; CHAIN; TECHNOLOGIES; MANAGEMENT; BLOCKCHAIN; IMPLEMENTATION; AGRICULTURE; CHALLENGES; SAFETY; ENERGY AB Traceability is key to ensure food quality and safety from farm to fork, yet high implementation costs and the complexity of the food supply chain pose challenges to its operation. Here we propose a mobile-based bidirectional tracing system for food products that integrates graph data and peer-to-peer architecture. Our system allows data synchronization to happen seamlessly between all connected nodes, as data are gathered through market transactions and all related product information is concatenated by scanning 2D product barcodes. The system's decentralized and flexible structure favours stakeholder involvement and is applicable to various and dynamic food networks. By promoting resource efficiency and transparency of origin, production and distribution, the system ensures mesh surveillance and sheds light on complex food networks, ultimately contributing to the advancement of food research. Traceability is key to food quality and safety, but its wider implementation is hindered by high costs and technical complexity. A newly proposed mobile-based bidirectional system based on information concatenation through products' 2D barcodes offers an effective, cheaper and more flexible alternative. C1 [Lin, Kaiyuan; Mizoguchi, Masaru] Univ Tokyo, Grad Sch Agr & Life Sci, Lab Agroinformat, Bunkyo City, Tokyo, Japan. [Lin, Kaiyuan; Chavalarias, David; Panahi, Maziyar] CNRS, Complex Syst Inst Paris Ile de France, Paris, France. [Chavalarias, David] EHESS, Ctr Anal & Math Sociales, Paris, France. C3 University of Tokyo; Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Universite Paris Cite RP Lin, KY (corresponding author), Univ Tokyo, Grad Sch Agr & Life Sci, Lab Agroinformat, Bunkyo City, Tokyo, Japan.; Lin, KY (corresponding author), CNRS, Complex Syst Inst Paris Ile de France, Paris, France. EM rgpstu15@kmd.keio.ac.jp CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Caswell J. A., 1998, Agricultural and Resource Economics Review, V27, P151 Clemens R.L., 2015, IOWA AG REV, V9, P2 de Wildt, 2017, 2017112 WAG EC RES Disney WT, 2001, REV SCI TECH OIE, V20, P385, DOI 10.20506/rst.20.2.1277 Dubey A, 2016, PROC VLDB ENDOW, V9, P852, DOI 10.14778/2983200.2983202 Eyal I, 2016, 13TH USENIX SYMPOSIUM ON NETWORKED SYSTEMS DESIGN AND IMPLEMENTATION (NSDI '16), P45 Fan B., 2010, P ACM CONEXT, DOI [10.1145/1921168.1921182, DOI 10.1145/1921168.1921182] FAO, 2017, FOOD TRAC GUID Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Funabashi M, 2018, NPJ SCI FOOD, V2, DOI 10.1038/s41538-018-0026-4 Galvao JA, 2010, FOOD CONTROL, V21, P1360, DOI 10.1016/j.foodcont.2010.03.010 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gao JZ, 2007, P INT COMP SOFTW APP, P49 Golan E., 2004, 830 USDA Herrero M, 2017, LANCET PLANET HEALTH, V1, pE33, DOI [10.1016/s2542-5196(17)30007-4, 10.1016/S2542-5196(17)30007-4] Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Krause MJ, 2018, NAT SUSTAIN, V1, P711, DOI 10.1038/s41893-018-0152-7 Litke A, 2019, LOGISTICS-BASEL, V3, DOI 10.3390/logistics3010005 Lua EK, 2005, IEEE COMMUN SURV TUT, V7, P72, DOI 10.1109/COMST.2005.1610546 Macias T, 2008, SOC SCI QUART, V89, P1086, DOI 10.1111/j.1540-6237.2008.00566.x Mao DH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15081627 NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Pettitt RG, 2001, REV SCI TECH OIE, V20, P584, DOI 10.20506/rst.20.2.1299 Randrup M, 2008, FOOD CONTROL, V19, P1064, DOI 10.1016/j.foodcont.2007.11.005 Singh K., 2005, Proceedings of the 15th International Workshop on Network and Operating Systems Support for Digital Audio and Video. NOSSDAV 2005, P63, DOI 10.1145/1065983.1065999 Skinner C., 2016, VALUEWEB FINTECH FIR Tang YR, 2018, FINANCIAL CRYPTOGRAP, P308 Tian F, 2017, I C SERV SYST SERV M Tribis Youness, 2018, MATEC Web of Conferences, V200, DOI 10.1051/matecconf/201820000020 Truby J, 2018, ENERGY RES SOC SCI, V44, P399, DOI 10.1016/j.erss.2018.06.009 Verneau F., 2004, P 82 SEM EUR ASS AGR, P14 Viotti P, 2016, PROCEEDINGS OF THE 2ND WORKSHOP ON THE PRINCIPLES AND PRACTICE OF CONSISTENCY FOR DISTRIBUTED DATA, PAPOC 2016, DOI 10.1145/2911151.2911162 Vitiello DJ, 2001, REV SCI TECH OIE, V20, P598, DOI 10.20506/rst.20.2.1298 Wilson T., 1998, SUPPLY CHAIN MANAG I, V3, P127, DOI [10.1108/13598549810230831, DOI 10.1108/13598549810230831] Xu XW, 2016, 2016 13TH WORKING IEEE/IFIP CONFERENCE ON SOFTWARE ARCHITECTURE (WICSA), P182, DOI 10.1109/WICSA.2016.21 Zheng XY, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9224731 2020, NAT FOOD, V1, P383 2011, SMALLSC FISH MAN, P1, DOI DOI 10.1079/9781845936075.0000 NR 47 TC 7 Z9 7 U1 16 U2 41 PD NOV PY 2020 VL 1 IS 11 BP 673 EP + DI 10.1038/s43016-020-00163-y WC Food Science & Technology SC Food Science & Technology UT WOS:000607142400008 DA 2022-12-14 ER PT J AU Thakur, M Tveit, GM Vevle, G Yurt, T AF Thakur, Maitri Tveit, Guro Moen Vevle, Geir Yurt, Tufan TI A framework for traceability of hides for improved supply chain coordination SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Traceability; Information exchange; Supply chain coordination; Hides supply chain; RFID AB Hides are an important co-product of the meat processing industry which are further used in leather production. However, there is a lack of automated traceability systems in this industry. A traceability system can improve data capture and information exchange between the stakeholders of a hide supply chain which can further improve supply chain coordination. Such a system can be used to provide important feedback to the producers about handling practices on the farm as well as provide relevant product information to the customers. A traceability system for the Norwegian hides supply chain is proposed in this paper. Various data capture technologies including RFID, dot peening and laser engraving were tested in a pilot setting. Pilot tests showed that traceability from the farm to the hide processor is possible using the RFID enabled hide tags up to the tanning process. If the machine-readable requirement is not necessary, laser engraving can be used for traceability covering the entire supply chain including the tanning process. Costs and benefits of proposed technologies are presented. Security concerns related to the use of RFID tags are also discussed. C1 [Thakur, Maitri; Tveit, Guro Moen] SINTEF Ocean, Brattorkaia 17C, Trondheim, Norway. [Vevle, Geir] RFID Solut AS, Maskinveien 6, Stavanger, Norway. [Yurt, Tufan] Norilia AS, Loenveien 1, N-1747 Skjeberg, Norway. C3 SINTEF RP Thakur, M (corresponding author), SINTEF Ocean, Brattorkaia 17C, Trondheim, Norway. EM maitri.thakur@sintef.no CR Abraham D, 2014, HEALTH TECHNOL-GER, V4, P171, DOI 10.1007/s12553-014-0081-z Agrawal TK, 2018, INT J ADV MANUF TECH, V99, P2563, DOI 10.1007/s00170-018-2638-x Bandyopadhyay S., 2003, J INT CONSUMER MARKE, V15, P85, DOI 10.1300/J046v15n02_06 Blancou J, 2001, REV SCI TECH OIE, V20, P420 Carrier S., 2014, DETERMINANTS CONSUMP Cataldo A, 2016, INT J ADV MANUF TECH, V86, P3563, DOI 10.1007/s00170-016-8489-4 Clemens R.L., 2003, MEAT TRACEABILITY CO Cline E.L., 2019, CAN LEATHER GO GREEN CORDELL VV, 1991, J ACADEMY MARKETING, V19, P123, DOI DOI 10.1007/BF02726004 European Commission DC Enterprise, 2013, RES STUD FEAS LEATH Fenu Gianni, 2009, IECON 2009 - 35th Annual Conference of IEEE Industrial Electronics (IECON 2009), P2672, DOI 10.1109/IECON.2009.5415251 Germani M, 2015, PROC CIRP, V29, P227, DOI 10.1016/j.procir.2015.02.199 Gibson J., 2016, INDIVIDUAL HIDE SKIN Grande Eliana Tiba Gomes, 2013, JISTEM J.Inf.Syst. Technol. Manag., V10, P99 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Index box UK, 2016, HID PROD NORW Kalicharan H.D., 2014, INT BUS EC REJ, V13, P897, DOI [10.19030/iber.v13i5.8760, DOI 10.19030/IBER.V13I5.8760] Luca A., 2016, 6 INT C ADV MAT SYST Mousavi A., 2002, British Food Journal, V104, P7, DOI 10.1108/00070700210418703 Sparling D., 2004, FOOD TRACEABILITY UN Thornews, 2014, THORNEWS Zhang YY, 2019, PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019), P2172, DOI 10.1109/ITNEC.2019.8729408 NR 22 TC 5 Z9 7 U1 1 U2 13 PD JUL PY 2020 VL 174 AR 105478 DI 10.1016/j.compag.2020.105478 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000540218000014 DA 2022-12-14 ER PT J AU Liu, X Gong, WW Fu, ZT Xian, P Li, WG AF Liu Xue Gong Weiwei Fu Zetian Xian Peng Li Weiguang TI Traceability and IT: implications for the future international competitiveness and structure of China's vegetable sector SO NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH DT Article DE information technology; traceability; vegetable supply; China ID PRODUCE AB This paper identifies the key aspects of traceability for vegetable producers, wholesalers, processors, trade associations, government and food hygiene and safety authorities and outlines the core elements of modem IT-based vegetable traceability systems. The internal and external pressures for a traceability system and the barriers to adoption in China are explored in the context of its vegetable sector. It then highlights current developments in China and some of the barriers to adopting and implementing IT based traceability which would meet accepted international norms, thereby integrating the Chinese vegetable industry with prevalent global best practices. We conclude that the Chinese vegetable industry may lose much of its competitive advantage in world vegetable markets if it fails to introduce and enforce, traceability systems but that the costs of introduction may be beyond certain sections of the industry, thereby leading to a two-speed and two-tier vegetable sector. C1 [Fu Zetian; Xian Peng; Li Weiguang] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China. [Gong Weiwei] China Agr Univ, Coll Econ & Management, Beijing 100083, Peoples R China. [Liu Xue] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. C3 China Agricultural University; China Agricultural University; China Agricultural University RP Fu, ZT (corresponding author), China Agr Univ, Coll Engn, POB 209, Beijing 100083, Peoples R China. EM fzt.2007@yahoo.com.cn CR ABDULRAOUF UM, 1993, APPL ENVIRON MICROB, V59, P2364, DOI 10.1128/AEM.59.8.2364-2368.1993 Beuchat LR, 1996, J FOOD PROTECT, V59, P204, DOI 10.4315/0362-028X-59.2.204 Chang J., 2000, Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, Minnesota, USA, 16-19 July, 2000, P1 Chen Q, 2004, NUTR CYCL AGROECOSYS, V69, P51, DOI 10.1023/B:FRES.0000025293.99199.ff CLARKE PT, 1986, BIENN C AGR ENG, P40 CLARKE PT, 1992, AGRIMATION, V1, P183 CLIVER DO, 1987, INT J FOOD MICROBIOL, V4, P269, DOI 10.1016/0168-1605(87)90001-8 *COD AL COMM, 2002, CL200224FL COD AL CO *COD AL COMM, 2002, COD COORD COMM EUR 2 *COD AL COMM, 2002, CCFICS WORK GROUP TR FDA Centre for Food Safety and Applied Nutrition (CFSAN), 1998, GUID MIN MICR FOOD S GOLAN E, 2004, AER830USDAERS LANGAN J, 2000, FARM FOOD AUT, P34 Liu X., 2007, IATRC S CHIN AGR TRA Liu XM, 2003, J MOL RECOGNIT, V16, P23, DOI 10.1002/jmr.604 Miraglia M, 2004, FOOD CHEM TOXICOL, V42, P1157, DOI 10.1016/j.fct.2004.02.018 MONTANARI DJ, 1995, INVENTORS METHODS TR Mousavi A., 2002, British Food Journal, V104, P7, DOI 10.1108/00070700210418703 PILZ P, 2000, P 1 INT MEAT AUT C, P49 Powell DA, 2002, J FOOD PROTECT, V65, P918, DOI 10.4315/0362-028X-65.6.918 Tauxe R, 1997, J FOOD PROTECT, V60, P1400, DOI 10.4315/0362-028X-60.11.1400 Viaene J., 1998, SUPPLY CHAIN MANAG I, V3, P139 Wilson T. P., 1998, SUPPLY CHAIN MANAG, V3, P127 Zhou P, 2004, CHEMOSPHERE, V57, P1691, DOI 10.1016/j.chemosphere.2004.06.025 NR 24 TC 6 Z9 6 U1 0 U2 16 PD DEC PY 2007 VL 50 IS 5 BP 911 EP 917 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000258308200047 DA 2022-12-14 ER PT J AU Smith, J AF Smith, Julia TI Coffee Landscapes: Specialty Coffee, Terroir, and Traceability in Costa Rica SO CULTURE AGRICULTURE FOOD AND ENVIRONMENT DT Article DE specialty coffee market; Costa Rica; traceability; landscapes; terroir AB Coffee with the right traits can command impressive prices in the specialty coffee market. While much of the attention to terroirthe taste of placein coffee has focused on region, the details of place matter too. For farmers, paying attention to the details of coffee landscapes is important to create the distinctive flavors prized in these markets. This article explores how farmers in the Tarrazu coffee region of Costa Rica use systems of traceability based on details of the landscape and the coffee that grows in it to create coffee that tastes of something. Instead of seeing traceability and terroir as different kinds of production with different logic, these farmers see traceability as a key piece of terroir-based high-value production. C1 [Smith, Julia] Eastern Washington Univ, Geog & Anthropol, Cheney, WA 99004 USA. C3 Eastern Washington University RP Smith, J (corresponding author), Eastern Washington Univ, Geog & Anthropol, Cheney, WA 99004 USA. CR Alliance for Coffee Excellence (ACE), 2015, GLOB ALL COFF LOV DE [Anonymous], 2002, BRAND MANAGEMENT, DOI DOI 10.1057/PALGRAVE.BM.2540076 Bender B, 2002, CURR ANTHROPOL, V43, pS103, DOI 10.1086/339561 Bender B., 2006, HDB MAT CULTURE, P303, DOI DOI 10.4135/9781848607972 Besky Sarah, 2014, KARATIRTHA Black RE., 2013, WINE CULTURE VINEYAR Bowen S., 2015, DIVIDED SPIRITS TEQU Coff C, 2008, INT LIBR ENVIRON AGR, V15, P1, DOI 10.1007/978-1-4020-8524-6 CYCON DEAN, 2007, JAVATREKKER DISPATCH Daynes S., 2013, WINE CULTURE VINEYAR, P15 Demossier M, 2011, J ROY ANTHROPOL INST, V17, P685, DOI 10.1111/j.1467-9655.2011.01714.x Hacienda la Esmeralda, 2014, ESM SPEC Hunevan Michelle, 1989, CALIFORNIA MAGAZINE, V14, P158 Kinro G., 2003, CUP ALOHA KONA COFFE Krissoff Barry, 2004, TRACEABILITY US FOOD Leung Leonard, 2014, ERODED COFFEE TRACEA Levy D, 2016, J MANAGE STUD, V53, P364, DOI 10.1111/joms.12144 Macdonald K, 2007, THIRD WORLD Q, V28, P793, DOI 10.1080/01436590701336663 Manzo J, 2010, HUM STUD, V33, P141, DOI 10.1007/s10746-010-9159-4 Meneley A, 2007, AM ANTHROPOL, V109, P678, DOI [10.1525/aa.2007.109.4.678, 10.1525/AA.2007.109.4.678] Mumbi Mungai Karen, 2013, THESIS Neilson J, 2008, WORLD DEV, V36, P1607, DOI 10.1016/j.worlddev.2007.09.005 Neumann RP, 2011, PROG HUM GEOG, V35, P843, DOI 10.1177/0309132510390870 Paxson Heather., 2013, LIFE CHEESE CRAFTING Raynolds LT, 2009, WORLD DEV, V37, P1083, DOI 10.1016/j.worlddev.2008.10.001 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Reichman Daniel, 2014, FOOD ACTIVISM AGENCY, P159 Roseberry W, 1996, AM ANTHROPOL, V98, P762, DOI 10.1525/aa.1996.98.4.02a00070 Sandi Morales Jose A., 2007, REV HISTORIQUE, P99 Schnell S.M., 2014, GEOGRAPHY BEER REGIO, P167 Sick Deborah, 1999, FARMERS GOLDEN BEAN Specialty Coffee Association of America SCAA, 2015, CUPP PROT Specialty Coffee Association of America (SCAA), 2015, SCAA STAND Topik Steven., 2006, SILVER COCAINE LATIN, P118 Trubek A., 2008, TASTE PLACE CULTURAL Weissman Michaele, 2011, GOD CUP OBSESSIVE QU West Paige, 2006, CONSERVATION IS OUR, DOI 10.1215/9780822388067 NR 37 TC 13 Z9 13 U1 4 U2 24 PD JUN PY 2018 VL 40 IS 1 BP 36 EP 44 DI 10.1111/cuag.12103 WC Agricultural Economics & Policy SC Agriculture UT WOS:000434218700005 DA 2022-12-14 ER PT J AU Bollen, AF Ridena, CP Cox, NR AF Bollen, A. F. Ridena, C. P. Cox, N. R. TI Agricultural supply system traceability, Part 1: Role of packing procedures and effects of fruit mixing SO BIOSYSTEMS ENGINEERING DT Article AB Traceability is becoming an integral component of modern agricultural supply chains. Higher-precision traceability and finer granularity of identifiable units of product offer the opportunity to add value to the conventional track and trace information in terms of improved feedback to producers and benefits to supply system efficiency. The packhouse is the major transformer of identifiable units in a horticultural supply system and is the only source of information on these transformations. The major influences on the precision of traceability possible through a packhouse are mixing in the infeed system to the grader, mixing in the packing system and the splitting of fruit stream to different packing outlets. A mixing model has been developed that is able to assign the probabilities of bin origin to individual fruit at the point they are packed into their final packs. In-feed mixing is essentially a mechanical process dependent on both packhouse design and operation. Simple design modification can significantly reduce fruit mixing and improve traceability. Packing lane mixing is a function of both mechanical design and operator factors. Traceability is not a definitive judgement, but a variable and statistical management process with inherent uncertainty. The research suggests there is potential to implement high-precision and fine granularity traceability in the agricultural supply system. (c) 2007 Published by Elsevier Ltd. C1 [Bollen, A. F.; Ridena, C. P.] Lincoln Ventures Ltd, Supply Chain Syst Grp, Hamilton, NZ, New Zealand. [Cox, N. R.] AgResearch, Hamilton, NZ, New Zealand. C3 AgResearch - New Zealand RP Bollen, AF (corresponding author), Lincoln Ventures Ltd, Supply Chain Syst Grp, Private Bag 3062, Hamilton, NZ, New Zealand. EM bollen@lvl.co.nz CR BOLLEN AF, 2004, ACTA HORTIC, V687, P279 *CLUB BOL, 2002, CIGR EJOURNAL, V4, P1 Hobbs J.E., 2004, AGRIBUSINESS, V20, P341 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 PRAAT JP, 2003, 3 INT MULT C 6 9 JUL, V604, P377 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 SARIG Y, 2003, CIGR EJOURNAL, V4, P1 ZASKE J, 2003, CIGR EJOURNAL, V5, P1 NR 10 TC 53 Z9 63 U1 0 U2 26 PD DEC PY 2007 VL 98 IS 4 BP 391 EP 400 DI 10.1016/j.biosystemseng.2007.07.011 WC Agricultural Engineering; Agriculture, Multidisciplinary SC Agriculture UT WOS:000252270200003 DA 2022-12-14 ER PT J AU Moysiadis, T Spanaki, K Kassahun, A Klaser, S Becker, N Alexiou, G Zotos, N Karali, I AF Moysiadis, Theocharis Spanaki, Konstantina Kassahun, Ayalew Klaeser, Sabine Becker, Nicolas Alexiou, George Zotos, Nikolaos Karali, Iliada TI AgriFood supply chain traceability: data sharing in a farm-to-fork case SO BENCHMARKING-AN INTERNATIONAL JOURNAL DT Article; Early Access DE SC traceability; AgriFood SC; Internet of things; Smart farming; Data sharing; EPCIS ID DESIGN SCIENCE; BIG DATA; FOOD; FRAMEWORK; SUSTAINABILITY; IMPLEMENTATION; COLLABORATION; MANAGEMENT; INTERNET; SYSTEMS AB Purpose Traceability of food is of paramount importance to the increasingly sustainability-conscious consumers. Several tracking and tracing systems have been developed in the AgriFood sector in order to prove to the consumers the origins and processing of food products. Critical challenges in realizing food's traceability include cooperating with multiple actors on common data sharing standards and data models. Design/methodology/approach This research applies a design science approach to showcase traceability that includes preharvest activities and conditions in a case study. The authors demonstrate how existing data sharing standards can be applied in combination with new data models suitable for capturing transparency information about plant production. Findings Together with existing studies on farm-to-fork transparency, our results demonstrate how to realize transparency from field to fork and enable producers to show a complete bill of sustainability. Originality/value The existing standards and data models address transparency challenges in AgriFood chains from the moment of harvest up to retail (farm-to-fork) relatively well, but not what happens before harvest. In order to address sustainability concerns, there is a need to collect data about production activities related to product quality and sustainability before harvesting and share it downstream the supply chain. The ability to gather data on sustainability practices such as reducing pesticide, herbicide, fertilizer and water use are crucial requirements for producers to market their produce as quality and sustainable products. C1 [Moysiadis, Theocharis; Alexiou, George; Zotos, Nikolaos] Future Intelligence Ltd, Athens, Greece. [Spanaki, Konstantina] Audencia Business Sch, Nantes, France. [Kassahun, Ayalew] Wageningen Univ, Wageningen, Netherlands. [Klaeser, Sabine] GS1 Germany, Cologne, Germany. [Becker, Nicolas] European EPC Competence Ctr GmbH, Neuss, Germany. [Karali, Iliada] GS1 Greece, Athens, Greece. C3 Audencia; Wageningen University & Research RP Spanaki, K (corresponding author), Audencia Business Sch, Nantes, France. EM kspanaki@audencia.com CR Anastasiadis F., 2014, AGR ENG INT CIGR J, DOI [10.1108/01443570610672220, DOI 10.1108/01443570610672220] [Anonymous], EPCIS CORE BUSINESS [Anonymous], TRACEABILITY CASE ST [Anonymous], GS1 ID KEYS Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Braziotis C, 2013, SUPPLY CHAIN MANAG, V18, P644, DOI 10.1108/SCM-07-2012-0260 Corallo A., 2018, INT J NUTR FOOD ENG, V12, P146, DOI DOI 10.5281/zenodo.1316618 Corallo A, 2020, INT J PROD RES, V58, P4789, DOI 10.1080/00207543.2020.1771455 Dentoni D, 2011, INT FOOD AGRIBUS MAN, V14, P83 Despoudi S, 2018, INT J PROD RES, V56, P4396, DOI 10.1080/00207543.2018.1440654 Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002 Garcia-Torres S, 2019, SUPPLY CHAIN MANAG, V24, P85, DOI 10.1108/SCM-04-2018-0152 Hevner A, 2010, INTEGR SER INFORM SY, V22, P9, DOI 10.1007/978-1-4419-5653-8_2 Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625 Jaffee S., 2008, RAPID AGR SUPPLY CHA, V47, P1 Kaloxylos A, 2012, COMPUT ELECTRON AGR, V89, P130, DOI 10.1016/j.compag.2012.09.002 Kamilaris A, 2017, COMPUT ELECTRON AGR, V143, P23, DOI 10.1016/j.compag.2017.09.037 Kittipanya-ngam P, 2020, PROD PLAN CONTROL, V31, P158, DOI 10.1080/09537287.2019.1631462 Kruize JW, 2016, COMPUT ELECTRON AGR, V125, P12, DOI 10.1016/j.compag.2016.04.011 Li F, 2016, PROD PLAN CONTROL, V27, P514, DOI 10.1080/09537287.2016.1147096 Linaza MT, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11061227 Moazzam M, 2018, PROD PLAN CONTROL, V29, P1258, DOI 10.1080/09537287.2018.1522847 Montecchi M, 2021, INT J PROD ECON, V238, DOI 10.1016/j.ijpe.2021.108152 Moysiadis T., 2021, BIOECONOMY AGRI PROD, P175 Nukala Revathi, 2016, 2016 27th Irish Signals and Systems Conference (ISSC), DOI 10.1109/ISSC.2016.7528456 O'Grady M. J., 2017, Information Processing in Agriculture, V4, P179, DOI 10.1016/j.inpa.2017.05.001 O'Keefe R, 2014, J OPER RES SOC, V65, P673, DOI 10.1057/jors.2012.175 O'Keefe RM, 2016, EUR J OPER RES, V249, P899, DOI 10.1016/j.ejor.2015.09.027 Patwa N, 2021, J BUS RES, V122, P725, DOI 10.1016/j.jbusres.2020.05.015 Pham X, 2018, BUS HORIZONS, V61, P125, DOI 10.1016/j.bushor.2017.09.011 Rogerson M, 2020, SUPPLY CHAIN MANAG, V25, P601, DOI 10.1108/SCM-08-2019-0300 Sodhi MS, 2019, PROD OPER MANAG, V28, P2946, DOI 10.1111/poms.13115 Sony M, 2020, PROD PLAN CONTROL, V31, P799, DOI 10.1080/09537287.2019.1691278 Spanaki K, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2021.102350 Spanaki K, 2022, PROD PLAN CONTROL, V33, P1498, DOI 10.1080/09537287.2021.1882688 Spanaki K, 2022, ANN OPER RES, V308, P491, DOI 10.1007/s10479-020-03922-z Standards and services, 2021, GS1 STAND WORK STAND Tsolakis N, 2019, AGRONOMY-BASEL, V9, DOI 10.3390/agronomy9070403 Tsolakis NK, 2014, BIOSYST ENG, V120, P47, DOI 10.1016/j.biosystemseng.2013.10.014 Verdouw C.N., 2017, INT TRICONFERENCE PR, DOI [10.5281/zenodo.1002903, DOI 10.5281/ZENODO.1002903] Villa-Henriksen A, 2020, BIOSYST ENG, V191, P60, DOI 10.1016/j.biosystemseng.2019.12.013 Vlachos IP, 2008, INT J LOGIST-RES APP, V11, P267, DOI 10.1080/13675560701768517 Vlajic JV, 2018, PROD PLAN CONTROL, V29, P522, DOI 10.1080/09537287.2018.1449264 Wolfert S, 2017, AGR SYST, V153, P69, DOI 10.1016/j.agsy.2017.01.023 Wolfert S, 2014, ANN SRII GLOB CONF, P266, DOI 10.1109/SRII.2014.47 World Bank, SMALL MEDIUM ENTERPR Yamoah FA, 2022, INT J PROD ECON, V244, DOI 10.1016/j.ijpe.2021.108388 Zissis D, 2017, TRANSP RES PROC, V22, P588, DOI 10.1016/j.trpro.2017.03.048 NR 48 TC 0 Z9 0 U1 3 U2 3 DI 10.1108/BIJ-01-2022-0006 EA AUG 2022 WC Management SC Business & Economics UT WOS:000836694000001 DA 2022-12-14 ER PT J AU Aparicio-Ruiz, R Tena, N Garcia-Gonzalez, DL AF Aparicio-Ruiz, Ramon Tena, Noelia Garcia-Gonzalez, Diego L. TI An International Survey on Olive Oils Quality and Traceability: Opinions from the Involved Actors SO FOODS DT Article DE olive oil; survey; traceability; quality; protected designation of origin; best before; sensory assessment ID GEOGRAPHICAL ORIGIN; HARVEST YEAR; VIRGIN; LABELS; PREFERENCES; CULTIVAR; ANTIOXIDANT; KNOWLEDGE; GREEK AB A survey was launched to understand the current problems and sensitivities of the olive oil market through a series of questions clustered around topics related to quality, traceability, regulation, standard methods and other issues. The questions were selected after a series of interviews with different actors to identify those aspects where some disagreement or different points of view may exist. These questions were grouped in topics such as geographical traceability, consumer perception and quality management. The survey was addressed to eight different olive oil actors independently: producers, retailers, importers, exporters, analysts, workers at regulatory bodies, and consumers. Approximately half of the respondents (67.0% for consumers and 56.0% for the rest of olive oil actors) claimed to understand the importance of the protected designation of origin. In fact, the traceability objectives that were selected as the most relevant were those related with geographical traceability (19.3%) followed by the detection of adulteration (15.6%). Most of the respondents (80%) would agree to share data for a common database; however, some concerns exist about the use of these data and the issue of paying to have access to this database. The respondents mostly expressed an affirmative answer concerning the efficiency of panel test (74%) and a negative answer (90%) concerning the proposal of removing from regulation, although 42% agree with their revision for improvement. The opinions on "best before" date and their relationship with quality and the willingness to apply non-targeted methods were also surveyed. C1 [Aparicio-Ruiz, Ramon; Tena, Noelia] Univ Seville, Fac Farm, Dept Quim Analit, Prof Garcia Gonzalez 2, Seville 41012, Spain. [Garcia-Gonzalez, Diego L.] Campus Univ Pablo Olavide, Inst Grasa, CSIC, Edificio 46,Ctra Utrera,Km 1, Seville 41013, Spain. C3 University of Sevilla; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de la Grasa (IG); Universidad Pablo de Olavide RP Garcia-Gonzalez, DL (corresponding author), Campus Univ Pablo Olavide, Inst Grasa, CSIC, Edificio 46,Ctra Utrera,Km 1, Seville 41013, Spain. EM aparicioruiz@us.es; ntena@us.es; dlgarcia@ig.csic.es CR Romo-Munoz RA, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0184585 Angerosa F., 2013, HDB OLIVE OIL ANAL P, V2nd ed., P523, DOI [10.1007/978-1-4614-7777-8_14, DOI 10.1007/978-1-4614-7777-8_14] [Anonymous], 2021, CONS VERS 26 05 2021 [Anonymous], 1991, OFFIC J EUR COMMUN, VL248, P1 Aparicio-Ruiz R, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201900202 Aparicio-Ruiz R, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201800133 Aparicio-Ruiz R, 2014, J AGR FOOD CHEM, V62, P554, DOI 10.1021/jf405220d Aprile MC, 2012, INT J CONSUM STUD, V36, P158, DOI 10.1111/j.1470-6431.2011.01092.x Ballco P, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101999 Bendini A, 2007, MOLECULES, V12, P1679, DOI 10.3390/12081679 Bernabeu R, 2016, SPAN J AGRIC RES, V14, DOI 10.5424/sjar/2016144-10200 Cafarelli B., 2017, RIV STUDI SOSTENIBIL, V1, P203, DOI [10.3280/RISS2017-001013, DOI 10.3280/RISS2017-001013] Carlucci D, 2014, BRIT FOOD J, V116, P1600, DOI 10.1108/BFJ-05-2013-0138 Cicerale S, 2012, CURR OPIN BIOTECH, V23, P129, DOI 10.1016/j.copbio.2011.09.006 Dekhili S, 2011, FOOD QUAL PREFER, V22, P757, DOI 10.1016/j.foodqual.2011.06.005 Del Giudice T, 2015, AGR FOOD EC, DOI [10.1186/s40100-015-0034-5, DOI 10.1186/S40100-015-0034-5] Di Lecce G, 2020, FOODS, V9, DOI 10.3390/foods9070904 DICHTER E, 1962, HARVARD BUS REV, V40, P113 Duman S, 2008, ACTA HORTIC, P747, DOI 10.17660/ActaHortic.2008.791.113 EC European Commission, 2010, GUID IMPL ART 11 12 Erraach Y, 2014, NEW MEDIT, V13, P11 European Commission (EC),, 66 COM EC Fotopoulos C, 2001, J INT FOOD AGRIBUS M, V12, P1, DOI 10.1300/J047v12n01_01 Fraga H, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11010056 Garcia-Gonzalez DL, 2017, GRASAS ACEITES, V68, DOI 10.3989/gya.0446171 Garcia-Gonzalez D.L., 2018, FOOD INTEGRITY HDB G, P335, DOI [10.32741/fihb.18.oliveoil, DOI 10.32741/FIHB.18.OLIVEOIL] Genovese A, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11041639 Giuffre AM, 2014, GRASAS ACEITES, V65, DOI 10.3989/gya.073913 Giuffre AM, 2013, EUR J LIPID SCI TECH, V115, P928, DOI 10.1002/ejlt.201200390 Giuffre AM, 2013, EUR J LIPID SCI TECH, V115, P549, DOI 10.1002/ejlt.201200235 Guney O. I., 2017, Agro Food Industry hi-tech, V28, P56 International Olive Council (IOC), 2021, WORLD OL OIL TABL OL IOC International Olive Council, 2018, COIT20DOC IOC IOC International Olive Council Trade Standard Applying to Olive Oils and Olive Pomace Oils, 2021, COIT15NC IOC Jimenez-Lopez C, 2020, FOODS, V9, DOI 10.3390/foods9081014 Lobo-Prieto A, 2020, FOODS, V9, DOI 10.3390/foods9121846 Lobo-Prieto A, 2020, MOLECULES, V25, DOI 10.3390/molecules25071686 Mansouri F, 2018, EMIR J FOOD AGR, V30, P549, DOI 10.9755/ejfa.2018.v30.i7.1738 Marakis G, 2021, NUTRIENTS, V13, DOI 10.3390/nu13113709 Martin-Pelaez S, 2013, MOL NUTR FOOD RES, V57, P760, DOI 10.1002/mnfr.201200421 Menapace L, 2011, EUR REV AGRIC ECON, V38, P193, DOI 10.1093/erae/jbq051 Mili S., 2016, International Journal on Food System Dynamics, V7, P311 Ollivier D, 2003, J AGR FOOD CHEM, V51, P5723, DOI 10.1021/jf034365p Panico T., 2014, Agricultural Economics Review, V15, P100 Perito MA, 2019, J FOOD PROD MARK, V25, P462, DOI 10.1080/10454446.2019.1582395 Romero N, 2016, J SCI FOOD AGR, V96, P583, DOI 10.1002/jsfa.7127 Salazar-Ordonez M, 2018, DATA BRIEF, V18, P1750, DOI 10.1016/j.dib.2018.04.084 Santosa M, 2010, FOOD QUAL PREFER, V21, P881, DOI 10.1016/j.foodqual.2010.05.011 Scarpa R., 2004, Journal of Agricultural & Food Industrial Organization, V2, P7, DOI 10.2202/1542-0485.1080 Schwingshackl L, 2020, BRIT J PHARMACOL, V177, P1241, DOI 10.1111/bph.14778 Skiada V, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10196733 Tena N., FOOD TRACEABILITY AU, P232, DOI [10.1201/9781351228435, DOI 10.1201/9781351228435] Tena N, 2015, J AGR FOOD CHEM, V63, P4509, DOI 10.1021/jf5062265 Vargas AJ, 2016, AM J EPIDEMIOL, V184, P23, DOI 10.1093/aje/kwv304 Virruso C, 2014, REJUV RES, V17, P217, DOI 10.1089/rej.2013.1532 Visioli F, 2020, BRIT J PHARMACOL, V177, P1316, DOI 10.1111/bph.14782 Wang Shiqian, 2013, IEEE Int Conf Rehabil Robot, V2013, P6650381, DOI 10.1109/ICORR.2013.6650381 NR 57 TC 0 Z9 0 U1 2 U2 2 PD APR PY 2022 VL 11 IS 7 AR 1045 DI 10.3390/foods11071045 WC Food Science & Technology SC Food Science & Technology UT WOS:000782099100001 DA 2022-12-14 ER PT J AU Manikas, I Manos, B AF Manikas, Ioannis Manos, Basil TI Design of an integrated supply chain model for supporting traceability of dairy products SO INTERNATIONAL JOURNAL OF DAIRY TECHNOLOGY DT Article DE Computer modelling; Traceability; Supply chain management ID MANUFACTURE AB An efficient traceability system must follow some rules that define which data must be gathered and stored in each stage of the supply chain. This is achieved by data standardization and typification of the messages that enable storing and communication of the data. By establishing and modelling these concepts, we developed a model that supports traceability in the food supply chain. The reference model presented in this paper consists of three distinct phases that represent stages of real-life supply chains, and is the basis for the development of a web application for traceability management in the dairy sector. C1 [Manikas, Ioannis; Manos, Basil] Aristotle Univ Thessaloniki, Sch Agr, GR-54006 Thessaloniki, Greece. C3 Aristotle University of Thessaloniki RP Manikas, I (corresponding author), Aristotle Univ Thessaloniki, Sch Agr, GR-54006 Thessaloniki, Greece. EM imanikas@agro.auth.gr CR Arvanitoyannis IS, 2005, INT J FOOD SCI TECH, V40, P1021, DOI 10.1111/j.1365-2621.2005.01113.x BOOCH G, 1998, UML XML SCHEMA MAPPI BROCK L, 2001, WH003 AUT ID Conrad R., 2000, P INT CONC MOD C US, P309 DAIVES C, 2004, SUPPLY CHAIN EUR JUN European Commission, 2000, WHIT PAP FOOD SAF FOLINAS D, 2003, P 10 INT C HUM COMP, P699 Folinas D., 2003, P EFITA 2003 C HUNG, V1, P143 *FOOD STAND AF, 2002, TRAC FOOD CHAIN PREL Golan E.H., 2004, AGR EC REPORTS, P1362 Hobbs JE, 2006, WAG UR FRON, V15, P87, DOI 10.1007/1-4020-4693-6_7 *IFSQN, 2008, 220052007 IFSQN ISO *ISO, 2005, TRAC FEED FOOD CHAIN Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Karkkainen M., 2003, INT J RETAIL DISTRIB, V31, P529, DOI DOI 10.1108/09590550310497058 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 KNIGHT C, 2002, KEY TOPICS FOOD SCI, V5, P91 LAURENT S, 1999, XML PRIMER Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 NOY N, 2000, ONTOLOGY DEV, V101 QUIN L, 2007, EXTENSIBLE MARKUP LA Rabade L.A., 2006, J PURCH SUPPLY MANAG, V12, p39?50, DOI DOI 10.1016/J.PURSUP.2006.02.003 *RAT, 2000, MIGR XML DTD XML SCH Routledge N., 2002, C RES PRACT INF TECH, V5 Salin V., 1998, INT FOOD AGRIBUS MAN, V1, P329, DOI [10.1016/S1096-7508(99)80003-2, DOI 10.1016/S1096-7508(99)80003-2] Van Dorp CA., 2003, P EFITA 2003 C, P280 VANDERVORST JGA, 2004, 6 INT C CHAIN NETW M Verdenius F, 2006, WOODHEAD PUBL FOOD S, P26, DOI 10.1533/9781845691233.1.26 Wilson T. P., 1998, SUPPLY CHAIN MANAG, V3, P127 NR 30 TC 13 Z9 14 U1 3 U2 29 PD FEB PY 2009 VL 62 IS 1 BP 126 EP 138 DI 10.1111/j.1471-0307.2008.00444.x WC Food Science & Technology SC Food Science & Technology UT WOS:000262470900020 DA 2022-12-14 ER PT J AU Varavallo, G Caragnano, G Bertone, F Vernetti-Prot, L Terzo, O AF Varavallo, Giuseppe Caragnano, Giuseppe Bertone, Fabrizio Vernetti-Prot, Luca Terzo, Olivier TI Traceability Platform Based on Green Blockchain: An Application Case Study in Dairy Supply Chain SO SUSTAINABILITY DT Article DE traceability platform; green blockchain; sustainability; dairy industry; supply chain ID SYSTEM; TECHNOLOGY; CHALLENGES; PRODUCTS AB Recent progress in IoT and software development has simplified data acquisition and immutability of information in the agri-food supply chain. In the last few years, several frameworks and applications were proposed to ensure traceability in the agri-food-sector using distributed ledger technologies (DLT) such as Blockchain technologies. Still, no other study has presented a Blockchain-based traceability platform with a lower impact on the environment and lower cost for each transaction sent by the supply chain. This article presents a traceability platform based on Green Blockchain with low energy consumption and costs savings applied to the Fontina PDO cheese supply chain, part of the project "Typicalp", funded by the European Union (EU). The proposed traceability system is based on Algorand Blockchain, which uses the Pure Proof-of-Stake mechanism of consensus that requires minimal computational power, is highly scalable and environmentally sustainable. In addition to the environmental and financial benefits, the developed traceability platform has made it possible to digitize the entire production chain, making the data immutable and available in real-time for Fontina consortium operators and final consumers. C1 [Varavallo, Giuseppe; Caragnano, Giuseppe; Bertone, Fabrizio; Terzo, Olivier] LINKS Fdn, Via Boggio 61, I-10138 Turin, Italy. [Vernetti-Prot, Luca] Inst Agr Reg IAR, Reg Rochere 1, I-11100 Aosta, Italy. RP Varavallo, G (corresponding author), LINKS Fdn, Via Boggio 61, I-10138 Turin, Italy. EM giuseppe.varavallo@linksfoundation.com; giuseppe.caragnano@linksfoundation.com; fabrizio.bertone@linksfoundation.com; l.vernetti@iaraosta.it; olivier.terzo@linksfoundation.com CR Adithela S., 2018, P IEEE, P1 Alturki M., 2019, P INT S FORM METH PO, P362 [Anonymous], 2017, 0060 SWISS EC Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bada AO, 2021, IEEE INT CONF DISTR, P503, DOI 10.1109/DCOSS52077.2021.00083 Barinov I., 2019, PROOF STAKE DECENTRA Bellini M., 2019, BLOCKCHAIN IMPRESE C Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Chaudhry N, 2018, 2018 12TH INTERNATIONAL CONFERENCE ON OPEN SOURCE SYSTEMS AND TECHNOLOGIES (ICOSST), P54, DOI 10.1109/ICOSST.2018.8632190 Chiap G., 2019, BLOCKCHAIN TECNOLOGI Conti M, 2019, 14TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY (ARES 2019), DOI 10.1145/3339252.3339255 de las Morenas J, 2014, COMPUT ELECTRON AGR, V101, P34, DOI 10.1016/j.compag.2013.12.011 de Vries A, 2021, JOULE, V5, P509, DOI 10.1016/j.joule.2021.02.006 Di Pierro M, 2017, COMPUT SCI ENG, V19, P92, DOI 10.1109/MCSE.2017.3421554 Fang C, 2022, PACKAG TECHNOL SCI, V35, P643, DOI 10.1002/pts.2579 Friedman N, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121403 Ge L., 2017, BLOCKCHAIN AGR FOOD Ghimire Devndra, 2020, COMP STUDY PYTHON WE Giacalone Massimiliano, 2021, Qual Quant, V55, P1945, DOI 10.1007/s11135-021-01095-w Giungato P, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9122214 Halkos GE, 2021, RENEW SUST ENERG REV, V144, DOI 10.1016/j.rser.2021.110981 ISMEA, I SERV MERC AGR AL Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kasten J., 2019, J SUPPLY CHAIN MANAG, V8, P45 Koirala RC, 2019, I C SOFTWARE KNOWL I Lepore C, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8101782 Longo F, 2020, INT J FOOD ENG, V16, DOI 10.1515/ijfe-2019-0109 Magliulo L., 2013, AGR SCI, V4, P41, DOI DOI 10.4236/as.2013.45B008 Makarov EI, 2019, STUD COMPUT INTELL, V826, P1059, DOI 10.1007/978-3-030-13397-9_109 Mangla SK, 2021, TRANSPORT RES E-LOG, V149, DOI 10.1016/j.tre.2021.102289 Manikas I, 2009, INT J DAIRY TECHNOL, V62, P126, DOI 10.1111/j.1471-0307.2008.00444.x Marin M., 2021, ARXIV210102026 Mukherjee AA, 2022, OPER MANAGE RES, V15, P46, DOI 10.1007/s12063-021-00180-5 Niya S. Rafati, 2021, 2 INT C SOC AUTOMATI, P1 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Platt M., 2021, ENERGY FOOTPRINT BLO Queiroz MM, 2019, INT J INFORM MANAGE, V46, P70, DOI 10.1016/j.ijinfomgt.2018.11.021 Rambim D, 2020, 2020 IST-AFRICA CONFERENCE (IST-AFRICA) Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Rejeb A, 2020, LOGFORUM, V16, P363, DOI 10.17270/J.LOG.2020.467 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Saleh F, 2021, REV FINANC STUD, V34, P1156, DOI 10.1093/rfs/hhaa075 Santhi AR, 2022, LOGISTICS-BASEL, V6, DOI 10.3390/logistics6010015 Schinckus C, 2020, ENERGY RES SOC SCI, V69, DOI 10.1016/j.erss.2020.101614 Shi N, 2016, FINANC INNOV, V2, DOI 10.1186/s40854-016-0045-6 Shingh S., 2020, ASIAN J EC BUS ACCOU, V14, P13, DOI DOI 10.9734/AJEBA/2020/V14I230189 Thume M., 2021, BLOCKCHAIN BASED TRA, DOI [10.31219/osf.io/uyb64, DOI 10.31219/OSF.IO/UYB64] Martinez JAD, 2018, J APPL ANIM RES, V46, P784, DOI 10.1080/09712119.2017.1403327 Westerlund M, 2021, TECHNOL INNOV MANAG, V11, P6, DOI 10.22215/timreview/1446 Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 Yim J., 2018, ELECT TELECOMMUN TRE, V33, P45 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 52 TC 8 Z9 8 U1 30 U2 46 PD MAR PY 2022 VL 14 IS 6 AR 3321 DI 10.3390/su14063321 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000776418400001 DA 2022-12-14 ER PT J AU Marin Haddad, F Toma, I Popa, ME Pipirigeanu, M AF Marin Haddad, Florentina Toma, Ioana Popa, Mona Elena Pipirigeanu, Mariana TI THE TRACEABILITY OF FOOD PRODUCTS IN RELATION WITH FOOD INTEGRITY - A REVIEW SO SCIENTIFIC PAPERS-SERIES D-ANIMAL SCIENCE DT Review DE food traceability; food integrity; food chain; food consumer AB Traceability expresses the ability to detect and track raw materials, food products of animal or plant origin, a food-producing animal, or a substance intended to be embedded or expected to be incorporated into a food product, throughout all stages of production, processing and distribution. A traceability system in practice involves systematically and continuously completing and keeping records that can be uniquely identified for each batch unit and the information required at each stage of the food chain (up to consumption). For agri-food products, traceability makes a link between raw materials, their origin, processing, distribution and location after marketing. Food traceability must in principle aim at two objectives: the first one is to provide information to product use and the second one, to contribute to the safety of the food, allowing, as appropriate, withdrawal of non-conforming batches and recall of the product. The way the food goes through "from farm to fork" is called the food chain. The food chain has several links: farmers producing raw material, food processors, distributors and consumers. One of the benefits of traceability is the implementation of food contamination monitoring programs. Traceability facilitates the identification of key products in a particular food chain where sampling of products is required, to monitor the concentration of chemical, microbiological and biological contaminants. C1 [Marin Haddad, Florentina; Toma, Ioana; Popa, Mona Elena] Univ Agron Sci & Vet Med Bucharest, 59 Marasti Blvd, Bucharest, Romania. [Pipirigeanu, Mariana] Romanian Acad, CSCBA INCE, Path13 September 13, Bucharest, Romania. C3 University of Agronomic Science & Veterinary Medicine - Bucharest; Romanian Academy of Sciences RP Marin Haddad, F (corresponding author), Univ Agron Sci & Vet Med Bucharest, 59 Marasti Blvd, Bucharest, Romania. EM haddad.florentina@ansvsa.ro CR Buhr B, 2003, J FOOD DISTRIBUTION, V34, P14 Gibbons R, 2005, J ECON PERSPECT, V19, P3, DOI 10.1257/0895330053147912 Iorga D., 2016, APC ROMANIA CERINTE Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Saak A.E., 2016, INT J PRODUCTION EC, V177 Skilton PF, 2009, J SUPPLY CHAIN MANAG, V45, P40, DOI 10.1111/j.1745-493X.2009.03170.x Souza Monteiro D.M, 2004, 20046 U MASS AMH DEP Wilson T., 1998, SUPPLY CHAIN MANAG I, V3, P127, DOI [10.1108/13598549810230831, DOI 10.1108/13598549810230831] NR 9 TC 0 Z9 0 U1 3 U2 6 PY 2019 VL 62 IS 2 BP 228 EP 232 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000509121700036 DA 2022-12-14 ER PT J AU Jurcaga, L Zidek, R Golian, J Belej, L Demianova, A Bobko, M Bobkova, A AF Jurcaga, Lukas Zidek, Radoslav Golian, Jozef Belej, L'ubomir Demianova, Alzbeta Bobko, Marek Bobkova, Alica TI VERIFICATION OF THE TRACEABILITY MODEL OF AUTOCHTHONOUS POULTRY BREEDS SO CARPATHIAN JOURNAL OF FOOD SCIENCE AND TECHNOLOGY DT Article DE Chicken; Microsatellite; Traceability; F1 generation ID GENETIC DIVERSITY; CHICKEN BREEDS; IDENTIFICATION; MICROSATELLITES; DNA; POPULATIONS; PARENTAGE; PRODUCTS; ANIMALS AB Microsatellite markers are reliable and cheap method for studying diversity among animal breeds. They are widely used for separation of related animal breeds on genetic level. When used in food industry, they have great potential to be used for authentication of animal food products. We are aiming to explore the variability of alleles in selected markers in modeled F1 generation of Slovak breeds of chicken. We want to compare, if previously proposed traceability model is relevant for next generation of chickens or it is limited to one, parental, generation. Our analysis was based on 7 selected microsatellite markers. We modeled genotypes of 42 F1 generation individuals of Oravka tawny and 42 of Oravka white, derived from 1 rooster and 7 chickens from each breed. In our study, we used PCoA analysis and neighbor joining (NJ) analysis. With usage of both analyzes, we proved, that both generations are unique and genetic distance between individuals of different color breed are wide enough. We proved, that we only need to genotype the parental generation of both Oravka chicken tawny and white breeds. After creating F1 generation, we are reliably able to separate those populations. There is no need to genotype whole F1 generation. This provide huge financial benefits. Furthermore, we are able to trace and authenticate whole F1 production generation. C1 [Jurcaga, Lukas; Bobko, Marek] Slovak Univ Agr, Dept Technol & Qual Anim Prod, Tr A Hlinku 2, Nitra 94976, Slovakia. [Zidek, Radoslav; Golian, Jozef; Belej, L'ubomir; Demianova, Alzbeta; Bobkova, Alica] Slovak Univ Agr, Dept Food Hyg & Safety, Tr A Hlinku 2, Nitra 94976, Slovakia. C3 Slovak University of Agriculture Nitra; Slovak University of Agriculture Nitra RP Jurcaga, L (corresponding author), Slovak Univ Agr, Dept Technol & Qual Anim Prod, Tr A Hlinku 2, Nitra 94976, Slovakia. EM xjurcaga@uniag.sk CR Abdurakhmonov I.Y., 2016, MICROSATELLITE MARKE Ammendrup S, 2001, REV SCI TECH OIE, V20, P437, DOI 10.20506/rst.20.2.1287 [Anonymous], 2019, EURASIA J BIOSCI [Anonymous], 2016, EUROPEAN INT J SCI T Barker J.S.F., 1989, REV MOL QUANTITATIVE, P75 Belej L., 2019, PotravinArstvo: Slovak Journal of Food Sciences, V13, P956, DOI 10.5219/1254 BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Chmelnicna L., 2004, OHROZENE PLEMENA ZVI, P37 Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Davila SG, 2009, POULTRY SCI, V88, P2518, DOI 10.3382/ps.2009-00347 Ellegren H, 2004, NAT REV GENET, V5, P435, DOI 10.1038/nrg1348 FAO, 2011, FAO ANIMAL PRODUCTIO Fernandez ME, 2013, GENET MOL BIOL, V36, P185, DOI 10.1590/S1415-47572013000200008 Granevitze Z, 2009, ANIM GENET, V40, P686, DOI 10.1111/j.1365-2052.2009.01902.x Hanusova E., 2017, Slovak Journal of Animal Science, V50, P112 Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 Hillel J, 2003, GENET SEL EVOL, V35, P533, DOI [10.1186/1297-9686-35-6-533, 10.1051/gse:2003038] Jobling MA, 2004, NAT REV GENET, V5, P739, DOI 10.1038/nrg1455 Jorde LB, 2000, AM J HUM GENET, V66, P979, DOI 10.1086/302825 Krawczak M, 1999, ELECTROPHORESIS, V20, P1676, DOI 10.1002/(SICI)1522-2683(19990101)20:8<1676::AID-ELPS1676>3.3.CO;2-4 Moioli B, 2001, LIVEST PROD SCI, V70, P203, DOI 10.1016/S0301-6226(01)00175-0 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Pariset L, 2003, J ANIM BREED GENET, V120, P425, DOI 10.1046/j.0931-2668.2003.00411.x Rosa AJM, 2013, SMALL RUMINANT RES, V113, P62, DOI 10.1016/j.smallrumres.2013.03.021 Rosenbom S, 2015, ANIM GENET, V46, P30, DOI 10.1111/age.12256 SAITOU N, 1987, MOL BIOL EVOL, V4, P406, DOI 10.1093/oxfordjournals.molbev.a040454 Sardina MT, 2015, FOOD RES INT, V74, P115, DOI 10.1016/j.foodres.2015.04.038 Seo DW, 2013, ASIAN AUSTRAL J ANIM, V26, P316, DOI 10.5713/ajas.2012.12469 Simianer H, 2005, ECOL ECON, V53, P559, DOI 10.1016/j.ecolecon.2004.11.016 Sokolowicz Z, 2016, ANN ANIM SCI, V16, P347, DOI 10.1515/aoas-2016-0004 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Tadano R, 2013, POULTRY SCI, V92, P2860, DOI 10.3382/ps.2013-03343 Toione M, 2012, SMALL RUMINANT RES, V102, P18, DOI 10.1016/j.smallrumres.2011.09.010 Witzenberger KA, 2011, BIODIVERS CONSERV, V20, P1843, DOI 10.1007/s10531-011-0074-4 Yilmaz O, 2015, TURK J VET ANIM SCI, V39, P576, DOI 10.3906/vet-1411-46 Zhou Y, 2008, J POULT SCI, V45, P241, DOI 10.2141/jpsa.45.241 NR 39 TC 0 Z9 0 U1 1 U2 1 PY 2020 VL 12 IS 4 BP 51 EP 59 DI 10.34302/crpjfst/2020.12.4.6 WC Food Science & Technology SC Food Science & Technology UT WOS:000606845900006 DA 2022-12-14 ER PT J AU Bertacchini, L Durante, C Marchetti, A Sighinolfi, S Silvestri, M Cocchi, M AF Bertacchini, Lucia Durante, Caterina Marchetti, Andrea Sighinolfi, Simona Silvestri, Michele Cocchi, Marina TI Use of X-ray diffraction technique and chemometrics to aid soil sampling strategies in traceability studies SO TALANTA DT Article DE Soil; Powder X-ray diffraction; Food geographical traceability; Multivariate data analysis; PCA; PARAFAC ID ICP-MS; MULTIELEMENT; PROVENANCE; ORIGIN; WINE AB Aim of this work is to assess the potentialities of the X-ray powder diffraction technique as fingerprinting technique, i.e. as a preliminary tool to assess soil samples variability, in terms of geochemical features, in the context of food geographical traceability. A correct approach to sampling procedure is always a critical issue in scientific investigation. In particular, in food geographical traceability studies, where the cause-effect relations between the soil of origin and the final foodstuff is sought, a representative sampling of the territory under investigation is certainly an imperative. This research concerns a pilot study to investigate the field homogeneity with respect to both field extension and sampling depth, taking also into account the seasonal variability. Four Lambrusco production sites of the Modena district were considered. The X-Ray diffraction spectra, collected on the powder of each soil sample, were treated as fingerprint profiles to be deciphered by multivariate and multi-way data analysis, namely PCA and PARAFAC. The differentiation pattern observed in soil samples, as obtained by this fast and non-destructive analytical approach, well matches with the results obtained by characterization with other costly analytical techniques, such as ICP/MS, GFAAS, FAAS, etc. Thus, the proposed approach furnishes a rational basis to reduce the number of soil samples to be collected for further analytical characterization, i.e. metals content, isotopic ratio of radiogenic element, etc., while maintaining an exhaustive description of the investigated production areas. (C) 2012 Elsevier B.V. All rights reserved. C1 [Bertacchini, Lucia; Durante, Caterina; Marchetti, Andrea; Sighinolfi, Simona; Silvestri, Michele; Cocchi, Marina] Univ Modena & Reggio Emilia, Dept Chem, I-41125 Modena, Italy. C3 Universita di Modena e Reggio Emilia RP Cocchi, M (corresponding author), Univ Modena & Reggio Emilia, Dept Chem, Via G Campi 183, I-41125 Modena, Italy. EM marina.cocchi@unimore.it CR Almeida CMR, 2003, J AGR FOOD CHEM, V51, P4788, DOI 10.1021/jf034145b Asfaha DG, 2011, J CEREAL SCI, V53, P170, DOI 10.1016/j.jcs.2010.11.004 Barbaste M, 2002, J ANAL ATOM SPECTROM, V17, P135, DOI 10.1039/b109559p Birge L, 1997, FESTSCHRIFT L LECAM, P55 Bro R, 1997, CHEMOMETR INTELL LAB, V38, P149, DOI 10.1016/S0169-7439(97)00032-4 Brunner M, 2010, EUR FOOD RES TECHNOL, V231, P623, DOI 10.1007/s00217-010-1314-7 Coifman R.R., 1993, PROGR WAVELET ANAL A Durante C., ANAL CHEM UNPUB Hao X., 2006, SOIL SAMPLING METHOD, P745 Holzl S., 2009, P FIN TRACE C LECT T Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Lorho G, 2006, CHEMOMETR INTELL LAB, V84, P119, DOI 10.1016/j.chemolab.2006.04.023 Margesin R., 2005, MANUAL SOIL ANAL MON Misiti M, 2010, WAVELET TOOLBOX 4 US Moens LJ, 2001, J ANAL ATOM SPECTROM, V16, P991, DOI 10.1039/b103707m Nigro G., 2008, ZONAZIONE VITICOLA P Prohaska T, 2005, INT J MASS SPECTROM, V242, P243, DOI 10.1016/j.ijms.2004.11.028 Savorani F, 2010, J MAGN RESON, V202, P190, DOI 10.1016/j.jmr.2009.11.012 Smart DR, 2006, AM J ENOL VITICULT, V57, P89 Taylor A, 2011, J ANAL ATOM SPECTROM, V26, P653, DOI 10.1039/c1ja90006d WALCZAK B, 2000, WAVELET CHEM Wold S., 1993, 3D QSAR DRUG DESIGN NR 22 TC 13 Z9 14 U1 2 U2 33 PD AUG 30 PY 2012 VL 98 BP 178 EP 184 DI 10.1016/j.talanta.2012.06.067 WC Chemistry, Analytical SC Chemistry UT WOS:000309327600026 DA 2022-12-14 ER PT J AU Rodriguez-Ramirez, R Gonzalez-Cordova, AF Arana, A Sanchez-Escalante, A Vallejo-Cordoba, B AF Rodriguez-Ramirez, Roberto Gonzalez-Cordova, Aaron F. Arana, Ana Sanchez-Escalante, Armida Vallejo-Cordoba, Belinda TI TRACEABILITY OF BOVINE MEAT: CONCEPTS, TECHNOLOGICAL ASPECTS AND PERSPECTIVES FOR MEXICO SO INTERCIENCIA DT Article ID IDENTIFICATION; RESIDUES; CATTLE AB Bovine meat traceability is of great importance as a tool for food safety since it ensures product identity and the possibility to follow. it up from as origin until commercialization Meat traceability starts with animal identification; however, traditional markers, although necessary are removed at slaughter. On the other hand, methods that use molecular markers, based on DNA fingerprinting, are permanent and unique. Therefore, it is important that a meat traceability system combines both methods so that the product could be traced along the production chain to ensure consumer safety in case of a Pod crisis In this review, a general overview of bovine meat traceability concepts and technological aspects are presented. Also, methods used for meat traceability, including the selection and evaluation of molecular markers are discussed Finally, the status of bovine meat traceability in different countries, with special emphasis on Mexico is addressed. C1 [Vallejo-Cordoba, Belinda] CIAD, Lab Calidad Autenticidad & Trazabilidad Alimentos, Hermosillo 83000, Sonora, Mexico. [Rodriguez-Ramirez, Roberto; Sanchez-Escalante, Armida] Univ Sonora, Hermosillo 83000, Sonora, Mexico. [Gonzalez-Cordova, Aaron F.] Inst Tecnol Veracruz, Cordoba, Mexico. [Arana, Ana; Sanchez-Escalante, Armida] Univ Zaragoza, E-50009 Zaragoza, Spain. [Arana, Ana] Univ Publ Navarra, Escuela Tecn Super Ingn Agronomos, Navarra, Spain. C3 CIAD - Centro de Investigacion en Alimentacion y Desarrollo; Universidad de Sonora; University of Zaragoza; Universidad Publica de Navarra RP Vallejo-Cordoba, B (corresponding author), CIAD, Lab Calidad Autenticidad & Trazabilidad Alimentos, Carretera La Victoria Km 06,Apartado 1735, Hermosillo 83000, Sonora, Mexico. CR Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Bicalho HMS, 2006, GENET MOL RES, V5, P432 Buntjer JB, 2002, HEREDITY, V88, P46, DOI 10.1038/sj.hdy.6800007 CANON J, 2003, 8 S AN AVEDILA LEON Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 EC-European Commission, 2002, J EUROP COMM, VL031, P1 Estrada-Montoya MC, 2008, CIENC TEC ALIMENTAR, V6, P130, DOI 10.1080/11358120809487637 *EUR COMM, 1997, OFF J EUR COMMUN L, V354, P19 European Parliament and European council, 2000, OFFICIAL J EUROPEA L, V204, P1 Felmer R, 2006, ARCH MED VET, V38, P197, DOI 10.4067/S0301-732X2006000300002 Luna Martinez E., 2006, SITUACION ACTUAL PER Mburu D., 2005, PRACTICAL APPROACH M Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Orbera Raton Teresa, 2004, Revista Iberoamericana de Micologia, V21, P15 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Pettitt RG, 2001, REV SCI TECH OIE, V20, P584, DOI 10.20506/rst.20.2.1299 RIOJAS VVM, 2006, CIEN, V9, P41 *SAGARPA, 2006, 25406 SAGARPA *SAGARPA, 2006, 08406 SAGARPA Sallam KI, 2008, FOOD CHEM, V108, P154, DOI 10.1016/j.foodchem.2007.10.066 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 SHACKELL GH, 2008, MEAT BIOTECHNOLOGY, P61 Sifuentes Rincon A. M., 2006, Tecnica Pecuaria en Mexico, V44, P389 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 SMITH GC, 2005, EXPLORING TRACEABILI Sofos JN, 2008, MEAT SCI, V78, P3, DOI 10.1016/j.meatsci.2007.07.027 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Yordanov D., 2006, BIOTECHNOL BIOTEC EQ, V20, P3, DOI [10.1080/13102818.2006.10817295, DOI 10.1080/13102818.2006.10817295] NR 32 TC 0 Z9 0 U1 2 U2 13 PD OCT PY 2010 VL 35 IS 10 BP 746 EP 751 WC Ecology SC Environmental Sciences & Ecology UT WOS:000283639600006 DA 2022-12-14 ER PT J AU Qian, JP Xing, B Zhang, BH Yang, H AF Qian, Jianping Xing, Bin Zhang, Baohui Yang, Han TI Optimizing QR code readability for curved agro-food packages using response surface methodology to improve mobile phone-based traceability SO FOOD PACKAGING AND SHELF LIFE DT Article DE QR code; Traceability; Response surface methodology; Food packaging ID FOOD; OPTIMIZATION; TECHNOLOGY; BARCODE; TRUST AB Quick response (QR) codes are two-dimensional (2D) barcodes that are widely used in food packaging. Moreover, scanning QR codes with mobile phones has become a convenient method to ensure product traceability. However, attaching QR codes to curved agro-food products to improve traceability effects and increase customer satisfaction presents a significant challenge. This paper reports an optimization method developed for curved surfaces that uses response surface methodology (RSM) analysis. The value ranges of the following four factors affecting QR readability were selected and analyzed: reading distance, ball diameter, code size, and coded characters. A central composite inscribed experiment using four factors with five levels was designed using RSM to obtain the optimal reading parameters, and experimental equipment was designed in-house. The results indicate the primary factors that significantly affect QR code readability are reading distance, ball diameter, the interactions between reading distance and code size, and the interactions between ball diameter and code size. Optimal parameters were obtained with the established model by using apples and melons. For actual experimental testing, two solutions for apples and one solution for melon were selected to determine the requisite average values for the parameters. Tests were performed using different mobile phones, including both iOS and Android platforms. An analysis of the results reveals only slight differences between simulated readability and actual readability for different fruits and mobile phone platforms. C1 [Qian, Jianping; Zhang, Baohui; Yang, Han] Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing AGRIRS, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. [Xing, Bin] Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Ministry of Agriculture & Rural Affairs; Beijing Academy of Agriculture & Forestry RP Qian, JP (corresponding author), Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing AGRIRS, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. EM qianjianping@caas.cn CR [Anonymous], 220052007 ISO Bas D, 2007, J FOOD ENG, V78, P836, DOI 10.1016/j.jfoodeng.2005.11.024 Bernard A, 2002, ENVIRON RES, V88, P1, DOI 10.1006/enrs.2001.4274 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Boys KA, 2019, RENEW AGR FOOD SYST, V34, P226, DOI 10.1017/S1742170518000030 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Froschle HK, 2009, COMPUT ELECTRON AGR, V66, P126, DOI 10.1016/j.compag.2009.01.002 Ghaani M, 2016, TRENDS FOOD SCI TECH, V51, P1, DOI 10.1016/j.tifs.2016.02.008 International Organization for Standardization, 2006, ISOIEC180042006 Jedermann R, 2017, FOOD PACKAGING SHELF, V14, P18, DOI 10.1016/j.fpsl.2017.08.006 Jiao SM, 2017, OPT COMMUN, V387, P235, DOI 10.1016/j.optcom.2016.11.066 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Khuri AI, 2010, WIRES COMPUT STAT, V2, P128, DOI 10.1002/wics.73 Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Lien CH, 2014, COMPUT HUM BEHAV, V41, P104, DOI 10.1016/j.chb.2014.08.013 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qian JP, 2018, SCI AGR, V75, P273, DOI [10.1590/1678-992X-2016-0152, 10.1590/1678-992x-2016-0152] Qian JP, 2017, COMPUT ELECTRON AGR, V139, P56, DOI 10.1016/j.compag.2017.05.009 Ramesh T, 2016, INNOV FOOD SCI EMERG, V38, P105, DOI 10.1016/j.ifset.2016.09.015 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Schumann B, 2018, INNOV FOOD SCI EMERG, V47, P88, DOI 10.1016/j.ifset.2018.02.005 So-In C, 2014, COMPUT ELECTRON AGR, V109, P287, DOI 10.1016/j.compag.2014.10.004 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Tseng CL, 2006, COMPUT ELECTRON AGR, V53, P45, DOI 10.1016/j.compag.2006.03.005 Wales C, 2006, APPETITE, V47, P187, DOI 10.1016/j.appet.2006.05.007 Wang CQ, 2016, J CLEAN PROD, V139, P866, DOI 10.1016/j.jclepro.2016.08.111 NR 29 TC 7 Z9 7 U1 6 U2 16 PD JUN PY 2021 VL 28 AR 100638 DI 10.1016/j.fpsl.2021.100638 EA FEB 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000663453800007 DA 2022-12-14 ER PT J AU Qi, LM Liu, HG Li, JQ Li, T Wang, YZ AF Qi, Luming Liu, Honggao Li, Jieqing Li, Tao Wang, Yuanzhong TI Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics SO SENSORS DT Article DE origin traceability; Boletus edulis; ICP-AES; UV-Vis; FT-MIR ID ANTIOXIDANT ACTIVITY; TRACE-ELEMENTS; REFLECTANCE SPECTROSCOPY; INFRARED-SPECTROSCOPY; PATTERN-RECOGNITION; EDIBLE MUSHROOMS; FRUITING BODIES; AGARICUS-BLAZEI; DISCRIMINATION; IDENTIFICATION AB Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 184 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms. C1 [Qi, Luming; Wang, Yuanzhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. [Qi, Luming] Chengdu Univ Tradit Chinese Med, State Key Lab Breeding Base Systemat Res Dev & Ut, Chengdu 611137, Sichuan, Peoples R China. [Liu, Honggao; Li, Jieqing] Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China. [Li, Tao] Yuxi Normal Univ, Coll Resources & Environm, Yuxi 653100, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Chengdu University of Traditional Chinese Medicine; Yunnan Agricultural University; Yuxi Normal University RP Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China.; Li, T (corresponding author), Yuxi Normal Univ, Coll Resources & Environm, Yuxi 653100, Peoples R China. EM 18669326801@163.com; honggaoliu@126.com; lijieqing2008@126.com; ltyx_1976@126.com; boletus@126.com CR Alamprese C, 2013, LWT-FOOD SCI TECHNOL, V53, P225, DOI 10.1016/j.lwt.2013.01.027 Anghileri A, 2007, J BIOTECHNOL, V127, P508, DOI 10.1016/j.jbiotec.2006.07.021 Barros L, 2007, EUR FOOD RES TECHNOL, V225, P151, DOI 10.1007/s00217-006-0394-x Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Biancolillo A, 2014, ANAL CHIM ACTA, V820, P23, DOI 10.1016/j.aca.2014.02.024 Botelho BG, 2017, FOOD CONTROL, V77, P25, DOI 10.1016/j.foodcont.2017.01.020 Casale M, 2016, TALANTA, V160, P729, DOI 10.1016/j.talanta.2016.08.004 Chen QS, 2007, SPECTROCHIM ACTA A, V66, P568, DOI 10.1016/j.saa.2006.03.038 Chen Y, 2008, ANAL CHIM ACTA, V618, P121, DOI 10.1016/j.aca.2008.04.055 Chudzynski K, 2008, CHEMOSPHERE, V73, P1230, DOI 10.1016/j.chemosphere.2008.07.055 Donno D, 2016, J FOOD SCI TECH MYS, V53, P1071, DOI 10.1007/s13197-015-2115-6 Donno D, 2012, VEGETOS, V25, P21 Dupuy N, 2010, ANAL CHIM ACTA, V666, P23, DOI 10.1016/j.aca.2010.03.034 Falandysz J, 2007, J ENVIRON SCI HEAL A, V42, P2089, DOI 10.1080/10934520701627058 Falandysz J, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0143608 Falandysz J, 2011, J ENVIRON SCI HEAL B, V46, P231, DOI 10.1080/03601234.2011.540528 Fernandes A, 2016, LWT-FOOD SCI TECHNOL, V69, P91, DOI 10.1016/j.lwt.2016.01.037 Fischer G, 2006, J MICROBIOL METH, V64, P63, DOI 10.1016/j.mimet.2005.04.005 Gachumi G, 2017, J AGR FOOD CHEM, V65, P10141, DOI 10.1021/acs.jafc.7b03785 Galtier O, 2011, VIB SPECTROSC, V55, P132, DOI 10.1016/j.vibspec.2010.09.012 Giannaccini G, 2012, ENVIRON MONIT ASSESS, V184, P7579, DOI 10.1007/s10661-012-2520-5 Gonzaga MLC, 2005, CARBOHYD POLYM, V60, P43, DOI 10.1016/j.carbpol.2004.11.022 Hirri A, 2016, FOOD ANAL METHOD, V9, P974, DOI 10.1007/s12161-015-0255-y Hong XZ, 2014, J FOOD ENG, V126, P89, DOI 10.1016/j.jfoodeng.2013.11.008 Hung YC, 2002, FOOD CHEM, V78, P233, DOI 10.1016/S0308-8146(01)00403-4 Jarzynska G, 2012, J GEOCHEM EXPLOR, V121, P69, DOI 10.1016/j.gexplo.2012.07.001 Jiang W, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16091509 Li Y, 2016, ADV MATER TECHNOL-US, V1, DOI 10.1002/admt.201600102 Li Y, 2016, SPECTROCHIM ACTA A, V165, P61, DOI 10.1016/j.saa.2016.04.012 Li Y, 2014, J ANAL METHODS CHEM, V2014, DOI 10.1155/2014/519424 Li Y, 2017, SPECTROCHIM ACTA A, V177, P20, DOI 10.1016/j.saa.2017.01.029 Lim J, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17102258 Ma DX, 2016, SPECTROSC SPECT ANAL, V36, P2479, DOI 10.3964/j.issn.1000-0593(2016)08-2479-08 Mahadevan S, 2008, ANAL CHEM, V80, P7562, DOI 10.1021/ac800954c Medyk M, 2017, J ENVIRON SCI HEAL B, V52, P361, DOI 10.1080/03601234.2017.1283145 Mellado-Mojica E, 2015, FOOD CHEM, V167, P349, DOI 10.1016/j.foodchem.2014.06.111 Mohacek-Grosev V, 2001, SPECTROCHIM ACTA A, V57, P2815, DOI 10.1016/S1386-1425(01)00584-4 Nasiri F., 2012, Annals of Biological Research, V3, P5677 Nikkarinen M, 2004, J FOOD COMPOS ANAL, V17, P301, DOI 10.1016/j.jfca.2004.03.013 Nnorom IC, 2013, J FOOD COMPOS ANAL, V29, P73, DOI 10.1016/j.jfca.2012.10.001 O'Gorman A, 2010, J AGR FOOD CHEM, V58, P7770, DOI 10.1021/jf101123a Palacios I, 2011, FOOD CHEM, V128, P674, DOI 10.1016/j.foodchem.2011.03.085 Peev CI, 2007, CHEM NAT COMPD+, V43, P259, DOI 10.1007/s10600-007-0100-7 Qi LM, 2017, INT J FOOD PROP, V20, pS56, DOI 10.1080/10942912.2017.1289387 Qi LM, 2017, ANAL LETT, V50, P389, DOI 10.1080/00032719.2016.1178757 Ribeiro B, 2008, FOOD CHEM, V110, P47, DOI 10.1016/j.foodchem.2008.01.054 Sarikurkcu C, 2008, BIORESOURCE TECHNOL, V99, P6651, DOI 10.1016/j.biortech.2007.11.062 Sitta N, 2008, ECON BOT, V62, P307, DOI 10.1007/s12231-008-9037-4 Sun WJ, 2017, SPECTROCHIM ACTA A, V171, P72, DOI 10.1016/j.saa.2016.07.039 Tesanovic K, 2017, J FOOD SCI TECH MYS, V54, P430, DOI 10.1007/s13197-016-2479-2 Tsai SY, 2007, LWT-FOOD SCI TECHNOL, V40, P1392, DOI 10.1016/j.lwt.2006.10.001 Ulziijargal E, 2011, INT J MED MUSHROOMS, V13, P343, DOI 10.1615/IntJMedMushr.v13.i4.40 Yao S., 2017, J SCI FOOD AGR Zervakis GI, 2012, FUNGAL BIOL-UK, V116, P715, DOI 10.1016/j.funbio.2012.04.006 Zhang AQ, 2011, INT J BIOL MACROMOL, V49, P1092, DOI 10.1016/j.ijbiomac.2011.09.005 NR 55 TC 24 Z9 27 U1 6 U2 30 PD JAN PY 2018 VL 18 IS 1 AR 241 DI 10.3390/s18010241 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000423286300240 DA 2022-12-14 ER PT J AU Shanahan, C Kernan, B Ayalew, G McDonnell, K Butler, F Ward, S AF Shanahan, C. Kernan, B. Ayalew, G. McDonnell, K. Butler, F. Ward, S. TI A framework for beef traceability from farm to slaughter using global standards: An Irish perspective SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Cattle; Traceability; Biometrics; BioTrack; EPC; RFID ID IDENTIFICATION; RECOGNITION; TECHNOLOGY; CATTLE AB A system that can be used to identify all aspects of beef traceability from farm to slaughter has been laid out based on pertinent European Union law and global standards. An integrated traceability system involving all of the stakeholders along the supply chain can serve to increase consumer confidence in beef products by making traceability data accessible to the consumer. The use of radio frequency identification (RFID) for the identification of individual cattle, and biometric identifiers for verification of cattle identity has been proposed. The use of a BioTrack database for the storage of retinal images has been outlined. A data structure for RFID tags has been proposed in accordance with ISO 11784 and a middleware to convert animal ID data to the EPC (electronic product code) data structure, in order to facilitate the use of EPCglobal Network for the exchange of traceability data. (C) 2008 Elsevier B.V. All rights reserved. C1 [Shanahan, C.; Ayalew, G.; McDonnell, K.; Butler, F.; Ward, S.] Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Dublin 4, Ireland. [Kernan, B.] GSI Ireland, Dublin 4, Ireland. C3 University College Dublin RP Shanahan, C (corresponding author), Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Room 125,Chem Bldg, Dublin 4, Ireland. EM conor.shanahan@ucdconnect.ie CR Allen A, 2008, LIVEST SCI, V116, P42, DOI 10.1016/j.livsci.2007.08.018 Barcos LO, 2001, REV SCI TECH OIE, V20, P640, DOI 10.20506/rst.20.2.1294 Barron UG, 2008, COMPUT ELECTRON AGR, V60, P156, DOI 10.1016/j.compag.2007.07.010 Barry B, 2007, T ASABE, V50, P1073, DOI 10.13031/2013.23121 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Butler J., 2008, COMMUNICATION *DEP AGR FISH FOOD, 2006, COMP AUTH MOD PASSP *EPCGLOBAL INC, 2008, EPCGLOBAL TAG DAT ST FALLON RJ, 2002, BEEF PRODUCTION SERI, V46 GANDINO F, 2007, RFID EUR 2007 1 ANN Golan E., 2004, AER830, P56 Golden B. L, 2004, US Pat, Patent No. [6766041, 6,766,041] *I ENG TECHN, 2005, RAD FREQ ID DEV TECH *IDEA PROJ TEAM, 2001, IDEA PROJ IDENTIFICA Kampers FWH, 1999, COMPUT ELECTRON AGR, V24, P27, DOI 10.1016/S0168-1699(99)00035-6 KELLY A, 2008, COMMUNICATION McGrann J, 2001, REV SCI TECH OIE, V20, P406, DOI 10.20506/rst.20.2.1283 McMeekin TA, 2006, INT J FOOD MICROBIOL, V112, P181, DOI 10.1016/j.ijfoodmicro.2006.04.048 Sahin E., 2002, IEEE INT C SYST MAN Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 ZAROKOSTAS N, 2007, P IEEE 18 INT S PERS NR 22 TC 64 Z9 78 U1 1 U2 29 PD APR PY 2009 VL 66 IS 1 BP 62 EP 69 DI 10.1016/j.compag.2008.12.002 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000264700900007 DA 2022-12-14 ER PT J AU Wang, MC Yang, CY AF Wang, M. C. Yang, C. Y. TI Analyzing traceability system within the Taiwanese herbal products SO PLANTA MEDICA DT Meeting Abstract CT 9th Joint Meeting of AFERP, ASP, GA, JSP, PSE and SIF CY JUL 24-27, 2016 CL Copenhagen, DENMARK DE Traceability system; managerial accounting; corporate governance; game theory C1 [Wang, M. C.] Chinese Culture Univ, Dept Accounting, Taipei 11114, Taiwan. [Yang, C. Y.] Natl Chung Hsing Univ, Dept Agron, Taichung 40227, Taiwan. C3 Chinese Culture University; National Chung Hsing University CR Gibbons Robert, 1992, PRIMER GAME THEORY Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Tirole J, 2001, ECONOMETRICA, V69, P1, DOI 10.1111/1468-0262.00177 Wilson WW, 2008, AGRIBUSINESS, V24, P85, DOI [10.1002/agr.20148, 10.1002/AGR.20148] NR 4 TC 0 Z9 0 U1 0 U2 4 PD DEC PY 2016 VL 82 SU 1 MA P1091 DI 10.1055/s-0036-1597062 WC Plant Sciences; Chemistry, Medicinal; Integrative & Complementary Medicine; Pharmacology & Pharmacy SC Plant Sciences; Pharmacology & Pharmacy; Integrative & Complementary Medicine UT WOS:000411789300885 DA 2022-12-14 ER PT J AU Shackell, GH Mathias, HC Cave, VM Dodds, KG AF Shackell, GH Mathias, HC Cave, VM Dodds, KG TI Evaluation of microsatellites as a potential tool for product tracing of ground beef mixtures SO MEAT SCIENCE DT Article DE DNA; microsatellites; traceability; ground beef ID MEAT-PRODUCTS; DNA POOLS; MARKERS; IDENTIFICATION; CATTLE; TRACEABILITY; CHAIN; LOCI; RAW AB Microsatellite genotyping was evaluated as a potential tool for DNA-based tracing of ground beef product. DNA from mixtures containing different numbers of individuals was analysed with a set of cattle microsatellite markers frequently used for parentage testing. As samples contained DNA from several animals, the microsatellite markers showed multiple peaks. The method could distinguish between mixtures containing equal amounts of meat from three different individuals, meat from three individuals mixed in different proportions, ground beef mixtures purchased in different cities, and different batches of ground beef patties. Limitations occurred when batches contained large numbers of individuals (> 10) and different batches used meat from the same individuals. We conclude that DNA microsatellites may be useful for DNA traceability of ground beef mixtures prepared from less than 10 individuals, but where larger numbers of animals contribute to a mixture the method is not consistently accurate. (c) 2005 Elsevier Ltd. All rights reserved. C1 AgRes Ltd, Invermay Agr Ctr, Mosgiel, New Zealand. C3 AgResearch - New Zealand RP Shackell, GH (corresponding author), AgRes Ltd, Invermay Agr Ctr, Private Bag 50 034, Mosgiel, New Zealand. EM grant.shackell@agresearch.co.nz CR Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 BISHOP MD, 1994, GENETICS, V136, P619 Calvo JH, 2002, J AGR FOOD CHEM, V50, P5265, DOI 10.1021/jf0201576 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Daniels J, 1998, AM J HUM GENET, V62, P1189, DOI 10.1086/301816 DODDS KG, 2004, 22 INT BIOM C CAIRNS, P433 Egeland T, 2003, INT J LEGAL MED, V117, P271, DOI 10.1007/s00414-003-0382-7 Evett IW, 1998, J FORENSIC SCI, V43, P62 GEORGES M, 1992, Patent No. 92113102 GILL P, 1998, P 9 INT S HUM ID PRO, P7 Hillel J, 2003, GENET SEL EVOL, V35, P533, DOI [10.1186/1297-9686-35-6-533, 10.1051/gse:2003038] Hobbs AL, 2002, FOOD POLICY, V27, P437, DOI 10.1016/S0306-9192(02)00048-9 Hunt DJ, 1997, FOOD CHEM, V60, P437, DOI 10.1016/S0308-8146(96)00364-0 Martinez I, 1998, FOOD RES INT, V31, P459, DOI 10.1016/S0963-9969(99)00013-7 Meghen C. N., 1998, Animal Genetics, V29, P48 Miraglia M, 2004, FOOD CHEM TOXICOL, V42, P1157, DOI 10.1016/j.fct.2004.02.018 Moore S. S., 1993, Animal Genetics, V24, P150 Piknova L., 2002, Industrie Alimentari, V41, P163 Saez R, 2004, MEAT SCI, V66, P659, DOI 10.1016/S0309-1740(03)00186-4 Seyboldt C, 2003, J FOOD PROTECT, V66, P644, DOI 10.4315/0362-028X-66.4.644 SHACKELL GH, 2001, P ASS ADVMT ANIM BRE, V14, P533 Slate J, 1998, ANIM GENET, V29, P307, DOI 10.1046/j.1365-2052.1998.00347.x Smith PG, 2004, CURR TOP MICROBIOL, V284, P161 STEFFEN P, 1993, ANIM GENET, V24, P121, DOI 10.1111/j.1365-2052.1993.tb00252.x TOLDO SS, 1993, MAMM GENOME, V4, P720, DOI 10.1007/BF00357796 VAIMAN D, 1994, MAMM GENOME, V5, P288, DOI 10.1007/BF00389543 Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] NR 27 TC 22 Z9 25 U1 0 U2 5 PD JUN PY 2005 VL 70 IS 2 BP 337 EP 345 DI 10.1016/j.meatsci.2005.01.020 WC Food Science & Technology SC Food Science & Technology UT WOS:000228966900016 DA 2022-12-14 ER PT J AU Xie, LN Zhao, SS Rogers, KM Xia, YN Zhang, B Suo, R Zhao, Y AF Xie, Lina Zhao, Shanshan Rogers, Karyne M. Xia, Yanan Zhang, Bin Suo, Ran Zhao, Yan TI A case of milk traceability in small-scale districts-Inner Mongolia of China by nutritional and geographical parameters SO FOOD CHEMISTRY DT Article DE Small-scale districts; Traceability; Milk; Amino acids; Stable isotopes; Elements ID STABLE-ISOTOPE RATIOS; ORIGIN; CHEESE; IDENTIFICATION; ELEMENTS; ACIDS; COW; PDO AB As far as we known, recent studies on the origin of agricultural products rarely focus on the source of cities or counties even small-scale districts, but traceability of small-scale districts of food is the research trend and difficulty of future researches. The most commonly used methods of origin tracing researches are stable isotope and element technology, because these indicators are directly related to local geographical environment. However, when the region of traceability is very close, it is necessary to find new parameters to enhance the accuracy of traceability in small-scale districts. This study uses a combination of nutritional (amino acids) and geographical parameters (stable isotopes, elements) to trace the origin of milk from eleven districts located in five cities of Inner Mongolia. The results showed that the combination of nutritional (amino acids) and geographical parameters (stable isotopes, elements) was the best source tracing method. C1 [Xie, Lina; Suo, Ran] Hebei Agr Univ, Coll Food Sci & Technol, Baoding 071000, Peoples R China. [Xie, Lina; Zhao, Shanshan; Zhao, Yan] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Rogers, Karyne M.] GNS Sci, Natl Isotope Ctr, 30 Gracefield Rd, Lower Hutt 5040, New Zealand. [Xia, Yanan] Inner Mongolia Agr Univ, Coll Food Sci & Engn, Hohhot 010018, Peoples R China. [Zhang, Bin] Henan Univ Sci & Technol, Coll Food & Bioengn, Luoyang 471023, Peoples R China. C3 Hebei Agricultural University; Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; GNS Science - New Zealand; Inner Mongolia Agricultural University; Henan University of Science & Technology RP Suo, R (corresponding author), Hebei Agr Univ, Coll Food Sci & Technol, Baoding 071000, Peoples R China. EM ransuo@qq.com CR Bontempo L, 2012, INT DAIRY J, V23, P99, DOI 10.1016/j.idairyj.2011.10.005 Bontempo L, 2019, FOOD CHEM, V285, P316, DOI 10.1016/j.foodchem.2019.01.160 Bostic JN, 2018, RAPID COMMUN MASS SP, V32, P561, DOI 10.1002/rcm.8069 Bowen GJ, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005186 Camin F, 2004, J AGR FOOD CHEM, V52, P6592, DOI 10.1021/jf040062z CHEN JS, 1991, WATER AIR SOIL POLL, V57-8, P699, DOI 10.1007/BF00282934 Chesson LA, 2010, J AGR FOOD CHEM, V58, P2358, DOI 10.1021/jf904151c Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 Denholm SJ, 2019, J DAIRY SCI, V102, P11180, DOI 10.3168/jds.2019-16960 Ehtesham E, 2013, GEOCHIM COSMOCHIM AC, V111, P105, DOI 10.1016/j.gca.2012.10.026 Elmastas M, 2008, ANAL LETT, V41, P725, DOI 10.1080/00032710801935020 Kabata-Pendias A., 1992, Trace elements in soils and plants. Korenovska M, 2007, EUR FOOD RES TECHNOL, V225, P707, DOI 10.1007/s00217-006-0473-z Luo DH, 2016, FOOD ANAL METHOD, V9, P437, DOI 10.1007/s12161-015-0204-9 Magdas DA, 2019, FOOD CHEM, V277, P307, DOI 10.1016/j.foodchem.2018.10.103 Mikulec N, 2013, INT J DAIRY TECHNOL, V66, P390, DOI 10.1111/1471-0307.12032 Molkentin J, 2010, ANAL BIOANAL CHEM, V398, P1493, DOI 10.1007/s00216-010-3995-y Necemer M, 2016, J FOOD COMPOS ANAL, V52, P16, DOI 10.1016/j.jfca.2016.07.002 Nkosi BD, 2019, TROP ANIM HEALTH PRO, V51, P1981, DOI 10.1007/s11250-019-01896-0 Paul D, 2007, RAPID COMMUN MASS SP, V21, P3006, DOI 10.1002/rcm.3185 Piliciauskas G, 2017, ARCHAEOL ANTHROP SCI, V9, P1421, DOI 10.1007/s12520-017-0463-z Pillonel L., 2004, Mitteilungen aus Lebensmitteluntersuchung und Hygiene, V95, P489 Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Renou JP, 2004, FOOD CHEM, V85, P63, DOI 10.1016/j.foodchem.2003.06.003 Rocchetti G, 2018, FOOD RES INT, V113, P407, DOI 10.1016/j.foodres.2018.07.029 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Scampicchio M, 2012, J AGR FOOD CHEM, V60, P11268, DOI 10.1021/jf302846j Wang FengZheng, 2007, Research of Agricultural Modernization, V28, P280 Zhao SS, 2020, FOOD CHEM, V310, DOI 10.1016/j.foodchem.2019.125826 NR 29 TC 20 Z9 21 U1 3 U2 73 PD JUN 30 PY 2020 VL 316 AR 126332 DI 10.1016/j.foodchem.2020.126332 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000517839500026 DA 2022-12-14 ER PT J AU Alagappan, S Hoffman, LC Mantilla, SMO Mikkelsen, D James, P Yarger, O Cozzolino, D AF Alagappan, Shanmugam Hoffman, Louwrens C. Mantilla, Sandra M. Olarte Mikkelsen, Deirdre James, Peter Yarger, Olympia Cozzolino, Daniel TI Near Infrared Spectroscopy as a Traceability Tool to Monitor Black Soldier Fly Larvae (Hermetia illucens) Intended as Animal Feed SO APPLIED SCIENCES-BASEL DT Article DE black soldier fly larvae; traceability; animal feed; NIR spectroscopy; organic side streams ID DIPTERA STRATIOMYIDAE; CHEMOMETRICS; AUTHENTICITY; LINNAEUS; INSECTS; FLIES; AGE AB The demand for animal proteins, especially from pork and poultry, is projected to increase significantly due to rapid growth in population and underlying socio-economic conditions. Livestock rearing using conventional feed ingredients is becoming challenging due to climate change and several other factors, thereby suggesting the need for alternative, viable and sustainable animal feed sources. The use of black soldier fly larvae (BSFL) (Hermetia illucens) as a component in animal feed is a promising candidate due to their ability to valorise different organic waste streams. The nutrient composition of BSFL reared on organic waste streams is also comparable to that of several conventional animal feed ingredients and varies depending upon the feed, rearing conditions, and the morphological stage of the larvae. The identification of organic waste is of importance as it can determine not only the composition but also the safety issues of BSFL as an animal feed ingredient. The objective of this study was to determine the ability of near-infrared (NIR) spectroscopy to trace the food waste used to grow BSFL. Samples of BSFL (5th and 6th instar BSFL; n = 50) obtained from a commercial production facility were analysed using NIR spectroscopy. Partial least squares discriminant analysis (PLS-DA) was employed to develop the models. The outcomes of this study revealed that NIR spectroscopy could distinguish different larval instars and suggested the importance of larval instars in developing calibration models for traceability applications. The developed PLS-DA model could predict the feed source used for rearing the 5th instar larvae (R-2 value: 0.89) and 6th instar pre-pupae (R-2 value: 0.91). This suggests that NIR spectroscopy could be used as a non-invasive traceability tool for BSFL and to assist in selecting the suitable time frame for larvae harvesting in commercial facilities. C1 [Alagappan, Shanmugam; Hoffman, Louwrens C.; Mantilla, Sandra M. Olarte; Mikkelsen, Deirdre; Cozzolino, Daniel] Univ Queensland, Ctr Nutr & Food Sci, Queensland Alliance Agr & Food Innovat QAAFI, Brisbane, Qld 4072, Australia. [Alagappan, Shanmugam; Hoffman, Louwrens C.] Fight Food Waste Cooperat Res Ctr, Wine Innovat Cent Bldg Level 1,Waite Campus, Urrbrae, SA 5064, Australia. [Hoffman, Louwrens C.] Univ Stellenbosch, Dept Anim Sci, Private Bag X1, ZA-7602 Stellenbosch, South Africa. [Mikkelsen, Deirdre] Univ Queensland, Fac Sci, Sch Agr & Food Sci, Brisbane, Qld 4072, Australia. [James, Peter] Univ Queensland, Ctr Anim Sci, Queensland Alliance Agr & Food Innovat QAAFI, Brisbane, Qld 4072, Australia. [Yarger, Olympia] Goterra, 14 Arnott St, Hume, ACT 2620, Australia. C3 University of Queensland; Stellenbosch University; University of Queensland; University of Queensland RP Cozzolino, D (corresponding author), Univ Queensland, Ctr Nutr & Food Sci, Queensland Alliance Agr & Food Innovat QAAFI, Brisbane, Qld 4072, Australia. EM d.cozzolino@uq.edu.au CR Alagappan S, 2022, J INSECTS FOOD FEED, V8, P343, DOI 10.3920/JIFF2021.0111 Anyidoho EK, 2020, ANAL METHODS-UK, V12, P4150, DOI 10.1039/d0ay00901f Banks IJ, 2014, TROP MED INT HEALTH, V19, P14, DOI 10.1111/tmi.12228 Barbi S, 2020, SCI TOTAL ENVIRON, V709, DOI 10.1016/j.scitotenv.2019.136209 Barragan-Fonseca KY, 2020, CURR OPIN INSECT SCI, V40, P85, DOI 10.1016/j.cois.2020.05.011 Barros LM, 2019, MICROSC RES TECHNIQ, V82, P178, DOI 10.1002/jemt.23127 Bec KB, 2021, MOLECULES, V26, DOI 10.3390/molecules26216390 Belghit I, 2019, AQUACULTURE, V503, P609, DOI 10.1016/j.aquaculture.2018.12.032 Benes E, 2022, FOOD CHEM X, V13, DOI 10.1016/j.fochx.2022.100266 Bessa LW, 2020, COMPR REV FOOD SCI F, V19, P2747, DOI 10.1111/1541-4337.12609 Bureau S, 2019, POSTHARVEST BIOL TEC, V148, P1, DOI 10.1016/j.postharvbio.2018.10.003 Cozzolino D, 2019, FOOD ANAL METHOD, V12, P2469, DOI 10.1007/s12161-019-01605-5 Cozzolino D, 2005, J AGR FOOD CHEM, V53, P4459, DOI 10.1021/jf050303i Cozzolino D, 2016, WOODHEAD PUBL FOOD S, V301, P119, DOI 10.1016/B978-0-08-100310-7.00007-7 Cozzolino D, 2012, APPL SPECTROSC REV, V47, P518, DOI 10.1080/05704928.2012.667858 Cozzolino D, 2004, NEAR INFRARED SPECTR, V44, P647 Cozzolino D, 2021, CYTA-J FOOD, V19, P183, DOI 10.1080/19476337.2021.1875052 Cozzolino D, 2009, PLANTA MED, V75, P746, DOI 10.1055/s-0028-1112220 EC-European Commission, 2002, J EUROP COMM, VL031, P1 Franco A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su131810198 Galassi G, 2021, J INSECT SCI, V21, DOI 10.1093/jisesa/ieaa148 Gold M, 2020, WASTE MANAGE, V102, P319, DOI 10.1016/j.wasman.2019.10.036 Gold M, 2018, WASTE MANAGE, V82, P302, DOI 10.1016/j.wasman.2018.10.022 Grassi S, 2018, FOOD CHEM, V243, P382, DOI 10.1016/j.foodchem.2017.09.145 Guelpa A, 2017, FOOD CONTROL, V73, P1388, DOI 10.1016/j.foodcont.2016.11.002 Heussler CD, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0197896 Hoc B, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0216160 Holmes LA, 2013, ENVIRON ENTOMOL, V42, P370, DOI 10.1603/EN12255 Hopkins I, 2021, INSECTS, V12, DOI 10.3390/insects12070608 Imathiu S., 2020, NFS J, V18, P1, DOI [10.1016/j.nfs.2019.11.002, DOI 10.1016/J.NFS.2019.11.002] Johnson JB, 2020, APPL SPECTROSC REV, V55, P810, DOI 10.1080/05704928.2019.1685532 Karoui R, 2010, CHEM REV, V110, P6144, DOI 10.1021/cr100090k Kaya C, 2021, BMC BIOL, V19, DOI 10.1186/s12915-021-01029-w Kim Wontae, 2010, International Journal of Industrial Entomology, V21, P185 Komarek AM, 2021, GLOBAL ENVIRON CHANG, V70, DOI 10.1016/j.gloenvcha.2021.102343 Liew CS, 2022, J HAZARD MATER, V423, DOI 10.1016/j.jhazmat.2021.126995 Liu NJ, 2018, TALANTA, V184, P128, DOI 10.1016/j.talanta.2018.02.097 Liu X, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0182601 Mayagaya VS, 2009, AM J TROP MED HYG, V81, P622, DOI 10.4269/ajtmh.2009.09-0192 McVey C, 2021, TRENDS FOOD SCI TECH, V118, P777, DOI 10.1016/j.tifs.2021.11.003 Mellado-Carretero J, 2020, J INSECTS FOOD FEED, V6, P141, DOI 10.3920/JIFF2019.0032 Minhas A.S, 2016, FOOD SAFETY 21 CENTU Nyakeri EM, 2017, J INSECTS FOOD FEED, V3, P193, DOI 10.3920/JIFF2017.0004 Oonincx DGAB, 2015, J INSECTS FOOD FEED, V1, P131, DOI 10.3920/JIFF2014.0023 Perez-Mendoza J, 2002, J MED ENTOMOL, V39, P499, DOI 10.1603/0022-2585-39.3.499 Pinotti L, 2021, J CLEAN PROD, V294, DOI 10.1016/j.jclepro.2021.126290 Purkayastha D, 2022, INT J ENVIRON SCI TE, V19, P12701, DOI 10.1007/s13762-021-03524-7 Ravi HK, 2020, EUR FOOD RES TECHNOL, V246, P2549, DOI 10.1007/s00217-020-03596-8 Santos PM, 2019, MICROCHEM J, V149, DOI 10.1016/j.microc.2019.104057 SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 Sheppard DC, 2002, J MED ENTOMOL, V39, P695, DOI 10.1603/0022-2585-39.4.695 Shumo M, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-46603-z Sikulu M, 2010, PARASITE VECTOR, V3, DOI 10.1186/1756-3305-3-49 Sivanantharaja Abirami, 2018, International Journal of Entomology Research, V3, P18 Spranghers T, 2017, J SCI FOOD AGR, V97, P2594, DOI 10.1002/jsfa.8081 Surendra KC, 2020, WASTE MANAGE, V117, P58, DOI 10.1016/j.wasman.2020.07.050 Tomberlin JK, 2009, ENVIRON ENTOMOL, V38, P930, DOI 10.1603/022.038.0347 van der Fels-Klerx HJ, 2018, COMPR REV FOOD SCI F, V17, P1172, DOI 10.1111/1541-4337.12385 van Hal O, 2019, J CLEAN PROD, V219, P485, DOI 10.1016/j.jclepro.2019.01.329 Weeranantanaphan J, 2011, J NEAR INFRARED SPEC, V19, P61, DOI 10.1255/jnirs.924 Weyer L., 2007, PRACTICAL GUIDE INTE Williams P, 2017, J NEAR INFRARED SPEC, V25, P85, DOI 10.1177/0967033517702395 Yu ZL, 2022, CRIT REV FOOD SCI, V62, P905, DOI [10.1080/10408398.2020.1830262, 10.1007/978-3-030-58529-7_1] NR 63 TC 1 Z9 1 U1 4 U2 4 PD AUG PY 2022 VL 12 IS 16 AR 8168 DI 10.3390/app12168168 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000847139500001 DA 2022-12-14 ER PT J AU Chen, X Liu, HG Li, JQ Wang, YZ AF Chen, Xiong Liu, Honggao Li, Jieqing Wang, Yuanzhong TI A geographical traceability method for Lanmaoa asiatica mushrooms from 20 township-level geographical origins by near infrared spectroscopy and ResNet image analysis techniques br SO ECOLOGICAL INFORMATICS DT Article DE Boletes; Near -infrared spectroscopy; Residual neural network; Geographical traceability; Key climate factors ID FRONTIERS; MUSHROOMS AB Food authenticity and traceability and climate change are key scientific issues that must be addressed in response to the food crisis in 2050. Lanmaoa asiatica mushroom is an expensive and nutritious fungi-based diets resource, it is necessary to identify its geographical origin and explore the impact of the climate on it. Thus, the purpose of this study is to establish a fast and accurate geographical traceability model based on L. asiatica mushrooms chemical information collected by near-infrared spectroscopy (NIRS) technology, and screen out key climate variables by competitive adaptive reweighted sampling (CARS) algorithm. Based on the NIRS information of L. asiatica mushrooms, two-dimensional correlation spectroscopy (2D-COS) images were generated and a residual neural network (ResNet) image recognition model was established to identify the geographical origin of L. asiatica mushrooms. The accuracy of training set and test set of ResNet model is 100%, and the loss value is 0.052, which indicates that the model is effective. In addition, the CARS algorithm was used to select the feature variables from 105 climate variables. Four important variables (February, March, and April precipitation and January minimum temperature) related to NIRS difference of L. asiatica mushroom were obtained by CARS algorithm. The results can provide a fast and accurate method for food authenticity and traceability research, and provide an innovative idea for screening key climate factors. C1 [Chen, Xiong; Liu, Honggao; Li, Jieqing] Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China. [Chen, Xiong; Wang, Yuanzhong] Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China. [Liu, Honggao] Zhaotong Univ, Zhaotong 657000, Peoples R China. C3 Yunnan Agricultural University; Yunnan Academy of Agricultural Sciences; Zhaotong University RP Li, JQ (corresponding author), Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China. EM lijieqing2008@126.com; boletus@126.com CR Agreda T, 2015, GLOBAL CHANGE BIOL, V21, P3499, DOI 10.1111/gcb.12960 Alikord M, 2022, CRIT REV FOOD SCI, V62, P4833, DOI 10.1080/10408398.2021.1879003 Amirvaresi A, 2021, FOOD CHEM, V344, DOI 10.1016/j.foodchem.2020.128647 Bellin N, 2022, ECOL INFORM, V69, DOI 10.1016/j.ecoinf.2022.101682 Brennan G, 2015, NAT CLIM CHANGE, V5, P892, DOI 10.1038/NCLIMATE2682 Cao CC, 2022, ECOL INDIC, V140, DOI 10.1016/j.ecolind.2022.108984 Chapman J, 2022, CRIT REV FOOD SCI, V62, P2845, DOI 10.1080/10408398.2020.1863328 Chen X, 2022, SPECTROCHIM ACTA A, V274, DOI 10.1016/j.saa.2022.121137 Chen X, 2021, APPL SPECTROSC REV, DOI 10.1080/05704928.2021.1994415 Chhaya R. S., 2022, Trends in Food Science & Technology, V126, P126, DOI 10.1016/j.tifs.2021.07.040 Cui YY, 2016, FUNGAL DIVERS, V81, P189, DOI 10.1007/s13225-015-0336-7 Debus B, 2021, TRAC-TREND ANAL CHEM, V145, DOI 10.1016/j.trac.2021.116459 Dong J. E., 2022, Microchemical Journal, V177, DOI 10.1016/j.microc.2022.107260 Dong JE, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108132 Ganguly S, 2022, ECOL INFORM, V69, DOI 10.1016/j.ecoinf.2022.101585 Halbwachs H, 2018, FUNGAL BIOL REV, V32, P143, DOI 10.1016/j.fbr.2018.04.001 Hui E, 2022, ECOL INFORM, V68, DOI 10.1016/j.ecoinf.2021.101539 Jenkins CL, 2022, ECOL INFORM, V69, DOI 10.1016/j.ecoinf.2022.101662 Li HD, 2009, ANAL CHIM ACTA, V648, P77, DOI 10.1016/j.aca.2009.06.046 Liang GF, 2022, ECOL INDIC, V139, DOI 10.1016/j.ecolind.2022.108928 Liang N, 2022, CRIT REV FOOD SCI, V62, P2963, DOI 10.1080/10408398.2020.1862045 Liu JM, 2021, ENERGIES, V14, DOI 10.3390/en14051460 Liu ZM, 2021, MICROCHEM J, V169, DOI 10.1016/j.microc.2021.106545 Taye ZM, 2016, FUNGAL ECOL, V23, P30, DOI 10.1016/j.funeco.2016.05.008 Miller SL, 2021, FUNGAL ECOL, V50, DOI 10.1016/j.funeco.2021.101040 Misiou O., 2022, Trends in Food Science & Technology, V126, P142, DOI 10.1016/j.tifs.2021.03.031 Mleczek M, 2021, CHEMOSPHERE, V263, DOI 10.1016/j.chemosphere.2020.128095 Nawy T, 2018, NAT METHODS, V15, P572, DOI 10.1038/s41592-018-0093-0 Negrao DR, 2021, FUNGAL BIOL-UK, V125, P860, DOI 10.1016/j.funbio.2021.05.007 Neves MD, 2022, FOOD CONTROL, V132, DOI 10.1016/j.foodcont.2021.108489 Ngarega BK, 2021, ECOL INFORM, V65, DOI 10.1016/j.ecoinf.2021.101419 Noda I, 2014, J MOL STRUCT, V1069, P23, DOI 10.1016/j.molstruc.2014.01.016 Noda I, 2014, J MOL STRUCT, V1069, P3, DOI 10.1016/j.molstruc.2014.01.025 Pandiselvam R, 2022, FOOD CONTROL, V133, DOI 10.1016/j.foodcont.2021.108588 Qi LM, 2018, FOOD FUNCT, V9, P5903, DOI 10.1039/c8fo01376d Rios-Reina R, 2021, TRAC-TREND ANAL CHEM, V134, DOI 10.1016/j.trac.2020.116121 Rizzo G, 2021, TRENDS FOOD SCI TECH, V117, P60, DOI 10.1016/j.tifs.2020.12.025 Sakamoto Y, 2018, FUNGAL BIOL REV, V32, P236, DOI 10.1016/j.fbr.2018.02.003 de Lima ABS, 2022, FOOD CHEM, V367, DOI 10.1016/j.foodchem.2021.130744 Schuetz D, 2022, FOOD CONTROL, V136, DOI 10.1016/j.foodcont.2022.108892 Shigyo N, 2021, FUNGAL ECOL, V50, DOI 10.1016/j.funeco.2020.101036 Soilhi Z, 2022, ECOL INFORM, V68, DOI 10.1016/j.ecoinf.2021.101533 Springmann M, 2018, NATURE, V562, P519, DOI 10.1038/s41586-018-0594-0 Struminska-Parulska D, 2021, TRENDS FOOD SCI TECH, V114, P672, DOI 10.1016/j.tifs.2021.06.015 Suman M, 2021, TRAC-TREND ANAL CHEM, V142, DOI 10.1016/j.trac.2021.116305 Sun T, 2020, ECOL INDIC, V109, DOI 10.1016/j.ecolind.2019.105771 Talari G., 2022, Trends in Food Science & Technology, V126, P192, DOI 10.1016/j.tifs.2021.08.032 Trendov N.M., 2019, FOOD AGR ORG UN Visciano P, 2021, TRENDS FOOD SCI TECH, V114, P424, DOI 10.1016/j.tifs.2021.06.010 Wang L, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107879 Wang L, 2021, ACS OMEGA, V6, P19665, DOI 10.1021/acsomega.1c02317 Wang N, 2022, MOLECULES, V27, DOI 10.3390/molecules27113373 Wang ZQ, 2022, INFRARED PHYS TECHN, V122, DOI 10.1016/j.infrared.2022.104085 Wu M, 2022, ECOL INDIC, V139, DOI 10.1016/j.ecolind.2022.108934 Wu ZF, 2019, PATTERN RECOGN, V90, P119, DOI 10.1016/j.patcog.2019.01.006 Xie LN, 2020, FOOD CHEM, V316, DOI 10.1016/j.foodchem.2020.126332 Yan ZY, 2022, LWT-FOOD SCI TECHNOL, V162, DOI 10.1016/j.lwt.2022.113490 Yang B, 2021, SPECTROCHIM ACTA A, V260, DOI 10.1016/j.saa.2021.119956 Yu ZL, 2022, CRIT REV FOOD SCI, V62, P905, DOI [10.1080/10408398.2020.1830262, 10.1007/978-3-030-58529-7_1] NR 59 TC 0 Z9 0 U1 2 U2 2 PD NOV PY 2022 VL 71 AR 101808 DI 10.1016/j.ecoinf.2022.101808 WC Ecology SC Environmental Sciences & Ecology UT WOS:000862299700003 DA 2022-12-14 ER PT J AU Yadav, S Garg, D Luthra, S AF Yadav, Sanjeev Garg, Dixit Luthra, Sunil TI Ranking of performance indicators in an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC) SO INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT DT Article DE Internet of Things (IoT); Agriculture supply chain (ASC); Additive ratio assessment (ARAS); COVID-19; Efficient traceability mechanism ID FOOD WASTE; DYNAMIC CAPABILITIES; COLD CHAIN; MANAGEMENT; CHALLENGES; INFORMATION; BLOCKCHAIN; FRAMEWORK; ICT; TECHNOLOGIES AB Purpose The prime aim of this paper is the identification and prioritization of performance indicators, which motivate the development of an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC). Also, this research aims for checking the robustness of obtained results. Design/methodology/approach Ten performance indicators have been identified based on the five "criteria in the IoT-based traceable system". Further, based on five criteria, performance indicators were ranked by using grey-based "Additive Ratio Assessment". Findings Sustainable practices obtained first rank, and certification of agri-products obtained worst ranking. Further, based on sensitivity analysis, tracking of agri-products and stakeholders' behavior have found high sensitivity. Also, information sharing and global distribution networks have found the least sensitive performance indicators. Research limitations/implications This research has some limitations of taking only a few criteria and alternatives. This study may also contribute as a practical insight to the practitioners and managers in decision-making in the adoption of an IoT-based traceable system within the ASC. Originality/value This research may motivate the implementation of an IoT-based efficient traceability mechanism that improved the sustainability and consumer's trust in the ASC during different types of hazardous activities and other outbreaks (COVID-19). Also, this research has provided a theoretical insight based on the dynamic capability theory (DCT). C1 [Yadav, Sanjeev] Natl Inst Technol Kurukshetra, Mech Engn, Kurukshetra, Haryana, India. [Garg, Dixit] Natl Inst Technol Kurukshetra, Dept Mech Engn, Kurukshetra, Haryana, India. [Luthra, Sunil] Ch Ranbir Singh State Inst Engn & Technol, Dept Mech Engn, Jhajjar, India. C3 National Institute of Technology (NIT System); National Institute of Technology Kurukshetra; National Institute of Technology (NIT System); National Institute of Technology Kurukshetra RP Yadav, S (corresponding author), Natl Inst Technol Kurukshetra, Mech Engn, Kurukshetra, Haryana, India. EM ysanjeev949@gmail.com CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Amarnath G, 2018, CLIM RISK MANAG, V22, P52, DOI 10.1016/j.crm.2018.10.001 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Azuara G, 2012, IND MANAGE DATA SYST, V112, P340, DOI 10.1108/02635571211210022 Badia-Melis R, 2018, FOOD CONTROL, V86, P170, DOI 10.1016/j.foodcont.2017.11.022 Badia-Melis R, 2015, SENSORS-BASEL, V15, P4781, DOI 10.3390/s150304781 Balaji M, 2016, RESOUR CONSERV RECY, V114, P153, DOI 10.1016/j.resconrec.2016.07.016 Barnett I, 2019, DEV PRACT, V29, P287, DOI 10.1080/09614524.2018.1557596 Ben-Daya M, 2019, INT J PROD RES, V57, P4719, DOI 10.1080/00207543.2017.1402140 Bernal P, 2019, IND INNOV, V26, P295, DOI 10.1080/13662716.2018.1465813 Beske P, 2014, INT J PROD ECON, V152, P131, DOI 10.1016/j.ijpe.2013.12.026 Bhanot N, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041517 Bharucha J, 2018, BRIT FOOD J, V120, P639, DOI [10.1108/BFJ-06-2017-0324, 10.1108/bfj-06-2017-0324] Bordel B, 2019, ADV INTELL SYST, V850, P224, DOI 10.1007/978-3-030-02351-5_27 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bravi L, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11041110 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Buyukozkan G, 2018, APPL SOFT COMPUT, V69, P634, DOI 10.1016/j.asoc.2018.04.040 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Castiaux A, 2012, INT J INNOV MANAG, V16, DOI 10.1142/S1363919612400130 Chang WJ, 2016, EUR MANAG J, V34, P282, DOI 10.1016/j.emj.2015.11.008 Chauhan A, 2020, ANN OPER RES, V290, P621, DOI 10.1007/s10479-019-03190-6 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Donnelly KAM, 2012, FOOD CONTROL, V27, P228, DOI 10.1016/j.foodcont.2012.03.021 Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Dubey R, 2019, MANAGE DECIS, V57, P767, DOI 10.1108/MD-04-2018-0396 FAO, 2017, FOOD OUTL BIANN REP FAO and WHO, 2020, COVID 19 FOOD SAF GU Farahani RZ, 2020, EUR J OPER RES, V287, P787, DOI 10.1016/j.ejor.2020.03.005 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fiorino M, 2019, SPRBRIEF MOLEC SCI, P1, DOI 10.1007/978-3-030-22553-7 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Ghaani M, 2016, TRENDS FOOD SCI TECH, V51, P1, DOI 10.1016/j.tifs.2016.02.008 Girotto F, 2015, WASTE MANAGE, V45, P32, DOI 10.1016/j.wasman.2015.06.008 Gupta H, 2017, J CLEAN PROD, V152, P242, DOI 10.1016/j.jclepro.2017.03.125 Haleem Abid, 2019, Information Processing in Agriculture, V6, P335, DOI 10.1016/j.inpa.2019.01.003 Han JH, 2017, INT J PROD ECON, V187, P196, DOI 10.1016/j.ijpe.2017.02.018 Haulder N, 2019, COMPUT IND ENG, V138, DOI 10.1016/j.cie.2019.106116 Helfat CE., 2007, DYNAMIC CAPABILITIES Hobbs JE, 2020, CAN J AGR ECON, V68, P171, DOI 10.1111/cjag.12237 Hong W., 2019, P 2018 1 IEEE INT C Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Jedermann R, 2006, SENSOR ACTUAT A-PHYS, V132, P370, DOI 10.1016/j.sna.2006.02.008 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2010, BRIT FOOD J, V112, P187, DOI 10.1108/00070701011018860 Kassem R, 2019, BENCHMARKING, V26, P117, DOI 10.1108/BIJ-03-2018-0068 Kataike J, 2019, SUPPLY CHAIN MANAG, V24, P484, DOI 10.1108/SCM-03-2018-0097 Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Kumar A, 2020, BENCHMARKING, V27, P1003, DOI 10.1108/BIJ-11-2019-0500 Kumar P, 2022, INT J LOGIST-RES APP, V25, P1401, DOI 10.1080/13675567.2021.1908524 Lago P, 2009, J SYST SOFTWARE, V82, P168, DOI 10.1016/j.jss.2008.08.026 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Linton T., 2020, HARWARD BUSINESS REV Mangla SK, 2018, INT J PROD ECON, V203, P379, DOI 10.1016/j.ijpe.2018.07.012 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 Naik G, 2018, IIMB MANAG REV, V30, P270, DOI 10.1016/j.iimb.2018.04.001 Narayanan S, 2015, ASIA PAC POLICY STUD, V2, P197, DOI 10.1002/app5.62 OECD, 2020, COVID 19 FOOD AGR SE Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Papargyropoulou E, 2014, J CLEAN PROD, V76, P106, DOI 10.1016/j.jclepro.2014.04.020 Perez-Aloe R., 2007, APPL RFID TAGS OVERA, P1, DOI [DOI 10.1109/RFIDEURAS1A.2007.4368136, 10.1109/RFIDEURASIA.2007.4368136] Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sarpong S, 2014, EUR BUS REV, V26, P271, DOI 10.1108/EBR-09-2013-0113 Schanes K, 2018, J CLEAN PROD, V182, P978, DOI 10.1016/j.jclepro.2018.02.030 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Singh RK, 2019, RESOUR CONSERV RECY, V147, P10, DOI 10.1016/j.resconrec.2019.04.014 Sivarajah U, 2017, J BUS RES, V70, P263, DOI 10.1016/j.jbusres.2016.08.001 Srivastava A, 2022, ANN OPER RES, V315, P2115, DOI 10.1007/s10479-021-04072-6 Taoukis PS, 2016, FOOD ENG SER, P285, DOI 10.1007/978-3-319-24040-4_16 Teece DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/smj.640 Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z Medialdea JT, 2018, J INNOV KNOWL, V3, P82, DOI 10.1016/j.jik.2017.12.007 Vahlne JE, 2017, INT BUS REV, V26, P57, DOI 10.1016/j.ibusrev.2016.05.006 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Wen X, 2019, INT J PROD ECON, V207, P34, DOI 10.1016/j.ijpe.2018.10.012 Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 Wong CY, 2011, J OPER MANAG, V29, P604, DOI 10.1016/j.jom.2011.01.003 Zambrano MV, 2019, TRENDS FOOD SCI TECH, V88, P484, DOI 10.1016/j.tifs.2019.04.006 Zavadskas EK, 2010, TECHNOL ECON DEV ECO, V16, P159, DOI 10.3846/tede.2010.10 Zhang J, 2009, J FOOD AGRIC ENVIRON, V7, P28 Zhou XY, 2022, OPER MANAGE RES, V15, P93, DOI 10.1007/s12063-021-00189-w NR 88 TC 1 Z9 1 U1 12 U2 17 PD FEB 22 PY 2022 VL 39 IS 3 SI SI BP 777 EP 803 DI 10.1108/IJQRM-03-2021-0085 EA OCT 2021 WC Management SC Business & Economics UT WOS:000705083300001 DA 2022-12-14 ER PT J AU Chen, YN Zhu, XH Fang, K Wu, YB Deng, YC He, X Zou, ZY Guo, T AF Chen, Yineng Zhu, Xinghui Fang, Kui Wu, Yanbin Deng, Yuechao He, Xiao Zou, Zhuoyang Guo, Ting TI An Optimization Model for Process Traceability in Case-Based Reasoning Based on Ontology and the Genetic Algorithm SO IEEE SENSORS JOURNAL DT Article DE Ontologies; Optimization; Production; Genetic algorithms; Cognition; Raw materials; Quality assessment; Ontology; GA; traceability optimization; CBR; processing information AB A traceability system can quickly and accurately query product supply chain circulation information. It is difficult to obtain processing information and achieve sharing due to the big data, long duration, large amount of equipment and scattered and easily lost processing information. In tracing the application of the case-based reasoning (CBR) to product processing, we can solve the problem of establishing an accurate and complete mathematical model between the processing information and product quality defects by finding a target record in a large amount of historical data. The traceability model of ontology and CBR uses the web ontology language (OWL) for case representation to simplify the CBR framework for big data, classifies process data and standardizes information storage. A joint optimization algorithm based on the genetic algorithm (GA) and CBR is proposed to establish an optimization model of process traceability, which is applied to case retrieval and reuse. This algorithm optimizes the genetic operator by combining the optimal preservation strategy and roulette selection method and uses an exponential-scale transformation method to stretch the fitness function. The experiments show that the optimized traceability model can infer information from garbled codes, wrong codes and missing messages to quickly determine the problematic products, thus effectively improving the traceability accuracy of product processing. C1 [Chen, Yineng; Zhu, Xinghui; Fang, Kui; Wu, Yanbin; Deng, Yuechao; He, Xiao; Zou, Zhuoyang] Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China. [Wu, Yanbin] Changsha Commerce & Tourism Coll, Network Secur & Informat Technol Ctr, Changsha 410116, Peoples R China. [Guo, Ting] Hezhou Univ, Coll Food & Bioengn, Hezhou 432829, Peoples R China. C3 Hunan Agricultural University; Hezhou University RP Zhu, XH; Fang, K (corresponding author), Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China. EM zhuxh@hunau.edu.cn; fk@hunau.edu.cn CR Bayar N, 2016, COMPUT IND, V81, P67, DOI 10.1016/j.compind.2015.09.004 Chen R.-Y., 2018, PROC J PHYS C, V1026 Chen SL, 2016, ADV ENG INFORM, V30, P564, DOI 10.1016/j.aei.2016.06.005 Chen Y., 2016, JIANGSU AGR SCI, V6, P426 Chen Z., 2014, THESIS NE U SHENYANG Chhim P, 2019, J INTELL MANUF, V30, P905, DOI 10.1007/s10845-016-1290-2 El Kadiri S, 2015, INT J PROD RES, V53, P5657, DOI 10.1080/00207543.2015.1052155 Fang K, 2015, J INVEST MED, V63, pS22 Gautam R, 2017, COMPUT IND ENG, V103, P46, DOI 10.1016/j.cie.2016.09.007 Guo S.T, 2019, COMPUT ENG DES, V40, P834 Ibrahim H, 2018, COMPUT ELECTR ENG, V67, P551, DOI 10.1016/j.compeleceng.2018.02.028 Jiang ZG, 2019, J INTELL MANUF, V30, P19, DOI 10.1007/s10845-016-1231-0 Ke C, 2020, J CLEAN PROD, V277, DOI 10.1016/j.jclepro.2020.123269 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Mabkhot MM, 2019, ADV MATER SCI ENG, V2019, DOI 10.1155/2019/2505183 Martins LD, 2016, 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), P2424, DOI 10.1109/ITSC.2016.7795946 Meena R, 2020, J INTELL SYST, V29, P653, DOI 10.1515/jisys-2017-0561 Pizzuti T, 2017, FOOD CONTROL, V72, P123, DOI 10.1016/j.foodcont.2016.07.038 Shan W, 2019, ROBOT CIM-INT MANUF, V58, P80, DOI 10.1016/j.rcim.2019.01.012 Vivek M., 2018, PROC INT C COGN RECO Wang ShanShan, 2018, Transactions of the Chinese Society of Agricultural Engineering, V34, P263, DOI 10.11975/j.issn.1002-6819.2018.14.034 Wu L., 2018, CHINA FOOD SAFETY DE Xing Bin, 2015, Transactions of the Chinese Society of Agricultural Engineering, V31, P309, DOI 10.11975/j.issn.1002-6819.2015.10.042 [杨从锐 Yang Congrui], 2018, [计算机应用研究, Application Research of Computers], V35, P1042 Zhang C., 2018, J TEST MEAS TECHNOL, V32, P526 Zhang YJ, 2019, FOOD CONTROL, V95, P283, DOI 10.1016/j.foodcont.2018.08.018 NR 26 TC 0 Z9 0 U1 8 U2 20 PD NOV 15 PY 2021 VL 21 IS 22 BP 25123 EP 25132 DI 10.1109/JSEN.2021.3065757 WC Engineering, Electrical & Electronic; Instruments & Instrumentation; Physics, Applied SC Engineering; Instruments & Instrumentation; Physics UT WOS:000717802500030 DA 2022-12-14 ER PT J AU Pafundo, S Agrimonti, C Maestri, E Marmiroli, N AF Pafundo, Simona Agrimonti, Caterina Maestri, Elena Marmiroli, Nelson TI Applicability of SCAR markers to food genomics: Olive oil traceability SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE food genomics; AFLPs; SCAR markers; olive oil traceability ID FRAGMENT LENGTH POLYMORPHISMS; FORENSIC APPLICATIONS; DNA EXTRACTION; OLEA-EUROPAEA; IDENTIFICATION; CONVERSION; GENE; CULTIVARS; LOCUS; RICE AB DNA analysis with molecular markers has opened a shortcut toward a genomic comprehension of complex organisms. The availability of micro-DNA extraction methods, coupled with selective amplification of the smallest extracted fragments with molecular markers, could equally bring a breakthrough in food genomics: the identification of original components in food. Amplified fragment length polymorphisms (AFLPs) have been instrumental in plant genomics because they may allow rapid and reliable analysis of multiple and potentially polymorphic sites. Nevertheless, their direct application to the analysis of DNA extracted from food matrixes is complicated by the low quality of DNA extracted: its high degradation and the presence of inhibitors of enzymatic reactions. The conversion of an AFLP fragment to a robust and specific single-locus PCR-based marker, therefore, could extend the use of molecular markers to large-scale analysis of complex agro-food matrixes. In the present study is reported the development of sequence characterized amplified regions (SCARs) starting from AFLP profiles of monovarietal olive oils analyzed on agarose gel; one of these was used to identify differences among 56 olive cultivars. All the developed markers were purposefully amplified in olive oils to apply them to olive oil traceability. C1 Univ Parma, Dept Environm Sci, Div Genet & Environm Biotechnol, I-43100 Parma, Italy. C3 University of Parma RP Marmiroli, N (corresponding author), Univ Parma, Dept Environm Sci, Div Genet & Environm Biotechnol, Viale GP Usberti 11-A, I-43100 Parma, Italy. EM nelson.marmiroli@unipr.it CR Angiolillo A, 1999, THEOR APPL GENET, V98, P411, DOI 10.1007/s001220051087 [Anonymous], 2003, OFF J EUR COMMUNIT L, V295, P57 Benham JJ, 2001, GENOGRAPHER Besnard G, 2002, THEOR APPL GENET, V104, P1353, DOI 10.1007/s00122-001-0832-x Bradeen JM, 1998, THEOR APPL GENET, V97, P960, DOI 10.1007/s001220050977 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f BROWN JWS, 1986, NUCLEIC ACIDS RES, V14, P9549, DOI 10.1093/nar/14.24.9549 Brugmans B, 2003, NUCLEIC ACIDS RES, V31, DOI 10.1093/nar/gng055 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Cresti M., 1997, Oliva, P36 Hellebrand M, 1998, Z LEBENSM UNTERS F A, V206, P237, DOI 10.1007/s002170050250 Hernandez M, 2004, J CEREAL SCI, V39, P99, DOI 10.1016/S0733-5210(03)00071-7 Hernandez P, 2001, THEOR APPL GENET, V103, P788, DOI 10.1007/s001220100603 Jobling MA, 2004, NAT REV GENET, V5, P739, DOI 10.1038/nrg1455 Lopez-Sese AI, 2003, THEOR APPL GENET, V108, P41, DOI 10.1007/s00122-003-1404-z Lumaret R, 2000, THEOR APPL GENET, V101, P547, DOI 10.1007/s001220051514 Meksem K, 2001, MOL GENET GENOMICS, V265, P207, DOI 10.1007/s004380000418 Muzzalupo I, 2002, EUR FOOD RES TECHNOL, V214, P528, DOI 10.1007/s00217-001-0482-x Naqvi NI, 1996, GENOME, V39, P26, DOI 10.1139/g96-004 Paabo S, 2004, ANNU REV GENET, V38, P645, DOI 10.1146/annurev.genet.37.110801.143214 Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x PALMIERI L, 2003, THESIS U PARMA Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 POSNO M, 1986, NUCLEIC ACIDS RES, V14, P3181, DOI 10.1093/nar/14.8.3181 Schmitz-Linneweber C, 2002, MOL BIOL EVOL, V19, P1602, DOI 10.1093/oxfordjournals.molbev.a004222 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 Wu YY, 2006, CLIN CHIM ACTA, V363, P165, DOI 10.1016/j.cccn.2005.07.010 Wurz A, 1999, FOOD CONTROL, V10, P385, DOI 10.1016/S0956-7135(99)00080-8 Yamanaka S, 2004, THEOR APPL GENET, V108, P1200, DOI 10.1007/s00122-003-1564-x 1992, OFF J EUR COMMUNIT L, V208, P1 2002, OFF J EUR COMMUNIT L, V155, P27 NR 33 TC 51 Z9 54 U1 0 U2 16 PD JUL 26 PY 2007 VL 55 IS 15 BP 6052 EP 6059 DI 10.1021/jf0701638 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000248085300024 DA 2022-12-14 ER PT J AU Zhang, Y Li, JQ Li, T Liu, HG Wang, YZ AF Zhang Yu Li Jie-qing Li Tao Liu Hong-gao Wang Yuan-zhong TI Study on the Geographical Traceability of Boletus Tomentipes Using Multi-Spectra Data Fusion SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE The geographical traceability; Data fusion; Boletus tomentipes; UV-Vis; FTIR ID ISOTOPE RATIO ANALYSIS; MASS-SPECTROMETRY; DIFFERENT REGIONS; ICP-OES; ORIGIN; SPECTROSCOPY; AUTHENTICATION; FTIR; PREDICTION; VINEGARS AB Currently, since the domestic and international food marketing on the safety supervision and traceability system is defective, as well as false labels used by agency, situation on the food safety is becoming more and more serious. In order to enhance food safety, it's essential to establish a fast and efficient geographical traceability method to protect the agricultural brand of Yunnan plateau. A total of 77 fruit bodies of Boletus tomentipes were collected from 8 geographical origins. Raw of ultraviolet-visible (UV-Vis) and Fourier transform infrared (FTIR) spectra were preprocessed by multiplicative scatter correction (MSC), standard normal variate (SNV), second derivative (2D), Savitzky-Golay (SG) smoothing. Based on pretreatment of UV and FUR spectra, low-level and mid-level data fusion strategy combined with partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to identify Boletus in different regions. The results indicated that: (1) that the best pretreatment, was SNV+2D with highest (RY)-Y-2 (61. 58%) and Q(2) (95. 09%) for UV-Vis spectra, and MSC+2D with highest (RY)-Y-2 (50. 85%) and Q(2) (82. 16%) for FTIR spectra; (2) For UV-Vis, FTIR spectra, low-level and mid-level data fusion, the number of error samples in the classification of PLS-DA and SVM analysis were 24, 6, 2, 2, and 6, 1, 1, 0, respectively; (3) In the mid-level data fusion, the best classification of SVM with none error sample was better than that of the PLSDA with 2 error samples; (4) The classification of HCA analysis in the mid-level data fusion with 4 error samples had the better performance than that in the low-level data fusion with 1 error sample. In addition, HCA analysis of mid-level data fusion showed that the distance of samples collected from same area were longer than that collected from different sites. It indicated that the differences of samples collected from different sites in the same area were less than that collected from different regions. Those results indicated that mid-level data fusion combined with SVM model using UV-Vis and FTIR spectroscopy can accurately identify Boletus collected from different geographical origins. It will provide a new strategy on the research of geographical traceability of wild edible fungus. C1 [Zhang Yu; Li Jie-qing; Liu Hong-gao] Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China. [Zhang Yu; Wang Yuan-zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. [Li Tao] Yuxi Normal Univ, Coll Resources & Environm, Yuxi 653100, Peoples R China. C3 Yunnan Agricultural University; Yunnan Academy of Agricultural Sciences; Yuxi Normal University RP Liu, HG (corresponding author), Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM m15343842322@163.com; honggaoliu@126.com; boletus@126.com CR Acierno V, 2016, FOOD RES INT, V84, P86, DOI 10.1016/j.foodres.2016.03.022 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Ballabio D, 2013, ANAL METHODS-UK, V5, P3790, DOI 10.1039/c3ay40582f Biancolillo A, 2014, ANAL CHIM ACTA, V820, P23, DOI 10.1016/j.aca.2014.02.024 Blanco M, 2011, TALANTA, V85, P2218, DOI 10.1016/j.talanta.2011.07.082 Carrera M, 2017, J AGR FOOD CHEM, V65, P1070, DOI 10.1021/acs.jafc.6b04972 Casian T, 2017, TALANTA, V167, P333, DOI 10.1016/j.talanta.2017.01.092 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Gad HA, 2013, PHYTOCHEM ANALYSIS, V24, P520, DOI 10.1002/pca.2426 Gambetta JM, 2016, FOOD ANAL METHOD, V9, P2842, DOI 10.1007/s12161-016-0467-9 Habte G, 2016, FOOD CHEM, V212, P512, DOI 10.1016/j.foodchem.2016.05.178 Huang ChenYang, 2010, Scientia Agricultura Sinica, V43, P1198 Jaiswal P, 2015, FOOD CHEM, V168, P41, DOI 10.1016/j.foodchem.2014.07.010 Lu YZ, 2014, COMPUT ELECTRON AGR, V107, P58, DOI 10.1016/j.compag.2014.06.005 Luo DH, 2016, FOOD ANAL METHOD, V9, P437, DOI 10.1007/s12161-015-0204-9 Nerin C, 2016, TRENDS FOOD SCI TECH, V48, P63, DOI 10.1016/j.tifs.2015.12.004 Nikolaou C., 2017, FOOD ANAL METHOD, P1 Ouyang Q, 2014, ANAL CHIM ACTA, V841, P68, DOI 10.1016/j.aca.2014.06.001 Paneque P, 2017, FOOD CONTROL, V75, P203, DOI 10.1016/j.foodcont.2016.12.006 Prins TW, 2016, FOOD CHEM, V213, P536, DOI 10.1016/j.foodchem.2016.07.009 Rashmi D, 2017, FOOD CONTROL, V79, P169, DOI 10.1016/j.foodcont.2017.03.025 Ruthes AC, 2015, CARBOHYD POLYM, V117, P753, DOI 10.1016/j.carbpol.2014.10.051 Sarbu C, 2012, FOOD CHEM, V130, P994, DOI 10.1016/j.foodchem.2011.07.120 Silva SD, 2014, FOOD CHEM, V150, P489, DOI 10.1016/j.foodchem.2013.11.028 Sobek S, 2007, LIMNOL OCEANOGR, V52, P1208, DOI 10.4319/lo.2007.52.3.1208 Spiteri M, 2016, ANAL BIOANAL CHEM, V408, P4389, DOI 10.1007/s00216-016-9538-4 Stefanovic V, 2016, ENVIRON SCI POLLUT R, V23, P22084, DOI 10.1007/s11356-016-7450-2 SUN Su-gin, 2010, ANAL TRADITIONAL CHI Valverde Maria Elena, 2015, Int J Microbiol, V2015, P376387, DOI 10.1155/2015/376387 Vapnik V., 2013, NATURE STAT LEARNING [魏益民 Wei Yimin], 2012, [中国农业科学, Scientia Agricultura Sinica], V45, P5073 Xiong C, 2016, FOOD ANAL METHOD, V9, P768, DOI 10.1007/s12161-015-0243-2 Yun ZY, 2017, FOOD SCI BIOTECHNOL, V26, P357, DOI 10.1007/s10068-017-0048-8 ZHANG Hai-xiang, 2013, SOCIAL SCI YUNNAN, P83 Zhang XF, 2010, TELECOMMUNICATIONS E, P1, DOI [10.3969/j.issn.1005-7641.2010.06.001, DOI 10.3969/J.ISSN.1005-7641.2010.06.001] Zhao HY, 2017, J FOOD COMPOS ANAL, V63, P15, DOI 10.1016/j.jfca.2017.07.030 NR 36 TC 1 Z9 3 U1 2 U2 24 PD AUG PY 2018 VL 38 IS 8 BP 2529 EP 2535 DI 10.3964/j.issn.1000-0593(2018)08-2529-07 WC Spectroscopy SC Spectroscopy UT WOS:000443104600035 DA 2022-12-14 ER PT J AU Chiaverini, A Lyu, SJ Garofolo, G Di Giannatale, E Migliorati, G AF Chiaverini, Alexandra Lyu, Shijie Garofolo, Giuliano Di Giannatale, Elisabetta Migliorati, Giacomo TI Assessment of a new microsatellites panel for traceability in Italian inbreed pigs using parentage test SO VETERINARIA ITALIANA DT Article DE Meat traceability; Parentage test; Pig; Microsatellite; Inbreeding ID IDENTIFICATION; PATERNITY; PROBABILITY; MARKERS AB The origin of meat and meat products can be traced by verifying the identity of an offspring from its parents' genotypes. Although there are many microsatellite panels applicable to swine population, efficiency of parental testing decreases when the population consists of consanguineous animals. The aims of the present study were to develop a new microsatellite panel for traceability using parentage test in inbreed pig population and to assess how hybridization can influence the efficiency of parental testing. A new genotyping assay, based on 20-microsatellite assay, was performed in 304 individuals consisting of related and unrelated animals. The results showed that the microsatellites used in this study display high level of polymorphism ensuring a parentage assignment of 100%. This genotyping panel can be a useful tool to test a 'parent-to-fork' traceability system based on 20 microsatellite loci and can overcome technical limitations in inbreed population. C1 [Chiaverini, Alexandra; Garofolo, Giuliano; Di Giannatale, Elisabetta; Migliorati, Giacomo] Ist Zooprofilatt Sperimentale Abruzzo & Molise G, I-64100 Campo Boario, Teramo, Italy. [Lyu, Shijie] Albrecht Daniel Thaer Inst Agr & Hort Sci, Breeding Biol & Mol Genet, Invalidenstr 42, D-10115 Berlin, Germany. C3 IZS Dell'abruzzo E Del Molise Giuseppe Caporale RP Chiaverini, A (corresponding author), Ist Zooprofilatt Sperimentale Abruzzo & Molise G, I-64100 Campo Boario, Teramo, Italy. EM a.chiaverini@izs.it CR Blasi M, 2003, ITAL J ANIM SCI, V2, P82, DOI 10.4081/ijas.2003.s1.82 Costa Vania, 2012, BMC Res Notes, V5, P479, DOI 10.1186/1756-0500-5-479 European Communities (EC), 2002, OFF J, V31 FAO, 2011, AN PROD HLTH GUID, V9 Guastella AM, 2010, GENET MOL BIOL, V33, P650, DOI 10.1590/S1415-47572010005000075 Jamieson A, 1997, ANIM GENET, V28, P397, DOI 10.1111/j.1365-2052.1997.00186.x Kalinowski ST, 2007, MOL ECOL, V16, P1099, DOI 10.1111/j.1365-294X.2007.03089.x Lin YC, 2014, FORENSIC SCI INT-GEN, V9, P12, DOI 10.1016/j.fsigen.2013.10.006 Marshall TC, 1998, MOL ECOL, V7, P639, DOI 10.1046/j.1365-294x.1998.00374.x Menendez J, 2015, ARCH TIERZUCHT, V58, P217, DOI 10.5194/aab-58-217-2015 Michailidou S, 2014, GENET MOL RES, V13, P2752, DOI 10.4238/2014.April.14.4 Nechtelberger D, 2001, ANIM BIOTECHNOL, V12, P141, DOI 10.1081/ABIO-100108340 Oh JD, 2014, ASIAN AUSTRAL J ANIM, V27, P926, DOI 10.5713/ajas.2013.13829 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Park SDE., 2001, TRYPANOTOLERANCE W A Putnova L, 2003, CZECH J ANIM SCI, V48, P307 RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Scarano D., 2014, Diversity, V6, P579 Waits LP, 2001, MOL ECOL, V10, P249, DOI 10.1046/j.1365-294X.2001.01185.x WEIR BS, 1984, EVOLUTION, V38, P1358, DOI [10.2307/2408641, 10.1111/j.1558-5646.1984.tb05657.x] NR 20 TC 0 Z9 0 U1 2 U2 6 PY 2021 VL 57 IS 3 BP 227 EP 232 DI 10.12834/VetIt.1700.8999.4 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000754642900007 DA 2022-12-14 ER PT J AU Mai, NTT Margeirsson, S Stefansson, G Arason, S AF Mai, Nga T. T. Margeirsson, Sveinn Stefansson, Gunnar Arason, Sigurjon TI Evaluation of a seafood firm traceability system based on process mapping information: More efficient use of recorded data SO JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT DT Article DE Traceability; effectiveness; fresh fish supply chain; process mapping; traceable resource unit; batch; identification; transformation; Pangasius; warm up time; shelf life ID FOOD; MANAGEMENT AB The purpose of this work was to develop a conceptual framework that can be used to evaluate the effectiveness of traceability systems at food producers based on information from process mapping. The framework was based on a broad literature review from the food processing industry. The proposed framework was then applied to evaluate the traceability system at a Vietnamese fresh farmed Pangasius catfish producer and validated by evaluating the ability to track and trace through the company. In addition, the studied traceability system was analyzed on its compliance with regulation on traceability of importing countries such as EU regulation No. 178/2002, as well as with the TraceFish standard. The paper also aimed to propose how to use recorded data more efficiently to improve quality management and supply chain management. The results show that the framework works well in the specified case, but further investigation for other cases is desirable. The company traceability system meets with EU regulation No. 178/2002, but not with the TraceFish standard as global trade item numbers (GTIN) are not used for dispatched products. It is suggested that the company also stores recorded data in electronic form in parallel with paper form to facilitate data access. It is proposed that the temperature data during storage and transportation are used to estimate the warm up time and the remaining shelf life (RSL) of the products. C1 [Mai, Nga T. T.; Stefansson, Gunnar; Arason, Sigurjon] Univ Iceland, IS-101 Reykjavik, Iceland. [Mai, Nga T. T.] Univ Nhatrang, Nha Trang, Vietnam. [Margeirsson, Sveinn; Arason, Sigurjon] Matis Ohf, IS-113 Reykjavik, Iceland. C3 University of Iceland RP Mai, NTT (corresponding author), Univ Iceland, Saemundargotu 2, IS-101 Reykjavik, Iceland. EM mtt2@hi.is; sveinn.margeirsson@matis.is; gunste@hi.is; sigurjon.arason@matis.is CR [Anonymous], 2000, 90002000 ISO Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 *CEN, 2003, 14659 CWA CEN EUR CO Dalgaard P., 2002, Safety and quality issues in fish processing, P191, DOI 10.1533/9781855736788.2.191 Dalgaard P, 2002, INT J FOOD MICROBIOL, V73, P343, DOI 10.1016/S0168-1605(01)00670-5 Deasy DJ, 2002, INT J DAIRY TECHNOL, V55, P1, DOI 10.1046/j.1364-727X.2001.00036.x DERRICK S, 2004, GUIDE TRACEABILITY F, P78 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 *DTU AQ, 2008, INTR REL RAT SPOIL R Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 *EAN INT, 2002, TRAC FISH GUID *EC, 2002, OFF J EUR COMM Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 FREDERIKSEN MT, 2007, INTEGRATING FOOD SAF Giannakourou MC, 2003, J FOOD SCI, V68, P201, DOI 10.1111/j.1365-2621.2003.tb14140.x *GSI, 2009, GSI STAND DOC GSI GL Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kim HM, 1999, BT TECHNOL J, V17, P131, DOI 10.1023/A:1009611528866 LE TC, 2009, WORLD ACAD SCI ENG T, P325 LEVINSON SR, 2009, TRACEABILITY FOOD SU LINDH H, 2008, P NOFIMA, P393 MARGEIRSSON B, 2009, 2009 MVK160 HEAT MAS Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 OLSEN P, 2005, MAT FLOW INFORM FLOW, P45 Olsson A., 2008, Open Food Science Journal, V2, P49, DOI 10.2174/1874256400802010049 Randrup M, 2008, FOOD CONTROL, V19, P1064, DOI 10.1016/j.foodcont.2007.11.005 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Singh SP, 2008, PACKAG TECHNOL SCI, V21, P25, DOI 10.1002/pts.773 TAOUKIS P, 2006, 13 WORLD C FOOD SCI, P765 Taoukis PS, 1998, ACTA HORTIC, P131, DOI 10.17660/ActaHortic.1998.476.14 Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x *TRACE, 2009, REC GOOD TRAC PRACT, P35 Van Dorp C.A., 2004, REFERENCE DATA MODEL Zueco J, 2004, J FOOD ENG, V64, P347, DOI 10.1016/j.jfoodeng.2003.10.017 NR 36 TC 4 Z9 4 U1 0 U2 13 PD APR PY 2010 VL 8 IS 2 BP 51 EP 59 PN 1 WC Food Science & Technology SC Food Science & Technology UT WOS:000279317500010 DA 2022-12-14 ER PT J AU Ferrandez-Pastor, FJ Mora-Pascual, J Diaz-Lajara, D AF Ferrandez-Pastor, Francisco-Javier Mora-Pascual, Jeronimo Diaz-Lajara, Daniel TI Agricultural traceability model based on IoT and Blockchain: Application in industrial hemp production SO JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION DT Article DE Blockchain; IoT; Industrial hemp; Tamper proof tech; Traceability AB Facilities based on the Internet of Things and embedded systems along with the application of ambient intelligence paradigms offer new scenarios for optimization services in agronomic processes, specifically in the hemp industry. The traceability of products and activities demonstrates the scope of these technologies. However, the technologies themselves introduce integration-related problems that can affect the planned benefits. This article proposes a model that balances agricultural expert knowledge (user-centered design), value chain planning (through blockchain implementation), and digital technology (Internet of Things protocols) for providing tamper proof, transparent, and secure traceability in this agricultural sector. The proposed approach is backed by a proof-of-concept implementation in a realist scenario, using embedded devices and a permissioned blockchain. The model and its deployment fully integrate a set of services that other proposals only partially integrate. On one hand, the design creates a permissioned blockchain that contemplates the different actors in the value chain, and on the other hand, it develops services that use applications with human-machine interfaces. Finally, it deploys a network of embedded devices with Internet of Things protocols and control algorithms with automated access to the blockchain for traceability services. Combining digital systems with interoperable human tasks it has been possible to deploy a model that provides a new approach for the development of value-added services. C1 [Ferrandez-Pastor, Francisco-Javier; Mora-Pascual, Jeronimo; Diaz-Lajara, Daniel] Dept Comp Technol & Computat, Ctra San Vicente Raspeig, Alicante 03690, Alicante, Spain. RP Ferrandez-Pastor, FJ (corresponding author), Dept Comp Technol & Computat, Ctra San Vicente Raspeig, Alicante 03690, Alicante, Spain. EM fjferran@dtic.ua.es; dani.diaz@ua.es CR Agencia Espanola de Medicamentos y Productos Sanitarios AEMS, 2020, GUIA NORM CORR FABR Akkerman R, 2010, OR SPECTRUM, V32, P863, DOI 10.1007/s00291-010-0223-2 Amazon, 2021, IOT SERV IND CONS CO Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Codeluppi G, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20072028 Corallo A, 2018, 2018 IEEE WORKSHOP ON ENVIRONMENTAL, ENERGY, AND STRUCTURAL MONITORING SYSTEMS (EESMS), P1 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Davcev D., 2018, 2018 14 IEEE INTERNA, P1, DOI DOI 10.1109/WFCS.2018.8402368 European Commission, 2020, RUL GOV MED PROD EUR Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Food Standards Agency, 2002, TRAC FOOD CHAIN PREL Hyperledger Explorer, 2021, GET START HYP EXPL IBM, 2021, INTERNET THINGS IOT IBM PartnerWorld, 2021, INT THINGS IBM CLOUD Interaction Design, US CENT DES International Trade Centre, 2020, TRAC FOOD AGR ISO Technical Committee, 2007, TRAC FEED FOOD CHAIN Ferrandez-Pastor FJ, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18061731 Ferrandez-Pastor FJ, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16071141 Kassim Mohamed Rawidean Mohd, 2014, 2014 International Conference on Computer, Information and Telecommunication Systems (CITS), DOI 10.1109/CITS.2014.6878963 Keshtgari M., 2012, WIRELESS SENSOR NETW, V4, P25, DOI [10.4236/wsn.2012.41004, DOI 10.4236/WSN.2012.41004] Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Mat I, 2016, IEEE CONF OPEN SYST, P24, DOI 10.1109/ICOS.2016.7881983 Microsoft Azure, 2021, WHAT IS AZ INT THING O'Grady M. J., 2019, Artificial Intelligence in Agriculture, V3, P42, DOI 10.1016/j.aiia.2019.12.001 Shi XJ, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19081833 Siddique A., 2019, LIFE, V100, P7000 Taskin D, 2018, ADV SCI TECHNOL-RES, V12, P88, DOI 10.12913/22998624/100342 Ubidots, 2021, POW YOUR IOT CLOUD A Verzijl E.R. Diederik, 2015, TRACEABILITY VALUE C Zamora-Izquierdo MA, 2019, BIOSYST ENG, V177, P4, DOI 10.1016/j.biosystemseng.2018.10.014 NR 32 TC 1 Z9 1 U1 17 U2 17 PD SEP PY 2022 VL 29 AR 100381 DI 10.1016/j.jii.2022.100381 WC Computer Science, Interdisciplinary Applications; Engineering, Industrial SC Computer Science; Engineering UT WOS:000840625300001 DA 2022-12-14 ER PT J AU Violino, S Pallottino, F Sperandio, G Figorilli, S Ortenzi, L Tocci, F Vasta, S Imperi, G Costa, C AF Violino, Simona Pallottino, Federico Sperandio, Giulio Figorilli, Simone Ortenzi, Luciano Tocci, Francesco Vasta, Simone Imperi, Giancarlo Costa, Corrado TI A Full Technological Traceability System for Extra Virgin Olive Oil SO FOODS DT Article DE EVOO; supply chain; RFID; infotracing; blockchain; Arduino; made in Italy AB The traceability of extra virgin olive oil (EVOO) could guarantee the authenticity of the product and the protection of the consumer if it is part of a system able to certify the traceability information. The purpose of this paper was to propose and apply a complete electronic traceability prototype along the entire EVOO production chain of a small Italian farm and to verify its economic sustainability. The full traceability of the EVOO extracted from 33 olive trees from three different cultivars (Carboncella, Frantoio and Leccino) was considered. The technological traceability system (TTS; infotracing) consists of several open source devices (based on radio frequency identification (RFID) and QR code technologies) able to track the EVOO from the standing olive tree to the final consumer. The infotracing system was composed of a dedicated open source app and was designed for easy blockchain integration. In addition, an economic analysis of the proposed TTS, with reference to the semi-mechanized olive harvesting process, was conducted. The results showed that the incidence of the TTS application on the whole production varies between 3% and 15.5%, (production from 5 to 60 kg tree(-1)). The application at the consortium level with mechanized harvesting is fully sustainable in economic terms. The proposed TTS could not only provide guarantees to the final consumer but could also direct the farmer towards precision farming management. C1 [Violino, Simona; Pallottino, Federico; Sperandio, Giulio; Figorilli, Simone; Ortenzi, Luciano; Tocci, Francesco; Vasta, Simone; Imperi, Giancarlo; Costa, Corrado] Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn & Trasformaz Agroaliment, Via Pascolare 16, I-00015 Rome, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Costa, C (corresponding author), Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn & Trasformaz Agroaliment, Via Pascolare 16, I-00015 Rome, Italy. EM simonaviolino@hotmail.com; federico.pallottino@crea.gov.it; giulio.sperandio@crea.gov.it; simone.figorilli@crea.gov.it; luciano.ortenzi@crea.gov.it; francesco.tocci@crea.gov.it; simone.vasta@crea.gov.it; giancarlo.imperi@crea.gov.it; corrado.costa@crea.gov.it CR Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Ben Ayed R, 2016, DATABASE-OXFORD, DOI 10.1093/database/bav090 Bernardi P, 2008, ECCSC 08: 4TH EUROPEAN CONFERENCE ON CIRCUITS AND SYSTEMS FOR COMMUNICATIONS, P227, DOI 10.1109/ECCSC.2008.4611682 Bibi F, 2017, TRENDS FOOD SCI TECH, V62, P91, DOI 10.1016/j.tifs.2017.01.013 Biondi P., 1999, MECCANICA AGRARIA MA Chatziantoniou SE, 2014, THERMOCHIM ACTA, V576, P9, DOI 10.1016/j.tca.2013.11.014 Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 Galtier O, 2007, ANAL CHIM ACTA, V595, P136, DOI 10.1016/j.aca.2007.02.033 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Garcia-Gonzalez DL, 2009, EUR J LIPID SCI TECH, V111, P1003, DOI 10.1002/ejlt.200900015 Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Kalaitzis P., 2016, Lipid Technology, V28, P173, DOI 10.1002/lite.201600048 Muzzalupo I, 2015, EUR FOOD RES TECHNOL, V241, P151, DOI 10.1007/s00217-015-2455-5 Pallottino F, 2018, PRECIS AGRIC, V19, P1011, DOI 10.1007/s11119-018-9569-2 Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 Pereira L, 2018, FOOD RES INT, V103, P170, DOI 10.1016/j.foodres.2017.10.026 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sebastiani L, 2017, PLANT CELL REP, V36, P1345, DOI 10.1007/s00299-017-2145-9 Nguyen SD, 2015, PROC INT CONF ADV, P258, DOI 10.1109/ATC.2015.7388330 Sperandio G., 2017, CHEM ENG TRANS, V58, P853, DOI [10.3303/CET1758143, DOI 10.3303/CET1758143] Timpe D., 2012, AM J IND BUSINESMA, V2, P128 Violino S, 2019, FOODS, V8, DOI 10.3390/foods8110529 NR 22 TC 18 Z9 18 U1 2 U2 14 PD MAY PY 2020 VL 9 IS 5 AR 624 DI 10.3390/foods9050624 WC Food Science & Technology SC Food Science & Technology UT WOS:000542281300061 DA 2022-12-14 ER PT J AU Busconi, M Lucini, L Soffritti, G Bernardi, J Bernardo, L Brunschwig, C Lepers-Andrzejewski, S Raharivelomanana, P Fernandez, JA AF Busconi, Matteo Lucini, Luigi Soffritti, Giovanna Bernardi, Jamila Bernardo, Letizia Brunschwig, Christel Lepers-Andrzejewski, Sandra Raharivelomanana, Phila Fernandez, Jose A. TI Phenolic Profiling for Traceability of Vanilla x tahitensis SO FRONTIERS IN PLANT SCIENCE DT Article DE Vanilla xtahitensis; food metabolomics; phenolics; traceability; authenticity ID RADICAL SCAVENGING ACTIVITY; TAHITIAN VANILLA; AROMA EXTRACT; GC-MS; ORIGIN; IDENTIFICATION; METABOLOMICS; QUALITY; OIL AB Vanilla is a flavoring recovered from the cured beans of the orchid genus Vanilla. Vanilla xtahitensis is traditionally cultivated on the islands of French Polynesia, where vanilla vines were first introduced during the nineteenth century and, since the 1960s, have been introduced to other Pacific countries such as Papua New Guinea (PNG), cultivated and sold as "Tahitian vanilla," although both sensory properties and aspect are different. From an economic point of view, it is important to ensure V. xtahitensis traceability and to guarantee that the marketed product is part of the future protected designation of the origin "Tahitian vanilla" (PDO), currently in progress in French Polynesia. The application of metabolomics, allowing the detection and simultaneous analysis of hundreds or thousands of metabolites from different matrices, has recently gained high interest in food traceability. Here, metabolomics analysis of phenolic compounds profiles was successfully applied for the first time to V. xtahitensis to deepen our knowledge of vanilla metabolome, focusing on phenolics compounds, for traceability purposes. Phenolics were screened through a quadrupole-time-of-flightmass spectrometer coupled to a UHPLC liquid chromatography system, and 260 different compounds were clearly evidenced and subjected to different statistical analysis in order to enable the discrimination of the samples based on their origin. Eighty-eight and twenty three compounds, with a prevalence of flavonoids, resulted to be highly discriminant through ANOVA and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) respectively. Volcano plot analysis and pairwise comparisons were carried out to determine those compounds, mainly responsible for the differences among samples as a consequence of either origin or cultivar. The samples from PNG were clearly different from the Tahitian samples that were further divided in two different groups based on the different phenolic patterns. Among the 260 compounds, metabolomics analysis enabled the detection of previously unreported phenolics in vanilla (such as flavonoids, lignans, stilbenes and other polyphenols). C1 [Busconi, Matteo; Soffritti, Giovanna; Bernardi, Jamila] Univ Cattolica Sacro Cuore, Dept Sustainable Crop Prod, Piacenza, Italy. [Lucini, Luigi; Bernardo, Letizia] Univ Cattolica Sacro Cuore, Inst Environm & Agr Chem, Piacenza, Italy. [Brunschwig, Christel; Raharivelomanana, Phila] Univ Polynesie Francaise, UMR EIO 241, Equipe EIMS Etude Integree Metabolites Secondaire, Tahiti, French Polynesi, France. [Brunschwig, Christel; Lepers-Andrzejewski, Sandra] Etab Vanille Tahiti, Dept Res & Dev, Raiatea, French Polynesi, France. [Fernandez, Jose A.] Univ Castilla La Mancha, IDR Lab Biotecnol & Recursos Nat, Albacete, Spain. C3 Catholic University of the Sacred Heart; Catholic University of the Sacred Heart; Ifremer; Institut de Recherche pour le Developpement (IRD); Universidad de Castilla-La Mancha RP Busconi, M (corresponding author), Univ Cattolica Sacro Cuore, Dept Sustainable Crop Prod, Piacenza, Italy.; Raharivelomanana, P (corresponding author), Univ Polynesie Francaise, UMR EIO 241, Equipe EIMS Etude Integree Metabolites Secondaire, Tahiti, French Polynesi, France. EM matteo.busconi@unicatt.it; phila.raharivelomanana@upf.pf CR Bavaresco L, 2016, NUTRIENTS, V8, DOI 10.3390/nu8040222 Bouriquet G., 1954, VANILLIER VANILLE MO Brunschwig C, 2012, FOOD RES INT, V46, P148, DOI 10.1016/j.foodres.2011.12.006 Brunschwig C, 2016, J SCI FOOD AGR, V96, P848, DOI 10.1002/jsfa.7157 Brunschwig C, 2009, NAT PROD COMMUN, V4, P1393 Bythrow JD., 2005, SEMINARS INTEGRATIVE, V3, P129, DOI [10.1016/j.sigm.2006.03.001, DOI 10.1016/J.SIGM.2006.03.001] Constantin D., 1915, CR HEBD ACAD SCI, V161, P196 Del Rio D, 2013, ANTIOXID REDOX SIGN, V18, P1818, DOI 10.1089/ars.2012.4581 Duke J. A., 2003, CRC HDB MED SPICE Florence J., 1996, ORCHIDEE, V13, P84 Gu FL, 2017, AMB EXPRESS, V7, DOI 10.1186/s13568-017-0413-2 Hansen AMS, 2014, J AGR FOOD CHEM, V62, P10326, DOI 10.1021/jf503055k Hernandez JH, 2011, MED AROMAT PLANTS-IN, P75 Hondrogiannis E, 2013, J FOOD SCI, V78, pC395, DOI 10.1111/1750-3841.12050 Journal Officiel de la Polynesie Francaise, 2016, J OFFICIEL POLYNESIE, P9169 Journal Officiel de la Polynesie Francaise, 2014, J OFFICIEL POLYNESIE, P8210 King J., 1898, KINGS AM DISPENSARY Klockmann S, 2016, J AGR FOOD CHEM, V64, P9253, DOI 10.1021/acs.jafc.6b04433 Kumar SS, 2002, REDOX REP, V7, P35, DOI 10.1179/135100002125000163 Lepers-Andrzejewski S, 2012, CROP SCI, V52, P795, DOI 10.2135/cropsci2010.11.0634 Lepers-Andrzejewski S, 2011, MED AROMAT PLANTS-IN, P205 Lepers-Andrzejewski S, 2011, AM J BOT, V98, P986, DOI 10.3732/ajb.1000415 Lubinsky P, 2008, AM J BOT, V95, P1040, DOI 10.3732/ajb.0800067 Lucini L, 2017, FOOD CONTROL, V73, P696, DOI 10.1016/j.foodcont.2016.09.020 Lucini L, 2015, FOOD CHEM, V170, P501, DOI 10.1016/j.foodchem.2014.08.034 Mac Gregor A., 2005, 2 AGSF UN FAO AGR SU Marques FZ, 2009, INT J BIOCHEM CELL B, V41, P2125, DOI 10.1016/j.biocel.2009.06.003 Maurya DK, 2007, MUTAT RES-GEN TOX EN, V634, P69, DOI 10.1016/j.mrgentox.2007.06.003 Oliveira RCS, 2009, J FOOD COMPOS ANAL, V22, P257, DOI 10.1016/j.jfca.2008.10.015 Oms-Oliu G, 2013, FOOD RES INT, V54, P1172, DOI 10.1016/j.foodres.2013.04.005 Palama TL, 2011, ENVIRON EXP BOT, V72, P258, DOI 10.1016/j.envexpbot.2011.03.015 Palama TL, 2010, PHYTOCHEMISTRY, V71, P567, DOI 10.1016/j.phytochem.2009.12.011 Palama TL, 2009, J AGR FOOD CHEM, V57, P7651, DOI 10.1021/jf901508f Perez-Silva A, 2006, FOOD CHEM, V99, P728, DOI 10.1016/j.foodchem.2005.08.050 Rain P, 2011, MED AROMAT PLANTS-IN, P251 Rohman A, 2012, CHEMOMETR INTELL LAB, V110, P129, DOI 10.1016/j.chemolab.2011.10.010 Rombouts C, 2017, SCI REP-UK, V7, DOI 10.1038/srep42514 Rothwell JA, 2013, DATABASE-OXFORD, DOI 10.1093/database/bat070 Ruiz-Samblas C, 2012, FOOD CHEM, V134, P589, DOI 10.1016/j.foodchem.2012.02.135 Soto Arenas M.A., 2010, LANKESTERIANA, V9, P285, DOI DOI 10.15517/LANK.V0I0.12065 Soto Arenas M. A., 2003, GENERA ORCHIDACEARUM, V3, P321 Takahashi M, 2013, BIOSCI BIOTECH BIOCH, V77, P601, DOI 10.1271/bbb.120840 Teuscher E., 2005, PLANTES AROMATIQUES Vardin H, 2008, FOOD CHEM, V108, P742, DOI 10.1016/j.foodchem.2007.11.027 NR 44 TC 4 Z9 4 U1 3 U2 27 PD OCT 12 PY 2017 VL 8 AR 1746 DI 10.3389/fpls.2017.01746 WC Plant Sciences SC Plant Sciences UT WOS:000412796200001 DA 2022-12-14 ER PT J AU El Sheikha, AF Chalier, C Zaremski, A Montet, D AF El Sheikha, A. F. Chalier, C. Zaremski, A. Montet, D. TI NOVEL MOLECULAR FINGERPRINTING FOR GEOGRAPHICAL TRACEABILITY OF TIMBER SO JOURNAL OF TROPICAL FOREST SCIENCE DT Article DE Tropical timber; PCR-DGGE; fungi communities; geographical origin ID PCR-DGGE; PHYSALIS FRUITS; ORIGIN; COMMUNITIES AB Traceability is defined according to ISO 9000 as the ability to retrieve the origin and use of an article or an activity through a registered method. Its implementation in the timber industry is delayed because of limits of classical identification systems with regard to the nature of timber and features of the manufacturing process. One hypothesis of tracing the source of timber and its products is by analysing in a global way the microbial communities of timber and linking this analysis statistically to its geographical origin. We proposed a very innovative tool of fungi ecology, the polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), that was used to characterise the fungi flora of two tropical timbers species, teak and limbali from four countries: Cote d'Ivoire, Cameroon, Central African Republic and French Polynesia. The aim was to show if there was statistical relation between the fungal communities of the timbers and their geographical origins. PCR-DGGE method is a new, simple and cheap traceability tool that can trace the original locations of timbers. C1 [El Sheikha, A. F.] Univ Putra Malaysia, Halal Prod Res Inst, Serdang 43400, Selangor Darul, Malaysia. [El Sheikha, A. F.] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm 32511, Minufiya Govern, Egypt. [Chalier, C.; Zaremski, A.] CIRAD, Ctr Cooperat Int Rech Agron Dev, UPR Genet Forestiere 39, F-34398 Montpellier 5, France. [Montet, D.] CIRAD, Ctr Cooperat Int Rech Agron Dev, UMR Qualisud, F-34398 Montpellier 5, France. C3 Universiti Putra Malaysia; Egyptian Knowledge Bank (EKB); Menofia University; CIRAD; CIRAD; Universite de Montpellier RP El Sheikha, AF (corresponding author), Univ Putra Malaysia, Halal Prod Res Inst, Serdang 43400, Selangor Darul, Malaysia. EM elsheikha_aly@yahoo.com CR Chiorescu S, 2003, FOREST PROD J, V53, P78 CHOFFEL D, 1999, BREVET INVENTION PRO EL SHEIKHA AF, 2011, DETERMINATION ORIGIN El Sheikha AF, 2010, THESIS MONTPELLIER U El Sheikha AF, 2011, FOOD BIOTECHNOL, V25, P115, DOI 10.1080/08905436.2011.576556 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Finkeldey R, 2010, APPL MICROBIOL BIOT, V85, P1251, DOI 10.1007/s00253-009-2328-6 FUENTEALBA C, 2006, NONDESTRUCTIVE CONTR HAVARD ML, 2007, BOIS FORETS TROPIQUE, V294, P65 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Le Quere A, 2002, FUNGAL GENET BIOL, V36, P234, DOI 10.1016/S1087-1845(02)00024-5 Li XY, 2008, J ENVIRON SCI-CHINA, V20, P619, DOI 10.1016/S1001-0742(08)62103-8 Lowe A., 2007, P INT WORKSH FING ME Miller RB, 1999, WOOD HDB WOOD ENG MA MONTAGNINI F, 2005, TROP FOR, P1 Montet D., 2008, Aspects of Applied Biology, P11 Montet D., 2004, SEM FOOD SAF INT TRA Nielsen LR, 2008, TRACING TIMBER FORES Parfitt D, 2010, FUNGAL ECOL, V3, P338, DOI 10.1016/j.funeco.2010.02.001 ROSSMOORE HW, 1995, HDB BIOCIDE PRESERVA, P283 SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 Tnah LH, 2009, FOREST ECOL MANAG, V258, P1918, DOI 10.1016/j.foreco.2009.07.029 Wu ZH, 2002, J ENVIRON MONITOR, V4, P377, DOI 10.1039/b200490a NR 23 TC 1 Z9 1 U1 0 U2 13 PD JUL PY 2013 VL 25 IS 3 BP 387 EP 392 WC Forestry SC Forestry UT WOS:000324006600012 DA 2022-12-14 ER PT J AU Liu, Y Ma, DH Wang, XC Liu, LP Fan, YX Cao, JX AF Liu, Yuan Ma, Dong-hong Wang, Xi-chang Liu, Li-ping Fan, Yu-xia Cao, Jin-xuan TI Prediction of chemical composition and geographical origin traceability of Chinese export tilapia fillets products by near infrared reflectance spectroscopy SO LWT-FOOD SCIENCE AND TECHNOLOGY DT Article DE Near infrared spectroscopy (NIRS); Origin traceability; Soft independent modeling of class analogy (SIMCA); Chemical composition; Tilapia ID DRY-MATTER; NIRS; FAT; DISCRIMINATION; IDENTIFICATION; PROTEIN; MUSCLE AB Near infrared reflectance spectroscopy (NIRS) analysis was used to predict proximate chemical composition of Chinese export tilapia fillets from four geographical origins (Guangdong Province, Hainan Province, Guangxi Province and Fujian Province, respectively). NIRS provided good reliability in the prediction of chemical composition of tilapia fillets but weak results in crude protein prediction. Origin traceability is an important part of food safety traceability system. The tilapia origin traceability model was developed by near infrared reflectance (NIR) spectroscopy coupled with soft independent modeling of class analogy (SIMCA). The result showed that when classifying tilapia by means of SIMCA, more than 80% of from the Guangdong, Hainan and Fujian systems and 75% of fillets from the Fujian system were correctly and exclusively assigned to the correctly and exclusively assigned to the corresponding clusters. No spectra were assigned to two or more clusters, while a certain number of spectra (10-18%) were not assigned to any class. Only 1-2% of samples were classified incorrectly. The results of this study indicated that NIRS coupled with pattern recognition methods was a feasible way for origin traceability of export tilapia fillets. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Liu, Yuan; Ma, Dong-hong; Wang, Xi-chang; Fan, Yu-xia] Shanghai Ocean Univ, Coll Food Sci & Technol, Shanghai 201306, Peoples R China. [Liu, Li-ping] Shanghai Ocean Univ, Coll Fisheries & Life Sci, Shanghai 201306, Peoples R China. [Cao, Jin-xuan] Ningbo Univ, Life Sci & Biotechnol Coll, Ningbo 315211, Zhejiang, Peoples R China. C3 Shanghai Ocean University; Shanghai Ocean University; Ningbo University RP Liu, Y (corresponding author), Shanghai Ocean Univ, Coll Food Sci & Technol, Lab Food Nutr & Qual Evaluat, 999 Huchenghuan Rd, Shanghai 201306, Peoples R China. EM yliu@shou.edu.cn CR Alomar D, 2003, MEAT SCI, V63, P441, DOI 10.1016/S0309-1740(02)00101-8 [Anonymous], 2008, 9695112008 GBT [Anonymous], 2008, 969512008 GBT [Anonymous], 2008, 9695152008 GBT Chen Y, 2008, ANAL CHIM ACTA, V618, P121, DOI 10.1016/j.aca.2008.04.055 Cozzolino D, 2002, AQUACULT NUTR, V8, P1, DOI 10.1046/j.1365-2095.2002.00176.x Downey G., 1996, Journal of Near Infrared Spectroscopy, V4, P47 Downey G, 1997, MEAT SCI, V45, P353, DOI 10.1016/S0309-1740(96)00127-1 Downey G, 1996, FOOD CHEM, V55, P305, DOI 10.1016/0308-8146(95)00118-2 Fjellanger K., 2001, PROXIMATE ANAL FISH ISAKSSON T, 1995, J SCI FOOD AGR, V69, P95, DOI 10.1002/jsfa.2740690115 LEE MH, 1992, J AGR FOOD CHEM, V40, P2176, DOI 10.1021/jf00023a026 Liu Wei, 2010, LIQUOR MAKING SCI TE, V7, P65 McElhinney J, 1999, J NEAR INFRARED SPEC, V7, P145, DOI 10.1255/jnirs.245 Nortvedt R, 1998, CHEMOMETR INTELL LAB, V42, P199, DOI 10.1016/S0169-7439(98)00012-4 Olivier Fumiere, 2000, J NEAR INFRARED SPEC, V8, P27 Ortiz-Somovilla V, 2007, FOOD CHEM, V101, P1031, DOI 10.1016/j.foodchem.2006.02.058 Shenk J. S., 1992, APPL NIRS SPECTROSCO Shenk J. S., 1994, APPL NEAR INFRARED R Solberg C, 2001, J NEAR INFRARED SPEC, V9, P221, DOI 10.1255/jnirs.308 Solberg C., 2000, DIODEARRAY NEAR INFR Sun T, 2009, SPECTROSC SPECT ANAL, V29, P122, DOI 10.3964/j.issn.1000-0593(2009)01-0122-05 Weeranantanaphan J, 2011, J NEAR INFRARED SPEC, V19, P61, DOI 10.1255/jnirs.924 Woo YA, 2005, J PHARMACEUT BIOMED, V36, P955, DOI 10.1016/j.jpba.2004.08.037 Xiccato G, 2004, FOOD CHEM, V86, P275, DOI 10.1016/j.foodchem.2003.09.026 Zhang N., 2008, T CHINESE SOC AGR EN, V12 NR 26 TC 30 Z9 32 U1 6 U2 53 PD MAR PY 2015 VL 60 IS 2 BP 1214 EP 1218 DI 10.1016/j.lwt.2014.09.009 PN 2 WC Food Science & Technology SC Food Science & Technology UT WOS:000347740800023 DA 2022-12-14 ER PT J AU Monteiro, DMS Caswel, JA AF Monteiro, Diogo M. Souza Caswel, Julie A. TI Traceability adoption at the farm level: An empirical analysis of the Portuguese pear industry SO FOOD POLICY DT Article DE Traceability; Portuguese pear industry; EurepGAP; Penalized likelihood estimation ID LOGISTIC-REGRESSION; FOOD-INDUSTRY; SYSTEM; STANDARDS AB Traceability is becoming a condition to operate in European food markets. Retailers impose more stringent standards than what is mandatory. An example is EurepGAP, a quality standard for good agricultural practices that imposes traceability as a main obligation. This research investigates the choice of traceability at the farm level in the Portuguese pear industry. Results suggest that in this industry farm-level adoption of EurepGAP traceability is best explained by the choice to sell to the United Kingdom (UK). For farmers selling to the UK, the odds of choosing the EurepGAP traceability level are significantly linked to membership in particular producer organizations, farm productivity, producing products under a protected designation of origin (PDO), and farmer's age. While retailers and farmer organizations seem to drive traceability, policy adjustments may be required to reduce adoption costs upstream and extend compliance among producers that sell directly to consumers and market independently. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Monteiro, Diogo M. Souza] Univ Kent, Kent Business Sch, Ashford TN25 5AH, Kent, England. [Caswel, Julie A.] Univ Massachusetts, Dept Resource Econ, Amherst, MA 01003 USA. C3 University of Kent; University of Massachusetts System; University of Massachusetts Amherst RP Monteiro, DMS (corresponding author), Univ Kent, Kent Business Sch, Wye Campus, Ashford TN25 5AH, Kent, England. EM D.M.Souza-Monteiro@kent.ac.uk CR Allen DW, 1999, J LAW ECON ORGAN, V15, P704, DOI 10.1093/jleo/15.3.704 Allison P.D., 2001, LOGISTIC REGRESSION, V2nd ed. BAER AG, 2006, AAEA ANN M LONG BEAC Banterle A., 2006, 99 EUR SEM EAAE TRUS, DOI [10.22004/ag.econ.7722, DOI 10.22004/AG.ECON.7722] Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Caswell J. A., 1998, Review of Agricultural Economics, V20, P547, DOI 10.2307/1350007 *EUREPGAP, 2004, CHECKL FRUITS VEG VE FIRTH D, 1993, BIOMETRIKA, V80, P27, DOI 10.2307/2336755 Fulponi L, 2006, FOOD POLICY, V31, P1, DOI 10.1016/j.foodpol.2005.06.006 GIRAUDHERAUD HEH, 2005, 11 EAAE C COP DENM 2 Golan E.H., 2004, AGR EC REPORTS, P1362 *GPP, 2007, PER *GPPAA, 2001, EST PREV COMP MERC P Hatanaka M, 2005, FOOD POLICY, V30, P354, DOI 10.1016/j.foodpol.2005.05.006 Heinze G, 2003, COMPUT METH PROG BIO, V71, P181, DOI 10.1016/S0169-2607(02)00088-3 Heinze G, 2002, STAT MED, V21, P2409, DOI 10.1002/sim.1047 Henson S, 2005, FOOD POLICY, V30, P241, DOI 10.1016/j.foodpol.2005.05.002 Hobbs J. E., 1996, British Food Journal, V98, P16, DOI 10.1108/00070709610131339 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x HUFFMAN WE, 1991, REV ECON STAT, V73, P541, DOI 10.2307/2109583 *INE, 1999, REC GER AGR 1999 *INT ORG STAND, 1986, 84021986 ISO Kennedy P., 1998, GUIDE ECONOMETRICS, V4th ed. MADDALA GS, 1983, LIMITED DEPENDENT QU MASTEN SE, 2000, REV EC IND, V92, P215, DOI DOI 10.3406/REI.2000.1048 Menard C, 2005, EUR REV AGRIC ECON, V32, P421, DOI 10.1093/eurrag/jbi013 PINTO A, 2005, SEM FRUTACONFIANCA P SILVA JM, 2005, P 9 INT PEAR S ACT H, V671 Smith A, 2004, J AGR RESOUR ECON, V29, P481 STERNS PA, 2001, AAEA ANN M CHIC IL 5 SUNDING D, 2000, HDB AGR EC A, V1 VANGOOR AR, 1996, PHYS DISTRIBUTION TH Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Vernede R., 2003, TRACEABILITY FOOD PR NR 34 TC 39 Z9 43 U1 6 U2 36 PD FEB PY 2009 VL 34 IS 1 BP 94 EP 101 DI 10.1016/j.foodpol.2008.07.003 WC Agricultural Economics & Policy; Economics; Food Science & Technology; Nutrition & Dietetics SC Agriculture; Business & Economics; Food Science & Technology; Nutrition & Dietetics UT WOS:000263531800012 DA 2022-12-14 ER PT J AU Lopes, MA Junqueira, LV Bruhn, FRP Demeu, AA Silva, MD AF Lopes, Marcos Aurelio Junqueira, Lucio Violin Bruhn, Fabio Raphael Pascoti Demeu, Andreia Alves Silva, Marilane das Dores TI Technical efficiency and economic viability of different cattle identification methods allowed by the Brazilian traceability system SO SEMINA-CIENCIAS AGRARIAS DT Article DE Automation; Cattle farming; Electronic identification; SISBOV ID COUNTRY-OF-ORIGIN; BEEF; IMPLANTATION; PREFERENCES; COST AB We aimed to evaluate the technical efficiency and economic viability of the implementation and use of four cattle identification methods allowed by the Brazilian traceability system. The study was conducted in a beef cattle production system located in the State of Mato Grosso, from January to June 2012. Four identification methods (treatments) were compared: T1: ear tag in one ear and ear button in the other ear (eabu); T2: ear tag and iron brand on the right leg (eaib); T3: ear tag in one ear and tattoo on the other ear (eata); and T4: ear tag in one ear and electronic ear tag (eael) on the other. Each treatment was applied to 60 Nelore animals, totaling 240 animals, divided equally into three life stages (calves, young cattle, adult cattle). The study had two phases: implementation (phase 1) and reading and transfer of identification numbers to an electronic database (phase 2). All operating expenses related to the two phases of the study were determined. The database was constructed, and the statistical analyses were performed using SPSS (R) 17.0 software. Regarding the time spent on implementation (phase 1), conventional ear tags and electronic ear tags produced similar results, which were lower than those of hot iron and tattoo methods, which differed from each other. Regarding the time required for reading the numbers on animals and their transcription into a database (phase 2), electronic ear-tagging was the fastest method, followed by conventional ear tag, hot iron and tattoo. Among the methods analyzed, the electronic ear tag had the highest technical efficiency because it required less time to implement identifiers and to complete the process of reading and transcription to an electronic database and because it did not exhibit any errors. However, the cost of using the electronic ear-tagging method was higher primarily due to the cost of the device. C1 [Lopes, Marcos Aurelio] Univ Fed Lavras, Dept Medicina Veterin ria, UFLA, Lavras, MG, Brazil. [Junqueira, Lucio Violin] UNIVAR, Fac Unidas Vale Araguaia, Barra Garcas, UF, Brazil. [Bruhn, Fabio Raphael Pascoti] Univ Fed Pelotas, Dept Med Vet Prevent, UFPel, Capao do Leao, RS, Brazil. [Demeu, Andreia Alves; Silva, Marilane das Dores] Discente, Grad Med Vet, UFLA, Lavras, MG, Brazil. C3 Universidade Federal de Lavras; Universidade Federal de Pelotas; Universidade Federal de Lavras RP Lopes, MA (corresponding author), Univ Fed Lavras, Dept Medicina Veterin ria, UFLA, Lavras, MG, Brazil. EM malopes@dmv.ufla.br; lucio@univar.edu.br; fabio_rpb@yahoo.com.br; marylanezoo@yahoo.com.br CR Casarotto Filho N., 2010, ANALISE INVESTIMENTO HOFFMANN R., 1981, ADM EMPRESA AGRICOLA Lagerkvist CJ, 2014, FOOD QUAL PREFER, V34, P50, DOI 10.1016/j.foodqual.2013.12.009 Lopes Marcos Aurélio, 2013, Rev. Ceres, V60, P757, DOI 10.1590/S0034-737X2013000600003 Lopes M.A., 2013, Arq. Inst. Biol., V80, P135 Lopes MA, 2006, B IND ANIM, V63, P177 Lopes Marcos Aurelio, 2007, Ciencia Animal Brasileira, V8, P657 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 MACHADO J. G. C. F., 2000, REV BRASILEIRA AGROC, V1, P13 Machado JGCF, 2000, REV BRASILEIRA AGROI, V3, P41 Maroco J., 2010, ANALISE ESTATISTICA SCHMIDEK A., 2009, BOAS PRATICAS MANEJO NR 12 TC 5 Z9 5 U1 0 U2 11 PY 2017 VL 38 IS 1 BP 467 EP 480 DI 10.5433/1679-0359.2017v38n1p467 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000396711100045 DA 2022-12-14 ER PT J AU Fontanesi, L AF Fontanesi, Luca TI Genetic authentication and traceability of food products of animal origin: new developments and perspectives SO ITALIAN JOURNAL OF ANIMAL SCIENCE DT Article; Proceedings Paper CT 18th Congress of the Scientific-Association-of-Animal-Production (ASPA) CY JUN 09-12, 2009 CL Palermo, ITALY DE Animal genomics; Authentication; Coat colour genetics; Traceability ID MELANOCORTIN-1-RECEPTOR MC1R GENE; SINGLE NUCLEOTIDE POLYMORPHISMS; CATTLE BREEDS; SEX-DETERMINATION; IDENTIFICATION; ASSIGNMENT; TESTS; SNP AB In recent years, both the demand and the supply for food of animal origin have experienced important changes making of fundamental importance the implementation of traceability systems. DNA analysis has the potential to overcome the limits of the conventional authentication and traceability procedures. Different levels can be considered: species identification, breed traceability, individual traceability, sex determination, and identification of genetically modified animals. DNA analysis for these levels makes use of endogenous DNA, i.e. DNA of animal origin that constitutes the fingerprinting of the animal itself or of its derived products. However, another source of DNA that can be analysed for authentication or traceability purposes is exogenous DNA, i.e. DNA added to the products that is not derived from the animals from which the products are obtained. Using exogenous DNA, other levels could be considered for traceability: year of production, consortium, farm, processing industry, etc. New technologies and innovative approaches are changing the way to consider and apply genetic authentication and traceability of food of animal origin. The advantages will be for both the consumers and producers creating added values for the animal production sector. C1 Univ Bologna, DIPROVAL, Sez Allevamenti Zootecn, Fac Agr, I-42100 Reggio Emilia, Italy. C3 University of Bologna RP Fontanesi, L (corresponding author), Univ Bologna, DIPROVAL, Sez Allevamenti Zootecn, Fac Agr, Via F Ili Rosselli 107, I-42100 Reggio Emilia, Italy. EM luca.fontanesi@unibo.it CR Bellis C, 2003, FORENSIC SCI INT, V134, P99, DOI 10.1016/S0379-0738(03)00128-2 Blott SC, 1999, HEREDITY, V82, P613, DOI 10.1046/j.1365-2540.1999.00521.x Chisholm J, 2008, EUR FOOD RES TECHNOL, V228, P39, DOI 10.1007/s00217-008-0904-0 Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 Crepaldi P, 2003, ITAL J ANIM SCI, V2, P13, DOI 10.4081/ijas.2003.s1.13 D'Alessandro E, 2007, VET RES COMMUN, V31, P389, DOI 10.1007/s11259-007-0063-y Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 de Roest K, 2000, SOCIOL RURALIS, V40, P439, DOI 10.1111/1467-9523.00159 Dove AW, 2005, NAT BIOTECHNOL, V23, P283, DOI 10.1038/nbt0305-283 ENNIS S, 1994, ANIM GENET, V25, P425, DOI 10.1111/j.1365-2052.1994.tb00533.x Fernandez A, 2004, J SCI FOOD AGR, V84, P1855, DOI 10.1002/jsfa.1829 Fontanesi L, 2008, MEAT SCI, V80, P1345, DOI 10.1016/j.meatsci.2008.06.014 Fontanesi L, 2007, ITAL J ANIM SCI, V6, P136, DOI 10.4081/ijas.2007.1s.136 Fontanesi L, 2006, ANIM GENET, V37, P489, DOI 10.1111/j.1365-2052.2006.01494.x FONTANESI L, 2005, P INT WORKSH ROL BIO FONTANESI L, 2009, BMC GENET IN PRESS Fontanesi L, 2008, MOL REPROD DEV, V75, P1662, DOI 10.1002/mrd.20903 Gandini GC, 2003, J ANIM BREED GENET, V120, P1, DOI 10.1046/j.1439-0388.2003.00365.x Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 Hill WG, 2008, J ANIM SCI, V86, P2508, DOI 10.2527/jas.2007-0276 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Maudet C, 2002, J DAIRY SCI, V85, P707, DOI 10.3168/jds.S0022-0302(02)74127-1 Maudet C, 2002, J ANIM SCI, V80, P942 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Negrini R, 2007, ANIM GENET, V38, P147, DOI 10.1111/j.1365-2052.2007.01573.x Oulmouden A., 2005, WO, Patent No. [2005/019473, 2005019473] Rohrer GA, 2007, ANIM GENET, V38, P253, DOI 10.1111/j.1365-2052.2007.01593.x RUSSO V, 2004, P 7 WORLD BROWN SWIS, P95 Russo V, 2007, ITAL J ANIM SCI, V6, P257 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Zeleny R, 2002, J AGR FOOD CHEM, V50, P4169, DOI 10.1021/jf020156d NR 32 TC 28 Z9 29 U1 0 U2 8 PY 2009 VL 8 SU 2 BP 9 EP 18 DI 10.4081/ijas.2009.s2.9 WC Agriculture, Dairy & Animal Science; Agriculture, Multidisciplinary; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000277211600002 DA 2022-12-14 ER PT J AU Mottese, AF Naccari, C Vadala, R Bua, GD Bartolomeo, G Rando, R Cicero, N Dugo, G AF Mottese, Antonio Francesco Naccari, Clara Vadala, Rossella Bua, Giuseppe Daniel Bartolomeo, Giovanni Rando, Rossana Cicero, Nicola Dugo, Giacomo TI Traceability of Opuntia ficus-indica L. Miller by ICP-MS multi-element profile and chemometric approach SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE Opuntia ficus-indica L; Miller; traceability; multi-element profile; ICP-MS; PCA; protection brand ID GEOGRAPHICAL ORIGIN; PRICKLY PEAR; CHEMICAL-CHARACTERIZATION; CACTUS-PEAR; FRUITS; CLADODES; JUICE; FOOD; NMR AB BACKGROUNDOpuntia ficus-indica L. Miller fruits, particularly Ficodindia dell'Etna' of Biancavilla (POD), Fico d'india tradizionale di Roccapalumba' with protected brand and samples from an experimental field in Pezzolo (Sicily) were analyzed by inductively coupled plasma mass spectrometry in order to determine the multi-element profile. A multivariate chemometric approach, specifically principal component analysis (PCA), was applied to individuate how mineral elements may represent a marker of geographic origin, which would be useful for traceability. RESULTSPCA has allowed us to verify that the geographical origin of prickly pear fruits is significantly influenced by trace element content, and the results found in Biancavilla PDO samples were linked to the geological composition of this volcanic areas. It was observed that two principal components accounted for 72.03% of the total variance in the data and, in more detail, PC1 explains 45.51% and PC2 26.52%, respectively. CONCLUSIONThis study demonstrated that PCA is an integrated tool for the traceability of food products and, at the same time, a useful method of authentication of typical local fruits such as prickly pear. (c) 2017 Society of Chemical Industry C1 [Mottese, Antonio Francesco; Naccari, Clara; Vadala, Rossella; Bua, Giuseppe Daniel; Bartolomeo, Giovanni; Rando, Rossana; Cicero, Nicola; Dugo, Giacomo] Univ Messina, Food Chem Sect, Dept BIOMORF, Messina, Italy. [Cicero, Nicola; Dugo, Giacomo] Univ Messina, Science4Life Srl, Acad Spin Off, Messina, Italy. C3 University of Messina; University of Messina RP Naccari, C (corresponding author), Univ Messina, Dept BIOMORF, Food Sect, Vle Annunziata,Polo Univ, I-98165 Messina, Italy. EM cnaccari@unime.it CR Antunes-Ricardo M, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/847320 Ariyama K, 2004, J AGR FOOD CHEM, V52, P5803, DOI 10.1021/jf049333w ASKAR A, 1981, DEUT LEBENSM-RUNDSCH, V77, P279 Barbera G., 1993, COLTURA FICODINDIA Benabdelkamel H, 2012, J AGR FOOD CHEM, V60, P3717, DOI 10.1021/jf2050075 Carrera M, 2017, J AGR FOOD CHEM, V65, P1070, DOI 10.1021/acs.jafc.6b04972 Cicero N, 2015, NAT PROD RES, V29, P1894, DOI 10.1080/14786419.2015.1012166 Corsaro C, 2016, FOOD RES INT, V89, P1085, DOI 10.1016/j.foodres.2016.09.033 D'Evoli L., 2015, Food and Nutrition Sciences, V6, P1267, DOI 10.4236/fns.2015.614132 de Andres-de Prado R, 2007, J AGR FOOD CHEM, V55, P779, DOI 10.1021/jf062446q De la Barrera E, 2004, J EXP BOT, V55, P719, DOI 10.1093/jxb/erh084 Di Bella G, 2016, INT J FOOD SCI NUTR, V4, P1 Medina EMD, 2007, FOOD CHEM, V103, P38, DOI 10.1016/j.foodchem.2006.06.064 El-Mostafa K, 2014, MOLECULES, V19, P14879, DOI 10.3390/molecules190914879 Ferrante Margherita, 2013, Ig Sanita Pubbl, V69, P47 Ghazi Z., 2015, J MAT ENV SCI, V6, P2338 Griffith MP, 2004, AM J BOT, V91, P1915, DOI 10.3732/ajb.91.11.1915 Gurrieri S, 2000, J AGR FOOD CHEM, V48, P5424, DOI 10.1021/jf9907844 HAMMER MM, 2006, J PROFESSIONAL ASS C, V8, P1 Hernandez-Urbiola MI, 2011, INT J ENV RES PUB HE, V8, P1287, DOI 10.3390/ijerph8051287 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 KRUSKAL WH, 1953, J AM STAT ASSOC, V48, P907, DOI 10.2307/2281082 Lahsasni S, 2004, J FOOD ENG, V61, P173, DOI 10.1016/S0260-8774(03)00084-0 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Marengo E, 2003, FOOD CHEM, V81, P621, DOI 10.1016/S0308-8146(02)00564-2 Monforte MT, 2014, J MED FOOD, V17, P455, DOI 10.1089/jmf.2012.0262 Monforte Maria Teresa, 2014, Journal of Coastal Life Medicine, V2, P14 Msaddak L, 2015, INT J FOOD SCI NUTR, V66, P851, DOI 10.3109/09637486.2015.1095862 Naccarato A, 2016, FOOD CHEM, V206, P217, DOI 10.1016/j.foodchem.2016.03.072 Nunez-Lopez MA, 2013, J AGR FOOD CHEM, V61, P10981, DOI 10.1021/jf403834x Piga A, 2004, J PROF ASSOC CACTUS, V6, P9 Ritota M, 2013, J SCI FOOD AGR, V93, P1665, DOI 10.1002/jsfa.5947 Saenz C, 2001, J PROF ASSOC CACTUS, V4, P3 Saenz CH, 1997, RIV FRUTTIC, V12, P47 Salim N, 2009, AFR J BIOTECHNOL, V8, P1623 Salvo A, 2016, NAT PROD RES, V30, P1517, DOI 10.1080/14786419.2015.1115999 Santoni F, 2016, CHEM BIODIVERS, V13, P748, DOI 10.1002/cbdv.201500236 Schirra M, 2002, J AGR FOOD CHEM, V50, P739, DOI 10.1021/jf011330l Vadala R, 2016, FOODS, V5, DOI 10.3390/foods5010020 NR 39 TC 17 Z9 19 U1 2 U2 30 PD JAN PY 2018 VL 98 IS 1 BP 198 EP 204 DI 10.1002/jsfa.8456 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000415716900025 DA 2022-12-14 ER PT J AU Bonizzi, I Feligini, M Aleandri, R Enne, G AF Bonizzi, I. Feligini, M. Aleandri, R. Enne, G. TI Genetic traceability of the geographical origin of typical Italian water buffalo Mozzarella cheese: a preliminary approach SO JOURNAL OF APPLIED MICROBIOLOGY DT Article DE bacterial community; cheese; fingerprinting; food traceability; internal transcribed spacers-PCR; PDO authentication; phylogeny; 16S-23S spacer ID LACTOCOCCUS-LACTIS; SEQUENCE; PCR AB Aims: To distinguish Italian Protected Designation of Origin (PDO) water buffalo Mozzarella from different producers on a molecular basis in relation to the place of manufacturing within the production district, and to develop a tool for genetic traceability of typical dairy products. Methods and Results: Microbial DNA was isolated from Mozzarella's governing liquid to amplify the whole microflora's ribosomal 16S-23S internal transcribed spacers (ITS)-PCR fingerprinting by means of an original primer pair. Phylogenetic distance analyses were performed on the obtained electrophoretic band patterns by maximum parsimony and neighbour-joining tree construction algorithms for discrete binary data, using a conventional bootstrap resampling test. The observed band profiles showed high repeatability and specificity, allowing unambiguous distinction of each sample; phylogenetic analyses yielded the same tree topology with good strength of nodal support. Moreover, a relationship between the genetic distances among samples and the actual geographical ones separating the respective producing dairies was observed. Conclusions: The genetic diversity of PDO water buffalo Mozzarella's microflora, observed by ITS-PCR fingerprinting, can be exploited to discriminate cheeses from differently located dairies. Significance and Impact of the Study: Given the increasing importance of food traceability for safety, quality and typicalness issues, the ITS-PCR fingerprinting protocol described here may represent a suitable tool for tracing the geographical origin of Italian Mozzarella. C1 Ist Sperimentale Italiano Lazzaro Spallanzani, Lodi, Italy. Univ Sassari, Fac Agr, Dipartimento Sci Zootech, I-07100 Sassari, Italy. C3 IRCCS Lazzaro Spallanzani; University of Sassari RP Bonizzi, I (corresponding author), Via Einstein,Local Cascina Codazza, I-26900 Lodi, Italy. EM ivan.bonizzi@isils.it CR Blaiotta G, 2002, SYST APPL MICROBIOL, V25, P520, DOI 10.1078/07232020260517652 Bolotin A, 2001, GENOME RES, V11, P731, DOI 10.1101/gr.GR-1697R Coppola S, 2001, J APPL MICROBIOL, V90, P414, DOI 10.1046/j.1365-2672.2001.01262.x Daffonchio D, 1998, INT J SYST BACTERIOL, V48, P1081, DOI 10.1099/00207713-48-3-1081 Feligini M, 2005, FOOD TECHNOL BIOTECH, V43, P91 FELSENSTEIN J, 2004, PHYLIP VERSION 3 6 D JENSEN MA, 1993, APPL ENVIRON MICROB, V59, P945, DOI 10.1128/AEM.59.4.945-952.1993 KIMURA M, 1980, J MOL EVOL, V16, P111, DOI 10.1007/BF01731581 KLUDGE AG, 1969, SYST ZOOL, V18, P1 Maoz A, 2003, APPL ENVIRON MICROB, V69, P4012, DOI 10.1128/AEM.69.7.4012-4018.2003 Mauriello G, 2003, J DAIRY SCI, V86, P486, DOI 10.3168/jds.S0022-0302(03)73627-3 Page RDM, 1996, COMPUT APPL BIOSCI, V12, P357 Popping B, 2002, J BIOTECHNOL, V98, P107, DOI 10.1016/S0168-1656(02)00089-5 SAITOU N, 1987, MOL BIOL EVOL, V4, P406, DOI 10.1093/oxfordjournals.molbev.a040454 WEISBURG WG, 1991, J BACTERIOL, V173, P697, DOI 10.1128/JB.173.2.697-703.1991 Widmer F, 1998, APPL ENVIRON MICROB, V64, P2545 1996, OFF J EUR COMMUNIT L, V148, P1 NR 17 TC 22 Z9 25 U1 0 U2 22 PD MAR PY 2007 VL 102 IS 3 BP 667 EP 673 DI 10.1111/j.1365-2672.2006.03131.x WC Biotechnology & Applied Microbiology; Microbiology SC Biotechnology & Applied Microbiology; Microbiology UT WOS:000244243900007 DA 2022-12-14 ER PT J AU Dickinson, DL Bailey, D AF Dickinson, DL Bailey, D TI Meat traceability: Are US consumers willing to pay for it? SO JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS DT Article DE auctions; experiments; red meat; traceability; willingness to pay ID FOOD SAFETY; EXPERIMENTAL AUCTION; MARKETS; PORK AB This article reports the results from a series of laboratory auction markets in which consumers bid on meat characteristics. The characteristics examined include meat traceability (i.e., the ability to trace the retail meat back to the farm or animal of origin), transparency (e.g., knowing the meat was produced without added growth hormones, or knowing the animal was humanely treated), and extra assurances (e.g., extra meat safety assurances). This laboratory study provides non-hypothetical bid data on consumer preferences for a sample of consumers in Logan, Utah, for traceability, transparency, and assurances (TTA) in red meat at a time when the United States currently lags other countries in development of TTA meat systems. Results suggest these consumers would be willing to pay for such TTA meat characteristics, and the magnitude of the consumer bids reveals that a profitable market for development of TTA systems in the United States might exist. C1 Utah State Univ, Dept Econ, Logan, UT 84322 USA. Utah State Univ, Dept Management & Human Resources, Logan, UT 84322 USA. C3 Utah System of Higher Education; Utah State University; Utah System of Higher Education; Utah State University RP Dickinson, DL (corresponding author), Utah State Univ, Dept Econ, Logan, UT 84322 USA. CR ABBATEMARICO K, 2001, COMMUNICATION SEP Baines R.N., 1998, 3 INT C CHAIN MAN AG BAINES RN, 2000, AGR FOR 2000 WORLD F BAINES RN, 2001, COMMUNICATION SEP BUHR B, 2002, NAT PORK IND FOR DEN COE M, 2000, COMMUNICATION DEWAAL CS, 2001, FOOD TRACEABILITY RE, V1, P12 Disney WT, 2001, REV SCI TECH OIE, V20, P385, DOI 10.20506/rst.20.2.1277 DOREY E, 2001, WALL STREET J EU SEP, P27 Early R., 1998, Journal of the Royal Agricultural Society of England, V159, P32 *FARML IND, 2001, DAT PRIV DEV TTA POR Friedman M, 1937, J AM STAT ASSOC, V32, P675, DOI 10.2307/2279372 GRANNIS J, 2000, ANN M W AGR EC ASS V HAYES DJ, 1995, AM J AGR ECON, V77, P40, DOI 10.2307/1243887 Hobbs J. E., 1996, Agribusiness (New York), V12, P509, DOI 10.1002/(SICI)1520-6297(199611/12)12:6<509::AID-AGR2>3.0.CO;2-7 Hobbs J. E., 1996, British Food Journal, V98, P16, DOI 10.1108/00070709610131339 JONES E, 2001, ANN M AM AGR LAW ASS Knetsch J. L., 2001, Experimental Economics, V4, P257, DOI 10.1007/BF01673537 *LAB COMM UT, WEEKL INC STAT Latouche K, 1998, FOOD POLICY, V23, P347, DOI 10.1016/S0306-9192(98)00048-7 LEWIS S, 2001, FOOD TRACEABILITY RE, V1, P10 LIDDELL S, 2001, INT FOOD AGRIBUS MAN, V4, P287, DOI DOI 10.1016/S1096-7508(01)00081-7 Loader R., 1996, British Food Journal, V98, P26, DOI 10.1108/00070709610153669 Lusk J.L., 2002, J AGRIC APPL ECON, V34, P27, DOI [10.1017/S1074070800002121, DOI 10.1017/S1074070800002121] LUSK JL, IN PRESS AM J AGR EC Melton BE, 1996, AM J AGR ECON, V78, P916, DOI 10.2307/1243848 Nakamoto M., 2001, FINANCIAL TIMES 1003 Palmer C. M., 1996, British Food Journal, V98, P17, DOI 10.1108/00070709610153650 *PEW IN FOOD BIOT, 2002, KNOW ITS GOING BRING Rutstrom EE, 1998, INT J GAME THEORY, V27, P427, DOI 10.1007/s001820050082 SHOGREN JF, 1994, AM ECON REV, V84, P255 SHOGREN JF, 1994, AM J AGR ECON, V76, P1089, DOI 10.2307/1243397 Shogren JF, 2001, J ECON BEHAV ORGAN, V46, P409, DOI 10.1016/S0167-2681(01)00165-2 Shogren JF, 1999, AM J AGR ECON, V81, P1192, DOI 10.2307/1244106 SHOGREN JF, 2002, NEGATIVE VALUES 2 PR *US BUR CENS, HOUS DEM INF *US BUR CENS, ANN MED HOUS INC *US BUR CENS, HOUS DEM INF SIZ COM *US BUR LAB STAT, US WEEKL INC STAT Verbeke W, 1999, MEAT SCI, V53, P77, DOI 10.1016/S0309-1740(99)00036-4 WIEMERS JF, 2001, C ATT GLOB FOOD AGR NR 41 TC 135 Z9 142 U1 1 U2 40 PD DEC PY 2002 VL 27 IS 2 BP 348 EP 364 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000183192300004 DA 2022-12-14 ER PT J AU Guo, BL Wei, YM Simon, KD Pan, JR Wei, SA AF Guo Bo-Li Wei Yi-Min Simon, Kelly D. Pan Jia-Rong Wei Shuai TI Application of Stable Hydrogen Isotope Analysis in Beef Geographical Origin Traceability SO CHINESE JOURNAL OF ANALYTICAL CHEMISTRY DT Article DE Beef; geographical origin; traceability; stable hydrogen isotope ID RATIOS; OXYGEN; TRACE; NITROGEN; CARBON; HAIR AB A method of measuring the hydrogen isotope ratio in cattle tissues using pyrolysis instrument and isotope ratio mass spectrometry (IRMS) was examined and the hydrogen isotope ratio in cattle tissues from different regions of China was measured. The relationship between the stable hydrogen isotope in cattle tissues and the longitude, latitude and altitude of the region was analyzed, and the feasibility of applying the stable hydrogen isotope to trace the beef geographical origin in China was discussed. The results indicated that there were significant differences of delta H-2 values in cattle tissues from different regions, they were closely related to the hydrogen composition of the local water, and the delta H-2 values in cattle tissues decreased with the latitude increased. In addition, there was very significant correlation of delta H-2 values between cattle tall hair and defatted beef. Stable hydrogen isotope in cattle tissues is the potential indicator in cattle geographical origin traceability, and both tissues of defatted beef and tall hair could yield useful information on cattle geographical origin. C1 [Guo Bo-Li; Wei Yi-Min; Pan Jia-Rong; Wei Shuai] Minist Agr, Inst Agrofood Sci & Technol, Key Lab Agrofood Proc & Qual Control, Beijing 100193, Peoples R China. [Simon, Kelly D.] Inst Food Res, Norwich NR4 7TJ, Norfolk, England. C3 Ministry of Agriculture & Rural Affairs; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Quadram Institute; University of East Anglia RP Guo, BL (corresponding author), Minist Agr, Inst Agrofood Sci & Technol, Key Lab Agrofood Proc & Qual Control, Beijing 100193, Peoples R China. EM guoboli.caas@yahoo.com.cn CR BETTINA MF, 2007, EUROPEAN FOOD RES TE, V225, P501 Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Camin F, 2004, J AGR FOOD CHEM, V52, P6592, DOI 10.1021/jf040062z Camin F, 2008, RAPID COMMUN MASS SP, V22, P1690, DOI 10.1002/rcm.3506 Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 Guo BoLi, 2008, Scientia Agricultura Sinica, V41, P2105 Guo BoLi, 2007, Scientia Agricultura Sinica, V40, P365 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Hobson KA, 2004, OECOLOGIA, V141, P477, DOI 10.1007/s00442-004-1671-7 Kelly JF, 2002, OECOLOGIA, V130, P216, DOI 10.1007/s004420100789 Manca G, 2001, J AGR FOOD CHEM, V49, P1404, DOI 10.1021/jf000706c MARTIN IG, 1999, MEAT SCI, V52, P437 O'Brien DM, 2007, RAPID COMMUN MASS SP, V21, P2422, DOI 10.1002/rcm.3108 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Rossmann A, 2000, EUR FOOD RES TECHNOL, V211, P32, DOI 10.1007/s002170050585 Schwertl M, 2005, AGR ECOSYST ENVIRON, V109, P153, DOI 10.1016/j.agee.2005.01.015 NR 18 TC 7 Z9 21 U1 2 U2 30 PD SEP PY 2009 VL 37 IS 9 BP 1333 EP 1336 WC Chemistry, Analytical SC Chemistry UT WOS:000270400500016 DA 2022-12-14 ER PT J AU Cuinas, I Newman, R Trebar, M Catarinucci, L Melcon, AA AF Cuinas, Inigo Newman, Robert Trebar, Mira Catarinucci, Luca Melcon, Alejandro A. TI RFID-Based Traceability Along the Food-Production Chain SO IEEE ANTENNAS AND PROPAGATION MAGAZINE DT Article DE Radiofrequency identification; RFID; wireless sensor networks; food technology; supply chain management; traceability; from farm to fork ID IDENTIFICATION AB This contribution explains and analyzes the use of RFID (radio-frequency identification) for defining a complete traceability system applied to the food-production chain. The paper contains a summary of the actual work developed to test the ability of radio technologies to perform traceability at different food companies in a variety of sectors: wine, fish, and meat. Each pilot experience is explained, with special emphasis on the radio segment implemented by RFID technologies and sensors, whether connected by wired or as elements of a wireless sensor network. The application of the new RFID-based system at the three investigated sectors, and the return on investment that the companies could obtain by its usage, are the core of the paper. C1 [Cuinas, Inigo] Univ Vigo, Dept Teoria Sinal & Comunicac, Vigo 36310, Spain. [Newman, Robert] Wolverhampton Univ, Sch Technol, Wolverhampton WV1 1SB, England. [Trebar, Mira] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia. [Catarinucci, Luca] Univ Salento, Dept Innovat Engn, Lecce, Italy. [Melcon, Alejandro A.] Univ Politecn Cartagena, Cartagena, Spain. C3 Universidade de Vigo; University of Wolverhampton; University of Ljubljana; University of Salento; Universidad Politecnica de Cartagena RP Cuinas, I (corresponding author), Univ Vigo, Dept Teoria Sinal & Comunicac, Vigo 36310, Spain. EM inhigo@uvigo.es; r.newman@wlv.ac.uk; mira.trebar@fri.uni-lj.si; luca.catarinucci@unisalento.it; alejandro.alvarez@upct.es CR [Anonymous], 2002, OFFICIAL J EUROPEAN [Anonymous], 2007, EPC INFORM SERVICES Bolic M., 2010, RFID SYSTEMS RES TRE Catarinucci L., 2013, INT J RADIO FREQUENC, V2, P122 Catarinucci L, 2012, J MED SYST, V36, P3451, DOI 10.1007/s10916-011-9790-2 Cha JR, 2005, 11TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS WORKSHOPS, VOL II, PROCEEDINGS,, P63 Cuinas I., 2011, PROGR EL RES S PIERS Exposito I, 2013, IEEE ANTENN PROPAG M, V55, P255, DOI 10.1109/MAP.2013.6529365 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Hendrickson MK, 2002, SOCIOL RURALIS, V42, P347, DOI 10.1111/1467-9523.00221 Jansen WJ, 2003, ECON LETT, V79, P89, DOI 10.1016/S0165-1765(02)00292-6 Kelepouris T., 2007, IND MANAGEMENT DATA, V107, P2 Michael K, 2005, ICMB 2005: International Conference on Mobile Business, P623, DOI 10.1109/ICMB.2005.103 Montoro Filho A. F., 2010, GS1 HEALTHCARE REFER Napolitano F, 2010, TRENDS FOOD SCI TECH, V21, P537, DOI 10.1016/j.tifs.2010.07.012 Parreno A. M., 2013, J SYSTEM MANAGEMENT, V3, P58 Pramatari K, 2007, SUPPLY CHAIN MANAG, V12, P210, DOI 10.1108/13598540710742527 Rao K. V. S., 1999, 1999 Asia Pacific Microwave Conference. APMC'99. Microwaves Enter the 21st Century. Conference Proceedings (Cat. No.99TH8473), P746, DOI 10.1109/APMC.1999.833700 Ratcliff J, 2009, COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE II, VOLUME 3, P2161 Roberts CM, 2006, COMPUT SECUR, V25, P18, DOI 10.1016/j.cose.2005.12.003 Swedberg C., 2011, RFID J Tajima May, 2007, Journal of Purchasing and Supply Management, V13, P261, DOI 10.1016/j.pursup.2007.11.001 Trebar M., 2011, P 19 INT C SOFTW TEL, P1 Wu NC, 2006, TECHNOVATION, V26, P1317, DOI 10.1016/j.technovation.2005.08.012 NR 24 TC 16 Z9 18 U1 5 U2 46 PD APR PY 2014 VL 56 IS 2 BP 196 EP 207 DI 10.1109/MAP.2014.6837090 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications UT WOS:000342398300015 DA 2022-12-14 ER PT J AU Porto, SMC Arcidiacono, C Cascone, G AF Porto, S. M. C. Arcidiacono, C. Cascone, G. TI Developing integrated computer-based information systems for certified plant traceability: Case study of Italian citrus-plant nursery chain SO BIOSYSTEMS ENGINEERING DT Article ID MANAGEMENT; FRAMEWORK; FARM AB Certified plant production process usually requires the handling of a number of sub-products deriving from various production centres located in different geographical sites. In this context, supply-chain traceability systems are suitable tools for controlling plant disease diffusion and they can be implemented by means of integrated computer-based information systems (ICBISs) which incorporate data from different production centres. This paper proposes a methodology for the design of ICBISs to implement supply-chain traceability procedures regarding certified plants for food, fresh fruit production, and agro-processing industries. The use of international standards and regulations, put forward by plant certification programs to develop the phase of 'Requirements analysis and specifications', addresses the lack of standards application in certified plant traceability procedures. Conceptual and logical models are suggested to model information content and functions of the ICBIS. An innovative system architecture is developed to fulfil supply-chain traceability system requirements and specifications. The proposed methodology was applied to a case study of the Italian certified citrus-plant nursery chain. The results of this research could constitute guidelines for developing ICBISs for certified citrus-plant nursery chain traceability in other major producing countries. (C) 2011 IAgrE. Published by Elsevier Ltd. All rights reserved. C1 [Porto, S. M. C.; Arcidiacono, C.; Cascone, G.] Univ Catania, Dept Agri Food & Environm Syst Management, Bldg & Land Engn Sect, I-95123 Catania, Italy. C3 University of Catania RP Arcidiacono, C (corresponding author), Univ Catania, Dept Agri Food & Environm Syst Management, Bldg & Land Engn Sect, Via S Sofia 100, I-95123 Catania, Italy. EM carcidi@unict.it CR Aubert B., 1998, CITRUS NURSERIES PLA Barbari M, 2006, BIOSYST ENG, V95, P271, DOI 10.1016/j.biosystemseng.2006.06.012 Batini C., 1992, CONCEPTUAL DATABASE Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 BOOCH G, 1999, UNIFIED MODELING LAN CASCONE G, 2010, P 8 WORLD C COMP AGR Catara A., 2006, Italus Hortus, V13, P49 Cherry C., 1999, Requirements Engineering, V4, P103, DOI 10.1007/s007660050017 CIFARELLI A, 2009, TERRA VITA, P19 Dunn C. L., 2005, International Journal of Accounting Information Systems, V6, P83, DOI 10.1016/j.accinf.2004.03.002 Fountas S, 2009, PRECIS AGRIC, V10, P247, DOI 10.1007/s11119-008-9098-5 Garcia FJM, 2006, BIOSYST ENG, V93, P253, DOI 10.1016/j.biosystemseng.2005.12.011 Gmitter FG, 2007, CITRUS GENETICS, BREEDING AND BIOTECHNOLOGY, P287, DOI 10.1079/9780851990194.0287 Hancevic K, 2009, J FOOD AGRIC ENVIRON, V7, P254 HEATHCOTE PM, 2003, A2 ICT *ISM, 2006, FIL AGR CAL *ISO, 2007, 220052007 UNI EN ISO LEE RF, 2004, DIS FRUIT VEGETABLE, V1 Leone A, 2008, BIOSYST ENG, V101, P270, DOI 10.1016/j.biosystemseng.2008.07.005 Luvisi A, 2010, SCI HORTIC-AMSTERDAM, V124, P349, DOI 10.1016/j.scienta.2010.01.015 Luvisi A, 2010, COMPUT ELECTRON AGR, V70, P256, DOI 10.1016/j.compag.2009.08.007 Mall R, 2004, FUNDAMENTALS SOFTWAR Niederhauser N, 2008, COMPUT ELECTRON AGR, V61, P241, DOI 10.1016/j.compag.2007.12.001 Nikkila R, 2010, COMPUT ELECTRON AGR, V70, P328, DOI 10.1016/j.compag.2009.08.013 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Provolo G, 2005, BIORESOURCE TECHNOL, V96, P145, DOI 10.1016/j.biortech.2004.05.002 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Rovira-Mas F, 2005, BIOSYST ENG, V90, P251, DOI 10.1016/j.biosystemseng.2004.11.013 Sorensen CG, 2010, COMPUT ELECTRON AGR, V72, P37, DOI 10.1016/j.compag.2010.02.003 Stafford JV, 2000, J AGR ENG RES, V76, P267, DOI 10.1006/jaer.2000.0577 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X 1993, OFFICIAL GAZETT 1111 2006, OFFICIAL GAZETT 0721 1991, OFFICIAL GAZETT 1106 2007, OFFICIAL GAZETT 0620 2003, OFFICIAL GAZETT 1015 NR 38 TC 19 Z9 20 U1 2 U2 17 PD JUN PY 2011 VL 109 IS 2 BP 120 EP 129 DI 10.1016/j.biosystemseng.2011.02.008 WC Agricultural Engineering; Agriculture, Multidisciplinary SC Agriculture UT WOS:000291180300003 DA 2022-12-14 ER PT J AU Islam, S Manning, L Cullen, JM AF Islam, Samantha Manning, Louise Cullen, Jonathan M. TI A Hybrid Traceability Technology Selection Approach for Sustainable Food Supply Chains SO SUSTAINABILITY DT Article DE cold food chain; traceability technology; technology selection; fuzzy AHP; fuzzy TOPSIS; integer linear programming ID RFID TECHNOLOGY; COLD CHAIN; SYSTEM; TOPSIS; BARCODE; CHALLENGES; SAFETY; MODEL; STATE; AHP AB Traceability technologies have great potential to improve sustainable performance in cold food supply chains by reducing food loss. In existing approaches, traceability technologies are selected either intuitively or through a random approach, that neither considers the trade-off between multiple cost-benefit technology criteria nor systematically translates user requirements for traceability systems into the selection process. This paper presents a hybrid approach combining the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with integer linear programming to select the optimum traceability technologies for improving sustainable performance in cold food supply chains. The proposed methodology is applied in four case studies utilising data collected from literature and expert interviews. The proposed approach can assist decision-makers, e.g., food business operators and technology companies, to identify what combination of technologies best suits a given food supply chain scenario and reduces food loss at minimum cost. C1 [Islam, Samantha; Cullen, Jonathan M.] Univ Cambridge, Dept Engn, Energy Fluids & Turbomachinery Div, Cambridge CB2 1PZ, England. [Manning, Louise] Royal Agr Univ, Sch Agr Food & Environm, Cirencester GL7 6JS, Glos, England. C3 University of Cambridge RP Islam, S (corresponding author), Univ Cambridge, Dept Engn, Energy Fluids & Turbomachinery Div, Cambridge CB2 1PZ, England. EM si313@cam.ac.uk; louise.manning@rau.ac.uk; jmc99@cam.ac.uk CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Anand MB, 2018, RAPID PROTOTYPING J, V24, P424, DOI 10.1108/RPJ-10-2016-0160 [Anonymous], 2010, RFID HDB FUNDAMENTAL Aroor SR, 2007, IEEE SYST J, V1, P168, DOI 10.1109/JSYST.2007.909179 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Balbinot-Alfaro E, 2019, FOOD ENG REV, V11, P235, DOI 10.1007/s12393-019-09198-9 Bertolini M, 2013, INT J RF TECHNOL-RES, V5, P123, DOI 10.3233/RFT-130052 Blom E.D., 2009, Patent, Patent No. [7,474,230, 7474230] Boss R.W., 2009, LIBR TECHNOL REPOR, V39, P18 Bukkapatnam S., 2005, SENSOR RFID NE UNPUB Buyukozkan G, 2017, MEASUREMENT, V112, P88, DOI 10.1016/j.measurement.2017.08.018 Chawla V, 2007, IEEE COMMUN MAG, P11 Chen CT, 2006, INT J PROD ECON, V101, P185, DOI 10.1016/j.ijpe.2005.05.003 Chou CH, 2013, QUAL QUANT, V47, P1, DOI 10.1007/s11135-011-9473-6 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Crossbow Technology datasheet, CROSSB TELOSB MOT TP D'Hont S., 2004, TEX INSTRUM TIRIS, V16, P1 Dinmohammadi A, 2017, ENERGIES, V10, DOI 10.3390/en10050642 Dobrucka R, 2014, POL J FOOD NUTR SCI, V64, P7, DOI 10.2478/v10222-012-0091-3 Erkan TE, 2014, TEH VJESN, V21, P87 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Fan TJ, 2014, INT J PROD ECON, V147, P659, DOI 10.1016/j.ijpe.2013.05.007 FAO, 2013, FOOD WASTAGE FOOTPRI Favre Ray, 2014, USING RADIO FREQUENC Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 GS1, EAN UPC BARC GS1, 2015, EPCTM RAD FREQ ID PR GS1, 2019, EPC TAG DAT STAND DE Haflioason T, 2012, INT J PHYS DISTR LOG, V42, P355, DOI 10.1108/09600031211231335 Hamzeh R, 2019, COMPUT IND ENG, V138, DOI 10.1016/j.cie.2019.106123 Howard J., 2003, U.S. Patent, Patent No. [6,614,392, 6614392] Huang Vincent, 2008, 2008 Second International Conference on Sensor Technologies and Applications (SENSORCOMM), P456, DOI 10.1109/SENSORCOMM.2008.23 Impinj, TYP RFID SYST Islam S., 2021, ADV TRACEABILITY SYS Islam S, 2021, TRENDS FOOD SCI TECH, V112, P708, DOI 10.1016/j.tifs.2021.04.020 Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 ISO/IEC, 2009, 1800022009 ISOIEC ISO/IEC, 2009, 159632009 ISOIEC ISO/IEC, 2013, 1800062013 ISOIEC ISO/IEC, 2010, 1800032010 ISOIEC Jadhav AS, 2009, INFORM SOFTWARE TECH, V51, P555, DOI 10.1016/j.infsof.2008.09.003 Jahanshahloo GR, 2006, APPL MATH COMPUT, V181, P1544, DOI 10.1016/j.amc.2006.02.057 Jedermann R, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0302 Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Jiang ZG, 2011, J CLEAN PROD, V19, P1939, DOI 10.1016/j.jclepro.2011.07.010 Kabli M.R., 2009, THESIS U NOTTINGHAM Kahraman C, 2007, J ENTERP INF MANAG, V20, P143, DOI 10.1108/17410390710725742 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Kumar P, 2009, J FOOD SCI, V74, pR101, DOI 10.1111/j.1750-3841.2009.01323.x Kummu M, 2012, SCI TOTAL ENVIRON, V438, P477, DOI 10.1016/j.scitotenv.2012.08.092 Lee CC, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20030744 Leenen MAM, 2009, PHYS STATUS SOLIDI A, V206, P588, DOI 10.1002/pssa.200824428 Li Q, 2013, EXPERT SYST APPL, V40, P1609, DOI 10.1016/j.eswa.2012.09.015 Liegeard J, 2020, CRIT REV FOOD SCI, V60, P1048, DOI 10.1080/10408398.2018.1556580 Liu Y, 2020, EXPERT SYST APPL, V161, DOI 10.1016/j.eswa.2020.113738 Luo H, 2016, INTERNET RES, V26, P435, DOI 10.1108/IntR-11-2014-0294 Lupo T, 2019, COMPUT IND ENG, V137, DOI 10.1016/j.cie.2019.106046 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Martinez-Sala AS, 2009, COMPUT IND, V60, P161, DOI 10.1016/j.compind.2008.12.003 Mavrotas G, 2006, EUR J OPER RES, V171, P296, DOI 10.1016/j.ejor.2004.07.069 McCathie L., 2005, P COLL EL COMM TECHN, P1 Mohammed A, 2018, J CLEAN PROD, V192, P99, DOI 10.1016/j.jclepro.2018.04.131 Musa A, 2014, EXPERT SYST APPL, V41, P176, DOI 10.1016/j.eswa.2013.07.020 Ndraha N, 2018, FOOD CONTROL, V89, P12, DOI 10.1016/j.foodcont.2018.01.027 Occhiuzzi C, 2013, IEEE ANTENN PROPAG M, V55, P14, DOI 10.1109/MAP.2013.6781700 Oskarsdottir K, 2019, J FOOD ENG, V240, P153, DOI 10.1016/j.jfoodeng.2018.07.013 Palmer R.C., 1989, BAR CODE BOOK READIN, V3rd Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qu ZJ, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0219344 Rajak M, 2019, TECHNOL SOC, V59, DOI 10.1016/j.techsoc.2019.101186 Rana RL, 2021, BRIT FOOD J, V123, P3471, DOI 10.1108/BFJ-09-2020-0832 RFID4U, SEL CORR TAG FREQ Ruiz-Garcia Luis, 2010, Sustainable Radio Frequency Identification Solutions, P37 Ruiz-Garcia L, 2009, SENSORS-BASEL, V9, P4728, DOI 10.3390/s90604728 Saaty T.L., 2014, ANAL HIERARCHY PROCE, DOI [10.1002/9781118445112.stat05310, DOI 10.1002/9781118445112.STAT05310] Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Tan J, 2014, IEEE INT CONF DISTR, P269, DOI 10.1109/DCOSS.2014.45 Tan J, 2019, IEEE J RADIO FREQ ID, V3, P35, DOI 10.1109/JRFID.2019.2896145 Tavana M, 2015, EXPERT SYST APPL, V42, P8432, DOI 10.1016/j.eswa.2015.06.057 Tec-IT, BARC OV Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Trafton A., DETECTING GASES WIRE Tu YJ, 2021, DECIS SUPPORT SYST, V142, DOI 10.1016/j.dss.2020.113471 Vaz A, 2010, IEEE T CIRCUITS-II, V57, P95, DOI 10.1109/TCSII.2010.2040314 Vijayakumar V, 2021, J AMB INTEL HUM COMP, V12, P8009, DOI 10.1007/s12652-020-02530-w Violino S, 2019, FOODS, V8, DOI 10.3390/foods8110529 Von Reischach F., 2010, P 2010 INT THINGS IO, P2, DOI [10.1109/IOT.2010.5678457, DOI 10.1109/IOT.2010.5678457] Wang JY, 2015, COMPUT ELECTRON AGR, V110, P196, DOI 10.1016/j.compag.2014.11.009 Wu JY, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107501 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X Zhang XS, 2010, FOOD CONTROL, V21, P825, DOI 10.1016/j.foodcont.2009.10.015 NR 93 TC 3 Z9 3 U1 7 U2 28 PD AUG PY 2021 VL 13 IS 16 AR 9385 DI 10.3390/su13169385 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000689950800001 DA 2022-12-14 ER PT J AU Conti, M AF Conti, Massimo TI EVO-NFC: Extra Virgin Olive Oil Traceability Using NFC Suitable for Small-Medium Farms SO IEEE ACCESS DT Article DE Oils; Supply chains; Safety; Costs; Companies; Blockchains; Databases; Food traceability; smart farm; EVO; olive oil; NFC; android app ID FOOD-SUPPLY CHAIN; SYSTEM AB Food traceability is a fundamental requirement for the agriculture of the future. A food traceability system should ensure food safety and quality control, allow authentication, fraud prevention and control by the authority, improve consumers' safety and confidence. The agri-food supply chain is complex and difficult to handle due to the presence of various stakeholders and control authorities. Consequently, the complexity and the cost of traceability systems make it inapplicable for small and medium enterprises (SMEs). This work defines of a food traceability system using existing low cost digital technologies with the possibility to be integrated into a database for public authority controls. Smartphone applications allow consumer involvement and a bidirectional interaction between the company and the consumer. This work proposes the use of smartphone with NFC technology in every phase of the food chain bringing the information to the final consumer. An advantage of the proposed system is the low cost and easy to use, allowing its diffusion in small and micro farms, regional typical products, bio productions. The applications developed and the database architecture have been customized to the extra virgin olive oil process. Final considerations evidence the economic advantage of the traceability system. C1 [Conti, Massimo] Univ Politecn Marche, Dipartimento Ingn Informaz, I-60131 Ancona, Italy. C3 Marche Polytechnic University RP Conti, M (corresponding author), Univ Politecn Marche, Dipartimento Ingn Informaz, I-60131 Ancona, Italy. EM m.conti@univpm.it CR Abenavoli L. M., 2016, Agronomy Research, V14, P1247 [Anonymous], 2020, ISMEA I SERVIZI MERC Antony AP, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12093750 Arena A, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2019), P173, DOI 10.1109/SMARTCOMP.2019.00049 Cocco L, 2021, IEEE ACCESS, V9, P62899, DOI 10.1109/ACCESS.2021.3074874 Conte L, 2020, TRENDS FOOD SCI TECH, V105, P483, DOI 10.1016/j.tifs.2019.02.025 Conti M., 2020, P 15 INT C AVAILABIL, P1 Dong YH, 2020, IEEE ACCESS, V8, P161261, DOI 10.1109/ACCESS.2020.3019593 EU Commission Communication from the Commission to the EU Parliament the Council the EU Economic and Social Committee and the Committee of the Regions, 2020, FARM FORK STRATEGY F Farooq MS, 2019, IEEE ACCESS, V7, P156237, DOI 10.1109/ACCESS.2019.2949703 Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Garlando U, 2020, IEEE INT SYMP CIRC S Giametta F, 2008, J AGRIC ENG, V39, P19, DOI 10.4081/jae.2008.4.19 Gonzalez D. L. G., 2018, FOOD INTEGRITY HDB G, DOI [10.32741/~hb, DOI 10.32741/~HB] Grimblatt V., 2019, 2019 IEEE INT S CIRC, P1, DOI [10.1109/ISCAS.2019.8702563, DOI 10.1109/ISCAS.2019.8702563] Guido R, 2020, INT J IND ENG MANAGE, V11, P50, DOI 10.24867/IJIEM-2020-1-252 Higgins L. M., 2014, Wine Economics and Policy, V3, P19, DOI 10.1016/j.wep.2014.01.002 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Morin J., 2018, FOOD INTEGRITY HDB G, DOI [10.32741/~hb, DOI 10.32741/~HB] Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Papaefthimiou D., 2017, P 8 INT C INF COMM T, P91 Patelli N, 2020, J FOOD SCI, V85, P3670, DOI 10.1111/1750-3841.15477 Pigini D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101910 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Sophocleous M., 2017, PAPER PRESENTED 2017, P1, DOI [10.1109/BIOCAS.2017.8325180, DOI 10.1109/BIOCAS.2017.8325180] Tao Q, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/6668339 Tharatipyakul A, 2021, IEEE ACCESS, V9, P82909, DOI 10.1109/ACCESS.2021.3085982 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Violino S, 2019, FOODS, V8, DOI 10.3390/foods8110529 Wang L, 2021, IEEE ACCESS, V9, P9296, DOI 10.1109/ACCESS.2021.3050112 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 33 TC 2 Z9 2 U1 3 U2 7 PY 2022 VL 10 BP 20345 EP 20356 DI 10.1109/ACCESS.2022.3151795 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000764166100001 DA 2022-12-14 ER PT J AU Shew, AM Snell, HA Nayga, RM Lacity, MC AF Shew, Aaron M. Snell, Heather A. Nayga, Rodolfo M., Jr. Lacity, Mary C. TI Consumer valuation of blockchain traceability for beef in the United States SO APPLIED ECONOMIC PERSPECTIVES AND POLICY DT Article DE blockchain; food safety; supply chain management; traceability; US beef demand ID WILLINGNESS-TO-PAY; CHEAP TALK; CHOICE EXPERIMENTS; FOOD SAFETY; PREFERENCE; BEHAVIOR; QUALITY; DEMAND; DESIGN; MODELS AB Blockchain (BC) technology, defined as a shared information system to validate, secure, and permanently store transactions among multiple parties on a distributed ledger, presents many applications in agricultural and food industries. This study examines the application of BC in food traceability for beef in the United States using a choice experiment. Findings indicate that consumers value USDA certifications over BC traceability to guide their meat preferences. Our study suggests a number of industry implications, the most important of which suggests focusing business and consumer education on the value of product data, rather than on the value of the technologies that manage data. C1 [Shew, Aaron M.] Arkansas State Univ, Coll Agr, Jonesboro, AR 72401 USA. [Snell, Heather A.; Nayga, Rodolfo M., Jr.] Univ Arkansas, Dept Agr Econ & Agribusiness, Fayetteville, AR 72701 USA. [Lacity, Mary C.] Univ Arkansas, Sam M Walton Coll Business, Fayetteville, AR 72701 USA. C3 Arkansas State University; University of Arkansas System; University of Arkansas Fayetteville; University of Arkansas System; University of Arkansas Fayetteville RP Shew, AM (corresponding author), Arkansas State Univ, Coll Agr, Jonesboro, AR 72401 USA. EM ashew@astate.edu CR ALLENBY GM, 1990, J MARKETING RES, V27, P379, DOI 10.2307/3172624 Ates AM, 2020, J AGRIC APPL ECON, V52, P308, DOI 10.1017/aae.2020.1 Balcombe K, 2010, FOOD POLICY, V35, P211, DOI 10.1016/j.foodpol.2009.12.005 Bazzani C, 2017, FOOD QUAL PREFER, V62, P144, DOI 10.1016/j.foodqual.2017.06.019 Bolfing A, 2020, CRYPTOGRAPHIC PRIMIT, P199 Brashears MM, 2017, MEAT SCI, V132, P59, DOI 10.1016/j.meatsci.2017.03.015 Carlsson F, 2005, ECON LETT, V89, P147, DOI 10.1016/j.econlet.2005.03.010 Charlebois S, 2016, TRENDS FOOD SCI TECH, V50, P211, DOI 10.1016/j.tifs.2016.02.003 Collart AJ, 2022, APPL ECON PERSPECT P, V44, P219, DOI 10.1002/aepp.13134 Crandall PG, 2013, MEAT SCI, V95, P137, DOI 10.1016/j.meatsci.2013.04.022 Dohmen T, 2011, J EUR ECON ASSOC, V9, P522, DOI 10.1111/j.1542-4774.2011.01015.x Fang D, 2021, AM J AGR ECON, V103, P142, DOI 10.1111/ajae.12118 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Fiebig DG, 2010, MARKET SCI, V29, P393, DOI 10.1287/mksc.1090.0508 FOLKES VS, 1988, J CONSUM RES, V14, P548, DOI 10.1086/209135 Gao ZF, 2016, FOOD POLICY, V64, P26, DOI 10.1016/j.foodpol.2016.09.001 Griffin TW, 2022, APPL ECON PERSPECT P, V44, P237, DOI 10.1002/aepp.13142 GUPTA S, 1994, J MARKETING RES, V31, P128, DOI 10.2307/3151952 Hoffmann S, 2017, FOODBORNE DISEASES, 3RD EDITION, P31, DOI 10.1016/B978-0-12-385007-2.00002-4 Imbault F, 2017, 2017 1ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2017 17TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE) Ishmael Wes., 2018, BEEF MAGAZINE 1002 KAMAKURA WA, 1989, J MARKETING RES, V26, P379, DOI 10.2307/3172759 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Knight Russell., 2019, CATTLE BEEF STAT INF KUHFELD WF, 1994, J MARKETING RES, V31, P545, DOI 10.2307/3151882 Lacity M.C., 2018, MANAGERS GUIDE BLOCK Lakkakula P, 2022, APPL ECON PERSPECT P, V44, P273, DOI 10.1002/aepp.13159 LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131 Li XG, 2016, FOOD POLICY, V64, P93, DOI 10.1016/j.foodpol.2016.09.003 Lin W, 2022, APPL ECON PERSPECT P, V44, P253, DOI 10.1002/aepp.13135 Liu RF, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107157 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lusk JL, 2018, FOOD POLICY, V77, P91, DOI 10.1016/j.foodpol.2018.04.011 Lusk JL, 2003, AM J AGR ECON, V85, P840, DOI 10.1111/1467-8276.00492 Lusk JL, 2003, AM J AGR ECON, V85, P16, DOI 10.1111/1467-8276.00100 Mallick P. K., 2018, PROCEDIA COMPUTER SC, V132, P1815, DOI [10.1016/j.procs.2018.05.140, DOI 10.1016/J.PROCS.2018.05.140] MANSKI CF, 1977, THEOR DECIS, V8, P229, DOI 10.1007/BF00133443 Mapperson Joshua., 2020, COINTELEGRAPH, V2020 McFadden D, 2000, J APPL ECONOMET, V15, P447, DOI 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1 McFadden D., 1974, FRONTIERS ECONOMETRI, P105, DOI DOI 10.1108/EB028592 O'Neal Stephen., 2019, COINTELEGRAPH 0707 Ohkubo M, 2005, COMMUN ACM, V48, P66, DOI 10.1145/1081992.1082022 OpenSC, 2020, AUSTRAL FISHERIES CA Ortega DL, 2020, EUR REV AGRIC ECON, V47, P1644, DOI 10.1093/erae/jbaa003 Palmer Daniel, 2019, COINDESK Pendell, 2020, FRONTIERS ANIMAL SCI, V1, DOI 10.3389/fanim.2020.552386 Pendell DL, 2013, FOOD POLICY, V43, P332, DOI 10.1016/j.foodpol.2013.05.013 Pirus Benjamin., FORBES, V2019 Powe NA, 2005, ECOL ECON, V52, P513, DOI 10.1016/j.ecolecon.2004.06.022 Sarrias M, 2017, J STAT SOFTW, V79, P1, DOI 10.18637/jss.v079.i02 Savage SJ, 2008, J APPL ECONOM, V23, P351, DOI 10.1002/jae.984 Scallan E, 2011, EMERG INFECT DIS, V17, P16, DOI [10.3201/eid1701.P21101, 10.3201/eid1701.091101p2] Scarpa R, 2008, AUST J AGR RESOUR EC, V52, P253, DOI 10.1111/j.1467-8489.2007.00436.x SCHANINGER CM, 1981, J CONSUM RES, V8, P208, DOI 10.1086/208857 Schroeder TC, 2007, BE J ECON ANAL POLI, V7 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Shear Hannah., 2019, IMPLEMENTATION EC IM Silva A, 2011, J AGR RESOUR ECON, V36, P280 Statista, 2018, TOT US RET FOOD SERV Syrengelas KG, 2018, APPL ECON PERSPECT P, V40, P445, DOI 10.1093/aepp/ppx042 Thiene M, 2018, FOOD POLICY, V80, P84, DOI 10.1016/j.foodpol.2018.09.004 TRAIN K, 2001, DISCRETE CHOICE METH Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 USDA FSIS, 2020, CURR REC AL Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Whalen, 2019, GRASS ROOTS FARMERS Wieck Marie., 2019, COINDESK Yu HY, 2018, FOOD CONTROL, V86, P83, DOI 10.1016/j.foodcont.2017.11.014 NR 70 TC 12 Z9 12 U1 11 U2 57 PD MAR PY 2022 VL 44 IS 1 SI SI BP 299 EP 323 DI 10.1002/aepp.13157 EA FEB 2021 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000623163300001 DA 2022-12-14 ER PT J AU Mateus, JC Russo-Almeida, PA AF Mateus, J. C. Russo-Almeida, P. A. TI Traceability of 9 Portuguese cattle breeds with PDO products in the market using microsatellites SO FOOD CONTROL DT Article DE Traceability; Microsatellites; Livestock products; Meat ID GENETIC DIVERSITY; MULTILOCUS GENOTYPES; POPULATION-STRUCTURE; ASSIGNMENT TESTS; DIFFERENTIATION; LOCI; DNA AB Assignment tests based on multilocus genotypes are becoming increasingly important to certify the origin of livestock products and assure food safety and authenticity. The potential of microsatellites for determining the origin of beef products among cattle breeds present in the Portuguese market with the Protected Denomination of Origin (PDO) was studied. Methodologies were used to establish the number of populations under study and to allocate individuals to their original population. The STRUCTURE program was used to perform the strictly Bayesian method and the GENECLASS 2 program was used to accomplish two types of assignment tests. The STRUCTURE program converged to 9 populations, precisely the number of populations under study. Regarding the individual allocation, the strictly Bayesian method implemented by the STRUCTURE program allowed 96% of correct allocations when running the program without the knowledge of the source populations and 98% when the STRUCTURE program was run knowing the source populations of the animals. In the assignment test performed by the GENECLASS 2, 95% and 97% of individuals were correctly allocated by the frequency and the Bayesian methods, respectively. These results show the potential feasibility for traceability scheme based on microsatellites. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Mateus, J. C.; Russo-Almeida, P. A.] Univ Tras Os Montes & Alto Douro, Dept Zootecnia, Escola Ciencias Agr & Vet, P-5000801 Vila Real, Portugal. C3 University of Tras-os-Montes & Alto Douro RP Mateus, JC (corresponding author), Univ Tras Os Montes & Alto Douro, Dept Zootecnia, Apartado 1013, P-5000801 Vila Real, Portugal. EM jmateus@utad.pt CR Banks MA, 2003, BIOINFORMATICS, V19, P1436, DOI 10.1093/bioinformatics/btg172 Canon J, 2001, GENET SEL EVOL, V33, P311, DOI 10.1051/gse:2001121 Ciampolini R, 2006, J ANIM SCI, V84, P11, DOI 10.2527/2006.84111x Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 Cornuet JM, 1999, GENETICS, V153, P1989 Ginja C, 2010, J HERED, V101, P201, DOI 10.1093/jhered/esp104 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Ibeagha-Awemu EM, 2005, J ANIM BREED GENET, V122, P12, DOI 10.1111/j.1439-0388.2004.00478.x Leite J. V., 2004, LIVRO RESUMOS 2 JORN Lucas A. V., 1996, GANADARIAS PORTUGUES MacHugh DE, 1998, ANIM GENET, V29, P333, DOI 10.1046/j.1365-2052.1998.295330.x Martin-Burriel I, 2011, J ANIM SCI, V89, P893, DOI 10.2527/jas.2010-3338 Mateus J. C., 2014, ANIMAL GENE IN PRESS Mateus JC, 2004, ANIM GENET, V35, P106, DOI 10.1111/j.1365-2052.2004.01089.x MATEUS Joao, 2008, THESIS Maudet C, 2002, J ANIM SCI, V80, P942 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 MILLER SA, 1988, NUCLEIC ACIDS RES, V16, P1215, DOI 10.1093/nar/16.3.1215 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Pritchard JK, 2000, GENETICS, V155, P945 Pritchard JK, 2003, DOCUMENTATION STRUCT Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 Stoyke M., 2013, Journal fur Verbraucherschutz und Lebensmittelsicherheit, V8, P91, DOI 10.1007/s00003-013-0820-5 NR 24 TC 16 Z9 17 U1 0 U2 33 PD JAN PY 2015 VL 47 BP 487 EP 492 DI 10.1016/j.foodcont.2014.07.038 WC Food Science & Technology SC Food Science & Technology UT WOS:000343612700069 DA 2022-12-14 ER PT J AU Agrawal, TK Pal, R AF Agrawal, Tarun Kumar Pal, Rudrajeet TI Traceability in Textile and Clothing Supply Chains: Classifying Implementation Factors and Information Sets via Delphi Study SO SUSTAINABILITY DT Article DE traceability; Delphi study; supply chain; textile and clothing ID FOOD; TRANSPARENCY; APPAREL; SYSTEM; LABEL AB The purpose of this study is twofold. First, to explore and classify factors influencing traceability implementation, and second, to cluster essential traceability-related information that demands recording and sharing with businesses and customers, in the context of the textile and clothing supply chain. A Delphi study is conducted with 23 experts (including research practitioners and industry experts) to explore, validate, and classify traceability factors and related information using distribution analyses and hierarchal clustering. As a result, 14 factors and 19 information sets are identified and classified with a moderately high agreement among the experts. Among these, risk management, product authentication, and visibility are the highest ranked and the most important factors influencing traceability implementation in the textile and clothing supply chain. While origin, composition, and sustainability-related information are crucial for sharing with customers, the information vital to businesses includes manufacturer/supplier details, product specifications, and composition. It is noteworthy that this research is among the few that classifies traceability factors and information through expert perspectives, and it creates decisive knowledge of traceability for the textile and clothing supply chain. It further provides insights on the extent to which this information can be shared among supply chain actors. Outcomes of this study can be helpful for the development of an information traceability framework. Policymakers can use the results to draft traceability guidelines/regulations, whilst top management can develop traceability-related strategies. C1 [Agrawal, Tarun Kumar; Pal, Rudrajeet] Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden. [Agrawal, Tarun Kumar] ENSAIT, GEMTEX, Lab Genie & Mat Text, F-59000 Lille, France. [Agrawal, Tarun Kumar] Univ Lille Nord France, F-59000 Lille, France. [Agrawal, Tarun Kumar] Soochow Univ, Coll Text & Clothing Engn, Suzhou 215021, Peoples R China. C3 University of Boras; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Universite de Lille - ISITE; Universite de Lille; Soochow University - China RP Agrawal, TK (corresponding author), Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden.; Agrawal, TK (corresponding author), ENSAIT, GEMTEX, Lab Genie & Mat Text, F-59000 Lille, France.; Agrawal, TK (corresponding author), Univ Lille Nord France, F-59000 Lille, France.; Agrawal, TK (corresponding author), Soochow Univ, Coll Text & Clothing Engn, Suzhou 215021, Peoples R China. EM tarun_kumar.agrawal@hb.se; rudrajeet.pal@hb.se CR Agrawal TK, 2018, SPR SER FASH BUS, P197, DOI 10.1007/978-981-13-0080-6_10 Alemanno A., 2009, SOLVING PROBLEM SCAL Alemanno A., 2010, ERASMUS LAW REV, V3, P203 Algayerova O., 2017, TEXTILE4SDG12 TRANSP, P1 Assmuth T., 2011, RISK MANAGEMENT GOVE Bar-Joseph Z, 2001, Bioinformatics, V17 Suppl 1, pS22 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bostrom M, 2016, J CONSUM POLICY, V39, P367, DOI 10.1007/s10603-016-9336-6 Bradu C, 2014, J BUS ETHICS, V124, P283, DOI 10.1007/s10551-013-1872-2 Brinch M, 2018, INT J LOGIST MANAG, V29, P555, DOI 10.1108/IJLM-05-2017-0115 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chaudhuri A, 2018, INT J LOGIST MANAG, V29, P839, DOI 10.1108/IJLM-03-2017-0059 CHENG MJ, 1994, INT J OPER PROD MAN, V14, P4, DOI 10.1108/01443579410067199 Cheng ZL, 2013, MATH PROBL ENG, V2013, DOI 10.1155/2013/629363 Corbellini S, 2006, IEEE IMTC P, P1331, DOI 10.1109/IMTC.2006.328556 Doorey DJ, 2011, J BUS ETHICS, V103, P587, DOI 10.1007/s10551-011-0882-1 Egels-Zanden N, 2015, J CLEAN PROD, V107, P95, DOI 10.1016/j.jclepro.2014.04.074 Forster B, 2014, INT J PHYS DISTR LOG, V44, P373, DOI 10.1108/IJPDLM-09-2012-0289 Freise Matthias, 2015, Logistics Research, V8, DOI 10.1007/s12159-015-0121-8 Giannakis Mihalis, 2016, International Journal of Production Economics, V171, P455, DOI 10.1016/j.ijpe.2015.06.032 Goswami S., 2014, TRACEABILITY FARM FA Grimm JH, 2016, J CLEAN PROD, V112, P1971, DOI 10.1016/j.jclepro.2014.11.036 Guercini S, 2009, IND MARKET MANAG, V38, P883, DOI 10.1016/j.indmarman.2009.03.016 Hsu C-C, 2007, PARE, V12, P1 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kembro J, 2017, INT J PROD ECON, V193, P77, DOI 10.1016/j.ijpe.2017.06.032 Kumar V., 2017, TEXTILES CLOTHING SU, V3, P5 Kumar V., 2017, EXPLORING FULLY INTE Kumar V, 2017, SYSTEMS, V5, DOI 10.3390/systems5020033 Kwok SK, 2009, IND MANAGE DATA SYST, V109, P1166, DOI 10.1108/02635570911002252 Legnani E, 2010, UNIQUE RADIO INNOVATION FOR THE 21ST CENTURY: BUILDING SCALABLE AND GLOBAL RFID NETWORKS, P309, DOI 10.1007/978-3-642-03462-6_14 Li Z, 2017, IND MANAGE DATA SYST, V117, P1906, DOI 10.1108/IMDS-11-2016-0489 Lumsden K, 2008, INT J PHYS DISTR LOG, V38, P659, DOI 10.1108/09600030810925953 Machado SM, 2018, INT J PHYS DISTR LOG, V48, P139, DOI 10.1108/IJPDLM-01-2017-0004 MacKenzie CA, 2017, INT J LOGIST MANAG, V28, P656, DOI 10.1108/IJLM-04-2016-0097 Mason R, 2013, INT J LOGIST MANAG, V24, P22, DOI 10.1108/IJLM-05-2013-0053 McMillen D., 2016, SECURITY TRENDS MANU, P1 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Morgan TR, 2018, INT J LOGIST MANAG, V29, P959, DOI 10.1108/IJLM-01-2017-0018 Nativi JJ, 2012, INT J PROD ECON, V136, P366, DOI 10.1016/j.ijpe.2011.12.024 OECD, 2017, RESPONSIBLE BUSINESS Okoli C, 2004, INFORM MANAGE-AMSTER, V42, P15, DOI 10.1016/j.im.2003.11.002 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Pal R, 2018, INT J LOGIST MANAG, V29, P1147, DOI 10.1108/IJLM-10-2017-0270 Paltriccia C, 2016, IND MANAGE DATA SYST, V116, P1493, DOI 10.1108/IMDS-12-2015-0500 Phau I, 2015, J FASH MARK MANAG, V19, P169, DOI 10.1108/JFMM-01-2014-0008 Pigni F., 2007, P 2 MED C INF SYST M RADHAKRISHNAN S, 1981, J NONLINEAR FUNCT AN, V978, P163 Richero R., 2016, BACKGROUND ANAL TRAN Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Ritch EL, 2015, INT J RETAIL DISTRIB, V43, P1162, DOI 10.1108/IJRDM-04-2014-0042 Roosen J., 2003, Journal of Food Distribution Research, V34, P77 Sarpong S, 2014, EUR BUS REV, V26, P271, DOI 10.1108/EBR-09-2013-0113 Shin S, 2000, J TEXT I, V91, P20, DOI 10.1080/00405000008659524 Srivastava SK, 2015, INT J LOGIST MANAG, V26, P568, DOI 10.1108/IJLM-02-2014-0032 STRAHLE J, 1981, SPRINGER, P269, DOI DOI 10.1007/978-981-10-2440-5_14 Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 Tao F, 2016, J IND INF INTEGR, V1, P26, DOI 10.1016/j.jii.2016.03.001 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x von der Gracht HA, 2016, INT J LOGIST MANAG, V27, P142, DOI 10.1108/IJLM-12-2013-0150 Wajsman N., 2015, EC COST IPR INFRINGE Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 Wyld D.C., 2010, INT J WEB SEMANTIC T, V1, P1 NR 65 TC 15 Z9 16 U1 4 U2 32 PD MAR 2 PY 2019 VL 11 IS 6 AR 1698 DI 10.3390/su11061698 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000465613000130 DA 2022-12-14 ER PT J AU Hobbs, JE Bailey, DV Dickinson, DL Haghiri, M AF Hobbs, JE Bailey, DV Dickinson, DL Haghiri, M TI Traceability in the Canadian red meat sector: Do consumers care? SO CANADIAN JOURNAL OF AGRICULTURAL ECONOMICS-REVUE CANADIENNE D AGROECONOMIE DT Article AB Increased traceability of food and food ingredients through the agri-food chain has featured in recent industry initiatives in the Canadian livestock sector and is an important facet of the new Canadian Agricultural Policy Framework (APF). While traceability is usually implicitly associated with ensuring food safety and delivering quality assurances, there has been very little economic analysis of the functions of traceability systems and the value that consumers place on traceability assurances. This paper examines the economic incentives for implementing traceability systems in the meat and livestock sector. Experimental auctions are used to assess the willingness to pay of Canadian consumers for a traceability assurance, a food safety assurance, and an on-farm production method assurance for beef and pork products. Results from these laboratory market experiments provide insights into the relative value for Canadian consumers of traceability and quality assurances. Traceability, in the absence of quality verification, is of limited value to individual consumers. Bundling traceability with quality assurances has the potential to deliver more value. C1 Univ Saskatchewan, Dept Agr Econ, Saskatoon, SK 57N 5A8, Canada. Utah State Univ, Dept Econ, Logan, UT 84322 USA. Appalachian State Univ, Dept Econ, Boone, NC 28608 USA. Mt Allison Univ, Dept Econ, Sackville, NB E0A 3C0, Canada. C3 University of Saskatchewan; Utah System of Higher Education; Utah State University; University of North Carolina; Appalachian State University; Mount Allison University RP Hobbs, JE (corresponding author), Univ Saskatchewan, Dept Agr Econ, 51 Campus Dr, Saskatoon, SK 57N 5A8, Canada. CR *CCIA, 2002, RAT WHY NEED ID CATT CLEMENS R, 2003, 03MBP5 MATRIC IOW ST DARBY MR, 1973, J LAW ECON, V16, P67, DOI 10.1086/466756 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 DICKINSON DL, 2003, ERI200312 UT STAT U Fearne A., 1998, SUPPLY CHAIN MANAG, V3, P214, DOI DOI 10.1108/13598549810244296 FOX JA, 1994, J DAIRY SCI, V77, P703, DOI 10.3168/jds.S0022-0302(94)77003-X Golan E., 2003, CURRENT AGR FOOD RES, V4, P27 GOLAN E, 2003, CHOICES, V18, P17 HAYES DJ, 1995, AM J AGR ECON, V77, P40, DOI 10.2307/1243887 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs J. E., 2003, CURRENT AGR FOOD RES, V4, P36 LAWRENCE JD, 2002, 02MBP1 MATRIC IOW ST LIDDELL S, 2001, INT FOOD AGRIBUSINES, V3, P287 *MEAT LIV AUSTR, 2003, AUSTR MEAT SAF AN HL NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630 Pettitt RG, 2001, REV SCI TECH OIE, V20, P584, DOI 10.20506/rst.20.2.1299 SHOGREN JF, 1994, AM ECON REV, V84, P255 NR 18 TC 165 Z9 176 U1 7 U2 51 PD MAR PY 2005 VL 53 IS 1 BP 47 EP 65 DI 10.1111/j.1744-7976.2005.00412.x WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000227638600003 DA 2022-12-14 ER PT J AU Barge, P Gay, P Merlino, V Tortia, C AF Barge, P. Gay, P. Merlino, V. Tortia, C. TI Radio frequency identification technologies for livestock management and meat supply chain traceability SO CANADIAN JOURNAL OF ANIMAL SCIENCE DT Article DE Animal identification; farm and livestock management; radio frequency identification; traceability ID ELECTRONIC IDENTIFICATION; INJECTABLE TRANSPONDERS; UNCERTAIN ENVIRONMENTS; OPTIMIZATION; REGISTRATION; PERSPECTIVE; CATTLE; SHEEP; GOAT AB Barge, P., Gay, P., Merlino, V. and Tortia, C. 2013. Radio frequency identification technologies for livestock management and meat supply chain traceability. Can. J. Anim. Sci. 93: 23-33. Animal electronic identification could be exploited by farmers as an interesting opportunity to increase the efficiency of herd management and traceability. Although radio frequency identification (RFID) solutions for animal identification have already been envisaged, the integration of a RFID traceability system at farm level has to be carried out carefully, considering different aspects (farm type, number and species of animals, barn structure). The tag persistence on the animal after application, the tag-to-tag collisions in the case of many animals contemporarily present in the reading area of the same antenna and the barn layout play determinant roles in system reliability. The goal of this paper is to evaluate the RFID identification system performance and determine the best practice to apply these devices in livestock management. RFID systems were tested both in laboratory, on the farm and in slaughterhouses for the implementation of a traceability system with automatic animal data capture. For this purpose a complete system for animal identification and tracking, accomplishing regulatory compliance as well as supply chain management requirements, has been developed and is described in the paper. Results were encouraging for identification of calves both in farms and slaughterhouses, while in swine breeding, identification was critical for small piglets. In this case, the design of a RFID gate where tag-to-tag collisions are avoided should be envisaged. C1 [Barge, P.; Gay, P.; Merlino, V.; Tortia, C.] Univ Turin, Dipartimento Sci Agr Forestali & Alimentari, I-10095 Turin, Italy. C3 University of Turin RP Tortia, C (corresponding author), Univ Turin, Dipartimento Sci Agr Forestali & Alimentari, Via Leonardo da Vinci 44, I-10095 Turin, Italy. EM cristina.tortia@unito.it CR Barge P., 2009, P 33 CIOSTA CIGR 5 C Burose F., 2010, LANDTECHNIK, V6, P446 Caja G, 2005, J ANIM SCI, V83, P2215 Caja G, 1999, COMPUT ELECTRON AGR, V24, P45, DOI 10.1016/S0168-1699(99)00036-8 Dabbene F, 2008, BIOSYST ENG, V99, P348, DOI 10.1016/j.biosystemseng.2007.11.011 Dabbene F, 2008, BIOSYST ENG, V99, P360, DOI 10.1016/j.biosystemseng.2007.11.012 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Gay P., 2008, AGENG INT C AGR ENG Gay P., 2007, 32 CIOSTA C ADV LAB Ghirardi J.J., 2004, ASAS ADSA JOINT ANN Hessel E.F., 2008, AGENG 2008 C AGR BIO International Committee for Animal Recording, 2005, AN ID LIST MAN COD International Organization for Standardization, 2003, 14223 ISO 1 International Organization for Standardization, 1996, 117841996E ISO ISO (International Organization for Standardization), 1996, 117851996E ISO, V1st Klindtworth M, 1999, COMPUT ELECTRON AGR, V24, P65, DOI 10.1016/S0168-1699(99)00037-X Lambooij E, 1999, COMPUT ELECTRON AGR, V24, P81, DOI 10.1016/S0168-1699(99)00038-1 LENG N M, 2005, P 2005 IEEE INT S MI Leong K. S., 2007, P IEEE ANT PROP INT Liu YC, 2012, INT J APPL LOGISTICS, V3, P54, DOI DOI 10.4018/JAL.2012010104 Mingxiu Z., 2012, PHYS PROCEDIA, V25, P2045 Mutenje T. J., 2012, P CIGR AG 2012 2012 Reiners K, 2009, COMPUT ELECTRON AGR, V68, P178, DOI 10.1016/j.compag.2009.05.010 Ribo O, 2001, REV SCI TECH OIE, V20, P426 Rossing W, 1999, COMPUT ELECTRON AGR, V24, P1, DOI 10.1016/S0168-1699(99)00033-2 Saa C, 2005, J ANIM SCI, V83, P1215 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Smith GC, 2008, MEAT SCI, V80, P66, DOI 10.1016/j.meatsci.2008.05.024 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Syd?nheimo L., 2006, INT J LOGIST-RES APP, V9, P143, DOI 10.1080/13675560600630818 Thurner S., 2007, Landtechnik, V62, P106 TREVARTHEN A, 2007, IEEE 6 INT C MAN MOB Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Wismans WMG, 1999, COMPUT ELECTRON AGR, V24, P99, DOI 10.1016/S0168-1699(99)00040-X NR 34 TC 30 Z9 30 U1 5 U2 65 PD MAR PY 2013 VL 93 IS 1 BP 23 EP 33 DI 10.4141/CJAS2012-029 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000316038700003 DA 2022-12-14 ER PT J AU Vietina, M Agrimonti, C Marmiroli, N AF Vietina, Michelangelo Agrimonti, Caterina Marmiroli, Nelson TI Detection of plant oil DNA using high resolution melting (HRM) post PCR analysis: A tool for disclosure of olive oil adulteration SO FOOD CHEMISTRY DT Article DE Olive oil; Seeds oil; DNA extraction; PCR high resolution melting (HRM); Traceability; Adulteration ID PROTECTED DESIGNATION; IDENTIFICATION; TRACEABILITY; AUTHENTICITY; MARKERS AB Extra virgin olive oil is frequently subjected to adulterations with addition of oils obtained from plants other than olive. DNA analysis is a fast and economic tool to identify plant components in oils. Extraction and amplification of DNA by PCR was tested in olives, in milled seeds and in oils, to investigate its use in olive oil traceability. DNA was extracted from different oils made of hazelnut, maize, sunflower, peanut, sesame, soybean, rice and pumpkin. Comparing the DNA melting profiles in reference plant materials and in the oils, it was possible to identify any plant components in oils and mixtures of oils. Real-Time PCR (RT-PCR) platform has been added of the new methodology of high resolution melting (HRM), both were used to analyse olive oils mixed with different percentage of other oils. Results showed HRM a cost effective method for efficient detection of adulterations in olive oils. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Vietina, Michelangelo] Univ Parma, SITEIA PARMA Interdeptr Ctr, I-43124 Parma, Italy. [Agrimonti, Caterina; Marmiroli, Nelson] Univ Parma, Dept Life Sci, I-43124 Parma, Italy. C3 University of Parma; University of Parma RP Marmiroli, N (corresponding author), Univ Parma, Dept Life Sci, Parco Area Sci 11-A, I-43124 Parma, Italy. EM nelson.marmiroli@unipr.it CR Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 Capote FP, 2007, ANAL BIOANAL CHEM, V388, P1859, DOI 10.1007/s00216-007-1422-9 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Doyle J. J., 1997, PHYTOCHEMISTRY B, V19, P11, DOI DOI 10.2307/4119796 Ganopoulos I, 2011, FOOD CONTROL, V22, P532, DOI 10.1016/j.foodcont.2010.09.040 Graham R, 2005, CLIN CHEM, V51, P1295, DOI 10.1373/clinchem.2005.051516 Kibbe WA, 2007, NUCLEIC ACIDS RES, V35, pW43, DOI 10.1093/nar/gkm234 Mackay JF, 2008, PLANT METHODS, V4, DOI 10.1186/1746-4811-4-8 Marmiroli N., 2009, ADV OLIVE RESOURCES, P1 Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Reed GH, 2007, PHARMACOGENOMICS, V8, P597, DOI 10.2217/14622416.8.6.597 Rozen S, 2000, Methods Mol Biol, V132, P365 Spaniolas S, 2008, J AGR FOOD CHEM, V56, P6886, DOI 10.1021/jf8008926 Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 Wu YJ, 2008, EUR FOOD RES TECHNOL, V227, P1117, DOI 10.1007/s00217-008-0827-9 Wu YJ, 2011, EUR FOOD RES TECHNOL, V233, P313, DOI 10.1007/s00217-011-1520-y Zhang L, 2009, J AGR FOOD CHEM, V57, P7227, DOI 10.1021/jf901172d NR 20 TC 57 Z9 64 U1 3 U2 164 PD DEC 15 PY 2013 VL 141 IS 4 BP 3820 EP 3826 DI 10.1016/j.foodchem.2013.06.075 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000324848000071 DA 2022-12-14 ER PT J AU Song, MX Yang, MX AF Song Mo-xi Yang Morgan Xin TI Leveraging core capabilities and environmental dynamism for food traceability and firm performance in a food supply chain: A moderated mediation model SO JOURNAL OF INTEGRATIVE AGRICULTURE DT Article DE food traceability; operating capabilities; marketing capabilities; organizational learning; environmental dynamism; food firm performance; China ID EXTERNAL INTEGRATION; PRODUCT DEVELOPMENT; MARKET ORIENTATION; INFORMATION; MANAGEMENT; STRATEGY; ANTECEDENTS; UNCERTAINTY; INCENTIVES; QUALITY AB This paper develops a moderated mediation model in which the interactive effects of food traceability and environmental dynamism on firm performance are mediated by the core capabilities (operating capabilities and marketing capabilities) in food supply chain context, by invoking the indirect view of organizational learning theory. Our hypotheses were tested using hierarchical regression and bootstrapping methods with a sample of 216 food manufacturing firms in China, and a survey-based, two-informant design was used to collect data. The results revealed that operating and marketing capabilities fully mediate the food traceability-performance link. In addition, environmental dynamism positively moderates the food traceability-core capabilities link. Moreover, it is found that operating and marketing capabilities transform the interactive impacts of environmental dynamism and food traceability into firm performance. Our study offers a fine-grained picture of the essential food traceability-performance link by revealing for the first time that there is an interactive impacts of food traceability and environmental dynamism on firm performance via core capabilities. C1 [Song Mo-xi] China Agr Univ, Coll Econ & Management, Dept Management, Beijing 100083, Peoples R China. [Yang Morgan Xin] Hang Seng Univ Hong Kong, Dept Mkt, Sch Business, Hong Kong, Peoples R China. C3 China Agricultural University; Hang Seng University of Hong Kong RP Song, MX (corresponding author), China Agr Univ, Coll Econ & Management, Dept Management, Beijing 100083, Peoples R China. EM songmoxi87@aliyun.com CR Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411 Anica-Popa I., 2012, MANAGEMENT MARKETING, V7, P749 Argyris C., 1996, ORG LEARNING BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173 Calisir F, 2013, LEARNING ORG, V20, P52 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chang WJ, 2016, EUR MANAG J, V34, P282, DOI 10.1016/j.emj.2015.11.008 Chavez R, 2015, DECIS SUPPORT SYST, V80, P83, DOI 10.1016/j.dss.2015.10.001 Cheung GW, 2002, STRUCT EQU MODELING, V9, P233, DOI 10.1207/S15328007SEM0902_5 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 DAY GS, 1994, J MARKETING, V58, P37, DOI 10.2307/1251915 De Meyer A., 1990, J OPERATIONS MANAGEM, V9, P168, DOI [10.1016/0272-6963(90)90094-T, DOI 10.1016/0272-6963(90)90094-T] Dutta S, 1999, MARKET SCI, V18, P547, DOI 10.1287/mksc.18.4.547 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Finger AB, 2014, INT J OPER PROD MAN, V34, P807, DOI 10.1108/IJOPM-09-2012-0386 Flynn BB, 2016, J SUPPLY CHAIN MANAG, V52, P3, DOI 10.1111/jscm.12106 Flynn BB, 2010, J OPER MANAG, V28, P58, DOI 10.1016/j.jom.2009.06.001 FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Frazier GL, 2009, J MARKETING, V73, P31, DOI 10.1509/jmkg.73.4.31 Fynes B., 2004, Journal of Purchasing and Supply Management, V10, P179, DOI 10.1016/j.pursup.2004.11.003 Grossler A, 2006, INT J OPER PROD MAN, V26, P458, DOI 10.1108/01443570610659865 Handfield R, 2009, INT J OPER PROD MAN, V29, P100, DOI 10.1108/01443570910932011 Hayes A.F., 2012, PROCESS VERSATILE CO Helfat CE, 2011, STRATEGIC MANAGE J, V32, P1243, DOI 10.1002/smj.955 Hosseini S. M, 2012, INT J BUSINESS MANAG, V7, P73 Huber GP, 1991, ORGAN SCI, V2, P88, DOI 10.1287/orsc.2.1.88 Hult GTM, 2007, STRATEG MANAGE J, V28, P1035, DOI 10.1002/smj.627 International Standard Organization, 2007, 220052007 ISO Jain AK, 2015, LEARN ORGAN, V22, P14, DOI 10.1108/TLO-05-2013-0024 JAWORSKI BJ, 1993, J MARKETING, V57, P53, DOI 10.2307/1251854 Jraisat L, 2013, INT MARKET REV, V30, P323, DOI 10.1108/IMR-03-2012-0056 Koufteros X, 2005, DECISION SCI, V36, P97, DOI 10.1111/j.1540-5915.2005.00067.x Kumar V, 2017, PROCEDIA MANUF, V11, P814, DOI 10.1016/j.promfg.2017.07.183 LEVITT B, 1988, ANNU REV SOCIOL, V14, P319, DOI 10.1146/annurev.so.14.080188.001535 March JG, 1991, ORGAN SCI, V2, P71, DOI 10.1287/orsc.2.1.71 MILLER D, 1987, STRATEGIC MANAGE J, V8, P55, DOI 10.1002/smj.4250080106 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Muller D, 2005, J PERS SOC PSYCHOL, V89, P852, DOI 10.1037/0022-3514.89.6.852 Noble MA, 1995, DECISION SCI, V26, P693, DOI 10.1111/j.1540-5915.1995.tb01446.x Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Ralston PM, 2015, J SUPPLY CHAIN MANAG, V51, P47, DOI 10.1111/jscm.12064 Schoenherr T, 2012, J OPER MANAG, V30, P99, DOI 10.1016/j.jom.2011.09.001 Selnes F, 2003, J MARKETING, V67, P80, DOI 10.1509/jmkg.67.3.80.18656 SINKULA JM, 1994, J MARKETING, V58, P35, DOI 10.2307/1252249 SLATER SF, 1995, J MARKETING, V59, P63, DOI 10.2307/1252120 Song XM, 1997, J MARKETING, V61, P1, DOI 10.2307/1251827 Srinivasan M, 2011, EUR MANAG J, V29, P260, DOI 10.1016/j.emj.2011.02.004 Starbird SA, 2006, J AGR RESOUR ECON, V31, P14 Stonebraker PW, 2006, SUPPLY CHAIN MANAG, V11, P34, DOI 10.1108/13598540610642457 Swink M, 2007, J OPER MANAG, V25, P148, DOI 10.1016/j.jom.2006.02.006 Tamayo-Torres J, 2017, INT J OPER PROD MAN, V37, P282, DOI 10.1108/IJOPM-06-2015-0378 Vorhies DW, 2005, J MARKETING, V69, P80, DOI 10.1509/jmkg.69.1.80.55505 Wiengarten F, 2014, J OPER MANAG, V32, P51, DOI 10.1016/j.jom.2013.07.001 Wilson WW, 2008, AGRIBUSINESS, V24, P85, DOI [10.1002/agr.20148, 10.1002/AGR.20148] Wong CY, 2011, J OPER MANAG, V29, P604, DOI 10.1016/j.jom.2011.01.003 Zheng YM, 2013, DECIS SUPPORT SYST, V56, P513, DOI 10.1016/j.dss.2012.11.008 NR 59 TC 14 Z9 14 U1 4 U2 19 PD AUG PY 2019 VL 18 IS 8 BP 1820 EP 1837 DI 10.1016/S2095-3119(19)62590-6 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000479139600016 DA 2022-12-14 ER PT J AU Silvestri, M Bertacchini, L Durante, C Marchetti, A Salvatore, E Cocchi, M AF Silvestri, Michele Bertacchini, Lucia Durante, Caterina Marchetti, Andrea Salvatore, Elisa Cocchi, Marina TI Application of data fusion techniques to direct geographical traceability indicators SO ANALYTICA CHIMICA ACTA DT Article DE Multivariate curve resolution; Hierarchical data fusion; X-ray powder diffraction; Soils characterization; Geographical traceability markers ID MULTIVARIATE CURVE RESOLUTION; MASS-SPECTROMETRY; CHEMOMETRICS AB A hierarchical data fusion approach has been developed proposing multivariate curve resolution (MCR) as a variable reduction tool. The case study presented concerns the characterization of soil samples of the Modena District. It was performed in order to understand, at a pilot study stage, the geographical variability of the zone prior to planning a representative soils sampling to derive geographical traceability models for Lambrusco Wines. Soils samples were collected from four producers of Lambrusco Wines, located in in-plane and hill areas. Depending on the extension of the sampled fields the number of points collected varies from three to five and, for each point, five depth levels were considered. The different data blocks consisted of X-ray powder diffraction (XRDP) spectra, metals concentrations relative to thirty-four elements and the Sr-87/Sr-86 isotopic abundance ratio, a very promising geographical traceability marker. A multi steps data fusion strategy has been adopted. Firstly, the metals concentrations dataset was weighted and concatenated with the values of strontium isotopic ratio and compressed. The resolved components described common patterns of variation of metals content and strontium isotopic ratio. The X-ray powder spectra profiles were resolved in three main components that can be referred to calcite, quartz and clays contributions. Then, a high-level data fusion approach was applied by combining the components arising from the previous data sets. The results show interesting links among the different components arising from XRDP, the metals pattern and to which of these Sr-87/Sr-86 Isotopic Ratio variation is closer. The combined information allowed capturing the variability of the analyzed soil samples. (C) 2013 Elsevier B.V. All rights reserved. C1 [Silvestri, Michele; Bertacchini, Lucia; Durante, Caterina; Marchetti, Andrea; Salvatore, Elisa; Cocchi, Marina] Univ Modena & Reggio Emilia, Dept Chem & Geol Sci, I-41125 Modena, Italy. C3 Universita di Modena e Reggio Emilia RP Cocchi, M (corresponding author), Univ Modena & Reggio Emilia, Dept Chem & Geol Sci, Via Campi 183, I-41125 Modena, Italy. EM marina.cocchi@unimore.it CR Bertacchini L., 2012, TALANTA Brown SD, 2009, COMPREHENSIVE CHEMOMETRICS: CHEMICAL AND BIOCHEMICAL DATA ANALYSIS, VOLS 1-4, P1 deJuan A, 1997, ANAL CHIM ACTA, V346, P307, DOI 10.1016/S0003-2670(97)90069-6 Eriksson L, 2004, ANAL BIOANAL CHEM, V380, P419, DOI 10.1007/s00216-004-2783-y Forshed J, 2007, CHEMOMETR INTELL LAB, V85, P102, DOI 10.1016/j.chemolab.2006.05.002 Gemperline PJ, 2007, J CHEMOMETR, V21, P507, DOI 10.1002/cem.1098 Gredilla A, 2012, ANAL METHODS-UK, V4, P676, DOI 10.1039/c2ay05636d Jaumot J, 2005, CHEMOMETR INTELL LAB, V76, P101, DOI 10.1016/j.chemolab.2004.12.007 Liu Y, 2004, ANAL BIOANAL CHEM, V380, P445, DOI 10.1007/s00216-004-2776-x Mas S, 2011, J CHROMATOGR A, V1218, P9260, DOI 10.1016/j.chroma.2011.10.035 Navea S, 2006, ANAL CHEM, V78, P4768, DOI 10.1021/ac052257r Ni YN, 2009, ANAL CHIM ACTA, V647, P149, DOI 10.1016/j.aca.2009.06.021 Pere-Trepat E, 2006, J CHROMATOGR A, V1131, P85, DOI 10.1016/j.chroma.2006.07.047 Pere-Trepat E, 2007, CHEMOMETR INTELL LAB, V88, P69, DOI 10.1016/j.chemolab.2006.09.009 Smilde AK, 2005, ANAL CHEM, V77, P6729, DOI 10.1021/ac051080y Van Mechelen I, 2010, CHEMOMETR INTELL LAB, V104, P83, DOI 10.1016/j.chemolab.2010.04.012 Westerhuis JA, 1998, J CHEMOMETR, V12, P301, DOI 10.1002/(SICI)1099-128X(199809/10)12:5<301::AID-CEM515>3.0.CO;2-S Windig W, 1997, CHEMOMETR INTELL LAB, V36, P3, DOI 10.1016/S0169-7439(96)00061-5 Wold S, 1996, J CHEMOMETR, V10, P463 NR 19 TC 25 Z9 26 U1 0 U2 47 PD MAR 26 PY 2013 VL 769 BP 1 EP 9 DI 10.1016/j.aca.2013.01.024 WC Chemistry, Analytical SC Chemistry UT WOS:000316591400001 DA 2022-12-14 ER PT J AU Qian, JP Du, XW Zhang, BY Fan, BL Yang, XT AF Qian, Jianping Du, Xiaowei Zhang, Baoyan Fan, Beilei Yang, Xinting TI Optimization of QR code readability in movement state using response surface methodology for implementing continuous chain traceability SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE QR code; Traceability; Response surface methodology; Barcode readability; Optimization ID ARTIFICIAL NEURAL-NETWORK; SYSTEM; PRODUCTS; MODELS; RSM AB Logistics and storage is the main processing for agro-food supply chain. Because of disconnection information between the two processing, it is difficult to trace continuously. An intelligent conveyer belt provides an effective method to associate storage and logistics by QR code scanning and information recording. Improving the QR code readability in movement state is the core of implementing continuous chain traceability with this belt. In this paper, a intelligent conveyer belt including automatic conveyer unit, barcode scanning unit, fault remove unit and control display unit was designed. Four factors affected QR readability were selected and the value range was confirmed, which was reading distance, code size, coded characters and belt moving speed. Based on the belt, an Central Composite Inscribed (CCI) experiment of four factors with five levels was designed using Response Surface Methodology (RSM) to obtain the optimal reading parameters. The result shows that the main factors of reading distance, belt moving speed and the interaction between reading distance and code size have the significant effect on QR code readability. Under the optimization condition of 141.45 mm reading distance, 34.58 mm code size, 100 bytes coded characters and 2.98 m/min belt moving speed, the average value of QR code readability was 95%. With the optimization parameters, the intelligent conveyer belt was used in an apple marketing enterprise. The result shows that the continuous traceability between storage and logistic can be implemented with the extended breadth, deepened depth and improved precision. (C) 2017 Elsevier B.V. All rights reserved. C1 [Qian, Jianping; Du, Xiaowei; Fan, Beilei; Yang, Xinting] Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. [Qian, Jianping; Du, Xiaowei; Fan, Beilei; Yang, Xinting] Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. [Zhang, Baoyan] Tianjin Rural Affairs Comm, Informat Ctr, Tianjin 3300061, Peoples R China. C3 Beijing Academy of Agriculture & Forestry RP Yang, XT (corresponding author), Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. EM xintingyang@nercita.org.cn CR Ampatzidis Y, 2009, PRECIS AGRIC, V10, P63, DOI 10.1007/s11119-008-9095-8 Astray G, 2016, IND CROP PROD, V92, P290, DOI 10.1016/j.indcrop.2016.08.011 Bas D, 2007, J FOOD ENG, V78, P836, DOI 10.1016/j.jfoodeng.2005.11.024 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Bingol D, 2012, BIORESOURCE TECHNOL, V112, P111, DOI 10.1016/j.biortech.2012.02.084 Cunha CR, 2010, COMPUT ELECTRON AGR, V73, P154, DOI 10.1016/j.compag.2010.05.007 Donis-Gonzalez IR, 2012, COMPUT ELECTRON AGR, V87, P94, DOI 10.1016/j.compag.2012.04.006 Golan E., 2004, AGR EC REPORT, V830, P1 Hamid HA, 2016, IND CROP PROD, V93, P108, DOI 10.1016/j.indcrop.2016.05.035 International Organization for Standardization, 2006, INF TECHN AUT ID DAT International Organization for Standardization, 2007, TRAC FEED FOOD CHAIN Nourbakhsh H, 2014, COMPUT ELECTRON AGR, V102, P1, DOI 10.1016/j.compag.2013.12.017 Qian JP, 2015, COMPUT ELECTRON AGR, V116, P101, DOI 10.1016/j.compag.2015.06.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Rakshit M, 2015, LWT-FOOD SCI TECHNOL, V63, P814, DOI 10.1016/j.lwt.2015.04.026 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 So-In C, 2014, COMPUT ELECTRON AGR, V109, P287, DOI 10.1016/j.compag.2014.10.004 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Wang CQ, 2016, J CLEAN PROD, V139, P866, DOI 10.1016/j.jclepro.2016.08.111 Wang G, 2016, FOOD CONTROL, V59, P743, DOI 10.1016/j.foodcont.2015.06.047 Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 NR 24 TC 10 Z9 10 U1 3 U2 43 PD JUN 15 PY 2017 VL 139 BP 56 EP 64 DI 10.1016/j.compag.2017.05.009 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000404320100006 DA 2022-12-14 ER PT J AU Felmer, R Chavez, R Catrileo, A Rojas, C AF Felmer, R. Chavez, R. Catrileo, A. Rojas, C. TI Current and emergent technologies for animal identification and their use in animal traceability SO ARCHIVOS DE MEDICINA VETERINARIA DT Article DE traceability; microsatellites; individual identification ID PASSIVE INJECTABLE TRANSPONDERS; ELECTRONIC IDENTIFICATION; INJECTION POSITION; GENETIC-MARKERS; CATTLE; DNA; SHEEP AB The use of labels for the identification of live animals and its products have been practiced for over 2000 years, being extensively used in Europe with the appearance of the first epidemic diseases. At present, the advances on crops and livestock genetic engineering, the appearance of new diseases related with food consumption (i.e. mad cow disease, E. coli O157, etc.), and the discovery of contaminants in the food chain have produced great concern among the consumers. This is why it is so important that producers provide consumers with a guarantee on the product quality, specially since they now demand to know origin and quality of the food. In international markets, new procedures based on the concept "from gate to plate" have been implemented through the food chain, in order to bring out trademarks and certified processes which guarantee the quality of the food that goes out in the market. As a result of this new consumer demand, the "traceability concept" in food has been gradually incorporated in our vocabulary and it is now one of the main concerns of the food industry. The traceability systems used to identify animals, monitor their movements and trace their products have considerably evolved over the last years. These systems must be convenient, easy to read, durable, and harmless. Many devices have been used pursuing this objective, such as tattoos, ear-tags, electronic chips, iris and retina identification and more recently, DNA fingerprinting and molecular markers. It has become important to harmonise approved systems in order to provide a better guarantee and to facilitate international trade of animals and their products. Today, traceability is an esssential requirement in terms of exporting high-value products to the global food markets providing the producer with an opportunity to reach better prices for a differentiated product. Thus, many meat industries have turned this concern in a great commercial opportunity. C1 INIA Carillanca, Lab Biotecnol Anim, Unidad Biotecnol, Inst Invest Agropecuarias, Temuco, Chile. C3 Instituto de Investigacion Agropecuaria (INIA) RP Felmer, R (corresponding author), INIA Carillanca, Lab Biotecnol Anim, Unidad Biotecnol, Inst Invest Agropecuarias, Camino Cajon Vilcun S-N Km 10,Casilla 58-D, Temuco, Chile. EM rfelmer@inia.cl CR Ammendrup S, 2001, REV SCI TECH OIE, V20, P437, DOI 10.20506/rst.20.2.1287 [Anonymous], 2000, BRIT FOOD J, V102, DOI [10.1108/bfj.2000.070102dab.005, DOI 10.1108/BFJ.2000.070102DAB.005] Blott SC, 1999, HEREDITY, V82, P613, DOI 10.1046/j.1365-2540.1999.00521.x BRAPPAT B, 1996, AUTOMATION WEIGHING, P4 Caja G, 1999, COMPUT ELECTRON AGR, V24, P45, DOI 10.1016/S0168-1699(99)00036-8 Caja G, 1998, LIVEST PROD SCI, V55, P279, DOI 10.1016/S0301-6226(98)00137-7 CAJA G, 2000, MEAT AUTOMATION, V1, P18 CAJA G, 1996, COMP DIFFERENT DEVIC, P5 CHAVEZ R, 2005, 30 REUN AN SOCHIPA T, P37 CHAVEZ R, 2004, 29 REUN AN SOCHIPA V, P151 Conill C, 2002, J ANIM SCI, V80, P919 Conill C, 2000, J ANIM SCI, V78, P3001 CONILL C, 1996, EAAP PUBL, V87, P341 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 FOLCH C, 2004, 29 REUN AN SOCHIPA V, P153 GUERRA D, 2003, SUBSECRETARIA AGR CH, P112 HOWE C, 1995, GENE CLONING MANIPUL JEANPIERRE M, 1987, NUCLEIC ACIDS RES, V15, P9611, DOI 10.1093/nar/15.22.9611 Lodish H., 1995, MOL CELL BIOL MASAHIKO S, 2001, SYSTEMS COMPUTERS JA, V32, P12 MILLER SA, 1988, NUCLEIC ACIDS RES, V16, P1215, DOI 10.1093/nar/16.3.1215 *MLC, 2002, MEAT LIV COMM BEEF Y, P95 MULLIS KB, 1986, QUANT BIOL, V51, P263 PAUW R, 2003, TRAZABILIDAD REQUISI, P81 Pettitt RG, 2001, REV SCI TECH OIE, V20, P584, DOI 10.20506/rst.20.2.1299 Ribo O, 2001, REV SCI TECH OIE, V20, P426 Sambrook J., 1989, MOL CLONING LAB MANU, V2nd ed Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Zamorano M. J., 1998, Archivos de Zootecnia, V47, P195 NR 30 TC 8 Z9 10 U1 1 U2 33 PY 2006 VL 38 IS 3 BP 197 EP 206 DI 10.4067/S0301-732X2006000300002 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000243336400002 DA 2022-12-14 ER PT J AU Xiao, XQ Fu, ZT Zhang, YJ Peng, ZH Zhang, XS AF Xiao, Xinqing Fu, Zetian Zhang, Yongjun Peng, Zhaohui Zhang, Xiaoshuan TI Developing an Intelligent Traceability System for Aquatic Products in Cold Chain Logistics Integrated WSN with SPC SO JOURNAL OF FOOD PROCESSING AND PRESERVATION DT Article ID STATISTICAL PROCESS-CONTROL; FOOD; QUALITY; FROZEN AB Aquatic products are well received by customers from around the world. Quality and safety of aquatic products have attracted increasing attention, especially in emerging economies. Cased by actual aquatic products cold chain logistics, this article introduces the efforts for developing and evaluating the intelligent monitoring system for aquatic products (IMS-CS): an intelligent traceability system integrated wireless sensor network with statistical process control. Observations, interviews, literature review and experts' questionnaires were used for system requirement and knowledge acquisition; actual aquatic products cold chain logistics scenario was applied to evaluate and validate the performance of IMS-CS. The results show that that the IMS-CS could trace and monitor the real time temperature fluctuation and the quality of aquatic products, which improve the transparency of the cold chain logistics and enables more effective control of the traceability, quality and safety. The valuable suggestion indicates that the process was under medium risk level and had potential to be improved. Practical ApplicationsThe proposed method could be exploited widely by future researchers or practitioners in cold chain monitoring and traceability tasks. C1 [Xiao, Xinqing; Fu, Zetian; Zhang, Yongjun; Zhang, Xiaoshuan] China Agr Univ, Beijing, Peoples R China. [Xiao, Xinqing; Fu, Zetian; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing, Peoples R China. [Zhang, Yongjun] Shandong Inst Commerce & Technol, Jinan, Shandong, Peoples R China. [Peng, Zhaohui] Beijing Beishui Food Ind Co Ltd, Beijing, Peoples R China. C3 China Agricultural University; Shandong Institute of Commerce & Technology RP Zhang, XS (corresponding author), China Agr Univ, Beijing, Peoples R China.; Zhang, XS (corresponding author), Beijing Lab Food Qual & Safety, Beijing, Peoples R China. EM zhxshuan@cau.edu.cn CR [Anonymous], 2013, CONTR CHARTS 2 Asadi G, 2014, SCI PAP-SER D-ANIM S, V57, P223 Badia-Melis R, 2014, COMPUT ELECTRON AGR, V103, P11, DOI 10.1016/j.compag.2014.01.014 Chen KY, 2011, ADV ENG INFORM, V25, P11, DOI 10.1016/j.aei.2010.05.003 Chen YY, 2014, J FOOD ENG, V141, P113, DOI 10.1016/j.jfoodeng.2014.05.014 Coates RW, 2013, COMPUT ELECTRON AGR, V96, P13, DOI 10.1016/j.compag.2013.04.013 Dudek-Burlikowska A, 2005, J MATER PROCESS TECH, V162, P736, DOI 10.1016/j.jmatprotec.2005.02.210 FAO, 2014, HANDL FISH FISH PROD ISO/IEC, 2004, INF TECHN PROC ASS Laguerre O, 2013, TRENDS FOOD SCI TECH, V29, P87, DOI 10.1016/j.tifs.2012.08.001 Lim SAH, 2014, TRENDS FOOD SCI TECH, V37, P137, DOI 10.1016/j.tifs.2014.03.010 Lunden J, 2014, FOOD CONTROL, V45, P109, DOI 10.1016/j.foodcont.2014.04.041 Mataragas M, 2012, FOOD CONTROL, V28, P205, DOI 10.1016/j.foodcont.2012.05.032 Myo M. A., 2014, FOOD CONTROL, V40, P198, DOI DOI 10.1016/J.F00DC0NT.2013.11.016 National Bureau of Statistics of China, 2014, CHIN STAT YB Pack EC, 2014, J TOXICOL ENV HEAL A, V77, P1477, DOI 10.1080/15287394.2014.955892 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Qi Lin, 2011, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V42, P129 Srbinovska M, 2015, J CLEAN PROD, V88, P297, DOI 10.1016/j.jclepro.2014.04.036 Srikaeo K, 2002, FOOD CONTROL, V13, P263, DOI 10.1016/S0956-7135(02)00024-5 Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 SYMPHONY TECHNOLOGIES, 2013, PROC CAP EV LOOK OBV Trebar M, 2015, J FOOD ENG, V159, P66, DOI 10.1016/j.jfoodeng.2015.03.007 Wang JY, 2015, COMPUT ELECTRON AGR, V110, P196, DOI 10.1016/j.compag.2014.11.009 Wu CW, 2009, INT J PROD ECON, V117, P338, DOI 10.1016/j.ijpe.2008.11.008 Xiao XQ, 2015, J SCI FOOD AGR, V95, P2693, DOI 10.1002/jsfa.7005 Xiao XinQing, 2013, Transactions of the Chinese Society of Agricultural Engineering, V29, P259 Xu GB, 2014, SENSORS-BASEL, V14, P16932, DOI 10.3390/s140916932 Yue J, 2013, MATH COMPUT MODEL, V58, P474, DOI 10.1016/j.mcm.2011.11.003 Zhang J, 2009, J FOOD AGRIC ENVIRON, V7, P28 Zhang XS, 2011, J SCI FOOD AGR, V91, P1316, DOI 10.1002/jsfa.4320 Zhou JH, 2013, FOOD CONTROL, V33, P528, DOI 10.1016/j.foodcont.2013.03.019 NR 32 TC 13 Z9 13 U1 5 U2 90 PD DEC PY 2016 VL 40 IS 6 BP 1448 EP 1458 DI 10.1111/jfpp.12730 WC Food Science & Technology SC Food Science & Technology UT WOS:000390351000034 DA 2022-12-14 ER PT J AU Zhao, J Li, TT Zhu, C Jiang, XL Zhao, Y Xu, ZZ Yang, SM Chen, AL AF Zhao, Jie Li, Tingting Zhu, Chao Jiang, Xiaoling Zhao, Yan Xu, Zhenzhen Yang, Shuming Chen, Ailiang TI Selection and use of microsatellite markers for individual identification and meat traceability of six swine breeds in the Chinese market SO FOOD SCIENCE AND TECHNOLOGY INTERNATIONAL DT Review DE Microsatellite markers; animal identification; pork traceability; Chinese market ID CATTLE BREEDS; GENETIC TRACEABILITY; POPULATION-STRUCTURE; ASSIGNMENT; DIVERSITY; PRODUCTS; SYSTEM; PANEL; BEEF AB Meat traceability based on molecular markers is exerting a great influence on food safety and will enhance its key role in the future. This study aimed to investigate and verify the polymorphism of 23 microsatellite markers and select the most suitable markers for individual identification and meat traceability of six swine breeds in the Chinese market. The mean polymorphism information content value of these 23 loci was 0.7851, and each locus exhibited high polymorphism in the pooled population. There were 10 loci showing good polymorphism in each breed, namely, Sw632, S0155, Sw2406, Sw830, Sw2525, Sw72, Sw2448, Sw911, Sw122 and CGA. When six highly polymorphic loci were combined, the match probability value for two random individual genotypes among the pig breeds (Beijing Black, Sanyuan and Taihu) was lower than 1.151 E-06. An increasing number of loci indicated a gradually decreasing match probability value and therefore enhanced traceability accuracy. The validation results of tracing 18 blood and corresponding meat samples based on five highly polymorphic loci (Sw2525, S0005, Sw0107, Sw911 and Sw857) were successful, with 100% conformation probability, which provided a foundation for establishing a traceability system for pork in the Chinese market. C1 [Zhao, Jie; Li, Tingting; Zhu, Chao; Jiang, Xiaoling; Zhao, Yan; Xu, Zhenzhen; Yang, Shuming; Chen, Ailiang] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing, Peoples R China. [Zhao, Jie; Li, Tingting; Zhu, Chao; Jiang, Xiaoling; Zhao, Yan; Xu, Zhenzhen; Yang, Shuming; Chen, Ailiang] Minist Agr, Key Lab Agroprod Qual & Safety, Beijing, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs RP Yang, SM; Chen, AL (corresponding author), Chinese Acad Agr Sci, Zhongguancun South St 12, Beijing 10081, Peoples R China. EM yangshumingcaas@sina.com; ailiang.chen@gmai.com CR Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Baldo A, 2010, MEAT SCI, V85, P671, DOI 10.1016/j.meatsci.2010.03.023 BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Espineira M, 2016, EUR FOOD RES TECHNOL, V242, P25, DOI 10.1007/s00217-015-2514-y Gamarra D, 2015, FORENS SCI INT-GEN S, V5, pE253, DOI 10.1016/j.fsigss.2015.09.101 Gaouar SBS, 2016, SMALL RUMINANT RES, V144, P23, DOI 10.1016/j.smallrumres.2016.07.021 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Liu RD, 2014, FOOD CONTROL, V46, P291, DOI 10.1016/j.foodcont.2014.05.033 Maretto F, 2012, LIVEST SCI, V150, P256, DOI 10.1016/j.livsci.2012.09.011 Maudet C, 2002, J ANIM SCI, V80, P942 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Peelman LJ, 1998, ANIM GENET, V29, P161, DOI 10.1111/j.1365-2052.1998.00280.x RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Rogberg-Munoz A, 2016, MEAT SCI, V111, P47, DOI 10.1016/j.meatsci.2015.08.014 Rogberg-Munoz A, 2014, MEAT SCI, V98, P822, DOI 10.1016/j.meatsci.2014.07.028 Ruan HongYue, 2010, Journal of Agricultural Biotechnology, V18, P1129 Sambrook J., 1989, MOL CLONING LAB MANU Shang HaiTao, 2001, Hereditas (Beijing), V23, P17 SLATKIN M, 1995, GENETICS, V139, P457 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Weir B. S., 1996, GENETIC DATA ANAL [吴潇 Wu Xiao], 2014, [食品与生物技术学报, Journal of Food Science and Biotechnology], V33, P624 Zhang Gui-xiang, 2003, Acta Genetica Sinica, V30, P225 [张小波 Zhang Xiaobo], 2011, [中国农业科技导报, Journal of Agricultural Science and Technology], V13, P85 赵方, 2003, [中国法医学杂志, Chinese Journal of Forensic Medicine], V18, P297 2005, ACTA ZOOLOGICA SINIC, V51, P142 NR 33 TC 8 Z9 9 U1 1 U2 37 PD JUN PY 2018 VL 24 IS 4 BP 292 EP 300 DI 10.1177/1082013217748457 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000432269800002 DA 2022-12-14 ER PT J AU Sun, SN Wang, XP Zhang, Y AF Sun, Shengnan Wang, Xinping Zhang, Yan TI Sustainable Traceability in the Food Supply Chain: The Impact of Consumer Willingness to Pay SO SUSTAINABILITY DT Article DE sustainable food supply chain; traceability; willingness to pay ID SAFETY; INCENTIVES; REPUTATION; INDUSTRY; DESIGN; ISSUES; SYSTEM; PORK AB This article addresses the sustainable traceability issue in the food supply chain from the sourcing perspective in which consumer willingness to pay for traceability is considered. There are two supplier types: traceable suppliers, which are costly but can carry a precise recall in food safety events, and non-traceable suppliers, which are less expensive but may suffer a higher cost in food safety events. A portion of consumers display traceability consciousness, and are willing to pay a premium for traceable food products. Four possible strategies in a transparent food supply chain and three sourcing strategies in a nontransparent food supply chain are identified and we determine when each strategy is optimal. We show that efforts to improve traceability that focus on consumers, by increasing their willingness to pay for traceability or expanding the portion of traceability consciousness consumers, may lead to an unintended consequence, such as a decrease in the provision of traceable food products. However, efforts that focus on revealing and penalizing the buyer always lead to a higher provision of traceable food products. We further find that efforts focusing on eliminating the information asymmetry may not be helpful for sustainable traceability in the food supply chain. C1 [Sun, Shengnan; Zhang, Yan] Southeast Univ, Sch Econ & Management, Sipailou 2, Nanjing 210096, Jiangsu, Peoples R China. [Wang, Xinping] Nanjing Agr Univ, Coll Econ & Management, Weigang 1, Nanjing 210095, Jiangsu, Peoples R China. C3 Southeast University - China; Nanjing Agricultural University RP Wang, XP (corresponding author), Nanjing Agr Univ, Coll Econ & Management, Weigang 1, Nanjing 210095, Jiangsu, Peoples R China. EM sun.shengnan@seu.edu.cn; wangxp@njau.edu.cn; yan.zhang@seu.edu.cn CR Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Banterle A, 2013, SUSTAINABILITY-BASEL, V5, P5272, DOI 10.3390/su5125272 Beske P, 2014, INT J PROD ECON, V152, P131, DOI 10.1016/j.ijpe.2013.12.026 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chen JY, 2015, INT J PROD ECON, V161, P17, DOI 10.1016/j.ijpe.2014.11.001 Dai HY, 2015, INT J PROD ECON, V170, P14, DOI 10.1016/j.ijpe.2015.08.010 De Castro P., MECH TRACEABILITY AG Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 EC-European Commission, 2002, J EUROP COMM, VL031, P1 Farooq U, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8090839 FSIS, SUMM REC CAS CAL YEA Golan E., 830 US DEP AGR Guo RX, 2016, MANAGE SCI, V62, P2722, DOI 10.1287/mnsc.2015.2256 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hsueh CF, 2014, INT J PROD ECON, V151, P214, DOI 10.1016/j.ijpe.2013.10.017 Huang S., 2014, CHINADIALOGUE 0730 International Organization for Standardization, 2005, 90002005 ISO Layton L., 2009, WASHINGTON POST Leat P, 2011, SUSTAINABILITY-BASEL, V3, P605, DOI 10.3390/su3040605 Li CH, 2013, MANAGE SCI, V59, P1389, DOI 10.1287/mnsc.1120.1649 Marucheck A, 2011, J OPER MANAG, V29, P707, DOI 10.1016/j.jom.2011.06.007 Mason R., 2013, TELEGRAPH Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 POLINSKY AM, 1979, AM ECON REV, V69, P880 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 Pouliot S, 2011, AM J AGR ECON, V93, P735, DOI 10.1093/ajae/aar019 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Roth AV, 2008, J SUPPLY CHAIN MANAG, V44, P22, DOI 10.1111/j.1745-493X.2008.00043.x Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Spencer R., 2009, TELEGRAPH Starbird S., 2004, AM AGR EC ASS ANN M United States, BIOT ACT 2002 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 43 TC 12 Z9 12 U1 7 U2 64 PD JUN PY 2017 VL 9 IS 6 AR 999 DI 10.3390/su9060999 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000404133200130 DA 2022-12-14 ER PT J AU Tsai, HT Hong, JT Yeh, SP Wu, TJ AF Tsai, Hsien-Tang Hong, Jai-Tsung Yeh, Shang-Pao Wu, Tung-Ju TI Consumers' Acceptance Model for Taiwan Agriculture and Food Traceability System SO ANTHROPOLOGIST DT Article DE Technology Acceptance Model; Mobile Anxiety; Social Influence; Perceived Limitations ID USER ACCEPTANCE; INFORMATION-TECHNOLOGY; SAFETY; CHAIN; SERVICES; ADOPTION; DETERMINANTS; PERCEPTIONS; SECURITY; ISSUES AB Due to globalization, the food-borne illness may cause damage to people's health in various countries. Such scandals resulted in consumers losing confidence in the government's ability to manage food safety. To regain consumers' trust and confidence, many countries placed more emphasis on food traceability, which is capable of recording the details of a specific food as it makes its way from the farm to the table. Accordingly, in 2004, the Taiwan Agriculture and Food Traceability System (TAFTS) were implemented to govern food safety. Being the key stakeholder along the food supply chain, consumers' acceptance to food traceability plays a critical role in the promotion and persistence of the policy. Based on the technology acceptance model (TAM), a consumers' acceptance model is proposed by adding three extra constructs of mobile anxiety (MA), social influence (SI), and perceived limitations (PL). A survey of 380 participants being recruited from two major metropolises in Taiwan is conducted to examine the proposed model via structural equation modeling analysis. The empirical results show that the proposed model possesses good model fits and explanatory power. Some findings identify that perceived usefulness (PU) and perceived ease of use (PEOU) exhibit significantly positive effects while PLhas a significantly negative effect on both attitude (ATT) and intention to use (ITU). Besides, ATT partially mediates the relationship between beliefs (PU, PEOU or PL) and ITU. However, due to high Internet usage rate and the popularity of social networks, there is no significant effect between two constructs of MA and ATT, MA and ITU, SI and ATT, or SI and ITU. Finally, some recommendations are provided to policymakers for better implementation of TAFTS. C1 [Tsai, Hsien-Tang; Hong, Jai-Tsung; Wu, Tung-Ju] Natl Sun Yat Sen Univ, Dept Business Management, Kaohsiung 80424, Taiwan. [Yeh, Shang-Pao] I Shou Univ, Dept Tourism, Kaohsiung 84001, Taiwan. C3 National Sun Yat Sen University RP Yeh, SP (corresponding author), I Shou Univ, Dept Tourism, TAIWAN 1,Sec 1,Syuecheng Rd, Kaohsiung 84001, Taiwan. EM shangpao@ms12.hinet.net CR Ahmed ZU, 2004, INT MARKET REV, V21, P102, DOI 10.1108/02651330410522925 AJZEN I, 1986, BASIC APPL SOC PSYCH, V7, P259, DOI 10.1207/s15324834basp0704_2 AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Arbore A, 2014, J ASSOC INF SYST, V15, P86 ASCH SE, 1955, SCI AM, V193, P31, DOI 10.1038/scientificamerican1155-31 Barling D, 2009, INT J AGR SUSTAIN, V7, P261, DOI 10.3763/ijas.2009.0331 Barton J, 1998, 21 U COLL DEP FOOD E Bosman MJC, 2014, BRIT FOOD J, V116, P30, DOI 10.1108/BFJ-12-2011-0298 Brummel L, 2014, RURAL COOPERATIVES, V81, P8 Charlebois S, 2014, BRIT FOOD J, V116, P317, DOI 10.1108/BFJ-05-2012-0124 Chau PYK, 2001, DECISION SCI, V32, P699, DOI 10.1111/j.1540-5915.2001.tb00978.x Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Chen YC, 2012, PUBLIC ADM POLICY, V55, P67 Cheng R, 2008, INTRO M TAIWAN APPL Chin WW, 1998, MIS QUART, V22, pVII Chong AYL, 2010, J COMPUT INFORM SYST, V51, P71 COMPEAU DR, 1995, MIS QUART, V19, P189, DOI 10.2307/249688 Cornelisse-Vermaat JR, 2008, TRENDS FOOD SCI TECH, V19, P669, DOI 10.1016/j.tifs.2008.08.003 Council of Agriculture, 2006, TRACK SYST DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982 DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 Doherty E, 2014, BRIT FOOD J, V116, P676, DOI 10.1108/BFJ-10-2011-0266 DOLL WJ, 1994, MIS QUART, V18, P453, DOI 10.2307/249524 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Evans P, 2013, TAIWAN TELECOMS MOBI Fernqvist F, 2014, FOOD QUAL PREFER, V32, P340, DOI 10.1016/j.foodqual.2013.10.005 Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231 FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Gellynck X., 2001, Agrarwirtschaft, V50, P368 Ghiselli E.E., 1981, MEASUREMENT THEORY B Hair J., 2005, MULTIVAR DATA ANAL Hamner M, 2009, GOV INFORM Q, V26, P128, DOI 10.1016/j.giq.2007.12.003 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Holden RJ, 2010, J BIOMED INFORM, V43, P159, DOI 10.1016/j.jbi.2009.07.002 Hu JI, 2009, DEV GAP TRACEABILITY Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118 Hu PJH, 2009, J AM SOC INF SCI TEC, V60, P292, DOI 10.1002/asi.20956 Hung SY, 2012, J COMPUT INFORM SYST, V52, P70 Hung SY, 2013, GOV INFORM Q, V30, P33, DOI 10.1016/j.giq.2012.07.008 Ilie V, 2009, DECISION SCI, V40, P213, DOI 10.1111/j.1540-5915.2009.00227.x Internet World Stats, 2012, INT WORLD STATS 2012 Lee MKO, 2007, J AM SOC INF SCI TEC, V58, P2066, DOI 10.1002/asi.20670 Legris P, 2003, INFORM MANAGE-AMSTER, V40, P191, DOI 10.1016/S0378-7206(01)00143-4 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Lim S, 2011, INT J MED INFORM, V80, pE189, DOI 10.1016/j.ijmedinf.2011.08.007 Liu P, 2012, TAIWANS MOBILE PHONE Liu SF, 2012, SERV IND J, V32, P1823, DOI 10.1080/02642069.2011.574695 Luo SC, 2012, EVALUATION US TRACEA Lur HS, 2005, INT WORKSH TECHN GAP Marucheck A, 2011, J OPER MANAG, V29, P707, DOI 10.1016/j.jom.2011.06.007 Meuter ML, 2003, J BUS RES, V56, P899, DOI 10.1016/S0148-2963(01)00276-4 Moon JW, 2001, INFORM MANAGE, V38, P217, DOI 10.1016/S0378-7206(00)00061-6 Moores TT, 2012, DECIS SUPPORT SYST, V53, P507, DOI 10.1016/j.dss.2012.04.014 Mort GS, 2007, J ADVERTISING RES, V47, P302, DOI 10.2501/S0021849907070328 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Egea JMO, 2011, COMPUT HUM BEHAV, V27, P319, DOI 10.1016/j.chb.2010.08.010 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Qin L, 2011, INT J HUM-COMPUT INT, V27, P885, DOI 10.1080/10447318.2011.555311 Ritzer G., 2007, BLACKWELL ENCY SOCIO Roth AV, 2008, J SUPPLY CHAIN MANAG, V44, P22, DOI 10.1111/j.1745-493X.2008.00043.x Sarpong S, 2014, EUR BUS REV, V26, P271, DOI 10.1108/EBR-09-2013-0113 Schepers J, 2007, INFORM MANAGE-AMSTER, V44, P90, DOI 10.1016/j.im.2006.10.007 Schierz PG, 2010, ELECTRON COMMER R A, V9, P209, DOI 10.1016/j.elerap.2009.07.005 Sridhar S, 2012, J MARKETING, V76, P70, DOI 10.1509/jm.10.0377 Szajna B, 1996, MANAGE SCI, V42, P85, DOI 10.1287/mnsc.42.1.85 van Dijk JAGM, 2008, GOV INFORM Q, V25, P379, DOI 10.1016/j.giq.2007.09.006 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Wezemael L, 2010, FOOD CONTROL, V21, P835, DOI 10.1016/j.foodcont.2009.11.010 Venkatesh V, 2007, J ASSOC INF SYST, V8, P267, DOI 10.17705/1jais.00120 Venkatesh V, 2006, DECISION SCI, V37, P497, DOI 10.1111/j.1540-5414.2006.00136.x Venkatesh V, 2012, MIS QUART, V36, P157 Voss MD, 2009, J BUS LOGIST, V30, P127, DOI 10.1002/j.2158-1592.2009.tb00102.x Wu CS, 2011, COMPUT STAND INTER, V33, P50, DOI 10.1016/j.csi.2010.03.002 Wu LH, 2011, BRIT FOOD J, V113, P519, DOI 10.1108/00070701111123998 NR 74 TC 13 Z9 13 U1 9 U2 71 PD MAY PY 2014 VL 17 IS 3 BP 845 EP 856 DI 10.1080/09720073.2014.11891499 WC Anthropology; Multidisciplinary Sciences; Social Sciences, Interdisciplinary SC Anthropology; Science & Technology - Other Topics; Social Sciences - Other Topics UT WOS:000340016100017 DA 2022-12-14 ER PT J AU Oh, JD Song, KD Seo, JH Kim, DK Kim, SH Seo, KS Lim, HT Lee, JB Park, HC Ryu, YC Kang, MS Cho, S Kim, ES Choe, HS Kong, HS Lee, HK AF Oh, Jae-Don Song, Ki-Duk Seo, Joo-Hee Kim, Duk-Kyung Kim, Sung-Hoon Seo, Kang-Seok Lim, Hyun-Tae Lee, Jae-Bong Park, Hwa-Chun Ryu, Youn-Chul Kang, Min-Soo Cho, Seoae Kim, Eui-Soo Choe, Ho-Sung Kong, Hong-Sik Lee, Hak-Kyo TI Genetic Traceability of Black Pig Meats Using Microsatellite Markers SO ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES DT Article DE Black Pig Populations; Genetic Traceability; Heterozygosity; Microsatellite Markers; Probability of Identity ID POPULATION-STRUCTURE; IDENTIFICATION; BERKSHIRE; PRODUCTS; DUROC; BEEF; SNP AB Pork from Jeju black pig (population 3) and Berkshire (population B) has a unique market share in Korea because of their high meat quality. Due to the high demand of this pork, traceability of the pork to its origin is becoming an important part of the consumer demand. To examine the feasibility of such a system, we aim to provide basic genetic information of the two black pig populations and assess the possibility of genetically distinguishing between the two breeds. Muscle samples were collected from slaughter houses in Jeju Island and Namwon, Chonbuk province, Korea, for populations J and B, respectively. In total 800 Jeju black pigs and 351 Berkshires were genotyped at thirteen microsatellite (MS) markers. Analyses on the genetic diversity of the two populations were carried out in the programs MS toolkit and FSTAT. The population structure of the two breeds was determined by a Bayesian clustering method implemented in structure and by a phylogenetic analysis in Phylip. Population J exhibited higher mean number of alleles, expected heterozygosity and observed heterozygosity value, and polymorphism information content, compared to population B. The F-IS values of population J and population B were 0.03 and -0.005, respectively, indicating that little or no inbreeding has occurred. In addition, genetic structure analysis revealed the possibility of gene flow from population B to population J. The expected probability of identify value of the 13 MS markers was 9.87x10(-14) in population J, 3.17x10(-9) in population B, and 1.03x10(-12) in the two populations. The results of this study are useful in distinguishing between the two black pig breeds and can be used as a foundation for further development of DNA markers. C1 [Oh, Jae-Don; Song, Ki-Duk; Seo, Joo-Hee; Kim, Duk-Kyung; Kim, Sung-Hoon; Kim, Eui-Soo; Kong, Hong-Sik; Lee, Hak-Kyo] Hankyong Natl Univ, Genom Informat Ctr, Anseong 456649, South Korea. [Seo, Kang-Seok] Sunchon Natl Univ, Dept Anim Sci & Technol, Sunchon 540742, South Korea. [Lim, Hyun-Tae; Lee, Jae-Bong] Gyeongsang Natl Univ, Inst Agr & Life Sci, Jinju 660701, South Korea. [Park, Hwa-Chun] Dasan Pig Breeding Co, Namwon 590831, South Korea. [Ryu, Youn-Chul; Kang, Min-Soo] Jeju Natl Univ, Cheju 690756, South Korea. [Cho, Seoae] C&K Genom, Seoul 151919, South Korea. [Kim, Eui-Soo] Iowa State Univ, Dept Anim Sci, Ames, IA 50011 USA. [Choe, Ho-Sung] Chonbuk Natl Univ, Dept Anim Biotechnol, Jeonju 561756, South Korea. C3 Hankyong National University; Sunchon National University; Gyeongsang National University; Jeju National University; Iowa State University; Jeonbuk National University RP Kong, HS (corresponding author), Hankyong Natl Univ, Genom Informat Ctr, Anseong 456649, South Korea. EM kebinkhs@hknu.ac.kr; breedlee@empas.com CR Alves E, 2009, ANIMAL, V3, P1216, DOI 10.1017/S1751731109004819 Ayres KL, 2004, MOL ECOL NOTES, V4, P315, DOI 10.1111/j.1471-8286.2004.00616.x BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 CAMERON ND, 1990, LIVEST PROD SCI, V26, P119, DOI 10.1016/0301-6226(90)90061-A CLIPLEF RL, 1993, CAN J ANIM SCI, V73, P483, DOI 10.4141/cjas93-053 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 FELSENSTEIN J, 2007, PHYLIP PHYLOGENY INF Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Goudet J., 2001, FSTAT PROGRAM ESTIMA Kim MJ, 2007, KOREAN J FOOD SCI AN, V27, P150, DOI 10.5851/kosfa.2007.27.2.150 Kim TH, 2005, J ANIM SCI, V83, P2255 Lee Yong Hwa, 2011, Journal of Animal Science and Technology, V53, P89, DOI 10.5187/JAST.2011.53.2.89 Lim H. T., 2005, Journal of Animal Science and Technology, V47, P491 Lim Hyun-Tae, 2009, Journal of Animal Science and Technology, V51, P201 MILLER SA, 1988, NUCLEIC ACIDS RES, V16, P1215, DOI 10.1093/nar/16.3.1215 Moon YoonHee, 2004, Korean Journal for Food Science of Animal Resources, V24, P238 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 NEI M, 1983, J MOL EVOL, V19, P153, DOI 10.1007/BF02300753 Park S., 2000, COMMUNICATION Pritchard JK, 2000, GENETICS, V155, P945 SAITOU N, 1987, MOL BIOL EVOL, V4, P406, DOI 10.1093/oxfordjournals.molbev.a040454 Shim JM, 2010, KOREAN J FOOD SCI AN, V30, P918, DOI 10.5851/kosfa.2010.30.6.918 Song J. Y., 2010, THESIS JEJU U JEJU Suzuki K, 2003, MEAT SCI, V64, P35, DOI 10.1016/S0309-1740(02)00134-1 WRIGHT S, 1965, EVOLUTION, V19, P395, DOI 10.2307/2406450 NR 26 TC 18 Z9 18 U1 0 U2 11 PD JUL PY 2014 VL 27 IS 7 BP 926 EP 931 DI 10.5713/ajas.2013.13829 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000338388600002 DA 2022-12-14 ER PT J AU Mattarozzi, M Laski, E Bertucci, A Giannetto, M Bianchi, F Zoani, C Careri, M AF Mattarozzi, Monica Laski, Eleni Bertucci, Alessandro Giannetto, Marco Bianchi, Federica Zoani, Claudia Careri, Maria TI Metrological traceability in process analytical technologies and point-of-need technologies for food safety and quality control: not a straightforward issue SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Review; Early Access DE Metrological traceability; Method validation; Rapid screening; Process analytical technology; Point of need; Food analysis ID ELECTRONIC TONGUE; YOGURT; VALIDATION; CHALLENGES; RESIDUES; GLUTEN; MODEL AB Traditional techniques for food analysis are based on off-line laboratory methods that are expensive and time-consuming and often require qualified personnel. Despite the high standards of accuracy and metrological traceability, these well-established methods do not facilitate real-time process monitoring and timely on-site decision-making as required for food safety and quality control. The future of food testing includes rapid, cost-effective, portable, and simple methods for both qualitative screening and quantification of food contaminants, as well as continuous, real-time measurement in production lines. Process automatization through process analytical technologies (PAT) is an increasing trend in the food industry as a way to achieve improved product quality, safety, and consistency, reduced production cycle times, minimal product waste or reworks, and the possibility for real-time product release. Novel methods of analysis for point-of-need (PON) screening could greatly improve food testing by allowing non-experts, such as consumers, to test in situ food products using portable instruments, smartphones, or even visual naked-eye inspections, or farmers and small producers to monitor products in the field. This requires the attention of the research community and devices manufacturers to ensure reliability of measurement results from PAT strategy and PON tests through the demonstration and critical evaluation of performance characteristics. The fitness for purpose of methods in real-life conditions is a priority that should not be overlooked in order to maintain an effective and harmonized food safety policy. C1 [Mattarozzi, Monica; Laski, Eleni; Bertucci, Alessandro; Giannetto, Marco; Bianchi, Federica; Careri, Maria] Univ Parma, Dept Chem Life Sci & Environm Sustainabil, Parco Area Sci 17-A, I-43124 Parma, Italy. [Mattarozzi, Monica; Giannetto, Marco; Careri, Maria] Univ Parma, Interdept Ctr SITEIA PARMA, Technopole Pad 33 Parco Area Sci, I-43124 Parma, Italy. [Bianchi, Federica] Univ Parma, Interdept Ctr CIPACK, Technopole Pad 33 Parco Area Sci, I-43124 Parma, Italy. [Zoani, Claudia] Italian Natl Agcy New Technol Energy & Sustainabl, Dept Sustainabil Biotechnol & Agroind Div SSPT BI, Casaccia Res Ctr, Via Anguillarese 301, I-00123 Rome, Italy. C3 University of Parma; University of Parma; University of Parma; Italian National Agency New Technical Energy & Sustainable Economics Development RP Careri, M (corresponding author), Univ Parma, Dept Chem Life Sci & Environm Sustainabil, Parco Area Sci 17-A, I-43124 Parma, Italy.; Careri, M (corresponding author), Univ Parma, Interdept Ctr SITEIA PARMA, Technopole Pad 33 Parco Area Sci, I-43124 Parma, Italy. EM maria.careri@unipr.it CR ALLIS, FOOD ALL SENS Anfossi L, 2019, ANAL BIOANAL CHEM, V411, P1905, DOI 10.1007/s00216-018-1451-6 [Anonymous], 2014, J AOAC INT, V97, P1492, DOI 10.5740/jaoacint.BinaryGuidelines [Anonymous], COD AL INT FOOD STAN [Anonymous], EURLEX32021R0808ENEU [Anonymous], EUR UN REF LAB GEN M [Anonymous], EURLEX32017R0625ENEU [Anonymous], 2021, CCQM STRAT DOC 2021 [Anonymous], EURLEX32002R0178ENEU [Anonymous], 2000, BRIT FOOD J, V102, DOI [10.1108/bfj.2000.070102dab.005, DOI 10.1108/BFJ.2000.070102DAB.005] [Anonymous], NIMA PARTNERS GLUTEN [Anonymous], EURLEX32014R0519ENEU Bakeev KA., 2010, PROCESS ANAL TECHNOL, V2 Bao HH, 2022, FOOD CHEM, V375, DOI 10.1016/j.foodchem.2021.131875 Bello A, 2007, ANAL CHIM ACTA, V603, P8, DOI 10.1016/j.aca.2007.09.037 Bianchi F, 2018, TRAC-TREND ANAL CHEM, V107, P142, DOI 10.1016/j.trac.2018.07.024 Bianchi V, 2021, IEEE ACCESS, V9, P141544, DOI 10.1109/ACCESS.2021.3120022 Brasil YL, 2022, FOOD CONTROL, V131, DOI 10.1016/j.foodcont.2021.108418 Careri M, 2006, ANAL BIOANAL CHEM, V386, P38, DOI 10.1007/s00216-006-0581-4 Chen D, 2019, INT DAIRY J, V96, P132, DOI 10.1016/j.idairyj.2019.04.006 Di Nardo F, 2019, TALANTA, V192, P288, DOI 10.1016/j.talanta.2018.09.037 Eifert T, 2020, ANAL BIOANAL CHEM, V412, P2037, DOI 10.1007/s00216-020-02421-1 Eisen K, 2020, ANAL BIOANAL CHEM, V412, P2027, DOI 10.1007/s00216-020-02420-2 Estevao ST, 2020, J AOAC INT, V103, P1654, DOI 10.1093/jaoacint/qsaa057 Ezhilan M, 2020, FOOD BIOPROCESS TECH, V13, P1193, DOI 10.1007/s11947-020-02473-2 Gancarz M., 2021, DETECTION MEASUREMEN, V127, P90, DOI [10.1016/j.fbp.2021.02.011, DOI 10.1016/J.FBP.2021.02.011] Gerzon G, 2022, J PHARMACEUT BIOMED, V207, DOI 10.1016/j.jpba.2021.114379 Gondim CD, 2014, ANAL CHIM ACTA, V830, P11, DOI 10.1016/j.aca.2014.04.050 Gong XY, 2019, FOOD CHEM, V295, P254, DOI 10.1016/j.foodchem.2019.05.127 Grassi S, 2019, FOODS, V8, DOI 10.3390/foods8090405 Gu DC, 2019, LWT-FOOD SCI TECHNOL, V101, P382, DOI 10.1016/j.lwt.2018.11.012 He XC, 2020, BIOSENS BIOELECTRON, V152, DOI 10.1016/j.bios.2020.112013 Hitzmann B, 2015, BIOTECHNOL J, V10, P1095, DOI 10.1002/biot.201400773 International Laboratory Accreditation Cooperation, P10072020 ILAC International Organization for Standardization, 302015 ISO GUIDE International Organization for Standardization, 170252017 ISOIEC Jafari S, 2021, FOODS, V10, DOI 10.3390/foods10061399 Jerome RE, 2019, J FOOD PROCESS ENG, V42, DOI 10.1111/jfpe.13143 Jiarpinijnun A, 2020, MEASUREMENT, V157, DOI 10.1016/j.measurement.2020.107561 Jung Y, 2020, J MICROBIOL METH, V168, DOI 10.1016/j.mimet.2019.105800 Kalinowska K, 2021, TRENDS FOOD SCI TECH, V111, P271, DOI 10.1016/j.tifs.2021.02.068 Soriano ML, 2019, TRAC-TREND ANAL CHEM, V114, P98, DOI 10.1016/j.trac.2019.03.005 Lei YA, 2022, FOOD CONTROL, V134, DOI 10.1016/j.foodcont.2021.108747 Lin HY, 2017, ACS NANO, V11, P10062, DOI 10.1021/acsnano.7b04318 Lintvedt TA, 2022, APPL SPECTROSC, V76, P559, DOI 10.1177/00037028211056931 Luo Y, 2021, SENSOR ACTUAT B-CHEM, V349, DOI 10.1016/j.snb.2021.130761 Magnusson B., 2014, EURACHEM GUIDE FITNE, V2nd Man Y, 2019, ANAL CHIM ACTA, V1092, P75, DOI 10.1016/j.aca.2019.09.039 Mattarozzi M, 2019, ANAL BIOANAL CHEM, V411, P4465, DOI 10.1007/s00216-019-01642-3 Mayer M, 2018, ANAL BIOANAL CHEM, V410, P5095, DOI 10.1007/s00216-018-1191-7 Merkyte V, 2018, ELECTROANAL, V30, P314, DOI 10.1002/elan.201700652 Muller-Maatsch J, 2021, TRENDS FOOD SCI TECH, V110, P841, DOI 10.1016/j.tifs.2021.01.091 Muncan J, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21010177 Navratil M, 2004, J AGR FOOD CHEM, V52, P415, DOI 10.1021/jf0304876 Nelis JLD, 2020, TRAC-TREND ANAL CHEM, V129, DOI 10.1016/j.trac.2020.115934 Nolasco-Perez IM, 2019, BIOSYST ENG, V183, P151, DOI 10.1016/j.biosystemseng.2019.04.013 Ogrinc N., 2021, MEASUREMENT SENSORS, V18, P100285, DOI [10.1016/j.measen.2021.100285, DOI 10.1016/J.MEASEN.2021.100285] Panikuttira B, 2020, INT J FOOD SCI TECH, V55, P175, DOI 10.1111/ijfs.14267 Panikuttira B, 2018, INT J FOOD SCI TECH, V53, P1803, DOI 10.1111/ijfs.13806 Popping B., 2020, AFFIDIA J FOOD DIAGN, V2, P48 Popping B, 2021, J AOAC INT, V104, P1, DOI 10.1093/jaoacint/qsaa091 Popping B, 2018, J AOAC INT, V101, P185, DOI 10.5740/jaoacint.17-0425 Pu YY, 2020, INT DAIRY J, V103, DOI 10.1016/j.idairyj.2019.104623 Rodrigues N, 2019, TALANTA, V197, P363, DOI 10.1016/j.talanta.2019.01.055 Schorn-Garcia D, 2021, MICROCHEM J, V166, DOI 10.1016/j.microc.2021.106215 Sergeyeva T, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20154304 Shrivas K., 2022, SMARTPHONE INTEGRATE, V383, P132449, DOI [10.1016/j.foodchem.2022.132449, DOI 10.1016/J.FOODCHEM.2022.132449] Soares RRG, 2018, ANALYST, V143, P1015, DOI [10.1039/c7an01762f, 10.1039/C7AN01762F] Sorbo A., 2022, SEPARATIONS, V9, P53, DOI [10.3390/separations9020053, DOI 10.3390/SEPARATIONS9020053] Sundhoro M, 2021, FOOD CHEM, V344, DOI 10.1016/j.foodchem.2020.128648 Tsagkaris AS, 2019, TRAC-TREND ANAL CHEM, V121, DOI 10.1016/j.trac.2019.115688 Tsagkaris AS, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19245579 Tsimidou MZ, 2022, FOODS, V11, DOI 10.3390/foods11040599 Umapathi R, 2022, TRENDS FOOD SCI TECH, V119, P69, DOI 10.1016/j.tifs.2021.11.018 van den Berg F, 2013, TRENDS FOOD SCI TECH, V31, P27, DOI 10.1016/j.tifs.2012.04.007 van den Berg FWJ, 2002, ANAL CHEM, V74, P3105, DOI 10.1021/ac020148w Wang HQ, 2020, J HAZARD MATER, V392, DOI 10.1016/j.jhazmat.2020.122506 Wehling P, 2011, J AOAC INT, V94, P335 Wei ZB, 2017, J FOOD ENG, V203, P41, DOI 10.1016/j.jfoodeng.2017.01.022 Winquist F, 2005, SENSOR ACTUAT B-CHEM, V111, P299, DOI 10.1016/j.snb.2005.05.003 Wu QF, 2020, POSTHARVEST BIOL TEC, V159, DOI 10.1016/j.postharvbio.2019.111016 Wu YY, 2019, ANAL BIOANAL CHEM, V411, P7511, DOI 10.1007/s00216-019-02149-7 Wu YY, 2020, FOOD CHEM, V304, DOI 10.1016/j.foodchem.2019.125377 Xie J, 2020, SPECTROCHIM ACTA A, V231, DOI 10.1016/j.saa.2020.118104 Yang MY, 2018, NANOSCALE, V10, P15865, DOI 10.1039/c8nr04138e Yang N, 2019, J FOOD PROCESS ENG, V42, DOI 10.1111/jfpe.12976 Ye XY, 2022, TALANTA, V245, DOI 10.1016/j.talanta.2022.123489 Ye YW, 2020, ACS APPL MATER INTER, V12, P14552, DOI 10.1021/acsami.9b23167 Zangheri M, 2021, ANAL CHIM ACTA, V1163, DOI 10.1016/j.aca.2021.338515 Zhang JQ, 2019, FOOD CHEM, V275, P446, DOI 10.1016/j.foodchem.2018.08.117 Zhang YF, 2021, FOODS, V10, DOI 10.3390/foods10122983 Zheng LY, 2019, BIOSENS BIOELECTRON, V124, P143, DOI 10.1016/j.bios.2018.10.006 Zhou HL, 2021, REV ANAL CHEM, V40, P173, DOI 10.1515/revac-2021-0132 NR 93 TC 0 Z9 0 U1 1 U2 1 DI 10.1007/s00216-022-04398-5 EA NOV 2022 WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000881929700001 DA 2022-12-14 ER PT J AU Lindh, H Olsson, A AF Lindh, Helena Olsson, Annika TI Communicating imperceptible product attributes through traceability: A case study in an organic food supply chain SO RENEWABLE AGRICULTURE AND FOOD SYSTEMS DT Article DE case study; consumer communication; food supply chain; food system; imperceptible product attributes; organic food; supply chain management; traceability ID MARKET; INCENTIVES AB Companies in the food industry are driven to improve their traceability for several reasons. The primary reasons are food safety and quality. Another is the response to the increased interest among consumers in imperceptible product attributes such as organic, fair trade, dolphin-safe and non genetically modified (non-GMO). Such attributes are hard to distinguish and thus require generally enhanced traceability in order to verify their existence. This has led to an emergent area in which actors engage in gaining and maintaining traceability and communicating it to the consumers. This paper describes the relations between the actors in a supply chain (SC) in the field of organic food systems. It examines the objectives each actor has for gaining and maintaining traceability throughout the SC. The focus on organic relates to the challenge for the companies to ensure this imperceptible product attribute throughout the entire food system. A single case study was conducted in an organic food system providing organic ice cream products. The data collection included semi-structured interviews, observations, a review of internal documents and a survey among the participating companies. The findings illustrate and elaborate on the objectives companies have for engaging in traceability. The objectives identified are divided into three categories: food safety and quality, managing the SC and internal resources and communication with consumers. The survey confirms the results from the interviews that all actors want to engage in traceability. They prioritize the objectives differently, however. The study highlights the value of close relations between the actors when addressing consumer concerns regarding product and process characteristics, such as the imperceptible organic attribute. C1 [Lindh, Helena; Olsson, Annika] Lund Univ, Div Packaging Logist, SE-22100 Lund, Sweden. C3 Lund University RP Lindh, H (corresponding author), Lund Univ, Div Packaging Logist, SE-22100 Lund, Sweden. EM helena.lindh@plog.lth.se CR Beekman V, 2008, ETHICAL THEORY MORAL, V11, P61, DOI 10.1007/s10677-007-9075-5 Beer S.C., 2001, FOOD SUPPLY CHAIN MA, P21 BEER SC, 1998, P CUL ARTS SCI C GLO, P225 Bowersox D.J., 2002, SUPPLY CHAIN LOGISTI Brom FWA, 2000, J AGR ENVIRON ETHIC, V12, P127, DOI 10.1023/A:1009586529518 Coff C, 2008, INT LIBR ENVIRON AGR, V15, P1, DOI 10.1007/978-1-4020-8524-6 Deblonde M, 2007, J AGR ENVIRON ETHIC, V20, P99, DOI 10.1007/s10806-006-9019-4 FSA, 2002, TRAC FOOD CHAIN PREL Furness A., 2003, Food authenticity and traceability, P473, DOI 10.1533/9781855737181.3.473 Ghosh A, 2008, BUS PROCESS MANAG J, V14, P453, DOI 10.1108/14637150810888019 Golan E., 2004, Amber Waves, V2, P14 GOLAN E, 2004, EC THEORY IND STUDIE, V830 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Lambert DM, 2000, IND MARKET MANAG, V29, P65, DOI 10.1016/S0019-8501(99)00113-3 Michalopoulos T, 2008, J AGR ENVIRON ETHIC, V21, P3, DOI 10.1007/s10806-007-9059-4 Min S., 2001, J BUSINESS LOGISTICS, V22, P1, DOI 10.1002/j.2158-1592.2001.tb00001.x Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Narayanan V, 2004, HARVARD BUS REV, V82, P94 *NAT FOOD ADM, 2009, MATF NORRMAN A, 2006, P 18 ANN NOF C NOF O, P1 Poppo L, 2002, STRATEGIC MANAGE J, V23, P707, DOI 10.1002/smj.249 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Seshadri S, 2004, IND MARKET MANAG, V33, P513, DOI 10.1016/j.indmarman.2004.03.004 Tavernier EM, 2004, RENEW AGR FOOD SYST, V19, P110, DOI 10.1079/RAFS200071 Viaene J., 1998, SUPPLY CHAIN MANAG I, V3, P139 Yin R.K., 2011, QUALITATIVE RES STAR Zsidisin G., 2008, SUPPLY CHAIN RISK HD NR 27 TC 7 Z9 7 U1 1 U2 32 PD DEC PY 2010 VL 25 IS 4 BP 263 EP 271 DI 10.1017/S1742170510000281 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000283802100003 DA 2022-12-14 ER PT J AU Barbera, M Saiano, F Tutone, L Massenti, R Pisciotta, A AF Barbera, Marcella Saiano, Filippo Tutone, Livia Massenti, Roberto Pisciotta, Antonino TI The Pattern of Rare Earth Elements Like a Possible Helpful Tool in Traceability and Geographical Characterization of the Soil-Olive System (Olea europaea L.) SO PLANTS-BASEL DT Article DE rare earth elements; normalized pattern; icp-ms; olive; traceability ID ORIGIN; OIL; BEHAVIOR; FOODS; TRACE AB The identification of a product, with its geographical origin, is a guaranty of the value of the foodstuff and protection from potential fraud. Extra virgin olive oil is produced or marketed as a single variety or a blend of two or more cultivars, often of different geographic origins. Therefore, to study a possible link between the soil and olive oil, we accounted crucial to analyse the behaviour of olive of different cultivars. We studied Rare Earth Elements (REE) amounts and their relationship to trace their distribution from soil to the olive pulp (Olea europea L.). The results obtained pointed out that the different cultivars of Olea did not drive significant differences in reciprocal ratios of REE in the uptake from the soil up to olive (except for Eu). However soil-plant Rare Earth relationships depend exclusively on the soil REE composition. This method can be the starting point to enforcing the laws, in fact, it is important to develop analytical methods to measure the authenticity of the samples, and to verify the geographical origin even when olive oil is blended. C1 [Barbera, Marcella; Saiano, Filippo; Tutone, Livia; Massenti, Roberto; Pisciotta, Antonino] Univ Palermo, Dipartimento Sci Agr Alimentari & Forestali, Viale Sci 4, I-90128 Palermo, Italy. [Barbera, Marcella] Sorbonne Univ, METIS, Dipartimento Geosci Ressources Nat & Environm, 4 Pl Jussieu, F-75005 Paris, France. C3 University of Palermo; UDICE-French Research Universities; Sorbonne Universite RP Saiano, F; Pisciotta, A (corresponding author), Univ Palermo, Dipartimento Sci Agr Alimentari & Forestali, Viale Sci 4, I-90128 Palermo, Italy. EM filippo.saiano@unipa.it; antonino.pisciotta@unipa.it CR Aceto M, 2019, FOOD CHEM, V298, DOI 10.1016/j.foodchem.2019.125047 Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 Balaram V, 2019, GEOSCI FRONT, V10, P1285, DOI 10.1016/j.gsf.2018.12.005 Barbera M, 2021, CHEMOSPHERE, V266, DOI 10.1016/j.chemosphere.2020.128993 Barbera M, 2020, J AOAC INT, V103, P906, DOI 10.1093/jaocint/qsz025 Beltran M, 2015, FOOD CHEM, V169, P350, DOI 10.1016/j.foodchem.2014.07.104 Brioschi L, 2013, PLANT SOIL, V366, P143, DOI 10.1007/s11104-012-1407-0 Bryla P, 2018, QUAL ASSUR SAF CROP, V10, P155, DOI 10.3920/QAS2017.1189 BYRNE RH, 1995, GEOCHIM COSMOCHIM AC, V59, P4575, DOI 10.1016/0016-7037(95)00303-7 Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Censi P, 2014, SCI TOTAL ENVIRON, V473, P597, DOI 10.1016/j.scitotenv.2013.12.073 Chiocchini F, 2016, FOOD CHEM, V202, P291, DOI 10.1016/j.foodchem.2016.01.146 Crescimanno G, 2006, SOIL SCI SOC AM J, V70, P1774, DOI 10.2136/sssaj2005.0335 Danezis GP, 2016, CURR OPIN FOOD SCI, V10, P22, DOI 10.1016/j.cofs.2016.07.003 Drivelos SA, 2016, FOOD CHEM, V213, P238, DOI 10.1016/j.foodchem.2016.06.088 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Durante C, 2016, FOOD CHEM, V210, P648, DOI 10.1016/j.foodchem.2016.04.108 Farmaki EG, 2012, ANAL LETT, V45, P920, DOI 10.1080/00032719.2012.655656 Garcia-Gonzalez DL, 2010, J AGR FOOD CHEM, V58, P12569, DOI 10.1021/jf102735n Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 Hidalgo MJ, 2018, MICROCHEM J, V142, P30, DOI 10.1016/j.microc.2018.06.002 Issaoui M, 2020, J AOAC INT, V103, P915, DOI 10.1093/jaocint/qsz018 Joebstl D, 2010, FOOD CHEM, V123, P1303, DOI 10.1016/j.foodchem.2010.06.009 Kabata-Pendias A, 2004, GEODERMA, V122, P143, DOI 10.1016/j.geoderma.2004.01.004 Kang XM, 2018, FOOD CONTROL, V94, P361, DOI 10.1016/j.foodcont.2018.07.019 Laveuf C, 2009, GEODERMA, V154, P1, DOI 10.1016/j.geoderma.2009.10.002 Liang T, 2008, J RARE EARTH, V26, P7, DOI 10.1016/S1002-0721(08)60027-7 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Markert B, 1993, INSTRUMENTAL MULTIE Medini S, 2015, FOOD CHEM, V171, P78, DOI 10.1016/j.foodchem.2014.08.121 Otero N, 2005, APPL GEOCHEM, V20, P1473, DOI 10.1016/j.apgeochem.2005.04.002 Pisciotta A, 2017, FOOD CHEM, V221, P1214, DOI 10.1016/j.foodchem.2016.11.037 Poscic F, 2020, PLANT SOIL, V448, P133, DOI 10.1007/s11104-019-04418-x Samczynski Z, 2012, SCI WORLD J, DOI 10.1100/2012/216380 Sayago A, 2018, FOOD CHEM, V261, P42, DOI 10.1016/j.foodchem.2018.04.019 Taylor A, 2018, J ANAL ATOM SPECTROM, V33, P338, DOI [10.1039/c8ja90005a, 10.1039/C8JA90005A] Tescione I, 2018, FOOD CHEM, V258, P374, DOI 10.1016/j.foodchem.2018.03.083 Tripoli E, 2005, NUTR RES REV, V18, P98, DOI 10.1079/NRR200495 Tyler G, 2004, PLANT SOIL, V267, P191, DOI 10.1007/s11104-005-4888-2 WEDEPOHL KH, 1995, GEOCHIM COSMOCHIM AC, V59, P1217, DOI 10.1016/0016-7037(95)00038-2 NR 40 TC 0 Z9 0 U1 2 U2 2 PD OCT PY 2022 VL 11 IS 19 AR 2579 DI 10.3390/plants11192579 WC Plant Sciences SC Plant Sciences UT WOS:000867921000001 DA 2022-12-14 ER PT J AU Verbeke, W Ward, RW AF Verbeke, Wim Ward, Ronald W. TI Consumer interest in information cues denoting quality, traceability and origin: An application of ordered probit models to beef labels SO FOOD QUALITY AND PREFERENCE DT Article DE beef; consumer; label; origin; quality; traceability ID FOOD QUALITY; FRESH MEAT; RED MEAT; SAFETY; PERCEPTION; ATTITUDE; SYSTEM AB The objective of this paper is, first, to determine which information cues on beef labels really attract consumer interest, specified as the level of perceived importance attached to and attention paid to label cues. The focus is (1) on indications of quality through a quality label and quality guarantee, (2) on indications referring to the mandatory European beef labelling regulation and traceability system, and (3) on indications reflecting country-of-origin. The second objective is to assess the impact of a campaign aiming at informing consumers about beef traceability, and at raising consumer interest in beef quality, traceability and origin. Data were collected from a sample of 278 beef consumers in Belgium. Ordered probit models were specified and estimated to assess the impact of individual characteristics and the beef labelling information campaign. Findings reveal that consumer interest is generally low for traceability, moderate for origin and high for direct indications of quality like a quality guarantee seal or expiration date. Interest in label cues is specifically low among younger males. Further, the information campaign had a measurable positive impact on consumer attention paid to direct indications of quality and country-of-origin. Strategies including traceability for backing up on-label indications of quality and origin, rather than providing consumers with detailed traceability information on-label, are recommended. (C) 2005 Elsevier Ltd. All rights reserved. C1 Univ Ghent, Dept Agr Econ, B-9000 Ghent, Belgium. Univ Florida, Dept Food & Resource Econ, Gainesville, FL 32611 USA. C3 Ghent University; State University System of Florida; University of Florida RP Verbeke, W (corresponding author), Univ Ghent, Dept Agr Econ, B-9000 Ghent, Belgium. EM wim.verbeke@ugent.be CR Acebron LB, 2000, FOOD QUAL PREFER, V11, P229 Becker T., 2000, British Food Journal, V102, P158, DOI 10.1108/00070700010371707 Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Bernues A, 2003, FOOD QUAL PREFER, V14, P265, DOI 10.1016/S0950-3293(02)00085-X Bonnet C, 2001, EUR REV AGRIC ECON, V28, P433, DOI 10.1093/erae/28.4.433 Bredahl L, 2004, FOOD QUAL PREFER, V15, P65, DOI 10.1016/S0950-3293(03)00024-7 BRUNSO K, 2002, 27 MAPP CTR AARH SCH Caswell J. A., 1998, Agricultural and Resource Economics Review, V27, P151 Chen S, 1999, DUAL-PROCESS THEORIES IN SOCIAL PSYCHOLOGY, P73 Crespi JM, 2003, REV AGR ECON, V25, P294, DOI 10.1111/1467-9353.00140 *EC EURLEX, 2004, COMM LEG FORC *FMI, 2000, FMI BACKGR MAND COUN Forker O.D., 1993, COMMODITY ADVERTISIN *GFK CONS, 2003, GFKS HOUS PAN FRESH GIRAUD G, 2002, COLLECTION ETUDES, V11 Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 Henson S, 2000, J AGR ECON, V51, P90, DOI 10.1111/j.1477-9552.2000.tb01211.x Herrmann R, 2002, BER LANDWIRTSCH, V80, P53 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x *IFIC, 2001, IFIC BACKGR MARCH 20 Issanchou S, 1996, MEAT SCI, V43, pS5, DOI 10.1016/0309-1740(95)00062-3 KRISSOFF B, 2004, WRS0402 USDAERS Long S. J., 1997, REGRESSION MODELS CA, V7 Loureiro ML, 2003, J AGR RESOUR ECON, V28, P287 Lusk J.L., 2002, J AGRIC APPL ECON, V34, P27, DOI [10.1017/S1074070800002121, DOI 10.1017/S1074070800002121] McCarthy M. B., 2004, Acta Agricultura Scandinavica. Section C, Economy, V1, P99, DOI 10.1080/16507540410035036 McCluskey J. J., 2004, American Journal of Agricultural Economics, V86, P1230, DOI 10.1111/j.0002-9092.2004.00670.x OPHUIS PAM, 1994, FOOD QUAL PREFER, V5, P173 Peter J.P., 1999, CONSUMER BEHAV MARKE ROBINSON L, 2003, TEXAS AGR 0117 Roosen J., 2003, Agribusiness (New York), V19, P77, DOI 10.1002/agr.10041 Salaun Y, 2001, INT J INFORM MANAGE, V21, P21, DOI 10.1016/S0268-4012(00)00048-7 SCHUPP AR, 2001, BEEF CATTLE RES REPO, P47 Steenkamp JBEM, 1996, EUR REV AGRIC ECON, V23, P195, DOI 10.1093/erae/23.2.195 Sudman S., 1976, APPL SAMPLING Teisl M. F., 1998, AGR RESOURCE EC REV, V27, P140, DOI [10.1017/S1068280500006468, DOI 10.1017/S1068280500006468] van der Lans IA, 2001, EUR REV AGRIC ECON, V28, P451, DOI 10.1093/erae/28.4.451 *VANC PUBL CORP, 2002, 2002 FRESH TRENDS Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Verbeke W, 2004, MEAT SCI, V67, P159, DOI 10.1016/j.meatsci.2003.09.017 Verbeke W, 1999, J INT FOOD AGRIBUS M, V10, P45, DOI 10.1300/J047v10n03_03 Verbeke W, 2002, FOOD POLICY, V27, P339, DOI 10.1016/S0306-9192(02)00037-4 Verbeke W, 2001, OUTLOOK AGR, V30, P249, DOI 10.5367/000000001101293733 Verbeke W, 2001, AGR ECON-BLACKWELL, V25, P359, DOI 10.1111/j.1574-0862.2001.tb00215.x Verbeke W, 2001, FOOD QUAL PREFER, V12, P489, DOI 10.1016/S0950-3293(01)00042-8 Viaene J., 1998, SUPPLY CHAIN MANAG I, V3, P139 Ward R. W., 2003, Agribusiness (New York), V19, P393, DOI 10.1002/agr.10068 ZEITHAML VA, 1988, J MARKETING, V52, P2, DOI 10.2307/1251446 2002, FED REG, V67, P63367 NR 49 TC 261 Z9 272 U1 6 U2 90 PD SEP PY 2006 VL 17 IS 6 BP 453 EP 467 DI 10.1016/j.foodqual.2005.05.010 WC Food Science & Technology SC Food Science & Technology UT WOS:000238588400004 DA 2022-12-14 ER PT J AU Thakur, M Wang, L Hurburgh, CR AF Thakur, Maitri Wang, Lizhi Hurburgh, Charles R. TI A multi-objective optimization approach to balancing cost and traceability in bulk grain handling SO JOURNAL OF FOOD ENGINEERING DT Article DE Lot aggregation; Multi-objective optimization; Traceability; Bulk grain handling; Food safety risk ID FOOD-INDUSTRY; CHAIN; MODEL; MANUFACTURE AB This paper proposes a multi-objective optimization model that provides an effective method for minimizing traceability effort by minimizing the food safety risk caused by lot aggregation at a grain elevator. A mathematical multi-objective mixed-integer programming (MIP) model is proposed with two objective functions that allow in calculating the minimum levels of lot aggregation and minimum total cost of blending grain. Constraints on the system include customer contract specifications, availability of grain at the elevators and the blending requirements. The solutions include quantities of grain from different storage bins to be used for blending for a shipment while using the minimum number of storage bins and minimum total cost. The numerical results are presented for a corn shipment scenario to demonstrate the application of this model. Pareto optimal front is computed for the problem for simultaneous optimization of lot aggregation and cost of blending. Sensitivity analysis is conducted to analyze the application of the model under different operating conditions. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Thakur, Maitri; Hurburgh, Charles R.] Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA. [Thakur, Maitri; Wang, Lizhi] Iowa State Univ, Dept Ind & Mfg Syst Engn, Ames, IA 50011 USA. [Hurburgh, Charles R.] Iowa State Univ, Dept Food Sci & Human Nutr, Ames, IA 50011 USA. C3 Iowa State University; Iowa State University; Iowa State University RP Thakur, M (corresponding author), Iowa State Univ, Dept Agr & Biosyst Engn, 1551 Food Sci Bldg, Ames, IA 50011 USA. EM maitri@iastate.edu CR Benayoun R., 1971, MATH PROGRAM, V1, P366, DOI DOI 10.1007/BF01584098 Bilgen B, 2007, INT J PROD ECON, V107, P555, DOI 10.1016/j.ijpe.2006.11.008 *CAN TRAC, 2003, AGR AGR CAN Carriquiry M, 2007, AM J AGR ECON, V89, P12, DOI 10.1111/j.1467-8276.2007.00959.x *CDC, 2005, FOODB ILLN Deb K., 2005, MULTIOBJECTIVE OPTIM, P273 Deb K, 2001, MULTIOBJECTIVE OPTIM Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Ekel PY, 2006, COMPUT MATH APPL, V52, P179, DOI 10.1016/j.camwa.2006.08.012 Fishburn P, 1970, UTILITY THEORY DECIS Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 GATTEGNO I, 2000, 2001 RIA REV IND AGR, V609, P46 HEMPHILL, 2009, COMMUNICATION Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 LAUX CM, 2007, THESIS IOWA STATE U Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Parreiras RO, 2005, ISE BOOK SERIES REAL, P1 *REUT, 2008, N AM TOM IND REEL GR Saaty T.L., 1980, ANAL HIERARCHY PROCE Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 SHIH JS, 1995, EUR J OPER RES, V83, P452, DOI 10.1016/0377-2217(94)00243-6 Singh A, 2000, J PROCESS CONTR, V10, P43, DOI 10.1016/S0959-1524(99)00037-2 Sivaraman E., 2002, Journal of Agribusiness, V20, P155 STEUER RE, 1996, J MULTICRITERIA DECI, V5, P195 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 *US FDA, 2002, BIOT ACT 2002 VINCKE P, 1986, EUR J OPER RES, V25, P160, DOI 10.1016/0377-2217(86)90082-2 2008, GLPK GNU LINEAR PROG 2002, OFFICIAL J EURO 0128 NR 33 TC 32 Z9 35 U1 0 U2 19 PD NOV PY 2010 VL 101 IS 2 BP 193 EP 200 DI 10.1016/j.jfoodeng.2010.07.001 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000281334000009 DA 2022-12-14 ER PT J AU Xiong, BH Fu, RT Lin, ZH Luo, QY Yang, LA Pan, JR AF Xiong Ben-hai Fu Run-ting Lin Zhao-hui Luo Qing-yao Yang Liang Pan Jia-rong TI A Solution on Pork Quality Traceability from Farm to Dinner Table in Tianjin City, China SO AGRICULTURAL SCIENCES IN CHINA DT Article DE animal identification; pork; tracking; traceability; PDA; GPRS AB In order to meet government supervision of pork production safety as well as consumer's right to know what they buy and protect the public safety of pork food, this study adopts animal identification, intelligent personal digital assistant (PDA) reading and writing, general packet radio service (GPRS), and other information technologies, proposes a pork tracking and traceability inferstructure based on pork production substrace flow and data flow, designs the metadata structure and related datatbases for farming, slaughtering, and retailing sector based on intensive pig farming and smallhold pig farming, develops three different data-recording systems, and finally establishes a public network platform for the information inquiry in light of "the administrative rules on identification and rearing files for animal and poultry" in China. The farming process information system supplies early warning for the usage of drugs and feed additives based on data of every individual pig and timely uploading all events data to remote traceability database when pigs are sold; the PDA data collecting system can collect farming events data for pigs fed by farmers and submit to the center database by GPRS; the web-based Tianjin's pork traceability platform can integrate all identifications and related pork quality data from farming, slaughtering to marketing by online, and achieve pork tracking from product origin to consumption and tracing in the turnover direction. It is feasible to realize pork quality traceability by identification technologies developed and/or integrated, metadata specifications designed, three data-recording systems developed, and web-inquiring platform established. Some individual technical bottlenecks will be resolved with the development of communication technologies. The full implementation in Tianjin, China, will supply technical support for guaranteeing the quality and safety of pork production and meeting consumer's demands. C1 [Xiong Ben-hai; Luo Qing-yao; Yang Liang] Chinese Acad Agr Sci, Inst Anim Sci, State Key Lab Anim Nutr, Beijing 100193, Peoples R China. [Fu Run-ting; Lin Zhao-hui] Anim Husb & Vet Bur, Tianjin 300210, Peoples R China. [Pan Jia-rong] Chinese Acad Agr Sci, Inst Manufacture Agr Food, Beijing 100193, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Animal Science, CAAS; Chinese Academy of Agricultural Sciences RP Xiong, BH (corresponding author), Chinese Acad Agr Sci, Inst Anim Sci, State Key Lab Anim Nutr, Beijing 100193, Peoples R China. EM bhxiong@iascaas.net.cn CR [Anonymous], 2006, PEOPLES DAILY O 0916 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 Fu X., 2005, WINDOWS MOBILE TELEP Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Liu Y., 2006, WINDOWS MOBILE PLATF Lu C.H., 2007, ANIMALS THEIR PRODUC, P46 Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 *MIN AGR PEOPL REP, 2006, MAN METH LIV POULTR *MIN AGR PEOPL REP, 2008, CHIN MOB HELPS AGR M National Bureau of Statistics of China (NBSC), 2006, COD ADM REG PEOPL RE SANCHEZ J, 2006, PEOPLES REPUBLIC FIS Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x *USDA, 2005, MARK REG PROGR AN S XIE JF, 2004, DEV MONITORING TRACE XIONG BH, 2005, PRECISION FEEDIONG T, P51 YANG ZH, 2003, SAFETY USAGE CRITERI ZHANG J, 2006, SWINE IND SCI, V24, P26 2008, GPRS OPERATION SYNOP 2006, PEOPLES DAILY O 0919 NR 19 TC 16 Z9 17 U1 2 U2 37 PD JAN 20 PY 2010 VL 9 IS 1 BP 147 EP 156 DI 10.1016/S1671-2927(09)60078-X WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000284136700018 DA 2022-12-14 ER PT J AU Xie, ZJ Kong, H Wang, B AF Xie, Zhenjun Kong, Hua Wang, Bin TI Dual-Chain Blockchain in Agricultural E-Commerce Information Traceability Considering the Viniar Algorithm SO SCIENTIFIC PROGRAMMING DT Article ID SCHEME AB In this paper, we consider the Vennia algorithm to conduct in-depth research and analysis on the traceability of dual-chain blockchain agricultural products' E-commerce information. This paper adds a collaborative verification module to the traceability system and carries out a detailed design of information storage, traceability consensus algorithm, and smart contract for agricultural products according to the characteristics of the agricultural products supply chain, among which the collaborative verification module adopts dynamic data storage technology; the ConsiderVinia consensus algorithm is improved by introducing the way of integral penalty mechanism to ensure the block data validity. After a comparative study of the features and differences of the three major blockchain technology platforms, this paper selects the super ledger to implement the agricultural traceability system based on blockchain technology, introduces the partitioning and credit mechanism into the ConsiderVinia algorithm, and elaborates the improvement process of the algorithm. The improved algorithm reduces the malicious behavior of nodes and maintains the system security through a credit mechanism while maintaining the consistency of blockchain. In the event of a transaction dispute, the third-party platform will determine the party at fault based on the transaction records and other evidence and make corresponding punishments and compensations. The experiment proves that the algorithm proposed in this paper can reduce the amount of network data transmission in the process of node consensus, which is better than the ConsiderVinia algorithm in terms of both throughput and latency, improves the consensus efficiency, and alleviates the communication bottleneck caused by the increase of users in blockchain applications, and the solution of applying the blockchain technology to the agricultural products traceability system is practical and feasible. The blockchain-based agricultural products information traceability system solves the problems of information asymmetry, difficult sharing, easy tampering, and storage centralization in the traditional IoT-based agricultural products traceability system and truly realizes the credible and reliable traceability of the whole chain of agricultural products information. The research content and results of this paper have certain theoretical and practical values. C1 [Xie, Zhenjun] Chongqing Business Vocat Coll, Sch Elect Commerce, Chongqing 401331, Peoples R China. [Kong, Hua] Neijiang Normal Univ, Neijiang 641100, Peoples R China. [Wang, Bin] Shanghai Univ Finance & Econ, Sch Humanities, Shanghai 200433, Peoples R China. C3 Neijiang Normal University; Shanghai University of Finance & Economics RP Xie, ZJ (corresponding author), Chongqing Business Vocat Coll, Sch Elect Commerce, Chongqing 401331, Peoples R China.; Wang, B (corresponding author), Shanghai Univ Finance & Econ, Sch Humanities, Shanghai 200433, Peoples R China. EM share87@163.com; 10000776@njtc.edu.cn; wangbin@mail.shufe.edu.cn CR Ali T, 2019, WIRELESS PERS COMMUN, V107, P1573, DOI 10.1007/s11277-019-06346-6 Belkadi F, 2019, J ENG DESIGN, V30, P311, DOI 10.1080/09544828.2019.1642463 Caetano I, 2020, FRONT ARCHIT RES, V9, P287, DOI 10.1016/j.foar.2019.12.008 Dong S, 2020, COMPUTING, V102, P2185, DOI 10.1007/s00607-020-00836-3 He RS, 2018, IEEE COMMUN MAG, V56, P177, DOI 10.1109/MCOM.2018.1700701 He RS, 2017, IEEE T WIREL COMMUN, V16, P7138, DOI 10.1109/TWC.2017.2740206 Hirnschall N, 2019, J CATARACT REFR SURG, V45, P738, DOI 10.1016/j.jcrs.2019.01.023 Leal F, 2021, J SUSTAIN TOUR, V29, P774, DOI 10.1080/09669582.2020.1778011 Li FH, 2019, ENGINEERING-PRC, V5, P1179, DOI 10.1016/j.eng.2019.09.002 Li JS, 2020, J INTERNET TECHNOL, V21, P1115, DOI 10.3966/160792642020072104020 Lindner M., 2020, CEAS AERONAUT J, V11, P321, DOI [10.1007/s13272-019-00430-0, DOI 10.1007/S13272-019-00430-0] Sales AK, 2021, THEOR APPL CLIMATOL, V146, P833, DOI 10.1007/s00704-021-03771-1 Sanzharov VV, 2019, PROGRAM COMPUT SOFT+, V45, P187, DOI 10.1134/S0361768819040078 Sato T, 2018, J NUCL SCI TECHNOL, V55, P684, DOI 10.1080/00223131.2017.1419890 Schneider M, 2021, INT J CARDIOVAS IMAG, V37, P577, DOI 10.1007/s10554-020-02046-6 Sun R, 2020, IEEE T VEH TECHNOL, V69, P4842, DOI 10.1109/TVT.2020.2983220 Teng JK, 2019, IET INFORM SECUR, V13, P703, DOI 10.1049/iet-ifs.2019.0177 Wang ZW, 2019, ROCK MECH ROCK ENG, V52, P183, DOI 10.1007/s00603-018-1585-z Wu GF, 2018, INT J EMBED SYST, V10, P225, DOI 10.1504/IJES.2018.091785 Xue WX, 2018, IEEE INTERNET THINGS, V5, P3031, DOI 10.1109/JIOT.2018.2829486 Zhang Z, 2017, SPE J, V22, P1946, DOI 10.2118/182635-PA NR 21 TC 2 Z9 2 U1 8 U2 21 PD FEB 10 PY 2022 VL 2022 AR 2604216 DI 10.1155/2022/2604216 WC Computer Science, Software Engineering SC Computer Science UT WOS:000772367400001 DA 2022-12-14 ER PT J AU Ding, JP Huang, JK Jia, XP Bai, JF Boucher, S Carter, M AF Ding Ji-ping Huang Ji-kun Jia Xiang-ping Bai Jun-fei Boucher, Steve Carter, Michael TI Direct farm, production base, traceability and food safety in China SO JOURNAL OF INTEGRATIVE AGRICULTURE DT Article DE direct farm; production base; marketing chain; traceability; food safety AB With the rapid growth of China's economy, rising demand for safety food has been accompanied by frequent food safety scandals. Given that China's farming is dominated by millions of small-scale farms, ensuring food safety is a major challenge facing the public and private sectors. The direct farm (DF) program, initiated in 2008, represents one of the government's major initiatives to modernize the distribution of fresh fruit and vegetables (FFV) and improve food safety. Under the DF program, participating national and international retailers are expected to establish more direct procurement relationships with farm communities. While it is often claimed that greater participation by retailers in the production and post-harvest processing implied the OF program will lead to improved quality, safety and traceability, systematic evidence remains elusive as existing studies are largely narrative, based on case studies, or theoretical inference. Little empirical evidence is available for a broader evaluation of the DF program. This paper aims to fill this gap by assessing the overall performance of a single retailer's OF experience with respect to the procurement and food safety of FFV. We use data from a survey of production managers of 35 OF production bases (PBs) spread across 11 provinces, 3 cities and 1 autonomous region in China. The results show a mixture of opportunities and challenges. On one hand, the DF program improves production practices and distribution channels of FFV produced on its PBs, thus facilitating the move of China's food system towards improved food safety compliance. On the other hand, significant heterogeneity in the traceability of food and the ability of OF to meet higher safety standards is evident both across major product categories and across household-operated vs. firm-operated PBs. The paper concludes with policy implications. C1 [Ding Ji-ping; Huang Ji-kun; Jia Xiang-ping; Bai Jun-fei] Chinese Acad Sci, Ctr Chinese Agr Policy, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China. [Ding Ji-ping] Univ Chinese Acad Sci, Beijing 100049, Peoples R China. [Jia Xiang-ping] Northwest A&F Univ, Coll Econ & Management, Yangling 712100, Peoples R China. [Bai Jun-fei] China Agr Univ, Coll Econ & Management, Beijing 100083, Peoples R China. [Boucher, Steve; Carter, Michael] Univ Calif Davis, Agr & Resource Econ, Davis, CA 95616 USA. C3 Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Northwest A&F University - China; China Agricultural University; University of California System; University of California Davis RP Huang, JK (corresponding author), Chinese Acad Sci, Ctr Chinese Agr Policy, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China. EM dingjp.11b@igsnrr.ac.cn; jkhuang.ccap@igsnrr.ac.cn CR CGFDC (China Green Food Development Center), 2012, MAIN GREEN FOOD QUAN Deng HS, 2010, CHINA ECON REV, V21, P495, DOI 10.1016/j.chieco.2010.04.009 ESWARAN M, 1985, AM ECON REV, V75, P352 FMRIC (Food Marketing Research and Information Center), 2008, HDB INTRO FOOD TRAC Gale H. F., 2009, EC INFORM B, V52 Gale HF, 2012, AM J AGR ECON, V94, P483, DOI 10.1093/ajae/aar069 Gu C, 2011, SOFT SCI, V25, P21 Hu D, 2010, FARMER SUPERMARKET D Hu D., 2006, CHINESE RURAL EC, V11, P17 Hu D H, 2006, ISSUE AGR EC, V1, P3 Hu D H, 2012, AGR PROCESSING, V1, P24 Huang G F, 1999, RES ENV SCI, V12, P54 Huang J.-Z., 2015, J FIXED INCOME, V25, P34, DOI [10.3905/jfi.2015.25.1.034, DOI 10.3905/JFI.2015.25.1.034] Huang JK, 2008, REV AGR ECON, V30, P469, DOI 10.1111/j.1467-9353.2008.00421.x Huang JK, 2003, AGR ECON, V29, P55, DOI 10.1016/S0169-5150(03)00044-6 Jia XP, 2011, FOOD POLICY, V36, P656, DOI 10.1016/j.foodpol.2011.06.007 Lam HM, 2013, LANCET, V381, P2044, DOI 10.1016/S0140-6736(13)60776-X Li Z, 2013, GANSU SOCIAL SCI, V2, P233 Liu RD, 2013, FOOD CONTROL, V33, P93, DOI 10.1016/j.foodcont.2013.01.051 Miyata S, 2009, WORLD DEV, V37, P1781, DOI 10.1016/j.worlddev.2008.08.025 NBSC (National Bureau of Statistics of China), 2013, CHIN YB RUR HOUS SUR Niu R F, 2000, ORG PATTERNS OPERATI [牛亚丽 Niu Yali], 2014, [四川农业大学学报, Journal of Sichuan Agricultural University], V32, P236 Shi S, 2012, CHINA RURAL SURV, V4, P14 Waldron S. A., 2009, THESIS Zhang Y, 2009, ISSUE AGR EC S, P93 Zhou JH, 2011, SOC SCI J, V48, P543, DOI 10.1016/j.soscij.2011.06.007 NR 27 TC 17 Z9 18 U1 1 U2 37 PY 2015 VL 14 IS 11 BP 2380 EP 2390 DI 10.1016/S2095-3119(15)61127-3 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000365247500021 DA 2022-12-14 ER PT J AU Resende, MA Buhr, BL AF Resende-Filho, Moises A. Buhr, Brian L. TI A principal-agent model for evaluating the economic value of a traceability system: A case study with injection-site lesion control in fed cattle SO AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS DT Article DE information asymmetry; principal-agent; supply chain management; traceability ID RISK-AVERSION; MORAL HAZARD; BEEF; INCENTIVES; QUALITY AB Traceability can link the identity of cattle feeders with retail beef cuts. The economic problem for the packer implementing traceability is to choose the level of investment in traceability and the level of incentive payments to cattle feeders so that cattle feeders will avoid production actions that can damage retail beef cuts. A case study of injection-site lesions in cattle is the basis for technical parameters to numerically solve this principal-agent problem. Results show that cattle feeders will give injections in sites preferred by the packer even with low rates of successful tracking and minimal incentives. C1 [Resende-Filho, Moises A.] Univ Fed Juiz de Fora, Dept Anal Econ, Juiz De Fora, Brazil. [Buhr, Brian L.] Univ Minnesota, Dept Appl Econ, Minneapolis, MN 55455 USA. C3 Universidade Federal de Juiz de Fora; University of Minnesota System; University of Minnesota Twin Cities RP Resende, MA (corresponding author), Univ Fed Juiz de Fora, Dept Anal Econ, Juiz De Fora, Brazil. CR Antle JM, 2001, HANDB ECON, V18, P1083 Basarab JA, 1997, CAN J ANIM SCI, V77, P525, DOI 10.4141/A97-047 Bogetoft P, 2003, AM J AGR ECON, V85, P234, DOI 10.1111/1467-8276.00115 Chalfant JA, 1999, J AGR RESOUR ECON, V24, P57 DEXTER DR, 1994, J ANIM SCI, V72, P824, DOI 10.2527/1994.724824x Dubois P, 2004, AM J AGR ECON, V86, P835, DOI 10.1111/j.0002-9092.2004.00634.x FIELD TG, 2004, BEEF PRODUCTION MANA Gibbons R., 1992, GAME THEORY APPL EC GOLAN E, 2004, 830 ESCS USDA GRIFFIN DD, 2002, MISSOURI STOCKER FEE, P1 GRIFFIN DD, 2005, AVERAGE NUMBER UNPUB GROSSMAN SJ, 1983, ECONOMETRICA, V51, P7, DOI 10.2307/1912246 Hardaker BJ, 2004, COPING RISK AGR HAUBRICH JG, 1994, J POLIT ECON, V102, P258, DOI 10.1086/261931 Hennessy DA, 1996, AM J AGR ECON, V78, P1034, DOI 10.2307/1243859 HILTON WM, 2005, VY60W PURD U *INT ORG STAND, 2000, 90002000 ISO COMM ST King RP, 2007, EUR REV AGRIC ECON, V34, P81, DOI 10.1093/erae/jbl030 MASCOLLEL A, 1996, MICROECONOMIC THEORY McKenna DR, 2002, J ANIM SCI, V80, P1212 Morgan JB, 2004, J ANIM SCI, V82, P3308 PAPE WR, 2003, FOOD TRACEABILITY RE, V2, P16 Prendergast C, 1999, J ECON LIT, V37, P7, DOI 10.1257/jel.37.1.7 ROBB JG, 2004, US LIVESTOCK IDENTIF Roeber DL, 2001, J ANIM SCI, V79, P2615 ROEBER DL, 2000, FINAL REPORT I UNPUB, P94 RTI International, 2007, GIPSA LIV MEAT MARK, V3 Salani? B, 1997, EC CONTRACTS PRIMER Starbird SA, 2005, AM J AGR ECON, V87, P15, DOI 10.1111/j.0002-9092.2005.00698.x NR 29 TC 29 Z9 29 U1 0 U2 21 PD NOV PY 2008 VL 90 IS 4 BP 1091 EP 1102 DI 10.1111/j.1467-8276.2008.01150.x WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000259640900015 DA 2022-12-14 ER PT J AU Adam, BD Holcomb, R Buser, M Mayfield, B Thomas, J O'Bryan, CA Crandall, P Knipe, D Knipe, R Ricke, SC AF Adam, Brian D. Holcomb, Rodney Buser, Michael Mayfield, Blayne Thomas, Johnson O'Bryan, Corliss A. Crandall, Philip Knipe, Dar Knipe, Richard Ricke, Steven C. TI Enhancing Foog Safety, Product Quality, and Value-Added in Food Supply Chains Using Whole-Chain Traceability SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Article DE whole chain traceability; fragmented supply chains; MarketMaker (R); proprietary centralized data; food safety; product recall; value-added; product tracking; biosecurity; big data ID WILLINGNESS-TO-PAY; ESCHERICHIA-COLI; ECONOMIC VALUE; BEEF; US; IDENTIFICATION; VALUATION; OUTBREAKS; DISEASE; SYSTEM AB A robust whole chain traceability system can limit consumers' exposure to potentially hazardous foods, improve supply chain management, and add value to consumer products. However, fragmented supply chains present special challenges. In the beef industry, for example, producers have resisted participation in whole chain traceability because of high cost relative to value and concerns about disclosing proprietary information, among others. A multi-disciplinary team from universities, private firms, and a foundation has developed and tested a pilot proprietary centralized data whole chain traceability system that addresses many of these obstacles. This system would facilitate a precision agriculture approach to beef production and marketing. While the remaining challenges are serious, the benefits to society, consumers, and businesses from widespread adoption of whole-chain traceability systems are potentially large. C1 [Adam, Brian D.; Holcomb, Rodney] Oklahoma State Univ, Dept Agr Econ, 413 Ag Hall & 114 Food & Agr Prod Ctr, Stillwater, OK 74078 USA. [Buser, Michael] Oklahoma State Univ, Dept Biosyst & Agr Engn, 113 Agr Hall, Stillwater, OK 74078 USA. [Mayfield, Blayne; Thomas, Johnson] Oklahoma State Univ, Dept Comp Sci, 232 MSCS, Stillwater, OK 74078 USA. [O'Bryan, Corliss A.; Crandall, Philip; Ricke, Steven C.] Univ Arkansas, Dept Food Sci, 2650 N Young Ave, Fayetteville, AR 72704 USA. [O'Bryan, Corliss A.; Crandall, Philip; Ricke, Steven C.] Univ Arkansas, Ctr Food Safety, 2650 N Young Ave, Fayetteville, AR 72704 USA. [Knipe, Dar; Knipe, Richard] MarketMaker Riverside Res, Food Syst SME, 4709 44th St,Suite 7, Rock Isl, IL 61201 USA. C3 Oklahoma State University System; Oklahoma State University - Stillwater; Oklahoma State University System; Oklahoma State University - Stillwater; Oklahoma State University System; Oklahoma State University - Stillwater; University of Arkansas System; University of Arkansas Fayetteville; University of Arkansas System; University of Arkansas Fayetteville RP Adam, BD (corresponding author), Oklahoma State Univ, Dept Agr Econ, 413 Ag Hall & 114 Food & Agr Prod Ctr, Stillwater, OK 74078 USA. EM Brian.Adam@okstate.edu CR Adam B., 2015, FOOD SAFETY EMERGING, P9 Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 [Anonymous], 2016, FOOD SAFETY NEWS APHIS-USDA, 2009, OV REP BEN COST AN N Armbruster W.J., 2014, P 28 ENVIROINFO 2014 Bennett D., 2014, BLOOMBERG BUSINESSWE Beuchat LR, 2006, BRIT FOOD J, V108, P38, DOI 10.1108/00070700610637625 Bhatt T, 2013, J FOOD SCI, V78, pB21, DOI 10.1111/1750-3841.12278 Bitsch V, 2014, INT FOOD AGRIBUS MAN, V17, P97 Blasi D., 2009, BENEFIT COST ANAL NA Bottemiller H., 2012, MORE ILLNESSES TIED Butler L. J., 2008, BENEFITS COSTS IMPLE Carestream Health, 2011, CISC VIS NETW IND GL Centers for Disease Control and Prevention (CDC), 2008, MMWR Morb Mortal Wkly Rep, V57, P929 Centers for Disease Control and Prevention (CDC), 1993, MMWR Morb Mortal Wkly Rep, V42, P258 Coffey B., 2005, EC IMPACT BSE US BEE Crandall PG, 2013, MEAT SCI, V95, P137, DOI 10.1016/j.meatsci.2013.04.022 den Bakker HC, 2014, EMERG INFECT DIS, V20, P1306, DOI 10.3201/eid2008.131399 Deselnicu OC, 2013, J AGR RESOUR ECON, V38, P204 DeVuyst EA, 2007, J AGR RESOUR ECON, V32, P291 Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dumitrescu O, 2011, CLIN MICROBIOL INFEC, V17, P649, DOI 10.1111/j.1469-0691.2011.03511.x Elzo MA, 2009, J ANIM SCI, V87, P3877, DOI 10.2527/jas.2008-1553 Fremaux B, 2008, VET MICROBIOL, V132, P1, DOI 10.1016/j.vetmic.2008.05.015 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Ge C, 2014, THESIS Global Food Traceability Center (GFTC), 2014, CONC INT ARCH SEAF T Goetz G., 2012, FOOD SAFETY NEWS GOLAN E, 2004, 830 US DEP AGR EC RE Golan E., 2003, CHOICES, V2, P17 Goldsmith P., 2003, INT FOOD AGRIBUS MAN, V6, P25 Grein TW, 2000, EMERG INFECT DIS, V6, P97, DOI 10.3201/eid0602.000201 Herring W.O., 2003, GENETICS FEED EFFICI, P31 Heymann D L, 2001, Lancet Infect Dis, V1, P345, DOI 10.1016/S1473-3099(01)00148-7 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 IFT, 2009, 223042503 IFT FOOD D, V1 of 2 Inns T, 2015, EUROSURVEILLANCE, V20, P15, DOI 10.2807/1560-7917.ES2015.20.16.21098 International Food Information Council [IFIC], 2010, 2010 FOOD HLTH SURV Johnson JYM, 2003, CAN J MICROBIOL, V49, P326, DOI [10.1139/w03-046, 10.1139/W03-046] KIM HM, 1995, ONTOLOGY QUALITY ENT Koser CU, 2012, PLOS PATHOG, V8, DOI 10.1371/journal.ppat.1002824 Kroenke D. M., 2014, DATABASE PROCESSING Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Lewis R. J., 2013, INDEPENDENT REV XL F Lim KH, 2013, CAN J AGR ECON, V61, P93, DOI 10.1111/j.1744-7976.2012.01260.x Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lusk JL, 2001, AM J AGR ECON, V83, P539, DOI 10.1111/0002-9092.00176 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Ng D, 2012, INT FOOD AGRIBUS MAN, V15, P21 Orsi RH, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-539 Ortega Carlos, J AGR APPL EC, V42, P551 Ozsu MT, 2011, PRINCIPLES OF DISTRIBUTED DATABASE SYSTEMS, THIRD EDITION, P1, DOI 10.1007/978-1-4419-8834-8_1 Pendell DL, 2010, AM J AGR ECON, V92, P927, DOI 10.1093/ajae/aaq037 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Resende-Filho M. A., 2006, INT ASS AGR EC AUG 1 Resende MA, 2008, AM J AGR ECON, V90, P1091, DOI 10.1111/j.1467-8276.2008.01150.x Scallan E, 2011, EMERG INFECT DIS, V17, P16, DOI [10.3201/eid1701.P21101, 10.3201/eid1701.091101p2] Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Seyoum Bruk, 2013, AAEA ANN M WASH DC A Sherman EL, 2010, J ANIM SCI, V88, P16, DOI 10.2527/jas.2008-1759 Shinbaum S, 2016, FOOD CONTROL, V60, P12, DOI 10.1016/j.foodcont.2015.07.014 Smyth S., 2002, AgBioForum, V5, P30 Soeder J., 1993, RESTAURANT HOSPITALI, V77, P34 Stasiewicz MJ, 2015, APPL ENVIRON MICROB, V81, P6024, DOI 10.1128/AEM.01049-15 States Department of Agriculture. Animal and Plant Health Inspection Service, 2009, OV REP BEN COST AN N Sterling B, 2015, COMPR REV FOOD SCI F, V14, P205, DOI 10.1111/1541-4337.12130 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Tonsor GT, 2006, J INT FOOD AGRIBUS M, V18, P103, DOI 10.1300/J047v18n03_07 United States Department of Agriculture [USDA] Portal, 2015, AN PLANT HLTH INSP S VCM International, 2014, LEARN AUSTR TRAC MEA Weaber RL, 2010, AM J AGR ECON, V92, P1456, DOI 10.1093/ajae/aaq062 Wilson K, 2008, CAN MED ASSOC J, V179, P44, DOI 10.1503/cmaj.080516 Wilson K, 2009, CAN MED ASSOC J, V180, P829, DOI [10.1503/cmaj.090215, 10.1503/cmaj.1090215] Yeung R, 2012, BRIT FOOD J, V114, P40, DOI 10.1108/00070701211197356 NR 74 TC 6 Z9 6 U1 2 U2 32 PY 2016 VL 19 IS A SI SI BP 191 EP 214 WC Agricultural Economics & Policy SC Agriculture UT WOS:000422721200013 DA 2022-12-14 ER PT J AU Pelaez, V Aquino, D Hofmann, R Melo, M AF Pelaez, Victor Aquino, Dayani Hofmann, Ruth Melo, Marcelo TI Implementation of a Traceability and Certification System for Non-genetically Modified Soybeans: The Experience of Imcopa Co. in Brazil SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Article DE non-GM soy; cost; benefit; traceability; certification ID 3RD-PARTY CERTIFICATION AB This paper analyses a productive opportunity taken by a family-owned Brazilian soybean crusher (Imcopa) as it adapted its production system to sell certified non-GM soybeans products. Imcopa was Brazil's first soybean crusher to implement a non-GM soybean traceability and certification system, in 1998. It is now held to be the world's largest non-GM lecithin exporter. The analysis adopted here is based on a microeconomic perspective of productive opportunities identified by the firm, which goes beyond a simple balance-sheet approach. Four fundamental elements were used to guide the analysis: benefit-cost ratio; information asymmetry; bounded rationality; and company's growth. The possibility of selling non-GM soy and soybean products on the international market has provided Imcopa with access to an even broader commercial network of feed and food products. This has given the company a better outlook on why it should diversify its activities and intensify its pace of growth. C1 [Pelaez, Victor; Aquino, Dayani; Hofmann, Ruth] Univ Fed Parana, Jardim Bot, BR-80210170 Curitiba, Parana, Brazil. [Melo, Marcelo] Inst Tecnol Parana Tecpar, BR-81350010 Curitiba, Parana, Brazil. C3 Universidade Federal do Parana RP Hofmann, R (corresponding author), Univ Fed Parana, Jardim Bot, Av Lothario Meissner 632, BR-80210170 Curitiba, Parana, Brazil. EM victor@ufpr.br; dayani.aquino@gmail.com; ruthofmann@gmail.com; marcelofmelo@gmail.com CR *AB BRAZ ASS VEG O, 2005, INST CAP VEG OILS IN Boulding K., 1961, IMAGE Braithwaite John, 2001, GLOBAL BUSINESS REGU Callon M, 1998, LAWS OF THE MARKETS, P1, DOI 10.1111/j.1467-954X.1998.tb03468.x Deaton BJ, 2004, FOOD CONTROL, V15, P615, DOI 10.1016/j.foodcont.2003.09.007 GHEZAN G, 2006, ARGENTINE NONGM SOYB *GRAINN, 1999, GMO TEST SERV Hatanaka M, 2005, FOOD POLICY, V30, P354, DOI 10.1016/j.foodpol.2005.05.006 Loader R, 1999, FOOD POLICY, V24, P685, DOI 10.1016/S0306-9192(99)00073-1 Loasby B. J., 1976, CHOICE COMPLEXITY IG Miller D, 1999, SOC SCI MED, V49, P1239, DOI 10.1016/S0277-9536(99)00163-X Millstone E., 2001, SCI PUBL POLICY, V28, P99, DOI [10.3152/147154301781781543, DOI 10.3152/147154301781781543] Morris S, 2000, TRENDS BIOTECHNOL, V18, P325, DOI 10.1016/S0167-7799(00)01469-4 PELAEZ V, 2006, TRACEABILITY SEGREGA Pelaez V, 2009, SCI PUBL POLICY, V36, P61, DOI 10.3152/030234209X403235 PELAEZ VLS, 2009, J ECON ISSUES, V43, P1 PENROSE E, 1962, TEORIA CRESCIMECIMIE Tanner B, 2000, FOOD CONTROL, V11, P415, DOI 10.1016/S0956-7135(99)00055-9 TRAVER E, 2009, COMMUNICATION FEB TRAVER E, 2005, WORKSH SOJ CONV GEN TRAVER E, 2006, COMMUNICATION JUN TRAVER E, 2006, COMMUNICATION DEC *UN EUR, 2003, J OFICIAL UNIAO EURO 2006, GAZETA POVO PR 2005, GAZETA MERCANTIL SP 2006, VALOR EC 0123 NR 26 TC 6 Z9 6 U1 0 U2 9 PY 2010 VL 13 IS 1 BP 27 EP 44 WC Agricultural Economics & Policy SC Agriculture UT WOS:000298318900003 DA 2022-12-14 ER PT J AU Agrawal, TK Koehl, L Campagne, C AF Agrawal, Tarun Kumar Koehl, Ludovic Campagne, Christine TI A secured tag for implementation of traceability in textile and clothing supply chain SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY DT Article DE Supply chain management; Manufacturing; Traceability; Textile and clothing; Secured tag ID AUTHENTICATION; IDENTIFICATION; TRACKING; SYSTEM AB Textile and clothing industry is one of the oldest manufacturing industries and is a major contributor in the economic growth of developing countries. However, from past few decades, it has been criticised for its opaque, unsecured and untraceable nature of supply chain. Addressing these challenges, the paper proposes a system approach to introduce an item-centric secured traceability concept to monitor and control manufacturing processes and supply chain activities. In order to implement such secured traceability system, the paper describes the process for manufacturing, encoding and validating an innovative two-factor secured tag based on particle randomness that is printed on the surface of textile. Being micro-sized, the particles are easy to read and validate with pattern recognition. Further, as achieved through an uncontrolled manufacturing process, the randomness is unclonable to produce counterfeit tags. Furthermore, a sequence of experimental analyses has been conducted using various simulated scenarios to verify its applicability. A secured tag can be a low-cost and durable substitute for detachable, unsecured identifiers commercially available in the market. C1 [Agrawal, Tarun Kumar; Koehl, Ludovic; Campagne, Christine] ENSAIT, GEMTEX Lab Genie & Mat Text, F-59000 Lille, France. [Agrawal, Tarun Kumar; Koehl, Ludovic; Campagne, Christine] Univ Lille Nord France, F-59000 Lille, France. [Agrawal, Tarun Kumar] Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden. [Agrawal, Tarun Kumar] Soochow Univ, Coll Text & Clothing Engn, Suzhou, Peoples R China. C3 Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Universite de Lille - ISITE; Universite de Lille; University of Boras; Soochow University - China RP Agrawal, TK (corresponding author), ENSAIT, GEMTEX Lab Genie & Mat Text, F-59000 Lille, France.; Agrawal, TK (corresponding author), Univ Lille Nord France, F-59000 Lille, France.; Agrawal, TK (corresponding author), Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden.; Agrawal, TK (corresponding author), Soochow Univ, Coll Text & Clothing Engn, Suzhou, Peoples R China. EM tarun-kumar.agrawal@ensait.fr CR [Anonymous], 2018, OXF DICT ENGL AURENHAMMER F, 1991, COMPUT SURV, V23, P345, DOI 10.1145/116873.116880 Azuara G, 2012, IND MANAGE DATA SYST, V112, P340, DOI 10.1108/02635571211210022 Baldini G, 2015, PUBL OFF EUR UNION, P1, DOI DOI 10.2788/186725 Bansal Dipika, 2013, Sci Pharm, V81, P1, DOI 10.3797/scipharm.1202-03 Burnotte L., 2006, TRACEABILITY RULES C Cataldo A, 2016, INT J ADV MANUF TECH, V86, P3563, DOI 10.1007/s00170-016-8489-4 Cheung HH, 2011, COMPUT IND, V62, P708, DOI 10.1016/j.compind.2011.04.001 Corbellini S, 2006, IEEE IMTC P, P1331, DOI 10.1109/IMTC.2006.328556 Cui Y, 2015, CHEM COMMUN, V51, P5363, DOI 10.1039/c4cc08596e Devadas S, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON RFID, P58, DOI 10.1109/RFID.2008.4519377 DUDA RO, 1972, COMMUN ACM, V15, P11, DOI 10.1145/361237.361242 Ekwall D., 2009, NOFOMA, P2009 Gennaro R, 2006, IEEE SECUR PRIV, V4, P64, DOI 10.1109/MSP.2006.49 Han S, 2012, ADV MATER, V24, P5924, DOI 10.1002/adma.201201486 He W., 2008, 2008 6th IEEE International Conference on Industrial Informatics (INDIN), P1364, DOI 10.1109/INDIN.2008.4618316 Hurry SM, APPL DNA SCI Ilie-Zudor E, 2014, PROC CIRP, V25, P337, DOI 10.1016/j.procir.2014.10.047 Ilie-Zudor E, 2011, COMPUT IND, V62, P227, DOI 10.1016/j.compind.2010.10.004 Jourova V, 2017, RAPID ALERT SYSTEM D, V2017, P1, DOI [10.2838/069613, DOI 10.2838/069613] Juels A, 2006, IEEE J SEL AREA COMM, V24, P381, DOI 10.1109/JSAC.2005.861395 Kang YS, 2013, COMPUT IND, V64, P609, DOI 10.1016/j.compind.2013.03.004 Katayama A., 2004, P 3 INT C MOBILE UBI, P109 Kumar V., 2017, TEXTILES CLOTHING SU, V3, P5 Kumar V., 2017, THESIS Kumar V, 2017, J MANUF SYST, V42, P124, DOI 10.1016/j.jmsy.2016.11.008 Kumar V, 2016, J MANUF SYST, V40, P76, DOI 10.1016/j.jmsy.2016.06.007 Lee SY, 2008, SUPPLY CHAIN MANAG, V13, P185, DOI 10.1108/13598540810871235 Lei P, 2005, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, Proceedings, P686 Li JM, 2016, J MANUF PROCESS, V21, P141, DOI 10.1016/j.jmapro.2015.12.007 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 McMillen D, 2016, CISC VIS NETW IND GL Myae AC, 2012, INT J CONSUM STUD, V36, P192, DOI 10.1111/j.1470-6431.2011.01084.x Nakamura T, 2006, INT J PATTERN RECOGN, V20, P543, DOI 10.1142/S0218001406004818 Nath B, 2006, IEEE PERVAS COMPUT, V5, P22, DOI 10.1109/MPRV.2006.13 OTSU N, 1979, IEEE T SYST MAN CYB, V9, P62, DOI 10.1109/TSMC.1979.4310076 Pateriya R. K., 2011, 2011 International Conference on Communication Systems and Network Technologies (CSNT), P115, DOI 10.1109/CSNT.2011.31 Periaswamy SCG, 2011, IEEE T DEPEND SECURE, V8, P938, DOI 10.1109/TDSC.2010.56 Philip B, 2016, J MANUF PROCESS, V22, P185, DOI 10.1016/j.jmapro.2016.03.001 Pigni F, 2007, 2 MED C INF SYST MCI, P2007 PREWITT JMS, 1966, ANN NY ACAD SCI, V128, P1035 Ranjan R, 2016, J MANUF PROCESS, V22, P237, DOI 10.1016/j.jmapro.2016.03.009 Richero R., 2016, BACKGROUND ANAL TRAN Rieback MR, 2006, IEEE PERVAS COMPUT, V5, P62, DOI 10.1109/MPRV.2006.17 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Soille P, 2004, MORPHOLOGICAL IMAGE, DOI [10.1007/978-3-662-05088-0, DOI 10.1007/978-3-662-05088-0] Sonka M, 2014, IMAGE PROCESSING ANA, V4th Sonka M., 1993, IMAGE PROCESSING ANA, DOI [10.1007/978-1-4899-3216-7_4, DOI 10.1007/978-1-4899-3216-7_4] Sparavigna A, 2008, ARXIV08012700 Surovy P, 2014, CENT EURO FOR J, V60, P244, DOI 10.1515/forj-2015-0007 Ting SL, 2013, COMPUT IND, V64, P268, DOI 10.1016/j.compind.2012.11.002 Varallyai L., 2012, Agrarinformatika Folyoirat, V3, P9 Vedel-Smith NK, 2012, J MANUF SYST, V31, P113, DOI 10.1016/j.jmsy.2011.12.001 Wajsman N., 2015, EC COST IPR INFRINGE Yoon B, 2013, J MATER CHEM C, V1, P2388, DOI 10.1039/c3tc00818e Zadeh L.A., 1975, SYNSES, V30, P407 NR 57 TC 18 Z9 19 U1 1 U2 26 PD DEC PY 2018 VL 99 IS 9-12 SI SI BP 2563 EP 2577 DI 10.1007/s00170-018-2638-x WC Automation & Control Systems; Engineering, Manufacturing SC Automation & Control Systems; Engineering UT WOS:000452076900039 DA 2022-12-14 ER PT J AU He, MH Shi, JH AF He, Muhan Shi, Jianhua TI Circulation traceability system of Chinese herbal medicine supply chain based on internet of things agricultural sensor SO SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS DT Article DE Internet of things; Agricultural sensor; Chinese herbal medicine; Supply chain AB In the past years, the supply of Chinese herbal medicine has become more and more significant. In order to promote the standardized production of Chinese herbal medicine, we should take effective quality control on its production, processing and purchase. It is difficult to control the production quality and production standards of various Chinese herbal medicines planted separately. It is imperative to create a safe and effective Chinese herbal medicine supply chain circulation traceability system. Therefore, this paper puts forward the research of Chinese herbal medicine supply chain circulation traceability system based on Internet of things agricultural sensor. According to the characteristics of the Chinese herbal medicine supply chain, this paper uses the agricultural sensors in the Internet of things technology platform to plan the implementation of the detailed information inspection and traceability system of the Chinese herbal medicine supply chain, and then numbers the planting, processing, purchasing and selling parts of Chinese herbal medicine. It mainly improves the information implementation of the whole process and realizes the high supervision mode of Chinese herbal medicine from planting to selling drugs. Through testing the feasibility of the system, it is found that the Chinese herbal medicine supply chain circulation traceability system based on the Internet of things agricultural sensor can significantly improve the circulation efficiency of Chinese herbal medicine. It not only reduces the circulation cost of Chinese herbal medicine, but also enables Chinese herbal medicine suppliers, manufacturers and sellers to enjoy accurate and convenient Chinese herbal medicine supply services. It is concluded that the first mode of Chinese herbal medicine supply chain is pre supervision mode, and the second is circulation traceability monitoring mode. The 100 % safety inspection makes citizens feel more confident about the use of Chinese herbal medicine, and the popularization of Chinese herbal medicine will be more extensive. C1 [He, Muhan] Yunnan Forestry Technol Coll, Kunming 650224, Yunnan, Peoples R China. [Shi, Jianhua] Yunnan Inst Water & Hydropower Engn Invest Design, Kunming 650000, Yunnan, Peoples R China. RP Shi, JH (corresponding author), Yunnan Inst Water & Hydropower Engn Invest Design, Kunming 650000, Yunnan, Peoples R China. EM hmhtianshui@163.com; hh80611343@126.com CR Al-Fuqaha A, 2015, IEEE COMMUN SURV TUT, V17, P2347, DOI 10.1109/COMST.2015.2444095 Anthony Vernon P., 2018, INTELLIGENCE, V7, P53 Breiner JM, 1999, J POLYM SCI POL PHYS, V37, P1421, DOI 10.1002/(SICI)1099-0488(19990701)37:13<1421::AID-POLB8>3.0.CO;2-M Chen C, 2018, IEEE COMMUN LETT, V22, P2407, DOI 10.1109/LCOMM.2018.2873320 Dong XB, 2017, INFORM TECHNOL PEOPL, V30, P117, DOI 10.1108/ITP-11-2015-0272 Hellmann B, 2001, J COMP NEUROL, V429, P94, DOI 10.1002/1096-9861(20000101)429:1<94::AID-CNE8>3.0.CO;2-5 Jayachandran P.T., 2016, RADIO SCI, V44, P1 Kwan MP, 2002, ANN ASSOC AM GEOGR, V92, P645, DOI 10.1111/1467-8306.00309 Lang PJ, 2016, BEHAV THER, V47, P688, DOI 10.1016/j.beth.2016.08.011 Li CL, 2016, DRUG DES DEV THER, V10, DOI 10.2147/DDDT.S96589 Li SC, 2015, INFORM SYST FRONT, V17, P243, DOI 10.1007/s10796-014-9492-7 Lin Y.B., 2015, WIREL COMMUN MOB COM, P77 Liu J, 2016, IEEE T MOBILE COMPUT, V15, P1348, DOI 10.1109/TMC.2015.2446461 Mader P, 2015, EMPIR SOFTW ENG, V20, P413, DOI 10.1007/s10664-014-9314-z Mahon T, 2016, MILITARY TECHNOL, V40, P68 Omoregie EM, 2001, AGR ECON-BLACKWELL, V26, P281, DOI 10.1111/j.1574-0862.2001.tb00070.x Schultz SR, 1999, HIPPOCAMPUS, V9, P582, DOI 10.1002/(SICI)1098-1063(1999)9:5<582::AID-HIPO12>3.0.CO;2-P Sicari S, 2015, COMPUT NETW, V76, P146, DOI 10.1016/j.comnet.2014.11.008 TERPSTRA C, 1976, ZBL VET MED B, V23, P809 Turkulainen V, 2017, J SUPPLY CHAIN MANAG, V53, P41, DOI 10.1111/jscm.12133 Valaker S, 2016, MIL PSYCHOL, V28, P390, DOI 10.1037/mil0000123 VARGA J, 1986, BIOCHEM BIOPH RES CO, V138, P974, DOI 10.1016/S0006-291X(86)80591-5 Wang P, 2015, COMPUT NETW, V92, P408, DOI 10.1016/j.comnet.2015.07.020 Wang XF, 2014, ADV MATER, V26, P4763, DOI 10.1002/adma.201400910 Zhong RY, 2015, INT J PROD ECON, V165, P260, DOI 10.1016/j.ijpe.2015.02.014 Zhou ZL, 2016, IEEE ACM T NETWORK, V24, P312, DOI 10.1109/TNET.2014.2361149 NR 26 TC 4 Z9 4 U1 7 U2 34 PD JUN PY 2021 VL 30 AR 100518 DI 10.1016/j.suscom.2021.100518 EA MAR 2021 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems SC Computer Science UT WOS:000663407000008 DA 2022-12-14 ER PT J AU Negrini, R Nicoloso, L Crepaldi, P Milanesi, E Marino, R Perini, D Pariset, L Dunner, S Leveziel, H Williams, JL Marsan, PA AF Negrini, R. Nicoloso, L. Crepaldi, P. Milanesi, E. Marino, R. Perini, D. Pariset, L. Dunner, S. Leveziel, H. Williams, J. L. Marsan, P. Ajmone TI Traceability of four European Protected Geographic Indication (PGI) beef products using Single Nucleotide Polymorphisms (SNP) and Bayesian statistics SO MEAT SCIENCE DT Article DE SNPs; Traceability; Breed; Cattle ID INDIVIDUAL ASSIGNMENT; POPULATION-STRUCTURE; MARKERS; ASSOCIATION; INFERENCE; TESTS AB The use of SNPs in combination with Bayesian statistics for the geographic traceability of cattle was evaluated using a dataset comprising 24 breeds from Italy, France, Spain, Denmark, the Netherlands, Switzerland and UK genotyped with 90 polymorphic markers. The percentage of correct assignment of the individuals to their Country of origin was 90%, with an average assignment probability of 93% and an average specificity of 92%. The higher value was observed for UK breeds (97% of correct assignment) while Swiss animals were the most difficult to allocate (77% of correct assignment). Tracing of Protected Geographic Indication (PGI) products, the approach correctly assigned 100% of Guaranteed Pure Highland Beef; 97% of "Vitellone dell'Appennino Centrale" breeds; 84% of Ternera de Navarra, and 80% of Boeuf de Chalosse. Methods to verify Products of Designated Origin (PDO) and Protected Geographic Indication (PGI) products will help to protect regional foods and promote the economic growth of marginal rural areas by encouraging the production of high quality niche market foods. (c) 2008 Elsevier Ltd. All rights reserved. C1 [Negrini, R.; Milanesi, E.; Marino, R.; Perini, D.; Marsan, P. Ajmone] Univ Cattolica Sacro Cuore, Ist Zootecn, I-29100 Piacenza, Italy. [Nicoloso, L.; Crepaldi, P.; Milanesi, E.] Univ Milan, Dipartimento Sci Anim, Sez Zootecn Agr, I-20122 Milan, Italy. [Pariset, L.] Univ Tuscia, Dipartimento Prod Anim, Viterbo, Italy. [Dunner, S.] Univ Complutense Madrid, Fac Vet, Dept Anim Prod, Madrid, Spain. [Leveziel, H.] Univ Limoges, INRA, Fac Sci & Tech, Unite Genet Mol Anim, Limoges, France. [Williams, J. L.] Polo Univ, I-26900 Lodi, Italy. C3 Catholic University of the Sacred Heart; University of Milan; Tuscia University; Complutense University of Madrid; INRAE; Universite de Limoges RP Negrini, R (corresponding author), Univ Cattolica Sacro Cuore, Ist Zootecn, Via E Parmense 84, I-29100 Piacenza, Italy. EM riccardo.negrini@unicatt.it CR Angius A, 2008, HUM HERED, V65, P9, DOI 10.1159/000106058 Belkhir K., 2004, LAB GENOME POPUL INT, V5000, P1996 Chen WM, 2007, AM J HUM GENET, V81, P913, DOI 10.1086/521580 Ciampolini R, 2006, J ANIM SCI, V84, P11, DOI 10.2527/2006.84111x Cymbron T, 2005, P ROY SOC B-BIOL SCI, V272, P1837, DOI 10.1098/rspb.2005.3138 DALVIT C, 2008, MEAT SCI, DOI DOI 10.1016/J.MEATSCI.2008.01.00 EXCOFFIER L, 1992, GENETICS, V131, P479 Falush D, 2007, MOL ECOL NOTES, V7, P574, DOI 10.1111/j.1471-8286.2007.01758.x Goodkin DA, 2004, AM J KIDNEY DIS, V44, pS16, DOI 10.1053/j.akjd.2004.08.006 Hall N, 2007, J EXP BIOL, V210, P1518, DOI 10.1242/jeb.001370 Hamada D, 2005, ARTHRITIS RHEUM-US, V52, P1371, DOI 10.1002/art.21013 Heaton MP, 2005, JAVMA-J AM VET MED A, V226, P1311, DOI 10.2460/javma.2005.226.1311 IIBERY B, 2000, J RURAL STUD, V16, P217 Ilbery B, 2005, LAND USE POLICY, V22, P331, DOI 10.1016/j.landusepol.2004.06.002 Kapferer J.N., 1992, STRATEGIC BRAND MANA Latch EK, 2006, CONSERV GENET, V7, P295, DOI 10.1007/s10592-005-9098-1 LENSTRA JA, 2003, FOOD AUTHENTICITY TR Lindblad-Toh K, 2000, NAT GENET, V24, P381, DOI 10.1038/74215 Lloyd TA, 2006, EUR REV AGRIC ECON, V33, P119, DOI 10.1093/erae/jbl001 Manel S, 2005, TRENDS ECOL EVOL, V20, P136, DOI 10.1016/j.tree.2004.12.004 Markovtsova L, 2000, GENETICS, V156, P1427 Marsden T, 2002, ENVIRON PLANN A, V34, P809, DOI 10.1068/a3427 Murdoch J, 2000, J RURAL STUD, V16, P407, DOI 10.1016/S0743-0167(00)00022-X PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Parrott N, 2002, EUR URBAN REG STUD, V9, P241, DOI 10.1177/096977640200900304 Peiris JSM, 2007, CLIN MICROBIOL REV, V20, P243, DOI 10.1128/CMR.00037-06 Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Puntaric D, 2000, CROAT MED J, V41, P150 Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 REYNOLDS J, 1983, GENETICS, V105, P767 Troy CS, 2001, NATURE, V410, P1088, DOI 10.1038/35074088 Weir B. S., 1996, GENETIC DATA ANAL Wollstein A, 2007, NUCLEIC ACIDS RES, V35, DOI 10.1093/nar/gkm621 NR 33 TC 37 Z9 39 U1 0 U2 27 PD DEC PY 2008 VL 80 IS 4 BP 1212 EP 1217 DI 10.1016/j.meatsci.2008.05.021 WC Food Science & Technology SC Food Science & Technology UT WOS:000260294000036 DA 2022-12-14 ER PT J AU Roncero-Diaz, M Panea, B Arguello, A Alcalde, MJ AF Roncero-Diaz, Mercedes Panea, Begona Arguello, Anastasio Alcalde, Maria J. TI How Management System Affects the Concentration of Retinol and alpha-Tocopherol in Plasma and Milk of Payoya Lactating Goats: Possible Use as Traceability Biomarkers SO ANIMALS DT Article DE biomarker; traceability; goat; carotenoids; retinol; alpha-tocopherol ID VITAMIN-E; CAROTENOID-PIGMENTS; ADIPOSE-TISSUE; FATTY-ACID; GRAZING SHEEP; BLOOD; COLOR; ENCAPSULATION; ASSOCIATIONS; STRATEGIES AB Simple Summary The milk production systems in goats are mainly intensive and semi-extensive. In the former, the goats are housed and are fed a total mixed ration with vitamin supplementation. In the latter, the feeding of the goats is based on grazing, although with some supplementation with compound feed. Retinol and alpha-tocopherol that appear in these animals come from the feeding regime, since the animal cannot synthesize them. The objective of this study was to verify if the vitamins provided in different management systems of Payoya lactating goats are good markers of the production system. For this purpose, the content of carotenoids, retinol and alpha-tocopherol in the milk and plasma of the goats was quantified. Results showed an inverse relationship of the amount of these vitamins between milk and plasma. On the other hand, the metabolism of different vitamins depends on their origin (natural/synthetic), with natural alpha-tocopherol and synthetic retinol showing the highest amount in milk. Finally, it was found that these compounds can be good traceability tools which allows to guaranty to the consumer the origin of the products derived from these animals. The retinol and alpha-tocopherol concentrations were quantified (mu g/mL) using high-performance liquid chromatography (HPLC) in both plasma and milk of goats from three management systems. The aim was to investigate if the compounds pass from feed to animals' fluids and to evaluate their potential use as feeding regime biomarkers. A total of 45 Payoya dams were distributed in three groups according to management system during the first month of lactation: mountain grazing (MG), cultivated meadow (CM) and total mixed ration (TMR). TMR group had higher concentrations of retinol in both plasma (25.92 +/- 3.61 at 30 days postpartum) and milk (8.26 +/- 0.79 at 10 days postpartum), and they were also the unique animals whose milk contained detectable concentrations of alpha-tocopherol (3.15 +/- 0.19 at parturition). However, MG and CM goats showed higher plasma concentrations of alpha-tocopherol (64.26 +/- 14.56 and 44.65 +/- 5.75 at 30 days postpartum, respectively). These results could imply differences in the bioavailability of supplemented vitamin A and natural beta-carotene and between the natural/synthetic forms of alpha-tocopherol. An inverse relationship between the fluids (plasma/milk) in the contents of alpha-tocopherol and retinol was observed as lactation progressed. Since 80% of the animals were correctly classified using a discriminant analysis based on these vitamins, these compounds could be used as traceability biomarkers of feeding system, but further studies are necessary to know the possible passage to kid meat. C1 [Roncero-Diaz, Mercedes; Alcalde, Maria J.] Univ Seville, Dept Agron, Ctra Utrera Km 1, Seville 41013, Spain. [Panea, Begona] Ctr Invest & Tecnol Agroalimentaria Aragon CITA, Unidad Prod & Sanidad Anim, Avda Montanana 930, Zaragoza 50059, Spain. [Panea, Begona] Univ Zaragoza, Inst Agroalimentario Aragon IA2, CITA, Zaragoza 50059, Spain. [Arguello, Anastasio] Univ Las Palmas Gran Canaria, Dept Anim Pathol Anim Prod Bromatol & Feed Techno, Sch Vet Sci, Univ Campus Arucas, Aruca 35416, Spain. C3 University of Sevilla; University of Zaragoza; Universidad de Las Palmas de Gran Canaria RP Alcalde, MJ (corresponding author), Univ Seville, Dept Agron, Ctra Utrera Km 1, Seville 41013, Spain. EM mroncerodiaz@gmail.com; bpanea@cita-aragon.es; tacho@ulpg.es; aldea@us.es CR Alvarez R, 2014, J FOOD COMPOS ANAL, V36, P59, DOI 10.1016/j.jfca.2014.08.001 Alvarez R, 2014, MEAT SCI, V98, P187, DOI 10.1016/j.meatsci.2014.05.026 [Anonymous], HPLC CHROMATOGRAPHY [Anonymous], 2010, OFFICIAL J EUROPEAN, VL276, P33 Aparicio A., 1996, FLORA PARQUE NATURAL, V1st Asadian A, 1996, ACTA VET HUNG, V44, P99 Avalos Funez A., 2016, REV COMPLUT CIENC VE, V10, P1, DOI [10.5209/rev_RCCV.2016.v10.n2.53544, DOI 10.5209/REV_RCCV.2016.V10.N2.53544] Ballet N, 2000, FORAGE EVALUATION IN RUMINANT NUTRITION, P399, DOI 10.1079/9780851993447.0399 Blagojevic Dusko P, 2011, Front Biosci (Schol Ed), V3, P416, DOI 10.2741/s161 Butler G, 2008, J SCI FOOD AGR, V88, P1431, DOI 10.1002/jsfa.3235 Calderon F, 2007, J DAIRY SCI, V90, P2335, DOI 10.3168/jds.2006-630 Capper JL, 2005, BRIT J NUTR, V93, P549, DOI 10.1079/BJN20051376 Cardinault N, 2006, ANIM SCI, V82, P49, DOI 10.1079/ASC200514 Chauveau-Duriot B., 2005, Rencontres Autour des Recherches sur les Ruminants, V12, P117 Chauveau-Duriot B, 2010, ANAL BIOANAL CHEM, V397, P777, DOI 10.1007/s00216-010-3594-y Coelho M.B., 2002, P 13 ANN FLOR RUM NU, V1, P127 Debier C, 2005, BRIT J NUTR, V93, P153, DOI 10.1079/BJN20041308 Delgado-Pertinez M, 2013, SMALL RUMINANT RES, V114, P167, DOI 10.1016/j.smallrumres.2013.06.001 Dian PHM, 2007, ANIMAL, V1, P1198, DOI 10.1017/S175173110700047X Dian PHM, 2007, J ANIM SCI, V85, P3054, DOI 10.2527/jas.2006-477 DONOGHUE S, 1984, CAN J ANIM SCI, V64, P255, DOI 10.4141/cjas84-249 Dunne PG, 2009, MEAT SCI, V81, P28, DOI 10.1016/j.meatsci.2008.06.013 El tiempo en Grazalema, 2021, DAT PLUV HIDROL Erdman R., 1992, LARGE DAIRY HERD MAN, P297 Fedele V, 2004, S AFR J ANIM SCI, V34, P148 FERNANDEZ AR, 1993, J VEG SCI, V4, P313, DOI 10.2307/3235589 Food and Agriculture Organization of the United Nations (FAO), 2020, AGR STAT FAOSTAT Gentili A, 2013, J AGR FOOD CHEM, V61, P1628, DOI 10.1021/jf302811a Gonzalez-Martinez A, 2014, SPAN J AGRIC RES, V12, P117, DOI 10.5424/sjar/2014121-4673 Guerrero M.Y., 2007, FAEGAS, V32, P143 Gutierrez-Pena R, 2018, J FOOD COMPOS ANAL, V72, P122, DOI 10.1016/j.jfca.2018.07.003 HERDT TH, 1991, VET CLIN N AM-FOOD A, V7, P391, DOI 10.1016/S0749-0720(15)30796-9 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Kondyli E, 2007, FOOD CHEM, V100, P226, DOI 10.1016/j.foodchem.2005.09.038 LeBlanc SJ, 2004, J DAIRY SCI, V87, P609, DOI 10.3168/jds.S0022-0302(04)73203-8 Lietz G, 2012, MOL NUTR FOOD RES, V56, P241, DOI 10.1002/mnfr.201100387 Lobo GP, 2012, BBA-MOL CELL BIOL L, V1821, P78, DOI 10.1016/j.bbalip.2011.04.010 Lyan B, 2001, J CHROMATOGR B, V751, P297, DOI 10.1016/S0378-4347(00)00488-6 Magnuson BA, 2011, J FOOD SCI, V76, pR126, DOI 10.1111/j.1750-3841.2011.02170.x MAPA, 2021, CAT OF RAZ GAN (MAPA) Ministerio de Agricultura Pesca y Alimentacion, 2021, AGR STAT MIN AGR SPA MARKUS A, 1989, J MICROENCAPSUL, V6, P389, DOI 10.3109/02652048909019921 Martin B., 2004, Land use systems in grassland dominated regions. Proceedings of the 20th General Meeting of the European Grassland Federation, Luzern, Switzerland, 21-24 June 2004, P876 Meglia GE, 2006, J DAIRY RES, V73, P227, DOI 10.1017/S0022029906001701 Melendez-Martinez AJ, 2006, PHYTOCHEMISTRY, V67, P771, DOI 10.1016/j.phytochem.2006.02.002 Mena Y, 2017, AGROECOL SUST FOOD, V41, P614, DOI 10.1080/21683565.2017.1320620 Mouly PP, 1999, J CHROMATOGR A, V844, P149, DOI 10.1016/S0021-9673(99)00337-4 NJERU CA, 1994, J ANIM SCI, V72, P1636, DOI 10.2527/1994.7261636x Noziere P, 2006, ANIM FEED SCI TECH, V131, P418, DOI 10.1016/j.anifeedsci.2006.06.018 Olmedilla-Alonso B, 2013, MEAT SCI, V95, P919, DOI 10.1016/j.meatsci.2013.03.030 Pickworth CL, 2012, J ANIM SCI, V90, P1553, DOI 10.2527/jas.2011-4217 Pizzoferrato L, 2007, J DAIRY SCI, V90, P4569, DOI 10.3168/jds.2007-0093 Prache S, 2003, ANIM SCI, V77, P225, DOI 10.1017/S1357729800058963 Prache S, 2003, J ANIM SCI, V81, P360 Reinoso V., 2019, REV VET ARGENT, V13, P1 Ruiz FA, 2008, SMALL RUMINANT RES, V77, P208, DOI 10.1016/j.smallrumres.2008.03.007 Ruiz F.A., 2014, FEAGAS, V38, P103 Sauvant P, 2012, FOOD RES INT, V46, P469, DOI 10.1016/j.foodres.2011.09.025 Sayago A, 2007, GRASAS ACEITES, V58, P74 Schweigert F. J., 1998, Carotenoids, volume 3: Biosynthesis and metabolism., P249 Serrano E, 2006, ANIM SCI, V82, P909, DOI 10.1017/ASC200698 Taylor TM, 2005, CRIT REV FOOD SCI, V45, P587, DOI 10.1080/10408390591001135 Torre G., 1998, UTILIZACION ADITIVOS, P1 Vagni S., 2011, Food and Nutrition Sciences, V2, P1088, DOI 10.4236/fns.2011.210146 WEIR WC, 1949, J ANIM SCI, V8, P381, DOI 10.2527/jas1949.83381x YANG A, 1992, AUST J AGR RES, V43, P1809, DOI 10.1071/AR9921809 NR 66 TC 4 Z9 4 U1 1 U2 7 PD AUG PY 2021 VL 11 IS 8 AR 2326 DI 10.3390/ani11082326 WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology SC Agriculture; Veterinary Sciences; Zoology UT WOS:000688641600001 DA 2022-12-14 ER PT J AU Barling, D Sharpe, R Lang, T AF Barling, David Sharpe, Rosalind Lang, Tim TI Traceability and ethical concerns in the UK wheat-bread chain: from food safety to provenance to transparency SO INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY DT Article DE bread; ethics; supply chain governance; traceability; wheat AB Traceability systems that track both physical entities and their less tangible attributes are increasingly widely used in contemporary food supply to meet a range of regulatory and commercial objectives, including a growing number of ethical concerns. Even with a traditional combinable and blended food crop such as wheat to bread there is clear evidence of traceability from the variety and crop in the field through to the mill to the bakery to the shelf. This study examines the traceability systems that have emerged in the wheat to bread supply in the UK, and the ethical concerns that have emerged within this supply process. The study reveals that these ethical concerns are dynamic and evolving and are contested. In the case of the supply chains studied, a priority concern with safety aspects has been followed by an emerging greater focus upon the provenance of the wheat and flour and upon the environmental impacts of the more industrialized supply chains. A study of the traceability schemes in the chains and the views of the stakeholders reflects quite restricted 'fields of ethical vision'. The governance and the transmission of information along the chains to the final consumer are quite restricted and partial, inhibiting transparency. The realization of greater transparency and ethical traceability to address different moral perspectives will need further changes in the governance and operation of the supply chains. C1 [Barling, David; Sharpe, Rosalind; Lang, Tim] City Univ London, Ctr Food Policy, London EC1V 0HB, England. C3 City University London RP Barling, D (corresponding author), City Univ London, Ctr Food Policy, London EC1V 0HB, England. EM d.barling@city.ac.uk CR [Anonymous], 2007, 22005 ISO Arienzo A, 2008, INT LIBR ENVIRON AGR, V15, P23 Barling D, 2008, INT LIBR ENVIRON AGR, V15, P43 Beekman V, 2008, INT LIBR ENVIRON AGR, V15, P277 BOEL MF, 2008, ETHICAL TRACEABILITY, P206 BURNETT J, 1996, PLENTY WANT SOCIAL H *CAMGR, 2007, SAINSB ANN UN MULT P Cauvain S.P., 2006, WOODH PUB FOOD SCI T CIWF, 2002, FARM ASS SCHEM AN WE Coff C, 2008, INT LIBR ENVIRON AGR, V15, P1, DOI 10.1007/978-1-4020-8524-6 *CSL, 2005, PEST US SURV REP 202 Curtis B.C., 2002, BREAD WHEAT IMPROVEM DAVID E, 1979, ENGLISH BREAD YEAST DAVIES J, 2007, FARMERS GUARDIA 1130, P1 *DEFR, 2005, BETT ORG BREAD INT R *DEFR, 2008, FURTH INF CROSS COMP FINCH J, 2006, GUARDIAN 1028, P41 *HGCA, 2009, HGCA PROV REP *MINT INT GROUP, 2005, BREAD UK 2005 *NAB, 2006, UK FLOUR MILL IND 20 *NAB, 2007, UK FLOUR MILL IND 20 Pena RJ, 2002, BREAD WHEAT IMPROVEM Sustainable Development Commission (SDC), 2005, SUST IMPL LITTL RED TACON C, 2009, FARMERS GUARDIA 0814, P9 *USDA, 2006, Q INT TRAD REP WHITLEY A, 2006, BREAD MATTERS STATE 2006, FINANCIAL TIMES 1017, P10 2009, CO NEWS 0703 NR 28 TC 24 Z9 24 U1 2 U2 26 PD NOV PY 2009 VL 7 IS 4 BP 261 EP 278 DI 10.3763/ijas.2009.0331 WC Agriculture, Multidisciplinary; Green & Sustainable Science & Technology SC Agriculture; Science & Technology - Other Topics UT WOS:000272724100004 DA 2022-12-14 ER PT J AU Farooq, U Tao, W Alfian, G Kang, YS Rhee, J AF Farooq, Umar Tao, Wu Alfian, Ganjar Kang, Yong-Shin Rhee, Jongtae TI ePedigree Traceability System for the Agricultural Food Supply Chain to Ensure Consumer Health SO SUSTAINABILITY DT Article DE agricultural food chain; food quality; food safety; social sustainability; electronic pedigree; RFID; sensors; EPCglobal network; performance analysis ID QUALITY; MANAGEMENT; SAFETY AB Sustainability relies on the environmental, social and economical systems: the three pillars of sustainability. The social sustainability mostly advocates the people's welfare, health, safety, and quality of life. In the agricultural food industry, the aspects of social sustainability, such as consumer health and safety have gained substantial attention due to the frequent cases of food-borne diseases. The food-borne diseases due to the food degradation, chemical contamination and adulteration of food products pose a serious threat to the consumer's health, safety, and quality of life. To ensure the consumer's health and safety, it is essential to develop an efficient system which can address these critical social issues in the food distribution networks. This research proposes an ePedigree (electronic pedigree) traceability system based on the integration of RFID and sensor technology for real-time monitoring of the agricultural food to prevent the distribution of hazardous and adulterated food products. The different aspects regarding implementation of the proposed system in food chains are analyzed and a feasible integrated solution is proposed. The performance of the proposed system is evaluated and finally, a comprehensive analysis of the proposed ePedigree system's impact on the social sustainability in terms of consumer health and safety is presented. C1 [Farooq, Umar; Tao, Wu; Rhee, Jongtae] Dongguk Univ, Dept Ind & Syst Engn, Seoul 100715, South Korea. [Alfian, Ganjar] Dongguk Univ, Nano Informat Technol Acad, U SCM Res Ctr, Seoul 100715, South Korea. [Kang, Yong-Shin] Sungkyunkwan Univ, Dept Syst Management Engn, Suwon 16419, South Korea. C3 Dongguk University; Dongguk University; Sungkyunkwan University (SKKU) RP Rhee, J (corresponding author), Dongguk Univ, Dept Ind & Syst Engn, Seoul 100715, South Korea. EM umar@dongguk.edu; wutao@dongguk.edu; ganjar@dongguk.edu; yskang7867@skku.edu; jtrhee@dongguk.edu CR Akkerman R, 2010, OR SPECTRUM, V32, P863, DOI 10.1007/s00291-010-0223-2 [Anonymous], 2007, EPCGLOBAL PEDIGREE R [Anonymous], 2010, EPCGLOBAL ARCHITECTU [Anonymous], 2015, 90002015 ISO Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Christian F., CHEAP SENSOR TAG ELKINGTON J, 1997, CANNIBALS FORKS TRIP, P402 EPC Information Services (EPCIS), 2014, VERS 1 1 SPEC GS1 ST EPCglobal Object Name Service (ONS), 2008, EPCGLOBAL STAND VERS European Commission, 2014, IMP ASS MEAS ADDR FO Fritz M, 2008, AGRIBUSINESS, V24, P440, DOI 10.1002/agr.20172 Garcia G., 2015, FOOD SAFETY EVERYONE GLADWIN TN, 1995, ACAD MANAGE REV, V20, P874, DOI 10.5465/amr.1995.9512280024 ICC-International Chamber of Commerce, 2011, EST GLOB EC SOC IMP Interpol Media Room, 2016, LARG EV SEIZ FAK FOO Joe W., 2007, COLD WAS IT KNOW WHO Johnson, FOOD FRAUD EC MOTIVA Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Kim H, 2012, IEEE INT TECHNOL MAN, P137, DOI 10.1109/ITMC.2012.6306405 Michal M., 2013, THESIS Mitsugi J, 2007, ARCHITECTURE DEV SEN Moore JC, 2012, J FOOD SCI, V77, pR118, DOI 10.1111/j.1750-3841.2012.02657.x Norton T., 2014, GUIDE TRACEABILITY P Piramuthu S., 2016, RFID SENSOR NETWORK, V1, P227 Saltini R, 2012, FOOD CONTROL, V23, P221, DOI 10.1016/j.foodcont.2011.07.015 Scharff RL, 2012, J FOOD PROTECT, V75, P123, DOI 10.4315/0362-028X.JFP-11-058 Sloof M, 1996, TRENDS FOOD SCI TECH, V7, P165, DOI 10.1016/0924-2244(96)81257-X Smallholders Food Security and the Environment, 2013, REP PREP INT FUND AG Solanki M, 2014, LECT NOTES COMPUT SC, V8796, P82, DOI 10.1007/978-3-319-11964-9_6 Stoecker W.F., 1998, IND REFRIGERATION HD, P567 Thakur M, 2010, J FOOD ENG, V101, P193, DOI 10.1016/j.jfoodeng.2010.07.001 Ting SL, 2014, INT J PROD ECON, V152, P200, DOI 10.1016/j.ijpe.2013.12.010 Tressler D.K., 2006, FREEZING PRESERVATIO, V2 United Nation Document, REPORT WORLD COMMISS Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 World Health Organization, 2007, FOOD SAF FOODB ILLN Zhang L, 2006, GCC 2006: FIFTH INTERNATIONAL CONFERENCE ON GRID AND COOPERATIVE COMPUTING WORKSHOPS, PROCEEDINGS, P463 NR 38 TC 20 Z9 20 U1 3 U2 59 PD SEP PY 2016 VL 8 IS 9 AR 839 DI 10.3390/su8090839 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000385529400011 DA 2022-12-14 ER PT J AU Liang, WJ Cao, J Fan, Y Zhu, KF Dai, QW AF Liang, Wanjie Cao, Jing Fan, Yan Zhu, Kefeng Dai, Qiwei TI Modeling and Implementation of Cattle/Beef Supply Chain Traceability Using a Distributed RFID-Based Framework in China SO PLOS ONE DT Article ID PRODUCT TRACEABILITY; EPCGLOBAL NETWORK; FOOD-PRODUCTS; TRACE SYSTEM; BUSINESS; TECHNOLOGIES; AQUACULTURE; RECALL; SAFETY AB In recent years, traceability systems have been developed as effective tools for improving the transparency of supply chains, thereby guaranteeing the quality and safety of food products. In this study, we proposed a cattle/beef supply chain traceability model and a traceability system based on radio frequency identification (RFID) technology and the EPCglobal network. First of all, the transformations of traceability units were defined and analyzed throughout the cattle/beef chain. Secondly, we described the internal and external traceability information acquisition, transformation, and transmission processes throughout the beef supply chain in detail, and explained a methodology for modeling traceability information using the electronic product code information service (EPCIS) framework. Then, the traceability system was implemented based on Fosstrak and FreePastry software packages, and animal ear tag code and electronic product code (EPC) were employed to identify traceability units. Finally, a cattle/beef supply chain included breeding business, slaughter and processing business, distribution business and sales outlet was used as a case study to evaluate the beef supply chain traceability system. The results demonstrated that the major advantages of the traceability system are the effective sharing of information among business and the gapless traceability of the cattle/beef supply chain. C1 [Liang, Wanjie; Cao, Jing; Fan, Yan; Zhu, Kefeng; Dai, Qiwei] Jiangsu Acad Agr Sci, Inst Agr Econ & Informat, Nanjing, Jiangsu, Peoples R China. C3 Jiangsu Academy of Agricultural Sciences RP Liang, WJ (corresponding author), Jiangsu Acad Agr Sci, Inst Agr Econ & Informat, Nanjing, Jiangsu, Peoples R China. EM wanjie.liang@163.com CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Attaran M, 2007, SUPPLY CHAIN MANAG, V12, P249, DOI 10.1108/13598540710759763 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Donnelly KAM, 2012, BRIT FOOD J, V114, P1016, DOI 10.1108/00070701211241590 Donnelly KAM, 2009, MEAT SCI, V83, P68, DOI 10.1016/j.meatsci.2009.04.006 Engelseth P, 2009, J BUS IND MARK, V24, P421, DOI 10.1108/08858620910966291 EPC Tag Data Standard, 2014, EPC TAG DAT STAND DE Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Framling K, 2006, COMPUT IND, V57, P72, DOI 10.1016/j.compind.2005.04.004 Frederiksen M, 2001, FOOD AUST, V53, P117 Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Jakkhupan W, 2011, J NETW COMPUT APPL, V34, P949, DOI 10.1016/j.jnca.2010.04.003 Jones P, 2004, INT J RETAIL DISTRIB, DOI DOI 10.1108/09590550410524957 Kang YS, 2013, COMPUT IND, V64, P609, DOI 10.1016/j.compind.2013.03.004 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Karlsen KM, 2011, FOOD CONTROL, V22, P1209, DOI 10.1016/j.foodcont.2011.01.020 Ko JM, 2011, EXPERT SYST APPL, V38, P1583, DOI 10.1016/j.eswa.2010.07.077 Koutsoumanis K, 2005, INT J FOOD MICROBIOL, V100, P253, DOI 10.1016/j.ijfoodmicro.2004.10.024 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Manzanares-Lopez P, 2011, J NETW COMPUT APPL, V34, P925, DOI 10.1016/j.jnca.2010.04.018 MOA Ministry of Agriculture of the People's Republic of China, 2006, REG ADM LIV POULTR I, V67 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Musa A, 2014, EXPERT SYST APPL, V41, P176, DOI 10.1016/j.eswa.2013.07.020 Nam T, 2011, J NETW COMPUT APPL, V34, P958, DOI 10.1016/j.jnca.2010.04.021 National Bureau of Statistics of China (NBSC), 2006, COD ADM REG PEOPL RE Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Munoz-Gea JP, 2010, COMPUT IND, V61, P480, DOI 10.1016/j.compind.2010.01.006 Piramuthu S, 2013, EUR J OPER RES, V225, P253, DOI 10.1016/j.ejor.2012.09.024 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Shi J, 2012, COMPUT IND, V63, P574, DOI 10.1016/j.compind.2012.03.006 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Twist DC, 2005, J FACIL MANAG, V3, P226, DOI 10.1108/14725960510808491 NR 45 TC 20 Z9 21 U1 0 U2 69 PD OCT 2 PY 2015 VL 10 IS 10 AR e0139558 DI 10.1371/journal.pone.0139558 WC Multidisciplinary Sciences SC Science & Technology - Other Topics UT WOS:000362178700077 DA 2022-12-14 ER PT J AU Durante, C Baschieri, C Bertacchini, L Bertelli, D Cocchi, M Marchetti, A Manzini, D Papotti, G Sighinolfi, S AF Durante, Caterina Baschieri, Carlo Bertacchini, Lucia Bertelli, Davide Cocchi, Marina Marchetti, Andrea Manzini, Daniela Papotti, Giulia Sighinolfi, Simona TI An analytical approach to Sr isotope ratio determination in Lambrusco wines for geographical traceability purposes SO FOOD CHEMISTRY DT Article DE Sr-87/Sr-56 ratio; Lambrusco PDO wines; Geographic traceability; MC-ICP/MS ID ABUNDANCE RATIOS; SOIL; FINGERPRINTS; PROVENANCE AB Geographical origin and authenticity of food are topics of interest for both consumers and producers. Among the different indicators used for traceability studies, Sr-87/Sr-86 isotopic ratio has provided excellent results. In this study, two analytical approaches for wine sample pre-treatment, microwave and low temperature mineralisation, were investigated to develop accurate and precise analytical method for Sr-87/Sr-86 determination. The two procedures led to comparable results (paired t-test, with t < t(crit)). Furthermore, the precision of the whole analytical procedure was evaluated by using a control sample (wine sample), processed during each sample batch (calculated Relative Standard Deviation, RSD%, equal to 0.002%. Larnbrusco PDO (Protected Designation of Origin) wines coming from four different vintages (2009, 2010, 2011 and 2012) were pre-treated according to the best procedure and their isotopic values were compared with isotopic data coming from (i) soils of their territory of origin and (ii) wines obtained by same grape varieties cultivated in different districts. The obtained results have shown no significant variability among the different vintages of wines and a perfect agreement between the isotopic range of the soils and wines has been observed. Nevertheless, the investigated indicator was not enough powerful to discriminate between similar products. To this regard, it is worth to note that more soil samples as well as wines coming from different districts will be considered to obtain more trustworthy results. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Durante, Caterina; Baschieri, Carlo; Bertacchini, Lucia; Cocchi, Marina; Marchetti, Andrea; Sighinolfi, Simona] Univ Modena & Reggio Emilia, Dept Chem & Geol Sci, I-41125 Modena, Italy. [Bertelli, Davide; Papotti, Giulia] Univ Modena & Reggio Emilia, Dept Pharmaceut Sci, I-41125 Modena, Italy. [Manzini, Daniela] Univ Modena & Reggio Emilia, Ctr Interdipartimentale Grandi Strumenti, I-41125 Modena, Italy. C3 Universita di Modena e Reggio Emilia; Universita di Modena e Reggio Emilia; Universita di Modena e Reggio Emilia RP Marchetti, A (corresponding author), Univ Modena & Reggio Emilia, Dept Chem & Geol Sci, Via Campi 183, I-41125 Modena, Italy. EM andrea.marchetti@unimore.it CR Adami L, 2010, RAPID COMMUN MASS SP, V24, P2943, DOI 10.1002/rcm.4726 Almeida CMR, 2003, J AGR FOOD CHEM, V51, P4788, DOI 10.1021/jf034145b [Anonymous], 2009, 19730 DINISO Asfaha DG, 2011, J CEREAL SCI, V53, P170, DOI 10.1016/j.jcs.2010.11.004 Barbaste M, 2002, J ANAL ATOM SPECTROM, V17, P135, DOI 10.1039/b109559p Baroni MV, 2011, J AGR FOOD CHEM, V59, P11117, DOI 10.1021/jf2023929 Baschieri C., 2012, FOOD TRACEABILITY MU Berglund M, 2011, PURE APPL CHEM, V83, P397, DOI 10.1351/PAC-REP-10-06-02 Bertacchini L, 2012, TALANTA, V98, P178, DOI 10.1016/j.talanta.2012.06.067 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Durante C, 2013, FOOD CHEM, V141, P2779, DOI 10.1016/j.foodchem.2013.05.108 Fortunato G, 2004, J ANAL ATOM SPECTROM, V19, P227, DOI 10.1039/b307068a HORWITZ EP, 1992, SOLVENT EXTR ION EXC, V10, P313, DOI 10.1080/07366299208918107 Laaks J, 2012, ANAL BIOANAL CHEM, V403, P2429, DOI 10.1007/s00216-012-5909-7 Marchetti Andrea, 2011, MICROWAVES THEORETIC Marchionni S, 2013, J AGR FOOD CHEM, V61, P6822, DOI 10.1021/jf4012592 Marisa C, 2004, FOOD CHEM, V85, P7, DOI 10.1016/j.foodchem.2003.05.003 Miller Jane C., 2010, STAT CHEMOMETRICS AN, V6th, DOI 10.7861/clinmedicine.14-6-677 MOORE LJ, 1982, J RES NAT BUR STAND, V87, P1, DOI 10.6028/jres.087.001 Riovanto R, 2011, J AGR FOOD CHEM, V59, P10356, DOI 10.1021/jf202578f Stein M, 1997, GEOCHIM COSMOCHIM AC, V61, P3975, DOI 10.1016/S0016-7037(97)00191-9 Swoboda S, 2008, ANAL BIOANAL CHEM, V390, P487, DOI 10.1007/s00216-007-1582-7 Tasev K, 2005, MICROCHIM ACTA, V149, P55, DOI 10.1007/s00604-004-0306-3 Totaro S, 2013, CHEMOMETR INTELL LAB, V124, P14, DOI 10.1016/j.chemolab.2013.03.001 Voerkelius S, 2010, FOOD CHEM, V118, P933, DOI 10.1016/j.foodchem.2009.04.125 NR 25 TC 55 Z9 58 U1 1 U2 118 PD APR 15 PY 2015 VL 173 BP 557 EP 563 DI 10.1016/j.foodchem.2014.10.086 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000347755800072 DA 2022-12-14 ER PT J AU Gao, GD Xiao, K Chen, MM AF Gao, Guandong Xiao, Ke Chen, Miaomiao TI An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Intelligent aquaculture; Water-quality forecast; Traceability; Fish food safety ID WIRELESS SENSOR NETWORK; MONITORING-SYSTEM; DENSITY; DESIGN AB The quality and safety of aquatic products are increasingly important in China. In this study, we developed an Internet of Things (IoT)-based intelligent fish farming and tracking control system that includes a forecasting method that enables automatic water quality management and supports tracking the breeding and selling of freshwater fish. This system can assist fish farmers to intelligently control and manage fishpond water-quality treatment equipment and assist consumers in tracking and viewing historical farming process data using the QR code tag of an aquatic product, which can raise revenue of fish farmers and safeguard the food safety of consumers. We also propose a set of water-quality indicator forecasting methods for a fishpond intelligent management module that first detect and remove abnormal data using the local outlier factor (LOF) algorithm after compared with DBSCAN. Then, the key fishpond data are analysed, modelled and predicted using the model tree algorithm, allowing water-quality indicators to be addressed in advance and maintained within a safe range that complies with standards. The experiments verified that the mean values of the predicted data generated by the M5 model tree algorithm were closer to those of the training data than Cubist, RF, GBM algorithm, and strong correlations were found between the predicted data and the verification data. Moreover, the mean absolute errors of our method are small relative to the data means, indicating that the proposed method can effectively and accurately forecast water-quality indicators. C1 [Gao, Guandong] Natl Police Univ Criminal Justice, Dept Informat Management, Baoding, Hebei, Peoples R China. [Xiao, Ke] Hebei Agr Univ, Coll Informat Sci & Technol, Baoding, Hebei, Peoples R China. [Chen, Miaomiao] Hebei Agr Univ, Acad Sci & Technol, Baoding, Hebei, Peoples R China. C3 National Police University for Criminal Justice; Hebei Agricultural University; Hebei Agricultural University RP Xiao, K (corresponding author), Hebei Agr Univ, Coll Informat Sci & Technol, Baoding, Hebei, Peoples R China. EM xiaoketeaching@sina.com CR Becke C, 2019, AQUACULTURE, V499, P348, DOI 10.1016/j.aquaculture.2018.09.048 Cario G, 2017, OC AB C AB ENGL JUN Chen JH, 2015, IEEE SYS MAN CYBERN, P1161, DOI 10.1109/SMC.2015.208 Davidson RS, 2010, J ANIM ECOL, V79, P1113, DOI 10.1111/j.1365-2656.2010.01708.x Elshabrawy T, 2018, IEEE COMMUN LETT, V22, P1778, DOI 10.1109/LCOMM.2018.2849718 Espinosa-Faller FJ, 2012, J APPL RES TECHNOL, V10, P380 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 FAO, CONTR FOOD SEC NUTR Haugen TO, 2007, ECOL MONOGR, V77, P483, DOI 10.1890/06-0163.1 Idachaba Francis E, 2017, P WORLD C ENG COMP S, VI Kumar S, 2019, INFORM FUSION, V52, P41, DOI 10.1016/j.inffus.2018.11.001 Lee HC, 2018, IEEE T INSTRUM MEAS, V67, P2177, DOI 10.1109/TIM.2018.2814082 Lorenzen K, 2016, FISH RES, V180, P4, DOI 10.1016/j.fishres.2016.01.006 Luo HP, 2015, INT J AGR BIOL ENG, V8, P136, DOI 10.3965/j.ijabe.20150806.1486 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Raju KRSR, 2017, IEEE INT ADV COMPUT, P318, DOI [10.1109/IACC.2017.0075, 10.1109/IACC.2017.67] Reddy KSS, 2019, MEASUREMENT, V144, P14, DOI 10.1016/j.measurement.2018.11.041 Shi B, 2018, BIOSYST ENG, V172, P57, DOI 10.1016/j.biosystemseng.2018.05.016 Simbeye DS, 2014, COMPUT ELECTRON AGR, V102, P31, DOI 10.1016/j.compag.2014.01.004 Tai HJ, 2012, SENSOR LETT, V10, P265, DOI 10.1166/sl.2012.1851 Tome MD, 2019, IEEE T IND ELECTRON, V66, P1629, DOI 10.1109/TIE.2018.2816006 Ullah I, 2018, PROCESSES, V6, DOI 10.3390/pr6060065 Veiga P, 2016, REDUCTION FISHERIES Vollestad LA, 2008, OIKOS, V117, P1752, DOI 10.1111/j.1600-0706.2008.16872.x Wang I., 1997, 9 EUR C MACH LEARN, P128 Wang X, 2011, PROCEDIA ENGINEER, V15, DOI 10.1016/j.proeng.2011.08.504 World Ocean Review, 2013, WORLD OC REV 2 FUT F Zahedi S, 2019, AQUACULTURE, V498, P271, DOI 10.1016/j.aquaculture.2018.07.044 Zhang YJ, 2019, FOOD CONTROL, V95, P283, DOI 10.1016/j.foodcont.2018.08.018 Zhang Y, 2013, APPL MECH MATER, V303-306, P1395, DOI 10.4028/www.scientific.net/AMM.303-306.1395 Zhou J, 2019, SAFETY SCI, V118, P505, DOI 10.1016/j.ssci.2019.05.046 Zhu XN, 2010, COMPUT ELECTRON AGR, V71, pS3, DOI 10.1016/j.compag.2009.10.004 NR 32 TC 34 Z9 34 U1 7 U2 45 PD NOV PY 2019 VL 166 AR 105013 DI 10.1016/j.compag.2019.105013 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000497247500019 DA 2022-12-14 ER PT J AU Lachance, PA AF Lachance, PA TI Nutraceutical/drug/anti-terrorism safety assurance through traceability SO TOXICOLOGY LETTERS DT Article DE global positioning; bar/chip coding; HACCP; safety; traceability AB Nutraceuticals are naturally occurring/derived bioactive compounds that are reported to have health benefits. The delivery systems for nutraceuticals are foods (functional foods), supplements, or both. Drugs are designed to have medicinal properties for the prevention and treatment of identified diseases or signs and symptoms of disease. Counterfeit drugs contain either placebo, materials not identified in the labeling or substandard or impure materials, which may produce untoward pharmacological or toxicological effects. In addition, the consumer has the right to microbiological safety and prevention from adverse exposure to hazardous chemical(s), and other adverse compounds. Nutraceutical/drug delivery systems are viewed as approaches to (1) enhanced consumer health, (2) decreased healthcare costs, and (3) enhanced economic development. Therefore, the nutra/pharma/ceutical industry is reliant upon a strong underpinning of diversified research that addresses safety and assures chemical and biological efficacy. Significant safety through traceability can be assured by the coupling of the technologies of (a) global positioning (GPS); (b) bar/chip coding; and (c) hazard analysis critical control point (HACCP) management, coupled to rapid nanotechnology marker assays now under development. (C) 2004 Elsevier Ireland Ltd. All rights reserved. C1 Nutraceut Inst, Food Sci & Ctr Adv Food Technol, New Jersey Agr Expt Stn, New Brunswick, NJ 08901 USA. C3 Rutgers State University New Brunswick RP Lachance, PA (corresponding author), Nutraceut Inst, Food Sci & Ctr Adv Food Technol, New Jersey Agr Expt Stn, 65 Dudley Rd, New Brunswick, NJ 08901 USA. EM lachance@aesop.rutgers.edu CR Childs N. M., 2000, Journal of Nutraceuticals, Functional and Medical Foods, V2, P85, DOI 10.1300/J133v02n03_06 Lachance PA, 1997, FOOD TECHNOL-CHICAGO, V51, P35 LACHANCE PA, 2002, ACS SYM SER, V803, P2 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 NR 4 TC 6 Z9 7 U1 0 U2 8 PD APR 15 PY 2004 VL 150 IS 1 BP 25 EP 27 DI 10.1016/j.toxlet.2003.05.001 WC Toxicology SC Toxicology UT WOS:000221049900004 DA 2022-12-14 ER PT J AU Agrawal, TK Kumar, V Pal, R Wang, LC Chen, Y AF Agrawal, Tarun Kumar Kumar, Vijay Pal, Rudrajeet Wang, Lichuan Chen, Yan TI Blockchain-based framework for supply chain traceability: A case example of textile and clothing industry SO COMPUTERS & INDUSTRIAL ENGINEERING DT Article DE Blockchain; Traceability; Manufacturing; Textile and Clothing; Information sharing; Supply chain ID TECHNOLOGY; CHALLENGES; MANAGEMENT; INTERNET; MODEL; TRANSPARENCY; OPERATIONS; ROLES AB Traceability has emerged as a prime requirement for a multi-tier and multi-site production. It enables visibility and caters to the consumer requirements of transparency and quality assurance. Textile and clothing industry is one such example that requires traceability implementation to address prevailing problems of information asymmetry and low visibility. Customers find it difficult to access product data that can facilitate ethical buying practices or assure product authenticity. Besides, it is challenging for stakeholders to share crucial information in an insecure environment with risk of data manipulations and fear of losing information advantage. In this context, this study investigates and proposes a blockchain-based traceability framework for traceability in multi-tier textile and clothing supply chain. It conceptualizes the interaction of supply chain partners, and related network architecture at the organizational level and smart contract and transaction validation rules at the operational level. To illustrate the application of the proposed framework, the study presents an example of organic cotton supply chain using blockchain with customized smart contract and transaction rules. It finally demonstrates the applicability of the developed blockchain by testing it under two parameters. The proposed system can build a technology-based trust among the supply chain partners, where the distributed ledger can be used to store and authenticate supply chain transactions. Further, the blockchain-based traceability system would provide a unique opportunity, flexibility, and authority to all partners to trace-back their supply network and create transparent and sustainable supply chain. C1 [Agrawal, Tarun Kumar; Kumar, Vijay; Pal, Rudrajeet] Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden. [Agrawal, Tarun Kumar; Wang, Lichuan; Chen, Yan] Soochow Univ, Coll Text & Clothing Engn, Suzhou 215168, Peoples R China. [Agrawal, Tarun Kumar] ENSAIT, GEMTEX Lab Genie & Mat Text, F-59000 Lille, France. [Agrawal, Tarun Kumar] Univ Lille Nord France, F-59000 Lille, France. C3 University of Boras; Soochow University - China; Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Universite de Lille - ISITE; Universite de Lille RP Agrawal, TK (corresponding author), KTH Royal Inst Technol, Kvarnbergagatan 12, S-15136 Sodertalje, Sweden. EM tkag@kth.se CR Agrawal TK, 2020, IFIP ADV INF COMM TE, V591, P259, DOI 10.1007/978-3-030-57993-7_30 Agrawal TK, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11061698 Ahmad RW, 2021, COMPUT IND ENG, V151, DOI 10.1016/j.cie.2020.106982 Algayerova O., 2017, SOCIAL HUMAN HLTH IM, P1 Alves B, 2014, LECT NOTES BUS INF P, V190, P68, DOI 10.1007/978-3-319-09492-2_5 Alzahrani N, 2020, CONCURR COMP-PRACT E, V32, DOI 10.1002/cpe.5232 Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Biswas B, 2019, COMPUT IND ENG, V136, P225, DOI 10.1016/j.cie.2019.07.005 Perez JJB, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12187491 Castro M, 2002, ACM T COMPUT SYST, V20, P398, DOI 10.1145/571637.571640 Chen YB, 2009, J MARKETING, V73, P214, DOI 10.1509/jmkg.73.6.214 Choi TM, 2019, TRANSPORT RES E-LOG, V131, P139, DOI 10.1016/j.tre.2019.09.019 Cruz E., 2020, P 22 INT C ENT INF S Egels-Zanden N, 2015, J CLEAN PROD, V107, P95, DOI 10.1016/j.jclepro.2014.04.074 Elmessiry M, 2019, LECT NOTES COMPUT SC, V11521, P157, DOI 10.1007/978-3-030-23404-1_11 ElMessiry M, 2018, LECT NOTES COMPUT SC, V10974, P213, DOI 10.1007/978-3-319-94478-4_15 European Commission, 2018, KEEP EUR CONS SAF RA Fiaidhi J, 2018, IT PROF, V20, P66, DOI 10.1109/MITP.2018.043141671 Frizzo-Barker J, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.10.014 Fu BL, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10041105 Guercini S, 2009, IND MARKET MANAG, V38, P883, DOI 10.1016/j.indmarman.2009.03.016 Gupta M., 2018, BLOCKCHAIN DUMMIES I, V2nd Helo P, 2019, COMPUT IND ENG, V136, P242, DOI 10.1016/j.cie.2019.07.023 Karumanchi MD, 2019, 2019 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMMUNICATION, COMPUTER TECHNOLOGIES AND OPTIMIZATION TECHNIQUES (ICEECCOT), P390, DOI 10.1109/ICEECCOT46775.2019.9114692 Korpela K, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P4182 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Kumar V, 2017, SYSTEMS, V5, DOI 10.3390/systems5020033 Kumar V, 2016, ENTROPY-SWITZ, V18, DOI 10.3390/e18120440 Lam OA, 2019, IEEE INT CONF MOB DA, P447, DOI 10.1109/MDM.2019.000-4 Lohmer J, 2020, COMPUT IND ENG, V149, DOI 10.1016/j.cie.2020.106789 Longo F, 2019, COMPUT IND ENG, V136, P57, DOI 10.1016/j.cie.2019.07.026 Lu ZH, 2019, INT CONF SOFTW ENG, P596, DOI 10.1109/ICSESS47205.2019.9040744 Luu L, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P254, DOI 10.1145/2976749.2978309 Madumidha S., 2019, 2019 TEQIP III Sponsored International Conference on Microwave Integrated Circuits, Photonics and Wireless Networks (IMICPW), P174, DOI 10.1109/IMICPW.2019.8933270 Mandolla C, 2019, COMPUT IND, V109, P134, DOI 10.1016/j.compind.2019.04.011 Meyer T, 2019, COMPUT IND ENG, V136, P5, DOI 10.1016/j.cie.2019.07.006 Muthu S. S., 2017, TEXTILE SCI CLOTHING, P9, DOI [10.1007/978-981-10-2639-3_2., DOI 10.1007/978-981-10-2639-3_2] Nakasumi M, 2017, CONF BUS INFORM, V1, P140, DOI 10.1109/CBI.2017.56 OECD/EUIPO, 2019, TRENDS TRAD COUNT PI, DOI [10.1787/g2g9f533-en, DOI 10.1787/G2G9F533-EN] Pal K, 2020, PROCEDIA COMPUT SCI, V170, P450, DOI 10.1016/j.procs.2020.03.088 Petersen M, 2018, IT-INF TECHNOL, V60, P263, DOI 10.1515/itit-2017-0031 Queiroz MM, 2019, INT J INFORM MANAGE, V46, P70, DOI 10.1016/j.ijinfomgt.2018.11.021 RIVEST RL, 1978, COMMUN ACM, V21, P120, DOI [10.1145/359340.359342, 10.1145/357980.358017] Shi YQ, 2020, COMM COM INF SC, V1156, P446, DOI 10.1007/978-981-15-2777-7_36 Short JL, 2016, STRATEGIC MANAGE J, V37, P1878, DOI 10.1002/smj.2417 Sinkovics N, 2016, ACCOUNT AUDIT ACCOUN, V29, P617, DOI 10.1108/AAAJ-07-2015-2141 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Swan M., 2015, BLOCKCHAIN BLUEPRINT Thomassey S, 2010, INT J PROD ECON, V128, P470, DOI 10.1016/j.ijpe.2010.07.018 Tu MR, 2018, IND MANAGE DATA SYST, V118, P65, DOI 10.1108/IMDS-11-2016-0503 Umeh Jude, 2016, IT Now, V58, P58, DOI 10.1093/itnow/bww026 Viriyasitavat W, 2019, J IND INF INTEGR, V13, P32, DOI 10.1016/j.jii.2018.07.004 Wang YL, 2019, INT J PROD ECON, V211, P221, DOI 10.1016/j.ijpe.2019.02.002 Weghofer S., 2020, UNIDO OPEN DATA PLAT Wills B. A., 2016, WILLS MINERAL PROCES, V8th, P41, DOI DOI 10.1016/B978-0-08-097053-0.00003-0 Yu CY, 2020, COMPUT IND ENG, V146, DOI 10.1016/j.cie.2020.106602 Zahnentferner J, 2018, IACR CRYPTOLOGY EPRI, V2018, P262 Zhang YP, 2019, COMPUT IND ENG, V135, P1025, DOI 10.1016/j.cie.2019.05.039 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zyskind G, 2015, 2015 IEEE SECURITY AND PRIVACY WORKSHOPS (SPW), P180, DOI 10.1109/SPW.2015.27 NR 60 TC 67 Z9 67 U1 43 U2 123 PD APR PY 2021 VL 154 AR 107130 DI 10.1016/j.cie.2021.107130 EA JAN 2021 WC Computer Science, Interdisciplinary Applications; Engineering, Industrial SC Computer Science; Engineering UT WOS:000632964300030 HC Y HP N DA 2022-12-14 ER PT J AU Yao, S Li, JQ Li, T Duan, ZL Wang, YZ AF Yao, Sen Li, Jieqing Li, Tao Duan, Zhili Wang, Yuanzhong TI Geographical traceability of Boletaceae mushrooms using data fusion of FT-IR, UV, and ICP-AES combined with SVM SO INTERNATIONAL JOURNAL OF FOOD PROPERTIES DT Article DE Geographical traceability; data fusion; Boletaceae mushrooms; principal components analysis (PCA); support vector machine (SVM) ID TRANSFORM INFRARED-SPECTROSCOPY; MEDICINAL MUSHROOM; FRUITING BODIES; CLASSIFICATION; SAMPLES; BIOCONCENTRATION; AUTHENTICATION; MINERALS; ORIGIN; FOREST AB Geographical traceability is important to consumer protection and quality control of edible mushrooms. In this work, Fourier transform infrared (FT-IR) spectroscopy, ultraviolet (UV) spectroscopy, and inductively coupled plasma-atomic emission spectrometry were used for traceability of 312 mushroom samples from eight different geographical origins in combination with multivariate statistical analysis. Initially, FT-IR, UV spectra, and 14 elements of 312 samples obtained from 8 geographical origins were analyzed, respectively. Meanwhile, the principal components of three techniques were extracted by principal components analysis for data fusion. Finally, classification models were established in the basis of UV, FT-IR, elements, and fusion datasets combined with support vector machine (SVM). Compared with individual technology, data fusion of multi-technique can obviously promote the classification performance in SVM models for geographical origins traceability. Especially, the accuracy of prediction in SVM model by data fusion of three instruments was 99.04%, which was higher than single technique and data fusion of two spectroscopies techniques. This result indicated that data fusion strategy combined with SVM can provide high synergic effect for geographical origins traceability of Boletaceae mushrooms. The more information is fused, the better performance of the model is. This method may be applied for quality control and evaluation of analogous food. C1 [Yao, Sen; Li, Jieqing; Duan, Zhili] Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China. [Yao, Sen; Wang, Yuanzhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. [Li, Tao] Yuxi Normal Univ, Coll Resources & Environm, Yuxi, Peoples R China. C3 Yunnan Agricultural University; Yunnan Academy of Agricultural Sciences; Yuxi Normal University RP Duan, ZL (corresponding author), Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM 292769220@qq.com; boletus@126.com CR Anghileri A, 2007, J BIOTECHNOL, V127, P508, DOI 10.1016/j.jbiotec.2006.07.021 Biancolillo A, 2014, ANAL CHIM ACTA, V820, P23, DOI 10.1016/j.aca.2014.02.024 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Bougrini M., 2014, J SENSORS, V2014, P1, DOI DOI 10.1155/2014/245831 Buckley K, 2017, APPL SPECTROSC, V71, P1085, DOI 10.1177/0003702817703270 Chen H., 2013, SOC PETROLEUM ENG SP, V2013, P1, DOI [10.2118/163614-MS, DOI 10.2118/163614-MS] Choong YK, 2014, J MOL STRUCT, V1069, P188, DOI 10.1016/j.molstruc.2014.04.001 Dankowska A, 2017, EUR J LIPID SCI TECH, V119, DOI 10.1002/ejlt.201600268 Devos O, 2014, FOOD CHEM, V148, P124, DOI 10.1016/j.foodchem.2013.10.020 Elmastas M, 2007, J FOOD COMPOS ANAL, V20, P337, DOI 10.1016/j.jfca.2006.07.003 Falandysz J, 2016, ENVIRON SCI POLLUT R, V23, P23730, DOI 10.1007/s11356-016-7580-6 Falandysz J, 2013, APPL MICROBIOL BIOT, V97, P477, DOI 10.1007/s00253-012-4552-8 Frankowska A, 2010, FOOD ADDIT CONTAM B, V3, P1, DOI 10.1080/19440040903505232 Gonzaga MLC, 2005, CARBOHYD POLYM, V60, P43, DOI 10.1016/j.carbpol.2004.11.022 Jarzynska G, 2012, J FOOD SCI, V77, pH202, DOI 10.1111/j.1750-3841.2012.02876.x KAISER HF, 1960, EDUC PSYCHOL MEAS, V20, P141, DOI 10.1177/001316446002000116 Kalac P, 2010, FOOD CHEM, V122, P2, DOI 10.1016/j.foodchem.2010.02.045 Kojta AK, 2016, FOOD CHEM, V200, P206, DOI 10.1016/j.foodchem.2016.01.006 Lau BF, 2017, TRENDS FOOD SCI TECH, V61, P116, DOI 10.1016/j.tifs.2016.11.017 Li Y, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0168998 Li Y, 2016, INT J MED MUSHROOMS, V18, P721, DOI 10.1615/IntJMedMushrooms.v18.i8.80 Lu J, 2012, FRONT PHARMACOL, V3, DOI 10.3389/fphar.2012.00057 O'Gorman A, 2010, J AGR FOOD CHEM, V58, P7770, DOI 10.1021/jf101123a Pizarro C, 2013, FOOD CHEM, V138, P915, DOI 10.1016/j.foodchem.2012.11.087 Pouladzadeh P, 2015, MULTIMED TOOLS APPL, V74, P5243, DOI 10.1007/s11042-014-2116-x Qureshi N.A, 2017, INDIAN J SCI TECHNOL, V10, P1, DOI DOI 10.17485/ijst/2017/v10i20/91294 Rodrigues PH, 2016, FOOD CHEM, V196, P584, DOI 10.1016/j.foodchem.2015.09.055 Roy IG, 2015, J COMPUT PHYS, V295, P307, DOI 10.1016/j.jcp.2015.04.015 Samec D, 2016, FOOD CHEM, V194, P828, DOI 10.1016/j.foodchem.2015.08.095 Saptoro A, 2012, CHEM PROD PROCESS MO, V7, DOI 10.1515/1934-2659.1645 Turkekul I, 2004, FOOD CHEM, V84, P389, DOI 10.1016/S0308-8146(03)00245-0 Tuzen M, 2003, MICROCHEM J, V74, P289, DOI 10.1016/S0026-265X(03)00035-3 Ulziijargal E, 2011, INT J MED MUSHROOMS, V13, P343, DOI 10.1615/IntJMedMushr.v13.i4.40 Vera L, 2010, ANAL BIOANAL CHEM, V397, P3043, DOI 10.1007/s00216-010-3852-z Wang X, 2015, ANAL METHODS-UK, V7, P787, DOI [10.1039/C4AY02106A, 10.1039/c4ay02106a] Wang XM, 2015, SPECTROSC SPECT ANAL, V35, P1398, DOI 10.3964/j.issn.1000-0593(2015)05-1398-06 Wang XM, 2014, FOOD CHEM, V151, P279, DOI 10.1016/j.foodchem.2013.11.062 Wang XM, 2017, J ENVIRON SCI HEAL B, V52, P178, DOI 10.1080/03601234.2017.1261545 Wisniewska P, 2017, SPECTROCHIM ACTA A, V173, P849, DOI 10.1016/j.saa.2016.10.042 Wu Su-Rui, 2010, Food Science and Technology, V35, P100 Yao S, 2017, ANAL LETT, V50, P2257, DOI 10.1080/00032719.2017.1279172 Ye Q, 2015, DIS MARKERS, V2015, P1, DOI [10.1155/2015/192108, 10.1155/2015/387382] Zhang J, 2015, BIOL TRACE ELEM RES, V164, P261, DOI 10.1007/s12011-014-0213-3 Zhu FK, 2011, ENVIRON MONIT ASSESS, V179, P191, DOI 10.1007/s10661-010-1728-5 Zhu Y., 2015, AM J ANAL CHEM, V6, P480, DOI DOI 10.4236/AJAC.2015.65047 NR 45 TC 7 Z9 7 U1 2 U2 16 PD JAN 1 PY 2019 VL 22 IS 1 BP 414 EP 426 DI 10.1080/10942912.2019.1588299 WC Food Science & Technology SC Food Science & Technology UT WOS:000462640900001 DA 2022-12-14 ER PT J AU Bellon-Maurel, V Peters, GM Clermidy, S Frizarin, G Sinfort, C Ojeda, H Roux, P Short, MD AF Bellon-Maurel, Veronique Peters, Gregory M. Clermidy, Sonia Frizarin, Gustavo Sinfort, Carole Ojeda, Hernan Roux, Philippe Short, Michael D. TI Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies - part II: application to viticulture SO JOURNAL OF CLEANER PRODUCTION DT Article DE Grape; Life cycle inventory; Traceability; LCA; Agriculture; Data ID N DYNAMICS; WATER-USE; PESTICIDES; NITROGEN; SOIL; EMISSIONS; VINEYARD; CULTIVATION; COMPACTION; IMPACTS AB Agricultural systems are increasingly subjected to environmental life cycle assessment (LCA) but generating life cycle inventory (LCI) data in agriculture remains a challenge. In Part I, it was suggested that traceability data are a good basis for generating precise LCI with reduced effort, especially when collected by efficient information and communication technologies (ICTs). The aim of this paper is to demonstrate this for wine grape production and generate a list of data to be collected for streamlined LCI generation. The study is carried out in the South of France, on a viticultural farm implementing electronic traceability of each cultivation operation, i.e. tillage, fertilisation, crop protection, weeding, canopy management and harvesting (no irrigation is needed at this vineyard). For each operation, specific emission models which satisfy the trade-off between accuracy and need for data have been identified. Traceability data must be supplemented with data related to the plot, equipment and inputs to feed the models. The sensitivity of the LCA outputs to plot soil type and year of cultivation was studied. Consistent with previous agricultural studies, the results show that operations such as pesticide spraying and fertilising have large environmental impacts in this Mediterranean vineyard. Notable variations occur in life cycle impact assessment indicators, principally due to variations in crop yield; however, the influence of secondary factors such as soil type and agricultural practices is also evident and this contribution allows us to better characterise the variability of grape production and to show that streamlined LCI can be created using traceability data. Ultimately, this paper delivers two results. It provides simple models, and relevant data and methodology to enable viticultural LCAs to be undertaken. Additionally, it demonstrates that accurate LCIs can be built based on data already collected for traceability when supplemented with other easily collectable data (weather and farm structural data). Overall, this work paves the way for streamlined LCI in agriculture. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Bellon-Maurel, Veronique; Clermidy, Sonia; Roux, Philippe] Irstea Montpellier Supagro, UMR ITAP, ELSA Grp, F-34033 Montpellier 1, France. [Peters, Gregory M.; Short, Michael D.] Univ New S Wales, Sch Civil & Environm Engn, UNSW Water Res Ctr, Sydney, NSW 2052, Australia. [Peters, Gregory M.] Chalmers, S-41296 Gothenburg, Sweden. [Frizarin, Gustavo; Sinfort, Carole] Montpellier Supagro Irstea, UMR ITAP, ELSA Grp, F-34000 Montpellier, France. [Ojeda, Hernan] INRA, Pech Rouge UE999, F-11430 Gruissan, France. [Short, Michael D.] Univ S Australia, Sch Nat & Built Environm, Ctr Water Management & Reuse, Adelaide, SA 5095, Australia. C3 INRAE; Institut Agro; Montpellier SupAgro; University of New South Wales Sydney; Chalmers University of Technology; INRAE; Institut Agro; Montpellier SupAgro; INRAE; University of South Australia RP Bellon-Maurel, V (corresponding author), Irstea Montpellier Supagro, UMR ITAP, ELSA Grp, BP5095, F-34033 Montpellier 1, France. EM veronique.bellon@irstea.fr; petersg@chalmers.se; sinfort@supagro.inra.fr; ojeda@supagro.inra.fr; philippe.roux@irstea.fr; michael.short@unisa.edu.au CR Abt V., 2007, MES DOCUMENTS EXPLOI, P389 [Anonymous], 2006, 14044 ISO, V1, P46, DOI DOI 10.1136/BMJ.332.7550.1107 Aranda Alfonso, 2005, International Journal of Agricultural Resources Governance and Ecology, V4, P178, DOI 10.1504/IJARGE.2005.007199 Askegaard M, 2004, MANAGING SOIL QUALITY: CHALLENGES IN MODERN AGRICULTURE, P85, DOI 10.1079/9780851996714.0085 Askegaard M, 2008, AGR ECOSYST ENVIRON, V123, P99, DOI 10.1016/j.agee.2007.05.008 Bayart JB, 2010, INT J LIFE CYCLE ASS, V15, P439, DOI 10.1007/s11367-010-0172-7 Bedos C, 2010, ENVIRON SCI TECHNOL, V44, P2522, DOI 10.1021/es9030547 Bellon-Maurel V, 2014, J CLEAN PROD, V69, P60, DOI 10.1016/j.jclepro.2014.01.079 Biala J., 2000, IFOAM 2000 - the world grows organic. Proceedings, 6th International Congress on Organic Viticulture, Convention Center, Basel, Switzerland, 25-26 August 2000, P130 Birkved M, 2006, ECOL MODEL, V198, P433, DOI 10.1016/j.ecolmodel.2006.05.035 Brentrup F, 2000, INT J LIFE CYCLE ASS, V5, P349, DOI 10.1007/BF02978670 Buendia L, 2006, NAT GREENH GAS INV P, V4 BURNS IG, 1975, J AGR SCI-CAMBRIDGE, V85, P443, DOI 10.1017/S0021859600062328 Cannavo P, 2008, ADV AGRON, V97, P131, DOI 10.1016/S0065-2113(07)00004-1 CRCV, 2006, GRAP NUTR EPA, 1995, STAT POINT AR SOURC, V1, P22 Freney JR, 1997, NUTR CYCL AGROECOSYS, V48, P155, DOI 10.1023/A:1009735901543 Gaviglio C., 2009, ETUDE PERFORMANCES E, P16 Gazulla C, 2010, INT J LIFE CYCLE ASS, V15, P330, DOI 10.1007/s11367-010-0173-6 Gil Y, 2008, BIOSYST ENG, V100, P184, DOI 10.1016/j.biosystemseng.2008.03.009 Gil Y, 2007, ATMOS ENVIRON, V41, P2945, DOI 10.1016/j.atmosenv.2006.12.019 Guilbaut P., 2006, FERTILISATION UTILE Kucke M, 1997, EUR J AGRON, V6, P89, DOI 10.1016/S1161-0301(96)02027-8 Lagacherie P, 2006, GEODERMA, V134, P207, DOI 10.1016/j.geoderma.2005.10.006 Langevin B, 2010, J CLEAN PROD, V18, P747, DOI 10.1016/j.jclepro.2009.12.015 Lee J, 2009, AGR ECOSYST ENVIRON, V129, P378, DOI 10.1016/j.agee.2008.10.012 McConnell S., 2003, CODE ENV BEST PRACTI Mercik S, 2000, J PLANT NUTR SOIL SC, V163, P273, DOI 10.1002/1522-2624(200006)163:3<273::AID-JPLN273>3.0.CO;2-A Nemecek T., 2007, FINAL REPORT ECOINVE Nunez M, 2010, INT J LIFE CYCLE ASS, V15, P67, DOI 10.1007/s11367-009-0126-0 Parnaudeau V., 2012, INNOVATIONS AGRONOMI, V21, P59 Peters GM, 2010, INT J LIFE CYCLE ASS, V15, P311, DOI 10.1007/s11367-010-0161-x Peters GM, 2011, INT J LIFE CYCLE ASS, V16, P431, DOI 10.1007/s11367-011-0279-5 Pizzigallo ACI, 2008, J ENVIRON MANAGE, V86, P396, DOI 10.1016/j.jenvman.2006.04.020 Poppe K.J., 2000, AGR DATA LIFE CYCLE, P122 Powers SE, 2007, INT J LIFE CYCLE ASS, V12, P399, DOI 10.1065/lca2007.02.307 Prichard T., 2000, RAISIN PRODUCTION MA, V3393, P57 Reicosky DC, 2007, SOIL TILL RES, V94, P109, DOI 10.1016/j.still.2006.07.004 Sinfort C., 2009, 39 C GROUP FRANC PES Smith KA, 2001, ENVIRON POLLUT, V112, P53, DOI 10.1016/S0269-7491(00)00098-1 Sommer SG, 2001, EUR J AGRON, V15, P1, DOI 10.1016/S1161-0301(01)00112-5 Steenwerth K, 2008, APPL SOIL ECOL, V40, P370, DOI 10.1016/j.apsoil.2008.06.004 Steenwerth KL, 2010, SOIL SCI SOC AM J, V74, P231, DOI 10.2136/sssaj2008.0346 Vadas P.A., 2009, J ENVIRON QUAL, V38, P1 van den Berg F, 1999, WATER AIR SOIL POLL, V115, P195, DOI 10.1023/A:1005234329622 van Dijck SJE, 2002, SOIL TILL RES, V63, P141, DOI 10.1016/S0167-1987(01)00237-9 van Zelm R, 2014, CHEMOSPHERE, V100, P175, DOI 10.1016/j.chemosphere.2013.11.037 Vazquez-Rowe I, 2012, J CLEAN PROD, V27, P92, DOI 10.1016/j.jclepro.2011.12.039 NR 48 TC 22 Z9 22 U1 3 U2 61 PD JAN 15 PY 2015 VL 87 BP 119 EP 129 DI 10.1016/j.jclepro.2014.09.095 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000347493700014 DA 2022-12-14 ER PT J AU Kim, MK Ukkestad, CM Tejeda, HA Bailey, D AF Kim, Man-Keun Ukkestad, C. Michael Tejeda, Hernan A. Bailey, Deevon TI BENEFITS OF AN ANIMAL TRACEABILITY SYSTEM FOR A FOOT-AND-MOUTH DISEASE OUTBREAK: A SUPPLY-DRIVEN SOCIAL ACCOUNTING MATRIX APPROACH SO JOURNAL OF AGRICULTURAL AND APPLIED ECONOMICS DT Article DE Animal traceability; foot-and-mouth disease; regional economic impact; supply-driven social accounting matrix ID CONTROL STRATEGIES; INPUT-OUTPUT; CATTLE; US AB This study reports the findings for an analysis using the computer program NAADSM (North American Animal Disease Spread Model) and a supply-driven social accounting matrix to examine the impact of a hypothetical foot-and-mouth disease (FMD) outbreak in a relatively isolated part of the United States, Utah, under various levels of livestock traceability. The analysis demonstrates that a significant regional economic impact in Utah would result from an FMD outbreak but that improved levels of traceability would be very important in helping to reduce the negative economic consequences of the outbreak. C1 [Kim, Man-Keun; Bailey, Deevon] Utah State Univ, Dept Appl Econ, Logan, UT 84322 USA. [Ukkestad, C. Michael] Goldman Sachs, Salt Lake City, UT USA. [Tejeda, Hernan A.] Univ Idaho, Dept Agr Econ & Rural Sociol, Moscow, ID 83843 USA. C3 Utah System of Higher Education; Utah State University; Idaho; University of Idaho RP Kim, MK (corresponding author), Utah State Univ, Dept Appl Econ, Logan, UT 84322 USA. EM mk.kim@usu.edu CR [Anonymous], 2007, J REGIONAL POLICY AN Bailey D., 2004, EC RES I STUDY PAPER, V283 Bailey D, 2007, J AGR RESOUR ECON, V32, P403 Brester G. W., 2011, EC ASSESSMENT EVOLVI Cozzens T., 2010, 1262 USDA NAT WILDL Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Ekboir J. M., 1999, POTENTIAL IMPACT FOO Fernandez-Macho J, 2008, FISH RES, V90, P225, DOI 10.1016/j.fishres.2007.10.019 GARNER MG, 1995, PREV VET MED, V23, P9, DOI 10.1016/0167-5877(94)00433-J GHOSH A, 1958, ECONOMICA, V25, P58, DOI 10.2307/2550694 Greene J. L., 2010, R40832 CRS Harvey N, 2007, PREV VET MED, V82, P176, DOI 10.1016/j.prevetmed.2007.05.019 Hill A., 2006, USERS GUIDE N AM ANI Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Holland D., 1993, RES B B, P1027 Jones Jane J., 2010, THESIS Jordan K., 2009, TESTIMONY KAREN JORD Klobuchar A., 2013, EC CONTRIBUTION AM F Korea Rural Economic Institute (KREI), 2011, CISC VIS NETW IND GL Lawrence J. D., 2004, NATL ANIMAL IDENTIFI Leontief W., 1941, STRUCTURE AM EC, P1 Leung P. S., 2001, Marine Resource Economics, V16, P251 LIDDELL S, 2001, INT FOOD AGRIBUS MAN, V4, P287, DOI DOI 10.1016/S1096-7508(01)00081-7 Lind M., 2014, FOODONLINE Mahul O, 1999, EUR REV AGRIC ECON, V26, P39, DOI 10.1093/erae/26.1.39 Mahul O, 2000, PREV VET MED, V47, P23, DOI 10.1016/S0167-5877(00)00166-5 Man-Keun Kim, 2015, [Journal of Rural Development, 농촌경제], V38, P173 Mardones F, 2010, VET RES, V41, DOI 10.1051/vetres/2010017 McReynolds S. W., 2013, THESIS MIG Inc, IMPLAN SYST DAT SOFT Morell S.F., 2006, UPDATE NATL ANIMAL I National Pork Producers Council (NPPC), 2009, NPPC URG C BACK AN I Paarlberg P.L., 2008, 57 USDA EC RES SERV Park M, 2008, AGR ECON-BLACKWELL, V39, P183, DOI 10.1111/j.1574-0862.2008.00325.x Pendell D. L., 2007, Journal of Agricultural and Applied Economics, V39, P19 Pendell DL, 2006, THESIS Pendell DL, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0129134 Polo C, 2012, ELGAR ORIG REF, P227 Premashthira Sith, 2011, Animal Health Research Reviews, V12, P225, DOI 10.1017/S146625231100017X Reeves A., 2012, USERS GUIDE N AM ANI Rich KM, 2005, REV SCI TECH OIE, V24, P833, DOI 10.20506/rst.24.3.1618 Schroeder T. C., 2015, Journal of Agricultural and Applied Economics, V47, P47 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Scudamore JM, 2002, ORIGIN UK FOOT MOUTH Seung CK, 2009, FISH RES, V97, P17, DOI 10.1016/j.fishres.2008.12.013 문수희, 2013, [Korean Journal of Agricultural Management and Policy, 농업경영.정책연구], V40, P511 Thompson D, 2002, REV SCI TECH OIE, V21, P675, DOI 10.20506/rst.21.3.1353 U. S. Department of Agriculture Animal and Plant Health Inspection Service, 2013, FED REGISTER, V78, P2040 U. S. Department of Agriculture Economic Research Service, 2014, CATTL BEEF U. S. Department of Agriculture National Agricultural Statistics Service (USDA- NASS) Utah Field Office, 2015, UT AGR STAT UT DEP A U. S. Department of Agriculture (USDA), 2006, NAT AN ID S IN PRESS Ukkestad C. M., 2014, THESIS Ward R., 2005, INT FOOD AGRIBUS MAN, V8, P92 Weston A. Price Foundation, 2013, FARM TO CONS LEG DEF NR 55 TC 3 Z9 3 U1 0 U2 1 PD AUG PY 2017 VL 49 IS 3 BP 438 EP 466 DI 10.1017/aae.2017.7 WC Agricultural Economics & Policy SC Agriculture UT WOS:000408927400007 DA 2022-12-14 ER PT J AU Jin, CY Levi, R Liang, Q Renegar, N Zhou, JH AF Jin Cang-yu Levi, Retsef Liang Qiao Renegar, Nicholas Zhou Jie-hong TI Food safety inspection and the adoption of traceability in aquatic wholesale markets: A game-theoretic model and empirical evidence SO JOURNAL OF INTEGRATIVE AGRICULTURE DT Article DE food safety; traceability; supply chain; wholesale markets; China ID INCENTIVES; REGRESSION; SYSTEM; FARM AB Supply chain traceability is key to reduce food safety risks, since it allows problems to be traced to their sources. Moreover, it allows regulatory agencies to understand where risk is introduced into the supply chain, and offers a major disincentive for upstream agricultural businesses engaging in economically motivated adulteration. This paper focuses on the aquatic supply chain in China, and seeks to understand the adoption of traceability both through an analytical model, and empirical analysis based on data collected through an extensive (largest ever) field survey of Chinese aquatic wholesale markets. The field survey includes 76 managers and 753 vendors, covering all aquatic wholesale markets in Zhejiang and Hunan provinces. The analytical and empirical results suggest that the adoption of traceability among wholesale market vendors is significantly associated with inspection intensity, their individual history of food safety problems, and their risk awareness. The effect of inspection intensity on traceability adoption is stronger in markets which are privately owned than in markets with state/collective ownership. The analysis offers insights into the current state of traceability in China. More importantly, it suggests several hypothesized factors that might affect the adoption of traceability and could be leveraged by regulatory organizations to improve it. C1 [Jin Cang-yu; Liang Qiao; Zhou Jie-hong] Zhejiang Univ, China Acad Rural Dev, Sch Publ Affairs, Hangzhou 310058, Peoples R China. [Levi, Retsef] MIT, Sloan Sch Management, 77 Massachusetts Ave, Cambridge, MA 02139 USA. [Renegar, Nicholas] MIT, Operat Res Ctr, 77 Massachusetts Ave, Cambridge, MA 02139 USA. C3 Zhejiang University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT) RP Liang, Q; Zhou, JH (corresponding author), Zhejiang Univ, China Acad Rural Dev, Sch Publ Affairs, Hangzhou 310058, Peoples R China. EM liangqiao2323@126.com; runzhou@zju.edu.cn CR AKERLOF GA, 1970, Q J ECON, V84, P488, DOI 10.2307/1879431 Boselie D, 2003, AM J AGR ECON, V85, P1155, DOI 10.1111/j.0092-5853.2003.00522.x Caswell J A., 2013, US GOVT PROGRAMS AFF Ding JP, 2015, J INTEGR AGR, V14, P2380, DOI 10.1016/S2095-3119(15)61127-3 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 *FISH BUR MIN AGR, 2018, CHINA FISHERY STAT Y Gautam D.M., 2014, INT J APPL SCI BIOTE, V2, P559, DOI DOI 10.3126/IJASBT.V2I4.11551 Hirschauer N, 2007, FOOD POLICY, V32, P246, DOI 10.1016/j.foodpol.2006.07.001 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hu Y., 2014, FISHERIES AQUACULTUR, V5, P1 Iarossi G, 2006, POWER OF SURVEY DESIGN: A USER'S GUIDE FOR MANAGING SURVEYS, INTERPRETING RESULTS, AND INFLUENCING RESPONDENTS, P1, DOI 10.1596/978-0-8213-6392-8 Jin CY, 2021, MANAGE SCI, V67, P2985, DOI 10.1287/mnsc.2020.3839 Karippacheril T. G., 2017, ICT AGR UPDATED EDIT, DOI [DOI 10.1596/978-1-4648-1002-2, 10.1596/978-1-4648-1002-2] Kyung M, 2010, BAYESIAN ANAL, V5, P369, DOI 10.1214/10-BA607 Li T., 2016, LOGISTICS SCI TECH, V3, P33 Lowder SK, 2016, WORLD DEV, V87, P16, DOI 10.1016/j.worlddev.2015.10.041 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Minten B, 2012, J DEV STUD, V48, P864, DOI 10.1080/00220388.2011.615919 Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Opara E U, 2003, J INT BUS ENTREP DEV, V2, P45 Orden D, 2007, AGR ECON-BLACKWELL, V37, P103, DOI 10.1111/j.1574-0862.2007.00238.x Pant RR, 2015, PROCD SOC BEHV, V189, P385, DOI 10.1016/j.sbspro.2015.03.235 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Ren Y, 2010, AGRIC AGRIC SCI PROC, V1, P344, DOI 10.1016/j.aaspro.2010.09.043 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Song MX, 2019, J INTEGR AGR, V18, P1820, DOI 10.1016/S2095-3119(19)62590-6 Starbird SA, 2006, J AGR RESOUR ECON, V31, P14 Starbird SA, 2005, AM J AGR ECON, V87, P15, DOI 10.1111/j.0002-9092.2005.00698.x Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x Umali-Deininger D, 2007, AGR ECON-BLACKWELL, V37, P135, DOI 10.1111/j.1574-0862.2007.00240.x Xie K., 2016, ECO RES J, V51, P174 Yiannas F, 2019, FOOD SAFETY IS EVERY Yu HL, 2018, AGR ECON-BLACKWELL, V49, P787, DOI 10.1111/agec.12460 Zhang YJ, 2019, FOOD CONTROL, V95, P283, DOI 10.1016/j.foodcont.2018.08.018 Zheng Y., 2019, ARTIFICIAL SHORTAGE Zhou JH, 2013, J INTEGR AGR, V12, P1112, DOI 10.1016/S2095-3119(13)60490-6 Zhou X., 2018, LOGISTICS SCI TECH, V41, P44 Zou H, 2006, J AM STAT ASSOC, V101, P1418, DOI 10.1198/016214506000000735 NR 39 TC 2 Z9 2 U1 12 U2 36 PD OCT PY 2021 VL 20 IS 10 BP 2807 EP 2819 DI 10.1016/S2095-3119(21)63624-9 EA AUG 2021 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000683529500017 DA 2022-12-14 ER PT J AU Starbird, SA Amanor-Boadu, V AF Starbird, SA Amanor-Boadu, V TI Do inspection and traceability provide incentives for food safety? SO JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS DT Article DE diagnostic error; food safety; inspection; sampling error; traceability ID QUALITY; SALMONELLA; LEMONS; RISK AB One of the goals of inspection and traceability is to motivate suppliers to deliver safer food. The ability of these policies to motivate suppliers depends on the accuracy of the inspection, the cost of failing inspection, the cost of causing a foodborne illness, and the proportion of these costs paid by the supplier. We develop a model of the supplier's expected cost as a function of inspection accuracy, the cost of failure, and the proportion of the failure cost that is allocated to suppliers. The model is used to identify the conditions under which the supplier is motivated to deliver uncontaminated lots. Surprisingly, our results show that when safety failure costs can be allocated to suppliers, minimum levels of inspection error are required to motivate a supplier to deliver uncontaminated lots. This result does not hold when costs cannot be allocated to suppliers. As a case study, we use our results to analyze the technical requirements for suppliers of frozen beef to the USDA's Agricultural Marketing Service. C1 Santa Clara Univ, Dept Operat Informat Syst, Santa Clara, CA 95053 USA. Kansas State Univ, Dept Agr Econ, Manhattan, KS 66506 USA. C3 Santa Clara University; Kansas State University RP Starbird, SA (corresponding author), Santa Clara Univ, Dept Operat Informat Syst, Santa Clara, CA 95053 USA. CR [Anonymous], 2001, COMPENDIUM METHODS M ATSUSHI M, 2004, AGR AN INT J, V20, P167 Baiman S, 2000, MANAGE SCI, V46, P776, DOI 10.1287/mnsc.46.6.776.11939 BARZEL Y, 1982, J LAW ECON, V25, P27, DOI 10.1086/467005 Bogetoft P, 2004, AM J AGR ECON, V86, P829, DOI 10.1111/j.0002-9092.2004.00633.x BUZBY JC, 2001, 799 USDA EC RES SERV Chalfant JA, 1999, J AGR RESOUR ECON, V24, P57 Chalfant JA, 2002, AM J AGR ECON, V84, P53, DOI 10.1111/1467-8276.00242 CLAYTON DA, 2003, INT J CONSUM STUD, V27, P233 DEY BP, 1998, USDA MICROBIOLOGY LA Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Elbasha E. H., 2003, Agribusiness (New York), V19, P29, DOI 10.1002/agr.10043 Golan E.H., 2004, AGR EC REPORTS, P1362 HEINKEL R, 1981, BELL J ECON, V12, P625, DOI 10.2307/3003577 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hueth B, 1999, AM J AGR ECON, V81, P512, DOI 10.2307/1244011 Malcolm S. A., 2004, Agribusiness (New York), V20, P109, DOI 10.1002/agr.10080 Marsh TL, 2004, APPL ECON, V36, P897, DOI 10.1080/0003684042000233113 Mayer KJ, 2004, MANAGE SCI, V50, P1064, DOI 10.1287/mnsc.1040.0235 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Nayga R. M., 2004, INT J CONSUM STUD, V28, P178, DOI [10.1111/j.1470-6431.2003.00362.x, DOI 10.1111/J.1470-6431.2003.00362.X] OLLINGER M, 2004, AMBER WAVES APR, P7 Patil SR, 2004, RISK ANAL, V24, P573, DOI 10.1111/j.0272-4332.2004.00460.x Schilling EG., 1982, ACCEPTANCE SAMPLING Smyth S., 2002, AgBioForum, V5, P30 *USDA AGR MARK SER, 2005, TECHN REQ SCHED GB 2 van der Gaag MA, 2004, EUR J OPER RES, V156, P782, DOI 10.1016/S0377-2217(03)00141-3 Winfree JA, 2005, AM J AGR ECON, V87, P206, DOI 10.1111/j.0002-9092.2005.00712.x NR 29 TC 33 Z9 34 U1 0 U2 29 PD APR PY 2006 VL 31 IS 1 BP 14 EP 26 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000236866600002 DA 2022-12-14 ER PT J AU Visconti, P de Fazio, R Velazquez, R Del-Valle-Soto, C Giannoccaro, NI AF Visconti, Paolo de Fazio, Roberto Velazquez, Ramiro Del-Valle-Soto, Carolina Giannoccaro, Nicola Ivan TI Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management SO SENSORS DT Article DE precision agriculture; IoT devices; on-cloud software platform; decision support systems; solar energy harvesting; node power consumption; BLE sensor tag ID DECISION-SUPPORT-SYSTEM; HARDWARE DESIGN; BIG DATA; AGRICULTURE; IMPLEMENTATION; NETWORKS; ART AB The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers' usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information; a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products' traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system. C1 [Visconti, Paolo; de Fazio, Roberto; Giannoccaro, Nicola Ivan] Univ Salento, Dept Innovat Engn, I-73100 Lecce, Italy. [Velazquez, Ramiro] Univ Panamericana, Fac Ingn, Aguascalientes 20290, Aguascalientes, Mexico. [Del-Valle-Soto, Carolina] Univ Panamericana, Fac Ingn, Alvaro Del Portillo 49, Zapopan 45010, Jalisco, Mexico. C3 University of Salento; Universidad Panamericana - Ciudad de Mexico; Universidad Panamericana - Aguascalientes; Universidad Panamericana - Ciudad de Mexico; Universidad Panamericana - Guadalajara RP Visconti, P (corresponding author), Univ Salento, Dept Innovat Engn, I-73100 Lecce, Italy. EM paolo.visconti@unisalento.it; roberto.defazio@unisalento.it; rvelazquez@up.edu.mx; cvalle@up.edu.mx; ivan.giannoccaro@unisalento.it CR Abdullahi S.I., 2019, B ELECT ENG INFORM, V8, P450, DOI [10.11591/eei.v8i2.1515, DOI 10.11591/EEI.V8I2.1515] Abdulsalam HM, 2014, PROCEDIA COMPUT SCI, V34, P499, DOI 10.1016/j.procs.2014.07.055 Altissimi S, 2017, ITAL J FOOD SAF, V6, P217, DOI 10.4081/ijfs.2017.6921 Ammari HM, 2015, AD HOC SENS WIREL NE, V25, P1 Ampatzidis Y, 2009, PRECIS AGRIC, V10, P63, DOI 10.1007/s11119-008-9095-8 [Anonymous], 2016, ELECT NOSES TONGUES Arjona L, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20092696 Arroyo P, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19030691 Arulnathan V, 2020, J CLEAN PROD, V256, DOI 10.1016/j.jclepro.2020.120410 Bhuvaneswari PTV, 2009, 2009 1ST INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATION SYSTEMS AND NETWORKS(CICSYN 2009), P57, DOI 10.1109/CICSYN.2009.91 BURG SP, 1962, PLANT PHYSIOL, V37, P179, DOI 10.1104/pp.37.2.179 Baseca CC, 2019, AGRONOMY-BASEL, V9, DOI 10.3390/agronomy9050216 Canadas Joaquin, 2017, Information Processing in Agriculture, V4, P50, DOI 10.1016/j.inpa.2016.12.002 Catarinucci L., 2014, P SOFTCOM 2011 19 IN, P1 Colella R., 2019, P 2019 4 INT C SMART, P1 Corallo A., 2018, INT J NUTR FOOD ENG, V12, P146, DOI DOI 10.5281/zenodo.1316618 Cuinas I, 2014, IEEE ANTENN PROPAG M, V56, P196, DOI 10.1109/MAP.2014.6837090 Del-Valle-Soto C, 2020, ENERGIES, V13, DOI 10.3390/en13030728 Dharini PU., 2018, INT J RECENT TECHNOL, V7, P362 Eom KH, 2014, INT J DISTRIB SENS N, DOI 10.1155/2014/591812 Escobedo-Araque P., 2015, P 2015 IEEE 15 MED M, P1 Estrada-Lopez JJ, 2018, IEEE SENS J, V18, P8913, DOI 10.1109/JSEN.2018.2867432 Fang XL, 2013, AD HOC SENS WIREL NE, V18, P203 Fry J, 2017, GEODERMA, V287, P105, DOI 10.1016/j.geoderma.2016.08.012 Gaetani F, 2019, IET SCI MEAS TECHNOL, V13, P354, DOI 10.1049/iet-smt.2018.5108 Geiges O, 1996, ADV SPACE RES, V18, P109, DOI 10.1016/0273-1177(96)00006-3 Grassini P, 2015, FIELD CROP RES, V177, P49, DOI 10.1016/j.fcr.2015.03.004 Han EJ, 2017, ENVIRON MODELL SOFTW, V95, P102, DOI 10.1016/j.envsoft.2017.06.024 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Jawad HM, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17081781 Jones JW, 2017, AGR SYST, V155, P269, DOI 10.1016/j.agsy.2016.09.021 Kamilaris A, 2018, COMPUT ELECTRON AGR, V147, P70, DOI 10.1016/j.compag.2018.02.016 Kamilaris A, 2017, COMPUT ELECTRON AGR, V143, P23, DOI 10.1016/j.compag.2017.09.037 Keresztes B., 2014, P INT C AGR ENG ZUR, P1 Khan R, 2018, IEEE ACCESS, V6, P25686, DOI 10.1109/ACCESS.2018.2836185 Kumar V., 2018, ADV RES, V14, P1, DOI [10.19080/ARTOAJ.2018.14.555924, 10.9734/AIR/2018/40525, DOI 10.9734/AIR/2018/40525] Kuswandi B., 2017, REFERENCE MODULE FOO, DOI [10.1016/b978-0-08-100596-5.21876-3, 10.1016/B978-0-08-100596-5.21876-3, DOI 10.1016/B978-0-08-100596-5.21876-3] LANDALUCE H, 2020, SENSORS BASEL, V0020 Maier D.E., 2010, P 10 INT WORK C STOR, P1174 Manzari S, 2014, EUR MICROW CONF, P263, DOI 10.1109/EuMC.2014.6986420 Matteucci M., 2018, ADV INTELLIGENT SYST, P249 Ramadan KM, 2018, COMPUT ELECTRON AGR, V148, P148, DOI 10.1016/j.compag.2017.12.038 Muller P, 2019, FOODS, V8, DOI 10.3390/foods8010016 Mustafa F, 2018, FOODS, V7, DOI 10.3390/foods7100168 Ojha T, 2015, COMPUT ELECTRON AGR, V118, P66, DOI 10.1016/j.compag.2015.08.011 Pallottino F, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10072209 Pinheiro CL, 2018, PROCESS SAF ENVIRON, V114, P16, DOI 10.1016/j.psep.2017.11.013 Primiceri P, 2016, INT J SMART SENS INT, V9, P1534, DOI 10.21307/ijssis-2017-929 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Rupnik R, 2019, COMPUT ELECTRON AGR, V161, P260, DOI 10.1016/j.compag.2018.04.001 Shaikh FK, 2016, RENEW SUST ENERG REV, V55, P1041, DOI 10.1016/j.rser.2015.11.010 Shin H. Y., 2006, KOREAN J FOOD SCI TE, V38, P325 Smits E., 2012, P IMCS 2012 NUR GERM, P403, DOI [10.5162/IMCS2012/4.5.2, DOI 10.5162/IMCS2012/4.5.2] Sohail M, 2018, CRIT REV FOOD SCI, V58, P2650, DOI 10.1080/10408398.2018.1449731 Szulczynski B, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4010021 Taiti C, 2015, EUR FOOD RES TECHNOL, V241, P91, DOI 10.1007/s00217-015-2438-6 Tan L, 2016, IFAC PAPERSONLINE, V49, P330, DOI 10.1016/j.ifacol.2016.10.061 Tarantino A., 2005, P INT S ADV EXP UNS, V1st Torres ABB, 2020, COMPUT ELECTRON AGR, V171, DOI 10.1016/j.compag.2020.105309 Van der Wee M., 2019, FUTURE INTERNET, P209 Visconti P, 2019, J COMMUN SOFTW SYS, V15, P89, DOI 10.24138/jcomss.v15i2.691 Visconti P, 2017, INT J RENEW ENERGY R, V7, P1281 Visconti P, 2015, MEASUREMENT, V76, P80, DOI 10.1016/j.measurement.2015.08.024 Visconti P, 2015, 2015 IEEE 15TH INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING (IEEE EEEIC 2015), P1933, DOI 10.1109/EEEIC.2015.7165468 Visconti P., 2016, P 2016 IEEE 16 INT C, P1, DOI [10.1109/EEEIC.2016.7555451, DOI 10.1109/EEEIC.2016.7555451] Visconti P., 2016, ARPN J ENG APPL SCI, V11, P4623, DOI DOI 10.13140/RG.2.2.35123.63526 Visconti P, 2017, IEEE SENS J, V17, P2507, DOI 10.1109/JSEN.2017.2669529 Wang YK, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P1, DOI 10.1109/AUS.2016.7748011 Wolfert S, 2017, AGR SYST, V153, P69, DOI 10.1016/j.agsy.2017.01.023 Yusianto R., 2019, 2019 INT SEM APPL TE, P1 Zhai ZY, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105256 Zhao LQ, 2011, PROCEDIA ENVIRON SCI, V11, P558, DOI 10.1016/j.proenv.2011.12.088 NR 72 TC 25 Z9 25 U1 6 U2 22 PD JUL PY 2020 VL 20 IS 13 AR 3632 DI 10.3390/s20133632 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000555050500001 DA 2022-12-14 ER PT J AU Seitz, S Manzin, A Jensen, HD Jakobsen, PT Spitzer, P AF Seitz, S. Manzin, A. Jensen, H. D. Jakobsen, P. T. Spitzer, P. TI Traceability of electrolytic conductivity measurements to the International System of Units in the sub mS m(-1) region and review of models of electrolytic conductivity cells SO ELECTROCHIMICA ACTA DT Review DE Metrological traceability; Electrolytic conductivity; Purified water; Electrolytic double layer; Adsorption processes ID HYDROCHLORIC-ACID; DOUBLE-LAYER; PURE WATER; CONDUCTANCE; IMPEDANCE; RESISTIVITY; DEPENDENCE AB The present paper focuses on the currently available reliability of the results of electrolytic conductivity measurements in the low conductivity range, emphasizing its weaknesses with respect to SI traceability. Electrochemical and physical effects at the electrode-solution interface influencing the measured impedance spectra are outlined, giving an overview of models that can be employed to investigate the frequency response of electrolytic conductivity cells to an applied AC voltage. Particular attention is devoted to the determination of the bulk resistance of the solution, which is necessary to derive the electrolytic conductivity of aqueous solutions traceable to the SI in the sub mS m(-1) range. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Seitz, S.; Spitzer, P.] Phys Tech Bundesanstalt, D-38116 Braunschweig, Germany. [Manzin, A.] Ist Nazl Ric Metrol, I-10135 Turin, Italy. [Jensen, H. D.; Jakobsen, P. T.] Danish Fundamental Metrol, DK-2800 Lyngby, Denmark. C3 Physikalisch-Technische Bundesanstalt (PTB); Istituto Nazionale di Ricerca Metrologica (INRIM); Danish Fundamental Metrology RP Seitz, S (corresponding author), Phys Tech Bundesanstalt, Bundesalle 100, D-38116 Braunschweig, Germany. EM steffen.seitz@ptb.de CR *A INT, 2007, D512707 A INT *ASTM INT, 1999, D539199 ASTM INT *ASTM INT, 1991, D112591 ASTM INT Barbero G, 2006, PHYS LETT A, V360, P179, DOI 10.1016/j.physleta.2006.08.001 Barbero G, 2007, J APPL PHYS, V101, DOI 10.1063/1.2433747 Bard Allen J., 1980, ELECTROCHEMICAL METH, V2nd, DOI 10.1021/ed060pa25 Barsoukov E, 2005, IMPEDANCE SPECTROSCOPY: THEORY, EXPERIMENT, AND APPLICATIONS, 2ND EDITION, pXII Barthel J. M. G., 1998, PHYS CHEM ELECTROLYT Bazant MZ, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.021506 Becchi M, 2005, J PHYS CHEM B, V109, P23444, DOI 10.1021/jp044443r BEVILACQUA AC, 1998, SEM PUR WAT CHEM C BEVILACQUA AC, 1996, P INT W SAN DIEG BEVILACQUA AC, 2004, ULTRAPURE WATER FEB Bohinc K, 2001, ELECTROCHIM ACTA, V46, P3033, DOI 10.1016/S0013-4686(01)00525-4 BONDARENKO AS, 2008, EIS SPECTRUM ANAL SO BORUCHOV I, 1997, PHYS REV LETT, P79 Bottauscio O, 2006, IEEE T MAGN, V42, P1423, DOI 10.1109/TMAG.2006.871443 BOUKAMP BA, 1995, J ELECTROCHEM SOC, V142, P1885, DOI 10.1149/1.2044210 BRAUNSTEIN J, 1971, J CHEM EDUC, V48, P52, DOI 10.1021/ed048p52 Brinkmann F, 2003, ACCREDIT QUAL ASSUR, V8, P346, DOI 10.1007/s00769-003-0645-5 Bruesch P, 2004, J APPL PHYS, V95, P2846, DOI 10.1063/1.1641517 Chevallier FG, 2005, J ELECTROANAL CHEM, V574, P217, DOI 10.1016/j.jelechem.2004.07.033 Crozier PS, 2000, J CHEM PHYS, V113, P9202, DOI 10.1063/1.1320825 De Bievre P, 2008, METROLOGIA, V45, P335, DOI 10.1088/0026-1394/45/3/011 Durbiano F, 2007, IEEE T INSTRUM MEAS, V56, P321, DOI 10.1109/TIM.2006.890612 *EUR PHARM, MON 1927 0169 Franks W, 2005, IEEE T BIO-MED ENG, V52, P1295, DOI 10.1109/TBME.2005.847523 Geddes LA, 1997, ANN BIOMED ENG, V25, P1, DOI 10.1007/BF02738534 GRASSO F, 1990, NUOVO CIMENTO D, V12, P1117, DOI 10.1007/BF02451954 GRAY DM, 1996, ULTRAPURE WATER JAN, P60 Guymon CG, 2005, CONDENS MATTER PHYS, V8, P335 Guymon CG, 2003, J CHEM PHYS, V118, P10195, DOI 10.1063/1.1571056 Harned H.S., 1958, PHYS CHEM ELECTROLYT HARNED HS, 1939, J AM CHEM SOC, V61, P3113 HOOVER TB, 1970, J PHYS CHEM-US, V74, P2667, DOI 10.1021/j100707a013 *ISO IEC, 2008, INT VOC METR IVERSON A, 1964, J PHYS CHEM-US, V68, P515, DOI 10.1021/j100785a012 JANZ GJ, 1977, J ELECTROCHEM SOC, V124, pC55, DOI 10.1149/1.2133291 Jorcin JB, 2006, ELECTROCHIM ACTA, V51, P1473, DOI 10.1016/j.electacta.2005.02.128 Kilic MS, 2007, PHYS REV E, V75, DOI 10.1103/PhysRevE.75.021502 Light T S, 1995, AM CHEM SOC ANN M AN Light TS, 2005, ELECTROCHEM SOLID ST, V8, pE16, DOI 10.1149/1.1836121 LIGHT TS, 1984, ANAL CHEM, V56, P1138, DOI 10.1021/ac00271a019 LIGHT TS, 1987, ANAL CHEM, V59, P2327, DOI 10.1021/ac00146a003 LIGHT TS, 1991, ULTRAPURE WATER APR, P59 Macdonald J.R, 1987, IMPEDANCE SPECTROSCO MANZIN A, 2009, SENS ACTUATORS B, V138 MARSH KN, 1964, AUST J CHEM, V17, P740, DOI 10.1071/CH9640740 MAYERGOYZ ID, 1986, J APPL PHYS, V59, P195, DOI 10.1063/1.336862 MORASH KR, 1994, ULTRAPURE WATER DEC MURPHY WD, 1992, J PHYS CHEM-US, V96, P9983, DOI 10.1021/j100203a074 Newman J., 2012, ELECTROCHEMICAL SYST NORMANN J, 1993, ANAL CHEM, V65, P1199 Owen BB, 1941, J AM CHEM SOC, V63, P2811, DOI 10.1021/ja01855a091 PAJKOSSY T, 1994, J ELECTROANAL CHEM, V364, P111, DOI 10.1016/0022-0728(93)02949-I PATE KT, 1991, ULTRAPURE WATER JAN, P26 Pham P., 2007, P COMSOL US C 2007 G Pratt KW, 2001, PURE APPL CHEM, V73, P1783, DOI 10.1351/pac200173111783 Prentice G, 1991, ELECTROCHEMICAL ENG Robinson R., 1959, ELECTROLYTE SOLUTION, DOI DOI 10.1073?PNAS.0305836101 Schiefelbein SL, 1998, REV SCI INSTRUM, V69, P3308, DOI 10.1063/1.1149095 SLUYTERSREHBACH M, 1970, SINE WAVE METHODS ST, V4 SONG X, 2007, ACCREDIT QUAL ASSUR, P12 Spitzer P, 2005, ACCREDIT QUAL ASSUR, V10, P78, DOI 10.1007/s00769-004-0880-4 SPITZER P, 2000, 146 PTB WORKSH REP STRONG LE, 1980, J CHEM ENG DATA, V25, P104, DOI 10.1021/je60085a025 THORNTON RD, 1989, ULTRAPURE WATER JUL Timmer B, 2002, LAB CHIP, V2, P121, DOI 10.1039/b201225a Tuckerman ME, 2002, NATURE, V417, P925, DOI 10.1038/nature00797 *US PHARM, 645 US PHARM WU YC, 1995, J RES NATL INST STAN, V100, P521, DOI 10.6028/jres.100.039 ZABARSKY O, 1991, ULTRAPURE WATER JAN, P59 GUIDE ULTRAPURE WATE GLYCEROL BASED CONDU COMPLETED KEY COMP C LIST CCQM PILOT STUD 2009, CCQM P83 PI IN PRESS NR 77 TC 20 Z9 21 U1 3 U2 27 PD SEP 1 PY 2010 VL 55 IS 22 BP 6323 EP 6331 DI 10.1016/j.electacta.2010.06.008 WC Electrochemistry SC Electrochemistry UT WOS:000281501100001 DA 2022-12-14 ER PT J AU Myae, AC Goddard, E Aubeeluck, A AF Myae, Aye Chan Goddard, Ellen Aubeeluck, Ashwina TI THE ROLE OF PSYCHOLOGICAL DETERMINANTS AND DEMOGRAPHIC FACTORS IN CONSUMER DEMAND FOR FARM-TO-FORK TRACEABILITY SYSTEMS SO JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH-PART A-CURRENT ISSUES DT Article ID INTERNAL-EXTERNAL-CONTROL; FOOD-CONSUMPTION; SAFETY; LOCUS; MEAT; PERCEPTIONS; PERSONALITY; ATTITUDES; BEHAVIOR; RISK AB Traceability systems are an important tool (1) for tracking, monitoring, and managing product flows through the supply chain for better efficiency and profitability of suppliers, and (2) to improve consumer confidence in the face of serious food safety incidents. After the global bovine spongiform encephalopathy (BSE) crisis affected producers, consumers, trade, and the health status of animals and humans, new systems to help confirm the status of cattle products along the supply chain from farm to fork were implemented in many countries (Trautman et al. 2008). In this study, people's overall food safety beliefs are explored with the main objective of measuring the link between their food safety beliefs and their attitudes toward traceability. A comparison is made among English-speaking Canadians, French-speaking Canadians, and Japanese consumers. In the study, an Internet-based survey was used to collect data from nationally representative samples of the population in Canada-English (1275), Canada-French (343), and Japanese (1940) in the summer of 2009. Respondents' interests in traceability systems are clearly linked to their sense that the industry is primarily responsible for any food safety outbreaks. Moreover, it is clear that certain segments of the population in all samples feel strongly about the importance of farm to fork traceability in beef; thus, policymakers may wish to consider extending traceability beyond the point of slaughter as a way of encouraging beef sales in Canada. C1 [Myae, Aye Chan; Goddard, Ellen] Univ Alberta, Dept Rural Econ, Edmonton, AB T6G 2H1, Canada. [Aubeeluck, Ashwina] Agr & Agri Food Canada, Strateg Policy Branch, Ottawa, ON, Canada. C3 University of Alberta; Agriculture & Agri Food Canada RP Goddard, E (corresponding author), Univ Alberta, Dept Rural Econ, 515 Gen Serv Bldg, Edmonton, AB T6G 2H1, Canada. EM ellen.goddard@ualberta.ca CR Abbot JM, 2009, EUR J CLIN NUTR, V63, P572, DOI 10.1038/sj.ejcn.1602961 *AGR AGR CAN, FACTS TRAC CAN *AGR AGR CAN, 2004, CONS PERC FOOD SAF Q *AGR AGR CAN, NAT AGR FOOD TRAC SY Altekruse SF, 1999, AM J PREV MED, V16, P216, DOI 10.1016/S0749-3797(98)00099-3 AUBEELUCK A, 2010, THESIS U ALBERTA EDM Baron J, 2000, RISK ANAL, V20, P413, DOI 10.1111/0272-4332.204041 BARTAL D, 1977, CONTEMP EDUC PSYCHOL, V2, P181, DOI 10.1016/0361-476X(77)90020-0 BLOCK J, 1968, PSYCHOL BULL, V70, P210, DOI 10.1037/h0026190 Bowling A, 2000, RES METHODS HLTH BROWN BR, 2009, THESIS IOWA STATE U Burger J, 1998, J TOXICOL ENV HEAL A, V53, P181, DOI 10.1080/009841098159321 Byrd-Bredbenner C, 2007, J FOOD PROTECT, V70, P1917, DOI 10.4315/0362-028X-70.8.1917 CALHOUN LG, 1974, J CONSULT CLIN PSYCH, V42, P736, DOI 10.1037/h0037048 *CAN FOOD INSP AG, EXP CATTL BIS THEIR *CAN FOOD INSP AG, TRAC CAN *CAN FOOD INSP AG, LESS LEARN CAN FOOD Christensen BB, 2005, RISK ANAL, V25, P49, DOI 10.1111/j.0272-4332.2005.00566.x CLEMENS R, 2003, 03MBP5 MATRIC IOW ST Coleman M, 2003, J HUM RESOUR, V38, P701, DOI 10.2307/1558773 DAVIS WL, 1967, J PERS, V35, P547, DOI 10.1111/j.1467-6494.1967.tb01447.x de Jonge J, 2008, FOOD QUAL PREFER, V19, P439, DOI 10.1016/j.foodqual.2008.01.002 de Jonge J, 2007, RISK ANAL, V27, P729, DOI 10.1111/j.1539-6924.2007.00917.x DEJONGE J, 2008, THESIS WAGENINGEN U Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Fischer ARH, 2008, J APPL SOC PSYCHOL, V38, P2859, DOI 10.1111/j.1559-1816.2008.00416.x FREWER LJ, 1994, J FOOD SAFETY, V14, P19, DOI 10.1111/j.1745-4565.1994.tb00581.x GELLYNCK X, 2006, NAT PUBL POL ED C CO Gellynck X, 2006, MEAT SCI, V74, P161, DOI 10.1016/j.meatsci.2006.04.013 Golan E., 2004, Agricultural Economic Report - Economic Research Service, US Department of Agriculture GORDON DA, 1977, J PERS ASSESS, V41, P383, DOI 10.1207/s15327752jpa4104_8 Gracia A., 2005, Journal of Food Distribution Research, V36, P45 *GS1, 2006, EXP GS1 EUR TRAC HERSCH PD, 1967, J CONSULT PSYCHOL, V31, P609, DOI 10.1037/h0025154 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x HOBBS JE, 2009, PUBLIC PRIVATE GOODS Kennedy J, 2008, BRIT FOOD J, V110, P691, DOI 10.1108/00070700810887167 Kogan N., 1964, RISK TAKING STUDY CO Kornelis M, 2007, RISK ANAL, V27, P327, DOI 10.1111/j.1539-6924.2007.00885.x KRAUS SJ, 1995, PERS SOC PSYCHOL B, V21, P58, DOI 10.1177/0146167295211007 LEFCOURT HM, 1982, LOCUS CONTROL CURREN LEVENSON H, 1974, J PERS ASSESS, V38, P377, DOI 10.1080/00223891.1974.10119988 Li Y., 2008, THESIS U ALBERTA EDM McCluskey JJ, 2005, AUST J AGR RESOUR EC, V49, P197, DOI 10.1111/j.1467-8489.2005.00282.x *MIN AGR FOR FISH, 2007, HDB INTR FOOD TRAC S *MIN INT AFF COMM, 2007, CENS 2007 MISCHEL W, 1969, AM PSYCHOL, V24, P1012, DOI 10.1037/h0028886 Mischel W., 1968, PERSONALITY ASSESSME Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 MOSS HA, 1961, J ABNORM SOC PSYCH, V63, P629, DOI 10.1037/h0040267 Nganje E. W., 2005, Agribusiness (New York), V21, P375, DOI 10.1002/agr.20053 Nganje W, 2010, AGRIBUSINESS, V26, P557, DOI 10.1002/agr.20240 Onozaka Y, 2011, AM J AGR ECON, V93, P689, DOI 10.1093/ajae/aar005 Pennings JME, 2002, INT J RES MARK, V19, P91, DOI 10.1016/S0167-8116(02)00050-2 Phares E.J., 1976, LOCUS CONTROL PERSON PINES HA, 1973, J PERS SOC PSYCHOL, V26, P262, DOI 10.1037/h0034390 Redmond EC, 2004, APPETITE, V43, P309, DOI 10.1016/j.appet.2004.05.003 Roosen J., 2003, Journal of Food Distribution Research, V34, P77 ROSELIUS T, 1971, J MARKETING, V35, P56, DOI 10.2307/1250565 Rotter J.B., 1972, APPL SOCIAL LEARNING ROTTER JB, 1966, PSYCHOL MONOGR, V80, P1, DOI 10.1037/h0092976 ROTTER JB, 1961, J PSYCHOL, V52, P161, DOI 10.1080/00223980.1961.9916516 Rotter JB., 1954, SOCIAL LEARNING CLIN, DOI DOI 10.1037/10788-000 SCHROEDER T, 2006, CONSUMER RISK PERCEP SEEMAN M, 1962, AM SOCIOL REV, V27, P772, DOI 10.2307/2090405 Souza-Monteiro D. M., 2004, Working Paper - Department of Resource Economics, University of Massachusetts SPARKS P, 1994, RISK ANAL, V14, P799, DOI 10.1111/j.1539-6924.1994.tb00291.x Statistics Canada, 2006, 2006 CENS Tanaka K, 2008, AGR HUM VALUES, V25, P567, DOI 10.1007/s10460-008-9152-y Teisl MF, 2009, FOOD QUAL PREFER, V20, P586, DOI 10.1016/j.foodqual.2009.07.001 TRAUTMAN D, 2008, 0802 U ALB DEP RUR E VEEMAN M, 2006, CMD0601 U ALB DEP RU VEEMAN M, 2007, CMD0702 U ALB DEP RU Verbeke W, 1999, FOOD QUAL PREFER, V10, P437, DOI 10.1016/S0950-3293(99)00031-2 Verbeke W, 2005, PUBLIC HEALTH NUTR, V8, P422, DOI 10.1079/PHN2004697 Wilcock A, 2004, TRENDS FOOD SCI TECH, V15, P56, DOI 10.1016/j.tifs.2003.08.004 YANG J, 2010, THESIS U ALB DEP RUR Yeung R. M. W., 2001, British Food Journal, V103, P170, DOI 10.1108/00070700110386728 ZHEN C, 2006, THESIS N CAR STAT U NR 80 TC 4 Z9 4 U1 0 U2 29 PY 2011 VL 74 IS 22-24 SI SI BP 1550 EP 1574 DI 10.1080/15287394.2011.618983 WC Environmental Sciences; Public, Environmental & Occupational Health; Toxicology SC Environmental Sciences & Ecology; Public, Environmental & Occupational Health; Toxicology UT WOS:000298322700010 DA 2022-12-14 ER PT J AU Jousten, K AF Jousten, Karl TI Traceability to SI units for vacuum measurement in industrial applications SO MEASUREMENT DT Article DE Vacuum metrology; Primary standard; Traceability; Vacuum gauges; Accuracy; Uncertainty ID LONG-TERM STABILITY; TRANSFER STANDARD; CALIBRATION; GAUGE; HOT; PA; PERFORMANCE; PRESSURES AB In the context of international standards like the ISO 9000 series or ISO 17025 the traceability of measurement instruments of physical units in industrial processes gained more importance in the last two decades, so to say also for vacuum measurement. Traceable calibrations of vacuum gauges ensure agreement with the SI units. For this purpose vacuum primary standards are needed. The international system of metrology ensures that the vacuum primary standards registered in the system are equivalent and fulfill their specifications. Secondary and reference standards are used to disseminate the pressure scale in vacuum to calibration laboratories, to the manufacturers of gauges, and finally to industrial processes or to research facilities. Suitable vacuum gauges for this purpose including their expected measurement uncertainties will be described. Notes for the measurement uncertainties at the place of the end user will be given. (C) 2011 Elsevier Ltd. All rights reserved. C1 Phys Tech Bundesanstalt, D-10587 Berlin, Germany. C3 Physikalisch-Technische Bundesanstalt (PTB) RP Jousten, K (corresponding author), Phys Tech Bundesanstalt, Abbestr 2-12, D-10587 Berlin, Germany. EM karl.jousten@ptb.de CR BENNETT SJ, 1975, J PHYS E SCI INSTRUM, V8, P5, DOI 10.1088/0022-3735/8/1/002 BERGOGLIO M, 1988, VACUUM, V38, P887, DOI 10.1016/0042-207X(88)90486-1 Calcatelli Anita, 2005, METROLOGIA S7004, V42 Delajoud P., 2003, VAKUUM FORSCHUNG PRA, V15, P24, DOI 10.1002/vipr.200390004 DITTMANN S, 1989, J VAC SCI TECHNOL A, V7, P3356, DOI 10.1116/1.576150 ELLIOTT KWT, 1978, 28 NPL MOM FILIPPELLI AR, 1995, J VAC SCI TECHNOL A, V13, P2582, DOI 10.1116/1.579454 FREMEREY JK, 1982, VACUUM, V32, P685, DOI 10.1016/0042-207X(82)94048-9 GROSSE G, 1970, VACUUM, V20, P373, DOI 10.1016/S0042-207X(70)80036-7 Harada K, 1999, SENSOR ACTUAT A-PHYS, V73, P261, DOI 10.1016/S0924-4247(98)00245-3 Hendricks JH, 2007, METROLOGIA, V44, P171, DOI 10.1088/0026-1394/44/3/002 HEYDEMANN PL, 1971, REV SCI INSTRUM, V42, P983, DOI 10.1063/1.1685319 HIRATA M, 1982, J VAC SCI TECHNOL, V20, P1159, DOI 10.1116/1.571508 HYLAND RW, 1985, J VAC SCI TECHNOL A, V3, P1731, DOI 10.1116/1.573009 JAGER J, 1994, METROLOGIA, V30, P553, DOI 10.1088/0026-1394/30/6/002 Jennings F.B., 1956, T ASME, P55 JITSCHIN W, 1992, J VAC SCI TECHNOL A, V10, P3344, DOI 10.1116/1.577823 JITSCHIN W, 1990, VACUUM, V40, P293, DOI 10.1016/0042-207X(90)90047-3 Jousten K, 2011, J VAC SCI TECHNOL A, V29, DOI 10.1116/1.3529023 Jousten K, 1999, VACUUM, V52, P491, DOI 10.1016/S0042-207X(98)00337-6 Jousten K, 1999, METROLOGIA, V36, P493, DOI 10.1088/0026-1394/36/6/2 Jousten K, 1997, J VAC SCI TECHNOL A, V15, P2395, DOI 10.1116/1.580754 Jousten K., 2005, METROLOGIA S7001, V42 Jousten K., 2007, PTBMA81 Jousten K., 2007, METROLOGIA S7007, V44 Jousten K., 2005, METROLOGIA S7002, V42 Li DT, 2003, J VAC SCI TECHNOL A, V21, P937, DOI 10.1116/1.1578654 MCCULLOH KE, 1981, J VAC SCI TECHNOL, V18, P994, DOI 10.1116/1.570971 MESSER G, 1989, METROLOGIA, V26, P183, DOI 10.1088/0026-1394/26/3/004 Miiller A.P., 2002, METROLOGIA S7001, V39 OOIWA A, 1994, METROLOGIA, V30, P607, DOI 10.1088/0026-1394/30/6/012 Perkin M, 1998, METROLOGIA, V35, P161, DOI 10.1088/0026-1394/35/3/4 Rendle CG, 1999, METROLOGIA, V36, P613, DOI 10.1088/0026-1394/36/6/25 ROHL P, 1988, VACUUM, V38, P507, DOI 10.1016/0042-207X(88)90010-3 Sabuga W., 2007, MAPAN-Journal of Metrology Society of India, V22, P3 SULLIVAN JJ, 1985, J VAC SCI TECHNOL A, V3, P1721, DOI 10.1116/1.573008 TILFORD CR, 1987, METROLOGIA, V24, P121, DOI 10.1088/0026-1394/24/3/003 TILFORD CR, 1985, J VAC SCI TECHNOL A, V3, P546, DOI 10.1116/1.572991 TILFORD CR, 1995, J VAC SCI TECHNOL A, V13, P485, DOI 10.1116/1.579384 TILFORD CR, 1988, J VAC SCI TECHNOL A, V6, P2853, DOI 10.1116/1.575612 WARSHAWSKY I, 1985, J VAC SCI TECHNOL A, V3, P430, DOI 10.1116/1.573234 Wolf A., 1946, J APPL MECH, P207 WOOD SD, 1985, J VAC SCI TECHNOL A, V3, P542, DOI 10.1116/1.572990 NR 43 TC 5 Z9 6 U1 0 U2 13 PD DEC PY 2012 VL 45 IS 10 BP 2420 EP 2425 DI 10.1016/j.measurement.2011.10.036 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000311016600020 DA 2022-12-14 ER PT J AU Zhou, JH Jin, Y Liang, Q AF Zhou, Jiehong Jin, Yu Liang, Qiao TI Effects of regulatory policy mixes on traceability adoption in wholesale markets: Food safety inspection and information disclosure SO FOOD POLICY DT Article DE Policy mixes; Food safety sampling; Information disclosure; Traceability; Wholesale market ID QUALITY; INNOVATION; INCENTIVES; MANAGEMENT; REPUTATION; LIABILITY; ECONOMICS; RISK AB The increasingly heavier burden of government spending on food safety supervision is a common problem faced by regulatory agencies in various countries. Traceability is an effective quality and safety management measure and plays an important role in food safety risk control in many developed countries. However, the agricultural product traceability system in wholesale markets has low coverage in China. This article investigates the effects of regulatory policy mixes, i.e., food safety sampling intensity and information disclosure, on vendors' traceability adoption, theoretically and empirically based on a dataset containing the information of all aquatic product wholesale markets in three provinces of China. The results show that both sampling intensity and information disclosure positively influence the traceability adoption of vendors. However, the effect of food safety sampling intensity on traceability adoption relies on information disclosure. Specifically, an increase of 10 sampling tests enhances the probability that vendors adopt traceability by 4 percentage points in markets with information disclosure. These effects are heterogeneous across vendors with different business scales and with different supply chain distances from farms. The effects are larger for vendors with larger business scales and/or are closer to farms. The above results are robust after the inclusion of the instrumental variable into the models. C1 [Zhou, Jiehong; Jin, Yu; Liang, Qiao] Zhejiang Univ, China Acad Rural Dev, Sch Publ Affairs, 866Yuhangtang Rd, Hangzhou 310058, Peoples R China. C3 Zhejiang University RP Liang, Q (corresponding author), Zhejiang Univ, China Acad Rural Dev, Sch Publ Affairs, 866Yuhangtang Rd, Hangzhou 310058, Peoples R China. EM runzhou@zju.edu.cn; jinyu@zju.edu.cn; liangqiao2323@zju.edu.cn CR Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Balachandran KR, 2005, MANAGE SCI, V51, P1266, DOI 10.1287/mnsc.1050.0408 Cantner U, 2016, RES POLICY, V45, P1165, DOI 10.1016/j.respol.2016.03.005 Chao GH, 2009, MANAGE SCI, V55, P1122, DOI 10.1287/mnsc.1090.1008 Chen B, 2006, AGR EC, P107 Chen Y., 2014, CHINESE RURAL EC, V12, P41 Clemens R.L.B, 2003, MEAT TRACEABILITY CO, P5 Costantini V, 2017, RES POLICY, V46, P799, DOI 10.1016/j.respol.2017.02.004 Ding J, 2011, RES AGR PRODUCTS WHO Dranove D, 2010, J ECON LIT, V48, P935, DOI 10.1257/jel.48.4.935 Dulleck U, 2011, AM ECON REV, V101, P526, DOI 10.1257/aer.101.2.526 Edmondson DL, 2019, RES POLICY, V48, DOI 10.1016/j.respol.2018.03.010 Elfenbein DW, 2015, AM ECON J-MICROECON, V7, P83, DOI 10.1257/mic.20130182 Foster ST, 2008, J OPER MANAG, V26, P461, DOI 10.1016/j.jom.2007.06.003 Gong Q., 2013, EC RES, V3, P135 Grennan M, 2020, AM ECON REV, V110, P120, DOI 10.1257/aer.20180946 Hennessy DA, 1996, AM J AGR ECON, V78, P1034, DOI 10.2307/1243859 Hu Q., 2012, ISSUES AGR EC, V33, P71 Jin C., 2019, FOOD SAFETY ADOPTION, DOI [10.2139/ssrn.3497135, DOI 10.2139/SSRN.3497135] Jin CY, 2021, MANAGE SCI, V67, P2985, DOI 10.1287/mnsc.2020.3839 Jin GZ, 2003, Q J ECON, V118, P409, DOI 10.1162/003355303321675428 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Kafetzopoulos DP, 2013, FOOD CONTROL, V33, P505, DOI 10.1016/j.foodcont.2013.03.044 LAFFONT JJ, 1991, Q J ECON, V106, P1089, DOI 10.2307/2937958 Li K., 2013, CHINESE RURAL EC, V5, P58 [李清光 Li Qingguang], 2015, [中国人口·资源与环境, China Population Resources and Environment], V25, P120 Li X., 2014, EC RES J CHINA, P169 Li X., 2013, EC RES J, V48, P98 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Menard C, 2005, EUR REV AGRIC ECON, V32, P421, DOI 10.1093/eurrag/jbi013 Monteiro DMS, 2009, FOOD POLICY, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Narrod C, 2009, FOOD POLICY, V34, P8, DOI 10.1016/j.foodpol.2008.10.005 Ollinger M, 2020, AM J AGR ECON, V102, P186, DOI 10.1093/ajae/aaz031 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Reichardt K, 2016, ENVIRON INNOV SOC TR, V18, P62, DOI 10.1016/j.eist.2015.08.001 Ren Y, 2010, AGRIC AGRIC SCI PROC, V1, P344, DOI 10.1016/j.aaspro.2010.09.043 Roasto M., 2012, ECOLOGY ANIMAL HLTH, P200 Robinson CJ, 2005, INT J PROD ECON, V96, P315, DOI 10.1016/j.ijpe.2004.06.055 Rosenow J, 2016, BUILD RES INF, V44, P562, DOI 10.1080/09613218.2016.1138803 ROTHSCHILD M, 1976, Q J ECON, V90, P629, DOI 10.2307/1885326 SAMR, 2018, Q REP AN SAMPL INSP SAMR, 2019, NOT ISS 2019 FOOD SA SAMR, 2019, FINANCIAL STATEMENT Sohn MG, 2014, J SCI FOOD AGR, V94, P1932, DOI 10.1002/jsfa.6278 SPENCE M, 1973, Q J ECON, V87, P355, DOI 10.2307/1882010 Staiger D, 1997, ECONOMETRICA, V65, P557, DOI 10.2307/2171753 Starbird SA, 2005, AM J AGR ECON, V87, P15, DOI 10.1111/j.0002-9092.2005.00698.x Stiglitz JE, 2002, AM ECON REV, V92, P460, DOI 10.1257/00028280260136363 USDA, 2020, BUDG SUMM Van Nispen F., 1998, PUBLIC POLICY INSTRU Wang Hongchang, 2013, CHINA IND EC, P98 Westgren RE, 1999, AM J AGR ECON, V81, P1107, DOI 10.2307/1244092 Wu L., 2013, CHINA POPULATION RES, V23, P129 Yang R.L., 2006, EC RES J, V41, P104 Ye J, 2010, ECOLOGICAL EC, V10, P110 Zhang B., 2015, CHINESE RURAL EC, P85 Zhou J., 2020, ISSUES AGR EC, V09, P76 NR 60 TC 2 Z9 2 U1 27 U2 49 PD FEB PY 2022 VL 107 AR 102218 DI 10.1016/j.foodpol.2022.102218 WC Agricultural Economics & Policy; Economics; Food Science & Technology; Nutrition & Dietetics SC Agriculture; Business & Economics; Food Science & Technology; Nutrition & Dietetics UT WOS:000747979000001 DA 2022-12-14 ER PT J AU Tharatipyakul, A Pongnumkul, S AF Tharatipyakul, Atima Pongnumkul, Suporn TI User Interface of Blockchain-Based Agri-Food Traceability Applications: A Review SO IEEE ACCESS DT Review DE Blockchain; User interfaces; Supply chains; Agriculture; Safety; Peer-to-peer computing; Stakeholders; Blockchain; food; literature review; supply chain; traceability; user interface ID SUPPLY CHAIN MANAGEMENT; TECHNOLOGY; CHALLENGES; SYSTEM; FRAMEWORK; INTERNET; AGRICULTURE; INFORMATION; OPPORTUNITIES; ARCHITECTURE AB Blockchain technology is a secure distributed ledger for lists of transactions, which has immense potential to solve traditional agri-food supply chain issues. An increasing number of research on blockchain-based traceability applications aims to improve food quality and safety. Still, relatively few works considered user interfaces when developing and reporting their applications, which could lead to usability issues. This paper aims to address this gap by reviewing existing works from user interface perspectives. We gathered 25 review papers on blockchain or agri-food supply chain and 39 research papers that presented screenshots of user interfaces of related applications. We first reviewed 7 review papers that focused on the blockchain-based agri-food supply chain to understand the benefits and challenges in the blockchain applications. We then analyzed 14 blockchain-based agri-food traceability applications and 10 non-blockchain-based agri-food traceability applications. The analysis resulted in categorizations of 5 target user groups, 3 main approaches for collecting data, 5 main approaches for visualizing data, and a discussion of other aspects of user interfaces. However, we found insufficient details and discussions on the user interfaces and design decisions of the applications for further usability assessment. Additionally, user involvement for evaluation is lower in blockchain-based researches than in non-blockchain-based researches. This trend could lead to usability problems of blockchain applications, causing blockchain technology to be underutilized. Finally, we discussed research gaps and future research directions related to user interface design, which should be addressed to ease future blockchain adoption. C1 [Tharatipyakul, Atima; Pongnumkul, Suporn] Natl Elect & Comp Technol Ctr NECTEC, Pathum Thani 12120, Thailand. C3 National Science & Technology Development Agency - Thailand; National Electronics & Computer Technology Center (NECTEC) RP Tharatipyakul, A (corresponding author), Natl Elect & Comp Technol Ctr NECTEC, Pathum Thani 12120, Thailand. EM atima.tharatipyakul@gmail.com CR Ahumada O, 2009, EUR J OPER RES, V196, P1, DOI 10.1016/j.ejor.2008.02.014 Alertbox J. N., 2003, USABILITY 101 INTRO Alharby M., 2017, 3 INT C ARTIFICIAL I, P125 Ali S. A., 2011, Proceedings of the 2011 International Conference on Internet Computing and Information Services (ICICIS 2011), P20, DOI 10.1109/ICICIS.2011.12 Andoni M, 2019, RENEW SUST ENERG REV, V100, P143, DOI 10.1016/j.rser.2018.10.014 [Anonymous], 2017, FARMSHARE [Anonymous], P WORLD EC FOR GEN [Anonymous], 2017, AGRILEDGER [Anonymous], 2019, ARBOLMARKET [Anonymous], 2007, TRAC FEED FOOD CHAIN Astarita V, 2020, INFORMATION, V11, DOI 10.3390/info11010021 Aste T, 2017, COMPUTER, V50, P18, DOI 10.1109/MC.2017.3571064 Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Atlam Hany F., 2018, International Journal of Intelligent Systems and Applications, V10, P40, DOI 10.5815/ijisa.2018.06.05 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Banafa A., 2017, IEEE INTERNET THINGS, V9 Banterle A., 2008, AGRIREGIONIEUROPA, V4, P15 Basnayake B. M. A. L., 2019, 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE). Proceedings, P103, DOI 10.23919/SCSE.2019.8842690 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Biswas K., 2017, PROC FUTURE TECHNOL, P1, DOI DOI 10.1007/978-3-319-54460-1_1 Bohanec M, 2017, FOOD CONTROL, V71, P168, DOI 10.1016/j.foodcont.2016.06.032 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Burke T., 2019, FOOD TRACEABILITY BI, DOI DOI 10.1007/978-3-030-10902-8_10 Carbone A., 2018, P 8 INT C ADV COMPUT, V2018, P51 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Cartier LE, 2018, J GEMMOL, V36, P212, DOI 10.15506/JoG.2018.36.3.212 Chan KY, 2019, INT J ADV COMPUT SC, V10, P149 Chang SE, 2019, TECHNOL FORECAST SOC, V144, P1, DOI 10.1016/j.techfore.2019.03.015 Chinaka, 2016, THESIS MIT CAMBRIDGE Coindesk, 2019, WILL ETH SCAL Cong An An, 2019, 2019 International Conference on Advanced Computing and Applications (ACOMP). Proceedings, P27, DOI 10.1109/ACOMP.2019.00012 Conoscenti M, 2016, I C COMP SYST APPLIC Corallo A, 2018, 2018 IEEE WORKSHOP ON ENVIRONMENTAL, ENERGY, AND STRUCTURAL MONITORING SYSTEMS (EESMS), P1 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 da Cruz A. M. R., 2020, LECT NOTES COMPUTER, V12423, P740 Dave D, 2019, PROCEDIA COMPUT SCI, V160, P740, DOI 10.1016/j.procs.2019.11.017 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Dong XY, 2019, IEEE INT SYMP PARAL, P1511, DOI 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00219 Dorri A, 2017, INT CONF PERVAS COMP Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067 Elmasry M, 2018, PIPELINES 2018: CONDITION ASSESSMENT, CONSTRUCTION, AND REHABILITATION, P1 Elsden C, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3174032 Eyal I, 2016, 13TH USENIX SYMPOSIUM ON NETWORKED SYSTEMS DESIGN AND IMPLEMENTATION (NSDI '16), P45 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng Q, 2019, J NETW COMPUT APPL, V126, P45, DOI 10.1016/j.jnca.2018.10.020 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fernandez-Carames TM, 2018, IEEE ACCESS, V6, P32979, DOI 10.1109/ACCESS.2018.2842685 Ferrer EC, 2019, ADV INTELL SYST, V881, P1037, DOI 10.1007/978-3-030-02683-7_77 Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 Foth M., 2017, P 29 AUSTR C COMPUTE, P513, DOI [10.1145/3152771.3156168, DOI 10.1145/3152771.3156168] Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Palacio MG, 2017, IBER CONF INF SYST Gopi K, 2019, TRENDS FOOD SCI TECH, V91, P294, DOI 10.1016/j.tifs.2019.07.010 Grossman T, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P649 Guido R, 2020, INT J IND ENG MANAGE, V11, P50, DOI 10.24867/IJIEM-2020-1-252 Gul MJJ, 2019, SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING, P709, DOI 10.1145/3297280.3297347 Guo M., 2018, WIT T BUILT ENV, V179, P391 Hackius N., 2017, P HAMB INT C LOG HIC, V23, P3 Hald KS, 2019, INT J PHYS DISTR LOG, V49, P376, DOI 10.1108/IJPDLM-02-2019-0063 Hasan H, 2019, COMPUT IND ENG, V136, P149, DOI 10.1016/j.cie.2019.07.022 Helo P, 2019, COMPUT IND ENG, V136, P242, DOI 10.1016/j.cie.2019.07.023 Hong JT, 2018, J CLEAN PROD, V172, P3508, DOI 10.1016/j.jclepro.2017.06.093 ICT4Ag, 2017, PERSP ICT AGR ACP CO Iftekhar A, 2020, J FOOD QUALITY, V2020, DOI 10.1155/2020/5385207 Interaction Design Foundation, HUMAN COMPUTER INTER Ismail Razimah, 2013, 2013 IEEE 4th Control and System Graduate Research Colloquium (ICSGRC), P43, DOI 10.1109/ICSGRC.2013.6653273 Jindal A, 2019, COMPUT NETW, V153, P36, DOI 10.1016/j.comnet.2019.02.002 Juma H, 2019, IEEE ACCESS, V7, P184115, DOI 10.1109/ACCESS.2019.2960542 Kafeza E, 2020, I C DATA ENGIN WORKS, P18, DOI 10.1109/ICDEW49219.2020.00-12 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamble SS, 2020, INT J PROD ECON, V219, P179, DOI 10.1016/j.ijpe.2019.05.022 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Keesstra S, 2018, LAND-BASEL, V7, DOI 10.3390/land7040133 Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Khaqqi KN, 2018, APPL ENERG, V209, P8, DOI 10.1016/j.apenergy.2017.10.070 Khare A.A., 2019, J COMPUT THEOR NANOS, V16, P4418, DOI DOI 10.1166/JCTN.2019.8535 Kiayias A, 2017, PROGR CRYPTOLOGY LAT, P327 Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Kittipanya-ngam P, 2020, PROD PLAN CONTROL, V31, P158, DOI 10.1080/09537287.2019.1631462 Korpela K, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P4182 Kosba A, 2016, P IEEE S SECUR PRIV, P839, DOI 10.1109/SP.2016.55 Koteska B., 2017, SQAMIA 2017 6 WORKSH, P11 Kronung, 2020, P 27 EUR C INF SYST, P1 Kshetri M, 2019, IT PROF, V21, P63, DOI 10.1109/MITP.2019.2906761 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Kshetri N, 2017, IT PROF, V19, P68, DOI 10.1109/MITP.2017.3051335 Lagarda-Leyva EA, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10134481 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Lee H.L., 2017, CISC VIS NETW IND GL Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Levitt T., 2016, BLOCKCHAIN TECHNOLOG Lezoche M, 2020, COMPUT IND, V117, DOI 10.1016/j.compind.2020.103187 Li XQ, 2020, FUTURE GENER COMP SY, V107, P841, DOI 10.1016/j.future.2017.08.020 Li Z, 2018, IND MANAGE DATA SYST, V118, P303, DOI 10.1108/IMDS-04-2017-0142 Liang GQ, 2019, IEEE T SMART GRID, V10, P3162, DOI 10.1109/TSG.2018.2819663 Lin I.C., 2017, IJ NETWORK SECURITY, V19, P653, DOI [DOI 10.6633/IJNS.201709.19(5).01, 10.6633/IJNS.201709.19(5).01] Lin J, 2017, PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING ICCSE 2017, P38, DOI 10.1145/3126973.3126980 Lin WJ, 2020, IEEE ACCESS, V8, P143920, DOI 10.1109/ACCESS.2020.3014522 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Loop P., 2016, MAT HANDLING LOGISTI Lu QH, 2017, IEEE SOFTWARE, V34, P21, DOI 10.1109/MS.2017.4121227 Lucena P., 2018, P S FDN APPL BLOCKCH, P1 Mallick P. K., 2018, PROCEDIA COMPUTER SC, V132, P1815, DOI [10.1016/j.procs.2018.05.140, DOI 10.1016/J.PROCS.2018.05.140] Mao DH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15081627 Maru A., 2018, GLOBAL FORUM AGR RES Min H, 2019, BUS HORIZONS, V62, P35, DOI 10.1016/j.bushor.2018.08.012 Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Mistry I, 2020, MECH SYST SIGNAL PR, V135, DOI 10.1016/j.ymssp.2019.106382 Mohanta BK, 2018, INT CONF COMPUT Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Myers B., 1985, ACM SIGCHI B, V16, P11 New S, 2010, HARVARD BUS REV, V88, P76 Olnes S, 2017, GOV INFORM Q, V34, P355, DOI 10.1016/j.giq.2017.09.007 Olnes S, 2019, LECT NOTES COMPUT SC, V9820, P253, DOI 10.1007/978-3-319-44421-5_20 Ometoruwa T., 2018, BLOCKCHAIN TRILEMMA Panarello A, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18082575 Papadopoulos S, 2017, P 27 INT S POW TIM M, P1 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Parmigiani A, 2011, J OPER MANAG, V29, P212, DOI 10.1016/j.jom.2011.01.001 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Peets S, 2009, PRECIS AGRIC, V10, P382, DOI 10.1007/s11119-009-9106-4 Peng YL, 2015, FOOD POLICY, V51, P44, DOI 10.1016/j.foodpol.2014.12.010 Perboli G, 2018, IEEE ACCESS, V6, P62018, DOI 10.1109/ACCESS.2018.2875782 Puthal D, 2018, IEEE CONSUM ELECTR M, V7, P18, DOI 10.1109/MCE.2017.2776459 Qadir, 2017, ACM INT C P SERIES, P1 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Queiroz MM, 2020, SUPPLY CHAIN MANAG, V25, P241, DOI 10.1108/SCM-03-2018-0143 Rabah K., 2018, LAKE I J, V1, P1 Rahman Md Abdur, 2020, 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), P262, DOI 10.1109/ICIoT48696.2020.9089613 Raskin Max, 2017, GEO L TECH REV, V1, P305, DOI [DOI 10.2139/SSRN.2842258, 10.2139/ssrn.2842258] Rejeb A., 2018, ACTA TECH JAURINENSI, V11, P104, DOI [DOI 10.14513/ACTATECHJAUR.V11.N2.462, 10.14513/actatechjaur.v11.n2.462] Rejeb A, 2020, LOGFORUM, V16, P363, DOI 10.17270/J.LOG.2020.467 Rejeb A, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11070161 Reyna A, 2018, FUTURE GENER COMP SY, V88, P173, DOI 10.1016/j.future.2018.05.046 Risius M, 2017, BUS INFORM SYST ENG+, V59, P385, DOI 10.1007/s12599-017-0506-0 Saad-Hussein A., 2011, Eastern Mediterranean Health Journal, V17, P468 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Schmidhuber, 2018, FOOD AGR ORG UN, V3, P21 Scuderi A, 2019, QUAL-ACCESS SUCCESS, V20, P580 Seitz A, 2018, 2018 FIFTH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SYSTEMS, MANAGEMENT AND SECURITY, P182, DOI 10.1109/IoTSMS.2018.8554484 Shaikh S, 2019, 2019 IEEE 5TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT) Sharma PK, 2019, IEEE T IND INFORM, V15, P4197, DOI 10.1109/TII.2018.2887101 Sharma R, 2020, COMPUT OPER RES, V119, DOI 10.1016/j.cor.2020.104926 Shih DH, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8111341 Singh M, 2018, 2018 IEEE 4TH WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P51 Soil Association, 2020, SOIL ASS CERT Speed C., 2018, GEOCOIN SUPPORTING I, P1, DOI [10.1145/3173574.3173737, DOI 10.1145/3173574.3173737] Stewart, 2018, TRACING SUPPLY CHAIN Surasak T, 2019, INT J ADV COMPUT SC, V10, P578 Sutopo W, 2013, LECT NOTES ENG COMP, VAo,, P1180 Tallyn Ella, 2018, Proceedings of the ACM on Human-Computer Interaction, V2, DOI 10.1145/3274439 Teruel MA, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124280 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thea, 2017, P 13 INT C WIR MOB C, P92 Thiruchelvam V., 2018, J TELECOMMUNICATION, V10, P121 Thomason J., 2018, TRANSFORMING CLIMATE, P137, DOI DOI 10.1016/B978-0-12-814447-3.00010-0 Tian F, 2017, I C SERV SYST SERV M Top J, 2017, BLOCKCHAIN AGR FOOD, P112 Torky M, 2020, COMPUT ELECTRON AGR, V178, DOI 10.1016/j.compag.2020.105476 Tradigo G., 2019, CEUR WORKSHOP PROC Tribis Youness, 2018, MATEC Web of Conferences, V200, DOI 10.1051/matecconf/201820000020 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Tseas, 2020, P 53 HAW INT C SYST, P1 Underwood S, 2016, COMMUN ACM, V59, P15, DOI 10.1145/2994581 UNESCO UNICEF UNFPA UN Women WHO and UNAIDS, 2018, USABILITY ENG MORGAN Verhoeven P, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2030020 Violino S, 2019, FOODS, V8, DOI 10.3390/foods8110529 Wang YL, 2019, INT J PROD ECON, V211, P221, DOI 10.1016/j.ijpe.2019.02.002 Wass S, 2017, FOOD CO UNITE ADV BL Watanabe H, 2015, 2015 IEEE 4TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS (GCCE), P577, DOI 10.1109/GCCE.2015.7398721 Weisbord E, 2018, DEMYSTIFYING BLOCK 1 WFP, 2017, WFP BUILD BLOCKS BLO White L.H., 2015, CATO J, V35, P383, DOI [DOI 10.2139/ssrn.2538290, DOI 10.2139/SSRN.2538290] WHO, 2015, WHO 10 FACTS FOOD SA Wickramarachchi R., 2020, P INT C IND ENG OP M, P1163 Wright C. S, 2019, BITCOIN PEER TO PEER Wu ML, 2019, IEEE INTERNET THINGS, V6, P8114, DOI 10.1109/JIOT.2019.2922538 Xu XW, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE (ICSA 2017), P243, DOI 10.1109/ICSA.2017.33 Yadav V.S., 2019, P INT C IND ENG OPER, P973 Yiannas F., 2018, Innovations: Technology / Governance / Globalization, V12, P46, DOI 10.1162/inov_a_00266 Yli-Huumo J, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0163477 Yuan HQ, 2020, INF SYST E-BUS MANAG, V18, P681, DOI 10.1007/s10257-018-0391-1 Zhang XH, 2020, IEEE ACCESS, V8, P141748, DOI 10.1109/ACCESS.2020.3013005 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zheng PL, 2018, 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING - SOFTWARE ENGINEERING IN PRACTICE TRACK (ICSE-SEIP 2018), P134, DOI 10.1145/3183519.3183546 Zheng ZB, 2018, INT J WEB GRID SERV, V14, P352, DOI 10.1504/IJWGS.2018.095647 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 Zoellner C, 2018, APPL ENVIRON MICROB, V84, DOI [10.1128/AEM.00813-18, 10.1128/aem.00813-18] NR 193 TC 10 Z9 10 U1 20 U2 72 PY 2021 VL 9 BP 82909 EP 82929 DI 10.1109/ACCESS.2021.3085982 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000673105300001 DA 2022-12-14 ER PT J AU Crandall, PG O'Bryan, CA Babu, D Jarvis, N Davis, ML Buser, M Adam, B Marcy, J Ricke, SC AF Crandall, Philip G. O'Bryan, Corliss A. Babu, Dinesh Jarvis, Nathan Davis, Mike L. Buser, Michael Adam, Brian Marcy, John Ricke, Steven C. TI Whole-chain traceability, is it possible to trace your hamburger to a particular steer, a U. S. perspective SO MEAT SCIENCE DT Review DE Traceability; Beef; Ground beef; Food safety; Animal disease ID SAFETY; BEEF AB Traceability through the entire food supply chain from conception to consumption is a pressing need for the food industry, consumers and government regulators. A robust, whole-chain traceability system is needed that will effectively address food quality, food safety and food defense issues by providing real-time, transparent and reliable information from beef production through slaughter and distribution to the consumer. Traceability is an expanding part of the food safety continuum that minimizes the risk of foodborne diseases, assures quality and cold-chain integrity. Traceability can be a positive competitive marketing edge for beef producers who can verify specific quality attributes such as humane production or grass fed or Certified Organic. In this review we address the benefits as well as the remaining issues for whole-chain traceability in the beef industry, with particular focus on ground beef for the markets in the United States. (c) 2013 Elsevier Ltd. All rights reserved. C1 [Crandall, Philip G.; O'Bryan, Corliss A.; Babu, Dinesh; Jarvis, Nathan; Davis, Mike L.; Marcy, John; Ricke, Steven C.] Univ Arkansas, Ctr Food Safety, Fayetteville, AR 72704 USA. [Crandall, Philip G.; O'Bryan, Corliss A.; Babu, Dinesh; Jarvis, Nathan; Davis, Mike L.; Marcy, John; Ricke, Steven C.] Univ Arkansas, Dept Food Sci, Fayetteville, AR 72704 USA. [Buser, Michael; Adam, Brian] Oklahoma State Univ, Stillwater, OK 74078 USA. [Marcy, John] Univ Arkansas, Dept Poultry Sci, Fayetteville, AR 72701 USA. C3 University of Arkansas System; University of Arkansas Fayetteville; University of Arkansas System; University of Arkansas Fayetteville; Oklahoma State University System; Oklahoma State University - Stillwater; University of Arkansas System; University of Arkansas Fayetteville RP Crandall, PG (corresponding author), Univ Arkansas, 2650 Young Ave, Fayetteville, AR 72704 USA. EM crandal@uark.edu CR Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 Animal and Plant Health Inspection Service (APHIS), 2009, OV REP BEN COST AN N [Anonymous], 2012, IDENTIGENS DNA TRACE APHIS, 2011, REG IMP AN IN REG FL APHIS, 2010, BOV TUB REQ APPL ACC Aune K, 2012, J WILDLIFE MANAGE, V76, P253, DOI 10.1002/jwmg.274 BeefU, 2012, FOODS GUID BEEF Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Blancou J., 2001, Revue Scientifique et Technique Office International des Epizooties, V20, P413 Bolte K., 2007, ADOPTING ANIMAL IDEN Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Caja G., 2004, ICAR Technical Series, P21 Crandall P, 2012, J FOOD PROTECT, V75, P1660, DOI 10.4315/0362-028X.JFP-11-550 ERS, 2012, US CATTL BEEF IND 20 Flynn F., 2011, 2 GO AN DIS TRAC HIT FSIS, 2008, VER INSTR REL SPEC R GLEDHILL J., 2002, FOOD PROCESSING MAR, V63, P54 Golan E.H., 2004, TRACEABILITY US FOOD GREENE J. L., 2010, ANIMAL IDENTIFICATIO GS 1, 2012, GS 1 IS OFFICIAL PRO Heaton MP, 2005, JAVMA-J AM VET MED A, V226, P1311, DOI 10.2460/javma.2005.226.1311 Information Technology Research Institute, 2011, RFID FOR Johnson A., 2012, BOARD ANIMAL HLTH AS Kramer MN, 2005, MEAT SCI, V71, P158, DOI 10.1016/j.meatsci.2005.04.001 Lackey J., 2011, US CATTLE BRANDING D Latouche K, 1998, FOOD POLICY, V23, P347, DOI 10.1016/S0306-9192(98)00048-7 Levs J., 2013, BATTLE BLAME HORSE M Loureiro M. L, 2004, 2004 ANN M AUG 1 4 D McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Mennecke B. E., 2005, RADIO FREQUENCY IDEN Morris D.L., 2008, PROF ANIM SCI, V24, P277 Neuman W., 2010, USDA PLANS DROP PROG Resende-Filho M. L., 2010, EC TRACEABILITY MITI Robb J. G., 2004, SOME ISSUES RELATED Schmidt R. H., 2007, REGULATORY REQUIREME Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 Shackell GH, 2008, INT J FOOD SCI TECH, V43, P2134, DOI 10.1111/j.1365-2621.2008.01812.x Smith GC, 2008, MEAT SCI, V80, P66, DOI 10.1016/j.meatsci.2008.05.024 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Souza-Monteiro D.M., 2004, EC IMPLEMENTING TRAC Stead Miriam, 1986, EGYPTIAN LIFE STEINSTRATER M, 2001, FLEISCHWIRTSCHAFT IN, V2, P52 Thomsen M.R., 2008, MANDATORY FOOD RECAL USDA, 2004, USDA PROCESS VERIFIE USDA NASS, 2007, CENSUS AGR CATTLE PR NR 45 TC 19 Z9 21 U1 2 U2 97 PD OCT PY 2013 VL 95 IS 2 BP 137 EP 144 DI 10.1016/j.meatsci.2013.04.022 WC Food Science & Technology SC Food Science & Technology UT WOS:000321407900001 DA 2022-12-14 ER PT J AU Radogna, AV Latino, ME Menegoli, M Prontera, CT Morgante, G Mongelli, D Giampetruzzi, L Corallo, A Bondavalli, A Francioso, L AF Radogna, Antonio Vincenzo Latino, Maria Elena Menegoli, Marta Prontera, Carmela Tania Morgante, Gabriele Mongelli, Diamantea Giampetruzzi, Lucia Corallo, Angelo Bondavalli, Andrea Francioso, Luca TI A Monitoring Framework with Integrated Sensing Technologies for Enhanced Food Safety and Traceability SO SENSORS DT Article DE Industry 4.0; Agriculture 4.0; food traceability; ICT for sensor networks; Internet of Things; model based system engineering; glyphosate low-cost detection ID SUPPLY CHAIN; GLYPHOSATE; TEMPERATURE; GROWTH AB A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO2. The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted. C1 [Radogna, Antonio Vincenzo; Prontera, Carmela Tania; Giampetruzzi, Lucia; Francioso, Luca] Natl Res Council Italy CNR IMM, Inst Microelect & Microsyst, Campus Ecotekne,Via Monteroni S-N, I-73100 Lecce, Italy. [Radogna, Antonio Vincenzo; Latino, Maria Elena; Menegoli, Marta; Corallo, Angelo] Univ Salento, Dept Innovat Engn, Campus Ecotekne,Via Monteroni S-N, I-73100 Lecce, Italy. [Morgante, Gabriele; Mongelli, Diamantea; Bondavalli, Andrea] Resiltech Srl, Piazza N Lotti 25, I-56025 Pontedera, Italy. C3 Consiglio Nazionale delle Ricerche (CNR); Istituto per la Microelettronica e Microsistemi (IMM-CNR); University of Salento RP Radogna, AV; Giampetruzzi, L (corresponding author), Natl Res Council Italy CNR IMM, Inst Microelect & Microsyst, Campus Ecotekne,Via Monteroni S-N, I-73100 Lecce, Italy.; Radogna, AV; Latino, ME; Menegoli, M (corresponding author), Univ Salento, Dept Innovat Engn, Campus Ecotekne,Via Monteroni S-N, I-73100 Lecce, Italy. EM antonio.radogna@le.imm.cnr.it; mariaelena.latino@unisalento.it; marta.menegoli@unisalento.it; lucia.giampetruzzi@le.imm.cnr.it CR Al Qudah A, 2021, MITIG ADAPT STRAT GL, V26, DOI 10.1007/s11027-021-09944-7 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Balafoutis A.T., 2017, PRECISION AGR TECHNO, P21, DOI [10.1007/978-3-319-68715-5_2, DOI 10.1007/978-3-319-68715-5_2] Benlloch-Gonzalez M, 2016, J PLANT PHYSIOL, V207, P22, DOI 10.1016/j.jplph.2016.10.001 Bjork A, 2011, COMPUT IND, V62, P830, DOI 10.1016/j.compind.2011.08.001 CAO HF, 1989, J APPL ECOL, V26, P763, DOI 10.2307/2403688 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 De Bortoli M., 1986, ENVIRON INT, V12, P343, DOI [10.1016/0160-4120(86)90048-6, DOI 10.1016/0160-4120(86)90048-6] Druart C, 2011, ANAL BIOANAL CHEM, V399, P1725, DOI 10.1007/s00216-010-4468-z FOCUS, 2008, PEST AIR CONS EXP AS Fu Y, 2013, INT C DIGIT MANUF, P296, DOI 10.1109/ICDMA.2013.71 Gillezeau C, 2019, ENVIRON HEALTH-GLOB, V18, DOI 10.1186/s12940-018-0435-5 Griffiths H, 2003, EFFECTS AIR POLLUTIO Han YH, 2017, J AGR FOOD CHEM, V65, P1750, DOI 10.1021/acs.jafc.6b03922 Jun HB, 2007, INT J COMPUT INTEG M, V20, P684, DOI 10.1080/09511920701566624 Kalpana S, 2019, TRENDS FOOD SCI TECH, V93, P145, DOI 10.1016/j.tifs.2019.09.008 Kodan R, 2020, FOOD REV INT, V36, P584, DOI 10.1080/87559129.2019.1657442 Kudsk P, 2020, WEED SCI, V68, P214, DOI 10.1017/wsc.2019.59 Kudzin ZH, 2002, J CHROMATOGR A, V947, P129, DOI 10.1016/S0021-9673(01)01603-X Kumari L, 2015, TRENDS FOOD SCI TECH, V43, P144, DOI 10.1016/j.tifs.2015.02.005 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Mafrica R, 2021, AGRICULTURE-BASEL, V11, DOI 10.3390/agriculture11020147 Molhave L., 1992, INDOOR AIR, V2, P65, DOI [10.1111/j.1600-0668.1992.01-22.x, DOI 10.1111/J.1600-0668.1992.01-22.X] Nematollahi M, 2020, J CLEAN PROD, V271, DOI 10.1016/j.jclepro.2020.122201 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Parashar S, 2020, J CLEAN PROD, V275, DOI 10.1016/j.jclepro.2020.122932 Perez-Lopez D, 2008, J HORTIC SCI BIOTECH, V83, P171, DOI 10.1080/14620316.2008.11512366 Perez-Priego O, 2014, AGROFOREST SYST, V88, P245, DOI 10.1007/s10457-014-9672-y Preftakes CJ, 2019, PEERJ, V7, DOI 10.7717/peerj.7136 Ruffer D, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18041052 Shi XQ, 2011, CONCURR COMP-PRACT E, V23, P972, DOI 10.1002/cpe.1625 Son Y, 2011, ENVIRON MONIT ASSESS, V176, P1, DOI 10.1007/s10661-010-1562-9 Tian F, 2017, I C SERV SYST SERV M Verdouw C, 2019, COMPUT ELECTRON AGR, V165, DOI 10.1016/j.compag.2019.104939 Zambrano-Intriago LA, 2021, SCI TOTAL ENVIRON, V793, DOI 10.1016/j.scitotenv.2021.148496 Zhang C, 2017, ANAL BIOANAL CHEM, V409, P7133, DOI 10.1007/s00216-017-0671-5 Zhao GQ, 2017, IFIP ADV INF COMM TE, V506, P739, DOI 10.1007/978-3-319-65151-4_66 NR 39 TC 0 Z9 0 U1 11 U2 11 PD SEP PY 2022 VL 22 IS 17 AR 6509 DI 10.3390/s22176509 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000851720000001 DA 2022-12-14 ER PT J AU Kondo, N AF Kondo, Naoshi TI Automation on fruit and vegetable grading system and food traceability SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review ID MACHINE; EXTRACTION AB In recent ten years, operations in grading systems for fruits and vegetables became highly automated with mechatronics, and robotics technologies. Especially, machine vision systems and near infrared inspection systems have been introduced to many grading facilities with mechanisms for inspecting all sides of fruits and vegetables. In this paper, automation technologies of several types of fruit and vegetable grading systems are described, while their potentials to give producers and consumers product information are discussed from a view point of food traceability. C1 Kyoto Univ, Grad Sch Agr, Sakyo Ku, Kyoto 6068502, Japan. C3 Kyoto University RP Kondo, N (corresponding author), Kyoto Univ, Grad Sch Agr, Sakyo Ku, Kyoto 6068502, Japan. EM kondonao@kais.kyoto-u.ac.jp CR Aleixos N, 2002, COMPUT ELECTRON AGR, V33, P121, DOI 10.1016/S0168-1699(02)00002-9 Chong VK, 2008, APPL ENG AGRIC, V24, P675 Chong VK, 2008, APPL ENG AGRIC, V24, P877 *FAO, 1989, PREV POSTH FOOD LOSS, P157 GUEDALIA ID, 1997, P SENS NOND TEST INT, P232 Heinemann P. H., 1995, Applied Engineering in Agriculture, V11, P901 KOHNO S, 2003, SCI FORUM Kondo N., 2007, Journal of the Japanese Society of Agricultural Machinery, V69, P68 Kondo Naoshi, 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P1, DOI 10.1007/s11694-008-9065-x KONDO N, 2006, ENV CONTROL BIOL, V44, P3 KONDO N, 2007, SYSTEMS ANAL MODELIN Leemans V, 1998, COMPUT ELECTRON AGR, V20, P117, DOI 10.1016/S0168-1699(98)00012-X Lu R, 2002, APPL ENG AGRIC, V18, P585 MILLER WM, 1997, P SENS NOND TEST INT, P249 MOLTO E, 1998, SPIE S PREC AGR BIOL NJOROGE J, P SICE ANN C 2002 OS REHKUGLER GE, 1986, T ASAE, V29, P1388 REYES MU, 1988, DESIGN CONCEPT OPERA Sagara Y., 1998, Journal of the Japanese Society of Agricultural Machinery, V60, P167 SARKAR N, 1985, T ASAE, V28, P970 NR 20 TC 66 Z9 74 U1 4 U2 77 PD MAR PY 2010 VL 21 IS 3 SI SI BP 145 EP 152 DI 10.1016/j.tifs.2009.09.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000276290700005 DA 2022-12-14 ER PT J AU Prashar, D Jha, N Jha, S Lee, Y Joshi, GP AF Prashar, Deepak Jha, Nishant Jha, Sudan Lee, Yongju Joshi, Gyanendra Prasad TI Blockchain-Based Traceability and Visibility for Agricultural Products: A Decentralized Way of Ensuring Food Safety in India SO SUSTAINABILITY DT Article DE Ethereum smart contracts; blockchain; traceability; visibility; throughput; supply chain; IPFS ID IDENTIFICATION RFID TECHNOLOGY; CONSENSUS; IMPACTS AB The globalization of the food supply chain industry has significantly emerged today. Due to this, farm-to-fork food safety and quality certification have become very important. Increasing threats to food security and contamination have led to the enormous need for a revolutionary traceability system, an important mechanism for quality control that ensures sufficient food supply chain product safety. In this work, we proposed a blockchain-based solution that removes the need for a secure centralized structure, intermediaries, and exchanges of information, optimizes performance, and complies with a strong level of safety and integrity. Our approach completely relies on the use of smart contracts to monitor and manage all communications and transactions within the supply chain network among all of the stakeholders. Our approach verifies all of the transactions, which are recorded and stored in a centralized interplanetary file system database. It allows a secure and cost-effective supply chain system for the stakeholders. Thus, our proposed model gives a transparent, accurate, and traceable supply chain system. The proposed solution shows a throughput of 161 transactions per second with a convergence time of 4.82 s, and was found effective in the traceability of the agricultural products. C1 [Prashar, Deepak; Jha, Nishant; Jha, Sudan] Lovely Profess Univ, Dept CSE, Phagwara 144411, Punjab, India. [Lee, Yongju] Kyungpook Natl Univ, Sch CSE, Daegu 702701, South Korea. [Joshi, Gyanendra Prasad] Sejong Univ, Dept CSE, Seoul 05006, South Korea. C3 Lovely Professional University; Kyungpook National University; Sejong University RP Lee, Y (corresponding author), Kyungpook Natl Univ, Sch CSE, Daegu 702701, South Korea.; Joshi, GP (corresponding author), Sejong Univ, Dept CSE, Seoul 05006, South Korea. EM deepak.prashar@lpu.co.in; nishant.11702196@lpu.co.in; sudhan.25850@lpu.co.in; yongju@knu.ac.kr; joshi@sejong.ac.kr CR Afshin A, 2019, LANCET, V393, P1958, DOI 10.1016/S0140-6736(19)30041-8 Aich S, 2019, INT CONF ADV COMMUN, P138, DOI 10.23919/ICACT.2019.8701910 Arena A, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2019), P173, DOI 10.1109/SMARTCOMP.2019.00049 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Boukema A., 2017, ADVANTAGES ANOMALY D, P1 Chinaka M., 2016, THESIS, P43 Dai HN, 2019, IEEE INTERNET THINGS, V6, P8076, DOI 10.1109/JIOT.2019.2920987 DWORK C, 1988, J ACM, V35, P288, DOI 10.1145/42282.42283 FISCHER MJ, 1985, J ACM, V32, P374, DOI 10.1145/3149.214121 Foroglou G., 2014, P COL U PHD SUST DEV, V1, P1 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kosba A, 2016, P IEEE S SECUR PRIV, P839, DOI 10.1109/SP.2016.55 Lucena P., 2018, P S FDN APPL BLOCKCH, P31 Mao DH, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093149 Paramithiotis S, 2017, CURR OPIN FOOD SCI, V18, P71, DOI 10.1016/j.cofs.2017.11.007 Sardar D.R., 2018, MAGGI STEWS LEAD MSG Sari K, 2010, EUR J OPER RES, V207, P174, DOI 10.1016/j.ejor.2010.04.003 Sayogo DS, 2014, J THEOR APPL EL COMM, V9, P1, DOI 10.4067/S0718-18762014000200002 Shakhbulatov D, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2019), P546, DOI 10.1109/Blockchain.2019.00079 Tian F, 2017, INT C SERV SYST SERV, V1, P6, DOI DOI 10.1109/ICSSSM.2017.7996119 Tripoli M., 2018, EMERGING OPPORTUNITI, P1 Ustundag A, 2009, TRANSPORT RES E-LOG, V45, P29, DOI 10.1016/j.tre.2008.09.001 World Health Organization, 2017, DEPR OTH COMM MENT D Yang XinTing, 2008, Transactions of the Chinese Society of Agricultural Engineering, V24, P162 NR 24 TC 45 Z9 46 U1 15 U2 72 PD APR PY 2020 VL 12 IS 8 AR 3497 DI 10.3390/su12083497 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000535598700430 DA 2022-12-14 ER PT J AU Violino, S Antonucci, F Pallottino, F Cecchini, C Figorilli, S Costa, C AF Violino, Simona Antonucci, Francesca Pallottino, Federico Cecchini, Cristina Figorilli, Simone Costa, Corrado TI Food traceability: a term map analysis basic review SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Review DE Supply chain; RFID; QR CODE; NFC; NIR; DNA barcoding ID RADIOFREQUENCY IDENTIFICATION; SUPPLY CHAIN; PRODUCTS; ORIGIN; SYSTEM; AUTHENTICATION; BARCODE AB The aim of this work is to realize a term map analysis on technological advancements, in the year and in the world, of scientific researches of food traceability. Quality protection needs efficient instruments to discriminate Protected Denomination of Origin and Protected Geographical Indication varieties in field and to trace them along the agri-food chain. This study attempts to analyze global scientific of food traceability researches (between 1999 and 2018). In this period, 2534 scientific publications by Scopus database were found. Publication trends, research topics and their geographical distribution were analyzed by science mapping (VOSviewer software). Term map evidenced four main groups: red cluster with terms about food product and analytical methods for the characterization of food; green cluster including terms related with consumer (e.g., "food safety" and "food packaging"); blue cluster associating terms with the technology for traceability and yellow cluster with identification of food by genetic marker. It is possible to observe many links (i.e., co-occurrence between terms) in the green and blue clusters and among them. The yellow cluster could be considered as a subcategory of red one. In addition, green cluster refers to consumer and food safety. Yellow and red clusters contain analytical methods to identify food product, while blue cluster refers to advancements technological transferring information about the product to the consumer. These clusters do not present many linkages, and the consumer is in-between these. Finally, this study contributes to a better knowing of food traceability, and to an enhanced scientific research of technological advancements in supply chain. C1 [Violino, Simona; Antonucci, Francesca; Pallottino, Federico; Figorilli, Simone; Costa, Corrado] Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn Trasformaz Agroalimentari, Via Pascolare 16, I-00015 Rome, Italy. [Cecchini, Cristina] Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn Trasformaz Agroalimentari, Via Manziana 30, I-00015 Rome, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Costa, C (corresponding author), Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn Trasformaz Agroalimentari, Via Pascolare 16, I-00015 Rome, Italy. EM corrado.costa@crea.gov.it CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Abdullah A, 2017, INT J FOOD SCI TECH, V52, P266, DOI 10.1111/ijfs.13278 Ahmed N, 2018, FOOD CONTROL, V90, P259, DOI 10.1016/j.foodcont.2018.02.012 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Becattini Giacomo, 2000, DAL DISTRETTO IND AL Benincasa C, 2007, ANAL CHIM ACTA, V585, P366, DOI 10.1016/j.aca.2006.12.040 Bianchi F, 2018, TRAC-TREND ANAL CHEM, V107, P142, DOI 10.1016/j.trac.2018.07.024 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Campanaro A, 2019, FOOD RES INT, V115, P1, DOI 10.1016/j.foodres.2018.07.031 Costa C., 2011, INSTRUMENTATION VIEW, V11, P48 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Dabbene F, 2016, WOODHEAD PUBL FOOD S, V301, P67, DOI 10.1016/B978-0-08-100310-7.00005-3 Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 EC-European Commission, 2002, J EUROP COMM, VL031, P1 Espineira M, 2016, WOODHEAD PUBL FOOD S, V301, P3, DOI 10.1016/B978-0-08-100310-7.00001-6 Fallon M, 2001, REV SCI TECH OIE, V20, P538, DOI 10.20506/rst.20.2.1289 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Hu H, 2009, INT C COMP COMP TECH Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Kalaitzis P., 2016, Lipid Technology, V28, P173, DOI 10.1002/lite.201600048 Kumari L, 2015, TRENDS FOOD SCI TECH, V43, P144, DOI 10.1016/j.tifs.2015.02.005 Lammers W, 2007, LVT LEBENSMITTEL IND, V6, P2 Maffei S, 2002, SOLE 24 ORE Mainetti L., 2013, INT J ANTENN PROPAG, V2013, DOI [DOI 10.1155/2013/531364, 10.1155/2013/531364] Mohammed A, 2017, INT J FOOD PROP, V20, P1145, DOI 10.1080/10942912.2016.1203933 Molkentin J, 2007, ANAL BIOANAL CHEM, V388, P297, DOI 10.1007/s00216-007-1222-2 Mortara A., 2016, RIV TRIMESTRALE SCI, P1 Nakamoto S., 2008, DECENTRALIZED BUS RE, P21260 Nardi P, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0155016 Ning Z, 2008, T CHIN SOC AGR ENG, DOI [10.3969/j.issn.1002-6819.2008.12.060, DOI 10.3969/J.ISSN.1002-6819.2008.12.060] Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Pigini D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101910 Porter M. E., 1990, COMPETITIVE ADVANTAG Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Rao R, 2009, ITAL J AGRON, V3, P95 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ritota M, 2013, J SCI FOOD AGR, V93, P1665, DOI 10.1002/jsfa.5947 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Nguyen SD, 2013, ELECTRON LETT, V49, P1588, DOI 10.1049/el.2013.3328 Tajima May, 2007, Journal of Purchasing and Supply Management, V13, P261, DOI 10.1016/j.pursup.2007.11.001 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Tian F, 2017, INT C SERV SYST SERV, V1, P6, DOI DOI 10.1109/ICSSSM.2017.7996119 Toti E., 2017, RIV SCI ALIMENTAZION, V46, P39 Trebar M, 2013, INT J ANTENN PROPAG, V2013, DOI 10.1155/2013/875973 Uncu AT, 2017, FOOD CHEM, V221, P1026, DOI 10.1016/j.foodchem.2016.11.059 Valentini P, 2017, ANGEW CHEM INT EDIT, V56, P8094, DOI 10.1002/anie.201702120 Van Eck N.J., 2011, ARXIV11092058 van Eck NJ, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0062395 van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3 Van Eck NJ., 2014, MEASURING SCHOLARLY, P285, DOI [DOI 10.1007/978-3-319-10377-8_13, 10.1007/978-3-319-10377-8_13(InEng.), 10.1007/978-3-319-10377-8_13] Van Raan AF, 2014, WENNER GREN INT SERI Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Vesentini I, 2018, IL SOLE 24 ORE Wang Y., 2012, J CONVERGENCE INFORM, V7, P86 Xiang B., 2015, J FOOD SCI TECH MYS, V8, P394 NR 61 TC 13 Z9 14 U1 9 U2 85 PD OCT PY 2019 VL 245 IS 10 BP 2089 EP 2099 DI 10.1007/s00217-019-03321-0 WC Food Science & Technology SC Food Science & Technology UT WOS:000487651100002 DA 2022-12-14 ER PT J AU Tedeschi, P Coisson, JD Maietti, A Cereti, E Stagno, C Travaglia, F Arlorio, M Brandolini, V AF Tedeschi, P. Coisson, J. D. Maietti, A. Cereti, E. Stagno, C. Travaglia, F. Arlorio, M. Brandolini, V. TI Chemotype and genotype combined analysis applied to tomato (Lycopersicon esculentum Mill.) analytical traceability SO JOURNAL OF FOOD COMPOSITION AND ANALYSIS DT Article DE Tomato (Lycopersicon esculentum Mill.); Traceability; Chemotype; RAPD fingerprinting; Lycopene; Ascorbic acid; Quercetin 3-rhamnosyl-glucoside (rutin); Food analysis; Food composition ID QUALITY-CONTROL METHODS; ANTIOXIDANT CAPACITY; MULTIVARIATE-ANALYSIS; LYCOPENE; FOODS; PHENOLICS; CHERRY; CLASSIFICATION; IDENTIFICATION; CAROTENOIDS AB A large number of fresh fruits and vegetables are primary sources of antioxidants; tomato (Lycopersicon esculentum Mill.) is accepted worldwide as a significant source of antioxidant functional compounds (vitamin C. lycopene, rutin). Many cultivars and hybrids of tomato, having different chemical and nutritional characteristics, are available on the market. Tomato cultivars for industrial processing are very different, not only in fruit characteristics (size, shape), but also in lycopene and antioxidant contents. The aim of this study was the chemotyping and genotyping of the tomato varieties Heinz 3402, Leader and Perfectpeel, (1) to evaluate the genetic traceability of these varieties, and (2) to determine whether their functional antioxidants compounds are useful markers of traceability. Principal component analysis (PCA) was first applied to the Random Polymorphic DNA (RAPD) fingerprints, confirming that this approach is a powerful identification method at intra-specific level. Heinz 3402 showed the highest antioxidant activity, followed by Perfectpeel and Leader varieties. Perfectpeel showed the lower lycopene content, while Leader and Heinz 3402 showed significantly higher values (13.68 and 15.78 mg/100 g, fresh weight, respectively). The highest rutin content was observed in Heinz 3402 (12.46 +/- 0.69. mg/100 g. fresh weight), followed by Leader (7.87 +/- 0.72) and Perfectpeel (2.70 +/- 0.68). Antioxidant capacity was significantly correlated with the lycopene and rutin content. Finally. PCA was applied to chemotype data-sets, confirming both mineral content and functional antioxidant compounds as useful markers to unambiguously identify these high-lycopene content varieties. (C) 2010 Elsevier Inc. All rights reserved. C1 [Coisson, J. D.; Cereti, E.; Travaglia, F.; Arlorio, M.] DiSCAFF, Dept Chem Food Pharmaceut & Pharmacol Sci, I-28100 Novara, Italy. [Coisson, J. D.; Cereti, E.; Travaglia, F.; Arlorio, M.] Univ Piemonte Orientale, Drug & Food Biotechnol DFB Ctr, I-28100 Novara, Italy. [Tedeschi, P.; Maietti, A.; Stagno, C.; Brandolini, V.] Univ Ferrara, Dept Pharmaceut Sci, I-44100 Ferrara, Italy. C3 University of Eastern Piedmont Amedeo Avogadro; University of Ferrara RP Arlorio, M (corresponding author), DiSCAFF, Dept Chem Food Pharmaceut & Pharmacol Sci, Largo Donegani 2, I-28100 Novara, Italy. EM arlorio@pharm.unipmn.it CR Abushita AA, 2000, J AGR FOOD CHEM, V48, P2075, DOI 10.1021/jf990715p Ali BA, 2004, REV FISH BIOL FISHER, V14, P443, DOI 10.1007/s11160-005-0815-0 Andreakis N, 2004, J AGR FOOD CHEM, V52, P3366, DOI 10.1021/jf049963y Arens P, 1995, ACTA HORTIC, P49, DOI 10.17660/ActaHortic.1995.412.3 Arvanitoyannis IS, 2008, INT J FOOD SCI TECH, V43, P1960, DOI 10.1111/j.1365-2621.2008.01799.x Arvanitoyannis IS, 2007, CRIT REV FOOD SCI, V47, P675, DOI 10.1080/10408390600948568 Beecher GR, 1998, P SOC EXP BIOL MED, V218, P98, DOI 10.3181/00379727-218-44282a Brandolini V, 2005, J AGR FOOD CHEM, V53, P678, DOI 10.1021/jf0489623 Brandolini V., 1998, International Journal of Cosmetic Science, V20, P69, DOI 10.1046/j.1467-2494.1998.171737.x Bredemeijer GMM, 2002, THEOR APPL GENET, V105, P1019, DOI 10.1007/s00122-002-1038-6 Consonni R, 2009, J AGR FOOD CHEM, V57, P4506, DOI 10.1021/jf804004z DADOMO M, 2006, AGRICOLTURA, P90 Fanasca S, 2006, J AGR FOOD CHEM, V54, P4319, DOI 10.1021/jf0602572 Fish WW, 2002, J FOOD COMPOS ANAL, V15, P309, DOI 10.1006/jfca.2002.1069 Frusciante L, 2007, MOL NUTR FOOD RES, V51, P609, DOI 10.1002/mnfr.200600158 George B, 2004, FOOD CHEM, V84, P45, DOI 10.1016/S0308-8146(03)00165-1 Gokmen V, 2009, TRENDS FOOD SCI TECH, V20, P278, DOI 10.1016/j.tifs.2009.03.010 HART DJ, 1995, FOOD CHEM, V54, P101, DOI 10.1016/0308-8146(95)92669-B Hernandez M, 2007, J AGR FOOD CHEM, V55, P8604, DOI 10.1021/jf071069u Kallithraka S, 2001, FOOD CHEM, V73, P501, DOI 10.1016/S0308-8146(00)00327-7 Kujala TS, 2000, J AGR FOOD CHEM, V48, P5338, DOI 10.1021/jf000523q Lenucci MS, 2006, J AGR FOOD CHEM, V54, P2606, DOI 10.1021/jf052920c Leonardi C, 2000, J AGR FOOD CHEM, V48, P4723, DOI 10.1021/jf000225t MEUNIER JR, 1993, RES MICROBIOL, V144, P373, DOI 10.1016/0923-2508(93)90194-7 Odriozola-Serrano I, 2007, J AGR FOOD CHEM, V55, P9036, DOI 10.1021/jf0709101 Omoni AO, 2005, TRENDS FOOD SCI TECH, V16, P344, DOI 10.1016/j.tifs.2005.02.002 Peng YY, 2008, J AGR FOOD CHEM, V56, P1838, DOI 10.1021/jf0727544 Popov I, 1999, METHOD ENZYMOL, V300, P437 Prior RL, 2005, J AGR FOOD CHEM, V53, P4290, DOI 10.1021/jf0502698 Raffo A, 2002, J AGR FOOD CHEM, V50, P6550, DOI 10.1021/jf020315t Rossi J.A., 1965, AM J ENOL VITICULT, V16, P144 Ruckert A, 2004, INT J FOOD MICROBIOL, V96, P263, DOI 10.1016/j.ijfoodmicro.2004.03.020 Schwarz S, 2008, J NUTR, V138, P49, DOI 10.1093/jn/138.1.49 Shen YC, 2007, J AGR FOOD CHEM, V55, P6475, DOI 10.1021/jf070799z Stewart AJ, 2000, J AGR FOOD CHEM, V48, P2663, DOI 10.1021/jf000070p Turci M, 2010, FOOD CONTROL, V21, P143, DOI 10.1016/j.foodcont.2009.04.012 Tzouros NE, 2001, CRIT REV FOOD SCI, V41, P287, DOI 10.1080/20014091091823 Venneria E, 2008, J AGR FOOD CHEM, V56, P9206, DOI 10.1021/jf8010992 Weder JKP, 2002, J AGR FOOD CHEM, V50, P4456, DOI 10.1021/jf020216f NR 39 TC 16 Z9 16 U1 0 U2 28 PD MAR PY 2011 VL 24 IS 2 BP 131 EP 139 DI 10.1016/j.jfca.2010.06.008 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000289021800001 DA 2022-12-14 ER PT J AU Canavari, M Centonze, R Hingley, M Spadoni, R AF Canavari, Maurizio Centonze, Roberta Hingley, Martin Spadoni, Roberta TI Traceability as part of competitive strategy in the fruit supply chain SO BRITISH FOOD JOURNAL DT Article DE Information control; Fruits; Supply chain management ID SYSTEM TRACEABILITY; PERCEPTIONS; INFORMATION; MANAGEMENT; QUALITY; SAFETY AB Purpose - The paper aims to focus on traceability as part of information management in the fruit supply chains of Emilia-Romagna, Italy A review of the rules in use for traceability distinguishes between baseline traceability and traceability plus (T +), which encompasses many further embedded value attributes Design/methodology/approach - The paper takes the form of a qualitative study involving in-depth interviews with key Informants in the Italian fresh produce chain Findings - Findings are discussed in terms of different themes including identification of three distinct types of supply chains and the impact upon them and categorisation of traceability systems across the different chains Identified are the impact of information systems management, purchasing management, product management transaction costs, and co-ordination issues Research limitations/implications - The study's findings are based on Italian fresh produce traceability context only Practical implications - Elements of competitive strategy are considered in the analysts of fruit supply chains of Emilia-Romagna, to demonstrate that not only strategic, but also operative choices determine the way a single firm or supply network manages traceability and information issues Applications of such elements to buyer and seller selection as well as to competing retailers of the fruit supply chain, verify the hypothesis. Originality/value - The paper adds to the body of knowledge surrounding prior studies on the development of traceability systems and develops further the analysis of legal and value-adding dimensions of traceability. C1 [Canavari, Maurizio; Spadoni, Roberta] Alma Mater Studiorum Univ Bologna, Dept Agr Econ & Engn, Bologna, Italy. [Centonze, Roberta] Alma Mater Studiorum Univ Bologna, European Off, Res Dept, Bologna, Italy. [Hingley, Martin] Harper Adams Univ Coll, Dept Business Management & Mkt, Newport, Shrops, England. C3 University of Bologna; University of Bologna RP Canavari, M (corresponding author), Alma Mater Studiorum Univ Bologna, Dept Agr Econ & Engn, Bologna, Italy. CR [Anonymous], 1997, MARKETING CONCEPTS S BARZEL Y, 1982, J LAW ECON, V25, P27, DOI 10.1086/467005 Berry D., 1994, INT J PHYS DISTRIB, V24, P20, DOI DOI 10.1108/09600039410074773 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 BUZZELL RD, 1983, HARVARD BUS REV, V61, P92 Cheung S. N. S., 1987, NEW PALGRAVE DICT EC, P55 Choi TM, 2008, OMEGA-INT J MANAGE S, V36, P565, DOI 10.1016/j.omega.2006.12.003 Choi TM, 2008, EUR J OPER RES, V184, P356, DOI 10.1016/j.ejor.2006.10.051 Feldmann M, 2003, OMEGA-INT J MANAGE S, V31, P63, DOI 10.1016/S0305-0483(02)00096-8 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Golan E., 2004, 830 AGR EC GUTMAN J, 1982, J MARKETING, V46, P60, DOI 10.2307/3203341 HAYENGA ML, 1996, STRUCTURAL CHANGES P Hingley M. K., 2001, INT J LOGIST MANAG, V12, P57, DOI DOI 10.1108/09574090110806299 Hingley MK, 2005, IND MARKET MANAG, V34, P848, DOI 10.1016/j.indmarman.2005.03.008 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Holmberg S., 2000, INT J PHYS DISTR LOG, V30, P847 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Kehagia O, 2007, SOCIOL RURALIS, V47, P400, DOI 10.1111/j.1467-9523.2007.00445.x Lambin J.J., 2004, MARKETING STRATEGICO Lindgreen A., 2003, British Food Journal, V105, P328, DOI 10.1108/00070700310481702 Lindgreen A., 2003, British Food Journal, V105, P310, DOI 10.1108/00070700310481694 Maslow AH, 1943, PSYCHOL REV, V50, P370, DOI 10.1037/h0054346 Mason-Jones R., 1997, SUPPLY CHAIN MANAG, V2, P137 Meinzen-Dick R., 2002, CAPRI WORKING PAPER Mentzer JT, 2000, J RETAILING, V76, P549, DOI 10.1016/S0022-4359(00)00040-3 Monczka RM, 1998, DECISION SCI, V29, P553, DOI 10.1111/j.1540-5915.1998.tb01354.x Porter M., 1980, COMPETITIVE STRATEGY Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 Schild SE, 2008, J THORAC ONCOL, V3, P1, DOI 10.1097/JTO.0b013e31815f662c STERN LW, 1980, J MARKETING, V44, P52, DOI 10.2307/1251111 Tompkins J, 1999, IIE SOLUTIONS, V31, P66 van Dorp K.-J., 2002, Logistics Information Management, V15, P24, DOI 10.1108/09576050210412648 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 WARD CE, 1997, W AGR EC ASS SEL PAP Williamson OliverE., 1975, MARKETS HIERARCHIES Yin R.K., 1994, CASE STUDY RES NR 39 TC 68 Z9 72 U1 1 U2 61 PY 2010 VL 112 IS 2-3 BP 171 EP 186 DI 10.1108/00070701011018851 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000276866500006 DA 2022-12-14 ER PT J AU Mai, N Bogason, SG Arason, S Arnason, SV Matthiasson, TG AF Mai, Nga Bogason, Sigurdur Gretar Arason, Sigurjon Arnason, Sveinn Vikingur Matthiasson, Thorolfur Geir TI Benefits of traceability in fish supply chains - case studies SO BRITISH FOOD JOURNAL DT Article DE Cost benefit analysis; Agricultural and fishing industries; Europe; Vietnam; Chile; Supply chain management ID FOOD SAFETY; SYSTEM; COST; US; PERSPECTIVE; QUALITY; MODEL; PAY AB Purpose - The purpose of this paper is to investigate how the seafood industry perceives benefits of traceability implementation. Furthermore, ex ante cost-benefit analyses (CBAs) of adopting new traceability systems are conducted for two firms, operating at different steps of the seafood supply chains, to obtain preliminary knowledge on the net benefits of the project and on how costs and benefits are distributed among the actors. Design/methodology/approach - This is a case-based study. Findings - The surveyed companies perceive improving supply chain management as the most important benefit of traceability. Other benefits are increase of the ability to retain existing customers; product quality improvement; product differentiation; and reduction of customer complaints. However, the quantifiable benefits are perceived differently by the actors at different steps in the supply chains, e.g. implementing radio frequency identification (RFID) tags on pallets in the seafood trading company case study shows tangibly quantifiable benefits. Originality/value - The paper is useful for both practitioners and academics regarding perceived benefits of traceability in fish supply chains. The research provides initial insight into seafood companies' perspectives on the benefits of adopting RFID-based traceability solutions. The paper suggests that the financial burden of implementing traceability may be borne by the processing firms, while gains are reaped by firms in the distribution business closer to the end consumer. This could provide a partial explanation as to why traceability has been slow to gain ground as a visible value-adding marketing tool, and is mainly being driven by food safety regulations. C1 [Mai, Nga; Bogason, Sigurdur Gretar; Arason, Sigurjon; Arnason, Sveinn Vikingur; Matthiasson, Thorolfur Geir] Univ Iceland, Reykjavik, Iceland. [Mai, Nga] Univ Nha Trang, Nha Trang, Vietnam. [Bogason, Sigurdur Gretar; Arnason, Sveinn Vikingur] MarkMar Ehf, Reykjavik, Iceland. [Arason, Sigurjon] Matis Ohf, Reykjavik, Iceland. C3 University of Iceland; Nha Trang University RP Mai, N (corresponding author), Univ Iceland, Reykjavik, Iceland. EM mtt2@hi.is CR Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 BELL C, 1983, Q J ECON, V97, P454 Boardman A. E., 2006, COST BENEFIT ANAL CO Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Can-Trace, 2007, COST TRAC CAN DEV ME Can-Trace, 2004, CAN TRAC DEC SUPP SY *CARBONTRUST, 2008, WHATS CARB FOOTPR YO Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Cicia G., 2005, CAHIERS OPTIONS MEDI, V64, P19 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 DREZE J, 2001, HDB PUBLIC EC, P909 *EU, 2002, OFFICAL J EUROPEAN L, V31, P24 Evans DJ, 2005, J ECON STUD, V32, P47, DOI 10.1108/01443580510574832 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 Golan E.H., 2004, AGR EC REPORTS, P1362 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x HOBBS JE, 2003, CURRENT AGR FOOD RES, P36 *INTR, 2009, WHAT TRUE VAL SEAFOO Karkkainen M., 2003, INT J RETAIL DISTRIB, V31, P529, DOI DOI 10.1108/09590550310497058 Kim HS, 2009, EUR J OPER RES, V194, P406, DOI 10.1016/j.ejor.2007.12.015 Leat P., 1998, SUPPLY CHAIN MANAG, V3, P115, DOI DOI 10.1108/EUM0000000004534 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Loureiro ML, 2003, J AGR RESOUR ECON, V28, P287 Montanari R, 2008, TRENDS FOOD SCI TECH, V19, P425, DOI 10.1016/j.tifs.2008.03.009 MOSCHINI G, 2007, WORKSH RISK COST BEN OLSSON, 2008, OPEN FOOD SCI J, V2, P49 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x RESENDEFILHO MA, 2007, EC TRACEABILITY MITI Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Souza-Monteiro D.M., 2004, EC IMPLEMENTING TRAC Sparling D., 2006, Journal of Food Distribution Research, V37, P154 *TESCO, 2008, CORP RESP REV 2008 *TESCO, 2007, CORP RESP REV 2007 TYEDMERS P, 2008, CO2 EMISSIONS CASE S UMBERGER WJ, 2003, J FOOD DISTRIBUTION, V34, P103 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 VASVIK S, 2006, DAGHGVAREHANDELEN Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Wang Tie-jun, 2009, Shengwu Jiagong Guocheng, V7, P64 Ward R., 2005, INT FOOD AGRIBUS MAN, V8, P92 2002, SEN HOUS REPR USA C [No title captured] NR 44 TC 73 Z9 75 U1 4 U2 89 PY 2010 VL 112 IS 8-9 BP 976 EP 1002 DI 10.1108/00070701011074354 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000283111300013 DA 2022-12-14 ER PT J AU Zhang, YJ Wang, WS Yan, L Glamuzina, B Zhang, XS AF Zhang, Yongjun Wang, Wensheng Yan, Liu Glamuzina, Branko Zhang, Xiaoshuan TI Development and evaluation of an intelligent traceability system for waterless live fish transportation SO FOOD CONTROL DT Article DE Waterless fish live transportation; WSN; Prediction model; RFID; Survival prediction; QR code ID COLD CHAIN LOGISTICS; SUPPLY CHAIN; CRUCIAN CARP; QR CODE; TEMPERATURE; STRESS; QUALITY; FRAMEWORK; MODEL; OPTIMIZATION AB Chinese consumers prefer to purchase fish that have only recently been killed. The supplying of live fish is routinely delivered by water. However, the traditional transportation of live fish with water results in small volume of transportation and may cost prohibitive. Therefore, cold anesthetized waterless live fish transport is considered an alternative and promising strategy, since this is likely to demand less energy with large freight volume. This paper aims to develop an intelligent traceability platform based on HACCP system that integrates wireless monitoring and quality control models to improve the quality control and safety transparency in waterless fish transportation. In this research Chinese sturgeon is taken as experiment subjects for long-distance transportation. Oxygen change model is established as a life-sustained key factor by hybrid prediction method can optimize fish survivability. Survival prediction model is also designed for improvement of live delivery quality with minimum stress accumulation under precise temperature condition. For tracing function evaluation, the QR Code combined with existing EPC traceability technology enables users to expediently query and quickly trace the safety transport information from aquaculture to markets. To demonstrate the automatic monitoring and intelligent traceable management in this platform, sturgeon delivery experiments have been evaluated and analyzed. The results illustrate this system can reduce the potential risks, implement quality control with high survival results, and improve the live fish transport volume at low-costly. In brief, application of this smart platform will provide an effective, suitable technical reference for aquatic enterprises to follow, and help them to adjust the waterless transport techniques for the other types of aquatic products. C1 [Zhang, Yongjun; Wang, Wensheng; Zhang, Xiaoshuan] China Agr Univ, Beijing, Peoples R China. [Wang, Wensheng; Zhang, Xiaoshuan] China Agr Univ, Beijing Lab Food Qual & Safety, Beijing, Peoples R China. [Zhang, Yongjun] Shandong Inst Commerce & Technol, Coll Informat & Art, Jinan, Shandong, Peoples R China. [Yan, Liu] Beijing Wuzi Univ, Beijing 101100, Peoples R China. [Glamuzina, Branko] Univ Dubrovnik, Dubrovnik, Croatia. C3 China Agricultural University; China Agricultural University; Shandong Institute of Commerce & Technology; Beijing Wuzi University; University of Dubrovnik RP Zhang, XS (corresponding author), China Agr Univ, Beijing, Peoples R China. EM zhxshuan@cau.edu.cn CR AMEND DF, 1982, T AM FISH SOC, V111, P603, DOI 10.1577/1548-8659(1982)111<603:TOFICS>2.0.CO;2 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Balamurugan J, 2016, AQUACULTURE, V454, P171, DOI 10.1016/j.aquaculture.2015.12.020 Barton BA, 2003, N AM J AQUACULT, V65, P210, DOI 10.1577/C02-030 CARMICHAEL GJ, 1988, PROG FISH CULT, V50, P155, DOI 10.1577/1548-8640(1988)050<0155:CSOFTE>2.3.CO;2 Chen YY, 2014, J FOOD ENG, V141, P113, DOI 10.1016/j.jfoodeng.2014.05.014 Das PC, 2015, INDIAN J FISH, V62, P39 Espinosa-Curiel I, 2016, INT CONF ELECTR COMM, P40, DOI 10.1109/CONIELECOMP.2016.7438550 Gan M, 2012, APPL MATH MODEL, V36, P2907, DOI 10.1016/j.apm.2011.09.066 Ghughuskar R. S. V. V., 2004, AQUACULTURE, V235, P297 Harmon TS, 2009, REV AQUACULT, V1, P58, DOI 10.1111/j.1753-5131.2008.01003.x Hoang MH, 2012, J FOOD ENG, V113, P389, DOI 10.1016/j.jfoodeng.2012.06.020 Hsiao HI, 2016, FOOD CONTROL, V64, P181, DOI 10.1016/j.foodcont.2015.12.020 Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Jakkhupan W, 2015, TELECOMMUN SYST, V58, P243, DOI 10.1007/s11235-014-9866-7 Juarros E. A. F. P., 2009, J FOOD ENG, V93, P394 Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Kim K, 2016, EXPERT SYST APPL, V46, P463, DOI 10.1016/j.eswa.2015.11.005 Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Langone R, 2015, ENG APPL ARTIF INTEL, V37, P268, DOI 10.1016/j.engappai.2014.09.008 Li B, 2010, CONTROL ENG PRACT, V18, P1406, DOI 10.1016/j.conengprac.2010.08.001 Li CP, 2016, SAFETY SCI, V89, P19, DOI 10.1016/j.ssci.2016.05.015 Long B, 2014, NEUROCOMPUTING, V133, P237, DOI 10.1016/j.neucom.2013.11.012 Lorite GS, 2017, J FOOD ENG, V193, P20, DOI 10.1016/j.jfoodeng.2016.06.016 Manzanares-Lopez P, 2011, J NETW COMPUT APPL, V34, P925, DOI 10.1016/j.jnca.2010.04.018 Marchante A. P., 2014, Journal of Food Engineering, V122, P99 Mi HB, 2012, FISH PHYSIOL BIOCHEM, V38, P1721, DOI 10.1007/s10695-012-9669-2 Moureh J, 2004, INT J REFRIG, V27, P464, DOI 10.1016/j.ijrefrig.2004.03.003 Muradzikwa G., 2014, INT J INNOVATIVE RES, V3 Niros AD, 2012, FUZZY SET SYST, V193, P62, DOI 10.1016/j.fss.2011.08.011 Oyoo-Okoth E, 2011, AQUACULTURE, V319, P226, DOI 10.1016/j.aquaculture.2011.06.052 Perez F. H. R., 2015, BRIT J APPL SCI TECH, V5, P173 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Rawat S, 2016, COMPUT IND, V75, P27, DOI 10.1016/j.compind.2015.10.012 Salin KR, 2005, AQUAC RES, V36, P300, DOI 10.1111/j.1365-2109.2005.01245.x Same M., 1996, TOLERANCE RESPIRATIO, V143, P205 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Shabani F, 2016, AQUACULTURE, V453, P110, DOI 10.1016/j.aquaculture.2015.11.040 Stieglitz JD, 2012, AQUACULTURE, V364, P293, DOI 10.1016/j.aquaculture.2012.08.038 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Summerfelt ST, 2000, AQUACULT ENG, V22, P87, DOI 10.1016/S0144-8609(00)00034-0 Summerfelt ST, 2015, AQUACULT ENG, V65, P46, DOI 10.1016/j.aquaeng.2014.11.002 Tacchi L, 2015, AQUACULTURE, V435, P120, DOI 10.1016/j.aquaculture.2014.09.027 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Tassou SA, 2009, APPL THERM ENG, V29, P1467, DOI 10.1016/j.applthermaleng.2008.06.027 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Trebar M., 2013, INT J ANTENN PROPAG, V2013, P35 Trebar M, 2015, J FOOD ENG, V159, P66, DOI 10.1016/j.jfoodeng.2015.03.007 Tsekouras GE, 2013, FUZZY SET SYST, V221, P65, DOI 10.1016/j.fss.2012.10.004 Walters T., 1994, N AM J AQUACULT, V56, P153 Wei Q, 2004, J APPL ICHTHYOL, V20, P321, DOI 10.1111/j.1439-0426.2004.00593.x Wu HY, 2015, BIOSENS BIOELECTRON, V67, P503, DOI 10.1016/j.bios.2014.09.015 Wu WY, 2005, APPL MATH COMPUT, V169, P198, DOI 10.1016/j.amc.2004.10.087 Xiao XP, 2014, APPL MATH MODEL, V38, P1896, DOI 10.1016/j.apm.2013.10.004 Xiao XQ, 2016, FOOD CONTROL, V60, P656, DOI 10.1016/j.foodcont.2015.09.012 Xiao XQ, 2015, J SCI FOOD AGR, V95, P2693, DOI 10.1002/jsfa.7005 Xinqing X., 2017, J FOOD PROCESS ENG, V40 Yonemori Y, 2009, ANAL CHIM ACTA, V633, P90, DOI 10.1016/j.aca.2008.11.023 Yoneyama Y, 2009, TALANTA, V80, P909, DOI 10.1016/j.talanta.2009.08.014 Yu HZ, 2016, INT J PHOTOENERGY, V2016, DOI 10.1155/2016/6725106 Zeng P, 2014, FISH PHYSIOL BIOCHEM, V40, P973, DOI 10.1007/s10695-013-9898-z Zhang X., 2018, DEV EVALUATION KEY A, V145, P43 Zhang Y., 2017, J FOOD PROCESS ENG, V5 Zhang YJ, 2017, APPL SCI-BASEL, V7, DOI 10.3390/app7090957 Zhang YJ, 2017, J FOOD PROCESS ENG, V40, DOI 10.1111/jfpe.12495 NR 66 TC 38 Z9 40 U1 11 U2 198 PD JAN PY 2019 VL 95 BP 283 EP 297 DI 10.1016/j.foodcont.2018.08.018 WC Food Science & Technology SC Food Science & Technology UT WOS:000447093700037 DA 2022-12-14 ER PT J AU Thakur, M Foras, E AF Thakur, Maitri Foras, Eskil TI EPCIS based online temperature monitoring and traceability in a cold meat chain SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Online temperature monitoring; EPCIS; Cold chain; Traceability; Food quality; Food safety ID QUALITY AB Temperature monitoring is the most critical requirement in the management of a cold products chain. A real-time temperature monitoring system can be useful in reducing losses in a cold food chain that occur due to product deterioration caused by fluctuating temperatures. A pilot test was conducted to evaluate the functionality of an EPCIS based online system for time-temperature monitoring and documenting traceability in a cold meat chain. The test was performed during transportation of chilled lamb products. The RFID based temperature sensors were used to record temperature of the product and ambient temperature inside the container at 10 min time intervals during transportation between processing plant and a distribution terminal 800 km away. The sensors communicated with the main unit installed in the truck and transmitted the data to an EPCIS based system which was accessible through a web interface. The temperature information was recorded as EPCIS events. The results of the application of EPCIS for online temperature monitoring are presented in this paper. Implementing a systematic EPCIS based cold chain monitoring tool can optimize the performance of the whole food supply chain by reducing losses caused by quality deterioration due to temperature variations. (C) 2015 Elsevier B.V. All rights reserved. C1 [Thakur, Maitri; Foras, Eskil] SINTEF Fisheries & Aquaculture, Trondheim, Norway. C3 SINTEF RP Thakur, M (corresponding author), SINTEF Fisheries & Aquaculture, Brattorkaia 17 C, Trondheim, Norway. EM maitri.thakur@sintef.no CR [Anonymous], 2009, KLIM FOR FORSKN [Anonymous], 2011, COM2011571 [Anonymous], 2011, GLOBAL FOOD LOSSES F Bobelyn E, 2006, POSTHARVEST BIOL TEC, V42, P104, DOI 10.1016/j.postharvbio.2006.05.011 Bottani E, 2008, INT J PROD ECON, V112, P548, DOI 10.1016/j.ijpe.2007.05.007 EPCglobal, 2007, EPCGLOBAL ARCH FRAM EPCIS standard, 2007, EPC INF SERV VERS 1 Hanssen O.J., 2011, NYTTBART MATSVINN NO Hartley G., 2013, USE EPC RFID STANDAR Jedermann Reiner, 2007, International Journal of Radio Frequency Identification Technology and Applications, V1, P247, DOI 10.1504/IJRFITA.2007.015849 Luning PA, 2006, TRENDS FOOD SCI TECH, V17, P378, DOI 10.1016/j.tifs.2006.01.012 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Montanari R, 2008, TRENDS FOOD SCI TECH, V19, P425, DOI 10.1016/j.tifs.2008.03.009 Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Schouten RE, 2006, ACTA HORTIC, P131, DOI 10.17660/ActaHortic.2006.712.12 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Sloof M, 1996, TRENDS FOOD SCI TECH, V7, P165, DOI 10.1016/0924-2244(96)81257-X Smith D, 2004, FOOD SUPPLY CHAIN MA, V1, P179 Stenmarck A., 2011, TEMANORD 2011, V2011, P548 Storoy J., 2007, TOK INT FOR OCT 2007 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Strandhagen J. O., 2011, MANAGING GLOBAL SUPP TAOUKIS PS, 1989, J FOOD SCI, V54, P783, DOI 10.1111/j.1365-2621.1989.tb07882.x Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Van der Vorst J.G.A., 2007, 14 INT ANN EUR C APR, P17 van Donk DP, 2008, BRIT FOOD J, V110, P218, DOI 10.1108/00070700810849925 WELLS JH, 1989, J FOOD PROCESS PRES, V12, P271, DOI 10.1111/j.1745-4549.1989.tb00086.x [No title captured] [No title captured] NR 29 TC 43 Z9 47 U1 3 U2 74 PD SEP PY 2015 VL 117 BP 22 EP 30 DI 10.1016/j.compag.2015.07.006 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000362135900003 DA 2022-12-14 ER PT J AU Sun, XY Zhang, F Gutierrez-Gamboa, G Ge, Q Xu, PK Zhang, QW Fang, YL Ma, TT AF Sun, Xiangyu Zhang, Fan Gutierrez-Gamboa, Gaston Ge, Qian Xu, Pingkang Zhang, Qianwen Fang, Yulin Ma, Tingting TI Real wine or not? Protecting wine with traceability and authenticity for consumers: chemical and technical basis, technique applications, challenge, and perspectives SO CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION DT Review DE adulteration; analytical techniques; authenticity; chemometrics methods; geographical origins; grape variety; multivariate analysis; traceability; vintage; wine AB Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity. C1 [Sun, Xiangyu; Zhang, Fan; Ge, Qian; Fang, Yulin; Ma, Tingting] Northwest A&F Univ, Viti Viniculture Engn Technol Ctr State Forestry, Shaanxi Engn Res Ctr Viti Viniculture,Coll Enol, Coll Food Sci & Engn,Heyang Viti Viniculture Stn, Yangling, Shaanxi, Peoples R China. [Gutierrez-Gamboa, Gaston] Univ Talca, Fac Ciencias Agr, Talca, Chile. [Ge, Qian] Qual Stand & Testing Inst Agr Technol, Yinchuan, Ningxia, Peoples R China. [Xu, Pingkang] Mississippi State Univ, Dept Plant & Soil Sci, Mississippi State, MS 39762 USA. [Zhang, Qianwen] Natl Univ Singapore, Coll Sci, Dept Chem, Food Sci & Technol Programme, Singapore, Singapore. C3 Northwest A&F University - China; Universidad de Talca; Mississippi State University; National University of Singapore RP Fang, YL; Ma, TT (corresponding author), Northwest A&F Univ, Viti Viniculture Engn Technol Ctr State Forestry, Shaanxi Engn Res Ctr Viti Viniculture,Coll Enol, Coll Food Sci & Engn,Heyang Viti Viniculture Stn, Yangling, Shaanxi, Peoples R China. EM fangyulin@nwsuaf.edu.cn; matingting@nwafu.edu.cn CR Aceto M, 2002, FOOD ADDIT CONTAM, V19, P126, DOI 10.1080/02652030110071336 ANDERSON K, 2003, WORLD ECON, V26 Annesley TM, 2003, CLIN CHEM, V49, P1041, DOI 10.1373/49.7.1041 Arvanitoyannis IS, 2007, CRIT REV FOOD SCI, V47, P675, DOI 10.1080/10408390600948568 Arvanitoyannis IS, 1999, TRENDS FOOD SCI TECH, V10, P321, DOI 10.1016/S0924-2244(99)00053-9 Augagneur S, 1996, J ANAL ATOM SPECTROM, V11, P713, DOI 10.1039/ja9961100713 Azcarate SM, 2015, FOOD CHEM, V184, P214, DOI 10.1016/j.foodchem.2015.03.081 Banc R, 2014, NOT BOT HORTI AGROBO, V42, P556, DOI 10.15835/nbha4229674 Barrias S, 2019, FOOD CHEM, V270, P299, DOI 10.1016/j.foodchem.2018.07.058 Basalekou M, 2017, J ANAL METHODS CHEM, V2017, DOI 10.1155/2017/5767613 BAXTER M, 1997, FOOD CHEM, V60 Bevin CJ, 2006, J AGR FOOD CHEM, V54, P9713, DOI 10.1021/jf062265o Bevin CJ, 2008, ANAL CHIM ACTA, V621, P19, DOI 10.1016/j.aca.2007.10.042 Bisotto A, 2015, AUST J GRAPE WINE R, V21, P194, DOI 10.1111/ajgw.12127 Boccacci P, 2020, FOOD CHEM, V312, DOI 10.1016/j.foodchem.2019.126100 Bockova J, 2017, APPL SPECTROSC, V71, P1750, DOI 10.1177/0003702817708337 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Brenna JT, 1997, MASS SPECTROM REV, V16, P227, DOI 10.1002/(SICI)1098-2787(1997)16:5<227::AID-MAS1>3.0.CO;2-J Bronzi B, 2020, FOOD CHEM, V315, DOI 10.1016/j.foodchem.2020.126248 Cabrita MJ, 2018, FOOD CONTROL, V92, P80, DOI 10.1016/j.foodcont.2018.04.041 Camara JS, 2006, TALANTA, V68, P1512, DOI 10.1016/j.talanta.2005.08.012 Cappiello A, 2008, ANAL CHEM, V80, P9343, DOI 10.1021/ac8018312 Casale M, 2010, ANAL CHIM ACTA, V668, P143, DOI 10.1016/j.aca.2010.04.021 Catalano V, 2016, J AGR FOOD CHEM, V64, P6969, DOI 10.1021/acs.jafc.6b02560 Cheynier V, 2012, PHYTOCHEM REV, V11, P153, DOI 10.1007/s11101-012-9242-8 Coetzee PP, 2014, FOOD CHEM, V164, P485, DOI 10.1016/j.foodchem.2014.05.027 Colquhoun I.J, 1993, NUTR FOOD SCI, V93, P8, DOI [10.1108/EUM0000000000975, DOI 10.1108/EUM0000000000975] Cordella C, 2002, J AGR FOOD CHEM, V50, P1751, DOI 10.1021/jf011096z Cozzolino D, 2011, ANAL BIOANAL CHEM, V401, P1475, DOI 10.1007/s00216-011-4946-y Daniel C, 2015, MOLECULES, V20, P726, DOI 10.3390/molecules20010726 Darwaish SF, 2014, PROCEDIA COMPUT SCI, V35, P832, DOI 10.1016/j.procs.2014.08.250 de Lima CM, 2020, FOOD CHEM, V312, DOI 10.1016/j.foodchem.2019.126060 DINATALE C, 1995, SENSOR ACTUAT B-CHEM, V25, P801, DOI 10.1016/0925-4005(95)85178-X Dordevic N, 2012, ANAL CHIM ACTA, V757, P19, DOI 10.1016/j.aca.2012.10.046 Dronov M, 2018, RAPID COMMUN MASS SP, V32, P149, DOI 10.1002/rcm.8018 Durante C, 2015, FOOD CHEM, V173, P557, DOI 10.1016/j.foodchem.2014.10.086 Efremov EV, 2008, ANAL CHIM ACTA, V606, P119, DOI 10.1016/j.aca.2007.11.006 Epova EN, 2019, FOOD CHEM, V294, P35, DOI 10.1016/j.foodchem.2019.04.068 Fan SX, 2018, FOOD CONTROL, V88, P113, DOI 10.1016/j.foodcont.2017.11.002 Fantoni R, 2008, SPECTROCHIM ACTA B, V63, P1097, DOI 10.1016/j.sab.2008.08.008 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Ferman AM, 2002, IEEE T IMAGE PROCESS, V11, P497, DOI 10.1109/TIP.2002.1006397 Flamini R, 2003, MASS SPECTROM REV, V22, P218, DOI 10.1002/mas.10052 Flamini R, 2006, MASS SPECTROM REV, V25, P741, DOI 10.1002/mas.20087 Galgano F, 2008, LWT-FOOD SCI TECHNOL, V41, P1808, DOI 10.1016/j.lwt.2008.01.015 Garcia-Beneytez E, 2003, J AGR FOOD CHEM, V51, P5622, DOI 10.1021/jf0302207 Garde-Cerdan T, 2009, FOOD CONTROL, V20, P269, DOI 10.1016/j.foodcont.2008.05.003 Garde-Cerdan T, 2008, FOOD CHEM, V111, P1025, DOI 10.1016/j.foodchem.2008.05.006 Geana EI, 2016, FOOD CONTROL, V62, P1, DOI 10.1016/j.foodcont.2015.10.003 Geana I, 2013, FOOD CHEM, V138, P1125, DOI 10.1016/j.foodchem.2012.11.104 German JB, 2000, ANNU REV NUTR, V20, P561, DOI 10.1146/annurev.nutr.20.1.561 Ghaani M, 2016, TRENDS FOOD SCI TECH, V51, P1, DOI 10.1016/j.tifs.2016.02.008 Godelmann R, 2013, J AGR FOOD CHEM, V61, P5610, DOI 10.1021/jf400800d Gomez-Alonso S, 2007, J FOOD COMPOS ANAL, V20, P618, DOI 10.1016/j.jfca.2007.03.002 Gonzalez G, 2000, EUR FOOD RES TECHNOL, V212, P100, DOI 10.1007/s002170000207 Gonzalvez A, 2009, FOOD CHEM, V112, P26, DOI 10.1016/j.foodchem.2008.05.043 Gosetti F, 2010, J CHROMATOGR A, V1217, P3929, DOI 10.1016/j.chroma.2009.11.060 Gougeon L, 2019, FOOD ANAL METHOD, V12, P956, DOI 10.1007/s12161-018-01425-z Granato D, 2018, COMPR REV FOOD SCI F, V17, P663, DOI 10.1111/1541-4337.12341 Greenberg RR, 2011, SPECTROCHIM ACTA B, V66, P208 Grindlay G, 2011, ANAL CHIM ACTA, V691, P18, DOI 10.1016/j.aca.2011.02.050 Guilbault GG, 2004, ANAL LETT, V37, P1481, DOI 10.1081/AL-120037582 Holmberg L., 2010, INT J WINE RES, V2, P105, DOI [10.2147/IJWR.S14102, DOI 10.2147/IJWR.S14102, 10.2147/ijwr.s14102] Hong E, 2017, J SCI FOOD AGR, V97, P3877, DOI 10.1002/jsfa.8364 HORN P, 1993, Z LEBENSM UNTERS FOR, V196, P407, DOI 10.1007/BF01190802 Isci B, 2014, J I BREWING, V120, P238, DOI 10.1002/jib.129 Jaitz L, 2010, FOOD CHEM, V122, P366, DOI 10.1016/j.foodchem.2010.02.053 Jiang W, 2015, INT J FOOD SCI TECH, V50, P774, DOI 10.1111/ijfs.12686 Perez-Elortondo FJ, 2018, FOOD CONTROL, V88, P159, DOI 10.1016/j.foodcont.2018.01.010 Kelly S. D., 2003, Food authenticity and traceability, P156, DOI 10.1533/9781855737181.1.156 Kment P, 2005, FOOD CHEM, V91, P157, DOI 10.1016/j.foodchem.2004.06.010 Kokkinofta R, 2017, FOOD ANAL METHOD, V10, P3902, DOI 10.1007/s12161-017-0959-2 Kosir I, 1998, ANALUSIS, V26, P97, DOI 10.1051/analusis:1998118 Kosir IJ, 2001, ANAL CHIM ACTA, V429, P195, DOI 10.1016/S0003-2670(00)01301-5 Lakowicz J. R., 1999, PRINCIPLES FLUORESCE, P1, DOI 10.1007/978-1-4757-3061-6_1 Lecat B, 2017, BRIT FOOD J, V119, P84, DOI [10.1108/BFJ-09-2016-0398, 10.1108/bfj-09-2016-0398] Lohumi S, 2015, TRENDS FOOD SCI TECH, V46, P85, DOI 10.1016/j.tifs.2015.08.003 Lorrain B, 2013, MOLECULES, V18, P1076, DOI 10.3390/molecules18011076 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Ma TT, 2014, S AFR J ENOL VITIC, V35, P321 Ma TT, 2019, FOOD FUNCT, V10, P1317, DOI 10.1039/c8fo02322k MACRAE R, 1981, J FOOD TECHNOL, V16, P1 Makris DP, 2006, TALANTA, V70, P1143, DOI 10.1016/j.talanta.2006.03.024 Mandrile L, 2016, FOOD CHEM, V211, P260, DOI 10.1016/j.foodchem.2016.05.011 Markechova D, 2014, FOOD CHEM, V159, P193, DOI 10.1016/j.foodchem.2014.02.085 Martin AE, 2012, FOOD CHEM, V133, P1081, DOI 10.1016/j.foodchem.2012.02.013 MARTIN GJ, 1988, J AGR FOOD CHEM, V36, P316, DOI 10.1021/jf00080a019 McDonald JG, 2007, METHOD ENZYMOL, V432, P145, DOI 10.1016/S0076-6879(07)32006-5 Merken HM, 2000, J AGR FOOD CHEM, V48, P577, DOI 10.1021/jf990872o Messai H, 2016, FOODS, V5, DOI 10.3390/foods5040077 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Monakhova YB, 2014, ANAL CHIM ACTA, V833, P29, DOI 10.1016/j.aca.2014.05.005 Moncayo S, 2016, TALANTA, V158, P185, DOI 10.1016/j.talanta.2016.05.059 Mugo SM, 2020, IEEE SENS J, V20, P656, DOI 10.1109/JSEN.2019.2943088 Mutavdzic M., 2013, Acta Agriculturae Serbica, V18, P169 Ogrinc N, 2003, ANAL BIOANAL CHEM, V376, P424, DOI 10.1007/s00216-003-1804-6 OIV, 2015, INT STANDARD LABELLI Otto M, 2016, CHEMOMETRICS, P135, DOI 10.1002/9783527699377.ch5 Pappas D, 2000, TALANTA, V51, P131, DOI 10.1016/S0039-9140(99)00254-4 Pepi S, 2019, ENVIRON MONIT ASSESS, V191, DOI 10.1007/s10661-019-7544-7 Pereira L, 2017, FOOD CHEM, V216, P80, DOI 10.1016/j.foodchem.2016.07.185 Perestrelo R, 2014, J SEP SCI, V37, P1974, DOI 10.1002/jssc.201400374 Peris M, 2016, TRENDS FOOD SCI TECH, V58, P40, DOI 10.1016/j.tifs.2016.10.014 Pirnau A, 2013, FOOD BIOPHYS, V8, P24, DOI 10.1007/s11483-012-9278-8 Pudney P.D.A., 2010, HDB VIBRATIONAL SPEC, DOI [10.1002/0470027320.s8939, DOI 10.1002/0470027320.S8939] Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Que ZL, 2020, J AGR FOOD CHEM, V68, P4799, DOI 10.1021/acs.jafc.0c01082 Raco B, 2015, FOOD CHEM, V168, P588, DOI 10.1016/j.foodchem.2014.07.043 Reid LM, 2006, TRENDS FOOD SCI TECH, V17, P344, DOI 10.1016/j.tifs.2006.01.006 Rossier JS, 2016, CHIMIA, V70, P338, DOI 10.2533/chimia.2016.338 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Rudnitskaya A, 2010, ANAL CHIM ACTA, V662, P82, DOI 10.1016/j.aca.2009.12.042 Rudnitskaya A, 2007, CHEMOMETR INTELL LAB, V88, P125, DOI 10.1016/j.chemolab.2006.07.005 Rusak DA, 1997, CRIT REV ANAL CHEM, V27, P257, DOI 10.1080/10408349708050587 Sayago I, 2003, SENSOR MATER, V15, P165 Selih VS, 2014, FOOD CHEM, V153, P414, DOI 10.1016/j.foodchem.2013.12.081 Serrano-Lourido D, 2012, FOOD CHEM, V135, P1425, DOI 10.1016/j.foodchem.2012.06.010 Shaw Tony B., 2017, Journal of Wine Research, V28, P13, DOI 10.1080/09571264.2016.1238349 Sivertsen HK, 1999, J SCI FOOD AGR, V79, P107, DOI 10.1002/(SICI)1097-0010(199901)79:1<107::AID-JSFA193>3.0.CO;2-A Springer AE, 2014, J AGR FOOD CHEM, V62, P6844, DOI 10.1021/jf502042c Sun X, 2015, S AFR J ENOL VITIC, V36, P393 Sun XY, 2020, WASTE MANAGE, V104, P119, DOI 10.1016/j.wasman.2020.01.018 Sun XY, 2020, FOOD RES INT, V127, DOI 10.1016/j.foodres.2019.108704 Sun XY, 2016, INNOV FOOD SCI EMERG, V33, P123, DOI 10.1016/j.ifset.2015.10.017 Sun XY, 2015, J FOOD SCI, V80, pC2170, DOI 10.1111/1750-3841.13011 Tang K, 2015, J FOOD SCI, V80, pC20, DOI 10.1111/1750-3841.12691 dos Santos CAT, 2017, TRAC-TREND ANAL CHEM, V88, P100, DOI 10.1016/j.trac.2016.12.012 Temerdashev ZA, 2019, FOOD RAW MATER, V7, P124, DOI 10.21603/2308-4057-2019-1-124-130 Tenenhaus M, 2005, COMPUT STAT DATA AN, V48, P159, DOI 10.1016/j.csda.2004.03.005 The national standard of China, GBT 19049 2008 The national standard of China, GBT 20820 The national standard of China, GBT 18966 2008 The national standard of China, GBT 19504 2008 The national standard of China, GBT 19265 2008 Thompson J.M., 2018, MASS SPECTROMETRY Tominaga T, 1998, FLAVOUR FRAG J, V13, P159, DOI 10.1002/(SICI)1099-1026(199805/06)13:3<159::AID-FFJ709>3.3.CO;2-Z Urvieta R, 2018, FOOD CHEM, V265, P120, DOI 10.1016/j.foodchem.2018.05.083 Van Leeuwen Cornelis, 2006, Journal of Wine Research, V17, P1, DOI 10.1080/09571260600633135 Vergara C., 2011, 1081 PROGR AUTHENTIC, P101, DOI [10.1021/bk-2011-1081.ch007, DOI 10.1021/BK-2011-1081.CH007] Vicol C., 2010, Journal of Agroalimentary Processes and Technologies, V16, P294 Volschenk H., 2006, SOUTH AFRICAN JOURNAL OF ENOLOGY AND VITICULTURE, V27, P123 von Baer D, 2008, ANAL CHIM ACTA, V621, P52, DOI 10.1016/j.aca.2007.11.034 WACHS T, 1991, J CHROMATOGR SCI, V29, P357, DOI 10.1093/chromsci/29.8.357 Winquist F, 1999, SENSOR ACTUAT B-CHEM, V58, P512, DOI 10.1016/S0925-4005(99)00155-0 Wu H, 2019, FOOD CHEM, V301, DOI 10.1016/j.foodchem.2019.125137 Yang H, 2013, REGULATION OVERSIGHT Yoo YJ, 2010, COMPR REV FOOD SCI F, V9, P530, DOI 10.1111/j.1541-4337.2010.00125.x Zinicovscaia I, 2017, FOOD ANAL METHOD, V10, P3523, DOI 10.1007/s12161-017-0913-3 NR 148 TC 3 Z9 3 U1 13 U2 57 PD AUG 29 PY 2022 VL 62 IS 24 BP 6783 EP 6808 DI 10.1080/10408398.2021.1906624 EA MAR 2021 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000637605700001 DA 2022-12-14 ER PT J AU Wang, Q Liu, HJ Bai, Y Zhao, Y Guo, J Chen, AL Yang, SM Zhao, SS Tan, LQ AF Wang, Qian Liu, Haijin Bai, Yang Zhao, Yan Guo, Jun Chen, Ailiang Yang, Shuming Zhao, Shanshan Tan, Liqin TI Research progress on mutton origin tracing and authenticity SO FOOD CHEMISTRY DT Review DE Origin tracing; Authenticity identification; Mutton; Stable isotope traceability technology; DNA traceability technology ID STABLE-ISOTOPE RATIO; LAMB MEAT; GEOGRAPHICAL ORIGIN; MULTIELEMENT ANALYSIS; GAS-CHROMATOGRAPHY; DIETARY-CHANGES; OVINE MUSCLES; PCR METHOD; IDENTIFICATION; TRACEABILITY AB With the globalization of the food market and the convenience of food transportation between countries, consumers are increasingly worried about the source and safety of the food they eat. Traceability has been identified as an important tool for ensuring food safety and quality. This review mainly introduces the principles of five food traceability technologies, summarizes the progress in mutton application, comprehensively compares and analyzes the five traceability technologies, and discusses their application prospects, advantages and disadvantages. It is aimed at promoting research and application of traceability technology in mutton safety, promoting establishment and improvement of food traceability system. C1 [Wang, Qian; Bai, Yang; Zhao, Yan; Chen, Ailiang; Yang, Shuming; Zhao, Shanshan] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Wang, Qian; Bai, Yang; Guo, Jun] Inner Mongolia Agr Univ, Coll Food Sci & Engn, Hohhot 010018, Peoples R China. [Liu, Haijin] Tibet Autonomous Reg Agr & Livestock Prod Qual &, Lhasa 850211, Peoples R China. [Tan, Liqin] Changgao Agr Technol Extens Stn, Beipiao 122109, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Inner Mongolia Agricultural University RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. CR Al-Taghlubee D., 2019, Journal of Food Quality and Hazards Control, V6, P8, DOI 10.18502/jfqhc.6.1.453 Aleixandre-Tudo JL, 2020, APPL SPECTROSC REV, V55, P873, DOI 10.1080/05704928.2019.1694936 Andre CM, 2020, FOODS, V9, DOI 10.3390/foods9070897 Aranishi F, 2005, J FOOD SCI, V70, pC235, DOI 10.1111/j.1365-2621.2005.tb07165.x Arvanitoyannis IS, 2003, CRIT REV FOOD SCI, V43, P173, DOI 10.1080/10408690390826482 Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bai Jing, 2019, Shipin Kexue / Food Science, V40, P287 Behkami S, 2017, FOOD CHEM, V217, P438, DOI 10.1016/j.foodchem.2016.08.130 Biondi L, 2013, ANIMAL, V7, P1559, DOI 10.1017/S1751731113000645 Bontempo L, 2016, RAPID COMMUN MASS SP, V30, P170, DOI 10.1002/rcm.7428 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Capuano E, 2013, J SCI FOOD AGR, V93, P12, DOI 10.1002/jsfa.5914 Chapman J, 2020, INT J FOOD SCI TECH, V55, P935, DOI 10.1111/ijfs.14367 Chen C., 2018, MODERN FOOD SCI TECH, V34 [陈通 Chen Tong], 2019, [中国食品学报, Journal of Chinese Institute Of Food Science and Technology], V19, P221 Cozzolino D, 2004, LEBENSM-WISS TECHNOL, V37, P447, DOI 10.1016/j.lwt.2003.10.013 Deng Z. D., 2018, DIGITAL PCR DETECTIO Devincenzi T, 2014, FOOD CHEM, V152, P456, DOI 10.1016/j.foodchem.2013.11.164 Di Stasio L, 2017, SMALL RUMINANT RES, V149, P85, DOI 10.1016/j.smallrumres.2017.01.013 Elmqvist N, 2010, IEEE T VIS COMPUT GR, V16, P439, DOI 10.1109/TVCG.2009.84 Erasmus SW, 2018, FOOD CHEM, V239, P926, DOI 10.1016/j.foodchem.2017.07.026 Erasmus SW, 2016, FOOD CHEM, V192, P997, DOI 10.1016/j.foodchem.2015.07.121 Fan YX, 2019, MEAT SCI, V157, DOI 10.1016/j.meatsci.2019.06.008 Fisher AV, 2000, MEAT SCI, V55, P141, DOI 10.1016/S0309-1740(99)00136-9 Fragni R, 2018, FOOD CONTROL, V93, P211, DOI 10.1016/j.foodcont.2018.06.002 Franke BM, 2008, EUR FOOD RES TECHNOL, V226, P761, DOI 10.1007/s00217-007-0588-x Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Gomez-Cortes P, 2019, ANIM PROD SCI, V59, P914, DOI 10.1071/AN17885 Granato D, 2018, COMPR REV FOOD SCI F, V17, P663, DOI 10.1111/1541-4337.12341 Guo BoLi, 2018, Scientia Agricultura Sinica, V51, P2391 Gurbanov R, 2018, SPECTROCHIM ACTA A, V189, P282, DOI 10.1016/j.saa.2017.08.038 Harrison SM, 2011, FOOD CHEM, V124, P291, DOI 10.1016/j.foodchem.2010.06.035 Harrison SM, 2010, FOOD CHEM, V123, P203, DOI 10.1016/j.foodchem.2010.04.032 He DeHua, 2019, Journal of Agricultural Science and Technology (Beijing), V21, P123 He WeiLing, 2012, Scientia Agricultura Sinica, V45, P1873 Hernandez OM, 2005, FOOD CHEM, V93, P449, DOI 10.1016/j.foodchem.2004.10.036 Hou C. C., 2019, CHINESE J ANIMAL SCI, V55, P106 Inacio CT, 2017, CRIT REV FOOD SCI, V57, P181, DOI 10.1080/10408398.2014.887056 Jiang Jie, 2015, Journal of Food Safety and Quality, V6, P3701 Karimi K, 2020, EUR J PLANT PATHOL, V156, P463, DOI 10.1007/s10658-019-01895-9 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Lange CN, 2019, FOOD CHEM, V300, DOI 10.1016/j.foodchem.2019.125145 Li L., 2016, RAPID DETECTION MUTT Li M. Q., 2017, J GANSU AGR U, V52 Li T. T., 2018, CURRENT BIOTECHNOLOG, V8, P522 Li TT, 2019, FOOD CHEM, V277, P554, DOI 10.1016/j.foodchem.2018.11.009 [李文博 Li Wenbo], 2019, [食品科学, Food Science], V40, P207 Liu F., 2020, MODERN FOOD, V11, P167 Liu M.L., 2017, MEAT RES, V31, P25 Loftus R, 2005, REV SCI TECH OIE, V24, P231, DOI 10.20506/rst.24.1.1563 Lopez-Maestresalas A, 2019, FOOD CONTROL, V98, P465, DOI 10.1016/j.foodcont.2018.12.003 Luo YuLong, 2018, Shipin Kexue / Food Science, V39, P103 Lv C. R., 2019, YUNNAN ANIMAL HUSBAN, P11 Lv J, 2017, FOOD ANAL METHOD, V10, P347, DOI 10.1007/s12161-016-0588-1 Ma DH, 2011, SPECTROSC SPECT ANAL, V31, P877, DOI 10.3964/j.issn.1000-0593(2011)04-0877-05 Ma LiNa, 2019, Animal Husbandry and Feed Science (Inner Mongolia), V40, P1 Mao P. C., 2017, J HENAN AGR SCI, V46 Margetin M, 2018, ARCH ANIM BREED, V61, P395, DOI 10.5194/aab-61-395-2018 Mekki I, 2016, J FOOD COMPOS ANAL, V53, P40, DOI 10.1016/j.jfca.2016.09.002 Monahan FJ, 2018, MEAT SCI, V144, P2, DOI 10.1016/j.meatsci.2018.05.008 Moreno-Rojas JM, 2008, RAPID COMMUN MASS SP, V22, P3701, DOI 10.1002/rcm.3773 Newby PK, 2004, NUTR REV, V62, P177, DOI [10.1111/j.1753-4887.2004.tb00040.x, 10.1301/rn.2004.may.177-203] Osorio MT, 2011, J AGR FOOD CHEM, V59, P3285, DOI 10.1021/jf1040433 Othman A, 2019, BMC RES NOTES, V12, DOI 10.1186/s13104-019-4263-7 Peng D, 2015, FOOD CHEM, V188, P415, DOI 10.1016/j.foodchem.2015.05.001 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 [齐安安 Qi Anan], 2018, [分析测试学报, Journal of Instrumental Analysis], V37, P955 Qiao L., 2016, SENSING AGR FOOD QUA, V9864 Rachmadhani, 2019, INT FOOD RES J, V26, P509 Reykdal O, 2011, J FOOD COMPOS ANAL, V24, P980, DOI 10.1016/j.jfca.2011.03.002 Rossmann A, 2000, EUR FOOD RES TECHNOL, V211, P32, DOI 10.1007/s002170050585 Sacco D, 2005, MEAT SCI, V71, P542, DOI 10.1016/j.meatsci.2005.04.038 Song J., 2012, J ANHUI AGR, V40 Song KY, 2017, FOOD CHEM, V229, P341, DOI 10.1016/j.foodchem.2017.02.085 Sturup S, 2004, ANAL BIOANAL CHEM, V378, P273, DOI 10.1007/s00216-003-2195-4 Sun F. M., 2009, THESIS CHINESE ACAD Sun H., 2018, GUIDE FOOD SAFETY, P139 Sun HaiXia, 2013, Acta Prataculturae Sinica, V22, P346 Sun S. M., 2012, THESIS NW AGRICULTUR Sun SM, 2011, SPECTROSC SPECT ANAL, V31, P937, DOI 10.3964/j.issn.1000-0593(2011)04-0937-05 Sun SM, 2016, FOOD CHEM, V213, P675, DOI 10.1016/j.foodchem.2016.07.013 Sun ShuMin, 2012, Transactions of the Chinese Society of Agricultural Engineering, V28, P237 Sun SM, 2011, FOOD CHEM, V124, P1151, DOI 10.1016/j.foodchem.2010.07.027 Tian C. X., 2016, MODERN FOOD SCI TECH, V32, P295 Tian X. X., 2016, MODERN FOOD SCI TECH, V32, P295 Tsakali E., 2019, Journal of Food Research, V8, P52, DOI 10.5539/jfr.v8n4p52 Vasta V, 2007, J AGR FOOD CHEM, V55, P4630, DOI 10.1021/jf063432n Wang BoHui, 2018, Shipin Kexue / Food Science, V39, P1 Wang J., 2018, SCI TECHNOLOGY FOOD, V39 Wang LiPing, 2018, China Oils and Fats, V43, P141 Wang P. P., 2012, STUDY BREED DISCRIMI Wang Q, 2021, MEAT SCI, V174, DOI 10.1016/j.meatsci.2020.108415 Wang Q, 2019, FOOD CONTROL, V98, P431, DOI 10.1016/j.foodcont.2018.11.038 [王綪 Wang Qian], 2017, [食品科学, Food Science], V38, P222 Wang WX, 2018, FOOD ANAL METHOD, V11, P2707, DOI 10.1007/s12161-018-1256-4 Wang Y., 2018, TRACEABILITY GEOGRAP Wu H, 2020, FOOD CONTROL, V109, DOI 10.1016/j.foodcont.2019.106905 Wu X., 2009, SWINE IND SCI, V26, P105 Xue S. Y., 2019, FEED IND, V40, P43 Yan XiangLin, 2018, Shipin Kexue / Food Science, V39, P80 Yanagi Y, 2012, FOOD CHEM, V134, P502, DOI 10.1016/j.foodchem.2012.02.107 Yang DongYan, 2015, Journal of Food Safety and Quality, V6, P555 Yuan Qian, 2019, Shipin Kexue / Food Science, V40, P29 Zhang C., 2019, CHINESE J ANIMAL SCI, P1 [张宏博 Zhang Hongbo], 2017, [食品工业科技, Science & Technology of Food Industry], V38, P347 Zhang Ning, 2008, Transactions of the Chinese Society of Agricultural Engineering, V24, P309 Zhang Y., 2018, HEILONGJIANG ANIMAL Zhang Y. H., 2015, SCI TECHNOLOGY FOOD, V36 Zhao QN, 2018, POULTRY SCI, V97, P2239, DOI 10.3382/ps/pey070 Zhao Y., 2015, QUALITY SAFETY AGROP, P35 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y NR 113 TC 0 Z9 0 U1 45 U2 114 PD MAR 30 PY 2022 VL 373 AR 131387 DI 10.1016/j.foodchem.2021.131387 EA OCT 2021 PN A WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000715124400014 DA 2022-12-14 ER PT J AU Cannavan, A Maestroni, BM AF Cannavan, Andrew Maestroni, Britt M. TI Analytical methodology for food safety and traceability in developing countries SO AGRO FOOD INDUSTRY HI-TECH DT Article DE Analytical methodology; food safety; traceability; developing countries; technology transfer ID MASS-SPECTROMETRY AB The implementation of integrated farm-to-fork food safety systems to meet today's stringent requirements for consumer protection and the global trade in food commodities requires a well developed analytical capacity for contaminants and residues. New technology for food safety and traceability applications is continuously being developed. Developing countries frequently struggle to keep pace with technological developments. Effort may be better invested in optimising existing and proven methodology to tackle food safety problems. Revisions of international guidelines must recognise this and be risk-rather than technology-based. Only those emerging techniques that are robust, wide scope, cost-effective and applicable in the medium to long term should be considered for use in developing countries to complement the more proven techniques. C1 [Cannavan, Andrew; Maestroni, Britt M.] Joint FAO IAEA Div Nucl Techn Food & Agr, Int Atom Energy Agcy Food & Environm Protect Lab, A-1400 Vienna, Austria. C3 International Atomic Energy Agency RP Cannavan, A (corresponding author), Joint FAO IAEA Div Nucl Techn Food & Agr, Int Atom Energy Agcy Food & Environm Protect Lab, Wagromer Str 5,POB 100, A-1400 Vienna, Austria. CR [Anonymous], 2002, OFFICIAL J EUROPEA L, V031, P1 [Anonymous], 2002, OFFICIAL J EUROPEA L, V221, P8 [Anonymous], 2009, 712009 CACGL [Anonymous], 2009, SANCO106842009 CANNAVAN A, 2004, P EUR 5 C NOORDW NET, P151 Codex Alimentarius Commission, 2003, 401993 CACGL Codex Alimentarius Commission, 2006, 602006 CACGL *IAEA, IAEA FOOD ENV PROT E Lehotay SJ, 2008, TRAC-TREND ANAL CHEM, V27, P1070, DOI 10.1016/j.trac.2008.10.004 Mooney MH, 2009, TRAC-TREND ANAL CHEM, V28, P665, DOI 10.1016/j.trac.2009.03.011 Schurek J, 2008, ANAL CHEM, V80, P9567, DOI 10.1021/ac8018137 NR 11 TC 0 Z9 0 U1 0 U2 8 PD MAY-JUN PY 2010 VL 21 IS 3 SU S BP 9 EP 12 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000279871200004 DA 2022-12-14 ER PT J AU Gautam, R Singh, A Karthik, K Pandey, S Scrimgeour, F Tiwari, MK AF Gautam, Rahul Singh, Agnisha Karthik, K. Pandey, S. Scrimgeour, F. Tiwari, M. K. TI Traceability using RFID and its formulation for a kiwifruit supply chain SO COMPUTERS & INDUSTRIAL ENGINEERING DT Article DE Kiwifruit supply chain; RFID-tags; Transportation costs; Plant Pollinator Optimization; Traceability ID IDENTIFICATION; ALLOCATION; ALGORITHM; NETWORKS AB Traceability is one of the most important requirements in a fruit supply chain. It helps in keeping a track over the quality, perishability and freshness of the fruits which are the foremost concern for maintaining customer satisfaction. In this paper, we consider a case of kiwifruit supply chain and analyze the impact of traceability using Radio Frequency Identification (RFID) tags. A multi-objective integer non-linear programming model (MOINLP) is formulated considering two objective functions which include (i) minimization of the total costs incorporating logistics costs and costs of implementing RFID tags and (ii) minimization of liability costs on occurrence of contamination. To compute this problem, a newly developed approach of Plant Pollinator Optimization Algorithm (PPO) has been implemented and a comparison with the well-known Non-dominated Sorting Genetic Algorithm-H (NSGA-II) is performed. Insights evolving out of this study can help the Supply Chain Managers in minimizing the risks of delivery of perished fruits by tracing it at an earlier stage and thus reducing the liability costs. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Gautam, Rahul; Singh, Agnisha; Karthik, K.; Tiwari, M. K.] IIT Kharagpur, Dept Ind & Syst Engn, Kharagpur 721302, W Bengal, India. [Scrimgeour, F.] Simla Pandey Consulting Agribusiness, Hamilton, New Zealand. [Pandey, S.] Univ Waikato, Waikato Management Sch, Hamilton, New Zealand. C3 Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kharagpur; University of Waikato RP Tiwari, MK (corresponding author), IIT Kharagpur, Dept Ind & Syst Engn, Kharagpur 721302, W Bengal, India. EM rahul.iitkgp13@outlook.com; agnisha7@gmail.com; kkarthikkgp@hotmail.com; drsppandey@gmail.com; scrim@waikato.ac.nz; mkt09@hotmail.com CR Alizadeh M, 2015, APPL SOFT COMPUT, V34, P551, DOI 10.1016/j.asoc.2015.05.020 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Deb K, 2002, IEEE T EVOLUT COMPUT, V6, P182, DOI 10.1109/4235.996017 Deguines N., 2010, COMPARTMENTALIZATION Fazlollahtabar H, 2013, APPL SOFT COMPUT, V13, P550, DOI 10.1016/j.asoc.2012.08.016 Gandino F., 2007, P 1 ANN RFID EUR, P1 Greer G., 2009, COMP PERFORMANCE ORG Hajiaghaei-Keshteli M, 2011, APPL SOFT COMPUT, V11, P2069, DOI 10.1016/j.asoc.2010.07.004 Huang W., 2012, ASIAN AGR RES, V4 Bravo JJ, 2013, EXPERT SYST APPL, V40, P6742, DOI 10.1016/j.eswa.2013.06.015 Khorram E, 2014, J COMPUT APPL MATH, V261, P158, DOI 10.1016/j.cam.2013.11.007 Ko M, 2010, APPL SOFT COMPUT, V10, P661, DOI 10.1016/j.asoc.2009.09.004 Laiopoulou N., 2002, LABOUR HOURS, V424, P400 MacLeod CJ, 2012, J APPL ECOL, V49, P652, DOI 10.1111/j.1365-2664.2012.02135.x Mohtashami A, 2015, APPL SOFT COMPUT, V31, P30, DOI 10.1016/j.asoc.2015.02.030 Piramuthu S, 2013, EUR J OPER RES, V225, P253, DOI 10.1016/j.ejor.2012.09.024 Rahmati SHA, 2013, APPL SOFT COMPUT, V13, P1728, DOI 10.1016/j.asoc.2012.12.016 Saavedra S, 2009, NATURE, V457, P463, DOI 10.1038/nature07532 Saunders C. M., 2005, CHINA NEW ZEALAND HO Soysal M, 2014, INT J PROD ECON, V152, P57, DOI 10.1016/j.ijpe.2013.12.012 Suweis S, 2013, NATURE, V500, P449, DOI 10.1038/nature12438 Tu M., 2013, ASIAN AGR RES, V5, P35 Ustundag A, 2009, TRANSPORT RES E-LOG, V45, P29, DOI 10.1016/j.tre.2008.09.001 Validi S, 2014, INT J PROD ECON, V152, P71, DOI 10.1016/j.ijpe.2014.02.003 Zitzler E, 2000, EVOL COMPUT, V8, P173, DOI 10.1162/106365600568202 NR 25 TC 42 Z9 45 U1 1 U2 38 PD JAN PY 2017 VL 103 BP 46 EP 58 DI 10.1016/j.cie.2016.09.007 WC Computer Science, Interdisciplinary Applications; Engineering, Industrial SC Computer Science; Engineering UT WOS:000393527400005 DA 2022-12-14 ER PT J AU Yusof, R Shuib, A Ramachandran, S Ali, A Ismail, I AF Yusof, Roba'a Shuib, Ahmad Ramachandran, Sridar Ali, Ahmad Ismail, Illisriyani TI FACTORS INFLUENCING THE EFFECTIVENESS OF SUPPLY CHAIN TRACEABILITY SYSTEM IMPLEMENTATION FOR SHARK AND RAY PRODUCTS IN PAHANG, MALAYSIA: INSIGHTS FROM KEY INFORMANT INTERVIEWS SO INTERNATIONAL JOURNAL OF BUSINESS AND SOCIETY DT Article DE supply chain; traceability; shark and ray products; key informant interviews; Pahang; Peninsular Malaysia AB The objective of this study is to obtain experts' opinions in identifying factors influencing the implementation of supply chain traceability for shark and ray products in Pahang, Malaysia. The information was obtained through structured key informant interviews (KIIs), which were conducted one-to-one and face-to-face with relevant experts from government agencies and Malaysia's National Plan of Action (NPOA-Shark) committee members. From the text discourse analysis, the study has identified seven indicators that need to be improved and explored before developing the full implementation of the supply chain traceability system in Pahang. The indicators are as follows: (i) policy and strategic management; (ii) acts and regulations; (iii) shark and ray resources; (iv) manpower and capability; (v) infrastructure and management information system; (vi) collaboration efforts; and (vii) buy-ins from stakeholders. It is recommended that the government reviews the current policy, acts, regulations, and strategic initiatives to increase the commitment from all relevant stakeholders to ensure sustainable utilisation of shark and ray species. C1 [Yusof, Roba'a] Univ Putra Malaysia, Inst Trop Agr & Food Secur, Serdang 43400, Selangor, Malaysia. [Shuib, Ahmad; Ramachandran, Sridar] Univ Putra Malaysia, Sch Business & Econ, Serdang, Selangor, Malaysia. [Ali, Ahmad] Southeast Asia Marine Resources Inst ISMAT, Taman Perikanan Chendering, Terengganu 21080, Malaysia. [Ismail, Illisriyani] Univ Putra Malaysia, Int Inst Aquaculture & Aquat Sci, Serdang, Selangor, Malaysia. C3 Universiti Putra Malaysia; Universiti Putra Malaysia; Universiti Putra Malaysia RP Yusof, R (corresponding author), Univ Putra Malaysia, Inst Trop Agr & Food Secur, Serdang 43400, Selangor, Malaysia. EM GS48497@student.upm.edu.my CR Ahmad A., 2004, SEAFDECMFRDMDSP8, P47 Ahmad A., 2014, SEAFDECMFRDMDSP25, P289 Ahmad A., 2012, SEAFDECMFRDMDSP18, P210 Ahmad A., 2017, SEAFDECMFRDMDSP31, P33 Ahmad S., 2019, STUDY DOMESTIC UNPUB, P32 Ahmad S., 2019, DOMESTIC MARKE UNPUB, P36 Ahmad S., 2018, STUDY FISHERS DEPEND Andre V., 2018, FISHERIES AQUACULTUR, P24 Martins APB, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0193969 Booth H., 2018, SHARK RAY CONSERVATI Brautigam A., 2015, GLOBAL PRIORITIES CO, P26 Clarke S., 2004, SHARK PRODUCT TRADE Creswell J.W., 2014, RES DESIGN QUALITATI, V4th Creswell J. W., 2015, ED RES PLANNING COND Cripps G., 2015, SF201534 Dent Felix, 2015, FAO Fisheries and Aquaculture Technical Paper, V590, P1 Department of Fisheries. (DOF), 2021, ANN FISH STAT Department of Fisheries. (DOF), 2014, MALAYSIA NATL PLAN A Dipper F.A., 2002, ELASMOBRANCH BIODIVE Duan Y., 2017, FRAMEWORK SUCCESSFUL Fatimah M. A., 2017, MARKETING SHARKS RAY, P73 Friedman K, 2018, FISH FISH, V19, P662, DOI 10.1111/faf.12281 Gardner H, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16173053 GS1 Global Traceability Standard. (GIS GTS), 2017, GS1S FRAM DES INT TR, P58 Hosch G., 2017, TECHNICAL PAPER NO 6, P102 International Organization for Standardization. (ISO), 2011, TRAC FINF PROD SPEC International Trade Centre, 2015, B NO 912015, P41 Kamalu I., 2015, CHAPTER 8 DISCOURSE Khan S, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010204 Lehr H., 2015, REPORT PREPARED CITE, P101 Lehr H, 2017, UN C TRADE DEV UNCTA, P77 Lehr H., 2016, CATCH DOCUMENTATION Lewis SG, 2017, J FOOD SCI, V82, pA13, DOI 10.1111/1750-3841.13743 Lewis S, 2015, HEALTH PROMOT PRACT, V16, P473, DOI 10.1177/1524839915580941 Ministry of Marine Affairs and Fisheries. (MMAF), 2019, EFFORT CHALLENGES MA Momballa M. C., 2020, RAPID ASSESSMENT ART Mundy V., 2015, TRAFFIC REPORT CITES, P90 Musick J.A., 2011, SHARKS FAO FISHERIES Musick J. A., 2005, VIMS BOOKS BOOK CHAP, V25 Okes N., 2019, OVERVIEW MAJOR SHARK Palinkas LA, 2015, ADM POLICY MENT HLTH, V42, P533, DOI 10.1007/s10488-013-0528-y Pavitt A., FAO FISHERIES AQUACU, P2021, DOI [10.4060/cb2971-n, DOI 10.4060/CB2971-N] SUCIU L, 2019, INTRO CHAPTER DISCOU Tongco M. D. C., 2007, Ethnobotany Research and Applications, V5, P147 United Nations Economic Commission for Europe. (UNECE), 2016, TRACEABILITY SUSTAIN USAID, 2018, MALAYSIA CDT GAP ANA, P38 Ussher J. M., 2014, CHAPTER 13 DISCOURSE Vannuccini S, 1999, FAO FISHERIES TECHNI NR 48 TC 0 Z9 0 U1 2 U2 2 PY 2022 VL 23 IS 1 BP 297 EP 325 DI 10.33736/ijbs.4614.2022 WC Business SC Business & Economics UT WOS:000782477300018 DA 2022-12-14 ER PT J AU Jing, RZ Li, P AF Jing, Rongzhi Li, Ping TI Quality Control System of Red Jujube by Hybrid Model: Development of an Efficient Framework SO FRONTIERS IN PLANT SCIENCE DT Article DE red jujube quality and safety; traceability system; blockchain; internet of things (IoTs); quality control system ID TRACEABILITY; BLOCKCHAIN; CHALLENGES; INTERNET AB Food traceability is very important for the quality and safety of agricultural products, which is related to the people's livelihood and national economy and has drawn great attention from governments and scientists around the world. The existing studies have not yet overcome the crisis characteristics comprehensively and systematically. A traceability system of red jujube is constructed by a hybrid mode of blockchain and the Internet of Things (IoTs). The system integrates the blockchain and the IoT technologies with characteristics of tamper-proof, decentralization, and distributed storage and solves the problem of date quality traceability by designing the technical process and architecture of date quality traceability and the big data of red jujube, jujube plantation, processing enterprise, commercial enterprises, and market administration. The whole process from planting to processing and sales of red jujube are recorded in the block to ensure the realization of quality traceability of red dates in the process. Through the whole process of big data processing, the key information collected in each process is stored in the database to ensure the realization of quality traceability of red dates in the framework. The system can help to minimize the production and distribution of unsafe or poor-quality products, thereby minimizing the potential for bad publicity, liability, and recalls. C1 [Jing, Rongzhi; Li, Ping] Sias Univ, Sch Elect & Informat Engn, Xinzheng, Peoples R China. RP Li, P (corresponding author), Sias Univ, Sch Elect & Informat Engn, Xinzheng, Peoples R China. EM 19360666@qq.com CR Almalki FA, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13115908 Alsamhi SH, 2022, IEEE T GREEN COMMUN, V6, P295, DOI 10.1109/TGCN.2021.3132561 Alsamhi SH, 2021, T EMERG TELECOMMUN T, V32 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Christidis K, 2016, IEEE ACCESS, V4, P2292, DOI 10.1109/ACCESS.2016.2566339 Cocco L, 2021, 2021 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING (SANER 2021), P669, DOI 10.1109/SANER50967.2021.00085 Corallo A, 2020, TRENDS FOOD SCI TECH, V101, P28, DOI 10.1016/j.tifs.2020.04.022 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Konstantinidis I, 2018, LECT NOTES BUS INF P, V320, P384, DOI 10.1007/978-3-319-93931-5_28 Mukkamala Raghava Rao, 2018, IEEE Engineering Management Review, V46, P94, DOI 10.1109/EMR.2018.2881149 [Ракитский В.Н. Rakitskii Valeriy N.], 2020, [Здравоохранение Российской Федерации, Health Care of the Russian Federation, Zdravookhranenie Rossiiskoi Federatsii], V64, P150, DOI 10.46563/0044-197X-2020-64-3-150-157 Ray P, 2021, INT J ENG APPL SCI, V12, P1 Tharatipyakul A, 2021, IEEE ACCESS, V9, P82909, DOI 10.1109/ACCESS.2021.3085982 Xu Zu, 2020, Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing (AISC 1190), P119, DOI 10.1007/978-3-030-49829-0_9 Yadav S, 2022, OPER MANAGE RES, V15, P1, DOI 10.1007/s12063-020-00164-x Yang C., 2020, INT C DATA MINING WO, DOI [10.3390/s20102990, DOI 10.3390/S20102990] Zhang XH, 2020, IEEE ACCESS, V8, P141748, DOI 10.1109/ACCESS.2020.3013005 Zhang Y., 2021, CYBER SECURITY INTEL, DOI [10.3390/s22041304, DOI 10.3390/S22041304] Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zhao YB, 2017, IEEE INT C COMPUT, P414, DOI 10.1109/CSE-EUC.2017.264 NR 22 TC 0 Z9 0 U1 7 U2 7 PD JUN 9 PY 2022 VL 13 AR 888978 DI 10.3389/fpls.2022.888978 WC Plant Sciences SC Plant Sciences UT WOS:000815009000001 DA 2022-12-14 ER PT J AU Collart, AJ Canales, E AF Collart, Alba J. Canales, Elizabeth TI How might broad adoption of blockchain-based traceability impact the US fresh produce supply chain? SO APPLIED ECONOMIC PERSPECTIVES AND POLICY DT Article DE blockchain; distributed ledger technology; food provenance; specialty crops; supply chain traceability AB Applications of blockchain in the food sector are growing and the adoption of farm-to-fork traceability systems is at the forefront. We review applications of blockchain across different dimensions while focusing on how broad adoption of the technology might help address major challenges faced by the U.S. fresh produce industry. These challenges include food safety, food fraud, food loss and waste, and the general need for better traceability systems. We discuss whether blockchain technologies might play a role in enhancing the resilience of the produce supply chain and highlight limitations and challenges of the technology stakeholders might consider going forward. C1 [Collart, Alba J.; Canales, Elizabeth] Mississippi State Univ, Dept Agr Econ, POB 5187, Mississippi State, MS 39762 USA. C3 Mississippi State University RP Collart, AJ (corresponding author), Mississippi State Univ, Dept Agr Econ, POB 5187, Mississippi State, MS 39762 USA. EM alba.collart@msstate.edu CR Agnoli L, 2016, BRIT FOOD J, V118, P1878, DOI 10.1108/BFJ-04-2016-0176 Al-Jaroodi J, 2019, IEEE ACCESS, V7, P36500, DOI 10.1109/ACCESS.2019.2903554 [Anonymous], 2017, WHATS IN STOR ONL GR [Anonymous], 2018, PWCS GLOB BLOCKCH SU [Anonymous], 2018, INTRO HYPERLEDGER Arnade C, 2009, REV AGR ECON, V31, P734, DOI 10.1111/j.1467-9353.2009.01464.x Arun J. S., 2019, BLOCKCHAIN BUSINESS Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Banerjee A, 2018, INTEGRATING BLOCKCHA Bellemare MF, 2017, AM J AGR ECON, V99, P1148, DOI 10.1093/ajae/aax034 Berger CN, 2010, ENVIRON MICROBIOL, V12, P2385, DOI 10.1111/j.1462-2920.2010.02297.x Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Branan, 2019, NC STATE EC APR Buzby J. C., 2014, Economic Information Bulletin - USDA Economic Research Service Calvin L., 2007, Amber Waves, V5, P24 Canales, 2021, APPL ECON PERSPECT P, P1 Capgemini Research Institute, 2018, DOES BLOCKCH HOLD KE Capri Alex., 2018, FORBES Carrefour, 2020, FOOD BLOCKCH CBRE, 2019, FOOD DEMAND SERIES C Chang SCE, 2020, IEEE ACCESS, V8, P62478, DOI 10.1109/ACCESS.2020.2983601 Cox Thomas., 2019, ONLINE PRESENTATION Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Everstine K, 2013, J FOOD PROTECT, V76, P723, DOI 10.4315/0362-028X.JFP-12-399 Ferguson M, 2006, PROD OPER MANAG, V15, P57 Ferreira, 2018, AGR APPL EC ASS ANN Forrester Research, 2018, EMERGING TECHNOLOGY Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Ganne E., 2018, CAN BLOCKCHAIN REVOL Ge L., 2017, BLOCKCHAIN AGR FOOD Griffin TW, 2022, APPL ECON PERSPECT P, V44, P237, DOI 10.1002/aepp.13142 Grunert K. G., 2011, International Journal on Food System Dynamics, V2, P207 Harvard T.H, 2020, FOOD SAFETY NUTR WEL Hasan H, 2019, COMPUT IND ENG, V136, P149, DOI 10.1016/j.cie.2019.07.022 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hobbs JE, 2020, CAN J AGR ECON, V68, P171, DOI 10.1111/cjag.12237 Hoffmann S., 2015, Economic Information Bulletin - USDA Economic Research Service Huff Andrew G, 2015, J Environ Stud Sci, V5, P337, DOI 10.1007/s13412-015-0275-3 Hussain MA, 2013, FOODS, V2, P585, DOI 10.3390/foods2040585 IBM, 2019, IBM FOOD TRUST IBM, 2018, PUBLIC VERSUS PRIVAT IBM, 2020, JOIN POW IBM FOOD TR Interagency Food Safety Analytics Collaboration (IFSAC), 2018, FOODB ILLN SOURC ARR Ishangulyyev S, 2019, FOODS, V8, DOI 10.3390/foods8080297 Jay-Russell MT, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0113433 Johnson Ren?e., 2016, RL34468 CRS Kendall H, 2019, TRENDS FOOD SCI TECH, V94, P79, DOI 10.1016/j.tifs.2019.10.005 Larue B, 2020, CAN J AGR ECON, V68, P231, DOI 10.1111/cjag.12233 Laskowski Marek., 2017, AGR BLOCKCHAIN SUSTA Liao R., 2020, SUPPLY CHAINS HAVE B Lin W, 2022, APPL ECON PERSPECT P, V44, P253, DOI 10.1002/aepp.13135 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Luna-Guevara JJ, 2019, INT J MICROBIOL, V2019, DOI 10.1155/2019/2894328 Lusk JL, 2017, APPL ECON LETT, V24, P1199, DOI 10.1080/13504851.2016.1265070 Marucheck A, 2011, J OPER MANAG, V29, P707, DOI 10.1016/j.jom.2011.06.007 Messer KD, 2017, APPL ECON PERSPECT P, V39, P407, DOI 10.1093/aepp/ppx028 Minor Travis., 2020, EC INFORM B, V216 Morris N., 2019, UPLOADING DATA IBMS Nakamoto, 2008, BITCOIN PEER TO PEER Nam SO, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10114332 Nielsen Thorkild., 2008, ETH TRAC COMM FOOD Orcutt M., 2020, MIT TECHNOL REV Petersson D., 2018, FORBES Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 Ribera LA, 2012, HORTTECHNOLOGY, V22, P150, DOI 10.21273/HORTTECH.22.2.150 Schmidhuber, 2018, EMERGING OPPORTUNITI Spink J, 2017, TRENDS FOOD SCI TECH, V62, P215, DOI 10.1016/j.tifs.2017.02.012 Spink J, 2011, J FOOD SCI, V76, pR157, DOI 10.1111/j.1750-3841.2011.02417.x Sylvester G, 2019, E AGR ACTION BLOCKCH Teisl MF, 2010, FOOD POLICY, V35, P521, DOI 10.1016/j.foodpol.2010.07.003 The Linux Foundation, 2019, CAS STUD WALM BROUGH Thomasson E., 2019, CARREFOUR SAYS BLOCK Toledo C, 2019, APPL ECON PERSPECT P, V41, P519, DOI 10.1093/aepp/ppy015 U.N. FAO (Food and Agriculture Organization), 2020, FOOD LOSS FOOD WAST U.S. FDA (Food and Drug Administration), 2019, FDA STRAT SAF IMP FO Vakil, 2020, HARVARD BUSINESS REV van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Violino S, 2019, FOODS, V8, DOI 10.3390/foods8110529 WHO, 2019, FACT SHEET FOOD SAF Williamson J, 2019, CONSUMER WILLINGNESS Yu Y, 2020, AM J AGR ECON, V102, P525, DOI 10.1002/ajae.12036 NR 83 TC 13 Z9 13 U1 24 U2 104 PD MAR PY 2022 VL 44 IS 1 SI SI BP 219 EP 236 DI 10.1002/aepp.13134 EA JAN 2021 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000607559500001 DA 2022-12-14 ER PT J AU Eramo, R de Kerchove, FM Colange, M Tucci, M Ouy, J Bruneliere, H Di Ruscio, D AF Eramo, Romina de Kerchove, Florent Marchand Colange, Maximilien Tucci, Michele Ouy, Julien Bruneliere, Hugo Di Ruscio, Davide TI Model-driven Design-Runtime Interaction in Safety Critical System Development: an Experience Report SO JOURNAL OF OBJECT TECHNOLOGY DT Article DE Model-Driven Engineering; Critical Systems; Design; Runtime; Interactions; Traceability AB Automotive, aerospace, industrial control, and railway systems are examples of application domains which are particularly characterized by the need for developing and managing critical systems. Model-driven engineering is recognized as an effective solution to leverage abstraction and automation while developing complex systems. One of the major and key challenges in the model-driven engineering of critical software systems is the integration of design and runtime aspects. Even though several methods and tools are available for performing measurements of runtime properties, the ability to trace them with design models is still limited. In the context of a real railway system, this paper presents a model-based approach that has been conceived to analyze runtime data (coming from different sensors), to produce corresponding traceability models and to automatically infer from them potential design issues that might need to be fixed in order to solve detected system malfunctionings. C1 [de Kerchove, Florent Marchand; Bruneliere, Hugo] CNRS, LS2N, IMT Atlantique, Paris, France. [de Kerchove, Florent Marchand; Bruneliere, Hugo] ARMINES, Ile De France, France. [Colange, Maximilien; Ouy, Julien] CLEARSY, Aix En Provence, France. [Eramo, Romina; Tucci, Michele; Di Ruscio, Davide] Univ Aquila, Laquila, Italy. C3 Centre National de la Recherche Scientifique (CNRS); IMT - Institut Mines-Telecom; IMT Atlantique; UDICE-French Research Universities; Universite Paris Cite; University of L'Aquila RP Eramo, R (corresponding author), Univ Aquila, Laquila, Italy. EM romina.eramo@univaq.it; florent.marchand-de-kerchove@imt-atlantique.fr; maximilien.colange@clearsy.com; michele.tucci@univaq.it; julien.ouy@clearsy.com; hugo.bruneliere@imt-atlantique.fr; davide.diruscio@univaq.it CR Abrial Jean-Raymond, 2005, B BOOK ASSIGNING PRO Addazi L, 2017, LECT NOTES COMPUT SC, V10376, P20, DOI 10.1007/978-3-319-61482-3_2 Afzal W, 2018, MICROPROCESS MICROSY, V61, P86, DOI 10.1016/j.micpro.2018.05.010 Agrawal R, 1998, LECT NOTES COMPUT SC, V1377, P469 Aizenbud-Reshef N, 2006, IBM SYST J, V45, P515, DOI 10.1147/sj.453.0515 Arcaini P, 2015, 2015 IEEE/ACM 10TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS, P13, DOI 10.1109/SEAMS.2015.10 Arcelli D., 2019, ICSA Boccara N., 2004, GRADUATE TEXTS COMTE Bruneliere Hugo, 2019, Software & Systems Modeling, V18, P1931, DOI 10.1007/s10270-017-0622-9 Bruneliere H., 2018, P STAF 2018 COLL WOR Bruneliere H., 2015, ER, P317 Bruneliere H, 2018, 21ST ACM/IEEE INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS (MODELS 2018), P334, DOI 10.1145/3239372.3239408 Bruneliere H, 2014, INFORM SOFTWARE TECH, V56, P1012, DOI 10.1016/j.infsof.2014.04.007 Cicchetti A, 2011, LECT NOTES COMPUT SC, V6563, P183, DOI 10.1007/978-3-642-19440-5_11 Del Fabro MD, 2009, SOFTW SYST MODEL, V8, P305, DOI 10.1007/s10270-008-0094-z Derler P, 2012, P IEEE, V100, P13, DOI 10.1109/JPROC.2011.2160929 Drivalos N, 2009, LECT NOTES COMPUT SC, V5452, P151, DOI 10.1007/978-3-642-00434-6_10 Drusinsky D., 2011, MODELING VERIFICATIO Eramo Romina, 2018, C COMP 2 INT C ART S, P36, DOI [10.1145/3191697.3191720, DOI 10.1145/3191697.3191720] Eysholdt M., 2010, P ACM INT C COMPANIO, P307, DOI DOI 10.1145/1869542.1869625 Jouault F., 2010, P 2010 ACM S APPL CO, P2011, DOI DOI 10.1145/1774088.1774511 Kolovos DS, 2009, LECT NOTES COMPUT SC, V5562, P146, DOI 10.1007/978-3-642-02674-4_11 Lecomte T., 2012, DS EV B WORKSH ICFME Lecomte T., SBMF, P70 LEHMANN A, 2010, MODELS, P209, DOI DOI 10.1007/978-0-8176-4924-1_14 Mitchell N, 2006, LECT NOTES COMPUT SC, V4067, P429 Paige RF, 2011, SOFTW SYST MODEL, V10, P469, DOI 10.1007/s10270-010-0158-8 Paige RF, 2009, 2009 14TH IEEE INTERNATIONAL CONFERENCE ON ENGINEERING OF COMPLEX COMPUTER SYSTEMS (ICECCS), P162, DOI 10.1109/ICECCS.2009.14 Schmidt DC, 2006, COMPUTER, V39, P25, DOI 10.1109/MC.2006.58 Walderhaug S., 2006, ECMDA TRAC WORKSH EC, P41 NR 30 TC 1 Z9 1 U1 0 U2 2 PD JUL PY 2019 VL 18 IS 2 DI 10.5381/jot.2019.18.2.a1 WC Computer Science, Software Engineering SC Computer Science UT WOS:000473336200002 DA 2022-12-14 ER PT J AU Anwar, S Perdana, T Rachmadi, M Noor, TI AF Anwar, Syaiful Perdana, Tomy Rachmadi, Meddy Noor, Trisna Insan TI Traceability Information Model for Sustainability of Black Soybean Supply Chain: A Systematic Literature Review SO SUSTAINABILITY DT Review DE traceability; information modelling; sustainability; supply chain; black soybean; PRISMA model ID BLOCKCHAIN; MANAGEMENT; FRAMEWORK; IMPLEMENTATION; PERSPECTIVES; FOOTPRINT; TOMATO AB Traceability information as a solution option becomes an important task for the industry in providing products, preparing sustainable raw materials, and ensuring adequate safety quality. The emergence of these demands makes the industry perform tracking in order to prepare product inventories ranging from raw materials to products that have been produced. Based on these reasons, the scope of this paper is to provide a systematic review of the literature on various aspects of implementing information traceability models and sustainability of supply chain on economic, social, environmental, technological, institutional, and infrastructural dimensions. For this purpose, we use the Scopus, Science Direct, EBSCO Host, and ProQuest databases. We used the PRISMA model to identify, filter, and test for the eligibility of articles to be included. We selected 52 articles contributed by this search engine. We found was that between 2018 to 2021 there was increasing interest in this research. The dominant traceability information model in the article uses blockchain, the rest use operations research (OR), Google Earth Engine (GEE), website-based, Unified Modeling Language (UML), Extensible Markup Language (XML), physical markup language (PML), logit, enterprise resource planning (ERP), soft independent modelling of class analogies (SIMCA), and Spatially Explicit Information on Production to Consumption Systems (SEI-PCS). C1 [Anwar, Syaiful] Univ Padjadjaran, Fac Agr, Doctorate Program Agr Sci, Sumedang 45363, Indonesia. [Perdana, Tomy; Noor, Trisna Insan] Univ Padjadjaran, Fac Agr, Dept Agro Socioecon, Sumedang 45363, Indonesia. [Rachmadi, Meddy] Univ Padjadjaran, Fac Agr, Dept Agron, Sumedang 45363, Indonesia. C3 Universitas Padjadjaran; Universitas Padjadjaran; Universitas Padjadjaran RP Anwar, S (corresponding author), Univ Padjadjaran, Fac Agr, Doctorate Program Agr Sci, Sumedang 45363, Indonesia. EM syaiful19003@mail.unpad.ac.id; tomy.perdana@unpad.ac.id; meddy.rachmadi@unpad.ac.id; trisna.insan.noor@unpad.ac.id CR Aldrighetti A., 2021, International Journal on Food System Dynamics, V12, P6, DOI 10.18461/ijfsd.v12i1.72 Allaoui H, 2019, J CLEAN PROD, V229, P761, DOI 10.1016/j.jclepro.2019.04.367 Anders S, 2021, TRENDS PLANT SCI, V26, P575, DOI 10.1016/j.tplants.2021.03.004 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bashiri M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020589 Bostrom M, 2015, J CLEAN PROD, V107, P1, DOI 10.1016/j.jclepro.2014.11.050 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Cheng ZL, 2013, MATH PROBL ENG, V2013, DOI 10.1155/2013/629363 Chowdhury MMH, 2021, J CLEAN PROD, V278, DOI 10.1016/j.jclepro.2020.123521 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Dasaklis TK, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14042439 Demestichas K., 2020, APPL SCI, V10, P22 Ding QY, 2020, IEEE ACCESS, V8, P6209, DOI 10.1109/ACCESS.2019.2962274 Dodgson JE, 2021, J HUM LACT, V37, P27, DOI 10.1177/0890334420977815 Duan YQ, 2017, INFORM SOC, V33, P226, DOI 10.1080/01972243.2017.1318325 Ekawati R, 2021, INT J ADV COMPUT SC, V12, P459 Ercin AE, 2012, ECOL INDIC, V18, P392, DOI 10.1016/j.ecolind.2011.12.009 Fearnside PM, 2001, ENVIRON CONSERV, V28, P23, DOI 10.1017/S0376892901000030 Fitry N., 2017, J ILM MHS AGROINFO G, V4, P352 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Franca ASL, 2020, J CLEAN PROD, V244, DOI 10.1016/j.jclepro.2019.118529 Gardner TA, 2019, WORLD DEV, V121, P163, DOI 10.1016/j.worlddev.2018.05.025 Garrett RD, 2013, LAND USE POLICY, V34, P265, DOI 10.1016/j.landusepol.2013.03.011 Gayialis SP, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14116666 Ginting E., 2015, PROSIDING SEMINAR AG Godar J, 2015, ECOL ECON, V112, P25, DOI 10.1016/j.ecolecon.2015.02.003 Guo FF, 2021, IEEE ACCESS, V9, P138082, DOI 10.1109/ACCESS.2021.3117906 Gurzawska A, 2020, PHILOS MANAG, V19, P267, DOI 10.1007/s40926-019-00114-z Haleem Abid, 2019, Information Processing in Agriculture, V6, P335, DOI 10.1016/j.inpa.2019.01.003 Hermiatin FR, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14138196 Hidalgo MJ, 2020, J CHEMOMETR, V34, DOI 10.1002/cem.3252 Hinkes C, 2020, SUSTAIN ACCOUNT MANA, V11, P1159, DOI 10.1108/SAMPJ-04-2019-0145 Hizbi M.S., 2019, B AGROHORTI, V7, P153, DOI [10.29244/agrob.7.2.153-161, DOI 10.29244/AGROB.7.2.153-161] Hong W, 2021, J CLEAN PROD, V303, DOI 10.1016/j.jclepro.2021.127044 Jia F, 2020, J CLEAN PROD, V255, DOI 10.1016/j.jclepro.2020.120254 Jose A, 2019, J ADV MANAG RES, V17, P19, DOI 10.1108/JAMR-02-2019-0010 Kang Y, 2021, SCI PROGRAMMING-NETH, V2021, DOI 10.1155/2021/1455814 Kaur R., 2021, TURK J COMPUT MATH E, V12, P3286 Kim K, 2014, FLEX SERV MANUF J, V26, P5, DOI 10.1007/s10696-012-9163-2 Kurniawan M., 2021, IOP Conference Series : Earth and Environmental Science, V924, DOI 10.1088/1755-1315/924/1/012050 Latif RMA, 2021, CLUSTER COMPUT, V24, P1, DOI 10.1007/s10586-020-03165-4 Liu FZ, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13031275 Liu HH, 2019, INT J NURS PRACT, V25, DOI 10.1111/ijn.12729 Liu Z.-Y., 2021, DISCRETE DYN NAT SOC, V2021, DOI DOI 10.1155/2021/5795547 Mao DH, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093149 Medina G, 2021, LOGISTICS-BASEL, V5, DOI 10.3390/logistics5030058 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Moher David, 2009, BMJ, V339, pb2535, DOI [10.1016/j.ijsu.2010.02.007, 10.1136/bmj.b2535] Monteiro ES, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11178149 Morales V., 2016, P ILS C Ng H, 2021, J AGR RESOUR ECON, V46, P101, DOI 10.22004/ag.econ.302465 Nurgazina J, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084206 Oliveira GDT, 2016, J PEASANT STUD, V43, P167, DOI 10.1080/03066150.2014.993625 Paliwal V, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12187638 Patel N, 2021, T EMERG TELECOMMUN T, DOI 10.1002/ett.4286 Pelaez V, 2010, INT FOOD AGRIBUS MAN, V13, P27 Pranto TH, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.407 Rajesh R, 2020, TECHNOL SOC, V61, DOI 10.1016/j.techsoc.2020.101230 Rana SK, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su131810008 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ronaghi M. H., 2021, Information Processing in Agriculture, V8, P398, DOI 10.1016/j.inpa.2020.10.004 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sanjaya S, 2015, PROCEDIA MANUF, V4, P513, DOI 10.1016/j.promfg.2015.11.070 Santana S, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14095469 Seuring S, 2008, J CLEAN PROD, V16, P1699, DOI 10.1016/j.jclepro.2008.04.020 Seuring S, 2008, J CLEAN PROD, V16, P1545, DOI 10.1016/j.jclepro.2008.02.002 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Sjauw-Koen-Fa AR, 2017, INT FOOD AGRIBUS MAN, V20, P709, DOI 10.22434/IFAMR2016.0171 Sjauw-Koen-Fa AR, 2018, J AGRIBUS DEV EMERG, V8, P656, DOI 10.1108/JADEE-06-2017-0064 Srivastava A, 2022, ANN OPER RES, V315, P2115, DOI 10.1007/s10479-021-04072-6 Stadtler H., 2005, SUPPLY CHAIN MANAGEM Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Susilowati I., 2018, ESPACIOS, V39, P27 Teuscher P., 2006, Corporate Social Responsibility and Environmental Management, V13, P1, DOI 10.1002/csr.081 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Tounakaki O, 2020, INT OPHTHALMOL, V40, P1163, DOI 10.1007/s10792-019-01282-7 Utami H. N., 2016, Journal of ISSAAS (International Society for Southeast Asian Agricultural Sciences), V22, P123 Utomo DS, 2018, EUR J OPER RES, V269, P794, DOI 10.1016/j.ejor.2017.10.041 Wang SP, 2019, IEEE ACCESS, V7, P115122, DOI 10.1109/ACCESS.2019.2935873 Wang X, 2011, IFIP ADV INF COMM TE, V345, P567 Wardani A.K., 2014, J PANGAN DAN AGROIND, V2, P58 Wiloso EI, 2019, INT J LIFE CYCLE ASS, V24, P1948, DOI 10.1007/s11367-019-01617-7 Wu XY, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.1894497 Xiaojun Wang, 2009, International Journal of Services Operations and Informatics, V4, P232, DOI 10.1504/IJSOI.2009.026951 You NS, 2020, ISPRS J PHOTOGRAMM, V161, P109, DOI 10.1016/j.isprsjprs.2020.01.001 Yusop SRM, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14095225 Zhang LJ, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/3298514 Zhang MY, 2021, GM CROPS FOOD, V12, P36, DOI 10.1080/21645698.2020.1807852 Zhang YJ, 2021, J FOOD PROCESS ENG, V44, DOI 10.1111/jfpe.13669 Zhao DA, 2009, ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL III, PROCEEDINGS, P562, DOI 10.1109/ICICTA.2009.601 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zhu WW, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10051510 Zhu ZG, 2018, INT J PROD RES, V56, P5700, DOI 10.1080/00207543.2018.1425014 NR 94 TC 0 Z9 0 U1 11 U2 11 PD AUG PY 2022 VL 14 IS 15 AR 9498 DI 10.3390/su14159498 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000838986400001 DA 2022-12-14 ER PT J AU Pereira, L Guedes-Pinto, H Martins-Lopes, P AF Pereira, Leonor Guedes-Pinto, Henrique Martins-Lopes, Paula TI An Enhanced Method for Vitis vinifera L. DNA Extraction from Wines SO AMERICAN JOURNAL OF ENOLOGY AND VITICULTURE DT Article DE wine; DNA extraction; grapevine identification; SSR marker; traceability ID MICROSATELLITE MARKERS; GRAPE; MUSTS; QUANTIFICATION; IDENTIFICATION; VARIETIES; DIFFERENTIATION; AUTHENTICATION; ANTHOCYANINS; CULTIVARS AB Wine quality and value largely depend on grape variety, which is of primary importance in wine identification. The aim of the present work was to enhance a wine DNA extraction protocol and, subsequently, grapevine variety identification. This enhanced method is an outcome from several previously developed extraction methods and effectively allows obtaining large amounts of high-quality DNA exhibiting an optimal 260/280 ratio. Grapevine variety DNA extracted from wine was amplifiable with a specific SSR primer. This procedure was applicable for monovarietal and older commercial red and white wines. The potential of this enhanced method relies on its use for traceability as part of protecting both consumer and producer interests. C1 [Pereira, Leonor; Guedes-Pinto, Henrique] Univ Tras Os Montes & Alto Douro IBB CGB UTAD, Inst Biotechnol & Bioengn, Ctr Genom & Biotechnol, P-5000911 Vila Real, Portugal. [Martins-Lopes, Paula] Sch Life Sci & Environm, Dept Genet & Biotechnol, P-5000911 Vila Real, Portugal. [Martins-Lopes, Paula] IBB CGB UTAD, P-5000911 Vila Real, Portugal. C3 University of Tras-os-Montes & Alto Douro; University of Tras-os-Montes & Alto Douro RP Pereira, L (corresponding author), Univ Tras Os Montes & Alto Douro IBB CGB UTAD, Inst Biotechnol & Bioengn, Ctr Genom & Biotechnol, POB 1013, P-5000911 Vila Real, Portugal. EM leopereira@utad.pt CR Almeida CMR, 2003, J AGR FOOD CHEM, V51, P4788, DOI 10.1021/jf034145b Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Coetzee PP, 2005, ANAL BIOANAL CHEM, V383, P977, DOI 10.1007/s00216-005-0093-7 Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 Drabek J, 2008, EUR FOOD RES TECHNOL, V226, P491, DOI 10.1007/s00217-007-0561-8 Faria MA, 2000, J AGR FOOD CHEM, V48, P1096, DOI 10.1021/jf990837h Faria MA, 2008, EUR FOOD RES TECHNOL, V227, P845, DOI 10.1007/s00217-007-0795-5 Garcia-Beneytez E, 2003, J AGR FOOD CHEM, V51, P5622, DOI 10.1021/jf0302207 Garcia-Beneytez E, 2002, J AGR FOOD CHEM, V50, P6090, DOI 10.1021/jf0202077 GONZALEZLARA R, 1989, FOOD CHEM, V34, P103, DOI 10.1016/0308-8146(89)90078-2 Lodhi M. A., 1994, Plant Molecular Biology Reporter, V12, P6, DOI 10.1007/BF02668658 Monaci Fabrizio, 2003, Journal of Trace Elements in Medicine and Biology, V17, P45 Moreno-Arribas MV, 1999, J AGR FOOD CHEM, V47, P114, DOI 10.1021/jf980483e MUNOZORGANERO G, 1997, RIV VITIC ENOL, V3, P55 Nakamura S, 2007, J AGR FOOD CHEM, V55, P10388, DOI 10.1021/jf072407u PUEYO E, 1993, AM J ENOL VITICULT, V44, P255 Revilla E, 2001, J CHROMATOGR A, V915, P53, DOI 10.1016/S0021-9673(01)00635-5 Rodriguez-Plaza P, 2006, EUR FOOD RES TECHNOL, V223, P625, DOI 10.1007/s00217-005-0244-2 Savazzini F, 2006, ANAL CHIM ACTA, V563, P274, DOI 10.1016/j.aca.2005.10.078 Sefc KM, 1999, GENOME, V42, P367, DOI 10.1139/gen-42-3-367 Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Siret R, 2002, J AGR FOOD CHEM, V50, P3822, DOI 10.1021/jf011462e VASCONCELOS AMP, 1989, J AGR FOOD CHEM, V37, P931, DOI 10.1021/jf00088a023 NR 23 TC 32 Z9 35 U1 2 U2 21 PY 2011 VL 62 IS 4 BP 547 EP 552 DI 10.5344/ajev.2011.10022 WC Biotechnology & Applied Microbiology; Food Science & Technology; Horticulture SC Biotechnology & Applied Microbiology; Food Science & Technology; Agriculture UT WOS:000298125500015 DA 2022-12-14 ER PT J AU Dong, KTP Saito, Y Hoa, NTN Dan, TY Matsuishi, T AF Khuu Thi Phuong Dong Saito, Yoko Nguyen Thi Ngoc Hoa Dan, Tong Yen Matsuishi, Takashi TI Pressure-State-Response of traceability implementation in seafood-exporting countries: evidence from Vietnamese shrimp products SO AQUACULTURE INTERNATIONAL DT Article DE Pressure-State-Response (PSR); Quality assurance practices; Regulations; Shrimp; Traceability ID FOOD SAFETY; QUALITY-ASSURANCE; CHAIN; GOVERNANCE; STANDARDS; INDUSTRY; SYSTEM; US AB Shrimp products play a vital role in the international trade of fisheries products. The main suppliers for shrimp products are developing nations such as Vietnam, Thailand, Bangladesh, and other countries in Southeast Asia. Among them, Vietnam is one of the largest exporters of shrimp products, and developed countries, especially the United States (US), Europe, and Japan, are key importers of shrimp in the global market. An increase in the demand for shrimp products has led to the development of traceability regulations in developed countries. In this study, Pressure-State-Response (PSR) concepts are applied to evaluate the implementation responses of traceability regulations by exporting countries to meet the mandatory requirements of global markets. The evaluation was based on the prepared questions that were developed to allow comparison of specified indicators in the traceability regulations of importing countries and those of Vietnam. The examination showed that importing countries have introduced stringent traceability regulations via legislation and quality assurance practices. Regarding measures taken by exporting countries, Vietnam has introduced traceability regulations for both shrimp and other seafood products. Thus, Vietnamese regulations were found to satisfy the regulations of importing countries. However, the implementation of these regulations has faced a number of challenges, largely because of complicated of distribution channels, small-scale production, price discrimination, and a lack capital to apply for international certificates. C1 [Khuu Thi Phuong Dong] Hokkaido Univ, Grad Sch Fisheries Sci, Hakodate, Hokkaido, Japan. [Khuu Thi Phuong Dong; Nguyen Thi Ngoc Hoa; Dan, Tong Yen] Can Tho Univ, Coll Econ, Can Tho, Vietnam. [Saito, Yoko] Hokkaido Univ, Global Inst Collaborat Res & Educ, Fac Agr, Sapporo, Hokkaido, Japan. [Matsuishi, Takashi] Hokkaido Univ, Fac Fisheries Sci, Global Inst Collaborat Res & Educ, 3-1-1 Minato Cho, Hakodate, Hokkaido 0418611, Japan. C3 Hokkaido University; Can Tho University; Hokkaido University; Hokkaido University RP Matsuishi, T (corresponding author), Hokkaido Univ, Fac Fisheries Sci, Global Inst Collaborat Res & Educ, 3-1-1 Minato Cho, Hakodate, Hokkaido 0418611, Japan. EM catm@fish.hokudai.ac.jp CR [Anonymous], ENH FOOD SAF ROL FOO [Anonymous], 2014, JAPAN RETAIL NEWS Baert K, 2012, FOOD RES INT, V48, P257, DOI 10.1016/j.foodres.2012.04.005 Bailey M, 2018, FISH FISH, V19, P782, DOI 10.1111/faf.12289 Bernard A, 2002, ENVIRON RES, V88, P1, DOI 10.1006/enrs.2001.4274 Caswell JA, 1998, AUST J AGR RESOUR EC, V42, P409, DOI 10.1111/1467-8489.00060 Chan K., 2016, MANUAL GOOD AGR PRAC Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Clemens RLB, 2003, MATRIC BRIEFING PAPE Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Duc NM, 2009, AQUACULT INT, V17, P15, DOI 10.1007/s10499-008-9176-8 EU Council, 2013, 13792013 EUR COUNC EU Council, 2011, 11692011 EU COUNC FAO, 2018, FISH COMM TRAD Golan E., 2004, AGR EC REPORT, V830, P1 Golan E., 2003, CURRENT AGR FOOD RES, V4, P27 Hall D, 2010, GEOFORUM, V41, P826, DOI 10.1016/j.geoforum.2010.05.005 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Karipidis P, 2009, FOOD CONTROL, V20, P93, DOI 10.1016/j.foodcont.2008.02.008 Lap DX, 2015, CURRENT SITUATION QU Loc N., 2006, THESIS U PHILIPPINES Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Marucheck A, 2011, J OPER MANAG, V29, P707, DOI 10.1016/j.jom.2011.06.007 Meyar-Naimi H, 2012, ENERG POLICY, V43, P351, DOI 10.1016/j.enpol.2012.01.012 Mohammad Taj Uddin, 2009, Asia-Pacific Journal of Rural Development, V19, P89 Tran N, 2013, WORLD DEV, V45, P325, DOI 10.1016/j.worlddev.2013.01.025 Palacios MRH, 2001, STUDY QUALITY MANAGE Phuong NT, 2010, SUCCESS STORIES IN ASIAN AQUACULTURE, P131, DOI 10.1007/978-90-481-3087-0_7 Poll C, 2005, DEV INDICATORS INTEG Portley N., 2016, REPORT SHRIMP SECTOR Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 SEAFDEC, 2016, TRAC FISH FISH PROD Suzuki A, 2013, 395 IDE Suzuki A, 2018, AQUACULT INT, V26, P469, DOI 10.1007/s10499-017-0228-9 Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Tunxi FL, 2015, TECHNICAL COMPILATIO USDA, 2016, COUNTR OR LAB COOL van der Vorst JGAJ, 2000, EUR J OPER RES, V122, P354, DOI 10.1016/S0377-2217(99)00238-6 Vasileiou KZ, 2002, THESIS Vietnam DARD, 2013, 482013BNNPTNT VIETN Vietnam DARD, 2017, 022017BNNPTNT VIETN Vietnam MARD, 2011, 032011TTBNNPTNT VIET Waheed B., 2009, SUSTAINABILITY, V1, P441, DOI [10.3390/su1030441, DOI 10.3390/SU1030441] Ziggers GW, 1999, INT J PROD ECON, V60-1, P271, DOI 10.1016/S0925-5273(98)00138-8 NR 48 TC 6 Z9 7 U1 3 U2 14 PD OCT PY 2019 VL 27 IS 5 BP 1209 EP 1229 DI 10.1007/s10499-019-00378-2 WC Fisheries SC Fisheries UT WOS:000484962500006 DA 2022-12-14 ER PT J AU Qie, MJ Zhang, B Li, Z Zhao, SS Zhao, Y AF Qie, Mengjie Zhang, Bin Li, Zheng Zhao, Shanshan Zhao, Yan TI Data fusion by ratio modulation of stable isotope, multi-element, and fatty acids to improve geographical traceability of lamb SO FOOD CONTROL DT Article DE Lamb; Data fusion; Geographical traceability; Stable isotope ratio; Multi-element; Fatty acid ID ORIGIN; MEAT; AUTHENTICATION; CLASSIFICATION; PROVENANCE; NITROGEN; CARBON; BEEF; FOOD; UV AB There is a growing enthusiasm among consumers for high value lamb, especially the lamb with certain geographical indications, which may result in possible fraudulent labeling due to the opportunity for large economic profits. On such a basis, the aim of this study was to improve the discrimination rate of geographical traceability of lamb. To achieve this objective, lamb samples obtained from four different regions from China and New Zealand were analyzed. The chemical profiles of lamb samples from different origins were characterized by stable isotope analysis, multi-element determination, and fatty-acid profile, and further subjected to a series of statistical analyses, including principal component analysis, hierarchical cluster analysis, and principal component analysis-linear discriminant analysis. Compared with the result obtained by using each method individually, data fusion by ratio modulation of multi-technique clearly improved the discrimination performance for origin traceability. A satisfactory discrimination rate of 100% was obtained after ratio modulation of these techniques. Comprehensively, this study offers proof of concept for a promising method that could be used for lamb provenance authentication and fraud detection. C1 [Qie, Mengjie; Li, Zheng; Zhao, Shanshan; Zhao, Yan] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agro Prod, Beijing 100081, Peoples R China. [Qie, Mengjie; Li, Zheng; Zhao, Shanshan; Zhao, Yan] Minist Agr & Rural Affairs, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Zhang, Bin] Henan Univ Sci & Technol, Coll Food & Bioengn, Luoyang 471023, Peoples R China. [Li, Zheng] Shandong Agr Univ, Coll Food Sci & Engn, Tai An 271018, Shandong, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs; Henan University of Science & Technology; Shandong Agricultural University RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agro Prod, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR Butler D, 2009, NATURE, V458, P1082, DOI 10.1038/4581082a Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Castro-Puyana M., 2017, TRAC TRENDS ANAL CHE, V93 Cyranoski D, 2001, NATURE, V412, P261, DOI 10.1038/35085715 Erasmus SW, 2017, J SCI FOOD AGR, V97, P1979, DOI 10.1002/jsfa.8180 Fisher AV, 2000, MEAT SCI, V55, P141, DOI 10.1016/S0309-1740(99)00136-9 Gong Y, 2018, RAPID COMMUN MASS SP, V32, P583, DOI 10.1002/rcm.8071 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Jia M., 2013, BUSINESS RES, P143 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Liang K. H., 2017, J APPL ANIM RES, V46, P1 Liang Y., 2013, ANAL CHEM, V41, P48 Lv J, 2017, FOOD ANAL METHOD, V10, P347, DOI 10.1007/s12161-016-0588-1 Mekki I, 2016, J FOOD COMPOS ANAL, V53, P40, DOI 10.1016/j.jfca.2016.09.002 Monahan FJ, 2018, MEAT SCI, V144, P2, DOI 10.1016/j.meatsci.2018.05.008 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Pizarro C, 2013, FOOD CHEM, V138, P915, DOI 10.1016/j.foodchem.2012.11.087 Qi LY, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18072037 Qie MJ, 2019, J CHROMATOGR A, V1608, DOI 10.1016/j.chroma.2019.460423 Richter B, 2019, FOOD CHEM, V286, P475, DOI 10.1016/j.foodchem.2019.01.105 Sacco D, 2005, MEAT SCI, V71, P542, DOI 10.1016/j.meatsci.2005.04.038 Serra F, 2005, RAPID COMMUN MASS SP, V19, P2111, DOI 10.1002/rcm.2034 Setbon M, 2005, RISK ANAL, V25, P813, DOI 10.1111/j.1539-6924.2005.00634.x Sun SM, 2016, FOOD CHEM, V213, P675, DOI 10.1016/j.foodchem.2016.07.013 Sun SM, 2012, FOOD CHEM, V135, P508, DOI 10.1016/j.foodchem.2012.05.004 Sun SM, 2011, FOOD CHEM, V124, P1151, DOI 10.1016/j.foodchem.2010.07.027 Sun ShuMin, 2010, Scientia Agricultura Sinica, V43, P1670 van der Merwe M, 2015, AGREKON, V54, P53, DOI 10.1080/03031853.2015.1019524 Wang Z., 2015, BUSINESS, P268 Wilcock A., 2014, FOOD SAFETY, P8 Woods VB, 2009, LIVEST SCI, V126, P1, DOI 10.1016/j.livsci.2009.07.002 Wu XM, 2019, SPECTROCHIM ACTA A, V212, P132, DOI 10.1016/j.saa.2019.01.008 Wu XM, 2018, SPECTROCHIM ACTA A, V205, P479, DOI 10.1016/j.saa.2018.07.067 Yan ZhongXin, 2017, Food Research and Development, V38, P11 Yao S, 2019, INT J FOOD PROP, V22, P414, DOI 10.1080/10942912.2019.1588299 Zhao SS, 2019, RAPID COMMUN MASS SP, V33, P803, DOI 10.1002/rcm.8411 Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y NR 39 TC 12 Z9 13 U1 10 U2 93 PD FEB PY 2021 VL 120 AR 107549 DI 10.1016/j.foodcont.2020.107549 WC Food Science & Technology SC Food Science & Technology UT WOS:000579185600044 DA 2022-12-14 ER PT J AU Cheng, WW Sun, DW Pu, HB Wei, QY AF Cheng, Weiwei Sun, Da-Wen Pu, Hongbin Wei, Qingyi TI Chemical spoilage extent traceability of two kinds of processed pork meats using one multispectral system developed by hyperspectral imaging combined with effective variable selection methods SO FOOD CHEMISTRY DT Article DE Spectral imaging; Chemical spoilage; Traceability; Processed meat; Salting; Cooking; Genetic algorithm-partial least squares ID VACUUM COOLING PROCESS; CARP CTENOPHARYNGODON-IDELLA; NITROGEN TVB-N; LEAST-SQUARES REGRESSION; FROZEN-THAWED PORK; NONDESTRUCTIVE DETERMINATION; INFRARED-SPECTROSCOPY; COOKED MEAT; GENETIC ALGORITHM; CHICKEN FILLETS AB The feasibility of hyperspectral imaging (HSI) (400-1000 nm) for tracing the chemical spoilage extent of the raw meat used for two kinds of processed meats was investigated. Calibration models established separately for salted and cooked meats using full wavebands showed good results with the determination coefficient in prediction (R-P(2)) of 0.887 and 0.832, respectively. For simplifying the calibration models, two variable selection methods were used and compared. The results showed that genetic algorithm-partial least squares (GA-PLS) with as much continuous wavebands selected as possible always had better performance. The potential of HSI to develop one multispectral system for simultaneously tracing the chemical spoilage extent of the two kinds of processed meats was also studied. Good result with an R-P(2) of 0.854 was obtained using GA-PLS as the dimension reduction method, which was thus used to visualize total volatile base nitrogen (TVB-N) contents corresponding to each pixel of the image. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Cheng, Weiwei; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi] South China Univ Technol, Sch Food Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China. [Cheng, Weiwei; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi] South China Univ Technol, Acad Contemporary Food Engn, Guangzhou Higher Educ Mega Ctr, Guangzhou 510006, Guangdong, Peoples R China. [Cheng, Weiwei; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi] Guangzhou Higher Educ Mega Ctr, Engn & Technol Res Ctr Guangdong Prov Intelligent, Guangzhou 510006, Guangdong, Peoples R China. [Sun, Da-Wen] Natl Univ Ireland, Univ Coll Dublin, Agr & Food Sci Ctr, Food Refrigerat & Computerized Food Technol, Dublin 4, Ireland. C3 South China University of Technology; South China University of Technology; University College Dublin RP Sun, DW (corresponding author), South China Univ Technol, Sch Food Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China. EM dawen.sun@ucd.ie CR Araujo MCU, 2001, CHEMOMETR INTELL LAB, V57, P65, DOI 10.1016/S0169-7439(01)00119-8 Barbin D, 2012, MEAT SCI, V90, P259, DOI 10.1016/j.meatsci.2011.07.011 Barbin DF, 2013, FOOD CHEM, V138, P1162, DOI 10.1016/j.foodchem.2012.11.120 Barbin DF, 2012, ANAL CHIM ACTA, V719, P30, DOI 10.1016/j.aca.2012.01.004 Cai JR, 2011, FOOD CHEM, V126, P1354, DOI 10.1016/j.foodchem.2010.11.098 Cheng JH, 2015, FOOD BIOPROCESS TECH, V8, P951, DOI 10.1007/s11947-014-1457-9 Cheng JH, 2014, FOOD BIOPROCESS TECH, V7, P3109, DOI 10.1007/s11947-014-1325-7 Cheng JH, 2015, FOOD CHEM, V171, P258, DOI 10.1016/j.foodchem.2014.08.124 Cheng JH, 2014, TRENDS FOOD SCI TECH, V37, P78, DOI 10.1016/j.tifs.2014.03.006 Cheng JH, 2014, INNOV FOOD SCI EMERG, V21, P179, DOI 10.1016/j.ifset.2013.10.013 Cheng WW, 2016, LWT-FOOD SCI TECHNOL, V73, P13, DOI 10.1016/j.lwt.2016.05.031 Cheng WW, 2016, LWT-FOOD SCI TECHNOL, V72, P322, DOI 10.1016/j.lwt.2016.05.003 Cui ZW, 2008, J FOOD ENG, V84, P582, DOI 10.1016/j.jfoodeng.2007.06.027 Da-Wen Sun, 1997, J ENERGY CONVERSION, V38, P479 Dai Q, 2016, FOOD CHEM, V197, P257, DOI 10.1016/j.foodchem.2015.10.073 Dai Q, 2015, CRIT REV FOOD SCI, V55, P1368, DOI 10.1080/10408398.2013.871692 Dai Q, 2015, J FOOD ENG, V149, P97, DOI 10.1016/j.jfoodeng.2014.10.001 Dissing BS, 2013, FOOD BIOPROCESS TECH, V6, P2268, DOI 10.1007/s11947-012-0886-6 Du CJ, 2005, J FOOD ENG, V66, P137, DOI 10.1016/j.jfoodeng.2004.03.011 ElMasry G, 2008, J AGR FOOD CHEM, V56, P7672, DOI 10.1021/jf801074s Elmasry G, 2012, CRIT REV FOOD SCI, V52, P689, DOI 10.1080/10408398.2010.507908 ElMasry G, 2011, FOOD RES INT, V44, P2624, DOI 10.1016/j.foodres.2011.05.001 ElMasry G, 2007, J FOOD ENG, V81, P98, DOI 10.1016/j.jfoodeng.2006.10.016 ElMasry G, 2013, J FOOD ENG, V117, P235, DOI 10.1016/j.jfoodeng.2013.02.016 Feng YZ, 2013, TALANTA, V109, P74, DOI 10.1016/j.talanta.2013.01.057 Feng YZ, 2013, FOOD CHEM, V138, P1829, DOI 10.1016/j.foodchem.2012.11.040 Feng YZ, 2012, CRIT REV FOOD SCI, V52, P1039, DOI 10.1080/10408398.2011.651542 Gowen AA, 2007, TRENDS FOOD SCI TECH, V18, P590, DOI 10.1016/j.tifs.2007.06.001 Herrero AM, 2008, J AGR FOOD CHEM, V56, P7119, DOI 10.1021/jf800925s Hu ZH, 2000, J FOOD ENG, V46, P189, DOI 10.1016/S0260-8774(00)00082-0 Huang L, 2014, FOOD CHEM, V145, P228, DOI 10.1016/j.foodchem.2013.06.073 Hung Y, 2016, MEAT SCI, V121, P119, DOI 10.1016/j.meatsci.2016.06.002 Jackman P, 2009, MEAT SCI, V83, P187, DOI 10.1016/j.meatsci.2009.03.010 Jeirani Z, 2006, J PETROL SCI ENG, V50, P11, DOI 10.1016/j.petrol.2005.09.002 Kamruzzaman M, 2013, FOOD CHEM, V141, P389, DOI 10.1016/j.foodchem.2013.02.094 Kamruzzaman M, 2012, INNOV FOOD SCI EMERG, V16, P218, DOI 10.1016/j.ifset.2012.06.003 Khulal U, 2016, FOOD CHEM, V197, P1191, DOI 10.1016/j.foodchem.2015.11.084 Kiani H, 2011, FOOD RES INT, V44, P2915, DOI 10.1016/j.foodres.2011.06.051 Leardi R, 1998, CHEMOMETR INTELL LAB, V41, P195, DOI 10.1016/S0169-7439(98)00051-3 Leardi R, 2000, J CHEMOMETR, V14, P643, DOI 10.1002/1099-128X(200009/12)14:5/6<643::AID-CEM621>3.0.CO;2-E Lee SY, 2016, FOOD CONTROL, V66, P53, DOI 10.1016/j.foodcont.2016.01.041 Li HH, 2015, LWT-FOOD SCI TECHNOL, V63, P268, DOI 10.1016/j.lwt.2015.03.052 Liao YT, 2012, J FOOD ENG, V109, P668, DOI 10.1016/j.jfoodeng.2011.11.029 Liu D, 2014, FOOD BIOPROCESS TECH, V7, P3100, DOI 10.1007/s11947-014-1327-5 Liu D, 2014, FOOD CHEM, V152, P197, DOI 10.1016/j.foodchem.2013.11.107 Liu D, 2014, FOOD BIOPROCESS TECH, V7, P307, DOI 10.1007/s11947-013-1193-6 Liu D, 2013, INNOV FOOD SCI EMERG, V20, P316, DOI 10.1016/j.ifset.2013.09.002 Lorente D, 2012, FOOD BIOPROCESS TECH, V5, P1121, DOI 10.1007/s11947-011-0725-1 Mc Donald K, 2001, J FOOD ENG, V48, P195, DOI 10.1016/S0260-8774(00)00158-8 Meersman F, 2002, BIOPHYS J, V82, P2635, DOI 10.1016/S0006-3495(02)75605-1 Pu HB, 2015, FOOD BIOPROCESS TECH, V8, P1, DOI 10.1007/s11947-014-1393-8 Pu HB, 2014, FOOD BIOPROCESS TECH, V7, P3088, DOI 10.1007/s11947-014-1330-x RIEDER RF, 1970, J CLIN INVEST, V49, P2369, DOI 10.1172/JCI106456 Sun DW, 1999, INT J REFRIG, V22, P472, DOI 10.1016/S0140-7007(99)00011-0 Sun DW, 2000, INT J REFRIG, V23, P508, DOI 10.1016/S0140-7007(99)00079-1 Sun DW, 2003, INT J REFRIG, V26, P19, DOI 10.1016/S0140-7007(02)00038-5 SUN DW, 1994, J STORED PROD RES, V30, P27, DOI 10.1016/0022-474X(94)90270-4 Tao FF, 2015, FOOD BIOPROCESS TECH, V8, P17, DOI 10.1007/s11947-014-1374-y Wang LJ, 2004, J FOOD ENG, V61, P231, DOI 10.1016/S0260-8774(03)00095-5 Wang LJ, 2002, INT J REFRIG, V25, P854, DOI 10.1016/S0140-7007(01)00094-9 Wang LJ, 2002, INT J REFRIG, V25, P862, DOI 10.1016/S0140-7007(01)00095-0 Wu D, 2013, INNOV FOOD SCI EMERG, V19, P1, DOI 10.1016/j.ifset.2013.04.014 Wu D, 2013, TRENDS FOOD SCI TECH, V29, P5, DOI 10.1016/j.tifs.2012.08.004 Wu X, 2016, MEAT SCI, V113, P92, DOI 10.1016/j.meatsci.2015.11.008 Xiong ZJ, 2015, FOOD CHEM, V175, P417, DOI 10.1016/j.foodchem.2014.11.161 Zheng LY, 2004, TRENDS FOOD SCI TECH, V15, P555, DOI 10.1016/j.tifs.2004.09.002 Zhu FL, 2013, FOOD BIOPROCESS TECH, V6, P2931, DOI 10.1007/s11947-012-0825-6 NR 67 TC 74 Z9 77 U1 4 U2 121 PD APR 15 PY 2017 VL 221 BP 1989 EP 1996 DI 10.1016/j.foodchem.2016.11.093 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000389909100253 DA 2022-12-14 ER PT J AU Wen, J Zeng, L Sun, YL Chen, DH Xu, YH Luo, P Zhao, Z Yu, ZH Fan, SG AF Wen, Jing Zeng, Ling Sun, Yulin Chen, Daohai Xu, Youhou Luo, Peng Zhao, Zhe Yu, Zonghe Fan, Sigang TI Authentication and traceability of fish maw products from the market using DNA sequencing SO FOOD CONTROL DT Article DE Fish maw; Genetic identification; Labeling; Traceability; 16S rRNA gene; BLAST ID COMMERCIAL FISH; IDENTIFICATION; FINS; PCR; EXTINCTION; FISHERIES; SHARK AB Fish maws (dried swimbladders of fishes) are regarded as traditional luxurious delicacies, medicine and tonics, which have been recommended and consumed in Asia over many centuries. At the commercial level, they are ranked as different values based on species. However, fish maw species and the trade are still unknown and undocumented. The processing treatments make them difficult for species identification based on morphological characterization. In the present study, the genetic identification of the main commercialized species of fish maws has been carried out, based on the amplification of a fragment of mitochondrial 16S rRNA gene and subsequent BLAST analysis. The applicability of all kinds of processed. products was verified, including dried, water soaked; salt fried and salt fried plus water soaked forms. The result indicated this method was applicable to all of them, showed that 53.3% of the products were incorrectly labeled and 58.3% of "croaker" products were substituted with catfish or perch species. Moreover, results indicated that besides traditional trade in Asia, the fish maw trade has been globally expanded to meet the growing demand of market. Therefore, this method can be useful in normative control of processed products, particularly in the authenticity of imported species, verifying the correct traceability in commercial trade, the correct labeling, and also for fisheries control of endangered species to conserve stocks biodiversity. (C) 2015 Elsevier Ltd. All rights' reserved. C1 [Wen, Jing; Zeng, Ling; Sun, Yulin; Chen, Daohai] Lingnan Normal Univ, Dept Biol, Zhanjiang 524048, Peoples R China. [Xu, Youhou] Qinzhou Univ, Guangxi Key Lab Beibu Gulf Marine Biodivers Conse, Qinzhou 535000, Peoples R China. [Luo, Peng; Zhao, Zhe; Yu, Zonghe] Chinese Acad Sci, South China Sea Inst Oceanol, Key Lab Trop Marine Bioresources & Ecol LMB, Guangzhou 510301, Guangdong, Peoples R China. [Fan, Sigang] Chinese Acad Fishery Sci, South China Sea Fisheries Res Inst, Minist Agr, Key Lab South China Sea Fishery Resources Utiliza, Guangzhou 510300, Guangdong, Peoples R China. C3 LingNan Normal University; Beibu Gulf University; Chinese Academy of Sciences; South China Sea Institute of Oceanology, CAS; Chinese Academy of Fishery Sciences; South China Sea Fisheries Research Institute, CAFS; Ministry of Agriculture & Rural Affairs RP Wen, J (corresponding author), Lingnan Normal Univ, Dept Biol, Zhanjiang 524048, Peoples R China. EM jw82123@126.com; karine126@126.com CR Ardura A, 2010, FOOD RES INT, V43, P2295, DOI 10.1016/j.foodres.2010.08.004 Ardura A, 2010, FOOD RES INT, V43, P1549, DOI 10.1016/j.foodres.2010.03.016 Balirwa JS, 2007, AFR J ECOL, V45, P120, DOI 10.1111/j.1365-2028.2007.00753.x Bellagamba F, 2001, J AGR FOOD CHEM, V49, P3775, DOI 10.1021/jf0010329 dos Santos APB, 2014, LWT-FOOD SCI TECHNOL, V57, P267, DOI 10.1016/j.lwt.2014.01.010 Chan WH, 2012, FOOD CONTROL, V23, P137, DOI 10.1016/j.foodcont.2011.06.024 Cheung WWL, 2005, BIOL CONSERV, V124, P97, DOI 10.1016/j.biocon.2005.01.017 Clarke S, 2004, FISH FISH, V5, P53, DOI 10.1111/j.1467-2960.2004.00137.x Clarke SC, 2006, CONSERV BIOL, V20, P201, DOI 10.1111/j.1523-1739.2005.00247.x Cutarelli A, 2014, FOOD CONTROL, V37, P46, DOI 10.1016/j.foodcont.2013.08.009 Espineira M, 2008, J AGR FOOD CHEM, V56, P10594, DOI 10.1021/jf801728q Galal-Khallaf A, 2014, FOOD CONTROL, V46, P441, DOI 10.1016/j.foodcont.2014.06.016 Haye PA, 2012, FOOD CONTROL, V25, P239, DOI 10.1016/j.foodcont.2011.10.034 Holmes BH, 2009, FISH RES, V95, P280, DOI 10.1016/j.fishres.2008.09.036 Huang YR, 2014, FOOD RES INT, V55, P294, DOI 10.1016/j.foodres.2013.11.027 Josupeit H., 2006, FAO GLOBEFISH RES PR, V84 Lago FC, 2013, EUR FOOD RES TECHNOL, V236, P171, DOI 10.1007/s00217-012-1875-8 Lago FC, 2011, J AGR FOOD CHEM, V59, P2223, DOI 10.1021/jf104505q Lin S. Y., 1939, Hong Kong Naturalist, V9, P108 Liu M, 2008, FISH FISH, V9, P219, DOI 10.1111/j.1467-2979.2008.00278.x Newman J. M., 2004, FOOD CULTURE CHINA Nyboer EA, 2013, FISH RES, V137, P18, DOI 10.1016/j.fishres.2012.08.003 Palumbi SR, 1996, MOL SYSTEMATICS, P205, DOI DOI 10.1080/17451000.2014.902536 Perez M, 2008, J AGR FOOD CHEM, V56, P10865, DOI 10.1021/jf801700x Rasmussen RS, 2008, COMPR REV FOOD SCI F, V7, P280, DOI 10.1111/j.1541-4337.2008.00046.x Sadovy Y, 2003, FISH FISH, V4, P86, DOI 10.1046/j.1467-2979.2003.00104.x Stone R, 2007, SCIENCE, V316, P1684, DOI 10.1126/science.316.5832.1684 Veneza I, 2014, FOOD CONTROL, V38, P116, DOI 10.1016/j.foodcont.2013.10.012 Wen J, 2012, FOOD CONTROL, V27, P380, DOI 10.1016/j.foodcont.2012.04.007 Wen J, 2011, FOOD CONTROL, V22, P72, DOI 10.1016/j.foodcont.2010.06.010 Wen J, 2010, FOOD CONTROL, V21, P403, DOI 10.1016/j.foodcont.2009.06.014 NR 31 TC 23 Z9 27 U1 0 U2 49 PD SEP PY 2015 VL 55 BP 185 EP 189 DI 10.1016/j.foodcont.2015.02.033 WC Food Science & Technology SC Food Science & Technology UT WOS:000353850500024 DA 2022-12-14 ER PT J AU Pasqualone, A Di Rienzo, V Nasti, R Blanco, A Gomes, T Montemurro, C AF Pasqualone, Antonella Di Rienzo, Valentina Nasti, Raffaella Blanco, Antonio Gomes, Tommaso Montemurro, Cinzia TI Traceability of Italian Protected Designation of Origin (PDO) Table Olives by Means of Microsatellite Molecular Markers SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE table olives; PDO and traditional food; traceability; DNA microsatellites ID OLEA-EUROPAEA L.; IDENTIFICATION; OIL AB The aim of this work was to develop a DNA microsatellite-based method of analysis to allow traceability of the three Italian Protected Designation of Origin (PDO) table olives in comparison with fruits of another seven highly diffused table olive cultivars. The analyses were carried out by using 16 primer pairs, with a mean of five different alleles detected per primer set, and power of discrimination from 0.56 to 0.90. Allelic error rates in the range of 0-3.8% were observed. By combining data from the most reliable and highly informative microsatellites (DCA3, DCA16, DCA17, DCA18, UDO-043, and GAPU101), it was possible to identify the PDO fruits over the panel of 10 cultivars, with the probability of a chance match between different cultivars as low as 10(-9) and with 0.5% error rate. The amplification profile is independent of environmental and processing conditions and is helpful to verify the authenticity of PDO samples. C1 [Pasqualone, Antonella; Nasti, Raffaella; Gomes, Tommaso] Univ Bari, Dept Soil Plant & Food Sci, Food Sci & Technol Unit, I-70126 Bari, Italy. [Di Rienzo, Valentina; Blanco, Antonio; Montemurro, Cinzia] Univ Bari, Dept Soil Plant & Food Sci, Plant Breeding Unit, I-70126 Bari, Italy. C3 Universita degli Studi di Bari Aldo Moro; Universita degli Studi di Bari Aldo Moro RP Pasqualone, A (corresponding author), Univ Bari, Dept Soil Plant & Food Sci, Food Sci & Technol Unit, Via Amendola 165-A, I-70126 Bari, Italy. EM antonella.pasqualone@agr.uniba.it CR Alba V, 2009, SCI HORTIC-AMSTERDAM, V123, P11, DOI 10.1016/j.scienta.2009.07.007 ANDERSON JA, 1993, GENOME, V36, P181, DOI 10.1139/g93-024 Bailin N. Z., 2011, CURRENT TOPICS FOOD, P73 Baldoni L., 2011, MANUALE IDENTIFICAZI, P1 Baldoni L, 2009, MOL BREEDING, V24, P213, DOI 10.1007/s11032-009-9285-8 Bianchi G, 2003, EUR J LIPID SCI TECH, V105, P229, DOI 10.1002/ejlt.200390046 Carriero F, 2002, THEOR APPL GENET, V104, P301, DOI 10.1007/s001220100691 Cipriani G, 2002, THEOR APPL GENET, V104, P223, DOI 10.1007/s001220100685 Cunha S. C., 2011, CURRENT TOPICS FOOD, P97 De La Rosa R, 2002, MOL ECOL NOTES, V2, P265, DOI 10.1046/j.1471-8286.2002.00217.x Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 Fernandez A. G., 1997, TABLE OLIVES PRODUCT KLOOSTERMAN AD, 1993, INT J LEGAL MED, V105, P257, DOI 10.1007/BF01370382 PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Pasqualone A, 2005, RIV ITAL SOSTANZE GR, V82, P173 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Pasqualone A., 2011, CURRENT TOPICS FOOD, P23 Pasqualone Antonella, 2003, Polish Journal of Food and Nutrition Sciences, V12, P96 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Pasqualone A, 2012, FOOD RES INT, V47, P188, DOI 10.1016/j.foodres.2011.05.008 Pasqualone A, 2010, EUR FOOD RES TECHNOL, V230, P723, DOI 10.1007/s00217-009-1210-1 Pasqualone C, 2008, RIV ITAL SOSTANZE GR, V85, P83 Pompanon F, 2005, NAT REV GENET, V6, P847, DOI 10.1038/nrg1707 Prevost A, 1999, THEOR APPL GENET, V98, P107, DOI 10.1007/s001220051046 Gomez AH, 2006, GRASAS ACEITES, V57, P86 Sefc KM, 2000, MOL ECOL, V9, P1171, DOI 10.1046/j.1365-294x.2000.00954.x TAUTZ D, 1989, NUCLEIC ACIDS RES, V17, P6463, DOI 10.1093/nar/17.16.6463 The International Olive Council, 2000, WORLD TABL OL FIG Vallejo-Cordoba B., 2001, CURRENT TOPICS FOOD, P41 Weir BS, 1990, GENETIC DATA ANAL Yeh FC., 2000, POPGENE VERSION 1 32 NR 31 TC 23 Z9 23 U1 0 U2 13 PD MAR 27 PY 2013 VL 61 IS 12 BP 3068 EP 3073 DI 10.1021/jf400014g WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000317031800022 DA 2022-12-14 ER PT J AU Heaps, E Yacoot, A Dongmo, H Picco, L Payton, OD Russell-Pavier, F Klapetek, P AF Heaps, Edward Yacoot, Andrew Dongmo, Herve Picco, Loren Payton, Oliver D. Russell-Pavier, Freddie Klapetek, Petr TI Bringing real-time traceability to high-speed atomic force microscopy SO MEASUREMENT SCIENCE AND TECHNOLOGY DT Article DE metrology; high-speed atomic force microscopy; traceability; nanometrology; nanotechnology ID DESIGN AB In recent years, there has been growth in the development of high-speed AFMs, which offer the possibility of video rate scanning and long-range scanning over several hundred micrometres. However, until recently these instruments have been lacking full traceable metrology. In this paper traceable metrology, using optical interferometry, has been added to an open-loop contact-mode high-speed AFM to provide traceability both for short-range video rate images and large-area scans made using a combination of a high-speed dual-axis scanner and long-range positioning system. Using optical interferometry to determine stages' positions and cantilever displacement enables the direct formation of images, obviating the need for complex post-processing corrections to compensate for lateral stage error. The application of metrology increases the spatial accuracy and linearisation of the high-speed AFM measurements, enabling the generation of very large traceable composite images. C1 [Heaps, Edward; Yacoot, Andrew; Dongmo, Herve; Russell-Pavier, Freddie] Natl Phys Lab, Teddington TW11 0LW, Middx, England. [Picco, Loren; Payton, Oliver D.; Russell-Pavier, Freddie] Univ Bristol, Tyndall Ave, Bristol BS8 1TL, Avon, England. [Picco, Loren] Virginia Commonwealth Univ, Dept Phys, Richmond, VA 23284 USA. [Klapetek, Petr] Czech Metrol Inst, Okruzni 31, Brno 63800, Czech Republic. [Klapetek, Petr] Brno Univ Technol, CEITEC, Purkynova 123, Brno 61200, Czech Republic. C3 National Physical Laboratory - UK; University of Bristol; Virginia Commonwealth University; Czech Metrology Institute; Brno University of Technology RP Yacoot, A (corresponding author), Natl Phys Lab, Teddington TW11 0LW, Middx, England. EM andrew.yacoot@npl.co.uk CR Ando T, 2019, CURR OPIN CHEM BIOL, V51, P105, DOI 10.1016/j.cbpa.2019.05.010 Ando T, 2013, ANNU REV BIOPHYS, V42, P393, DOI 10.1146/annurev-biophys-083012-130324 Ando T, 2012, NANOTECHNOLOGY, V23, DOI 10.1088/0957-4484/23/6/062001 Bakucz P, 2008, MEAS SCI TECHNOL, V19, DOI 10.1088/0957-0233/19/6/065101 BIRCH KP, 1994, METROLOGIA, V31, P315, DOI 10.1088/0026-1394/31/4/006 Dai GL, 2018, MEAS SCI TECHNOL, V29, DOI 10.1088/1361-6501/aaaf8a Danzebrink HU, 2006, CIRP ANN-MANUF TECHN, V55, P841, DOI 10.1016/j.cirp.2006.10.010 DOWNS MJ, 1993, PRECIS ENG, V15, P281, DOI 10.1016/0141-6359(93)90111-M Fantner GE, 2006, ULTRAMICROSCOPY, V106, P881, DOI 10.1016/j.ultramic.2006.01.015 Haycocks J, 2005, PRECIS ENG, V29, P168, DOI 10.1016/j.precisioneng.2004.06.002 HEYDEMANN PLM, 1981, APPL OPTICS, V20, P3382, DOI 10.1364/AO.20.003382 ISO 1:2016:, 12016 ISO Keyvani A, 2015, PROC SPIE, V9424, DOI 10.1117/12.2185848 Klapetek P, 2015, NANOTECHNOLOGY, V26, DOI 10.1088/0957-4484/26/6/065501 Klapetek P, 2017, MEAS SCI TECHNOL, V28, DOI 10.1088/1361-6501/28/3/034015 Klapetek P, 2013, MEAS SCI TECHNOL, V24, DOI 10.1088/0957-0233/24/2/025006 Liu L, 2019, REV SCI INSTRUM, V90, DOI 10.1063/1.5089534 Mikheikin A, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-01891-9 Necas D, 2012, CENT EUR J PHYS, V10, P181, DOI 10.2478/s11534-011-0096-2 Payton OD, 2016, INT MATER REV, V61, P473, DOI 10.1080/09506608.2016.1156301 Payton OD, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4747455 Payton OD, 2011, REV SCI INSTRUM, V82, DOI 10.1063/1.3575321 Picco LM, 2008, NANOTECHNOLOGY, V19, DOI 10.1088/0957-4484/19/38/384018 PTB, 2019, TRAC 3 DIM NAN PTB, 2019, METR MOV POS 6 DEGR Russell-Pavier FS, 2018, MEAS SCI TECHNOL, V29, DOI 10.1088/1361-6501/aad771 Schitter G, 2008, MATER TODAY, V11, P40, DOI 10.1016/S1369-7021(09)70006-9 Yacoot A, 2000, MEAS SCI TECHNOL, V11, P1126, DOI 10.1088/0957-0233/11/8/305 Yacoot A, 2011, MEAS SCI TECHNOL, V22, DOI 10.1088/0957-0233/22/12/122001 Yong YK, 2009, IEEE T NANOTECHNOL, V8, P46, DOI 10.1109/TNANO.2008.2005829 NR 30 TC 3 Z9 3 U1 1 U2 12 PD JUL PY 2020 VL 31 IS 7 AR 074005 DI 10.1088/1361-6501/ab7ca9 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000531259600001 DA 2022-12-14 ER PT J AU Dandage, K AF Dandage, Keshav TI FOOD TRACEABILITY THROUGH WEB AND SMART PHONE FOR FARMER'S AGRICULTURE PRODUCTS IN INDIA WITH HELP OF WEB API'S TECHNOLOGY SO AGROLIFE SCIENTIFIC JOURNAL DT Article DE cloud computing; farmers; food traceability; ICTs; India; web APIs AB Indian farming sector is mainly occupied by marginal, small, medium and large landholding farmers. These farmers are working consistently on their own or contract farm for financial sustainability, and to feed themselves and the nation; simultaneously they are also geared up for healthy food access and food production. Though food traceability of agriculture products in India is at the initial stage, many public and private enterprises have taken initiatives to establish the best agro-food produce traceability system in the country. This paper focuses on smart phone and other devices that provide Indian farmers a new identity and an easy platform to access domestic and international market. In addition, this paper presents the way of collecting information through web-based traceability portal from government databases like UIDAI, APEDA, GS1 India, Soil Health, InfoLNet, and AGMARK to mitigate the food fraud vulnerability, consumer health risk hazards, and recall issues. Furthermore, this paper aims to create a compatible one-touch secure remote access, which would give third-party services to consumer, retailer, exporter, and food business, and ultimately it would help the farmers to raise their income (earning) level and provide them the best market valuation through the online cloud-based web portal. C1 [Dandage, Keshav] Univ Politecn Madrid, Dept Ingn Agroforestal, ETSI Agron Alimentaria & Biosistemas, Ciudad Univ S-N, E-28040 Madrid, Spain. C3 Universidad Politecnica de Madrid RP Dandage, K (corresponding author), Univ Politecn Madrid, Dept Ingn Agroforestal, ETSI Agron Alimentaria & Biosistemas, Ciudad Univ S-N, E-28040 Madrid, Spain. EM k.dandage@alumnos.upm.es CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 AZEVEDO ELAINE DE, 2015, Ambient. soc., V18, P81, DOI 10.1590/1809-4422ASOC740V1832015 Boomi, 2015, INFORM TECHNOLOGY NE Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 FAO, 2011, WHAT DIES INT PROCEV Gartner, 2014, WHICH NEW OLD APPL W Gartner, 2016, GRANT SYA WORLDW PUB IBEF, 2014, INDIAN FOOD IND FOOD IBM, 2016, API EC API CONNECT India Brand Equity Foundation, 2017, AGR INDIA IND OVERVI Mahendra Dev S., 2012, SMALL FARMERS INDIA, P2012 McEntire Jennifer C., 2010, COMPREHENSIVE REV FO, V1, P92 Milne A, 2013, J BANK REGUL, V14, P241, DOI 10.1057/jbr.2013.16 *** Ministry of Health and Family Welfare and Food Safety and Standards Authority of India, 2016, FOOD SAFETY STANDARD Mintel, 2017, GLOB PACK TRENDS 201 Mulesoft, 2017, API STRATEGY RESOURC Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Organisation Economic Cooperation Development (OECD), 2014, COMPETITION ISSUES F, P489 Painter K., 2007, ANAL FOOD CHAIN DEMA, P3 Pakstaite S., 2015, BIO REACTIVE FOOD EX Pen J. B., 2010, S SPONS FRD RES BANK Pratt S., 2015, WHAT DOES YOUR GUT S Ray S., 2017, HINDUSTAN TIMES Singh R B., 2002, SMALLHOLDER FARMERS Spink J, 2011, J FOOD SCI, V76, pR157, DOI 10.1111/j.1750-3841.2011.02417.x Sterling B, 2015, COMPR REV FOOD SCI F, V14, P205, DOI 10.1111/1541-4337.12130 TechSci Research, 2015, INDIA CLOUD COMPUTIN Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 TNN, 2017, TIMES INDIA Yadav IC, 2015, SCI TOTAL ENVIRON, V511, P123, DOI 10.1016/j.scitotenv.2014.12.041 NR 30 TC 2 Z9 2 U1 0 U2 15 PD DEC PY 2018 VL 7 IS 2 BP 31 EP 42 WC Agronomy SC Agriculture UT WOS:000454271200004 DA 2022-12-14 ER PT J AU Cavite, HJ Mankeb, P Suwanmaneepong, S AF Cavite, Harry Jay Mankeb, Panya Suwanmaneepong, Suneeporn TI Community enterprise consumers' intention to purchase organic rice in Thailand: the moderating role of product traceability knowledge SO BRITISH FOOD JOURNAL DT Article DE Organic rice; Purchase intention; Product traceability; Theory of planned behaviour; Structural equation modelling ID FOOD TRACEABILITY; YOUNG CONSUMERS; CONSUMPTION; SYSTEM; WILLINGNESS; BEHAVIOR; INFORMATION; INVOLVEMENT; AWARENESS; MODEL AB Purpose Organic rice forms the largest portion of the Thai organic food market. Because of its increasing popularity, marketers need to better understand consumer behaviour to address emerging concerns regarding product safety and quality and to tailor better marketing strategies relevant to the development of organic rice. As such, this study aims to examine consumers' purchase intention towards organic rice, using traceability information, and to investigate the direct and moderating roles of product traceability knowledge, using the theory of planned behaviour. Design/methodology/approach Responses were collected from 243 organic rice consumers in a farmers' market in Chachoengsao Province, Thailand, following a convenience sampling approach. The gathered data were analysed using structural equation modelling to evaluate the strength of the relationship between the constructs. Findings The findings reveal that subjective norms, health consciousness and product traceability knowledge have a significant positive influence on consumers' intention to purchase organic rice. This study also establishes the moderating role of product traceability knowledge in perceived behavioural control and purchase intention, indicating that elaborated product information through traceability is essential for consumers who feel capable of buying the product. However, the direct effects of attitude and perceived behavioural control are insignificant, indicating the presence of external barriers to the purchase of organic rice, and that people may have a negative attitude towards the product. In addition, the cost perception result reveals that consumers consider price as an indicator of organic product quality, thereby increasing their desirability. Social implications The findings of this study will help community enterprises in Thailand develop a more effective marketing strategy based on the identified motivators of organic rice purchase intention. Originality/value This study develops a model that integrates important factors related to organic food consumption to generate a more comprehensive analysis of this mainstream research. To the best of the authors' knowledge, this is also the first study to investigate the moderating role of product traceability knowledge to obtain a new and more focused understanding of how this factor influences purchase intention when applied explicitly to organic food. Finally, the findings provide theoretical contributions and implications for both the community enterprise and policymakers on developing strategies for organic rice marketing among community enterprises in Thailand. C1 [Cavite, Harry Jay; Mankeb, Panya; Suwanmaneepong, Suneeporn] King Mongkuts Inst Technol Ladkrabang, Sch Agr Technol, Bangkok, Thailand. C3 King Mongkuts Institute of Technology Ladkrabang RP Cavite, HJ (corresponding author), King Mongkuts Inst Technol Ladkrabang, Sch Agr Technol, Bangkok, Thailand. EM harryjaycavite@gmail.com; panmankeb@gmail.com; ksuneeporn@gmail.com CR Adam BD, 2016, INT FOOD AGRIBUS MAN, V19, P191 Ahmed N, 2021, J ENVIRON PLANN MAN, V64, P796, DOI 10.1080/09640568.2020.1785404 Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Al-Swidi A, 2014, BRIT FOOD J, V116, P1561, DOI 10.1108/BFJ-05-2013-0105 Appelhanz S, 2016, J CLEAN PROD, V110, P132, DOI 10.1016/j.jclepro.2015.02.034 Arun TM, 2021, BUS STRATEG ENVIRON, V30, P2224, DOI 10.1002/bse.2755 Asif M, 2018, FOOD QUAL PREFER, V63, P144, DOI 10.1016/j.foodqual.2017.08.006 Atthirawong W, 2017, P 31 EUR C MOD SIM B Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bagozzi RP, 2012, J ACAD MARKET SCI, V40, P8, DOI 10.1007/s11747-011-0278-x Bai L, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11113045 BENTLER PM, 1987, SOCIOL METHOD RES, V16, P78, DOI 10.1177/0049124187016001004 Boobalan K, 2021, FOOD QUAL PREFER, V87, DOI 10.1016/j.foodqual.2020.104070 Bradu C, 2014, J BUS ETHICS, V124, P283, DOI 10.1007/s10551-013-1872-2 Canova L, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.575820 Chaiyasoonthorn W., 2019, INT J INNOVATION CRE, V8, P30 Chang A, 2013, BRIT FOOD J, V115, P1361, DOI 10.1108/BFJ-11-2011-0286 Chang SH, 2017, BRIT FOOD J, V119, P284, DOI 10.1108/BFJ-04-2016-0156 Charlebois S, 2015, J DAIRY SCI, V98, P3514, DOI 10.3168/jds.2014-9247 Chekima B, 2019, FOOD QUAL PREFER, V74, P49, DOI 10.1016/j.foodqual.2018.12.010 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Chin WW, 1997, INFORM SYST RES, V8, P342, DOI 10.1287/isre.8.4.342 Chu KM, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10124690 Corallo A, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11040613 De Farias F, 2019, J FOOD PROD MARK, DOI 10.1080/10454446.2019.1698484 Demirtas B, 2019, FOOD SCI TECH-BRAZIL, V39, P881, DOI 10.1590/fst.10518 Dorce LC, 2021, FOOD QUAL PREFER, V91, DOI 10.1016/j.foodqual.2021.104191 Ferraris A, 2020, INT MARKET REV, V37, P651, DOI 10.1108/IMR-11-2018-0322 Fishbein M., 1975, BELIEF ATTITUDES INT FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Fuller CM, 2016, J BUS RES, V69, P3192, DOI 10.1016/j.jbusres.2015.12.008 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Ghali-Zinoubi Z, 2019, TRENDS FOOD SCI TECH, V90, P175, DOI 10.1016/j.tifs.2019.02.028 Guo QZ, 2020, FOOD RES INT, V137, DOI 10.1016/j.foodres.2020.109518 Hair JF, 1998, MULTIVAR DATA ANAL, V5, P207, DOI DOI 10.14267/CJSSP.2016.02.04 Ham M, 2018, BRIT FOOD J, V120, P734, DOI 10.1108/BFJ-02-2017-0090 Nguyen HV, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16061037 Jager J, 2017, MONOGR SOC RES CHILD, V82, P13, DOI 10.1111/mono.12296 Jitrawang P, 2019, J FOOD PROD MARK, V25, P805, DOI 10.1080/10454446.2019.1679690 Jose H, 2021, BRIT FOOD J, V123, P3999, DOI 10.1108/BFJ-10-2020-0916 Torres-Ruiz FJ, 2018, BUS STRATEG ENVIRON, V27, P588, DOI 10.1002/bse.2022 Kim RB, 2016, QUAL INNOV PROSPER, V20, P18, DOI 10.12776/QIP.V20I1.625 Konuk FA, 2018, BRIT FOOD J, V120, P1561, DOI 10.1108/BFJ-11-2017-0631 Kushwah S, 2019, APPETITE, V143, DOI 10.1016/j.appet.2019.104402 Kushwah S, 2019, FOOD QUAL PREFER, V77, P1, DOI 10.1016/j.foodqual.2019.04.003 Laiprakobsup T, 2018, ASIAN GEOGR, V35, P53, DOI 10.1080/10225706.2017.1422767 Lee KH, 2015, INT J CONTEMP HOSP M, V27, P1157, DOI 10.1108/IJCHM-02-2014-0060 Li G, 2022, ETHICS BEHAV, V32, P532, DOI 10.1080/10508422.2021.1932502 Li SW, 2019, J INTEGR AGR, V18, P1793, DOI [10.1016/S2095-3119(19)62589-X, 10.1016/s2095-3119(19)62589-x] Liang RD, 2016, BRIT FOOD J, V118, P183, DOI 10.1108/BFJ-06-2015-0215 Lin HC, 2018, J RES EDUC SCI, V63, P291, DOI 10.6209/JORIES.201809_63(3).0010 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Marian L, 2014, FOOD QUAL PREFER, V37, P52, DOI 10.1016/j.foodqual.2014.05.001 Massey M, 2018, APPETITE, V125, P418, DOI 10.1016/j.appet.2018.02.029 My NHD, 2018, FOOD POLICY, V79, P283, DOI 10.1016/j.foodpol.2018.08.004 Nagaraj S, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102423 National Organic Farming Development Board, 2017, NAT ORG FARM DEV STR Ngobo PV, 2011, J RETAILING, V87, P90, DOI 10.1016/j.jretai.2010.08.001 O'Brien RM, 2007, QUAL QUANT, V41, P673, DOI 10.1007/s11135-006-9018-6 Osburg VS, 2020, MANAGE DECIS, V58, P1084, DOI 10.1108/MD-10-2017-1012 Osburg VS, 2017, J CLEAN PROD, V162, P1582, DOI 10.1016/j.jclepro.2017.06.112 Pandey Deepak, 2019, Organic Agriculture, V9, P357, DOI 10.1007/s13165-018-0240-z Petcho W, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11195445 Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879 Pomsanam P., 2014, Asian Journal of Scientific Research, V7, P434, DOI 10.3923/ajsr.2014.434.446 Pomsanam P., 2014, Asian Journal of Applied Sciences, V7, P294, DOI 10.3923/ajaps.2014.294.305 Pracharuengwit P., 2015, BUS ADM J, V146, P52 Prakash G, 2017, J CLEAN PROD, V141, P385, DOI 10.1016/j.jclepro.2016.09.116 Purwandoko PB, 2019, INFORMATION, V10, DOI 10.3390/info10060218 Qi X, 2019, APPETITE, V133, P414, DOI 10.1016/j.appet.2018.12.004 Rahman K.M., 2016, INT BUS MANAG, V10, P4292 Rana J, 2020, INT J CONSUM STUD, V44, P162, DOI 10.1111/ijcs.12556 Roitner-Schobesberger B, 2008, FOOD POLICY, V33, P112, DOI 10.1016/j.foodpol.2007.09.004 Sadiq MA, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102352 Santos V, 2021, J CLEAN PROD, V304, DOI 10.1016/j.jclepro.2021.127066 Satyapriya, 2019, INDIAN J AGR SCI, V89, P588 Secretariat of Community Enterprise Promotion Board, 2019, REP REG COMM ENT THA Ahangarkolaee SS, 2021, INT J CONSUM STUD, V45, P273, DOI 10.1111/ijcs.12619 Shin YH, 2019, J QUAL ASSUR HOSP TO, V20, P107, DOI 10.1080/1528008X.2018.1483288 Social Development Council, 2018, NATL STRAT 2018 2037 Spence M, 2018, FOOD CONTROL, V91, P138, DOI 10.1016/j.foodcont.2018.03.035 Sultan P, 2020, FOOD QUAL PREFER, V81, DOI 10.1016/j.foodqual.2019.103838 Teng CC, 2016, APPETITE, V105, P95, DOI 10.1016/j.appet.2016.05.006 Teng CC, 2015, BRIT FOOD J, V117, P1066, DOI 10.1108/BFJ-12-2013-0361 Thailand Board of Investment, 2020, THAIL FOOD IND Vega-Zamora M, 2019, J CLEAN PROD, V216, P511, DOI 10.1016/j.jclepro.2018.12.129 Violino S, 2019, EUR FOOD RES TECHNOL, V245, P2089, DOI 10.1007/s00217-019-03321-0 von Meyer-Hofer M, 2015, J FOOD PROD MARK, V21, P626 Voon JP, 2011, INT FOOD AGRIBUS MAN, V14, P103 Wang JH, 2020, FOOD CONTROL, V108, DOI 10.1016/j.foodcont.2019.106825 Wang JH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15122879 Wang JS, 2019, EKOLOJI, V28, P3783 Wang XH, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11010209 Wu WH, 2019, J FOOD PROD MARK, V25, P549, DOI 10.1080/10454446.2019.1611515 Wu X, 2021, J MARKET MANAG-UK, V37, P1267, DOI [10.3171/2020.9.JNS202965, 10.1080/0267257X.2021.1910328] Yadav R, 2016, APPETITE, V96, P122, DOI 10.1016/j.appet.2015.09.017 Yanakittkul P, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2019.e03039 You JJ, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.579274 Zagata L, 2012, APPETITE, V59, P81, DOI 10.1016/j.appet.2012.03.023 Zarei A, 2018, J FOOD PROD MARK, V24, P96, DOI 10.1080/10454446.2017.1266548 Zerbini C, 2019, FOOD RES INT, V122, P167, DOI 10.1016/j.foodres.2019.04.008 Zhang YJ, 2019, FOOD CONTROL, V95, P283, DOI 10.1016/j.foodcont.2018.08.018 Zhu LJ, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9050682 Zhu WW, 2018, BRIT FOOD J, V120, P2182, DOI 10.1108/BFJ-11-2017-0622 NR 106 TC 9 Z9 9 U1 6 U2 26 PD FEB 23 PY 2022 VL 124 IS 4 BP 1124 EP 1148 DI 10.1108/BFJ-02-2021-0148 EA AUG 2021 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000686179500001 DA 2022-12-14 ER PT J AU Lee, M Wang, YR Huang, CF AF Lee, Marco Wang, Yu-Ren Huang, Chung-Fah TI Design and development of a friendly user interface for building construction traceability system SO MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS DT Article AB The traceability systems of agricultural and food products have been developed for a few years. Due to the fact that building construction projects are typically more complex and have long project durations, the builders are less willing to engage in the development of building traceability systems for construction projects. Starting from 2016, the Kaohsiung city government took the first step to develop and promote a building construction traceability system for building construction quality assurance in Taiwan. In the future, builders are required to input the construction inspection records into the system during the building construction process. However, this system is designed in such a way that records are input from personal computers. This creates more job load for site engineers because they need to first keep the records on paper in the field and then input the data into the system using the computer in the office. In light of this, this research would like to design a more user-friendly interface so that it's easier for the engineer to directly input the inspection data using their hand-held devices on site. To achieve this, Responsive Web Design and Bootstrap framework are adopted to develop the new interface. Also, feedbacks and suggestions from current users are collected and incorporated into the new design. C1 [Lee, Marco; Wang, Yu-Ren; Huang, Chung-Fah] Natl Kaohsiung Univ Sci & Technol, Dept Civil Engn, Kaohsiung, Taiwan. C3 National Kaohsiung University of Science & Technology RP Wang, YR (corresponding author), Natl Kaohsiung Univ Sci & Technol, Dept Civil Engn, Kaohsiung, Taiwan. EM 1103412101@nkust.edu.tw; yrwang@nkust.edu.tw CR Almeida F, 2017, INT J INF TECHNOL WE, V12, P49, DOI 10.4018/IJITWE.2017040103 [Anonymous], 2018, BOOTSTRAP 4 CLASS RE Cascading Style Sheets Standard Boasts Unprecedented Interoperability, 2011, W3C IMM REL Cheng HF, 2016, THESIS NATL KAOHSIUN Faghih Behnam, 2013, International Journal of Soft Computing and Software Engineering, V3, P786, DOI 10.7321/jscse.v3.n3.119 HTML5 is a W3C recommendation. W3C News, 2014, W3C NEWS Hu CH, 2017, THESIS KUN SHAN U TA Hu Z, 2005, OFFICIALS CONDUCT SU Huang CH., 2017, J PROP MANAG, V8, P45 Koch PP, 2014, META VIEWPORT QUIRKS Lai GW, 2014, THESIS KUN SHANG U T Lestari D.M., 2014, INT J SOFTWARE ENG I, V8, P53 Mutua F, 2018, TROP ANIM HEALTH PRO, V50, P299, DOI 10.1007/s11250-017-1431-4 Nielsen j., 1994, C COMP CHI 94 BOST Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Ping Zhang, 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers, DOI 10.1109/HICSS.1999.772668 Shahzad F, 2017, PROCEDIA COMPUT SCI, V110, P410, DOI 10.1016/j.procs.2017.06.105 Walsh TA, 2017, PROCEEDINGS OF THE 26TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS (ISSTA'17), P192, DOI 10.1145/3092703.3092712 Yang XinTing, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P212 NR 19 TC 0 Z9 0 U1 2 U2 7 PD APR PY 2021 VL 27 IS 4 SI SI BP 1773 EP 1785 DI 10.1007/s00542-019-04547-4 WC Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied SC Engineering; Science & Technology - Other Topics; Materials Science; Physics UT WOS:000647800200069 DA 2022-12-14 ER PT J AU Zhao, Y Zan, FG Deng, J Zhao, PF Zhao, J Wu, CW Liu, JY Zhang, YB AF Zhao, Yong Zan, Fenggang Deng, Jun Zhao, Peifang Zhao, Jun Wu, Caiwen Liu, Jiayong Zhang, Yuebin TI Improvements in Sugarcane (Saccharum spp.) Varieties and Parent Traceability Analysis in Yunnan, China SO AGRONOMY-BASEL DT Article DE sugarcane (Saccharum spp.); variety improvements; agronomic/industrial characteristics; disease incidence; parent traceability ID SELECTION; TRAITS; GROWTH AB Sugarcane (Saccharum spp.) breeding in China has a history of nearly 70 years. Yunnan province represents the second largest sugarcane planting area in China; therefore, by studying the evolution of sugarcane varieties in this region, it is possible to gain an understanding of the process of improvement since the foundation of sugarcane hybrid breeding. In this study, we compared the main industrial and agronomical characteristics of 107 sugarcane varieties, developed between 1952 and 2020, and discussed the reasons for replacement and exchange. Overall, significant differences were observed (p < 0.01), highlighting notable improvements, especially in terms of yield; however, disease incidence remains a serious issue and the fundamental reason for variety replacement. Meanwhile, analysis of parent traceability revealed that the main varieties cultivated at present have a similar parental relationship based around CP, F, and YC series germplasms. Taken together, these findings suggest that disease-resistant breeding should be strengthened, and susceptible varieties eliminated, while making full use of existing varieties as core parents. C1 [Zhao, Yong; Zan, Fenggang; Deng, Jun; Zhao, Peifang; Zhao, Jun; Wu, Caiwen; Liu, Jiayong; Zhang, Yuebin] Yunnan Acad Agr Sci, Sugarcane Res Inst, Kaiyuan 661699, Peoples R China. C3 Yunnan Academy of Agricultural Sciences RP Liu, JY; Zhang, YB (corresponding author), Yunnan Acad Agr Sci, Sugarcane Res Inst, Kaiyuan 661699, Peoples R China. EM 18087395132@163.com; fengang88@126.com; dj@yaas.org.cn; hnzpf@163.com; junzhao_ky@126.com; gksky_wcw@163.com; lljjyy1976@163.com; ynzyb@sohu.com CR Asinari F, 2021, TROP PLANT PATHOL, V46, P37, DOI 10.1007/s40858-019-00322-y Chen J., 2020, SUGAR CROPS CHIN, V3, P8 Dal-Bianco M, 2012, CURR OPIN BIOTECH, V23, P265, DOI 10.1016/j.copbio.2011.09.002 Deng HaiHua, 2004, Sugarcane, V11, P7 [邓海华 DENG Haihua], 2006, [广东农业科学, Guangdong Agricultural Sciences], P7 Huang YingKun, 2007, Journal of Yunnan Agricultural University, V22, P935 Jackson P, 2001, CROP SCI, V41, P315, DOI 10.2135/cropsci2001.412315x Jackson PA, 2005, FIELD CROP RES, V92, P277, DOI 10.1016/j.fcr.2005.01.024 Khumla N, 2022, SUGAR TECH, V24, P193, DOI 10.1007/s12355-021-00996-2 Li WenFeng, 2018, Agricultural Biotechnology, V7, P148 [梁强 Liang Qiang], 2021, [热带作物学报, Chinese Journal of Tropical Crops], V42, P982 Lingle SE, 2010, FIELD CROP RES, V118, P152, DOI 10.1016/j.fcr.2010.05.002 Lingle SE, 2009, FIELD CROP RES, V113, P306, DOI 10.1016/j.fcr.2009.06.015 Liu JY, 2016, FIELD CROP RES, V196, P418, DOI 10.1016/j.fcr.2016.07.022 Luo Jun, 2015, Acta Agronomica Sinica, V41, P214 Matsuoka S, 2009, IN VITRO CELL DEV-PL, V45, P372, DOI 10.1007/s11627-009-9220-z Nair NV, 1998, GENET RESOUR CROP EV, V45, P459, DOI 10.1023/A:1008696617524 Ostengo S, 2022, SUGAR TECH, V24, P166, DOI 10.1007/s12355-021-00999-z Qi Y., 2012, CROP RES, V5, P443 Que Y, 2012, PLANT DIS, V96, P1519, DOI 10.1094/PDIS-08-11-0663-RE Ram B, 2022, SUGAR TECH, V24, P4, DOI 10.1007/s12355-021-01015-0 Roach B, 1989, P AUSTR SOC SUGAR CA, V11, P34 Shabbir R, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11061042 Shan HL, 2021, EUPHYTICA, V217, DOI 10.1007/s10681-021-02924-7 Snyman SJ, 2011, IN VITRO CELL DEV-PL, V47, P234, DOI 10.1007/s11627-011-9354-7 Todd James, 2015, Int Sch Res Notices, V2015, P257417, DOI 10.1155/2015/257417 Viswanathan R., 2021, Indian Phytopathology, V74, P573, DOI 10.1007/s42360-021-00391-7 Wang XY, 2021, J PLANT PATHOL, V103, P985, DOI 10.1007/s42161-021-00870-w Wang XY, 2014, TROP PLANT PATHOL, V39, P184, DOI 10.1590/S1982-56762014000200010 Wei XM, 2022, SUGAR TECH, V24, P151, DOI 10.1007/s12355-021-00969-5 Wu CaiWen, 2005, Southwest China Journal of Agricultural Sciences, V18, P858 Wu J, 2021, J ANIM PLANT SCI-PAK, V31, P719, DOI 10.36899/JAPS.2021.3.0262 Wu JT, 2019, AGRONOMY-BASEL, V9, DOI 10.3390/agronomy9080449 Yadav S, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10040585 Yang R., 2021, SUGAR CROPS CHIN, V43, P18 You Q, 2016, SUGAR TECH, V18, P380, DOI 10.1007/s12355-015-0395-9 Zhang C., 2009, SUGARCANE CANESUGAR, V5, P1, DOI [10.3969/j.issn.1005-9695.2009.05.001, DOI 10.3969/J.ISSN.1005-9695.2009.05.001] [张琼 ZHANG Qiong], 2009, [广东农业科学, Guangdong Agricultural Sciences], P44 Zhang Y., 2016, SUGAR CROPS CHIN, V38, P71, DOI [10.13570/j.cnki.scc.2016.06.024, DOI 10.13570/J.CNKI.SCC.2016.06.024] Zhao DL, 2015, INT J AGRON, V2015, DOI 10.1155/2015/547386 Zhao DL, 2012, J CROP IMPROV, V26, P60, DOI 10.1080/15427528.2011.611926 Zhao PF, 2019, J PLANT REGIST, V13, P362, DOI 10.3198/jpr2018.10.0068crc Zhao Y., 2019, SUBTROP AGR RES, V15, P7, DOI [10.13321/j.cnki.subtrop.agric.res.2019.01.002, DOI 10.13321/J.CNKI.SUBTROP.AGRIC.RES.2019.01.002] Zhao Yong, 2019, Scientia Agricultura Sinica, V52, P602, DOI 10.3864/j.issn.0578-1752.2019.04.003 Zhou H, 2012, J SW AGR, V25, P390, DOI [10.16213/j.cnki.scjas.2012.02.036, DOI 10.16213/J.CNKI.SCJAS.2012.02.036] NR 45 TC 1 Z9 1 U1 1 U2 1 PD MAY PY 2022 VL 12 IS 5 AR 1211 DI 10.3390/agronomy12051211 WC Agronomy; Plant Sciences SC Agriculture; Plant Sciences UT WOS:000801387200001 DA 2022-12-14 ER PT J AU Bojang, KP Kuna, A Pushpavalli, SNCVL Sarkar, S Sreedhar, M AF Bojang, Keotshepile Precious Kuna, Aparna Pushpavalli, Sreerangam N. C. V. L. Sarkar, Supta Sreedhar, M. TI Evaluation of DNA extraction methods for molecular traceability in cold pressed, solvent extracted and refined groundnut oils SO JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE DT Article DE Food adulteration; Groundnut oil; DNA extraction; Fingerprinting; SSR markers ID REAL-TIME PCR; OLIVE OIL; FOOD; AUTHENTICATION; AMPLIFICATION; MARKERS AB Groundnut oil (GNO)/peanut oil is one of the agro-food products with great economic value and hence an attractive target for adulteration and mislabeling. Simple Sequence Repeats (SSR) are markers of choice for DNA fingerprinting studies as they exhibit high polymorphism due to variable number of repeats. Hence, this study was designed to evaluate and optimize a method for DNA isolation from groundnut oil and study the possibility of using the isolated DNA for molecular traceability using SSR markers. Four methods to isolate DNA from groundnut oil were evaluated. All the four methods were modified CTAB protocols, but differed in procedures for extraction, buffer compositions, amount of oil used and DNA carriers. For molecular traceability of oils, extraction and recovery of DNA from edible oil is a key step, especially in refined oils. A method that employed DNA enrichment prior to extraction with CTAB buffer yielded amplifiable DNA from cold pressed GNO, crude hexane extracted GNO and refined GNO. The optimized method for isolation of DNA from groundnut oil is simple, efficient, less costly and reproducible when compared to chromatography and spectroscopy based techniques. C1 [Bojang, Keotshepile Precious] PJTS Agr Univ, Post Grad & Res Ctr, Hyderabad, India. [Kuna, Aparna] PJTS Agr Univ, 2MFPI Qual Control Lab, Hyderabad, India. [Pushpavalli, Sreerangam N. C. V. L.] PJTS Agr Univ, Inst Biotechnol, Hyderabad, India. [Sarkar, Supta] PJTS Agr Univ, Dept Foods & Nutr, Coll Community Sci, Hyderabad, India. [Sreedhar, M.] PJTS Agr Univ, 5Reg Rice & Sugarcane Res Ctr, Rudrur, India. RP Bojang, KP (corresponding author), PJTS Agr Univ, Post Grad & Res Ctr, Hyderabad, India. EM preciousbojang@gmail.com CR Arya SS, 2016, J FOOD SCI TECH MYS, V53, P31, DOI 10.1007/s13197-015-2007-9 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Carrin ME, 2010, EUR J LIPID SCI TECH, V112, P697, DOI 10.1002/ejlt.200900176 Cheng XX, 2018, ARAB J CHEM, V11, P815, DOI 10.1016/j.arabjc.2017.12.025 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Costa J, 2010, FOOD RES INT, V43, P301, DOI 10.1016/j.foodres.2009.10.003 Devasena N., 2017, J OILS RES, V34, P75 Dorni C, 2018, FOOD CHEM, V238, P9, DOI 10.1016/j.foodchem.2017.05.072 Doveri S, 2007, J AGR FOOD CHEM, V55, P4640, DOI 10.1021/jf063259v Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Gomes S, 2012, GENETIC DIVERSITY IN PLANTS, P15 Gryson N, 2002, J AM OIL CHEM SOC, V79, P171, DOI 10.1007/s11746-002-0453-2 He J, 2013, FOOD CONTROL, V31, P71, DOI 10.1016/j.foodcont.2012.07.001 Herrero M, 2010, ELECTROPHORESIS, V31, P205, DOI 10.1002/elps.200900365 Li YunJing, 2018, Oil Crop Science, V3, P122 List GR, 2016, PEAN GEN PROC UT, P405 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Morello L, 2019, GENES-BASEL, V10, DOI 10.3390/genes10030229 Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Muzzalupo I, 2015, EUR FOOD RES TECHNOL, V241, P151, DOI 10.1007/s00217-015-2455-5 Nikolic Z, 2014, FOOD CHEM, V145, P1072, DOI 10.1016/j.foodchem.2013.09.017 Ramos-Gomez S, 2014, FOOD CHEM, V158, P374, DOI 10.1016/j.foodchem.2014.02.142 Shayan P., 2017, IRAN J VET RES, V11, P311, DOI DOI 10.1007/S00217-008-0863-5 Shoba D, 2012, EUPHYTICA, V188, P265, DOI 10.1007/s10681-012-0718-9 Sujay V, 2012, MOL BREEDING, V30, P773, DOI 10.1007/s11032-011-9661-z Wu YJ, 2011, EUR FOOD RES TECHNOL, V233, P313, DOI 10.1007/s00217-011-1520-y Yang RN, 2018, TRENDS FOOD SCI TECH, V74, P26, DOI 10.1016/j.tifs.2018.01.013 NR 29 TC 2 Z9 2 U1 3 U2 11 PD SEP PY 2021 VL 58 IS 9 BP 3561 EP 3567 DI 10.1007/s13197-021-05079-4 EA MAR 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000632292500001 DA 2022-12-14 ER PT J AU Germak, A Herrmann, K Low, S AF Germak, Alessandro Herrmann, Konrad Low, Samuel TI Traceability in hardness measurements: from the definition to industry SO METROLOGIA DT Article AB The measurement of hardness has been and continues to be of significant importance to many of the world's manufacturing industries. Conventional hardness testing is the most commonly used method for acceptance testing and production quality control of metals and metallic products. Instrumented indentation is one of the few techniques available for obtaining various property values for coatings and electronic products in the micrometre and nanometre dimensional scales. For these industries to be successful, it is critical that measurements made by suppliers and customers agree within some practical limits. To help assure this measurement agreement, a traceability chain for hardness measurement traceability from the hardness definition to industry has developed and evolved over the past 100 years, but its development has been complicated. A hardness measurement value not only requires traceability of force, length and time measurements but also requires traceability of the hardness values measured by the hardness machine. These multiple traceability paths are needed because a hardness measurement is affected by other influence parameters that are often difficult to identify, quantify and correct. This paper describes the current situation of hardness measurement traceability that exists for the conventional hardness methods (i.e. Rockwell, Brinell, Vickers and Knoop hardness) and for special-application hardness and indentation methods (i.e. elastomer, dynamic, portables and instrumented indentation). C1 [Germak, Alessandro] INRIM, I-10135 Turin, Italy. [Herrmann, Konrad] Phys Tech Bundesanstalt, D-38116 Braunschweig, Germany. [Low, Samuel] NIST, Gaithersburg, MD 20899 USA. C3 Istituto Nazionale di Ricerca Metrologica (INRIM); Physikalisch-Technische Bundesanstalt (PTB); National Institute of Standards & Technology (NIST) - USA RP Germak, A (corresponding author), INRIM, Str Cacce 73, I-10135 Turin, Italy. CR [Anonymous], 2005, 650712005 ISO [Anonymous], 650722005 ISO [Anonymous], 650812005 ISO *ASTM, E10082008 ASTM *ASTM, E384092008 ASTM *ASTM, E1808B2008 ASTM *ASTM, E92822003E22008 ASTM *ASTM, A1038052005 ASTM *ASTM, A956062006 ASTM Barbato G., 1998, P HARDMENKO 98 SEPT, P53 Barbato G., 1994, P 13 IMEKO TOR, V1, P761 BARBATO G, 1978, 128 IMGC BARBATO G, 1995, P VDI S HARDN DUSS G, P11 CZICHOS H, 2006, SPRINGER HDB MAT MEA, V311 *DIN, 5015612007 DIN *DIN, 5015722008 DIN *DIN, 5015812008 DIN *DIN, 1457722005 DIN *DIN, 5015922008 DIN *DIN, 5015632007 DIN *DIN, 501572007 DIN *DIN, 5015822008 DIN *DIN, 5015912008 DIN *EUR ASS NAT METR, EURAMETCG16V01 EUR A Fruhauf J., 2007, P HARDMEKO 2007 TSUK, P138 Gao S, 2008, P SPIE, V6993 Germak A., 2006, VDI BER, V1948, P13 GERMAK A, 2007, P IMEKO TC5 HARDMEKO, P78 IIZUKA K, 2007, P HARDMEKO 2007 TSUK, P1 *ISO, 1457712005 ISO *ISO, 482005 ISO *ISO, 454512005 ISO *ISO, 761912005 ISO *ISO, 650822005 ISO *ISO, 454532005 ISO *ISO, 650622005 ISO *ISO, 650832005 ISO *ISO, 650612005 ISO *ISO, 650732005 ISO *ISO, 454522005 ISO *ISO, 188982005 ISO *ISO, 650632005 ISO LEHMANN R, 1940, WERKSTATTSTECHNIK WE, V34, P73 LOW SR, 1999, P 1999 WORKSH S CHAR MARRINER RS, 1957, MACHINERY, V91, P1225 MARRINER RS, 1972, MC8 NPL MARRINER RS, 1967, METALLURGIA, V87, P87 MEYER K, 1957, VDI BER, V11, P103 NESTEROV V, 2008, P EUSP 2008 ZUR SWIT, V2, P302 *OIML, SP19SR41984 OIML OLIVER WC, 1992, J MATER RES, V7, P1564, DOI 10.1557/JMR.1992.1564 PETIK F, 1988, OIML B, V113, P35 PETIK F, 1983, BUREAU INT METROLOGI Pratt JR, 2004, J MATER RES, V19, P366, DOI 10.1557/jmr.2004.19.1.366 Simunek A, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.085501 YAMAMOTO K, 1958, REPORT NRLM, V7, P10 YAMAMOTO K, 1966, B NRLM, V13, P10 NR 57 TC 8 Z9 9 U1 0 U2 9 PD APR PY 2010 VL 47 IS 2 BP S59 EP S66 DI 10.1088/0026-1394/47/2/S07 WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000275302900008 DA 2022-12-14 ER PT J AU Galvez, JF Mejuto, JC Simal-Gandara, J AF Galvez, Juan F. Mejuto, J. C. Simal-Gandara, J. TI Future challenges on the use of blockchain for food traceability analysis SO TRAC-TRENDS IN ANALYTICAL CHEMISTRY DT Review DE Blockchain; Food authentication; Agricultural and farming applications; Food chain; Traceability; Data analysis and management ID MULTIVARIATE DATA-ANALYSIS; SUPPLY CHAIN MANAGEMENT; FRAMEWORK; TRANSPARENCY; CLASSIFICATION; PRINCIPLES; SYSTEM AB The steady increase in food falsification, which has caused large economic losses and eroded consumers' trust, has become a pressing issue for producers, researchers, governments, consumers and other stakeholders. Tracking and authenticating the food supply chain to understand provenance is critical with a view to identifying and addressing sources of contamination in the food supply chain worldwide. One way of solving traceability issues and ensuring transparency is by using blockchain technology to store data from chemical analysis in chronological order so that they are impossible to manipulate afterwards. This review examines the potential of blockchain technology for assuring traceability and authenticity in the food supply chain. It can be considered a true innovation and relevant approach to assure the quality of the third step of the analytical processes: data acquisition and management. (C) 2018 Elsevier B.V. All rights reserved. C1 [Galvez, Juan F.] Univ Vigo, ESEI, Dept Informat, Ourense Campus, Orense, Spain. [Mejuto, J. C.] Univ Vigo, Fac Sci, Dept Phys Chem, Ourense Campus, Orense, Spain. [Simal-Gandara, J.] Univ Vigo, Fac Sci, Dept Analyt & Food Chem, Nutr & Bromatol Grp, Ourense Campus, Orense, Spain. C3 Universidade de Vigo; Universidade de Vigo; Universidade de Vigo RP Simal-Gandara, J (corresponding author), Univ Vigo, Fac Sci, Dept Analyt & Food Chem, Nutr & Bromatol Grp, Ourense Campus, Orense, Spain. EM galvez@uvigo.es; xmejuto@uvigo.es; jsimal@uvigo.es CR Aljazzaf Zainab M., 2010, Proceedings 2010 5th International Multi-Conference on Computing in the Global Information Technology (ICCGI 2010), P163, DOI 10.1109/ICCGI.2010.17 Anderson R.J., 2017, P PRAG ARC NET Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Benkler Y., 2007, WEALTH NETWORKS SOCI Beske-Janssen P, 2015, SUPPLY CHAIN MANAG, V20, P664, DOI 10.1108/SCM-06-2015-0216 Bhardwaj S., 2018, SMART INNOVATION SYS, V78 Bonneau Joseph, 2015, IEEE SECURITY PRIVAC Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bozarth C.C., 2019, INTRO OPERATIONS SUP Carter CR, 2008, INT J PHYS DISTR LOG, V38, P360, DOI 10.1108/09600030810882816 Charlebois S, 2017, CONVERSATION Crosby M., 2016, APPL INNOVATION, V2, P6, DOI DOI 10.21626/innova/2016.1/01 Crossey S., 2017, NEW FOOD Cuadros-Rodriguez L, 2016, ANAL CHIM ACTA, V909, P9, DOI 10.1016/j.aca.2015.12.042 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 De Meijer C.R.W., 2016, FINEXTRA Dunkel S., 2015, TECHNOLOGY BASED BLO Engelhardt MA, 2017, TECHNOL INNOV MANAG, V7, P22, DOI 10.22215/timreview/1111 English Matthew S., 2017, APPL BITCOIN DATA ST Esteki M, 2017, FOOD ANAL METHOD, V10, P3312, DOI 10.1007/s12161-017-0903-5 Filiba J., 2017, BLOCKCHAIN CAN REGUL Fombrun CJ., 1996, REPUTATION Franco P., 2014, UNDERSTANDING BITCOI, DOI [10.1002/9781119019138.ch1, DOI 10.1002/9781119019138.CH1] Fraser E., 2017, CANADA SHOULD ADOPT Future Thinkers, 2018, 19 IND BLOCKCH WILL Gao W, 2012, J CHROMATOGR A, V1245, P109, DOI 10.1016/j.chroma.2012.05.027 Gerbig S, 2017, ANAL CHEM, V89, P10717, DOI 10.1021/acs.analchem.7b01689 Golan E., 2004, AGR EC REPORT, V830, P1 Gord M., 2016, BITCOIN MAGAZINE GS1, 2017, GLOB TRAC STAND Gualandris J, 2015, J OPER MANAG, V38, P1, DOI 10.1016/j.jom.2015.06.002 HABER S, 1991, LECT NOTES COMPUT SC, V537, P437 Heinen D., 2017, CAPGEMINI CONSULTING Hyperledger, 2016, HYP WHIT Hyperledger, 2017, HYP FABR DOC Kharif O., 2016, BLOOMBERG BUSINESSWE, V4501, P20 Kim DJ, 2008, DECIS SUPPORT SYST, V44, P544, DOI 10.1016/j.dss.2007.07.001 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Leibowitz J., 2016, COINDESK Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Lifton R., 2016, BLOCKCHAIN IMPACT DI Loop P., 2016, MAT HANDLING LOGISTI Lumb David, 2017, ENGADGET Mattila J., 2016, ETLA WORKING PAPERS, V38 Mejia C, 2010, COMPR REV FOOD SCI F, V9, P159, DOI 10.1111/j.1541-4337.2009.00098.x Merkle RC, 1982, US Patent, Patent No. [4,309,569, 4309569] Meyer R, 1996, FOOD SCI TECHNOL-LEB, V29, P1, DOI 10.1006/fstl.1996.0001 Mol APJ, 2015, J CLEAN PROD, V107, P154, DOI 10.1016/j.jclepro.2013.11.012 Nakomoto S., 2008, BLOCKCHAIN Nakomoto Satoshi, 2009, BITCOIN PEER TO PEER, V3 New S, 2010, HARVARD BUS REV, V88, P76 Newman Peter, 2017, BUSINESS INSIDER Nystrom M., 1999, USENIX WORKSH SMARTC Parker L., 2016, IND RES PAPERS HIGHL Parmigiani A, 2011, J OPER MANAG, V29, P212, DOI 10.1016/j.jom.2011.01.001 Pass R., 2017, FRUITCHAINS FAIR BLO Pinna A., 2016, ECB OCCASIONAL PAPER Pizarro C, 2013, FOOD CHEM, V138, P915, DOI 10.1016/j.foodchem.2012.11.087 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Podio NS, 2013, J AGR FOOD CHEM, V61, P3763, DOI 10.1021/jf305258r Provenance, 2016, SHOR PLAT TRACK TUN Provenance, 2015, BLOCKCH SOL TRANSP P Raskin Max, 2017, GEO L TECH REV, V1, P305 Redman J., 2016, WALMART IBM IMPROVES Rodriguez-Bermudez R, 2018, FOOD CHEM, V240, P686, DOI 10.1016/j.foodchem.2017.08.011 Schneier B., 1998, 7 USENIX SEC S P, V53-62 Seibold S, 2016, KPMG, V26, P2001 Sforza S, 2011, CHEM SOC REV, V40, P221, DOI 10.1039/b907695f Shermin V, 2017, STRATEG CHANG, V26, P499, DOI 10.1002/jsc.2150 Smith BG, 2008, PHILOS T R SOC B, V363, P849, DOI 10.1098/rstb.2007.2187 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Svensson G, 2009, SUPPLY CHAIN MANAG, V14, P259, DOI 10.1108/13598540910970090 Tian F., 13 INT C SERV SYST S Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 Universa Blockchain, WHAT HIND BLOCKCH TE World Economic Forum, 2017, 110517 REF WORLD EC Wright A., 2017, DECENTRALIZED BLOCKC, P58 Zhang J, 2011, FOOD CONTROL, V22, P1126, DOI 10.1016/j.foodcont.2011.01.019 NR 79 TC 266 Z9 275 U1 43 U2 496 PD OCT PY 2018 VL 107 BP 222 EP 232 DI 10.1016/j.trac.2018.08.011 WC Chemistry, Analytical SC Chemistry UT WOS:000444837000017 HC Y HP N DA 2022-12-14 ER PT J AU Exposito, I Cuinas, I AF Exposito, Isabel Cuinas, Inigo TI Exploring the Limitations on RFID Technology in Traceability Systems at Beverage Factories SO INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION DT Article ID UHF; BENEFITS; TAGS AB The application of RFID in traceability of products in beverage factories is analyzed in terms of the electromagnetic conditions defined by the massive presence of metallic elements and liquids. Various experiments are reported to determine the maximum reading range from RFID tags installed on tanks or to read RFID information around bottles, both empty and full of wine, trying to put in context the possible problems that could apper-when installing an RFID-based traceability system within a winery, a brewery, or any other beverage factory. C1 [Exposito, Isabel; Cuinas, Inigo] Univ Vigo, Dept Teoria Sinal & Comunicac, Vigo 36310, Spain. C3 Universidade de Vigo RP Cuinas, I (corresponding author), Univ Vigo, Dept Teoria Sinal & Comunicac, Vigo 36310, Spain. EM inhigo@uvigo.es CR Alien Technology, 2012, ALR 9900 EMA ENT RFI Alien Technology, 2009, ALR 8611 C HIGH PERF Alonso M., 1967, FUNDAMENTAL U PHYS, V2 ASPIRE, ADV SENS LIGHT WEIGH ATID Company Limited, AT570 REF GUID VERS Catarinucci Luca, 2011, Journal of Communication Software & Systems, V7, P59 Catarinucci L, 2012, J MED SYST, V36, P3451, DOI 10.1007/s10916-011-9790-2 Confidex Limited, 2010, CONF HAL PROD DAT de Cos ME, 2012, INT J ANTENN PROPAG, V2012, DOI 10.1155/2012/804536 EPC Information Services (EPCIS), 2007, EPC INFORM SERVICES Fuschini F, 2010, IEEE T ANTENN PROPAG, V58, P1759, DOI 10.1109/TAP.2010.2044328 Guido AL, 2012, INT J HEALTHC TECHNO, V13, P198, DOI 10.1504/IJHTM.2012.050625 Landt J, 2005, IEEE POTENTIALS, V24, P8, DOI 10.1109/MP.2005.1549751 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Nikitin P. V., 2006, 2006 IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No. 06CH37758C), P1011, DOI 10.1109/APS.2006.1710704 Nikitin PV, 2010, P IEEE, V98, P1629, DOI 10.1109/JPROC.2010.2047821 Nikitin PV, 2009, IEEE T IND ELECTRON, V56, P2374, DOI 10.1109/TIE.2009.2018434 Rao KVS, 2005, IEEE T ANTENN PROPAG, V53, P3870, DOI 10.1109/TAP.2005.859919 RAPPAPORT TS, 1989, IEEE T ANTENN PROPAG, V37, P1058, DOI 10.1109/8.34144 Trebar M., 2011, 2011 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2011) Virtanen J, 2012, INT J ANTENN PROPAG, V2012, DOI 10.1155/2012/801014 Yan Ji, 2013, Research Journal of Applied Sciences, Engineering and Technology, V5, P2520 Zhang Y., 2009, P INT S IEEE ANT PRO NR 23 TC 4 Z9 4 U1 2 U2 13 PY 2013 VL 2013 AR 916526 DI 10.1155/2013/916526 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications UT WOS:000321833300001 DA 2022-12-14 ER PT J AU Mainetti, L Patrono, L Stefanizzi, ML Vergallo, R AF Mainetti, Luca Patrono, Luigi Stefanizzi, Maria Laura Vergallo, Roberto TI An innovative and low-cost gapless traceability system of fresh vegetable products using RF technologies and EPCglobal standard SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE RFID (Radio Frequency IDentification); Item-level tracing system; EPC (Electronic Product Code); Food supply chain; NFC (Near Field Communication); Enterprise Service Bus AB Traceability requirements in supply chain management are getting more and more strict in order to ensure product quality and public safety. Such requirements are particularly difficult to reach in the agro-food sector, especially for fresh ready-to-eat (RTE) vegetables, where specific needs exist: for example, mixed RTE salads are made of different produces, and there is the need to track and trace the treatments all the ingredients separately receive, avoiding gaps in the electronic histories. Traceability global standards, along with the adoption of Radio Frequency (RF) technologies have been widely experimented in this field; nevertheless, there are still many difficulties. Wireless Sensor Networks (WSN) cause a big impact on the existing information system, and meet the opposition of professionals in the field such as agronomists who feel out of the process. Additionally, Ultra-High Frequency (UHF) Radio Frequency IDentification (RFID)-based item-level traceability is still too expensive. In this paper, we propose an integrated and innovative solution for the "gapless" traceability of fresh RTE vegetables produced by an Italian agro-food company. Most approaches to sensor-based implementations completely replace agronomists. By contrast, our solution keeps the agronomists in the greenhouses but empowers them with smart technology. The Agronomist Android mobile App uses Near Field Communication (NFC) technology to allow the linking of plants and traceability information, following the EPCglobal standard. We achieve low costs by using DataMatrix technology for item-level tagging, while restricting the use of UHF RFID to coarse-grained grouping levels (case and pallet). We adopt the Enterprise Service Bus (ESB) architectural style for granting flexibility and scalability while preserving compatibility with legacy applications. We obtained the experimental results we report by using a Living Laboratory approach; the experiments we carried on have demonstrated the good performances of RFID tags and readers when used in conjunction with fresh vegetables products, as well as the actual effectiveness of the proposed gapless traceability system. (C) 2013 Elsevier B.V. All rights reserved. C1 [Mainetti, Luca; Patrono, Luigi; Stefanizzi, Maria Laura; Vergallo, Roberto] Salento Univ, Dept Innovat Engn, I-73100 Lecce, Italy. C3 University of Salento RP Vergallo, R (corresponding author), Salento Univ, Dept Innovat Engn, Via Monteroni, I-73100 Lecce, Italy. EM luca.mainetti@unisalento.it; luigi.patrono@unisalento.it; laura.stefanizzi@unisalento.it; roberto.vergallo@unisalento.it CR Ahson S, 2008, RFID HDB APPL TECHNO Anastasi G., 2009, P 42 HAW INT C SYST, P1, DOI DOI 10.1109/HICSS.2009.313 [Anonymous], 2011, OFFICIAL J EUROPEAN, V157, P1 Apache Software Foundation, 2005, AP SYN Barchetti U., 2009, P INT C ULTR TEL WOR, P1, DOI DOI 10.1109/ICUMT.2009.5345580 Barchetti U, 2009, 2009 INTERNATIONAL CONFERENCE ON SOFTWARE, TELECOMMUNICATIONS AND COMPUTER NETWORKS, P80 Bendavid Ygal, 2010, International Journal of Project Organisation and Management, V2, P84, DOI 10.1504/IJPOM.2010.031883 Calcagnini G, 2012, IEEE T INF TECHNOL B, V16, P1051, DOI 10.1109/TITB.2012.2204895 Catarinucci Luca, 2011, Journal of Communication Software & Systems, V7, P59 Catarinucci L, 2012, J MED SYST, V36, P3451, DOI 10.1007/s10916-011-9790-2 Chappell D.A., 2009, ENTERPRISE SERVICE B Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Duan Y., 2011, INT COMP TECHN AUT I, P1045 EPCglobal, 2007, EPC TAG DAT STAND TD EPCglobal, 2008, CLASS 1 GEN 2 UHF RF Finkenzeller K., 2003, RFID HDB FUNDAMENTAL Fulton L., 2011, FORRESTER WAVE ENTER Gandino Filippo, 2009, International Journal of Advanced Pervasive and Ubiquitous Computing, V1, P49, DOI 10.4018/japuc.2009040104 Google, 2013, ANDR GS1, 2003, EPCGLOBAL Guido AL, 2012, INT J HEALTHC TECHNO, V13, P198, DOI 10.1504/IJHTM.2012.050625 Maffia M., 2012, INT J RF TECHNOLOGIE, V3, P101 Mainetti L., 2012, J COMMUNICATION SOFT, V8, P1 Mainetti L, 2011, P 4 INT S APL SCI BI Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 Rida A, 2010, ARTECH HSE INTEGR MI, P1 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Santa J, 2012, COMPUT ELECTRON AGR, V80, P31, DOI 10.1016/j.compag.2011.10.010 Vellidis G, 2008, COMPUT ELECTRON AGR, V61, P44, DOI 10.1016/j.compag.2007.05.009 Wamba SF, 2012, BUS PROCESS MANAG J, V18, P58, DOI 10.1108/14637151211215019 Wolfert J, 2010, COMPUT ELECTRON AGR, V70, P389, DOI 10.1016/j.compag.2009.07.015 WSO2, 2005, WS02 LEAN ENT MIDDL Wu CX, 2009, 2009 INTERNATIONAL FORUM ON INFORMATION TECHNOLOGY AND APPLICATIONS, VOL 2, PROCEEDINGS, P669, DOI 10.1109/IFITA.2009.200 Zurich ETH, 2008, FOSSTR OP SOURC RFID NR 34 TC 33 Z9 35 U1 1 U2 53 PD OCT PY 2013 VL 98 BP 146 EP 157 DI 10.1016/j.compag.2013.07.015 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000326210600018 DA 2022-12-14 ER PT J AU Sallabi, F Fadel, M Hussein, A Jaffar, A El Khatib, H AF Sallabi, Farag Fadel, Moustafa Hussein, Ahmed Jaffar, Ahmad El Khatib, Hazem TI Design and implementation of an electronic mobile poultry production documentation system SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Mobile computing; Agriculture; Traceability; Poultry; Handheld device; Internet ID TRACEABILITY AB Documentation is a major component of any traceability system where traceability is defined in the ISO Regulation 8402:1994 as the ability to trace the history, application and location of what is under consideration. Traceability systems are record keeping systems designed to track the flow of product or product attributes through the production process or supply chain. All international supply chains are forced to comply with traceability requirements. In this paper, we develop and implement an end-to-end mobile application prototype that traces the poultry production. This application consists of front-end and back-end systems. At the front-end, the worker uses a GPRS enabled handheld device (cell phone, PDA, etc.) to capture information on poultry operations collected at a remote chicken farm and transmit it to a back-end server in the main office. Through customized application the back-end server analyses all information received from the front-end and based on a built-in business process and business rules, intelligently updates various stakeholders of any breach of bio-security measures that requires immediate attention. The proposed system administrators can also access this application via Internet for management decision making. The back-end system consists of web server, defined business application logic and database server. (C) 2011 Elsevier B.V. All rights reserved. C1 [Sallabi, Farag; Jaffar, Ahmad; El Khatib, Hazem] United Arab Emirates Univ, Fac Informat Technol, Al Ain, U Arab Emirates. [Fadel, Moustafa; Hussein, Ahmed] United Arab Emirates Univ, Dept Aridland Agr, Al Ain, U Arab Emirates. C3 United Arab Emirates University; United Arab Emirates University RP Sallabi, F (corresponding author), United Arab Emirates Univ, Fac Informat Technol, POB 17551, Al Ain, U Arab Emirates. EM f.sallabi@uaeu.ac.ae CR Barbash A, 2001, J Ambul Care Manage, V24, P54 Curbera F, 2002, IEEE INTERNET COMPUT, V6, P86, DOI 10.1109/4236.991449 *FAO, 2007, TECHN M HIGHL PATH A GLEDHILL J., 2002, FOOD PROCESSING MAR, V63, P54 Golan E., 2004, 830 USDA *IBM, 2010, WEBSPHERE International Organization for Standardization, 1994, 84021994 ISO KALAKOTA R, 1996, 29 ANN HAW INT C SYS, P354 MEI C, 2005, ADV INFORM NETWORKIN, V2, P277 OPARA L, 2002, AGR ENG INT CIGR J S, V4 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Salman MD, 2003, REV SCI TECH OIE, V22, P689, DOI 10.20506/rst.22.2.1431 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 *SPARKS CO INC, 2002, FOOD TRAC STAND SYST STEINFIELD C, 2004, ELIFE DOT COM BUST, P177, DOI DOI 10.1007/978-3-662-11659-3_10 *TRAC MAN POULTR I, 2005, VERS 3 SPONS APA Turisco F, 2000, Healthc Financ Manage, V54, P78 York J, 2004, INT J HUM-COMPUT ST, V60, P771, DOI 10.1016/j.ijhcs.2003.07.004 2002, FORMAL EU NEWSLETT L, V31 NR 19 TC 12 Z9 12 U1 0 U2 19 PD MAR PY 2011 VL 76 IS 1 BP 28 EP 37 DI 10.1016/j.compag.2010.12.016 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000291178900004 DA 2022-12-14 ER PT J AU Gonzalez-Compean, JL Sosa-Sosa, VJ Garcia-Hernandez, JJ Galeana-Zapien, H Reyes-Anastacio, HG AF Luis Gonzalez-Compean, Jose Jesus Sosa-Sosa, Victor Juan Garcia-Hernandez, Jose Galeana-Zapien, Hiram German Reyes-Anastacio, Hugo TI A Blockchain and Fingerprinting Traceability Method for Digital Product Lifecycle Management SO SENSORS DT Article DE DPLM; blockchain; fingerprinting; digital value chain; security AB The rise of digitalization, sensory devices, cloud computing and internet of things (IoT) technologies enables the design of novel digital product lifecycle management (DPLM) applications for use cases such as manufacturing and delivery of digital products. The verification of the accomplishment/violations of agreements defined in digital contracts is a key task in digital business transactions. However, this verification represents a challenge when validating both the integrity of digital product content and the transactions performed during multiple stages of the DPLM. This paper presents a traceability method for DPLM based on the integration of online and offline verification mechanisms based on blockchain and fingerprinting, respectively. A blockchain lifecycle registration model is used for organizations to register the exchange of digital products in the cloud with partners and/or consumers throughout the DPLM stages as well as to verify the accomplishment of agreements at each DPLM stage. The fingerprinting scheme is used for offline verification of digital product integrity and to register the DPLM logs within digital products, which is useful in either dispute or violation of agreements scenarios. We built a DPLM service prototype based on this method, which was implemented as a cloud computing service. A case study based on the DPLM of audios was conducted to evaluate this prototype. The experimental evaluation revealed the ability of this method to be applied to DPLM in real scenarios in an efficient manner. C1 [Luis Gonzalez-Compean, Jose; Jesus Sosa-Sosa, Victor; Juan Garcia-Hernandez, Jose; Galeana-Zapien, Hiram; German Reyes-Anastacio, Hugo] Ctr Invest & Estudios Avanzados IPN Cinvestav Tam, Victoria City 87130, Mexico. RP Sosa-Sosa, VJ (corresponding author), Ctr Invest & Estudios Avanzados IPN Cinvestav Tam, Victoria City 87130, Mexico. EM vjsosa@cinvestav.mx CR Aich S, 2019, INT CONF ADV COMMUN, P138, DOI 10.23919/ICACT.2019.8701910 Bagga P, 2022, TELECOMMUN SYST, V81, P125, DOI 10.1007/s11235-022-00938-7 Cao Y, 2020, IEEE T IND INFORM, V16, P6004, DOI 10.1109/TII.2019.2942211 Chaabane F, 2013, INT C INFORM ASSUR S, P85, DOI 10.1109/ISIAS.2013.6947738 Cui P, 2019, IEEE ACCESS, V7, P157113, DOI 10.1109/ACCESS.2019.2949951 De Mel S., 2016, DEV ENG, V1, P4, DOI [10.1016/j.deveng.2015.06.001, DOI 10.1016/J.DEVENG.2015.06.001] Furon T, 2010, IEEE SECUR PRIV, V8, P69, DOI 10.1109/MSP.2010.167 Haji M, 2021, METHOD PROTOCOL, V4, DOI 10.3390/mps4040085 Hassoun A, 2022, CRIT REV FOOD SCI, DOI 10.1080/10408398.2022.2110033 Hyperledger-Foundation, 2022, HYPERLEDGER FABR IBM, 2022, BLOCKCH SUPPL CHAIN Garcia-Hernandez JJ, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0065985 Kiyavash N, 2009, IEEE T INF FOREN SEC, V4, P318, DOI 10.1109/TIFS.2009.2025855 Kuribayashi M, 2011, EURASIP J INF SECUR, DOI 10.1155/2011/502782 Leng JW, 2022, IEEE T SERV COMPUT, V15, P2490, DOI 10.1109/TSC.2020.3038641 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Li JR, 2015, INT J ADV MANUF TECH, V81, P667, DOI 10.1007/s00170-015-7151-x Li M, 2020, IEEE SIGNAL PROC LET, V27, P1794, DOI 10.1109/LSP.2020.3028037 Liu K.J.R., 2005, MULTIMEDIA FINGERPRI, VVolume 4 Luu L, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P254, DOI 10.1145/2976749.2978309 MarketStudyReport, 2020, GLOB DIG CONT MARK 2 Martinez-Rendon C, 2022, CLUSTER COMPUT, V25, P2179, DOI 10.1007/s10586-021-03252-0 McDonald P., 2021, DIGITAL MEDIA DISTRI Mehta J., 2021, P ASIAN C INNOVATION, P1, DOI [10.1109/ASIANCON51346.2021.9544543, DOI 10.1109/ASIANCON51346.2021.9544543] Mussomeli A., 2021, CISC VIS NETW IND GL Nakamoto S., 2008, CONSULTED, P21260 Perboli G, 2018, IEEE ACCESS, V6, P62018, DOI 10.1109/ACCESS.2018.2875782 Reddy P., 2022, DIGITAL FOOD SUPPLY, V82, P9, DOI [10.3390/proceedings2022082009, DOI 10.3390/PROCEEDINGS2022082009] Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Swan M., 2015, BLOCKCHAIN BLUEPRINT Trappe W, 2003, IEEE T SIGNAL PROCES, V51, P1069, DOI 10.1109/TSP.2003.809378 Wang B, 2020, COMPUT IND, V123, DOI 10.1016/j.compind.2020.103324 Wang J, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10124822 Wang SP, 2019, IEEE ACCESS, V7, P115122, DOI 10.1109/ACCESS.2019.2935873 Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 Wang ZJ, 2005, IEEE T IMAGE PROCESS, V14, P804, DOI 10.1109/TIP.2005.847284 Westerkamp M, 2020, DIGIT COMMUN NETW, V6, P167, DOI 10.1016/j.dcan.2019.01.007 Yasui T, 2020, IEEE T INF FOREN SEC, V15, P2069, DOI 10.1109/TIFS.2019.2956587 Zhao J, 2021, IEEE SIGNAL PROC LET, V28, P1833, DOI 10.1109/LSP.2021.3108903 NR 39 TC 0 Z9 0 U1 0 U2 0 PD NOV PY 2022 VL 22 IS 21 AR 8400 DI 10.3390/s22218400 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000883584900001 DA 2022-12-14 ER PT J AU Islam, S Cullen, JM AF Islam, Samantha Cullen, Jonathan M. TI Food traceability: A generic theoretical framework SO FOOD CONTROL DT Review AB Numerous studies have been performed in food traceability, but there is no common, clear understanding of its theoretical concepts which are scattered and disjointed across the literature. Existing studies are mainly concerned with practical implementation and the theoretical concepts derive from that approach. As a result, various definitions, classifications and inconsistent principles have been proposed which hamper clear understanding and further development of the field. Thus, this study aims to coalesce the proposed and emergent fundamental concepts of food traceability in a generic theoretical framework. To this end, we have used an iterative approach to review and synthesize the papers in the field most relevant to our enquiry, consolidate proposed drivers and beneficiaries, highlight the main typologies, and as a result, propose a revised definition of food traceability and four associated principles. Different information is recorded in a traceability system, depending on the underlying drivers, for example, legislation, food safety, sustainability, or consumer satisfaction. In this paper traceability approaches are categorised by an iterative typology, as internal or external and the implementation of traceability systems is organised according to four consolidated principles: identification, data recording, data integration and accessibility. It is proposed that the collation of existing approaches into a cohesive theoretical framework will improve understanding and the effective implementation of food traceability systems. C1 [Islam, Samantha; Cullen, Jonathan M.] Univ Cambridge, Dept Engn, Trumpington St, Cambridge CB2 1PZ, England. C3 University of Cambridge RP Islam, S (corresponding author), Univ Cambridge, Dept Engn, Trumpington St, Cambridge CB2 1PZ, England. EM si313@cam.ac.uk; jmc99@cam.ac.uk CR Aftab H, 2020, DIGIT COMMUN NETW, V6, P333, DOI 10.1016/j.dcan.2019.05.003 Aitken J, 2003, INT J PROD ECON, V85, P127, DOI 10.1016/S0925-5273(03)00105-1 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Alpa G., 1994, ANN SURV INT COMP L, V1, P1 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Azuara G, 2012, IND MANAGE DATA SYST, V112, P340, DOI 10.1108/02635571211210022 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bendaoud M, 2012, INT FOOD AGRIBUS MAN, V15, P103 Bhatt T, 2013, J FOOD SCI, V78, pB9, DOI 10.1111/j.1750-3841.2011.02617.x Bibi F, 2017, TRENDS FOOD SCI TECH, V62, P91, DOI 10.1016/j.tifs.2017.01.013 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 Borit M, 2015, J CLEAN PROD, V104, P13, DOI 10.1016/j.jclepro.2015.05.003 Borit M, 2012, MAR POLICY, V36, P96, DOI 10.1016/j.marpol.2011.03.012 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bourlakis M, 2006, J ENTERP INF MANAG, V19, P389, DOI 10.1108/17410390610678313 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 Christopher M., 2011, LOGISTICS SUPPLY CHA, V4th Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Donnelly KAM, 2012, FOOD CONTROL, V27, P228, DOI 10.1016/j.foodcont.2012.03.021 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Forgacs E., 2003, FOOD AUTHENTICITY TR, V1st ed, P197 Frederiksen M, 2001, FOOD AUST, V53, P117 Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Golan E., 2004, Agricultural Economic Report - Economic Research Service, US Department of Agriculture Hernandez-Jover M, 2009, AUST VET J, V87, P387, DOI 10.1111/j.1751-0813.2009.00483.x Highfield R., 2004, TELEGRAPH, DOI [1457001/Tutankhamun-wine-mystery-is-solved.html., DOI 1457001/TUTANKHAMUN-WINE-MYSTERY-IS-SOLVED.HTML] Hovy E, 1998, P 1 INT C LANG RES E, P535 Islam S, 2016, J CLEAN PROD, V136, P266, DOI 10.1016/j.jclepro.2016.05.144 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Jedermann R, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0302 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Jonson G, 2010, P WORLD C TRANSP RES Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Khabbazi MR, 2011, INT J PROD RES, V49, P731, DOI 10.1080/00207540903530810 Dong KTP, 2019, AQUACULT INT, V27, P1209, DOI 10.1007/s10499-019-00378-2 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 Kowalska A, 2021, CRIT REV FOOD SCI, V61, P906, DOI 10.1080/10408398.2020.1747978 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu Y, 2016, J IND INF INTEGR, V3, P1, DOI 10.1016/j.jii.2016.06.001 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Marmiroli N, 2011, WOODHEAD PUBL FOOD S, P51 Mattevi M, 2016, BRIT FOOD J, V118, P1107, DOI 10.1108/BFJ-07-2015-0261 Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mohammad Taj Uddin, 2009, Asia-Pacific Journal of Rural Development, V19, P89 Nakandala D, 2017, BUS PROCESS MANAG J, V23, P108, DOI 10.1108/BPMJ-09-2015-0130 Norton T., 2014, GUIDE TRACEABILITY P Olsen P., 2018, FOOD TRACEABILITY TH Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Rodriguez-Salvador B, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107142 Rose A, 2005, INDEPENDENT Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Salampasis Michail, 2012, Journal of Systems and Information Technology, V14, P302, DOI 10.1108/13287261211279053 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Senk I, 2013, IFIP ADV INF COMM TE, V394, P155 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Smith I., 2006, IMPROVING TRACEABILI Storoy J, 2008, WOODHEAD PUBL FOOD S, P516, DOI 10.1533/9781845694586.5.516 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 Thakur M, 2011, COMPUT ELECTRON AGR, V75, P327, DOI 10.1016/j.compag.2010.12.010 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Tian F, 2017, I C SERV SYST SERV M Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 Ybarra J, 2014, U.S. Patent Application, Patent No. [13/ 688,579, 13688579] Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhang JR, 2014, COMPR REV FOOD SCI F, V13, P1074, DOI 10.1111/1541-4337.12103 NR 84 TC 25 Z9 25 U1 14 U2 44 PD MAY PY 2021 VL 123 AR 107848 DI 10.1016/j.foodcont.2020.107848 EA JAN 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000618050800005 DA 2022-12-14 ER PT J AU Souza-Monteiro, DM Caswell, JA AF Souza-Monteiro, Diogo M. Caswell, Julie A. TI The Economics of Voluntary Traceability in Multi-Ingredient Food Chains SO AGRIBUSINESS DT Article ID INFORMATION AB The consumption of multi-ingredient foods is increasing across the globe. Traceability can be used as a tool to gather information about and manage food safety risks associated with these types of products. The authors investigate the choice of voluntary traceability in three-tiered multi-ingredient food supply chains, They propose a framework based on vertical control and agency theory to model three dimensions of traceability systems: depth, breadth, and precision. Their analysis has three main results. First, full traceability is feasible as long as there are net benefits to a downstream firm that demands traceability across all ingredients. Second, horizontal network externalities are positive because an increase in the level of traceability in one ingredient requires a similar increase in others. Finally, vertical network effects will be positive insofar as willingness to pay and probabilities of food safety hazards increase. [EconLit Classification: Q130, L140]. (C) 2010 Wiley Periodicals. Inc. C1 [Souza-Monteiro, Diogo M.] Univ Kent, Kent Business Sch, Canterbury CT2 7PE, Kent, England. [Caswell, Julie A.] Univ Massachusetts, Dept Resource Econ, Amherst, MA 01003 USA. C3 University of Kent; University of Massachusetts System; University of Massachusetts Amherst RP Souza-Monteiro, DM (corresponding author), Univ Kent, Kent Business Sch, Canterbury CT2 7PE, Kent, England. EM D.M.Souza-Monteiro@kent.ac.uk; caswell@resecon.umass.edu CR Baldani Jeffrey, 1996, MATH EC Banterle A, 2008, AGRIBUSINESS, V24, P320, DOI 10.1002/agr.20169 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 EC-European Commission, 2002, J EUROP COMM, VL031, P1 FOLBERT JP, 2000, VEILIG VERTROUWD VOE Golan E.H., 2004, AGR EC REPORTS, P1362 Goldsmith P., 2004, Journal on Chain and Network Science, V4, P111, DOI 10.3920/JCNS2004.x046 Gracia A., 2005, Journal of Food Distribution Research, V36, P45 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hofstede GJ, 2002, CHALLENGE OF GLOBAL CHAINS, P73 Jansen-Vullers MH, 2003, INT J INFORM MANAGE, V23, P395, DOI 10.1016/S0268-4012(03)00066-5 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Resende MA, 2008, AM J AGR ECON, V90, P1091, DOI 10.1111/j.1467-8276.2008.01150.x Royer J. S., 1998, The industrialization of agriculture: vertical coordination in the US food system., P73 Sporleder TL, 2002, AM J AGR ECON, V84, P1345, DOI 10.1111/1467-8276.00400 Starbird S., 2004, AM AGR EC ASS ANN M Starbird SA, 2007, J AGR FOOD IND ORG, V5 TRIENEKENS JH, 2001, INT FOOD AGR MAN ASS VELTHUIS AGJ, 2008, ORG SESS EC TRAC 12 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 VERNEDE R, 2003, 015 WAG U AGR FOOD S NR 25 TC 20 Z9 20 U1 0 U2 28 PD WIN PY 2010 VL 26 IS 1 BP 122 EP 142 DI 10.1002/agr.20233 WC Agricultural Economics & Policy; Economics; Food Science & Technology SC Agriculture; Business & Economics; Food Science & Technology UT WOS:000274031100007 DA 2022-12-14 ER PT J AU Lee, HS Lee, JM Park, SR Lee, JH Kim, YG AF Lee, Hwa Shim Lee, Jong Man Park, Sang Ryoul Lee, Je Hoon Kim, Yong Goo TI Development and Validation of Primary Method for the Determination of Glucose in Human Serum by Isotope Dilution Liquid Chromatography Tandem Mass Spectrometry and Comparison with Field Methods SO BULLETIN OF THE KOREAN CHEMICAL SOCIETY DT Article DE Glucose; ID LC/MS/MS; Primary method; Traceability; Proficiency testing ID CLINICAL-CHEMISTRY; DEFINITIVE METHOD; ACCURACY AB Glucose is a common medical analyte measuring in human serum or blood samples. The development of a primary method is necessary for the establishment of traceability in measurements. We have developed an isotope dilution liquid chromatography tandem mass spectrometry as a primary method for the measurement of glucose in human serum. Glucose and glucose-C-13(6) in sample were ionized in ESI negative mode and monitored at mass transfers of m/z 179/89 and 185/92 in MRM, respectively. Glucose was separated on NH2P-50 2D column, and the mobile phase was 20 mM NH4OAc in 30% acetonitrile/70% water. Verification of this method was performed by the comparison with NIST SRMs. Our results agreed well with the SRM values. We have developed two levels of glucose strum certified reference material using this method and distributed them to the clinical laboratories in Korea as samples for proficiency testings. The expended uncertainty was about 1.2% on 95% confidence level. In proficiency testings, the results obtained from the clinical laboratories showed about 3.6% and 3.9% RSD to the certified values. Primary method can provide the traceability to the field laboratories through proficiency testings or certified reference materials. C1 [Lee, Hwa Shim; Lee, Jong Man; Park, Sang Ryoul] Korea Res Inst Stand & Sci, Div Metrol Qual Life, Ctr Bioanal, Taejon 305600, South Korea. [Lee, Je Hoon; Kim, Yong Goo] Catholic Univ Korea, Coll Med, Dept Lab Med, Seoul, South Korea. C3 Korea Research Institute of Standards & Science (KRISS); Catholic University of Korea RP Lee, HS (corresponding author), Korea Res Inst Stand & Sci, Div Metrol Qual Life, Ctr Bioanal, Taejon 305600, South Korea. EM eclhs@kriss.re.kr CR [Anonymous], 2008, 9832008 ISOIEC BJORKHEM I, 1981, CLIN CHEM, V27, P733 Chen YZ, 2012, CLIN CHIM ACTA, V413, P808, DOI 10.1016/j.cca.2012.01.025 David B. S., 1999, TIETZ TXB CLIN CHEM, P750 DEBIEVRE P, 1993, ANAL PROC, V30, P328, DOI DOI 10.1039/AP9933000328 DELEENHEER AP, 1992, MASS SPECTROM REV, V11, P249, DOI 10.1002/mas.1280110402 HEUMANN KG, 1992, MASS SPECTROM REV, V11, P41, DOI 10.1002/mas.1280110104 Jongoh C., 2003, ACCREDIT QUA ASS, V8, P13 MAGNI F, 1992, CLIN CHEM, V38, P381 McIntosh TS, 2002, ANAL BIOCHEM, V300, P163, DOI 10.1006/abio.2001.5455 PELLETIER O, 1987, CLIN CHEM, V33, P1397 Phinney CS, 1998, FRESEN J ANAL CHEM, V361, P71, DOI 10.1007/s002160050837 Prendergast JL, 2010, ANAL BIOANAL CHEM, V397, P1779, DOI 10.1007/s00216-010-3710-z Sacks DB, 1997, CLIN CHEM, V43, P2230 SCHAFFER R, 1976, PURE APPL CHEM, V45, P75, DOI 10.1351/pac197645020075 Taormina CR, 2007, J AM SOC MASS SPECTR, V18, P332, DOI 10.1016/j.jasms.2006.10.002 WHITE E, 1982, BIOMED MASS SPECTROM, V9, P395, DOI 10.1002/bms.1200090907 NR 17 TC 7 Z9 7 U1 2 U2 24 PD JUN 20 PY 2013 VL 34 IS 6 BP 1698 EP 1702 DI 10.5012/bkcs.2013.34.6.1698 WC Chemistry, Multidisciplinary SC Chemistry UT WOS:000321234300017 DA 2022-12-14 ER PT J AU Zhang, LJ Zeng, WM Jin, ZL Su, YS Chen, HL AF Zhang, Lejun Zeng, Weimin Jin, Zilong Su, Yansen Chen, Huiling TI A Research on Traceability Technology of Agricultural Products Supply Chain Based on Blockchain and IPFS SO SECURITY AND COMMUNICATION NETWORKS DT Article ID IDENTIFICATION; SYSTEM; SCHEME AB Blockchain technology, the fundamental technology of Bitcoin, is featured with high transparency, decentralization, traceability, tamperproof nature, and anonymousness. In this thesis, a case study of the traceability of agricultural products is to explain a traceability solution of agricultural products supply chain based on blockchain and IPFS. The latter one is used to store large quantities of transactions data; and the former one is used for the safety of data storage and circulation. And consumers can know the quality of agricultural products in the shortest time through the evaluation function. As shown in the experiment, the solution is more efficient and secure compared with existing supply chain traceability methods, meeting the traceability requirements of security, transparency, and reliability. Furthermore, the traceability, safety, and performance of the scheme are also analyzed here. C1 [Zhang, Lejun; Zeng, Weimin] Yangzhou Univ, Coll Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China. [Zhang, Lejun] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China. [Jin, Zilong] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 21004, Peoples R China. [Su, Yansen] Anhui Univ, Sch Comp Sci & Technol, Key Lab Intelligent Comp & Signal Proc, Minist Educ, Hefei 230601, Peoples R China. [Chen, Huiling] Wenzhou Univ, Dept Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China. C3 Yangzhou University; Guangzhou University; Anhui University; Wenzhou University RP Zhang, LJ (corresponding author), Yangzhou Univ, Coll Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China.; Zhang, LJ (corresponding author), Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China. EM zhanglejun@yzu.edu.cn CR [Anonymous], 2021, HYPERLEDGER FABRIC D Chen YL, 2022, INT J INTELL SYST, V37, P1204, DOI 10.1002/int.22666 Crosby M., 2016, APPL INNOVATION REV, V2, P1 Hao J.T., 2018, J COMPUT, V29, P158, DOI [10.3966/199115992018122906015, DOI 10.3966/199115992018122906015] Kumar M, 2021, J NETW COMPUT APPL, V179, DOI 10.1016/j.jnca.2021.102975 Kumar R, 2021, J PARALLEL DISTR COM, V152, P128, DOI 10.1016/j.jpdc.2021.02.022 Kumar R, 2021, J SUPERCOMPUT, V77, P7916, DOI 10.1007/s11227-020-03570-x Li T, 2021, INT J INTELL SYST, DOI 10.1002/int.22656 Li T, 2021, INT J INTELL SYST, V36, P3596, DOI 10.1002/int.22428 Lohachab A, 2021, FUTURE GENER COMP SY, V118, P392, DOI 10.1016/j.future.2021.01.023 Nyaletey E, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2019), P18, DOI 10.1109/Blockchain.2019.00012 Politou E, 2020, FUTURE GENER COMP SY, V112, P956, DOI 10.1016/j.future.2020.06.037 Qiu J, 2020, IEEE INTERNET THINGS, V7, P4682, DOI 10.1109/JIOT.2020.2969326 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sato T, 2018, INT CONF NEW TECHNOL Shafiq M, 2021, IEEE INTERNET THINGS, V8, P3242, DOI 10.1109/JIOT.2020.3002255 Shafiq M, 2020, COMPUT SECUR, V94, DOI 10.1016/j.cose.2020.101863 Shafiq M, 2020, FUTURE GENER COMP SY, V107, P433, DOI 10.1016/j.future.2020.02.017 Shafiq SM, 2020, SUSTAIN CITIES SOC, V60, DOI 10.1016/j.scs.2020.102177 Su S, 2020, IEEE WIREL COMMUN, V27, P46, DOI 10.1109/MWC.001.1900456 Thakkar P, 2018, I S MOD ANAL SIM COM, P264, DOI 10.1109/MASCOTS.2018.00034 Tian F, 2017, I C SERV SYST SERV M Uddin M, 2021, INT J PHARMACEUT, V597, DOI 10.1016/j.ijpharm.2021.120235 Wang SP, 2019, IEEE ACCESS, V7, P115122, DOI 10.1109/ACCESS.2019.2935873 Xie C, 2017, 2017 3RD INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING AND COMMUNICATIONS (BIGCOM), P45, DOI 10.1109/BIGCOM.2017.43 Xue X, 2022, IEEE T SERV COMPUT, V15, P1760, DOI 10.1109/TSC.2020.3016660 Xue X, 2019, IEEE T IND INFORM, V15, P3343, DOI 10.1109/TII.2018.2871167 Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 Yu WJ, 2018, INT SYM COMPUT INTEL, P339, DOI 10.1109/ISCID.2018.00083 Yuan X, 2016, FOOD SAFETY GUIDE, P32 [张乐君 Zhang Lejun], 2021, [自动化学报, Acta Automatica Sinica], V47, P594 Zhang LJ, 2021, COMPUT SECUR, V105, DOI 10.1016/j.cose.2021.102249 Zhang LJ, 2021, CMC-COMPUT MATER CON, V66, P499, DOI 10.32604/cmc.2020.012205 NR 33 TC 6 Z9 6 U1 11 U2 12 PD NOV 12 PY 2021 VL 2021 AR 3298514 DI 10.1155/2021/3298514 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000781636200004 DA 2022-12-14 ER PT J AU Luo, SH Zhou, ZM Huang, JY Pan, C Li, LL Zheng, SF Zhang, ZM Liu, GK AF Luo, Si-Heng Zhou, Zhi-Ming Huang, Jian-Ying Pan, Cheng Li, Ling-Ling Zheng, Shu-Feng Zhang, Zhi-Min Liu, Guo-Kun TI Rapid Identification of Active Ingredient and Geographic Traceability of Bifonazole Drugs by Raman Spectroscopy SO CHINESE JOURNAL OF ANALYTICAL CHEMISTRY DT Article DE Raman spectroscopy; Principal component analysis; Identification of active ingredients; Geographic traceability ID QUANTIFICATION; CLASSIFICATION AB The qualitative and quantitative analysis of the effective components is one of the focus of drug analysis. Normally, during routine measurement, sample pretreatment is always necessary to decrease or eliminate the interference of complex drug excipients, which is time-consuming and hard to handle. Therefore, it is highly demanded to develop rapid screening techniques towards high qualification and high efficiency for drug analysis. In this work, by using the bifonazole medicine as the model target, the rapid qualitative analysis of effective components was realized with the combination of Raman spectroscopy and chemmometric techniques including principal component analysis (PCA) and support vector machine (SVM). To exclude the unavoidable random interference from the complex composition of drug excipients to the qualitative analysis of target with low concentration (1%), the PCA method was used to precisely locate and extract the characteristic Raman signal of bifonazole. Furthermore, PCA combined with SVM classifier was applied to extract the tiny difference of Raman spectra, especially the two peaks at 1600 cm(-1) and 1650 cm(-1), from different manufactories. The result showed that the strategy could successfully distinguish and identify commercial drugs from different manufactories. This research implies that Raman spectroscopy is a very promising nondestructive and fast traceability analysis technique for drug analysis. C1 [Luo, Si-Heng] Xiamen Univ, Coll Chem & Chem Engn, State Key Lab Phys Chem Solid Surfaces, Xiamen 361005, Peoples R China. [Zhou, Zhi-Ming; Liu, Guo-Kun] Xiamen Univ, Coll Environm & Ecol, State Key Lab Marine Environm Sci, Xiamen 361102, Peoples R China. [Huang, Jian-Ying; Li, Ling-Ling; Zheng, Shu-Feng] Xiamen Food & Drug Qual Inspect Inst, Xiamen 361102, Peoples R China. [Pan, Cheng] Fujian Inspect & Res Inst Prod Qual, China Natl Qual Supervis & Testing Ctr Processed, Fuzhou 350002, Peoples R China. [Zhang, Zhi-Min] Cent South Univ, Coll Chem & Chem Engn, Changsha 410083, Peoples R China. C3 Xiamen University; Xiamen University; Central South University RP Liu, GK (corresponding author), Xiamen Univ, Coll Environm & Ecol, State Key Lab Marine Environm Sci, Xiamen 361102, Peoples R China.; Zhang, ZM (corresponding author), Cent South Univ, Coll Chem & Chem Engn, Changsha 410083, Peoples R China. EM zmzhang@csu.edu.cn; guokunliu@xmu.edu.cn CR Al-Kindy SMZ, 2007, LUMINESCENCE, V22, P294, DOI 10.1002/bio.962 Atri V, 2019, ANAL CHEM, V91 CAO Lu, 2019, J LIGHT SCATTERING, V31, P102 Chan JW, 2006, BIOPHYS J, V90, P648, DOI 10.1529/biophysj.105.066761 Das G, 2010, J BIOMED OPT, V15, DOI 10.1117/1.3368687 Dies H, 2018, SENSOR ACTUAT B-CHEM, V257, P382, DOI 10.1016/j.snb.2017.10.181 Fitzgerald RL, 1999, CLIN CHEM, V45, P1224 Gao Q, 2012, SPECTROSC SPECT ANAL, V32, P3258, DOI 10.3964/j.issn.1000-0593(2012)12-3258-04 Huang SS, 2011, CHINESE J ANAL CHEM, V39, P521, DOI 10.3724/SP.J.1096.2011.00521 Le LMM, 2018, EUR J PHARM SCI, V111, P158, DOI 10.1016/j.ejps.2017.09.046 Li SX, 2013, J BIOMED OPT, V18, DOI 10.1117/1.JBO.18.2.027008 [柳艳 Liu Yan], 2011, [计算机与应用化学, Computers and Applied Chemistry], V28, P1433 QUAN Chun.Mei, 2019, SHANDONG CHEM IND, V332, P35 Roggo Y, 2010, TALANTA, V81, P988, DOI 10.1016/j.talanta.2010.01.046 Wang XS, 2019, CHIN OPT, V12, P888, DOI 10.3788/CO.20191204.0888 Widjaja E, 2008, INT J ONCOL, V32, P653 [张海鹏 Zhang Haipeng], 2013, [吉林大学学报. 医学版, Journal of Jilin Univeristy. Medicine edition], V39, P938 Zhang X, 2016, CHINESE J ANAL CHEM, V44, P1846, DOI 10.11895/j.issn.0253-3820.160392 Zhu P, 2011, J PHARMACEUT BIOMED, V54, P198, DOI 10.1016/j.jpba.2010.07.016 冯尚源, 2011, [中国科学. 生命科学, Scientia Sinica Vitae], V41, P550 NR 20 TC 0 Z9 1 U1 6 U2 28 PD SEP PY 2020 VL 48 IS 9 BP 1210 EP 1218 DI 10.19756/j.issn.0253.3820.191462 WC Chemistry, Analytical SC Chemistry UT WOS:000569417900011 DA 2022-12-14 ER PT J AU Lee, KO Woo, MH Nakaji, K AF Lee, Kang Oh Woo, Myeong Ho Nakaji, Kei TI Development of a Ubiquitous Information Technology Based Distribution Traceability Management System for Imported Beef in Korea SO JOURNAL OF THE FACULTY OF AGRICULTURE KYUSHU UNIVERSITY DT Article AB A distribution traceability management system for imported beef based on ubiquitous information technology (u-IT) was developed by the Ministry for Food, Agriculture, Forestry and Fisheries in Korea to manage the distribution traceability and to trade reliable beef. The system was developed for the seller, government official, and consumer, separately by considering users' convenience, Distributors of imported beef registered and managed the distribution traceability on the computerized system, and government officials could monitor distribution managements and deal with recall immediately when the beef at risk was alerted. The information for the imported risk beef is rapidly transferred by the Sort Message Service (SMS), and sales of beef can be prevented by an electronic balance based on u-IT or by a credit card terminal. The developed system will be broadly used from December 2010 after testing the system within restricted areas as a model. C1 [Nakaji, Kei] Kyushu Univ, Div Agron Environm Sci, Dept Agroenvironm Sci, Lab Agr Ecol,Fac Agr, Fukuoka 8112307, Japan. C3 Kyushu University EM leeko2@affis.net CR *CRIC, 2009, NA FOOD SAT SURV RES *GALL KOR, 2010, TRAC IMP BEEF SURV R HUH D, 2006, TRACEABILITY CATTLE *MAF, 2005, STUD EST TRAC SYST L *MIFAFF, 2010, STUD U IT BUS DIFF P *MIFAFF, 2010, U IT BAS DIST TRAC M NR 6 TC 0 Z9 0 U1 0 U2 5 PD FEB PY 2011 VL 56 IS 1 BP 115 EP 122 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000288104900023 DA 2022-12-14 ER PT J AU Lee, KM Armstrong, PR Thomasson, JA Sui, RX Casada, M Herrman, TJ AF Lee, Kyung-Min Armstrong, Paul R. Thomasson, J. Alex Sui, Ruixiu Casada, Mark Herrman, Timothy J. TI Application of binomial and multinomial probability statistics to the sampling design process of a global grain tracing and recall system SO FOOD CONTROL DT Article DE Grain traceability system; Sampling design process; Binomial probability distribution; Multinomial probability distribution; Sample size ID SIMULTANEOUS CONFIDENCE-INTERVALS; WHEAT; SIZE AB Small, coded, pill sized tracers embedded in grains are proposed as a method to store a historical record of grains and retrieve coded information for grain traceability. This study aimed to develop and validate a statistical sampling procedure to securely collect sample sizes (kg) and number of tracers since the sampling accuracy is critical in the proposed traceability system for capturing information and data related to grain lots to trace the grain back through the route in a grain supply chain. The statistical results and observations showed similar concentrations and insignificant segregation of tracers in bin and truck operations. The number of tracers required for identification of grain sources fell within the confidence intervals and sample sizes (kg) estimated by statistical probability methods. Truck sampling appeared more feasible in collecting the secure number of tracers over bin sampling. The designed sampling process was empirically proven to be practically applicable and provide better scientific assurance of sampling accuracy, which may reduce economic risks and their consequent costs caused by unfavorable sampling in the propose traceability system. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Lee, Kyung-Min; Herrman, Timothy J.] Texas A&M Univ Syst, Texas Agr Expt Stn, Off Texas State Chemist, College Stn, TX 77841 USA. [Armstrong, Paul R.; Casada, Mark] ARS, Ctr Grain & Anim Hlth Res, USDA, Manhattan, KS 66502 USA. [Thomasson, J. Alex] Texas A&M Univ, Biol & Agr Engn Dept, College Stn, TX 77843 USA. [Sui, Ruixiu] ARS, Cotton Ginning Res Unit, USDA, Stoneville, MS 38776 USA. C3 Texas A&M University System; Texas A&M University College Station; Texas A&M AgriLife Research; United States Department of Agriculture (USDA); Texas A&M University System; Texas A&M University College Station; United States Department of Agriculture (USDA) RP Herrman, TJ (corresponding author), Texas A&M Univ Syst, Texas Agr Expt Stn, Off Texas State Chemist, College Stn, TX 77841 USA. EM tjh@otsc.tamu.edu CR Agresti A, 1998, AM STAT, V52, P119, DOI 10.2307/2685469 ANGERS C, 1974, TECHNOMETRICS, V16, P469, DOI 10.2307/1267680 Angers C, 1984, SIMULATION OCT, P175 [Anonymous], 2000, J STAT SOFTWARE, DOI DOI 10.18637/JSS.V005.I06 [Anonymous], 2002, OFFICIAL J EUROPEAN *CAN TRAC, 2003, AGR AGR CAN Coker Raymond D., 1995, Natural Toxins, V3, P257, DOI 10.1002/nt.2620030417 *ERS, 2001, KNOW GOING BRIN FOOD FITZPATRICK S, 1987, J AM STAT ASSOC, V82, P875, DOI 10.2307/2288799 Golan E.H., 2004, AGR EC REPORTS, P1362 GOODMAN LA, 1965, TECHNOMETRICS, V7, P247, DOI 10.2307/1266673 HAGSTRUM D, 1995, E9126569 OKL STAT U Hart LP, 1998, PLANT DIS, V82, P625, DOI 10.1094/PDIS.1998.82.6.625 HELLEVANG KJ, 1992, N DAKOTASTATE U EXTE Herrman T., 2002, WHITE PAPER TRACEABI Hirai Y, 2006, APPL ENG AGRIC, V22, P747 *ISO, 2007, 22005 ISO STAND Kendall MG, 1938, J R STAT SOC, V101, P147, DOI 10.2307/2980655 LARSEN RJ, 1985, INTRO PROBABILITY AP, P96 May WL, 1997, COMPUT METH PROG BIO, V53, P153, DOI 10.1016/S0169-2607(97)01809-9 Meyer S., 2004, GRAIN TRANSPORTATION Park DL, 2000, J AOAC INT, V83, P1247 QUESENBERRY CP, 1964, TECHNOMETRICS, V6, P191, DOI 10.2307/1266151 SAS Institute, 2004, SAS US GUID STAT VER SIMS F, 2005, P I FOOD TECHN 1 ANN Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 THOMPSON SK, 1987, AM STAT, V41, P42, DOI 10.2307/2684318 TORTORA RD, 1978, AM STAT, V32, P100, DOI 10.2307/2683352 *USDA, 2005, GLOBAL TRACEABILITY Vachal K., 2001, REGIONAL ELEVATOR SU Wald A, 1943, T AM MATH SOC, V54, P426, DOI 10.2307/1990256 Whitaker TB, 2006, FOOD ADDIT CONTAM, V23, P50, DOI 10.1080/02652030500241587 Wilson EB, 1927, J AM STAT ASSOC, V22, P209, DOI 10.2307/2276774 2004, FED REG 1209, V69, P71561 NR 34 TC 7 Z9 7 U1 0 U2 10 PD JUL PY 2011 VL 22 IS 7 BP 1085 EP 1094 DI 10.1016/j.foodcont.2010.12.016 WC Food Science & Technology SC Food Science & Technology UT WOS:000288882500011 DA 2022-12-14 ER PT J AU Wu, QY Zhou, GH Yang, SS Xu, XL Li, CB AF Wu, Qiayu Zhou, Guanghong Yang, Sasa Xu, Xinglian Li, Chunbao TI Combined SNPs and miRNAs technologies for beef traceability SO JOURNAL OF FOOD SAFETY DT Article ID MELTING ANALYSIS; CATTLE BREEDS; EAR TAGS; IDENTIFICATION; PCR; MARKERS; PANEL AB The independent and highly heterozygous single nucleotide polymorphisms (SNPs) have been shown suitable for meat traceability. The microRNAs (miRNAs) that were differently expressed among regions could be used for meat origin traceability. In the present study, ten SNPs from 148 Chinese beef cattle were screened by HRM. The results showed that these SNPs were all suitable for beef traceability with the probability error rate of .308. These cattle could not be clustered into different regions based on the SNP genotyping, which indicated that the SNPs assay was suitable for individual traceability. High throughput sequencing revealed that 16 miRNAs were up-regulated expression and 18 miRNAs were down-regulated expression in JL group (eastern part) compared to XJ group (western part). RT-PCR confirmed that bta-miR-2284ab and bta-miR-486 were differently expressed in Jilin and Xinjiang samples. Thus, the two miRNAs could be used as biomarkers for origin traceability. Practical applicationsTo solve meat safety problems, meat traceability is extremely important. SNPs-based technology can trace animal individuals and miRNAs-based technology can do origin traceability. Once suitable primers could be designed for a sufficient number of SNP loci, these primers could be loaded on an array, and then traceability could be realized by HRM. We identified the miRNAs that were differently expressed among regions and beef origin traceability could be realized by analyzing them using RT-PCR. All the detection methods are rapidly and cost-effectively. A combination of SNPs- and miRNAs-based technologies can implement traceability management of beef cattle. C1 [Wu, Qiayu; Zhou, Guanghong; Yang, Sasa; Xu, Xinglian; Li, Chunbao] Nanjing Agr Univ, Jiangsu Synerget Innovat Ctr Meat Prod Proc & Qua, Key Lab Meat Proc & Qual Control, Key Lab Anim Prod Proc,MOE,MOA, Nanjing 210095, Jiangsu, Peoples R China. C3 Nanjing Agricultural University RP Li, CB (corresponding author), Nanjing Agr Univ, Nanjing 210095, Jiangsu, Peoples R China. EM chunbao.li@njau.edu.cn CR Ana F., 2004, J SCI FOOD AGR, V84, P1855 Arslan A, 2006, MEAT SCI, V72, P326, DOI 10.1016/j.meatsci.2005.08.001 Biasiolo M, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0023854 Caja G, 2005, J ANIM SCI, V83, P2215 Carne S, 2009, J ANIM SCI, V87, P2419, DOI 10.2527/jas.2008-1670 Ciampolini R, 2006, J ANIM SCI, V84, P11, DOI 10.2527/2006.84111x Emebiri LC, 2014, J SCI FOOD AGR, V94, P1422, DOI 10.1002/jsfa.6434 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Karniol B, 2009, ANIM GENET, V40, P353, DOI 10.1111/j.1365-2052.2008.01846.x Kidd KK, 2006, FORENSIC SCI INT, V164, P20, DOI 10.1016/j.forsciint.2005.11.017 Koechl Silvano, 2005, V297, P13 Lee HY, 2005, FORENSIC SCI INT, V148, P107, DOI 10.1016/j.forsciint.2004.04.073 Li DW, 2014, MOL BIOL REP, V41, P6475, DOI 10.1007/s11033-014-3530-x Li X, 2013, ADV DIFFER EQU, V2013, P140 Li YJ, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0131958 Li Z., 2013, ANIMAL GENETICS, V45, P20 Lipsky RH, 2001, CLIN CHEM, V47, P635 Livak KJ, 2001, METHODS, V25, P402, DOI 10.1006/meth.2001.1262 [罗艳 Luo Yan], 2012, [中国科学. 生命科学, Scientia Sinica Vitae], V42, P96 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 Reed GH, 2004, CLIN CHEM, V50, P1748, DOI 10.1373/clinchem.2003.029751 Ritter KB, 2007, EUPHYTICA, V157, P161, DOI 10.1007/s10681-007-9408-4 Sevane N, 2011, LIVEST SCI, V137, P141, DOI 10.1016/j.livsci.2010.10.011 Shen GQ, 2009, METHODS MOL BIOL, V578, P293, DOI 10.1007/978-1-60327-411-1_19 Sheng XH, 2011, MOL BIOL REP, V38, P3161, DOI 10.1007/s11033-010-9987-3 Weller JI, 2006, ANIM GENET, V37, P387, DOI 10.1111/j.1365-2052.2006.01455.x Werner FAO, 2004, ANIM GENET, V35, P44, DOI 10.1046/j.1365-2052.2003.01071.x Xu LL, 2010, J SCI FOOD AGR, V90, P1368, DOI 10.1002/jsfa.3985 Yang SS, 2014, EUR FOOD RES TECHNOL, V239, P473, DOI 10.1007/s00217-014-2241-9 Yao LJ, 2015, GENOME BIOL, V16, DOI 10.1186/s13059-015-0668-3 NR 30 TC 1 Z9 1 U1 1 U2 23 PD NOV PY 2017 VL 37 IS 4 AR e12360 DI 10.1111/jfs.12360 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000415019500024 DA 2022-12-14 ER PT J AU Qian, JP Wu, WB Yu, QY Ruiz-Garcia, L Xiang, Y Jiang, L Shi, Y Duan, YL Yang, P AF Qian, Jianping Wu, Wenbin Yu, Qiangyi Ruiz-Garcia, Luis Xiang, Yang Jiang, Li Shi, Yun Duan, Yulin Yang, Peng TI Filling the trust gap of food safety in food trade between the EU and China: An interconnected conceptual traceability framework based on blockchain SO FOOD AND ENERGY SECURITY DT Article DE blockchain; cross-border food trade; food supply chain; food traceability; smart contract ID CHALLENGES; REGULATIONS; TECHNOLOGY; MANAGEMENT AB Global food trade has become an increasingly crucial element for feeding the world's population. Enhancing bilateral or multilateral trust in food safety in international food trade is not only important for promoting the sustainable development of trade but is also beneficial for cooperation when facing a global food crisis. However, highly credible traceability systems (TSs) for the cross-border movement of food are still absent in many countries and regions. Blockchain is regarded as a promising technology that can help build trust for transparency and security issues. In this paper, an interconnected conceptual traceability framework based on blockchain is proposed in order to increase trust in food safety during food trade. Taking the food trade between China and the European Union as an example, a conceptual framework is designed in order to take full advantage of existing TSs in these two locations, and the features of logistical flow, data flow, and blockchain flow are analyzed. Considering the data capacity and data privacy level, a hybrid data storage method combining on-chain and off-chain is adopted. Smart contracts according to the features of cross-border food trade-including the recording of exportation data, exporter inspection data, shipment data, importer inspection data, importation data, and tracing queries-are packaged and deployed to a blockchain network. An effective operation mechanism involving the distribution of related rights for different roles is presented. The blockchain-based TS framework has the advantages of enhancing bilateral trust in cross-border food trade, providing a flexible and intelligent technical framework, and having effective operability. Future challenges, such as data security, special smart contracts, and consensus mechanisms, and interoperability with other systems, are discussed. C1 [Qian, Jianping; Wu, Wenbin; Yu, Qiangyi; Shi, Yun; Duan, Yulin; Yang, Peng] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. [Ruiz-Garcia, Luis] Univ Politecn Madrid, Dept Agroforestry Engn, Madrid, Spain. [Xiang, Yang] Swinburne Univ Technol, Sch Software & Elect Engn, Hawthorn, Vic, Australia. [Jiang, Li] Chinese Acad Inspect & Quarantine, Beijing, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Agricultural Resources & Regional Planning, CAAS; Universidad Politecnica de Madrid; Swinburne University of Technology; Chinese Academy of Inspection & Quarantine RP Qian, JP; Yang, P (corresponding author), Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. EM qianjianping@caas.cn; yangpeng@caas.cn CR Ahmed S, 2017, NATURE, V550, P43, DOI 10.1038/550043e Andoni M, 2019, RENEW SUST ENERG REV, V100, P143, DOI 10.1016/j.rser.2018.10.014 [Anonymous], **NON-TRADITIONAL** Banerjee M., 2017, BLOCKCHAIN FUTURE IN Beestermoller M, 2018, CHINA ECON REV, V48, P66, DOI 10.1016/j.chieco.2017.11.004 Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 Cawthorn DM, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-12301-x COMTRADE, 2017, UN STAT DIV TRAD STA D'Odorico P, 2014, EARTHS FUTURE, V2, P458, DOI 10.1002/2014EF000250 Essaji A, 2008, J INT ECON, V76, P166, DOI 10.1016/j.jinteco.2008.06.008 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Friel S, 2020, NAT FOOD, V1, P51, DOI 10.1038/s43016-019-0014-0 Frizzo-Barker J, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.10.014 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gartner, 2017, TOP TRENDS GARTNER H Geng S, 2015, J INTEGR AGR, V14, P2136, DOI 10.1016/S2095-3119(15)61164-9 Golan E., 2004, TRACEABILITY US FOOD Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Helo P, 2019, COMPUT IND ENG, V136, P242, DOI 10.1016/j.cie.2019.07.023 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kostadinov A., 2017, EU CHINA FOOD TRADE Krittanawong C, 2020, NAT REV CARDIOL, V17, P1, DOI 10.1038/s41569-019-0294-y Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Liu ZY, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102059 Lopez D, 2020, TRANSPORT RES C-EMER, V111, P588, DOI 10.1016/j.trc.2020.01.002 Maesa DD, 2020, J PARALLEL DISTR COM, V138, P99, DOI 10.1016/j.jpdc.2019.12.019 Markets and Markets, 2018, BLOCKCH AGR MARK FOO Olsen P, 2010, TRENDS FOOD SCI TECH, V21, P313, DOI 10.1016/j.tifs.2010.03.002 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Prakash J, 2014, J SCI FOOD AGR, V94, P1962, DOI 10.1002/jsfa.6147 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Sohu, 2018, GEN ADM CUST ANN QUA Tama BA, 2017, 2017 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND COMPUTER SCIENCE (ICECOS), P109 Torero M, 2020, NATURE, V580, P588, DOI 10.1038/d41586-020-01181-3 Unnevehr L, 2015, GLOB FOOD SECUR-AGR, V4, P24, DOI 10.1016/j.gfs.2014.12.001 Venkatesh VG, 2020, ROBOT CIM-INT MANUF, V63, DOI 10.1016/j.rcim.2019.101896 Villoria N. B., 2019, ENCY FOOD SECURITY S, P64, DOI [10.1016/B978-0-08-100596-5.21965-3, DOI 10.1016/B978-0-08-100596-5.21965-3] Walls H, 2019, GLOB FOOD SECUR-AGR, V21, P69, DOI 10.1016/j.gfs.2019.05.005 Wong DR, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-08874-y NR 39 TC 16 Z9 16 U1 7 U2 56 PD NOV PY 2020 VL 9 IS 4 AR e249 DI 10.1002/fes3.249 EA OCT 2020 WC Agronomy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000575558500001 DA 2022-12-14 ER PT J AU Power, A Cozzolino, D AF Power, Aoife Cozzolino, Daniel TI How Fishy Is Your Fish? Authentication, Provenance and Traceability in Fish and Seafood by Means of Vibrational Spectroscopy SO APPLIED SCIENCES-BASEL DT Review DE fish; seafood; traceability; authenticity; provenance; infrared ID INFRARED REFLECTANCE SPECTROSCOPY; RAMAN-SPECTROSCOPY; FOOD FRAUD; SPECIES SUBSTITUTION; CHEMOMETRICS; PRODUCTS; QUALITY; ADULTERATION; SAFETY; FRESH AB Food authenticity, traceability and provenance are emerging issues of major concern for consumers, industries and regulatory bodies worldwide. In addition, both food safety and security are an intrinsic component of food quality where the above issues are key in modern traceability and management systems. It has been reported that substitution of a high-quality species by less expensive ones might be a frequent practice in seafood products such as fish and shellfish. In this type of products, the source (e.g., origin) and identification of the species are complex. Although different countries have implemented strict regulations and labelling protocols, these issues still are of concern. This article briefly reviews some of the most recent applications of vibrational spectroscopy (near and mid infrared, Raman) combined with chemometrics to target some of these issues in the seafood and fish industries. C1 [Power, Aoife] TU Dublin, CREST Technol Gateway, Dublin 8, Ireland. [Cozzolino, Daniel] Univ Queensland, Queensland Alliance Agr & Food Innovat QAAFI, Ctr Nutr & Food Sci, Brisbane, Qld 4072, Australia. C3 University of Queensland RP Cozzolino, D (corresponding author), Univ Queensland, Queensland Alliance Agr & Food Innovat QAAFI, Ctr Nutr & Food Sci, Brisbane, Qld 4072, Australia. EM aoife.power@TUDublin.ie; d.cozzolino@uq.edu.au CR Alamprese C, 2015, LWT-FOOD SCI TECHNOL, V63, P720, DOI 10.1016/j.lwt.2015.03.021 Amigo JM, 2013, DATA HANDL SCI TECHN, V28, P343, DOI 10.1016/B978-0-444-59528-7.00009-0 [Anonymous], 2007, SEAFOOD MARKETING CO Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bateman TS, 2016, NAT CLIM CHANGE, V6, P1052, DOI 10.1038/nclimate3166 Bene C, 2015, FOOD SECUR, V7, P261, DOI 10.1007/s12571-015-0427-z Bjorksten B, 2008, REGUL TOXICOL PHARM, V51, P42, DOI 10.1016/j.yrtph.2008.01.002 Bro R, 2014, ANAL METHODS-UK, V6, P2812, DOI 10.1039/c3ay41907j Brodersen K, 2001, LEBENSM-WISS TECHNOL, V34, P533, DOI 10.1006/fstl.2001.0802 BROWN SD, 1994, ANAL CHEM, V66, pR315, DOI 10.1021/ac00084a014 Carcea M, 2009, QUAL ASSUR SAF CROP, V1, P93, DOI 10.1111/j.1757-837X.2009.00011.x Cawthorn DM, 2013, FOOD CONTROL, V32, P440, DOI 10.1016/j.foodcont.2013.01.008 Cheng JH, 2013, TRENDS FOOD SCI TECH, V34, P18, DOI 10.1016/j.tifs.2013.08.005 Cozzolino D, 2019, FOOD ANAL METHOD, V12, P2469, DOI 10.1007/s12161-019-01605-5 Cozzolino D, 2015, CURR OPIN FOOD SCI, V4, P39, DOI 10.1016/j.cofs.2015.05.003 Cozzolino D, 2012, APPL SPECTROSC REV, V47, P207, DOI 10.1080/05704928.2011.639106 D'Amico P, 2016, MAR POLICY, V71, P147, DOI 10.1016/j.marpol.2016.05.026 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dai Q, 2015, J FOOD ENG, V149, P97, DOI 10.1016/j.jfoodeng.2014.10.001 Defernez M, 1997, TRAC-TREND ANAL CHEM, V16, P216, DOI 10.1016/S0165-9936(97)00015-0 Ding R, 2014, ANAL METHODS-UK, V6, P9675, DOI [10.1039/c4ay01839g, 10.1039/C4AY01839G] Echols MA, 1998, COLUM J EUR L, V4, P525 Ellis DI, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-12263-0 Ellis DI, 2015, ANAL METHODS-UK, V7, P9401, DOI [10.1039/C5AY02048D, 10.1039/c5ay02048d] Esslinger S, 2014, FOOD RES INT, V60, P189, DOI 10.1016/j.foodres.2013.10.015 Everstine K, 2013, J FOOD PROTECT, V76, P723, DOI 10.4315/0362-028X.JFP-12-399 Falci K.J., 2001, FOOD SAFETY MAGAZINE FAO, 2018, FAO FISH AQUAC CIRC, P1 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gayo J, 2006, J AGR FOOD CHEM, V54, P1130, DOI 10.1021/jf051636i Gayo J., 2006, THESIS Gayo J, 2007, J AGR FOOD CHEM, V55, P585, DOI 10.1021/jf061801+ Granato D, 2010, COMPR REV FOOD SCI F, V9, P292, DOI 10.1111/j.1541-4337.2010.00110.x Grassi S, 2018, FOOD CHEM, V243, P382, DOI 10.1016/j.foodchem.2017.09.145 Griffiths AM, 2014, FOOD CONTROL, V45, P95, DOI 10.1016/j.foodcont.2014.04.020 HALLERMAN EM, 1990, FISHERIES, V15, P12, DOI 10.1577/1548-8446(1990)015<0012:TFAPPR>2.0.CO;2 Hansen H, 2011, MAR RESOUR ECON, V26, P281, DOI 10.5950/0738-1360-26.4.281 He HJ, 2015, CRIT REV FOOD SCI, V55, P864, DOI 10.1080/10408398.2012.746638 Hellberg RSR, 2011, JALA-J LAB AUTOM, V16, P308, DOI 10.1016/j.jala.2010.07.004 Hernandez-Martinez D.M, 2014, J FOOD, V12, P369, DOI [10.1080/19476337.2014.889213, DOI 10.1080/19476337.2014.889213] Hofherr J, 2016, SEAFOOD AUTHENTICITY AND TRACEABILITY: A DNA-BASED PESPECTIVE, P47, DOI 10.1016/B978-0-12-801592-6.00003-6 Hooker NH, 1999, FOOD POLICY, V24, P653, DOI 10.1016/S0306-9192(99)00069-X Hu YX, 2018, FOOD CONTROL, V94, P38, DOI 10.1016/j.foodcont.2018.06.023 Jacquet JL, 2008, MAR POLICY, V32, P309, DOI 10.1016/j.marpol.2007.06.007 Kaferstein F, 2003, INT J ENVIRON HEAL R, V13, pS161, DOI 10.1080/0960312031000102949 Kamal M, 2015, TRENDS FOOD SCI TECH, V46, P27, DOI 10.1016/j.tifs.2015.07.007 Karoui R, 2010, CHEM REV, V110, P6144, DOI 10.1021/cr100090k King T, 2017, TRENDS FOOD SCI TECH, V68, P160, DOI 10.1016/j.tifs.2017.08.014 Leal MC, 2015, TRENDS BIOTECHNOL, V33, P331, DOI 10.1016/j.tibtech.2015.03.003 Liu D, 2013, APPL SPECTROSC REV, V48, P609, DOI 10.1080/05704928.2013.775579 Lv H, 2017, J NEAR INFRARED SPEC, V25, P54, DOI 10.1177/0967033516678801 Manning L., 2004, British Food Journal, V106, P598, DOI 10.1108/00070700410553594 Marshall D., 2001, FOOD PEOPLE SOC EURO, P317 McGrath TF, 2018, TRENDS FOOD SCI TECH, V76, P38, DOI 10.1016/j.tifs.2018.04.001 Muir J, 2005, PHILOS T R SOC B, V360, P191, DOI 10.1098/rstb.2004.1572 Nguyen A.V.T, 2009, P 2009 ANN M ATL GA Nielsen H. A., 2009, FISHERY PRODUCTS QUA, P89 Nilsen H, 2002, J FOOD SCI, V67, P1821, DOI 10.1111/j.1365-2621.2002.tb08729.x O'Brien N, 2013, J NEAR INFRARED SPEC, V21, P299, DOI 10.1255/jnirs.1063 Ortea I, 2016, J PROTEOMICS, V147, P212, DOI 10.1016/j.jprot.2016.06.033 Ottavian M, 2012, J AGR FOOD CHEM, V60, P639, DOI 10.1021/jf203385e Ozaki Y., 2007, PHARM APPL RAMAN SPE, P1, DOI [10.1002/ 9780470225882, DOI 10.1002/9780470225882.CH1] Qu JH, 2015, LWT-FOOD SCI TECHNOL, V62, P202, DOI 10.1016/j.lwt.2015.01.018 Ramirez D.A, 2018, P C INFR SENS DEV AP Raskovic B, 2016, FOOD ANAL METHOD, V9, P1301, DOI 10.1007/s12161-015-0312-6 Raspor P, 2008, CRIT REV FOOD SCI, V48, P276, DOI 10.1080/10408390701326219 Rodriguez EM, 2017, PROTEOMICS IN FOOD SCIENCE: FROM FARM TO FORK, P331, DOI 10.1016/B978-0-12-804007-2.00020-5 Saraiva C, 2017, INT J FOOD MICROBIOL, V241, P331, DOI 10.1016/j.ijfoodmicro.2016.10.038 Smith MD, 2010, SCIENCE, V327, P784, DOI 10.1126/science.1185345 Spanjersberg MQI, 2010, FOOD ADDIT CONTAM A, V27, P169, DOI 10.1080/19440040903317513 Szymanska E, 2015, TRAC-TREND ANAL CHEM, V69, P34, DOI 10.1016/j.trac.2015.02.015 Tacon AGJ, 2008, AQUACULTURE, V285, P146, DOI 10.1016/j.aquaculture.2008.08.015 Teletchea F, 2009, REV FISH BIOL FISHER, V19, P265, DOI 10.1007/s11160-009-9107-4 Tidwell JH, 2001, EMBO REP, V2, P958, DOI 10.1093/embo-reports/kve236 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Velioglu HM, 2015, FOOD CHEM, V172, P283, DOI 10.1016/j.foodchem.2014.09.073 Ward RD, 2009, J FISH BIOL, V74, P329, DOI 10.1111/j.1095-8649.2008.02080.x Washburn KE, 2017, J FOOD ENG, V205, P64, DOI 10.1016/j.jfoodeng.2017.02.025 Weeranantanaphan J, 2011, J NEAR INFRARED SPEC, V19, P61, DOI 10.1255/jnirs.924 WOLD S, 1987, CHEMOMETR INTELL LAB, V2, P37, DOI 10.1016/0169-7439(87)80084-9 Workman JJ, 1996, APPL SPECTROSC REV, V31, P73, DOI 10.1080/05704929608000565 Wu D, 2013, J FOOD ENG, V119, P680, DOI 10.1016/j.jfoodeng.2013.06.039 Wu T, 2018, FOOD ANAL METHOD, V11, P1501, DOI 10.1007/s12161-017-1135-4 Zhang HL, 2018, SPECTROSC SPECT ANAL, V38, P559, DOI 10.3964/j.issn.1000-0593(2018)02-0559-05 Zheng JK, 2014, COMPR REV FOOD SCI F, V13, P317, DOI 10.1111/1541-4337.12062 Zhou JJ, 2019, LWT-FOOD SCI TECHNOL, V106, P145, DOI 10.1016/j.lwt.2019.01.056 Zhu FL, 2013, FOOD BIOPROCESS TECH, V6, P2931, DOI 10.1007/s11947-012-0825-6 NR 87 TC 15 Z9 15 U1 6 U2 33 PD JUN PY 2020 VL 10 IS 12 AR 4150 DI 10.3390/app10124150 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000553484100001 DA 2022-12-14 ER PT J AU Garcia-Casco, JM Delgado-Chavero, CL Zapata, E Paredes, A Munoz, M Rey, AI AF Garcia-Casco, Juan M. Delgado-Chavero, Carmen L. Zapata, Elena Paredes, Andres Munoz, Maria Rey, Ana, I TI Discriminant analysis using fatty acids profile, stable carbon isotopes and tocopherols content as tool for feeding system prediction in Iberian pigs SO SPANISH JOURNAL OF AGRICULTURAL RESEARCH DT Article DE traceability; analytical methods; feeding background; autochthonous breed ID RATIO MASS-SPECTROMETRY; SUBCUTANEOUS FAT; ALPHA; DIET; QUANTIFICATION; CLASSIFICATION; REGIME; MODEL AB Aim of study: The application of three analytical methods (fatty acids: FA, tocopherols: TOC, and isotope ratio: ISO) to distinguish the feeding type received by Iberian pigs during the fattening stage. Area of study: This distinction is very important for the labelling of Iberian high-quality products in the Quercus forest located on the southwest of Iberian Peninsula, where several production systems coexist. Material and methods: Discriminant analysis on fat samples with unknown background obtained from commercial pigs was applied. The feasibility of the combination method to determine the authentication of feeding background was studied on samples from different fattening system: free-range feeding with acorn and pastures (BE); free-range feeding acorn and pastures plus commercial feeds (RE); open-air feeding with commercial feeds (CA); standard feeding with commercial feeds (CE). Main results: In a first application of the methods, the overall success rate was 60.1% for FA, 49.7% for ISO and 49.3% for TOC. When some of the batches were reclassified attending to those previous results and additional information available about farm characteristics, ISO and TOC analyses had a 70% of success rate in the four categories, whereas FA showed 40.5%, attributable to the use of high-oleic commercial diets. The predictions improved with the method combination. The ISO+TOC combination achieved a 84.1% of success in prediction. When it was reduced to just two categories (acorn vs non-acorn), the success reached a 95% for FA+TOC and ISO+TOC. Research highlights: The use of these methods as a complementary tool for quality controls is highly recommended to avoid undesirable misclassifications. C1 [Garcia-Casco, Juan M.; Munoz, Maria] INIA, Ctr I D Cerdo Iber, Ctra EX 101,Km 4,7, Zafra 06300, Spain. [Delgado-Chavero, Carmen L.; Zapata, Elena; Paredes, Andres] Assoc Interprofes Cerdo Iber ASICI, Ctra EX 101,Km 4,7, Zafra 06300, Spain. [Rey, Ana, I] Univ Complutense Madrid, Fac Vet, Dept Prod Anim, Madrid 28040, Spain. C3 Complutense University of Madrid RP Garcia-Casco, JM (corresponding author), INIA, Ctr I D Cerdo Iber, Ctra EX 101,Km 4,7, Zafra 06300, Spain. EM garcia.juan@inia.es CR Alonso R, 2008, TALANTA, V76, P591, DOI 10.1016/j.talanta.2008.03.052 Carrasco JA, 2013, GRASAS ACEITES, V64, P166, DOI 10.3989/gya.130512 Delgado-Chavero CL, 2013, GRASAS ACEITES, V64, P157, DOI 10.3989/gya.130412 EC, 2007, B1049 EC Casco JMG, 2013, GRASAS ACEITES, V64, P191, DOI 10.3989/gya.130812 Garcia-Olmo J, 2009, GRASAS ACEITES, V60, P233, DOI 10.3989/gya.130408 Gonzalez-Dominguez R, 2020, FOODS, V9, DOI 10.3390/foods9020149 GOODMAN KJ, 1992, ANAL CHEM, V64, P1088, DOI 10.1021/ac00034a004 Lopez-Bote CJ, 1998, MEAT SCI, V49, pS17, DOI 10.1016/S0309-1740(98)00072-2 Lopez-Bote CJ, 1999, 15 CURS ESP FEDNA AV, P223 MINSON DJ, 1975, NATURE, V256, P602, DOI 10.1038/256602a0 OLEARY MH, 1988, BIOSCIENCE, V38, P328, DOI 10.2307/1310735 Ordonez JA, 1996, FOOD SCI TECHNOL INT, V2, P383, DOI 10.1177/108201329600200604 Recio C, 2013, GRASAS ACEITES, V64, P181, DOI 10.3989/gya.130712 Rey AI, 2006, ANIM SCI, V82, P901, DOI 10.1017/ASC2006113 Rey AI, 2013, GRASAS ACEITES, V64, P138, DOI 10.3989/gya.130212 Rey AI, 2014, J SCI FOOD AGR, V94, P2649, DOI 10.1002/jsfa.6603 Sanabria C, 2013, GRASAS ACEITES, V64, P148, DOI 10.3989/gya.130312 Tejeda JF, 1999, FOOD SCI TECHNOL INT, V5, P229, DOI 10.1177/108201329900500305 TIESZEN LL, 1979, OECOLOGIA, V37, P351, DOI 10.1007/BF00347911 Viera-Alcaide I, 2007, ANAL CHIM ACTA, V596, P319, DOI 10.1016/j.aca.2007.06.026 NR 21 TC 0 Z9 0 U1 0 U2 4 PY 2020 VL 18 IS 4 AR e0614 DI 10.5424/sjar/2020184-16713 WC Agriculture, Multidisciplinary; Soil Science SC Agriculture UT WOS:000617035900012 DA 2022-12-14 ER PT J AU Cocco, L Mannaro, K Tonelli, R Mariani, L Lodi, MB Melis, A Simone, M Fanti, A AF Cocco, Luisanna Mannaro, Katiuscia Tonelli, Roberto Mariani, Lorena Lodi, Matteo B. Melis, Andrea Simone, Marco Fanti, Alessandro TI A Blockchain-Based Traceability System in Agri-Food SME: Case Study of a Traditional Bakery SO IEEE ACCESS DT Article DE Supply chains; Blockchain; Sensors; Radiofrequency identification; Monitoring; Companies; Food products; Ethereum smart contracts; Interplanetary File System; agri-food supply chain; wireless sensors network; decentralized application AB In this paper we present a blockchain based system for the supply chain management of a particular Italian bread. Goal of the system is to guarantee a transparent and auditable traceability of the Carasau bread where each actor of the supply chain can verify the quality of the products and the conformity to the normative about the hygienic-sanitary conditions along the chain. To realize this system we relied on the Blockchain and the Internet of Thing technologies in order to provide a trustless environment, in which trust is placed in cryptography, in mathematical operations and on the network, and not in public or private companies. Thanks to the use of digital technologies the system aims to reduce the data entry errors and the risk of tampering. Our system is designed so that along the supply chain, the nodes equipped with several sensors directly communicate their data to Raspberry Pi units that elaborate and transmit them to Interplanetary File System and to the Ethereum Blockchain. Furthermore, we designed ad hoc Radio Frequency Identification and Near Field communication tags to shortly supply the proposed system with information about the products and batches. The dedicated RFID tags robustness during on-bread operation was numerically tested. The system will easily allow end consumers to have a transparent view on the whole journey from raw material to purchased final product and a supervisory authority to perform online inspections on the products' quality and on the good working practices. C1 [Cocco, Luisanna; Mannaro, Katiuscia; Tonelli, Roberto] Univ Cagliari, Dept Math & Comp Sci, I-09123 Cagliari, Italy. [Mariani, Lorena; Lodi, Matteo B.; Melis, Andrea; Simone, Marco; Fanti, Alessandro] Univ Cagliari, Dept Elect & Elect Engn, I-09123 Cagliari, Italy. [Mariani, Lorena] Studio A Automaz Srl, I-09038 Serramanna, Italy. C3 University of Cagliari; University of Cagliari RP Mannaro, K (corresponding author), Univ Cagliari, Dept Math & Comp Sci, I-09123 Cagliari, Italy.; Fanti, A (corresponding author), Univ Cagliari, Dept Elect & Elect Engn, I-09123 Cagliari, Italy. EM katiuscia.mannaro@unica.it; alessandro.fanti@unica.it CR Ali M, 2017, IEEE GLOB COMM CONF Apaiah RK, 2005, TRENDS FOOD SCI TECH, V16, P204, DOI 10.1016/j.tifs.2004.09.004 Arvanitoyannis IS, 2005, CRIT REV FOOD SCI, V45, P327, DOI 10.1080/10408390590967694 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Baire M, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8121541 Baire M, 2018, 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR), P245 Baralla G, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5857 Baralla G, 2019, 2019 IEEE/ACM 2ND INTERNATIONAL WORKSHOP ON EMERGING TRENDS IN SOFTWARE ENGINEERING FOR BLOCKCHAIN (WETSEB 2019), P40, DOI 10.1109/WETSEB.2019.00012 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Benet J., 2014, CORR Bibi F, 2017, TRENDS FOOD SCI TECH, V62, P91, DOI 10.1016/j.tifs.2017.01.013 Carrefour, TECHNOLOGICAL INNOVA Casula GA, 2013, IEEE ANTENN WIREL PR, V12, P1400, DOI 10.1109/LAWP.2013.2287307 Chen YL, 2017, IEEE INT CONF BIG DA, P2652 Chin NL, 2005, J FOOD ENG, V70, P211, DOI 10.1016/j.jfoodeng.2004.09.024 Cocco L., 2021, P IEEE INT WORKSH BL, P669, DOI [10.1109/SANER50967.2021.00085, DOI 10.1109/SANER50967.2021.00085] Conoscenti M, 2017, PROC IEEE ACM INT C, P288, DOI 10.1109/ICSE-C.2017.60 Dobkin D. M, 2007, RF RFID PASSIVE UHF, P504 Fanari F., 2019, CHEM ENG T, V76, P1207 Fanari F., 2019, CHEM ENG TRANS, V75, P343, DOI [10.3303/CET1975058, DOI 10.3303/CET1975058] Fanari F., 2019, CHEM ENG TRANS, V76, P703, DOI DOI 10.3303/CET1976118 Fanti A, 2015, IEEE ANTENNAS PROP, P356, DOI 10.1109/APS.2015.7304564 Ferdousi T, 2020, IEEE ACCESS, V8, P154833, DOI 10.1109/ACCESS.2020.3019000 Galli F., 2015, AGR FOOD ECON, V3, P21, DOI [10.1186/s40100-015-0039-0, DOI 10.1186/S40100-015-0039-0] Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Gist R., 2018, BUILDING ETHEREUM OR Gonczol P, 2020, IEEE ACCESS, V8, P11856, DOI 10.1109/ACCESS.2020.2964880 Grover FW, 2004, INDUCTANCE CALCULATI Huang H., 2019, **DROPPED REF** Huang HW, 2020, IEEE ACCESS, V8, P50574, DOI 10.1109/ACCESS.2020.2979881 IBM Food Trust, MODULAR SOLUTION BUI ISO Technical Committee, STANDARD ISO 8402199 ISO Technical Committee, 2016, DOCUMENT ISO 22005 2 Jedermann R, 2014, PHILOS T R SOC A, V372, DOI 10.1098/rsta.2013.0304 Manyika J., 2015, CONNECTING TALENT OP Marchesi L., 2020, ARXIV200804761 Marchesi L, 2020, PROCEEDINGS OF THE 2020 IEEE 3RD INTERNATIONAL WORKSHOP ON BLOCKCHAIN ORIENTED SOFTWARE ENGINEERING (IWBOSE '20), P9, DOI 10.1109/IWBOSE50093.2020.9050163 Marchesi Lodovica, 2020, BLOCKCHAIN RES APPL, V1 Mariage P., 2014, J COMMUN SOFTW SYS, V10, P76 Marrocco G, 2009, IEEE ANTENN PROPAG M, V51, P44, DOI 10.1109/MAP.2009.5433096 Marrocco G, 2008, IEEE ANTENN PROPAG M, V50, P66, DOI 10.1109/MAP.2008.4494504 Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Mussigmann B, 2020, IEEE T ENG MANAGE, V67, P988, DOI 10.1109/TEM.2020.2980733 Naz M, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11247054 Norvill R, 2018, IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, P1121, DOI 10.1109/Cybermatics_2018.2018.00204 Parker Luke, 2016, BRAVE NEW COIN Peres C., 2020, INT J ADV NETWORKS S, V13, P45 Marques NRP, 2012, POL J FOOD NUTR SCI, V62, P215, DOI 10.2478/v10222-012-0057-5 Rao KVS, 2005, IEEE T ANTENN PROPAG, V53, P3870, DOI 10.1109/TAP.2005.859919 Rejeb A, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11070161 Saghlatoon H, 2014, IEEE ANTENN WIREL PR, V13, P915, DOI 10.1109/LAWP.2014.2322572 Steichen M, 2018, IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, P1499, DOI 10.1109/Cybermatics_2018.2018.00253 Tian F, 2017, I C SERV SYST SERV M Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 Wood G, 2014, ETHEREUM SECURE DECE, V151, P1, DOI DOI 10.1017/CBO9781107415324.004 Xu Q, 2018, 2018 5TH IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (IEEE CSCLOUD 2018) / 2018 4TH IEEE INTERNATIONAL CONFERENCE ON EDGE COMPUTING AND SCALABLE CLOUD (IEEE EDGECOM 2018), P1, DOI 10.1109/CSCloud/EdgeCom.2018.00010 Yu B, 2020, IEEE ACCESS, V8, P12479, DOI 10.1109/ACCESS.2020.2966020 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zheng QH, 2018, 2018 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2018), P704, DOI 10.1109/WI.2018.000-8 ZUERCHER J, 1990, J MICROWAVE POWER EE, V25, P161 NR 61 TC 22 Z9 23 U1 15 U2 50 PY 2021 VL 9 BP 62899 EP 62915 DI 10.1109/ACCESS.2021.3074874 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000645842600001 DA 2022-12-14 ER PT J AU Zhao, HY Guo, BL Wei, YM Zhang, B AF Zhao, Haiyan Guo, Boli Wei, Yimin Zhang, Bo TI Effects of Wheat Origin, Genotype, and Their Interaction on Multielement Fingerprints for Geographical Traceability SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE traceability; multielement analysis; wheat kernel; geographical origin; genotype ID ZINC; GRAIN; CADMIUM; IRON; SOIL; CHEMOMETRICS; ENVIRONMENT; ELEMENT; RATIOS; JAPAN AB The objective of this study was to investigate the effect of origin, genotype, and their interaction on multielement fingerprints in wheat kernels to provide theoretical basis for geographical traceability. Ten varieties were grown in three different regions of China during the 2010-2011 growing seasons. The concentrations of 10 elements (Na, Mg, Ca, V, Mn, Fe, Cu, Zn, Mo, and Ba) were determined in 90 wheat kernel samples and 30 provenance soil samples. Multiway analysis of variance results demonstrated that both origin and genotype had significant influences on the content of each element, and their interaction had significant influences on the contents of Mn, Fe, Cu, Zn, Mo, and Ba. The elements Na, Ca, Fe, Zn, and Mo were associated with origin, and Mg, Mn, Cu, and Ba were related to genotype. Na, Ca, Fe, Zn, and Mo were proved to be good indicators to discriminate wheat geographical origin. C1 [Zhao, Haiyan; Guo, Boli; Wei, Yimin; Zhang, Bo] Chinese Acad Agr Sci, Inst Agroprod Proc Sci & Technol, Key Lab Agroprod Proc, Minist Agr, Beijing 100193, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs RP Wei, YM (corresponding author), Chinese Acad Agr Sci, Inst Agroprod Proc Sci & Technol, Key Lab Agroprod Proc, Minist Agr, POB 5109, Beijing 100193, Peoples R China. EM weiyimin36@126.com CR Ariyama K, 2004, J AGR FOOD CHEM, V52, P5803, DOI 10.1021/jf049333w Ariyama K, 2007, J AGR FOOD CHEM, V55, P347, DOI 10.1021/jf062613m Ariyama K, 2012, J AGR FOOD CHEM, V60, P1628, DOI 10.1021/jf204296p Benincasa C, 2007, ANAL CHIM ACTA, V585, P366, DOI 10.1016/j.aca.2006.12.040 Coetzee PP, 2005, J AGR FOOD CHEM, V53, P5060, DOI 10.1021/jf048268n DONG XH, 1991, ENV MONIT CHINA, V7, P1 Ficco DBM, 2009, FIELD CROP RES, V111, P235, DOI 10.1016/j.fcr.2008.12.010 Gonzalvez A, 2011, FOOD CHEM, V126, P1254, DOI 10.1016/j.foodchem.2010.11.032 Herawati N, 2000, B ENVIRON CONTAM TOX, V64, P33, DOI 10.1007/s001289910006 Joshi AK, 2010, FIELD CROP RES, V116, P268, DOI 10.1016/j.fcr.2010.01.004 Khoshgoftarmanesh AH, 2006, SOIL SCI SOC AM J, V70, P582, DOI 10.2136/sssaj2005.0136 Laursen KH, 2011, J AGR FOOD CHEM, V59, P4385, DOI 10.1021/jf104928r Lu Lu, 2010, Chinese Journal of Applied and Environmental Biology, V16, P646, DOI 10.3724/SP.J.1145.2010.00646 Morgounov A, 2007, EUPHYTICA, V155, P193, DOI 10.1007/s10681-006-9321-2 Oury FX, 2006, EUR J AGRON, V25, P177, DOI 10.1016/j.eja.2006.04.011 Perilli P, 2010, J SCI FOOD AGR, V90, P813, DOI 10.1002/jsfa.3889 PETERSON CJ, 1986, CEREAL CHEM, V63, P183 Pilgrim TS, 2010, FOOD CHEM, V118, P921, DOI 10.1016/j.foodchem.2008.08.077 Purvis OW, 2008, SCI TOTAL ENVIRON, V390, P558, DOI 10.1016/j.scitotenv.2007.10.040 The People's Republic of China, 1993, METHODS CHEM ANAL SI Zhang Y, 2010, EUPHYTICA, V174, P303, DOI 10.1007/s10681-009-0082-6 Zhao FJ, 2009, J CEREAL SCI, V49, P290, DOI 10.1016/j.jcs.2008.11.007 Zhao HY, 2011, J AGR FOOD CHEM, V59, P4397, DOI 10.1021/jf200108d ZHAO SP, 1992, ACTA SCI CIRCUMSTANT, V12, P168 ZHENG CJ, 1992, ENV MONIT CHINA, V8, P8 NR 25 TC 33 Z9 39 U1 2 U2 52 PD NOV 7 PY 2012 VL 60 IS 44 BP 10957 EP 10962 DI 10.1021/jf3021283 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000311192200010 DA 2022-12-14 ER PT J AU Salina, AB Hassan, L Saharee, AA Jajere, SM Stevenson, MA Ghazali, K AF Salina, A. B. Hassan, Latiffah Saharee, A. A. Jajere, S. M. Stevenson, M. A. Ghazali, K. TI Assessment of knowledge, attitude, and practice on livestock traceability among cattle farmers and cattle traders in peninsular Malaysia and its impact on disease control SO TROPICAL ANIMAL HEALTH AND PRODUCTION DT Article DE Knowledge; Attitude; Practice; Traceability; Disease control; Risk factors; Malaysia AB The ability to trace the movement of animals and their related products is key to success in animal disease control. To ensure that a traceability system is optimized, livestock farmers and traders must have good appreciation and understanding about animal tracing. The present study examined the traceability of cattle in Malaysia vis-a-vis the domains of knowledge, attitude, and practice among cattle farmers and traders. A total of 543 farmers and traders in Peninsular Malaysia were interviewed. The results revealed that over 60% of the respondents had satisfactory knowledge and attitude about cattle movement and traceability. A lower proportion of the respondents (49%) were involved in appropriate practice that facilitated traceability of cattle. We found that the type of husbandry system and stakeholders' participation in livestock management-specific short courses were positively associated with satisfactory knowledge, attitude, and practice. A structured education and training program should be formulated to improve these domains so that the benefit of traceability becomes clear, paving the way to a successful traceability program. C1 [Salina, A. B.; Hassan, Latiffah; Saharee, A. A.; Jajere, S. M.] Univ Putra Malaysia, Fac Vet Med, Serdang 43400, Selangor, Malaysia. [Salina, A. B.; Ghazali, K.] Dept Vet Serv, Wisma Tani 62630, Putrajaya, Malaysia. [Stevenson, M. A.] Univ Melbourne, Fac Vet & Agr Sci, Parkville, Vic 3010, Australia. C3 Universiti Putra Malaysia; University of Melbourne RP Hassan, L (corresponding author), Univ Putra Malaysia, Fac Vet Med, Serdang 43400, Selangor, Malaysia. EM latiffah@upm.edu.my CR Abdulla I., 2016, AM J APPL SCI, V13, P976, DOI DOI 10.3844/AJASSP.2016.976.983 Abila R., 2006, P 11 INT S VET EPID Adesokan HK, 2014, TROP ANIM HEALTH PRO, V46, P159, DOI 10.1007/s11250-013-0467-3 Anka MS, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0108673 [Anonymous], 2016, 23 C OIE REG COMM AM, DOI [10.20506/TT.2585, DOI 10.20506/TT.2585] Ariff O. M., 2015, Malaysian Journal of Animal Science, V18, P1 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Coker RJ, 2011, LANCET, V377, P599, DOI 10.1016/S0140-6736(10)62004-1 HUSSAIN SS, 1994, AGR ECON, V10, P39, DOI 10.1016/0169-5150(94)90038-8 Jors E, 2016, J AGROMEDICINE, V21, P200, DOI 10.1080/1059924X.2016.1143428 OIE, 2017, AN WELF BEEF CATTL P Salina A. B., 2013, Malaysian Journal of Animal Science, V16, P83 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 NR 13 TC 2 Z9 2 U1 0 U2 48 PD JAN PY 2021 VL 53 IS 1 AR 15 DI 10.1007/s11250-020-02458-5 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000594992400004 DA 2022-12-14 ER PT J AU Cui, DS Liu, Y Yu, HS Wang, ZH Mao, XF AF Cui, Dongsheng Liu, Yang Yu, Hansong Wang, Zhaohui Mao, Xuefei TI Geographical traceability of soybean based on elemental fingerprinting and multivariate analysis SO JOURNAL OF CONSUMER PROTECTION AND FOOD SAFETY DT Article DE Food fraud; Element fingerprint; Visualisation analysis; Geographical traceability ID HARVEST YEAR; DISCRIMINATION; ORIGIN; RICE AB In this study, elemental composition differences of soybeans were analysed from four regions of the Chinese Heilongjiang Province (Daqing, Suihua, Heihe, and Jiamusi), and the characteristic fingerprints representative of the producing areas were screened. The contents of 25 elements in soybeans from the four producing areas were determined using an inductively coupled plasma mass spectrometer (ICP-MS), and the geographical sources of soybean were identified using difference, correlation, cluster heat map, and orthogonal partial least squares discriminant analyses (OPLS-DA). The results of the difference and correlation analyses showed that the elemental compositions were significantly affected by the soil environment for growth, and there were significant differences in element content among the four soybean-producing areas with regional characteristics. Heat map clustering showed the aggregation of the element content among different producing areas, distinguished the samples, and allowed classification of all elements. A discriminant model was established for the samples in the training set using the indices of 17 screened elements, including Mg, Al, K, Mn, Mo, p, Cu, Cr, Rb, Ni, Ca, Fe, Se, Pd, Zn, Ga, and Pb, and was used for the prediction and analysis of soybean samples in the testing set. Overall, the correct discrimination rate of the four soybean samples was 93.33%, which indicated these 17 elements contained sufficient information representative of the soybean-producing areas. Furthermore, they could be used as stable and effective traceability indicators to identify the production area of soybean samples from the four producing areas in Heilongjiang Province. C1 [Cui, Dongsheng; Yu, Hansong; Wang, Zhaohui] Jilin Agr Univ, Sch Food Sci & Engn, Changchun 130118, Peoples R China. [Liu, Yang] Jilin Agr Univ, Sch Informat Technol, Changchun 130118, Peoples R China. [Mao, Xuefei] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Mao, Xuefei] Minist Agr & Rural Affairs, Key Lab Agrofood Safety & Qual, Beijing 100081, Peoples R China. C3 Jilin Agricultural University; Jilin Agricultural University; Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs RP Wang, ZH (corresponding author), Jilin Agr Univ, Sch Food Sci & Engn, Changchun 130118, Peoples R China.; Mao, XF (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China.; Mao, XF (corresponding author), Minist Agr & Rural Affairs, Key Lab Agrofood Safety & Qual, Beijing 100081, Peoples R China. EM wzhjlndsp@aliyun.com; maoxuefei@caas.cn CR Chung IM, 2015, J CEREAL SCI, V65, P252, DOI 10.1016/j.jcs.2015.08.001 [崔玉军 CUI Yujun], 2008, [现代地质, Geoscience], V22, P929 Drivelos SA, 2016, FOOD CHEM, V213, P238, DOI 10.1016/j.foodchem.2016.06.088 Kawasaki A, 2002, SOIL SCI PLANT NUTR, V48, P635, DOI 10.1080/00380768.2002.10409251 Liao, 2004, 6 NAT S RES PROGR TR Lim DK, 2018, J FOOD DRUG ANAL, V26, P769, DOI 10.1016/j.jfda.2017.09.004 Liu, 2004, J NE PET U, V24 Liu LF, 2013, ECOLOGICAL GEOCHEMIS Opatic AM, 2018, J FOOD COMPOS ANAL, V71, P17, DOI 10.1016/j.jfca.2018.04.005 Perez-Castano E, 2019, FOOD CHEM, V274, P518, DOI 10.1016/j.foodchem.2018.08.128 Tang T., 2020, SCI TECHNOLOGY FOOD, V41, P360 Wang F, 2020, J SCI FOOD AGR, V100, P1294, DOI 10.1002/jsfa.10144 [汪江胜 Wang Jiangsheng], 2017, [发光学报, Chinese Journal of Luminescence], V38, P91 [王朝辉 Wang Zhaohui], 2019, [中国粮油学报, Journal of the Chinese Cereals and Oils Association], V34, P113 Wang ZhaoHui, 2019, Shipin Kexue / Food Science, V40, P318 [吴帅 Wu Shuai], 2015, [食品与机械, Food and Machinery], V31, P249 Xi, 1988, SOIL FERTIL SCI CHIN, V6, P10 Xia LY, 2013, STUDY CHARACTERISTIC Yang Jian, 2018, Zhongguo Zhong Yao Za Zhi, V43, P2676, DOI 10.19540/j.cnki.cjcmm.20180307.012 [张立 Zhang Li], 2019, [现代地质, Geoscience], V33, P1046 Zhang Yong, 2018, Journal of Food Safety and Quality, V9, P6161 Zhao HY, 2014, FOOD CHEM, V152, P316, DOI 10.1016/j.foodchem.2013.11.122 Zhao SS., 2020, QUAL SAF AGRO PROD, V1, P61 NR 23 TC 0 Z9 0 U1 15 U2 25 PD DEC PY 2021 VL 16 IS 4 BP 323 EP 331 DI 10.1007/s00003-021-01340-2 EA AUG 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000687095900001 DA 2022-12-14 ER PT J AU Zhang, YJ Liu, YF Jiong, Z Zhang, XS Li, BT Chen, EX AF Zhang, Yongjun Liu, Yanfeng Jiong, Zhang Zhang, Xiaoshan Li, Baotian Chen, Enxiu TI Development and assessment of blockchain-IoT-based traceability system for frozen aquatic product SO JOURNAL OF FOOD PROCESS ENGINEERING DT Article AB This study puts forward a novel frozen aquatic product traceability system (BIOT-TS) that based on blockchain and Internet of things (IoT) technology, which can alleviate the drawbacks of current tracing management during cold chain logistic, such as the weak security performance, inefficient centralized data management as well as the easy-tampered traceability information. The proposed system possesses the advantages of decentralization, safety, reliability, and tamper-proofing when it compares with traditional tracking technology. It is particularly suitable for multi-stages tracking management of cold chain logistics from aquaculture, production, logistics, and circulation to sales. In terms of such blockchain-IoT-based technology, the system is deployed and evaluated with the case of frozen turbot (Scophthalmus maximus) that is circulated and sold under the background of e-commerce cold chain logistics. It can significantly improve the quality and safety of the frozen aquatic product by using smart contracts, consensus strategy, and data on-chain verification technology. The overall involved participants can share more reliable tracing information after adopting this tracing system. Furthermore, the performance of this hybrid tracing technology has been evaluated by the operations of on-chain process and tracing queries through the real experiments, and has testified to be effective. Therefore, the application of this system is beneficial for the improvement of the aquatic food logistics industry and ensures credit in its ecosystem. Practical Applications The blockchain-IoT-based frozen aquatic product traceability management is one of the most promising technologies that possess the advantages of tamper-proof, transparent, and irrefutable transactions combined with distributed monitoring and tracking functionalities. Through the decentralized data management, smart contract, and mechanism of consensus, the traceability information is partitioned and encrypted storage, which can be used for government supervision and consumers' retroactive queries. This novel frozen aquatic food product tracing technology can fundamentally improve the reliability of quality tracing applications and make the closely related tracing management more transparent, secure, and efficient. C1 [Zhang, Yongjun; Liu, Yanfeng; Li, Baotian] Shandong Youth Univ Polit Sci, Coll Informat Engn, Jinan, Peoples R China. [Zhang, Xiaoshan] China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing, Peoples R China. [Jiong, Zhang; Chen, Enxiu] Shandong Inst Commerce & Technol, Coll Informat & Art, Jinan, Peoples R China. C3 Shandong Youth University of Political Science; China Agricultural University; Shandong Institute of Commerce & Technology RP Zhang, XS (corresponding author), China Agr Univ, Coll Engn, Beijing Lab Food Qual & Safety, Beijing, Peoples R China. EM zhxshuan@cau.edu.cn CR Alfian G, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107016 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Badia-Melis R, 2018, FOOD CONTROL, V86, P170, DOI 10.1016/j.foodcont.2017.11.022 Baralla G, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5857 Bhatt T, 2016, COMPR REV FOOD SCI F, V15, P392, DOI 10.1111/1541-4337.12187 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Corallo A, 2020, KNOWL PROCESS MANAG, V27, P150, DOI 10.1002/kpm.1634 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng HH, 2019, FOOD CONTROL, V98, P348, DOI 10.1016/j.foodcont.2018.11.050 Fernandez-Carames TM, 2018, IEEE ACCESS, V6, P32979, DOI 10.1109/ACCESS.2018.2842685 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Gooch M, 2017, J FOOD SCI, V82, pA45, DOI 10.1111/1750-3841.13744 Grassi S, 2018, J FOOD ENG, V234, P16, DOI 10.1016/j.jfoodeng.2018.04.012 Grema HA, 2020, PREV VET MED, V181, DOI 10.1016/j.prevetmed.2020.105038 Jiang YM, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19092042 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Kostal K, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19040856 Kumar G, 2020, SUSTAIN CITIES SOC, V62, DOI 10.1016/j.scs.2020.102361 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Liu ZY, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102059 Mazzei D, 2020, FUTURE GENER COMP SY, V105, P432, DOI 10.1016/j.future.2019.12.020 Moin S, 2019, FUTURE GENER COMP SY, V100, P325, DOI 10.1016/j.future.2019.05.023 Ndraha N, 2018, FOOD CONTROL, V89, P12, DOI 10.1016/j.foodcont.2018.01.027 O'Leary DE, 2017, INTELL SYST ACCOUNT, V24, P138, DOI 10.1002/isaf.1417 Peng YQ, 2018, J FOOD PROCESS ENG, V41, DOI 10.1111/jfpe.12685 Pigini D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101910 Qian JP, 2020, FOOD ENERGY SECUR, V9, DOI 10.1002/fes3.249 Reyna A, 2018, FUTURE GENER COMP SY, V88, P173, DOI 10.1016/j.future.2018.05.046 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Trektere K, 2017, J SOFTW-EVOL PROC, V29, DOI 10.1002/smr.1861 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Vivaldi F, 2020, SENSOR ACTUAT A-PHYS, V313, DOI 10.1016/j.sna.2020.112182 Xiao XQ, 2015, J SCI FOOD AGR, V95, P2693, DOI 10.1002/jsfa.7005 Xinqing X., 2017, J FOOD PROCESS ENG, V40 Yang K, 2019, COMPUT NETW, V148, P318, DOI 10.1016/j.comnet.2018.11.013 Zhang YJ, 2021, J FOOD PROCESS ENG, V44, DOI 10.1111/jfpe.13669 Zhang YJ, 2020, IEEE ACCESS, V8, P40955, DOI 10.1109/ACCESS.2020.2976509 NR 39 TC 12 Z9 12 U1 24 U2 99 PD MAY PY 2021 VL 44 IS 5 AR e13669 DI 10.1111/jfpe.13669 EA FEB 2021 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000621318200001 DA 2022-12-14 ER PT J AU Dac, HH Viejo, CG Lipovetzky, N Tongson, E Dunshea, FR Fuentes, S AF Dac, Hai Ho Gonzalez Viejo, Claudia Lipovetzky, Nir Tongson, Eden Dunshea, Frank R. Fuentes, Sigfredo TI Livestock Identification Using Deep Learning for Traceability SO SENSORS DT Article DE cow identification system; deep learning in agriculture; computer vision; edge computing ID ANIMAL BIOMETRICS; FACE RECOGNITION; CATTLE AB Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group. C1 [Dac, Hai Ho; Gonzalez Viejo, Claudia; Tongson, Eden; Dunshea, Frank R.; Fuentes, Sigfredo] Univ Melbourne, Sch Agr & Food,, Fac Vet & Agr Sci, Digital Agr,Food & Wine Sci Grp, Melbourne, Vic 3010, Australia. [Lipovetzky, Nir] Univ Melbourne, Fac Engn, Sch Comp & Informat Syst, Informat Technol, Parkville, Vic 3010, Australia. [Dunshea, Frank R.] Univ Leeds, Fac Biol Sci, Leeds LS2 9JT, England. C3 University of Melbourne; University of Melbourne; University of Leeds RP Fuentes, S (corresponding author), Univ Melbourne, Sch Agr & Food,, Fac Vet & Agr Sci, Digital Agr,Food & Wine Sci Grp, Melbourne, Vic 3010, Australia. EM sfuentes@unimelb.edu.au CR [Anonymous], DAIRY AUSTR COW FARM Awad AI, 2016, COMPUT ELECTRON AGR, V123, P423, DOI 10.1016/j.compag.2016.03.014 Bergqvist AS, 2015, LIVEST SCI, V180, P233, DOI 10.1016/j.livsci.2015.06.025 Bochkovskiy A., 2020, ARXIV200410934 Cai C, 2013, ASIAPAC SIGN INFO PR Clapham M, 2020, ECOL EVOL, V10, P12883, DOI 10.1002/ece3.6840 Deng J., 2009, 2009 IEEE C COMPUTER, P248, DOI [DOI 10.1109/CVPR.2009.5206848, 10.1109/CVPR.2009.5206848] Deng JK, 2019, PROC CVPR IEEE, P4685, DOI 10.1109/CVPR.2019.00482 Fisher R.A., 2009, ENCY BIOMETRICS, P899, DOI [10.1007/978-0-387-73003-5_349, DOI 10.1007/978-0-387-73003-5_349] Fuentes S, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21206844 Fuentes S, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20216334 Givens GH, 2013, COMPUT STAT DATA AN, V67, P236, DOI 10.1016/j.csda.2013.05.025 Grother P., 2017, NIST INTERAGENCY REP Grother Patrick, 2019, FACE RECOGNITION VEN He K., 2015, ARXIV Intel Corporation, 2018, TOOLK Jocher Glenn, 2021, ULTRALYTICS YOLOV5 Jorquera-Chavez M, 2020, ANIMALS-BASEL, V10, DOI 10.3390/ani10030451 Jorquera-Chavez M, 2019, ANIMALS-BASEL, V9, DOI 10.3390/ani9121089 Kalim A.R., 2020, FACE LANDMARKS DETEC Kingma DP., ADAM METHOD STOCHAST Kumar S, 2020, P NATL A SCI INDIA A, V90, P689, DOI 10.1007/s40010-019-00610-x Kumar S, 2017, IET IMAGE PROCESS, V11, P805, DOI 10.1049/iet-ipr.2016.0799 Kumar S, 2016, P NATL A SCI INDIA A, V86, P137, DOI 10.1007/s40010-016-0264-2 Lin Tsung-Yi, 2020, IEEE Trans Pattern Anal Mach Intell, V42, P318, DOI 10.1109/TPAMI.2018.2858826 Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48 Loshchilov I., 2017, FIXING WEIGHT DECAY Lu Y, 2014, INT J BIOMETRICS, V6, P18, DOI 10.1504/IJBM.2014.059639 Matkowski WM, 2019, IEEE IMAGE PROC, P1680, DOI 10.1109/ICIP.2019.8803125 Nason J., 2020, TAG RETENTION NLIS T Redmon J., 2015, ARXIV Sammut C., 2010, ENCY MACHINE LEARNIN, V653, DOI [10.1007/978-0-387-30164-8_528, DOI 10.1007/978-0-387-30164-8_528, 10.1007/978- 0- 387- 30164- 8 _ 528] Sandler M, 2018, PROC CVPR IEEE, P4510, DOI 10.1109/CVPR.2018.00474 Stark KDC, 1998, LIVEST PROD SCI, V53, P143, DOI 10.1016/S0301-6226(97)00154-1 Thi Thi Zin, 2018, International MultiConference of Engineers and Computer Scientists 2018. Proceedings, P320 Xue HC, 2021, MATH PROBL ENG, V2021, DOI 10.1155/2021/3375394 NR 36 TC 0 Z9 0 U1 3 U2 3 PD NOV PY 2022 VL 22 IS 21 AR 8256 DI 10.3390/s22218256 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000882185500001 DA 2022-12-14 ER PT J AU Ko, D Kwak, Y Song, S AF Ko, Daesik Kwak, Yunsik Song, Seokil TI Real Time Traceability and Monitoring System for Agricultural Products Based on Wireless Sensor Network SO INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS DT Article ID FOOD-INDUSTRY AB A system to monitor and trace the yields and distribution of agricultural products is an important precision agricultural application. The system can be used to predict the next year's yields or provide the distribution channel of agricultural products from farmers to customers. Existing traceability and monitoring systems, usually, are implemented by using both wireless sensor network (WSN) and radio frequency identification (RFID) techniques. In this paper, we propose a system architecture that only requires WSN techniques and implement it. The implementing system consists of sensor nodes, communication hubs, a communication protocol, and an event detection engine. We define events to trace and monitor agricultural products, and our event detection engine detects those events by using the current location and the status of each sensor node. We describe the overall architecture of the proposed system and implementation details. C1 [Ko, Daesik] Mokwon Univ, Dept Elect Engn, Taejon 302729, South Korea. [Kwak, Yunsik; Song, Seokil] Korea Natl Univ Transportat, Dept Comp Engn, Chungju 380702, Chungbuk, South Korea. C3 Mokwon University; Korea National University of Transportation RP Song, S (corresponding author), Korea Natl Univ Transportat, Dept Comp Engn, Chungju 380702, Chungbuk, South Korea. EM sisong@ut.ac.kr CR BAGGIO A, 2005, P ACM WORKSH REAL WO Beckwith R, 2004, C LOCAL COMPUT NETW, P471 Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Riquelme JAL, 2009, COMPUT ELECTRON AGR, V68, P25, DOI 10.1016/j.compag.2009.04.006 Ruiz-Garcia L, 2009, SENSORS-BASEL, V9, P4728, DOI 10.3390/s90604728 Sugahara K, 2009, COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE II, VOLUME 3, P2293 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 NR 7 TC 15 Z9 15 U1 0 U2 26 PY 2014 AR 832510 DI 10.1155/2014/832510 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000338538900001 DA 2022-12-14 ER PT J AU Bonello, F Cravero, MC Dell'Oro, V Tsolakis, C Ciambotti, A AF Bonello, Federica Cravero, Maria Carla Dell'Oro, Valentina Tsolakis, Christos Ciambotti, Aldo TI Wine Traceability Using Chemical Analysis, Isotopic Parameters, and Sensory Profiles SO BEVERAGES DT Article DE NMR; IRMS; sensory analyses; traceability; geographical origin; isotopes ID ELEMENTS AB NMR/IRMS techniques are now widely used to assess the geographical origin of wines. The sensory profile of a wine is also an interesting method of characterizing its origin. This study aimed at elaborating chemical, isotopic, and sensory parameters by means of statistical analysis. The data were determined in some Italian white wines-Verdicchio and Fiano- and red wines-Refosco dal Peduncolo Rosso and Nero d'Avola-produced from grapes grown in two different regions with different soil and climatic conditions during the years 2009-2010. The grapes were cultivated in Veneto (northwest Italy) and Marches (central Italy). The results show that the multivariate statistical analysis PCA (Principal Component Analysis) of all the data can be a useful tool to characterize the vintage and identify the origin of wines produced from different varieties. Moreover, it could discriminate wines of the same variety produced in regions with different soil and climatic conditions. C1 [Bonello, Federica; Cravero, Maria Carla; Dell'Oro, Valentina; Tsolakis, Christos; Ciambotti, Aldo] CREA Res Ctr Viticulture & Enol, Via Micca 35, I-14100 Asti, Italy. RP Cravero, MC (corresponding author), CREA Res Ctr Viticulture & Enol, Via Micca 35, I-14100 Asti, Italy. EM federica.bonello@crea.gov.it; mariacarla.cravero@crea.gov.it; valentina.delloro@crea.gov.it; christos.tsolakis@crea.gov.it; aldo.ciambotti@crea.gov.it CR Aghemo C, 2011, J SCI FOOD AGR, V91, P2088, DOI 10.1002/jsfa.4510 Cravero MC, 2012, ITAL J FOOD SCI, V24, P384 Di Stefano R., 1989, LENOTECNICO MAGGIO, V25, P83 Durante C, 2016, FOOD CHEM, V210, P648, DOI 10.1016/j.foodchem.2016.04.108 Dutra SV, 2013, FOOD CHEM, V141, P2148, DOI 10.1016/j.foodchem.2013.04.106 Garcia-Munoz S, 2014, FOOD QUAL PREFER, V32, P241, DOI 10.1016/j.foodqual.2013.09.005 Geana EI, 2016, FOOD CHEM, V192, P1015, DOI 10.1016/j.foodchem.2015.07.112 Gremaud G, 2004, EUR FOOD RES TECHNOL, V219, P97, DOI 10.1007/s00217-004-0919-0 Guaita M, 2013, J FOOD SCI, V78, pC160, DOI 10.1111/1750-3841.12022 GUINARD JX, 1986, SCI ALIMENT, V6, P657 Marchionni S, 2013, J AGR FOOD CHEM, V61, P6822, DOI 10.1021/jf4012592 MARTIN GJ, 1988, J AGR FOOD CHEM, V36, P316, DOI 10.1021/jf00080a019 Moreira C, 2017, S AFR J ENOL VITIC, V38, P82 Rochfort S, 2010, FOOD CHEM, V121, P1296, DOI 10.1016/j.foodchem.2010.01.067 Santesteban LG, 2015, AUST J GRAPE WINE R, V21, P157, DOI 10.1111/ajgw.12124 Versini G., 1996, Enotecnico, V32, P77 [No title captured] [No title captured] NR 18 TC 6 Z9 7 U1 1 U2 13 PD SEP PY 2018 VL 4 IS 3 AR 54 DI 10.3390/beverages4030054 WC Food Science & Technology SC Food Science & Technology UT WOS:000455152300008 DA 2022-12-14 ER PT J AU Valletta, M Ragucci, S Landi, N Di Maro, A Pedone, PV Russo, R Chambery, A AF Valletta, Mariangela Ragucci, Sara Landi, Nicola Di Maro, Antimo Pedone, Paolo Vincenzo Russo, Rosita Chambery, Angela TI Mass spectrometry-based protein and peptide profiling for food frauds, traceability and authenticity assessment SO FOOD CHEMISTRY DT Review DE Mass spectrometry; Matrix-assisted laser desorption ionization; mass spectrometry (MALDI-TOF MS); Liquid-chromatography-electrospray ionization; tandem mass spectrometry (LC-ESI MS; MS); Protein and peptide marker; Food authenticity; Food traceability ID NEAR-INFRARED SPECTROSCOPY; MS/MS SCREENING METHOD; LIQUID-CHROMATOGRAPHY; HIGH-RESOLUTION; MARKER PEPTIDES; MEAT-PRODUCTS; SPECIES IDENTIFICATION; TARGETED PROTEOMICS; MILK ADULTERATIONS; FISH PRODUCTS AB The ever-growing use of mass spectrometry (MS) methodologies in food authentication and traceability originates from their unrivalled specificity, accuracy and sensitivity. Such features are crucial for setting up analytical strategies for detecting food frauds and adulterations by monitoring selected components within food matrices. Among MS approaches, protein and peptide profiling has become increasingly consolidated. This review explores the current knowledge on recent MS techniques using protein and peptide biomarkers for assessing food traceability and authenticity, with a specific focus on their use for unmasking potential frauds and adulterations. We provide a survey of the current state-of-the-art instrumentation including the most reliable and sensitive acquisition modes highlighting advantages and limitations. Finally, we summarize the recent applications of MS to protein/peptide analyses in food matrices and examine their potential in ensuring the quality of agro-food products. C1 [Valletta, Mariangela; Ragucci, Sara; Landi, Nicola; Di Maro, Antimo; Pedone, Paolo Vincenzo; Russo, Rosita; Chambery, Angela] Univ Campania Luigi Vanvitelli, Dept Environm Biol & Pharmaceut Sci & Technol, I-81100 Caserta, Italy. C3 Universita della Campania Vanvitelli RP Russo, R; Chambery, A (corresponding author), Univ Campania Luigi Vanvitelli, Dept Environm Biol & Pharmaceut Sci & Technol, I-81100 Caserta, Italy. EM rosita.russo@unicampania.it; angela.chambery@unicampania.it CR Agapito-Tenfen SZ, 2013, PROTEOME SCI, V11, DOI 10.1186/1477-5956-11-46 Ansari P, 2012, ANAL BIOANAL CHEM, V402, P2607, DOI 10.1007/s00216-011-5218-6 Ansari P, 2011, ANAL BIOANAL CHEM, V399, P1105, DOI 10.1007/s00216-010-4422-0 Arena S, 2016, J PROTEOMICS, V147, P56, DOI 10.1016/j.jprot.2016.02.016 Artigaud S, 2014, J PROTEOMICS, V105, P164, DOI 10.1016/j.jprot.2014.03.026 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Barik SK, 2013, APPL BIOCHEM BIOTECH, V171, P1011, DOI 10.1007/s12010-013-0384-y Basalekou M, 2017, INT J FOOD SCI TECH, V52, P1307, DOI 10.1111/ijfs.13424 Bonick J, 2017, J FOOD COMPOS ANAL, V58, P82, DOI 10.1016/j.jfca.2017.01.019 Cacciola F, 2012, LC GC EUR, P15 Caira S, 2016, ANAL BIOANAL CHEM, V408, P5609, DOI 10.1007/s00216-016-9663-0 Calvano CD, 2013, J AGR FOOD CHEM, V61, P1609, DOI 10.1021/jf302999s Calvano CD, 2012, J MASS SPECTROM, V47, P1141, DOI 10.1002/jms.2995 Camin F, 2016, COMPR REV FOOD SCI F, V15, P868, DOI 10.1111/1541-4337.12219 Motta TMC, 2014, TALANTA, V120, P498, DOI 10.1016/j.talanta.2013.11.093 Caporaso N, 2018, APPL SPECTROSC REV, V53, P667, DOI 10.1080/05704928.2018.1425214 Carrera M, 2011, ANAL CHEM, V83, P5688, DOI 10.1021/ac200890w Cereda A, 2010, J PROTEOMICS, V73, P1732, DOI 10.1016/j.jprot.2010.05.010 Colgrave ML, 2016, J PROTEOMICS, V147, P169, DOI 10.1016/j.jprot.2016.03.045 Colgrave ML, 2015, J PROTEOME RES, V14, P2659, DOI 10.1021/acs.jproteome.5b00187 Consonni R, 2019, MAGN RESON CHEM, V57, P558, DOI 10.1002/mrc.4807 Cozzolino D, 2011, APPL SPECTROSC REV, V46, P523, DOI 10.1080/05704928.2011.587857 Cozzolino D, 2012, APPL SPECTROSC REV, V47, P207, DOI 10.1080/05704928.2011.639106 Cuollo M, 2010, RAPID COMMUN MASS SP, V24, P1687, DOI 10.1002/rcm.4564 D'Amato A, 2010, J PROTEOMICS, V73, P2370, DOI 10.1016/j.jprot.2010.08.010 Dalabasmaz S, 2017, J AGR FOOD CHEM, V65, P10781, DOI 10.1021/acs.jafc.7b03801 Dawson C, 2018, PEPTIDE SCI, V110, DOI 10.1002/pep2.24045 Di Girolamo F, 2014, INT J MOL SCI, V15, P13697, DOI 10.3390/ijms150813697 Di Giuseppe AMA, 2015, FOOD CHEM, V169, P241, DOI 10.1016/j.foodchem.2014.07.126 Du LJ, 2020, FOOD SCI NUTR, V8, P1471, DOI 10.1002/fsn3.1430 Esteki M, 2019, FOOD RES INT, V122, P303, DOI 10.1016/j.foodres.2019.04.025 Fanali C, 2016, WOODHEAD PUBL FOOD S, P253, DOI 10.1016/B978-0-08-100220-9.00010-2 Fiorino GM, 2019, FOOD RES INT, V116, P1258, DOI 10.1016/j.foodres.2018.10.013 Fornal E, 2019, FOOD CHEM, V283, P489, DOI 10.1016/j.foodchem.2019.01.074 Gavage M, 2019, J CHROMATOGR A, V1584, P115, DOI 10.1016/j.chroma.2018.11.036 Gavage M, 2020, FOOD CHEM, V304, DOI 10.1016/j.foodchem.2019.125428 Grundy HH, 2016, FOOD CHEM, V190, P276, DOI 10.1016/j.foodchem.2015.05.054 Guarino C, 2010, RAPID COMMUN MASS SP, V24, P705, DOI 10.1002/rcm.4426 Guo SW, 2018, RSC ADV, V8, P3768, DOI 10.1039/c7ra12539a Herrero M, 2012, MASS SPECTROM REV, V31, P49, DOI 10.1002/mas.20335 Herrero M, 2009, J CHROMATOGR A, V1216, P7110, DOI 10.1016/j.chroma.2009.08.014 Hoffmann B, 2017, FOOD CONTROL, V71, P200, DOI 10.1016/j.foodcont.2016.06.021 Lerma-Garcia MJ, 2014, BBA-PROTEINS PROTEOM, V1844, P1493, DOI 10.1016/j.bbapap.2014.04.022 Jira W, 2019, FOOD CHEM, V275, P214, DOI 10.1016/j.foodchem.2018.09.041 Karoui R, 2010, CHEM REV, V110, P6144, DOI 10.1021/cr100090k Karoui R, 2011, FOOD BIOPROCESS TECH, V4, P364, DOI 10.1007/s11947-010-0370-0 Katerinopoulou K, 2020, FOODS, V9, DOI 10.3390/foods9040489 Korte R, 2016, ANAL BIOANAL CHEM, V408, P3059, DOI 10.1007/s00216-016-9384-4 Kuckova S, 2019, J SEP SCI, V42, P3487, DOI 10.1002/jssc.201900659 Li YY, 2018, FOOD CHEM, V245, P125, DOI 10.1016/j.foodchem.2017.09.066 Lu JA, 2010, MOL BIOL REP, V37, P2141, DOI 10.1007/s11033-009-9684-2 Lu WY, 2017, J DAIRY SCI, V100, P6980, DOI 10.3168/jds.2017-12574 Lutter P, 2011, J AOAC INT, V94, P1043 Makarov A, 2010, J CHROMATOGR A, V1217, P3938, DOI 10.1016/j.chroma.2010.02.022 Mamone G., 2013, PROTEOMICS FOODS PRI, P465 Mamone G, 2009, J CHROMATOGR A, V1216, P7130, DOI 10.1016/j.chroma.2009.07.052 Manning L, 2016, CURR OPIN FOOD SCI, V10, P16, DOI 10.1016/j.cofs.2016.07.001 Martinez-Esteso MJ, 2016, J PROTEOMICS, V147, P156, DOI 10.1016/j.jprot.2016.03.015 MARTINEZMAQUEDA D, 2013, PROTEOMICS FOODS, P21, DOI DOI 10.1007/978-1-4614-5626-1_2 Mazzeo MF, 2016, J PROTEOMICS, V147, P119, DOI 10.1016/j.jprot.2016.03.007 McWilliam V, 2015, CURR ALLERGY ASTHM R, V15, DOI 10.1007/s11882-015-0555-8 Mikolajczak B, 2019, MOLECULES, V24, DOI 10.3390/molecules24010018 Monaci L, 2010, J CHROMATOGR A, V1217, P4300, DOI 10.1016/j.chroma.2010.04.035 Monaci L, 2013, RAPID COMMUN MASS SP, V27, P2009, DOI 10.1002/rcm.6662 Monaci L, 2011, J AOAC INT, V94, P1034 Montowska M, 2019, FOOD CONTROL, V104, P122, DOI 10.1016/j.foodcont.2019.04.022 Montowska M, 2018, J FOOD SCI TECH MYS, V55, P4984, DOI 10.1007/s13197-018-3437-y Montowska M, 2019, FOOD CHEM, V274, P857, DOI 10.1016/j.foodchem.2018.08.131 Montowska M, 2018, LWT-FOOD SCI TECHNOL, V87, P310, DOI 10.1016/j.lwt.2017.08.091 Montowska M, 2017, FOOD CHEM, V237, P1092, DOI 10.1016/j.foodchem.2017.06.059 Montowska M, 2013, FOOD CHEM, V136, P1461, DOI 10.1016/j.foodchem.2012.09.072 Montowska M, 2012, PROTEOMICS, V12, P2879, DOI 10.1002/pmic.201200043 Nalazek-Rudnicka K, 2019, FOOD CHEM, V283, P367, DOI 10.1016/j.foodchem.2019.01.007 Nardiello D, 2018, FOOD CHEM, V244, P317, DOI 10.1016/j.foodchem.2017.10.052 Naveena BM, 2018, J SCI FOOD AGR, V98, P1188, DOI 10.1002/jsfa.8572 Nessen MA, 2016, J AGR FOOD CHEM, V64, P3669, DOI 10.1021/acs.jafc.5b05322 Orduna AR, 2017, FOOD ADDIT CONTAM A, V34, P1110, DOI 10.1080/19440049.2017.1329951 Orduna AR, 2015, FOOD ADDIT CONTAM A, V32, P1709, DOI 10.1080/19440049.2015.1064173 Ortea I, 2011, J CHROMATOGR A, V1218, P4445, DOI 10.1016/j.chroma.2011.05.032 Ortea I, 2010, FOOD CHEM, V121, P569, DOI 10.1016/j.foodchem.2009.12.049 Ozenc N, 2015, J SCI FOOD AGR, V95, P1956, DOI 10.1002/jsfa.6911 Panchaud A, 2008, J PROTEOMICS, V71, P19, DOI 10.1016/j.jprot.2007.12.001 Pascoal A, 2012, ANAL BIOCHEM, V421, P56, DOI 10.1016/j.ab.2011.10.029 Pepe T, 2010, VET RES COMMUN, V34, pS153, DOI 10.1007/s11259-010-9400-7 Pepe T., 2012, PROTEOMIC APPL BIOL, P191 Pico Y., 2015, COMPREHENSIVE ANAL C, V68 Pinto G, 2012, ANAL BIOANAL CHEM, V402, P1961, DOI 10.1007/s00216-011-5627-6 Piovesana S, 2016, J CHROMATOGR A, V1428, P193, DOI 10.1016/j.chroma.2015.07.049 Ponce-Alquicira E, 2000, FOOD CHEM, V69, P81, DOI 10.1016/S0308-8146(99)00243-5 Prandi B, 2019, FOOD CONTROL, V97, P15, DOI 10.1016/j.foodcont.2018.10.016 Prandi B, 2013, FOOD CHEM, V140, P141, DOI 10.1016/j.foodchem.2013.02.039 Prandi B, 2012, ANAL BIOANAL CHEM, V403, P2909, DOI 10.1007/s00216-012-5731-2 Prieto N, 2017, APPL SPECTROSC, V71, P1403, DOI 10.1177/0003702817709299 Resetar D, 2016, FOOD CONTROL, V64, P157, DOI 10.1016/j.foodcont.2015.12.035 Russo R, 2019, FOOD CHEM, V285, P111, DOI 10.1016/j.foodchem.2019.01.127 Russo R, 2016, RAPID COMMUN MASS SP, V30, P497, DOI 10.1002/rcm.7463 Russo R, 2014, J MASS SPECTROM, V49, P1239, DOI 10.1002/jms.3451 Russo R, 2012, J MASS SPECTROM, V47, P1407, DOI 10.1002/jms.3064 Salla V, 2013, ANAL CHIM ACTA, V794, P55, DOI 10.1016/j.aca.2013.07.014 Samperi R., 2015, COMPREHENSIVE ANAL C, P309, DOI DOI 10.1007/978-94-007-6401-9_11 Santos J, 2017, FOOD AUTHENTICATION: MANAGEMENT, ANALYSIS AND REGULATION, P200 Sarah SA, 2016, FOOD CHEM, V199, P157, DOI 10.1016/j.foodchem.2015.11.121 Sassi M, 2015, J AGR FOOD CHEM, V63, P7093, DOI 10.1021/acs.jafc.5b03524 Schalk K, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0192804 Sealey-Voyksner J, 2016, FOOD CHEM, V194, P201, DOI 10.1016/j.foodchem.2015.07.043 Sentandreu MA, 2010, J PROTEOME RES, V9, P3374, DOI 10.1021/pr9008942 Stephan R, 2014, FOOD CONTROL, V46, P6, DOI 10.1016/j.foodcont.2014.04.047 TAYLOR AJ, 1993, MEAT SCI, V33, P75, DOI 10.1016/0309-1740(93)90095-Y Tranchida PQ, 2012, LC GC EUR, P25 Van Vlierberghe K, 2020, FOOD CHEM, V309, DOI 10.1016/j.foodchem.2019.125679 von Bargen C, 2014, J AGR FOOD CHEM, V62, P9428, DOI 10.1021/jf503468t von Bargen C, 2013, J AGR FOOD CHEM, V61, P11986, DOI 10.1021/jf404121b Wang GJ, 2018, J FOOD COMPOS ANAL, V73, P47, DOI 10.1016/j.jfca.2018.07.004 Wang X, 2013, TRAC-TREND ANAL CHEM, V52, P170, DOI 10.1016/j.trac.2013.08.005 Watson AD, 2015, ANAL CHEM, V87, P10315, DOI 10.1021/acs.analchem.5b02318 Wulff T, 2013, J PROTEOME RES, V12, P5253, DOI 10.1021/pr4006525 Yang Charles T, 2018, Food Addit Contam Part A Chem Anal Control Expo Risk Assess, V35, P599, DOI 10.1080/19440049.2017.1416680 Yang JH, 2019, FOOD SCI NUTR, V7, P56, DOI 10.1002/fsn3.791 Yilmaz MT, 2013, FOOD CHEM, V141, P2450, DOI 10.1016/j.foodchem.2013.05.096 Zubarev RA, 2013, ANAL CHEM, V85, P5288, DOI 10.1021/ac4001223 NR 120 TC 7 Z9 7 U1 20 U2 87 PD DEC 15 PY 2021 VL 365 AR 130456 DI 10.1016/j.foodchem.2021.130456 EA JUL 2021 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000687701800001 DA 2022-12-14 ER PT J AU Zhang, SQ Liao, JH Wu, SC Zhong, JR Xue, XP AF Zhang, Shaqing Liao, Jinhui Wu, Shuangcheng Zhong, Junrui Xue, Xiaoping TI A Traceability Public Service Cloud Platform Incorporating IDcode System and Colorful QR Code Technology for Important Product SO MATHEMATICAL PROBLEMS IN ENGINEERING DT Article ID TRACING SYSTEMS; CHAIN; OPTIMIZATION; READABILITY; MANAGEMENT; PROVENANCE; EQUIPMENT; TRACKING; BARCODE AB At present, the epidemic situation of COVID-19 is raging rampantly in the whole world, affecting the hearts of billions of people. The new coronavirus has been detected in many foods and agricultural products. At the same time, vaccines and medicines to prevent or treat COVID-19 are also stepping up research and development and gradually put into use. The quality and safety of foods, medicines, and agricultural products are directly related to the lives and health of people. There are many potential dangers and hidden risks of accidents in the production, sale, and transportation of dangerous goods and special equipment. Therefore, it is necessary to effectively monitor and record the workflow of the above productions or goods. In this paper, we developed an important product traceability public service cloud platform (IPTPSCP) based on batch identification and record keeping with International Two-Dimensional Code Object Identifier System (IDcode) coding rules. Through a case study of the tea factory that produces and sells Xinyang Maojian tea, a test and implementation of IPTPSCP was shown by designing a colorful QR code to prevent the traceability information from being forged in batches. Judging from the overall effect of the practical application of more than a dozen settled enterprises, IPTPSCP has improved the efficiency of data collection and monitoring by about 13%. The results show that the IPTPSCP can be considered as an effective tool to guarantee the quality and safety of products. Besides, since it is not required for the enterprise to invest much money and manpower to develop software, IPTPSCP reduces the cost of implementing product traceability by about 36%. C1 [Zhang, Shaqing; Liao, Jinhui; Wu, Shuangcheng] Guangdong Univ Technol, Sch Management, Guangzhou 510520, Peoples R China. [Zhang, Shaqing] Huizhou Guangdong Univ Technol, IoT Cooperat Innovat Inst Co Ltd, Huizhou 516025, Peoples R China. [Zhong, Junrui] Jinan Univ, Affiliated Hosp 1, Informat Ctr, Guangzhou 510630, Peoples R China. [Xue, Xiaoping] Huizhou Econ & Polytech Coll, Sch Informat Engn, Huizhou 516057, Peoples R China. C3 Guangdong University of Technology; Jinan University RP Zhong, JR (corresponding author), Jinan Univ, Affiliated Hosp 1, Informat Ctr, Guangzhou 510630, Peoples R China. EM zhangshaqing@126.com; 2218514809@qq.com; 734391487@qq.com; ecis@163.com; 307244112@qq.com CR Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Alfian G, 2017, J FOOD ENG, V212, P65, DOI 10.1016/j.jfoodeng.2017.05.008 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bong YS, 2016, FOOD CONTROL, V60, P378, DOI 10.1016/j.foodcont.2015.08.017 Cai Y, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0165263 Cao Y, 2017, PROCEDIA COMPUT SCI, V122, P617, DOI 10.1016/j.procs.2017.11.414 Chen TB, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106770 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Fernandes EAD, 2020, FOOD CHEM, V333, DOI 10.1016/j.foodchem.2020.127462 Gao GD, 2019, COMPUT ELECTRON AGR, V166, DOI 10.1016/j.compag.2019.105013 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Hu SS, 2021, COMPUT IND ENG, V153, DOI 10.1016/j.cie.2020.107079 Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Li BD, 2019, KNOWL-BASED SYST, V186, DOI 10.1016/j.knosys.2019.104989 Liang K, 2019, COMPUT ELECTRON AGR, V162, P709, DOI 10.1016/j.compag.2019.04.039 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Paunescu D, 2016, POWDER TECHNOL, V291, P344, DOI 10.1016/j.powtec.2015.12.035 Peng YQ, 2018, J FOOD PROCESS ENG, V41, DOI 10.1111/jfpe.12685 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Qian JP, 2017, COMPUT ELECTRON AGR, V139, P56, DOI 10.1016/j.compag.2017.05.009 Tang J., 2017, SOFTWARE ENG, V20, P36 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Wang X, 2018, FOOD CONTROL, V88, P169, DOI 10.1016/j.foodcont.2018.01.008 Zhao HY, 2017, FOOD CONTROL, V76, P82, DOI 10.1016/j.foodcont.2017.01.006 ZIIOT, 2018, INT 2 DIM COD OBJ ID NR 36 TC 8 Z9 8 U1 7 U2 13 PD JUN 8 PY 2021 VL 2021 AR 5535535 DI 10.1155/2021/5535535 WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications SC Engineering; Mathematics UT WOS:000665773000003 DA 2022-12-14 ER PT J AU Schraml, R Charwat-Pessler, J Petutschnigg, A Uhl, A AF Schraml, R. Charwat-Pessler, J. Petutschnigg, A. Uhl, A. TI Towards the applicability of biometric wood log traceability using digital log end images SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Biometric log traceability; Roundwood tracking; Log end biometrics; Cross-section analysis; Log end face analysis ID FINGERPRINT; SAWMILL; TRADE; YARD AB Log traceability in the timber based industries is a basic requirement to fulfil economical, social and legal requirements. This work introduces biometric log recognition using digital log end images and explores the robustness to a set of log end cross-section (CS) variations. In order to investigate longitudinal and surface CS variations three tree logs were sliced and captured in different sessions. A texture feature-based technique well known from fingerprint recognition is adopted to compute and match biometric templates of CS images captured from log ends. In the experimental evaluation insights and constraints on the general applicability and robustness of log end biometrics to identify logs in an industrial application are presented. Results for different identification performance scenarios indicate that the matching procedure which is based on annual ring pattern and shape information is very robust to log length cutting using different cutting tools. The findings of this study are a further step towards the development of a biometric log recognition system. (C) 2015 The Authors. Published by Elsevier B.V. C1 [Schraml, R.; Uhl, A.] Salzburg Univ, A-5020 Salzburg, Austria. [Charwat-Pessler, J.; Petutschnigg, A.] Univ Appl Sci Salzburg, A-5431 Kuchl, Austria. C3 Salzburg University RP Schraml, R (corresponding author), Salzburg Univ, Jakob Haringer Str 2, A-5020 Salzburg, Austria. EM rschraml@cosy.sbg.ac.at; johann.charwat-pessler@fh-salzburg.ac.at CR Barrett WA, 2008, Advances in Computer and Informatiom Sciences and Engineering, P562, DOI 10.1007/978-1-4020-8741-7_100 Chiorescu S, 2004, SCAND J FOREST RES, V19, P374, DOI 10.1080/02827580410030118 Chiorescu S, 2003, FOREST PROD J, V53, P78 Dykstra D.P., 2003, TECHNICAL REPORT Flodin J, 2008, FOREST PROD J, V58, P100 Flodin J, 2008, FOREST PROD J, V58, P21 Flodm J., 2007, P QUAL CONTR WOOD WO Hong L, 1998, IEEE T PATTERN ANAL, V20, P777, DOI 10.1109/34.709565 Jain A, 2001, 2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS, P282, DOI 10.1109/ICIP.2001.958106 Jain A.K., 2007, HDB BIOMETRICS HDB B Jain A. K., 2011, INTRO BIOMETRICS Jain AK, 2000, IEEE T IMAGE PROCESS, V9, P846, DOI 10.1109/83.841531 Kastner T, 2011, GLOBAL ENVIRON CHANG, V21, P947, DOI 10.1016/j.gloenvcha.2011.05.003 Knutsson H., 1983, IEEE COMP SOC WORKSH Kuemmerle T, 2009, REMOTE SENS ENVIRON, V113, P1194, DOI 10.1016/j.rse.2009.02.006 Maltoni D., 2009, HDB FINGERPRINT RECO Marjanen IC, 2008, P SPIE C IM PROC ALG Mattila JP, 1999, METHOD MEASURING APP Norell I, 2009, P INT C IM AN PROC, P307 Norell K, 2008, COMPUT ELECTRON AGR, V63, P155, DOI 10.1016/j.compag.2008.02.006 Pahlberg T, 2015, COMPUT ELECTRON AGR, V111, P164, DOI 10.1016/j.compag.2014.12.014 Richards M, 2003, INT FOREST REV, V5, P282, DOI 10.1505/IFOR.5.3.282.19153 Schram R., 2013, P 14 C COMP GRAPH IM Schraml R., 2015, P IEEE INT C IM PROC Schraml R., 2014, ARTIF INTELL, P614 Schraml R., 2015, P 16 INT C COMP AN I, P752 Schraml R, 2014, IEEE IMAGE PROC, P5706, DOI 10.1109/ICIP.2014.7026154 Smith J, 2003, INT FOREST REV, V5, P293, DOI 10.1505/IFOR.5.3.293.19138 Tzoulis I, 2013, PROC TECH, V8, P606, DOI 10.1016/j.protcy.2013.11.087 United Nations, 1992, UN C ENV DEV AG 21 R NR 30 TC 6 Z9 6 U1 1 U2 11 PD NOV PY 2015 VL 119 BP 112 EP 122 DI 10.1016/j.compag.2015.10.003 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000366781500012 DA 2022-12-14 ER PT J AU Li, JS Huang, M Wei, W Yang, H Yuan, JC Liu, PK AF Li, Jiasheng Huang, Ming Wei, Wei Yang, Hong Yuan, Jinchun Liu, Pinkuan TI Study on traceability and suppression method of medium-frequency error for ultra-precision machining optical crystals SO OPTICS EXPRESS DT Article ID LASER-INDUCED DAMAGE; KDP CRYSTAL; SURFACE-TOPOGRAPHY; TOOL; GENERATION; REMOVAL; SPINDLE AB The medium-frequency error on the surface of ultraprecision flycutting has an important effect on the performance of the optical crystal. In this paper, firstly, the characteristic phenomenon of "knife-like grain" in the medium frequency surface of the square and circular optical crystal machined by ultraprecision fly-cutting is revealed. Besides, the error traceability is realized and the results show that the periodic low-frequency fluctuation of 0.3 Hz between the tool tip and the workpiece is the cause of the medium frequency error of "knife-like grain". Secondly, through the frequency domain waterfall diagram of vibration signal and the analysis of spindle speed signal, it is proved that the surface shape characteristic is caused by the fluctuation of spindle speed during the cutting process. Then, the variation rule of the cutting amount caused by the fluctuation of spindle speed is explored theoretically and experimentally, and the formation mechanism of medium frequency error in flycutting is revealed. Finally, in order to reduce the medium frequency error of "knife-like grain", the control parameters of the aerostatic spindle are reasonably optimized based on the analysis of the mechanical and electrical coupling control performance of the spindle, so that the RMS values in the medium frequency band of the workpiece are greatly improved after machining. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement C1 [Li, Jiasheng; Huang, Ming; Wei, Wei; Yang, Hong] China Acad Engn Phys, Inst Machinery Mfg Technol, Mianyang 621900, Sichuan, Peoples R China. [Li, Jiasheng; Yuan, Jinchun; Liu, Pinkuan] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China. C3 Chinese Academy of Engineering Physics; Shanghai Jiao Tong University RP Huang, M; Wei, W (corresponding author), China Acad Engn Phys, Inst Machinery Mfg Technol, Mianyang 621900, Sichuan, Peoples R China. EM hmhy1972@163.com; weiwei_caep@163.com CR An C, 2019, INT J ADV MANUF TECH, V103, P3013, DOI 10.1007/s00170-019-03751-w Burnham AK, 2003, APPL OPTICS, V42, P5483, DOI 10.1364/AO.42.005483 Chen DJ, 2017, INT J ADV MANUF TECH, V91, P2185, DOI 10.1007/s00170-016-9918-0 Chen GD, 2018, CHIN J MECH ENG-EN, V31, DOI 10.1186/s10033-018-0208-7 Chen WQ, 2015, OPT ENG, V54, DOI 10.1117/1.OE.54.2.024101 Cheng J, 2014, OPT EXPRESS, V22, P28740, DOI 10.1364/OE.22.028740 Gao W, 2019, OPT EXPRESS, V27, P6268, DOI 10.1364/OE.27.006268 HARVEY JE, 1995, P SOC PHOTO-OPT INS, V2576, P155, DOI 10.1117/12.215588 Izdebski M, 2019, J APPL CRYSTALLOGR, V52, P158, DOI 10.1107/S1600576719000074 LAWSON JK, 1995, P SOC PHOTO-OPT INS, V2536, P38, DOI 10.1117/12.218430 Lee GI, 2018, INT J PRECIS ENG MAN, V19, P265, DOI 10.1007/s12541-018-0031-1 Lee WB, 2001, INT J MECH SCI, V43, P961, DOI 10.1016/S0020-7403(00)00050-3 Lee WJ, 2009, INT J PRECIS ENG MAN, V10, P115, DOI [10.1007/S12541-009-0102-4, 10.1007/s12541-009-0102-4] Li HY, 2019, OPT EXPRESS, V27, P24885, DOI 10.1364/OE.27.024885 Li JS, 2020, INT J ADV MANUF TECH, V108, P2895, DOI 10.1007/s00170-020-05557-7 Li JS, 2020, FRONT MECH ENG-PRC, V15, P227, DOI 10.1007/s11465-020-0587-1 Li JS, 2019, J MECH SCI TECHNOL, V33, P5199, DOI 10.1007/s12206-019-1009-3 Liang YC, 2013, INT J ADV MANUF TECH, V69, P237, DOI 10.1007/s00170-013-5020-z Liu L, 2017, INT J ADV MANUF TECH, V93, P4169, DOI 10.1007/s00170-017-0850-8 Liu Q, 2018, INT J ADV MANUF TECH, V99, P2777, DOI 10.1007/s00170-018-2622-5 Lu HJ, 2020, INT J PRECIS ENG MAN, V21, P189, DOI 10.1007/s12541-019-00239-1 Sun YZ, 2015, INT J ADV MANUF TECH, V76, P1215, DOI 10.1007/s00170-014-6373-7 Sun ZW, 2019, INT J ADV MANUF TECH, V101, P1583, DOI 10.1007/s00170-018-3013-7 Wang SF, 2019, OPT EXPRESS, V27, P15142, DOI 10.1364/OE.27.015142 Wang X, 2016, INT J ADV MANUF TECH, V85, P1347, DOI 10.1007/s00170-015-8019-9 Wei W, 2020, P I MECH ENG B-J ENG, V234, P1742, DOI 10.1177/0954405420928738 Yang X, 2016, INT J ADV MANUF TECH, V87, P1957, DOI 10.1007/s00170-016-8583-7 NR 27 TC 3 Z9 3 U1 5 U2 13 PD JUL 5 PY 2021 VL 29 IS 14 BP 22252 EP 22265 DI 10.1364/OE.432500 WC Optics SC Optics UT WOS:000670054200094 DA 2022-12-14 ER PT J AU Bezerra, AC Pandorfi, H Carvalho, FFRE Guiselini, C De Lima, TAS AF Bezerra, Alan C. Pandorfi, Heliton Carvalho, Francisco F. R. E. Guiselini, Cristiane De Lima, Thiago A. S. TI TECHNICAL EFFICIENCY AND IMPLEMENTATION COSTS OF SHEEP IDENTIFICATION SYSTEM SO ENGENHARIA AGRICOLA DT Article DE animal identification; sheep production; traceability ID TRACEABILITY; CATTLE AB The objective of this research was to characterize the efficiency and convenience of sheep identification system and its implementation costs. Therefore we quantified the time for implementation, reading and data transfer to a management program in a manual system (earrings) and electronic system (subcutaneous transponders). Furthermore, it was evaluated the implementation costs of the traceability systems by analysis of the absorption costs and comparison between systems. It was observed that the implementation time for the manual system was shorter (2.2 s animal(-1)) than the electronic system (4.67 s animal(-1)). As for reading time, the electronic system presented shorter time (2.89 s animal(-1)) than the manual system (4.67 s animal(-1)). As for the data transfer time, the electronic system also presented shorter time (16 s) than manual system (6 min and 42 s). In terms of economic costs, the manual system presented lower implementation costs and lower values per tracked animal to ensure the economic viability. In conclusion, the producer must analyze the context around the production unit to choose the best traceability system. C1 [Bezerra, Alan C.] Univ Fed Rural Pernambuco, Unidade Acad Serra Talhada, Serra Talhada, PE, Brazil. [Pandorfi, Heliton; Carvalho, Francisco F. R. E.; Guiselini, Cristiane; De Lima, Thiago A. S.] Univ Fed Rural Pernambuco, Recife, PE, Brazil. C3 Universidade Federal Rural de Pernambuco (UFRPE); Universidade Federal Rural de Pernambuco (UFRPE) RP Bezerra, AC (corresponding author), Univ Fed Rural Pernambuco, Unidade Acad Serra Talhada, Serra Talhada, PE, Brazil. EM cezaralan.a@gmail.com CR Caja G, 1998, LIVEST PROD SCI, V55, P279, DOI 10.1016/S0301-6226(98)00137-7 Coronetti J, 2012, REV ELECT GESTAO ORG, V10 Costa RG, 2011, SMALL RUMINANT RES, V98, P51, DOI 10.1016/j.smallrumres.2011.03.017 FAOSTAT, 2013, FOOD SUPPL LIV FISH Farias J.L. de S., 2014, Arch. zootec., V63, P13 Klindtworth M, 1999, COMPUT ELECTRON AGR, V24, P65, DOI 10.1016/S0168-1699(99)00037-X Lopes Marcos Aurélio, 2013, Rev. Ceres, V60, P757, DOI 10.1590/S0034-737X2013000600003 Lopes M.A., 2013, Arq. Inst. Biol., V80, P135 Lopes MA, 2008, CIENC AGROTEC, V32, P288, DOI 10.1590/S1413-70542008000100041 Nassar V, 2014, PERSPECTIVAS GESTAO, V5, P98 Oliveira CA, 2013, REV CIENCIAS VIDA, V33, P25 R Core Team, 2020, R LANG ENV STAT COMP Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Silva DED, 2014, REV COMPUTACAO APLIC, V3, P21 Souza D, 2012, SYNERGISMUS SCYENTIF, V7 NR 15 TC 0 Z9 0 U1 0 U2 0 PD SEP-OCT PY 2017 VL 37 IS 5 BP 1073 EP 1080 DI 10.1590/1809-4430-eng.agric.v37n5p1073-1080/2017 WC Agricultural Engineering SC Agriculture UT WOS:000411815600022 DA 2022-12-14 ER PT J AU Pearson, S May, D Leontidis, G Swainson, M Brewer, S Bidaut, L Frey, JG Parr, G Maull, R Zisman, A AF Pearson, Simon May, David Leontidis, Georgios Swainson, Mark Brewer, Steve Bidaut, Luc Frey, Jeremy G. Parr, Gerard Maull, Roger Zisman, Andrea TI Are Distributed Ledger Technologies the panacea for food traceability? SO GLOBAL FOOD SECURITY-AGRICULTURE POLICY ECONOMICS AND ENVIRONMENT DT Review DE Distributed Ledger Technology; Blockchain; Food supply chain; Governance; Scalability; Traceability; Food Safety; Food Security AB Distributed Ledger Technology (DLT), such as blockchain, has the potential to transform supply chains. It can provide a cryptographically secure and immutable record of transactions and associated metadata (origin, contracts, process steps, environmental variations, microbial records, etc.) linked across whole supply chains. The ability to trace food items within and along a supply chain is legally required by all actors within the chain. It is critical to food safety, underpins trust and global food trade. However, current food traceability systems are not linked between all actors within the supply chain. Key metadata on the age and process history of a food is rarely transferred when a product is bought and sold through multiple steps within the chain. Herein, we examine the potential of massively scalable DLT to securely link the entire food supply chain, from producer to end user. Under such a paradigm, should a food safety or quality issue ever arise, authorized end users could instantly and accurately trace the origin and history of any particular food item. This novel and unparalleled technology could help underpin trust for the safety of all food, a critical component of global food security. In this paper, we investigate the (i) data requirements to develop DLT technology across whole supply chains, (ii) key challenges and barriers to optimizing the complete system, and (iii) potential impacts on production efficiency, legal compliance, access to global food markets and the safety of food. Our conclusion is that while DLT has the potential to transform food systems, this can only be fully realized through the global development and agreement on suitable data standards and governance. In addition, key technical issues need to be resolved including challenges with DLT scalability, privacy and data architectures. C1 [Pearson, Simon; May, David; Brewer, Steve] Univ Lincoln, Lincoln Inst Agrifood Technol, Lincoln LN6 7TS, England. [Leontidis, Georgios; Bidaut, Luc] Univ Lincoln, Sch Comp Sci, Isaac Newton Bldg, Lincoln LN6 7TS, England. [Swainson, Mark] Univ Lincoln, Natl Ctr Food Mfg, Holbeach PE12 7PT, England. [Frey, Jeremy G.] Univ Southampton, Sch Chem, Southampton SO17 1BJ, Hants, England. [Parr, Gerard] Univ East Anglia, Sch Comp Sci, Norwich Res Pk, Norwich NR4 7TJ, Norfolk, England. [Maull, Roger] Exeter Business Sch, Initiat Digital Econ Exeter INDEX, London, England. [Zisman, Andrea] Open Univ, Dept Comp & Commun, Walton Hall, Milton Keynes MK7 6AA, Bucks, England. C3 University of Lincoln; University of Lincoln; University of Lincoln; University of Southampton; University of East Anglia; Open University - UK RP Pearson, S (corresponding author), Univ Lincoln, Lincoln Inst Agrifood Technol, Lincoln LN6 7TS, England. EM spearson@lincoln.ac.uk; dmay@lincoln.ac.uk; gleontidis@lincoln.ac.uk; mswainson@lincoln.ac.uk; sbrewer@lincoln.ac.uk; lbidaut@lincoln.ac.uk; j.g.frey@soton.ac.uk; g.parr@uea.ac.uk; r.maull@exeter.ac.uk; andrea.zisman@open.ac.uk CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 CAC, 2006, PRINC TRAC PROD TRAC Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Elliott C., 2014, INDEPENDENT REPORT E Ercsey-Ravasz M, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0037810 Food Standards Agency, 2007, FOOD IND GUID GOOD H, P63 Forbes, 2018, FORBES JUL Foteinis S, 2018, NATURE, V554, P169, DOI 10.1038/d41586-018-01625-x Kewell B, 2017, STRATEG CHANG, V26, P429, DOI 10.1002/jsc.2143 Manning L, 2014, FOOD POLICY, V49, P23, DOI 10.1016/j.foodpol.2014.06.005 MAO DH, 2018, SUSTAINABILITY-BASEL, V10, DOI DOI 10.3390/SU10093149 Maull R, 2017, STRATEG CHANG, V26, P481, DOI 10.1002/jsc.2148 Moyer DC, 2017, FOOD CONTROL, V71, P358, DOI 10.1016/j.foodcont.2016.07.015 O'Mahony PJ, 2013, QJM-INT J MED, V106, P595, DOI 10.1093/qjmed/hct087 Olsen P, 2018, TRENDS FOOD SCI TECH, V77, P143, DOI 10.1016/j.tifs.2018.05.004 PwC, 2016, FOOD FRAUD VULN ASS Swanson T, 2015, R3CEV WHITE PAPER Tian F, 2016, IEEE 2016 13 INT C S, DOI [10.1109/ICSSSM.2016, DOI 10.1109/ICSSSM.2016] Tian F, 2017, I C SERV SYST SERV M Walport M., 2015, DISTRIBUTED LEDGER T WHO, 2015, ESTIMATES GLOBAL BUR Yli-Huumo J, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0163477 NR 23 TC 73 Z9 75 U1 11 U2 76 PD MAR PY 2019 VL 20 BP 145 EP 149 DI 10.1016/j.gfs.2019.02.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000461483300017 DA 2022-12-14 ER PT J AU Yue, JQ Li, ZM Zuo, ZT Liao, YJ Huang, HY Wang, YZ AF Yue, JiaQi Li, ZhiMin Zuo, ZhiTian Liao, YiJun Huang, HengYu Wang, YuanZhong TI Geographical traceability and multielement analysis of edible and medicinal fungi: Taking Wolfiporia cocos (FA Wolf) Ryvarden and Gilb. as an example SO JOURNAL OF FOOD SCIENCE DT Article DE chemometrics; data fusion; geographical traceability; multielement analysis; Wolfiporia cocos ID PORIA-COCOS; MUSHROOM; POLYSACCHARIDES; QUALITY; ORIGIN; AUTHENTICATION; IDENTIFICATION; MYCELIA; FOREST; FOOD AB Different geographical environment has a certain influence on the accumulation of fungi elements and chemical components. However, our knowledge is limited to elucidate the fungi elements in response to heterogeneous environmental and the quality differences among different habitats. Here, multielement analysis, FTIR spectrum, and feature-level fusion technique combined with chemometrics were used to study Wolfiporia cocos from different geographical areas, different sampling sites and different altitude sources. From the results, (1) there is significant difference in element content of samples from different sampling sites and no positive correlation with geographical ranges. (2) There is a correlation between elevation and elements, and relatively low elevation (<1,800 m) is conducive to the enrichment of elements. (3) From the perspective of elements, the W. cocos in Yuxi have relatively better quality. (4) FTIR and feature-level models can well realize origin identification. The SVM models are better than the PLS-DA models, and the feature-level model is better than the single FTIR models. In summary, this study demonstrated that the developed method was reliable and could realize the genuineness evaluation and origin identification of W. cocos. The results have implications for the establishment of the technology system of geographical traceability and the development of high-quality geographical indication products of W. cocos. C1 [Yue, JiaQi; Huang, HengYu] Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. [Yue, JiaQi; Li, ZhiMin; Zuo, ZhiTian; Wang, YuanZhong] Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. [Liao, YiJun] Chengdu Technol Univ, Sch Mat & Environm Engn, Chengdu 611730, Peoples R China. C3 Yunnan University of Chinese Medicine; Yunnan Academy of Agricultural Sciences; Chengdu Technological University RP Huang, HY (corresponding author), Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. EM hhyhhy96@163.com; boletus@126.com CR Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Brzezicha-Cirocka J, 2016, ENVIRON SCI POLLUT R, V23, P21517, DOI 10.1007/s11356-016-7371-0 Brzostowski A, 2011, J ENVIRON SCI HEAL A, V46, P581, DOI 10.1080/10934529.2011.562827 Chen JB, 2014, SPECTROCHIM ACTA A, V128, P629, DOI 10.1016/j.saa.2014.03.010 Cho IH, 2007, J PHARMACEUT BIOMED, V43, P900, DOI 10.1016/j.jpba.2006.09.002 Cory L., 2017, MASTERING MACHINE LE, DOI DOI 10.1016/j.foodchem.2013.11.062 Drewnowska M, 2015, ECOTOX ENVIRON SAFE, V113, P9, DOI 10.1016/j.ecoenv.2014.11.028 Falandysz J, 2020, CHEMOSPHERE, V247, DOI 10.1016/j.chemosphere.2020.125928 Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Gutierrez D.D., 2015, MACHINE LEARNING DAT Huang QL, 2007, CARBOHYD POLYM, V70, P324, DOI 10.1016/j.carbpol.2007.04.015 Jafarzadegan M, 2019, EXPERT SYST APPL, V137, P1, DOI 10.1016/j.eswa.2019.06.064 Jawaid S, 2014, ANAL METHODS-UK, V6, P5269, DOI 10.1039/c4ay00558a Kuldo E, 2014, CHEM PAP, V68, P484, DOI 10.2478/s11696-013-0477-7 Kwon YK, 2014, FOOD CHEM, V161, P168, DOI 10.1016/j.foodchem.2014.03.124 Lee KY, 2004, INT IMMUNOPHARMACOL, V4, P1029, DOI 10.1016/j.intimp.2004.03.014 Lee S, 2018, CELLS-BASEL, V7, DOI 10.3390/cells7090116 Li Y., 2016, PLOS ONE, V11 Li Y, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-017-19131-x Li Y, 2017, SPECTROCHIM ACTA A, V177, P20, DOI 10.1016/j.saa.2017.01.029 Li Z.H., 2003, STUDIES TRACE ELEMEN, V20, P29 Ling Y, 2012, BIOMED CHROMATOGR, V26, P1109, DOI 10.1002/bmc.1756 Liu YT, 2016, FOOD CHEM, V211, P83, DOI 10.1016/j.foodchem.2016.05.032 Lu MK, 2010, FOOD CHEM, V118, P349, DOI 10.1016/j.foodchem.2009.04.126 Mameli O, 2001, BRAIN RES BULL, V55, P269, DOI 10.1016/S0361-9230(01)00467-1 Maquelin K, 2002, J MICROBIOL METH, V51, P255, DOI 10.1016/S0167-7012(02)00127-6 Meenu M, 2019, FOOD CHEM, V289, P545, DOI 10.1016/j.foodchem.2019.03.091 Podlasinska J, 2015, POL J ENVIRON STUD, V24, P651 Rathore Himanshi, 2017, PharmaNutrition, V5, P35, DOI 10.1016/j.phanu.2017.02.001 Sun J, 2016, INT J MED MUSHROOMS, V18, P433, DOI 10.1615/IntJMedMushrooms.v18.i5.70 Wang JY, 2018, J FUNCT FOODS, V48, P134, DOI 10.1016/j.jff.2018.07.015 Wang WH, 2015, J PHARMACEUT BIOMED, V102, P203, DOI 10.1016/j.jpba.2014.09.014 Wang XM, 2014, FOOD CHEM, V151, P279, DOI 10.1016/j.foodchem.2013.11.062 Wang YT, 2020, FOOD CHEM, V303, DOI 10.1016/j.foodchem.2019.125404 Wang YZ, 2013, J ETHNOPHARMACOL, V147, P265, DOI 10.1016/j.jep.2013.03.027 [魏益民 Wei Yimin], 2012, [中国农业科学, Scientia Agricultura Sinica], V45, P5073 Wu K, 2018, INT J BIOL MACROMOL, V114, P137, DOI 10.1016/j.ijbiomac.2018.03.107 Yang FL, 2020, FOOD CHEM, V324, DOI 10.1016/j.foodchem.2020.126854 Yang YM, 2004, BIODIVERS CONSERV, V13, P813, DOI 10.1023/B:BIOC.0000011728.46362.3c Yoon JJ, 2013, AM J CHINESE MED, V41, P71, DOI 10.1142/S0192415X13500067 [於小波 Yu Xiaobo], 2011, [时珍国医国药, Lishizhen Medicine and Materia Medica Research], V22, P714 Yue JM, 2022, CURR PSYCHOL, V41, P5723, DOI 10.1007/s12144-020-01191-4 Zhang XW, 2012, ENVIRON MONIT ASSESS, V184, P2261, DOI 10.1007/s10661-011-2115-6 Zhao C, 2020, ENERG FUEL, V34, P5938, DOI 10.1021/acs.energyfuels.0c00293 Zhu FK, 2011, ENVIRON MONIT ASSESS, V179, P191, DOI 10.1007/s10661-010-1728-5 Zhu LX, 2019, J FOOD DRUG ANAL, V27, P766, DOI 10.1016/j.jfda.2019.03.002 NR 46 TC 3 Z9 3 U1 5 U2 9 PD MAR PY 2021 VL 86 IS 3 BP 770 EP 778 DI 10.1111/1750-3841.15649 EA FEB 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000617997400001 DA 2022-12-14 ER PT J AU Palocci, C Presser, K Kabza, A Pucci, E Zoani, C AF Palocci, Caterina Presser, Karl Kabza, Agnieszka Pucci, Emilia Zoani, Claudia TI A Search Engine Concept to Improve Food Traceability and Transparency: Preliminary Results SO FOODS DT Article DE smart data; search engine concept; search engine visualisation; interoperability; food supply chain; food safety; nutritional quality; traceability; authenticity; food transparency ID BLOCKCHAIN AB In recent years, the digital revolution has involved the agrifood sector. However, the use of the most recent technologies is still limited due to poor data management. The integration, organisation and optimised use of smart data provides the basis for intelligent systems, services, solutions and applications for food chain management. With the purpose of integrating data on food quality, safety, traceability, transparency and authenticity, an EOSC-compatible (European Open Science Cloud) traceability search engine concept for data standardisation, interoperability, knowledge extraction, and data reuse, was developed within the framework of the FNS-Cloud project (GA No. 863059). For the developed model, three specific food supply chains were examined (olive oil, milk, and fishery products) in order to collect, integrate, organise and make available data relating to each step of each chain. For every step of each chain, parameters of interest and parameters of influence-related to nutritional quality, food safety, transparency and authenticity-were identified together with their monitoring systems. The developed model can be very useful for all actors involved in the food supply chain, both to have a quick graphical visualisation of the entire supply chain and for searching, finding and re-using available food data and information. C1 [Palocci, Caterina] Univ Roma Tor Vergata, Dept Enterprise Engn, I-00133 Rome, Italy. [Presser, Karl; Kabza, Agnieszka] Premotec GmbH, CH-8400 Winterthur, Switzerland. [Pucci, Emilia; Zoani, Claudia] Italian Natl Agcy New Technol Energy & Sustainabl, Dept Sustainabil, Biotechnol & Agroind Div SSPT BIOAG, Casaccia Res Ctr, CH-8400 Winterthur, Switzerland. C3 University of Rome Tor Vergata RP Palocci, C (corresponding author), Univ Roma Tor Vergata, Dept Enterprise Engn, I-00133 Rome, Italy. EM caterina.palocci@uniroma2.it; karl.presser@premotec.ch; agnieszka.kabza@premotec.ch; emilia.pucci@enea.it; claudia.zoani@enea.it CR Al-Mekhlal M, 2019, IEEE INT C COMPUT, P314, DOI 10.1109/CSE/EUC.2019.00067 Buckley J, IS BIG DATA GOING CH Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 Cox M, 1997, VISUALIZATION '97 - PROCEEDINGS, P235, DOI 10.1109/VISUAL.1997.663888 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 European Commission, 2019, EUR OP SCI CLOUD NEW FAO, WHO FAOS STRAT FOOD Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Katal A, 2013, INT CONF CONTEMP, P404, DOI 10.1109/IC3.2013.6612229 Lacam Jean-Sebastien, 2021, Journal of High Technology Management Research, V32, DOI 10.1016/j.hitech.2021.100406 Leible S, 2019, FRONT BLOCKCHAIN, V2, DOI 10.3389/fbloc.2019.00016 Liu Y, 2021, IEEE T IND INFORM, V17, P4322, DOI 10.1109/TII.2020.3003910 Meulenberg MTG, 2005, INNOVATION IN AGRI-FOOD SYSTEMS: PRODUCT QUALITY AND CONSUMER ACCEPTANCE, P17 Moates G., 2016, FUSIONS ESTIMATES EU Nakamoto S, 2008, BITCOIN P2P E CASH P Palocci C., 2020, 5 INT C METR FOOD NU, P118 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Pollard S., 2018, ENCY FOOD CHEM, DOI [10.1016/B978-0-08-100596-5.21839-8, DOI 10.1016/B978-0-08-100596-5.21839-8] Rao SK, 2018, WIRELESS PERS COMMUN, V100, P145, DOI 10.1007/s11277-018-5615-7 Rychlik M, 2018, FRONT CHEM, V6, DOI 10.3389/fchem.2018.00049 Serazetdinova L, 2019, J SCI FOOD AGR, V99, P3213, DOI 10.1002/jsfa.9545 Tao DD, 2020, COMPR REV FOOD SCI F, V19, P875, DOI 10.1111/1541-4337.12540 Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18 Wolfert S, 2017, AGR SYST, V153, P69, DOI 10.1016/j.agsy.2017.01.023 World Health Organization, 2015, WHO ESTIMATES GLOBAL, DOI DOI 10.1007/S13213-015-1147-5 NR 27 TC 2 Z9 2 U1 11 U2 15 PD APR PY 2022 VL 11 IS 7 AR 989 DI 10.3390/foods11070989 WC Food Science & Technology SC Food Science & Technology UT WOS:000783152700001 DA 2022-12-14 ER PT J AU Morrison, M Moraia, LB Steele, JC AF Morrison, Michael Moraia, Linda Briceno Steele, Jane C. TI Traceability in stem cell research: from participant sample to induced pluripotent stem cell and back SO REGENERATIVE MEDICINE DT Article DE biobanking; induced pluripotent stem cells; innovative medicines initiative; legal requirements; privacy; research collaboration; tissues and cells directive; traceability ID PRIVACY AB This paper describes a traceability system developed for the Stem cells for Biological Assays of Novel drugs and prediCtive toxiCology consortium. The system combines records and labels that to biological material across geographical locations and scientific processes from sample donation to induced pluripotent stem cell line. The labeling system uses a unique identification number to link every aliquot of sample at every stage of the reprogramming pathway back to the original donor. Only staff at the clinical recruitment site can reconnect the unique identification number to the identifying details of a specific donor. This ensures the system meets ethical and legal requirements for protecting privacy while allowing full traceability of biological material. The system can be adapted to other projects and for use with different primary sample types. C1 [Morrison, Michael; Moraia, Linda Briceno] Univ Oxford, HeLEX Ctr Hlth Law & Emerging Technol, Nuffield Dept Populat Hlth, Oxford OX2 7DD, England. [Steele, Jane C.] Univ Birmingham, Human Biol Resource Ctr, Birmingham B15 2TT, W Midlands, England. C3 University of Oxford; University of Birmingham RP Steele, JC (corresponding author), Univ Birmingham, Human Biol Resource Ctr, Birmingham B15 2TT, W Midlands, England. EM j.c.steele@bham.ac.uk CR Altshuler JS, 2010, SCI TRANSL MED, V2, DOI 10.1126/scitranslmed.3001515 Brindley DA, 2013, STEM CELLS DEV, V22, P63, DOI 10.1089/scd.2013.0403 Isasi R, 2014, CELL STEM CELL, V14, P427, DOI 10.1016/j.stem.2014.03.014 Isasi R, 2011, REGEN MED, V6, P783, DOI [10.2217/RME.11.93, 10.2217/rme.11.93] Knoppers BM, 2010, GENOME MED, V2, DOI 10.1186/gm194 Kurtz A, 2014, STEM CELLS DEV, V23, P51, DOI 10.1089/scd.2014.0319 Lim MD, 2014, SCI TRANSL MED, V6, DOI 10.1126/scitranslmed.3009024 Luong MX, 2011, CELL STEM CELL, V8, P357, DOI 10.1016/j.stem.2011.03.002 Noel L, 2010, TRANSPLANTATION, V90, P229, DOI 10.1097/TP.0b013e3181ec29f0 Ogbogu U, 2014, BMC MED ETHICS, V15, DOI 10.1186/1472-6939-15-7 Tallacchini M, 2015, INT LIBR ETH LAW TEC, V14, P21, DOI 10.1007/978-94-017-9573-9_3 NR 11 TC 2 Z9 2 U1 1 U2 16 PY 2016 VL 11 IS 1 BP 73 EP 79 DI 10.2217/rme.15.66 WC Cell & Tissue Engineering; Engineering, Biomedical SC Cell Biology; Engineering UT WOS:000367160500008 DA 2022-12-14 ER PT J AU Li, C Li, MZ Li, DX Wei, SB Cui, ZH Xiang, LL Huang, XZ AF Li Chao Li Meng-zhi Li Dan-xia Wei Shi-bing Cui Zhan-hu Xiang Li-ling Huang Xian-zhang TI Study on Geographical Traceability of Artemisia argyi by Employing the Fourier Transform Infrared Spectral Fingerprinting SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE Artemisia argyi; Fourier Transform Infrared Spectrum; Pattern recognition; Origin traceability AB The geographical distribution of medicinal plants significantly affect the quality and safety of Chinese medicinal materials. From the biological point of view, Chinese medicinal materials are formed during the long-term ecological adaptation of species affected by a specific ecological environment. The climate, soil, hydrology, and other ecological factors required for the growth of medicinal materials are closely related to their growth and quality and have fingerprint characteristics of geographical information. In recent years, the rapid development of the Chinese medicine industry has brought about a surge in demand for Chinese medicine resources. However, at the same time, there are also many potential safety hazards. The difficulty in distinguishing and tracing the origin of Chinese medicinal materials has become one of the main bottlenecks restricting the development of traditional Chinese medicine. In this study, 75 A. argyi samples from 5 major producing areas of 4 provinces in China were analyzed by FTIR for characteristic analysis and data mining. Spectral signal preprocessing methods include Gaussian filtering, multivariate scattering correction, standard normal transformation, first/second derivative, etc. and pattern recognition techniques include BP neural network model, random forest, K-nearest neighbor, Bayesian algorithm, particle swarm optimization support vector machine, etc. were applied to explore the feasibility of traceability for A. argyi. The results indicate that the algorithms of K-nearest neighbor, Bayesian, and particle swarm optimization support vector machine show the ideal recognition effect, with an accuracy of 100%. Considering the comprehensive factors of running time, identification accuracy, and model stability, the algorithm of K-nearest neighbor is determined as the best method to trace the origin of A. argyi. In general, FTIR technology combined with appropriate chemometrics methods can be used to trace the origin of A. argyi successfully. The results of this study can provide technical support for the evaluation and quality control of A. argyi, and also contribute useful reference for the isotropic research of other medicinal materials. C1 [Li Chao; Li Meng-zhi; Li Dan-xia; Wei Shi-bing; Xiang Li-ling; Huang Xian-zhang] Nanyang Inst Technol, Henan Key Lab Zhang Zhongjing Formulae & Herbs Im, Nanyang 473000, Peoples R China. [Cui Zhan-hu] Fujian Agr & Forestry Univ, Coll Agr, Fuzhou 350002, Peoples R China. C3 Nanyang Institute of Technology; Fujian Agriculture & Forestry University RP Huang, XZ (corresponding author), Nanyang Inst Technol, Henan Key Lab Zhang Zhongjing Formulae & Herbs Im, Nanyang 473000, Peoples R China. EM lichaotcm@126.com; nylgxyhxz@126.com CR CAO Ling, 2018, DRUG EVALUATION RES, V41, P216 Chinese Pharmacopoeia Commission, 2020, PHARMACOPOEIA PEOPLE, P91 HU Ji-qing, 2019, CHINA J TRADITIONAL, V34, P123 Huang Xian-Zhang, 2017, Zhongguo Zhong Yao Za Zhi, V42, P3504, DOI 10.19540/j.cnki.cjcmm.20170814.009 Li C, 2021, SPECTROSC SPECT ANAL, V41, P1343, DOI 10.3964/j.issn.1000-0593(2021)05-1343-07 LI Chao, 2021, JIANGSU AGR SCI, V49, P186 Song XW, 2019, J FUNCT FOODS, V52, P648, DOI 10.1016/j.jff.2018.11.029 [田胜尼 Tian Shengni], 2021, [生物学杂志, Journal of Biology], V38, P65 Xiao JQ, 2019, BIOORG CHEM, V92, DOI 10.1016/j.bioorg.2019.103268 ZHENG Si-hao, 2021, MODERN CHINESE MED, V23, P2037 NR 10 TC 0 Z9 0 U1 3 U2 3 PD AUG PY 2022 VL 42 IS 8 BP 2532 EP 2537 DI 10.3964/j.issn.1000-0593(2022)08-2532-06 WC Spectroscopy SC Spectroscopy UT WOS:000852883200033 DA 2022-12-14 ER PT J AU Westerlund, M Nene, S Leminen, S Rajahonka, M AF Westerlund, Mika Nene, Soham Leminen, Seppo Rajahonka, Mervi TI An Exploration of Blockchain-based Traceability in Food Supply Chains: On the Benefits of Distributed Digital Records from Farm to Fork SO TECHNOLOGY INNOVATION MANAGEMENT REVIEW DT Article DE Food safety; Food supply chain; Blockchain; Distributed ledger; Food innovation; Traceability; Supply chain management ID TECHNOLOGY; MANAGEMENT; AGRICULTURE; LOGISTICS; ISSUES AB There are growing internal and external pressures for traceability in food supply chains due to food scandals. Traceability refers to tracking food from the consumer back to the farm and vice versa for quality control and management. However, many traceability solutions have failed to meet the needs of supply chain stakeholders. Blockchain is a novel distributed database technology that could solve some issues of traditional traceability systems, such as cost of adoption and vulnerabilities to hacking and data tampering. This study aims to gain insights on the benefits of applying blockchain technology for traceability in food supply chains through literature review and an investigation of five companies that are experimenting with blockchain-based food traceability. Our findings suggest that, upon implementation and contribution by all supply chain participants, blockchain-based traceability can provide cost-savings, reduced response time to food scandals and food-borne illness outbreaks, improved security and accuracy, better compliance with government regulations, and thus increase consumer trust. C1 [Westerlund, Mika; Leminen, Seppo; Rajahonka, Mervi] Carleton Univ, Ottawa, ON, Canada. [Westerlund, Mika] Univ Calif Berkeley, Haas Sch Business, Berkeley, CA 94720 USA. [Westerlund, Mika] Aalto Univ, Sch Econ, Helsinki, Finland. [Leminen, Seppo] Univ South Eastern Norway, USN Sch Business, Innovat & Entrepreneurship, Notodden, Norway. [Leminen, Seppo] Aalto Univ, Business Dev, Helsinki, Finland. [Rajahonka, Mervi] South Eastern Finland Univ Appl Sci XAMK, Small Business Ctr SBC, Kouvola, Finland. [Rajahonka, Mervi] SBC, Kouvola, Finland. C3 Carleton University; University of California System; University of California Berkeley; Aalto University; University College of Southeast Norway; Aalto University; South-Eastern Finland University of Applied Sciences RP Westerlund, M (corresponding author), Carleton Univ, Ottawa, ON, Canada. CR Apte S., 2016, J EXCIPIENTS FOOD CH, V7, P76 Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Beck R, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P5390 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bhatt T, 2013, J FOOD SCI, V78, pB21, DOI 10.1111/1750-3841.12278 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 Chang SCE, 2020, IEEE ACCESS, V8, P62478, DOI 10.1109/ACCESS.2020.2983601 Chen HL, 2021, IEEE ACCESS, V9, P36008, DOI 10.1109/ACCESS.2021.3062410 Chen RY, 2015, FOOD CONTROL, V51, P70, DOI 10.1016/j.foodcont.2014.11.004 Corina E, 2013, EKON POLJOPR, V60, P287 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Etemadi N, 2021, INFORMATION, V12, DOI 10.3390/info12020070 Evans, 2020, OPERATIONS SUPPLY CH, V14, P111 Garaus M, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108082 Grecuccio J, 2020, ENERGIES, V13, DOI 10.3390/en13153820 Gurtu A, 2019, INT J PHYS DISTR LOG, V49, P881, DOI 10.1108/IJPDLM-11-2018-0371 He Y, 2018, J FOOD QUALITY, DOI 10.1155/2018/7279491 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kayikci Y, 2022, PROD PLAN CONTROL, V33, P301, DOI 10.1080/09537287.2020.1810757 Kim Henry M., 2018, SUPPLY CHAIN REVOLUT Kramer MP, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13042168 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Kumar V, 2017, SYSTEMS, V5, DOI 10.3390/systems5020033 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Lemieux VL, 2016, REC MANAG J, V26, P110, DOI 10.1108/RMJ-12-2015-0042 Lim MK, 2021, COMPUT IND ENG, V154, DOI 10.1016/j.cie.2021.107133 Lin X, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18030912 Mansfield-Devine Steve, 2017, Computer Fraud & Security, V2017, P14, DOI 10.1016/S1361-3723(17)30042-8 Min H, 2019, BUS HORIZONS, V62, P35, DOI 10.1016/j.bushor.2018.08.012 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mussigmann B, 2020, IEEE T ENG MANAGE, V67, P988, DOI 10.1109/TEM.2020.2980733 MUKRI B, 2018, INT RES J ENG TECHNO, V5, P2497 Vu N, 2021, PROD PLAN CONTROL, DOI 10.1080/09537287.2021.1939902 Ongena G., 2020, FRONTIERS, V3 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Paliwal V, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12187638 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Pournader M, 2020, INT J PROD RES, V58, P2063, DOI 10.1080/00207543.2019.1650976 Sarpong S, 2014, EUR BUS REV, V26, P271, DOI 10.1108/EBR-09-2013-0113 Shahbazi Z, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10010041 Tandon A, 2021, TECHNOL FORECAST SOC, V166, DOI 10.1016/j.techfore.2021.120649 Tapscott D, 2017, MIT SLOAN MANAGE REV, V58, P10 Tayal A, 2021, INT J COMMUN SYST, V34, DOI 10.1002/dac.4696 Teodorescu M., 2021, J INNOV TECH MAR COM, V7, P80, DOI [10.3390/joitmc7010080, DOI 10.3390/JOITMC7010080] Treiblmaier H, 2018, SUPPLY CHAIN MANAG, V23, P545, DOI 10.1108/SCM-01-2018-0029 Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 Xiong H, 2020, FRONT BLOCKCHAIN, V3, DOI 10.3389/fbloc.2020.00007 Ying WC, 2018, INT J INFORM MANAGE, V39, P1, DOI 10.1016/j.ijinfomgt.2017.10.004 Yoo M, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10114037 Zhao JL, 2016, FINANC INNOV, V2, DOI [10.1186/s40854-016-0049-2, 10.1186/s40854-017-0059-8] NR 56 TC 4 Z9 4 U1 12 U2 47 PD JUN PY 2021 VL 11 IS 6 BP 6 EP 18 DI 10.22215/timreview/1446 WC Management SC Business & Economics UT WOS:000669666300002 DA 2022-12-14 ER PT J AU Lin, XL AF Lin, Xiuli TI Analysis of Agricultural Product Information Traceability and Customer Preference Based on Blockchain SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING DT Article ID INTERNET AB For traditional agricultural product traceability system, there are some problems, such as information asymmetry and serious centralized storage. To solve the problems, a set of an agricultural product traceability information analysis system based on blockchain is designed. The overall framework of the agricultural product information traceability system is built, and the traceability model and process are designed in detail from the data layer, consensus layer, and contract layer. Collaborative verification function modules are added in the data layer to ensure the authenticity of data on the blockchain. The integral penalty mechanism is introduced to improve the practical Byzantine fault tolerance algorithm (PBFT), which ensures the security of the block network and the validity of data. Smart contracts are adopted to ensure that changes in agricultural information are actually recorded on blockchain. The results show that the designed blockchain-based agricultural product information traceability system solves the problem of difficult sharing and traceability of agricultural product information, which realizes the traceability of agricultural product production information, processing information, transportation information, and transaction query information, and has certain theoretical and practical value for the information traceability of agricultural products and other products. C1 [Lin, Xiuli] Guangzhou Huaxia Vocat Coll, Sch Informat Engn, Guangzhou 510935, Peoples R China. RP Lin, XL (corresponding author), Guangzhou Huaxia Vocat Coll, Sch Informat Engn, Guangzhou 510935, Peoples R China. EM lxl_lpw@outlook.com CR Ahmad A., 2021, IET BLOCKCHAIN, V1, P56, DOI [10.1049/blc2.12007, DOI 10.1049/BLC2.12007] Bao F., 2014, J FOOD SCI TECHNOLOG, V6, P1008 Chen XM, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph182211761 Chiniforoush TA, 2021, APPL RADIAT ISOTOPES, V170, DOI 10.1016/j.apradiso.2021.109596 Chiu WY, 2021, PEER PEER NETW APPL, V14, P2874, DOI 10.1007/s12083-021-01119-0 Donghan L., 2021, SUSTAIN COMPUT-INFOR, V31, P100587 Emna M., 2021, J HIGH TECHNOLOGY MA, V32, P100416 Firdaus M, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21072410 Gimenez-Aguilar M, 2021, FUTURE GENER COMP SY, V124, P91, DOI 10.1016/j.future.2021.05.007 Goyat R, 2021, FUTURE GENER COMP SY, V125, P221, DOI 10.1016/j.future.2021.06.039 Kim Y, 2020, HUM-CENT COMPUT INFO, V10, DOI 10.1186/s13673-020-00238-6 Li WenYong, 2012, Transactions of the Chinese Society of Agricultural Engineering, V28, P253 Li XR, 2021, PERVASIVE MOB COMPUT, V75, DOI 10.1016/j.pmcj.2021.101434 Li YX, 2021, PEER PEER NETW APPL, V14, P2826, DOI 10.1007/s12083-021-01103-8 Ok Park Eun, 2015, [The Journal of Internet Electronic Commerce Research, 인터넷전자상거래연구], V15, P365 Ouyang LW, 2021, INFORM SCIENCES, V570, P124, DOI 10.1016/j.ins.2021.04.021 Soman R, 2021, INT J DIGIT CRIME FO, V13, P65, DOI 10.4018/IJDCF.20210901.oa4 Wu X. F., 2013, APPL MECH MAT, V380-384, P2344 Xiandong Zheng, 2021, Journal of Physics: Conference Series, V1802, DOI 10.1088/1742-6596/1802/3/032022 Xin Qian, 2014, Applied Mechanics and Materials, V644-650, P3160, DOI 10.4028/www.scientific.net/AMM.644-650.3160 Yang C., 2020, OPEN ACCESS LIB J, V07, P1 Yang ZL, 2021, J CLEAN PROD, V290, DOI 10.1016/j.jclepro.2020.125191 Zhang C, 2021, COMPUT STAND INTER, V77, DOI 10.1016/j.csi.2021.103520 Zhao G, 2015, INT J ELECTROCHEM SC, V10, P3387 Zhao SL, 2020, PEERJ, V8, DOI 10.7717/peerj.8928 NR 25 TC 0 Z9 0 U1 7 U2 7 PD JUN 23 PY 2022 VL 2022 AR 1935233 DI 10.1155/2022/1935233 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000838053900002 DA 2022-12-14 ER PT J AU Zhou, J Cheng, H Zeng, JM Wang, LY Wei, K He, W Wang, WF Liu, X AF Zhou Jian Cheng Hao Zeng Jian-ming Wang Li-yuan Wei Kang He Wei Wang Wei-feng Liu Xu TI Study on Identification and Traceability of Tea Material Cultivar by Combined Analysis of Multi-Partial Least Squares Models Based on Near Infrared Spectroscopy SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE Tea; material cultivar; Traceability; Near infrared; Partial least squares; Euclidean Distance AB The present study attempted to achieve the identification and traceability of tea material cultivar through combination of multi-partial least squares models and Euclidean distance, etc. The results indicate that with the samples manufactured with tea fresh leaves of cultivar Longjing 43, Qunti, Yingshuang and Wuniuzao as the analysis objects, 4 models were established in this study so as to identify the tea with material cultivar being tea fresh leaves of cultivar Longjing 43, Qunti, Yingshuang and Wuniuzao separately by PLS. Their accuracy rate of identification of samples in the calibration set were 89.8%, 90.9%, 96.1% and 99.5%, respectively, while those in test set were 87.1%, 84.2%, 96.1% and 97.5%, respectively. After the "first identification" through the combined analysis of the four models for identification of tea material cultivar and the "second identification" adopting the Euclidean distance, the accuracy rate of material cultivar recognition for the tea samples was 90.3% (calibration set) and 83.5% (test set), respectively. This study provided a reference method for the identification of tea manufactured with a specific material cultivar and the material cultivar traceability of the manufactured tea. C1 [Zhou Jian; Cheng Hao; Zeng Jian-ming; Wang Li-yuan] Chinese Acad Agr Sci, Tea Res Inst, Natl Ctr Tea Improvement, Hangzhou 310008, Zhejiang, Peoples R China. [Wei Kang; He Wei; Wang Wei-feng; Liu Xu] Nanjing Agr Univ, Dept Tea Sci, Nanjing 210095, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Tea Research Institute, CAAS; Nanjing Agricultural University RP Cheng, H (corresponding author), Chinese Acad Agr Sci, Tea Res Inst, Natl Ctr Tea Improvement, Hangzhou 310008, Zhejiang, Peoples R China. EM zjph263@126.com; chenghao@mail.tricaas.com CR [Anonymous], 2001, NEAR INFRARED TECHNO Chen Quansheng, 2006, Food Science, China, V27, P186 Cheng Hao, 2008, Scientia Agricultura Sinica, V41, P2413 Cozzolino D, 2005, J AGR FOOD CHEM, V53, P4459, DOI 10.1021/jf050303i [杜国明 Du Guoming], 2008, [广东农业科学, Guangdong Agricultural Sciences], P101 Koolaard JP, 1996, COMMUN STAT THEORY, V25, P2989, DOI 10.1080/03610929608831882 [李广领 LI Guang-ling], 2009, [湖南农业科学, Hunan Agricultural Sciences], P120 Lutz U, 2006, ANAL CHEM, V78, P4564, DOI 10.1021/ac0522299 Ortiz C, 2004, ANAL BIOCHEM, V332, P245, DOI 10.1016/j.ab.2004.06.013 [张晓慧 ZHANG Xiaohui], 2008, [激光与红外, Laser and Infrared], V38, P342 [周健 Zhou Jian], 2009, [光学学报, Acta Optica Sinica], V29, P1117 Zhou J, 2009, SPECTROSC SPECT ANAL, V29, P1251, DOI 10.3964/j.issn.1000-0593(2009)05-1251-04 NR 12 TC 2 Z9 16 U1 1 U2 10 PD OCT PY 2010 VL 30 IS 10 BP 2650 EP 2653 DI 10.3964/j.issn.1000-0593(2010)10-2650-04 WC Spectroscopy SC Spectroscopy UT WOS:000283267900014 DA 2022-12-14 ER PT J AU Opara, LU Mazaud, F AF Opara, LU Mazaud, F TI Food traceability from field to plate SO OUTLOOK ON AGRICULTURE DT Article ID SAFETY; QUALITY; HACCP AB Several factors have led to intensified public scrutiny of the human food supply chain. Consumer concerns for food safety, animal welfare, and the environmental and ecological impact of food production and agro-processing have become increasingly important. These concerns have been exacerbated by several factors, including the trend towards further globalization of the food supply chain, the incidence of new and emerging safety hazards such as the human form of BSE (mad cow disease), and illnesses and deaths resulting from contamination of fresh and processed food. As a consequence of these growing concerns, consumers and other stakeholders in agroindustry now demand transparency in the way food is grown and handled throughout the supply chain, resulting in the emergence of 'traceability' as an important policy issue in food quality and safety. This paper provides a global overview of 'traceability' as a quality index in food trade, and discusses some of the drivers in both developed and developing countries. Policy changes are necessary specifically to incorporate traceability into existing food safety regulations and trade agreements. This will require further investments in information technology for data capture, storage and retrieval. Small-scale farmers in many developing regions moving towards market orientation face considerable technical and financial challenges in implementing appropriate food traceability systems in order to meet marketing compliance requirements. C1 Massey Univ, Ctr Postharvest & Refrigerat Res, Inst Technol & Engn, Palmerston North, New Zealand. UN, Food & Agr Org, Agro Ind & Post Harvest Management Serv, I-0100 Rome, Italy. C3 Massey University; Food & Agriculture Organization of the United Nations (FAO) RP Opara, LU (corresponding author), Massey Univ, Ctr Postharvest & Refrigerat Res, Inst Technol & Engn, Private Bag 11-222, Palmerston North, New Zealand. EM L.U.Opara@massey.ac.nz; Francois.Mazaud@fao.org CR Antle JM, 1999, FOOD POLICY, V24, P605, DOI 10.1016/S0306-9192(99)00068-8 Botta J. R, 1995, EVALUATION SEAFOOD F Bremner HA, 2000, CRIT REV FOOD SCI, V40, P83, DOI 10.1080/10408690091189284 Buzby JC, 1999, FOOD POLICY, V24, P637, DOI 10.1016/S0306-9192(99)00070-6 CRUTCHFIELD SR, 1997, 755 EC RES SERV DOLAN C, 2000, INSIGHTS GARVIN DA, 1984, BUS HORIZONS, V27, P40, DOI 10.1016/0007-6813(84)90024-7 Gregory NG, 2000, OUTLOOK AGR, V29, P251, DOI 10.5367/000000000101293310 Kidd M., 2000, Nutrition & Food Science, P53, DOI 10.1108/00346650010314250 KRUITHOF J, 1994, QUALITY STANDARDS HD LANGAN J, 2000, FARM FOOD AUT, P34 MCKECHNIE S, 1997, ADV BIOTECHNOLOGY PR, P69 MULE HM, 2000, IFAD PUBL LECT 24 OC *OECD, 1999, FOOD SAF QUAL TRAD C OPARA LU, 2000, ACIAR PUBL, V100, P244 Pimentel D., 1993, PESTICIDE QUESTION E Ropkins K, 2000, TRENDS FOOD SCI TECH, V11, P10, DOI 10.1016/S0924-2244(00)00036-4 Sloan A.E., 1996, FOODTECHNOL, V50, P55 Sloan AE, 1998, FOOD TECHNOL-CHICAGO, V52, P32 Unnevehr LJ, 1999, FOOD POLICY, V24, P625, DOI 10.1016/S0306-9192(99)00074-3 WOOD E, 1999, ACIAR POSTH TECHN WO 2001, FOOD TECHNOLOGY NZ, V35, P23 2000, METRO LONDON 0719, P19 NR 23 TC 95 Z9 99 U1 6 U2 62 PD DEC PY 2001 VL 30 IS 4 BP 239 EP 247 DI 10.5367/000000001101293724 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000172762400002 DA 2022-12-14 ER PT J AU de Abajo, FJ Albanell, J Sanchez, OD Klein, K Moreno-Muelas, JV Ruiz, S Ferrando, MJS Thorpe, R Zaragoza, F AF de Abajo, Francisco Jose Albanell, Joan Sanchez, Olga Delgado Klein, Kevin Moreno-Muelas, Jose Vicente Ruiz, Sol Ferrando, Maria Jesus Sanz Thorpe, Robin Zaragoza, Francisco TI Roundtable on biosimilars: pharmacovigilance, traceability, immunogenicity, 15 November 2016, Madrid, Spain SO GABI JOURNAL-GENERICS AND BIOSIMILARS INITIATIVE JOURNAL DT Article DE Biologicals; immunogenicity; interchangeability; pharmacovigilance; Spain; traceability AB Introduction: Biosimilars can off er a lower-cost alternative to current biological therapies and could help contribute to the much-needed savings for the healthcare systems. All biosimilars approved by the regulators must show comparable efficacy and safety with the reference biologic. However, the acceptance by the healthcare professionals and by patients is not uniform within Europe. Therefore, it is important to understand the barriers to biosimilar uptake. Increased awareness amongst the stakeholders regulators, prescribing physicians, medical societies, pharmacists and patients - to the barriers and plausible solutions could help improve further uptake of biosimilars. Methods: GaBI organized a workshop where regulators, academics, prescribing physicians and pharmacists met to discuss practical challenges in the uptake of biosimilars. Specifically, key elements unique to biosimilars such as extrapolation of indications and the implementation of pharmacovigilance systems were discussed with experts from Spain, The Netherlands and the UK. Results: Some of the key concerns of the physicians include the structure and post-translational differences between the biosimilars and the reference product. It appears that they are not entirely convinced by the regulatory decision of approval alone or from inputs from the hospital pharmacists that these drugs are comparably safe and effective. From the discussions the pharmacists, on the other hand, appear to accept the regulatory approval by the European Medicines Agency (EMA) as adequate for their use in all settings. Conclusion: Several concerns raised by the physicians in Spain on the role of appropriate evidence prior the biosimilar use in all settings must be heard and addressed to improve further uptake of biosimilars in the market. The role of education was also emphasized along with the need for more interactions between the regulators, physicians and the medical societies (including patients). Broad agreement on the importance of pharmacovigilance was reached including the importance of prescription by brand name and tracking by both the brand name and batch number. CR Hernandez MAA, 2015, REUMATOL CLIN, V11, P269, DOI 10.1016/j.reuma.2015.03.009 Annese V, 2016, GABI J, V5, P74, DOI 10.5639/gabij.2016.0502.019 European Medicines Agency, 2016, EMA1684022014 GaBI Online - Generics and Biosimilars Initiative, BIOS APPR EUR NR 4 TC 0 Z9 0 U1 0 U2 1 PY 2017 VL 6 IS 1 BP 31 EP 37 DI 10.5639/gabij.2017.0601.007 WC Pharmacology & Pharmacy SC Pharmacology & Pharmacy UT WOS:000404101600008 DA 2022-12-14 ER PT J AU Jerome, M Martinsohn, JT Ortega, D Carreau, P Verrez-Bagnis, V Mouchel, O AF Jerome, Marc Martinsohn, Jann Thorsten Ortega, Delphine Carreau, Philippe Verrez-Bagnis, Veronique Mouchel, Olivier TI Toward fish and seafood traceability: Anchovy species determination in fish products by molecular markers and support through a public domain database SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE species identification; food product; anchovy; Clupeomorpha; Engraulis; cytochrome b gene; cytochrome oxidase subunit I gene; 16S rRNA gene; direct sequencing; FINS; public domain database ID POLYMERASE-CHAIN-REACTION; MITOCHONDRIAL CYTOCHROME-B; DIRECT SEQUENCING METHOD; ENGRAULIS-ENCRASICOLUS; CONTROL REGION; DNA SEQUENCE; CANNED TUNA; PCR-RFLP; IDENTIFICATION; ATLANTIC AB Traceability in the fish food sector plays an increasingly important role for consumer protection and confidence building. This is reflected by the introduction of legislation and rules covering traceability on national and international levels. Although traceability through labeling is well established and supported by respective regulations, monitoring and enforcement of these rules are still hampered by the lack of efficient diagnostic tools. We describe protocols using a direct sequencing method based on 212-274-bp diagnostic sequences derived from species-specific mitochondria DNA cytochrome b, 16S rRNA, and cytochrome oxidase subunit I sequences which can efficiently be applied to unambiguously determine even closely related fish species in processed food products labeled "anchovy". Traceability of anchovy-labeled products is supported by the public online database AnchovyID (http://anchovyid.jrc.ec.europa.eu), which provided data obtained during our study and tools for analytical purposes. C1 [Jerome, Marc; Verrez-Bagnis, Veronique; Mouchel, Olivier] IFREMER, Dept Sci & Tech Alimentaires Marines, F-44037 Nantes 03, France. [Martinsohn, Jann Thorsten; Ortega, Delphine; Carreau, Philippe] IPSC, JRC, I-21027 Ispra, Va, Italy. C3 Ifremer; European Commission Joint Research Centre; EC JRC ISPRA Site RP Jerome, M (corresponding author), IFREMER, Dept Sci & Tech Alimentaires Marines, Rue Ile Yeu,BP 21105, F-44037 Nantes 03, France. EM Marc.Jerome@ifremer.fr CR Akasaki T, 2006, FISHERIES SCI, V72, P686, DOI 10.1111/j.1444-2906.2006.01200.x BARTLETT SE, 1991, CAN J FISH AQUAT SCI, V48, P309, DOI 10.1139/f91-043 BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 Carrera E., 1999, Journal of Food Science, V64, P410, DOI 10.1111/j.1365-2621.1999.tb15053.x Chapela MJ, 2002, J FOOD SCI, V67, P1672, DOI 10.1111/j.1365-2621.2002.tb08703.x Davidson W. S., 1998, ANAL METHODS FOOD AU, P182 *EUR COMM, 2006, EC ASS EUR FISH EC P *FAO FISH AQ DEP, 2007, STAT WORLD FISH AQ Folmer O., 1994, Molecular Marine Biology and Biotechnology, V3, P294 Grant WS, 2005, J FISH BIOL, V67, P1242, DOI 10.1111/j.1095-8649.2005.00820.x Grant WS, 2005, GENETICA, V125, P293, DOI 10.1007/s10709-005-0717-z HALL T. A., 1999, NUCL ACIDS S SER, V41, P9598 Infante C, 2006, FOOD RES INT, V39, P1023, DOI 10.1016/j.foodres.2006.02.006 Jerome M, 2003, J AGR FOOD CHEM, V51, P43, DOI 10.1021/jf020713w Jerome M, 2003, J AGR FOOD CHEM, V51, P7326, DOI 10.1021/jf034652t Klanjscek J, 2007, ECOL MODEL, V201, P312, DOI 10.1016/j.ecolmodel.2006.09.020 Kumar S, 2004, BRIEF BIOINFORM, V5, P150, DOI 10.1093/bib/5.2.150 Magoulas A, 2006, MOL PHYLOGENET EVOL, V39, P734, DOI 10.1016/j.ympev.2006.01.016 Miya M, 2000, MOL PHYLOGENET EVOL, V17, P437, DOI 10.1006/mpev.2000.0839 Pepe T, 2007, J AGR FOOD CHEM, V55, P3681, DOI 10.1021/jf063321o Quinteiro J, 1998, J AGR FOOD CHEM, V46, P1662, DOI 10.1021/jf970552+ Quinteiro J, 2001, J AGR FOOD CHEM, V49, P5108, DOI 10.1021/jf010421f Ram JL, 1996, J AGR FOOD CHEM, V44, P2460, DOI 10.1021/jf950822t SAITOU N, 1987, MOL BIOL EVOL, V4, P406, DOI 10.1093/oxfordjournals.molbev.a040454 Sebastio P, 2001, J AGR FOOD CHEM, V49, P1194, DOI 10.1021/jf000875x Sevilla RG, 2007, MOL ECOL NOTES, V7, P730, DOI 10.1111/j.1471-8286.2007.01863.x Takashima Y, 2006, FISHERIES SCI, V72, P1054, DOI 10.1111/j.1444-2906.2006.01256.x TAMURA K, 1993, MOL BIOL EVOL, V10, P512, DOI 10.1093/oxfordjournals.molbev.a040023 Terol J, 2002, J AGR FOOD CHEM, V50, P963, DOI 10.1021/jf011032o Trotta M, 2005, J AGR FOOD CHEM, V53, P2039, DOI 10.1021/jf048542d Yu ZN, 2005, FISHERIES SCI, V71, P299, DOI 10.1111/j.1444-2906.2005.00964.x NR 31 TC 30 Z9 31 U1 1 U2 19 PD MAY 28 PY 2008 VL 56 IS 10 BP 3460 EP 3469 DI 10.1021/jf703704m WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000256034800005 DA 2022-12-14 ER PT J AU Cibecchini, G Cecere, P Tumino, G Morcia, C Ghizzoni, R Carnevali, P Terzi, V Pompa, PP AF Cibecchini, Giulia Cecere, Paola Tumino, Giorgio Morcia, Caterina Ghizzoni, Roberta Carnevali, Paola Terzi, Valeria Pompa, Pier Paolo TI A Fast, Naked-Eye Assay for Varietal Traceability in the Durum Wheat Production Chain SO FOODS DT Article DE durum wheat variety; genetic traceability; single nucleotide polymorphisms; colorimetric tests; authenticity; point of care test ID SINGLE NUCLEOTIDE POLYMORPHISM; MEDIATED ISOTHERMAL AMPLIFICATION; DIVERSITY; LANDRACES; LAMP AB The development of a colorimetric mono-varietal discriminating assay, aimed at improving traceability and quality control checks of durum wheat products, is described. A single nucleotide polymorphism (SNP) was identified as a reliable marker for wheat varietal discrimination, and a rapid test for easy and clear identification of specific wheat varieties was developed. Notably, an approach based on the loop-mediated isothermal amplification reaction (LAMP) as an SNP discrimination tool, in combination with naked-eye visualization of the results, was designed and optimized. Our assay was proven to be effective in the detection of adulterated food products, including both substitution and mixing with different crop varieties. C1 [Cibecchini, Giulia; Cecere, Paola; Pompa, Pier Paolo] Ist Italiano Tecnol Nanobiointeract & Nanodiagnos, Via Morego 30, I-16163 Genoa, Italy. [Cibecchini, Giulia] Univ Genoa, Dept Chem & Ind Chem, Via Dodecaneso 31, I-16146 Genoa, Italy. [Tumino, Giorgio; Morcia, Caterina; Ghizzoni, Roberta; Terzi, Valeria] Council Agr Res & Econ, Res Ctr Genom & Bioinformat, Via San Protaso 302, I-29017 Fiorenzuola Darda Pc, Italy. [Carnevali, Paola] Barilla SpA, Via Mantova 166, I-43122 Parma Pr, Italy. C3 University of Genoa; Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Pompa, PP (corresponding author), Ist Italiano Tecnol Nanobiointeract & Nanodiagnos, Via Morego 30, I-16163 Genoa, Italy.; Terzi, V (corresponding author), Council Agr Res & Econ, Res Ctr Genom & Bioinformat, Via San Protaso 302, I-29017 Fiorenzuola Darda Pc, Italy. EM giulia.cibecchini@iit.it; paola.cecere@iit.it; giorgiotumino@hotmail.it; caterina.morcia@crea.gov.it; roberta.ghizzoni@crea.gov.it; paola.carnevali@barilla.com; valeria.terzi@crea.gov.it; pierpaolo.pompa@iit.it CR Badolo A, 2015, MALARIA J, V14, DOI 10.1186/s12936-015-0968-9 Banterle A, 2013, SUSTAINABILITY-BASEL, V5, P5272, DOI 10.3390/su5125272 Carlos Fabio Ferreira, 2017, Biotechnol Rep (Amst), V16, P21, DOI 10.1016/j.btre.2017.10.003 Cavanagh CR, 2013, P NATL ACAD SCI USA, V110, P8057, DOI 10.1073/pnas.1217133110 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Ding S, 2019, THERANOSTICS, V9, P3723, DOI 10.7150/thno.33980 Gao LF, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0150947 Gill Pooria, 2020, Avicenna Journal of Medical Biotechnology, V12, P2 Huang XQ, 2002, THEOR APPL GENET, V105, P699, DOI 10.1007/s00122-002-0959-4 Ikeda S, 2007, PATHOL INT, V57, P594, DOI 10.1111/j.1440-1827.2007.02144.x Itonaga M, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0151654 Jaroenram W, 2019, J MICROBIOL METH, V156, P9, DOI 10.1016/j.mimet.2018.11.020 Kwong KM, 2018, CLIN CHIM ACTA, V478, P45, DOI 10.1016/j.cca.2017.12.013 Laido G, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0067280 Maccaferri M, 2019, NAT GENET, V51, P885, DOI 10.1038/s41588-019-0381-3 Mangini G, 2018, CEREAL RES COMMUN, V46, P377, DOI 10.1556/0806.46.2018.033 Magallanes-Lopez AM, 2017, J CEREAL SCI, V75, P1, DOI 10.1016/j.jcs.2017.03.005 Morcia C, 2020, FOODS, V9, DOI 10.3390/foods9070911 Nadeem MA, 2018, BIOTECHNOL BIOTEC EQ, V32, P261, DOI 10.1080/13102818.2017.1400401 Nagamine K, 2002, MOL CELL PROBE, V16, P223, DOI 10.1006/mcpr.2002.0415 Nazari L, 2014, FOOD MICROBIOL, V39, P19, DOI 10.1016/j.fm.2013.10.009 Notomi T, 2000, NUCLEIC ACIDS RES, V28, DOI 10.1093/nar/28.12.e63 Pasqualone A., 2011, CURRENT TOPICS FOOD, P23 Singh R, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-41204-2 Tanner NA, 2015, BIOTECHNIQUES, V58, P59, DOI 10.2144/000114253 Wang SC, 2014, PLANT BIOTECHNOL J, V12, P787, DOI 10.1111/pbi.12183 Yongkiettrakul S, 2017, PARASITOL INT, V66, P964, DOI 10.1016/j.parint.2016.10.024 NR 27 TC 5 Z9 5 U1 1 U2 10 PD NOV PY 2020 VL 9 IS 11 AR 1691 DI 10.3390/foods9111691 WC Food Science & Technology SC Food Science & Technology UT WOS:000593454100001 DA 2022-12-14 ER PT J AU Trebar, M Lotric, M Fonda, I Pletersek, A Kovacic, K AF Trebar, Mira Lotric, Metka Fonda, Irena Pletersek, Anton Kovacic, Kosta TI RFID Data Loggers in Fish Supply Chain Traceability SO INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION DT Article ID FOOD; SYSTEM AB Radio frequency identification (RFID) is an innovative and well-recognized technology that supports all kinds of traceability systems in many areas. It becomes very important in the food industry where the electronic systems are used to capture the data in the supply chain. Additionally, RFID data loggers with sensors are available to perform a cold chain optimization for perishable foods. This paper presents the temperature monitoring solution at the box level in the fish supply chain as part of the traceability system implemented with RFID technology. RFID data loggers are placed inside the box to measure the temperature of the product and on the box for measuring ambient temperature. The results show that the system is very helpful during the phases of storage and transportation of fish to provide the quality control. The sensor data is available immediately at the delivery to be checked on the mobile RFID reader and afterwards stored in the traceability systems database to be presented on a web to stakeholders and private consumers. C1 [Trebar, Mira] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia. [Lotric, Metka; Fonda, Irena] Fonda Si Doo, Portoroz, Slovenia. [Pletersek, Anton; Kovacic, Kosta] Ams R&D Doo, Ljubljana, Slovenia. C3 University of Ljubljana RP Trebar, M (corresponding author), Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia. EM mira.trebar@fri.uni-lj.si CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 [Anonymous], 2013, FONDA FISH FARM [Anonymous], 2013, FOSSTRACK OPEN SOURC [Anonymous], 2013, RFID F2F RFID FARM F [Anonymous], 2013, NORDICID MORPHIC [Anonymous], 2013, AMS R D D O O [Anonymous], 2013, EPC INFORM SERVICES [Anonymous], 2013, IMPINJ SPEEDWAY Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Cuinas I, 2011, PR ELECTROMAGN RES S, P1370 Kim SA, 2013, FOOD CONTROL, V29, P66, DOI 10.1016/j.foodcont.2012.05.064 Raab V, 2011, BRIT FOOD J, V113, P1267, DOI 10.1108/00070701111177683 Shi J, 2010, J BUS IND MARK, V25, P596, DOI 10.1108/08858621011088338 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Zhang J, 2009, J FOOD AGRIC ENVIRON, V7, P28 NR 16 TC 20 Z9 20 U1 1 U2 10 PY 2013 VL 2013 AR 875973 DI 10.1155/2013/875973 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications UT WOS:000324192700001 DA 2022-12-14 ER PT J AU Agrawal, TK Campagne, C Koehl, L AF Agrawal, Tarun Kumar Campagne, Christine Koehl, Ludovic TI Development and characterisation of secured traceability tag for textile products by printing process SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY DT Article DE Secured tag; Traceability; Screen printing; Adhesion ID LOW-COST; TRACKING; TECHNOLOGY; SUBSTRATE; SURFACES; SYSTEM; ENERGY AB Product security is one of the major concerns in the textile industry. Every year, fashion brands suffer significant loss due to counterfeit products. Addressing this, the paper introduces a secured tag for traceability and security of textile products. The proposed tag is unclonable, which can be manufactured using conventional screen-printing process. Further, it can be read using a smartphone camera to authenticate the product and trace its history. Consequently, imparting additional functionality to the textile through surface modification. To validate its applicability, the study experimentally investigates the durability and readability of the developed secured tag using three different binders on polyester and cotton textiles substrates. A comparison is presented with an in-depth analysis of surfaces and binders interaction at different stages of the secured tag lifecycle, i.e. before print, after print, after wash and after abrasion cycles. The methodology and findings of the study can also be useful for other manufacturing domains dealing with the printing process. C1 [Agrawal, Tarun Kumar; Campagne, Christine; Koehl, Ludovic] ENSAIT, GEMTEX, Lab Genie & Mat Text, F-59000 Lille, France. [Agrawal, Tarun Kumar; Campagne, Christine; Koehl, Ludovic] Univ Lille Nord de France, F-59000 Lille, France. [Agrawal, Tarun Kumar] Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden. [Agrawal, Tarun Kumar] Soochow Univ, Coll Text & Clothing Engn, Suzhou, Peoples R China. C3 Ecole Nationale Superieure des Arts et Industries Textiles (ENSAIT); Universite de Lille - ISITE; Universite de Lille; University of Boras; Soochow University - China RP Agrawal, TK (corresponding author), ENSAIT, GEMTEX, Lab Genie & Mat Text, F-59000 Lille, France.; Agrawal, TK (corresponding author), Univ Lille Nord de France, F-59000 Lille, France.; Agrawal, TK (corresponding author), Univ Boras, Swedish Sch Text, S-50190 Boras, Sweden.; Agrawal, TK (corresponding author), Soochow Univ, Coll Text & Clothing Engn, Suzhou, Peoples R China. EM Tarun_kumar.agrawal@hb.se CR Abe T, 2012, ADV MATER RES-SWITZ, V441, P23, DOI 10.4028/www.scientific.net/AMR.441.23 Agrawal TK, 2018, INT J ADV MANUF TECH, V99, P2563, DOI 10.1007/s00170-018-2638-x Agrawal TK, 2017, IEEE SENS J, V17, P4239, DOI 10.1109/JSEN.2017.2703633 [Anonymous], COAT SIL SILBIONE TC Bahadir MC, 2015, INT J ADV MANUF TECH, V76, P1719, DOI 10.1007/s00170-014-6399-x Bansal Dipika, 2013, Sci Pharm, V81, P1, DOI 10.3797/scipharm.1202-03 Cao JL, 2018, APPL SURF SCI, V440, P177, DOI 10.1016/j.apsusc.2018.01.094 Chen YM, 2018, APPL SURF SCI, V436, P111, DOI 10.1016/j.apsusc.2017.11.288 Cherenack K, 2012, J APPL PHYS, V112, DOI 10.1063/1.4742728 Cui Y, 2015, CHEM COMMUN, V51, P5363, DOI 10.1039/c4cc08596e Devadas S, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON RFID, P58, DOI 10.1109/RFID.2008.4519377 Di JF, 2001, J TEXT I, V92, P184, DOI 10.1080/00405000108659569 DUDA RO, 1972, COMMUN ACM, V15, P11, DOI 10.1145/361237.361242 FOWKES FM, 1962, J PHYS CHEM-US, V66, P382, DOI 10.1021/j100808a524 Guan YJ, 2011, ACTA PHYSIOL PLANT, V33, P1271, DOI 10.1007/s11738-010-0657-9 Gupta D, 2011, INDIAN J FIBRE TEXT, V36, P321 Hu HB, 2012, J MATER CHEM, V22, P11048, DOI 10.1039/c2jm30169e Huang SH, 2013, INT J ADV MANUF TECH, V67, P1191, DOI 10.1007/s00170-012-4558-5 Ilie-Zudor E, 2011, COMPUT IND, V62, P227, DOI 10.1016/j.compind.2010.10.004 Kelly FM, 2013, DISPLAYS, V34, P1, DOI 10.1016/j.displa.2012.10.001 Kumar V, 2017, J MANUF SYST, V42, P124, DOI 10.1016/j.jmsy.2016.11.008 Kumar V, 2016, J MANUF SYST, V40, P76, DOI 10.1016/j.jmsy.2016.06.007 Lipatov Y. S., 1995, POLYM REINFORCEMENT Little AF, 2010, COLOR TECHNOL, V126, P164, DOI 10.1111/j.1478-4408.2010.00242.x Locher I, 2007, TEXT RES J, V77, P837, DOI 10.1177/0040517507080679 Luttropp C, 2010, J CLEAN PROD, V18, P346, DOI 10.1016/j.jclepro.2009.10.023 Merilampi SL, 2011, INT J ADV MANUF TECH, V53, P577, DOI 10.1007/s00170-010-2869-y Morent R, 2011, WOODHEAD PUBL SER TE, P3 Oner M, 2017, INT J ADV MANUF TECH, V90, P591, DOI 10.1007/s00170-016-9385-7 OWENS DK, 1969, J APPL POLYM SCI, V13, P1741, DOI 10.1002/app.1969.070130815 Pulit-Prociak J, 2016, APPL SURF SCI, V385, P543, DOI 10.1016/j.apsusc.2016.05.167 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Sowade E, 2015, APPL SURF SCI, V332, P500, DOI 10.1016/j.apsusc.2015.01.113 Street RA, 2015, P IEEE, V103, P607, DOI 10.1109/JPROC.2015.2408552 Strickland LS, 2005, J AM SOC INF SCI TEC, V56, P221, DOI 10.1002/asi.20122 Weremczuk J, 2012, PROCEDIA ENGINEER, V47, P1366, DOI 10.1016/j.proeng.2012.09.410 Yang K, 2014, SENSOR ACTUAT A-PHYS, V213, P108, DOI 10.1016/j.sna.2014.03.025 Young Thomas, 1855, MISCELLANEOUS WORKS NR 38 TC 4 Z9 4 U1 6 U2 11 PD APR PY 2019 VL 101 IS 9-12 BP 2907 EP 2922 DI 10.1007/s00170-018-3134-z WC Automation & Control Systems; Engineering, Manufacturing SC Automation & Control Systems; Engineering UT WOS:000463669500056 DA 2022-12-14 ER PT J AU Nguyen, DH Nguyen, HT Pham, HA AF Duc-Hiep Nguyen Nguyen Huynh-Tuong Hoang-Anh Pham TI A Blockchain-Based Framework for Developing Traceability Applications towards Sustainable Agriculture in Vietnam SO SECURITY AND COMMUNICATION NETWORKS DT Article AB Recently, many governments in the world have been focusing on building sustainable agriculture to improve the life quality of farmers and significantly increase their income. In Vietnam, however, the farmers still face the problems of "good harvest-low prices, and vice versa" and lack capital for scaling or transforming the production model. One of the main reasons for this phenomenon is that the price of agricultural products does not depend on farmers' efforts but is based on the purchase price of the trader or the market price. Besides, the farmers also maintain farming habits based on regional culture or follow trendy and profitable agricultural products. Those production strategies make this type of product oversupplied, leading to a down in price shortly, so the farmers' income will decrease. The above problems stem from the lack of information and communication tools between actors in the agricultural value chain, especially between cooperatives, farmers, and consumers. This paper presents a Blockchain-based framework for developing a traceability solution as an effective method of communication between actors in the agricultural value chain toward a sustainable agricultural model. The proposed approach helps to fully convey the production and distribution of agricultural products and the ability to verify traceability information, thereby helping to increase prices and protect the brand of agricultural products. C1 [Duc-Hiep Nguyen; Nguyen Huynh-Tuong; Hoang-Anh Pham] Ho Chi Minh City Univ Technol HCMUT, 268 Ly Thuong Kiet St,Dist 10, Ho Chi Minh City, Vietnam. [Duc-Hiep Nguyen; Nguyen Huynh-Tuong; Hoang-Anh Pham] Vietnam Natl Univ Ho Chi Minh City VNU HCM, Linh Trung Ward, Ho Chi Minh City, Vietnam. [Duc-Hiep Nguyen] Vietnam Blockchain Corp, Dist 11, Ho Chi Minh City, Vietnam. C3 Ho Chi Minh City University of Technology (HCMCUT); Vietnam National University Hochiminh City; Vietnam National University Hochiminh City RP Pham, HA (corresponding author), Ho Chi Minh City Univ Technol HCMUT, 268 Ly Thuong Kiet St,Dist 10, Ho Chi Minh City, Vietnam.; Pham, HA (corresponding author), Vietnam Natl Univ Ho Chi Minh City VNU HCM, Linh Trung Ward, Ho Chi Minh City, Vietnam. EM anhpham@hcmut.edu.vn CR Ali MH, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120870 Arati Baliga, 2018, Arxiv, DOI arXiv:1809.03421 Arslan C, 2021, ADV SCI TECHNOLOGY E, V6, P279 Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Bai CG, 2022, J CLEAN PROD, V358, DOI 10.1016/j.jclepro.2022.131896 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Ben E, 2017, P 13 INT C WIRELESS, V07 Sezer BB, 2021, Arxiv, DOI arXiv:2103.10519 Dey K, 2021, J CLEAN PROD, V316, DOI 10.1016/j.jclepro.2021.128254 Nguyen DH, 2020, I C INF COMM TECH CO, P737, DOI 10.1109/ICTC49870.2020.9289297 Jahanbin P, 2020, P 27 EUROPEAN C INFO Jeppsson A., 2017, THESIS LUND U SWEDEN Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Karamchandani Amit, 2021, International Journal of Technology Intelligence and Planning, V13, P1, DOI 10.1504/IJTIP.2021.117994 Leng JW, 2022, IEEE T SERV COMPUT, V15, P2490, DOI 10.1109/TSC.2020.3038641 Leng JW, 2021, IEEE T SYST MAN CY-S, V51, P237, DOI 10.1109/TSMC.2020.3040789 Leng JW, 2020, RENEW SUST ENERG REV, V132, DOI 10.1016/j.rser.2020.110112 Leng JW, 2020, IEEE T SYST MAN CY-S, V50, P182, DOI 10.1109/TSMC.2019.2930418 Leng JW, 2019, J CLEAN PROD, V234, P767, DOI 10.1016/j.jclepro.2019.06.265 Li XH, 2020, J CLEAN PROD, V271, DOI 10.1016/j.jclepro.2020.122503 National Institute of Food and Agriculture,, US CODE TITLE, V7 Nguyen H. N, 2020, SCI TECHNOLOGY DEV J, V3, pSI10 Park L, 2020, IBM STUDY PURPOSE PR Saurabh S, 2021, J CLEAN PROD, V284, DOI 10.1016/j.jclepro.2020.124731 Sekhar Bhusal C, 2021, INT J COMPUTER SCI I, V10, P31 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Toyoda K, 2020, IEEE ACCESS, V8, P141611, DOI 10.1109/ACCESS.2020.3011876 Zheng ZB, 2018, INT J WEB GRID SERV, V14, P352, DOI 10.1504/IJWGS.2018.095647 NR 28 TC 0 Z9 0 U1 0 U2 0 PD JUL 14 PY 2022 VL 2022 AR 1834873 DI 10.1155/2022/1834873 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000875615000002 DA 2022-12-14 ER PT J AU Wang, YX Gong, Y Zhang, J Tang, Y Shi, XF Shi, JA AF Wang, Yaxin Gong, Yi Zhang, Jian Tang, Yi Shi, Xiaofei Shi, Jiangao TI Intra- and Inter-Specific Variation in Edible Jellyfish Biomarkers and Implications for Origin Traceability and Authentication SO FRONTIERS IN MARINE SCIENCE DT Article DE Rhopilema esculentum; Nemopilema nomurai; jellyfish; fatty acids; stable isotopes; origin traceability ID RHOPILEMA-ESCULENTUM KISHINOUYE; FATTY-ACID-COMPOSITION; ISOTOPIC ANALYSES; COMMERCIAL FISH; STABLE-ISOTOPES; WILD; IDENTIFICATION; CARBON; CHINA; FRACTIONATION AB With the continuous development of jellyfish fisheries and food products around the world, an effective traceability system has become increasingly prominent. This study provides insight into the origin traceability and authentication of two commercially important jellyfish species, flame jellyfish Rhopilema esculentum and Nomura's jellyfish Nemopilema nomurai, while investigating the intra- and inter-specific variation in fatty acid (FA) profiles and carbon and nitrogen stable isotope ratios (delta C-13 and delta N-15). Results showed significant differences in FA profiles and isotopic values in fresh bell tissues between wild and farmed R. esculentum and among geographic origins, possibly due to different food sources, nutritional status, and energy costs that each group experiences at a given location. The linear discriminant analysis indicated that delta C-13, delta N-15, C16:0, C17:0, C18:0, C16:1n7, and C20:5n3 were suitable discriminatory variables with a high rate of correct classification for distinguishing origins of R. esculentum. In addition, inter-specific FA profiles/biomarkers, combined with isotopic values, suggests the variety of dietary sources and trophic positions of sympatric similar-sized R. esculentum and N. nomurai and the potential use of biomarkers, especially stable isotope analysis, for distinguishing sympatric jellyfish species. These results highlighted the complementarity of FA and stable isotope analyses and provide an alternative approach for improving the origin traceability and authenticity evaluation of untreated edible jellyfish. Furthermore, this study adds new information regarding the biochemical compositions of jellyfish species. C1 [Wang, Yaxin; Gong, Yi; Zhang, Jian; Shi, Xiaofei] Shanghai Ocean Univ, Coll Marine Sci, Shanghai, Peoples R China. [Gong, Yi; Zhang, Jian] Minist Educ, Key Lab Sustainable Exploitat Ocean Fisheries Res, Shanghai, Peoples R China. [Gong, Yi; Zhang, Jian] Shanghai Ocean Univ, Natl Engn Res Ctr Ocean Fisheries, Shanghai, Peoples R China. [Gong, Yi; Zhang, Jian] Minist Agr & Rural Affairs, Key Lab Ocean Fisheries Explorat, Shanghai, Peoples R China. [Tang, Yi] Shanghai Ocean Univ, Coll Marine Culture & Law, Shanghai, Peoples R China. [Shi, Jiangao] Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai, Peoples R China. C3 Shanghai Ocean University; National Engineering Research Center for Oceanic Fisheries; Shanghai Ocean University; Ministry of Agriculture & Rural Affairs; Shanghai Ocean University; Chinese Academy of Fishery Sciences; East China Sea Fisheries Research Institute, CAFS RP Zhang, J (corresponding author), Shanghai Ocean Univ, Coll Marine Sci, Shanghai, Peoples R China.; Zhang, J (corresponding author), Minist Educ, Key Lab Sustainable Exploitat Ocean Fisheries Res, Shanghai, Peoples R China.; Zhang, J (corresponding author), Shanghai Ocean Univ, Natl Engn Res Ctr Ocean Fisheries, Shanghai, Peoples R China.; Zhang, J (corresponding author), Minist Agr & Rural Affairs, Key Lab Ocean Fisheries Explorat, Shanghai, Peoples R China. EM j-zhang@shou.edu.cn CR Armani A, 2014, J AGR FOOD CHEM, V62, P12134, DOI 10.1021/jf504654b Armani A, 2013, FOOD RES INT, V54, P1383, DOI 10.1016/j.foodres.2013.10.003 Auel H, 2002, POLAR BIOL, V25, P374, DOI 10.1007/s00300-001-0354-7 Bleve G, 2019, FOODS, V8, DOI 10.3390/foods8070263 Boero F., 2013, STUD REV GEN FISH CO, V92, P53 Brotz L, 2017, REV FISH BIOL FISHER, V27, P1, DOI 10.1007/s11160-016-9445-y Busetto ML, 2008, J AGR FOOD CHEM, V56, P2742, DOI 10.1021/jf0734267 D'Ambra I, 2014, MAR BIOL, V161, P473, DOI 10.1007/s00227-013-2345-y D'Amico P, 2014, FOOD CONTROL, V35, P7, DOI 10.1016/j.foodcont.2013.06.029 Dalsgaard J, 2003, ADV MAR BIOL, V46, P225, DOI 10.1016/S0065-2881(03)46005-7 DENIRO MJ, 1977, SCIENCE, V197, P261, DOI 10.1126/science.327543 Fleming NEC, 2015, PEERJ, V3, DOI 10.7717/peerj.1110 FOLCH J, 1957, J BIOL CHEM, V226, P497 Gong Y, 2020, FRONT MAR SCI, V7, DOI 10.3389/fmars.2020.00642 Gopi K, 2019, AQUACULTURE, V502, P56, DOI 10.1016/j.aquaculture.2018.12.012 Javidpour J, 2016, MAR BIOL, V163, DOI 10.1007/s00227-016-2892-0 Jiang XY, 2019, FOOD CONTROL, V105, P52, DOI 10.1016/j.foodcont.2019.05.018 Kim H, 2015, FOOD CHEM, V172, P523, DOI 10.1016/j.foodchem.2014.09.058 Leone A, 2015, MAR DRUGS, V13, P4654, DOI 10.3390/md13084654 MacKenzie KM, 2017, MAR BIOL, V164, DOI 10.1007/s00227-017-3242-6 Marques R, 2021, LIMNOL OCEANOGR, V66, P141, DOI 10.1002/lno.11593 Milisenda G, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-30474-x Nogueira N, 2017, J FOOD COMPOS ANAL, V59, P68, DOI 10.1016/j.jfca.2016.11.005 Parrish C.C., 2013, ISRN OCEANOGR, V2013 Patil V, 2007, AQUACULT INT, V15, P1, DOI 10.1007/s10499-006-9060-3 Post DM, 2007, OECOLOGIA, V152, P179, DOI 10.1007/s00442-006-0630-x Post DM, 2002, ECOLOGY, V83, P703, DOI 10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2 Querouil S, 2013, MAR BIOL, V160, P1325, DOI 10.1007/s00227-013-2184-x Rasmussen RS, 2008, COMPR REV FOOD SCI F, V7, P280, DOI 10.1111/j.1541-4337.2008.00046.x Ricardo F, 2015, SCI REP-UK, V5, DOI 10.1038/srep11125 Stenvers V, 2020, J PLANKTON RES, V42, P440, DOI 10.1093/plankt/fbaa026 Sun Ming, 2016, Yingyong Shengtai Xuebao, V27, P1103, DOI 10.13287/j.1001-9332.201604.007 Thomas F, 2008, J AGR FOOD CHEM, V56, P989, DOI 10.1021/jf072370d Tocher DR, 2003, REV FISH SCI, V11, P107, DOI 10.1080/713610925 Torri L, 2020, FOOD QUAL PREFER, V79, DOI 10.1016/j.foodqual.2019.103782 Vasconi M, 2019, FOOD CONTROL, V102, P112, DOI 10.1016/j.foodcont.2019.03.004 Wang PP, 2020, J PLANKTON RES, V42, P689, DOI 10.1093/plankt/fbaa042 Ying C, 2012, J MAR BIOL ASSOC UK, V92, P1325, DOI 10.1017/S0025315412000082 You K, 2007, AQUACULT INT, V15, P479, DOI 10.1007/s10499-007-9114-1 NR 39 TC 0 Z9 0 U1 4 U2 12 PD OCT 11 PY 2021 VL 8 AR 755048 DI 10.3389/fmars.2021.755048 WC Environmental Sciences; Marine & Freshwater Biology SC Environmental Sciences & Ecology; Marine & Freshwater Biology UT WOS:000716685800001 DA 2022-12-14 ER PT J AU Greenberg, RR Bode, P Fernandes, EAD AF Greenberg, Robert R. Bode, Peter De Nadai Fernandes, Elisabete A. TI Neutron activation analysis: A primary method of measurement SO SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY DT Review DE Neutron activation analysis; Metrology; Primary method of measurement; Uncertainty budget; Metrological traceability ID QUALITY-ASSURANCE; SAMPLES AB Neutron activation analysis (NAA), based on the comparator method, has the potential to fulfill the requirements of a primary ratio method as defined in 1998 by the Comite Consultatif pour la Quantite de Matiere - Metrologie en Chimie (CCQM, Consultative Committee on Amount of Substance - Metrology in Chemistry). This thesis is evidenced in this paper in three chapters by: demonstration that the method is fully physically and chemically understood; that a measurement equation can be written down in which the values of all parameters have dimensions in SI units and thus having the potential for metrological traceability to these units; that all contributions to uncertainty of measurement can be quantitatively evaluated, underpinning the metrological traceability; and that the performance of NAA in CCQM key-comparisons of trace elements in complex matrices between 2000 and 2007 is similar to the performance of Isotope Dilution Mass Spectrometry (IDMS), which had been formerly designated by the CCQM as a primary ratio method. Published by Elsevier B.V. C1 [Greenberg, Robert R.] Natl Inst Stand & Technol, Gaithersburg, MD 20899 USA. [Bode, Peter] Delft Univ Technol, Delft, Netherlands. [De Nadai Fernandes, Elisabete A.] Univ Sao Paulo, Ctr Energia Nucl Agr, Piracicaba, SP, Brazil. C3 National Institute of Standards & Technology (NIST) - USA; Delft University of Technology; Universidade de Sao Paulo RP Greenberg, RR (corresponding author), Natl Inst Stand & Technol, Gaithersburg, MD 20899 USA. EM robert.greenberg@nist.gov; p.bode@tudelft.nl; lis@cena.usp.br CR [Anonymous], 2007, INT VOCABULARY METRO, V3rd BODE P, 1992, J RADIOAN NUCL CH AR, V157, P301, DOI 10.1007/BF02047445 BODE P, 1994, J RADIOAN NUCL CH AR, V179, P141, DOI 10.1007/BF02037934 BODE P, 1993, J RADIOAN NUCL CH AR, V167, P169, DOI 10.1007/BF02035477 BODE P, 1998, ENCY ENV ANAL REMEDI, P68 BODE P, 1996, THESIS DELFT U TECHN Bowen, 1963, RADIOACTIVATION ANAL BOYD GE, 1949, ANAL CHEM, V21, P335, DOI 10.1021/ac60027a005 BROWN H, 1949, SCIENCE, V109, P347, DOI 10.1126/science.109.2832.347 COOK GB, 1960, PURE APPL CHEM, V1, P15 CURRIE LA, 1968, ANAL CHEM, V40, P586, DOI 10.1021/ac60259a007 DANIEL H, 1979, ATOM ENERGY REV, V17, P287 De Corte F, 2003, ATOM DATA NUCL DATA, V85, P47, DOI 10.1016/S0092-640X(03)00036-6 De Soete D., 1972, NEUTRON ACTIVATION A DEBRUIN M, 1975, RADIOCHEM RADIOA LET, V21, P287 DECORTE F, 1993, J RADIOAN NUCL CH AR, V169, P125, DOI 10.1007/BF02046790 EGAN A, 1977, RADIOCHEM RADIOA LET, V28, P369 ELVING PJ, 1986, TREATISE ANAL CHEM 1, V14 Firestone R., 1996, TABLE ISOTOPES GEHRKE RJ, 1977, NUCL INSTRUM METHODS, V147, P405, DOI 10.1016/0029-554X(77)90276-2 GIRARDI F, 1965, ANAL CHEM, V37, P1085, DOI 10.1021/ac60228a002 HELMER RG, 1999, GAMMA RAY SPECTRUM C HEVESY G, 1938, DET KGL DANSK VIDENS, V15, P11 Hevesy G, 1936, KGL DANSK VIDENS MFM, VXIV, P3 Hogdahl OT, 1962, MMPP2261 U MICH IAEA, 2002, TECDOC1285 IAEA *JCGM, 2008, JCGM200 Kawamura H, 2000, J RADIOANAL NUCL CH, V245, P123, DOI 10.1023/A:1006712813369 KOHMAN TP, 1949, ANAL CHEM, V21, P352, DOI 10.1021/ac60027a007 Koster-Ammerlaan MJJ, 2008, APPL RADIAT ISOTOPES, V66, P1964, DOI 10.1016/j.apradiso.2008.06.001 LELIAERT G, 1958, ANAL CHIM ACTA, V19, P100, DOI 10.1016/S0003-2670(00)88122-2 LEVI H, SEM LECT HELD JUN 23 LEWIS WB, 1954, NUCLEONICS, V12, P30 LEWIS WB, 1955, NUCLEONICS, V13, P82 MEINKE WW, 1956, ANAL CHEM, V28, P736, DOI 10.1021/ac60112a025 Molnar G., 2004, HDB PROMPT GAMMA ACT OPELANIO LR, 1983, ANAL CHEM, V55, P677, DOI 10.1021/ac00255a022 PARR RM, 1984, ANAL CHIM ACTA, V165, P1 PLUMB RC, 1955, NUCLEONICS, V13, P42 RAO RR, 1991, ANAL CHEM, V63, P1298, DOI 10.1021/ac00013a022 RIETJENS LHT, 1955, PHYSICA, V21, P110 Rossbach M, 2006, ACCREDIT QUAL ASSUR, V10, P583, DOI 10.1007/s00769-005-0066-8 SIMONITS A, 1975, J RADIOANAL CHEM, V24, P31, DOI 10.1007/BF02514380 SOOD DD, 2000, FUNDAMENTALS RADIOCH STEINNES E, 1969, TALANTA, V16, P1326, DOI 10.1016/0039-9140(69)80010-X NR 45 TC 241 Z9 247 U1 0 U2 61 PD MAR-APR PY 2011 VL 66 IS 3-4 BP 193 EP 207 DI 10.1016/j.sab.2010.12.011 WC Spectroscopy SC Spectroscopy UT WOS:000292361600001 DA 2022-12-14 ER PT J AU Li, MH Li, JM Li, JH Zhang, LD Zhao, LL AF Li Meng-hua Li Jing-ming Li Jun-hui Zhang Lu-da Zhao Long-lian TI Traceability of Wine Varieties Using Near Infrared Spectroscopy Combined with Cyclic Voltammetry SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE Red wine; Variety; Near Infrared Spectroscopy; Cyclic Voltammetry; D-S evidence theory AB To achieve the traceability of wine varieties, a method was proposed to fuse Near-infrared (NW) spectra and cyclic voltammograms (CV) which contain different information using D-S evidence theory. NW spectra and CV curves of three different varieties of wines (cabernet sauvignon, merlot, cabernet gernischt) which come from seven different geographical origins were collected separately. The discriminant models were built using PLS-DA method. Based on this, D-S evidence theory was then applied to achieve the integration of the two kinds of discrimination results. After integrated by D-S evidence theory, the accuracy rate of cross-validation is 95. 69% and validation set is 94. 12% for wine variety identification. When only considering the wine that come from Yantai, the accuracy rate of cross-validation is 99. 46% and validation set is 100%. All the traceability models after fusion achieved better results on classification than individual method. These results suggest that the proposed method combining electrochemical information with spectral information using the D-S evidence combination formula is benefit to the improvement of model discrimination effect, and is a promising tool for discriminating different kinds of wines. C1 [Li Meng-hua; Li Jun-hui; Zhang Lu-da; Zhao Long-lian] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. [Li Jing-ming] China Agr Univ, Coll Food Sci & Nutr Engn, Beijing 100083, Peoples R China. C3 China Agricultural University; China Agricultural University RP Li, JM (corresponding author), China Agr Univ, Coll Food Sci & Nutr Engn, Beijing 100083, Peoples R China. EM lyma@cau.edu.cn; zhaolonglian@aliyun.com CR CHEN Yu-qing, 2004, LIQUOR MAKING SCI TE, V31, P311 Kilmartin PA, 2002, AM J ENOL VITICULT, V53, P294 Kilmartin PA, 2001, J AGR FOOD CHEM, V49, P1957, DOI 10.1021/jf001044u [赖宇坤 Lai Yukun], 2011, [电化学, Electrochemistry], V17, P102 LI Hua, 2006, WINE TASTING GLOSSAR LIANG Ying-ping, 2009, LIQUOR MAKING SCI TE, V7, P43 LUN San-lan, 2011, MICROELECTRONICS COM, V28, P95 Makhotkina O, 2009, J ELECTROANAL CHEM, V633, P165, DOI 10.1016/j.jelechem.2009.05.007 Tian Junfeng, 2005, J ELECT CHINA, V30, P261 YAN Y, 2005, FDN NIR SPECTRAL ANA Yang F., 2010, COMBINATION METHOD C ZHUO Li-bo, 2010, MICROCOMPUTER INFORM, V26, P120 NR 12 TC 1 Z9 3 U1 1 U2 18 PD JUN PY 2015 VL 35 IS 6 BP 1551 EP 1555 DI 10.3964/j.issn.1000-0593(2015)06-1551-05 WC Spectroscopy SC Spectroscopy UT WOS:000355883400019 DA 2022-12-14 ER PT J AU Wang, RK Chen, X AF Wang, Rongkuan Chen, Xi TI Research on Agricultural Product Traceability Technology (Economic Value) Based on Information Supervision and Cloud Computing SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE DT Article ID BLOCKCHAIN; LOGISTICS AB Traditional agricultural product traceability system adopts centralized storage, and the traceability process is solidified, which results in the low reliability of traceability results and the poor flexibility of the system. Aiming to solve this problem, blockchain technology is applied to supply chain traceability, and a supply chain traceability system based on sidechain technology is proposed. Goods management, information sharing, and product traceability in supply chain are realized through Ethereum smart contract. The sidechain technology is adopted to expand Ethereum so that it can meet the needs of practical applications. The experiment results show that the proposed system has a transaction function and information sharing function. Compared with similar trading systems, the proposed system has more advantages in throughput and security. C1 [Wang, Rongkuan] Zhongnan Univ Econ & Law, Sch Business Adm, Wuhan 430073, Hubei, Peoples R China. [Chen, Xi] Jiangxi Agr Univ, Nanchang Business Coll, Jiujiang 332020, Jiangxi, Peoples R China. C3 Zhongnan University of Economics & Law; Jiangxi Agricultural University RP Wang, RK (corresponding author), Zhongnan Univ Econ & Law, Sch Business Adm, Wuhan 430073, Hubei, Peoples R China. EM 202011080133@stu.zuel.edu.cn; rkwang2020@163.com CR Choi S, 2020, IEEE ACCESS, V8, P37518, DOI 10.1109/ACCESS.2020.2975920 Choi TM, 2019, TRANSPORT RES E-LOG, V127, P178, DOI 10.1016/j.tre.2019.05.007 Dong YuDe, 2016, Transactions of the Chinese Society of Agricultural Engineering, V32, P280, DOI 10.11975/j.issn.1002-6819.2016.01.039 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gao K, 2020, INT J COMPUT SCI ENG, V23, P185, DOI 10.1504/IJCSE.2020.110547 Jamil F, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8050505 Liang W, 2020, COMM COM INF SC, V1156, P702, DOI 10.1007/978-981-15-2777-7_57 Makhdoom I, 2020, COMPUT SECUR, V88, DOI 10.1016/j.cose.2019.101653 Maksimovic M., 2015, CEUR WORKSHOP PROC, V1498, P583 Ning ZL, 2018, IEEE INTERNET THINGS, V5, P2506, DOI 10.1109/JIOT.2017.2764259 Ostapenko R, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083416 Peng H, 2013, IEEE J BIOMED HEALTH, V17, P600, DOI 10.1109/JBHI.2013.2253614 Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497 Shankar R, 2018, TRANSPORT RES E-LOG, V119, P205, DOI 10.1016/j.tre.2018.03.006 Sharma P, 2020, ACM COMPUT SURV, V53, DOI 10.1145/3403954 Sidhu J, 2017, P 26 INT C COMP COMM, P1, DOI DOI 10.1109/ICCCN.2017.8038518 Wang C, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (IEEE ICWS 2019), P214, DOI 10.1109/ICWS.2019.00044 Xie C., 2018, P 4 ANN INT C SOCIAL Yang XinTing, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P212 Zhu ZQ, 2020, SIMUL MODEL PRACT TH, V102, DOI 10.1016/j.simpat.2019.101987 NR 20 TC 2 Z9 2 U1 15 U2 32 PD JAN 30 PY 2022 VL 2022 AR 4687639 DI 10.1155/2022/4687639 WC Mathematical & Computational Biology; Neurosciences SC Mathematical & Computational Biology; Neurosciences & Neurology UT WOS:000766942100008 DA 2022-12-14 ER PT J AU Anastasiadis, F Manikas, I Apostolidou, I Wahbeh, S AF Anastasiadis, Foivos Manikas, Ioannis Apostolidou, Ioanna Wahbeh, Sabreen TI The role of traceability in end-to-end circular agri-food supply chains SO INDUSTRIAL MARKETING MANAGEMENT DT Article DE Categorical regression; Holistic approach; Blockchain; Greece; Consumer centric supply chain ID SUSTAINABLE DEVELOPMENT; CONSUMER PERCEPTIONS; USER ACCEPTANCE; ECONOMY; SYSTEM; WASTE; MANAGEMENT; DELPHI; INFORMATION; FUTURE AB The transition to a circular supply chain is a prerequisite for the agri-food sector to address growing consumer pressure for sustainability while meeting the required standards of quality and safety. Although traceability systems contribute notably to agri-food sustainability, their function as a core mechanism for monitoring and managing the transition from linear to circular supply chains has been neglected. Our objective in this study is to explore the role of traceability in the transition to sustainability-driven circular agri-food supply chains. We employ an end-to-end supply chain investigation, using a consumer-focused survey and a Delphi study of supply chain stakeholders from farm to shelf. Our results suggest that traceability plays a pivotal role in this process. The acceptance of traceability systems begins with the consumer and moves upstream, from fork to farm, acting as a catalyst through stakeholders' adoption towards a circular economy. A change at the consumer level drives changes in the supply chain and eventually at the company level. This study is the first to introduce a traceability angle to the current literature on sustainable agri-food supply chains and its transition to the circular economy. C1 [Anastasiadis, Foivos; Apostolidou, Ioanna] Aristotle Univ Thessaloniki, Fac Agr Forestry & Nat Environm, Sch Agr, Dept Agr Econ, Thessaloniki 54124, Greece. [Manikas, Ioannis; Wahbeh, Sabreen] Univ Wollongong Dubai, Fac Business, Dubai Knowledge Pk, Dubai, U Arab Emirates. C3 Aristotle University of Thessaloniki; University of Wollongong RP Anastasiadis, F (corresponding author), Aristotle Univ Thessaloniki, Fac Agr Forestry & Nat Environm, Sch Agr, Dept Agr Econ, Thessaloniki 54124, Greece. EM anastasiadis.f@gmail.com CR Abd Rahman A, 2017, FOOD CONTROL, V73, P1318, DOI 10.1016/j.foodcont.2016.10.058 Achrol RS, 2012, J ACAD MARKET SCI, V40, P35, DOI 10.1007/s11747-011-0255-4 Adami L, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020925 Agrawal TK, 2021, COMPUT IND ENG, V154, DOI 10.1016/j.cie.2021.107130 AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Altieri MA, 2015, AGRON SUSTAIN DEV, V35, P869, DOI 10.1007/s13593-015-0285-2 Alyami SH, 2013, RENEW SUST ENERG REV, V27, P43, DOI 10.1016/j.rser.2013.06.011 Anastasiadis F., 2014, AGR ENG INT CIGR J, P11 Anastasiadis F, 2021, FOODS, V10, DOI 10.3390/foods10030543 Anastasiadis F, 2020, FOODS, V9, DOI 10.3390/foods9050539 Anastasiadis F, 2015, SUPPLY CHAIN MANAG, V20, P353, DOI 10.1108/SCM-08-2014-0259 [Anonymous], 2003, EUR J MARKETING, DOI DOI 10.1108/03090560310465099 [Anonymous], 2007, 22005 ISO Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783 Aschemann-Witzel J, 2017, J CLEAN PROD, V155, P33, DOI 10.1016/j.jclepro.2016.11.173 Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Bacarella S., 2015, Advances in Horticultural Science, V29, P145 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Baker JA, 2007, J PSYCHIATR MENT HLT, V14, P478, DOI 10.1111/j.1365-2850.2007.01112.x Barros MV, 2020, RENEW SUST ENERG REV, V131, DOI 10.1016/j.rser.2020.109958 Bartelmus P, 2013, ENVIRON DEV, V7, P165, DOI 10.1016/j.envdev.2013.04.001 Bauer HH, 2013, J BUS RES, V66, P1035, DOI 10.1016/j.jbusres.2011.12.028 Berg H., 2019, NACHHALT SUSTAIN MAN, V27, P1, DOI [10.1007/s00550-018-0468-9, DOI 10.1007/S00550-018-0468-9] Birat JP, 2015, METALL RES TECHNOL, V112, DOI 10.1051/metal/2015009 Borrello M, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9010141 Bougdira A, 2020, J AMB INTEL HUM COMP, V11, P3355, DOI 10.1007/s12652-019-01532-7 Boyer RHW, 2021, SUSTAIN PROD CONSUMP, V27, P61, DOI 10.1016/j.spc.2020.10.010 Bradu C, 2014, J BUS ETHICS, V124, P283, DOI 10.1007/s10551-013-1872-2 Bressanelli G, 2019, INT J PROD RES, V57, P7395, DOI 10.1080/00207543.2018.1542176 Carter CR, 2011, INT J PHYS DISTR LOG, V41, P46, DOI 10.1108/09600031111101420 Centobelli P, 2021, INT J PROD ECON, V242, DOI 10.1016/j.ijpe.2021.108297 Chang A, 2013, BRIT FOOD J, V115, P1361, DOI 10.1108/BFJ-11-2011-0286 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Cheng H, 2019, J CLEAN PROD, V212, P381, DOI 10.1016/j.jclepro.2018.12.014 Clark N, 2019, PACKAG TECHNOL SCI, V32, P577, DOI 10.1002/pts.2474 Coderoni S, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119870 Cooper J, 2007, WOODHEAD PUBL FOOD S, P1 Corona B, 2019, RESOUR CONSERV RECY, V151, DOI 10.1016/j.resconrec.2019.104498 DALKEY N, 1963, MANAGE SCI, V9, P458, DOI 10.1287/mnsc.9.3.458 DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982 DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 de Jesus A, 2018, ECOL ECON, V145, P75, DOI 10.1016/j.ecolecon.2017.08.001 Doinea Mihai, 2015, Informatica Economica, V19, P87, DOI 10.12948/issn14531305/19.1.2015.08 du Plessis HJ, 2012, FOOD RES INT, V47, P210, DOI 10.1016/j.foodres.2011.05.029 Dubey R, 2019, MANAGE DECIS, V57, P767, DOI 10.1108/MD-04-2018-0396 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Esper TL, 2020, J BUS LOGIST, V41, P286, DOI 10.1111/jbl.12267 Esposito B, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12187401 European Commission, 2020, CIRC EC ACT PLAN CLE, DOI [10.2779/717149, DOI 10.2779/717149, DOI 10.1017/CBO9781107415324.004] FAO, 2019, MOV FORW FOOD LOSS W Farooque M, 2019, SUPPLY CHAIN MANAG, V24, P677, DOI 10.1108/SCM-10-2018-0345 Farooque M, 2019, J CLEAN PROD, V228, P882, DOI 10.1016/j.jclepro.2019.04.303 Fassam L, 2017, BRIT FOOD J, V119, P67, DOI [10.1108/BFJ-07-2016-0314, 10.1108/bfj-07-2016-0314] Ferronato N, 2019, J ENVIRON MANAGE, V230, P366, DOI 10.1016/j.jenvman.2018.09.095 Fogarassy C, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10050616 Foley JA, 2011, NATURE, V478, P337, DOI 10.1038/nature10452 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Franco MA, 2019, J CLEAN PROD, V241, DOI 10.1016/j.jclepro.2019.118327 Garcia-Torres S, 2019, SUPPLY CHAIN MANAG, V24, P85, DOI 10.1108/SCM-04-2018-0152 Geissdoerfer M, 2017, J CLEAN PROD, V143, P757, DOI 10.1016/j.jclepro.2016.12.048 Geist MR, 2010, EVAL PROGRAM PLANN, V33, P147, DOI 10.1016/j.evalprogplan.2009.06.006 Genovese A, 2017, OMEGA-INT J MANAGE S, V66, P344, DOI 10.1016/j.omega.2015.05.015 Ghisellini P, 2016, J CLEAN PROD, V114, P11, DOI 10.1016/j.jclepro.2015.09.007 Giagnocavo C, 2017, INT J AGR BIOL ENG, V10, P115, DOI 10.25165/j.ijabe.20171005.3089 Giraud G., 2006, 98 SEM EUR ASS AGR E Gnatzy T, 2011, TECHNOL FORECAST SOC, V78, P1681, DOI 10.1016/j.techfore.2011.04.006 Godfray HCJ, 2010, SCIENCE, V327, P812, DOI 10.1126/science.1185383 Golan E.H., 2004, TRACEABILITY US FOOD Gracia A., 2005, Journal of Food Distribution Research, V36, P45 Grisham T, 2009, INT J MANAG PROJ BUS, V2, P112, DOI 10.1108/17538370910930545 Guo B, 2017, J CLEAN PROD, V142, P2177, DOI 10.1016/j.jclepro.2016.11.063 Gupta H, 2021, J CLEAN PROD, V295, DOI 10.1016/j.jclepro.2021.126253 Halloran A, 2014, FOOD POLICY, V49, P294, DOI 10.1016/j.foodpol.2014.09.005 Hamam M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063453 Hansstein F. V., 2014, 2 INT C ENV EN BIOT Hasson F, 2000, J ADV NURS, V32, P1008, DOI 10.1046/j.1365-2648.2000.01567.x Hernandez B, 2009, EUR J MARKETING, V43, P1232, DOI 10.1108/03090560910976465 HILL RJ, 1977, CONTEMP SOCIOL, V6, P244, DOI 10.2307/2065853 Homburg C, 2017, J ACAD MARKET SCI, V45, P377, DOI 10.1007/s11747-015-0460-7 Homrich AS, 2018, J CLEAN PROD, V175, P525, DOI 10.1016/j.jclepro.2017.11.064 Hoogland CT, 2007, APPETITE, V49, P47, DOI 10.1016/j.appet.2006.11.009 Horvath B, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11102961 Hou JC, 2020, TECHNOL FORECAST SOC, V151, DOI 10.1016/j.techfore.2019.119744 HOUSTON FS, 1986, J MARKETING, V50, P81, DOI 10.2307/1251602 Hsu C.-C., 2007, RES EVALUAT, V12, P10, DOI [10.7275/pdz9-th90, DOI 10.7275/PDZ9-TH90] Huang HF, 2018, J CLEAN PROD, V180, P280, DOI 10.1016/j.jclepro.2018.01.152 Huber F., 2001, J PRODUCT BRAND MANA, V10, P160, DOI [10.1108/10610420110395403, DOI 10.1108/10610420110395403] Ingrassia M., 2017, CHEM ENG TRANS, V58, P865, DOI [10.3303/CET1758145, DOI 10.3303/CET1758145] Islam S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13169385 Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 Jeppsson A., 2017, THESIS Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Kaczorowska J, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11247240 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Karantininis K., 2017, NEW PARADIGM GREEK A, P5 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Kayikci Y, 2022, PROD PLAN CONTROL, V33, P301, DOI 10.1080/09537287.2020.1810757 Kazancoglu Y, 2018, J CLEAN PROD, V195, P1282, DOI 10.1016/j.jclepro.2018.06.015 Kehagia O, 2007, SOCIOL RURALIS, V47, P400, DOI 10.1111/j.1467-9523.2007.00445.x Kher SV, 2013, INT J CONSUM STUD, V37, P73, DOI 10.1111/j.1470-6431.2011.01054.x Kirchherr J, 2017, RESOUR CONSERV RECY, V127, P221, DOI 10.1016/j.resconrec.2017.09.005 Klonaris S., 2021, MODELING EC GROWTH C, P221 Koizumi T, 2015, RENEW SUST ENERG REV, V52, P829, DOI 10.1016/j.rser.2015.06.041 Koo C, 2015, INT J INFORM MANAGE, V35, P64, DOI 10.1016/j.ijinfomgt.2014.10.001 Korhonen J, 2018, ECOL ECON, V143, P37, DOI 10.1016/j.ecolecon.2017.06.041 Kouhizadeh M, 2021, INT J PROD ECON, V231, DOI 10.1016/j.ijpe.2020.107831 Kouhizadeh M, 2020, PROD PLAN CONTROL, V31, P950, DOI 10.1080/09537287.2019.1695925 Kouhizadeh M, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9081712 Kowalski Z, 2021, J CLEAN PROD, V284, DOI 10.1016/j.jclepro.2020.124751 Lehtokunnas T, 2022, J CONSUM CULT, V22, P227, DOI 10.1177/1469540520926252 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Lim SFWT, 2018, INT J PHYS DISTR LOG, V48, P308, DOI 10.1108/IJPDLM-02-2017-0081 Lipinski B., 2013, WORLD RESOURCES INST, DOI DOI 10.2499/9780896295827_03 Lipinski B., 2013, WORKING PAPER INSTAL Liu A, 2017, FOOD CONTROL, V79, P185, DOI 10.1016/j.foodcont.2017.03.038 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Lobell DB, 2011, SCIENCE, V333, P616, DOI [10.1126/science.1204531, 10.1126/science.1206376] MacArthur E., 2013, CIRCULAR EC EC BUSIN, P21 Mahlamaki T, 2020, IND MARKET MANAG, V91, P162, DOI 10.1016/j.indmarman.2020.08.024 Malhotra NK, 2006, MANAGE SCI, V52, P1865, DOI 10.1287/mnsc.1060.0597 Malhotra NK., 2016, MARKETING RES APPL O, V6 ed Mangla SK, 2021, J BUS RES, V135, P1, DOI 10.1016/j.jbusres.2021.06.013 Manikas Ioannis, 2009, International Journal of Postharvest Technology and Innovation, V1, P430, DOI 10.1504/IJPTI.2009.030691 MarketsandMarkets, 2020, FOOD TRAC MARK TECHN Marston S, 2011, DECIS SUPPORT SYST, V51, P176, DOI 10.1016/j.dss.2010.12.006 Merli R, 2018, J CLEAN PROD, V178, P703, DOI 10.1016/j.jclepro.2017.12.112 Mhatre P, 2021, SUSTAIN PROD CONSUMP, V26, P187, DOI 10.1016/j.spc.2020.09.008 Millar N, 2019, ECOL ECON, V158, P11, DOI 10.1016/j.ecolecon.2018.12.012 Murray A, 2017, J BUS ETHICS, V140, P369, DOI 10.1007/s10551-015-2693-2 Nandi S, 2021, IND MANAGE DATA SYST, V121, P333, DOI 10.1108/IMDS-09-2020-0560 Nikolaou IE, 2021, SUSTAIN PROD CONSUMP, V28, P600, DOI 10.1016/j.spc.2021.06.017 Nonhebel S, 2012, ENERGY, V37, P115, DOI 10.1016/j.energy.2011.09.019 Oskarsdottir K, 2019, J FOOD ENG, V240, P153, DOI 10.1016/j.jfoodeng.2018.07.013 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Parfitt J, 2010, PHILOS T R SOC B, V365, P3065, DOI 10.1098/rstb.2010.0126 Patel SK, 2020, ENERGY ECOL ENVIRON, V5, P253, DOI 10.1007/s40974-020-00158-2 Phungpracha E., 2016, Kasetsart Journal of Social Sciences, V37, P82 Pieroni MPP, 2019, J CLEAN PROD, V215, P198, DOI 10.1016/j.jclepro.2019.01.036 Pillet Jean-Charles, 2018, AMCIS 2018 P, P8 Priefer C, 2016, RESOUR CONSERV RECY, V109, P155, DOI 10.1016/j.resconrec.2016.03.004 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Raheem D, 2019, AGRICULTURE-BASEL, V9, DOI 10.3390/agriculture9080168 Ranta V, 2020, IND MARKET MANAG, V87, P291, DOI 10.1016/j.indmarman.2019.10.007 Rashid A, 2013, J CLEAN PROD, V57, P166, DOI 10.1016/j.jclepro.2013.06.012 Ray P. K., 1990, International Journal of Operations & Production Management, V10, P25, DOI 10.1108/01443579010005245 Resende MA, 2008, AM J AGR ECON, V90, P1091, DOI 10.1111/j.1467-8276.2008.01150.x Reynolds CJ, 2014, WASTE MANAGE RES, V32, DOI 10.1177/0734242X14549797 Rodriguez-Salvador B, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107142 Roy V, 2022, INT J MANAG REV, V24, P3, DOI 10.1111/ijmr.12258 de Maya SR, 2011, ECOL ECON, V70, P1767, DOI 10.1016/j.ecolecon.2011.04.019 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Sasazaki S, 2004, MEAT SCI, V67, P275, DOI 10.1016/j.meatsci.2003.10.016 Sauve S, 2016, ENVIRON DEV, V17, P48, DOI 10.1016/j.envdev.2015.09.002 Schoggl JP, 2020, RESOUR CONSERV RECY, V163, DOI 10.1016/j.resconrec.2020.105073 Schroeder L. D., 2017, UNDERSTANDING REGRES, V2nd Schroeder P, 2019, J IND ECOL, V23, P77, DOI 10.1111/jiec.12732 Schweizer L, 2015, PALGRAVE HANDBOOK OF RESEARCH DESIGN IN BUSINESS AND MANAGEMENT, P319 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Sharma YK, 2019, MANAGE DECIS, V57, P995, DOI 10.1108/MD-09-2018-1056 Sharples M, 2016, LECT NOTES COMPUT SC, V9891, P490, DOI 10.1007/978-3-319-45153-4_48 Shen KL, 2020, WORLD J PEDIATR, V16, P223, DOI 10.1007/s12519-020-00343-7 SHEPARD LA, 1985, J EDUC MEAS, V22, P77, DOI 10.1111/j.1745-3984.1985.tb01050.x Sheth JN, 2011, J ACAD MARKET SCI, V39, P21, DOI 10.1007/s11747-010-0216-3 Siegrist M, 2020, APPETITE, V155, DOI 10.1016/j.appet.2020.104814 Slorach PC, 2020, WASTE MANAGE, V113, P359, DOI 10.1016/j.wasman.2020.06.012 Smolders P., 2012, CISC VIS NETW IND GL Stranieri S, 2021, FOOD CONTROL, V119, DOI 10.1016/j.foodcont.2020.107495 Suarez-Eiroa B, 2019, J CLEAN PROD, V214, P952, DOI 10.1016/j.jclepro.2018.12.271 Subramaniam Y, 2020, ENERGY RES SOC SCI, V68, DOI 10.1016/j.erss.2020.101549 Sun RY, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13168861 Taghikhah F, 2019, J CLEAN PROD, V229, P652, DOI 10.1016/j.jclepro.2019.05.051 Tan S. L, 2018, CIRCULAR EC NEEDS TR Tarver T, 2012, J CONS HLTH INTERNET, V16, P366, DOI 10.1080/15398285.2012.701177 Tessitore S., 2020, International Journal on Food System Dynamics, V11, P425, DOI 10.18461/ijfsd.v11i5.65 Thogersen J, 2005, J CONSUM POLICY, V28, P143, DOI 10.1007/s10603-005-2982-8 Thogersen J, 2012, J MARKET MANAG-UK, V28, P313, DOI 10.1080/0267257X.2012.658834 Thyberg KL, 2016, RESOUR CONSERV RECY, V106, P110, DOI 10.1016/j.resconrec.2015.11.016 Tong X, 2018, RESOUR CONSERV RECY, V135, P163, DOI 10.1016/j.resconrec.2017.10.039 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Van der Kooij A. J., 1997, SoftStat '97. Advances in Statistical Software 6. 9th Conference on the Scientific Use of Statistical Software, P99 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Velenturf APM, 2021, SUSTAIN PROD CONSUMP, V27, P1437, DOI 10.1016/j.spc.2021.02.018 Vinnari M, 2009, FUTURES, V41, P269, DOI 10.1016/j.futures.2008.11.014 Vlajic JV, 2018, PROD PLAN CONTROL, V29, P522, DOI 10.1080/09537287.2018.1449264 Vogel J., 2019, DISCOVERING BLOCKCHA von der Gracht HA, 2012, TECHNOL FORECAST SOC, V79, P1525, DOI 10.1016/j.techfore.2012.04.013 Voordouw J, 2011, FOOD QUAL PREFER, V22, P384, DOI 10.1016/j.foodqual.2011.01.009 Wang M, 2021, IND MARKET MANAG, V94, P52, DOI 10.1016/j.indmarman.2021.02.007 Webster FE, 2013, J ACAD MARKET SCI, V41, P389, DOI 10.1007/s11747-013-0331-z Wee C.S., 2014, REV INTEGRATIVE BUSI, V3, P378 Wurster S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126762 Xu XW, 2019, FUTURE GENER COMP SY, V92, P399, DOI 10.1016/j.future.2018.10.010 Yarimoglu E, 2019, BUS STRATEG ENVIRON, V28, P642, DOI 10.1002/bse.2270 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Zhang L, 2016, J CLEAN PROD, V134, P269, DOI 10.1016/j.jclepro.2015.09.078 Zhao HR, 2018, ENVIRON DEV SUSTAIN, V20, P1229, DOI 10.1007/s10668-017-9936-6 NR 200 TC 2 Z9 2 U1 24 U2 24 PD JUL PY 2022 VL 104 BP 196 EP 211 DI 10.1016/j.indmarman.2022.04.021 WC Business; Management SC Business & Economics UT WOS:000807115800001 DA 2022-12-14 ER PT J AU Li, Y Wang, YZ AF Li, Yun Wang, Yuanzhong TI Synergistic strategy for the geographical traceability of wild Boletus tomentipes by means of data fusion analysis SO MICROCHEMICAL JOURNAL DT Article DE Boletus tomentipes; Geographical traceability; FT-MIR; ICP-AES; Data fusion; Quality control ID INFRARED-SPECTROSCOPY; DISCRIMINANT-ANALYSIS; PROTECTED DESIGNATION; CHEMICAL-COMPOSITION; GROWING MUSHROOMS; METALLIC ELEMENTS; NUTRITIONAL-VALUE; ORIGIN; FOOD; EDULIS AB Due to the worldwide importance and public interest concerning to the food quality and safety, there is a growing trend in the geographical trace of food products with various techniques. The purpose of the present study is to evaluate integrated information as an effective strategy for the geographical traceability of wild Boletus tomentipes. To this goal, 76 fruiting bodies were collected, and two mushroom parts (pileus and stipe) were analyzed by Fourier transform-mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES). Five related data matrices (FT-MIR_P, FT-MIR_S, ELE_P, ELE_S and ELE_Q(C/S)) were fused at low- and mid-level, support vector machine (SVM) and random forest (RF) classification algorithms combined with data matrices were used for authenticating the geographical origin. The results of multivariate statistical analysis indicate that data fusion, taking advantage of the information synergy, shows the better classification performance than individual decision making. Also, the comparison of categorized result between two fusion levels suggests that, mid-level data fusion is more stable than low-level among different type models, which could be used as a reliable method for the geographical authentication purposes of wild porcini mushrooms. (C) 2018 Elsevier B.V. All rights reserved. C1 [Li, Yun; Wang, Yuanzhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences RP Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM boletus@126.com CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bagnasco L, 2015, TALANTA, V144, P1225, DOI 10.1016/j.talanta.2015.07.071 Baroni MV, 2015, J AGR FOOD CHEM, V63, P4638, DOI 10.1021/jf5060112 Bassbasi M, 2014, J FOOD COMPOS ANAL, V33, P210, DOI 10.1016/j.jfca.2013.11.010 Bergner N, 2012, CHEMOMETR INTELL LAB, V117, P224, DOI 10.1016/j.chemolab.2012.02.008 Boa E., 2004, NONWOOD FOREST PRODU Bonizzi I, 2007, J APPL MICROBIOL, V102, P667, DOI 10.1111/j.1365-2672.2006.03131.x Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324 Brereton RG, 2010, ANALYST, V135, P230, DOI 10.1039/b918972f Casale M, 2016, TALANTA, V160, P729, DOI 10.1016/j.talanta.2016.08.004 Chen Y, 2008, J PHARMACEUT BIOMED, V47, P469, DOI 10.1016/j.jpba.2008.01.039 Choong YK, 2011, VIB SPECTROSC, V57, P87, DOI 10.1016/j.vibspec.2011.05.008 Chung IM, 2015, J CEREAL SCI, V65, P252, DOI 10.1016/j.jcs.2015.08.001 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Corvucci F, 2015, FOOD CHEM, V169, P297, DOI 10.1016/j.foodchem.2014.07.122 Costas-Rodriguez M, 2010, ANAL CHIM ACTA, V664, P121, DOI 10.1016/j.aca.2010.03.003 Cubero-Leon E, 2014, FOOD RES INT, V60, P95, DOI 10.1016/j.foodres.2013.11.041 D'Archivio AA, 2017, FOOD CHEM, V219, P408, DOI 10.1016/j.foodchem.2016.09.169 Danezis GP, 2016, CURR OPIN FOOD SCI, V10, P22, DOI 10.1016/j.cofs.2016.07.003 Dankowska A, 2017, TALANTA, V172, P215, DOI 10.1016/j.talanta.2017.05.036 Pinho PG, 2008, J AGR FOOD CHEM, V56, P1704, DOI 10.1021/jf073181y de Toledo PRAB, 2017, FOOD CONTROL, V73, P164, DOI 10.1016/j.foodcont.2016.08.001 Dietterich TG, 2000, MACH LEARN, V40, P139, DOI 10.1023/A:1007607513941 Ding X, 2012, BIOCHEM SYST ECOL, V44, P233, DOI 10.1016/j.bse.2012.06.003 DURRANTWHYTE HF, 1988, INT J ROBOT RES, V7, P97, DOI 10.1177/027836498800700608 Elmastas M, 2007, J FOOD COMPOS ANAL, V20, P337, DOI 10.1016/j.jfca.2006.07.003 Falandysz J, 2017, ECOTOX ENVIRON SAFE, V142, P497, DOI 10.1016/j.ecoenv.2017.04.055 Falandysz J, 2011, J ENVIRON SCI HEAL B, V46, P231, DOI 10.1080/03601234.2011.540528 Godelmann R, 2013, J AGR FOOD CHEM, V61, P5610, DOI 10.1021/jf400800d Guo Y, 2016, SPECTROCHIM ACTA A, V153, P79, DOI 10.1016/j.saa.2015.08.006 Heleno SA, 2011, LWT-FOOD SCI TECHNOL, V44, P1343, DOI 10.1016/j.lwt.2011.01.017 Ho TK, 1998, IEEE T PATTERN ANAL, V20, P832, DOI 10.1109/34.709601 KAISER HF, 1960, EDUC PSYCHOL MEAS, V20, P141, DOI 10.1177/001316446002000116 Kalac P, 2009, FOOD CHEM, V113, P9, DOI 10.1016/j.foodchem.2008.07.077 Kasprzyk I, 2018, FOOD CONTROL, V84, P33, DOI 10.1016/j.foodcont.2017.07.015 KENNARD RW, 1969, TECHNOMETRICS, V11, P137, DOI 10.2307/1266770 Kim CS, 2015, J ANTIBIOT, V68, P414, DOI 10.1038/ja.2015.2 Klockmann S, 2017, J AGR FOOD CHEM, V65, P1456, DOI 10.1021/acs.jafc.6b05007 Kuligowski J, 2015, ANALYST, V140, P4521, DOI 10.1039/c5an00706b Li W, 2014, SPECTROSC SPECT ANAL, V34, P3235, DOI 10.3964/j.issn.1000-0593(2014)12-3235-06 Li Y, 2017, SPECTROCHIM ACTA A, V177, P20, DOI 10.1016/j.saa.2017.01.029 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Maguire A, 2015, ANALYST, V140, P2473, DOI 10.1039/c4an01887g Men H, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17071656 Mohacek-Grosev V, 2001, SPECTROCHIM ACTA A, V57, P2815, DOI 10.1016/S1386-1425(01)00584-4 O'Gorman A, 2010, J AGR FOOD CHEM, V58, P7770, DOI 10.1021/jf101123a Paneque P, 2017, FOOD CONTROL, V75, P203, DOI 10.1016/j.foodcont.2016.12.006 Ramirez-Anguiano AC, 2007, J SCI FOOD AGR, V87, P2272, DOI 10.1002/jsfa.2983 Rebentrost P, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.130503 Shen SG, 2013, ANAL METHODS-UK, V5, P6177, DOI 10.1039/c3ay40700d Silva CS, 2017, TRAC-TREND ANAL CHEM, V95, P23, DOI 10.1016/j.trac.2017.07.026 Sitta N, 2008, ECON BOT, V62, P307, DOI 10.1007/s12231-008-9037-4 Teichmann A, 2007, LWT-FOOD SCI TECHNOL, V40, P815, DOI 10.1016/j.lwt.2006.04.003 Tsai SY, 2008, FOOD CHEM, V107, P977, DOI 10.1016/j.foodchem.2007.07.080 Vamanu E, 2013, BIOMED RES INT, V2013, DOI 10.1155/2013/313905 van der Maaten L, 2008, J MACH LEARN RES, V9, P2579 Vidovic SS, 2010, FOOD BIOPHYS, V5, P49, DOI 10.1007/s11483-009-9143-6 Wang D, 2014, CARBOHYD POLYM, V105, P127, DOI 10.1016/j.carbpol.2013.12.085 Wang X.C., 2016, PARAMETERS OPTIMIZAT, P127 Wang XM, 2014, FOOD CHEM, V151, P279, DOI 10.1016/j.foodchem.2013.11.062 Wani BA, 2010, J MED PLANTS RES, V4, P2598 Wong HY, 2016, ANAL CHIM ACTA, V938, P90, DOI 10.1016/j.aca.2016.07.028 NR 64 TC 25 Z9 27 U1 4 U2 23 PD JUL PY 2018 VL 140 BP 38 EP 46 DI 10.1016/j.microc.2018.04.001 WC Chemistry, Analytical SC Chemistry UT WOS:000437391000006 DA 2022-12-14 ER PT J AU Xu, C Chen, K Zuo, M Liu, HZ Wu, YA AF Xu, Cheng Chen, Kai Zuo, Min Liu, Hongzhe Wu, Yanan TI Urban Fruit Quality Traceability Model Based on Smart Contract for Internet of Things SO WIRELESS COMMUNICATIONS & MOBILE COMPUTING DT Article AB In Internet of things, compared with the traditional traceability system, the existing system has difficulties in operation, data are easily lost, data are uncoordinated, standards are not unified, and so on. In this paper, based on the characteristics of blockchain, the traceability model of urban fruits is established to reduce the risk of counterfeit and shoddy urban fruits. The collected data is integrated into the chain for diversified display, providing different permissions and platforms for multiple roles. Blockchain and supporting intelligent hardware are used to realize the real record and tracking of the whole process of fruit. Improve the transparency and efficiency of supply chain, and reduce the supply chain cost. The experimental results show that the proposed algorithm model can be applied to the fruit service field and effectively improve the service level of smart city. C1 [Xu, Cheng; Chen, Kai; Liu, Hongzhe; Wu, Yanan] Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China. [Xu, Cheng; Zuo, Min] Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China. [Liu, Hongzhe; Wu, Yanan] Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing, Peoples R China. C3 Beijing Union University; Beijing Technology & Business University; Beijing Jiaotong University RP Liu, HZ (corresponding author), Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.; Liu, HZ (corresponding author), Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing, Peoples R China. EM liuhongzhe@buu.edu.cn CR Alabbasi Y., 2020, INT J INNOVATION DIG, V11 Chen Y., 2020, J AGR BIG DATA, V2, P61 Dong YuDe, 2016, Transactions of the Chinese Society of Agricultural Engineering, V32, P280, DOI 10.11975/j.issn.1002-6819.2016.01.039 Hongwei W., 2020, MODERN AGR SCI TECHN, V19, P223 Jie L., 2020, J XIDIAN U, V47, P1 Jing Z., 2020, CREDIT, V38, P49 Julie E. Golden, 2020, BLOCKCHAIN TECHNOLOG Luo X., 2020, INFORM SYSTEM ENG, V6, P28 Nan Z., 2020, COOPERATIVE EC TECHN, V20, P151 Ping D., 2020, COMPUTER MEASUREMENT, V28, P76 Shen X., 2016, CHIN J NETW INF SECU, V2, P11 Tingting C., 2020, CYBERSPACE SECURITY, V11, P1 Wang H., 2020, COMPUTER APPL SOFTWA, V37, P1 Wang X., 2020, CHINA BUSINESS FORUM, V20, P30 Yang XinTing, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P212 Yong Y., 2016, ZIDONGHUA XUEBAOACTA, V42, P481 [张宁 Zhang Ning], 2016, [中国电机工程学报, Proceedings of the Chinese Society of Electrical Engineering], V36, P4011 Zhiwei Z., 2020, J SOFTWARE, V31, P2903 Zhu Y., 2020, J ENG SCI, V42, P1267 Zongmei L., 2020, FOOD MACHINERY, V36, P102 NR 20 TC 2 Z9 2 U1 11 U2 22 PD AUG 15 PY 2021 VL 2021 AR 9369074 DI 10.1155/2021/9369074 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000687445200018 DA 2022-12-14 ER PT J AU Wang, JS Wang, JS Liu, CM AF Wang, Jui-Sheng Wang, Jau-Shyong Liu, Chia-Ming TI Based on Analytic Hierarchy Process (AHP) to discuss the Key Success Factors in the Establishment of Product Traceability Systems for Eco-Agriculture SO EKOLOJI DT Article DE farmer goal; industrial policy; marketing channel; education and training; technological innovation ID WILLINGNESS-TO-PAY; FOOD-SUPPLY CHAIN; SAFETY; PREFERENCES; MANAGEMENT; FRAMEWORK; INDUSTRY; QUALITY; MILK AB After the international trade circulation of global products, food safety issues have been an alerting living habit in human life. A food traceability system is a rapid and effective solution to trace the source of problematic food for pulling off shelves. The listed manufacturers who pass the certificate of Centers for Eco-agricultural Product Certification are randomly distributed the questionnaire in this study. Total 300 copies of questionnaire are distributed and 226 valid copies are retrieved, with the retrieval rate 75%. The research result reveals that production and marketing management are stressed the most by the experts for the establishment of product traceability systems for agriculture. The top 5 emphasized indicators contain (1) quality control capability, (2) international standards, (3) marketing and promotion, (4) market demand trend, and (5) industrial policy. Conclusion and suggestions are eventually proposed, according to the result, expecting to offer reference for the promotion of traceability systems for Eco-agricultural products, to accelerate the transformation of Eco-agriculture in Taiwan to establish product traceability systems for agriculture, and to have high-quality produce which is long-term created by farmers in Taiwan acquire the safety certificate equivalent to international standards. C1 [Wang, Jui-Sheng] Natl Univ Kaohsiung, Inst Business & Management, 700 Kaohsiung Univ Rd, Kaohsiung 811, Taiwan. [Wang, Jau-Shyong] Shu Te Univ, Dept Business Adm, Kaohsiung, Taiwan. [Liu, Chia-Ming] Chang Jung Christian Univ, Dept Int Business, 1 Changda Rd, Tainan 71101, Taiwan. C3 National University Kaohsiung; Shu-Te University; Chang Jung Christian University RP Liu, CM (corresponding author), Chang Jung Christian Univ, Dept Int Business, 1 Changda Rd, Tainan 71101, Taiwan. EM cm2815@mail.cjcu.edu.tw CR Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Azuara G, 2012, IND MANAGE DATA SYST, V112, P340, DOI 10.1108/02635571211210022 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chalak A, 2012, FOOD QUAL PREFER, V26, P81, DOI 10.1016/j.foodqual.2012.04.001 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Donnelly KAM, 2012, FOOD CONTROL, V27, P228, DOI 10.1016/j.foodcont.2012.03.021 Fancovicova J, 2018, EURASIA J MATH SCI T, V14, P721, DOI 10.12973/ejmste/80817 Gracia A, 2016, FOOD CONTROL, V61, P39, DOI 10.1016/j.foodcont.2015.09.023 Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hong IH, 2011, J FOOD ENG, V106, P119, DOI 10.1016/j.jfoodeng.2011.04.014 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 Konedrali G, 2017, EURASIA J MATH SCI T, V13, P7539, DOI 10.12973/ejmste/78609 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Luo YZ, 2018, REV CERCET INTERV SO, V61, P187 Lxe, 2011, TRAC FOOD SUPPL CHAI Manfreda G, 2014, INT J FOOD MICROBIOL, V184, P2, DOI 10.1016/j.ijfoodmicro.2014.01.013 Narsimhalu U, 2015, PROCD SOC BEHV, V189, P17, DOI 10.1016/j.sbspro.2015.03.188 Oznacar B, 2017, EURASIA J MATH SCI T, V13, P4513, DOI 10.12973/eurasia.2017.00944a Saaty T.L., 1980, ANAL HIERARCHY PROCE Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Wu LH, 2012, CAN J AGR ECON, V60, P317, DOI 10.1111/j.1744-7976.2011.01236.x Wu LH, 2011, BRIT FOOD J, V113, P519, DOI 10.1108/00070701111123998 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 28 TC 1 Z9 1 U1 1 U2 9 PY 2019 VL 28 IS 107 BP 3783 EP 3789 AR UNSP e107422 WC Ecology SC Environmental Sciences & Ecology UT WOS:000461678300421 DA 2022-12-14 ER PT J AU Yang, F Wang, KY Han, YY Qiao, Z AF Yang, Feng Wang, Kaiyi Han, Yanyun Qiao, Zhong TI A Cloud-Based Digital Farm Management System for Vegetable Production Process Management and Quality Traceability SO SUSTAINABILITY DT Article DE farm management; auto-identification technology; production process; quality traceability; production cost ID INFORMATION-SYSTEMS; FOOD SAFETY; ADOPTION; TECHNOLOGIES AB Farm Management Information Systems (FMISs) are being expanded to improve operation efficiency, reduce inputs, and ensure compliance with standards and regulations. However, this goal is difficult to attain in the vegetable sector, where data acquisition is time-consuming and data at different stages is fragmented by the potential diversity of crops and multiple batches cultivated at any given farm. This applies, in particular, to farms in China, which have small areas and low degrees of mechanization. This study presents an integrated approach to track and trace production efficiently through our Digital Farm Management System (DFMS), which adopts the cloud framework and utilizes Quick Response (QR) codes and Radio Frequency Identification (RFID) technology. Specifically, a data acquisition system is proposed that runs on a smartphone for the efficient gathering of planting information in the field. Moreover, DFMS generates statistics and analyses of planting areas, costs, and yields. DFMS meets the FMIS requirements and provides the accurate tracking and tracing of the production for each batch in an efficient manner. The system has been applied in a large-scale vegetable production enterprise, consisting of 12 farms distributed throughout China. This application shows that DFMS is a highly efficient solution for precise vegetable farm management. C1 [Yang, Feng; Qiao, Zhong] China Agr Univ, Coll Econ & Management, Beijing 100083, Peoples R China. [Yang, Feng; Wang, Kaiyi; Han, Yanyun] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. C3 China Agricultural University; Beijing Academy of Agriculture & Forestry RP Wang, KY; Han, YY (corresponding author), Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. EM yangf@nercita.org.cn; wangky@nercita.org.cn; hanyy@nercita.org.cn; qiaozhong_dr@163.com CR Abdelfattah AS, 2018, J KING SAUD UNIV-COM, V30, P164, DOI 10.1016/j.jksuci.2017.03.002 Ampatzidis Y, 2016, COMPUT ELECTRON AGR, V122, P161, DOI 10.1016/j.compag.2016.01.032 Ding JP, 2015, J INTEGR AGR, V14, P2380, DOI 10.1016/S2095-3119(15)61127-3 Fountas S, 2015, COMPUT ELECTRON AGR, V115, P40, DOI 10.1016/j.compag.2015.05.011 Fountas S, 2015, COMPUT ELECTRON AGR, V110, P131, DOI 10.1016/j.compag.2014.11.011 Husemann C, 2014, EKON POLJOPR, V61, P441, DOI 10.5937/ekoPolj1402441H Jin SS, 2011, FOOD CONTROL, V22, P204, DOI 10.1016/j.foodcont.2010.06.021 Kaloxylos A, 2014, COMPUT ELECTRON AGR, V100, P168, DOI 10.1016/j.compag.2013.11.014 Kaloxylos A, 2012, COMPUT ELECTRON AGR, V89, P130, DOI 10.1016/j.compag.2012.09.002 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kim YW, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9111995 Knuth U, 2018, J ENVIRON MANAGE, V220, P109, DOI 10.1016/j.jenvman.2018.04.087 Korada SK, 2018, FOOD SAFETY AND PRESERVATION: MODERN BIOLOGICAL APPROACHES TO IMPROVING CONSUMER HEALTH, P1, DOI 10.1016/B978-0-12-814956-0.00001-9 Lu L, 2006, LECT NOTES ARTIF INT, V4198, P1 Mainetti L, 2013, COMPUT ELECTRON AGR, V98, P146, DOI 10.1016/j.compag.2013.07.015 Nikkila R, 2010, COMPUT ELECTRON AGR, V70, P328, DOI 10.1016/j.compag.2009.08.013 Pagano M., 2014, Journal of Food, Agriculture & Environment, V12, P706 Paraforos D.S., 2017, ADV ANIM BIOSCI, V8, P650 Paraforos DS, 2017, COMPUT ELECTRON AGR, V142, P504, DOI 10.1016/j.compag.2017.11.022 Paraforos DS, 2016, IFAC PAPERSONLINE, V49, P320, DOI 10.1016/j.ifacol.2016.10.060 Pigini D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101910 Pivoto D., 2018, Information Processing in Agriculture, V5, P21, DOI 10.1016/j.inpa.2017.12.002 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sarac A, 2010, INT J PROD ECON, V128, P77, DOI 10.1016/j.ijpe.2010.07.039 Sorensen CG, 2011, COMPUT ELECTRON AGR, V76, P266, DOI 10.1016/j.compag.2011.02.005 Sorensen CG, 2010, COMPUT ELECTRON AGR, V73, P44, DOI 10.1016/j.compag.2010.04.003 Sorensen CG, 2010, COMPUT ELECTRON AGR, V72, P37, DOI 10.1016/j.compag.2010.02.003 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Thakur M, 2015, COMPUT ELECTRON AGR, V117, P22, DOI 10.1016/j.compag.2015.07.006 van de Weerd I, 2016, INFORM MANAGE-AMSTER, V53, P915, DOI 10.1016/j.im.2016.05.008 Wang J, 2017, FOOD CONTROL, V79, P363, DOI 10.1016/j.foodcont.2017.04.013 Wu XH, 2013, ECOL ENG, V61, P335, DOI 10.1016/j.ecoleng.2013.09.060 Yang F., 2010, IFIP ADV INFORM COMM, V346 Zhong Z, 2018, J CLEAN PROD, V172, P2266, DOI 10.1016/j.jclepro.2017.11.184 NR 34 TC 8 Z9 8 U1 12 U2 43 PD NOV PY 2018 VL 10 IS 11 AR 4007 DI 10.3390/su10114007 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000451531700181 DA 2022-12-14 ER PT J AU Perez, L Lasso, R AF Perez, Luiyiana Lasso, Rolando TI Business Management App for cattle ranches, articulated with the bovine traceability program SO INGENIERIA SOLIDARIA DT Article DE Growing Complexity; Organizational Structure; Information Processing Systems AB Introduction: The article is the result of Research "Development and implementation of a software for the management and control of livestock farms, articulated with the bovine traceability program". This initiative was financed by SENACYT and carried out at the Universidad Tecnologica de Panama, Azuero Campus, with the collaboration of Consultant Luiyiana Perez during 2017 and 2018 Problem: This project arises from lack of producers in the field information registration and bovine traceability program. Objective: To develop an app that allows you to manage the management of livestock farms, integrated to Livestock Management System (SIGEPE in its Spanish acronym) from the agencies of the Ministry of Agricultural Development. Methodology: A study of the level of the digital divide index (IBD) in the sector was carried out; a proportional stratified sampling was used for the selection of the producers and the view-controller model was used to develop the app. Result: A database was created, with information about nutrition, health programs, veterinary products among others. An application was developed, including its respective modules and a training plan for the implementation of the app. Conclusion: The livestock business competitiveness vs. bovine traceability regulation demands the appropriate field information registration at all points of animal production. Originality: The incorporation of livestock control by the various agencies and their articulation with the bovine traceability. Limitations: The users' deficient technological skills in the management and use of ICTs. C1 [Perez, Luiyiana; Lasso, Rolando] Univ Tecnol Panama, Panama City, Panama. C3 Universidad Tecnologica de Panama RP Perez, L (corresponding author), Univ Tecnol Panama, Panama City, Panama. EM luiyiana.perez@utp.ac.pa CR Avalos I., 1992, APROXIMACION GERENCI Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Gaynor G., 1999, MANUAL GESTION TECNO Kebebe E, 2019, TECHNOL SOC, V57, P30, DOI 10.1016/j.techsoc.2018.12.002 Luiyiana P., 2017, 6 C ING CIENC TECN E Manjarres Henriquez L., 2012, DIMENS EMPRESARIAL, V10, P18 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Moreno L., 2011, TELEFONIA MOVIL AREA Perez L., 2017, 15 LACCEI INT MULT E Perez L., 2017, REV I D, V13, P54 NR 10 TC 0 Z9 0 U1 0 U2 2 PY 2019 VL 15 IS 27 DI 10.16925/2357-6014.2019.01.10 WC Engineering, Multidisciplinary SC Engineering UT WOS:000496460200001 DA 2022-12-14 ER PT J AU Liu, LN Liu, PZ Wen, FJ Zhang, C Zhao, R Yan, ML Yu, XR AF Liu, Lining Liu, Pingzeng Wen, Fujiang Zhang, Chao Zhao, Rui Yan, Maoling Yu, Xueru TI Information collection system of duck products based on IoT SO EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING DT Article DE Information collection system; Duck product traceability; IoT; Industrial chain; RFID ID TRACEABILITY SYSTEM; SUPPLY CHAIN; INTERNET; THINGS; FRAMEWORK; QUALITY; SAFETY; CATTLE; CHINA AB In view of the problems existing in the processing of duck products, such as complicated technology, difficulties in information collection and information linkage, and lack of dedicated information collection equipment, the duck product traceability information of the Institute of Things automatic collection system was developed. Acquisition system is mainly composed of sensing terminals, bus structure, and host computer management system. Perceiving the terminal can automatically perceive the information of each link and transmit the perception information to the upper computer management system through the bus. The upper computer management system realizes the functions of storing the sensing information, intelligent analysis, and alarm prompting. The long-term operation results show that the system performance is stable and reliable; the collection of data is efficient, complete, and accurate; and the degree of automation of the system is high, which significantly improves the product quality and safety supervision capabilities of the company. C1 [Liu, Lining; Liu, Pingzeng; Zhang, Chao; Zhao, Rui; Yan, Maoling; Yu, Xueru] Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China. [Wen, Fujiang] Shandong Agr Univ, Tai An 271018, Shandong, Peoples R China. C3 Shandong Agricultural University; Shandong Agricultural University RP Liu, PZ (corresponding author), Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China. EM lpz8565@126.com CR Ambrosin M, 2016, IEEE MICRO, V36, P25, DOI 10.1109/MM.2016.101 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Bevilacqua M, 2009, J FOOD ENG, V93, P13, DOI 10.1016/j.jfoodeng.2008.12.020 Broll G, 2009, IEEE INTERNET COMPUT, V13, P74, DOI 10.1109/MIC.2009.120 Choi JH, 2017, ETRI J, V39, P202, DOI 10.4218/etrij.17.2816.0109 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Kaloxylos A, 2012, COMPUT ELECTRON AGR, V89, P130, DOI 10.1016/j.compag.2012.09.002 Kranz M, 2010, IEEE INTERNET COMPUT, V14, P46, DOI 10.1109/MIC.2009.141 Ma CongGuo, 2008, Transactions of the Chinese Society of Agricultural Engineering, V24, P121 Mc Inerney B, 2011, COMPUT ELECTRON AGR, V77, P1, DOI 10.1016/j.compag.2011.03.001 Pang Chao, 2011, Transactions of the Chinese Society of Agricultural Engineering, V27, P147 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Ren ShouGang, 2010, Transactions of the Chinese Society of Agricultural Engineering, V26, P229 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Sugiura Katsuaki, 2008, Vet Ital, V44, P519 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Welbourne E, 2009, IEEE INTERNET COMPUT, V13, P48, DOI 10.1109/MIC.2009.52 Zhao J, 2017, FOOD CONTROL, V78, P469, DOI 10.1016/j.foodcont.2017.03.017 NR 18 TC 1 Z9 1 U1 2 U2 17 PD MAY 18 PY 2018 AR 124 DI 10.1186/s13638-018-1144-z WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications UT WOS:000432558300001 DA 2022-12-14 ER PT J AU Littler, ICM Atkinson, EG Manson, PJ Ballico, M Kosubek, E Taubert, D Hollandt, J AF Littler, I. C. M. Atkinson, E. G. Manson, P. J. Ballico, M. Kosubek, E. Taubert, D. Hollandt, J. TI Aperture area measurement using two different traceability routes SO MEASUREMENT SCIENCE AND TECHNOLOGY DT Article DE aperture area; solid angle; metrology; radiometry; photometry; traceability AB Given the advances in cryogenic radiometry, area measurement is now one of the limiting factors in the realisation of radiometric and photometric units as well as radiometric temperature scales. An inter-comparison of aperture area measurements using complementary methods at two different national metrology institutes has shown excellent agreement in the values measured, with the measurements agreeing well within the 95% confidence intervals. Importantly, the statistical scatter of the values is consistent with the magnitude of the confidence intervals, implying that the estimates of uncertainty are not overly conservative. This provides increased confidence to both institutes that systematic errors have not been overlooked and that the uncertainty contributions have been realistically assessed. C1 [Littler, I. C. M.; Atkinson, E. G.; Manson, P. J.; Ballico, M.] Natl Measurement Inst, West Lindfield, NSW 2070, Australia. [Kosubek, E.; Taubert, D.; Hollandt, J.] Phys Tech Bundesanstalt, D-10587 Berlin, Germany. C3 National Measurement Institute Australia - NMI; Physikalisch-Technische Bundesanstalt (PTB) RP Littler, ICM (corresponding author), Natl Measurement Inst, POB 264, West Lindfield, NSW 2070, Australia. EM ian.littler@measurement.gov.au CR [Anonymous], 2008, JCGM, V100, P2008 Carol J B, 1993, METROLOGIA, V30, P309 FOWLER JB, 1995, J RES NATL INST STAN, V100, P277, DOI 10.6028/jres.100.020 Hartmann J, 2001, MEAS SCI TECHNOL, V12, P1678, DOI 10.1088/0957-0233/12/10/309 Jones N, 2009, NATURE, V459, P902, DOI 10.1038/459902a Littler ICM, 2013, METROLOGIA, V50, P596, DOI 10.1088/0026-1394/50/6/596 Littler I C M, 2014, NEWRAD, V2014, P170 Neugebauer M, 2015, C EXH LE BELG Taubert DR, 2003, METROLOGIA, V40, pS35, DOI 10.1088/0026-1394/40/1/309 NR 9 TC 2 Z9 2 U1 0 U2 3 PD DEC PY 2015 VL 26 IS 12 AR 125201 DI 10.1088/0957-0233/26/12/125201 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000366448200021 DA 2022-12-14 ER PT J AU Sillanpapa, S Heinonen, M AF Sillanpapa, S. Heinonen, M. TI A mixing method for traceable air velocity measurements SO MEASUREMENT SCIENCE AND TECHNOLOGY DT Article; Proceedings Paper CT 8th International Symposium on Measurement Technology and Intelligent Instruments CY SEP 24-27, 2007 CL Tohoku Univ, Sendai, JAPAN HO Tohoku Univ DE air velocity; wind tunnel; traceability; mass flow ID HOT-WIRE; CALIBRATION; FLOW; UNCERTAINTY AB A novel and quite simple method to establish a traceability link between air velocity and the national standards of mass and time is presented in this paper. The method is based on the humidification of flowing air before the blower of a wind tunnel with a known mass flow of water. Then air velocity can be calculated as a function of humidification water flow. The method is compared against a Pitot-tube-based velocity measurement in a wind tunnel at the MIKES. The results of these two different methods agreed well, with a maximum difference of 0.7%. C1 [Sillanpapa, S.; Heinonen, M.] Ctr Metrol & Accreditat MIKES, FI-02151 Espoo, Finland. RP Sillanpapa, S (corresponding author), Ctr Metrol & Accreditat MIKES, POB 9, FI-02151 Espoo, Finland. EM sampo.sillanpaa@mikes.fi; martti.heinonen@mikes.fi CR Al-Garni AM, 2007, FLOW MEAS INSTRUM, V18, P95, DOI 10.1016/j.flowmeasinst.2007.01.003 Bean VE, 1999, METROLOGY - AT THE THRESHOLD OF THE CENTURY ARE WE READY?, P413 BELL JH, 1989, AIAA J, V27, P372, DOI 10.2514/3.10122 Chua LP, 2000, INT COMMUN HEAT MASS, V27, P507, DOI 10.1016/S0735-1933(00)00133-0 ELGER DF, 1989, J PHYS E SCI INSTRUM, V22, P166, DOI 10.1088/0022-3735/22/3/008 Gullman-Strand J, 2004, INT J HEAT FLUID FL, V25, P451, DOI 10.1016/j.ijheatfluidflow.2004.02.012 International Organization for Standardization, 1993, GUID EXPR UNC MEAS International Organization for Standardization, 1993, INT VOC BAS GEN TERM Iyer VA, 2005, AIAA J, V43, P512, DOI 10.2514/1.8283 LAINE S, 1975, 75A2 HELS U TECHN LA LEE T, 1991, MEAS SCI TECHNOL, V2, P643, DOI 10.1088/0957-0233/2/7/011 MEHTA RD, 1979, AERONAUT J, V83, P443 Picard A, 2008, METROLOGIA, V45, P149, DOI 10.1088/0026-1394/45/2/004 SANCHEZ JR, 2004, P 12 INT S APPL LAS Sillanpaa S, 2006, MEASUREMENT, V39, P26, DOI 10.1016/j.measurement.2005.10.002 Sillanpaa S, 2008, METROLOGIA, V45, P249, DOI 10.1088/0026-1394/45/2/015 Sonntag D., 1994, Meteorologische Zeitschrift, V3, P51 Yue Z, 1998, MEAS SCI TECHNOL, V9, P1506, DOI 10.1088/0957-0233/9/9/020 NR 18 TC 0 Z9 0 U1 0 U2 5 PD AUG PY 2008 VL 19 IS 8 AR 085409 DI 10.1088/0957-0233/19/8/085409 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000257841800036 DA 2022-12-14 ER PT J AU Bai, Y Liu, HJ Zhang, B Zhang, JK Wu, H Zhao, SS Qie, MJ Guo, J Wang, Q Zhao, Y AF Bai, Yang Liu, Haijin Zhang, Bin Zhang, Jiukai Wu, Hao Zhao, Shanshan Qie, Mengjie Guo, Jun Wang, Qian Zhao, Yan TI Research Progress on Traceability and Authenticity of Beef SO FOOD REVIEWS INTERNATIONAL DT Review; Early Access DE Traceability; authenticity; stable isotope; dna technology; spectroscopic technology; volatilomics technology; metabolomic analysis; mineral elements analysis; fatty acid analysis ID STABLE-ISOTOPE COMPOSITION; FATTY-ACID-COMPOSITION; GEOGRAPHICAL ORIGIN; MINCED BEEF; PCR-RFLP; FOOD AUTHENTICATION; MEAT ADULTERATION; NIR SPECTROSCOPY; ELECTRONIC NOSE; POTENTIAL TOOL AB Nowadays, stable isotope technology, DNA technology, spectroscopic technology, volatilomics technology, metabolomic analysis, mineral elements analysis and fatty acid analysis have been used to trace the origin of beef. Stable isotope technique is often combined with mineral elements analysis. Both of them have the advantages of high accuracy and low detection limit. Stable isotopes may be affected by geography and dietary history. The mineral elements analysis has high requirements for experimental operation. DNA technology is not affected by geography and dietary history, so it has unique advantages. It faces the problem of selecting molecular markers. Spectroscopic technology and volatilomics technology based on electronic nose do not require sample pretreatment. The limitation is that infrared and Raman are insensitive, and the data processing of hyperspectral image is difficult. Raman is much less sensitive than Infrared. The use of electronic nose has higher requirements for environment. Volatilomics technology based on mass spectrometry (MS), metabolomic analysis and fatty acid analysis have been used to analyze organic components. They can provide comprehensive information, but they have higher requirements for sample preservation. Metabolomic analysis equipment is expensive. The research on traceability and authenticity of beef can help to ensure the security and quality of beef. C1 [Bai, Yang; Zhao, Shanshan; Qie, Mengjie; Wang, Qian; Zhao, Yan] Chinese Acad Agr Sci, Lab Qual & Safety Anim Prod, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing, Peoples R China. [Bai, Yang; Guo, Jun; Wang, Qian] Inner Mongolia Agr Univ, Coll Food Sci & Engn, Hohhot, Peoples R China. [Liu, Haijin] Tibet Autonomous Reg Agr & Livestock Prod Qual &, Lhasa, Peoples R China. [Zhang, Bin] Henan Univ Sci & Technol, Coll Food & Bioengn, Luoyang, Peoples R China. [Zhang, Jiukai] Chinese Acad Inspect & Quarantine, Agroprod Safety Res Ctr, Beijing, Peoples R China. [Wu, Hao] Shenzhen Customs, Food Inspect & Quarantine Ctr, Shenzhen, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Inner Mongolia Agricultural University; Henan University of Science & Technology; Chinese Academy of Inspection & Quarantine RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR Adebo OA, 2021, INT J FOOD SCI TECH, V56, P1514, DOI 10.1111/ijfs.14794 Alamprese C, 2016, MEAT SCI, V121, P175, DOI 10.1016/j.meatsci.2016.06.018 Alamprese C, 2013, LWT-FOOD SCI TECHNOL, V53, P225, DOI 10.1016/j.lwt.2013.01.027 Bahar B, 2008, FOOD CHEM, V106, P1299, DOI 10.1016/j.foodchem.2007.07.053 Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Bahar B, 2014, RAPID COMMUN MASS SP, V28, P1011, DOI 10.1002/rcm.6872 Bai Jing, 2019, Shipin Kexue / Food Science, V40, P287 Bai Y.B., 2015, J NORMAL U NAT SCI, V1, P270 Balakrishna K, 2019, FOOD ADDIT CONTAM A, V36, P1435, DOI 10.1080/19440049.2019.1633477 Bao X.P., 2018, CHINE I FOOD SCI TEC, P2 Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Bong YS, 2012, FOOD SCI BIOTECHNOL, V21, P233, DOI 10.1007/s10068-012-0030-4 Bong YS, 2010, RAPID COMMUN MASS SP, V24, P155, DOI 10.1002/rcm.4366 Boyaci I.H., 2014, ABSTRACTS PAPERS AM, P248 Cai XianFeng, 2011, Scientia Agricultura Sinica, V44, P4272 Chen SY, 2010, J GENET GENOMICS, V37, P763, DOI 10.1016/S1673-8527(09)60093-X Chen YingLi, 2019, Food Research and Development, V40, P141 [程碧君 Cheng Bijun], 2012, [核农学报, Acta Agriculturae Nucleatae Sinica], V26, P517 Chongtham N, 2021, J FOOD COMPOS ANAL, V95, DOI 10.1016/j.jfca.2020.103662 Cornale P, 2009, AIP CONF PROC, V1137, P267, DOI 10.1063/1.3156522 Cozzolino D, 2004, LEBENSM-WISS TECHNOL, V37, P447, DOI 10.1016/j.lwt.2003.10.013 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 De Smet S, 2004, RAPID COMMUN MASS SP, V18, P1227, DOI 10.1002/rcm.1471 Di Rosa AR, 2017, J FOOD ENG, V210, P62, DOI 10.1016/j.jfoodeng.2017.04.024 Domaradzki P, 2016, BIOL TRACE ELEM RES, V171, P328, DOI 10.1007/s12011-015-0549-3 El Hadi MAM, 2013, MOLECULES, V18, P8200, DOI 10.3390/molecules18078200 Esteki M, 2018, FOOD CONTROL, V93, P165, DOI 10.1016/j.foodcont.2018.06.015 Ghidini S., 2006, Annali della Facolta di Medicina Veterinaria, Universita di Parma, V26, P193 Gopi K, 2019, TRENDS FOOD SCI TECH, V91, P294, DOI 10.1016/j.tifs.2019.07.010 Guo BL, 2009, CHINESE J ANAL CHEM, V37, P1333 Guo BoLi, 2018, Scientia Agricultura Sinica, V51, P2391 Guo BoLi, 2007, Scientia Agricultura Sinica, V40, P365 Guo BoLi, 2006, Scientia Agricultura Sinica, V39, P1885 Hao LZ, 2019, KAFKAS UNIV VET FAK, V25, P93, DOI 10.9775/kvfd.2018.20366 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Heo EJ, 2014, KOREAN J FOOD SCI AN, V34, P763, DOI 10.5851/kosfa.2014.34.6.763 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Hu Y., 2018, J AGR BIOTECHNOL, V26 Jain R, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0229340 Jakes W, 2015, FOOD CHEM, V175, P1, DOI 10.1016/j.foodchem.2014.11.110 Jiang HZ, 2019, FOOD ANAL METHOD, V12, P2205, DOI 10.1007/s12161-019-01577-6 Jung Y, 2010, J AGR FOOD CHEM, V58, P10458, DOI 10.1021/jf102194t Kamal M, 2015, TRENDS FOOD SCI TECH, V46, P27, DOI 10.1016/j.tifs.2015.07.007 Kamruzzaman M, 2016, J FOOD ENG, V170, P8, DOI 10.1016/j.jfoodeng.2015.08.023 Kamruzzaman M, 2015, ANAL METHODS-UK, V7, P7496, DOI 10.1039/c5ay01617g Kamruzzaman M, 2015, FOOD BIOPROCESS TECH, V8, P1054, DOI 10.1007/s11947-015-1470-7 Kim MJ, 2019, LWT-FOOD SCI TECHNOL, V114, DOI 10.1016/j.lwt.2019.108390 Kim Sang Wook, 2009, Journal of Animal Science and Technology, V51, P273 Kumar D, 2014, J FOOD SCI TECH MYS, V51, P3458, DOI 10.1007/s13197-012-0864-z Li A, 2010, J AM OIL CHEM SOC, V87, P731, DOI 10.1007/s11746-010-1550-9 Li Y, 2009, SPECTROSC SPECT ANAL, V29, P647, DOI 10.3964/j.issn.1000-0593(2009)03-0647-05 Liu H.F., 2017, ANIMALS BREEDING FEE, V9, P3 Liu T, 2015, J FOOD SCI TECH MYS, V52, P1656, DOI 10.1007/s13197-013-1117-5 Liu W.D., 2015, J CHINA U METROLOGY, V26 Liu XL, 2013, FOOD CHEM, V140, P135, DOI 10.1016/j.foodchem.2013.02.020 [刘泽鑫 LIU Zexin], 2008, [核农学报, Acta Agriculturae Nucleatae Sinica], V22, P834 Lopez-Maestresalas A, 2019, FOOD CONTROL, V98, P465, DOI 10.1016/j.foodcont.2018.12.003 Lozicki A., 2012, J ANN ANIMAL SCI, V12, P1 Lu J., 2015, QUALITY SAFETY AGRO, V3, P32 Lytou AE, 2019, CURR OPIN FOOD SCI, V28, P88, DOI 10.1016/j.cofs.2019.10.003 Morsy N, 2013, MEAT SCI, V93, P292, DOI 10.1016/j.meatsci.2012.09.005 Nakashita R, 2008, ANAL CHIM ACTA, V617, P148, DOI 10.1016/j.aca.2008.03.048 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Osorio MT, 2012, ANIMAL, V6, P167, DOI 10.1017/S1751731111001418 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3285, DOI 10.1021/jf1040433 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3295, DOI 10.1021/jf1040959 Pavlidis DE, 2019, MEAT SCI, V151, P43, DOI 10.1016/j.meatsci.2019.01.003 Perestam AT, 2017, FOOD CONTROL, V71, P346, DOI 10.1016/j.foodcont.2016.07.017 Portarena S, 2019, FOOD CONTROL, V105, P151, DOI 10.1016/j.foodcont.2019.05.029 Qi J, 2021, FOOD CHEM, V337, DOI 10.1016/j.foodchem.2020.127779 Qie MJ, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107549 Qin PZ, 2019, J FOOD SCI TECH MYS, V56, P1266, DOI 10.1007/s13197-019-03591-2 Qin P, 2016, J FOOD SAFETY, V36, P367, DOI 10.1111/jfs.12252 Rummel S, 2012, ANAL BIOANAL CHEM, V402, P2837, DOI 10.1007/s00216-012-5759-3 Santos PM, 2014, FOOD CONTROL, V38, P204, DOI 10.1016/j.foodcont.2013.10.026 Sasazaki S, 2006, ASIAN AUSTRAL J ANIM, V19, P1106, DOI 10.5713/ajas.2006.1106 Sasazaki S, 2004, MEAT SCI, V67, P275, DOI 10.1016/j.meatsci.2003.10.016 Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 [沙坤 Sha Kun], 2017, [食品科学, Food Science], V38, P48 [沙坤 Sha Kun], 2015, [农业机械学报, Transactions of the Chinese Society for Agricultural Machinery], V46, P233 Shim JM, 2010, KOREAN J FOOD SCI AN, V30, P918, DOI 10.5851/kosfa.2010.30.6.918 Soliman LC, 2016, FOOD ANAL METHOD, V9, P630, DOI 10.1007/s12161-015-0229-0 임채란, 2008, [Korean Journal of Food Science and Technology, 한국식품과학회지], V40, P717 Sun F.M., 2012, ACTA PRATACULTURAE S, V40, P275 [孙丰梅 SUN Feng-mei], 2009, [分析测试学报, Journal of Instrumental Analysis], V28, P310 Trivedi DK, 2016, ANALYST, V141, P2155, DOI [10.1039/c6an00108d, 10.1039/C6AN00108D] Ueda S, 2019, BIOSCI BIOTECH BIOCH, V83, P137, DOI 10.1080/09168451.2018.1528139 Verkaar ELC, 2002, MEAT SCI, V60, P365, DOI 10.1016/S0309-1740(01)00144-9 Wang C.X., 2019, SCI TECH FOOD IND, V40, P241 [魏晋梅 WEI Jinmei], 2011, [食品工业科技, Science & Technology of Food Industry], V32, P73 [吴潇 Wu Xiao], 2010, [食品科学, Food Science], V31, P308 Xu YanYang, 2019, Scientia Agricultura Sinica, V52, P3163, DOI 10.3864/j.issn.0578-1752.2019.18.009 Yanagi Y, 2012, FOOD CHEM, V134, P502, DOI 10.1016/j.foodchem.2012.02.107 Yang D.Y., 2015, CHIN J PREVENTIVE ME, V16, P528 Yang D, 2018, INT J ROBOT AUTOM, V33, P293, DOI 10.2316/Journal.206.2018.3.206-5440 Yang Hua, 2017, Acta Agriculturae Zhejiangensis, V29, P994 Yang Y, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0208031 Zaima N, 2011, ANAL BIOANAL CHEM, V400, P1865, DOI 10.1007/s00216-011-4818-5 [张娟 Zhang Juan], 2018, [食品科学, Food Science], V39, P296 Zhang Y.H., 2015, SCI TECH FOOD IND, V36 Zhao J, 2018, FOOD CONTROL, V87, P94, DOI 10.1016/j.foodcont.2017.11.039 Zhao J, 2017, FOOD CONTROL, V78, P469, DOI 10.1016/j.foodcont.2017.03.017 Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y Zhou J.Q., 2013, CHIN I FOOD SCI TECH, P2 [周九庆 Zhou Jiuqing], 2014, [中国农业科学, Scientia Agricultura Sinica], V47, P977 Zhou Y.L, 2018, MEAT RES, V32, P26 Zhu Yang, 2018, Scientia Agricultura Sinica, V51, P4352 NR 108 TC 2 Z9 2 U1 39 U2 82 DI 10.1080/87559129.2021.1936000 EA JUN 2021 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000662072000001 DA 2022-12-14 ER PT J AU Adrian, J Gratacos-Cubarsi, M Sanchez-Baeza, F Regueiro, JAG Castellari, M Marco, MP AF Adrian, Javier Gratacos-Cubarsi, Marta Sanchez-Baeza, Francisco Garcia Regueiro, Jose-Antonio Castellari, Massimo Marco, M. -Pilar TI Traceability of sulfonamide antibiotic treatment by immunochemical analysis of farm animal hair samples SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article DE Sulfonamide antibiotics; Sulfamethazine; Hair analysis; ELISA; Immunoassay; HPLC-DAD; Treatment traceability ID COZART(R) MICROPLATE ELISA; ENZYME-IMMUNOASSAY; VETERINARY DRUGS; RESIDUE ANALYSIS; VALIDATION; MILK; CIPROFLOXACIN; ENROFLOXACIN; METHADONE; OPIATES AB The use of hair to trace use of unauthorized substances, therapeutic agents, or their misuse is becoming very attractive since residues can be detected for a long time after treatment. For this purpose, an indirect enzyme-linked immunosorbent assay (ELISA) has been evaluated for its capability to trace sulfonamide antibiotic treatment by analyzing cattle and pig hair samples. Pigmented and nonpigmented hair samples from control and sulfamethazine (SMZ)-treated pigs and calves were collected, extracted under different alkaline conditions, and analyzed by ELISA after just diluting the extracts with the assay buffer. Data analysis following the European recommendations for screening methods demonstrates that the ELISA can detect SMZ in hair samples with a limit of detection (90% of the zero dose (IC90)) between 30 and 75 ng g(-1). The same samples have been analyzed by HPLC after a dual solid-phase extraction. The ELISA results matched very well those obtained by the chromatographic method, demonstrating that the immunochemical method can be used as a screening tool to trace animal treatments. Between the benefits of this method are the possibility to directly analyze hair extracts with sufficient detectability and its high-throughput capability. Preliminary validation data are reported using an experimental approach inspired on the Commission Decision 2002/657/EC criteria for screening methods. C1 [Adrian, Javier; Sanchez-Baeza, Francisco; Marco, M. -Pilar] CSIC, IQAC, Networking Res Ctr Bioengn Biomat & Nanomed, AMRg, ES-08034 Barcelona, Spain. [Gratacos-Cubarsi, Marta; Garcia Regueiro, Jose-Antonio; Castellari, Massimo] IRTA Monells, Inst Food & Agr Res & Technol, Food Chem Unit, Girona 17121, Spain. C3 CIBER - Centro de Investigacion Biomedica en Red; CIBERBBN; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Centro de Investigacion y Desarrollo Pascual Vila (CID-CSIC); CSIC - Instituto de Quimica Avanzada de Cataluna (IQAC); IRTA RP Marco, MP (corresponding author), CSIC, IQAC, Networking Res Ctr Bioengn Biomat & Nanomed, AMRg, Jorge Girona 18-26, ES-08034 Barcelona, Spain. EM pilar.marco@cid.csic.es CR Adrian J, 2009, J AGR FOOD CHEM, V57, P385, DOI 10.1021/jf8027655 Anielski P, 2005, ANAL BIOANAL CHEM, V383, P903, DOI 10.1007/s00216-005-0104-8 Cirimele V, 2004, FORENSIC SCI INT, V143, P153, DOI 10.1016/j.forsciint.2004.02.035 Cooper G, 2005, CLIN CHIM ACTA, V355, pS424 Cooper G, 2005, J ANAL TOXICOL, V29, P678, DOI 10.1093/jat/29.7.678 Cooper G, 2003, J ANAL TOXICOL, V27, P581, DOI 10.1093/jat/27.8.581 Cromwell GL, 2002, ANIM BIOTECHNOL, V13, P7, DOI 10.1081/ABIO-120005767 Dunnett M, 2004, CHROMATOGRAPHIA, V59, pS69, DOI 10.1365/s10337-004-0250-8 Dunnett M, 2004, RES VET SCI, V77, P143, DOI 10.1016/j.rvsc.2004.03.004 Dunnett M, 2003, RES VET SCI, V75, P89, DOI 10.1016/S0034-5288(03)00074-2 European Commission, 2002, OFFICIAL J EUROPEAN, P8, DOI DOI 10.1017/CBO9781107415324.004 Font H, 2008, J AGR FOOD CHEM, V56, P736, DOI 10.1021/jf072550n Gaillard Y, 1999, J CHROMATOGR B, V733, P231, DOI 10.1016/S0378-4347(99)00263-7 Gaskins HR, 2002, ANIM BIOTECHNOL, V13, P29, DOI 10.1081/ABIO-120005768 Gratacos-Cubarsi M, 2006, J CHROMATOGR B, V832, P121, DOI 10.1016/j.jchromb.2006.01.002 Gratacos-Cubarsi M, 2007, ANAL BIOANAL CHEM, V387, P1991, DOI 10.1007/s00216-006-1000-6 Han EY, 2006, J ANAL TOXICOL, V30, P380, DOI 10.1093/jat/30.6.380 Lachenmeier K, 2006, FORENSIC SCI INT, V159, P189, DOI 10.1016/j.forsciint.2005.08.009 Miller EI, 2006, J ANAL TOXICOL, V30, P441, DOI 10.1093/jat/30.7.441 Moore C, 1999, J FORENSIC SCI, V44, P609 Nielen MWF, 2008, ANAL BIOANAL CHEM, V391, P199, DOI 10.1007/s00216-007-1760-7 Paul V, 2008, ANAL CHIM ACTA, V607, P106, DOI 10.1016/j.aca.2007.11.022 Pujol ML, 2007, FORENSIC SCI INT, V170, P189, DOI 10.1016/j.forsciint.2007.02.032 Scortichini G, 2005, ANAL CHIM ACTA, V535, P43, DOI 10.1016/j.aca.2004.12.004 Stolker AAM, 2007, TRAC-TREND ANAL CHEM, V26, P967, DOI 10.1016/j.trac.2007.09.008 Stolker AAM, 2005, J CHROMATOGR A, V1067, P15, DOI 10.1016/j.chroma.2005.02.037 Tagliaro F, 1997, J CHROMATOGR B, V689, P261, DOI 10.1016/S0378-4347(96)00320-9 NR 27 TC 12 Z9 15 U1 0 U2 20 PD OCT PY 2009 VL 395 IS 4 BP 1009 EP 1016 DI 10.1007/s00216-009-2878-6 WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000270539200009 DA 2022-12-14 ER PT J AU Mayr, CM De Rosso, M Dalla Vedova, A Flamini, R AF Mayr, Christine M. De Rosso, Mirko Dalla Vedova, Antonio Flamini, Riccardo TI High-Resolution Mass Spectrometry Identification of Secondary Metabolites in Four Red Grape Varieties Potentially Useful as Traceability Markers of Wines SO BEVERAGES DT Article DE wine; grape; traceability; metabolomics; high-resolution mass spectrometry; Amarone; Recioto; Raboso; Primitivo; Negro Amaro ID LIQUID-CHROMATOGRAPHY; AUTHENTICITY MARKERS; ANTHOCYANIN; EVOLUTION; DIFFERENTIATION; CLASSIFICATION; RESVERATROL; PRECURSORS; RESISTANCE; STILBENES AB Liquid chromatography coupled to high-resolution mass spectrometry (LC-Q/TOF) is a powerful tool to perform chemotaxonomic studies through identification of grape secondary metabolites. In the present work, the metabolomes of four autochthonous Italian red grape varieties including the chemical classes of anthocyanins, flavonols/flavanols/flavanones, and terpenol glycosides, were studied. By using this information, the metabolites that can potentially be used as chemical markers for the traceability of the corresponding wines were proposed. In Raboso wines, relatively high abundance of both anthocyanic and non-anthocyanic acyl derivatives, is expected. Potentially, Primitivo wines are characterized by high tri-substituted flavonoids, while Corvina wines are characterized by higher di-substituted compounds and lower acyl derivatives. Negro Amaro wine's volatile fraction is characterized by free monoterpenes, such as -terpineol, linalool, geraniol, and Ho-diendiol I. A similar approach can be applied for the traceability of other high-quality wines. C1 [Mayr, Christine M.; De Rosso, Mirko; Dalla Vedova, Antonio; Flamini, Riccardo] Council Agr Res & Econ Viticulture & Enol CREA, Viale 28 Aprile 26, I-31015 Conegliano, TV, Italy. [Mayr, Christine M.] Univ Padua, Dept Agron Food Nat Resources Anim & Environm DAF, I-35020 Legnaro, PD, Italy. C3 University of Padua RP Flamini, R (corresponding author), Council Agr Res & Econ Viticulture & Enol CREA, Viale 28 Aprile 26, I-31015 Conegliano, TV, Italy. EM mayrchristine@gmx.de; mirko.derosso@crea.gov.it; tonidallavedova@tin.it; riccardo.flamini@crea.gov.it CR Arapitsas P, 2016, J CHROMATOGR A, V1429, P155, DOI 10.1016/j.chroma.2015.12.010 Arapitsas P, 2016, FOOD CHEM, V197, P1331, DOI 10.1016/j.foodchem.2015.09.084 Arapitsas P, 2012, J AGR FOOD CHEM, V60, P10461, DOI 10.1021/jf302617e Arbulu M, 2015, ANAL CHIM ACTA, V858, P32, DOI 10.1016/j.aca.2014.12.028 Baer D. von, 2005, Bulletin de l'OIV, V78, P45 Bavaresco L, 2012, MINI-REV MED CHEM, V12, P1366 Bimpilas A, 2015, FOOD CHEM, V178, P164, DOI 10.1016/j.foodchem.2015.01.090 CACHO J, 1992, AM J ENOL VITICULT, V43, P244 Carreno J, 1997, FOOD SCI TECHNOL-LEB, V30, P259, DOI 10.1006/fstl.1996.0174 Castillo-Munoz N, 2007, J AGR FOOD CHEM, V55, P992, DOI 10.1021/jf062800k Cordente AG, 2012, APPL MICROBIOL BIOT, V96, P601, DOI 10.1007/s00253-012-4370-z De Rosso M, 2012, ANAL CHIM ACTA, V732, P120, DOI 10.1016/j.aca.2011.10.045 De Rosso M, 2010, J AGR FOOD CHEM, V58, P11364, DOI 10.1021/jf102551f DelGaudio S, 1960, PRINCIPALI VITIGNI V Dipalmo T, 2016, LWT-FOOD SCI TECHNOL, V71, P1, DOI 10.1016/j.lwt.2016.03.012 Ebeler SE, 2009, J AGR FOOD CHEM, V57, P8098, DOI 10.1021/jf9000555 Favretto D, 2000, AM J ENOL VITICULT, V51, P55 Fernandez-Lopez JA, 1998, FOOD RES INT, V31, P667, DOI 10.1016/S0963-9969(99)00043-5 Figueiredo-Gonzalez M, 2013, FOOD CHEM, V141, P3230, DOI 10.1016/j.foodchem.2013.05.142 Figueiredo-Gonzalez M, 2012, FOOD CHEM, V130, P9, DOI 10.1016/j.foodchem.2011.06.006 Flamini R., 2001, Rivista di Viticoltura e di Enologia, V54, P35 Flamini R., 2018, J MASS SPECTROM, V53, P1, DOI [10.1002/jms.421229907998, DOI 10.1002/JMS.421229907998] Flamini R, 2015, J ANAL METHODS CHEM, V2015, DOI 10.1155/2015/350259 Flamini R, 2014, J MASS SPECTROM, V49, P1214, DOI 10.1002/jms.3441 Flamini R, 2013, METABOLOMICS, V9, P1243, DOI 10.1007/s11306-013-0530-0 Fulcrand H, 2006, AM J ENOL VITICULT, V57, P289 Ghaste M, 2015, FOOD CHEM, V188, P309, DOI 10.1016/j.foodchem.2015.04.056 Goetz G, 1999, PHYTOCHEMISTRY, V52, P759, DOI 10.1016/S0031-9422(99)00351-9 HELLER W, 1980, FLAVONOIDS ADV RES 1, P399 Holmberg L., 2010, INT J WINE RES, V2, P105, DOI [10.2147/IJWR.S14102, DOI 10.2147/IJWR.S14102, 10.2147/ijwr.s14102] Jeong ST, 2006, PLANT SCI, V170, P61, DOI 10.1016/j.plantsci.2005.07.025 Liang NN, 2014, FOOD RES INT, V64, P264, DOI 10.1016/j.foodres.2014.06.048 Lingua MS, 2016, FOOD CHEM, V208, P228, DOI 10.1016/j.foodchem.2016.04.009 Makris DP, 2006, TALANTA, V70, P1143, DOI 10.1016/j.talanta.2006.03.024 Mattivi F, 2006, J AGR FOOD CHEM, V54, P7692, DOI 10.1021/jf061538c Monagas M, 2005, EUR FOOD RES TECHNOL, V220, P607, DOI 10.1007/s00217-004-1108-x Nasi A, 2008, FOOD CHEM, V110, P762, DOI 10.1016/j.foodchem.2008.03.001 Ortega-Regules A, 2006, J SCI FOOD AGR, V86, P1460, DOI 10.1002/jsfa.2511 Pezet R, 2003, J AGR FOOD CHEM, V51, P5488, DOI 10.1021/jf030227o Ragusa A, 2017, FOODS, V6, DOI 10.3390/foods6040024 Rentzsch M, 2007, J AGR FOOD CHEM, V55, P4883, DOI 10.1021/jf0702491 Rubert J, 2014, ANAL BIOANAL CHEM, V406, P6791, DOI 10.1007/s00216-014-7864-y Squadrito M., 2007, Rivista di Viticoltura e di Enologia, V60, P59 Stefano R. di, 2008, Hyphenated techniques in grape and wine chemistry, P33, DOI 10.1002/9780470754320.ch2 Styger G, 2011, J IND MICROBIOL BIOT, V38, P1145, DOI 10.1007/s10295-011-1018-4 Tamborra P, 2010, ANAL CHIM ACTA, V660, P221, DOI 10.1016/j.aca.2009.11.014 Vaclavik L, 2011, ANAL CHIM ACTA, V685, P45, DOI 10.1016/j.aca.2010.11.018 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Villano C, 2017, FOOD CONTROL, V80, P1, DOI 10.1016/j.foodcont.2017.04.020 Welke JE, 2013, FOOD CHEM, V141, P3897, DOI 10.1016/j.foodchem.2013.06.100 Xia Jianguo, 2016, Curr Protoc Bioinformatics, V55, DOI 10.1002/cpbi.11 NR 51 TC 20 Z9 20 U1 0 U2 6 PD DEC PY 2018 VL 4 IS 4 AR 74 DI 10.3390/beverages4040074 WC Food Science & Technology SC Food Science & Technology UT WOS:000455154000004 DA 2022-12-14 ER PT J AU Ktari, J Frikha, T Chaabane, F Hamdi, M Hamam, H AF Ktari, Jalel Frikha, Tarek Chaabane, Faten Hamdi, Monia Hamam, Habib TI Agricultural Lightweight Embedded Blockchain System: A Case Study in Olive Oil SO ELECTRONICS DT Article DE olive oil; Ethereum; Quorum; traceability; raspberry PI; IoT; Blockchain; smart contract ID TRACEABILITY AB In Tunisia, one of the major problems of the olive oil industry is marketing. Several factors have an impact, such as quality, originality, lobbying, subsidies and the certification of extra virgin olive oil. The major problem remains the traceability of the production process to guarantee the origin of the food at all times. This fine-grained traceability can be achieved by applying Blockchain technologies. Blockchain can be used as a solution that could bring visibility to the oil supply chain. It is proposed in order to guarantee the veracity of the product information at different stages. In this paper, a multi-Blockchain, multi-sensor traceability system using IoT will be presented. Two Blockchains that can be programmed via Smart Contract will be used. The first one is Quorum, which is a private Blockchain used by the actors of our system, and the second one is Ethereum, which is public and connects the different actors who have access to our system. This smart contract allows us to conta our system to track the olive oil manufacturing process from the farmer, through the oil mill, the transporter and the quality controller to the customer. A general approach for managing the olive oil supply chain is presented. This approach offers the possibility for the system to be configurable. It is based on smart contracts and applications that interact with the same smart contracts. The IoT is used to configure sensors. These sensors are the source of data for the supply chain process. These sensors are connected to the embedded platforms that host Quorum. C1 [Ktari, Jalel; Frikha, Tarek] Univ Sfax, CES Lab, ENIS, POB 3038, Sfax, Tunisia. [Chaabane, Faten] Univ Sfax, Regim Lab, ENIS, POB 3038, Sfax, Tunisia. [Hamdi, Monia] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Technol, POB 84428, Riyadh 11671, Saudi Arabia. [Hamam, Habib] Uni Moncton, Fac Engn, Moncton, NB E1A 3E9, Canada. [Hamam, Habib] Int Inst Technol & Management, POB 1989, Libreville, Gabon. [Hamam, Habib] Spectrum Knowledge Prod & Skills Dev, POB 3027, Sfax, Tunisia. [Hamam, Habib] Univ Johannesburg, Sch Elect Engn, Dept Elect & Elect Engn Sci, ZA-2006 Johannesburg, South Africa. C3 Universite de Sfax; Ecole Nationale dIngenieurs de Sfax (ENIS); Universite de Sfax; Ecole Nationale dIngenieurs de Sfax (ENIS); Princess Nourah bint Abdulrahman University; University of Johannesburg RP Frikha, T (corresponding author), Univ Sfax, CES Lab, ENIS, POB 3038, Sfax, Tunisia.; Hamdi, M (corresponding author), Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Technol, POB 84428, Riyadh 11671, Saudi Arabia. EM tarek.frikha@enis.tn; mshamdi@pnu.edu.sa CR Alkhudary R, 2022, IFAC PAPERSONLINE, V55, P469, DOI 10.1016/j.ifacol.2022.04.238 Allouche M, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11157169 Alsirhani A., 2022, ACM T INTERNET TECHN, DOI [10.1145/3511903, DOI 10.1145/3511903] [Anonymous], ETHEREUM DEV ONBOARD Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Arena A, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2019), P173, DOI 10.1109/SMARTCOMP.2019.00049 Biagianti A., 2019, ORACLE CUSTOMER STOR Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Chaabane F, 2022, FUTURE INTERNET, V14, DOI 10.3390/fil4090269 Conti M, 2022, IEEE ACCESS, V10, P20345, DOI 10.1109/ACCESS.2022.3151795 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Dhouioui M, 2021, J REAL-TIME IMAGE PR, V18, P2403, DOI 10.1007/s11554-021-01117-8 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Frikha T, 2021, J HEALTHC ENG, V2021, DOI 10.1155/2021/9978863 Frikha T, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/9918697 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Ghorbel O., 2022, COMPUT INTEL NEUROSC, V2022 Ghorbel O, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/9776776 Guido R, 2020, INT J IND ENG MANAGE, V11, P50, DOI 10.24867/IJIEM-2020-1-252 Hao J.T., 2018, J COMPUT, V29, P158, DOI [10.3966/199115992018122906015, DOI 10.3966/199115992018122906015] Haque B., 2021, IET BLOCKCHAIN, V1, P95, DOI [10.1049/blc2.12005, DOI 10.1049/BLC2.12005] Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Krithika LB, 2022, AGRICULTURE-BASEL, V12, DOI 10.3390/agriculture12091333 Ktari J., 2007, Journal of Computer Sciences, V3, P168, DOI 10.3844/jcssp.2007.168.173 Ktari Jalel, 2008, Proceedings of the 2008 International Conference on Embedded Systems & Applications (ESA 2008), P10 Ktari J., 2022, P 2022 IEEE INT C DE, P1, DOI [10.1109/dts55284.2022.9809847, DOI 10.1109/DTS55284.2022.9809847] Ktari J, 2007, IDT 2007: SECOND INTERNATIONAL DESIGN AND TEST WORKSHOP, PROCEEDINGS, P218 Ktari J, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11152314 Ktari J, 2009, J LOW POWER ELECTRON, V5, P17, DOI 10.1166/jolpe.2009.1003 Lee H, 2022, IEEE INTERNET THINGS, V9, P7916, DOI 10.1109/JIOT.2021.3118928 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Polge J, 2021, ICT EXPRESS, V7, P229, DOI 10.1016/j.icte.2020.09.002 Singh M., 2022, PREPRINTS, DOI [10.22541/au.164192086.60828523/v1, DOI 10.22541/AU.164192086.60828523/V1] Tian F, 2017, I C SERV SYST SERV M Violino S, 2019, FOODS, V8, DOI 10.3390/foods8110529 Xie C, 2017, 2017 3RD INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING AND COMMUNICATIONS (BIGCOM), P45, DOI 10.1109/BIGCOM.2017.43 Xu J., 2020, ARTIF INTELL AGR, V4, P153, DOI [10.1016/j.aiia.2020.08.002, DOI 10.1016/J.AIIA.2020.08.002] Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 Yu KP, 2021, IEEE T IND INFORM, V17, P7669, DOI 10.1109/TII.2021.3049141 Yuhan Bai, 2021, Journal of Cleaner Production, V310, DOI 10.1016/j.jclepro.2021.127407 NR 42 TC 0 Z9 0 U1 4 U2 4 PD OCT PY 2022 VL 11 IS 20 AR 3394 DI 10.3390/electronics11203394 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied SC Computer Science; Engineering; Physics UT WOS:000872616000001 DA 2022-12-14 ER PT J AU Marin, IA AF Marin, Ilinca-Andreea TI Blockchain technology and food traceability SO ROMANIAN JOURNAL OF INFORMATION TECHNOLOGY AND AUTOMATIC CONTROL-REVISTA ROMANA DE INFORMATICA SI AUTOMATICA DT Article DE traceability; transparency; smart contracts; supply chain; producer-consumer relationship AB Blockchain technology offers a wide range of applications that go beyond cryptocurrencies. This technology is expected to radically transform a large number of industries, from health and law, to real estate and banking systems. Blockchain technology has the potential to completely revolutionize the agricultural industry. The route of goods from producer to consumer is a complex one that makes it difficult to track and manage goods. However, Blockchain technology contains records and details of transactions along the route to help fully distribute the goods and reduce cases of fraud or illegal harvesting. (The Modex Team, 2018). In this paper I will analyze the benefits of blockchain technology for agricultural stakeholders, as well as how this technology works in agriculture. C1 [Marin, Ilinca-Andreea] Acad Studii Econ Bucuresti, Bucharest, Romania. C3 Bucharest University of Economic Studies RP Marin, IA (corresponding author), Acad Studii Econ Bucuresti, Bucharest, Romania. EM Ilinca.marin89@gmail.com CR Bosona T., 2019, BASED ANAL INTEGRATE Caro M, 2018, BLOCKCHAIN BASED TRA Dehbasteh K, 2019, ROM J INF TECH AUT C, V29, P17, DOI 10.33436/v29i4y201902 Fabbe-Costes N., 2010, REV EC SOC SERIE SYS Lansiti M, 2017, HARVARD BUS REV, V95, P119 Magee S. P. C, 2017, INCREASING IRRELEVAN Pilkington M, 2016, RESEARCH HANDBOOK ON DIGITAL TRANSFORMATIONS, P225 Radulescu C. Z, 2006, REV ROMANA INFORMATI The Modex Team, 2019, HELPING AGR BLOCKCHA Tian F., 2016, AGRIFOOD SUPPLY CHAI NR 10 TC 0 Z9 0 U1 1 U2 8 PY 2021 VL 31 IS 2 BP 125 EP 130 DI 10.33436/v31i2y202110 WC Computer Science, Interdisciplinary Applications SC Computer Science UT WOS:000700753400010 DA 2022-12-14 ER PT J AU Zhang, Q AF Zhang, Qi TI Analysis of Agricultural Products Supply Chain Traceability System Based on Internet of Things and Blockchain SO MATHEMATICAL PROBLEMS IN ENGINEERING DT Article ID TECHNOLOGY AB The quality and safety of agricultural products cannot be separated from rural revitalization. This is also related to the interests and development of participating in the agricultural supply chain. The traditional unitary agricultural product quality supervision system meets the growing market demand and requirements. This paper constructs the traceability and blockchain of agricultural products supply chain, as well as the trusted computing model of Internet of things nodes based on blockchain. The results show that the trusted computing model of IOT nodes based on blockchain can effectively identify and remove malicious nodes, ensure the effectiveness, tamperability, security, and reliability of agricultural product process data, greatly reduce the transaction delay, and provide a guarantee. C1 [Zhang, Qi] Wuxi Taihu Univ, Business Sch, Wuxi 214000, Jiangsu, Peoples R China. RP Zhang, Q (corresponding author), Wuxi Taihu Univ, Business Sch, Wuxi 214000, Jiangsu, Peoples R China. EM zhangq1@wxu.edu.cn CR Aik J, 2020, FOOD CONTROL, V117, DOI 10.1016/j.foodcont.2020.107324 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Behzadi G, 2020, EUR J OPER RES, V287, P145, DOI 10.1016/j.ejor.2020.04.040 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Coteur I, 2019, J CLEAN PROD, V209, P472, DOI 10.1016/j.jclepro.2018.10.066 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hao ZH, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17072300 Hou DY, 2020, NAT REV EARTH ENV, V1, P366, DOI 10.1038/s43017-020-0061-y Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 King TS, 2020, NURS EDUC, V45, P219, DOI 10.1097/NNE.0000000000000781 Li Tianming, 2021, Computer Engineering and Applications, V57, P50, DOI 10.3778/j.issn.1002-8331.2105-0106 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Longo F, 2019, COMPUT IND ENG, V136, P57, DOI 10.1016/j.cie.2019.07.026 Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Ren Shougang, 2020, Transactions of the Chinese Society of Agricultural Engineering, V36, P279, DOI 10.11975/j.issn.1002-6819.2020.03.034 Tao M, 2020, INFORM SCIENCES, V532, P155, DOI 10.1016/j.ins.2020.03.053 Wei PC, 2020, FUTURE GENER COMP SY, V102, P902, DOI 10.1016/j.future.2019.09.028 Yuan JJ, 2020, ECOSYST HEALTH SUST, V6, DOI 10.1080/20964129.2020.1741325 [张长贵 Zhang Changgui], 2020, [计算机科学, Computer Science], V47, P282 Zhang J., 2020, NO HORTICULTURE, V44, P166 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 21 TC 0 Z9 0 U1 18 U2 18 PD JUN 25 PY 2022 VL 2022 AR 3162871 DI 10.1155/2022/3162871 WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications SC Engineering; Mathematics UT WOS:000825318400005 DA 2022-12-14 ER PT J AU Zhang, R Jia, W AF Zhang, Rong Jia, Wei TI Authenticity and traceability of goat milk: Molecular mechanism of ?-carotene biotransformation and accessibility SO FOOD CHEMISTRY DT Article DE 0-Carotene; Authenticity; Goat milk; Non-targeted metabolomics; APCI-UHPLC-Q-Orbitrap ID BETA-CAROTENE; ALPHA-TOCOPHEROL; MASS; COW AB The efficiently extraction and accurately quantify of 0-carotene and its metabolites are crucial for authenticity and traceability in goat milk. Nevertheless, its reliability can be largely improved. In this study, meticulously designed native ESI-MS, fluorescence spectroscopy and molecular docking in combination with cold-induced acetonitrile aqueous two-phase separation system weaken the interaction between 0-lactoglobulin and 0-carotene metabolites and realized the efficiently extraction. Furthermore, established non-targeted quantitative metabolomics with optimal ion source and variable data-independent acquisition minimized the matrix effects and potential ion suppression. Validated atmospheric pressure chemical ionization-ultra high performance liquid chromatography-Orbitrap method showed that 0-carotene as distinctive biomarker in cow milk, and retinol, retinaldehyde, retinoic acid and abscisic acid in goat milk. Collectively, the proposed method is a powerful tool to detect cow adulteration risks in goat milk samples and provides valuable information for availability on authenticity of goat milk. C1 [Zhang, Rong; Jia, Wei] Shaanxi Univ Sci & Technol, Sch Food & Biol Engn, Xian 710021, Peoples R China. [Jia, Wei] Shaanxi Res Inst Agr Prod Proc Technol, Xian 710021, Peoples R China. C3 Shaanxi University of Science & Technology RP Jia, W (corresponding author), Shaanxi Univ Sci & Technol, Sch Food & Biol Engn, Xian 710021, Peoples R China. EM jiawei@sust.edu.cn CR Allahdad Z, 2019, FOOD CHEM, V277, P96, DOI 10.1016/j.foodchem.2018.10.057 Anantharamkrishnan V, 2020, J AGR FOOD CHEM, V68, P13121, DOI 10.1021/acs.jafc.9b07978 Barreto MC, 2021, FOOD CHEM, V364, DOI 10.1016/j.foodchem.2021.130407 Belyaeva OV, 2017, J BIOL CHEM, V292, P5884, DOI 10.1074/jbc.M117.776914 Bertolini F, 2019, J DAIRY SCI, V102, P10039, DOI 10.3168/jds.2019-16361 Caboni P, 2019, FOOD RES INT, V119, P869, DOI 10.1016/j.foodres.2018.10.071 Chen WJ, 2022, FOOD CHEM, V375, DOI 10.1016/j.foodchem.2021.131706 Condict L, 2022, FOOD HYDROCOLLOID, V124, DOI 10.1016/j.foodhyd.2021.107219 D'Ambrosio DN, 2011, NUTRIENTS, V3, P63, DOI 10.3390/nu3010063 Hu SP, 2021, J CHROMATOGR A, V1649, DOI 10.1016/j.chroma.2021.462235 Jia W, 2022, FOOD RES INT, V156, DOI 10.1016/j.foodres.2022.111157 Jia W, 2022, FOOD CHEM, V375, DOI 10.1016/j.foodchem.2021.131889 Jia W, 2022, J FOOD COMPOS ANAL, V105, DOI 10.1016/j.jfca.2021.104233 Jia W, 2022, FOOD CHEM, V369, DOI 10.1016/j.foodchem.2021.130948 Jia W, 2022, MEAT SCI, V183, DOI 10.1016/j.meatsci.2021.108655 Kim YK, 2011, FASEB J, V25, P1641, DOI 10.1096/fj.10-175448 Kritikou AS, 2022, FOOD CHEM, V370, DOI 10.1016/j.foodchem.2021.131057 Marino VM, 2014, J DAIRY SCI, V97, P543, DOI 10.3168/jds.2013-7005 Marino VM, 2012, DAIRY SCI TECHNOL, V92, P501, DOI 10.1007/s13594-012-0069-2 Marty MT, 2015, ANAL CHEM, V87, P4370, DOI 10.1021/acs.analchem.5b00140 Mung D, 2018, ANAL CHIM ACTA, V1001, P78, DOI 10.1016/j.aca.2017.11.019 Murugesu S, 2019, J PHARM ANAL, V9, P91, DOI 10.1016/j.jpha.2018.11.001 Pinto PA, 2021, J FOOD COMPOS ANAL, V104, DOI 10.1016/j.jfca.2021.104176 Potter CM, 2021, J FOOD COMPOS ANAL, V96, DOI 10.1016/j.jfca.2020.103760 Pulina G, 2018, J DAIRY SCI, V101, P6715, DOI 10.3168/jds.2017-14015 Roy D, 2020, FRONT NUTR, V7, DOI 10.3389/fnut.2020.577759 Salzano A, 2020, MOLECULES, V25, DOI 10.3390/molecules25020304 Scano P., 2020, Dairy, V1, P30, DOI 10.3390/dairy1010004 Shabtai Y, 2016, BIOCHEM J, V473, P1423, DOI 10.1042/BCJ20160101 Strickland JM, 2021, J DAIRY SCI, V104, P915, DOI 10.3168/jds.2020-18693 Verruck S, 2019, J FUNCT FOODS, V52, P243, DOI 10.1016/j.jff.2018.11.017 Wang FQ, 2019, FOOD CHEM, V275, P530, DOI 10.1016/j.foodchem.2018.09.142 Widjaja-Adhi MAK, 2020, BBA-MOL CELL BIOL L, V1865, DOI 10.1016/j.bbalip.2019.158571 Yilmaz-Ersan L, 2018, J DAIRY SCI, V101, P3788, DOI 10.3168/jds.2017-13871 Yuan XM, 2016, J CHROMATOGR A, V1443, P145, DOI 10.1016/j.chroma.2016.03.041 Zhang R, 2022, FOOD CHEM, V366, DOI 10.1016/j.foodchem.2021.130554 Zhang YD, 2018, J DAIRY SCI, V101, P9630, DOI 10.3168/jds.2018-14441 NR 37 TC 1 Z9 1 U1 14 U2 19 PD SEP 15 PY 2022 VL 388 AR 133073 DI 10.1016/j.foodchem.2022.133073 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000803019400010 DA 2022-12-14 ER PT J AU Zhang, Q AF Zhang, Qi TI Analysis of Agricultural Products Supply Chain Traceability System Based on Internet of Things and Blockchain SO MATHEMATICAL PROBLEMS IN ENGINEERING DT Article ID TECHNOLOGY AB The quality and safety of agricultural products cannot be separated from rural revitalization. This is also related to the interests and development of participating in the agricultural supply chain. The traditional unitary agricultural product quality supervision system meets the growing market demand and requirements. This paper constructs the traceability and blockchain of agricultural products supply chain, as well as the trusted computing model of Internet of things nodes based on blockchain. The results show that the trusted computing model of IOT nodes based on blockchain can effectively identify and remove malicious nodes, ensure the effectiveness, tamperability, security, and reliability of agricultural product process data, greatly reduce the transaction delay, and provide a guarantee. C1 [Zhang, Qi] Wuxi Taihu Univ, Business Sch, Wuxi 214000, Jiangsu, Peoples R China. RP Zhang, Q (corresponding author), Wuxi Taihu Univ, Business Sch, Wuxi 214000, Jiangsu, Peoples R China. EM zhangq1@wxu.edu.cn CR Aik J, 2020, FOOD CONTROL, V117, DOI 10.1016/j.foodcont.2020.107324 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Behzadi G, 2020, EUR J OPER RES, V287, P145, DOI 10.1016/j.ejor.2020.04.040 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Coteur I, 2019, J CLEAN PROD, V209, P472, DOI 10.1016/j.jclepro.2018.10.066 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hao ZH, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17072300 Hou DY, 2020, NAT REV EARTH ENV, V1, P366, DOI 10.1038/s43017-020-0061-y Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 King TS, 2020, NURS EDUC, V45, P219, DOI 10.1097/NNE.0000000000000781 Li Tianming, 2021, Computer Engineering and Applications, V57, P50, DOI 10.3778/j.issn.1002-8331.2105-0106 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Longo F, 2019, COMPUT IND ENG, V136, P57, DOI 10.1016/j.cie.2019.07.026 Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Ren Shougang, 2020, Transactions of the Chinese Society of Agricultural Engineering, V36, P279, DOI 10.11975/j.issn.1002-6819.2020.03.034 Tao M, 2020, INFORM SCIENCES, V532, P155, DOI 10.1016/j.ins.2020.03.053 Wei PC, 2020, FUTURE GENER COMP SY, V102, P902, DOI 10.1016/j.future.2019.09.028 Yuan JJ, 2020, ECOSYST HEALTH SUST, V6, DOI 10.1080/20964129.2020.1741325 [张长贵 Zhang Changgui], 2020, [计算机科学, Computer Science], V47, P282 Zhang J., 2020, NO HORTICULTURE, V44, P166 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 21 TC 0 Z9 0 U1 0 U2 0 PD JUN 25 PY 2022 VL 2022 AR 3162871 DI 10.1155/2022/3162871 WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications SC Engineering; Mathematics UT WOS:000886352800050 DA 2022-12-14 ER PT J AU Elliott, LCC Pintar, AL Copeland, CR Renegar, TB Dixson, RG Ilic, BR Verkouteren, RM Stavis, SM AF Elliott, Lindsay C. C. Pintar, Adam L. Copeland, Craig R. Renegar, Thomas B. Dixson, Ronald G. Ilic, B. Robert Verkouteren, R. Michael Stavis, Samuel M. TI Sub-picoliter Traceability of Microdroplet Gravimetry and Microscopy SO ANALYTICAL CHEMISTRY DT Article ID KNIFE-EDGE; DIFFRACTION; LIGHT; METROLOGY; ACCURACY; GEOMETRY; SIZE AB Gravimetry typically lacks the resolution to measure single microdroplets, whereas microscopy is often inaccurate beyond the resolution limit. To address these issues, we advance and integrate these complementary methods, introducing simultaneous measurements of the same microdroplets, comprehensive calibrations that are independently traceable to the International System of Units (SI), and Monte-Carlo evaluations of volumetric uncertainty. We achieve sub-picoliter agreement of measurements of microdroplets in flight with volumes of approximately 70 pL, with ensemble gravimetry and optical microscopy both yielding 95% coverage intervals of +/- 0.6 pL, or relative uncertainties of +/- 0.9%, and root-mean-square deviations of mean values between the two methods of 0.2 pL or 0.3%. These uncertainties match previous gravimetry results and improve upon previous microscopy results by an order of magnitude. Gravimetry precision depends on the continuity of droplet formation, whereas microscopy accuracy requires that optical diffraction from an edge reference matches that from a microdroplet. Applying our microscopy method, we jet and image water microdroplets suspending fluorescent nanoplastics, count nanoplastic particles after deposition and evaporation, and transfer volumetric traceability to the number concentrations of single microdroplets. We expect that our methods will impact diverse fields involving dimensional metrology and volumetric analysis of microdroplets, including inkjet microfabrication, disease transmission, and industrial sprays. C1 [Elliott, Lindsay C. C.; Pintar, Adam L.; Copeland, Craig R.; Renegar, Thomas B.; Dixson, Ronald G.; Ilic, B. Robert; Verkouteren, R. Michael; Stavis, Samuel M.] NIST, Gaithersburg, MD 20899 USA. [Elliott, Lindsay C. C.] Univ Maryland, College Pk, MD 20742 USA. C3 National Institute of Standards & Technology (NIST) - USA; University System of Maryland; University of Maryland College Park RP Stavis, SM (corresponding author), NIST, Gaithersburg, MD 20899 USA. EM samuel.stavis@nist.gov CR Aydin O, 2020, EXTREME MECH LETT, V40, DOI 10.1016/j.eml.2020.100924 BARNETT JD, 1962, J OPT SOC AM, V52, P637, DOI 10.1364/JOSA.52.000637 BIPM IEC IFCC ILAC ISO IUPAC IUPAP and OIML, 2008, GUID EXPR UNC MEAS Blaisot JB, 2005, EXP FLUIDS, V39, P977, DOI 10.1007/s00348-005-0026-4 Bourouiba L, 2020, JAMA-J AM MED ASSOC, V323, P1837, DOI 10.1001/jama.2020.4756 Castrejon-Pita J.R., 2013, FLUID DYNAMICS PHYS, P121 Chao CYH, 2009, J AEROSOL SCI, V40, P122, DOI 10.1016/j.jaerosci.2008.10.003 Copeland C. R., 2021, ARXIV210610221 Copeland CR, 2021, NAT COMMUN, V12, DOI 10.1038/s41467-021-23419-y Copeland CR, 2018, LIGHT-SCI APPL, V7, DOI 10.1038/s41377-018-0031-z ENGLISH RE, 1988, APPL OPTICS, V27, P1581, DOI 10.1364/AO.27.001581 FUKAYA J, 1995, APPL OPTICS, V34, P7820, DOI 10.1364/AO.34.007820 Goodman J. W., 2005, FOURIER OPTICS Gralton J, 2011, J INFECTION, V62, P1, DOI 10.1016/j.jinf.2010.11.010 Han ZY, 2013, J R SOC INTERFACE, V10, DOI 10.1098/rsif.2013.0560 Huber C, 2016, OPT EXPRESS, V24, P8214, DOI 10.1364/OE.24.008214 Hutchings IM, 2007, J IMAGING SCI TECHN, V51, P438, DOI 10.2352/J.ImagingSci.Technol.(2007)51:5(438) Jiang HC, 2018, J IMAGING SCI TECHN, V62, DOI 10.2352/J.ImagingSci.Technol.2018.62.6.060502 KELLER JB, 1985, IEEE T ANTENN PROPAG, V33, P123, DOI 10.1109/TAP.1985.1143546 Kwon KS, 2016, REV SCI INSTRUM, V87, DOI 10.1063/1.4940934 LANGLOIS P, 1983, OPT ACTA, V30, P1373, DOI 10.1080/713821070 LANGLOIS P, 1977, J OPT SOC AM, V67, P87, DOI 10.1364/JOSA.67.000087 LANGLOIS P, 1985, APPL OPTICS, V24, P1107, DOI 10.1364/AO.24.001107 Lau YM, 2013, CHEM ENG SCI, V94, P20, DOI 10.1016/j.ces.2013.02.043 Lee C, 2019, INT J ADV MANUF TECH, V102, P2465, DOI 10.1007/s00170-019-03319-8 Legrand M., 2014, 17 INT S APPL LAS TE, P7 Lemeshko YA, 2007, OPTOELECTRON INSTRUM, V43, P284, DOI 10.3103/S8756699007030120 Liao KT, 2018, LAB CHIP, V18, P139, DOI 10.1039/c7lc01047h LIN BJ, 1980, IEEE T ELECTRON DEV, V27, P931, DOI 10.1109/T-ED.1980.19959 LOCK JA, 1993, AM J PHYS, V61, P698, DOI 10.1119/1.17440 Martin G. D., 2016, NIP DIG FABR C, V2016, P94 Melbourne W. G, 2005, RADIO OCCULTATIONS U, P171 NYYSSONEN D, 1982, J OPT SOC AM, V72, P1425, DOI 10.1364/JOSA.72.001425 Sanchez-Brea LM, 2008, APPL OPTICS, V47, P4804, DOI 10.1364/AO.47.004804 SCHRODER KP, 1995, OPT COMMUN, V115, P568, DOI 10.1016/0030-4018(94)00673-I SHEPPARD CJR, 1989, P SOC PHOTO-OPT INS, V1139, P32, DOI 10.1117/12.961770 Shin DY, 2017, REV SCI INSTRUM, V88, DOI 10.1063/1.4975094 SOMMARGREN GE, 1990, APPL OPTICS, V29, P4646, DOI 10.1364/AO.29.004646 Staat HJJ, 2017, EXP FLUIDS, V58, DOI 10.1007/s00348-016-2284-8 Stavis SM, 2009, NANOTECHNOLOGY, V20, DOI 10.1088/0957-4484/20/16/165302 Strychalski EA, 2008, NANOTECHNOLOGY, V19, DOI 10.1088/0957-4484/19/31/315301 Thurow K., 2009, ANAL METHODS CHEM, V2009, P1 Umul YZ, 2008, J OPT SOC AM A, V25, P2896, DOI 10.1364/JOSAA.25.002896 Umul YZ, 2011, ADV IMAG ELECT PHYS, V165, P265, DOI 10.1016/B978-0-12-385861-0.00006-3 van der Bos A, 2014, PHYS REV APPL, V1, DOI 10.1103/PhysRevApplied.1.014004 Verkouteren R. M., 2013, TECHN P 2013 NSTI NA, V2, P224 Verkouteren RM, 2011, LANGMUIR, V27, P9644, DOI 10.1021/la201728f Verkouteren RM, 2009, ANAL CHEM, V81, P8577, DOI 10.1021/ac901563j von Diezmann A, 2015, OPTICA, V2, P985, DOI 10.1364/OPTICA.2.000985 Wright J., 2021, 685O000003439421 NIS YOUNG M, 1993, J RES NATL INST STAN, V98, P203, DOI 10.6028/jres.098.015 Yu F., 2014, GRAVIT SPACE RES, V2, P82 NR 52 TC 0 Z9 0 U1 6 U2 8 PD JAN 18 PY 2022 VL 94 IS 2 BP 678 EP 686 DI 10.1021/acs.analchem.1c02640 EA DEC 2021 WC Chemistry, Analytical SC Chemistry UT WOS:000734465700001 DA 2022-12-14 ER PT J AU Zhao, SS Zhao, Y Rogers, KM Chen, G Chen, AL Yang, SM AF Zhao, Shanshan Zhao, Yan Rogers, Karyne M. Chen, Gang Chen, Ailiang Yang, Shuming TI Application of multi-element (C, N, H, O) stable isotope ratio analysis for the traceability of milk samples from China SO FOOD CHEMISTRY DT Article DE Cow milk; Isotope ratio mass spectrometry (IRMS); Lactation; Sampling time; Geographic origin; Traceability ID GEOGRAPHICAL ORIGIN; NITROGEN ISOTOPES; CARBON; OXYGEN; DISCRIMINATION; AUTHENTICITY; COMBINATION; CHEESE; FOOD; TOOL AB Cow milk samples from various provinces in China were collected, and the effects of lactation stage, sampling time, and geographic origin on the samples were studied by elemental analysis-isotope ratio mass spectrometry (EA-IRMS). Traceability accuracy was determined using delta C-13, delta N-15, delta H-2 and delta O-18 values to specifically assign geographic origin. Stable isotope ratios of C, N, H and O were not significantly different among three lactation stages; however the delta C-13, delta N-15, and delta O-18 values of milk were influenced by sampling time. Furthermore, there were highly significant regional differences in the mean delta C-13 and delta N-15 values of milk. In summary, the lactation stage had no effect on the traceability of milk, whereas sampling time and geographic origin did affect milk traceability. Different geographic locations with a separation distance greater than 0.7 km can be distinguished using multi-element (C, N, H, O) stable isotope ratio analysis. C1 [Zhao, Shanshan; Zhao, Yan; Chen, Gang; Chen, Ailiang; Yang, Shuming] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Zhao, Shanshan; Zhao, Yan; Chen, Gang; Chen, Ailiang; Yang, Shuming] Minist Agr & Rural Affairs, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Rogers, Karyne M.] GNS Sci, Natl Isotope Ctr, 30 Gracefield Rd, Lower Hutt 5040, New Zealand. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs; GNS Science - New Zealand RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR Almeida CM, 2001, J ANAL ATOM SPECTROM, V16, P607, DOI 10.1039/b100307k Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Bostic JN, 2018, RAPID COMMUN MASS SP, V32, P561, DOI 10.1002/rcm.8069 Bowen GJ, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005186 Brand WA, 2014, PURE APPL CHEM, V86, P425, DOI 10.1515/pac-2013-1023 Cabanero AI, 2006, J AGR FOOD CHEM, V54, P9719, DOI 10.1021/jf062067x Camin F, 2008, RAPID COMMUN MASS SP, V22, P1690, DOI 10.1002/rcm.3506 Chung IM, 2018, FOOD CHEM, V261, P112, DOI 10.1016/j.foodchem.2018.04.017 Coplen TB, 2006, RAPID COMMUN MASS SP, V20, P3165, DOI 10.1002/rcm.2727 DUNBAR J, 1983, PLANT PHYSIOL, V72, P725, DOI 10.1104/pp.72.3.725 Ehtesham E, 2015, INT DAIRY J, V47, P37, DOI 10.1016/j.idairyj.2015.02.008 Garbaras A, 2018, LITH J PHYS, V58, P277 Garbariene I, 2016, AEROSOL AIR QUAL RES, V16, P1356, DOI 10.4209/aaqr.2015.07.0443 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 Guyon F, 2014, FOOD CHEM, V146, P36, DOI 10.1016/j.foodchem.2013.09.020 HE K, 2017, FOOD RES DEV, V35, P205, DOI DOI 10.1016/J.JFF.2017.04.041 Hobson KA, 2015, ECOL EVOL, V5, P799, DOI 10.1002/ece3.1383 Krivachy N, 2015, FOOD CONTROL, V48, P143, DOI 10.1016/j.foodcont.2014.06.002 Liang KH, 2018, J APPL ANIM RES, V46, DOI 10.1080/09712119.2017.1360186 Luo DH, 2016, FOOD ANAL METHOD, V9, P437, DOI 10.1007/s12161-015-0204-9 Lv J, 2017, FOOD ANAL METHOD, V10, P347, DOI 10.1007/s12161-016-0588-1 Magdas DA, 2013, AIP CONF PROC, V1565, P304, DOI 10.1063/1.4833750 Necemer M, 2016, J FOOD COMPOS ANAL, V52, P16, DOI 10.1016/j.jfca.2016.07.002 Oftedal OT, 2000, P NUTR SOC, V59, P99, DOI 10.1017/S0029665100000124 OFTEDAL OT, 1993, J DAIRY SCI, V76, P3234, DOI 10.3168/jds.S0022-0302(93)77660-2 Paul D, 2007, RAPID COMMUN MASS SP, V21, P3006, DOI 10.1002/rcm.3185 Piliciauskas G, 2017, ARCHAEOL ANTHROP SCI, V9, P1421, DOI 10.1007/s12520-017-0463-z Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Rutkowska J, 2015, FOOD CHEM, V178, P26, DOI 10.1016/j.foodchem.2015.01.036 Schimmelmann A, 2009, RAPID COMMUN MASS SP, V23, P3513, DOI 10.1002/rcm.4277 Sponheimer M, 2003, INT J OSTEOARCHAEOL, V13, P80, DOI 10.1002/oa.655 Valenti B, 2017, RAPID COMMUN MASS SP, V31, P737, DOI 10.1002/rcm.7840 Werner RA, 2001, RAPID COMMUN MASS SP, V15, P501, DOI 10.1002/rcm.258 Zhao Y, 2016, J SCI FOOD AGR, V96, P3950, DOI 10.1002/jsfa.7567 Zhao Y, 2016, CYTA-J FOOD, V14, P163, DOI 10.1080/19476337.2015.1057235 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y NR 36 TC 20 Z9 22 U1 8 U2 124 PD APR 25 PY 2020 VL 310 AR 125826 DI 10.1016/j.foodchem.2019.125826 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000505957800026 DA 2022-12-14 ER PT J AU Liang, K Thomasson, JA Lee, KM Shen, MX Ge, YF Herrman, TJ AF Liang, Kun Thomasson, John A. Lee, Kyung-Min Shen, Mingxia Ge, Yufeng Herrman, Timothy J. TI Printing data matrix code on food-grade tracers for grain traceability SO BIOSYSTEMS ENGINEERING DT Article ID ROUGHNESS AB A traceability system that enables grain to be traced from its point of origin throughout the supply chain is needed to improve food safety in cereal grains. Food-grade tracers - pill-sized particles with identity information printed on their surfaces - added to grain at harvest have been proposed as a means to realise a grain traceability system. Two types of such tracers (sugar-based and cellulose-based) have shown promise during previous research, but a means of applying identity information to them remains to be developed. In this study, a specialised ink-jet printer was used to print data matrix (DM) code symbols onto the tracers with food-grade ink, and the method's efficacy and the readability of the code were considered. Factors related to readability, including surface roughness of the tracers, were also considered. The printing of DM codes on sugar- and cellulose-based tracer particles was found to be readily feasible, and readability for both types was above 90%. The surface roughness of the sugar-based tracers was higher than that of the cellulose-based tracers, and readability of cellulose-based tracers was higher. However, for a given tracer type, readability increased slightly with surface roughness, indicating that some factor other than roughness - such as thermal conductivity of the tracer material - may be influencing the printing process and thus the readability of the code. Fundamentally, however, the successful printing of DM code onto food-grade tracers with food-grade ink is a novel and important step in developing a grain traceability system. (C) 2012 IAgrE. Published by Elsevier Ltd. All rights reserved. C1 [Thomasson, John A.; Ge, Yufeng] Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX 77843 USA. [Liang, Kun; Shen, Mingxia] Nanjing Agr Univ, Dept Elect Engn, Nanjing, Jiangsu, Peoples R China. [Lee, Kyung-Min; Herrman, Timothy J.] Texas A&M Agrilife Res, Off Texas State Chemist, College Stn, TX USA. C3 Texas A&M University System; Texas A&M University College Station; Nanjing Agricultural University; Texas A&M University System; Texas A&M University College Station; Texas A&M AgriLife Research RP Thomasson, JA (corresponding author), Texas A&M Univ, Dept Biol & Agr Engn, 2117 TAMU, College Stn, TX 77843 USA. EM thomasson@tamu.edu CR Aarnisalo K., 2007, VTT RES NOTES [Anonymous], 1995, B4611995 ASME Bogataj U, 2010, J IMAGING SCI TECHN, V54, DOI 10.2352/J.ImagingSci.Technol.2010.54.3.030502 Ceruti F. C., 2006, Proceedings of the 9th International Working Conference on Stored-Product Protection, ABRAPOS, Passo Fundo, RS, Brazil, 15-18 October 2006, P1198 CUCU TC, 2008, ED INFORM TECHNOLOGY, P585 Froschle HK, 2009, COMPUT ELECTRON AGR, V66, P126, DOI 10.1016/j.compag.2009.01.002 Herrman T., 2002, WHITE PAPER TRACEABI Hornbaker R. H., 2007, U.S. Patent, Patent No. [7,162,328 B2, 7162328] Lee KM, 2010, J AGR FOOD CHEM, V58, P10945, DOI 10.1021/jf101370k Mc Inerney B, 2010, COMPUT ELECTRON AGR, V73, P112, DOI 10.1016/j.compag.2010.06.004 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Podczeck F, 1999, PART PART SYST CHAR, V16, P185, DOI 10.1002/(SICI)1521-4117(199908)16:4<185::AID-PPSC185>3.0.CO;2-P Robinson M. C., 2001, United States patent application publication, Patent No. [US 2001/0029996 A1, 20010029996] Seitavuopio P, 2005, EUR J PHARM BIOPHARM, V59, P351, DOI 10.1016/j.ejpb.2004.08.009 Sui R., 2007, ASABE ANN INT M, VAsabe Annual International Meeting, P076032 Tambascio S., 2003, Tooling and Production, V69, P42 Taylor R. D., 2002, U.S. Patent, Patent No. [6,406,725 B1, 6406725] Thakur M, 2010, J FOOD ENG, V99, P98, DOI 10.1016/j.jfoodeng.2010.02.004 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 NR 19 TC 21 Z9 21 U1 1 U2 23 PD DEC PY 2012 VL 113 IS 4 BP 395 EP 401 DI 10.1016/j.biosystemseng.2012.09.012 WC Agricultural Engineering; Agriculture, Multidisciplinary SC Agriculture UT WOS:000311760900008 DA 2022-12-14 ER PT J AU Manos, B Manikas, I AF Manos, Basil Manikas, Ioannis TI Traceability in the Greek fresh produce sector: drivers and constraints SO BRITISH FOOD JOURNAL DT Article DE Fresh foods; Supply chain management; Greece; Tracking AB Purpose - In this paper, the key drivers and constraints for implementing traceability are examined in the Greek fresh produce supply chain. The main objective is to identify the main factors affecting the implementation of traceability schemes, under the current supply chain structure and evaluate the theoretical framework identified in the literature. Design/methodology/approach - A specific executive research was conducted, including interviews with key representatives of the sector. The scope of the research was to collect qualitative data with the aid of an unstructured questionnaire with no close-ended questions. The research sample included 22 agricultural cooperatives and private packinghouses located in northern Greece where the core value adding activities of the fresh produce supply chain are taking place. Northern Greece is of high importance for the examined sector as a high percentage of all value adding activities, from production to distribution, are taking place within this region. Findings - In the fresh produce supply chains ephemeral dynamic collaborations prevail which do not allow particular transparency with regard to the exchange of information between their members. Severe inequities recognized between supply chain members regarding their ability to imply traceability systems effectively, their current technological and operational status and the availability to undertake the cost of investment in such systems. Tight profit margins and inadequate knowledge on potential benefits of traceability systems are reported as some of the main factors that hinder investments on sophisticated traceability schemes. Adequate labeling automation with the implementation of machine-readable labeling technologies and the introduction of web-based technologies as a low cost solution are estimated to improve fresh produce traceability and logistics efficiency. Originality/value - Within a limited number of research papers on fresh produce traceability, there is no reference to the Greek produce sector. Thus, this paper progresses knowledge of the Greek produce industry regarding perspectives and key drivers for traceability implementation, supported by the thorough review of the literature. C1 [Manos, Basil; Manikas, Ioannis] Aristotle Univ Thessaloniki, Sch Agr, GR-54006 Thessaloniki, Greece. C3 Aristotle University of Thessaloniki RP Manikas, I (corresponding author), Aristotle Univ Thessaloniki, Sch Agr, GR-54006 Thessaloniki, Greece. EM manosb@agro.auth.gr CR AAKER DA, 1995, MARKETING RES Bourlakis M. A., 2001, SUPPLY CHAIN MANAG, V6, P189, DOI [DOI 10.1108/13598540110402728, 10.1108/13598540110402728] *CAN TRAC STAND, 2006, DEC SUPP TEMPL VERS DAIVES C, 2004, SUPPLY CHAIN EUR JUN Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 *DUTCH MIN AGR NAT, 2004, AGR PUBL EYE WHO IS *EUR UN FOOD TRAC, 2006, STAK QUEST European Union, 2002, OFFICIAL J EUROPEAN *EXP ASS NO GREEC, 2005, SEVE RES PROGR GREEK *FAO, 2005, COUNTR PROF GREEC Fearne A., 1999, SUPPLY CHAIN MANAG, V4, P120, DOI [10.1108/13598549910279567, DOI 10.1108/13598549910279567] *FOOD STRAT AG, 2002, TRAC FOOD CHAIN PREL GALANOPOULOS K, 2006, MEDFROL PROJECT 6 FR GARCIA M, 2003, BENCHMARKING SAFETY HERDON M, 2006, HAICTA 2006 INT C IN, P187 Hobbs JE, 2006, WAG UR FRON, V15, P87, DOI 10.1007/1-4020-4693-6_7 Karkkainen M., 2003, INT J RETAIL DISTRIB, V31, P529, DOI DOI 10.1108/09590550310497058 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 KNIGHT C, 2002, KEY TOPICS FOOD SCI, V5, P91 PEZAROS DP, 2004, REMOTE SENSING AGR E RUSSEL I, 2003, FOOD TRACE REV Salin V., 1998, INT FOOD AGRIBUS MAN, V1, P329, DOI [10.1016/S1096-7508(99)80003-2, DOI 10.1016/S1096-7508(99)80003-2] SOUZAMONTEIRO DM, 2006, ANN M AM AGR EC ASS Van der Vorst J. G. A. J., 1998, International Transactions in Operational Research, V5, P487, DOI 10.1016/S0969-6016(98)00049-5 van der Vorst JGAJ, 2004, DYNAMICS IN CHAINS AND NETWORKS, P175 VANDORP CA, 2004, THESIS WAGENINGEN U Wilson T. P., 1998, SUPPLY CHAIN MANAG, V3, P127 Yin R., 1994, CASE STUDY RES DESIG 2003, USE INNOVATIVE TECHN NR 29 TC 40 Z9 42 U1 0 U2 35 PY 2010 VL 112 IS 6-7 BP 640 EP 652 DI 10.1108/00070701011052727 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000281181300007 DA 2022-12-14 ER PT J AU Li, C Yang, SC Guo, QS Zheng, KY Wang, PL Meng, ZG AF Li, Chao Yang, Sheng-Chao Guo, Qiao-Sheng Zheng, Kai-Yan Wang, Ping-Li Meng, Zhen-Gui TI Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics SO SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY DT Article DE Marsdenia tenacissima; FTIR spectroscopy; Geographical traceability; Pattern recognition; Chemometrics ID PERFORMANCE LIQUID-CHROMATOGRAPHY; POLYOXYPREGNANE GLYCOSIDES; PREGNANE GLYCOSIDES; FTIR SPECTROSCOPY; STEMS; ORIGIN; DISCRIMINATION AB A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way. (C) 2015 Elsevier B.V. All rights reserved. C1 [Li, Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan] Nanjing Agr Univ, Inst Chinese Med Mat, Nanjing 210095, Jiangsu, Peoples R China. [Yang, Sheng-Chao; Meng, Zhen-Gui] Yunnan Agr Univ, Inst Chinese Med Mat, Kunming 605201, Peoples R China. [Wang, Ping-Li] Yunnan Xintong Bot Pharmaceut Co Ltd, Mengzi 661100, Peoples R China. C3 Nanjing Agricultural University; Yunnan Agricultural University RP Yang, SC (corresponding author), Yunnan Agr Univ, Inst Chinese Med Mat, Kunming 605201, Peoples R China. EM shengchaoyang@163.com; gqs@njau.edu.cn CR Alsberg BK, 1997, ANALYST, V122, P645, DOI 10.1039/a608255f Blockeel H, 2003, J MACH LEARN RES, V3, P621, DOI 10.1162/jmlr.2003.3.4-5.621 Bombayda I, 2008, ANAL CHIM ACTA, V613, P31, DOI 10.1016/j.aca.2008.02.038 Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324 Chen JJ, 2008, TALANTA, V77, P152, DOI 10.1016/j.talanta.2008.05.054 Chen QS, 2009, SPECTROCHIM ACTA A, V72, P845, DOI 10.1016/j.saa.2008.12.002 Chiang Su New Medical College, 1977, ZHONG DAC Dai Z., 2010, CHINA PHARM, V47, P4470 Deng J, 2006, J CHROMATOGR A, V1116, P83, DOI 10.1016/j.chroma.2006.03.021 Dunteman G.H., 1989, PRINCIPAL COMPONENTS Einax JW, 2008, ANAL BIOANAL CHEM, V390, P1225, DOI 10.1007/s00216-007-1786-x Gok S, 2015, FOOD CHEM, V170, P234, DOI 10.1016/j.foodchem.2014.08.040 Gori A, 2012, INT DAIRY J, V23, P115, DOI 10.1016/j.idairyj.2011.11.005 Gumbel Emil Julius, 2012, STAT EXTREMES Han SY, 2012, LUNG CANCER, V75, P30, DOI 10.1016/j.lungcan.2011.06.001 Hastie T, 1996, IEEE T PATTERN ANAL, V18, P607, DOI 10.1109/34.506411 Hu YJ, 2008, J NAT PROD, V71, P1049, DOI 10.1021/np070458f Huang ZR, 2013, ONCOL LETT, V5, P917, DOI 10.3892/ol.2013.1105 Kuligowski J, 2011, ANAL BIOANAL CHEM, V399, P1305, DOI 10.1007/s00216-010-4457-2 Leung AKM, 1998, ANAL CHEM, V70, P5222, DOI 10.1021/ac9803737 Li C, 2014, CHEMOMETR INTELL LAB, V136, P115, DOI 10.1016/j.chemolab.2014.05.008 Li Q. F., 2006, J SW U NATL, V32, P1185 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 McGarvey BD, 2012, J MASS SPECTROM, V47, P687, DOI 10.1002/jms.2991 Meza-Marquez OG, 2010, MEAT SCI, V86, P511, DOI 10.1016/j.meatsci.2010.05.044 MIYAKAWA S, 1986, PHYTOCHEMISTRY, V25, P2861, DOI 10.1016/S0031-9422(00)83756-5 Movasaghi Z, 2008, APPL SPECTROSC REV, V43, P134, DOI 10.1080/05704920701829043 Nakanishi K., 1977, INFRARED ABSORPTION, V2nd ed. National Pharmacopoeia Committee, 2010, CHINESE PHARMACOPOEI PARK J, 1993, NEURAL COMPUT, V5, P305, DOI 10.1162/neco.1993.5.2.305 Pavia D.L., 2008, INTRO SPECTROSCOPY Socrates G., 2004, INFRARED RAMAN CHARA, DOI DOI 10.1002/JRS.1238 Thennadil SN, 2006, APPL SPECTROSC, V60, P315, DOI 10.1366/000370206776342535 Wang XL, 2006, HELV CHIM ACTA, V89, P2738, DOI 10.1002/hlca.200690245 Wu ZC, 2010, VIB SPECTROSC, V53, P222, DOI 10.1016/j.vibspec.2010.03.008 Zeng QH, 2014, BIOMED CHROMATOGR, V28, P223, DOI 10.1002/bmc.3009 Zhang H, 2010, STEROIDS, V75, P176, DOI 10.1016/j.steroids.2009.11.003 NR 37 TC 8 Z9 10 U1 0 U2 25 PD JAN 5 PY 2016 VL 152 BP 391 EP 396 DI 10.1016/j.saa.2015.07.086 WC Spectroscopy SC Spectroscopy UT WOS:000365367100048 DA 2022-12-14 ER PT J AU Xiao, XQ He, QL Fu, ZT Xu, M Zhang, XS AF Xiao, Xinqing He, Qile Fu, Zetian Xu, Mark Zhang, Xiaoshuan TI Applying CS and WSN methods for improving efficiency of frozen and chilled aquatic products monitoring system in cold chain logistics SO FOOD CONTROL DT Article DE Food safety and traceability; Cold chain logistics; Monitoring system; Wireless sensor network; Compressed sensing ID ORTHOGONAL MATCHING PURSUIT; SHELF-LIFE; SIGNAL RECOVERY; TEMPERATURE; COMPRESSION; INFORMATION; PREDICTION; ORGANISMS; BEHAVIOR; NETWORK AB Wireless Sensor Network (WSN) is applied widely in food cold chain logistics. However, traditional monitoring systems require significant real-time sensor data transmission which will result in heavy data traffic and communication systems overloading, and thus reduce the data collection and transmission efficiency. This research aims to develop a temperature Monitoring System for Frozen and Chilled Aquatic Products (MS-FCAP) based on WSN integrated with Compressed Sending (CS) to improve the efficiency of MS-FCAP. Through understanding the temperature and related information requirements of frozen and chilled aquatic products cold chain logistics, this paper illustrates the design of the CS model which consists of sparse sampling and data reconstruction, and shelf-life prediction. The system was implemented and evaluated in cold chain logistics between Hainan and Beijing in China. The evaluation result suggests that MS-FCAP has a high accuracy in reconstructing temperature data under variable temperature condition as well as under constant temperature condition. The result shows that MS-FCAP is capable of recovering the sampled sensor data accurately and efficiently, reflecting the real-time temperature change in the refrigerated truck during cold chain logistics, and providing effective decision support traceability for quality and safety assurance of frozen and chilled aquatic products. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Xiao, Xinqing; Fu, Zetian; Zhang, Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Xiao, Xinqing; Fu, Zetian; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [He, Qile] Coventry Univ, Coventry CV1 5FB, W Midlands, England. [Xu, Mark] Univ Portsmouth, Portsmouth PO1 3DE, Hants, England. C3 China Agricultural University; Coventry University; University of Portsmouth RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Alayev Y, 2014, IEEE T WIREL COMMUN, V13, P4066, DOI 10.1109/TWC.2014.2315196 Asadi G, 2014, SCI PAP-SER D-ANIM S, V57, P223 Baraniuk RG, 2010, P IEEE, V98, P906, DOI 10.1109/JPROC.2010.2047424 Boari CA, 2008, CIENCIA TECNOL ALIME, V28, P863, DOI 10.1590/S0101-20612008000400015 Bytnerowicz TA, 2014, ENVIRON EXP BOT, V104, P44, DOI 10.1016/j.envexpbot.2014.03.006 Caione C, 2014, IEEE T IND INFORM, V10, P382, DOI 10.1109/TII.2013.2266097 Angelini MFC, 2013, PESQUI AGROPECU BRAS, V48, P1080, DOI 10.1590/S0100-204X2013000800038 Candes EJ, 2006, IEEE T INFORM THEORY, V52, P489, DOI 10.1109/TIT.2005.862083 Candes EJ, 2008, IEEE SIGNAL PROC MAG, V25, P21, DOI 10.1109/MSP.2007.914731 Candes EJ, 2006, IEEE T INFORM THEORY, V52, P5406, DOI 10.1109/TIT.2006.885507 Chen F, 2012, IEEE J SOLID-ST CIRC, V47, P744, DOI 10.1109/JSSC.2011.2179451 Chen W, 2012, IET WIREL SENS SYST, V2, P1, DOI 10.1049/iet-wss.2011.0009 Chen YY, 2014, J FOOD ENG, V141, P113, DOI 10.1016/j.jfoodeng.2014.05.014 CHINA CATFISH INSTITUTE, 2012, CHIN AQ IND REP 2011 Cho GY, 2015, J MED SYST, V39, DOI 10.1007/s10916-014-0161-7 Coates RW, 2013, COMPUT ELECTRON AGR, V96, P13, DOI 10.1016/j.compag.2013.04.013 Cortes P, 2014, J FOOD ENG, V140, P19, DOI 10.1016/j.jfoodeng.2014.04.005 Cruz RMS, 2009, J FOOD ENG, V94, P90, DOI 10.1016/j.jfoodeng.2009.03.006 Donoho DL, 2012, IEEE T INFORM THEORY, V58, P1094, DOI 10.1109/TIT.2011.2173241 Donoho DL, 2006, IEEE T INFORM THEORY, V52, P1289, DOI 10.1109/TIT.2006.871582 Fallah AA, 2013, FOOD CONTROL, V34, P630, DOI 10.1016/j.foodcont.2013.06.015 Farag H. E. M., 2009, Assiut Veterinary Medical Journal, V55, P156 Gram L, 1996, INT J FOOD MICROBIOL, V33, P121, DOI 10.1016/0168-1605(96)01134-8 Haupt J, 2008, IEEE SIGNAL PROC MAG, V25, P92, DOI 10.1109/MSP.2007.914732 Kotta J, 2014, MAR ENVIRON RES, V102, P88, DOI 10.1016/j.marenvres.2014.05.002 Li Xie, 2012, Signal Processing, V28, P1226 Liu Jing, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P214 Mosqueda-Melgar J, 2012, FOOD BIOPROD PROCESS, V90, P205, DOI 10.1016/j.fbp.2011.03.004 Myo M. A., 2014, FOOD CONTROL, V40, P198, DOI DOI 10.1016/J.F00DC0NT.2013.11.016 Pack EC, 2014, J TOXICOL ENV HEAL A, V77, P1477, DOI 10.1080/15287394.2014.955892 Pang YH, 2015, FOOD CONTROL, V47, P326, DOI 10.1016/j.foodcont.2014.07.030 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Qi Lin, 2011, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V42, P129 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Raven JA, 2014, PHOTOSYNTH RES, V121, P111, DOI 10.1007/s11120-013-9962-7 Shen W, 2013, WIREL NETW, V19, P1155, DOI 10.1007/s11276-012-0524-2 Smelt JP, 2013, FOOD CONTROL, V29, P358, DOI 10.1016/j.foodcont.2012.04.021 Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 Suryadevara NK, 2015, IEEE-ASME T MECH, V20, P564, DOI 10.1109/TMECH.2014.2301716 Tarrega A, 2011, J FOOD ENG, V104, P356, DOI 10.1016/j.jfoodeng.2010.12.028 Trebar M, 2013, INT J ANTENN PROPAG, V2013, DOI 10.1155/2013/875973 Tropp JA, 2007, IEEE T INFORM THEORY, V53, P4655, DOI 10.1109/TIT.2007.909108 Tsaig Y, 2006, SIGNAL PROCESS, V86, P549, DOI [10.1016/j.sigpro.2005.05.029, 10.1016/j.sigpro.2005.05.028] Wang TingMan, 2011, Transactions of the Chinese Society of Agricultural Engineering, V27, P141 Weimer J, 2012, INT J GREENH GAS CON, V9, P243, DOI 10.1016/j.ijggc.2012.04.001 Xiao XQ, 2014, SENSORS-BASEL, V14, P19877, DOI 10.3390/s141019877 Xiao XinQing, 2013, Transactions of the Chinese Society of Agricultural Engineering, V29, P259 Xing ShaoHua, 2013, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V44, P194 Xu GB, 2014, SENSORS-BASEL, V14, P16932, DOI 10.3390/s140916932 Yunhe Li, 2013, Information Technology Journal, V12, P1737, DOI 10.3923/itj.2013.1737.1745 Zhao JX, 2015, SIGNAL PROCESS, V106, P106, DOI 10.1016/j.sigpro.2014.06.010 Zhou JH, 2013, FOOD CONTROL, V33, P528, DOI 10.1016/j.foodcont.2013.03.019 NR 52 TC 56 Z9 61 U1 4 U2 173 PD FEB PY 2016 VL 60 BP 656 EP 666 DI 10.1016/j.foodcont.2015.09.012 WC Food Science & Technology SC Food Science & Technology UT WOS:000364882900087 DA 2022-12-14 ER PT J AU Kim, B Ju, CY Son, HI AF Kim, Bosung Ju, Chanyoung Son, Hyoung Il TI Field evaluation of UAV-based tracking method for localization of small insects SO ENTOMOLOGICAL RESEARCH DT Article DE behavior test; small insects; traceability test; tracking; unmanned aerial vehicle ID UNMANNED AERIAL VEHICLE; HARMONIC RADAR; DESIGN AB Small invasive insects cause a reduction or disappearance of native insects, causing biodiversity problems. Therefore, tracking small insects is emerging as a method for biodiversity protection and ecosystem management; studies using tracking techniques, such as harmonic radar, RFID, and radio telemetry, are being conducted. A system using a mobile vehicle and a mobile robot that enhances the mobility of the existing passive tracking is currently being studied. We confirmed that radio telemetry is suitable for tracking insects by comparing the communication distance, weight, and lifespan of the transmitter in our previous study and developed a UAV-based tracking method. In this study, we quantified the size of insects and conducted behavior, traceability, and field tests to verify the feasibility and performance of the developed system. We confirmed that the transmitter attachment did not affect the behavior of the insect, and the three-dimensional movement of the insect did not affect the tracking performance. Furthermore, the tracking experiment was successfully conducted in a real environment. Thus, we quantitatively evaluated the performance of the proposed system and suggested a method to trace small-sized insects. C1 [Kim, Bosung; Ju, Chanyoung; Son, Hyoung Il] Chonnam Natl Univ, Dept Convergence Biosyst Engn, Gwangju, South Korea. [Kim, Bosung; Ju, Chanyoung; Son, Hyoung Il] Chonnam Natl Univ, Interdisciplinary Program IT Bio Convergence Syst, Gwangju, South Korea. C3 Chonnam National University; Chonnam National University RP Son, HI (corresponding author), Chonnam Natl Univ, Coll Agr & Life Sci, Dept Convergence Biosyst Engn, Gwangju 61186, South Korea. EM hison@jnu.ac.kr CR chanyoung Ju, 2020, [Journal of The Korean Society of Industry Convergence, 한국산업융합학회논문집], V23, P125 Chapman JW, 2011, ANNU REV ENTOMOL, V56, P337, DOI 10.1146/annurev-ento-120709-144820 Christie KS, 2016, FRONT ECOL ENVIRON, V14, P242, DOI 10.1002/fee.1281 Cliff O. M., 2015, P ROB SCI SYST C, P1 Cliff OM, 2018, SCI ROBOT, V3, DOI 10.1126/scirobotics.aat8409 Colpitts BG, 2004, IEEE T ANTENN PROPAG, V52, P2825, DOI 10.1109/TAP.2004.835166 Ditmer MA, 2015, CURR BIOL, V25, P2278, DOI 10.1016/j.cub.2015.07.024 Dressel L, 2019, IEEE INT CONF ROBOT, P1905, DOI 10.1109/ICRA.2019.8794243 Dvoracek J, 2020, INSECTS, V11, DOI 10.3390/insects11020120 Gutierrez AP, 2014, CABI INVASIVE SER, V4, P45, DOI 10.1079/9781780641645.0045 Nguyen HV, 2019, J FIELD ROBOT, V36, P617, DOI 10.1002/rob.21857 Hoehn P, 2008, P ROY SOC B-BIOL SCI, V275, P2283, DOI 10.1098/rspb.2008.0405 Hui N., 2019, EFFICIENT DRONE BASE Ju C, 2022, IEEE ACCESS, V10, P4048, DOI 10.1109/ACCESS.2022.3140488 Julier SJ, 1997, P SOC PHOTO-OPT INS, V3068, P182, DOI 10.1117/12.280797 Kennedy PJ, 2018, COMMUN BIOL, V1, DOI 10.1038/s42003-018-0092-9 Kim Hae-Young, 2014, Restor Dent Endod, V39, P74, DOI 10.5395/rde.2014.39.1.74 Kim J, 2019, IEEE ACCESS, V7, P105100, DOI 10.1109/ACCESS.2019.2932119 Kim S, 2019, IEEE ACCESS, V7, P176998, DOI 10.1109/ACCESS.2019.2958153 Kim TK, 2015, KOREAN J ANESTHESIOL, V68, P540, DOI 10.4097/kjae.2015.68.6.540 Kirkpatrick DM, 2019, ENTOMOL EXP APPL, V167, P1020, DOI 10.1111/eea.12861 Kissling WD, 2014, BIOL REV, V89, P511, DOI 10.1111/brv.12065 Lavrenko A, 2020, IEEE MICROW WIREL CO, V30, P445, DOI 10.1109/LMWC.2020.2972744 MASCANZONI D, 1986, ECOL ENTOMOL, V11, P387, DOI 10.1111/j.1365-2311.1986.tb00317.x Muller CG, 2019, WILDLIFE RES, V46, P145, DOI 10.1071/WR17147 Olivares-Mendez, 2015, SENSORS-BASEL, V15, p31 362 Rice KB, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0129175 Stumph B, 2019, IEEE INT CONF ROBOT, P648, DOI 10.1109/ICRA.2019.8794116 Sung S, 2018, ENTOMOL RES, V48, P505, DOI 10.1111/1748-5967.12325 Tsai ZM, 2013, IEEE T MICROW THEORY, V61, P666, DOI 10.1109/TMTT.2012.2230020 Vogel G, 2017, SCIENCE, V356, P576, DOI 10.1126/science.356.6338.576 Wang SJ, 2020, IEEE T INTELL TRANSP, V21, P3409, DOI 10.1109/TITS.2019.2927838 Zhang ZY, 2020, ENTOMOL RES, V50, P50, DOI 10.1111/1748-5967.12411 NR 33 TC 1 Z9 1 U1 9 U2 10 PD MAR PY 2022 VL 52 IS 3 BP 135 EP 147 DI 10.1111/1748-5967.12573 EA MAR 2022 WC Entomology SC Entomology UT WOS:000769931300001 DA 2022-12-14 ER PT J AU Palade, LM Croitoru, C Albu, C Radu, GL Popa, ME AF Palade, Laurentiu Mihai Croitoru, Constantin Albu, Camelia Radu, Gabriel Lucian Popa, Mona Elena TI Identification of Tentative Traceability Markers with Direct Implications in Polyphenol Fingerprinting of Red Wines: Application of LC-MS and Chemometrics Methods SO SEPARATIONS DT Article DE red wine; traceability; polyphenols; markers; authenticity; fingerprinting; chemometrics ID PHENOLIC COMPOSITION; MASS-SPECTROMETRY; ANTHOCYANIN PROFILE; GEOGRAPHICAL ORIGIN; ARGENTINEAN WINES; GRAPE VARIETIES; COLOR; AUTHENTICATION; EXTRACTION; IMPACT AB This study investigated the potential of using the changes in polyphenol composition of red wine to enable a more comprehensive chemometric differentiation and suitable identification of authentication markers. Based on high performance liquid chromatography-mass spectrometry (HPLC-MS) data collected from Feteasca Neagra, Merlot, and Cabernet Sauvignon finished wines, phenolic profiles of relevant classes were investigated immediately after vinification (Stage 1), after three months (Stage 2) and six months (Stage 3) of storage, respectively. The data were subjected to multivariate analysis, and resulted in an initial vintage differentiation by principal component analysis (PCA), and variety grouping by canonical discriminant analysis (CDA). Based on polyphenol common biosynthesis route and on the PCA correlation matrix, additional descriptors were investigated. We observed that the inclusion of specific compositional ratios into the data matrix allowed for improved sample differentiation. We obtained simultaneous discrimination according to the considered oenological factors (variety, vintage, and geographical origin) as well as the respective clustering applied during the storage period. Subsequently, further discriminatory investigations to assign wine samples to their corresponding classes relied on partial least squares-discriminant analysis (PLS-DA); the classification models confirmed the clustering initially obtained by PCA. The benefits of the presented fingerprinting approach might justify its selection and warrant its potential as an applicable tool with improved authentication capabilities in red wines. C1 [Palade, Laurentiu Mihai] Natl Res Dev Inst Anim Biol & Nutr IBNA Balotesti, 1 Calea Bucuresti St, Balotesti 077015, Romania. [Palade, Laurentiu Mihai; Popa, Mona Elena] Univ Agron Sci & Vet Med Bucharest, Fac Biotechnol, 59 Marasti Blvd, Bucharest 011464, Romania. [Croitoru, Constantin] Acad Agr & Forestry Sci, Dept Food Sci, 61 Marasti Blvd, Bucharest 011464, Romania. [Albu, Camelia; Radu, Gabriel Lucian] Natl Inst Biol Sci, Ctr Bioanal, 296 Splaiul Independentei, Bucharest 060031, Romania. [Radu, Gabriel Lucian] Univ Politehn Bucuresti, Fac Appl Chem & Mat Sci, 1-7 Gheorghe Polizu St, Bucharest 011061, Romania. C3 University of Agronomic Science & Veterinary Medicine - Bucharest; National Institute of Gerontology & Geriatrics "Ana Aslan"; National Research Institute for Biological Sciences; Polytechnic University of Bucharest RP Croitoru, C (corresponding author), Acad Agr & Forestry Sci, Dept Food Sci, 61 Marasti Blvd, Bucharest 011464, Romania. EM palade_laurentiu_mihai@yahoo.com; c.croitoru@sodinal.com; camelia_barsan2000@yahoo.com; glradu2006@gmail.com; monapopa@agral.usamv.ro CR Albu C, 2017, ANAL LETT, V50, P591, DOI 10.1080/00032719.2016.1192641 Alecu A, 2016, FOOD ANAL METHOD, V9, P300, DOI 10.1007/s12161-015-0197-4 Anesi A, 2015, BMC PLANT BIOL, V15, DOI 10.1186/s12870-015-0584-4 Arcena MR, 2020, FOOD RES INT, V127, DOI 10.1016/j.foodres.2019.108767 Avizcuri JM, 2016, FOOD CHEM, V213, P123, DOI 10.1016/j.foodchem.2016.06.050 Bellomarino SA, 2009, TALANTA, V80, P833, DOI 10.1016/j.talanta.2009.08.001 Bertacchini L, 2013, DATA HANDL SCI TECHN, V28, P371, DOI 10.1016/B978-0-444-59528-7.00010-7 Blanco-Vega D, 2014, FOOD CHEM, V158, P449, DOI 10.1016/j.foodchem.2014.02.154 Bonada M, 2015, AUST J GRAPE WINE R, V21, P240, DOI 10.1111/ajgw.12142 Carrascon V, 2018, FOOD CHEM, V241, P206, DOI 10.1016/j.foodchem.2017.08.090 Castellarin SD, 2012, BIOCHEMISTRY OF THE GRAPE BERRY, P89 Costa E, 2014, J INT SCI VIGNE VIN, V48, P51 Cozzolino D, 2016, FOOD ANAL METHOD, V9, P2986, DOI 10.1007/s12161-016-0502-x Cravero MC, 2019, BEVERAGES, V5, DOI 10.3390/beverages5040059 Croitoru C., 2012, OENOLOGIE INOVARI SI de Freitas V, 2019, REC ADV POLYPHEN RES, V6, P263 de Lima CM, 2020, FOOD CHEM, V312, DOI 10.1016/j.foodchem.2019.126060 de Oliveira JB, 2019, LWT-FOOD SCI TECHNOL, V114, DOI 10.1016/j.lwt.2019.108415 Degu A, 2014, BMC PLANT BIOL, V14, DOI 10.1186/s12870-014-0188-4 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Diaz R, 2016, J CHROMATOGR A, V1433, P90, DOI 10.1016/j.chroma.2016.01.010 Donno D, 2016, J FOOD SCI TECH MYS, V53, P1071, DOI 10.1007/s13197-015-2115-6 Fabani MP, 2013, FOOD CHEM, V141, P1055, DOI 10.1016/j.foodchem.2013.04.046 Favre G, 2019, FOOD CHEM, V277, P391, DOI 10.1016/j.foodchem.2018.10.085 Ferreira V, 2016, FOOD CHEM, V194, P117, DOI 10.1016/j.foodchem.2015.07.142 Figueiredo-Gonzalez M, 2012, FOOD CHEM, V130, P9, DOI 10.1016/j.foodchem.2011.06.006 Flamini R, 2013, INT J MOL SCI, V14, P19651, DOI 10.3390/ijms141019651 Gambacorta G, 2019, FOOD SCI NUTR, V7, P483, DOI 10.1002/fsn3.817 Garcia-Beneytez E, 2002, EUR FOOD RES TECHNOL, V215, P32, DOI 10.1007/s00217-002-0526-x Garrido J, 2013, FOOD RES INT, V54, P1843, DOI 10.1016/j.foodres.2013.08.001 Geana EI, 2016, FOOD CONTROL, V62, P1, DOI 10.1016/j.foodcont.2015.10.003 Gonzalez-Neves G, 2015, J FOOD SCI TECH MYS, V52, P3449, DOI 10.1007/s13197-014-1410-y Harrison R, 2018, INT J FOOD SCI TECH, V53, P3, DOI 10.1111/ijfs.13480 He F, 2012, MOLECULES, V17, P1571, DOI 10.3390/molecules17021571 Jackson R, 2014, WINE SCI, V4th ed., P69 Jackson R. S., 2014, WINE SCI, P143, DOI [10.1016/B978-0-12-381468-5.00004-X, DOI 10.1016/B978-0-12-381468-5.00004-X] Jackson RS, 2014, WINE SCI, P307, DOI DOI 10.1016/B978-0-12-381468-5.00005-1 Jackson RS., 2014, WINE SCI, V4th ed, P535 Jaitz L, 2010, FOOD CHEM, V122, P366, DOI 10.1016/j.foodchem.2010.02.053 Kallithraka S, 2006, FOOD CHEM, V99, P784, DOI 10.1016/j.foodchem.2005.07.059 Kamiloglu S, 2019, FOOD CHEM, V277, P12, DOI 10.1016/j.foodchem.2018.10.091 Kumsta M, 2014, FOOD TECHNOL BIOTECH, V52, P383, DOI 10.17113/ftb.52.04.14.3650 Kyraleou M, 2020, J FOOD COMPOS ANAL, V92, DOI 10.1016/j.jfca.2020.103547 Lerno L, 2015, AM J ENOL VITICULT, V66, P444, DOI 10.5344/ajev.2015.14129 Li SY, 2019, CRIT REV FOOD SCI, V59, P1840, DOI 10.1080/10408398.2018.1431762 Lingua MS, 2016, FOOD CHEM, V208, P228, DOI 10.1016/j.foodchem.2016.04.009 Aleixandre-Tudo JL, 2018, FOOD CONTROL, V85, P11, DOI 10.1016/j.foodcont.2017.09.014 Makris DP, 2006, TALANTA, V70, P1143, DOI 10.1016/j.talanta.2006.03.024 Malacarne M, 2016, FOOD CHEM, V206, P274, DOI 10.1016/j.foodchem.2016.03.038 Marquez A, 2014, FOOD CHEM, V146, P507, DOI 10.1016/j.foodchem.2013.09.103 Mattivi F, 2006, J AGR FOOD CHEM, V54, P7692, DOI 10.1021/jf061538c Mayr CM, 2018, BEVERAGES, V4, DOI 10.3390/beverages4040074 Merkyte V, 2020, FOODS, V9, DOI 10.3390/foods9121785 Moreno J, 2012, ENOLOGICAL CHEMISTRY, P53, DOI 10.1016/B978-0-12-388438-1.00005-4 Muccillo L, 2014, FOOD CHEM, V143, P506, DOI 10.1016/j.foodchem.2013.07.133 Ortega-Regules A, 2006, J SCI FOOD AGR, V86, P1460, DOI 10.1002/jsfa.2511 Palade LM, 2018, BEVERAGES, V4, DOI 10.3390/beverages4040075 Pavlousek P, 2013, CZECH J FOOD SCI, V31, P474, DOI 10.17221/40/2013-CJFS Perez-Navarro J, 2018, INT J FOOD PROP, V21, P996, DOI 10.1080/10942912.2018.1479856 Perini M, 2020, J AGR FOOD CHEM, V68, P3322, DOI 10.1021/acs.jafc.9b05952 Piccardo D, 2019, FERMENTATION-BASEL, V5, DOI 10.3390/fermentation5030080 Pisano PL, 2015, FOOD CHEM, V175, P174, DOI 10.1016/j.foodchem.2014.11.124 Puertolas E, 2010, FOOD CHEM, V119, P1063, DOI 10.1016/j.foodchem.2009.08.018 Quaglieri C, 2017, MOLECULES, V22, DOI 10.3390/molecules22020192 Radovanovic BC, 2010, J SCI FOOD AGR, V90, P2455, DOI 10.1002/jsfa.4106 Rentzsch Michael, 2009, P509, DOI 10.1007/978-0-387-74118-5_19 Romisch U, 2009, EUR FOOD RES TECHNOL, V230, P31, DOI 10.1007/s00217-009-1141-x Rousserie P, 2019, J AGR FOOD CHEM, V67, P1325, DOI 10.1021/acs.jafc.8b05768 Rubert J, 2014, ANAL BIOANAL CHEM, V406, P6791, DOI 10.1007/s00216-014-7864-y Sacchi KL, 2005, AM J ENOL VITICULT, V56, P197 Salvatore E, 2013, ANAL CHIM ACTA, V761, P34, DOI 10.1016/j.aca.2012.11.015 Santos MC, 2019, FOOD RES INT, V116, P223, DOI 10.1016/j.foodres.2018.08.018 Saurina J, 2010, TRAC-TREND ANAL CHEM, V29, P234, DOI 10.1016/j.trac.2009.11.008 Schlesier K, 2009, EUR FOOD RES TECHNOL, V230, P1, DOI 10.1007/s00217-009-1140-y Setford PC, 2017, TRENDS FOOD SCI TECH, V69, P106, DOI 10.1016/j.tifs.2017.09.005 Siracusa L, 2014, POLYPHENOLS IN PLANTS: ISOLATION, PURIFICATION AND EXTRACT PREPARATION, P15, DOI 10.1016/B978-0-12-397934-6.00002-4 Sun XY, 2015, J FOOD SCI, V80, pC2170, DOI 10.1111/1750-3841.13011 Teixeira A, 2013, INT J MOL SCI, V14, P18711, DOI 10.3390/ijms140918711 Terrier Nancy, 2009, P463, DOI 10.1007/978-0-387-74118-5_18 Tonietto J., 2014, SPECIAL LACCAVE J IN, P19 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Vilanova M, 2015, FOOD CHEM, V169, P187, DOI 10.1016/j.foodchem.2014.08.015 Villano C, 2017, FOOD CONTROL, V80, P1, DOI 10.1016/j.foodcont.2017.04.020 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Wu Q, 2021, FOOD ANAL METHOD, V14, P1895, DOI 10.1007/s12161-021-02032-1 Zhang B, 2015, FOOD RES INT, V78, P313, DOI 10.1016/j.foodres.2015.09.026 NR 86 TC 1 Z9 1 U1 2 U2 13 PD DEC PY 2021 VL 8 IS 12 AR 233 DI 10.3390/separations8120233 WC Chemistry, Analytical SC Chemistry UT WOS:000744011500001 DA 2022-12-14 ER PT J AU Sadovykh, A Afzal, W Truscan, D Pierini, P Bruneliere, H Bagnato, A Gomez, A Cabot, J Avila-Garcia, O AF Sadovykh, Andrey Afzal, Wasif Truscan, Dragos Pierini, Pierluigi Bruneliere, Hugo Bagnato, Alessandra Gomez, Abel Cabot, Jordi Avila-Garcia, Orlando TI On a tool-supported model-based approach for building architectures and roadmaps: The MegaM@Rt2 project experience SO MICROPROCESSORS AND MICROSYSTEMS DT Article DE Model-driven engineering; Requirement engineering; Architecture; Roadmap; UML; Sysml; Traceability; Document generation; Modelio ID RUNTIME VALIDATION AB MegaM@Rt2 is a large European project dedicated to the provisioning of a model-based methodology and supporting tooling for system engineering at a wide scale. It notably targets the continuous development and runtime validation of such complex systems by developing a framework addressing a large set of engineering processes and application domains. This collaborative project involves 27 partners from 6 different countries, 9 industrial case studies as well as over 30 different software tools from project partners (and others). In the context of the MegaM@Rt2 project, we elaborated on a pragmatic model driven approach to specify the case study requirements, design the high-level architecture of a framework, perform the gap analysis between the industrial needs and current state-of-the-art, and plan a first framework development roadmap accordingly. The present paper describes the generic tool-supported approach that came out as a result. It also details its concrete application in the MegaM@Rt2 project. In particular, we discuss the collaborative modeling process, the requirement definition tooling, the approach for components modeling, as well as the traceability and document generation. In addition, we show how we used the proposed solution to specify the MegaM@Rt2 framework's conceptual tool components centered around three complementary tool sets: the MegaM@Rt2 System Engineering Tool Set, the MegaM@Rt2 Runtime Analysis Tool Set and the MegaM@Rt2 Model & Traceability Management Tool Set. The paper ends with a discussion on the practical lessons we have learned from this work so far. (C) 2019 Elsevier B.V. All rights reserved. C1 [Sadovykh, Andrey] Innopolis Univ, Innopolis 420500, Respublika Tata, Russia. [Sadovykh, Andrey; Bagnato, Alessandra] Softeam, 21 Ave Victor Hugo, F-75016 Paris, France. [Afzal, Wasif] Malardalen Univ, Vasteras, Sweden. [Truscan, Dragos] Abo Akad Univ, FIN-20520 Turku, Finland. [Pierini, Pierluigi] Intecs SpA, Via U Forti 5, I-56121 Pisa, Italy. [Bruneliere, Hugo] IMT Atlantique, CNRS, LS2N, F-44000 Nantes, France. [Bruneliere, Hugo] ARMINES, F-44000 Nantes, France. [Gomez, Abel] Univ Oberta Catalunya, IN3, Barcelona, Spain. [Cabot, Jordi] ICREA, Barcelona, Spain. [Avila-Garcia, Orlando] Atos, Subida Mayorazgo 24B, Tenerife 38110, Spain. C3 Innopolis University; Malardalen University; Abo Akademi University; Centre National de la Recherche Scientifique (CNRS); IMT - Institut Mines-Telecom; IMT Atlantique; UOC Universitat Oberta de Catalunya; ICREA RP Afzal, W (corresponding author), Malardalen Univ, Vasteras, Sweden. EM a.sadovykh@innopolis.ru; wasif.afzal@mdh.se; dragos.truscan@abo.fi; pierluigi.pierini@intecs.it; hugo.bruneliere@imt-atlantique.fr; alessandra.bagnato@softeam.fr; agomezlla@uoc.edu; jordi.cabot@icrea.cat; orlando.avila@atos.net CR Afzal W., 2017, P EUR C DIG SYST DES Afzal W, 2018, MICROPROCESS MICROSY, V61, P86, DOI 10.1016/j.micpro.2018.05.010 Ali S., 2013, P IEEE 6 INT C SOFTW Ali S, 2014, SOFTW SYST MODEL, V13, P1189, DOI 10.1007/s10270-012-0293-5 Ali S, 2011, LECT NOTES COMPUT SC, V6981, P108, DOI 10.1007/978-3-642-24485-8_9 Bagnato A., 2014, HDB RES EMBEDDED SYS, P283 Bayazit AA, 2005, IEEE IC CAD, P1052, DOI 10.1109/ICCAD.2005.1560217 Blair G, 2009, COMPUTER, V42, P22, DOI 10.1109/MC.2009.326 Brahneborg D., 2017, P IEEE INT C SOFTW Q Bruneliere H., 2017, P 22 INT C REL SOFTW Bruneliere H, 2018, 21ST ACM/IEEE INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS (MODELS 2018), P334, DOI 10.1145/3239372.3239408 Cariou E., 2013, P 9 EUR C MOD FDN AP Chiba S., 2005, P 19 EUR C OBJ OR PR Desfray P, 2015, COMM COM INF SC, V506, P3, DOI 10.1007/978-3-319-25156-1_1 Di Ruscio D, 2014, SCI COMPUT PROGRAM, V89, P69, DOI 10.1016/j.scico.2013.12.006 Filieri A., 2011, P 33 INT C SOFTW ENG Fitzgerald B, 2017, J SYST SOFTWARE, V123, P176, DOI 10.1016/j.jss.2015.06.063 Flemstriim D., 2018, P IEEE INT C SOFTW Q International Labour Organization, 2018, 291482018E ISOIECIEE, V2nd, P1, DOI DOI 10.1109/IEEESTD.2018.8559686 ISO, 2011, 250102011 ISOIEC, P1, DOI DOI 10.1109/IEEESTD.2011.6146379 Jamro M, 2015, ADV INTELL SYST, V350, P91, DOI 10.1007/978-3-319-15796-2_10 Koudri A, 2010, LECT NOTES COMPUT SC, V6138, P189, DOI 10.1007/978-3-642-13595-8_16 Liehr A.W., GENERATION MARTE ALL Marques M.R. Sena, 2014, P 12 IEEE INT C IND Robert T., 2019, COFLUENT METHODOLOGY Sadovykh A., P 6 INT C SOFTW ENG Selic B, 2003, IEEE SOFTWARE, V20, P19, DOI 10.1109/MS.2003.1231146 Szvetits M, 2016, SOFTW SYST MODEL, V15, P31, DOI 10.1007/s10270-013-0394-9 Tsadimas A., 2012, P 27 ANN ACM S APPL Vidal J., 2009, P DES AUT TEST EUR C Wimmer M, 2011, ACM COMPUT SURV, V43, DOI 10.1145/1978802.1978807 NR 31 TC 1 Z9 1 U1 0 U2 1 PD NOV PY 2019 VL 71 AR 102848 DI 10.1016/j.micpro.2019.102848 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering UT WOS:000500052000032 DA 2022-12-14 ER PT J AU Liu, P AF Liu, Pan TI Investment Decisions of Blockchain-Based Anti-Counterfeiting Traceability Services in a High-Quality Fresh Supply Chain of China SO AGRICULTURE-BASEL DT Article DE blockchain; anti-counterfeiting traceability; fresh supply chain; investment decision; coordination ID MANAGEMENT; OPTIMIZATION; TECHNOLOGY; SYSTEM AB The application of a blockchain-based anti-counterfeiting traceability system (hereafter, blockchain-based ACTS) presents a positive effect on improving the unreliability of the freshness information. However, using a blockchain-based ACTS requires additional expenditures from chain members. Chain members want to know the investment conditions of a blockchain-based ACTS and how to coordinate their supply chain. To solve these problems, a supply chain with one fresh producer and one retailer was chosen as the study subject. Afterwards, considering the unreliability of the freshness information, the demand function was revised. Then, the profit functions before and after adopting a blockchain-based ACTS were constructed, and then a price discount and revenue-sharing contract was put forward to coordinate the supply chain. Findings: With the growth of the unreliability coefficient of the freshness information, benefits to chain members in the proposed three situations would be reduced. Thus, we can know that after using a blockchain-based ACTS, if chain members want to gain more benefits, they should try their best to excavate the value of the blockchain-based ACTS and reduce the unreliability coefficient of the freshness information. C1 [Liu, Pan] Henan Agr Univ, Informat & Management Coll, Zhengzhou 450002, Peoples R China. C3 Henan Agricultural University RP Liu, P (corresponding author), Henan Agr Univ, Informat & Management Coll, Zhengzhou 450002, Peoples R China. EM hnycliupan@163.com CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Appelhanz S, 2016, J CLEAN PROD, V110, P132, DOI 10.1016/j.jclepro.2015.02.034 Boehm VAJ, 2018, LECT NOTES COMPUT SC, V10763, P184, DOI 10.1007/978-3-319-93563-8_16 Bumbudsanpharoke N, 2015, J FOOD SCI, V80, pR910, DOI 10.1111/1750-3841.12861 Chen HL, 2021, IEEE ACCESS, V9, P36008, DOI 10.1109/ACCESS.2021.3062410 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Dai B, 2021, EUR J OPER RES, V290, P116, DOI 10.1016/j.ejor.2020.08.003 Dai HY, 2015, INT J PROD RES, V53, P511, DOI 10.1080/00207543.2014.955922 Dai JB, 2017, INT J PROD RES, V55, P5465, DOI 10.1080/00207543.2017.1321800 Ehmke MD, 2019, AUST J AGR RESOUR EC, V63, P685, DOI 10.1111/1467-8489.12346 Fan ZP, 2022, ANN OPER RES, V309, P837, DOI 10.1007/s10479-020-03729-y Faye PS, 2017, AEBMR ADV ECON, V31, P38 Figorilli S, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18093133 Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 Gartner, 2020, BLOCKCHAIN TECHNOLOG Hayrutdinov S, 2020, J ADV TRANSPORT, V2020, DOI 10.1155/2020/5635404 He XJ, 2019, KSII T INTERNET INF, V13, P619, DOI 10.3837/tiis.2019.02.008 Kamble SS, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120465 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Li Shuguang, 2014, Journal of Chinese Institute of Food Science and Technology, V14, P1 [李文立 Li Wenli], 2019, [运筹与管理, Operations Research and Management Science], V28, P98 Liu P, 2020, J CLEAN PROD, V277, DOI 10.1016/j.jclepro.2020.123646 Madichie NO, 2017, THUNDERBIRD INT BUS, V59, P663, DOI 10.1002/tie.21841 Maersk Sohu, WORLDS LARGEST SHIPP Nakamoto S, BITCOIN PEER TO PEER News S, BUMBLE BEE FOODS SAP Nofer M, 2017, BUS INFORM SYST ENG+, V59, P183, DOI 10.1007/s12599-017-0467-3 Pedersen AB, 2019, MIS Q EXEC, V18, P99, DOI 10.17705/2msqe.00010 Piramuthu S, 2013, EUR J OPER RES, V225, P253, DOI 10.1016/j.ejor.2012.09.024 Pouliot S, 2008, DISS THESES GRADWORK, V70, P179 Rejeb A., 2018, ACTA TECH JAURINENSI, V11, DOI [DOI 10.14513/ACTATECHJAUR.V11.N4.467, 10.14513/actatechjaur.v11.n4.467] Robson K, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107516 Roseiro P, 2020, ADCAIJ-ADV DISTRIB C, V9, P95, DOI 10.14201/ADCAIJ20209495106 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sambrekar K, 2019, INT J CLOUD APPL COM, V9, P33, DOI 10.4018/IJCAC.2019010103 Saurabh S, 2021, J CLEAN PROD, V284, DOI 10.1016/j.jclepro.2020.124731 Shears P, 2010, BRIT FOOD J, V112, P198, DOI 10.1108/00070701011018879 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Taste S.-L., WORLDS 1 BLOCKCHAIN Tayal A, 2021, INT J COMMUN SYST, V34, DOI 10.1002/dac.4696 Tian F, 2017, I C SERV SYST SERV M Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Von Stackelberg H., 2010, MARKET STRUCTURE EQU Wu X.-Y., 2021, INT J PROD RES, V5, P1 Yao SQ, 2020, EUR J OPER RES, V282, P559, DOI 10.1016/j.ejor.2019.09.031 Yiu NCK, 2021, FUTURE INTERNET, V13, DOI 10.3390/fi13040086 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 49 TC 0 Z9 0 U1 11 U2 11 PD JUN PY 2022 VL 12 IS 6 AR 829 DI 10.3390/agriculture12060829 WC Agronomy SC Agriculture UT WOS:000817333200001 DA 2022-12-14 ER PT J AU Galliano, D Orozco, L AF Galliano, Danielle Orozco, Luis TI The Determinants of Electronic Traceability Adoption: A Firm-Level Analysis of French Agribusiness SO AGRIBUSINESS DT Article ID FOOD-INDUSTRY; INFORMATIONAL CASCADES; NETWORK EXTERNALITIES; PROCESS TECHNOLOGIES; DIFFUSION; ICT; ECONOMICS; COORDINATION; PERSPECTIVE; MANAGEMENT AB This paper aims to understand what factors influence firms to adopt electronic traceability systems (ETS) and notably the respective effects of the firm's internal characteristics, its vertical relations and its external environment. Traceability systems based on information and communication technologies (ICT) allow firms to collect, track, stock and transfer information on a range of product attributes. This study contributes to further understand traceability adoption by applying ICT adoption models to the case of ETS, and by using an original dataset, the 2002 ICT Survey, representative of all French agribusiness. The results suggest that a firm's degree of complexity (growing size, belonging to a group) and the development of its information system play a significant role in its adoption behavior. Moreover, they show that ETS adpotion is more driven by a firm's narrow relations with specialized suppliers and downstream processors than by retailers [EconLit classifications: O33; Q130]. (C) 2011 Wiley Periodicals, Inc. C1 [Galliano, Danielle] INRA, UMR AGIR 1248, F-31326 Castanet Tolosan, France. [Orozco, Luis] Univ Toulouse, UT1, LEREPS, EA 4212, Toulouse, France. C3 INRAE; Universite de Toulouse; Universite Toulouse 1 Capitole; Universite Toulouse III - Paul Sabatier; Universite Federale Toulouse Midi-Pyrenees (ComUE); Institut d'Etudes Politiques Toulouse (SciencePo Toulouse) RP Galliano, D (corresponding author), INRA, UMR AGIR 1248, F-31326 Castanet Tolosan, France. EM galliano@toulouse.inra.fr; luis.orozco-noguera@univ-tlse1.fr CR *ACTA ACTIA, 2007, TRAC PRACT GUID AGR Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 ARTHUR WB, 1989, ECON J, V99, P116, DOI 10.2307/2234208 Baldwin John R., 2004, EC IMPACT ICT MEASUR, P153 Banterle A, 2008, AGRIBUSINESS, V24, P320, DOI 10.1002/agr.20169 Battisti G, 2005, INT J IND ORGAN, V23, P1, DOI 10.1016/j.ijindorg.2004.12.002 Battisti G, 2003, RES POLICY, V32, P1641, DOI 10.1016/S0048-7333(03)00055-6 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bikhchandani S, 1998, J ECON PERSPECT, V12, P151, DOI 10.1257/jep.12.3.151 Bocquet R, 2007, RES POLICY, V36, P367, DOI 10.1016/j.respol.2006.12.005 BRAGA H, 1991, J IND ECON, V39, P421, DOI 10.2307/2098441 BRIZ J, 2007, INT MARKETING INT TR, P605 Brousseau E., 1994, Information Economics and Policy, V6, P319, DOI 10.1016/0167-6245(94)90007-8 Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 Chryssochoidis G, 2009, BRIT FOOD J, V111, P565, DOI 10.1108/00070700910966023 Codron Jean-Marie, 2007, International Journal of Agricultural Resources Governance and Ecology, V6, P415, DOI 10.1504/IJARGE.2007.012845 COHEN WM, 1990, ADMIN SCI QUART, V35, P128, DOI 10.2307/2393553 DAVID PA, 1985, AM ECON REV, V75, P332 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Falk M, 2005, TECHNOVATION, V25, P1229, DOI 10.1016/j.technovation.2004.07.004 FARRELL J, 1985, RAND J ECON, V16, P70, DOI 10.2307/2555589 Fischer M.M., 1994, PATTERNS NETWORK EC, P261 FUDENBERG D, 1985, REV ECON STUD, V52, P383, DOI 10.2307/2297660 Gale HF, 1998, AM J AGR ECON, V80, P347, DOI 10.2307/1244507 Galliano D, 2001, ENVIRON PLANN A, V33, P1643, DOI 10.1068/a3423 Galliano D, 2008, ANN REGIONAL SCI, V42, P425, DOI 10.1007/s00168-007-0157-z Geroski PA, 2000, RES POLICY, V29, P603, DOI 10.1016/S0048-7333(99)00092-X GOLAN E, 2004, 830 USDA EC RES Greenan N, 2003, CAMB J ECON, V27, P287, DOI 10.1093/cje/27.2.287 Greene W.H., 2003, ECONOMETRIC ANAL Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hollenstein H., 2004, Structural Change and Economic Dynamics, V15, P315, DOI 10.1016/j.strueco.2004.01.003 KARSHENAS M, 1993, RAND J ECON, V24, P503, DOI 10.2307/2555742 KATZ ML, 1986, J POLIT ECON, V94, P822, DOI 10.1086/261409 Kumar N, 1996, RES POLICY, V25, P713, DOI 10.1016/0048-7333(95)00854-3 Kumar S, 2006, TECHNOVATION, V26, P739, DOI 10.1016/j.technovation.2005.05.006 Lucchetti R, 2004, SMALL BUS ECON, V23, P151, DOI 10.1023/B:SBEJ.0000027667.55821.53 MANSFIELD E, 1961, ECONOMETRICA, V29, P741, DOI 10.2307/1911817 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 MILGROM P, 1990, AM ECON REV, V80, P511 Pinto DB, 2006, FOOD RES INT, V39, P772, DOI 10.1016/j.foodres.2006.01.015 Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Rabade L.A., 2006, J PURCH SUPPLY MANAG, V12, p39?50, DOI DOI 10.1016/J.PURSUP.2006.02.003 Reinganum J., 1989, HDB IND ORG Sans P, 2008, INT J CONSUM STUD, V32, P407, DOI 10.1111/j.1470-6431.2008.00708.x SETBOONSARNG S, 2009, ADBI WORKING PAPER S, V139 Souza Monteiro D. M., 2009, Food Policy, V34, P94, DOI 10.1016/j.foodpol.2008.07.003 Souza-Monteiro DM, 2010, AGRIBUSINESS, V26, P122, DOI 10.1002/agr.20233 Steinmueller E.W., 2000, IND CORP CHANGE, V9, P361, DOI [DOI 10.1093/ICC/9.2.361, 10.1093/icc/9.2.361] Terzi Sergio, 2007, International Journal of Product Lifecycle Management, V2, P253, DOI 10.1504/IJPLM.2007.016292 van der Vorst J, 2005, INNOVATION IN AGRI-FOOD SYSTEMS: PRODUCT QUALITY AND CONSUMER ACCEPTANCE, P245 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Vicente J, 2007, REG STUD, V41, P173, DOI 10.1080/00343400601108424 von Tunzelmann N., 2005, OXFORD HDB INNOVATIO, P407 Yannis Bakos J., 1991, Journal of Management Information Systems, V8, P31 NR 57 TC 14 Z9 15 U1 0 U2 22 PD SUM PY 2011 VL 27 IS 3 BP 379 EP 397 DI 10.1002/agr.20272 WC Agricultural Economics & Policy; Economics; Food Science & Technology SC Agriculture; Business & Economics; Food Science & Technology UT WOS:000293083500008 DA 2022-12-14 ER PT J AU Ruviaro, CF Barcellos, JOJ Dewes, H AF Ruviaro, Clandio Favarini Jardim Barcellos, Julio Otavio Dewes, Homero TI Market-oriented cattle traceability in the Brazilian Legal Amazon SO LAND USE POLICY DT Article DE Agribusiness; Beef production; Sustainability; LCA; Supply-chain; Consumer choice ID LIFE-CYCLE ASSESSMENT; GREENHOUSE-GAS EMISSIONS; BEEF-PRODUCTION; ENVIRONMENTAL IMPACTS; ORIENTATION; SYSTEM AB The purpose of this paper is to note the importance of market orientation in agribusiness and to describe the relevance of market-oriented traceability in the export of beef from the Brazilian Legal Amazon, one of the most scrutinised areas of the world in terms of environmental risks. The study is of a descriptive nature and it uses bibliographic references and secondary data to discuss bovine traceability in the context of deforestation of the Brazilian Legal Amazon and its consequences for international beef trade. Analysed data include those related to the Amazon Region and the following aspects are considered: deforestation dynamics, consumer demands, the volume of exported meat and traceability as a prerequisite for meat export based on the market orientation theory. The results indicated that, according to market orientation, beef certification is a prerequisite for meat produced in the Brazilian Amazon Region for maintaining and expanding a sustainable share of the international markets without the burden of presumptive deforestation. The findings markedly affect primary beef production in the region analysed and the local production systems are forced to adapt to the demands of consumers who are anxious to be assured that the environmental footprint of livestock produced is mitigated worldwide, particularly in the Brazilian Amazon. Concerns regarding the environmental impact of animal production are crucial in the promotion of sustainability of agriculture production, furthermore the major drivers of sustainability in agriculture are the demands of the food market. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Ruviaro, Clandio Favarini] UFGD, Fac Adm Ciencias Contabeis & Econ, BR-79804970 Dourados, MS, Brazil. [Ruviaro, Clandio Favarini; Jardim Barcellos, Julio Otavio] Univ Fed Rio Grande do Sul, Nucleo Estudos Sistemas Prod Bovinos Corte & Cade, BR-91540000 Porto Alegre, RS, Brazil. [Ruviaro, Clandio Favarini; Dewes, Homero] Univ Fed Rio Grande do Sul, Ctr Estudos & Pesquisas Agronegocios CEPAN, BR-91540000 Porto Alegre, RS, Brazil. C3 Universidade Federal da Grande Dourados; Universidade Federal do Rio Grande do Sul; Universidade Federal do Rio Grande do Sul RP Ruviaro, CF (corresponding author), UFGD, FACE, Rodovia Dourados Itahum,Km 12,Caixa Postal 364, BR-79804970 Dourados, MS, Brazil. EM clandioruviaro@ufgd.edu.br; julio.barcellos@ufrgs.br; hdewes@ufrgs.br CR ABRAFRIGO, 2011, EXP BRAS CARN DER BO Allianz S., 2009, G8 CLIMATE SCORECARD [Anonymous], 2006, ISO 140402006 AMD 12 [Anonymous], 2007, 22005 ISO ANUALPEC, 2010, AN AN PEC BRAS, P360 ANUALPEC, 2011, AN PEC BRAS, P360 Barcellos J. O. J., 2005, PECUARIA CORTE BRASI Batalha M. O., 2007, CAD PROD CAR BOV Beauchemin KA, 2010, AGR SYST, V103, P371, DOI 10.1016/j.agsy.2010.03.008 Beverland MB, 2007, IND MARKET MANAG, V36, P430, DOI 10.1016/j.indmarman.2005.12.003 Cadogan J. W., 2003, INT MARKETING REV, V20 Cadogan J. W., 1999, J INT BUSINESS STUDI, V30 Cadogan J. W., 1995, J STRATEGIC MARKETIN, V3 Cano C. R., 2004, INT J RES MARKETING, V21 Carfantan J. Y., 2006, AGRONEGOCIO BRASILEI Casey JW, 2006, AGR SYST, V90, P79, DOI 10.1016/j.agsy.2005.11.008 Cederberg C, 2003, INT J LIFE CYCLE ASS, V8, P350, DOI 10.1007/BF02978508 Cederberg C., 2009, LIFE CYCLE INVENTORY Cochoy F., 2001, RECHERCHE Crosson P, 2011, ANIM FEED SCI TECH, V166-67, P29, DOI 10.1016/j.anifeedsci.2011.04.001 DAY GS, 1994, J MARKETING, V58, P37, DOI 10.2307/1251915 Eurobarometer, 2012, SPEC SURV Fortes G., 2008, MILAGRE BOI BRASILER INPE, 2011, I NAC PESQ ESP TAX D Kirca A.H., 2009, INT MARKETING REV, P26 KOHLI AK, 1990, J MARKETING, V54, P1, DOI 10.2307/1251866 Koneswaran G., 2008, ENV HLTH PERSPECTIVE, P116 Larney FJ, 2006, J ENVIRON QUAL, V35, P1844, DOI 10.2134/jeq2005.0440 Macera A. P., 2004, REV ADM CONT, V8 Malafaia G. C., 2008, SCMS J INDIAN MANAGE, V5 MAPA, 2004, MIN AGR PEC AB SERV Marques D. S. P., 2005, INT PENSA C AGR CHA NARVER JC, 1990, J MARKETING, V54, P20, DOI 10.2307/1251757 Ogino A, 2004, J ANIM SCI, V82, P2115 Ogino A, 2007, ANIM SCI J, V78, P424, DOI 10.1111/j.1740-0929.2007.00457.x Pelletier N, 2010, AGR SYST, V103, P380, DOI 10.1016/j.agsy.2010.03.009 Pereira P. R. X. P., 2011, REV BRAS ZOOTECN, V40, P9 Peters GM, 2010, INT J LIFE CYCLE ASS, V15, P311, DOI 10.1007/s11367-010-0161-x Place S. E., 2012, MEAT SCI Portelle D., 2000, BIOTECHNOLOGY AGRONO, P4 Ribeiro C. F. A., 2005, 5 ENC LAT AM POS GRA Ridoutt BG, 2012, INT J LIFE CYCLE ASS, V17, P165, DOI 10.1007/s11367-011-0346-y Rose G. M., 2002, J BUSINESS RES, P55 Ruviaro CF, 2012, J CLEAN PROD, V28, P9, DOI 10.1016/j.jclepro.2011.10.015 Schlich E., 2008, 6 INT C LCA AGR SECT, P325 Slater S. F, 2001, MANAGING SERVICE QUA, P11 Steinman C., 2000, ACAD MARKETING SCI, P28 Suassuna K.R., 2009, MESMO COM REGRAS ATU Urdan F. T, 2004, MEDINDO ORIENTACAO M USDA, 2011, INT TRADE Zaks DPM, 2009, ENVIRON RES LETT, V4, DOI 10.1088/1748-9326/4/4/044010 Zeidan R. M, 2011, CERTIFICACAO CADEIA NR 52 TC 13 Z9 14 U1 2 U2 59 PD MAY PY 2014 VL 38 BP 104 EP 110 DI 10.1016/j.landusepol.2013.08.019 WC Environmental Studies SC Environmental Sciences & Ecology UT WOS:000335424100011 DA 2022-12-14 ER PT J AU Sobrinho, OG Cugnasca, CE Fialho, FB Guerra, CC AF Gogliano Sobrinho, Osvaldo Cugnasca, Carlos E. Fialho, Flavio B. Guerra, Celito C. TI MODELING OF AN INFORMATION SYSTEM FOR WINE TRACEABILITY BASED ON A SERVICE ORIENTED ARCHITECTURE SO ENGENHARIA AGRICOLA DT Article DE food safety; information technology; internet; web services; UML AB The purpose is to present a scientific research that led to the modeling of an information system which aimed at the maintenance of traceability data in the Brazilian wine industry, according to the principles of a service-oriented architecture (SOA). Since 2005, traceability data maintenance is an obligation for all producers that intend to export to any European Union country. Also, final customers, including the Brazilian ones, have been asking for information about food products. A solution that collectively contemplated the industry was sought in order to permit that producer consortiums of associations could share the costs and benefits of such a solution. Following an extensive bibliographic review, a series of interviews conducted with Brazilian researchers and wine producers in Bento Goncalves - RS, Brazil, elucidated many aspects associated with the wine production process. Information technology issues related to the theme were also researched. The software was modeled with the Unified Modeling Language (UML) and uses web services for data exchange. A model for the wine production process was also proposed. A functional prototype showed that the adopted model is able to fulfill the demands of wine producers. The good results obtained lead us to consider the use of this model in other domains. C1 [Gogliano Sobrinho, Osvaldo; Cugnasca, Carlos E.] Univ Sao Paulo, LAA, Escola Politecn, BR-09500900 Sao Paulo, Brazil. [Fialho, Flavio B.; Guerra, Celito C.] Embrapa Uva & Vinho, Empresa Brasileira Pesquisa Agropecuaria, Bento Goncalves, RS, Brazil. C3 Universidade de Sao Paulo; Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) RP Sobrinho, OG (corresponding author), Univ Sao Paulo, LAA, Escola Politecn, BR-09500900 Sao Paulo, Brazil. EM ogogli@abili.com.br CR *ABNT, 2006, 22000 NBR ISO ABNT Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 BOEHM BW, 1988, COMPUTER, V21, P61, DOI 10.1145/12944.12948 Henley Mark, 2008, Computer Law & Security Report, V24, P77, DOI 10.1016/j.clsr.2007.11.003 Jardim-Goncalves R, 2006, COMPUT IND, V57, P679, DOI 10.1016/j.compind.2006.04.013 McEachern MG, 2005, BRIT FOOD J, V107, P572, DOI 10.1108/00070700510610986 NATALE OR, 2002, IEEE C DEC CONTR 200, V4 Rombaldi CV, 2007, REV BRAS FRUTIC, V29, P681, DOI 10.1590/S0100-29452007000300049 SOBRINHO OG, 2005, REV BRASILEIRA AGROI, V7, P44 *WORLD WID WEB CON, 2007, SOAP SPEC World Wide Web Consortium, 2004, WEB SERV GLOSS NR 11 TC 2 Z9 2 U1 0 U2 8 PD JAN-FEB PY 2010 VL 30 IS 1 BP 100 EP 109 DI 10.1590/S0100-69162010000100011 WC Agricultural Engineering SC Agriculture UT WOS:000277832200011 DA 2022-12-14 ER PT J AU Doluschitz, R Engler, B Hoffmann, C AF Doluschitz, Reiner Engler, Barbara Hoffmann, Christa TI Quality assurance and traceability of foods of animal origin: major findings from the research project IT FoodTrace SO JOURNAL FUR VERBRAUCHERSCHUTZ UND LEBENSMITTELSICHERHEIT-JOURNAL OF CONSUMER PROTECTION AND FOOD SAFETY DT Article DE Food supply chain integration; Meat and meat products; Traceability; Quality assurance; Data standards; Integrated IT concept; Costs and benefits AB Based on the information needs of stakeholders (from animal feed to consumers, including the authorities and organizations involved) an integrated IT-system without structural fractures and barriers shall be developed. It will be designed to enable the merging, internal exchange and utilization of relevant data and parameters. Subprojects of the interdisciplinary research consortium cover the entire supply chain and also address cross-section issues, such as logistics, costs and benefits, veterinarian services, quality assurance systems, a comprehensive IT-solution including data format standards (agroXML), and requirements for sustainability. Substantial added value has been generated from intensive interdisciplinary co-operation. The interdisciplinary research project IT FoodTrace ( "http://www.itfoodtrace.de ) aims at achieving traceability and quality assurance along the food chain of "meat and meat products". The aim of this paper is the presentation of (a) the complexity of underlying problems, (b) the project structure, (c) available results from selected sub-projects and (d) the added values from interdisciplinary co-operation. Selected findings include an optimized single animal data collection and information management in livestock systems, benefits gained by linking animal-health-related information to an integrated animal-health system, requirements and features of an integrated IT-System, including consequences for data protection and security, and findings from a Delphi-based cost-benefit-analysis of an integrated quality assurance and traceability system. C1 [Doluschitz, Reiner; Hoffmann, Christa] Univ Hohenheim, Dept Farm Management 410C, D-70593 Stuttgart, Germany. [Engler, Barbara] Univ Hohenheim, Life Sci Ctr 760, D-70593 Stuttgart, Germany. C3 University Hohenheim; University Hohenheim RP Doluschitz, R (corresponding author), Univ Hohenheim, Dept Farm Management 410C, D-70593 Stuttgart, Germany. EM doluschitz@uni-hohenheim.de CR Bahlmann J., 2008, ZUKUNFTSPERSPEKTIVEN, P97 BREITMAYER E, 2009, AKZEPTANZ QUALITATS Commission of the European Communities, 2000, COM1999719 COMM EUR DOLUSCHITZ R, 2007, AGROXML INFORM ZUKUN, P9 HERD D, 2008, UNTERNEHMENS IT FUHR, P67 KLOTZ B, 2008, AUSWIRKUNGEN S UNPUB KUHLMANN A, 2008, LANDTECHNIK, V4, P234 Kunisch Martin, 2009, STAND ENTWICKLUNG AG Lichtenberg L., 2008, 12 C EUR ASS AGR EC, P1 LICHTENBERG L, 2008, ELEKT Z AGRARINFORMA ROGGE C, 2008, ELEKT Z AGRARINOFORM ROTH M, 2009, KOSTEN NUTZENASPEKTE, P137 NR 12 TC 5 Z9 6 U1 1 U2 25 PD FEB PY 2010 VL 5 IS 1 BP 11 EP 19 DI 10.1007/s00003-009-0527-9 WC Food Science & Technology SC Food Science & Technology UT WOS:000273810900003 DA 2022-12-14 ER PT J AU Cappai, MG Rubiu, NG Pinna, W AF Cappai, M. G. Rubiu, N. G. Pinna, W. TI Economic assessment of a smart traceability system (RFID plus DNA) for origin and brand protection of the pork product labelled "suinetto di Sardegna" SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article ID IDENTIFICATION; PIGS AB The suckling piglet is a typical niche meat product traditionally slaughtered before weaning at nearly one month of age. Due to African Swine Fever (ASF) foci and dumping phenomenon the pork production system of the island suffers from exportation restriction and competition with imported of pork products from GDO circuits, often claimed as local. A bottom-up analysis highlighted the need to establish a robust system of origin and brand protection for the suckling piglet labelled "Suinetto di Sardegna". Previous experiences tested the opportunity offered by the integrated system of identification of animal and products based on the RFID technology, paired with molecular analysis (DNA) for meat traceability. This study reports the economic evaluation of such a smart traceability system, through the introduction of technologically advanced tools in the traditional process of production. A comparative evaluation of conventional vs. RFID + DNA system was carried out and costs are compared. The break even point (BEP) calculated following the potential investment for the adoption of the RFID + DNA system for origin and brand protection highlighted that the abattoir of the circuit "Suinetto di Sardegna" should sell at least 17 piglets or the equivalent of 1305.00 euros in sales before any profits could be realized. In the light of production capacity of an extensive traditional farming system, this value requires high numbers of production units (unit = whole carcass of suckling piglet). In addition, this economic evaluation was only related to representative farms and did not take into account seasonal fluctuations or effective geo-distribution of farms, but was based on average yearly production and spatial distance of farms from abattoir. Decreasing trend of costs due to massive deployment of RFID and the diffusion of molecular analyses for traceability purposes may be forecasted and minimum acceptable levels of economic BEP can be achieved in a short period. If the new technology becomes a standard for the traceability of the suckling piglet, incremental benefits can arise from the reinforcement of the brand "Suinetto di Sardegna". C1 [Cappai, M. G.; Pinna, W.] Univ Sassari, Dept Agr, Res Unit Anim Breeding Sci, Sassari, Italy. [Rubiu, N. G.] NurEID Fdn, Cagliari, Italy. C3 University of Sassari RP Cappai, MG (corresponding author), Univ Sassari, Dept Vet Med, Via Vienna 2, I-07100 Sassari, Italy. EM mgcappai@uniss.it CR Babot D, 2006, J ANIM SCI, V84, P2575, DOI 10.2527/jas.2006-119 Caja G, 2005, J ANIM SCI, V83, P2215 Cappai MG, 2014, SMALL RUMINANT RES, V117, P169, DOI 10.1016/j.smallrumres.2013.12.031 EC, 2002, OJ, VL31, P1 European Commission Implementing Regulation (EU), 2013, OFF J EUR UNION L, V335, P19 European Council of the European Union, 2008, OFF J EUR COMMUN L, V31, P1 Hemandez-Jover M., 2008, ANIMAL, V2, P1692 International Organization for Standardization (ISO), 1996, 117851996E ISO International Organization for Standardization (ISO), 1996, 117841996E ISO Lim Hyun-Tae, 2009, Journal of Animal Science and Technology, V51, P201 Pinna W, 2007, J ANIM PHYSIOL AN N, V91, P252, DOI 10.1111/j.1439-0396.2007.00700.x Pinna W., 2007, 58 EAAP M, P275 NR 12 TC 8 Z9 8 U1 2 U2 23 PD FEB PY 2018 VL 145 BP 248 EP 252 DI 10.1016/j.compag.2018.01.003 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000425577400026 DA 2022-12-14 ER PT J AU Trapmann, S Corbisier, P Schimmel, H Emons, H AF Trapmann, Stefanie Corbisier, Philippe Schimmel, Heinz Emons, Hendrik TI Towards future reference systems for GM analysis SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article DE PCR; GMO; Calibration; Traceability; Measurement systems; Copy number ratio; Mass fraction ID DNA FRAGMENT RATIOS; REAL-TIME PCR; METROLOGICAL TRACEABILITY; QUANTITATIVE-DETERMINATION; MON-810 CORN; QUANTIFICATION; MAIZE; BT176 AB Despite the fact that the measurement unit for the quantification of GMOs in food and feed products has not yet been unambiguously agreed upon in Europe, international trade requires reliable GMO analysis measuring comparably the GMO content of products. The two reference systems, based either on mass fractions or on copy number ratios, and their metrological traceability chains are presented and discussed. It is concluded that, properly established and expressed, measurement results in copy number ratios can provide a metrologically sound reference system. In this case, certified reference materials used for calibration and quality control can be independent of each other and the uncertainty derived from calibration can correctly be included in the overall uncertainty of the GMO measurement. However, further efforts are required to establish this metrological system. C1 [Trapmann, Stefanie; Corbisier, Philippe; Schimmel, Heinz; Emons, Hendrik] Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements, B-2440 Geel, Belgium. C3 European Commission Joint Research Centre; EC JRC Institute for Reference Materials & Measurements (IRMM) RP Trapmann, S (corresponding author), Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements, Retieseweg 111, B-2440 Geel, Belgium. EM stefanie.trapmann@ec.europa.eu CR [Anonymous], 2003, 175112003 ISO Bhat S, 2009, ANAL BIOANAL CHEM, V394, P457, DOI 10.1007/s00216-009-2729-5 BROOTHAERTS W, 2009, 23986 EC EUR CHARELS D, 2007, 23028 EC EUR Charels D, 2007, J AGR FOOD CHEM, V55, P3268, DOI 10.1021/jf0629336 Charels D, 2007, J AGR FOOD CHEM, V55, P3258, DOI 10.1021/jf062932d *COMM REF LAB GMO, 2009, DEF MIN PERF REQ AN COMMISSION E, 2003, OFF J EUR UNION, V268, P24 COMMISSION E, 2004, OFF J EUR UNION, V348, P18 Corbisier P., 2007, 22948 EC EUR CORBISIER P, 2009, ANAL BIOANAL CHEM, DOI DOI 10.1007/S00216-009-2729-5 Corbisier P, 2007, J AGR FOOD CHEM, V55, P3249, DOI 10.1021/jf062931l Davison J, 2007, CAB REV, V2 *EUR REF MAT, 2009, APPL NOT 4 US CERT R Holst-Jensen A, 2006, J AGR FOOD CHEM, V54, P2799, DOI 10.1021/jf052849a Holst-Jensen A, 2004, J AOAC INT, V87, P927 *ISO, 2008, 301992 ISO *ISO, 2007, 992007 ISOIEC *ISO, 2000, 342000 ISO KOEBER R, ACCRED QUAL AS UNPUB Linsinger TPJ, 2001, ACCREDIT QUAL ASSUR, V6, P20, DOI 10.1007/s007690000261 Milton MJT, 2001, METROLOGIA, V38, P289, DOI 10.1088/0026-1394/38/4/1 Taverniers I, 2005, J AGR FOOD CHEM, V53, P3041, DOI 10.1021/jf0483467 Taverniers Isabel, 2008, Environmental Biosafety Research, V7, P197, DOI 10.1051/ebr:2008018 Trapmann S, 2005, ANAL BIOANAL CHEM, V381, P72, DOI 10.1007/s00216-004-2901-x TRAPMANN S, 2005, 21567 EC EUR TRAPMANN S, 2001, 20111 EC EUR TRAPMANN S, 2005, 21574 EC EUR Zhang D, 2008, TRANSGENIC RES, V17, P393, DOI 10.1007/s11248-007-9114-y NR 29 TC 15 Z9 16 U1 0 U2 5 PD MAR PY 2010 VL 396 IS 6 BP 1969 EP 1975 DI 10.1007/s00216-009-3321-8 WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000275454900004 DA 2022-12-14 ER PT J AU Ben Ayed, R Hanana, M Ercisli, S Karunakaran, R Rebai, A Moreau, F AF Ben Ayed, Rayda Hanana, Mohsen Ercisli, Sezai Karunakaran, Rohini Rebai, Ahmed Moreau, Fabienne TI Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil SO PLANTS-BASEL DT Review DE artificial intelligence; big data; blockchain; DNA technologies; internet of things; smart agriculture; olive fruit ID ARTIFICIAL-INTELLIGENCE MODELS; NUCLEAR-MAGNETIC-RESONANCE; UNITED-STATES; MICROSATELLITE MARKERS; PRECISION AGRICULTURE; COMPUTER VISION; PRODUCTS SOLD; FISH PRODUCTS; BIG DATA; IDENTIFICATION AB Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture. C1 [Ben Ayed, Rayda; Rebai, Ahmed] Ctr Biotechnol Sfax, Lab Mol & Cellular Screening Proc, PB 1177, Sfax 3018, Tunisia. [Hanana, Mohsen] Ctr Biotechnol Borj Cedria, Lab Extremophile Plants, BP 901, Hammam Lif 2050, Tunisia. [Ercisli, Sezai] Ataturk Univ, Fac Agr, Dept Hort, TR-25240 Erzurum, Turkey. [Karunakaran, Rohini] AIMST Univ, Fac Med, Unit Biochem, Bedong 08100, Malaysia. [Karunakaran, Rohini] Saveetha Inst Med & Tech Sci SIMATS, Inst Bioinformat, Saveetha Sch Engn SSE, Dept Computat Biol, Chennai 602105, Tamil Nadu, India. [Karunakaran, Rohini] AIMST Univ, Ctr Excellence Biomat Sci, Bedong 08100, Malaysia. [Moreau, Fabienne] Inst Natl Rech Agron INRA, 2 Pl Pierre Viala, F-34000 Montpellier, France. C3 Centre de Biotechnologie de Sfax; Universite de Sfax; Centre de Biotechnologie de Borj Cedria; Ataturk University; AIMST University; AIMST University; INRAE RP Karunakaran, R (corresponding author), AIMST Univ, Fac Med, Unit Biochem, Bedong 08100, Malaysia.; Karunakaran, R (corresponding author), Saveetha Inst Med & Tech Sci SIMATS, Inst Bioinformat, Saveetha Sch Engn SSE, Dept Computat Biol, Chennai 602105, Tamil Nadu, India.; Karunakaran, R (corresponding author), AIMST Univ, Ctr Excellence Biomat Sci, Bedong 08100, Malaysia. EM raydabenayed@yahoo.fr; mohsen.hnana@cbbc.rnrt.tn; sercisli@gmail.com; rohini@aimst.edu.my; ahmed.rebai@cbs.rnrt.tn; fabienne.moreau@qualtech-groupe.com CR Agu C. M., 2020, Artificial Intelligence in Agriculture, V4, P1, DOI 10.1016/j.aiia.2020.01.001 Alkan A, 2021, TURK J AGRIC FOR, V45, P717, DOI 10.3906/tar-2007-105 Alonso-Salces R., 2011, AUTHENTICATION VIRGI, P1 Anami BS., 2020, ARTIF INTELL AGR, V4, P12, DOI [10.1016/j.aiia.2020.03.001, DOI 10.1016/J.AIIA.2020.03.001] Anggraeni Wiwik, 2018, 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), P347, DOI 10.1109/ISRITI.2018.8864442 [Anonymous], 2014, WHY INT THINGS IS CA [Anonymous], 2013, GARTNERS BIG DATA DE [Anonymous], 2017, BBVA 5 VS BIG DATA Aouadi B, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20195479 Arena A, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2019), P173, DOI 10.1109/SMARTCOMP.2019.00049 Arnpatzidis Y, 2019, COMPUT ELECTRON AGR, V164, DOI 10.1016/j.compag.2019.104900 Belaud JP, 2019, COMPUT IND, V111, P41, DOI 10.1016/j.compind.2019.06.006 Ben Ayed R, 2021, J FOOD QUALITY, V2021, DOI 10.1155/2021/5584754 Ben Ayed R, 2016, DATABASE-OXFORD, DOI 10.1093/database/bav090 Ben Ayed Rayda, 2014, J Genet, V93, pe148 Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 Bolandnazar E, 2020, ENERG SOURCE PART A, V42, P1618, DOI 10.1080/15567036.2019.1604872 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Calo F, 2022, FOODS, V11, DOI 10.3390/foods11010113 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Chakraborty S, 2020, STUD BIG DATA, V63, P51, DOI 10.1007/978-981-13-9177-4_3 Chammem N, 2018, J AOAC INT, V101, P923, DOI 10.5740/jaoacint.17-0446 Chang CH, 2016, FOOD CONTROL, V66, P38, DOI 10.1016/j.foodcont.2016.01.034 Colizzi L, 2020, AGRICULTURAL INTERNET OF THINGS AND DECISION SUPPORT FOR PRECISION SMART FARMING, P1, DOI 10.1016/B978-0-12-818373-1.00001-9 Corallo A, 2020, 2020 9TH INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY AND MANAGEMENT (ICITM 2020), P197, DOI 10.1109/ICITM48982.2020.9080349 Cruz A, 2019, COMPUT ELECTRON AGR, V157, P63, DOI 10.1016/j.compag.2018.12.028 Cruz AC, 2017, FRONT PLANT SCI, V8, DOI 10.3389/fpls.2017.01741 De Mauro A, 2015, AIP CONF PROC, V1644, P97, DOI 10.1063/1.4907823 Del Coco L, 2012, NUTRIENTS, V4, P343, DOI 10.3390/nu4050343 Diaz A, 2006, J AM SOC HORTIC SCI, V131, P250, DOI 10.21273/JASHS.131.2.250 Dobrota CT, 2021, TURK J AGRIC FOR, V45, P730, DOI 10.3906/tar-2012-79 Dona A, 2009, CRIT REV FOOD SCI, V49, P164, DOI 10.1080/10408390701855993 Doveri S, 2008, SCI HORTIC-AMSTERDAM, V116, P367, DOI 10.1016/j.scienta.2008.02.005 Espitia A., 2020, WORLD BANK POLICY RE Etminan A, 2019, CEREAL RES COMMUN, V47, P170, DOI 10.1556/0806.46.2018.057 fao, WORLD AGR 2030 2050 FAO, FUT FOOD AGR Favenza A, 2019, 2019 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR AGRICULTURE AND FORESTRY (METROAGRIFOR), P7 Fiorini M., 2020, VOX Firouz M. S., 2019, Agricultural Engineering International: CIGR Journal, V21, P224 food-safety, MOST COMMON FOOD SAF Freitas S, 2019, INT J ADV ROBOT SYST, V16, DOI 10.1177/1729881419842991 Gao Z., 2020, ARTIF INTELL AGR, V4, P31, DOI DOI 10.1016/J.AIIA.2020.04.003 gartner, IMPACT INTERNET THIN Gazeli O, 2020, FOOD CHEM, V302, DOI 10.1016/j.foodchem.2019.125329 Gebremedhin S, 2021, WATER-SUI, V13, DOI 10.3390/w13040574 Girelli CR, 2020, FOODS, V9, DOI 10.3390/foods9121797 Girelli CR, 2018, METABOLITES, V8, DOI 10.3390/metabo8040060 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 Gradisek A, 2021, J AGR FOOD CHEM, V69, P12073, DOI 10.1021/acs.jafc.1c00622 Guido R, 2020, INT J IND ENG MANAGE, V11, P50, DOI 10.24867/IJIEM-2020-1-252 Gulluce Y, 2020, TURK J AGRIC FOR, V44, P127, DOI 10.3906/tar-1907-11 Guo HZ, 2020, AAAI CONF ARTIF INTE, V34, P13294 Gupta N, 2022, INT J PARALLEL PROG, V50, P1, DOI 10.1007/s10766-020-00671-1 Gyftokostas N, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-84941-z Han J-W, 2019, ARTIFICIAL INTELLIGE, V3, P11, DOI 10.1016/j.aiia.2019.10.001 Hoekman B, 2020, EXPORT RESTRICTIONS Hoekman B.M., 2020, LEARNING TRADE POLIC Jaafari A, 2019, AGR FOREST METEOROL, V266, P198, DOI 10.1016/j.agrformet.2018.12.015 Janin M, 2014, EUR FOOD RES TECHNOL, V239, P745, DOI 10.1007/s00217-014-2279-8 Jellali A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020722 Jha K., 2019, ARTIF INTELL AGR, V2, P1, DOI DOI 10.1016/J.AIIA.2019.05.004 Jung JH, 2021, CURR OPIN BIOTECH, V70, P15, DOI 10.1016/j.copbio.2020.09.003 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kane DE, 2016, FOOD CONTROL, V59, P158, DOI 10.1016/j.foodcont.2015.05.020 Keatley K L, 2000, Qual Assur, V8, P33, DOI 10.1080/105294100753209174 Koksal ES, 2021, TURK J AGRIC FOR, V45, P743, DOI 10.3906/tar-2011-5 Kouadio L, 2018, COMPUT ELECTRON AGR, V155, P324, DOI 10.1016/j.compag.2018.10.014 Kramer MP, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13042168 Kumar S.P., 2020, ARTIF INTELL AGR, V4, P116, DOI [10.1016/j.aiia.2020.06.004, DOI 10.1016/J.AIIA.2020.06.004] LAMPARTE A.M.G, USE BLOCKCHAIN TECHN Lezoche M, 2020, COMPUT IND, V117, DOI 10.1016/j.compind.2020.103187 Li Zhang, 2020, Journal of Physics: Conference Series, V1574, DOI 10.1088/1742-6596/1574/1/012121 Lou SJ, 2021, WATER-SUI, V13, DOI 10.3390/w13050640 Marchesi L., 2021, ARXIV mckinsey, INTERNET THINGS MCKI mckinsey, AGR TRENDS DISRUPTIN Minaev G, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21020361 Miranda J, 2019, COMPUT IND, V108, P21, DOI 10.1016/j.compind.2019.02.002 mongodb, BIG DATA EXPLIQU Mookerjee S, 2005, THEOR APPL GENET, V111, P1174, DOI 10.1007/s00122-005-0049-5 Motta GA, 2020, FRONT BLOCKCHAIN, V3, DOI 10.3389/Blockchain.2020.00006 Muzzalupo I, 2002, EUR FOOD RES TECHNOL, V214, P528, DOI 10.1007/s00217-001-0482-x Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Nadafzadeh M, 2019, PRECIS AGRIC, V20, P857, DOI 10.1007/s11119-018-9618-x oecd, BUILDING RESILIENT R oecd, COVID 19 FOOD AGR SE Oeschger MP, 2007, ADV BIOCHEM ENG BIOT, V107, P57, DOI 10.1007/10_2007_051 Okinda C., 2020, ARTIF INTELL AGR, V4, P184, DOI [10.1016/j.aiia.2020.09.002, DOI 10.1016/J.AIIA.2020.09.002] Onu C. E., 2020, Artificial Intelligence in Agriculture, V4, P39, DOI 10.1016/j.aiia.2020.04.001 Ordukaya E, 2017, J FOOD QUALITY, DOI 10.1155/2017/9272404 Ozden C, 2021, TURK J AGRIC FOR, V45, P775, DOI 10.3906/tar-2010-100 Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pandey PK, 2020, COMPUT ELECTRON AGR, V179, DOI 10.1016/j.compag.2020.105838 Partel V, 2019, COMPUT ELECTRON AGR, V162, P328, DOI 10.1016/j.compag.2019.04.022 Partel V, 2019, COMPUT ELECTRON AGR, V157, P339, DOI 10.1016/j.compag.2018.12.048 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Pasqualone A, 2001, EUR FOOD RES TECHNOL, V213, P240, DOI 10.1007/s002170100367 Patelli N, 2020, J FOOD SCI, V85, P3670, DOI 10.1111/1750-3841.15477 Patricio DI, 2018, COMPUT ELECTRON AGR, V153, P69, DOI 10.1016/j.compag.2018.08.001 Perri E, 2012, OLIVE GERMPLASM - THE OLIVE CULTIVATION, TABLE OLIVE AND OLIVE OIL INDUSTRY IN ITALY, P265, DOI 10.5772/51796 Petrakis PV, 2008, J AGR FOOD CHEM, V56, P3200, DOI 10.1021/jf072957s Rahul K., 2020, ARTIFICIAL INTELLIGE, V4, P172 Rallo P, 2000, THEOR APPL GENET, V101, P984, DOI 10.1007/s001220051571 Rana RL, 2021, BRIT FOOD J, V123, P3471, DOI 10.1108/BFJ-09-2020-0832 Reilly K, 2011, AUTOMATA AND MIMESIS ON THE STAGE OF THEATRE HISTORY, P148 Rohani A., 2019, ARTIF INTELL AGR, V1, P27, DOI [10.1016/j.aiia.2019.03.002, DOI 10.1016/J.AIIA.2019.03.002] Sabanci K, 2020, J SCI FOOD AGR, V100, P817, DOI 10.1002/jsfa.10093 Santangeli A, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-67898-3 sas, BIG DATA WHAT IT IS Schikowski AB, 2018, AN ACAD BRAS CIENC, V90, P3389, DOI 10.1590/0001-3765201820170569 Schmidhuber, 2018, EMERGING OPPORTUNITI Schmidt L.B, 1940, AGR HIST, V14, P117 semanticscholar, RONGAI 13C NMR ANAL Shadrin D, 2019, IEEE SENS J, V19, P11573, DOI 10.1109/JSEN.2019.2935812 Shafaei SM, 2016, COMPUT ELECTRON AGR, V128, P34, DOI 10.1016/j.compag.2016.08.014 Shafaei S. M., 2019, ARTIF INTELL AGR, V2, P38, DOI [10.1016/j.aiia.2019.06.003, DOI 10.1016/J.AIIA.2019.06.003] Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Silletti S, 2019, FOOD CHEM, V271, P410, DOI 10.1016/j.foodchem.2018.07.178 Singh VK, 2018, COMPUT ELECTRON AGR, V150, P205, DOI 10.1016/j.compag.2018.04.019 Sipos L, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20236768 Soh YW, 2018, COMPUT ELECTRON AGR, V144, P164, DOI 10.1016/j.compag.2017.12.002 Splitter J, DECADE GMO CONTROVER Su W.-H., 2020, ARTIF INTELLI AGR, V4, P262, DOI [10.1016/j.aiia.2020.11.001, DOI 10.1016/J.AIIA.2020.11.001] Talaviya T., 2020, ARTIF INTELL AGR, V4, P58, DOI [DOI 10.1016/J.AIIA.2020.04.002, 10.1016/j.aiia.2020.04.002] Tang DH, 2018, COMPUT ELECTRON AGR, V152, P375, DOI 10.1016/j.compag.2018.07.029 Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Tewari V.K., 2020, ARTIF INTELL AGR, V4, P21, DOI [DOI 10.1016/J.AIIA.2020.01.002, 10.1016/j.aiia.2020.01.002] Tian X., 2020, ARTIF INTELL AGR, V4, P48, DOI [10.1016/j.aiia.2020.05.001, DOI 10.1016/J.AIIA.2020.05.001] Tzachor A, 2020, ARTIF INTELL Vellinga A, 2002, EMERG INFECT DIS, V8, P19, DOI 10.3201/eid0801.010129 Venturini F, 2021, FOODS, V10, DOI 10.3390/foods10051010 Vij A, 2020, PROCEDIA COMPUT SCI, V167, P1250, DOI 10.1016/j.procs.2020.03.440 Violino S, 2021, EUR FOOD RES TECHNOL, V247, P1013, DOI 10.1007/s00217-021-03683-4 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9060834 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Waleed M, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10103365 WILESMITH JW, 1988, VET REC, V123, P638 Xiong X, 2019, J FOOD PROTECT, V82, P1200, DOI 10.4315/0362-028X.JFP-18-514 Xu Y., 2019, ARTIFICIAL INTELLIGE, V1, P35, DOI [DOI 10.1016/J.AIIA.2019.05.001, 10.1016/j.aiia.2019.05.001] Yigit E, 2019, COMPUT ELECTRON AGR, V156, P369, DOI 10.1016/j.compag.2018.11.036 Yin S., 2020, ARTIFICIAL INTELLIGE, V4, P140, DOI DOI 10.1016/J.AIIA.2020.07.002 Yu HJ, 2020, COMPUT ELECTRON AGR, V176, DOI 10.1016/j.compag.2020.105653 Zarezadeh MR, 2021, FOOD SCI NUTR, V9, P180, DOI 10.1002/fsn3.1980 NR 151 TC 0 Z9 0 U1 12 U2 12 PD MAY PY 2022 VL 11 IS 9 AR 1230 DI 10.3390/plants11091230 WC Plant Sciences SC Plant Sciences UT WOS:000799328900001 DA 2022-12-14 ER PT J AU Dokuzlu, S AF Dokuzlu, Sertac TI Geographical indications, implementation and traceability: Gemlik table olives SO BRITISH FOOD JOURNAL DT Article DE Turkey; Traceability; Gemlik olives; Geographical indication; Label; QR ID PROTECTION; ATTITUDES; CULTURE AB Purpose - Geographical indications (GIs) have been implemented across the EU for agricultural and food products for many years and consumers know them well. However, developing countries and/or transitioning economies do not have sufficient experience to apply GIs. The purpose of this paper is to demonstrate ways to implement GI in domestic markets in countries in which there are no common logo and control/tracking systems to help GI holders manage the process. Design/methodology/approach - This study uses a qualitative case study to describe a process to implement GI registered food products in domestic markets. The system was developed by the author and applied by the Gemlik Commodity Exchange (GTB). Findings - A registration system that allows inspection of producers in GI limits should be established. For good practice and to ensure correct registration, an efficient, established control system, promotion, and conservancy of GI-holding organizations are essential. Practical implications - GTB implemented two projects, for which a GI logo and labels with quick-response (QR) codes were created. A regional, GI promotion project was conducted, and after one year, there was no increase in consumer demand, but entering markets became easier, and traders of PDO products began to experience increases in orders and/or shortening of intervals. Long-term implications of the system could not be measured since one year had passed. Originality/value - This study develops and demonstrates a QR tracking system for implementation of GIs. C1 [Dokuzlu, Sertac] Uludag Univ, Fac Agr, Dept Agr Econ, Bursa, Turkey. C3 Uludag University RP Dokuzlu, S (corresponding author), Uludag Univ, Fac Agr, Dept Agr Econ, Bursa, Turkey. EM sdokuzlu@uludag.edu.tr CR Addor Felix, 2002, J WORLD INTELLECT PR, V5, P865, DOI DOI 10.1111/J.1747-1796.2002.TH00185.X Besirli H, 2010, MILLI FOLKLOR, P159 Biscaia N.C., 2014, PROTECTION GEOGRAPHI Bosworth RC, 2015, J FOOD PROD MARK, V21, P274, DOI 10.1080/10454446.2013.843488 Bramley C., 2009, EC INTELLECT PROP, V1, P109 Broude T, 2005, U PA J INT ECON LAW, V26, P623 Broude T., 2005, BRIDGES, V9, P20 Cacic J, 2011, BRIT FOOD J, V113, P66, DOI 10.1108/00070701111097349 Das K, 2006, J WORLD INTELLECT PR, V9, P459, DOI 10.1111/j.1422-2213.2006.00300.x Fancesco B.B., 2015, TAIEX WORKSH M UNPUB Folkeson C., 2005, GEOGRAPHICAL INDICAT Gervais D., 2013, COGNAC SPANISH CHAMP Gonenc S., 2006, J COOPERATION, V41, P43 GTHB, 2013, STRAT PLAN FOOD AGR IPR, 2014, INTR TRAD GEOGR IND ITB, 2016, AEG COTT GEOGR IND I Ittersum K., 2002, THESIS Jena PR, 2010, DEV POLICY REV, V28, P217, DOI 10.1111/j.1467-7679.2010.00482.x Langinier C., 2008, Journal of Agricultural & Food Industrial Organization, V6, P10, DOI 10.2202/1542-0485.1187 Marie-Vivien D, 2014, DEV POLICY REV, V32, P379, DOI 10.1111/dpr.12060 Marie-Vivien D, 2008, J WORLD INTELLECT PR, V11, P321, DOI 10.1111/j.1747-1796.2008.00341.x Nijssen EJ, 2011, J INT MARKETING, V19, P113, DOI 10.1509/jimk.19.3.113 Nizam D., 2011, J PRAKSIS, V2, P87 Reviron S., 2009, 14 SWISS NAT CTR COM Steenkamp JBEM, 2010, J MARKETING, V74, P18, DOI 10.1509/jmkg.74.6.18 T.C. Basbakanlik Devlet Arsivleri Genel Mudurlugu, 2015, STATE ARCH OTTOMAN E Thual D.D., 2013, STUDY GEOGRAPHICAL I Tonkin E, 2015, BRIT FOOD J, V117, P318, DOI 10.1108/BFJ-07-2014-0244 TPE, 2014, GEOGR IND STAT TURK TPE, 2014, TURK PAT I WIPO, 2014, COFF WAR ETH STARB S WIPO, 2014, GEOGR IND WORLD INT WIPO, 2014, WORLD INT PROP ORG WTO, 2014, UR ROUND AGR TRIPS 2 NR 34 TC 7 Z9 7 U1 2 U2 37 PY 2016 VL 118 IS 9 BP 2074 EP 2085 DI 10.1108/BFJ-09-2015-0341 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000382520600001 DA 2022-12-14 ER PT J AU Zhao, TJ Nakano, A AF Zhao, Tiejun Nakano, Akimasa TI Agricultural Product Authenticity and Geographical Origin Traceability - Use of Nondestructive Measurement SO JARQ-JAPAN AGRICULTURAL RESEARCH QUARTERLY DT Review DE electromagnetic spectrum; infrared; terahertz; ultraviolet; visible ID DISCRIMINATION; QUALITY AB As the security of agricultural products has become a serious issue faced by people worldwide in recent decades, measures for an effective supervision system for agricultural product quality and safety are continuously being developed. In such a system, the assessment of agricultural product authenticity and geographical origin traceability could play a very important role. Recently, researchers have been focusing on some successful techniques, including the stable isotopic technique, compositional analysis technique, spectroscopic technique, and sensor technology. The benefits from advances made in spectroscopy and imaging technology have facilitated the development of imaging spectrometry techniques that offer such advantages as being nondestructive, rapid, and requiring minimal to no sample preparation. This paper discusses several nondestructive technologies used for the assessment of agricultural product authenticity and geographical origin traceability, with a special focus on the nondestructive technology of imaging using an electromagnetic spectrum for agricultural product safety and quality. It specifically discusses the technology of ultraviolet imaging, hyperspectral imaging, fluorescence spectrometry, nuclear magnetic resonance, and terahertz spectroscopy according to different wavelengths and frequency of this electromagnetic spectrum. Although the application of nondestructive measurements performed using the electromagnetic spectrum for identifying agricultural product authenticity and geographical origin traceability is increasing along with advanced technology and lower equipment costs, the accuracy of such measurements must still be improved and the advantages in practical applications need to be determined. C1 [Zhao, Tiejun; Nakano, Akimasa] Natl Agr & Food Res Org, Inst Vegetable & Floriculture Sci, Tsukuba, Ibaraki 3058666, Japan. C3 National Agriculture & Food Research Organization - Japan RP Nakano, A (corresponding author), Natl Agr & Food Res Org, Inst Vegetable & Floriculture Sci, Tsukuba, Ibaraki 3058666, Japan. EM anakano@affrc.go.jp CR Al-Mallahi A, 2010, BIOSYST ENG, V105, P257, DOI 10.1016/j.biosystemseng.2009.11.004 [Anonymous], 2016, ADV FOOD AUTHENTICIT, DOI DOI 10.1016/C2014-0-01962-4 Bard AJ., 2000, ELECTROCHEMICAL METH, P864 Bernewitz R., 2014, COLLOID SURFACE A, V458, P1 Butcher Ginger, 2016, TOUR ELECTROMAGNETIC Cartz L., 1995, NONDESTRUCTIVE TESTI Deng YM, 2011, SENSORS-BASEL, V11, P11774, DOI 10.3390/s111211774 Downard Kevin, 2004, MASS SPECTROMETRY FD Fan Z. H., 2009, 2 DIMENSIONAL ELECTR Fitch MJ, 2004, J HOPKINS APL TECH D, V25, P348 Gao JF, 2013, COMPUT ELECTRON AGR, V99, P186, DOI 10.1016/j.compag.2013.09.011 Gowen AA, 2012, TRENDS FOOD SCI TECH, V25, P40, DOI 10.1016/j.tifs.2011.12.006 Guo Z., 2013, SENSING AGR FOOD QUA, VV Haiduc AM, 2007, FOOD RES INT, V40, P425, DOI 10.1016/j.foodres.2006.05.010 Han MY, 2014, FOOD RES INT, V62, P1175, DOI 10.1016/j.foodres.2014.05.062 Hashimoto A., 2006, 2006 SICE ICASE INT, P3559 Hong YS, 2009, FOOD CHEM, V112, P267, DOI 10.1016/j.foodchem.2008.05.109 Kamruzzaman M., 2014, P INT C AGR ENG ZUR, P6 Kondo N., 2016, BIOSENSING ENG OPTIC Ling NNA, 2014, COLLOID SURFACE A, V462, P244, DOI 10.1016/j.colsurfa.2014.08.031 Mahajan S, 2015, TRENDS FOOD SCI TECH, V42, P116, DOI 10.1016/j.tifs.2015.01.001 Manickavasagan A, 2014, IMAGING ELECTROMAGNE Momin M. A., 2011, P SPIE INT SOC OPTIC, P8027 Nakamura Y., 2013, JAPAN J FOOD ENG, V14, P125 Nakamura Y, 2012, J JPN SOC FOOD SCI, V59, P387, DOI 10.3136/nskkk.59.387 Nakano A., 2009, B NATL I VEG TEA SCI, V8, P157 Nakano A., 2010, B NATL I VEG TEA SCI, V9, P205 Nakano A., 2008, B NATL I VEG TEA SCI, V7, P1 Nakano A, 2018, JARQ-JPN AGR RES Q, V52, P105, DOI 10.6090/jarq.52.105 Saito T, 2008, J RADIOANAL NUCL CH, V278, P409, DOI 10.1007/s10967-008-0810-8 Sekiyama Y., 2012, METABOLOMICS, P25 Sekiyama Y., 2016, JAPANESE SOC AGR MAC, V25, P283 Skoog D A, 1988, FUNDAMENTALS ANAL CH Smith Randall B., 2012, INTRO HYPERSPECTRAL, P5 Sparkman OD, 2000, MASS SPECTROMETRY DE Sugiyama J., 2016, JAPANESE SOC AGR MAC, V78, P21 Sugiyama J, 2013, J JPN SOC FOOD SCI, V60, P457 Tiejun Zhao, 2010, Engineering in Agriculture, Environment and Food, V3, P105 Yuan YW, 2014, J AGR FOOD CHEM, V62, P11386, DOI 10.1021/jf502627c Zhang L, 2013, LWT-FOOD SCI TECHNOL, V53, P402, DOI 10.1016/j.lwt.2013.03.011 Zhang Z., 2016, ENCY FOOD HLTH, P79 Zhao TieJun, 2012, Journal of Agricultural Science and Applications, V1, P131, DOI 10.14511/jasa.2012.010407 Zou X., 2015, NONDESTRUCTIVE MEASU NR 43 TC 2 Z9 4 U1 9 U2 32 PY 2018 VL 52 IS 2 BP 115 EP 122 DI 10.6090/jarq.52.115 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000434112000002 DA 2022-12-14 ER PT J AU Jiang, YT Lei, YL AF Jiang, Yunting Lei, Yalin TI Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management SO COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE DT Article ID INTEGRATION AB To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), Tetris*, Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems. C1 [Jiang, Yunting; Lei, Yalin] China Univ Geosci, Sch Econ & Management, Beijing 100083, Peoples R China. [Jiang, Yunting; Lei, Yalin] Minist Nat Resources Peoples Republ China, Key Lab Carrying Capac Assessment Resource & Envir, Beijing 100083, Peoples R China. C3 China University of Geosciences; Ministry of Natural Resources of the People's Republic of China RP Lei, YL (corresponding author), China Univ Geosci, Sch Econ & Management, Beijing 100083, Peoples R China.; Lei, YL (corresponding author), Minist Nat Resources Peoples Republ China, Key Lab Carrying Capac Assessment Resource & Envir, Beijing 100083, Peoples R China. EM yunting.j1218@gmail.com; leiyalin@cugb.edu.cn CR Baas J, 2020, QUANT SCI STUD, V1, P377, DOI 10.1162/qss_a_00019 Birkle C, 2020, QUANT SCI STUD, V1, P363, DOI 10.1162/qss_a_00018 Cao B, 2021, IEEE T INTELL TRANSP, V22, P3841, DOI 10.1109/TITS.2021.3059455 Chen JB, 2019, CONCURR COMP-PRACT E, V31, DOI 10.1002/cpe.4766 Chen XF, 2020, IEEE T WIREL COMMUN, V19, P2268, DOI 10.1109/TWC.2019.2963667 Ejaz M, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21072502 Gai KK, 2018, IEEE NETWORK, V32, P34, DOI 10.1109/MNET.2018.1700407 Garrocho CTB, 2020, COMPUTER, V53, P46, DOI 10.1109/MC.2020.3002686 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Huang XH, 2021, COMPUT NETW, V189, DOI 10.1016/j.comnet.2021.107877 Jia N, 2020, SECUR COMMUN NETW, V2020, DOI 10.1155/2020/8872853 Kongsted A, 2020, CLIN EPIDEMIOL, V12, P1015, DOI 10.2147/CLEP.S266220 Kuhn M, 2021, J MANUF SYST, V59, P617, DOI 10.1016/j.jmsy.2021.04.013 Li WJ, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/5563312 Liang HG, 2021, ROBOT CIM-INT MANUF, V67, DOI 10.1016/j.rcim.2020.101991 Liu Boyan, 2020, Electronics Optics & Control, V27, P64, DOI 10.3969/j.issn.1671-637X.2020.08.013 Liu S, 2022, COMPUT IND ENG, V169, DOI 10.1016/j.cie.2022.108228 Liu X, 2022, IEEE T IND INFORM, V18, P5628, DOI 10.1109/TII.2022.3144016 Liu Z., 2021, IEEE IOT J, V10, P3 Lv ZH, 2022, ACM T SENSOR NETWORK, V18, DOI 10.1145/3519301 Lv ZH, 2020, IEEE INTERNET THINGS, V7, P5706, DOI 10.1109/JIOT.2019.2942719 Lv ZH, 2020, IEEE T IND INFORM, V16, P1957, DOI 10.1109/TII.2019.2913535 Markovic M, 2020, FRONT SUSTAIN FOOD S, V4, DOI 10.3389/fsufs.2020.563424 Munaye YY, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11052163 Ortega-Martorell S, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0220809 Oruc O., 2020, INT J NATURAL LANGUA, V9, P33, DOI [10.5121/ijnlc.2020.9403, DOI 10.5121/IJNLC.2020.9403] Ostad-Ali-Askari Kaveh, 2021, Arabian Journal of Geosciences, V14, DOI 10.1007/s12517-021-08336-0 Ostad-Ali-Askari K, 2017, KSCE J CIV ENG, V21, P134, DOI 10.1007/s12205-016-0572-8 Potter D, 2020, J CLIN ONCOL, V38 Predescu A, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11041449 Schirmann ML, 2020, OCEAN ENG, V216, DOI 10.1016/j.oceaneng.2020.107610 Sun QD, 2022, ACM T SENSOR NETWORK, V18, DOI 10.1145/3508392 Vidovic I, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10176016 Vikaliana R., 2021, TEST ENG MANAGEMENT, V83, P6503 Wang JY, 2020, LANDSC ECOL ENG, V16, P47, DOI 10.1007/s11355-019-00402-w Yan L, 2021, IEEE ACCESS, V9, P123764, DOI 10.1109/ACCESS.2021.3108178 Yan QB, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/9914048 Yang HL, 2020, IEEE INTERNET THINGS, V7, P5677, DOI 10.1109/JIOT.2020.2980586 Yang J, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11020273 Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 Yang Y, 2022, SOIL BIOL BIOCHEM, V170, DOI 10.1016/j.soilbio.2022.108688 Yang ZL, 2021, J CLEAN PROD, V290, DOI 10.1016/j.jclepro.2020.125191 Yin LR, 2022, ATMOSPHERE-BASEL, V13, DOI 10.3390/atmos13040522 Zhang Y, 2018, IEEE ACCESS, V6, P49721, DOI 10.1109/ACCESS.2018.2868476 Zheng WF, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12084059 Zheng WF, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12073416 Zhu Peng, 2021, Inf Process Manag, V58, P102570, DOI 10.1016/j.ipm.2021.102570 NR 47 TC 0 Z9 0 U1 1 U2 1 PD SEP 19 PY 2022 VL 2022 AR 6559517 DI 10.1155/2022/6559517 WC Mathematical & Computational Biology; Neurosciences SC Mathematical & Computational Biology; Neurosciences & Neurology UT WOS:000865649400015 DA 2022-12-14 ER PT J AU Panebianco, S Mazzoleni, P Barone, G Musumarra, A Pellegriti, MG Pulvirenti, A Scordino, A Cirvilleri, G AF Panebianco, S. Mazzoleni, P. Barone, G. Musumarra, A. Pellegriti, M. G. Pulvirenti, A. Scordino, A. Cirvilleri, G. TI Feasibility study of tomato fruit characterization by fast XRF analysis for quality assessment and food traceability SO FOOD CHEMISTRY DT Article DE Tomato fruits; Protected Geographical Indication (PGI); X-ray fluorescence (XRF); Elemental composition; Principal Component Analysis (PCA); Food authentication; Food traceability ID X-RAY-FLUORESCENCE; TRACE-ELEMENT CONCENTRATIONS; SOLANUM-LYCOPERSICON; CLUSTER-ANALYSIS; CHERRY TOMATOES; HEAVY-METALS; PLANTS; SOILS AB Food product nutritional and sensory characteristics are often deeply linked to its territory of origin; therefore, its authentication by means of elemental composition becomes crucial for traceability and fighting food fraud. This study aims to establish a fast and reproducible procedure for origin and quality assessment of Sicilian tomato fruits, including PGI "Pomodoro di Pachino", by using the X-ray fluorescence (XRF) technique. Measurements were performed on different parts of PGI Pachino tomatoes belonging to the same production lot. Principal Component and Cluster Analyses show that the samples cluster accordingly with the production lot, disentangling the different parts of the fruit. This procedure, which uses XRF yield elemental pattern and statistical analysis, establishes a solid basis for characterizing elemental profiles by a fast XRF in-situ campaign, supporting the traceability system. The reliability of XRF results was confirmed by comparing elemental concentrations with ICP-MS measurements, performed for comparison, and tomato literature values. C1 [Panebianco, S.; Musumarra, A.; Scordino, A.] Univ Catania, Dipartimento Fis & Astron, Catania, Italy. [Mazzoleni, P.; Barone, G.] Univ Catania, Dipartimento Sci Biol Geol & Ambientali, Catania, Italy. [Musumarra, A.; Pellegriti, M. G.] Ist Nazl Fis Nucl, Sez Catania, Catania, Italy. [Pulvirenti, A.] Univ Catania, Unita Bioinformat, Dipartimento Med Clin & Sperimentale, Catania, Italy. [Scordino, A.] Ist Nazl Fis Nucl, Lab Nazl Sud, Catania, Italy. [Cirvilleri, G.] Univ Catania, Dipartimento Agr Alimentaz & Ambiente, Catania, Italy. C3 University of Catania; University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania; Istituto Nazionale di Fisica Nucleare (INFN); University of Catania RP Mazzoleni, P (corresponding author), Univ Catania, Dipartimento Sci Biol Geol & Ambientali, Catania, Italy. EM paolo.mazzoleni@unict.it CR Barchitta M, 2017, BMJ OPEN, V7, DOI 10.1136/bmjopen-2016-014756 Barone G, 2019, DYES PIGMENTS, V171, DOI 10.1016/j.dyepig.2019.107766 Byers HL, 2019, FOOD CHEM X, V1, DOI 10.1016/j.fochx.2018.100001 Chailapakul O, 2008, TALANTA, V74, P683, DOI 10.1016/j.talanta.2007.06.034 Colmenero-Flores JM, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20194686 Commission Regulation, 2015, EUR UN REG FEED ADD CREA, 2019, TABELLE COMPOSIZIONE Cuartero J, 1999, SCI HORTIC-AMSTERDAM, V78, P83, DOI 10.1016/S0304-4238(98)00191-5 del Amor FM, 2001, HORTSCIENCE, V36, P1260, DOI 10.21273/HORTSCI.36.7.1260 Demir K, 2010, SCI HORTIC-AMSTERDAM, V127, P16, DOI 10.1016/j.scienta.2010.08.009 Dimartino M, 2011, J PLANT PATHOL, V93, P79 FoodData Central, 2019, 321360 FDC USDA Fuge R, 2015, APPL GEOCHEM, V63, P282, DOI 10.1016/j.apgeochem.2015.09.013 GABRIEL KR, 1971, BIOMETRIKA, V58, P453, DOI 10.1093/biomet/58.3.453 Gallardo H, 2016, J FOOD COMPOS ANAL, V50, P1, DOI 10.1016/j.jfca.2016.04.007 Ghidotti M, 2021, FOOD CHEM, V342, DOI 10.1016/j.foodchem.2020.128350 Granato D, 2018, TRENDS FOOD SCI TECH, V72, P83, DOI 10.1016/j.tifs.2017.12.006 Gundersen V, 2001, J AGR FOOD CHEM, V49, P3808, DOI 10.1021/jf0103774 Hansen P, 1997, MATH PROGRAM, V79, P191, DOI 10.1007/BF02614317 Suarez MH, 2007, FOOD CHEM, V104, P489, DOI 10.1016/j.foodchem.2006.11.072 Istat, 2020, CROPS AR PROD FRESH Jolliffe I. T., 1986, PRINCIPAL COMPONENT, DOI DOI 10.1007/B98835 Maathuis FJM, 2009, CURR OPIN PLANT BIOL, V12, P250, DOI 10.1016/j.pbi.2009.04.003 Mallamace D, 2014, PHYSICA A, V401, P112, DOI 10.1016/j.physa.2013.12.054 Martinez-Ballesta MC, 2010, AGRON SUSTAIN DEV, V30, P295, DOI 10.1051/agro/2009022 MathWorks, 2020, MATLAB R2020A Melquiades FL, 2004, J RADIOANAL NUCL CH, V262, P533, DOI 10.1023/B:JRNC.0000046792.52385.b2 Mundt F., 2020, FACTOEXTRA EXTRACT V, DOI DOI 10.1111/ECOG.03905 Natesh H.N, 2017, HORTIC INT J, V1, P1, DOI 10.15406/hij.2017.01.00011 Pearson K, 1901, PHILOS MAG, V2, P559, DOI 10.1080/14786440109462720 Pushie MJ, 2014, CHEM REV, V114, P8499, DOI 10.1021/cr4007297 R Core Team, 2021, R R PROJECT STAT COM, DOI DOI 10.1007/978-3-540-74686-7 Raffo A, 2006, J FOOD COMPOS ANAL, V19, P11, DOI 10.1016/j.jfca.2005.02.003 Rodriguez-Iruretagoiena A, 2015, FOOD CHEM, V173, P1083, DOI 10.1016/j.foodchem.2014.10.133 Sole VA, 2007, SPECTROCHIM ACTA B, V62, P63, DOI 10.1016/j.sab.2006.12.002 Toth G, 2016, SCI TOTAL ENVIRON, V565, P1054, DOI 10.1016/j.scitotenv.2016.05.115 Trebolazabala J, 2017, MICROCHEM J, V131, P137, DOI 10.1016/j.microc.2016.12.009 Vainikka P, 2012, FUEL, V95, P1, DOI 10.1016/j.fuel.2011.11.068 Vives AES, 2006, J RADIOANAL NUCL CH, V270, P147, DOI 10.1007/s10967-006-0322-3 Xin SZ, 2009, HORTIC SCI, V36, P133, DOI 10.17221/18/2009-HORTSCI NR 40 TC 2 Z9 2 U1 10 U2 19 PD JUL 30 PY 2022 VL 383 AR 132364 DI 10.1016/j.foodchem.2022.132364 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000778289900009 DA 2022-12-14 ER PT J AU Chambery, A del Monaco, G Di Maro, A Parente, A AF Chambery, Angela del Monaco, Giovanni Di Maro, Antimo Parente, August TI Peptide fingerprint of high quality Campania white wines by MALDI-TOF mass spectrometry SO FOOD CHEMISTRY DT Article DE MALDI-TOF MS; Peptide fingerprint; Mass code; Food traceability; Food authenticity ID PROTEIN; TRACEABILITY; GRAPE; SERUM; IDENTIFICATION; VARIETIES; CULTIVAR; SAFETY; TOOL; DNA AB Food traceability is essential to preserve the identity of unique quality traits against frauds or commercial disputes, Therefore, there is a growing demand of new traceability systems for the collection of information related to units/batches of food ingredients and products. A rapid method based on peptide profiles obtained from tryptic digests of whole wine proteins by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry is described. Reliable peptide fingerprints were obtained for high quality Campania white wines, providing a signature Of the finished products. The MALDI spectra revealed the presence of common diagnostic ions, but also evidenced differences between wines. Furthermore, the MALDI-TOF spectral traces were converted into Simulated images to obtain a graphical representation of spectra. the resulting "mass codes" Constitute a simple tool to display differences between samples, Suggesting their potential use as "biological bar codes" for food authenticity and traceability, probably applicable to other classes of certified food products. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Chambery, Angela; del Monaco, Giovanni; Di Maro, Antimo; Parente, August] Univ Naples 2, Dept Life Sci, I-81100 Caserta, Italy. C3 Universita della Campania Vanvitelli RP Chambery, A (corresponding author), Univ Naples 2, Dept Life Sci, Via Vivaldi 43, I-81100 Caserta, Italy. EM angela.chambery@unina2.it CR Adam BL, 2002, CANCER RES, V62, P3609 Albrethsen J, 2007, CLIN CHEM, V53, P852, DOI 10.1373/clinchem.2006.082644 Ammendrup S, 2006, REV SCI TECH OIE, V25, P763, DOI 10.20506/rst.25.2.1689 Caldwell RL, 2005, MOL CELL PROTEOMICS, V4, P394, DOI 10.1074/mcp.R500006-MCP200 Carpentieri A, 2007, ANAL BIOANAL CHEM, V389, P969, DOI 10.1007/s00216-007-1476-8 Catharino RR, 2006, J MASS SPECTROM, V41, P185, DOI 10.1002/jms.976 Chambery A, 2006, J PROTEOME RES, V5, P1176, DOI 10.1021/pr0504743 Chambery A, 2006, BIOL CHEM, V387, P1261, DOI 10.1515/BC.2006.156 Chaurand P, 2002, CURR OPIN CHEM BIOL, V6, P676, DOI 10.1016/S1367-5931(02)00370-8 Dworzanski JP, 2005, EXPERT REV PROTEOMIC, V2, P863, DOI 10.1586/14789450.2.6.863 Dworzanski JP, 2006, J PROTEOME RES, V5, P76, DOI 10.1021/pr050294t Ferreira RB, 2001, TRENDS FOOD SCI TECH, V12, P230, DOI 10.1016/S0924-2244(01)00080-2 Flamini R, 2006, MASS SPECTROM REV, V25, P741, DOI 10.1002/mas.20087 Flamini R, 2006, EXPERT REV PROTEOMIC, V3, P321, DOI 10.1586/14789450.3.3.321 Garcia-Beneytez E, 2002, J AGR FOOD CHEM, V50, P6090, DOI 10.1021/jf0202077 Hayasaka Y, 2001, J AGR FOOD CHEM, V49, P1830, DOI 10.1021/jf001163+ Koomen JM, 2005, RAPID COMMUN MASS SP, V19, P1624, DOI 10.1002/rcm.1963 Koomen JM, 2004, RAPID COMMUN MASS SP, V18, P2537, DOI 10.1002/rcm.1657 Kwon SW, 2004, J AGR FOOD CHEM, V52, P7258, DOI 10.1021/jf048940g Lupien JR, 2005, CRIT REV FOOD SCI, V45, P119, DOI 10.1080/10408690490911774 Moreno-Arribas MV, 2002, ANAL CHIM ACTA, V458, P63, DOI 10.1016/S0003-2670(01)01531-8 Muller A, 2007, FOOD CHEM, V102, P436, DOI 10.1016/j.foodchem.2006.10.015 Pascal G, 2001, CELL MOL BIOL, V47, P1329 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Pesavento IC, 2008, J MASS SPECTROM, V43, P234, DOI 10.1002/jms.1295 Petricoin EF, 2002, LANCET, V359, P572, DOI 10.1016/S0140-6736(02)07746-2 PINDER R, 2003, WINE SCI EXPLORATION Pusch W, 2005, CURR PHARM DESIGN, V11, P2577, DOI 10.2174/1381612054546932 Raspor P, 2005, ACTA BIOCHIM POL, V52, P659 Sarmento MR, 2001, INT J FOOD SCI TECH, V36, P759, DOI 10.1046/j.1365-2621.2001.00524.x Schwartz SA, 2004, CLIN CANCER RES, V10, P981, DOI 10.1158/1078-0432.CCR-0927-3 Siret R, 2002, J AGR FOOD CHEM, V50, P3822, DOI 10.1021/jf011462e Szilagyi Z, 1996, RAPID COMMUN MASS SP, V10, P1141, DOI 10.1002/(SICI)1097-0231(19960715)10:9<1141::AID-RCM607>3.0.CO;2-4 VANLOON LC, 1985, PLANT MOL BIOL, V4, P111, DOI 10.1007/BF02418757 Wang J, 1999, J AGR FOOD CHEM, V47, P2009, DOI 10.1021/jf981008j Won Y, 2003, PROTEOMICS, V3, P2310, DOI 10.1002/pmic.200300590 NR 36 TC 41 Z9 46 U1 5 U2 48 PD APR 15 PY 2009 VL 113 IS 4 BP 1283 EP 1289 DI 10.1016/j.foodchem.2008.08.031 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000261857100070 DA 2022-12-14 ER PT J AU Zaninelli, M Pace, MR AF Zaninelli, Mauro Pace, Matias Reyes TI The O3-Farm Project: First Evaluation of a Business Process Management (BPM) Approach through the Development of an Experimental Farm Management System for Milk Traceability SO AGRICULTURE-BASEL DT Article DE farm management systems; business process management; workflows; data sharing; dairy farms ID INFORMATION-SYSTEMS; VETERINARY HOSPITALS; O3-VET PROJECT; MODEL; AGRICULTURE; ARCHITECTURE; RECORD AB The modeling of farm workflows, and the use of a business process management (BPM) paradigm, could enable improvement in the development of farm management information systems (FMIS). A rapid design of software applications could be possible and quick development, intrinsically service oriented, could be achieved through the use of a software suite for the implementation of BPM diagrams. As the first evaluation of this paradigm, an experimental FMIS was developed considering a use-case whose target was to develop a hardware and software solution for the traceability of milk. The outcomes of this activity have shown that the software application developed (O3-Farm) was able to provide all features of the database application previously used for the traceability of milk. At the same time, it was able to provide some new features such as increased usability, portability and efficiency. Also, the chance to integrate it with other possible software applications was increased as a result of a better sharing of agricultural data. This seems to suggest that a design, and a software suite, based on the BPM paradigm, could be a valid way for the development of FMIS also in line with the farm software environment models if its abilities to describe, use and deploy, workflows and software services are taken into consideration. C1 [Zaninelli, Mauro] Univ Telemat San Raffaele Roma, Via Val Cannuta 247, I-00166 Rome, Italy. [Pace, Matias Reyes] Univ Chile, Fac Ciencias Vet & Pecuarias, Ctr Tecnol Informac, Santa Rosa 11735, Santiago 8820808, Chile. C3 Universita Telematica San Raffaele; Universidad de Chile RP Zaninelli, M (corresponding author), Univ Telemat San Raffaele Roma, Via Val Cannuta 247, I-00166 Rome, Italy. EM mauro.zaninelli@unisanraffaele.gov.it; matreyes@gmail.com CR Bai CG, 2013, INT J PROD ECON, V146, P281, DOI 10.1016/j.ijpe.2013.07.011 Digital Imaging and Communications in Medicine, 2017, PS 3 1 INTR OV Fountas S, 2006, AGR SYST, V87, P192, DOI 10.1016/j.agsy.2004.12.003 Fountas S, 2015, COMPUT ELECTRON AGR, V115, P40, DOI 10.1016/j.compag.2015.05.011 Health Level Seven, 2014, HLTH INF HL7 VERS 3 Hori M, 2010, FUJITSU SCI TECH J, V46, P446 Integrating the Healthcare Enterprise, 2014, IHE TECHN FRAM GEN I Jones JW, 2017, AGR SYST, V155, P269, DOI 10.1016/j.agsy.2016.09.021 Kaloxylos A, 2014, COMPUT ELECTRON AGR, V100, P168, DOI 10.1016/j.compag.2013.11.014 Kaloxylos A, 2012, COMPUT ELECTRON AGR, V89, P130, DOI 10.1016/j.compag.2012.09.002 Kruize JW, 2016, COMPUT ELECTRON AGR, V125, P12, DOI 10.1016/j.compag.2016.04.011 La Rosa M, 2011, IEEE T IND INFORM, V7, P255, DOI 10.1109/TII.2011.2124467 Lewis KA, 1998, ENVIRON MODELL SOFTW, V13, P123, DOI 10.1016/S1364-8152(98)00010-3 Lewis T, 1998, COMPUT ELECTRON AGR, V19, P233, DOI 10.1016/S0168-1699(97)00040-9 Murakami E, 2007, COMPUT ELECTRON AGR, V58, P37, DOI 10.1016/j.compag.2006.12.010 Niederhauser N, 2008, COMPUT ELECTRON AGR, V61, P241, DOI 10.1016/j.compag.2007.12.001 Nikkila R, 2010, COMPUT ELECTRON AGR, V70, P328, DOI 10.1016/j.compag.2009.08.013 Nuthall PL, 1996, COMPUT ELECTRON AGR, V14, P23, DOI 10.1016/0168-1699(95)00035-6 Paraforos DS, 2016, IFAC PAPERSONLINE, V49, P320, DOI 10.1016/j.ifacol.2016.10.060 Sorensen CG, 2010, COMPUT ELECTRON AGR, V72, P37, DOI 10.1016/j.compag.2010.02.003 Sorensen CG, 2010, BIOSYST ENG, V105, P41, DOI 10.1016/j.biosystemseng.2009.09.009 Tangorra F.M., 2006, COMPUT AGR NAT RESOU, P475 Wolfert J, 2010, COMPUT ELECTRON AGR, V70, P389, DOI 10.1016/j.compag.2009.07.015 Wolfert S, 2017, AGR SYST, V153, P69, DOI 10.1016/j.agsy.2017.01.023 Xu LD, 2011, IEEE T IND INFORM, V7, P630, DOI 10.1109/TII.2011.2167156 Zaninelli M, 2007, COMPUT METH PROG BIO, V87, P68, DOI 10.1016/j.cmpb.2007.04.005 Zaninelli M, 2015, LARGE ANIM REV, V21, P81 Zaninelli M, 2012, COMPUT METH PROG BIO, V108, P760, DOI 10.1016/j.cmpb.2012.04.011 NR 28 TC 2 Z9 2 U1 1 U2 6 PD SEP PY 2018 VL 8 IS 9 AR 139 DI 10.3390/agriculture8090139 WC Agronomy SC Agriculture UT WOS:000447906100011 DA 2022-12-14 ER PT J AU Liu, Z Yuan, YW Zhang, YZ Shi, YZ Hu, GX Zhu, JH Rogers, KM AF Liu, Zhi Yuan, Yuwei Zhang, Yongzhi Shi, Yuanzhi Hu, Guixian Zhu, Jiahong Rogers, Karyne M. TI Geographical traceability of Chinese green tea using stable isotope and multi-element chemometrics SO RAPID COMMUNICATIONS IN MASS SPECTROMETRY DT Article ID ORIGIN; RATIO; AUTHENTICATION; WINES; RICE; SOIL AB Rationale: Deliberate and fraudulent origin mislabeling of Chinese green tea motivated by large price differences often brings significant food safety risks and damages consumer trust. Currently, there is no reliable method to verify the origin of green tea produced in China. Stable isotope and multi-element analyses combined with statistical models are widely acknowledged as useful traceability techniques for many agro-products, and could be developed to confirm the geographical origin of Chinese green tea and, more importantly, combat illegal green tea mislabeling and fraud. Methods: An analytical strategy combining elemental analyzer/isotope ratio mass spectrometry (EA/IRMS) and inductively plasma coupled mass spectrometry (ICP-MS) with chemometrics tools was used to confirm the origin of green tea grown in the main tea production provinces around China. Stable C, N, H,O isotope ratios and twenty elements were measured to build mathematical discriminant models using unsupervised principal component analysis (PCA) and supervised linear discriminant analysis (LDA). Two main problems: (i) tracing the origin of Chinese green tea from different tea growing provinces (Zhejiang, Shandong, and other provinces); (ii) authentication of high-value Westlake Longjing tea from the Westlake region and surrounding areas in Zhejiang province, were investigated and assessed. Results: The results demonstrated that PCA and follow-up LDA based on stable isotope and multi-element signatures can verify the geographical origin of Chinese green tea from different provinces, and even localized zones in the same province could be distinguishable, with discrimination accuracies higher than 92.3% and 87.8%, respectively. Conclusions: Geochemical fingerprinting techniques coupled with chemometric tools offer an accurate and effective verification method for the geographical origin of Chinese green tea, providing a promising tool to combat fraudulent mislabeling of high-value green tea. C1 [Liu, Zhi; Yuan, Yuwei; Zhang, Yongzhi; Hu, Guixian; Zhu, Jiahong; Rogers, Karyne M.] Zhejiang Acad Agr Sci, Inst Qual & Stand Agr Prod, Hangzhou 310021, Zhejiang, Peoples R China. [Liu, Zhi; Yuan, Yuwei; Zhang, Yongzhi; Hu, Guixian; Zhu, Jiahong] Minist Agr, Key Lab Informat Traceabil Agr Prod, Hangzhou 310021, Zhejiang, Peoples R China. [Shi, Yuanzhi] Chinese Acad Agr Sci, Tea Res Inst, Hangzhou 310008, Zhejiang, Peoples R China. [Rogers, Karyne M.] Natl Isotope Ctr, GNS Sci, 30 Gracefield Rd, Lower Hutt 5040, New Zealand. C3 Zhejiang Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; Chinese Academy of Agricultural Sciences; Tea Research Institute, CAAS; GNS Science - New Zealand RP Yuan, YW (corresponding author), Zhejiang Acad Agr Sci, Inst Qual & Stand Agr Prod, Hangzhou 310021, Zhejiang, Peoples R China.; Rogers, KM (corresponding author), Natl Isotope Ctr, GNS Sci, 30 Gracefield Rd, Lower Hutt 5040, New Zealand. EM ywytea@163.com; k.rogers@gns.cri.nz CR Ariyama K, 2012, J AGR FOOD CHEM, V60, P1628, DOI 10.1021/jf204296p Baggs EM, 2008, RAPID COMMUN MASS SP, V22, P1664, DOI 10.1002/rcm.3456 Barker M, 2012, MAGN RESON IMAGING, V30, P446 Capron X, 2007, FOOD CHEM, V101, P1585, DOI 10.1016/j.foodchem.2006.04.019 Chen ZongMao, 2012, International Journal of Tea Science (IJTS), V8, P1 Conrad R, 2002, CHEMOSPHERE, V47, P797, DOI 10.1016/S0045-6535(02)00120-0 Coplen TB, 2011, RAPID COMMUN MASS SP, V25, P2538, DOI 10.1002/rcm.5129 Diomande D, 2015, FOOD CHEM, V188, P576, DOI 10.1016/j.foodchem.2015.05.040 Forina M, 1993, ANALUSIS Gehrels JC, 1998, HYDROLOG SCI J, V43, P579, DOI 10.1080/02626669809492154 GELADI P, 1986, ANAL CHIM ACTA, V185, P1, DOI 10.1016/0003-2670(86)80028-9 Ghee C, 2013, EGU GEN ASSEMBLY, V102, P161 Higdon JV, 2003, CRIT REV FOOD SCI, V43, P89, DOI 10.1080/10408690390826464 Huang ZG, 2006, J ZHEJIANG U Jolliffe I. T., 1986, PRINCIPAL COMPONENT, DOI DOI 10.1007/B98835 Jolliffe L., 2007, TEA TOURISM TOURISTS Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 [李清光 LI Qingguang], 2011, [食品科学, Food Science], V32, P341 Lifschitz D, 2010, EGU GEN ASSEMBLY, V2, P9465 Lin X, 2015, J FOOD SAF QUAL, V9, P354 Liu Jianping, 2008, Chin Med, V3, P12, DOI 10.1186/1749-8546-3-12 Liu Z, 2019, FOOD CONTROL, V99, P1, DOI 10.1016/j.foodcont.2018.12.011 Liu Z, 2019, RAPID COMMUN MASS SP, V33, P625, DOI 10.1002/rcm.8387 Liu Z, 2017, ANAL METHODS-UK, V9, P3361, DOI 10.1039/c7ay00415j Luo D, 2015, FOOD ANAL METHOD, V9, P1 Luo DH, 2015, FOOD CHEM, V174, P197, DOI 10.1016/j.foodchem.2014.11.006 Ma GC, 2016, FOOD CONTROL, V59, P714, DOI 10.1016/j.foodcont.2015.06.037 Profeta A, 2010, J INT FOOD AGRIBUS M, V22, P179, DOI 10.1080/08974430903007783 Rodrigues CI, 2009, J FOOD COMPOS ANAL, V22, P463, DOI 10.1016/j.jfca.2008.06.010 Rodrigues SM, 2011, J FOOD COMPOS ANAL, V24, P548, DOI 10.1016/j.jfca.2010.12.003 Schellenberg A, 2010, FOOD CHEM, V121, P770, DOI 10.1016/j.foodchem.2009.12.082 Spangenberg JE, 2001, J AGR FOOD CHEM, V49, P1534, DOI 10.1021/jf001291y Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Tharwat A, 2009, LDA LINEAR DISCRIMIN Timmerman M.E., 2003, J AM STAT ASSOC, V98, P464, DOI [DOI 10.1198/jasa.2003.s308, 10.1198/jasa.2003.s308] Wirth DA, 2016, GEOGRAPHICAL INDICAT Wu YL, 2015, FOOD CHEM, V174, P553, DOI 10.1016/j.foodchem.2014.11.096 Ye NS, 2012, CRIT REV FOOD SCI, V52, P775, DOI 10.1080/10408398.2010.508568 Yuan YW, 2013, J NUCL AGR SCI, V1, P51 NR 39 TC 35 Z9 37 U1 3 U2 105 PD APR 30 PY 2019 VL 33 IS 8 BP 778 EP 788 DI 10.1002/rcm.8405 WC Biochemical Research Methods; Chemistry, Analytical; Spectroscopy SC Biochemistry & Molecular Biology; Chemistry; Spectroscopy UT WOS:000462355400003 DA 2022-12-14 ER PT J AU Anastasiadis, F Apostolidou, I Michailidis, A AF Anastasiadis, Foivos Apostolidou, Ioanna Michailidis, Anastasios TI Food Traceability: A Consumer-Centric Supply Chain Approach on Sustainable Tomato SO FOODS DT Article DE food supply chains; food safety; tomato; Greece; end-to-end approach; sustainability AB Technological advances result in new traceability configurations that, however, cannot always secure transparency and food safety. Even in cases where a system guarantees transparency, the actual consumer involvement and a real consumer-based perspective cannot always be ensured. The importance of such consumer centricity is vital, since it is strongly associated with effective supply chains that properly fulfil their end-users' needs and requests. Thus, the objective of this paper was to explore the level of consumer centricity in food supply chains under a traceability system. The methodological approach employed a framework of two studies validating subsequently a similar set of variables, using initially consumers data and then supply chain actors data. The supply chain of sustainable tomato was selected to design the studies. The level of agreement between datasets suggested the level of the supply chain consumer centricity. Findings showed health, trust, quality, nutrition, and safety-related values to be significant for the consumers towards accepting a traceability system. The supply chain actors also accepted a traceability system based on the fact that their customers' needs rely on the exact same beliefs, indicating a high level of consumer centricity. The current work underlines the magnitude of consumer centricity in food supply chains and provides an easy and straightforward framework for its exploration. Key implications suggest the design of more effective supply chain and consumer-based strategies for the food industry. Policymakers could also adopt the concept of consumer centricity to further improve the food industry. C1 [Anastasiadis, Foivos; Apostolidou, Ioanna; Michailidis, Anastasios] Aristotle Univ Thessaloniki, Sch Agr, Dept Agr Econ, Thessaloniki 54124, Greece. C3 Aristotle University of Thessaloniki RP Anastasiadis, F (corresponding author), Aristotle Univ Thessaloniki, Sch Agr, Dept Agr Econ, Thessaloniki 54124, Greece. EM anastasiadis.f@gmail.com; ioanapost3@yahoo.gr; tassosm@auth.gr CR Anastasiadis F., 2018, P 47 EMAC C PEOPL MA Anastasiadis F., 2014, AGR ENG INT CIGR J, P11 Anastasiadis F, 2017, P 21 CAMBR INT MAN S Anastasiadis F, 2020, FOODS, V9, DOI 10.3390/foods9050539 Anderson D.L., 2007, SUPPLY CHAIN MANAG R, V11, P41 [Anonymous], 2003, EUR J MARKETING, DOI DOI 10.1108/03090560310465099 [Anonymous], FAO's Strategy for a Food Chain Approach to Food Safety and Quality: A Framework Document for the Development of Future Strategic Direction [Anonymous], Assuring Food Safety and Quality Guidelines for Strengthening National Food Control Systems Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Burgess K, 2006, INT J OPER PROD MAN, V26, P703, DOI 10.1108/01443570610672202 Buurma JS, 2012, ACTA HORTIC, V930, P69, DOI 10.17660/ActaHortic.2012.930.8 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Chamhuri N, 2015, BRIT FOOD J, V117, P1168, DOI 10.1108/BFJ-08-2013-0235 Chang A, 2013, BRIT FOOD J, V115, P1361, DOI 10.1108/BFJ-11-2011-0286 Chopra S, 2007, SUPPLY CHAIN MANAG, P265, DOI DOI 10.1007/978-3-8349-9320-5_22 Christopher M., 1999, INT J LOGIST-RES APP, V2, P103 Cope S, 2010, FOOD POLICY, V35, P349, DOI 10.1016/j.foodpol.2010.04.002 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Dordevic D, 2020, QUAL ASSUR SAF CROP, V12, P24, DOI [10.15586/qas.v12i3.646, 10.15586/QAS2019.646] Elghannam A, 2020, FOODS, V9, DOI 10.3390/foods9010022 Field AP., 2013, DISCOVERING STAT USI Ger K., 2017, INDEPENDENT Heyder M, 2012, FOOD POLICY, V37, P102, DOI 10.1016/j.foodpol.2011.11.006 Hofstede GJ, 2010, BRIT FOOD J, V112, P671, DOI 10.1108/00070701011058226 Ilieva J, 2002, INT J MARKET RES, V44, P361 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Jreissat M, 2017, PROC CIRP, V63, P698, DOI 10.1016/j.procir.2017.03.314 Kayikci Y, 2022, PROD PLAN CONTROL, V33, P301, DOI 10.1080/09537287.2020.1810757 Kehagia O, 2007, SOCIOL RURALIS, V47, P400, DOI 10.1111/j.1467-9523.2007.00445.x Kehagia OC, 2017, BRIT FOOD J, V119, P803, DOI 10.1108/BFJ-07-2016-0333 Kher SV, 2010, BRIT FOOD J, V112, P261, DOI 10.1108/00070701011029138 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Lawo D, 2021, SUSTAIN PROD CONSUMP, V27, P282, DOI 10.1016/j.spc.2020.11.007 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Levitt T., 2017, GUARDIAN Malhotra N.K., 2016, MARKETING RES APPL O Mishra N, 2017, PROD PLAN CONTROL, V28, P945, DOI 10.1080/09537287.2017.1336789 Nikolova HD, 2015, J MARKETING RES, V52, P817, DOI 10.1509/jmr.13.0270 Nuttavuthisit K, 2017, J BUS ETHICS, V140, P323, DOI 10.1007/s10551-015-2690-5 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Paul J, 2012, J CONSUM MARK, V29, P412, DOI 10.1108/07363761211259223 Peano C, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9020261 Pekkirbizli T., 2015, EC AGRO ALIMENT, V17, P31, DOI [10.3280/ECAG2015-003003, DOI 10.3280/ECAG2015-003003] Rimpeekool W, 2015, FOOD POLICY, V56, P59, DOI 10.1016/j.foodpol.2015.07.011 Roberts R., 2017, INDEPENDENT Schulze-Ehlers B, 2018, RENEW AGR FOOD SYST, V33, P73, DOI 10.1017/S1742170517000059 Sheu C, 2006, INT J OPER PROD MAN, V26, P24, DOI 10.1108/01443570610637003 Simons R, 2014, HARVARD BUS REV, V92, P48 Singh K., 2007, QUANTITATIVE SOCIAL, DOI DOI 10.4135/9789351507741 Storey J, 2006, INT J OPER PROD MAN, V26, P754, DOI 10.1108/01443570610672220 Sun SN, 2019, J CLEAN PROD, V217, P658, DOI 10.1016/j.jclepro.2019.01.296 Ulku MA, 2017, J CLEAN PROD, V142, P4230, DOI 10.1016/j.jclepro.2016.11.050 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Zhu LJ, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9050682 NR 56 TC 11 Z9 11 U1 1 U2 13 PD MAR PY 2021 VL 10 IS 3 AR 543 DI 10.3390/foods10030543 WC Food Science & Technology SC Food Science & Technology UT WOS:000633668400001 DA 2022-12-14 ER PT J AU Bailey, M Bush, SR Miller, A Kochen, M AF Bailey, Megan Bush, Simon R. Miller, Alex Kochen, Momo TI The role of traceability in transforming seafood governance in the global South SO CURRENT OPINION IN ENVIRONMENTAL SUSTAINABILITY DT Article ID ENVIRONMENTAL GOVERNANCE; FOOD SAFETY; INFORMATION AGE; SUPPLY CHAIN; TRANSPARENCY; AGRICULTURE; BENEFITS; PATTERNS; INDUSTRY; TRADE AB Business-to-business traceability has historically played an important role in coordinating value chain activities and helping businesses to manage reputational risk. Its use and value, however, have recently extended beyond industry value chain actors alone, and traceability information may now contribute to improving government regulation, and via consumer-facing traceability (CFT) systems to sustainable seafood governance. Implementing traceability can be costly and requires coordination, consequently most systems utilized till date have been in the global North. Yet seafood value chains remain incredibly complex, and the majority of seafood is sourced from the South. This paper synthesizes the traceability literature through an informational governance perspective, analyzing if and how information can transform production practices while at the same time empowering producing nations. Traceability has gone beyond simply facilitating improved recall coordination, but the future value of traceability lies in how to design and organize systems in such a way that information flows can be harnessed to improve global seafood governance. C1 [Bailey, Megan; Bush, Simon R.] Wageningen Univ, Environm Policy Grp, NL-6700 AP Wageningen, Netherlands. [Miller, Alex] Gulf States Marine Fisheries Commiss, Ocean Springs, MS USA. [Kochen, Momo] Masyarakat & Perikanan, Bali, Indonesia. C3 Wageningen University & Research RP Bailey, M (corresponding author), Wageningen Univ, Environm Policy Grp, NL-6700 AP Wageningen, Netherlands. EM megan.bailey@wur.nl CR Agnew DJ, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0004570 Akkerman R, 2010, QUALITY SAFETY SUSTA Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Boyle M, 2012, WITHOUT A TRACE Bush S, J CLEAN PROD Bush SR, 2011, GEOFORUM, V42, P185, DOI 10.1016/j.geoforum.2010.12.007 Caswell JA, 1998, AUST J AGR RESOUR EC, V42, P409, DOI 10.1111/1467-8489.00060 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Coff C, 2008, INT LIBR ENVIRON AGR, V15, P1, DOI 10.1007/978-1-4020-8524-6 Donnelly KAM, 2012, FOOD CONTROL, V27, P228, DOI 10.1016/j.foodcont.2012.03.021 FAO, 1995, COD COND RESP FISH FAO, 2001, INT PLAN ACT PREV DE FAO, 2014, STAT WORLD FISH AQ, P223 Fiorillo J, 2014, WEGMANS USING TRACEA Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 Fuchs D, 2011, AGR HUM VALUES, V28, P353, DOI 10.1007/s10460-009-9236-3 Gooch M, 2013, TRACEABILITY IS FREE Government Accountability Office, 2009, SEAF FRAUD FDA PROGR Gupta A, 2014, EARTH SYST GOV, P3 Gupta A, 2010, GLOBAL ENVIRON POLIT, V10, P1, DOI 10.1162/GLEP_e_00011 Hallstein E, 2013, J ENVIRON ECON MANAG, V66, P52, DOI 10.1016/j.jeem.2013.01.003 Helyar SJ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098691 Humphrey J, 2013, WHAT DO FOOD TRACEAB, P3 Iles A, 2007, J CLEAN PROD, V15, P577, DOI 10.1016/j.jclepro.2006.06.001 Jacquet J, 2010, ORYX, V44, P45, DOI 10.1017/S0030605309990470 Jacquet JL, 2008, MAR POLICY, V32, P309, DOI 10.1016/j.marpol.2007.06.007 Jensen HH, 2006, MAR POLLUT BULL, V53, P591, DOI 10.1016/j.marpolbul.2006.08.014 Magera A., 2009, SEAFOOD TRACEABILITY Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Martinsohn J., 2011, DETERRING ILLEGAL AC Miller A, 2014, ELECT SEAFOOD TRACEA Miller D, 2012, FISH FISH, V13, P345, DOI 10.1111/j.1467-2979.2011.00426.x Mol A., 2013, J CLEAN PROD Mol A. P. J, 2008, ENV REFORM INFORM AG Mol APJ, 2008, ORG TRANSNATIONAL AC, P296 Mol APJ, 2006, ENVIRON PLANN C, V24, P497, DOI 10.1068/c0508j Mol APJ, 2014, EARTH SYST GOV, P39 Morrissey MT, 2005, J FOOD ENG, V67, P135, DOI 10.1016/j.jfoodeng.2004.05.057 Neilson J, 2007, GREEN COFFEE CONTRAD, P311 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Rayner J., 2004, MANAGING REPUTATIONA Raynolds LT, 2009, WORLD DEV, V37, P1083, DOI 10.1016/j.worlddev.2008.10.001 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Roheim CA, 2009, THALASSORAMA EVALUAT, P301 Schroder U, 2008, J VERBRAUCH LEBENSM, V3, P45, DOI 10.1007/s00003-007-0302-8 Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x van Veen TWS, 2005, FOOD CONTROL, V16, P491, DOI 10.1016/j.foodcont.2003.10.014 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Warner K., 2013, OCEANA STUDY REVEALS, P1 Wessells C. R., 2002, Marine Resource Economics, V17, P153 Wilson T, 2006, SUPPLY CHAIN MANAG I, V3, P127 Wognum PM, 2011, ADV ENG INFORM, V25, P65, DOI 10.1016/j.aei.2010.06.001 WWF, 2014, TUN PROC GUID NR 54 TC 63 Z9 64 U1 5 U2 31 PD FEB PY 2016 VL 18 BP 25 EP 32 DI 10.1016/j.cosust.2015.06.004 WC Green & Sustainable Science & Technology; Environmental Sciences SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000373540900005 DA 2022-12-14 ER PT J AU Liu, ZM Yang, SB Wang, YZ Zhang, JY AF Liu, Zhimin Yang, Shaobing Wang, Yuanzhong Zhang, Jinyu TI Multi-platform integration based on NIR and UV-Vis spectroscopies for the geographical traceability of the fruits of Amomum tsao-ko SO SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY DT Article DE Geographical traceability; Amomum tsao-ko; NIR; UV-Vis; Multi-block; Data fusion ID DATA FUSION STRATEGY; QUALITY ASSESSMENT; FT-MIR; ADULTERATION; FOOD; RESOLUTION; CREVOST AB Due to the world-wide concern relating to herb quality and safety, there is a momentum to authenticate the geographical origin of herb with multi-platform techniques. This study attempted to assess multi platform information as a practical strategy for the geographical traceability of the fruits of Amomum tsao-ko. To this aim, one hundred and eighty dried fruits of A. tsao-ko from five geographical regions were analyzed by near infrared (NIR) and ultraviolet visible (UV-Vis) spectroscopy. On this basis, two variable dimension reduction strategies, including principal component analysis (PCA) and sequential and orthogonalized partial-least squares (SO-PLS), and two variables selection strategies, including variable importance in projection (VIP) and sequential and orthogonalized covariance selection (SO-CovSel), were performed to extract the feature information in the two blocks. Partial least squares discriminant analysis (PLS-DA) classification algorithm combined with fused matrices was used to identify the geographical origins. The results of PLS-DA models indicated that SO-PLS and SO-CovSel, taking advantage of the sequential modeling coupled to orthogonalization, could not only identify the common information presented in the two blocks but also provide more concise methods without any loss of classification ability, which could be employed in authenticating the geographical regions of the fruits of A. tsao-ko, effectively. (c) 2021 Elsevier B.V. All rights reserved. C1 [Liu, Zhimin; Yang, Shaobing; Wang, Yuanzhong; Zhang, Jinyu] Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. [Liu, Zhimin; Zhang, Jinyu] Yunnan Univ, Sch Agr, Kunming 650500, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University RP Wang, YZ; Zhang, JY (corresponding author), Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. EM boletus@126.com; jyzhang2008@126.com CR Alamprese C, 2013, LWT-FOOD SCI TECHNOL, V53, P225, DOI 10.1016/j.lwt.2013.01.027 Bian XH, 2020, ANAL METHODS-UK, V12, P3499, DOI [10.1039/d0ay00285b, 10.1039/D0AY00285B] Biancolillo A., 2019, DATA FUSION METHODOL, VVolume 31, P157, DOI DOI 10.1016/B978-0-444-63984-4.00006-5 Biancolillo A, 2021, FOOD CHEM, V340, DOI 10.1016/j.foodchem.2020.127904 Biancolillo A, 2020, J FOOD COMPOS ANAL, V86, DOI 10.1016/j.jfca.2019.103351 Biancolillo A, 2015, CHEMOMETR INTELL LAB, V141, P58, DOI 10.1016/j.chemolab.2014.12.001 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Calvani R, 2021, GEROSCIENCE, V43, P727, DOI 10.1007/s11357-020-00197-x Castanedo F, 2013, SCI WORLD J, DOI 10.1155/2013/704504 Chong IG, 2005, CHEMOMETR INTELL LAB, V78, P103, DOI 10.1016/j.chemolab.2004.12.011 Cubero-Leon E, 2014, FOOD RES INT, V60, P95, DOI 10.1016/j.foodres.2013.11.041 Ellis DI, 2012, CHEM SOC REV, V41, P5706, DOI 10.1039/c2cs35138b Engel J, 2013, TRAC-TREND ANAL CHEM, V50, P96, DOI 10.1016/j.trac.2013.04.015 Giannetti V, 2020, MICROCHEM J, V157, DOI 10.1016/j.microc.2020.104896 He TT, 2015, EUR J INTEGR MED, V7, P55, DOI 10.1016/j.eujim.2014.11.007 He XF, 2021, IND CROP PROD, V160, DOI 10.1016/j.indcrop.2020.112908 He Y, 2021, CRIT REV FOOD SCI, V61, P2351, DOI 10.1080/10408398.2020.1777526 Hong SS, 2015, TETRAHEDRON LETT, V56, P6681, DOI 10.1016/j.tetlet.2015.10.045 KAISER HF, 1960, EDUC PSYCHOL MEAS, V20, P141, DOI 10.1177/001316446002000116 Khaleghi B, 2013, INFORM FUSION, V14, P28, DOI 10.1016/j.inffus.2011.08.001 Kuligowski J, 2015, ANALYST, V140, P4521, DOI 10.1039/c5an00706b Li B, 2014, AM J CHINESE MED, V42, P1229, DOI 10.1142/S0192415X14500773 Li Y, 2020, J PHARMACEUT BIOMED, V185, DOI 10.1016/j.jpba.2020.113215 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Liu CY, 2018, ANAL METHODS-UK, V10, P743, DOI 10.1039/c7ay02248d Ma ML, 2017, AIP CONF PROC, V1864, DOI 10.1063/1.4992888 Martin TS, 2000, J AM OIL CHEM SOC, V77, P667, DOI 10.1007/s11746-000-0107-4 Mishra P, 2021, TALANTA, V223, DOI 10.1016/j.talanta.2020.121733 Naes T, 2011, J CHEMOMETR, V25, P28, DOI 10.1002/cem.1357 Ning ZC, 2013, PLANTA MED, V79, P897, DOI 10.1055/s-0032-1328656 Rabatel G, 2020, J CHEMOMETR, V34, DOI 10.1002/cem.3164 Rahman MRT, 2017, SAUDI J BIOL SCI, V24, P324, DOI 10.1016/j.sjbs.2015.09.034 Rinnan A, 2009, TRAC-TREND ANAL CHEM, V28, P1201, DOI 10.1016/j.trac.2009.07.007 Roger J.M., 2020, COMPREHENSIVE CHEMOM, V3, P1, DOI [10.1016/B978-0-12-409547-2.14878-4., DOI 10.1016/B978-0-12-409547-2.14878-4] Ruiz-Perez D, 2020, BMC BIOINFORMATICS, V21, DOI 10.1186/s12859-019-3310-7 SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 Silvestri M, 2014, CHEMOMETR INTELL LAB, V137, P181, DOI 10.1016/j.chemolab.2014.06.012 Spiteri M, 2016, ANAL BIOANAL CHEM, V408, P4389, DOI 10.1007/s00216-016-9538-4 Sun WJ, 2017, SPECTROCHIM ACTA A, V171, P72, DOI 10.1016/j.saa.2016.07.039 Wu XM, 2018, SPECTROCHIM ACTA A, V205, P479, DOI 10.1016/j.saa.2018.07.067 Yu LQ, 2010, J NUTR SCI VITAMINOL, V56, P171, DOI 10.3177/jnsv.56.171 Zhang J, 2021, MICROCHEM J, V160, DOI 10.1016/j.microc.2020.105662 Zhao YL, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0089100 Zheng D., 2015, INTRO PHYS GEOGRAPHY Zhou L, 2020, TRAC-TREND ANAL CHEM, V127, DOI 10.1016/j.trac.2020.115901 Zimmermann B, 2013, APPL SPECTROSC, V67, P892, DOI 10.1366/12-06723 NR 46 TC 9 Z9 9 U1 3 U2 35 PD SEP 5 PY 2021 VL 258 AR 119872 DI 10.1016/j.saa.2021.119872 EA MAY 2021 WC Spectroscopy SC Spectroscopy UT WOS:000652383800006 DA 2022-12-14 ER PT J AU Barcellos, JOJ Abicht, AD Brandao, FS Canozzi, MEA Collares, FC AF Jardim Barcellos, Julio Otavio Abicht, Alexandre de Melo Brandao, Fernanda Scharnberg Andrighetto Canozzi, Maria Eugenia Collares, Fernando Carbonari TI Consumer perception of Brazilian traced beef SO REVISTA BRASILEIRA DE ZOOTECNIA-BRAZILIAN JOURNAL OF ANIMAL SCIENCE DT Article DE agribusiness; certification; differentiation; quality; traceability ID QUALITY; PURCHASE; WILLINGNESS AB The objective of this study was to determine consumers understanding of beef traceability, identifying how consumers value this meat and traceability elements to be presented on retail shelves. The method used in this study was a survey through the internet applying the Sphinx software. The sample consisted of 417 consumers, mostly living in Porto Alegre, Brazil. Consumers are aware of certified beef, consider it important, but this is not a demand. As to traced beef, most consumers (62.4%) are in favor of mandatory traceability of beef cattle in Brazil, but 86.6% disagree with the destination of traced beef only to the foreign market. The majority of people are willing to pay more for traced beef and consider traceability a market opportunity, used as a differentiating tool. C1 [Jardim Barcellos, Julio Otavio; Abicht, Alexandre de Melo; Brandao, Fernanda Scharnberg] Univ Fed Rio Grande do Sul, CEPAN, Programa Posgrad Agronegocios, Porto Alegre, RS, Brazil. [Jardim Barcellos, Julio Otavio; Abicht, Alexandre de Melo; Brandao, Fernanda Scharnberg; Andrighetto Canozzi, Maria Eugenia; Collares, Fernando Carbonari] Univ Fed Rio Grande do Sul, Nucleo Estudos Sistemas Prod Bovinos Corte & Cade, Porto Alegre, RS, Brazil. [Jardim Barcellos, Julio Otavio] CNPq, Campinas, SP, Brazil. C3 Universidade Federal do Rio Grande do Sul; Universidade Federal do Rio Grande do Sul RP Barcellos, JOJ (corresponding author), Univ Fed Rio Grande do Sul, CEPAN, Programa Posgrad Agronegocios, Porto Alegre, RS, Brazil. EM julio.barcellos@ufrgs.br CR Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 BARCELLOS J.O.J., 2004, CECLO ATUALIZACAO ME, V11 Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Bernues A, 2003, FOOD QUAL PREFER, V14, P265, DOI 10.1016/S0950-3293(02)00085-X Castro, 2005, CADERNO PESQUISAS AD, V12, P81 Caswell JA, 2006, MAR POLLUT BULL, V53, P650, DOI 10.1016/j.marpolbul.2006.08.007 CODEX ALIMENTARIUS, 2007, FOOD IMP EXP INSP CE, P91 COLTRO A, 2007, REA REV ELETRONICA A, V124, P132 FERREIRA G.C., 2007, AGROANALYSIS, V27, P44 GOMEZ M.A.R, 2004, DISTRIBUCION CONSUMO, V14, P53 MACHADO R.T.M, 2005, ORG RURAIS AGROINDUS, V7, P227 MALAFAIA G.C., 2006, SEMINARIOS ADM SEMEA, V9 Neves MF, 2006, WAG UR FRON, V14, P141 Poghosyan A., 2004, INT FOOD AGRIBUS MAN, V7, P118 Rijswijk W. van, 2008, Food Quality and Preference, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Sepulveda W, 2008, MEAT SCI, V80, P1282, DOI 10.1016/j.meatsci.2008.06.012 Tootelian DH, 2004, J FOOD PROD MARK, V10, P27, DOI 10.1300/J038v10n03_03 Velho JP, 2009, REV BRAS ZOOTECN, V38, P399, DOI 10.1590/S1516-35982009000200025 VIEIRA L., 2007, AGROANALYSIS, V27, P26 Villalobos P, 2010, CHIL J AGR RES, V70, P85, DOI 10.4067/S0718-58392010000100009 NR 20 TC 10 Z9 14 U1 1 U2 19 PD MAR PY 2012 VL 41 IS 3 BP 771 EP 774 DI 10.1590/S1516-35982012000300041 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000303451600041 DA 2022-12-14 ER PT J AU Gamarra, D Taniguchi, M Aldai, N Arakawa, A Lopez-Oceja, A de Pancorbo, MM AF Gamarra, David Taniguchi, Masaaki Aldai, Noelia Arakawa, Aisaku Lopez-Oceja, Andres de Pancorbo, Marian M. TI Genetic Characterization of the Local Pirenaica Cattle for Parentage and Traceability Purposes SO ANIMALS DT Article DE structure; identity; assignment test; microsatellite; multiplex PCR; Salers; Holstein-Friesian; Terreñ a; Blonde d´ Aquitaine; Limousin ID MICROSATELLITE MARKERS; BEEF-CATTLE; POPULATION-STRUCTURE; MEAT TRACEABILITY; BOVINE BREEDS; DIVERSITY; IDENTIFICATION; INFERENCE; SYSTEM AB Simple Summary Domestic livestock diversity is an important component of global biodiversity and molecular data have become essential for the characterization of genetic diversity in cattle. The aim of this study was to assess the effectiveness of a 30-short tandem repeat (STR) panel and reveal the genetic structure of a local Pirenaica breed compared with other breeds (Terrena, Blonde d'Aquitaine, Limousin, Salers and Holstein-Friesian) typically raised in the same geographic Basque region. The proposed STR panel could be used as an appropriate genetic tool to trace Pirenaica animals and their Protected Geographic Indication (PGI) products. Pirenaica is the most important autochthonous cattle breed within the Protected Geographic Indication (PGI) beef quality label in the Basque region, in northern Spain. The short tandem repeats (STRs) are powerful markers to elucidate forensic cases and traceability across the agri-food sector. The main objective of the present work was to study the phylogenetic relationships of Pirenaica cattle and other breeds typically raised in the region and provide the minimum number of STR markers for parentage and traceability purposes. The 30-STR panel recommended by the International Society of Animal Genetics-Food and Agriculture Organization of the United Nations (ISAG-FAO) was compared against other commercial STR panels. The 30-STR panel showed a combined matching probability of 1.89 x 10(-25) and a power of exclusion for duos of 0.99998. However, commercial STR panels showed a limited efficiency for a reliable parentage analysis in Pirenaica, and at least a 21-STR panel is needed to reach a power of exclusion of 0.9999. Machine-learning analysis also demonstrated a 95% accuracy in assignments selecting the markers with the highest F-ST in Pirenaica individuals. Overall, the present study shows the genetic characterization of Pirenaica and its phylogeny compared with other breeds typically raised in the Basque region. Finally, a 21-STR panel with the highest F-ST markers is proposed for a confident parentage analysis and high traceability. C1 [Gamarra, David; Lopez-Oceja, Andres; de Pancorbo, Marian M.] Univ Basque Country UPV EHU, Lascaray Res Ctr, Biom Res Grp, Vitoria 01006, Spain. [Taniguchi, Masaaki; Arakawa, Aisaku] Natl Agr & Food Res Org NARO, Anim Genome Unit, Inst Livestock & Grassland Sci, Tsukuba, Ibaraki 3050901, Japan. [Aldai, Noelia] Univ Basque Country UPV EHU, Dept Pharm & Food Sci, Lactiker Res Grp, Vitoria 01006, Spain. C3 University of Basque Country; National Agriculture & Food Research Organization - Japan; University of Basque Country RP de Pancorbo, MM (corresponding author), Univ Basque Country UPV EHU, Lascaray Res Ctr, Biom Res Grp, Vitoria 01006, Spain. EM davidgamarrafdz@gmail.com; masaakit@affrc.go.jp; noelia.aldai@ehu.eus; aisaku@affrc.go.jp; andreslopezoceja@gmail.com; marian.mdepancorbo@ehu.eus CR Amigues Y, 2011, J ANIM BREED GENET, V128, P201, DOI 10.1111/j.1439-0388.2010.00890.x Anderson EC, 2010, MOL ECOL RESOUR, V10, P701, DOI 10.1111/j.1755-0998.2010.02846.x [Anonymous], **NON-TRADITIONAL** [Anonymous], **NON-TRADITIONAL** Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Baldo A, 2010, MEAT SCI, V85, P671, DOI 10.1016/j.meatsci.2010.03.023 Beja-Pereira A, 2003, J HERED, V94, P243, DOI 10.1093/jhered/esg055 Belkhir K., GENETIX 4 05 LOGICIE Canon J, 2001, GENET SEL EVOL, V33, P311, DOI 10.1051/gse:2001121 Carolino I, 2009, GENET MOL BIOL, V32, P306, DOI 10.1590/S1415-47572009005000026 Chen KY, 2018, METHODS ECOL EVOL, V9, P439, DOI 10.1111/2041-210X.12897 Cymbron T, 1999, P ROY SOC B-BIOL SCI, V266, P597, DOI 10.1098/rspb.1999.0678 Dalvit C, 2008, J ANIM BREED GENET, V125, P137, DOI 10.1111/j.1439-0388.2007.00707.x Evanno G, 2005, MOL ECOL, V14, P2611, DOI 10.1111/j.1365-294X.2005.02553.x Excoffier L, 2010, MOL ECOL RESOUR, V10, P564, DOI 10.1111/j.1755-0998.2010.02847.x Fernandez ME, 2013, GENET MOL BIOL, V36, P185, DOI 10.1590/S1415-47572013000200008 Food and Agriculture Organization of the United Nations (FAO), MOL GEN CHAR AN GEN Gamarra D, 2017, ANIMAL, V11, P24, DOI 10.1017/S1751731116001063 Gamarra D, 2018, BMC VET RES, V14, DOI 10.1186/s12917-018-1481-5 Gamarra D, 2015, FORENS SCI INT-GEN S, V5, pE253, DOI 10.1016/j.fsigss.2015.09.101 Ginja C, 2010, J HERED, V101, P201, DOI 10.1093/jhered/esp104 GUO SW, 1992, BIOMETRICS, V48, P361, DOI 10.2307/2532296 ISAG, CATTL MOL MARK PAR T ISAG, ISAG CATTL COR ADD S ISAG, CATTL MOL MARK PAR T James G., 2013, INTRO STATISCAL LEAR Jamieson A, 1997, ANIM GENET, V28, P397, DOI 10.1111/j.1365-2052.1997.00186.x Kopelman NM, 2015, MOL ECOL RESOUR, V15, P1179, DOI 10.1111/1755-0998.12387 Letunic I, 2007, BIOINFORMATICS, V23, P127, DOI 10.1093/bioinformatics/btl529 Lopez-Oceja A, 2017, FOOD CHEM, V237, P701, DOI 10.1016/j.foodchem.2017.06.004 Lopez-Oceja A, 2016, MITOCHONDRIAL DNA A, V27, P3597, DOI 10.3109/19401736.2015.1079823 MacHugh D.E., 1996, THESIS Marshall TC, 1998, MOL ECOL, V7, P639, DOI 10.1046/j.1365-294x.1998.00374.x Martin-Burriel I, 1999, ANIM GENET, V30, P177, DOI 10.1046/j.1365-2052.1999.00437.x Maudet C, 2002, J ANIM SCI, V80, P942 Mendizabal J. A., 1998, Archivos de Zootecnia, V47, P387 PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Pritchard JK, 2000, GENETICS, V155, P945 Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Rendo F, 2004, BIOCHEM GENET, V42, P99, DOI 10.1023/B:BIGI.0000020465.62447.00 SHACKELL GH, 2001, P ASS ADVMT ANIM BRE, V14, P533 Suh S, 2014, ASIAN AUSTRAL J ANIM, V27, P1548, DOI 10.5713/ajas.2014.14435 Tian F, 2008, J GENET GENOMICS, V35, P279, DOI 10.1016/S1673-8527(08)60040-5 van de Goor LHP, 2011, INT J LEGAL MED, V125, P111, DOI 10.1007/s00414-009-0353-8 Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] Wang JL, 2017, MOL ECOL RESOUR, V17, P981, DOI 10.1111/1755-0998.12650 Weir BS, 1996, SINAUER ASS SUNDERL Zhao J, 2017, FOOD CONTROL, V78, P469, DOI 10.1016/j.foodcont.2017.03.017 Zilhao J., 1993, J MEDITERR ARCHAEOL, V6, P5, DOI DOI 10.1558/JMEA.V6I1.5 NR 51 TC 2 Z9 3 U1 0 U2 7 PD SEP PY 2020 VL 10 IS 9 AR 1584 DI 10.3390/ani10091584 WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology SC Agriculture; Veterinary Sciences; Zoology UT WOS:000581465600001 DA 2022-12-14 ER PT J AU Azemard, S Vassileva, E AF Azemard, Sabine Vassileva, Emilia TI Determination of methylmercury in marine biota samples with advanced mercury analyzer: Method validation SO FOOD CHEMISTRY DT Article DE Methyl mercury; Advanced mercury analyzer; Marine biota; Sample preparation; Method validation; Traceability; Uncertainty ID SOUTHERN NEVADA; ORGANIC MERCURY; FISH; EXTRACTION; SPECIATION; SEDIMENTS; HPLC AB In this paper, we present a simple, fast and cost-effective method for determination of methyl mercury (MeHg) in marine samples. All important parameters influencing the sample preparation process were investigated and optimized. Full validation of the method was performed in accordance to the ISO-17025 (ISO/IEC, 2005) and Eurachem guidelines. Blanks, selectivity, working range (0.09-3.0 ng), recovery (92-108%), intermediate precision (1.7-4.5%), traceability, limit of detection (0.009 ng), limit of quantification (0.045 ng) and expanded uncertainty (15%, k = 2) were assessed. Estimation of the uncertainty contribution of each parameter and the demonstration of traceability of measurement results was provided as well. Furthermore, the selectivity of the method was studied by analyzing the same sample extracts by advanced mercury analyzer (AMA) and gas chromatography-atomic fluorescence spectrometry (GC-AFS). Additional validation of the proposed procedure was effectuated by participation in the IAEA-461 worldwide inter-laboratory comparison exercises. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Azemard, Sabine; Vassileva, Emilia] IAEA, Dept Nucl Sci & Applicat, Environm Labs, Monaco 98000, Monaco. RP Vassileva, E (corresponding author), IAEA, Dept Nucl Sci & Applicat, Environm Labs, 4 Quai Antoine 1er, Monaco 98000, Monaco. EM e.vasileva-veleva@iaea.org CR [Anonymous], 2017, 17025 ISOIEC [Anonymous], 2013, DETERMINATION METHYL [Anonymous], 1994, 5725 ISOIEC Carbonell G, 2009, B ENVIRON CONTAM TOX, V83, P210, DOI 10.1007/s00128-009-9720-x Carrasco L, 2014, TALANTA, V122, P106, DOI 10.1016/j.talanta.2014.01.027 Carrasco L, 2011, ENVIRON INT, V37, P1213, DOI 10.1016/j.envint.2011.05.004 Carrasco L, 2009, J CHROMATOGR A, V1216, P8828, DOI 10.1016/j.chroma.2009.10.043 Clemens S, 2011, ANAL BIOANAL CHEM, V401, P2699, DOI 10.1007/s00216-011-5040-1 Cordeiro F., 2013, JRC TECHNICAL REPORT da Rocha MS, 2001, J ANAL ATOM SPECTROM, V16, P951, DOI 10.1039/b101638p Feinberg M, 2007, J CHROMATOGR A, V1158, P174, DOI 10.1016/j.chroma.2007.02.021 Gerstenberger S, 2002, B ENVIRON CONTAM TOX, V69, P210, DOI 10.1007/s00128-002-0049-y Gerstenberger SL, 2004, ENVIRON TOXICOL, V19, P35, DOI 10.1002/tox.10149 Hight SC, 2006, ANAL CHIM ACTA, V567, P160, DOI 10.1016/j.aca.2006.03.048 Hintelmann H, 2005, ANAL BIOANAL CHEM, V381, P360, DOI 10.1007/s00216-004-2878-5 IAEA, 2012, IAEA AN QUAL NUCL AP, V23 Jagtap R, 2011, TALANTA, V85, P49, DOI 10.1016/j.talanta.2011.03.022 JCGM, 2008, EV MEAS DAT GUID EXP KRAGTEN J, 1994, ANALYST, V119, P2161, DOI 10.1039/an9941902161 Li YB, 2010, ENVIRON SCI TECHNOL, V44, P6661, DOI 10.1021/es1010434 Lopez I, 2010, TALANTA, V82, P594, DOI 10.1016/j.talanta.2010.05.013 Maggi C, 2009, ANAL CHIM ACTA, V641, P32, DOI 10.1016/j.aca.2009.03.033 Mason RP, 2003, ORGANOMETALLIC COMPO, P57, DOI [10.1002/0470867868, DOI 10.1002/0470867868, DOI 10.1002/0470867868.CH2] Mermet JM, 2012, SPECTROCHIM ACTA B, V76, P214, DOI 10.1016/j.sab.2012.06.003 Nevado JJB, 2005, J CHROMATOGR A, V1093, P21, DOI 10.1016/j.chroma.2005.07.054 Nordtest, 2012, HDB CALC MEAS UNC EN Qiu J, 2013, NATURE, V493, P144, DOI 10.1038/493144a Reyes LH, 2008, ANAL BIOANAL CHEM, V390, P2123, DOI 10.1007/s00216-008-1966-3 Scerbo R, 1998, ENVIRON TECHNOL, V19, P339, DOI 10.1080/09593331908616689 Senn DB, 2010, ENVIRON SCI TECHNOL, V44, P1630, DOI 10.1021/es902361j Siciliano SD, 2002, ENVIRON SCI TECHNOL, V36, P3064, DOI 10.1021/es010774v Sunderland EM, 2007, ENVIRON HEALTH PERSP, V115, P235, DOI 10.1289/ehp.9377 Thompson M, 2002, PURE APPL CHEM, V74, P835, DOI 10.1351/pac200274050835 USFDA, 2004, EPA823R04005 USFDA Valega M, 2006, WATER AIR SOIL POLL, V174, P223, DOI 10.1007/s11270-006-9100-7 Vassileva E, 2014, MICROCHEM J, V116, P197, DOI 10.1016/j.microc.2014.05.011 Vessman J, 2001, PURE APPL CHEM, V73, P1381, DOI 10.1351/pac200173081381 Yang DY, 2009, ANAL CHIM ACTA, V633, P157, DOI 10.1016/j.aca.2008.10.045 NR 38 TC 16 Z9 16 U1 3 U2 93 PD JUN 1 PY 2015 VL 176 BP 367 EP 375 DI 10.1016/j.foodchem.2014.12.085 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000349723100048 DA 2022-12-14 ER PT J AU Zsakai, A Munoz, A Diez, A Roman, R Marco, E Garcia, A Ibarra, A AF Zsakai, A. Munoz, A. Diez, A. Roman, R. Marco, E. Garcia, A. Ibarra, A. TI IFMIF-DONES systems engineering approach SO FUSION ENGINEERING AND DESIGN DT Article DE Fusion roadmap; Neutron source accelerator; Systems engineering; Requirements; Traceability; Interfaces AB In the framework of the EU fusion roadmap implementing activities, an accelerator-based Li(d,n) neutron source called DONES (Demo-Oriented early NEutron Source) is being designed as an essential irradiation facility for testing candidate materials for DEMO reactor and future fusion power plants. DONES facility is being developed within the EUROfusion workpackage WPENS (Work Package Early Neutron Source), which main objective is to be ready for IFMIF (International Fusion Materials Irradiation Facility)-DONES construction as soon as 2020. Fourteen Research Units (RU) around Europe, as well as industry Third Parties, are involved working in different aspects of the DONES scope of works. Taking into account the complexity of the facility and the geographical dispersion of the partners involved, it is of a paramount importance to properly develop the DONES Systems Engineering, creating and executing interdisciplinary processes to ensure that the DONES defined objectives are reached and that the facility fulfils the expected criteria. This paper presents the Systems Engineering processes that are being developed for the DONES Project as an interdisciplinary approach to enable the realization of successful Structure, Systems and Components (SSCs). In order to reach this objective (defining, controlling and documenting the requirements and interfaces) a dedicated group has been set up. A matrix for the management (traceability) of the requirements is being developed and populated. Also, a specific tool is being used for the management of the project interfaces (physical, functional, etc.). These works are to be considered as part of the development phase, started as early as possible in the project and then used in further stages (design synthesis activities, SSC validation, subcontractors' control, etc.) following the Systems Engineering works. C1 [Zsakai, A.] Wigner Res Ctr, Budapest, Hungary. [Munoz, A.; Marco, E.; Garcia, A.] Empresarios Agrupados, Madrid, Spain. [Diez, A.] Visure Solut, Madrid, Spain. [Roman, R.; Garcia, A.; Ibarra, A.] CIEMAT, Madrid, Spain. C3 Hungarian Academy of Sciences; Hungarian Wigner Research Centre for Physics; Centro de Investigaciones Energeticas, Medioambientales Tecnologicas RP Munoz, A (corresponding author), Empresarios Agrupados, Madrid, Spain. EM amcervantes@empre.es CR Garcia A., DONES PLANT DESIGN R Garcia A., DONES TOP LEVEL PLAN Roman R., PLANT DESIGN DESCRIP NR 3 TC 3 Z9 3 U1 0 U2 2 PD DEC PY 2019 VL 149 AR 111326 DI 10.1016/j.fusengdes.2019.111326 WC Nuclear Science & Technology SC Nuclear Science & Technology UT WOS:000493943400009 DA 2022-12-14 ER PT J AU Xiao, XQ Fu, ZT Zhang, YJ Peng, ZH Zhang, XS AF Xiao, Xinqing Fu, Zetian Zhang, Yongjun Peng, Zhaohui Zhang, Xiaoshuan TI SMS-CQ: A QUALITY AND SAFETY TRACEABILITY SYSTEM FOR AQUATIC PRODUCTS IN COLD-CHAIN INTEGRATED WSN AND QR CODE SO JOURNAL OF FOOD PROCESS ENGINEERING DT Article AB As one of the widely consumed foods, aquatic products are prone to spoilage and deteriorate in the cold chain. It is very important and necessary to trace and track the aquatic products' quality and safety in the cold chain. This paper aims to develop a quality and safety traceability system integrated with wireless sensor network (WSN) and quick response (QR) code (SMS-CQ) for aquatic products. This paper analyzed temperature fluctuations of aquatic products in cold-chain logistics that is implemented and evaluated in an actual cold chain. The results show that compared with the traditional system, SMS-CQ is an effective quality management tool that leads to real-time monitoring and tracing of the aquatic products in the cold chain: WSN technology enables SMS-CQ to automatically realize the real-time temperature acquisition, wireless remote transmission and monitoring; QR code provides valid means for the QR code generation, error correction and static and sensed dynamic information inquiry. C1 [Xiao, Xinqing; Fu, Zetian; Zhang, Yongjun; Zhang, Xiaoshuan] China Agr Univ, Beijing, Peoples R China. [Xiao, Xinqing; Fu, Zetian; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing, Peoples R China. [Zhang, Yongjun] Shandong Inst Commerce & Technol, Jinan, Shandong, Peoples R China. [Peng, Zhaohui] Beijing Beishui Food Ind Co Ltd, Beijing, Peoples R China. C3 China Agricultural University; Shandong Institute of Commerce & Technology RP Zhang, XS (corresponding author), China Agr Univ, Beijing, Peoples R China.; Zhang, XS (corresponding author), Beijing Lab Food Qual & Safety, Beijing, Peoples R China. EM zhxshuan@cau.edu.cn CR Abdelhak S, 2011, COMPUT J, V54, P373, DOI 10.1093/comjnl/bxq017 Alayev Y, 2014, IEEE T WIREL COMMUN, V13, P4066, DOI 10.1109/TWC.2014.2315196 Asadi G, 2014, SCI PAP-SER D-ANIM S, V57, P223 Balfour S. T., 2014, International Food Research Journal, V21, P1279 Bytnerowicz TA, 2014, ENVIRON EXP BOT, V104, P44, DOI 10.1016/j.envexpbot.2014.03.006 Chen YY, 2014, J FOOD ENG, V141, P113, DOI 10.1016/j.jfoodeng.2014.05.014 Chiang YJ, 2013, KSII T INTERNET INF, V7, P2527, DOI 10.3837/tiis.2013.10.012 CHINA CATFISH INSTITUTE, 2012, CHIN AQ IND REP 2011 Han S, 2012, ADV MATER, V24, P5924, DOI 10.1002/adma.201201486 Kaushik S, 2011, INT J ADV COMPUT SC, V2, P28 Liu ZG, 2013, ANN OPER RES, V208, P251, DOI 10.1007/s10479-011-1006-0 Mgonja JT, 2013, J FOOD ENG, V118, P188, DOI 10.1016/j.jfoodeng.2013.04.009 Myo M. A., 2014, FOOD CONTROL, V40, P198, DOI DOI 10.1016/J.F00DC0NT.2013.11.016 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Qi Lin, 2012, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V43, P134 Qi Lin, 2011, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V42, P129 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Raven JA, 2014, PHOTOSYNTH RES, V121, P111, DOI 10.1007/s11120-013-9962-7 Sanz-Valero J, 2013, ANN NUTR METAB, V63, P366 Shen W, 2013, WIREL NETW, V19, P1155, DOI 10.1007/s11276-012-0524-2 Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 Suryadevara NK, 2015, IEEE-ASME T MECH, V20, P564, DOI 10.1109/TMECH.2014.2301716 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Trebar M, 2013, INT J ANTENN PROPAG, V2013, DOI 10.1155/2013/875973 Weimer J, 2012, INT J GREENH GAS CON, V9, P243, DOI 10.1016/j.ijggc.2012.04.001 Xiao XQ, 2014, SENSORS-BASEL, V14, P19877, DOI 10.3390/s141019877 Xiao XinQing, 2013, Transactions of the Chinese Society of Agricultural Engineering, V29, P259 Xu GB, 2014, SENSORS-BASEL, V14, P16932, DOI 10.3390/s140916932 Zhou JH, 2013, FOOD CONTROL, V33, P528, DOI 10.1016/j.foodcont.2013.03.019 NR 30 TC 9 Z9 10 U1 0 U2 42 PD FEB PY 2017 VL 40 IS 1 AR e12303 DI 10.1111/jfpe.12303 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000399307200029 DA 2022-12-14 ER PT J AU Kestens, V Coleman, VA De Temmerman, PJ Minelli, C Woehlecke, H Roebben, G AF Kestens, Vikram Coleman, Victoria A. De Temmerman, Pieter-Jan Minelli, Caterina Woehlecke, Holger Roebben, Gert TI Improved Metrological Traceability of Particle Size Values Measured with Line-Start Incremental Centrifugal Liquid Sedimentation SO LANGMUIR DT Article ID ELECTRON-MICROSCOPY; EFFECTIVE DENSITY; NANOPARTICLES; NANOMATERIALS; DISPERSIONS AB Line-start incremental centrifugal liquid sedimentation (disc-CLS) is a powerful method to determine particle size based on the principles of Stokes' law. Because several of the input quantities of the Stokes equation cannot be easily determined for this case of a rotating disc, the disc-CLS approach relies on calibrating the sedimentation time scale with reference particles. To use these calibrant particles for establishing metrological traceability, they must fulfill the same requirements as those imposed on a certified reference material, i.e., their certified Stokes diameter and density value must come with a realistic measurement uncertainty and with a traceability statement. As is the case for several other techniques, the calibrants do not always come with uncertainties for the assigned modal diameter and effective particle density. The lack of such information and the absence of a traceability statement make it difficult for the end-user to estimate the uncertainty of the measurement results and to compare them with results obtained by others. We present the results of a collaborative study that aimed at demonstrating the traceability of particle size results obtained with disc-CLS. For this purpose, the particle size and effective particle density of polyvinyl chloride calibrants were measured using different validated methods, and measurement uncertainties were estimated according to the Guide to the Expression of Uncertainty in Measurement. The results indicate that the modal Stokes diameter and effective particle density that are assigned to the calibrants are accurate within 5% and 3.5%, respectively, and that they can be used to establish traceability of particle size results obtained with disc-CLS. This conclusion has a great impact on the traceability statement of certified particle size reference materials, for which the traceability is limited to the size and density values of the calibrant particles. C1 [Kestens, Vikram; Roebben, Gert] European Commiss, Directorate Gen Joint Res Ctr, B-2440 Geel, Belgium. [Coleman, Victoria A.] Natl Measurement Inst Australia, Nanometrol Sect, West Lindfield, NSW 2070, Australia. [De Temmerman, Pieter-Jan] Vet & Agrochem Res Ctr CODA CERVA, Serv Trace Elements & Nanomat, B-1180 Brussels, Belgium. [Minelli, Caterina] Natl Phys Lab, Chem Med & Environm Sci Div, Teddington TW11 0LW, Middx, England. [Woehlecke, Holger] Dr Lerche KG, D-12489 Berlin, Germany. C3 National Measurement Institute Australia - NMI; National Physical Laboratory - UK RP Kestens, V (corresponding author), European Commiss, Directorate Gen Joint Res Ctr, B-2440 Geel, Belgium. EM vikram.kestens@ec.europa.eu CR [Anonymous], 2003, TRAC CHEM MEAS GUID [Anonymous], 2008, E76698 ASTM [Anonymous], 2012, E283412 ASTM [Anonymous], 2009, 981 ISO IEC 1 [Anonymous], 2001, DET PART SIZ DISTR C [Anonymous], 2007, DET PART SIZ DISTR C Au KM, 2012, ACS NANO, V6, P8261, DOI 10.1021/nn302968j Bell NC, 2012, LANGMUIR, V28, P10860, DOI 10.1021/la301351k Bohren C. F., 2004, ABSORPTION SCATTERIN Braun A, 2011, ADV POWDER TECHNOL, V22, P766, DOI 10.1016/j.apt.2010.11.001 De Bievre P., 2011, PURE APPL CHEM, V83, P1873, DOI DOI 10.1351/PAC-REP-07-09-39 De Temmerman PJ, 2014, J NANOPART RES, V16, DOI 10.1007/s11051-014-2628-3 De Temmerman PJ, 2013, J NANOPART RES, V16, DOI 10.1007/s11051-013-2177-1 De Temmerman PJ, 2012, J NANOBIOTECHNOL, V10, DOI 10.1186/1477-3155-10-24 DeLoid G, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms4514 Fielding LA, 2012, LANGMUIR, V28, P2536, DOI 10.1021/la204841n Gardiner C, 2013, J EXTRACELL VESICLES, V2, DOI 10.3402/jev.v2i0.19671 Hanus LH, 1999, LANGMUIR, V15, P3091, DOI 10.1021/la980958w International Organization for Standardization (ISO), 2014, 133221 ISO ISO, 2016, 19430 ISO Kamiti M, 2012, ANAL CHEM, V84, P10526, DOI 10.1021/ac3022086 Kestens V., 2014, ERMFD102EUR26656EN Kestens V, 2016, J NANOPART RES, V18, DOI 10.1007/s11051-016-3474-2 Laidlaw I, 2005, ANALYTICAL ULTRACENTRIFUGATION: TECHNIQUES AND METHODS, P270 Lerche D, 2007, POWDER TECHNOL, V174, P46, DOI 10.1016/j.powtec.2006.10.020 Linsinger T., 2010, COMPARISON MEASUREME Mahl D, 2011, COLLOID SURFACE A, V377, P386, DOI 10.1016/j.colsurfa.2011.01.031 Mast J, 2009, DIAGN PATHOL, V4, DOI 10.1186/1746-1596-4-5 MCDONALD SA, 1977, J COLLOID INTERF SCI, V59, P342, DOI 10.1016/0021-9797(77)90017-0 Merkus HG, 2009, PART TECHNOL SER, V17, P1, DOI 10.1007/978-1-4020-9015-8_1 Nadler M, 2008, CARBON, V46, P1384, DOI 10.1016/j.carbon.2008.05.024 Neumann A, 2013, COLLOID SURFACE B, V104, P27, DOI 10.1016/j.colsurfb.2012.11.014 Patois E, 2012, J DRUG DELIV SCI TEC, V22, P427, DOI 10.1016/S1773-2247(12)50069-9 Planken KL, 2010, NANOSCALE, V2, P1849, DOI 10.1039/c0nr00215a PREWITT JMS, 1966, ANN NY ACAD SCI, V128, P1035 Walczyk D, 2010, J AM CHEM SOC, V132, P5761, DOI 10.1021/ja910675v Woehlecke H., 2013, INT C PART TECHN U E Wojdyr M, 2010, J APPL CRYSTALLOGR, V43, P1126, DOI 10.1107/S0021889810030499 NR 38 TC 18 Z9 18 U1 0 U2 3 PD AUG 22 PY 2017 VL 33 IS 33 BP 8213 EP 8224 DI 10.1021/acs.langmuir.7b01714 WC Chemistry, Multidisciplinary; Chemistry, Physical; Materials Science, Multidisciplinary SC Chemistry; Materials Science UT WOS:000408520500013 DA 2022-12-14 ER PT J AU Musio, B Todisco, S Antonicelli, M Garino, C Arlorio, M Mastrorilli, P Latronico, M Gallo, V AF Musio, Biagia Todisco, Stefano Antonicelli, Marica Garino, Cristiano Arlorio, Marco Mastrorilli, Piero Latronico, Mario Gallo, Vito TI Non-Targeted NMR Method to Assess the Authenticity of Saffron and Trace the Agronomic Practices Applied for Its Production SO APPLIED SCIENCES-BASEL DT Article DE food counterfeit; traceability; quality control; spectroscopy; organic farming; conventional farming; metabolic composition; fingerprint; chemometrics; biomarker ID CROCUS-SATIVUS L.; QUALITY-CONTROL; PLANT ADULTERANTS; SPECTROSCOPY; PICROCROCIN; QUANTIFICATION; IDENTIFICATION; METABOLOMICS; COMPONENT; CROCETIN AB Featured Application The analytical approach described in the present paper could find applications in the omics analytical tools devoted to food traceability, quality control, and authenticity assessment by reliable methods. The development of analytical methods aimed at tracing agri-food products and assessing their authenticity is essential to protect food commercial value and human health. An NMR-based non-targeted method is applied here to establish the authenticity of saffron samples. Specifically, 40 authentic saffron samples were compared with 18 samples intentionally adulterated by using turmeric and safflower at three different concentration levels, i.e., 5, 10, and 20 wt%. Statistical processing of NMR data furnished useful information about the main biomarkers contained in aqueous and dimethyl sulfoxide extracts, which are indicative of the presence of adulterants within the analyzed matrix. Furthermore, a discrimination model was developed capable of revealing the type of agronomic practice adopted during the production of this precious spice, distinguishing between organic and conventional cultivation. The main objective of this work was to provide the scientific community involved in the quality control of agri-food products with an analytical methodology able to extract useful information quickly and reliably for traceability and authenticity purposes. The proposed methodology turned out to be sensitive to minor variations in the metabolic composition of saffron that occur in the presence of the two adulterants studied. Both adulterants can be detected in aqueous extracts at a concentration of 5 wt%. A lower limit of detection was observed for safflower contained in organic extracts in which case the lowest detectable concentration was 20%. C1 [Musio, Biagia; Todisco, Stefano; Antonicelli, Marica; Mastrorilli, Piero; Latronico, Mario; Gallo, Vito] Politecn Bari, DICATECh, Dept Civil Environm Land Bldg & Chem Engn, Via Edoardo Orabona 4, I-70125 Bari, Italy. [Garino, Cristiano; Arlorio, Marco] Univ Piemonte Orientate, Dept Pharmaceut Sci, Food Chem Biotechnol & Nutr Unit, I-28100 Novara, Italy. [Mastrorilli, Piero; Latronico, Mario; Gallo, Vito] Innovat Solut Srl, Zona H 150-B, I-70015 Noci, Italy. C3 Politecnico di Bari; University of Eastern Piedmont Amedeo Avogadro RP Musio, B; Gallo, V (corresponding author), Politecn Bari, DICATECh, Dept Civil Environm Land Bldg & Chem Engn, Via Edoardo Orabona 4, I-70125 Bari, Italy.; Gallo, V (corresponding author), Innovat Solut Srl, Zona H 150-B, I-70015 Noci, Italy. EM biagia.musio@poliba.it; stefano.todisco@poliba.it; marica.antonicelli@poliba.it; cristiano.garino@bfr.bund.de; marco.arlorio@uniupo.it; piero.mastrorilli@poliba.it; mario.latronico@poliba.it; vito.gallo@poliba.it CR Aiello D, 2018, RSC ADV, V8, P36104, DOI 10.1039/c8ra07484d Ballin NZ, 2019, TRENDS FOOD SCI TECH, V86, P537, DOI 10.1016/j.tifs.2018.09.025 Bathaie SZ, 2014, BIOTECH HISTOCHEM, V89, P401, DOI 10.3109/10520295.2014.890741 Belmonte-Sanchez E, 2021, J SCI FOOD AGR, V101, P3541, DOI 10.1002/jsfa.11051 Bijlsma S, 2006, ANAL CHEM, V78, P567, DOI 10.1021/ac051495j Cagliani LR, 2015, FOOD CONTROL, V50, P342, DOI 10.1016/j.foodcont.2014.09.017 Carmona M, 2006, J AGR FOOD CHEM, V54, P973, DOI 10.1021/jf052297w Chrysanthou A, 2016, J FOOD SCI, V81, pS189, DOI 10.1111/1750-3841.13152 Consonni R, 2019, MAGN RESON CHEM, V57, P558, DOI 10.1002/mrc.4807 Cusano E, 2018, PHYTOCHEM ANALYSIS, V29, P476, DOI 10.1002/pca.2753 Dai HC, 2020, FOOD ANAL METHOD, V13, P2128, DOI 10.1007/s12161-020-01828-x Dowlatabadi R, 2017, METABOLOMICS, V13, DOI 10.1007/s11306-016-1155-x Er SV, 2017, FOOD ANAL METHOD, V10, P1547, DOI 10.1007/s12161-016-0710-4 FAO, 2021, FOOD FRAUD INT DET M, P44 Gad HA, 2017, FOOD CHEM, V237, P857, DOI 10.1016/j.foodchem.2017.06.022 Gallo V, 2020, FOOD ANAL METHOD, V13, P530, DOI 10.1007/s12161-019-01664-8 Gallo V, 2015, ANAL CHEM, V87, P6709, DOI 10.1021/acs.analchem.5b00919 Gao BY, 2019, J AGR FOOD CHEM, V67, P8425, DOI 10.1021/acs.jafc.9b03085 Giuliani A., 2016, ENCY FOOD HLTH, P273 Hagh-Nazari S, 2007, ACTA HORTIC, P411, DOI 10.17660/ActaHortic.2007.739.54 Hatzakis E, 2019, COMPR REV FOOD SCI F, V18, P189, DOI 10.1111/1541-4337.12408 Heidarbeigi K, 2015, INT J FOOD PROP, V18, P1391, DOI 10.1080/10942912.2014.915850 Kassambara A., 2019, PRACTICAL STAT R 2 C Kim S.B., 2020, UNTARGETED CAPABILIT, DOI [10.26434/CHEMRXIV.13173698.V1, DOI 10.26434/CHEMRXIV.13173698.V1] Kuballa T, 2018, CURR OPIN FOOD SCI, V19, P57, DOI 10.1016/j.cofs.2018.01.007 Kumar R, 2009, FOOD REV INT, V25, P44, DOI 10.1080/87559120802458503 Kumari L, 2021, TRENDS FOOD SCI TECH, V111, P301, DOI 10.1016/j.tifs.2021.02.061 Marieschi M, 2012, J AGR FOOD CHEM, V60, P10998, DOI 10.1021/jf303106r McGrath TF, 2018, TRENDS FOOD SCI TECH, V76, P38, DOI 10.1016/j.tifs.2018.04.001 Medina S, 2019, FOOD CHEM, V278, P144, DOI 10.1016/j.foodchem.2018.11.046 Mevik BH, 2007, J STAT SOFTW, V18, P1, DOI 10.18637/jss.v018.i02 Musio B, 2020, TALANTA, V214, DOI 10.1016/j.talanta.2020.120855 Ordoudi SA, 2017, FOOD CONTROL, V81, P147, DOI 10.1016/j.foodcont.2017.05.046 Ordoudi SA, 2015, FOOD RES INT, V70, P1, DOI 10.1016/j.foodres.2015.01.021 Petrakis EA, 2017, TALANTA, V162, P558, DOI 10.1016/j.talanta.2016.10.072 Petrakis EA, 2017, FOOD CHEM, V217, P418, DOI 10.1016/j.foodchem.2016.08.078 Petrakis EA, 2015, FOOD CHEM, V173, P890, DOI 10.1016/j.foodchem.2014.10.107 Praveen A, 2021, FOOD CHEM, V341, DOI 10.1016/j.foodchem.2020.128646 Ragone R, 2020, FOOD CHEM, V332, DOI 10.1016/j.foodchem.2020.127339 Righi V, 2015, J AGR FOOD CHEM, V63, P8439, DOI 10.1021/acs.jafc.5b03284 Sabatino L, 2011, NAT PROD COMMUN, V6, P1873 Sanchez AM, 2011, J AGR FOOD CHEM, V59, P249, DOI 10.1021/jf102828v Schuhmacher S., 2016, 19 WORLD C ONNON DES, P1, DOI DOI 10.1255/MRFS.3 Sobolev AP, 2019, TRENDS FOOD SCI TECH, V91, P347, DOI 10.1016/j.tifs.2019.07.035 Sobolev AP, 2014, FOODS, V3, P403, DOI 10.3390/foods3030403 Sobolev AP, 2015, MOLECULES, V20, P4088, DOI 10.3390/molecules20034088 Vignolini P, 2008, NAT PROD COMMUN, V3, DOI [10.1177/1934578X0800301203, DOI 10.1177/1934578X0800301203] Whang WK, 2007, B KOREAN CHEM SOC, V28, P557 Xu J., 2020, ARTIF INTELL AGR, V4, P153, DOI [10.1016/j.aiia.2020.08.002, DOI 10.1016/J.AIIA.2020.08.002] Yilmaz A, 2010, METABOLOMICS, V6, P511, DOI 10.1007/s11306-010-0221-z NR 50 TC 1 Z9 1 U1 4 U2 4 PD MAR PY 2022 VL 12 IS 5 AR 2583 DI 10.3390/app12052583 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000776081900001 DA 2022-12-14 ER PT J AU Catalano, V Moreno-Sanz, P Lorenzi, S Grando, MS AF Catalano, Valentina Moreno-Sanz, Paula Lorenzi, Silvia Grando, Maria Stella TI Experimental Review of DNA-Based Methods for Wine Traceability and Development of a Single-Nucleotide Polymorphism (SNP) Genotyping Assay for Quantitative Varietal Authentication SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE wine; grapevine; genetic traceability; single-nucleotide polymorphisms; DNA extraction; quantitative PCR ID CHLOROPLAST MICROSATELLITE MARKERS; VINIFERA L. DNA; VITIS-VINIFERA; PROTEINASE-K; GRAPE; IDENTIFICATION; MUSTS; PCR; QUANTIFICATION; EXTRACTION AB The genetic varietal authentication of wine was investigated according to DNA isolation procedures reported for enological matrices and also, by testing 11 commercial extraction kits and various protocol modifications. Samples were collected at different stages of the winemaking process of renowned Italian wines Brunello di Montalcino, Lambruschi Modenesi, and Trento DOC. Results demonstrated not only that grape DNA loss is produced by the fermentation process but also that clarification and stabilization operations contribute to the reduction of double-stranded DNA content on wine. Despite the presence of inhibitors, downstream PCIUgenotyping yielded reliable nuclear and chloroplast SSR markers for must samples, whereas no amplification or inconsistent results were obtained at later stages of the vinification. In addition, a TaqMan genotyping assay based on cultivar-specific single-nucleotide polymorphisms (SNPs) was designed, which allowed assessment of grapevine DNA mixtures. Once the wine matrix limitations are overcome, this sensitive tool may be implemented for the relative quantification of cultivars used for blend wines or frauds. C1 [Catalano, Valentina; Moreno-Sanz, Paula; Lorenzi, Silvia; Grando, Maria Stella] Fdn Edmund Mach, Res & Innovat Ctr, Via E Mach 1, I-38010 San Michele All Adige, Trento, Italy. C3 Fondazione Edmund Mach RP Grando, MS (corresponding author), Fdn Edmund Mach, Res & Innovat Ctr, Via E Mach 1, I-38010 San Michele All Adige, Trento, Italy. EM stella.grando@fmach.it CR Arapitsas P, 2012, J AGR FOOD CHEM, V60, P10461, DOI 10.1021/jf302617e Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Baxter MJ, 1997, FOOD CHEM, V60, P443, DOI 10.1016/S0308-8146(96)00365-2 Bigliazzi J, 2012, AM J ENOL VITICULT, V63, P568, DOI 10.5344/ajev.2012.12014 Boccacci P, 2012, EUR FOOD RES TECHNOL, V235, P439, DOI 10.1007/s00217-012-1770-3 Bowers JE, 1999, AM J ENOL VITICULT, V50, P243 Bowers JE, 1996, GENOME, V39, P628, DOI 10.1139/g96-080 Chung SM, 2003, THEOR APPL GENET, V107, P757, DOI 10.1007/s00122-003-1311-3 Drabek J, 2008, EUR FOOD RES TECHNOL, V226, P491, DOI 10.1007/s00217-007-0561-8 Emanuelli F, 2013, BMC PLANT BIOL, V13, DOI 10.1186/1471-2229-13-39 Faria MA, 2000, J AGR FOOD CHEM, V48, P1096, DOI 10.1021/jf990837h Faria MA, 2008, EUR FOOD RES TECHNOL, V227, P845, DOI 10.1007/s00217-007-0795-5 Garcia-Beneytez E, 2002, J AGR FOOD CHEM, V50, P6090, DOI 10.1021/jf0202077 Grassi F, 2002, VITIS, V41, P157 Hall T, 2011, GERF B BIOSCI, V2, P60, DOI [10.1017/S0317167100012865, DOI 10.1002/PROT.24632] Hall T.A., 1999, NUCL ACIDS S SER, V41, P95, DOI DOI 10.1021/BK-1999-0734.CH008 Harta M., 2011, Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Horticulture, V68, P143 HILZ H, 1975, EUR J BIOCHEM, V56, P103, DOI 10.1111/j.1432-1033.1975.tb02211.x Kacaniova M, 2012, J ENVIRON SCI HEAL B, V47, P571, DOI 10.1080/03601234.2012.665750 Le Bourse D, 2010, ANAL CHIM ACTA, V667, P33, DOI 10.1016/j.aca.2010.03.062 Leon C, 2011, J CHROMATOGR A, V1218, P7550, DOI 10.1016/j.chroma.2011.01.052 Lodhi M. A., 1994, Plant Molecular Biology Reporter, V12, P6, DOI 10.1007/BF02668658 Marques AP, 2010, INT J FOOD MICROBIOL, V142, P251, DOI 10.1016/j.ijfoodmicro.2010.06.006 Maul E, 2015, VITIS, V54, P5 Moreno-Arribas MV, 1999, J AGR FOOD CHEM, V47, P114, DOI 10.1021/jf980483e Mulligan CJ, 2005, METHOD ENZYMOL, V395, P87, DOI 10.1016/S0076-6879(05)95007-6 Nakamura S, 2007, J AGR FOOD CHEM, V55, P10388, DOI 10.1021/jf072407u Ogrinc N, 2003, ANAL BIOANAL CHEM, V376, P424, DOI 10.1007/s00216-003-1804-6 Papotti G, 2013, J AGR FOOD CHEM, V61, P1741, DOI 10.1021/jf302728b Pereira L, 2012, FOOD ANAL METHOD, V5, P1252, DOI 10.1007/s12161-012-9369-7 Pereira L, 2011, AM J ENOL VITICULT, V62, P547, DOI 10.5344/ajev.2011.10022 Powell R., 2002, OXFORD PRACT APPROAC, P1 Provan J, 1999, P ROY SOC B-BIOL SCI, V266, P633, DOI 10.1098/rspb.1999.0683 Rapeanu G., 2009, Innovative Romanian Food Biotechnology, V5, P1 Rodriguez-Plaza P, 2006, EUR FOOD RES TECHNOL, V223, P625, DOI 10.1007/s00217-005-0244-2 Savazzini F, 2006, ANAL CHIM ACTA, V563, P274, DOI 10.1016/j.aca.2005.10.078 Scali M., 2014, Advances in Bioscience and Biotechnology, V5, P142, DOI 10.4236/abb.2014.52018 Sefc KM, 1999, GENOME, V42, P367, DOI 10.1139/gen-42-3-367 Sefc KM, 2001, MOLECULAR BIOLOGY & BIOTECHNOLOGY OF THE GRAPEVINE, P433 Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Siret R, 2002, J AGR FOOD CHEM, V50, P3822, DOI 10.1021/jf011462e STEENKAMP J, 1994, AM J ENOL VITICULT, V45, P102 This P, 2004, THEOR APPL GENET, V109, P1448, DOI 10.1007/s00122-004-1760-3 THOMAS MR, 1993, THEOR APPL GENET, V86, P985, DOI 10.1007/BF00211051 TULLIS RH, 1980, ANAL BIOCHEM, V107, P260, DOI 10.1016/0003-2697(80)90519-9 Weising K, 1999, GENOME, V42, P9, DOI 10.1139/gen-42-1-9 Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 Zyprian E., 2005, DEV MICROSATELLITE D NR 48 TC 28 Z9 30 U1 0 U2 39 PD SEP 21 PY 2016 VL 64 IS 37 BP 6969 EP 6984 DI 10.1021/acs.jafc.6b02560 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000384037700008 DA 2022-12-14 ER PT J AU Khanna, A Jain, S Burgio, A Bolshev, V Panchenko, V AF Khanna, Abhirup Jain, Sapna Burgio, Alessandro Bolshev, Vadim Panchenko, Vladimir TI Blockchain-Enabled Supply Chain platform for Indian Dairy Industry: Safety and Traceability SO FOODS DT Article DE dairy products; food safety; traceability; supply chain management; blockchain ID FOOD; ADULTERATION AB Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters. C1 [Khanna, Abhirup] Univ Petr & Energy Studies, Sch Comp Sci, Dept Syst, Dehra Dun 248007, Uttarakhand, India. [Jain, Sapna] Univ Petr & Energy Studies, Dept Appl Sci & Humanities Chem, Dehra Dun 248007, Uttarakhand, India. [Bolshev, Vadim] Fed Sci Agroengn Ctr VIM, Moscow 109428, Russia. [Panchenko, Vladimir] Russian Univ Transport, Dept Theoret & Appl Mech, Moscow 127994, Russia. C3 University of Petroleum & Energy Studies (UPES); University of Petroleum & Energy Studies (UPES); Federal Scientific Agroengineering Center VIM; Russian University of Transport RP Jain, S (corresponding author), Univ Petr & Energy Studies, Dept Appl Sci & Humanities Chem, Dehra Dun 248007, Uttarakhand, India.; Bolshev, V (corresponding author), Fed Sci Agroengn Ctr VIM, Moscow 109428, Russia. EM sapnaj22@gmail.com; vadimbolshev@gmail.com CR [Anonymous], 1858, NEW YORK TIMES 13 MA Balamurugan S., 2022, International Journal of Information Technology, V14, P1087, DOI 10.1007/s41870-020-00581-y Biscotti A, 2020, 2020 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP), P440, DOI 10.1109/SMARTCOMP50058.2020.00091 Cannas VG, 2020, INT J PROD RES, V58, P7314, DOI 10.1080/00207543.2020.1809731 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Chauhan S.L., 2019, INT J CHEM STUD, V7, P2055, DOI 10.22271/chemi Choudhury T., 2021, BLOCKCHAIN APPL IOT Chugh Riya, 2022, IOP Conference Series: Materials Science and Engineering, V1225, DOI 10.1088/1757-899X/1225/1/012046 Ellis DI, 2012, CHEM SOC REV, V41, P5706, DOI 10.1039/c2cs35138b Erol I, 2021, ENERGY SUSTAIN DEV, V65, P130, DOI 10.1016/j.esd.2021.10.004 ethereum.org, HOME Fang C, 2022, PACKAG TECHNOL SCI, V35, P643, DOI 10.1002/pts.2579 Feng HH, 2022, COMPUT ELECTRON AGR, V193, DOI 10.1016/j.compag.2021.106642 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Hassan A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13179500 Hastig GM, 2020, PROD OPER MANAG, V29, P935, DOI 10.1111/poms.13147 Hussain M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132413646 Iftekhar A, 2021, FOODS, V10, DOI 10.3390/foods10102323 Iftekhar A, 2021, FOODS, V10, DOI 10.3390/foods10061289 Jabir E, 2022, INT J PROD RES, V60, P912, DOI 10.1080/00207543.2020.1846219 Jo J, 2022, J CLEAN PROD, V363, DOI 10.1016/j.jclepro.2022.132646 Khanna Abhirup, 2020, Information Systems. 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020. Proceedings. Lecture Notes in Business Information Processing (LNBIP 402), P99, DOI 10.1007/978-3-030-63396-7_7 Khanna A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132111840 Madhavan M., 2020, J XIAN U ARCHITECTUR, V12, P1610 Montgomery H, 2020, GLOB FOOD SECUR-AGR, V26, DOI 10.1016/j.gfs.2020.100447 Mor R.S., 2018, J OPERATIONS SUPPLY, V11, P14, DOI DOI 10.12660/JOSCMV11N1P14-25 Negi D., 2021, BLOCKCHAIN APPL IOT, P65, DOI [10.1007/978-3-030-65691-1_5, DOI 10.1007/978-3-030-65691-1_5] Nirmala DAR, 2022, MATER TODAY-PROC, V49, P3657, DOI 10.1016/j.matpr.2021.09.243 Niya S. Rafati, 2021, 2 INT C SOC AUTOMATI, P1 Nyokabi SN, 2021, FOOD CONTROL, V119, DOI 10.1016/j.foodcont.2020.107482 Peng XZ, 2022, AGRICULTURE-BASEL, V12, DOI 10.3390/agriculture12050689 Peng XZ, 2022, FOODS, V11, DOI 10.3390/foods11091269 Prakash S, 2017, BENCHMARKING, V24, P2, DOI 10.1108/BIJ-07-2015-0070 Raj PVRP, 2022, COMPUT IND ENG, V167, DOI 10.1016/j.cie.2022.108038 Raza SA, 2022, J ENTERP INF MANAG, V35, P617, DOI 10.1108/JEIM-08-2020-0322 scop, US Sharma YK, 2019, LECT NOTE NETW SYST, V40, P409, DOI 10.1007/978-981-13-0586-3_41 Shoaib M, 2020, IND MANAGE DATA SYST, V120, P2103, DOI 10.1108/IMDS-04-2020-0194 Siddh MM, 2015, J ADV MANAG RES, V12, P292, DOI 10.1108/JAMR-01-2015-0002 Swar S.O., 2021, CONTINENTAL VET J, V1, P1 Tan A., 2020, SUSTAINABLE FUTURES, V2, P100034 Varavallo G, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14063321 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Wang LX, 2022, FOODS, V11, DOI 10.3390/foods11050744 Xin H, 2008, SCIENCE, V322, P1310, DOI 10.1126/science.322.5906.1310 Yadav S, 2020, RESOUR CONSERV RECY, V152, DOI 10.1016/j.resconrec.2019.104505 NR 46 TC 0 Z9 0 U1 21 U2 21 PD SEP PY 2022 VL 11 IS 17 AR 2716 DI 10.3390/foods11172716 WC Food Science & Technology SC Food Science & Technology UT WOS:000851020800001 DA 2022-12-14 ER PT J AU Vietina, M Agrimonti, C Marmiroli, M Bonas, U Marmiroli, N AF Vietina, Michelangelo Agrimonti, Caterina Marmiroli, Marta Bonas, Urbana Marmiroli, Nelson TI Applicability of SSR markers to the traceability of monovarietal olive oils SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE olive oil; food genomics; SSR markers; shortened SSR; DNA extraction ID OLEA-EUROPAEA L.; MICROSATELLITE MARKERS; DNA EXTRACTION; IDENTIFICATION; IDENTITY; ALLELES AB BACKGROUND: To protect the features and authenticity of food products, the European Commission enforces two certification labels: Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI). EEC Regulation No. 510/2006 imposes criteria for labelling, production and commercialisation of olive oil. Since plant genotype is a major determinant in establishing the PDO and PGI labels, methods to ascertain the varieties present in a batch of olive oil are essential in validating product conformity. The traceability of olive oil can be assessed through simple sequence repeat (SSR) co-dominant markers targeted to specific regions of DNA from olive cultivars. RESULTS: Twenty-one monovarietal olive oils were analysed with nine nuclear and two shortened SSRs. For each marker the correspondence of allelic profile with the reference cultivar, the reproducibility of profiles in different DNA extractions and the polymorphism information content were determined. CONCLUSION: The results showed that using a panel of SSR markers such as those described in this paper allows one to make a reliable attribution of an olive oil to a specific cultivar. (C) 2011 Society of Chemical Industry C1 [Vietina, Michelangelo; Agrimonti, Caterina; Marmiroli, Marta; Bonas, Urbana; Marmiroli, Nelson] Univ Parma, Div Genet & Environm Biotechnol, Dept Environm Sci, I-43100 Parma, Italy. C3 University of Parma RP Marmiroli, N (corresponding author), Univ Parma, Div Genet & Environm Biotechnol, Dept Environm Sci, Via GP Usberti 11-A, I-43100 Parma, Italy. EM nelson.marmiroli@unipr.it CR Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Baldoni L, 2009, MOL BREEDING, V24, P213, DOI 10.1007/s11032-009-9285-8 Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Carriero F, 2002, THEOR APPL GENET, V104, P301, DOI 10.1007/s001220100691 Cipriani G, 2002, THEOR APPL GENET, V104, P223, DOI 10.1007/s001220100685 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 De La Rosa R, 2002, MOL ECOL NOTES, V2, P265, DOI 10.1046/j.1471-8286.2002.00217.x Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a Doyle J. J., 1997, PHYTOCHEMISTRY B, V19, P11, DOI DOI 10.2307/4119796 Fito M, 2008, EUR J CLIN NUTR, V62, P570, DOI 10.1038/sj.ejcn.1602724 Lopes MS, 2004, HORTSCIENCE, V39, P1562, DOI 10.21273/HORTSCI.39.7.1562 Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z MURRAY V, 1993, NUCLEIC ACIDS RES, V21, P2395, DOI 10.1093/nar/21.10.2395 Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Muzzalupo I, 2009, SCI AGR, V66, P685, DOI 10.1590/S0103-90162009000500014 Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Perez-Jimenez F, 2005, EUR J CLIN INVEST, V35, P421, DOI 10.1111/j.1365-2362.2005.01516.x Sefc KM, 2000, MOL ECOL, V9, P1171, DOI 10.1046/j.1365-294x.2000.00954.x Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Waits LP, 2001, MOL ECOL, V10, P249, DOI 10.1046/j.1365-294X.2001.01185.x Walsh PS, 1996, NUCLEIC ACIDS RES, V24, P2807, DOI 10.1093/nar/24.14.2807 NR 27 TC 32 Z9 33 U1 0 U2 18 PD JUN PY 2011 VL 91 IS 8 BP 1381 EP 1391 DI 10.1002/jsfa.4317 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000290860500005 DA 2022-12-14 ER PT J AU Baralla, G Pinna, A Tonelli, R Marchesi, M Ibba, S AF Baralla, Gavina Pinna, Andrea Tonelli, Roberto Marchesi, Michele Ibba, Simona TI Ensuring transparency and traceability of food local products: A blockchain application to a Smart Tourism Region SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE DT Article DE blockchain; IoT; smart contract; Smart Tourism; supply chain; traceability ID SUPPLY-CHAIN; CONTRACTS AB This article proposes a blockchain oriented platform to guarantee the origin and provenance of food items in a Smart Tourism Region context. Local food and beverage, in fact, can become a good combination to attract tourist and to promote the area provided that their provenance is clearly certified. We designed and developed a blockchain-based system to manage an agri-food supply chain for tracking food items. By using smart contracts the platform guarantees transparency, efficiency and trustworthiness. Our system is particularly suitable to manage cold chain since the system interfaces with IoT network devices providing detailed information about data monitoring food such as storage temperature, environment humidity, and GPS data. All involved actors can share data and information in a more efficient, transparent, and tamper proof way than traditional systems. The final consumer can access with transparency to all the agri-food chain of the purchased product and verify provenance by retrieving all detailed information registered in the blockchain public ledger. The proposed system has been designed according to the ABCDE method, an agile development process recently conceived, to obtain a higher software quality to design a general blockchain system by means software engineering practices. A real case study applied to local products from Sardinia, Italy, is proposed at the end of the article. C1 [Baralla, Gavina; Pinna, Andrea; Tonelli, Roberto; Marchesi, Michele; Ibba, Simona] Univ Cagliari, Dept Math & Comp Sci, Cagliari, Italy. C3 University of Cagliari RP Baralla, G (corresponding author), Univ Cagliari, Dept Math & Comp Sci, Cagliari, Italy. EM gavina.baralla@diee.unica.it CR Ali J, 2019, INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS (COINS), P152, DOI 10.1145/3312614.3312646 [Anonymous], 2018, EUROPEAN COMMISSION Bahga A., 2016, J SOFTWARE ENG APPL, P533, DOI [10.4236/jsea.2016.910036, DOI 10.4236/JSEA.2016.910036] Baralla G, 2019, P 2019 IEEE ACM 2 IN Baralla G, 2019, LECT NOTES COMPUT SC, V11339, P379, DOI 10.1007/978-3-030-10549-5_30 Bateman A. H., 2015, SUPPLY CHAIN MANAGEM, V9, P8 Biswas K, 2016, PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS), P1392, DOI [10.1109/HPCC-SmartCity-DSS.2016.178, 10.1109/HPCC-SmartCity-DSS.2016.0198] Buhalis D., 2015, INFORM COMMUNICATION, P377 Buterin V, 2019, PROOF OF STAKE FAQ Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Christidis K, 2016, IEEE ACCESS, V4, P2292, DOI 10.1109/ACCESS.2016.2566339 Conoscenti M, 2017, PROC IEEE ACM INT C, P288, DOI 10.1109/ICSE-C.2017.60 Dabbagh M, 2019, P INT C BLOCKCH APPL, P27 Dorri A, 2016, BLOCKCHAIN INTERNET Dorri A, 2017, INT CONF PERVAS COMP Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gervais L, 2016, REV FR ETUD AMER, P3, DOI 10.3917/rfea.148.0003 Gretzel U, 2015, ELECTRON MARK, V25, P179, DOI 10.1007/s12525-015-0196-8 Hammi MT, 2018, COMPUT SECUR, V78, P126, DOI 10.1016/j.cose.2018.06.004 Hang L, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20041207 Huh S, 2017, INT CONF ADV COMMUN, P464, DOI 10.23919/ICACT.2017.7890132 Ibba S., 2017, P XP2017 SCI WORKSH, P1, DOI DOI 10.1145/3120459.3120472 Jeppsson A., 2017, BLOCKCHAINS SOLUTION Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Kim HM, 2018, INTELL SYST ACCOUNT, V25, P18, DOI 10.1002/isaf.1424 Li B, 2018, IEEE TRUST BIG, P1164, DOI 10.1109/TrustCom/BigDataSE.2018.00161 Lopez de Avila, 2015, ENTER2015 C INF COMM, P4 Malik S, 2018, 2018 IEEE 17TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA) Marchesi M, 2018, CEE-SECR'18: PROCEEDINGS OF THE 14TH CENTRAL AND EASTERN EUROPEAN SOFTWARE ENGINEERING CONFERENCE RUSSIA, DOI 10.1145/3290621.3290627 McConaghy T, 2016, CISC VIS NETW IND GL Pinna A, 2019, IEEE ACCESS, V7, P78194, DOI 10.1109/ACCESS.2019.2921936 Porru S, 2017, PROC IEEE ACM INT C, P169, DOI 10.1109/ICSE-C.2017.142 Regione Sardegna, EL NAZ PROD AGR TRAD Sun JJ, 2016, FINANC INNOV, V2, DOI 10.1186/s40854-016-0040-y Swan M., 2015, BLOCKCHAIN BLUEPRINT Tapscott Don, 2016, BLOCKCHAIN REVOLUTIO Tian F, 2017, I C SERV SYST SERV M Toyoda K, 2017, IEEE ACCESS, V5, P17465, DOI 10.1109/ACCESS.2017.2720760 Wang YK, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P1, DOI 10.1109/AUS.2016.7748011 NR 40 TC 31 Z9 31 U1 14 U2 100 PD JAN 10 PY 2021 VL 33 IS 1 SI SI AR e5857 DI 10.1002/cpe.5857 EA JUN 2020 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science UT WOS:000540210900001 DA 2022-12-14 ER PT J AU Prenesti, E Fisicaro, P Berto, S Ferrara, E Daniele, PG AF Prenesti, Enrico Fisicaro, Paola Berto, Silvia Ferrara, Enzo Daniele, Pier Giuseppe TI Monitoring the traceability of the pH of a primary tetraborate buffer: comparison of results from primary and secondary apparatus SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article; Proceedings Paper CT 10th International Symposium on Biological and Environmental Reference Materials 9BERM 10) CY APR 30-MAY 04, 2006 CL Charleston, SC DE tetraborate buffer; primary pH measurement; glass electrode; ionic strength; traceability of pH ID GLASS ELECTRODES; CALIBRATION; STANDARDS; CONSTANTS; SCALES AB This paper reports evaluation of the behaviour of different combined glass electrodes applied to measurement of the pH of a primary, 0.01 mol kg(-1), tetraborate buffer. Measurements were first performed by use of a primary Harned cell (at 15, 25, and 37 degrees C); these results were then compared with those obtained for the same solution by use of three combined glass electrodes (25 degrees C) with different membranes and liquid-junction designs, calibrated by use of commercial pH-metric buffers. The pH of the same solution was also measured in terms of the molal concentration of hydrogen ions, using acid-base titration to evaluate the formal potential difference K of each cell at fixed ionic strength, I, adjusted by addition of KCl or Et4NI (tetraethylammonium iodide). The reference value from primary measurement, paH=9.171, was slightly closer to the mean value obtained by determination of concentration, rather than that obtained by direct measurement of activity; the differences were smaller than the extended uncertainty characteristics of the secondary measurements. The importance of evaluation of the ionic strength of the solution under study is emphasised. We verified that for tetraborate buffer slight modification of the value of I used to calculate gamma (i) (the activity coefficient of a single ion) in the calculation of paH from the acidity function at zero molality of chloride can significantly affect the reference value of the calibrator tool. This is true, in general, for low values of the ionic strength, such as those considered in this work; an approximate value of I can then cause distortions along the pH traceability chain. Application of the concepts of thermodynamics to this traceability chain is discussed. C1 Univ Turin, Dipartimento Chim Analit, I-10125 Turin, Italy. Ist Naz Ric Metrol, I-10135 Turin, Italy. C3 University of Turin; Istituto Nazionale di Ricerca Metrologica (INRIM) RP Prenesti, E (corresponding author), Univ Turin, Dipartimento Chim Analit, Via Pietro Giuria 5, I-10125 Turin, Italy. EM enrico.prenesti@unito.it CR Bates R., 1960, PURE APPL CHEM, V1, P163, DOI [10.1351/pac196001010163, DOI 10.1351/PAC196001010163, 10.1351/PAC196001010163] BECK WH, 1968, ANAL CHEM, V40, P501, DOI 10.1021/ac60259a032 BRAIBANTI A, 1987, PURE APPL CHEM, V59, P1721, DOI 10.1351/pac198759121721 Brandariz I, 2004, MONATSH CHEM, V135, P1475, DOI 10.1007/s00706-004-0239-x Brown RJC, 2003, ACCREDIT QUAL ASSUR, V8, P505, DOI 10.1007/s00769-003-0689-6 Buck RP, 2002, PURE APPL CHEM, V74, P2169, DOI 10.1351/pac200274112169 CSEFALVAYOVA B, 1989, CHEM LISTY, V83, P550 DAVIES CW, 1992, ION ASS DESTEFANO C, 1987, ANN CHIM-ROME, V77, P643 Fisicaro P, 2005, ANAL BIOANAL CHEM, V383, P341, DOI 10.1007/s00216-005-3417-8 FISICARO P, 2006, IN PRESS P ROYALI SO Kadis R, 2002, ANAL BIOANAL CHEM, V374, P817, DOI 10.1007/s00216-002-1457-x Martell AE, 2001, NIST DATABASE Meinrath G, 2002, ANAL BIOANAL CHEM, V374, P796, DOI 10.1007/s00216-002-1547-9 NANCOLLAS GH, 1982, PURE APPL CHEM, V54, P2675, DOI 10.1351/pac198254122675 Naumann R, 2002, ANAL BIOANAL CHEM, V374, P778, DOI 10.1007/s00216-002-1506-5 Spitzer P, 2002, ANAL BIOANAL CHEM, V374, P765, DOI 10.1007/s00216-002-1585-3 Spitzer P, 2001, ACCREDIT QUAL ASSUR, V6, P55, DOI 10.1007/PL00010440 SPITZER P, 2006, CCQM P82 PILOT STUDY NR 19 TC 1 Z9 1 U1 0 U2 8 PD APR PY 2007 VL 387 IS 7 BP 2595 EP 2600 DI 10.1007/s00216-007-1117-2 WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000244748500031 DA 2022-12-14 ER PT J AU Poltronieri, P D'Urso, OF Blaiotta, G Morea, M AF Poltronieri, Palmiro D'Urso, Oscar Fernando Blaiotta, Giuseppe Morea, Maria TI DNA Arrays and Membrane Hybridization Methods for Screening of Six Lactobacillus Species Common in Food Products SO FOOD ANALYTICAL METHODS DT Article DE Lactobacillus Species; Dairy Products; Traceability; Elongation Factor Tu (tuf); PCR Amplification; Fluorescent Probes; DNA Hybridization ID COMPLEX MICROBIAL COMMUNITIES; LACTOBACILLUS COMMUNITY; GENE-SEQUENCES; IDENTIFICATION; BACTERIA; ACID; DIVERSITY; TARGET; LENGTH AB Dot blot and deoxyribonucleic acid (DNA) array hybridization assays for the traceability of Lactobacillus species in food have been developed to monitor and validate typical food products. A primer set was designed to amplify the 540-bp region located at +157 of the tuf (Elongation factor Tu) gene of the Lactobacillus genus. An oligonucleotide array, containing 73 Lactobacillus species-specific tuf sequences representing 21 species, was developed and tested for identifying L. paracasei, L. rhamnosus, L. plantarum, and L. buchneri. We also tested a rapid screening method for monitoring the species present in airy samples. Dot blot hybridization identified polymerase chain reaction amplicons immobilized on nylon membranes, using six tuf-based cyanine-3-labeled 18-mer oligonucleotides, specific for L. paracasei, L. zeae, L. fermentum, L. plantarum, L. rhamnosus, and L. buchneri. This method discriminates between multiple species of Lactobacilli isolated directly from cheese samples, simultaneously. The tuf gene sequences, verified here with the DNA array method and used in dot blot hybridization, were shown to be a reliable tool for the simultaneous detection and differentiation of four Lactobacillus species. The hybridization techniques developed in this study may be useful in food processing and the analysis of food origin traceability. C1 [Morea, Maria] ISPA, CNR, I-73100 Lecce, Italy. [Poltronieri, Palmiro; Morea, Maria] ISPA, CNR, Inst Sci Food Prod, Natl Res Council, Bari, Italy. [D'Urso, Oscar Fernando] Biotecgen Srl, Ecotekne, Lecce, Italy. [Blaiotta, Giuseppe] Univ Napoli Federico II, Dept Food Sci, Portici, Italy. C3 Consiglio Nazionale delle Ricerche (CNR); Istituto Scienze delle Produzioni Alimentari (ISPA-CNR); Consiglio Nazionale delle Ricerche (CNR); Istituto Scienze delle Produzioni Alimentari (ISPA-CNR); University of Naples Federico II RP Poltronieri, P (corresponding author), ISPA, CNR, Via Monteroni Km 7, I-73100 Lecce, Italy. EM palmiro.poltronieri@ispa.cnr.it CR Baruzzi F, 2005, J RAPID METH AUT MIC, V13, P177, DOI 10.1111/j.1745-4581.2005.00018.x Baruzzi F, 2000, J APPL MICROBIOL, V89, P807, DOI 10.1046/j.1365-2672.2000.01183.x Blaiotta G, 2008, APPL ENVIRON MICROB, V74, P208, DOI 10.1128/AEM.01711-07 Burton JP, 2003, LETT APPL MICROBIOL, V36, P145, DOI 10.1046/j.1472-765X.2003.01281.x Cappello M. S., 2001, Mededelingen Faculteit Landbouwkundige en Toegepaste Biologische Wetenschappen Universiteit Gent, V66, P569 Chavagnat F, 2002, FEMS MICROBIOL LETT, V217, P177, DOI 10.1111/j.1574-6968.2002.tb11472.x Cole JR, 2005, NUCLEIC ACIDS RES, V33, pD294, DOI 10.1093/nar/gki038 Corsetti A, 2001, INT J FOOD MICROBIOL, V64, P95, DOI 10.1016/S0168-1605(00)00447-5 Dellaglio F, 2005, PROBIOTICS AND PREBIOTICS: SCIENTIFIC ASPECTS, P25 Dobson CM, 2004, CAN J MICROBIOL, V50, P482, DOI [10.1139/w04-044, 10.1139/W04-044] Druzhinina IS, 2005, FUNGAL GENET BIOL, V42, P813, DOI 10.1016/j.fgb.2005.06.007 Ercolini D, 2004, J APPL MICROBIOL, V96, P263, DOI 10.1046/j.1365-2672.2003.02146.x Ercolini D, 2001, SYST APPL MICROBIOL, V24, P610, DOI 10.1078/0723-2020-00076 Hall TA., 1999, NUCL ACIDS S SER, V41, P95, DOI DOI 10.1021/BK-1999-0734.CH008 Hill JE, 2004, GENOME RES, V14, P1669, DOI 10.1101/gr.2649204 HORFAT J, 2004, J APPL MICROBIOL, V96, P221 Letowski J, 2004, J MICROBIOL METH, V57, P269, DOI 10.1016/j.mimet.2004.02.002 Liu WT, 2007, APPL ENVIRON MICROB, V73, P73, DOI 10.1128/AEM.01468-06 Morea M, 1998, INT J FOOD MICROBIOL, V43, P53, DOI 10.1016/S0168-1605(98)00096-8 Peplies J, 2003, APPL ENVIRON MICROB, V69, P1397, DOI 10.1128/AEM.69.3.1397-1407.2003 Picard FJ, 2004, J CLIN MICROBIOL, V42, P3686, DOI 10.1128/jcm.42.8.3686-3695.2004 Rantsiou K, 2004, FOOD MICROBIOL, V21, P481, DOI 10.1016/j.fm.2003.10.002 Reyes-Lopez M, 2003, NUCLEIC ACIDS RES, V31, P779, DOI 10.1093/nar/gkg132 Rudi K, 2005, BIOTECHNIQUES, V39, P116, DOI 10.2144/05391GT02 Rudi K, 2002, APPL ENVIRON MICROB, V68, P1146, DOI 10.1128/AEM.68.3.1146-1156.2002 Tannock GW, 1999, APPL ENVIRON MICROB, V65, P4264 Ventura M, 2003, APPL ENVIRON MICROB, V69, P6908, DOI 10.1128/AEM.69.11.6908-6922.2003 Wang LT, 2007, INT J SYST EVOL MICR, V57, P1846, DOI 10.1099/ijs.0.64685-0 Wang RF, 2004, MOL CELL PROBE, V18, P223, DOI 10.1016/j.mcp.2004.03.002 Yao G, 2004, ANAL BIOCHEM, V331, P216, DOI 10.1016/j.ab.2003.12.005 NR 30 TC 9 Z9 9 U1 0 U2 8 PD SEP PY 2008 VL 1 IS 3 BP 171 EP 180 DI 10.1007/s12161-008-9015-6 WC Food Science & Technology SC Food Science & Technology UT WOS:000262755100003 DA 2022-12-14 ER PT J AU Zhang, GF Chen, X Feng, B Guo, XC Hao, X Ren, HG Dong, CY Zhang, YA AF Zhang, Guofeng Chen, Xiao Feng, Bin Guo, Xuchao Hao, Xia Ren, Henggang Dong, Chunyan Zhang, Yanan TI BCST-APTS: Blockchain and CP-ABE Empowered Data Supervision, Sharing, and Privacy Protection Scheme for Secure and Trusted Agricultural Product Traceability System SO SECURITY AND COMMUNICATION NETWORKS DT Article AB Blockchain provides new technologies and ideas for the construction of agricultural product traceability system (APTS). However, if data is stored, supervised, and distributed on a multiparty equal blockchain, it will face major security risks, such as data privacy leakage, unauthorized access, and trust issues. How to protect the privacy of shared data has become a key factor restricting the implementation of this technology. We propose a secure and trusted agricultural product traceability system (BCST-APTS), which is supported by blockchain and CP-ABE encryption technology. It can set access control policies through data attributes and encrypt data on the blockchain. This can not only ensure the confidentiality of the data stored in the blockchain, but also set flexible access control policies for the data. In addition, a whole-chain attribute management infrastructure has been constructed, which can provide personalized attribute encryption services. Furthermore, a reencryption scheme based on ciphertext-policy attribute encryption (RE-CP-ABE) is proposed, which can meet the needs of efficient supervision and sharing of ciphertext data. Finally, the system architecture of the BCST-APTS is designed to successfully solve the problems of mutual trust, privacy protection, fine-grained, and personalized access control between all parties. C1 [Zhang, Guofeng; Feng, Bin; Ren, Henggang; Dong, Chunyan] Taishan Univ, Sch Informat Sci & Technol, Tai An 271000, Shandong, Peoples R China. [Chen, Xiao] Taishan Univ, Sch Econ & Management, Tai An 271000, Shandong, Peoples R China. [Guo, Xuchao] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China. [Hao, Xia] Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271000, Shandong, Peoples R China. [Zhang, Yanan] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Shandong, Peoples R China. C3 Taishan University; Taishan University; China Agricultural University; Shandong Agricultural University; University of Jinan RP Zhang, GF; Feng, B (corresponding author), Taishan Univ, Sch Informat Sci & Technol, Tai An 271000, Shandong, Peoples R China. EM zhangguofeng@tsu.edu.cn; binfeng@tsu.edu.cn CR Alniamy A, 2020, 2020 2ND INTERNATIONAL CONFERENCE ON BLOCKCHAIN TECHNOLOGY (ICBCT 2020), P135, DOI 10.1145/3390566.3391688 Bethencourt J, 2007, P IEEE S SECUR PRIV, P321, DOI 10.1109/sp.2007.11 Boehlje M., 1995, Agribusiness (New York), V11, P493, DOI 10.1002/1520-6297(199511/12)11:6<493::AID-AGR2720110602>3.0.CO;2-G Cachon GP, 2000, MANAGE SCI, V46, P1032, DOI 10.1287/mnsc.46.8.1032.12029 Castiglione A, 2016, IEEE T INF FOREN SEC, V11, P850, DOI 10.1109/TIFS.2015.2512533 Chen YL, 2021, IEEE T INF FOREN SEC, V16, P5239, DOI 10.1109/TIFS.2021.3127023 Chen YL, 2022, INT J INTELL SYST, V37, P1204, DOI 10.1002/int.22666 Ding Q., 2019, J NETWORK INFORM SEC, V5, P1 [高举红 Gao Juhong], 2017, [系统工程学报, Journal of Systems Engineering], V32, P78 Gao WanLin, 2021, Smart Agriculture, V1, P8, DOI 10.12133/j.smartag.2019.1.1.201812-SA015 Goyal V., 2006, P 13 ACM C COMP COMM, P89 Graves S.C., 1998, OPER RES, V46, P35, DOI [DOI 10.1287/OPRE.46.3.S35, 10.1287/opre.46.3.s35] Hu VC, 2015, COMPUTER, V48, P85, DOI 10.1109/MC.2015.33 Huang S., 2019, COMPUTER SYSTEMS APP, V28, P79 Huang Z., 2018, CYBERSPACE SECURITY, V9, P25 Jemel M, 2017, INT CONF E BUS ENG, P177, DOI 10.1109/ICEBE.2017.35 Khan AA, 2021, IEEE ACCESS, V9, P103637, DOI 10.1109/ACCESS.2021.3099037 Li T, 2021, INT J INTELL SYST, DOI 10.1002/int.22656 Li T, 2021, INT J INTELL SYST, V36, P3596, DOI 10.1002/int.22428 [刘家稷 Liu Jiaji], 2018, [信息安全学报, Journal of Cyber Security], V3, P17 [闵新平 Min Xinping], 2018, [计算机学报, Chinese Journal of Computers], V41, P1005 Namasudra S, 2021, ARCH COMPUT METHOD E, V28, P1497, DOI 10.1007/s11831-020-09426-0 Sahai A, 2005, LECT NOTES COMPUT SC, V3494, P457, DOI 10.1007/11426639_27 Tian F, 2017, I C SERV SYST SERV M Tsai WT, 2016, PROCEEDINGS 2016 IEEE SYMPOSIUM ON SERVICE-ORIENTED SYSTEM ENGINEERING SOSE 2016, P450, DOI 10.1109/SOSE.2016.66 Uddin M, 2021, INT J PHARMACEUT, V597, DOI 10.1016/j.ijpharm.2021.120235 Wang P, 2020, P I CIVIL ENG-GEOTEC, V173, P254, DOI 10.1680/jgeen.19.00048 Wang Xiu-Li, 2019, Journal of Software, V30, P1661, DOI 10.13328/j.cnki.jos.005742 Yang Xinting, 2019, Transactions of the Chinese Society of Agricultural Engineering, V35, P323, DOI 10.11975/j.issn.1002-6819.2019.22.038 Yu L., 2017, NONGYE JIXIE XUEBAOT, V48, P387, DOI 10.6041/j.issn.1000-1298.2017.S0.059 Zhang Y, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9020285 Zhu Jian-Ming, 2019, Journal of Software, V30, P1594, DOI 10.13328/j.cnki.jos.005738 NR 32 TC 3 Z9 3 U1 11 U2 11 PD JAN 15 PY 2022 VL 2022 AR 2958963 DI 10.1155/2022/2958963 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000810728100003 DA 2022-12-14 ER PT J AU Hassoun, A Abdullah, NA Ait-Kaddour, A Ghellam, M Besir, A Zannou, O Onal, B Aadil, RM Lorenzo, JM Khaneghah, AM Regenstein, JM AF Hassoun, Abdo Abdullah, Nour Alhaj Ait-Kaddour, Abderrahmane Ghellam, Mohamed Besir, Aysegul Zannou, Oscar Onal, Begum Aadil, Rana Muhammad Lorenzo, Jose M. Khaneghah, Amin Mousavi Regenstein, Joe M. TI Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies SO CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION DT Review; Early Access DE Artificial intelligence; big data; Internet of things; blockchain; digital transformation; food fraud; Industry 4; 0; IoT; real-time surveillance; smart sensors ID BIG DATA; BLOCKCHAIN TECHNOLOGY; ELECTRONIC NOSE; SAFETY; INTERNET; QUALITY; THINGS; FUTURE; PRODUCTS; FRAUD AB Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible. C1 [Hassoun, Abdo; Abdullah, Nour Alhaj] Sustainable AgriFoodtech Innovat & Res SAFIR, Arras, France. [Hassoun, Abdo] Syrian Acad Expertise SAE, Gaziantep, Turkey. [Ait-Kaddour, Abderrahmane] Univ Clermont Auvergne, INRAE, UMRF, VetAgro Sup, Aurillac, France. [Ghellam, Mohamed; Besir, Aysegul; Zannou, Oscar] Ondokuz Mayis Univ, Fac Engn, Food Engn Dept, Samsun, Turkey. [Onal, Begum] Gourmet Int Ltd, Izmir, Turkey. [Aadil, Rana Muhammad] Univ Agr Faisalabad, Natl Inst Food Sci & Technol, Faisalabad, Pakistan. [Lorenzo, Jose M.] Ctr Tecnol Carne Galicia, Orense, Spain. [Khaneghah, Amin Mousavi] Prof Waclaw Dabrowski Inst Agr & Food Biotechnol, Dept Fruit & Vegetable Prod Technol, State Res Inst, Warsaw, Poland. [Regenstein, Joe M.] Cornell Univ, Dept Food Sci, Ithaca, NY 14853 USA. C3 INRAE; Universite Clermont Auvergne (UCA); VetAgro Sup; Ondokuz Mayis University; University of Agriculture Faisalabad; Waclaw Dabrowski Institute of Biotechnology of Agricultural & Food; Cornell University RP Hassoun, A (corresponding author), Sustainable AgriFoodtech Innovat & Res SAFIR, Arras, France.; Hassoun, A (corresponding author), Syrian Acad Expertise SAE, Gaziantep, Turkey. EM a.hassoun@saf-ir.com CR Addanki M., 2022, APPL FOOD RES, V2, P100126, DOI [10.1016/j.afres.2022.100126, DOI 10.1016/J.AFRES.2022.100126] Aditya Sinha, 2021, Artificial Intelligence in Agriculture, V5, P252, DOI 10.1016/j.aiia.2021.11.002 Al-Sarayreh M, 2020, FOOD CONTROL, V117, DOI 10.1016/j.foodcont.2020.107332 Alfian G, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107016 Ali MM, 2020, TRENDS FOOD SCI TECH, V99, P1, DOI 10.1016/j.tifs.2020.02.028 Altoukhov AV, 2020, IOP C SER EARTH ENV, V421, DOI 10.1088/1755-1315/421/4/042021 Amentae TK, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132112181 Aouadi B, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20195479 Ashfaq A, 2022, J AGR FOOD RES, V7, DOI 10.1016/j.jafr.2022.100270 Askew K., 2021, FOODNAVIGATOR Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Ayres LB, 2021, ANAL CHIM ACTA, V1161, DOI 10.1016/j.aca.2021.338403 Bai CG, 2020, INT J PROD ECON, V229, DOI 10.1016/j.ijpe.2020.107776 Bai HW, 2017, FOOD CONTROL, V79, P35, DOI 10.1016/j.foodcont.2017.02.040 Baralla G, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5857 Ben-Daya M., 2020, ROLE INTERNET THINGS, V28, P17, DOI [10.1080/10686967.2020.1838978, DOI 10.1080/10686967.2020.1838978] Ben-Daya M, 2019, INT J PROD RES, V57, P4719, DOI 10.1080/00207543.2017.1402140 Bhat SA, 2022, AGRICULTURE-BASEL, V12, DOI 10.3390/agriculture12010040 Bouzembrak Y, 2019, TRENDS FOOD SCI TECH, V94, P54, DOI 10.1016/j.tifs.2019.11.002 Brooks C, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108171 Chai JJK, 2022, TRENDS FOOD SCI TECH, V124, P182, DOI 10.1016/j.tifs.2022.04.021 Chausali N, 2022, J AGR FOOD RES, V7, DOI 10.1016/j.jafr.2021.100257 Cheng H, 2022, FOOD CHEM, V375, DOI 10.1016/j.foodchem.2021.131738 Choe JY, 2021, INT J CONTEMP HOSP M, V33, P1276, DOI 10.1108/IJCHM-08-2020-0839 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Cruz EF, 2020, ICSOFT: PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGIES, P501, DOI 10.5220/0009889705010508 Daikuzono CM, 2019, ANALYST, V144, P2827, DOI [10.1039/C8AN01934G, 10.1039/c8an01934g] de Oliveira AC, 2022, FOOD CONTROL, V131, DOI 10.1016/j.foodcont.2021.108414 Deng M., 2020, J COMPUTER COMMUNICA, V08, P17, DOI [10.4236/jcc.2020.89002, DOI 10.4236/JCC.2020.89002] Diallo TML, 2016, WOODHEAD PUBL FOOD S, V301, P263, DOI 10.1016/B978-0-08-100310-7.00014-4 Dutta P. K., 2021, AGR INFORM AUTOM USI, P67, DOI DOI 10.1002/9781119769231.CH4 Esteki M, 2019, COMPR REV FOOD SCI F, V18, P425, DOI 10.1111/1541-4337.12419 Ethuin P, 2015, FOOD CHEM, V176, P294, DOI 10.1016/j.foodchem.2014.12.065 Fanelli V, 2021, FOODS, V10, DOI 10.3390/foods10071644 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng L, 2021, FRONT NUTR, V8, DOI 10.3389/fnut.2021.680357 Fengou LC, 2021, FOODS, V10, DOI 10.3390/foods10040861 Firouz MS, 2021, FOOD RES INT, V141, DOI 10.1016/j.foodres.2021.110113 Francois G, 2020, PHYTOCHEMISTRY, V173, DOI 10.1016/j.phytochem.2020.112291 Friha O, 2021, IEEE-CAA J AUTOMATIC, V8, P718, DOI 10.1109/JAS.2021.1003925 Fuentes S, 2021, CURR OPIN FOOD SCI, V41, P99, DOI 10.1016/j.cofs.2021.03.014 Galanakis CM, 2021, TRENDS FOOD SCI TECH, V110, P193, DOI 10.1016/j.tifs.2021.02.002 Galvan D, 2022, CRIT REV FOOD SCI, V62, P6605, DOI 10.1080/10408398.2021.1903384 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Goyal K, 2022, ARCH COMPUT METHOD E, V29, P397, DOI 10.1007/s11831-021-09600-y Hashemi-Nasab FS, 2022, FOOD CHEM, V393, DOI 10.1016/j.foodchem.2022.133450 Hassoun A, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12031703 Hassoun A, 2022, CRIT REV FOOD SCI, DOI 10.1080/10408398.2022.2034735 Hassoun A, 2020, MOLECULES, V25, DOI 10.3390/molecules25194472 Hassoun A, 2020, FOODS, V9, DOI 10.3390/foods9081069 Hassoun A, 2019, LWT-FOOD SCI TECHNOL, V103, P279, DOI 10.1016/j.lwt.2019.01.021 He Y, 2021, CRIT REV FOOD SCI, V61, P2351, DOI 10.1080/10408398.2020.1777526 Hussain CM, 2021, SMARTPHONE BASED DET Iftekhar A, 2021, FOODS, V10, DOI 10.3390/foods10061289 Iymen G, 2020, INNOV FOOD SCI EMERG, V66, DOI 10.1016/j.ifset.2020.102527 Jagtap S., 2021, FOOD TECHNOLOGY DISR, P175, DOI [10.1016/B978-0-12-821470-1.00009-4, DOI 10.1016/B978-0-12-821470-1.00009-4] Jagtap S., 2021, PROCEDIA CIRP, V104, P1137, DOI [10.1016/j.procir.2021.11.191, DOI 10.1016/J.PROCIR.2021.11.191] Jagtap S, 2021, LOGISTICS-BASEL, V5, DOI 10.3390/logistics5010002 Jagtap S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11113173 Jeevanandam J., 2022, FUTURE FOODS GLOBAL, P733, DOI [10.1016/B978-0-323-91001-9.00036-0, DOI 10.1016/B978-0-323-91001-9.00036-0] Jiang HZ, 2021, SPECTROCHIM ACTA A, V249, DOI 10.1016/j.saa.2020.119307 Jideani A. I. O., 2020, Journal of Food Research, V9, P42, DOI 10.5539/jfr.v9n5p42 Jimenez-Carvelo AM, 2021, TALANTA, V224, DOI 10.1016/j.talanta.2020.121904 Jin CY, 2020, CURR OPIN FOOD SCI, V36, P24, DOI 10.1016/j.cofs.2020.11.006 Kailaku S. I., 2022, IOP Conference Series: Earth and Environmental Science, V1024, DOI 10.1088/1755-1315/1024/1/012079 Kakani V, 2020, J AGR FOOD RES, V2, DOI 10.1016/j.jafr.2020.100033 Kalpana S, 2019, TRENDS FOOD SCI TECH, V93, P145, DOI 10.1016/j.tifs.2019.09.008 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kamilaris A, 2017, COMPUT ELECTRON AGR, V143, P23, DOI 10.1016/j.compag.2017.09.037 Karuppuswami S, 2020, IEEE SENS J, V20, P4679, DOI 10.1109/JSEN.2020.2964676 Khan HH, 2022, J CLEAN PROD, V347, DOI 10.1016/j.jclepro.2022.131268 Khan M. Akhtaruzzaman, 2022, SMART AGR TECHNOL, V2, P2022 Kittichotsatsawat Y, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084593 Kler R, 2022, J FOOD QUALITY, V2022, DOI 10.1155/2022/8521236 Kumar I, 2021, J FOOD QUALITY, V2021, DOI 10.1155/2021/4535567 Laponogov I, 2021, HUM GENOMICS, V15, DOI 10.1186/s40246-020-00297-x Lebelo K, 2022, BRIT FOOD J, V124, P1149, DOI 10.1108/BFJ-04-2021-0366 Li FJ, 2022, CRIT REV ANAL CHEM, DOI 10.1080/10408347.2022.2073433 Li SC, 2021, FOOD CHEM, V348, DOI 10.1016/j.foodchem.2020.128701 Liu ZQ, 2022, FOOD CHEM, V386, DOI 10.1016/j.foodchem.2022.132748 Longobardi F, 2019, FOOD ANAL METHOD, V12, P1229, DOI 10.1007/s12161-019-01458-y Lu Y, 2022, PROCEDIA COMPUT SCI, V199, P629, DOI 10.1016/j.procs.2022.01.077 Ma J, 2019, ANNU REV FOOD SCI T, V10, P197, DOI 10.1146/annurev-food-032818-121155 Ma TT, 2022, FOOD RES INT, V152, DOI 10.1016/j.foodres.2021.110918 Manning L, 2022, TRENDS FOOD SCI TECH, V125, P33, DOI 10.1016/j.tifs.2022.04.025 Marvin HJP, 2022, TRENDS FOOD SCI TECH, V120, P344, DOI 10.1016/j.tifs.2022.01.020 Marvin HJP, 2017, CRIT REV FOOD SCI, V57, P2286, DOI 10.1080/10408398.2016.1257481 Marvin HJP, 2016, FOOD RES INT, V89, P463, DOI 10.1016/j.foodres.2016.08.028 Massaro A, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108364 Mavani NR, 2021, FOOD ENG REV, DOI 10.1007/s12393-021-09290-z Maynard AD, 2015, NAT NANOTECHNOL, V10, P1005, DOI 10.1038/nnano.2015.286 McVey C, 2021, TRENDS FOOD SCI TECH, V118, P777, DOI 10.1016/j.tifs.2021.11.003 Medeiros MLD, 2022, J FOOD COMPOS ANAL, V107, DOI 10.1016/j.jfca.2022.104403 Mirzaee-Ghaleh E, 2020, FOOD ANAL METHOD, V13, P678, DOI 10.1007/s12161-019-01682-6 Misra NN, 2022, IEEE INTERNET THINGS, V9, P6305, DOI 10.1109/JIOT.2020.2998584 Molkentin J, 2015, FOOD CONTROL, V53, P55, DOI 10.1016/j.foodcont.2015.01.003 Morella P, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11122526 Oliveira J, 2021, J AGR FOOD RES, V5, DOI 10.1016/j.jafr.2021.100169 Onwude DI, 2020, PROCESSES, V8, DOI 10.3390/pr8111431 Ozdemir V, 2018, OMICS, V22, P65, DOI 10.1089/omi.2017.0194 Oztemel E, 2020, J INTELL MANUF, V31, P127, DOI 10.1007/s10845-018-1433-8 Panprommin D, 2022, FOOD CONTROL, V136, DOI 10.1016/j.foodcont.2022.108895 Pearson S, 2019, GLOB FOOD SECUR-AGR, V20, P145, DOI 10.1016/j.gfs.2019.02.002 Penco L, 2021, J MANAG GOV, V25, P1179, DOI 10.1007/s10997-020-09526-w Qian JP, 2022, CRIT REV FOOD SCI, V62, P679, DOI 10.1080/10408398.2020.1825925 Qian JP, 2020, TRENDS FOOD SCI TECH, V99, P402, DOI 10.1016/j.tifs.2020.03.025 Qin JW, 2020, FOOD CONTROL, V114, DOI 10.1016/j.foodcont.2020.107234 Rahman LF, 2021, FOODS, V10, DOI 10.3390/foods10102265 Rahmani N, 2022, J FOOD COMPOS ANAL, V112, DOI 10.1016/j.jfca.2022.104650 Ramirez-Asis E, 2022, MATER TODAY-PROC, V51, P2462, DOI 10.1016/j.matpr.2021.11.616 Rana RL, 2021, BRIT FOOD J, V123, P3471, DOI 10.1108/BFJ-09-2020-0832 Rejeb A., 2021, J DATA INFO MANAG, P167, DOI [10.1007/s42488-021-00045-3, DOI 10.1007/S42488-021-00045-3] Ren QS, 2022, TRENDS FOOD SCI TECH, V119, P133, DOI 10.1016/j.tifs.2021.12.006 Robson K, 2021, FOOD CONTROL, V120, DOI 10.1016/j.foodcont.2020.107516 Rocchetti G, 2021, CURR OPIN FOOD SCI, V40, P168, DOI 10.1016/j.cofs.2021.04.005 Rodriguez-Saona L, 2020, CURR OPIN FOOD SCI, V31, P136, DOI 10.1016/j.cofs.2020.04.008 Sadeghi K, 2022, COMPR REV FOOD SCI F, V21, P2615, DOI 10.1111/1541-4337.12932 Seddaoui N, 2021, TALANTA, V230, DOI 10.1016/j.talanta.2021.122346 Shang JQ, 2022, FOOD RES INT, V157, DOI 10.1016/j.foodres.2022.111441 Sharma S, 2021, LOGISTICS-BASEL, V5, DOI 10.3390/logistics5040066 Skladal P, 2020, TRAC-TREND ANAL CHEM, V127, DOI 10.1016/j.trac.2020.115887 Sobolev AP, 2019, TRENDS FOOD SCI TECH, V91, P347, DOI 10.1016/j.tifs.2019.07.035 Song WR, 2021, MICROCHEM J, V164, DOI 10.1016/j.microc.2021.106088 Steinegger A, 2020, CHEM REV, V120, P12357, DOI 10.1021/acs.chemrev.0c00451 Styliaras G., 2021, DIGITAL, V1, P216, DOI [10.3390/digital1040016, DOI 10.3390/DIGITAL1040016] Sun RY, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13168861 Tagarakis AC, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11167596 Tahir HE, 2022, FOOD CHEM, V366, DOI 10.1016/j.foodchem.2021.130633 Tan J., 2020, ARTIF INTELL AGR, V4, P104, DOI [10.1016/j.aiia.2020.06.003., DOI 10.1016/J.AIIA.2020.06.003, 10.1016/J.AIIA.2020.06.003] Tao Q, 2021, FOODS, V10, DOI 10.3390/foods10092203 Temiz HT., 2021, PHOTOCHEM, V1, P125, DOI [10.3390/photochem1020008, DOI 10.3390/PHOTOCHEM1020008] Tian F, 2017, I C SERV SYST SERV M Todorovi V., 2019, IEEE 18 INT C SMART, P1 Torky M, 2020, COMPUT ELECTRON AGR, V178, DOI 10.1016/j.compag.2020.105476 Tsolakis N, 2021, J BUS RES, V131, P495, DOI 10.1016/j.jbusres.2020.08.003 Valletta M, 2021, FOOD CHEM, V365, DOI 10.1016/j.foodchem.2021.130456 Varavallo G, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14063321 Varra MO, 2021, FOOD CHEM, V356, DOI 10.1016/j.foodchem.2021.129687 Varra MO, 2021, FOODS, V10, DOI 10.3390/foods10020270 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9060834 Visconti P, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20133632 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Wang XX, 2022, COMPR REV FOOD SCI F, V21, P416, DOI 10.1111/1541-4337.12868 Wang YY, 2022, J FOOD COMPOS ANAL, V107, DOI 10.1016/j.jfca.2021.104359 Wang ZZ, 2022, FOOD CONTROL, V138, DOI 10.1016/j.foodcont.2022.108970 Wolfert S, 2017, AGR SYST, V153, P69, DOI 10.1016/j.agsy.2017.01.023 Wunsche JF, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14137739 Xiao XY, 2021, TRENDS FOOD SCI TECH, V111, P68, DOI 10.1016/j.tifs.2021.02.045 Xing RR, 2019, FOOD CONTROL, V101, P173, DOI 10.1016/j.foodcont.2019.02.034 Xu JY, 2021, TRENDS FOOD SCI TECH, V109, P83, DOI 10.1016/j.tifs.2021.01.027 Xu Y, 2020, TRAC-TREND ANAL CHEM, V131, DOI 10.1016/j.trac.2020.116017 Yousefi H, 2019, ACS SENSORS, V4, P808, DOI 10.1021/acssensors.9b00440 Yu ZL, 2022, CRIT REV FOOD SCI, V62, P905, DOI [10.1080/10408398.2020.1830262, 10.1007/978-3-030-58529-7_1] Yue Kangning, 2022, Aquaculture and Fisheries, V7, P111, DOI 10.1016/j.aaf.2021.04.009 Zhang YQ, 2022, TRENDS FOOD SCI TECH, V124, P1, DOI 10.1016/j.tifs.2022.03.030 Zhang YJ, 2021, J FOOD PROCESS ENG, V44, DOI 10.1111/jfpe.13669 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zhao X, 2019, SENSOR ACTUAT B-CHEM, V296, DOI 10.1016/j.snb.2019.126641 Zheng MM, 2021, IEEE ACCESS, V9, P70571, DOI 10.1109/ACCESS.2021.3078536 Zhou QQ, 2022, INT J FOOD ENG, V18, P1, DOI 10.1515/ijfe-2021-0299 Zhuang QB, 2022, J FOOD ENG, V316, DOI 10.1016/j.jfoodeng.2021.110840 NR 162 TC 3 Z9 3 U1 21 U2 21 DI 10.1080/10408398.2022.2110033 EA AUG 2022 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000839562800001 DA 2022-12-14 ER PT J AU van der Hagen, EAE Weykamp, C Sandberg, S Stavelin, AV MacKenzie, F Miller, WG AF van der Hagen, Eline A. E. Weykamp, Cas Sandberg, Sverre Stavelin, Anne, V MacKenzie, Finlay Miller, W. Greg TI Feasibility for aggregation of commutable external quality assessment results to evaluate metrological traceability and agreement among results SO CLINICAL CHEMISTRY AND LABORATORY MEDICINE DT Article DE commutability; external quality assessment; harmonization; metrological traceability; standardization ID WORKING GROUP RECOMMENDATIONS; CANDIDATE REFERENCE METHODS; INTERNAL ACCURACY CONTROL; DETERMINING TARGET VALUES; URIC-ACID; CHOLESTEROL; CREATININE; GLUCOSE AB Objectives: External quality assessment (EQA) with commutable samples is used for assessing agreement of results for patients' samples. We investigated the feasibility to aggregate results from four different EQA schemes to determine the bias between different measurement procedures and a reference target value. Methods: We aggregated EQA results for creatinine from programs that used commutable EQA material by calculating the relative difference between individual participant results and the reference target value for each sample. The means and standard errors of the means were calculated for the relative differences. Results were partitioned by methods, manufacturers and instrument platforms to evaluate the biases for the measurement procedures. Results: Data aggregated for enzymatic methods had biases that varied from -8.2 to 3.8% among seven instrument platforms for creatinine at normal concentrations (61-85 mu mol/L). EQA schemes differed in the evidence provided about the commutability of their samples, and in the amount of detail collected from participants regarding the measurement procedures which limited the ability to sub-divide aggregated data by instrument platforms and models. Conclusions: EQA data could be aggregated from four different programs using different commutable samples to determine bias among different measurement procedures. Criteria for commutability for EQA samples as well as standardization of reporting the measurement methods, reagents, instrument platforms and models used by participants are needed to improve the ability to aggregate the results for optimal assessment of performance of measurement procedures. Aggregating data from a larger number of EQA schemes is feasible to assess trueness on a global scale. C1 [van der Hagen, Eline A. E.; Weykamp, Cas] Dutch Fdn Qual Assessment Med Labs SKML, Nijmegen, Netherlands. [van der Hagen, Eline A. E.; Weykamp, Cas] Queen Beatrix Hosp, Dept Clin Chem, Winterswijk, Netherlands. [Sandberg, Sverre; Stavelin, Anne, V] Haraldsplass Deaconess Hosp, Norwegian Org Qual Improvement Lab Examinat Noklu, Bergen, Norway. [MacKenzie, Finlay] Univ Hosp Birmingham NHS Fdn Trust, Birmingham Qual UK NEQAS, Birmingham, W Midlands, England. [Miller, W. Greg] Virginia Commonwealth Univ, Dept Pathol, POB 980286, Richmond, VA 23298 USA. C3 University of Birmingham; Virginia Commonwealth University RP Miller, WG (corresponding author), Virginia Commonwealth Univ, Dept Pathol, POB 980286, Richmond, VA 23298 USA. EM greg.miller@vcuhealth.org CR [Anonymous], 2020, 17511 ISO Braga F, 2018, CLIN BIOCHEM, V57, P23, DOI 10.1016/j.clinbiochem.2018.02.004 International vocabulary of metrology -basic and general concepts and associated terms (VIM) JCGM, 2012, INT VOCABULARY METRO, V200 Miller WG, 2018, CLIN CHEM, V64, P447, DOI 10.1373/clinchem.2017.277525 Miller WG, 2011, CLIN CHEM, V57, P1670, DOI 10.1373/clinchem.2011.168641 Moore D. S., 2009, INTRO PRACTICE STAT Nilsson G, 2018, CLIN CHEM, V64, P455, DOI 10.1373/clinchem.2017.277541 STOCKL D, 1993, CLIN CHEM, V39, P993 THIENPONT LM, 1993, CLIN CHEM, V39, P1001 Weykamp C, 2018, CLIN CHEM, V64, P1183, DOI 10.1373/clinchem.2018.288795 Weykamp C, 2017, CLIN CHEM LAB MED, V55, P203, DOI 10.1515/cclm-2016-0220 NR 11 TC 2 Z9 2 U1 0 U2 2 PD JAN PY 2021 VL 59 IS 1 BP 117 EP 125 DI 10.1515/cclm-2020-0736 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000599400500020 DA 2022-12-14 ER PT J AU Lin, WJ Huang, XH Fang, H Wang, V Hua, YN Wang, JJ Yin, HN Yi, DW Yau, LH AF Lin, Weijun Huang, Xinghong Fang, Hui Wang, Victoria Hua, Yining Wang, Jingjie Yin, Haining Yi, Dewei Yau, Laihung TI Blockchain Technology in Current Agricultural Systems: From Techniques to Applications SO IEEE ACCESS DT Article DE Cryptography; Data integrity; Supply chains; Memory; Ecosystems; Agriculture; Blockchain technology; agricultural applications; food supply chains management; data integrity; traceability ID FOOD TRACEABILITY; SMART CONTRACTS; IOT SECURITY; CHALLENGES; INFRASTRUCTURE; ARCHITECTURE; MANAGEMENT; INTERNET; SCHEME AB Increasingly, blockchain technology is attracting significant attentions in various agricultural applications. These applications could satisfy the diverse needs in the ecosystem of agricultural products, e.g., increasing transparency of food safety and IoT based food quality control, provenance traceability, improvement of contract exchanges, and transactions efficiency. As multiple untrusted parties, including small-scale farmers, food processors, logistic companies, distributors and retailers, are involved into the complex farm-to-fork pipeline, it becomes vital to achieve optimal trade-off between efficiency and integrity of the agricultural management systems as required in contexts. In this paper, we provide a survey to study both techniques and applications of blockchain technology used in the agricultural sector. First, the technical elements, including data structure, cryptographic methods, and consensus mechanisms are explained in detail. Secondly, the existing agricultural blockchain applications are categorized and reviewed to demonstrate the use of the blockchain techniques. In addition, the popular platforms and smart contract are provided to show how practitioners use them to develop these agricultural applications. Thirdly, we identify the key challenges in many prospective agricultural systems, and discuss the efforts and potential solutions to tackle these problems. Further, we conduct an improved food supply chain in the post COVID-19 pandemic economy as an illustration to demonstrate an effective use of blockchain technology. C1 [Lin, Weijun; Huang, Xinghong; Wang, Jingjie] Inst Agr Econ & Rural Dev, Guangzhou 510640, Peoples R China. [Fang, Hui; Hua, Yining] Loughborough Univ, Dept Comp Sci, Loughborough LE11 3TU, Leics, England. [Wang, Victoria] Univ Portsmouth, Inst Criminal Justice Studies, Portsmouth PO1 2UP, Hants, England. [Yin, Haining] SAP China, Shanghai 200040, Peoples R China. [Yi, Dewei] Univ Aberdeen, Dept Comp Sci, Aberdeen AB24 3FX, Scotland. [Yau, Laihung] Asia Pacific Appl Nano Technol Res Ctr, Hong Kong, Peoples R China. C3 Loughborough University; University of Portsmouth; University of Aberdeen RP Fang, H (corresponding author), Loughborough Univ, Dept Comp Sci, Loughborough LE11 3TU, Leics, England. EM h.fang@lboro.ac.uk CR Abadi FA, 2018, IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, P1666, DOI 10.1109/Cybermatics_2018.2018.00278 Ahmed S, 2017, NATURE, V550, P43, DOI 10.1038/550043e Al-Jaroodi J, 2019, IEEE ACCESS, V7, P36500, DOI 10.1109/ACCESS.2019.2903554 Albert E., 2018, GASTAP GAS ANAL SMAR Alladi T, 2019, IEEE ACCESS, V7, P176935, DOI 10.1109/ACCESS.2019.2956748 Anjana P.S., 2018, ARXIV180901326 Anjum A, 2017, IEEE CLOUD COMPUT, V4, P84, DOI 10.1109/MCC.2017.3791019 [Anonymous], 2017, DPOS CONSENSUS ALGOR Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Atzei N., 2016, IACR CRYPTOLOGY EPRI, V2016, P1007 Bamasag O., 2020, DECENTRALISED INTERN, P169 Bhargavan K, 2016, PROCEEDINGS OF THE 2016 ACM WORKSHOP ON PROGRAMMING LANGUAGES AND ANALYSIS FOR SECURITY (PLAS'16), P91, DOI 10.1145/2993600.2993611 Boone T, 2019, INT J FORECASTING, V35, P170, DOI 10.1016/j.ijforecast.2018.09.003 Bordel B, 2019, I SYMP CONSUM ELECTR Bragagnolo S, 2018, 2018 IEEE 1ST INTERNATIONAL WORKSHOP ON BLOCKCHAIN ORIENTED SOFTWARE ENGINEERING (IWBOSE), P9 Brown R.G, 2018, CORDA PLATFORM INTRO Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Cachin C., 2017, ARXIV PREPRINT ARXIV, DOI 10.4230/LIPIcs.DISC.2017.1 Casey M., 2018, TECH REP Castro M, 1999, USENIX ASSOCIATION PROCEEDINGS OF THE THIRD SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDI '99), P173, DOI 10.1145/571637.571640 Chang S. E., 2019, SUSTAINABILITY, V12, P1 Chang YL, 2020, INT J PROD RES, V58, P2082, DOI 10.1080/00207543.2019.1651946 Charlier J., 2017, P EUR C MACH LEARN P Chatterjee S., 2018, ARXIV PREPRINT ARXIV Chen S, 2017, INT CONF E BUS ENG, P172, DOI 10.1109/ICEBE.2017.34 Cohn A., 2017, GEORGET LAW TECHNOL, V1, P273 Cole R, 2019, SUPPLY CHAIN MANAG, V24, P469, DOI 10.1108/SCM-09-2018-0309 Crosby M., 2016, APPL INNOVATION, V2, P6, DOI DOI 10.21626/innova/2016.1/01 Dai HN, 2019, IEEE INTERNET THINGS, V6, P8076, DOI 10.1109/JIOT.2019.2920987 Dhillon MDH., 2017, BLOCKCHAIN ENABLED A, P139, DOI DOI 10.1007/978-1-4842-3081-7_10 Dickerson T, 2020, DISTRIB COMPUT, V33, P209, DOI 10.1007/s00446-019-00357-z DOLEV D, 1982, J ALGORITHM, V3, P14, DOI 10.1016/0196-6774(82)90004-9 Dorri A, 2019, J PARALLEL DISTR COM, V134, P180, DOI 10.1016/j.jpdc.2019.08.005 Farooq MS, 2019, IEEE ACCESS, V7, P156237, DOI 10.1109/ACCESS.2019.2949703 Fernandez-Carames TM, 2018, IEEE ACCESS, V6, P32979, DOI 10.1109/ACCESS.2018.2842685 Ferrer EC, 2019, ADV INTELL SYST, V881, P1037, DOI 10.1007/978-3-030-02683-7_77 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Ge L., 2017, BLOCKCHAIN AGR FOOD Gorenflo C, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN AND CRYPTOCURRENCY (ICBC), P455, DOI [10.1002/nem.2099, 10.1109/BLOC.2019.8751452] Gramoli V, 2020, FUTURE GENER COMP SY, V107, P760, DOI 10.1016/j.future.2017.09.023 Greenspan G., 2018, INNOV TECHNOL GOVERN, V12, P58 Greenspan G., 2015, MULTICHAIN PRIVATE B Gupta M, 2020, IEEE ACCESS, V8, P34564, DOI 10.1109/ACCESS.2020.2975142 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hao J.T., 2018, J COMPUT, V29, P158, DOI [10.3966/199115992018122906015, DOI 10.3966/199115992018122906015] Hobbs JE, 2020, CAN J AGR ECON, V68, P171, DOI 10.1111/cjag.12237 Howard H., 2014, UCAMCLTR857 U CAMBR Huh S, 2019, PROCEEDINGS WEB3D 2019: THE 24TH INTERNATIONAL ACM CONFERENCE ON 3D WEB TECHNOLOGY, DOI 10.1145/3329714.3338137 Jo BW, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18124268 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Katz, 2019, FIELD ACTIONS SCI RE, P96 Kawakura S., 2019, EUR J AGR FOOD SCI, V1 Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Kos D, 2019, CURR OPIN ENV SUST, V41, P56, DOI 10.1016/j.cosust.2019.10.011 Kosba A, 2016, P IEEE S SECUR PRIV, P839, DOI 10.1109/SP.2016.55 KUMAR Y, 2011, INT J COMPUT SCI MAN, V11, P60 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Lezoche M, 2020, COMPUT IND, V117, DOI 10.1016/j.compind.2020.103187 Li H, 2013, INT BHURBAN C APPL S, P1, DOI 10.1109/IBCAST.2013.6512120 Li XQ, 2020, FUTURE GENER COMP SY, V107, P841, DOI 10.1016/j.future.2017.08.020 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Lozupone V, 2018, INT J INFORM MANAGE, V38, P42, DOI 10.1016/j.ijinfomgt.2017.08.004 Lucena P., 2018, P S FDN APPL BLOCKCH, P1 Luo K, 2018, 2018 IEEE 18TH INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C), P139, DOI 10.1109/QRS-C.2018.00037 Luu L, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P254, DOI 10.1145/2976749.2978309 Ma Y, 2020, WORLD WIDE WEB, V23, P393, DOI 10.1007/s11280-019-00735-4 Machado T. B., 2020, TRENDS FOOD SCI TECH Manski S, 2017, STRATEG CHANG, V26, P511, DOI 10.1002/jsc.2151 Mao DH, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093149 McConaghy T., 2016, CISC VIS NETW IND GL Mohanta BK, 2018, INT CONF COMPUT Molina-Jimenez C, 2018, 2018 IEEE 8TH INTERNATIONAL SYMPOSIUM ON CLOUD AND SERVICE COMPUTING (SC2), P83, DOI 10.1109/SC2.2018.00018 Moniz H., 2020, ISTANBUL BFT CONSENS Munir MS, 2019, COMPUT ELECTR ENG, V77, P109, DOI 10.1016/j.compeleceng.2019.05.006 Nakamoto S., 2008, CONSULTED, P21260 Novo O, 2018, IEEE INTERNET THINGS, V5, P1184, DOI 10.1109/JIOT.2018.2812239 Ojo A, 2017, PUB ADMIN INF TECH, V32, P283, DOI 10.1007/978-3-319-63743-3_11 Olson K., 2018, LINUX FDN Perboli G, 2018, IEEE ACCESS, V6, P62018, DOI 10.1109/ACCESS.2018.2875782 Goncalves MCP, 2019, PROCESS BIOCHEM, V76, P95, DOI 10.1016/j.procbio.2018.09.016 Pinna Andrea, 2019, Intelligent Computing. Proceedings of the 2018 Computing Conference. Advances in Intelligent Systems and Computing (AISC 857), P1231, DOI 10.1007/978-3-030-01177-2_88 Qu B, 2020, IEEE ACCESS, V8, P48325, DOI 10.1109/ACCESS.2020.2978614 Rachmawati D, 2018, J PHYS CONF SER, V978, DOI 10.1088/1742-6596/978/1/012116 Rouhani S, 2019, IEEE ACCESS, V7, P50759, DOI 10.1109/ACCESS.2019.2911031 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Salah K, 2019, IEEE ACCESS, V7, P10127, DOI 10.1109/ACCESS.2018.2890507 Samaniego M, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA), P433, DOI 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.102 Schaefer C, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN AND CRYPTOCURRENCY (ICBC), P65, DOI 10.1109/BLOC.2019.8751339 Si HP, 2019, FUTURE GENER COMP SY, V101, P1028, DOI 10.1016/j.future.2019.07.036 Singhal N., 2020, ADV CYBERNETICS COGN, V643, P77 Sylvester G., 2019, E AGR ACT BLOCKCHAIN Szabo N., 1997, First Monday, V2 Tasatanattakool P, 2018, 2018 32ND INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN), P473, DOI 10.1109/ICOIN.2018.8343163 Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Thomason J., 2018, TRANSFORMING CLIMATE, P137, DOI DOI 10.1016/B978-0-12-814447-3.00010-0 Tian F, 2017, INT C SERV SYST SERV, V1, P6, DOI DOI 10.1109/ICSSSM.2017.7996119 Ban TQ, 2019, 2019 8TH INTERNATIONAL CONFERENCE ON SOFTWARE AND COMPUTER APPLICATIONS (ICSCA 2019), P472, DOI 10.1145/3316615.3316671 Tsankov P, 2018, PROCEEDINGS OF THE 2018 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'18), P67, DOI 10.1145/3243734.3243780 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Vasin P., 2014, BLACKCOINS PROOF OF Wang SP, 2019, IEEE ACCESS, V7, P115122, DOI 10.1109/ACCESS.2019.2935873 Wang YK, 2016, 2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS), P1, DOI 10.1109/AUS.2016.7748011 WOOD G, 2014, ETHEREUM PROJECT YEL, V1, P151 Xiong H, 2020, FRONT BLOCKCHAIN, V3, DOI 10.3389/fbloc.2020.00007 Xu XW, 2019, FUTURE GENER COMP SY, V92, P399, DOI 10.1016/j.future.2018.10.010 Yeoh P., 2017, J FINANCIAL REGULATI, V25, P196, DOI [DOI 10.1108/JFRC-08-2016-0068, 10.1108/JFRC-08-2016-0068] Yli-Huumo J, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0163477 Yu B, 2020, IEEE ACCESS, V8, P12479, DOI 10.1109/ACCESS.2020.2966020 Zhang Q, 2020, COMPUT ELECTRON AGR, V173, DOI 10.1016/j.compag.2020.105395 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zheng ZB, 2020, FUTURE GENER COMP SY, V105, P475, DOI 10.1016/j.future.2019.12.019 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 NR 115 TC 40 Z9 41 U1 21 U2 21 PY 2020 VL 8 BP 143920 EP 143937 DI 10.1109/ACCESS.2020.3014522 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000560344100001 DA 2022-12-14 ER PT J AU Burger, S Balsley, SD Baumann, S Berger, J Boulyga, SF Cunningham, JA Kappel, S Koepf, A Poths, J AF Buerger, S. Balsley, S. D. Baumann, S. Berger, J. Boulyga, S. F. Cunningham, J. A. Kappel, S. Koepf, A. Poths, J. TI Uranium and plutonium analysis of nuclear material samples by multi-collector thermal ionisation mass spectrometry: Quality control, measurement uncertainty, and metrological traceability SO INTERNATIONAL JOURNAL OF MASS SPECTROMETRY DT Article DE Multi-collector TIMS; Isotope ratio analysis; Uncertainty in Measurement; Metrological traceability; Quality control ID ISOTOPE RATIO MEASUREMENTS; ENVIRONMENTAL-SAMPLES; AGE-DETERMINATION; ATOMIC-WEIGHT; MC-ICPMS; ALPHA-SPECTROMETRY; TIMS; EFFICIENCY; FISSION; SAFEGUARDS AB Metrological studies and recent improvements in multi-collector thermal ionisation mass spectrometry (MC-TIMS) of uranium and plutonium in bulk nuclear material samples are presented with a focus on nuclear safeguards. Using total evaporation and modified total evaporation methods, experimental data are presented for isotope ratio measurements in routine mode spanning a range of almost ten orders of magnitude, with n(U-236)/n(U-238) measurements as low as a few parts per billion. Based upon these data, measurement reproducibility, associated measurement uncertainties with comparison to International Target Values (ITV), an upper limit of the instrumental uranium memory effect and of the hydride formation, and process and instrumental blank levels are examined. A comparison of measurement performance between the latest and previous generation of instruments for the total evaporation method is conducted. In addition, the implementation of a quality control procedure including control charts is presented and, in this context, commercially available U or Pu certified reference materials for isotope ratio and isotope dilution mass spectrometry are surveyed. The implementation of the Guide to the expression of Uncertainty in Measurement (GUM) is discussed for the modified total evaporation method. To address the importance of metrological traceability of measurement results to the SI units, the agreement between the certified values of two independently produced series of uranium certified reference materials (CRMs) - NBL and IRMM U series - is probed. Using IRMM CRMs as calibration standards, a new set of values for selected NBL U series CRMs is presented with expanded uncertainties of about 0.035% for major isotope ratios and at an order of magnitude of 0.1-0.5% for minor ratios. (C) 2011 Elsevier B.V. All rights reserved. C1 [Buerger, S.; Balsley, S. D.; Baumann, S.; Berger, J.; Boulyga, S. F.; Cunningham, J. A.; Koepf, A.; Poths, J.] IAEA, A-1400 Vienna, Austria. [Kappel, S.] VIRIS Lab, Dept Chem, Div Analyt Chem, A-3430 Tulln, Austria. [Kappel, S.] Univ Nat Resources & Life Sci, Vienna, Austria. C3 International Atomic Energy Agency; University of Natural Resources & Life Sciences, Vienna RP Burger, S (corresponding author), IAEA, A-1400 Vienna, Austria. EM s.buerger@iaea.org CR Aggarwal SK, 2006, RADIOCHIM ACTA, V94, P397, DOI 10.1524/ract.2006.94.8.397 [Anonymous], CERT REF MAT CAT [Anonymous], 2010, INT TARG VALU 2010 M [Anonymous], GUM WORKB [Anonymous], 2010, CERT AN [Anonymous], 2008, IS REF MAT CATAL [Anonymous], 2008, CERT AN [Anonymous], 1999, EUROPEAN COOPERATION [Anonymous], 2010, REFERENCE MAT CATALO Baiter M., 2008, SCIENCE, V320, P1704 Beard BL, 2000, J FORENSIC SCI, V45, P1049 Boulyga SF, 2006, J ENVIRON RADIOACTIV, V88, P1, DOI 10.1016/j.jenvrad.2005.12.007 Boulyga SF, 2002, J ANAL ATOM SPECTROM, V17, P1143, DOI 10.1039/b202196j Brennecka GA, 2010, SCIENCE, V327, P449, DOI 10.1126/science.1180871 Brunner M, 2010, EUR FOOD RES TECHNOL, V231, P623, DOI 10.1007/s00217-010-1314-7 Burger S, 2007, J ALLOY COMPD, V444, P660, DOI 10.1016/j.jallcom.2006.11.019 Burger S, 2010, INT J MASS SPECTROM, V294, P65, DOI 10.1016/j.ijms.2010.05.003 Burger S, 2009, INT J MASS SPECTROM, V286, P70, DOI 10.1016/j.ijms.2009.06.010 Burger S, 2007, J RADIOANAL NUCL CH, V274, P491, DOI 10.1007/s10967-006-6930-0 CALLIS EL, 1991, INT J MASS SPECTROM, V103, P93, DOI 10.1016/0168-1176(91)80081-W CHEN JH, 1986, EARTH PLANET SC LETT, V80, P241, DOI 10.1016/0012-821X(86)90108-1 Collerson KD, 2007, SCIENCE, V317, P1907, DOI 10.1126/science.1147013 De Laeter JR, 2007, MASS SPECTROM REV, V26, P683, DOI 10.1002/mas.20141 de Laeter JR, 2003, ANAL BIOANAL CHEM, V375, P62, DOI 10.1007/s00216-002-1570-x de Oliveira OP, 2010, INT J MASS SPECTROM, V291, P48, DOI 10.1016/j.ijms.2010.01.005 de Oliveira OP, 2005, INT J MASS SPECTROM, V246, P35, DOI 10.1016/j.ijms.2005.08.004 DeLaeter JR, 1996, MASS SPECTROM REV, V15, P261, DOI 10.1002/(SICI)1098-2787(1996)15:4<261::AID-MAS3>3.0.CO;2-G DIETZ LA, 1962, ANAL CHEM, V34, P709, DOI 10.1021/ac60186a001 Donohue DL, 1998, J ALLOY COMPD, V271, P11, DOI 10.1016/S0925-8388(98)00015-2 Douthitt CB, 2008, ANAL BIOANAL CHEM, V390, P437, DOI 10.1007/s00216-007-1660-x EDWARDS RL, 1986, EARTH PLANET SC LETT, V81, P175 FIEDLER R, 1995, INT J MASS SPECTROM, V146, P91, DOI 10.1016/0168-1176(95)04197-S Fiedler R., 1999, INMM C P Font L, 2007, J ANAL ATOM SPECTROM, V22, P513, DOI 10.1039/b616328a Grousset FE, 2005, CHEM GEOL, V222, P149, DOI 10.1016/j.chemgeo.2005.05.006 Heumann KG, 1998, J ANAL ATOM SPECTROM, V13, P1001 Hoffmann DL, 2008, INT J MASS SPECTROM, V275, P75, DOI 10.1016/j.ijms.2008.05.033 Inn KGW, 2001, J RADIOANAL NUCL CH, V249, P121 Jakopic R, 2010, J ANAL ATOM SPECTROM, V25, P815, DOI 10.1039/b925918j Jakopic R, 2009, INT J MASS SPECTROM, V279, P87, DOI 10.1016/j.ijms.2008.10.014 Joint Committee for Guides in Metrology, 2008, 200 JCGM Joint Committee for Guides in Metrology, 2008, 100 JCGM, V100 Kacker R, 2007, METROLOGIA, V44, P513, DOI 10.1088/0026-1394/44/6/011 Keegan E, 2008, APPL GEOCHEM, V23, P765, DOI 10.1016/j.apgeochem.2007.12.004 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kraiem M, 2011, ANAL CHIM ACTA, V688, P1, DOI 10.1016/j.aca.2010.12.003 Lee CG, 2007, J RADIOANAL NUCL CH, V272, P299, DOI 10.1007/s10967-007-0519-0 Mayer K, 2005, ANALYST, V130, P433, DOI 10.1039/b412922a Miller D.L., 2006, ANAL CONCENTRATED UR Moody K.J., 2015, NUCL FORENSIC ANAL Morgenstern A, 2002, ANAL CHEM, V74, P5513, DOI 10.1021/ac0203948 Neumann K.G., 1995, ANALYST, V120, P1291 Pajo L, 2001, J RADIOANAL NUCL CH, V250, P79 Ranebo Y, 2009, J ANAL ATOM SPECTROM, V24, P277, DOI 10.1039/b810474c Raptis K., 2011, ESARDA C P Richter S, 2011, J ANAL ATOM SPECTROM, V26, P550, DOI 10.1039/c0ja00173b Richter S, 2011, INT J MASS SPECTROM, V299, P120, DOI 10.1016/j.ijms.2010.09.034 Richter S, 2003, INT J MASS SPECTROM, V229, P181, DOI 10.1016/S1387-3806(03)00338-5 Richter S, 2010, INT J MASS SPECTROM, V295, P94, DOI 10.1016/j.ijms.2010.06.004 Richter S, 2009, INT J MASS SPECTROM, V281, P115, DOI 10.1016/j.ijms.2009.01.002 Saito-Kokubu Y, 2012, INT J MASS SPECTROM, V310, P52, DOI 10.1016/j.ijms.2011.11.008 Shinonaga T, 2008, SPECTROCHIM ACTA B, V63, P1324, DOI 10.1016/j.sab.2008.09.001 Smith D.H., 1994, ORNLTM1263 Svedkauskaite-LeGore J, 2008, J RADIOANAL NUCL CH, V278, P201, DOI 10.1007/s10967-007-7215-y Tamborini G, 2002, MIKROCHIM ACTA, V139, P185, DOI 10.1007/s006040200059 Taylor BN, 1994, NIST GUIDELINES EVAL Tuttas D., 2003, AN310015E THERM EL C Usuda S, 2006, INT J ENVIRON AN CH, V86, P663, DOI 10.1080/03067310600583733 Vanhaecke F, 2009, J ANAL ATOM SPECTROM, V24, P863, DOI 10.1039/b903887f Varga Z, 2010, J ANAL ATOM SPECTROM, V25, P1958, DOI 10.1039/c0ja00048e Varga Z, 2009, ANAL CHEM, V81, P8327, DOI 10.1021/ac901100e VOGEL JC, 1990, NATURE, V346, P747, DOI 10.1038/346747a0 Wakaki S, 2007, INT J MASS SPECTROM, V264, P157, DOI 10.1016/j.ijms.2007.04.006 Wallenius M, 2006, FORENSIC SCI INT, V156, P55, DOI 10.1016/j.forsciint.2004.12.029 Wallenius M, 2000, FRESEN J ANAL CHEM, V366, P234, DOI 10.1007/s002160050046 Wang J, 2011, INT J MASS SPECTROM, V308, P65, DOI 10.1016/j.ijms.2011.07.023 Warneke T, 2002, EARTH PLANET SC LETT, V203, P1047, DOI 10.1016/S0012-821X(02)00930-5 Watrous MG, 2010, INT J MASS SPECTROM, V296, P21, DOI 10.1016/j.ijms.2010.07.015 Wayne DM, 2002, INT J MASS SPECTROM, V216, P41, DOI 10.1016/S1387-3806(02)00551-1 Yang L, 2009, MASS SPECTROM REV, V28, P990, DOI 10.1002/mas.20251 Zhai LH, 2011, INT J MASS SPECTROM, V305, P45, DOI 10.1016/j.ijms.2011.05.015 Zhang HT, 2008, RADIOCHIM ACTA, V96, P327, DOI 10.1524/ract.2008.1499 Zhao MT, 2005, RAPID COMMUN MASS SP, V19, P2743, DOI 10.1002/rcm.2114 NR 83 TC 53 Z9 55 U1 0 U2 57 PD FEB 1 PY 2012 VL 311 BP 40 EP 50 DI 10.1016/j.ijms.2011.11.016 WC Physics, Atomic, Molecular & Chemical; Spectroscopy SC Physics; Spectroscopy UT WOS:000300913900006 DA 2022-12-14 ER PT J AU Ritota, M Casciani, L Valentini, M AF Ritota, Mena Casciani, Lorena Valentini, Massimiliano TI PGI chicory (Cichorium intybus L.) traceability by means of HRMAS-NMR spectroscopy: a preliminary study SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE Red chicory; red-spotted chicory; metabolomics; PLS-DA ID NUCLEAR-MAGNETIC-RESONANCE; GEOGRAPHICAL CHARACTERIZATION; H-1-NMR SPECTROSCOPY; DISCRIMINATION; JUICE; CHEMOMETRICS; VARIETIES; CHEESE; ORIGIN; TOOL AB BACKGROUND Analytical traceability of PGI and PDO foods (Protected Geographical Indication and Protected Denomination Origin respectively) is one of the most challenging tasks of current applied research. RESULTS Here we proposed a metabolomic approach based on the combination of 1H high-resolution magic angle spinningnuclear magnetic resonance (HRMAS-NMR) spectroscopy with multivariate analysis, i.e. PLS-DA, as a reliable tool for the traceability of Italian PGI chicories (Cichorium intybus L.), i.e. Radicchio Rosso di Treviso and Radicchio Variegato di Castelfranco, also known as red and red-spotted, respectively. The metabolic profile was gained by means of HRMAS-NMR, and multivariate data analysis allowed us to build statistical models capable of providing clear discrimination among the two varieties and classification according to the geographical origin. CONCLUSION Based on Variable Importance in Projection values, the molecular markers for classifying the different types of red chicories analysed were found accounting for both the cultivar and the place of origin. (c) 2012 Society of Chemical Industry C1 [Ritota, Mena; Casciani, Lorena; Valentini, Massimiliano] Instrumental Ctr Tor Mancina, Res Ctr Soil Plant Syst, Consiglio Ric & Sperimentaz Agr, I-00015 Rome, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Valentini, M (corresponding author), Instrumental Ctr Tor Mancina, Res Ctr Soil Plant Syst, Consiglio Ric & Sperimentaz Agr, Str Neve SP Pascolare Km 1, I-00015 Rome, Italy. EM massimiliano.valentini@entecra.it CR Cevallos-Cevallos JM, 2009, TRENDS FOOD SCI TECH, V20, P557, DOI 10.1016/j.tifs.2009.07.002 Ciampa A, 2010, J FOOD QUALITY, V33, P199, DOI 10.1111/j.1745-4557.2010.00306.x Consonni R, 2008, J AGR FOOD CHEM, V56, P6873, DOI 10.1021/jf801332r CROASMUN WR, 1994, 2 DIMENSIONAL NMR SP, P785 Cuny M, 2008, ANAL BIOANAL CHEM, V390, P419, DOI 10.1007/s00216-007-1708-y Heinzmann SS, 2010, AM J CLIN NUTR, V92, P436, DOI 10.3945/ajcn.2010.29672 Holmes E, 2002, ANALYST, V127, P1549, DOI 10.1039/b208254n Innocenti M, 2005, J AGR FOOD CHEM, V53, P6497, DOI 10.1021/jf050541d Kind T, 2007, ANAL BIOCHEM, V363, P185, DOI 10.1016/j.ab.2007.01.028 Koda M, 2012, J AGR FOOD CHEM, V60, P1158, DOI 10.1021/jf2041438 Le Gall G, 2003, J AGR FOOD CHEM, V51, P2447, DOI 10.1021/jf0259967 Le Gall G, 2001, J AGR FOOD CHEM, V49, P580, DOI 10.1021/jf001046e Mannina L, 2001, J AGR FOOD CHEM, V49, P2687, DOI 10.1021/jf001408i Mazzei P, 2012, FOOD CHEM, V132, P1620, DOI 10.1016/j.foodchem.2011.11.142 National Research Institute for Food and Nutrition (INRAN), TABL FOOD COMP Nestor G, 2010, J AGR FOOD CHEM, V58, P10799, DOI 10.1021/jf103338j Perez-Enciso M, 2003, HUM GENET, V112, P581, DOI 10.1007/s00439-003-0921-9 Ritota M, 2012, MEAT SCI, V92, P754, DOI 10.1016/j.meatsci.2012.06.034 Ritota M, 2012, FOOD CHEM, V135, P684, DOI 10.1016/j.foodchem.2012.05.032 Ritota M, 2010, J AGR FOOD CHEM, V58, P9675, DOI 10.1021/jf1015957 Rossetto M, 2005, J AGR FOOD CHEM, V53, P8169, DOI 10.1021/jf051116n Sacco D, 2005, MEAT SCI, V71, P542, DOI 10.1016/j.meatsci.2005.04.038 Perez EMS, 2011, FOOD RES INT, V44, P3212, DOI 10.1016/j.foodres.2011.08.012 Shintu L, 2006, J AGR FOOD CHEM, V54, P4148, DOI 10.1021/jf060532k Trygg J, 2007, J PROTEOME RES, V6, P469, DOI 10.1021/pr060594q Valentini M, 2011, MAGN RESON CHEM, V49, pS121, DOI 10.1002/mrc.2826 NR 26 TC 16 Z9 16 U1 0 U2 38 PD MAY PY 2013 VL 93 IS 7 BP 1665 EP 1672 DI 10.1002/jsfa.5947 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000317614000019 DA 2022-12-14 ER PT J AU Pascal, G Mahe, S AF Pascal, G Mahe, S TI Identity, traceability, acceptability and substantial equivalence of food SO CELLULAR AND MOLECULAR BIOLOGY DT Article DE genetically modified organisms; nutrition; microsatellites; PCR; novel food ID MASS-SPECTROMETRY; MEAT AB The numerous food crises that Europe has experienced during the past five years have raised new consumer demands concerning the characterization, traceability, and safety of foods which are proposed on the market. The consumer has, at the same time, vigorously placed into question the modes of agricultural production in industrialized countries, as well as the structures and means of evaluating the food risks and the conditions of the consumer's participation in the public debate in these domains. For certain groups of consumers, one also attends a contestation of the expertise and the application to the food domain of the considerable progress that has taken place in the field of biotechnology. So it is that the development of genetically modified organisms (mainly plants, the raw material of food products) has experienced a slowing down in the European Union. The answers afforded to these new exigencies of consumers in matter of identity traceability and acceptability of the foods are dealt with in this paper, as well as the elements which may concur with the evaluation of their safety. The positive role that biotechnology can afford to the different domains is emphasized. A source of uneasiness, biotechnology is also a powerful tool for ameliorating the evaluation of the sanitary risks and for answering the hopes of the citizen in the food domain. C1 INRA, DS NHSA, F-75338 Paris 07, France. C3 INRAE RP Pascal, G (corresponding author), INRA, DS NHSA, 147 Rue Univ, F-75338 Paris 07, France. CR Antignac JP, 2000, RAPID COMMUN MASS SP, V14, P33, DOI 10.1002/(SICI)1097-0231(20000115)14:1<33::AID-RCM829>3.3.CO;2-I BECK E, 1982, GENE, V19, P327, DOI 10.1016/0378-1119(82)90023-3 Cornuet JM, 1996, CR ACAD SCI III-VIE, V319, P1167 DANHO D, 1992, ANALUSIS, V20, P179 DELVIN RH, 2001, NATURE, V409, P781 *FAO OMS, 2000, 00010015 FAO OMS *FAO OMS, 2001, 01010111 FAO OMS Ferchaud V, 2000, RAPID COMMUN MASS SP, V14, P652 GROSCLAUDE F, 1987, GENET SEL EVOL, V19, P399, DOI [10.1051/gse:19870402, 10.1186/1297-9686-19-4-399] HINFRAY J, 2000, BIOFUTUR, V200, P17 Joly P. B., 2001, Confiance et rationalite, Dijon, France, 5-6 mai 1999, P131 JOLY PB, 2001, DEMETER EC STRATEGIE, P73 Le Roy P, 2000, GENET SEL EVOL, V32, P165, DOI 10.1051/gse:2000112 Lipp M, 1999, J AOAC INT, V82, P923 Lipp M, 2000, J AOAC INT, V83, P919 Marchand P, 2000, J CHROMATOGR A, V867, P219, DOI 10.1016/S0021-9673(99)01114-0 Martin P, 2000, PROD ANIM, P125 Milan D, 2000, SCIENCE, V288, P1248, DOI 10.1126/science.288.5469.1248 MoazamiGoudarzi K, 1997, ANIM GENET, V28, P338, DOI 10.1111/j.1365-2052.1997.00176.x Momma K, 2000, BIOSCI BIOTECH BIOCH, V64, P1881, DOI 10.1271/bbb.64.1881 MULLIS KB, 1990, SCI AM, V262, P56, DOI 10.1038/scientificamerican0490-56 Naveau J., 1986, JOURNEES RECHERCHE P, V18, P265 *OCDE, 1993, 931993042P1 OCDE, P88 OLLIVIER L, 2000, GENETIQUE MOL PRINCI, P247 OULMOUDEN A, 1998, Patent No. 9805809 Pascal G., 1996, PLANTES TRANSGENIQUE, P49 Renou J. P., 1998, Viandes et Produits Carnes, V19, P77 Rouzaud F, 2000, PROD ANIM, P243 RUFFIEUX B, 2001, ANAL EC DISPOSITION Sancristobal-Gaudy M, 2000, PROD ANIM, V13, P269 THONAT C, 1999, ANN FALSIF EXP CHIM, V948, P365 VIALLON C, 1999, QUALITE PRODUITS LIE, P60 VOGELS JTW, 1996, P EUR 3 C RES VET DR, P968 NR 33 TC 24 Z9 29 U1 2 U2 10 PD DEC PY 2001 VL 47 IS 8 BP 1329 EP 1342 WC Biochemistry & Molecular Biology; Cell Biology SC Biochemistry & Molecular Biology; Cell Biology UT WOS:000173589900009 DA 2022-12-14 ER PT J AU Kun, L Zhang, LL Wei, L Okinda, CS Shen, MX AF Kun, Liang Zhang Lingling Wei, Lu Okinda, Cedric Sean Shen Mingxia TI Optimization of compression formulation and load of food-grade tracers for grain traceability using central composite design SO INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING DT Article DE grain traceability; food-grade tracer; optimization; food safety; formulation; mechanical property; central composite design ID SUPPLY CHAIN; DELIVERY-SYSTEM; FLOW PROPERTIES; BULK; MOISTURE; SAFETY; CODE AB Food-grade tracers have been developed as an identification technology for grain traceability from original harvest to final destination for transportation. The characteristics of food-grade tracers must be able to satisfy the environmental demands for grain traceability. To optimize the food-grade tracer production process, the effects of direct compression formulation and load on the mechanical characteristics were studied using response surface methodology (RSM) with central composite design (CCD). Among the four tested formulations, Formulations #2 (consisting of 35.00% lactose 100 mesh, 64.50% microcrystalline cellulose 102 and 0.50% magnesium stearate) and #4 (consisting of 38.00% lactose 100 mesh, 50.00% microcrystalline cellulose 102, 11.00% pregelatinized starch and 1.00% magnesium stearate) were selected for tracer production based on their physical properties as powders. The value of Carr's flowability index was 68 for both Formulations #2 and #4, which was the highest among all the formulations. Therefore, Formulations #2 and #4 also had the best powder flowability. The magnesium stearate ratio (1.00%-3.00%) and pressure (6.00-16.00 kgf) were used as independent variables to detect changes in the breaking rate, peak shear force and friction coefficient of tracers compressed by the selected formulations. The optimal production parameters could be achieved at a magnesium stearate ratio of 2.25% and pressure of 16.00 kgf for Formulation #2 and at a magnesium stearate ratio of 1.02% and pressure of 16.00 kgf for Formulation #4. Under these optimal conditions, the tracers had good impact characteristics (breaking rate), compression characteristics (peak shear force) and frictional characteristics (friction coefficient). Moreover, Formulation #2 was more suitable for production because compared to Formulation #4, its breaking rate and friction coefficient values were lower, and its peak shear force value was higher. C1 [Kun, Liang; Zhang Lingling; Wei, Lu; Okinda, Cedric Sean; Shen Mingxia] Nanjing Agr Univ, Coll Engn, Key Lab Intelligent Equipment Agr Jiangsu Prov, Nanjing 210031, Jiangsu, Peoples R China. C3 Nanjing Agricultural University RP Shen, MX (corresponding author), 40,Dianjiangtai Rd, Nanjing 210031, Jiangsu, Peoples R China. EM lkbb2006@126.com; zhangllling@126.com; njaurobot@njau.edu.cn; cedsean@hotmail.com; mingxia@njau.edu.cn CR ASAE Standard, 2008, S3684 ASAE STAND Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Can-trace, 2003, CAN TRAC DEV TRAC ST Carr RL., 1965, CHEM ENG-NEW YORK, V18, P163, DOI DOI 10.1016/J.JAEROSCI.2007.10.003 Ceruti F. C., 2006, Proceedings of the 9th International Working Conference on Stored-Product Protection, ABRAPOS, Passo Fundo, RS, Brazil, 15-18 October 2006, P1198 Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Faqih AMN, 2007, INT J PHARM, V336, P338, DOI 10.1016/j.ijpharm.2006.12.024 Ganesan V, 2009, FOOD BIOPROCESS TECH, V2, P156, DOI 10.1007/s11947-007-0026-x Guzel E, 2007, J FOOD ENG, V80, P385, DOI 10.1016/j.jfoodeng.2005.11.019 Herrman T J., WHITE PAPER TRACEABI Hirai Y, 2006, APPL ENG AGRIC, V22, P747 IRANLOYE TA, 1978, J PHARM SCI, V67, P535, DOI 10.1002/jps.2600670424 Kamst GF, 2002, T ASAE, V45, P145, DOI 10.13031/2013.7857 Kvarnstrom B, 2011, QUAL ENG, V23, P343, DOI 10.1080/08982112.2011.602278 Lee KM, 2011, FOOD CONTROL, V22, P1085, DOI 10.1016/j.foodcont.2010.12.016 Lee KM, 2010, J AGR FOOD CHEM, V58, P10945, DOI 10.1021/jf101370k Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Liang K, 2012, BIOSYST ENG, V113, P395, DOI 10.1016/j.biosystemseng.2012.09.012 Matsumoto R, 2007, INT J PHARMACEUT, V341, P44, DOI 10.1016/j.ijpharm.2007.03.055 Nakahira S., 2017, AAPS PHARMSCITECH, P10729 Narsimhalu U, 2015, PROCD SOC BEHV, V189, P17, DOI 10.1016/j.sbspro.2015.03.188 Otsuka T, 2011, INT J PHARMACEUT, V409, P81, DOI 10.1016/j.ijpharm.2011.02.044 Sacchetti M, 2017, AAPS PHARMSCITECH, P1 Sakurai Y, 2017, J FOOD ENG, V206, P118, DOI 10.1016/j.jfoodeng.2017.03.010 SANGHVI PP, 1993, PHARMACEUT RES, V10, P1597, DOI 10.1023/A:1018968502586 Shafaei S. M., 2016, Information Processing in Agriculture, V3, P133, DOI 10.1016/j.inpa.2016.05.003 Subramanian S, 2007, J FOOD ENG, V81, P118, DOI 10.1016/j.jfoodeng.2006.09.026 Sui R X, 2007, AS ANN INT M Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Wang X, 2017, COMPUT ELECTRON AGR, V135, P195, DOI 10.1016/j.compag.2016.12.019 Yang ZuoMei, 2016, Transactions of the Chinese Society of Agricultural Engineering, V32, P258, DOI 10.11975/j.issn.1002-6819.2016.16.035 Zhang Yeli, 2003, AAPS PharmSciTech, V4, pE62, DOI 10.1208/pt040462 Zhu HY, 2017, FOOD CONTROL, V73, P1256, DOI 10.1016/j.foodcont.2016.10.045 NR 33 TC 4 Z9 4 U1 0 U2 9 PD NOV PY 2017 VL 10 IS 6 BP 221 EP 230 DI 10.25165/j.ijabe.20171006.3531 WC Agricultural Engineering SC Agriculture UT WOS:000417884400023 DA 2022-12-14 ER PT J AU Heyder, M Theuvsen, L Hollmann-Hespos, T AF Heyder, Matthias Theuvsen, Ludwig Hollmann-Hespos, Thorsten TI Investments in tracking and tracing systems in the food industry: A PLS analysis SO FOOD POLICY DT Article DE Traceability; Tracking and tracing; Investments; Food industry; Investment model ID INFORMATION-TECHNOLOGY; QUALITY-ASSURANCE; USER ACCEPTANCE; SAFETY; TRACEABILITY; REPUTATION; ADOPTION; RELIABILITY; INSTRUMENT; MANAGEMENT AB Traceability and related concepts, such as trust and transparency have gained greatly in relevance in food supply chains. This study seeks to answer what exactly determines firms' investments in traceability systems by developing and testing a theoretical framework using partial least squares methodology and empirical data from 234 companies of the German food industry. The results reveal that high external pressure to implement improves the image of tracking and tracing systems in the sense that their use enhances a firm's status, increases the intention to use those systems and fosters their perceived usefulness in the eyes of agribusiness executives. The hypothesized negative effect of costs on perceived usefulness and the intention to invest could not be verified. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Heyder, Matthias; Theuvsen, Ludwig] Univ Gottingen, Dept Agr Econ & Rural Dev, D-37073 Gottingen, Germany. [Hollmann-Hespos, Thorsten] Chamber Agr Lower Saxony, D-30159 Hannover, Germany. C3 University of Gottingen RP Heyder, M (corresponding author), Univ Gottingen, Dept Agr Econ & Rural Dev, Pl Goettinger Sieben 5, D-37073 Gottingen, Germany. EM mheyder@uni-goettingen.de; theuvsen@uni-goettingen.de; Thorsten.Hollmann-Hespos@LWK-Niedersachsen.de CR Agarwal R, 1997, DECISION SCI, V28, P557, DOI 10.1111/j.1540-5915.1997.tb01322.x AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Ajzen I., 1985, UNDERSTANDING ATTITU, P11, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2] AKERLOF GA, 1970, Q J ECON, V84, P488, DOI 10.2307/1879431 Amanor-Boadu V, 2004, DYNAMICS IN CHAINS AND NETWORKS, P238 [Anonymous], 2004, AGRIBUSINESS SOC COR [Anonymous], FOOD SAFETY LAW EURO [Anonymous], PROBLEME VALIDIERUNG Arienzo A, 2008, INT LIBR ENVIRON AGR, V15, P23 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Bhuptani M., 2005, RFID FIELD GUIDE DEP Blackman S., 1999, DESIGN ANAL MODERN T Bocker A., 2003, QUALITY ASSURANCE RI, P51 Boudreau, 2000, COMMUN AIS, V4, P1, DOI [10.17705/1cais.00407, DOI 10.17705/1CAIS.00407] Bracken J., 2005, BEEF TRACEABILITY CA Brunner M, 2005, EDUC PSYCHOL MEAS, V65, P227, DOI 10.1177/0013164404268669 Bubb H, 2005, HUM FACTOR ERGON MAN, V15, P353, DOI 10.1002/hfm.20032 Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Buhr B.L., 2003, 035 INT AGR RES CONS Bullock D.S., 2000, EC NONGMO SEGREGATIO Bulut H., 2007, SURVEY MEAT SLAUGHTE Cass A.O., 2001, AUSTR MARKETING J, V9, P46, DOI 10.1016/s1441-3582(01)70166-8 Chin WW, 1998, MIS QUART, V22, pVII Chin WW, 1998, QUANT METH SER, P295 Coff C, 2008, INT LIBR ENVIRON AGR, V15, P195 Coff C, 2008, INT LIBR ENVIRON AGR, V15, P1 Cohen J., 1988, STAT POWER ANAL BEHA, DOI 10.4324/9780203771587 COHEN MD, 1972, ADMIN SCI QUART, V17, P1, DOI 10.2307/2392088 Cyert Richard M., 1992, BEHAV THEORY FIRM DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 de Jonge J, 2008, FOOD QUAL PREFER, V19, P439, DOI 10.1016/j.foodqual.2008.01.002 Deimel M., 2008, Journal on Chain and Network Science, V8, P21, DOI 10.3920/JCNS2008.x086 Demsetz H., 1997, EC BUSINESS FIRM 7 C Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Fearne A., 1998, SUPPLY CHAIN MANAG, V3, P214, DOI DOI 10.1108/13598549810244296 Fitzgerald A.I., 1999, P 12 INT FARM MAN C Flynn A, 2003, POLIT QUART, V74, P38, DOI 10.1111/1467-923X.00510 FMRIC-Food Marketing Research and Information Center, 2008, HDB INTR FOOD TRAC S Fombrun CJ., 1996, REPUTATION FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Fowler F. J., 2013, SURV RES METHODS-GER Fritz M., 2007, INT FOOD AGRIBUS MAN, V10, P141, DOI DOI 10.22004/AG.ECON.8185 Gawron JC, 2009, J INT FOOD AGRIBUS M, V21, P239, DOI 10.1080/08974430802589683 Gellynck X., 2007, Quality management in food chains, P45 Golan E., 2004, 830 USDA Goldsmith P., 2004, 2004 C AM AGR EC ASS Haertel I., 2007, NEUE HAFTUNGSRISIKEN, P21 Hair J., 2005, MULTIVAR DATA ANAL Hanf J., 2007, Quality management in food chains, P489 Hardgrave B.C., 2006, RFIDS IMPACT OUT STO HENSON S, 1993, FOOD POLICY, V18, P152, DOI 10.1016/0306-9192(93)90023-5 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hobbs JE, 2002, FOOD CONTROL, V13, P77, DOI 10.1016/S0956-7135(01)00103-7 Hofstede G. J., 2003, INFORM TECHNOLOGY BE, P17 Holleran E, 1999, FOOD POLICY, V24, P669, DOI 10.1016/S0306-9192(99)00071-8 Hornibrook S., 2005, 2 EUR FOR MARK DRIV Hutton JG, 2001, PUBLIC RELAT REV, V27, P247, DOI 10.1016/S0363-8111(01)00085-6 Igbaria M, 1997, MIS QUART, V21, P279, DOI 10.2307/249498 Jonas K, 1996, Z SOZIALPSYCHOL, V27, P18 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Lai TL, 2004, INFORM SYST FRONT, V6, P353, DOI 10.1023/B:ISFI.0000046377.32617.3d Lee JS, 2003, EDUC TECHNOL SOC, V6, P50 Lindgreen A., 2003, British Food Journal, V105, P328, DOI 10.1108/00070700310481702 LISKA AE, 1984, SOC PSYCHOL QUART, V47, P61, DOI 10.2307/3033889 Maltsbarger R., 2000, AGBIOFORUM, V3, P236 March J.G., 1958, ORGANIZATIONS Margin B., 2009, P 42 HAW INT C SYST, P1 Marwick N., 1997, EUR J MARKETING, V31, P396, DOI DOI 10.1108/EB060639 MEYER JW, 1977, AM J SOCIOL, V83, P340, DOI 10.1086/226550 Miller K., 2005, COMMUNICATION THEORI Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192 Mousavi A, 2005, INNOV FOOD SCI EMERG, V6, P91, DOI 10.1016/j.ifset.2004.10.006 Mulder RWAW, 2007, J APPL POULTRY RES, V16, P92, DOI 10.1093/japr/16.1.92 Musshoff O, 2008, AGR ECON-BLACKWELL, V39, P135, DOI 10.1111/j.1574-0862.2008.00321.x Neville BA, 2005, EUR J MARKETING, V39, P1184, DOI 10.1108/03090560510610798 Nunnally J, 1994, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882 Perez MP., 2004, EUR J INNOV MANAG, V7, P280 Porter M.E., 1980, COMPETITIVE STRATEGY Raynaud E, 2009, IND CORP CHANGE, V18, P835, DOI 10.1093/icc/dtp026 Resende-Filho M., 2007, EC TRACEABILITY MITI Ringle M.C., 2005, SMARTPIS 2 0 BETA Rogers E.M., 1995, DIFFUSION INNOVATION Rosada M., 2003, CCG FOR OCT 12 2003 SCHOEMAKER PJH, 1993, J MANAGE STUD, V30, P107, DOI 10.1111/j.1467-6486.1993.tb00297.x Schulze H, 2008, INT FOOD AGRIBUS MAN, V11, P99 Smith G.C., 2000, TRACEBACK TRACEABILI Smith ME, 2009, INT SER OPER RES MAN, V124, P219 Souza-Monteiro D.M., 2004, EC IMPLEMENTING TRAC Spiller A., 2008, ZUKUNFTSPERSPEKTIVEN, pV Spriggs J.M., 2000, INT FOOD AGRIBUS MAN, V3, P95 Theuvsen L., 2004, Quality assurance, risk management and environmental control in agriculture and food supply networks: Proceedings of the 82nd Seminar of the European Association of Agricultural Economists (EAAE) held in Bonn, Germany on 14-16 May 2003 (Volumes A and B), P223 Theuvsen L., 2004, Journal on Chain and Network Science, V4, P125, DOI 10.3920/JCNS2004.x047 Theuvsen L., 2005, Z AGRARINFORMATIK, V13, P49 Theuvsen L., 2005, UMWELT PRODUKTQUALIT Theuvsen L, 2004, INTEGRATION DATENSIC, P49 Theuvsen L., 2005, P EFITA WCCA 2005 JO, P914 Trautman D., 2008, 022008 U ALB Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 UNGSON GR, 1981, ADMIN SCI QUART, V26, P116, DOI 10.2307/2392604 Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926 Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540 Venkatesh V, 2007, J ASSOC INF SYST, V8, P267, DOI 10.17705/1jais.00120 Wold H., 1973, MULTIVARIATE ANAL, P383, DOI [https://doi.org/10.1016/B978-0-12-426653-7.50032-6, DOI 10.1016/B978-0-12-426653-7.50032-6, 10.1016/B978-0-12-426653-7.50032-6] YOON ES, 1993, J BUS RES, V27, P215, DOI 10.1016/0148-2963(93)90027-M Zhang C, 2009, INT J PROD ECON, V120, P252, DOI 10.1016/j.ijpe.2008.07.023 NR 105 TC 50 Z9 51 U1 5 U2 72 PD FEB PY 2012 VL 37 IS 1 BP 102 EP 113 DI 10.1016/j.foodpol.2011.11.006 WC Agricultural Economics & Policy; Economics; Food Science & Technology; Nutrition & Dietetics SC Agriculture; Business & Economics; Food Science & Technology; Nutrition & Dietetics UT WOS:000300130600011 DA 2022-12-14 ER PT J AU Xu, XW Lu, QH Liu, Y Zhu, LM Yao, HN Vasilakos, AV AF Xu, Xiwei Lu, Qinghua Liu, Yue Zhu, Liming Yao, Haonan Vasilakos, Athanasios V. TI Designing blockchain-based applications a case study for imported product traceability SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE DT Article DE Blockchain; Smart contract; Adaptability; Software architecture AB Blockchain technology enables decentralization as new forms of distributed software architectures, where components can reach agreements on the shared system states without trusting on a central integration point. Since blockchain is an emerging technology which is still at an early stage of development, there is limited experience on applying blockchain to real-world software applications. We applied blockchain application design approaches proposed in software architecture community in a real-world project called originChain, which is a blockchain-based traceability system that restructures the current system by replacing the central database with blockchain. In this paper, we share our experience of building originChain. By using blockchain and designing towards security, originChain provides transparent tamper-proof traceability data with high availability and enables automated regulatory-compliance checking and adaptation in product traceability scenarios. We also demonstrate both qualitative and quantitative analysis of the software architecture of originChain. Based on our experience and analysis, we found that the structural design of smart contracts has large impact on the quality of the system. (C) 2018 Elsevier B.V. All rights reserved. C1 [Xu, Xiwei; Lu, Qinghua; Zhu, Liming] CSIRO, Data61, Sydney, NSW, Australia. [Xu, Xiwei; Lu, Qinghua; Zhu, Liming] UNSW, Sch Comp Sci & Engn, Sydney, NSW, Australia. [Liu, Yue; Yao, Haonan] China Univ Petr East China, Coll Comp & Commun Engn, Qingdao, Peoples R China. [Vasilakos, Athanasios V.] Lulea Univ Technol, Lulea, Sweden. C3 Commonwealth Scientific & Industrial Research Organisation (CSIRO); University of New South Wales Sydney; China University of Petroleum; Lulea University of Technology RP Lu, QH (corresponding author), CSIRO, Data61, Sydney, NSW, Australia. EM xiwei.xu@data61.csiro.au CR Ali M., 2016, USENIX ATC [Anonymous], 2016, TECHNICAL REPORT Bartoletti M., 2017, ARXIV E PRINTS Chen S., 2016, 13 WORK IEEE IFIP C Eberhardt J, 2017, LECT NOTES COMPUT SC, V10465, P3, DOI 10.1007/978-3-319-67262-5_1 Kharif O., 2016, WALMART TACKLES FOOD Liang X., 2017, CCGRID Lo S.K., 2017, 22 INT C ENG COMPL C Lu QH, 2017, IEEE SOFTWARE, V34, P21, DOI 10.1109/MS.2017.4121227 Matsumoto S., 2017, IEEE SSP Mehta N. R., 2000, ICSE Mu N, 2017, IEEE INT C ELECTR TA Omohundro S., 2014, AI MATTERS, V1, P19, DOI [10.1145/2685328.2685334, DOI 10.1145/2685328.2685334] Staples M., 2017, TECHNICAL REPORT Swan M., 2015, BLOCKCHAIN BLUEPRINT Tschorsch F, 2016, IEEE COMMUN SURV TUT, V18, P2084, DOI 10.1109/COMST.2016.2535718 Weber I., 2017, SRDS Weber I, 2016, LECT NOTES COMPUT SC, V9850, P329, DOI 10.1007/978-3-319-45348-4_19 Zhang P., 2017, ARXIV E PRINTS Zyskind G., 2015, SPW NR 20 TC 78 Z9 86 U1 8 U2 181 PD MAR PY 2019 VL 92 BP 399 EP 406 DI 10.1016/j.future.2018.10.010 WC Computer Science, Theory & Methods SC Computer Science UT WOS:000454370600034 DA 2022-12-14 ER PT J AU Amenta, M Fabroni, S Costa, C Rapisarda, P AF Amenta, M. Fabroni, S. Costa, C. Rapisarda, P. TI Traceability of 'Limone di Siracusa PGI' by a multidisciplinary analytical and chemometric approach SO FOOD CHEMISTRY DT Article DE Citrus limon (L.) Burm. F.; Authenticity; Near Infrared Spectroscopy; Multi-Elemental Analysis; Isotopic Ratio Mass Spectrometry ID STABLE-ISOTOPE; GEOGRAPHICAL ORIGIN; NIR SPECTROSCOPY; SOLUBLE-SOLIDS; JUICES; AUTHENTICITY; MULTIELEMENT; PERFORMANCE; SELECTION; FIRMNESS AB Food traceability is increasingly relevant with respect to safety, quality and typicality issues. Lemon fruits grown in a typical lemon-growing area of southern Italy (Siracusa), have been awarded the PGI (Protected Geographical Indication) recognition as 'Limone di Siracusa'. Due to its peculiarity, consumers have an increasing interest about this product. The detection of potential fraud could be improved by using the tools linking the composition of this production to its typical features. This study used a wide range of analytical techniques, including conventional techniques and analytical approaches, such as spectral (NIR spectra), multi-elemental (Fe, Zn, Mn, Cu, Li, Sr) and isotopic (C-13/C-12, O-18/O-16) marker investigations, joined with multivariate statistical analysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Discriminant Analysis), to implement a traceability system to verify the authenticity of 'Limone di Siracusa' production. The results demonstrated a very good geographical discrimination rate. (C) 2016 Elsevier Ltd. All rights reserved. C1 Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Agrumicoltura & Colture Mediterranee CRE, Cso Savoia 190, I-95024 Acireale, CT, Italy. Unita Ric Ingn Agr CREA ING, Consiglio Ric Agr & Anal Econ Agr CREA, Via Pascolare 16, I-00015 Rome, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Rapisarda, P (corresponding author), Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Agrumicoltura & Colture Mediterranee CRE, Corso Savoia 190, I-95024 Acireale, CT, Italy. EM paolo.rapisarda@crea.gov.it CR Abramo G, 2015, SCIENTOMETRICS, V102, P1755, DOI 10.1007/s11192-014-1474-5 Angerosa F, 1999, J AGR FOOD CHEM, V47, P1013, DOI 10.1021/jf9809129 [Anonymous], [No title captured] [Anonymous], 2001, NEAR INFRARED TECHNO Arijama K., 2006, JAPAN AGR RES Q, V40, P333 Arijama K., 2006, J AGR FOOD CHEM, V54, P3341 Bengoechea L. M., 1997, J AGR FOOD CHEM, V45, P4071 Blanch GP, 1998, J AGR FOOD CHEM, V46, P3153, DOI 10.1021/jf9800209 Bontempo L, 2014, J MASS SPECTROM, V49, P785, DOI 10.1002/jms.3420 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Chong IG, 2005, CHEMOMETR INTELL LAB, V78, P103, DOI 10.1016/j.chemolab.2004.12.011 Cozzolino D, 2003, J AGR FOOD CHEM, V51, P7703, DOI 10.1021/jf034959s Forina M, 2008, CHEMOMETR INTELL LAB, V93, P132, DOI 10.1016/j.chemolab.2008.05.003 Forina M, 2008, ANAL CHIM ACTA, V622, P85, DOI 10.1016/j.aca.2008.05.065 Giammanco S, 1996, WATER RES, V30, P378, DOI 10.1016/0043-1354(95)00183-2 Giammanco S, 2007, PURE APPL GEOPHYS, V164, P2523, DOI 10.1007/s00024-007-0286-4 Gomez AH, 2006, J FOOD ENG, V77, P313, DOI 10.1016/j.jfoodeng.2005.06.036 Guyon F, 2014, FOOD CHEM, V146, P36, DOI 10.1016/j.foodchem.2013.09.020 KAWANO S, 1993, J JPN SOC HORTIC SCI, V62, P465, DOI 10.2503/jjshs.62.465 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 KENNARD RW, 1969, TECHNOMETRICS, V11, P137, DOI 10.2307/1266770 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Manley M., 2008, MODERN TECHNIQUES FO, P65 McGlone VA, 1998, POSTHARVEST BIOL TEC, V13, P131, DOI 10.1016/S0925-5214(98)00007-6 Menesatti P, 2014, FOOD BIOPROCESS TECH, V7, P1364, DOI 10.1007/s11947-013-1138-0 PALMER KF, 1974, J OPT SOC AM, V64, P1107, DOI 10.1364/JOSA.64.001107 Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Perez AL, 2006, J AGR FOOD CHEM, V54, P4506, DOI 10.1021/jf0600455 Rapisarda P, 1996, ITAL J FOOD SCI, V8, P251 Rodriguez-Saona LE, 2001, CARBOHYD RES, V336, P63, DOI 10.1016/S0008-6215(01)00244-0 Sabatier R., 2003, DATA SCI APPL DATA A Silva BM, 2000, J AGR FOOD CHEM, V48, P2853, DOI 10.1021/jf9911040 Sjostrom M., 1986, PATTERN RECOGNITION, VII Swierenga H, 1998, CHEMOMETR INTELL LAB, V41, P237, DOI 10.1016/S0169-7439(98)00055-0 Taiti C, 2015, J SCI FOOD AGR, V95, P1757, DOI 10.1002/jsfa.6761 Williams P.C., 1987, NEAR INFRARED TECHNO, P241 Yuntseover Y., 1981, IAEA TECHNICAL REPOR NR 38 TC 10 Z9 12 U1 1 U2 54 PD NOV 15 PY 2016 VL 211 BP 734 EP 740 DI 10.1016/j.foodchem.2016.05.119 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000377543500090 DA 2022-12-14 ER PT J AU Chen, SC Chiu, KKS Chen, HH Kao, Y Chang, CF AF Chen, Shih-Chih Chiu, Kevin Kuan-Shun Chen, Huei-Huang Kao, Yucheng Chang, Ching-Fen TI A reference model of RFID-enabled application for traceability of foods production and distribution SO AFRICAN JOURNAL OF AGRICULTURAL RESEARCH DT Article DE Radio frequency identification (RFID); architecture of integrated information system (ARIS) ID TECHNOLOGY AB This paper employs architecture of integrated information system (ARIS) as the holistic modeling methodology for analyzing step-by-step processes and radio frequency identification (RFID) as the tracking device for recording all procedures in logistics. The major purpose of this study is to establish an information traceability system on agricultural product for coordination and quality control over production, distribution, and consumption. The proposed model incorporates EPCglobal Network (TM) standards to generate, integrate, and disseminate the comprehensive records of paddy rice among farmers, distributors, inspectors, and consumers. C1 [Chen, Shih-Chih; Chen, Huei-Huang; Kao, Yucheng; Chang, Ching-Fen] Tatung Univ, Dept Informat Management, Taipei 104, Taiwan. [Chiu, Kevin Kuan-Shun] Lunghwa Univ Sci & Technol, Grad Sch Business & Management, Tao Yuan, Taiwan. C3 Tatung University RP Chen, SC (corresponding author), Tatung Univ, Dept Informat Management, 40,Sec 3,Chungshan N Rd, Taipei 104, Taiwan. EM scchen@ttu.edu.tw CR *AUT ID CTR, 2011, TECHN GUID Chang TH, 2007, PROD PLAN CONTROL, V18, P117, DOI 10.1080/09537280600913447 CHEN SC, 2006, J GLOB BUS MANAGE, V2, P259 Domdouzis K, 2007, ADV ENG INFORM, V21, P350, DOI 10.1016/j.aei.2006.09.001 Hossain MM, 2008, IEEE T ENG MANAGE, V55, P316, DOI 10.1109/TEM.2008.919728 Juan YC, 2005, INT J ADV MANUF TECH, V26, P191, DOI 10.1007/s00170-003-1970-x Leimeister S, 2009, INT J INFORM MANAGE, V29, P37, DOI 10.1016/j.ijinfomgt.2008.05.006 Ou-Yang C, 2008, INT J ADV MANUF TECH, V35, P943, DOI 10.1007/s00170-006-0779-9 Poon TC, 2009, EXPERT SYST APPL, V36, P8277, DOI 10.1016/j.eswa.2008.10.011 Reyes P. M., 2007, International Journal of Integrated Supply Management, V3, P125, DOI 10.1504/IJISM.2007.011972 Sari K, 2010, EUR J OPER RES, V207, P174, DOI 10.1016/j.ejor.2010.04.003 SCHEER AW, 2001, BUSINESS PROCESS MOD Wang SJ, 2008, INT J PROD ECON, V112, P570, DOI 10.1016/j.ijpe.2007.05.002 Wen W, 2010, EXPERT SYST APPL, V37, P3024, DOI 10.1016/j.eswa.2009.09.030 Wu NC, 2006, TECHNOVATION, V26, P1317, DOI 10.1016/j.technovation.2005.08.012 Yin SYL, 2009, AUTOMAT CONSTR, V18, P677, DOI 10.1016/j.autcon.2009.02.004 NR 16 TC 1 Z9 1 U1 1 U2 14 PD OCT 12 PY 2011 VL 6 IS 22 BP 5192 EP 5197 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000298503700023 DA 2022-12-14 ER PT J AU Zhou, JH Li, Y Zhao, J Xue, XF Wu, LM Chen, F AF Zhou, Jinhui Li, Yi Zhao, Jing Xue, Xiaofeng Wu, Liming Chen, Fang TI Geographical traceability of propolis by high-performance liquid-chromatography fingerprints SO FOOD CHEMISTRY DT Article DE Chinese propolis; geographical traceability; fingerprint; HPLC; ultrasound-assisted extraction ID QUALITY-CONTROL; CHEMICAL FINGERPRINT; ANTIBACTERIAL; FLAVONOIDS; EXTRACTION AB A rapid fingerprint method was developed for investigating and inferring geographical origin of Chinese propolis by using high performance liquid chromatography-ultraviolet detection (HPLC-UV). 120 samples were analyzed from 17 different locations of 10 provinces of China in this study. In the HPLC chromatograms, eight major compounds were identified as flavonoids, including rutin, myricetin, quercetin, kaempferol, apigenin, pinocembrine, chrysin and galangin. Both correlation coefficient of similarity in chromatograms and relative peak areas of characteristic compounds were calculated for quantitative expression of the HPLC fingerprints. Our results revealed that the presence or absence of specific peaks and similarity evaluation in simulative mean chromatograms among different regions could efficiently identify and distinguish Chinese propolis from different geographical origins. (C) 2007 Elsevier Ltd. All rights reserved. C1 [Zhou, Jinhui; Li, Yi; Zhao, Jing; Xue, Xiaofeng; Wu, Liming; Chen, Fang] Chinese Acad Agr Sci, Bee Res Inst, Beijing 100093, Peoples R China. C3 Chinese Academy of Agricultural Sciences RP Li, Y (corresponding author), Chinese Acad Agr Sci, Bee Res Inst, Beijing 100093, Peoples R China. EM wittyzhou@126.com CR Bankova VS, 2000, APIDOLOGIE, V31, P3, DOI 10.1051/apido:2000102 Banskota AH, 2002, J ETHNOPHARMACOL, V80, P67, DOI 10.1016/S0378-8741(02)00022-3 Banskota AH, 2001, PHYTOTHER RES, V15, P561, DOI 10.1002/ptr.1029 Barak V, 2002, ISR MED ASSOC J, V4, P919 Bazo AP, 2002, TERATOGEN CARCIN MUT, V22, P183, DOI 10.1002/tcm.10011 Boue SM, 2003, J CHROMATOGR A, V991, P61, DOI 10.1016/S0021-9673(03)00209-7 BRONNER WE, 1995, J CHROMATOGR A, V705, P247, DOI 10.1016/0021-9673(95)00304-6 Castaldo S, 2002, FITOTERAPIA, V73, pS1, DOI 10.1016/S0367-326X(02)00185-5 CHRISTOPHER RH, 2005, J CHROMATOGR A, V1062, P199 Ji YB, 2005, J CHROMATOGR A, V1066, P97, DOI 10.1016/j.chroma.2005.01.035 Jiang FQ, 2007, J ETHNOPHARMACOL, V111, P265, DOI 10.1016/j.jep.2006.11.024 Justesen U, 1998, J CHROMATOGR A, V799, P101, DOI 10.1016/S0021-9673(97)01061-3 KELLY L, 2001, INT S QUAL TCM CHROM, V259, P57 Kimoto T, 2001, ANTICANCER RES, V21, P221 Kujumgiev A, 1999, J ETHNOPHARMACOL, V64, P235, DOI 10.1016/S0378-8741(98)00131-7 Liang YZ, 2004, J CHROMATOGR B, V812, P53, DOI 10.1016/j.jchromb.2004.08.041 LUIS B, 2007, J CHROMATOGR A, V1157, P369 Murad JM, 2002, J ETHNOPHARMACOL, V79, P331, DOI 10.1016/S0378-8741(01)00404-4 Ng TB, 2000, LIFE SCI, V66, P709, DOI 10.1016/S0024-3205(99)00642-6 PIERRE M, 1998, J CHROMATOGR A, V800, P171 Pietta PG, 2002, FITOTERAPIA, V73, pS7, DOI 10.1016/S0367-326X(02)00186-7 Qi SD, 2006, J CHROMATOGR A, V1109, P300, DOI 10.1016/j.chroma.2006.01.045 Qiao CF, 2007, J SEP SCI, V30, P813, DOI 10.1002/jssc.200600339 SFDA. State Drug Administration of China, 2000, CHIN TRADIT PAT MED, V22, P671 Sforcin JM, 2000, J ETHNOPHARMACOL, V73, P243, DOI 10.1016/S0378-8741(00)00320-2 STREHL E, 1994, Z NATURFORSCH C, V49, P39 Volpi N, 2006, J PHARMACEUT BIOMED, V42, P354, DOI 10.1016/j.jpba.2006.04.017 Wang Long-xing, 2002, Yaoxue Xuebao, V37, P713 WANG QF, 2004, CHINESE J HUAIHAI I, V13, P60 Wang X, 2005, J CHROMATOGR A, V1090, P188, DOI 10.1016/j.chroma.2005.07.023 Wollenweber E., 1990, Bulletin de Liaison - Groupe Polyphenols, V15, P112 XIAO CH, 1989, CHEM TRADITIONAL CHI, P192 XIE PS, 2005, CHROMATOGRAPHY FINGE, P18 Yu Zhang, 2005, Journal of Chromatography A, V1065, P177, DOI 10.1016/j.chroma.2004.12.086 Zhang JL, 2005, J PHARMACEUT BIOMED, V36, P1029, DOI 10.1016/j.jpba.2004.09.009 ZHAO J, 2005, APICULT CHINA, V56, P9 ZHOU P, 2005, CHINESE J BEE, V8, P5 Zhu En-Yuan, 2005, Zhongguo Zhong Yao Za Zhi, V30, P1423 NR 38 TC 45 Z9 56 U1 1 U2 26 PD MAY 15 PY 2008 VL 108 IS 2 BP 749 EP 759 DI 10.1016/j.foodchem.2007.11.009 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000253033400040 DA 2022-12-14 ER PT J AU Holman, LE de la Serrana, DG Onoufriou, A Hillestad, B Johnston, IA AF Holman, Luke E. de la Serrana, Daniel Garcia Onoufriou, Aubrie Hillestad, Borghild Johnston, Ian A. TI A workflow used to design low density SNP panels for parentage assignment and traceability in aquaculture species and its validation in Atlantic salmon SO AQUACULTURE DT Article DE SNP; Parentage assignment; Atlantic salmon; Traceability; Pedigree; Workflow ID FISH POPULATIONS; MULTIPLEX PCR; SALAR; RELATEDNESS; RESISTANCE; ESCAPEES; FORMAT AB Accurate parentage assignment is key for the development of a successful breeding program, allowing pedigree reconstruction from mixed families and control of inbreeding. In the present study we developed a workflow for the design of an efficient single nucleotide polymorphism (SNP) panel for paternity assignment and validated it in Atlantic salmon (Salmo salar L.). A total of 86,468 SNPs were identified from Restriction Site Associated DNA Sequencing (RAD-seq) libraries, and reduced to 1517 following the application of quality control filters and stringent selection criteria. A subsample of SNPs were chosen for the design of high-throughput SNP assays and a training set of known parents and offspring was then used to achieve further filtering. A panel comprising 94 SNPs balanced across the salmon genome were identified, providing 100% assignment accuracy in known pedigrees. Additionally, the panel was able to assign individuals to one of three farmed salmon populations used in this study with 100% accuracy. We conclude that the workflow described is suitable for the design of cost effective parentage assignment and traceability tools for aquaculture species. C1 [Holman, Luke E.; de la Serrana, Daniel Garcia; Johnston, Ian A.] Univ St Andrews, Sch Biol, Scottish Oceans Inst, St Andrews KY16 8LB, Fife, Scotland. [Holman, Luke E.; Onoufriou, Aubrie; Johnston, Ian A.] Xelect Ltd, Horizon House, St Andrews KY16 9LB, Fife, Scotland. [Hillestad, Borghild] SalmoBreed AS, Sandviksboder 3A, N-5035 Bergen, Norway. C3 University of St Andrews RP Holman, LE (corresponding author), Univ St Andrews, Sch Biol, Scottish Oceans Inst, St Andrews KY16 8LB, Fife, Scotland. EM lh535@st-andrews.ac.uk CR Anderson EC, 2006, GENETICS, V172, P2567, DOI 10.1534/genetics.105.048074 Baird NA, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0003376 Borrell YJ, 2011, AQUACULTURE, V314, P58, DOI 10.1016/j.aquaculture.2011.01.028 Danecek P, 2011, BIOINFORMATICS, V27, P2156, DOI 10.1093/bioinformatics/btr330 FAO, 2014, FAO YB GJEDREM T, 1991, AQUACULTURE, V98, P41, DOI 10.1016/0044-8486(91)90369-I Glover KA, 2010, AQUACULT ENV INTERAC, V1, P1, DOI 10.3354/aei00002 Glover KA, 2013, BMC GENET, V14, DOI 10.1186/1471-2156-14-74 Gonen S, 2015, HEREDITY, V115, P405, DOI 10.1038/hdy.2015.37 Gonen S, 2014, BMC GENOMICS, V15, DOI 10.1186/1471-2164-15-166 HINDAR K, 1991, CAN J FISH AQUAT SCI, V48, P945, DOI 10.1139/f91-111 Houston RD, 2014, BMC GENOMICS, V15, DOI 10.1186/1471-2164-15-90 Jonas E, 2015, FRONT GENET, V6, DOI 10.3389/fgene.2015.00049 Jones OR, 2010, MOL ECOL RESOUR, V10, P551, DOI 10.1111/j.1755-0998.2009.02787.x Kaiser S.A., 2016, MOL ECOL RESOUR KINCAID HL, 1983, AQUACULTURE, V33, P215, DOI 10.1016/0044-8486(83)90402-7 Langmead B, 2012, NAT METHODS, V9, P357, DOI [10.1038/NMETH.1923, 10.1038/nmeth.1923] Li H, 2009, BIOINFORMATICS, V25, P2078, DOI 10.1093/bioinformatics/btp352 Lien S, 2016, NATURE, V533, P200, DOI 10.1038/nature17164 Lien S, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-615 Liu SX, 2016, AQUACULTURE, V452, P178, DOI 10.1016/j.aquaculture.2015.11.001 Macqueen DJ, 2014, P ROY SOC B-BIOL SCI, V281, DOI 10.1098/rspb.2013.2881 Moen T, 2015, GENETICS, V200, P1313, DOI 10.1534/genetics.115.175406 Morvezen R, 2013, AQUAT LIVING RESOUR, V26, P207, DOI 10.1051/alr/2013052 Norris AT, 2000, AQUACULTURE, V182, P73, DOI 10.1016/S0044-8486(99)00247-1 O'Reilly PT, 1998, ANIM GENET, V29, P363, DOI 10.1046/j.1365-2052.1998.295359.x Pardo BG, 2005, SCI MAR, V69, P531, DOI 10.3989/scimar.2005.69n4531 Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Purcell S, 2007, AM J HUM GENET, V81, P559, DOI 10.1086/519795 R Development Core Team, 2014, R LANG ENV STAT COMP Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 Tange O., 2011, USENIX MAGAZINE, V36, P42 Vandeputte M, 2014, FRONT GENET, V5, DOI 10.3389/fgene.2014.00432 Weinman LR, 2015, MOL ECOL RESOUR, V15, P502, DOI 10.1111/1755-0998.12330 Yanez J.M., 2016, MOL ECOL RESOUR Yue GH, 2014, J WORLD AQUACULT SOC, V45, P89, DOI 10.1111/jwas.12107 Zheng XW, 2012, BIOINFORMATICS, V28, P3326, DOI 10.1093/bioinformatics/bts606 NR 37 TC 17 Z9 17 U1 0 U2 50 PD JUL 1 PY 2017 VL 476 BP 59 EP 64 DI 10.1016/j.aquaculture.2017.04.001 WC Fisheries; Marine & Freshwater Biology SC Fisheries; Marine & Freshwater Biology UT WOS:000402466100008 DA 2022-12-14 ER PT J AU Mullejans, H Zaaiman, W Merli, F Dunlop, ED Ossenbrink, HA AF Mullejans, H Zaaiman, W Merli, F Dunlop, ED Ossenbrink, HA TI Comparison of traceable calibration methods for primary photovoltaic reference cells SO PROGRESS IN PHOTOVOLTAICS DT Article DE calibration; primary reference cells; traceability; international standards; uncertainty analysis ID SOLAR-CELLS; RADIOMETRY AB The calibration of photovoltaic reference cells used as primary laboratory standards for the calibration of photovoltaic devices needs to be traceable to international radiometric standards and SI units. As a contribution to the development of an international standard this paper describes three methods for the calibration of primary photovoltaic reference cells, establishing two independent traceability chains. The solar simulator method is traceable via a standard lamp to the international irradiance scale whereas the global sunlight method and the modified global sunlight method are traceable to the world radiometric reference. The calibration values obtained by the three methods agree with each other within their respective uncertainties and with the world photovoltaic scale within +/- 0.8%. Copyright (c) 2005 John Wiley & Sons, Ltd. C1 Commiss European Communities, Joint Res Ctr, Directorate Gen, Inst Environm & Sustainabil,Renewable Energies Un, I-21020 Ispra, Italy. C3 European Commission Joint Research Centre; EC JRC ISPRA Site RP Mullejans, H (corresponding author), Commiss European Communities, Joint Res Ctr, Directorate Gen, Inst Environm & Sustainabil,Renewable Energies Un, TP 450,Via Fermi 1, I-21020 Ispra, Italy. EM harald.muellejans@cec.eu.int CR *ASTM E, 112599 ASTM E BUCHER K, 1993, IEEE PHOT SPEC CONF, P1188, DOI 10.1109/PVSC.1993.346954 CURTIS HB, 1980, P 14 IEEE SAN DIEG, P500 Emery K, 2002, CONFERENCE RECORD OF THE TWENTY-NINTH IEEE PHOTOVOLTAIC SPECIALISTS CONFERENCE 2002, P1725, DOI 10.1109/PVSC.2002.1190954 Emery K., 2000, NRELTP52027942 FIELD H, 1993, IEEE PHOT SPEC CONF, P1180, DOI 10.1109/PVSC.1993.346955 FROHLICH C, 1991, METROLOGIA, V28, P111, DOI 10.1088/0026-1394/28/3/001 *IEC, 1995, 609049 IEC *IEC, 1989, 609043 IEC *IEC, 1998, 609047 IEC *INT PYRH COMP IPC, 2001, 197 INT PYRH COMP IP International Organisation for Standardisation, 1995, GUID EXPR UNC MEAS Keogh WM, 2004, PROG PHOTOVOLTAICS, V12, P1, DOI 10.1002/pip.517 METZDORF J, 1987, APPL OPTICS, V26, P1701, DOI 10.1364/AO.26.001701 Metzdorf J, 2000, METROLOGIA, V37, P573, DOI 10.1088/0026-1394/37/5/52 MULLEJANS H, IN PRESS MEASUREMENT MULLEJANS H, UNPUB METROLOGIA Ossenbrink F, 2003, WORL CON PHOTOVOLT E, P2177 Osterwald C., 1998, NRELTP52023477 Osterwald C. R., 1990, P 21 IEEE PHOT SPEC, P1062 Osterwald CR, 1999, PROG PHOTOVOLTAICS, V7, P287, DOI 10.1002/(SICI)1099-159X(199907/08)7:4<287::AID-PIP259>3.0.CO;2-I ROES RFM, 1994, P 1 WCPEC HAW, P863 Romero J, 1996, METROLOGIA, V32, P523, DOI 10.1088/0026-1394/32/6/25 SHIMOKAWA R, 1987, JPN J APPL PHYS 1, V26, P86, DOI 10.1143/JJAP.26.86 WHITAKER RD, 1982, SOL CELLS, V7, P135, DOI 10.1016/0379-6787(82)90098-9 NR 25 TC 14 Z9 14 U1 0 U2 13 PD DEC PY 2005 VL 13 IS 8 BP 661 EP 671 DI 10.1002/pip.625 WC Energy & Fuels; Materials Science, Multidisciplinary; Physics, Applied SC Energy & Fuels; Materials Science; Physics UT WOS:000234005700002 DA 2022-12-14 ER PT J AU Mol, APJ Oosterveer, P AF Mol, Arthur P. J. Oosterveer, Peter TI Certification of Markets, Markets of Certificates: Tracing Sustainability in Global Agro-Food Value Chains SO SUSTAINABILITY DT Article DE voluntary certification initiatives; agro-food supply chains; traceability; sustainability; marketization ID CARBON FLOWS; FOOD; TRACEABILITY; CERTIFY AB There is a blossoming of voluntary certification initiatives for sustainable agro-food products and production processes. With these certification initiatives come traceability in supply chains, to guarantee the sustainability of the products consumed. No systematic analysis exists of traceability systems for sustainability in agro-food supply chains. Hence, the purpose of this article is to analyze the prevalence of four different traceability systems to guarantee sustainability; to identify the factors that determine the kind of traceability systems applied in particular supply chains; and to assess what the emergence of economic and market logics in traceability mean for sustainability. Two conclusions are drawn. Globalizing markets for sustainable agro-food products induces the emergence of book-and-claim traceability systems, but the other three systems (identity preservation, segregation and mass balance) will continue to exist as different factors drive traceability requirements in different supply chains. Secondly, traceability itself is becoming a market driven by economic and market logics, and this may have consequences for sustainability in agro-food supply chains in the future. C1 [Mol, Arthur P. J.; Oosterveer, Peter] Wageningen Univ, Environm Policy Grp, NL-6700 EW Wageningen, Netherlands. C3 Wageningen University & Research RP Oosterveer, P (corresponding author), Wageningen Univ, Environm Policy Grp, POB 8130, NL-6700 EW Wageningen, Netherlands. EM arthur.mol@wur.nl; peter.oosterveer@wur.nl CR [Anonymous], 2014, STATE SUSTAINABILITY [Anonymous], 2010, TRAC MARK CLAIM WORK [Anonymous], 2012, GREEN CARBON BLACK T Beall E, 2012, 50 FAO Bonsucro, 2013, GUID BONS Book & Claim, 2014, TRAD CERT SUPP ENV Bostrom M, 2008, CONSUM PUBLIC LIFE, P1, DOI 10.1057/9780230584006 Bullock DS, 2002, FOOD POLICY, V27, P81, DOI 10.1016/S0306-9192(02)00004-0 Bush SR, 2013, SCIENCE, V341, P1067, DOI 10.1126/science.1237314 Bush SR, 2015, J CLEAN PROD, V107, P8, DOI 10.1016/j.jclepro.2014.10.019 Byrne J., 2011, NEW CSPO DEAL MEANS Castree N, 2008, ENVIRON PLANN A, V40, P153, DOI 10.1068/a39100 Commission of the European Communities, 2008, ANN IMP ASS DOC ACC Dallinger Jonas, 2011, OIL PALM EXPANSION S, P24 Esty D., 2003, SSRN ELECT J, DOI [10.2139/ssrn.429580, DOI 10.2139/SSRN.429580] European Commission, 2015, VOL SCHEM Fiorillo J., ARE WORLDS RETAILERS Fuchs D, 2011, AGR HUM VALUES, V28, P353, DOI 10.1007/s10460-009-9236-3 Glin LC, 2013, AGR HUM VALUES, V30, P539, DOI 10.1007/s10460-013-9435-9 Global Roundtable for Sustainable Beef, 2014, PRINC CRIT IN PRESS Greenpalm, 2015, MARK VOL PRIC CHARTS Greenpalm, 2014, REC DEM 2014 RSPO CE Hatanaka M, 2005, FOOD POLICY, V30, P354, DOI 10.1016/j.foodpol.2005.05.006 INTERPOL, 2013, GUID CARB TRAD CRIM Kjaernes U, 2007, TRUST IN FOOD: A COMPARATIVE AND INSTITUTIONAL ANALYSIS, P1, DOI 10.1057/9780230627611 Kleindorfer PR, 1998, RISK ANAL, V18, P155, DOI 10.1111/j.1539-6924.1998.tb00927.x Laurent B, 2015, ENVIRON POLIT, V24, P138, DOI 10.1080/09644016.2014.927190 Manning L, 2014, FOOD POLICY, V49, P23, DOI 10.1016/j.foodpol.2014.06.005 McDonalds, 2015, OUR JOURN SUST BEEF Meyer S., 2013, GLOBAL BIOFUEL TRADE Miller A.M.M., 2014, GOVERNANCE INNOVATIO Mol A., 1996, ENVIRON POLIT, V5, P302, DOI DOI 10.1080/09644019608414266 Mol APJ, 2006, ENVIRON PLANN C, V24, P497, DOI 10.1068/c0508j Mol APJ, 2014, FOOD CONTROL, V43, P49, DOI 10.1016/j.foodcont.2014.02.034 Mol APJ, 2014, SOCIOL RURALIS, V54, P1, DOI 10.1111/soru.12026 Mol APJ, 2012, ENVIRON DEV, V1, P10, DOI 10.1016/j.envdev.2011.12.003 Neuendorff J., 2007, Quality management in food chains, P209 Newell P., 2010, CLIMATE CAPITALISM G Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Pacini H., 2013, EUR ENERGY J, V3, P17 Partzsch L, 2011, AGR HUM VALUES, V28, P413, DOI 10.1007/s10460-009-9235-4 Raadal HL, 2012, ENERG POLICY, V42, P419, DOI 10.1016/j.enpol.2011.12.006 Rainforest Alliance, 2014, RAINF ALL CONTR BLEN Richardson B, 2015, NEW POLIT ECON, V20, P545, DOI 10.1080/13563467.2014.923829 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 RSPO, 2012, RSPO SUPPL CHAIN CER RTRS, 2014, RTRS US LOG CLAIMS P RTRS, 2015, SCOP OV SUPPL CHAIN Scarlat N, 2011, ENERG POLICY, V39, P1630, DOI 10.1016/j.enpol.2010.12.039 Schaltegger S, 2014, SUPPLY CHAIN MANAG, V19, P232, DOI [10.1108/SCM-02-2014-0061, 10.1108/SCM-02-2014-0083] Shears P, 2010, BRIT FOOD J, V112, P198, DOI 10.1108/00070701011018879 Smyth S., 2002, AgBioForum, V5, P30 Spaargaren G, 2013, ENVIRON POLIT, V22, P174, DOI 10.1080/09644016.2013.755840 Staaij J.v.d., 2012, ANAL OPERATION MASS Stetter A., 2012, CERTIFYING NATURAL 2 Sustainable Agriculture Network (SAN) Secretariat, 2012, LIST PERM MASS BAL P UTZ, 2014, COCOA Van Riel MC, 2015, FISH FISH, V16, P453, DOI 10.1111/faf.12066 Veldstra MD, 2014, FOOD POLICY, V49, P429, DOI 10.1016/j.foodpol.2014.05.010 NR 59 TC 39 Z9 39 U1 4 U2 52 PD SEP PY 2015 VL 7 IS 9 BP 12258 EP 12278 DI 10.3390/su70912258 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000362553400046 DA 2022-12-14 ER PT J AU Liu, XL Guo, BL Wei, YM Shi, JL Sun, SM AF Liu, Xiaoling Guo, Boli Wei, Yimin Shi, Junling Sun, Shumin TI Stable isotope analysis of cattle tail hair: A potential tool for verifying the geographical origin of beef SO FOOD CHEMISTRY DT Article DE Carbon; Nitrogen; Hydrogen; IRMS; Traceability database ID RATIO ANALYSIS; FOOD SAFETY; CARBON; ASSIGNMENT; NITROGEN; DIET AB Stable isotope ratios of cattle tail hair were investigated for their potential to classify beef from different regions in China. The delta C-13, delta N-15 and delta H-2 values in 167 cattle tail hair samples from 7 sampling subregions belonging to four beef production regions were measured by IRMS. Variance analysis and linear discriminant analysis (LDA) were employed for this purpose. The results showed that significant differences existed in delta C-13, delta N-15 and delta H-2 values of tail hair among different sampling regions. An overall correct classification rate of 82.6% and cross-validation rate of 79.6% were obtained for the four beef production regions based on delta C-13, delta N-15 and delta H-2 values, compared to 70.7% and 70.1% for the seven sampling subregions. These results demonstrated the potential usefulness of stable isotope analysis of cattle tail hair for establishing beef traceability database. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Liu, Xiaoling; Guo, Boli; Wei, Yimin] Chinese Acad Agr Sci, Comprehens Key Lab Agroprod Proc, Minist Agr, Inst Agroprod Proc Sci & Technol, Beijing 100193, Peoples R China. [Liu, Xiaoling; Shi, Junling; Sun, Shumin] Northwest A&F Univ, Coll Food Sci & Engn, Yangling 712100, Shaanxi, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; Northwest A&F University - China RP Wei, YM (corresponding author), Chinese Acad Agr Sci, Comprehens Key Lab Agroprod Proc, Minist Agr, Inst Agroprod Proc Sci & Technol, POB 5109, Beijing 100193, Peoples R China. EM lxl12580394@hotmail.com; guoboli2007@126.com; weiyimin36@hotmail.com; sjlshi2004@yahoo.com.cn; xia-nyun730@163.com CR AMBROSE SH, 1986, OECOLOGIA, V69, P395, DOI 10.1007/BF00377062 Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Cai Deling, 2002, Journal of Ocean University of Qingdao, V32, P287 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Cardona CJ, 2009, COMP IMMUNOL MICROB, V32, P255, DOI 10.1016/j.cimid.2008.01.001 De Smet S, 2004, RAPID COMMUN MASS SP, V18, P1227, DOI 10.1002/rcm.1471 FISHER DD, 1985, AM J VET RES, V46, P2235 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 Hobson KA, 2003, OECOLOGIA, V136, P302, DOI 10.1007/s00442-003-1271-y Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Kornexl BE, 1997, Z LEBENSM UNTERS F A, V205, P19, DOI 10.1007/s002170050117 Latouche K, 1998, FOOD POLICY, V23, P347, DOI 10.1016/S0306-9192(98)00048-7 Leforban Y, 2002, COMP IMMUNOL MICROB, V25, P373, DOI 10.1016/S0147-9571(02)00033-4 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Macko SA, 1999, PHILOS T ROY SOC B, V354, P65, DOI 10.1098/rstb.1999.0360 Nakashita R, 2008, ANAL CHIM ACTA, V617, P148, DOI 10.1016/j.aca.2008.03.048 O'Regan HJ, 2008, J HUM EVOL, V55, P617, DOI 10.1016/j.jhevol.2008.05.001 Pan J. R., 2009, TECHNOLOGICAL STUDY, P47 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Rossmann A, 2000, EUR FOOD RES TECHNOL, V211, P32, DOI 10.1007/s002170050585 Schellenberg A, 2010, FOOD CHEM, V121, P770, DOI 10.1016/j.foodchem.2009.12.082 Schwertl M, 2005, AGR ECOSYST ENVIRON, V109, P153, DOI 10.1016/j.agee.2005.01.015 Schwertl M, 2003, RAPID COMMUN MASS SP, V17, P1312, DOI 10.1002/rcm.1042 Simpkins WA, 2000, FOOD CHEM, V70, P385, DOI 10.1016/S0308-8146(00)00086-8 Swoboda S, 2008, ANAL BIOANAL CHEM, V390, P487, DOI 10.1007/s00216-007-1582-7 West AG, 2004, FUNCT ECOL, V18, P616, DOI 10.1111/j.0269-8463.2004.00862.x NR 27 TC 26 Z9 26 U1 0 U2 100 PD SEP 15 PY 2013 VL 140 IS 1-2 BP 135 EP 140 DI 10.1016/j.foodchem.2013.02.020 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000318193500018 DA 2022-12-14 ER PT J AU Pasqualone, A Montemurro, C di Rienzo, V Summo, C Paradiso, VM Caponio, F AF Pasqualone, Antonella Montemurro, Cinzia di Rienzo, Valentina Summo, Carmine Paradiso, Vito Michele Caponio, Francesco TI Evolution and perspectives of cultivar identification and traceability from tree to oil and table olives by means of DNA markers SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Review DE olive oil; table olives; DNA; molecular markers; cultivar identification; traceability ID OLEA-EUROPAEA L.; NUCLEAR-MAGNETIC-RESONANCE; SINGLE-NUCLEOTIDE POLYMORPHISMS; RESOLUTION MELTING ANALYSIS; SEQUENCE REPEATS SSRS; GENETIC DIVERSITY; MOLECULAR CHARACTERIZATION; MICROSATELLITE MARKERS; PROTECTED DESIGNATION; SCAR MARKERS AB In recent years, an increasing number of typicality marks has been awarded to high-quality olive oils produced from local cultivars. In this case, quality control requires effective varietal checks of the starting materials. Moreover, accurate cultivar identification is essential in vegetative-propagated plants distributed by nurseries and is a pre-requisite to register new cultivars. Food genomics provides many tools for cultivar identification and traceability from tree to oil and table olives. The results of the application of different classes of DNA markers to olive with the purpose of checking cultivar identity and variability of plant material are extensively discussed in this review, with special regard to repeatability issues and polymorphism degree. The characterization of olive germplasm from all countries of the Mediterranean basin and from less studied geographical areas is described and innovative high-throughput molecular tools to manage reference collections are reviewed. Then the transferability of DNA markers to processed products-virgin olive oils and table olives-is overviewed to point out strengths and weaknesses, with special regard to (i) the influence of processing steps and storage time on the quantity and quality of residual DNA, (ii) recent advances to overcome the bottleneck of DNA extraction from processed products, (iii) factors affecting whole comparability of DNA profiles between fresh plant materials and end-products, (iv) drawbacks in the analysis of multi-cultivar versus single-cultivar end-products and (v) the potential of quantitative polymerase chain reaction (PCR)-based techniques. (c) 2016 Society of Chemical Industry C1 [Pasqualone, Antonella; Montemurro, Cinzia; di Rienzo, Valentina; Summo, Carmine; Paradiso, Vito Michele; Caponio, Francesco] Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, Via Amendola 165-A, I-70126 Bari, Italy. C3 Universita degli Studi di Bari Aldo Moro RP Pasqualone, A (corresponding author), Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, Via Amendola 165-A, I-70126 Bari, Italy. EM antonella.pasqualone@uniba.it CR Abdelhamid S, 2015, MOL PLANT BREED, V6, P1 Abdessemed S, 2015, SCI HORTIC-AMSTERDAM, V192, P10, DOI 10.1016/j.scienta.2015.05.015 Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 Alagna F, 2012, BMC PLANT BIOL, V12, DOI 10.1186/1471-2229-12-162 Alagna F, 2009, BMC GENOMICS, V10, DOI 10.1186/1471-2164-10-399 Alba V, 2009, SCI HORTIC-AMSTERDAM, V123, P11, DOI 10.1016/j.scienta.2009.07.007 Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Angiolillo A, 2006, GENET RESOUR CROP EV, V53, P289, DOI 10.1007/s10722-004-6126-9 Angiolillo A, 1999, THEOR APPL GENET, V98, P411, DOI 10.1007/s001220051087 Atienza SG, 2013, FOOD RES INT, V54, P2045, DOI 10.1016/j.foodres.2013.08.015 Baldoni L, 2002, PLANT BIOLOGY, V4, P346, DOI 10.1055/s-2002-32338 Baldoni L, 2009, MOL BREEDING, V24, P213, DOI 10.1007/s11032-009-9285-8 Bandelj D, 2004, EUPHYTICA, V136, P93, DOI 10.1023/B:EUPH.0000019552.42066.10 Bandelj D, 2002, ACTA HORTIC, P133, DOI 10.17660/ActaHortic.2002.586.20 Barranco D, 2000, HORTSCIENCE, V35, P1323, DOI 10.21273/HORTSCI.35.7.1323 Bartolini G., 1998, OLIVE GERMPLASM CULT Bautista R, 2003, EUPHYTICA, V129, P33, DOI 10.1023/A:1021528122049 Bazakos C, 2012, FOOD CHEM, V134, P2411, DOI 10.1016/j.foodchem.2012.04.031 Beghe D, 2015, SCI HORTIC-AMSTERDAM, V189, P122, DOI 10.1016/j.scienta.2015.04.003 Belaj A, 2003, EUPHYTICA, V134, P261, DOI 10.1023/B:EUPH.0000004954.93250.f5 Belaj A, 2003, THEOR APPL GENET, V107, P736, DOI 10.1007/s00122-003-1301-5 Belaj A, 2003, EUPHYTICA, V130, P387, DOI 10.1023/A:1023042014081 Belaj A, 2001, J AM SOC HORTIC SCI, V126, P64, DOI 10.21273/JASHS.126.1.64 Belaj A, 2002, THEOR APPL GENET, V105, P638, DOI 10.1007/s00122-002-0981-6 Belaj A, 2012, TREE GENET GENOMES, V8, P365, DOI 10.1007/s11295-011-0447-6 Ben Ayed R, 2015, J FUNDAM APPL SCI, V7, P408, DOI 10.4314/jfas.v7i3.8 Ben Ayed Rayda, 2014, J Genet, V93, pe148 Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 Ben-Ayed R, 2014, BIOCHEM GENET, V52, P258, DOI 10.1007/s10528-014-9645-x Ben-Ayed R, 2012, EUR FOOD RES TECHNOL, V234, P263, DOI 10.1007/s00217-011-1631-5 Besnard G, 2003, MOL ECOL NOTES, V3, P651, DOI 10.1046/j.1471-8286.2003.00547.x Besnard G, 2002, THEOR APPL GENET, V105, P139, DOI 10.1007/s00122-002-0868-6 Besnard G, 2001, J AM SOC HORTIC SCI, V126, P668, DOI 10.21273/JASHS.126.6.668 Besnard G, 2001, GENET RESOUR CROP EV, V48, P165, DOI 10.1023/A:1011239308132 Besnard G, 2016, NEW PHYTOL, V209, P466, DOI 10.1111/nph.13518 Besnard G, 2011, BMC PLANT BIOL, V11, DOI 10.1186/1471-2229-11-80 Biton I, 2015, MOL BREEDING, V35, DOI 10.1007/s11032-015-0304-7 Bogani P., 1994, Acta Horticulturae, P98 Boucheffa S, 2017, GENET RESOUR CROP EV, V64, P379, DOI 10.1007/s10722-016-0365-4 Bracci T, 2011, PLANT CELL REP, V30, P449, DOI 10.1007/s00299-010-0991-9 Bracci T, 2009, SCI HORTIC-AMSTERDAM, V122, P209, DOI 10.1016/j.scienta.2009.04.010 Brake M, 2014, SCI HORTIC-AMSTERDAM, V176, P282, DOI 10.1016/j.scienta.2014.07.012 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Brookes AJ, 1999, GENE, V234, P177, DOI 10.1016/S0378-1119(99)00219-X Bryan GJ, 1997, THEOR APPL GENET, V94, P557, DOI 10.1007/s001220050451 Bubola KB, 2014, FOOD TECHNOL BIOTECH, V52, P342 Busconi M, 2006, MOL BREEDING, V17, P59, DOI 10.1007/s11032-005-1395-3 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Carriero F, 2002, THEOR APPL GENET, V104, P301, DOI 10.1007/s001220100691 Caruso T, 2014, SCI HORTIC-AMSTERDAM, V180, P130, DOI 10.1016/j.scienta.2014.10.019 Casas GL, 2014, BIOCHEM SYST ECOL, V57, P15, DOI 10.1016/j.bse.2014.07.010 Chiappetta A, 2015, AGRICULTURAL AND FOOD BIOTECHNOLOGIES OF OLEA EUROPAEA AND STONE FRUITS, P75 Cipriani G, 2002, THEOR APPL GENET, V104, P223, DOI 10.1007/s001220100685 Claros MG, 2000, EUPHYTICA, V116, P131, DOI 10.1023/A:1004011829274 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Consolandi C, 2007, J BIOTECHNOL, V129, P565, DOI 10.1016/j.jbiotec.2007.01.025 Corrado G, 2009, GENOME, V52, P692, DOI [10.1139/G09-044, 10.1139/g09-044] Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Cresti M., 1996, Advances in Horticultural Science, V10, P105 Dais P, 2013, ANAL CHIM ACTA, V765, P1, DOI 10.1016/j.aca.2012.12.003 de Caraffa VB, 2002, EUPHYTICA, V123, P263, DOI 10.1023/A:1014902210530 de la Rosa R, 2004, HORTSCIENCE, V39, P351, DOI 10.21273/HORTSCI.39.2.351 De La Rosa R, 2002, MOL ECOL NOTES, V2, P265, DOI 10.1046/j.1471-8286.2002.00217.x De la Rosa R, 2013, J AM SOC HORTIC SCI, V138, P290, DOI 10.21273/JASHS.138.4.290 de la Torre F, 2004, J FOOD AGRIC ENVIRON, V2, P84 Del Coco L, 2016, J AM OIL CHEM SOC, V93, P373, DOI 10.1007/s11746-015-2778-1 Del Coco L, 2012, NUTRIENTS, V4, P343, DOI 10.3390/nu4050343 Delgado-Martinez FJ, 2012, GENET MOL RES, V11, P918, DOI 10.4238/2012.April.10.7 Deschamps Stephane, 2012, Biology (Basel), V1, P460, DOI 10.3390/biology1030460 di Rienzo V, 2016, FOOD CONTROL, V60, P124, DOI 10.1016/j.foodcont.2015.07.015 Diaz A, 2006, J AM SOC HORTIC SCI, V131, P250, DOI 10.21273/JASHS.131.2.250 Diaz A, 2006, TREE GENET GENOMES, V2, P165, DOI 10.1007/s11295-006-0041-5 Diez CM, 2015, NEW PHYTOL, V206, P436, DOI 10.1111/nph.13181 Dominguez-Garcia MC, 2012, J HORTIC SCI BIOTECH, V87, P95, DOI 10.1080/14620316.2012.11512837 Dominguez-Garcia MC, 2012, SCI HORTIC-AMSTERDAM, V136, P50, DOI 10.1016/j.scienta.2011.12.017 Doveri S, 2008, SCI HORTIC-AMSTERDAM, V116, P367, DOI 10.1016/j.scienta.2008.02.005 Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a Ercisli S, 2012, J APPL BOT FOOD QUAL, V85, P144 Ercisli S, 2009, GENET MOL RES, V8, P414, DOI 10.4238/vol8-2gmr576 Ercisli S, 2011, BIOCHEM GENET, V49, P555, DOI 10.1007/s10528-011-9430-z Erre P, 2010, GENET RESOUR CROP EV, V57, P41, DOI 10.1007/s10722-009-9449-8 Essadki M, 2006, GENET RESOUR CROP EV, V53, P475, DOI 10.1007/s10722-004-1931-8 FABBRI A, 1995, J AM SOC HORTIC SCI, V120, P538, DOI 10.21273/JASHS.120.3.538 Fendri M., 2014, Academia Journal of Agricultural Research, V2, P58 Marti AFI, 2015, ACTA PHYSIOL PLANT, V37, DOI 10.1007/s11738-014-1726-2 Ferri E, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/365794 Figueiredo E, 2013, SCI HORTIC-AMSTERDAM, V156, P24, DOI 10.1016/j.scienta.2013.03.011 Galla G, 2009, BMC PLANT BIOL, V9, DOI 10.1186/1471-2229-9-128 Galtier O, 2007, ANAL CHIM ACTA, V595, P136, DOI 10.1016/j.aca.2007.02.033 Ganino T, 2007, GENET RESOUR CROP EV, V54, P1531, DOI 10.1007/s10722-006-9145-x Ganopoulos I, 2013, J SCI FOOD AGR, V93, P2281, DOI 10.1002/jsfa.6040 Ganopoulos I, 2011, FOOD CHEM, V129, P652, DOI 10.1016/j.foodchem.2011.04.109 Gemas VJ, 2000, J HORTIC SCI BIOTECH, V75, P312, DOI 10.1080/14620316.2000.11511243 Gemas VJV, 2004, GENET RESOUR CROP EV, V51, P501, DOI 10.1023/B:GRES.0000024152.16021.40 Gil FS, 2006, MOL ECOL NOTES, V6, P1275, DOI 10.1111/j.1471-8286.2006.01513.x Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Grati-Kamoun N, 2006, GENET RESOUR CROP EV, V53, P265, DOI 10.1007/s10722-004-6130-0 Gupta PK, 2001, CURR SCI INDIA, V80, P524 Hagidimitriou M, 2005, J AM SOC HORTIC SCI, V130, P211, DOI 10.21273/JASHS.130.2.211 Hakim I. R., 2010, Diversity, V2, P17 Harbi I, 2012, J LIFE SCI, V6, P1343 Harbi IM, 2012, IRAQI J SCI, V53, P73 Hegazi E.S., 2012, J HORTIC SCI ORN PLA, V4, P148 Hennessy S, 2009, J AGR FOOD CHEM, V57, P1735, DOI 10.1021/jf803714g Hernandez P, 2001, THEOR APPL GENET, V103, P788, DOI 10.1007/s001220100603 Hess J, 2000, MOL ECOL, V9, P857, DOI 10.1046/j.1365-294x.2000.00942.x Intrieri MC, 2007, J HORTIC SCI BIOTECH, V82, P109, DOI 10.1080/14620316.2007.11512206 Ipek A, 2012, SCI AGR, V69, P327, DOI 10.1590/S0103-90162012000500007 Ipek M, 2015, GENET MOL RES, V14, P2762, DOI 10.4238/2015.March.31.6 Jaccoud D, 2001, NUCLEIC ACIDS RES, V29, DOI 10.1093/nar/29.4.e25 Kalogianni DP, 2015, J AGR FOOD CHEM, V63, P3121, DOI 10.1021/jf5054657 Kaya HB, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0073674 Khadari B, 2003, THEOR APPL GENET, V106, P521, DOI 10.1007/s00122-002-1079-x Koehmstedt AM, 2011, GENET RESOUR CROP EV, V58, P519, DOI 10.1007/s10722-010-9595-z Leva AR, 2012, BIOL PLANTARUM, V56, P373, DOI 10.1007/s10535-012-0102-6 Linos A, 2014, SCI HORTIC-AMSTERDAM, V175, P33, DOI 10.1016/j.scienta.2014.05.034 Longobardi F, 2012, FOOD CHEM, V130, P177, DOI 10.1016/j.foodchem.2011.06.045 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Macedo ES, 2009, PHYSIOL PLANTARUM, V137, P532, DOI 10.1111/j.1399-3054.2009.01302.x Mackay JF, 2008, PLANT METHODS, V4, DOI 10.1186/1746-4811-4-8 Marra FP, 2013, TREE GENET GENOMES, V9, P961, DOI 10.1007/s11295-013-0609-9 Martins-Lopes P, 2008, J AGR FOOD CHEM, V56, P11786, DOI 10.1021/jf801146z Martins-Lopes P, 2007, GENET RESOUR CROP EV, V54, P117, DOI 10.1007/s10722-005-2640-7 Mekuria GT, 1999, J HORTIC SCI BIOTECH, V74, P309, DOI 10.1080/14620316.1999.11511114 Metzker Michael L, 2010, Nat Rev Genet, V11, P31, DOI 10.1038/nrg2626 Montemurro C, 2008, ACTA HORTIC, P603, DOI 10.17660/ActaHortic.2008.791.93 Montemurro C, 2005, J HORTIC SCI BIOTECH, V80, P105, DOI 10.1080/14620316.2005.11511899 Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z Montemurro C, 2015, J CHEM-NY, V2015, DOI 10.1155/2015/496986 Morgante M, 1994, US Patent, Patent No. [08/326456, 08326456] Mousavi S, 2014, GENET RESOUR CROP EV, V61, P775, DOI 10.1007/s10722-013-0071-4 Muleo R, 2009, GENOME, V52, P252, DOI 10.1139/G09-002 Muzzalupo I, 2002, EUR FOOD RES TECHNOL, V214, P528, DOI 10.1007/s00217-001-0482-x Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Muzzalupo I, 2015, EUR FOOD RES TECHNOL, V241, P151, DOI 10.1007/s00217-015-2455-5 Naqvi NI, 1996, GENOME, V39, P26, DOI 10.1139/g96-004 Negi MS, 2000, THEOR APPL GENET, V101, P146, DOI 10.1007/s001220051463 Nikoloudakis N, 2003, J AM SOC HORTIC SCI, V128, P741, DOI 10.21273/JASHS.128.5.0741 Noormohammadi Z, 2014, SCI HORTIC-AMSTERDAM, V178, P175, DOI 10.1016/j.scienta.2014.08.002 Ouazzani N, 1996, EUPHYTICA, V91, P9, DOI 10.1007/BF00035271 Ouazzani N, 2002, ACTA HORTIC, P233, DOI 10.17660/ActaHortic.2002.586.44 Owen CA, 2005, THEOR APPL GENET, V110, P1169, DOI 10.1007/s00122-004-1861-z Ozkaya MT, 2006, SCI HORTIC-AMSTERDAM, V108, P205, DOI 10.1016/j.scienta.2006.01.016 Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Pafundo S, 2010, FOOD CHEM, V123, P787, DOI 10.1016/j.foodchem.2010.05.027 Papadia P, 2011, J AM OIL CHEM SOC, V88, P1463, DOI 10.1007/s11746-011-1812-1 Pasqualone A., 2004, Rivista Italiana delle Sostanze Grasse, V81, P221 Pasqualone A, 2005, RIV ITAL SOSTANZE GR, V82, P173 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Pasqualone A, 2001, EUR FOOD RES TECHNOL, V213, P240, DOI 10.1007/s002170100367 Pasqualone A., 2013, CULTIVARS CHEM PROPE, P83 Pasqualone Antonella, 2003, Polish Journal of Food and Nutrition Sciences, V12, P96 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Pasqualone A, 2015, EUR J LIPID SCI TECH, V117, P2044, DOI 10.1002/ejlt.201400654 Pasqualone A, 2013, J AGR FOOD CHEM, V61, P3068, DOI 10.1021/jf400014g Pasqualone A, 2012, FOOD RES INT, V47, P188, DOI 10.1016/j.foodres.2011.05.008 Pasqualone A, 2010, EUR FOOD RES TECHNOL, V230, P723, DOI 10.1007/s00217-009-1210-1 Pasqualone C, 2008, RIV ITAL SOSTANZE GR, V85, P83 Perez-Jimenez M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070507 Perri E, 2002, ACTA HORTIC, P87, DOI 10.17660/ActaHortic.2002.586.9 Petrakis PV, 2008, J AGR FOOD CHEM, V56, P3200, DOI 10.1021/jf072957s Poland JA, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0032253 Poljuha D, 2008, SCI HORTIC-AMSTERDAM, V115, P223, DOI 10.1016/j.scienta.2007.08.018 Rabiei Zohreh, 2010, Iranian Journal of Biotechnology, V8, P24 Raieta K, 2015, FOOD CHEM, V172, P596, DOI 10.1016/j.foodchem.2014.09.101 Rallo P, 2000, THEOR APPL GENET, V101, P984, DOI 10.1007/s001220051571 Ramos-Gomez S, 2016, FOOD CHEM, V194, P447, DOI 10.1016/j.foodchem.2015.08.036 Reale S, 2006, GENOME, V49, P1193, DOI 10.1139/G06-068 Reed GH, 2007, PHARMACOGENOMICS, V8, P597, DOI 10.2217/14622416.8.6.597 Rehman AU, 2012, J HORTIC SCI BIOTECH, V87, P647, DOI 10.1080/14620316.2012.11512925 Resta P, 2002, ACTA HORTIC, P73, DOI 10.17660/ActaHortic.2002.586.6 REYSENBACH AL, 1992, APPL ENVIRON MICROB, V58, P3417, DOI 10.1128/AEM.58.10.3417-3418.1992 Rony C, 2009, TREE GENET GENOMES, V5, P109, DOI 10.1007/s11295-008-0170-0 Roselli G., 2002, Journal of Genetics and Breeding, V56, P51 Rotondi A, 2003, EUPHYTICA, V132, P129, DOI 10.1023/A:1024670321435 Sacco A, 2000, J AM OIL CHEM SOC, V77, P619, DOI 10.1007/s11746-000-0100-y Saito K, 2008, TRENDS PLANT SCI, V13, P36, DOI 10.1016/j.tplants.2007.10.006 Salimia RB, 2009, JORDAN J AGR SCI, V5, P282 Salimonti A, 2013, SCI HORTIC-AMSTERDAM, V162, P204, DOI 10.1016/j.scienta.2013.08.005 Sanz-Cortes F, 2001, J AM SOC HORTIC SCI, V126, P7, DOI 10.21273/JASHS.126.1.07 Sanz-Cortes F, 2003, PLANT BREEDING, V122, P173, DOI 10.1046/j.1439-0523.2003.00808.x Sarri V, 2006, GENOME, V49, P1606, DOI 10.1139/G06-126 Sefc KM, 2000, MOL ECOL, V9, P1171, DOI 10.1046/j.1365-294x.2000.00954.x Sensi E, 2003, SCI HORTIC-AMSTERDAM, V97, P379, DOI 10.1016/S0304-4238(02)00163-2 Spaniolas S, 2008, EUR FOOD RES TECHNOL, V227, P175, DOI 10.1007/s00217-007-0707-8 TAUTZ D, 1989, NUCLEIC ACIDS RES, V17, P6463, DOI 10.1093/nar/17.16.6463 Terzopoulos PJ, 2005, SCI HORTIC-AMSTERDAM, V105, P45, DOI 10.1016/j.scienta.2005.01.011 Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Trujillo I, 2014, TREE GENET GENOMES, V10, P141, DOI 10.1007/s11295-013-0671-3 Uncu AT, 2015, J AGR FOOD CHEM, V63, P2284, DOI 10.1021/acs.jafc.5b00090 Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 VOS P, 1995, NUCLEIC ACIDS RES, V23, P4407, DOI 10.1093/nar/23.21.4407 Wang DG, 1998, SCIENCE, V280, P1077, DOI 10.1126/science.280.5366.1077 Wiesman Z, 1998, J AM SOC HORTIC SCI, V123, P837, DOI 10.21273/JASHS.123.5.837 WILLIAMS JGK, 1990, NUCLEIC ACIDS RES, V18, P6531, DOI 10.1093/nar/18.22.6531 Woodcock T, 2008, J AGR FOOD CHEM, V56, P11520, DOI 10.1021/jf802792d Xanthopoulou A, 2014, PLANT GENET RESOUR-C, V12, P273, DOI 10.1017/S147926211400001X Zhan MM, 2015, GENET MOL RES, V14, P5958, DOI 10.4238/2015.June.1.13 ZIETKIEWICZ E, 1994, GENOMICS, V20, P176, DOI 10.1006/geno.1994.1151 ZOHARY D, 1975, SCIENCE, V187, P319, DOI 10.1126/science.187.4174.319 NR 202 TC 38 Z9 38 U1 0 U2 56 PD AUG 30 PY 2016 VL 96 IS 11 BP 3642 EP 3657 DI 10.1002/jsfa.7711 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000379680800003 DA 2022-12-14 ER PT J AU Copani, V Guarnaccia, P Biondi, L Lesina, SC Longo, S Testa, G Cosentino, SL AF Copani, Venera Guarnaccia, Paolo Biondi, Luisa Lesina, Salvatore Cala Longo, Stefania Testa, Giorgio Cosentino, Salvatore Luciano TI Pasture quality and cheese traceability index of Ragusano PDO cheese SO ITALIAN JOURNAL OF AGRONOMY DT Article DE Carotenoids; feeding system; Iblei plateau; Mediterranean environment; Modicana cattle; pasture quality ID REFLECTANCE SPECTRUM; CAROTENOIDS AB In the Iblei plateau (Sicily, Southern Italy) the native dairy cattle breed Modicana during the spring season grazes exclusively on natural pastures for the production of the Ragusano protected denomination of origin cheese. Along the grazing season, herbage undergoes to changes on protein, fibre and moisture content, affecting quality parameters such as plant carotenoids concentration, involved in the colour and nutritional characteristics of dairy products and potential biomarkers for authenticating fed green pasture-based diets. The aim of this work was to assess whether the cheese traceability index, based on the carotenoids spectra data elaboration, could be related to seasonal variations of floral composition and pasture quality. Four herbage and cheese samples were collected every two weeks in two representative farms of this area, from March to May 2013. Pasture characteristics as pastoral vegetation composition and pastoral value were analysed using the methodology developed for pastoral resources studies. Traceability index showed a significant positive correlation with pasture moisture and crude protein content (r=0.729* and 0.853**, respectively), while it was negatively correlated with fibre content (r=-0.719*). C1 [Copani, Venera; Guarnaccia, Paolo; Biondi, Luisa; Lesina, Salvatore Cala; Longo, Stefania; Testa, Giorgio; Cosentino, Salvatore Luciano] Univ Catania, Dept Agr Food & Environm, I-95123 Catania, Italy. C3 University of Catania RP Copani, V (corresponding author), Univ Catania, Dept Agr Food & Environm Di3A, Via Valdisavoia 5, I-95123 Catania, Italy. EM v.copani@unict.it CR Argenti G, 2012, ITAL J AGRON, V7, P293, DOI 10.4081/ija.2012.e39 Carpino S, 2004, J DAIRY SCI, V87, P308, DOI 10.3168/jds.S0022-0302(04)73169-0 Cavallero A, 2002, COLTIVAZIONI ERBACEE, P269 CoRFiLaC - Consorzio Ricerca Filiera Lattiero-Casearia, 2001, MET CAMP VAL NUTR ES Cosentino S., 1992, Rivista di Agronomia, V26, P404 Daget P, 1969, 50 CNRS CEP Daget PH, 1972, FOURRAGES, V49, P31 Gresta F., 2003, Optimal forage systems for animal production and the environment. Proceedings of the 12th Symposium of the European Grassland Federation, Pleven, Bulgaria, 26-28 May 2003, P81 Latimer G., 2012, OFFICIAL METHODS ANA, V19th Marino VM, 2012, DAIRY SCI TECHNOL, V92, P501, DOI 10.1007/s13594-012-0069-2 Noziere P, 2006, ANIM FEED SCI TECH, V131, P418, DOI 10.1016/j.anifeedsci.2006.06.018 Patane C., 1993, Rivista di Agronomia, V27, P412 Prache S, 1999, ANIM SCI, V69, P29, DOI 10.1017/S1357729800051067 Priolo A, 2003, SMALL RUMINANT RES, V48, P103, DOI 10.1016/S0921-4488(03)00006-3 Priolo A, 2002, J ANIM SCI, V80, P886 Roggero P. P., 2002, Rivista di Agronomia, V36, P149 VANSOEST PJ, 1991, J DAIRY SCI, V74, P3583, DOI 10.3168/jds.S0022-0302(91)78551-2 NR 17 TC 1 Z9 1 U1 0 U2 10 PY 2015 VL 10 IS 4 BP 220 EP 223 DI 10.4081/ija.2015.667 WC Agronomy SC Agriculture UT WOS:000366217100008 DA 2022-12-14 ER PT J AU Guo, BL Wei, YM Pan, JR Li, Y AF Guo, B. L. Wei, Y. M. Pan, J. R. Li, Y. TI Stable C and N isotope ratio analysis for regional geographical traceability of cattle in China SO FOOD CHEMISTRY DT Article DE Cattle; Beef; Traceability; Stable isotope ratios; Isotope ratio mass spectrometry (IRMS) ID MASS-SPECTROMETRY; ORIGIN ASSIGNMENT; CARBON; NITROGEN; TRACE; BEEF; TOOL; RECONSTRUCTION; C-13/C-12; ELEMENTS AB The aim of this study was to examine the variation in carbon and nitrogen stable isotope ratios in cattle tissues from different provinces in China, and to investigate the correlations of delta C-13 and delta N-15 values between different cattle tissues. Furthermore, the success rate of classification using delta C-13 and delta N-15 values to distinguish the geographical origin of cattle was analyzed. Fifty nine cattle samples were collected from Jilin, Ningxia, Guizhou and Hebei provinces in China, and the delta C-13 and delta N-15 values of de-fatted beef, crude fat and tail hair were measured using isotope ratio mass spectrometry (IRMS). There were highly significant regional differences in the mean values of delta C-13 and delta N-15 values in the cattle tissues. A significant correlation was found in delta C-13 and delta N-15 between de-fatted beef, crude fat and tail hair, which indicated that all of these matrices could be used to trace cattle to their geographical origin. The results of discriminant analysis showed that delta C-13 was the better indicator for cattle origin traceability than delta N-15. The classification success rate could be improved greatly by combining the two indicators. It was concluded that stable isotope analysis of C and N in cattle tissue can be used to trace cattle diet and origin in China. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Guo, B. L.; Wei, Y. M.; Pan, J. R.; Li, Y.] CAAS, Key Lab Agr Nucl Technol & Agrofood Proc, Minist Agr, Inst Agrofood Sci & Technol, Beijing 100193, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs RP Wei, YM (corresponding author), CAAS, Key Lab Agr Nucl Technol & Agrofood Proc, Minist Agr, Inst Agrofood Sci & Technol, POB 5109, Beijing 100193, Peoples R China. EM guoboli.caas@yahoo.com.cn; weiyimin36@hotmail.com; panjr@263.net; liyong1979@126.com CR Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Branch S, 2003, J ANAL ATOM SPECTROM, V18, P17, DOI 10.1039/b207055n Cai Deling, 2002, Journal of Ocean University of Qingdao, V32, P287 Camin F, 2007, ANAL BIOANAL CHEM, V389, P309, DOI 10.1007/s00216-007-1302-3 Camin F, 2004, J AGR FOOD CHEM, V52, P6592, DOI 10.1021/jf040062z Camin F, 2008, RAPID COMMUN MASS SP, V22, P1690, DOI 10.1002/rcm.3506 Coetzee PP, 2005, ANAL BIOANAL CHEM, V383, P977, DOI 10.1007/s00216-005-0093-7 Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 De Smet S, 2004, RAPID COMMUN MASS SP, V18, P1227, DOI 10.1002/rcm.1471 Gremaud G, 2004, EUR FOOD RES TECHNOL, V219, P97, DOI 10.1007/s00217-004-0919-0 Harrison SM, 2007, RAPID COMMUN MASS SP, V21, P479, DOI 10.1002/rcm.2861 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Huang ZY, 2003, CHINESE J ANAL CHEM, V31, P1036 Kelly S, 2002, EUR FOOD RES TECHNOL, V214, P72, DOI 10.1007/s002170100400 Kornexl BE, 1997, Z LEBENSM UNTERS F A, V205, P19, DOI 10.1007/s002170050117 Manca G, 2001, J AGR FOOD CHEM, V49, P1404, DOI 10.1021/jf000706c Marisa C, 2004, FOOD CHEM, V85, P7, DOI 10.1016/j.foodchem.2003.05.003 Nakashita R, 2008, ANAL CHIM ACTA, V617, P148, DOI 10.1016/j.aca.2008.03.048 Padovan GJ, 2003, FOOD CHEM, V82, P633, DOI 10.1016/S0308-8146(02)00504-6 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Renou JP, 2004, FOOD CHEM, V85, P63, DOI 10.1016/j.foodchem.2003.06.003 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Rossmann A, 2000, EUR FOOD RES TECHNOL, V211, P32, DOI 10.1007/s002170050585 Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 Schwertl M, 2005, AGR ECOSYST ENVIRON, V109, P153, DOI 10.1016/j.agee.2005.01.015 Schwertl M, 2003, RAPID COMMUN MASS SP, V17, P1312, DOI 10.1002/rcm.1042 Serra F, 2005, RAPID COMMUN MASS SP, V19, P2111, DOI 10.1002/rcm.2034 Simpkins WA, 2000, FOOD CHEM, V70, P385, DOI 10.1016/S0308-8146(00)00086-8 Yi Xian-feng, 2004, Zoological Research, V25, P232 NR 31 TC 66 Z9 79 U1 6 U2 50 PD FEB 15 PY 2010 VL 118 IS 4 SI SI BP 915 EP 920 DI 10.1016/j.foodchem.2008.09.062 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000271145300006 DA 2022-12-14 ER PT J AU Guerrero, ED Marin, RN Mejias, RC Barroso, CG AF Duran Guerrero, Enrique Natera Marin, Ramon Castro Mejias, Remedios Garcia Barroso, Carmelo TI Traceability of Phytosanitary Products in the Production of a Sherry Wine Vinegar SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE SBSE; Sherry wine vinegar; phytosanitary products; pesticides; traceability ID BAR SORPTIVE EXTRACTION; MICROWAVE-ASSISTED EXTRACTION; RED WINES; PESTICIDES; FUNGICIDES; METALAXYL; RESIDUES; FATE; PHOTODEGRADATION; CHLORPYRIFOS AB In the present work, the monitoring of the evolution of the different phytosanitary products employed in the production of a Sherry wine vinegar has been carried out. The study covers the complete process, from the grape ripening to the vinegar fermentation. For the liquid sample analysis, a method based on SBSE (stir bar sorptive extraction) coupled to GC-MS and previously developed was used. For the grape samples, the use of two different extraction methods (ultrasound assisted extraction and microwave assisted extraction) was considered. Both methods were correctly optimized by means of factorial designs and were finally compared to each other. Considering the obtained results, the ultrasound extraction method was chosen to make the extraction of the solid samples. After the extraction process, the different extracts were analyzed by means of SBSE-GC-MS. The achieved results show the decrease of the phytosanitary product residues during the grape ripening, most of them being removed completely before the final product. C1 [Duran Guerrero, Enrique; Natera Marin, Ramon; Castro Mejias, Remedios; Garcia Barroso, Carmelo] Univ Cadiz, Fac Sci, Dept Analyt Chem, E-11510 Puerto Real, Spain. C3 Universidad de Cadiz RP Guerrero, ED (corresponding author), Univ Cadiz, Fac Sci, Dept Analyt Chem, POB 40, E-11510 Puerto Real, Spain. EM enrique.duranguerrero@uca.es CR Andrey Daniel, 2000, Mitteilungen aus Lebensmitteluntersuchung und Hygiene, V91, P300 Bouaid A, 2000, FRESEN J ANAL CHEM, V367, P291, DOI 10.1007/s002160000363 Cabras P, 1999, J AGR FOOD CHEM, V47, P3854, DOI 10.1021/jf990005j Cabras P, 1998, J AGR FOOD CHEM, V46, P3249, DOI 10.1021/jf980186+ Cabras P, 1997, J AGR FOOD CHEM, V45, P476, DOI 10.1021/jf960353a Cabras P, 1997, J AGR FOOD CHEM, V45, P2708, DOI 10.1021/jf960939x CASAS JF, 1985, 3 JORN U JER, P333 DELINAN C, 2000, VADEMECUM PRODUCTOS Dzyadevych SV, 2002, ANAL CHIM ACTA, V459, P33, DOI 10.1016/S0003-2670(02)00083-1 Guerrero ED, 2007, J CHROMATOGR A, V1165, P144, DOI 10.1016/j.chroma.2007.07.058 Hayasaka Y, 2003, ANAL BIOANAL CHEM, V375, P948, DOI 10.1007/s00216-003-1837-x Jimenez JJ, 2007, FOOD CHEM, V104, P216, DOI 10.1016/j.foodchem.2006.11.027 Juan-Garcia A, 2004, J CHROMATOGR A, V1050, P119, DOI 10.1016/j.chroma.2004.08.026 Katagi T, 2004, REV ENVIRON CONTAM T, V182, P1 Nagayama T, 1997, J FOOD HYG SOC JPN, V38, P270, DOI 10.3358/shokueishi.38.4_270 Navarro S, 2000, J AGR FOOD CHEM, V48, P3537, DOI 10.1021/jf990741n Navarro S, 2000, J CHROMATOGR A, V882, P221, DOI 10.1016/S0021-9673(00)00337-X Navarro S, 1999, J AGR FOOD CHEM, V47, P264, DOI 10.1021/jf980801+ Navarro S, 2001, AM J ENOL VITICULT, V52, P35 OTTENEDER H, 2005, BULL OIV, V889, P173 Sala C, 1996, J AGR FOOD CHEM, V44, P3668, DOI 10.1021/jf960218y Sandra P, 2003, J CHROMATOGR A, V1000, P299, DOI 10.1016/S0021-9673(03)00508-9 Sandra P, 2001, J CHROMATOGR A, V928, P117, DOI 10.1016/S0021-9673(01)01113-X Singh SB, 2004, J AGR FOOD CHEM, V52, P105, DOI 10.1021/jf030358p Zuin VG, 2006, J CHROMATOGR A, V1114, P180, DOI 10.1016/j.chroma.2006.03.035 NR 25 TC 7 Z9 7 U1 0 U2 12 PD MAR 25 PY 2009 VL 57 IS 6 BP 2193 EP 2199 DI 10.1021/jf803729y WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000264303300014 DA 2022-12-14 ER PT J AU Tavoletti, S Iommarini, L Pasquini, M AF Tavoletti, Stefano Iommarini, Linda Pasquini, Marina TI A DNA method for qualitative identification of plant raw materials in feedstuff SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE Traceability; Feed; Molecular markers; STS; PCR ID POLYMERASE-CHAIN-REACTION; REAL-TIME PCR; QUANTIFICATION; CEREAL; FOOD; DISCRIMINATION; MIXTURES; SEQUENCE; PEA AB This research developed a simple and not expensive DNA method for the qualitative identification of plant raw materials used as feed mixtures. Specific simple sequence tagged (STS) markers were developed to detect faba bean (Lectin A gene), field pea (Convicilin A gene), grain sorghum (UDP-glucosyltransferase gene) and barley (Hordoindoline-a gene), whereas identification of durum and common wheat (lipid transfer protein gene), soybean (Gly m Bd 30K allergen gene) and maize (invertase gene) was carried out using markers available from the literature. Cross-reactivity of the primer pairs was also checked against oat, rye, kidney bean and lentil. The method was effectively applied to the analysis of flour mixtures and extruded feedstuff. It could be included in traceability and certification of animal feeding systems within high quality animal production chains which are strictly related to the production area by the valorisation of locally grown raw materials. C1 [Tavoletti, Stefano; Iommarini, Linda; Pasquini, Marina] Univ Politecn Marche, Dipartimento Sci Alimentari Agroingegnerist Fis E, I-60131 Ancona, Italy. C3 Marche Polytechnic University RP Tavoletti, S (corresponding author), Univ Politecn Marche, Dipartimento Sci Alimentari Agroingegnerist Fis E, Via Brecce Bianche, I-60131 Ancona, Italy. EM s.tavoletti@univpm.it CR Alary R, 2007, EUR FOOD RES TECHNOL, V225, P427, DOI 10.1007/s00217-006-0434-6 Bellagamba F, 2001, J AGR FOOD CHEM, V49, P3775, DOI 10.1021/jf0010329 BOWN D, 1988, BIOCHEM J, V251, P717, DOI 10.1042/bj2510717 Brezna B, 2006, EUR FOOD RES TECHNOL, V222, P600, DOI 10.1007/s00217-005-0168-x Dellaporta SL., 1983, PLANT MOL BIOL REP, V1, P19, DOI [DOI 10.1007/BF02712670, 10.1007/BF02712670] DIERYCK W, 1992, PLANT MOL BIOL, V19, P707, DOI 10.1007/BF00026798 Eugster A, 2008, EUR FOOD RES TECHNOL, V227, P17, DOI 10.1007/s00217-007-0686-9 Forgacs E., 2003, FOOD AUTHENTICITY TR, V1st ed, P197 Gryson N, 2008, EUR FOOD RES TECHNOL, V227, P345, DOI 10.1007/s00217-007-0727-4 Hernandez M, 2004, J AGR FOOD CHEM, V52, P4632, DOI 10.1021/jf049789d Jones PR, 1999, J BIOL CHEM, V274, P35483, DOI 10.1074/jbc.274.50.35483 Krahulcova J, 2003, EUR FOOD RES TECHNOL, V217, P80, DOI 10.1007/s00217-003-0703-6 Lioi L, 2006, GENET RESOUR CROP EV, V53, P1615, DOI 10.1007/s10722-005-8719-3 Lopez-Andreo M, 2005, ANAL BIOCHEM, V339, P73, DOI 10.1016/j.ab.2004.11.045 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Martin I, 2007, J ANIM SCI, V85, P452, DOI 10.2527/jas.2006-350 Massa AN, 2006, J MOL EVOL, V63, P526, DOI 10.1007/s00239-005-0292-z Piknova L, 2008, J FOOD NUTR RES, V47, P114 Sandberg M, 2003, EUR FOOD RES TECHNOL, V217, P344, DOI 10.1007/s00217-003-0758-4 Sokal R. R., 1981, BIOMETRY, V2nd Sun Da-Wen, 2008, MODERN TECHNIQUES FO Terzi V, 2005, J CEREAL SCI, V41, P213, DOI 10.1016/j.jcs.2004.08.003 Torp AM, 2006, FOOD CONTROL, V17, P30, DOI 10.1016/j.foodcont.2004.08.005 Zeltner D, 2009, EUR FOOD RES TECHNOL, V228, P321, DOI 10.1007/s00217-008-0937-4 NR 24 TC 6 Z9 6 U1 1 U2 6 PD JUL PY 2009 VL 229 IS 3 BP 475 EP 484 DI 10.1007/s00217-009-1077-1 WC Food Science & Technology SC Food Science & Technology UT WOS:000267242000014 DA 2022-12-14 ER PT J AU Verbeke, W AF Verbeke, W TI The emerging role of traceability and information in demand-oriented livestock production SO OUTLOOK ON AGRICULTURE DT Article ID BSE; SAFETY; BEEF AB Consumer concerns about food safety in general and meat safety in particular have led to an increased demand for information and transparency in food chains, and acted as the major driver for the development of traceability systems. This paper focuses on the current and future role of traceability in demand-oriented meat and livestock production. Consumer demand for information is taken as the starting point. Information about and practice relating to traceability are reported and illustrated from historical and current experience in Belgium. It was found that organizational and operational aspects of traceability were clearly dealt with. However, questions remain with respect to the management of information flows and the proactive, rather than defensive use of traceability realizations. Key points for future success in livestock production chains pertain to market orientation substantiation of claimed benefit and effective management of information flows. C1 Univ Ghent, Dept Agr Econ, B-9000 Ghent, Belgium. C3 Ghent University RP Verbeke, W (corresponding author), Univ Ghent, Dept Agr Econ, Coupure Links 653, B-9000 Ghent, Belgium. EM wim.verbeke@rug.ac.be CR BAINES R, 1998, P 3 INT C CHAIN MAN, P213 Burton M, 1996, APPL ECON, V28, P687, DOI 10.1080/000368496328434 Calder R., 1998, SUPPLY CHAIN MANAG, V3, P123 Caskie P, 1998, FOOD POLICY, V23, P231, DOI 10.1016/S0306-9192(98)00035-9 Coase RH, 1937, ECONOMICA-NEW SER, V4, P386, DOI 10.1111/j.1468-0335.1937.tb00002.x DENOUDEN M, 1996, THESIS WAGENINGEN U Douzain N., 1998, Viandes et Produits Carnes, V19, P21 DOWNEY W, 1996, P 2 INT C CHAIN MAN, P3 ELLIOT K, 1998, LONG TERM PROSPECTS, P211 ENTWISTLE G, 1997, IMPACT QUALITY FOOD FEARNE A, 1998, SUPPLY CHAIN MANAG, V3, P112 Gregory NG, 2000, OUTLOOK AGR, V29, P251, DOI 10.5367/000000000101293310 Henson S, 2000, J AGR ECON, V51, P90, DOI 10.1111/j.1477-9552.2000.tb01211.x JACK D, 1998, SUPPLY CHAIN MANAG, V3, P134 Kennett J., 1998, SUPPLY CHAIN MANAG, V3, P157, DOI DOI 10.1108/13598549810230912 Latouche K, 1998, FOOD POLICY, V23, P347, DOI 10.1016/S0306-9192(98)00048-7 Leat P., 1998, SUPPLY CHAIN MANAG, V3, P115, DOI DOI 10.1108/EUM0000000004534 Luttighuis PHWMO, 2000, CHAIN MANAGEMENT IN AGRIBUSINESS AND THE FOOD INDUSTRY, P275 Meulenberg M.T., 1997, AGR MARKETING CONSUM, P95 MIGCHELS N, 1996, P 2 INT C CHAIN MAN, P239 PALMER C, 1996, P 2 INT C CHAIN MAN, P223 PORIN F, 1998, LONG TERM PROSPECTS, P125 Simpson B., 1998, SUPPLY CHAIN MANAG, V3, P118, DOI DOI 10.1108/13598549810230813 Storer CE, 2000, CHAIN MANAGEMENT IN AGRIBUSINESS AND THE FOOD INDUSTRY, P283 Sundrum A, 2001, LIVEST PROD SCI, V67, P207, DOI 10.1016/S0301-6226(00)00188-3 TIMON D, 1998, LONG TERM PROSPECTS, P219 Van der Gaag MA, 2000, CHAIN MANAGEMENT IN AGRIBUSINESS AND THE FOOD INDUSTRY, P139 Verbeke W, 1999, FOOD QUAL PREFER, V10, P437, DOI 10.1016/S0950-3293(99)00031-2 Verbeke W., 2000, Agribusiness (New York), V16, P215, DOI 10.1002/(SICI)1520-6297(200021)16:2<215::AID-AGR6>3.0.CO;2-S Verbeke W, 1999, MEAT SCI, V53, P77, DOI 10.1016/S0309-1740(99)00036-4 VERBEKE W, 1999, THESIS GHENT U GENT Viaene J., 1998, SUPPLY CHAIN MANAG I, V3, P139 Ward R. W., 1996, Choices. The Magazine of Food, Farm, and Resources Issues, P40 WILLIAMSON OE, 1979, J LAW ECON, V22, P233, DOI 10.1086/466942 WILSON N, 1996, P 2 INT C CHAIN MAN, P265 WILSON N, 1998, SUPPLY CHAIN MANAG, V3, P127 Wortmann JC, 2000, CHAIN MANAGEMENT IN AGRIBUSINESS AND THE FOOD INDUSTRY, P3 NR 37 TC 38 Z9 44 U1 0 U2 19 PD DEC PY 2001 VL 30 IS 4 BP 249 EP 255 DI 10.5367/000000001101293733 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000172762400003 DA 2022-12-14 ER PT J AU Mennecke, BE Townsend, AM Hayes, DJ Lonergan, SM AF Mennecke, B. E. Townsend, A. M. Hayes, D. J. Lonergan, S. M. TI A study of the factors that influence consumer attitudes toward beef products using the conjoint market analysis tool SO JOURNAL OF ANIMAL SCIENCE DT Article DE conjoint market analysis; consumer preferences; country of origin; steak quality; traceability; transaction cost ID PREFERENCES; PERCEPTIONS; QUALITY; ORIGIN AB This study utilizes an analysis technique commonly used in marketing, the conjoint analysis method, to examine the relative utilities of a set of beef steak characteristics considered by a national sample of 1,432 US consumers, as well as additional localized samples representing undergraduate students at a business college and in an animal science department. The analyses indicate that among all respondents, region of origin is by far the most important characteristic; this is followed by animal breed, traceability, animal feed, and beef quality. Alternatively, the cost of cut, farm ownership, the use ( or nonuse) of growth promoters, and whether the product is guaranteed tender were the least important factors. Results for animal science undergraduates are similar to the aggregate results, except that these students emphasized beef quality at the expense of traceability and the nonuse of growth promoters. Business students also emphasized region of origin but then emphasized traceability and cost. The ideal steak for the national sample is from a locally produced, choice Angus fed a mixture of grain and grass that is traceable to the farm of origin. If the product was not produced locally, respondents indicated that their preferred production states are, in order from most to least preferred, Iowa, Texas, Nebraska, and Kansas. C1 Iowa State Univ, Coll Business, Dept Management Informat Syst, Ames, IA 50011 USA. Iowa State Univ, Coll Agr, Dept Econ, Ames, IA 50011 USA. Iowa State Univ, Coll Business, Dept Finance, Ames, IA 50011 USA. Iowa State Univ, Coll Agr, Dept Anim Sci, Ames, IA 50011 USA. C3 Iowa State University; Iowa State University; Iowa State University; Iowa State University RP Mennecke, BE (corresponding author), Iowa State Univ, Coll Business, Dept Management Informat Syst, Ames, IA 50011 USA. EM mennecke@iastate.edu CR Agarwal M. K., 1991, INT J RES MARK, V8, P141 AGARWAL MK, 1988, EMPIRICAL COMPARISON, P88 AKAAH IP, 1983, J MARKETING RES, V20, P187, DOI 10.2307/3151685 AKAAH IP, 1991, J ACAD MARKET SCI, V19, P309 BaiduForson J, 1997, AGR SYST, V54, P463, DOI 10.1016/S0308-521X(96)00094-7 Bredahl L, 2004, FOOD QUAL PREFER, V15, P65, DOI 10.1016/S0950-3293(03)00024-7 Bredahl L, 1998, FOOD QUAL PREFER, V9, P273, DOI 10.1016/S0950-3293(98)00007-X CAMPICHE J, 2004, IMPACTS CONSUMER CHA CATTIN P, 1982, J MARKETING, V46, P44, DOI 10.2307/1251701 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 DIGBY A, 1996, BIOL SEX HLTH WELFAR DREIFUS C, 1977, SEIZING BODIES POLIT *FARM FDN, 2006, FUTURE ANIMAL AGR N, pCH6 Gellynck X, 2002, J AGR ECON, V53, P531, DOI 10.1111/j.1477-9552.2002.tb00036.x GILLESPIE JG, 1998, OPINIONS PROFESSIONA, V14, P247 Goode J., 2002, BRIT FOOD J, V104, P470, DOI [10.1108/00070700210418767, DOI 10.1108/00070700210418767] GREEN PE, 1991, J MARKETING RES, V28, P215, DOI 10.2307/3172809 GREEN PE, 1989, EUR J OPER RES, V41, P127, DOI 10.1016/0377-2217(89)90375-5 GREEN PE, 1991, J PROD INNOVAT MANAG, V8, P189, DOI 10.1111/1540-5885.830189 Grunert K. G., 2004, Agribusiness (New York), V20, P95, DOI 10.1002/agr.10086 Grunert K. G., 1996, MARKET ORIENTATION F Grunert KG, 1997, FOOD QUAL PREFER, V8, P157, DOI 10.1016/S0950-3293(96)00038-9 Hair J. F., 2009, CLUSTER ANAL MULTIVA Harrison R. W., 2002, Agricultural and Resource Economics Review, V31, P157 HAUSER JR, 1981, MANAGE SCI, V27, P33, DOI 10.1287/mnsc.27.1.33 Hobbs J. E., 1996, British Food Journal, V98, P16, DOI 10.1108/00070709610131339 HUBER J, 1987, SAWTOOTH C PERCEPTUA, P2 *I EUR FOOD STUD, 1996, 2 PAN SURV CONS ATT Jekanowski M. D., 2000, Agricultural and Resource Economics Review, V29, P43 Johnson R.M., 1987, SAWTOOTH SOFTWARE C, P253 Loureiro M. L., 2005, Journal of Agricultural and Applied Economics, V37, P49, DOI 10.1017/S1074070800007094 Loureiro ML, 2003, J AGR RESOUR ECON, V28, P287 Lusk JL, 2003, AM J AGR ECON, V85, P16, DOI 10.1111/1467-8276.00100 MAHAJAN V, 1992, J PROD INNOVAT MANAG, V9, P128, DOI 10.1111/1540-5885.920128 MCGARRYWOLF M, 2000, J FOOD DISTRIBUTION, V32, P193 Mesias FJ, 2005, J SCI FOOD AGR, V85, P2487, DOI 10.1002/jsfa.2283 Moore WL, 1999, J PROD INNOVAT MANAG, V16, P27, DOI 10.1111/1540-5885.1610027 Murphy M., 2000, British Food Journal, V102, P585, DOI 10.1108/00070700010348424 NAYAGA RM, 2004, INT J CONSUM STUD, V28, P178 Nayga RM, 2005, REV AGR ECON, V27, P37, DOI 10.1111/j.1467-9353.2004.00206.x ORME B, 1999, ACA CBC BOTH EFFECTI Orth U. R., 2003, Agribusiness (New York), V19, P137, DOI 10.1002/agr.10051 PAGE AL, 1987, J PROD INNOVAT MANAG, V4, P120, DOI 10.1111/1540-5885.420120 PINNELL J, 1994, SAWTOOTH SOFTWARE SE Pollard T M, 1999, SEX GENDER HLTH Quagrainie KK, 1998, CAN J AGR ECON, V46, P201, DOI 10.1111/j.1744-7976.1998.tb00363.x RAPPOPORT L, 1993, APPETITE, V20, P33, DOI 10.1006/appe.1993.1004 Roosen J., 2003, Agribusiness (New York), V19, P77, DOI 10.1002/agr.10041 SAVELL JW, 1989, J FOOD QUALITY, V12, P251, DOI 10.1111/j.1745-4557.1989.tb00328.x *SAWT SOFTW, 2002, ACA 5 0 *SAWT SOFTW, 2005, CBCHB SYST HIER BAY Tendero A, 2005, BRIT FOOD J, V107, P60, DOI 10.1108/00070700510579144 Thilmany D., 2003, Journal of Agribusiness, V21, P149 TUMBUSH JJ, 1991, SAWTOOTH SOFTWARE C, P177 UMBERGER WJ, 2004, CHOICES, V19, P15 UMBERGER WJ, 2006, BEEF QUALITY BEEF DE, pCH10 UMBERGER WJ, 2003, J FOOD DISTRIBUTION, V34, P103 Unterschultz J., 1997, Agribusiness (New York), V13, P457, DOI 10.1002/(SICI)1520-6297(199709/10)13:5<457::AID-AGR1>3.3.CO;2-2 Unterschultz J, 1998, CAN J AGR ECON, V46, P53, DOI 10.1111/j.1744-7976.1998.tb00081.x URBAN GL, 1990, MANAGE SCI, V36, P401, DOI 10.1287/mnsc.36.4.401 Urban GL, 1996, J MARKETING, V60, P47, DOI 10.2307/1251887 Valeeva NI, 2005, J DAIRY SCI, V88, P1601, DOI 10.3168/jds.S0022-0302(05)72829-0 Verbeke W, 2004, MEAT SCI, V67, P159, DOI 10.1016/j.meatsci.2003.09.017 Walley K., 1999, British Food Journal, V101, P148, DOI 10.1108/00070709910261936 WEINBERG BD, 1990, ROLES RES MODELS IMP WILKINSON S, 1994, WOMEN HLTH FEMINIST WIND J, 1989, INTERFACES, V19, P25, DOI 10.1287/inte.19.1.25 WITHGEN A, 2005, AGRIBUSINESS, V21, P191 Wittink DR., 1994, INT J RES MARKET, V11, P41, DOI [DOI 10.1016/0167-8116(94)90033-7, 10.1016/0167-8116(94)90033-7] ZIEHL A, 2005, J FOOD DISTRIBUTION, V36, P210 [No title captured] NR 71 TC 92 Z9 97 U1 7 U2 22 PD OCT PY 2007 VL 85 IS 10 BP 2639 EP 2659 DI 10.2527/jas.2006-495 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000249557700031 DA 2022-12-14 ER PT J AU Schnettler , B Silva , R Sepulveda , N AF Schnettler M, Berta Silva F, Roberto Sepulveda B, Nestor TI UTILITY TO CONSUMERS AND CONSUMER ACCEPTANCE OF INFORMATION ON BEEF LABELS IN SOUTHERN CHILE SO CHILEAN JOURNAL OF AGRICULTURAL RESEARCH DT Article DE beef; label; animal welfare; production systems; traceability ID ANIMAL-WELFARE; CARCASS CHARACTERISTICS; QUALITY; MEAT; PERCEPTION; TRACEABILITY; CONSUMPTION; PRODUCTS; CATTLE; ORIGIN AB Credence attributes (production system, animal welfare, traceability, among others) have acquired importance for meat products in developed countries, representing information that must be included on label. A personal survey was administered to 770 consumers in the Bio-Bio and La Araucan a Regions, Chile, to determine the utility of information contained on the label and acceptance of information referred to as credence attributes, as well as to distinguish different consumer segments. The packaging and expiry dates were the most useful aspects of the current information. The greatest degree of agreement with respect to information that should be included was a quality seal, type of animal, handling regarding animal welfare, production system and feeding. Using cluster analysis, three segments were distinguished. The largest (49.2%) agreed most strongly with including information about feeding, transport conditions, slaughtering, traceability, and production system. The second group (34.4%) was indifferent to information about transport, slaughter and traceability. The smallest segment (16.4%) disagreed with including information on slaughtering. Therefore, the information currently present on meat labels was useful for consumers, who would also value having information on the credence attributes associated with cattle production, such as production system, feeding and animal welfare. C1 [Schnettler M, Berta; Silva F, Roberto; Sepulveda B, Nestor] Univ La Frontera, Fac Ciencias Agropecuarias & Forestales, Temuco, Chile. C3 Universidad de La Frontera RP Schnettler , B (corresponding author), Univ La Frontera, Fac Ciencias Agropecuarias & Forestales, Casilla 54-D, Temuco, Chile. EM bschnett@ufro.cl CR Adimark, 2004, MAP SOC CHIL Alfnes F, 2004, EUR REV AGRIC ECON, V31, P19, DOI 10.1093/erae/31.1.19 Altmann M., 1997, Agro-food marketing., P279 Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Bernues A, 2003, FOOD QUAL PREFER, V14, P265, DOI 10.1016/S0950-3293(02)00085-X Blokhuis HJ, 2003, ANIM WELFARE, V12, P445 Bredahl L, 2004, FOOD QUAL PREFER, V15, P65, DOI 10.1016/S0950-3293(03)00024-7 Caswell JA, 1996, AM J AGR ECON, V78, P1248, DOI 10.2307/1243501 Choat WT, 2006, J ANIM SCI, V84, P1820, DOI 10.2527/jas.2004-418 Corcoran K., 2001, Options Mediterraneennes. Serie A, Seminaires Mediterraneens, P75 *FAENACAR, 2004, NUEV REGL MAT FERNANDEZ A, 2002, INVESTIGACION TECNIC Gellynck X., 2001, Agrarwirtschaft, V50, P368 Glitsch K., 2000, British Food Journal, V102, P177, DOI 10.1108/00070700010332278 Goode J., 2002, BRIT FOOD J, V104, P470, DOI [10.1108/00070700210418767, DOI 10.1108/00070700210418767] Grunert KG, 2001, LIVEST PROD SCI, V72, P83, DOI 10.1016/S0301-6226(01)00269-X Hair J. F., 1999, ANALISIS MULTIVARIAN *INE, 2003, CENS 2002 RES, V1 KINNEAR TC, 1989, INVESTIGACION MERCAD Kobrich K, 2001, EC AGRARIA, V6, P251 Kotler P., 2006, MARK MANAG Lea P, 1997, ANAL VARIANCE SENSOR Maria GA, 2006, LIVEST SCI, V103, P250, DOI 10.1016/j.livsci.2006.05.011 McEachern MG, 2005, BRIT FOOD J, V107, P572, DOI 10.1108/00070700510610986 Napolitano F, 2007, FOOD QUAL PREFER, V18, P305, DOI 10.1016/j.foodqual.2006.02.002 Olaizola Tolosana A. M., 2005, Spanish Journal of Agricultural Research, V3, P418 Oliver MA, 2006, MEAT SCI, V74, P435, DOI 10.1016/j.meatsci.2006.03.010 Realini CE, 2004, MEAT SCI, V66, P567, DOI 10.1016/S0309-1740(03)00160-8 Roosen J., 2003, Agribusiness (New York), V19, P77, DOI 10.1002/agr.10041 *SAG, 2008, REGL GEN SIST CLAS G SCHEAFFER R, 1996, ELEMENTOS MUESTREO Schnettler B., 2004, Ciencia e Investigacion Agraria, V31, P91 Schnettler B., 2006, 9 REGION CHILE, V24, P15 Schnettler B, 2008, FOOD QUAL PREFER, V19, P372, DOI 10.1016/j.foodqual.2007.11.005 Schnettler B, 2008, CHIL J AGR RES, V68, P80, DOI 10.4067/S0718-58392008000100008 Schroder M.J.A., 2004, INT J CONSUM STUD, V28, P168, DOI [DOI 10.1111/J.1470-6431.2003.00357.X, 10.1111/J.1470-6431.2003.00357.X, 10.1111/j.1470-6431.2003.00357.x] SPSS, 2005, STAT PACK SOC SCI SP van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Verbeke W, 1999, FOOD QUAL PREFER, V10, P437, DOI 10.1016/S0950-3293(99)00031-2 Verbeke W., 2000, Agribusiness (New York), V16, P215, DOI 10.1002/(SICI)1520-6297(200021)16:2<215::AID-AGR6>3.0.CO;2-S Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 NR 41 TC 4 Z9 6 U1 1 U2 9 PD JUL-SEP PY 2009 VL 69 IS 3 BP 373 EP 382 DI 10.4067/S0718-58392009000300010 WC Agriculture, Multidisciplinary; Agronomy SC Agriculture UT WOS:000272709400010 DA 2022-12-14 ER PT J AU Su, TF Wei, PF Wu, LL Guo, YW Zhao, WQ Zhang, Y Chi, ZY Qiu, LY AF Su, Tongfu Wei, Pengfei Wu, Lulu Guo, Yawen Zhao, Wanqian Zhang, Yan Chi, Zhiyong Qiu, Liyou TI Development of nucleic acid isolation by non-silica-based nanoparticles and real-time PCR kit for edible vegetable oil traceability SO FOOD CHEMISTRY DT Article DE Non-silicon based dipolar nanocomposites; DNA extraction; Real-time PCR; Edible vegetable oils; Traceability ID COATED MAGNETIC NANOPARTICLES; OLIVE OIL; DNA; AUTHENTICATION; IDENTIFICATION; QUANTIFICATION; EXTRACTION; ABSOLUTE AB For efficient extraction of amplifiable DNA from edible vegetable oils, we developed a novel DNA extraction approach based on the non-silica-based dipolar nanocomposites. The nanoparticle comprises a hydrophilic polymethyl methacrylate core with abundant capillaries, hydrophilic vesicles decorated with molecules having DNA affinity and a coating hydrophobic polystyrene layer. The nanoparticles are soluble in oil, adsorb the DNA from the aqueous phase and gave a high DNA recovery ratio. All DNA extracts from fully refined vegetable oil soybean, peanut, rapeseed, and cottonseed oils, including their blends, were sufficiently pure to be amplified by real-time PCR targeting the chloroplast ribulose-1,5-bisphosphate gene (rbcL), therefore, the species of origin and their ratios in mixed vegetable oils blended from two or three oil-species could be determined. These results indicate that the novel DNA isolation and real-time PCR kit is a simple, sensitive and efficient tool for the species identification and traceability in refined vegetable oils. C1 [Su, Tongfu; Wu, Lulu; Chi, Zhiyong] Henan Agr Univ, Coll Sci, Zhengzhou 450002, Henan, Peoples R China. [Wei, Pengfei; Guo, Yawen; Zhang, Yan; Qiu, Liyou] Henan Agr Univ, Coll Life Sci, Zhengzhou 450002, Henan, Peoples R China. [Su, Tongfu; Wei, Pengfei; Wu, Lulu; Guo, Yawen; Zhang, Yan; Chi, Zhiyong; Qiu, Liyou] Minist Agr, Key Lab Enzyme Engn Agr Microbiol, Zhengzhou 450002, Henan, Peoples R China. [Zhao, Wanqian] Jiangsu Jiayu Biomed Technol Co Ltd, Jiangyin 214432, Peoples R China. C3 Henan Agricultural University; Henan Agricultural University; Ministry of Agriculture & Rural Affairs RP Qiu, LY (corresponding author), 95 Wenhua Rd, Zhengzhou 450002, Henan, Peoples R China. EM qliyou@henau.edu.cn CR Abbas O, 2016, WOODHEAD PUBL FOOD S, P519, DOI 10.1016/B978-0-08-100220-9.00019-9 Amaral J, 2016, WOODHEAD PUBL FOOD S, P369, DOI 10.1016/B978-0-08-100220-9.00014-X Amaral JS, 2014, FOOD RES INT, V60, P140, DOI 10.1016/j.foodres.2013.11.003 Aparicio R, 2013, FOOD RES INT, V54, P2025, DOI 10.1016/j.foodres.2013.07.039 Busby E., 2014, Journal of the Association of Public Analysts, V42, P35 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Cao HH, 2017, LWT-FOOD SCI TECHNOL, V82, P243, DOI 10.1016/j.lwt.2017.04.037 CARTER MJ, 1993, NUCLEIC ACIDS RES, V21, P1044, DOI 10.1093/nar/21.4.1044 Chen XW, 2012, TALANTA, V100, P107, DOI 10.1016/j.talanta.2012.07.095 Corrado G, 2016, TRENDS FOOD SCI TECH, V52, P80, DOI 10.1016/j.tifs.2016.04.003 Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Costa J, 2010, EUR FOOD RES TECHNOL, V230, P915, DOI 10.1007/s00217-010-1238-2 Costa J, 2010, FOOD RES INT, V43, P301, DOI 10.1016/j.foodres.2009.10.003 Esslinger S, 2014, FOOD RES INT, V60, P189, DOI 10.1016/j.foodres.2013.10.015 Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Gliszczynska-Swiglo A, 2017, FOOD ANAL METHOD, V10, P1800, DOI 10.1007/s12161-016-0739-4 Gryson N, 2004, J AM OIL CHEM SOC, V81, P231, DOI 10.1007/s11746-004-0887-6 He XX, 2007, TALANTA, V73, P764, DOI 10.1016/j.talanta.2007.04.056 Huang FR, 2016, J RAMAN SPECTROSC, V47, P860, DOI 10.1002/jrs.4895 Kang K, 2009, J PHYS CHEM B, V113, P536, DOI 10.1021/jp807081b Lee C, 2006, J BIOTECHNOL, V123, P273, DOI 10.1016/j.jbiotec.2005.11.014 Li S, 2017, INT J ENVIRON AN CH, V97, P124, DOI 10.1080/03067319.2017.1291806 Li YunJing, 2018, Oil Crop Science, V3, P122 McKee AM, 2015, BIOL CONSERV, V183, P70, DOI 10.1016/j.biocon.2014.11.031 Muzzalupo I, 2002, EUR FOOD RES TECHNOL, V214, P528, DOI 10.1007/s00217-001-0482-x Pafundo S, 2010, FOOD CHEM, V123, P787, DOI 10.1016/j.foodchem.2010.05.027 Pauli U, 1998, Z LEBENSM UNTERS F A, V207, P264, DOI 10.1007/s002170050330 Ren JN, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0173567 Rezadoost MH, 2016, 3 BIOTECH, V6, DOI 10.1007/s13205-016-0375-0 Rozen S, 2000, Methods Mol Biol, V132, P365 Scollo F, 2016, FOOD CHEM, V213, P388, DOI 10.1016/j.foodchem.2016.06.086 Shayan P., 2017, IRAN J VET RES, V11, P311, DOI DOI 10.1007/S00217-008-0863-5 Soares S, 2017, COMPR REV FOOD SCI F, V16, P1072, DOI 10.1111/1541-4337.12278 Spaniolas S, 2008, EUR FOOD RES TECHNOL, V227, P175, DOI 10.1007/s00217-007-0707-8 Tiwari AP, 2015, RSC ADV, V5, P8463, DOI 10.1039/c4ra15806g Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Wang HF, 2016, PROCEEDINGS OF SYMPOSIUM OF POLICING DIPLOMACY AND THE BELT & ROAD INITIATIVE, 2016, P265 Wu YJ, 2008, EUR FOOD RES TECHNOL, V227, P1117, DOI 10.1007/s00217-008-0827-9 Wu YJ, 2011, EUR FOOD RES TECHNOL, V233, P313, DOI 10.1007/s00217-011-1520-y Zhu WR, 2017, FOOD CHEM, V216, P268, DOI 10.1016/j.foodchem.2016.08.051 NR 40 TC 1 Z9 2 U1 4 U2 66 PD DEC 1 PY 2019 VL 300 AR 125205 DI 10.1016/j.foodchem.2019.125205 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000483990100017 DA 2022-12-14 ER PT J AU Xu, YL Yang, WZ Wu, XW Wang, YZ Zhang, JY AF Xu, Yulin Yang, Weize Wu, Xuewei Wang, Yuanzhong Zhang, Jinyu TI ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum SO FOODS DT Article DE ResNet model; FT-NIR analysis; preprocessing; feature extraction; geographical traceability ID VARIABLE SELECTION METHODS; DATA FUSION; SPECTROSCOPY; AUTHENTICATION; QUALITY; MIR AB Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants. C1 [Xu, Yulin; Yang, Weize; Wang, Yuanzhong; Zhang, Jinyu] Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. [Xu, Yulin; Wu, Xuewei] Yunnan Univ, Sch Agr, Kunming 650504, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University RP Wang, YZ; Zhang, JY (corresponding author), Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. EM boletus@126.com; jyzhang2008@126.com CR Abasi S, 2018, TRENDS FOOD SCI TECH, V78, P197, DOI 10.1016/j.tifs.2018.05.009 Amirvaresi A, 2021, FOOD CHEM, V344, DOI 10.1016/j.foodchem.2020.128647 Chong IG, 2005, CHEMOMETR INTELL LAB, V78, P103, DOI 10.1016/j.chemolab.2004.12.011 Dai Q, 2014, COMPR REV FOOD SCI F, V13, P891, DOI 10.1111/1541-4337.12088 Daszykowski M, 2007, CHEMOMETR INTELL LAB, V85, P203, DOI 10.1016/j.chemolab.2006.06.016 Ding YG, 2021, J ETHNOPHARMACOL, V278, DOI 10.1016/j.jep.2021.114293 Dong JE, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108132 Dong JE, 2020, CHEMOMETR INTELL LAB, V197, DOI 10.1016/j.chemolab.2019.103913 Guyon I., 2003, Journal of Machine Learning Research, V3, P1157, DOI 10.1162/153244303322753616 Janeczko B, 2022, INTERNET MULTIMEDIA, P133, DOI [10.1016/b978-0-32-385845-8.00011-3, DOI 10.1016/B978-0-32-385845-8.00011-3] Li HD, 2009, ANAL CHIM ACTA, V648, P77, DOI 10.1016/j.aca.2009.06.046 Li L, 2022, MICROCHEM J, V178, DOI 10.1016/j.microc.2022.107430 Li XP, 2020, INT J FOOD PROP, V23, P227, DOI 10.1080/10942912.2020.1722159 Li Y, 2020, J PHARMACEUT BIOMED, V185, DOI 10.1016/j.jpba.2020.113215 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Liang N, 2022, CRIT REV FOOD SCI, V62, P2963, DOI 10.1080/10408398.2020.1862045 Lin SW, 2008, EXPERT SYST APPL, V35, P1817, DOI 10.1016/j.eswa.2007.08.088 Liu L, 2020, MICROCHEM J, V159, DOI 10.1016/j.microc.2020.105360 Liu Y, 2021, TRENDS FOOD SCI TECH, V113, P193, DOI 10.1016/j.tifs.2021.04.042 Mayerich D., 2023, MICROSCOPE IMAGE PRO, V2nd, P431, DOI [10.1016/b978-0-12-821049-9.00015-0, DOI 10.1016/B978-0-12-821049-9.00015-0] Meng T, 2020, INFORM FUSION, V57, P115, DOI 10.1016/j.inffus.2019.12.001 Mishra P, 2020, TRAC-TREND ANAL CHEM, V132, DOI 10.1016/j.trac.2020.116045 Negi A, 2021, FOOD CONTROL, V127, DOI 10.1016/j.foodcont.2021.108113 Nicolai BM, 2007, POSTHARVEST BIOL TEC, V46, P99, DOI 10.1016/j.postharvbio.2007.06.024 NODA I, 1993, APPL SPECTROSC, V47, P1329, DOI 10.1366/0003702934067694 Noda I, 2018, J MOL STRUCT, V1160, P471, DOI 10.1016/j.molstruc.2018.01.091 Oliveira MM, 2019, COMPR REV FOOD SCI F, V18, P670, DOI 10.1111/1541-4337.12436 Rinnan A, 2009, TRAC-TREND ANAL CHEM, V28, P1201, DOI 10.1016/j.trac.2009.07.007 Roger J.M., 2020, COMPREHENSIVE CHEMOM, V3, P1, DOI [10.1016/B978-0-12-409547-2.14878-4., DOI 10.1016/B978-0-12-409547-2.14878-4] Schonbichler SA, 2013, J PHARMACEUT BIOMED, V84, P97, DOI 10.1016/j.jpba.2013.04.038 Sun WJ, 2017, SPECTROCHIM ACTA A, V171, P72, DOI 10.1016/j.saa.2016.07.039 Wang CY, 2021, IND CROP PROD, V160, DOI 10.1016/j.indcrop.2020.113090 Wu XM, 2018, MICROCHEM J, V143, P367, DOI 10.1016/j.microc.2018.08.035 Wu ZF, 2019, PATTERN RECOGN, V90, P119, DOI 10.1016/j.patcog.2019.01.006 Yue JQ, 2021, FRONT PLANT SCI, V12, DOI 10.3389/fpls.2021.752863 Yun YH, 2019, TRAC-TREND ANAL CHEM, V113, P102, DOI 10.1016/j.trac.2019.01.018 Zhang J, 2021, MICROCHEM J, V160, DOI 10.1016/j.microc.2020.105662 Zhang XH, 2018, CHEMOMETR INTELL LAB, V183, P147, DOI 10.1016/j.chemolab.2018.10.016 Zhao P, 2018, J ETHNOPHARMACOL, V214, P274, DOI 10.1016/j.jep.2017.12.006 Zhou L, 2019, COMPR REV FOOD SCI F, V18, P1793, DOI 10.1111/1541-4337.12492 Zou XB, 2010, ANAL CHIM ACTA, V667, P14, DOI 10.1016/j.aca.2010.03.048 NR 41 TC 0 Z9 0 U1 1 U2 1 PD NOV PY 2022 VL 11 IS 22 AR 3568 DI 10.3390/foods11223568 WC Food Science & Technology SC Food Science & Technology UT WOS:000887185000001 DA 2022-12-14 ER PT J AU Jiang, LF Shi, Z Xia, JY Liang, JY Lu, XJ Wang, Y Luo, YQ AF Jiang, Lifen Shi, Zheng Xia, Jianyang Liang, Junyi Lu, Xingjie Wang, Ying Luo, Yiqi TI Transient Traceability Analysis of Land Carbon Storage Dynamics: Procedures and Its Application to Two Forest Ecosystems SO JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS DT Article DE carbon storage capacity; carbon storage potential; model intercomparison; residence time; traceability analysis; transient carbon dynamics ID EARTH SYSTEM MODELS; SOIL CARBON; ATMOSPHERIC CO2; TERRESTRIAL; CLIMATE; NITROGEN; CYCLE; SEQUESTRATION; UNCERTAINTIES; PRODUCTIVITY AB Uptake of anthropogenically emitted carbon (C) dioxide by terrestrial ecosystem is critical for determining future climate. However, Earth system models project large uncertainties in future C storage. To help identify sources of uncertainties in model predictions, this study develops a transient traceability framework to trace components of C storage dynamics. Transient C storage (X) can be decomposed into two components, C storage capacity (X-c) and C storage potential (X-p). X-c is the maximum C amount that an ecosystem can potentially store and X-p represents the internal capacity of an ecosystem to equilibrate C input and output for a network of pools. X-c is codetermined by net primary production (NPP) and residence time (tau(N)), with the latter being determined by allocation coefficients, transfer coefficients, environmental scalar, and exit rate. X-p is the product of redistribution matrix (tau(ch)) and net ecosystem exchange. We applied this framework to two contrasting ecosystems, Duke Forest and Harvard Forest with an ecosystem model. This framework helps identify the mechanisms underlying the responses of carbon cycling in the two forests to climate change. The temporal trajectories of X are similar between the two ecosystems. Using this framework, we found that different mechanisms lead to a similar trajectory between the two ecosystems. This framework has potential to reveal mechanisms behind transient C storage in response to various global change factors. It can also identify sources of uncertainties in predicted transient C storage across models and can therefore be useful for model intercomparison. C1 [Jiang, Lifen; Lu, Xingjie; Luo, Yiqi] No Arizona Univ, Ctr Ecosyst Sci & Soc, Flagstaff, AZ 86011 USA. [Shi, Zheng; Liang, Junyi] Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA. [Xia, Jianyang] East China Normal Univ, Sch Ecol & Environm Sci, Shanghai, Peoples R China. [Liang, Junyi] Oak Ridge Natl Lab, Environm Sci Div, Oak Ridge, TN USA. [Liang, Junyi] Oak Ridge Natl Lab, Climate Change Sci Inst, Oak Ridge, TN USA. [Wang, Ying] Univ Oklahoma, Dept Math, Norman, OK 73019 USA. [Luo, Yiqi] Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China. C3 Northern Arizona University; University of Oklahoma System; University of Oklahoma - Norman; East China Normal University; United States Department of Energy (DOE); Oak Ridge National Laboratory; United States Department of Energy (DOE); Oak Ridge National Laboratory; University of Oklahoma System; University of Oklahoma - Norman; Tsinghua University RP Jiang, LF; Luo, YQ (corresponding author), No Arizona Univ, Ctr Ecosyst Sci & Soc, Flagstaff, AZ 86011 USA.; Shi, Z (corresponding author), Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA. EM Lifen.Jiang@nau.edu; zheng.shi@ou.edu; Yiqi.Luo@nau.edu CR Ahlstrom A, 2013, BIOGEOSCIENCES, V10, P1517, DOI 10.5194/bg-10-1517-2013 Ahlstrom A, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/5/054019 Anav A, 2013, J CLIMATE, V26, P6801, DOI 10.1175/JCLI-D-12-00417.1 Arora VK, 2013, J CLIMATE, V26, P5289, DOI 10.1175/JCLI-D-12-00494.1 Ball J., 1987, PROGR PHOTOSYNTHESIS, V4, P221, DOI [10.1007/978-94-017-0519-6_48, DOI 10.1007/978-94-017-0519-6_48] Barford CC, 2001, SCIENCE, V294, P1688, DOI 10.1126/science.1062962 Brovkin V, 2013, J CLIMATE, V26, P6859, DOI 10.1175/JCLI-D-12-00623.1 Carvalhais N, 2014, NATURE, V514, P213, DOI 10.1038/nature13731 Cox PM, 2013, NATURE, V494, P341, DOI 10.1038/nature11882 FARQUHAR GD, 1980, PLANTA, V149, P78, DOI 10.1007/BF00386231 Fisher JB, 2014, ANNU REV ENV RESOUR, V39, P91, DOI 10.1146/annurev-environ-012913-093456 Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1 Friedlingstein P, 2014, J CLIMATE, V27, P511, DOI 10.1175/JCLI-D-12-00579.1 Friend AD, 2014, P NATL ACAD SCI USA, V111, P3280, DOI 10.1073/pnas.1222477110 Hararuk O, 2014, ECOSPHERE, V5, DOI 10.1890/ES14-00092.1 Hararuk O, 2014, J GEOPHYS RES-BIOGEO, V119, P403, DOI 10.1002/2013JG002535 He YJ, 2016, SCIENCE, V353, P1419, DOI 10.1126/science.aad4273 Jiang LF, 2015, J CLIMATE, V28, P5217, DOI 10.1175/JCLI-D-14-00270.1 Jones C, 2013, J CLIMATE, V26, P4398, DOI 10.1175/JCLI-D-12-00554.1 Koven C.D., 2013, TECHNICAL DESCRIPTIO, DOI DOI 10.1073/pnas.1407302112 Le Quere C, 2015, EARTH SYST SCI DATA, V7, P47, DOI 10.5194/essd-7-47-2015 Lichter J, 2005, ECOLOGY, V86, P1835, DOI 10.1890/04-1205 Luo YQ, 2017, BIOGEOSCIENCES, V14, P145, DOI 10.5194/bg-14-145-2017 Luo YQ, 2016, GLOBAL BIOGEOCHEM CY, V30, P40, DOI 10.1002/2015GB005239 Luo YQ, 2015, GLOBAL CHANGE BIOL, V21, P1737, DOI 10.1111/gcb.12766 Luo YQ, 2011, TRENDS ECOL EVOL, V26, P96, DOI 10.1016/j.tree.2010.11.003 Manzoni S, 2009, SOIL BIOL BIOCHEM, V41, P1355, DOI 10.1016/j.soilbio.2009.02.031 McCarthy HR, 2006, P NATL ACAD SCI USA, V103, P19356, DOI 10.1073/pnas.0609448103 McCarthy HR, 2010, NEW PHYTOL, V185, P514, DOI 10.1111/j.1469-8137.2009.03078.x Melillo JM, 2011, P NATL ACAD SCI USA, V108, P9508, DOI 10.1073/pnas.1018189108 Peng J, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0093094 Qian TT, 2006, J HYDROMETEOROL, V7, P953, DOI 10.1175/JHM540.1 Shi Z, 2015, ECOSPHERE, V6, DOI 10.1890/ES14-00335.1 Sierra CA, 2015, ECOL MONOGR, V85, P505, DOI 10.1890/15-0361.1 Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x Tian HQ, 2015, GLOBAL BIOGEOCHEM CY, V29, P775, DOI 10.1002/2014GB005021 Todd-Brown KEO, 2014, BIOGEOSCIENCES, V11, P2341, DOI 10.5194/bg-11-2341-2014 Todd-Brown KEO, 2013, BIOGEOSCIENCES, V10, P1717, DOI 10.5194/bg-10-1717-2013 Urbanski S, 2007, J GEOPHYS RES-BIOGEO, V112, DOI 10.1029/2006JG000293 Wang WL, 2011, GLOBAL CHANGE BIOL, V17, P1367, DOI 10.1111/j.1365-2486.2010.02315.x Wang YP, 1998, AGR FOREST METEOROL, V91, P89, DOI 10.1016/S0168-1923(98)00061-6 Weng ES, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000539 Wieder WR, 2015, NAT GEOSCI, V8, P441, DOI 10.1038/NGEO2413 Xia JY, 2013, GLOBAL CHANGE BIOL, V19, P2104, DOI 10.1111/gcb.12172 NR 44 TC 10 Z9 10 U1 5 U2 19 PD DEC PY 2017 VL 9 IS 8 BP 2822 EP 2835 DI 10.1002/2017MS001004 WC Meteorology & Atmospheric Sciences SC Meteorology & Atmospheric Sciences UT WOS:000422718400002 DA 2022-12-14 ER PT J AU Li, QB Bi, ZQ Shi, DD AF Li Qing-bo Bi Zhi-qi Shi Dong-dong TI Near Infrared Spectral Analysis Algorithms for Traceability of Fishmeal Origin SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE Near infrared spectroscopy; Preprocessing; Gray wolf algorithm; Support vector machine; Traceability of fish meal; origin ID SPECTROSCOPY AB Fish meal is a kind of high-protein feed made up of one or more kinds of fish, which occupies a very important position in the aquaculture industry. In order to maintain market order, a method of tracing the origin of the fish meal should be established to identify and analyze the quality of the fish meal more accurately. In this paper, near-infrared spectroscopy (NIRS) and chemometrics are used to trace the origin of fish meal from different habitats quickly and accurately. The support vector machine with radial basis function (RBF-SVM) as the kernel function is used for pattern recognition, and the gray wolf algorithm is used to select the key parameters of RBF-SVM. By simulating the hunting behavior of wolves, a hierarchical system is set up according to the fitness level. The target parameters gradually approximate the movement of encirclement. After each movement, the adaptability is re-evaluated. The prey is finally captured through the iteration of wolf pack rank, and the optimal penalty factor and the radius of the kernel function are searched. Finally, the optimal parameters are used to establish the support vector machine model to trace the origin of fish meal from different origins. Grey Wolf algorithm can improve the speed and accuracy of selecting key parameters in the support vector machine algorithm, and improve the classification accuracy of support vector machine. In this paper, 144 spectra of fish meal samples from four fishmeal producing areas in ZhejiangWenling, Shandong Rongcheng, Shandong Weihai and Liaoning Dalian were obtained. The spectrum ranges from 3700 to 12500 cm(-1). The origin of fish meal was traced by the obtained spectra. Seventy percent of the samples from each producing area was randomly selected as the training sample set for modeling and 30 percent as the test sample set. First, the original near infrared spectra are pretreated, and the average spectra of all the collected spectra are calculated by multivariate scattering correction as "ideal spectra". The other spectra are linearly regressed, and the baseline correction of spectral translation and migration is carried out. The original signal is decomposed by wavelet transform, and the high-frequency signal is thresholded to eliminate the high-frequency noise so as to achieve the smooth denoising effect of the spectral curve. Ten parallel experiments were carried out by support vector machine to reduce error interference, and the classification results were obtained as follows: Zhejiang Wenling, Shandong Rongcheng, Shandong Weihai and Liaoning Dalian were 100% 98.89% 96.43% and 97.78% respectively. Compared with the grid search method, the Improved Grey Wolf algorithm searches for the penalty factor and the radius of the kernel function faster and more accurately, and the classification accuracy is high. It can be seen that the improved grey wolf algorithm's support vector machine (GWO-SVM) is feasible for tracing the origin of fish meal. C1 [Li Qing-bo; Bi Zhi-qi] Beihang Univ, Sch Instrumentat & Optoelectron Engn, Key Lab Precis Opto Mechatron Technol, Minist Educ, Beijing 100191, Peoples R China. [Shi Dong-dong] Chinese Acad Agr Sci, Feed Res Inst, Beijing 100081, Peoples R China. C3 Beihang University; Chinese Academy of Agricultural Sciences; Feed Research Institute, CAAS RP Li, QB (corresponding author), Beihang Univ, Sch Instrumentat & Optoelectron Engn, Key Lab Precis Opto Mechatron Technol, Minist Educ, Beijing 100191, Peoples R China. CR Cozzolino D, 2015, AQUACULT NUTR, V8, P149 Faris H, 2018, NEURAL COMPUT APPL, V30, P413, DOI 10.1007/s00521-017-3272-5 Guo ZM, 2017, I COMP CONF WAVELET, P89 Khan S, 2016, BIOMED OPT EXPRESS, V7, P2249, DOI 10.1364/BOE.7.002249 LIN Yi-qun, 2014, CHINESE ABSTRACTS AN, P38 Mabood F, 2017, FOOD CHEM, V221, P746, DOI 10.1016/j.foodchem.2016.11.109 Mirjalili S, 2014, ADV ENG SOFTW, V69, P46, DOI 10.1016/j.advengsoft.2013.12.007 Samuel PP, 2015, MATER TODAY-PROC, V2, P949, DOI 10.1016/j.matpr.2015.06.014 [宋涛 Song Tao], 2015, [食品科学, Food Science], V36, P260 TENG Xu-xia, 2014, MODERN ANIMAL HUSBAN, P63 NR 10 TC 1 Z9 2 U1 0 U2 14 PD SEP PY 2020 VL 40 IS 9 BP 2804 EP 2808 DI 10.3964/j.issn.1000-0593(2020)09-2804-05 WC Spectroscopy SC Spectroscopy UT WOS:000576358300025 DA 2022-12-14 ER PT J AU Gaspar, P Diaz-Caro, C del Puerto, I Ortiz, A Escribano, M Tejerina, D AF Gaspar, Paula Diaz-Caro, Carlos del Puerto, Ines Ortiz, Alberto Escribano, Miguel Tejerina, David TI What effect does the presence of sustainability and traceability certifications have on consumers of traditional meat products? The case of Iberian cured products in Spain SO MEAT SCIENCE DT Article DE Consumption habits; Iberian cured products; QR code; Choice experiment; Bayesian methods ID FOOD TRACEABILITY; CONSUMPTION; QUALITY AB The objective of this study was to determine the differences in the behaviour and perception of consumers from three regions in Spain with different levels of Iberian ham consumption (high, medium and low) on the attributes related to traceability and differentiated quality certifications. The study was carried out with a sample of 1501 consumers using an online questionnaire in which respondents were asked about their consumption habits regarding cured products and a choice experiment for Iberian ham was performed. The results showed that consumers give greater utility to the attributes of mandatory certification established for Iberian products (quality standards) and to the price, followed by nonmandatory quality certifications (organic, animal welfare, carbon footprint, and PDO). Specifically, animal welfare certification has been the most valued in all areas, especially in the areas with the lowest consumption. C1 [Gaspar, Paula; Escribano, Miguel] Univ Extremadura, Sch Agr Engn, Dept Anim Prod & Food Sci, Avda Adolfo Suarez S-N, Badajoz 06007, Spain. [Diaz-Caro, Carlos] Univ Extremadura, Sch Business Finance & Tourism, Dept Accounting & Finance, Avda Univ S-N, Caceres 10071, Spain. [del Puerto, Ines] Univ Extremadura, Fac Sci, Dept Math, Avda Elvas S-N, Badajoz 06006, Spain. [Ortiz, Alberto; Tejerina, David] Ctr Sci & Technol Res Extremadura CICYTEX La Orde, Meat Qual Area, Junta Extremadura, Autovia A5 Km 372, Badajoz 06187, Spain. C3 Universidad de Extremadura; Universidad de Extremadura; Universidad de Extremadura RP Ortiz, A (corresponding author), Ctr Sci & Technol Res Extremadura CICYTEX La Orde, Meat Qual Area, Junta Extremadura, Autovia A5 Km 372, Badajoz 06187, Spain. EM alberto.ortiz@juntaex.es CR Adamowicz W, 1998, RESOURCE VALUATION B Apostolidis C, 2016, FOOD POLICY, V65, P74, DOI 10.1016/j.foodpol.2016.11.002 Chamorro A., 2008, DISTRIBUCION CONSUMO, P50 CoreTeam R., 2017, R LANG ENV STAT COMP, V2 Dudinskaya EC, 2021, ANIMALS-BASEL, V11, DOI 10.3390/ani11020556 den Herder M, 2017, AGR ECOSYST ENVIRON, V241, P121, DOI 10.1016/j.agee.2017.03.005 Diaz-Caro C, 2019, MEAT SCI, V158, DOI 10.1016/j.meatsci.2019.107908 Dieguez E., 2016, SOLO CERDO IBERICO, V36, P122 European Commission, 2020, FARM FORK STRATEGY Gadema Z, 2011, FOOD POLICY, V36, P815, DOI 10.1016/j.foodpol.2011.08.001 Garcia-Gudino J, 2021, MEAT SCI, V172, DOI 10.1016/j.meatsci.2020.108317 Grunert KG, 2018, MEAT SCI, V137, P123, DOI 10.1016/j.meatsci.2017.11.022 Grymshi D, 2022, AGRIBUSINESS, V38, P93, DOI 10.1002/agr.21714 Hanley N, 2001, J ECON SURV, V15, P435, DOI 10.1111/1467-6419.00145 Higgins L. M., 2014, Wine Economics and Policy, V3, P19, DOI 10.1016/j.wep.2014.01.002 Holker S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11143907 ICEX, 2019, ESP EXP INV Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Koutsimanis G, 2012, APPETITE, V59, P270, DOI 10.1016/j.appet.2012.05.012 LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131 Li TZ, 2019, J AGR RESOUR ECON, V44, P311 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Lockshin L, 2006, FOOD QUAL PREFER, V17, P166, DOI 10.1016/j.foodqual.2005.03.009 Lorido L., 2016, EVALUACION SENSORIAL Lorido L, 2019, MEAT SCI, V149, P126, DOI 10.1016/j.meatsci.2018.11.015 MAPA, 2014, CONS JAM IB HOG COM MAPA, 2019, INF CONS AL ESP 2018 Mazzocchi Chiara, 2019, Wine Economics and Policy, V8, P155, DOI 10.1016/j.wep.2019.09.002 Mesias FJ, 2009, MEAT SCI, V83, P684, DOI 10.1016/j.meatsci.2009.08.004 Monteiro ANTR, 2019, J CLEAN PROD, V237, DOI 10.1016/j.jclepro.2019.117843 Morales R, 2013, MEAT SCI, V95, P652, DOI 10.1016/j.meatsci.2013.05.012 Orme B., 2009, P SAET SOFTW C Ortiz A, 2021, MEAT SCI, V173, DOI 10.1016/j.meatsci.2020.108373 Ortiz A, 2020, J SENS STUD, V35, DOI 10.1111/joss.12575 Pugliese C, 2012, MEAT SCI, V90, P511, DOI 10.1016/j.meatsci.2011.09.019 RD 4/2014, 2014, 42014 SPAN MIN AGR F Resano H, 2012, FOOD POLICY, V37, P355, DOI 10.1016/j.foodpol.2012.03.006 Rossi PE, 2005, WILEY SER PROBAB ST, P1 Sahelices A, 2017, ITAL J ANIM SCI, V16, P9, DOI [10.1080/1828051X.2016.1266704, 10.1080/1828051x.2016.1266704] Tait P, 2016, J CLEAN PROD, V124, P65, DOI 10.1016/j.jclepro.2016.02.088 Van Loo EJ, 2014, FOOD POLICY, V49, P137, DOI 10.1016/j.foodpol.2014.07.002 Violino S, 2019, EUR FOOD RES TECHNOL, V245, P2089, DOI 10.1007/s00217-019-03321-0 NR 42 TC 2 Z9 2 U1 7 U2 10 PD MAY PY 2022 VL 187 AR 108752 DI 10.1016/j.meatsci.2022.108752 WC Food Science & Technology SC Food Science & Technology UT WOS:000793126600015 DA 2022-12-14 ER PT J AU Monteiro, ES Righi, RD Barbosa, JLV Alberti, AM AF Monteiro, Emiliano Soares Righi, Rodrigo da Rosa Victoria Barbosa, Jorge Luis Alberti, Antonio Marcos TI APTM: A Model for Pervasive Traceability of Agrochemicals SO APPLIED SCIENCES-BASEL DT Article DE reverse supply chain; blockchain; agrochemicals; IoT; multi-sensor; gamification ID SUPPLY CHAIN MANAGEMENT; ARTIFICIAL-INTELLIGENCE; BLOCKCHAIN AB As the world population increases and the need for food monoculture farms are using more and more agrochemicals, there is also an increase in the possibility of theft, misuse, environmental damage, piracy of products, and health problems. This article addresses these issues by introducing the agrochemical pervasive traceability model (APTM), which integrates machine learning, sensors, microcontrollers, gamification, and two blockchains. It contributes in two dimensions: (I) the study of the environmental, product piracy and regulatory of agrochemical control; (II) the technological dimension: application of an adequate set of sensors collecting multiple data; modeling and implementation of a system via machine learning for analyzing and predicting the behavior and use of agrochemicals; development of a scoring system via gamification for reverse use of agrochemicals; and presenting a record of transactions in a consortium of two blockchains, simultaneously. Its main advantage is to be a flexible, adaptable, and expansive model. Results indicated that the model has positive aspects, from detecting the agrochemical, its handling, and disposal, recording of transactions, and data visualization along the reverse supply chain. This study obtained a round trip time of 0.510 ms on average; data transfers between layer one and its persistence in the database were between 4 to 5 s. Thus, blockchain nodes consumed only 34 to 38% of CPU and recorded transactions between 2 to 4 s. These results point to a horizon of applicability in real situations within agricultural farms. C1 [Monteiro, Emiliano Soares] Univ Estado Mato Grosso Carlos Alberto Reyes Mald, UNEMAT, Campus Imperial,Av Ingas, BR-3001 Sinop, Brazil. [Righi, Rodrigo da Rosa; Victoria Barbosa, Jorge Luis] Univ Vale Rio dos Sinos, UNISINOS, Programa Posgrad Comp Aplicada, Av Unisinos 950, BR-93022750 Sao Leopoldo, RS, Brazil. [Alberti, Antonio Marcos] INATEL, Inst Nacl Telecomunicacoes, Av Joao de Camargo 510, BR-37540000 Santa Rita Do Sapucai, MG, Brazil. C3 Universidade do Estado de Mato Grosso; Universidade do Vale do Rio dos Sinos (Unisinos); Instituto Nacional de Telecomunicacoes (INATEL) RP Monteiro, ES (corresponding author), Univ Estado Mato Grosso Carlos Alberto Reyes Mald, UNEMAT, Campus Imperial,Av Ingas, BR-3001 Sinop, Brazil. EM emiliano@unemat.br; rrrighi@unisinos.br; jbarbosa@unisinos.br; alberti@inatel.br CR Alsawaier RS, 2018, INT J INF LEARN TECH, V35, P56, DOI 10.1108/IJILT-02-2017-0009 [Anonymous], 2013, P 11 IFAC WORKSH INT, DOI DOI 10.3182/20130522-3-BR-4036.00101 Apeh CC, 2018, J HEALTH POLLUT, V8, DOI 10.5696/2156-9614-8.19.180901 Awan FM, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20010322 Aydin, 2018, LEARNING DESIGN TECH, P1, DOI [10.1007/978-3-319-17727-4_115-1, DOI 10.1007/978-3-319-17727-4_115-1] Azoulay D., 2019, ILLEGAL TRADE CHEM, P110 Bendavid Y, 2009, INF SYST E-BUS MANAG, V7, P1, DOI 10.1007/s10257-008-0092-2 Bernardi A.C.A., 2018, PERSPECTIVA, V42, P15 Blankenburg M, 2015, PROC CIRP, V26, P430, DOI 10.1016/j.procir.2014.07.062 Chagnon M, 2015, ENVIRON SCI POLLUT R, V22, P119, DOI 10.1007/s11356-014-3277-x Dib O., 2018, INT J ADV TELECOMMUN, V11, P51 Escobar N, 2020, GLOBAL ENVIRON CHANG, V62, DOI 10.1016/j.gloenvcha.2020.102067 Esmaeilian B, 2020, RESOUR CONSERV RECY, V163, DOI 10.1016/j.resconrec.2020.105064 FAO, 2016, INT COD COND PEST MA, P38 Fuertes G, 2016, J SENSORS, V2016, DOI 10.1155/2016/4046061 Gai KK, 2022, IEEE INTERNET THINGS, V9, P14247, DOI 10.1109/JIOT.2020.3024694 Govindan K, 2015, EUR J OPER RES, V240, P603, DOI 10.1016/j.ejor.2014.07.012 Guo ZL, 2010, INT CONF COMP SCI, P518, DOI 10.1109/ICCSIT.2010.5565170 Gupta M, 2020, IEEE ACCESS, V8, P34564, DOI 10.1109/ACCESS.2020.2975142 Hu SS, 2021, COMPUT IND ENG, V153, DOI 10.1016/j.cie.2020.107079 Huici O, 2017, ENVIRON HEALTH INSIG, V11, DOI 10.1177/1178630217716917 Imbault F., 2017, 2017 IEEE INT C ENV, P1, DOI 10.1109/EEEIC.2017.7977613 Johann D., 2017, SCI INITIAT POSTGRAD, V6, P1 Kapsoulis N, 2020, FUTURE INTERNET, V12, DOI 10.3390/fi12080134 Kovacs T., 2021, BIOECONOMY AGRI PROD, P279, DOI [10.1016/B978-0-12-819774-5.00017-5, DOI 10.1016/B978-0-12-819774-5.00017-5] Kristoffersen P, 2008, WEED RES, V48, P201, DOI 10.1111/j.1365-3180.2008.00619.x Kumar V, 2017, SYSTEMS, V5, DOI 10.3390/systems5020033 Majeed A., 2018, J FOOD SCI TOXICOL, V2, P1 Mattah MM, 2015, J ENVIRON PUBLIC HEA, V2015, DOI 10.1155/2015/547272 Michael K, 2005, ICMB 2005: International Conference on Mobile Business, P623, DOI 10.1109/ICMB.2005.103 Michelucci U., 2020, ENG P, V2, P96, DOI [10.3390/engproc2020002096, DOI 10.3390/ENGPROC2020002096] Min H, 2010, INT J LOGIST-RES APP, V13, P13, DOI 10.1080/13675560902736537 Mukhamedjanova K.A., 2020, J CRIT REV, V7, P759, DOI [10.31838/jcr.07.02.139, DOI 10.31838/JCR.07.02.139] Peng CY, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21134288 Plourde JD, 2013, AGR ECOSYST ENVIRON, V165, P50, DOI 10.1016/j.agee.2012.11.011 Rejeb A, 2020, INTERNET THINGS-NETH, V12, DOI 10.1016/j.iot.2020.100318 Shamsuddoha M., 2011, P CGSB RES FOR EM RE, DOI [10.2139/ssrn.1957883, DOI 10.2139/SSRN.1957883] Sila-Nowicka K, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0210090 Silva F., 2020, EXACTA, V18, P686, DOI [10.5585/exactaep.v18n4.8457, DOI 10.5585/EXACTAEP.V18N4.8457] Sultan K., 2018, ARXIV180603693 Toorajipour R, 2021, J BUS RES, V122, P502, DOI 10.1016/j.jbusres.2020.09.009 Tyagi R.K., 2012, INT J MANAG INF SYST, V16, P137, DOI [10.19030/ijmis.v16i2.6913, DOI 10.19030/IJMIS.V16I2.6913] van den Berg H, 2020, SCI TOTAL ENVIRON, V742, DOI 10.1016/j.scitotenv.2020.140598 WHO, 2020, INT COD COND PEST MA WHO FAO, 2015, INT COD COND PEST MA, P68 Wood L.C., 2012, P IADIS INT C INT TE Wu QS, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20123504 Yusoff N, 2017, ANN RES REV BIOL, V13, P1, DOI [10.9734/ARRB/2017/33986, DOI 10.9734/ARRB/2017/33986] Zhao FY, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12093942 Zhao WT, 2019, ENERGIES, V12, DOI 10.3390/en12203812 Zikankuba VL, 2019, COGENT FOOD AGR, V5, DOI 10.1080/23311932.2019.1601544 NR 51 TC 3 Z9 3 U1 3 U2 8 PD SEP PY 2021 VL 11 IS 17 AR 8149 DI 10.3390/app11178149 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000694117700001 DA 2022-12-14 ER PT J AU Isanta, F Barba, C Leon, JM de Rena, AG Angon, E Moyano, R AF Isanta, Fernando Barba, Cecilio Manuel Leon, Jose Garcia de Rena, Agustin Angon, Elena Moyano, Rosario TI Analysis of the traceability system of sheep milk in southern Spain (Andalucia). Preliminary results SO REVISTA CIENTIFICA-FACULTAD DE CIENCIAS VETERINARIAS DT Article DE Veterinary management; letra Q program; milk quality; food security ID SOMATIC-CELL COUNT; MANAGEMENT AB Letra Q program is the official information system of European Union that allows registration and identification of agents, establishments and containers of dairy sector to improve the traceability of raw milk for human consumption from farm to dairy industry. In this paper, the objective was to analyze Letra Q program functionig in raw sheep milk in the South of Spain (Andalusia). Five process management variables were studied: sample status, sample result (VA: analyzed valid sample; VI: incomplete valid sample, ER: sample in reserve,and RE: rejected sample), time from sample collection to laboratory reception (TT-R), time from sample reception to analysis (TR-A), time total from sampling to analysis (TT-A); as well as four bromatological variables: fat content (G), protein content (P) and lean dry extract (ESM), and cryoscopic point (PC); and finally three hygienic-sanitary variables: somatic cell count (RCS), colony forming number (NFC) and inhibitors presence (PI). A descriptive and comparative statistical analysis on a universe of 7,507 samples belonging to 53 farms was performed. The results showed VA (77.67 +/- 0.54), VI (20.65 +/- 0.53), ER (1.05 +/- 0.01), RE (0.63 +/- 0.01%), TT- R (1.27 +/- 1.82d), TR-A (1.32 +/- 1.67d), TTA (2.53 +/- 1.77d), G (6.98 +/- 0.48 g/100g) , P (5.40 +/- 0.48 g/100g), ESM (12.23 +/- 2.71 g/100g), PC (-0.57 +/- 0.01 degrees C), RCS (971.69 +/- 764.72x103) cells/mL) NFC (121.24 +/- 280.675x103 cells/mL) and PI (0.12 +/- 0.09%). The comparative analysis revealed significant differences in management variables for factors year and type of laboratory, as well as differences for bromatological and hygienic-sanitary variables between farm size and breed type. It is concluded that Letter Q program contributes to guarantee food innocuousness in consumers and constitutes a tool of great interest for the management and improvement of information in dairy sector. C1 [Isanta, Fernando; Garcia de Rena, Agustin] Junta Andalucia, Consejeria Agr Pesca & Desarrollo Rural, Seville, Spain. [Barba, Cecilio; Angon, Elena] Univ Cordoba, Dept Prod Anim, Cordoba, Spain. [Manuel Leon, Jose] Univ Cordoba, Dept Genet, Cordoba, Spain. [Moyano, Rosario] Univ Cordoba, Dept Farmacol Toxicol & Med Legal & Forense, Cordoba, Spain. C3 Universidad de Cordoba; Universidad de Cordoba; Universidad de Cordoba RP Barba, C (corresponding author), Univ Cordoba, Dept Prod Anim, Cordoba, Spain. EM cjbarba@uco.es CR Arias R, 2012, SMALL RUMINANT RES, V106, P92, DOI 10.1016/j.smallrumres.2012.03.019 ARIAS R., ARCH ZOOT, V65, P469 BAI J., 2013, AGR EC, V44, P4 Bencini R, 1997, AUST J EXP AGR, V37, P485, DOI 10.1071/EA96014 BROWN B, THESIS, P2009 Castro J. A., 2009, FEAGAS, V35, P98 Consejera de Agricultura Pesca y Desarrollo Rural (CAGPDS), 2015, CAR SECT OV CAPR AND CONSEJERIA DE AGRICULTURA PESCA Y DESARROLLO RURAL (CAPDER), 2016, PROGR CONTR OF HIG T FONDO ESPANOL DE GARANTIA AGRARIA, 2017, DECL OBL SECT OV CAP Gonzalo C, 2005, J DAIRY SCI, V88, P969, DOI 10.3168/jds.S0022-0302(05)72764-8 GONZALO C, 2014, 39 C NAC 15 INT SOC, V1, P88 HAILE A., 2017, J EXPT BIOL AGR SCI, V5, P69 Sobrino LJ, 2018, ITAL J ANIM SCI, V17, P477, DOI [10.1080/1828051X.2017.1383860, 10.1080/1828051x.2017.1383860] LEGAZ H. E. A, THESIS MARTIN M, 2008, REV FRISONA, V163, P82 MINISTERIO DE AGRICULTURA Y PESCA ALIMENTACION Y MEDIO AMBIENTE (MAPAMA), 2017, INF SECT OV CAP Molina A, 2010, SPAN J AGRIC RES, V8, P334, DOI 10.5424/sjar/2010082-1213 Morantes M, 2017, SMALL RUMINANT RES, V149, P62, DOI 10.1016/j.smallrumres.2017.01.005 ORGANIZACION PARA LA COOPERACION Y EL DESARROLLO ECONOMICOS Y ORGANIZACION DE LAS NACIONES UNIDAS PARA LA ALIMENTACION Y LA AGRICULTURA OECD/ FAO, 2015, PERSP AGR 2015 2024 PARTIDA L. E, THESIS Rivas J., 2016, Archivos de Zootecnia, V65, P429 Rivas J, 2015, ITAL J ANIM SCI, V14, P179, DOI 10.4081/ijas.2015.3513 ROCA M., 2009, P 34 C NAC SOC ESP O, V1, P113 STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES (SPSS), 2010, SAT 19 UNION EUROPEA, 2004, REGL CE 853 2004 PAR, P55 Vara Martinez J. A. de la, 2018, Journal of Applied Animal Research, V46, P784, DOI 10.1080/09712119.2017.1403327 YAMAKI M., 2005, JPN J SHEEP SCI, V42, P1 NR 27 TC 1 Z9 1 U1 0 U2 4 PD SEP-OCT PY 2018 VL 28 IS 5 BP 360 EP 368 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000459020600006 DA 2022-12-14 ER PT J AU Papetti, P Costa, C Antonucci, F Figorilli, S Solaini, S Menesatti, P AF Papetti, Patrizia Costa, Corrado Antonucci, Francesca Figorilli, Simone Solaini, Silvia Menesatti, Paolo TI A RFID web-based infotracing system for the artisanal Italian cheese quality traceability SO FOOD CONTROL DT Article DE RFID; Spectrophotometry; Tracing web-based architecture; PLS; Chemical analyses; Buffalo milk cheese ID FOOD-PRODUCTS; SPECTROSCOPY; TOOL AB The aim of this study is the integration of an electronic tracing system with a non-destructive quality analysis system for single product of a typical Italian cheese, prepared with buffalo milk and called "Caciottina rnassaggiata di Amaseno", a typical diary product of Lazio Region. The tracing and quality information are combined on a web platform to obtain a complete procedure to develop what we define as an "infotracing system". Quality analyses (chemical, sensorial and spectrophotometric) were carried Out on a total of 23 cheese wheels (8 with TAGs) and for three cheese maturation classes (3, 6 or 9 months after production). Two typologies of RFID tags were tested. Results were screened by Partial Least Squares regressions (PLS) on reflectance values for the prediction of chemical content, while classification of cheese maturation classes (3, 6 or 9 months) was carried out by Partial Least Squares.Discriminant Analysis (PLSDA) on reflectance values. The RFID system turned out as effective, reliable and compatible with the production process tool. A good estimation of maturation degree by spectral and chemical analysis was obtained. Moreover an infotracing web-based system was designed to acquire and link basic information that can be made available to the final consumer or to different food chain actors before or after purchasing, using the RFID code to identify the single and specific cheese product. The projected web-based tracing system could improve the products commerce by increasing the information transparency for the consumer. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Costa, Corrado; Antonucci, Francesca; Figorilli, Simone; Solaini, Silvia; Menesatti, Paolo] Agr Res Council, CRA ING Agr Engn Res Unit, I-00015 Rome, Italy. [Papetti, Patrizia] Univ Cassino, Dept Econ, I-03043 Cassino, FR, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); University of Cassino RP Costa, C (corresponding author), Agr Res Council, CRA ING Agr Engn Res Unit, Via Pascolare 16, I-00015 Rome, Italy. EM corrado.costa@entecra.it CR [Anonymous], 2001, NEAR INFRARED TECHNO Antonucci F, 2011, FOOD BIOPROCESS TECH, V4, P809, DOI 10.1007/s11947-010-0414-5 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Breyer Didier, 2007, Biotechnology Journal, V2, P1081, DOI 10.1002/biot.200700115 Brofman-Epelbaum F., 2007, 17 ANN FOR S IAMA C Brown SD, 2009, COMPREHENSIVE CHEMOMETRICS: CHEMICAL AND BIOCHEMICAL DATA ANALYSIS, VOLS 1-4, pB345 Costa C, 2008, J ZOOL, V276, P71, DOI 10.1111/j.1469-7998.2008.00469.x Costa C., 2011, INSTRUMENTATION VIEW, V11, P48 Curda L, 2004, J FOOD ENG, V61, P557, DOI 10.1016/S0260-8774(03)00215-2 DEJONG S, 1993, CHEMOMETR INTELL LAB, V18, P251, DOI 10.1016/0169-7439(93)85002-X Dolgui A., 2008, P 17 WORLD C INT FED, P4464 Downey G, 2005, INT DAIRY J, V15, P701, DOI 10.1016/j.idairyj.2004.06.013 Dubeuf JP, 2010, SMALL RUMINANT RES, V93, P67, DOI 10.1016/j.smallrumres.2010.03.001 Fossa E., 2007, SCI TECNICA LATTIERO, V58, P243 Fukatsu T, 2009, SENSORS-BASEL, V9, P6171, DOI 10.3390/s90806171 Galvao RKH, 2005, TALANTA, V67, P736, DOI 10.1016/j.talanta.2005.03.025 Gandino F., 2007, P 1 ANN RFID EUR, P1 Giusti AM, 2008, FOOD BIOPROCESS TECH, V1, P130, DOI 10.1007/s11947-007-0043-9 Ilbery B, 1999, ENVIRON PLANN A, V31, P2207, DOI 10.1068/a312207 ISO/IEC, 2005, 27001 ISOIEC Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Kahn G., 2005, WALL STREET J 0707, pB1 KENNARD RW, 1969, TECHNOMETRICS, V11, P137, DOI 10.2307/1266770 Lammers W, 2007, LVT LEBENSMITTEL IND, V6, P2 Lee SJ, 1997, J FOOD SCI, V62, P53, DOI 10.1111/j.1365-2621.1997.tb04367.x Lwoga E. T., 2006, Quarterly Bulletin of IAALD, V51, P177 Martin-del-Campo ST, 2007, J DAIRY SCI, V90, P3018, DOI 10.3168/jds.2006-656 Martin-del-Campo ST, 2007, INT DAIRY J, V17, P835, DOI 10.1016/j.idairyj.2006.10.003 Menesatti P, 2010, BIOSYST ENG, V105, P448, DOI 10.1016/j.biosystemseng.2010.01.003 Menesatti P, 2008, BIOSYST ENG, V101, P417, DOI 10.1016/j.biosystemseng.2008.09.013 Perez-Aloe R., 2007, APPL RFID TAGS OVERA, P1, DOI [DOI 10.1109/RFIDEURAS1A.2007.4368136, 10.1109/RFIDEURASIA.2007.4368136] Ramakrishnan R., 1999, DATABASE MANAGEMENT, V2nd Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Resmini P., 1997, SCI TECNICA LATTIERO, V48, P73 Rodriguez-Saona LE, 2006, J DAIRY SCI, V89, P1407, DOI 10.3168/jds.S0022-0302(06)72209-3 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Ruiz-Garcia L, 2009, SENSORS-BASEL, V9, P4728, DOI 10.3390/s90604728 Samad A, 2010, COMPUT ELECTRON AGR, V73, P213, DOI 10.1016/j.compag.2010.05.001 Sarria D., 2009, P OC 09 IEEE BREM, P1, DOI [10.1109/OCEANSE.2009.5278280, DOI 10.1109/OCEANSE.2009.5278280] Tosi F., 2007, Scienza e Tecnica Lattiero-Casearia, V58, P291 Tosi F., 2008, Scienza e Tecnica Lattiero-Casearia, V59, P507 Varese E., 2008, SCI TECHNOLOGY QUALI, V47, P171 Viscarra-Rossel R., 2007, EUR J SOIL SCI, V58, P343, DOI DOI 10.1111/J.1365-2389.2006.00859.X Wold S, 2001, CHEMOMETR INTELL LAB, V58, P109, DOI 10.1016/S0169-7439(01)00155-1 Woodcock T, 2008, FOOD BIOPROCESS TECH, V1, P117, DOI 10.1007/s11947-007-0033-y Xiong BH, 2007, NEW ZEAL J AGR RES, V50, P725 Zanasi C., 2008, 16 IFOAM ORG WORLD C Zapparoli G. A., 1997, Scienza e Tecnica Lattiero-Casearia, V48, P297 NR 48 TC 49 Z9 54 U1 2 U2 50 PD SEP PY 2012 VL 27 IS 1 BP 234 EP 241 DI 10.1016/j.foodcont.2012.03.025 WC Food Science & Technology SC Food Science & Technology UT WOS:000304790800034 DA 2022-12-14 ER PT J AU de Magalhaes, RF Danilevicz, ADF Palazzo, J AF de Magalhaes, Ruane Fernandes Ferreira Danilevicz, Angela de Moura Palazzo, Joseph TI Managing trade-offs in complex scenarios: A decision-making tool for sustainability projects SO JOURNAL OF CLEANER PRODUCTION DT Review DE Trade-off management; Sustainability projects; Complex projects management; Decision-making tool ID CORPORATE SUSTAINABILITY; MULTICRITERIA EVALUATION; EFFECTIVE IMPLEMENTATION; AGRICULTURAL PRODUCTION; PERFORMANCE EVALUATION; URBAN SUSTAINABILITY; ECODESIGN TOOLS; POWER-SYSTEM; MODEL; MANAGEMENT AB The inclusion of sustainability factors in projects is considered a challenge for technicians, managers and decision makers. Sustainability projects deal with a large number of criteria from different areas, making the decision-making process more complex and uncertain. Multicriteria methods can guide project choices to reach their objectives; however, they are not able to manage the overlap and conflicting aspects of these objectives, called trade-offs. Trade-offs are considered to be an integral part of any sustainability project, since they address conflicting objectives, taking into account environmental, social and economic aspects. In some studies, trade-offs have been approached from the view of their formation process, or from how they can be identified in projects. However, there is a gap in the literature related to structured procedures to support decision-makers after the identification of trade-offs. Therefore, this paper proposes a tool to support trade-off management in the decision-making process in complex sustainability-focused projects. The Trade-Off Decision-Making tool assists the project planning stage, unfolding into two sequential phases: guidelines to be considered for the management of trade-offs; and trade-offs management operationalization. The guidelines were developed based on literature best practices, while the trade-offs management originated from the operationalization of five comparative analyses carried out between conflicting objectives, using a structured worksheet. The proposed tool contributes to the proper handling of conflicting objectives in sustainability projects not only in managerial but also operational areas. Our guidance structure for handling trade-offs provides greater robustness, objectivity and traceability to the choices made during the planning of sustainability projects. (C) 2018 Elsevier Ltd. All rights reserved. C1 [de Magalhaes, Ruane Fernandes; Ferreira Danilevicz, Angela de Moura] Univ Fed Rio Grande do Sul, Ind & Transportat Dept, DEPROT, Ave Osvaldo Aranha 99,5th Floor, Porto Alegre, RS, Brazil. [Palazzo, Joseph] UCSB, Bren Sch Environm Sci & Management, 2400A Bren Hall, Santa Barbara, CA 93106 USA. C3 Universidade Federal do Rio Grande do Sul; University of California System; University of California Santa Barbara RP de Magalhaes, RF (corresponding author), Univ Fed Rio Grande do Sul, Ind & Transportat Dept, DEPROT, Ave Osvaldo Aranha 99,5th Floor, Porto Alegre, RS, Brazil. EM ruane.magalhaes@ufrgs.br; angelamfd@producao.ufrgs.br; jpalazzo@bren.ucsb.edu CR Agudo-Valiente JM, 2015, CORP SOC RESP ENV MA, V22, P13, DOI 10.1002/csr.1324 Ahern J, 2011, LANDSCAPE URBAN PLAN, V100, P341, DOI 10.1016/j.landurbplan.2011.02.021 Akadiri PO, 2013, AUTOMAT CONSTR, V30, P113, DOI 10.1016/j.autcon.2012.10.004 Alberti M, 1996, ENVIRON IMPACT ASSES, V16, P381, DOI 10.1016/S0195-9255(96)00083-2 Alberti M., 2016, CITIES THINK PLANETS Ali-Toudert F, 2017, ECOL INDIC, V73, P597, DOI 10.1016/j.ecolind.2016.09.046 Sanchez MA, 2015, J CLEAN PROD, V96, P319, DOI 10.1016/j.jclepro.2013.12.087 [Anonymous], 2007, 140402006 ISO [Anonymous], 1987, REPORT WORLD COMMISS Arena Marika, 2009, International Journal of Product Lifecycle Management, V4, P207, DOI 10.1504/IJPLM.2009.031674 Arushanyan Y, 2014, COMPUT IND, V65, P211, DOI 10.1016/j.compind.2013.10.003 Bartke S, 2015, J ENVIRON MANAGE, V153, P11, DOI 10.1016/j.jenvman.2015.01.040 Braganca Luis, 2010, Sustainability, V2, P2010, DOI 10.3390/su2072010 Brones F, 2014, J CLEAN PROD, V80, P106, DOI 10.1016/j.jclepro.2014.05.088 Byggeth S, 2006, J CLEAN PROD, V14, P1420, DOI 10.1016/j.jclepro.2005.03.024 Calik E, 2016, PROC CIRP, V40, P449, DOI 10.1016/j.procir.2016.01.091 Callistus T, 2016, PROCEDIA ENGINEER, V164, P389, DOI 10.1016/j.proeng.2016.11.635 Chow JF, 2014, PROCEDIA ENGINEER, V70, P343, DOI 10.1016/j.proeng.2014.02.039 Cinelli M, 2014, ECOL INDIC, V46, P138, DOI 10.1016/j.ecolind.2014.06.011 Coleman K, 2017, SCI TOTAL ENVIRON, V609, P1483, DOI 10.1016/j.scitotenv.2017.07.193 Crawford L., 2004, International Journal of Project Management, V22, P645, DOI 10.1016/j.ijproman.2004.04.004 Da Silveira G, 2001, INT J OPER PROD MAN, V21, P949, DOI 10.1108/01443570110393432 de Magalhaes RF, 2017, WASTE MANAGE, V67, P265, DOI 10.1016/j.wasman.2017.05.025 de Villiers C, 2016, J CLEAN PROD, V136, P78, DOI 10.1016/j.jclepro.2016.01.107 Casanovas-Rubio MD, 2018, RENEW SUST ENERG REV, V91, P741, DOI 10.1016/j.rser.2018.04.040 Dhar TK, 2017, URBAN CLIM, V19, P72, DOI 10.1016/j.uclim.2016.12.004 Dyllick T., 2002, BUSINESS STRATEGY EN, V11, P130, DOI [DOI 10.1002/BSE.323, 10.1002/bse.323] Egilmez G, 2015, CITIES, V42, P31, DOI 10.1016/j.cities.2014.08.006 Elkington J., 1999, TRIPLE BOTTOM LINE R, V69, P75 Engert S, 2016, J CLEAN PROD, V112, P2833, DOI 10.1016/j.jclepro.2015.08.031 FANTINATTI P.A.P., 2015, INDICADORES SUSTENTA Franke U, 2018, INFORM SYST, V74, P86, DOI 10.1016/j.is.2017.07.004 Frew BA, 2016, ENERGY, V117, P198, DOI 10.1016/j.energy.2016.10.074 Garcia S, 2016, J CLEAN PROD, V136, P181, DOI 10.1016/j.jclepro.2016.01.110 Gibson R.B., 2006, IMPACT ASSESS PROJ A, V24, P170, DOI [DOI 10.3152/147154606781765147, 10.3152/147154606781765147] Glavic P, 2007, J CLEAN PROD, V15, P1875, DOI 10.1016/j.jclepro.2006.12.006 Hafliger IF, 2017, J CLEAN PROD, V156, P805, DOI 10.1016/j.jclepro.2017.04.052 Hallstedt SI, 2017, J CLEAN PROD, V140, P251, DOI 10.1016/j.jclepro.2015.06.068 Holling C.S., 1973, Annual Rev Ecol Syst, V4, P1, DOI 10.1146/annurev.es.04.110173.000245 Invidiata A, 2018, BUILD ENVIRON, V139, P58, DOI 10.1016/j.buildenv.2018.04.041 Iraldo F, 2017, J CLEAN PROD, V140, P1353, DOI 10.1016/j.jclepro.2016.10.017 Jiang Y, 2018, J ENVIRON MANAGE, V211, P42, DOI 10.1016/j.jenvman.2018.01.047 Jin SW, 2018, ENVIRON RES, V164, P367, DOI 10.1016/j.envres.2018.03.010 Kamali M, 2018, BUILD ENVIRON, V138, P21, DOI 10.1016/j.buildenv.2018.04.019 Kamali M, 2017, J CLEAN PROD, V142, P3592, DOI 10.1016/j.jclepro.2016.10.108 Kang H, 2016, ENVIRON IMPACT ASSES, V58, P34, DOI 10.1016/j.eiar.2016.02.003 Karatas A, 2015, AUTOMAT CONSTR, V53, P83, DOI 10.1016/j.autcon.2015.02.010 KERZNER H, 2014, PROJECT MANAGEMENT B Khalili NR, 2013, J CLEAN PROD, V47, P188, DOI 10.1016/j.jclepro.2012.10.044 Khoshnava SM, 2018, J CLEAN PROD, V173, P82, DOI 10.1016/j.jclepro.2016.10.066 Kiridena S, 2016, PROJ MANAG J, V47, P56, DOI 10.1177/875697281604700605 Labuschagne C., 2005, International Journal of Project Management, V23, P159, DOI 10.1016/j.ijproman.2004.06.003 Lame G, 2017, J CLEAN PROD, V148, P60, DOI 10.1016/j.jclepro.2017.01.173 Lombardi P, 2016, RENEW ENERG, V90, P532, DOI 10.1016/j.renene.2016.01.016 Maletic M, 2014, J CLEAN PROD, V79, P182, DOI 10.1016/j.jclepro.2014.05.045 Marcelino-Sadaba S, 2015, J CLEAN PROD, V99, P1, DOI 10.1016/j.jclepro.2015.03.020 Medineckiene M, 2015, ARCH CIV MECH ENG, V15, P11, DOI 10.1016/j.acme.2014.09.001 Meerow S, 2016, LANDSCAPE URBAN PLAN, V147, P38, DOI 10.1016/j.landurbplan.2015.11.011 Modak M, 2017, RESOUR POLICY, V52, P181, DOI 10.1016/j.resourpol.2017.03.002 Morioka SN, 2016, J CLEAN PROD, V136, P134, DOI 10.1016/j.jclepro.2016.01.104 Morrison-Saunders A, 2013, ENVIRON IMPACT ASSES, V38, P54, DOI 10.1016/j.eiar.2012.06.003 Munda G, 2006, LAND USE POLICY, V23, P86, DOI 10.1016/j.landusepol.2004.08.012 Munda Giuseppe, 2005, Environment Development and Sustainability, V7, P117, DOI 10.1007/s10668-003-4713-0 Nielsen AN, 2016, BUILD ENVIRON, V103, P165, DOI 10.1016/j.buildenv.2016.04.009 Olazabal M, 2016, ENVIRON INNOV SOC TR, V18, P18, DOI 10.1016/j.eist.2015.06.006 Pearce AR, 2008, CIV ENG ENVIRON SYST, V25, P291, DOI 10.1080/10286600802002973 Petit-Boix A, 2017, J CLEAN PROD, V166, P939, DOI 10.1016/j.jclepro.2017.08.030 Plevin RJ, 2014, J IND ECOL, V18, P73, DOI 10.1111/jiec.12074 Pohekar SD, 2004, RENEW SUST ENERG REV, V8, P365, DOI 10.1016/j.rser.2003.12.007 Prendeville SM, 2017, J CLEAN PROD, V143, P1327, DOI 10.1016/j.jclepro.2016.11.095 Project Management Institute, 2008, GUID PROJ MAN BOD KN, V4th Ren JZ, 2018, J CLEAN PROD, V172, P438, DOI 10.1016/j.jclepro.2017.10.167 Rohrbach B, 2018, LANDSCAPE URBAN PLAN, V176, P38, DOI 10.1016/j.landurbplan.2018.04.002 Rosburg A, 2016, ENERG ECON, V58, P77, DOI 10.1016/j.eneco.2016.06.020 Rossi M, 2016, J CLEAN PROD, V129, P361, DOI 10.1016/j.jclepro.2016.04.051 Rousseaux P, 2017, J CLEAN PROD, V151, P546, DOI 10.1016/j.jclepro.2017.03.089 SAATY TL, 1977, J MATH PSYCHOL, V15, P234, DOI 10.1016/0022-2496(77)90033-5 Santos MK, 2017, J CLEAN PROD, V159, P374, DOI 10.1016/j.jclepro.2017.05.035 Silvius AJG, 2017, INT J PROJ MANAG, V35, P1133, DOI 10.1016/j.ijproman.2017.01.011 Simsek Y, 2018, RENEW SUST ENERG REV, V93, P421, DOI 10.1016/j.rser.2018.04.090 Snowden DJ, 2007, HARVARD BUS REV, V85, P68 Tian Z, 2018, AGR SYST, V159, P175, DOI 10.1016/j.agsy.2017.04.006 Turkelboom F, 2018, ECOSYST SERV, V29, P566, DOI 10.1016/j.ecoser.2017.10.011 Umer A, 2017, TRANSPORT RES D-TR E, V53, P88, DOI 10.1016/j.trd.2017.04.011 UN, 2015, TRANSF OUR WORLD 203, VA/R Vahidi R, 2013, PROCD SOC BEHV, V74, P71, DOI 10.1016/j.sbspro.2013.03.020 Valdivia RO, 2012, AGR SYST, V110, P17, DOI 10.1016/j.agsy.2012.03.003 Varmazyar M, 2016, EVAL PROGRAM PLANN, V58, P125, DOI 10.1016/j.evalprogplan.2016.06.005 Vishnupriyan J, 2018, RENEW ENERG, V121, P474, DOI 10.1016/j.renene.2018.01.008 Wang L, 2018, WATER RES, V129, P394, DOI 10.1016/j.watres.2017.11.027 Wang XB, 2018, J DEV ECON, V135, P222, DOI 10.1016/j.jdeveco.2018.04.007 Wang ZJ, 2017, Q REV ECON FINANC, V65, P314, DOI 10.1016/j.qref.2016.10.001 Williams T., 1999, INT J PROJECT MANAGE, V17, P269, DOI DOI 10.1016/S0263-7863(98)00047-7 Zhang L, 2016, J CLEAN PROD, V131, P491, DOI 10.1016/j.jclepro.2016.04.153 [No title captured] [No title captured] [No title captured] NR 97 TC 17 Z9 17 U1 4 U2 29 PD MAR 1 PY 2019 VL 212 BP 447 EP 460 DI 10.1016/j.jclepro.2018.12.023 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000457952500039 DA 2022-12-14 ER PT J AU Querci, M Foti, N Bogni, A Kluga, L Broll, H Van den Eede, G AF Querci, Maddalena Foti, Nicoletta Bogni, Alessia Kluga, Linda Broll, Hermann Van den Eede, Guy TI Real-Time PCR-Based Ready-to-Use Multi-Target Analytical System for GMO Detection SO FOOD ANALYTICAL METHODS DT Article DE GMO; Detection; Traceability; Food Control; Real-Time PCR ID GENETICALLY-MODIFIED ORGANISMS; POLYMERASE-CHAIN-REACTION; MODIFIED MAIZE; FOOD; QUANTIFICATION; IDENTIFICATION; TRACEABILITY; QUANTITATION; METHODOLOGY; QUALITY AB This paper describes the development, production, and testing of a high-throughput analytical system, i.e., a unique screening tool for the unequivocal simultaneous identification of all currently EU-approved and all unapproved genetically modified organisms (GMOs) known to the Community Reference Laboratory for GM Food and Feed (CRL-GMFF), established according to Regulation (EC) No 1829/2003. The rationale and comparative advantage of the strategy selected as well as the formulation, potentiality, and flexibility of the system are illustrated here. The approach, developed in response to the worldwide growing testing needs, allows the event-specific simultaneous detection of 39 single-insert GMOs and their derived stacked events. System performance (specificity, efficiency, etc) has been successfully confirmed by experimental testing conducted within the CRL-GMFF and in collaboration with European control laboratories. The limit of detection (LOD) has been determined to be at least 0.045% expressed in haploid genome copies, thus in full compliance with EU requirements for method LOD. The "real-time PCR-based ready-to-use multi-target analytical system for GMO detection" developed by the Joint Research Centre is the first analytical tool worldwide allowing the simultaneous detection of so many genetic modification events using event-specific targets. C1 [Querci, Maddalena; Foti, Nicoletta; Bogni, Alessia; Kluga, Linda; Broll, Hermann; Van den Eede, Guy] European Commission Joint Res Ctr, Mol Biol & Genom Unit, IHCP, I-21027 Ispra, Va, Italy. C3 European Commission Joint Research Centre; EC JRC ISPRA Site RP Querci, M (corresponding author), European Commission Joint Res Ctr, Mol Biol & Genom Unit, IHCP, Via Fermi 2749, I-21027 Ispra, Va, Italy. EM maddalena.querci@jrc.ec.europa.eu CR Arumuganathan K., 1991, PLANT MOL BIOL REP, V9, P208, DOI [10.1007/BF02672069, DOI 10.1007/BF02672069] Brodmann PD, 2002, J AOAC INT, V85, P646 *EUR COMM, 2005, GUID DOC WEBS COMM R *EUR COMM, 2003, OFF J EUR UNION L, V102, P14 European Commission, 2003, OFF J EUR UNION European Commission, 2003, OFF J EUR UNION L, VL268, P24 Garcia-Canas V, 2004, ANAL CHEM, V76, P2306, DOI 10.1021/ac035481u Hernandez M, 2004, J CEREAL SCI, V39, P99, DOI 10.1016/S0733-5210(03)00071-7 Hernandez M, 2005, CURR ANAL CHEM, V1, P203, DOI 10.2174/1573411054021574 Holst-Jensen A, 2006, J AGR FOOD CHEM, V54, P2799, DOI 10.1021/jf052849a Holst-Jensen A, 2003, ANAL BIOANAL CHEM, V375, P985, DOI 10.1007/s00216-003-1767-7 JAMES C, 2008, 39 ISAAA Kuribara H, 2002, J AOAC INT, V85, P1077 Made D, 2006, EUR FOOD RES TECHNOL, V224, P271, DOI 10.1007/s00217-006-0467-x Marmiroli N, 2008, ANAL BIOANAL CHEM, V392, P369, DOI 10.1007/s00216-008-2303-6 Matsuoka T, 2000, J FOOD HYG SOC JPN, V41, P137, DOI 10.3358/shokueishi.41.137 Miraglia M, 2004, FOOD CHEM TOXICOL, V42, P1157, DOI 10.1016/j.fct.2004.02.018 MURRAY MG, 1980, NUCLEIC ACIDS RES, V8, P4321, DOI 10.1093/nar/8.19.4321 PADGETTE SR, 1995, CROP SCI, V35, P1451, DOI 10.2135/cropsci1995.0011183X003500050032x QUERCI M, 2007, COLLECTION BIOSAFETY Ramessar K, 2008, NAT BIOTECHNOL, V26, P975, DOI 10.1038/nbt0908-975 Reiting R, 2007, J VERBRAUCH LEBENSM, V2, P116, DOI 10.1007/s00003-007-0189-4 Rodriguez-Lazaro D, 2007, TRENDS FOOD SCI TECH, V18, P306, DOI 10.1016/j.tifs.2007.01.009 Ronning SB, 2003, EUR FOOD RES TECHNOL, V216, P347, DOI 10.1007/s00217-002-0653-4 The European Parliament and the Council of the European Union, 1997, OFF J EUR UNION, VL43, P1 VANDENEEDE G, 2000, 19676 IHCPEUR EN EUR Windels P, 2003, EUR FOOD RES TECHNOL, V216, P259, DOI 10.1007/s00217-002-0652-5 Zel J, 2008, FOOD ANAL METHOD, V1, P61, DOI 10.1007/s12161-008-9016-5 Zimmermann A, 1998, Z LEBENSM UNTERS F A, V207, P81, DOI 10.1007/s002170050299 [No title captured] NR 30 TC 55 Z9 55 U1 1 U2 14 PD DEC PY 2009 VL 2 IS 4 BP 325 EP 336 DI 10.1007/s12161-009-9093-0 WC Food Science & Technology SC Food Science & Technology UT WOS:000271256300010 DA 2022-12-14 ER PT J AU Turchini, GM Quinn, GP Jones, PL Palmeri, G Gooley, G AF Turchini, Giovanni M. Quinn, Gerry P. Jones, Paul L. Palmeri, Giorgio Gooley, Geoff TI Traceability and Discrimination among Differently Farmed Fish: A Case Study on Australian Murray Cod SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE Aquaculture; chemiometric; discriminant function analysis; fatty acids; Maccullochella peelii peelii; stable isotopes; product tracing ID MACCULLOCHELLA-PEELII-PEELII; BASS DICENTRARCHUS-LABRAX; FATTY-ACID-METABOLISM; FRESH-WATER FISH; QUALITY CHARACTERISTICS; ATLANTIC SALMON; NATIVE FISH; WILD; CONSUMERS; SYSTEM AB The development of traceability methods to distinguish between farmed and wild-caught fish and seafood is becoming increasingly important. However, very little is known about how to distinguish fish originating from different farms. The present study addresses this issue by attempting to discriminate among intensively farmed freshwater Murray cod originating from different farms (indoor recirculating, outdoor floating cage, and flow through systems) in different geographical areas, using a combination of morphological, chemical, and isotopic analyses. The results show that stable isotopes are the most informative variables. in particular, delta C-13 and/or delta N-15 clearly linked fish to a specific commercial diet, while delta O-18 linked fish to a specific water source. Thus, the combination of these isotopes can distinguish among fish originating from different farms. On the contrary, fatty acid and tissue proximate compositions and morphological parameters, which are useful in distinguishing between farmed and wild fish, are less informative in discriminating among fish originating from different farms. C1 [Turchini, Giovanni M.; Quinn, Gerry P.; Jones, Paul L.; Palmeri, Giorgio] Deakin Univ, Sch Life & Environm Sci, Warrnambool, Vic 3280, Australia. [Gooley, Geoff] Fisheries Victoria, Dept Primary Ind, Mornington, Vic 3931, Australia. C3 Deakin University; Victorian Department of Environment & Primary Industries RP Turchini, GM (corresponding author), Deakin Univ, Sch Life & Environm Sci, POB 423, Warrnambool, Vic 3280, Australia. EM giovanni.turchini@deakin.edu.au CR ABERY N, 2007, MARKET DEV EVALUATIO Bell JG, 2007, J AGR FOOD CHEM, V55, P5934, DOI 10.1021/jf0704561 BJERNER, 2006, STUDY ECOLABELLING A Botonaki A, 2006, BRIT FOOD J, V108, P77, DOI 10.1108/00070700610644906 Bowen GJ, 2005, OECOLOGIA, V143, P337, DOI 10.1007/s00442-004-1813-y Busetto ML, 2008, J AGR FOOD CHEM, V56, P2742, DOI 10.1021/jf0734267 Einen O, 1999, AQUACULTURE, V178, P149, DOI 10.1016/S0044-8486(99)00126-X Francis DS, 2007, J AGR FOOD CHEM, V55, P1582, DOI 10.1021/jf062153x Gooley, 2005, WORLD AQUACULTURE, V36, P37 Jaffry S, 2004, FOOD POLICY, V29, P215, DOI 10.1016/j.foodpol.2004.04.001 Luten J. B., 2003, Quality of fish from catch to consumer: labelling, monitoring and traceability Masoum S, 2007, ANAL BIOANAL CHEM, V387, P1499, DOI 10.1007/s00216-006-1025-x Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Morkore T, 2001, J FOOD SCI, V66, P1348, DOI 10.1111/j.1365-2621.2001.tb15213.x Orban E, 2003, J FOOD SCI, V68, P128, DOI 10.1111/j.1365-2621.2003.tb14127.x Orban E, 2002, J FOOD SCI, V67, P542, DOI 10.1111/j.1365-2621.2002.tb10635.x Orban E., 1996, Rivista di Scienza dell'Alimentazione, V25, P27 Orban E, 2000, FOOD CHEM, V70, P27, DOI 10.1016/S0956-7135(99)00112-7 Palmeri G, 2008, FOOD CHEM, V107, P1605, DOI 10.1016/j.foodchem.2007.09.079 Palmeri G, 2007, FOOD CHEM, V102, P796, DOI 10.1016/j.foodchem.2006.06.018 PETTINGER C, 2004, APPETITE, P42 Pond DW, 1997, MAR ECOL PROG SER, V156, P167, DOI 10.3354/meps156167 QUINN GP, 2002, EXPT DESIGN DATA ANA, P520 Rezzi S, 2007, J AGR FOOD CHEM, V55, P9963, DOI 10.1021/jf070736g Sargent J.R., 2001, FARMED FISH QUALITY, P3 Sargent John R., 2002, P181 SHEARER KD, 2001, FARMED FISH QUALITY, P31 Stuart-Williams H, 2008, RAPID COMMUN MASS SP, V22, P1117, DOI 10.1002/rcm.3474 Torjusen H, 2001, FOOD QUAL PREFER, V12, P207, DOI 10.1016/S0950-3293(00)00047-1 TURCHENKO I, 2004, INT SCI J COMPUTING, V3, P140 Turchini GM, 2006, AQUAC RES, V37, P570, DOI 10.1111/j.1365-2109.2006.01465.x Turchini GM, 2004, AQUAC RES, V35, P378, DOI 10.1111/j.1365-2109.2004.01026.x Turchini GM, 2006, COMP BIOCHEM PHYS B, V144, P110, DOI 10.1016/j.cbpb.2006.01.013 Turchini GM, 2003, AQUACULTURE, V225, P251, DOI 10.1016/S0044-8486(03)00294-1 Turchini GM, 2003, AQUAC RES, V34, P697, DOI 10.1046/j.1365-2109.2003.00870.x NR 35 TC 38 Z9 41 U1 1 U2 49 PD JAN 14 PY 2009 VL 57 IS 1 BP 274 EP 281 DI 10.1021/jf801962h WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000262292700037 DA 2022-12-14 ER PT J AU Wilson, WW Henry, X Dahl, BL AF Wilson, William W. Henry, Xavier Dahl, Bruce L. TI Costs and risks of conforming to EU traceability requirements: The case of hard red spring wheat SO AGRIBUSINESS DT Article AB European Union (EU) traceability requirements impose added costs and risks on suppliers. A stochastic simulation model is developed to determine optimal testing strategies and marginal costs to conform to EU traceability requirements for exports of non-genetically modified (non-GM) wheat from the United States. The optimal strategy is chosen to maximize an integrator's utility. Cost components include certified seed, certification and auditing, testing, traceability, quality loss, and a premium for the added risk of a dual traceability system over a single non-traceability system. Adventitious commingling risks are defined stochastically. Results indicate that traceability requirements can be conformed to with reasonable buyer and seller risk at a total cost of $18/non-GM mt. [EconLit Subject Descriptors: C150, C610, D810] (c) 2007 Wiley Periodicals, Inc. C1 [Wilson, William W.; Dahl, Bruce L.] N Dakota State Univ, Dept Agribusiness & Appl Econ, Fargo, ND 58105 USA. [Henry, Xavier] Plantureux, F-04100 Manosque, France. C3 North Dakota State University Fargo RP Wilson, WW (corresponding author), N Dakota State Univ, Dept Agribusiness & Appl Econ, Fargo, ND 58105 USA. EM William.Wilson@ndsu.edu; xhenry@plantureux.com; Bruce.Dahl@ndsu.edu CR Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 *ENV SYST RES I, 2004, ID PRES AGR CROPS *EUR PARL, 2003, OFF J EUR UNI FERRIERE J, 2003, OVERVIEW EU BIOTECH Furtan WH, 2003, CONTEMP ECON POLICY, V21, P433, DOI 10.1093/cep/byg023 *GENC EAN FRANC, 2001, TRAC SUPPL CHAIN STR GOLAN E, 2004, 830 US DEP AGR WASH Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hobbs JE, 2006, FOOD POLICY, V31, P78, DOI 10.1016/j.foodpol.2005.08.001 Institute of Electrical and Electronics Engineers (IEEE), 1990, STAND COMP DICT COMP *INT I BEET RES, 2003, GMO CULT SOON AUTH E Loureiro M. L., 2003, Eurochoices, V2, P18, DOI 10.1111/j.1746-692X.2003.tb00038.x MACDONALD J, 2006, EC INFROM B EIB, V9 Matus-Cadiz MA, 2004, CROP SCI, V44, P718, DOI 10.2135/cropsci2004.0718 MONSANTO, 2004, MONSANTO REALIGN RES Moschini G, 2005, J AGR ECON, V56, P347, DOI 10.1111/j.1477-9552.2005.00022.x *PAL CORP, 1998, RISK OPT OPT SIM MIC PETERSON D, 2002, THESIS N DAKOTA STAT SERRAO A, 2000, AM AGR ASS ANN M Sheldon I. M., 1996, Review of Agricultural Economics, V18, P7, DOI 10.2307/1349662 Shepherd R, 2006, J AGR ECON, V57, P313, DOI 10.1111/j.1477-9552.2006.00054.x SWENSON A, 2003, FARM MANAGEMENT PLAN SYKUTA M, 2005, J AGR APPL EC, V37 *US DEP AGR, 2004, NATL AGR STAT SERV *US WHEAT ASS, 2006, POS BIOT JOINT STAT WILSON W, 2007, J AGR ECON, V36, P39 Wilson WW, 2006, CAN J AGR ECON, V54, P341, DOI 10.1111/j.1744-7976.2006.00054.x Wilson WW, 2005, REV AGR ECON, V27, P212, DOI 10.1111/j.1467-9353.2005.00222.x WRIGHT Y, 2004, SE AGR EC ASS ANN M 2004, MILLING BAKING NEWS, P8 [No title captured] NR 31 TC 19 Z9 22 U1 0 U2 10 PD WIN PY 2008 VL 24 IS 1 BP 85 EP 101 DI 10.1002/AGR.20148 WC Agricultural Economics & Policy; Economics; Food Science & Technology SC Agriculture; Business & Economics; Food Science & Technology UT WOS:000252453200006 DA 2022-12-14 ER PT J AU Sezer, BB Topal, S Nuriyev, U AF Sezer, Bora Bugra Topal, Selcuk Nuriyev, Urfat TI TPPSUPPLY : A traceable and privacy-preserving blockchain system architecture for the supply chain SO JOURNAL OF INFORMATION SECURITY AND APPLICATIONS DT Article DE Blockchain; Supply chain; Privacy; Traceability; Smart contract; Off-chain ID TECHNOLOGY; MANAGEMENT; IDENTIFICATION AB Traceability and auditability are key structures in supply chain management and construction. However, trust is the most important aspect of customers in these systems. Also, relying on third parties to trade in centralized systems is indispensable. Although current exist frameworks for these solutions in the supply chain, these have work poor traceability and lack of real-time information, and especially lack of privacy. In this article, we propose a framework for supply chain traceability that preserves privacy from third parties by using smart contracts. In the proposed framework, digital signature and verification are provided by using existing cryptographic techniques in off-chain and on-chain smart contract integration, and it is demonstrated the applicability of architecture by testing. The architecture also provides both anonymity and traceability depending on the user's request. Finally, thanks to traceability and auditability in the proposed system, customers and other parties can view with a single product ID and also verify with digital signature the claims of the actors in the system. C1 [Sezer, Bora Bugra] Ege Univ, Sch Nat & Appl Sci, Dept Math, TR-35100 Izmir, Turkey. [Topal, Selcuk] Bitlis Eren Univ, Dept Math, TR-13000 Bitlis, Turkey. [Nuriyev, Urfat] Ege Univ, Dept Math, TR-35100 Izmir, Turkey. [Nuriyev, Urfat] Azerbaijan State Agr Univ, AZ-2000 Ganja, Azerbaijan. C3 Ege University; Bitlis Eren University; Ege University; Azerbaijan State Agricultural University RP Topal, S (corresponding author), Bitlis Eren Univ, Dept Math, TR-13000 Bitlis, Turkey. EM s.topal@beu.edu.tr CR Abbas K, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/5597679 Abd El-Latif AA, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102549 Abeyratne S. A., 2016, INT J RES ENG TECHNO, V5, P1, DOI DOI 10.15623/IJRET.2016.0509001 Agrawal TK, 2021, COMPUT IND ENG, V154, DOI 10.1016/j.cie.2021.107130 Ahmad RW, 2021, COMPUT IND ENG, V151, DOI 10.1016/j.cie.2020.106982 Alvarez-Diaz N, 2017, PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND MACHINE LEARNING (IML'17), DOI 10.1145/3109761.3158384 Amrioui S, 2012, IEEE INT C DIGITAL E [Anonymous], 2013, 1864 FIPS PUB, P130 Back A., 2014, ENABLING BLOCKCHAIN, V72 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Badra M, 2014, IEEE T INF FOREN SEC, V9, P321, DOI 10.1109/TIFS.2013.2296441 Ben-Sasson E, 2014, P IEEE S SECUR PRIV, P459, DOI 10.1109/SP.2014.36 Bertoni G., 2011, KECCAK SHA 3 SUBMISS, V6, P16 Bocek Thomas, 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), P772, DOI 10.23919/INM.2017.7987376 Buterin V., 2014, NEXT GENERATION SMAR Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Chang SE, 2019, TECHNOL FORECAST SOC, V144, P1, DOI 10.1016/j.techfore.2019.03.015 Cheung JCL, 2011, GLOB TELECOMM CONF Cui Y, 2018, J FOOD PROCESS ENG, V41, DOI 10.1111/jfpe.12864 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dietrich F, 2021, PROCEDIA COMPUT SCI, V180, P724, DOI 10.1016/j.procs.2021.01.295 Du XJ, 2007, AD HOC NETW, V5, P24, DOI 10.1016/j.adhoc.2006.05.012 Dwivedi SK, 2020, J INF SECUR APPL, V54, DOI 10.1016/j.jisa.2020.102554 Efthymiou C, 2010, INT CONF SMART GRID, P238, DOI 10.1109/SMARTGRID.2010.5622050 Exposito I, 2013, IEEE ANTENN PROPAG M, V55, P255, DOI 10.1109/MAP.2013.6529365 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Gans J. S, 2017, SOME SIMPLE EC BLOCK, DOI [10.2139/ssrn.2874598, DOI 10.2139/SSRN.2874598, DOI 10.3386/W22952] Nguyen GN, 2021, J PARALLEL DISTR COM, V153, P150, DOI 10.1016/j.jpdc.2021.03.011 Gilad Y, 2017, PROCEEDINGS OF THE TWENTY-SIXTH ACM SYMPOSIUM ON OPERATING SYSTEMS PRINCIPLES (SOSP '17), P51, DOI 10.1145/3132747.3132757 Gurtu A, 2019, INT J PHYS DISTR LOG, V49, P881, DOI 10.1108/IJPDLM-11-2018-0371 Han S, 2016, IEEE T INF FOREN SEC, V11, P1940, DOI 10.1109/TIFS.2015.2472369 Hankerson D., 2006, GUIDE ELLIPTIC CURVE, DOI DOI 10.1007/0-387-21846-73 Helo P, 2020, ROBOT CIM-INT MANUF, V63, DOI 10.1016/j.rcim.2019.101909 Ivanov D, 2019, INT J PROD RES, V57, P829, DOI 10.1080/00207543.2018.1488086 Jakkhupan W, 2015, TELECOMMUN SYST, V58, P243, DOI 10.1007/s11235-014-9866-7 Jansma N., 2004, TECHNICAL REPORT Johnson D., 2001, International Journal of Information Security, V1, P36, DOI 10.1007/s102070100002 Kane E., 2017, IS BLOCKCHAIN GEN PU, DOI 10.2139/ssrn.2932585 Kang YS, 2013, COMPUT IND, V64, P609, DOI 10.1016/j.compind.2013.03.004 Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Korpela K, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P4182 Kosba A, 2016, P IEEE S SECUR PRIV, P839, DOI 10.1109/SP.2016.55 Lavelli V, 2013, FOOD CONTROL, V33, P148, DOI 10.1016/j.foodcont.2013.02.022 Lee JH, 2017, IEEE CONSUM ELECTR M, V6, P19, DOI 10.1109/MCE.2017.2684916 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Liang WJ, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0139558 Lin C, 2021, COMPUT STAND INTER, V75, DOI 10.1016/j.csi.2020.103505 Lin C, 2021, IEEE SYST J, V15, P4367, DOI 10.1109/JSYST.2020.3019923 Ma SL, 2021, IEEE T DEPEND SECURE, V18, P641, DOI 10.1109/TDSC.2020.2969418 Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Min H, 2019, BUS HORIZONS, V62, P35, DOI 10.1016/j.bushor.2018.08.012 Nakamoto S, BITCOIN PEER TO PEER Pan SL, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103462 Philipp R, 2019, TRANSP TELECOMMUN J, V20, P365, DOI 10.2478/ttj-2019-0030 Rejeb A, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11070161 RIVEST RL, 1978, COMMUN ACM, V21, P120, DOI [10.1145/359340.359342, 10.1145/357980.358017] Rocket T, 2019, ARXIV PREPRINT ARXIV Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Schmidt CG, 2019, J PURCH SUPPLY MANAG, V25, DOI 10.1016/j.pursup.2019.100552 Sheel A, 2019, MANAG RES REV, V42, P1353, DOI 10.1108/MRR-12-2018-0490 Silverman JH, 1998, LECT NOTES COMPUT SC, V1514, P110 Steffen S, 2019, PROCEEDINGS OF THE 2019 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'19), P1759, DOI 10.1145/3319535.3363222 Sun SF, 2017, LECT NOTES COMPUT SC, V10493, P456, DOI 10.1007/978-3-319-66390-9_25 Sunmola FT, 2021, PROCEDIA COMPUT SCI, V180, P887, DOI 10.1016/j.procs.2021.01.339 Sunny J, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106895 Surjandy, 2019, ICIC Express Letters, V13, P913, DOI 10.24507/icicel.13.10.913 Szabo N, 1994, SMART CONTRACTS Szewczyk P., 2016, FINANCE TODAY TOMORR, V63 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Dinh TTA, 2017, SIGMOD'17: PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1085, DOI 10.1145/3035918.3064033 van der Vorst JGAJ, 2009, INT J PROD RES, V47, P6611, DOI 10.1080/00207540802356747 Viotti P, 2016, PROCEEDINGS OF THE 2ND WORKSHOP ON THE PRINCIPLES AND PRACTICE OF CONSISTENCY FOR DISTRIBUTED DATA, PAPOC 2016, DOI 10.1145/2911151.2911162 Vo V.D., 2016, SUPPLY CHAIN UM I, V17, P125, DOI [10.1080/16258312.2016.1188588, DOI 10.1080/16258312.2016.1188588] Wang B, 2020, COMPUT IND, V123, DOI 10.1016/j.compind.2020.103324 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 Westerkamp M, 2018, IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, P1595, DOI 10.1109/Cybermatics_2018.2018.00267 Wong LW, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.08.005 Wood G, 2014, ETHEREUM SECURE DECE, V151, P1, DOI DOI 10.1017/CBO9781107415324.004 Xu P, 2021, INT J PHYS DISTR LOG, V51, P305, DOI 10.1108/IJPDLM-08-2019-0234 Yang K, 2017, ACM T DES AUTOMAT EL, V22, DOI 10.1145/3005346 Zhang C, 2021, COMPUT STAND INTER, V77, DOI 10.1016/j.csi.2021.103520 Zhou ZY, 2017, IEEE COMMUN MAG, V55, P52, DOI 10.1109/MCOM.2017.1700169 Zou T., 2017, PLOS ONE, V2, P1 NR 86 TC 1 Z9 1 U1 4 U2 4 PD MAY PY 2022 VL 66 AR 103116 DI 10.1016/j.jisa.2022.103116 WC Computer Science, Information Systems SC Computer Science UT WOS:000820170800001 DA 2022-12-14 ER PT J AU Kim, S Ju, C Kim, J Son, HI AF Kim, Seungwon Ju, Chanyoung Kim, Jeongeun Son, Hyoung Il TI A Tracking Method for the Invasive Asian Hornet: A Brief Review and Experiments SO IEEE ACCESS DT Review DE Tracking; Asian hornet; radio-telemetry; flight capability; traceability test ID TELEMETRY; BEHAVIOR AB Radio-telemetry tracking uses a lightweight transmitter and receivers capable of good mobility. This method can be used to track Asian hornets, which have a significant impact on bees and beekeepers. This is because hornet acts as a group and exhibit dangerous aggressiveness. The most efficient way to prevent the invasion of Asian hornets is to destroy their nest. To this end, fire, pesticides, and experts can be used. However, the nest is not destroyed based on the location (e.g., treetops, rocks, urban areas). Therefore, we propose a method for tracking Asian hornets to effectively destroy their nests. Firstly, we investigate the existing insect-tracking methods. Secondly, we select sensors and conduct flight capability and traceability tests. Finally, we analyze and discuss the feasibility of the proposed tracking method using the experimental results. C1 [Kim, Seungwon; Ju, Chanyoung; Kim, Jeongeun; Son, Hyoung Il] Chonnam Natl Univ, Dept Rural & Biosyst Engn, Gwangju 61186, South Korea. C3 Chonnam National University RP Son, HI (corresponding author), Chonnam Natl Univ, Dept Rural & Biosyst Engn, Gwangju 61186, South Korea. EM hison@jnu.ac.kr CR Abrol D. P., 1994, Korean Journal of Apiculture, V9, P5 Barbet-Massin M, 2013, BIOL CONSERV, V157, P4, DOI 10.1016/j.biocon.2012.09.015 Benaets K, 2017, P ROY SOC B-BIOL SCI, V284, DOI 10.1098/rspb.2016.2149 Biswas J, 2012, IEEE INT CONF ROBOT, P1697, DOI 10.1109/icra.2012.6224766 Brenneke C, 2003, IROS 2003: PROCEEDINGS OF THE 2003 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, P188 Capaldi EA, 2000, NATURE, V403, P537, DOI 10.1038/35000564 Cliff OM, 2018, SCI ROBOT, V3, DOI 10.1126/scirobotics.aat8409 DANFORTH B, 2007, CURR BIOL, V17, pR15 Day W. B., IMPORTANCE BEES Decourtye A, 2011, ECOTOXICOLOGY, V20, P429, DOI 10.1007/s10646-011-0594-4 Dopico N. I., 2011, P 17 EUR WIR SUST WI, P1 Fischer J, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0091364 Franklin DN, 2017, APPL ENTOMOL ZOOL, V52, P221, DOI 10.1007/s13355-016-0470-z Hagen M, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0019997 Hedin J, 2002, COMPUT ELECTRON AGR, V35, P171, DOI 10.1016/S0168-1699(02)00017-0 Heidinger Ina Monika Margret, 2014, Insects, V5, P513, DOI 10.3390/insects5030513 Henry M, 2012, SCIENCE, V336, P348, DOI 10.1126/science.1215039 Juliette P., 2017, P COLOSS TASKF VESP, P1 Kennedy PJ, 2018, COMMUN BIOL, V1, DOI 10.1038/s42003-018-0092-9 Kissling WD, 2014, BIOL REV, V89, P511, DOI 10.1111/brv.12065 Maitra P., 2009, 2009 WORKSH APPL COM, p[1, 1] Milanesio D, 2016, ECOL EVOL, V6, P2170, DOI 10.1002/ece3.2011 Monceau K, 2014, J PEST SCI, V87, P1, DOI 10.1007/s10340-013-0537-3 O'Neal M. E., 2004, American Entomologist, V50, P212 Poidatz J, 2018, ECOL EVOL, V8, P7588, DOI 10.1002/ece3.4182 Psychoudakis D, 2008, IEEE ANTENN WIREL PR, V7, P444, DOI 10.1109/LAWP.2008.2004512 Riley JR, 2003, P ROY SOC B-BIOL SCI, V270, P2421, DOI 10.1098/rspb.2003.2542 Roberts B. R., 2010, J APICULT RES, V58, P494 Rojas-Nossa SV, 2018, APIDOLOGIE, V49, P872, DOI 10.1007/s13592-018-0612-0 Rome Quentin, 2011, Aliens (Auckland), V31, P7 Royer F, 2008, J EXP MAR BIOL ECOL, V359, P1, DOI 10.1016/j.jembe.2008.01.026 Sauvard D, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0198597 Schneider CW, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0030023 Schofield G, 2007, J EXP MAR BIOL ECOL, V347, P58, DOI 10.1016/j.jembe.2007.03.009 Semmence N., ASIAN HORNET UPDATE Shearwood J, 2018, IEEE MTT S INT MICR, P957, DOI 10.1109/MWSYM.2018.8439173 Streit S, 2003, ZOOLOGY, V106, P169, DOI 10.1078/0944-2006-00113 Sustain, WHY BEES ARE IMP Tenczar P, 2014, ANIM BEHAV, V95, P41, DOI 10.1016/j.anbehav.2014.06.006 Tremblay JA, 2017, J UNMANNED VEH SYST, V5, P102, DOI 10.1139/juvs-2016-0021 Van Nguyen H., 2017, ARXIV171201491 Wikelski M, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0010738 Wolf S, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0103989 Won SHP, 2011, MEAS SCI TECHNOL, V22, DOI 10.1088/0957-0233/22/12/125108 [No title captured] NR 45 TC 3 Z9 3 U1 8 U2 16 PY 2019 VL 7 BP 176998 EP 177008 DI 10.1109/ACCESS.2019.2958153 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000509405300046 DA 2022-12-14 ER PT J AU Suzuki, Y AF Suzuki, Yaeko TI Achieving Food Authenticity and Traceability Using an Analytical Method Focusing on Stable Isotope Analysis SO ANALYTICAL SCIENCES DT Review DE Stable isotope analysis; food authenticity; rice; beef; honey ID VERIFYING GEOGRAPHICAL ORIGIN; ORYZA-SATIVA L.; RATIO ANALYSIS; TRACE-ELEMENT; DISCRIMINATION; CARBON; RICE; BEEF; MULTIELEMENT; OXYGEN AB High-value agricultural products are characterized by the geographical conditions of the production areas such as climatic and soil conditions. These products are protected by the geographical indication (GI) protection system, which has been introduced in more than 100 countries. Because GI products are expensive in the market, products are often mislabeled as GI. Thus, there is an urgent need for the development of analytical methods that enable the tracing of geographical origins of food materials. Stable isotope analysis is used to trace the geographical origin of food materials. In this study, we review the applications for tracing the geographical origin of agricultural products (especially rice, beef, and honey) focusing on an analytical method for analyzing stable isotopes (delta D, delta C-13, delta N-15, delta O-18, and delta S-34). C1 [Suzuki, Yaeko] Natl Agr & Food Res Org NARO, Food Res Inst, 2-1-12 Kannondai, Tsukuba, Ibaraki 3058642, Japan. C3 National Agriculture & Food Research Organization - Japan RP Suzuki, Y (corresponding author), Natl Agr & Food Res Org NARO, Food Res Inst, 2-1-12 Kannondai, Tsukuba, Ibaraki 3058642, Japan. EM yaekos@affrc.go.jp CR Aoyagi K, 2013, J JPN SOC FOOD SCI, V60, P138, DOI 10.3136/nskkk.60.138 Baroni MV, 2015, J AGR FOOD CHEM, V63, P4638, DOI 10.1021/jf5060112 Bateman AS, 2007, J AGR FOOD CHEM, V55, P2664, DOI 10.1021/jf0627726 Bligh HFJ, 2000, INT J FOOD SCI TECH, V35, P257, DOI 10.1046/j.1365-2621.2000.00390.x Bowen G, GRIDDED MAPS ISOTOPI Brescia MA, 2002, RAPID COMMUN MASS SP, V16, P2286, DOI 10.1002/rcm.860 Camin F, 2004, J AGR FOOD CHEM, V52, P6592, DOI 10.1021/jf040062z Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Camin F, 2016, COMPR REV FOOD SCI F, V15, P868, DOI 10.1111/1541-4337.12219 Camin F, 2012, ANAL CHIM ACTA, V711, P54, DOI 10.1016/j.aca.2011.10.047 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Carter JF, 2015, J AGR FOOD CHEM, V63, P5771, DOI 10.1021/acs.jafc.5b01526 Carter JF., GOOD PRACTICE GUIDE, V2nd Cengiz MF, 2017, INT J FOOD PROP, V20, P3234, DOI 10.1080/10942912.2017.1283327 Chen TJ, 2016, FOOD CHEM, V209, P95, DOI 10.1016/j.foodchem.2016.04.029 Chesson LA, 2010, J AGR FOOD CHEM, V58, P2358, DOI 10.1021/jf904151c Chiocchini F, 2016, FOOD CHEM, V202, P291, DOI 10.1016/j.foodchem.2016.01.146 Chung IM, 2015, J CEREAL SCI, V65, P252, DOI 10.1016/j.jcs.2015.08.001 CRAIG H, 1961, SCIENCE, V133, P1702, DOI 10.1126/science.133.3465.1702 DeLaGuardia M, 2013, COMP ANAL C, V60, P1 DENIRO MJ, 1978, GEOCHIM COSMOCHIM AC, V42, P495, DOI 10.1016/0016-7037(78)90199-0 DENIRO MJ, 1977, SCIENCE, V197, P261, DOI 10.1126/science.327543 Diefendorf AF, 2010, P NATL ACAD SCI USA, V107, P5738, DOI 10.1073/pnas.0910513107 Dinca OR, 2015, FOOD ANAL METHOD, V8, P401, DOI 10.1007/s12161-014-9903-x Driscoll AW, 2020, RAPID COMMUN MASS SP, V34, DOI 10.1002/rcm.8626 Franke BM, 2008, MEAT SCI, V80, P944, DOI 10.1016/j.meatsci.2008.03.018 Fry B., 2007, STABLE ISOTOPE ECOLO Gat JR, 1996, ANNU REV EARTH PL SC, V24, P225, DOI 10.1146/annurev.earth.24.1.225 Geana EI, 2017, FOOD ANAL METHOD, V10, P63, DOI 10.1007/s12161-016-0550-2 Gotoh T, 2018, ASIAN AUSTRAL J ANIM, V31, P933, DOI 10.5713/ajas.18.0333 Harrison SM, 2011, FOOD CHEM, V124, P291, DOI 10.1016/j.foodchem.2010.06.035 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Isshiki M, 2015, J JPN SOC FOOD SCI, V62, P257, DOI 10.3136/nskkk.62.257 Kawasaki A, 2002, SOIL SCI PLANT NUTR, V48, P635, DOI 10.1080/00380768.2002.10409251 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kelly S, 2002, EUR FOOD RES TECHNOL, V214, P72, DOI 10.1007/s002170100400 Kobayashi A, 2018, RICE, V11, DOI 10.1186/s12284-018-0207-4 Korenaga T., 2013, ANAL SCI, V29, P143 Kropf U, 2010, J AGR FOOD CHEM, V58, P12794, DOI 10.1021/jf102940s Kukusamude C, 2018, FOOD CONTROL, V91, P357, DOI 10.1016/j.foodcont.2018.04.018 Li A, 2015, QUAL ASSUR SAF CROP, V7, P343, DOI 10.3920/QAS2013.0378 Liu HC, 2020, INFRARED PHYS TECHN, V111, DOI 10.1016/j.infrared.2020.103513 Liu Z, 2019, RAPID COMMUN MASS SP, V33, P625, DOI 10.1002/rcm.8387 Luo DH, 2015, FOOD CHEM, V174, P197, DOI 10.1016/j.foodchem.2014.11.006 Magdas DA, 2021, FOOD CHEM, V334, DOI 10.1016/j.foodchem.2020.127599 Manca G, 2006, J DAIRY SCI, V89, P831, DOI 10.3168/jds.S0022-0302(06)72146-4 Manca G, 2001, J AGR FOOD CHEM, V49, P1404, DOI 10.1021/jf000706c MEINTS VW, 1975, SOIL SCI, V119, P421, DOI 10.1097/00010694-197506000-00003 Nakano A, 2018, JARQ-JPN AGR RES Q, V52, P105, DOI 10.6090/jarq.52.105 Nakashita R, 2008, ANAL CHIM ACTA, V617, P148, DOI 10.1016/j.aca.2008.03.048 Nakashita R, 2009, BUNSEKI KAGAKU, V58, P1023, DOI 10.2116/bunsekikagaku.58.1023 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3285, DOI 10.1021/jf1040433 PARK R, 1961, PLANT PHYSIOL, V36, P133, DOI 10.1104/pp.36.2.133 Peng CY, 2019, J SCI FOOD AGR, V99, P2596, DOI 10.1002/jsfa.9475 Pianezze S., 2019, J MASS SPECTROM, V55, pe4451 Pillonel L, 2005, INT DAIRY J, V15, P547, DOI 10.1016/j.idairyj.2004.07.028 Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Portarena S, 2017, FOOD CHEM, V215, P1, DOI 10.1016/j.foodchem.2016.07.135 Rees G, 2016, FOOD CONTROL, V67, P144, DOI 10.1016/j.foodcont.2016.02.018 Rodrigues C, 2011, J AGR FOOD CHEM, V59, P10239, DOI 10.1021/jf200788p Rodrigues CI, 2009, J FOOD COMPOS ANAL, V22, P463, DOI 10.1016/j.jfca.2008.06.010 Rogers KM, 2014, J AGR FOOD CHEM, V62, P2605, DOI 10.1021/jf404766f Rossmann A., 2013, NEW ANAL APPROACHES, P272 Santato A, 2012, J MASS SPECTROM, V47, P1132, DOI 10.1002/jms.3018 SAURER M, 1995, TELLUS B, V47, P320, DOI 10.1034/j.1600-0889.47.issue3.4.x Schellenberg A, 2010, FOOD CHEM, V121, P770, DOI 10.1016/j.foodchem.2009.12.082 Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 Shin WJ, 2018, RAPID COMMUN MASS SP, V32, P1843, DOI 10.1002/rcm.8251 Suzuki Y, 2008, J JPN SOC FOOD SCI, V55, P250, DOI 10.3136/nskkk.55.250 Suzuki Y, 2013, COMP ANAL C, V60, P461, DOI 10.1016/B978-0-444-59562-1.00018-9 Suzuki Y, 2009, BUNSEKI KAGAKU, V58, P1053, DOI 10.2116/bunsekikagaku.58.1053 Tanz N, 2010, J AGR FOOD CHEM, V58, P3139, DOI 10.1021/jf903251k Wadood SA, 2019, J MASS SPECTROM, V54, P178, DOI 10.1002/jms.4312 Zhao SS, 2020, RAPID COMMUN MASS SP, V34, DOI 10.1002/rcm.8795 Zhao Y, 2020, MEAT SCI, V165, DOI 10.1016/j.meatsci.2020.108129 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y Zhou JQ, 2015, FOOD CHEM, V182, P23, DOI 10.1016/j.foodchem.2015.02.116 NR 79 TC 2 Z9 2 U1 8 U2 42 PD JAN PY 2021 VL 37 IS 1 BP 189 EP 199 DI 10.2116/analsci.20SAR14 WC Chemistry, Analytical SC Chemistry UT WOS:000608835000020 DA 2022-12-14 ER PT J AU Phukphatthanachai, P Panne, U Traub, H Pfeifer, J Vogl, J AF Phukphatthanachai, Pranee Panne, Ulrich Traub, Heike Pfeifer, Jens Vogl, Jochen TI Quantification of sulphur in copper and copper alloys by GDMS and LA-ICP-MS, demonstrating metrological traceability to the international system of units SO JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY DT Article AB The quantification of the sulphur mass fraction in pure copper and copper alloys by GDMS and LA-ICP-MS revealed a lack of traceability mainly due to a lack of suitable certified reference materials for calibrating the instruments. Within this study GDMS and LA-ICP-MS were applied as routine analytical tools to quantify sulphur in copper samples by applying reference materials as calibrators, which were characterized for their sulphur mass fraction by IDMS beforehand. Different external calibration strategies were applied including a matrix cross type calibration. Both techniques with all calibration strategies were validated by using certified reference materials (others than those used for calibration) and good agreement with the reference values was achieved except for the matrix cross type calibration, for which the agreement was slightly worse. All measurement results were accompanied by an uncertainty statement. For GDMS, the relative expanded (k = 2) measurement uncertainty ranged from 3% to 7%, while for LA-ICP-MS it ranged from 11% to 33% when applying matrix-matched calibration in the sulphur mass fraction range between 25 mg kg(-1) and 1300 mg kg(-1). For cross-type calibration the relative expanded (k = 2) measurement uncertainty need to be increased to at least 12% for GDMS and to at least 54% for LA-ICP-MS to yield metrological compatibility with the reference values. The so obtained measurement results are traceable to the international system of units (SI) via IDMS reference values, which is clearly illustrated by the unbroken chain of calibrations in the metrological traceability scheme. C1 [Phukphatthanachai, Pranee] Natl Inst Metrol Thailand, 3-4-5 Moo 3,Klong 5, Pathum Thani 12120, Thailand. [Panne, Ulrich; Traub, Heike; Pfeifer, Jens; Vogl, Jochen] Bundesanstalt Materialforsch & Prufung BAM, Richard Willstaetter Str 11, D-12489 Berlin, Germany. [Panne, Ulrich] Humboldt Univ, Dept Chem, Brook Taylor 2, D-12489 Berlin, Germany. C3 National Institute of Metrology Thailand; Federal Institute for Materials Research & Testing; Humboldt University of Berlin RP Phukphatthanachai, P (corresponding author), Natl Inst Metrol Thailand, 3-4-5 Moo 3,Klong 5, Pathum Thani 12120, Thailand.; Vogl, J (corresponding author), Bundesanstalt Materialforsch & Prufung BAM, Richard Willstaetter Str 11, D-12489 Berlin, Germany. EM pranee@nimt.or.th; jochen.vogl@bam.de CR Caprioli, 2010, ENCY MASS SPECTROMET CCQM Guidance note, 2013, CCQM1322 Gusarova T, 2010, J ANAL ATOM SPECTROM, V25, P314, DOI 10.1039/b921649a Lange B, 2008, MICROCHIM ACTA, V160, P97, DOI 10.1007/s00604-007-0849-1 Li J., 2016, J ANAL ATOM SPECTROM, V31, P10 MARCUS RK, 2003, GLOW DISCHARGE PLASM Matschat R, 2002, MATER TRANS, V43, P90, DOI 10.2320/matertrans.43.90 Matschat R., 1999, FRESEN J ANAL CHEM, V364, P7 Matschat R, 2006, ANAL BIOANAL CHEM, V386, P125, DOI 10.1007/s00216-006-0645-5 NELMS SM, 2005, ICP MASS SPECTROMETR Phukphatthanachai P, 2018, J ANAL ATOM SPECTROM, V33, P1506, DOI 10.1039/c8ja00116b Phukphatthanachai P, 2018, J ANAL ATOM SPECTROM, V33, P90, DOI 10.1039/c7ja00338b Pisonero J, 2009, J ANAL ATOM SPECTROM, V24, P1145, DOI 10.1039/b904698d Traub H, 2010, J ANAL ATOM SPECTROM, V25, P690, DOI 10.1039/b915564c Traub H, 2009, ANAL BIOANAL CHEM, V395, P1471, DOI 10.1007/s00216-009-3061-9 Ulrich, 2013, SPECTROCHIM ACTA B, P79 Vogl J, 2018, METROLOGIA, V55, DOI 10.1088/1681-7575/aaa677 NR 17 TC 1 Z9 1 U1 7 U2 19 PD NOV 3 PY 2021 VL 36 IS 11 BP 2404 EP 2414 DI 10.1039/d1ja00137j EA SEP 2021 WC Chemistry, Analytical; Spectroscopy SC Chemistry; Spectroscopy UT WOS:000700550600001 DA 2022-12-14 ER PT J AU Yadav, S Carvalho, J Trujillo, I Prado, M AF Yadav, Shambhavi Carvalho, Joana Trujillo, Isabel Prado, Marta TI Microsatellite Markers in Olives (Olea europaea L.): Utility in the Cataloging of Germplasm, Food Authenticity and Traceability Studies SO FOODS DT Review DE authentication; cultivar identification; Olea europaea; olive oil; simple sequence repeats; traceability; table olive ID MELTING SSR-HRM; SEQUENCE REPEATS SSRS; GENETIC DIVERSITY; CULTIVAR IDENTIFICATION; DNA EXTRACTION; PROTECTED DESIGNATION; LINKAGE MAP; MOLECULAR CHARACTERIZATION; OIL AUTHENTICATION; WILD OLIVES AB The olive fruit, a symbol of Mediterranean diets, is a rich source of antioxidants and oleic acid (55-83%). Olive genetic resources, including cultivated olives (cultivars), wild olives as well as related subspecies, are distributed widely across the Mediterranean region and other countries. Certain cultivars have a high commercial demand and economical value due to the differentiating organoleptic characteristics. This might result in economically motivated fraudulent practices and adulteration. Hence, tools to ensure the authenticity of constituent olive cultivars are crucial, and this can be achieved accurately through DNA-based methods. The present review outlines the applications of microsatellite markers, one of the most extensively used types of molecular markers in olive species, particularly referring to the use of these DNA-based markers in cataloging the vast olive germplasm, leading to identification and authentication of the cultivars. Emphasis has been given on the need to adopt a uniform platform where global molecular information pertaining to the details of available markers, cultivar-specific genotyping profiles (their synonyms or homonyms) and the comparative profiles of oil and reference leaf samples is accessible to researchers. The challenges of working with microsatellite markers and efforts underway, mainly advancements in genotyping methods which can be effectively incorporated in olive oil varietal testing, are also provided. Such efforts will pave the way for the development of more robust microsatellite marker-based olive agri-food authentication platforms. C1 [Yadav, Shambhavi] Forest Res Inst, Genet & Tree Improvement Div, PO New Forest, Dehra Dun 248001, Uttarakhand, India. [Carvalho, Joana; Prado, Marta] Int Iberian Nanotechnol Lab INL, Food Qual & Safety Res Grp, P-4715330 Braga, Portugal. [Carvalho, Joana] Univ Santiago Compostela, Coll Pharm, Sch Vet Sci, Dept Analyt Chem Nutr & Food Sci, Campus Vida, E-15782 Santiago De Compostela, Spain. [Trujillo, Isabel] Univ Cordoba, Int Campus Excellence Agrofood CeiA3, Dept Agron, Excellence Unit Maria Maeztu, Rabanales Campus, Cordoba 14014, Spain. C3 Indian Council of Forestry Research & Education (ICFRE); Forest Research Institute (FRI); International Iberian Nanotechnology Laboratory; Universidade de Santiago de Compostela; Universidad de Cordoba RP Yadav, S (corresponding author), Forest Res Inst, Genet & Tree Improvement Div, PO New Forest, Dehra Dun 248001, Uttarakhand, India.; Trujillo, I (corresponding author), Univ Cordoba, Int Campus Excellence Agrofood CeiA3, Dept Agron, Excellence Unit Maria Maeztu, Rabanales Campus, Cordoba 14014, Spain. EM shambhaviy@icfre.org; joana.rr.carvalho@gmail.com; ag2trnai@uco.es; marta.prado@inl.int CR Abdessemed S, 2015, SCI HORTIC-AMSTERDAM, V192, P10, DOI 10.1016/j.scienta.2015.05.015 Adawy S.S., 2015, INT J SCI RES, V14, P1063 Aksehirli-Pakyurek M, 2017, PLANT MOL BIOL REP, V35, P575, DOI 10.1007/s11105-017-1046-y Alagna F, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0152943 Alagna F, 2009, BMC GENOMICS, V10, DOI 10.1186/1471-2164-10-399 Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Angiolillo A, 1999, THEOR APPL GENET, V98, P411, DOI 10.1007/s001220051087 Arbeiter AB, 2017, SCI AGR, V74, P215, DOI [10.1590/1678-992x-2016-0111, 10.1590/1678-992X-2016-0111] Bajoub A, 2018, CRIT REV FOOD SCI, V58, P832, DOI 10.1080/10408398.2016.1225666 Baldoni L, 2009, HANDB PLANT BREED, V4, P397, DOI 10.1007/978-0-387-77594-4_13 Baldoni L, 2009, MOL BREEDING, V24, P213, DOI 10.1007/s11032-009-9285-8 Bandelj D, 2004, EUPHYTICA, V136, P93, DOI 10.1023/B:EUPH.0000019552.42066.10 Bandelj D, 2002, FOOD TECHNOL BIOTECH, V40, P185 Barranco D, 2000, HORTSCIENCE, V35, P1323, DOI 10.21273/HORTSCI.35.7.1323 Barranco D, 2000, WORLD CATALOGUE OLIV Bartolini G., OLIVE GERMPLASM OLEA Bartolini G., 1998, OLIVE GERMPLASM CULT Beghe D, 2015, SCI HORTIC-AMSTERDAM, V189, P122, DOI 10.1016/j.scienta.2015.04.003 Belaj A, 2003, THEOR APPL GENET, V107, P736, DOI 10.1007/s00122-003-1301-5 Belaj A., 2020, P INT OL COUNC IOC N Belaj A, 2007, ANN BOT-LONDON, V100, P449, DOI 10.1093/aob/mcm132 Belaj A, 2016, COMPEND PL GENOME, P27, DOI 10.1007/978-3-319-48887-5_3 Belaj A, 2011, SCI HORTIC-AMSTERDAM, V129, P561, DOI 10.1016/j.scienta.2011.04.025 Belaj A, 2010, SCI HORTIC-AMSTERDAM, V124, P323, DOI 10.1016/j.scienta.2010.01.010 Ben Ayed R, 2016, DATABASE-OXFORD, DOI 10.1093/database/bav090 Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 Ben-Ayed R, 2013, COMPR REV FOOD SCI F, V12, P218, DOI 10.1111/1541-4337.12003 Ben-Ayed R, 2012, EUR FOOD RES TECHNOL, V234, P263, DOI 10.1007/s00217-011-1631-5 Besnard G, 2013, ANN BOT-LONDON, V112, P1293, DOI 10.1093/aob/mct196 Boucheffa S, 2017, GENET RESOUR CROP EV, V64, P379, DOI 10.1007/s10722-016-0365-4 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Breton C, 2006, J BIOGEOGR, V33, P1916, DOI 10.1111/j.1365-2699.2006.01544.x Cantini C, 2008, J AM SOC HORTIC SCI, V133, P598, DOI 10.21273/JASHS.133.4.598 Carmona R, 2015, FRONT PLANT SCI, V6, DOI 10.3389/fpls.2015.00625 Vieira MLC, 2016, GENET MOL BIOL, V39, P312, DOI 10.1590/1678-4685-GMB-2016-0027 Carriero F, 2002, THEOR APPL GENET, V104, P301, DOI 10.1007/s001220100691 Carvalho J, 2018, ANAL CHIM ACTA, V1020, P30, DOI 10.1016/j.aca.2018.02.079 Casadei E, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107902 Chedid E, 2020, FOOD CHEM X, V6, DOI 10.1016/j.fochx.2020.100082 Chiappetta A, 2017, SCI HORTIC-AMSTERDAM, V226, P42, DOI 10.1016/j.scienta.2017.08.022 Cicerale S, 2010, INT J MOL SCI, V11, P458, DOI 10.3390/ijms11020458 Cipriani G, 2002, THEOR APPL GENET, V104, P223, DOI 10.1007/s001220100685 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Corrado G, 2011, J HORTIC SCI BIOTECH, V86, P461, DOI 10.1080/14620316.2011.11512789 Corrado G, 2012, BMC PLANT BIOL, V12, DOI 10.1186/1471-2229-12-86 Corrado G, 2009, GENOME, V52, P692, DOI [10.1139/G09-044, 10.1139/g09-044] Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Crawford LM, 2020, FOOD CONTROL, V114, DOI 10.1016/j.foodcont.2020.107264 Crawford LM, 2020, J AGR FOOD CHEM, V68, P1110, DOI 10.1021/acs.jafc.9b06890 de la Rosa R, 2003, THEOR APPL GENET, V106, P1273, DOI 10.1007/s00122-002-1189-5 De La Rosa R, 2002, MOL ECOL NOTES, V2, P265, DOI 10.1046/j.1471-8286.2002.00217.x De la Rosa R, 2013, J AM SOC HORTIC SCI, V138, P290, DOI 10.21273/JASHS.138.4.290 Dervishi A, 2018, TREE GENET GENOMES, V14, DOI 10.1007/s11295-018-1269-6 Diaz A, 2006, TREE GENET GENOMES, V2, P165, DOI 10.1007/s11295-006-0041-5 Distefano G, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0044202 Doveri S, 2008, SCI HORTIC-AMSTERDAM, V116, P367, DOI 10.1016/j.scienta.2008.02.005 Downey G, 1996, J SCI FOOD AGR, V71, P41, DOI [10.1002/(SICI)1097-0010(199605)71:1<41::AID-JSFA546>3.3.CO;2-9, 10.1002/(SICI)1097-0010(199605)71:1<41::AID-JSFA546>3.0.CO;2-I] El Bakkali A, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0061265 Ercisli S, 2011, BIOCHEM GENET, V49, P555, DOI 10.1007/s10528-011-9430-z Erre P, 2010, GENET RESOUR CROP EV, V57, P41, DOI 10.1007/s10722-009-9449-8 Marti AFI, 2015, ACTA PHYSIOL PLANT, V37, DOI 10.1007/s11738-014-1726-2 Ganino T., 2008, Advances in Horticultural Science, V22, P149 Ganino T, 2007, GENET RESOUR CROP EV, V54, P1531, DOI 10.1007/s10722-006-9145-x Ganopoulos I, 2011, FOOD CONTROL, V22, P532, DOI 10.1016/j.foodcont.2010.09.040 Gil FS, 2006, MOL ECOL NOTES, V6, P1275, DOI 10.1111/j.1471-8286.2006.01513.x Gomes S, 2008, J HORTIC SCI BIOTECH, V83, P395, DOI 10.1080/14620316.2008.11512397 Gomes S, 2018, J FOOD SCI, V83, P2415, DOI 10.1111/1750-3841.14333 Guasch-Ferre M, 2014, BMC MED, V12, DOI 10.1186/1741-7015-12-78 Gupta PK, 2000, EUPHYTICA, V113, P163, DOI 10.1023/A:1003910819967 Hannachi H., 2010, NAT RESOUR, V01, P95, DOI [10.4236/nr.2010.12010., DOI 10.4236/nr.2010.12010, 10.4236/nr.2010.12010, DOI 10.4236/NR.2010.12010] Hannachi H, 2008, SCI HORTIC-AMSTERDAM, V116, P280, DOI 10.1016/j.scienta.2008.01.004 Hannachi H, 2008, ACTA BOT GALLICA, V155, P531, DOI 10.1080/12538078.2008.10516132 Haouane H, 2011, GENETICA, V139, P1083, DOI 10.1007/s10709-011-9608-7 Hmmam I, 2018, GENET RESOUR CROP EV, V65, P1733, DOI 10.1007/s10722-018-0650-5 Hodgkin T., 2000, CORE COLLECTIONS PLA Ipek A, 2009, GENET MOL RES, V8, P1264, DOI 10.4238/vol8-4gmr659 Jimenez-Morillo NT, 2020, FOODS, V9, DOI 10.3390/foods9121855 Kalia RK, 2011, EUPHYTICA, V177, P309, DOI 10.1007/s10681-010-0286-9 Kaya HB, 2016, BIOCHEM GENET, V54, P506, DOI 10.1007/s10528-016-9738-9 Kaya HB, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0073674 Khadari B, 2010, J AM SOC HORTIC SCI, V135, P548, DOI 10.21273/JASHS.135.6.548 Kiritsakis A., 2000, HDB OLIVE OIL, P129 Lavee S, 1994, FRUTICULTURA, V62, P29 Lazovic B, 2016, SCI HORTIC-AMSTERDAM, V209, P117, DOI 10.1016/j.scienta.2016.06.022 Li DY, 2020, PEERJ, V8, DOI 10.7717/peerj.8573 Linos A, 2014, SCI HORTIC-AMSTERDAM, V175, P33, DOI 10.1016/j.scienta.2014.05.034 Lukic I, 2020, MOLECULES, V25, DOI 10.3390/molecules25010004 Lumaret R, 2004, HEREDITY, V92, P343, DOI 10.1038/sj.hdy.6800430 Mackay JF, 2008, PLANT METHODS, V4, DOI 10.1186/1746-4811-4-8 Mariotti R, 2016, TREE GENET GENOMES, V12, DOI 10.1007/s11295-016-1077-9 Martins-Lopes P, 2008, J AGR FOOD CHEM, V56, P11786, DOI 10.1021/jf801146z Melchiade D, 2007, FOOD BIOTECHNOL, V21, P33, DOI 10.1080/08905430701191114 Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Montemurro C, 2005, J HORTIC SCI BIOTECH, V80, P105, DOI 10.1080/14620316.2005.11511899 Montemurro C, 2015, J CHEM-NY, V2015, DOI 10.1155/2015/496986 Morello P., 2020, P INT OL COUNC IOC N MORGANTE M, 1993, PLANT J, V3, P175, DOI 10.1111/j.1365-313X.1993.tb00020.x Mouly PP, 1997, J AGR FOOD CHEM, V45, P373, DOI 10.1021/jf9605097 Munoz-Merida A, 2013, DNA RES, V20, P93, DOI 10.1093/dnares/dss036 Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Muzzalupo I, 2015, EUR FOOD RES TECHNOL, V241, P151, DOI 10.1007/s00217-015-2455-5 Ninot A, 2018, SCI HORTIC-AMSTERDAM, V231, P253, DOI 10.1016/j.scienta.2017.11.025 Noormohammadi Zahra, 2009, Acta Biologica Szegediensis, V53, P27 Omrani-Sabbaghi A, 2007, SCI HORTIC-AMSTERDAM, V112, P439, DOI 10.1016/j.scienta.2006.12.051 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Pasqualone A, 2016, J CHEM-NY, V2016, DOI 10.1155/2016/4347207 Pasqualone A, 2015, EUR J LIPID SCI TECH, V117, P2044, DOI 10.1002/ejlt.201400654 Pasqualone A, 2013, J AGR FOOD CHEM, V61, P3068, DOI 10.1021/jf400014g Perez-Jimenez F, 2007, MOL NUTR FOOD RES, V51, P1199, DOI [10.1002/mnfr.200600273, 10.1002/mnfr.200790023] Perez-Jimenez M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070507 Piarulli L, 2019, FOODS, V8, DOI 10.3390/foods8100462 Poljuha D, 2008, SCI HORTIC-AMSTERDAM, V115, P223, DOI 10.1016/j.scienta.2007.08.018 Raieta K, 2015, FOOD CHEM, V172, P596, DOI 10.1016/j.foodchem.2014.09.101 Rallo L., 2018, ADV PLANT BREEDING S, VVolume 3, P535, DOI [10.1007/978-3-319-91944-7_14, DOI 10.1007/978-3-319-91944-7_14] Rallo L, 2013, HORTICUL RE, V41, P303 Rallo P, 2003, THEOR APPL GENET, V107, P940, DOI 10.1007/s00122-003-1332-y Rallo P, 2000, THEOR APPL GENET, V101, P984, DOI 10.1007/s001220051571 Reboredo-Rodriguez P, 2018, SCI HORTIC-AMSTERDAM, V232, P269, DOI 10.1016/j.scienta.2018.01.015 Rehman AU, 2012, J HORTIC SCI BIOTECH, V87, P647, DOI 10.1080/14620316.2012.11512925 Rekik I, 2008, HORTSCIENCE, V43, P1371, DOI 10.21273/HORTSCI.43.5.1371 Resetic T, 2013, MOL BREEDING, V32, P211, DOI 10.1007/s11032-013-9863-7 Rotondi A, 2018, PLANT BIOSYST, V152, P1067, DOI 10.1080/11263504.2017.1415993 Rotondi A, 2011, FOOD CHEM, V129, P1825, DOI 10.1016/j.foodchem.2011.05.122 Sakar E, 2016, NOT BOT HORTI AGROBO, V44, P557, DOI 10.15835/nbha44210439 Sakar E, 2016, BIOCHEM GENET, V54, P348, DOI 10.1007/s10528-016-9723-3 Sefc KM, 2000, MOL ECOL, V9, P1171, DOI 10.1046/j.1365-294x.2000.00954.x Stambuk S, 2007, CROAT MED J, V48, P556 Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Trujillo I., 2019, P INT SEM IOC NETW G Trujillo I, 2014, TREE GENET GENOMES, V10, P141, DOI 10.1007/s11295-013-0671-3 Unver H, 2016, GENETIKA-BELGRADE, V48, P1017, DOI 10.2298/GENSR1603017U Varshney RK, 2005, TRENDS BIOTECHNOL, V23, P48, DOI 10.1016/j.tibtech.2004.11.005 Veral G.M., 2020, P INT OL COUNC IOC N Gomez-Rodriguez MV, 2021, GENET RESOUR CROP EV, V68, P117, DOI 10.1007/s10722-020-00971-y Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 Wu SB, 2004, GENOME, V47, P26, DOI 10.1139/G03-091 Xanthopoulou A, 2014, PLANT GENET RESOUR-C, V12, P273, DOI 10.1017/S147926211400001X Yoruk B, 2014, PLANT SYST EVOL, V300, P1247, DOI 10.1007/s00606-014-1002-3 ZOHARY D, 1975, SCIENCE, V187, P319, DOI 10.1126/science.187.4174.319 NR 140 TC 1 Z9 2 U1 2 U2 9 PD AUG PY 2021 VL 10 IS 8 AR 1907 DI 10.3390/foods10081907 WC Food Science & Technology SC Food Science & Technology UT WOS:000690453800001 DA 2022-12-14 ER PT J AU Cheong, KH Doihara, R Furuichi, N Terao, Y Shimada, T AF Cheong, Kar-Hooi Doihara, Ryouji Furuichi, Noriyuki Terao, Yoshiya Shimada, Takashi TI Primary standard for traceability in low liquid hydrocarbon fuel flow rates SO METROLOGIA DT Article DE low liquid hydrocarbon fuel flow rates; measurement traceability; primary standard; gravimetric method; uncertainty analysis; CMC validation through intra- and inter-facility comparisons ID FINAL REPORT; FACILITY AB We developed a primary standard for low liquid hydrocarbon flow rates that works on light oil (diesel), kerosene and industrial gasoline. To achieve a calibration accuracy of below 0.1% over a wide flow range from 0.02 L h(-1) to 100 L h(-1) as required by the industry, this primary standard adopts the gravimetric calibration method performing static weighing with flying-start-and-finish, incorporating a compactly designed conical rotating double-wing diverter for the upper flow range (1 L h(-1) to 100 L h(-1)) and a pair of high-speed switching valves as a diverter for the lower flow range (0.02 L h(-1) to 6 L h(-1)). The uncertainty of liquid mass measurement (in which the evaporation error is a dominant factor) as well as the uncertainty of liquid collection time (in which the diverter timing error is a major factor) are minimized to a level that curbs their dominance to the overall mass flow uncertainty. On the other hand, the uncertainty due to the estimation of liquid density at the flowmeter under test, which is a significant contributing factor to the overall uncertainty of volumetric flow, is being reduced by temperature control measures through a self-developed thermostatic chamber. Intra-facility and inter-facility comparisons within NMIJ were performed, all showing a good agreement, and hence providing supporting evidence for the calibration and measurement capability (CMC), which are 0.020%-0.044% for the mass flow rates and 0.044%-0.066% for the volumetric flow rates as claimed by the facility. C1 [Cheong, Kar-Hooi; Doihara, Ryouji; Furuichi, Noriyuki; Terao, Yoshiya; Shimada, Takashi] Natl Metrol Inst Japan NMIJ, Natl Inst Adv Ind Sci & Technol AIST, I-1-1 Umezono, Tsukuba, Ibaraki 3058563, Japan. C3 National Institute of Advanced Industrial Science & Technology (AIST); National Metrology Institute of Japan RP Cheong, KH (corresponding author), Natl Metrol Inst Japan NMIJ, Natl Inst Adv Ind Sci & Technol AIST, I-1-1 Umezono, Tsukuba, Ibaraki 3058563, Japan. EM kh.cheong@aist.go.jp CR [Anonymous], 1989, 9770 ISO [Anonymous], 2011, KEY COMP HYDROCARBON [Anonymous], 1998, 72783 ISO [Anonymous], 1980, 4185 ISO Automotive Quality Management System Standard, 2016, 16949 IATF AUT QUAL Baker RC, 2013, FLOW MEAS INSTRUM, V29, P9, DOI 10.1016/j.flowmeasinst.2012.09.001 Batista E, 2020, FLOW MEAS INSTRUM, V71, DOI 10.1016/j.flowmeasinst.2020.101691 Bissig H, 2015, FLOW MEAS INSTRUM, V44, P34, DOI 10.1016/j.flowmeasinst.2014.11.008 Bissig H, 2015, BIOMED ENG-BIOMED TE, V60, P301, DOI 10.1515/bmt-2014-0145 CCM/WGFF BIPM., 2013, GUID COMP WORK GROUP Cheong KH, 2018, MEAS SCI TECHNOL, V29, DOI 10.1088/1361-6501/aac47d Cheong KH, 2017, FLOW MEAS INSTRUM, V56, P1, DOI 10.1016/j.flowmeasinst.2017.05.006 Chun S, 2014, MEASUREMENT, V51, P367, DOI 10.1016/j.measurement.2013.12.002 Cox MG, 2002, METROLOGIA, V39, P589, DOI 10.1088/0026-1394/39/6/10 Doihara R, 2017, METROLOGIA, V54, P262, DOI 10.1088/1681-7575/aa6399 Doihara R, 2016, FLOW MEAS INSTRUM, V50, P90, DOI 10.1016/j.flowmeasinst.2016.06.014 Furuichi N, 2016, MEASUREMENT, V91, P548, DOI 10.1016/j.measurement.2016.05.088 ISO, 2008, 9832008 ISOIEC Lee KB, 2011, METROLOGIA, V48, DOI 10.1088/0026-1394/48/1A/07003 Lee SH, 2018, FLOW MEAS INSTRUM, V62, P105, DOI 10.1016/j.flowmeasinst.2018.03.009 Paik JS, 2007, METROLOGIA, V44, DOI 10.1088/0026-1394/44/1A/07005 Paton R, 2008, METROLOGIA, V45, DOI 10.1088/0026-1394/45/1A/07019 Shimada T, 2017, METROLOGIA, V54, DOI 10.1088/0026-1394/54/1A/07007 Shimada T, 2016, METROLOGIA, V53, DOI 10.1088/0026-1394/53/1A/07018 Shimada Takashi, 2007, J FLUID SCI TECHNOL, V2, P23 Su C.-M, 2008, P NCSL INT WORKSH S Wright JD., 2019, P FLOMEKO NR 27 TC 0 Z9 0 U1 0 U2 0 PD DEC PY 2021 VL 58 IS 6 AR 065003 DI 10.1088/1681-7575/ac190f WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000708654800001 DA 2022-12-14 ER PT J AU Cabral, AE Ricardo, F Patinha, C da Silva, EF Correia, M Palma, J Planas, M Calado, R AF Cabral, Ana Elisa Ricardo, Fernando Patinha, Carla da Silva, Eduardo Ferreira Correia, Miguel Palma, Jorge Planas, Miquel Calado, Ricardo TI Successful Use of Geochemical Tools to Trace the Geographic Origin of Long-Snouted Seahorse Hippocampus guttulatus Raised in Captivity SO ANIMALS DT Article DE bony plates; elemental fingerprints; ICP-MS; traceability ID STOCK DISCRIMINATION; ICP-MS; FISH; FINGERPRINTS; OTOLITHS; TRADE; CONSERVATION; CHALLENGES; CHEMISTRY; RELEVANCE AB Simple Summary Seahorses (Hippocampus spp.) are currently exposed to a multitude of anthropogenic pressures worldwide. The illegal, unreported, and unregulated (IUU) fisheries and trade of these flagship species undermine the efforts to manage and protect their wild populations. Here we aim to validate a forensic tool to identify the geographic origin of seahorses and contribute to the ongoing fight against the illegal capture and trade of these organisms. The elemental fingerprints of long-snouted seahorse (Hippocampus guttulatus) bony structures, including the subdermal bony plates that cover their body, revealed that they can be successfully employed to confirm their geographic origin. The results of this first study using seahorses raised in captivity indicate that this tool may also allow to discriminate between different populations of wild specimens and enhance the traceability of traded specimens. The global market of dried seahorses mainly supplies Traditional Chinese Medicine and still relies on blurry trade chains that often cover less sustainable practices targeting these pricey and endangered fish. As such, reliable tools that allow the enforcement of traceability, namely to confirm the geographic origin of traded seahorses, are urgently needed. The present study evaluated the use of elemental fingerprints (EF) in the bony structures of long-snouted seahorses Hippocampus guttulatus raised in captivity in two different locations (southern Portugal and Northern Spain) to discriminate their geographic origin. The EF of different body parts of H. guttulatus were also evaluated as potential proxies for the EF of the whole body, in order to allow the analysis of damaged specimens and avoid the use of whole specimens for analysis. The contrasting EF of H. guttulatus raised in the two locations allowed their reliable discrimination. Although no single body part exactly mimicked the EF of the whole body, seahorse trunks, as well as damaged specimens, could still be correctly allocated to their geographic origin. This promising forensic approach to discriminate the geographic origin of seahorses raised in captivity should now be validated for wild conspecifics originating from different locations, as well as for other species within genus Hippocampus. C1 [Cabral, Ana Elisa; Ricardo, Fernando; Calado, Ricardo] Univ Aveiro, Dept Biol, ECOMARE, CESAM Ctr Environm & Marine Studies, Santiago Univ Campus, P-3810193 Aveiro, Portugal. [Patinha, Carla; da Silva, Eduardo Ferreira] Univ Aveiro, GEOBIOTEC Dept Geosci, Santiago Univ Campus, P-3810193 Aveiro, Portugal. [Correia, Miguel; Palma, Jorge] Univ Algarve, CCMAR Ctr Ciencias Mar, Campus Gambelas, P-8005139 Faro, Portugal. [Planas, Miquel] IIM CSIC Inst Invest Marinas IIM, Dept Ecol & Marine Resources, Vigo 36208, Spain. C3 Universidade de Aveiro; Universidade de Aveiro; Universidade do Algarve RP Calado, R (corresponding author), Univ Aveiro, Dept Biol, ECOMARE, CESAM Ctr Environm & Marine Studies, Santiago Univ Campus, P-3810193 Aveiro, Portugal. EM anacabral@ua.pt; fafr@ua.pt; cpatinha@ua.pt; eafsilva@ua.pt; mtcorreia@ualg.pt; jpalma@ualg.pt; mplanas@iim.csic.es; rjcalado@ua.pt CR Anderson M.J., 2008, PERMANOVA PRIMER GUI, P214 Arechavala-Lopez P., 2016, International Aquatic Research, V8, P263, DOI 10.1007/s40071-016-0142-1 Aurelio M, 2013, MAR BIOL, V160, P2663, DOI 10.1007/s00227-013-2259-8 Avigliano E, 2020, FISH RES, V230, DOI 10.1016/j.fishres.2020.105625 Avigliano E, 2019, MAR ECOL PROG SER, V614, P147, DOI 10.3354/meps12895 Aylesworth L, 2018, ICES J MAR SCI, V75, P642, DOI 10.1093/icesjms/fsx193 Bruner E, 2008, INT J MORPHOL, V26, P247, DOI 10.4067/S0717-95022008000200002 Campana SE, 2000, FISH RES, V46, P343, DOI 10.1016/S0165-7836(00)00158-2 Campana Steven E., 2005, P227, DOI 10.1016/B978-012154351-8/50013-7 Clarke LM, 2011, CAN J FISH AQUAT SCI, V68, P105, DOI 10.1139/F10-147 Cohen FPA, 2018, AQUACULTURE, V492, P259, DOI 10.1016/j.aquaculture.2018.04.020 Cohen FPA, 2017, REV FISH SCI AQUAC, V25, P100, DOI 10.1080/23308249.2016.1237469 Cohen FPA, 2013, REV FISH SCI, V21, P98, DOI 10.1080/10641262.2012.760522 Curtis JMR, 2017, J FISH BIOL, V91, P1603, DOI 10.1111/jfb.13473 Faleiro F, 2015, CONSERV PHYSIOL, V3, DOI 10.1093/conphys/cov009 Foster S, 2016, AQUAT CONSERV, V26, P154, DOI 10.1002/aqc.2493 Gillanders BM, 2003, ESTUAR COAST SHELF S, V57, P1049, DOI 10.1016/S0272-7714(03)00009-X Gillanders BM, 2001, FISH B-NOAA, V99, P410 Kerr L.A., 2014, STOCK IDENTIFICATION, P205, DOI DOI 10.1016/B978-0-12-397003-9.00011-4 Kuo TC, 2018, MAR POLICY, V88, P48, DOI 10.1016/j.marpol.2017.10.031 Lall SP, 2007, AQUACULTURE, V267, P3, DOI 10.1016/j.aquaculture.2007.02.053 Leal MC, 2015, TRENDS BIOTECHNOL, V33, P331, DOI 10.1016/j.tibtech.2015.03.003 Longmore C, 2011, MAR ECOL PROG SER, V435, P209, DOI 10.3354/meps09197 Lourie S., 2003, MEASURING SEAHORSES, P15 LOURIE S, 2003, PROJECT SEAHORSE TEC, V3, P1 Luque PL, 2017, J EXP MAR BIOL ECOL, V486, P127, DOI 10.1016/j.jembe.2016.09.016 Neutens C, 2017, ZOOLOGY, V120, P62, DOI 10.1016/j.zool.2016.11.002 Novelli B, 2017, FISH PHYSIOL BIOCHEM, V43, P833, DOI 10.1007/s10695-017-0339-2 Porter MM, 2013, ACTA BIOMATER, V9, P6763, DOI 10.1016/j.actbio.2013.02.045 Praet T, 2012, INT J NUMER METH BIO, V28, P1028, DOI 10.1002/cnm.2499 Ramirez MD, 2019, MAR ECOL PROG SER, V608, P247, DOI 10.3354/meps12796 Raubenheimer EJ, 1998, ARCH ORAL BIOL, V43, P641, DOI 10.1016/S0003-9969(98)00051-X Ricardo F, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107383 Ricardo F, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-03381-w Thorrold SR, 2002, B MAR SCI, V70, P291 Thorrold SR, 2001, SCIENCE, V291, P297, DOI 10.1126/science.291.5502.297 Vincent ACJ, 2011, J FISH BIOL, V78, P1681, DOI 10.1111/j.1095-8649.2011.03003.x Zhang Y.Y., 2017, ANN RES REV BIOL, V14, P1, DOI [10.9734/ARRB/2017/34152, DOI 10.9734/ARRB/2017/34152] NR 38 TC 1 Z9 1 U1 3 U2 10 PD JUN PY 2021 VL 11 IS 6 AR 1534 DI 10.3390/ani11061534 WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology SC Agriculture; Veterinary Sciences; Zoology UT WOS:000665392900001 DA 2022-12-14 ER PT J AU Ruiz-Garcia, L Steinberger, G Rothmund, M AF Ruiz-Garcia, L. Steinberger, G. Rothmund, M. TI A model and prototype implementation for tracking and tracing agricultural batch products along the food chain SO FOOD CONTROL DT Article DE Traceability; Web service; Monitoring logistics; IT-farming; Automated documentation ID SUPPLY-SYSTEM TRACEABILITY; MANAGEMENT; NETWORKS; DESIGN AB There is an increasing demand of traceability in the food chain, statutory requirements are growing stricter and there is increasing pressure to develop standardized traceability systems. Each event in the chain, like production of transportation, packing, distribution or processing results in a different product which can have its own information associated within the tracing system. From the raw material to the sale of goods, more and more information needs to be gathered and made available. Supplementary information may also be collected at any step, in order to provide data for analysis and optimization of production practices. Using web-based systems for data processing, storage and transfer makes possible a flexible way of information access, networking and usability. In this paper an architectural proposal is presented and the proposed solution is tested by the implementation of a prototype. The software architecture presented makes use of a series of standards than offer new possibilities in traceability control and management. For testing the prototype, information from precision farming together with the information recorded during the transport and delivery was used. The system enables full traceability and it complies with all existing traceability standards. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Ruiz-Garcia, L.] Univ Politecn Madrid, Lab Propiedades Fis & Tecnol Avanzadas Agroalimen, E-28040 Madrid, Spain. [Steinberger, G.] Tech Univ Munich, Ctr Life Sci Weihenstephan, Dept Life Sci Engn, D-85354 Freising Weihenstephan, Germany. [Rothmund, M.] OSB AG, D-80469 Munich, Germany. C3 Universidad Politecnica de Madrid; Technical University of Munich RP Ruiz-Garcia, L (corresponding author), Univ Politecn Madrid, Lab Propiedades Fis & Tecnol Avanzadas Agroalimen, Avda Complutense S-N, E-28040 Madrid, Spain. EM luis.ruiz@upm.es CR [Anonymous], 2007, 22005 ISO ARIMA S, 2003, P 2003 IEEE ASME INT AUERNHAMMER H, 2002, AGR ENG INT CIGR J S, V4 AUERNHAMMER H, 2000, AGENG WARW 2000 C P Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bell M., 2008, INTRO SERVICE ORIENT Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Bollen AF, 2007, BIOSYST ENG, V98, P391, DOI 10.1016/j.biosystemseng.2007.07.011 CLAPP S, 2002, BRIEF HIST TRACEABIL *COD AL COMM, 1999, COD AL Coulomb D, 2005, INT J REFRIG, V28, P459, DOI 10.1016/j.ijrefrig.2005.03.008 Curbera F, 2003, COMMUN ACM, V46, P29, DOI 10.1145/944217.944234 Diaz P, 2007, J SYST SOFTWARE, V80, P1375, DOI 10.1016/j.jss.2006.10.042 Dupuy C, 2005, J FOOD ENG, V70, P333, DOI 10.1016/j.jfoodeng.2004.05.074 DUPUY C, 2002, IEEE INT C, V1, P494 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 HSU YC, 2008, P IEEE INT C AUT LOG Jedermann R, 2006, SENSOR ACTUAT A-PHYS, V132, P370, DOI 10.1016/j.sna.2006.02.008 Kim H. M., 1995, Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE '95 (Cat. No.95TB8030), P105, DOI 10.1109/ENABL.1995.484554 LATTEIER A, 2001, ZOPE BOOK LEYMANN F, 2002, IBM SYSTEMS J, V41 *LPFTAG, 2006, WEBS PHYS PROP ADV T Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Ort E., 2005, SERVICE ORIENTED ARC *PREAGR, 2008, COLL RES PROJ PREAGR *REFR RES, 2008, POSTGIS WEBS Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Riden CP, 2007, BIOSYST ENG, V98, P401, DOI 10.1016/j.biosystemseng.2007.07.004 ROTHMUND M, 2004, WEB BASED INFORM MAN, P846 Ruiz-Garcia L, 2008, J FOOD ENG, V87, P405, DOI 10.1016/j.jfoodeng.2007.12.033 Ruiz-Garcia L, 2007, SPAN J AGRIC RES, V5, P142, DOI 10.5424/sjar/2007052-234 Sarig Y., 2003, AGR ENG INT CIGR J S, P5 Serrano S, 2008, COMPUT ELECTRON AGR, V64, P307, DOI 10.1016/j.compag.2008.07.001 Shimomura T, 2005, J VISUAL LANG COMPUT, V16, P213, DOI 10.1016/j.jvlc.2004.08.005 STEINBERGER G, 2006, AGR PROCESS DATA SER, P271 Thomas AG, 2005, TOP CAN WEED SCI, V1, P1 VANDORP CA, 2004, THESIS WAGENINGEN U Wang XB, 2006, PROCEEDINGS OF THE 2006 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING (13TH), VOLS 1-3, P493, DOI 10.1109/SOLI.2006.329074 World Wide Web Consortium (W3C), 2004, WEB SERV ARCH NR 39 TC 74 Z9 89 U1 1 U2 62 PD FEB 10 PY 2010 VL 21 IS 2 BP 112 EP 121 DI 10.1016/j.foodcont.2008.12.003 WC Food Science & Technology SC Food Science & Technology UT WOS:000270768400002 DA 2022-12-14 ER PT J AU Liu, Y Zhang, XF Li, Y Wang, HX AF Liu, Yu Zhang, Xufeng Li, Ying Wang, Haixia TI The application of compound-specific isotope analysis of fatty acids for traceability of sea cucumber (Apostichopus japonicus) in the coastal areas of China SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE CSIA; GC-C-IRMS; seadood; food quality; geographical origin; authentication ID GAS-CHROMATOGRAPHY; MASS-SPECTROMETRY; STABLE-ISOTOPES; CARBON; MULTIELEMENT; RATIO; AUTHENTICATION; DISCRIMINATION; FRACTIONATION; SUBSTANCES AB BACKGROUNDGeographical origin traceability is an important issue for controlling the quality of seafood and safeguarding the interest of consumers. In the present study, a new method of compound-specific isotope analysis (CSIA) of fatty acids was established to evaluate its applicability in establishing the origin traceability of Apostichopus japonicus in the coastal areas of China. Moreover, principal component analysis (PCA) and discriminant analysis (DA) were applied to distinguish between the origins of A. japonicus. RESULTSThe results show that the stable carbon isotope compositions of fatty acids of A. japonicus significantly differ in terms of both season and origin. They also indicate that the stable carbon isotope composition of fatty acids could effectively discriminate between the origins of A. japonicus, except for between Changhai Island and Zhangzi Island in the spring of 2016 because of geographical proximity or the similarity of food sources. The fatty acids that have the highest contribution to identifying the geographical origins of A. japonicus are C22:6n-3, C16:1n-7, C20:5n-3, C18:0 and C23:1n-9, when considering the fatty acid contents, the stable carbon isotope composition of fatty acids and the results of the PCA and DA. CONCLUSIONSWe conclude that CSIA of fatty acids, combined with multivariate statistical analysis such as PCA and DA, may be an effective tool for establishing the traceability of A. japonicus in the coastal areas of China. The relevant conclusions of the present study provide a new method for determining the traceability of seafood or other food products. (c) 2017 Society of Chemical Industry C1 [Liu, Yu; Zhang, Xufeng] Dalian Maritime Univ, Coll Environm Sci & Engn, Dalian, Peoples R China. [Li, Ying; Wang, Haixia] Dalian Maritime Univ, Nav Coll, Dalian, Peoples R China. C3 Dalian Maritime University; Dalian Maritime University RP Zhang, XF (corresponding author), Dalian Maritime Univ, Coll Environm Sci & Engn, Dalian, Peoples R China. EM zjzhangxufeng@163.com CR Abrajano TA, 1999, ORG GEOCHEM, V30, pV Alfaro AC, 2008, ESTUAR COAST SHELF S, V79, P718, DOI 10.1016/j.ecss.2008.06.016 Barrie A., 1984, SPECTROSC INT J, V3, P259 Boschker HTS, 2002, FEMS MICROBIOL ECOL, V40, P85, DOI 10.1111/j.1574-6941.2002.tb00940.x Canuel EA, 1997, LIMNOL OCEANOGR, V42, P1570, DOI 10.4319/lo.1997.42.7.1570 Chaguri MP, 2017, J FOOD PROCESS PRES, V41, DOI 10.1111/jfpp.13312 DENIRO MJ, 1977, SCIENCE, V197, P261, DOI 10.1126/science.327543 EVERSHED RP, 1994, ANALYST, V119, P909, DOI 10.1039/an9941900909 FARQUHAR GD, 1989, ANNU REV PLANT PHYS, V40, P503, DOI 10.1146/annurev.pp.40.060189.002443 FOLCH J, 1957, J BIOL CHEM, V226, P497 Fujibayashi M, 2016, OECOLOGIA, V180, P589, DOI 10.1007/s00442-015-3486-0 Gruber N, 1999, GLOBAL BIOGEOCHEM CY, V13, P307, DOI 10.1029/1999GB900019 Guschina IA, 2009, LIPIDS IN AQUATIC ECOSYSTEMS, P1, DOI 10.1007/978-0-387-89366-2_1 Iverson SJ, 2009, LIPIDS IN AQUATIC ECOSYSTEMS, P281, DOI 10.1007/978-0-387-89366-2_12 Kattner G, 2009, LIPIDS IN AQUATIC ECOSYSTEMS, P257, DOI 10.1007/978-0-387-89366-2_11 Kiljunen M, 2006, J APPL ECOL, V43, P1213, DOI 10.1111/j.1365-2664.2006.01224.x Larsen T, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0073441 Larsen T, 2009, ECOLOGY, V90, P3526, DOI 10.1890/08-1695.1 Laursen KH, 2014, TRAC-TREND ANAL CHEM, V59, P73, DOI 10.1016/j.trac.2014.04.008 Li L, 2016, FOOD CHEM, V194, P1238, DOI 10.1016/j.foodchem.2015.08.123 Lichtfouse E, 2000, RAPID COMMUN MASS SP, V14, P1337, DOI 10.1002/1097-0231(20000815)14:15<1337::AID-RCM9>3.0.CO;2-B Lichtfouse E, 1997, NATURWISSENSCHAFTEN, V84, P23, DOI 10.1007/s001140050342 McMahon KW, 2015, LIMNOL OCEANOGR, V60, P1076, DOI 10.1002/lno.10081 MOSANDL A, 1995, FOOD REV INT, V11, P597, DOI 10.1080/87559129509541063 MURPHY DE, 1994, ESTUAR COAST SHELF S, V39, P261, DOI 10.1006/ecss.1994.1063 Ortea I, 2015, FOOD CHEM, V170, P145, DOI 10.1016/j.foodchem.2014.08.049 Oxtoby LE, 2016, POLAR BIOL, V39, P473, DOI 10.1007/s00300-015-1800-2 Parrish CC, 2000, HANDB ENVIRON CHEM, V5, P193 Pond DW, 1997, MAR ECOL PROG SER, V157, P221, DOI 10.3354/meps157221 Ravet JL, 2010, ECOLOGY, V91, P180, DOI 10.1890/08-2037.1 Rennie MJ, 1996, BIOCHEM SOC T, V24, P927, DOI 10.1042/bst0240927 Ricardo F., 2015, SCI REP-UK, V5, P1 Ricardo F, 2015, SCI REP-UK, V5, DOI 10.1038/srep11125 RIELEY G, 1991, NATURE, V352, P425, DOI 10.1038/352425a0 SCHOELL M, 1994, ORG GEOCHEM, V21, pR5, DOI 10.1016/0146-6380(94)90001-9 Snyder RJ, 2003, FISH PHYSIOL BIOCHEM, V29, P117, DOI 10.1023/B:FISH.0000035920.60817.11 Tanaka H, 2010, FISH RES, V102, P217, DOI 10.1016/j.fishres.2009.11.002 Thomas F, 2008, J AGR FOOD CHEM, V56, P989, DOI 10.1021/jf072370d Yanagisawa T, 1998, TROPICAL MARICULTURE, P291, DOI 10.1016/B978-012210845-7/50009-X Zhang XF, 2017, FOOD CHEM, V218, P269, DOI 10.1016/j.foodchem.2016.08.083 NR 40 TC 19 Z9 24 U1 3 U2 56 PD NOV PY 2017 VL 97 IS 14 BP 4912 EP 4921 DI 10.1002/jsfa.8367 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000413156000032 DA 2022-12-14 ER PT J AU Pacifico, D Casciani, L Ritota, M Mandolino, G Onofri, C Moschella, A Parisi, B Cafiero, C Valentin, M AF Pacifico, Daniela Casciani, Lorena Ritota, Mena Mandolino, Giuseppe Onofri, Chiara Moschella, Anna Parisi, Bruno Cafiero, Caterina Valentin, Massimiliano TI NMR-Based Metabolomics for Organic Farming Traceability of Early Potatoes SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE Solanum tuberosum L.; HRMAS-NMR; PLS-DA; CHN ID SPECTROSCOPY; METABOLITES; VEGETABLES; CHEMISTRY; QUALITY; FOODS; L. AB H-1 HRMAS-NMR spectroscopy was successfully used to determine the metabolic profiles of 78 tubers obtained from three early genotypes grown under organic and conventional management. The variation in total hydrogen, carbon, and nitrogen contents was also assessed. A PLS-DA multivariate statistical analysis provided good discrimination among the varieties and cropping systems (100% unknown samples placed in a cross-validation blind test), suggesting that this method is a powerful and rapid tool for tracing organic potatoes. As a result of the farming system, the nitrogen content decreased by 11-14% in organic tubers, whereas GABA and lysine accumulated in the organic tubers of all clones. Clear variations in primary metabolites are discussed to provide a better understanding of the metabolic pathway modifications resulting from agronomical practices. C1 [Pacifico, Daniela; Mandolino, Giuseppe; Onofri, Chiara; Moschella, Anna; Parisi, Bruno] Res Ctr Ind Crops CRA CIN, Agr Res Council, Consiglio Ric & Sperimentaz Agr, I-40128 Bologna, Italy. [Casciani, Lorena; Ritota, Mena; Cafiero, Caterina] Res Ctr Soil Plant Syst CRA RPS, Agr Res Council, Consiglio Ric & Sperimentaz Agr, Instrumental Ctr Tor Mancina, I-00016 Rome, Italy. [Valentin, Massimiliano] Res Ctr Food & Nutr CRA NUT, Agr Res Council, Consiglio Ric & Sperimentaz Agr, I-00178 Rome, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Pacifico, D (corresponding author), Res Ctr Ind Crops CRA CIN, Agr Res Council, Consiglio Ric & Sperimentaz Agr, Via Corticella 133, I-40128 Bologna, Italy. EM daniela.pacifico@entecra.it CR Araujo WL, 2011, PHYTOCHEMISTRY, V72, P838, DOI 10.1016/j.phytochem.2011.02.028 Bogdanov S, 2002, APIDOLOGIE, V33, P399, DOI 10.1051/apido:2002029 Burgos G, 2009, J FOOD COMPOS ANAL, V22, P503, DOI 10.1016/j.jfca.2008.08.008 Centeno DC, 2011, PLANT CELL, V23, P162, DOI 10.1105/tpc.109.072231 Cheng HN, 2012, POLYM REV, V52, P81, DOI 10.1080/15583724.2012.668154 Clausen MR, 2012, J AGR FOOD CHEM, V60, P9495, DOI 10.1021/jf302067m Defernez M, 2004, J AGR FOOD CHEM, V52, P6075, DOI 10.1021/jf049522e Defernez M, 2003, PHYTOCHEMISTRY, V62, P1009, DOI 10.1016/S0031-9422(02)00704-5 Eppendorfer WH, 1996, J SCI FOOD AGR, V71, P449, DOI 10.1002/(SICI)1097-0010(199608)71:4<449::AID-JSFA601>3.0.CO;2-N Fait A, 2008, TRENDS PLANT SCI, V13, P14, DOI 10.1016/j.tplants.2007.10.005 Herencia JF, 2011, SCI HORTIC-AMSTERDAM, V129, P882, DOI 10.1016/j.scienta.2011.04.008 Friedman M, 1997, J AGR FOOD CHEM, V45, P1523, DOI 10.1021/jf960900s Gennaro L, 2003, ACTA HORTIC, P675, DOI 10.17660/ActaHortic.2003.614.100 Hajslova J, 2005, FOOD ADDIT CONTAM A, V22, P514, DOI 10.1080/02652030500137827 Hoefkens C, 2009, BRIT FOOD J, V111, P1078, DOI 10.1108/00070700910992934 Hoefkens C, 2009, BRIT FOOD J, V111, P1062, DOI 10.1108/00070700910992916 Kim HS, 2009, BIOTECHNOL BIOPROC E, V14, P738, DOI 10.1007/s12257-009-0168-y King RR, 2012, MAGN RESON CHEM, V50, P627, DOI 10.1002/mrc.3844 Kumpulainen J., 2001, P 2001 DAHL GREID S Lawson LD, 1997, J AGR FOOD CHEM, V45, P542, DOI 10.1021/jf950806w Le Gall G, 2001, J AGR FOOD CHEM, V49, P580, DOI 10.1021/jf001046e Lineback DR, 2012, ANNU REV FOOD SCI T, V3, P15, DOI 10.1146/annurev-food-022811-101114 Lisinska G, 1989, POTATO SCI TECHNOLOG Lombardo S., 2012, RENEW AGR FOOD SYST, P1 Lopez-Rituerto E, 2012, J AGR FOOD CHEM, V60, P3452, DOI 10.1021/jf204361d Maggio A, 2008, EUR J AGRON, V28, P343, DOI 10.1016/j.eja.2007.10.003 Manetti C, 2004, PHYTOCHEMISTRY, V65, P3187, DOI 10.1016/j.phytochem.2004.10.015 Oksman-Caldentey KM, 2004, P NATL ACAD SCI USA, V101, P9949, DOI 10.1073/pnas.0403636101 Perez-Enciso M, 2003, HUM GENET, V112, P581, DOI 10.1007/s00439-003-0921-9 Ritota M, 2012, MEAT SCI, V92, P754, DOI 10.1016/j.meatsci.2012.06.034 Ritota M, 2010, J AGR FOOD CHEM, V58, P9675, DOI 10.1021/jf1015957 Rodriguez B, 2005, MAGN RESON CHEM, V43, P82, DOI 10.1002/mrc.1500 Sidhu OP, 2010, PLANTA, V232, P85, DOI 10.1007/s00425-010-1159-0 Stepansky A, 2006, AMINO ACIDS, V30, P121, DOI 10.1007/s00726-005-0246-1 Szymanska E, 2012, METABOLOMICS, V8, pS3, DOI 10.1007/s11306-011-0330-3 Tanokura M., 2012, J AGR FOOD CHEM, V60, P10118 Valentini M, 2011, MAGN RESON CHEM, V49, pS121, DOI 10.1002/mrc.2826 Vermathen M, 2011, J AGR FOOD CHEM, V59, P12784, DOI 10.1021/jf203733u Wichrowska D, 2009, POL J ENVIRON STUD, V18, P487 Yiridoe EK, 2005, RENEW AGR FOOD SYST, V20, P193, DOI 10.1079/RAF2005113 NR 40 TC 19 Z9 19 U1 1 U2 43 PD NOV 20 PY 2013 VL 61 IS 46 BP 11201 EP 11211 DI 10.1021/jf402961m WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000327103400044 DA 2022-12-14 ER PT J AU Li, L Li, ZM Wang, YZ AF Li, Lian Li, Zhi Min Wang, Yuan Zhong TI A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves SO PLANT METHODS DT Article DE Eucommia ulmoides leaf; Two-dimensional correlation spectroscopy; Residual neural network; Drying methods; Geographical traceability ID GEOGRAPHICAL TRACEABILITY; MULTIVARIATE METHODS; OLIV.; DISCRIMINATION; IDENTIFICATION; PHYTOCHEMISTRY AB Background Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy. Results (1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions. Conclusions This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus. C1 [Li, Lian; Li, Zhi Min; Wang, Yuan Zhong] Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. [Li, Lian] Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University of Chinese Medicine RP Li, ZM; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. EM 393891330@qq.com; boletus@126.com CR Chen JB, 2018, J MOL STRUCT, V1163, P327, DOI 10.1016/j.molstruc.2018.02.061 Committee for the Pharmacopoeia of PR China, 2015, LAW PEOPL REP CHIN A, P133 Cortes V, 2017, POSTHARVEST BIOL TEC, V133, P113, DOI 10.1016/j.postharvbio.2017.07.015 Ding YG, 2021, SPECTROCHIM ACTA A, V261, DOI 10.1016/j.saa.2021.120070 Dong JE, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108132 Dong JE, 2011, IND CROP PROD, V34, P1607, DOI 10.1016/j.indcrop.2011.06.007 Greg Corrado, 2013, Arxiv, DOI arXiv:1301.3781 He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90 He XR, 2014, J ETHNOPHARMACOL, V151, P78, DOI 10.1016/j.jep.2013.11.023 HU SY, 1979, AM J CHINESE MED, V7, P5, DOI 10.1142/S0192415X79000039 LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539 Li Y, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-31264-1 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Liu ZM, 2021, MICROCHEM J, V169, DOI 10.1016/j.microc.2021.106545 Noda I, 2007, ANAL SCI, V23, P139, DOI 10.2116/analsci.23.139 Pei YF, 2019, ANAL METHODS-UK, V11, P113, DOI 10.1039/c8ay02363h Rambla FJ, 1997, ANAL CHIM ACTA, V344, P41, DOI 10.1016/S0003-2670(97)00032-9 Rossi GB, 2020, LWT-FOOD SCI TECHNOL, V126, DOI 10.1016/j.lwt.2020.109290 Su WH, 2016, TALANTA, V155, P347, DOI 10.1016/j.talanta.2016.04.041 Walkowiak A, 2019, SPECTROCHIM ACTA A, V208, P222, DOI 10.1016/j.saa.2018.10.008 Wang CY, 2021, IND CROP PROD, V160, DOI 10.1016/j.indcrop.2020.113090 Wang CY, 2020, FRONT PLANT SCI, V11, DOI 10.3389/fpls.2020.00079 Wang CY, 2019, AM J CHINESE MED, V47, P259, DOI 10.1142/S0192415X19500137 Wang L, 2021, MICROCHEM J, V170, DOI 10.1016/j.microc.2021.106670 Xu ZS, 2010, J AGR FOOD CHEM, V58, P7289, DOI 10.1021/jf100304t Yang RJ, 2020, J MOL STRUCT, V1214, DOI 10.1016/j.molstruc.2020.128219 Yang RJ, 2015, ANAL METHODS-UK, V7, P4302, DOI [10.1039/c5ay00134j, 10.1039/C5AY00134J] Yang RJ, 2014, ANAL METHODS-UK, V6, P3436, DOI 10.1039/c4ay00442f Yen GC, 2000, J AGR FOOD CHEM, V48, P3431, DOI 10.1021/jf000150t Yue JQ, 2021, FRONT PLANT SCI, V12, DOI 10.3389/fpls.2021.752863 Zhu MQ, 2016, IND CROP PROD, V83, P124, DOI 10.1016/j.indcrop.2015.12.049 NR 31 TC 1 Z9 1 U1 3 U2 3 PD AUG 13 PY 2022 VL 18 IS 1 AR 102 DI 10.1186/s13007-022-00935-6 WC Biochemical Research Methods; Plant Sciences SC Biochemistry & Molecular Biology; Plant Sciences UT WOS:000840321600002 DA 2022-12-14 ER PT J AU Ou, GZ Hu, R Zhang, LX Li, PW Luo, XJ Zhang, ZW AF Ou, Gaozhi Hu, Rui Zhang, Liangxiao Li, Peiwu Luo, Xinjian Zhang, Zhaowei TI Advanced detection methods for traceability of origin and authenticity of olive oils SO ANALYTICAL METHODS DT Review ID REAL-TIME PCR; VEGETABLE-OILS; INFRARED-SPECTROSCOPY; RAMAN-SPECTROSCOPY; FTIR SPECTROSCOPY; ELECTRONIC NOSE; ADULTERATION; TOOL; CLASSIFICATION; CHEMOMETRICS AB The adulteration of olive oils based on advanced sensors has attracted high interest owing to its health benefits in the prevention and treatment of certain pathologies. Concerning its health and commercial aspects, lower grade oil blending and other illegal additives in virgin olive oil can negatively affect the nutritive value of olive oil. This review focuses on the advances in the sensing and identification of adulteration of olive oil. Optical sensing, chromatography (usually coupled with mass spectrometry), and nuclear magnetic resonance are discussed in detail. Other methods including a DNA-based method, dielectric spectroscopy, differential scanning calorimetry, thermogravimetric analysis and electronic nose among others, are overviewed as well. C1 [Ou, Gaozhi; Luo, Xinjian] China Univ Geosci, Dept Sports, Wuhan 430074, Peoples R China. [Zhang, Liangxiao; Li, Peiwu; Zhang, Zhaowei] Chinese Acad Agr Sci, Oil Crops Res Inst, Key Lab Biol & Genet Improvement Oil Crops,Minist, Key Lab Detect Mycotoxins,Lab Risk Assessment Oil, Wuhan 430062, Peoples R China. [Hu, Rui] Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China. C3 China University of Geosciences; Chinese Academy of Agricultural Sciences; Oil Crops Research Institute, CAAS; Chinese Academy of Sciences; Institute of Hydrobiology, CAS RP Luo, XJ (corresponding author), China Univ Geosci, Dept Sports, 388 Lumo Rd, Wuhan 430074, Peoples R China. EM ougz@foxmail.com; hurui@ihb.ac.cn; liangxiao_zhang@hotmail.com; peiwuli@oilcrops.cn; xinjianluo@foxmail.com; zwzhang@whu.edu.cn CR Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Alves JO, 2014, ANAL METHODS-UK, V6, P7502, DOI [10.1039/c4ay00967c, 10.1039/C4AY00967C] Arvanitoyannis IS, 2007, CRIT REV FOOD SCI, V47, P441, DOI 10.1080/10408390600846325 Azadmard-Damirchi S, 2010, FOOD ADDIT CONTAM A, V27, P1, DOI 10.1080/02652030903225773 Ben-Ayed R, 2013, COMPR REV FOOD SCI F, V12, P218, DOI 10.1111/1541-4337.12003 Bevilacqua M, 2012, ANAL CHIM ACTA, V717, P39, DOI 10.1016/j.aca.2011.12.035 Calvano CD, 2012, FOOD CHEM, V134, P1192, DOI 10.1016/j.foodchem.2012.02.154 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Casale M, 2014, J NEAR INFRARED SPEC, V22, P59, DOI 10.1255/jnirs.1106 Cataldo A, 2012, J FOOD ENG, V112, P338, DOI 10.1016/j.jfoodeng.2012.04.012 Cataldo A, 2010, MEASUREMENT, V43, P1031, DOI 10.1016/j.measurement.2010.02.008 Cataldo A, 2009, IEEE SENS J, V9, P1226, DOI 10.1109/JSEN.2009.2029454 Chen HL, 2011, FOOD CHEM, V125, P1423, DOI 10.1016/j.foodchem.2010.10.026 Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Dias LG, 2014, FOOD CHEM, V160, P321, DOI 10.1016/j.foodchem.2014.03.072 Dong W, 2012, ANAL METHODS-UK, V4, P2772, DOI 10.1039/c2ay25431j Ellis DI, 2012, CHEM SOC REV, V41, P5706, DOI 10.1039/c2cs35138b Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Graham SF, 2012, FOOD CHEM, V132, P1614, DOI 10.1016/j.foodchem.2011.11.136 Jafari M, 2009, J AM OIL CHEM SOC, V86, P103, DOI 10.1007/s11746-008-1333-8 Karoui R, 2010, CHEM REV, V110, P6144, DOI 10.1021/cr100090k Kim M, 2012, ANAL CHIM ACTA, V748, P58, DOI 10.1016/j.aca.2012.08.028 Kumar S, 2011, FOOD CHEM, V127, P1335, DOI 10.1016/j.foodchem.2011.01.094 Maggio RM, 2010, FOOD CONTROL, V21, P890, DOI 10.1016/j.foodcont.2009.12.006 Man YBC, 2002, PHYTOCHEM ANALYSIS, V13, P142, DOI 10.1002/pca.634 Martin YG, 1999, ANAL CHIM ACTA, V384, P83 Mignani AG, 2011, ANAL BIOANAL CHEM, V399, P1315, DOI 10.1007/s00216-010-4408-y Mildner-Szkudlarz S, 2010, J FOOD QUALITY, V33, P21, DOI 10.1111/j.1745-4557.2009.00286.x Moros J, 2010, TRAC-TREND ANAL CHEM, V29, P578, DOI 10.1016/j.trac.2009.12.012 Mu TT, 2014, ANAL METHODS-UK, V6, P940, DOI 10.1039/c3ay41552j Nunes CA, 2014, FOOD RES INT, V60, P255, DOI 10.1016/j.foodres.2013.08.041 Ogrinc N, 2003, ANAL BIOANAL CHEM, V376, P424, DOI 10.1007/s00216-003-1804-6 Owen RW, 2004, EUR J CANCER PREV, V13, P319, DOI 10.1097/01.cej.0000130221.19480.7e Poulli KI, 2007, FOOD CHEM, V105, P369, DOI 10.1016/j.foodchem.2006.12.021 Raieta K, 2015, FOOD CHEM, V172, P596, DOI 10.1016/j.foodchem.2014.09.101 Rohman A, 2010, FOOD RES INT, V43, P886, DOI 10.1016/j.foodres.2009.12.006 Rohman A, 2014, J AM OIL CHEM SOC, V91, P207, DOI 10.1007/s11746-013-2370-5 Rohman A, 2012, APPL SPECTROSC REV, V47, P1, DOI 10.1080/05704928.2011.619020 Smejkalova D, 2010, FOOD CHEM, V118, P153, DOI 10.1016/j.foodchem.2009.04.088 Torrecilla JS, 2011, ANAL CHIM ACTA, V688, P140, DOI 10.1016/j.aca.2011.01.009 Torrecilla JS, 2010, TALANTA, V83, P404, DOI 10.1016/j.talanta.2010.09.048 Venkatesh MS, 2004, BIOSYST ENG, V88, P1, DOI 10.1016/j.biosystemseng.2004.01.007 Wu YJ, 2008, EUR FOOD RES TECHNOL, V227, P1117, DOI 10.1007/s00217-008-0827-9 Wu YJ, 2011, EUR FOOD RES TECHNOL, V233, P313, DOI 10.1007/s00217-011-1520-y Zhang HL, 2012, FOOD CONTROL, V27, P322, DOI 10.1016/j.foodcont.2012.03.027 NR 46 TC 21 Z9 21 U1 4 U2 122 PY 2015 VL 7 IS 14 BP 5731 EP 5739 DI 10.1039/c5ay00048c WC Chemistry, Analytical; Food Science & Technology; Spectroscopy SC Chemistry; Food Science & Technology; Spectroscopy UT WOS:000357825700001 DA 2022-12-14 ER PT J AU Costa, C Antonucci, F Pallottino, F Aguzzi, J Sarria, D Menesatti, P AF Costa, Corrado Antonucci, Francesca Pallottino, Federico Aguzzi, Jacopo Sarria, David Menesatti, Paolo TI A Review on Agri-food Supply Chain Traceability by Means of RFID Technology SO FOOD AND BIOPROCESS TECHNOLOGY DT Review DE Radio Frequency; Infotracking; Supply chain; Food control; Logistic; Wireless sensing ID REFRIGERATED SEA CONTAINER; BAKERY PRODUCTS; SYSTEM; AGRICULTURE; READABILITY; INDUSTRY; FIELD; FISH; TAG AB Radio Frequency Identification (RFID) is a technology which provides appealing opportunities to improve the management of information flow within the supply chain and security in the agri-food sector. Nowadays, food safety is considered a major requirement in several countries, in particular, the traceability of food products which is mandatory by law. Thus, technological implementation leading to traceability strengthening in the agri-food sector is crucial. The first aim of this review is to analyze the current developments in RFID technology in the agri-food sector, through an operative framework which organizes the literature and facilitate a quick content analysis identifying future research direction. RFID technology seems to be able to bring great opportunities to this sector; nevertheless, several constraints are slowing its adoption. This survey may provide readers with an exhaustive overview of opportunities and constraints for the wide adoption of RFID. The second aim of this review is to provide an updated analysis on the current developments of RFID technology for different product typologies within the agri-food industry, discussing at the same time its potential in technological and logistical development regarding different sectors of the production/distribution chain. As referenced here, RFID implementations in the agri-food sector are increasing at a fast rate, and technological advancement follows the applicability opportunities. However, real applications of RFID technologies are still limited because of various technical and economical obstacles which are also discussed. C1 [Costa, Corrado; Antonucci, Francesca; Pallottino, Federico; Menesatti, Paolo] Agr Res Council, CRA, ING, Agr Engn Res Unit, I-00015 Rome, Italy. [Aguzzi, Jacopo] CSIC, ICM, E-08003 Barcelona, Spain. [Sarria, David] Tech Univ Catalonia UPC, Dept Elect, Technol Dev Ctr Remote Acquisit & Data Proc Syst, Vilanova I La Geltru Bar 08800, Spain. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); ING Group; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Centro Mediterraneo de Investigaciones Marinas y Ambientales (CMIMA); CSIC - Instituto de Ciencias del Mar (ICM); Universitat Politecnica de Catalunya RP Costa, C (corresponding author), Agr Res Council, CRA, ING, Agr Engn Res Unit, Via Pascolare 16, I-00015 Rome, Italy. EM corrado.costa@entecra.it CR AAVV, 2006, CON TAG RFID Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Aguzzi J, 2011, SENSORS-BASEL, V11, P9532, DOI 10.3390/s111009532 Amador Cecilia, 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P26, DOI 10.1007/s11694-009-9072-6 Ampatzidis YG, 2009, COMPUT ELECTRON AGR, V66, P166, DOI 10.1016/j.compag.2009.01.008 Ampatzidis Y, 2009, PRECIS AGRIC, V10, P63, DOI 10.1007/s11119-008-9095-8 Anastasi G., 2009, 42 HAW INT C SYST SC [Anonymous], 2002, OFFICIAL J EUROPEA L, V31, P1 [Anonymous], 2005, 270012005 ISOIEC, P1 Antonucci F., 2009, RFID FRESH CUT PIU E, P58 Aung M. M., 2010, 15 INT S LOG ISL 201, P66 Bagchi U, 2007, SPRINGER SER ADV MAN, P71, DOI 10.1007/978-1-84628-607-0_4 Bandinelli R., 2009, Advances in Horticultural Science, V23, P75 Bernardi P, 2008, ECCSC 08: 4TH EUROPEAN CONFERENCE ON CIRCUITS AND SYSTEMS FOR COMMUNICATIONS, P227, DOI 10.1109/ECCSC.2008.4611682 Bhattacharyya Rahul, 2010, 2010 IEEE International Conference on RFID (IEEE RFID 2010), P126, DOI 10.1109/RFID.2010.5467235 Biedeman D., 2006, J COMMERCE, V7, P36 Bono G, 2010, AFR J BIOTECHNOL, V9, P2811 Cai W.-G, 2011, RES IMPLEMENTATION S Collins J., 2004, RFID J Costa C., 2011, INSTRUMENTATION VIEW, V11, P48 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P2190, DOI 10.1007/s11947-011-0773-6 Costa C, 2011, FOOD BIOPROCESS TECH, V4, P673, DOI 10.1007/s11947-011-0556-0 Deale C, 2008, J CULIN SCI TECHNOL, V6, P5, DOI 10.1080/15428050701884121 Dobkin DM, 2005, IEEE MTT-S, P135, DOI 10.1109/MWSYM.2005.1516541 Gandino Filippo, 2009, International Journal of Advanced Pervasive and Ubiquitous Computing, V1, P49, DOI 10.4018/japuc.2009040104 Grabacki S. T., 2007, INT SMOK SEAF C P AL, P101 Hertog MLATM, 2008, ACTA HORTIC, P407, DOI 10.17660/ActaHortic.2008.768.53 HSU YC, 2008, P IEEE INT C AUT LOG Hsu YC, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P81, DOI 10.1109/ICAL.2008.4636124 Jain P. C., 2010, P ASCNT 2010, P1 Jedermann R, 2008, DYNAMICS IN LOGISTICS, P231, DOI 10.1007/978-3-540-76862-3_22 Jedermann R, 2006, SENSOR ACTUAT A-PHYS, V132, P370, DOI 10.1016/j.sna.2006.02.008 Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Karlsen KM, 2011, FOOD CONTROL, V22, P1339, DOI 10.1016/j.foodcont.2011.02.010 Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kerry JP, 2006, MEAT SCI, V74, P113, DOI 10.1016/j.meatsci.2006.04.024 Ketzenberg M. E., 2009, ERIM REPORT SERIES Kotsianis IS, 2002, TRENDS FOOD SCI TECH, V13, P319, DOI 10.1016/S0924-2244(02)00162-0 Kumar P, 2009, J FOOD SCI, V74, pR101, DOI 10.1111/j.1750-3841.2009.01323.x Laniel M, 2011, TRANSPORT RES C-EMER, V19, P1071, DOI 10.1016/j.trc.2011.06.008 Laniel M, 2010, INNOV FOOD SCI EMERG, V11, P703, DOI 10.1016/j.ifset.2010.06.005 Liu L, 2010, SENSOR LETT, V8, P47, DOI 10.1166/sl.2010.1198 Luo QY, 2011, IFIP ADV INF COMM TE, V346, P710 Luvisi A, 2010, SCI HORTIC-AMSTERDAM, V124, P349, DOI 10.1016/j.scienta.2010.01.015 Luvisi A, 2010, HORTTECHNOLOGY, V20, P1037, DOI 10.21273/HORTSCI.20.6.1037 Luvisi A, 2010, COMPUT ELECTRON AGR, V70, P256, DOI 10.1016/j.compag.2009.08.007 Mc Carthy U., 2009, AGR ENG INT CIGR EJO, VXI Menesatti P, 2012, PACKAG TECHNOL SCI, V25, P203, DOI 10.1002/pts.974 Meyer GG, 2009, COMPUT IND, V60, P137, DOI 10.1016/j.compind.2008.12.005 Michael K, 2005, ICMB 2005: International Conference on Mobile Business, P623, DOI 10.1109/ICMB.2005.103 Microchip Technology Inc, 2004, DS21299E MICR TECHN Milczarek RR, 2011, J FOOD PROCESS PRES, V35, P631, DOI 10.1111/j.1745-4549.2011.00512.x Myo Min Aung, 2011, International Journal of Manufacturing Research, V6, P91, DOI 10.1504/IJMR.2011.040005 Nambiar Arun N., 2010, Proceedings of 2010 International Symposium on Information Technology (ITSim 2010), P874, DOI 10.1109/ITSIM.2010.5561567 Nambiar A. N., 2009, P WORLD C ENG COMP S, V2, P1 Ngai EWT, 2008, INT J PROD ECON, V112, P630, DOI 10.1016/j.ijpe.2007.05.011 Ngai EWT, 2008, INT J PROD ECON, V112, P510, DOI 10.1016/j.ijpe.2007.05.004 OnTrace, 2007, TRAC BACKGR Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 PEREIRA DP, 2008, 11 IEEE INT C COMP S Perez-Aloe R., 2007, APPL RFID TAGS OVERA, P1, DOI [DOI 10.1109/RFIDEURAS1A.2007.4368136, 10.1109/RFIDEURASIA.2007.4368136] Qiang Kong, 2009, 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC 2009), P709, DOI 10.1109/ICICIC.2009.326 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Rizzi A., 2011, LOGISTICA TECNOLOGIA, P69 Roberts CM, 2006, COMPUT SECUR, V25, P18, DOI 10.1016/j.cose.2005.12.003 Ruiz-Garcia L, 2007, SPAN J AGRIC RES, V5, P142, DOI 10.5424/sjar/2007052-234 Ruiz-Garcia L., 2010, SUSTAINABLE RADIO FR, P356 Ruiz-Garcia L., 2008, THESIS U POLITECNICA Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Ruiz-Garcia L, 2009, SENSORS-BASEL, V9, P4728, DOI 10.3390/s90604728 Sarac A, 2010, INT J PROD ECON, V128, P77, DOI 10.1016/j.ijpe.2010.07.039 Sarria D., 2009, P OC 09 IEEE BREM, P1, DOI [10.1109/OCEANSE.2009.5278280, DOI 10.1109/OCEANSE.2009.5278280] Sarria D., 2009, INSTRUMENTATION VIEW, P7 Schroder U, 2008, J VERBRAUCH LEBENSM, V3, P45, DOI 10.1007/s00003-007-0302-8 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Shi Y. -D., 2009, RES J APPL SCI, V4, P57 Shougang R., 2010, INT C COMP APPL SYST, pV8 Singer T., 2006, IND MAINTENANCE PLAN, V67, P18 Singh J., 2007, J APPL PACKAGING RES, V2, P45 Sioen I., 2007, Open Food Science Journal, V1, P33, DOI 10.2174/1874256400701010033 Smith JP, 2004, CRIT REV FOOD SCI, V44, P19, DOI 10.1080/10408690490263774 Swedberg C., 2006, RFID J 1030 Takai K, 2010, LECT NOTES ARTIF INT, V6278, P254, DOI 10.1007/978-3-642-15393-8_29 Tang X.-D., 2009, APPL RADIO FREQUENCY Texas Instruments, 2006, WEDG TRANSP RI TRP R Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x Tomes J., 2009, Agricultura Tropica et Subtropica, V42, P98 Trautman D., 2008, 0802 U ALB DEP RUR E, P149 Trebar M., 2011, P 19 INT C SOFTW TEL, P1 Uldrich J., 2007, DOLE LET THANK RFID Varese E., 2008, Journal of Commodity Science, Technology and Quality, V47, P171 Vergara A, 2007, SENSOR ACTUAT B-CHEM, V127, P143, DOI 10.1016/j.snb.2007.07.107 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 Wang TM, 2010, AFR J BIOTECHNOL, V9, P6146 Wu CX, 2009, 2009 INTERNATIONAL FORUM ON INFORMATION TECHNOLOGY AND APPLICATIONS, VOL 2, PROCEEDINGS, P669, DOI 10.1109/IFITA.2009.200 Xin H, 2008, SCIENCE, V322, P1310, DOI 10.1126/science.322.5906.1310 Yang F., 2012, INT C SYST INF ICSAI Yasothkumar N., 2010, RADIOFREQUENCY IDENT, P343 Zhang J., 2009, J FOOD AGRIC ENVIRON, V7, P132 Zhang L, 2006, GCC 2006: FIFTH INTERNATIONAL CONFERENCE ON GRID AND COOPERATIVE COMPUTING WORKSHOPS, PROCEEDINGS, P463 Zhang Z.-Y., 2010, STUDY RISK CONTROL D NR 102 TC 160 Z9 174 U1 15 U2 381 PD FEB PY 2013 VL 6 IS 2 BP 353 EP 366 DI 10.1007/s11947-012-0958-7 WC Food Science & Technology SC Food Science & Technology UT WOS:000313656600003 DA 2022-12-14 ER PT J AU Gligora, S Antunac, N AF Gligora, Sime Antunac, Neven TI Implementation of HACCP system in production of Paski cheese SO MLJEKARSTVO DT Article DE HACCP system; Paski cheese; traceability; "Sirena mala sirana" AB Since August 2006 all participants in Republic of Croatia dealing with the food have an obligation to introduce Hazard Analysis of Critical Control Point (HACCP) system. Therefore, all producers of dairy products, as well as registered producers of Paski cheese have to implement HACCP system in their facilities. The aim of this work is to describe implementation and use of HACCP system in "Sirena - mala sirana" which is a small-scale cheese factory situated in a place Kolan on the island of Pag. For this reason, EU and Croatian legislative related to HACCP system is firstly described. After that, procedure of certification is presented, as well as prerequisite programs of the system which are the base for successful implementation of the HACCP system. Furthermore, appliance of HACCP in Paski cheese production through system's documentation is described Flow diagram is presented, analysis of hazardous and determination of critical control points is described trough defined production processes. Next, control, monitoring and corrective measures for production processes are described Finally, HACCP plan and Standard Sanitation Operative Procedures (SSOP) are presented Besides that, traceability system and training plan are shown, as well as all required record lists and other documentation for HA CCP system. With implementation of the HACCP system in "Sirena" cheese facility, general hygiene was improved as well as hygiene of equipment and personnel. Risk of product contamination is reduced to a minimum level. With effective management of control measures and records, quality of produced Paski cheese was also improved. C1 [Gligora, Sime] Sirena Mala Sirana, Kolan, Otok Pag, Croatia. [Antunac, Neven] Agron Fak Sveucilista Zagrebu, Zavod Mljekarstvo, Zagreb, Croatia. C3 University of Zagreb RP Gligora, S (corresponding author), Sirena Mala Sirana, Kolan, Otok Pag, Croatia. CR BAINES RN, 2004, 2004 IAMA WORLD FOOD Basic M., 2005, Mljekarstvo, V55, P51 BRENTON P, 2001, HACCP PLAN ASSESSMEN Ingham SC, 2000, J FOOD PROTECT, V63, P1697, DOI 10.4315/0362-028X-63.12.1697 MORTIMORE S, 1998, HACCP PRACTICAL APRO *PRAV, 1992, 60 PRAV *PRAV, 7497 PRAV *PRAV, 1291999 PRAV Soliman F., 2000, Journal of Knowledge Management, V4, P287, DOI 10.1108/13673270010379830 *ZAK, 2004, 11703 ZAK NR 10 TC 0 Z9 0 U1 0 U2 7 PD APR-JUN PY 2007 VL 57 IS 2 BP 127 EP 152 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000257856200004 DA 2022-12-14 ER PT J AU Zhou, JH Yan, Z Liu, QY AF Zhou Jie-hong Yan Zhen Liu Qing-yu TI Identification of Behavior of Voluntary Traceability and Analysis of Its Determinants: A Case Study of Hog Slaughtering and Processing Firms in Zhejiang Province, China SO JOURNAL OF INTEGRATIVE AGRICULTURE DT Article DE voluntary traceable behavior; product quality improvement; capital ability; role perception; hog slaughtering and processing firm ID FOOD; INCENTIVES; SYSTEM AB In recent years, the Chinese government has highlighted the importance of adopting hog safety/quality traceability, and a growing amount of research continues to entice firms to adopt traceability systems. In this study, a survey was conducted on a sample of pig slaughtering and processing firms in Zhejiang, China through personal interviews and emails. The aim of this study was to examine the determinants of firm behavior on the implementation of voluntary traceability systems with more stringent standards and controls than those of the mandatory system in China. The results revealed that motivation based on product quality improvement, capital ability and role perception (business type) had significantly positive relationships with a firm's voluntary traceability. Other incentives, such as operation improvement, recall risk reduction, reduced occurrence of safety issues, and technical strength were not found to be supportive in our study. This study provides an opportunity to better understand the determinants of firm behavior on voluntary traceability, particularly in light of the fact that some Chinese firms are facing the threat of criminal action for the use of illegal additives and the abuse of Clenbuterol. Policy recommendations on encouraging the implementation of pork safety voluntary traceability by hog slaughtering and processing firms are also discussed. C1 [Zhou Jie-hong; Yan Zhen; Liu Qing-yu] Zhejiang Univ, Ctr Agr & Rural Dev, Hangzhou 310058, Zhejiang, Peoples R China. C3 Zhejiang University RP Zhou, JH (corresponding author), Zhejiang Univ, Ctr Agr & Rural Dev, Hangzhou 310058, Zhejiang, Peoples R China. EM runzhou@zju.edu.cn CR Alfnes F, 2010, EUR REV AGRIC ECON, V37, P147, DOI 10.1093/erae/jbq012 [Anonymous], 2011, CHANGCHUN EVENI 0322 Banterle A, 2008, FOOD POLICY, V33, P560, DOI 10.1016/j.foodpol.2008.06.002 Buhr B. L., 2003, Journal of Food Distribution Research, V34, P13 Chen HH, 2007, MARKET MODERNIZATION, V21, P5 China's National People's Congress Standing Committee, 2009, FOOD SAF LAW PEOPL R Golan E., 2002, Agricultural Outlook, P21 GOLAN E, 2003, CHOICES, V18, P17 Golan E.H., 2004, TRACEABILITY US FOOD Heyder M., 2010, International Journal on Food System Dynamics, V1, P133 Hobbs J. E., 1996, British Food Journal, V98, P16, DOI 10.1108/00070709610131339 Hobbs J. E., 2003, CURRENT AGR FOOD RES, V4, P36 Hobbs J.E., 1998, SUPPLY CHAIN MANAG, V3, P68, DOI DOI 10.1108/13598549810215388 Hobbs J. E., 2007, IDENTIFICATION ANAL Hollmann-Hespos T., 2005, EC TRACEABILITY MODE, P914 Jin SS, 2008, FOOD CONTROL, V19, P823, DOI 10.1016/j.foodcont.2008.01.008 Li Y, 2010, J HUAZHONG AGR U SOC, V89, P48 Liu W., 2007, J AGROTECHNICAL EC, P80 McShane S.L, 2005, ORG BEHAV Menard C, 2005, EUR REV AGRIC ECON, V32, P421, DOI 10.1093/eurrag/jbi013 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 National Bureau of Statistics of China, 2011, CHIN STAT YB Pouliot S, 2008, AM J AGR ECON, V90, P15, DOI 10.1111/j.1467-8276.2007.01061.x Ritson, 1998, NUTR FOOD SCI, V98, P253, DOI DOI 10.1108/00346659810224163.R0ININEN Starbird SA, 2006, J AGR RESOUR ECON, V31, P14 [孙世民 SUN Shimin], 2006, [农业经济问题, Issues in Agricultural Economy], V27, P70 Xiong BH, 2010, AGR SCI CHINA, V9, P147, DOI 10.1016/S1671-2927(09)60078-X Yang Qiuhong, 2009, J AGROTECHNICAL EC, P69 Yuan C, 2009, THESIS SICHUAN AGR U Zhao RX, 2010, AGR SCI CHINA, V9, P764, DOI 10.1016/S1671-2927(09)60153-X Zhejiang Province Bureau of Statistics, 2010, ZHEJIANG STAT YB Zhou J., 2007, ISSUES AGR EC, V8, P55 Zhou J., 2007, J ZHEJIANG U, V37, P118 NR 33 TC 4 Z9 4 U1 2 U2 25 PY 2013 VL 12 IS 6 BP 1112 EP 1121 DI 10.1016/S2095-3119(13)60490-6 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000324348700020 DA 2022-12-14 ER PT J AU Dayana, DS Kalpana, G AF Dayana, D. S. Kalpana, G. TI Augmented System for Food Crops Production in Agricultural Supply Chain using Blockchain Technology SO INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS DT Article DE Blockchain; distributed ledger; consensus; decentralized; stakeholders; agricultural insurance; payouts; trust-based farming system; food safety; panel of advisers; agri-supply chain ID TRACEABILITY; MANAGEMENT AB The elevated version of the Agricultural Traceability System dealing with food production holds utmost significance in not only assuring food insecure, smart contracts and agri-insurance to farmers but also guaranteeing insurance to them during natural disaster. The proposed and improved system for food cultivation traceability deals with agri-crops and farmers that hires the Blockchain technology guarantee at par safety, agreement, distributed ledger, immediate payment, de-centralization and thereby achieving the goal of minimizing the cost incurred in the food processing system and building trust. Smart contracts play a pivot role in the field of agricultural insurance. Agricultural insurance based upon Blockchain comprises of major weather incidents and associated payouts enlisted on a smart contract, connected to the mobile wallets with timely weather updates notified by the field sensors and interrelated with data from proximity weather stations would enable prompt payout during any natural calamity such as flood or drought. A panel of advisers in the decentralized system which is professionally governed and managed by certain retired officers makes the traceability system more trustworthy. These professionals can offer wise suggestions to the planters aiding them to acquire productive outcome. C1 [Dayana, D. S.; Kalpana, G.] SRM Inst Sci & Technol, Dept Comp Sci, Chennai, Tamil Nadu, India. C3 SRM Institute of Science & Technology Chennai RP Dayana, DS (corresponding author), SRM Inst Sci & Technol, Dept Comp Sci, Chennai, Tamil Nadu, India. CR [Anonymous], 2018, JMR INFORMATICS Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bermeo-Almeida O, 2018, COMM COM INF SC, V883, P44, DOI 10.1007/978-3-030-00940-3_4 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chen HL, 2021, IEEE ACCESS, V9, P36008, DOI 10.1109/ACCESS.2021.3062410 Christidis K, 2016, IEEE ACCESS, V4, P2292, DOI 10.1109/ACCESS.2016.2566339 Currie Richard J., 2018, INFLATION ITS IMPACT Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dayana D. S., 2022, 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), P267, DOI 10.1109/ICSSIT53264.2022.9716366 Dayana DS, 2021, PROCEEDINGS OF THE 2021 FIFTH INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC 2021), P1619, DOI 10.1109/I-SMAC52330.2021.9640768 Dayana D. S., 2014, Advanced Materials Research, V984-985, P1269, DOI 10.4028/www.scientific.net/AMR.984-985.1269 Foteinis S, 2018, NATURE, V554, P169, DOI 10.1038/d41586-018-01625-x Huppe Gabriel A., FOOD PRICE INFLATION Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kewell B, 2017, STRATEG CHANG, V26, P429, DOI 10.1002/jsc.2143 Kumar M.V., 2017, ADV SCI TECHNOLOGY L, V146, P125, DOI DOI 10.14257/AST1.2017.146.22 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Mafini Chengedzai, 2017, TRACEABILITY FOOD AG Manski S, 2017, STRATEG CHANG, V26, P511, DOI 10.1002/jsc.2151 matta Brigadier j, 2016, DOCTOR PHILOS MANAGE Mohan M, FOOD SUPPLY CHAIN US Nguegan Nguegan Catherine A., 2017, Acta Commer., V17, P1, DOI 10.4102/ac.v17i1.485 Osmanoglu M, 2020, IEEE T ENG MANAGE, V67, P1157, DOI 10.1109/TEM.2020.2978829 Patil AS, 2018, LECT NOTES ELECTR EN, V474, P1162, DOI 10.1007/978-981-10-7605-3_185 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Viotti P, 2016, PROCEEDINGS OF THE 2ND WORKSHOP ON THE PRINCIPLES AND PRACTICE OF CONSISTENCY FOR DISTRIBUTED DATA, PAPOC 2016, DOI 10.1145/2911151.2911162 Wang SP, 2018, IEEE ACCESS, V6, P38437, DOI 10.1109/ACCESS.2018.2851611 Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 Zhang Y., 2017, IMECS NR 30 TC 0 Z9 0 U1 3 U2 5 PD APR PY 2022 VL 13 IS 4 BP 579 EP 589 DI 10.14569/IJACSA.2022.0130468 WC Computer Science, Theory & Methods SC Computer Science UT WOS:000798652100001 DA 2022-12-14 ER PT J AU Kwon, T Yoon, J Heo, J Lee, W Kim, H AF Kwon, Taehyung Yoon, Joon Heo, Jaeyoung Lee, Wonseok Kim, Heebal TI Tracing the breeding farm of domesticated pig using feature selection (Sus scrofa) SO ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES DT Article DE Pig; Traceability; Breed Differences; Single Nucleotide Polymorphism ID GENETIC DIVERSITY; TRACEABILITY; SIGNATURES AB Objective: Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP) markers strictly selected through least absolute shrinkage and selection operator (LASSO) feature selection. Methods: We performed farm tracing of domesticated pig (Sus scrofa) from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers. Results: We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs. Conclusion: The study represents a successful application of LASSO feature selection on multibreed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability. C1 [Kwon, Taehyung; Lee, Wonseok; Kim, Heebal] Seoul Natl Univ, Dept Agr Biotechnol, Seoul 08826, South Korea. [Kwon, Taehyung; Lee, Wonseok; Kim, Heebal] Seoul Natl Univ, Res Inst Agr & Life Sci, Seoul 08826, South Korea. [Yoon, Joon] Seoul Natl Univ, Interdisciplinary Program Bioinformat, Dept Nat Sci, Seoul 08826, South Korea. [Heo, Jaeyoung] Chonbuk Natl Univ, Int Agr Dev & Cooperat Ctr, Jeonju 54896, South Korea. [Kim, Heebal] Shinshu Univ, Inst Biomed Sci, Nagano 3900802, Japan. C3 Seoul National University (SNU); Seoul National University (SNU); Seoul National University (SNU); Jeonbuk National University; Shinshu University RP Kim, H (corresponding author), Seoul Natl Univ, Dept Agr Biotechnol, Seoul 08826, South Korea.; Kim, H (corresponding author), Seoul Natl Univ, Res Inst Agr & Life Sci, Seoul 08826, South Korea.; Kim, H (corresponding author), Shinshu Univ, Inst Biomed Sci, Nagano 3900802, Japan. EM heebal@snu.ac.kr CR Alexander DH, 2009, GENOME RES, V19, P1655, DOI 10.1101/gr.094052.109 BERNARD CS, 1954, J ANIM SCI, V13, P389, DOI 10.2527/jas1954.132389x Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Even-Zohar Y, CS01060442001 FOWLER VR, 1976, ANIM PROD, V23, P365, DOI 10.1017/S0003356100031482 Friedman J, 2010, J STAT SOFTW, V33, P1, DOI 10.18637/jss.v033.i01 Ghosh D, 2005, J BIOMED BIOTECHNOL, P147, DOI 10.1155/JBB.2005.147 Guyon I., 2003, J MACH LEARN RES, V3, P1157, DOI DOI 10.1162/153244303322753616 Hornik K, 2009, COMPUTATION STAT, V24, P225, DOI 10.1007/s00180-008-0119-7 Laval G, 2000, GENET SEL EVOL, V32, P187, DOI 10.1051/gse:2000113 Makhoul J, 1999, P DARPA BROADC NEWS, P249 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Moon S, 2015, BMC GENOMICS, V16, DOI 10.1186/s12864-015-1330-x Purcell S, 2007, AM J HUM GENET, V81, P559, DOI 10.1086/519795 R Core Team, 2019, R LANG ENV STAT COMP Rubin CJ, 2012, P NATL ACAD SCI USA, V109, P19529, DOI 10.1073/pnas.1217149109 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 Tang GQ, 2013, ASIAN AUSTRAL J ANIM, V26, P755, DOI 10.5713/ajas.2012.12645 Wiener P, 2011, P ROY SOC B-BIOL SCI, V278, P3161, DOI 10.1098/rspb.2011.1376 Wilkinson S, 2013, PLOS GENET, V9, DOI 10.1371/journal.pgen.1003453 Zheng XW, 2012, BIOINFORMATICS, V28, P3326, DOI 10.1093/bioinformatics/bts606 NR 21 TC 5 Z9 5 U1 2 U2 5 PD NOV PY 2017 VL 30 IS 11 BP 1540 EP 1549 DI 10.5713/ajas.17.0561 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000417180300003 DA 2022-12-14 ER PT J AU Mangla, SK Kazancoglu, Y Ekinci, E Liu, MQ Ozbiltekin, M Sezer, MD AF Mangla, Sachin Kumar Kazancoglu, Yigit Ekinci, Esra Liu, Mengqi Ozbiltekin, Melisa Sezer, Muruvvet Deniz TI Using system dynamics to analyze the societal impacts of blockchain technology in milk supply chainsrefer SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW DT Article DE Food waste; Milk supply chain and distribution; Societal impacts; Blockchain; Sustainability; Transparency and traceability; System Dynamics ID CHAIN MANAGEMENT; FOOD LOSS; TRACEABILITY; LOGISTICS; CHALLENGES; QUALITY; HEALTH; SUSTAINABILITY; AGRICULTURE; PERFORMANCE AB The integration of blockchain technologies in the food sector has significant social impacts. The objectives of this research are firstly, to map the milk supply chains to explore information flow among different members for higher traceability; secondly, to investigate the societal impacts of blockchain technology in a milk supply chain to build social sustainability. The systems theory in integration with system dynamics (SD) provides the necessary theoretical underpinning to this research. We collect data from an agricultural development cooperative founded to support dairy farmers in Turkey. This work evaluates the societal impacts of blockchain technology on farmers, the community and animals using parameters such as local embedding, rural development, decreasing food fraud, animal health and welfare, proximity to food markets, food security, educating and promoting people towards healthy eating, assisting food access and social acceptability for transparency. In the last 18 years, the cooperative has encouraged dairy farmers in the district to become partners with a resultant increase in milk production from 30 thousand tons in 2002 to 330 thousand tons in 2019. According to our findings, population growth of the country and adult population increases in the district, it is expected that by 2025 the number of partners will rise to approximately 2800. The increase in number of partners proves the network expansion. Furthermore, blockchain technology can be incorporated into the existing system so that transparent and end-to-end accurate tracking of the supply chain is made possible, while creating decentralized recording of transactions. Moreover, the critical traceability points of a milk supply chain are evaluated with the blockchain adoption. This will help achieve the sustainable development goals (SDGs) of providing safe food, promoting good health and better well-being for everyone. C1 [Mangla, Sachin Kumar] OP Jindal Global Univ, Jindal Global Business Sch, Sonipat, Haryana, India. [Mangla, Sachin Kumar] Univ Plymouth, Plymouth Business Sch, Plymouth, Devon, England. [Kazancoglu, Yigit; Ekinci, Esra] Yasar Univ, Int Logist Management Dept, Izmir, Turkey. [Liu, Mengqi] Hunan Univ, Business Sch, Changsha, Peoples R China. [Ozbiltekin, Melisa] Yasar Univ, Int Logist Management Dept, Izmir, Turkey. [Sezer, Muruvvet Deniz] Yasar Univ, Business Adm Dept, TR-35100 Izmir, Turkey. C3 O.P. Jindal Global University; University of Plymouth; Yasar University; Hunan University; Yasar University; Yasar University RP Liu, MQ (corresponding author), Hunan Univ, Business Sch, Changsha, Peoples R China. EM sachin.kumar@plymouth.ac.uk; yigit.kazancoglu@yasar.edu.tr; esra.ekinci@yasar.edu.tr; liumengqi76@163.com; melisa.ozbiltekin@yasar.edu.tr; deniz.sezer@yasar.edu.tr CR Ala-Harja H, 2015, TRANSPORT RES E-LOG, V74, P11, DOI 10.1016/j.tre.2014.12.005 Alexandre A, 2018, WALMART IS READY USE Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Balaman SY, 2019, DECISION-MAKING FOR BIOMASS-BASED PRODUCTION CHAINS: THE BASIC CONCEPTS AND METHODOLOGIES, P77, DOI 10.1016/B978-0-12-814278-3.00004-2 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Biswal AK, 2018, TRANSPORT RES E-LOG, V109, P205, DOI 10.1016/j.tre.2017.11.010 Borchers MR, 2016, J DAIRY SCI, V99, P7458, DOI 10.3168/jds.2015-10843 Broom DM, 2010, J VET MED EDUC, V37, P83, DOI 10.3138/jvme.37.1.83 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Cai YJ, 2021, DECISION SCI, V52, P866, DOI 10.1111/deci.12475 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Chang YL, 2020, INT J PROD RES, V58, P2082, DOI 10.1080/00207543.2019.1651946 Charlebois S, 2015, J DAIRY SCI, V98, P3514, DOI 10.3168/jds.2014-9247 Choi TM, 2021, J OPER RES SOC, V72, P2580, DOI 10.1080/01605682.2020.1800419 Choi TM, 2020, EUR J OPER RES, V284, P1031, DOI 10.1016/j.ejor.2020.01.049 Choi TM, 2020, TRANSPORT RES E-LOG, V135, DOI 10.1016/j.tre.2020.101860 Choi TM, 2020, INT J PROD ECON, V221, DOI 10.1016/j.ijpe.2019.08.008 Choi TM, 2019, TRANSPORT RES E-LOG, V131, P139, DOI 10.1016/j.tre.2019.09.019 Choi TM, 2019, TRANSPORT RES E-LOG, V127, P178, DOI 10.1016/j.tre.2019.05.007 Cole R, 2019, SUPPLY CHAIN MANAG, V24, P469, DOI 10.1108/SCM-09-2018-0309 Connolly A, 2018, WHAT ARE IMPLICATION Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Crosby M., 2016, APPL INNOVATION, V2, P6, DOI DOI 10.21626/innova/2016.1/01 Crossey S., 2018, NEW FOOD MAGAZINE Daud A. R., 2015, Livestock Research for Rural Development, V27, P137 de las Morenas J, 2014, COMPUT ELECTRON AGR, V101, P34, DOI 10.1016/j.compag.2013.12.011 Deloitte, 2017, GLOB DAIR SECT TREND Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067 Ekinci E, 2020, SCI TOTAL ENVIRON, V715, DOI 10.1016/j.scitotenv.2020.136948 European Bank for Reconstruction and Development, 2019, FOOD LOSS WAST SECT Fahim A, 2017, INDIAN J ANIM SCI, V87, P1396 Fan ZP, 2022, ANN OPER RES, V309, P837, DOI 10.1007/s10479-020-03729-y FAO, FOOD LOSS WAST TURK FAO, 2012, BAL FEED IMPR LIV PR FAO: Food Agriculture Organization of the United Nations, 2013, STATE FOOD INSECURIT Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fernando AL., 2018, PERENNIAL GRASSES BI, P245, DOI 10.1016/b978-0-12-812900-5.00008-4 FORRESTER JW, 1994, SYST DYNAM REV, V10, P245, DOI 10.1002/sdr.4260100211 Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 Frohling A, 2010, FOOD BIOPROCESS TECH, V3, P892, DOI 10.1007/s11947-010-0366-9 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Georgiadis P, 2005, J FOOD ENG, V70, P351, DOI 10.1016/j.jfoodeng.2004.06.030 Ghadge A, 2021, PROD PLAN CONTROL, V32, P1191, DOI 10.1080/09537287.2020.1796140 Gnansounou E, 2015, BIORESOURCE TECHNOL, V196, P364, DOI 10.1016/j.biortech.2015.07.072 Hao ZH, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17072300 Hastig GM, 2020, PROD OPER MANAG, V29, P935, DOI 10.1111/poms.13147 Hingley M., 2011, International Journal on Food System Dynamics, V2, P340 Hosseini S, 2019, TRANSPORT RES E-LOG, V125, P285, DOI 10.1016/j.tre.2019.03.001 Huang KC, 2019, TRANSPORT RES E-LOG, V129, P325, DOI 10.1016/j.tre.2018.01.018 Hume DA, 2011, J AGR SCI, V149, P9, DOI 10.1017/S0021859610001188 IBM, 2017, IBM WATS INT THINGS Ingalls W, 2011, PROCEDURES PRODUCTS Irani Z, 2018, COMPUT OPER RES, V98, P367, DOI 10.1016/j.cor.2017.10.007 Ivanov D, 2019, INT J PROD RES, V57, P829, DOI 10.1080/00207543.2018.1488086 Jagtap S, 2019, WASTE MANAGE, V87, P387, DOI 10.1016/j.wasman.2019.02.017 Kaiyue Ling Eric, 2020, International Journal of Productivity and Quality Management, V29, P216 Kamble SS, 2020, INT J PROD ECON, V219, P179, DOI 10.1016/j.ijpe.2019.05.022 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kayikci Y, 2022, PROD PLAN CONTROL, V33, P301, DOI 10.1080/09537287.2020.1810757 Koh L, 2020, INT J PROD RES, V58, P2054, DOI 10.1080/00207543.2020.1736428 Leat P, 2011, SUSTAINABILITY-BASEL, V3, P605, DOI 10.3390/su3040605 Lemma DH, 2018, FOOD QUAL SAF-OXFORD, V2, P135, DOI 10.1093/fqsafe/fyy009 Li G, 2020, DECISION SCI, V51, P1521, DOI 10.1111/deci.12437 Li G, 2020, J OPER MANAG, V66, P958, DOI 10.1002/joom.1061 Li G, 2020, DECISION SCI, V51, P691, DOI 10.1111/deci.12340 Li G, 2019, TRANSPORT RES E-LOG, V126, P32, DOI 10.1016/j.tre.2019.03.019 Liu HA, 2012, BRIT FOOD J, V114, P372, DOI 10.1108/00070701211213474 Lotta F., 2015, European Food and Feed Law Review, P114 Millogo V, 2010, FOOD CONTROL, V21, P1070, DOI 10.1016/j.foodcont.2009.12.029 Minegishi S, 2000, SIMULAT PRACT THEORY, V8, P321, DOI 10.1016/S0928-4869(00)00026-4 MIRANDA MG, 2014, INDIAN J SCI TECHN S, V7, P16, DOI DOI 10.17485/ijst/2014/v7i3S/48521 Monasterolo I, 2016, AGRICULTURE-BASEL, V6, DOI 10.3390/agriculture6030044 Morkunas VJ, 2019, BUS HORIZONS, V62, P295, DOI 10.1016/j.bushor.2019.01.009 Navarro E.F., 2014, THESIS U COLL CORK I Nielsen NI, 2005, J DAIRY SCI, V88, P3186, DOI 10.3168/jds.S0022-0302(05)73002-2 Orji IJ, 2020, TRANSPORT RES E-LOG, V141, DOI 10.1016/j.tre.2020.102025 Pant RR, 2015, PROCD SOC BEHV, V189, P385, DOI 10.1016/j.sbspro.2015.03.235 Simoes ARP, 2020, INT FOOD AGRIBUS MAN, V23, P71, DOI 10.22434/IFAMR2019.0033 Pohlmann CR, 2020, J CLEAN PROD, V245, DOI 10.1016/j.jclepro.2019.118798 Pournader M, 2020, INT J PROD RES, V58, P2063, DOI 10.1080/00207543.2019.1650976 Rabbani H, 2015, LAB MANUAL QUALITY C Rebs T, 2019, J CLEAN PROD, V208, P1265, DOI 10.1016/j.jclepro.2018.10.100 Redlingshofer B, 2017, J CLEAN PROD, V164, P703, DOI 10.1016/j.jclepro.2017.06.173 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Rogerson M, 2020, SUPPLY CHAIN MANAG, V25, P601, DOI 10.1108/SCM-08-2019-0300 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Saetta S, 2020, PROCEDIA MANUF, V42, P333, DOI 10.1016/j.promfg.2020.02.083 SafeFood, 2008, REV MILK SUPPL CHAIN Salihoglu G, 2018, BIORESOURCE TECHNOL, V248, P88, DOI 10.1016/j.biortech.2017.06.083 Sanchez-Flores RB, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12176972 Shankar R, 2018, TRANSPORT RES E-LOG, V119, P205, DOI 10.1016/j.tre.2018.03.006 Shen B, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102066 SNV, 2017, HYG QUAL MILK PROD T Statista, 2020, MILK PROD PER COW US Stewart M, 2017, J DAIRY SCI, V100, P3893, DOI 10.3168/jds.2016-12055 Tan A., 2020, SUSTAINABLE FUTURES, V2, P100034 Tang CS, 2019, TRANSPORT RES E-LOG, V129, P1, DOI 10.1016/j.tre.2019.06.004 Tarnanidis T, 2020, SUPPLY CHAIN LOGISTI, P108 Tatlidil FF., 2013, FOOD LOSSES WASTE TU Tian ZG, 2021, INT J PROD RES, V59, P2229, DOI 10.1080/00207543.2020.1809733 Tipmontian J, 2020, MULTIDISCIP DIGIT PU, V39, P14, DOI [10.3390/proceedings2019039014, DOI 10.3390/PROCEEDINGS2019039014] Toni F, 2011, J DAIRY SCI, V94, P1772, DOI 10.3168/jds.2010-3389 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 World Health Organization, 2015, WHOS 1 EV GLOB EST F Wu X, 2018, TRANSPORT RES E-LOG, V111, P186, DOI 10.1016/j.tre.2018.01.002 Yang CS, 2019, TRANSPORT RES E-LOG, V131, P108, DOI 10.1016/j.tre.2019.09.020 Zelbst PJ, 2020, J MANUF TECHNOL MANA, V31, P441, DOI 10.1108/JMTM-03-2019-0118 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 111 TC 27 Z9 27 U1 25 U2 90 PD MAY PY 2021 VL 149 AR 102289 DI 10.1016/j.tre.2021.102289 EA MAR 2021 WC Economics; Engineering, Civil; Operations Research & Management Science; Transportation; Transportation Science & Technology SC Business & Economics; Engineering; Operations Research & Management Science; Transportation UT WOS:000670306600013 DA 2022-12-14 ER PT J AU Giannetti, V Mariani, MB Mannino, P Marini, F AF Giannetti, Vanessa Mariani, Maurizio Boccacci Mannino, Paola Marini, Federico TI Volatile fraction analysis by HS-SPME/GC-MS and chemometric modeling for traceability of apples cultivated in the Northeast Italy SO FOOD CONTROL DT Article DE Apples; Volatiles; Ancient cultivars; Organic farming; HS-SPME/GC-MS; Multivariate analysis; Chemometrics ID FRUIT; QUALITY; TOOL; NORMALIZATION; BIOSYNTHESIS; MATURATION; STORAGE; ODOR; STEP AB The present study aimed at characterising the flavour composition of apple cultivars grown in the Northeast Italy through different cultivation methods, by combining Head Space-Solid Phase Micro Extraction/Gas Chromatography Mass Spectrometry (HS-SPME/GC-MS) analysis of volatile fraction with chemometric tools for class modeling. In order to represent the overall production in the target area, the investigation included 42 apples varieties consisting of ancient, non-native and new hybrid cultivars grown in Friuli Venezia Giulia and Alto Adige-South Tyrol, respectively. Moreover, apple samples from both conventional and organic agricultural practices were considered. Overall 118 volatile compounds were identified in the samples and Partial Least Squares-Discriminant Analysis (PLS-DA) was used to classify apples based on their different geographical origin or growing conditions. Models highlighted good classification results both in calibration (over 91%) and cross-validation (over 87%), enabling to obtain a good separation between apple categories with high prediction accuracy (over 90%). In addition, the Variable Importance in Projection (VIP) scores of the PLS-DA models were calculated, allowing to identify a reduced number of volatiles (e.g., ethanol, ethyl acetate, isobutyl acetate, propyl propanoate, 1-hexanol, D-limonene, (Z)-2-hexen-l-ol acetate and others) which are relevant for the discrimination of different apple groups. The proposed approach may represent a powerful tool for fruit traceability. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Giannetti, Vanessa; Mariani, Maurizio Boccacci; Mannino, Paola] Sapienza Univ Rome, Dept Management, Via Castro Laurenziano 9, I-00161 Rome, Italy. [Marini, Federico] Sapienza Univ Rome, Dept Chem, Ple Aldo Moro 5, I-00185 Rome, Italy. C3 Sapienza University Rome; Sapienza University Rome RP Giannetti, V (corresponding author), Sapienza Univ Rome, Dept Management, Via Castro Laurenziano 9, I-00161 Rome, Italy. EM vanessa.giannetti@uniroma1.it CR Aprea E, 2012, FOOD RES INT, V49, P677, DOI 10.1016/j.foodres.2012.09.023 Aprea E, 2011, J CHROMATOGR A, V1218, P4517, DOI 10.1016/j.chroma.2011.05.019 Barker M, 2003, J CHEMOMETR, V17, P166, DOI 10.1002/cem.785 Bevilacqua M, 2014, J AOAC INT, V97, P19, DOI 10.5740/jaoacint.SGEBevilacqua Corrigan VK, 1997, NEW ZEAL J CROP HORT, V25, P375, DOI 10.1080/01140671.1997.9514029 Dal Piaz A., 2015, SPECIALE MELE PRODUZ Dieterle F, 2006, ANAL CHEM, V78, P4281, DOI 10.1021/ac051632c Dixon J, 2000, NEW ZEAL J CROP HORT, V28, P155, DOI 10.1080/01140671.2000.9514136 Dunemann F, 2009, MOL BREEDING, V23, P501, DOI 10.1007/s11032-008-9252-9 Echeverria G, 2004, POSTHARVEST BIOL TEC, V32, P29, DOI 10.1016/j.postharvbio.2003.09.017 Echeverria G, 2004, POSTHARVEST BIOL TEC, V31, P217, DOI 10.1016/j.postharvbio.2003.09.003 El Hadi MAM, 2013, MOLECULES, V18, P8200, DOI 10.3390/molecules18078200 FAO, 2014, APPL PROD FAM FARMS Fellman J.K, 2019, HORTSCIENCE, V32, P554, DOI [10.21273/hortsci.32.3.554c, DOI 10.21273/HORTSCI.32.3.554C] Filzmoser P, 2014, J CHROMATOGR A, V1362, P194, DOI 10.1016/j.chroma.2014.08.050 Forconi V., 2015, FRUTTI DIMENTICATI B, V7, P59 Hampson CR, 2000, EUPHYTICA, V111, P79, DOI 10.1023/A:1003769304778 Hecke K, 2006, EUR J CLIN NUTR, V60, P1136, DOI 10.1038/sj.ejcn.1602430 Lara I, 2006, POSTHARVEST BIOL TEC, V39, P19, DOI 10.1016/j.postharvbio.2005.09.001 Lopez ML, 2000, J SCI FOOD AGR, V80, P311, DOI [10.1002/1097-0010(200002)80:3<311::AID-JSFA519>3.0.CO;2-F, 10.1002/(SICI)1097-0010(200002)80:3<311::AID-JSFA519>3.0.CO;2-F] Maarse H., 1991, VOLATILE COMPOUNDS F, V44 Mattheis JP, 1999, POSTHARVEST BIOL TEC, V15, P227, DOI 10.1016/S0925-5214(98)00087-8 Mehinagic E, 2006, J AGR FOOD CHEM, V54, P2678, DOI 10.1021/jf052288n Mehinagic E, 2003, FOOD QUAL PREFER, V14, P473, DOI 10.1016/S0950-3293(03)00012-0 Peck GM, 2006, HORTSCIENCE, V41, P99, DOI 10.21273/HORTSCI.41.1.99 Rattan RS, 2010, CROP PROT, V29, P913, DOI 10.1016/j.cropro.2010.05.008 Roth E, 2007, POSTHARVEST BIOL TEC, V45, P11, DOI 10.1016/j.postharvbio.2007.01.006 Rowan DD, 2009, J AGR FOOD CHEM, V57, P7944, DOI 10.1021/jf901359r Savorani F, 2010, J MAGN RESON, V202, P190, DOI 10.1016/j.jmr.2009.11.012 Schaffer RJ, 2007, PLANT PHYSIOL, V144, P1899, DOI 10.1104/pp.106.093765 SNEE RD, 1977, TECHNOMETRICS, V19, P415, DOI 10.2307/1267881 STAHLE L, 1987, Journal of Chemometrics, V1, P185, DOI 10.1002/cem.1180010306 Sumner LW, 2007, METABOLOMICS, V3, P211, DOI 10.1007/s11306-007-0082-2 Tagliavini M., 2014, APPLE IND S TYROL EX Tomasi G, 2011, J CHROMATOGR A, V1218, P7832, DOI 10.1016/j.chroma.2011.08.086 Valavanidis A, 2009, INT J FOOD SCI TECH, V44, P1167, DOI 10.1111/j.1365-2621.2009.01937.x Villatoro C, 2008, POSTHARVEST BIOL TEC, V47, P286, DOI 10.1016/j.postharvbio.2007.07.003 Watada A. E., 1985, EVALUATION QUALITY F, P63 Westad F, 2015, ANAL CHIM ACTA, V893, P14, DOI 10.1016/j.aca.2015.06.056 WOLD S, 1983, LECT NOTES MATH, V973, P286 Wold S., 1993, 3D QSAR DRUG DESIGN, P523, DOI DOI 10.1007/0-306-46858-1 World Apple and Pear Association (WAPA), 2014, WORLD DAT REP Yahia E. M., 1994, Horticultural Reviews, V16, P197, DOI 10.1002/9780470650561.ch6 NR 43 TC 58 Z9 59 U1 2 U2 90 PD AUG PY 2017 VL 78 BP 215 EP 221 DI 10.1016/j.foodcont.2017.02.036 WC Food Science & Technology SC Food Science & Technology UT WOS:000401878400029 DA 2022-12-14 ER PT J AU Chen, RY AF Chen, Rui-Yang TI Autonomous tracing system for backward design in food supply chain SO FOOD CONTROL DT Article DE Fuzzy cognitive maps; Internet of things; Food supply chain; Fuzzy rule ID FUZZY COGNITIVE MAPS; TRACEABILITY; QUALITY; SAFETY; INDUCTION AB Food safety and quality issues generally occur due to incurring problem of food products handling processing. It led to a growing interest in developing systems for food supply chain traceability. At present, there are lacking in modeling the traceability process for developing "autonomous" traceability system in comparative to "automation" and little research has been conducted where the product problem information related to the cause of problem, the responsibility attribution simultaneously. This paper design the autonomous agent-based tracing system based on IoT (intemet of things) architecture using FCM (fuzzy cognitive maps) and fuzzy rule method for product usage life cycle. The case study for agriculture food product is discussed. It aimed to simulating food tracing complex system with imprecise relationships while quantifying the performance impact of backward design process efficiency using the total effects algorithm. (C) 2014 Elsevier Ltd. All rights reserved. C1 Aletheia Univ, Dept Business Adm, New Taipei City 25103, Peoples R China. RP Chen, RY (corresponding author), Aletheia Univ, Dept Business Adm, 32 Zhenli St, New Taipei City 25103, Peoples R China. EM a168.cloudy@msa.hinet.net CR Amaral LA, 2011, J NETW COMPUT APPL, V34, P972, DOI 10.1016/j.jnca.2010.04.005 Atzori L, 2010, COMPUT NETW, V54, P2787, DOI 10.1016/j.comnet.2010.05.010 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Barr A., 1982, HDB ARTIFICIAL INTEL, V3 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bryson N, 1997, INTELLIGENT INFORMATION SYSTEMS, (IIS'97) PROCEEDINGS, P231, DOI 10.1109/IIS.1997.645234 Carvalho J. P., 1999, INT C COMP INT MOD C, P1 Carvalho JP, 2013, FUZZY SET SYST, V214, P6, DOI 10.1016/j.fss.2011.12.009 Chen RY, 2009, EUR J OPER RES, V196, P266, DOI 10.1016/j.ejor.2008.03.009 COOPER RG, 1994, J PROD INNOVAT MANAG, V11, P3, DOI 10.1111/1540-5885.1110003 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Gandino F, 2009, IEEE T IND ELECTRON, V56, P2357, DOI 10.1109/TIE.2009.2019569 Giusto D., 2010, INTERNET THINGS Glykas M, 2010, STUD FUZZ SOFT COMP, V247, P1, DOI 10.1007/978-3-642-03220-2 Hamilton V. L., 1991, P IEE C TOOLS TECHN HAYESROTH F, 1984, COMPUTER, V17, P263, DOI 10.1109/MC.1984.1658976 Huang YM, 2008, EXPERT SYST APPL, V34, P446, DOI 10.1016/j.eswa.2006.09.037 JOO Y, 1995, COMPUT IND ENG, V28, P561, DOI 10.1016/0360-8352(94)00209-6 Kang YS, 2013, COMPUT IND, V64, P609, DOI 10.1016/j.compind.2013.03.004 Kardaras D, 1999, INFORM SOFTWARE TECH, V41, P197, DOI 10.1016/S0950-5849(98)00125-6 Khalgui M, 2010, INFORM SOFTWARE TECH, V52, P1259, DOI 10.1016/j.infsof.2010.06.001 Kiritsis D, 2011, COMPUT AIDED DESIGN, V43, P479, DOI 10.1016/j.cad.2010.03.002 KOSKO B, 1986, INT J MAN MACH STUD, V24, P65, DOI 10.1016/S0020-7373(86)80040-2 Kumar P, 2009, J FOOD SCI, V74, pR101, DOI 10.1111/j.1750-3841.2009.01323.x Lee J, 2004, FUZZY SET SYST, V144, P105, DOI 10.1016/j.fss.2003.10.016 Lee KC, 2013, IND MARKET MANAG, V42, P552, DOI 10.1016/j.indmarman.2013.03.007 Manos B, 2010, BRIT FOOD J, V112, P640, DOI 10.1108/00070701011052727 Marconi A, 2006, LECT NOTES COMPUT SC, V4294, P459 Mendonca M, 2013, ENG APPL ARTIF INTEL, V26, P1199, DOI 10.1016/j.engappai.2012.11.007 Myhre B, 2009, P 5 EUR WORKSH RFID Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Qi L, 2011, MATH COMPUT MODEL, V53, P2162, DOI 10.1016/j.mcm.2010.08.023 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Salton G., 1983, INTRO MODERN INFORM Samantaray SR, 2013, APPL SOFT COMPUT, V13, P928, DOI 10.1016/j.asoc.2012.09.010 Stylios C. D., 2001, 9 IFAC S LARG SCAL S Thakur M, 2011, J FOOD ENG, V103, P417, DOI 10.1016/j.jfoodeng.2010.11.012 Theodorou S., 2007, THESIS U CAMBRIDGE Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 van Rijsenbergen Cornelis Joost, 1979, INFORM RETRIEVAL Xirogiannis G, 2004, IEEE T ENG MANAGE, V51, P334, DOI 10.1109/TEM.2004.830861 YUAN YF, 1995, FUZZY SET SYST, V69, P125, DOI 10.1016/0165-0114(94)00229-Z Zhang GB, 2011, COMPUT IND ENG, V60, P863, DOI 10.1016/j.cie.2011.02.002 NR 46 TC 44 Z9 45 U1 7 U2 113 PD MAY PY 2015 VL 51 BP 70 EP 84 DI 10.1016/j.foodcont.2014.11.004 WC Food Science & Technology SC Food Science & Technology UT WOS:000350538100010 DA 2022-12-14 ER PT J AU Alizadeh, J Safkhani, M Allahdadi, A AF Alizadeh, Javad Safkhani, Masoumeh Allahdadi, Amir TI ISAKA: Improved Secure Authentication and Key Agreement protocol for WBAN SO WIRELESS PERSONAL COMMUNICATIONS DT Article DE IoT; WBAN; AKA; Sensor node traceability attack; Impersonation attack; BAN logic; ProVerif tool ID ANONYMOUS MUTUAL AUTHENTICATION; AREA; SCHEME AB Internet of Things (IoT) is a revolution which has influenced the lifestyle of human. Wireless Body Area Networks (WBAN)s are IoT-based applications which have a crucial role in the current healthcare systems. A WBAN is used to collect some health-related information of patients and transport and monitor them in a healthcare system. This information is crucial in the sense of the patient's life. Then the privacy of the patient and the security of his/her information are some main challenges in the WBAN. Another challenge in the WBAN is the resources limitation of the sensor nodes. This limitation imposes that a suitable scheme for the WBAN should be a lightweight one. In order to response these challenges, several lightweight Authentication and Key Agreement (AKA) schemes have been presented for WBAN so far. However, approximately none of them could reach their security and cost goals. In 2020, Narwal and Mohapatra proposed a claimed to be secure lightweight AKA protocol for WBAN named SEEMAKA. In this paper, we show that this scheme suffers from attacks including sensor node traceability, disclosure of the secret parameters of the sensor nodes and master nodes, sensor node impersonation, extracting the session key, and Denial of Service attacks. Besides that, we focus to overcome these vulnerabilities and present an improved version of SEEMAKA named ISAKA. ISAKA improves the security level and also the efficiency level of SEEMAKA. More precisely, ISAKA is safe against mentioned attacks and it improves ROM and RAM storage requirements and also computational and communication costs. We prove the security of ISAKA using two formal methods, i.e. BAN logic method and ProVerif tool. C1 [Alizadeh, Javad; Allahdadi, Amir] Imam Hossein Univ, Fac & Res Ctr Comp, Fath Ctr, Tehran, Iran. [Safkhani, Masoumeh] Shahid Rajaee Teacher Training Univ, Comp Engn Dept, Tehran, Iran. [Safkhani, Masoumeh] Inst Res Fundamental Sci IPM, Tehran, Iran. C3 Shahid Rajaee Teacher Training University (SRTTU) RP Safkhani, M (corresponding author), Shahid Rajaee Teacher Training Univ, Comp Engn Dept, Tehran, Iran.; Safkhani, M (corresponding author), Inst Res Fundamental Sci IPM, Tehran, Iran. EM Jaalizadeh@ihu.ac.ir; Safkhani@sru.ac.ir; Aallandad@ihu.ac.ir CR Agha DES, 2018, WIRELESS PERS COMMUN, V103, P2877, DOI 10.1007/s11277-018-5968-y Alzahrani BA, 2021, ARAB J SCI ENG, V46, P3017, DOI 10.1007/s13369-020-04905-9 Amin R, 2015, J MED SYST, V39, DOI 10.1007/s10916-015-0262-y Arshad H, 2016, J MED SYST, V40, DOI 10.1007/s10916-016-0585-3 Blanchet B., 2012, PROVERIF AUTOMATIC C BURROWS M, 1990, ACM T COMPUT SYST, V8, P18, DOI [10.1145/77648.77649, 10.1145/74851.74852] Fotouhi M, 2020, COMPUT NETW, V177, DOI 10.1016/j.comnet.2020.107333 Giri D, 2015, J MED SYST, V39, DOI [10.1007/s10916-014-0145-7, 10.1007/s10916-015-0262-y] Hussain SJ, 2021, WIRELESS PERS COMMUN, V116, P1, DOI 10.1007/s11277-020-07702-7 Ibrahim MH, 2016, COMPUT METH PROG BIO, V135, P37, DOI 10.1016/j.cmpb.2016.07.022 Karthigaiveni M, 2019, J AMB INTEL HUM COMP, DOI 10.1007/s12652-019-01513-w Li X, 2018, TELECOMMUN SYST, V67, P323, DOI 10.1007/s11235-017-0340-1 Li X, 2017, COMPUT NETW, V129, P429, DOI 10.1016/j.comnet.2017.03.013 Narwal B, 2021, ARAB J SCI ENG, V46, P9197, DOI 10.1007/s13369-021-05707-3 Narwal B, 2020, WIRELESS PERS COMMUN, V113, P1985, DOI 10.1007/s11277-020-07304-3 Nikolic MV, 2020, IEEE INT SYMP DESIGN Ostad-Sharif A, 2019, INT J COMMUN SYST, V32, DOI 10.1002/dac.3974 Ostad-Sharif A, 2019, INT J COMMUN SYST, V32, DOI 10.1002/dac.3913 Shaik MF, 2018, WIRELESS PERS COMMUN, V98, P2333, DOI 10.1007/s11277-017-4977-6 Soni M, 2022, WIRELESS PERS COMMUN, V127, P1067, DOI 10.1007/s11277-021-08565-2 Xu ZS, 2019, CONCURR COMP-PRACT E, V31, DOI 10.1002/cpe.5295 Zimmerman TG, 1996, IBM SYST J, V35, P609, DOI 10.1147/sj.353.0609 NR 22 TC 0 Z9 0 U1 3 U2 3 PD OCT PY 2022 VL 126 IS 4 BP 2911 EP 2935 DI 10.1007/s11277-022-09844-2 EA JUN 2022 WC Telecommunications SC Telecommunications UT WOS:000815753000003 DA 2022-12-14 ER PT J AU Stanford, K Stitt, J Kellar, JA McAllister, T AF Stanford, K Stitt, J Kellar, JA McAllister, T TI Traceability in cattle and small ruminants in Canada SO REVUE SCIENTIFIQUE ET TECHNIQUE-OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE Canada; cattle; deoxyribonucleic acid fingerprinting; ear tags; goats; identification; rumen bolus; sheep; traceability; transponders ID ELECTRONIC IDENTIFICATION; BEEF-CATTLE; HOT-IRON; ANIMAL IDENTIFICATION; PASSIVE TRANSPONDERS; SHEEP; FREEZE; BEHAVIOR; COWS AB Traceback systems for cattle and small ruminants are of international concern after the outbreaks of bovine spongiform encephalopathy in the European Union and foot and mouth disease in the United Kingdom and South America. Implementation of a national or international identification system depends on meeting a balance between cost, reliability/durability, ease of use, data transfer speed, protection from fraud, avoidance of entry into the food chain and animal welfare issues. As of 1 January 2001, Canada has instituted a national identification programme for cattle, which will have annual operating and administrative costs of Can$0.20 per head, excluding ear tags. The system will provide herd of origin traceback and individual animal identification by ear tags for all beef cattle. A number of identification technologies are available that would have advantages over visual tags, but these are currently too costly without government support (electronic identification, deoxyribonucleic acid [DNA] fingerprinting, too slow (DNA fingerprinting) or have not been tested sufficiently (retinal imaging) to warrant mandatory inclusion in a national traceback/identification system. C1 Alberta Agr Food & Rural Dev, Lethbridge, AB T1J 4V6, Canada. Canadian Cattle Identificat Agcy, Calgary, AB T2E 7H7, Canada. Canadian Food Inspect Agcy, Nepean, ON K1A 0Y9, Canada. Agr & Agri Food Canada, Lethbridge, AB T1J 4B1, Canada. C3 Alberta Agriculture Food & Rural Development; Canadian Food Inspection Agency; Agriculture & Agri Food Canada RP Stanford, K (corresponding author), Alberta Agr Food & Rural Dev, 100,5401 1st Ave S, Lethbridge, AB T1J 4V6, Canada. CR Basarab JA, 1997, CAN J ANIM SCI, V77, P525, DOI 10.4141/A97-047 *BRIT BROADC CORP, 2000, BSE RIS BAFFL FRANC Caja G, 1999, COMPUT ELECTRON AGR, V24, P45, DOI 10.1016/S0168-1699(99)00036-8 Caja G, 1998, LIVEST PROD SCI, V55, P279, DOI 10.1016/S0301-6226(98)00137-7 CAJA G, 1998, J ANIM SCI S1, V76, P271 *CAN CATTL ASS, 1995, ANN GEN M 23 25 MARC *CAN CATTL ASS, 1997, ANN GEN M 5 8 MARCH *CANF RES, 1998, CAN CATTL ID AG CCIA *CANF RES, 2000, CAN CATTL ID AG CCIA Cheng KJ, 1998, J ANIM SCI, V76, P299 CONILL C, 1998, J ANIM SCI S1, V76, P270 Eradus WJ, 1999, COMPUT ELECTRON AGR, V24, P91, DOI 10.1016/S0168-1699(99)00039-3 EWBANK R, 1988, MANAGEMENT WELFARE F, P17 Fallon R. J., 1996, Farm & Food, V6, P7 Geers R, 1997, ELECT INDENTIFICATIO, P25 GOLDEN B, 2000, P NAT BEEF SCI SEM 2 GRACEY C, 2000, SELLING NATL IDENTIF HASKER PJS, 1992, AUST J EXP AGR, V32, P689, DOI 10.1071/EA9920689 Hasker PJS, 1996, AUST J EXP AGR, V36, P19, DOI 10.1071/EA9960019 HOLSTEIN USA, 2000, ALTERNATIVE ID METHO HOSIE B, 1995, VET REC, V137, P571, DOI 10.1136/vr.137.22.571-b HULL BL, 1992, EXTENSION GOAT HDB M, P4 HUNT ER, 1994, AUST J EXP AGR, V34, P741, DOI 10.1071/EA9940741 KELLAR JA, 2000, CANADIAN FOOD INSPEC KELLAR JA, 2000, LIVESTOCK IDENTIFICA KELLAR JA, 1998, REIDENTIFICATION CAN KIRKEIDE MA, 1982, CATTLE IDENTIFICATIO Klindtworth M, 1999, COMPUT ELECTRON AGR, V24, P65, DOI 10.1016/S0168-1699(99)00037-X Kolver ES, 1998, J DAIRY SCI, V81, P1403, DOI 10.3168/jds.S0022-0302(98)75704-2 Lambooij E, 1999, COMPUT ELECTRON AGR, V24, P81, DOI 10.1016/S0168-1699(99)00038-1 LAY DC, 1992, J ANIM SCI, V70, P1121, DOI 10.2527/1992.7041121x Maher K. D., 1991, Proceedings - Annual Meeting of the United States Animal Health Association, V95, P283 McAllister TA, 2000, CAN J ANIM SCI, V80, P381, DOI 10.4141/A99-099 *MIN AGR FISH FOOD, 2000, CATTL TRAC TAGG NEHRING R, 1998, J ANIM SCI S1, V76, P267 NIGGEMEYER H, 1992, LANDTECHNIK, V47, P141 PINKERTON F, 1991, PUBLICATION LANGST M, V1 *QC DAT INT, 1999, CAN CATTL ID AG TRAC Robinson R., 1995, Quality and grading of carcasses of meat animals., P201 Rossing W, 1999, COMPUT ELECTRON AGR, V24, P1, DOI 10.1016/S0168-1699(99)00033-2 SCHWARTZKOPF KS, 1994, J ANIM SCI, V72, P1393, DOI 10.2527/1994.7261393x Schwartzkopf-Genswein KS, 1998, J ANIM SCI, V76, P972 SchwartzkopfGenswein KS, 1997, J ANIM SCI, V75, P2064 Sutton MD, 1998, ANIM GENET, V29, P168, DOI 10.1111/j.1365-2052.1998.00286.x TAYLOR MA, 1998, BENEFITS COSTS ASS R VANHOUWELINGEN P, 1991, AUTOMATIC ELECT IDEN, P7 Wismans WMG, 1999, COMPUT ELECTRON AGR, V24, P99, DOI 10.1016/S0168-1699(99)00040-X NR 47 TC 43 Z9 54 U1 0 U2 15 PD AUG PY 2001 VL 20 IS 2 BP 510 EP 522 DI 10.20506/rst.20.2.1291 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000170689800014 DA 2022-12-14 ER PT J AU Lai, HQ Xi, JL Sun, JC He, WZ Wang, ZH Zheng, CM Mao, XF AF Lai, Hanqing Xi, Jialin Sun, Jianchun He, Weizhong Wang, Zhaohui Zheng, Chuangmu Mao, Xuefei TI Multi-elemental Analysis by Energy Dispersion X-ray Fluorescence Spectrometry and Its Application on the Traceability of Soybean Origin SO ATOMIC SPECTROSCOPY DT Article ID VIRGIN OLIVE OILS; GEOGRAPHICAL ORIGIN; NEURAL-NETWORKS; TRACE-ELEMENTS; CLASSIFICATION; IDENTIFICATION; XRF; AUTHENTICATION; APPROXIMATION; SPECTROSCOPY AB In this work, a commercial energy dispersion X-ray fluorescence (ED-XRF) spectrometer was utilized to measure the trace elements in soybean samples. After optimizing the radiation time and calibration strategy, ED-XRF was able to successfully measure 9 elements (Mg, K, Ca, Mn, Fe, Ni, Cu, Zn, and Rb) in 296 soybean samples from five producing areas of northern China (Henan, Inner Mongolia, Xinjiang, Heilongjiang, and Liaoning). Since principal component analysis (PCA) is not able to distinguish all growing areas completely, the multi-layer perceptron (MLP) procedure was employed which demonstrated to have a powerful classification capacity with an accuracy rate of 96.2%. XRF is a type of solid sampling analytical method, which is fast, accurate and partly portable for multi-elemental analysis. The combination of MLP and ED-XRF overcomes the analytical disadvantages found with ICP-MS or wavelength dispersion XRF analysis, and provides a novel and fast testing method for on-site recognition of food origin traceability. C1 [Lai, Hanqing; Wang, Zhaohui] Jilin Agr Univ, Coll Food Sci & Engn, Changchun 130118, Peoples R China. [Lai, Hanqing; Zheng, Chuangmu; Mao, Xuefei] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Lai, Hanqing; Zheng, Chuangmu; Mao, Xuefei] Minist Agr & Rural Affairs, Key Lab Agrofood Safety & Qual, Beijing 100081, Peoples R China. [Xi, Jialin] Municipal Stn Agroenvironm Monitoring, Beijing 100029, Peoples R China. [Sun, Jianchun; Zheng, Chuangmu] Inspect & Testing Ctr Agr & Livestock Prod Tibet, Lhasa 850000, Peoples R China. [He, Weizhong] Xinjiang Acad Agr Sci, Inst Agr Qual Stand & Testing Technol, Urumqi 830091, Peoples R China. [Wang, Zhaohui] Chinese Agr Res Syst, Soybean Res & Dev Ctr, Div Soybean Proc, Changchun 130118, Peoples R China. C3 Jilin Agricultural University; Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs RP Wang, ZH (corresponding author), Jilin Agr Univ, Coll Food Sci & Engn, Changchun 130118, Peoples R China.; Mao, XF (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Beijing 100081, Peoples R China.; Mao, XF (corresponding author), Minist Agr & Rural Affairs, Key Lab Agrofood Safety & Qual, Beijing 100081, Peoples R China.; Wang, ZH (corresponding author), Chinese Agr Res Syst, Soybean Res & Dev Ctr, Div Soybean Proc, Changchun 130118, Peoples R China. EM wzhjlndsp@aliyun.com; maoxuefei@caas.cn CR Alonso-Salces RM, 2009, J AGR FOOD CHEM, V57, P4224, DOI 10.1021/jf8037117 An J, 2015, MICROCHEM J, V122, P76, DOI 10.1016/j.microc.2015.03.015 Ariyama K, 2007, J AGR FOOD CHEM, V55, P347, DOI 10.1021/jf062613m Beltran M, 2015, FOOD CHEM, V169, P350, DOI 10.1016/j.foodchem.2014.07.104 Bertoldi D, 2016, FOOD CONTROL, V65, P46, DOI 10.1016/j.foodcont.2016.01.013 Bosco GL, 2013, TRAC-TREND ANAL CHEM, V45, P121, DOI 10.1016/j.trac.2013.01.006 Cabrera-Vique C, 2012, FOOD CHEM, V134, P434, DOI 10.1016/j.foodchem.2012.02.088 Chen JW, 2018, SPECTROSC SPECT ANAL, V38, P2600, DOI 10.3964/j.issn.1000-0593(2018)08-2600-06 Costa VC, 2019, FOOD CHEM, V273, P91, DOI 10.1016/j.foodchem.2018.02.016 De La Calle I, 2013, FOOD CHEM, V138, P234, DOI 10.1016/j.foodchem.2012.09.105 Fragni R, 2018, FOOD CONTROL, V93, P211, DOI 10.1016/j.foodcont.2018.06.002 Gallardo H, 2016, J FOOD COMPOS ANAL, V50, P1, DOI 10.1016/j.jfca.2016.04.007 Gonzalvez A, 2011, FOOD CHEM, V126, P1254, DOI 10.1016/j.foodchem.2010.11.032 Gopi K, 2019, AQUACULTURE, V502, P56, DOI 10.1016/j.aquaculture.2018.12.012 Hamed MM, 2019, PROCESS SAF ENVIRON, V124, P31, DOI 10.1016/j.psep.2019.01.033 Herreros-Chavez L, 2019, FOOD CHEM, V278, P373, DOI 10.1016/j.foodchem.2018.11.065 HORNIK K, 1990, NEURAL NETWORKS, V3, P551, DOI 10.1016/0893-6080(90)90005-6 HORNIK K, 1991, NEURAL NETWORKS, V4, P251, DOI 10.1016/0893-6080(91)90009-T Jansen JHF, 1998, MAR GEOL, V151, P143, DOI 10.1016/S0025-3227(98)00074-7 Kalnicky DJ, 2001, J HAZARD MATER, V83, P93, DOI 10.1016/S0304-3894(00)00330-7 Kawahara N., 2006, HDB PRACTICAL XRAY F, P284 Korenovska M, 2005, EUR FOOD RES TECHNOL, V221, P550, DOI 10.1007/s00217-005-1193-5 Latorre MJ, 2000, ANALYST, V125, P307, DOI 10.1039/a905978d Liu M. B., 2019, METALLURGICAL ANAL, V39, P19 Margui E, 2014, TRAC-TREND ANAL CHEM, V53, P73, DOI 10.1016/j.trac.2013.09.009 Markowicz, 1993, HDB XRAY SPECTROMETR McLaren TI, 2012, SOIL SCI SOC AM J, V76, P1446, DOI 10.2136/sssaj2011.0355 McWhirt A, 2012, COMPOST SCI UTIL, V20, P185 Mirabal-Gallardo Y, 2018, CIENC INVESTIG AGRAR, V45, P181, DOI 10.7764/rcia.v45i2.1883 Mohammed W., 2019, FOOD CHEM, V290, P295 Moreda-Pineiro A, 2003, J FOOD COMPOS ANAL, V16, P195, DOI 10.1016/S0889-1575(02)00163-1 National Health Commission of PRC (NHCPRC), 2016, GB 5009268 2016 Otaka A, 2014, FOOD CHEM, V147, P318, DOI 10.1016/j.foodchem.2013.09.142 Paltridge NG, 2012, PLANT SOIL, V361, P261, DOI 10.1007/s11104-012-1423-0 Parus J, 2000, X-RAY SPECTROM, V29, P192, DOI 10.1002/(SICI)1097-4539(200003/04)29:2<192::AID-XRS421>3.3.CO;2-K Perez-Rodriguez M, 2019, FOOD CONTROL, V95, P232, DOI 10.1016/j.foodcont.2018.08.001 Pince P, 2019, ARCHAEOL ANTHROP SCI, V11, P1241, DOI 10.1007/s12520-018-0598-6 Richardson JA, 2019, CHEM GEOL, V523, P59, DOI 10.1016/j.chemgeo.2019.05.036 Rowe H, 2012, CHEM GEOL, V324, P122, DOI 10.1016/j.chemgeo.2011.12.023 Stanimirova I, 2007, TALANTA, V72, P172, DOI 10.1016/j.talanta.2006.10.011 Tjallingii R, 2007, GEOCHEM GEOPHY GEOSY, V8, DOI 10.1029/2006GC001393 Tong Cheng-ying, 2018, Shengtaixue Zazhi, V37, P1574, DOI 10.13292/j.1000-4890.201805.016 Urickova V, 2015, SPECTROCHIM ACTA A, V148, P131, DOI 10.1016/j.saa.2015.03.111 Wang G. X., 2017, NUCL TECHNIQUES, V40, P1 [魏益民 Wei Yimin], 2012, [中国农业科学, Scientia Agricultura Sinica], V45, P5073 Weindorf DC, 2014, ADV AGRON, V128, P1, DOI 10.1016/B978-0-12-802139-2.00001-9 Zhang GQP, 2000, IEEE T SYST MAN CY C, V30, P451, DOI 10.1109/5326.897072 Zhao HY, 2017, FOOD CONTROL, V76, P82, DOI 10.1016/j.foodcont.2017.01.006 ZUPAN J, 1991, ANAL CHIM ACTA, V248, P1, DOI 10.1016/S0003-2670(00)80865-X NR 49 TC 7 Z9 7 U1 4 U2 27 PD JAN-FEB PY 2020 VL 41 IS 1 BP 20 EP 28 DI 10.46770/AS.2020.01.003 WC Spectroscopy SC Spectroscopy UT WOS:000521635300003 DA 2022-12-14 ER PT J AU Muzzalupo, I Pisani, F Greco, F Chiappetta, A AF Muzzalupo, Innocenzo Pisani, Francesca Greco, Federica Chiappetta, Adriana TI Direct DNA amplification from virgin olive oil for traceability and authenticity SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE SSR loci; Extra virgin olive oil; Direct DNA amplification; Engineered DNA polymerase ID OLEA-EUROPAEA L.; IDENTIFICATION; MARKERS; EXTRACTION; PCR AB The olive tree is one of the most common fruit species cultivated for oil and table olives in Italy and, particularly, in the Mediterranean area. DNA fingerprinting methods include different markers; however, our work is based on the identification of olive oil cultivars by using simple sequence repeats analysis. As previously reported in the literature, this proposed method shows good capability to amplify, for example, DNA from wine and table grape varieties. In our paper, we suggest an easy methodology, which allows direct amplification of DNA and bypassing of the extraction of DNA using an engineered DNA polymerase, KAPA3G Plant DNA polymerase, improved to tolerate plant PCR inhibitors. This new procedure is more efficient, faster and cheaper than traditional methods of DNA extraction and amplification and leads to more accurate results. This innovative protocol, without the addition of chemical solutions, has provided good results and has permitted traceability of virgin olive oils. C1 [Muzzalupo, Innocenzo; Pisani, Francesca; Greco, Federica] Consiglio Ric & Sperimentaz Agr, Ctr Ric Olivicoltura & Ind Olearia CRA OLI, I-87036 Arcavacata Di Rende, CS, Italy. [Greco, Federica] ISOLAB Srl, Ctr Serv Acquario, I-89132 Reggio Di Calabria, Italy. [Chiappetta, Adriana] Univ Calabria, Dipartimento Biol Ecol & Sci Terra DiBEST, I-87036 Arcavacata Di Rende, CS, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); University of Calabria RP Muzzalupo, I (corresponding author), Consiglio Ric & Sperimentaz Agr, Ctr Ric Olivicoltura & Ind Olearia CRA OLI, C Li Rocchi Vermicelli, I-87036 Arcavacata Di Rende, CS, Italy. EM innocenzo.muzzalupo@entecra.it CR Bracci T, 2011, PLANT CELL REP, V30, P449, DOI 10.1007/s00299-010-0991-9 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Carriero F, 2002, THEOR APPL GENET, V104, P301, DOI 10.1007/s001220100691 Chambers GK, 2000, COMP BIOCHEM PHYS B, V126, P455, DOI 10.1016/S0305-0491(00)00233-9 Cipriani G, 2002, THEOR APPL GENET, V104, P223, DOI 10.1007/s001220100685 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 de la Torre F, 2004, J FOOD AGRIC ENVIRON, V2, P84 Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Jobling MA, 2004, NAT REV GENET, V5, P739, DOI 10.1038/nrg1455 Marmiroli N., 2009, ADV OLIVE RESOURCES, P157 Migliaro D, 2013, PLANT GENET RESOUR-C, V11, P182, DOI 10.1017/S1479262112000433 Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z Muzzalupo I, 2002, EUR FOOD RES TECHNOL, V214, P528, DOI 10.1007/s00217-001-0482-x Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Muzzalupo I, 2014, SCI WORLD J, DOI 10.1155/2014/296590 Muzzalupo I, 2011, PLANT FOOD HUM NUTR, V66, P1, DOI 10.1007/s11130-011-0208-6 Omar S. H., 2008, Pharmacognosy Reviews, V2, P135 Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pasqualone A, 2001, EUR FOOD RES TECHNOL, V213, P240, DOI 10.1007/s002170100367 Sefc KM, 2000, MOL ECOL, V9, P1171, DOI 10.1046/j.1365-294x.2000.00954.x Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 NR 24 TC 15 Z9 15 U1 1 U2 42 PD JUL PY 2015 VL 241 IS 1 BP 151 EP 155 DI 10.1007/s00217-015-2455-5 WC Food Science & Technology SC Food Science & Technology UT WOS:000356090700015 DA 2022-12-14 ER PT J AU Liu, RF Gao, ZF Nayga, RM Snell, HA Ma, HY AF Liu, Ruifeng Gao, Zhifeng Nayga, Rodolfo M., Jr. Snell, Heather Arielle Ma, Hengyun TI Consumers' valuation for food traceability in China: Does trust matter? SO FOOD POLICY DT Article DE Choice experiment; Interaction effects; Fuji apple products; Willingness to pay; Chinese consumer ID WILLINGNESS-TO-PAY; COUNTRY-OF-ORIGIN; FRESH PRODUCE; EXPERIMENTAL-DESIGN; CHOICE EXPERIMENT; MIXED LOGIT; CHEAP TALK; GREEN FOOD; SUSTAINABILITY LABELS; SAFETY ATTRIBUTES AB Food safety is a very important topic in China. We investigate Chinese consumers' preferences and willingness to pay (WTP) for food traceability using a choice experiment. Given that consumers' trust in the food system may affect their preferences and WTP, we also assess the interaction between consumers' trust in government's supervision of food safety and food labels and consumers' preferences for traceable food products. Using data collected from a choice experiment on Fuji apples in a face-to-face survey in six Chinese cities, the results show that (i) consumers are willing to pay for traceable food but their valuations can differ upon the degree of their trust in government's supervision of food safety and food labels; (ii) consumers are willing to pay for traceability with strong evidence of preference heterogeneity; (iii) government is not the most trusted safety inspection and certificate authority as found in prior studies using animal food products in China. C1 [Liu, Ruifeng; Ma, Hengyun] Henan Agr Univ, Coll Econ & Management, 15 Longzi Lake Coll Pk, Zhengzhou 450046, Henan, Peoples R China. [Gao, Zhifeng] Univ Florida, Food & Resource Econ Dept, POB 110240, Gainesville, FL 32611 USA. [Nayga, Rodolfo M., Jr.; Snell, Heather Arielle] Univ Arkansas, Dept Agr Econ & Agribusiness, 217 Agr Bldg, Fayetteville, AR 72701 USA. C3 Henan Agricultural University; State University System of Florida; University of Florida; University of Arkansas System; University of Arkansas Fayetteville RP Ma, HY (corresponding author), Henan Agr Univ, Coll Econ & Management, 15 Longzi Lake Coll Pk, Zhengzhou 450046, Henan, Peoples R China. EM ruifeng076@163.com; zfgao@ufl.edu; rnayga@uark.edu; haprice@uark.edu; h.y.ma@163.com CR Akaichi F, 2013, AM J AGR ECON, V95, P949, DOI 10.1093/ajae/aat013 Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Banerji A, 2016, FOOD POLICY, V62, P133, DOI 10.1016/j.foodpol.2016.06.003 Bazzani C, 2017, FOOD QUAL PREFER, V62, P144, DOI 10.1016/j.foodqual.2017.06.019 Bazzani C, 2017, ECON INQ, V55, P383, DOI 10.1111/ecin.12377 Brownstone D, 1999, J ECONOMETRICS, V89, P109 Calvin L., 2006, Amber Waves, V4, P16 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Caputo V, 2018, J CHOICE MODEL, V28, P10, DOI 10.1016/j.jocm.2018.04.003 Caputo V, 2013, AUST J AGR RESOUR EC, V57, P465, DOI 10.1111/1467-8489.12014 CARTER DP, 2018, J CONSU AFF, V5, P1 Chamorro A, 2015, BRIT FOOD J, V117, P820, DOI 10.1108/BFJ-03-2014-0112 Chang A, 2013, BRIT FOOD J, V115, P1361, DOI 10.1108/BFJ-11-2011-0286 Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Farrell J, 1996, J ECON PERSPECT, V10, P103, DOI 10.1257/jep.10.3.103 Fiebig DG, 2010, MARKET SCI, V29, P393, DOI 10.1287/mksc.1090.0508 Gao ZF, 2019, FOOD QUAL PREFER, V71, P475, DOI 10.1016/j.foodqual.2018.03.016 Gao ZF, 2016, FOOD POLICY, V64, P26, DOI 10.1016/j.foodpol.2016.09.001 Gao ZF, 2009, AM J AGR ECON, V91, P795, DOI 10.1111/j.1467-8276.2009.01259.x Golan E., 2004, AER830 USDA EC RES S Gracia A, 2016, FOOD CONTROL, V61, P39, DOI 10.1016/j.foodcont.2015.09.023 Gracia A, 2014, J AGR ECON, V65, P49, DOI 10.1111/1477-9552.12036 Greene D, 2012, DENISE LEVERTOV: A POET'S LIFE, P5 Greene W. H., 1993, ECONOMETRIC ANAL Grunert KG, 2015, FOOD QUAL PREFER, V42, P37, DOI 10.1016/j.foodqual.2015.01.001 Hamzaoui-Essoussi L, 2013, J RETAIL CONSUM SERV, V20, P292, DOI 10.1016/j.jretconser.2013.02.002 Hasimu H, 2017, APPETITE, V108, P191, DOI 10.1016/j.appet.2016.09.019 Hensher DA, 2015, APPLIED CHOICE ANALYSIS, 2ND EDITION, P1, DOI 10.1017/CBO9781316136232 Hensher DA, 2003, TRANSPORTATION, V30, P133, DOI 10.1023/A:1022558715350 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hofstede GJ, 2010, BRIT FOOD J, V112, P671, DOI 10.1108/00070701011058226 Hu WY, 2005, CAN J AGR ECON, V53, P83, DOI 10.1111/j.1744-7976.2005.04004.x International Standard Organization, 2007, 220052007 ISO Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Johnson FR, 2013, VALUE HEALTH, V16, P3, DOI 10.1016/j.jval.2012.08.2223 Johnson R, 2003, SAWTOOTH SOFTWARE RE, P1 Kathuria LM, 2016, J FOOD PROD MARK, V22, P501, DOI 10.1080/10454446.2014.885865 Kehagia O, 2007, SOCIOL RURALIS, V47, P400, DOI 10.1111/j.1467-9523.2007.00445.x Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Kirezieva K, 2013, FOOD RES INT, V52, P230, DOI 10.1016/j.foodres.2013.03.023 Kjaemes U., 2013, TRUST FOOD COMP I AN, P228 KRINSKY I, 1986, REV ECON STAT, V68, P715, DOI 10.2307/1924536 KROSNICK JA, 1991, APPL COGNITIVE PSYCH, V5, P213, DOI 10.1002/acp.2350050305 KUHFELD WF, 1994, J MARKETING RES, V31, P545, DOI 10.2307/3151882 LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 LIDDELL S, 2001, INT FOOD AGRIBUS MAN, V4, P287, DOI DOI 10.1016/S1096-7508(01)00081-7 Liu XL, 2015, BRIT FOOD J, V117, P1440, DOI 10.1108/BFJ-08-2014-0295 Lobb A., 2005, Acta Agricultura Scandinavica. Section C, Food Economics, V2, P3, DOI 10.1080/16507540510033424 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Louviere J, 2002, MARKET LETT, V13, P177, DOI 10.1023/A:1020258402210 Louviere J, 2006, P 2006 SAWT SOFTW C, P211 Louviere J.J., 1999, MARKET LETT, V10, P205, DOI DOI 10.1023/A:1008050215270 Louviere J. J., 2007, REV MARKETING RES, V4, P3 Louviere JJ, 2008, J CHOICE MODEL, V1, P128 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Lusk JL, 2006, REV AGR ECON, V28, P284, DOI 10.1111/j.1467-9353.2006.00288.x Lusk JL, 2018, FOOD POLICY, V77, P91, DOI 10.1016/j.foodpol.2018.04.011 Lusk JL, 2005, AM J AGR ECON, V87, P771, DOI 10.1111/j.1467-8276.2005.00761.x Lusk JL, 2004, REV AGR ECON, V26, P152, DOI 10.1111/j.1467-9353.2004.00168.x Lusk JL, 2003, AM J AGR ECON, V85, P840, DOI 10.1111/1467-8276.00492 Lusk JL, 2003, AM J AGR ECON, V85, P16, DOI 10.1111/1467-8276.00100 MADDALA GS, 1997, LTD DEPENDENT QUALIT McCarthy BL, 2015, J ECON SOC POLICY, V17 McFadden D, 2000, J APPL ECONOMET, V15, P447, DOI 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1 McFadden D, 1974, CONDITIONAL LOGIT AN, P105 Meas T, 2015, AM J AGR ECON, V97, P1044, DOI 10.1093/ajae/aau108 Muringai V, 2017, CAN J AGR ECON, V65, P477, DOI 10.1111/cjag.12138 Murphy JJ, 2005, ENVIRON RESOUR ECON, V30, P327, DOI 10.1007/s10640-004-4224-y Nuttavuthisit K, 2017, J BUS ETHICS, V140, P323, DOI 10.1007/s10551-015-2690-5 Onozaka Y, 2011, AM J AGR ECON, V93, P689, DOI 10.1093/ajae/aar005 Orme B., 2013, SAWTOOTH SOFTWARE Ortega DL, 2016, MEAT SCI, V121, P317, DOI 10.1016/j.meatsci.2016.06.032 Ortega DL, 2012, AM J AGR ECON, V94, P489, DOI 10.1093/ajae/aar074 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pekkirbizli T., 2015, EC AGRO ALIMENT, V17, P31, DOI [10.3280/ECAG2015-003003, DOI 10.3280/ECAG2015-003003] Perrea T, 2014, ASIA PAC J MARKET LO, V26, P296, DOI 10.1108/APJML-09-2013-0106 Revelt D, 1998, REV ECON STAT, V80, P647, DOI 10.1162/003465398557735 Rimpeekool W, 2015, FOOD POLICY, V56, P59, DOI 10.1016/j.foodpol.2015.07.011 Sanders R, 2006, DEV CHANGE, V37, P201, DOI 10.1111/j.0012-155X.2006.00475.x Sarrias M, 2017, J STAT SOFTW, V79, P1, DOI 10.18637/jss.v079.i02 Savage SJ, 2008, J APPL ECONOM, V23, P351, DOI 10.1002/jae.984 Scarpa R., 2005, APPL SIMULATION METH, P247 Scarpa R, 2008, AM J AGR ECON, V90, P994, DOI 10.1111/j.1467-8276.2008.01155.x Scarpa R, 2008, AUST J AGR RESOUR EC, V52, P253, DOI 10.1111/j.1467-8489.2007.00436.x Scarpa R, 2004, NEW HORIZ ENVIRON EC, P316 Shi LJ, 2018, AGR ECON-BLACKWELL, V49, P353, DOI 10.1111/agec.12421 Silva A, 2011, J AGR RESOUR ECON, V36, P280 Su LianFan, 2017, Journal of Agricultural and Resource Economics, V42, P255 Train K., 2005, APPL SIMULATION METH, P1, DOI [10.1007/1-4020-3684-1_1, DOI 10.1007/1-4020-3684-1_1] Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1, DOI 10.1017/CBO9780511805271 Train K, 2016, J CHOICE MODEL, V19, P40, DOI 10.1016/j.jocm.2016.07.004 Trenz M., 2015, MULTICHANNEL COMMERC, P71 Tsakiridou E, 2011, J FOOD PROD MARK, V17, P211, DOI 10.1080/10454446.2011.548749 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 Van Loo EJ, 2015, ECOL ECON, V118, P215, DOI 10.1016/j.ecolecon.2015.07.011 Van Loo EJ, 2014, FOOD POLICY, V49, P137, DOI 10.1016/j.foodpol.2014.07.002 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Wezemael L, 2014, FOOD POLICY, V44, P167, DOI 10.1016/j.foodpol.2013.11.006 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wongprawmas R, 2015, QUAL ASSUR SAF CROP, V7, P789, DOI 10.3920/QAS2013.0359 Wongprawmas R, 2015, QUAL ASSUR SAF CROP, V7, P73, DOI 10.3920/QAS2013.0255 Wongprawmas R, 2017, FOOD POLICY, V69, P25, DOI 10.1016/j.foodpol.2017.03.004 Wu LH, 2017, AGRIBUSINESS, V33, P424, DOI 10.1002/agr.21509 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Wu LH, 2012, CAN J AGR ECON, V60, P317, DOI 10.1111/j.1744-7976.2011.01236.x Xie J, 2016, AGR ECON-BLACKWELL, V47, P181, DOI 10.1111/agec.12193 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] Yu XH, 2014, FOOD POLICY, V45, P80, DOI 10.1016/j.foodpol.2014.01.003 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhong B, 2015, COMPUT ELECTRON AGR, V117, P81, DOI 10.1016/j.compag.2015.07.009 Zhou JH, 2015, J INTEGR AGR, V14, P2189, DOI 10.1016/S2095-3119(15)61115-7 NR 116 TC 65 Z9 66 U1 26 U2 103 PD OCT PY 2019 VL 88 AR 101768 DI 10.1016/j.foodpol.2019.101768 WC Agricultural Economics & Policy; Economics; Food Science & Technology; Nutrition & Dietetics SC Agriculture; Business & Economics; Food Science & Technology; Nutrition & Dietetics UT WOS:000494888700009 DA 2022-12-14 ER PT J AU Muhammad, SA Seow, EK Omar, AM Rodhi, AM Hassan, HM Lalung, J Lee, SC Ibrahim, B AF Muhammad, Syahidah Akmal Seow, Eng-Keng Omar, Ak Mohd Rodhi, Ainolsyakira Mohd Hassan, Hasnuri Mat Lalung, Japareng Lee, Sze-Chi Ibrahim, Baharudin TI Variation of delta H-2, delta O-18 & delta C-13 in crude palm oil from different regions in Malaysia: Potential of stable isotope signatures as a key traceability parameter SO SCIENCE & JUSTICE DT Article DE Stable isotopes; Crude palm oil; Traceability; Hierarchical Cluster Analysis; Principal Component Analysis; Orthogonal Partial Least Square-Discriminant Analysis ID GEOGRAPHICAL ORIGIN; FATTY-ACID; RATIOS; HOUSE; CAVE AB A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (delta C-13) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (delta H-2 and delta O-18) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk delta H-2, delta O-18 and delta C-13 of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. delta O-18 was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils. (C) 2017 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved. C1 [Muhammad, Syahidah Akmal; Omar, Ak Mohd; Lalung, Japareng; Lee, Sze-Chi] Univ Sains Malaysia, Sch Ind Technol, Environm Technol Div, George Town 11800, Malaysia. [Muhammad, Syahidah Akmal; Rodhi, Ainolsyakira Mohd] Univ Sains Malaysia, Analyt Biochem Res Ctr, George Town 11800, Malaysia. [Seow, Eng-Keng] Univ Sains Malaysia, Sch Ind Technol, Food Technol Div, George Town 11800, Malaysia. [Hassan, Hasnuri Mat] Univ Sains Malaysia, Sch Biol Sci, Agrobiol Div, George Town 11800, Malaysia. [Ibrahim, Baharudin] Univ Sains Malaysia, Sch Pharmaceut Sci, Discipline Clin Pharm, George Town 11800, Malaysia. C3 Universiti Sains Malaysia; Universiti Sains Malaysia; Universiti Sains Malaysia; Universiti Sains Malaysia; Universiti Sains Malaysia RP Muhammad, SA (corresponding author), Univ Sains Malaysia, Sch Ind Technol, Environm Technol Div, George Town 11800, Malaysia. EM syahidah.muhammad@usm.my CR Abdi H, 2010, WIRES COMPUT STAT, V2, P433, DOI 10.1002/wics.101 Barcelos E, 2015, FRONT PLANT SCI, V6, DOI 10.3389/fpls.2015.00190 Basiron Y, 2007, EUR J LIPID SCI TECH, V109, P289, DOI 10.1002/ejlt.200600223 Bontempo L, 2016, J MASS SPECTROM, V51, P675, DOI 10.1002/jms.3816 Bontempo L, 2009, RAPID COMMUN MASS SP, V23, P1043, DOI 10.1002/rcm.3968 Bowen GJ, 2010, ISOSCAPES: UNDERSTANDING MOVEMENT, PATTERN, AND PROCESS ON EARTH THROUGH ISOTOPE MAPPING, P139, DOI 10.1007/978-90-481-3354-3_7 BRIDGES CC, 1966, PSYCHOL REP, V18, P851, DOI 10.2466/pr0.1966.18.3.851 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Chiocchini F, 2016, FOOD CHEM, V202, P291, DOI 10.1016/j.foodchem.2016.01.146 Dawson TE, 2002, ANNU REV ECOL SYST, V33, P507, DOI 10.1146/annurev.ecolsys.33.020602.095451 Donald PF, 2004, CONSERV BIOL, V18, P17, DOI 10.1111/j.1523-1739.2004.01803.x Duijn G. van, 2013, Lipid Technology, V25, P15, DOI 10.1002/lite.201300251 Edem DO, 2002, PLANT FOOD HUM NUTR, V57, P319, DOI 10.1023/A:1021828132707 Gat J. R., 1967, IS HYDR S P 1996 INT Georgi M, 2005, PLANT SOIL, V275, P93, DOI 10.1007/s11104-005-0258-3 Gunnarsson-Ostling U, 2011, INT J URBAN REGIONAL, V35, P1048, DOI 10.1111/j.1468-2427.2010.01002.x Kelly SD, 2002, GRASAS ACEITES, V53, P34 Lam MK, 2009, RENEW SUST ENERG REV, V13, P1456, DOI 10.1016/j.rser.2008.09.009 Lamade E, 2016, PLANT CELL ENVIRON, V39, P199, DOI 10.1111/pce.12606 Longo P, 2016, AGRIC AGRIC SCI PROC, V8, P31, DOI 10.1016/j.aaspro.2016.02.005 Morel AC, 2011, FOREST ECOL MANAG, V262, P1786, DOI 10.1016/j.foreco.2011.07.008 Naidoo R, 2009, CONSERV LETT, V2, P35, DOI 10.1111/j.1755-263X.2008.00041.x Portarena S, 2015, FOOD CONTROL, V57, P129, DOI 10.1016/j.foodcont.2015.03.052 Ruiz-Samblas C, 2013, TALANTA, V116, P788, DOI 10.1016/j.talanta.2013.07.054 Seow E. K., 2016, Pertanika Journal of Tropical Agricultural Science, V39, P181 Seow EK, 2016, LWT-FOOD SCI TECHNOL, V65, P428, DOI 10.1016/j.lwt.2015.08.047 Tan KT, 2009, RENEW SUST ENERG REV, V13, P420, DOI 10.1016/j.rser.2007.10.001 Tres A, 2013, FOOD CHEM, V137, P142, DOI 10.1016/j.foodchem.2012.09.094 NR 29 TC 14 Z9 15 U1 0 U2 24 PD JAN PY 2018 VL 58 IS 1 BP 59 EP 66 DI 10.1016/j.scijus.2017.05.008 WC Medicine, Legal; Pathology SC Legal Medicine; Pathology UT WOS:000424183600007 DA 2022-12-14 ER PT J AU Zhao, HY Wang, F Yang, QL AF Zhao, Haiyan Wang, Feng Yang, Qingli TI Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE peanut; multi-elements; geographical origin; traceability; regional scale; chemometrics ID ARACHIS-HYPOGAEA L.; GEOGRAPHICAL ORIGIN; SOIL TYPE; ICP-MS; ELEMENTAL COMPOSITION; MINERAL-COMPOSITION; TEA; DISCRIMINATION; YIELD; ZINC AB BACKGROUND Multi-elements have been widely used to identify the geographical origins of various agricultural products. The objective of this study was to investigate the feasibility of identifying the geographical origins of peanut kernels at different regional scales by using the multi-element fingerprinting technique. The concentrations of 20 elements [boron (B), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), etc.] were determined in 135 peanut samples from Jilin Province, Jiangsu Province, and Shandong Province of China. Data obtained were processed by one-way analysis of variance (ANOVA), principal components analysis (PCA), k nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM). RESULTS Peanut kernels from different regions had their own element fingerprints. The k-NN, LDA, and SVM were all suitable to predict peanut kernels according to their grown provinces with the total correct classification rates of 91.2%, 91.1%, and 91.1%, respectively. While SVM was the best to identify different grown cities of peanut kernels with the prediction accuracy of 91.3%, compared to 72.2% and 78.3% for k-NN and LDA, respectively. CONCLUSION It was an effective method to identify producing areas of peanut kernels at different regional scales using multi-element fingerprinting combined with SVM to enhance regional capabilities for quality assurance and control. (c) 2020 Society of Chemical Industry C1 [Zhao, Haiyan; Wang, Feng; Yang, Qingli] Qingdao Agr Univ, Coll Food Sci & Engn, 700 Changcheng Rd, Qingdao 266109, Peoples R China. C3 Qingdao Agricultural University RP Zhao, HY; Yang, QL (corresponding author), Qingdao Agr Univ, Coll Food Sci & Engn, 700 Changcheng Rd, Qingdao 266109, Peoples R China. EM rice407@163.com; xinyuyuanyin@163.com CR AKRIDADEMERTZI K, 1985, FOOD CHEM, V16, P133, DOI 10.1016/0308-8146(85)90006-8 Branch W. D., 1983, Peanut Science, V10, P5, DOI 10.3146/i0095-3679-10-1-3 D'Archivio AA, 2019, FOOD CHEM, V275, P333, DOI 10.1016/j.foodchem.2018.09.088 DERISE NL, 1974, J FOOD SCI, V39, P264, DOI 10.1111/j.1365-2621.1974.tb02871.x Elsheikh EAE, 1998, FOOD CHEM, V63, P253, DOI 10.1016/S0308-8146(97)00223-9 GAINES T P, 1981, Peanut Science, V8, P16, DOI 10.3146/i0095-3679-8-1-5 GALVAO LCA, 1976, J FOOD SCI, V41, P1305, DOI 10.1111/j.1365-2621.1976.tb01158.x Hidalgo MJ, 2016, FOOD CHEM, V210, P228, DOI 10.1016/j.foodchem.2016.04.120 Janila P, 2015, J AGR SCI-CAMBRIDGE, V153, P975, DOI 10.1017/S0021859614000525 Joy EJM, 2015, SCI TOTAL ENVIRON, V505, P587, DOI 10.1016/j.scitotenv.2014.10.038 Phan-Thien KY, 2012, FOOD CHEM, V134, P453, DOI 10.1016/j.foodchem.2012.02.095 Kukusamude C, 2018, FOOD CONTROL, V91, P357, DOI 10.1016/j.foodcont.2018.04.018 Li L, 2018, FOOD CONTROL, V90, P18, DOI 10.1016/j.foodcont.2018.02.031 Matsuoka K, 2018, HORTICULT J, V87, P155, DOI [10.2503/hortj.OKD-100, 10.2503/hortj.okd-100] Nadaf SA, 2015, LEGUME RES, V38, P598 Opatic AM, 2018, FOOD CONTROL, V89, P133, DOI 10.1016/j.foodcont.2017.11.013 Phan-Thien KY, 2010, J AGR FOOD CHEM, V58, P9204, DOI 10.1021/jf101332z Pongrac P, 2019, PLANT SOIL, V434, P151, DOI 10.1007/s11104-018-3628-3 Shokunbi OS, 2012, GRASAS ACEITES, V63, P14, DOI 10.3989/gya.056611 Thavarajah D, 2010, FOOD CHEM, V122, P254, DOI 10.1016/j.foodchem.2010.02.073 Thornton ST, 2015, CROP SCI, V55, P211, DOI 10.2135/cropsci2014.04.0302 Wang F, 2020, J SCI FOOD AGR, V100, P1294, DOI 10.1002/jsfa.10144 Wang SS, 2014, ECOTOX ENVIRON SAFE, V108, P23, DOI 10.1016/j.ecoenv.2014.06.029 Wang XB, 2020, PEDOSPHERE, V30, P555, DOI 10.1016/S1002-0160(17)60457-0 Yanai J, 2012, SOIL SCI PLANT NUTR, V58, P1, DOI 10.1080/00380768.2012.658349 Zhao HY, 2019, J SCI FOOD AGR, V99, P6509, DOI 10.1002/jsfa.9930 Zhao HY, 2019, INT J FOOD SCI TECH, V54, P249, DOI 10.1111/ijfs.13935 Zhao HY, 2017, J FOOD COMPOS ANAL, V63, P15, DOI 10.1016/j.jfca.2017.07.030 Zhao HY, 2017, FOOD CONTROL, V76, P82, DOI 10.1016/j.foodcont.2017.01.006 Zhu YB, 2013, ANAL SCI, V29, P1027, DOI 10.2116/analsci.29.1027 Zhuang H, 2014, CHEMOMETR INTELL LAB, V135, P183, DOI 10.1016/j.chemolab.2014.04.018 NR 31 TC 14 Z9 15 U1 5 U2 41 PD AUG PY 2020 VL 100 IS 10 BP 4040 EP 4048 DI 10.1002/jsfa.10449 EA MAY 2020 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000531900500001 DA 2022-12-14 ER PT J AU Szemethy, D Mihalik, B Frank, K Nagy, T Ujvary, D Kusza, S Szemethy, L Barta, E Steger, V AF Szemethy, Daniel Mihalik, Bendeguz Frank, Krisztian Nagy, Tibor ujvary, Dora Kusza, Szilvia Szemethy, Laszlo Barta, Endre Steger, Viktor TI Development of Wild Boar Species-Specific DNA Markers for a Potential Quality Control and Traceability Method in Meat Products SO FOOD ANALYTICAL METHODS DT Article DE Wild boar; Food safety; Venison; InDel markers; Species identification ID DEER CAPREOLUS-CAPREOLUS; SUS-SCROFA; CERVUS-ELAPHUS; PIG BREEDS; DAMA-DAMA; IDENTIFICATION; PCR; QUANTIFICATION; AUTHENTICATION; MULTIPLEX AB In the food supply chain, quality control has a very important role in maintaining customer confidence. In the EU, food safety aspects are strictly regulated; however, composition requirements and standard control methods are generally undefined. The rapidly increasing wild boar population has a growing market share in venison or game meat production. Several methods have been described for species identification and control of composition in food products, but only some of these are suitable for routine measurements. The aim of our research was to design a rapid, reliable and simple PCR insertion/deletion (InDel)-based genetic tool suitable for species identification in food quality control laboratories. In total, 59 different swine (Sus scrofa) whole genomes were tested with bioinformatic tools to identify wild boar-specific insertions or deletions. Three independent InDels were suitable for marker development, multiplex PCR amplification and separation in agarose gel. Altogether, 209 samples of wild boar and ten other domestic pig breeds were taken for DNA extraction and validation of the three multiplexed InDel markers. Statistical analysis showed a very high combined predictive value (0.996), indicating the capability of the newly developed markers to detect wild boars with a probability over 99%. Breed assignment tests confirm that the InDel markers developed are suitable for rapid, sensitive and reliable identification of the wild boar meat content of food products. The use of the reported method in food quality control can mean a simple and cost-effective way to maintain consumer confidence and to support the competitiveness of fair producers. C1 [Szemethy, Daniel] Szent Istvan Univ, Fac Agr & Environm Sci, Godollo, Hungary. [Szemethy, Daniel; Mihalik, Bendeguz; Frank, Krisztian; Nagy, Tibor; Barta, Endre; Steger, Viktor] Natl Agr Res & Innovat Ctr, Agr Biotechnol Inst, Godollo, Hungary. [ujvary, Dora] Fdn Preservat European Wildlife, Godollo, Hungary. [Kusza, Szilvia] Univ Debrecen, Fac Agr & Food Sci & Environm Management, Debrecen, Hungary. [Szemethy, Laszlo] Univ Pecs, Fac Reg Dev, Szekszard, Hungary. C3 Hungarian University of Agriculture & Life Sciences; University of Debrecen; University of Pecs RP Steger, V (corresponding author), Natl Agr Res & Innovat Ctr, Agr Biotechnol Inst, Godollo, Hungary. EM steger.viktor@abc.naik.hu CR Arslan A, 2006, MEAT SCI, V72, P326, DOI 10.1016/j.meatsci.2005.08.001 Ballin NZ, 2008, MEAT SCI, V80, P151, DOI 10.1016/j.meatsci.2007.12.024 Bhattramakki D, 2002, PLANT MOL BIOL, V48, P539, DOI 10.1023/A:1014841612043 Bieber C, 2005, J APPL ECOL, V42, P1203, DOI 10.1111/j.1365-2664.2005.01094.x Caratti S, 2010, FORENSIC SCI INT-GEN, V4, P339, DOI 10.1016/j.fsigen.2010.07.001 Conyers CM, 2012, J AGR FOOD CHEM, V60, P3341, DOI 10.1021/jf205109b Crespo-Piazuelo D, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0218862 Csanyi S, 2014, J MOUNTAIN ECOL, V3, P222 Earl DA, 2012, CONSERV GENET RESOUR, V4, P359, DOI 10.1007/s12686-011-9548-7 Ellegren H, 2004, NAT REV GENET, V5, P435, DOI 10.1038/nrg1348 Evanno G, 2005, MOL ECOL, V14, P2611, DOI 10.1111/j.1365-294X.2005.02553.x Fajardo V, 2007, MEAT SCI, V76, P234, DOI 10.1016/j.meatsci.2006.11.004 Fajardo V, 2008, MEAT SCI, V79, P289, DOI 10.1016/j.meatsci.2007.09.013 Fang MY, 2006, P R SOC B, V273, P1803, DOI 10.1098/rspb.2006.3514 Fernandez A, 2004, J SCI FOOD AGR, V84, P1855, DOI 10.1002/jsfa.1829 Floren C, 2015, FOOD CHEM, V173, P1054, DOI 10.1016/j.foodchem.2014.10.138 Fondevila M, 2012, INT J LEGAL MED, V126, P725, DOI 10.1007/s00414-012-0721-7 Frank K, 2017, MITOCHONDRIAL DNA B, V2, P730, DOI 10.1080/23802359.2017.1390415 Groenen MAM, 2012, NATURE, V491, P393, DOI 10.1038/nature11622 Lehmann DJ, 2005, AM J EPIDEMIOL, V162, P305, DOI 10.1093/aje/kwi202 Li H, 2009, BIOINFORMATICS, V25, P1754, DOI 10.1093/bioinformatics/btp324 Lin YC, 2014, FORENSIC SCI INT-GEN, V9, P12, DOI 10.1016/j.fsigen.2013.10.006 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 Manunza A, 2016, SCI REP-UK, V6, DOI 10.1038/srep29913 Massei Giovanna, 2004, Galemys, V16, P135 Massei G, 2015, PEST MANAG SCI, V71, P492, DOI 10.1002/ps.3965 Molnar J, 2014, BMC GENOMICS, V15, DOI 10.1186/1471-2164-15-761 Pascal G, 2001, CELL MOL BIOL, V47, P1329 Peakall R, 2012, BIOINFORMATICS, V28, P2537, DOI 10.1093/bioinformatics/bts460 Pereira R, 2009, ELECTROPHORESIS, V30, P3682, DOI 10.1002/elps.200900274 Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Pompanon F, 2005, NAT REV GENET, V6, P847, DOI 10.1038/nrg1707 Pritchard JK, 2000, GENETICS, V155, P945 Ramos AM, 2011, ANIM GENET, V42, P613, DOI 10.1111/j.1365-2052.2011.02198.x Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 Ren F, 2017, ANIM REPROD SCI, V178, P59, DOI 10.1016/j.anireprosci.2017.01.009 Robinson JT, 2011, NAT BIOTECHNOL, V29, P24, DOI 10.1038/nbt.1754 Rozen S, 2000, Methods Mol Biol, V132, P365 Syvanen AC, 2001, NAT REV GENET, V2, P930, DOI 10.1038/35103535 Szanto-Egesz R, 2016, FOOD ANAL METHOD, V9, P889, DOI 10.1007/s12161-015-0261-0 Vali U, 2008, BMC GENET, V9, DOI 10.1186/1471-2156-9-8 Velickovic N, 2014, P 3 INT S HUNT Wilkinson S, 2012, BMC GENOMICS, V13, DOI 10.1186/1471-2164-13-580 Zhang TF, 2017, BMC GENOMICS, V18, DOI 10.1186/s12864-017-4093-8 Zsolnai A, 2013, ARCH TIERZUCHT, V56, P200, DOI 10.7482/0003-9438-56-019 NR 45 TC 1 Z9 2 U1 2 U2 8 PD JAN PY 2021 VL 14 IS 1 BP 18 EP 27 DI 10.1007/s12161-020-01840-1 EA SEP 2020 WC Food Science & Technology SC Food Science & Technology UT WOS:000567723500001 DA 2022-12-14 ER PT J AU Boulo, S Hanisch, K Bidlingmaier, M Arsene, CG Panteghini, M Auclair, G Sturgeon, C Schimmel, H Zegers, I AF Boulo, Sebastien Hanisch, Katja Bidlingmaier, Martin Arsene, Cristian-Gabriel Panteghini, Mauro Auclair, Guy Sturgeon, Catharine Schimmel, Heinz Zegers, Ingrid TI Gaps in the Traceability Chain of Human Growth Hormone Measurements SO CLINICAL CHEMISTRY DT Article ID CONSENSUS STATEMENT; STANDARDIZATION; ASSAY; QUANTIFICATION; INTERFERENCE; SOMATROPIN; SERUM; GH AB BACKGROUND: Human growth hormone (hGH) is measured for the diagnosis of secretion disorders. These measurements fall under the EU Directive 98/79/EC on in vitro diagnostic medical devices requiring traceability of commercial calibrator values to higher-order reference materials or procedures (Off J Eur Communities 1998 Dec 7; L 331:1-37). External quality assessment schemes show large discrepancies between results from different methods, even though most methods provide results traceable to the recommended International Standard (IS 98/574). The aim of this study was to investigate possible causes for these discrepancies. METHODS: We investigated the commutability and recovery of hGH in reconstituted IS 98/574. We tested different reconstitution protocols and used 4 different serum matrices for spiking. These IS preparations were measured together with serum samples. We quantified hGH by 5 different methods in 4 different laboratories. RESULTS: Results from the different methods correlated well for the serum samples. Mean discrepancies between results from different methods were <= 20%. None of the IS preparations was commutable for all the method comparisons. The recovery of hGH in preparations of IS 98/574 depended on the reconstitution protocol (>10-fold differences) and background matrix (relative differences <= 17% for different serum matrices). CONCLUSIONS: The use of different protocols for reconstitution and spiking of hGH reference preparations affects quantification by immunoassays, potentially leading to a bias between commercial methods, despite the use of calibrators with values claimed to be traceable to the same higher-order reference material. (C) 2013 American Association for Clinical Chemistry C1 [Boulo, Sebastien; Hanisch, Katja; Auclair, Guy; Schimmel, Heinz; Zegers, Ingrid] European Commiss, Joint Res Ctr, IRMM, B-2440 Geel, Belgium. [Bidlingmaier, Martin] Klinikum Univ Munchen, Med Klin & Poliklin 4, Endocrine Lab, Munich, Germany. [Arsene, Cristian-Gabriel] PTB, Braunschweig, Germany. [Panteghini, Mauro] Univ Milan, Ctr Metrol Traceabil Lab Med, Milan, Italy. [Sturgeon, Catharine] Royal Infirm, UK Natl External Qual Assessment Serv NEQAS, Dept Lab Med, Edinburgh, Midlothian, Scotland. C3 European Commission Joint Research Centre; EC JRC Institute for Reference Materials & Measurements (IRMM); University of Munich; Physikalisch-Technische Bundesanstalt (PTB); University of Milan; Royal Infirmary of Edinburgh RP Zegers, I (corresponding author), European Commiss, Joint Res Ctr, IRMM, Retieseweg 111, B-2440 Geel, Belgium. EM ingrid.zegers@ec.europa.eu CR [Anonymous], 2010, C53A CLSI [Anonymous], 2011, D405211 ASTM Arsene CG, 2010, ANAL BIOCHEM, V401, P228, DOI 10.1016/j.ab.2010.03.005 Bengtsson BA, 2004, J CLIN ENDOCR METAB, V89, P3099, DOI 10.1210/jc.2003-031138 Bidlingmaier M, 2010, GROWTH HORM IGF RES, V20, P19, DOI 10.1016/j.ghir.2009.09.005 Bristow AF, 2001, BIOLOGICALS, V29, P97, DOI 10.1006/biol.2001.0281 Clemmons DR, 2011, CLIN CHEM, V57, P555, DOI 10.1373/clinchem.2010.150631 De Palo EF, 2006, CLIN CHIM ACTA, V364, P67, DOI 10.1016/j.cca.2005.06.009 Hansen TK, 2002, CLIN ENDOCRINOL, V57, P779, DOI 10.1046/j.1365-2265.2002.01668.x Israel E, 2000, J CLIN ENDOCR METAB, V85, P3990 Manolopoulou J, 2012, CLIN CHEM, V58, P1446, DOI 10.1373/clinchem.2012.188128 Markkanen H, 2006, CLIN CHEM, V52, P468, DOI 10.1373/clinchem.2005.060236 Muller A, 2011, CLIN CHEM LAB MED, V49, P1135, DOI 10.1515/CCLM.2011.201 Popii V, 2004, CLIN CHIM ACTA, V350, P1, DOI 10.1016/j.cccn.2004.06.007 Pritchard C, 2009, CLIN CHEM, V55, P1984, DOI 10.1373/clinchem.2009.124354 R Core Team, 2012, R LANG ENV STAT COMP Ross HA, 2008, CLIN CHEM LAB MED, V46, P1334, DOI 10.1515/CCLM.2008.261 Ross HA, 2011, CLIN CHEM, V57, P1463, DOI 10.1373/clinchem.2011.169771 Strasburger CJ, 2005, HORM RES, V64, P1, DOI 10.1159/000087745 Tong J, 2012, J CLIN ENDOCR METAB, V97, P3366, DOI 10.1210/jc.2012-2012 Trainer PJ, 2006, EUR J ENDOCRINOL, V155, P1, DOI 10.1530/eje.1.02186 WHO International Standard, 2012, SOM REC DNA DER HUM Zegers I, 2008, CERTIFICATION PROTEI NR 23 TC 17 Z9 17 U1 0 U2 12 PD JUL PY 2013 VL 59 IS 7 BP 1074 EP 1082 DI 10.1373/clinchem.2012.199489 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000321549400013 DA 2022-12-14 ER PT J AU Blanc-Jolivet, C Liesebach, M AF Blanc-Jolivet, C. Liesebach, M. TI Tracing the origin and species identity of Quercus robur and Quercus petraea in Europe: a review SO SILVAE GENETICA DT Review DE Quercus; oak; molecular markers; traceability; Europe; forest reproductive material; timber ID CHLOROPLAST DNA VARIATION; (GA)(N) MICROSATELLITE LOCI; WHITE OAKS; GENETIC-VARIATION; GEOGRAPHIC ORIGIN; ASSIGNMENT TESTS; POSTGLACIAL RECOLONIZATION; DIFFERENT PROVENANCES; CATTLE BREEDS; L. AB Traceability of forest material has received recently increasing interest and European regulations already apply on forest reproductive material and timber. DNA fingerprinting methods allow identification of species and control of geographic origin, providing that genetic reference data is available. In this review, we focus on the two economically important European oak species, Quercus robur and Q. petraea. We describe the available molecular markers and data, and discuss their applicability for traceability systems of forest reproductive material at a European scale. We also provide insights on the use of DNA fingerprinting on timber material. C1 [Blanc-Jolivet, C.; Liesebach, M.] Thunen Inst Forest Genet, Sieker Landstr 2, D-22927 Grosshansdorf, Germany. C3 Johann Heinrich von Thunen Institute RP Blanc-Jolivet, C (corresponding author), Thunen Inst Forest Genet, Sieker Landstr 2, D-22927 Grosshansdorf, Germany. EM celine.blanc-jolivet@thuenen.de CR AAS G., 2002, ENZYKLOPADIE HOLZGEW Aas G, 2000, ENZYKLOPADIE HOLZGEW Abadie P, 2012, J EVOLUTION BIOL, V25, P157, DOI 10.1111/j.1420-9101.2011.02414.x Alberto F, 2010, MOL ECOL, V19, P2626, DOI 10.1111/j.1365-294X.2010.04631.x Bakker EG, 2003, PLANT BIOLOGY, V5, P393, DOI 10.1055/s-2003-42706 Ballian D, 2010, ACTA SOC BOT POL, V79, P189, DOI 10.5586/asbp.2010.024 Bekkevold D, 2015, ICES J MAR SCI, V72, P1790, DOI 10.1093/icesjms/fsu247 Bordacs S, 2002, FOREST ECOL MANAG, V156, P197, DOI 10.1016/S0378-1127(01)00643-0 Buschbom J, 2011, J HERED, V102, P464, DOI 10.1093/jhered/esr023 Chmielewski M, 2015, IFOREST, V8, P765, DOI 10.3832/ifor1597-008 Chybicki IJ, 2010, MOL ECOL, V19, P2137, DOI 10.1111/j.1365-294X.2010.04632.x Chybicki IJ, 2013, ANN BOT-LONDON, V112, P561, DOI 10.1093/aob/mct131 Cornuet JM, 1999, GENETICS, V153, P1989 Cottrell JE, 2002, FOREST ECOL MANAG, V156, P181, DOI 10.1016/S0378-1127(01)00642-9 Csaikl UM, 2002, FOREST ECOL MANAG, V156, P211, DOI 10.1016/S0378-1127(01)00644-2 Csaikl UM, 2002, FOREST ECOL MANAG, V156, P131, DOI 10.1016/S0378-1127(01)00639-9 Curtu AL, 2007, PLANT BIOLOGY, V9, P116, DOI 10.1055/s-2006-924542 Curtu AL, 2007, BMC EVOL BIOL, V7, DOI 10.1186/1471-2148-7-218 Degen B, 2013, FORENSIC SCI INT-GEN, V7, P55, DOI 10.1016/j.fsigen.2012.06.003 Degen B, 2010, SILVAE GENET, V59, P268, DOI 10.1515/sg-2010-0038 Degen B, 1999, HEREDITY, V83, P597, DOI 10.1038/sj.hdy.6886220 Deguilloux MF, 2004, ANN FOREST SCI, V61, P97, DOI 10.1051/forest:2003089 Deguilloux MF, 2003, MOL ECOL, V12, P1629, DOI 10.1046/j.1365-294X.2003.01836.x Deguilloux MF, 2003, MOL ECOL NOTES, V3, P24, DOI 10.1046/j.1471-8286.2003.00339.x Deguilloux MF, 2002, P ROY SOC B-BIOL SCI, V269, P1039, DOI 10.1098/rspb.2002.1982 Derory J, 2010, HEREDITY, V104, P438, DOI 10.1038/hdy.2009.134 DKV, 2022, STIEL QUERC ROB DKV DOW BD, 1995, THEOR APPL GENET, V91, P137, DOI 10.1007/BF00220870 Ducousso A., 2004, EUFORGEN TECHNICAL G Duforet-Frebourg N, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/s12859-015-0661-6 Dumolin S, 1995, THEOR APPL GENET, V91, P1253, DOI 10.1007/BF00220937 Dumolin-Lapegue S, 1999, EVOLUTION, V53, P1406, DOI 10.1111/j.1558-5646.1999.tb05405.x Dumolin-Lapegue S, 1998, MOL BIOL EVOL, V15, P1321, DOI 10.1093/oxfordjournals.molbev.a025860 Durand J, 2010, BMC GENOMICS, V11, DOI 10.1186/1471-2164-11-570 Fineschi S, 2002, FOREST ECOL MANAG, V156, P103, DOI 10.1016/S0378-1127(01)00637-5 Finkeldey R, 2003, THEOR APPL GENET, V106, P346, DOI 10.1007/s00122-002-1002-5 Fortini P, 2015, PLANT SYST EVOL, V301, P375, DOI 10.1007/s00606-014-1080-2 Frantz AC, 2006, MOL ECOL, V15, P3191, DOI 10.1111/j.1365-294X.2006.03022.x Gailing O, 2007, J APPL BOT FOOD QUAL, V81, P165 Gailing O, 2007, ALLG FORST JAGDZTG, V178, P85 Gailing O, 2003, ALLG FORST JAGDZTG, V174, P227 Gailing O, 2009, PHYSIOL PLANTARUM, V137, P509, DOI 10.1111/j.1399-3054.2009.01263.x Gerber S, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0085130 Gill P, 2012, FORENSIC SCI INT-GEN, V6, P679, DOI 10.1016/j.fsigen.2012.06.002 Glover KA, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-2 Goicoechea PG, 2012, HEREDITY, V109, P361, DOI 10.1038/hdy.2012.51 Gomory D., 2000, Forest Genetics, V7, P167 Gugerli F, 2007, ANN BOT-LONDON, V99, P713, DOI 10.1093/aob/mcm006 Guichoux E, 2011, MOL ECOL RESOUR, V11, P578, DOI 10.1111/j.1755-0998.2011.02983.x GUICHOUX E., 2013, MOL ECOL, V22, p[Molecular, 540] Hedrick PW, 2005, EVOLUTION, V59, P1633, DOI 10.1111/j.0014-3820.2005.tb01814.x Hertel Heike, 2000, Forest Snow and Landscape Research, V75, P169 Hoeltken AM., 2012, ALLGEMEINE FORST JAG, V183, P5 Honjo M, 2008, CONSERV GENET, V9, P1139, DOI 10.1007/s10592-007-9427-7 Howard C, 2009, J FORENSIC SCI, V54, P556, DOI 10.1111/j.1556-4029.2009.01014.x Jensen JS, 2002, FOREST ECOL MANAG, V156, P167, DOI 10.1016/S0378-1127(01)00641-7 Jolivet C, 2012, FORENSIC SCI INT-GEN, V6, P487, DOI 10.1016/j.fsigen.2011.11.002 Kampfer S, 1998, HEREDITAS, V129, P183, DOI 10.1111/j.1601-5223.1998.00183.x Kelleher CT, 2004, FOREST ECOL MANAG, V189, P123, DOI 10.1016/j.foreco.2003.07.032 Konnert M., 2006, AFZ/Der Wald, Allgemeine Forst Zeitschrift fur Waldwirtschaft und Umweltvorsorge, V61, P430 Konnert M., 2002, ALLG FORST U JAGDZTG, V6, P97 Krahl- Urban J., 1959, DIE EICHEN Kremer A, 2002, FOREST ECOL MANAG, V156, P75, DOI 10.1016/S0378-1127(01)00635-1 Lagache L, 2014, MOL ECOL, V23, P4331, DOI 10.1111/mec.12766 Lagache L, 2013, MOL ECOL, V22, P423, DOI 10.1111/mec.12121 Lepais O, 2011, EVOLUTION, V65, P156, DOI 10.1111/j.1558-5646.2010.01101.x Lepoittevin C, 2015, MOL ECOL RESOUR, V15, P1446, DOI 10.1111/1755-0998.12407 Lowe A, 2004, FORESTRY, V77, P335, DOI 10.1093/forestry/77.4.335 Manel S, 2005, TRENDS ECOL EVOL, V20, P136, DOI 10.1016/j.tree.2004.12.004 Manel S, 2002, CONSERV BIOL, V16, P650, DOI 10.1046/j.1523-1739.2002.00576.x Mateus JC, 2015, FOOD CONTROL, V47, P487, DOI 10.1016/j.foodcont.2014.07.038 Maudet C, 2002, J ANIM SCI, V80, P942 Muller-Starck G., 1993, ANN FOREST SCI, V50, p233S Nazareno AG, 2014, CONSERV GENET, V15, P441, DOI 10.1007/s10592-013-0552-1 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Neophytou C, 2015, TREE GENET GENOMES, V11, DOI 10.1007/s11295-015-0905-7 Neophytou C, 2014, TREE GENET GENOMES, V10, P273, DOI 10.1007/s11295-013-0680-2 Neophytou C, 2013, FOREST ECOL MANAG, V304, P89, DOI 10.1016/j.foreco.2013.04.020 Ogden R, 2015, FORENSIC SCI INT-GEN, V18, P152, DOI 10.1016/j.fsigen.2015.02.008 Olalde M, 2002, FOREST ECOL MANAG, V156, P89, DOI 10.1016/S0378-1127(01)00636-3 Petit RJ, 2002, FOREST ECOL MANAG, V156, P49, DOI 10.1016/S0378-1127(01)00634-X Petit RJ, 2004, NEW PHYTOL, V161, P151, DOI 10.1046/j.1469-8137.2003.00944.x Petit RJ, 1997, P NATL ACAD SCI USA, V94, P9996, DOI 10.1073/pnas.94.18.9996 Petit RJ, 2002, FOREST ECOL MANAG, V156, P115, DOI 10.1016/S0378-1127(01)00638-7 Petit RJ, 2002, FOREST ECOL MANAG, V156, P5, DOI 10.1016/S0378-1127(01)00645-4 Plomion C, 2016, MOL ECOL RESOUR, V16, P254, DOI 10.1111/1755-0998.12425 Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 Sebastiani F, 2004, MOL ECOL NOTES, V4, P259, DOI 10.1111/j.1471-8286.2004.00635.x Slade D, 2008, SILVAE GENET, V57, P227, DOI 10.1515/sg-2008-0035 Steinkellner H, 1997, MOL ECOL, V6, P1189, DOI 10.1046/j.1365-294X.1997.00288.x Streiff R, 1999, MOL ECOL, V8, P831, DOI 10.1046/j.1365-294X.1999.00637.x Streiff R, 1998, MOL ECOL, V7, P317, DOI 10.1046/j.1365-294X.1998.00360.x Vidalis A, 2013, PLANT BIOLOGY, V15, P126, DOI 10.1111/j.1438-8677.2012.00575.x Wasser SK, 2015, SCIENCE, V349, P84, DOI 10.1126/science.aaa2457 Weising K, 1999, GENOME, V42, P9, DOI 10.1139/gen-42-1-9 Yucedag C, 2013, TURK J BOT, V37, P619, DOI 10.3906/bot-1205-18 Zanetto A., 1994, Forest Genetics, V1, P111 Ziegenhagen B, 2003, TREES-STRUCT FUNCT, V17, P345, DOI 10.1007/s00468-002-0244-9 NR 98 TC 11 Z9 11 U1 0 U2 21 PY 2015 VL 64 IS 4 BP 182 EP 193 DI 10.1515/sg-2015-0017 WC Forestry; Genetics & Heredity SC Forestry; Genetics & Heredity UT WOS:000382797500005 DA 2022-12-14 ER PT J AU Wang, L Wang, QN Wang, YZ Wang, YM AF Wang, Li Wang, Qinqin Wang, Yuanzhong Wang, Yunmei TI Comparison of Geographical Traceability of Wild and Cultivated Macrohyporia cocos with Different Data Fusion Approaches SO JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY DT Article ID QUALITY ASSESSMENT; CLASSIFICATION; SPECTROSCOPY; AUTHENTICATION; MECHANISM; LIQUID; GRAPE; FOOD; UV AB Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos, this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques, like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid-, and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method-Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis-were considered in data fusion. The results indicate the following: (1) The difference between wild and cultivated samples did exist in terms of the content analysis of vital chemical components and fingerprint analysis. (2) Wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The mid-level Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification. C1 [Wang, Li; Wang, Yunmei] Yunnan Acad Agr Sci, Qual Stand & Testing Technol Res Inst, Kunming 650205, Yunnan, Peoples R China. [Wang, Li] Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Yunnan, Peoples R China. [Wang, Qinqin] Yunnan Univ Tradit Chinese Med, Affiliated Hosp 1, Kunming 650021, Yunnan, Peoples R China. [Wang, Qinqin; Wang, Yuanzhong] Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan Agricultural University; Yunnan University of Chinese Medicine; Yunnan Academy of Agricultural Sciences RP Wang, YM (corresponding author), Yunnan Acad Agr Sci, Qual Stand & Testing Technol Res Inst, Kunming 650205, Yunnan, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. EM wlwanglidong@126.com; wqq6501@163.com; boletus@126.com; 1347914675@qq.com CR Ballabio D, 2013, ANAL METHODS-UK, V5, P3790, DOI 10.1039/c3ay40582f Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Breiman L., 2001, MACH LEARN, V45, P5, DOI [10.1023/A:1010933404324, 10.1201/9780429469275-8] de Santana FB, 2018, FOOD ANAL METHOD, V11, P1927, DOI 10.1007/s12161-017-1142-5 Horn B, 2018, FOOD CHEM, V257, P112, DOI 10.1016/j.foodchem.2018.03.007 Jimenez-Carvelo AM, 2019, TALANTA, V195, P69, DOI 10.1016/j.talanta.2018.11.033 KENNARD RW, 1969, TECHNOMETRICS, V11, P137, DOI 10.2307/1266770 Kursa MB, 2010, J STAT SOFTW, V36, P1, DOI 10.18637/jss.v036.i11 Lee S, 2017, J FUNCT FOODS, V32, P27, DOI 10.1016/j.jff.2017.02.012 Li Y, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-31264-1 Li Y, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0168998 Li Y, 2016, SPECTROCHIM ACTA A, V165, P61, DOI 10.1016/j.saa.2016.04.012 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Liu XF, 2019, INT J BIOL MACROMOL, V127, P39, DOI 10.1016/j.ijbiomac.2019.01.029 Miao H, 2016, J AGR FOOD CHEM, V64, P969, DOI 10.1021/acs.jafc.5b05350 Obisesan KA, 2017, TALANTA, V170, P413, DOI 10.1016/j.talanta.2017.04.035 Oliveri P, 2012, TRAC-TREND ANAL CHEM, V35, P74, DOI 10.1016/j.trac.2012.02.005 Orlandi G, 2019, TALANTA, V195, P181, DOI 10.1016/j.talanta.2018.11.046 Qi LM, 2018, J PHARMACEUT BIOMED, V161, P436, DOI 10.1016/j.jpba.2018.09.012 Rios-Reina R, 2019, TALANTA, V198, P560, DOI 10.1016/j.talanta.2019.01.100 Roussel S, 2003, J FOOD ENG, V60, P407, DOI 10.1016/S0260-8774(03)00064-5 Shi CY, 2017, J ETHNOPHARMACOL, V209, P24, DOI 10.1016/j.jep.2017.07.003 Skov T, 2006, J CHEMOMETR, V20, P484, DOI 10.1002/cem.1031 STAHLE L, 1987, Journal of Chemometrics, V1, P185, DOI 10.1002/cem.1180010306 Wang M, 2018, BRIT J PHARMACOL, V175, P2689, DOI 10.1111/bph.14333 Wang N, 2018, BIOMED PHARMACOTHER, V102, P865, DOI 10.1016/j.biopha.2018.03.134 Wang QQ, 2019, MOLECULES, V24, DOI 10.3390/molecules24071320 Wang QQ, 2020, SPECTROCHIM ACTA A, V226, DOI 10.1016/j.saa.2019.117633 WOLD S, 1987, CHEMOMETR INTELL LAB, V2, P37, DOI 10.1016/0169-7439(87)80084-9 Wold S., 1993, 3D QSAR DRUG DESIGN, P523, DOI DOI 10.1007/0-306-46858-1 Wu K, 2018, INT J BIOL MACROMOL, V114, P137, DOI 10.1016/j.ijbiomac.2018.03.107 Wu LF, 2016, MOLECULES, V21, DOI 10.3390/molecules21020227 Wu XM, 2018, MICROCHEM J, V143, P367, DOI 10.1016/j.microc.2018.08.035 Yuan TJ, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-017-18458-9 ZADEH LA, 1968, INFORM CONTROL, V12, P94, DOI 10.1016/S0019-9958(68)90211-8 Zhu LX, 2018, MOLECULES, V23, DOI 10.3390/molecules23092200 NR 36 TC 2 Z9 2 U1 1 U2 8 PD JUL 22 PY 2021 VL 2021 AR 5818999 DI 10.1155/2021/5818999 WC Chemistry, Analytical SC Chemistry UT WOS:000680195600001 DA 2022-12-14 ER PT J AU Henao-Jaramillo, D Montoya-Tamayo, DA Alvarez-Rios, Y Aristizabal-Tique, VH AF Henao-Jaramillo, Daniel Montoya-Tamayo, Daniel Alejandro Alvarez-Rios, Yolanda Aristizabal-Tique, Victor Hugo TI Implementation of RFID Middleware Based on Client-Multiserver Architecture for Traceability of Autoparts SO IEEE LATIN AMERICA TRANSACTIONS DT Article DE RFID; Middleware; Radio Frequency Identification; Object Identification; Production Line ID TECHNOLOGY; MANAGEMENT; IDENTIFICATION; MODEL AB The implementation of RFID systems allows automation of information flow along the production chains. This is a desirable condition for increasing efficiency, productivity and management, and it simplifies other activities such as traceability records along these supply and production chains, especially in the automobile assembly supply chain as in the case of Renault-SOFASA-Colombia, where the traceability of automotive parts is realized manually by means of mobile terminals, which is time consuming and laborious, with low automotive parts rate and high error rate. In this work, a RFID solution for the traceability of automotive parts in real time in Renault-SOFASA-Colombia is implemented, where a middleware under client-multiserver architecture for Siemens readers is developed. The terminal, where the middleware is located, behaves like a client and links to several RFID readers which behave like servers, forming a star type network topology. This architecture allows a cost reduction in the implementation and operation of RFID solutions by reducing hardware acquisition and energy consumption, being an alternative that can compete with the solution provided by a manufacturer as Siemens without neglecting the robustness and the proper functioning of the system. Moreover, the proposed solution concentrates all the maintenance efforts of the system at a single point, which is due to the fact that a terminal attends all RFID portals. The tests performed, where the transport carts with the automotive parts and their respective RFID tags are passed through the RFID portals of the assembly plant, are in agreement with the results reported by the developed middleware. C1 [Henao-Jaramillo, Daniel; Montoya-Tamayo, Daniel Alejandro] JDL SOLUT SAS, Medellin, Colombia. [Alvarez-Rios, Yolanda] ITM, Fac Ciencias Exactas & Aplicadas, Medellin, Colombia. [Aristizabal-Tique, Victor Hugo] UCC, Fac Ingn, Medellin 50630, Colombia. RP Henao-Jaramillo, D (corresponding author), JDL SOLUT SAS, Medellin, Colombia. EM daniel.henao@jdl-solutions.co; daniel.montoya@jdl-solutions.co; yolandaalvarez@itm.edu.co; vharisti@yahoo.com CR Baskoro H, 2017, PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND TECHNOLOGY (ICIMTECH), P210 Bouhouche T, 2014, INT CONF MULTIMED, P1025, DOI 10.1109/ICMCS.2014.6911314 Brandao FB, 2018, IEEE LAT AM T, V16, P391 Centeno J. P. Ferreira, 2018, REV MEDIO AMBIENT MI, P59 Chamekh M, 2017, INT WIREL COMMUN, P1915, DOI 10.1109/IWCMC.2017.7986576 Chan HL, 2016, WOODHEAD PUBL SER TE, V179, P41, DOI 10.1016/B978-0-08-100571-2.00003-8 Cherif A, 2019, ACM T EMBED COMPUT S, V18, DOI 10.1145/3274667 Chih-Yung Chen, 2010, 2010 International Conference on Machine Learning and Cybernetics (ICMLC 2010), P2956, DOI 10.1109/ICMLC.2010.5580759 Fan TJ, 2014, INT J PROD ECON, V147, P659, DOI 10.1016/j.ijpe.2013.05.007 Feng B., 2006, P INF COMMUN TECHNOL, V2, P2754 Foina AG, 2009, IEEE LAT AM T, V7, P688, DOI 10.1109/TLA.2009.5419367 Gaynor M., 2016, HEAL POLICY TECH JUL Guinard D., 2010, 2010 INTERNET THINGS Hashim SZM, 2008, PROCEEDINGS OF NINTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING, P858, DOI 10.1109/SNPD.2008.170 Li ZX, 2012, PHYSCS PROC, V33, P587, DOI 10.1016/j.phpro.2012.05.108 Liao XX, 2017, 2017 3RD INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT (ICIM 2017), P232, DOI 10.1109/INFOMAN.2017.7950382 Liu H, 2019, ASSEMBLY AUTOM, V39, P86, DOI 10.1108/AA-09-2018-0144 Liu HY, 2008, 2008 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER THEORY AND ENGINEERING, P657, DOI 10.1109/ICACTE.2008.112 Montaser A, 2014, AUTOMAT CONSTR, V39, P167, DOI 10.1016/j.autcon.2013.06.012 Peng SL, 2019, INT J AUTOM COMPUT, V16, P52, DOI 10.1007/s11633-018-1164-5 Prakasam S. A., 2012, 2012 International Conference on Advances in Engineering, Science and Management (ICAESM), P423 Rouchdi Y, 2018, 2018 IEEE 5TH INTERNATIONAL CONGRESS ON INFORMATION SCIENCE AND TECHNOLOGY (IEEE CIST'18), P642, DOI 10.1109/CIST.2018.8596629 Sobrinho OG, 2013, IEEE LAT AM T, V11, P1053, DOI 10.1109/TLA.2013.6601749 Song YQ, 2008, 2008 PROCEEDINGS OF INFORMATION TECHNOLOGY AND ENVIRONMENTAL SYSTEM SCIENCES: ITESS 2008, VOL 2, P1 Tanner D., 2016, REFERENCE MODULE FOO Tudora E., 2011, 15 WSEAS INT C COMPU, P15 Vimos V, 2018, IEEE LAT AM T, V16, P2496, DOI 10.1109/TLA.2018.8789574 Xu H, 2019, ADV INTELL SYST, V797, P437, DOI 10.1007/978-981-13-1165-9_41 Zang K, 2020, ADV INTELL SYST COMP, V993, P637, DOI 10.1007/978-3-030-22354-0_57 Zhang JS, 2017, ADV MATER SCI ENG, V2017, DOI 10.1155/2017/4563164 Zheng MX, 2012, PHYSCS PROC, V25, P2045, DOI 10.1016/j.phpro.2012.03.348 NR 31 TC 0 Z9 0 U1 1 U2 14 PD JUN PY 2019 VL 17 IS 6 BP 930 EP 936 DI 10.1109/TLA.2019.8896815 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering UT WOS:000497680400006 DA 2022-12-14 ER PT J AU Panea, B Carrasco, S Ripoll, G Joy, M AF Panea, B. Carrasco, S. Ripoll, G. Joy, M. TI Diversification of feeding systems for light lambs: sensory characteristics and chemical composition of meat SO SPANISH JOURNAL OF AGRICULTURAL RESEARCH DT Article DE grazing; indoors; meat quality; relationships; traceability ID FATTY-ACID-COMPOSITION; CONJUGATED LINOLEIC-ACID; CARCASS COMPOSITION; QUALITY CHARACTERISTICS; LONGISSIMUS-THORACIS; ADIPOSE-TISSUE; FED GRASS; CONCENTRATE; MUSCLE; GROWTH AB Forty-eight light lambs were used to study the effect of feeding systems on the sensory quality and chemical composition of their meat. Animals were divided into four batches as follows: GR: lambs with dams continuously on pasture until slaughter; GR+S: like GR, but the lambs had free access to concentrate; DRL-GRE: lambs in drylot and ewes with rationed grazing; DRL: lambs in drylot with dams indoors. DRL-GRE and DRL lambs were weaned at age 45 days. All lambs were slaughtered on reaching 22-24 kg live weight. Sensory attributes were not affected by the feeding system; grazing systems would therefore be a good alternative to indoor feeding systems. Meat from grazing lambs (GR and GR+S) presented the lowest values of C18:2 n-6/C18:3 n-3 and n-6/n-3, DRL-GRE lambs yielded intermediate values and DRL lambs the highest values. Ewes' diet during lactation affects the fatty acid composition of the meat of light lambs. The feeding system affected the relationships between the meat's sensory characteristics and chemical properties. Discriminant analysis using fatty acid composition was able to distinguish between lambs from each feeding system, and could therefore be used as a tool for traceability. C1 [Panea, B.; Carrasco, S.; Ripoll, G.; Joy, M.] Ctr Invest & Tecnol Agroalimentaria Aragon, Zaragoza 50059, Spain. RP Panea, B (corresponding author), Ctr Invest & Tecnol Agroalimentaria Aragon, Avda Montanana 930, Zaragoza 50059, Spain. EM bpanea@aragon.es CR Alvarez-Rodriguez J, 2007, LIVEST SCI, V107, P152, DOI 10.1016/j.livsci.2006.09.011 Angood KM, 2008, MEAT SCI, V78, P176, DOI 10.1016/j.meatsci.2007.06.002 AOAC, 1999, OFF METH AN *AOCS, 2004, 504 AOCS Arana A, 2006, SMALL RUMINANT RES, V63, P75, DOI 10.1016/j.smallrumres.2005.02.006 Atti N, 2006, LIVEST SCI, V104, P121, DOI 10.1016/j.livsci.2006.03.014 Aurousseau B, 2007, MEAT SCI, V76, P417, DOI 10.1016/j.meatsci.2006.12.001 Aurousseau B, 2007, MEAT SCI, V76, P241, DOI 10.1016/j.meatsci.2006.11.005 Aurousseau B, 2004, MEAT SCI, V66, P531, DOI 10.1016/S0309-1740(03)00156-6 Caneque V, 2004, MEAT SCI, V67, P595, DOI 10.1016/j.meatsci.2004.01.002 Carrasco S, 2009, LIVEST SCI, V121, P56, DOI 10.1016/j.livsci.2008.05.017 CROUSE JD, 1978, J ANIM SCI, V47, P1207, DOI 10.2527/jas1978.4761207x Demirel G, 2006, MEAT SCI, V72, P229, DOI 10.1016/j.meatsci.2005.07.006 Enser M, 1998, MEAT SCI, V49, P329, DOI 10.1016/S0309-1740(97)00144-7 ENSER M, 1995, P 2 DUMM MUSCL WORKS, P125 FHAMY DC, 1992, J FOOD SCI, V37, P226 FIELD RA, 1978, J ANIM SCI, V47, P858, DOI 10.2527/jas1978.474858x Fisher AV, 2000, MEAT SCI, V55, P141, DOI 10.1016/S0309-1740(99)00136-9 French P, 2001, MEAT SCI, V57, P379, DOI 10.1016/S0309-1740(00)00115-7 Hopkins DL, 1999, MEAT SCI, V51, P91, DOI 10.1016/S0309-1740(98)00105-3 Jeremiah LE, 2003, MEAT SCI, V65, P1013, DOI 10.1016/S0309-1740(02)00309-1 Joy M, 2008, SMALL RUMINANT RES, V78, P123, DOI 10.1016/j.smallrumres.2008.05.011 Joy M, 2008, SMALL RUMINANT RES, V75, P24, DOI 10.1016/j.smallrumres.2007.07.005 KEMP JD, 1980, J ANIM SCI, V51, P321, DOI 10.2527/jas1980.512321x KEMP JD, 1976, J ANIM SCI, V42, P575, DOI 10.2527/jas1976.423575x Kosulwat S, 2003, MEAT SCI, V65, P1413, DOI 10.1016/S0309-1740(03)00064-0 Lee JH, 2008, SMALL RUMINANT RES, V75, P177, DOI 10.1016/j.smallrumres.2007.10.003 Nuernberg K, 2008, SMALL RUMINANT RES, V74, P279, DOI 10.1016/j.smallrumres.2007.07.009 Okeudo NJ, 2005, MEAT SCI, V69, P1, DOI 10.1016/j.meatsci.2004.04.011 Oriani G, 2005, MEAT SCI, V71, P557, DOI 10.1016/j.meatsci.2005.04.040 PAUL PAULINE C., 1964, FOOD TECHNOL, V18, P121 PETROVA I, 1994, SMALL RUMINANT RES, V13, P263, DOI 10.1016/0921-4488(94)90074-4 Prache S, 2005, SMALL RUMINANT RES, V59, P157, DOI 10.1016/j.smallrumres.2005.05.004 Priolo A, 2002, MEAT SCI, V62, P179, DOI 10.1016/S0309-1740(01)00244-3 Raes K, 2004, ANIM FEED SCI TECH, V113, P199, DOI 10.1016/j.anifeedsci.2003.09.001 Rhee K. S., 1992, FATTY ACIDS FOODS TH, P65 Ripoll G, 2008, MEAT SCI, V80, P239, DOI 10.1016/j.meatsci.2007.11.025 RoussetAkrim S, 1997, MEAT SCI, V45, P169, DOI 10.1016/S0309-1740(96)00099-X Rowe A, 1999, MEAT SCI, V51, P283, DOI 10.1016/S0309-1740(98)00063-1 Rule DC, 1997, MEAT SCI, V46, P23, DOI 10.1016/S0309-1740(97)00008-9 Santos-Silva J, 2002, LIVEST PROD SCI, V77, P187, DOI 10.1016/S0301-6226(02)00059-3 Santos-Silva J, 2002, LIVEST PROD SCI, V76, P17, DOI 10.1016/S0301-6226(01)00334-7 Sanudo C, 1998, MEAT SCI, V49, pS29, DOI 10.1016/S0309-1740(98)00073-4 Scerra M, 2007, MEAT SCI, V76, P390, DOI 10.1016/j.meatsci.2006.04.033 Scollan N, 2006, MEAT SCI, V74, P17, DOI 10.1016/j.meatsci.2006.05.002 Solomon M. B., 1996, Journal of Animal Science, V74, P162 SOLOMON MB, 1986, J ANIM SCI, V62, P139, DOI 10.2527/jas1986.621139x SUMMERS RL, 1978, J ANIM SCI, V47, P622, DOI 10.2527/jas1978.473622x Valvo MA, 2005, ANIM SCI, V81, P431, DOI 10.1079/ASC50480431 Velasco S, 2004, MEAT SCI, V66, P457, DOI [10.1016/S0309-1740(03)00134-7, 10.1016/s0309-1740(03)00134-7] Velasco S, 2001, MEAT SCI, V59, P325, DOI 10.1016/S0309-1740(01)00135-8 VIPOND JE, 1995, ANIM SCI, V60, P231, DOI 10.1017/S1357729800008390 Wood JD, 2008, MEAT SCI, V78, P343, DOI 10.1016/j.meatsci.2007.07.019 Wood JD, 1997, BRIT J NUTR, V78, pS49, DOI 10.1079/BJN19970134 NR 54 TC 15 Z9 15 U1 1 U2 10 PD MAR PY 2011 VL 9 IS 1 BP 74 EP 85 WC Agriculture, Multidisciplinary; Soil Science SC Agriculture UT WOS:000289377900009 DA 2022-12-14 ER PT J AU Werner, A Kniel, B Berg, U AF Werner, A Kniel, B Berg, U TI The new regulations for the labelling and traceability of GMOs - Are they applicable? SO DEUTSCHE LEBENSMITTEL-RUNDSCHAU DT Article DE genetic modification; labelling; traceability; care obligations; distinction between "from GMO" and with the help of GMO" AB The application of Regulations (EC) No 1829/2003 and (EC) No 1830/ 2003 is to be co-ordinated systematically as follows: For the placing on the market of products consisting of or containing GMOs, Article 4 of Regulation (EC) No 1830/2003 should be applied. For products for food and feed produced from GMOs, Article 5 of Regulation (EC) 1830/2003 has to be applied. At both stages of the manufacturing process, it should be ensured that certain specific information is transmitted to the client and the traceability guaranteed. If a GMO or food containing or consisting of GMOs or containing ingredients produced from GMOs is to be delivered to the final consumer or communal caterers, Regulation (EC) No 1829/2003 has to be applied. In the general context of those rules concerning GMOs in Regulation (EEC) No 2092/91 on the organic production of agricultural products and indications referring therein to agricultural products and foodstuffs and the German NLV ("without genetic modification"), a clear distinction between "from" and "with the help of" has to be drawn. The criteria of Regulations (EC) No 1829/2003 and (EC) No 1830/2003 have to be homogeneously interpreted and applied in order to reach a consistent regulation system for application purposes. Regulations (EC) No. 1829/2003 and (EC) No 1830/2003 are poorly worked out and formulated, leaving open interpretational questions that are too large with incalculable legal, economic and public risks for the legal user. Even if interpretational questions are to be treated more restrictively as shown above, the scope of the regulations still allows labelling exceptions by way of reference to relevant legal definitions (for example the definition of ingredients in Directive 2000/13/EC on the approximation of the laws of the Member States relating to the labelling, presentation and advertising of foodstuffs or the definition of GMOs in Directive 2001/18/EC on the deliberate release into the environment of genetically modified organisms and repealing Council Directive 90/220/ EEC. The legally determined purpose of guaranteeing a complete labelling of GMOs has been unsuccessful. Instead of this, considerable administrative and personal expense has been incurred by the food industry in order to comply with the requirements of the regulations. Thus, extensive queries within the manufacturing chain concerning raw materials and guarantees have already been raised, caused by uncertainties connected with the regulation requirements, which are partly beyond legal justification and which incur costs for the companies concerned. Without the interpretations shown in this article, the regulations would be incapable of being applied. C1 Verband Backmittel & Backgrundstoffhersteller eV, D-53111 Bonn, Germany. Biotask AG, D-73728 Esslingen, Germany. Gerb Jung GmbH, D-60437 Frankfurt, Germany. RP Werner, A (corresponding author), Verband Backmittel & Backgrundstoffhersteller eV, Marktstr 9, D-53111 Bonn, Germany. EM Backmittelverband.Werner@t-online.de CR FELDMANN UC, 1997, GENTECHNIKFREI ERLAU HAGENMEYER M, 2002, MODERN FOOD SAFETY R, P4431 LOOSEN P, 2000, ZLR, P434 MEYER AH, 2002, GEN FOOD NOVEL FOOD RABE H, 2003, GRUNDFRAGEN EG LEBEN SCHROETER KA, 2003, GEBURT RUCKVERFOLGBA WERNER A, 2002, RUCKVERFOLGBARKEIT R 2000, 200013EG 2001, 200118EG 1991, 209291 NR 1991, METHODENLEHRE RECHTS, P320 2003, 1782002 EG 1999, 18041999 NR NR 13 TC 0 Z9 0 U1 1 U2 12 PD MAY PY 2004 VL 100 IS 5 BP 165 EP 176 WC Food Science & Technology SC Food Science & Technology UT WOS:000221278700001 DA 2022-12-14 ER PT J AU Gellynck, X Verbeke, W Vermeire, B AF Gellynck, Xavier Verbeke, Wim Vermeire, Bert TI Pathways to increase consumer trust in meat as a safe and wholesome food SO MEAT SCIENCE DT Article; Proceedings Paper CT 52nd International Congress of Meat Science and Technology (52nd ICoMST) CY AUG 13-18, 2006 CL Dublin, IRELAND DE food safety; information; traceability; quality assurance schemes ID QUALITY; TRACEABILITY; PERCEPTION; ATTITUDE AB This paper focuses on the effect of information about meat safety and wholesomeness on consumer trust based on several studies with data collected in Belgium. The research is grounded in the observation that despite the abundant rise of information through labelling, traceability systems and quality assurance schemes, the effect on consumer trust in meat as a safe and wholesome product is only limited. The overload and complexity of information on food products results in misunderstanding and misinterpretation. Functional traceability attributes such as organisational efficiency and chain monitoring are considered to be highly important but not as a basis for market segmentation. However, process traceability attributes such as origin and production method are of interest for particular market segments as a response to meat quality concerns. Quality assurance schemes and associated labels have a poor impact on consumers' perception. It is argued that the high interest of retailers in such schemes is driven by procurement management efficiency rather than safety or overall quality. Future research could concentrate on the distribution of costs and benefits associated with meat quality initiatives among the chain participants. (c) 2006 Elsevier Ltd. All rights reserved. C1 Univ Ghent, Fac Biosci Engn, Dept Agr Econ, B-9000 Ghent, Belgium. C3 Ghent University RP Gellynck, X (corresponding author), Univ Ghent, Fac Biosci Engn, Dept Agr Econ, Coupure Links 653, B-9000 Ghent, Belgium. EM Xavier.Gellynck@UGent.be CR Berg L, 2004, APPETITE, V42, P21, DOI 10.1016/S0195-6663(03)00112-0 Bernues A, 2003, MEAT SCI, V65, P1095, DOI 10.1016/S0309-1740(02)00327-3 Bocker A, 2000, J ECON BEHAV ORGAN, V43, P471, DOI 10.1016/S0167-2681(00)00131-1 Caswell JA, 1996, AM J AGR ECON, V78, P1248, DOI 10.2307/1243501 FISCHER C, 2006, P 99 EUR SEM EAAE TR, P63 Frewer L, 2005, INNOVATION IN AGRI-FOOD SYSTEMS: PRODUCT QUALITY AND CONSUMER ACCEPTANCE, P125 Gellynck X., 2001, Agrarwirtschaft, V50, P368 GELLYNCK X, 2004, QUALITY ASSURANCE RI, VB, P443 GELLYNCK X, 2005, NAME QUALITY WHAT KI, P223 Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x JACOBY J, 1977, J CONSUM RES, V4, P119, DOI 10.1086/208687 Knight AJ, 2005, RURAL SOCIOL, V70, P253, DOI 10.1526/0036011054776389 KOLA J, 2003, P 13 ANN WORLD FOOD, P1 Latouche K, 1998, FOOD POLICY, V23, P347, DOI 10.1016/S0306-9192(98)00048-7 Leat P., 1998, SUPPLY CHAIN MANAG, V3, P115, DOI DOI 10.1108/EUM0000000004534 Luhmann N., 1988, TRUST MAKING BREAKIN, V6, P94 Luning PA, 2005, INNOVATION IN AGRI-FOOD SYSTEMS: PRODUCT QUALITY AND CONSUMER ACCEPTANCE, P293 Malhotra N.K., 2010, MARKETING RES APPL O McCluskey J. J., 2004, American Journal of Agricultural Economics, V86, P1230, DOI 10.1111/j.0002-9092.2004.00670.x Miles S, 2001, FOOD QUAL PREFER, V12, P47, DOI 10.1016/S0950-3293(00)00029-X Rohr A, 2005, FOOD CONTROL, V16, P649, DOI 10.1016/j.foodcont.2004.06.001 ROSA F, 2006, P 99 EUR SEM EAAE TR, P235 Salaun Y, 2001, INT J INFORM MANAGE, V21, P21, DOI 10.1016/S0268-4012(00)00048-7 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Teisl M. F., 1998, AGR RESOURCE EC REV, V27, P140, DOI [10.1017/S1068280500006468, DOI 10.1017/S1068280500006468] Theuvsen L., 2003, QUALITY ASSURANCE RI, VB, P223 van Trijp JCM, 2005, INNOVATION IN AGRI-FOOD SYSTEMS: PRODUCT QUALITY AND CONSUMER ACCEPTANCE, P87 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Verbeke W, 2004, MEAT SCI, V67, P159, DOI 10.1016/j.meatsci.2003.09.017 Verbeke W, 1999, J INT FOOD AGRIBUS M, V10, P45, DOI 10.1300/J047v10n03_03 Verbeke W, 2002, FOOD POLICY, V27, P339, DOI 10.1016/S0306-9192(02)00037-4 Verbeke W, 2001, FOOD QUAL PREFER, V12, P489, DOI 10.1016/S0950-3293(01)00042-8 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 VONALVENSLEBEN R, 1993, AGRARWIRTSCHAFT, V40, P247 Yee WMS, 2005, BRIT FOOD J, V107, P841, DOI 10.1108/00070700510629788 NR 36 TC 58 Z9 60 U1 2 U2 25 PD SEP PY 2006 VL 74 IS 1 BP 161 EP 171 DI 10.1016/j.meatsci.2006.04.013 WC Food Science & Technology SC Food Science & Technology UT WOS:000239349600015 DA 2022-12-14 ER PT J AU Barry, B Barron, UG Butler, F Ward, S McDonnell, K AF Barry, B. Barron, U. Gonzales Butler, F. Ward, S. McDonnell, K. TI VERIFICATION OF SHEEP IDENTITY BY MEANS OF A RETINAL RECOGNITION SYSTEM SO TRANSACTIONS OF THE ASABE DT Article DE Biometrics; Electronic identification; Retina; Sheep; Traceability AB In January 2010, electronic identification of sheep was introduced within the European Union. This article presents an alternative method of verification of sheep identity. Current identification protocols implement a system in which the tag number is traced throughout the traceability system and not the animal; this can lead to misidentification and the possibility of tampering with identifiers. Retinal identification offers a non-invasive, accurate, and tamper-proof method of identification in which the retinal vascular patterns of both eyes are recorded for identification. There are more than 200 distinct breeds of sheep worldwide, so it is important to determine if breed affects the retinal image matching performance as a result of different eye physiology, such as the amount of myelinization (dark pigmentation present on the ocular fundus) that forms within the retinal vascular area that is examined for identification. A previous study determined that the best condition in which to achieve optimum quality retinal images was indoors; therefore, all the retinal images in this experiment were collected indoors. This study sets out the matching score criteria that are required to successfully implement retinal imaging as a viable method of sheep identification. The objectives of this study were to examine the effects of breed and age of sheep on the matching scores recorded and to set a matching score decision criterion for a retinal imaging system. A representative sample (n = 160) of four breeds of sheep (Cheviot, Charollais, Suffolk, and Texel) categorized into two age groups (<= 2 years and >2 years) were examined. A non-parametric statistical analysis of matching scores showed that there was no observable effect of breed (p = 0.209) or age category (p = 0.181) on the matching performance. The recognition performance of a single-eye retinal system (either the left or right eye retinal image was used for identity) was estimated to have a false match error of 0.43% and a false non-match error of 0.96% at a matching score threshold of 75 when sheep retinal images were obtained indoors. When the recognition system was based on the two retinas, the false non-match rate achieved was 2.66 x 10(-7), or approximately one false non-match in 3.7 million matching attempts. C1 [Barron, U. Gonzales] Univ Coll Dublin, Dept Biosyst Engn, Agr & Food Sci Ctr, Dublin 4, Ireland. C3 University College Dublin RP Barron, UG (corresponding author), Univ Coll Dublin, Dept Biosyst Engn, Agr & Food Sci Ctr, Dublin 4, Ireland. EM ursula.gonzalesbarron@ucd.ie CR Barry B, 2008, COMPUT ELECTRON AGR, V64, P202, DOI 10.1016/j.compag.2008.05.011 DAUGMAN J, 2000, 482 TR U CAMBR COMP Delac K, 2004, PROCEEDINGS ELMAR-2004: 46TH INTERNATIONAL SYMPOSIUM ELECTRONICS IN MARINE, P184 Gonzales-Barron U, 2008, COMPUT ELECTRON AGR, V60, P156 Ollivier FJ, 2004, VET OPHTHALMOL, V7, P11, DOI 10.1111/j.1463-5224.2004.00318.x Osborne NN, 2004, PROG RETIN EYE RES, V23, P91, DOI 10.1016/j.preteyeres.2003.12.001 Prabhakar S, 2002, PATTERN RECOGN, V35, P861, DOI 10.1016/S0031-3203(01)00103-0 Thompson D, 2002, REV SCI TECH OIE, V21, P675, DOI 10.20506/rst.21.3.1353 WHITTIER JC, 2003, P W SECTION AM SOC A, V54, P339 NR 9 TC 1 Z9 1 U1 0 U2 9 PD MAY-JUN PY 2011 VL 54 IS 3 BP 1161 EP 1167 WC Agricultural Engineering SC Agriculture UT WOS:000292800800040 DA 2022-12-14 ER PT J AU Figorilli, S Antonucci, F Costa, C Pallottino, F Raso, L Castiglione, M Pinci, E Del Vecchio, D Colle, G Proto, AR Sperandio, G Menesatti, P AF Figorilli, Simone Antonucci, Francesca Costa, Corrado Pallottino, Federico Raso, Luciano Castiglione, Marco Pinci, Edoardo Del Vecchio, Davide Colle, Giacomo Proto, Andrea Rosario Sperandio, Giulio Menesatti, Paolo TI A Blockchain Implementation Prototype for the Electronic Open Source Traceability of Wood along the Whole Supply Chain SO SENSORS DT Article DE IoT; sensors; infotracing; RFID; ARDUINO (R) ID TECHNOLOGIES; PERFORMANCE; SYSTEM AB This is the first work to introduce the use of blockchain technology for the electronic traceability of wood from standing tree to final user. Infotracing integrates the information related to the product quality with those related to the traceability [physical and digital documents (Radio Frequency IDentification-RFID-architecture)] within an online information system whose steps (transactions) can be made safe to evidence of alteration through the blockchain. This is a decentralized and distributed ledger that keeps records of digital transactions in such a way that makes them accessible and visible to multiple participants in a network while keeping them secure without the need of a centralized certification organism. This work implements a blockchain architecture within the wood chain electronic traceability. The infotracing system is based on RFID sensors and open source technology. The entire forest wood supply chain was simulated from standing trees to the final product passing through tree cutting and sawmill process. Different kinds of Internet of Things (IoT) open source devices and tags were used, and a specific app aiming the forest operations was engineered to collect and store in a centralized database information (e.g., species, date, position, dendrometric and commercial information). C1 [Figorilli, Simone; Antonucci, Francesca; Costa, Corrado; Pallottino, Federico; Sperandio, Giulio; Menesatti, Paolo] Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn & Trasformazioni Agroalimentari, Via Pascolare 16, I-00015 Monterotondo, Italy. [Raso, Luciano; Castiglione, Marco; Pinci, Edoardo; Del Vecchio, Davide] Microsoft Srl, Viale Pasubio 21, I-20154 Milan, Italy. [Colle, Giacomo] Effetreseizero Srl, Spinoff CREA, Via Solteri 37-1, I-38121 Trento, Italy. [Proto, Andrea Rosario] Mediterranean Univ Reggio Calabria, Dept AGRARIA, I-89122 Reggio Di Calabria, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Universita Mediterranea di Reggio Calabria RP Costa, C (corresponding author), Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn & Trasformazioni Agroalimentari, Via Pascolare 16, I-00015 Monterotondo, Italy. EM simone.figorilli@crea.gov.it; francesca.antonucci@crea.gov.it; corrado.costa@crea.gov.it; federico.pallottino@crea.gov.it; luraso@microsoft.com; marco.castiglione@microsoft.com; epinci@microsoft.com; davide.delvecchio@microsoft.com; giacomo.colle@f360.it; andrea.proto@unirc.it; giulio.sperandio@crea.gov.it; paolo.menesatti@crea.gov.it CR Abenavoli L. M., 2016, Agronomy Research, V14, P1247 Angeles R, 2005, INFORM SYST MANAGE, V22, P51, DOI 10.1201/1078/44912.22.1.20051201/85739.7 Bjork A, 2011, COMPUT IND, V62, P830, DOI 10.1016/j.compind.2011.08.001 Corona P., 2014, P INT C INT SELV FIR, P631 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Gibbs T., 2014, INT J SOCIAL BEHAV E, V8, P2334 Kaul C., 2010, P FORMEC C PAD IT 11, P1 Kumar M.V., 2017, ADV SCI TECHNOLOGY L, V146, P125, DOI DOI 10.14257/AST1.2017.146.22 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Paletto A, 2018, FOREST POLICY ECON, V92, P65, DOI 10.1016/j.forpol.2018.04.002 Panarello A, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18082575 Pazaitis A, 2017, TECHNOL FORECAST SOC, V125, P105, DOI 10.1016/j.techfore.2017.05.025 Picchi G, 2015, CROAT J FOR ENG, V36, P165 Pinna A., 2018, P 2018 COMP C LOND U Pizzi A, 2006, J ADHES SCI TECHNOL, V20, P427, DOI 10.1163/156856106777144327 Qu T., 2016, International Journal of Advanced Manufacturing Technology, V84, P147, DOI 10.1007/s00170-015-7220-1 Sperandio G., 2017, Forest@, V14, P124, DOI 10.3832/efor2267-014 Srinivasan S, 2013, INT J COMPUT SCI ENG, V8, P154, DOI 10.1504/IJCSE.2013.053081 Tacconi L, 2012, ILLEGAL LOGGING LAW, P320 Tian F., 2018, THESIS Timpe D., 2006, RFID IN FORESTRY PRO Tzoulis I, 2013, PROC TECH, V8, P606, DOI 10.1016/j.protcy.2013.11.087 Xu LD, 2011, INT J PROD RES, V49, P183, DOI 10.1080/00207543.2010.508944 NR 23 TC 91 Z9 93 U1 8 U2 97 PD SEP PY 2018 VL 18 IS 9 AR 3133 DI 10.3390/s18093133 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000446940600392 DA 2022-12-14 ER PT J AU Thienpont, LM Van Uytfanghe, K Marriot, J Stokes, P Siekmann, L Kessler, A Bunk, D Tai, S AF Thienpont, LM Van Uytfanghe, K Marriot, J Stokes, P Siekmann, L Kessler, A Bunk, D Tai, S TI Metrologic traceability of total thyroxine measurements in human serum: Efforts to establish a network of reference measurement laboratories SO CLINICAL CHEMISTRY DT Article ID DILUTION-MASS-SPECTROMETRY; CLINICAL-CHEMISTRY; ELECTROSPRAY-IONIZATION; DEFINITIVE METHODS; STANDARDIZATION; BLOOD; MODEL; SPECIFICATIONS; PART AB Background: Assuring/demonstrating metrologic traceability of in vitro diagnostics necessitates the availability of measurand-specific reference measurement systems (RMSs) and the possibility for industry to work with competent reference measurement laboratories (RMLs). Here we report the results of a European project to investigate the feasibility of developing a RMS for serum total thyroxine. Methods: Four candidate RMLs (cRMLs) developed/implemented variants of a candidate reference measurement procedure (cRMP) based on isotope dilution-liquid chromatography-mass spectrometry. The sole constraint implemented was calibration with a common thyroxine primary calibrator. The RMPs were externally validated and assessed for comparability in round-robin trials using common samples, i.e., 5 lyophilized and 33 frozen native sera. At the same time, the performance of the cRMLs organized in a network was assessed. For uniform external quality assessment, common performance specifications were agreed on. Results: All cRMLs performed the cRMPs with fulfillment of the predefined specifications: total and between-laboratory CVs less than or equal to2.0% and 2.5%, respectively, and a systematic deviation less than or equal to0.9%, estimated with a target assigned from the mean of means obtained by the cRMLs. The mean expanded uncertainty for value assignment to the native sera was 2.1%. Conclusions: A network of cRMLs, with externally conformed competence to properly perform RMPs, has been established. Performance specifications were defined and will form the basis for admittance of new network members. A serum panel, successfully targeted during the validation process, is available for split-sample measurements with commercial routine measurement procedures. The model can now be used for other measurands for which traceability to the Systeme International d'Unites is needed. (C) 2005 American Association for Clinical Chemistry. C1 Ghent Univ, Fac Pharmaceut Sci, Analyt Chem Lab, B-9000 Ghent, Belgium. Lab Govt Chemist, Teddington TW11 0LY, Middx, England. Univ Bonn, Inst Clin Biochem, D-5300 Bonn, Germany. Natl Inst Stand & Technol, Gaithersburg, MD USA. C3 Ghent University; University of Bonn; National Institute of Standards & Technology (NIST) - USA RP Thienpont, LM (corresponding author), Ghent Univ, Fac Pharmaceut Sci, Analyt Chem Lab, Harelbekestr 72, B-9000 Ghent, Belgium. EM linda.thienpont@ugent.be CR *BUR INT POID MES, 2004, DECL COOP BETW CIPM CALI JP, 1973, CLIN CHEM, V19, P291 De Brabandere VI, 1998, RAPID COMMUN MASS SP, V12, P1099, DOI 10.1002/(SICI)1097-0231(19980831)12:16<1099::AID-RCM290>3.0.CO;2-J EKINS R, 1993, CLIN LAB MED, V13, P599, DOI 10.1016/S0272-2712(18)30428-1 EKINS R, 1990, ENDOCR REV, V11, P5, DOI 10.1210/edrv-11-1-5 EKINS R, 1987, CLIN CHEM, V33, P2137 *EN ISO, 2003, 17511 ENISO, P140 *EN ISO, 2002, 15193 ENISO, P140 *EUR COMM, 2000, 5 FRAM PROGR COMP SU Hopley CJ, 2004, RAPID COMMUN MASS SP, V18, P1033, DOI 10.1002/rcm.1441 *INT ORG STAND, 1995, ISO GUID EXPR UNC ME Jeppsson JO, 2002, CLIN CHEM LAB MED, V40, P78, DOI 10.1515/CCLM.2002.016 MAAS AHJ, 1987, J CLIN CHEM CLIN BIO, V25, P281 MARCOVINA SM, 1991, CLIN CHEM, V37, P1676 McNamara JR, 1997, CLIN CHEM, V43, P1306 MENDEL CM, 1989, ENDOCR REV, V10, P232, DOI 10.1210/edrv-10-3-232 MILLER WG, 1993, ARCH PATHOL LAB MED, V117, P343 Myers GL, 2000, CLIN CHEM, V46, P1762 Siekmann L, 1995, EUR J CLIN CHEM CLIN, V33, P1013 SIEKMANN L, 1982, J CLIN CHEM CLIN BIO, V20, P883 Siekmann L, 2002, CLIN CHEM LAB MED, V40, P631, DOI 10.1515/CCLM.2002.109 SIEKMANN L, 1987, BIOMED ENVIRON MASS, V14, P683, DOI 10.1002/bms.1200141120 STAMM D, 1979, J CLIN CHEM CLIN BIO, V17, P283 Stockl D, 1996, EUR J CLIN CHEM CLIN, V34, P319 Tai SSC, 2002, CLIN CHEM, V48, P637 Thienpont L, 1995, EUR J CLIN CHEM CLIN, V33, P949 Thienpont L. M., 1999, CHARACTERISATION COR Thienpont LM, 1996, J MASS SPECTROM, V31, P1119, DOI 10.1002/(SICI)1096-9888(199610)31:10<1119::AID-JMS404>3.0.CO;2-X TIETZ NW, 1979, CLIN CHEM, V25, P833 URIANO GA, 1977, CRC CR REV ANAL CHEM, V6, P361, DOI 10.1080/10408347708542696 Van Uytfanghe K, 2004, RAPID COMMUN MASS SP, V18, P1539, DOI 10.1002/rcm.1510 1998, OFF J EUROPEAN COMMU, V7, pL331 NR 32 TC 12 Z9 12 U1 0 U2 3 PD JAN PY 2005 VL 51 IS 1 BP 161 EP 168 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000225991100027 DA 2022-12-14 ER PT J AU Morcia, C Rattotti, E Stanca, AM Tumino, G Rossi, V Ravaglia, S Germeier, CU Herrmann, M Polisenska, I Terzi, V AF Morcia, Caterina Rattotti, Elisa Stanca, A. Michele Tumino, Giorgio Rossi, Vittorio Ravaglia, Stefano Germeier, Christoph U. Herrmann, Matthias Polisenska, Ivana Terzi, Valeria TI Fusarium genetic traceability: Role for mycotoxin control in small grain cereals agro-food chains SO JOURNAL OF CEREAL SCIENCE DT Article DE Mycotoxins; qPCR; Fusarium; Small grain cereals ID REAL-TIME PCR; MEDIATED ISOTHERMAL AMPLIFICATION; GRAMINEARUM SPECIES COMPLEX; HEAD BLIGHT; TRICHOTHECENE GENOTYPES; DEOXYNIVALENOL CONTENT; QUANTITATIVE DETECTION; F GRAMINEARUM; QPCR ASSAY; CROWN ROT AB Risks associated with mycotoxin contamination of cereals, that are included in the ten major staple foods and greatly contribute to the dietary energy intake, are of worldwide relevance. In small grain cereals, mycotoxins are produced by fungi such as Aspergillus, Penicillium, Alternaria and Fusarium that colonize the plant in the field and can grow during the post-harvest period, producing several classes of mycotoxins. The identification of mycotoxigenic fungal species and strains is essential for developing effective strategies for control. For this purpose, genetic traceability has proved to be a valuable tool that can be applied along the whole production chain, starting in the field for early diagnosis of FHB (Fusarium Head Blight) disease to the final processing steps, such as malting or pasta making. In this paper, DNA-based analytical tools that are currently available for the identification and quantification of mycotoxigenic fungal species and strains are reviewed, with particular emphasis on Fusarium, and their possible applications in mycotoxin control in small grain cereal chains are discussed. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Morcia, Caterina; Rattotti, Elisa; Terzi, Valeria] Genom Res Ctr, CRA GPG, Consiglio Ric & Sperimentaz Agr, I-29017 Fiorenzuola Darda, PC, Italy. [Stanca, A. Michele; Tumino, Giorgio] Univ Modena & Reggio Emilia, Dpt Agr & Food Sci, I-42122 Reggio Emilia, Italy. [Rossi, Vittorio] Univ Cattolica Sacro Cuore, Ist Entomol & Patol Vegetale, I-29122 Piacenza, Italy. [Ravaglia, Stefano] SIS, I-40068 San Lazzaro Di Savena, BO, Italy. [Germeier, Christoph U.; Herrmann, Matthias] Fed Res Ctr Cultivated Plants, Inst Breeding Res Agr Crops, Julius Kuhn Inst, D-06484 Quedlinburg, Germany. [Polisenska, Ivana] Agrotest Fyto Ltd, CZ-76701 Kromeriz, Czech Republic. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Universita di Modena e Reggio Emilia; Catholic University of the Sacred Heart; Julius Kuhn-Institut; Agricultural Research Institute Kromeriz, Ltd; Agrotest Fyto RP Terzi, V (corresponding author), Genom Res Ctr, CRA GPG, Consiglio Ric & Sperimentaz Agr, Via San Protaso 302, I-29017 Fiorenzuola Darda, PC, Italy. EM valeria.terzi@entecra.it CR Al-Reedy RM, 2012, FUNGAL GENET BIOL, V49, P2, DOI 10.1016/j.fgb.2011.11.008 Astolfi P, 2011, INT J FOOD MICROBIOL, V148, P197, DOI 10.1016/j.ijfoodmicro.2011.05.019 Begerow D, 2010, APPL MICROBIOL BIOT, V87, P99, DOI 10.1007/s00253-010-2585-4 Beyer M, 2006, J PLANT DIS PROTECT, V113, P241, DOI 10.1007/BF03356188 Bluhm BH, 2004, J FOOD PROTECT, V67, P536, DOI 10.4315/0362-028X-67.3.536 Buerstmayr H, 2009, PLANT BREEDING, V128, P1, DOI 10.1111/j.1439-0523.2008.01550.x Burlakoti RR, 2007, PHYTOPATHOLOGY, V97, P835, DOI 10.1094/PHYTO-97-7-0835 Dall'Asta C, 2010, MYCOTOXINS IN FOOD, FEED AND BIOWEAPONS, P385, DOI 10.1007/978-3-642-00725-5_22 Davari M, 2012, J MICROBIOL METH, V89, P63, DOI 10.1016/j.mimet.2012.01.017 Demeke T, 2010, INT J FOOD MICROBIOL, V141, P45, DOI [10.1016/j.ijfoodmicro.2010.04.02, 10.1016/j.ijfoodmicro.2010.04.020] Denschlag C., 2012, INT J FOOD MICROBIOL Desjardins A.E., 2006, FUSARIUM MYCOTOXINS Fredlund E, 2008, J MICROBIOL METH, V73, P33, DOI 10.1016/j.mimet.2008.01.007 Goswami RS, 2004, MOL PLANT PATHOL, V5, P515, DOI [10.1111/j.1364-3703.2004.00252.x, 10.1111/J.1364-3703.2004.00252.X] Hallen-Adams HE, 2011, PHYTOPATHOLOGY, V101, P1091, DOI 10.1094/PHYTO-01-11-0023 Hogg AC, 2007, PLANT DIS, V91, P1021, DOI 10.1094/PDIS-91-8-1021 Hogg AC, 2010, PHYTOPATHOLOGY, V100, P49, DOI 10.1094/PHYTO-100-1-0049 Horevaj P, 2011, J APPL MICROBIOL, V111, P396, DOI 10.1111/j.1365-2672.2011.05049.x Kimura M, 2007, BIOSCI BIOTECH BIOCH, V71, P2105, DOI 10.1271/bbb.70183 Kulik T, 2011, FEMS MICROBIOL LETT, V314, P49, DOI 10.1111/j.1574-6968.2010.02145.x Leisova L, 2006, J PHYTOPATHOL, V154, P603, DOI 10.1111/j.1439-0434.2006.01154.x Levin RE, 2012, INT J FOOD MICROBIOL, V156, P1, DOI 10.1016/j.ijfoodmicro.2012.03.001 Ma LJ, 2010, NATURE, V464, P367, DOI 10.1038/nature08850 Magan N, 2011, PLANT PATHOL, V60, P150, DOI 10.1111/j.1365-3059.2010.02412.x Reynoso MM, 2011, INT J FOOD MICROBIOL, V145, P444, DOI 10.1016/j.ijfoodmicro.2011.01.020 Moradi M, 2010, MICROBIOLOGY+, V79, P646, DOI 10.1134/S0026261710050097 Munaut F, 2011, WOODHEAD PUBL FOOD S, P298 Nayaka SC, 2011, APPL MICROBIOL BIOT, V90, P1625, DOI 10.1007/s00253-011-3209-3 Nicolaisen M, 2009, J MICROBIOL METH, V76, P234, DOI 10.1016/j.mimet.2008.10.016 Nielsen LK, 2012, INT J FOOD MICROBIOL, V157, P384, DOI 10.1016/j.ijfoodmicro.2012.06.010 Niessen L, 2010, INT J FOOD MICROBIOL, V140, P183, DOI 10.1016/j.ijfoodmicro.2010.03.036 Notomi T, 2000, NUCLEIC ACIDS RES, V28, DOI 10.1093/nar/28.12.e63 Park B, 2011, NUCLEIC ACIDS RES, V39, pD640, DOI 10.1093/nar/gkq1166 Pasquali M, 2010, INT J FOOD MICROBIOL, V137, P246, DOI 10.1016/j.ijfoodmicro.2009.11.009 Pasquali M, 2011, J PHYTOPATHOL, V159, P700, DOI 10.1111/j.1439-0434.2011.01824.x Paterson RRM, 2009, J APPL MICROBIOL, V106, P1070, DOI 10.1111/j.1365-2672.2008.04024.x Paterson RRM, 2006, PROCESS BIOCHEM, V41, P1467, DOI 10.1016/j.procbio.2006.02.019 Paterson RRM, 2010, FOOD RES INT, V43, P1902, DOI 10.1016/j.foodres.2009.07.010 Prodi A, 2009, J PLANT PATHOL, V91, P727 Ramos AJ, 2011, WORLD MYCOTOXIN J, V4, P101, DOI 10.3920/WMJ2010.1268 Reischer GH, 2004, J MICROBIOL METH, V59, P141, DOI 10.1016/j.mimet.2004.06.003 Rep M, 2010, CURR OPIN PLANT BIOL, V13, P420, DOI 10.1016/j.pbi.2010.04.004 Rios G, 2009, J CEREAL SCI, V49, P387, DOI 10.1016/j.jcs.2009.01.003 Rossi V., 2007, Bulletin OEPP, V37, P359, DOI 10.1111/j.1365-2338.2007.01138.x Rossi V, 2007, FOOD ADDIT CONTAM A, V24, P1121, DOI 10.1080/02652030701551818 Santamaria M, 2011, WOODHEAD PUBL FOOD S, P349 Sarlin T, 2006, EUR J PLANT PATHOL, V114, P371, DOI 10.1007/s10658-006-0001-9 Schnerr H, 2001, INT J FOOD MICROBIOL, V71, P53, DOI 10.1016/S0168-1605(01)00579-7 Shephard GS, 2012, WORLD MYCOTOXIN J, V5, P3, DOI 10.3920/WMJ2011.1338 Shephard GS, 2011, WORLD MYCOTOXIN J, V4, P3, DOI 10.3920/WMJ2010.1249 Stakheev AA, 2011, FOOD CONTROL, V22, P462, DOI 10.1016/j.foodcont.2010.09.028 Strausbaugh CA, 2005, CAN J PLANT PATHOL, V27, P430 Suanthie Y, 2009, J STORED PROD RES, V45, P139, DOI 10.1016/j.jspr.2008.12.001 Szemes M, 2005, NUCLEIC ACIDS RES, V33, DOI 10.1093/nar/gni069 Terzi V, 2007, INT J FOOD SCI TECH, V42, P1390, DOI 10.1111/j.1365-2621.2006.01344.x Vegi A, 2011, INT J FOOD MICROBIOL, V150, P150, DOI 10.1016/j.ijfoodmicro.2011.07.032 Waalwijk C, 2004, EUR J PLANT PATHOL, V110, P481, DOI 10.1023/B:EJPP.0000032387.52385.13 Ward TJ, 2002, P NATL ACAD SCI USA, V99, P9278, DOI 10.1073/pnas.142307199 Yin Y, 2009, LETT APPL MICROBIOL, V48, P680, DOI 10.1111/j.1472-765X.2009.02595.x Yli-Mattila T., 2008, Archives of Phytopathology and Plant Protection, V41, P243, DOI 10.1080/03235400600680659 Yli-Mattila T, 2004, INT J FOOD MICROBIOL, V95, P267, DOI 10.1016/j.ijfoodmicro.2003.12.006 NR 61 TC 19 Z9 19 U1 2 U2 58 PD MAR PY 2013 VL 57 IS 2 SI SI BP 175 EP 182 DI 10.1016/j.jcs.2012.09.016 WC Food Science & Technology SC Food Science & Technology UT WOS:000317162500004 DA 2022-12-14 ER PT J AU Contreras, R Porcile, V Guggiana-Nilo, D Aguayo, F AF Contreras, Roberto Porcile, Vincenzo Guggiana-Nilo, Drago Aguayo, Fernanda TI AN EFFICIENT PROTOCOL TO PERFORM GENETIC TRACEABILITY OF TISSUE AND FOODS FROM Geoffroea decorticans SO CHILEAN JOURNAL OF AGRICULTURAL & ANIMAL SCIENCES DT Article DE DNA isolation; chanar; Geoffroea decorticans; quality control; ISSR; RAPD; SSR ID DNA EXTRACTION METHOD; PCR; QUALITY; MARKERS; STRESS; FRUITS; TREE AB The quality of a DNA isolation method depends, among others, on the target tissue and the metabolites therein. Geoffroea decorticans Burkart (chanar) is a species that has nutritional and pharmacological potential. However, an effective method of DNA extraction capable of facilitating population studies and food genetic traceability has not been studied yet. The objective of the present work was to evaluate four methods of DNA extraction from leaves and chanar-based foods. The methods were evaluated based on yield, DNA purity, and molecular markers. The CCI-P (CTAB/Chloroform-Isoamylalcohol/pellet) method showed the highest yield of DNA obtained from leaves. However, the CPCI-SC (CTAB/Phenol-Chloroform-Isoamylalcohol/silica-column) method was the only one that resulted in acceptable DNA quality with both parameters (A260/A280 and A260/A230). The leaf DNA obtained with this method showed a greater amount of fragments with RAPD, and an acceptable amount of fragments with ISSR. On the other hand, the CCI-P method showed a higher yield of DNA from arrope de chanar (syrup). However, the CPCI-SC method was the only one that had relatively better DNA quality, which allowed the amplification of molecular markers. Regarding chanar flour, the CPCI-SC method showed the highest yield, DNA quality and good amplification with molecular markers. Therefore, the CPCI-SC extraction method is efficient for obtaining DNA from different matrices, and can support studies for a possible designation of origin of chanar-based foods. C1 [Contreras, Roberto; Porcile, Vincenzo; Aguayo, Fernanda] Univ Atacama, Ctr Invest Desarrollo Sustentable Atacama CRIDESA, Av Copayapu 485, Copiapo, Chile. [Guggiana-Nilo, Drago] Max Planck Inst Neurobiol, Klopferspitz 18, D-82152 Planegg, Germany. C3 Universidad de Atacama; Max Planck Society RP Contreras, R (corresponding author), Univ Atacama, Ctr Invest Desarrollo Sustentable Atacama CRIDESA, Av Copayapu 485, Copiapo, Chile. EM roberto.contreras@uda.cl CR Hurrell JA, 2011, B LATINOAM CARIBE PL, V10, P443 Aleksic JM, 2012, BIOCHEM GENET, V50, P881, DOI 10.1007/s10528-012-9528-y Allen GC, 2006, NAT PROTOC, V1, P2320, DOI 10.1038/nprot.2006.384 Amane D, 2019, FOOD CONTROL, V104, P193, DOI 10.1016/j.foodcont.2019.04.041 Charpentier M., 1998, VALORES NUTR PLANTAS Chomczynski P, 2006, NAT PROTOC, V1, P581, DOI 10.1038/nprot.2006.83 Diaz RC, 2018, BOSQUE, V39, P321, DOI 10.4067/S0717-92002018000200321 Contreras Roberto, 2018, Idesia, V36, P95, DOI 10.4067/S0718-34292018005001501 Contreras R, 2018, CHIL J AGRIC ANIM SC, V34, P126, DOI 10.4067/S0719-38902018005000402 Costamagna MS, 2016, FOOD CHEM, V190, P392, DOI 10.1016/j.foodchem.2015.05.068 Demeke T, 2010, ANAL BIOANAL CHEM, V396, P1977, DOI 10.1007/s00216-009-3150-9 Di Bernardo G, 2007, BIOTECHNOL PROGR, V23, P297, DOI 10.1021/bp060182m Fahmy T., 2003, XLSTAT VERSION 7 0 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Ginwal HS, 2010, INDIAN J BIOTECHNOL, V9, P69 Inglis PW, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0206085 Jain SA, 2013, FOOD SCI TECH-BRAZIL, V33, P753, DOI 10.1590/S0101-20612013000400022 Jimenez-Aspee F, 2017, MOLECULES, V22, DOI 10.3390/molecules22091565 Kopecka J, 2014, J AM SOC BREW CHEM, V72, P1, DOI 10.1094/ASBCJ-2014-0110-01 Liu PF, 2009, ANAL BIOCHEM, V389, P165, DOI 10.1016/j.ab.2009.03.028 Lorenz TC, 2012, JOVE-J VIS EXP, DOI 10.3791/3998 Lucena-Aguilar G, 2016, BIOPRESERV BIOBANK, V14, P264, DOI 10.1089/bio.2015.0064 Maestri DM, 2001, J FOOD COMPOS ANAL, V14, P585, DOI 10.1006/jfca.2001.1020 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Naciri-Graven Y, 2005, MOL ECOL NOTES, V5, P542, DOI 10.1111/j.1471-8286.2005.00982.x Ohmori K, 2008, J FOOD HYG SOC JPN, V49, P63, DOI 10.3358/shokueishi.49.63 Orrabalis Camilo J., 2013, Multequina, V22, P15 Paracchini V, 2019, FOOD ADDIT CONTAM A, V36, P1, DOI 10.1080/19440049.2018.1556402 Pereira L, 2016, SPRINGER PROTOC HAND, P195, DOI 10.1007/978-1-4939-3185-9_14 Popovic BM, 2016, PLANT PHYSIOL BIOCH, V105, P242, DOI 10.1016/j.plaphy.2016.04.036 Reynoso M. A., 2016, Journal of Nutrition and Food Sciences, V6, P485 Sairkar P, 2013, J GENET ENG BIOTECHN, V11, P17, DOI DOI 10.1016/J.JGEB.2013.02.001 Scarano D., 2014, Diversity, V6, P579 Schrader C, 2012, J APPL MICROBIOL, V113, P1014, DOI 10.1111/j.1365-2672.2012.05384.x Souza HAV, 2012, GENET MOL RES, V11, P756, DOI 10.4238/2012.March.22.6 Sucher N.J., 2012, PLANT DNA FINGERPRIN Turci M, 2010, FOOD CONTROL, V21, P143, DOI 10.1016/j.foodcont.2009.04.012 Vicente O, 2018, NOT BOT HORTI AGROBO, V46, P14, DOI 10.15835/nbha46110992 WILLIAMS JGK, 1990, NUCLEIC ACIDS RES, V18, P6531, DOI 10.1093/nar/18.22.6531 Wilson IG, 1997, APPL ENVIRON MICROB, V63, P3741, DOI 10.1128/AEM.63.10.3741-3751.1997 Yayla M.E., 2018, J CONSUMER PROTECTIO, V14, P147 ZIETKIEWICZ E, 1994, GENOMICS, V20, P176, DOI 10.1006/geno.1994.1151 NR 42 TC 4 Z9 4 U1 0 U2 2 PY 2019 VL 35 IS 3 BP 224 EP 237 DI 10.4067/S0719-38902019005000402 WC Agronomy SC Agriculture UT WOS:000509988700002 DA 2022-12-14 ER PT J AU Xia, YM Chen, FS Jiang, LZ Li, SS Zhang, JY AF Xia, Yimiao Chen, Fusheng Jiang, Lianzhou Li, Shanshan Zhang, Jinyang TI Development of an Efficient Method to Extract DNA from Refined Soybean Oil SO FOOD ANALYTICAL METHODS DT Article DE DNA extraction; Round-up ready soybean; Refined oil; Traceability; Transgenic detection ID FOOD; AUTHENTICATION; OPTIMIZATION; DETECT; SIZE AB Recently, soybean oil has become the most consumed genetically modified (GM) vegetable oil globally. To monitor products derived from GM sources, DNA-based analysis methods are being widely adopted, particularly to control the authenticity of several food products including oil. Considering that DNA isolation from foodstuffs is the first step in the detection of GM organisms, we compared five different methods to extract high quality DNA from refined soybean oil. We used ultraviolet spectrophotometry, nested polymerase chain reaction (PCR), and real-time PCR to evaluate DNA yield and purity, and the sensitivity of the optimized method was evaluated. According to our results, the most effective DNA extraction method was the cetyltrimethylammonium bromide-NucleoSpin Food Kit method. Using this method, 21-225 DNA copies were isolated from 1 mL soybean or blended oils, and the minimum ratio of soybean oil that could be detected from a mixture of soybean and peanut oil was 5%. To summarize, we report a novel method that combines pre-concentrating oils and commercial kits. This optimized DNA extraction method advances the traceability of soybean oils for authenticity issues and transgenic detection. C1 [Xia, Yimiao; Chen, Fusheng; Li, Shanshan; Zhang, Jinyang] Henan Univ Technol, Coll Food Sci & Technol, 100,Lianhua St, Zhengzhou 450001, Peoples R China. [Jiang, Lianzhou] Northeast Agr Univ, Coll Food Sci, Harbin 150030, Peoples R China. C3 Henan University of Technology; Northeast Agricultural University - China RP Chen, FS (corresponding author), Henan Univ Technol, Coll Food Sci & Technol, 100,Lianhua St, Zhengzhou 450001, Peoples R China. EM fushengc@haut.edu.cn CR Alonso-Rebollo A, 2017, FOOD CHEM, V232, P827, DOI 10.1016/j.foodchem.2017.04.078 [Anonymous], 2003, DET GEN MOD PLANT OR Bogani P, 2009, FOOD CHEM, V113, P658, DOI 10.1016/j.foodchem.2008.07.056 Chen Y, 2005, J AGR FOOD CHEM, V53, P10239, DOI 10.1021/jf0519820 Cheng XX, 2018, ARAB J CHEM, V11, P815, DOI 10.1016/j.arabjc.2017.12.025 Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Costa J, 2010, EUR FOOD RES TECHNOL, V230, P915, DOI 10.1007/s00217-010-1238-2 Debode F, 2012, J AM OIL CHEM SOC, V89, P1249, DOI 10.1007/s11746-012-2007-0 ENGL, 2015, DEF MIN PERF REQ AN European Commission, 2003, OFF J EUR UNION FEREIDOON S, 2005, BAILEYS IND OIL FAT Foreign Agricultural Service and United States Department of Agriculture, 2019, OILS WORLD MARK TRAD Grazina L, 2017, FOOD CONTROL, V73, P1053, DOI 10.1016/j.foodcont.2016.10.020 Greilhuber J, 1997, HEREDITY, V78, P547, DOI 10.1038/hdy.1997.85 Gryson N, 2004, J AM OIL CHEM SOC, V81, P231, DOI 10.1007/s11746-004-0887-6 Gryson N, 2002, J AM OIL CHEM SOC, V79, P171, DOI 10.1007/s11746-002-0453-2 He J, 2013, FOOD CONTROL, V31, P71, DOI 10.1016/j.foodcont.2012.07.001 Leitch I.J., 2019, ANGIOSPERM DNA C VAL Lipp M, 1999, J AOAC INT, V82, P923 Lucchetti S, 2018, FOOD CHEM, V245, P812, DOI 10.1016/j.foodchem.2017.11.107 Mafra I, 2008, FOOD CONTROL, V19, P1183, DOI 10.1016/j.foodcont.2008.01.004 Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Nicolia A, 2014, CRIT REV BIOTECHNOL, V34, P77, DOI 10.3109/07388551.2013.823595 Nikolic Z, 2017, FOOD TECHNOL BIOTECH, V55, P439, DOI 10.17113/ftb.55.04.17.5192 Nikolic Z, 2014, FOOD CHEM, V145, P1072, DOI 10.1016/j.foodchem.2013.09.017 Pauli U, 1998, Z LEBENSM UNTERS F A, V207, P264, DOI 10.1007/s002170050330 Piarulli L, 2019, FOODS, V8, DOI 10.3390/foods8100462 Ramos-Gomez S, 2014, FOOD CHEM, V158, P374, DOI 10.1016/j.foodchem.2014.02.142 Sambrook J, 2001, MOL CLONING LAB MANU Turkec A, 2016, FOOD CONTROL, V59, P766, DOI 10.1016/j.foodcont.2015.06.052 Uncu AO, 2018, FOOD ANAL METHOD, V11, P939, DOI 10.1007/s12161-017-1070-4 Xia YM, 2019, BIOSCIENCE REP, V39, DOI 10.1042/BSR20182271 NR 32 TC 2 Z9 2 U1 5 U2 39 PD JAN PY 2021 VL 14 IS 1 BP 196 EP 207 DI 10.1007/s12161-020-01867-4 EA OCT 2020 WC Food Science & Technology SC Food Science & Technology UT WOS:000575029900001 DA 2022-12-14 ER PT J AU Pasqualone, A Alba, V Mangini, G Blanco, A Montemurro, C AF Pasqualone, Antonella Alba, Vittorio Mangini, Giacomo Blanco, Antonio Montemurro, Cinzia TI Durum wheat cultivar traceability in PDO Altamura bread by analysis of DNA microsatellites SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE Durum wheat; PDO Altamura bread; Cultivar traceability; DNA microsatellites; Polymerase chain reaction ID IDENTIFICATION; VARIETIES; SEQUENCES; MARKERS AB Altamura bread is an Italian baking product that obtained the European mark of protected designation of origin (PDO). The varietal requirements of the official production protocol of this bread require it to be prepared from the durum wheat cultivars Appulo, Duilio, Arcangelo and Simeto (single or in combination, accounting for minimum 80%), and eventually other cultivars diffused in the production area. The aim of this work was to set up a microsatellite-based method for verifying the presence of the four required durum wheat cultivars in PDO Altamura bread, also in the presence of other cultivars up to 20%. Ten microsatellites were tested and the combination of the amplification profiles of four of them, characterised by high polymorphism and simple electrophoretic patterns, enabled to distinguish and identify breads from all the possible combinations of the cultivars required for PDO mark. The obtained amplicons were all in the range of molecular weight between 115 and 272 bp, and were analysed by capillary electrophoresis. The contribution of the single cultivars was detectable in the amplification profiles, enabling to verify their presence. The analysis was also effective in the case of additional cultivars. C1 [Pasqualone, Antonella] Univ Bari, Sect Agri Food Ind, PROGESA Dept, I-70126 Bari, Italy. [Alba, Vittorio; Mangini, Giacomo; Blanco, Antonio; Montemurro, Cinzia] Univ Bari, Sect Genet & Breeding, DIBCA Dept, I-70126 Bari, Italy. C3 Universita degli Studi di Bari Aldo Moro; Universita degli Studi di Bari Aldo Moro RP Pasqualone, A (corresponding author), Univ Bari, Sect Agri Food Ind, PROGESA Dept, Via Amendola 165-A, I-70126 Bari, Italy. EM antonella.pasqualone@agr.uniba.it CR [Anonymous], 2006, OFFICIAL J EUROPEA L, VL93, P12 *APPL BIOS US, 2000, GENESCAN REF GUID, pCH8 Bryan GJ, 1997, THEOR APPL GENET, V94, P557, DOI 10.1007/s001220050451 Chiavaro E, 2008, LWT-FOOD SCI TECHNOL, V41, P58, DOI 10.1016/j.lwt.2007.01.018 Cooke RJ, 2003, EUPHYTICA, V132, P331, DOI 10.1023/A:1025046919570 Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 *EUR COMM, 2003, OFF J EUR COMMUN L, V181, P12 *EUR COMM, 2001, OFF J EUR COMMUN C, V156, P10 European Commission, 1992, OFF J EUR COMMUN L, V208, P1 Ma ZQ, 1996, GENOME, V39, P123, DOI 10.1139/g96-017 MANGINI G, 2008, P 52 ANN C IT SOC AG, pA5 Pasqualone A., 2002, Tecnica Molitoria, V53, P770 Pasqualone A, 1999, EUR FOOD RES TECHNOL, V210, P144, DOI 10.1007/s002170050551 PASQUALONE A, 2005, CEREALI BIOTECNOLOGI, P83 PASQUALONE A, 2006, P 3 AACC INT S SOURD, P12 PASQUALONE A, 2006, P 50 ANN C IT SOC AG, pB6 Pasqualone A, 2007, J FOOD SCI, V72, pS191, DOI 10.1111/j.1750-3841.2007.00293.x Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3312, DOI 10.1021/jf063383e Perry DJ, 2004, THEOR APPL GENET, V109, P55, DOI 10.1007/s00122-004-1597-9 Pestsova E, 2000, GENOME, V43, P689, DOI 10.1139/gen-43-4-689 Prasad M, 2000, THEOR APPL GENET, V100, P584, DOI 10.1007/s001229900102 Raffo A, 2003, EUR FOOD RES TECHNOL, V218, P49, DOI 10.1007/s00217-003-0793-1 RODER MS, 1995, MOL GEN GENET, V246, P327, DOI 10.1007/BF00288605 Roder MS, 1998, GENETICS, V149, P2007 Roder MS, 2002, THEOR APPL GENET, V106, P67, DOI 10.1007/s00122-002-1061-7 Simeone R., 2001, Tecnica Molitoria, V52, P34 Song QJ, 2005, THEOR APPL GENET, V110, P550, DOI 10.1007/s00122-004-1871-x TAUTZ D, 1989, NUCLEIC ACIDS RES, V17, P6463, DOI 10.1093/nar/17.16.6463 Terzi V, 2004, EUR FOOD RES TECHNOL, V219, P428, DOI 10.1007/s00217-004-0965-7 Tilley M, 2004, CEREAL CHEM, V81, P44, DOI 10.1094/CCHEM.2004.81.1.44 *USDA, 2009, GRAING 2 0 DAT TRIT VOSMAN B, 2001, ACTA HORTIC, V546, P307 NR 32 TC 33 Z9 33 U1 0 U2 5 PD MAR PY 2010 VL 230 IS 5 BP 723 EP 729 DI 10.1007/s00217-009-1210-1 WC Food Science & Technology SC Food Science & Technology UT WOS:000274385400005 DA 2022-12-14 ER PT J AU Vermeulen, P Brereton, P Lofthouse, J Smith, J Kehagia, O Krafft, A Baeten, V AF Vermeulen, Philippe Brereton, Paul Lofthouse, Janice Smith, Joel Kehagia, Olga Krafft, Alain Baeten, Vincent TI Web-based communication tools in a European research project: the example of the TRACE project SO BIOTECHNOLOGIE AGRONOMIE SOCIETE ET ENVIRONNEMENT DT Article DE Databases; Internet; project management; computer systems (applications); information systems; communication technology AB The multi-disciplinary and international nature of large European projects requires powerful managerial and communicative tools to ensure the transmission of information to the end-users. One such project is TRACE entitled "Tracing Food Commodities in Europe". One of its objectives is to provide a communication system dedicated to be the central source of information on food authenticity and traceability in Europe. This paper explores the web tools used and communication vehicles offered to scientists involved in the TRACE project to communicate internally as well as to the public. Two main tools have been built: an Intranet and a public website. The TRACE website can be accessed at http://www.trace.eu.org. A particular emphasis was placed on the efficiency, the relevance and the accessibility of the information, the publicity of the website as well as the use of the collaborative utilities. The rationale of web space design as well as integration of proprietary software solutions are presented. Perspectives on the using of web tools in the research projects are discussed. C1 [Vermeulen, Philippe; Krafft, Alain; Baeten, Vincent] Walloon Agr Res Ctr, Qual Dept Agrofood Prod, B-5030 Gembloux, Belgium. [Brereton, Paul; Lofthouse, Janice; Smith, Joel] FERA, Sand Hutton YO41 1LZ, Yorks, England. [Kehagia, Olga] AUA, GR-11855 Athens, Greece. C3 Food & Environment Research Agency; Agricultural University of Athens RP Vermeulen, P (corresponding author), Walloon Agr Res Ctr, Qual Dept Agrofood Prod, Chaussee Namur 24, B-5030 Gembloux, Belgium. EM vermeulen@cra.wallonie.be CR *AD, 2005, AD FLASH PLAYER BOERRESEN T, 2006, P C COMM EUR RES 200, P217 CLAESSENS M, 2005, RTD INFO MAGAZINE EU, P17 *DESIGNTECH INN PR, 2006, PROJECTCOORDINATOR W *EUR COMM, 2005, SPEC EUR 63 1 SOC VA *EUR COMM, 2004, EUR RES GUID SUCC CO *EUR COMM, 2001, EUR 55 2 EUR SCI TEH *EUR COMM, 2004, RAIS PUBL AW SCI TEC *EUR COMM, 2005, P C COMM EUR RES 200 *EUR COMM, 2007, KNOWL BAS BIOEC KBBE *GOOGL, 2006, GOOGL AN *MICR, 2006, ENT PROJ MAN EPM SOL *MICR, 2006, WIND SHAREPOINTSERVI MILLER S, 2000, ENSCOT EUROPEAN SCI *THOMS RESEARCHSOF, 2004, REF MAN WEB PUBL Vermeulen P., 2003, Biotechnologie, Agronomie, Societe et Environnement, V7, P161 VERMEULEN P, 2006, P TRAC 2 ANN M TRAC, P157 Vermeulen P., 2005, STRATEGIES METHODS D, P1 *WALL AGR RES CTR, 2003, EUROPEANFP5PROJECT G NR 19 TC 3 Z9 3 U1 0 U2 5 PY 2009 VL 13 IS 4 BP 509 EP 520 WC Agronomy; Biotechnology & Applied Microbiology; Environmental Sciences SC Agriculture; Biotechnology & Applied Microbiology; Environmental Sciences & Ecology UT WOS:000272743400003 DA 2022-12-14 ER PT J AU Sun, SM Guo, BL Wei, YM Fan, MT AF Sun Shu-min Guo Bo-li Wei Yi-min Fan Ming-tao TI Application of Near Infrared Spectral Fingerprint Technique in Lamb Meat Origin Traceability SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE Near infrared reflectance spectroscopy; Lamb meat; Geographical origin; PCA plus LDA; PLS-DA ID GEOGRAPHICAL ORIGIN; SPECTROSCOPY; VARIETIES; WINES; FOOD AB Near infrared spectra of 99 lamb meat samples from three pasturing areas and two farming areas of China were scanned and analyzed to seek a cheap, rapid and effective method for lamb meat origin traceability. Two chemometric methods including linear discriminant analysis based on principal component analysis (PCA+LDA) and partial least squares discriminant analysis (PLS-DA) were used to develop the discriminate models. It was showed that there were significantly differences among the lamb meat samples from five regions based on NIR spectra after second derivative (Savitzky-Golay, 9 point) and multiplicative scattering correction(MSC) transformation in the whole wavelength. The discrimination of two models was best for classification of pasturing area and farming area, with both correctly classified by 100%. The correct classification rate of samples from five different regions using PCA-FLDA model was 91. 2%, higher than using PLS-DA model (76. 7%). These results demonstrate that near infrared reflectance spectroscopy (NIRS) combined with chemometric analysis can be used as an effective method to classify lamb meat according to its geographical origin. C1 [Sun Shu-min; Guo Bo-li; Wei Yi-min] Chinese Acad Agr Sci, Key Lab Agr Prod Proc & Qual Control, Minist Agr, Inst Agrofood Sci & Technol, Beijing 100193, Peoples R China. [Sun Shu-min; Fan Ming-tao] NW A&F Univ, Coll Food Sci & Engn, Yangling 712100, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; Northwest A&F University - China RP Wei, YM (corresponding author), Chinese Acad Agr Sci, Key Lab Agr Prod Proc & Qual Control, Minist Agr, Inst Agrofood Sci & Technol, Beijing 100193, Peoples R China. EM xianyun730@163.com; weiyimin36@hotmail.com CR [Anonymous], 2002, OFFICIAL J EUROPEAN Chen QS, 2008, CZECH J FOOD SCI, V26, P360, DOI 10.17221/1125-CJFS Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 He Y, 2007, J FOOD ENG, V79, P1238, DOI 10.1016/j.jfoodeng.2006.04.042 Karoui R, 2005, INT DAIRY J, V15, P287, DOI 10.1016/j.idairyj.2004.07.005 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Lin P, 2012, FOOD BIOPROCESS TECH, V5, P235, DOI 10.1007/s11947-009-0302-z Liu L, 2008, FOOD CHEM, V106, P781, DOI 10.1016/j.foodchem.2007.06.015 Liu L, 2006, J AGR FOOD CHEM, V54, P6754, DOI 10.1021/jf061528b Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Roggo Y, 2003, ANAL CHIM ACTA, V477, P187, DOI 10.1016/S0003-2670(02)01422-8 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Sun DW, 2009, INFRARED SPECTROSCOPY FOR FOOD QUALITY ANALYSIS AND CONTROL, P1 Tewari JC, 2008, SPECTROCHIM ACTA A, V71, P1119, DOI 10.1016/j.saa.2008.03.005 Viljoen M, 2007, SMALL RUMINANT RES, V69, P88, DOI 10.1016/j.smallrumres.2005.12.019 Woodcock T, 2009, FOOD CHEM, V114, P742, DOI 10.1016/j.foodchem.2008.10.034 Zhang Ning, 2008, Transactions of the Chinese Society of Agricultural Engineering, V24, P309 NR 17 TC 7 Z9 23 U1 3 U2 22 PD APR PY 2011 VL 31 IS 4 BP 937 EP 941 DI 10.3964/j.issn.1000-0593(2011)04-0937-05 WC Spectroscopy SC Spectroscopy UT WOS:000291227500017 DA 2022-12-14 ER PT J AU Tescione, I Marchionni, S Casalini, M Vignozzi, N Mattei, M Conticelli, S AF Tescione, Ines Marchionni, Sara Casalini, Martina Vignozzi, Nadia Mattei, Massimo Conticelli, Sandro TI Sr-87/Sr-86 isotopes in grapes of different cultivars: A geochemical tool for geographic traceability of agriculture products SO FOOD CHEMISTRY DT Article DE Sr-87/Sr-86 of fresh grapes; White and red grapes; Geographic traceability; Geologic and pedologic fingerprints; Pitigliano area; Vulsini Mountains; Central Italy ID WINES; ORIGIN; RATIOS; SOIL; VALIDATE; QUEBEC; TRACER; FOOD; RED; SR AB Sr-87/Sr-86 was determined on fresh red and white grapes, soils and rocks from three selected vineyards to verify the isotopic relationships between the fruit of the vine and geologic substrata of vineyards. Sr-87/Sr-86 were determined on sampled grapes of four different harvest years and different grape varieties, on bioavailable fraction of soils, on whole soils, and on bedrocks from the geo-pedological substratum of the vineyards. The vineyards chosen for the experimental works belong to an organic farming winery and thus cultivation procedures were strictly controlled. Grapes were sampled during the harvests of four different but consecutive years with Sr-87/Sr-86 that does not change reflecting the values of the soil bioavailable fraction. No variations among grapes from different vine cultivars were observed. A strict isotope relationship with soil bio-available fraction was observed. These findings demonstrate the reliability of Sr-87/Sr-86, even at a very small scale, for food products geographic origin assessment. C1 [Tescione, Ines; Marchionni, Sara; Mattei, Massimo] Univ Roma TRE, Dipartimento Sci, Largo S Leonardo Murialdo 1, I-00146 Rome, Italy. [Tescione, Ines; Marchionni, Sara; Casalini, Martina; Conticelli, Sandro] Univ Florence, Dipartimento Sci Terra, Via G La Pira 4, I-50121 Florence, Italy. [Vignozzi, Nadia] Consiglio Ric Agr & Anal Econ Agr, Ctr Ric Agr & Ambiente, Via Lanciola 12-A, I-50125 Florence, Italy. [Conticelli, Sandro] CNR, Sede Secondaria Firenze, Ist Geosci & Georisorse, Via G La Pira 4, I-50121 Florence, Italy. C3 Roma Tre University; University of Florence; Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Consiglio Nazionale delle Ricerche (CNR); Istituto di Geoscienze e Georisorse (IGG-CNR) RP Mattei, M (corresponding author), Univ Roma TRE, Dipartimento Sci, Largo S Leonardo Murialdo 1, I-00146 Rome, Italy.; Conticelli, S (corresponding author), Univ Florence, Dipartimento Sci Terra, Via G La Pira 4, I-50121 Florence, Italy. EM sandro.conticelli@unifi.it CR Amoros O.-V., 2012, VITIS, V51, P111 Avanzinelli R, 2005, PERIOD MINERAL, V74, P147 Barbaste M, 2002, J ANAL ATOM SPECTROM, V17, P135, DOI 10.1039/b109559p Boari E., 2008, OIV 2008 31 WORLD C, P1 Braschi E., 2018, SCI TOTAL ENVIRON, P628 Bravo J. A., 2017, J GEOCHEMICAL EXPLOR Censi P, 2014, SCI TOTAL ENVIRON, V473, P597, DOI 10.1016/j.scitotenv.2013.12.073 Christoph Norbert, 2004, Mitteilungen Klosterneuburg, V54, P144 Christoph Norbert, 2003, Mitteilungen Klosterneuburg, V53, P23 CONTICELLI S, 1991, J VOLCANOL GEOTH RES, V46, P187, DOI 10.1016/0377-0273(91)90083-C Conticelli S., 2010, GEOLOGY ITALY J VIRT, V36, DOI [10.3809/jvirtex.2010.00251, DOI 10.3809/JVIRTEX.2009.00251, DOI 10.3809/JVIRTEX.2010.00251] Conticelli S, 1987, PERIOD MINERAL, V56, P175 Conticelli S., 2018, BEHAV STRONTIUM PLAN, P145, DOI [10.1007/978-3-319-66574-0_10, DOI 10.1007/978-3-319-66574-0_10] Conticelli S, 2015, LITHOS, V232, P174, DOI 10.1016/j.lithos.2015.07.002 de Rijke E, 2016, FOOD CHEM, V204, P122, DOI 10.1016/j.foodchem.2016.01.134 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 DOBERMANN A, 1994, COMMUN SOIL SCI PLAN, V25, P1329, DOI 10.1080/00103629409369119 Durante C, 2016, FOOD CHEM, V210, P648, DOI 10.1016/j.foodchem.2016.04.108 Durante C, 2013, FOOD CHEM, V141, P2779, DOI 10.1016/j.foodchem.2013.05.108 Evans JA, 2015, SCI TOTAL ENVIRON, V537, P447, DOI 10.1016/j.scitotenv.2015.07.133 Faure G., 1986, PRINCIPLES ISOTOPE G, P350 Food and Agriculture Organization (FAO), 2016, FAO OIV FOC REP TABL Garcia-Ruiz S, 2007, ANAL CHIM ACTA, V590, P55, DOI 10.1016/j.aca.2007.03.016 HORN P, 1993, Z LEBENSM UNTERS FOR, V196, P407, DOI 10.1007/BF01190802 Jaeger E., 1979, THE RB SR METHOD, P13 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Marchionni S, 2016, FOOD CHEM, V190, P777, DOI 10.1016/j.foodchem.2015.06.026 Marchionni S, 2013, J AGR FOOD CHEM, V61, P6822, DOI 10.1021/jf4012592 Marisa C, 2004, FOOD CHEM, V85, P7, DOI 10.1016/j.foodchem.2003.05.003 Medini S, 2015, FOOD CHEM, V171, P78, DOI 10.1016/j.foodchem.2014.08.121 Petrini R, 2015, FOOD CHEM, V170, P138, DOI 10.1016/j.foodchem.2014.08.051 Rummel S, 2010, FOOD CHEM, V118, P890, DOI 10.1016/j.foodchem.2008.05.115 Skogley EO, 1996, J ENVIRON QUAL, V25, P13, DOI 10.2134/jeq1996.00472425002500010004x Techer I, 2011, FOOD CHEM, V126, P718, DOI 10.1016/j.foodchem.2010.11.035 Tescione I, 2015, PROCED EARTH PLAN SC, V13, P169, DOI 10.1016/j.proeps.2015.07.039 Tommasini S, 2000, APPL GEOCHEM, V15, P891, DOI 10.1016/S0883-2927(99)00106-7 Vezzoli L., 1987, PERIOD MINERAL, V56, P89 Victor V, 2015, PROCED EARTH PLAN SC, V13, P252, DOI 10.1016/j.proeps.2015.07.059 Vinciguerra V, 2016, FOOD CHEM, V210, P121, DOI 10.1016/j.foodchem.2016.04.017 Voerkelius S, 2010, FOOD CHEM, V118, P933, DOI 10.1016/j.foodchem.2009.04.125 NR 40 TC 10 Z9 13 U1 1 U2 45 PD AUG 30 PY 2018 VL 258 BP 374 EP 380 DI 10.1016/j.foodchem.2018.03.083 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000430241500049 DA 2022-12-14 ER PT J AU Bai, JF Zhang, CP Jiang, J AF Bai, Junfei Zhang, Caiping Jiang, Jing TI The role of certificate issuer on consumers' willingness-to-pay for milk traceability in China SO AGRICULTURAL ECONOMICS DT Article DE Certificate issuer; Food safety; Traceable milk; D12; Q18 ID COUNTRY-OF-ORIGIN; FOOD SAFETY; PREFERENCES; QUALITY; MARKET; BEEF; PORK; SYSTEM; US AB In response to increasing concerns about domestic food safety issues, establishing tracking systems in the food industry is mandatorily required under newly launched food safety laws. However, the kinds of monitoring and certification systems that should be set up to ensure practical adoption and the effectiveness of the regulation remain unclear. This study aims to analyze consumers' preferences for milk traceability, with particular interest in investigating how consumers' preferences could be affected by monitoring and certification systems of the regarding system. Survey data from a choice-based conjoint (CBC) experiment are used to achieve this objective. In the experiment, milk is defined by a set of attributes in which we assume that milk traceability can be certified by three entities: the government, an industrial association, and a third party. The CBC data are then analyzed by using the alternative-specific form of a conditional Logit (McFadden's Choice) model. We found that urban Chinese consumers have a strong desire for traceable milk, but their preference for traceable milk is significantly related to the associated certificate issuers. Currently, the highest willingness-to-pay goes to government certificated traceable milk, followed by industrial association certificated and third-party certificated milks. In the future, however, consumers are likely to give more credit to third-party certification with rising income and knowledge. C1 [Bai, Junfei; Jiang, Jing] Chinese Acad Sci, Ctr Chinese Agr Policy, Beijing 100101, Peoples R China. [Zhang, Caiping] Cent Univ Finance & Econ, Sch Econ, Beijing 100081, Peoples R China. C3 Chinese Academy of Sciences; Central University of Finance & Economics RP Zhang, CP (corresponding author), Cent Univ Finance & Econ, Sch Econ, 39 South Coll Rd, Beijing 100081, Peoples R China. EM caipingzhang@gmail.com CR AKERLOF GA, 1970, Q J ECON, V84, P488, DOI 10.2307/1879431 Anderson D. A., 1992, MARKET LETT, V3, P357 Bryan S, 2002, APPL ECON, V34, P561, DOI 10.1080/00036840110103733 Caswell JA, 1996, AM J AGR ECON, V78, P1248, DOI 10.2307/1243501 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Ehmke MD, 2008, AGR ECON-BLACKWELL, V38, P277, DOI 10.1111/j.1574-0862.2008.00299.x ELROD T, 1992, J MARKETING RES, V29, P368, DOI 10.2307/3172746 Golan E., 2004, AER830 USDA EC RES S Hensher D.A., 1993, MARKET LETT, V4, P139, DOI DOI 10.1007/BF00994072 Hobbs J. E., 2003, CURRENT AGR FOOD RES, V4, P36 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Innes BG, 2011, CAN J AGR ECON, V59, P87, DOI 10.1111/j.1744-7976.2010.01194.x Kim Y, 2005, APPL ECON, V37, P817, DOI 10.1080/0003684042000337398 LAZARI AG, 1994, J MARKETING RES, V31, P375, DOI 10.2307/3152224 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Louviere J. J., 2003, 04002 CENSOC LOUVIERE JJ, 1988, J TRANSP ECON POLICY, V22, P93 McCluskey J. J., 2000, Agricultural and Resource Economics Review, V29, P1 McFadden D., 1973, FRONTIERS ECONOMETRI, P105 Mittelhammer Ron C, 2000, ECONOMETRIC FDN PACK Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Pan E., 2012, FOOD SAFETY RANKS 1, P64 Roosen J., 2003, Agribusiness (New York), V19, P77, DOI 10.1002/agr.10041 San Miguel F, 2000, APPL ECON, V32, P823 Song M, 2008, J FAC AGR KYUSHU U, V53, P569 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 Uzea AD, 2011, J AGR ECON, V62, P281, DOI 10.1111/j.1477-9552.2011.00297.x Verbeke W, 2002, FOOD POLICY, V27, P339, DOI 10.1016/S0306-9192(02)00037-4 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Wang ZG, 2008, FOOD POLICY, V33, P27, DOI 10.1016/j.foodpol.2007.05.006 Xiu CB, 2010, FOOD POLICY, V35, P463, DOI 10.1016/j.foodpol.2010.05.001 Yang B., 2009, RURAL EC, V8, P57 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhang CP, 2010, CHINA ECON REV, V20, pS45, DOI 10.1016/j.chieco.2010.05.008 NR 35 TC 68 Z9 70 U1 4 U2 107 PD JUL PY 2013 VL 44 IS 4-5 BP 537 EP 544 DI 10.1111/agec.12037 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000321442000017 DA 2022-12-14 ER PT J AU Curro, S Balzan, S Serva, L Boffo, L Ferlito, JC Novelli, E Fasolato, L AF Curro, Sarah Balzan, Stefania Serva, Lorenzo Boffo, Luciano Ferlito, Jacopo Carlo Novelli, Enrico Fasolato, Luca TI Fast and Green Method to Control Frauds of Geographical Origin in Traded Cuttlefish Using a Portable Infrared Reflective Instrument SO FOODS DT Article DE traceability; authenticity; machine learning; NIRS; cephalopods; mislabeling ID CHEMICAL-COMPOSITION; AUTHENTICATION; SPECTROSCOPY; TRACEABILITY; MULTIVARIATE; IMPACT; NIR AB An appropriate seafood origin identification is essential for labelling regulation but also economic and ecological issues. Near infrared (NIRS) reflectance spectroscopy was employed to assess the origins of cuttlefish caught from five fishing FAO areas (Adriatic Sea, northeastern and eastern central Atlantic Oceans, and eastern Indian and western central Pacific Oceans). A total of 727 cuttlefishes of the family Sepiidae (Sepia officinalis and Sepiella inermis) were collected with a portable spectrophotometer (902-1680 nm) in a wholesale fish plant. NIR spectra were treated with standard normal variate, detrending, smoothing, and second derivative before performing chemometric approaches. The random forest feature selection procedure was executed to select the most significative wavelengths. The geographical origin classification models were constructed on the most informative bands, applying support vector machine (SVM) and K nearest neighbors algorithms (KNN). The SVM showed the best performance of geographical classification through the hold-out validation according to the overall accuracy (0.92), balanced accuracy (from 0.83 to 1.00), sensitivity (from 0.67 to 1.00), and specificity (from 0.88 to 1.00). Thus, being one of the first studies on cuttlefish traceability using NIRS, the results suggest that this represents a rapid, green, and non-destructive method to support on-site, practical inspection to authenticate geographical origin and to contrast fraudulent activities of cuttlefish mislabeled as local. C1 [Curro, Sarah; Balzan, Stefania; Novelli, Enrico; Fasolato, Luca] Univ Padua, Agripolis, Dept Comparat Biomed & Food Sci, Viale Univ 16, I-35020 Legnaro, Italy. [Serva, Lorenzo] Univ Padua, Agripolis, Dept Anim Med Prod & Hlth, Viale Univ 16, I-35020 Legnaro, Italy. [Boffo, Luciano] ITPhotonics Srl, Via Astico 39, I-36030 Fara Vicentino, Italy. [Ferlito, Jacopo Carlo] BluPesca Srl, Isola Saloni 59, I-30015 Chioggia, Italy. C3 University of Padua; University of Padua RP Balzan, S (corresponding author), Univ Padua, Agripolis, Dept Comparat Biomed & Food Sci, Viale Univ 16, I-35020 Legnaro, Italy. EM sarah.curro@phd.unipd.it; stefania.balzan@unipd.it; lorenzo.serva@unipd.it; luc.boffo@gmail.com; j.ferlito@itphotonics.com; enrico.novelli@unipd.it; luca.fasolato@unipd.it CR Ayyildiz H, 2020, CHEMOMETR INTELL LAB, V196, DOI 10.1016/j.chemolab.2019.103886 BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Bisenius S, 2019, J CONSUM PROT FOOD S, V14, P329, DOI 10.1007/s00003-019-01247-z Bisutti V, 2019, J NEAR INFRARED SPEC, V27, P65, DOI 10.1177/0967033518824765 Camin F, 2018, FOOD CHEM, V267, P288, DOI 10.1016/j.foodchem.2017.06.017 Cevik M, 2018, J FOOD PROCESS ENG, V41, DOI 10.1111/jfpe.12675 Espineira M, 2016, WOODHEAD PUBL FOOD S, V301, P91, DOI 10.1016/B978-0-08-100310-7.00006-5 European Medicines Agency, 2019, EMA2946742019 European Ombudsman, 2018, ECDC ANN EP REP 2016, P1, DOI DOI 10.2790/3042 FAO, 2018, FISH AQ STAT AQ PROD FAO, 2020, STATE WORLD FISHERIE, P224, DOI [DOI 10.4060/CA9229EN, 10.4060/ca9229en] Farquad MAH, 2012, DECIS SUPPORT SYST, V53, P226, DOI 10.1016/j.dss.2012.01.016 Fasolato L, 2012, J AQUAT FOOD PROD T, V21, P493, DOI 10.1080/10498850.2011.615103 Feng XD, 2013, CHEM RES CHINESE U, V29, P15, DOI 10.1007/s40242-013-2191-y Fox M, 2018, FOOD SECUR, V10, P939, DOI 10.1007/s12571-018-0826-z Ghidini S, 2019, MOLECULES, V24, DOI 10.3390/molecules24091812 Ghidini S, 2019, FOOD CHEM, V280, P321, DOI 10.1016/j.foodchem.2018.12.075 Ghosh Prabal K., 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P3, DOI 10.1007/s11694-008-9068-7 Giraud G, 2006, EUROPEAN ASS AGR ECO, P1, DOI [10.22004/ag.econ.10047, DOI 10.22004/AG.ECON.10047] Grujic S., 2013, Journal of Food Research, V2, P57 Guo XH, 2018, ROY SOC OPEN SCI, V5, DOI 10.1098/rsos.170714 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hassoun A, 2020, FOODS, V9, DOI 10.3390/foods9081069 Kuhn M, 2008, J STAT SOFTW, V28, P1, DOI 10.18637/jss.v028.i05 Kursa MB, 2010, J STAT SOFTW, V36, P1, DOI 10.18637/jss.v036.i11 Liu Y, 2015, LWT-FOOD SCI TECHNOL, V60, P1214, DOI 10.1016/j.lwt.2014.09.009 Luque GM, 2019, BIOL CONSERV, V236, P556, DOI 10.1016/j.biocon.2019.04.006 Mouritsen O.G., 2018, FRONT COMMUN, V3, P38, DOI [10.3389/fcomm.2018.00038, DOI 10.3389/FCOMM.2018.00038] Naaum AM, 2016, SEAFOOD AUTHENTICITY AND TRACEABILITY: A DNA-BASED PESPECTIVE, P1 Newton M, 2018, HYDROBIOLOGIA, V806, P251, DOI 10.1007/s10750-017-3364-3 Ozogul Y, 2008, FOOD CHEM, V108, P847, DOI 10.1016/j.foodchem.2007.11.048 Panero F.S., 2020, J AGR SCI, V12, P105, DOI [10.5539/jas.v12n7p105, DOI 10.5539/JAS.V12N7P105] Pennisi F, 2021, FOODS, V10, DOI 10.3390/foods10030528 Callao MP, 2018, FOOD CONTROL, V86, P283, DOI [10.1016/J.foodcont.2017.11.034, 10.1016/j.foodcont.2017.11.034] Pramod G, 2014, MAR POLICY, V48, P102, DOI 10.1016/j.marpol.2014.03.019 Richter B, 2019, FOOD CONTROL, V104, P318, DOI 10.1016/j.foodcont.2019.04.032 Rinnan A, 2009, TRAC-TREND ANAL CHEM, V28, P1201, DOI 10.1016/j.trac.2009.07.007 Saito H, 1997, J SCI FOOD AGR, V73, P53, DOI 10.1002/(SICI)1097-0010(199701)73:1<53::AID-JSFA707>3.0.CO;2-5 Sannia M, 2019, J FOOD SCI TECH MYS, V56, P4437, DOI 10.1007/s13197-019-03957-6 Standal IB, 2012, J AM OIL CHEM SOC, V89, P1173, DOI 10.1007/s11746-012-2031-0 Sterling B, 2014, GLOB FOOD TRACEABIL, P1, DOI [10.13140/2.1.1884.3526, DOI 10.13140/2.1.1884.3526] Su WH, 2017, COMPUT ELECTRON AGR, V139, P41, DOI 10.1016/j.compag.2017.04.017 Varra MO, 2021, FOOD CHEM, V356, DOI 10.1016/j.foodchem.2021.129687 Varra MO, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107778 Williams M, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107429 Workman J., 2012, PRACTICAL GUIDE SPEC, DOI 10.1201/b11894 Yin HM, 2020, ACTA GEOCHIM, V39, P326, DOI 10.1007/s11631-020-00407-5 NR 47 TC 1 Z9 1 U1 3 U2 18 PD AUG PY 2021 VL 10 IS 8 AR 1678 DI 10.3390/foods10081678 WC Food Science & Technology SC Food Science & Technology UT WOS:000690479600001 DA 2022-12-14 ER PT J AU Maciel, ED Santosvasconcelos, J Savay-Da-Silva, LK Galvao, JA Sonati, JG Christofoletti, JC Oetterer, M AF Maciel, Erika Da Silva Santosvasconcelos, Julia Savay-Da-Silva, Luciana Kimie Galvao, Juliana Antunes Sonati, Jaqueline Girnos Christofoletti, Jefferson Cristiano Oetterer, Marilia TI LABEL DESIGNING FOR MINIMALLY PROCESSED TILAPIA AIMING THE TRACEABILITY OF THE PRODUCTIVE CHAIN SO BOLETIM DO CENTRO DE PESQUISA DE PROCESSAMENTO DE ALIMENTOS DT Article DE TILAPIA; TRACED FISH; FISH PRODUCTION CHAIN; ELECTRONIC IDENTIFICATION ID FOOD LABEL AB The aim of this study was to develop label for Nile tilapia minimally processed. Analyses were performed to quantify the nutritional components of the fillets; and information about freshness, date of processing and batch specification was collected. Such data were essential for designing labels providing information about the traceability system and presenting codes for quality identification such as the Quick Response Code (QR Code), which allows the electronic encoding of the product on a database. Mandatory food labeling associated with the use of QR Code are tools that convey information to consumers about food quality and the entire production chain. One hundred sixty-two potential consumers were surveyed about the general characteristics of the product packaged in poly nylon film type, vacuum-packed, used for the packaging of traced fresh-cut fillets of tilapia minimally processed and kept under refrigeration (0 +/- 1 degrees C). According to the Spearman correlation the variables packaging, general appearance and willingness to purchase the product presented a correlation, with coefficients estimated at 0.437, 0.466 and 0.497 respectively. C1 [Maciel, Erika Da Silva] Univ Sao Paulo, ESALQ, Dept Agroind Alimentos & Nutr, Piracicaba, SP, Brazil. [Santosvasconcelos, Julia; Galvao, Juliana Antunes] Univ Sao Paulo, ESALQ, Piracicaba, SP, Brazil. [Savay-Da-Silva, Luciana Kimie; Oetterer, Marilia] Univ Sao Paulo, ESALQ, BR-09500900 Sao Paulo, Brazil. [Sonati, Jaqueline Girnos] Univ Estadual Campinas UNICAMP, Fac Ciencias Med, Sao Paulo, Brazil. [Christofoletti, Jefferson Cristiano] Embrapa Pesca & Aquicultura, Palmas, TO, Brazil. C3 Universidade de Sao Paulo; Universidade de Sao Paulo; Universidade de Sao Paulo; Universidade Estadual de Campinas; Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) RP Maciel, ED (corresponding author), Univ Sao Paulo, ESALQ, Dept Agroind Alimentos & Nutr, Piracicaba, SP, Brazil. EM erikasmaciel@gmail.com; julia.vasconcelos31@gmail.com; kimie@usp.br; jugalvao@usp.br; j.girnos@gmail.com; jefferson.christofoletti@embrapa.br; moettere@esalq.usp.br CR Maia MCA, 2008, CIENCIA TECNOL ALIME, V28, P341, DOI 10.1590/S0101-20612008000200011 Asenslo L, 2008, FOOD CONTROL, V19, P795, DOI 10.1016/j.foodcont.2007.08.005 BARBOSA NETO G. E., 2008, MOBILE PAYMENT ESTUD Borgmeier I, 2009, BMC PUBLIC HEALTH, V9, DOI 10.1186/1471-2458-9-184 BRASIL. Agencia Nacional de Vigilancia Sanitaria (ANVISA), 2005, ROT NUTR OBR BRASIL. Ministerio da Pesca e Aquicultura, 2010, CONS CAP AP PESC BRA BRASIL. Ministerio da Pesca e Aquicultura, 2010, PROD PESQ AQ BRASIL. Ministerio da Pesca e Aquicultura, 2012, B EST PESC AQ Cartasegna D., 2010, SENS MICROSYS, V54, P381 CHHORN L., 2006, TILAPIA BIOL CULTURE Camara MCC, 2008, REV PANAM SALUD PUBL, V23, P52, DOI 10.1590/S1020-49892008000100007 FAO, 2012, STATE WORLD FISHERIE FAO, 2008, FISH AQ STAT FAO, 2009, STAT WORLD FISH AQ 2, P196 FAO. Food and Agriculture Organization, 2010, FAO AN PROD HLTH FAO. Food and Agriculture Organization, 2005, PRES FUT MARK FISH F, P33 FENG W., 2003, FOOD CONTROL GUILDFO, V20, P918 FIESP. Federacao das Industrias do Estado de Sao Paulo, 2009, BRAS FOOD TRENDS 202 GALVAO J., 2011, THESIS U SAO PAULO P Galvao JA, 2010, FOOD CONTROL, V21, P1360, DOI 10.1016/j.foodcont.2010.03.010 GONCALVES A.A., 2009, ESTUDOS TECNOLOGICOS, V5, P14 Hartman L, 1973, Lab Pract, V22, P475 HORWITZ W., 2005, 98535 AOAC Jaffry S, 2004, FOOD POLICY, V29, P215, DOI 10.1016/j.foodpol.2004.04.001 JOHNSON C.M., 1974, ANALISES QUIMICAS PL, P56 Jory D.E., 2000, PANORAMA AQU COLA CI, V5, P50 LEVIN J., 2004, ESTATISTICA APLICADA, P392 MACIEL E.S., 2011, THESIS U SAO PAULO S, P304 MOORE I, 1963, METHOD ENZYMOL, V6, P919 Oetterer M., 2002, IND PESCADICULTIVADO, P200 Ollberding NJ, 2010, J AM DIET ASSOC, V110, P1233, DOI 10.1016/j.jada.2010.05.007 Pieniak Z, 2011, FOOD CONTROL, V22, P843, DOI 10.1016/j.foodcont.2010.09.022 Pieniak Z, 2010, FOOD POLICY, V35, P448, DOI 10.1016/j.foodpol.2010.05.002 Pregnolatto W., 1985, NORMAS ANALITICAS I Ribeiro MM, 2008, CIENCIA TECNOL ALIME, V28, P395, DOI 10.1590/S0101-20612008000200019 SALES R.O., 1990, CIENCIAS AGRONOMICAS, V1-2, P27 SAVAY-DA-SILVA L. K., 2009, THESIS U SAO PAULO S, P315 Schmarr HG, 1996, J AGR FOOD CHEM, V44, P512, DOI 10.1021/jf950193n Schroder U, 2008, J VERBRAUCH LEBENSM, V3, P45, DOI 10.1007/s00003-007-0302-8 Seino K, 2004, OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4, P476 Simoes MR, 2007, CIENCIA TECNOL ALIME, V27, P608, DOI 10.1590/S0101-20612007000300028 SPACKMAN DH, 1958, ANAL CHEM, V30, P1190, DOI 10.1021/ac60139a006 SPIES JR, 1967, ANAL CHEM, V39, P1412, DOI 10.1021/ac60256a004 SPSS. Statistical Package for the Social Sciences, 2006, US GUID BAS 15 0 Taylor CL, 2008, J AM DIET ASSOC, V108, P618, DOI 10.1016/j.jada.2008.01.009 WINTERS S., 2005, OFFICIAL METHODS ANA, P8 Yanar Y, 2006, FOOD CHEM, V97, P244, DOI 10.1016/j.foodchem.2005.03.043 Zhou J., 2004, J CHINA AGR EC, V11, P44 NR 48 TC 1 Z9 1 U1 1 U2 9 PY 2012 VL 30 IS 2 BP 157 EP 168 WC Food Science & Technology SC Food Science & Technology UT WOS:000317895400001 DA 2022-12-14 ER PT J AU Brem, G AF Brem, G TI Techniques and possibilities of traceability of food: genotyping as an innovative contribution for food safety. SO DEUTSCHE TIERARZTLICHE WOCHENSCHRIFT DT Article AB Traceability of meat has become a very important aspect of quality assurance of food. DNA analyses could be used for identification and verification of farm animals and animal derived products. A prerequisite is the collection of qualified samples from entire populations of production animals or from regionally or specially characterised animal populations. The expenditure for conventional carrying out collection, preservation, cataloguing, and storage would be enormous. Therefore we have developed a simple, reliable, and inexpensive method for the collection using the ear tagging process and for preservation of samples at room temperature. A similar collection technology can also be used for sampling of carcasses, meat and meat products. Isolation of DNA from these tissue samples can be preformed using a new single step technology. For identifying individuals microsatellites and single nucleotide polymorphisms are analysed. Comparison of DNA fingerprints or SNP signatures allows to traceback samples collected from products to the animals they are coming from. If the system will be tal costs would be less than 0,05 EUR per kilogramm meat sold. C1 Agrobiogen GmbH, D-86567 Hilgertshausen, Germany. RP Brem, G (corresponding author), Agrobiogen GmbH, Thalmannsdorf 25, D-86567 Hilgertshausen, Germany. EM gottfried.brem@agrobiogen.de NR 0 TC 4 Z9 4 U1 0 U2 5 PD JUL PY 2004 VL 111 IS 7 BP 273 EP 276 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000223028400002 DA 2022-12-14 ER PT J AU Feligini, M Panelli, S Sacchi, R Ghitti, M Capelli, E AF Feligini, Maria Panelli, Simona Sacchi, Roberto Ghitti, Michele Capelli, Enrica TI Tracing the origin of raw milk from farm by using Automated Ribosomal Intergenic Spacer Analysis (ARISA) fingerprinting of microbiota SO FOOD CONTROL DT Article DE Milk; Traceability; Microbiota; PCR; ARISA; Fingerprinting ID FOOD; TRACEABILITY; CHEESE AB The aim of the study was to distinguish the raw milk from different farms in relation to their geographical sites within a narrowed territorial district. The goal was achieved by applying a molecular-based system for traceability that uses microbial DNA barcodes present in milk. Microbiota of milk were fingerprinted by PCR of the 16S-23S intergenic transcribed spacer using the Automated Ribosomal Intergenic Spacer Analysis (ARISA). A total of 64 markers within the range 279-756 bp were detected on the thirty-eight bulk milk samples, none of which was common to all the patterns. Overall samples did not show relevant differences across the two years of sampling. In fact, every farm maintained a specific core profile over time, thus demonstrating that the interaction between site and year of sampling is not significant and that the variability between years does not affect the distinction between grouping of farms. The system was able to trace the geographical origin of raw milk with a resolution of less than 5 km. According to the European regulations for the protection of the geographical names of foodstuffs which have a tangible link to the territory, the ARISA system described here may represent a suitable analytical tool for tracing the origin of milk integrating and reinforcing traceability processes of the dairy chain. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Feligini, Maria; Panelli, Simona] Ist Sperimentale Italiano Lazzaro Spallanzani, Lab Qual Prodotti, I-26027 Rivolta Dadda, Cremona, Italy. [Sacchi, Roberto; Ghitti, Michele] Univ Pavia, Dipartimento Sci Terra & Ambiente, Lab Anal Stat & Bioinformat, I-27100 Pavia, Italy. [Capelli, Enrica] Univ Pavia, Dipartimento Sci Ambiente & Terr, Lab Immunol & Anal Genet, I-27100 Pavia, Italy. C3 IRCCS Lazzaro Spallanzani; University of Pavia; University of Pavia RP Feligini, M (corresponding author), Ist Sperimentale Italiano Lazzaro Spallanzani, Lab Qual Prodotti, I-26027 Rivolta Dadda, Cremona, Italy. EM maria.feligini@istitutospallanzani.it CR Anderson MJ, 2006, BIOMETRICS, V62, P245, DOI 10.1111/j.1541-0420.2005.00440.x Anderson MJ, 2001, AUSTRAL ECOL, V26, P32, DOI 10.1111/j.1442-9993.2001.01070.pp.x Bonizzi I, 2007, J APPL MICROBIOL, V102, P667, DOI 10.1111/j.1365-2672.2006.03131.x Bonizzi I, 2009, J APPL MICROBIOL, V107, P1319, DOI 10.1111/j.1365-2672.2009.04311.x Cardinale M, 2004, APPL ENVIRON MICROB, V70, P6147, DOI 10.1128/AEM.70.10.6147-6156.2004 Chen L, 2013, PLOS ONE, V8, DOI [10.1371/journal.pone.0060523, 10.1371/journal.pone.0081232] Feligini M, 2014, FOOD CONTROL, V42, P71, DOI 10.1016/j.foodcont.2014.02.002 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Gilg AW, 1998, FOOD POLICY, V23, P25, DOI 10.1016/S0306-9192(98)00020-7 Hantsis-Zacharov E, 2007, APPL ENVIRON MICROB, V73, P7162, DOI 10.1128/AEM.00866-07 Ilbery B, 1998, EUR URBAN REG STUD, V5, P329 Kovacs A, 2010, RES MICROBIOL, V161, P192, DOI 10.1016/j.resmic.2010.01.006 Legendre L, 1983, NUMERICAL ECOLOGY McArdle BH, 2001, ECOLOGY, V82, P290, DOI 10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2 Oger R, 2010, BIOTECHNOL AGRON SOC, V14, P633 Oksanen J, 2013, COMMUNITY ECOLOGY PA, V2, P1 Quigley L, 2011, INT J FOOD MICROBIOL, V150, P81, DOI 10.1016/j.ijfoodmicro.2011.08.001 Quinn G., 2002, EXPT DESIGN DATA ANA R Core Team, 2014, R LANG ENV STAT COMP Rastogi G, 2011, MICROBES AND MICROBIAL TECHNOLOGY: AGRICULTURAL AND ENVIRONMENTAL APPLICATIONS, P29, DOI 10.1007/978-1-4419-7931-5_2 Zuur Alain F., 2009, P1 NR 21 TC 11 Z9 11 U1 0 U2 69 PD APR PY 2015 VL 50 BP 51 EP 56 DI 10.1016/j.foodcont.2014.08.024 WC Food Science & Technology SC Food Science & Technology UT WOS:000347581100008 DA 2022-12-14 ER PT J AU Li, Y Zhang, J Li, T Liu, HG Li, JQ Wang, YZ AF Li, Yun Zhang, Ji Li, Tao Liu, Honggao Li, Jieqing Wang, Yuanzhong TI Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM) SO SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY DT Article DE Boletus edulis; Geographical traceability; Fourier transform mid infrared (FT-MIR) spectroscopy; Inductively coupled plasma-atomic emission spectrometry (ICP-AES); Data fusion; Support vector machine (SVM); Quality control ID CHEMICAL-COMPOSITION; NUTRITIONAL-VALUE; INFRARED-SPECTROSCOPY; MEDICINAL MUSHROOM; RAMAN-SPECTROSCOPY; QUALITY ASSESSMENT; AGARICUS-BLAZEI; ELECTRONIC NOSE; ORIGIN; CLASSIFICATION AB In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR Finally, the classification models were established by SVM, and the combination of parameter C and gamma (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms. (C) 2017 Published by Elsevier B.V. C1 [Li, Yun; Zhang, Ji; Wang, Yuanzhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Peoples R China. [Li, Yun; Zhang, Ji; Wang, Yuanzhong] Yunnan Tech Ctr Qual Chinese Mat Med, Kunming 650200, Peoples R China. [Li, Yun] Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Peoples R China. [Liu, Honggao; Li, Jieqing] Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China. [Li, Tao] Yuxi Normal Univ, Coll Resources & Environm, Yuxi 653100, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University of Chinese Medicine; Yunnan Agricultural University; Yuxi Normal University RP Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Peoples R China.; Wang, YZ (corresponding author), Yunnan Tech Ctr Qual Chinese Mat Med, Kunming 650200, Peoples R China.; Li, JQ (corresponding author), Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China. EM lijieqing2008@126.com; boletus@126.com CR Auria L, 2008, 811 DIW, P1 Aydin I, 2011, APPL SOFT COMPUT, V11, P120, DOI 10.1016/j.asoc.2009.11.003 Bagnasco L, 2015, TALANTA, V144, P1225, DOI 10.1016/j.talanta.2015.07.071 Barbosa RM, 2016, J FOOD COMPOS ANAL, V45, P95, DOI 10.1016/j.jfca.2015.09.010 BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Barros L, 2008, J AGR FOOD CHEM, V56, P3856, DOI 10.1021/jf8003114 Bassbasi M, 2014, J FOOD COMPOS ANAL, V33, P210, DOI 10.1016/j.jfca.2013.11.010 Bergner N, 2012, CHEMOMETR INTELL LAB, V117, P224, DOI 10.1016/j.chemolab.2012.02.008 Biancolillo A, 2014, ANAL CHIM ACTA, V820, P23, DOI 10.1016/j.aca.2014.02.024 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Cao CL, 2013, INT J MED MUSHROOMS, V15, P57, DOI 10.1615/IntJMedMushr.v15.i1.70 Casale M, 2016, TALANTA, V160, P729, DOI 10.1016/j.talanta.2016.08.004 Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199 Chen Y, 2008, J PHARMACEUT BIOMED, V47, P469, DOI 10.1016/j.jpba.2008.01.039 [陈永义 Chen Yongyi], 2004, [应用气象学报, Journal of Applied Meteorolgical Science], V15, P345 Cheung PCK, 2010, NUTR BULL, V35, P292, DOI 10.1111/j.1467-3010.2010.01859.x Choong YK, 2011, VIB SPECTROSC, V57, P87, DOI 10.1016/j.vibspec.2011.05.008 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Corvucci F, 2015, FOOD CHEM, V169, P297, DOI 10.1016/j.foodchem.2014.07.122 Cvetkovic JS, 2015, ANAL LETT, V48, P2107, DOI 10.1080/00032719.2015.1010118 D'Archivio AA, 2017, FOOD CHEM, V219, P408, DOI 10.1016/j.foodchem.2016.09.169 Danezis GP, 2016, CURR OPIN FOOD SCI, V10, P22, DOI 10.1016/j.cofs.2016.07.003 Pinho PG, 2008, J AGR FOOD CHEM, V56, P1704, DOI 10.1021/jf073181y [邓百万 Deng Baiwan], 2004, [食品科学, Food Science], V25, P255 Firenzuoli F, 2008, EVID-BASED COMPL ALT, V5, P3, DOI 10.1093/ecam/nem007 Gromski PS, 2014, ANAL BIOANAL CHEM, V406, P7581, DOI 10.1007/s00216-014-8216-7 Huang CL, 2006, EXPERT SYST APPL, V31, P231, DOI 10.1016/j.eswa.2005.09.024 Huang LX, 2015, FOOD BIOPROCESS TECH, V8, P359, DOI 10.1007/s11947-014-1407-6 Kalac P, 2009, FOOD CHEM, V113, P9, DOI 10.1016/j.foodchem.2008.07.077 Kalac P, 2013, J SCI FOOD AGR, V93, P209, DOI 10.1002/jsfa.5960 Li D, 2010, J MOL STRUCT, V974, P68, DOI 10.1016/j.molstruc.2010.01.031 Li HD, 2009, ANAL CHIM ACTA, V648, P77, DOI 10.1016/j.aca.2009.06.046 Li W, 2014, SPECTROSC SPECT ANAL, V34, P3235, DOI 10.3964/j.issn.1000-0593(2014)12-3235-06 Li Y., LIBSVM FARUTO ULTIMA Li Y, 2016, SPECTROCHIM ACTA A, V165, P61, DOI 10.1016/j.saa.2016.04.012 Longobardi F, 2012, FOOD CHEM, V130, P177, DOI 10.1016/j.foodchem.2011.06.045 Maguire A, 2015, ANALYST, V140, P2473, DOI 10.1039/c4an01887g Maione C, 2016, COMPUT ELECTRON AGR, V121, P101, DOI 10.1016/j.compag.2015.11.009 Marquez C, 2016, TALANTA, V161, P80, DOI 10.1016/j.talanta.2016.08.003 Mleczek M, 2015, J ENVIRON SCI HEAL B, V50, P207, DOI 10.1080/03601234.2015.982427 O'Gorman A, 2010, J AGR FOOD CHEM, V58, P7770, DOI 10.1021/jf101123a Pillonel L, 2003, EUR FOOD RES TECHNOL, V216, P179, DOI 10.1007/s00217-002-0629-4 Rebentrost P, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.130503 Reis FS, 2012, FOOD CHEM TOXICOL, V50, P191, DOI 10.1016/j.fct.2011.10.056 Saptoro A, 2012, CHEM PROD PROCESS MO, V7, DOI 10.1515/1934-2659.1645 Shen F, 2012, FOOD BIOPROCESS TECH, V5, P786, DOI 10.1007/s11947-010-0347-z Shen TT, 2016, FOOD ANAL METHOD, V9, P68, DOI 10.1007/s12161-015-0175-x Subasi A, 2013, COMPUT BIOL MED, V43, P576, DOI 10.1016/j.compbiomed.2013.01.020 Sullivan R, 2006, PERSPECT BIOL MED, V49, P159, DOI 10.1353/pbm.2006.0034 Tsai SY, 2007, LWT-FOOD SCI TECHNOL, V40, P1392, DOI 10.1016/j.lwt.2006.10.001 Wang D, 2014, CARBOHYD POLYM, V105, P127, DOI 10.1016/j.carbpol.2013.12.085 Wang XM, 2014, FOOD CHEM, V151, P279, DOI 10.1016/j.foodchem.2013.11.062 Wani BA, 2010, J MED PLANTS RES, V4, P2598 Wu ZZ, 2016, FOOD CHEM, V194, P671, DOI 10.1016/j.foodchem.2015.08.071 Yang Y, 2017, SPECTROCHIM ACTA A, V171, P351, DOI 10.1016/j.saa.2016.08.033 Zimmermann B, 2013, APPL SPECTROSC, V67, P892, DOI 10.1366/12-06723 NR 56 TC 57 Z9 65 U1 3 U2 68 PD APR 15 PY 2017 VL 177 BP 20 EP 27 DI 10.1016/j.saa.2017.01.029 WC Spectroscopy SC Spectroscopy UT WOS:000397076500005 DA 2022-12-14 ER PT J AU Karalis, P Poutouki, AE Nikou, T Halabalaki, M Proestos, C Tsakalidou, E Gougoura, S Diamantopoulos, G Tassi, M Dotsika, E AF Karalis, Petros Poutouki, Anastasia Elektra Nikou, Theodora Halabalaki, Maria Proestos, Charalampos Tsakalidou, Effie Gougoura, Sofia Diamantopoulos, George Tassi, Maria Dotsika, Elissavet TI Isotopic Traceability (C-13 and O-18) of Greek Olive Oil SO MOLECULES DT Article DE isotopic analysis; C-13; O-18; greek olive oil origin; traceability; authenticity; biophenols ID FRACTIONATION; RATIOS AB In recent years, isotopic analysis has been proven a valuable tool for the determination of the origin of various materials. In this article, we studied the O-18 and C-13 isotopic values of 210 olive oil samples that were originated from different regions in Greece in order to verify how these values are affected by the climate regime. We observed that the delta O-18 isotopic values range from 19.2 parts per thousand to 25.2 parts per thousand and the delta C-13 values range from -32.7 parts per thousand to -28.3 parts per thousand. These differences between the olive oils' isotopic values depended on the regional temperature, the meteoric water, and the distance from the sea. Furthermore, we studied the C-13 isotopic values of biophenolic extracts, and we observed that they have same capability to differentiate the geographic origin. Finally, we compared the isotopic values of Greek olive oils with samples from Italy, and we concluded that there is a great dependence of oxygen isotopes on the climatic characteristics of the different geographical areas. C1 [Karalis, Petros; Gougoura, Sofia; Diamantopoulos, George; Tassi, Maria; Dotsika, Elissavet] NCSR Demokritos, Stable Isotope Unit, Inst Nanosci & Nanotechnol, Agia Paraskevi Attiki 15310, Greece. [Karalis, Petros; Poutouki, Anastasia Elektra; Dotsika, Elissavet] Inst Geosci & Earth Resources, Via G Moruzzi 1, I-56124 Pisa, Italy. [Nikou, Theodora; Halabalaki, Maria] Univ Athens, Fac Pharm, Dept Pharmacognosy & Nat Prod Chem, Athens 15772, Greece. [Proestos, Charalampos] Natl & Kapodistrian Univ Athens, Dept Chem, Food Chem Lab, Athens 15771, Greece. [Tsakalidou, Effie] Agr Univ Athens, Sch Food & Nutr Sci, Dept Food Sci & Human Nutr, Lab Dairy Res, Iera Odos 75, Athens 11855, Greece. C3 National Centre of Scientific Research "Demokritos"; Consiglio Nazionale delle Ricerche (CNR); Istituto di Geoscienze e Georisorse (IGG-CNR); National & Kapodistrian University of Athens; National & Kapodistrian University of Athens; Agricultural University of Athens RP Karalis, P (corresponding author), NCSR Demokritos, Stable Isotope Unit, Inst Nanosci & Nanotechnol, Agia Paraskevi Attiki 15310, Greece.; Karalis, P (corresponding author), Inst Geosci & Earth Resources, Via G Moruzzi 1, I-56124 Pisa, Italy. EM p.karalis@inn.demokritos.gr; anastasia_294@hotmail.com; th-nikou@pharm.uoa.gr; mariahal@pharm.uoa.gr; harpro@chem.uoa.gr; et@aua.gr; sofi2gr@yahoo.gr; g.diamantopoulos@inn.demokritos.gr; m.tassi@inn.demokritos.gr; e.dotsika@inn.demokritos.gr CR BENDER MM, 1971, PHYTOCHEMISTRY, V10, P1239, DOI 10.1016/S0031-9422(00)84324-1 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Cimato A., 1989, P INT S OL GROW 286, P457 Clark I., 1997, ENV ISOTOPES HYDROLO Council I.O., 2009, DETERMINATION BIOPHE Dotsika E, 2018, GEOSCIENCES, V8, DOI 10.3390/geosciences8070238 Dotsika E, 2010, GLOBAL PLANET CHANGE, V71, P141, DOI 10.1016/j.gloplacha.2009.10.007 FREGA N, 1992, J AM OIL CHEM SOC, V69, P447, DOI 10.1007/BF02540946 Hermann A, 2008, AM J ENOL VITICULT, V59, P194 Lerman J.C, 1975, ENV BIOL CONTROL PHO, P323, DOI 10.1007/978-94-010-1957-6 MARIANI C, 1991, Rivista Italiana delle Sostanze Grasse, V68, P179 MARTIN GJ, 1988, J AGR FOOD CHEM, V36, P316, DOI 10.1021/jf00080a019 Modi G., 1992, P INT C OL OIL QUAL MONTEDORO G., 1969, Rivista Italiana delle Sostanze Grasse, V46, P115 Montedoro G., 1990, P 6 INT FLAV C, P881 Nikou T, 2020, FRONT PUBLIC HEALTH, V8, DOI 10.3389/fpubh.2020.558226 O'Leary M.H., 1995, STABLE ISOTOPES BIOS, P78, DOI DOI 14710615/#CIT OLEARY MH, 1981, PHYTOCHEMISTRY, V20, P553, DOI 10.1016/0031-9422(81)85134-5 PARK R, 1961, PLANT PHYSIOL, V36, P133, DOI 10.1104/pp.36.2.133 ROSSELL JB, 1994, FETT WISS TECHNOL, V96, P304, DOI 10.1002/lipi.19940960806 Rossmann A, 1996, Z LEBENSM UNTERS FOR, V203, P293, DOI 10.1007/BF01192881 Schmidt HL, 2001, PHYTOCHEMISTRY, V58, P9, DOI 10.1016/S0031-9422(01)00017-6 SMITH BN, 1973, AM J BOT, V60, P505, DOI 10.2307/2441373 Versini G., 1997, MONITORING AUTHENTIC Zhang BL, 1998, J AGR FOOD CHEM, V46, P1374, DOI 10.1021/jf970794+ NR 26 TC 3 Z9 3 U1 0 U2 5 PD DEC PY 2020 VL 25 IS 24 AR 5816 DI 10.3390/molecules25245816 WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary SC Biochemistry & Molecular Biology; Chemistry UT WOS:000603266200001 DA 2022-12-14 ER PT J AU Kandemir, C Baytore, C Taskin, T Kosum, N Tekin, BA AF Kandemir, Cagri Baytore, Cem Taskin, Turgay Kosum, Nedim Tekin, Behic Arif TI Performance evaluation of leg and ear numbers in radio frequency identification systems (RFID) in sensitive livestock products in goat breeding SO CIENCIA RURAL DT Article DE animal welfare; goat; traceability; RFID; tags ID SMALL RUMINAL BOLUSES; ELECTRONIC IDENTIFICATION; TAGS; TRACEABILITY; DEVICES; BIRTH; PIGS AB This study, evaluated the readability of electronic leg and ear tags in Saanen goats. Fifty-seven goats were identified with the electronic leg tap (ELT) and electronic ear tags (EET) from birth until the lactation period ends. Readability of FIT and EET was %.30% and 90.55% respectively in static conditions at the end of 12 months. Foot and udder, with no infection rates for ELI and EET in calm and aggressive goats were 95.70% and 100%, respectively. No infection rates of foot and udder for ELI and EET in calm and aggressive goats were 95.70% and 100%, respectively. Tagging method and animal temperament was not statistically significant. As a result, low animal traceability with ear tags was determined by this study. Besides, it is suggested that smaller-sized tagging materials would be more accurate when the ankle was selected as a body area to place identification tags in goats. The resulting issue to be considered is that the leg tagging should not negatively affect the animal welfare and the foot and udder health. In the future, using a leg band in the identification of goats will become more widespread as it does not damage animals and has a high readability capacity. C1 [Kandemir, Cagri; Taskin, Turgay; Kosum, Nedim] Ege Univ, Fac Agr, Dept Anim Sci, TR-35100 Izmir, Turkey. [Baytore, Cem] Dokuz Eylul Univ, Fac Engn, Dept Elect & Elect Engn, Izmir, Turkey. [Tekin, Behic Arif] Ege Univ, Fac Agr, Dept Agr Engn & Technol, Izmir, Turkey. C3 Ege University; Dokuz Eylul University; Ege University RP Kandemir, C (corresponding author), Ege Univ, Fac Agr, Dept Anim Sci, TR-35100 Izmir, Turkey. EM cagri.kandemir@ege.edu.tr CR ABECIA J. A, 2009, ALBEITAR, V129, P54 ABECIA J. A, 2004, PEQUENOS RUMIANTES, V5, P10 Ait-Saidi A, 2008, J DAIRY SCI, V91, P1438, DOI 10.3168/jds.2007-0815 Ait-Saidi A., 2013, XV Jornadas sobre Produccion Animal, Zaragoza 14 y 15 de mayo de 2013, P91 AIT-SAIDI A, 2008, 59 M EAAP [Anonymous], 2006, RFID UHF PRESCRIPTIO BALWAY B., 2010, IDENTIFICATION ELECT Caja G., 2004, ICAR Technical Series, P21 Caja G, 2005, J ANIM SCI, V83, P2215 Caja G, 1999, COMPUT ELECTRON AGR, V24, P45, DOI 10.1016/S0168-1699(99)00036-8 Cantor AB., 2003, SAS SURVIVAL ANAL TE CAPOTE J, 2005, ITEA PRODUCTION ANIM, V26, P297 CAR International Committee for Animal Recording, 2012, GEN ASSEMBLY HELD CO Carne S, 2010, J DAIRY SCI, V93, P5157, DOI 10.3168/jds.2010-3188 Carne S, 2009, J ANIM SCI, V87, P2419, DOI 10.2527/jas.2008-1670 Carne S, 2009, J DAIRY SCI, V92, P1500, DOI 10.3168/jds.2008-1577 CASTRO A, 2004, 29 JORNADAS CIENTIFI, P88 Ching SH, 2009, J ACAD LIBR, V35, P347, DOI 10.1016/j.acalib.2009.04.005 COOKE A., 2010, US UHF TAGS DEER SHE COX DR, 1972, J R STAT SOC B, V34, P187 CURTIS M., 2002, FARMERS WEEKLY INTER, V28 Ermetin O, 2021, ANIM SCI PAP REP, V39, P19 FONSECA M. S., 1994, RESTOR ECOL, V2, P198, DOI DOI 10.1111/J.1526-100X.1994.TB00067.X Garin D, 2005, LIVEST PROD SCI, V92, P47, DOI 10.1016/j.livprodsci.2004.08.007 Garin D, 2003, J ANIM SCI, V81, P879 Ghirardi JJ, 2006, J ANIM SCI, V84, P2865, DOI 10.2527/jas.2006-157 Gosalvez LF, 2007, J ANIM SCI, V85, P2746, DOI 10.2527/jas.2007-0173 Haskell MJ, 2014, FRONT GENET, V5, DOI 10.3389/fgene.2014.00368 HILPERT J. J. J. H., 2009, International Cooperation Treaty (PCT) Publ., Patent No. [WO2009/034058, 2009034058] ICAR International Committee for Animal Recording, 2014, ICAR INT COMMITTEE A ICAR International Committee for Animal Recording, 2007, GEN ASSEMBLY HELD KU ISO (International Organization for Standardization), 1996, 117851996E ISO, V1st ISO (International Organization for Standardization), 117841996E ISO, V2nd JAUME J, 2012, EAAP SCI SERIES, V129, P237 Karakus F, 2015, ARCH TIERZUCHT, V58, DOI 10.5194/aab-58-287-2015 Karakus M, 2017, ARCH ANIM BREED, V60, P297, DOI 10.5194/aab-60-297-2017 KLEINBAUM D. G, 2005, SURVIVAL ANAL SELFLE, V2nd Kowalski LH, 2014, REV BRAS ZOOTECN, V43, P100, DOI 10.1590/S1516-35982014000200008 Marina A, 2020, SCI PAP-SER D-ANIM S, V63, P306 Mc Carthy U, 2009, COMPUT ELECTRON AGR, V69, P135, DOI 10.1016/j.compag.2009.07.018 MEULMAN J. J, 1999, SPSS CATEGORIES 10 0 Ocak S., 2013, Journal of Agricultural Science and Technology A, V3, P417 Pinna W, 2006, SMALL RUMINANT RES, V66, P286, DOI 10.1016/j.smallrumres.2005.09.012 QUEIROGA M., 1994, SUBCUTANEOUS TISSUE Silva SR, 2022, ANIMALS-BASEL, V12, DOI 10.3390/ani12070885 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 TASKIN T., 2016, J ANIMAL PRODUCTION, V57, P42 Thurner S., 2007, Landtechnik, V62, P106 TORRAS X., 2006, Patent, Patent No. [WO2006/050835A8, 2006050835] NR 49 TC 0 Z9 0 U1 1 U2 1 PY 2023 VL 53 IS 6 AR e20210801 DI 10.1590/0103-8478cr20210801 WC Agronomy SC Agriculture UT WOS:000875821800001 DA 2022-12-14 ER PT J AU Theocharopoulos, SP Mitsios, IK Arvanitoyannis, I AF Theocharopoulos, SP Mitsios, IK Arvanitoyannis, I TI Traceabilty of environmental soil measurements SO TRAC-TRENDS IN ANALYTICAL CHEMISTRY DT Article DE environmental; soil; measurement; traceability ID QUALITY ASSURANCE; PROJECT; POLLUTION AB Traceability is a key concept in environmental soil measurements, and should be linked with any soil measurement, especially long-term soil monitoring, which needs to demonstrate measurement quality. This article presents the main soil measurements of environmental concern, including traceability, uncertainties and possible errors, for each step in the process. It starts with sampling the soil in the field, treatment and conditioning in the field, transportation, storage and preservation of the sample in the laboratory, then goes on to the pre-analytical treatment and preparation for analyses, including the main extraction methods and analytical techniques and instruments employed. The article highlights the traceability links that should be considered for soil measurements to achieve data comparability. (C) 2004 Elsevier Ltd. All rights reserved. C1 NAGREF, Inst Soil Sci, Athens 14123, Greece. Univ Thessaly, Lab Soil Sci, GK-38446 N Ionia, Magnesias, Greece. Univ Thessaly, Dept Crop Sci & Agr Environm, GK-38446 N Ionia, Magnesias, Greece. C3 University of Thessaly; University of Thessaly RP Theocharopoulos, SP (corresponding author), NAGREF, Inst Soil Sci, Athens 14123, Greece. EM sid_theo@nagref.gr CR Bezdicek D.F., 1996, METHODS ASSESS SOIL, V49, P1 Cline MG, 1944, SOIL SCI, V58, P275, DOI 10.1097/00010694-194410000-00003 COFINO WP, 1994, AKKREDITIERUNG QUALI, P173 CONACHER AJ, 1997, GEODERMA, V1, P1 Doran J. W., 1994, SSSA SPECIAL PUBLICA, V35, P3, DOI DOI 10.1016/0016-7061(95)90042-X Doran J.W., 1996, METHODS ASSESSING SO, P25, DOI [10.2136/sssaspecpub49.c2, DOI 10.2136/SSSASPECPUB49.C2] DORAN JW, 1998, DEFINING ASSESSING S, P1 Food and Agriculture Organization of the United Nations, 1976, FRAM LAND EV FORTUNATI GU, 1994, QUIM ANAL S1, V13, P5 GORLITZ G, 1994, QUALITAT ANAL LABOR, P159 Greenland D.J., 1994, SOIL RESILIENCE SUST Gregorich E.G C.M., 1997, SOIL QUALITY CROP PR, V1 JAMES AT, 1952, BIOCHEM J, V50, P679, DOI 10.1042/bj0500679 Lightfoot N.F., 1998, MICROBIOLOGICAL ANAL LYNCH JM, 1998, BIOINDICATORS PERSPE, P79 Markert B, 1995, QUALITY ASSURANCE EN, P215 Mausback M.J., 1998, SOIL QUALITY AGR SUS, P33 MIEHLICH G, 1989, ASSESSMENT HEAVY MET, P9 Muntau H, 2001, SCI TOTAL ENVIRON, V264, P27, DOI 10.1016/S0048-9697(00)00610-0 OLSON GL, 1996, SSSA SPEC PUBL, V49, P357 Page AL., 1982, METHODS SOIL ANAL, V2 Pankhurst C., 1998, BIOL INDICATORS SOIL Pansu M., 2001, SOIL ANAL SAMPLING I Quevauviller P, 2001, TRAC-TREND ANAL CHEM, V20, P600, DOI 10.1016/S0165-9936(01)00116-9 QUEVAUVILLER P, 1995, QUALITY ASSURANCE EN, P215 Quevauviller P., 1995, QUALITY ASSURANCE EN, P2 Russell JR., 1994, DESCRIPTION SAMPLING SMITH JL, 1993, SOIL SCI SOC AM J, V57, P743, DOI 10.2136/sssaj1993.03615995005700030020x Theocharopoulos SP, 2001, SCI TOTAL ENVIRON, V264, P51, DOI 10.1016/S0048-9697(00)00611-2 Theocharopoulos SP, 2001, SCI TOTAL ENVIRON, V264, P63, DOI 10.1016/S0048-9697(00)00612-4 URE AM, 1991, SOIL ANAL MODERN INS, P1 VOINOVITCH IA, 1998, ANAL SOLS ROCHES SED, P14 Wagner G, 1995, SCI TOTAL ENVIRON, V176, P63, DOI 10.1016/0048-9697(95)04830-8 Wagner G, 2001, SCI TOTAL ENVIRON, V264, P103, DOI 10.1016/S0048-9697(00)00614-8 Wagner G., 1995, QUALITY ASSURANCE EN, P25 WEGSCHEIDER W, 1994, AKKREDITIERUNG QUALI, P105 Wells DE, 1997, MAR POLLUT BULL, V35, P146, DOI 10.1016/S0025-326X(97)80880-6 NR 37 TC 10 Z9 11 U1 0 U2 3 PD MAR PY 2004 VL 23 IS 3 BP 237 EP 251 DI 10.1016/S0165-9936(04)00317-6 WC Chemistry, Analytical SC Chemistry UT WOS:000220324900019 DA 2022-12-14 ER PT J AU Bayano-Tejero, S Sola-Guirado, RR Gil-Ribes, JA Blanco-Roldan, GL AF Bayano-Tejero, Sergio Sola-Guirado, Rafael R. Gil-Ribes, Jesus A. Blanco-Roldan, Gregorio L. TI Machine to machine connections for integral management of the olive production SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Traceability; RFID; GPS; QR; Field notebook; Machinery ID TRACEABILITY SYSTEM; TECHNOLOGY; AGRICULTURE; CHAIN; CLOUD AB Most of the advances made in olive traceability are focusing on the arrival of the fruit to industry but it is necessary to manage all the operations involved in the production chain. This work introduces a new machineto-machine system that allows integrating all the information generated from the field to the market through a methodology for the real-time management of all operations carried out. The consumer can check the product history, farmers can consult and optimise their resources, and industry can control the operations performed. The system developed is composed of an electronic device, iOlivetrack-D, mounted on agricultural machinery which identify plots and sends the information generated to a web application, iOlivetrack-W. The system was tested in real working conditions obtaining good results in the identification of plots using RFID and GPS although the limitation about this technology must be considered for a commercial purpose. The registration of information associated in the web application has been carried successfully by an adaptable system that allow the access to the data in configurable ways. To complete the product chain information, industrial processing operations were simulated by entering the data manually through the application. Finally, a QR code was generated to provide consumer access to product traceability information that may be consulted in this paper. The work shows the pros and cons of this system which allows assured traceability of the entire production chain and the proper manage of the production. C1 [Bayano-Tejero, Sergio; Gil-Ribes, Jesus A.; Blanco-Roldan, Gregorio L.] Univ Cordoba, ETSIAM, Dept Rural Engn, Campus Rabanales,Ctra Nacl 4 Km 396, Cordoba, Spain. [Sola-Guirado, Rafael R.] Univ Cordoba, EPS, Dept Mech Engn, Campus Rabanales,Ctra Nacl 4 Km 396, Cordoba, Spain. C3 Universidad de Cordoba; Universidad de Cordoba RP Sola-Guirado, RR (corresponding author), Univ Cordoba, EPS, Dept Mech Engn, Campus Rabanales,Ctra Nacl 4 Km 396, Cordoba, Spain. EM ir2sogur@uco.es CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Alfaro JA, 2009, INT J PROD ECON, V118, P104, DOI 10.1016/j.ijpe.2008.08.030 Bartlett AC, 2015, COMPUT ELECTRON AGR, V111, P127, DOI 10.1016/j.compag.2014.12.021 Channe H., 2015, INT J COMPUTER TECHN, V6, P374 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Choudhary S.K., 2016, ROLE CLOUD COMPUTING Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Cunha CR, 2010, COMPUT ELECTRON AGR, V73, P154, DOI 10.1016/j.compag.2010.05.007 Fountas S, 2015, COMPUT ELECTRON AGR, V115, P40, DOI 10.1016/j.compag.2015.05.011 Hamrita TK, 2005, APPL ENG AGRIC, V21, P139 Li Z, 2017, IND MANAGE DATA SYST, V117, P1906, DOI 10.1108/IMDS-11-2016-0489 Lopez-Riquelme JA, 2017, AGR WATER MANAGE, V183, P123, DOI 10.1016/j.agwat.2016.10.020 Lu QH, 2017, IEEE SOFTWARE, V34, P21, DOI 10.1109/MS.2017.4121227 Magalhaes PSG, 2007, BIOSYST ENG, V96, P1, DOI 10.1016/j.biosystemseng.2006.10.002 Nieto LM, 2009, J PHOTOCH PHOTOBIO A, V203, P1, DOI 10.1016/j.jphotochem.2008.11.025 Ning Y, 2013, ELEKTRON ELEKTROTECH, V19, P105, DOI 10.5755/j01.eee.19.8.5405 Paraforos DS, 2017, COMPUT ELECTRON AGR, V142, P504, DOI 10.1016/j.compag.2017.11.022 Perez-Ruiz M, 2011, PRECIS AGRIC, V12, P564, DOI 10.1007/s11119-010-9200-7 Perez-Ruiz M, 2012, BIOSYST ENG, V111, P64, DOI 10.1016/j.biosystemseng.2011.10.009 Prinsloo J, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16060825 Privette CV, 2011, REMOTE SENS ENVIRON, V115, P3582, DOI 10.1016/j.rse.2011.08.019 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Rangarajan B, 2014, I C CONT AUTOMAT ROB, P17, DOI 10.1109/ICARCV.2014.7064272 Resende MA, 2012, INT J PROD ECON, V139, P596, DOI 10.1016/j.ijpe.2012.05.034 Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Suprem A, 2013, COMPUT STAND INTER, V35, P355, DOI 10.1016/j.csi.2012.09.002 Tan L, 2015, SYMP NETW CLOUD, P91, DOI 10.1109/NCCA.2015.23 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Tian F, 2017, I C SERV SYST SERV M Valencia-garcia R., 2018, TECHNOLOGIES INNOVAT Yifan Bo, 2011, 2011 International Joint Conference on Service Sciences (IJCSS), P168, DOI 10.1109/IJCSS.2011.40 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zheng JY, 2005, 2005 IEEE Networking, Sensing and Control Proceedings, P777 NR 34 TC 3 Z9 3 U1 5 U2 12 PD NOV PY 2019 VL 166 AR 104980 DI 10.1016/j.compag.2019.104980 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000497247500001 DA 2022-12-14 ER PT J AU Adamashvili, N State, R Tricase, C Fiore, M AF Adamashvili, Nino State, Radu Tricase, Caterina Fiore, Mariantonietta TI Blockchain-Based Wine Supply Chain for the Industry Advancement SO SUSTAINABILITY DT Article DE blockchain technology; wine; supply chain; traceability; time-to-recall ID FOOD; TECHNOLOGY; CHALLENGES; TRACEABILITY; MANAGEMENT; HEALTH; TOOLS AB The wine sector is one of the most 'amazing' and significant agri-food sectors worldwide since ancient times, considering revenue or employment as well as health aspects. This article aims to describe the impact of the implementation of blockchain technology (BCT) in the wine supply chain. After the literature review, the study is based on Agent Based Models (ABMs) and carried out by the GAMA program. Then, the model and simulation of BCT wine supply chain is designed. Finally, the paper compares traditional and BCT-based supply chains, and the advantages of the last one are evident. Blockchain is a useful tool to ensure a traceability system and to protect the production from any type of fraud and contamination. C1 [Adamashvili, Nino; Tricase, Caterina; Fiore, Mariantonietta] Univ Foggia, Dept Econ, I-71121 Foggia, Italy. [State, Radu] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, L-1855 Luxembourg, Luxembourg. C3 University of Foggia; University of Luxembourg RP Adamashvili, N (corresponding author), Univ Foggia, Dept Econ, I-71121 Foggia, Italy. EM nino.adamashvili@unifg.it; radu.state@uni.lu; caterina.tricase@unifg.it; mariantonietta.fiore@unifg.it CR Adamashvili N, 2020, EUR COUNTRYS, V12, P242, DOI 10.2478/euco-2020-0014 Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Alketbi A, 2018, 2018 15TH LEARNING AND TECHNOLOGY CONFERENCE (L&T), P112 Astill J, 2018, FRONT VET SCI, V5, DOI 10.3389/fvets.2018.00263 Batty, 2012, AGENT BASED MODELS G, P85, DOI [DOI 10.1007/978-90-481-8927-4, 10.1007/978-90-481-8927-4_5, DOI 10.1007/978-90-481-8927-4_5] Baxter P, 2008, QUAL REP, V13, P544 Beaman RS, 2012, ZOOKEYS, P7, DOI 10.3897/zookeys.209.3313 Bermeo-Almeida O, 2018, COMM COM INF SC, V883, P44, DOI 10.1007/978-3-030-00940-3_4 Burgers C, 2019, J PRAGMATICS, V145, P102, DOI 10.1016/j.pragma.2019.04.004 BUTERIN V, 2015, PUBLIC PRIVATE BLOCK Caballero Ricardo, 2019, 2019 7th International Engineering, Sciences and Technology Conference (IESTEC), P46, DOI 10.1109/IESTEC46403.2019.00017 Cales L., 2019, EUR 29813, DOI [10.2760/901029, DOI 10.2760/901029] Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Chaudhry N, 2018, 2018 12TH INTERNATIONAL CONFERENCE ON OPEN SOURCE SYSTEMS AND TECHNOLOGIES (ICOSST), P54, DOI 10.1109/ICOSST.2018.8632190 Collier E, FOOD PRODUCT RECALL Condos J., 2016, BLOCKCHAIN TECHNOLOG Conto Francesco, 2016, Recent Pat Food Nutr Agric, V8, P48, DOI 10.2174/221279840801160304144309 Crooks A, 2018, COMPREHENSIVE GEOGRAPHIC INFORMATION SYSTEMS, VOL 1: GIS METHODS AND TECHNIQUES, P218 D'Alessandro A, 2012, J PROTEOME RES, V11, P26, DOI 10.1021/pr2008829 Dave D, 2019, PROCEDIA COMPUT SCI, V160, P740, DOI 10.1016/j.procs.2019.11.017 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Desai H, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2019), P34, DOI 10.1109/Blockchain.2019.00014 Durante C, 2018, FOOD CHEM, V255, P139, DOI 10.1016/j.foodchem.2018.02.084 EISENHARDT KM, 1991, ACAD MANAGE REV, V16, P620, DOI 10.2307/258921 EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557 el Bilali H., 2020, SCI EXPERTS C AGR FO, V78, DOI [10.1007/978-3-030-40049-1_41, DOI 10.1007/978-3-030-40049-1_41] Esteki M, 2019, COMPR REV FOOD SCI F, V18, P425, DOI 10.1111/1541-4337.12419 Exposito I, 2013, PROC EUR CONF ANTENN, P3539 Fabiano N, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA), P727, DOI 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.112 Falazi G, 2020, SICS SOFTWARE, V35, P49, DOI 10.1007/s00450-019-00411-y FAO Aquaculture Development, 2001, 1 GOOD AQ FEED MAN P FAO International Telecommunication Union, 2019, E AGR ACT BLOCKCH AG Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fernandez Ahan, 2020, Proceedings of International Conference on Intelligent Manufacturing and Automation. ICIMA 2020. Lecture Notes in Mechanical Engineering (LNME), P127, DOI 10.1007/978-981-15-4485-9_14 Fernandez-Novales J, 2011, J FOOD PROCESS ENG, V34, P1028, DOI 10.1111/j.1745-4530.2009.00530.x Fiore M, 2020, BRIT FOOD J, V122, P2707, DOI 10.1108/BFJ-05-2019-0344 Fiore M, 2017, J CLEAN PROD, V142, P4085, DOI 10.1016/j.jclepro.2016.10.026 Fiore M, 2016, BRIT FOOD J, V118, P1926, DOI 10.1108/BFJ-05-2016-0201 Fotakis C, 2013, FOOD RES INT, V54, P1184, DOI 10.1016/j.foodres.2013.03.032 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gemeliarana I. Gusti Ayu Kusdiah, 2018, 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), P126, DOI 10.1109/ISRITI.2018.8864381 Gomes, 2012, OLIVE CONSUM HLTH, P211 Gorshkova Natalia V., 2020, Competitive Russia: Foresight Model of Economic and Legal Development in the Digital Age. Proceedings of the International Scientific Conference in Memory of Oleg Inshakov (1952-2018). Lecture Notes in Networks and Systems (LNNS 110), P164, DOI 10.1007/978-3-030-45913-0_19 Gourisetti SNG, 2020, IEEE T ENG MANAGE, V67, P1142, DOI 10.1109/TEM.2019.2928280 Grignard A., 2017, AAAI 17 WORKSH HUM M, P670 Guo M., 2018, WIT T BUILT ENV, V179, P391 Haber S., 1991, Journal of Cryptology, V3, P99, DOI 10.1007/BF00196791 Hackett R, 2017, FORTUNE Harrison S., 2016, OPEN DATA I Hebert C, 2019, PERVASIVE MOB COMPUT, V59, DOI 10.1016/j.pmcj.2019.101038 Hill G. N., 2010, New Zealand Plant Protection, V63, P174 Huck CW, 2016, CURR OPIN FOOD SCI, V10, P32, DOI 10.1016/j.cofs.2016.07.004 Jabir B., 2020, P 2020 IEEE 6 INT C Jennath HS, 2019, STUD COMPUT INTELL, V771, P333, DOI 10.1007/978-981-10-8797-4_35 Kadariya J, 2014, BIOMED RES INT, V2014, DOI 10.1155/2014/827965 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Katsikouli P, 2021, J SCI FOOD AGR, V101, P2175, DOI 10.1002/jsfa.10883 Kennedy J, 2016, SAYS PWC EXPERT Ketokivi M, 2014, J OPER MANAG, V32, P232, DOI 10.1016/j.jom.2014.03.004 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kshetri N, 2019, IT PROF, V21, P11, DOI 10.1109/MITP.2018.2881307 Lin WJ, 2020, IEEE ACCESS, V8, P143920, DOI 10.1109/ACCESS.2020.3014522 Malik S, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2019), P184, DOI 10.1109/Blockchain.2019.00032 Malik S, 2018, 2018 IEEE 17TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA) Ciruela-Lorenzo AM, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041325 Meroni G, 2018, LECT NOTES BUS INF P, V316, P103, DOI 10.1007/978-3-319-92898-2_8 Miles M.B., 1994, QUALITATIVE DATA ANA, VSecond, P338 Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Mollet B, 2002, CURR OPIN BIOTECH, V13, P483, DOI 10.1016/S0958-1669(02)00375-0 Moreno-Arribas MV., 2009, WINE CHEM BIOCH, V1, DOI [10.1007/978-0-387-74118-5, DOI 10.1007/978-0-387-74118-5] Nakamoto S, BITCOIN PEER TO PEER Okada H, 2017, INT CONF ADV COMMUN, P593, DOI 10.23919/ICACT.2017.7890159 Pagell M, 2009, J SUPPLY CHAIN MANAG, V45, P37, DOI 10.1111/j.1745-493X.2009.03162.x Palfreyman John, 2016, DISTRIBUTED LEDGER T Patton M.Q., 2002, QUALITATIVE RES EVAL Pouliot S, 2013, EUR REV AGRIC ECON, V40, P121, DOI 10.1093/erae/jbs006 POUR FSA, 2018, P SPRINGSIM 18 2018 Raikwar M, 2019, IEEE ACCESS, V7, P148550, DOI 10.1109/ACCESS.2019.2946983 Rana RL, 2021, BRIT FOOD J, V123, P3471, DOI 10.1108/BFJ-09-2020-0832 Recker J, 2013, BEGINNERS GUIDE Reyna A, 2018, FUTURE GENER COMP SY, V88, P173, DOI 10.1016/j.future.2018.05.046 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Scharff RL, 2012, J FOOD PROTECT, V75, P123, DOI 10.4315/0362-028X.JFP-11-058 Schieritz N., 2003, 36th Hawaii International Conference on Systems Sciences Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Sheldon MD, 2019, CURR ISS AUDIT, V13, pA15, DOI 10.2308/ciia-52356 Song L, 2020, PEERJ, V8, DOI 10.7717/peerj.10302 Soudoplatoff Y, 2016, FOND LINNOVATION POL Stake RE., 1995, ART CASE STUDY RES Stubbs M., 2016, BIG DATA US AGR Taillandier P, 2019, GEOINFORMATICA, V23, P299, DOI 10.1007/s10707-018-00339-6 Taillandier P, 2019, JASSS-J ARTIF SOC S, V22, DOI 10.18564/jasss.3964 Terzi S, 2019, P 23 PAN HELLENIC C, P9 Tian F, 2017, I C SERV SYST SERV M Umamaheswari S, 2019, INT CONF ADV COMPU, P324, DOI 10.1109/ICoAC48765.2019.246860 Valmori I, 2018, AGRONOTIZIE LE NOVIT Westerkamp M, 2020, DIGIT COMMUN NETW, V6, P167, DOI 10.1016/j.dcan.2019.01.007 WHO, FOOD SAF KEY FACTS 2 Yang HT, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20092643 Yao ZY, 2020, COMM COM INF SC, V1156, P240, DOI 10.1007/978-981-15-2777-7_20 Yin R.K., 2011, QUALITATIVE RES STAR Yin S, 2016, INFORM SCIENCES, V355, P229, DOI 10.1016/j.ins.2016.03.035 YOUNG Y, 2000, FUNCTIONAL FOODS 2 C Zhang KW, 2018, INT CON DISTR COMP S, P1337, DOI 10.1109/ICDCS.2018.00134 Zhang QingAn, 2020, Scientia Agricultura Sinica, V53, P1029, DOI 10.3864/j.issn.0578-1752.2020.05.014 Zhang ZH, 2018, LECT NOTES COMPUT SC, V10974, P32, DOI 10.1007/978-3-319-94478-4_3 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 Zheng ZB, 2017, IEEE INT CONGR BIG, P557, DOI 10.1109/BigDataCongress.2017.85 NR 109 TC 9 Z9 9 U1 8 U2 25 PD DEC PY 2021 VL 13 IS 23 AR 13070 DI 10.3390/su132313070 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000734647000001 DA 2022-12-14 ER PT J AU Vriezen, R Plishka, M Cranfield, J AF Vriezen, Rachael Plishka, Mikayla Cranfield, John TI Consumer willingness to pay for traceable food products: a scoping review SO BRITISH FOOD JOURNAL DT Review; Early Access DE Evidence synthesis; Traceable food products; Traceability; Scoping review; Willingness to pay; WTP ID CONTINGENT VALUATION; INFORMATION; PORK AB Purpose Traceability is an increasingly important tool for reducing food safety risks and managing supply logistics. Given the costs of implementing and maintaining traceability systems, it is crucial to understand consumer willingness to pay (WTP) for traceable products. Design/methodology/approach The authors conducted a scoping review to collate the existing literature on consumer WTP for traceability in food products to determine the nature of the evidence base and to identify research gaps. Findings A total of 77 articles were included in the review. The number of studies published per year generally increased over the review period, and China and the United States were the most common countries in which studies were conducted (43.6 and 14.1% of total studies, respectively). All but one of the studies investigated at least one factor that might influence consumer WTP for traceability, the most common of which was socio-demographic characteristics (72.7%). Three-quarters of studies used hypothetical methods to elicit WTP values (75.3%), whereas one-quarter used non-hypothetical methods (24.7%). Most studies included some measure of preference heterogeneity (83.1%). Research limitations/implications There is some potential for systematic bias in the evidence due to the predominance of studies from only a few countries and the possible presence of hypothetical bias. These potential biases could be corrected through future research. Originality/value To the authors' knowledge, no previous study systematically and comprehensively identifies and summarizes the evidence base on consumer WTP for traceable food products. C1 [Vriezen, Rachael; Plishka, Mikayla; Cranfield, John] Univ Guelph, Dept Food Agr & Resource Econ, Guelph, ON, Canada. C3 University of Guelph RP Vriezen, R (corresponding author), Univ Guelph, Dept Food Agr & Resource Econ, Guelph, ON, Canada. EM rvriezen@uoguelph.ca CR Arksey H, 2005, INT J SOC RES METHOD, V8, P19, DOI [10.1080/1364557032000119616, DOI 10.1080/1364557032000119616] Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bateman I.J., 2002, EC VALUATION STATED Boyle KJ, 2017, ECON NON-MARK GOOD, V13, P83, DOI 10.1007/978-94-007-7104-8_4 Brown T.C., 2003, PRIMER NONMARKET VAL, DOI 10.1007/978-94-007-0826-6 Canadian Food Inspection Agency, 2022, LIV ID TRAC Carson RT, 2001, ENVIRON RESOUR ECON, V19, P173, DOI 10.1023/A:1011128332243 Cicia G., 2010, International Journal on Food System Dynamics, V1, P252 Department of Justice, 2022, HLTH AN REG C R C C Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 EFSA (European Food Safety Authority), 2015, EFSA SUPP PUBL, DOI [10.2903/sp.efsa.2015.EN-836, DOI 10.2903/SP.EFSA.2015.EN-836] Golan E.H., 2004, TRACEABILITY US FOOD Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Hou B, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11051464 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x Levac D, 2010, IMPLEMENT SCI, V5, DOI 10.1186/1748-5908-5-69 Loomis J, 2011, J ECON SURV, V25, P363, DOI 10.1111/j.1467-6419.2010.00675.x Macdonald Geraldine, 2012, Cochrane Database Syst Rev, pCD001930, DOI 10.1002/14651858.CD001930.pub3 McDonald's Corporation, 2019, SUST AGR BEEF Moher D, 2009, J CLIN EPIDEMIOL, V62, P1006, DOI 10.1016/j.jclinepi.2009.06.005 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Pham MT, 2014, RES SYNTH METHODS, V5, P371, DOI 10.1002/jrsm.1123 Ringsberg H, 2014, SUPPLY CHAIN MANAG, V19, P558, DOI 10.1108/SCM-01-2014-0026 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Sun SN, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9060999 Thi Phuong Dong Khuu, 2019, International Journal of Food and Agricultural Economics, V7, P127 Tricco AC, 2018, ANN INTERN MED, V169, P467, DOI 10.7326/M18-0850 Venkatachalam L, 2004, ENVIRON IMPACT ASSES, V24, P89, DOI 10.1016/S0195-9255(03)00138-0 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Xu LL, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16193616 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 34 TC 0 Z9 0 U1 6 U2 6 DI 10.1108/BFJ-01-2022-0085 EA AUG 2022 WC Agricultural Economics & Policy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000844522400001 DA 2022-12-14 ER PT J AU Jilberto, F Araneda, C Larrain, MA AF Jilberto, Felipe Araneda, Cristian Angelica Larrain, Maria TI High resolution melting analysis for identification of commercially-important Mytilus species SO FOOD CHEMISTRY DT Article DE Mytilus; Species identification; High resolution melting; Traceability ID DNA MICROSATELLITES; STATISTICS NOTES; MUSSELS; PCR; GALLOPROVINCIALIS; POPULATIONS; FOOD; HRM; TRACEABILITY; SPECIFICITY AB Mytilus are edible mussels, including commercially-significant species such as M. chilensis, M. galloprovincialis and M. edulis. The scientific name of the species must be indicated on commercial products to satisfy labelling and traceability requirements. Species identification using morphological criteria is difficult due the plasticity of these characteristics and the absence of shells in processed products, and conventional PCR-based methods are laborious and time-intensive. As alternative, we propose high resolution melting (HRM) analysis as a simple tool to detect and identify SNP (single nucleotide polymorphisms) and length polymorphisms in Mytilus spp. We designed HRM-specific primers for the Mytilus genus to identify M. chilensis, M. galloprovincialis, M. edulis and their hybrids through clearly-distinguishable melting curves. HRM analysis showed high sensitivity (0.9639), specificity (1.0000) and precision (1.0000) compared to a conventional PCR-RFLP test. HRM is a fast and low cost method, being a reliable tool for species identification within the Mytilus genus. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Jilberto, Felipe; Araneda, Cristian] Univ Chile, Fac Ciencias Agron, Dept Prod Anim, Santiago, Chile. [Jilberto, Felipe; Angelica Larrain, Maria] Univ Chile, Fac Ciencias Quim & Farmaceut, Dept Ciencia Alimentos & Tecnol Quim, Sergio Livingstone 1007, Santiago, Chile. C3 Universidad de Chile; Universidad de Chile RP Larrain, MA (corresponding author), Univ Chile, Fac Ciencias Quim & Farmaceut, Dept Ciencia Alimentos & Tecnol Quim, Sergio Livingstone 1007, Santiago, Chile. EM fjilberto@ug.uchile.cl; craraned@uchile.cl; mlarrain@uchile.cl CR ALTMAN DG, 1994, BRIT MED J, V308, P1552, DOI 10.1136/bmj.308.6943.1552 Larrain MA, 2012, LAT AM J AQUAT RES, V40, P1077, DOI 10.3856/vol40-issue4-fulltext-23 Deeks JJ, 2004, BRIT MED J, V329, P168, DOI 10.1136/bmj.329.7458.168 Druml B, 2014, FOOD CHEM, V158, P245, DOI 10.1016/j.foodchem.2014.02.111 EU, 2013, OFFICIAL J EUROPEA L, VL354, P13 FAO, 2016, FAO FISH AQ DEP STAT Fernandez-Tajes J, 2011, EUR FOOD RES TECHNOL, V233, P791, DOI 10.1007/s00217-011-1574-x Gerard K, 2008, MOL PHYLOGENET EVOL, V49, P84, DOI 10.1016/j.ympev.2008.07.006 Groenenberg DSJ, 2011, CONTRIB ZOOL, V80, P95, DOI 10.1163/18759866-08002001 Herrmann MG., 2006, JALA-J LAB AUTOM, V11, P273 Hilbish TJ, 2002, MAR BIOL, V140, P137, DOI 10.1007/s002270100631 Iacumin L, 2015, FOOD MICROBIOL, V46, P357, DOI 10.1016/j.fm.2014.08.007 Inoue K, 1995, BIOL BULL, V189, P370, DOI 10.2307/1542155 Jin Y., 2014, J MOLLUS STUD, V81, P167 Koressaar T, 2007, BIOINFORMATICS, V23, P1289, DOI 10.1093/bioinformatics/btm091 Krapivka S, 2007, AQUAC RES, V38, P1770, DOI 10.1111/j.1365-2109.2007.01839.x Lever J, 2016, NAT METHODS, V13, P603, DOI 10.1038/nmeth.3945 Loong TW, 2003, BMJ-BRIT MED J, V327, P716, DOI 10.1136/bmj.327.7417.716 Madesis P, 2014, FOOD RES INT, V60, P163, DOI 10.1016/j.foodres.2013.10.042 Ogden R, 2008, FISH FISH, V9, P462, DOI 10.1111/j.1467-2979.2008.00305.x Oyarzun PA, 2016, BIOL J LINN SOC, V117, P574, DOI 10.1111/bij.12687 Pasqualone A, 1999, EUR FOOD RES TECHNOL, V210, P144, DOI 10.1007/s002170050551 Pasqualone A, 2016, J SCI FOOD AGR, V96, P3642, DOI 10.1002/jsfa.7711 Pasqualone A, 2015, EUR J LIPID SCI TECH, V117, P2044, DOI 10.1002/ejlt.201400654 Rego I, 2002, J AGR FOOD CHEM, V50, P1780, DOI 10.1021/jf0110957 Santaclara FJ, 2006, J AGR FOOD CHEM, V54, P8461, DOI 10.1021/jf061400u Seipp Michael T, 2010, J Biomol Tech, V21, P163 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 Untergasser A, 2012, NUCLEIC ACIDS RES, V40, DOI 10.1093/nar/gks596 Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Westfall KM, 2013, BIOL INVASIONS, V15, P1493, DOI 10.1007/s10530-012-0385-8 Wittwer CT, 2009, HUM MUTAT, V30, P857, DOI 10.1002/humu.20951 Yang SS, 2014, EUR FOOD RES TECHNOL, V239, P473, DOI 10.1007/s00217-014-2241-9 Ye J, 2012, BMC BIOINFORMATICS, V13, DOI 10.1186/1471-2105-13-134 NR 34 TC 22 Z9 22 U1 0 U2 53 PD AUG 15 PY 2017 VL 229 BP 716 EP 720 DI 10.1016/j.foodchem.2017.02.109 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000400033000090 DA 2022-12-14 ER PT J AU Li, X Zhang, LX Zhang, Y Wang, D Wang, XF Yu, L Zhang, W Li, PW AF Li, Xue Zhang, Liangxiao Zhang, Yong Wang, Du Wang, Xuefang Yu, Li Zhang, Wen Li, Peiwu TI Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review DE Near infrared spectroscopy; Oilseed; Quality; Authentication; Geographical origin traceability; Nondestructive measurement; Edible oils ID NEAR-INFRARED SPECTROSCOPY; VIRGIN OLIVE OIL; LEAST-SQUARES REGRESSION; FATTY-ACID-COMPOSITION; FOOD SAFETY EVALUATION; BRASSICA-NAPUS L.; VEGETABLE-OILS; REFLECTANCE SPECTROSCOPY; MULTIVARIATE-ANALYSIS; CHEMOMETRIC METHODS AB Background: Edible oils play a vital role in our daily life, which provide human beings with energy, essential fatty acids and nutrients. The quality of edible oils can be dependent on the quality of the oilseeds they originate from, and their adulteration during processing. In recent years, near infrared spectroscopy was widely used in the rapid assessment of quality of oilseeds and edible oils. However, to the best of our knowledge, a comprehensive review on near infrared spectroscopy for the quality of oilseeds remains to be published. Scope and approach: The applications of near infrared spectroscopy in the quality of oilseeds and edible oils have been emphasized in this review. This article briefly summarizes the basic knowledge of near infrared spectroscopy. In addition, we highlight the application of this technique on the detection of physicochemical properties and quality, specific nutrient components, authentication, and geographical origin traceability of oilseeds and edible oils. Moreover, the application of near infrared hyperspectral imaging technology in oilseeds has been addressed. Key findings and conclusions: Near infrared spectroscopy possesses the advantages of rapid, green and low-cost analysis. Meanwhile, it is also a nondestructive method and therefore suitable to quality analysis of oilseeds in agricultural sciences. It can be used to detect macro nutrients in oilseeds and edible oils by combining NIR and advanced chemometrics. In the future, NIR could be applied in the rapid detection and online detection of hazardous substances and nutrients in oilseeds by developing the new instrument and chemometric methods. C1 [Li, Xue; Zhang, Liangxiao; Zhang, Yong; Wang, Du; Wang, Xuefang; Yu, Li; Zhang, Wen; Li, Peiwu] Chinese Acad Agr Sci, Oil Crops Res Inst, Wuhan 430062, Peoples R China. [Li, Xue; Zhang, Yong] Minist Agr, Key Lab Biol & Genet Improvement Oil Crops, Wuhan 430062, Peoples R China. [Zhang, Liangxiao; Yu, Li; Li, Peiwu] Minist Agr, Lab Qual & Safety Risk Assessment Oilseed Prod Wu, Wuhan 430062, Peoples R China. [Wang, Du; Li, Peiwu] Minist Agr, Key Lab Detect Mycotoxins, Wuhan 430062, Peoples R China. [Zhang, Liangxiao; Wang, Du; Wang, Xuefang; Yu, Li; Zhang, Wen; Li, Peiwu] Minist Agr, Qual Inspect & Test Ctr Oilseed Prod, Wuhan 430062, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Oil Crops Research Institute, CAAS; Ministry of Agriculture & Rural Affairs; Ministry of Agriculture & Rural Affairs; Ministry of Agriculture & Rural Affairs; Ministry of Agriculture & Rural Affairs RP Zhang, LX; Li, PW (corresponding author), Chinese Acad Agr Sci, Oil Crops Res Inst, Wuhan 430062, Peoples R China. EM zhanglx@caas.cn; peiwuli@oilcrops.cn CR Corro-Herrera VA, 2016, BIOTECHNOL PROGR, V32, P510, DOI 10.1002/btpr.2222 Amar S, 2009, PLANT BREEDING, V128, P78, DOI 10.1111/j.1439-0523.2008.01531.x Angelopoulou T, 2017, WATER AIR SOIL POLL, V228, DOI 10.1007/s11270-017-3609-9 Anjos O, 2015, FOOD CHEM, V169, P218, DOI 10.1016/j.foodchem.2014.07.138 Arendse E, 2018, J FOOD ENG, V217, P11, DOI 10.1016/j.jfoodeng.2017.08.009 Azizian H, 2015, LIPIDS, V50, P705, DOI 10.1007/s11745-015-4038-4 Barbin DF, 2014, FOOD RES INT, V61, P23, DOI 10.1016/j.foodres.2014.01.005 Beljkas B, 2010, ACCREDIT QUAL ASSUR, V15, P555, DOI 10.1007/s00769-010-0677-6 BEWIG KM, 1994, J AM OIL CHEM SOC, V71, P195, DOI 10.1007/BF02541556 Budic-Leto I., 2011, Croatian Journal of Food Science and Technology, V3, P9 Buning-Pfaue H, 2003, FOOD CHEM, V82, P107, DOI 10.1016/S0308-8146(02)00583-6 Bureau S, 2009, FOOD CHEM, V113, P1323, DOI 10.1016/j.foodchem.2008.08.066 Casale M, 2008, J NEAR INFRARED SPEC, V16, P39, DOI 10.1255/jnirs.759 Cayuela JA, 2017, J FOOD ENG, V202, P79, DOI 10.1016/j.jfoodeng.2017.01.015 Sanchez JAC, 2013, J AGR FOOD CHEM, V61, P8056, DOI 10.1021/jf4021575 Chen B, 2012, ANAL METHODS-UK, V4, P4310, DOI 10.1039/c2ay25962a Chen GL, 2011, ANIM FEED SCI TECH, V165, P111, DOI 10.1016/j.anifeedsci.2011.02.004 Chen L. J., 2014, J ENVIRON QUAL, V42, P1015 Chen QS, 2009, FOOD CHEM, V113, P1272, DOI 10.1016/j.foodchem.2008.08.042 Cheng JH, 2017, ANAL METHODS-UK, V9, P6148, DOI [10.1039/C7AY02115A, 10.1039/c7ay02115a] Cheng JH, 2017, FOOD ENG REV, V9, P36, DOI 10.1007/s12393-016-9147-1 Choi YH, 2016, FOOD SCI BIOTECHNOL, V25, P433, DOI 10.1007/s10068-016-0059-x Cozzolino D, 2014, FOOD RES INT, V60, P262, DOI 10.1016/j.foodres.2013.08.034 Cui XY, 2019, SCI CHINA CHEM, V62, P583, DOI 10.1007/s11426-018-9398-2 Daszykowski M, 2008, ANALYST, V133, P1523, DOI 10.1039/b803687j Daun J. K., 2012, Lipid Technology, V24, P134, DOI 10.1002/lite.201200204 Moreira ACD, 2018, FOOD ANAL METHOD, V11, P1867, DOI 10.1007/s12161-017-1079-8 de Santana FB, 2019, FOOD CHEM, V293, P323, DOI 10.1016/j.foodchem.2019.04.073 Deepak Prem, 2012, Journal of Oilseed Brassica, V3, P88 Dossa K, 2018, CROP J, V6, P202, DOI 10.1016/j.cj.2017.10.003 Dumalisile P, 2020, FOOD ANAL METHOD, V13, P1220, DOI 10.1007/s12161-020-01739-x ElMasry G, 2012, J FOOD ENG, V110, P127, DOI 10.1016/j.jfoodeng.2011.11.028 Ferreira DS, 2013, FOOD RES INT, V51, P53, DOI 10.1016/j.foodres.2012.09.015 Forina M, 2015, TALANTA, V144, P1070, DOI 10.1016/j.talanta.2015.07.067 Fu XP, 2016, CRIT REV FOOD SCI, V56, P1913, DOI 10.1080/10408398.2013.807418 Gad HA, 2013, PHYTOCHEM ANALYSIS, V24, P1, DOI 10.1002/pca.2378 Galtier O, 2007, ANAL CHIM ACTA, V595, P136, DOI 10.1016/j.aca.2007.02.033 Gambhir P. N., 1992, Trends in Food Science & Technology, V3, P191, DOI 10.1016/0924-2244(92)90188-3 Garcia-Ayuso LE, 2000, CHROMATOGRAPHIA, V52, P103, DOI 10.1007/BF02490801 Gendrin C, 2008, J PHARMACEUT BIOMED, V48, P533, DOI 10.1016/j.jpba.2008.08.014 Georgieva M., 2013, Hrvatski Casopis za Prehrambenu Tehnologiju Biotehnologiju i Nutricionizam - Croatian Journal of Food Technology, Biotechnology and Nutrition, V8, P67 Gomez-Caravaca AM, 2016, ANAL CHIM ACTA, V913, P1, DOI 10.1016/j.aca.2016.01.025 Gotor AA, 2007, EUR J LIPID SCI TECH, V109, P525, DOI 10.1002/ejlt.200600236 Guo Y, 2016, SPECTROCHIM ACTA A, V153, P79, DOI 10.1016/j.saa.2015.08.006 Han SI, 2014, J AM OIL CHEM SOC, V91, P229, DOI 10.1007/s11746-013-2369-y Haughey SA, 2013, FOOD CHEM, V136, P1557, DOI 10.1016/j.foodchem.2012.01.068 Hom NH, 2007, EUPHYTICA, V153, P27, DOI 10.1007/s10681-006-9195-3 Hong SJ, 2017, J SPECTROSC, V2017, DOI 10.1155/2017/1082612 Hu H. Y., 2010, P JOINT INT AGR C, P415 Hubik J, 1965, Cesk Farm, V14, P425 Hussain N, 2019, TRENDS FOOD SCI TECH, V91, P598, DOI 10.1016/j.tifs.2019.07.018 Inarejos-Garcia AM, 2013, FOOD RES INT, V50, P250, DOI 10.1016/j.foodres.2012.10.029 Jamshidi B, 2016, MEASUREMENT, V89, P1, DOI 10.1016/j.measurement.2016.03.069 Jiang H, 2019, MOLECULES, V24, DOI 10.3390/molecules24112134 Jin HL, 2016, FOOD ANAL METHOD, V9, P2060, DOI 10.1007/s12161-015-0384-3 Jin HL, 2015, FOOD ANAL METHOD, V8, P2524, DOI 10.1007/s12161-015-0147-1 Johnson J. B., 2020, J STORED PRODUCTS RE, V86, P1 Kahriman F., 2019, SPECTROSC LETT, V52, P1 Kardash-Strochkova E, 2001, TALANTA, V54, P411, DOI 10.1016/S0039-9140(00)00649-4 Kasemsumran S., 2007, SPECTROSC LETT, V38, P839 Kumar S, 2010, J FOOD SCI TECH MYS, V47, P690, DOI 10.1007/s13197-010-0120-3 Lang HH, 2019, LWT-FOOD SCI TECHNOL, V107, P221, DOI 10.1016/j.lwt.2019.03.018 Lee S, 2012, J ELECTROCHEM SCI TE, V3, P85, DOI 10.5229/JECST.2012.3.2.85 Li SF, 2012, J FOOD SCI, V77, pC374, DOI 10.1111/j.1750-3841.2012.02622.x Li X, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201800078 Li YQ, 2011, ACTA PHYS SIN-CH ED, V60 LICHTER R, 1988, PLANT BREEDING, V100, P209, DOI 10.1111/j.1439-0523.1988.tb00242.x Lopez-Maestresalas A, 2013, J AGR FOOD CHEM, V61, P5413, DOI 10.1021/jf401292j Louw ED, 2010, POSTHARVEST BIOL TEC, V58, P176, DOI 10.1016/j.postharvbio.2010.07.001 Luna AS, 2013, SPECTROCHIM ACTA A, V100, P115, DOI 10.1016/j.saa.2012.02.085 Luo J, 2012, IFIP ADV INF COMM TE, V369, P24 Luo QS, 2018, DESTECH TRANS COMP, P287 Mailer RJ, 2004, J AM OIL CHEM SOC, V81, P823, DOI 10.1007/s11746-004-0986-4 Mainali D, 2014, J PHARMACEUT BIOMED, V95, P169, DOI 10.1016/j.jpba.2014.03.001 Mark H, 2007, CHEMOMETRICS IN SPECTROSCOPY, P471, DOI 10.1016/B978-012374024-3/50070-2 Melchert HU, 2002, J CHROMATOGR A, V976, P215, DOI 10.1016/S0021-9673(02)00941-X Mendes TO, 2015, FOOD ANAL METHOD, V8, P2339, DOI 10.1007/s12161-015-0121-y Neveu V, 2010, DATABASE-OXFORD, DOI 10.1093/database/bap024 Nicolai BM, 2007, POSTHARVEST BIOL TEC, V46, P99, DOI 10.1016/j.postharvbio.2007.06.024 Nordey T, 2017, SCI HORTIC-AMSTERDAM, V216, P51, DOI 10.1016/j.scienta.2016.12.023 Ogrinc N, 2003, ANAL BIOANAL CHEM, V376, P424, DOI 10.1007/s00216-003-1804-6 Olivos-Trujillo M, 2015, 2015 CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (CHILECON), P25, DOI 10.1109/Chilecon.2015.7400347 Ozdemir IS, 2018, LWT-FOOD SCI TECHNOL, V91, P125, DOI 10.1016/j.lwt.2018.01.045 Parasoglou P., P 34 INT C INFR MIL, P1 Pathmanaban P, 2019, TRENDS FOOD SCI TECH, V94, P32, DOI 10.1016/j.tifs.2019.10.004 Petrovic M, 2010, FOOD CHEM, V122, P285, DOI 10.1016/j.foodchem.2010.02.018 Callao MP, 2018, FOOD CONTROL, V86, P283, DOI [10.1016/J.foodcont.2017.11.034, 10.1016/j.foodcont.2017.11.034] Porep JU, 2015, TRENDS FOOD SCI TECH, V46, P211, DOI 10.1016/j.tifs.2015.10.002 Puertas G, 2020, J FOOD COMPOS ANAL, V86, DOI 10.1016/j.jfca.2019.103350 Qu JH, 2015, CRIT REV FOOD SCI, V55, P1939, DOI 10.1080/10408398.2013.871693 Rani SNBA, 2013, IEEE CONF OPEN SYST, P38, DOI 10.1109/ICOS.2013.6735044 Roggo Y, 2007, J PHARMACEUT BIOMED, V44, P683, DOI 10.1016/j.jpba.2007.03.023 Sato T, 1998, J AM OIL CHEM SOC, V75, P1877, DOI 10.1007/s11746-998-0344-9 SATO T, 1994, J AM OIL CHEM SOC, V71, P293, DOI 10.1007/BF02638055 Sen R, 2018, J SCI FOOD AGR, V98, P4050, DOI 10.1002/jsfa.8919 So CL, 2004, FOREST PROD J, V54, P6 Sundaram J, 2010, J AM OIL CHEM SOC, V87, P1103, DOI 10.1007/s11746-010-1589-7 Szlyk E, 2005, J AGR FOOD CHEM, V53, P6980, DOI 10.1021/jf050672e Tao FF, 2018, TRAC-TREND ANAL CHEM, V100, P65, DOI 10.1016/j.trac.2017.12.017 Vithu P, 2016, TRENDS FOOD SCI TECH, V56, P13, DOI 10.1016/j.tifs.2016.07.011 Wang HL, 2006, CEREAL CHEM, V83, P402, DOI 10.1094/CC-83-0402 Wang Jinshui, 2011, Proceedings 2011 International Conference on New Technology of Agricultural Engineering (ICAE 2011), P1092, DOI 10.1109/ICAE.2011.5943978 Wang L, 2006, FOOD CHEM, V95, P529, DOI 10.1016/j.foodchem.2005.04.015 Wang L, 2013, J SCI FOOD AGR, V93, P118, DOI 10.1002/jsfa.5738 Wang Q, 2017, J INTEGR AGR, V16, P2886, DOI 10.1016/S2095-3119(17)61799-4 Wang Y., 2018, GRAIN OIL SCI TECHNO, V1, P40, DOI [10.3724/sp.j.1447.gost.2018.18025, DOI 10.3724/SP.J.1447.GOST.2018.18025, 10.3724/SP.J.1447.GOST.2018.18025] Weeranantanaphan J, 2011, J NEAR INFRARED SPEC, V19, P61, DOI 10.1255/jnirs.924 Woodcock T, 2008, J AGR FOOD CHEM, V56, P11520, DOI 10.1021/jf802792d Workman Jr J, 1996, J NEAR INFRARED SPEC, V4, P69 Wu JG, 2007, FOOD CHEM, V103, P1054, DOI 10.1016/j.foodchem.2006.07.063 Wu R, 2016, FOOD CHEM, V204, P334, DOI 10.1016/j.foodchem.2016.02.086 Xie CQ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098522 Yang J, 2019, ANAL CHIM ACTA, V1081, P6, DOI 10.1016/j.aca.2019.06.012 Yang MX, 2017, MEASUREMENT, V103, P179, DOI 10.1016/j.measurement.2017.02.037 Yang RN, 2018, TRENDS FOOD SCI TECH, V74, P26, DOI 10.1016/j.tifs.2018.01.013 Yang XS, 2013, FOOD SCI BIOTECHNOL, V22, P1495, DOI 10.1007/s10068-013-0243-1 Yildiz G, 2002, J AM OIL CHEM SOC, V79, P1085, DOI 10.1007/s11746-002-0608-1 Yuan Z, 2020, LWT-FOOD SCI TECHNOL, V125, DOI 10.1016/j.lwt.2020.109247 Yun YH, 2019, TRAC-TREND ANAL CHEM, V113, P102, DOI 10.1016/j.trac.2019.01.018 Zareef M, 2020, FOOD ENG REV, V12, P173, DOI 10.1007/s12393-020-09210-7 Zhang C, 2020, FOOD CHEM, V319, DOI 10.1016/j.foodchem.2020.126536 Zhang LX, 2019, FOOD CHEM, V289, P313, DOI 10.1016/j.foodchem.2019.03.067 Zhang LX, 2014, ANAL CHIM ACTA, V839, P44, DOI 10.1016/j.aca.2014.06.040 NR 123 TC 41 Z9 43 U1 24 U2 150 PD JUL PY 2020 VL 101 BP 172 EP 181 DI 10.1016/j.tifs.2020.05.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000541893100015 DA 2022-12-14 ER PT J AU Barron, UG Corkery, G Barry, B Butler, R McDonnell, K Ward, S AF Barron, U. Gonzales Corkery, G. Barry, B. Butler, R. McDonnell, K. Ward, S. TI Assessment of retinal recognition technology as a biometric method for sheep identification SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE sheep; retina; identification; traceability; biometrics; multimodal ID ANIMAL IDENTIFICATION; EAR TAGS; INFORMATION; VERIFICATION; FUSION; CATTLE AB In order to assure effective traceability, food-producing animals must be identified by a tamper-proof and durable technique. With the advance in human biometric technologies, the deployment of retinal recognition technology for cattle identification and verification has been prompted. The objective of this study was to assess the accuracy of a commercially available retina biometric technology for sheep identification (i) by determining whether light conditions during retinal image capture (indoors and outdoors with shade) and different operators exerted any significant effect on the matching score of the built-in pattern matching algorithm; and (ii) by evaluating the recognition performance of the biometric system for enrolment of one retinal image per sheep and two retinal images per sheep (bimodal biometric system). Neither the light conditions nor the operators were found to have a statistically significant effect on the matching score values of the built-in algorithm; yet it was clear that the pupillary light reflex phenomenon played a major role in obtaining lower matching score values for retinal images taken outdoors. The recognition errors of the one-retina biometric system were estimated to be 0.25% for false matches and 0.82% for false non-matches. An improved bimodal biometric system, i.e., two retinas, that applies a decision criterion based on a simple OR logical operator and a sum of matching scores, has been proposed in this study in order to reduce both probabilities of false matches and false non-matches to near zero. (C) 2007 Elsevier B.V. All rights reserved. C1 [Barron, U. Gonzales; Corkery, G.; Barry, B.; Butler, R.; McDonnell, K.; Ward, S.] Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Dublin 2, Ireland. C3 University College Dublin RP Barron, UG (corresponding author), Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Dublin 2, Ireland. EM ursula.gonzalesbarron@ucd.ie CR Aslani MR, 1998, VET REC, V142, P518, DOI 10.1136/vr.142.19.518 Barry B, 2007, T ASABE, V50, P1073, DOI 10.13031/2013.23121 Corkery GP, 2007, T ASABE, V50, P313, DOI 10.13031/2013.22395 Daugman J., 2000, 482 U CAMBR COMP LAB Delac K, 2004, PROCEEDINGS ELMAR-2004: 46TH INTERNATIONAL SYMPOSIUM ELECTRONICS IN MARINE, P184 DESCHAEPDRIJVER L, 1989, RES VET SCI, V47, P34 Dziuk P, 2003, ANIM REPROD SCI, V79, P319, DOI 10.1016/S0378-4320(03)00170-2 Edwards DS, 1999, VET REC, V144, P603, DOI 10.1136/vr.144.22.603 Edwards DS, 2001, ANIM WELFARE, V10, P141 Eradus WJ, 1999, COMPUT ELECTRON AGR, V24, P91, DOI 10.1016/S0168-1699(99)00039-3 Fosgate GT, 2006, PREV VET MED, V73, P287, DOI 10.1016/j.prevetmed.2005.09.006 Galan A, 2006, VET OPHTHALMOL, V9, P7, DOI 10.1111/j.1463-5224.2005.00425.x Jimenez-Gamero I, 2006, SMALL RUMINANT RES, V65, P266, DOI 10.1016/j.smallrumres.2005.07.019 Loftus R, 2005, REV SCI TECH OIE, V24, P231, DOI 10.20506/rst.24.1.1563 Masters BR, 2004, ANNU REV BIOMED ENG, V6, P427, DOI 10.1146/annurev.bioeng.6.040803.140100 MOSS GE, 2004, P 2004 ASAS W SECT M, P134 Ollivier FJ, 2004, VET OPHTHALMOL, V7, P11, DOI 10.1111/j.1463-5224.2004.00318.x Peltier MR, 1998, J ANIM SCI, V76, P847 Prabhakar S, 2002, PATTERN RECOGN, V35, P861, DOI 10.1016/S0031-3203(01)00103-0 Raschke A, 2006, FOOD CONTROL, V17, P65, DOI 10.1016/j.foodcont.2004.09.004 Ross A, 2003, PATTERN RECOGN LETT, V24, P2115, DOI 10.1016/S0167-8655(03)00079-5 ROSS A, 2004, P 12 EUR SIGN PROC C, P1221 RUSK CP, 2006, J EXTENS, V44, DOI ARTN 5FEA7 Sanderson C, 2004, DIGIT SIGNAL PROCESS, V14, P449, DOI 10.1016/j.dsp.2004.05.001 SHADDUCK JA, 2002, P ID INFO EXP 2002 N Simon C., 1935, NEW YORK STATE J MED, V35, P901 Smith WL, 2005, CURR OPIN CELL BIOL, V17, P174, DOI 10.1016/j.ceb.2005.02.005 TOWER P, 1955, ARCH OPHTHALMOL-CHIC, V54, P225, DOI 10.1001/archopht.1955.00930020231010 WHITTIER JC, 2003, P 2003 ASAS W SECT M, P339 NR 29 TC 36 Z9 38 U1 2 U2 42 PD MAR PY 2008 VL 60 IS 2 BP 156 EP 166 DI 10.1016/j.compag.2007.07.010 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000253333700005 DA 2022-12-14 ER PT J AU Pafundo, S Busconi, M Agrimonti, C Fogher, C Marmiroli, N AF Pafundo, Simona Busconi, Matteo Agrimonti, Caterina Fogher, Corrado Marmiroli, Nelson TI Storage-time effects on olive oil DNA assessed by Amplified Fragments Length Polymorphisms SO FOOD CHEMISTRY DT Article DE Olive oil traceability; AFLPs on food; Time-storage effects ID MARKERS; TRACEABILITY; AFLP; AUTHENTICITY; EXTRACTION; PARAMETERS; STABILITY; QUALITY AB In this study Amplified Fragments Length Polymorphisms (AFLPs) analysis was applied on DNA extracted from different monovarietal olive oils. The aim was to study how the length of storage after milling of the oil can affect the use of DNA as an analyte for molecular traceability. Results, all assessed by statistical analyses, showed that the authentication of olive oil with molecular methods should be performed within a month from olive oil production. After this period, a significant decrease of quality of DNA extracted from olive oil was observed, with a consequent loss of information, that can affect the reliability of the results. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Pafundo, Simona; Agrimonti, Caterina; Marmiroli, Nelson] Univ Parma, Dept Environm Sci, Div Genet & Environm Biotechnol, I-43124 Parma, Italy. [Busconi, Matteo; Fogher, Corrado] Univ Cattolica Sacro Cuore, Fac Agr, Inst Agron Genet & Crop Sci, I-29122 Piacenza, Italy. C3 University of Parma; Catholic University of the Sacred Heart RP Marmiroli, N (corresponding author), Univ Parma, Dept Environm Sci, Div Genet & Environm Biotechnol, Viale GP Usberti 11-A, I-43124 Parma, Italy. EM nelson.marmiroli@unipr.it CR Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Angiolillo A, 1999, THEOR APPL GENET, V98, P411, DOI 10.1007/s001220051087 Bjelland S, 2003, MUTAT RES-FUND MOL M, V531, P37, DOI 10.1016/j.mrfmmm.2003.07.002 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Choe E, 2006, COMPR REV FOOD SCI F, V5, P169, DOI 10.1111/j.1541-4337.2006.00009.x Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 d'Abbadie M, 2007, NAT BIOTECHNOL, V25, P939, DOI 10.1038/nbt1321 DEVITA OZ, 2003, EXTRAVERGINE, P14 Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a El SN, 2009, NUTR REV, V67, P632, DOI 10.1111/j.1753-4887.2009.00248.x Gomez-Alonso S, 2007, FOOD CHEM, V100, P36, DOI 10.1016/j.foodchem.2005.09.006 Hamilton R. J., 1983, RANCIDITY FOODS, P1 KRUSKAL WH, 1952, J AM STAT ASSOC, V47, P583, DOI 10.1080/01621459.1952.10483441 KRUSKAL WH, 1952, ANN MATH STAT, V23, P525, DOI 10.1214/aoms/1177729332 KUSHI L, 2002, AM J MED S9B, V30, pS63 Lavelli V, 2006, J AGR FOOD CHEM, V54, P3002, DOI 10.1021/jf052918l MANN HB, 1947, ANN MATH STAT, V18, P50, DOI 10.1214/aoms/1177730491 Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Psomiadou E, 2003, EUR J LIPID SCI TECH, V105, P403, DOI 10.1002/ejlt.200300780 Roca M, 2003, J AM OIL CHEM SOC, V80, P1237, DOI 10.1007/s11746-003-0848-0 Roche HM, 2000, FOOD RES INT, V33, P227, DOI 10.1016/S0963-9969(00)00043-0 Salvador MD, 2001, FOOD CHEM, V74, P267, DOI 10.1016/S0308-8146(01)00148-0 Sanz-Cortes F, 2003, PLANT BREEDING, V122, P173, DOI 10.1046/j.1439-0523.2003.00808.x Sensi E, 2003, SCI HORTIC-AMSTERDAM, V97, P379, DOI 10.1016/S0304-4238(02)00163-2 Spyros A, 2004, J AGR FOOD CHEM, V52, P157, DOI 10.1021/jf030586j Velasco J, 2002, EUR J LIPID SCI TECH, V104, P661, DOI 10.1002/1438-9312(200210)104:9/10<661::AID-EJLT661>3.0.CO;2-D VOS P, 1995, NUCLEIC ACIDS RES, V23, P4407, DOI 10.1093/nar/23.21.4407 Willett WC, 2006, PUBLIC HEALTH NUTR, V9, P105, DOI 10.1079/PHN2005931 Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 NR 32 TC 30 Z9 32 U1 0 U2 12 PD DEC 1 PY 2010 VL 123 IS 3 BP 787 EP 793 DI 10.1016/j.foodchem.2010.05.027 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000280917800033 DA 2022-12-14 ER PT J AU Li Qing, B Bi, ZQ Shi, DD AF Li Qing-bo Bi Zhi-qi Shi Dong-dong TI The Method of Fishmeal Origin Tracing Based on EDXRF Spectrometry Analysis SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE Energy Dispersion X-ray fluorescence spectrum; Spectral pretreatment; Whale optimization algorithm; Adaptive net analyte signal weight K_local hyperplane; Fishmeal origin traceability AB Fishmeal is a kind of high protein feed material which plays an important role in aquaculture. There is a great market demand for fishmeal in China, but the quality of fishmeal from different places is different. In order to ensure the quality and safety of fishmeal, it is very important to establish a traceability system of fishmeal origin. The energy dispersion X-ray fluorescence spectrum is able to detect the type and content of mineral elements in the sample depending on the energy of the element's radiation X-ray fluorescent photons. The types and contents of mineral elements contained in fishmeal may vary depending on the origin of fishmeal, so this paper proposes for the first time to use the energy dispersion X-ray fluorescence spectroscopy (EDXRF) method to scan the fishmeal to obtain the element information of fishmeal elements. After preprocessing the original spectrum, whale optimization algorithm is used to improve the adaptive net analyte signal weight K_lacal hyperplane method can identify the spectrum vector of fishmeal samples, and then identify the origin of fishmeal samples. Firstly, 51 fishmeal samples from Liaoning and Zhejiang were pressed, and different filters were set in the detection program of EDXRF spectrometer, 51 groups (6 spectra in each group) of spectra were obtained. Then the spectrum is preprocessed, and the baseline is corrected based on the adaptive iterative reweighted penalty least squares algorithm (airPLS) so as to eliminate the impact of baseline drift and improve the accuracy. Wavelet transform is used to smooth the spectrum and remove the high-frequency noise of the spectrum curve. The 16-dimensional vector representing the element content of each fishmeal sample was obtained by calculating the peak area of six effective spectral regions. Finally, whale optimization algorithm is used to select the key parameters (neighbor number, principal component fraction, adjustment parameter) of the adaptive net analyte signal weight K_local hyperplane (ANWKH) method, and then the adaptive net analyte signal weight K_local hyperplane model is established by using the found optimal parameters. 70% of the fishmeal samples from each place of origin are selected as the training set, 30% as the test set to identify the fishmeal place of origin. Fishmeal samples are from Liaoning and Zhejiang provinces. The accuracy of the prediction model is 94.3% and 100% respectively. The total accuracy is 97.3%, which is higher than the accuracy of the adaptive net analyte signal weight K_local hyperplane classification. The results show that the method based on energy dispersive X-ray fluorescence spectrum can accurately realize the origin traceability of fishmeal, and the adaptive net analyte signal weight K_local hyperplane method improved by whale optimization algorithm can find the optimal parameters and establish a model with higher classification accuracy. This paper provides a reference for more detailed origin traceability of fishmeal at home and abroad in the future. C1 [Li Qing-bo; Bi Zhi-qi] Beihang Univ, Sch Instrumentat & Optoelect Engn, Minist Educ, Key Lab Precis Optomechatron Technol, Beijing 100191, Peoples R China. [Shi Dong-dong] Chinese Acad Agr Sci, Feed Res Inst, Beijing 100081, Peoples R China. C3 Beihang University; Chinese Academy of Agricultural Sciences; Feed Research Institute, CAAS RP Li Qing, B (corresponding author), Beihang Univ, Sch Instrumentat & Optoelect Engn, Minist Educ, Key Lab Precis Optomechatron Technol, Beijing 100191, Peoples R China. EM qbleebuaa@buaa.edu.cn CR Buchele D, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-53426-5 DENG Sai-wen, 2019, METALLURGICAL ANAL, V39, P30 [邓玉福 Deng Yufu], 2020, [农业机械学报, Transactions of the Chinese Society for Agricultural Machinery], V51, P358 Herreros-Chavez L, 2019, J FOOD COMPOS ANAL, V82, DOI 10.1016/j.jfca.2019.103240 Li QB, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17030627 Mirjalili S, 2016, ADV ENG SOFTW, V95, P51, DOI 10.1016/j.advengsoft.2016.01.008 [宋涛 Song Tao], 2015, [食品科学, Food Science], V36, P260 WEI Zi-yu, 2019, CEREALS OILS, V32, P66 XUE Xiao-kang, 2018, CHINESE J INORG CHEM, V8, P66 Zhang ZM, 2010, ANALYST, V135, P1138, DOI 10.1039/b922045c NR 10 TC 0 Z9 1 U1 5 U2 17 PD MAR PY 2021 VL 41 IS 3 BP 745 EP 749 DI 10.3964/j.issn.1000-0593(2021)03-0745-05 WC Spectroscopy SC Spectroscopy UT WOS:000636048700011 DA 2022-12-14 ER PT J AU Maretto, F Reffo, E Dalvit, C Barcaccia, G Mantovani, R AF Maretto, F. Reffo, E. Dalvit, C. Barcaccia, G. Mantovani, R. TI Finding 16S rRNA gene-based SNPs for the genetic traceability of commercial species belonging to Gadiformes SO ITALIAN JOURNAL OF ANIMAL SCIENCE DT Article; Proceedings Paper CT 17th Congress of the Scientific-Association-of-Animal-Production CY MAY 29-JUN 01, 2007 CL Alghero, ITALY DE genetic traceability; gadiformes; SNPs; 16S rRNA ID IDENTIFICATION AB A SNPs (Single Nucleotide Polymorphism) based analysis was developed to differentiate four economically important species belonging to the Gadiformes order: Pacific cod Gadus macrocephalus, Atlantic cod Gadus morhua, Haddock Melanogrammus aeglefinus and Ling Molva molua. A 430bp fragment of the 16s rRNA gene was amplified using interspecific conserved primer and sequenced. The sequences were aligned and analyzed for the presence of SNPs; three SNPs (MerSNP1, MerSNP7 and MerSNP9) were identified and selected to allow discrimination between the four species. Aplotypes were TCC, CCC, CAT and CAC for Pacific cod, Atlantic cod, Haddock and Ling respectively. Confirmation of results was achieved by sequencing 16s rRNA gene fragments of 16 G. morhua, 7 G. macrocephalus, 15 M. aeglefinus and 5 M. molva samples collected at different fish catching campaign. Nucleatide sequence of 16s rRNA mitachandial gene has been shown to be a useful tool to allow rapid reliable and fully automatable for discrimination of 4 economically important species in fisheries industry. C1 Univ Padua, Dipartimento Sci Anim, I-35020 Legnaro, Italy. Univ Padua, Dept Environm Agron & Crop Sci, I-35100 Padua, Italy. C3 University of Padua; University of Padua RP Maretto, F (corresponding author), Univ Padua, Dipartimento Sci Anim, Viale Univ 16, I-35020 Legnaro, Italy. EM fabio.maretto@unipd.it CR Aranishi Futoshi, 2005, Journal of Applied Genetics, V46, P69 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 MACKIE I M, 1990, Analytical Proceedings, V27, P89 Perez M, 2005, J AGR FOOD CHEM, V53, P5239, DOI 10.1021/jf048012h Quinteiro J, 1998, J AGR FOOD CHEM, V46, P1662, DOI 10.1021/jf970552+ SOTELO CG, 1993, TRENDS FOOD SCI TECH, V4, P395, DOI 10.1016/0924-2244(93)90043-A NR 6 TC 8 Z9 8 U1 1 U2 1 PY 2007 VL 6 SU 1 BP 161 EP 163 DI 10.4081/ijas.2007.1s.161 WC Agriculture, Dairy & Animal Science; Agriculture, Multidisciplinary; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000248276300063 DA 2022-12-14 ER PT J AU Bettinelli, M Spezia, S Baffi, C Beone, GM Rocchetta, R Nassisi, A AF Bettinelli, M Spezia, S Baffi, C Beone, GM Rocchetta, R Nassisi, A TI ICP-MS determination of REEs in tomato plants and related products: A new analytical tool to verify traceability SO ATOMIC SPECTROSCOPY DT Article ID RARE-EARTH-ELEMENTS; GROWN PLANTS; SOILS; FRACTIONATION; SAMPLES AB The rare earth element (REE) concentration in tomato plants and soils of three agricultural farms in the Province of Piacenza, Italy, was evaluated. The REE distribution in the different parts of the plant follows the order: root > leaf > stem > edible part; the concentrations reflect those observed in the soil. The most reliable REE concentrations are obtained from soil having the lowest pH and organic matter. The analytical procedure, using inductively coupled plasma mass spectrometry (ICP-MS), proved to be suitable for the determination of REEs in plants and soil, and was validated using certified samples. The precision of the method resulted in better than 10-15% for all REEs, with recoveries ranging from 70-150%. The method detection limit was 10 ng/g for soil and I ng/g for plant material. This technique seems to be promising for its application in tracing REE concentrations in tomato plants and its use in ensuring high quality of typical food products. C1 CMB Cent Lab, Piacenza, Italy. Maugeri Fdn, Lab Environm Hlth & Ind Toxicol, Pavia, Italy. Univ Sacred Heart, Fac Agr, Inst Agr & Environm Chem, Soil Sect, Piacenza, Italy. ARPA Reg Agcy Environm Protect, Piacenza, Italy. C3 Istituti Clinici Scientifici Maugeri IRCCS; Catholic University of the Sacred Heart; Regional Environmental Protection Agency - Italy RP Bettinelli, M (corresponding author), CMB Cent Lab, Piacenza, Italy. EM maurizio.bettinelli@libero.it CR CASTELLINI A, 2004, INFORMATORE AGRARIO, V11, P49 DAVIE RD, 1980, 139 TR WRC DUDDY IR, 1980, CHEM GEOL, V30, P363 GUO B, 1985, P INT C RAR EARTH DE, V2, P1522 GUO B, 1987, P C NAT MEAS LAB DEP, P237 Guo FQ, 1996, J RADIOAN NUCL CH AR, V209, P91, DOI 10.1007/BF02063534 ICHIHASHI H, 1992, ENVIRON POLLUT, V76, P157, DOI 10.1016/0269-7491(92)90103-H Kabata-Pendias A., 2001, TRACE ELEMENTS SOILS, P169 Krachler M, 2002, J ANAL ATOM SPECTROM, V17, P844, DOI 10.1039/b200780k Kuang Y. H., 1991, Acta Agriculturae Nucleatae Sinica, V5, P146 KUANG YH, 1981, ENV SCI, V2, P40 Laul J. C., 1979, Physics and Chemistry of the Earth, V11, P819 Li FL, 1998, ENVIRON POLLUT, V102, P269, DOI 10.1016/S0269-7491(98)00063-3 Liu Z., 1988, P INT S NEW RES RES, P23 MAHESWARAN J, 2001, RIRDC PUBLICATION, V1 MARKERT B, 1989, RADIAT ENVIRON BIOPH, V28, P213, DOI 10.1007/BF01211258 MARKERT B, 1987, PHYTOCHEMISTRY, V26, P3167, DOI 10.1016/S0031-9422(00)82463-2 MEEHAN B, 1996, RMI2 RES DEV CORP WANG L, 1988, J CHINESE RARE EARTH, V6, P85 Weinberg ED, 1977, MICROORGANISMS MINER, P492 Wen B, 2001, CHEM SPEC BIOAVAILAB, V13, P39, DOI 10.3184/095422901783726825 Wyttenbach A, 1998, PLANT SOIL, V199, P267, DOI 10.1023/A:1004331826160 WYTTENBACH A, 1994, BIOL TRACE ELEM RES, V41, P13, DOI 10.1007/BF02917214 Wyttenbach A, 1996, J RADIOAN NUCL CH AR, V204, P401, DOI 10.1007/BF02060328 Wyttenbach A., 1997, P 4 INT C BIOG TRAC, P299 Xiong B, 1995, P RAR EARTHS AGR SEM, P5 ZHANG Y, 1988, P 1 INT C MET MAT SC, V1, P1275 ZHU J, 1977, P 4 INT C BIOG TRAC NR 28 TC 19 Z9 20 U1 0 U2 8 PD MAR-APR PY 2005 VL 26 IS 2 BP 41 EP 50 WC Spectroscopy SC Spectroscopy UT WOS:000228654200001 DA 2022-12-14 ER PT J AU El Sheikha, AF AF El Sheikha, A. F. TI Tracing insect pests: is there new potential in molecular techniques? SO INSECT MOLECULAR BIOLOGY DT Review DE insect pests; challenges facing insect traceability; molecular techniques; innovative approaches; PCR-DGGE ID POLYMERASE-CHAIN-REACTION; MONITORING RANDOM-WALKS; POLYMORPHIC DNA RAPD; PCR-DGGE; GEL-ELECTROPHORESIS; BACTERIAL COMMUNITIES; MICROBIAL COMMUNITIES; GEOGRAPHICAL ORIGIN; MULTISCALE APPROACH; BIOLOGICAL-CONTROL AB Insects are amongst the greatest pests of agriculture, horticulture and forestry worldwide, inflicting damage and economic costs both directly and by transmitting plant viruses. Many kinds of insects are now resistant or cross-resistant to pesticides. Tracking studies have become very important for combatting insect pests and for better understanding their biology (eg insect population dynamics, movements, feeding behaviour and other ecological interactions). A wide variety of tracing approaches have been used including discriminative, tracer and molecular methods. The perfect technique for insect tracking is the technique that harmonizes with insects' 'normal' biology. Furthermore, the technique should be environmentally safe, cost-effective and easy to use. This paper reviews the current techniques used for insect traceability, documents the advantages and drawbacks of each method, and puts special focus on molecular techniques, including PCR-denaturing gradient gel electrophoresis as a new and promising traceability tool that could provide insects with a unique biological barcode and thus make it possible to trace their movements. C1 [El Sheikha, A. F.] Jiangxi Agr Univ, Coll Biosci & Bioengn, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. [El Sheikha, A. F.] Jiangxi Agr Univ, Bioengn & Technol Res Ctr Edible & Med Fungi, Nanchang, Jiangxi, Peoples R China. [El Sheikha, A. F.] Jiangxi Agr Univ, Jiangxi Key Lab Conservat & Utilizat Fungal Resou, Nanchang, Jiangxi, Peoples R China. [El Sheikha, A. F.] McMaster Univ, Dept Biol, Hamilton, ON, Canada. [El Sheikha, A. F.] Menoufia Univ, Minufiya Govt, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm, Egypt. C3 Jiangxi Agricultural University; Jiangxi Agricultural University; Jiangxi Agricultural University; McMaster University; Egyptian Knowledge Bank (EKB); Menofia University RP El Sheikha, AF (corresponding author), Jiangxi Agr Univ, Coll Biosci & Bioengn, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. EM elsheikha_aly@yahoo.com CR AKEY DH, 1991, SOUTHWEST ENTOMOL S, V14, P25 Ameziane N., 2005, PRINCIPES BIOL MOL B Bartlett A.C., 1982, P75 Bartlett A. C., 1982, STIPUB595 IAEA, P451 BLACKMAN RL, 1995, HEREDITY, V75, P297, DOI 10.1038/hdy.1995.138 BLOEM KA, 1994, ENVIRON ENTOMOL, V23, P629, DOI 10.1093/ee/23.3.629 Bommarco R, 2013, TRENDS ECOL EVOL, V28, P230, DOI 10.1016/j.tree.2012.10.012 Buchner P., 1965, ENDOSYMBIOSIS ANIMAL Cant ET, 2005, P ROY SOC B-BIOL SCI, V272, P785, DOI 10.1098/rspb.2004.3002 Centre for Animal Movement Research, 2015, INS TRACK Chang H. T., 1946, Mosquito News, V6, P122 Chen BS, 2016, SCI REP-UK, V6, DOI 10.1038/srep29505 Claridge Mike, 2003, Antenna, V27, P304 Codling EA, 2014, PHYS LIFE REV, V11, P533, DOI 10.1016/j.plrev.2014.06.011 Das M, 2007, APPL ENVIRON MICROB, V73, P756, DOI 10.1128/AEM.01170-06 Diez B, 2001, APPL ENVIRON MICROB, V67, P2942, DOI 10.1128/AEM.67.7.2942-2951.2001 DILLON RJ, 1995, J INVERTEBR PATHOL, V66, P72, DOI 10.1006/jipa.1995.1063 Dobzhansky T, 1943, GENETICS, V28, P304 Drake VA, 2012, RADAR ENTOMOLOGY: OBSERVING INSECT FLIGHT AND MIGRATION, P1, DOI 10.1079/9781845935566.0000 Duarte S, 2008, FRESHWATER BIOL, V53, P91, DOI 10.1111/j.1365-2427.2007.01869.x Duarte S, 2010, MICROBIOL RES, V165, P351, DOI 10.1016/j.micres.2009.06.002 Duarte S, 2009, APPL ENVIRON MICROB, V75, P6211, DOI 10.1128/AEM.00971-09 Duarte S, 2009, FRESHWATER BIOL, V54, P1683, DOI 10.1111/j.1365-2427.2009.02217.x Dubois G, 2009, THESIS El Fantroussi S, 1999, APPL ENVIRON MICROB, V65, P982 El Sheikha A. F., 2010, THESIS El Sheikha A.F., 2011, 200643 FSP El Sheikha A.F., 2011, FRANCO BENIN SCI POS El Sheikha AF, 2019, INT J TROP INSECT SC, V39, P9, DOI 10.1007/s42690-019-00002-z El Sheikha AF, 2016, CRIT REV FOOD SCI, V56, P306, DOI 10.1080/10408398.2012.745478 El Sheikha AF, 2011, FOOD BIOTECHNOL, V25, P115, DOI 10.1080/08905436.2011.576556 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Ercolini D, 2004, J MICROBIOL METH, V56, P297, DOI 10.1016/j.mimet.2003.11.006 Feng HQ, 2004, J ECON ENTOMOL, V97, P1874, DOI 10.1603/0022-0493-97.6.1874 Ferris MJ, 1996, APPL ENVIRON MICROB, V62, P340, DOI 10.1128/AEM.62.2.340-346.1996 FISCHER SG, 1983, P NATL ACAD SCI-BIOL, V80, P1579, DOI 10.1073/pnas.80.6.1579 Follett PA, 2000, NONTARGET EFFECTS OF BIOLOGICAL CONTROL, P77 Fraval A., 2001, INSECTES, V122, P33 Frederick B. A., 2000, Proceedings of the X International Symposium on Biological Control of Weeds, Bozeman, Montana, USA, 4-14 July, 1999, P261 Gasperi G, 2002, GENETICA, V116, P125, DOI 10.1023/A:1020971911612 Ghashghaie J., 2001, ECOLE THEMATIQUE BIO, P1 Godfray HCJ, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2012.0273 Gouda AC, 2011, THESIS GRAHAM HM, 1971, J ECON ENTOMOL, V64, P376, DOI 10.1093/jee/64.2.376 Green S.J., 2010, HDB HYDROCARBON LIPI, P4139 Greenstone MH, 1996, SYST ASSOC, P265 Gurr GM, 2016, FRONT PLANT SCI, V6, DOI 10.3389/fpls.2015.01255 Hadrys H, 1992, MOL ECOL, V1, P55, DOI 10.1111/j.1365-294X.1992.tb00155.x Hagler J. R., 1997, TREND ENTOMOL, V1, P105 HAGLER JR, 1992, ENVIRON ENTOMOL, V21, P20, DOI 10.1093/ee/21.1.20 Hagler JR, 1997, ENVIRON ENTOMOL, V26, P1079, DOI 10.1093/ee/26.5.1079 Hagler JR, 1998, BIOL CONTROL, V12, P25, DOI 10.1006/bcon.1998.0612 Hagler JR, 2001, ANNU REV ENTOMOL, V46, P511, DOI 10.1146/annurev.ento.46.1.511 Hagler JR, 1998, ENVIRON ENTOMOL, V27, P1010, DOI 10.1093/ee/27.4.1010 HALL HG, 1992, ARCH INSECT BIOCHEM, V19, P163, DOI 10.1002/arch.940190303 Hall HG, 1998, BIOCHEM GENET, V36, P351, DOI 10.1023/A:1018797429804 HAYES JL, 1989, ANN ENTOMOL SOC AM, V82, P340, DOI 10.1093/aesa/82.3.340 Holtzhauer M., 2006, BASIC METHODS BIOCH, P181 Hui XA, 2006, CAN J MICROBIOL, V52, P1085, DOI 10.1139/W06-064 Husheer T, 2005, STABLE ISOTOPE INVES Isaacs J., 2015, TRACKING INSECT MOVE Jones G.D., 2001, P 9 INT PALYN C AM A, P505 Jones G.D., 1995, POLLEN SE US EMPHASI Jones Gretchen D., 2014, Journal of Pollination Ecology, V13, P203 Jones Gretchen D., 2001, Neotropical Entomology, V30, P341, DOI 10.1590/S1519-566X2001000300001 Kalle Elena, 2014, Biomol Detect Quantif, V2, P11 Kim KS, 2004, INSECT MOL BIOL, V13, P293, DOI 10.1111/j.0962-1075.2004.00487.x Kissling WD, 2014, BIOL REV, V89, P511, DOI 10.1111/brv.12065 Lavandero B, 2004, INT J PEST MANAGE, V50, P147, DOI 10.1080/09670870410001731853 Le Nguyen DD, 2008, FRUITS, V63, P75, DOI 10.1051/fruits:2007049 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Le QS, 2017, INT CONF UBIQ ROBOT, P242 Leesing R., 2005, THESIS U MONTPELLIER Lou KF, 1998, J HERED, V89, P329, DOI 10.1093/jhered/89.4.329 Loxdale HD, 1998, B ENTOMOL RES, V88, P577, DOI 10.1017/S0007485300054250 Lushai G, 2004, INT J PEST MANAGE, V50, P307, DOI 10.1080/09670870412331286049 LYNCH M, 1994, MOL ECOL, V3, P91, DOI 10.1111/j.1365-294X.1994.tb00109.x Macdonald C, 2004, INT J PEST MANAGE, V50, P215, DOI 10.1080/09670870410001731952 Magdeldin S, 2012, GEL ELECTROPHORESIS - PRINCIPLES AND BASICS, P1, DOI 10.5772/2205 Marsh J.H., 1999, CANADIAN ENCY Martinez-Porchas M, 2017, PEERJ, V5, DOI 10.7717/peerj.3036 Massias B., 2008, THESIS MCDONALD IC, 1976, ENVIRON ENTOMOL, V5, P815, DOI 10.1093/ee/5.5.815 Menozzi P, 2007, Commun Agric Appl Biol Sci, V72, P375 Menozzi P., 2010, MONITORING PEST INSE MESSING RH, 1993, BIOL CONTROL, V3, P140, DOI 10.1006/bcon.1993.1021 Mikac KM, 2006, B ENTOMOL RES, V96, P523, DOI 10.1079/BER2006453 Mille-Lindblom C, 2006, FRESHWATER BIOL, V51, P730, DOI 10.1111/j.1365-2427.2006.01532.x MILLER LR, 1993, SOCIOBIOLOGY, V23, P127 Mrazek J, 2008, FOLIA MICROBIOL, V53, P229, DOI 10.1007/s12223-008-0032-z Muyzer G, 1999, CURR OPIN MICROBIOL, V2, P317, DOI 10.1016/S1369-5274(99)80055-1 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Muyzer G., 1996, MOL MICROBIAL ECOLOG, P1 Muyzer G., 1998, MOL MICROBIAL ECOLOG, P1 Nardi JB, 2002, J INSECT PHYSIOL, V48, P751, DOI 10.1016/S0022-1910(02)00105-1 Nielsen DI, 2000, ANN ENTOMOL SOC AM, V93, P1, DOI 10.1603/0013-8746(2000)093[0001:IPCRBM]2.0.CO;2 Nikolausz M, 2005, FEMS MICROBIOL LETT, V244, P385, DOI 10.1016/j.femsle.2005.02.013 O'Neal M. E., 2004, American Entomologist, V50, P212 ODONALD P, 1992, HEREDITY, V69, P521, DOI 10.1038/hdy.1992.167 Orr D., 2010, BIOL CONTROL PESTS Q Osborne J, 2002, DISPERSAL ECOLOGY, P24 Pashley D.P., 1979, P333 Petrovskii S, 2014, PHYS LIFE REV, V11, P467, DOI 10.1016/j.plrev.2014.02.001 Piterina A.V., 2010, DIVERSITY-BASEL, V2, P502, DOI DOI 10.3390/d2040505 Pradhan A, 2011, MICROB ECOL, V62, P58, DOI 10.1007/s00248-011-9861-4 Reeson AF, 2003, INSECT MOL BIOL, V12, P85, DOI 10.1046/j.1365-2583.2003.00390.x ROEHRDANZ RL, 1993, ENTOMOPHAGA, V38, P479, DOI 10.1007/BF02373082 Roskam JC, 1999, BIOL J LINN SOC, V66, P345, DOI 10.1111/j.1095-8312.1999.tb01895.x Senderovich Y, 2012, J INSECT SCI, V12, DOI 10.1673/031.012.14901 Service M. W., 1993, FIELD SAMPLING METHO SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 SHOWERS WB, 1989, ENVIRON ENTOMOL, V18, P447, DOI 10.1093/ee/18.3.447 Stevens J, 1995, B ENTOMOL RES, V85, P549, DOI 10.1017/S0007485300033058 Stock M.W., 1979, P328 SU NY, 1991, SOCIOBIOLOGY, V19, P349 Teanpaisan R, 2006, ORAL MICROBIOL IMMUN, V21, P79, DOI 10.1111/j.1399-302X.2006.00259.x Tilman D, 2011, P NATL ACAD SCI USA, V108, P20260, DOI 10.1073/pnas.1116437108 Turnock W.J., 2012, INSECT PESTS van Lenteren JC., 2012, IOBC INTERNET BOOK B VANSTEENWYK RA, 1992, J ECON ENTOMOL, V85, P2357, DOI 10.1093/jee/85.6.2357 Vialatte A, 2006, ECOL APPL, V16, P839, DOI 10.1890/1051-0761(2006)016[0839:TIMOAR]2.0.CO;2 Weinstein P., 2013, ENCY NATURAL HAZARDS, P540 WELSH J, 1990, NUCLEIC ACIDS RES, V18, P7213, DOI 10.1093/nar/18.24.7213 Westbrook JK, 1998, SOUTHWEST ENTOMOL, V23, P209 WILLIAMS JGK, 1990, NUCLEIC ACIDS RES, V18, P6531, DOI 10.1093/nar/18.22.6531 Zera AJ, 1997, ANNU REV ENTOMOL, V42, P207, DOI 10.1146/annurev.ento.42.1.207 Zhang H, 2008, J APPL MICROBIOL, V105, P1277, DOI 10.1111/j.1365-2672.2008.03867.x Zouache K, 2011, FEMS MICROBIOL ECOL, V75, P377, DOI 10.1111/j.1574-6941.2010.01012.x Zouache K, 2009, APPL ENVIRON MICROB, V75, P3755, DOI 10.1128/AEM.02964-08 NR 129 TC 8 Z9 8 U1 1 U2 21 PD DEC PY 2019 VL 28 IS 6 BP 759 EP 772 DI 10.1111/imb.12601 WC Biochemistry & Molecular Biology; Entomology SC Biochemistry & Molecular Biology; Entomology UT WOS:000493661600002 DA 2022-12-14 ER PT J AU Agrimonti, C Vietina, M Pafundo, S Marmiroli, N AF Agrimonti, Caterina Vietina, Michelangelo Pafundo, Simona Marmiroli, Nelson TI The use of food genomics to ensure the traceability of olive oil SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review ID REAL-TIME PCR; LIGATION DETECTION REACTION; POLYMERASE-CHAIN-REACTION; UAA INTRON POLYMORPHISMS; MICROSATELLITE MARKERS; MOLECULAR MARKERS; BOTANICAL ORIGIN; DNA EXTRACTION; SCAR MARKERS; IDENTIFICATION AB Fraudulent practices damage the market for premium olive oil; these can involve blending premium oil with oil produced from poor quality fruit, or adulteration with other plant oils. Methods based on assaying the DNA present in the oil are developing into a workable analytical tool. This review describes the use of "Food Genomics" to identify the varietal composition of olive oils, to ensure their conformity with legislation. The whole procedure is discussed, in the context of assembling an analytical platform suitable for the elaboration of an "identity card" for premium olive oils. C1 [Agrimonti, Caterina; Vietina, Michelangelo; Pafundo, Simona; Marmiroli, Nelson] Univ Parma, Dept Environm Sci, Div Genet & Environm Biotechnol, I-43121 Parma, Italy. C3 University of Parma RP Marmiroli, N (corresponding author), Univ Parma, Dept Environm Sci, Div Genet & Environm Biotechnol, V Le GP Usberti 11-A, I-43121 Parma, Italy. EM nelson.marmiroli@unipr.it CR Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Arvanitoyannis IS, 2007, CRIT REV FOOD SCI, V47, P441, DOI 10.1080/10408390600846325 Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 Bordoni R, 2004, J AGR FOOD CHEM, V52, P1049, DOI 10.1021/jf034871e Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Breton CM, 2009, ADV OLIVE RESOURCES, P105 Busconi M, 2006, MOL BREEDING, V17, P59, DOI 10.1007/s11032-005-1395-3 Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Carcea M, 2009, QUAL ASSUR SAF CROP, V1, P93, DOI 10.1111/j.1757-837X.2009.00011.x Christopoulou E, 2004, FOOD CHEM, V84, P463, DOI 10.1016/S0308-8146(03)00273-5 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Consolandi C, 2007, J BIOTECHNOL, V129, P565, DOI 10.1016/j.jbiotec.2007.01.025 Diaz A, 2007, J AM SOC HORTIC SCI, V132, P830 Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a Doyle J. J., 1991, Molecular techniques in taxonomy., P283 Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 Gerry NP, 1999, J MOL BIOL, V292, P251, DOI 10.1006/jmbi.1999.3063 Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 KIRITSAKIS K, 2000, HDB OLIVE OIL, P129 Marmiroli N., 2009, ADV OLIVE RESOURCES, P157 Marmiroli N, 2008, ANAL BIOANAL CHEM, V392, P369, DOI 10.1007/s00216-008-2303-6 Martins-Lopes P, 2008, J AGR FOOD CHEM, V56, P11786, DOI 10.1021/jf801146z Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z Mookerjee S, 2005, THEOR APPL GENET, V111, P1174, DOI 10.1007/s00122-005-0049-5 Muzzalupo I, 2002, EUR FOOD RES TECHNOL, V214, P528, DOI 10.1007/s00217-001-0482-x Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Pafundo S, 2005, J AGR FOOD CHEM, V53, P6995, DOI 10.1021/jf050775x Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Pafundo S, 2010, ANAL BIOANAL CHEM, V396, P1831, DOI 10.1007/s00216-009-3419-z Pafundo S, 2010, FOOD CHEM, V123, P787, DOI 10.1016/j.foodchem.2010.05.027 Pafundo S, 2009, FOOD CHEM, V116, P811, DOI 10.1016/j.foodchem.2009.03.040 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3857, DOI 10.1021/jf063708r Peano C, 2005, ANAL BIOCHEM, V346, P90, DOI 10.1016/j.ab.2005.08.004 Peano C, 2005, ANAL BIOCHEM, V344, P174, DOI 10.1016/j.ab.2005.04.009 Rabiei Zohreh, 2010, Iranian Journal of Biotechnology, V8, P24 Reale S, 2006, GENOME, V49, P1193, DOI 10.1139/G06-068 Samson MC, 2010, J SCI FOOD AGR, V90, P1437, DOI 10.1002/jsfa.3961 Spaniolas S, 2008, J AGR FOOD CHEM, V56, P6886, DOI 10.1021/jf8008926 Spaniolas S, 2008, EUR FOOD RES TECHNOL, V227, P175, DOI 10.1007/s00217-007-0707-8 Spaniolas S, 2010, FOOD CHEM, V122, P850, DOI 10.1016/j.foodchem.2010.02.039 Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Turci M, 2010, FOOD CONTROL, V21, P143, DOI 10.1016/j.foodcont.2009.04.012 Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 Wahrburg U, 2002, EUR J LIPID SCI TECH, V104, P698, DOI 10.1002/1438-9312(200210)104:9/10<698::AID-EJLT698>3.0.CO;2-A WAIBLINGER HU, 1999, LEBENSMITTELCHEMIE, V53, P11 Wu YJ, 2008, EUR FOOD RES TECHNOL, V227, P1117, DOI 10.1007/s00217-008-0827-9 Zhang L, 2009, J AGR FOOD CHEM, V57, P7227, DOI 10.1021/jf901172d 2010, Patent No. 101546885 NR 50 TC 59 Z9 64 U1 1 U2 55 PD MAY PY 2011 VL 22 IS 5 BP 237 EP 244 DI 10.1016/j.tifs.2011.02.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000291517500004 DA 2022-12-14 ER PT J AU Bunger, L Anhalt, K Taubert, RD Kruger, U Schmidt, F AF Buenger, L. Anhalt, K. Taubert, R. D. Krueger, U. Schmidt, F. TI Traceability of a CCD-Camera System for High-Temperature Measurements SO INTERNATIONAL JOURNAL OF THERMOPHYSICS DT Article; Proceedings Paper CT 12th International Symposium on Temperature, Humidity, Moisture and Thermal Measurements in Industry and Science CY OCT 14-18, 2013 CL Funchal, PORTUGAL DE Absolute spectral radiance responsivity; Charge-coupled device (CCD); Dark-signal-non-uniformity (DSNU); High temperature; Imaging radiometer; IRMD; Non-linearity (NL); Photo-response-non-uniformity (PRNU); Size-of-source effect (SSE); Thermodynamic temperature AB A CCD camera, which has been specially equipped with narrow-band interference filters in the visible spectral range for temperature measurements above 1200 K, was characterized with respect to its temperature response traceable to ITS-90 and with respect to absolute spectral radiance responsivity. The calibration traceable to ITS-90 was performed at a high-temperature blackbody source using a radiation thermometer as a transfer standard. Use of Planck's law and the absolute spectral radiance responsivity of the camera system allows the determination of the thermodynamic temperature. For the determination of the absolute spectral radiance responsivity, a monochromator-based setup with a supercontinuum white-light laser source was developed. The CCD-camera system was characterized with respect to the dark-signal-non-uniformity, the photo-response-non-uniformity, the non-linearity, and the size-of-source effect. The influence of these parameters on the calibration and measurement was evaluated and is considered for the uncertainty budget. The results of the two different calibration schemes for the investigated temperature range from 1200 K to 1800 K are in good agreement considering the expanded uncertainty (k = 2). The uncertainty for the absolute spectral responsivity of the camera is 0.56 % (k = 2). C1 [Buenger, L.; Anhalt, K.; Taubert, R. D.] Phys Tech Bundesanstalt, D-10587 Berlin, Germany. [Krueger, U.; Schmidt, F.] TechnoTeam Bildverarbeitung GmbH, D-98693 Ilmenau, Germany. C3 Physikalisch-Technische Bundesanstalt (PTB) RP Bunger, L (corresponding author), Phys Tech Bundesanstalt, Abbestr 2-12, D-10587 Berlin, Germany. EM lars.buenger@ptb.de CR BENNETT HE, 1966, APPL OPTICS, V5, P1265, DOI 10.1364/AO.5.001265 Bloembergen P, 2009, METROLOGIA, V46, P534, DOI 10.1088/0026-1394/46/5/018 Fischer J, 2011, INT J THERMOPHYS, V32, P12, DOI 10.1007/s10765-011-0922-1 Hartmann J, 2000, METROLOGIA, V37, P637, DOI 10.1088/0026-1394/37/5/67 Janesick J.R., 2001, SCI CHARGE COUPLED D JUNG HJ, 1979, METROLOGIA, V15, P173, DOI 10.1088/0026-1394/15/4/002 JUNG HJ, 1973, OPTIK, V38, P95 Keawprasert T, 2011, INT J THERMOPHYS, V32, P1697, DOI 10.1007/s10765-011-1031-x Kruger U, 2009, METROLOGIA, V46, pS252, DOI 10.1088/0026-1394/46/4/S23 Machin G., 2010, REALISATION DISSEMIN PRESTONTHOMAS H, 1990, METROLOGIA, V27, P3, DOI 10.1088/0026-1394/27/1/002 Sakuma F., 1982, TEMPERATURE ITS MEAS, VVolume 5, P421 Saunders P, 2011, INT J THERMOPHYS, V32, P1633, DOI 10.1007/s10765-011-0988-9 Saunders P, 2003, METROLOGIA, V40, P93, DOI 10.1088/0026-1394/40/2/315 Saunders P., 1997, P TEMPMEKO 96 6 INT, P329 Saunders P, 2009, METROLOGIA, V46, P62, DOI 10.1088/0026-1394/46/1/008 Yoon H, 2017, INT J CULT POLICY, V23, P634, DOI 10.1080/10286632.2015.1084298 NR 17 TC 8 Z9 8 U1 0 U2 11 PD AUG PY 2015 VL 36 IS 8 SI SI BP 1784 EP 1802 DI 10.1007/s10765-015-1915-2 WC Thermodynamics; Chemistry, Physical; Mechanics; Physics, Applied SC Thermodynamics; Chemistry; Mechanics; Physics UT WOS:000360556100008 DA 2022-12-14 ER PT J AU Bin, L Wang, C Liu, Z He, WZ Zhao, DY Fang, YY Li, Y Zhang, ZH Chen, P Liu, W Rogers, KM AF Bin, Li Wang, Cheng Liu, Zhi He, Weizhong Zhao, Duoyong Fang, Ying-ying Li, Ying Zhang, Zihong Chen, Piao Liu, Wei Rogers, Karyne M. TI Geographical origin traceability of muskmelon from Xinjiang province using stable isotopes and multi-elements with chemometrics SO JOURNAL OF FOOD COMPOSITION AND ANALYSIS DT Article DE Muskmelon; Stable isotopes; Multi-element composition; Origin traceability; LDA modeling; Food integrity ID VIRGIN OLIVE OILS; FOOD; AUTHENTICATION; PATTERNS; PLANTS; CHINA AB Xinjiang muskmelon is a popular fruit in China that is exported worldwide. In particular, Hami muskmelon is classified as a protected geographical indication (PGI) product, and consequently it is more desirable and sells at a higher market price than products from other regions. Origin mislabeling and fraudulent substitution of Hami PGI muskmelon products by non-PGI products are frequently reported, which damages its reputation and market advantage. In this study, the geographical origin of muskmelon from Xinjiang was investigated using stable isotopes and multi-element analyses with chemometrics. Four stable isotopes (delta C-13, delta N-15, delta H-2, delta O-18) and seventeen elemental contents (Na, Al, P, K, Ca, Cr, Mn, Co, Ni, Cu, As, Sr, Mo, Cd, Sb, Ba, Pb) of 239 batches of muskmelon were collected from 13 administrative regions distributed across Xinjiang Province. One-way analysis of variance (ANOVA) was used to compare these regional differences using stable isotopes and multi-elements, and showed that a single variable could not fully classify all sample origins. A multivariate model was developed using linear discriminant analysis (LDA) based on stable isotope and multi-element data. The discriminant accuracies of 13 localities across Xinjiang Province was unsatisfactory with values as low as 46.2 % for Changji. Muskmelon from Kashi were misclassified as Aksu (20 %) and Bazhou (16.7 %) respectively. Further LDA modeling was undertaken after the 13 administrative regions were reduced to 6 geographical production regions from Xinjiang. Classification accuracies improved significantly; 100 % for Altay, 91.9 % for Hami and more than 79.1 % for other regions. Therefore, this strategy may be a useful and complementary tool to combat origin mislabeling of Hami PGI muskmelons from other Xinjiang regions, effectively ensuring food integrity and promoting origin certainty of PGI Hami muskmelon from Xinjiang. C1 [Bin, Li; Liu, Zhi; Li, Ying; Zhang, Zihong; Chen, Piao; Liu, Wei] Hunan Univ Humanities Sci & Technol, Coll Agr & Biotechnol, Loudi 417000, Peoples R China. [Wang, Cheng; He, Weizhong; Zhao, Duoyong; Fang, Ying-ying] Minist Agr & Rural Affairs, Agr Prod Qual & Safety Risk Assessment Lab, Urumqi 830091, Peoples R China. [Wang, Cheng; He, Weizhong; Zhao, Duoyong; Fang, Ying-ying] Xinjiang Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Urumqi 830091, Peoples R China. [Liu, Zhi; Rogers, Karyne M.] Zhejiang Acad Agr Sci, Inst Agroprod Safety & Nutr, Hangzhou 310021, Peoples R China. [Liu, Zhi; Rogers, Karyne M.] Minist Agr, Key Lab Informat Traceabil Agr Prod, Hangzhou 310021, Peoples R China. [Rogers, Karyne M.] GNS Sci, Natl Isotope Ctr, 30 Gracefield Rd, Lower Hutt 5040, New Zealand. C3 Hunan University Of Humanities, Science & Technology; Ministry of Agriculture & Rural Affairs; Zhejiang Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; GNS Science - New Zealand RP Liu, Z (corresponding author), Hunan Univ Humanities Sci & Technol, Coll Agr & Biotechnol, Loudi 417000, Peoples R China.; Rogers, KM (corresponding author), Zhejiang Acad Agr Sci, Inst Agroprod Safety & Nutr, Hangzhou 310021, Peoples R China. EM liu_schoolar@163.com; K.Rogers@gns.cri.nz CR Arca C., 2020, WATER RESX, V9, P1 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Beltran M, 2015, FOOD CHEM, V169, P350, DOI 10.1016/j.foodchem.2014.07.104 Bianchi T, 2016, SCI HORTIC-AMSTERDAM, V201, P46, DOI 10.1016/j.scienta.2016.01.028 Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Chung IM, 2018, FOOD CHEM, V240, P840, DOI 10.1016/j.foodchem.2017.08.023 Cozzolino D, 2016, WOODHEAD PUBL FOOD S, V301, P119, DOI 10.1016/B978-0-08-100310-7.00007-7 Dias C, 2018, FOOD RES INT, V103, P492, DOI 10.1016/j.foodres.2017.09.059 Fu HY, 2021, J FOOD COMPOS ANAL, V102, DOI 10.1016/j.jfca.2021.103972 Herrero M., 2013, FOODOMICS ADV MASS S Hobbie EA, 2004, NEW PHYTOL, V161, P371, DOI 10.1111/j.1469-8137.2004.00970.x Igamberdiev AU, 2004, PHOTOSYNTH RES, V81, P139, DOI 10.1023/B:PRES.0000035026.05237.ec Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Kumar S, 2015, POSTHARVEST BIOL TEC, V100, P16, DOI 10.1016/j.postharvbio.2014.09.021 Li SC, 2021, FOOD CHEM, V348, DOI 10.1016/j.foodchem.2020.128701 Lignou S, 2014, FOOD CHEM, V148, P218, DOI 10.1016/j.foodchem.2013.10.045 Liu CJ, 2020, WATER-SUI, V12, DOI 10.3390/w12010265 Liu Z, 2020, J AGR FOOD CHEM, V68, P1213, DOI 10.1021/acs.jafc.9b06847 Liu Z, 2019, RAPID COMMUN MASS SP, V33, P778, DOI 10.1002/rcm.8405 Lu GH, 2005, J CHROMATOGR A, V1068, P209, DOI 10.1016/j.chroma.2005.01.082 Martinez-Gaitan C, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10050741 Ni K, 2018, J FOOD COMPOS ANAL, V67, P104, DOI 10.1016/j.jfca.2018.01.005 Peck WH, 2010, J AGR FOOD CHEM, V58, P2364, DOI 10.1021/jf100104s Pilgrim TS, 2010, FOOD CHEM, V118, P921, DOI 10.1016/j.foodchem.2008.08.077 Portarena S, 2014, FOOD CHEM, V164, P12, DOI 10.1016/j.foodchem.2014.04.115 Qiu X, 2016, ENVIRON EARTH SCI, V75, DOI 10.1007/s12665-016-6299-5 Rummel S, 2010, FOOD CHEM, V118, P890, DOI 10.1016/j.foodchem.2008.05.115 Teng C.H., 2009, LDA LINEAR DISCRIMIN Vallone S, 2013, FOOD CHEM, V139, P171, DOI 10.1016/j.foodchem.2012.12.042 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Wang BL, 2013, QUATERN INT, V298, P141, DOI 10.1016/j.quaint.2012.09.010 Wang C, 2020, J FOOD COMPOS ANAL, V92, DOI 10.1016/j.jfca.2020.103577 Wang J, 2021, J FOOD COMPOS ANAL, V96, DOI 10.1016/j.jfca.2020.103756 Wu H, 2019, FOOD CHEM, V301, DOI 10.1016/j.foodchem.2019.125137 Wu YF, 2015, J ARID LAND, V7, P527, DOI 10.1007/s40333-015-0125-x [项锦欣 Xiang Jinxin], 2014, [食品科学, Food Science], V35, P345 Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 Zheng L.C., 2010, J ANHUI AGR SCI, V26, P554 Zhou XW, 2021, J FOOD COMPOS ANAL, V101, DOI 10.1016/j.jfca.2021.103940 NR 40 TC 0 Z9 0 U1 13 U2 13 PD MAR PY 2022 VL 106 AR 104320 DI 10.1016/j.jfca.2021.104320 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000803854200045 DA 2022-12-14 ER PT J AU Jaanson, P Bialek, A Greenwell, C Mantynen, H Widlowski, JL Manoocheri, F Lassila, A Fox, N Ikonen, E AF Jaanson, Priit Bialek, Agnieszka Greenwell, Claire Mantynen, Henrik Widlowski, Jean-Luc Manoocheri, Farshid Lassila, Antti Fox, Nigel Ikonen, Erkki TI Toward SI Traceability of a Monte Carlo Radiative Transfer Model in the Visible Range SO IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING DT Article DE Accuracy; bidirectional reflectance factor (BRF); cost function; model validation; Monte Carlo (MC) methods; parameter estimation; radiative transfer (RT); ray tracing; SI traceability; uncertainty ID DIFFUSE-REFLECTANCE; SURFACES; SCATTERING; CAVEATS AB A 3-D Monte Carlo (MC) ray-tracing radiative transfer model is tested for its ability to simulate the bidirectional reflectance factors (BRFs) of a grooved artificial target given SI-traceable measurements of the optical and topographic properties of the target's surface. The optical properties of a grooved target and an identical flat target were measured with the goniospectrophotometer at the National Metrology Institute of U.K. (NPL) and are traceable to the NPL scales of radiance factor. The topographic measurements were performed with the coordinate measuring machine at the National Metrology Institute of Finland (MIKES), and are traceable to the realization of the meter. The BRFs of the flat target were used to parameterize analytical scattering functions for rough surfaces. Similarly, the topographic measurement results were used to construct a structural model of the grooved target. Each element within this structural model then had its optical properties defined by the parameterized scattering function before the 3-D MC model simulated the BRFs of the grooved target under well-defined illumination and viewing conditions. The measured and modeled BRFs agreed for 72% of the measured geometries in the plane of incidence within the measurement and modeling uncertainties. The relative root-mean-squared (RMSE) error was 0.19. In the plane orthogonal to the plane of incidence, the measured and modeled BRFs agreed for 45% of the measured geometries, and the relative RMSE between measured and modeled values was 0.65. C1 [Jaanson, Priit; Lassila, Antti; Ikonen, Erkki] VTT Tech Res Ctr Finland Ltd, MIKES Metrol, 02150 Espoo, Finland. [Jaanson, Priit; Mantynen, Henrik; Manoocheri, Farshid; Ikonen, Erkki] Aalto Univ, Metrol Res Inst, 02150 Espoo, Finland. [Bialek, Agnieszka; Greenwell, Claire; Fox, Nigel] Natl Phys Lab, Teddington TW11 0LW, Middx, England. [Widlowski, Jean-Luc] European Commiss, Joint Res Ctr, I-21027 Ispra, Italy. C3 VTT Technical Research Center Finland; Aalto University; National Physical Laboratory - UK; European Commission Joint Research Centre; EC JRC ISPRA Site RP Jaanson, P (corresponding author), VTT Tech Res Ctr Finland Ltd, MIKES Metrol, 02150 Espoo, Finland. EM priit.jaanson@vtt.fi; agnieszka.bialek@npl.co.uk; claire.greenwell@npl.co.uk; henrik.mantynen@aalto.fi; jean-luc.widlowski@ec.europa.eu; farshid.manoocheri@aalto.fi; antti.lassila@vtt.fi; nigel.fox@npl.co.uk; erkki.ikonen@aalto.fi CR Adams J, 2016, J QUANT SPECTROSC RA, V180, P126, DOI 10.1016/j.jqsrt.2016.04.005 [Anonymous], [No title captured] [Anonymous], 2008, 1012008 JCGM BUR INT [Anonymous], [No title captured] Bass M., 1995, HDB OPTICS, VI Blinn James F., 1977, COMPUT GRAPHICS-US, V11, P192, DOI [DOI 10.1145/965141.563893, 10.1145/965141, DOI 10.1145/965141] Bousquet L, 2005, REMOTE SENS ENVIRON, V98, P201, DOI 10.1016/j.rse.2005.07.005 Butler SD, 2015, OPT EXPRESS, V23, P29100, DOI 10.1364/OE.23.029100 Chunnilall CJ, 2003, METROLOGIA, V40, pS192, DOI 10.1088/0026-1394/40/1/344 Disney M.I., 2000, REMOTE SENS REV, V18, P163, DOI DOI 10.1080/02757250009532389 Govaerts YM, 1998, IEEE T GEOSCI REMOTE, V36, P493, DOI 10.1109/36.662732 Hyde MW, 2009, OPT EXPRESS, V17, P22138, DOI 10.1364/OE.17.022138 International Vocabulary of Metrology-Basic and General Concepts and Associated Terms, 2012, 2002012 JCGM BUR INT Jaanson P, 2014, METROLOGIA, V51, pS314, DOI 10.1088/0026-1394/51/6/S314 KOECHLER C, 1994, INT GEOSCI REMOTE SE, P2375, DOI 10.1109/IGARSS.1994.399742 Kuusk A, 2009, REMOTE SENS ENVIRON, V113, P889, DOI 10.1016/j.rse.2009.01.005 Loew A, 2014, BIOGEOSCIENCES, V11, P1873, DOI 10.5194/bg-11-1873-2014 Nadal M, 2013, METROLOGIA, V50 Nevas S, 2004, APPL OPTICS, V43, P6391, DOI 10.1364/AO.43.006391 Nicodemus FE, 1977, NBS MONOGRAPH, V160 Pinty B, 2002, IEEE T GEOSCI REMOTE, V40, P1560, DOI 10.1109/TGRS.2002.801148 Pointer MR, 2005, COLOR TECHNOL, V121, P96 Priest RG, 2002, OPT ENG, V41, P988, DOI 10.1117/1.1467360 Schneider FD, 2014, REMOTE SENS ENVIRON, V152, P235, DOI 10.1016/j.rse.2014.06.015 Takatsuji T, 2014, METROLOGIA, V51, P4, DOI 10.1088/0026-1394/51/1A/04003 TORRANCE KE, 1967, J OPT SOC AM, V57, P1105, DOI 10.1364/JOSA.57.001105 VANDONGEN V, 1991, PHOT VEG INT APPL, V505, P191 Widlowski JL, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007821 Widlowski JL, 2015, REMOTE SENS ENVIRON, V169, P418, DOI 10.1016/j.rse.2015.08.016 Widlowski JL, 2015, ENVIRON SCI POLICY, V51, P149, DOI 10.1016/j.envsci.2015.03.018 Widlowski JL, 2005, IEEE T GEOSCI REMOTE, V43, P2008, DOI 10.1109/TGRS.2005.853718 NR 31 TC 0 Z9 0 U1 0 U2 7 PD MAR PY 2018 VL 56 IS 3 BP 1360 EP 1373 DI 10.1109/TGRS.2017.2761988 WC Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology SC Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology UT WOS:000426789800011 DA 2022-12-14 ER PT J AU Castellini, G Sesini, G Iannello, P Lombi, L Lozza, E Lucini, L Graffigna, G AF Castellini, Greta Sesini, Giulia Iannello, Paola Lombi, Linda Lozza, Edoardo Lucini, Luigi Graffigna, Guendalina TI "Omics" technologies for the certification of organic vegetables: Consumers' orientation in Italy and the main determinants of their acceptance SO FOOD CONTROL DT Article DE Omics" technology; Consumer psychology; Organic food; Food traceability technology ID WILLINGNESS-TO-PAY; FOOD TRACEABILITY SYSTEM; INTENTIONS; SAFETY; PERCEPTIONS; TRUST; EXPECTATIONS; INFORMATION; CONSUMPTION; ATTITUDES AB Traceability of food is considered as an important tool for ensuring food quality and safety, in turn increasing consumers' trust. Verification of food authenticity is currently carried out by officials and producers, typically through the use of paper documents that may not ensure a proper level of integrity. Therefore, independent analytical methods and forgery-proof labels are required by the agri-food sector, driving the development of "omics" traceability technologies for the authentication of foods. Despite the growing interest in these technologies from a technical-scientific point of view, no research has been carried out on consumers' perceptions. Given these premises, this study aimed at understanding consumers' orientations towards "omics" technologies applied to organic vegetables and unravelling the main psychological and socio-demographic variables that influence their adoption. Data were collected with a self-report questionnaire filled out by a sample of 807 Italians, representative of the population. We used descriptive statistics and multiple linear regression to analyze data. Results show that, despite the lack of subjective knowledge of "omics" technologies, Italians have positive attitudes and are interested in using and buying organic vegetables certified by this approach, even paying more. Furthermore, young people with positive attitudes and higher environmental concerns have a more positive attitude towards "omics" technologies. This research points out the need to educate consumers about what the "omics" technology is, enhancing consumers' attitudes and underlining its environmental contribution. This will increase consumers' acceptance and support the implementation of the technology. C1 [Castellini, Greta; Graffigna, Guendalina] Univ Cattolica Sacro Cuore, Fac Agr Food & Environm Sci, Via Bissolati 74, I-26100 Cremona, Italy. [Castellini, Greta; Graffigna, Guendalina] Univ Cattolica Sacro Cuore, EngageMinds HUB Consumer Food & Hlth Engagement R, I-20123 Milan, Italy. [Sesini, Giulia; Iannello, Paola; Lozza, Edoardo] Univ Cattolica Sacro Cuore, Fac Psychol, I-20123 Milan, Italy. [Lombi, Linda] Univ Cattolica Sacro Cuore, Dept Sociol, I-20123 Milan, Italy. [Lucini, Luigi] Univ Cattolica Sacro Cuore, Dept Sustainable Food Proc, I-29122 Piacenza, Italy. C3 Catholic University of the Sacred Heart; Catholic University of the Sacred Heart; Catholic University of the Sacred Heart; Catholic University of the Sacred Heart; Catholic University of the Sacred Heart RP Castellini, G (corresponding author), Univ Cattolica Sacro Cuore, Fac Agr Food & Environm Sci, Via Bissolati 74, I-26100 Cremona, Italy. EM greta.castellini@unicatt.it CR Ahmed N, 2021, J ENVIRON PLANN MAN, V64, P796, DOI 10.1080/09640568.2020.1785404 AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T Angulo AM, 2005, J FOOD PROD MARK, V11, P89, DOI 10.1300/J038v11n03_06 Asif M, 2018, FOOD QUAL PREFER, V63, P144, DOI 10.1016/j.foodqual.2017.08.006 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bohme K, 2019, TRAC-TREND ANAL CHEM, V110, P221, DOI 10.1016/j.trac.2018.11.005 BRUCKS M, 1985, J CONSUM RES, V12, P1, DOI 10.1086/209031 Brunso K, 2021, FOOD QUAL PREFER, V91, DOI 10.1016/j.foodqual.2021.104192 Capuano E, 2013, J SCI FOOD AGR, V93, P12, DOI 10.1002/jsfa.5914 Cardello AV, 2003, APPETITE, V40, P217, DOI 10.1016/S0195-6663(03)00008-4 Chang A, 2013, BRIT FOOD J, V115, P1361, DOI 10.1108/BFJ-11-2011-0286 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Creydt M, 2018, ELECTROPHORESIS, V39, P1569, DOI 10.1002/elps.201800004 Davies H., 2010, Food Control, V21, P1601 Frewer LJ, 2011, TRENDS FOOD SCI TECH, V22, P442, DOI 10.1016/j.tifs.2011.05.005 Gefen, 2003, E SERVICE J, V2, P7, DOI [10.2979/esj.2003.2.2.7, DOI 10.2979/ESJ.2003.2.2.7, https://doi.org/10.2979/esj.2003.2.2.7] Giraud G., 2006, 98 SEM EUR ASS AGR E Hansstein F. V., 2014, 2 INT C ENV EN BIOT Ingrassia M., 2017, CHEM ENG TRANS, V58, P865, DOI [10.3303/CET1758145, DOI 10.3303/CET1758145] ISTAT, IT NAT I STAT Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Cuevas FJ, 2019, FOOD CONTROL, V104, P63, DOI 10.1016/j.foodcont.2019.04.012 Kamrath C, 2019, COMPR REV FOOD SCI F, V18, P798, DOI 10.1111/1541-4337.12442 Kim G, 2009, INFORM SYST J, V19, P283, DOI 10.1111/j.1365-2575.2007.00269.x Kim YG, 2016, FOOD RES INT, V85, P266, DOI 10.1016/j.foodres.2016.05.002 Krystallis A, 2005, BRIT FOOD J, V107, P320, DOI 10.1108/00070700510596901 Lin W, 2019, FOOD QUAL PREFER, V76, P10, DOI 10.1016/j.foodqual.2019.03.007 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Lockie S, 2002, SOCIOL RURALIS, V42, P23, DOI 10.1111/1467-9523.00200 Bueno MJM, 2018, J CHROMATOGR A, V1546, P66, DOI 10.1016/j.chroma.2018.03.002 McKnight DH, 2002, J STRATEGIC INF SYST, V11, P297, DOI 10.1016/S0963-8687(02)00020-3 Mihailova A, 2021, TRENDS FOOD SCI TECH, V110, P142, DOI 10.1016/j.tifs.2021.01.071 Nassivera F, 2020, GREEN METAMORPHOSES: AGRICULTURE, FOOD, ECOLOGY, P85, DOI 10.3920/978-90-8686-898-8_4 Nunnally J.C., 1978, PSYCHOMETRIC THEORY Rainero C, 2021, BRIT FOOD J, V123, P4284, DOI 10.1108/BFJ-10-2020-0921 Rodriguez-Salvador B, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107142 Roininen K, 1999, APPETITE, V33, P71, DOI 10.1006/appe.1999.0232 Rollin F, 2011, TRENDS FOOD SCI TECH, V22, P99, DOI 10.1016/j.tifs.2010.09.001 Sacchettini G., 2021, Science of the Total Environment, V789, DOI 10.1016/j.scitotenv.2021.148049 Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Shew AM, 2022, APPL ECON PERSPECT P, V44, P299, DOI 10.1002/aepp.13157 Siegrist M, 2000, RISK ANAL, V20, P195, DOI 10.1111/0272-4332.202020 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Wu X, 2021, J MARKET MANAG-UK, V37, P1267, DOI [10.3171/2020.9.JNS202965, 10.1080/0267257X.2021.1910328] Xu LL, 2010, J SCI FOOD AGR, V90, P1368, DOI 10.1002/jsfa.3985 Yeh J. -Y., 2019, UNDERSTANDING CONSUM, P1, DOI [10.1109/sitim.2019.8910212, DOI 10.1109/SITIM.2019.8910212] Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Zaichkowsky J. L., 1987, PERSONAL INVOLVEMENT Zhang AR, 2020, J CONSUM PROT FOOD S, V15, P99, DOI 10.1007/s00003-020-01277-y Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 52 TC 0 Z9 0 U1 4 U2 4 PD NOV PY 2022 VL 141 AR 109209 DI 10.1016/j.foodcont.2022.109209 WC Food Science & Technology SC Food Science & Technology UT WOS:000833386800009 DA 2022-12-14 ER PT J AU Xia, Y Jia, LJ Zhang, K Xie, J Yu, EM Tian, JJ Gong, WB Li, ZF Li, HY Wang, GJ Liu, YR AF Xia, Yun Jia, Lijuan Zhang, Kai Xie, Jun Yu, Ermeng Tian, Jingjing Gong, Wangbao Li, Zhifei Li, Hongyan Wang, Guangjun Liu, Yarong TI Geographical Origin Traceability of Procambarus clarkii Based on Mineral Elements and Stable Isotopes SO FOODS DT Article DE Procambarus clarkii; geographical origin traceability; mineral elements; stable isotope ID MULTIELEMENT; AUTHENTICATION; CHEMOMETRICS; RICE AB We explore the prospect of applying mineral element and stable isotope data in origin tracing Procambarus clarkii to establish an origin tracing system. Microwave digestion-atomic absorption spectrometry and stable isotope ratio mass spectrometry determined the contents of 14 mineral elements (Na, Mg, Al, K, Ca, Mn, Zn, Cu, Fe, Sr, Ba, As, Se and Cd) and the abundances of C and N stable isotopes in the muscle tissue of P. clarkii from Guangdong, Hunan and Hubei regions. The one-way ANOVA and Duncan multiple comparison results revealed Na, Sr, Ba, Cu, Mn, Fe, Al, Se, delta C-13 and delta N-15 varied significantly between the three regions (p < 0.05). A systematic clustering analysis revealed the stable isotopes combined with the mineral elements easily distinguished samples into the three different regions. Multivariate statistical analysis allowed us to establish a discriminant model for distinguishing P. clarkii from the three geographical regions. When stable isotopes were combined with mineral elements, the accuracy of the linear discriminant analysis of the samples from Guangdong, Hunan and Hubei were 95%, 95% and 100%, respectively. The initial overall discriminant accuracy was 96.7%, and the cross-validation discriminant accuracy was 93.3%. Principal component analysis identified three main components which were based on eleven major factors, including Cu, Ba, Cd, Mn, delta C-13, delta N-15, Al and Mg, resulting in a cumulative variance contribution rate of 78.77%. We established a three-dimensional coordinate system using the three principal components to create scatter diagrams with the samples from the three regions in the coordinate system. The results revealed the samples clearly differentiated into the three regions. Therefore, mineral elements combined with stable isotopes can distinguish the regional origin of P. clarkii. C1 [Xia, Yun; Jia, Lijuan; Zhang, Kai; Xie, Jun; Yu, Ermeng; Tian, Jingjing; Gong, Wangbao; Li, Zhifei; Li, Hongyan; Wang, Guangjun; Liu, Yarong] Chinese Acad Fishery Sci, Pearl River Fisheries Res Inst, Key Lab Trop & Subtrop Fishery Resource Applicat, Minist Agr, Guangzhou 510380, Peoples R China. C3 Chinese Academy of Fishery Sciences; Pearl River Fisheries Research Institute, CAFS; Ministry of Agriculture & Rural Affairs RP Wang, GJ (corresponding author), Chinese Acad Fishery Sci, Pearl River Fisheries Res Inst, Key Lab Trop & Subtrop Fishery Resource Applicat, Minist Agr, Guangzhou 510380, Peoples R China. EM gjwang@prfri.ac.cn CR Anderson R.K., 1987, Journal of the World Aquaculture Society, V18, P148, DOI 10.1111/j.1749-7345.1987.tb00433.x Ariyama K, 2012, J AGR FOOD CHEM, V60, P1628, DOI 10.1021/jf204296p Bloomfield AL, 2011, J EXP MAR BIOL ECOL, V399, P48, DOI 10.1016/j.jembe.2011.01.015 Carrera M, 2017, J AGR FOOD CHEM, V65, P1070, DOI 10.1021/acs.jafc.6b04972 Cheajesadagul P, 2013, FOOD CHEM, V141, P3504, DOI 10.1016/j.foodchem.2013.06.060 Curtis JM, 2014, FISH RES, V153, P31, DOI 10.1016/j.fishres.2013.12.013 Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Gopi K, 2019, AQUACULTURE, V502, P56, DOI 10.1016/j.aquaculture.2018.12.012 Guo J., 2014, J ISOT, V27, P1, DOI [10.7538/tws.2014.27.01.0001, DOI 10.7538/TWS.2014.27.01.0001] Guo Lipan, 2015, Journal of Chinese Institute of Food Science and Technology, V15, P214 [郭小溪 Guo Xiaoxi], 2015, [食品科学, Food Science], V36, P294 KIM KW, 1993, APPLIED GEOCHEMISTRY, SUPPLEMENTARY ISSUE NO 2, JANUARY 1993, P249 Li L, 2016, FOOD CHEM, V194, P1238, DOI 10.1016/j.foodchem.2015.08.123 Luo RenJun, 2020, Shipin Kexue / Food Science, V41, P298, DOI 10.7506/spkx1002-6630-20190109-108 Luo RJ, 2019, FOOD CHEM, V274, P1, DOI 10.1016/j.foodchem.2018.08.104 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Lv J., 2015, QUAL SAF AGRO PROD, V3, P32, DOI [10.3969/j.issn.1674-8255.2015.03.011, DOI 10.3969/J.ISSN.1674-8255.2015.03.011] Ma HuiJun, 2017, Food and Fermentation Industries, V43, P277 Ma N., 2016, MOD AGR SCI TECHNOL, V9, P296, DOI [10.3969/j.issn.1007-5739.2016.09.176, DOI 10.3969/J.ISSN.1007-5739.2016.09.176] Molkentin J, 2007, EUR FOOD RES TECHNOL, V224, P535, DOI 10.1007/s00217-006-0314-0 National Aquatic Technology Promotion Station Chinese Fisheries Society China Aquatic Products Circulation and Processing Association, 2021, CHINA FISH, V7, P27 National Aquatic Technology Promotion Station Chinese Fisheries Society Fishery Administration of the Ministry of Agriculture and Rural Areas, 2021, CHINA FISHERIES STAT Ortea I, 2015, FOOD CHEM, V170, P145, DOI 10.1016/j.foodchem.2014.08.049 Peng Kai-xiu, 2021, Yingyong Shengtai Xuebao, V32, P2021, DOI 10.13287/j.1001-9332.202106.025 Sant'Ana LS, 2010, FOOD CHEM, V122, P74, DOI 10.1016/j.foodchem.2010.02.016 Shi Y., 2017, J ZHEJIANG AGR SCI, V58, P1541, DOI [10.16178/j.issn.0528-9017.20170913, DOI 10.16178/J.ISSN.0528-9017.20170913] Siano F, 2017, AQUACULT NUTR, V23, P796, DOI 10.1111/anu.12446 Sun ShuMin, 2012, Transactions of the Chinese Society of Agricultural Engineering, V28, P237 [王洁 Wang Jie], 2017, [核农学报, Journal of Nuclear Agricultural Sciences], V31, P547 Wu Hao, 2021, Shipin Kexue / Food Science, V42, P304, DOI 10.7506/spkx1002-6630-20200723-321 [项洋 Xiang Yang], 2015, [食品科学, Food Science], V36, P191 [张政权 Zhang Zhengquan], 2020, [食品科学, Food Science], V41, P125 Zhu Y., 2019, CHINA BUS, V3, P234, DOI [10.19699/j.cnki.issn2096-0298.2019.03.234, DOI 10.19699/J.CNKI.ISSN2096-0298.2019.03.234] NR 33 TC 0 Z9 0 U1 3 U2 3 PD OCT PY 2022 VL 11 IS 19 AR 3060 DI 10.3390/foods11193060 WC Food Science & Technology SC Food Science & Technology UT WOS:000866824600001 DA 2022-12-14 ER PT J AU Brown, AS Brown, RJC Corns, WT Stockwell, PB AF Brown, Andrew S. Brown, Richard J. C. Corns, Warren T. Stockwell, Peter B. TI Establishing SI traceability for measurements of mercury vapour SO ANALYST DT Article AB The majority of measurements of mercury vapour, for example those to determine mass concentration in air, are currently ultimately traceable to the vapour pressure of mercury, usually via a bell-jar calibration apparatus. This allows a saturated concentration of mercury vapour in air to develop in a confined space in equilibrium with ambient conditions, from which a known mass of mercury can be removed for calibration purposes. Several empirical equations are available to describe the vapour pressure of mercury at a given temperature, but the agreement between them is not good, with data from different equations sometimes differing by 5% or more. In order to remove the dependence of mercury vapour measurement on these empirical equations, and to provide stability, comparability and coherence for mercury vapour measurements, this paper describes work undertaken to link directly mercury vapour measurements to standards of mass, and therefore to establish traceability for these measurements to the SI system of units. This has been achieved by measuring the mass output rate of a dynamic mercury vapour generator gravimetrically, and linking this to the expected mass concentration in the bell-jar apparatus. The SI traceable mercury vapour measurements have been shown to agree with the predicted output from the bell-jar, as defined by the most commonly used empirical mercury vapour pressure equation, within the uncertainty of the measurement. C1 [Brown, Andrew S.; Brown, Richard J. C.] Natl Phys Lab, Analyt Sci Team, Teddington TW11 0LW, Middx, England. [Corns, Warren T.; Stockwell, Peter B.] PS Analyt, Orpington BR5 3HP, Kent, England. C3 National Physical Laboratory - UK RP Brown, AS (corresponding author), Natl Phys Lab, Analyt Sci Team, Teddington TW11 0LW, Middx, England. EM andrew.brown@npl.co.uk CR Brown RJC, 2008, ATMOS ENVIRON, V42, P2504, DOI 10.1016/j.atmosenv.2007.12.012 Brown RJC, 2007, CHEM SOC REV, V36, P904, DOI 10.1039/b507452p BROWN RJC, 2008, ENV MONIT ASSESS CORNS WT, UNPUB Cox M., 2004, 2403 CMSC NAT PHYS L DAVIS RS, 1992, METROLOGIA, V29, P67, DOI 10.1088/0026-1394/29/1/008 DUMAREY R, 1985, ANAL CHIM ACTA, V170, P337, DOI 10.1016/S0003-2670(00)81759-6 Huber ML, 2006, IND ENG CHEM RES, V45, P7351, DOI 10.1021/ie060560s *ISO, 2003, 697822003 ISO INT OR Lide D. R., 2007, CRC HDB CHEM PHYS, V129, P724, DOI 10.1021/ja069813z Long S, 2007, AM LAB, V39, P26 MITCHELL GD, 2006, ESTABLISHING MEASURE Salit ML, 1998, ANAL CHEM, V70, P3184, DOI 10.1021/ac980095b Smith IM, 2007, CMSCM06657 NAT PHYS Witt TJ, 2003, IEEE T INSTRUM MEAS, V52, P487, DOI 10.1109/TIM.2003.811653 NR 15 TC 32 Z9 33 U1 0 U2 4 PY 2008 VL 133 IS 7 BP 946 EP 953 DI 10.1039/b803724h WC Chemistry, Analytical SC Chemistry UT WOS:000257125100019 DA 2022-12-14 ER PT J AU Schaarschmidt, S Spradau, F Mank, H Hiller, P Appel, B Braunig, J Wichmann-Schauer, H Mader, A AF Schaarschmidt, Sara Spradau, Franziska Mank, Helmut Hiller, Petra Appel, Bernd Braeunig, Juliane Wichmann-Schauer, Heidi Mader, Anneluise TI Reporting of traceability and food safety data in the culinary herb and spice chains SO FOOD CONTROL DT Article DE Business-to-business reporting; Chemical hazards; Electronic reports; Microbiological hazards; Recording; Food tracing ID PRIVATE STANDARDS AB Reporting of information is crucial to enable backward and forward tracing of food along the chain, which is of main importance in case of non-compliance with legal obligations on food safety. Thus, food business operators in the European Union (EU) must enable tracing of any foodstuff one step forward and one step back. However, flux of information relevant for traceability and food safety can be a challenge in the supply chains of dried culinary herbs and spices. Results of a survey among herb/spice businesses - either located within the EU or exporting dried herbs/spices to the EU - showed the widespread use of electronic systems for recording and processing of traceability/food safety data. However, automated capture of transaction data and automated readout/processing of reported data were rare. The survey indicates that besides electronic documents, typed paper documents are often exchanged between businesses. For data delivery along the chain, paper documents filled in by hand are still used - even in the EU or upon import to the EU. The document type as well as the forms varied, particularly in case of incoming herbs/spices. The forms used for reporting by the survey participants or by their suppliers covered mostly individual/company-specific forms. Standardised forms provided by herb/spice associations were rarely shared between these businesses. The extent of reporting of traceability data upon import to the EU and within the EU appears to be sufficient. Some additional traceability data that promote product tracing, such as the country of harvest, were frequently reported. Same was true for information on food safety hazards. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Schaarschmidt, Sara; Hiller, Petra; Appel, Bernd; Braeunig, Juliane; Wichmann-Schauer, Heidi; Mader, Anneluise] German Fed Inst Risk Assessment, Dept Biol Safety, Max Dohrn Str 8-10, D-10589 Berlin, Germany. [Spradau, Franziska; Mank, Helmut] Fuchs Gewurze GmbH, Ind Str 25, D-49201 Dissen Atw, Germany. C3 Federal Institute for Risk Assessment RP Schaarschmidt, S (corresponding author), German Fed Inst Risk Assessment, Dept Biol Safety, Max Dohrn Str 8-10, D-10589 Berlin, Germany. EM s.schaarschmidt@posteo.de CR CAC, 2003, GEN PRINC FOOD HYG CAC, 2003, COD PRACT RAD PROC F CAC, 2014, 421995 CACRCP CAC, 2010, GEN STAND LAB PREP F CAC, 2006, PRINC TRAC PROD TRAC CAC (Codex Alimentarius Commission), 2003, GEN STAND IRR FOODS EFSA. European Food Safety Authority, 2016, EFSA J, V14, P4040 ESA, 2003, ESA PROD INF EUROPAM, EUROPAM BATCH DOC FAO, 2011, SPIC HERBS HOM MARK GS1, 2016, GLOB TRAD IT GTIN IFT, 2012, PIL PROJ IMPR PROD T ISO, 1994, QUAL MAN QUAL ASS VO Novak J, 2014, J APPL RES MED AROMA, V1, P70, DOI 10.1016/j.jarmap.2014.05.001 Schaarschmidt S, 2016, FOOD CONTROL, V70, P339, DOI 10.1016/j.foodcont.2016.06.004 Schaarschmidt S, 2016, FOOD CONTROL, V70, P360, DOI 10.1016/j.foodcont.2016.06.003 UN, 2002, INT EUR, P1 World Bank, 2011, ICT AGR SOURC NR 18 TC 3 Z9 3 U1 1 U2 46 PD JAN PY 2018 VL 83 SI SI BP 18 EP 27 DI 10.1016/j.foodcont.2016.11.029 WC Food Science & Technology SC Food Science & Technology UT WOS:000414882700003 DA 2022-12-14 ER PT J AU El Sheikha, AF Hu, DM AF El Sheikha, Aly Farag Hu, Dian-Ming TI How to trace the geographic origin of mushrooms? SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Article DE Mushroom; Multidimensional importance; Geo-traceability; Certification system; Analytical approaches; DNA barcoding ID NONVOLATILE TASTE COMPONENTS; BOLETE BOLETUS-EDULIS; EDIBLE MUSHROOMS; AGROCYBE-CYLINDRACEA; NUTRITIONAL-VALUE; ANTIOXIDANT PROPERTIES; TRICHOLOMA-MATSUTAKE; CHEMICAL-COMPOSITION; FRUITING BODIES; AGARICUS-BLAZEI AB Mushrooms became one of the most popular food resources worldwide that are very appreciated by consumers. Fresh produce properties including mushrooms are obviously varied based on their geographical origins. In view of the significant increase in the world's production of mushrooms, this will be accompanied by the increasing attention of all actors in the international food trade system (producers, traders, consumers and the authorities) to obtain a safe and high-quality commodity. This can be achieved through the implementation of an efficient and universal geo-tracing technique. Current approaches of geo-tracing mushrooms are few and have several limitations. Our intent is to suggest the DNA barcoding as a potent and universal method for mushrooms geo-traceability. C1 [El Sheikha, Aly Farag; Hu, Dian-Ming] Jiangxi Agr Univ, Jiangxi Key Lab Conservat & Utilizat Fungal Resou, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. [El Sheikha, Aly Farag] McMaster Univ, Dept Biol, 1280 Main St West, Hamilton, ON L8S 4K1, Canada. [El Sheikha, Aly Farag] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibitt El Kom 32511, Minufiya Govt, Egypt. C3 Jiangxi Agricultural University; McMaster University; Egyptian Knowledge Bank (EKB); Menofia University RP El Sheikha, AF (corresponding author), Jiangxi Agr Univ, Jiangxi Key Lab Conservat & Utilizat Fungal Resou, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. EM elsheikha_aly@yahoo.com; hudianming1@163.com CR Ayaz FA, 2011, TURK J BIOCHEM, V36, P213 Barcaccia G, 2016, DIVERSITY-BASEL, V8, DOI 10.3390/d8010002 Bellettini MB, 2019, SAUDI J BIOL SCI, V26, P633, DOI 10.1016/j.sjbs.2016.12.005 Borit M., 2016, FAO Fisheries and Aquaculture Circular Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Castro-Puyana M, 2017, TRAC-TREND ANAL CHEM, V93, P102, DOI 10.1016/j.trac.2017.05.004 Chang S.T., 2017, OXFORD RES ENCY ENV, P1 Chang S. T., 1992, MYCOLOGIST, V6, P64, DOI [10.1016/S0269-915X(09)80449-7, DOI 10.1016/S0269-915X(09)80449-7] Chang Shu-Ting, 2006, International Journal of Medicinal Mushrooms, V8, P297, DOI 10.1615/IntJMedMushr.v8.i4.10 Chang ST, 2013, MUSHROOM FARMING LIF Chang ST., 1989, EDIBLE MUSHROOMS THE Chang ST, 2005, P 5 INT C MUSHR BIOL Cheung PCK, 2010, NUTR BULL, V35, P292, DOI 10.1111/j.1467-3010.2010.01859.x Crisan EV, 1978, BIOL CULTIVATION EDI, P137 Dai E., 2010, CHINA DAILY De Mattia F, 2011, FOOD RES INT, V44, P693, DOI 10.1016/j.foodres.2010.12.032 Diana JS, 2013, BIOSCIENCE, V63, P255, DOI 10.1525/bio.2013.63.4.5 EC-European Commission, 2002, J EUROP COMM, VL031, P1 El Sheikha A., 2017, ASIA PACIFIC J FOOD, V3, P1 El Sheikha A. F, 2010, THESIS U MONTPELLIER, P2 El Sheikha A. F., 2018, EMERGING TRENDS DEV, VI-XX El Sheikha A. F., 2015, ADV FOOD TECHNOLOGY, V1, pS1, DOI [10.17140/AFTNSOJ-SE-1-101, DOI 10.17140/AFTNSOJ-SE-1-101] El Sheikha A.F., 2018, FOOD BIOTECHNOL El Sheikha A.F., 2015, NUTR FOOD TECHNOLOGY, V1, P1, DOI [10.16966/nftoa.103, DOI 10.16966/2470-6086.103] El Sheikha AF, 2017, BIOTECHNOLOGY AND PRODUCTION OF ANTI-CANCER COMPOUNDS, P1, DOI 10.1007/978-3-319-53880-8_1 El Sheikha AF, 2018, MOLECULAR TECHNIQUES IN FOOD BIOLOGY: SAFETY, BIOTECHNOLOGY, AUTHENTICITY AND TRACEABILITY, P423 El Sheikha AF, 2018, MOLECULAR TECHNIQUES IN FOOD BIOLOGY: SAFETY, BIOTECHNOLOGY, AUTHENTICITY AND TRACEABILITY, P3 El Sheikha AF, 2017, FOOD BIOTECHNOL, V31, P281, DOI 10.1080/08905436.2017.1369886 El Sheikha AF, 2017, REV FISH SCI AQUAC, V25, P158, DOI 10.1080/23308249.2016.1254158 El Sheikha AF, 2016, CRIT REV FOOD SCI, V56, P306, DOI 10.1080/10408398.2012.745478 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Faier L, 2011, ENVIRON PLANN A, V43, P1079, DOI 10.1068/a4382 Falandysz J, 2007, J ENVIRON SCI HEAL A, V42, P2089, DOI 10.1080/10934520701627058 Falandysz J, 2017, ECOTOX ENVIRON SAFE, V137, P265, DOI 10.1016/j.ecoenv.2016.12.014 Falandysz J, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0143608 Fernandes A, 2016, LWT-FOOD SCI TECHNOL, V69, P91, DOI 10.1016/j.lwt.2016.01.037 Fisher W., 2017, FOOD SAFETY MAG 0606 Frankowska A, 2010, FOOD ADDIT CONTAM B, V3, P1, DOI 10.1080/19440040903505232 Galimberti A, 2016, ADVANCES IN FOOD BIOTECHNOLOGY, P37 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Geng XR, 2016, SCI REP-UK, V6, DOI 10.1038/srep24130 Gevelt T. van, 2013, Forests, Trees and Livelihoods, V22, P156, DOI 10.1080/14728028.2013.809670 Guillamon E, 2010, FITOTERAPIA, V81, P715, DOI 10.1016/j.fitote.2010.06.005 He J, 2010, INT FOREST REV, V12, P27, DOI 10.1505/ifor.12.1.27 Hebert PDN, 2003, P ROY SOC B-BIOL SCI, V270, P313, DOI [10.1098/rspb.2002.2218, 10.1098/rsbl.2003.0025] Hollingsworth PM, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0019254 Hoshi H, 2005, J AGR FOOD CHEM, V53, P8948, DOI 10.1021/jf0510743 Hrudayanath Thatoi, 2014, African Journal of Biotechnology, V13, P523 International Trade Center (ITC), 2015, INT TRAD CTR ITC B, V91 Jo Feeney Mary, 2014, Nutr Today, V49, P301 Johnsy G., 2011, BOT RES INT, V4, P69, DOI DOI 10.1002/JSFA.5960 Kalac P, 2009, FOOD CHEM, V113, P9, DOI 10.1016/j.foodchem.2008.07.077 Kamal S., 2009, TXB MOL BIOTECHNOLOG, V3rd ed., P573 Koutrotsios G, 2014, FOOD CHEM, V161, P127, DOI 10.1016/j.foodchem.2014.03.121 Kundakovic T, 2013, CURR TOP MED CHEM, V13, P2734, DOI 10.2174/15680266113136660196 Lu J, 2012, FRONT PHARMACOL, V3, DOI 10.3389/fphar.2012.00057 Maloy L., 2014, IS DNA BARCODING FUT Manikandan K., 2011, MUSHROOMS CULTIVATIO, P11, DOI [10.1615/intjmedmushrooms.v18.i10.40, DOI 10.1615/INTJMEDMUSHROOMS.V18.I10.40] Manimozhi M, 2013, INT J ADV BIOTECHNOL, V4, P78 Manjunathan J., 2011, International Journal of Biodiversity and Conservation, V3, P386 Mau JL, 2001, FOOD CHEM, V73, P461, DOI 10.1016/S0308-8146(00)00330-7 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x Meng X, 2016, CARBOHYD RES, V424, P30, DOI 10.1016/j.carres.2016.02.008 Miller K, 2013, DISCOVER Murata H, 2008, APPL ENVIRON MICROB, V74, P2023, DOI 10.1128/AEM.02411-07 OECD, 2007, EC IMP COUNT PIR EX Pala SA, 2014, NUSANT BIOSCI, V6, P173, DOI 10.13057/nusbiosci/n060211 Palacios I, 2011, FOOD CHEM, V128, P674, DOI 10.1016/j.foodchem.2011.03.085 Panjikkaran ST, 2013, J SCI FOOD AGR, V93, P973, DOI 10.1002/jsfa.5827 Pushpa H, 2010, WORLD J DAIRY FOOD S, V5, P140 Qi LM, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18010241 Raja HA, 2017, FOOD CHEM, V214, P383, DOI 10.1016/j.foodchem.2016.07.052 Ribeiro B, 2008, FOOD CHEM, V110, P47, DOI 10.1016/j.foodchem.2008.01.054 Roncero-Ramos I, 2017, CURR OPIN FOOD SCI, V14, P122, DOI 10.1016/j.cofs.2017.04.002 Roupas P, 2012, J FUNCT FOODS, V4, P687, DOI 10.1016/j.jff.2012.05.003 Sabaratnam Vikineswary, 2013, J Tradit Complement Med, V3, P62, DOI 10.4103/2225-4110.106549 Sarikurkcu C, 2008, BIORESOURCE TECHNOL, V99, P6651, DOI 10.1016/j.biortech.2007.11.062 Shamtsyan M., 2008, RES PROGR BIOTECHNOL, P137 Shang HM, 2016, J SCI FOOD AGR, V96, P215, DOI 10.1002/jsfa.7084 Singh M., 2011, MUSHROOM CULTIVATION, P1 Sitta N, 2008, ECON BOT, V62, P307, DOI 10.1007/s12231-008-9037-4 Stamets P., 2005, MYCELIUM RUNNING MUS Sun LP, 2017, MOLECULES, V22, DOI 10.3390/molecules22030350 Tesanovic K, 2017, J FOOD SCI TECH MYS, V54, P430, DOI 10.1007/s13197-016-2479-2 Tsai SY, 2008, FOOD CHEM, V107, P977, DOI 10.1016/j.foodchem.2007.07.080 Tsai SY, 2007, LWT-FOOD SCI TECHNOL, V40, P1392, DOI 10.1016/j.lwt.2006.10.001 Tseng YH, 2005, FOOD CHEM, V90, P409, DOI 10.1016/j.foodchem.2004.03.054 Ulziijargal E, 2011, INT J MED MUSHROOMS, V13, P343, DOI 10.1615/IntJMedMushr.v13.i4.40 WANG J, 2015, MUSHROOM TIMES, V5, P5, DOI DOI 10.1016/j.jtrangeo.2015.05.005 Wang XM, 2014, FOOD CHEM, V151, P279, DOI 10.1016/j.foodchem.2013.11.062 Wasser Solomon P, 2014, Biomed J, V37, P345, DOI 10.4103/2319-4170.138318 Yao S, 2018, J SCI FOOD AGR, V98, P2215, DOI 10.1002/jsfa.8707 Zhang AQ, 2011, INT J BIOL MACROMOL, V49, P1092, DOI 10.1016/j.ijbiomac.2011.09.005 NR 93 TC 39 Z9 39 U1 4 U2 37 PD AUG PY 2018 VL 78 BP 292 EP 303 DI 10.1016/j.tifs.2018.06.008 WC Food Science & Technology SC Food Science & Technology UT WOS:000440960900025 DA 2022-12-14 ER PT J AU Diaz, O Contell, JP AF Diaz, Oscar Contell, Jeremias P. TI Developing research questions in conversation with the literature: operationalization & tool support SO EMPIRICAL SOFTWARE ENGINEERING DT Article DE Research question; Literature review; Inductive Top-Down Theorizing; Reference management systems ID SOFTWARE; TECHNOLOGY; ACCEPTANCE; DESIGN; USER AB Empirical Software Engineering rests on the understanding of practical problems and their solution counterparts. Frequently, solutions are not absolute but relative to the context where the problem is observed. This tends to imply that the solution and the problem unveil gradually together, and hence, researchers are not always in the position to state the research question (RQ) at the onset. Like software engineers when facing blurred requirements, researchers might not be familiar enough with the problem in the early phases of a research to properly scope their RQs (hereafter referred to as RQ Scoping). Here, the literature may play the role of the stakeholders in Agile methods: keeping the focus on the aspects that are essential (vs. accidental) of the RQ. Informed by Inductive Top-Down Theorizing, this article acknowledges RQ Scoping as iterative and incremental, entailing a conversation between the experimental work and literature reviewing. Yet, for literature reviewing to become "Agile" it is not only required to be driven by the RQ but also to have tool support. Tools might bring transparency and traceability, both factors especially welcome in a scenario characterized by testing (is my RQ relevant?) and adjustment (how can I make my RQ relevant?). Specifically, the advent of the RQ in close relationship with the literature advises for "Agile" literature reviewing to be conducted at the place where the literature is naturally kept: the Reference Management System (e.g., Mendeley). This article introduces the theoretical underpinnings, design principles, proof of concept and evaluation for FRAMEndeley, a Mendeley-integrated utility for RQ Scoping. C1 [Diaz, Oscar; Contell, Jeremias P.] Univ Basque Country, ONEKIN Res Grp, UPV EHU, San Sebastian, Spain. C3 University of Basque Country RP Diaz, O (corresponding author), Univ Basque Country, ONEKIN Res Grp, UPV EHU, San Sebastian, Spain. EM oscar.diaz@ehu.eus; jeremias.perez@ehu.eus CR Al-Zubidy A, 2019, EMPIR SOFTW ENG, V24, P139, DOI 10.1007/s10664-018-9626-5 Alvesson M., 2013, CONSTRUCTING RES QUE, DOI [10.4135/9781446270035, DOI 10.4135/9781446270035] Anderson R.C., 1984, SCH ACQUISITION KNOW Anderson R.E., 2010, MULTIVARIATE DATA AN Bandara W, 2006, P 3 INT C QUALITATIV, P6 BASILI VR, 1986, IEEE T SOFTWARE ENG, V12, P733, DOI 10.1109/TSE.1986.6312975 Baxter G, 2011, INTERACT COMPUT, V23, P4, DOI 10.1016/j.intcom.2010.07.003 Beelmann A, 2006, EUR PSYCHOL, V11, P244, DOI 10.1027/1016-9040.11.3.244 Benmerikhi M, 2015, XXIVE C INT MANAGEME Bitzer P., 2014, P 22 EUR C INF SYST Boell SK, 2014, COMMUN ASSOC INF SYS, V34, P257 Bonasio A, 2013, MENDELEY HAS 2 5 MIL Bosetti G, 2022, COMPUT STAND INTER, V82, DOI 10.1016/j.csi.2022.103633 Bowes D, 2012, P INT WORKSHOP EVIDE, P33, DOI [10.1145/2372233.2372243, DOI 10.1145/2372233.2372243] Boyatzis RE., 1998, TRANSFORMING QUALITA Braun V., 2006, QUALITATIVE RES PSYC, V3, P77, DOI [DOI 10.1191/1478088706QP063OA, 10.1191/1478088706qp063oa] Brereton P., 2015, EVIDENCE BASED SOFTW, V4 Charmaz K, 2014, CONSTRUCTING GROUND, V2nd Chauhan S, 2016, INT J MANAG EDUC-OXF, V14, P248, DOI 10.1016/j.ijme.2016.05.005 Cooper D. R, 2014, BUSINESS RES METHODS DeSantis L, 2000, WESTERN J NURS RES, V22, P351, DOI 10.1177/019394590002200308 Diaz O, 2017, LECT NOTES COMPUT SC, V10243, P231, DOI 10.1007/978-3-319-59144-5_14 Diaz O, 2015, ACM T WEB, V9, DOI 10.1145/2735633 DiMaggio P, 1997, ANNU REV SOCIOL, V23, P263, DOI 10.1146/annurev.soc.23.1.263 Dyba T, 2012, INT SYMP EMP SOFTWAR, P19, DOI 10.1145/2372251.2372256 EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557 Ellemers N, 2021, BRIT J SOC PSYCHOL, V60, P1, DOI 10.1111/bjso.12430 Evers JC, 2018, QUAL REP, V23, P61 Fabbri S, 2016, PROCEEDINGS OF THE 20TH INTERNATIONAL CONFERENCE ON EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING 2016 (EASE '16), DOI 10.1145/2915970.2916013 Felizardo KR, 2017, 2017 43RD EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA), P202, DOI 10.1109/SEAA.2017.17 Felizardo KR, 2020, CONT EMPIRICAL METHO, P327, DOI [10.1007/978-3-030-32489-6_12, DOI 10.1007/978-3-030-32489-6_12] Fernandez-Saez AM, 2010, ICSOFT 2010: PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES, VOL 2, P157 Fink, 2020, CONDUCTING RES LIT R Fitzgibbons M, 2010, J ACAD LIBR, V36, P144, DOI 10.1016/j.acalib.2010.01.005 FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Fowler M., 2001, Software Development, V9, P28 Frankland J, 2002, FOCUS GROUPS SOCIAL, DOI DOI 10.4135/9781849209175 Glaser B.G., 1999, DISCOV GROUNDED THEO, V1st, DOI [10.4324/9780203793206, DOI 10.4324/9780203793206] Granieri N, 2019, FANTASTIC REFERENCE Gregor S, 2007, J ASSOC INF SYST, V8, P312, DOI 10.17705/1jais.00129 Gupta S, 2013, INFORM SYST RES, V24, P454, DOI 10.1287/isre.1120.0433 Hassan N R, 2017, P 23 AMERICAS C INFO Thuan NH, 2019, COMMUN ASSOC INF SYS, V44, P332, DOI 10.17705/1CAIS.04420 Hoda R, 2022, IEEE T SOFTWARE ENG, V48, P3808, DOI 10.1109/TSE.2021.3106280 Iivari J., 2007, SCANDINAVIAN J INFOR, V19, P39 Kitchenham Barbara., 2007, GUIDELINES PERFORMIN Kitchenham BA, 2011, INFORM SOFTWARE TECH, V53, P638, DOI 10.1016/j.infsof.2010.12.011 Klopper R, 2007, ALTERNATION, V14, P262 Kontio J., 2008, FOCUS GROUP METHOD E, P93, DOI [10.1007/978-1-84800-044-5{$\_$}4, DOI 10.1007/978-1-84800-044-5{$] Kuhrmann M, 2017, EMPIR SOFTW ENG, V22, P2852, DOI 10.1007/s10664-016-9492-y Kwan BSC, 2008, ENGL SPECIF PURP, V27, P42, DOI 10.1016/j.esp.2007.05.002 Madera MM., 2012, APERTURA, V4, P96 Marijan D, 2021, INFORM SOFTWARE TECH, V132, DOI 10.1016/j.infsof.2020.106473 Marzano R., 2001, CLASSROOM INSTRUCTIO MAXWELL JA, 1992, HARVARD EDUC REV, V62, P279, DOI 10.17763/haer.62.3.8323320856251826 Molleri J S, 2015, P 19 INT C EV ASS SO, P1, DOI [10.1145/2745802.2745825, DOI 10.1145/2745802.2745825] Mussweiler T, 2012, COGNITION, V122, P236, DOI 10.1016/j.cognition.2011.10.005 Nielsen PA, 2016, COMMUN ASSOC INF SYS, V38, P720, DOI 10.17705/1CAIS.03835 Ortlipp M, 2008, QUAL REP, V13, P695 Parker A, 2006, INT J RES METHOD EDU, V29, P23, DOI 10.1080/01406720500537304 Rai A, 2017, MIS QUART, V41, pIII Recker J., 2013, SCI RES INFORM SYSTE Ridder HG, 2014, BRIT J MANAGE, V25, P373, DOI 10.1111/1467-8551.12000 Ruben A, 2016, READ SCI PAPER, DOI [10.1126/science.caredit.a1600012, DOI 10.1126/SCIENCE.CAREDIT.A1600012] Russo D, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447580 Service RW, 2009, ORGAN RES METHODS, V12, P614, DOI 10.1177/1094428108324514 Shepherd DA, 2011, ACAD MANAGE REV, V36, P361, DOI 10.5465/AMR.2011.59330952 Stol KJ, 2016, PROC INT CONF SOFTW, P120, DOI 10.1145/2884781.2884833 Studycom, 2013, WRIT RES QUEST PURP Timonen V, 2018, INT J QUAL METH, V17, DOI 10.1177/1609406918758086 Venable J, 2016, EUR J INFORM SYST, V25, P77, DOI 10.1057/ejis.2014.36 Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540 Wagner G., 2020, P EUR C INF SYST ECI, P44 Walton Douglas., 2014, ABDUCTIVE REASONING Webster J, 2002, MIS QUART, V26, pXIII Wieringa RJ., 2014, DESIGN SCI METHODOLO, DOI [10.1007/978-3-662-43839-8, DOI 10.1007/978-3-662-43839-8] Wohlin C., 2012, EXPT SOFTWARE ENG, DOI DOI 10.1007/978-3-642-29044-2 Wohlin C, 2014, P 18 INT C EV ASS SO, P1, DOI [10.1145/2601248.2601268, DOI 10.1145/2601248.2601268] Wolfswinkel JF, 2013, EUR J INFORM SYST, V22, P45, DOI 10.1057/ejis.2011.51 Zaugg H, 2011, TECHTRENDS, V55, P32 NR 80 TC 0 Z9 0 U1 2 U2 2 PD DEC PY 2022 VL 27 IS 7 AR 174 DI 10.1007/s10664-022-10204-8 WC Computer Science, Software Engineering SC Computer Science UT WOS:000859469500017 DA 2022-12-14 ER PT J AU Schwagele, F AF Schwagele, F TI Determination of origin and animal species in meat and meat products SO FLEISCHWIRTSCHAFT DT Article DE traceability; meat; meat products ID MUSCLE MEAT; SYSTEM; BSE AB On a pan-European level there is a need for traceability systems giving information on origin, processing, retailing and final destination of food stuffs. Such systems shall enhance consumer confidence in food, enable the regulatory authorities to identify and to withdraw unsafe and not consumable foodstuffs from the market. A pan-European food traceability protocol would greatly assist authorities in detecting fraud. The food chain comprises a range of sequential and parallel stages bridging the full spectrum from agricultural production to the consumable food stuff in the hands of the consumer. This contribution summarises the existing possibilities of traceability in the area of meat and meat products. C1 Standort Kulmbach, Inst Chem & Phys Bundesforsch Anstalt Ernahrung &, D-95326 Kulmbach, Germany. RP Schwagele, F (corresponding author), Standort Kulmbach, Inst Chem & Phys Bundesforsch Anstalt Ernahrung &, EC Baumann Str 20, D-95326 Kulmbach, Germany. CR Al-Jowder O, 2002, J AGR FOOD CHEM, V50, P1325, DOI 10.1021/jf0108967 Altmann K, 2004, FLEISCHWIRTSCHAFT, V84, P115 Anderson RM, 1996, NATURE, V382, P779, DOI 10.1038/382779a0 Binke R, 2003, ARCH LEBENSMITTELHYG, V54, P52 BINKE R, MITTEILUNGSBLATT BAF, V43, P155 BINKE S, 2003, INNOVATIONS FOOD TEC, V21, P130 COWIE WP, 1968, J SCI FOOD AGR, V19, P226, DOI 10.1002/jsfa.2740190411 Cozzolino D, 2002, ANIM SCI, V74, P477, DOI 10.1017/S1357729800052632 DEBECKER G, 2001, PESTICIDE OUTLOOK, V12, P118 European Commission, 2000, OP SCI COMM AN NUTR Ferguson NM, 1997, P ROY SOC B-BIOL SCI, V264, P1445, DOI 10.1098/rspb.1997.0201 Gonzalez-Martin I, 2002, ANAL CHIM ACTA, V468, P293, DOI 10.1016/S0003-2670(02)00657-8 Hilger C, 2004, ALLERGY, V59, P653, DOI 10.1111/j.1398-9995.2004.00436.x HOFMANN K, 1986, FLEISCHWIRTSCHAFT, V66, P916 HOFMANN K, 1986, FLEISCHWIRTSCHAFT, V66, P91 Honikel K. O., 2002, Mitteilungsblatt der Bundesanstalt fuer Fleischforschung, Kulmbach, V41, P125 ISAKSSON T, 1991, P 3 INT C NEAR INFR Kim SM, 1999, J AGR ENG RES, V74, P293, DOI 10.1006/jaer.1999.0465 MULLIS KB, 1987, METHOD ENZYMOL, V155, P335 OLEARY MH, 1981, PHYTOCHEMISTRY, V20, P553, DOI 10.1016/0031-9422(81)85134-5 Schwagele F, 2003, FLEISCHWIRTSCHAFT, V83, P78 Schwagele F, 2001, FLEISCHWIRTSCHAFT, V81, P78 ZIEGLER H, 1976, PLANTA, V128, P85, DOI 10.1007/BF00397183 1990, OFFICIAL J EUROEAP L, V33 NR 24 TC 0 Z9 0 U1 0 U2 6 PY 2005 VL 85 IS 6 BP 108 EP 112 WC Food Science & Technology SC Food Science & Technology UT WOS:000229883800017 DA 2022-12-14 ER PT J AU El Khamlichi, M Alvarez-Melcon, A El Mrabet, O Ennasar, MA Hinojosa, J AF El Khamlichi, Mohamed Alvarez-Melcon, Alejandro El Mrabet, Otman Ennasar, Mohammed Ali Hinojosa, Juan TI A Flexible and Low-Cost UHF RFID Tag Antenna for Blood Bag Traceability SO ELECTRONICS DT Article DE antenna; blood bag monitoring; radiofrequency identification (RFID) ID DESIGN; FIELD; TEMPERATURE; SENSOR; EXPERIMENTATION; TECHNOLOGY AB A new low-profile flexible RFID tag antenna operating in the ultra-high frequency (UHF) European band (865 MHz-868 MHz) is proposed for blood bag traceability. Its structure combines inductive and capacitive parts with nested slots allowing for the achieving of conjugate impedance matching with the IC-chip. The whole electrical parameters of the environment (substrate, bag, and blood) were considered for the design of the tag antenna. A good agreement was obtained between the measurements and electromagnetic simulations for the input impedance of the tag antenna in the UHF band. A reading range close to 2.5 m was experimentally obtained. Therefore, this tag antenna could be effective and useful in future RFID systems for blood bag monitoring, thus improving patient safety in healthcare infrastructures. C1 [El Khamlichi, Mohamed; El Mrabet, Otman; Ennasar, Mohammed Ali] Abdelmalek Essaadi Univ, Syst Informat & Telecommun Lab LaSIT, Fac Sci, Tetouan 93000, Morocco. [Alvarez-Melcon, Alejandro] Univ Politecn Cartagena, Dept Informat & Commun Technol, Cartagena 30202, Spain. [Hinojosa, Juan] Univ Politecn Cartagena, Dept Elect & Comp Engn, Cartagena 30202, Spain. C3 Abdelmalek Essaadi University of Tetouan; Universidad Politecnica de Cartagena; Universidad Politecnica de Cartagena RP Alvarez-Melcon, A (corresponding author), Univ Politecn Cartagena, Dept Informat & Commun Technol, Cartagena 30202, Spain. EM mohamed.elkhamlichi@edu.upct.es; alejandro.alvarez@upct.es; oelmrabet@uae.ac.ma; ennasar.ali55@gmail.com; juan.hinojosa@upct.es CR Amendola S, 2016, IEEE SENS J, V16, P7250, DOI 10.1109/JSEN.2016.2594582 Andrade L, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10040491 Benouakta S, 2021, TEXTILES, V1, P547, DOI [10.3390/textiles1030029, DOI 10.3390/TEXTILES1030029] Byondi FK, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21165380 Caccami MC, 2018, IEEE SENS J, V18, P8893, DOI 10.1109/JSEN.2018.2867208 Camera F, 2021, IEEE SENS J, V21, P421, DOI 10.1109/JSEN.2020.3014404 Camera F, 2020, IEEE SENSOR LETT, V4, DOI 10.1109/LSENS.2020.3036486 Choi J., 2011, P INT S ANT PROP ISA, P712 Chung Y, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21072521 Colella R, 2016, IEEE T INSTRUM MEAS, V65, P905, DOI 10.1109/TIM.2016.2516322 El Khamlichi M, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19224903 Ennasar M.Ali, 2019, PROGR ELECTROMAGNE C, V94, P273, DOI [10.2528/PIERC19052402, DOI 10.2528/PIERC19052402] Erman F, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8060713 Fanti A, 2015, IEEE ANTENNAS PROP, P356, DOI 10.1109/APS.2015.7304564 Fanti A, 2016, ELECTRONICS-SWITZ, V5, DOI 10.3390/electronics5040077 Gao MJ, 2021, BIOSENSORS-BASEL, V11, DOI 10.3390/bios11120480 Hohberger C, 2012, BIOLOGICALS, V40, P209, DOI 10.1016/j.biologicals.2011.10.008 Hussain M, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20195713 Ibrahim G.T., 2015, P AM SOC ENG ED ANN, P1 Islam MT, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18124212 Jeong MG, 2019, IEEE T ANTENN PROPAG, V67, P1837, DOI 10.1109/TAP.2018.2884875 Khamlichi M.E., 2019, P 19 MED MICR S MMS Kim S, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9101636 Kiruthika S., 2021, ANN ROM SOC CELL BIO, V25, P182 Lin YF, 2017, ELECTRON LETT, V53, P1627, DOI 10.1049/el.2017.1859 Lippi G, 2017, J MED BIOCHEM, V36, P107, DOI 10.1515/jomb-2017-0003 Mezzanotte Paolo, 2021, IEEE Journal of Microwaves, V1, P55, DOI 10.1109/JMW.2020.3035020 Moraru A, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20123451 Occhiuzzi C, 2021, IEEE SENS J, V21, P5359, DOI 10.1109/JSEN.2020.3031664 Otin R, 2011, PROG ELECTROM RES LE, V22, P129, DOI 10.2528/PIERL11021002 Panescu D, 1997, P ANN INT IEEE EMBS, V19, P154, DOI 10.1109/IEMBS.1997.754490 Pradhan Nihar Ranjan, 2021, Advances in VLSI, Communication, and Signal Processing. Select Proceedings of VCAS 2019. Lecture Notes in Electrical Engineering (LNEE 683), P313, DOI 10.1007/978-981-15-6840-4_25 Qing XM, 2009, IEEE T MICROW THEORY, V57, P1268, DOI 10.1109/TMTT.2009.2017288 Rogers RT, DUROID LAMINATES RTD Sharif A, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10131603 Sharif A, 2019, IEEE J ELECTROMAG RF, V3, P261, DOI 10.1109/JERM.2019.2924823 Wagih M, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20123435 Xiao ZB, 2015, IEEE J BIOMED HEALTH, V19, P910, DOI 10.1109/JBHI.2015.2415836 Zaid J, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19091982 Zaric A, 2015, IEEE ANTENN PROPAG M, V57, P54, DOI 10.1109/MAP.2015.2420491 Zhang BH, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21041513 Zhu XW, 2012, J ENG TECHNOL MANAGE, V29, P152, DOI 10.1016/j.jengtecman.2011.09.011 NR 42 TC 1 Z9 1 U1 4 U2 10 PD FEB PY 2022 VL 11 IS 3 AR 439 DI 10.3390/electronics11030439 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Physics, Applied SC Computer Science; Engineering; Physics UT WOS:000759841600001 DA 2022-12-14 ER PT J AU Fountas, S Carli, G Sorensen, CG Tsiropoulos, Z Cavalaris, C Vatsanidou, A Liakos, B Canavari, M Wiebensohn, J Tisserye, B AF Fountas, S. Carli, G. Sorensen, C. G. Tsiropoulos, Z. Cavalaris, C. Vatsanidou, A. Liakos, B. Canavari, M. Wiebensohn, J. Tisserye, B. TI Farm management information systems: Current situation and future perspectives SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Farm software; Precision agriculture; Farm machinery; Decision support system; Adoption; Profitability ID DECISION-SUPPORT-SYSTEM; MODEL; AGRICULTURE; SIMULATION; TECHNOLOGIES AB Farm Management Information Systems (FMIS) in agriculture have evolved from simple farm record-keeping into sophisticated and complex systems to support production management. The purpose of current FMIS is to meet the increased demands to reduce production costs, comply with agricultural standards, and maintain high product quality and safety. This paper presents current advancements in the functionality of academic and commercial FMIS. The study focuses on open-field crop production and centeres on farm managers as the primary users and decision makers. Core system architectures and application domains, adoption and profitability, and FMIS solutions for precision agriculture as the most information-intensive application area were analyzed. Our review of commercial solutions involved the analysis of 141 international software packages, categorized into 11 functions. Cluster analysis was used to group current commercial FMIS as well as examine possible avenues for further development. Academic FMIS involved more sophisticated systems covering compliance to standards applications, automated data capture as well as interoperability between different software packages. Conversely, commercial FMIS applications targeted everyday farm office tasks related to budgeting and finance, such as recordkeeping, machinery management, and documentation, with emerging trends showing new functions related to traceability, quality assurance and sales. (C) 2015 Elsevier B.V. All rights reserved. C1 [Fountas, S.] Agr Univ Athens, Dept Nat Resource Management & Agr Engn, GR-11855 Athens, Greece. [Carli, G.] Univ Bologna, Dept Management, I-40131 Bologna, Italy. [Sorensen, C. G.] Aarhus Univ, Dept Engn, DK-8000 Aarhus, Denmark. [Tsiropoulos, Z.; Cavalaris, C.; Vatsanidou, A.; Liakos, B.] Univ Thessaly, Dept Crop Prod & Rural Environm, Volos 38446, Greece. [Canavari, M.] Univ Bologna, Dept Agr Sci, I-40127 Bologna, Italy. [Wiebensohn, J.] Univ Rostock, Fac Agr & Environm Sci, Geodesy & Geoinformat, D-18059 Rostock, Germany. [Tisserye, B.] SupAgro, Irstea, UMR ITAP, F-34060 Montpellier, France. C3 Agricultural University of Athens; University of Bologna; Aarhus University; University of Thessaly; University of Bologna; University of Rostock; INRAE; Institut Agro; Montpellier SupAgro RP Fountas, S (corresponding author), Agr Univ Athens, Dept Nat Resource Management & Agr Engn, Iera Odos 75, GR-11855 Athens, Greece. EM sfountas@aua.gr CR Abt V., 2006, 4 WORLD C COMP AGR N Attonaty JM, 1999, COMPUT ELECTRON AGR, V22, P157, DOI 10.1016/S0168-1699(99)00015-0 Bange MP, 2004, COMPUT ELECTRON AGR, V43, P131, DOI 10.1016/j.compag.2003.12.003 Berry JK, 2003, J SOIL WATER CONSERV, V58, P332 Blackie M. J., 1976, Agricultural Systems, V1, P23, DOI 10.1016/0308-521X(76)90019-6 Boehlje M.D., 1984, FARM MANAGEMENT Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 Cardin-Pedrosa M, 2012, COMPUT ELECTRON AGR, V82, P87, DOI 10.1016/j.compag.2011.12.004 Chaudhary S., 2004, P 2004 IEEE INT C SE Cohen Y, 2008, COMPUT ELECTRON AGR, V62, P107, DOI 10.1016/j.compag.2007.12.005 DOLUSCHITZ R, 1988, Computers and Electronics in Agriculture, V2, P173, DOI 10.1016/0168-1699(88)90022-1 Engel BA, 2003, COMPUT ELECTRON AGR, V39, P241, DOI 10.1016/S0168-1699(03)00078-4 Fountas S., 2005, Precision Agriculture, V6, P121, DOI 10.1007/s11119-004-1030-z Fountas S, 2006, AGR SYST, V87, P192, DOI 10.1016/j.agsy.2004.12.003 GLADWIN H, 1989, ETHNOGRAPHIC DECISIO HAIR JR, 2010, CENGAGE LEARNING Hameed IA, 2012, COMPUT ELECTRON AGR, V81, P24, DOI 10.1016/j.compag.2011.11.003 Harwood TD, 2010, COMPUT ELECTRON AGR, V71, P57, DOI 10.1016/j.compag.2009.12.003 Hearn AB, 2002, AGR SYST, V74, P27, DOI 10.1016/S0308-521X(02)00019-7 Jensen AL, 2000, COMPUT ELECTRON AGR, V25, P271, DOI 10.1016/S0168-1699(99)00074-5 Kaloxylos A, 2014, COMPUT ELECTRON AGR, V100, P168, DOI 10.1016/j.compag.2013.11.014 Kaloxylos A, 2012, COMPUT ELECTRON AGR, V89, P130, DOI 10.1016/j.compag.2012.09.002 Karetsos Sotiris, 2007, Information Services & Use, V27, P123 Kitchen NR, 2008, COMPUT ELECTRON AGR, V61, P1, DOI 10.1016/j.compag.2007.06.007 Kok R., 1986, Computers and Electronics in Agriculture, V1, P125, DOI 10.1016/0168-1699(86)90001-3 Kruize JW, 2013, COMPUT ELECTRON AGR, V96, P75, DOI 10.1016/j.compag.2013.04.017 Kuhlmann F, 2001, COMPUT ELECTRON AGR, V30, P71, DOI 10.1016/S0168-1699(00)00157-5 Lawson LG, 2011, COMPUT ELECTRON AGR, V77, P7, DOI 10.1016/j.compag.2011.03.002 Lewis T, 1998, COMPUT ELECTRON AGR, V19, P233, DOI 10.1016/S0168-1699(97)00040-9 Lilburne L, 1998, COMPUT ELECTRON AGR, V21, P195, DOI 10.1016/S0168-1699(98)00035-0 Mackrell D, 2009, DECIS SUPPORT SYST, V47, P143, DOI 10.1016/j.dss.2009.02.004 Magne MA, 2010, ANIMAL, V4, P842, DOI 10.1017/S1751731110000637 Moghaddam KS, 2011, COMPUT ELECTRON AGR, V77, P229, DOI 10.1016/j.compag.2011.05.006 Murakami E, 2007, COMPUT ELECTRON AGR, V58, P37, DOI 10.1016/j.compag.2006.12.010 Nash E, 2009, PRECIS AGRIC, V10, P546, DOI 10.1007/s11119-009-9134-0 Nash E, 2009, COMPUT ELECTRON AGR, V66, P25, DOI 10.1016/j.compag.2008.11.005 Nikkila R, 2010, COMPUT ELECTRON AGR, V70, P328, DOI 10.1016/j.compag.2009.08.013 Norusis M.J., 2011, IBM SPSS STAT 19 STA OHLMER B, 1991, AGR ECON, V5, P279, DOI 10.1016/0169-5150(91)90049-Q Papadopoulos A, 2011, COMPUT ELECTRON AGR, V78, P130, DOI 10.1016/j.compag.2011.06.007 Parker C., 1999, Farm Management, V10, P273 Parsons DJ, 2009, COMPUT ELECTRON AGR, V65, P155, DOI 10.1016/j.compag.2008.08.007 Peets S, 2012, COMPUT ELECTRON AGR, V81, P104, DOI 10.1016/j.compag.2011.11.011 Pesonen L., 2008, INFOXT USER CENTRIC, VVolume 05103 PLANT RE, 1989, AGR SYST, V31, P127, DOI 10.1016/0308-521X(89)90017-6 Plenet D, 2009, AGR SYST, V100, P1, DOI 10.1016/j.agsy.2008.11.002 Robbemond R., 2011, DATA STANDARDS USED Sahuy RK, 2008, COMPUT ELECTRON AGR, V60, P76, DOI 10.1016/j.compag.2007.07.001 Sante-Riveira I, 2008, COMPUT ELECTRON AGR, V63, P257, DOI 10.1016/j.compag.2008.03.007 Schweik CM, 2005, COMPUT ELECTRON AGR, V47, P221, DOI 10.1016/j.compag.2004.12.006 Shaffer MJ, 1998, COMPUT ELECTRON AGR, V21, P135, DOI 10.1016/S0168-1699(98)00031-3 Sonka S. T., 1985, Computers and Electronics in Agriculture, V1, P75, DOI 10.1016/0168-1699(85)90007-9 Sorensen CG, 2011, COMPUT ELECTRON AGR, V76, P266, DOI 10.1016/j.compag.2011.02.005 Sorensen CG, 2010, COMPUT ELECTRON AGR, V73, P44, DOI 10.1016/j.compag.2010.04.003 Sorensen CG, 2010, COMPUT ELECTRON AGR, V72, P37, DOI 10.1016/j.compag.2010.02.003 Sorensen C.G., 1999, THESIS TU DENMARK Stafford JV, 2000, J AGR ENG RES, V76, P267, DOI 10.1006/jaer.2000.0577 Steffe J., 2000, AGENDA 2000 FADN AGE, P88 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Taragola N., 2004, INFORM COMMUNICATION Teye F., 2011, THESIS HELSINKI METR THOMPSON SC, 1976, AGR ADMIN EXT, V3, P181, DOI 10.1016/0309-586X(76)90013-3 Thomson AJ, 2004, COMPUT ELECTRON AGR, V42, P43, DOI 10.1016/S0168-1699(03)00085-1 Thorp KR, 2008, COMPUT ELECTRON AGR, V64, P276, DOI 10.1016/j.compag.2008.05.022 Tozer PR, 2009, AGR SYST, V100, P80, DOI 10.1016/j.agsy.2009.02.001 Trepos R, 2012, COMPUT ELECTRON AGR, V86, P75, DOI 10.1016/j.compag.2012.01.006 Tsiropoulos Z, 2013, PRECISION AGRICULTURE '13, P349 Tsiropoulos Z., 2013, EFITA WCCA CIGR 2013 Verstegen JAAM, 1995, COMPUT ELECTRON AGR, V13, P273, DOI 10.1016/0168-1699(95)00019-4 Zhang T., 1996, P 1996 ACM SIGMOD IN, P103, DOI [10.1145/235968.233324, DOI 10.1145/235968.233324] NR 70 TC 106 Z9 107 U1 5 U2 143 PD JUL PY 2015 VL 115 BP 40 EP 50 DI 10.1016/j.compag.2015.05.011 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000358099900006 DA 2022-12-14 ER PT J AU Maiwore, J Tatsadjieu, NL Montet, D Loiseau, G Mbofung, CMF AF Maiwore, J. Tatsadjieu, N. L. Montet, D. Loiseau, G. Mbofung, C. M. F. TI Comparison of bacterial communities of tilapia fish from Cameroon and Vietnam using PCR-DGGE (polymerase chain reaction-denaturing gradient gel electrophoresis) SO AFRICAN JOURNAL OF BIOTECHNOLOGY DT Article DE Traceability; PCR-DGGE; bacterial community ID WATER; DIVERSITY; POPULATIONS; MICROFLORA; FRAGMENTS; FLORA AB Fishes in general and tilapia in particular are traded all over the world. However, it is difficult to find out their exact geographical location. One of the techniques used in the traceability of fish and its by-products consist in analysing in a global way the whole viable and non viable bacterial communities. For this purpose, the molecular technique employing the bacterial 16S DNA banding profiles generated by PCR-DGGE ( polymerase chain reaction-Denaturing gradient gel electrophoresis) was used to evaluate the differences between the bacterial profiles of fishes from Vietnam ( An Giang, south province) and those of Cameroon ( Yagoua, Maga, Lagdo). The different PCR-DGGE 16S rDNA banding profiles obtained were analysed and results showed that there were specific bands for each geographical location though some bands common to Cameroon and Vietnam were observed. This method could be used as a rapid analytical traceability tool for fish products and could be considered as a provider of a unique biological bar code. C1 [Tatsadjieu, N. L.] Univ Ngaoundere, IUT, Microbiol Lab, Ngaoundere, Cameroon. [Maiwore, J.; Mbofung, C. M. F.] Univ Ngaoundere, Dept Food Sci & Nutr, Natl Adv Sch Agroind Sci, Ngaoundere, Cameroon. [Montet, D.; Loiseau, G.] CIRAD, UMR Qualisud 95, F-34398 Montpellier 5, France. C3 CIRAD; Universite de Montpellier RP Tatsadjieu, NL (corresponding author), Univ Ngaoundere, IUT, Microbiol Lab, POB 454, Ngaoundere, Cameroon. EM tatsadjieu@yahoo.fr CR Al-Harbi AH, 2003, AQUAC RES, V34, P43, DOI 10.1046/j.1365-2109.2003.00791.x Ampe F, 1999, APPL ENVIRON MICROB, V65, P5464 [Anonymous], 84021994 ISO Billard Roland, 1995, Cahiers Agricultures, V4, P9 de Sousa JA, 2001, BRAZ ARCH BIOL TECHN, V44, P373, DOI 10.1590/S1516-89132001000400007 Dewettinck T, 2001, APPL MICROBIOL BIOT, V57, P412, DOI 10.1007/s002530100797 Diez B, 2001, APPL ENVIRON MICROB, V67, P2942, DOI 10.1128/AEM.67.7.2942-2951.2001 Ercolini D, 2004, J APPL MICROBIOL, V96, P263, DOI 10.1046/j.1365-2672.2003.02146.x GIOVANNONI SJ, 1990, NATURE, V345, P60, DOI 10.1038/345060a0 Grisez L, 1997, AQUACULTURE, V155, P387, DOI 10.1016/S0044-8486(97)00113-0 Head IM, 1998, MICROB ECOL, V35, P1, DOI 10.1007/s002489900056 Heyndrickx M, 1996, J MICROBIOL METH, V26, P247, DOI 10.1016/0167-7012(96)00916-5 HORSLEY RW, 1973, J APPL BACTERIOL, V36, P377, DOI 10.1111/j.1365-2672.1973.tb04119.x Hugenholtz P, 1998, J BACTERIOL, V180, P4765, DOI 10.1128/JB.180.18.4765-4774.1998 Hugenholtz P, 1996, TRENDS BIOTECHNOL, V14, P190, DOI 10.1016/0167-7799(96)10025-1 LAZARD J, 2007, CAHIERS AGR, V6, P123 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Leesing R, 2005, THESIS U MONTPELLIER Liston J., 1980, In 'Advances in fish science and technology' [see FSTA (1981) 13 6R300]., P138 Mauriello G, 2003, J DAIRY SCI, V86, P486, DOI 10.3168/jds.S0022-0302(03)73627-3 Montet D., 2004, SEM FOOD SAF INT TRA MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Muyzer G., 1996, MOL MICROBIAL ECOLOG, P1 Ovreas L, 1997, APPL ENVIRON MICROB, V63, P3367 SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 Shewan J. M., 1977, In 'Proceedings of the Conference on the handling, processing and marketing of tropical fish' [see FSTA (1978) 10 2R48]., P51 Spanggaard B, 2000, AQUACULTURE, V182, P1, DOI 10.1016/S0044-8486(99)00250-1 Van der Gucht K, 2005, FEMS MICROBIOL ECOL, V53, P205, DOI 10.1016/j.femsec.2004.12.006 Yang CH, 2001, FEMS MICROBIOL ECOL, V35, P129, DOI 10.1111/j.1574-6941.2001.tb00796.x NR 29 TC 8 Z9 10 U1 0 U2 5 PD DEC 15 PY 2009 VL 8 IS 24 BP 7156 EP 7163 WC Biotechnology & Applied Microbiology SC Biotechnology & Applied Microbiology UT WOS:000273902200063 DA 2022-12-14 ER PT J AU Qian, JP Shi, C Wang, SS Song, YZ Fan, BL Wu, XM AF Qian, Jianping Shi, Ce Wang, Shanshan Song, Yingzhuo Fan, Beilei Wu, Xiaoming TI Cloud-based system for rational use of pesticide to guarantee the source safety of traceable vegetables SO FOOD CONTROL DT Article DE Pesticide; Food safety; Cloud-based platform; Mobile phone; Vegetable; Traceability ID SUPPLY CHAIN; MANAGEMENT; FRAMEWORK; PRODUCTS; CROPS; PEST; WEB AB Recent legal requirements and market demands have motivated more food companies to implement traceability systems. Ensuring safe farming practices is the first step in food supply chain traceability, and reasonable pesticide use is a main feature of food safety and sustainable production. This study describes the design and development of a cloud-based platform for rational pesticide use to guarantee the source safety of traceable vegetables. The system includes a pesticide use control cloud platform (PUCC) and a pesticide user application (PUA), which interactively guide users through the steps of pesticide purchasing, pesticide application, harvest time, and pesticide evaluation. Models for evaluating and recommending potential pesticides were developed based on an open library of pesticide use rules. The PUCC, which includes the main functions of farmer registration, authentication of platform administrator, and information management for plant protection service agencies, was developed using Microsoft Visual Studio 2010 and deployed on the Internet. The PUA provides interfaces for pesticide purchasing guideline, pesticide application, optimal harvest time, and feedback. As a case study, the system was used for about a year in 24 vegetable bases in Tianjin. The effectiveness of the system was evaluated by investigating 8 management center staff members and 41 farmers. Management agencies noted the positive effects of promoting reasonable pesticide use, facilitating information accessibility, and enhancing management. Advantages to farmers included reducing the risk of unreasonable pesticide usage, decreasing the risk of counterfeit pesticides, and improving vegetable quality and safety; disadvantages included increased costs and reduced efficiency. In addition, the system improved external and internal traceability to ensure crop quality and safety. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Qian, Jianping; Shi, Ce; Fan, Beilei; Wu, Xiaoming] Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. [Qian, Jianping; Shi, Ce; Wang, Shanshan; Song, Yingzhuo; Fan, Beilei; Wu, Xiaoming] Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China. C3 Beijing Academy of Agriculture & Forestry RP Qian, JP (corresponding author), Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China. EM qianjp@nercita.org.cn CR Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Barriere V, 2015, EUR J AGRON, V71, P34, DOI 10.1016/j.eja.2015.07.003 Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Borit M, 2015, J CLEAN PROD, V104, P13, DOI 10.1016/j.jclepro.2015.05.003 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Damos P, 2015, AGRON SUSTAIN DEV, V35, P1347, DOI 10.1007/s13593-015-0319-9 Garcia-Valls M, 2014, J SYST ARCHITECT, V60, P726, DOI 10.1016/j.sysarc.2014.07.004 Hossard L, 2014, SCI REP-UK, V4, DOI 10.1038/srep04405 Jula A, 2014, EXPERT SYST APPL, V41, P3809, DOI 10.1016/j.eswa.2013.12.017 Li M, 2010, COMPUT ELECTRON AGR, V70, P69, DOI 10.1016/j.compag.2009.09.009 Mailly F, 2017, EUR J AGRON, V84, P23, DOI 10.1016/j.eja.2016.12.005 McEntire J. C., 2010, COMPREHENSIVE REV FO, V1, P92 Nansen C, 2015, AGRON SUSTAIN DEV, V35, P1075, DOI 10.1007/s13593-015-0309-y Notarnicola B, 2012, J CLEAN PROD, V28, P1, DOI 10.1016/j.jclepro.2012.02.007 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Poole NF, 2014, CAN J PLANT PATHOL, V36, P1, DOI 10.1080/07060661.2013.870230 Popp J, 2013, AGRON SUSTAIN DEV, V33, P243, DOI 10.1007/s13593-012-0105-x Qian JP, 2015, COMPUT ELECTRON AGR, V116, P101, DOI 10.1016/j.compag.2015.06.003 Qian JP, 2012, COMPUT ELECTRON AGR, V89, P76, DOI 10.1016/j.compag.2012.08.004 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sharma Y, 2016, J NETW COMPUT APPL, V74, P66, DOI 10.1016/j.jnca.2016.08.010 So-In C, 2014, COMPUT ELECTRON AGR, V109, P287, DOI 10.1016/j.compag.2014.10.004 Steinberger G, 2009, COMPUT ELECTRON AGR, V65, P238, DOI 10.1016/j.compag.2008.10.005 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Thiollet-Scholtus M, 2015, EUR J AGRON, V62, P13, DOI 10.1016/j.eja.2014.09.001 Walklate PJ, 2011, COMPUT ELECTRON AGR, V75, P355, DOI 10.1016/j.compag.2010.12.015 Xing K, 2016, J CLEAN PROD, V139, P191, DOI 10.1016/j.jclepro.2016.08.042 Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 NR 28 TC 11 Z9 13 U1 3 U2 63 PD MAY PY 2018 VL 87 BP 192 EP 202 DI 10.1016/j.foodcont.2017.12.015 WC Food Science & Technology SC Food Science & Technology UT WOS:000428606500024 DA 2022-12-14 ER PT J AU Russo, V Fontanesi, L Scotti, E Tazzoli, M Dall'Olio, S Davoli, R AF Russo, Vincenzo Fontanesi, Luca Scotti, Emilio Tazzoli, Marco Dall'Olio, Stefania Davoli, Roberta TI Analysis of melanocortin 1 receptor (MC1R) gene polymorphisms in some cattle breeds: their usefulness and application for breed traceability and authentication of Parmigiano Reggiano cheese SO ITALIAN JOURNAL OF ANIMAL SCIENCE DT Article DE breed traceability; dairy cattle products; food authentication; MC1R polymorphisms; parmigiano reggiano cheese ID COAT COLOR; MSH RECEPTOR; PHARMACOLOGICAL CHARACTERIZATION; DNA; MUTATIONS; MILK; EXTENSION; ALLELES; LOCUS; MICROSATELLITE AB In cattle, the MC1R gene has been the subject of several studies with the aim to elucidate the biology of coat colour. Then, polymorphisms of this gene have been proposed as tools for breed identification and animal products authentication. As a first step to identify breed specific DNA markers that can be used for the traceability of mono-breed dairy cattle products we investigated, using PCR-RFLP and PCR-APLP protocols, the presence and distribution of some alleles at the MC1R locus in 18 cattle breeds for a total of 1360 animals. For each of seven breeds (Italian Holstein, Italian Brown, Italian Simmental, Rendena, Jersey, Reggiana and Modenese) a large number of animals (>70) was genotyped so the obtained results can be considered with more confidence. Allele E-D was identified only in black pied cattle (Italian Holstein and Black Pied Valdostana). Allele E (this nomenclature includes all alleles except ED, El and e) was observed in Italian Brown, Rendena, Jersey, Modenese, Italian Simmental, Grigio Alpina, Piedmontese, Chianina, Romagnola, Marchigiana, Swedish Red and White and Danish Red. Allele El was identified in Italian Brown, Rendena, Grigio Alpina, Piedmontese, Swedish Red and White and Danish Red. The recessive allele e, known to cause red coat colour, was fixed in Reggiana and almost fixed in Italian Simmental. This allele was observed also in Italian Holstein, Italian Brown, Rendena, Jersey and Modenese albeit with low frequency. Moreover, this allele was detected in Valdostana, Pezzata Rossa d'Oropa, Piedmontese, Romagnola, Swedish Red and White, Danish Red, Charoleis and Salers. In the case of the Reggiana breed, which is fixed for allele e, the MC1R locus is highly informative with respect to breeds that carry other alleles or in which allele e is at very low frequency. In theory, using the MC1R locus it is possible to identify the presence of milk from some other breeds in Parmigiano Reggiano cheese labelled as exclusively from the Reggiana breed. This possibility was practically tested by setting up protocols to extract and analyse polymorphisms of the MC1R locus in several dairy products, including Parmigiano Reggiano cheese cured for 30 months. The lower detection limit was estimated to be 5% of non expected DNA. This test can represent a first deterrent against fraud and an important tool for the valorisation and authentication of Parmigiano Reggiano cheese obtained from only Reggiana milk. C1 Univ Bologna, Sezione Allevamenti Zootecn, DIPROVAL, I-42100 Reggio Emilia, Italy. Univ Bologna, Dipartimento Protezione & Valorizzanione Agr, I-40126 Bologna, Italy. C3 University of Bologna; University of Bologna RP Russo, V (corresponding author), Univ Bologna, Sezione Allevamenti Zootecn, DIPROVAL, Via F lli Rosselli 107, I-42100 Reggio Emilia, Italy. EM vincenzo.russo@unibo.it CR ADALSTEINSSON S, 1995, J HERED, V86, P395, DOI 10.1093/oxfordjournals.jhered.a111609 *AIA IT BREED ASS, 2006, REG AN POP BOV AUT G *ASS NAZ ALL BOV R, 2000, RAZZ REGG Berryere TG, 2003, ANIM GENET, V34, P169, DOI 10.1046/j.1365-2052.2003.00985.x Blott SC, 1999, HEREDITY, V82, P613, DOI 10.1046/j.1365-2540.1999.00521.x Branciari R, 2000, J FOOD PROTECT, V63, P408, DOI 10.4315/0362-028X-63.3.408 Breen G, 2000, BIOTECHNIQUES, V28, P464, DOI 10.2144/00283st03 Carrion D., 2003, Archivos de Zootecnia, V52, P237 Chung E. R., 2000, Korean Journal of Animal Science, V42, P379 Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 Crepaldi P, 2003, ITAL J ANIM SCI, V2, P13, DOI 10.4081/ijas.2003.s1.13 Crepaldi R, 2005, ITAL J ANIM SCI, V4, P43 DAVOLI R, 1998, P 41 NAT C SISVET SI, V41, P493 de Roest K, 2000, SOCIOL RURALIS, V40, P439, DOI 10.1111/1467-9523.00159 Fernandez A, 2004, J SCI FOOD AGR, V84, P1855, DOI 10.1002/jsfa.1829 Fontanesi L, 2006, ANIM GENET, V37, P489, DOI 10.1111/j.1365-2052.2006.01494.x Gandini GC, 2003, J ANIM BREED GENET, V120, P1, DOI 10.1046/j.1439-0388.2003.00365.x Graphodatskaya D, 2002, J RECEPT SIGNAL TR R, V22, P421, DOI 10.1081/RRS-120014611 Grosz MD, 1999, J HERED, V90, P233, DOI 10.1093/jhered/90.1.233 HEALY PJ, 1995, AUST VET J, V72, P392, DOI 10.1111/j.1751-0813.1995.tb06178.x Jackson IJ, 1997, HUM MOL GENET, V6, P1613, DOI 10.1093/hmg/6.10.1613 Joerg H, 1996, MAMM GENOME, V7, P317, DOI 10.1007/s003359900090 Kantanen J, 2000, GENET SEL EVOL, V32, P561, DOI 10.1051/gse:2000137 Kijas JMH, 1998, GENETICS, V150, P1177 KLUNGLAND H, 1995, MAMM GENOME, V6, P636, DOI 10.1007/BF00352371 Kriegesmann B, 2001, J DAIRY SCI, V84, P1768, DOI 10.3168/jds.S0022-0302(01)74612-7 LIPKIN E, 1993, J DAIRY SCI, V76, P2025, DOI 10.3168/jds.S0022-0302(93)77536-0 MARILLI M, 2005, P 4 WORLD C IT BEEF Marks D F, 1996, J Health Psychol, V1, P7, DOI 10.1177/135910539600100102 Mason I. L., 1996, WORLD DICT LIVESTOCK Maudet C, 2002, J DAIRY SCI, V85, P707, DOI 10.3168/jds.S0022-0302(02)74127-1 Maudet C, 2002, J ANIM SCI, V80, P942 Maudet C, 2001, J DAIRY RES, V68, P229, DOI 10.1017/S0022029901004794 MOUNTJOY KG, 1992, SCIENCE, V257, P1248, DOI 10.1126/science.1325670 Negrini R, 2003, ITAL J ANIM SCI, V2, P22, DOI 10.4081/ijas.2003.s1.22 Olsen HG, 2000, ANIM GENET, V31, P71, DOI 10.1111/j.1365-2052.2000.579-4.x Olson T, 1999, GENETICS OF CATTLE, P33 Reinsch N, 1999, J HERED, V90, P629, DOI 10.1093/jhered/90.6.629 ROBBINS LS, 1993, CELL, V72, P827, DOI 10.1016/0092-8674(93)90572-8 Rolando A, 2006, ITAL J ANIM SCI, V5, P87 Rouzaud F, 2000, GENET SEL EVOL, V32, P511, DOI 10.1051/gse:2000102 ROYO LJ, 2003, P 10 MEET AN PROD IN RUSSO V, 2004, P 7 WORLD BROWN SWIS, P95 RUSSO V, 1975, ITALIA AGRICOLA, V113, P56 Searle A.G., 1968, COMP GENETICS COAT C Vage DI, 1999, MAMM GENOME, V10, P39, DOI 10.1007/s003359900939 VALVERDE P, 1995, NAT GENET, V11, P328, DOI 10.1038/ng1195-328 VANETTI M, 1994, FEBS LETT, V348, P268, DOI 10.1016/0014-5793(94)00619-9 WALSH PS, 1991, BIOTECHNIQUES, V10, P506, DOI 10.2144/000114018 NR 49 TC 59 Z9 60 U1 0 U2 11 PD JUL-SEP PY 2007 VL 6 IS 3 BP 257 EP 272 WC Agriculture, Dairy & Animal Science; Agriculture, Multidisciplinary; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000250647500003 DA 2022-12-14 ER PT J AU Archer, M de Vos, BJ Visser, MS AF Archer, Marcelle de Vos, Betty-Jayne Visser, Maria S. TI The preparation, assay and certification of aqueous ethanol reference solutions SO ACCREDITATION AND QUALITY ASSURANCE DT Article; Proceedings Paper CT 10th International Symposium on Biological and Environmental Reference Materials 9BERM 10) CY APR 30-MAY 04, 2006 CL Charleston, SC DE ethanol; certified reference material; traceability; titrimetry; gravimetry; primary methods AB Internationally, certified ethanol reference materials are required to calibrate breathalysers and blood-alcohol measurement instruments. The CSIR National Metrology Laboratory of South Africa provides certified aqueous ethanol solutions with traceability to the SI. Ethanol solutions in the concentration range 10 mg/100 g to 20 g/100 g are prepared gravimetrically by mixing ethanol and reagent quality water. To verify the concentration of the ethanol it is oxidized to acetic acid with potassium dichromate in the presence of sulphuric acid. The unreacted potassium dichromate is back-titrated with sodium thiosulphate. The method utilizes gravimetry and titrimetry, which are both primary methods. This paper addresses aspects of ethanol-certified reference materials that have not been previously published: traceability, stability of unpreserved ethanol solutions, homogeneity, quality control measures and the effect of reproducibility on the measurement uncertainty. C1 CSIR, Natl Metrol Lab, ZA-0001 Pretoria, South Africa. C3 Council for Scientific & Industrial Research (CSIR) - South Africa; National Metrology Institute of South Africa (NMISA) RP Archer, M (corresponding author), CSIR, Natl Metrol Lab, POB 395, ZA-0001 Pretoria, South Africa. EM marcher@csir.co.za CR BARWICK VJ, 2000, VAM PROJECT 3 2 1 DE BARWICK VJ, 1997, CERTIFICATION FORENS International Organization for Standardization, 1993, GUID EXPR UNC MEAS VANGENT PKF, 1966, THESIS U S AFRICA Vogel A, 1951, VOGELS TXB QUANTITAT, P271 VOGEL AI, 1978, VOGELS TXB QUANTITAT, P375 [No title captured] [No title captured] 2000, QUANTIFYING ANAL MEA [No title captured] NR 10 TC 6 Z9 8 U1 2 U2 17 PD APR PY 2007 VL 12 IS 3-4 BP 188 EP 193 DI 10.1007/s00769-006-0212-y WC Chemistry, Analytical; Instruments & Instrumentation SC Chemistry; Instruments & Instrumentation UT WOS:000245291300012 DA 2022-12-14 ER PT J AU Zimmerman, BE Meghzifene, A Shortt, KR AF Zimmerman, B. E. Meghzifene, A. Shortt, K. R. TI Establishing measurement traceability for national laboratories: Results of an IAEA comparison of I-131 SO APPLIED RADIATION AND ISOTOPES DT Article; Proceedings Paper CT 16th International Conference on Radionuclide Metrology and Its Applications (ICRM 2007) CY SEP 03-07, 2007 CL Cape Town, SOUTH AFRICA AB A radioactivity measurement comparison for solutions of I-131 was conducted by the International Atomic Energy Agency for participants in one of its Cooperative Research Projects aimed at enhancing quality assurance practices in nuclear medicine. The comparison solutions were prepared from a single master stock solution and distributed to the participating laboratories, who measured the activity concentration of the solution using either the laboratory's radionuclide activity calibrator or primary standardization methods. From the 7 results received, a Comparison Reference Value was calculated to be 37.35(78) M Bq g(-1) at the reference time. Degrees of equivalence, as defined by the Mutual Recognition Agreement (MRA) of the Comite International des Poids et Mesures (CIPM), were calculated for each laboratory, demonstrating that equivalence to within +/- 4% could be achieved. The comparison has been registered as a supplementary comparison with the CIPM, Consultative Committee for Ionizing Radiation, Section II-measurement of radionuclides (CCRI(II)) for the purposes of allowing the participants to establish traceability to international standards for this radionuclide. Published by Elsevier Ltd. C1 [Zimmerman, B. E.] NIST, Phys Lab, Gaithersburg, MD 20899 USA. [Zimmerman, B. E.; Meghzifene, A.; Shortt, K. R.] IAEA, Dosimetry & Med Radiat Phys Sect, A-1400 Vienna, Austria. C3 National Institute of Standards & Technology (NIST) - USA; International Atomic Energy Agency RP Zimmerman, BE (corresponding author), NIST, Phys Lab, Gaithersburg, MD 20899 USA. EM bez@nist.gov CR Baker M, 2005, APPL RADIAT ISOTOPES, V63, P71, DOI 10.1016/j.apradiso.2005.01.004 BE MM, 2004, MONOGRAPHIE BPIM 5 T CIPM MRA, 1999, MUTUAL RECOGNITION N Ratel G, 2003, METROLOGIA, V40, DOI 10.1088/0026-1394/40/1A/06023 RATEL G, 2005, METROLOGIA S, V40, P6004 RYTZ A, 1983, INT J APPL RADIAT IS, V34, P1047, DOI 10.1016/0020-708X(83)90170-9 Thomas, 2005, BIPM0506 Woods MJ, 2000, APPL RADIAT ISOTOPES, V52, P313, DOI 10.1016/S0969-8043(99)00171-2 ZIMMERMAN BE, 2007, METROLOGIA T S UNPUB NR 9 TC 11 Z9 12 U1 0 U2 2 PD JUN-JUL PY 2008 VL 66 IS 6-7 BP 954 EP 959 DI 10.1016/j.apradiso.2008.02.067 WC Chemistry, Inorganic & Nuclear; Nuclear Science & Technology; Radiology, Nuclear Medicine & Medical Imaging SC Chemistry; Nuclear Science & Technology; Radiology, Nuclear Medicine & Medical Imaging UT WOS:000256203000057 DA 2022-12-14 ER PT J AU Arts, B Heukels, B Turnhout, E AF Arts, Bas Heukels, Bas Turnhout, Esther TI Tracing timber legality in practice: The case of Ghana and the EU SO FOREST POLICY AND ECONOMICS DT Article DE Traceability; FLEGT; VPA; Ghana; Practice-based approach ID FOREST LAW-ENFORCEMENT; FLEGT; GOVERNANCE; TRADE; TRACEABILITY; SUSTAINABILITY; IMPLEMENTATION AB The traceability of products has become an ever more important topic in global value chains because governments, producers and consumers wish to have in-depth information on the origin, quality, safety and sustainability of the products they regulate, trade or buy. However, traceability systems come with criticisms and challenges. This article describes how timber traceability is being realized in Ghana in the context of the Voluntary Partnership Agreement (VPA) between Ghana and the EU, which is part of the Forest Law Enforcement, Governance and Trade (FLEGT) initiative. Building on practice theory, this article conceptualizes traceability systems as ensembles of procedures, interpretations and activities. Empirically, it presents an analysis of the Ghanaian Legality Assurance System (LAS) and Wood Tracking System (WTS). Results show that the LAS/ WTS moved from a 'digitalized regulatory track-and-trace system' on the design table towards a more hybrid one in practice, integrating elements of a communication governance mode and of a mass-balance model too, and keeping alive a parallel paper-based infrastructure. While particularly governmental officials are satisfied with the LAS/WTS, it is also important to recognize that stakeholders interpret aspects of the system quite differently, and deal with implementation issues on the ground quite differently, implying that 'legality-on-paper' and 'legality-in-practice' are not necessarily the same. C1 [Arts, Bas; Turnhout, Esther] Wageningen Univ & Res, Wageningen, Netherlands. [Heukels, Bas] Rijksdienst Ondernemend Nederland, The Hague, Netherlands. C3 Wageningen University & Research RP Arts, B (corresponding author), Wageningen Univ & Res, Wageningen, Netherlands. EM bas.arts@wur.nl CR Acheampong E, 2020, FOREST POLICY ECON, V111, DOI 10.1016/j.forpol.2019.102047 Arts B, 2014, FOREST POLICY ECON, V49, P4, DOI 10.1016/j.forpol.2014.04.001 Ayana AN, 2017, CRIT POLICY STUD, V11, P19, DOI 10.1080/19460171.2015.1024703 Bailey M, 2016, CURR OPIN ENV SUST, V18, P25, DOI 10.1016/j.cosust.2015.06.004 Beeko C, 2010, INT FOREST REV, V12, P221, DOI 10.1505/ifor.12.3.221 Behagel JH, 2019, J ENVIRON POL PLAN, V21, P479, DOI 10.1080/1523908X.2017.1295841 Bourdieu Pierre, 1990, REPROD ELEMENTS THEO Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Cleaver F., 2012, Development through bricolage: rethinking institutions for natural resource management Cleaver F, 2015, INT J COMMONS, V9, P1, DOI 10.18352/ijc.605 Cook W, 2016, CURR OPIN ENV SUST, V18, P33, DOI 10.1016/j.cosust.2015.07.016 Crowe S, 2011, BMC MED RES METHODOL, V11, DOI 10.1186/1471-2288-11-100 Derous M, 2019, EUR FOREIGN AFF REV, V24, P327 Doddema M, 2020, SOC NATUR RESOUR, V33, P1232, DOI 10.1080/08941920.2020.1739358 Eden S, 2008, ENVIRON PLANN D, V26, P1018, DOI 10.1068/d3208 EU-Ghana VPA, 2010, OFFICIAL J EUROPEA L, V70, P1 Gardner TA, 2019, WORLD DEV, V121, P163, DOI 10.1016/j.worlddev.2018.05.025 Goncalves M.P., 2012, EMBRAPA CLIMA TEMPER Gupta A, 2014, 2014 RECENT ADVANCES IN ENGINEERING AND COMPUTATIONAL SCIENCES (RAECS) Hajer M., 2003, DELIBERATIVE POLICY, DOI http://dx.doi Hansen CP, 2008, INT FOREST REV, V10, P573, DOI 10.1505/ifor.10.4.573 Hansen CP, 2018, FOREST POLICY ECON, V96, P75, DOI 10.1016/j.forpol.2018.08.012 Hansen CP, 2015, SMALL-SCALE FOR, V14, P401, DOI 10.1007/s11842-015-9295-9 Heukels B., 2018, LEGALITY TRACEABILIT Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Karsent A, 2019, FOREST POLICY ECON, V106, DOI 10.1016/j.forpol.2019.101974 Kleinschmit D., 2016, IUFRO WORLD SERIES Lesniewska F, 2014, FOREST POLICY ECON, V48, P16, DOI 10.1016/j.forpol.2014.01.005 Lipsky M., 1980, STREET LEVEL BUREAUC, DOI DOI 10.1007/s13178-012-0092-3 Maryudi A, 2020, SOC NATUR RESOUR, V33, P859, DOI 10.1080/08941920.2020.1725201 Maryudi A, 2018, GEOFORUM, V97, P46, DOI 10.1016/j.geoforum.2018.10.008 McDermott CL, 2020, SOC NATUR RESOUR, V33, P261, DOI 10.1080/08941920.2018.1544679 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Mol APJ, 2015, SUSTAINABILITY-BASEL, V7, P12258, DOI 10.3390/su70912258 Myers R, 2020, J POLIT ECOL, V27, P125, DOI 10.2458/v27i1.23208 Nathan I, 2014, FOREST POLICY ECON, V48, P1, DOI 10.1016/j.forpol.2014.11.001 Overdevest C, 2018, REGUL GOV, V12, P64, DOI 10.1111/rego.12180 Rutt RL, 2018, ENVIRON SCI POLICY, V89, P266, DOI 10.1016/j.envsci.2018.08.012 Schatzki T., 2001, PRACTICE TURN CONT T Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Setyowati A, 2017, SOC NATUR RESOUR, V30, P750, DOI 10.1080/08941920.2016.1239295 Shove E., 2012, DYNAMICS SOCIAL PRAC, DOI 10.4135/9781446250655 Tegegne YT, 2017, FOREST POLICY ECON, V75, P1, DOI 10.1016/j.forpol.2016.11.005 Turnhout E, 2014, ENVIRON PLANN A, V46, P581, DOI 10.1068/a4629 van der Arend S, 2011, CRIT POLICY STUD, V5, P169, DOI 10.1080/19460171.2011.576529 van Heeswijk L, 2013, FOREST POLICY ECON, V32, P6, DOI 10.1016/j.forpol.2012.10.009 Wagenaar H., 2011, MEANING ACTION INTER Wester F., 1991, STRATEGIEEN KWALITAT Yanow D., 1996, DOES POLICY MEAN INT Yin R., 2014, CASE STUDY RES DESIG, V5 NR 50 TC 3 Z9 3 U1 1 U2 6 PD SEP PY 2021 VL 130 AR 102532 DI 10.1016/j.forpol.2021.102532 EA JUN 2021 WC Economics; Environmental Studies; Forestry SC Business & Economics; Environmental Sciences & Ecology; Forestry UT WOS:000667293600005 DA 2022-12-14 ER PT J AU Mc Inerney, B Corkery, G Ayalew, G Ward, S Mc Donnell, K AF Mc Inerney, Barry Corkery, Gerard Ayalew, Gashaw Ward, Shane Mc Donnell, Kevin TI Preliminary in vivo study on the potential application of a novel method of e-tracking to facilitate traceability in the poultry food chain SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE GS1 DataMatrix barcodes; Laser printing; Poultry beaks; Layer hens; Readability; Proportion of identification ID CONSUMER PERCEPTIONS; INFORMATION; PRODUCTS; PERSPECTIVE; QUALITY; SAFETY AB The feasibility of using GS1 DataMatrix (GS1 DM) barcodes laser printed onto the beaks of poultry as a possible method of identification and, therefore, traceability of the individual were examined in this study, including a preliminary live trial on layer hens. The optimal laser type and settings for this particular application had been selected during previous in vitro and in vivo trials. GS1 DM barcodes were printed on both sides of the beaks of mature layer hens and read using a high specification camera based 1-Dimensional/2-Dimensional (1-D/2-D) DataMan 7500 barcode reader. The reading procedure was repeated on a number of occasions over a 5 week period to examine the effects of time in a commercial environment on the clarity and readability of the GS1 DM barcode, and the ability of the printed GS1 DM barcodes to resist the physical and chemical challenges of such a setting. The results show a very short timeframe during which all barcodes, both right and left combined, remain readable. Thereafter the readability deteriorates rapidly, due to the growth and healing of the beaks of the layer hens. Results also show that there was no significant difference in the readability between GS1 DM barcodes printed on the right or left side of the beak. The proportion of identification ( i) (i.e. number of layers identifiable by either one or two readable GS1 DM barcodes) was also calculated. All layer hens were fully identifiable for a seven day period by either one or two readable GS1 DM barcodes. Further analysis showed that the proportion of identification (,)was significantly higher for layer hens when identified with two GS1 DM barcodes as opposed to just one. Secure movement control of live mature poultry at vulnerable points in the food chain, such as transfer of ownership, could well be facilitated by the use of this technology, thereby preventing fraud or substitution at these points. (C) 2011 Elsevier B.V. All rights reserved. C1 [Mc Inerney, Barry; Corkery, Gerard; Ayalew, Gashaw; Ward, Shane; Mc Donnell, Kevin] Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Dublin 4, Ireland. C3 University College Dublin RP Mc Inerney, B (corresponding author), Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Room 201,Engn Bldg, Dublin 4, Ireland. EM Barry.Mc-Inerney@ucdconnect.ie CR Bertolini M, 2006, FOOD CONTROL, V17, P137, DOI 10.1016/j.foodcont.2004.09.013 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 CHRYSSOCHOIDIS GM, 2006, TRACEABILITY EUROPEA, P1 *DG SANCO, 2007, FOOD TRAC TRAC FOOD, P1 Dziuk P, 2003, ANIM REPROD SCI, V79, P319, DOI 10.1016/S0378-4320(03)00170-2 *EFSA, 2005, WELF ASP VAR SYST KE, P1 Fallon M, 2001, REV SCI TECH OIE, V20, P538, DOI 10.20506/rst.20.2.1289 Froschle HK, 2009, COMPUT ELECTRON AGR, V66, P126, DOI 10.1016/j.compag.2009.01.002 Gellynck X., 2001, Agrarwirtschaft, V50, P368 Gentle MJ, 2007, VET REC, V160, P145, DOI 10.1136/vr.160.5.145 GLATZ P, 2004, LASER BEAK TRIMMING, P1 Glatz PC, 2000, ASIAN AUSTRAL J ANIM, V13, P1619, DOI 10.5713/ajas.2000.1619 Golan E.H., 2004, TRACEABILITY US FOOD Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x JENSEN HH, 2006, IATC SUMM S BONN GER, P1 LayWel, 2006, WELF IMPL CHANG PROD, P1 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 *SP COMP INC, 2002, FOOD TRAC STAND SYST, P1 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Verbeke W, 2001, OUTLOOK AGR, V30, P249, DOI 10.5367/000000001101293733 Verbeke W, 2007, ANAL CHIM ACTA, V586, P2, DOI 10.1016/j.aca.2006.07.065 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 NR 26 TC 8 Z9 8 U1 0 U2 10 PD JUN PY 2011 VL 77 IS 1 BP 1 EP 6 DI 10.1016/j.compag.2011.03.001 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000292711500001 DA 2022-12-14 ER PT J AU El Sheikha, AF Condur, A Metayer, I Le Nguyen, DD Loiseau, G Montet, D AF El Sheikha, Aly Farag Condur, Ana Metayer, Isabelle Le Nguyen, Doan Duy Loiseau, Gerard Montet, Didier TI Determination of fruit origin by using 26S rDNA fingerprinting of yeast communities by PCR-DGGE: preliminary application to Physalis fruits from Egypt SO YEAST DT Article DE traceability; PCR-DGGE; Physalis; Egypt; yeast communities; origin ID GRADIENT GEL-ELECTROPHORESIS; BACTERIAL; IDENTIFICATION; PHYLOGENY; IMPACT AB The determination of geographical origin is a demand of the traceability system of import-export food products. One hypothesis for tracing the source of a product is by global analysis of the microbial communities of the food and statistical linkage of this analysis to the geographical origin of the food. For this purpose, a molecular technique employing 26S rDNA profiles generated by PCR-DGGE was used to detect the variation in yeast community structures of three species of Physalis fruit (Physalis ixocarpa Brat, Physalis pubescens L, Physalis pruinosa L) from four Egyptian regions (Qalyoubia, Minufiya, Beheira and Alexandria Governments). When the 26S rDNA profiles were analysed by multivariate analysis, distinct microbial communities were detected. The band profiles of Physalis yeasts from different Governments were specific for each location and could be used as a bar code to discriminate the origin of the fruits. This method is a new traceability tool which provides fruit products with a unique biological bar code and makes it possible to trace back the fruits to their original location. Copyright (C) 2009 John Wiley & Sons, Ltd. C1 [El Sheikha, Aly Farag] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm 32511, Minufiya Govt, Egypt. [El Sheikha, Aly Farag; Condur, Ana; Metayer, Isabelle; Le Nguyen, Doan Duy; Loiseau, Gerard; Montet, Didier] Ctr Cooperat Int Rech Agron Dev, UMR Qualisud, TA 95B16, F-34398 Montpellier 5, France. [Le Nguyen, Doan Duy] Can Tho Univ, Fac Agr, Can Tho, Vietnam. C3 Egyptian Knowledge Bank (EKB); Menofia University; CIRAD; Universite de Montpellier; Can Tho University RP El Sheikha, AF (corresponding author), Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm 32511, Minufiya Govt, Egypt. EM elsheikha_aly@yahoo.com CR Cocolin L, 2000, FEMS MICROBIOL LETT, V189, P81, DOI 10.1016/S0378-1097(00)00257-3 El Sheikha A., 2008, FOOD, V2, P124 El Sheikha AF, 2010, J FOOD PROCESS PRES, V34, P541, DOI 10.1111/j.1745-4549.2009.00382.x ELSHEIKHA AF, 2004, THESIS MINUFIYA U EG, P174 Fleet GH, 2007, CURR OPIN BIOTECH, V18, P170, DOI 10.1016/j.copbio.2007.01.010 FOUQUE A, 1972, FRUITS, V17, P62 Ghidini S., 2006, Annali della Facolta di Medicina Veterinaria, Universita di Parma, V26, P193 Heyndrickx M, 1996, J MICROBIOL METH, V26, P247, DOI 10.1016/0167-7012(96)00916-5 Kowalchuk GA, 1997, APPL ENVIRON MICROB, V63, P1489, DOI 10.1128/AEM.63.4.1489-1497.1997 Kurtzman CP, 1998, ANTON LEEUW INT J G, V73, P331, DOI 10.1023/A:1001761008817 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Leesing R, 2005, THESIS U MONTPELLIER, P183 Masoud W, 2004, YEAST, V21, P549, DOI 10.1002/yea.1124 Montet D., 2008, Aspects of Applied Biology, P11 Montet D., 2004, SEM FOOD SAF INT TRA MUYZER G, 1995, ARCH MICROBIOL, V164, P165, DOI 10.1007/BF02529967 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 PRAKITCHAIWATTA.CJ, 2007, FEMS YEAST RES, V4, P865 Ramadan MF, 2007, J SCI FOOD AGR, V87, P452, DOI 10.1002/jsfa.2728 RANDALL RP, 2001, PLANT DATABASE COMPR Ros-Chumillas M, 2007, FOOD CONTROL, V18, P33, DOI 10.1016/j.foodcont.2005.08.004 SODEKO OO, 1987, MICROBIOS, V51, P133 Tournas VH, 2006, FOOD MICROBIOL, V23, P684, DOI 10.1016/j.fm.2006.01.003 *USDA, 2006, USDA GRIN NRCS DAT van Hannen EJ, 1999, APPL ENVIRON MICROB, V65, P795 2006, BAYER CROP SCI MAGAZ [No title captured] NR 28 TC 55 Z9 55 U1 0 U2 27 PD OCT PY 2009 VL 26 IS 10 BP 567 EP 573 DI 10.1002/yea.1707 WC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Microbiology; Mycology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Microbiology; Mycology UT WOS:000270859500004 DA 2022-12-14 ER PT J AU Hu, XZ Liu, SQ Li, XH Wang, CX Ni, XL Liu, X Wang, Y Liu, Y Xu, CH AF Hu, Xiao-Zhen Liu, Si-Qi Li, Xiao-Hong Wang, Chuan-Xian Ni, Xin-Lu Liu, Xia Wang, Yang Liu, Yuan Xu, Chang-Hua TI Geographical origin traceability of Cabernet Sauvignon wines based on Infrared fingerprint technology combined with chemometrics SO SCIENTIFIC REPORTS DT Article ID GRAPE-GROWING REGIONS; TRI-STEP IR; QUANTITATIVE-EVALUATION; PROTECTED DESIGNATION; MIR SPECTROSCOPY; MERLOT WINES; DISCRIMINATION; AUTHENTICITY; FORMALDEHYDE; QUALITY AB Mid-infrared (MIR) and near-infrared (NIR) spectroscopy combined with chemometrics were explored to classify Cabernet Sauvignon wines from different countries (Australia, Chile and China). Commercial wines (n = 540) were scanned in transmission mode using MIR and NIR, and their characteristic fingerprint bands were extracted at 1750-1000 cm(-1) and 4555-4353 cm(-1). Through the identification system of Tri-step infrared spectroscopy, the correlation between macroscopic chemical fingerprints and geographical regions was explored more deeply. Furthermore, Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra were used to visualize or discriminate differences between samples and to realize geographical origin traceability of Cabernet Sauvignon wines. Through "external test set (n = 157)" validation, SIMCA models correctly classified 97%, 97% and 92% of Australian, Chilean and Chinese Cabernet Sauvignon wines, while the DA models correctly classified 86%, 85% and 77%, respectively. Based on unique digital fingerprints of spectroscopy (FT-MIR and FT-NIR) associated with chemometrics, geographical origin traceability was achieved in a more comprehensive, effective and rapid manner. The developed database models based on IR fingerprint spectroscopy with chemometrics could provide scientific basis and reference for geographical origin traceability of Cabernet Sauvignon wines (Australia, Chile and China). C1 [Hu, Xiao-Zhen; Liu, Si-Qi; Xu, Chang-Hua] Shanghai Ocean Univ, Coll Food Sci & Technol, Shanghai 201306, Peoples R China. [Liu, Si-Qi; Li, Xiao-Hong; Wang, Chuan-Xian; Ni, Xin-Lu; Liu, Xia] Shanghai Entry Exit Inspect & Quarantine Bur, Shanghai 200135, Peoples R China. [Wang, Yang] Tianjin Univ Tradit Chinese Med, Teaching Hosp 1, Tianjin 300193, Peoples R China. [Liu, Yuan] Shanghai Jiao Tong Univ, Sch Agr & Biol, Shanghai 200240, Peoples R China. [Xu, Chang-Hua] Yale Univ, Dept Pharmacol, New Haven, CT 06520 USA. [Xu, Chang-Hua] Shanghai Engn Res Ctr Aquat Prod Proc & Preservat, Shanghai 201306, Peoples R China. [Xu, Chang-Hua] Minist Agr, Lab Qual & Safety Risk Assessment Aquat Prod Stor, Shanghai 201306, Peoples R China. [Xu, Chang-Hua] Ctr Freshwater Aquat Prod Proc Technol Shanghai, Natl R&D Branch, Shanghai 201306, Peoples R China. C3 Shanghai Ocean University; Tianjin University of Traditional Chinese Medicine; Shanghai Jiao Tong University; Yale University; Ministry of Agriculture & Rural Affairs RP Xu, CH (corresponding author), Shanghai Ocean Univ, Coll Food Sci & Technol, Shanghai 201306, Peoples R China.; Liu, Y (corresponding author), Shanghai Jiao Tong Univ, Sch Agr & Biol, Shanghai 200240, Peoples R China.; Xu, CH (corresponding author), Yale Univ, Dept Pharmacol, New Haven, CT 06520 USA.; Xu, CH (corresponding author), Shanghai Engn Res Ctr Aquat Prod Proc & Preservat, Shanghai 201306, Peoples R China.; Xu, CH (corresponding author), Minist Agr, Lab Qual & Safety Risk Assessment Aquat Prod Stor, Shanghai 201306, Peoples R China.; Xu, CH (corresponding author), Ctr Freshwater Aquat Prod Proc Technol Shanghai, Natl R&D Branch, Shanghai 201306, Peoples R China. EM y_liu@sjtu.edu.cn; chxu@shou.edu.cn CR Banc R, 2014, NOT BOT HORTI AGROBO, V42, P556, DOI 10.15835/nbha4229674 Bowers JE, 1997, NAT GENET, V16, P84, DOI 10.1038/ng0597-84 Chandra S, 2017, FOOD ANAL METHOD, V10, P3947, DOI 10.1007/s12161-017-0968-1 Cocciardi RA, 2005, J AGR FOOD CHEM, V53, P2803, DOI 10.1021/jf048663d Cordella C, 2002, J AGR FOOD CHEM, V50, P1751, DOI 10.1021/jf011096z Cozzolino D, 2003, J AGR FOOD CHEM, V51, P7703, DOI 10.1021/jf034959s Cozzolino D, 2008, TALANTA, V74, P711, DOI 10.1016/j.talanta.2007.06.045 Cozzolino D, 2011, ANAL BIOANAL CHEM, V401, P1475, DOI 10.1007/s00216-011-4946-y Cozzolino D, 2011, FOOD RES INT, V44, P181, DOI 10.1016/j.foodres.2010.10.043 Cozzolino D, 2011, FOOD CHEM, V126, P673, DOI 10.1016/j.foodchem.2010.11.005 Cozzolino D, 2009, FOOD CHEM, V116, P761, DOI 10.1016/j.foodchem.2009.03.022 Cuadrado MU, 2005, ANAL BIOANAL CHEM, V381, P953, DOI 10.1007/s00216-004-2954-x Di Egidio V, 2010, EUR FOOD RES TECHNOL, V230, P947, DOI 10.1007/s00217-010-1227-5 Gan JH, 2015, CHINESE CHEM LETT, V26, P215, DOI 10.1016/j.cclet.2015.01.012 Green JA, 2011, FOOD RES INT, V44, P2788, DOI 10.1016/j.foodres.2011.06.005 Gu DC, 2019, LWT-FOOD SCI TECHNOL, V101, P382, DOI 10.1016/j.lwt.2018.11.012 Gu DC, 2017, FOOD CHEM, V229, P458, DOI 10.1016/j.foodchem.2017.02.082 Guo XX, 2015, J MOL STRUCT, V1099, P393, DOI 10.1016/j.molstruc.2015.06.081 Hou SW, 2019, SPECTROCHIM ACTA A, V215, P1, DOI 10.1016/j.saa.2019.02.080 Hu W, 2016, FOOD ANAL METHOD, V9, P831, DOI 10.1007/s12161-015-0258-8 Industry C.I.O.F.F, 2006, 150372006 CIOFF GBT Jiang B, 2013, FOOD RES INT, V51, P482, DOI 10.1016/j.foodres.2013.01.001 Jiang B, 2012, MOLECULES, V17, P8804, DOI 10.3390/molecules17088804 Kamiloglu S, 2019, FOOD CHEM, V277, P12, DOI 10.1016/j.foodchem.2018.10.091 Li SJ, 2014, J FOOD MEAS CHARACT, V8, P356, DOI 10.1007/s11694-014-9196-1 Liu L, 2008, FOOD CHEM, V106, P781, DOI 10.1016/j.foodchem.2007.06.015 Liu L, 2006, J AGR FOOD CHEM, V54, P6754, DOI 10.1021/jf061528b Liu SQ, 2018, SPECTROCHIM ACTA A, V189, P265, DOI 10.1016/j.saa.2017.08.031 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Mandrile L, 2016, FOOD CHEM, V211, P260, DOI 10.1016/j.foodchem.2016.05.011 Rios-Reina R, 2018, FOOD CONTROL, V89, P108, DOI 10.1016/j.foodcont.2018.01.031 Shen F, 2012, FOOD BIOPROCESS TECH, V5, P786, DOI 10.1007/s11947-010-0347-z Smyth H, 2013, CHEM REV, V113, P1429, DOI 10.1021/cr300076c Tregear A, 1998, FOOD POLICY, V23, P383, DOI 10.1016/S0306-9192(98)00044-X Urickova V, 2015, SPECTROCHIM ACTA A, V148, P131, DOI 10.1016/j.saa.2015.03.111 Xu CH, 2013, PLANTA MED, V79, P1068, DOI 10.1055/s-0032-1328764 Zhang XY, 2017, FOOD CONTROL, V73, P1124, DOI 10.1016/j.foodcont.2016.10.030 Zhang YL, 2010, J MOL STRUCT, V974, P144, DOI 10.1016/j.molstruc.2010.03.021 Zhu L, 2018, FOOD ANAL METHOD, V11, P3201, DOI 10.1007/s12161-018-1284-0 Ziegel E. R, 2004, TECHNOMETRICS, V46, P3 NR 40 TC 26 Z9 29 U1 7 U2 38 PD JUN 4 PY 2019 VL 9 AR 8256 DI 10.1038/s41598-019-44521-8 WC Multidisciplinary Sciences SC Science & Technology - Other Topics UT WOS:000470075600005 DA 2022-12-14 ER PT J AU Shahid, A Almogren, A Javaid, N Al-Zahrani, FA Zuair, M Alam, M AF Shahid, Affaf Almogren, Ahmad Javaid, Nadeem Al-Zahrani, Fahad Ahmad Zuair, Mansour Alam, Masoom TI Blockchain-Based Agri-Food Supply Chain: A Complete Solution SO IEEE ACCESS DT Article DE Supply chains; Contracts; Government; Electronic mail; Accountability; blockchain; credibility; reputation; supply chain; traceability; trust ID CHALLENGES; TRACEABILITY; TECHNOLOGY; SYSTEMS; PRIVACY AB Supply chains are evolving into automated and highly complex networks and are becoming an important source of potential benefits in the modern world. At the same time, consumers are now more interested in food product quality. However, it is challenging to track the provenance of data and maintain its traceability throughout the supply chain network. The traditional supply chains are centralized and they depend on a third party for trading. These centralized systems lack transparency, accountability and auditability. In our proposed solution, we have presented a complete solution for blockchain-based Agriculture and Food (Agri-Food) supply chain. It leverages the key features of blockchain and smart contracts, deployed over ethereum blockchain network. Although blockchain provides immutability of data and records in the network, it still fails to solve some major problems in supply chain management like credibility of the involved entities, accountability of the trading process and traceability of the products. Therefore, there is a need of a reliable system that ensures traceability, trust and delivery mechanism in Agri-Food supply chain. In the proposed system, all transactions are written to blockchain which ultimately uploads the data to Interplanetary File Storage System (IPFS). The storage system returns a hash of the data which is stored on blockchain and ensures efficient, secure and reliable solution. Our system provides smart contracts along with their algorithms to show interaction of entities in the system. Furthermore, simulations and evaluation of smart contracts along with the security and vulnerability analyses are also presented in this work. C1 [Shahid, Affaf; Javaid, Nadeem; Alam, Masoom] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad 44000, Pakistan. [Almogren, Ahmad] King Saud Univ, Comp Sci Dept, Coll Comp & Informat Sci, Chair Cyber Secur, Riyadh 11633, Saudi Arabia. [Al-Zahrani, Fahad Ahmad] Umm AlQura Univ, Comp Engn Dept, Mecca 24381, Saudi Arabia. [Zuair, Mansour] King Saud Univ, Comp Engn Dept, Coll Comp & Informat Sci, Chair Cyber Secur, Riyadh 11543, Saudi Arabia. C3 COMSATS University Islamabad (CUI); King Saud University; Umm Al Qura University; King Saud University RP Javaid, N (corresponding author), COMSATS Univ Islamabad, Dept Comp Sci, Islamabad 44000, Pakistan.; Almogren, A (corresponding author), King Saud Univ, Comp Sci Dept, Coll Comp & Informat Sci, Chair Cyber Secur, Riyadh 11633, Saudi Arabia. EM ahalmogren@ksu.edu.sa; nadeemjavaidqau@gmail.com CR AlTawy R, 2017, ANN CONF PRIV SECUR, P15, DOI 10.1109/PST.2017.00013 Andoni M, 2019, RENEW SUST ENERG REV, V100, P143, DOI 10.1016/j.rser.2018.10.014 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Chen YL, 2017, IEEE INT CONF BIG DA, P2652 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Hao J.T., 2018, J COMPUT, V29, P158, DOI [10.3966/199115992018122906015, DOI 10.3966/199115992018122906015] Hasan HR, 2018, IEEE ACCESS, V6, P46781, DOI 10.1109/ACCESS.2018.2866512 Josang A, 2007, DECIS SUPPORT SYST, V43, P618, DOI 10.1016/j.dss.2005.05.019 Li Z, 2018, PLAST RECON SURG-SER, P187, DOI 10.1007/978-981-10-3400-8_7 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Lu QH, 2017, IEEE SOFTWARE, V34, P21, DOI 10.1109/MS.2017.4121227 Luu L, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P254, DOI 10.1145/2976749.2978309 Malhotra K., 2019, OPERATIONS MANAGEMEN Nakasumi M, 2017, CONF BUS INFORM, V1, P140, DOI 10.1109/CBI.2017.56 Reyna A, 2018, FUTURE GENER COMP SY, V88, P173, DOI 10.1016/j.future.2018.05.046 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Schaub A, 2016, IFIP ADV INF COMM TE, V471, P398, DOI 10.1007/978-3-319-33630-5_27 Shahid Affaf, 2020, Advanced Information Networking and Applications. Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020). Advances in Intelligent Systems and Computing (AISC 1151), P12, DOI 10.1007/978-3-030-44041-1_2 Tian F, 2017, INT C SERV SYST SERV, V1, P6, DOI DOI 10.1109/ICSSSM.2017.7996119 Toyoda K, 2017, IEEE ACCESS, V5, P17465, DOI 10.1109/ACCESS.2017.2720760 Tripoli M., 2018, 3 CC BYNCSA FAO ICTS Truffle Suite, GAN GAN QUICKST DOC Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Turri AM, 2017, J CONSUM AFF, V51, P329, DOI 10.1111/joca.12133 Wang S., 2019, IEEE ACCESS, V7 Wang SP, 2018, IEEE ACCESS, V6, P38437, DOI 10.1109/ACCESS.2018.2851611 Wood G., 2015, HDB DIGITAL CURRENCY Yang L, 2019, J IND INF INTEGR, V15, P80, DOI 10.1016/j.jii.2019.04.002 Yang ZY, 2017, 2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI), DOI 10.1145/3025453.3025842 NR 31 TC 93 Z9 96 U1 15 U2 95 PY 2020 VL 8 BP 69230 EP 69243 DI 10.1109/ACCESS.2020.2986257 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000549823900008 DA 2022-12-14 ER PT J AU Pisciotta, A Tutone, L Saiano, F AF Pisciotta, Antonino Tutone, Livia Saiano, Filippo TI Distribution of YLOID in soil-grapevine system (Vitis vinifera L.) as tool for geographical characterization of agro-food products. A two years case study on different grafting combinations SO FOOD CHEMISTRY DT Article DE Lanthanoids; Cabernet Sauvignon; Nero d'Avola; Geographical origin; ICP-MS; Traceability ID RARE-EARTH-ELEMENTS; CHARDONNAY BERRIES; TRACE-ELEMENTS; PLANT; ORIGIN; TRACEABILITY; BEHAVIOR; EUROPIUM; RATIOS; GROWTH AB The knowledge of a chemistry relationship between the soil and the agricultural products is an important tool for the quality assessment of food. We studied YLOID (Y, La and lanthanoids), recognized as very useful tracers due their coherent and predictable behavior, to trace and evaluate their distribution from soil to the grape in Vitis vinifera L. Because much of the world's viticulture is based on grafting, and rootstocks have proved affect vine growth, yield, fruit and wine quality, we carried out experimental trials to analyse the YLOID distribution of two different red cultivars, grafted onto six different rootstocks, on the same soil. The YLOID amounts, the relationship Heavy vs Light YLOID and the pattern of YLOID were calculated. The results showed that the different grafting combinations were not able to induce significant differences in YLOID uptake from the soil maintaining the same fingerprint (with the exception of Eu). (C) 2016 Elsevier Ltd. All rights reserved. C1 [Pisciotta, Antonino; Tutone, Livia; Saiano, Filippo] Univ Palermo, Dipartimento Sci Agr & Forestali, Viale Sci & 4, I-90128 Palermo, Italy. C3 University of Palermo RP Saiano, F (corresponding author), Univ Palermo, Dipartimento Sci Agr & Forestali, Viale Sci & 4, I-90128 Palermo, Italy. EM filippo.saiano@unipa.it CR Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 Ortiz-Villajos JAA, 2012, VITIS, V51, P111 [Anonymous], 2001, TRACE METALS SOILS P, DOI DOI 10.1201/9781420039900 Baroni MV, 2015, J AGR FOOD CHEM, V63, P4638, DOI 10.1021/jf5060112 Bertoldi D, 2009, VITIS, V48, P49 Bertoldi D, 2011, J AGR FOOD CHEM, V59, P7224, DOI 10.1021/jf2006003 Bibak A, 1999, COMMUN SOIL SCI PLAN, V30, P2409, DOI 10.1080/00103629909370382 Brioschi L, 2013, PLANT SOIL, V366, P143, DOI 10.1007/s11104-012-1407-0 BYRNE RH, 1995, GEOCHIM COSMOCHIM AC, V59, P4575, DOI 10.1016/0016-7037(95)00303-7 Cao XD, 2000, INT J ENVIRON AN CH, V76, P295, DOI 10.1080/03067310008034137 Censi P, 2014, SCI TOTAL ENVIRON, V473, P597, DOI 10.1016/j.scitotenv.2013.12.073 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Durante C, 2016, FOOD CHEM, V210, P648, DOI 10.1016/j.foodchem.2016.04.108 Durante C, 2015, FOOD CHEM, V173, P557, DOI 10.1016/j.foodchem.2014.10.086 Fu FF, 2001, PLANT SOIL, V235, P53, DOI 10.1023/A:1011837326556 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 Henderson P., 1984, DEV GEOCHEMISTRY, V2 Johnson GV, 2007, PLANT PHYSIOL BIOCH, V45, P302, DOI 10.1016/j.plaphy.2007.03.012 Kabata-Pendias A, 2004, GEODERMA, V122, P143, DOI 10.1016/j.geoderma.2004.01.004 Kruk J, 2003, BIOCHEMISTRY-US, V42, P14862, DOI 10.1021/bi0351413 Laveuf C, 2009, GEODERMA, V154, P1, DOI 10.1016/j.geoderma.2009.10.002 Li FL, 1998, ENVIRON POLLUT, V102, P269, DOI 10.1016/S0269-7491(98)00063-3 Liang T, 2008, J RARE EARTH, V26, P7, DOI 10.1016/S1002-0721(08)60027-7 Lorenzo R. di, 2005, XIV International GESCO Viticulture Congress, Geisenheim, Germany, 23-27 August, 2005, P493 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Marchionni S, 2016, FOOD CHEM, V190, P777, DOI 10.1016/j.foodchem.2015.06.026 Ouyang J, 2003, J BIOTECHNOL, V102, P129, DOI 10.1016/S0168-1656(03)00019-1 Pepi S, 2016, ENVIRON MONIT ASSESS, V188, DOI [10.1007/s10661-016-5490-1, 10.1007/s10661-016-] Rao CRM, 2010, ANAL CHIM ACTA, V662, P128, DOI 10.1016/j.aca.2010.01.006 Reid LM, 2006, TRENDS FOOD SCI TECH, V17, P344, DOI 10.1016/j.tifs.2006.01.006 Samczynski Z, 2012, SCI WORLD J, DOI 10.1100/2012/216380 Sastri V.R., 2003, MODERN ASPECTS RARE Tian HE, 2003, BIOL TRACE ELEM RES, V93, P257, DOI 10.1385/BTER:93:1-3:257 Tyler G, 2004, PLANT SOIL, V267, P191, DOI 10.1007/s11104-005-4888-2 Wahid PA, 2000, J PLANT NUTR, V23, P329, DOI 10.1080/01904160009382019 Wang YQ, 1997, J RADIOANAL NUCL CH, V219, P99, DOI 10.1007/BF02040273 WEDEPOHL KH, 1995, GEOCHIM COSMOCHIM AC, V59, P1217, DOI 10.1016/0016-7037(95)00038-2 Xu XK, 2002, SCI TOTAL ENVIRON, V293, P97, DOI 10.1016/S0048-9697(01)01150-0 YANG XC, 1989, SCIENCE, V243, P1068, DOI 10.1126/science.2466333 Zeng FL, 2003, BIOL TRACE ELEM RES, V93, P271, DOI 10.1385/BTER:93:1-3:271 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y NR 41 TC 11 Z9 11 U1 0 U2 53 PD APR 15 PY 2017 VL 221 BP 1214 EP 1220 DI 10.1016/j.foodchem.2016.11.037 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000389909100154 DA 2022-12-14 ER PT J AU Dominik, S Duff, CJ Byrne, AI Daetwyler, H Reverter, A AF Dominik, S. Duff, C. J. Byrne, A., I Daetwyler, H. Reverter, A. TI Ultra-small SNP panels to uniquely identify individuals in thousands of samples SO ANIMAL PRODUCTION SCIENCE DT Article DE supply-chain traceability; provenance; genomics; beef ID ANIMAL IDENTIFICATION; TRACEABILITY; MARKERS AB Context. Genomic profiles are the only information source that can uniquely identify an individual but have not yet been strongly considered in the context of paddock to plate traceability due to the lack of value proposition. Aim. The aim of this study was to define the minimum number of single nucleotide polymorphisms (SNP) required to distinguish a unique genotype profile for each individual sample within a large given population. At the same time, ad hoc approaches were explored to reduce SNP density, and therefore, the size of the dataset to improve computing efficiency and storage requirements while maintaining informativeness to distinguish individuals. Methods. Data for this study included two datasets. One included 78 411 high-density SNP genotypes from commercial Angus cattle and the other 2107 from a research data (1000-bull genome data). In a stepwise approach, different-size SNP panels were explored, with the last step being a successive removal resulting in the smallest set of SNPs that still produced the maximum number of unique genotypes. Key results. First study that has demonstrated for large datasets, that ultra-small SNP panels with 20-23 SNPs can generate unique genotypes for up to similar to 80 000 individuals, allowing for 100% matching accuracy. Conclusions. Ultra-small SNP panels could provide an efficient method to approach the large-scale task of the traceability of beef products through the beef supply chain. C1 [Dominik, S.] CSIRO Agr & Food, FD McMasters Labs, 9308 New England Highway, Armidale, NSW 2350, Australia. [Duff, C. J.; Byrne, A., I] Angus Australia, 86 Glen Innes Rd, Armidale, NSW 2350, Australia. [Daetwyler, H.] Agr Victoria, AgriBio Ctr, 5 Ring Rd, Bundoora, Vic 3083, Australia. [Daetwyler, H.] La Trobe Univ, Plenty Rd & Kingsbury Dr, Bundoora, Vic 3083, Australia. [Reverter, A.] CSIRO Agr & Food, Queensland Bioprecinct, 306 Carmody Rd, St Lucia, Qld 4067, Australia. C3 Commonwealth Scientific & Industrial Research Organisation (CSIRO); La Trobe University; Commonwealth Scientific & Industrial Research Organisation (CSIRO) RP Dominik, S (corresponding author), CSIRO Agr & Food, FD McMasters Labs, 9308 New England Highway, Armidale, NSW 2350, Australia. EM sonja.dominik@csiro.au CR Aliloo H, 2021, ANIM PROD SCI, V61, P1958, DOI 10.1071/AN21098 Allen AR, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-5 Australian Bureau of Statistics, 2019, 71210 AUSTR BUR STAT Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Hayes BJ, 2019, ANNU REV ANIM BIOSCI, V7, P89, DOI 10.1146/annurev-animal-020518-115024 Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Nicoloso Letizia, 2013, Recent Pat Food Nutr Agric, V5, P9 Reverter A, 2020, J ANIM SCI, V98, DOI 10.1093/jas/skaa337 Weller JI, 2006, ANIM GENET, V37, P387, DOI 10.1111/j.1365-2052.2006.01455.x Werner FAO, 2004, ANIM GENET, V35, P44, DOI 10.1046/j.1365-2052.2003.01071.x Zhao J, 2020, BIOTECHNOL BIOTEC EQ, V34, P48, DOI 10.1080/13102818.2019.1711185 Zhao J, 2017, FOOD CONTROL, V78, P469, DOI 10.1016/j.foodcont.2017.03.017 NR 13 TC 1 Z9 1 U1 1 U2 2 PY 2021 VL 61 IS 18 BP 1796 EP 1800 DI 10.1071/AN21123 EA JUL 2021 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000674152500001 DA 2022-12-14 ER PT J AU Rogberg-Munoz, A Wei, S Ripoli, MV Guo, BL Carino, MH Castillo, N Castagnaso, EEV Liron, JP Durand, HFM Melucci, L Villarreal, E Peral-Garcia, P Wei, YM Giovambattista, G AF Rogberg-Munoz, A. Wei, S. Ripoli, M. V. Guo, B. L. Carino, M. H. Castillo, N. Villegas Castagnaso, E. E. Liron, J. P. Morales Durand, H. F. Melucci, L. Villarreal, E. Peral-Garcia, P. Wei, Y. M. Giovambattista, G. TI Foreign meat identification by DNA breed assignment for the Chinese market SO MEAT SCIENCE DT Article DE Traceability; Breed; DNA; China; Meat; Microsatellites ID SINGLE NUCLEOTIDE POLYMORPHISMS; POPULATION-STRUCTURE; GENETIC TRACEABILITY; CATTLE BREEDS; DIVERSITY; PRODUCTS; SNP AB Methods for individual identification are usually employed for traceability, whereas breed identification is useful to detect commercial frauds. In this study, Chinese Yellow Cattle (CYC) samples plus data from six Bos taurus breeds, two Bos indicus breeds, and one composite breed were used to develop an allocation test based on 22 microsatellites. The test allowed discriminating all foreign breeds from the CYC, although some CYC individuals were wrongly allocated as Limousin or Holstein, probably due to the recent introduction of these breeds into China. In addition, CYC evidenced a previously reported Zebu dine (south-north) and a possible structure within the B. taurus component that should be confirmed. An independent test performed with meat samples of unknown breed origin from Argentina allocated 92% of them to either Angus, Hereford, or their crossbreed, but none was identified as CYC. We conclude that the test is a suitable tool to certify meat of foreign breed origin and to detect adulterations of CYC beef labeled as imported meat. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Rogberg-Munoz, A.; Ripoli, M. V.; Carino, M. H.; Castillo, N.; Villegas Castagnaso, E. E.; Liron, J. P.; Morales Durand, H. F.; Peral-Garcia, P.; Giovambattista, G.] Univ Nacl La Plata, Fac Ciencias Vet, CONICET, Inst Genet Vet IGEVET,CCT La Plata, La Plata, Buenos Aires, Argentina. [Wei, S.; Guo, B. L.; Wei, Y. M.] Chinese Acad Agr Sci, Inst Agroprod Proc Sci & Technol, Key Lab Agroprod Proc & Qual Control, Minist Agr, Beijing 100193, Peoples R China. [Melucci, L.; Villarreal, E.] EEA INTA, Fac Ciencias Agr UNMDP, Unidad Integrada Balcarce, Balcarce, Argentina. C3 Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); National University of La Plata; Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; Instituto Nacional de Tecnologia Agropecuaria (INTA); National University of Mar del Plata RP Rogberg-Munoz, A (corresponding author), Univ Nacl La Plata, Fac Ciencias Vet, CONICET, IGEVET,CCT La Plata,FCV, B1900AVW,CC 296, La Plata, Buenos Aires, Argentina. EM arogberg@fcv.unlp.edu.ar CR Allen AR, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-5 Baldo A, 2010, MEAT SCI, V85, P671, DOI 10.1016/j.meatsci.2010.03.023 Cannings, 2007, HDB STAT GENETICS, P945 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Frost & Sullivan, 2012, STAT QUO FUT PROJ CH Garcia D, 2006, MEAT SCI, V72, P560, DOI 10.1016/j.meatsci.2005.09.005 Giovambattista G, 2001, J FORENSIC SCI, V46, P1484 Jia Shangang, 2007, Journal of Genetics and Genomics, V34, P510, DOI 10.1016/S1673-8527(07)60056-3 Kantanen J, 2000, J HERED, V91, P446, DOI 10.1093/jhered/91.6.446 Li Y, 2009, SPECTROSC SPECT ANAL, V29, P647, DOI 10.3964/j.issn.1000-0593(2009)03-0647-05 Longworth J. W., 2001, BEEF CHINA MacHugh DE, 1998, ANIM GENET, V29, P333, DOI 10.1046/j.1365-2052.1998.295330.x Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Nicoloso Letizia, 2013, Recent Pat Food Nutr Agric, V5, P9 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Patterson N, 2006, PLOS GENET, V2, P2074, DOI 10.1371/journal.pgen.0020190 Pritchard JK, 2000, GENETICS, V155, P945 Qiu H., 1993, WORLD REV ANIMAL, V76, pV0600 Schneider S., 2000, ARLEQUIN VERSION 200 Sun WB, 2008, GENET SEL EVOL, V40, P681, DOI 10.1051/gse:2008027 Syntesa Heiner Lehr, 2013, Recent Pat Food Nutr Agric, V5, P19, DOI 10.2174/2212798411305010005 USDA (United State Department of Agriculture), 2014, LIVESTOCK POULTRY WO van de Goor LHP, 2011, INT J LEGAL MED, V125, P111, DOI 10.1007/s00414-009-0353-8 Vignal A, 2002, GENET SEL EVOL, V34, P275, DOI [10.1186/1297-9686-34-3-275, 10.1051/gse:2002009] Zhang GX, 2007, ANIM GENET, V38, P550, DOI 10.1111/j.1365-2052.2007.01644.x NR 26 TC 9 Z9 9 U1 0 U2 28 PD DEC PY 2014 VL 98 IS 4 BP 822 EP 827 DI 10.1016/j.meatsci.2014.07.028 WC Food Science & Technology SC Food Science & Technology UT WOS:000342869600036 DA 2022-12-14 ER PT J AU Opatic, AM Necemer, M Lojen, S Vidrih, R AF Opatic, Anja Mahne Necemer, Marijan Lojen, Sonja Vidrih, Rajko TI Stable isotope ratio and elemental composition parameters in combination with discriminant analysis classification model to assign country of origin to commercial vegetables - A preliminary study SO FOOD CONTROL DT Article DE Food traceability; Geographical origin; Vegetables; Stable isotopes; Elemental content ID GEOGRAPHICAL ORIGIN; MASS-SPECTROMETRY; FOOD; TRACE; TRACEABILITY; TOOL; PRODUCTS; POTATOES; SAFETY; CARBON AB Recently, increased public attention has been paid to the geographical authentication of food, including vegetables, which are considered to be one of the major health-promoting components in a balanced diet. The purpose of the present study was to investigate the suitability of the use of isotopic compositions of light elements (delta C-13, delta N-15, delta O-18, delta S-34) in combination with multi-elemental fingerprinting (P, S, Cl, K, Ca, Mn, Fe, Zn, Br, Rb, Sr) to provide rapid, robust and inexpensive screening methods for distinguishing lettuce, sweet pepper, and tomato samples according to their given country of origin (i.e., Slovenia, Austria, Spain, Morocco, Italy, Greece), and thus ensuring their traceability in terms of their authenticity. The classification efficiency of the proposed multivariate statistical models using supervised pattern-recognition analysis, namely multivariate discriminant analysis, was sufficient for rapid and robust screening purpose. The predictions of the suggested discriminant analysis models per kind using cross-validation leave-one-out were 86.2%, 71.1% and 74.4% for lettuce, sweet pepper and tomato, respectively. The first use of the proposed methodology on vegetable samples on European and Mediterranean scales provides a valuable and necessary contribution to the development and implementation of a new national surveillance system that can be used to trace the geographical origins of vegetables. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Opatic, Anja Mahne; Lojen, Sonja] Jozef Stefan Inst, Dept Environm Sci, Jamova Cesta 39, Ljubljana, Slovenia. [Opatic, Anja Mahne; Lojen, Sonja] Jozef Stefan Int Postgrad Sch, Jamova Cesta 39, Ljubljana, Slovenia. [Necemer, Marijan] Jozef Stefan Inst, Dept Low & Medium Energy Phys, Jamova Cesta 39, Ljubljana, Slovenia. [Lojen, Sonja] Univ Nova Gorica, Fac Environm Sci, Vipayska 13, Nova Gorica, Slovenia. [Vidrih, Rajko] Biotech Fac, Dept Food Sci & Technol, Jamnikarjeva 101, Ljubljana, Slovenia. C3 Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Nova Gorica RP Opatic, AM (corresponding author), Jozef Stefan Inst, Dept Environm Sci, Jamova Cesta 39, Ljubljana, Slovenia. EM anja.mahne00@gmail.com CR Almeida CMR, 2003, J AGR FOOD CHEM, V51, P4788, DOI 10.1021/jf034145b [Anonymous], 1998, 121411998 SIST ENV Asfaha DG, 2011, J CEREAL SCI, V53, P170, DOI 10.1016/j.jcs.2010.11.004 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bat KB, 2016, FOOD CHEM, V203, P86, DOI 10.1016/j.foodchem.2016.02.039 Benson S, 2006, FORENSIC SCI INT, V157, P1, DOI 10.1016/j.forsciint.2005.03.012 Bontempo L, 2011, RAPID COMMUN MASS SP, V25, P899, DOI 10.1002/rcm.4935 Bourn D, 2002, CRIT REV FOOD SCI, V42, P1, DOI 10.1080/10408690290825439 Brand WA, 2014, PURE APPL CHEM, V86, P425, DOI 10.1515/pac-2013-1023 Brescia MA, 2002, RAPID COMMUN MASS SP, V16, P2286, DOI 10.1002/rcm.860 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Chung IM, 2016, FOOD CHEM, V212, P48, DOI 10.1016/j.foodchem.2016.05.161 Coplen TB, 2016, PURE APPL CHEM, V88, P1203, DOI 10.1515/pac-2016-0302 Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 Di Giacomo F, 2007, J AGR FOOD CHEM, V55, P860, DOI 10.1021/jf062690h European Commission, 2016, EU QUAL LOG Fontes J.C.H., 1980, HDB ENV ISOTOPE GEOC, P75, DOI [10.1016/B978-0-444-41780-0.50009-2, DOI 10.1016/B978-0-444-41780-0.50009-2.A] Furia E, 2011, J AGR FOOD CHEM, V59, P8450, DOI 10.1021/jf201556e Gat JR, 1996, ANNU REV EARTH PL SC, V24, P225, DOI 10.1146/annurev.earth.24.1.225 Inacio CT, 2015, CRIT REV FOOD SCI, V55, P1206, DOI 10.1080/10408398.2012.689380 Kendall C., 1995, SOLUTE MODELING CATC, P261 Krivachy N, 2015, FOOD CONTROL, V48, P143, DOI 10.1016/j.foodcont.2014.06.002 Kropf U, 2010, FOOD CHEM, V121, P839, DOI 10.1016/j.foodchem.2009.12.094 Longobardi F, 2011, FOOD CHEM, V124, P1708, DOI 10.1016/j.foodchem.2010.07.092 Luo DH, 2015, FOOD CHEM, V174, P197, DOI 10.1016/j.foodchem.2014.11.006 Mizota C, 1996, GEODERMA, V71, P77, DOI 10.1016/0016-7061(95)00091-7 Necemer M., 2010, HDB PHYTOREMEDIATION, P1 Necemer M, 2016, J FOOD COMPOS ANAL, V52, P16, DOI 10.1016/j.jfca.2016.07.002 Necemer M, 2008, SPECTROCHIM ACTA B, V63, P1240, DOI 10.1016/j.sab.2008.07.006 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3285, DOI 10.1021/jf1040433 Otero N, 2005, APPL GEOCHEM, V20, P1473, DOI 10.1016/j.apgeochem.2005.04.002 Palacios-Morillo A, 2014, TALANTA, V128, P15, DOI 10.1016/j.talanta.2014.04.025 Pilgrim TS, 2010, FOOD CHEM, V118, P921, DOI 10.1016/j.foodchem.2008.08.077 Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Rogers KM, 2008, J AGR FOOD CHEM, V56, P4078, DOI 10.1021/jf800797w Rummel S, 2010, FOOD CHEM, V118, P890, DOI 10.1016/j.foodchem.2008.05.115 Schellenberg A, 2010, FOOD CHEM, V121, P770, DOI 10.1016/j.foodchem.2009.12.082 Schmidt HL, 2005, ISOT ENVIRON HEALT S, V41, P223, DOI 10.1080/10256010500230072 Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 Serret MD, 2008, ANN APPL BIOL, V153, P243, DOI 10.1111/j.1744-7348.2008.00259.x SMITH BN, 1971, PLANT PHYSIOL, V47, P380, DOI 10.1104/pp.47.3.380 Smith RG, 2005, J AGR FOOD CHEM, V53, P4041, DOI 10.1021/jf040166+ Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Walcroft AS, 1997, AUST J PLANT PHYSIOL, V24, P57, DOI 10.1071/PP96025 Wu YL, 2015, FOOD CHEM, V174, P553, DOI 10.1016/j.foodchem.2014.11.096 Yun Seok-In, 2008, Journal of Applied Biological Chemistry, V51, P262, DOI 10.3839/jabc.2008.041 Zhang L, 2012, J ZHEJIANG UNIV-SC B, V13, P824, DOI 10.1631/jzus.B1200046 NR 48 TC 19 Z9 21 U1 2 U2 59 PD OCT PY 2017 VL 80 BP 252 EP 258 DI 10.1016/j.foodcont.2017.05.010 WC Food Science & Technology SC Food Science & Technology UT WOS:000404198800032 DA 2022-12-14 ER PT J AU Costa, TE Santos, DFL Rodrigues, SV AF Costa, Tiago Eid Lopes Santos, David Ferreira Rodrigues, Santiago Valcacer TI Economic viability in bovine confinement system with traceability SO CUSTOS E AGRONEGOCIO ON LINE DT Article ID BEEF-CATTLE; MATO-GROSSO; FEEDLOT; PERFORMANCE; STATE; TERMINATION; PROPERTY; NELLORE; CARCASS C1 [Costa, Tiago Eid; Lopes Santos, David Ferreira] Univ Estadual Paulista, UNESP, Via de Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil. [Rodrigues, Santiago Valcacer] Univ Fortaleza UNIFOR, Adm Empresas, Rua Deputado Hesiquio Fernandes 202, BR-59920000 Sao Miguel, RN, Brazil. C3 Universidade Estadual Paulista; Universidade Fortaleza RP Costa, TE (corresponding author), Univ Estadual Paulista, UNESP, Via de Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil. EM tiago@fncosta.com.br; david.lopes@unesp.br; santiago.valcacer@gmail.com CR Assaf Neto A., 2014, FINANCAS CORPORATIVA Barbieri Rayner Sversut, 2016, Interações (Campo Grande), V17, P357, DOI 10.20435/1984-042X-2016-v.17-n.3(01) BERK J., 2010, FUNDAMENTOS FINANCAS Bicalho FL, 2014, ARQ BRAS MED VET ZOO, V66, P1112, DOI 10.1590/1678-6369 BRANDAO F. T., 2007, REV CIENCIAS EMPRESA, V4, P7 Cáceres Daniel M, 2015, Mundo agrar., V16 Cardoso E. O., 2014, Semina: Ciencias Agrarias (Londrina), V35, P2643 Cócaro Henri, 2007, JISTEM J.Inf.Syst. Technol. Manag., V4, P353 Conceicao EV, 2019, AGRIC FINANCE REV, V79, P519, DOI 10.1108/AFR-10-2018-0088 COSTA I. R. B., 2019, CUSTOS GRONEGOCIO ON, V15, P81 COSTA JR, 2014, J ANIM SCI, V91, P1811 Cunha Moisés Ferreira da, 2014, Rev. Adm. (São Paulo), V49, P251, DOI 10.5700/rausp1144 DA SILVA S. Z., 2011, REV POLITICA AGRICOL, P23 Damodaran A., 2007, VALUATION APPROACHES DANTHINE JP, 2005, INTERMEDIATE FINANCI DEL BASTIAN-PINTO C., 2015, BRAZILIAN BUSINESS R, V12, P102 DIAS-FILHO M.B., 2011, REV BRAS ZOOTECN, V40, P243 EHRHARDT M. C., 2012, ADM FINANCEIRA Estremote M, 2017, CUST AGRONEGOCIO, V13, P20 Fabricio ED, 2017, CIENC RURAL, V47, DOI 10.1590/0103-8478cr20160516 Ferrazza RDA, 2013, B IND ANIM, V70, P110, DOI 10.17523/bia.v70n2p110 Geron L. J. V., 2014, Semina: Ciencias Agrarias (Londrina), V35, P2673 GIMENES R. M. T, 2015, CONTABILIDADE VISTA, V26, P90 Leite M, 2017, CUST AGRONEGOCIO, V13, P203 Lopes LS, 2011, CIENC AGROTEC, V35, P774, DOI 10.1590/S1413-70542011000400017 Lopes Marcos Aurélio, 2013, Rev. Ceres, V60, P465 Lopes MA, 2008, CIENC AGROTEC, V32, P288, DOI 10.1590/S1413-70542008000100041 Santos DFL, 2016, CUST AGRONEGOCIO, V12, P222 Santos DFL, 2013, CUST AGRONEGOCIO, V9, P129 Neto ORM, 2011, REV BRAS ZOOTECN, V40, P1080, DOI 10.1590/S1516-35982011000500020 Mandarino RA, 2013, ARQ BRAS MED VET ZOO, V65, P1463, DOI 10.1590/S0102-09352013000500027 Melz Laércio Juarez, 2014, Rev. Econ. Sociol. Rural, V52, P743, DOI 10.1590/S0103-20032014000400007 Mendes Ricardo Evandro, 2006, Cienc. Rural, V36, P1524, DOI 10.1590/S0103-84782006000500028 Montoro SB, 2019, J CLEAN PROD, V226, P1082, DOI 10.1016/j.jclepro.2019.04.148 MONTORO SB, 2017, REV ENGENHARIA AGRIC, V37, P353, DOI DOI 10.1590/1809-4430-ENG.AGRIC.V37N2P353-365/2017 Moreira SA, 2009, CUST AGRONEGOCIO, V5, P132 NAAS ID, 2015, REV ENGENHARIA AGRIC, V35, P340, DOI DOI 10.1590/1809-4430-ENG.AGRIC.V35N2P340-349/2015 NICHELE Evelyn Mangilli, 2015, Rev. bras. saúde prod. anim., V16, P699, DOI 10.1590/S1519-99402015000300020 Nicoloso C, 2013, REV AGRONEGOCIOS MEI, V6, P79 PACHECO P. S., 2016, AGROPAMPA, V1, P86 Pacheco PS, 2015, CIENC RURAL, V45, P492, DOI 10.1590/0103-8478cr20140631 Pacheco Paulo Santana, 2014, Ciênc. anim. bras., V15, P420, DOI 10.1590/1089-6891v15i425747 Parish J. A., 2014, Professional Animal Scientist, V30, P43 Moi PCP, 2017, CUST AGRONEGOCIO, V13, P350 Retallick KM, 2013, J ANIM SCI, V91, P5954, DOI 10.2527/jas.2013-6156 Rodrigues L. C., 2010, INFORM EC, V40, P31 Rodrigues Rinaldo, 2012, Rev. bras. saúde prod. anim., V13, P244 Yin R. K., 2015, ESTUDO CASOPLANEJAME ZANETTE P. M., 2012, REV BRAS MILHO SORGO, V11, P86 NR 49 TC 3 Z9 3 U1 0 U2 2 PD JUL-SEP PY 2019 VL 15 IS 3 BP 206 EP 237 WC Agricultural Economics & Policy; Business; Economics SC Agriculture; Business & Economics UT WOS:000502818300012 DA 2022-12-14 ER PT J AU Wu, LH Gong, XR Qin, SS Chen, XJ Zhu, D Hu, WY Li, QG AF Wu, Linhai Gong, Xiaoru Qin, Shasha Chen, Xiujuan Zhu, Dian Hu, Wuyang Li, Qingguang TI Consumer preferences for pork attributes related to traceability, information certification, and origin labeling: Based on China's Jiangsu Province SO AGRIBUSINESS DT Article ID WILLINGNESS-TO-PAY; CHOICE EXPERIMENT; FRESH PRODUCE; FOOD SAFETY; BEEF; ACCEPTANCE; QUALITY; MILK; US AB In this study, 110 consumers in Wuxi, China's Jiangsu Province were surveyed for their preferences for traceable pork in a real choice experiment. Using random parameters logit and latent class logit models, results revealed that consumers had the highest willingness to pay (WTP) for government certification of traceability information authenticity. Consumers also had higher WTP for origin labeling compared to uncertified traceability information. Moreover, traceability to slaughter and processing was viewed as a substitute for local farming labeling and complement to non-local farming labeling. Despite the heterogeneity among consumer groups, all consumers had some positive WTP for the local farming labeling attribute of traceable pork. Therefore, it is beneficial to include origin labeling in the traceable food attribute systems during the initial construction of traceable food markets in China. C1 [Wu, Linhai; Gong, Xiaoru; Qin, Shasha; Chen, Xiujuan; Li, Qingguang] Jiangnan Univ, Sch Business, Food Safety Res Base Jiangsu Prov, Wuxi, Peoples R China. [Wu, Linhai] Synerget Innovat Ctr Food Safety & Nutr, Wuxi, Peoples R China. [Chen, Xiujuan] Hong Kong Polytech Univ, Synerget Innovat Ctr Food Safety & Nutr, Hong Kong, Hong Kong, Peoples R China. [Zhu, Dian] Soochow Univ, Sch Dongwu Business, Suzhou, Peoples R China. [Hu, Wuyang] Univ Kentucky, Dept Agr Econ, Lexington, KY 40506 USA. C3 Jiangnan University; Hong Kong Polytechnic University; Soochow University - China; University of Kentucky RP Wu, LH (corresponding author), 1800 Lihu Ave, Wuxi 214122, Jiangsu, Peoples R China. EM wlh6799@126.com; gxrhiahia@163.com; qinshasha11@163.com; cxj8710@163.com; 15150157673@139.com; wuyang.hu@uky.edu; rainyb@yeah.net CR Abidoye B. O., 2011, Journal of Agricultural and Applied Economics, V43, P1 Adamowicz W, 1998, AM J AGR ECON, V80, P64, DOI 10.2307/3180269 Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Banterle A, 2008, AGRIBUSINESS, V24, P320, DOI 10.1002/agr.20169 Ben-Akiva M. E., 1985, DISCRETE CHOICE ANAL, V9 Bernabeu R, 2005, MEAT SCI, V71, P464, DOI 10.1016/j.meatsci.2005.04.027 Bolliger C., 2008, 12 C EUR ASS AGR EC Bu Fan, 2013, Asian Agricultural Research, V5, P121 Bureau of Statistics of Wuxi & National Bureau of Statistics Survey Office in Wuxi, 2015, WUX STAT YB 2015 CASWELL JA, 1992, AM J AGR ECON, V74, P460, DOI 10.2307/1242500 Chang KL, 2013, J INT FOOD AGRIBUS M, V25, P42, DOI 10.1080/08974438.2013.724002 Chen Q, 2013, FOOD QUAL PREFER, V28, P419, DOI 10.1016/j.foodqual.2012.10.008 Chern WS, 2012, FOOD POLICY, V37, P511, DOI 10.1016/j.foodpol.2012.04.002 Clemens R, 2002, WHY CANT US BEEF COM DARBY MR, 1973, J LAW ECON, V16, P67, DOI 10.1086/466756 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Dong Y. D, 2010, J CHINESE ANIMAL QUA, V27, P25 Enneking U, 2004, EUR REV AGRIC ECON, V31, P205, DOI 10.1093/erae/31.2.205 Fang F, 2008, MEAT RES, V6, P3 Galliano D, 2011, AGRIBUSINESS, V27, P379, DOI 10.1002/agr.20272 Gracia A, 2011, AM J AGR ECON, V93, P1358, DOI 10.1093/ajae/aar054 Grunert KG, 1997, FOOD QUAL PREFER, V8, P157, DOI 10.1016/S0950-3293(96)00038-9 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hu WY, 2005, CAN J AGR ECON, V53, P83, DOI 10.1111/j.1744-7976.2005.04004.x Jaeger SR, 2004, FOOD QUAL PREFER, V15, P701, DOI 10.1016/j.foodqual.2004.04.002 Jiang L. H., 2009, J CHINESE I FOOD SCI, V2, P87 KRINSKY I, 1986, REV ECON STAT, V68, P715, DOI 10.2307/1924536 Lancaster K. J, 1996, J POLITICAL EC, V4, P132 Lim KH, 2014, AGRIBUSINESS, V30, P17, DOI 10.1002/agr.21365 Liu J. P., 2009, THESIS Loureiro M. L., 2004, AM AGR EC ASS ANN M, V08 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lusk JL, 2005, AM J AGR ECON, V87, P393, DOI 10.1111/j.1467-8276.2005.00730.x Mennecke BE, 2007, J ANIM SCI, V85, P2639, DOI 10.2527/jas.2006-495 NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630 Ortega DL, 2014, CHINA ECON REV, V28, P17, DOI 10.1016/j.chieco.2013.11.001 Ouma E, 2007, AM J AGR ECON, V89, P1005, DOI 10.1111/j.1467-8276.2007.01022.x Pouta E, 2010, FOOD QUAL PREFER, V21, P539, DOI 10.1016/j.foodqual.2010.02.004 Rayner A. J., 1993, CURRENT ISSUES AGR E Rossi PE, 1996, MARKET SCI, V15, P321, DOI 10.1287/mksc.15.4.321 Skreli E, 2012, INT FOOD AGRIBUSINES, V15 Starbird SA, 2006, J AGR RESOUR ECON, V31, P14 Sun S. M., 2012, CHINESE RURAL EC, V10, P24 Tsakiridou E, 2011, J FOOD PROD MARK, V17, P211, DOI 10.1080/10454446.2011.548749 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 Van Loo EJ, 2011, FOOD QUAL PREFER, V22, P603, DOI 10.1016/j.foodqual.2011.02.003 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Verbeke W., 2009, Estey Centre Journal of International Law and Trade Policy, V10, P20 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wu L., 2014, INTRO 2013 CHINA DEV, P341 Wu L.H., 2014, CHINA POPUL RESOUR E, V24, P35 Wu L. H., 2012, CHINAS RURAL EC, V334, P13 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LinHai, 2014, Chinese Rural Economy, P58 Wu LH, 2015, CHINA AGR ECON REV, V7, P303, DOI 10.1108/CAER-11-2013-0153 Wu LH, 2012, CAN J AGR ECON, V60, P317, DOI 10.1111/j.1744-7976.2011.01236.x Wu LH, 2011, BRIT FOOD J, V113, P519, DOI 10.1108/00070701111123998 Wu X.M., 2007, CHINESE RURAL EC, V9, P17 Yue CY, 2009, HORTSCIENCE, V44, P366, DOI 10.21273/HORTSCI.44.2.366 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhang M., 2014, US JAPAN WORLD AGR, V2, P136 Zhang T., 2013, BUSINESS TRADE IND, V15, P25 Zhu D., 2013, J PUBLIC MANAGEMENT, V10, P129 NR 64 TC 25 Z9 25 U1 2 U2 67 PD SUM PY 2017 VL 33 IS 3 BP 424 EP 442 DI 10.1002/agr.21509 WC Agricultural Economics & Policy; Economics; Food Science & Technology SC Agriculture; Business & Economics; Food Science & Technology UT WOS:000405533200008 DA 2022-12-14 ER PT J AU Liu, L Zuo, ZT Xu, FR Wang, YZ AF Liu, Lu Zuo, Zhi-tian Xu, Fu-rong Wang, Yuan-zhong TI Study on Quality Response to Environmental Factors and Geographical Traceability of WildGentiana rigescens Franch SO FRONTIERS IN PLANT SCIENCE DT Article DE Gentiana rigescens; environmental factors; iridoid content; quantitative determination; Fourier transform infrared; quality control; geographical traceability ID INFRARED-SPECTROSCOPY; PRECIPITATION; TEMPERATURE; DISCRIMINATION; ANTIOXIDANT; VEGETATION; PATTERNS; ROOTS AB Gentiana rigescensFranch. ex Hemsl. is an important medicinal plant in China and the over exploitation of wild resources has affected its quality and clinical efficacy. The accumulation of plant secondary metabolites is not only determined by their genetic characteristics but also influenced by environmental factors. At present, many studies on evaluating the environmental conditions of its planting area are still in the qualitative stage. Therefore, it is necessary to establish a systematic evaluation method to deeply analyze the impact of environmental factors on the quality of medicinal materials and quickly verify the geographical origin. In this study, the contents of five iridoids (loganic acid, swertiamarin, sweroside, gentiopicroside and 6'-O-beta-D-glucopyranosylgentiopicroside) ofG. rigescensfrom 45 different origins (including 441 individuals) of Yunnan Province in China were analyzed by high performance liquid chromatography. Analytical procedures of one-way analysis of variance, correlation analysis, principal components analysis, and hierarchical cluster analysis were employed to interpret the correlation of iridoid content and environmental factors. Fourier transform infrared spectroscopy (FT-IR) combined with two multivariate analysis methods (partial least squares discriminant analysis; support vector machines, SVM) was used to discriminate four major producing areas (158 individuals). The combination of SVM with grid search algorithm achieved an accuracy of 100% in the test set. One-way analysis of variance showed that the contents of five iridoids in root tissues ofG. rigescensvaried significantly among different origins, which was also verified by the chemometrics analysis results of hierarchical cluster analysis. The results of correlation analysis indicated that the high value of altitude and precipitation were unfavorable for the accumulation of these five iridoids. A correlation between increase of temperature and iridoid accumulation was observed. This study provided a certain theoretical basis for the resource protection and development ofG. rigescensbased on the correlation analysis between the ecological environment factors and quality. C1 [Liu, Lu; Xu, Fu-rong] Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming, Yunnan, Peoples R China. [Liu, Lu; Zuo, Zhi-tian; Wang, Yuan-zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming, Yunnan, Peoples R China. C3 Yunnan University of Chinese Medicine; Yunnan Academy of Agricultural Sciences RP Xu, FR (corresponding author), Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming, Yunnan, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming, Yunnan, Peoples R China. EM xfrong99@163.com; boletus@126.com CR Ackerly DD, 2000, BIOSCIENCE, V50, P979, DOI 10.1641/0006-3568(2000)050[0979:TEOPET]2.0.CO;2 Assogbadjo AE, 2006, ANN BOT-LONDON, V97, P819, DOI 10.1093/aob/mcl043 Ballabio D, 2013, ANAL METHODS-UK, V5, P3790, DOI 10.1039/c3ay40582f Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Bowling DR, 2002, OECOLOGIA, V131, P113, DOI 10.1007/s00442-001-0851-y Chao M. X., 2013, PHARM BIOTECHNOL, V20, P365, DOI [10.1109/CONTEL.2003.1215870, DOI 10.1109/CONTEL.2003.1215870] Chen GH, 2014, J FOOD DRUG ANAL, V22, P303, DOI 10.1016/j.jfda.2013.12.001 Chen QS, 2007, SPECTROCHIM ACTA A, V66, P568, DOI 10.1016/j.saa.2006.03.038 Chen Z. Y., 2001, CLIMATE YUNNAN GEN [褚博文 Chu Bowen], 2016, [中国实验方剂学杂志, Chinese Journal of Experimental Traditional Medical Formulae], V22, P213 Dhanoa M.S., 1994, J NEAR INFRARED SPEC, V2, P43, DOI [DOI 10.1255/JNIRS.30, 10.1255/jnirs.30] Gao LJ, 2010, BIOORGAN MED CHEM, V18, P2131, DOI 10.1016/j.bmc.2010.02.004 He TN, 2001, WORLDWIDE MONOGRAPH HERMS DA, 1992, Q REV BIOL, V67, P283, DOI 10.1086/417659 [黄林芳 Huang Linfang], 2012, [中草药, Chinese Traditional and Herbal Drugs], V43, P1249 Inouye H., 1996, TETRAHEDRON LETT, V7, P5229, DOI 10.1016/S0040-4039(01)89261-3 Inouye H, 1971, PHARMACOGNOSY PHYTOC, P290, DOI DOI 10.1007/978-3-642-65136-6-13 Jaishree V, 2010, J ETHNOPHARMACOL, V130, P103, DOI 10.1016/j.jep.2010.04.019 Jamieson MA, 2013, J CHEM ECOL, V39, P1204, DOI 10.1007/s10886-013-0340-x Kattel DB, 2013, THEOR APPL CLIMATOL, V113, P671, DOI 10.1007/s00704-012-0816-6 Kawabata A, 2001, INT J REMOTE SENS, V22, P1377, DOI 10.1080/01431160119381 KENNARD RW, 1969, TECHNOMETRICS, V11, P137, DOI 10.2307/1266770 Krithika R, 2015, SCI REP-UK, V5, DOI 10.1038/srep08258 Lim JH, 2007, ARCH PHARM RES, V30, P1590, DOI 10.1007/BF02977329 Lin H, 2009, CZECH J FOOD SCI, V27, P393, DOI 10.17221/82/2009-CJFS Linhart YB, 1996, ANNU REV ECOL SYST, V27, P237, DOI 10.1146/annurev.ecolsys.27.1.237 Liu Y, 2017, SCI REP-UK, V7, DOI 10.1038/srep43108 Liu ZJ, 2000, PHYSIOL PLANTARUM, V110, P483, DOI 10.1111/j.1399-3054.2000.1100409.x Ma YH, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-017-18422-7 Martelo-Vidal MJ, 2013, AUST J GRAPE WINE R, V19, P62, DOI 10.1111/ajgw.12003 [米丽菊 Mi Liju], 2015, [时珍国医国药, Lishizhen Medicine and Materia Medica Research], V26, P2656 Morison JIL, 1999, PLANT CELL ENVIRON, V22, P659, DOI 10.1046/j.1365-3040.1999.00443.x Mustafa AM, 2015, J FUNCT FOODS, V19, P164, DOI 10.1016/j.jff.2015.09.018 National Pharmacopoeia Committee, 2015, CHINESE PHARMACOPOEI Ozturk N, 2006, PLANTA MED, V72, P289, DOI 10.1055/s-2005-916198 Pei YF, 2019, ANAL METHODS-UK, V11, P113, DOI 10.1039/c8ay02363h Qi LM, 2017, J ANAL METHODS CHEM, V2017, DOI 10.1155/2017/3194146 Ramakrishna A, 2011, PLANT SIGNAL BEHAV, V6, P1720, DOI 10.4161/psb.6.11.17613 Seidel V, 2008, PHYTOTHER RES, V22, P1256, DOI 10.1002/ptr.2480 [苏文华 Su Wenhua], 2005, [中草药, Chinese Traditional and Herbal Drugs], V36, P1415 Tang R. P., 2013, J MT AGR BIOL, V32, P445, DOI [10.15958/j.cnki.sdnyswxb.2013.05.021, DOI 10.15958/J.CNKI.SDNYSWXB.2013.05.021] TREIMER JF, 1979, EUR J BIOCHEM, V101, P225, DOI 10.1111/j.1432-1033.1979.tb04235.x Wang CX, 2015, AGR WATER MANAGE, V161, P9, DOI 10.1016/j.agwat.2015.07.010 Wang Li, 2017, Southwest China Journal of Agricultural Sciences, V30, P267 Wang YM, 2013, J ETHNOPHARMACOL, V147, P341, DOI 10.1016/j.jep.2013.03.016 Wu DH, 2014, INT GEOSCI REMOTE SE, P855, DOI 10.1109/IGARSS.2014.6946559 Wu X, 2018, PHYTOMEDICINE, V44, P103, DOI 10.1016/j.phymed.2018.01.016 [徐关丽 Xu Guanli], 2013, [激光杂志, Laser Journal], V34, P96 Zhan H, 2017, SPECTROCHIM ACTA A, V183, P75, DOI 10.1016/j.saa.2017.04.034 Zhao YL, 2015, J AOAC INT, V98, P22, DOI 10.5740/jaoacint.13-395 Zhao ZZ, 2012, J ETHNOPHARMACOL, V140, P476, DOI 10.1016/j.jep.2012.01.048 Zheng D., 2015, INTRO PHYS GEOGRAPHY NR 52 TC 6 Z9 7 U1 3 U2 17 PD JUL 22 PY 2020 VL 11 AR 1128 DI 10.3389/fpls.2020.01128 WC Plant Sciences SC Plant Sciences UT WOS:000559205400001 DA 2022-12-14 ER PT J AU Bhatt, T Hickey, C McEntire, JC AF Bhatt, Tejas Hickey, Caitlin McEntire, Jennifer C. TI Pilot Projects for Improving Product Tracing along the Food Supply System SO JOURNAL OF FOOD SCIENCE DT Article DE FSMA; product tracing; public policy; traceability pilots AB In September 2011, the U. S. Food and Drug Administration (FDA) asked the Institute of Food Technologists (IFT) to execute product tracing pilot projects as described in Section 204 of the FDA Food Safety Modernization Act (FSMA). IFT collaborated with representatives from more than 100 organizations-including the U. S. Dept. of Agriculture, state departments of agriculture and public health, industry, and consumer groups, as well as not-for-profit organizations-to implement the pilots. The objectives of the pilot projects were 1) to identify and gather information on methods to improve product tracing of foods in the supply chain and 2) to explore and evaluate methods to rapidly and effectively identify the recipient of food to prevent or mitigate a foodborne illness outbreak and to address credible threats of serious adverse health consequences or death to humans or animals as a result of such food being adulterated or misbranded. IFT conducted evaluations to determine the impact of currently available technologies, types of data and formats, and the data acquisition process, as well as the use of technology on the ability to follow product movement through the supply chain. Results fromthe pilots found inconsistencies in the terminology, numbering systems, formatting, legibility, and occasionally the language that sometimes required IFT to contact the submitting firm to gain clarity, thus increasing the time required to capture data before any meaningful analysis could begin. However, the pilot participants appeared to have many of the tools and processes in place which are required to allow the capture and communication of critical track and trace information (such as, key data elements) at critical points of product transfer and transformation (such as, critical tracking events). IFT determined that costs associated with implementing a product tracing system can vary widely as determined by numerous factors: the size of the firm/facility, the method of product tracing already in use (manual or electronic), and the range of each firm's capabilities to implement or improve its product tracing system, to name a few. IFT found that there are several areas (such as uniformity and standardization, improved recordkeeping, enhanced planning and preparedness, better coordination and communication, and the use of technology) in which industry improvements and enhancements to FDA's processes would enable tracebacks and traceforwards to occur more rapidly. IFT developed 10 recommendations for FDA to consider for improving the state of system-wide food product tracing. The recommendations outlined in the report will enable FDA to conduct more rapid and effective investigations during foodborne illness outbreaks and other product tracing investigations, thus significantly enhancing protection of public health. C1 [Bhatt, Tejas] Inst Food Technologists, Washington, DC 20036 USA. [Hickey, Caitlin] Deloitte Consulting, Mclean, VA 22102 USA. [McEntire, Jennifer C.] Leavitt Partners, Salt Lake City, UT 84111 USA. C3 Deloitte Touche Tohmatsu Limited RP Bhatt, T (corresponding author), Inst Food Technologists, 1025 Connecticut Ave NW,Suite 503, Washington, DC 20036 USA. EM tbhatt@ift.org CR McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x U.S. Food and Drug Administration, 2004, EST MAINT REC PUBL H NR 2 TC 10 Z9 11 U1 1 U2 25 PD DEC PY 2013 VL 78 SU 2 SI SI BP B34 EP B39 DI 10.1111/1750-3841.12298 WC Food Science & Technology SC Food Science & Technology UT WOS:000331148000007 DA 2022-12-14 ER PT J AU Quevedo-Silva, F Lucchese-Cheung, T Spers, EE Alves, FV de Almeida, RG AF Quevedo-Silva, Filipe Lucchese-Cheung, Thelma Spers, Eduardo Eugenio Alves, Fabiana Villa de Almeida, Roberto Giolo TI The effect of Covid-19 on the purchase intention of certified beef in Brazil SO FOOD CONTROL DT Article DE Food retail; Beef consumption; Subjective knowledge; Traceability ID WILLINGNESS-TO-PAY; FOOD TRACEABILITY; RISK PERCEPTION; CONSUMER INVOLVEMENT; SUBJECTIVE KNOWLEDGE; MEAT SECTOR; INFORMATION; QUALITY; TRUST; SAFETY AB Some events in recent years have weakened consumers' trust regarding food safety in a number of countries, including bird flu, hormones and residue of veterinary medicine in meat and, more recently, Operation Weak Flesh. COVID-19 also led to a focus on the urgent need to seek strict production standards that ensure food safety. In this scenario of uncertainty, traceability and certification could be useful tools to improve the perception of trust and safety in production processes. Thus, the aim of this study is to analyze the effect of the COVID-19 pandemic on the purchase intention of certified beef. A quantitative study was conducted with 862 Brazilian consumers. The data were treated using structural equation modeling. The results show that the level of subjective knowledge of certification is related to the importance attributed to traceability and purchase intention. The concern towards legality of slaughterhouses and to traceability was shown to be related to purchase intention. Furthermore, the higher the level of concern over COVID-19, the more important the influence of traceability becomes with regard to meat purchase intention. C1 [Quevedo-Silva, Filipe; Lucchese-Cheung, Thelma] Univ Fed Mato Grosso do Sul, Sch Business & Adm, Av Sen Filinto Muler,1015 Cidade Univ, BR-79046460 Campo Grande, MS, Brazil. [Spers, Eduardo Eugenio] Univ Sao Paulo ESALQ, Dept Econ Adm & Social Sci, Padua Dias Ave 11, BR-13418900 Piracicaba, SP, Brazil. [Alves, Fabiana Villa; de Almeida, Roberto Giolo] Brazilian Agr Res Corp, Embrapa Beef Cattle Radio Maia Ave 830, BR-79106550 Campo Grande, MS, Brazil. C3 Universidade Federal de Mato Grosso do Sul; Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) RP Quevedo-Silva, F (corresponding author), Rua Cristiano Machado,214, BR-79112060 Campo Grande, MS, Brazil. EM filquevedo@gmail.com; thelma.lucchese@gmail.com; edespers@usp.br; fabiana.alves@embrapa.br; roberto.giolo@embrapa.br CR Aertsens J, 2011, BRIT FOOD J, V113, P1353, DOI 10.1108/00070701111179988 Alves F. V., 2015, CARNE CARBONO NEUTRO, P210 Pardo MA, 2016, FOOD CONTROL, V62, P277, DOI 10.1016/j.foodcont.2015.10.048 Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 Aprile MC, 2012, INT J CONSUM STUD, V36, P158, DOI 10.1111/j.1470-6431.2011.01092.x Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Biasini B, 2021, TRENDS FOOD SCI TECH, V111, P191, DOI 10.1016/j.tifs.2021.02.062 Bitzios M, 2017, EUR J RISK REGUL, V8, P541, DOI 10.1017/err.2017.27 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Brazilian Institute of Geography and Statistics-IBGE, 2020, PESQUISA NACL AMOSTR BRUCKS M, 1985, J CONSUM RES, V12, P1, DOI 10.1086/209031 Burnier PC, 2019, J INT FOOD AGRIBUS M, pNIL_1, DOI 10.1080/08974438.2019.1599755 Carlson JP, 2009, J CONSUM RES, V35, P864, DOI 10.1086/593688 Rossi MDC, 2017, FOOD CONTROL, V73, P681, DOI 10.1016/j.foodcont.2016.09.016 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Chen SE, 2020, J FOOD SCI, V85, P517, DOI 10.1111/1750-3841.15046 Cornish LS, 2015, PSYCHOL MARKET, V32, P558, DOI 10.1002/mar.20800 Cunha L. M., 2008, SEGURANCA QUALIDADE, V4, P2008 Dandage K, 2017, FOOD CONTROL, V71, P217, DOI 10.1016/j.foodcont.2016.07.005 Freire OBD, 2017, REV GEST TECNOL, V17, P10, DOI 10.20397/2177-6652/2017.v17i3.1206 FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Gellynck X, 2006, MEAT SCI, V74, P161, DOI 10.1016/j.meatsci.2006.04.013 Grunert KG, 2004, MEAT SCI, V66, P259, DOI 10.1016/S0309-1740(03)00130-X Gurhan-Canli Z, 2003, J CONSUM RES, V30, P105 Hair J. F, 2010, MULTIVARIATE DATA AN, V7 Hair J.F., 2018, ADV ISSUES PARTIAL L, DOI DOI 10.1007/978-3-319-71691-6 Henchion MM, 2017, MEAT SCI, V128, P1, DOI 10.1016/j.meatsci.2017.01.006 Hobbs JE, 2016, WOODHEAD PUBL FOOD S, V301, P321, DOI 10.1016/B978-0-08-100310-7.00017-X Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x IBGE, 2010, I BRASILEIRO GEOGRAF Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 Kehagia O, 2007, EUROMED J BUS, V2, P173, DOI 10.1108/14502190710826040 Kemeny Z, 2016, WOODHEAD PUBL FOOD S, V301, P49, DOI 10.1016/B978-0-08-100310-7.00004-1 Kendall H, 2019, TRENDS FOOD SCI TECH, V94, P79, DOI 10.1016/j.tifs.2019.10.005 Kozup JC, 2003, J MARKETING, V67, P19, DOI 10.1509/jmkg.67.2.19.18608 Liu RD, 2015, FOOD QUAL PREFER, V41, P103, DOI 10.1016/j.foodqual.2014.11.007 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Lobb AE, 2007, FOOD QUAL PREFER, V18, P384, DOI 10.1016/j.foodqual.2006.04.004 Mazzocchi M, 2008, J AGR ECON, V59, P2, DOI 10.1111/j.1477-9552.2007.00142.x Menozzi D, 2019, BRIT FOOD J, V121, P3119, DOI 10.1108/BFJ-06-2019-0400 Menozzi D, 2015, FOOD CONTROL, V49, P40, DOI 10.1016/j.foodcont.2013.09.006 Mussell A., 2020, AGRIFOOD SUPPLY CHAI PARK CW, 1994, J CONSUM RES, V21, P71, DOI 10.1086/209383 Pennings JME, 2002, INT J RES MARK, V19, P91, DOI 10.1016/S0167-8116(02)00050-2 Peschel AO, 2016, APPETITE, V106, P78, DOI 10.1016/j.appet.2016.02.162 Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879 Quevedo-Silva F., 2020, PURCHASE BRIT FOOD J, V122, P722, DOI [10.1108/BFJ-07-2019-0491, DOI 10.1108/BFJ-07-2019-0491] Quevedo-Silva F, 2016, BRIT FOOD J, V118, P572, DOI 10.1108/BFJ-09-2015-0305 Ringle CM, 2014, REV BRASIL MARK, V13, P54, DOI 10.5585/remark.v13i2.2717 Rodrigues JF, 2017, INT J CONSUM STUD, V41, P735, DOI 10.1111/ijcs.12386 Ruby MB, 2016, APPETITE, V96, P546, DOI 10.1016/j.appet.2015.10.018 Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Song H., 2017, AM J IND BUS MANAG, V7, P1128, DOI DOI 10.4236/AJIBM.2017.710081 Stefani G, 2008, AGRIBUSINESS, V24, P523, DOI 10.1002/agr.20177 Tsakiridou E, 2008, INT J RETAIL DISTRIB, V36, P158, DOI 10.1108/09590550810853093 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Vanderroost M, 2014, TRENDS FOOD SCI TECH, V39, P47, DOI 10.1016/j.tifs.2014.06.009 Verbeke W, 2004, MEAT SCI, V67, P159, DOI 10.1016/j.meatsci.2003.09.017 Verbeke W, 2007, ANAL CHIM ACTA, V586, P2, DOI 10.1016/j.aca.2006.07.065 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Wang EST, 2019, FOOD QUAL PREFER, V78, DOI 10.1016/j.foodqual.2019.103723 Wu LH, 2011, BRIT FOOD J, V113, P519, DOI 10.1108/00070701111123998 Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 NR 64 TC 4 Z9 4 U1 7 U2 15 PD MAR PY 2022 VL 133 AR 108652 DI 10.1016/j.foodcont.2021.108652 EA NOV 2021 PN B WC Food Science & Technology SC Food Science & Technology UT WOS:000722679700015 DA 2022-12-14 ER PT J AU Schulz, L Tonsor, G AF Schulz, Lee Tonsor, Glynn TI "Cow-Calf Producer Valuations of Traceability System Attributes." SO JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS DT Meeting Abstract C1 [Schulz, Lee; Tonsor, Glynn] Michigan State Univ, E Lansing, MI 48824 USA. C3 Michigan State University NR 0 TC 0 Z9 0 U1 0 U2 1 PD DEC PY 2008 VL 33 IS 3 BP 502 EP 502 WC Agricultural Economics & Policy; Economics SC Agriculture; Business & Economics UT WOS:000262138900084 DA 2022-12-14 ER PT J AU Bergqvist, AS Forsberg, F Eliasson, C Wallenbeck, A AF Bergqvist, Ann-Sofi Forsberg, Frida Eliasson, Christina Wallenbeck, Anna TI Individual identification of pigs during rearing and at slaughter using microchips SO LIVESTOCK SCIENCE DT Article DE Swine; Ears tags; Electronic ID; Traceability ID ELECTRONIC IDENTIFICATION; INJECTABLE TRANSPONDERS; DEVICES; TRACEABILITY AB Identification of individual pigs is essential for management, traceability, breeding, trading and disease control in commercial pig production. Conventional identification methods used for pigs, such as ear tags and tattoos, are not sufficiently reliable due to losses and code erasing. This study investigated the retention rate, functionality and tissue damage of microchips compared with conventional electronic ear tags and assessed the effects of chip size and pig age at microchip injection. A larger proportion of small (95.2%) than large (82.5%) microchips were readable throughout the rearing period (p < 0.031). It was better to inject microchips when the piglets were 9-10 weeks old compared with 1-2 weeks (p = 0.058). Ear tags caused significantly more tissue damage than microchips (p=0.001). However, although microchips met the requirements of an identification system for pigs that is unique, easy to read, does not produce apparent disturbance to the animals and causes minimal pathological changes, the proportion of lost microchips was unacceptably high. Further research on chip type, pig age at marking and marking site is needed to find suitable methods for identification of individual pigs. (C) 2015 Elsevier B.V. All rights reserved. C1 [Bergqvist, Ann-Sofi] Swedish Univ Agr Sci, Div Reprod, Dept Clin Sci, Fac Vet Med & Anim Sci,SLU, S-75007 Uppsala, Sweden. [Forsberg, Frida; Eliasson, Christina; Wallenbeck, Anna] Swedish Univ Agr Sci, Dept Anim Breeding & Genet, Fac Vet Med & Anim Sci, SLU, Uppsala, Sweden. C3 Swedish University of Agricultural Sciences; Swedish University of Agricultural Sciences RP Bergqvist, AS (corresponding author), Swedish Univ Agr Sci, Div Reprod, Dept Clin Sci, Fac Vet Med & Anim Sci,SLU, POB 5054, S-75007 Uppsala, Sweden. EM Ann-Sofi.Bergqvist@slu.se CR Babot D, 2006, J ANIM SCI, V84, P2575, DOI 10.2527/jas.2006-119 Caja G, 2005, J ANIM SCI, V83, P2215 Janssens S, 1996, PREV VET MED, V25, P249, DOI 10.1016/0167-5877(95)00495-5 LAMMERS GH, 1995, VET REC, V136, P606, DOI 10.1136/vr.136.24.606 Leslie E, 2010, APPL ANIM BEHAV SCI, V127, P86, DOI 10.1016/j.applanim.2010.09.006 Madec F, 2001, REV SCI TECH OIE, V20, P523, DOI 10.20506/rst.20.2.1290 Marchi Enrico, 2007, Vet Ital, V43, P97 Merks J., 1999, PIG NEWS INFO, V11, P35 Prola L, 2010, ITAL J ANIM SCI, V9, P183, DOI 10.4081/ijas.2010.e35 Santamarina C, 2007, J ANIM SCI, V85, P497, DOI 10.2527/jas.2006-317 SJV, 2013, MARKN JOURN REG Stark KDC, 1998, LIVEST PROD SCI, V53, P143, DOI 10.1016/S0301-6226(97)00154-1 NR 12 TC 4 Z9 4 U1 0 U2 12 PD OCT PY 2015 VL 180 BP 233 EP 236 DI 10.1016/j.livsci.2015.06.025 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000362382200032 DA 2022-12-14 ER PT J AU Peng, YQ Zhang, LX Song, ZX Yan, J Li, XX Li, ZB AF Peng, Yaoqi Zhang, Lingxian Song, Zhixing Yan, Jin Li, Xinxing Li, Zhenbo TI A QR code based tracing method for fresh pork quality in cold chain SO JOURNAL OF FOOD PROCESS ENGINEERING DT Article ID NEAR-INFRARED SPECTROSCOPY; TRACEABILITY SYSTEM; FOOD TRACEABILITY; FACE RECOGNITION; ELECTRONIC NOSE; PREDICTION; SAFETY; MEAT; EFFICIENT; PRODUCTS AB Fresh pork is a meat that many people choose to eat in daily lives, and it is becoming more important to devise a method for ensuring the quality and safety of fresh pork. Combined with the meat quality of the cold chain and the environmental information collection program, this study presents a QR code based tracing method for a quality tracing system. This method includes a correction recognition test for the final design of the QR code. The results show that QR code can store large amounts of traceability information, with strong error correction capability, and can provide a great advantage in scanning recognition. In the process of consumption, consumers can easily obtain fresh pork quality information by scanning the QR code with a mobile phone, rather than having to choose a piece of meat to purchase based only on a visual observation of the meat. Practical applicationsIn this study, the design of the temperature acquisition scheme was verified in SMEs (small and medium enterprises), the results show that the data transmission is stable. The generated QR codes were tested in some supermarkets, have strong error correction capability, and can meet the retroactive requirements. According to this research, when the damage to the traceability barcode is beyond the range of 30% before the barcode enters circulation, the barcode needs to be reprinted. C1 [Peng, Yaoqi; Zhang, Lingxian; Song, Zhixing; Yan, Jin; Li, Xinxing; Li, Zhenbo] China Agr Univ, 17 Qinghua Donglu, Beijing 100083, Peoples R China. [Peng, Yaoqi; Song, Zhixing; Yan, Jin; Li, Xinxing] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [Song, Zhixing; Yan, Jin] Minist Agr, Key Lab Agr Informat, Acquisit Technol, Beijing 100083, Peoples R China. C3 China Agricultural University; Ministry of Agriculture & Rural Affairs RP Li, XX; Li, ZB (corresponding author), China Agr Univ, 17 Qinghua Donglu, Beijing 100083, Peoples R China. EM lxxcau@cau.edu.cn; lizb@cau.edu.cn CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Abate AF, 2007, PATTERN RECOGN LETT, V28, P1885, DOI 10.1016/j.patrec.2006.12.018 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Zubeldia BB, 2016, FOOD CONTROL, V59, P614, DOI 10.1016/j.foodcont.2015.06.046 Barbon APAC, 2016, COMPUT ELECTRON AGR, V127, P368, DOI 10.1016/j.compag.2016.06.028 Bui TV, 2014, 2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014), P520, DOI 10.1109/IIH-MSP.2014.135 D'Ostuni V, 2016, FOOD CONTROL, V62, P104, DOI 10.1016/j.foodcont.2015.10.025 Gendel Y, 2013, AQUACULT ENG, V52, P27, DOI 10.1016/j.aquaeng.2012.07.005 Hammer N, 2016, LIVEST SCI, V187, P125, DOI 10.1016/j.livsci.2016.03.007 Huang L, 2014, FOOD CHEM, V145, P228, DOI 10.1016/j.foodchem.2013.06.073 Jung H, 2014, INFORM SCIENCES, V274, P156, DOI 10.1016/j.ins.2014.02.061 Kitsos P, 2003, MICROELECTRON J, V34, P975, DOI 10.1016/S0026-2692(03)00172-1 Kong SG, 2005, COMPUT VIS IMAGE UND, V97, P103, DOI 10.1016/j.cviu.2004.04.001 Li RP, 2016, FOOD CONTROL, V68, P14, DOI 10.1016/j.foodcont.2016.03.009 Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Ma JC, 2015, COMPUT ELECTRON AGR, V111, P92, DOI 10.1016/j.compag.2014.12.007 Maralit BA, 2013, FOOD CONTROL, V33, P119, DOI 10.1016/j.foodcont.2013.02.018 Margeirsson B, 2012, J FOOD ENG, V113, P87, DOI 10.1016/j.jfoodeng.2012.05.017 Miao JY, 2015, FOOD CONTROL, V56, P53, DOI 10.1016/j.foodcont.2015.03.013 Neyrinck E, 2015, FOOD BIOPROCESS TECH, V8, P2383, DOI 10.1007/s11947-015-1583-z Sanz-Valero J, 2016, CRIT REV FOOD SCI, V56, P973, DOI 10.1080/10408398.2012.742865 Sciortino R, 2016, COMPUT ELECTRON AGR, V127, P451, DOI 10.1016/j.compag.2016.07.004 Shih CW, 2016, COMPUT STAND INTER, V45, P62, DOI 10.1016/j.csi.2015.12.004 Simpson R, 2012, J FOOD PROCESS ENG, V35, P742, DOI 10.1111/j.1745-4530.2010.00623.x Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 Suvitha S, 2015, ASIAN J BIOMED PHARM, V04, P38 Tao FF, 2014, J FOOD ENG, V126, P98, DOI 10.1016/j.jfoodeng.2013.11.006 Tarjan L, 2014, COMPUT ELECTRON AGR, V109, P1, DOI 10.1016/j.compag.2014.08.015 Tian XJ, 2013, J FOOD ENG, V119, P744, DOI 10.1016/j.jfoodeng.2013.07.004 Wachter-Zeh A., 2012, DES CODES CRYPT, V71, P261 Wang LX, 2013, COMPUT ELECTRON AGR, V90, P14, DOI 10.1016/j.compag.2012.10.004 Wang X, 2017, COMPUT ELECTRON AGR, V135, P195, DOI 10.1016/j.compag.2016.12.019 Wang XW, 2013, COMPUT ELECTRON AGR, V99, P41, DOI 10.1016/j.compag.2013.08.025 Wu W. -C., 2013, LNEE, V253, P597, DOI [10.1007/978-94-007-6996-063, DOI 10.1007/978-94-007-6996-0_63] Zhang YJ, 2017, J FOOD PROCESS ENG, V40, DOI 10.1111/jfpe.12495 NR 35 TC 12 Z9 13 U1 4 U2 63 PD JUN PY 2018 VL 41 IS 4 AR e12685 DI 10.1111/jfpe.12685 WC Engineering, Chemical; Food Science & Technology SC Engineering; Food Science & Technology UT WOS:000435276700011 DA 2022-12-14 ER PT J AU El Sheikha, AF Menozzi, P AF El Sheikha, Aly Farag Menozzi, Philippe TI Potential geo-tracing tool for migrant insects by using 16S rDNA fingerprinting of bacterial communities by PCR-DGGE SO INTERNATIONAL JOURNAL OF TROPICAL INSECT SCIENCE DT Article DE Geo-location fingerprinting; Migrant insects; Insect pests; Helicoverpa armigera; Bacterial community; PCR-DGGE; Biological barcoding ID DIVERSITY; POPULATIONS; FRAGMENTS; ECOLOGY; GENES AB Insect movement in the landscape remains often poorly known and in some cases does not make it possible to understand the role of the different cultivated and wild habitats in the dynamics of useful and pest insects. Insects are among the greatest pests of agriculture, horticulture and forestry worldwide, inflicting damage and economic costs both directly and by transmitting plant viruses. There is a need for tracking the migrant insects in agroecosystems through space and time to establish their migation. Furthermore, tracing the origin of pest insects allows to design more rapid, efficient and environment-friendly control systems (less use of insecticides). Tracking insects can also help us better understanding their biology (e.g. insect population dynamics, geo-traceability, feeding behavior and other ecological interactions). However. tracking insects presents a considerable challenge as they are often small, cryptic and highly mobile organisms. The most common methods of analysis currently proposed (microsatellite markers, stable isotopes) do not allow for the moment to determine their geographical origin. The ecological niche occupied by the insects highly influences the bacterial communities associated with the insects. Hence understanding the diversity of bacterial communities could be an option to trace the orgins of the pest. This paper presents two new protocols used to extract bacterial DNA from the Cotton Bollworm. Helicoverpa armigera and the analysis of this DNA by PCR-DGGE. This promising method is proposed as a new traceability tool which provides insects with a unique biological barcode and makes it possible to trace back the insects to their original location. C1 [El Sheikha, Aly Farag] Jiangxi Agr Univ, Sch Biol Sci & Engn, Jiangxi Key Lab Conservat & Utilizat Fungal Resou, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. [El Sheikha, Aly Farag] McMaster Univ, Dept Biol, 1280 Main St West, Hamilton, ON L8S 4K1, Canada. [El Sheikha, Aly Farag] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm, Minufiya Govern, Egypt. [Menozzi, Philippe] Ctr Cooperat Int Rech Agron Dev CIRAD, F-34398 Montpellier 5, France. [Menozzi, Philippe] AfricaRice, 01 BP 2031, Cotonou, Benin. C3 Jiangxi Agricultural University; McMaster University; Egyptian Knowledge Bank (EKB); Menofia University; CIRAD; CGIAR; Africa Rice Center RP El Sheikha, AF (corresponding author), Jiangxi Agr Univ, Sch Biol Sci & Engn, Jiangxi Key Lab Conservat & Utilizat Fungal Resou, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China.; El Sheikha, AF (corresponding author), McMaster Univ, Dept Biol, 1280 Main St West, Hamilton, ON L8S 4K1, Canada.; El Sheikha, AF (corresponding author), Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm, Minufiya Govern, Egypt. EM elsheikha_aly@yahoo.com CR Ampe F, 1999, APPL ENVIRON MICROB, V65, P5464 ARONSON AI, 1986, MICROBIOL REV, V50, P1 Buchner P., 1965, ENDOSYMBIOSIS ANIMAL Chen BS, 2016, SCI REP-UK, V6, DOI 10.1038/srep29505 Diez B, 2001, APPL ENVIRON MICROB, V67, P2942, DOI 10.1128/AEM.67.7.2942-2951.2001 DILLON RJ, 1995, J INVERTEBR PATHOL, V66, P72, DOI 10.1006/jipa.1995.1063 El Sheikha A. F., 2010, THESIS El Sheikha AF, 2011, FRANCOBENIN SCI POST El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 FITT GP, 1989, ANNU REV ENTOMOL, V34, P17, DOI 10.1146/annurev.en.34.010189.000313 Frederick B. A., 2000, Proceedings of the X International Symposium on Biological Control of Weeds, Bozeman, Montana, USA, 4-14 July, 1999, P261 Gouda AC, 2011, THESIS Halpern M, 2007, MICROB ECOL, V53, P285, DOI 10.1007/s00248-006-9094-0 Hobson KA, 1999, OECOLOGIA, V120, P397, DOI 10.1007/s004420050872 Hui XA, 2006, CAN J MICROBIOL, V52, P1085, DOI 10.1139/W06-064 Husheer T, 2005, STABLE ISOTOPE INVES Kriticos D. J., 2005, New Zealand Plant Protection, V58, P1 Leesing R., 2005, THESIS U MONTPELLIER Menozzi P, 2007, Commun Agric Appl Biol Sci, V72, P375 Montet D., 2004, SEM FOOD SAF INT TRA Mrazek J, 2008, FOLIA MICROBIOL, V53, P229, DOI 10.1007/s12223-008-0032-z Muyzer G, 1999, CURR OPIN MICROBIOL, V2, P317, DOI 10.1016/S1369-5274(99)80055-1 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Nardi JB, 2002, J INSECT PHYSIOL, V48, P751, DOI 10.1016/S0022-1910(02)00105-1 Reeson AF, 2003, INSECT MOL BIOL, V12, P85, DOI 10.1046/j.1365-2583.2003.00390.x Rubenstein DR, 2004, TRENDS ECOL EVOL, V19, P256, DOI 10.1016/j.tree.2004.03.017 Sanchez-Contreras M, 2008, BIOTECHNOL GENET ENG, V25, P203, DOI 10.5661/bger-25-203 Senderovich Y, 2012, J INSECT SCI, V12, DOI 10.1673/031.012.14901 SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 Vilmos P, 1998, IMMUNOL LETT, V62, P59, DOI 10.1016/S0165-2478(98)00023-6 Zahner V, 2008, CURR MICROBIOL, V57, P564, DOI 10.1007/s00284-008-9243-4 Zhang H, 2008, J APPL MICROBIOL, V105, P1277, DOI 10.1111/j.1365-2672.2008.03867.x Zouache K, 2011, FEMS MICROBIOL ECOL, V75, P377, DOI 10.1111/j.1574-6941.2010.01012.x Zouache K, 2009, APPL ENVIRON MICROB, V75, P3755, DOI 10.1128/AEM.02964-08 NR 34 TC 10 Z9 11 U1 1 U2 7 PD MAR PY 2019 VL 39 IS 1 BP 9 EP 16 DI 10.1007/s42690-019-00002-z WC Entomology SC Entomology UT WOS:000473835000002 DA 2022-12-14 ER PT J AU Damak, F Bougi, MSM Araoka, D Baba, K Furuya, M Ksibi, M Tamura, K AF Damak, Fadwa Bougi, Mohamed Seddik Mahmoud Araoka, Daisuke Baba, Koji Furuya, Manami Ksibi, Mohamed Tamura, Kenji TI Soil geochemistry, edaphic and climatic characteristics as components of Tunisian olive terroirs: Relationship with the multielemental composition of olive oils for their geographical traceability SO EURO-MEDITERRANEAN JOURNAL FOR ENVIRONMENTAL INTEGRATION DT Article DE Biogeochemistry; ICP-MS; XRF; LA-ICP-MS; Elemental fingerprinting ID TRACE-ELEMENT; MINERAL ELEMENTS; ORIGIN; RICE; BIOAVAILABILITY; AUTHENTICITY; SEDIMENTS; IGNITION; CARBON AB Olive oil traceability based on the intrinsic chemical composition of the oil is becoming increasingly important due to the prevalence of fraudulent geographical labelling of olive oils. For a traceability tool to be valid, it should be based on olive oil properties that are clearly related to provenance factors. However, multielement analysis of the oil has been used as a traceability tool without any proof of a direct link between the multielemental composition and the geographical origin of the oil. In order to verify this link, Tunisian olive terroir components from the 11 major olive-producing regions were sampled to evaluate the influences of these components (especially soil and climate) on the geochemical composition of Tunisian olive oil. Overall, geochemical processes relating to strontium and rare-earth element (REE) enrichment were found to control the multelemental compositions of Tunisian soils. Even though olive oils from the 11 Tunisian olive-growing regions considered did not strongly reflect the geochemical signatures of the corresponding Tunisian soils, the concentrations of four elements in the oils, namely Fe, Ti, Ni and Ba, showed significant positive Spearman correlations with their concentrations in the bioavailable extracts from those soils. Moreover, there were numerous significant correlations of elements in the olive oil with soil chemical and climate parameters. Our results clearly confirm that the complex interactions of the olives with the climate and soil chemistry during cultivation significantly affect the multielemental composition of the resulting olive oil. This finding implies that the elemental profile of the olive oil is an effective and valid marker of the geographical origin of the oil, as it is significantly linked to oil provenance factors. It also explains the discrepancies between the geochemical signature of an oil and that of the soil in which the olives were grown, as climate parameters affect the transfer of that signature from soil to olives. This work therefore provides the basis for a scientifically based approach to olive oil traceability. The results of this work can be utilized by agricultural authorities to realise the multielement-based traceability of olive oils from various Tunisian regions. [GRAPHICS] . C1 [Damak, Fadwa; Tamura, Kenji] Univ Tsukuba, Lab Environm Soil Chem, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058577, Japan. [Bougi, Mohamed Seddik Mahmoud; Ksibi, Mohamed] Univ Sfax, Natl Sch Engineers Sfax ENIS, Environm Engn & Ecotechnol Lab LGEET, Route Soukra Km 4,Post Box 1173, Sfax 3038, Tunisia. [Damak, Fadwa; Araoka, Daisuke] AIST, Geol Survey Japan GSJ, 1-1-1 Higashi, Tsukuba, Ibaraki 3058567, Japan. [Baba, Koji; Furuya, Manami] Natl Agr & Food Res Org NARO, 3-1-3 Kannondai, Tsukuba, Ibaraki 3050856, Japan. [Damak, Fadwa] Univ Tokyo, Grad Sch Agr & Life Sci, Bunkyo Ku, 1-1-1 Yayoi, Tokyo 1138657, Japan. C3 University of Tsukuba; Universite de Sfax; Ecole Nationale dIngenieurs de Sfax (ENIS); National Institute of Advanced Industrial Science & Technology (AIST); National Agriculture & Food Research Organization - Japan; University of Tokyo RP Damak, F (corresponding author), Univ Tsukuba, Lab Environm Soil Chem, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058577, Japan.; Damak, F (corresponding author), AIST, Geol Survey Japan GSJ, 1-1-1 Higashi, Tsukuba, Ibaraki 3058567, Japan.; Damak, F (corresponding author), Univ Tokyo, Grad Sch Agr & Life Sci, Bunkyo Ku, 1-1-1 Yayoi, Tokyo 1138657, Japan. EM f.damak@yahoo.fr CR [Anonymous], 2013, J ENV ANAL TOXICOL Bakircioglu D, 2011, CLEAN-SOIL AIR WATER, V39, P728, DOI 10.1002/clen.201000501 Bazon Iva, 2013, Agriculturae Conspectus Scientificus, V78, P95 Beltran M, 2015, FOOD CHEM, V169, P350, DOI 10.1016/j.foodchem.2014.07.104 Ben Khedher M, 2012, ISOFAR NEWSLETT, V15, P7 Benincasa C, 2007, ANAL CHIM ACTA, V585, P366, DOI 10.1016/j.aca.2006.12.040 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Chandrajith R, 2005, CHEMOSPHERE, V58, P1415, DOI 10.1016/j.chemosphere.2004.09.090 Chen JY, 2010, J RARE EARTH, V28, P517, DOI 10.1016/S1002-0721(10)60271-2 Chiocchini F, 2016, FOOD CHEM, V202, P291, DOI 10.1016/j.foodchem.2016.01.146 Damak F, 2019, METHOD PROTOCOL, V2, DOI 10.3390/mps2030072 Damak F, 2019, FOOD CHEM, V283, P656, DOI 10.1016/j.foodchem.2019.01.082 Ejima T, 2018, ISL ARC, V27, DOI 10.1111/iar.12222 Greenough JD, 2005, GEOSCI CAN, V32, P129 Greenough JD, 2010, CAN J EARTH SCI, V47, P1093, DOI 10.1139/E10-055 Halim MA, 2015, ARAB J GEOSCI, V8, P3391, DOI 10.1007/s12517-014-1480-1 Han WX, 2011, ECOL LETT, V14, P788, DOI 10.1111/j.1461-0248.2011.01641.x Heiri O, 2001, J PALEOLIMNOL, V25, P101, DOI 10.1023/A:1008119611481 Houba VJG, 2000, COMMUN SOIL SCI PLAN, V31, P1299, DOI 10.1080/00103620009370514 Intawongse M, 2006, FOOD ADDIT CONTAM A, V23, P36, DOI 10.1080/02652030500387554 International Olive Council, 2017, OLIVAE OFF J INT OLI, P124 International Olive Council, 2016, COIT15NCNO3REV11 INT Japan International Cooperation Agency (JICA), 2014, NEWS TUN HAS YET CAP Kon Y, 2015, GEOCHEM J, V49, P351, DOI 10.2343/geochemj.2.0362 Laroussi-Mezghani S, 2015, FOOD CHEM, V173, P122, DOI 10.1016/j.foodchem.2014.10.002 Lernoud J., 2019, WORLD ORGANIC AGR ST Likudis Z, 2016, PRODUCTS FROM OLIVE TREE, P175, DOI 10.5772/64909 Longobardi F, 2014, VIRGIN OLIVE OIL PRO Ogidi E. G. O., 2018, Journal of Experimental Biology and Agricultural Sciences, V6, P116, DOI 10.18006/2018.6(1).116.123 Oliveri P, 2016, WOODHEAD PUBL FOOD S, P701, DOI 10.1016/B978-0-08-100220-9.00025-4 Pal DK, 2000, GLOBAL CLIMATE CHANG Papangelakis V.G., 2014, ERES 20141ST EUROPEA, P191 Paye HD, 2016, J GEOCHEM EXPLOR, V161, P27, DOI 10.1016/j.gexplo.2015.09.003 Pepi S, 2017, CHEM ERDE-GEOCHEM, V77, P121, DOI 10.1016/j.chemer.2017.01.003 Rahmani SM, 2019, EURO-MEDITERR J ENVI, V4, DOI 10.1007/s41207-019-0118-9 Ranalli A, 1999, GRASAS ACEITES, V50, P249, DOI 10.3989/gya.1999.v50.i4.663 Rayment G.E., 2002, AUSTR HDB SOIL WATER Reinhardt N, 2018, MINERALS-BASEL, V8, DOI 10.3390/min8120562 Shen SG, 2013, ANAL METHODS-UK, V5, P6177, DOI 10.1039/c3ay40700d Tyler G, 2001, PLANT SOIL, V230, P307, DOI 10.1023/A:1010314400976 Wali A, 2015, B ENVIRON CONTAM TOX, V94, P511, DOI 10.1007/s00128-015-1469-9 Wilson RA, 2007, GEOSCI CAN, V34, P77 Wright AL, 2008, COMMUN SOIL SCI PLAN, V39, P3074, DOI 10.1080/00103620802432931 Zeng FR, 2011, ENVIRON POLLUT, V159, P84, DOI 10.1016/j.envpol.2010.09.019 NR 44 TC 2 Z9 2 U1 1 U2 5 PD MAR 10 PY 2021 VL 6 IS 1 AR 37 DI 10.1007/s41207-021-00241-y WC Environmental Sciences SC Environmental Sciences & Ecology UT WOS:000627796700001 DA 2022-12-14 ER PT J AU Sengupta, U Kim, HM AF Sengupta, Ushnish Kim, Henry Michael TI Meeting Changing Customer Requirements in Food and Agriculture Through the Application of Blockchain Technology SO FRONTIERS IN BLOCKCHAIN DT Article DE agriculture; food; supply chains; blockchain; distributed database systems; canada; traceability; consumer ID TRACEABILITY; IMPLEMENTATION; DESIGN AB This research summarizes the implementation of blockchain technology in the food and agriculture industry in Canada. Our research indicates that blockchain solutions are an existing and proven set of technologies. We also describe how blockchain based supply chain traceability information has many more benefits than its current use for food safety and product recalls. We recommend that costs for development of blockchain based solutions should also be distributed across stakeholders, and apportioned by the relevant industry associations. Our research indicates that adoption of blockchain technology in agriculture will achieve critical mass earlier when the industry applies a consortium approach, in a regulatory environment that is supported by government. This report also makes recommendations relevant to the integration of blockchain for end consumers of food. C1 [Sengupta, Ushnish; Kim, Henry Michael] York Univ, Schulich Sch Business, Toronto, ON, Canada. C3 York University - Canada RP Sengupta, U; Kim, HM (corresponding author), York Univ, Schulich Sch Business, Toronto, ON, Canada. EM ushnish@yorku.ca; hmkim@yorku.ca CR Akaichi F, 2020, NUTRIENTS, V12, DOI 10.3390/nu12010120 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Bellantuono Nicola, 2018, International Journal of Technology, Policy and Management, V18, P336 Bloom JD, 2017, ENVIRON PLANN A, V49, P168, DOI 10.1177/0308518X16663207 Bowen Tan, 2018, Smart Blockchain. First International Conference, SmartBlock 2018. Proceedings: Lecture Notes in Computer Science (LNCS 121373), P167, DOI 10.1007/978-3-030-05764-0_18 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Canadian Agri-Food Trade Alliance, 2018, AGR FOOD EXP CAN AGR AGR FOOD EXP CAN AGR Cao SF, 2021, COMPUT ELECTRON AGR, V180, DOI 10.1016/j.compag.2020.105886 Casey MJ, 2018, TRUTH MACHINE BLOCKC Cho CH, 2020, ACCOUNT PERSPECT, V19, P181, DOI 10.1111/1911-3838.12232 Deloitte, 2018, BREAK BLOCKCH OP 201 BREAK BLOCKCH OP 201 Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Ding QY, 2020, IEEE ACCESS, V8, P6209, DOI 10.1109/ACCESS.2019.2962274 Edmiston J., 2020, FINANCIAL POST FINANCIAL POST Flood J, 2020, KNOWL ENG REV, V35, DOI 10.1017/S0269888920000016 Franke L, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12031068 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Government of Canada Canadian Food Inspection Agency, 2019, TARG REG REV AGR FOO TARG REG REV AGR FOO Grassroots Farmers Cooperative, 2019, GRASS ROOTS COOP GS1, 2018, GS1 DIG LINK TEXT GS1 DIG LINK TEXT GS1, 2017, GS1 GLOB TRAC STAND Gunday G., 2020, EUROPEAN SHOPPER WIL Halaburda H, 2018, COMMUN ACM, V61, P27, DOI 10.1145/3225619 He CY, 2020, CAN J AGR ECON, V68, P359, DOI 10.1111/cjag.12222 Hou B, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11051464 IFT, 2020, LEAF GREEN TRAC PIL LEAF GREEN TRAC PIL Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Keogh J. G., 2020, BUILDING FUTURE FOOD, V171, P171, DOI DOI 10.1016/B978-0-12-818956-6.00017-8 Kim H., 2018, SUPPLY CHAIN REVOLUT, P23 Kim HM, 2018, INTELL SYST ACCOUNT, V25, P18, DOI 10.1002/isaf.1424 Label Insight Inc, 2016, CONS DEM TRANSP IS S CONS DEM TRANSP IS S Lassoued R, 2015, FOOD POLICY, V52, P99, DOI 10.1016/j.foodpol.2014.12.003 Macready AL, 2020, FOOD POLICY, V92, DOI 10.1016/j.foodpol.2020.101880 Manning L, 2019, J HORTIC SCI BIOTECH, V94, P413, DOI 10.1080/14620316.2019.1574613 Mao DH, 2019, ARAB J SCI ENG, V44, P3439, DOI 10.1007/s13369-018-3537-z Markovic M, 2020, FRONT SUSTAIN FOOD S, V4, DOI 10.3389/fsufs.2020.563424 Mellizo P.P., 2018, J PARTICIPATION EMPL, V1, P162, DOI [10.1108/JPEO-10-2017-0001, DOI 10.1108/JPEO-10-2017-0001] Nelson D., 2018, GRAND RAPIDS BUSINES Omale G., 2019, GARTNER Produce Marketing Association, 2020, PROD TRAC IN PROD TRAC IN Public Health Agency of Canada, 2016, AEM Qian JP, 2020, FOOD ENERGY SECUR, V9, DOI 10.1002/fes3.249 Redman R., 2018, SUPERMARKET NEWS Redman R., 2019, SUPERMARKET NEWS Rejeb A, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11070161 Restaurants Canada, 2020, CAN REST NEED NAT WO CAN REST NEED NAT WO Sai K, 2019, 2019 FIRST IEEE INTERNATIONAL CONFERENCE ON TRUST, PRIVACY AND SECURITY IN INTELLIGENT SYSTEMS AND APPLICATIONS (TPS-ISA 2019), P36, DOI 10.1109/TPS-ISA48467.2019.00014 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sambo P., 2020, FINANCIALPOST Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Sayogo D.S., 2014, P 15 ANN INT C DIG G P 15 ANN INT C DIG G, P137 Sheikh A, 2020, IEEE ACCESS, V8, P8554, DOI 10.1109/ACCESS.2019.2963325 Simangunsong E, 2016, INT J OPER PROD MAN, V36, P1272, DOI 10.1108/IJOPM-12-2014-0599 Smart Virginia., 2020, CBC NEWS Smith R, 2017, W PRODUCER Statistics Canada, 2020, CAN CONS AD COVID 19 Sternberg HS, 2021, J BUS LOGIST, V42, P71, DOI 10.1111/jbl.12240 Stevens A., 2018, FOLLOW 4 EVALUATION, P13 Sylvester G, 2019, E AGR ACTION BLOCKCH Szabo N, 1996, EXTROPY J TRANSHUMAN Tao Q, 2019, IEEE ACCESS, V7, P51817, DOI 10.1109/ACCESS.2019.2911265 Tapscott Don, 2016, BLOCKCHAIN REVOLUTIO The Canadian Press, 2020, GLOBAL NEWS Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Wang WB, 2019, IEEE ACCESS, V7, P22328, DOI 10.1109/ACCESS.2019.2896108 Whelan P., 2020, CANADIAN CATTLEMEN Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2014, CAN J AGR ECON, V62, P545, DOI 10.1111/cjag.12050 Yang Q, 2018, LECT NOTES COMPUT SC, V11373, P111, DOI 10.1007/978-3-030-05764-0_12 Yuan P, 2019, IEEE ACCESS, V7, P6109, DOI 10.1109/ACCESS.2018.2888929 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 NR 71 TC 6 Z9 6 U1 8 U2 30 PD FEB 15 PY 2021 VL 4 AR 613346 DI 10.3389/fbloc.2021.613346 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science UT WOS:000678215300001 DA 2022-12-14 ER PT J AU Adams, F AF Adams, F TI Synchrotron radiation micro-X-ray fluorescence analysis: A tool to increase accuracy in-microscopic analysis SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS DT Article; Proceedings Paper CT 3rd International Conference on Synchrotron Radiation in Materials Science CY JAN 21-24, 2002 CL SINGAPORE, SINGAPORE DE microanalysis; synchrotron X-ray analysis; traceability ID SCATTERING AB Microscopic X-ray fluorescence (XRF) analysis has potential for development as a certification method and as a calibration tool for other microanalytical techniques. The interaction of X-rays with matter is well understood and modelling studies show excellent agreement between experimental data and calculations using Monte Carlo simulation. The method can be used for a direct iterative calculation of concentrations using available high accuracy physical constants. Average accuracy is in the range of 3-5% for micron sized objects at concentration levels of less than 1 ppm. with focused radiation from SR sources. The end-station ID18F of the ESRF is dedicated to accurate quantitative micro-XRF analysis including fast 2D scanning with collection of full X-ray spectra. Important aspects of the beamline are the precise monitoring of the intensity of the polarized, variable energy beam and the high reproducibility of the setup measurement geometry, instrumental parameters and long-term stability. (C) 2002 Elsevier Science B.V. All rights reserved. C1 Univ Instelling Antwerp, Dept Chem, B-261 Wilrijk, Belgium. C3 University of Antwerp RP Adams, F (corresponding author), Univ Instelling Antwerp, Dept Chem, Univ Pl 1, B-261 Wilrijk, Belgium. CR Adams F, 1998, ACCREDIT QUAL ASSUR, V3, P308, DOI 10.1007/s007690050252 ADAMS F, ENCY ANAL CHEM, V15, P13636 Grime GW, 1996, NUCL INSTRUM METH B, V109, P170, DOI 10.1016/0168-583X(95)00901-9 Janssens K, 1996, NUCL INSTRUM METH B, V109, P179, DOI 10.1016/0168-583X(95)01211-7 Janssens K.H.A., 2000, MICROSCOPIC XRAY FLU KEMPENAERS L, COMMUNICATION Richter W, 1999, FRESEN J ANAL CHEM, V365, P569, DOI 10.1007/s002160051524 Somogyi A, 2001, X-RAY SPECTROM, V30, P242, DOI 10.1002/xrs.494 Tougaard S, 1997, SURF INTERFACE ANAL, V25, P137, DOI 10.1002/(SICI)1096-9918(199703)25:3<137::AID-SIA230>3.0.CO;2-L Vincze L, 2002, ANAL CHEM, V74, P1128, DOI 10.1021/ac010789b Vincze L, 1999, J ANAL ATOM SPECTROM, V14, P529, DOI 10.1039/a808040b Vincze L, 1999, SPECTROCHIM ACTA B, V54, P1711, DOI 10.1016/S0584-8547(99)00094-4 NR 12 TC 10 Z9 13 U1 0 U2 1 PD JAN PY 2003 VL 199 BP 375 EP 381 AR PII S0168-583X(02)01563-X DI 10.1016/S0168-583X(02)01563-X WC Instruments & Instrumentation; Nuclear Science & Technology; Physics, Atomic, Molecular & Chemical; Physics, Nuclear SC Instruments & Instrumentation; Nuclear Science & Technology; Physics UT WOS:000180925400073 DA 2022-12-14 ER PT J AU Bohanec, M Boshkoska, BM Prins, TW Kok, EJ AF Bohanec, Marko Boshkoska, Biljana Mileva Prins, Theo W. Kok, Esther J. TI SIGMO: A decision support System for Identification of genetically modified food or feed products SO FOOD CONTROL DT Article DE Genetically modified crops; Food and feed products; Traceability; Unauthorised GMOs; Decision support system; Qualitative multi-attribute model ID MODIFIED ORGANISMS; TRACEABILITY; TOMATO AB Since their introduction in 1994, more and more genetically modified (GM) crops are grown worldwide and introduced in food or feed products. In the European Union (EU), the production, trade and marketing of GM products is strictly regulated, but the situation is becoming more complex due to the increasing number and complexity of GM crops, and asynchronic approval procedures with the major GM crop producing countries. Importers and traders are obliged to assess their respective supply chains for the potential presence of authorised and unauthorised GM organisms (GMOs), where wrong decisions may lead to substantial economic losses. This article presents a decision support system SIGMO aimed at guiding producers and traders with the assessment of the likelihood that their products may comprise authorised or unauthorised GM materials. The assessment is based on traceability data about the product (nature and origin of the raw materials, transportation aspects), as well as analytical results of the presence of GMOs in the final product or its ingredients. The approach uses a combination of data driven and model-driven decision support. SIGMO is composed of (1) a data base providing data about GMO crop species produced and approved in counties worldwide, (2) a multi-attribute model for the assessment of GMO presence in food/feed products, and (3) an on-line user interface. SIGMO helps producers and traders to better comply to valid EU GMO regulations and to better control their products and supply chains in terms of the unintended presence of (unauthorised) GMOs in a cost-effective way. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Bohanec, Marko; Boshkoska, Biljana Mileva] Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia. [Prins, Theo W.; Kok, Esther J.] RIKILT Wageningen UR, NL-6700 AE Wageningen, Netherlands. [Boshkoska, Biljana Mileva] Fac Informat Studies, Ljubljanska Cesta 32, Novo Mesto 8000, Slovenia. C3 Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Wageningen University & Research RP Bohanec, M (corresponding author), Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia. EM marko.bohanec@ijs.si CR Arulandhu A. J., 2016, ANAL BIOANALYTICAL C Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bertheau Y, 2013, GENETICALLY MODIFIED Bohanec M., 2013, GENETICALLY MODIFIED, P461 Bohanec M., 2015, DP11897 IJS J STEF I Bohanec M, 2013, INFORM-J COMPUT INFO, V37, P49 Broeders SRM, 2012, J BIOMED BIOTECHNOL, DOI 10.1155/2012/402418 CERA, 2012, GM CROP DAT COGEM, 2014, 201404 COGEM CGM Coleno FC, 2008, J FOOD ENG, V88, P306, DOI 10.1016/j.jfoodeng.2008.02.013 EU, 2015, EU REG AUTH GMOS FAO, 2014, FAO J Figueira J., 2005, MULTICRITERIA DECISI Fraiture MA, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/392872 Ishizaka A., 2013, MULTICRITERIA DECISI James C., 2014, GLOBAL STATUS COMMER, P32 JENSEN AH, 2009, BIOTECHNOL ADV, V27, P1071 Karlsen KM, 2013, FOOD CONTROL, V32, P409, DOI 10.1016/j.foodcont.2012.12.011 KRAMER MG, 1994, EUPHYTICA, V79, P293, DOI 10.1007/BF00022530 Krieger EK, 2008, HORTSCIENCE, V43, P962, DOI 10.21273/HORTSCI.43.3.962 Liang CJ, 2014, ANAL BIOANAL CHEM, V406, P2603, DOI 10.1007/s00216-014-7667-1 Philippidis G, 2010, SPAN J AGRIC RES, V8, P3, DOI 10.5424/sjar/2010081-1138 Power D.J., 2002, DECIS SUPPORT SYST Power D. J., 2012, DECISION SUPPORT ANA Scholtens I, 2013, J AGR FOOD CHEM, V61, P9097, DOI 10.1021/jf4018146 Singh M, 2016, FOOD CONTROL, V68, P20, DOI 10.1016/j.foodcont.2016.03.032 Turban E., 2007, DECISION SUPPORT BUS NR 27 TC 9 Z9 10 U1 1 U2 162 PD JAN PY 2017 VL 71 BP 168 EP 177 DI 10.1016/j.foodcont.2016.06.032 WC Food Science & Technology SC Food Science & Technology UT WOS:000384778400023 DA 2022-12-14 ER PT J AU Sharma, RK Rab, S Kumar, L Zafer, A Yadav, S AF Sharma, Raman Kumar Rab, Shanay Kumar, Lalit Zafer, Afaqul Yadav, Sanjay TI Design and Development of an Indigenous Cross-Floating Pressure Calibration System up to 140 MPa SO MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA DT Article DE Pressure balance; Pressure metrology; Accuracy; Precision; Traceability ID GAUGE AB Precise and accurate pressure measurements play a prominent role in several scientific applications. The pressure balances (PBs) are used as a primary standard for pressure measurements which requires utmost accuracy. In the case of calibration and characterization of these PBs, the method of cross-floating is an internationally accepted practice used for the comparison of 2 PBs for determining their metrological parameters, i.e. effective areas, distortion coefficient and zero pressure area etc. The present paper describes the work which has been carried out to develop a cross-floating pressure balance calibration system for an organization engaged in R&D activities and providing calibration services to the process industries. The study also describes some of the newly designed, developed, upgraded and fabricated components and devices for a cross-floating system in hydrostatic pressure range up to 150 MPa. The whole system consists of an upgraded hydraulic screw pump, newly designed, developed and fabricated oil reservoir, cross-float valve, use of two commercially available needle valves in combination with a pressure balance supplied by the customer with associated pressure fittings and connector in the pressure transmitting circuit. The characterization of the pressure balance was also done using the newly developed components/parts and instruments at 12 calibration points in the high pressure range (7-140) MPa and 11 calibration points in the low pressure range (0.2-7) MPa. The development process was started in the year 2018 and completed in the month of November 2020. The combined relative measurement uncertainty (at coverage factor, k = 2) associated in high pressure range is found to be 59.3 x 10(-6) and 42.3 x 10(-6) with associated pressure and effective area, respectively. Similarly, in low pressure range the combined relative measurement uncertainty (k = 2) is found to be 62.2 x 10(-6) and 47.3 x 10(-6) with associated pressure and effective area, respectively. The results obtained are found in excellent agreement with the values of uncertainty and the effective area reported by the manufacturer. This newly developed system will help the customer in providing in-house traceability to their own instruments as well services to the industries. C1 [Sharma, Raman Kumar; Rab, Shanay; Kumar, Lalit; Zafer, Afaqul; Yadav, Sanjay] Natl Phys Lab, CSIR, New Delhi 110012, India. C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR - National Physical Laboratory (NPL) RP Sharma, RK (corresponding author), Natl Phys Lab, CSIR, New Delhi 110012, India. EM ramansharma2007@gmail.com CR Bandyopadhyay AK., 2005, MED J MEAS CONTRL, V1, P138 Bandyopadhyay AK., 2005, MEASUREMENT SCI REV, V5, P104 Dadson R S, 1982, PRESSURE BALANCE THE Ehtesham B, 2020, MAPAN-J METROL SOC I, V35, P585, DOI 10.1007/s12647-020-00392-3 Gupta AC., 2005, J I ENG ID, V86, P49 Indicating and Recording Pressure Gauges,, 1991, VAC GAUG PRESS VAC G NABL, 2016, GUID EST EXPR UNC ME Pressure Balances, 1994, R110 OIML Rab, 2017, 5 ADMET C ADV METR N, P123 Rab S, 2020, MAPAN-J METROL SOC I, V35, P475, DOI 10.1007/s12647-020-00400-6 Rab S, 2019, MAPAN-J METROL SOC I, V34, P305, DOI 10.1007/s12647-019-00333-9 Rab S, 2021, INDIAN J PURE AP PHY, V59, P202 Rab S, 2019, REV SCI INSTRUM, V90, DOI 10.1063/1.5089953 Sabuga W, 2012, METROLOGIA, V49, DOI 10.1088/0026-1394/49/1A/07006 Voder B., 1975, EXPT THERMODYNAMICS, VII Yadav S, 2018, MAPAN-J METROL SOC I, V33, P347, DOI 10.1007/s12647-018-0270-8 Yadav S, 2007, METROLOGIA, V44, P222, DOI 10.1088/0026-1394/44/3/009 Yadav S, 2011, MAPAN-J METROL SOC I, V26, P133, DOI 10.1007/s12647-011-0014-5 Yadav S, 2007, METROL MEAS SYST, V14, P453 NR 19 TC 0 Z9 0 U1 0 U2 1 PD JUN PY 2021 VL 36 IS 2 SI SI BP 295 EP 303 DI 10.1007/s12647-021-00450-4 EA APR 2021 WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000645291600001 DA 2022-12-14 ER PT J AU Li, L Wen, B Zhang, XL Zhao, Y Duan, Y Song, XF Ren, S Wang, YH Fang, WP Zhu, XJ AF Li, Lei Wen, Bo Zhang, Xiaolei Zhao, Yue Duan, Yu Song, Xiangfei Ren, Shuang Wang, Yuhua Fang, Wanping Zhu, Xujun TI Geographical origin traceability of tea based on multi-element spatial distribution and the relationship with soil in district scale SO FOOD CONTROL DT Article DE Tea leaves; Soil; Mineral elements; Correlation; Traceability ID ICP-MS; INFRARED-SPECTROSCOPY; ELEMENTS; SAMPLES; IDENTIFICATION; HONEY; WINES; OES AB In this study, a discriminant model was established by determining mineral element contents in tea leaves and the soil, collected from Lishui, Jiangsu Province, China. The contents of 12 elements (Se, Zn, Ni, Mn, Cr, Pb, Mg, Ca, Cu, Al, Na, and K) were determined in both tea leaves and soil samples. Cluster analysis and principal component analysis (PCA) were employed for regional classification of tea samples. After data conversion and correlation analysis, spatial and quantitative prediction models were established by ordinary Kriging interpolation and multiple linear regressions. The results indicated a corresponding relationship of elements between tea and soil, and the cluster analysis and PCA showed a clear distinction between tea from the north to that from the middle and south of Lishui. Kriging interpolation predicted the levels of 12 elements, and among them, Se, Ca, and Cr showed a related spatial distribution. Three linear regression equations were established using Mn, Al, Ni, and K contents and soil pH, and these equations fitted well between predicted and actual values. The established linear equations can be used to identify the predominant mineral elements in tea plants and soil from Lishui and to identify the geographical origin of the tea product. (C) 2018 Elsevier Ltd. All rights reserved. C1 [Li, Lei; Wen, Bo; Zhang, Xiaolei; Zhao, Yue; Duan, Yu; Song, Xiangfei; Ren, Shuang; Wang, Yuhua; Fang, Wanping; Zhu, Xujun] Nanjing Agr Univ, Coll Hort, 1 Weigang, Nanjing 210095, Jiangsu, Peoples R China. C3 Nanjing Agricultural University RP Fang, WP; Zhu, XJ (corresponding author), Nanjing Agr Univ, Coll Hort, 1 Weigang, Nanjing 210095, Jiangsu, Peoples R China. EM 2015104087@njau.edu.cn; njauwb@njau.edu.cn; 2016104089@njau.edu.cn; 2016104088@njau.edu.cn; 2016804201@njau.edu.cn; 2016804142@njau.edu.cn; 2017804138@njau.edu.cn; wangyuhua@njau.edu.cn; fangwp@njau.edu.cn; zhuxujun@njau.edu.cn CR Altundag H, 2016, BIOL TRACE ELEM RES, V170, P508, DOI 10.1007/s12011-015-0468-3 Anderberg M.R., 2014, CLUSTER ANAL APPL PR, V19 [Anonymous], 2012, INTRO LINEAR REGRESS Azcarate SM, 2015, FOOD CONTROL, V57, P268, DOI 10.1016/j.foodcont.2015.04.025 Beltran M, 2015, FOOD CHEM, V169, P350, DOI 10.1016/j.foodchem.2014.07.104 Brown CE., 1998, APPL MULTIVARIATE ST, P155, DOI DOI 10.1007/978-3-642-80328-4_13 Brzezicha-Cirocka J, 2017, BIOL TRACE ELEM RES, V176, P429, DOI 10.1007/s12011-016-0849-2 Brzezicha-Cirocka J, 2016, ENVIRON MONIT ASSESS, V188, DOI 10.1007/s10661-016-5157-y Chudzinska M, 2011, FOOD CHEM TOXICOL, V49, P2741, DOI 10.1016/j.fct.2011.08.014 Cozzolino D, 2014, FOOD RES INT, V60, P262, DOI 10.1016/j.foodres.2013.08.034 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Durdic S, 2017, RSC ADV, V7, P2151, DOI 10.1039/c6ra25105f Fageria VD, 2001, J PLANT NUTR, V24, P1269, DOI 10.1081/PLN-100106981 Fang WP, 2014, HORTIC RES-ENGLAND, V1, DOI 10.1038/hortres.2014.35 Fang WP, 2016, CROP J, V4, P304, DOI 10.1016/j.cj.2016.02.001 Fung KF, 1999, ENVIRON POLLUT, V104, P197, DOI 10.1016/S0269-7491(98)00187-0 Geana I, 2013, FOOD CHEM, V138, P1125, DOI 10.1016/j.foodchem.2012.11.104 Gerdol R, 2006, CHEMOSPHERE, V64, P810, DOI 10.1016/j.chemosphere.2005.10.053 Gliszczynska-Swiglo A, 2017, FOOD ANAL METHOD, V10, P1800, DOI 10.1007/s12161-016-0739-4 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 Gringarten E, 2001, MATH GEOL, V33, P507, DOI 10.1023/A:1011093014141 Habte G, 2016, FOOD CHEM, V212, P512, DOI 10.1016/j.foodchem.2016.05.178 He Y, 2007, J FOOD ENG, V79, P1238, DOI 10.1016/j.jfoodeng.2006.04.042 Karak T, 2017, BIOL TRACE ELEM RES, V175, P475, DOI 10.1007/s12011-016-0783-3 Karak T, 2010, FOOD RES INT, V43, P2234, DOI 10.1016/j.foodres.2010.08.010 LEGENDRE P, 1998, DEV ENV MODELLING Lendl B, 2005, TRAC-TREND ANAL CHEM, V24, P488, DOI 10.1016/j.trac.2005.03.010 Lin GF, 2004, J HYDROL, V288, P288, DOI 10.1016/j.jhydrol.2003.10.008 Liu G., 2016, ENV EARTH SCI, V75 Lou YX, 2017, J ANAL METHODS CHEM, V2017, DOI 10.1155/2017/5454231 Ma GC, 2016, FOOD CONTROL, V59, P714, DOI 10.1016/j.foodcont.2015.06.037 Malvi U.R., 2011, KARNATAKA J AGRIC SC, V24 MATSUMOTO H, 1976, PLANT CELL PHYSIOL, V17, P627 Ndraha N, 2017, FOOD CONTROL, V78, P331, DOI 10.1016/j.foodcont.2017.02.051 Orlowski G, 2016, ENVIRON POLLUT, V219, P288, DOI 10.1016/j.envpol.2016.10.048 Ran J, 2016, SCI TOTAL ENVIRON, V544, P422, DOI 10.1016/j.scitotenv.2015.11.105 Ruey-Shun Chen, 2008, WSEAS Transactions on Information Science and Applications, V5, P1551 Scordino M, 2011, EUR FOOD RES TECHNOL, V232, P275, DOI 10.1007/s00217-010-1386-4 Szymczycha-Madeja A, 2013, J BRAZIL CHEM SOC, V24, P777, DOI 10.5935/0103-5053.20130102 Tavakoli L, 2008, J HAZARD MATER, V152, P737, DOI 10.1016/j.jhazmat.2007.07.039 Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 Tyler G, 2001, PLANT SOIL, V230, P307, DOI 10.1023/A:1010314400976 Zhao HY, 2017, FOOD CONTROL, V76, P82, DOI 10.1016/j.foodcont.2017.01.006 NR 43 TC 38 Z9 39 U1 4 U2 99 PD AUG PY 2018 VL 90 BP 18 EP 28 DI 10.1016/j.foodcont.2018.02.031 WC Food Science & Technology SC Food Science & Technology UT WOS:000432100800003 DA 2022-12-14 ER PT J AU Sica, D Esposito, B Malandrino, O Supino, S AF Sica, Daniela Esposito, Benedetta Malandrino, Ornella Supino, Stefania TI The role of digital technologies for the LCA empowerment towards circular economy goals: a scenario analysis for the agri-food system SO INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT DT Article; Early Access DE Circular economy; Agri-food system; Sustainability; Life cycle assessment; Digital technologies ID LIFE-CYCLE ASSESSMENT; OF-THE-ART; SUPPLY CHAIN; INDUSTRY 4.0; BIG DATA; SUSTAINABILITY ASSESSMENT; COEFFICIENT ALPHA; PERFORMANCE; MANAGEMENT; RELIABILITY AB Purpose This paper aims to develop a scenario analysis on the experts' perceptions of benefits and barriers related to adopting digital technologies for the life cycle assessment (LCA) to catalyse a circular economy transition in the agri-food system. Methods A literature review was performed to identify LCA's digital technologies that can be implemented within the agri-food system. Furthermore, an in-depth interview with a panel of senior researchers was conducted to establish a set of items and assess the perceived benefits and barriers associated with an "empowered LCA", i.e. a future-oriented LCA based on digital technologies. To this end, a two-stage exploratory factor analysis relying on the principal component analysis technique was carried out to refine the set of items. Finally, a covariance-based structural equation model was performed, built on a confirmatory factor analysis, to test the measurement model. Results and discussion The study's findings provide five constructs to explore the potential benefits and barriers related to adopting a digital technologies-based LCA (empowered LCA) for a circular economy transition in the agri-food system. More specifically, the benefits can be assessed using the following constructs: "benefits for the data collection and analysis", "benefits for the LCA analysts", "benefits for the management" and "benefits for traceability". In addition, the barriers have been evaluated using a single construct labelled "general barriers". Conclusions The study highlights the relevance of digital technologies for a circular economy transition to develop a more reliable LCA, enhancing legislative compliance and supporting the traceability processes in the agri-food system. The associated implications for LCA experts, agri-food managers and policymakers are presented. Furthermore, limitations and future research directions are also discussed. C1 [Sica, Daniela; Esposito, Benedetta; Malandrino, Ornella] Univ Salerno, Dept Management & Innovat Syst DISA MIS, I-84084 Salerno, Italy. [Supino, Stefania] San Raffaele Roma Univ, Dept Human Sci Promot Qual Life, I-00166 Rome, Italy. C3 University of Salerno RP Esposito, B (corresponding author), Univ Salerno, Dept Management & Innovat Syst DISA MIS, I-84084 Salerno, Italy. EM dsica@unisa.it; besposito@unisa.it; ornellam@unisa.it; stefania.supino@uniroma5.it CR Acquaye AA, 2015, INT J PROD ECON, V164, P472, DOI 10.1016/j.ijpe.2014.12.014 Alexandratos N., 2012, WORLD AGR 2030 2050, DOI [10.22004/ag.econ.288998, DOI 10.22004/AG.ECON.288998] Anderson R.E., 2010, MULTIVARIATE DATA AN [Anonymous], 2022, PACE PLATFORM ACCELE Aryal A, 2020, SUPPLY CHAIN MANAG, V25, P141, DOI 10.1108/SCM-03-2018-0149 Arzoumanidis I, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132111853 Arzoumanidis I, 2017, J CLEAN PROD, V149, P406, DOI 10.1016/j.jclepro.2017.02.059 Bagozzi RP, 2010, J CONSUM PSYCHOL, V20, P208, DOI 10.1016/j.jcps.2010.03.001 Bagozzi RP., 1984, INT J RES MARK, V1, P295, DOI [10.1016/0167-8116(84)90017-X, DOI 10.1016/0167-8116(84)90017-X] Bai CG, 2020, INT J PROD ECON, V229, DOI 10.1016/j.ijpe.2020.107776 Belaud JP, 2019, COMPUT IND, V111, P41, DOI 10.1016/j.compind.2019.06.006 Belaud JP, 2014, COMPUT IND, V65, P521, DOI 10.1016/j.compind.2014.01.009 Bewley JM, 2010, P 1 N AM C PREC DAIR, P12 Bhinge R, 2015, PROC CIRP, V29, P396, DOI 10.1016/j.procir.2015.02.192 Borrello Massimiliano, 2016, Recent Pat Food Nutr Agric, V8, P39, DOI 10.2174/221279840801160304143939 Brankatschk G, 2014, J CLEAN PROD, V73, P72, DOI 10.1016/j.jclepro.2014.02.005 Broadbent E, 2006, J PSYCHOSOM RES, V60, P631, DOI 10.1016/j.jpsychores.2005.10.020 Brown J, 2009, JALT TEST EVALUATION, V13 Carlson KD, 2012, ORGAN RES METHODS, V15, P17, DOI 10.1177/1094428110392383 Carrieres V, 2021, IFIP ADV INF COMM TE, V630, P124, DOI 10.1007/978-3-030-85874-2_13 Chau CK, 2015, APPL ENERG, V143, P395, DOI 10.1016/j.apenergy.2015.01.023 Chen Y, 2014, EUR J INFORM SYST, V23, P326, DOI 10.1057/ejis.2013.4 Christensen TH, 2007, WASTE MANAGE RES, V25, P257, DOI 10.1177/0734242X07079184 Cooper J, 2013, J IND ECOL, V17, P796, DOI 10.1111/jiec.12069 Cordella M, 2020, INT J LIFE CYCLE ASS, V25, P921, DOI 10.1007/s11367-019-01608-8 Cronbach LJ, 1951, PSYCHOMETRIKA, V16, P297 CUDECK R, 1994, PSYCHOL BULL, V115, P475, DOI 10.1037/0033-2909.115.3.475 Neto GCD, 2022, INT J ENVIRON SCI TE, DOI 10.1007/s13762-022-04234-4 De Pascale A, 2021, J CLEAN PROD, V281, DOI 10.1016/j.jclepro.2020.124942 Dev NK, 2020, RESOUR CONSERV RECY, V153, DOI 10.1016/j.resconrec.2019.104583 DeVellis RF, 1991, SCALE DEV THEORY APP, V26 Dieterle M., 2021, PROCEDIA CIRP, V98, P354, DOI [10.1016/j.procir.2021.01.116, DOI 10.1016/J.PROCIR.2021.01.116] Dieterle M, 2018, PROC CIRP, V69, P764, DOI 10.1016/j.procir.2017.11.058 DZIUBAN CD, 1974, PSYCHOL BULL, V81, P358, DOI 10.1037/h0036316 Elia V, 2017, J CLEAN PROD, V142, P2741, DOI 10.1016/j.jclepro.2016.10.196 Esposito B., 2020, Sustainability, V12, DOI 10.3390/su12187401/ Facchini F, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12010086 FAO-Food and Agriculture Organization of the United Nations, 2014, WATER ENERGY FOOD NE, DOI DOI 10.1039/C4EW90001D Faraci P, 2013, MODELLI EQUAZIONI ST, P111 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 FLEISHMAN J, 1987, EDUC PSYCHOL MEAS, V47, P925, DOI 10.1177/0013164487474008 FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Foster C, 2006, ENV IMPACTS FOOD PRO Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 Garg P, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120407 Gbededo MA, 2018, J CLEAN PROD, V184, P1002, DOI 10.1016/j.jclepro.2018.02.310 Genovese A, 2017, OMEGA-INT J MANAGE S, V66, P344, DOI 10.1016/j.omega.2015.05.015 Ghisellini P, 2020, J CLEAN PROD, V243, DOI 10.1016/j.jclepro.2019.118360 Guo M, 2012, SCI TOTAL ENVIRON, V435, P230, DOI 10.1016/j.scitotenv.2012.07.006 Gusmerotti N M, 2020, MANAGEMENT EC CIRCOL Hamid AB, 2017, J PHYS CONF SER, V890 Haupt M, 2017, INT J LIFE CYCLE ASS, V22, P832, DOI 10.1007/s11367-017-1267-1 Hellweg S, 2016, INT J LIFE CYCLE ASS, V21, P1215, DOI 10.1007/s11367-016-1126-5 Hofmann E, 2017, COMPUT IND, V89, P23, DOI 10.1016/j.compind.2017.04.002 Hospido A, 2010, INT J LIFE CYCLE ASS, V15, P44, DOI 10.1007/s11367-009-0130-4 Huang SH, 2020, J MANUF SYST, V54, P361, DOI 10.1016/j.jmsy.2020.01.009 Iacobucci D, 2010, J CONSUM PSYCHOL, V20, P90, DOI 10.1016/j.jcps.2009.09.003 Ingoglia S, 2013, MODELLI EQUAZIONI ST, P59 Ingrao C, 2021, ENVIRON IMPACT ASSES, V88, DOI 10.1016/j.eiar.2021.106569 Jensen AA, 1998, EUR COMMUNITY KAISER HF, 1974, PSYCHOMETRIKA, V39, P31, DOI 10.1007/BF02291575 Kaluza A, 2018, PROC CIRP, V69, P37, DOI 10.1016/j.procir.2017.11.128 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamble SS, 2020, INT J PROD ECON, V219, P179, DOI 10.1016/j.ijpe.2019.05.022 Kelly S, 2017, DISRUPTIVE TECHNOLOG Kim J., 1978, SAGE U PAPER SERIES, V07&014 Klerkx L, 2020, GLOB FOOD SECUR-AGR, V24, DOI 10.1016/j.gfs.2019.100347 Korhonen J, 2018, ECOL ECON, V143, P37, DOI 10.1016/j.ecolecon.2017.06.041 Kouhizadeh M, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103652 Kumar S, 2021, J CLEAN PROD, V293, DOI 10.1016/j.jclepro.2021.126023 Leader J, 2020, DT AFS Lehmann RJ, 2012, COMPUT ELECTRON AGR, V89, P158, DOI 10.1016/j.compag.2012.09.005 Li J., 2018, 2018 2 IEEE ADV INFO, P2637 Jabbour ABLD, 2018, ANN OPER RES, V270, P273, DOI 10.1007/s10479-018-2772-8 MacCallum RC, 2000, ANNU REV PSYCHOL, V51, P201, DOI 10.1146/annurev.psych.51.1.201 Majeau-Bettez G, 2011, ENVIRON SCI TECHNOL, V45, P10170, DOI 10.1021/es201308x Marshal K, 2012, CALL ARMS CONTRIBUTI Matos S, 2007, J OPER MANAG, V25, P1083, DOI 10.1016/j.jom.2007.01.013 Nascimento DLM, 2019, J MANUF TECHNOL MANA, V30, P607, DOI 10.1108/JMTM-03-2018-0071 Merli R, 2018, J CLEAN PROD, V178, P703, DOI 10.1016/j.jclepro.2017.12.112 Mieras E., 2019, CHALLENGES, V10, P8, DOI [DOI 10.3390/CHALLE10010008, 10.3390/challe10010008] Mondello G, 2020, ATTI 29 CONGRESSO NA, P455 Muller JM, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010247 Nayal K, 2021, J ENTERP INF MANAG, DOI 10.1108/JEIM-09-2020-0381 Niero M, 2018, PROC CIRP, V69, P793, DOI 10.1016/j.procir.2017.11.022 Notarnicola B, 2022, INT J LIFE CYCLE ASS, DOI 10.1007/s11367-021-02020-x Notarnicola B, 2011, INT J LIFE CYCLE ASS, V16, P102 Notarnicola B., 2015, LIFE CYCLE ASSESSMEN, DOI DOI 10.1007/978-3-319-11940-3 Notarnicola B, 2017, J CLEAN PROD, V140, P399, DOI 10.1016/j.jclepro.2016.06.071 Notarnicola B, 2012, J CLEAN PROD, V28, P1, DOI 10.1016/j.jclepro.2012.02.007 Nunnally J.C., 1994, PSYCHOL THEORY Nworie J, 2011, TECHTRENDS, V55, P24, DOI 10.1007/s11528-011-0524-6 Padilla-Rivera A, 2021, SUSTAIN PROD CONSUMP, V26, P101, DOI 10.1016/j.spc.2020.09.015 Panarello A, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18082575 Peacock N, 2011, INT J LIFE CYCLE ASS, V16, P189, DOI 10.1007/s11367-011-0250-5 Pehnt M, 2006, RENEW ENERG, V31, P55, DOI 10.1016/j.renene.2005.03.002 Pena C, 2021, INT J LIFE CYCLE ASS, V26, P215, DOI 10.1007/s11367-020-01856-z Pereira Raphael Dias de Mello, 2015, Esc. Anna Nery, V19, P174, DOI 10.5935/1414-8145.20150024 Pieper M, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-19474-6 Poponi S, 2022, RESOUR CONSERV RECY, V176, DOI 10.1016/j.resconrec.2021.105916 Proto M., 2009, STATO DELLARTE DINAM, V42, P1 Rana RL, 2021, BRIT FOOD J, V123, P3471, DOI 10.1108/BFJ-09-2020-0832 Reap J, 2008, INT J LIFE CYCLE ASS, V13, P290, DOI 10.1007/s11367-008-0008-x Reisch L., 2013, Sustainability: Science, Practice & Policy, V9, P7 Jambrak AR, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11020686 Rolinck M., 2021, PROCEDIA CIRP, V98, P394, DOI [10.1016/j.procir.2021.01.123, DOI 10.1016/J.PROCIR.2021.01.123] Roser M., 2020, ENERGY PRODUCTION CO Roy P, 2009, J FOOD ENG, V90, P1, DOI 10.1016/j.jfoodeng.2008.06.016 Ruggieri R, 2021, IAPE 20 RUST RT, 1994, J MARKETING RES, V31, P1, DOI 10.2307/3151942 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Salmond SS, 2008, ORTHOP NURS, V27, P28, DOI 10.1097/01.NOR.0000310608.00743.54 Salomone R, 2010, P 7 INT C LCA AFS BA Salomone R, 2011, LIFE CYCLE MANAGEMEN Sassanelli C, 2019, J CLEAN PROD, V229, P440, DOI 10.1016/j.jclepro.2019.05.019 Schau EM, 2008, INT J LIFE CYCLE ASS, V13, P255, DOI 10.1065/lca2007.12.372 Schulz M., 2020, PROCEDIA CIRP, V90, P182, DOI [10.1016/j.procir.2020.01.134, DOI 10.1016/J.PROCIR.2020.01.134] Serna-Guerrero R, 2022, J CLEAN PROD, V374, DOI 10.1016/j.jclepro.2022.133984 Smaldone F, 2020, EUR MANAG J, V38, P19, DOI 10.1016/j.emj.2019.12.001 Song ML, 2018, ANN OPER RES, V270, P459, DOI 10.1007/s10479-016-2158-8 Steiner J, 2015, BLOCKCHAIN CAN BRING STEWART DW, 1981, J MARKETING RES, V18, P51, DOI 10.2307/3151313 Straub D., 2004, COMMUN ASSOC INF SYS, V13, P24 Streiner DL, 2003, J PERS ASSESS, V80, P217, DOI 10.1207/S15327752JPA8003_01 Sun HY, 2002, TECHNOVATION, V22, P699, DOI 10.1016/S0166-4972(01)00066-9 Swan M., 2015, BLOCKCHAIN BLUEPRINT Teh David, 2020, Environment Systems & Decisions, V40, DOI 10.1007/s10669-020-09761-4 Toop TA, 2017, ENRGY PROCED, V123, P76, DOI 10.1016/j.egypro.2017.07.269 Tortorella GL, 2020, INT J PROD ECON, V219, P284, DOI 10.1016/j.ijpe.2019.06.023 van Stijn A, 2021, RESOUR CONSERV RECY, V174, DOI 10.1016/j.resconrec.2021.105683 Verdouw CN, 2016, J FOOD ENG, V176, P128, DOI 10.1016/j.jfoodeng.2015.11.009 Viola I, 2016, AGRIC AGRIC SCI PROC, V8, P317, DOI 10.1016/j.aaspro.2016.02.026 Voglhuber-Slavinsky A, 2022, EUR J FUTURES RES, V10, DOI 10.1186/s40309-022-00203-9 Walzberg J, 2021, FRONT SUSTAIN SEC QU, DOI [10.3389/frsus.2020.620047, DOI 10.3389/FRSUS.2020.620047] Wang L., 2018, APPL MECH MAT, V260, P1086 Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 Xing K, 2016, J CLEAN PROD, V139, P191, DOI 10.1016/j.jclepro.2016.08.042 Xu LD, 2018, INT J PROD RES, V56, P2941, DOI 10.1080/00207543.2018.1444806 Yong AG, 2013, TUTOR QUANT METHODS, V9, P79, DOI 10.20982/tqmp.09.2.p079 Zamagni A, 2013, INT J LIFE CYCLE ASS, V18, P1637, DOI 10.1007/s11367-013-0648-3 Zhang A, 2020, RESOUR CONSERV RECY, V152, DOI 10.1016/j.resconrec.2019.104512 Zhang YZ, 2015, J CLEAN PROD, V86, P146, DOI 10.1016/j.jclepro.2014.08.053 NR 142 TC 0 Z9 0 U1 2 U2 2 DI 10.1007/s11367-022-02104-2 EA OCT 2022 WC Engineering, Environmental; Environmental Sciences SC Engineering; Environmental Sciences & Ecology UT WOS:000868972000002 DA 2022-12-14 ER PT J AU Xu, LL Yang, XX Wu, LH Chen, XJ Chen, L Tsai, FS AF Xu, Lingling Yang, Xixi Wu, Linhai Chen, Xiujuan Chen, Lu Tsai, Fu-Sheng TI Consumers' Willingness to Pay for Food with Information on Animal Welfare, Lean Meat Essence Detection, and Traceability SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH DT Article DE consumer; willingness to pay; pork; real choice experiment; China ID CHOICE EXPERIMENT; QUALITY PERCEPTION; PREFERENCES; BEEF; ATTRIBUTES; SAFETY; SUSTAINABILITY; CLENBUTEROL; DEMAND; ORIGIN AB Amid high-profile food scares, health concerns and threats of information imperfection and asymmetry, the Chinese pork industry faces increasing demands from consumers for assurances regarding quality and production methods in both the domestic and export markets. Using a real choice experiment (RCE), 316 consumers in Wuxi, located in China's Jiangsu Province, were randomly surveyed to examine the impact of various factors (e.g., traceability, lean meat essence testing, animal welfare, appearance, and price) on consumers' preference and willingness to pay (WTP) for pork products. A random parameter logit model was estimated, and the results show that having a traceable code is the second important factor after price for consumers, corresponding to a WTP of 4.76 yuan per catty, followed by a bright red appearance, a national stocking density standard of animal welfare, and detected no lean meat essence, corresponding to a WTP of more than 2 yuan per catty. In addition, there is a complementary interrelationship between a traceable code and a bright red appearance, detected no lean meat essence, and a national stocking density standard of animal welfare. The results concerning the latent class model (LCM) indicate that 56.9% of consumers are "quality-focused" consumers who are willing to pay a high price for traceable code, detected no lean meat essence, a national stocking density standard of animal welfare, and bright red appearance attributes. A further 28.1% are "price-sensitive" consumers who pay significant attention to the price, and the price that they pay for each product is meagre. The consumers with "preference combination attributes" attach greater value to interaction attributes, such as a traceable code combined with detected no lean meat essence or a bright red appearance and detected no lean meat essence combined with a national stocking density standard of animal welfare or a bright red appearance, accounting for 15% of consumers. The government should improve the traceability system, increase the intensity of lean meat essence testing, promote the welfare level of pigs, and promote public education and publicity on pork quality and safety attributes. Meanwhile, enterprises can formulate "differentiated" pork products, according to different consumer groups, and appropriately increase prices, according to production costs, in order to meet the requirements for pork quality and safety for consumers. C1 [Xu, Lingling; Yang, Xixi; Wu, Linhai; Chen, Xiujuan] Jiangnan Univ, Sch Business, Inst Food Safety Risk Management, Wuxi 214122, Jiangsu, Peoples R China. [Chen, Lu] Northeast Agr Univ, Sch Humanity & Law, Haerbin 150030, Peoples R China. [Tsai, Fu-Sheng] Cheng Shiu Univ, Dept Business Adm, Kaohsiung 83347, Taiwan. [Tsai, Fu-Sheng] Cheng Shiu Univ, Ctr Environm Toxin & Emerging Contaminant Res, Kaohsiung 83347, Taiwan. [Tsai, Fu-Sheng] Cheng Shiu Univ, Super Micro Mass Res & Technol Ctr, Kaohsiung 83347, Taiwan. C3 Jiangnan University; Northeast Agricultural University - China; Cheng Shiu University; Cheng Shiu University; Cheng Shiu University RP Chen, L (corresponding author), Northeast Agr Univ, Sch Humanity & Law, Haerbin 150030, Peoples R China. EM 8383800028@jiangnan.edu.cn; 6180906013@stu.jiangnan.edu.cn; wlh6799@jiangnan.edu.cn; xjchen@jiangnan.edu.cn; chenlu1999lucy@neau.edu.cn; tsaifs@gcloud.csu.edu.tw CR Abidoye B. O., 2011, Journal of Agricultural and Applied Economics, V43, P1 Adamowicz W, 1998, AM J AGR ECON, V80, P64, DOI 10.2307/3180269 Alfnes F, 2006, AM J AGR ECON, V88, P1050, DOI 10.1111/j.1467-8276.2006.00915.x Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Ben-Akiva M., 2019, FDN TRENDS EC, V10, P1, DOI [10.1561/0800000036, DOI 10.1561/0800000036] Berges M., 2015, MICROBIOLOGY, V160, P279 Blanca J, 2005, ANAL CHIM ACTA, V529, P199, DOI 10.1016/j.aca.2004.09.061 Blokhuis H. J., 2008, TRENDS FOOD SCI TECH, V19, P79, DOI DOI 10.1016/J.TIFS.2008.09.007 Borra D., 2015, CONSUMATORE EUROPEO Chen Q, 2013, FOOD QUAL PREFER, V28, P419, DOI 10.1016/j.foodqual.2012.10.008 Chen XJ, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15061126 [陈秀娟 Chen Xiujuan], 2016, [中国人口·资源与环境, China Population Resources and Environment], V26, P92 Clark B, 2017, FOOD POLICY, V68, P112, DOI 10.1016/j.foodpol.2017.01.006 Contini C., 2017, Agricultural and Food Economics, V5, DOI 10.1186/s40100-017-0092-y Curtis K., 2006, INT J SHEEP WOOL SCI, V54, P7 de-Magistris T, 2016, PUBLIC HEALTH, V135, P83, DOI 10.1016/j.puhe.2016.02.004 Denver S, 2017, MEAT SCI, V129, P140, DOI 10.1016/j.meatsci.2017.02.018 Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dopico D.C., 2016, SPANISH J MARKETING, V20, P93, DOI [DOI 10.1016/J.SJME.2016.07.001, 10.1016/j.sjme.2016.07.001] du Plessis HJ, 2012, FOOD RES INT, V47, P210, DOI 10.1016/j.foodres.2011.05.029 Ellison B, 2017, AGR HUM VALUES, V34, P819, DOI 10.1007/s10460-017-9777-9 Francesc J., 2017, NUTRIENTS, V9, P132 Frey UJ, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0202193 Garcia-Torres S, 2016, MEAT SCI, V114, P114, DOI 10.1016/j.meatsci.2015.12.019 Gracia A, 2011, AM J AGR ECON, V93, P1358, DOI 10.1093/ajae/aar054 Grebitus C, 2013, J FOOD PROTECT, V76, P99, DOI 10.4315/0362-028X.JFP-12-045 Gregory NG., 1998, ANIMAL WELFARE MEAT Harper G.C., 1999, NATURE CONSUMER ANIM Hartung J., 2009, ANIMAL WELFARE MEAT Hieger MA, 2016, J EMERG MED, V51, P259, DOI 10.1016/j.jemermed.2016.05.047 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Kuhfeld W.F., 2001, INTRO DESIGNING CHOI Lagerkvist CJ, 2014, FOOD QUAL PREFER, V34, P50, DOI 10.1016/j.foodqual.2013.12.009 Lagerkvist CJ, 2013, FOOD QUAL PREFER, V29, P77, DOI 10.1016/j.foodqual.2013.02.005 Lai J, 2018, FOOD CONTROL, V85, P423, DOI 10.1016/j.foodcont.2017.09.032 LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131 Lewis KE, 2017, J AGR ECON, V68, P451, DOI 10.1111/1477-9552.12187 Li XG, 2018, J AGRIC APPL ECON, V50, P1, DOI 10.1017/aae.2017.17 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Lusk JL, 2018, FOOD POLICY, V77, P91, DOI 10.1016/j.foodpol.2018.04.011 Lusk JL, 2003, AM J AGR ECON, V85, P16, DOI 10.1111/1467-8276.00100 Magalhães Danielle Rodrigues, 2016, Arq. Inst. Biol., V83, pe1182013, DOI 10.1590/1808-1657001182013 Merlino VM, 2018, MEAT SCI, V143, P119, DOI 10.1016/j.meatsci.2018.04.023 Merritt MG, 2018, J AGRIC APPL ECON, V50, P233, DOI 10.1017/aae.2017.35 Miele M., EUROPEAN ANIMAL WELF Miranda-de la Lama GC, 2017, MEAT SCI, V125, P106, DOI 10.1016/j.meatsci.2016.12.001 Rossi PE, 1996, MARKET SCI, V15, P321, DOI 10.1287/mksc.15.4.321 Samant SS, 2016, FOOD QUAL PREFER, V49, P151, DOI 10.1016/j.foodqual.2015.12.004 Shen J.Z., 2011, CHINA ANIM INSP, V28, P27 Shi XL, 2016, J ADOLESCENCE, V53, P55, DOI 10.1016/j.adolescence.2016.08.015 So Y., 1995, SUGI 20 C P, P1227 Sonoda Y, 2018, MEAT SCI, V146, P75, DOI 10.1016/j.meatsci.2018.07.030 Spain CV, 2018, ANIMALS, V8, DOI 10.3390/ani8080128 STEENKAMP JBEM, 1990, J BUS RES, V21, P309, DOI 10.1016/0148-2963(90)90019-A Stranieri S., 2015, INT EUR FOR INNSBR I Swait J, 1994, J RETAIL CONSUM SERV, V1, P77, DOI [10.1016/0969-6989(94)90002-7, DOI 10.1016/0969-6989(94)90002-7] Torquati B, 2019, SUSTAIN PROD CONSUMP, V19, P238, DOI 10.1016/j.spc.2019.04.005 Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1, DOI 10.1017/CBO9780511805271 Troiano S, 2019, ENERGIES, V12, DOI 10.3390/en12132632 Troy DJ, 2010, MEAT SCI, V86, P214, DOI 10.1016/j.meatsci.2010.05.009 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 Udomkun P, 2018, FOOD SCI NUTR, V6, P2321, DOI 10.1002/fsn3.813 Van Loo EJ, 2011, FOOD QUAL PREFER, V22, P603, DOI 10.1016/j.foodqual.2011.02.003 Velarde A, 2015, MEAT SCI, V109, P13, DOI 10.1016/j.meatsci.2015.05.010 Viegas I, 2014, J AGR ECON, V65, P600, DOI 10.1111/1477-9552.12067 Wang C.W., 2016, J FINANC ECON, V42, P16 Wang JH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15122879 Wang JH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15081716 Wu LH, 2017, AGRIBUSINESS, V33, P424, DOI 10.1002/agr.21509 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Yangi ShangHo, 2016, Journal of Food Distribution Research, V47, P50 Yue CY, 2009, HORTSCIENCE, V44, P366, DOI 10.21273/HORTSCI.44.2.366 NR 73 TC 21 Z9 21 U1 12 U2 40 PD OCT PY 2019 VL 16 IS 19 AR 3616 DI 10.3390/ijerph16193616 WC Environmental Sciences; Public, Environmental & Occupational Health SC Environmental Sciences & Ecology; Public, Environmental & Occupational Health UT WOS:000494748600112 DA 2022-12-14 ER PT J AU Nakano, A Zhao, TJ AF Nakano, Akimasa Zhao, Tiejun TI Authenticity of the Geographical Origin and Production Methods of Agricultural Products - Application of Element Composition and Stable Isotope Analyses SO JARQ-JAPAN AGRICULTURAL RESEARCH QUARTERLY DT Review DE agricultural product; authenticity; geographical origin traceability; element composition; stable isotope ID DELTA-C-13 VALUES; MULTIELEMENT; DELTA-N-15; RATIOS; JAPAN; RICE; TARO; TOOL AB Multi-element analysis including stable isotopes can be used as a possible indicator for food safety and security. For the certification of geographical origin, the analytical methods can be performed in two ways: one where multivariate analysis is used to determine the concentrations of such omnipresent elements as Al, Ca, Cl, Mg, Mn, Fe and Zn, and one that focuses on such special elements as the stable isotope ratios of Sr, O, and H. For the certification of production methods, especially those regarding organic products, delta N-15 values could be a potential indicator, particularly in such protected cultivations as in a plant factory (advanced-type greenhouse horticulture). Because the accuracy of these values is affected by production conditions, the delta N-15 values of products can be predicted more accurately under controlled conditions, such as in a plant factory using delta N-15-evaluated fertilizer, medium, and water. Non-destructive systems have been developed for measuring both the level of elements in a product and the production environment, such as soil conditions. In the near future, the results of chemically analyzed and those of non-destructively analyzed elemental composition will become interconnected to non-destructively certify the geographical origin and production method of agricultural products. All these destructive methods have been used to a limited extent for practical regulation as an analysis guideline; however, a combined system involving the use of the newly developed detector (non-destructive), data collection, and analysis using artificial intelligence could address the issue of falsely labeled products for practical application. Particularly in a plant factory, production is controlled and regulated, allowing the tracing of products from the farm to the table. In this context, the greenhouse production system would be an advanced example for the practical use of food safety combined with analytical chemistry and information communication technology. C1 [Nakano, Akimasa; Zhao, Tiejun] Natl Agr & Food Res Org, Inst Vegetable & Floriculture Sci, Tsukuba, Ibaraki 3058519, Japan. [Nakano, Akimasa; Zhao, Tiejun] Minist Agr Forestry & Fisheries, Res Councils Secretariat, Tokyo, Japan. C3 National Agriculture & Food Research Organization - Japan; Ministry of Agriculture Forestry & Fisheries - Japan RP Nakano, A (corresponding author), Natl Agr & Food Res Org, Inst Vegetable & Floriculture Sci, Tsukuba, Ibaraki 3058519, Japan.; Nakano, A (corresponding author), Minist Agr Forestry & Fisheries, Res Councils Secretariat, Tokyo, Japan. EM anakano@affrc.go.jp CR Almeida CM, 2001, J ANAL ATOM SPECTROM, V16, P607, DOI 10.1039/b100307k Ariyama K, 2004, J AGR FOOD CHEM, V52, P5803, DOI 10.1021/jf049333w Ariyama K, 2007, J AGR FOOD CHEM, V55, P347, DOI 10.1021/jf062613m Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Flores P, 2007, J AGR FOOD CHEM, V55, P5740, DOI 10.1021/jf0701180 Georgi M, 2005, PLANT SOIL, V275, P93, DOI 10.1007/s11104-005-0258-3 Honda N., 2014, ISSUE BRIEF, V810, P1 Kawasaki A, 2002, SOIL SCI PLANT NUTR, V48, P635, DOI 10.1080/00380768.2002.10409251 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kobayashi NI, 2011, J AGR FOOD CHEM, V59, P4412, DOI 10.1021/jf200264n Magdas DA, 2011, ISOT ENVIRON HEALT S, V47, P372, DOI 10.1080/10256016.2011.600454 Nakamura S, 2012, FOOD SCI TECHNOL RES, V18, P723, DOI 10.3136/fstr.18.723 Nakano A., 2003, Japanese Journal of Soil Science and Plant Nutrition, V74, P737 Nakano A., 2012, Journal of Science and High Technology in Agriculture, V24, P219, DOI 10.2525/shita.24.219 Nakano A, 2003, PLANT SOIL, V255, P343, DOI 10.1023/A:1026180700963 Nakano A., 2006, B NATL I VEG TEA SCI, V5, P7 Nakano A., 2005, B NATL I VEG TEA SCI, V4, P1 Nakano A., 2002, JPN J SOIL SCI PLANT, V73, P307 Nakano A., 2009, B NATL I VEG TEA SCI, V8, P157 Nakano A., 2005, B NATL I VEG TEA SCI, V4, P9 Nakano A., 2010, PRODUCTION SYSTEM AN, P114 Nakano A., 2005, AGR HORTICULTURE, V80, P363 Nakano A., 2010, B NATL I VEG TEA SCI, V9, P205 Nakano A., 2006, B NATL I VEG TEA SCI, V5, P15 Nakano A., 2008, B NATL I VEG TEA SCI, V7, P1 Nishida M, 2015, PLANT PROD SCI, V18, P180, DOI 10.1626/pps.18.180 Pilgrim TS, 2010, FOOD CHEM, V118, P921, DOI 10.1016/j.foodchem.2008.08.077 Rapisarda P, 2005, J AGR FOOD CHEM, V53, P2664, DOI 10.1021/jf048733g Saito T, 2008, J RADIOANAL NUCL CH, V278, P409, DOI 10.1007/s10967-008-0810-8 Saito T., 2008, KAJITSU NIHON, V63, P76 Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Suzuki Y, 2012, J JPN SOC FOOD SCI, V59, P69, DOI 10.3136/nskkk.59.69 Tsuchida H, 2014, BUNSEKI KAGAKU, V63, P625, DOI 10.2116/bunsekikagaku.63.625 Voerkelius S, 2010, FOOD CHEM, V118, P933, DOI 10.1016/j.foodchem.2009.04.125 Yanada Y, 2007, BUNSEKI KAGAKU, V56, P1053, DOI 10.2116/bunsekikagaku.56.1053 Yasuba K., 2013, AGR INFO RES JAPAN, V22, P247 Zen-Noh, 2015, RES REP INJ FERT MAN Zhao TJ, 2018, JARQ-JPN AGR RES Q, V52, P115, DOI 10.6090/jarq.52.115 NR 38 TC 3 Z9 3 U1 4 U2 38 PY 2018 VL 52 IS 2 BP 105 EP 113 DI 10.6090/jarq.52.105 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000434112000010 DA 2022-12-14 ER PT J AU Sun, LL Sun, HR Cao, N Han, XL Cao, GS Huo, W Zhu, DJ Higgs, R AF Sun, Lili Sun, Hairui Cao, Ning Han, Xiuli Cao, Guangsheng Huo, Wei Zhu, Dongjie Higgs, Russell TI Intelligent Agriculture Technology Based on Internet of Things SO INTELLIGENT AUTOMATION AND SOFT COMPUTING DT Article DE Intelligent agriculture; IoT; traceability of agricultural products AB Although the application of agricultural product traceability technology is a key point to realize Modern Agricultural IoT, it has still encountered various food safety problems. For example, immature environmental monitoring technol-ogy of agricultural products, weak product traceability and imperfect product monitoring equipment. For this reason, this paper studies and compares several emerging technologies of the things Internet, then it analyzes the functional diver-sity and practicability of the Modern Agricultural IoT. It builds the experimental environment based on the agricultural product traceability technology, so as to realize the monitoring of crop growth environment and traceability. The result plays a positive role in popularizing the traceability technology of agricultural products and has a certain impact on the development of intelligent agriculture. It has designed and developed a set of crop growth monitoring terminal equip-ment through experimental debugging. Then it could obtain temperature and humidity, illumination, CO2 concentration, soil data, and other crop growth envir-onment parameters in real time. The relevant data has provided strong support for the traceability model to realize the multi-point monitoring, intelligent control, and automatic operation of crop growing environment. Then it can be sorted and analyzed through the application layer. This system can promote the intelli-gent processing, improve the utilization efficiency of agricultural resources, pro -mote the development of modern agriculture, save agricultural production costs, and increase the production and marketing. It is an innovative application of the Internet of things technology applied to agricultural production. C1 [Sun, Lili; Cao, Ning] Sanming Univ, Sch Informat Engn, Sanming 365004, Peoples R China. [Sun, Hairui] Jiujiang Univ, Sch Foreign Languages, Jiujiang 332005, Peoples R China. [Han, Xiuli; Cao, Guangsheng; Huo, Wei] Qingdao Tech Coll, Publ Teaching Dept, Qingdao 266555, Peoples R China. [Zhu, Dongjie] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China. [Higgs, Russell] Univ Coll Dublin, Sch Math Sci, Dublin 4, Ireland. C3 Sanming University; Jiujiang University; Harbin Institute of Technology; University College Dublin RP Zhu, DJ (corresponding author), Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China. EM zhudongjie@hit.edu.cn CR Bi P. J., 2017, SHANDONG COMMUNICATI, V37, P1 Chang Y, 2019, CMC-COMPUT MATER CON, V59, P1005, DOI 10.32604/cmc.2019.06297 Dai G, 2016, 2016 GUANGD HON INT, V1, P48 Fang LM, 2020, IEEE INTERNET THINGS, V7, P5745, DOI 10.1109/JIOT.2019.2944301 Guo J., 2018, THESIS YANGZHOU U CH Hou H, 2017, COMMUN WORLD, V1, P1 Peng X., 2017, POST TELECOMMUNICATI, V1, P58 Ren YJ, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20010207 Wang C, 2017, ELECT TECHNOLOGY SOF, V1, P20 Xu J, 2021, MOBILE NETW APPL, V26, P1475, DOI 10.1007/s11036-019-01484-4 Xu J, 2020, IEEE T COMPUT SOC SY, V7, P261, DOI 10.1109/TCSS.2019.2960857 Xu J, 2020, FUTURE GENER COMP SY, V108, P1287, DOI 10.1016/j.future.2018.04.018 Xu J, 2018, J NETW COMPUT APPL, V107, P113, DOI 10.1016/j.jnca.2018.01.014 Yan LL, 2019, CMC-COMPUT MATER CON, V61, P877, DOI 10.32604/cmc.2019.06222 Zhang F., 2019, CHINA NEW COMMUNICAT, V21, P11 Zhang S., 2020, COMPUTERS MAT CONTIN, V61, P1145 Zhu DJ, 2021, IEEE INTERNET THINGS, V8, P3201, DOI 10.1109/JIOT.2020.3020951 Zhu DJ, 2020, INT J EMBED SYST, V12, P72, DOI 10.1504/IJES.2020.105280 Zou YL, 2017, ZTE TECHNOL, V23, P43 NR 20 TC 1 Z9 1 U1 15 U2 38 PY 2022 VL 32 IS 1 BP 429 EP 439 DI 10.32604/iasc.2022.021526 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science UT WOS:000712025300002 DA 2022-12-14 ER PT J AU Gerostathopoulos, I Bures, T Hnetynka, P Keznikl, J Kit, M Plasil, F Plouzeau, N AF Gerostathopoulos, Ilias Bures, Tomas Hnetynka, Petr Keznikl, Jaroslav Kit, Michal Plasil, Frantisek Plouzeau, Noel TI Self-adaptation in software-intensive cyber-physical systems: From system goals to architecture configurations SO JOURNAL OF SYSTEMS AND SOFTWARE DT Article DE Cyber physical systems; Self-adaptivity; Dependability ID CONSENSUS; SUPPORT; MODELS AB Design of self-adaptive software-intensive cyber-physical systems (siCPS) operating in dynamic environments is a significant challenge when a sufficient level of dependability is required. This stems partly from the fact that the concerns of self-adaptivity and dependability are to an extent contradictory. In this paper, we introduce IRM-SA (Invariant Refinement Method for Self-Adaptation) a design method and associated formally grounded model targeting siCPS that addresses self-adaptivity and supports dependability by providing traceability between system requirements, distinct situations in the environment, and predefined configurations of system architecture. Additionally, IRM-SA allows for architecture self adaptation at runtime and integrates the mechanism of predictive monitoring that deals with operational uncertainty. As a proof of concept, it was implemented in DEECo, a component framework that is based on dynamic ensembles of components. Furthermore, its feasibility was evaluated in experimental settings assuming decentralized system operation. (C) 2016 Elsevier Inc. All rights reserved. C1 [Gerostathopoulos, Ilias] Tech Univ Munich, Fak Informat Munich, Munich, Germany. [Bures, Tomas; Hnetynka, Petr; Keznikl, Jaroslav; Kit, Michal; Plasil, Frantisek] Charles Univ Prague, Fac Math & Phys, Prague, Czech Republic. [Bures, Tomas; Keznikl, Jaroslav] Acad Sci Czech Republ, Inst Comp Sci, Prague, Czech Republic. [Plouzeau, Noel] Univ Rennes 1, IRISA, Rennes, France. C3 Technical University of Munich; Charles University Prague; Czech Academy of Sciences; Institute of Computer Science of the Czech Academy of Sciences; Universite de Rennes 1 RP Gerostathopoulos, I (corresponding author), Tech Univ Munich, Fak Informat Munich, Munich, Germany. EM gerostat@in.tum.de; bures@d3s.mff.cuni.cz; hnetynka@d3s.mff.cuni.cz; keznikl@d3s.mff.cuni.cz; kit@d3s.mff.cuni.cz; plasil@d3s.mff.cuni.cz; noel.plouzeau@irisa.fr CR Al Ali R., 2014, P WICSA 14 SYDN AUST, P1 Amato C, 2009, INT C AUT AG MULT SY, P593 [Anonymous], 2002, P 1 WORKSH SELF HEAL Arcaini P., 2015, P 2015 IEEE 8 INT C, P1 Baresi L., 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), P125, DOI 10.1109/RE.2010.25 Baresi L, 2011, 2011 9TH WORKING IEEE/IFIP CONFERENCE ON SOFTWARE ARCHITECTURE (WICSA), P161, DOI 10.1109/WICSA.2011.29 Barrett S., 2013, P 27 AAAI C ART INT Belov A., 2014, P SAT COMP 2014 SOLV Bernstein DS, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P1287 Bernstein DS, 2002, MATH OPER RES, V27, P819, DOI 10.1287/moor.27.4.819.297 Berry D.M., 2005, P 11 INT WORKSH REQ, P95 Bhave A, 2011, ACM IEEE INT CONF CY, P151, DOI 10.1109/ICCPS.2011.17 Bresciani P, 2004, AUTON AGENT MULTI-AG, V8, P203, DOI 10.1023/B:AGNT.0000018806.20944.ef Bruneton E, 2006, SOFTWARE PRACT EXPER, V36, P1257, DOI 10.1002/spe.767 Bures T, 2006, FOURTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATIONS, PROCEEDINGS, P40, DOI 10.1109/SERA.2006.62 Bures T, 2014, LECT NOTES COMPUT SC, V8627, P250, DOI 10.1007/978-3-319-09970-5_23 Bures Tomas, 2013, CBSE 13 P 16 ACM SIG Chang N, 2010, IEEE T CONSUM ELECTR, V56, P1860, DOI 10.1109/TCE.2010.5606338 Chen B, 2009, 6 ANN IEEE COMM SOC, P1, DOI DOI 10.1007/978-3-642-04425-0_36 Cheng BHC, 2009, LECT NOTES COMPUT SC, V5525, P1, DOI 10.1007/978-3-642-02161-9_1 CHUNG LE, 1999, NONFUNCTIONAL REQUIR Clements P., 2002, SOFTWARE PRODUCT LIN Dalpiaz F, 2010, LECT NOTES COMPUT SC, V6412, P31, DOI 10.1007/978-3-642-16373-9_3 De Nicola R., 2013, FORMAL METHODS COMPO, V7542, P25 Esfahani N., 2011, P ESEC FSE, P234 Feather MS, 1998, NINTH INTERNATIONAL WORKSHOP ON SOFTWARE SPECIFICATION AND DESIGN, PROCEEDINGS, P50, DOI 10.1109/IWSSD.1998.667919 Fickas S., 1995, Proceedings of the Second IEEE International Symposium on Requirements Engineering (Cat. No.95TH8040), P140, DOI 10.1109/ISRE.1995.512555 FISCHER MJ, 1985, J ACM, V32, P374, DOI 10.1145/3149.214121 Fouquet F., 2012, DISTRIBUTED APPL INT, P16 Garlan D, 2004, COMPUTER, V37, P46, DOI 10.1109/MC.2004.175 Gerostathopoulos I., 2015, D3STR201501 Goldsby HJ, 2008, FIFTEENTH IEEE INTERNATIONAL CONFERENCE AND WORKSHOPS ON THE ENGINEERING OF COMPUTER-BASED SYSTEMS, PROCEEDINGS, P36, DOI 10.1109/ECBS.2008.22 Gupta RA, 2010, IEEE T IND ELECTRON, V57, P2527, DOI 10.1109/TIE.2009.2035462 Hall Richard S., 2011, OSGI ACTION CREATING Hansen K.M., 2012, SAC 10, P2257 Hasan MS, 2009, IET COMMUN, V3, P1297, DOI 10.1049/iet-com.2008.0536 Hellerstein J.L., 2004, FEEDBACK CONTROL COM Hinchey M, 2012, COMPUTER, V45, P22, DOI 10.1109/MC.2012.332 Hirsch D, 2006, LECT NOTES COMPUT SC, V4344, P113 Holzl Matthias, 2008, Software-Intensive Systems and New Computing Paradigms, P1 Host M., 2000, Empirical Software Engineering, V5, P201, DOI 10.1023/A:1026586415054 Jadbabaie A, 2003, IEEE T AUTOMAT CONTR, V48, P988, DOI 10.1109/TAC.2003.812781 Kang KC, 2002, IEEE SOFTWARE, V19, P58, DOI 10.1109/MS.2002.1020288 Kephart JO, 2003, COMPUTER, V36, P41, DOI 10.1109/MC.2003.1160055 Keznikl J., 2012, 2012 Joint Working IEEE/IFIP Conference on Software Architecture (WICSA 2012) & European Conference on Software Architecture (ECSA 2012), P249, DOI 10.1109/WICSA-ECSA.212.39 Keznikl J., 2013, P CBSE 2013, P91, DOI DOI 10.1145/2465449.2465457 Kramer J, 2007, FOSE 2007: FUTURE OF SOFTWARE ENGINEERING, P259, DOI 10.1109/FOSE.2007.19 Le Berre D., 2010, SAT4J LIB RELEASE 2, V7, P59 McKinley PK, 2004, COMPUTER, V37, P56, DOI 10.1109/MC.2004.48 Morandini Mirko, 2008, 2008 23rd IEEE/ACM International Conference on Automated Software Engineering, P485, DOI 10.1109/ASE.2008.83 Morandini M., 2008, SEAMS 2008, P9, DOI DOI 10.1145/1370018.1370021 Morin B, 2009, PROC INT CONF SOFTW, P122, DOI 10.1109/ICSE.2009.5070514 Morin B, 2009, COMPUTER, V42, P44, DOI 10.1109/MC.2009.327 Murguzur A., 2014, P 18 INT SOFTWARE PR, VVolume 2, P2 Murray R. M., 2003, IEEE CONTROL SYST, V23, P1 Mustafiz S., 2012, INT WORKSHOP MULTIPA, P13 Olfati-Saber R, 2007, P IEEE, V95, P215, DOI 10.1109/JPROC.2006.887293 Oreizy P, 1999, IEEE INTELL SYST APP, V14, P54, DOI 10.1109/5254.769885 Robinson WN, 2006, REQUIR ENG, V11, P17, DOI 10.1007/s00766-005-0016-3 Salehie M, 2009, ACM T AUTON ADAP SYS, V4, DOI 10.1145/1516533.1516538 Sawyer P., 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), P95, DOI 10.1109/RE.2010.21 Sawyer P, 2012, COMPUTER, V45, P56, DOI 10.1109/MC.2012.286 Sentilles S, 2008, LECT NOTES COMPUT SC, V5282, P310, DOI 10.1007/978-3-540-87891-9_21 Seuken S., 2012, P 23 C UNC ART INT Sha Lui, 2008, Software-Intensive Systems and New Computing Paradigms, P92 Sheskin DJ, 2011, HDB PARAMETRIC NONPA Shoham Y., 2008, MULTIAGENT SYSTEMS A, DOI DOI 10.1017/CBO9780511811654 Souza V. E., 2013, LECT NOTES COMPUTER, V7475, P133 Spaan M. T.J., 2008, P ICAPS, V8, P338 Valtazanos A., 2014, P WORKSH DISTR MULT, P45 van Lamsweerde A, 2000, IEEE T SOFTWARE ENG, V26, P978, DOI 10.1109/32.879820 van Lamsweerde A., 2008, P 16 ACM SIGSOFT INT, P238 van Lamsweerde A., 2009, GOAL ORIENTED REQUIR Whittle J, 2010, REQUIR ENG, V15, P177, DOI 10.1007/s00766-010-0101-0 Wohlin C., 2012, EXPT SOFTWARE ENG, DOI DOI 10.1007/978-3-642-29044-2 Wu F., 2011, P INT JOINT C ART IN, P439 Wu F, 2011, ARTIF INTELL, V175, P487, DOI 10.1016/j.artint.2010.09.008 NR 77 TC 31 Z9 32 U1 1 U2 19 PD DEC PY 2016 VL 122 BP 378 EP 397 DI 10.1016/j.jss.2016.02.028 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science UT WOS:000387627200024 DA 2022-12-14 ER PT J AU Dufosse, L Donadio, C Valla, A Meile, JC Montet, D AF Dufosse, Laurent Donadio, Clara Valla, Alain Meile, Jean-Christophe Montet, Didier TI Determination of speciality food salt origin by using 16S rDNA fingerprinting of bacterial communities by PCR-DGGE: An application on marine salts produced in solar salterns from the French Atlantic Ocean SO FOOD CONTROL DT Article DE Bacteria; Salt; Microbial ecology; Food traceability; Geographical origin; PCR-DGGE ID GRADIENT GEL-ELECTROPHORESIS; SP-NOV.; HALOPHILIC MICROORGANISMS; MICROBIAL DIVERSITY; GENUS; PONDS AB The determination of geographical origin is part of the demand of the traceability system of food products. A hypothesis of tracing the source of a product is to analyse in a global way the bacterial communities of the food samples after their production. For this purpose, molecular techniques employing 16S rDNA profiles generated by PCR-DGGE were used to detect the variation in bacterial community structures of salts from four French regions. When the 16S rDNA profiles were analysed by multivariate analysis, distinct microbial communities were detected. The band profiles of the salt bacteria from different prodaing areas were different and were specific for each location and could be used as a bar code to certify the origin of salts. These band profiles can be used as specific markers for a specific location. This method is proposed as a new traceability tool which provides salts with a unique bar code that permits to trace back salts from store shelves to their original location. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Dufosse, Laurent; Donadio, Clara] Univ La Reunion, Lab Chim Subst Nat & Sci Aliments, Ecole Super Ingn Reunion Ocean Indien, Dept Innovat & Dev Agroalimentaire Integre, 2 Rue Joseph Wetzell,Parc Technol Univ, F-97490 St Clotilde, Reunion, France. [Valla, Alain] CNRS, F-29000 Quimper, France. [Meile, Jean-Christophe; Montet, Didier] CIRAD, UMR Qualisud 95, F-34398 Montpellier 5, France. C3 University of La Reunion; Centre National de la Recherche Scientifique (CNRS); CIRAD; Universite de Montpellier RP Dufosse, L (corresponding author), Univ La Reunion, Lab Chim Subst Nat & Sci Aliments, Ecole Super Ingn Reunion Ocean Indien, Dept Innovat & Dev Agroalimentaire Integre, 2 Rue Joseph Wetzell,Parc Technol Univ, F-97490 St Clotilde, Reunion, France. EM laurent.dufosse@univ-reunion.fr CR Alderman MH, 2002, INT J EPIDEMIOL, V31, P311, DOI 10.1093/ije/31.2.311 Ampe F, 1999, APPL ENVIRON MICROB, V65, P5464 [Anonymous], 2012, OFFICIAL J EUROPEA L, pL80/4 Anton J, 2000, APPL ENVIRON MICROB, V66, P3052, DOI 10.1128/AEM.66.7.3052-3057.2000 Arfini F., 1999, EUR ASS AGR EC SEM N Bardavid RE, 2008, EXTREMOPHILES, V12, P5, DOI 10.1007/s00792-006-0053-y Baxter BK, 2005, CELL ORIGIN LIFE EXT, V9, P9 Brown IJ, 2009, INT J EPIDEMIOL, V38, P791, DOI 10.1093/ije/dyp139 Choi DH, 2009, INT J SYST EVOL MICR, V59, P1167, DOI 10.1099/ijs.0.005512-0 Codex Alimentarius, 2006, NORM COD SEL QUAL AL Donadio C, 2011, J FOOD COMPOS ANAL, V24, P801, DOI 10.1016/j.jfca.2011.03.005 El Sheikha A. F., 2011, MANSOURA J BIOL, V37, P35 El Sheikha AF, 2011, FRUITS, V66, P79, DOI 10.1051/fruits/2011001 He FJ, 2010, PROG CARDIOVASC DIS, V52, P363, DOI 10.1016/j.pcad.2009.12.006 He FJ, 2003, HYPERTENSION, V42, P1093, DOI 10.1161/01.HYP.0000102864.05174.E8 Heyndrickx M, 1996, J MICROBIOL METH, V26, P247, DOI 10.1016/0167-7012(96)00916-5 Jaenicke L, 1998, PROTIST, V149, P381, DOI 10.1016/S1434-4610(98)70044-6 Kowalchuk GA, 1997, APPL ENVIRON MICROB, V63, P1489, DOI 10.1128/AEM.63.4.1489-1497.1997 Kurlansky M, 2002, SALT A WORLD HIST Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Leesing R., 2005, THESIS U MONTPELLIER Margesin R, 2001, EXTREMOPHILES, V5, P73, DOI 10.1007/s007920100184 Montet D., 2008, Aspects of Applied Biology, P11 Muramatsu Y, 2010, INT J SYST EVOL MICR, V60, P1735, DOI 10.1099/ijs.0.016113-0 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Muyzer G., 1996, MOL MICROBIAL ECOLOG, P1 Pecqueur B., 2001, CONOMIE RURALE, V261, P37, DOI [https://doi.org/10.3406/ecoru.2001.5217, DOI 10.3406/ECORU.2001.5217, DOI 10.3406/EC0RU.2001.5217] RAINEY FA, 1995, ANAEROBE, V1, P185, DOI 10.1006/anae.1995.1018 SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 Silva I, 2010, J CHROMATOGR A, V1217, P5511, DOI 10.1016/j.chroma.2010.06.050 Silva I, 2009, ANAL CHIM ACTA, V635, P167, DOI 10.1016/j.aca.2009.01.011 Sorokin DY, 2006, INT J SYST EVOL MICR, V56, P487, DOI 10.1099/ijs.0.63965-0 Tatsadjieu NL, 2010, FOOD CONTROL, V21, P673, DOI 10.1016/j.foodcont.2009.10.006 Urios L, 2006, INT J SYST EVOL MICR, V56, P1883, DOI 10.1099/ijs.0.64285-0 van Hannen EJ, 1999, APPL ENVIRON MICROB, V65, P795 Wen HY, 2009, WORLD J MICROB BIOT, V25, P1727, DOI 10.1007/s11274-009-0068-5 NR 36 TC 19 Z9 19 U1 1 U2 73 PD AUG PY 2013 VL 32 IS 2 BP 644 EP 649 DI 10.1016/j.foodcont.2013.01.045 WC Food Science & Technology SC Food Science & Technology UT WOS:000317440900046 DA 2022-12-14 ER PT J AU Wu, W Zhang, AR van Klinken, RD Schrobback, P Muller, JM AF Wu, Wen Zhang, Airong van Klinken, Rieks Dekker Schrobback, Peggy Muller, Jane Marie TI Consumer Trust in Food and the Food System: A Critical Review SO FOODS DT Review DE assurance; food actor; packaging label; traceability; supply chain operator; food industry influencer ID WILLINGNESS-TO-PAY; INFANT MILK FORMULA; COUNTRY-OF-ORIGIN; BEHAVIORAL-RESPONSES; RISK COMMUNICATION; SOCIAL PRESENCE; SAFETY; PREFERENCES; PERCEPTIONS; TRACEABILITY AB Increased focus towards food safety and quality is reshaping food purchasing decisions around the world. Although some food attributes are visible, many of the attributes that consumers seek and are willing to pay a price premium for are not. Consequently, consumers rely on trusted cues and information to help them verify the food quality and credence attributes they seek. In this study, we synthesise the findings from previous research to generate a framework illustrating the key trust influencing factors that are beyond visual and brand-related cues. Our framework identifies that consumer trust in food and the food system is established through the assurances related to individual food products and the actors of the food system. Specifically, product assurance builds consumer trust through food packaging labels communicating food attribute claims, certifications, country or region of origin, and food traceability information. In addition, producers, processors, and retailers provide consumers with food safety and quality assurances, while government agencies, third-party institutions, advocacy groups, and the mass media may modify how labelling information and food operators are perceived by consumers. We hope our framework will guide future research efforts to test these trust factors in various consumer and market settings.
C1 [Wu, Wen] Commonwealth Sci & Ind Res Org CSIRO, Data 61, Brisbane, Qld 4102, Australia. [Zhang, Airong; van Klinken, Rieks Dekker; Muller, Jane Marie] Commonwealth Sci & Ind Res Org CSIRO, Hlth & Biosecur, Brisbane, Qld 4102, Australia. [Schrobback, Peggy] Commonwealth Sci & Ind Res Org CSIRO, Agr & Food, Brisbane, Qld 4067, Australia. C3 Commonwealth Scientific & Industrial Research Organisation (CSIRO); Commonwealth Scientific & Industrial Research Organisation (CSIRO); Commonwealth Scientific & Industrial Research Organisation (CSIRO) RP Wu, W (corresponding author), Commonwealth Sci & Ind Res Org CSIRO, Data 61, Brisbane, Qld 4102, Australia. EM drwenwu.uq@gmail.com; airong.zhang@csiro.au; peggy.schrobback@csiro.au CR Agnoli L, 2016, BRIT FOOD J, V118, P1878, DOI 10.1108/BFJ-04-2016-0176 Alfnes F., 2010, INT J REVENUE MANAGE, V4, P238, DOI [https://doi.org/10.1504/IJRM.2010.035955, DOI 10.1504/IJRM.2010.035955] Andehn M, 2016, J CONSUM BEHAV, V15, P225, DOI 10.1002/cb.1550 Anderson WA, 2000, BRIT MED BULL, V56, P254, DOI 10.1258/0007142001902932 Dang AK, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15050981 [Anonymous], 2018, ABC NEWS Ariyawardana A, 2017, FOOD CONTROL, V73, P193, DOI 10.1016/j.foodcont.2016.08.006 Arnot C, 2011, FOOD TECHNOL-CHICAGO, V65, P132 Batra R, 2000, J CONSUM PSYCHOL, V9, P83, DOI 10.1207/S15327663JCP0902_3 Bauman Antonina, 2017, Journal of Technology Management & Innovation, V12, P68 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Benson T, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.106988 Bozic B, 2017, EUR MANAG J, V35, P538, DOI 10.1016/j.emj.2017.02.007 Bray D. B., 2002, J APPL BUS RES, V5, P429, DOI DOI 10.19030/jabr.v30i2.8414 Bray HJ, 2017, ANTHROZOOS, V30, P213, DOI 10.1080/08927936.2017.1310986 Buddle EA, 2019, J AGR ENVIRON ETHIC, V32, P357, DOI 10.1007/s10806-019-09778-z Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Cao SF, 2021, COMPUT ELECTRON AGR, V180, DOI 10.1016/j.compag.2020.105886 Carfora V, 2019, FOOD QUAL PREFER, V76, P1, DOI 10.1016/j.foodqual.2019.03.006 Chen CY, 2014, J CONSUM RES, V41, P1033, DOI 10.1086/678194 Chen MF, 2008, RISK ANAL, V28, P1553, DOI 10.1111/j.1539-6924.2008.01115.x Chopdar PK, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102106 Costa C, 2016, INT BUS REV, V25, P1066, DOI 10.1016/j.ibusrev.2016.01.003 Daoud M.K., 2019, INT C DIG EC Denver S, 2019, J FOOD PROD MARK, V25, P668, DOI 10.1080/10454446.2019.1640159 Dong BB, 2019, DECISION SCI, V50, P537, DOI 10.1111/deci.12339 El Benni N, 2019, FOOD QUAL PREFER, V71, P25, DOI 10.1016/j.foodqual.2018.05.006 Esteki M, 2019, COMPR REV FOOD SCI F, V18, P425, DOI 10.1111/1541-4337.12419 Feng NN, 2021, BRIT FOOD J, V123, P2216, DOI 10.1108/BFJ-07-2020-0575 Frasquet M, 2017, INT J RETAIL DISTRIB, V45, P608, DOI 10.1108/IJRDM-07-2016-0118 Gao ZF, 2019, FOOD QUAL PREFER, V71, P475, DOI 10.1016/j.foodqual.2018.03.016 Giampietri E, 2018, FOOD QUAL PREFER, V64, P160, DOI 10.1016/j.foodqual.2017.09.012 Gorostidi-Martinez H, 2017, ASIA PAC J MARKET LO, V29, P589, DOI 10.1108/APJML-09-2016-0160 Haas R, 2021, FOODS, V10, DOI 10.3390/foods10010160 Halkias G, 2016, J BUS RES, V69, P3621, DOI 10.1016/j.jbusres.2016.03.022 Hartmann C, 2018, FOOD QUAL PREFER, V68, P377, DOI 10.1016/j.foodqual.2017.12.009 Hastie M, 2020, FOODS, V9, DOI 10.3390/foods9020126 Heide M., 2015, INT J STRAT COMMUN, V9, P134, DOI [https://doi.org/10.1080/1553118X.2015.1008636, DOI 10.1080/1553118X.2015.1008636] Henderson J, 2012, HEALTH RISK SOC, V14, P257, DOI 10.1080/13698575.2012.662948 Henderson J, 2010, AUSTRALAS MED J, V3, P164, DOI 10.4066/AMJ.2010.202 Henderson J, 2011, AUST NZ J PUBL HEAL, V35, P319, DOI 10.1111/j.1753-6405.2011.00725.x Hoque MZ, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103722 Hou B, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11051464 Jacob CJ, 2011, PUBLIC UNDERST SCI, V20, P261, DOI 10.1177/0963662509355737 Jensen KK, 2004, J AGR ENVIRON ETHIC, V17, P405, DOI 10.1007/s10806-004-5186-3 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Joshi Y., 2015, INT INT STRATEGIC MA, V3, P128, DOI [10.1016/j.ism.2015.04.001, DOI 10.1016/J.ISM.2015.04.001] Kendall H, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0195817 Kendall H, 2019, TRENDS FOOD SCI TECH, V94, P79, DOI 10.1016/j.tifs.2019.10.005 Kendall H, 2019, FOOD CONTROL, V95, P339, DOI 10.1016/j.foodcont.2018.08.006 Kjaernes U, 2012, J AGR ENVIRON ETHIC, V25, P145, DOI 10.1007/s10806-011-9315-5 Li SW, 2019, J DAIRY SCI, V102, P8807, DOI 10.3168/jds.2019-16638 Lin XL, 2019, INT J ELECTRON COMM, V23, P328, DOI 10.1080/10864415.2019.1619907 Liu C, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0206793 Liu RD, 2014, FOOD CONTROL, V46, P291, DOI 10.1016/j.foodcont.2014.05.033 Liu RF, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107157 Liu RF, 2019, FOOD POLICY, V88, DOI 10.1016/j.foodpol.2019.101768 Lu BZ, 2016, COMPUT HUM BEHAV, V56, P225, DOI 10.1016/j.chb.2015.11.057 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Lupton DA, 2005, SOCIOL HEALTH ILL, V27, P448, DOI 10.1111/j.1467-9566.2005.00451.x Macready AL, 2020, FOOD POLICY, V92, DOI 10.1016/j.foodpol.2020.101880 Maehle N, 2015, BRIT FOOD J, V117, P3039, DOI 10.1108/BFJ-04-2015-0148 Manning L, 2016, J FOOD SCI, V81, pR823, DOI 10.1111/1750-3841.13256 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 McCarthy BL, 2015, J ECON SOC POLICY, V17 Meat and Livestock Australia, 2020, COMM APPL SUPPL CHAI Menozzi D, 2015, FOOD CONTROL, V49, P40, DOI 10.1016/j.foodcont.2013.09.006 Meyerding SGH, 2019, J CLEAN PROD, V207, P30, DOI 10.1016/j.jclepro.2018.09.224 Mirosa M, 2018, J FOOD PROD MARK, V24, P216, DOI 10.1080/10454446.2017.1266555 Moruzzo R, 2020, FOODS, V9, DOI 10.3390/foods9091153 My NHD, 2018, FOOD POLICY, V79, P283, DOI 10.1016/j.foodpol.2018.08.004 Nawi N.M., 2018, INT FOOD RES J, pS157 Nuttavuthisit K, 2019, J INT CONSUM MARK, V31, P225, DOI 10.1080/08961530.2018.1544529 Nuttavuthisit K, 2017, J BUS ETHICS, V140, P323, DOI 10.1007/s10551-015-2690-5 Ortega DL, 2016, MEAT SCI, V121, P317, DOI 10.1016/j.meatsci.2016.06.032 Osman H, 2019, PSYCHOL MARKET, V36, P1162, DOI 10.1002/mar.21264 Papadopoulos A, 2012, HEALTH POLICY, V107, P98, DOI 10.1016/j.healthpol.2012.05.010 Pedersen S, 2018, APPETITE, V130, P134, DOI 10.1016/j.appet.2018.08.016 Regan A, 2016, J RISK RES, V19, P119, DOI 10.1080/13669877.2014.961517 Rezai G, 2012, PERTANIKA J SOC SCI, V20, P33 Richards C, 2011, FOOD CULT SOC, V14, P29, DOI 10.2752/175174411X12810842291146 Rohr A, 2005, FOOD CONTROL, V16, P649, DOI 10.1016/j.foodcont.2004.06.001 Rupprecht CDD, 2020, FOOD CHEM TOXICOL, V137, DOI 10.1016/j.fct.2020.111170 Sander F, 2018, BRIT FOOD J, V120, P2066, DOI 10.1108/BFJ-07-2017-0365 Santeramo FG, 2020, FOOD RES INT, V131, DOI 10.1016/j.foodres.2020.108995 Schjoll A., 2017, Organic Agriculture, V7, P315, DOI 10.1007/s13165-016-0159-1 Seo S, 2020, BRIT FOOD J, V122, P2895, DOI 10.1108/BFJ-08-2019-0639 Song Y, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11040973 Soon JM, 2020, FOOD CONTROL, V115, DOI 10.1016/j.foodcont.2020.107298 Thogersen J, 2019, FOOD QUAL PREFER, V72, P10, DOI 10.1016/j.foodqual.2018.09.003 Thomson B, 2012, QUAL ASSUR SAF CROP, V4, P77, DOI 10.1111/j.1757-837X.2012.00129.x Tonkin E, 2021, BRIT FOOD J, V123, P702, DOI 10.1108/BFJ-05-2020-0394 Tonkin E, 2019, BRIT FOOD J, V121, P561, DOI 10.1108/BFJ-05-2018-0291 Tonkin E, 2019, FOOD CONTROL, V101, P112, DOI 10.1016/j.foodcont.2019.02.012 Tonkin E, 2016, APPETITE, V103, P118, DOI 10.1016/j.appet.2016.04.004 van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 Van Wezemael L, 2010, FOOD CONTROL, V21, P835, DOI 10.1016/j.foodcont.2009.11.010 Wang JH, 2020, FOOD CONTROL, V108, DOI 10.1016/j.foodcont.2019.106825 Wilson AM, 2017, HEALTH PROMOT INT, V32, P988, DOI 10.1093/heapro/daw024 Wu LH, 2017, AGRIBUSINESS, V33, P424, DOI 10.1002/agr.21509 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Wu LH, 2014, CAN J AGR ECON, V62, P545, DOI 10.1111/cjag.12050 Yamoah F., 2014, INT REV MANAG MARK, V4, P98 Ye C, 2020, PSYCHOL MARKET, V37, P1539, DOI 10.1002/mar.21400 Yeh CH, 2010, FOOD QUAL PREFER, V21, P849, DOI 10.1016/j.foodqual.2010.05.005 Yeh CH, 2020, FOODS, V9, DOI 10.3390/foods9091212 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] Yin SJ, 2017, CHINA AGR ECON REV, V9, P141, DOI [10.1108/caer-11-2015-0147, 10.1108/CAER-11-2015-0147] Yu HY, 2018, FOOD CONTROL, V86, P83, DOI 10.1016/j.foodcont.2017.11.014 Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Yue LQ, 2017, BRIT FOOD J, V119, P2724, DOI [10.1108/BFJ-09-2016-0421, 10.1108/bfj-09-2016-0421] Zachmann K, 2011, HIST TECHNOL, V27, P1, DOI 10.1080/07341512.2011.548970 Zander K, 2018, J INT FOOD AGRIBUS M, V30, P251, DOI 10.1080/08974438.2017.1413611 Zhang AR, 2021, FOODS, V10, DOI 10.3390/foods10010056 Zhang AR, 2020, J CONSUM PROT FOOD S, V15, P99, DOI 10.1007/s00003-020-01277-y Zhang CP, 2012, FOOD CONTROL, V27, P21, DOI 10.1016/j.foodcont.2012.03.001 Zhang L, 2016, J CLEAN PROD, V134, P269, DOI 10.1016/j.jclepro.2015.09.078 Zhllima E, 2015, J INTEGR AGR, V14, P1142, DOI 10.1016/S2095-3119(14)60997-7 Zhu B, 2019, INT J RETAIL DISTRIB, V48, P53, DOI 10.1108/IJRDM-04-2018-0071 NR 121 TC 9 Z9 9 U1 14 U2 27 PD OCT PY 2021 VL 10 IS 10 AR 2490 DI 10.3390/foods10102490 WC Food Science & Technology SC Food Science & Technology UT WOS:000713976700001 DA 2022-12-14 ER PT J AU Nie, J Shao, SZ Zhang, YZ Li, CL Liu, Z Rogers, KM Wu, MC Lee, CP Yuan, YW AF Nie, Jing Shao, Shengzhi Zhang, Yongzhi Li, Chunlin Liu, Zhi Rogers, Karyne M. Wu, Ming-Chee Lee, Chuan-Pin Yuan, Yuwei TI Discriminating protected geographical indication Chinese Jinxiang garlic from other origins using stable isotopes and chemometrics SO JOURNAL OF FOOD COMPOSITION AND ANALYSIS DT Article DE Garlic; Geographical origin; Stable isotopes; Protected geographical indication; Traceability; Chemometrics ID ORYZA-SATIVA L.; OXYGEN ISOTOPES; FRACTIONATION; HYDROGEN; RATIO; FERTILIZER; NITROGEN; FOOD; SOIL AB Jinxiang garlic is a well-known commodity with protected geographical indication (PGI) status in China and is also registered PGI in the European Union. A stable isotope profile (C-13, N-15, H-2, O-18, and S-34) was used to construct a discrimination model to differentiate PGI Jinxiang garlic from other major garlic export countries (Argentina and Southeast Asian countries; Thailand and Vietnam), as well as garlic produced in other Chinese provinces (Guizhou and Hainan). Results showed that there were no significant isotope differences for Jinxiang garlic sampled from 13 towns across the county, especially for delta N-15 and delta H-2. delta O-18 value was not a suitable classification variable for origin, but delta H-2, delta N-15, and delta S-34 values were found to be useful to characterize garlic origin. Chemometrics showed pair-wise partial least squares discriminant analysis (PLS-DA) model was an appropriate method to distinguish Jinxiang garlic from other locations, with a total discriminant accuracy of more than 89.5 % and 100% classification of PGI Jinxiang garlic. This study has potential to provide a suitable traceability method to authenticate Jinxiang garlic with PGI status in international markets. C1 [Nie, Jing; Shao, Shengzhi; Li, Chunlin; Rogers, Karyne M.; Yuan, Yuwei] State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou 310021, Peoples R China. [Nie, Jing; Shao, Shengzhi; Zhang, Yongzhi; Li, Chunlin; Rogers, Karyne M.; Yuan, Yuwei] Zhejiang Acad Agr Sci, Inst Qual & Stand Agr Prod, 198 Shiqiao Rd, Hangzhou 310021, Peoples R China. [Liu, Zhi] Hunan Univ Humanities Sci & Technol, Coll Agr & Biotechnol, Loudi 417000, Peoples R China. [Rogers, Karyne M.] GNS Sci, Natl Isotope Ctr, Lower Hutt 5040, New Zealand. [Wu, Ming-Chee; Lee, Chuan-Pin] Natl Cheng Kung Univ, Dept Earth Sci, Tainan 70101, Taiwan. C3 Zhejiang Academy of Agricultural Sciences; Hunan University Of Humanities, Science & Technology; GNS Science - New Zealand; National Cheng Kung University RP Rogers, KM; Yuan, YW (corresponding author), Zhejiang Acad Agr Sci, Inst Qual & Stand Agr Prod, 198 Shiqiao Rd, Hangzhou 310021, Peoples R China. EM k.rogers@gns.cri.nz; ywytea@163.com CR Barbour MM, 2007, FUNCT PLANT BIOL, V34, P83, DOI 10.1071/FP06228 Baroni MV, 2011, J AGR FOOD CHEM, V59, P11117, DOI 10.1021/jf2023929 Bateman AS, 2007, ISOT ENVIRON HEALT S, V43, P237, DOI 10.1080/10256010701550732 Chen TJ, 2016, FOOD CHEM, V209, P95, DOI 10.1016/j.foodchem.2016.04.029 Chesson LA, 2009, RAPID COMMUN MASS SP, V23, P1275, DOI 10.1002/rcm.4000 Choi SH, 2020, FOOD CONTROL, V111, DOI 10.1016/j.foodcont.2019.107064 Choi WJ, 2003, SOIL BIOL BIOCHEM, V35, P1289, DOI 10.1016/S0038-0717(03)00199-8 Chung IM, 2018, FOOD CHEM, V240, P840, DOI 10.1016/j.foodchem.2017.08.023 Crumsey JM, 2019, OECOLOGIA, V190, P769, DOI 10.1007/s00442-019-04457-2 DANSGAARD W, 1964, TELLUS, V16, P436 El Sheikha AF, 2016, CRIT REV FOOD SCI, V56, P306, DOI 10.1080/10408398.2012.745478 European Commission, 2019, AGR RUR DEV FARQUHAR GD, 1989, ANNU REV PLANT PHYS, V40, P503, DOI 10.1146/annurev.pp.40.060189.002443 FARQUHAR GD, 1982, OECOLOGIA, V52, P121, DOI 10.1007/BF00349020 Galimov E.M., 1985, BIOL FRACTIONATION I, P16 Galimov E.M., 1985, BIOL FRACTIONATION I, P174 Gori Y, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0118941 Guo QJ, 2016, J GEOCHEM EXPLOR, V161, P112, DOI 10.1016/j.gexplo.2015.11.010 Inacio CT, 2015, CRIT REV FOOD SCI, V55, P1206, DOI 10.1080/10408398.2012.689380 Jinxiang County Government, 2012, REP WORK JINX COUNT Kelly JF, 2009, RAPID COMMUN MASS SP, V23, P2316, DOI 10.1002/rcm.4150 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kelly SD, 2010, FOOD CHEM, V119, P738, DOI 10.1016/j.foodchem.2009.07.022 KORNER C, 1991, OECOLOGIA, V88, P30, DOI 10.1007/BF00328400 Kriszan M, 2014, AGR ECOSYST ENVIRON, V184, P158, DOI 10.1016/j.agee.2013.11.028 LANE GA, 1956, SCIENCE, V123, P574, DOI 10.1126/science.123.3197.574 Liu PX, 2020, FOOD CHEM, V305, DOI 10.1016/j.foodchem.2019.125499 Liu TS, 2018, J FORENSIC SCI, V63, P1366, DOI 10.1111/1556-4029.13731 Liu Z, 2019, RAPID COMMUN MASS SP, V33, P778, DOI 10.1002/rcm.8405 Lu GH, 2005, J CHROMATOGR A, V1068, P209, DOI 10.1016/j.chroma.2005.01.082 Martins N, 2016, FOOD CHEM, V211, P41, DOI 10.1016/j.foodchem.2016.05.029 Muccio Z, 2009, ANALYST, V134, P213, DOI 10.1039/b808232d Opatic AM, 2017, ACTA CHIM SLOV, V64, P1048, DOI 10.17344/acsi.2017.3476 Perini M, 2018, FOOD CHEM, V239, P48, DOI 10.1016/j.foodchem.2017.06.023 Pianezze S, 2019, FOOD CHEM TOXICOL, V134, DOI 10.1016/j.fct.2019.110862 Rogers KM, 2017, APPL GEOCHEM, V82, P15, DOI 10.1016/j.apgeochem.2017.05.006 Rubenstein DR, 2004, TRENDS ECOL EVOL, V19, P256, DOI 10.1016/j.tree.2004.03.017 Schmitz H, 2000, J DEV STUD, V37, P177, DOI 10.1080/713600073 SMITH BN, 1990, BOT ACTA, V103, P335, DOI 10.1111/j.1438-8677.1990.tb00171.x Sun J, 2017, J HYDROL, V551, P245, DOI 10.1016/j.jhydrol.2017.06.006 Suzuki Y, 2019, BUNSEKI KAGAKU, V68, P671, DOI 10.2116/bunsekikagaku.68.671 Tanz N, 2010, J AGR FOOD CHEM, V58, P3139, DOI 10.1021/jf903251k Wadood SA, 2019, J MASS SPECTROM, V54, P178, DOI 10.1002/jms.4312 West JB, 2006, TRENDS ECOL EVOL, V21, P408, DOI 10.1016/j.tree.2006.04.002 NR 44 TC 7 Z9 7 U1 11 U2 21 PD JUN PY 2021 VL 99 AR 103856 DI 10.1016/j.jfca.2021.103856 EA FEB 2021 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000701770600010 DA 2022-12-14 ER PT J AU Cawthorn, DM Mariani, S AF Cawthorn, Donna-Maree Mariani, Stefano TI Global trade statistics lack granularity to inform traceability and management of diverse and high-value fishes SO SCIENTIFIC REPORTS DT Article ID WILDLIFE TRADE AB Illegal, unreported and unregulated (IUU) fishing and seafood supply chain fraud are multifaceted problems that demand multifaceted solutions. Here, we investigate the extent to which global fisheries trade data analyses can support effective seafood traceability and promote sustainable seafood markets using one of the world's most highly prized, yet misunderstood, groups of fishes as a model: the snappers, family Lutjanidae. By collating and comparing production, import and export data from international and national statistical collections for the period 2006-2013, we show that official trade data severely lack the level of detail required to track snapper trade flows, uncover potential IUU activities and/or inform exploitation management of snappers and related species. Moreover, we contend that the lack of taxonomic granularity and use of vague generic names in trade records represent one of the most insidious impediments to seafood traceability, and suggest that widely used harmonised commodity classification systems should evolve to address these gaps. C1 [Cawthorn, Donna-Maree; Mariani, Stefano] Univ Salford, The Crescent, Sch Environm & Life Sci, Ecosyst & Environm Res Ctr, Peel Bldg, Manchester M5 4WT, Lancs, England. C3 University of Manchester; University of Salford RP Cawthorn, DM (corresponding author), Univ Salford, The Crescent, Sch Environm & Life Sci, Ecosyst & Environm Res Ctr, Peel Bldg, Manchester M5 4WT, Lancs, England. EM cawthorndonna@gmail.com CR Agnew DJ, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0004570 Allen G.R., 1985, FAO FISHERIES SYNOPS, V6 [Anonymous], [No title captured] BUrgener M., 2007, S AFRICAS DEMERSAL S Cawthorn DM, 2012, FOOD RES INT, V46, P30, DOI 10.1016/j.foodres.2011.11.011 Chan HK, 2015, SCIENCE, V348, P291, DOI 10.1126/science.aaa3141 Claus S., 2017, MARINEREGIONS Eschmeyer W. N., 2017, CATALOG FISHES SPECI FAO, 2017, GLOB PROD STAT 1950 FAO, 2016, CONTRIBUTING FOOD SE FAO (Food and agriculture Organization), 2014, CWP HDB FISH STAT ST Food and Agriculture Organization, 2017, FISH COMM TRAD 1976 Froese R., 2020, FISHBASE Fund World Wildlife, 2014, ILL RUSS CRAB INV TR Gagern A, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0069959 Gerson H, 2008, CONSERV BIOL, V22, P4, DOI 10.1111/j.1523-1739.2007.00857.x Guo D., 2010, 192009 UN IND DEV OR Hamanaka S., 2011, 88 ADB Hofherr J, 2016, SEAFOOD AUTHENTICITY AND TRACEABILITY: A DNA-BASED PESPECTIVE, P47, DOI 10.1016/B978-0-12-801592-6.00003-6 Krzywinski M, 2009, GENOME RES, V19, P1639, DOI 10.1101/gr.092759.109 Lack M., 2001, PATAGONIAN TOOTHFISH Logan CA, 2008, BIOL CONSERV, V141, P1591, DOI 10.1016/j.biocon.2008.04.007 Marko PB, 2004, NATURE, V430, P309, DOI 10.1038/430309b National Oceanic and Atmospheric Administration, 2015, PRES TASK FORC COMB Pauly D, 2016, GLOBAL ATLAS MARINE Pramod G, 2014, MAR POLICY, V48, P102, DOI 10.1016/j.marpol.2014.03.019 Presidential Task Force on Combating IUU Fishing and Seafood Fraud, 2015, ACT PLAN IMPL TASK F Raemaekers S, 2011, OCEAN COAST MANAGE, V54, P433, DOI 10.1016/j.ocecoaman.2011.02.001 Robinson L., 1997, MANAGEMENT DATA SERI Statistics New Zealand, 2012, NZ HARM SYST CLASS United Nations Office on Drugs and Crime, 2011, TRANSN ORG CRIM FISH United States Census Bureau, 2016, US IMP MERCH Wagey G.A., 2009, STUDY ILLEGAL UNREPO Washington S., 2011, FAO Fisheries and Aquaculture Technical Paper Willock A., 2004, FISH PIRACY COMBATIN, P67 Willock A, 2004, 1 CHOICE FALLBACK EX Wong EHK, 2008, FOOD RES INT, V41, P828, DOI 10.1016/j.foodres.2008.07.005 World Wildlife Fund, 2012, WWF UNC MASS UNR TRA NR 38 TC 29 Z9 29 U1 2 U2 22 PD OCT 9 PY 2017 VL 7 AR 12852 DI 10.1038/s41598-017-12301-x WC Multidisciplinary Sciences SC Science & Technology - Other Topics UT WOS:000412661700001 DA 2022-12-14 ER PT J AU Pegels, N Garcia, T Martin, R Gonzalez, I AF Pegels, Nicolette Garcia, Teresa Martin, Rosario Gonzalez, Isabel TI Market Analysis of Food and Feed Products for Detection of Horse DNA by a TaqMan Real-Time PCR SO FOOD ANALYTICAL METHODS DT Article DE 12SrRNA gene; TaqMan real-time PCR; Horse; Human and pet foods; Traceability ID LENGTH-POLYMORPHISM ANALYSIS; MEAT-PRODUCTS; SPECIES IDENTIFICATION; ASSAY; AUTHENTICITY; COMPONENTS; CHICKEN; BEEF; PORK; DOG AB Food-labelling regulations require that meat species in food and feed are accurately declared to the consumer. Traceability systems based on species identification through DNA analysis are potent tools for the supervision of food adulteration. In this study, a TaqMan real-time polymerase chain reaction (PCR) assay targeting a short mitochondrial 12S ribosomal RNA (rRNA) gene fragment of 73 base pair (bp) was developed for detection of horse DNA in different commercial meat products for human and pet consumption. The method was found to be specific for horse and did not show any cross-reactivity with different species of mammals, birds, fish and plants. The assay complies with the acceptance criteria required for real-time PCR methods in terms of applicability, linear dynamic range, accuracy and PCR efficiency and showed to be sensitive allowing the detection of 1 pg of horse DNA. A range of food and feed products (n = 171) was screened with the horse-specific real-time PCR to determine whether a correct labelling had been employed at the market level. Results obtained when testing the meat products for human consumption were in agreement with the labelling description provided by the suppliers. In the case of the pet foods, undeclared horse meat was detected in 21 % of the samples at low levels, suggesting unintentional cross-contamination during processing. The reported real-time PCR methodology may represent a suitable tool for the detection of food mislabelling. C1 [Pegels, Nicolette; Garcia, Teresa; Martin, Rosario; Gonzalez, Isabel] Univ Complutense Madrid, Fac Vet, Dept Nutr Bromatol & Tecnol Alimentos, E-28040 Madrid, Spain. C3 Complutense University of Madrid RP Gonzalez, I (corresponding author), Univ Complutense Madrid, Fac Vet, Dept Nutr Bromatol & Tecnol Alimentos, E-28040 Madrid, Spain. EM gonzalzi@vet.ucm.es CR Ballin NZ, 2012, MEAT SCI, V90, P438, DOI 10.1016/j.meatsci.2011.09.002 Bustin SA, 2009, CLIN CHEM, V55, P611, DOI 10.1373/clinchem.2008.112797 Cawthorn DM, 2013, FOOD CONTROL, V32, P440, DOI 10.1016/j.foodcont.2013.01.008 Cawthraw S, 2009, J FOOD PROTECT, V72, P1055, DOI 10.4315/0362-028X-72.5.1055 Chisholm J, 2005, MEAT SCI, V70, P727, DOI 10.1016/j.meatsci.2005.03.009 Dalmasso A, 2004, MOL CELL PROBE, V18, P81, DOI 10.1016/j.mcp.2003.09.006 Doosti A, 2014, J FOOD SCI TECH MYS, V51, P148, DOI 10.1007/s13197-011-0456-3 European Network of GMO Laboratories, 2008, DEF MIN PERF REQU AN Fairbrother KS, 1998, MEAT SCI, V50, P105, DOI 10.1016/S0309-1740(98)00020-5 Girish PS, 2004, MEAT SCI, V66, P551, DOI 10.1016/S0309-1740(03)00158-X Hird H, 2006, FOOD ADDIT CONTAM, V23, P645, DOI 10.1080/02652030600603041 Huber I, 2013, J AGR FOOD CHEM, V61, P10293, DOI 10.1021/jf402448y Ilhak OI, 2007, TURK J VET ANIM SCI, V31, P159 Jonker KM, 2008, FOOD ADDIT CONTAM A, V25, P527, DOI 10.1080/02652030701584041 Kesmen Z., 2010, GIDA - Journal of Food, V35, P81 Kesmen Z, 2012, J FOOD SCI, V77, pC167, DOI 10.1111/j.1750-3841.2011.02536.x Koppel R, 2011, EUR FOOD RES TECHNOL, V232, P151, DOI 10.1007/s00217-010-1371-y Lenstra J. A., 2003, Food authenticity and traceability, P34, DOI 10.1533/9781855737181.1.34 Martin I, 2009, J SCI FOOD AGR, V89, P1202, DOI 10.1002/jsfa.3576 Matsunaga T, 1999, MEAT SCI, V51, P143, DOI 10.1016/S0309-1740(98)00112-0 Meyer R, 1996, FOOD SCI TECHNOL-LEB, V29, P1, DOI 10.1006/fstl.1996.0001 Meyer R, 1995, J AOAC INT, V78, P1542 Montowska M, 2011, FOOD REV INT, V27, P84, DOI 10.1080/87559129.2010.518297 Myers MJ, 2004, AM J VET RES, V65, P99, DOI 10.2460/ajvr.2004.65.99 Nader W, 2013, AGRO FOOD IND HI TEC, V24, P42 O'Mahony PJ, 2013, QJM-INT J MED, V106, P595, DOI 10.1093/qjmed/hct087 Partis L, 2000, MEAT SCI, V54, P369, DOI 10.1016/S0309-1740(99)00112-6 Pegels N, 2013, FOOD ADDIT CONTAM A, V30, P771, DOI 10.1080/19440049.2013.794978 Pegels N, 2012, FOOD ADDIT CONTAM A, V29, P1402, DOI 10.1080/19440049.2012.696284 Pegels N, 2012, POULTRY SCI, V91, P1709, DOI 10.3382/ps.2011-01954 Pegels N, 2013, FOOD ANAL METHOD, V6, P1040, DOI 10.1007/s12161-012-9555-7 Pegels N, 2011, FOOD CONTROL, V22, P1189, DOI 10.1016/j.foodcont.2011.01.015 Pereira F, 2010, NUCLEIC ACIDS RES, V38, DOI 10.1093/nar/gkq865 Prado M, 2013, FOOD CONTROL, V34, P19, DOI 10.1016/j.foodcont.2013.04.007 Rojas M, 2011, ANIM FEED SCI TECH, V169, P128, DOI 10.1016/j.anifeedsci.2011.05.006 Santaclara FJ, 2007, J AGR FOOD CHEM, V55, P305, DOI 10.1021/jf061840l Soares S, 2013, MEAT SCI, V94, P115, DOI 10.1016/j.meatsci.2012.12.012 Tanabe S, 2007, BIOSCI BIOTECH BIOCH, V71, P3131, DOI 10.1271/bbb.70683 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 Wang HC, 2004, J VET MED SCI, V66, P855, DOI 10.1292/jvms.66.855 Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 Xu WT, 2008, J SCI FOOD AGR, V88, P2631, DOI 10.1002/jsfa.3382 Yusop MHM, 2012, FOOD ANAL METHOD, V5, P422, DOI 10.1007/s12161-011-9260-y NR 43 TC 16 Z9 18 U1 3 U2 39 PD FEB PY 2015 VL 8 IS 2 BP 489 EP 498 DI 10.1007/s12161-014-9914-7 WC Food Science & Technology SC Food Science & Technology UT WOS:000347681100025 DA 2022-12-14 ER PT J AU Tao, HW Hu, YH Li, H Fan, DQ Chen, HR AF Tao, Hongwei Hu, Yinghui Li, Hui Fan, Deqiang Chen, Haoran TI The Credibility Measurement Model of Food Safety On-chain Data based on Blockchain SO JOURNAL OF INTERNET TECHNOLOGY DT Article DE Blockchain; Food safety traceability; Data credibility measurement; D-S evidence theory ID TECHNOLOGY AB Food safety is related to the national economy and people's livelihood and has always been the focus of the people and the government. Blockchain technology has characteristics of being decentralized, tamper-free, and having underlying openness. It can record and trace product information, prevent data tampering, effectively enhance the transparency of product information, and provide new methods and ideas for food safety traceability. At present, research hotspots mainly focus on the design and construction of a trusted blockchain traceability system, but the provided blockchain traceability system cannot provide a way to verify the authenticity of the information. This paper studies the credibility evaluation model of the members involved in the blockchain and the on-chain data quality model and provides a method to solve the credibility of the on-chain data. Meanwhile, the effectiveness of the method is validated by a case study. C1 [Tao, Hongwei; Hu, Yinghui; Chen, Haoran] Zhengzhou Univ Light Ind, Coll Comp & Commun Engn, Zhengzhou, Henan, Peoples R China. [Li, Hui] Qingdao Agr Univ, Coll Econ, Qingdao, Shandong, Peoples R China. [Fan, Deqiang] Henan New Landmark Construct Engn Ltd Co, Qingdao, Peoples R China. C3 Zhengzhou University of Light Industry; Qingdao Agricultural University RP Li, H (corresponding author), Qingdao Agr Univ, Coll Econ, Qingdao, Shandong, Peoples R China. EM tthhww_811@163.com; hyingh6@163.com; leephil@163.com; 93613155@qq.com; chenhaoran@zzuli.edu.cn CR Back Adam, 2002, HASHCASH A DENIAL SE Batini C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541883 Chen Y. X., 2019, SOFTWARE CREDIBILITY DEMPSTER AP, 1967, ANN MATH STAT, V38, P325, DOI 10.1214/aoms/1177698950 Dongcheng Li, 2020, 2020 7th International Conference on Dependable Systems and Their Applications (DSA), P69, DOI 10.1109/DSA51864.2020.00018 Glowalla P, 2014, P ANN HICSS, P4700, DOI 10.1109/HICSS.2014.575 HABER S, 1991, LECT NOTES COMPUT SC, V537, P437 Hao J.T., 2018, J COMPUT, V29, P158, DOI [10.3966/199115992018122906015, DOI 10.3966/199115992018122906015] Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Li D., 2020, PROC 6 INT C DEPENDA, P71, DOI [10.1109/DSA.2019.00017, DOI 10.1109/DSA.2019.00017] Li D. C., 2021, INT J PERFORMABILITY, V17, P411 Li DC, 2020, COMPANION OF THE 2020 IEEE 20TH INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY (QRS-C 2020), P153, DOI 10.1109/QRS-C51114.2020.00035 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Massias H., 1999, 20 S INFORM THEORY, P1 McGilvray D, 2008, EXECUTING DATA QUALI Nakamoto, 2008, BITCOIN PEER TO PEER Pan G. Y, 2020, INT J PERFORMABILITY, V16, P1608 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Shell ZL, 2018, INT SYMP PARA DISTR, P118, DOI 10.1109/ISPDC2018.2018.00025 Sidi F., 2012, 2012 International Conference on Information Retrieval & Knowledge Management (CAMP), P300, DOI 10.1109/InfRKM.2012.6204995 State Council, 2015, FOOD SAF LOW PEOPL R Tiwari A., 2021, INT J PERFORMABILITY, V17, P722 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Turri AM, 2017, J CONSUM AFF, V51, P329, DOI 10.1111/joca.12133 NR 25 TC 0 Z9 0 U1 2 U2 2 PY 2022 VL 23 IS 4 BP 719 EP 725 DI 10.53106/160792642022072304007 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications UT WOS:000866388400007 DA 2022-12-14 ER PT J AU Ishizuka, AND de Carvalho, MAG Araujo, RGAC Denadai, JC Pimenta, GEM Sartori, MMP Costa, VE AF Dias Ishizuka, Adriele Nayara Gonzales de Carvalho, Marco Antonio Aparecido Cardoso Araujo, Robert Guaracy Denadai, Juliana Celia Mendes Pimenta, Guilherme Emygdio Pereira Sartori, Maria Marcia Costa, Vladimir Eliodoro TI Traceability of animal meals in quail eggs using carbon and nitrogen stable isotopes SO PESQUISA AGROPECUARIA BRASILEIRA DT Article DE Coturnix coturnix japonica; bovine meal; certification; feather meal; poultry viscera meal ID POULTRY OFFAL MEAL; BY-PRODUCTS; DIETS; C-13/C-12; QUALITY AB The objective of this work was to detect the inclusion of animal meal in Japanese quail (Coturnix coturnix japonica) diets, by analyzing eggs and their fractions (albumen and yolk) through the technique of carbon (C-13/C-12) and nitrogen (N-15/N-14) stable isotopes. Four hundred and thirty-two Japanese quails, 45-day-old females, were distributed in a completely randomized experimental design, in eight treatments: T1, strictly vegetable diet (VEG), with corn (Zea mays) and soybean (Glycine max) meal: T2, bovine meat and bone meal (BM); T3, poultry viscera meal (OM); T4, feather meal (FM); T5, BM+OM; T6, BM+FM; T7, OM+FM; and T8, BM+OM+FM. Sixteen eggs were randomly collected from each treatment - eight for whole egg analysis and eight for separate yolk and albumen analyses. To determine the turnover rate, the exponential isotope dilution model was used. The application of C and N stable isotopes allows identifying the use of animal meal in coded diets through the analysis of whole eggs and their fractions, which suggests that this technique is a promising tool for the traceability and certification of products of animal origin. C1 [Dias Ishizuka, Adriele Nayara; Gonzales de Carvalho, Marco Antonio; Denadai, Juliana Celia; Mendes Pimenta, Guilherme Emygdio; Costa, Vladimir Eliodoro] Univ Estadual Paulista, Dept Fis & Biofis, Inst Biociencias, Ctr Isotopes Estaveis Ambientais Ciencias Vida, BR-18618970 Botucatu, SP, Brazil. [Aparecido Cardoso Araujo, Robert Guaracy] Univ Estadual Paulista, Fac Med Vet & Zootecnia, Rua Prof Doutor Walter Mauricio Correa S-N, BR-18618681 Botucatu, SP, Brazil. [Pereira Sartori, Maria Marcia] Univ Estadual Paulista, Fac Ciencias Agron, Fazenda Expt Lageado, BR-18618970 Botucatu, SP, Brazil. C3 Universidade Estadual Paulista; Universidade Estadual Paulista; Universidade Estadual Paulista RP Araujo, RGAC (corresponding author), Univ Estadual Paulista, Fac Med Vet & Zootecnia, Rua Prof Doutor Walter Mauricio Correa S-N, BR-18618681 Botucatu, SP, Brazil. EM adriele_drik@hotmail.com; gonzalezmarco@hotmail.com; robertzootecnista@gmail.com; denadaijc@gmail.com; gmpimenta@zootecnista.com.br; mmpsartori@fca.unesp.br; vladimir.costa@unesp.br CR BERTECHINI A.G., 2010, 4 S INT 4 C BRAS COT, V3 Bottero MT, 2011, VET J, V190, P34, DOI 10.1016/j.tvjl.2010.09.024 Carrijo AS, 2006, Rev. Bras. Cienc. Avic., V8, P63, DOI 10.1590/S1516-635X2006000100010 CRAIG H, 1953, GEOCHIM COSMOCHIM AC, V3, P53, DOI 10.1016/0016-7037(53)90001-5 Cruz VC, 2012, POULTRY SCI, V91, P478, DOI 10.3382/ps.2011-01512 Denadai JC, 2008, BRAZ J POULTRY SCI, V10, P189, DOI 10.1590/S1516-635X2008000300010 DENIRO MJ, 1978, GEOCHIM COSMOCHIM AC, V42, P495, DOI 10.1016/0016-7037(78)90199-0 Gottmann R, 2008, PESQUI AGROPECU BRAS, V43, P1641, DOI 10.1590/S0100-204X2008001200001 Hendriks WH, 2002, ASIAN AUSTRAL J ANIM, V15, P1507, DOI 10.5713/ajas.2002.1507 Herzka SZ, 2013, AQUAT CONSERV, V23, P546, DOI 10.1002/aqc.2326 HOBSON KA, 1995, CONDOR, V97, P752, DOI 10.2307/1369183 Mori C, 2008, BRAZ J POULT SCI, V10, P45, DOI 10.1590/S1516-635X2008000100007 Mori C, 2007, BRAZ J POULT SCI, V9, P263, DOI 10.1590/S1516-635X2007000400010 Mori C, 2013, BRAZ J POULTRY SCI, V15, P59, DOI 10.1590/S1516-635X2013000100010 Oliveira RP, 2010, BRAZ J POULTRY SCI, V12, P13, DOI 10.1590/S1516-635X2010000100002 Prache S, 2005, SMALL RUMINANT RES, V59, P157, DOI 10.1016/j.smallrumres.2005.05.004 Rock L, 2012, TRENDS FOOD SCI TECH, V28, P62, DOI 10.1016/j.tifs.2012.04.002 Rock L, 2013, FOOD CHEM, V136, P1551, DOI 10.1016/j.foodchem.2012.03.041 Rogers KM, 2009, J AGR FOOD CHEM, V57, P4236, DOI 10.1021/jf803760s Rostagno H.S., 2011, COMPOSICAO ALIMENTOS, V2, P186 Vinci G, 2013, J SCI FOOD AGR, V93, P439, DOI 10.1002/jsfa.5970 Wang X, 1998, POULTRY SCI, V77, P834, DOI 10.1093/ps/77.6.834 NR 22 TC 0 Z9 0 U1 0 U2 3 PY 2020 VL 55 AR e01394 DI 10.1590/S1678-3921.pab2020.v55.01394 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000548649400001 DA 2022-12-14 ER PT J AU El Sheikha, AF Durand, N Sarter, S Okullo, JBL Montet, D AF El Sheikha, Aly F. Durand, Noel Sarter, Samira Okullo, John B. L. Montet, Didier TI Study of the microbial discrimination of fruits by PCR-DGGE: Application to the determination of the geographical origin of Physalis fruits from Colombia, Egypt, Uganda and Madagascar SO FOOD CONTROL DT Article DE Physalis; PCR-DGGE; 26S rDNA fingerprinting; Traceability; Geographical origin ID GRADIENT GEL-ELECTROPHORESIS; BACTERIAL; YEASTS; IDENTIFICATION; COMMUNITIES; PHYLOGENY AB Traceability of fruits is only documentary. In case of doubt or fraud, no standardized analysis makes it possible to trace back the origin of the fruit. The aim of this study is to apply PCR-DGGE method to analyze in a unique step all the yeasts present on the fruit to create the linkage between yeast communities and their geographical origins. PCR-DGGE is a method of yeast ecology which was used to characterize all the yeast flora of three species of Physalis fruit (Physalis ixocarpa Brot, Physalis pruinosa Physalis peruviana L) from Colombia, Egypt, Uganda, Madagascar. DGGE fingerprints analyzed by multivariate analysis permitted to distinguish different fruit origins by their yeast communities. PCR-DGGE method is then proposed as a new traceability tool which provides to the fruits in general and Physalis in particular with a unique bar code for each country by using 26S rDNA fingerprinting of yeasts. (C) 2011 Elsevier Ltd. All rights reserved. C1 [El Sheikha, Aly F.; Durand, Noel; Montet, Didier] UMR Qualisud, CIRAD, Ctr Cooperat Int Rech Agron Dev, F-34398 Montpellier 5, France. [El Sheikha, Aly F.] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Minufiya Govt, Shibin Al Kawm 32511, Egypt. [Sarter, Samira] UMR Qualisud, CIRAD Madagascar, Ctr Cooperat Int Rech Agron Dev, Antananarivo, Madagascar. [Okullo, John B. L.] Makerere Univ, Fac Forestry & Nat Conservat, Dept Forest Biol & Ecosyst Management, Kampala, Uganda. C3 CIRAD; Universite de Montpellier; Egyptian Knowledge Bank (EKB); Menofia University; CIRAD; Makerere University RP El Sheikha, AF (corresponding author), UMR Qualisud, CIRAD, Ctr Cooperat Int Rech Agron Dev, TA 958-16, F-34398 Montpellier 5, France. EM elsheikha_aly@yahoo.com CR Agrios GN., 1997, PLANT PATHOL, V4th ed Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 *CAPMAS, 2001, STAT SECT PLANT PROD Cocolin L, 2000, FEMS MICROBIOL LETT, V189, P81, DOI 10.1016/S0378-1097(00)00257-3 Drake JW, 1998, GENETICS, V148, P1667 El Sheikha A., 2008, FOOD, V2, P124 El Sheikha A. F., 2009, AFR J FOOD AGR NUTR, V9, P1388 El Sheikha A.F., 2010, THESIS U MONTPELLIER EL SHEIKHA A F, 2010, J LIFE SCI, V4, P9 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Fleet GH, 2007, CURR OPIN BIOTECH, V18, P170, DOI 10.1016/j.copbio.2007.01.010 GHIDINI S, 2006, STABLE ISOTOPES DETE, V26 Heyndrickx M, 1996, J MICROBIOL METH, V26, P247, DOI 10.1016/0167-7012(96)00916-5 *ISO, 2007, QUAL MAN SYST TRAC F Kowalchuk GA, 1997, APPL ENVIRON MICROB, V63, P1489, DOI 10.1128/AEM.63.4.1489-1497.1997 Kurtzman CP, 1998, ANTON LEEUW INT J G, V73, P331, DOI 10.1023/A:1001761008817 Le Nguyen DD, 2008, FRUITS, V63, P75, DOI 10.1051/fruits:2007049 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Montet D., 2008, Aspects of Applied Biology, P11 Montet D., 2004, SEM FOOD SAF INT TRA MUYZER G, 1995, ARCH MICROBIOL, V164, P165, DOI 10.1007/BF02529967 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Novoa R. H., 2006, AGRON COLOMB, V24, P68 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Prakitchaiwattana CJ, 2004, FEMS YEAST RES, V4, P865, DOI 10.1016/j.femsyr.2004.05.004 RAMANAMIDONA JY, 2004, BIMONTHLY B HORTICUL, V2 Ros-Chumillas M, 2007, FOOD CONTROL, V18, P33, DOI 10.1016/j.foodcont.2005.08.004 SASAKI T, 1987, APPL ENVIRON MICROB, V53, P1504, DOI 10.1128/AEM.53.7.1504-1511.1987 SODEKO OO, 1987, MICROBIOS, V51, P133 Suzuki R., 2004, 15 INT C GEN INF PAC, pP034 Tournas VH, 2006, FOOD MICROBIOL, V23, P684, DOI 10.1016/j.fm.2006.01.003 van Hannen EJ, 1999, APPL ENVIRON MICROB, V65, P795 2006, BAYER CROP SCI MAGAZ NR 33 TC 20 Z9 21 U1 0 U2 26 PD MAR-APR PY 2012 VL 24 IS 1-2 BP 57 EP 63 DI 10.1016/j.foodcont.2011.09.003 WC Food Science & Technology SC Food Science & Technology UT WOS:000297659100009 DA 2022-12-14 ER PT J AU Ranaweera, RKR Gilmore, AM Bastian, SEP Capone, DL Jeffery, DW AF Ranaweera, Ranaweera K. R. Gilmore, Adam M. Bastian, Susan E. P. Capone, Dimitra L. Jeffery, David W. TI Spectrofluorometric analysis to trace the molecular fingerprint of wine during the winemaking process and recognise the blending percentage of different varietal wines SO OENO ONE DT Article DE Authenticity; excitation-emission matrix; traceability; chemometrics; vinification ID TRACEABILITY AB As a robust analytical method, spectrofluorometric analysis with machine learning modelling has recently been used to authenticate wine from different regions, vintages and varieties. This preliminary study investigated whether the molecular fingerprint obtained with this approach is maintained throughout the winemaking process, along with assessing different percentages of wine in a blend. Monovarietal wine samples were collected at different stages of the winemaking process and analysed with the absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique. Wines were clustered tightly according to origin for the different winemaking stages, with some clear separation of different regions and varieties based on principal component analysis. In addition, wines were classified with 100 % accuracy according to varietal origin using extreme gradient boosting (XGB) discriminant analysis. The sensitivity of the A-TEEM technique was such that it allowed for accurate modelling of wine blends containing as little as 1 % of Cabernet-Sauvignon or Grenache in Shiraz wine when employing XGB regression, which performed better than partial least squares regression. The overall results indicated the potential for applying A-TEEM and machine learning modelling to wine chemical traceability through production to guarantee the provenance of wine or identify the composition of a blend. C1 [Ranaweera, Ranaweera K. R.; Bastian, Susan E. P.; Capone, Dimitra L.; Jeffery, David W.] Univ Adelaide, Dept Wine Sci, PMB 1, Glen Osmond, SA 5064, Australia. [Ranaweera, Ranaweera K. R.; Bastian, Susan E. P.; Capone, Dimitra L.; Jeffery, David W.] Univ Adelaide, Waite Res Inst, PMB 1, Glen Osmond, SA 5064, Australia. [Gilmore, Adam M.] HORIBA Instruments Inc, 20 Knightsbridge Rd, Piscataway, NJ 08854 USA. [Bastian, Susan E. P.; Capone, Dimitra L.; Jeffery, David W.] Univ Adelaide, Australian Res Council, Training Ctr Innovat Wine Prod, PMB 1, Glen Osmond, SA 5064, Australia. C3 University of Adelaide; University of Adelaide; University of Adelaide RP Jeffery, DW (corresponding author), Univ Adelaide, Dept Wine Sci, PMB 1, Glen Osmond, SA 5064, Australia.; Jeffery, DW (corresponding author), Univ Adelaide, Waite Res Inst, PMB 1, Glen Osmond, SA 5064, Australia.; Jeffery, DW (corresponding author), Univ Adelaide, Australian Res Council, Training Ctr Innovat Wine Prod, PMB 1, Glen Osmond, SA 5064, Australia. EM david.jeffery@adelaide.edu.au CR Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 Arcena MR, 2020, FOOD RES INT, V127, DOI 10.1016/j.foodres.2019.108767 Boccacci P, 2020, FOOD CHEM, V312, DOI 10.1016/j.foodchem.2019.126100 Catalano V, 2016, J AGR FOOD CHEM, V64, P6969, DOI 10.1021/acs.jafc.6b02560 Dooley LM, 2012, AM J ENOL VITICULT, V63, P241, DOI 10.5344/ajev.2012.11086 Ghanem E, 2015, MOLECULES, V20, P9170, DOI 10.3390/molecules20059170 Gilmore A., 2020, SCIX C SCIX VIRT EV Gilmore A., 2017, READOUT, V2, P41 Gilmore AM, 2014, METHODS MOL BIOL, V1076, P3, DOI 10.1007/978-1-62703-649-8_1 Gomez MDM, 2004, J AGR FOOD CHEM, V52, P2953, DOI 10.1021/jf035119g Imparato G, 2011, J AGR FOOD CHEM, V59, P4429, DOI 10.1021/jf200587n Jones-Moore HR, 2022, FOOD HYDROCOLLOID, V123, DOI 10.1016/j.foodhyd.2021.107150 Li S.-Y., 2020, BLENDING STRATEGIES Lonvaud-Funel A, 2010, WOODHEAD PUBL FOOD S, V192, P60, DOI 10.1533/9781845699987.1.60 Marisa C, 2004, FOOD CHEM, V85, P7, DOI 10.1016/j.foodchem.2003.05.003 Preserova J, 2015, J FOOD SCI TECH MYS, V52, P6405, DOI 10.1007/s13197-014-1644-8 Ranaweera RKR, 2021, FOOD CHEM, V335, DOI 10.1016/j.foodchem.2020.127592 Ranaweera R. K. R., 2021, COMPREHENSIVE FOODOM, V3, P452, DOI DOI 10.1016/B978-0-08-100596-5.22876-X Ranaweera RKR, 2021, FOOD CHEM, V361, DOI 10.1016/j.foodchem.2021.130149 Gonzaga LS, 2021, AUST J GRAPE WINE R, V27, P246, DOI 10.1111/ajgw.12474 Styger G, 2011, J IND MICROBIOL BIOT, V38, P1145, DOI 10.1007/s10295-011-1018-4 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Wine Australia, 2017, HIST EV REV AUSTR RE Wine Australia, 2018, BLEND RUL NR 24 TC 0 Z9 0 U1 3 U2 4 PY 2022 VL 56 IS 1 BP 189 EP 196 DI 10.20870/oeno-one.2022.56.1.4904 WC Food Science & Technology SC Food Science & Technology UT WOS:000783867700002 DA 2022-12-14 ER PT J AU Tulli, F Moreno-Rojas, JM Messina, CM Trocino, A Xiccato, G Munoz-Redondo, JM Santulli, A Tibaldi, E AF Tulli, Francesca Moreno-Rojas, Jose M. Messina, Concetta Maria Trocino, Angela Xiccato, Gerolamo Munoz-Redondo, Jose M. Santulli, Andrea Tibaldi, Emilio TI The Use of Stable Isotope Ratio Analysis to Trace European Sea Bass (D. labrax) Originating from Different Farming Systems SO ANIMALS DT Article DE aquaculture; Dicentrarchus labrax; stable isotopes; traceability; farming system; geographic origin; IRMS; sea bass; fish; authentication ID BREAM SPARUS-AURATA; NEAR-INFRARED SPECTROSCOPY; FATTY-ACID-COMPOSITION; DICENTRARCHUS-LABRAX; GEOGRAPHICAL ORIGIN; LIPID EXTRACTION; TROPHIC POSITION; ATLANTIC SALMON; FEEDING ECOLOGY; OLIVE OILS AB Simple Summary European sea bass is one of the most economically important fish species in the Mediterranean area. The potential effects of farming systems on the final quality of this product and the recent popular demand for labels to certify the animal rearing origin, which is increasingly used as a marketing tool, have raised the use of analytical techniques that make it possible to differentiate this fish product according to the rearing farming system and authenticate their geographical origin. The aim of this study was to determine whether isotopic ratio mass spectrometry (IRMS) can discriminate farmed European sea bass according to different farming systems (concrete tank inland, sea cages, and extensive methods in valleys or salt works) and geographic origins (different locations scattered throughout Italy). The results of this study showed the viability of delta C-13 and delta N-15 to discriminate cultured sea bass from different farming systems (extensive vs. intensive) reared at different geographical sites in Italy. Meanwhile, the measurement of delta O-18 and delta H-2 made it possible to distinguish the geographical origin of the sea bass farmed extensively and intensively (in cages). This study aimed to determine whether isotopic ratio mass spectrometry (IRMS) can discriminate farmed European sea bass according to different farming systems and geographic origins. Dicentrarchus labrax of commercial size from three different rearing systems (concrete tank inland, sea cages, and extensive methods in valleys or salt works) were collected at the trading period (autumn-winter). For each farming type, different locations spread over Italy were monitored. Once the fish were harvested, the muscle and feed were sampled. For both muscle and feed, delta C-13 and delta N-15 were measured by continuous flow elemental analyzer isotope ratio mass spectrometry (CF-EA-IRMS) with the goal of discriminating samples based on the rearing system. Additional delta H-2 and delta O-18 measurements of fish samples were performed by continuous flow total combustion elemental analyzer isotope ratio mass spectrometry (CF-TC/EA-IRMS) to track the geographical origin. The measurements of delta C-13 and delta N-15 made it possible to discriminate cultured sea bass from different farming systems (extensive vs. intensive) reared at different geographical sites in Italy. Additional information was obtained from delta O-18 and delta H-2, which enabled the geographical areas of origin of the sea bass farmed extensively and intensively (in cages) to be distinguished. C1 [Tulli, Francesca; Tibaldi, Emilio] Univ Udine, Dept Agr Food Environm & Anim Sci, Via Sondrio 2, I-33100 Udine, Italy. [Moreno-Rojas, Jose M.; Munoz-Redondo, Jose M.] Alameda Obispo Ctr, Andalusian Inst Agr & Fisheries Res & Training IF, Dept Food Sci & Hlth, Avda Menendez Pidal S-N, Cordoba 14004, Spain. [Messina, Concetta Maria; Santulli, Andrea] Univ Palermo, Dept Earth & Sea Sci, Lab Marine Biochem & Ecotoxicol, Via Barlotta 4, I-91100 Trapani, Italy. [Trocino, Angela] Univ Padua, Dept Comparat Biomed & Food Sci, Viale Univ 16, I-35020 Padua, Italy. [Xiccato, Gerolamo] Univ Padua, Dept Agron Food Nat Resources Anim & Environm, Viale Univ 16, I-35020 Padua, Italy. C3 University of Udine; University of Palermo; University of Padua; University of Padua RP Moreno-Rojas, JM (corresponding author), Alameda Obispo Ctr, Andalusian Inst Agr & Fisheries Res & Training IF, Dept Food Sci & Hlth, Avda Menendez Pidal S-N, Cordoba 14004, Spain. EM francesca.tulli@uniud.it; josem.moreno.rojas@juntadeandalucia.es; concetta.messina@unipa.it; angela.trocino@unipd.it; gerolamo.xiccato@unipd.it; josem.munoz.redondo@juntadeandalucia.es; andrea.santulli@unipa.it; emilio.tibaldi@uniud.it CR Alonso-Salces RM, 2015, EUR J LIPID SCI TECH, V117, P1991, DOI 10.1002/ejlt.201400243 Araghipour N, 2008, FOOD CHEM, V108, P374, DOI 10.1016/j.foodchem.2007.10.056 Arvanitoyannis IS, 2005, INT J FOOD SCI TECH, V40, P237, DOI 10.1111/j.1365-2621.2004.00917.x Barnes C, 2007, FUNCT ECOL, V21, P356, DOI 10.1111/j.1365-2435.2006.01224.x Bell JG, 2007, J AGR FOOD CHEM, V55, P5934, DOI 10.1021/jf0704561 Benson S, 2006, FORENSIC SCI INT, V157, P1, DOI 10.1016/j.forsciint.2005.03.012 Bertoldi D, 2019, FOOD CHEM, V275, P585, DOI 10.1016/j.foodchem.2018.09.098 Bodin N, 2007, J EXP MAR BIOL ECOL, V341, P168, DOI 10.1016/j.jembe.2006.09.008 Bontempo L, 2009, RAPID COMMUN MASS SP, V23, P1043, DOI 10.1002/rcm.3968 Busetto ML, 2008, J AGR FOOD CHEM, V56, P2742, DOI 10.1021/jf0734267 Camin F, 2013, FOOD CONTROL, V29, P107, DOI 10.1016/j.foodcont.2012.05.055 Camin F, 2018, FOOD CHEM, V267, P288, DOI 10.1016/j.foodchem.2017.06.017 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Chiesa L, 2016, FOOD CHEM, V212, P296, DOI 10.1016/j.foodchem.2016.05.180 de Rijke E, 2016, FOOD CHEM, V204, P122, DOI 10.1016/j.foodchem.2016.01.134 Dempson JB, 2004, ECOL FRESHW FISH, V13, P176, DOI 10.1111/j.1600-0633.2004.00057.x DENIRO MJ, 1978, GEOCHIM COSMOCHIM AC, V42, P495, DOI 10.1016/0016-7037(78)90199-0 DENIRO MJ, 1981, GEOCHIM COSMOCHIM AC, V45, P341, DOI 10.1016/0016-7037(81)90244-1 Di Marco P, 2017, AQUACULTURE, V471, P92, DOI 10.1016/j.aquaculture.2017.01.012 Domi N, 2005, MAR ENVIRON RES, V60, P551, DOI 10.1016/j.marenvres.2005.03.001 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Dubois S, 2007, MAR ECOL PROG SER, V336, P151, DOI 10.3354/meps336151 El Sheikha AF, 2016, CRIT REV FOOD SCI, V56, P306, DOI 10.1080/10408398.2012.745478 EUMOFA, 2019, HIGHL EU WORLD EU MA European Commission, 2015, REP COMM EUR PARL CO, P2 FAO, 2016, CONTRIBUTING FOOD SE FOLCH J, 1957, J BIOL CHEM, V226, P497 Fuentes A, 2010, FOOD CHEM, V119, P1514, DOI 10.1016/j.foodchem.2009.09.036 Gamboa-Delgado J, 2014, CAN J FISH AQUAT SCI, V71, P1520, DOI 10.1139/cjfas-2014-0005 Gannes LZ, 1998, COMP BIOCHEM PHYS A, V119, P725, DOI 10.1016/S1095-6433(98)01016-2 Gaye-Siessegger J, 2007, J FISH BIOL, V71, P90, DOI 10.1111/j.1095-8649.2007.01469.x Ghidini S, 2019, FOOD CHEM, V280, P321, DOI 10.1016/j.foodchem.2018.12.075 Grigorakis K, 2007, AQUACULTURE, V272, P55, DOI 10.1016/j.aquaculture.2007.04.062 HOBSON KA, 1992, CONDOR, V94, P181, DOI 10.2307/1368807 Hong E, 2017, J SCI FOOD AGR, V97, P3877, DOI 10.1002/jsfa.8364 Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Hussey NE, 2012, J EXP MAR BIOL ECOL, V434, P7, DOI 10.1016/j.jembe.2012.07.012 Ingram T, 2007, LIMNOL OCEANOGR-METH, V5, P338, DOI 10.4319/lom.2007.5.338 Cuevas FJ, 2019, FOOD CONTROL, V104, P63, DOI 10.1016/j.foodcont.2019.04.012 Kambikambi MJ, 2019, RAPID COMMUN MASS SP, V33, P613, DOI 10.1002/rcm.8393 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kim H, 2015, FOOD CHEM, V172, P523, DOI 10.1016/j.foodchem.2014.09.058 Kim SL, 2012, ENVIRON BIOL FISH, V95, P37, DOI 10.1007/s10641-011-9919-7 Kusche H, 2018, ISOT ENVIRON HEALT S, V54, P28, DOI 10.1080/10256016.2017.1361419 Li L, 2016, FOOD CHEM, V194, P1238, DOI 10.1016/j.foodchem.2015.08.123 Lv WT, 2012, J ANIM SCI BIOTECHNO, V3, DOI 10.1186/2049-1891-3-14 Mattarucchi E, 2010, J AGR FOOD CHEM, V58, P12089, DOI 10.1021/jf102632g Michener RH, 2007, ECOL METHOD CONCEPT, P238, DOI 10.1002/9780470691854.ch9 MINAGAWA M, 1984, GEOCHIM COSMOCHIM AC, V48, P1135, DOI 10.1016/0016-7037(84)90204-7 Mokrani D, 2018, AQUACULTURE, V490, P120, DOI 10.1016/j.aquaculture.2018.02.032 Moreno-Rojas JM, 2008, RAPID COMMUN MASS SP, V22, P3706, DOI 10.1002/rcm.3775 Moreno-Rojas JM, 2008, RAPID COMMUN MASS SP, V22, P3701, DOI 10.1002/rcm.3773 Ottavian M, 2012, J AGR FOOD CHEM, V60, P639, DOI 10.1021/jf203385e Park HJ, 2018, FISH RES, V204, P297, DOI 10.1016/j.fishres.2018.03.006 PETERSON BJ, 1987, ANNU REV ECOL SYST, V18, P293, DOI 10.1146/annurev.es.18.110187.001453 Petrovic M, 2015, ITAL J FOOD SCI, V27, P151 Post DM, 2002, ECOLOGY, V83, P703, DOI 10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2 Reilly A., 2018, 1165 FAO Rojas JMM, 2007, RAPID COMMUN MASS SP, V21, P207, DOI 10.1002/rcm.2836 Roncarati A, 2010, EUR J LIPID SCI TECH, V112, P770, DOI 10.1002/ejlt.200900286 Sant'Ana LS, 2010, FOOD CHEM, V122, P74, DOI 10.1016/j.foodchem.2010.02.016 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Serrano R, 2007, CHEMOSPHERE, V69, P1075, DOI 10.1016/j.chemosphere.2007.04.034 Shiffman DS, 2014, MAR COAST FISH, V6, P156, DOI 10.1080/19425120.2014.920742 Smichi N, 2017, FISH RES, V188, P74, DOI 10.1016/j.fishres.2016.12.003 Steffens W, 2016, AQUACULT INT, V24, P787, DOI 10.1007/s10499-015-9885-8 Sweeting CJ, 2007, J EXP MAR BIOL ECOL, V340, P1, DOI 10.1016/j.jembe.2006.07.023 Thomas F, 2008, J AGR FOOD CHEM, V56, P989, DOI 10.1021/jf072370d Trocino A, 2012, FOOD CHEM, V134, P333, DOI 10.1016/j.foodchem.2012.02.153 Turchini GM, 2009, J AGR FOOD CHEM, V57, P274, DOI 10.1021/jf801962h Valladares S, 2020, ANIMALS-BASEL, V10, DOI 10.3390/ani10091571 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 Vasconi M, 2019, FOOD CONTROL, V102, P112, DOI 10.1016/j.foodcont.2019.03.004 Oliveira EJVM, 2011, EUR FOOD RES TECHNOL, V232, P97, DOI 10.1007/s00217-010-1367-7 Vidal NP, 2016, J SCI FOOD AGR, V96, P1181, DOI 10.1002/jsfa.7201 Vidal NP, 2012, FOOD CHEM, V135, P1583, DOI 10.1016/j.foodchem.2012.06.002 Wang JS, 2020, FOOD CHEM, V313, DOI 10.1016/j.foodchem.2019.126093 Xiccato G, 2004, FOOD CHEM, V86, P275, DOI 10.1016/j.foodchem.2003.09.026 NR 78 TC 2 Z9 3 U1 1 U2 9 PD NOV PY 2020 VL 10 IS 11 AR 2042 DI 10.3390/ani10112042 WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology SC Agriculture; Veterinary Sciences; Zoology UT WOS:000592764700001 DA 2022-12-14 ER PT J AU Jesus, G Aguiar, ML Gaspar, PD AF Jesus, Guilherme Aguiar, Martim L. Gaspar, Pedro D. TI Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants SO ENERGIES DT Article DE computational tool; HFCs; objective function; environmental impact; alternative refrigerants; sustainable refrigerants; sustainability ID AGRIFOOD INDUSTRIES; PERFORMANCE; SYSTEM AB There have been consequences regarding the increment of the greenhouse effect, such as the rise in the planet's global temperature, and climate change. Refrigerants have an important contribution to the aforementioned environmental impact. In particular, hydrofluorocarbons (HFCs) contribute to the destruction of the ozone layer and the increase of the greenhouse effect. Protocols, international agreements, and legislation were developed to slow down the emission of greenhouse gases. Prohibition and definition of deadlines for the gradual elimination of various refrigerants have been proposed to replace them with others that are environmentally sustainable. Soon, the refrigeration sector will have to replace some refrigerants with others that are alternative and/or sustainable with minimal or zero environmental impact. A computational tool to support decision-making regarding the selection of alternative and/or sustainable refrigerant to replace the old one is developed to be used by refrigeration companies, manufacturers, and installers. A suggestion of refrigerants with reduced environmental impact is provided, ensuring similar thermal performance and energy efficiency, considering the safety level and renovation cost of the installation and refrigerant itself. This decision support system (DSS) uses an objective function that includes the technical specifications and properties of alternative and sustainable refrigerants. The computational tool is applied in the agri-food sector in three case studies. The results show not only the consistency of the computational tool, but also its flexibility, objectivity, and simplicity. Its use allows companies to choose refrigerants with reduced environmental impact, reduced or zero ozone depletion potential and global warming impact, thus contributing to environmental sustainability. C1 [Jesus, Guilherme; Aguiar, Martim L.; Gaspar, Pedro D.] Univ Beira Interior, Dept Electromech Engn, Rua Marques DAvila & Bolama, P-6201001 Covilha, Portugal. [Aguiar, Martim L.; Gaspar, Pedro D.] C MAST Ctr Mech & Aerosp Sci & Technol, Rua Marques DAvila & Bolama, P-6201001 Covilha, Portugal. C3 Universidade da Beira Interior RP Gaspar, PD (corresponding author), Univ Beira Interior, Dept Electromech Engn, Rua Marques DAvila & Bolama, P-6201001 Covilha, Portugal.; Gaspar, PD (corresponding author), C MAST Ctr Mech & Aerosp Sci & Technol, Rua Marques DAvila & Bolama, P-6201001 Covilha, Portugal. EM dinis@ubi.pt CR ACR, 2008, QUE PROBL CON REFR C Agencia Portuguesa do Ambiente, 2021, POL MIT Agencia Portuguesa do Ambiente Protocolo de Quioto, 2021, US Alibabaei Khadijeh, 2020, 2020 International Conference on Decision Aid Sciences and Application (DASA), P132, DOI 10.1109/DASA51403.2020.9317100 Alibabaei K, 2022, COMPUTERS, V11, DOI 10.3390/computers11070104 Alibabaei K, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14030638 Alibabaei K, 2022, AGR WATER MANAGE, V263, DOI 10.1016/j.agwat.2022.107480 Alibabaei K, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11115029 Alibabaei K, 2021, ENERGIES, V14, DOI 10.3390/en14113004 Ananias E, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10192394 [Anonymous], 2003, UNEP HDB INT TREAT P [Anonymous], 2022, ALDIFRIO GAS REFRIGE Ara I, 2021, AGR WATER MANAGE, V257, DOI 10.1016/j.agwat.2021.107161 Ascencao P.O. COMPETE, 2016, AL 113 MILH EUR INV Assuncao Eduardo, 2020, 2020 International Conference on Decision Aid Sciences and Application (DASA), P652, DOI 10.1109/DASA51403.2020.9317219 ATM Revolution, 2009, CAM ATM TERR Bandarra D.E.P., 2011, USO FLUIDOS ALTERNAT Boa J., 2012, THESIS U BEIRA INTER Bwambale E, 2022, AGR WATER MANAGE, V260, DOI 10.1016/j.agwat.2021.107324 Cardoso BJ, 2017, INT J REFRIG, V83, P60, DOI 10.1016/j.ijrefrig.2017.07.013 Dufrio R., 2021, DUFRIO REFRIGER 0729 Fonseca M., 2017, THESIS U FEDERAL RIO Gaspar P., 2020, P X CONGRESSO IB RIC Gomes DE, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10182298 Gupta A, 2016, RENEW SUST ENERG REV, V62, P164, DOI 10.1016/j.rser.2016.04.035 Harby K, 2017, RENEW SUST ENERG REV, V73, P1247, DOI 10.1016/j.rser.2017.02.039 INTARCON, 2021, F GAS PROH FLUOR GAS Lopes P., 2019, VALVULAS EXPANSAO CO Maciel V., 2021, COMPUTATIONAL MANAGE, P487, DOI [10.1007/978-3-030-72929-5_23, DOI 10.1007/978-3-030-72929-5_23] Mendes Adriana, 2020, 2020 International Conference on Decision Aid Sciences and Application (DASA), P173, DOI 10.1109/DASA51403.2020.9317068 Norman J., 2022, ROWLAND MOLINA SUGGE Nunes J, 2014, ENERG CONVERS MANAGE, V88, P758, DOI 10.1016/j.enconman.2014.09.018 Paul S., 2013, INT J EMERG TECHNOL, V3, P400 Pavkovic B., 2013, REHVA EUR HVAC J, V50, P28 Pina M, 2021, APPL SYST INNOV, V4, DOI 10.3390/asi4040080 Ramos A., 2016, THESIS ISEL LISBON Rocha R., 2022, THESIS U FEDERAL RIO Rosa D., 2022, INSTRUCOES ELSE C EM Roy R, 2020, J THERM ANAL CALORIM, V139, P3247, DOI 10.1007/s10973-019-08710-x Saldanha P., 2019, THESIS U PORTO PORTO Silva PD, 2014, APPL MECH MATER, V675-677, P1880, DOI 10.4028/www.scientific.net/AMM.675-677.1880 Singh KK, 2021, ARAB J SCI ENG, V46, P12235, DOI 10.1007/s13369-021-05924-w Tazzetti Fluidos Refrigerantes, 2022, R422A UE, 2014, REG CE N 842 2006 P UE, 2014, REG EU N 517 2014 PA UE, 2009, REG CE N 1005 2009 P Verde M, 2016, ENERGY, V116, P526, DOI 10.1016/j.energy.2016.09.113 Zhang JF, 2018, J HYDROL, V561, P918, DOI 10.1016/j.jhydrol.2018.04.065 Zinkernagel J, 2020, AGR WATER MANAGE, V242, DOI 10.1016/j.agwat.2020.106404 Zocca R., 2018, P 48 INT C COMPUTERS Zocca R, 2019, ENRGY PROCED, V161, P100, DOI 10.1016/j.egypro.2019.02.063 NR 51 TC 0 Z9 0 U1 0 U2 0 PD NOV PY 2022 VL 15 IS 22 AR 8497 DI 10.3390/en15228497 WC Energy & Fuels SC Energy & Fuels UT WOS:000887517600001 DA 2022-12-14 ER PT J AU Smetana, S Aganovic, K Heinz, V AF Smetana, Sergiy Aganovic, Kemal Heinz, Volker TI Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition SO FOOD ENGINEERING REVIEWS DT Article DE Cyber-physical systems; Food chains; Food systems; Traceability; Sustainability ID INDUSTRY 4.0; BIG DATA; DIGITAL TWIN; GENETIC ALGORITHM; NEURAL-NETWORK; DATA SCIENCE; OPTIMIZATION; INTERNET; BLOCKCHAIN; CHALLENGES AB Current food system evolved in a great degree because of the development of processing and food engineering technologies: people learned to bake bread long before the advent of agriculture; salting and smoking supported nomad lifestyles; canning allowed for longer military marches; etc. Food processing technologies went through evolution and significant optimization and currently rely on minor fraction of energy comparing with initial prototypes. Emerging processing technologies (high-pressure, pulsed electric fields, ohmic heating, ultrasound) and novel food systems (cultured biomass, 3-D bioprinting, cyber-physical chains) try to challenge the existing chains by developing potentially more nutritious and sustainable food solutions. However, new food systems rely on low technology readiness levels and estimation of their potential future benefits or drawbacks is a complex task mostly due to the lack of integrated data. The research is aimed for the development of conceptual guidelines of food production system structuring as cyber-physical systems. The study indicates that cyber-physical nature of modern food is a key for the engineering of more nutritious and sustainable paths for novel food systems. Implementation of machine learning methods for the collection, integration, and analysis of data associated with biomass production and processing on different levels from molecular to global, leads to the precise analysis of food systems and estimation of upscaling benefits, as well as possible negative rebound effects associated with societal attitude. Moreover, such data-integrated assessment systems allow transparency of chains, integration of nutritional and environmental properties, and construction of personalized nutrition technologies. C1 [Smetana, Sergiy; Aganovic, Kemal; Heinz, Volker] German Inst Food Technol DIL eV, Quakenbruck, Germany. RP Smetana, S (corresponding author), German Inst Food Technol DIL eV, Quakenbruck, Germany. EM s.smetana@dil-ev.de CR Accorsi R, 2019, PROCEDIA MANUF, V38, P341, DOI 10.1016/j.promfg.2020.01.044 AIELLO LC, 1995, CURR ANTHROPOL, V36, P199, DOI 10.1086/204350 Alguliyev R, 2018, COMPUT IND, V100, P212, DOI 10.1016/j.compind.2018.04.017 An W, 2017, INTELL DAT CENT SYST, P399, DOI 10.1016/B978-0-12-803801-7.00025-0 Anil A, 2019, 2019 INT C ISS CHALL, P1 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Asgari S, 2017, COMPUT ELECTRON AGR, V140, P422, DOI 10.1016/j.compag.2017.06.025 Baheti R., 2011, ITHE IMPACT CONTROL, V12, P161, DOI DOI 10.1145/1795194.1795205 Baire M, 2018, 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR), P245 Banga JR, 2008, COMPR REV FOOD SCI F, V7, P168, DOI 10.1111/j.1541-4337.2007.00023.x Barbosa-Canovas G.V., 2004, NOVEL FOOD PROCESSIN Barr A, 1981, HDB ARTIFICIAL INTEL Berntsen J, 2006, PRECIS AGRIC, V7, P65, DOI 10.1007/s11119-006-9000-2 Bettenhausen KD, 2013, CYBER PHYS SYSTEMS C, P9 Bogataj D, 2017, INT J PROD ECON, V193, P51, DOI 10.1016/j.ijpe.2017.06.028 Bordel B, 2017, PERVASIVE MOB COMPUT, V40, P156, DOI 10.1016/j.pmcj.2017.06.011 BORNKESSEL S, 2019, FRONT NUTR, V6 Braun T, 2018, SUSTAIN CITIES SOC, V39, P499, DOI 10.1016/j.scs.2018.02.039 Brewster C, 2017, IEEE COMMUN MAG, V55, P26, DOI 10.1109/MCOM.2017.1600528 Buche P, 2019, COMPUT ELECTRON AGR, V163, DOI 10.1016/j.compag.2019.05.052 Burg A, 2018, P IEEE, V106, P38, DOI 10.1109/JPROC.2017.2780172 Castelluccia C, 2009, CCS'09: PROCEEDINGS OF THE 16TH ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P400 Chaurasia P, 2019, J FOOD PROCESS ENG, V42, DOI 10.1111/jfpe.12966 Chen MJ, 2004, J FOOD SCI, V69, pE344, DOI 10.1111/j.1365-2621.2004.tb13640.x Chen RY, 2018, J PHYS CONF SER, V1026, DOI 10.1088/1742-6596/1026/1/012017 Chen RY, 2017, FOOD CONTROL, V71, P124, DOI 10.1016/j.foodcont.2016.06.042 Chowhan R. S., 2019, SMART DEVICES APPL P, P189, DOI 10.4018/978-1-5225-7811-6.ch009. Chyan Y, 2018, ACS NANO, V12, P2176, DOI 10.1021/acsnano.7b08539 Clarke KC, 1998, INT J GEOGR INF SCI, V12, P699, DOI 10.1080/136588198241617 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Dallasega P, 2018, COMPUT IND, V99, P205, DOI 10.1016/j.compind.2018.03.039 Darwish A, 2018, J AMB INTEL HUM COMP, V9, P1541, DOI 10.1007/s12652-017-0575-4 Deb K, 2002, IEEE T EVOLUT COMPUT, V6, P182, DOI 10.1109/4235.996017 Defraeye T, 2014, APPL ENERG, V131, P323, DOI 10.1016/j.apenergy.2014.06.027 Delgado JA, 2019, FRONT SUSTAIN FOOD S, V3, DOI 10.3389/fsufs.2019.00054 Deng SG, 2013, J FOOD ENG, V119, P159, DOI 10.1016/j.jfoodeng.2013.05.024 Fang ZX, 2017, TRENDS FOOD SCI TECH, V61, P60, DOI 10.1016/j.tifs.2017.01.002 Fernandez MR, 2019, APPL SOFT COMPUT, V79, P326, DOI 10.1016/j.asoc.2019.03.050 Ferrandez MR, 2019, J SUPERCOMPUT, V75, P1187, DOI 10.1007/s11227-018-2351-4 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Ghasemi-Varnamkhasti M, 2019, LWT-FOOD SCI TECHNOL, V111, P85, DOI 10.1016/j.lwt.2019.04.099 Gill SS, 2017, J ORGAN END USER COM, V29, P1, DOI 10.4018/JOEUC.2017100101 Gunes V, 2014, KSII T INTERNET INF, V8, P4242, DOI 10.3837/tiis.2014.12.001 Guo P, 2018, COMPUT ELECTRON AGR, V150, P439, DOI 10.1016/j.compag.2018.05.022 Hamilton CA, 2018, J FOOD ENG, V220, P83, DOI 10.1016/j.jfoodeng.2017.01.008 Hang MYLP, 2016, J CLEAN PROD, V135, P1065, DOI 10.1016/j.jclepro.2016.06.194 Haque SA, 2014, INT J DISTRIB SENS N, DOI 10.1155/2014/217415 Hardie M, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19194232 Henze N, 2015, PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS MULTIMEDIA (MUM 2015), P258, DOI 10.1145/2836041.2836068 Humayed A, 2017, IEEE INTERNET THINGS, V4, P1802, DOI 10.1109/JIOT.2017.2703172 Hwang C, 2019, SENSING AGR FOOD QUA, VXI, P23 Iqbal J, 2017, FOOD SCI TECH-BRAZIL, V37, P159, DOI [10.1590/1678-457X.14616, 10.1590/1678-457x.14616] Jambrak AR, 2019, J FOOD QUALITY, DOI 10.1155/2019/2171375 Joardder MUH, 2017, CRIT REV FOOD SCI, V57, P1190, DOI 10.1080/10408398.2014.971354 Junge K, 2020, IEEE ROBOT AUTOM LET, V5, P760, DOI 10.1109/LRA.2020.2965418 Kalpana S, 2019, TRENDS FOOD SCI TECH, V93, P145, DOI 10.1016/j.tifs.2019.09.008 Khalilpourazari S, 2020, NEURAL COMPUT APPL, V32, P3987, DOI 10.1007/s00521-018-3872-8 Khan ZH, 2018, INNOV FOOD SCI EMERG, V48, P11, DOI 10.1016/j.ifset.2018.05.011 Kim J, 2017, ADV HEALTHC MATER, V6, DOI 10.1002/adhm.201700770 Kim KD, 2012, P IEEE, V100, P1287, DOI 10.1109/JPROC.2012.2189792 Knorr D, 2011, ANNU REV FOOD SCI T, V2, P203, DOI 10.1146/annurev.food.102308.124129 Kochhar A, 2019, COMPUT ELECTRON AGR, V163, DOI 10.1016/j.compag.2019.104877 Kumar SA, 2018, WIRELESS PERS COMMUN, V98, P685, DOI 10.1007/s11277-017-4890-z Lanzani G, 2014, NAT MATER, V13, P775, DOI 10.1038/nmat4021 Lee Jay, 2013, Manufacturing Letters, V1, P38, DOI 10.1016/j.mfglet.2013.09.005 LEE J, 2015, MANUF LETT, V3, P18, DOI [DOI 10.1016/j.mfglet.2014.12.001, 10.1016/j.mfglet.2014.12.001] Li Z, 2017, IND MANAGE DATA SYST, V117, P1906, DOI 10.1108/IMDS-11-2016-0489 Liegeard J, 2020, CRIT REV FOOD SCI, V60, P1048, DOI 10.1080/10408398.2018.1556580 Liu R, 2020, IEEE ACCESS, V8, P38501, DOI 10.1109/ACCESS.2020.2975672 Lopes JF, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19132953 Luo S., 2009, INT J MULTIMEDIA UBI, V4, P69, DOI [10.14257/ijmue.2009.4.2.07, DOI 10.14257/IJMUE.2009.4.2.07] Marwedel P., 2018, EMBEDDED SYSTEM DESI, V3rd ed. Monostori L, 2016, CIRP ANN-MANUF TECHN, V65, P621, DOI 10.1016/j.cirp.2016.06.005 Moreno G, 2019, 2019 IEEE/ACM 14TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2019), P181, DOI 10.1109/SEAMS.2019.00031 Mosterman PJ, 2016, SOFTW SYST MODEL, V15, P17, DOI 10.1007/s10270-015-0493-x Muller P, 2019, FOODS, V8, DOI 10.3390/foods8010016 Neugebauer R, 2007, CIRP ANN-MANUF TECHN, V56, P657, DOI 10.1016/j.cirp.2007.10.007 Pandey G, 2018, IEEE ACCESS, V6, P43179, DOI 10.1109/ACCESS.2018.2862634 Perrot N, 2016, TRENDS FOOD SCI TECH, V48, P88, DOI 10.1016/j.tifs.2015.10.003 Pirvu BC, 2016, MECHATRONICS, V34, P147, DOI 10.1016/j.mechatronics.2015.08.010 Poovendran R, 2010, P IEEE, V98, P1363, DOI 10.1109/JPROC.2010.2050377 Poyatos-Racionero E, 2018, J CLEAN PROD, V172, P3398, DOI 10.1016/j.jclepro.2017.11.075 Priyadarshini A, 2019, CRIT REV FOOD SCI, V59, P3082, DOI 10.1080/10408398.2018.1483890 Provost F, 2013, BIG DATA, V1, P51, DOI 10.1089/big.2013.1508 Qi QL, 2018, IEEE ACCESS, V6, P3585, DOI 10.1109/ACCESS.2018.2793265 Rad CR, 2015, AGRIC AGRIC SCI PROC, V6, P73, DOI 10.1016/j.aaspro.2015.08.041 Rajkumar R, 2010, DES AUT CON, P731 Romdhana H, 2016, DRY TECHNOL, V34, P1253, DOI 10.1080/07373937.2015.1104348 Selmani A, 2019, BIOSYST ENG, V177, P18, DOI 10.1016/j.biosystemseng.2018.06.007 Shi XJ, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19081833 Shukla N, 2019, COMPUT IND ENG, V128, P905, DOI 10.1016/j.cie.2018.12.026 Silva AR, 2010, P 1 ACM IEEE INT C C, P79, DOI DOI 10.1145/1795194.1795206 Silva BN, 2019, BIG DATA ANAL, P13 Smajic H, 2018, LECT NOTE NETW SYST, V22, P546, DOI 10.1007/978-3-319-64352-6_51 Smetana S, 2018, RESOUR CONSERV RECY, V133, P229, DOI 10.1016/j.resconrec.2018.02.020 Smetana SM, 2019, FRONT NUTR, V6, DOI 10.3389/fnut.2019.00039 Sundmaeker H, 2016, RIVER PUBL SER COMM, V49, P129 Tao F, 2019, ENGINEERING-PRC, V5, P653, DOI 10.1016/j.eng.2019.01.014 Tao F, 2018, INT J ADV MANUF TECH, V94, P3563, DOI 10.1007/s00170-017-0233-1 Tao H, 2012, ADV MATER, V24, P1067, DOI 10.1002/adma.201103814 Thakur D., 2018, PROCEDIA COMPUT SCI, V132, P507, DOI [10.1016/j.procs.2018.05.003, DOI 10.1016/J.PROCS.2018.05.003] Thomopoulos R, 2019, FOOD ENG REV, V11, P44, DOI 10.1007/s12393-018-9186-x Tian F, 2017, I C SERV SYST SERV M Khoa TA, 2019, J SENS ACTUAR NETW, V8, DOI 10.3390/jsan8030045 Uhlemann THJ, 2017, PROC CIRP, V61, P335, DOI 10.1016/j.procir.2016.11.152 Vanderroost M, 2017, COMPUT IND, V87, P15, DOI 10.1016/j.compind.2017.01.004 Verboven P, 2020, CURR OPIN FOOD SCI, V35, P79, DOI 10.1016/j.cofs.2020.03.002 Verdouw CN, 2016, J FOOD ENG, V176, P128, DOI 10.1016/j.jfoodeng.2015.11.009 Verdouw CN, 2015, COMPUT IND, V68, P116, DOI 10.1016/j.compind.2014.12.011 Vuran MC, 2018, AD HOC NETW, V81, P160, DOI 10.1016/j.adhoc.2018.07.017 Waller MA, 2013, J BUS LOGIST, V34, P77, DOI 10.1111/jbl.12010 Wang J, 2017, FOOD CONTROL, V73, P223, DOI 10.1016/j.foodcont.2016.09.048 Wang X, 2016, ADV MATER TECHNOL-US, V1, DOI 10.1002/admt.201600059 Wolfert J, 2010, COMPUT ELECTRON AGR, V70, P389, DOI 10.1016/j.compag.2009.07.015 Wright Paul, 2014, Manufacturing Letters, V2, P49, DOI 10.1016/j.mfglet.2013.10.001 Xinyue Deng, 2020, ICTE 2019 - Proceedings of the Sixth International Conference on Transportation Engineering, P671 Xu LD, 2019, ENTERP INF SYST-UK, V13, P148, DOI 10.1080/17517575.2018.1442934 Xu WW, 2017, ADV MATER TECHNOL-US, V2, DOI 10.1002/admt.201700181 Xu YL, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18124245 Yan B, 2016, IND MANAGE DATA SYST, V116, P1397, DOI 10.1108/IMDS-12-2015-0512 Yan JH, 2019, PROCEDIA MANUF, V35, P1178, DOI 10.1016/j.promfg.2019.06.074 Yuan J, 2008, INT J MACH TOOL MANU, V48, P47, DOI 10.1016/j.ijmachtools.2007.07.011 Zhang SG, 2015, 2015 INTERNATIONAL CONFERENCE OF EDUCATIONAL INNOVATION THROUGH TECHNOLOGY - EITT 2015, P115, DOI 10.1109/EITT.2015.31 Zhang WS, 2018, COMPUT IND, V95, P15, DOI 10.1016/j.compind.2017.09.001 Zhou I, 2020, IEEE INTERNET THINGS, V7, P6514, DOI 10.1109/JIOT.2020.2972936 NR 125 TC 17 Z9 17 U1 12 U2 60 PD MAR PY 2021 VL 13 IS 1 SI SI BP 92 EP 103 DI 10.1007/s12393-020-09243-y EA AUG 2020 WC Food Science & Technology SC Food Science & Technology UT WOS:000561727300001 DA 2022-12-14 ER PT J AU Tate, JR Bunk, DM Christenson, RH Katrukha, A Noble, JE Porter, RA Schimmel, H Wang, LL Panteghini, M AF Tate, Jillian R. Bunk, David M. Christenson, Robert H. Katrukha, Alexei Noble, James E. Porter, Robert A. Schimmel, Heinz Wang, Lili Panteghini, Mauro CA IFCC Working Grp Standardization TI Standardisation of cardiac troponin I measurement: past and present SO PATHOLOGY DT Review DE Cardiac troponin I; standardisation; measurement traceability; reference material; reference measurement procedure ID ACUTE MYOCARDIAL-INFARCTION; CIRCULATING TROPONIN; 99TH PERCENTILE; ASSAYS; HARMONIZATION; IMMUNOASSAYS; TRACEABILITY; UNCERTAINTY; DEFINITION; OUTCOMES AB The laboratory measurement of cardiac troponin (cTn) concentration is a critical tool in the diagnosis of acute myocardial infarction (MI). Current cTnI assays produce different absolute troponin numbers and use different clinical cut-off values; hence cTnI values cannot be interchanged, with consequent confusion for clinicians. A recent Australian study compared patient results for seven cTnI assays and showed that between-method variation was approximately 2-to 5-fold. A major reason for poor method agreement is the lack of a suitable common reference material for the calibration of cTnI assays by manufacturers. Purified complexed troponin material lacks adequate commutability for all assays; hence a serum-based secondary reference material is required for cTnI with value assignment by a higher order reference measurement procedure. There is considerable debate about how best to achieve comparability of results for heterogeneous analytes such as cTnI, whether it should be via the harmonisation or the standardisation process. Whereas harmonisation depends upon consensus value assignment and uses those commercial methods which give the closest agreement at the time, standardisation comes closer to the true value through a reference measurement system that is based upon long-term calibration traceability. The current paper describes standardisation efforts by the International Federation of Clinical Chemistry and Laboratory Medicine Working Group on Standardization of cTnI (IFCC WG-TNI) to establish a reference immunoassay measurement procedure for cTnI of a higher order than current commercial immunoassay methods and a commutable secondary reference material for cTnI to which companies can reference their calibration materials. C1 [Tate, Jillian R.] Royal Brisbane & Womens Hosp, Dept Chem Pathol, Herston, Qld 4029, Australia. [Bunk, David M.] NIST, Chem Sci & Technol Lab, Gaithersburg, MD 20899 USA. [Wang, Lili] NIST, Div Biochem Sci, Gaithersburg, MD 20899 USA. [Christenson, Robert H.] Univ Maryland, Sch Med, Dept Pathol, Baltimore, MD 21201 USA. [Katrukha, Alexei] HyTest Ltd, Turku, Finland. [Noble, James E.; Porter, Robert A.] Natl Phys Lab, Analyt Sci Grp, Teddington TW11 0LW, Middx, England. [Schimmel, Heinz] Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements, Geel, Belgium. [Panteghini, Mauro] Univ Milan, Ctr Metrol Traceabil Lab Med CIRME, Milan, Italy. C3 Royal Brisbane & Women's Hospital; National Institute of Standards & Technology (NIST) - USA; National Institute of Standards & Technology (NIST) - USA; University System of Maryland; University of Maryland Baltimore; National Physical Laboratory - UK; European Commission Joint Research Centre; EC JRC Institute for Reference Materials & Measurements (IRMM); University of Milan RP Tate, JR (corresponding author), Royal Brisbane & Womens Hosp, Dept Chem Pathol, Herston, Qld 4029, Australia. EM jill_tate@health.qld.gov.au CR Adamczyk M, 2009, ANN NY ACAD SCI, V1173, P67, DOI 10.1111/j.1749-6632.2009.04617.x Antman EM, 1996, NEW ENGL J MED, V335, P1342, DOI 10.1056/NEJM199610313351802 Apple FS, 2007, CLIN CHEM, V53, P547, DOI 10.1373/clinchem.2006.084715 Apple FS, 1999, CLIN CHEM, V45, P206 Bunk David M, 2007, Clin Biochem Rev, V28, P131 Bunk DM, 2006, CLIN CHEM, V52, P212, DOI 10.1373/clinchem.2005.051359 Christenson RH, 2006, CLIN CHEM, V52, P1685, DOI 10.1373/clinchem.2006.068437 Eggers KM, 2009, CLIN CHEM, V55, P85, DOI 10.1373/clinchem.2007.101683 Eriksson S, 2005, CLIN CHEM, V51, P839, DOI 10.1373/clinchem.2004.040063 [International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on the Standardization of Markers of Cardiac Damage (C-SMCD)], TROP ASS AN CHAR James S, 2006, CLIN CHEM, V52, P832, DOI 10.1373/clinchem.2005.064857 James S, 2003, AM J MED, V115, P178, DOI 10.1016/S0002-9343(03)00348-6 Katrukha A, 1999, SCAND J CLIN LAB INV, V59, P124 Katrukha AG, 1997, CLIN CHEM, V43, P1379 Katrukha AG, 1998, CLIN CHEM, V44, P2433 Kontos MC, 2004, J AM COLL CARDIOL, V43, P958, DOI 10.1016/j.jacc.2003.10.036 Labugger R, 2000, CIRCULATION, V102, P1221, DOI 10.1161/01.CIR.102.11.1221 Lippi G, 2009, CLIN CHEM LAB MED, V47, P1183, DOI 10.1515/CCLM.2009.322 McDonough JL, 2004, PROG CARDIOVASC DIS, V47, P207, DOI 10.1016/j.pcad.2004.07.001 Mingels A, 2009, CLIN CHEM, V55, P101, DOI 10.1373/clinchem.2008.106427 *NHS PURCH SUPPL A, 2005, 05085 NHS PURCH SUPP *NIST, 2004, 2921 NIST Noble JE, 2008, CLIN CHEM LAB MED, V46, P1033, DOI 10.1515/CCLM.2008.182 Panteghini M, 2005, CLIN CHEM, V51, P1594, DOI 10.1373/clinchem.2005.054551 Panteghini M, 2005, CLIN CHEM, V51, P803, DOI 10.1373/clinchem.2005.049239 Panteghini M, 2004, CLIN CHEM LAB MED, V42, P3, DOI 10.1515/CCLM.2004.002 Panteghini M, 2004, CLIN CHEM, V50, P327, DOI 10.1373/clinchem.2003.026815 Panteghini M, 2001, CLIN CHEM LAB MED, V39, P175, DOI 10.1515/cclm.2001.39.2.175 Panteghini Mauro, 2007, Clin Biochem Rev, V28, P97 Panteghini M, 2010, CLIN CHEM LAB MED, V48, P7, DOI 10.1515/CCLM.2010.020 Panteghini M, 2009, CLIN CHEM LAB MED, V47, P1179, DOI 10.1515/CCLM.2009.295 Panteghini M, 2009, CLIN CHIM ACTA, V402, P88, DOI 10.1016/j.cca.2008.12.037 Panteghini M, 2008, CLIN CHEM LAB MED, V46, P1501, DOI 10.1515/CCLM.2008.291 Sabatine MS, 2009, EUR HEART J, V30, P162, DOI 10.1093/eurheartj/ehn504 Schulz O, 2006, CLIN CHEM, V52, P1614, DOI 10.1373/clinchem.2006.071498 Stenman UH, 2001, CLIN CHEM, V47, P815 Tate JR, 2008, ANN CLIN BIOCHEM, V45, P275, DOI 10.1258/acb.2007.007185 Tate JR, 2008, CLIN CHEM LAB MED, V46, P1489, DOI 10.1515/CCLM.2008.292 Tate JR, 2002, CLIN CHIM ACTA, V324, P13, DOI 10.1016/S0009-8981(02)00214-0 Thienpont LM, 2002, CLIN CHIM ACTA, V323, P73, DOI 10.1016/S0009-8981(02)00188-2 Thygesen K, 2007, CIRCULATION, V116, P2634, DOI 10.1161/CIRCULATIONAHA.107.187397 Venge P, 2002, AM J CARDIOL, V89, P1035, DOI 10.1016/S0002-9149(02)02271-3 Vesper Hubert W, 2007, Clin Biochem Rev, V28, P139 Vesper HW, 2009, CLIN CHEM, V55, P1067, DOI 10.1373/clinchem.2008.107052 Wilson SR, 2009, AM HEART J, V158, P386, DOI 10.1016/j.ahj.2009.06.011 Wu AHB, 1998, CLIN CHEM, V44, P1198 NR 46 TC 63 Z9 71 U1 1 U2 23 PD AUG PY 2010 VL 42 IS 5 BP 402 EP 408 DI 10.3109/00313025.2010.495246 WC Pathology SC Pathology UT WOS:000280831900002 DA 2022-12-14 ER PT J AU Zappi, A Marassi, V Kassouf, N Giordani, S Pasqualucci, G Garbini, D Roda, B Zattoni, A Reschiglian, P Melucci, D AF Zappi, Alessandro Marassi, Valentina Kassouf, Nicholas Giordani, Stefano Pasqualucci, Gaia Garbini, Davide Roda, Barbara Zattoni, Andrea Reschiglian, Pierluigi Melucci, Dora TI A Green Analytical Method Combined with Chemometrics for Traceability of Tomato Sauce Based on Colloidal and Volatile Fingerprinting SO MOLECULES DT Article DE green analytical methods; asymmetric flow field-flow fractionation AF4; AF4-multidetection; food colloids; volatile compounds (VOC); Gas Chromatography; ion-mobility spectroscopy; chemometric analysis; principal component analysis (PCA); FFF-chemometrics ID FIELD-FLOW FRACTIONATION; ANGLE LIGHT-SCATTERING; ECONOMICALLY MOTIVATED ADULTERATION; QUALITY-CONTROL; TOTAL PHENOLICS; CHROMATOGRAPHY; NANOPARTICLES; COMPONENTS; DATABASE; ORIGIN AB Tomato sauce is a world famous food product. Despite standards regulating the production of tomato derivatives, the market suffers frpm fraud such as product adulteration, origin mislabelling and counterfeiting. Methods suitable to discriminate the geographical origin of food samples and identify counterfeits are required. Chemometric approaches offer valuable information: data on tomato sauce is usually obtained through chromatography (HPLC and GC) coupled to mass spectrometry, which requires chemical pretreatment and the use of organic solvents. In this paper, a faster, cheaper, and greener analytical procedure has been developed for the analysis of volatile organic compounds (VOCs) and the colloidal fraction via multivariate statistical analysis. Tomato sauce VOCs were analysed by GC coupled to flame ionisation (GC-FID) and to ion mobility spectrometry (GC-IMS). Instead of using HPLC, the colloidal fraction was analysed by asymmetric flow field-fractionation (AF4), which was applied to this kind of sample for the first time. The GC and AF4 data showed promising perspectives in food-quality control: the AF4 method yielded comparable or better results than GC-IMS and offered complementary information. The ability to work in saline conditions with easy pretreatment and no chemical waste is a significant advantage compared to environmentally heavy techniques. The method presented here should therefore be taken into consideration when designing chemometric approaches which encompass a large number of samples. C1 [Zappi, Alessandro; Marassi, Valentina; Kassouf, Nicholas; Giordani, Stefano; Pasqualucci, Gaia; Roda, Barbara; Zattoni, Andrea; Reschiglian, Pierluigi; Melucci, Dora] Univ Bologna, Dept Chem Giacomo Ciamician, I-40126 Bologna, Italy. [Marassi, Valentina; Roda, Barbara; Zattoni, Andrea; Reschiglian, Pierluigi] ByFlow srl, I-40129 Bologna, Italy. [Garbini, Davide] COOP ITALIA Soc, Cooperativa, I-40033 Casalecchio Di Reno, Italy. [Melucci, Dora] Univ Bologna, CIRI Agrifood, I-47521 Cesena, Italy. C3 University of Bologna; University of Bologna RP Marassi, V (corresponding author), Univ Bologna, Dept Chem Giacomo Ciamician, I-40126 Bologna, Italy.; Marassi, V (corresponding author), ByFlow srl, I-40129 Bologna, Italy. EM valentina.marassi@unibo.it CR Abbate RA, 2019, FOOD HYDROCOLLOID, V92, P117, DOI 10.1016/j.foodhyd.2019.01.043 Ali MY, 2021, FOODS, V10, DOI 10.3390/foods10010045 [Anonymous], 2022, SPECIAL EUROBAROMETE Antonella F., 2017, ISMEA NUMERI DELLAFI Arrizabalaga-Larranaga A, 2021, ANAL CHIM ACTA, V1164, DOI 10.1016/j.aca.2021.338519 Arvanitoyannis IS, 2007, CRIT REV FOOD SCI, V47, P675, DOI 10.1080/10408390600948568 Boukid F, 2021, EUR FOOD RES TECHNOL, V247, P2345, DOI 10.1007/s00217-021-03794-y Bro R, 2014, ANAL METHODS-UK, V6, P2812, DOI 10.1039/c3ay41907j BUTTERY RG, 1993, ACS SYM SER, V525, P23 BUTTERY RG, 1990, J AGR FOOD CHEM, V38, P336, DOI 10.1021/jf00091a074 Coelho C, 2017, ANAL BIOANAL CHEM, V409, P2757, DOI 10.1007/s00216-017-0221-1 Cumeras R, 2015, ANALYST, V140, P1376, DOI 10.1039/c4an01100g Dong WJ, 2015, MOLECULES, V20, P16687, DOI 10.3390/molecules200916687 Eiceman G. A., 2004, ION MOBILITY SPECTRO Everstine K, 2013, J FOOD PROTECT, V76, P723, DOI 10.4315/0362-028X.JFP-12-399 Forleo T, 2020, EUR FOOD RES TECHNOL, V246, P1805, DOI 10.1007/s00217-020-03534-8 Fragni R, 2018, FOOD CONTROL, V93, P211, DOI 10.1016/j.foodcont.2018.06.002 Geiss O, 2019, ANAL BIOANAL CHEM, V411, P5817, DOI 10.1007/s00216-019-01964-2 Granato D, 2018, TRENDS FOOD SCI TECH, V72, P83, DOI 10.1016/j.tifs.2017.12.006 Guyomarc'h F, 2010, J AGR FOOD CHEM, V58, P12592, DOI 10.1021/jf102808f Jackson LS, 2009, J AGR FOOD CHEM, V57, P8161, DOI 10.1021/jf900628u KADER AA, 1978, J AM SOC HORTIC SCI, V103, P6 Khorramifar A, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21175836 Koltun SJ, 2021, FLAVOUR FRAG J, V36, P121, DOI 10.1002/ffj.3622 Krebs G, 2021, FOOD CHEM, V342, DOI 10.1016/j.foodchem.2020.128253 Li J, 2019, MOLECULES, V24, DOI 10.3390/molecules24142594 Lie-Piang A, 2021, CURR RES FOOD SCI, V4, P83, DOI 10.1016/j.crfs.2021.02.004 Lo Feudo G, 2010, J AGR FOOD CHEM, V58, P3801, DOI 10.1021/jf903868j Luthria DL, 2006, J FOOD COMPOS ANAL, V19, P771, DOI 10.1016/j.jfca.2006.04.005 Marassi V, 2014, J CHROMATOGR A, V1372, P196, DOI 10.1016/j.chroma.2014.10.072 Marassi V, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12136762 Marassi V, 2022, ANTIBIOTICS-BASEL, V11, DOI 10.3390/antibiotics11030358 Marassi V, 2021, J CHROMATOGR A, V1638, DOI 10.1016/j.chroma.2020.461861 Marassi V, 2021, J CHROMATOGR A, V1637, DOI 10.1016/j.chroma.2020.461806 Marassi V, 2021, J CHROMATOGR A, V1636, DOI 10.1016/j.chroma.2020.461739 Marassi V, 2021, FOOD HYDROCOLLOID, V110, DOI 10.1016/j.foodhyd.2020.106204 Marassi V, 2019, ANAL CHIM ACTA, V1087, P121, DOI 10.1016/j.aca.2019.08.003 Marassi V, 2018, ROY SOC OPEN SCI, V5, DOI 10.1098/rsos.171113 Marassi V, 2018, MICROCHEM J, V136, P149, DOI 10.1016/j.microc.2016.12.015 Marassi V, 2015, J PHARMACEUT BIOMED, V106, P92, DOI 10.1016/j.jpba.2014.11.031 Marengo E, 2017, CURR ANAL CHEM, V13, P187, DOI 10.2174/1573411012666160504125330 Medina B., 2013, USING NEW ANALYTICAL, P149 Melucci D, 2016, FOOD CHEM, V204, P263, DOI 10.1016/j.foodchem.2016.02.131 Ministero Delle Politiche Agricole E Forestali, 2006, PASS POM OR POM FRES Mohammad-Razdari A, 2019, J FOOD PROCESS ENG, V42, DOI 10.1111/jfpe.13119 Moore JC, 2012, J FOOD SCI, V77, pR118, DOI 10.1111/j.1750-3841.2012.02657.x Moriello M.S.L., 2020, REPORT MINISTERO POL Morozzi P, 2019, MOLECULES, V24, DOI 10.3390/molecules24142602 Nijhuis A, 1997, CHEMOMETR INTELL LAB, V38, P51, DOI 10.1016/S0169-7439(97)00054-3 Nilsson L, 2013, FOOD HYDROCOLLOID, V30, P1, DOI 10.1016/j.foodhyd.2012.04.007 Opara UL, 2012, FOOD BIOPROCESS TECH, V5, P3236, DOI 10.1007/s11947-011-0693-5 Osorio-Macias DE, 2022, FOOD CHEM, V381, DOI 10.1016/j.foodchem.2022.132123 Palianskikh AI, 2022, FOOD CHEM, V369, DOI 10.1016/j.foodchem.2021.130947 Pascotto K, 2021, FOOD CHEM, V361, DOI 10.1016/j.foodchem.2021.130104 Raffo A, 2002, J AGR FOOD CHEM, V50, P6550, DOI 10.1021/jf020315t Rasekh M., 2022, LWT, V164 Rios JJ, 2008, FOOD CHEM, V106, P1145, DOI 10.1016/j.foodchem.2007.07.045 Roda B, 2018, ANAL BIOANAL CHEM, V410, P5245, DOI 10.1007/s00216-018-1176-6 Rusinek R, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21082812 Servili M, 2000, FOOD CHEM, V71, P407, DOI 10.1016/S0308-8146(00)00187-4 Slimani S, 2020, CHEMOSENSORS, V8, DOI 10.3390/chemosensors8030060 Socaci SA, 2014, PHYTOCHEM ANALYSIS, V25, P161, DOI 10.1002/pca.2483 Son HS, 2009, FOOD RES INT, V42, P1483, DOI 10.1016/j.foodres.2009.08.006 Song HL, 2018, FOOD RES INT, V114, P187, DOI 10.1016/j.foodres.2018.07.037 Spink J, 2019, J FOOD SCI, V84, P2705, DOI 10.1111/1750-3841.14705 Tibola CS, 2018, J FOOD SCI, V83, P2028, DOI 10.1111/1750-3841.14279 Vallverdu-Queralt A, 2013, J AGR FOOD CHEM, V61, P1044, DOI 10.1021/jf304631c Ventouri IK, 2022, ANAL CHIM ACTA, V1193, DOI 10.1016/j.aca.2021.339396 Vitalis F, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20216059 Wang LB, 2018, J FOOD PROCESS PRES, V42, DOI 10.1111/jfpp.13387 Wankar J, 2018, MOL PHARMACEUT, V15, P3823, DOI 10.1021/acs.molpharmaceut.8b00321 WRIGHT DH, 1985, J AGR FOOD CHEM, V33, P355, DOI 10.1021/jf00063a009 Zappi A, 2018, EUR FOOD RES TECHNOL, V244, P2149, DOI 10.1007/s00217-018-3123-3 Zattoni A, 2014, J PHARMACEUT BIOMED, V87, P53, DOI 10.1016/j.jpba.2013.08.018 NR 74 TC 0 Z9 0 U1 3 U2 3 PD SEP PY 2022 VL 27 IS 17 AR 5507 DI 10.3390/molecules27175507 WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary SC Biochemistry & Molecular Biology; Chemistry UT WOS:000851945500001 DA 2022-12-14 ER PT J AU Klobucar, R Acko, B AF Klobucar, R. Acko, B. TI EXPERIMENTAL EVALUATION OF BALL BAR STANDARD THERMAL PROPERTIES BY SIMULATING REAL SHOP FLOOR CONDITIONS SO INTERNATIONAL JOURNAL OF SIMULATION MODELLING DT Article DE Traceability; Co-Ordinate Measurement; Measurement Standard; Thermal Expansion ID COMPENSATION; TRACEABILITY AB Monitoring quality of production processes is a complex task consisting of different measurements of product properties and process parameters, as well as visual checks and other activities. One of the most important measurement tasks is measuring complex product geometry. In order to get information about measured quantities as quickly as possible, measurements are made directly on the shop floor. However, assuring traceability of complex co-ordinate measurements in uncontrolled shop floor conditions is an advanced metrological task requiring special measurement standards and procedures. European project EMRP IND62 TIM that was agreed between EC and European metrology association Euramet is aimed to introduce a traceability chain into in-process geometrical measurements by offering different solutions for calibrating machine tools in harsh environmental conditions. One of the tasks of this project was to develop a highly accurate robust 1D measurement standard with very low expansion coefficient. The article presents basic design of this standard and experimental verification of its thermal expansion characteristics in a laboratory, as well as in harsh environment in production companies. Thermal expansion verification was performed by means of measurements on a co-ordinate measuring machine at different temperatures simulating real shop floor conditions. C1 [Klobucar, R.; Acko, B.] Univ Maribor, Fac Mech Engn, Smetanova 17, SI-2000 Maribor, Slovenia. C3 University of Maribor RP Klobucar, R (corresponding author), Univ Maribor, Fac Mech Engn, Smetanova 17, SI-2000 Maribor, Slovenia. EM rok.klobucar@um.si; bojan.acko@um.si CR Acko B, 2014, ADV PROD ENG MANAG, V9, P44, DOI 10.14743/apem2014.1.175 Acko B., 2014, P 18 INT RES EXP C T, P297 Acko B, 2015, PROCEDIA ENGINEER, V100, P376, DOI 10.1016/j.proeng.2015.01.381 [Anonymous], 2012, 2301 ISO COP OFF [Anonymous], 2012, 2309 ISOTR COP OFF Cuesta E, 2015, INT J SIMUL MODEL, V14, P609, DOI 10.2507/IJSIMM14(4)4.311 Cus F, 2013, T FAMENA, V37, P41 Klancnik S, 2015, INT J SIMUL MODEL, V14, P571, DOI 10.2507/IJSIMM14(4)1.301 Koren R, 2015, ADV PROD ENG MANAG, V10, P27 Mgwatu MI, 2013, ADV PROD ENG MANAG, V8, P209 Mijuskovic G, 2013, TEH VJESN, V20, P629 Mudronja V, 2014, T FAMENA, V38, P37 Ostasevicius V, 2013, STROJ VESTN-J MECH E, V59, P351, DOI 10.5545/sv-jme.2012.856 Schwenke H, 2008, CIRP ANN-MANUF TECHN, V57, P660, DOI 10.1016/j.cirp.2008.09.008 Wendt K., 2013, IND62 TIM TACEABLE I NR 15 TC 5 Z9 6 U1 0 U2 6 PD SEP PY 2016 VL 15 IS 3 BP 511 EP 521 DI 10.2507/IJSIMM15(3)10.356 WC Engineering, Industrial; Engineering, Manufacturing SC Engineering UT WOS:000388365800010 DA 2022-12-14 ER PT J AU Amentae, TK Gebresenbet, G AF Amentae, Tadesse Kenea Gebresenbet, Girma TI Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications SO SUSTAINABILITY DT Review DE agro-food supply chain management; blockchain; digitalization; IoT; sustainability; sustainable food system; traceability ID GREEN PUBLIC PROCUREMENT; FOOD; BLOCKCHAIN; OPTIMIZATION; TRACEABILITY; PERFORMANCE; MODELS; AGRICULTURE; METHODOLOGY; CHALLENGES AB Achieving transition towards sustainable and resilient food systems is a critical issue on the current societal agenda. This study examined the potential contribution of digitalization of the food system to such transition by reviewing 76 relevant journal articles, indexed on the Scopus database, using the integrative literature review approach and descriptive content analysis with MAXQDA 2020 software. 'Blockchain' was the top hit among keywords and main concepts applied to the food system. The UK as a country and Europe as a continent were found to lead the scientific research on food system digitalization. Use of digital technologies such as blockchain, the Internet of Things, big-data analytics, artificial intelligence, and related information and communications technologies were identified as enablers. Traceability, sustainability, resilience to crises such as the COVID-19 pandemic, and reducing food waste were among the key benefit areas associated with digitalization for different food commodities. Challenges to practical applications related to infrastructure and cost, knowledge and skill, law and regulations, the nature of the technologies, and the nature of the food system were identified. Developing policies and regulations, supporting infrastructure development, and educating and training people could facilitate fuller digitalization of the food system. C1 [Amentae, Tadesse Kenea] Ambo Univ, Dept Management, POB 19, Ambo, Ethiopia. [Gebresenbet, Girma] Swedish Univ Agr Sci, Div Automat, Dept Energy & Technol, POB 7032, SE-75007 Uppsala, Sweden. C3 Ambo University; Swedish University of Agricultural Sciences RP Amentae, TK (corresponding author), Ambo Univ, Dept Management, POB 19, Ambo, Ethiopia. EM tadesse.kenea@ambou.edu.et; girma.gebresenbet@slu.se CR Accorsi R, 2018, J CLEAN PROD, V203, P1039, DOI 10.1016/j.jclepro.2018.08.275 Ahmad Tarmizi H., 2020, FOOD RES, V4, P256, DOI [10.26656/fr.2017.4(S1).S26, DOI 10.26656/FR.2017.4(S1).S26] Aldrighetti A., 2021, International Journal on Food System Dynamics, V12, P6, DOI 10.18461/ijfsd.v12i1.72 Alkahtani M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020816 Amin M.R., 2021, INDIAN J COMPUT SCI, V12, P193, DOI 10.21817/indjcse/2021/v12i1/211201018 Anastasiadis F, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10124850 Annosi MC, 2021, IND MARKET MANAG, V93, P208, DOI 10.1016/j.indmarman.2021.01.005 [Anonymous], 2016, LOGISTICS SUPPLY CHA [Anonymous], INT FOOD POLICY RES Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Bahn RA, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063223 Ben-Daya M., 2020, ROLE INTERNET THINGS, V28, P17, DOI [10.1080/10686967.2020.1838978, DOI 10.1080/10686967.2020.1838978] Bergier I, 2021, SCI TOTAL ENVIRON, V761, DOI 10.1016/j.scitotenv.2020.143276 Bhutta MNM, 2021, IEEE ACCESS, V9, P65660, DOI 10.1109/ACCESS.2021.3076373 Botos S., 2020, AGRIS On-line Papers in Economics and Informatics, V12, P41, DOI 10.7160/aol.2020.120204 Bucea-Manea-Tonis R, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13010012 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Canas H, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12197978 Caro M.P., P 2018 IOT VERT TOP, P1 Cerutti AK, 2016, FOOD POLICY, V58, P82, DOI 10.1016/j.foodpol.2015.12.001 Chauhan C, 2021, J CLEAN PROD, V295, DOI 10.1016/j.jclepro.2021.126438 Chen HL, 2021, IEEE ACCESS, V9, P36008, DOI 10.1109/ACCESS.2021.3062410 Cheng WJ, 2018, J CLEAN PROD, V176, P770, DOI 10.1016/j.jclepro.2017.12.027 Cocco L, 2021, IEEE ACCESS, V9, P62899, DOI 10.1109/ACCESS.2021.3074874 De A, 2021, J CLEAN PROD, V283, DOI 10.1016/j.jclepro.2020.124577 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Dey S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063486 Di Vaio A, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12124851 Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067 El Bilali Hamid, 2018, Information Processing in Agriculture, V5, P456, DOI 10.1016/j.inpa.2018.06.006 Etemadi N, 2021, INFORMATION, V12, DOI 10.3390/info12020070 Fallahpour A, 2021, IND MANAGE DATA SYST, V121, P1997, DOI 10.1108/IMDS-06-2020-0343 FAO, 2018, FRONT SUSTAIN FOOD S Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Flores-Siguenza P, 2021, MATH BIOSCI ENG, V18, P2206, DOI 10.3934/mbe.2021111 Frederico GF, 2020, SUPPLY CHAIN MANAG, V25, P262, DOI 10.1108/SCM-09-2018-0339 Fu H, 2020, INT FOOD AGRIBUS MAN, V23, P667, DOI 10.22434/IFAMR2019.0152 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Garcia DJ, 2015, COMPUT CHEM ENG, V81, P153, DOI 10.1016/j.compchemeng.2015.03.015 Haseli G, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9161881 Herrera M. M., 2021, International Journal on Food System Dynamics, V12, P37, DOI 10.18461/ijfsd.v12i1.74 Hirata E, 2021, MARIT BUS REV, V6, P114, DOI 10.1108/MABR-07-2020-0043 Hofmann E, 2019, INT J PHYS DISTR LOG, V49, P945, DOI 10.1108/IJPDLM-11-2019-399 Jacobsen H, 2022, PROD PLAN CONTROL, V33, P1548, DOI 10.1080/09537287.2021.1882691 Jagtap S, 2019, WASTE MANAGE, V87, P387, DOI 10.1016/j.wasman.2019.02.017 Jula P, 2011, TRANSPORT RES E-LOG, V47, P593, DOI 10.1016/j.tre.2011.02.006 Kamble SS, 2020, INT J PROD ECON, V219, P179, DOI 10.1016/j.ijpe.2019.05.022 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kittichotsatsawat Y, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084593 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Kolesnikov A.V., 2020, INT J SUPPLY CHAIN M, V9, P820 Kollia I, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10111223 Kopyto M, 2020, TECHNOL FORECAST SOC, V161, DOI 10.1016/j.techfore.2020.120330 Kramer MP, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13042168 Kumar T.B., 2021, INT J CUR RES REV, V13, P143 Lin WJ, 2020, IEEE ACCESS, V8, P143920, DOI 10.1109/ACCESS.2020.3014522 Lindstrom H, 2020, ECOL ECON, V172, DOI 10.1016/j.ecolecon.2020.106622 Lioutas ED, 2020, LAND USE POLICY, V94, DOI 10.1016/j.landusepol.2020.104541 Liu JQ, 2021, J PUBLIC PROCUR, V21, P138, DOI 10.1108/JOPP-04-2020-0035 Lundberg S., 2013, ENV EC, V4, P75 Luo JL, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10051573 Ma YL, 2021, CORP SOC RESP ENV MA, V28, P1002, DOI 10.1002/csr.2101 Mahamuni A., 2018, DEF TRANSP J, V74, P14 Mangla SK, 2021, TRANSPORT RES E-LOG, V149, DOI 10.1016/j.tre.2021.102289 Ciruela-Lorenzo AM, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041325 Martindale W, 2020, SCI TOTAL ENVIRON, V724, DOI 10.1016/j.scitotenv.2020.137871 Mavilia R, 2022, AFR J SCI TECHNOL IN, V14, P845, DOI 10.1080/20421338.2021.1908660 Mishakov VY, 2021, RES DEVELOP, P265, DOI 10.1007/978-3-030-70194-9_26 Mondejar ME, 2021, SCI TOTAL ENVIRON, V794, DOI 10.1016/j.scitotenv.2021.148539 Mor R.S., 2018, J OPERATIONS SUPPLY, V11, P14, DOI DOI 10.12660/JOSCMV11N1P14-25 Moraes NV, 2021, J ENVIRON MANAGE, V286, DOI 10.1016/j.jenvman.2021.112268 Musavi M, 2017, COMPUT IND ENG, V113, P766, DOI 10.1016/j.cie.2017.07.039 Neethirajan S, 2021, SENS BIO-SENS RES, V32, DOI 10.1016/j.sbsr.2021.100408 Ni D, 2020, INT J MACH LEARN CYB, V11, P1463, DOI 10.1007/s13042-019-01050-0 Nordmark L., P INT S PREC MAN ORC, P235 Nosratabadi S, 2020, FOODS, V9, DOI 10.3390/foods9020132 Nurgazina J, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084206 Olan F, 2022, INT J PROD RES, V60, P4418, DOI 10.1080/00207543.2021.1915510 Park A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13041726 Preininger E.M., 2021, GEOGR HELV, V76, P249, DOI 10.5194/gh-76-249-2021 Qian JP, 2017, COMPUT ELECTRON AGR, V139, P56, DOI 10.1016/j.compag.2017.05.009 Qian JP, 2017, FOOD CONTROL, V74, P98, DOI 10.1016/j.foodcont.2016.11.034 Jambrak AR, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11020686 Ringler C., 2021, FOOD SYSTEMS SUMMIT, P1 Sahay N, 2013, AICHE J, V59, P4612, DOI 10.1002/aic.14226 Saryatmo MA, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13095109 Saurabh S, 2021, J CLEAN PROD, V284, DOI 10.1016/j.jclepro.2020.124731 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Sharma R, 2020, COMPUT OPER RES, V119, DOI 10.1016/j.cor.2020.104926 Shepherd M, 2020, J SCI FOOD AGR, V100, P5083, DOI 10.1002/jsfa.9346 Shew AM, 2022, APPL ECON PERSPECT P, V44, P299, DOI 10.1002/aepp.13157 Sitek P, 2017, IND MANAGE DATA SYST, V117, P2115, DOI 10.1108/IMDS-10-2016-0465 Snyder H, 2019, J BUS RES, V104, P333, DOI 10.1016/j.jbusres.2019.07.039 Sufiyan M, 2019, SUSTAIN PROD CONSUMP, V20, P40, DOI 10.1016/j.spc.2019.03.004 Teodorescu M., 2021, J INNOV TECH MAR COM, V7, P80, DOI [10.3390/joitmc7010080, DOI 10.3390/JOITMC7010080] Tirkolaee EB, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9111304 Tirkolaee EB, 2020, J CLEAN PROD, V250, DOI 10.1016/j.jclepro.2019.119517 Torky M, 2020, COMPUT ELECTRON AGR, V178, DOI 10.1016/j.compag.2020.105476 Tsai JF, 2007, EUR J OPER RES, V177, P982, DOI 10.1016/j.ejor.2006.01.034 Tsolakis N, 2021, J BUS RES, V131, P495, DOI 10.1016/j.jbusres.2020.08.003 Tzounis A, 2017, BIOSYST ENG, V164, P31, DOI 10.1016/j.biosystemseng.2017.09.007 Visciano P, 2021, TRENDS FOOD SCI TECH, V114, P424, DOI 10.1016/j.tifs.2021.06.010 Visconti P, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20133632 Wenzel H, 2019, HAMB INT C LOG, V27, P413, DOI [10.15480/882.2478 18.08.2021, DOI 10.15480/882.2478] Wolfert S, 2017, AGR SYST, V153, P69, DOI 10.1016/j.agsy.2017.01.023 Jing X, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12051874 Yazdani M, 2022, OPER MANAGE RES, V15, P116, DOI 10.1007/s12063-021-00186-z Yazdani M, 2020, J ENTERP INF MANAG, V33, P965, DOI 10.1108/JEIM-09-2019-0294 Zekhnini K, 2021, BENCHMARKING, V28, P465, DOI 10.1108/BIJ-04-2020-0156 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhong R, 2017, IND MANAGE DATA SYST, V117, P2085, DOI 10.1108/IMDS-09-2016-0391 NR 112 TC 9 Z9 9 U1 35 U2 94 PD NOV PY 2021 VL 13 IS 21 AR 12181 DI 10.3390/su132112181 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000718856200001 DA 2022-12-14 ER PT J AU El Sheikha, AF AF El Sheikha, Aly Farag TI Why the importance of geo-origin tracing of edible bird nests is arising? SO FOOD RESEARCH INTERNATIONAL DT Review DE Edible bird's nest; Multidimensional value; Geo-traceability; Automation methods; Physical approaches; Analytical techniques; Polymerase chain reaction-denaturing gradient; gel electrophoresis ID SWIFTLET AERODRAMUS-FUCIPHAGUS; LINKED-IMMUNOSORBENT-ASSAY; COLLOCALIA-ESCULENTA L.; PCR-DGGE; GEOGRAPHICAL ORIGIN; MICROBIAL ECOLOGY; PHYSALIS FRUITS; SIALIC-ACID; AMINO-ACID; BACTERIAL COMMUNITIES AB Edible bird's nest (EBN) swiftlet existed naturally 48,000 years ago in caves as their natural dwellings. Nowadays, edible bird's nest has become a very important industry due to its high nutritional, medicinal and economic value. Additionally, edible bird's nest has a long quality guarantee period. Obviously, the nutritional components and medicinal functions vary depending on geographical origins. Recently, the global demand for edible bird's nest has markedly increased, accompanied by the increasing attention of all key players of the global food trade system, i.e., producers, consumers, traders and the authorities to obtain safe and high-quality edible bird's nest. Hence, this target can be accomplished via the enforcement of an efficient and universal geo-tracing technique. Current methods of the geo-tracking of edible bird's nest, i.e., automation, physical and analytical techniques have several limitations and all of them fail to discriminate different quality grades of edible bird's nest. Meanwhile, in many studies and applications, polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) has proven to be a "cutting edge" technique for greatly enhance food traceability from field to fork through its ability in distinguishing the food products in terms of their quality and safety. This article provides an overview of (1) edible bird's nest as a multiuse strategic food product, (2) quality issues associated with edible bird's nest including implications that the site of acquisition of the edible bird's nest has food safety implications, (3) current regulations and geo-tracking approaches to ensure the safety and quality of edible bird's nest with the special focus on polymerase chain reaction-denaturing gradient gel electrophoresis technique as a vigorous and universal geo-tracing tool to be suggested for edible bird's nest geo-traceability. C1 [El Sheikha, Aly Farag] Jiangxi Agr Univ, Coll Biosci & Bioengn, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. [El Sheikha, Aly Farag] McMaster Univ, Dept Biol, 1280 Main St West, Hamilton, ON L8S 4K1, Canada. [El Sheikha, Aly Farag] Univ Ottawa, Fac Hlth Sci, Sch Nutr Sci, 25 Univ Private, Ottawa, ON K1N 6N5, Canada. [El Sheikha, Aly Farag] Jiangxi Agr Univ, Bioengn & Technol Res Ctr Edible & Med Fungi, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. [El Sheikha, Aly Farag] Jiangxi Agr Univ, Jiangxi Key Lab Conservat & Utilizat Fungal Resou, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China. [El Sheikha, Aly Farag] Menoufia Univ, Dept Food Sci & Technol, Fac Agr, Minufiya Govt, Shibin Al Kawm 32511, Egypt. C3 Jiangxi Agricultural University; McMaster University; University of Ottawa; Jiangxi Agricultural University; Jiangxi Agricultural University; Egyptian Knowledge Bank (EKB); Menofia University RP El Sheikha, AF (corresponding author), Jiangxi Agr Univ, Coll Biosci & Bioengn, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China.; El Sheikha, AF (corresponding author), McMaster Univ, Dept Biol, 1280 Main St West, Hamilton, ON L8S 4K1, Canada.; El Sheikha, AF (corresponding author), Univ Ottawa, Fac Hlth Sci, Sch Nutr Sci, 25 Univ Private, Ottawa, ON K1N 6N5, Canada.; El Sheikha, AF (corresponding author), Jiangxi Agr Univ, Bioengn & Technol Res Ctr Edible & Med Fungi, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China.; El Sheikha, AF (corresponding author), Jiangxi Agr Univ, Jiangxi Key Lab Conservat & Utilizat Fungal Resou, 1101 Zhimin Rd, Nanchang 330045, Jiangxi, Peoples R China.; El Sheikha, AF (corresponding author), Menoufia Univ, Dept Food Sci & Technol, Fac Agr, Minufiya Govt, Shibin Al Kawm 32511, Egypt. EM elsheikha_aly@yahoo.com CR Abidin FZ, 2011, BMC COMPLEM ALTERN M, V11, DOI 10.1186/1472-6882-11-94 Adenan Mohd Noor Hidayat, 2020, AIP Conference Proceedings, V2295, DOI 10.1063/5.0031893 Andrade P, 1997, FOOD CHEM, V60, P79, DOI 10.1016/S0308-8146(96)00313-5 Aowphol A, 2008, ZOOL SCI, V25, P372, DOI 10.2108/zsj.25.372 Arcuri EF, 2013, FOOD CONTROL, V30, P1, DOI 10.1016/j.foodcont.2012.07.007 Babji AS, 2018, INT FOOD RES J, V25, P1936 Babji AS., 2015, UTAR AGR SCI J, V1, P32 Bailon-Salas AM, 2017, BIORESOURCES, V12, P4384, DOI 10.15376/biores.12.2.Bailon_Salas Bandoim L., 2020, HEALTHLINE Barcaccia G, 2016, DIVERSITY-BASEL, V8, DOI 10.3390/d8010002 Basir M. M., 1996, P CITES TECHN WORKSH Bernard L, 2001, CYTOMETRY, V43, P314, DOI 10.1002/1097-0320(20010401)43:4<314::AID-CYTO1064>3.0.CO;2-H Bigot C, 2015, FOOD CONTROL, V48, P123, DOI 10.1016/j.foodcont.2014.03.035 Bush L. M., 2021, MERCK MANUAL CONSUME But PPH, 2013, J ETHNOPHARMACOL, V145, P378, DOI 10.1016/j.jep.2012.10.050 Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Careena S, 2018, EVID-BASED COMPL ALT, V2018, DOI 10.1155/2018/9318789 Carmona M, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0037353 Centers for Disease Control and Prevention (CDC), 2020, CAND FUNG DIS TYP FU Centers for Disease Control and Prevention (CDC), 2019, AC HEALTHC SETT HEAL Centers for Disease Control and Prevention (CDC), 2021, ASP COM FUNG DIS TYP Chan G.K., 2018, J COMPLEMENT MED, V6, DOI [10.19080/JCMAH.2018.06.555683, DOI 10.19080/JCMAH.2018.06.555683] Chan G.K.L., 2015, J COSMET DERMATOLOGI, V5, P262, DOI [10.4236/jcdsa.2015.54032, DOI 10.4236/JCDSA.2015.54032] Chan G. K. L., 2013, THESIS U SCI TECHNOL, DOI [10.14711/thesis-b1213319, DOI 10.14711/THESIS-B1213319] Chan GKL, 2013, FOOD CONTROL, V34, P637, DOI 10.1016/j.foodcont.2013.06.010 Chau Q, 2003, SUPPORT CARE CANCER, V11, P795, DOI 10.1007/s00520-003-0520-2 Chen JXJ, 2015, FOOD ADDIT CONTAM A, V32, P2138, DOI 10.1080/19440049.2015.1101494 Chen TT, 2011, AFR J BIOTECHNOL, V10, P9387 Chitmanat C., 2015, Journal of Agricultural Science (Toronto), V7, P254 Chua KH, 2013, BMC COMPLEM ALTERN M, V13, DOI 10.1186/1472-6882-13-19 Chua LS, 2016, J INTEGR MED-JIM, V14, P415, DOI 10.1016/S2095-4964(16)60282-0 Chua YG, 2015, J AGR FOOD CHEM, V63, P279, DOI 10.1021/jf503157n Chua YG, 2014, RAPID COMMUN MASS SP, V28, P1387, DOI 10.1002/rcm.6914 Colloff MJ, 2009, DUST MITES, P273, DOI 10.1007/978-90-481-2224-0_7 Colombo JP, 2003, ACTA PAEDIATR, V92, P42, DOI 10.1080/08035320310010437 Dai YW, 2021, FOOD RES INT, V140, DOI 10.1016/j.foodres.2020.109875 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Daud N, 2021, FOOD REV INT, V37, P177, DOI 10.1080/87559129.2019.1696359 Deng YE, 2006, SPECTROSC SPECT ANAL, V26, P1242 Deshpande S D, 1999, Indian J Pathol Microbiol, V42, P81 Diana JS, 2013, BIOSCIENCE, V63, P255, DOI 10.1525/bio.2013.63.4.5 Dolinsky LCB, 2002, NEUROMUSCULAR DISORD, V12, P845, DOI 10.1016/S0960-8966(02)00069-X Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Dufosse L, 2013, FOOD CONTROL, V32, P644, DOI 10.1016/j.foodcont.2013.01.045 Durand N., 2012, THESIS U MONTPELLIER, P2 Durand N, 2013, FOOD CONTROL, V34, P466, DOI 10.1016/j.foodcont.2013.05.017 Ecker C, 2012, FOOD CHEM, V130, P759, DOI 10.1016/j.foodchem.2011.07.100 El Sheikha A., 2017, ASIA PACIFIC J FOOD, V3, P1 El Sheikha A.F., 2010, THESIS U MONTPELLIER El Sheikha A.F., 2018, MOL TECHNIQUES FOOD, V1st, DOI [10.1002/9781119374633, DOI 10.1002/9781119374633] El Sheikha A. F., 2015, ADV FOOD TECHNOLOGY, V1, pS1, DOI [10.17140/AFTNSOJ-SE-1-101, DOI 10.17140/AFTNSOJ-SE-1-101] El Sheikha A.F., 2015, NUTR FOOD TECHNOLOGY, V1, P1, DOI [10.16966/nftoa.103, DOI 10.16966/2470-6086.103] El Sheikha AF., 2021, FOOD AUTHENTICATION, DOI [10.1016/B978-0-12-821104-5.00006-4, DOI 10.1016/B978-0-12-821104-5.00006-4] El Sheikha AF, 2012, FOOD CONTROL, V24, P57, DOI 10.1016/j.foodcont.2011.09.003 El Sheikha AF, 2011, QUAL ASSUR SAF CROP, V3, P40, DOI 10.1111/j.1757-837X.2010.00090.x El Sheikha AF, 2019, SCI BEVERAGES, V1, P179, DOI 10.1016/B978-0-12-815260-7.00006-7 El Sheikha AF, 2019, TRENDS FOOD SCI TECH, V86, P544, DOI 10.1016/j.tifs.2018.11.012 El Sheikha AF, 2019, INT J TROP INSECT SC, V39, P9, DOI 10.1007/s42690-019-00002-z El Sheikha AF, 2019, FOOD BIOTECHNOL, V33, P54, DOI 10.1080/08905436.2018.1547644 El Sheikha AF, 2020, CRIT REV FOOD SCI, V60, P11, DOI 10.1080/10408398.2018.1506906 El Sheikha AF, 2017, BIOTECHNOLOGY AND PRODUCTION OF ANTI-CANCER COMPOUNDS, P1, DOI 10.1007/978-3-319-53880-8_1 El Sheikha AF, 2018, MOLECULAR TECHNIQUES IN FOOD BIOLOGY: SAFETY, BIOTECHNOLOGY, AUTHENTICITY AND TRACEABILITY, P423 El Sheikha AF, 2018, MOLECULAR TECHNIQUES IN FOOD BIOLOGY: SAFETY, BIOTECHNOLOGY, AUTHENTICITY AND TRACEABILITY, P3 El Sheikha AF, 2018, TRENDS FOOD SCI TECH, V78, P292, DOI 10.1016/j.tifs.2018.06.008 El Sheikha AF, 2017, REV FISH SCI AQUAC, V25, P158, DOI 10.1080/23308249.2016.1254158 El Sheikha AF, 2016, CRIT REV FOOD SCI, V56, P306, DOI 10.1080/10408398.2012.745478 El Sheikha AF, 2014, BIOLOGICAL CONTROLS FOR PREVENTING FOOD DETERIORATION: STRATEGIES FOR PRE- AND POSTHARVEST MANAGEMENT, P409 El Sheikha AF, 2011, FOOD BIOTECHNOL, V25, P115, DOI 10.1080/08905436.2011.576556 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Elfita Lina, 2020, Biodiversitas, V21, P2362 Ercolini D, 2004, J MICROBIOL METH, V56, P297, DOI 10.1016/j.mimet.2003.11.006 FISCHER SG, 1979, CELL, V16, P191, DOI 10.1016/0092-8674(79)90200-9 Fisher W., 2017, FOOD SAFETY MAG 0606 Food and Agriculture Organization (FAO), FOOD FRAUD INT DET M Gan S. H., 2016, THESIS U NOTTINGHAM GMA Marketing to China, 2020, WHY IS CHIN MOST ATT Goh DLM, 2000, J PEDIATR-US, V137, P277, DOI 10.1067/mpd.2000.107108 Goh DLM, 1999, ALLERGY, V54, P84, DOI 10.1034/j.1398-9995.1999.00925.x Goh DLM, 2001, J ALLERGY CLIN IMMUN, V107, P1082, DOI 10.1067/mai.2001.114342 Grisez L, 1997, AQUACULTURE, V155, P387, DOI 10.1016/S0044-8486(97)00113-0 Gunnars K., 2020, HEALTHLINE Guo CT, 2006, ANTIVIR RES, V70, P140, DOI 10.1016/j.antiviral.2006.02.005 Guo L., 2014, THESIS CHINA AGR U B Guo LL, 2018, J SCI FOOD AGR, V98, P3057, DOI 10.1002/jsfa.8805 Guo LL, 2017, FOOD CONTROL, V80, P259, DOI 10.1016/j.foodcont.2017.05.007 Guo LL, 2014, FOOD CONTROL, V44, P220, DOI 10.1016/j.foodcont.2014.04.006 Haghani A, 2016, J ETHNOPHARMACOL, V185, P327, DOI 10.1016/j.jep.2016.03.020 Hamdouche Y, 2016, FOOD CONTROL, V65, P112, DOI 10.1016/j.foodcont.2016.01.022 Hamdouche Y, 2015, FOOD CONTROL, V48, P117, DOI 10.1016/j.foodcont.2014.05.031 Hanspal S, 2017, SAGE OPEN, V7, DOI 10.1177/2158244016677325 Head IM, 1998, MICROB ECOL, V35, P1, DOI 10.1007/s002489900056 Helmi, 2018, HVM Bioflux, V10, P62 Hobbs JJ, 2004, BIODIVERS CONSERV, V13, P2209, DOI 10.1023/B:BIOC.0000047905.79709.7f Hou ZP, 2015, BIOSCI BIOTECH BIOCH, V79, P1570, DOI 10.1080/09168451.2015.1050989 Howard DH, 2003, PATHOGENIC FUNGI HUM Hu XJ, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0151976 Huan XW, 2019, J FOOD DRUG ANAL, V27, P876, DOI 10.1016/j.jfda.2019.06.004 Huang XW, 2018, TRENDS FOOD SCI TECH, V81, P90, DOI 10.1016/j.tifs.2018.09.001 Huang XW, 2020, J FOOD MEAS CHARACT, V14, P514, DOI 10.1007/s11694-019-00251-z Hun LT, 2016, ANAL METHODS-UK, V8, P526, DOI 10.1039/c5ay02170g Ito Y., 2020, EDIBLE BIRDS NESTS W, DOI [10.2139/ssrn.3531323, DOI 10.2139/SSRN.3531323] Jamalluddin NH, 2019, FOOD CONTROL, V104, P247, DOI 10.1016/j.foodcont.2019.04.042 Jandam K., 2020, GLOBAL BIRDS NEST IN Jing T. S., 2020, INVESTMENT BIRDS NES Johnson R., 2014, C RES SERVICE, V43358, P2 JONES PJH, 1990, CAN J PHYSIOL PHARM, V68, P935, DOI 10.1139/y90-142 Jong CH, 2013, COMPUT ELECTRON AGR, V96, P90, DOI 10.1016/j.compag.2013.04.015 Kamaruddin R., 2019, INT J SUP CHAIN MANA, V8, P724 Kawai M, 2002, APPL ENVIRON MICROB, V68, P699, DOI 10.1128/AEM.68.2.699-704.2002 Kew PE, 2015, TROP BIOMED, V32, P761 Kew PE, 2014, TROP BIOMED, V31, P63 Koay M. Y., 2018, ICBBT 18 P 2018 10 I, P25, DOI [10.1145/3232059.3232075, DOI 10.1145/3232059.3232075] Koe T., 2020, MIDYEAR IMPORT REPOR KONG YC, 1987, COMP BIOCHEM PHYS B, V87, P221, DOI 10.1016/0305-0491(87)90133-7 Kouakou AC, 2012, FISHERIES SCI, V78, P1125, DOI 10.1007/s12562-012-0526-0 Lau SM., 1994, INT TRADE SWIFTLET N Le Nguyen DD, 2008, FRUITS, V63, P75, DOI 10.1051/fruits:2007049 Lee MS, 2019, J FOOD DRUG ANAL, V27, P154, DOI 10.1016/j.jfda.2018.08.003 Lee Ting Hun, 2020, Chiang Mai University Journal of Natural Sciences, V19, P379, DOI 10.12982/CMUJNS.2020.0025 Lee TH, 2017, FOOD RES INT, V100, P14, DOI 10.1016/j.foodres.2017.07.036 Leesing R., 2011, DYNAMIC BIOCH PROCES, V5, P83 Leesing R., 2005, IDENTIFICATION VALID, V2 Leh C., 2000, HORNBILL, V4, P102 Lerner A, 2006, SOIL BIOL BIOCHEM, V38, P1188, DOI 10.1016/j.soilbio.2005.10.006 Liljas P., 2015, ANCIENT PRACTICE HAR Lin JR, 2009, FOOD RES INT, V42, P1053, DOI 10.1016/j.foodres.2009.04.014 Lin Jie-Ru, 2006, Zhong Yao Cai, V29, P219 Liu B, 2013, PLANT MOL BIOL REP, V31, P272, DOI 10.1007/s11105-012-0500-0 Liu KF, 2020, ANAL METHODS-UK, V12, P2710, DOI [10.1039/c9ay02548k, 10.1039/C9AY02548K] Liu X., 1995, CHINESE J TOUR MED S, V1, P26 Lv DY, 2021, FRONT GENET, V12, DOI 10.3389/fgene.2021.632232 Ly F. D., 2007, DENATURING GRADIENT Lyttle DJ, 2011, AOB PLANTS, DOI 10.1093/aobpla/plr008 Ma FuCui, 2012, African Journal of Pharmacy and Pharmacology, V6, P2875 Ma FC, 2012, FOOD RES INT, V48, P559, DOI 10.1016/j.foodres.2012.06.001 Marcone MF, 2005, FOOD RES INT, V38, P1125, DOI 10.1016/j.foodres.2005.02.008 Martin NH, 2016, FRONT MICROBIOL, V7, DOI 10.3389/fmicb.2016.01549 Matsukawa N, 2011, BIOSCI BIOTECH BIOCH, V75, P590, DOI 10.1271/bbb.100705 Meei Chien Quek, 2015, Information Processing in Agriculture, V2, P1, DOI 10.1016/j.inpa.2014.12.002 Merriam-Webster Dictionary, 2021, EDIBLE BIRDS NEST DE Michaelsen A, 2006, INT BIODETER BIODEGR, V58, P133, DOI 10.1016/j.ibiod.2006.06.019 Montet, 2015, EGYPTIAN J BASIC APP, V2, P327, DOI [10.1016/j.ejbas.2015.06.002, DOI 10.1016/J.EJBAS.2015.06.002] Montet D., 2008, Aspects of Applied Biology, P11 Montet D, 2012, AQUACULTURE, P93 Murugan DD, 2020, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.01624 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Nasir M, 2011, INT ARAB J INF TECHN, V8, P204 Nazari T., 2020, MADE MALAYSIA NUSANT Neilson JW, 2013, J MICROBIOL METH, V92, P256, DOI 10.1016/j.mimet.2012.12.021 Nganou N. D., 2014, British Microbiology Research Journal, V4, P1 Nhari RMHR, 2019, QUAL ASSUR SAF CROP, V11, P449, DOI 10.3920/QAS2018.1415 Norhayati M K Jr, 2010, Malays J Nutr, V16, P389 Norrakiah Abdullah Sani, 2015, Kasetsart Journal, Natural Science, V49, P880 Organisation for Economic Co-operation and Development (OECD), 2007, AC OR TOX PROC UDP, P1 Oshikata C, 2017, PREHOSP DISASTER MED, V32, P688, DOI [10.1017/s1049023x17006914, 10.1017/S1049023X17006914] Pak J., 2012, BBC BUSINESS NE 0529 Pal A. K., 2017, INT J RES GRANTHAALA, V5, P176 Panikov NS, 2010, HANDB ENVIRON ENG, V10, P121, DOI 10.1007/978-1-60327-140-0_4 Panyaarvudh J., 2018, THE NATION THAILAND Paydar M, 2013, J FOOD SCI, V78, pT1940, DOI 10.1111/1750-3841.12313 Petrescu DC, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17010169 Pisei H., 2020, BIRDS NEST EXPORTS C Piterina Anna V, 2013, ISRN Biotechnol, V2013, P162645, DOI 10.5402/2013/162645 Probst L., 2015, TRACEABILITY VALUE C Property Hunter, 2019, SWIFTLET HOME 1 SELL Putri E., 2018, INDONESIAS BIRDS NES Quek MC, 2018, INT J FOOD PROP, V21, P1680, DOI 10.1080/10942912.2018.1503303 Rahman, 2016, PERTANIKA J SCH RES, V2, P32 Rakow NA, 2000, NATURE, V406, P710, DOI 10.1038/35021028 Ramlan M., 2018, Malaysian Journal of Veterinary Research, V9, P81 RIEDEL GE, 1990, THEOR APPL GENET, V80, P1, DOI 10.1007/BF00224008 Roh KB, 2012, EVID-BASED COMPL ALT, V2012, DOI 10.1155/2012/797520 Rohaizan M. A., 2017, APPL EXPORT BIRD NES ROSNER MH, 1991, ENVIRON HEALTH PERSP, V94, P131, DOI 10.2307/3431306 Rychlik T, 2017, FOOD CONTROL, V73, P1074, DOI 10.1016/j.foodcont.2016.10.024 Saengkrajang W, 2013, J FOOD COMPOS ANAL, V31, P41, DOI 10.1016/j.jfca.2013.05.001 Sam C. T., 1991, ISFM MED SCI REV, V3, P4 SanchezBorges M, 1997, J ALLERGY CLIN IMMUN, V99, P738, DOI 10.1016/S0091-6749(97)80005-X Seow EK, 2016, LWT-FOOD SCI TECHNOL, V65, P428, DOI 10.1016/j.lwt.2015.08.047 Set J., 2012, FAST EFFECTIVE EVALU, P1 Shen YD, 2008, MOLECULES, V13, P2238, DOI 10.3390/molecules13092238 Shi JY, 2017, FOOD CHEM, V229, P235, DOI 10.1016/j.foodchem.2017.02.075 Shim E., 2017, SPECTROSCOPYEUROPE, V29, P10 Shim EKS, 2017, FOOD RES INT, V95, P9, DOI 10.1016/j.foodres.2017.02.018 Shim EKS, 2016, J FOOD SCI TECH MYS, V53, P3602, DOI 10.1007/s13197-016-2344-3 Short SM, 2002, APPL ENVIRON MICROB, V68, P1290, DOI 10.1128/AEM.68.3.1290-1296.2002 Sia Y. H., 2014, United States Patent, Patent, Patent No. [US 8,651,061 B2, 8651061] Smit S, 2007, NUCLEIC ACIDS RES, V35, P3339, DOI 10.1093/nar/gkm101 Spanggaard B, 2000, AQUACULTURE, V182, P1, DOI 10.1016/S0044-8486(99)00250-1 Sun SQ, 2001, CHINESE J ANAL CHEM, V29, P552 Tan KokHong, 2014, International Proceedings of Chemical, Biological and Environmental Engineering (IPCBEE), V63, P17 Teo PS, 2013, J CHEM-NY, V2013, DOI 10.1155/2013/325372 Thorburn C, 2014, FOOD CULT SOC, V17, P535, DOI 10.2752/175174414X14006746101439 Thorburn CC, 2015, HUM ECOL, V43, P179, DOI 10.1007/s10745-014-9713-1 Tong SR, 2020, VET WORLD, V13, P304, DOI 10.14202/vetworld.2020.304-316 Tsumuraya T, 2010, TOXICON, V56, P797, DOI 10.1016/j.toxicon.2009.06.003 Tukiran NA, 2016, FOOD CONTROL, V59, P561, DOI 10.1016/j.foodcont.2015.06.039 Tung CH, 2008, J FOOD DRUG ANAL, V16, P86 Turaki AA, 2017, VIRUSES-BASEL, V9, DOI 10.3390/v9070181 Valaskova V, 2009, PLANT SOIL ENVIRON, V55, P413, DOI 10.17221/132/2009-PSE Vimala B, 2012, FOOD AGR IMMUNOL, V23, P303, DOI 10.1080/09540105.2011.625494 Wang B, 2003, EUR J CLIN NUTR, V57, P1351, DOI 10.1038/sj.ejcn.1601704 Wang CC, 1921, J BIOL CHEM, V49, P429 [王慧 WANG Hui], 2006, [药物分析杂志, Chinese Journal of Pharmaceutical Analysis], V26, P1251 Whitehead R., 2019, BEVERAGEDAILY WHO (World Health Organization), 2020, HAZ FOOD INF Wishart DS, 2008, TRENDS FOOD SCI TECH, V19, P482, DOI 10.1016/j.tifs.2008.03.003 Wong CF, 2017, FOOD QUAL SAF-OXFORD, V1, P83, DOI 10.1093/fqs/fyx002 Wong RSY, 2013, CHIN J INTEGR MED, V19, P643, DOI 10.1007/s11655-013-1563-y Wong SF, 2018, INT FOOD RES J, V25, P966 Wu R. H., 2007, INSPECTION QUARANTIN, V17, P60 Wu YJ, 2010, FOOD RES INT, V43, P2020, DOI 10.1016/j.foodres.2010.05.020 Yang M, 2014, FOOD CHEM, V151, P271, DOI 10.1016/j.foodchem.2013.11.007 Yeo BH, 2021, FRONT PHARMACOL, V12, DOI 10.3389/fphar.2021.631136 Yew MY, 2014, BMC COMPLEM ALTERN M, V14, DOI 10.1186/1472-6882-14-391 Yida Z, 2014, BMC COMPLEM ALTERN M, V14, DOI 10.1186/1472-6882-14-468 Yiran Z., 2018, CHINA DAILY Yu YQ, 2000, J CHROMATOGR SCI, V38, P27, DOI 10.1093/chromsci/38.1.27 Yusuf B, 2020, IOP C SER EARTH ENV, V486, DOI 10.1088/1755-1315/486/1/012008 Zainab Hamzah, 2013, Advances in Environmental Biology, V7, P3758 Zainab Hamzah, 2013, Journal of Asian Scientific Research, V3, P600 Zhang HF, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107031 Zhang SW, 2013, J IMMUNOASS IMMUNOCH, V34, P49, DOI 10.1080/15321819.2012.680527 Zhang Y., 2015, THESIS U VIRGINIA Zhang YD, 2015, BMC COMPLEM ALTERN M, V15, DOI 10.1186/s12906-015-0843-9 Zhao X., 1765, SUPPLEMENT COMPENDIU Ziolkowska A, 2016, FOOD CHEM, V213, P714, DOI 10.1016/j.foodchem.2016.06.120 NR 227 TC 3 Z9 3 U1 9 U2 22 PD DEC PY 2021 VL 150 AR 110806 DI 10.1016/j.foodres.2021.110806 EA NOV 2021 PN B WC Food Science & Technology SC Food Science & Technology UT WOS:000722657600001 DA 2022-12-14 ER PT J AU Yang, L Babu, VS Zou, J Cai, XC Wu, T Lin, L AF Yang, Ling Babu, V. Sarath Zou, Juan Cai, Xu Can Wu, Ting Lin, Li TI The Development of an Intelligent Monitoring System for Agricultural Inputs Basing on DBN-SOFTMAX SO JOURNAL OF SENSORS DT Article ID CLASSIFICATION; TRACEABILITY; FOOD AB To solve the problem of unreliability of traceability information in the traceability system, we developed an intelligent monitoring system to realize the real-time online acquisition of physicochemical parameters of the agricultural inputs and to predict the varieties of input products accurately. Firstly, self-developed monitoring equipment was used to realize real-time acquisition, format conversion and pretreatment of the physicochemical parameters of inputs, and real-time communication with the cloud platform server. In this process, LoRa technology was adopted to solve the wireless communication problems between long-distance, low-power, and multinode environments. Secondly, a deep belief network (DBN) model was used to learn unsupervised physicochemical parameters of input products and extract the input features. Finally, these input features were utilized on the softmax classifier to establish the classification model, which could accurately predict the varieties of agricultural inputs. The results showed that when six kinds of pesticides, chemical fertilizers, and other agricultural inputs were predicted through the system, the prediction accuracy could reach 98.5%. Therefore, the system can be used to monitor the varieties of agrarian inputs effectively and use in real-time to ensure the authenticity and accuracy of the traceability information. C1 [Yang, Ling; Zou, Juan; Cai, Xu Can; Wu, Ting] Zhongkai Univ Agr & Engn, Sch Informat Sci & Technol, Guangzhou 510225, Guangdong, Peoples R China. [Babu, V. Sarath; Lin, Li] Zhongkai Univ Agr & Engn, Guangdong Prov Water Environm & Aquat Prod Secur, Guangzhou Key Lab Aquat Anim Dis & Waterfowl Bree, Guangdong Prov Key Lab Waterfowl Hlth Breeding,Co, Guangzhou 510225, Guangdong, Peoples R China. C3 Zhongkai University of Agriculture & Engineering; Zhongkai University of Agriculture & Engineering RP Wu, T (corresponding author), Zhongkai Univ Agr & Engn, Sch Informat Sci & Technol, Guangzhou 510225, Guangdong, Peoples R China.; Lin, L (corresponding author), Zhongkai Univ Agr & Engn, Guangdong Prov Water Environm & Aquat Prod Secur, Guangzhou Key Lab Aquat Anim Dis & Waterfowl Bree, Guangdong Prov Key Lab Waterfowl Hlth Breeding,Co, Guangzhou 510225, Guangdong, Peoples R China. EM 405684932@qq.com; linli@zhku.edu.cn CR Alcocer MJC, 2000, J AGR FOOD CHEM, V48, P2228, DOI 10.1021/jf990691m Aref Mohamed, 2014, 2014 2nd International Symposium on Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems, P19, DOI 10.1109/IDAACS-SWS.2014.6954616 Azmi N, 2018, IOP CONF SER-MAT SCI, V318, DOI 10.1088/1757-899X/318/1/012051 Bengio Y., 2006, P 19 INT C NEUR INF, P153 Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Burrell J, 2004, IEEE PERVAS COMPUT, V3, P38, DOI 10.1109/MPRV.2004.1269130 Chaudhary D.D., 2011, INT J WIRELESS MOBIL, V3, P140, DOI [DOI 10.5121/IJWMN.2011.3113, 10.5121/ijwmn.2011.3113140] Chen LF, 2018, INFORM SCIENCES, V428, P49, DOI 10.1016/j.ins.2017.10.044 Chough S. H., 2015, ELECTROANAL, V14, P273 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 [丁慧瑛 DING Hui-ying], 2009, [分析测试学报, Journal of Instrumental Analysis], V28, P970 Duan KB, 2003, LECT NOTES COMPUT SC, V2709, P125 Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647 Hinton GE., 2012, NEURAL NETWORKS TRIC, P599, DOI DOI 10.1007/978-3-642-35289-8_32 Jia H., 2010, P 2010 INT C E PRODU, P1 Kearns M, 1999, NEURAL COMPUT, V11, P1427, DOI 10.1162/089976699300016304 Kumaran S., 2010, ELECTROANAL, V4, P949 Li Bing, 2008, Computer Engineering and Applications, V44, P136 Li N., 2016, COMMUN COMPUT PHYS, V681, P438 Liao B, 2015, IETE TECH REV, V32, P294, DOI 10.1080/02564602.2015.1015631 LIU DC, 1989, MATH PROGRAM, V45, P503, DOI 10.1007/BF01589116 Long PM, 2010, MACH LEARN, V78, P287, DOI 10.1007/s10994-009-5165-z Nagabandi A, 2018, IEEE INT CONF ROBOT, P7579 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 SELISKER MY, 1995, J AGR FOOD CHEM, V43, P544, DOI 10.1021/jf00050a053 Srbinovska M, 2015, J CLEAN PROD, V88, P297, DOI 10.1016/j.jclepro.2014.04.036 van Tulder G, 2016, IEEE T MED IMAGING, V35, P1262, DOI 10.1109/TMI.2016.2526687 WOLD S, 1987, CHEMOMETR INTELL LAB, V2, P37, DOI 10.1016/0169-7439(87)80084-9 Yang B, 2002, 2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, P64, DOI 10.1109/ICMLC.2002.1176710 Yang J., 2017, INT J MACHINE LEARNI, V9, P1733 Zheng JH, 2009, CHIN J CHROMATOGR, V27, P254 NR 32 TC 2 Z9 2 U1 0 U2 3 PY 2018 VL 2018 AR 6025381 DI 10.1155/2018/6025381 WC Engineering, Electrical & Electronic; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000449836600001 DA 2022-12-14 ER PT J AU Radulescu, C Olteanu, RL Nicolescu, CM Bumbac, M Buruleanu, LC Holban, GC AF Radulescu, Cristiana Olteanu, Radu Lucian Nicolescu, Cristina Mihaela Bumbac, Marius Buruleanu, Lavinia Claudia Holban, Georgeta Carmen TI Vibrational Spectroscopy Combined with Chemometrics as Tool for Discriminating Organic vs. Conventional Culture Systems for Red Grape Extracts SO FOODS DT Article DE vibrational spectroscopy; red grape extracts; organic; conventional vineyards; chemometrics ID NEAR-INFRARED SPECTROSCOPY; QUANTITATIVE-ANALYSIS; WINE TANNINS; FTIR; IDENTIFICATION; QUANTIFICATION; FLAVONOIDS; SPECTRA; RAMAN; PHENOLICS AB Food plants provide a regulated source of delivery of functional compounds, plant secondary metabolites production being also tissue specific. In grape berries, the phenolic compounds, flavonoids and non-flavonoids, are distributed in the different parts of the fruit. The aim of this study was to investigate the applicability of FTIR and Raman screening spectroscopic techniques combined with multivariate statistical tools to find patterns in red grape berry parts (skin, seeds and pulp) according to grape variety and vineyard type (organic and conventional). Spectral data were acquired and processed using the same pattern for each different berry part (skin, seeds and pulp). Multivariate analysis has allowed separation between extracts obtained from organic and conventional vineyards for each grape variety for all grape berry parts. The innovative approach presented in this work is low-cost and feasible, being expected to have applications in studies referring to the authenticity and traceability of foods. The findings of this study are useful as well in solving a great challenge that producers are confronting, namely the consumers' distrust of the organic origin of food products. Further analyses of the chemical composition of red grapes may enhance the capability of the method of using both vibrational spectroscopy and chemometrics for discriminating the hydroalcoholic extracts according to grape varieties. C1 [Radulescu, Cristiana; Bumbac, Marius] Valahia Univ Targoviste, Fac Sci & Arts, Targoviste 130004, Romania. [Radulescu, Cristiana; Olteanu, Radu Lucian; Nicolescu, Cristina Mihaela; Bumbac, Marius] Valahia Univ Targoviste, Inst Multidisciplinary Res Sci & Technol, Targoviste 130004, Romania. [Buruleanu, Lavinia Claudia] Valahia Univ Targoviste, Fac Environm Engn & Food Sci, Targoviste 130004, Romania. [Holban, Georgeta Carmen] Univ Agron Sci & Vet Med Bucharest, Doctoral Sch, Bucharest 011464, Romania. C3 Valahia University of Targoviste; Valahia University of Targoviste; Valahia University of Targoviste; University of Agronomic Science & Veterinary Medicine - Bucharest RP Olteanu, RL (corresponding author), Valahia Univ Targoviste, Inst Multidisciplinary Res Sci & Technol, Targoviste 130004, Romania. EM cristiana.radulescu@valahia.ro; radmolteanu@valahia.ro; cristina.nicolescu@valahia.ro; marius.bumbac@valahia.ro; lavinia.buruleanu@valahia.ro; carmenholban@yahoo.com CR Agatonovic-Kustrin S., 2013, MOD CHEM APPL, V1, P1, DOI DOI 10.4172/2329-6798.1000110 Agatonovic-Kustrin S, 2017, EUR FOOD RES TECHNOL, V243, P659, DOI 10.1007/s00217-016-2779-9 Alecu GC, 2020, J SCI ARTS, P475 [Anonymous], 2007, SELECTED CONTRIBUTIO [Anonymous], 2018, OIV STANDARD MINIMUM Averilla JN, 2019, FOOD SCI BIOTECHNOL, V28, P1607, DOI 10.1007/s10068-019-00628-2 Baranska M, 2006, VIB SPECTROSC, V42, P341, DOI 10.1016/j.vibspec.2006.08.004 Bauer R, 2008, ANAL CHEM, V80, P1371, DOI 10.1021/ac086051c Biancolillo A, 2018, FRONT CHEM, V6, DOI 10.3389/fchem.2018.00576 Brereton R.G., 2007, APPL CHEMOMETRICS SC, P145, DOI [10.1002/9780470057780.ch5, DOI 10.1002/9780470057780.CH5, 10.1002/9780470057780, DOI 10.1002/9780470057780] Brereton R.G., 2003, CHEMOMETRICS DATA AN, P183, DOI [DOI 10.1002/0470863242.CH4, 10.1002/0470863242, DOI 10.1002/0470863242] Brezoiu AM, 2019, FOOD CHEM TOXICOL, V133, DOI 10.1016/j.fct.2019.110787 Bro R, 2014, ANAL METHODS-UK, V6, P2812, DOI 10.1039/c3ay41907j Bunea CI, 2012, CHEM CENT J, V6, DOI 10.1186/1752-153X-6-66 Buruleanu LC, 2018, ANAL LETT, V51, P1039, DOI 10.1080/00032719.2017.1366499 Campo E, 2005, J AGR FOOD CHEM, V53, P5682, DOI 10.1021/jf047870a Carbonaro M, 2002, J AGR FOOD CHEM, V50, P5458, DOI 10.1021/jf0202584 Casassa L. F., 2017, PHENOLIC COMPOUNDS N, P153, DOI [10.5772/67452, DOI 10.5772/67452] Coates J., 2000, ENCY ANAL CHEM Conlin AK, 2000, J CHEMOMETR, V14, P725, DOI 10.1002/1099-128X(200009/12)14:5/6<725::AID-CEM611>3.0.CO;2-8 Cosme F, 2018, BEVERAGES, V4, DOI 10.3390/beverages4010022 Cozzolino D., 2009, INT J WINE RES, V1, P123, DOI [DOI 10.2147/IJWR.S4585, 10.2147/ijwr.s4585] da Rocha JC, 2012, INT J ELECTROCHEM SC, V7, P11941 Dani C, 2007, FOOD CHEM TOXICOL, V45, P2574, DOI 10.1016/j.fct.2007.06.022 Du J, 2011, J MED PLANTS RES, V5, P4001 Everitt B.S., 2010, APPL MULTIVARIATE DA, V2nd, P125 Fernandez K, 2007, J AGR FOOD CHEM, V55, P7294, DOI 10.1021/jf071193d Gautam R, 2015, EPJ TECH INSTRUM, V2, DOI 10.1140/epjti/s40485-015-0018-6 Geana EI, 2019, MOLECULES, V24, DOI 10.3390/molecules24224166 Godelmann R, 2013, J AGR FOOD CHEM, V61, P5610, DOI 10.1021/jf400800d Grasel FD, 2016, SPECTROCHIM ACTA A, V153, P94, DOI 10.1016/j.saa.2015.08.020 Greenacre M, 2009, COMPUT STAT DATA AN, V53, P3107, DOI 10.1016/j.csda.2008.09.001 Grinder-Pedersen L, 2003, J AGR FOOD CHEM, V51, P5671, DOI 10.1021/jf030217n Grootveld, 2012, METABOLIC PROFILING, P1, DOI [10.1039/9781849735162-00001, DOI 10.1039/9781849735162-00001] Hasanaliyeva G, 2021, FOODS, V10, DOI 10.3390/foods10020476 He F, 2010, MOLECULES, V15, P9057, DOI 10.3390/molecules15129057 Heraud P, 2006, J CHEMOMETR, V20, P193, DOI 10.1002/cem.990 Heredia-Guerrero JA, 2014, FRONT PLANT SCI, V5, DOI 10.3389/fpls.2014.00305 Holban G.C., 2021, Patent Application, Patent No. [A000097, 000097] Husson F, 2005, FOOD QUAL PREFER, V16, P245, DOI 10.1016/j.foodqual.2004.04.019 Ivanova BB, 2012, TALANTA, V94, P9, DOI 10.1016/j.talanta.2011.12.016 Jensen JS, 2008, J AGR FOOD CHEM, V56, P3493, DOI 10.1021/jf703573f Jolliffe I.T., 2002, ENCYCL STAT BEHAV SC, Vsecond, DOI [DOI 10.2307/1270093, 10.2307/1270093] Kamil M., 2015, MALAYS J FORENSIC SC, V6, P48 Karoui R, 2010, CHEM REV, V110, P6144, DOI 10.1021/cr100090k Kemsley EK, 1996, CHEMOMETR INTELL LAB, V33, P47, DOI 10.1016/0169-7439(95)00090-9 Kohler A., 2010, APPL VIBRATIONAL SPE, P89, DOI [10.1002/0470027320.S8937, DOI 10.1002/0470027320.S8937] Laakso O, 2000, J ANAL TOXICOL, V24, P250, DOI 10.1093/jat/24.4.250 Lam C, 2014, IEEE INT PROF COMMUN Lasch P, 2012, CHEMOMETR INTELL LAB, V117, P100, DOI 10.1016/j.chemolab.2012.03.011 Liang ZC, 2011, FOOD CHEM, V129, P940, DOI 10.1016/j.foodchem.2011.05.050 Lindsay D.G., 2001, FUNCTIONAL FOODS CON, V2nd ed., P183 Lindsay DG, 2000, TRENDS FOOD SCI TECH, V11, P145, DOI 10.1016/S0924-2244(00)00048-0 Lu HY, 2012, J PHARMACEUT BIOMED, V59, P44, DOI 10.1016/j.jpba.2011.09.037 Lucarini M, 2020, FOODS, V9, DOI 10.3390/foods9010010 Luypaert J, 2003, ANAL CHIM ACTA, V478, P303, DOI 10.1016/S0003-2670(02)01509-X Mazet V, 2005, CHEMOMETR INTELL LAB, V76, P121, DOI 10.1016/j.chemolab.2004.10.003 Mokgalaka NS, 2013, PURE APPL CHEM, V85, P2197, DOI 10.1351/PAC-CON-13-02-09 Moncada A, 2021, SCI HORTIC-AMSTERDAM, V275, DOI 10.1016/j.scienta.2020.109733 Mungkarndee R, 2015, ANAL METHODS-UK, V7, P7431, DOI 10.1039/c5ay00797f Nicolescu CM, 2019, J SCI ARTS, P201 Nogales-Bueno J, 2017, FOOD CHEM, V232, P602, DOI 10.1016/j.foodchem.2017.04.049 Noh C.H.C., 2017, ADV SCI TECHNOL ENG, V2, P435, DOI [10.25046/aj020356, DOI 10.25046/AJ020356] Oliveira RN, 2016, MATERIA-BRAZIL, V21, P767, DOI 10.1590/S1517-707620160003.0072 Pang T, 2021, J ANAL METHODS CHEM, V2021, DOI 10.1155/2021/8874827 Ping L, 2012, IND CROP PROD, V40, P13, DOI 10.1016/j.indcrop.2012.02.039 Radulescu C, 2020, PLANTS-BASEL, V9, DOI 10.3390/plants9111470 Radulescu C, 2020, J CHEMOMETR, V34, DOI 10.1002/cem.3234 Radulescu C, 2019, ANAL LETT, V52, P2393, DOI 10.1080/00032719.2019.1590379 Radulescu C, 2017, ANAL LETT, V50, P2839, DOI 10.1080/00032719.2016.1264409 Randolph Timothy W., 2006, Cancer Biomarkers, V2, P135 Ranganathan Priya, 2017, Perspect Clin Res, V8, P148, DOI 10.4103/picr.PICR_87_17 Rayat A, 2014, COLOR RES APPL, V39, P136, DOI 10.1002/col.21771 Roychoudhury P, 2006, ANAL CHIM ACTA, V571, P159, DOI 10.1016/j.aca.2006.04.086 SAKIA RM, 1992, J ROY STAT SOC D-STA, V41, P169, DOI 10.2307/2348250 Sandasi M, 2016, MOLECULES, V21, DOI 10.3390/molecules21040472 Schulz H, 2007, VIB SPECTROSC, V43, P13, DOI 10.1016/j.vibspec.2006.06.001 Shen XC, 2018, OPT EXPRESS, V26, pA609, DOI 10.1364/OE.26.00A609 Shi JY, 2012, SPECTROCHIM ACTA A, V94, P271, DOI 10.1016/j.saa.2012.03.078 Siebert KJ, 2001, J AM SOC BREW CHEM, V59, P147 Smith B. C., 2011, FUNDAMENTALS FOURIER, DOI [10.1201/b10777, DOI 10.1201/B10777] Smith B.C., 1998, INFRARED SPECTRAL IN, DOI DOI 10.1201/9780203750841 Soleas GJ, 1997, CLIN BIOCHEM, V30, P91, DOI 10.1016/S0009-9120(96)00155-5 Stajner N, 2009, VITIS, V48, P145 Teixeira A, 2013, INT J MOL SCI, V14, P18711, DOI 10.3390/ijms140918711 Treutter D, 2010, INT J MOL SCI, V11, P807, DOI 10.3390/ijms11030807 Tur J.A., 2016, ENCY FOOD HLTH, V1st ed., P157 Turker-Kaya S, 2017, MOLECULES, V22, DOI 10.3390/molecules22010168 Unsalan O, 2009, J RAMAN SPECTROSC, V40, P562, DOI 10.1002/jrs.2166 Vlad V., 2015, Research Journal of Agricultural Science, V47, P173 Westfall A, 2020, ANTIOXIDANTS-BASEL, V9, DOI 10.3390/antiox9060486 Xia EQ, 2010, INT J MOL SCI, V11, P622, DOI 10.3390/ijms11020622 Yendle P.W., 1989, J CHEMOMETR, V3, P589, DOI [10.1002/cem.1180030407, DOI 10.1002/CEM.1180030407] NR 93 TC 1 Z9 1 U1 2 U2 11 PD AUG PY 2021 VL 10 IS 8 AR 1856 DI 10.3390/foods10081856 WC Food Science & Technology SC Food Science & Technology UT WOS:000689303300001 DA 2022-12-14 ER PT J AU Shen, M Tu, MM Zhang, W Zou, JH Zhang, M Cao, Z Zou, BD AF Shen, Min Tu, Minmin Zhang, Wei Zou, Jihua Zhang, Man Cao, Zheng Zou, Bingde TI Ion chromatography as candidate reference method for the determination of chloride in human serum SO JOURNAL OF CLINICAL LABORATORY ANALYSIS DT Article DE candidate reference method; ion chromatography; serum chloride ID POTENTIAL REFERENCE METHODOLOGY; PLASMA; ELECTROLYTES; POTASSIUM AB Background We developed an ion chromatography (IC) method for measurement of chloride in human serum which was regarded as a simple, rapid, accurate, and sensitive technique. The method will be hopefully selected as a candidate reference method. Method Serum aliquots of 0.1 mL were diluted 500 times with Milli-Q water, and chloride in serum samples was measured by IC with a gradient elution procedure using a KOH eluent generator. Results Based on the data, chloride in human serum was well detected by IC. The calibration curve for chloride was linear in the concentration range from 0 to 0.42 mmol/L with a correlation coefficient of .99995 under the optimum experimental conditions. The chloride concentration had a good linear relationship with the peak areas of chloride. This method was sensitive because of the low limit of detection (LOD) and the low limit of quantification (LOQ) 9.87 x 10(-5) mmol/L and 3.27 x 10(-4) mmol/L, respectively. Besides, the method was highly precise with the within-run coefficient of variations (CVs) for the measurement of low, medium, and high concentration level samples 0.32%, 0.73%, and 0.50%. As for the evaluation of accuracy, the biases were less than +/- 1% and 2% by comparing with National Institute of Science and Technology (NIST) standard material SRM 956d and 2013-2018 IFCC-RELA samples, respectively. Finally, the biases between IC method and the inductively coupled plasma mass spectrometry (ICP-MS) method were less than 1% which showed good agreement. Conclusion Ion chromatography is a simple sample treatment procedure for the determination of chloride in human serum with high sensitivity and specificity. The proposed method could be recommended as a candidate reference method for the determination of serum chloride in human serum. C1 [Shen, Min; Tu, Minmin; Zhang, Wei; Zou, Jihua; Zou, Bingde] MedicalSyst Biotechnol Co Ltd, Reference Lab, Ningbo, Peoples R China. [Shen, Min; Zhang, Man] Beijing Shijitan Hosp, Dept Lab Med, Beijing, Peoples R China. [Cao, Zheng] Capital Med Univ, Beijing Obstet & Gynecol Hosp, Dept Lab Med, Beijing, Peoples R China. C3 Capital Medical University RP Zou, JH (corresponding author), MedicalSyst Biotechnol Co Ltd, Reference Lab, Ningbo, Peoples R China. EM jihua.zou@nbmedicalsystem.com CR Ben Rayana MC, 2006, CLIN CHEM LAB MED, V44, P346, DOI 10.1515/CCLM.2006.060 Collie JT, 2016, CLIN CHEM LAB MED, V54, P561, DOI 10.1515/cclm-2015-0506 Grote-Koska D, 2018, METROLOGIA, V55, P245, DOI 10.1088/1681-7575/aaaa3f HULANICKI A, 1982, MIKROCHIM ACTA, V1, P203 LINGANE JJ, 1954, ANAL CHEM, V26, P622, DOI 10.1021/ac60088a002 LYON TDB, 1988, J ANAL ATOM SPECTROM, V3, P601, DOI 10.1039/ja9880300601 Rohker K, 2004, ACCREDIT QUAL ASSUR, V9, P671, DOI 10.1007/s00769-004-0887-x SCHOENFIELD RG, 1964, CLIN CHEM, V10, P533 Thienpont LM, 1997, J CHROMATOGR A, V789, P557, DOI 10.1016/S0021-9673(97)00692-4 THIENPONT LM, 1994, ANAL CHEM, V66, P2404, DOI 10.1021/ac00086a029 THIENPONT LM, 1995, J CHROMATOGR A, V706, P443, DOI 10.1016/0021-9673(95)00152-D Velapoldi RA, 1979, STANDARD REFERENCE M Zhang R, 2013, CLIN CHIM ACTA, V420, P146, DOI 10.1016/j.cca.2012.10.020 Zou JH, 2018, J CLIN LAB ANAL, V32, DOI 10.1002/jcla.22429 NR 14 TC 6 Z9 6 U1 1 U2 9 PD AUG PY 2020 VL 34 IS 8 AR e23296 DI 10.1002/jcla.23296 EA JUL 2020 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000551476400001 DA 2022-12-14 ER PT J AU Strong, DM Seem, D Taylor, G Parker, J Stewart, D Kuehnert, MJ AF Strong, D. Michael Seem, Debbie Taylor, Gloria Parker, Jory Stewart, Darren Kuehnert, Matthew J. TI Development of a transplantation transmission sentinel network to improve safety and traceability of organ and tissues SO CELL AND TISSUE BANKING DT Article DE Tissue banking; Transplantation; Surveillance; Sentinel networks; Traceability; Biovigilance; Organ and tissue safety ID RECIPIENTS; DONOR AB The US lags behind other developed countries in creating a system to monitor disease transmission and other complications from human allograft use, despite a pressing need. The risks of transmission are amplified in transplantation, since at least 8 organs and more than 100 tissues can be recovered from a single common organ and tissue donor. Moreover, since many allografts collected in the US are distributed internationally, tissue safety is a global concern. In June 2005, participants of a US government-sponsored workshop concluded that a communication network for the tracking and reporting of disease transmissions for tissues and organs was critically needed. The United Network for Organ Sharing (UNOS) entered into a cooperative agreement with the Centers for Disease Control and Prevention (CDC) in 2006 to develop a system prototype. Over the following 3 years, the Transplantation Transmission Sentinel Network (TTSN) was developed and piloted with the participation of organ procurement organizations, tissue banks and transplant centers. The prototype centered around three elements of data entry: (1) donation, (2) tissue implantation, and (3) adverse event. The pilot proved that a system can be built and operated successfully, but also suggested that users may be hesitant to report adverse events. CDC has requested further input on scope and cost to build a transplant surveillance infrastructure for a fully functional national system. For tissues however, in contrast to organs, tracking from recovery to implantation will be necessary before a system is operable, requiring common identifiers and nomenclature. Until a US sentinel network is operational, future transmission events that are preventable may result nationally and globally due to its absence. C1 [Seem, Debbie; Kuehnert, Matthew J.] Ctr Dis Control & Prevent, Off Blood Organ & Other Tissue Safety, Div Healthcare Qual Promot, Atlanta, GA 30333 USA. [Strong, D. Michael] Univ Washington, Dept Orthopaed & Sports Med, Seattle, WA 98195 USA. [Taylor, Gloria; Parker, Jory; Stewart, Darren] UNOS, Richmond, VA USA. C3 Centers for Disease Control & Prevention - USA; University of Washington; University of Washington Seattle; United Network for Organ Sharing RP Kuehnert, MJ (corresponding author), Ctr Dis Control & Prevent, Off Blood Organ & Other Tissue Safety, Div Healthcare Qual Promot, 1600 Clifton Rd,Mailstop A-07, Atlanta, GA 30333 USA. EM mkuehnert@cdc.gov CR [Anonymous], 2005, FED REG, V70, P32620 CHANEY A, 2006, BODY BROKERS Department of Health and Human Services, 2009, BIOV US EFF BRIDG CR Eastlund T., 2004, ADV TISSUE BANKING, V7, P51, DOI DOI 10.1142/9789812796646_0003 Feyerick D, 2009, MAYORS RABBIS ARREST Fishman JA, 2007, NEW ENGL J MED, V357, P2601, DOI 10.1056/NEJMra064928 Fishman JA, 2009, CELL TISSUE BANK, V10, P271, DOI 10.1007/s10561-008-9114-z Ison MG, 2009, AM J TRANSPLANT, V9, P1929, DOI 10.1111/j.1600-6143.2009.02700.x JOYCE M, 2010, US MUSCULOS IN PRESS, V5 KELLER M, 2009, INSIDE CREEPY GLOBAL *OPTN UNOS, 2007, REG HIV HEP C TRANSM Strong DM, 2008, ISBT SCI SER, V3, P77, DOI 10.1111/j.1751-2824.2008.00168.x TROTTER JF, 2008, ARCH OPHTHALMOL-CHIC, V126, P235, DOI DOI 10.1001/ARCHOPHTHALMOL.2007.45 Tugwell BD, 2005, ANN INTERN MED, V143, P648, DOI 10.7326/0003-4819-143-9-200511010-00008 WARREN J, 2006, TRANSPLANT NEWS, V16 2009, FED REG, V74, P49881 NR 16 TC 12 Z9 12 U1 0 U2 2 PD NOV PY 2010 VL 11 IS 4 SI SI BP 335 EP 343 DI 10.1007/s10561-010-9198-0 WC Cell Biology; Engineering, Biomedical SC Cell Biology; Engineering UT WOS:000288436100004 DA 2022-12-14 ER PT J AU Liu, HY Guo, BL Zhang, B Zhang, YQ Wei, S Li, M Wadood, SA Wei, YM AF Liu, Hongyan Guo, Boli Zhang, Bo Zhang, Yingquan Wei, Shuai Li, Ming Wadood, Syed Abdul Wei, Yimin TI Characterizations of stable carbon and nitrogen isotopic ratios in wheat fractions and their feasibility for geographical traceability: A preliminary study SO JOURNAL OF FOOD COMPOSITION AND ANALYSIS DT Article DE Food analysis; Food composition; Triticum aestivum L.; Geographical origin; Isotope ratio mass spectrometry; delta C-13; delta N-15; Milling fraction ID MASS-SPECTROMETRY; FISH-TISSUES; LAMB MEAT; DELTA-D; ORIGIN; MULTIELEMENT; GRAINS; RICE; DIFFERENTIATION; FINGERPRINTS AB The study aims to investigate the characterization of stable isotopic ratios in wheat milling fractions (bran, wheat shorts, and flour) and extracts (defatted flour, gluten, lipid, starch, and crude fiber) and correlations among different matrices for each isotope, which could provide some references for the development and application of stable isotopes for geographical traceability of wheat and its milling products. Wheat samples with three genotypes were collected from three regions in China. delta C-13 and delta N-15 values in wholemeal, milling fractions and extracts were determined. Results showed that delta C-13 varied significantly among milling fractions and extracts, while no significant difference was found among delta N-15 values of different milling fractions or extracts. Each isotope shows significantly positive correlations among wheat fractions (p < 0.01). The variations of delta C-13 and delta N-15 were most contributed by fraction and region, respectively. Therefore, delta N-15 is suitable for geographical traceability of wheat and its milling products. C1 [Liu, Hongyan; Guo, Boli; Zhang, Bo; Zhang, Yingquan; Wei, Shuai; Li, Ming; Wadood, Syed Abdul; Wei, Yimin] Chinese Acad Agr Sci, Inst Food Sci & Technol, Minist Agr, Key Lab Agroprod Proc, Beijing, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Food Science & Technology, CAAS; Ministry of Agriculture & Rural Affairs RP Wei, YM (corresponding author), Chinese Acad Agr Sci, Inst Food Sci & Technol, Minist Agr, Key Lab Agroprod Proc, Beijing, Peoples R China. EM weiyimin36@hotmail.com CR [Anonymous], 2000, 68650 ISO Asfaha DG, 2011, J CEREAL SCI, V53, P170, DOI 10.1016/j.jcs.2010.11.004 Bateman AS, 2007, ISOT ENVIRON HEALT S, V43, P237, DOI 10.1080/10256010701550732 Bateman AS, 2005, J AGR FOOD CHEM, V53, P5760, DOI 10.1021/jf050374h Beltran M, 2009, AQUACULT NUTR, V15, P9, DOI 10.1111/j.1365-2095.2008.00563.x Boner M, 2004, ANAL BIOANAL CHEM, V378, P301, DOI 10.1007/s00216-003-2347-6 Bowling DR, 2008, NEW PHYTOL, V178, P24, DOI 10.1111/j.1469-8137.2007.02342.x Brescia MA, 2002, RAPID COMMUN MASS SP, V16, P2286, DOI 10.1002/rcm.860 Chen TJ, 2016, FOOD CHEM, V209, P95, DOI 10.1016/j.foodchem.2016.04.029 Chen W., 2007, CEREALS OILS PROCESS, V6, P97 Choi WJ, 2002, PLANT SOIL, V245, P223, DOI 10.1023/A:1020475017254 Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 DENIRO MJ, 1977, SCIENCE, V197, P261, DOI 10.1126/science.327543 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Diomande D, 2015, FOOD CHEM, V188, P576, DOI 10.1016/j.foodchem.2015.05.040 Dong H, 2017, RSC ADV, V7, P18946, DOI 10.1039/c7ra00722a Dong H, 2016, J AGR FOOD CHEM, V64, P3258, DOI 10.1021/acs.jafc.6b00691 Gaston TF, 2004, J EXP MAR BIOL ECOL, V304, P17, DOI 10.1016/j.jembe.2003.11.022 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 Kawasaki A, 2002, SOIL SCI PLANT NUTR, V48, P635, DOI 10.1080/00380768.2002.10409251 Kendall C, 1998, ISOTOPE TRACERS IN CATCHMENT HYDROLOGY, P519 Liu HY, 2016, FOOD CHEM, V212, P367, DOI 10.1016/j.foodchem.2016.06.002 Liu HY, 2015, FOOD CHEM, V171, P56, DOI 10.1016/j.foodchem.2014.08.111 Araus JL, 2013, FUNCT PLANT BIOL, V40, P595, DOI 10.1071/FP12254 Luo DH, 2015, FOOD CHEM, V174, P197, DOI 10.1016/j.foodchem.2014.11.006 Mihailova A, 2015, FOOD CHEM, V173, P114, DOI 10.1016/j.foodchem.2014.10.003 Nietner T, 2014, FOOD RES INT, V60, P146, DOI 10.1016/j.foodres.2013.11.002 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Shewry PR, 2013, J AGR FOOD CHEM, V61, P8295, DOI 10.1021/jf3054092 Sotiropoulos MA, 2004, ECOL FRESHW FISH, V13, P155, DOI 10.1111/j.1600-0633.2004.00056.x Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Wu YL, 2015, FOOD CHEM, V174, P553, DOI 10.1016/j.foodchem.2014.11.096 Zhao HY, 2011, J AGR FOOD CHEM, V59, P4397, DOI 10.1021/jf200108d Zheng X.L., 2008, J HENAN U TECHNOL NA, V29, P9 NR 35 TC 11 Z9 14 U1 4 U2 36 PD JUN PY 2018 VL 69 BP 149 EP 155 DI 10.1016/j.jfca.2018.01.009 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000433014600020 DA 2022-12-14 ER PT J AU Accorsi, R Cholette, S Manzini, R Tufano, A AF Accorsi, Riccardo Cholette, Susan Manzini, Riccardo Tufano, Alessandro TI A hierarchical data architecture for sustainable food supply chain management and planning SO JOURNAL OF CLEANER PRODUCTION DT Article DE Food operations; Sustainable planning; Traceability; Food supply chain; Data architecture; JOT ID BIOFUEL PRODUCTION POTENTIALS; LIFE-CYCLE ASSESSMENT; LAND-USE COMPETITION; ENVIRONMENTAL IMPACTS; TRACEABILITY SYSTEMS; GENERAL FRAMEWORK; CULTIVATED LAND; QUALITY; DESIGN; RETAIL AB The agro-food industry is one of the largest parts of the European Union's economy and faces economic and environmental stresses. While food traceability systems (FTSs) inform supply chain actors of product and logistical attributes, large scale implementations are scarce and are do not support active decision making. We present a framework developed for FUTUREMED project used to perform a data-driven analysis that considers both micro and macro aspects of a food supply chain (FSC). With its comprehensive multiple-depth data architecture incorporated within a tailored decision-support platform, this framework and the resulting decision-support tool is the first to move beyond simple traceability implementation to the sustainable planning of food logistics, bridging the gap between research techniques and real-world data availability. We define KPIs that measure a subset of economic and environmental factors to quantify the impact of logistical decisions. We validate the framework with the case study of an Italian fruit trader that is considering opening a new warehouse. We conclude by suggesting that this framework be applied to more complex case studies and be enhanced through including more dimensions of sustainability. (C) 2018 Elsevier Ltd. All rights reserved. C1 [Accorsi, Riccardo; Manzini, Riccardo; Tufano, Alessandro] Alma Mater Studiorum Univ Bologna, Dept Ind Engn, Bologna, Italy. [Cholette, Susan] San Francisco State Univ, Decis Sci, San Francisco, CA 94132 USA. C3 University of Bologna; California State University System; San Francisco State University RP Accorsi, R (corresponding author), Alma Mater Studiorum Univ Bologna, Dept Ind Engn, Bologna, Italy. EM riccardo.accorsi2@unibo.it CR Accorsi R, 2018, INT J LOGIST-RES APP, V21, P35, DOI 10.1080/13675567.2017.1354978 Accorsi R, 2017, PROCEDIA MANUF, V11, P889, DOI 10.1016/j.promfg.2017.07.192 Accorsi R, 2017, J CLEAN PROD, V165, P917, DOI 10.1016/j.jclepro.2017.07.170 Accorsi R, 2016, J CLEAN PROD, V112, P158, DOI 10.1016/j.jclepro.2015.06.082 Accorsi R, 2015, J TRANSP GEOGR, V48, P121, DOI 10.1016/j.jtrangeo.2015.09.005 Ahumada O, 2015, HDB OPERATIONS RES A, P19 Ahumada O, 2011, INT J PROD ECON, V133, P677, DOI 10.1016/j.ijpe.2011.05.015 Ahumada O, 2009, EUR J OPER RES, V196, P1, DOI 10.1016/j.ejor.2008.02.014 Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Akkerman R, 2010, OR SPECTRUM, V32, P863, DOI 10.1007/s00291-010-0223-2 [Anonymous], 2017, HUFFINGTON POST [Anonymous], 2015, GUARDIAN Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Ayyad Z, 2017, ITAL J FOOD SCI, V29, P38, DOI 10.14674/1120-1770%2Fijfs.v483 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bartholdi JJ, 2016, MARIT ECON LOGIST, V18, P231, DOI 10.1057/mel.2016.5 Bechini A, 2005, INT FED INFO PROC, V189, P497 Bechini A, 2008, INFORM SOFTWARE TECH, V50, P342, DOI 10.1016/j.infsof.2007.02.017 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bottani E, 2008, INT J PROD ECON, V112, P548, DOI 10.1016/j.ijpe.2007.05.007 Boye JI, 2013, FOOD ENG REV, V5, P1, DOI 10.1007/s12393-012-9062-z Cholette S, 2013, J CONSUM MARK, V30, P563, DOI 10.1108/JCM-04-2013-0544 Cobuloglu HI, 2015, APPL ENERG, V140, P418, DOI 10.1016/j.apenergy.2014.11.080 Convery FJ, 2009, ENVIRON RESOUR ECON, V43, P391, DOI 10.1007/s10640-009-9275-7 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Dabbene F, 2011, COMPUT ELECTRON AGR, V75, P139, DOI 10.1016/j.compag.2010.10.009 Dai HY, 2015, INT J PROD ECON, V170, P14, DOI 10.1016/j.ijpe.2015.08.010 de Keizer M, 2017, EUR J OPER RES, V262, P535, DOI 10.1016/j.ejor.2017.03.049 Epelbaum FMB, 2014, INT J PROD ECON, V150, P215, DOI 10.1016/j.ijpe.2014.01.007 Etemadnia H, 2015, EUR J OPER RES, V244, P648, DOI 10.1016/j.ejor.2015.01.044 Fan SG, 2016, GLOB FOOD SECUR-AGR, V11, P11, DOI 10.1016/j.gfs.2016.03.005 Farahani P, 2013, INT J PROD ECON, V144, P383, DOI 10.1016/j.ijpe.2013.03.004 Fischer G, 2010, BIOMASS BIOENERG, V34, P159, DOI 10.1016/j.biombioe.2009.07.008 Fischer G, 2010, BIOMASS BIOENERG, V34, P173, DOI 10.1016/j.biombioe.2009.07.009 Gallo A, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9112044 Garrone P, 2014, FOOD POLICY, V46, P129, DOI 10.1016/j.foodpol.2014.03.014 Gerdes L, 2012, FOOD ANAL METHOD, V5, P1368, DOI 10.1007/s12161-012-9378-6 Giuseppe A, 2014, WASTE MANAGE, V34, P1306, DOI 10.1016/j.wasman.2014.02.018 Gwanpua SG, 2015, J FOOD ENG, V148, P2, DOI 10.1016/j.jfoodeng.2014.06.021 Handayati Y., 2015, LOGIST RES, V8, P1 Higgins AJ, 2010, J OPER RES SOC, V61, P964, DOI 10.1057/jors.2009.57 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Kucukvar M, 2015, J CLEAN PROD, V108, P395, DOI 10.1016/j.jclepro.2015.08.117 Kummu M, 2012, SCI TOTAL ENVIRON, V438, P477, DOI 10.1016/j.scitotenv.2012.08.092 La Scalia G, 2016, J FOOD PROCESS ENG, V39, P140, DOI 10.1111/jfpe.12207 Lebersorger S, 2014, WASTE MANAGE, V34, P1911, DOI 10.1016/j.wasman.2014.06.013 LeBlanc DI, 2015, J FOOD ENG, V147, P24, DOI 10.1016/j.jfoodeng.2014.09.026 Lenton D., 2010, BERL MUNCH TIERARZTL, V118, P377 Li Y, 2010, COMPUT IND, V61, P852, DOI 10.1016/j.compind.2010.07.010 Li Z, 2017, IND MANAGE DATA SYST, V117, P1906, DOI 10.1108/IMDS-11-2016-0489 Manzini R, 2014, BRIT FOOD J, V116, P2069, DOI 10.1108/BFJ-11-2013-0338 Manzini R, 2014, INT J PROD RES, V52, P89, DOI 10.1080/00207543.2013.828168 Manzini R, 2013, J FOOD ENG, V115, P251, DOI 10.1016/j.jfoodeng.2012.10.026 McEntire JC, 2010, COMPR REV FOOD SCI F, V9, P92, DOI 10.1111/j.1541-4337.2009.00097.x McLaughlin D, 2015, WATER RESOUR RES, V51, P4966, DOI 10.1002/2015WR017053 Notarnicola B, 2017, J CLEAN PROD, V140, P753, DOI 10.1016/j.jclepro.2016.06.080 Notarnicola B, 2017, J CLEAN PROD, V140, P399, DOI 10.1016/j.jclepro.2016.06.071 Pang ZB, 2015, INFORM SYST FRONT, V17, P289, DOI 10.1007/s10796-012-9374-9 Penazzi S, 2017, INT J LOGIST MANAG, V28, P782, DOI 10.1108/IJLM-11-2015-0204 Pizzuti T, 2015, J FOOD ENG, V159, P16, DOI 10.1016/j.jfoodeng.2015.03.001 Pizzuti T, 2014, J FOOD ENG, V120, P17, DOI 10.1016/j.jfoodeng.2013.07.017 Rathmann R, 2010, RENEW ENERG, V35, P14, DOI 10.1016/j.renene.2009.02.025 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Roy P, 2009, J FOOD ENG, V90, P1, DOI 10.1016/j.jfoodeng.2008.06.016 Saak AE, 2016, INT J PROD ECON, V177, P149, DOI 10.1016/j.ijpe.2016.04.008 Sadler RC, 2016, GEOJOURNAL, V81, P443, DOI 10.1007/s10708-015-9634-6 Sala S, 2017, J CLEAN PROD, V140, P387, DOI 10.1016/j.jclepro.2016.09.054 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Solanki M, 2014, INT J SEMANT WEB INF, V10, P45, DOI 10.4018/IJSWIS.2014070102 Sorrell S, 2009, ENERG POLICY, V37, P29, DOI 10.1016/j.enpol.2008.08.009 Soto-Silva WE, 2016, EUR J OPER RES, V251, P345, DOI 10.1016/j.ejor.2015.08.046 Storoy J, 2013, J FOOD ENG, V115, P41, DOI 10.1016/j.jfoodeng.2012.09.018 Suh NP., 2001, AXIOMATIC DESIGN ADV Thomas B., 2010, WORLD ACAD SCI ENG T, V67, P19 Ting SL, 2014, INT J PROD ECON, V152, P200, DOI 10.1016/j.ijpe.2013.12.010 Tsolakis NK, 2014, BIOSYST ENG, V120, P47, DOI 10.1016/j.biosystemseng.2013.10.014 Valli E, 2013, RIV ITAL SOSTANZE GR, V90, P163 Verdouw CN, 2016, J FOOD ENG, V176, P128, DOI 10.1016/j.jfoodeng.2015.11.009 Yan B, 2016, IND MANAGE DATA SYST, V116, P1397, DOI 10.1108/IMDS-12-2015-0512 Yang X. W., 2014, Advances in Materials Science and Engineering, DOI 10.1155/2014/697170 Zhang JR, 2014, COMPR REV FOOD SCI F, V13, P1074, DOI 10.1111/1541-4337.12103 Zhang YF, 2017, IND MANAGE DATA SYST, V117, P1890, DOI 10.1108/IMDS-10-2016-0456 NR 84 TC 49 Z9 49 U1 1 U2 63 PD DEC 1 PY 2018 VL 203 BP 1039 EP 1054 DI 10.1016/j.jclepro.2018.08.275 WC Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences SC Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology UT WOS:000447568700083 DA 2022-12-14 ER PT J AU Nasr, EG Epova, EN Sebilo, M Lariviere, D Hammami, M Souissi, R Abderrazak, H Donard, OFX AF Nasr, Emna G. Epova, Ekaterina N. Sebilo, Mathieu Lariviere, Dominic Hammami, Mohamed Souissi, Radhia Abderrazak, Houyem Donard, Olivier F. X. TI Olive Oil Traceability Studies Using Inorganic and Isotopic Signatures: A Review SO MOLECULES DT Review DE olive oil; geographical authentication; trace elements; stable isotopes of light elements; Sr-87; Sr-86; sample preparation; detection techniques; statistical data treatment ID FATTY-ACID COMPOSITIONS; ULTRASOUND-ASSISTED EXTRACTION; GEOGRAPHICAL ORIGIN; EDIBLE OILS; TRACE-ELEMENTS; STABLE-ISOTOPE; MASS-SPECTROMETRY; MULTIELEMENTAL ANALYSIS; OXIDATIVE STABILITY; VEGETABLE-OILS AB The olive oil industry is subject to significant fraudulent practices that can lead to serious economic implications and even affect consumer health. Therefore, many analytical strategies have been developed for olive oil's geographic authentication, including multi-elemental and isotopic analyses. In the first part of this review, the range of multi-elemental concentrations recorded in olive oil from the main olive oil-producing countries is discussed. The compiled data from the literature indicates that the concentrations of elements are in comparable ranges overall. They can be classified into three categories, with (1) Rb and Pb well below 1 mu g kg(-1); (2) elements such as As, B, Mn, Ni, and Sr ranging on average between 10 and 100 mu g kg(-1); and (3) elements including Cr, Fe, and Ca ranging between 100 to 10,000 mu g kg(-1). Various sample preparations, detection techniques, and statistical data treatments were reviewed and discussed. Results obtained through the selected analytical approaches have demonstrated a strong correlation between the multi-elemental composition of the oil and that of the soil in which the plant grew. The review next focused on the limits of olive oil authentication using the multi-elemental composition method. Finally, different methods based on isotopic signatures were compiled and critically assessed. Stable isotopes of light elements have provided acceptable segregation of oils from different origins for years already. More recently, the determination of stable isotopes of strontium has proven to be a reliable tool in determining the geographical origin of food products. The ratio Sr-87/Sr-86 is stable over time and directly related to soil geology; it merits further study and is likely to become part of the standard tool kit for olive oil origin determination, along with a combination of different isotopic approaches and multi-elemental composition. C1 [Nasr, Emna G.; Sebilo, Mathieu; Donard, Olivier F. X.] Univ Pau & Pays Adour, Inst Sci Analyt & Physicochim Environm & Mat, F-64000 Pau, France. [Nasr, Emna G.; Hammami, Mohamed; Souissi, Radhia; Abderrazak, Houyem] Inst Natl Rech & Anal Physicochim, Lab Mat Utiles, Technopole Sidi Thabet, Ariana 2020, Tunisia. [Nasr, Emna G.] Univ Tunis El Manar, Fac Sci, Farhat Hached Univ Campus, Tunis 1068, Tunisia. [Epova, Ekaterina N.] Adv Isotop Anal Helioparc, F-64000 Pau, France. [Sebilo, Mathieu] Sorbonne Univ, Inst Ecol & Sci Environm Paris IEES Paris, CNRS, F-75005 Paris, France. [Lariviere, Dominic] Univ Laval, Dept Chim, Quebec City, PQ G1V 0A6, Canada. C3 Universite de Pau et des Pays de l'Adour; Institut National de Recherche d'Analyse Physico-Chimique; Universite de Tunis-El-Manar; Faculte des Sciences de Tunis (FST); Centre National de la Recherche Scientifique (CNRS); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; Universite Paris-Est-Creteil-Val-de-Marne (UPEC); Laval University RP Nasr, EG (corresponding author), Univ Pau & Pays Adour, Inst Sci Analyt & Physicochim Environm & Mat, F-64000 Pau, France.; Nasr, EG (corresponding author), Inst Natl Rech & Anal Physicochim, Lab Mat Utiles, Technopole Sidi Thabet, Ariana 2020, Tunisia.; Nasr, EG (corresponding author), Univ Tunis El Manar, Fac Sci, Farhat Hached Univ Campus, Tunis 1068, Tunisia. EM emna.nsr@gmail.com; ekaterina.epova@ai-analysis.com; mathieu.sebilo@sorbonne-universite.fr; dominic.lariviere@chm.ulaval.ca; mohamed.hammami@inrap.rnrt.tn; souissiradhia@yahoo.fr; houyem.snani@yahoo.fr; olivier.donard@univ-pau.fr CR Abdi H, 2010, WIRES COMPUT STAT, V2, P433, DOI 10.1002/wics.101 Aceto M, 2019, FOOD CHEM, V298, DOI 10.1016/j.foodchem.2019.125047 ALLOWAY BJ, 1971, GEODERMA, V5, P197, DOI 10.1016/0016-7061(71)90009-7 Angerosa F, 1999, J AGR FOOD CHEM, V47, P1013, DOI 10.1021/jf9809129 Angioni A, 2006, FOOD CHEM, V99, P525, DOI 10.1016/j.foodchem.2005.08.016 Bajoub A, 2015, J AGR FOOD CHEM, V63, P4376, DOI 10.1021/jf506097u Bakircioglu D, 2013, FOOD CHEM, V138, P770, DOI 10.1016/j.foodchem.2012.10.089 Banerjee S, 2015, J FOOD COMPOS ANAL, V42, P98, DOI 10.1016/j.jfca.2015.03.011 Beltran M, 2015, FOOD CHEM, V169, P350, DOI 10.1016/j.foodchem.2014.07.104 Benincasa C, 2007, ANAL CHIM ACTA, V585, P366, DOI 10.1016/j.aca.2006.12.040 Benincasa C, 2012, SCI WORLD J, DOI 10.1100/2012/535781 Bimbo F, 2019, AUST J AGR RESOUR EC, V63, P701, DOI 10.1111/1467-8489.12318 Bottino A, 2008, EUR J LIPID SCI TECH, V110, P1109, DOI 10.1002/ejlt.200800075 Brkljaca M, 2013, ANAL LETT, V46, P2912, DOI 10.1080/00032719.2013.814056 Cabrera-Vique C, 2012, FOOD CHEM, V134, P434, DOI 10.1016/j.foodchem.2012.02.088 Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Camin F, 2016, FOOD CHEM, V196, P98, DOI 10.1016/j.foodchem.2015.08.132 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Capo RC, 1998, GEODERMA, V82, P197, DOI 10.1016/S0016-7061(97)00102-X Carini F, 2001, J ENVIRON RADIOACTIV, V52, P215, DOI 10.1016/S0265-931X(00)00034-5 Cernusak LA, 2013, NEW PHYTOL, V200, P950, DOI 10.1111/nph.12423 Cesur H, 2002, TURK J CHEM, V26, P599 Chiavaro E, 2011, EUR J LIPID SCI TECH, V113, P1509, DOI 10.1002/ejlt.201100174 Chiocchini F, 2016, FOOD CHEM, V202, P291, DOI 10.1016/j.foodchem.2016.01.146 Cindric IJ, 2007, MICROCHEM J, V85, P136, DOI 10.1016/j.microc.2006.04.011 Coelho I, 2017, TRAC-TREND ANAL CHEM, V90, P45, DOI 10.1016/j.trac.2017.02.005 Costa LM, 2001, SPECTROCHIM ACTA B, V56, P1981, DOI 10.1016/S0584-8547(01)00308-1 Dabbou S, 2010, J AM OIL CHEM SOC, V87, P1199, DOI 10.1007/s11746-010-1600-3 Damak F, 2019, METHOD PROTOCOL, V2, DOI 10.3390/mps2030072 Damak F, 2019, FOOD CHEM, V283, P656, DOI 10.1016/j.foodchem.2019.01.082 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 De Leonardis A, 2000, INT J FOOD SCI TECH, V35, P371, DOI 10.1046/j.1365-2621.2000.00389.x Dekhili S, 2011, FOOD QUAL PREFER, V22, P757, DOI 10.1016/j.foodqual.2011.06.005 dos Santos EJ, 2007, J ANAL ATOM SPECTROM, V22, P1300, DOI 10.1039/b702563g Ekezie FGC, 2017, TRENDS FOOD SCI TECH, V67, P160, DOI 10.1016/j.tifs.2017.06.006 Elflein L, 2008, APIDOLOGIE, V39, P574, DOI 10.1051/apido:2008042 Epova EN, 2019, FOOD CHEM, V294, P35, DOI 10.1016/j.foodchem.2019.04.068 Fausto L., 2000, HDB OLIVE OIL Flori L, 2019, NUTRIENTS, V11, DOI 10.3390/nu11091962 Frankel EN, 2010, J AGR FOOD CHEM, V58, P5991, DOI 10.1021/jf1007677 Garcia-Gonzalez DL, 2004, EUR FOOD RES TECHNOL, V218, P484, DOI 10.1007/s00217-003-0855-4 Gertz C, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201900281 Gonzalvez A, 2013, COMP ANAL C, V60, P51, DOI 10.1016/B978-0-444-59562-1.00003-7 Goodarzi F, 2019, CAN J CHEM ENG, V97, P281, DOI 10.1002/cjce.23336 Gouvinhas I, 2016, J AM OIL CHEM SOC, V93, P813, DOI 10.1007/s11746-016-2827-4 Granato D, 2018, COMPR REV FOOD SCI F, V17, P663, DOI 10.1111/1541-4337.12341 Greenough JD, 2010, CAN J EARTH SCI, V47, P1093, DOI 10.1139/E10-055 Gumus ZP, 2017, EUR FOOD RES TECHNOL, V243, P1719, DOI 10.1007/s00217-017-2876-4 Gupta N, 2019, SCI TOTAL ENVIRON, V651, P2927, DOI 10.1016/j.scitotenv.2018.10.047 Higgins P., 2018, METHODS PALEOECOLOGY, P99 Jimenez MS, 2003, J ANAL ATOM SPECTROM, V18, P1154, DOI 10.1039/b303131d Kalogiouri NP, 2020, ANAL CHIM ACTA, V1134, P150, DOI 10.1016/j.aca.2020.07.029 Kara D, 2015, TALANTA, V144, P219, DOI 10.1016/j.talanta.2015.05.056 Kara D, 2015, FOOD CHEM, V188, P143, DOI 10.1016/j.foodchem.2015.04.057 Karabagias I, 2013, FOOD RES INT, V54, P1950, DOI 10.1016/j.foodres.2013.09.023 Karalis P, 2020, MOLECULES, V25, DOI 10.3390/molecules25245816 Kelly S. D., 2003, Food authenticity and traceability, P156, DOI 10.1533/9781855737181.1.156 Khan A, 2015, ENVIRON SCI POLLUT R, V22, P13772, DOI 10.1007/s11356-015-4881-0 Khan MA, 2017, SCI TOTAL ENVIRON, V601, P1591, DOI 10.1016/j.scitotenv.2017.06.030 Lauteri M, 2004, J EVOLUTION BIOL, V17, P1286, DOI 10.1111/j.1420-9101.2004.00765.x Lepri FG, 2011, APPL SPECTROSC REV, V46, P175, DOI 10.1080/05704928.2010.529628 Liu HY, 2016, FOOD CHEM, V212, P367, DOI 10.1016/j.foodchem.2016.06.002 Liu HC, 2014, FOOD CHEM, V142, P439, DOI 10.1016/j.foodchem.2013.07.082 Llorent-Martinez EJ, 2011, FOOD CHEM, V127, P1257, DOI 10.1016/j.foodchem.2011.01.064 Lozano-Sanchez J, 2010, TRENDS FOOD SCI TECH, V21, P201, DOI 10.1016/j.tifs.2009.12.004 Malechaux A, 2020, FOODS, V9, DOI 10.3390/foods9050556 Manjusha R, 2019, FOOD CHEM, V294, P384, DOI 10.1016/j.foodchem.2019.04.104 Markhali FS, 2021, PROCESSES, V9, DOI 10.3390/pr9060953 Martinez S, 2020, J ANAL ATOM SPECTROM, V35, P1897, DOI 10.1039/d0ja00112k Mathieu A, 2008, ENVIRON RISQUE SANTE, V7, P112, DOI 10.1684/ers.2008.0142 Matthaus B, 2019, OCL OILS FAT CROP LI, V26, DOI 10.1051/ocl/2019010 Mdluli NS, 2022, CRIT REV ANAL CHEM, V52, P1, DOI 10.1080/10408347.2020.1781591 Medini S., 2015, THESIS U AIX MARSEIL Medini S, 2015, FOOD CHEM, V171, P78, DOI 10.1016/j.foodchem.2014.08.121 Menapace L, 2011, EUR REV AGRIC ECON, V38, P193, DOI 10.1093/erae/jbq051 Mohebbi M, 2018, J ANAL CHEM+, V73, P30, DOI 10.1134/S1061934818010069 Nasr EG, 2022, FOODS, V11, DOI 10.3390/foods11010082 Noorali M, 2014, J AM OIL CHEM SOC, V91, P1571, DOI 10.1007/s11746-014-2497-z Pin C, 2003, J ANAL ATOM SPECTROM, V18, P135, DOI 10.1039/b211832g Poljsak N, 2020, PHYTOTHER RES, V34, P254, DOI 10.1002/ptr.6524 Portarena S, 2015, FOOD CONTROL, V57, P129, DOI 10.1016/j.foodcont.2015.03.052 Portarena S, 2014, FOOD CHEM, V164, P12, DOI 10.1016/j.foodchem.2014.04.115 Poscic F, 2019, J FOOD COMPOS ANAL, V77, P39, DOI 10.1016/j.jfca.2019.01.002 Quintanilla-Casas B, 2020, FOOD CHEM, V307, DOI 10.1016/j.foodchem.2019.125556 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Rummel S, 2010, FOOD CHEM, V118, P890, DOI 10.1016/j.foodchem.2008.05.115 Rutkowska M., 2017, APPL GREEN SOLVENTS, P301, DOI [10.1016/B978-0-12-805297-6.00010-3, DOI 10.1016/B978-0-12-805297-6.00010-3] Savio M, 2014, FOOD CHEM, V159, P433, DOI 10.1016/j.foodchem.2014.03.041 Sayago A, 2018, FOOD CHEM, V261, P42, DOI 10.1016/j.foodchem.2018.04.019 Schimmelmann A, 2020, J AGR FOOD CHEM, V68, P10852, DOI 10.1021/acs.jafc.0c02610 Shahzad B, 2017, ENVIRON SCI POLLUT R, V24, P103, DOI 10.1007/s11356-016-7898-0 Shears P, 2010, BRIT FOOD J, V112, P198, DOI 10.1108/00070701011018879 Skrzydlewska E, 2003, ANAL CHIM ACTA, V479, P191, DOI 10.1016/S0003-2670(02)01527-1 Stewart BW, 1998, GEODERMA, V82, P173, DOI 10.1016/S0016-7061(97)00101-8 Tarapoulouzi M, 2021, FOODS, V10, DOI 10.3390/foods10020336 Techer I, 2017, APPL GEOCHEM, V82, P1, DOI 10.1016/j.apgeochem.2017.05.010 Tres A, 2013, COMP ANAL C, V60, P543, DOI 10.1016/B978-0-444-59562-1.00021-9 Valasques GS, 2017, APPL SPECTROSC REV, V52, P729, DOI 10.1080/05704928.2017.1294599 Viola P, 2009, CLIN DERMATOL, V27, P159, DOI 10.1016/j.clindermatol.2008.01.008 Voerkelius S, 2010, FOOD CHEM, V118, P933, DOI 10.1016/j.foodchem.2009.04.125 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 WINKLER FJ, 1980, Z LEBENSM UNTERS FOR, V171, P85, DOI 10.1007/BF01140746 Woods GD, 2007, ANAL BIOANAL CHEM, V389, P753, DOI 10.1007/s00216-007-1439-0 Yan J, 2020, FOOD CONTROL, V111, DOI 10.1016/j.foodcont.2019.107081 Yorulmaz HO, 2017, J FOOD SCI TECH MYS, V54, P4067, DOI 10.1007/s13197-017-2879-y Youseff R., 2014, INT J PHARM SCI REV, V28, P229 Zeiner M, 2005, MICROCHEM J, V81, P171, DOI 10.1016/j.microc.2004.12.002 NR 108 TC 1 Z9 1 U1 4 U2 12 PD MAR PY 2022 VL 27 IS 6 AR 2014 DI 10.3390/molecules27062014 WC Biochemistry & Molecular Biology; Chemistry, Multidisciplinary SC Biochemistry & Molecular Biology; Chemistry UT WOS:000774459700001 DA 2022-12-14 ER PT J AU Ejeahalaka, KK Cheng, L Kulasiri, D Edwards, GR On, SLW AF Ejeahalaka, K. K. Cheng, L. Kulasiri, D. Edwards, G. R. On, S. L. W. TI Efficacy of near infrared spectroscopy to segregate raw milk from individual cows between herds for product innovation and traceability SO QUALITY ASSURANCE AND SAFETY OF CROPS & FOODS DT Article DE milk segregation; near infrared spectroscopy; partial least square; soft independent modelling ID PARTIAL LEAST-SQUARES; FATTY-ACIDS; QUALITY; QUANTIFICATION; AUTHENTICATION; PARAMETERS; REGRESSION; RESOLUTION; CHEESE; NIRS AB Cows with specialised characteristics and requirements can be aggregated into different herds for targeted nutritional management and to facilitate on-farm segregation of raw milk for the production of high-value niche dairy products, offering improved economic returns. Rapid methods for independent verification of product quality and origin are desirable to support validation and traceability of such products. This study examined the use of near infrared spectroscopy (NIRS) to segregate raw milk from individual cows of multiple breeds from different herds fed on the same or differing feeding regimes, and to correlate and evaluate the efficacy of the predictions for crude protein and the milk fatty acid (FA) phenotypes for each of the herds. Reference values and near infrared spectra were obtained from representative freeze-dried raw milk samples (n = 220) collected from 847 lactating cows of 3 breeds from the Lincoln University dairy farm in New Zealand. The feed sources (i.e. pasture or pasture with lucerne silage) significantly influenced the protein and the FA values, and these differences were reflected in NIRS analyses. The partial least square regression models for crude protein determination showed excellent results, whereas for the most dominant FA, they were not appreciable. Maximum separation was obtained between the herds on the same feeding regime (mean specificity = 95.2%) using the partial least square discriminant analysis, and its overall performance in differentiating the objects was better than that of the soft independent modelling of class analogy. The multiclass analyses conducted in this study offer improvements to current approaches for evaluating and validating raw milk for the manufacture of specific dairy products, and for enhancing product traceability. C1 [Ejeahalaka, K. K.; Kulasiri, D.; On, S. L. W.] Lincoln Univ, Dept Wine Food & Mol Biosci, POB 85084, Canterbury, New Zealand. [Cheng, L.] Univ Melbourne, Fac Vet & Agr Sci, Dookie Campus, Melbourne, Vic 3647, Australia. [Edwards, G. R.] Lincoln Univ, Dept Agr Sci, POB 85084, Canterbury, New Zealand. C3 Lincoln University - New Zealand; University of Melbourne; Lincoln University - New Zealand RP On, SLW (corresponding author), Lincoln Univ, Dept Wine Food & Mol Biosci, POB 85084, Canterbury, New Zealand. EM stephen.on@lincoln.ac.nz CR Andueza D, 2013, FOOD CHEM, V141, P209, DOI 10.1016/j.foodchem.2013.02.086 Ballabio D, 2009, INFRARED SPECTROSCOPY FOR FOOD QUALITY ANALYSIS AND CONTROL, P83, DOI 10.1016/B978-0-12-374136-3.00004-3 Barker M, 2003, J CHEMOMETR, V17, P166, DOI 10.1002/cem.785 Carr B. T., 1999, SENSORY EVALUATION T Chan Y H, 2003, Singapore Med J, V44, P614 Collomb M, 2002, INT DAIRY J, V12, P649, DOI 10.1016/S0958-6946(02)00061-4 Coppa M, 2012, J DAIRY SCI, V95, P5544, DOI 10.3168/jds.2011-5272 de la Roza-Delgado B, 2017, FOOD CONTROL, V76, P74, DOI 10.1016/j.foodcont.2017.01.004 Dierking RM, 2010, CROP SCI, V50, P391, DOI 10.2135/cropsci2008.12.0741 Dooley AE, 2005, AGR SYST, V85, P82, DOI 10.1016/j.agsy.2004.07.012 Ejeahalaka KK, 2020, FOOD CHEM, V309, DOI 10.1016/j.foodchem.2019.125785 Ejeahalaka KK, 2019, FOOD CHEM, V295, P198, DOI 10.1016/j.foodchem.2019.05.120 Fleming A, 2017, J DAIRY SCI, V100, P5073, DOI 10.3168/jds.2016-12102 FRANKHUIZEN R, 2001, HDB NEAR INFRARED AN, P00499 Heinrichs J., 1997, DAIRY ANIMAL SCI FAC, V5, p1e Hurtaud C, 2014, DAIRY SCI TECHNOL, V94, P103, DOI 10.1007/s13594-013-0147-0 Karoui R, 2006, LAIT, V86, P83, DOI 10.1051/lait:2005040 Katz G, 2016, J DAIRY SCI, V99, P4178, DOI 10.3168/jds.2015-10599 Lavine B, 2004, ANAL CHEM, V76, P3365, DOI 10.1021/ac040053p Marchitelli C, 2013, J DAIRY RES, V80, P165, DOI 10.1017/S002202991300006X MARTENS H, 1991, J PHARMACEUT BIOMED, V9, P625, DOI 10.1016/0731-7085(91)80188-F Martin B., 2004, Land use systems in grassland dominated regions. Proceedings of the 20th General Meeting of the European Grassland Federation, Luzern, Switzerland, 21-24 June 2004, P876 Moriasi DN, 2007, T ASABE, V50, P885, DOI 10.13031/2013.23153 Mouazen AM, 2009, BIOSYST ENG, V104, P353, DOI 10.1016/j.biosystemseng.2009.08.001 Norgaard L, 2000, APPL SPECTROSC, V54, P413, DOI 10.1366/0003702001949500 Nunez-Sanchez N, 2016, FOOD CHEM, V190, P244, DOI 10.1016/j.foodchem.2015.05.083 Oliveri P, 2017, ANAL CHIM ACTA, V982, P9, DOI 10.1016/j.aca.2017.05.013 Oliveri P, 2012, TRAC-TREND ANAL CHEM, V35, P74, DOI 10.1016/j.trac.2012.02.005 Powers D. M. W., 2011, J MACH LEARN TECHNOL, V2, P37 R Core Team, 2021, R R PROJECT STAT COM, DOI DOI 10.1007/978-3-540-74686-7 Rugoho I, 2014, NEW ZEAL J AGR RES, V57, P165, DOI 10.1080/00288233.2014.899505 SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 Tsenkova R, 2000, J ANIM SCI, V78, P515 Wishart DS, 2007, BRIEF BIOINFORM, V8, P279, DOI 10.1093/bib/bbm030 Wold H., 1966, MULTIVARIATE ANAL, P391 WOLD S, 1984, SIAM J SCI STAT COMP, V5, P735, DOI 10.1137/0905052 Wold S., 1977, CHEMOMETRICS THEORY, V52, P243, DOI [DOI 10.1021/BK-1977-0052.CH012, 10.1021/bk-1977-0052.ch012] WOODWARD S, 2010, P NZ SOC ANIMAL PROD, P57 Zimmermann B, 2013, APPL SPECTROSC, V67, P892, DOI 10.1366/12-06723 NR 39 TC 2 Z9 2 U1 1 U2 3 PY 2020 VL 12 IS 3 BP 1 EP 11 DI 10.15586/qas.v12i3.659 WC Food Science & Technology SC Food Science & Technology UT WOS:000607541400001 DA 2022-12-14 ER PT J AU Zazo-Moratalla, A Troncoso-Gonzalez, I Moreira-Munoz, A AF Zazo-Moratalla, Ana Troncoso-Gonzalez, Isidora Moreira-Munoz, Andres TI Regenerative Food Systems to Restore Urban-Rural Relationships: Insights from the Concepcion Metropolitan Area Foodshed (Chile) SO SUSTAINABILITY DT Article DE Local Food Systems; diversity; flexibility; self-reliance; local scale ID AGRICULTURE; PLACE; GLOBALIZATION; PARIS; CITY AB Cities, in recent years, have seen their functional and metabolic relationships with their agrarian hinterland being either broken off completely or substantially damaged. Within this context, Local Food Systems (LFS) can play a key role in restoring the supply relationships under regenerative assumptions. This paper analyses LFS within the Concepcion Metropolitan Area (CMA) as a representative case of Metropolitan Areas in Chile. The aim of the paper is to evaluate whether LFS are regenerating sustainable rural-urban relationships, and to accomplish this goal, foodsheds have been used as a methodological tool to both characterise and represent food traceability. For this purpose, three quantitative foodshed indicators have been applied and three qualitative spatial analytical categories of the Regenerative Food Systems (RFS) defined to decode the behaviour of LFS in the CMA. The proposed method has been successful as an initial exploratory attempt to characterize the regenerative potential of RFS. The results highlight that LFS in the CMA are certainly restoring relationships between the city and its surrounding farmland by establishing new and renewed supply linkages. Further, the application of this method has shed light on some key aspects that show how an LFS is being converted into a potential RFS. C1 [Zazo-Moratalla, Ana; Troncoso-Gonzalez, Isidora] Univ Bio Bio, Dept Planning & Urban Design, Av Collao 1202, Concepcion 4030000, Chile. [Moreira-Munoz, Andres] Pontificia Univ Catolica Valparaiso, Inst Geog, Av Brasil 2241, Valparaiso 2340000, Chile. C3 Universidad del Bio-Bio; Pontificia Universidad Catolica de Valparaiso RP Zazo-Moratalla, A (corresponding author), Univ Bio Bio, Dept Planning & Urban Design, Av Collao 1202, Concepcion 4030000, Chile. EM azazo@ubiobio.cl; itroncoso@icloud.com; andres.moreira@pucv.cl CR Aguayo M, 2009, REV CHIL HIST NAT, V82, P361, DOI 10.4067/S0716-078X2009000300004 Aliste Almuna Enrique, 2012, Rev. geogr. Norte Gd., P5 Alvarez del Valle L., 2017, TERRITORIOS FORMACIO, P3, DOI [10.20868/tf.2017.12.3645, DOI 10.20868/TF.2017.12.3645] [Anonymous], 2010, LOCAL FOOD SYSTEMS C BENGOA J, 2017, ANALES, V12, P73, DOI DOI 10.5354/0717-8883.2017.47176 Billen G, 2012, REG ENVIRON CHANGE, V12, P325, DOI 10.1007/s10113-011-0244-7 Billen G, 2009, REG ENVIRON CHANGE, V9, P13, DOI 10.1007/s10113-008-0051-y Breitbach C, 2007, LANDSCAPE RES, V32, P533, DOI 10.1080/01426390701552696 Brunori G, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8050449 Bryant C.R., 1992, AGR CITYS COUNTRYSID Butt A, 2013, GEOGR RES-AUST, V51, P204, DOI 10.1111/1745-5871.12005 Canales Alejandro I, 2013, Polis, V12, P31 Cardoso AS, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9112003 Chisholm M., 1962, RURAL SETTLEMENT LAN Cid Aguayo B., 2011, AGROALIMENTARIA, V20, P15 Aguayo BC, 2015, ANN ASSOC AM GEOGR, V105, P397, DOI 10.1080/00045608.2014.985626 Cid B., 2014, AGROALIMENTARIA, V20, P65 Curran-Cournane F, 2016, LAND USE POLICY, V58, P241, DOI 10.1016/j.landusepol.2016.07.031 Dahlberg K., 2006, MANAGEMENT AGR FORES, V2, P172 Dahlberg K. A., 1993, Food for the future: conditions and contradictions of sustainability., P75 DAHLBERG KA, 1994, FUTURES, V26, P170, DOI 10.1016/0016-3287(94)90106-6 DELGADO M, 2010, REV EC CRITICA, V10, P32 Dias BD, 2019, MANAGEMENT AND APPLICATIONS OF COMPLEX SYSTEMS, P147, DOI 10.2495/DNE-V13-N3-315-323 Dockemdorff E, 2000, ENVIRON URBAN, V12, P171, DOI 10.1177/095624780001200112 du Plessis C, 2012, BUILD RES INF, V40, P7, DOI 10.1080/09613218.2012.628548 DUBBELING M, 2016, ROLE PRIVATE SECTOR Duram L, 2010, RENEW AGR FOOD SYST, V25, P99, DOI 10.1017/S1742170510000104 Eriksen SN, 2013, ACTA AGR SCAND B-S P, V63, P47, DOI 10.1080/09064710.2013.789123 Feagan R, 2007, PROG HUM GEOG, V31, P23, DOI 10.1177/0309132507073527 Feenstra G. W., 1997, American Journal of Alternative Agriculture, V12, P28, DOI 10.1017/S0889189300007165 Fonte M, 2008, SOCIOL RURALIS, V48, P200, DOI 10.1111/j.1467-9523.2008.00462.x Freudenberger C., 1988, PRO REGE, V16, P15 Galzki JC, 2017, J AGRIC FOOD SYST CO, V7, P181, DOI 10.5304/jafscd.2017.073.013 Galzki JC, 2015, RENEW AGR FOOD SYST, V30, P364, DOI 10.1017/S1742170514000039 Getz A., 1991, PERMAC ACT, V24, P26 Gibson-Graham J.K., 2017, RETOMEMOS EC GUIA ET Giombolini KJ, 2011, AGR HUM VALUES, V28, P247, DOI 10.1007/s10460-010-9282-x Hedden W. P., 1929, GREAT CITIES ARE FED Henriquez C., 2017, INVESTIG GEOGR CHILE, V54, P5, DOI [10.5354/0719-5370.2017.48039, DOI 10.5354/0719-5370.2017.48039] Hinrichs Clare., 2015, J ENVIRON STUD SCI, V6, P759, DOI [DOI 10.1007/S13412-015-0266-4, 10.1007/s13412-015-0266-4] Holling C. S., 2002, PANARCHY UNDERSTANDI, P25 Hu GP, 2011, J AGRIC FOOD SYST CO, V2, P195, DOI 10.5304/jafscd.2011.021.004 Jacobs J., 1961, DEATH LIFE GT AM CIT Jennings S., 2015, FOOD URBANIZED WORLD Kennedy C, 2007, J IND ECOL, V11, P43, DOI 10.1162/jie.2007.1107 Kloppenburg J. Jr., 1996, Agriculture and Human Values, V13, P33, DOI 10.1007/BF01538225 Knieling J, 2017, SPRING TRACT CIV ENG, P31, DOI 10.1007/978-3-319-41022-7_4 Knight L, 2010, INT J AGR SUSTAIN, V8, P116, DOI 10.3763/ijas.2009.0478 Kremer P, 2011, J AGRIC FOOD SYST CO, V2, P171, DOI 10.5304/jafscd.2012.022.005 Kremer P, 2011, APPL GEOGR, V31, P1252, DOI 10.1016/j.apgeog.2011.01.007 Marchant C., 2016, WELT VERSTEHEN GEOGR, P221 Maturana F., 2017, CYBERGEO EUR J GEOGR, V43 McKenzie FH, 2005, AUST J AGR RES, V56, P537, DOI 10.1071/AR04197 McMichael P, 2009, J PEASANT STUD, V36, P139, DOI 10.1080/03066150902820354 MINSAL, 2016, MARC CONC FACT COND Moreira-Munoz A., 2016, WELT VERSTEHEN GEOGR, P235 Mumford Lewis, 1961, CITY HIST ITS ORIGIN, DOI DOI 10.1016/j.jaa.2011.06.005 ODEPA, 2012, IMP EXP URB SECT AGR Oosterveer P., 2012, FOOD GLOBALIZATION S Panez-Pinto A, 2018, BITACORA URBANO TERR, V28, P153, DOI 10.15446/bitacora.v28n3.72210 Paredes M, 2016, ICONOS, V20, P11 Paul V, 2013, LAND USE POLICY, V30, P94, DOI 10.1016/j.landusepol.2012.02.009 Peters CJ, 2009, RENEW AGR FOOD SYST, V24, P72, DOI 10.1017/S1742170508002457 Piorr A., 2011, PERI URBANISATION EU Pretty J., 2000, International Journal of Agricultural Resources, Governance and Ecology, V1, P77, DOI 10.1504/IJARGE.2000.006912 Proctor F., 2016, WORKING PAPER SERIES, V194 Quinlan AE, 2016, J APPL ECOL, V53, P677, DOI 10.1111/1365-2664.12550 Ramon M., 1976, REV GEOGRAFIA, V10, P11 Rodriguez B., 2005, AN 10 ENC GEOGR AM L Rojas C, 2015, REV GEOGR NORTE GD, P181, DOI 10.4067/S0718-34022015000200010 ROJAS QUEZADA CAROLINA ALEJANDRA, 2009, EURE (Santiago), V35, P47 Sali G, 2014, ADV ENG FORUM, V11, P259, DOI 10.4028/www.scientific.net/AEF.11.259 Sarricolea P, 2017, J MAPS, V13, P66, DOI 10.1080/17445647.2016.1259592 Sasaki Y., 2003, J ARTIFICAL SOC SOCI, V6, P9 SERCOTEC, 2016, CAT NAC FER LIBR Servicio Nacional de Aduanas, 2016, REG IMP EXP SINCLAIR R, 1967, ANN ASSOC AM GEOGR, V57, P72, DOI 10.1111/j.1467-8306.1967.tb00591.x Soja Edward, 2000, POSTMETROPOLIS CRITI Sonnino R, 2013, ACTA AGR SCAND B-S P, V63, P2, DOI 10.1080/09064710.2013.800130 Stagl S., 2002, EMPIRICA, V29, P145, DOI [10.1023/A:1015656400998, DOI 10.1023/A:1015656400998, DOI 10.1023/A] Steel C., 2008, HUNGRY CITY FOOD SHA Stohr W., 1984, IIR DISCUSS PAP, V19, P1 Sundkvist A, 2005, FOOD POLICY, V30, P224, DOI 10.1016/j.foodpol.2005.02.003 Swiader M, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10030882 Tedesco C, 2017, FOOD POLICY, V69, P35, DOI 10.1016/j.foodpol.2017.03.006 Tendall DM, 2015, GLOB FOOD SECUR-AGR, V6, P17, DOI 10.1016/j.gfs.2015.08.001 Torreggiani D, 2012, CITIES, V29, P412, DOI 10.1016/j.cities.2011.12.006 Tsuchiya K, 2015, LANDSCAPE URBAN PLAN, V143, P192, DOI 10.1016/j.landurbplan.2015.07.008 Van der Lans C.J.M., 2011, GTB1072 MIN EC AFF A Wilhelmina Q, 2010, INT J CONSUM STUD, V34, P357, DOI 10.1111/j.1470-6431.2010.00868.x Wiskerke J. S. C., 2004, Agrarwirtschaft und Agrarsoziologie, P39 Wiskerke JSC, 2009, INT PLAN STUD, V14, P369, DOI 10.1080/13563471003642803 Zasada I., 2019, CITY CULTURE SOC, V16, P25, DOI 10.1016/j.ccs.2017.06.002 Zazo A., 2018, INFINITE RURAL SYSTE, P67 NR 94 TC 5 Z9 5 U1 5 U2 30 PD MAY 2 PY 2019 VL 11 IS 10 AR 2892 DI 10.3390/su11102892 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000471010300175 DA 2022-12-14 ER PT J AU Awad, AI AF Awad, Ali Ismail TI From classical methods to animal biometrics: A review on cattle identification and tracking SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Review DE Beef animals; Cattle identification; Cattle tracking; Biometrics science; Muzzle prints; Iris patterns; Retinal vascular patterns ID IRIS RECOGNITION; PERFORMANCE EVALUATION; SYSTEM SECURITY; EAR TAGS; VERIFICATION; INFORMATION; TECHNOLOGY; PATTERN; FACE; TRACEABILITY AB Cattle, buffalo and cow, identification has recently played an influential role towards understanding disease trajectory, vaccination and production management, animal traceability, and animal ownership assignment. Cattle identification and tracking refers to the process of accurately recognizing individual cattle and their products via a unique identifier or marker. Classical cattle identification and tracking methods such as ear tags, branding, tattooing, and electrical methods have long been in use; however, their performance is limited due to their vulnerability to losses, duplications, fraud, and security challenges. Owing to their uniqueness, immutability, and low costs, biometric traits mapped into animal identification systems have emerged as a promising trend. Biometric identifiers for beef animals include muzzle print images, iris patterns, and retinal vascular patterns. Although using biometric identifiers has replaced human experts with computerized systems, it raises additional challenges in terms of identifier capturing, identification accuracy, processing time, and overall system operability. This article reviews the evolution in cattle identification and tracking from classical methods to animal biometrics. It reports on traditional animal identification methods and their advantages and problems. Moreover, this article describes the deployment of biometric identifiers for effectively identifying beef animals. The article presents recent research findings in animal biometrics, with a strong focus on cattle biometric identifiers such as muzzle prints, iris patterns, and retinal vascular patterns. A discussion of current challenges involved in the biometric-based identification systems appears in the conclusions, which may drive future research directions. (C) 2016 Elsevier B.V. All rights reserved. C1 [Awad, Ali Ismail] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97187 Lulea, Sweden. [Awad, Ali Ismail] Al Azhar Univ, Fac Engn, POB 83513, Qena, Egypt. C3 Lulea University of Technology; Egyptian Knowledge Bank (EKB); Al Azhar University RP Awad, AI (corresponding author), Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97187 Lulea, Sweden.; Awad, AI (corresponding author), Al Azhar Univ, Fac Engn, POB 83513, Qena, Egypt. EM ali.awad@ltu.se CR Allen A, 2008, LIVEST SCI, V116, P42, DOI 10.1016/j.livsci.2007.08.018 Arymurthy A.M., 2012, P 3 EUR C COMP SCI E, V110, P114 Awad AI, 2013, FED CONF COMPUT SCI, P529 Awad AI, 2013, INFORM-J COMPUT INFO, V37, P279 Awad AI, 2014, STUD COMPUT INTELL, V555, P47, DOI 10.1007/978-3-319-05885-6_3 Awad AI, 2013, COMM COM INF SC, V381, P143 Babu SC, 2009, FOOD SECURITY, POVERTY, AND NUTRITION POLICY ANALYSIS: STATISTICAL METHODS AND APPLICATIONS, P1 BARANOV AS, 1993, J ANIM BREED GENET, V110, P385, DOI 10.1111/j.1439-0388.1993.tb00751.x Barron UG, 2009, IRISH VET J, V62, P204 Barron UG, 2008, COMPUT ELECTRON AGR, V60, P156, DOI 10.1016/j.compag.2007.07.010 Barry B, 2008, COMPUT ELECTRON AGR, V64, P202, DOI 10.1016/j.compag.2008.05.011 Barry B, 2007, T ASABE, V50, P1073, DOI 10.13031/2013.23121 Barry B., 2008, THESIS U COLL DUBLIN Barry B., 2006, P IUFOST 13 WORLD C Belcher C, 2008, IEEE T INF FOREN SEC, V3, P572, DOI 10.1109/TIFS.2008.924606 Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Cappelli R, 2006, IEEE T PATTERN ANAL, V28, P3, DOI 10.1109/TPAMI.2006.20 Carne S, 2009, J DAIRY SCI, V92, P1500, DOI 10.3168/jds.2008-1577 Chen WK, 2013, INFORM SCIENCES, V221, P439, DOI 10.1016/j.ins.2012.09.021 Chollet G., 2009, GUIDE BIOMETRIC REFE, P1, DOI DOI 10.1007/978-1-84800-292-0_ Corkery GP, 2007, T ASABE, V50, P313, DOI 10.13031/2013.22395 DAUGMAN JG, 1993, IEEE T PATTERN ANAL, V15, P1148, DOI 10.1109/34.244676 Daugman J, 2013, PATTERN RECOGN, V46, P609, DOI 10.1016/j.patcog.2012.08.005 de Luis-Garcia R, 2003, SIGNAL PROCESS, V83, P2539, DOI 10.1016/j.sigpro.2003.08.001 Dunstone T, 2008, BIOMETRIC SYSTEM DAT, P111 Dziuk P, 2003, ANIM REPROD SCI, V79, P319, DOI 10.1016/S0378-4320(03)00170-2 Egawa S., 2012, CCIS, V294, P231 Eradus WJ, 1999, COMPUT ELECTRON AGR, V24, P91, DOI 10.1016/S0168-1699(99)00039-3 Fosgate GT, 2006, PREV VET MED, V73, P287, DOI 10.1016/j.prevetmed.2005.09.006 Frost AR, 1997, COMPUT ELECTRON AGR, V17, P139, DOI 10.1016/S0168-1699(96)01301-4 Gabor D., 1946, J I ELECTR ENG 3, V93, P429, DOI [10.1049/ji-1.1947.0015, DOI 10.1049/JI-3-2.1946.0074] Geers R., 1994, Computers and Electronics in Agriculture, V10, P1, DOI 10.1016/0168-1699(94)90032-9 Giot R, 2013, FUTURE GENER COMP SY, V29, P788, DOI 10.1016/j.future.2012.02.003 Goudelis G, 2008, J MULTIMODAL USER IN, V2, P217, DOI 10.1007/s12193-009-0020-x Gragnaniello D, 2015, PATTERN RECOGN LETT, V57, P81, DOI 10.1016/j.patrec.2014.10.018 Gupta P, 2015, APPL SOFT COMPUT, V29, P411, DOI 10.1016/j.asoc.2015.01.027 He XF, 2008, IEEE T INSTRUM MEAS, V57, P1369, DOI 10.1109/TIM.2007.915437 HOSIE B, 1995, VET REC, V137, P571, DOI 10.1136/vr.137.22.571-b Huhtala A, 2007, BIOSYST ENG, V96, P399, DOI 10.1016/j.biosystemseng.2006.11.013 Islam SMS, 2013, PATTERN RECOGN, V46, P613, DOI 10.1016/j.patcog.2012.09.016 Jain A.K., 2005, BIOMETRICS PERSONAL Jain A. K., 2011, INTRO BIOMETRICS Jain AK, 2004, IEEE T CIRC SYST VID, V14, P4, DOI 10.1109/TCSVT.2003.818349 Jain AK, 2006, IEEE T INF FOREN SEC, V1, P125, DOI 10.1109/TIFS.2006.873653 Jain AK, 2012, COMPUTER, V45, P87, DOI 10.1109/MC.2012.364 Jillela RR, 2015, PATTERN RECOGN LETT, V57, P4, DOI 10.1016/j.patrec.2014.09.014 Jimenez-Gamero I, 2006, SMALL RUMINANT RES, V65, P266, DOI 10.1016/j.smallrumres.2005.07.019 Johnston AM, 1996, VET REC, V138, P612, DOI 10.1136/vr.138.25.612 Juhola M, 2013, COMPUT BIOL MED, V43, P42, DOI 10.1016/j.compbiomed.2012.10.005 Klindtworth M, 1999, COMPUT ELECTRON AGR, V24, P65, DOI 10.1016/S0168-1699(99)00037-X Lahiri M., 2011, P 1 ACM INT C MULT R, P61 Lee HC, 2001, ADV FINGERPRINT TECH Lee Y, 2013, COMPUT VIS IMAGE UND, V117, P532, DOI 10.1016/j.cviu.2013.01.003 Leslie E, 2010, APPL ANIM BEHAV SCI, V127, P86, DOI 10.1016/j.applanim.2010.09.006 Li Yongping, 2006, [Nuclear Science and Techniques, 核技术(英文版)], V17, P97, DOI 10.1016/S1001-8042(06)60020-1 Lowe D. G., 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision, P1150, DOI 10.1109/ICCV.1999.790410 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Lowrence D, 2014, BIOMETRIC TECHNOLOGI, V2014, P7, DOI [10.1016/S0969-4765(14)70032-3, DOI 10.1016/S0969-4765(14)70032-3] Lu Y, 2014, INT J BIOMETRICS, V6, P18, DOI 10.1504/IJBM.2014.059639 Maio D, 2004, LECT NOTES COMPUT SC, V3072, P1 Maio D, 2002, INT C PATT RECOG, P811, DOI 10.1109/ICPR.2002.1048144 Maltoni D., 2009, HDB FINGERPRINT RECO Maltoni D, 2009, IMAGE VISION COMPUT, V27, P258, DOI 10.1016/j.imavis.2007.01.005 Marchant J, 2002, SECURE ANIMAL IDENTI MARIA Vlad, 2012, P 13 WSEAS INT C AUT, P165 Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188 Mikolajczyk K., 2009, ENCY BIOMETRICS, P939, DOI DOI 10.1007/978-0-387-73003-5_224 Minagawa H., 2002, AFITA 2002: Asian agricultural information technology & management. Proceedings of the Third Asian Conference for Information Technology in Agriculture, Beijing, China, 26-28 October, 2002, P596 Mishra S., 1995, ASIAN LIVESTOCK, P91 Most C.M., 2007, BIOMETRICS MARKET DE Nigam I, 2015, INFORM FUSION, V26, P1, DOI 10.1016/j.inffus.2015.03.005 NOONAN GJ, 1994, APPL ANIM BEHAV SCI, V39, P203, DOI 10.1016/0168-1591(94)90156-2 Noviyanto A, 2013, COMPUT ELECTRON AGR, V99, P77, DOI 10.1016/j.compag.2013.09.002 Petersen W., 1922, J DAIRY SCI, V5, P249, DOI DOI 10.3168/JDS.S0022-0302(22)94150-5 Rankin DM, 2013, PATTERN RECOGN, V46, P611, DOI 10.1016/j.patcog.2012.08.008 Rankin DM, 2012, PATTERN RECOGN, V45, P145, DOI 10.1016/j.patcog.2011.07.019 Ratha NK, 2001, IBM SYST J, V40, P614, DOI 10.1147/sj.403.0614 Rathgeb C, 2011, EURASIP J INF SECUR, DOI 10.1186/1687-417X-2011-3 Roberts CM, 2006, COMPUT SECUR, V25, P18, DOI 10.1016/j.cose.2005.12.003 Rojas-Olivares MA, 2011, J ANIM SCI, V89, P2603, DOI 10.2527/jas.2010-3197 Rossing W, 1999, COMPUT ELECTRON AGR, V24, P1, DOI 10.1016/S0168-1699(99)00033-2 Rotter P, 2008, IEEE PERVAS COMPUT, V7, P70, DOI 10.1109/MPRV.2008.22 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Samad A, 2010, COMPUT ELECTRON AGR, V73, P213, DOI 10.1016/j.compag.2010.05.001 Schouten B, 2009, IMAGE VISION COMPUT, V27, P305, DOI 10.1016/j.imavis.2008.05.008 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Sofos JN, 2008, MEAT SCI, V78, P3, DOI 10.1016/j.meatsci.2007.07.027 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Sun SN, 2013, NEUROCOMPUTING, V120, P310, DOI 10.1016/j.neucom.2012.08.068 Toledano DT, 2006, INTERACT COMPUT, V18, P1101, DOI 10.1016/j.intcom.2006.01.004 Tresadern P, 2013, IEEE PERVAS COMPUT, V12, P79, DOI 10.1109/MPRV.2012.54 Trevarthen A., 2007, Journal of Theoretical and Applied Electronic Commerce Research, V2 Tuytelaars T, 2007, FOUND TRENDS COMPUT, V3, P177, DOI 10.1561/0600000017 Unar JA, 2014, PATTERN RECOGN, V47, P2673, DOI 10.1016/j.patcog.2014.01.016 Velez JF, 2013, SPAN J AGRIC RES, V11, P945, DOI 10.5424/sjar/2013114-3924 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Wallace L. E., 2008, Professional Animal Scientist, V24, P384 Wardrope DD, 1995, VET REC, V137, P675 Watson, 1992, NIST SPECIAL DATABAS Whittenburg B., 2006, YANR0170 AL A M U AU Yager A., 2002, AS556W PURD U Yang K, 2013, NEUROCOMPUTING, V100, P153, DOI 10.1016/j.neucom.2011.12.044 Zhao LD, 2011, INT J INNOV COMPUT I, V7, P2201 NR 103 TC 63 Z9 65 U1 7 U2 70 PD APR PY 2016 VL 123 BP 423 EP 435 DI 10.1016/j.compag.2016.03.014 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000375166400045 DA 2022-12-14 ER PT J AU Chan, KY Abdullah, J Khan, AS AF Chan, Kok Yong Abdullah, Johari Khan, Adnan Shahid TI A Framework for Traceable and Transparent Supply Chain Management for Agri-food Sector in Malaysia using Blockchain Technology SO INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS DT Article DE Supply chain; blockchain; consensus algorithm; traceability; transparency AB This paper presents a framework for traceable and transparent supply chain management (SCM) system for the agri-food sector using blockchain technology in Malaysia. Numerous researchers believed that the current SCM system consists of several weak points, especially when multiple enterprise resource planning (ERP) system utilizing centralized SCM. Thus, data transparency and traceability are limited. This study hypothesized that if blockchain technology correlates with transparency and traceability of SCM, the above limitation can be minimized, as blockchain technology works in a distributed manner. This research uses "pepper" as an agri-food domain. The research also recommends that permissioned blockchain is a better fit as compared to permissionless blockchain. C1 [Chan, Kok Yong; Abdullah, Johari; Khan, Adnan Shahid] Univ Malaysia Sarawak, Fac Comp Sci & Informat Technol, Kota Samarahan, Malaysia. C3 University of Malaysia Sarawak RP Chan, KY (corresponding author), Univ Malaysia Sarawak, Fac Comp Sci & Informat Technol, Kota Samarahan, Malaysia. CR Ahmad A, 2019, INT J ADV COMPUT SC, V10, P77 AKERLOF GA, 1970, Q J ECON, V84, P488, DOI 10.2307/1879431 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 ChainThat Limited, 2015, SIMPL INTR SMART CON Chetak Logistics, 2015, DIFF 1PL 2PL 3PL 4PL Egels-Zanden N, 2016, J CONSUM POLICY, V39, P377, DOI 10.1007/s10603-015-9283-7 Fletcher N., 2017, LLEGAL CROSS BORDER Gupta M., 2017, BLOCKCHAIN DUMMIES Handfield R., 2002, SUPPLY CHAIN REDESIG Hofstedel C., 2005, HIDE CONFIDE DILEMMA Koo A., 2017, AGRIBUSINESS Laney J., 2018, BLOCKCHAIN CAN REVOL Lee H. H., 2013, 3 D SUPPLY CHAIN PRO Mah S.K, 2017, STAR ONLINE Manzini R, 2013, J FOOD ENG, V115, P251, DOI 10.1016/j.jfoodeng.2012.10.026 Duong-Trung N, 2019, INT J ADV COMPUT SC, V10, P553 Le NTT, 2019, INT J ADV COMPUT SC, V10, P677 Razzaq A, 2019, INT J ADV COMPUT SC, V10, P685 Surasak T, 2019, INT J ADV COMPUT SC, V10, P578 Tapscott D., 2003, NAKED CORPORATION AG Treiblmaier H, 2018, SUPPLY CHAIN MANAG, V23, P545, DOI 10.1108/SCM-01-2018-0029 Tsai K., 2018, TRANSPARENCY VS TRAC Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 Yagoob S, 2019, INT J ADV COMPUT SC, V10, P644 NR 26 TC 13 Z9 13 U1 4 U2 30 PD NOV PY 2019 VL 10 IS 11 BP 149 EP 156 WC Computer Science, Theory & Methods SC Computer Science UT WOS:000504404900020 DA 2022-12-14 ER PT J AU Ciliberti, S Bartolini, F Brunori, A Mariano, E Metta, M Brunori, G Frascarelli, A AF Ciliberti, Stefano Bartolini, Fabio Brunori, Antonio Mariano, Eleonora Metta, Matteo Brunori, Gianluca Frascarelli, Angelo TI EUTR implementation in the Italian wood-energy sector: Role and impact of (ongoing) digitalisation SO FOREST POLICY AND ECONOMICS DT Article DE Digitalisation; Forestry; Traceability; Italy; Living lab ID TIMBER REGULATION; EUROPEAN-UNION; SUPPORT; SYSTEM; TRACEABILITY; GOVERNANCE; EMERGENCE; TRADE AB Illegal logging is a global problem associated with deforestation, climate change, and biodiversity loss with significant negative economic, environmental, and social impacts. In response to this phenomenon, European Union has enacted the European Timber Regulation (EUTR) that imposes economic operators to exercise due diligence thanks to traceability verifications. These are mainly based on a "paper-based" approach, with implementation issues as a consequence.Italy is a interesting case study, since it is the first importer of wood-energy biomass worldwide, where tons of fuelwood without clear traceability are imported every year, and EUTR enforcement still lags behind. Here, a Living Lab involving stakeholders and key informants, carried out a participatory and open assessment of the impact of digital technologies on EUTR enforcement and traceability in the national wood-energy sector.Results reveals that, even if digitalisation is at the first stage, it is far from being only a technological phenomenon since it is already able to impact on different aspects (social, economic, territorial, institutional) related to the EUTR application.Policymakers are therefore recommended to rely on holistic evaluations and approaches that recognise such a complexity, in order to foster a creation of a viable digital ecosystem in favour of EUTR implementation and traceability in the wood-energy sector. C1 [Ciliberti, Stefano; Metta, Matteo; Brunori, Gianluca] Univ Pisa, Dept Agr Food & Agri Environm Sci, Pisa Agr Econ Grp PAGE, Via Borghetto 80, I-56124 Pisa, Italy. [Ciliberti, Stefano; Frascarelli, Angelo] Univ Perugia, Dept Agr Food & Environm Sci, Via Borgo xx Giugno 74, I-06121 Perugia, Italy. [Bartolini, Fabio] Univ Ferrara, Dept Chem Pharmaceut & Agr Sci, Borsari 46, I-44121 Ferrara, Italy. [Brunori, Antonio; Mariano, Eleonora] Programme Endorsement Forest Certificat PEFC schem, Italian Secretariat, Via Pietro Cestellini 17, I-06135 Perugia, Italy. C3 University of Pisa; University of Perugia; University of Ferrara RP Ciliberti, S (corresponding author), Univ Pisa, Dept Agr Food & Agri Environm Sci, Pisa Agr Econ Grp PAGE, Via Borghetto 80, I-56124 Pisa, Italy.; Ciliberti, S (corresponding author), Univ Perugia, Dept Agr Food & Environm Sci, Via Borgo xx Giugno 74, I-06121 Perugia, Italy. EM stefano.ciliberti@unipg.it CR Acheampong E, 2020, FOREST POLICY ECON, V111, DOI 10.1016/j.forpol.2019.102047 Agnoletti M, 2022, FOREST ECOL MANAG, V503, DOI 10.1016/j.foreco.2021.119655 Appelhanz S, 2016, J CLEAN PROD, V110, P132, DOI 10.1016/j.jclepro.2015.02.034 Arnould M, 2022, FOREST POLICY ECON, V139, DOI 10.1016/j.forpol.2022.102716 Brunori A., 2021, KEY DIGITAL GAME CHA Brusselaers J, 2021, FOREST POLICY ECON, V123, DOI 10.1016/j.forpol.2020.102338 Carstens N, 2021, GER POLIT, DOI 10.1080/09644008.2021.1887851 Cashore B, 2012, FOREST POLICY ECON, V18, P13, DOI [10.1016/j.forpol.2011.12.005, 10.1016/j.forpol.2012.03.001] Corona P., 2017, Forest@, V14, P1, DOI 10.3832/efor2285-014 Corona P., 2019, RAF ITALIA RAPPORTO, P98 CREA and Carabinieri Forestali, 2015, INV NAZ FOR SERB CAR Crivellaro A., 2020, Forest@, V17, P88, DOI 10.3832/efor3678-017 Dietrich T, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.634102 Ebinger F, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12156129 Ehlers MH, 2021, FOOD POLICY, V100, DOI 10.1016/j.foodpol.2020.102019 Eurostat, 2021, DAT FAO and UNEP, 2020, STAT WORLDS FOR FOR, DOI 10.4060/ca8642-n Ferrari A, 2022, INFORM SOFTWARE TECH, V145, DOI 10.1016/j.infsof.2021.106816 Gamache G, 2020, ENVIRON INNOV SOC TR, V37, P93, DOI 10.1016/j.eist.2020.08.002 Gavrilut I, 2016, FORESTS, V7, DOI 10.3390/f7010003 Giurca A, 2015, IFOREST, V8, DOI 10.3832/ifor1271-008 Giurca A, 2013, FORESTS, V4, P730, DOI 10.3390/f4040730 Istat, 2020, CENS PERM IMPR Ituarte-Lima C, 2019, INT ENVIRON AGREEM-P, V19, P255, DOI 10.1007/s10784-019-09439-6 Kalinauskaite I., 2021, FACING SOC CHALLENGE, P1 Kothke M, 2020, FOREST POLICY ECON, V111, DOI 10.1016/j.forpol.2019.102028 Legambiente, 2016, EC 2016 STOR NUM CRI Leipold S, 2017, FOREST POLICY ECON, V82, P41, DOI 10.1016/j.forpol.2016.11.009 Leipold S, 2016, GLOBAL ENVIRON CHANG, V39, P294, DOI 10.1016/j.gloenvcha.2016.06.005 Maryudi A, 2020, SOC NATUR RESOUR, V33, P859, DOI 10.1080/08941920.2020.1725201 Masiero M, 2015, FORESTS, V6, P3452, DOI 10.3390/f6103452 McDermott CL, 2018, FOREST POLICY ECON, V90, P180, DOI 10.1016/j.forpol.2017.12.015 Moral-Pajares E, 2020, FORESTS, V11, DOI 10.3390/f11091009 Moser C, 2021, REGUL GOV, V15, P115, DOI 10.1111/rego.12268 Pallotta E, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12052474 Pra A., 2016, Italia Forestale e Montana, V71, P49 Pynnonen S, 2021, FOREST POLICY ECON, V125, DOI 10.1016/j.forpol.2021.102404 Rete Rurale Nazionale, 2017, FOR IT STAT HLTH MAN Rijswijk K, 2021, J RURAL STUD, V85, P79, DOI 10.1016/j.jrurstud.2021.05.003 Rolandi S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13095172 Schraml R, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8071071 Schroeder K., 2021, WHATS COOKING DIGITA Secco L, 2017, LAND USE POLICY, V62, P79, DOI 10.1016/j.landusepol.2016.11.013 Sikkema R, 2017, SCAND J FOREST RES, V32, P551, DOI 10.1080/02827581.2016.1240228 Sotirov M, 2017, FOREST POLICY ECON, V81, P69, DOI 10.1016/j.forpol.2017.05.001 Sperandio G., 2017, Forest@, V14, P124, DOI 10.3832/efor2267-014 Storti D, 2016, AREE INTERNE SVILUPP, P45 Trishkin M, 2015, FORESTS, V6, P1380, DOI 10.3390/f6041380 Tzoulis I. K., 2014, Journal of Agricultural Informatics, V5, P9 UNEP-WCMC, 2020, BRAZ REL ITS TIMB EX United Nations International Trade Statistics Database, 2017, UN COMTR DAT Watkinson CJ, 2020, FORESTS, V11, DOI 10.3390/f11080862 NR 52 TC 1 Z9 1 U1 5 U2 5 PD AUG PY 2022 VL 141 AR 102758 DI 10.1016/j.forpol.2022.102758 WC Economics; Environmental Studies; Forestry SC Business & Economics; Environmental Sciences & Ecology; Forestry UT WOS:000802638000003 DA 2022-12-14 ER PT J AU Cho, KS Hong, SY Yun, BK Won, HS Yoon, YH Kwon, KB Mekapogu, M AF Cho, Kwang-Soo Hong, Su-Young Yun, Bong-Kyoung Won, Hong-Sik Yoon, Young-Ho Kwon, Ki-Beom Mekapogu, Manjulatha TI Application of InDel Markers Based on the Chloroplast Genome Sequences for Authentication and Traceability of Tartary and Common Buckwheat SO CZECH JOURNAL OF FOOD SCIENCES DT Article DE quantitative PCR; processed foods; insertion/deletion markers ID FAGOPYRUM; VARIABILITY; FOODS AB A reliable, qualitative PCR-based detection method for the traceability and authentication of common and Tartary buckwheat was developed. Five InDel markers developed from chloroplast genome variation between the two species were applied for 96 buckwheat accessions and all accessions were easily differentiated as Tartary and common buckwheat using these markers. We also determined the sample detection limit by PCR and qPCR as 0.001 and 0.02 ng/mu l, respectively. InDel markers could detect the mixture of two species flour up to 10% contamination. InDel markers were also applied to processed foods such as noodles and tea, and we found that species-specific PCR bands could be used to identify buckwheat even after processing. Hence, these InDel markers are simple with higher specificity and sensitivity and are reliable for the authentication of buckwheat processed foods. C1 [Cho, Kwang-Soo; Hong, Su-Young; Yun, Bong-Kyoung; Won, Hong-Sik; Yoon, Young-Ho; Kwon, Ki-Beom; Mekapogu, Manjulatha] Rural Dev Adm, Highland Agr Res Inst, Natl Inst Crop Sci, Pyeongchang, South Korea. C3 Rural Development Administration (RDA), Republic of Korea; National Institute of Crop Science RP Mekapogu, M (corresponding author), Rural Dev Adm, Highland Agr Res Inst, Natl Inst Crop Sci, Pyeongchang, South Korea. EM manjubio7@gmail.com CR Abeywardena MY, 2001, PROSTAG LEUKOTR ESS, V65, P91, DOI 10.1054/plef.2001.0294 Barcaccia G, 2016, GENET RESOUR CROP EV, V63, P639, DOI 10.1007/s10722-015-0273-z Bonafaccia G, 2003, FOOD CHEM, V80, P9, DOI 10.1016/S0308-8146(02)00228-5 Chauhan RS., 2010, EUROPEAN J PLANT SCI, V4, P33 Cho KS, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0125332 Fabjan N, 2003, J AGR FOOD CHEM, V51, P6452, DOI 10.1021/jf034543e Futo S, 2002, J FOOD HYG SOC JPN, V43, pJ280 Guo XN, 2010, INT J MOL SCI, V11, P5202, DOI 10.3390/ijms11125201 Hirao T, 2005, BIOSCI BIOTECH BIOCH, V69, P724, DOI 10.1271/bbb.69.724 HOWES NK, 1992, GENOME, V35, P120, DOI 10.1139/g92-020 Ikeda Kiyokazu, 2002, Adv Food Nutr Res, V44, P395, DOI 10.1016/S1043-4526(02)44008-9 Ikeda S., 1994, FAGOPYRUM, V14, P29 Janes D, 2012, J FOOD SCI, V77, pC746, DOI 10.1111/j.1750-3841.2012.02778.x Kim JK, 2004, KOREAN J FOOD SCI TE, V36, P598 Kreft I., 1994, B RES I FOOD SCI, V57, P1 Kump B, 1996, PLANT SCI, V114, P149, DOI 10.1016/0168-9452(95)04321-7 Li SQ, 2001, CRIT REV FOOD SCI, V41, P451, DOI 10.1080/20014091091887 Liu Z, 2001, J NUTR, V131, P1850, DOI 10.1093/jn/131.6.1850 Luthar Z., 1992, Fagopyrum, V12, P36 Nagai T, 2001, FOOD CHEM, V75, P237, DOI 10.1016/S0308-8146(01)00193-5 Nair Arun, 2002, ScientificWorldJournal, V2, P818 Ohnishi O, 1996, GENES GENET SYST, V71, P383, DOI 10.1266/ggs.71.383 Ohsako T, 2002, GENES GENET SYST, V77, P399, DOI 10.1266/ggs.77.399 Pacurar DI, 2012, J EXP BOT, V63, P2491, DOI 10.1093/jxb/err422 Paradkar MM, 2002, FOOD CHEM, V76, P231, DOI 10.1016/S0308-8146(01)00292-8 Park CheolHo, 2000, Fagopyrum, V17, P63 Qin PY, 2011, J FOOD SCI, V76, pS401, DOI 10.1111/j.1750-3841.2011.02223.x Vimla Bisht, 2007, Journal of Tropical Agriculture, V45, P48 WOJCICKI J, 1995, PHARMAZIE, V50, P560 Yamakawa H, 2008, BIOSCI BIOTECH BIOCH, V72, P2228, DOI 10.1271/bbb.80237 Yamaki S, 2013, BREEDING SCI, V63, P246, DOI 10.1270/jsbbs.63.246 Yoon JW, 2010, J CEREAL SCI, V52, P321, DOI 10.1016/j.jcs.2010.06.015 전영준, 2007, Applied Biological Chemistry, V50, P276 NR 33 TC 3 Z9 3 U1 1 U2 6 PY 2017 VL 35 IS 2 BP 122 EP 130 DI 10.17221/116/2016-CJFS WC Food Science & Technology SC Food Science & Technology UT WOS:000400934000004 DA 2022-12-14 ER PT J AU Zhang, R Wang, QT AF Zhang, Rui Wang, Qingtao TI Comparability of four clinical laboratory measurement methods for GGT and commutability of candidate reference materials SO JOURNAL OF CLINICAL LABORATORY ANALYSIS DT Article DE commutability; comparability; gamma-glutamyl transferase; reference method ID GAMMA-GLUTAMYL-TRANSFERASE; CATALYTIC CONCENTRATION; INTERNATIONAL FEDERATION; RISK; GLUTAMYLTRANSFERASE; TRACEABILITY; STABILITY; CHEMISTRY; ENZYMES; VALUES AB Background This study was conducted to evaluate the progress in the standardization of the gamma-glutamyl transferase (GGT) to achieve metrological traceability of routine in vitro diagnosis (IVD) medical devices. Methods We collected 25 single fresh frozen serum samples for GGT analysis. Candidate reference materials (RMs), calibrators, internal quality controls (IQC), and external quality assessment (EQA) materials from the National Center for Clinical Laboratory (NCCL), Beijing Center for Clinical Laboratory (BCCL), and College of American Pathologists (CAP) were randomly added to these serum samples. A total of 42 samples were examined using IFCC reference method and four different IVD medical devices to perform the comparability and commutability study. Results The four IVD medical devices achieved trueness assessment within the measurement range. Linear analysis showed the agreement of Siemens ADVIA 2400, Hitachi 7600-020/BioSino, Beckman AU 5800, and Roche Cobas 501 with the reference method. These assay pairs were comparable at the medical decision levels. The GGT in-house candidate RMs, and Beckmann and Roche calibrators were all within the limits of the 95% prediction intervals, the commutability of BioSino calibrators was indeterminate, and some internal and external quality controls were not commutable for comparisons of certain IVD medical devices vs the reference method. Conclusions By comparing with the reference method, we found that performance of GGT conventional measurement systems to be traceable to the higher order references was improved. The commutable materials for calibration and trueness controls of routine methods were significant to promote the standardization of GGT analysis. C1 [Zhang, Rui; Wang, Qingtao] Capital Med Univ, Beijing Chaoyang Hosp, Dept Clin Lab, 8 Gongtinan Rd, Beijing 100020, Peoples R China. [Wang, Qingtao] Beijing Ctr Clin Labs, 8 Gongtinan Rd, Beijing 100020, Peoples R China. C3 Capital Medical University RP Wang, QT (corresponding author), Capital Med Univ, Beijing Chaoyang Hosp, Dept Clin Lab, 8 Gongtinan Rd, Beijing 100020, Peoples R China.; Wang, QT (corresponding author), Beijing Ctr Clin Labs, 8 Gongtinan Rd, Beijing 100020, Peoples R China. EM wqt36@163.com CR [Anonymous], 2006, REF MAT GEN STAT PRI [Anonymous], 2013, AESTHET SURG J, V33, p1S Arasteh S, 2018, INDIAN HEART J, V70, P788, DOI 10.1016/j.ihj.2017.11.017 Bulusu S, 2016, ANN CLIN BIOCHEM, V53, P312, DOI 10.1177/0004563215597010 Canalias F, 2010, CLIN CHIM ACTA, V411, P7, DOI 10.1016/j.cca.2009.09.029 Candas-Estebanez B, 2012, CLIN CHEM LAB MED, V50, P2237, DOI 10.1515/cclm-2011-0738 Clinical and Laboratory Standards Institute (CLSI), 2014, EP14A3 CLSI, P34 Cuhadar S, 2013, BIOCHEM MEDICA, V23, P70, DOI 10.11613/BM.2013.009 Cuhadar S, 2012, BIOCHEM MEDICA, V22, P202 International Organization for Standardization, 2003, 151952003 ISO ISO, 2005, 170252005 ISO IEC Kristensen GBB, 2016, CLIN CHEM, V62, P1255, DOI 10.1373/clinchem.2016.258962 Liu X, 2014, METABOLISM, V63, P773, DOI 10.1016/j.metabol.2014.03.008 Martins MD, 2010, ACTA MEDICA PORT, V23, P579 Miller WG, 2013, CLIN CHEM, V59, P1291, DOI 10.1373/clinchem.2013.208785 Ndrepepa G, 2018, CLIN CHIM ACTA, V476, P130, DOI 10.1016/j.cca.2017.11.026 Ndrepepa G, 2016, ANN TRANSL MED, V4, DOI 10.21037/atm.2016.12.27 Ricos C, 1999, SCAND J CLIN LAB INV, V59, P491 Scharnhorst V, 2004, CLIN CHEM LAB MED, V42, P1401, DOI 10.1515/CCLM.2004.261 Schumann G, 2002, CLIN CHEM LAB MED, V40, P734, DOI 10.1515/CCLM.2002.126 SchumannG BR, 2002, CLIN CHEM LAB MED, V40, P734 SHAW LM, 1983, J CLIN CHEM CLIN BIO, V21, P633 Tong Q, 2018, SCAND J CLIN LAB INV, V78, P74, DOI 10.1080/00365513.2017.1413715 Xia CY, 2010, ANN CLIN BIOCHEM, V47, P189, DOI 10.1258/acb.2009.009210 NR 24 TC 1 Z9 1 U1 1 U2 2 PD DEC PY 2020 VL 34 IS 12 AR e23557 DI 10.1002/jcla.23557 EA SEP 2020 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000567842800001 DA 2022-12-14 ER PT J AU Bhutta, MNM Ahmad, M AF Bhutta, Muhammad Nasir Mumtaz Ahmad, Muneer TI Secure Identification, Traceability and Real-Time Tracking of Agricultural Food Supply During Transportation Using Internet of Things SO IEEE ACCESS DT Article DE Supply chains; Blockchain; Stakeholders; Supply chain management; Internet of Things; Business; Security; Supply chain management (SCM); Internet of Things (IoT); real-time tracking; business management; smart transportation; secure supply chain management AB Food supply chain process comprises crops collection, processing of food, shipping & delivery to the whole seller in the market. Harvested foods decompose from the moment they are harvested due to attacks from enzymes, oxidation, and microorganisms. These include bacteria, mold, yeast, moisture, temperature, and chemical reaction. The spoilage of fresh food has increased over time due to the multistage slow food supply chain process. The identification, traceability, and real-time tracking of goods in supply chains have always been a challenge. The advent of the Internet of Things and cloud computing has brought a new approach to the food supply chain process for better cooperation among supply chain partners. The supply chain management (SCM) benefit greatly through automation based on key technologies of IoT, Radio Frequency Identification (RFID), and Wireless Sensor Networks (WSN). These technologies collect the data relevant to the food supply chain system, such as identifying tag-possessed objects or individuals and sensing capabilities of the surrounding environment. However, the collected data can be tempered or modified by attackers to provide false information about environmental conditions. They can destroy or damage the product due to false identification of dynamic environmental conditions. Furthermore, the current automation systems in industry-based retail logistics and SCM do not provide efficient solutions for monitoring the quality of perishable products with integrated solutions. This research aims to develop a secure monitoring and reporting system based on IoT to update the quality of the perishables along with the SCM with a focus on transportation without any human intervention. C1 [Bhutta, Muhammad Nasir Mumtaz] King Faisal Univ, Dept Informat Syst, Coll Comp Sci & Informat Technol, Al Hasa 31982, Saudi Arabia. [Ahmad, Muneer] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Informat Syst, Kuala Lumpur 50603, Malaysia. C3 King Faisal University; Universiti Malaya RP Bhutta, MNM (corresponding author), King Faisal Univ, Dept Informat Syst, Coll Comp Sci & Informat Technol, Al Hasa 31982, Saudi Arabia.; Ahmad, M (corresponding author), Univ Malaya, Fac Comp Sci & Informat Technol, Dept Informat Syst, Kuala Lumpur 50603, Malaysia. EM mmbhutta@kfu.edu.sa; mmalik@um.edu.my CR Abdirad M, 2021, ENG MANAG J, V33, P187, DOI 10.1080/10429247.2020.1783935 Ahlqvist V, 2020, OPER SUPPLY CHAIN MA, V13, P382, DOI 10.31387/oscm0430278 Alora A, 2019, INT J VALUE CHAIN MA, V10, P1, DOI 10.1504/IJVCM.2019.096538 Amiri SAHS, 2020, COMPUT IND ENG, V139, DOI 10.1016/j.cie.2019.106156 Arora R., 2020, SMART INNOVATION SYS, DOI [10.1007/978-981-15-2647- 3_46, DOI 10.1007/978-981-15-2647-3_46] Attauabi M, 2021, SCAND J GASTROENTERO, V56, P53, DOI 10.1080/00365521.2020.1854848 Batwa A, 2020, OPER SUPPLY CHAIN MA, V13, P294, DOI 10.31387/oscm0420271 Bencic FM, 2019, IEEE ACCESS, V7, P46198, DOI 10.1109/ACCESS.2019.2909170 Borah M. D., 2020, ADV APPL BLOCKCHAIN, P27 Bottani E, 2019, COMPUT IND ENG, V135, P177, DOI 10.1016/j.cie.2019.05.011 Chang SCE, 2020, IEEE ACCESS, V8, P62478, DOI 10.1109/ACCESS.2020.2983601 Chen CY, 2019, J INTELL FUZZY SYST, V37, P5809, DOI 10.3233/JIFS-179162 de Vass T, 2018, AUSTRALAS J INF SYST, V22 Evtodieva T. E., 2020, ADV INTELLIGENT SYST, DOI [10.1007/978-3-030-11367-4_38, DOI 10.1007/978-3-030-11367-4_38] Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Garrido-Hidalgo C, 2019, COMPUT IND, V112, DOI 10.1016/j.compind.2019.103127 Grida M, 2020, NEUTROSOPHIC SETS SY, V33, P323 Hahn GJ, 2020, INT J PROD RES, V58, P1425, DOI 10.1080/00207543.2019.1641642 Hatefi SM, 2019, INT J INTEGR ENG, V11, P80 Irfan M, 2019, OPER MANAGE RES, V12, P113, DOI 10.1007/s12063-019-00142-y Ivanov D, 2020, TRANSPORT RES E-LOG, V136, DOI 10.1016/j.tre.2020.101922 Jiang M., 2019, INT J ASIAN SOCIAL S, V9, P516 Kesharwani S., 2019, CYBERNOMICS, V1, P18 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Kose B. Ozdenizci, 2020, INTERNET THINGS IOT, DOI [10.4018/978-1-7998-3175-4.ch007, DOI 10.4018/978-1-7998-3175-4.CH007] Kousiouris G, 2019, ICT EXPRESS, V5, P141, DOI 10.1016/j.icte.2019.04.002 Kuandeet W, 2019, INT J ONLINE BIOMED, V15, P4, DOI 10.3991/ijoe.v15i03.8533 Layaq W., 2019, INT J TRANSP ENG TEC, V5, P50 Longo F, 2019, COMPUT IND ENG, V136, P57, DOI 10.1016/j.cie.2019.07.026 Luo LZ, 2019, J MANAGE ENG, V35, DOI 10.1061/(ASCE)ME.1943-5479.0000675 Machado TB, 2020, TRENDS FOOD SCI TECH, V102, P261, DOI 10.1016/j.tifs.2020.03.043 Manavalan E, 2019, COMPUT IND ENG, V127, P925, DOI 10.1016/j.cie.2018.11.030 Mastos TD, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122377 Naskar S., 2019, SUPPLY CHAIN LOGISTI, DOI [10.4018/978-1-7998-0945-6.ch096, DOI 10.4018/978-1-7998-0945-6.CH096] Nawaz F, 2019, KNOWL-BASED SYST, V180, P133, DOI 10.1016/j.knosys.2019.05.024 Putri AN, 2020, IOP C SER EARTH ENV, V466, DOI 10.1088/1755-1315/466/1/012007 Rejeb A, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11070161 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Sachdev D., 2019, J MANAGE, V6, P66 Sawik T, 2019, INT J PROD RES, V57, P4502, DOI 10.1080/00207543.2018.1504246 Schmidt CG, 2019, J PURCH SUPPLY MANAG, V25, DOI 10.1016/j.pursup.2019.100552 Shahzad A, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20133760 Sharma Apoorva., 2020, SN COMPUTER SCI, V1, P232, DOI 10.1007/s42979-020-00248-2 Sun J, 2020, COMPUT IND ENG, V144, DOI 10.1016/j.cie.2020.106457 Taboada I, 2021, INT J LOGIST-RES APP, V24, P392, DOI 10.1080/13675567.2020.1762850 Tsang YP, 2019, IEEE ACCESS, V7, P129000, DOI 10.1109/ACCESS.2019.2940227 Wamba SF, 2020, INT J PROD ECON, V229, DOI 10.1016/j.ijpe.2020.107791 Wong LW, 2020, INT J PROD RES, V58, P2100, DOI 10.1080/00207543.2020.1730463 Yadav Sanjeev, 2020, International Journal of Logistics Systems and Management, V35, P204 Yoon J, 2020, INT J PROD RES, V58, P1362, DOI 10.1080/00207543.2019.1634296 Zagurskiy O. N., 2019, Journal of Automation and Information Sciences, V51, P63 NR 52 TC 13 Z9 13 U1 28 U2 77 PY 2021 VL 9 BP 65660 EP 65675 DI 10.1109/ACCESS.2021.3076373 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000647305200001 DA 2022-12-14 ER PT J AU Corrado, G AF Corrado, Giandomenico TI Advances in DNA typing in the agro-food supply chain SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review DE Traceability; Barcoding; SSR; SNP; Next generation sequencing ID GENOME-WIDE ASSOCIATION; REAL-TIME PCR; OLIVE OIL; MOLECULAR MARKERS; GENETIC DIVERSITY; CAPILLARY-ELECTROPHORESIS; CULTIVAR IDENTIFICATION; EXTRACTION METHODS; FOOD FORENSICS; MULTIPLEX PCR AB Background: DNA typing is increasingly being applied to assess the genetic origin and authenticity of products entering and exiting the food supply chain. The growing interest in DNA typing has arisen from an expanding array of contexts, such as the need to protect manufacturers, ensure compliance with food regulations, validate labels, fight misbranding, evaluate product ingredients and defend consumers' rights and freedom of choice. Scope and approach: This review presents current practices and emerging technologies about the genetic traceability in the agro-food chain, providing an overview of the specificity and challenges related to the analysis of commercial products of plant origin. We also discuss unsolved needs and specific features of DNA testing in the agro-food supply chain. These include the biochemical and physical variability of the samples under investigation, the possible DNA degradation, and the necessity to distinguish among plant varieties and not only different species. Key findings and conclusions: We acknowledge that a number of DNA typing systems have been successfully used, and the vast majority are based on the PCR technique. Advances in next-generation sequencing technologies are expected to greatly expand data range and the amount of information accessible to a DNA analysis. The evaluation and implementation of novel technologies and tools, along with concerted efforts to increase information sharing and to establish standard operating protocols, are main priorities of genetic typing in the agro-food chain. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Corrado, Giandomenico] Univ Naples Federico II, Dipartimento Agr, Via Univ 100, I-80055 Portici, NA, Italy. C3 University of Naples Federico II RP Corrado, G (corresponding author), Univ Naples Federico II, Dipartimento Agr, Via Univ 100, I-80055 Portici, NA, Italy. EM giandomenico.corrado@unina.it CR Adamo P, 2012, J GEOCHEM EXPLOR, V121, P62, DOI 10.1016/j.gexplo.2012.07.006 Akbari M, 2005, J MOL DIAGN, V7, P36, DOI 10.1016/S1525-1578(10)60006-2 Alary R, 2007, EUR FOOD RES TECHNOL, V225, P427, DOI 10.1007/s00217-006-0434-6 Archak S, 2007, ELECTROPHORESIS, V28, P2396, DOI 10.1002/elps.200600646 Arlorio M, 2007, FOOD CONTROL, V18, P140, DOI 10.1016/j.foodcont.2005.09.005 Arlorio M, 2003, EUR FOOD RES TECHNOL, V216, P253, DOI 10.1007/s00217-002-0634-7 Atwell S, 2010, NATURE, V465, P627, DOI 10.1038/nature08800 BALDING DJ, 1995, P NATL ACAD SCI USA, V92, P11741, DOI 10.1073/pnas.92.25.11741 Bauer T, 2003, EUR FOOD RES TECHNOL, V217, P338, DOI 10.1007/s00217-003-0743-y Benschop CCG, 2011, FORENSIC SCI INT-GEN, V5, P316, DOI 10.1016/j.fsigen.2010.06.006 Bonnet C, 2001, EUR REV AGRIC ECON, V28, P433, DOI 10.1093/erae/28.4.433 Bush JF, 2002, FOOD DRUG LAW J, V57, P573 Butler JM, 2004, ELECTROPHORESIS, V25, P1397, DOI 10.1002/elps.200305822 Butler John M, 2007, Forensic Sci Med Pathol, V3, P200, DOI 10.1007/s12024-007-0018-1 Caramante M, 2011, FOOD CONTROL, V22, P549, DOI 10.1016/j.foodcont.2010.10.002 Caramante M, 2009, SCI HORTIC-AMSTERDAM, V120, P560, DOI 10.1016/j.scienta.2008.12.004 Carpenter ML, 2013, AM J HUM GENET, V93, P852, DOI 10.1016/j.ajhg.2013.10.002 Caulfield T, 2012, ANNU REV MED, V63, P23, DOI 10.1146/annurev-med-062110-123753 Chen SG, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0010848 Coghlan ML, 2012, PLOS GENET, V8, P436, DOI 10.1371/journal.pgen.1002657 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Corrado G, 2011, J HORTIC SCI BIOTECH, V86, P461, DOI 10.1080/14620316.2011.11512789 Corrado G, 2014, SCI HORTIC-AMSTERDAM, V168, P138, DOI 10.1016/j.scienta.2014.01.027 Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Cottrell CE, 2014, J MOL DIAGN, V16, P89, DOI 10.1016/j.jmoldx.2013.10.002 Coyle H. M., 2004, FORENSIC BOT PRINCIP Craft KJ, 2007, FORENSIC SCI INT, V165, P64, DOI 10.1016/j.forsciint.2006.03.002 Dahinden I, 2001, EUR FOOD RES TECHNOL, V212, P228, DOI 10.1007/s002170000252 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 De Mattia F, 2011, FOOD RES INT, V44, P693, DOI 10.1016/j.foodres.2010.12.032 Demeke T, 2010, ANAL BIOANAL CHEM, V396, P1977, DOI 10.1007/s00216-009-3150-9 Di Pinto A, 2007, FOOD CONTROL, V18, P76, DOI 10.1016/j.foodcont.2005.08.011 Dooley JJ, 2004, MEAT SCI, V68, P431, DOI 10.1016/j.meatsci.2004.04.010 Duvick DN, 2001, NAT REV GENET, V2, P69, DOI 10.1038/35047587 Elsanhoty RM, 2011, FOOD CHEM, V126, P1883, DOI 10.1016/j.foodchem.2010.12.013 Ercolini D, 2013, APPL ENVIRON MICROB, V79, P3148, DOI 10.1128/AEM.00256-13 Fang WP, 2014, J AGR FOOD CHEM, V62, P481, DOI 10.1021/jf404402v Federico S., 2014, PLANT BIOSYSTEMS INT, P1 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Ganopoulos I, 2013, J SCI FOOD AGR, V93, P2281, DOI 10.1002/jsfa.6040 Gansauge MT, 2014, GENOME RES, V24, P1543, DOI 10.1101/gr.174201.114 Gargis AS, 2012, NAT BIOTECHNOL, V30, P1033, DOI 10.1038/nbt.2403 Gettings KB, 2015, FORENSIC SCI INT-GEN, V19, P1, DOI 10.1016/j.fsigen.2015.04.010 Gilder JR, 2007, J FORENSIC SCI, V52, P97, DOI 10.1111/j.1556-4029.2006.00318.x Gill P, 2000, FORENSIC SCI INT, V112, P17, DOI 10.1016/S0379-0738(00)00158-4 Gryson N, 2010, ANAL BIOANAL CHEM, V396, P2003, DOI 10.1007/s00216-009-3343-2 Ha WY, 2002, J AGR FOOD CHEM, V50, P1871, DOI 10.1021/jf011365l Hall D.W., 2012, FORENSIC BOT PRACTIC Heckenberger M, 2005, THEOR APPL GENET, V111, P598, DOI 10.1007/s00122-005-2052-2 Hollingsworth PM, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0019254 Holzhauser T, 2000, EUR FOOD RES TECHNOL, V211, P360, DOI 10.1007/s002170000152 Houlton S, 2009, CHEM WORLD-UK, V6, P87 Hrncirova Z, 2008, J FOOD NUTR RES-SLOV, V47, P23 Jain N, 2008, PLANTA MED, V74, P296, DOI 10.1055/s-2008-1034314 Jobling MA, 2004, NAT REV GENET, V5, P739, DOI 10.1038/nrg1455 Jones ES, 2007, THEOR APPL GENET, V115, P361, DOI 10.1007/s00122-007-0570-9 Kalia RK, 2011, EUPHYTICA, V177, P309, DOI 10.1007/s10681-010-0286-9 Kaundun SS, 2003, J AGR FOOD CHEM, V51, P1765, DOI 10.1021/jf020821i Kayser M, 2011, NAT REV GENET, V12, P179, DOI 10.1038/nrg2952 Kelly F, 2007, J FOOD QUALITY, V30, P237, DOI 10.1111/j.1745-4557.2007.00118.x Kloosterman Ate D., 2003, Journal de la Societe de Biologie, V197, P351 Knight A, 2000, AGRO FOOD IND HI TEC, V11, P7 Korir NK, 2013, CRIT REV BIOTECHNOL, V33, P111, DOI 10.3109/07388551.2012.675314 Kosoy R, 2009, HUM MUTAT, V30, P69, DOI 10.1002/humu.20822 Kress WJ, 2005, P NATL ACAD SCI USA, V102, P8369, DOI 10.1073/pnas.0503123102 Laube I, 2010, FOOD CHEM, V118, P979, DOI 10.1016/j.foodchem.2008.09.063 Little DP, 2014, GENOME, V57, P513, DOI 10.1139/gen-2014-0130 Lum MR, 2005, PLANTA MED, V71, P841, DOI 10.1055/s-2005-871230 Madesis P, 2014, FOOD RES INT, V60, P163, DOI 10.1016/j.foodres.2013.10.042 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Mafra I, 2008, FOOD CONTROL, V19, P1183, DOI 10.1016/j.foodcont.2008.01.004 Martinez I, 2003, TRENDS FOOD SCI TECH, V14, P489, DOI 10.1016/j.tifs.2003.07.005 Martins-Lopes P, 2013, FOOD TECHNOL BIOTECH, V51, P198 Melchiade D, 2007, FOOD BIOTECHNOL, V21, P33, DOI 10.1080/08905430701191114 Meldrum Cliff, 2011, Clin Biochem Rev, V32, P177 Meyer R, 1999, FOOD CONTROL, V10, P391, DOI 10.1016/S0956-7135(99)00081-X Mihalov JJ, 2000, J AGR FOOD CHEM, V48, P3744, DOI 10.1021/jf000011b Morisset D, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0062583 Moritz C, 2004, PLOS BIOL, V2, P1529, DOI 10.1371/journal.pbio.0020354 Moshelion M, 2015, TRENDS BIOTECHNOL, V33, P337, DOI 10.1016/j.tibtech.2015.03.001 Muzzalupo I, 2007, EUR FOOD RES TECHNOL, V224, P469, DOI 10.1007/s00217-006-0340-y Newmaster SG, 2013, BMC MED, V11, DOI 10.1186/1741-7015-11-222 Nielsen R, 2011, NAT REV GENET, V12, P443, DOI 10.1038/nrg2986 Nybom Hilde, 2014, Investig Genet, V5, P1, DOI 10.1186/2041-2223-5-1 Ogden R, 2008, FISH FISH, V9, P462, DOI 10.1111/j.1467-2979.2008.00305.x Ortola-Vidal A, 2007, FOOD CONTROL, V18, P921, DOI 10.1016/j.foodcont.2006.04.013 Palmieri L, 2009, NUTRIENTS, V1, P316, DOI 10.3390/nu1020316 Parvathy VA, 2014, FOOD BIOTECHNOL, V28, P25, DOI 10.1080/08905436.2013.870078 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Porebski S, 1997, PLANT MOL BIOL REP, V15, P8, DOI 10.1007/BF02772108 Premanandh J, 2013, FOOD CONTROL, V34, P568, DOI 10.1016/j.foodcont.2013.05.033 Primrose S, 2010, TRENDS FOOD SCI TECH, V21, P582, DOI 10.1016/j.tifs.2010.09.006 Provan J, 2001, TRENDS ECOL EVOL, V16, P142, DOI 10.1016/S0169-5347(00)02097-8 Rasmussen RS, 2008, COMPR REV FOOD SCI F, V7, P280, DOI 10.1111/j.1541-4337.2008.00046.x Reid LM, 2006, TRENDS FOOD SCI TECH, V17, P344, DOI 10.1016/j.tifs.2006.01.006 Remund KM, 2001, SEED SCI RES, V11, P101 Rico C, 2013, SCI REP-UK, V3, DOI 10.1038/srep03376 Ripp F, 2014, BMC GENOMICS, V15, DOI 10.1186/1471-2164-15-639 Rodriguez GR, 2011, PLANT PHYSIOL, V156, P275, DOI 10.1104/pp.110.167577 Sandberg M, 2003, EUR FOOD RES TECHNOL, V217, P344, DOI 10.1007/s00217-003-0758-4 Scarano D, 2014, SCI HORTIC-AMSTERDAM, V180, P72, DOI 10.1016/j.scienta.2014.10.013 Scarano D, 2015, FOOD CONTROL, V51, P397, DOI 10.1016/j.foodcont.2014.12.006 Schlotterer C, 2004, NAT REV GENET, V5, P63, DOI 10.1038/nrg1249 Schneider PM, 2004, FORENSIC SCI INT, V139, P123, DOI 10.1016/j.forsciint.2003.10.002 Schrader C, 2012, J APPL MICROBIOL, V113, P1014, DOI 10.1111/j.1365-2672.2012.05384.x Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Siret R, 2002, J AGR FOOD CHEM, V50, P3822, DOI 10.1021/jf011462e Skuras D., 2002, British Food Journal, V104, P898, DOI 10.1108/00070700210454622 Smith C. J. S., 2014, MANAGING SEQUENCING, P49 Sonnante G, 2009, J AGR FOOD CHEM, V57, P10199, DOI 10.1021/jf902624z Spaniolas S, 2006, J AGR FOOD CHEM, V54, P7466, DOI 10.1021/jf061164n Spink J, 2011, J FOOD SCI, V76, pR157, DOI 10.1111/j.1750-3841.2011.02417.x Stoeckle MY, 2011, SCI REP-UK, V1, DOI 10.1038/srep00042 Sun D.-W., 2008, MODERN TECHNIQUES FO Sundaram RM, 2008, EUPHYTICA, V163, P215, DOI 10.1007/s10681-007-9630-0 Sze SCW, 2008, BIOTECHNOL APPL BIOC, V51, P15, DOI 10.1042/BA20070096 Taylor G, 2010, FORENSIC ENFORCEMENT: THE ROLE OF THE PUBLIC ANALYST, P1 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 Tian F, 2014, APPL BIOCHEM BIOTECH, V172, P3686, DOI 10.1007/s12010-014-0760-2 Tian F, 2011, NAT GENET, V43, P159, DOI 10.1038/ng.746 Tillmar AO, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0083761 Turkec A, 2015, J FOOD SCI TECH MYS, V52, P5164, DOI 10.1007/s13197-014-1547-8 Ujihara T, 2005, FOOD SCI TECHNOL RES, V11, P43, DOI 10.3136/fstr.11.43 Valentini A., 2010, Diversity, V2, P610 van Oorschot Roland Ah, 2010, Investig Genet, V1, P14, DOI 10.1186/2041-2223-1-14 Vemireddy LR, 2007, J AGR FOOD CHEM, V55, P8112, DOI 10.1021/jf0714517 Veteluinen M., 2009, EUROPEAN LANDRACES O Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Waits LP, 2001, MOL ECOL, V10, P249, DOI 10.1046/j.1365-294X.2001.01185.x Weber-Lehmann J, 2014, FORENSIC SCI INT-GEN, V9, P42, DOI 10.1016/j.fsigen.2013.10.015 Weiler NEC, 2012, FORENSIC SCI INT-GEN, V6, P102, DOI 10.1016/j.fsigen.2011.03.002 Weiss MM, 2013, HUM MUTAT, V34, P1313, DOI 10.1002/humu.22368 Wenz HM, 1998, GENOME RES, V8, P69, DOI 10.1101/gr.8.1.69 Wong EHK, 2008, FOOD RES INT, V41, P828, DOI 10.1016/j.foodres.2008.07.005 Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 NR 135 TC 20 Z9 21 U1 0 U2 58 PD JUN PY 2016 VL 52 BP 80 EP 89 DI 10.1016/j.tifs.2016.04.003 WC Food Science & Technology SC Food Science & Technology UT WOS:000377316500007 DA 2022-12-14 ER PT J AU Dolle, D Fages, A Mata, X Schiavinato, S Tonasso-Calviere, L Chauvey, L Wagner, S Sarkissian, CD Fromentier, A Seguin-Orlando, A Orlando, L AF Dolle, Dirk Fages, Antoine Mata, Xavier Schiavinato, Stephanie Tonasso-Calviere, Laure Chauvey, Lorelei Wagner, Stefanie Der Sarkissian, Clio Fromentier, Aurore Seguin-Orlando, Andaine Orlando, Ludovic TI CASCADE: A Custom-Made Archiving System for the Conservation of Ancient DNA Experimental Data SO FRONTIERS IN ECOLOGY AND EVOLUTION DT Article DE ancient DNA; laboratory management; database; LIMS; traceability; conservation; collaborative sharing ID LIBRARY PREPARATION; GENOME SEQUENCE; HISTORY; CAVE AB The field of ancient genomics has undergone a true revolution during the last decade. Input material, time requirements and processing costs have first limited the number of specimens amenable to genome sequencing. However, the discovery that archeological material such as petrosal bones can show increased ancient DNA preservation rates, combined with advances in sequencing technologies, molecular methods for the recovery of degraded DNA fragments and bioinformatics, has vastly expanded the range of samples compatible with genome-wide investigation. Experimental procedures for DNA extraction, genomic library preparation and target enrichment have become more streamlined, and now also include automation. These procedures have considerably reduced the amount of work necessary for data generation, effectively adapting the processing capacity of individual laboratories to the increasing numbers of analyzable samples. Handling vast amounts of samples, however, comes with logistical challenges. Laboratory capacities, equipment, and people need to be efficiently coordinated, and the progress of each sample through the different experimental stages needs to be fully traceable, especially as archeological remains of animals or plants are often provided and/or handled by many different collaborators. Here we present CASCADE, a laboratory information management system (LIMS) dealing with the specificities of ancient DNA sample processing and tracking, applicable by large and small laboratories alike, and scalable to large projects involving the analysis of thousands of samples and more. By giving an account of the specimen's progress at any given analytical step, CASCADE not only optimizes the collaborative experience, including real-time information sharing with third parties, but also improves the efficacy of data generation and traceability in-house. C1 [Dolle, Dirk; Fages, Antoine; Mata, Xavier; Schiavinato, Stephanie; Tonasso-Calviere, Laure; Chauvey, Lorelei; Wagner, Stefanie; Der Sarkissian, Clio; Fromentier, Aurore; Seguin-Orlando, Andaine; Orlando, Ludovic] Fac Med Purpan, CNRS UMR 5288, Lab Anthropol & Imagerie Synth, Toulouse, France. C3 Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE) RP Orlando, L (corresponding author), Fac Med Purpan, CNRS UMR 5288, Lab Anthropol & Imagerie Synth, Toulouse, France. EM ludovic.orlando@univ-tlse3.fr CR Boessenkool S, 2017, MOL ECOL RESOUR, V17, P742, DOI 10.1111/1755-0998.12623 Briggs AW, 2007, P NATL ACAD SCI USA, V104, P14616, DOI 10.1073/pnas.0704665104 Brunson K, 2019, TRENDS GENET, V35, P319, DOI 10.1016/j.tig.2019.02.006 Caroe C, 2018, METHODS ECOL EVOL, V9, P410, DOI 10.1111/2041-210X.12871 CODD EF, 1970, COMMUN ACM, V13, P377, DOI 10.1145/357980.358007 Dabney J, 2013, P NATL ACAD SCI USA, V110, P15758, DOI 10.1073/pnas.1314445110 Damgaard PB, 2015, SCI REP-UK, V5, DOI 10.1038/srep11184 Damgaard PD, 2018, NATURE, V557, P369, DOI 10.1038/s41586-018-0094-2 Date C.J., 2003, INTRO DATABASE SYSTE Etxebarria IG, 2019, J HIGH ENERGY PHYS, DOI 10.1007/JHEP10(2019)169 Fages A, 2019, CELL, V177, P1419, DOI 10.1016/j.cell.2019.03.049 Gansauge MT, 2017, NUCLEIC ACIDS RES, V45, DOI 10.1093/nar/gkx033 Gansauge MT, 2014, GENOME RES, V24, P1543, DOI 10.1101/gr.174201.114 Gansauge MT, 2013, NAT PROTOC, V8, P737, DOI 10.1038/nprot.2013.038 Glocke I, 2017, GENOME RES, V27, P1230, DOI 10.1101/gr.219675.116 Goodwin S, 2016, NAT REV GENET, V17, P333, DOI 10.1038/nrg.2016.49 Green RE, 2010, SCIENCE, V328, P710, DOI 10.1126/science.1188021 Haak W, 2015, NATURE, V522, P207, DOI 10.1038/nature14317 Harney E, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-05649-9 Kistler L, 2018, SCIENCE, V362, P1309, DOI 10.1126/science.aav0207 Korlevic P, 2019, METHODS MOL BIOL, V1963, P15, DOI 10.1007/978-1-4939-9176-1_2 Lander ES, 2001, NATURE, V409, P860, DOI 10.1038/35057062 Mann AE, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-28091-9 Marciniak S, 2017, NAT REV GENET, V18, DOI 10.1038/nrg.2017.65 Mathieson I, 2018, NATURE, V555, P197, DOI 10.1038/nature25778 Metzker ML, 2010, NAT REV GENET, V11, P31, DOI 10.1038/nrg2626 Olalde I, 2019, SCIENCE, V363, P1230, DOI 10.1126/science.aav4040 Olalde I, 2018, NATURE, V555, P190, DOI 10.1038/nature25738 Pedersen MW, 2015, PHILOS T R SOC B, V370, DOI 10.1098/rstb.2013.0383 Pinhasi R, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0129102 Rasmussen M, 2010, NATURE, V463, P757, DOI 10.1038/nature08835 Reich D, 2010, NATURE, V468, P1053, DOI 10.1038/nature09710 Rohland N, 2018, NAT PROTOC, V13, DOI 10.1038/s41596-018-0050-5 Rohland N, 2015, PHILOS T R SOC B, V370, DOI 10.1098/rstb.2013.0624 Schubert M, 2014, NAT PROTOC, V9, P1056, DOI 10.1038/nprot.2014.063 Spyrou MA, 2019, NAT REV GENET, V20, P323, DOI 10.1038/s41576-019-0119-1 Venter JC, 2001, SCIENCE, V291, P1304, DOI 10.1126/science.1058040 NR 37 TC 0 Z9 0 U1 1 U2 4 PD JUN 23 PY 2020 VL 8 AR 185 DI 10.3389/fevo.2020.00185 WC Ecology SC Environmental Sciences & Ecology UT WOS:000551667700001 DA 2022-12-14 ER PT J AU Maestrello, V Solovyev, P Bontempo, L Mannina, L Camin, F AF Maestrello, Valentina Solovyev, Pavel Bontempo, Luana Mannina, Luisa Camin, Federica TI Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication SO COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY DT Review DE EVOO; NMR Spectroscopy; Metabolomics; Traceability ID MULTIVARIATE STATISTICAL-ANALYSIS; HS-GC-IMS; NMR-SPECTROSCOPY; EDIBLE OILS; H-1-NMR SPECTROSCOPY; GEOGRAPHICAL ORIGIN; MASS-SPECTROMETRY; GAS-CHROMATOGRAPHY; FTIR SPECTROSCOPY; SOYBEAN OIL AB Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents. C1 [Maestrello, Valentina; Solovyev, Pavel; Bontempo, Luana; Camin, Federica] Fdn Edmund Mach FEM, San Michele All Adige, Italy. [Camin, Federica] Univ Trento, Ctr Agr Food Environm C3A, San Michele All Adige, Italy. [Mannina, Luisa] Sapienza Univ Roma, Dipartimento Chim & Tecnol Farmaco, Rome, Italy. [Camin, Federica] Vienna Int Ctr, Int Atom Energy Agcy, POB 100, A-1400 Vienna, Austria. C3 Fondazione Edmund Mach; University of Trento; Sapienza University Rome; International Atomic Energy Agency RP Camin, F (corresponding author), Vienna Int Ctr, Int Atom Energy Agcy, POB 100, A-1400 Vienna, Austria. EM federica.camin@unitn.it CR Agiomyrgianaki A, 2012, FOOD CHEM, V135, P2561, DOI 10.1016/j.foodchem.2012.07.050 Agiomyrgianaki A, 2010, TALANTA, V80, P2165, DOI 10.1016/j.talanta.2009.11.024 Alonso-Salces RM, 2010, FOOD CHEM, V118, P956, DOI 10.1016/j.foodchem.2008.09.061 Alonso-Salces RM, 2015, EUR J LIPID SCI TECH, V117, P1991, DOI 10.1002/ejlt.201400243 Alonso-Salces RM, 2012, OLIVE OIL - CONSTITUENTS, QUALITY, HEALTH PROPERTIES AND BIOCONVERSIONS, P185 Alonso-Salces RM, 2010, J AGR FOOD CHEM, V58, P5586, DOI 10.1021/jf903989b Alonso-Salces RM, 2022, FOOD CHEM, V366, DOI 10.1016/j.foodchem.2021.130588 Anderson SL, 2017, J CHEM EDUC, V94, P1377, DOI 10.1021/acs.jchemed.7b00012 [Anonymous], 2021, FOODSCREENER [Anonymous], 2019, CONSORZIO OLIVICOLO [Anonymous], OFFICIAL J EUROPEA L, V136, P40, DOI DOI 10.1016/B978-0-08-100922-2.00001-2 Aparicio R, 2013, FOOD RES INT, V54, P2025, DOI 10.1016/j.foodres.2013.07.039 Aykas DP, 2020, FOODS, V9, DOI 10.3390/foods9020221 Bajoub A, 2017, FOOD CHEM, V215, P245, DOI 10.1016/j.foodchem.2016.07.140 Barison A, 2010, MAGN RESON CHEM, V48, P642, DOI 10.1002/mrc.2629 Behmer, 2018, ANAL OLIVES OLIVE OI Ben Mansour A, 2016, J SCI FOOD AGR, V96, P4432, DOI 10.1002/jsfa.7654 Beteinakis S, 2020, MOLECULES, V25, DOI 10.3390/molecules25153339 Boskou D., 2006, OLIVE OIL, P41, DOI DOI 10.1016/B978-1-893997-88-2.50008-0 Brescia MA, 2003, J AM OIL CHEM SOC, V80, P945, DOI 10.1007/s11746-003-0801-2 Cabrita MJ, 2021, FOODS, V10, DOI 10.3390/foods10020399 Calo F, 2022, FOODS, V11, DOI 10.3390/foods11010113 Calo F, 2021, MOLECULES, V26, DOI 10.3390/molecules26082233 Camin F, 2016, FOOD CHEM, V196, P98, DOI 10.1016/j.foodchem.2015.08.132 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Castejon D, 2016, NUTRIENTS, V8, DOI 10.3390/nu8020093 Castejon D, 2014, FOOD ANAL METHOD, V7, P1285, DOI 10.1007/s12161-013-9747-9 Christy AA, 2006, CHEMOMETR INTELL LAB, V82, P130, DOI 10.1016/j.chemolab.2005.06.019 Circi S, 2018, METABOLITES, V8, DOI 10.3390/metabo8030043 Conte L, 2020, TRENDS FOOD SCI TECH, V105, P483, DOI 10.1016/j.tifs.2019.02.025 Corsaro C, 2015, J ANAL METHODS CHEM, V2015, DOI 10.1155/2015/175696 Crawford LM, 2020, FOOD CONTROL, V114, DOI 10.1016/j.foodcont.2020.107264 Culeddu N, 2017, EUR J LIPID SCI TECH, V119, DOI 10.1002/ejlt.201700035 D'Imperio M, 2007, FOOD CHEM, V105, P1256, DOI 10.1016/j.foodchem.2007.02.045 Da Ros A, 2019, MOLECULES, V24, DOI 10.3390/molecules24162896 del Cano-Ochoa S, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201800137 Del Coco L, 2021, FOOD CHEM, V345, DOI 10.1016/j.foodchem.2020.128778 Del Coco L, 2014, FOODS, V3, P238, DOI 10.3390/foods3020238 Del Coco L, 2016, J AM OIL CHEM SOC, V93, P373, DOI 10.1007/s11746-015-2778-1 Del Coco L, 2012, NUTRIENTS, V4, P343, DOI 10.3390/nu4050343 Contreras MD, 2019, FOOD CONTROL, V98, P82, DOI 10.1016/j.foodcont.2018.11.001 Meras ID, 2018, TALANTA, V178, P751, DOI 10.1016/j.talanta.2017.09.095 European Commission, 2006, OFFICIAL J EUROPEA L, V93, P12 European Commission, 2022, COMM COMM ORG AGR MA European Commission, 1992, OFF J EUR COMMUN L, V208, P1 European Commission, 2009, OFFICIAL J L, V63, P6 European Commission,, 2013, OFF J EUR COMMUN L, VL338, P31 European Union Commission, 1991, J EURO COMM, VL248, P1 Fauhl C, 2000, MAGN RESON CHEM, V38, P436, DOI 10.1002/1097-458X(200006)38:6<436::AID-MRC672>3.3.CO;2-O Fragaki G, 2005, J AGR FOOD CHEM, V53, P2810, DOI 10.1021/jf040279t Gaforio JJ, 2019, NUTRIENTS, V11, DOI 10.3390/nu11092039 Gerhardt N, 2017, ANAL BIOANAL CHEM, V409, P3933, DOI 10.1007/s00216-017-0338-2 Ghisoni S, 2019, FOOD RES INT, V121, P746, DOI 10.1016/j.foodres.2018.12.052 Girelli CR, 2020, FOODS, V9, DOI 10.3390/foods9121797 Girelli CR, 2018, METABOLITES, V8, DOI 10.3390/metabo8040060 Girelli CR, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9081471 Girelli CR, 2016, EUR J LIPID SCI TECH, V118, P1380, DOI 10.1002/ejlt.201500401 Gomez-Caravaca AM, 2016, ANAL CHIM ACTA, V913, P1, DOI 10.1016/j.aca.2016.01.025 Gorzynik-Debicka M, 2018, INT J MOL SCI, V19, DOI 10.3390/ijms19030686 Gouilleux B, 2018, FOOD CHEM, V244, P153, DOI 10.1016/j.foodchem.2017.10.016 Guillen MD, 2003, EUR J LIPID SCI TECH, V105, P688, DOI 10.1002/ejlt.200300866 Gunning Y, 2022, FOOD CHEM, V370, DOI 10.1016/j.foodchem.2021.131333 Hou XW, 2021, J SCI FOOD AGR, V101, P2389, DOI 10.1002/jsfa.10862 Ingallina C, 2019, METABOLITES, V9, DOI 10.3390/metabo9040065 IOC, 2022, WP CONT Janin M, 2014, EUR FOOD RES TECHNOL, V239, P745, DOI 10.1007/s00217-014-2279-8 Jiang XY, 2018, GRASAS ACEITES, V69, DOI 10.3989/gya.1221172 Kalogiouri NP, 2020, ANAL CHIM ACTA, V1134, P150, DOI 10.1016/j.aca.2020.07.029 Kalogiouri NP, 2016, ANAL BIOANAL CHEM, V408, P7955, DOI 10.1007/s00216-016-9891-3 Karkoula E, 2012, J AGR FOOD CHEM, V60, P11696, DOI 10.1021/jf3032765 Karunathilaka SR, 2016, J FOOD SCI, V81, pC2390, DOI 10.1111/1750-3841.13432 Knothe G, 2004, EUR J LIPID SCI TECH, V106, P88, DOI 10.1002/ejlt.200300880 Laincer F, 2016, FOOD RES INT, V89, P1123, DOI 10.1016/j.foodres.2016.04.024 Lia F, 2020, FOODS, V9, DOI 10.3390/foods9060689 Lia F, 2020, FOODS, V9, DOI 10.3390/foods9040498 Lioupi A, 2020, J CHROMATOGR B, V1150, DOI 10.1016/j.jchromb.2020.122161 Longobardi F, 2012, FOOD CHEM, V130, P177, DOI 10.1016/j.foodchem.2011.06.045 Lukic I, 2020, MOLECULES, V25, DOI 10.3390/molecules25010004 Mannina L, 2003, J AGR FOOD CHEM, V51, P120, DOI 10.1021/jf025656l Mannina L, 2005, RIV ITAL SOSTANZE GR, V82, P59 Mannina L, 2010, TALANTA, V80, P2141, DOI 10.1016/j.talanta.2009.11.021 Mannina L, 2012, PROG NUCL MAG RES SP, V66, P1, DOI 10.1016/j.pnmrs.2012.02.001 Mannina L, 2011, MAGN RESON CHEM, V49, pS3, DOI 10.1002/mrc.2856 Mannina L, 2009, J AGR FOOD CHEM, V57, P11550, DOI 10.1021/jf902426b Meenu M, 2019, TRENDS FOOD SCI TECH, V91, P391, DOI 10.1016/j.tifs.2019.07.045 Merchak N, 2017, FOOD CHEM, V217, P379, DOI 10.1016/j.foodchem.2016.08.110 Morin E.J.-F., 2018, FOOD INTEGRITY HDB G, DOI [10.32741/fihb, DOI 10.32741/FIHB] Nagy K, 2005, J CHROMATOGR A, V1078, P90, DOI 10.1016/j.chroma.2005.05.008 Nigri S, 2016, RIV ITAL SOSTANZE GR, V93, P125 Nikou T, 2020, FRONT PUBLIC HEALTH, V8, DOI 10.3389/fpubh.2020.558226 Ok S, 2014, GRASAS ACEITES, V65, DOI 10.3989/gya.122413 Olmo-Cunillera A, 2020, J SCI FOOD AGR, V100, P1842, DOI 10.1002/jsfa.10173 Owen R W, 2000, Lancet Oncol, V1, P107, DOI 10.1016/S1470-2045(00)00015-2 Ozdemir IS, 2019, TURK J AGRIC FOR, V43, P299, DOI 10.3906/tar-1805-81 Ozdemir IS, 2018, LWT-FOOD SCI TECHNOL, V92, P10, DOI 10.1016/j.lwt.2018.02.015 Pan ZZ, 2007, ANAL BIOANAL CHEM, V387, P525, DOI 10.1007/s00216-006-0687-8 Paolini M, 2017, TALANTA, V174, P38, DOI 10.1016/j.talanta.2017.05.080 Parker T, 2014, TRAC-TREND ANAL CHEM, V57, P147, DOI 10.1016/j.trac.2014.02.006 Perri E, 2012, OLIVE GERMPLASM - THE OLIVE CULTIVATION, TABLE OLIVE AND OLIVE OIL INDUSTRY IN ITALY, P265, DOI 10.5772/51796 Piccinonna S, 2016, FOOD CHEM, V199, P675, DOI 10.1016/j.foodchem.2015.12.064 Plante, 2013, DETERMINATION OLIVE Poiana MA, 2015, OPEN CHEM, V13, P689, DOI 10.1515/chem-2015-0110 Popescu R, 2015, FOOD CONTROL, V48, P84, DOI 10.1016/j.foodcont.2014.04.046 Portarena, 2012, ANALISI SPETTROMETRI, P11 Poulli KI, 2007, FOOD CHEM, V105, P369, DOI 10.1016/j.foodchem.2006.12.021 Quintanilla-Casas B, 2020, LWT-FOOD SCI TECHNOL, V121, DOI 10.1016/j.lwt.2019.108936 Quintanilla-Casas B, 2020, FOOD CHEM, V307, DOI 10.1016/j.foodchem.2019.125556 Rabiei Z, 2012, OLIVE OIL - CONSTITUENTS, QUALITY, HEALTH PROPERTIES AND BIOCONVERSIONS, P163 Ray CL, 2022, MOLECULES, V27, DOI 10.3390/molecules27010213 Rezzi S, 2005, ANAL CHIM ACTA, V552, P13, DOI 10.1016/j.aca.2005.07.057 Rifna EJ, 2022, FOOD CHEM, V369, DOI 10.1016/j.foodchem.2021.130898 Rohman A, 2010, FOOD RES INT, V43, P886, DOI 10.1016/j.foodres.2009.12.006 Rongai D, 2017, FOODS, V6, DOI 10.3390/foods6110096 Sayago A, 2019, LWT-FOOD SCI TECHNOL, V111, P99, DOI 10.1016/j.lwt.2019.05.009 Smejkalova D, 2010, FOOD CHEM, V118, P153, DOI 10.1016/j.foodchem.2009.04.088 Solovyev PA, 2021, COMPR REV FOOD SCI F, V20, P2040, DOI 10.1111/1541-4337.12700 Stilo F, 2019, J AGR FOOD CHEM, V67, P5289, DOI 10.1021/acs.jafc.9b01661 Tahir HE, 2022, FOOD CHEM, V366, DOI 10.1016/j.foodchem.2021.130633 Tang FF, 2022, FOOD CONTROL, V137, DOI 10.1016/j.foodcont.2022.108939 Milanez KDTM, 2017, LWT-FOOD SCI TECHNOL, V85, P9, DOI 10.1016/j.lwt.2017.06.060 Valli E, 2016, EUR J LIPID SCI TECH, V118, P1601, DOI 10.1002/ejlt.201600065 Vigli G, 2003, J AGR FOOD CHEM, V51, P5715, DOI 10.1021/jf030100z Violino S, 2020, FOODS, V9, DOI 10.3390/foods9060834 Vlahov G, 2009, J AOAC INT, V92, P1747 Wang SH, 2021, FOOD ANAL METHOD, V14, P1322, DOI 10.1007/s12161-021-01973-x Wang X, 2020, FOOD ANAL METHOD, V13, P1894, DOI 10.1007/s12161-020-01799-z Winkelmann O, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201900027 Xu Y, 2015, INT J FOOD PROP, V18, P2085, DOI 10.1080/10942912.2014.963869 NR 128 TC 1 Z9 1 U1 13 U2 13 PD SEP PY 2022 VL 21 IS 5 BP 4056 EP 4075 DI 10.1111/1541-4337.13005 EA JUL 2022 WC Food Science & Technology SC Food Science & Technology UT WOS:000829392500001 DA 2022-12-14 ER PT J AU Aceto, M Gulino, F Cala, E Robotti, E Petrozziello, M Tsolakis, C Cassino, C AF Aceto, Maurizio Gulino, Federica Cala, Elisa Robotti, Elisa Petrozziello, Maurizio Tsolakis, Christos Cassino, Claudio TI Authentication and Traceability Study on Barbera d'Asti and Nizza DOCG Wines: The Role of Trace- and Ultra-Trace Elements SO BEVERAGES DT Article DE ICP-MS; trace elements; wine; Nizza; Barbera; authentication AB Barbera d'Asti-including Barbera d'Asti superiore-and Nizza are two DOCG (Denominazione di Origine Controllata e Garantita) wines produced in Piemonte (Italy) from the Barbera grape variety. Differences among them arise in the production specifications in terms of purity, ageing, and zone of production, in particular with concern to Nizza, which follows the most stringent rules, sells at three times the average price, and is considered to have the highest market value. To guarantee producers and consumers, authentication methods must be developed in order to distinguish among the different wines. As the production zones totally overlap, it is important to verify whether the distinction is possible or not according to metals content, or whether chemical markers more linked to winemaking are needed. In this work, Inductively Coupled Plasma (ICP) elemental analysis and multivariate data analysis are used to study the authentication and traceability of samples from the three designations of 2015 vintage. The results show that, as far as elemental distribution in wine is concerned, work in the cellar, rather than geographic provenance, is crucial for the possibility of distinction. C1 [Aceto, Maurizio; Gulino, Federica; Cala, Elisa; Robotti, Elisa; Cassino, Claudio] Univ Piemonte Orientale, Dipartimento Sci & Innovaz Tecnol, Viale T Michel 11, I-15121 Alessandria, Italy. [Petrozziello, Maurizio; Tsolakis, Christos] Ctr Ric Viticoltura Enol, CREA Consiglio Ric Agr & Anal Econ Agr, Via Pietro Micca 35, I-14100 Asti, Italy. C3 University of Eastern Piedmont Amedeo Avogadro; Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Aceto, M (corresponding author), Univ Piemonte Orientale, Dipartimento Sci & Innovaz Tecnol, Viale T Michel 11, I-15121 Alessandria, Italy. EM maurizio.aceto@uniupo.it; federica.gulino@uniupo.it; elisa.cala@uniupo.it; elisa.robotti@uniupo.it; maurizio.petrozziello@crea.gov.it; christos.tsolakis@crea.gov.it; claudio.cassino@uniupo.it CR AA. VV, 1970, GAZZ UFF G, V73, P1836 AA. VV, 2019, OFF J EUR UNION, V154, P33 AA. VV, 2012, 12914 ISO AA. VV, 2008, GAZZ UFF G, V169, P21 Aceto M, 2016, WOODHEAD PUBL FOOD S, V301, P137, DOI 10.1016/B978-0-08-100310-7.00008-9 Aceto M, 2019, FOOD CHEM, V298, DOI 10.1016/j.foodchem.2019.125047 Aceto M, 2018, BEVERAGES, V4, DOI 10.3390/beverages4010023 Aceto M, 2017, J AGR FOOD CHEM, V65, P4200, DOI [10.1021/acs.jafc.7b00916, 10.1021/acs.jafc.7b009] Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 Alvarez M, 2007, MICROCHEM J, V87, P72, DOI 10.1016/j.microc.2007.05.007 Bronzi B, 2020, FOOD CHEM, V315, DOI 10.1016/j.foodchem.2020.126248 Catarino S, 2008, CIENC TEC VITIVINIC, V23, P3 Catarino S, 2008, J AGR FOOD CHEM, V56, P158, DOI 10.1021/jf0720180 Catarino S, 2018, BEVERAGES, V4, DOI 10.3390/beverages4040085 Ebeler SE, 2015, ACS SYM SER, V1203, P3 Gomez MDM, 2004, J AGR FOOD CHEM, V52, P2953, DOI 10.1021/jf035119g Gonzalvez A, 2013, COMP ANAL C, V60, P51, DOI 10.1016/B978-0-444-59562-1.00003-7 Griboff J, 2019, FOOD CHEM, V283, P549, DOI 10.1016/j.foodchem.2019.01.067 Hopfer H, 2015, FOOD CHEM, V172, P486, DOI 10.1016/j.foodchem.2014.09.113 Jakubowski N, 1999, FRESEN J ANAL CHEM, V364, P424, DOI 10.1007/s002160051361 Kaya AD, 2017, J AGR FOOD CHEM, V65, P4766, DOI 10.1021/acs.jafc.7b01510 Magnusson B., 2014, EURACHEM GUIDE FITNE, V2nd Martin AE, 2012, FOOD CHEM, V133, P1081, DOI 10.1016/j.foodchem.2012.02.013 May TW, 1998, ATOM SPECTROSC, V19, P150 Mihucz VG, 2006, TALANTA, V70, P984, DOI 10.1016/j.talanta.2006.05.080 Nicolini G., 2003, Rivista di Viticoltura e di Enologia, V56, P45 Nicolini G, 2004, VITIS, V43, P41 Oddone M, 2009, J AGR FOOD CHEM, V57, P3404, DOI 10.1021/jf900312p Pohl P, 2007, TRAC-TREND ANAL CHEM, V26, P941, DOI 10.1016/j.trac.2007.07.005 Razic S, 2010, AM J ENOL VITICULT, V61, P506, DOI 10.5344/ajev.2010.10002 Rossano EC, 2007, J AGR FOOD CHEM, V55, P311, DOI 10.1021/jf061828t Tatar E, 2007, MICROCHEM J, V85, P132, DOI 10.1016/j.microc.2006.05.009 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Worku M, 2019, FOOD CHEM, V290, P295, DOI 10.1016/j.foodchem.2019.03.135 NR 34 TC 5 Z9 5 U1 1 U2 5 PD DEC PY 2020 VL 6 IS 4 AR 63 DI 10.3390/beverages6040063 WC Food Science & Technology SC Food Science & Technology UT WOS:000616120500006 DA 2022-12-14 ER PT J AU Sartore, S Soglia, D Maione, S Sacchi, P De Marco, M Schiavone, A Sponza, S Dalmasso, A Bottero, MT Pattono, D Zoccarato, I Gasco, L Brugiapaglia, A Tarantola, M Giacobini, M Bertolotti, L Rasero, R AF Sartore, Stefano Soglia, Dominga Maione, Sandra Sacchi, Paola De Marco, Michele Schiavone, Achille Sponza, Simone Dalmasso, Alessandra Bottero, Maria Teresa Pattono, Daniele Zoccarato, Ivo Gasco, Laura Brugiapaglia, Alberto Tarantola, Martina Giacobini, Mario Bertolotti, Luigi Rasero, Roberto TI Genetic traceability of two local chicken populations, Bianca di Saluzzo and Bionda Piemontese, versus some current commercial lines SO ITALIAN JOURNAL OF AGRONOMY DT Article DE Bianca di Saluzzo; Bionda Piemontese; chicken; local breeds; microsatellites; traceabilily ID BREED ASSIGNMENT; DIVERSITY; IDENTIFICATION; MEAT; SOFTWARE; SPANISH; TESTS; TIME AB The aims of this investigation were to analyse the genetic variation of two Piemonte chicken local breeds, Bionda Piemontese and Bianca di Saluzzo, and to set them against some commercial lines. A panel of 19 microsatellite markers was used. On the overall, the results of different analyses highlight the genetic uniqueness of the two breeds; therefore they should be considered genetic resources worthy of preservation. The panel of microsatellites used in this investigation turns out to be a consistent and reliable tool for traceability. In fact, these markers are able to distinguish the two local populations from the commercial lines and they are able to confirm the existence of two genetically different clusters within the Bionda Piemontese, namely the ecotypes standard and Cuneo. Mating policies implemented to avoid inbreeding and, if necessary, a marker assisted conservation scheme would be sufficient to solve the problem of inbreeding. C1 [Sartore, Stefano; Soglia, Dominga; Maione, Sandra; Sacchi, Paola; De Marco, Michele; Schiavone, Achille; Sponza, Simone; Dalmasso, Alessandra; Bottero, Maria Teresa; Pattono, Daniele; Zoccarato, Ivo; Gasco, Laura; Brugiapaglia, Alberto; Tarantola, Martina; Giacobini, Mario; Bertolotti, Luigi; Rasero, Roberto] Univ Turin, Scuola Agr & Med Vet, I-10124 Turin, Italy. C3 University of Turin RP Sartore, S (corresponding author), Dipartimento Sci Vet, Largo Paolo Braccini 2, I-10095 Grugliasco, TO, Italy. EM stefano.sartore@unito.it CR [Anonymous], 2010, INTRO VET GENETICS Aviandiv Project, 2011, DEV STRAT APPL MOL T Bianchi M, 2011, ITAL J ANIM SCI, V10, P205, DOI 10.4081/ijas.2011.e39 Boitard S, 2010, ANIM GENET, V41, P608, DOI 10.1111/j.1365-2052.2010.02061.x Bramante A, 2011, ITAL J FOOD SAF, V1, P41, DOI 10.4081/ijfs.2011.1.41 Ciampolini R, 2006, J ANIM SCI, V84, P11, DOI 10.2527/2006.84111x Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Dalvit C, 2008, MEAT SCI, V80, P389, DOI 10.1016/j.meatsci.2008.01.001 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Dalvit C, 2009, ITAL J ANIM SCI, V8, P63, DOI 10.4081/ijas.2009.s2.63 Davila SG, 2009, POULTRY SCI, V88, P2518, DOI 10.3382/ps.2009-00347 Earl DA, 2012, CONSERV GENET RESOUR, V4, P359, DOI 10.1007/s12686-011-9548-7 Evanno G, 2005, MOL ECOL, V14, P2611, DOI 10.1111/j.1365-294X.2005.02553.x Gandini GC, 2003, J ANIM BREED GENET, V120, P1, DOI 10.1046/j.1439-0388.2003.00365.x Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Goudet J, 1995, J HERED, V86, P485, DOI 10.1093/oxfordjournals.jhered.a111627 Granevitze Z, 2014, ANIM GENET, V45, P87, DOI 10.1111/age.12088 Gutierrez JP, 2005, J HERED, V96, P718, DOI 10.1093/jhered/esi118 Hillel J, 2003, GENET SEL EVOL, V35, P533, DOI [10.1186/1297-9686-35-6-533, 10.1051/gse:2003038] Lasagna E, 2011, SMALL RUMINANT RES, V96, P111, DOI 10.1016/j.smallrumres.2010.11.014 Lewis PO, 2001, GENETIC DATA ANAL GD Maudet C, 2002, J ANIM SCI, V80, P942 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Moioli B, 2004, J HERED, V95, P250, DOI 10.1093/jhered/esh032 Nakamura A, 2006, POULTRY SCI, V85, P2124, DOI 10.1093/ps/85.12.2124 Negrini R, 2008, MEAT SCI, V80, P1212, DOI 10.1016/j.meatsci.2008.05.021 Nicoloso Letizia, 2013, Recent Pat Food Nutr Agric, V5, P9 Oh JD, 2014, ASIAN AUSTRAL J ANIM, V27, P926, DOI 10.5713/ajas.2013.13829 Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Peakall R, 2006, MOL ECOL NOTES, V6, P288, DOI 10.1111/j.1471-8286.2005.01155.x Peakall R, 2012, BIOINFORMATICS, V28, P2537, DOI 10.1093/bioinformatics/bts460 Pompanon F, 2005, NAT REV GENET, V6, P847, DOI 10.1038/nrg1707 Pritchard JK, 2000, GENETICS, V155, P945 Rikimaru K, 2007, POULTRY SCI, V86, P1881, DOI 10.1093/ps/86.9.1881 Rodriguez-Ramirez R, 2011, GENET MOL RES, V10, P2358, DOI 10.4238/2011.October.6.1 Rogberg-Munoz A, 2014, MEAT SCI, V98, P822, DOI 10.1016/j.meatsci.2014.07.028 Rosenberg NA, 2001, GENETICS, V159, P699 Tadano R, 2007, POULTRY SCI, V86, P2301, DOI 10.3382/ps.2007-00233 Wilkinson S, 2011, HEREDITY, V106, P261, DOI 10.1038/hdy.2010.80 Yue GH, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0052721 Zanetti E, 2011, POULTRY SCI, V90, P2195, DOI 10.3382/ps.2011-01527 Zanetti E, 2010, POULTRY SCI, V89, P420, DOI 10.3382/ps.2009-00324 Zanon A., 2001, Annali della Facolta di Medicina Veterinaria, Universita di Parma, V21, P117 NR 46 TC 6 Z9 6 U1 0 U2 4 PY 2014 VL 9 IS 4 BP 176 EP 181 DI 10.4081/ija.2014.605 WC Agronomy SC Agriculture UT WOS:000366211900007 DA 2022-12-14 ER PT J AU Agrawal, K Aggarwal, M Tanwar, S Sharma, G Bokoro, PN Sharma, R AF Agrawal, Kanika Aggarwal, Mayank Tanwar, Sudeep Sharma, Gulshan Bokoro, Pitshou N. Sharma, Ravi TI An Extensive Blockchain Based Applications Survey: Tools, Frameworks, Opportunities, Challenges and Solutions SO IEEE ACCESS DT Article DE Blockchain; ethereum; security; privacy; trust; banking; e-voting; agriculture; healthcare ID SUPPLY-CHAIN; HEALTH-CARE; COMPREHENSIVE SURVEY; IOT SECURITY; TECHNOLOGY; SYSTEM; MANAGEMENT; ARCHITECTURE; INFORMATION; BANKING AB Many security standards and cryptographic solutions exist for different applications such as agriculture, aircraft, banking systems and etc. but a more effective and efficient solution can be given by combining existing technologies with blockchain. This work addresses the problems of previous works such as scalability, immutability, robustness, network latency, auditability, and traceability. Satoshi Nakamoto introduced Blockchain (BC) to tackle the Address Resolution Protocol (ARP) spoofing attacks, Distributed Denial of Service (DDoS), phishing problems and various security issues. Blockchain is a technology that stores the data using a chain of blocks in an encrypted form with hashing algorithms. It uses the decentralized architecture to store the information that helps users and customers to have transparency on records. The data is stored in a distributed ledger that is tamperproof and immutable. To amalgamate the research done so far, this paper presents a systematic review of ten different applications and tools used in blockchain. The applications include academics and education, agriculture, aircraft, banking, car sharing, e-voting, healthcare, Internet of Things (IoT), Intellectual Property Rights (IPR), and Supplychain (SC). Moreover, this paper presented a taxonomy for these applications and analyzed the implementation of tools used in different domains. Different open issues and challenges and key takeaways of blockchain technology were also highlighted. Hence, this paper helps give a new insight into working with blockchain and deciding on appropriate tools and approaches for a particular application. C1 [Agrawal, Kanika; Aggarwal, Mayank] Gurukula Kangri Univ, Fac Engn & Technol, Dept Comp Sci & Engn, Haridwar 249404, India. [Tanwar, Sudeep] Nirma Univ, Inst Technol, Dept Comp Sci & Engn, Ahmadabad 382481, Gujarat, India. [Sharma, Gulshan; Bokoro, Pitshou N.] Univ Johannesburg, Dept Elect Engn Technol, ZA-2006 Johannesburg, South Africa. [Sharma, Ravi] Univ Petr & Energy Studies, Ctr Interdisciplinary Res & Innovat, Dehra Dun 248001, India. C3 Gurukul Kangri Vishwavidyalaya; Nirma University; University of Johannesburg; University of Petroleum & Energy Studies (UPES) RP Aggarwal, M (corresponding author), Gurukula Kangri Univ, Fac Engn & Technol, Dept Comp Sci & Engn, Haridwar 249404, India.; Tanwar, S (corresponding author), Nirma Univ, Inst Technol, Dept Comp Sci & Engn, Ahmadabad 382481, Gujarat, India. EM mayank@gkv.ac.in; sudeep.tanwar@nirmauni.ac.in CR AboSamra KM, 2017, J INF SECUR APPL, V36, P69, DOI 10.1016/j.jisa.2017.08.002 Abuidris Y, 2021, ETRI J, V43, P357, DOI 10.4218/etrij.2019-0362 Agbo CC, 2019, INTERNET TECHNOL LET, V2, DOI 10.1002/itl2.122 Agrawal D, 2022, COMPUT BIOL MED, V140, DOI 10.1016/j.compbiomed.2021.105100 Ahmad M., 2020, INT J DISTRIB SENSOR, V16 Ahmad RW, 2021, IEEE ACCESS, V9, P44905, DOI 10.1109/ACCESS.2021.3066503 Al Omar A, 2019, FUTURE GENER COMP SY, V95, P511, DOI 10.1016/j.future.2018.12.044 Alagiah M., 2020, TEST ENG MANAGE, V83, P3436 Alammary A, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9122400 Alfa Abraham Ayegba, 2021, Journal of Reliable Intelligent Environments, V7, P115, DOI 10.1007/s40860-020-00116-z Ali M, 2021, COMPUT SECUR, V108, DOI 10.1016/j.cose.2021.102355 Alkhodre A, 2019, INT J ADV COMPUT SC, V10, P708 [Anonymous], 2022, WHAT IS HYPERLEDGER Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Arenas R, 2018, INT ICE CONF ENG Astarita V, 2020, INFORMATION, V11, DOI 10.3390/info11010021 Aste T, 2017, COMPUTER, V50, P18, DOI 10.1109/MC.2017.3571064 Auer S, 2022, J NETW COMPUT APPL, V200, DOI 10.1016/j.jnca.2021.103316 Azbeg K, 2022, EGYPT INFORM J, V23, P329, DOI 10.1016/j.eij.2022.02.004 Bai Y., 2021, J CLEANER PROD, V310, P1 Bano S., 2017, USENIX LOGIN MAG, V42, P31 Baudier P, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120397 Baza M, 2021, IEEE T NETW SCI ENG, V8, P1214, DOI 10.1109/TNSE.2019.2959230 Benet J., 2014, ARXIV Bhattacharya P, 2020, LECT NOTES ELECTR EN, V597, P797, DOI 10.1007/978-3-030-29407-6_57 Bhavin M, 2021, J INF SECUR APPL, V56, DOI 10.1016/j.jisa.2020.102673 Bhowmik D., 2018, PROC IEEE INT C MULT, P1 Bhuiyan M. Z. A., 2018, P INT C DATA PROCESS, P62, DOI 10.1145/3224207.3224220 Bhutta MNM, 2021, IEEE ACCESS, V9, P61048, DOI 10.1109/ACCESS.2021.3072849 Bothos E, 2019, 2019 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE WORKSHOPS (WI 2019 COMPANION), P292, DOI 10.1145/3358695.3361844 Buccafurri F, 2017, PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY (ARES 2017), DOI 10.1145/3098954.3098983 Buterin V., 2013, GITHUB REPOSITORY, V1, P22 Cachin C, 2016, P WORKSH DISTR CRYPT, V31, P1 Cai CW, 2021, ACCOUNT FINANC, V61, P71, DOI 10.1111/acfi.12556 Capetillo A, 2022, INT J INTERACT DES M, V16, P791, DOI 10.1007/s12008-022-00886-1 Casino F, 2019, TELEMAT INFORM, V36, P55, DOI 10.1016/j.tele.2018.11.006 Chanson M, 2017, PROCEEDINGS OF THE 2017 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2017 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (UBICOMP/ISWC '17 ADJUNCT), P13, DOI 10.1145/3123024.3123078 Charters, 2007, EBSE200701 KEEL U SC Chen MJ, 2021, J INF SECUR APPL, V58, DOI 10.1016/j.jisa.2021.102771 Choi TM, 2019, TRANSPORT RES E-LOG, V127, P178, DOI 10.1016/j.tre.2019.05.007 Collart AJ, 2022, APPL ECON PERSPECT P, V44, P219, DOI 10.1002/aepp.13134 Composer H, 2022, HYP COMP Cooper B.F., 2010, 1 ACM S CLOUD COMPUT, P143, DOI DOI 10.1145/1807128.1807152 Cucari N, 2022, TECHNOL ANAL STRATEG, V34, P138, DOI 10.1080/09537325.2021.1891217 Dai Qiongjie, 2022, Cyber Security Intelligence and Analytics: The 4th International Conference on Cyber Security Intelligence and Analytics (CSIA 2022). Lecture Notes on Data Engineering and Communications Technologies (125), P752, DOI 10.1007/978-3-030-97874-7_101 De Filippi P, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101284 Dedeoglu V, 2019, PROCEEDINGS OF THE 16TH EAI INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES (MOBIQUITOUS'19), P190, DOI 10.1145/3360774.3360822 Deenmahomed HAM, 2021, COMPUT APPL ENG EDUC, V29, P1234, DOI 10.1002/cae.22381 Denter N. M., 2022, INT J INFORM MANAGE Desai S, 2019, PROCEEDINGS OF THE 20TH ANNUAL CONFERENCE ON INFORMATION TECHNOLOGY EDUCATION (SIGITE '19), P166, DOI 10.1145/3349266.3351386 Dhall S, 2022, ACM T ASIAN LOW-RESO, V21, DOI 10.1145/3467019 Dhulavvagol PM, 2020, PROCEDIA COMPUT SCI, V167, P2506, DOI 10.1016/j.procs.2020.03.303 Dimitriou T, 2020, COMPUT NETW, V174, DOI 10.1016/j.comnet.2020.107234 Divya K., 2022, PROC INT C INNOV TRE, P1 Djenouri Y, 2021, T EMERG TELECOMMUN T, DOI 10.1002/ett.4332 Dorri A, 2019, J PARALLEL DISTR COM, V134, P180, DOI 10.1016/j.jpdc.2019.08.005 Dozier PD, 2020, IEEE T ENG MANAGE, V67, P1129, DOI 10.1109/TEM.2019.2948142 Durach CF, 2021, J BUS LOGIST, V42, P7, DOI 10.1111/jbl.12238 Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067 Engelmann F, 2018, ADV TRANSDISCIPL ENG, V7, P103, DOI 10.3233/978-1-61499-898-3-103 Esmaeilian B., 2019, P ASME INT DES ENG T, P1 Ethereum G, 2022, GETH DOC Explorer B, 2022, BITC TESTNET Faye PS, 2017, AEBMR ADV ECON, V31, P38 Fontela, 1972, WORLD PROBLEMS INVIT, P1 Fraga-Lamas P, 2019, IEEE ACCESS, V7, P17578, DOI 10.1109/ACCESS.2019.2895302 Gambhire G, 2018, 2018 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA) Garg P, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120407 Garriga M, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.5992 Gervais A., 2016, P 2016 ACM SIGSAC C, P3 Gervais A, 2014, IEEE SECUR PRIV, V12, P54, DOI 10.1109/MSP.2014.49 Gethersphere Swarm, 2022, SWARM CENS RES STOR Gopenethereum Parity Ethereum, 2022, PAR ETH FAST LIGHT R Goyal M, 2021, SUSTAINABLE E INFRAS, P221 Guo D., 2021, PROC INT S ELECT ELE, P608 Gupta N., 2019, NAT C COMM NCC FEB, P1 Gupta R, 2020, INT CONF COMP INFO, P184 Gupta R, 2021, T EMERG TELECOMMUN T, V32, DOI 10.1002/ett.4176 Gupta R, 2021, IEEE NETWORK, V35, P160, DOI 10.1109/MNET.011.2000439 Gupta R, 2021, T EMERG TELECOMMUN T, V32, DOI 10.1002/ett.4009 Gupta S., 2018, PROC 21 INT C EXTEND, P157 Gurkaynak G, 2018, COMPUT LAW SECUR REV, V34, P847, DOI 10.1016/j.clsr.2018.05.027 H. Foundation, 2022, HYP CAL H. Foundation, 2022, HYPERLEDGER IR Halloush Z. A., 2019, PROC 2 INT C DATA SC, P1 Hammi MT, 2018, COMPUT SECUR, V78, P126, DOI 10.1016/j.cose.2018.06.004 Han X, 2019, IEEE T COMPUT SOC SY, V6, P922, DOI 10.1109/TCSS.2019.2938841 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hassani H, 2018, J MANAG ANAL, V5, P256, DOI 10.1080/23270012.2018.1528900 Hathaliya JJ, 2020, COMPUT COMMUN, V153, P311, DOI 10.1016/j.comcom.2020.02.018 Heilman E, 2014, LECT NOTES COMPUT SC, V8438, P161, DOI 10.1007/978-3-662-44774-1_12 Ho G., 2021, EXPERT SYST APPL, V179, P1 Huang J, 2022, ACM COMPUT SURV, V54, DOI 10.1145/3439725 Huo R, 2022, IEEE COMMUN SURV TUT, V24, P88, DOI 10.1109/COMST.2022.3141490 Iqbal N, 2021, IEEE ACCESS, V9, P8069, DOI 10.1109/ACCESS.2021.3049325 Ishmaev G, 2017, METAPHILOSOPHY, V48, P666, DOI 10.1111/meta.12277 Ismail L, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11101198 Jabbar R, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20143928 Jaramillo M. P., 2020, PROC 15 CONFERENCIA, P1 Larios-Hernandez GJ, 2017, BUS HORIZONS, V60, P865, DOI 10.1016/j.bushor.2017.07.012 Jing N, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102518 Johar S, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11146252 Kakkar R, 2021, IEEE SYST J, DOI 10.1109/JSYST.2021.3126620 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kanza Y, 2018, 26TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2018), P540, DOI 10.1145/3274895.3274986 Karajovic M, 2019, AUST ACCOUNT REV, V29, P319, DOI 10.1111/auar.12280 Kato K, 2018, 2018 8TH INTERNATIONAL CONFERENCE ON LOGISTICS, INFORMATICS AND SERVICE SCIENCES (LISS) Kaushik K, 2020, IEEE INT CONF MOB, P32, DOI 10.1109/MASS50613.2020.00014 Khan HH, 2022, J CLEAN PROD, V347, DOI 10.1016/j.jclepro.2022.131268 Khan KM, 2021, COMPUT SECUR, V100, DOI 10.1016/j.cose.2020.102081 Kim HM, 2018, INTELL SYST ACCOUNT, V25, P18, DOI 10.1002/isaf.1424 Kim M, 2021, IEEE ACCESS, V9, P54796, DOI 10.1109/ACCESS.2021.3071499 King S., 2012, SELFPUBLISHED PAPER Kitchenham B, 2009, INFORM SOFTWARE TECH, V51, P7, DOI 10.1016/j.infsof.2008.09.009 Kiviat TI, 2015, DUKE LAW J, V65, P569 Kohler W., 1991, OVERVIEW TPC BENCHMA Kosba A, 2016, P IEEE S SECUR PRIV, P839, DOI 10.1109/SP.2016.55 Koshechkin KA, 2018, PROCEDIA COMPUT SCI, V126, P1323, DOI 10.1016/j.procs.2018.08.082 Kouhizadeh M, 2021, INT J PROD ECON, V231, DOI 10.1016/j.ijpe.2020.107831 Kouhizadeh M, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103652 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Kuhle P, 2021, COMPUT IND, V126, DOI 10.1016/j.compind.2020.103393 Kumar M., 2017, FUTURE GENER COMMUN, P125 Kumari A, 2021, COMPUT COMMUN, V172, P102, DOI 10.1016/j.comcom.2021.03.005 Kumari A, 2020, COMPUT COMMUN, V161, P304, DOI 10.1016/j.comcom.2020.07.042 Leduc G., 2021, J CLEANER PROD, V306, P1 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Leutenegger S. T., 1993, SIGMOD Record, V22, P22, DOI 10.1145/170036.170042 Li DM, 2021, T EMERG TELECOMMUN T, V32, DOI 10.1002/ett.3938 Li M, 2021, IEEE ACCESS, V9, P56457, DOI 10.1109/ACCESS.2021.3072196 Liang XP, 2017, 2017 IEEE 28TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC), DOI 10.1109/PIMRC.2017.8292361 Lim J, 2018, INT J TRADE EC FINAN, V9, P159 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Lin J, 2017, PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING ICCSE 2017, P38, DOI 10.1145/3126973.3126980 Litke A, 2019, LOGISTICS-BASEL, V3, DOI 10.3390/logistics3010005 Liu JG, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.1930239 Liu W, 2021, J CLEAN PROD, V298, DOI 10.1016/j.jclepro.2021.126763 Lopes DP, 2021, RES TRANSP BUS MANAG, V41, DOI 10.1016/j.rtbm.2021.100669 Maesa DD, 2020, J PARALLEL DISTR COM, V138, P99, DOI 10.1016/j.jpdc.2019.12.019 Makhdoom I, 2019, J NETW COMPUT APPL, V125, P251, DOI 10.1016/j.jnca.2018.10.019 Makkes MX, 2019, 2019 SIXTH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SYSTEMS, MANAGEMENT AND SECURITY (IOTSMS), P466 Malkawi M., 2021, INT J ELECT COMPUT E, V11, P4325 Manjunath A, 2022, IEEE T INTELL TRANSP, V23, P7028, DOI 10.1109/TITS.2021.3066439 Mathivathanan D, 2021, INT J PROD RES, V59, P3338, DOI 10.1080/00207543.2020.1868597 Mavilia R, 2022, AFR J SCI TECHNOL IN, V14, P845, DOI 10.1080/20421338.2021.1908660 Menon M., 2021, MARKETING EDU REV, V32, P1 Minoli D, 2018, INTERNET THINGS-NETH, V1-2, P1, DOI 10.1016/j.iot.2018.05.002 Mofokeng N.E.M, 2018, AFR J HOSP TOUR LEIS, V7, P1 Mohammed S, 2021, ACM T INTERNET TECHN, V21, DOI 10.1145/3445788 Mohan V, 2019, RES POLICY, V48, DOI 10.1016/j.respol.2019.103805 Monrat AA, 2019, IEEE ACCESS, V7, P117134, DOI 10.1109/ACCESS.2019.2936094 Moura T, 2017, DG.O 2017: THE PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH: INNOVATIONS AND TRANSFORMATIONS IN GOVERNMENT, P574, DOI 10.1145/3085228.3085263 Nakamoto S., 2008, CONSULTED, P21260 Vu N, 2021, PROD PLAN CONTROL, DOI 10.1080/09537287.2021.1939902 O'Leary DE, 2017, INTELL SYST ACCOUNT, V24, P138, DOI 10.1002/isaf.1417 Omar IA, 2021, ARAB J SCI ENG, V46, P3001, DOI 10.1007/s13369-020-04989-3 Omar IAA, 2022, COMPUT IND ENG, V167, DOI 10.1016/j.cie.2022.107995 Orjuela KG, 2021, ACTA AGR SCAND B-S P, V71, P1, DOI 10.1080/09064710.2020.1840618 Pal S, 2022, J NETW COMPUT APPL, V203, DOI 10.1016/j.jnca.2022.103371 Panja S, 2021, J INF SECUR APPL, V59, DOI 10.1016/j.jisa.2021.102815 Panja S, 2020, IEEE T ENG MANAGE, V67, P1323, DOI 10.1109/TEM.2020.2986371 Pasha SH, 2022, J CIRCUIT SYST COMP, V31, DOI 10.1142/S0218126622500025 Patel MM, 2020, J INF SECUR APPL, V55, DOI 10.1016/j.jisa.2020.102583 Patel N, 2021, T EMERG TELECOMMUN T, DOI 10.1002/ett.4286 Pawlak M, 2019, 17TH INTERNATIONAL CONFERENCE ON ADVANCES IN MOBILE COMPUTING & MULTIMEDIA (MOMM2019), P145, DOI 10.1145/3365921.3365927 Putz B, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2020.102425 Qian YF, 2018, COMPUT ELECTR ENG, V72, P266, DOI 10.1016/j.compeleceng.2018.08.021 Raddatz N, 2021, EUR J INFORM SYST, DOI 10.1080/0960085X.2021.1944823 Raju S., 2017, PROC 10 INT C THEORY, P538 Ramchandra MV, 2022, MATER TODAY-PROC, V56, P2221, DOI 10.1016/j.matpr.2021.11.554 Ray PP, 2021, IEEE SYST J, V15, P85, DOI 10.1109/JSYST.2020.2963840 Rupa C, 2021, MATH BIOSCI ENG, V18, P7010, DOI 10.3934/mbe.2021349 Ruta M, 2017, PROCEEDINGS OF THE 15TH ACM CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS (SENSYS'17), DOI 10.1145/3131672.3136974 Saha S, 2020, IEEE ICC Saheb T., 2021, J HIGH TECHNOLOGY MA, V32, P1 Salah K, 2019, IEEE ACCESS, V7, P10127, DOI 10.1109/ACCESS.2018.2890507 Savelyev A, 2018, COMPUT LAW SECUR REV, V34, P550, DOI 10.1016/j.clsr.2017.11.008 Schmidt CG, 2019, J PURCH SUPPLY MANAG, V25, DOI 10.1016/j.pursup.2019.100552 Shafagh H., 2017, P 2017 CLOUD COMPUTI, P45 Shanker M, 2019, INT J SCI RES COMPUT, V7, P1 Shuaib M., 2021, MATER TODAY-PROC, DOI 10.1016/j.matpr.2021.03.083 Shynu PG, 2021, IEEE ACCESS, V9, P45706, DOI 10.1109/ACCESS.2021.3065440 Singh M, 2018, COMPUT NETW, V145, P219, DOI 10.1016/j.comnet.2018.08.016 Soltanisehat L, 2020, IEEE T ENG MANAG EAR, DOI [10.1109/TEM.2020.3013507, DOI 10.1109/TEM.2020.3013507] Song K, 2021, J CLEANER PROD, V281, P1 Soni M., 2021, MATER TODAY-PROC, P1 Sravan N. P. V., 2018, INT J SCI ENG RES, V9, P1664 SUITE T, 2022, TRUFFL SMART CONTR M Suite T, 2022, GAN OV Suralkar S., 2019, INT J RES ANAL REV, V6, P77 Gupta S, 2021, Arxiv, DOI arXiv:2107.11592 Swati V., 2018, PROC INT C CIRCUITS, P1 Syed F, 2021, T EMERG TELECOMMUN T, V32, DOI 10.1002/ett.4133 T. L. F. Projects, 2022, HYP SAWT HYP FDN Tanwar S, 2020, FOG DATA ANAL IOT AP Tanwar S, 2020, J INF SECUR APPL, V50, DOI 10.1016/j.jisa.2019.102407 Thomas C, 2022, ASCRIBE ANNOUNCES SC Tsai WT, 2017, 2017 11TH IEEE SYMPOSIUM ON SERVICE-ORIENTED SYSTEM ENGINEERING (SOSE), P174, DOI 10.1109/SOSE.2017.35 Udegbe S, 2017, INT J INNOV RES ADV, V4, P257 Ul Hassan M, 2019, FUTURE GENER COMP SY, V97, P512, DOI 10.1016/j.future.2019.02.060 Wang D, 2021, IEEE INTERNET THINGS, V8, P2976, DOI 10.1109/JIOT.2020.3023920 Wang J, 2021, FUTURE GENER COMP SY, V123, P233, DOI 10.1016/j.future.2021.05.002 Wang S, 2018, IEEE T COMPUT SOC SY, V5, P942, DOI 10.1109/TCSS.2018.2865526 Wang WB, 2019, IEEE ACCESS, V7, P22328, DOI 10.1109/ACCESS.2019.2896108 Wang XD, 2021, IEEE T IND INFORM, V17, P7725, DOI 10.1109/TII.2021.3049405 Wang YL, 2019, SUPPLY CHAIN MANAG, V24, P62, DOI 10.1108/SCM-03-2018-0148 Weerasinghe N, 2021, IEEE OPEN J COMM SOC, V2, P575, DOI 10.1109/OJCOMS.2021.3066284 Williams P, 2019, J HIGH EDUC POLICY M, V41, P104, DOI 10.1080/1360080X.2018.1520491 Xu RH, 2018, PROCEEDINGS OF THE 15TH EAI INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES (MOBIQUITOUS 2018), P449, DOI 10.1145/3286978.3287022 Xu Y, 2020, IEEE T SERV COMPUT, V13, P289, DOI 10.1109/TSC.2019.2953033 Xu ZY, 2020, PROCEEDINGS OF THE AUSTRALASIAN COMPUTER SCIENCE WEEK MULTICONFERENCE (ACSW 2020), DOI 10.1145/3373017.3373022 Yadav A.S., 2021, INGENIERIE SYSTEMES, V26, P13, DOI 10.18280/isi.260102 Yadav AS, 2022, IETE TECH REV, V39, P799, DOI 10.1080/02564602.2021.1908859 Yadav Janardan Krishna, 2022, ICT Analysis and Applications. Lecture Notes in Networks and Systems (314), P475, DOI 10.1007/978-981-16-5655-2_46 Yadav Janardan Krishna, 2021, Journal of High Technology Management Research, V32, DOI 10.1016/j.hitech.2021.100404 Yaqoob I, 2022, NEURAL COMPUT APPL, V34, P11475, DOI 10.1007/s00521-020-05519-w Yu HX, 2020, PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING (ICDCN 2020), DOI 10.1145/3369740.3369776 Yuan X, 2021, WIREL COMMUN MOB COM, V2021, DOI 10.1155/2021/9952218 Zeadally S, 2019, INTERNET TECHNOL LET, V2, DOI 10.1002/itl2.130 Zhang PY, 2022, IEEE T IND INFORM, V18, P3551, DOI 10.1109/TII.2021.3116037 Zhang SJ, 2020, ICT EXPRESS, V6, P93, DOI 10.1016/j.icte.2019.08.001 Zhu P, 2021, IEEE T ENG MANAGE, DOI 10.1109/TEM.2021.3066090 Zou YJ, 2020, IEEE ACCESS, V8, P187182, DOI 10.1109/ACCESS.2020.3030491 NR 223 TC 0 Z9 0 U1 2 U2 2 PY 2022 VL 10 BP 116858 EP 116906 DI 10.1109/ACCESS.2022.3219160 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000886177600001 DA 2022-12-14 ER PT J AU Cappelli, G Di Vuolo, G Gerini, O Noschese, R Bufano, F Capacchione, R Rosini, S Limone, A De Carlo, E AF Cappelli, Giovanna Di Vuolo, Gabriele Gerini, Oreste Noschese, Rosario Bufano, Francesca Capacchione, Roberta Rosini, Stefano Limone, Antonio De Carlo, Esterina TI Italian Tracing System for Water Buffalo Milk and Processed Milk Products SO ANIMALS DT Article DE water buffalo; traceability milk; farmers; dairies ID MOZZARELLA CHEESE; PERFORMANCE; QUALITY AB Simple Summary As buffalo milk is important in Italy, especially in the southern regions where Protected Designation of Origin (PDO) buffalo mozzarella from Campania is produced, the need has arisen to trace the path of milk from the farm to the dairy. We aimed to verify the applicability of a buffalo tracing system throughout the national territory, following the Ministerial Decree of 9 September 2014. Use of the online system is mandatory for breeders, dairies, and intermediaries, and is required to record and communicate all milk that is produced, bought, and sold. In Italy, the number of registrations is more representative in the Campania region, which has the highest concentration of dairy buffalo herds; for the other regions, the number of farms registered in the system is less representative. The system highlights that the circuit of non-PDO products absorbs 35% of the total milk produced, while 65% of the milk is processed in PDO dairies, which allows for the control of milk production throughout the territory and facilitates the control of milk that proceeds to be frozen or that can only be used in non-PDO productions; this increases the transparency of the supply chain. The system is a computerized archive of the productions for all the operators in the sector. This document describes the development of a tracing system for the buffalo supply chain, namely an online computer system in which farmers, dairies, and brokers must maintain records of the production of milk through to the production of derivatives. The system is jointly used throughout the Italian national territory by the Istituto Zooprofilattico Sperimentale del Mezzogiorno (IZSM) and the Sistema Informativo Agricolo Nazionale Italiano (SIAN), after being made mandatory and regulated with the publication of the Ministerial Decree of 9 September 2014. Farmers are obligated to communicate their daily production of bulk milk, the number of animals milked, the number of the delivery note of the sale, and the name of the purchaser; within the first week of the month, they must communicate the milk production of each animal milked. Dairies are required to communicate the milk and the processed product (mozzarella, yogurt, etc.) purchased on a daily basis. The intermediaries are required to communicate the daily milk purchased, both fresh and frozen, the semi-finished product, and the sale of the same. The tracing system linked to the project authorized by the Ministry of Health, called "Development, validation and verification of the applicability of an IT system to be used for the management of traceability in the buffalo industry", provides operators with the monitoring of production and sales in real time through alerts and access logs. Currently, there are 1531 registered farmers, 601 non-PDO dairies, 102 PDO dairies, 68 non-PDO intermediaries, and 17 PDO intermediaries in Italy. The system provides support for the recovery of the buffalo sector; from the analysis of the data extrapolated from the tracing system of the buffalo supply chain for the years 2016 to 2019, this paper highlights that the application of the Ministerial Decree No. 9406 of 9 September 2014 and the tracing of the supply chain have increased the price of buffalo milk at barns from EUR 1.37/kg to EUR 1.55/kg from 2016 to 2019. C1 [Cappelli, Giovanna; Di Vuolo, Gabriele; Noschese, Rosario; Bufano, Francesca; Capacchione, Roberta; Limone, Antonio; De Carlo, Esterina] Ist Zooprofilatt Sperimentale Mezzogiorno, Natl Reference Lab Hyg & Technol Breeding & Buffa, I-84131 Salerno, Italy. [Gerini, Oreste] Minist Agr Food & Forestry Policies, I-00187 Rome, Italy. [Rosini, Stefano] Agribusiness Qual Dept, I-00187 Rome, Italy. C3 IZS del Mezzogiorno RP Di Vuolo, G (corresponding author), Ist Zooprofilatt Sperimentale Mezzogiorno, Natl Reference Lab Hyg & Technol Breeding & Buffa, I-84131 Salerno, Italy. EM giovanna.cappelli@izsmportici.it; gabriele.divuolo@izsmportici.it; o.gerini@politicheagricole.it; rosario.noschese@izsmportici.it; francesca.bufano@izsmportici.it; roberta.capacchione@izsmportici.it; rosini.s@dqacertificazioni.it; antolim@izsmportici.it; direzionesanitaria@izsmportici.it CR Altieri S, 2020, J SCI FOOD AGR, V100, P995, DOI 10.1002/jsfa.10100 [Anonymous], DATI FORNITI DALLA B Brescia MA, 2005, FOOD CHEM, V89, P139, DOI 10.1016/j.foodchem.2004.02.016 Campanile G, 2009, PREGNANCY PROTEIN RESEARCH, P31 CLAL, 2019, PRODUCTION MOZZARELL Infascelli F, 2004, VET RES COMMUN, V28, P143, DOI 10.1023/B:VERC.0000045392.42902.7e Mazzei P, 2012, FOOD CHEM, V132, P1620, DOI 10.1016/j.foodchem.2011.11.142 Minervino AHH, 2020, FRONT VET SCI, V7, DOI 10.3389/fvets.2020.570413 Mwanga G, 2020, ScientificWorldJournal, V2020, P1279569, DOI 10.1155/2020/1279569 Naveena B. M., 2014, Animal Frontiers, V4, P18, DOI 10.2527/af.2014-0029 Phogat J.B., 2016, INT J PLANT ANIM ENV, V6, P46 Zicarelli L, 2004, VET RES COMMUN, V28, P127, DOI 10.1023/B:VERC.0000045390.81982.4d Zicarelli L, 2010, SOC REPROD FERTIL, V67, P443 Zicarelli L., 1997, Bubalus Bubalis, P29 NR 14 TC 4 Z9 4 U1 3 U2 9 PD JUN PY 2021 VL 11 IS 6 AR 1737 DI 10.3390/ani11061737 WC Agriculture, Dairy & Animal Science; Veterinary Sciences; Zoology SC Agriculture; Veterinary Sciences; Zoology UT WOS:000665326600001 DA 2022-12-14 ER PT J AU Maes, KA Bruch, S Hersberger, KE Lampert, ML AF Maes, Karen A. Bruch, Sophia Hersberger, Kurt E. Lampert, Markus L. TI Documentation of pharmaceutical care: development of an intervention oriented classification system SO INTERNATIONAL JOURNAL OF CLINICAL PHARMACY DT Article DE Classification system; Community pharmacy practice; Drug-related problem; Mixed method; Pharmaceutical care; Pharmaceutical intervention ID DRUG-RELATED PROBLEMS; VALIDATION AB Background A standardised classification system of pharmaceutical interventions (PI) is in use in several Swiss hospitals, whereas none exists for community pharmacies to date. To promote information exchange between both settings, a compatible structure of the classification system is needed. Objective To develop an intervention oriented classification system for community pharmacies named PharmDISC based on the hospital system; to test it on interrater reliability, appropriateness, interpretability, and face and content validity; to assess pharmacists' opinions. Setting Seventy-seven Swiss community pharmacies. Method Based on previous studies, a modified classification system was developed. Fifth-year pharmacy students (n = 77) received a two-hour training and classified three model PIs with which Fleiss-Kappa coefficients K were calculated to determine interrater reliability. In the community pharmacies, each student consecutively collected ten prescriptions that required a PI. A focus group interview was conducted with pharmacists (n = 9). The anonymised transcript was analysed using thematic analysis. Main outcome measure Number of classified PIs, interrater reliability, pharmacists' opinion/suggestions. Results The classification system includes 5 categories and 52 subcategories. Most of the 725 PIs (94.6%) were completely classified. The PharmDISC system reached an overall substantial user agreement (K = 0.61). Despite some points for optimisation, the pharmacists were satisfied with the PharmDISC system. They recognised the importance of PI documentation and believed that this may allow traceability, facilitate communication within the team and other healthcare professionals, and increase quality of care. Conclusion The PharmDISC system was valid and reached substantial interrater reliability. Refinement based on the pharmacists' suggestions resulted in a final version to be tested in an observational study with community pharmacists. C1 [Maes, Karen A.; Bruch, Sophia; Hersberger, Kurt E.; Lampert, Markus L.] Univ Basel, Dept Pharmaceut Sci, Pharmaceut Care Res Grp, Klingelbergstr 50, CH-4056 Basel, Switzerland. [Lampert, Markus L.] Solothurner Spitaler, Inst Hosp Pharm, Olten, Switzerland. C3 University of Basel; Kantonsspital Olten RP Maes, KA (corresponding author), Univ Basel, Dept Pharmaceut Sci, Pharmaceut Care Res Grp, Klingelbergstr 50, CH-4056 Basel, Switzerland. EM karen.maes@unibas.ch CR Allenet B, 2006, PHARM WORLD SCI, V28, P181, DOI 10.1007/s11096-006-9027-5 American Coll Clinical Pharm, 2008, PHARMACOTHERAPY, V28, P816, DOI 10.1592/phco.28.6.816 Basger BJ, 2015, ANN PHARMACOTHER, V49, P405, DOI 10.1177/1060028014568008 Basger BJ, 2014, EUR J CLIN PHARMACOL, V70, P799, DOI 10.1007/s00228-014-1686-x Bjorkman IK, 2008, RES SOC ADMIN PHARM, V4, P320, DOI 10.1016/j.sapharm.2007.10.006 Brock KA, 2006, J AM PHARM ASSOC, V46, P378, DOI 10.1331/154434506777069642 Comite de Consenso, 2007, ARS PHARM, V48, P5 Craig P, 2008, BMJ-BRIT MED J, V337, DOI 10.1136/bmj.a1655 Driscoll D.L., 2007, ECOLOG ENV ANTHR, V3, P19 Eichenberger PM, 2010, PHARM WORLD SCI, V32, P362, DOI 10.1007/s11096-010-9377-x GANSO M, 2007, KRANKENHAUSPHARMAZIE, V28, P273 Hammerlein A, 2007, ANN PHARMACOTHER, V41, P1825, DOI 10.1345/aph.1K207 Hersberger KE, 2009, J CLIN PHARM THER, V34, P387, DOI 10.1111/j.1365-2710.2009.01049.x Hohmann C, 2012, J CLIN PHARM THER, V37, P276, DOI 10.1111/j.1365-2710.2011.01281.x Kaufmann CP, 2015, BMJ OPEN, V5, DOI 10.1136/bmjopen-2014-006376 King J. E., RESOURCE PAGE GEN KA Krahenbuhl JM, 2008, PHARM WORLD SCI, V30, P777, DOI 10.1007/s11096-008-9217-4 LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310 Maes KA, 2015, INT J CLIN PHARM-NET, V37, P1162, DOI 10.1007/s11096-015-0179-z Nicolas A, 2013, INT J CLIN PHARM-NET, V35, P476, DOI 10.1007/s11096-013-9769-9 Ryan C, 2015, PHARM PRACTICE RES M, P107 Schaefer M, 2002, PHARM WORLD SCI, V24, P120, DOI 10.1023/A:1019543029936 STRAND LM, 1990, DICP ANN PHARMAC, V24, P1093, DOI 10.1177/106002809002401114 Tong A, 2007, INT J QUAL HEALTH C, V19, P349, DOI 10.1093/intqhc/mzm042 van Mil JWF, 2004, ANN PHARMACOTHER, V38, P859, DOI 10.1345/aph.1D182 Westerlund LOT, 2003, ANN PHARMACOTHER, V37, P354, DOI 10.1345/aph.1C182 Westerlund T., 1999, INT J PHARM PRACT, V7, P40, DOI DOI 10.1111/J.2042-7174.1999.TB00947.X Williams M, 2012, INT J CLIN PHARM-NET, V34, P43, DOI 10.1007/s11096-011-9583-1 NR 28 TC 9 Z9 9 U1 0 U2 3 PD APR PY 2017 VL 39 IS 2 BP 354 EP 363 DI 10.1007/s11096-017-0442-6 WC Pharmacology & Pharmacy SC Pharmacology & Pharmacy UT WOS:000399049800002 DA 2022-12-14 ER PT J AU Pegels, N Lopez-Calleja, I Garcia, T Martin, R Gonzalez, I AF Pegels, N. Lopez-Calleja, I. Garcia, T. Martin, R. Gonzalez, I. TI Detection of rabbit and hare processed material in compound feeds by TaqMan real-time PCR SO FOOD ADDITIVES AND CONTAMINANTS PART A-CHEMISTRY ANALYSIS CONTROL EXPOSURE & RISK ASSESSMENT DT Article DE real-time PCR; 12S rRNA gene; rabbit; hare; leporids; animal feeds; pet foods; traceability ID POLYMERASE-CHAIN-REACTION; ANIMAL PROTEINS; DNA; MEAT; FOOD; IDENTIFICATION; ASSAY; QUANTIFICATION; AUTHENTICATION; FORENSICS AB Food and feed traceability has become a priority for governments due to consumer demand for comprehensive and integrated safety policies. In the present work, a TaqMan real-time PCR assay targeting the mitochondrial 12S rRNA gene was developed for specific detection of rabbit and hare material in animal feeds and pet foods. The technique is based on the use of three species-specific primer/probe detection systems targeting three 12S rRNA gene fragments: one from rabbit species, another one from hare species and a third fragment common to rabbit and hare (62, 102 and 75bp length, respectively). A nuclear 18S rRNA PCR system, detecting a 77-bp amplicon, was used as positive amplification control. Assay performance and sensitivity were assessed through the analysis of a batch of laboratory-scale feeds treated at 133 degrees C at 3 bar for 20min to reproduce feed processing conditions dictated by European regulations. Successful detection of highly degraded rabbit and hare material was achieved at the lowest target concentration assayed (0.1%). Furthermore, the method was applied to 96 processed commercial pet food products to determine whether correct labelling had been used at the market level. The reported real-time PCR technique detected the presence of rabbit tissues in 80 of the 96 samples analysed (83.3%), indicating a possible labelling fraud in some pet foods. The real-time PCR method reported may be a useful tool for traceability purposes within the framework of feed control. C1 [Pegels, N.; Lopez-Calleja, I.; Garcia, T.; Martin, R.; Gonzalez, I.] Univ Complutense Madrid, Fac Vet, Dept Nutr Bromatol & Tecnol Alimentos, Madrid, Spain. C3 Complutense University of Madrid RP Gonzalez, I (corresponding author), Univ Complutense Madrid, Fac Vet, Dept Nutr Bromatol & Tecnol Alimentos, Madrid, Spain. EM gonzalzi@vet.ucm.es CR Ali ME, 2012, FOOD ANAL METHOD, V5, P935, DOI 10.1007/s12161-011-9357-3 Bellagamba F, 2006, J FOOD PROTECT, V69, P891, DOI 10.4315/0362-028X-69.4.891 Benedetto A, 2011, FOOD CHEM, V126, P1436, DOI 10.1016/j.foodchem.2010.11.131 Bottero MT, 2003, J FOOD PROTECT, V66, P2307, DOI 10.4315/0362-028X-66.12.2307 Bustin SA, 2009, CLIN CHEM, V55, P611, DOI 10.1373/clinchem.2008.112797 Chapman JA, 1990, RABBITS HARES PIKAS Chianini F, 2012, P NATL ACAD SCI USA, V109, P5080, DOI 10.1073/pnas.1120076109 Combelles E, 2004, PREMIUMISATION PRIVA CORBET GB, 1986, MAMMAL REV, V16, P105, DOI 10.1111/j.1365-2907.1986.tb00029.x Dalmasso A, 2004, MOL CELL PROBE, V18, P81, DOI 10.1016/j.mcp.2003.09.006 De Silva SS, 2008, J AGR ENVIRON ETHIC, V21, P459, DOI 10.1007/s10806-008-9109-6 European Commission, 2003, OFF J EUR UNION L, V173, P6 European Commission, 2009, OFFICIAL J EUROPEA L, V300, P1 European Commission, 2009, OFF J EUR UNION, VL 229, P1 Fajardo V, 2008, MEAT SCI, V79, P289, DOI 10.1016/j.meatsci.2007.09.013 Fajardo V, 2010, TRENDS FOOD SCI TECH, V21, P408, DOI 10.1016/j.tifs.2010.06.002 Frezza D, 2008, INNOV FOOD SCI EMERG, V9, P18, DOI 10.1016/j.ifset.2007.04.008 Fumiere O, 2006, ANAL BIOANAL CHEM, V385, P1045, DOI 10.1007/s00216-006-0533-z Fumiere O, 2009, BIOTECHNOL AGRON SOC, V13, P59 Hird H, 2006, FOOD ADDIT CONTAM, V23, P645, DOI 10.1080/02652030600603041 La Neve F, 2008, MEAT SCI, V80, P216, DOI 10.1016/j.meatsci.2007.11.027 Lebas F, 1997, FAO ANIMAL PRODUCTIO, V21 Lee C, 2008, APPL MICROBIOL BIOT, V78, P371, DOI 10.1007/s00253-007-1300-6 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 Martin I, 2009, WORLD RABBIT SCI, V17, P27 Montowska M, 2011, FOOD REV INT, V27, P84, DOI 10.1080/87559129.2010.518297 Pegels N, 2012, FOOD ADDIT CONTAM A, V29, P1402, DOI 10.1080/19440049.2012.696284 Pegels N, 2012, POULTRY SCI, V91, P1709, DOI 10.3382/ps.2011-01954 Pegels N, 2013, FOOD ANAL METHODS, DOI 10.1007/s12161-012-9555-7 Pegels N, 2011, FOOD CONTROL, V22, P1189, DOI 10.1016/j.foodcont.2011.01.015 Peiretti PG, 2012, ANIMALS-BASEL, V2, P55, DOI 10.3390/ani2010055 Pereira F, 2010, NUCLEIC ACIDS RES, V38, DOI 10.1093/nar/gkq865 Pereira Filipe, 2008, Recent Pat DNA Gene Seq, V2, P187, DOI 10.2174/187221508786241738 Prado M, 2007, J AGR FOOD CHEM, V55, P7495, DOI 10.1021/jf0707583 Rojas M, 2011, ANIM FEED SCI TECH, V169, P128, DOI 10.1016/j.anifeedsci.2011.05.006 Santos CG, 2012, MEAT SCI, V90, P836, DOI 10.1016/j.meatsci.2011.10.018 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 NR 38 TC 8 Z9 8 U1 1 U2 29 PD MAY 1 PY 2013 VL 30 IS 5 BP 771 EP 779 DI 10.1080/19440049.2013.794978 WC Chemistry, Applied; Food Science & Technology; Toxicology SC Chemistry; Food Science & Technology; Toxicology UT WOS:000320571000001 DA 2022-12-14 ER PT J AU Liu, C Li, JY Steele, W Fang, XM AF Liu, Cheng Li, Jiaoyuan Steele, William Fang, Xiangming TI A study on Chinese consumer preferences for food traceability information using best-worst scaling SO PLOS ONE DT Article ID CONJOINT-ANALYSIS APPLICATIONS; WILLINGNESS-TO-PAY; COUNTRY-OF-ORIGIN; SAFETY ATTRIBUTES; QUALITY; CHOICE; BEEF; HEALTH AB Food safety is a global public health issue, which often arises from asymmetric information between consumers and suppliers. With the development of information technology in human life, building a food traceability information sharing platform is viewed as one of the best ways to overcome the trust crisis and resolve the problem of information asymmetry in China. However, among the myriad information available from the food supply chain, there is a lack of knowledge on consumer preference. Based on the best-worst scaling approach, this paper investigated consumer preferences for vegetable, pork, and dairy product traceability information. Specifically, this paper measured the relative importance that consumers place on the traceable information. The results indicate that consumers have varying priorities for information in different cases. "Pesticide/veterinary use," "picking/slaughtering date," and "fertilizer/feed use" are the most preferred traceable information for Chinese consumers in the case of vegetables, while "picking/slaughtering date" and "history of illness and taking protective measures" are the most preferred information in the case of pork. In the case of dairy products, consumers prefer "processing information," "environmental information of the origin," and "traceable tag certification information" most. The results of this study call for the direct involvement of the Chinese government in the food safety information sharing system as following. First, given consumers' diverse preferences, different types of traceable information should be recorded into the information sharing platform depending on food types. Second, the government could promote the step-by-step construction of such a platform based on the priority of consumers' preferences. Third, new technology should be applied to guarantee the reliability of traceable information. Finally, local preferences in terms of the way consumers receive and understand information should be taken into consideration. C1 [Liu, Cheng; Li, Jiaoyuan; Fang, Xiangming] China Agr Univ, Coll Econ & Management, Beijing, Peoples R China. [Steele, William] Univ Georgia, Dept Agr & Appl Econ, Athens, GA 30602 USA. [Fang, Xiangming] Georgia State Univ, Sch Publ Hlth, Atlanta, GA 30303 USA. C3 China Agricultural University; University System of Georgia; University of Georgia; University System of Georgia; Georgia State University RP Fang, XM (corresponding author), China Agr Univ, Coll Econ & Management, Beijing, Peoples R China.; Fang, XM (corresponding author), Georgia State Univ, Sch Publ Hlth, Atlanta, GA 30303 USA. EM xmfang@cau.edu.cn CR Alcorn T, 2012, LANCET, V379, P789, DOI 10.1016/S0140-6736(12)60330-4 Angulo AM, 2007, FOOD QUAL PREFER, V18, P1106, DOI 10.1016/j.foodqual.2007.05.008 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Baumgartner H, 2001, J MARKETING RES, V38, P143, DOI 10.1509/jmkr.38.2.143.18840 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bridges JFP, 2011, VALUE HEALTH, V14, P403, DOI 10.1016/j.jval.2010.11.013 Calvin L., 2006, Amber Waves, V4, P16 Campbell D.T., 1979, QUASI EXPERIMENTATIO Caswell JA, 1996, AM J AGR ECON, V78, P1248, DOI 10.2307/1243501 Craig C.S., 2005, INT MARKETING RES Ehmke MD, 2008, AGR ECON-BLACKWELL, V38, P277, DOI 10.1111/j.1574-0862.2008.00299.x Fang B, 2014, FOOD CONTROL, V39, P62, DOI 10.1016/j.foodcont.2013.10.039 Grace D, 2015, INT J ENV RES PUB HE, V12, P10490, DOI 10.3390/ijerph120910490 GREEN PE, 1974, J CONSUM RES, V1, P61, DOI 10.1086/208592 Han Y., 2014, CHINA SOFT SCI, V2, P32 Jin SS, 2017, FOOD CONTROL, V77, P163, DOI 10.1016/j.foodcont.2017.02.012 Jin SS, 2014, FOOD QUAL PREFER, V36, P144, DOI 10.1016/j.foodqual.2014.04.005 KIRK MD, 2015, PLOS MED, V12 Lanasier EV., 2015, J MARK COMMUN, V21, P425, DOI [10.1080/13527266.2013.828769, DOI 10.1080/13527266.2013.828769] Lee JA, 2008, J PERS ASSESS, V90, P335, DOI 10.1080/00223890802107925 Lee JA, 2007, PSYCHOL MARKET, V24, P1043, DOI 10.1002/mar.20197 Liu LN, 2017, WHICEB 2017 P, P54 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Louviere J.J., 2000, STATED CHOICE METHOD, DOI DOI 10.1017/CBO9780511753831 Louviere J, 2013, INT J RES MARK, V30, P292, DOI 10.1016/j.ijresmar.2012.10.002 Lu J, 2016, BRIT FOOD J, V118, P2140, DOI 10.1108/BFJ-12-2015-0461 Marshall D, 2010, PATIENT, V3, P249, DOI 10.2165/11539650-000000000-00000 Menozzi D, 2015, FOOD CONTROL, V49, P40, DOI 10.1016/j.foodcont.2013.09.006 Mesias FJ, 2005, J SCI FOOD AGR, V85, P2487, DOI 10.1002/jsfa.2283 Mori T, 2017, SSM POPULATION HLTH, V3, P624 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Smith TG, 2011, FOOD POLICY, V36, P239, DOI 10.1016/j.foodpol.2010.11.021 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Wang ZG, 2008, FOOD POLICY, V33, P27, DOI 10.1016/j.foodpol.2007.05.006 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Yam-Kwang Chen, 2012, Information Technology Journal, V11, P1154, DOI 10.3923/itj.2012.1154.1165 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] NR 37 TC 16 Z9 16 U1 3 U2 27 PD NOV 2 PY 2018 VL 13 IS 11 AR e0206793 DI 10.1371/journal.pone.0206793 WC Multidisciplinary Sciences SC Science & Technology - Other Topics UT WOS:000449289800060 DA 2022-12-14 ER PT J AU Inaba, M Fort, A Bringloe, T Mols-Mortensen, A Ghriofa, CN Sulpice, R AF Inaba, Masami Fort, Antoine Bringloe, Trevor Mols-Mortensen, Agnes Ghriofa, Cliodhna Ni Sulpice, Ronan TI Branding and tracing seaweed: Development of a high-resolution genetic kit to identify the geographic provenance of Alaria esculenta SO ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS DT Article DE Next generation sequencing (NGS) technology; Single nucleotide polymorphisms (SNPs); Traceability; Cleaved amplified polymorphic sequence (CAPS) method ID INHERITANCE AB Global seaweed production increased rapidly in the past 50 years, mostly in Asia. Recently seaweed aquaculture has been attracting more attention worldwide because of its potential as a form of sustainable agriculture that provides food, feed, fertiliser, bio-stimulants and other valuable products. Quality characteristics of seaweeds are species-dependent, influenced by the genetic makeup of the individual, and by the environmental conditions where the seaweeds are grown. Those two factors largely depend on their place of origin. Traceability in terms of geographic provenance would therefore add commercial value to seaweed products. Here we describe the development of a method to identify/certify the place of origin of the blown alga Alaria esculenta. We identified Single Nucleotide Polymorphisms (SNPs) on the nuclear, plastid and mitochondrial genomes by analysing whole-genome sequences of individuals originated from Ireland, Northern Ireland, Faroe Islands, Greenland, Norway and Atlantic Canada. Multi-dimensional scaling (MDS) plots of the nuclear SNPs, but not of the organellar SNPs, showed a clear separation of individual samples by the country of origin. We then identified country-specific nuclear SNPs and designed a set of cleaved amplified polymorphic sequence (CAPS) assays to detect two or three of specific SNPs per country. The SNPs were tested on individuals, including those whose genome sequence had not been determined, and were shown to successfully identify individual's country of origin. This method allows accurate determination of the geographic provenance of Alaria esculenta. This tool could form the base for branding seaweed products with certified origin, and for the development of mass identification kits using metabarcoding. It also has the potential to be used to monitor seaweed farms for invasion of non-native varieties of a seaweed species, and, more broadly, to monitor effects of climate change on the diversity of seaweed populations. C1 [Inaba, Masami; Fort, Antoine; Sulpice, Ronan] Natl Univ Ireland Galway, Plant Syst Biol Lab, Ryan Inst, SFI MaREI Ctr Climate Energy & Marine,Sch Nat Sci, Galway H91 TK33, Ireland. [Fort, Antoine] Athlone Inst Technol, Dublin Rd, Athlone N37H D68, Co Westmeath, Ireland. [Bringloe, Trevor] Univ Melbourne, Sch BioSci, Parkville, Vic 3010, Australia. [Mols-Mortensen, Agnes] Faroe Seaweed, TARI, Vipuvegur 14, FO-100 Torshavn, Faroe Islands. [Ghriofa, Cliodhna Ni] Marine Innovat Dev Ctr Pairc Na Mara, Galway, Ireland. C3 Technological University of the Shannon: Midlands Midwest; University of Melbourne RP Sulpice, R (corresponding author), Natl Univ Ireland Galway, Plant Syst Biol Lab, Ryan Inst, SFI MaREI Ctr Climate Energy & Marine,Sch Nat Sci, Galway H91 TK33, Ireland. EM ronan.sulpice@nuigalway.ie CR Afonso C, 2021, J APPL PHYCOL, V33, P501, DOI 10.1007/s10811-020-02298-8 Asher David M, 2018, Handb Clin Neurol, V153, P1, DOI 10.1016/B978-0-444-63945-5.00001-5 BIRKY CW, 1995, P NATL ACAD SCI USA, V92, P11331, DOI 10.1073/pnas.92.25.11331 Choi JW, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-58817-7 Fort A, 2018, ALGAL RES, V32, P308, DOI 10.1016/j.algal.2018.04.015 Francois G, 2020, PHYTOCHEMISTRY, V173, DOI 10.1016/j.phytochem.2020.112291 Gao G, 2022, ENVIRON RES LETT, V17, DOI 10.1088/1748-9326/ac3fd9 Gao G, 2020, BOT MAR, V63, P355, DOI 10.1515/bot-2019-0065 Jueterbock A, 2018, BMC EVOL BIOL, V18, DOI 10.1186/s12862-018-1213-2 Kerrison PD, 2020, J APPL PHYCOL, V32, P2173, DOI 10.1007/s10811-020-02069-5 Kim Kyung Seok, 2013, Methods Mol Biol, V1006, P271, DOI 10.1007/978-1-62703-389-3_19 Kraan S, 2000, PHYCOLOGIA, V39, P554, DOI 10.2216/i0031-8884-39-6-554.1 Kumagai Susumu, 2019, Food Saf (Tokyo), V7, P21, DOI 10.14252/foodsafetyfscj.2018009 Li H, 2009, BIOINFORMATICS, V25, P1754, DOI 10.1093/bioinformatics/btp324 Mignerot L, 2019, MOL BIOL EVOL, V36, P2778, DOI 10.1093/molbev/msz186 Miyamura S, 2010, J PLANT RES, V123, P171, DOI 10.1007/s10265-010-0309-6 Vilanova S, 2020, PLANT METHODS, V16, DOI 10.1186/s13007-020-00652-y NR 17 TC 0 Z9 0 U1 0 U2 0 PD SEP PY 2022 VL 67 AR 102826 DI 10.1016/j.algal.2022.102826 WC Biotechnology & Applied Microbiology SC Biotechnology & Applied Microbiology UT WOS:000859509500002 DA 2022-12-14 ER PT J AU Li, HH Geng, WH Hassan, MM Zuo, M Wei, WY Wu, XY Ouyang, Q Chen, QS AF Li, Huanhuan Geng, Wenhui Hassan, Md Mehedi Zuo, Min Wei, Wenya Wu, Xiangyang Ouyang, Qin Chen, Quansheng TI Rapid detection of chloramphenicol in food using SERS flexible sensor coupled artificial intelligent tools SO FOOD CONTROL DT Article DE Chloramphenicol; Surface-enhanced Raman scattering; Flower-like AgNPs; Flexible paper-based SERS sensor; Artificial intelligence tools ID LIQUID-CHROMATOGRAPHY; COMBINATION; EXTRACTION; SUBSTRATE; ARRAY AB The abuse of antibiotics is causing gradually increases drug-resistant bacterial strains, which pose a threat to economic development and human health around the world. Therefore, surface-enhanced Raman scattering (SERS) active flower-like silver nanoparticles (AgNPs) was used to fabricate flexible paper-based SERS sensor to acquire magnified Raman signal of chloramphenicol in food samples for the development of a suitable prediction model at picogram levels employing artificial intelligence tools. Among the employed artificial intelligent tools, the multivariate scattering correction integrated competitive adaptive weighted-partial least squares (MSCCARS-PLS) model showed best prediction efficiency over the concentration range 102 to 10-5 mu g/mL with correlation coefficient of test = 0.9635, residual predictive deviation = 3.6686 and a limit of detection = 10-5 mu g/mL. The recovery results range from 90 to 102% in real sample analysis and RSD was recorded 3.3% suggested that proposed sensor was rapid, reproducible and reliable for predicting CAP residue in food samples. C1 [Li, Huanhuan; Geng, Wenhui; Hassan, Md Mehedi; Wei, Wenya; Ouyang, Qin; Chen, Quansheng] Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China. [Li, Huanhuan; Wu, Xiangyang] Jiangsu Univ, Sch Environm & Safety Engn, Zhenjiang 212013, Jiangsu, Peoples R China. [Li, Huanhuan; Zuo, Min] Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China. [Zuo, Min] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 100048, Peoples R China. C3 Jiangsu University; Jiangsu University; Beijing Technology & Business University; Beijing Technology & Business University RP Chen, QS (corresponding author), Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China. EM qschen@ujs.edu.cn CR ang S., 2021, OPT MATER, V112 Azzouz A, 2015, FOOD CHEM, V178, P63, DOI 10.1016/j.foodchem.2015.01.044 Berendsen B, 2013, J AGR FOOD CHEM, V61, P4004, DOI 10.1021/jf400570c Bonerba E, 2021, FOOD CHEM, V334, DOI 10.1016/j.foodchem.2020.127575 Bonoldi L, 2018, ENERG FUEL, V32, P8955, DOI 10.1021/acs.energyfuels.8b01093 Chen J, 2020, CHEM ENG J, V392, DOI 10.1016/j.cej.2019.123670 Chen J, 2015, J FOOD SCI, V80, pN834, DOI 10.1111/1750-3841.12825 Chen J, 2019, J AGR FOOD CHEM, V67, P7569, DOI 10.1021/acs.jafc.9b01334 Du XJ, 2016, J AGR FOOD CHEM, V64, P2971, DOI 10.1021/acs.jafc.6b00639 Fernandez-Torres R, 2011, ANAL LETT, V44, P2357, DOI 10.1080/00032719.2010.551693 Fraiture MA, 2020, FOOD CONTROL, V108, DOI 10.1016/j.foodcont.2019.106873 Hassan MM, 2021, FOOD CHEM, V338, DOI 10.1016/j.foodchem.2020.127796 He LL, 2014, FOOD CHEM, V148, P42, DOI 10.1016/j.foodchem.2013.10.023 Huang Y, 2019, BIOSENS BIOELECTRON, V127, P45, DOI 10.1016/j.bios.2018.12.016 Jiao T., 2020, FOOD CHEM, V337 Khulal U, 2016, FOOD CHEM, V197, P1191, DOI 10.1016/j.foodchem.2015.11.084 Li HD, 2009, ANAL CHIM ACTA, V648, P77, DOI 10.1016/j.aca.2009.06.046 Li HH, 2021, FOOD CHEM, V339, DOI 10.1016/j.foodchem.2020.127843 Li HH, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106761 Li HH, 2017, BIOSENS BIOELECTRON, V92, P192, DOI 10.1016/j.bios.2017.02.009 Li QQ, 2019, SPECTROCHIM ACTA A, V214, P129, DOI 10.1016/j.saa.2019.02.023 Liang HY, 2009, ADV MATER, V21, P4614, DOI 10.1002/adma.200901139 Liu ZP, 2019, NANO, V14, DOI 10.1142/S1793292019501443 Lu Z., 2020, ANAL BIOANAL CHEM, V412, P1 Ma XY, 2020, FOOD CHEM, V302, DOI 10.1016/j.foodchem.2019.125359 Rodriguez MP, 2016, SPECTROCHIM ACTA A, V153, P386, DOI 10.1016/j.saa.2015.08.048 Wang HX, 2017, FOOD CONTROL, V80, P217, DOI 10.1016/j.foodcont.2017.04.034 Xiao D., 2020, MICROCHIM ACTA, V187, P1 Xie X, 2018, FOOD CHEM, V269, P542, DOI 10.1016/j.foodchem.2018.07.045 Xu Y, 2020, FOOD CHEM, V315, DOI 10.1016/j.foodchem.2020.126300 Yan WJ, 2016, BIOSENS BIOELECTRON, V78, P67, DOI 10.1016/j.bios.2015.11.011 Yang K, 2016, BIOSENS BIOELECTRON, V80, P373, DOI 10.1016/j.bios.2016.01.064 Yu SH, 2018, ANALYST, V143, P883, DOI 10.1039/c7an01547j Yun YH, 2015, ANAL CHIM ACTA, V862, P14, DOI 10.1016/j.aca.2014.12.048 Zhang JY, 2020, WATER RES, V187, DOI 10.1016/j.watres.2020.116397 Zhang S, 2018, ACS OMEGA, V3, P12886, DOI 10.1021/acsomega.8b01812 Zhao CY, 2020, TALANTA, V217, DOI 10.1016/j.talanta.2020.121054 Zhu CH, 2017, ACS APPL MATER INTER, V9, P39618, DOI 10.1021/acsami.7b13479 Zhu CH, 2016, ADV MATER, V28, P4871, DOI [10.1002/adma.201506251, 10.1002/adma.201670168] Zhu YH, 2019, BIOSENS BIOELECTRON, V131, P79, DOI 10.1016/j.bios.2019.02.008 NR 40 TC 18 Z9 18 U1 24 U2 100 PD OCT PY 2021 VL 128 AR 108186 DI 10.1016/j.foodcont.2021.108186 EA MAY 2021 WC Food Science & Technology SC Food Science & Technology UT WOS:000663760300001 DA 2022-12-14 ER PT J AU Li, L Boyd, CE Sun, ZL AF Li, Li Boyd, Claude E. Sun, Zhenlong TI Authentication of fishery and aquaculture products by multi-element and stable isotope analysis SO FOOD CHEMISTRY DT Review DE Multi-element analysis; Stable-isotope analysis; Traceability; Authenticity; Fishery and aquaculture products ID GEOGRAPHICAL ORIGIN; FATTY-ACID; WILD; DISCRIMINATION; IDENTIFICATION; TRACEABILITY; CROAKER; REGION; SALMON; JAPAN AB The market of fishery and aquaculture products is globalized with increasing numbers of mislabeled products. This highlights the need for approaches to indentify the origin of these products. Among the measures used to identify the origin of other agro-products, multi-element and stable isotope analysis are promising approaches to identify the authenticity and traceability of fishery and aquaculture products. The present paper reviews the use of multi-element and stable isotope analysis to determine the origin of fishery and aquaculture products. Principles and limitations of each method will be illustrated and perspectives for traceability of fishery and aquaculture products will be discussed. The aim of this review is to mediate fundamental knowledge for the interpretation of experimental data on authentication of aquaculture products. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Li, Li; Sun, Zhenlong] Ocean Univ China, Coll Fisheries, Minist Educ, Key Lab Mariculture, Qingdao 266003, Peoples R China. [Boyd, Claude E.] Auburn Univ, Sch Fisheries Aquaculture & Aquat Sci, Auburn, AL 36849 USA. C3 Ocean University of China; Auburn University System; Auburn University RP Li, L (corresponding author), Ocean Univ China, Coll Fisheries, Minist Educ, Key Lab Mariculture, Qingdao 266003, Peoples R China. EM l_li@ouc.edu.cn CR Alasalvar C, 2002, FOOD CHEM, V79, P145, DOI 10.1016/S0308-8146(02)00122-X Anderson KA, 2005, J AGR FOOD CHEM, V53, P410, DOI 10.1021/jf048907u Anderson KA, 1999, J AGR FOOD CHEM, V47, P1568, DOI 10.1021/jf980677u Anderson KA, 2010, J AGR FOOD CHEM, V58, P11768, DOI 10.1021/jf102046b Boyd C.E., 2000, WATER QUALITY INTRO, P69, DOI 10.1007/978-1-4615-4485-2_5 Boyd CE, 2015, AQUACULTURE, RESOURCE USE, AND THE ENVIRONMENT, P1, DOI 10.1002/9781118857915 Busetto ML, 2008, J AGR FOOD CHEM, V56, P2742, DOI 10.1021/jf0734267 Camargo AB, 2010, J FOOD COMPOS ANAL, V23, P586, DOI 10.1016/j.jfca.2010.01.002 Carter JF, 2015, FOOD CHEM, V170, P241, DOI 10.1016/j.foodchem.2014.08.037 Chaguri MP, 2015, LWT-FOOD SCI TECHNOL, V61, P194, DOI 10.1016/j.lwt.2014.11.006 Gamboa-Delgado J, 2014, CAN J FISH AQUAT SCI, V71, P1520, DOI 10.1139/cjfas-2014-0005 Iguchi J, 2014, FISHERIES SCI, V80, P1089, DOI 10.1007/s12562-014-0775-1 Iguchi J, 2013, FISHERIES SCI, V79, P977, DOI 10.1007/s12562-013-0659-9 Jacquet JL, 2008, MAR POLICY, V32, P309, DOI 10.1016/j.marpol.2007.06.007 Kim H, 2015, FOOD CHEM, V172, P523, DOI 10.1016/j.foodchem.2014.09.058 Krivachy N, 2015, FOOD CONTROL, V48, P143, DOI 10.1016/j.foodcont.2014.06.002 Kwon YK, 2014, FOOD CHEM, V161, P168, DOI 10.1016/j.foodchem.2014.03.124 Laursen KH, 2014, TRAC-TREND ANAL CHEM, V59, P73, DOI 10.1016/j.trac.2014.04.008 Lavilla I, 2013, COMP ANAL C, V60, P657, DOI 10.1016/B978-0-444-59562-1.00025-6 Li L, 2015, FOOD CONTROL, V50, P18, DOI 10.1016/j.foodcont.2014.08.014 Li L, 2014, FOOD CONTROL, V45, P70, DOI 10.1016/j.foodcont.2014.03.013 Li L, 2013, J WORLD AQUACULT SOC, V44, P405, DOI 10.1111/jwas.12033 Liu HY, 2015, FOOD CHEM, V171, P56, DOI 10.1016/j.foodchem.2014.08.111 Liu HC, 2014, FOOD CHEM, V142, P439, DOI 10.1016/j.foodchem.2013.07.082 Liu XF, 2012, FOOD CONTROL, V23, P522, DOI 10.1016/j.foodcont.2011.08.025 Luo DH, 2015, FOOD CHEM, V174, P197, DOI 10.1016/j.foodchem.2014.11.006 Molkentin J, 2007, EUR FOOD RES TECHNOL, V224, P535, DOI 10.1007/s00217-006-0314-0 Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Nietner T, 2014, FOOD RES INT, V60, P146, DOI 10.1016/j.foodres.2013.11.002 Niu J, 2012, ANIM FEED SCI TECH, V174, P86, DOI 10.1016/j.anifeedsci.2012.03.003 Ortea I, 2015, FOOD CHEM, V170, P145, DOI 10.1016/j.foodchem.2014.08.049 Ostermeyer U, 2014, EUR FOOD RES TECHNOL, V239, P1015, DOI 10.1007/s00217-014-2298-5 Pilgrim TS, 2010, FOOD CHEM, V118, P921, DOI 10.1016/j.foodchem.2008.08.077 Raco B, 2015, FOOD CHEM, V168, P588, DOI 10.1016/j.foodchem.2014.07.043 Rochfort SJ, 2013, FOOD RES INT, V54, P1302, DOI 10.1016/j.foodres.2013.03.004 Rossier JS, 2014, CHIMIA, V68, P696, DOI 10.2533/chimia.2014.696 Roy PK, 2006, J FISH BIOL, V68, P1460, DOI 10.1111/j.0022-1112.2006.001031.x Russo M, 2014, FOOD CHEM, V165, P467, DOI 10.1016/j.foodchem.2014.05.142 Sant'Ana LS, 2010, FOOD CHEM, V122, P74, DOI 10.1016/j.foodchem.2010.02.016 Schroder V, 2011, BIOL INVASIONS, V13, P203, DOI 10.1007/s10530-010-9802-z Sciubba F, 2014, FOOD RES INT, V62, P66, DOI 10.1016/j.foodres.2014.02.039 Serrano R, 2007, CHEMOSPHERE, V69, P1075, DOI 10.1016/j.chemosphere.2007.04.034 Smith RG, 2009, J AGR FOOD CHEM, V57, P8244, DOI 10.1021/jf901658f TACON AGJ, 1983, AQUACULTURE, V31, P11, DOI 10.1016/0044-8486(83)90253-3 Tanz N, 2010, J AGR FOOD CHEM, V58, P3139, DOI 10.1021/jf903251k Techen N, 2014, CURR OPIN BIOTECH, V25, P103, DOI 10.1016/j.copbio.2013.09.010 Turchini GM, 2009, J AGR FOOD CHEM, V57, P274, DOI 10.1021/jf801962h Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Oliveira EJVM, 2011, EUR FOOD RES TECHNOL, V232, P97, DOI 10.1007/s00217-010-1367-7 Weber PK, 2002, CAN J FISH AQUAT SCI, V59, P587, DOI [10.1139/f02-038, 10.1139/F02-038] Yamashita Y, 2006, FISHERIES SCI, V72, P1109, DOI 10.1111/j.1444-2906.2006.01263.x Zhao Y, 2014, FOOD CHEM, V145, P300, DOI 10.1016/j.foodchem.2013.08.062 NR 52 TC 56 Z9 59 U1 2 U2 122 PD MAR 1 PY 2016 VL 194 BP 1238 EP 1244 DI 10.1016/j.foodchem.2015.08.123 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000364248900159 DA 2022-12-14 ER PT J AU Scanga, JA Hoffman, T Picanso, J Rajopadhye, SV Kim, DG Gupta, A Forbes, R Ladd, J Burns, PJ AF Scanga, J. A. Hoffman, T. Picanso, J. Rajopadhye, S. V. Kim, D. G. Gupta, A. Forbes, R. Ladd, J. Burns, P. J. TI Development of computational models for the purpose of conducting individual livestock and premises traceback investigations utilizing National Animal Identification System-compliant data SO JOURNAL OF ANIMAL SCIENCE DT Article DE animal identification; national animal identification system; traceability; traceback; traceforward AB Many of the efforts surrounding the development of the National Animal Identification System have encompassed the identification of livestock production and handling premises as well as individuals or herds of animals, whereas little effort has been directed toward the ultimate goal of animal traceback within 48 h. A mock data set representative of the Colorado cattle population was created for modeling of cattle traceability. Using this data set, algorithms were developed to complete rapid and accurate traceback and traceforward of animals or premises or both. On July 19, 2005, the Colorado Department of Public Health and Environment, in conjunction with the Colorado Department of Agriculture, conducted a test exercise pertaining to homeland security. The exercise team randomly identified animal number 926,583 (of the 2 million total animals) as a potentially infected animal of interest and requested a traceback of this animal. Traceback was accomplished in 215 s, and 540 primary coresident animals were identified. However, due to animal movements, the number of coresidents (animals exposed, directly or indirectly, to the animal of interest) expanded with coresidency level (level 1 = direct contact; level 2 = direct contact with an animal that had direct contact with the animal of interest; level 3 = direct contact with an animal that had contact with an animal that had direct contact with the animal of interest, etc.) to more than 1.2 million coresidents at level 4, and more than 90% of all animals identified as a coresident at some level. In addition to the coresidency results, the premises containing the coresidents were identified and sorted by the number of coresidents. Because of animal movement, all 19,391 premises included in the data set had coresidents at some level. This exercise demonstrated the capability of the developed algorithms to complete rapid traceback and the complexity of the resulting animal traceback output. C1 Colorado State Univ, Dept Anim Sci, Ft Collins, CO 80523 USA. Governors Off Innovat & Technol, Denver, CO 80203 USA. Colorado State Univ, Dept Comp Sci, Ft Collins, CO 80523 USA. Colorado State Univ, Acad Comp & Network Serv, Ft Collins, CO 80523 USA. C3 Colorado State University; Colorado State University; Colorado State University RP Scanga, JA (corresponding author), Colorado State Univ, Dept Anim Sci, Ft Collins, CO 80523 USA. EM john.scanga@csumeats.com CR DAVIS J, 2006, PROF ANIM, V22, P139 MILGRAM S, 1967, PSYCHOL TODAY, V2, P60, DOI DOI 10.1145/335305.335325 *NIDT, 2003, US AN ID PLAN VERS 4 *USDA, 2006, ALB BSE INF FIN EP *USDA, 2006, NAT AGR STAT SERV QU *USDA, 2004, CAS BOV SPON ENC BSE *USDA, 2005, TEX BSE INV FIN EP R *USDA, 2006, NAT AN ID SYST STRAT NR 8 TC 1 Z9 3 U1 0 U2 5 PD FEB PY 2007 VL 85 IS 2 BP 503 EP 511 DI 10.2527/jas.2006-352 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000245678100027 DA 2022-12-14 ER PT J AU Pei, YF Wu, LH Zhang, QZ Wang, YZ AF Pei, Yi-Fei Wu, Li-Hua Zhang, Qing-Zhi Wang, Yuan-Zhong TI Geographical traceability of cultivated Paris polyphylla var. yunnanensis using ATR-FTMIR spectroscopy with three mathematical algorithms SO ANALYTICAL METHODS DT Article ID FT-MIR; MEDICINAL-PLANTS; DATA FUSION; SAPONINS; IDENTIFICATION; WILD; NIR; DISCRIMINATION; STRATEGY; HARVEST AB Paris polyphylla has been used by multiple nationalities as a traditional herb medicine to treat diseases in different regions of China. Since Paris quality is influenced by geographical regions, a fast and effective geographical traceability method is necessary. In our study, the geographical origin discrimination of 789 P. yunnanensis species from central, western, northwest, southeast and southwest Yunnan with 3rd to 8th cultivation years was carried out by chemometric of partial least squares discriminant analysis (PLS-DA), random forest (RF) and hierarchical cluster analysis (HCA) combined with attenuated total reflection-Fourier transform mid infrared (ATR-FTMIR). The results indicated that principal component analysis (PCA) was successfully used as an exploration data analysis method to remove outliers. Additionally, classification ability of PLS-DA model, established by variable importance for the projection (VIP), was similar to that of PLS-DA model, which indicated that raw ATR-FTMIR spectra contained abundant redundant information useless for classifying samples. For each model, the range of VIP values of PLS-DA is wider than the range of the important variables of RF, and PLS-DA was more time-efficient and high-ability than RF model. Besides, the geographical origin PLS-DA classification model with all collected samples was used to verify the results with accuracy rates of calibration set and validation set of 98.34% and 93.78%, respectively. HCA verified that the impact of geographical origin on P. yunnanensis characteristics is greater than that of the number of cultivation years. In short, PLS-DA model can assure the accuracy and limit time for identification analysis of cultivated P. yunnanensis quality assessment from different geographical origins. C1 [Pei, Yi-Fei; Wu, Li-Hua; Wang, Yuan-Zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. [Pei, Yi-Fei; Zhang, Qing-Zhi] Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University of Chinese Medicine RP Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM boletus@126.com CR Amjad A, 2018, VIB SPECTROSC, V99, P124, DOI 10.1016/j.vibspec.2018.09.003 BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324 C. P. Commission, 2015, PHARM PEOPL REP CH 1, P260 Chen GB, 2018, ENVIRON POLLUT, V242, P605, DOI 10.1016/j.envpol.2018.07.012 [陈铁柱 Chen Tiezhu], 2017, [中成药, Chinese Traditional Patent Medicine], V39, P2345 Cunningham AB, 2018, J ETHNOPHARMACOL, V222, P208, DOI 10.1016/j.jep.2018.04.048 [戴雪雯 Dai Xuewen], 2018, [中国实验方剂学杂志, Chinese Journal of Experimental Traditional Medical Formulae], V24, P41 Deng DW, 2008, PLANTA MED, V74, P1397, DOI 10.1055/s-2008-1081345 Gorski L, 2016, TALANTA, V146, P231, DOI 10.1016/j.talanta.2015.08.027 Gredilla A, 2013, TRAC-TREND ANAL CHEM, V46, P59, DOI 10.1016/j.trac.2013.01.014 HWANG SJ, 1992, J HEPATOL, V16, P320, DOI 10.1016/S0168-8278(05)80663-4 Li P, 2011, CHIN J INTEGR MED, V17, P283, DOI 10.1007/s11655-011-0704-4 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Liu F. R., 1992, J CHIN MED MAT, V15, P40 Liu W, 2018, FOOD CHEM, V251, P86, DOI 10.1016/j.foodchem.2018.01.081 Magdalena E. S., 2017, FOOD SCI NUTR, V58, P1747 Mayumi Oshiro Thais, 2012, Machine Learning and Data Mining in Pattern Recognition. Proceedings 8th International Conference, MLDM 2012, P154, DOI 10.1007/978-3-642-31537-4_13 Qi JJ, 2013, BMC GENOMICS, V14, DOI 10.1186/1471-2164-14-358 Qi LM, 2017, INT J FOOD PROP, V20, pS56, DOI 10.1080/10942912.2017.1289387 SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 SUN Su-gin, 2010, ANAL TRADITIONAL CHI Wang GX, 2010, PHYTOMEDICINE, V17, P1102, DOI 10.1016/j.phymed.2010.04.012 Wang X, 2018, TALANTA, V183, P320, DOI 10.1016/j.talanta.2018.02.080 Wang YZ, 2018, PLANT GROWTH REGUL, V84, P373, DOI 10.1007/s10725-017-0348-2 Wu M, 2018, J LUMIN, V202, P239, DOI 10.1016/j.jlumin.2018.05.036 Wu X, 2013, CARBOHYD RES, V368, P1, DOI 10.1016/j.carres.2012.11.027 Wu XM, 2018, SPECTROCHIM ACTA A, V205, P479, DOI 10.1016/j.saa.2018.07.067 Wu Z, 2017, SPECTROSC SPECT ANAL, V37, P1754, DOI 10.3964/j.issn.1000-0593(2017)06-1754-05 [谢俊大 Xie Junda], 2015, [药物分析杂志, Chinese Journal of Pharmaceutical Analysis], V35, P1585 Xie LJ, 2009, FOOD CHEM, V114, P1135, DOI 10.1016/j.foodchem.2008.10.076 Yang LF, 2018, J MOL STRUCT, V1165, P37, DOI 10.1016/j.molstruc.2018.03.061 Yang XD, 2018, SPECTROCHIM ACTA A, V205, P457, DOI 10.1016/j.saa.2018.07.056 Yang YG, 2017, BIOMED CHROMATOGR, V31, DOI 10.1002/bmc.3913 Yang YG, 2018, ANAL LETT, V51, P1730, DOI 10.1080/00032719.2017.1385618 Yang YG, 2017, J NAT MED-TOKYO, V71, P148, DOI 10.1007/s11418-016-1044-7 [杨远贵 Yang Yuangui], 2016, [中草药, Chinese Traditional and Herbal Drugs], V47, P3301 Zhang JY, 2012, SPECTROSC SPECT ANAL, V32, P2176, DOI 10.3964/j.issn.1000-0593(2012)08-2176-05 [张绍山 Zhang Shaoshan], 2016, [中草药, Chinese Traditional and Herbal Drugs], V47, P4257 Zhao YL, 2014, SPECTROSC SPECT ANAL, V34, P1831, DOI 10.3964/j.issn.1000-0593(2014)07-1831-05 NR 40 TC 16 Z9 17 U1 0 U2 28 PD JAN 7 PY 2019 VL 11 IS 1 BP 113 EP 122 DI 10.1039/c8ay02363h WC Chemistry, Analytical; Food Science & Technology; Spectroscopy SC Chemistry; Food Science & Technology; Spectroscopy UT WOS:000454084300012 DA 2022-12-14 ER PT J AU Cuevas, FJ Moreno-Rojas, M Ruiz-Moreno, MJ AF Julian Cuevas, Francisco Moreno-Rojas, Manuel Jose Ruiz-Moreno, Maria TI Assessing a traceability technique in fresh oranges (Citrus sinensis L. Osbeck) with an HS-SPME-GC-MS method. Towards a volatile characterisation of organic oranges SO FOOD CHEMISTRY DT Article DE Citrus sinensis; Organic farming; PLS-DA; Volatile compounds; HS-SPME-GC-MS ID GAS-CHROMATOGRAPHY-OLFACTOMETRY; STRAWBERRY AROMA; ACTIVE COMPOUNDS; BRANCHED-CHAIN; FOOD ANALYSIS; JUICE; FLAVOR; ACID; ODOR; CLASSIFICATION AB A targeted approach using HS-SPME-GC-MS was performed to compare flavour compounds of 'Navelina' and 'Salustiana' orange cultivars from organic and conventional management systems. Both varieties of conventional oranges showed higher content of ester compounds. On the other hand, higher content of some compounds related with the geranyl-diphosphate pathway (neryl and geranyl acetates) and some terpenoids were found in the organic samples. Furthermore, the partial least square discriminant analysis (PLS-DA) achieved an effective classification for oranges based on the farming system using their volatile profiles (90 and 100% correct classification). To our knowledge, it is the first time that a comparative study dealing with farming systems and orange aroma profile has been performed. These new insights, taking into account local databases, cultivars and advanced analytical tools, highlight the potential of volatile composition for organic orange discrimination. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Julian Cuevas, Francisco; Moreno-Rojas, Manuel; Jose Ruiz-Moreno, Maria] Andalusian Inst Agr & Fisheries Res & Training IF, Technol Postharvest & Food Ind Area, Alameda del Obispo Avda,Menendez Pidal S-N, Cordoba 14071, Andalucia, Spain. RP Moreno-Rojas, M; Ruiz-Moreno, MJ (corresponding author), Andalusian Inst Agr & Fisheries Res & Training IF, Technol Postharvest & Food Ind Area, Alameda del Obispo Avda,Menendez Pidal S-N, Cordoba 14071, Andalucia, Spain. EM josem.moreno.rojas@juntadeandalucia.es; mariaj.ruiz.moreno@juntadeandalucia.es CR AHMED EM, 1978, J AGR FOOD CHEM, V26, P187, DOI 10.1021/jf60215a074 Alvarez R, 2012, J AGR FOOD CHEM, V60, P774, DOI 10.1021/jf203353h Arena E, 2006, FOOD CHEM, V98, P59, DOI 10.1016/j.foodchem.2005.04.035 Bai JH, 2016, FOODS, V5, DOI 10.3390/foods5010004 Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Gonzalez-Mas MC, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0022016 Cerdan-Calero M, 2012, J CHROMATOGR A, V1241, P84, DOI 10.1016/j.chroma.2012.04.014 Cuevas FJ, 2016, FOOD CHEM, V199, P479, DOI 10.1016/j.foodchem.2015.12.049 Cynkar W, 2010, ANAL CHIM ACTA, V660, P227, DOI 10.1016/j.aca.2009.09.030 Davidovich-Rikanati R, 2007, NAT BIOTECHNOL, V25, P899, DOI 10.1038/nbt1312 Dharmawan J, 2009, J AGR FOOD CHEM, V57, P239, DOI 10.1021/jf801070r Dziadas M, 2016, FOOD CHEM, V190, P412, DOI 10.1016/j.foodchem.2015.05.089 Francis S, 1999, NEUROREPORT, V10, P453, DOI 10.1097/00001756-199902250-00003 Goldenberg L, 2016, J SCI FOOD AGR, V96, P57, DOI 10.1002/jsfa.7191 Gonda I, 2010, J EXP BOT, V61, P1111, DOI 10.1093/jxb/erp390 Gromski PS, 2015, ANAL CHIM ACTA, V879, P10, DOI 10.1016/j.aca.2015.02.012 Hognadottir A, 2003, J CHROMATOGR A, V998, P201, DOI 10.1016/S0021-9673(03)00524-7 Kelebek H, 2011, J SCI FOOD AGR, V91, P1855, DOI 10.1002/jsfa.4396 LASKA M, 1993, J COMP PHYSIOL A, V173, P249 Lewinsohn E, 2009, PLANT SCI, V176, P161, DOI 10.1016/j.plantsci.2008.09.018 MAGRAMA, 2013, STAT ORG PROD Mahattanatawee K, 2005, J AGR FOOD CHEM, V53, P393, DOI 10.1021/jf049012k Mithofer A, 2012, ANNU REV PLANT BIOL, V63, P431, DOI 10.1146/annurev-arplant-042110-103854 Miyazaki T, 2011, J SCI FOOD AGR, V91, P449, DOI 10.1002/jsfa.4205 Obenland D, 2012, POSTHARVEST BIOL TEC, V71, P41, DOI 10.1016/j.postharvbio.2012.03.006 Papini P. C., 2015, VITIS J GRAPEVINE RE, V49, P121 Perez AG, 2002, J AGR FOOD CHEM, V50, P4037, DOI 10.1021/jf011465r Plotto A, 2004, FLAVOUR FRAG J, V19, P491, DOI 10.1002/ffj.1470 Rowan DD, 1999, J AGR FOOD CHEM, V47, P2553, DOI 10.1021/jf9809028 Perez-Cacho PR, 2008, J AGR FOOD CHEM, V56, P9785, DOI 10.1021/jf801244j Sahota A., 2010, WORLD ORGANIC AGR ST Souza-Silva EA, 2015, TRAC-TREND ANAL CHEM, V71, P236, DOI 10.1016/j.trac.2015.04.018 Sugimoto N, 2011, J AM SOC HORTIC SCI, V136, P429, DOI 10.21273/JASHS.136.6.429 Urruty L, 2002, J AGR FOOD CHEM, V50, P3129, DOI 10.1021/jf0116799 Vallverdu-Queralt A., 2015, ELECTROPHORESIS van den Berg RA, 2006, BMC GENOMICS, V7, DOI 10.1186/1471-2164-7-142 Wang C., 2014, SCI REPORTS, V4 Willer H., 2016, WORLD ORGANIC AGR ST, P199 NR 38 TC 36 Z9 39 U1 5 U2 140 PD APR 15 PY 2017 VL 221 BP 1930 EP 1938 DI 10.1016/j.foodchem.2016.11.156 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000389909100246 DA 2022-12-14 ER PT J AU Favaro, G Magno, F Boaretto, A Bailoni, L Mantovani, R AF Favaro, G Magno, F Boaretto, A Bailoni, L Mantovani, R TI Traceability of Asiago mountain cheese: A rapid, low-cost analytical procedure for its identification based on solid-phase microextraction SO JOURNAL OF DAIRY SCIENCE DT Article DE terpenes; sesquiterpenes; solid-phase microextraction; gas chromatography-mass spectrometry ID TERRINCHO EWE CHEESE; PURGE-AND-TRAP; VOLATILE COMPOUNDS; GEOGRAPHIC ORIGIN; EMMENTALER CHEESE; MILK; FRACTION; FLAVOR; SPME; SESQUITERPENES AB The traceability of Asiago mountain cheese was established by analyzing samples of herbaceous species, milk, and cheese of mountain origin using the headspace solid-phase microextraction sampling procedure coupled with gas chromatography-mass spectrometry. As preliminary work had highlighted the characteristic presence of sesquiterpenes in Asiago mountain cheese, these species were considered effective markers of mountain origin. Systematic qualitative analysis, carried out using a carboxen/ polydimethylsiloxane fiber, revealed several sesquiterpenes in mountain herbage and milk, in particular beta-caryophyllene and alpha-humulene, in Asiago mountain cheese, confirming sesquiterpenes as markers of cheese produced from animals grazing on mountain pastures. Analysis was performed on 19 samples of herbage, 8 of milk, and 8 of cheese, collected in summer from 4 mountain farms on the Asiago plateau. For quantitative analysis of caryophyllene in cheese, polydimethylsiloxane fiber sampling, coupled with the standard addition method to eliminate matrix effect, was preferred. The amount of beta-caryophyllene found ranged from 21 to 65 mu g/kg. C1 Univ Padua, Dept Chem Sci, I-35131 Padua, Italy. Univ Padua, Agripolis, Dept Anim Sci, I-35020 Legnaro, PD, Italy. C3 University of Padua; University of Padua RP Favaro, G (corresponding author), Univ Padua, Dept Chem Sci, Via Marzolo 1, I-35131 Padua, Italy. EM gabriella.favaro@unipd.it CR Ai J, 1997, ANAL CHEM, V69, P1230, DOI 10.1021/ac9609541 Almeida CMM, 2004, J ENVIRON MONITOR, V6, P80, DOI 10.1039/b307053k Black L, 2001, ENVIRON SCI TECHNOL, V35, P3190, DOI 10.1021/es010539c Bugaud C, 2001, LAIT, V81, P401, DOI 10.1051/lait:2001140 Bugaud C, 2001, LAIT, V81, P593, DOI 10.1051/lait:2001152 Bugaud C, 2001, LAIT, V81, P757, DOI 10.1051/lait:2001162 Cardinal M, 2000, ANALUSIS, V28, P825, DOI 10.1051/analusis:2000150 Careri M, 2003, RAPID COMMUN MASS SP, V17, P479, DOI 10.1002/rcm.944 Contarini G, 2002, J AGR FOOD CHEM, V50, P7350, DOI 10.1021/jf025713a Cozzolino D, 2002, J NEAR INFRARED SPEC, V10, P187, DOI 10.1255/jnirs.334 Fernandez C, 2003, INT J FOOD SCI TECH, V38, P445, DOI 10.1046/j.1365-2621.2003.00708.x Gorecki T, 1999, ANALYST, V124, P643, DOI 10.1039/a808487d Gorecki T, 1997, ANALYST, V122, P1079, DOI 10.1039/a701303e Jaillais B, 1999, TALANTA, V48, P747, DOI 10.1016/S0039-9140(98)00091-5 Lecanu L, 2002, J AGR FOOD CHEM, V50, P3810, DOI 10.1021/jf0117107 Lee JH, 2003, J AGR FOOD CHEM, V51, P1136, DOI 10.1021/jf025910+ Mariaca RG, 1997, J AGR FOOD CHEM, V45, P4423, DOI 10.1021/jf970216t Mauriello G, 2003, J DAIRY SCI, V86, P486, DOI 10.3168/jds.S0022-0302(03)73627-3 Murray RA, 2001, ANAL CHEM, V73, P1646, DOI 10.1021/ac001176m Peres C, 2001, ANAL CHEM, V73, P1030, DOI 10.1021/ac001146j Pillonel L, 2003, ITAL J FOOD SCI, V15, P49 Pillonel L, 2003, EUR FOOD RES TECHNOL, V216, P174, DOI 10.1007/s00217-002-0628-5 Pillonel L, 2002, LEBENSM-WISS TECHNOL, V35, P1, DOI 10.1006/fstl.2001.0804 Pillonel L, 2003, EUR FOOD RES TECHNOL, V216, P179, DOI 10.1007/s00217-002-0629-4 Pinho O, 2004, INT DAIRY J, V14, P455, DOI 10.1016/j.idairyj.2003.08.007 Pinho O, 2003, J CHROMATOGR A, V1011, P1, DOI 10.1016/S0021-9673(03)01066-5 Pinho O, 2002, ANAL CHEM, V74, P5199, DOI 10.1021/ac020296m PLASTOW GS, 2003, Patent No. 2003087765 Povolo M, 2003, J CHROMATOGR A, V985, P117, DOI 10.1016/S0021-9673(02)01395-X Vial J, 1999, ANAL CHEM, V71, P2672, DOI 10.1021/ac981179n Viallon C, 1999, J DAIRY RES, V66, P319, DOI 10.1017/S0022029999003520 Viallon C, 2000, LAIT, V80, P635, DOI 10.1051/lait:2000150 NR 32 TC 29 Z9 29 U1 0 U2 18 PD OCT PY 2005 VL 88 IS 10 BP 3426 EP 3434 DI 10.3168/jds.S0022-0302(05)73026-5 WC Agriculture, Dairy & Animal Science; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000231835200006 DA 2022-12-14 ER PT J AU Rami, L Roura, M Canalias, F AF Rami, Laura Roura, Montserrat Canalias, Francesca TI Evaluation of commutability of several materials for harmonization alkaline phosphatase catalytic concentration measurements SO CLINICA CHIMICA ACTA DT Article DE Alkaline phosphatase; IFCC reference procedure; Commutability; Traceability ID EXTERNAL QUALITY ASSESSMENT; METROLOGICAL TRACEABILITY; HUMAN SERUM; VALUES; CALIBRATOR; ENZYMES AB Background: The International Standard ISO 18153 establish that one of the requirements to assure the metrological traceability of values for catalytic concentration of enzymes is the commutability of calibrator and control materials used in the reference measurement systems. This approach was applied to verify the commutability of several commercial stabilized materials using the recently published alkaline phosphatase IFCC primary reference procedure and two routine procedures. Methods: ALP catalytic activity was measured in 50 serum samples and 16 commercial materials, including control materials from EQAS programs, using primary reference measurement procedure and two routine measurement procedures with AMP and DEA as buffers. Calibration materials with a value assigned by reference procedure which were proved to be commutable were used to recalculate the serum values obtained by routine procedures. Results: All commercial materials showed a similar behaviour to the patient specimens when AMP vs IFCC procedures were compared. For DEA vs IFCC comparison only one calibration material and two quality control materials were commutable. Recalculation of serum results with a commutable common calibrator improves the agreement between methods changing the ratio AMP vs IFCC from 1.44 to 1.04 and DEA vs IFCC from 3.02 to 1.05. Conclusions: The use of a common commutable calibration material allows harmonizing ALP measurements and made traceable patient results to reference procedure. (C) 2012 Elsevier B.V. All rights reserved. C1 [Rami, Laura; Roura, Montserrat; Canalias, Francesca] Univ Autonoma Barcelona, Dept Bioquim & Biol Mol, LREC, Unitat Bioquim Med, Bellaterra 08193, Spain. C3 Autonomous University of Barcelona RP Canalias, F (corresponding author), Univ Autonoma Barcelona, Dept Bioquim & Biol Mol, LREC, Unitat Bioquim Med, Edifici M, Bellaterra 08193, Spain. EM francesca.canalias@uab.cat CR Baadenhuijsen H, 2005, CLIN CHEM LAB MED, V43, P304, DOI 10.1515/CCLM.2005.052 BLAND JM, 1986, LANCET, V1, P307, DOI 10.1016/s0140-6736(86)90837-8 Canalias F, 2006, CLIN CHEM LAB MED, V44, P333, DOI 10.1515/CCLM.2006.058 Canalias F, 2010, CLIN CHIM ACTA, V411, P7, DOI 10.1016/j.cca.2009.09.029 Cattozzo G, 2008, CLIN CHEM, V54, P1349, DOI 10.1373/clinchem.2007.100081 Cattozzo G, 2010, CLIN CHIM ACTA, V411, P882, DOI 10.1016/j.cca.2010.03.008 Clinical and Laboratory Standards Institute, 2010, CHAR QUAL COMM REF M, VEP30-A *COMM ENZ SCAND SO, 1974, SCAND J CLIN LAB INV, V33, P291 Infusino I, 2010, CLIN CHEM LAB MED, V48, P301, DOI 10.1515/CCLM.2010.075 International Organization for Standardization, 2003, 18153 ISO Joint committee for guides In measurements 2008 Joint committee for guides In measurements, 2008, JCGM, V100 Joint Committee for Guides in Metrology International vocabulary of metrology-basic and general concepts and associated terms (VIM), 2012, JCGM, V200 Miller WG, 2011, CLIN CHEM, V57, P1108, DOI 10.1373/clinchem.2011.164012 Miller WG, 2003, CLIN CHIM ACTA, V327, P25, DOI 10.1016/S0009-8981(02)00370-4 MOSS DW, 1992, CLIN CHEM, V38, P2486 Panteghini M, 2001, CLIN CHEM LAB MED, V39, P795, DOI 10.1515/CCLM.2001.131 Panteghini M, 2010, CLIN CHEM LAB MED, V48, P7, DOI 10.1515/CCLM.2010.020 PASSING H, 1983, J CLIN CHEM CLIN BIO, V21, P709 PRICE CP, 1993, ANN CLIN BIOCHEM, V30, P355, DOI 10.1177/000456329303000403 RELA-IFCC, EXT QUAL ASS SCHEM R Ricos C, 1997, CLIN CHIM ACTA, V263, P225, DOI 10.1016/S0009-8981(97)00062-4 Schumann G, 2011, CLIN CHEM LAB MED, V49, P1439, DOI 10.1515/CCLM.2011.621 TIETZ NW, 1983, J CLIN CHEM CLIN BIO, V21, P731 NR 23 TC 5 Z9 6 U1 0 U2 8 PD AUG 16 PY 2012 VL 413 IS 15-16 BP 1249 EP 1254 DI 10.1016/j.cca.2012.04.004 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000305370300017 DA 2022-12-14 ER PT J AU Fang, X Fu, YX Wu, Q Zhang, L Zhang, F AF Fang, Xi Fu, Yixin Wu, Qi Zhang, Lei Zhang, Fan TI Volterra Expansion Based Intra Channel Nonlinear Effect Equalization Method for Optical OFDM/OQAM Systems SO IEEE ACCESS DT Article DE Optical fiber communication; orthogonal frequency division multiplexing offset quadrature amplitude modulation (OFDM; OQAM); digital signal processing; nonlinear distortion ID DFT-SPREAD OFDM; CHROMATIC DISPERSION; COMPENSATION; TRANSMISSION; SUPPRESSION; SSMF; COMMUNICATION; SUPERCHANNEL; INTERFERENCE AB Optical orthogonal frequency-division multiplexing offset quadrature amplitude modulation (O-OFDM/OQAM) system relaxes the orthogonal condition of the sub-carriers from the complex domain to the real field. Inter-symbol-interference (ISI) and inter-carrier-interference (ICI) could be suppressed by using filter banks with promising time-frequency-localization (TFL) properties. Therefore, cyclic prefix (CP) inserted between consecutive OFDM blocks could be removed for O-OFDM/OQAM system to improve system spectral efficiency. When passing through fiber channel, O-OFDM/OQAM faces serious intrinsic imaginary interference (IMI) induced by chromatic dispersion (CD), and fiber nonlinear effect, which would deteriorate system performance evidently. Fiber nonlinear effect induced interference is a great impairment for long haul transmission O-OFDM/OQAM system, and can not be equalized directly by using nonlinear equalization method designed for traditional optical OFDM. There is still much room for improvement of nonlinear equalization method for O-OFDM/OQAM. In this paper, we systematically study Volterra expansion based nonlinear equalization method (VENE) for O-OFDM/OQAM. We theoretically deduce simplified Volterra series expansion nonlinear transmission matrix (SVEM) for O-OFDM/OQAM based on mathematic deduction. With SVEM, intra channel nonlinear effect induced distortions could be modeled and estimated. By using specially designed pilot blocks, we obtain approximate solution of SVEM and perform effective VENE with very limited complexities. As shown in multiple Montel Carlo simulation results, nonlinear robustness for O-OFDM/OQAM has been improved significantly thanks to VENE, with various transmission distances and system parameters. C1 [Fang, Xi; Fu, Yixin; Zhang, Lei] Beijing Elect Sci & Technol Inst, Beijing 100070, Peoples R China. [Wu, Qi] Tsinghua Univ, Inst Microelect, Beijing 100084, Peoples R China. [Zhang, Fan] Peking Univ, Dept Elect, Beijing 100871, Peoples R China. [Zhang, Lei] Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China. C3 Beijing Electronic Science & Technology Institute; Tsinghua University; Peking University; Beijing Technology & Business University RP Fang, X (corresponding author), Beijing Elect Sci & Technol Inst, Beijing 100070, Peoples R China.; Zhang, F (corresponding author), Peking Univ, Dept Elect, Beijing 100871, Peoples R China. EM xfang@besti.edu.cn; fzhang@pku.edu.cn CR Abdzadeh-Ziabari H, 2011, IEEE T VEH TECHNOL, V60, P3646, DOI 10.1109/TVT.2011.2163194 Bi MH, 2018, IEEE PHOTONICS J, V10, DOI 10.1109/JPHOT.2018.2848248 Bodinier Q, 2016, IEEE ICC, DOI 10.1109/ICC.2016.7511285 Bouhadda H, 2014, EURASIP J ADV SIG PR, DOI 10.1186/1687-6180-2014-60 Cvijetic N, 2010, IEEE COMMUN MAG, V48, P70, DOI 10.1109/MCOM.2010.5496880 Dashti S, 2014, 2014 7TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATIONS (IST), P383, DOI 10.1109/ISTEL.2014.7000734 Fang X., 2019, P AS COMM PHOT C ACP, P1 Fang X., 2019, P AS COMM PHOT C ACP, P1 Fang X, 2019, IEEE PHOTONIC TECH L, V31, P1281, DOI 10.1109/LPT.2019.2925662 Fang X, 2017, J LIGHTWAVE TECHNOL, V35, P1837, DOI 10.1109/JLT.2017.2665464 Fang X, 2016, J LIGHTWAVE TECHNOL, V34, P891, DOI 10.1109/JLT.2015.2507605 Fang X, 2015, J LIGHTWAVE TECHNOL, V33, P2743, DOI 10.1109/JLT.2015.2410281 Fang X, 2014, IEEE PHOTONIC TECH L, V26, P376, DOI 10.1109/LPT.2013.2293515 Gao Y., 2008, P COH OPT TECHN APPL Giacoumidis E, 2016, OPT LETT, V41, P2509, DOI 10.1364/OL.41.002509 Giacoumidis E, 2014, IEEE PHOTONIC TECH L, V26, P1383, DOI 10.1109/LPT.2014.2321434 Guiomar FP, 2013, J LIGHTWAVE TECHNOL, V31, P3879, DOI 10.1109/JLT.2013.2288781 Horlin F, 2013, OPT EXPRESS, V21, P6409, DOI 10.1364/OE.21.006409 Kofidis E, 2013, SIGNAL PROCESS, V93, P2038, DOI 10.1016/j.sigpro.2013.01.013 Lele C, 2008, IEEE ICC, P1302, DOI 10.1109/ICC.2008.253 Li A, 2012, J LIGHTWAVE TECHNOL, V30, P3931, DOI 10.1109/JLT.2012.2206369 Li ZH, 2013, OPT EXPRESS, V21, P21924, DOI 10.1364/OE.21.021924 Liu X, 2013, NAT PHOTONICS, V7, P560, DOI [10.1038/NPHOTON.2013.109, 10.1038/nphoton.2013.109] Mao TQ, 2017, IEEE COMMUN LETT, V21, P761, DOI 10.1109/LCOMM.2016.2635634 Nawawi N. M., 2015, 2015 International Conference on Computer, Communications and Control Technology (I4CT), P346, DOI 10.1109/I4CT.2015.7219595 Pan J, 2011, J LIGHTWAVE TECHNOL, V29, P215, DOI 10.1109/JLT.2010.2098017 Pechenkin V, 2011, J LIGHTWAVE TECHNOL, V29, P1678, DOI 10.1109/JLT.2011.2138677 Peddanarappagari KV, 1997, J LIGHTWAVE TECHNOL, V15, P2232, DOI 10.1109/50.643545 Qiao YJ, 2011, CHINESE PHYS LETT, V28, DOI 10.1088/0256-307X/28/6/064214 Ranzini SM, 2019, TELECOMMUN INF TECH, P93, DOI 10.1007/978-3-319-97187-2_5 Rottenberg F, 2017, IEEE PHOTONICS J, V9, DOI 10.1109/JPHOT.2017.2773667 Saeedi-Sourck H, 2011, IEEE T SIGNAL PROCES, V59, P1907, DOI 10.1109/TSP.2010.2104148 Schaich Frank, 2010, 2010 European Wireless Conference (EW), P1051, DOI 10.1109/EW.2010.5483518 Schaich F, 2014, 2014 6TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS, CONTROL AND SIGNAL PROCESSING (ISCCSP), P457, DOI 10.1109/ISCCSP.2014.6877912 Sengupta S.K., 1995, TECHNOMETRICS, DOI [10.2307/1269750, DOI 10.2307/1269750] Shaoliang Z., 2012, P OFC COLL NAT FIB O Shieh W, 2008, OPT EXPRESS, V16, P841, DOI 10.1364/OE.16.000841 Shulkind G, 2012, OPT EXPRESS, V20, P25884, DOI 10.1364/OE.20.025884 Tang RG, 2017, 2017 17TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT 2017), P249 Tian Y, 2019, IEEE T BROADCAST, V65, P260, DOI 10.1109/TBC.2018.2847453 Nguyen TH, 2019, J LIGHTWAVE TECHNOL, V37, P4340, DOI 10.1109/JLT.2019.2923763 Nguyen TH, 2017, J LIGHTWAVE TECHNOL, V35, P2909, DOI 10.1109/JLT.2017.2707179 Tsonev D, 2014, IEEE PHOTONIC TECH L, V26, P637, DOI 10.1109/LPT.2013.2297621 You BY, 2019, IEEE PHOTONICS J, V11, DOI 10.1109/JPHOT.2019.2896930 Zakaria R, 2014, PHYS COMMUN-AMST, V11, P15, DOI 10.1016/j.phycom.2013.10.005 Zhang L, 2018, OPT LETT, V43, P182, DOI 10.1364/OL.43.000182 Zhang L, 2016, OPT COMMUN, V364, P129, DOI 10.1016/j.optcom.2015.11.032 Zhang QW, 2017, OPT COMMUN, V387, P12, DOI 10.1016/j.optcom.2016.11.032 Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029 Zhang T, 2019, IEEE ACCESS, V7, P82571, DOI 10.1109/ACCESS.2019.2921310 Zhang XB, 2014, OPT EXPRESS, V22, P12079, DOI 10.1364/OE.22.012079 Zhou X, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9071454 Zhou Z, 2017, IEEE J SEL AREA COMM, V35, P1524, DOI 10.1109/JSAC.2017.2699338 NR 53 TC 2 Z9 2 U1 0 U2 2 PY 2020 VL 8 BP 205657 EP 205668 DI 10.1109/ACCESS.2020.3036861 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000591800000001 DA 2022-12-14 ER PT J AU Sillanpaa, S Niederhauser, B Heinonen, M AF Sillanpaa, S Niederhauser, B Heinonen, M TI Comparison of the primary low gas flow standards between MIKES and METAS SO MEASUREMENT DT Article DE gas flow; comparison; traceability AB At Centre for Metrology and Accreditation (MIKES). a primary calibration system for gas mass flows between 0.42 mg/s and 625 mg/s is based on dynamic weighing providing traceability directly to the national mass and time standards. To evaluate the agreement of the system with a volumetric primary calibrator, the international comparison between MIKES and the Swiss Federal Office of Metrology and Accreditation (METAS, Switzerland) was carried out. At METAS, the primary low gas flow standard provides traceability to the Swiss national measurement standards for length and time. The results of the comparison showed that the two systems do not deviate more than +/-0.15% in the mass flow range 0.42-625 mg/s. (C) 2005 Elsevier Ltd. All rights reserved. C1 Ctr Metrol & Accreditat MIKES, Helsinki 00181, Finland. Swiss Fed Off Metrol & Accreditat METAS, CH-3003 Bern, Switzerland. C3 Swiss Federal Institute for Metrology (METAS) RP Sillanpaa, S (corresponding author), Ctr Metrol & Accreditat MIKES, POB 239, Helsinki 00181, Finland. EM sampo.sillanpaa@mikes.fi; bernhard.niederhauser@metas.ch; martti.hei-nonen@mikes.fi CR Cox MG, 2002, METROLOGIA, V39, P589, DOI 10.1088/0026-1394/39/6/10 International Organization for Standardization, 1993, GUID EXPR UNC MEAS Niederhauser B, 2002, METROLOGIA, V39, P573, DOI 10.1088/0026-1394/39/6/7 NIEDERHAUSER B, 2001, METR 2001 FRANC 22 2 SILLANPAA S, 2003, FLOMEKO 2003 NETH 12 SILLANPAA S, 2003, THESIS HELSINKI U TE NR 6 TC 11 Z9 11 U1 1 U2 3 PD JAN PY 2006 VL 39 IS 1 BP 26 EP 33 DI 10.1016/j.measurement.2005.10.002 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000234740800004 DA 2022-12-14 ER PT S AU Mitani, Y AF Mitani, Y BE Geni, M Kikuchi, M TI Traceable measurements in materials characterization SO PROGRESS IN EXPERIMENTAL AND COMPUTATIONAL MECHANICS IN ENGINEERING SE KEY ENGINEERING MATERIALS DT Article; Proceedings Paper CT International Conference on Experimental and Computational Mechanics in Engineering CY AUG 24-27, 2002 CL DUNHUANG, PEOPLES R CHINA DE test methods; materials behavior; measurement standards; traceability of measurement AB International standardization of the measurements has gained enormous importance in the recent globalization of the market, due to the necessity of reducing the technical barriers to trade (TBT), in which conformity assessment by comparable measurements among countries is one of the main issues in the free trade agreement. Some activities in establishing internationally acceptable traceability measurements and comparability within the framework of BIPM-CIPM are described and mention is made on the Mutual Recognition Arrangement (MRA) among National Metrology Institutes. A national metrology infrastructure which supports commerce and trade depends on the policy of each nation, but has been affected by the establishment of regional free trade blocks. The comparability of measurements and calibration among them can be obtained by the sound scientific and technological principles which cover instrumentation, methodology and measurement standards, including reference materials. C1 Ctr Nacl Metrol, El Marques, Queretaro, Mexico. C3 Centro Nacional de Metrologia (CENAM) RP Mitani, Y (corresponding author), Ctr Nacl Metrol, Km 4-5,Carretera Cues, El Marques, Queretaro, Mexico. CR BETTIN H, 1995, PTB JAHRES BERICHT *BIPM, 1998, INT SYST UN FIGUEROA JM, 1996, CENAM INTERNAL REPOR International Organization for Standardization, 1993, INT VOC BAS GEN TERM MITANI Y, 2000, P CRMS 21 CENT BAM G NR 5 TC 1 Z9 1 U1 0 U2 0 PY 2003 VL 243-2 BP 165 EP 170 DI 10.4028/www.scientific.net/KEM.243-244.165 WC Materials Science, Ceramics; Mechanics; Materials Science, Composites SC Materials Science; Mechanics UT WOS:000183458800028 DA 2022-12-14 ER PT J AU Zhao, HY Guo, BL Wei, YM Zhang, B AF Zhao, Haiyan Guo, Boli Wei, Yimin Zhang, Bo TI Effects of grown origin, genotype, harvest year, and their interactions of wheat kernels on near infrared spectral fingerprints for geographical traceability SO FOOD CHEMISTRY DT Article DE Wheat kernel; NIR; Geographical origin; Genotype; Harvest year; Traceability ID QUALITY; SPECTROSCOPY; CONFIRMATION; ENVIRONMENT; PROVENANCE; SAMPLES; STARCH AB The effects of origin, genotype, harvest year, and their interactions on wheat near infrared (NIR) spectra were studied to find the reasons for differences in NIR fingerprints of wheat from different geographical origins and the stability of NIR fingerprints among different years. Ten varieties were grown in three regions of China for 2 years. 180 kernel samples were analysed by NIR. The spectra after pre-treatment were analysed by principal component analysis, multi-way analysis of variance, and discriminant partial least-squares. The results showed that origin, genotype, year, and their interactions all had significant effects on wheat NIR fingerprints. The second overtones of N-H and C-H stretching vibrations and a combination of stretch and deformation of C-H group in wheat were mainly influenced by the geographical origin. The wavelength ranges 975-990 nm, 1200 nm, and 1355-1380 nm contained plenty of origin information to build robust discriminant models of wheat geographical origin. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Zhao, Haiyan; Guo, Boli; Wei, Yimin; Zhang, Bo] Chinese Acad Agr Sci, Minist Agr, Comprehens Key Lab Agroprod Proc, Inst Agroprod Proc Sci & Technol, Beijing 100193, Peoples R China. [Zhao, Haiyan] Qingdao Agr Univ, Coll Food Sci & Engn, Qingdao 266109, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; Qingdao Agricultural University RP Wei, YM (corresponding author), Chinese Acad Agr Sci, Minist Agr, Comprehens Key Lab Agroprod Proc, Inst Agroprod Proc Sci & Technol, POB 5109, Beijing 100193, Peoples R China. EM xinyuyuanyin@163.com; guoboli2007@126.com; weiyimin36@hotmail.com; zjzb1978@126.com CR Ames NP, 1999, CEREAL CHEM, V76, P582, DOI 10.1094/CCHEM.1999.76.4.582 Bontempo L, 2009, RAPID COMMUN MASS SP, V23, P1043, DOI 10.1002/rcm.3968 Casale M, 2010, FOOD CHEM, V118, P163, DOI 10.1016/j.foodchem.2009.04.091 Chen QS, 2009, SPECTROCHIM ACTA A, V72, P845, DOI 10.1016/j.saa.2008.12.002 Dornez E, 2008, J CEREAL SCI, V47, P180, DOI 10.1016/j.jcs.2007.03.008 Galtier O, 2007, ANAL CHIM ACTA, V595, P136, DOI 10.1016/j.aca.2007.02.033 [何凤丽 HE Feng-li], 2009, [山东农业大学学报. 自然科学版, Journal of Shandong Agricultural University. Natural Science], V40, P169 He W, 2012, SPECTROCHIM ACTA A, V86, P399, DOI 10.1016/j.saa.2011.10.056 Hristov N, 2010, EUPHYTICA, V174, P315, DOI 10.1007/s10681-009-0100-8 Kindred DR, 2008, J CEREAL SCI, V48, P46, DOI 10.1016/j.jcs.2007.07.010 Kucerova J, 2005, PLANT SOIL ENVIRON, V51, P101, DOI 10.17221/3562-PSE Li, 2004, SOIL GEOGRAPHY LUKOW OM, 1991, CEREAL CHEM, V68, P597 Ma J. L., 2008, SHANDONG AGR SCI, V2, P1 Mahesh S, 2008, BIOSYST ENG, V101, P50, DOI 10.1016/j.biosystemseng.2008.05.017 Murray I., 1987, NEAR INFRARED TECHNO, P17 Rharrabti Y, 2003, FIELD CROP RES, V80, P123, DOI 10.1016/S0378-4290(02)00176-4 Rharrabti Y, 2003, FIELD CROP RES, V80, P133, DOI 10.1016/S0378-4290(02)00177-6 Stuart B., 2004, INFRARED SPECTROSCOP, DOI [10.1002/0470011149, DOI 10.1002/0470011149] Triboi E, 2000, EUR J AGRON, V13, P47, DOI 10.1016/S1161-0301(00)00059-9 Wickramasinghe HAM, 2005, FOOD CHEM, V93, P9, DOI 10.1016/j.foodchem.2004.08.049 Woodcock T, 2009, FOOD CHEM, V114, P742, DOI 10.1016/j.foodchem.2008.10.034 Woodcock T, 2008, J AGR FOOD CHEM, V56, P11520, DOI 10.1021/jf802792d Xu L, 2012, FOOD RES INT, V49, P771, DOI 10.1016/j.foodres.2012.08.016 Yan Y. L., 2005, FUNDAMENTALS APPL NE, P31 Zecevic V, 2009, GENETIKA-BELGRADE, V41, P247, DOI 10.2298/GENSR0903247Z Zhao HY, 2013, FOOD CHEM, V138, P1902, DOI 10.1016/j.foodchem.2012.11.037 NR 27 TC 30 Z9 32 U1 1 U2 55 PD JUN 1 PY 2014 VL 152 BP 316 EP 322 DI 10.1016/j.foodchem.2013.11.122 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000332132300045 DA 2022-12-14 ER PT J AU Usui, S Nakamura, M Jitsukata, K Nara, M Hosaki, S Okazaki, M AF Usui, S Nakamura, M Jitsukata, K Nara, M Hosaki, S Okazaki, M TI Assessment of between-instrument variations in a HPLC method for serum lipoproteins and its traceability to reference methods for total cholesterol and HDL-cholesterol SO CLINICAL CHEMISTRY DT Article ID EDUCATION-PROGRAM RECOMMENDATIONS; APOLIPOPROTEIN-B; PRECIPITATION; PARTICLES; DISEASE AB Background: The main purpose of this study was to evaluate the between-instrument variation of the HPLC method for the measurement of total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C), VLDL-cholesterol (VLDL-C), chylomicron cholesterol (CM-C), LDL size, and HDL Size. Furthermore, the accuracy of the HPLC was assessed for the determination of TC and HDL-C compared with CDC reference methods. Methods: We used four HPLC instruments with different column-load numbers from 250 to 5000. For accuracy assessment of TC and HDL-C, we used the reference methods recommended by the CDC. Results: The values measured by the four instruments were highly correlated with each other (mean r = 0.965), and the absolute mean differences were 4-43 mg/L for TC, 4-30 mg/L for HDL-C, 0-48 mg/L for LDL-C, 7-66 mg/L for VLDL-C, 0-7 mg/L for CM-C, 0.1-0.3 nm for LDL size, and 0-0.1 nm for HDL size. For TC, the HPLC instruments showed high correlation and good agreement with the reference method: r = 0.997; total error <6.6%; absolute mean bias <1.2%. For HDL-C, the results from the HPLC method were significantly higher (10.8% absolute mean bias) than those of the CDC reference method, in spite of good correlation between the two methods (r = 0.998). Conclusions: The between-instrument variation in serum lipoprotein analysis by HPLC was confirmed to be very small. This method met the US National Cholesterol Education Program's performance criteria for TC but not for HDL-C. (C) 2000 American Association for Clinical Chemistry. C1 Tokyo Med & Dent Univ, Coll Liberal Arts & Sci, Chem Lab, Chiba 2720827, Japan. SRL Inc, Dept Clin Chem, Tokyo 1928535, Japan. Osaka Med Ctr Canc & Cardiovasc Dis, Dept Epidemiol & Mass Examinat CVD, Higashinari Ku, Osaka 5378511, Japan. Tokyo Med & Dent Univ, Sch Allied Hlth Sci, Bunkyo Ku, Tokyo 1138519, Japan. C3 Tokyo Medical & Dental University (TMDU); SRL Inc.; Osaka Medical Center for Cancer & Cardiovascular Diseases; Tokyo Medical & Dental University (TMDU) RP Okazaki, M (corresponding author), Tokyo Med & Dent Univ, Coll Liberal Arts & Sci, Chem Lab, 2-8-30 Kohnodai, Chiba 2720827, Japan. CR AUSTIN MA, 1988, JAMA-J AM MED ASSOC, V260, P1917 BACHORIK PS, 1995, CLIN CHEM, V41, P1414 CHUNG BH, 1981, J LIPID RES, V22, P1003 ELLERBE P, 1990, CLIN CHEM, V36, P370 Gardner CD, 1996, JAMA-J AM MED ASSOC, V276, P875, DOI 10.1001/jama.276.11.875 GIBSON JC, 1984, CLIN CHEM, V30, P1784 Hara I, 1986, Methods Enzymol, V129, P57 MARZ W, 1993, CLIN CHEM, V39, P2276 MILLER GJ, 1975, LANCET, V1, P16 MYERS GL, 1997, HDB LIPOPROTEIN TEST, P223 Nakajima K, 1996, J CLIN LIGAND ASSAY, V19, P177 *NAT CHOL ED PROGR, 1994, CIRCULATION, V89, P1333 Okazaki M, 1997, CLIN CHEM, V43, P1885 OKAZAKI M, 1997, HDB LIPOPROTEIN TEST, P531 OKAZAKI M, 1995, JPN J CLIN CHEM, V24, P245 SASAMOTO K, 1996, JPN J CLIN CHEM, V25, P28 SASAMOTO K, 1996, JPN J CLIN CHEM, V25, P235 TANAKA M, 1995, ATHEROSCLEROSIS, V114, P73, DOI 10.1016/0021-9150(94)05468-X WARNICK GR, 1995, CLIN CHEM, V41, P1427 WIEBE DA, 1985, CLIN CHEM, V31, P746 Wiebe DA, 1997, HDB LIPOPROTEIN TEST, P127 WILSON PWF, 1988, ARTERIOSCLEROSIS, V8, P737, DOI 10.1161/01.ATV.8.6.737 Yagyu H, 1999, J LIPID RES, V40, P1677 NR 23 TC 50 Z9 51 U1 0 U2 5 PD JAN PY 2000 VL 46 IS 1 BP 63 EP 72 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000084793100009 DA 2022-12-14 ER PT J AU Ritota, M Casciani, L Han, BZ Cozzolino, S Leita, L Sequi, P Valentini, M AF Ritota, Mena Casciani, Lorena Han, Bei-Zhong Cozzolino, Sara Leita, Liviana Sequi, Paolo Valentini, Massimiliano TI Traceability of Italian garlic (Allium sativum L.) by means of HRMAS-NMR spectroscopy and multivariate data analysis SO FOOD CHEMISTRY DT Article DE Metabolomics; HRMAS-NMR; PLS-DA; SPME-GC-MS; Organosulphurs ID MAGNETIC-RESONANCE-SPECTROSCOPY; ANGLE-SPINNING NMR; METABOLOMIC CHARACTERIZATION; ORGANOSULFUR COMPOUNDS; QUANTITATIVE-ANALYSIS; H-1-NMR SPECTROSCOPY; JUICE; TOOL; DISCRIMINATION; IDENTIFICATION AB H-1 High Resolution Magic Angle Spinning-Nuclear Magnetic Resonance (HRMAS-NMR) spectroscopy was used to analyse garlic (Allium sativum L) belonging to red and white varieties and collected in different Italian regions, in order to address the traceability issue. 1D and 2D NMR spectra, performed directly on untreated small pieces of garlic, so without any sample manipulation, allowed the assignment of several compounds: organic acids, sugars, fatty acids, amino acids and the nutritionally important fructo-oligosaccharides and allyl-organosulphur compounds. Application of Partial Least Squares projections to latent structures-Discrimination Analysis provided an excellent model for the discrimination of both the variety and, most important, the place origin, allowing the identification of the metabolites contributing to such classifications. The presence of organosulphurs, allicin and some allyl-organosulphurs found by HRMAS-NMR, was confirmed also by SPME-GC-MS; 11 molecules were identified, containing from one up to three sulphur atoms and with and without allyl moieties. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Ritota, Mena; Casciani, Lorena; Cozzolino, Sara; Leita, Liviana; Sequi, Paolo; Valentini, Massimiliano] Instrumental Ctr Tor Mancina, Agr Res Council, Res Ctr Soil Plant Syst, I-00015 Rome, Italy. [Han, Bei-Zhong] China Agr Univ, Coll Food Sci & Nutr Engn, Beijing 100083, Peoples R China. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); China Agricultural University RP Valentini, M (corresponding author), Instrumental Ctr Tor Mancina, Agr Res Council, Res Ctr Soil Plant Syst, Str Neve,SP Pascolare Km 1, I-00015 Rome, Italy. EM massimiliano.valentini@entecra.it CR Agarwal KC, 1996, MED RES REV, V16, P111, DOI 10.1002/(SICI)1098-1128(199601)16:1<111::AID-MED4>3.0.CO;2-5 Arnault I, 2006, J CHROMATOGR A, V1112, P23, DOI 10.1016/j.chroma.2006.01.036 Baumgartner S, 2000, CARBOHYD RES, V328, P177, DOI 10.1016/S0008-6215(00)00097-5 Boffo EF, 2009, LWT-FOOD SCI TECHNOL, V42, P1455, DOI 10.1016/j.lwt.2009.05.008 Brescia MA, 2007, FOOD CHEM, V104, P429, DOI 10.1016/j.foodchem.2006.09.043 Cevallos-Cevallos JM, 2009, TRENDS FOOD SCI TECH, V20, P557, DOI 10.1016/j.tifs.2009.07.002 Chandrashekar PM, 2011, PHYTOCHEMISTRY, V72, P255, DOI 10.1016/j.phytochem.2010.11.015 Ciampa A, 2010, J FOOD QUALITY, V33, P199, DOI 10.1111/j.1745-4557.2010.00306.x CROASMUN WR, 1994, 2 DIMENSIONAL NMR SP, P785 Cuny M, 2008, ANAL BIOANAL CHEM, V390, P419, DOI 10.1007/s00216-007-1708-y Donarski JA, 2008, J AGR FOOD CHEM, V56, P5451, DOI 10.1021/jf072402x Donarski JA, 2010, FOOD CHEM, V118, P987, DOI 10.1016/j.foodchem.2008.10.033 Duarte IF, 2005, SPECTROSC LETT, V38, P319, DOI 10.1081/SL-200058713 Ernst MK, 1998, J PLANT PHYSIOL, V153, P53, DOI 10.1016/S0176-1617(98)80044-8 Hu QH, 2002, J AGR FOOD CHEM, V50, P1059, DOI 10.1021/jf011182z KAMANNA VS, 1980, J AM OIL CHEM SOC, V57, P175, DOI 10.1007/BF02883781 Kharazi PR, 2005, PHOSPHORUS SULFUR, V180, P1399, DOI 10.1080/10426500590912727 Khoo YSK, 2009, J CLIN PHARM THER, V34, P133, DOI 10.1111/j.1365-2710.2008.00998.x Koch HP., 1996, GARLIC SCI THERAPEUT Le Gall G, 2003, J AGR FOOD CHEM, V51, P2447, DOI 10.1021/jf0259967 Le Gall G, 2001, J AGR FOOD CHEM, V49, P580, DOI 10.1021/jf001046e Losso DN, 1997, J AGR FOOD CHEM, V45, P4342, DOI 10.1021/jf970433u Mannina L, 2001, J AGR FOOD CHEM, V49, P2687, DOI 10.1021/jf001408i Mazzei P, 2012, FOOD CHEM, V132, P1620, DOI 10.1016/j.foodchem.2011.11.142 Nestor G, 2010, J AGR FOOD CHEM, V58, P10799, DOI 10.1021/jf103338j PAULING L, 1971, P NATL ACAD SCI USA, V68, P2374, DOI 10.1073/pnas.68.10.2374 PETESCH BL, 1999, TRENDS FOOD SCI TECH, V9, P415 Ramaa CS, 2006, CURR PHARM BIOTECHNO, V7, P15, DOI 10.2174/138920106775789647 Reuter HD, 1995, PHYTOMEDICINE, V2, P73, DOI 10.1016/S0944-7113(11)80052-8 Ritota M, 2010, J AGR FOOD CHEM, V58, P9675, DOI 10.1021/jf1015957 Sacco A, 1998, J AGR FOOD CHEM, V46, P4242, DOI 10.1021/jf971113d Sacco D, 2005, MEAT SCI, V71, P542, DOI 10.1016/j.meatsci.2005.04.038 Schievano E, 2010, J AGR FOOD CHEM, V58, P57, DOI 10.1021/jf9022977 Shintu L, 2004, MAGN RESON CHEM, V42, P396, DOI 10.1002/mrc.1359 SHIOMI N, 1979, AGR BIOL CHEM TOKYO, V43, P2233, DOI 10.1080/00021369.1979.10863800 Sobolev AP, 2005, MAGN RESON CHEM, V43, P625, DOI 10.1002/mrc.1618 Sobolev AP, 2003, MAGN RESON CHEM, V41, P237, DOI 10.1002/mrc.1176 Son HS, 2008, J AGR FOOD CHEM, V56, P8007, DOI 10.1021/jf801424u Son HS, 2009, J AGR FOOD CHEM, V57, P4801, DOI 10.1021/jf9005017 SPIES T, 1992, CARBOHYD RES, V235, P221, DOI 10.1016/0008-6215(92)80090-N Spraul M, 2009, MAGN RESON CHEM, V47, pS130, DOI 10.1002/mrc.2528 STOLL A, 1951, ADV ENZYMOL REL S BI, V11, P377 Tarachiwin L, 2007, J AGR FOOD CHEM, V55, P9330, DOI 10.1021/jf071956x Valentini M, 2011, MAGN RESON CHEM, V49, pS121, DOI 10.1002/mrc.2826 Wang HX, 1999, LIFE SCI, V65, P2663, DOI 10.1016/S0024-3205(99)00253-2 Yang Q, 2003, Z NATURFORSCH C, V58, P408 NR 46 TC 50 Z9 54 U1 1 U2 74 PD NOV 15 PY 2012 VL 135 IS 2 BP 684 EP 693 DI 10.1016/j.foodchem.2012.05.032 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000308574300054 DA 2022-12-14 ER PT J AU Durante, C Bertacchini, L Cocchi, M Manzini, D Marchetti, A Rossi, MC Sighinolfi, S Tassi, L AF Durante, Caterina Bertacchini, Lucia Cocchi, Marina Manzini, Daniela Marchetti, Andrea Rossi, Maria Cecilia Sighinolfi, Simona Tassi, Lorenzo TI Development of Sr-87/Sr-86 maps as targeted strategy to support wine quality SO FOOD CHEMISTRY DT Article DE Geographical traceability; Sr-87/Sr-86 values; Food; Isotopic maps ID STRONTIUM; SOIL; ISOTOPES; RATIOS; TRACEABILITY; SYSTEMATICS; TRENDS AB This study summarizes the results obtained from a systematic and long-term project aimed at the development of tools to assess the provenance of food in the oenological sector. In particular, Sr-87/Sr-86 isotope ratios were measured on statistically representative set of soils, vine branches and wines sampled in the production district of Modena, worldwide known for the Lambrusco wines production. The obtained data were used to build strontium isotopic maps able to objectively support the Lambrusco PDO wines origin as well as other products of the Modena district. Finally, a strong relationship was found between the Sr-87/Sr-86 isotope ratios of soils and vine branches on a large scale, highlighting and confirming once more the idea that plants can also represent an optimal sampling device to support geographical traceability. C1 [Durante, Caterina] Spin Univ Modena & Reggio Emilia, ChemStamp Srl, Via Campi 183, I-41125 Modena, Italy. [Bertacchini, Lucia; Cocchi, Marina; Marchetti, Andrea; Sighinolfi, Simona; Tassi, Lorenzo] Univ Modena & Reggio Emilia, Dept Geol & Chem Sci, Via Campi 183, I-41125 Modena, Italy. [Manzini, Daniela; Rossi, Maria Cecilia] Univ Modena & Reggio Emilia, Ctr Interdipartimentale Grandi Strumenti, Via Campi 203, I-41125 Modena, Italy. C3 Universita di Modena e Reggio Emilia; Universita di Modena e Reggio Emilia RP Marchetti, A (corresponding author), Dept Chem & Geol Sci, Via G Campi 103, I-41125 Modena, Italy. EM andrea.marchetti@unimore.it CR Alonso A, 2015, FRONT BIOENG BIOTECH, V3, DOI 10.3389/fbioe.2015.00023 [Anonymous], 2009, 19730 DIN ISO Banner JL, 2004, EARTH-SCI REV, V65, P141, DOI 10.1016/S0012-8252(03)00086-2 Bertacchini L, 2012, TALANTA, V98, P178, DOI 10.1016/j.talanta.2012.06.067 Capo RC, 1998, GEODERMA, V82, P197, DOI 10.1016/S0016-7061(97)00102-X Coelho I, 2017, TRAC-TREND ANAL CHEM, V90, P45, DOI 10.1016/j.trac.2017.02.005 Danezis GP, 2017, FOOD AUTHENTICATION: MANAGEMENT, ANALYSIS AND REGULATION, P19 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 Degryse P, 2006, J ARCHAEOL SCI, V33, P494, DOI 10.1016/j.jas.2005.09.003 Durante C, 2016, FOOD CHEM, V210, P648, DOI 10.1016/j.foodchem.2016.04.108 Durante C, 2015, FOOD CHEM, V173, P557, DOI 10.1016/j.foodchem.2014.10.086 Durante C, 2013, FOOD CHEM, V141, P2779, DOI 10.1016/j.foodchem.2013.05.108 Evans JA, 2006, ARCHAEOMETRY, V48, P309, DOI 10.1111/j.1475-4754.2006.00258.x HORN P, 1993, Z LEBENSM UNTERS FOR, V196, P407, DOI 10.1007/BF01190802 HORWITZ EP, 1992, SOLVENT EXTR ION EXC, V10, P313, DOI 10.1080/07366299208918107 Lee M., 2003, FOOD AUTHENTICITY TR Lugli S, 2007, GEOL S AM S, P57, DOI 10.1130/2006.2420(05) Marchetti A, 2017, FOOD AUTHENTICATION Marchetti Dori S, 2006, THESIS Marchionni S, 2016, FOOD CHEM, V190, P777, DOI 10.1016/j.foodchem.2015.06.026 Marchionni S, 2013, J AGR FOOD CHEM, V61, P6822, DOI 10.1021/jf4012592 MOORE LJ, 1982, J RES NAT BUR STAND, V87, P1, DOI 10.6028/jres.087.001 Moreira C, 2017, S AFR J ENOL VITIC, V38, P82 Petrini R, 2015, FOOD CHEM, V170, P138, DOI 10.1016/j.foodchem.2014.08.051 Price TD, 2002, ARCHAEOMETRY, V44, P117, DOI 10.1111/1475-4754.00047 Stein M, 1997, GEOCHIM COSMOCHIM AC, V61, P3975, DOI 10.1016/S0016-7037(97)00191-9 Totaro S, 2013, CHEMOMETR INTELL LAB, V124, P14, DOI 10.1016/j.chemolab.2013.03.001 Vinciguerra V, 2016, FOOD CHEM, V210, P121, DOI 10.1016/j.foodchem.2016.04.017 White W.M., 2013, GEOCHEMISTRY NR 29 TC 22 Z9 23 U1 2 U2 29 PD JUL 30 PY 2018 VL 255 BP 139 EP 146 DI 10.1016/j.foodchem.2018.02.084 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000428402000018 DA 2022-12-14 ER PT J AU Albergamo, A Mottese, AF Bua, GD Caridi, F Sabatino, G Barrega, L Costa, R Dugo, G AF Albergamo, Ambrogina Mottese, Antonio F. Bua, Giuseppe D. Caridi, Francesco Sabatino, Giuseppe Barrega, Luna Costa, Rosaria Dugo, Giacomo TI Discrimination of the Sicilian Prickly Pear (Opuntia Ficus-Indica L., CV. Muscaredda) According to the Provenance by Testing Unsupervised and Supervised Chemometrics SO JOURNAL OF FOOD SCIENCE DT Article DE food traceability; inorganic elements; multivariate statistics; prickly pears ID ICP-MS; TRACE-ELEMENTS; CACTUS PEAR; TRACEABILITY; PERFORMANCE; PROFILE; FRUIT AB Different multivariate techniques were tested in an attempt to build up a statistical model for predicting the origin of prickly pears (Opuntia ficus-indica L., cv. Muscaredda) from several localities within the Sicilian region. Specifically, two areas known for producing fruits marked respectively by TAP (traditional agri-food product) and PDO (protected designation of origin) brands, and three sites producing non-branded fruits, were considered. A validated inductively coupled plasma mass spectrometry (ICP-MS) method allowed to obtain elemental fingerprints of prickly pears, which were subsequently elaborated by unsupervised tools, such as hierarchical clustering analysis (HCA) and principal component analysis (PCA), and supervised techniques, such as stepwise-canonical discriminant analysis (CDA) and partial least squares-discriminant analysis (PLS-DA). With the exception of HCA, which was not enough powerful to correctly cluster all selected samples, PCA successfully investigated the effect of subregional provenance on prickly pears, thus, differentiating labeled products from the non-labeled counterpart. Also, stepwise CDA and PLS-DA allowed to build up reliable models able to correctly classify 100% of fruits on the basis of the production areas, by exploiting a restricted pool of metals. Both statistical models, including unsupervised (PCA) and supervised techniques (stepwise CDA or PLS-DA), may guarantee the provenance of prickly pears protected by quality labels and safeguard producers and consumers. Practical Application Based on elemental analysis and chemometrics, the reliable traceability models herein proposed, could be applied to commercial Sicilian prickly pears protected by TAP and PDO logos to guarantee their provenance and, at the same time, to safeguard producers and consumers. C1 [Albergamo, Ambrogina; Mottese, Antonio F.; Bua, Giuseppe D.; Dugo, Giacomo] Science4Life Srl, Messina, Italy. [Albergamo, Ambrogina; Mottese, Antonio F.; Bua, Giuseppe D.; Barrega, Luna; Costa, Rosaria; Dugo, Giacomo] Univ Messina, Dipto Sci Biomed Odontoiatr & Immagini Morfol & F, Viale Annunziata, I-98168 Messina, Italy. [Caridi, Francesco] Agenzia Reg Protez Ambiente Calabria ARPACal, Via Troncovito SNC, I-89135 Reggio Di Calabria, Italy. [Sabatino, Giuseppe] Univ Messina, Dipto Sci Matemat & Informat Sci Fis & Terra MIFT, Viale F Stagno Alcontres 31, I-98166 Messina, Italy. C3 University of Messina; Regional Environmental Protection Agency - Italy; University of Messina RP Albergamo, A (corresponding author), Science4Life Srl, Messina, Italy.; Albergamo, A (corresponding author), Univ Messina, Dipto Sci Biomed Odontoiatr & Immagini Morfol & F, Viale Annunziata, I-98168 Messina, Italy. EM aalbergamo@unime.it CR Albergamo A, 2018, J FOOD COMPOS ANAL, V66, P212, DOI 10.1016/j.jfca.2017.12.026 Albergamo A, 2017, NAT PROD RES, V31, P990, DOI 10.1080/14786419.2016.1258563 Assessorato Regionale Agricoltura e foreste, 2008, PFR SIC AN CON Bua GD, 2017, FOOD ANAL METHOD, V10, P1181, DOI 10.1007/s12161-016-0680-6 Butera D, 2002, J AGR FOOD CHEM, V50, P6895, DOI 10.1021/jf025696p Caridi F, 2016, EUR PHYS J PLUS, V131, DOI 10.1140/epjp/i2016-16155-x Caridi F, 2016, ENVIRON EARTH SCI, V75, DOI 10.1007/s12665-016-5393-z Chong IG, 2005, CHEMOMETR INTELL LAB, V78, P103, DOI 10.1016/j.chemolab.2004.12.011 Currie LA, 1999, ANAL CHIM ACTA, V391, P105, DOI 10.1016/S0003-2670(99)00104-X Di Bella G, 2016, INT J FOOD SCI NUTR, V67, P239, DOI 10.3109/09637486.2016.1153610 Di Bella G, 2015, J FOOD COMPOS ANAL, V44, P25, DOI 10.1016/j.jfca.2015.05.003 Medina EMD, 2007, FOOD CHEM, V103, P38, DOI 10.1016/j.foodchem.2006.06.064 Fernandez-Lopez JA, 2001, J CHROMATOGR A, V913, P415, DOI 10.1016/S0021-9673(00)01224-3 Feugang JM, 2006, FRONT BIOSCI-LANDMRK, V11, P2574, DOI 10.2741/1992 Gomes T, 2017, CURR ORG CHEM, V21, P402, DOI 10.2174/1385272820666161102121232 Granato D, 2018, COMPR REV FOOD SCI F, V17, P663, DOI 10.1111/1541-4337.12341 Granato D, 2018, TRENDS FOOD SCI TECH, V72, P83, DOI 10.1016/j.tifs.2017.12.006 Italian Ministerial Decree, 2000, GAZETTA UFFICIALE RE, V194 Kozak M, 2008, J SCI FOOD AGR, V88, P1115, DOI 10.1002/jsfa.3215 Ministry of Agricultural Food and Forestry Policies, 2010, DISC PROD DEN OR PRO Mottese AF, 2018, J FOOD COMPOS ANAL, V72, P66, DOI 10.1016/j.jfca.2018.05.009 Mottese AF, 2018, J SCI FOOD AGR, V98, P198, DOI 10.1002/jsfa.8456 O P La Deliziosa Soc Coop Agr, 2018, THE PRICKL PEAR Piga A, 2004, J PROF ASSOC CACTUS, V6, P9 Potorti AG, 2018, J FOOD COMPOS ANAL, V69, P122, DOI 10.1016/j.jfca.2018.03.001 Potorti AG, 2017, NAT PROD RES, V31, P1000, DOI 10.1080/14786419.2016.1261341 Potorti AG, 2013, J FOOD COMPOS ANAL, V31, P161, DOI 10.1016/j.jfca.2013.05.006 SIAS Servizio Informativo Agrometereologico Siciliano, 2017, SIST INF TERR AGR Vadala R, 2016, FOODS, V5, DOI 10.3390/foods5010020 NR 29 TC 18 Z9 18 U1 0 U2 27 PD DEC PY 2018 VL 83 IS 12 BP 2933 EP 2942 DI 10.1111/1750-3841.14382 WC Food Science & Technology SC Food Science & Technology UT WOS:000452797900006 DA 2022-12-14 ER PT J AU Wu, XM Zhang, QZ Wang, YZ AF Wu, Xue-Mei Zhang, Qing-Zhi Wang, Yuan-Zhong TI Traceability of wild Paris polyphylla Smith var. yunnanensis based on data fusion strategy of FT-MIR and UV-Vis combined with SVM and random forest SO SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY DT Article DE Paris polyphylla Smith var. yunnanensis; Data fusion; Support vector machine gird search (SVM-GS); Random forest; Fourier transform mid infrared (FT-MIR); Ultraviolet-visible (UV-Vis) ID LIQUID-CHROMATOGRAPHY; GEOGRAPHICAL ORIGIN; ELECTRONIC TONGUE; CLASSIFICATION; SPECTROSCOPY; IDENTIFICATION; DISCRIMINATION; PLANTS; FLUORESCENCE; PERFORMANCE AB Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz (PPY) was a frequently used herbal medicine in pharmaceutical field and different provenances might affect the clinical efficacy. Tracing the geographical origin was an important portion for PPY authentication and quality assessment. Present study was compared low-, mid and high-level data fusion methodology for geographical traceability of PPY samples (161 batches) combined with multivariate classification methods such as support vector machine gird search (SVM-GS) and random forest (RF) on the basis of Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV-Vis) spectra. Compared with the low-and mid-level data fusion strategy results basing on SVM-GS algorithm, result of high-level data fusion method (calculated by RF) was more satisfying. Result of RF basing on high-level data fusion strategy showed that merely two samples were misclassified and one sample was multiple assigned after voting with fuzzy set theory. Values of specificity, sensitivity, and accuracy rates were exceeded 0.91, 0.99 and 90.91%, for each class respectively, satisfying results of these were shown in training and test sets for high-level data fusion method. This feasible result indicated that the RF algorithm could establish a reliable and good performance model in geographical traceability on the basis of high-level data fusion strategy. Combination of high-level data fusion and RF algorithm could consider as a good choice for establishing a discrimination multivariate model for origins identification of PPY samples. (C) 2018 Published by Elsevier B.V. C1 [Wu, Xue-Mei; Wang, Yuan-Zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. [Wu, Xue-Mei; Zhang, Qing-Zhi] Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University of Chinese Medicine RP Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China.; Zhang, QZ (corresponding author), Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. EM ynkzqz@126.com; boletus@126.com CR Abidoye LK, 2018, J CLEAN PROD, V175, P123, DOI 10.1016/j.jclepro.2017.12.013 Anjos O, 2015, FOOD CHEM, V169, P218, DOI 10.1016/j.foodchem.2014.07.138 BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Borras E, 2016, FOOD CHEM, V203, P314, DOI 10.1016/j.foodchem.2016.02.038 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324 Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199 Chen JB, 2017, SPECTROCHIM ACTA A, V182, P81, DOI 10.1016/j.saa.2017.03.070 Chen P, 2016, J SEP SCI, V39, P3550, DOI 10.1002/jssc.201600259 Chen X, 2012, GENOMICS, V99, P323, DOI 10.1016/j.ygeno.2012.04.003 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 de Santana FB, 2018, FOOD ANAL METHOD, V11, P1927, DOI 10.1007/s12161-017-1142-5 Devos O, 2014, FOOD CHEM, V148, P124, DOI 10.1016/j.foodchem.2013.10.020 Di Anibal CV, 2011, TALANTA, V84, P829, DOI 10.1016/j.talanta.2011.02.014 Di Natale C, 2002, ANAL CHIM ACTA, V459, P107, DOI 10.1016/S0003-2670(02)00107-1 Doeswijk TG, 2011, ANAL CHIM ACTA, V705, P41, DOI 10.1016/j.aca.2011.03.025 Hall D. L., 2004, MATH TECHNIQUES MULT Hayta S, 2014, J ETHNOPHARMACOL, V154, P613, DOI 10.1016/j.jep.2014.04.026 Hu LQ, 2018, MICROCHEM J, V137, P456, DOI 10.1016/j.microc.2017.12.012 Hu LQ, 2018, SPECTROCHIM ACTA A, V193, P87, DOI 10.1016/j.saa.2017.12.011 Huang JL, 2018, SPECTROCHIM ACTA A, V198, P198, DOI 10.1016/j.saa.2018.03.017 Jing SS, 2017, NAT PROD RES, V31, P660, DOI 10.1080/14786419.2016.1219861 KAISER HF, 1960, EDUC PSYCHOL MEAS, V20, P141, DOI 10.1177/001316446002000116 Kang LP, 2017, J PHARMACEUT BIOMED, V142, P252, DOI 10.1016/j.jpba.2017.05.019 KIERS HAL, 1994, PSYCHOMETRIKA, V59, P81, DOI 10.1007/BF02294267 Li Y. J., 2017, SENSORS, V17, P1, DOI DOI 10.1109/JSEN.2017.2671241 Li Y, 2018, MICROCHEM J, V140, P38, DOI 10.1016/j.microc.2018.04.001 Lin SW, 2008, EXPERT SYST APPL, V35, P1817, DOI 10.1016/j.eswa.2007.08.088 Liu M, 2013, SENSOR ACTUAT B-CHEM, V177, P970, DOI 10.1016/j.snb.2012.11.071 Liu Yang, 2014, ScientificWorldJournal, V2014, P476219, DOI 10.1155/2014/476219 Maguire A, 2015, ANALYST, V140, P2473, DOI 10.1039/c4an01887g Man SL, 2010, J CHROMATOGR B, V878, P2943, DOI 10.1016/j.jchromb.2010.08.033 Marquez C, 2016, TALANTA, V161, P80, DOI 10.1016/j.talanta.2016.08.003 Escamilla MN, 2013, TALANTA, V114, P304, DOI 10.1016/j.talanta.2013.05.046 Pardo M, 2005, SENSOR ACTUAT B-CHEM, V107, P730, DOI 10.1016/j.snb.2004.12.005 Qi LM, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18010241 Rajer-Kanduc K, 2003, CHEMOMETR INTELL LAB, V65, P221, DOI 10.1016/S0169-7439(02)00110-7 Ramos PM, 2007, ANAL CHIM ACTA, V584, P360, DOI 10.1016/j.aca.2006.11.051 Rudnitskaya A, 2006, SENSOR ACTUAT B-CHEM, V116, P23, DOI 10.1016/j.snb.2005.11.069 Saptoro A, 2012, CHEM PROD PROCESS MO, V7, DOI 10.1515/1934-2659.1645 SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 Shen S, 2018, INT J BIOL MACROMOL, V107, P1613, DOI 10.1016/j.ijbiomac.2017.10.026 Song Y, 2017, MITOCHONDRIAL DNA A, V28, P159, DOI 10.3109/19401736.2015.1115489 Strobl C, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-25 Sun S.Q., 2011, INFRARED SPECTROSCOP SUN Su-gin, 2010, ANAL TRADITIONAL CHI Sun SQ, 2010, PLANTA MED, V76, P1987, DOI 10.1055/s-0030-1250520 Sun WJ, 2017, SPECTROCHIM ACTA A, V171, P72, DOI 10.1016/j.saa.2016.07.039 Tan J, 2015, FOOD CHEM, V184, P30, DOI 10.1016/j.foodchem.2015.03.085 The State Pharmacopoeia Commission, 2015, CHINESE PHARMACOPOEI, P260 Tohda C, 2012, SCI REP-UK, V2, DOI 10.1038/srep00535 Turker-Kaya S, 2017, MOLECULES, V22, DOI 10.3390/molecules22010168 Wang YZ, 2018, PLANT GROWTH REGUL, V84, P373, DOI 10.1007/s10725-017-0348-2 Wu X, 2013, CARBOHYD RES, V368, P1, DOI 10.1016/j.carres.2012.11.027 Wu X, 2018, CHIN MED-UK, V13, DOI 10.1186/s13020-018-0163-3 Wu Z, 2017, MOLECULES, V22, DOI 10.3390/molecules22071238 Wu Z, 2017, J NAT MED-TOKYO, V71, P139, DOI 10.1007/s11418-016-1043-8 Xu CH, 2015, BIOMED SPECTROSC IMA, V4, P139, DOI 10.3233/BSI-150112 Yang LF, 2018, J MOL STRUCT, V1165, P37, DOI 10.1016/j.molstruc.2018.03.061 Yang T. M., 2017, J CHIN MED MAT, V12, P2834, DOI [10.13863/j.issn1001-4454.2017.12.019, DOI 10.13863/J.ISSN1001-4454.2017.12.019] Yang YG, 2017, J NAT MED-TOKYO, V71, P148, DOI 10.1007/s11418-016-1044-7 Yao N, 2017, AM J CHINESE MED, V45, P575, DOI [10.1142/S0192415X17500343, 10.1142/s0192415x17500343] Zhao YL, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0089100 Zhu Y., 2015, AM J ANAL CHEM, V6, P480, DOI DOI 10.4236/AJAC.2015.65047 NR 64 TC 31 Z9 33 U1 4 U2 69 PD DEC 5 PY 2018 VL 205 BP 479 EP 488 DI 10.1016/j.saa.2018.07.067 WC Spectroscopy SC Spectroscopy UT WOS:000445713600055 DA 2022-12-14 ER PT J AU Orji, NG Dixson, RG Garcia-Gutierrez, DI Bunday, BD Bishop, M Cresswell, MW Allen, RA Allgair, JA AF Orji, Ndubuisi G. Dixson, Ronald G. Garcia-Gutierrez, Domingo I. Bunday, Benjamin D. Bishop, Michael Cresswell, Michael W. Allen, Richard A. Allgair, John A. TI Transmission electron microscope calibration methods for critical dimension standards SO JOURNAL OF MICRO-NANOLITHOGRAPHY MEMS AND MOEMS DT Article DE linewidth metrology; high-resolution transmission electron microscope; high angle annular dark field scanning transmission electron microscope; critical dimension atomic force microscope; uncertainty; SI traceability ID CD; METROLOGY AB One of the key challenges in critical dimension (CD) metrology is finding suitable dimensional calibration standards. The transmission electron microscope (TEM), which produces lattice-resolved images having scale traceability to the SI (International System of Units) definition of length through an atomic lattice constant, has gained wide usage in different areas of CD calibration. One such area is critical dimension atomic force microscope (CD-AFM) tip width calibration. To properly calibrate CD-AFM tip widths, errors in the calibration process must be quantified. Although the use of TEM for CD-AFM tip width calibration has been around for about a decade, there is still confusion on what should be considered in the uncertainty analysis. We characterized CD-AFM tip-width samples using high-resolution TEM and high angle annular dark field scanning TEM and two CD-AFMs that are implemented as reference measurement systems. The results are used to outline how to develop a rigorous uncertainty estimate for TEM/CD-AFM calibration, and to compare how information from the two electron microscopy modes are applied to practical CD-AFM measurements. The results also represent a separate validation of previous TEM/CD-AFM calibration. Excellent agreement was observed. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) C1 [Orji, Ndubuisi G.; Dixson, Ronald G.; Cresswell, Michael W.; Allen, Richard A.] NIST, 100 Bur Dr, Gaithersburg, MD 20899 USA. [Garcia-Gutierrez, Domingo I.] UANL, FIME, Ave Univ S-N,Ciudad Univ, San Nicolas De Los Garza 66450, Nuevo Leon, Mexico. [Bunday, Benjamin D.] SUNY Poly SEMATECH, 257 Fuller Rd,Suite 2200, Albany, NY 12203 USA. [Bishop, Michael] 608 Oak Hollow Dr, Kerrville, TX 78028 USA. [Allgair, John A.] AWG, 2028 East Ben White Blvd,240-2308, Austin, TX 78741 USA. C3 National Institute of Standards & Technology (NIST) - USA; Universidad Autonoma de Nuevo Leon; SUNY Polytechnic Institute RP Orji, NG (corresponding author), NIST, 100 Bur Dr, Gaithersburg, MD 20899 USA. EM ndubuisi.orji@nist.gov CR Allen RA, 2001, IEEE T SEMICONDUCT M, V14, P26, DOI 10.1109/66.909652 Bunday B, 2016, PROC SPIE, V9778, DOI 10.1117/12.2218375 Dai GL, 2016, OPT ENG, V55, DOI 10.1117/1.OE.55.9.091407 Dai GL, 2015, MEAS SCI TECHNOL, V26, DOI 10.1088/0957-0233/26/11/115006 Diebold AC, 2003, MICROSC MICROANAL, V9, P493, DOI 10.1017/S1431927603030629 DIEBOLD AC, 2001, HDB SILICON SEMICOND, P851 Dixson R, 2003, P SOC PHOTO-OPT INS, V5038, P150, DOI 10.1117/12.483667 Dixson RG, 2005, J VAC SCI TECHNOL B, V23, P3028, DOI 10.1116/1.2130347 Dixson R, 2015, J VAC SCI TECHNOL B, V33, DOI 10.1116/1.4919090 Dixson R, 2012, J MICRO-NANOLITH MEM, V11, DOI 10.1117/1.JMM.11.1.011006 Dixson RG, 2016, J MICRO-NANOLITH MEM, V15, DOI 10.1117/1.JMM.15.1.014503 International Organisation for Standardisation, 1995, GUID EXPR UNC MEAS MANDEL J, 1984, J QUAL TECHNOL, V16, P1 Mohr PJ, 2012, REV MOD PHYS, V84, P1527, DOI 10.1103/RevModPhys.84.1527 Orji NG, 2007, MEAS SCI TECHNOL, V18, P448, DOI 10.1088/0957-0233/18/2/S17 Orji NG, 2007, J MICRO-NANOLITH MEM, V6, DOI 10.1117/1.2728742 Orji NG, 2016, ULTRAMICROSCOPY, V162, P25, DOI 10.1016/j.ultramic.2015.12.003 Orji NG, 2008, PROC SPIE, V6922, DOI 10.1117/12.774426 Orji NG, 2007, PROC SPIE, V6518, DOI 10.1117/12.713368 Pennycook S. J, 1997, ELECT MICROSCOPY PRI, P361 Rana Narender, 2014, Journal of Micro/Nanolithography, MEMS, and MOEMS, V13, DOI 10.1117/1.JMM.13.4.041415 Rana N., 2009, P SOC PHOTO-OPT INS, V7272 Robert J, 2007, PROC SPIE, V6518, DOI 10.1117/12.713101 Scott JHJ, 2007, MEAS SCI TECHNOL, V18, P2755, DOI 10.1088/0957-0233/18/9/003 Sendelbach Matthew, 2014, Journal of Micro/Nanolithography, MEMS, and MOEMS, V13, DOI 10.1117/1.JMM.13.4.041414 Takamasu K., 2014, P SOC PHOTO-OPT INS, V9050, P9050 Taylor BN, 1994, NIST GUIDELINES EVAL Taylor JR, 1997, INTRO ERROR ANAL STU Tortonese M, 2004, P SOC PHOTO-OPT INS, V5375, P647, DOI 10.1117/12.536812 Ukraintsev V, 2012, J MICRO-NANOLITH MEM, V11, DOI 10.1117/1.JMM.11.1.011010 Williams D.B., 1996, TRANSMISSION ELECT M NR 31 TC 11 Z9 13 U1 2 U2 23 PD OCT PY 2016 VL 15 IS 4 AR 044002 DI 10.1117/1.JMM.15.4.044002 WC Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Optics SC Engineering; Science & Technology - Other Topics; Materials Science; Optics UT WOS:000397068400013 DA 2022-12-14 ER PT J AU Moros, B Toval, A Rosique, F Sanchez, P AF Moros, Begona Toval, Ambrosio Rosique, Francisca Sanchez, Pedro TI Transforming and tracing reused requirements models to home automation models SO INFORMATION AND SOFTWARE TECHNOLOGY DT Article DE Requirements metamodel; Requirements reuse; Requirements traceability; Models transformation; Model driven software development; Home automation models ID TRACEABILITY AB Context: Model-Driven Software Development (MDSD) has emerged as a very promising approach to cope with the inherent complexity of modern software-based systems. Furthermore, it is well known that the Requirements Engineering (RE) stage is critical for a project's success. Despite the importance of RE, MDSD approaches commonly leave textual requirements specifications to one side. Objective: Our aim is to integrate textual requirements specifications into the MDSD approach by using the MDSD techniques themselves, including metamodelling and model transformations. The proposal is based on the assumption that a reuse-based Model-Driven Requirements Engineering (MDRE) approach will improve the requirements engineering stage, the quality of the development models generated from requirements models, and will enable the traces from requirements to other development concepts (such as analysis or design) to be maintained. Method: The approach revolves around the Requirements Engineering Metamodel, denominated as REMM, which supports the definition of the boilerplate based textual requirements specification languages needed for the definition of model transformation from application requirements models to platform-specific application models and code. Results: The approach has been evaluated through its application to Home Automation (HA) systems. The HA Requirement Specification Language denominated as HAREL is used to define application requirements models which will be automatically transformed and traced to the application model conforming to the HA Domain Specific Language. Conclusions: An anonymous online survey has been conducted to evaluate the degree of acceptance by both HA application developers and MDSD practitioners. The main conclusion is that 66.7% of the HA experts polled strongly agree that the automatic transformation of the requirements models to HA models improves the quality of the HA models. Moreover, 58.3% of the HA participants strongly agree with the usefulness of the traceability matrix which links requirements to HA functional units in order to discover which devices are related to a specific requirement. We can conclude that the experts we have consulted agree with the proposal we are presenting here, since the average mark given is 4 out of 5. (C) 2012 Elsevier B.V. All rights reserved. C1 [Moros, Begona; Toval, Ambrosio] Univ Murcia, Software Engn Res Grp, Dept Informat & Sistemas, E-30071 Murcia, Spain. [Rosique, Francisca; Sanchez, Pedro] Univ Politecn Cartagena, Syst & Elect Engn Div DSIE, Cartagena 30202, Spain. C3 University of Murcia; Universidad Politecnica de Cartagena RP Moros, B (corresponding author), Univ Murcia, Software Engn Res Grp, Dept Informat & Sistemas, Campus Espinardo, E-30071 Murcia, Spain. EM bmoros@um.es; atoval@um.es; paqui.rosique@upct.es; pedro.sanchez@upct.es CR Albinet A., 2008, THE MEMVATEX METHODO Almeida JPA, 2007, INFORM SYST FRONT, V9, P327, DOI 10.1007/s10796-007-9038-3 [Anonymous], 2008, DOMAIN SPECIFIC MODE [Anonymous], 2001, SIGSOFT SOFTW ENG NO, DOI DOI 10.1145/505532.505535 Baudry B., 2007, 11TH IEEE INTERNATIO Braganca A, 2007, FOURTH INTERNATIONAL WORKSHOP ON MODEL-BASED METHODOLOGIES FOR PERVASIVE AND EMBEDDED SOFTWARE, PROCEEDINGS, P91, DOI 10.1109/MOMPES.2007.2 Brambilla M., 2008, 4TH INTERNATIONAL WO Brattier E., 2007, 11TH IEEE INTERNATIO Bryant B. R., 2010, PROCEEDINGS OF THE F Ceron R, 2005, LECT NOTES COMPUT SC, V3547, P173 Champeau J., 2003, SIVOES MDA WORKSHOP Cheng B. H. C., 2007, INTERNATIONAL CONFER Chernak Y., REQUIREMENTS REUSE T Clements P., 2002, SOFTWARE PRODUCT LIN Cybulski JL, 2000, LECT NOTES COMPUT SC, V1844, P190 Damian D, 2006, IEEE T SOFTWARE ENG, V32, P433, DOI 10.1109/TSE.2006.61 Davis A. M., 1995, 201 PRINCIPLES OF SO Debnath N., 2008, FIFTH INTERNATIONAL Domges R, 1998, COMMUN ACM, V41, P54, DOI 10.1145/290133.290149 dos Santos Soares Michel, 2008, Journal of Software, V3, P57 Doyle D., 2006, 3RD INTERNATIONAL WO Fernandez-Medina E., 2009, EDITORIAL INFORMATIO, V51 Ferreira D. d. Almeida, 2009, FOURTH INTERNATIONAL Goknil A, 2008, LECT NOTES COMPUT SC, V5095, P310, DOI 10.1007/978-3-540-69100-6_21 Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), P94, DOI 10.1109/ICRE.1994.292398 Herzog E., 2005, 15TH INCOSE INTERNAT Hull E., 2005, REQUIREMENTS ENG IEEE, 1999, IEEE STD 830 Jimenez M, 2009, IEEE SOFTWARE, V26, P30, DOI 10.1109/MS.2009.93 Kaindl H., 2007, REQUIREMENTS SPECIFI Kaindl H., 2008, THE THIRD INTERNATIO Kalnins A, 2010, LECT NOTES COMPUT SC, V5968, P161 Karban R., 2009, 12TH INTERNATIONAL C Kasunic M., 2005, CMU SEI 2005 HB 004 Kherraf S., 2008, 19TH AUSTRALIAN CONF Kitchenham B., 2002, Software Engineering Notes, V27, P20, DOI 10.1145/638574.638580 Kitchenham BA., 2002, SIGSOFT SOFTWARE ENG, V27, P18, DOI [10.1145/566493.566495, DOI 10.1145/566493.566495] Kleppe A., 2003, MDA EXPLAINED THE MO Le Dang H, 2008, INNOV SYST SOFTW ENG, V4, P189, DOI 10.1007/s11334-008-0053-4 Leffingwell D., 2002, THE ROLE OF REQUIREM Letelier P., 2002, 1ST INTERNATIONAL WO Likert, 1932, ARCH PSYCHOL, DOI DOI 10.4135/9781412961288.N454 Limon A. E., 2005, ECMDA TRACEABILITY W Martin A., 2008, 1ST INTERNATIONAL WO Melby S. J., 2007, THESIS Mellegard N., 2009, THE FIRST INTERNATIO Mitschke A., 2010, COST EFFICIENT METHO Mohagheghi P., 2009, 3RD WORKSHOP ON QUAL Molina F, 2010, INT J INTELL SYST, V25, P757, DOI 10.1002/int.20430 Moros B., 2008, METAMODELING VARIABI Moros B, 2007, ICSOFT 2007: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES, VOL SE, P296 Moros B, 2008, LECT NOTES COMPUT SC, V5231, P530, DOI 10.1007/978-3-540-87877-3_46 Object Management Group (OMG), META OBJECT FACILITY Oldevik J., 2006, 2ND ECMDA EUROPEAN C OMG, OMG SYSTEMS MODELING OMG, MDA GUIDE VERSION 1 Pinheiro F. A. C., 2003, KLUWER INTERNATION S, V753 Pires PF, 2011, REQUIR ENG, V16, P133, DOI 10.1007/s00766-011-0116-1 Poernomo I., 2008, 1ST INTERNATIONAL WO Probasco L., 1999, RATIONAL WHITE PAPER Ramesh B, 2001, IEEE T SOFTWARE ENG, V27, P58, DOI 10.1109/32.895989 Robert F. J. F., 2009, SURVEY METHODOLOGY Sanchez Cuadrado J, 2006, LECT NOTES COMPUT SC, V4066, P158 Sanchez P, 2011, J SYST SOFTWARE, V84, P1008, DOI 10.1016/j.jss.2011.01.052 Santos J., 2008, FIRST INTERNATIONAL Schatz B., 2005, INFORMATIK 2005 Schmidt D. C., 2006, IEEE COMPUT, P39 SEI-CMU, CMMI FOR DEVELOPMENT Shehata M. S., 2002, 15TH INTERNATIONAL C Silva A., 2006, 2 INT C INN VIEWS NE Sommerville I, 2005, ACM T SOFTW ENG METH, V14, P85, DOI 10.1145/1044834.1044837 Standish-Group, 1994, CHAOS REPORT Standish-Group, 2009, CHAOS SUMMARY 2009 Toval A, 2008, COMPUT SYST SCI ENG, V23, P373 Van Der Straeten R, 2009, LECT NOTES COMPUT SC, V5421, P35, DOI 10.1007/978-3-642-01648-6_4 Vicente-Chicote C., 2007, JOURNAL OF OBJECT TE, V6 Vicente-Chicote C., 2007, INTERNATIONAL JOURNA, V16, P394 Walderhaug S., 2006, 5TH ECMDA TRACEABILI Wiegers K.E., 2003, SOFTWARE REQUIREMENT, V2nd Yue T, 2011, REQUIR ENG, V16, P75, DOI 10.1007/s00766-010-0111-y NR 80 TC 10 Z9 10 U1 0 U2 19 PD JUN PY 2013 VL 55 IS 6 BP 941 EP 965 DI 10.1016/j.infsof.2012.12.003 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science UT WOS:000318584800003 DA 2022-12-14 ER PT J AU Peltoniemi, JI Hakala, T Suomalainen, J Honkavaara, E Markelin, L Gritsevich, M Eskelinen, J Jaanson, P Ikonen, E AF Peltoniemi, Jouni I. Hakala, Teemu Suomalainen, Juha Honkavaara, Eija Markelin, Lauri Gritsevich, Maria Eskelinen, Juho Jaanson, Priit Ikonen, Erkki TI Technical notes: A detailed study for the provision of measurement uncertainty and traceability for goniospectrometers SO JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER DT Article DE Bidirectional reflectance; BRF; Spectrum; Polarisation; Calibration ID REFLECTANCE DISTRIBUTION FUNCTION; LABORATORY GONIOMETER SYSTEM; BIDIRECTIONAL REFLECTANCE; FIELD; POLARIZATION; RADIOMETER; STANDARD; PLANE; VIEW; BRDF AB The measurement uncertainty and traceability of the Finnish Geodetic Institutes's field gonio-spectro-polarimeter FIGIFIGO have been assessed. First, the reference standard (Spectralon sample) was measured at the National Standard Laboratory of MIKES-Aalto. This standard was transferred to FGI's field reference standard (larger Spectralon sample), and from that to the unmanned aerial vehicle (UAV), reference standards (1 m(2) plates). The reflectance measurement uncertainty of FIGIFIGO has been estimated to be 0.01 in ideal laboratory conditions, but about 0.02-0.05 in typical field conditions, larger at larger solar or observation zenith angles. Target specific uncertainties can increase total uncertainty even to 0.1-0.2. The angular reading uncertainty is between 1 degrees and 3 degrees, depending on user selection, and the polarisation uncertainty is around 0.01. For UAV, the transferred reflectance uncertainty is about 0.05-0.1, depending on, how ideal the measurement conditions are. The design concept of FIGIFIGO has been proved to have a number of advantages, such as a well-adopted user-friendly interface, a high level of automation and excellent suitability for the field measurements. It is a perfect instrument for collection of reference data on a given target in natural (and well-recorded) conditions. In addition to the strong points of FIGIFIGO, the current study reveals several issues that need further attention, such as the field of view, illumination quality, polarisation calibration, Spectralon reflectance and polarisation properties in the 1000-2400 nm range. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Peltoniemi, Jouni I.; Hakala, Teemu; Suomalainen, Juha; Honkavaara, Eija; Markelin, Lauri; Gritsevich, Maria; Eskelinen, Juho] Finnish Geodet Inst, Masala 02431, Finland. [Peltoniemi, Jouni I.; Gritsevich, Maria; Eskelinen, Juho] Univ Helsinki, FIN-00014 Helsinki, Finland. [Suomalainen, Juha] Wageningen Univ, Lab Geoinformat Sci & Remote Sensing, NL-6700 AP Wageningen, Netherlands. [Jaanson, Priit; Ikonen, Erkki] Mikes Aalto Univ, Espoo, Finland. [Gritsevich, Maria] Moscow MV Lomonosov State Univ, Inst Mech, Moscow 117234, Russia. C3 The National Land Survey of Finland; Finnish Geospatial Research Institute (FGI); University of Helsinki; Wageningen University & Research; Aalto University; Lomonosov Moscow State University RP Peltoniemi, JI (corresponding author), Finnish Geodet Inst, POB 15, Masala 02431, Finland. EM jouni.Peltoniemi@fgi.fi CR Abdou, 2000, REMOTE SENS REV, V19, P75, DOI [DOI 10.1080/02757250009532411, 10.1080/02757250009532411] Anderson K, 2012, REMOTE SENS LETT, V3, P131, DOI 10.1080/01431161.2010.543181 Bhandari A, 2011, APPL OPTICS, V50, P2431, DOI 10.1364/AO.50.002431 Bourgeois CS, 2006, J ATMOS OCEAN TECH, V23, P573, DOI 10.1175/JTECH1870.1 Burkart A, 2014, IEEE SENS J, V14, P62, DOI 10.1109/JSEN.2013.2279720 Coburn CA, 2006, CAN J REMOTE SENS, V32, P244, DOI 10.5589/m06-021 Courreges-Lacoste GB, 2003, OPT ENG, V42, P3600, DOI 10.1117/1.1622961 Deadman AJ, 2011, INT GEOSCI REMOTE SE, P3883, DOI 10.1109/IGARSS.2011.6050079 DEERING DW, 1986, REMOTE SENS ENVIRON, V19, P1, DOI 10.1016/0034-4257(86)90038-6 DEMIRCAN A, 2000, REMOTE SENSING REV, V19, P95 Ferrero A, 2012, APPL OPTICS, V51, P8535, DOI 10.1364/AO.51.008535 Georgiev GT, 2004, P SOC PHOTO-OPT INS, V5570, P492, DOI 10.1117/12.56586 Germer TA, 2011, PROC SPIE, V8160, DOI 10.1117/12.892713 GHOSH R, 1993, INT J REMOTE SENS, V14, P2501, DOI 10.1080/01431169308904288 Goldstein DH, 1999, P SOC PHOTO-OPT INS, V3754, P126, DOI 10.1117/12.366323 Grenzdorffer G, 2011, INT ARCH PHOTOGRAMM, V38 Guyot G, 1980, P INT S ISP HAMBURG, P372 Hakala T, 2010, REMOTE SENS-BASEL, V2, P819, DOI 10.3390/rs2030819 Haner DA, 1999, APPL OPTICS, V38, P6350, DOI 10.1364/AO.38.006350 HAPKE B, 1993, THEORY REFLECTANCE E, DOI DOI 10.1017/CBO9780511524998 Hope A, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.3692755 Honkavaara E, 2008, PHOTOGRAMM ENG REM S, V74, P95, DOI 10.14358/PERS.74.1.95 Honkavaara E, 2014, PHOTOGRAMM FERNERKUN, P175, DOI 10.1127/1432-8364/2014/0218 Honkavaara E, 2010, REMOTE SENS-BASEL, V2, P1892, DOI 10.3390/rs2081892 HOSGOOD B, 2000, ENCY ELECT ENG, V8, P424 Hueni A, 2013, ESA PV2013 WORKSH Johnson JR, 2013, ICARUS, V223, P383, DOI 10.1016/j.icarus.2012.12.004 Kaasalainen S, 2005, APPL OPTICS, V44, P1485, DOI 10.1364/AO.44.001485 KRIEBEL KT, 1978, APPL OPTICS, V17, P253, DOI 10.1364/AO.17.000253 KUUSK A, 1991, REMOTE SENS ENVIRON, V37, P207, DOI 10.1016/0034-4257(91)90082-H Liang S., 2004, WILEY SERIES REMOTE Mac Arthur A, 2012, IEEE T GEOSCI REMOTE, V50, P3892, DOI 10.1109/TGRS.2012.2185055 Matsapey N, 2013, MEAS SCI TECHNOL, V24, DOI 10.1088/0957-0233/24/6/065901 Milton EJ, 2009, REMOTE SENS ENVIRON, V113, pS92, DOI 10.1016/j.rse.2007.08.001 Munoz O, 2010, J QUANT SPECTROSC RA, V111, P187, DOI 10.1016/j.jqsrt.2009.06.011 Nevas S, 2004, APPL OPTICS, V43, P6391, DOI 10.1364/AO.43.006391 Nicodemus F.E., 1977, TECHNICAL REPORT, V160 Painter TH, 2003, REV SCI INSTRUM, V74, P5179, DOI 10.1063/1.1626011 Patrick HJ, 2012, PROC SPIE, V8495, DOI 10.1117/12.930742 Pegrum H, 2006, INT GEOSCI REMOTE SE, P1119, DOI 10.1109/IGARSS.2006.289 Peltoniemi J, REFLECTANCE POLARISA Peltoniemi JI, 2005, IEEE T GEOSCI REMOTE, V43, P2294, DOI 10.1109/TGRS.2005.855131 Peltoniemi J, 2009, J QUANT SPECTROSC RA, V110, P1940, DOI 10.1016/j.jqsrt.2009.04.008 Peltoniemi JI, 2007, ISPRS J PHOTOGRAMM, V62, P434, DOI 10.1016/j.isprsjprs.2007.07.009 Peltoniemi JI, 2010, S-P B ENVIRON SCI, P393, DOI 10.1007/978-3-642-10336-0_9 Podobedov VB, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4752762 Rabal AM, 2012, METROLOGIA, V49, P213, DOI 10.1088/0026-1394/49/3/213 RONDEAUX G, 1991, REMOTE SENS ENVIRON, V38, P63, DOI 10.1016/0034-4257(91)90072-E Roosjen PPJ, 2012, SENSORS-BASEL, V12, P17358, DOI 10.3390/s121217358 Sandmeier SR, 1999, IEEE T GEOSCI REMOTE, V37, P978, DOI 10.1109/36.752216 Sanz JM, 2013, APPL OPTICS, V52, P6051, DOI 10.1364/AO.52.006051 Schopfer JT, 2008, THESIS U ZURICH Schwarzbach M, 2009, P ASME 2009 INT DES Shepard MK, 2011, ICARUS, V215, P526, DOI 10.1016/j.icarus.2011.07.033 Shibayama M, 2011, PLANT PROD SCI, V14, P64, DOI 10.1626/pps.14.64 Solheim I, 1996, 17268 EUR EN JOINT R Stokes G. G., 1851, T CAMBRIDGE PHILOS S, V9, P399 Sun ZQ, 2011, J QUANT SPECTROSC RA, V112, P2372, DOI 10.1016/j.jqsrt.2011.05.011 Suomalainen J, 2009, J QUANT SPECTROSC RA, V110, P1044, DOI 10.1016/j.jqsrt.2009.02.017 Suomalainen J, 2009, SENSORS-BASEL, V9, P3891, DOI 10.3390/s90503891 Svensen O, 2012, OPT EXPRESS, V20, P15045, DOI 10.1364/OE.20.015045 Turner M., 1998, NASA COMM REM SENS V, P1 Xie DH, 2010, SPECTROSC SPECT ANAL, V30, P3324, DOI 10.3964/j.issn.1000-0593(2010)12-3324-05 [No title captured] NR 64 TC 25 Z9 25 U1 3 U2 28 PD OCT PY 2014 VL 146 SI SI BP 376 EP 390 DI 10.1016/j.jqsrt.2014.04.011 WC Optics; Spectroscopy SC Optics; Spectroscopy UT WOS:000339697300034 DA 2022-12-14 ER PT J AU Zhang, TW Wang, Q Li, JR Zhao, SS Qie, MJ Wu, XL Bai, Y Zhao, Y AF Zhang, Tangwei Wang, Qian Li, Jirong Zhao, Shanshan Qie, Mengjie Wu, Xuelian Bai, Yang Zhao, Yan TI Study on the origin traceability of Tibet highland barley (Hordeum vulgare L.) based on its nutrients and mineral elements SO FOOD CHEMISTRY DT Article DE Tibet; Highland barley; Soil; Geographic origin; Nutrients and mineral elements ID GEOGRAPHICAL ORIGIN; SATIVA L.; RICE; SOIL; DISCRIMINATION; ACCUMULATION; FINGERPRINTS; GRAINS AB The potential of traceability by nutrients and mineral elements in highland barley (Hordeum vulgare L.) from five cities in Tibet were investigated. The results showed that there were significant differences in nutrients and mineral elements in highland barley from different regions (P < 0.05). The original classification accuracy of linear discriminant analysis (LDA) was 78.3%, and the discrimination accuracy of training set samples based on partial least-squares discriminant analysis (PLS-DA) model was over 65%. The results of correlation analysis show that five elements (Fe, Zn, K, Mn and P) in highland barley are related to the concentration of elements in soil, while three elements (Ca, Cu and Mg) in highland barley have no obvious correlation with soil, because the special natural environment in Tibet affecting the growth of highland barley. This indicates that the origin traceability of highland barley can be achieved by measuring its nutrients and mineral elements. C1 [Zhang, Tangwei; Li, Jirong] Tibet Acad Agr & Anim Husb Sci, Inst Agr Prod Qual Stand & Testing Res, Lhasa 850001, Tibet, Peoples R China. [Wang, Qian; Zhao, Shanshan; Qie, Mengjie; Bai, Yang; Zhao, Yan] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Wang, Qian; Wu, Xuelian; Bai, Yang] Inner Mongolia Agr Univ, Food Sci & Engn, Hohhot 010018, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Inner Mongolia Agricultural University RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR Agegnehu G, 2016, SOIL TILL RES, V160, P1, DOI 10.1016/j.still.2016.02.003 Arvanitoyannis IS, 2009, CRIT REV FOOD SCI, V49, P501, DOI 10.1080/10408390802068140 Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Bianba ZM, 2019, CHINESE AGR SCI B, V35, P79 Birsin MA, 2010, J PLANT NUTR, V33, P267, DOI 10.1080/01904160903435391 Cheajesadagul P, 2013, FOOD CHEM, V141, P3504, DOI 10.1016/j.foodchem.2013.06.060 Chen TJ, 2016, FOOD CHEM, V209, P95, DOI 10.1016/j.foodchem.2016.04.029 Du MJ, 2018, INT J FOOD SCI TECH, V53, P2088, DOI 10.1111/ijfs.13795 Gao Jia-jia, 2019, Journal of Ecology and Rural Environment, V35, P1484, DOI 10.19741/j.issn.1673-4831.2018.0731 Geng Y., 1999, J BEIJING FOREST TRY, V06, P3 Gonzalvez A, 2011, FOOD CHEM, V126, P1254, DOI 10.1016/j.foodchem.2010.11.032 Guan W., 2019, TIBET J AGR SCI, V41, P32 Han HJ, 2016, FOOD SCI BIOTECHNOL, V25, P695, DOI 10.1007/s10068-016-0121-8 He DeHua, 2019, Journal of Agricultural Science and Technology (Beijing), V21, P123 Karami M, 2009, J AGR FOOD CHEM, V57, P10876, DOI 10.1021/jf902074f Kaye JP, 2003, BIOGEOCHEMISTRY, V63, P1, DOI 10.1023/A:1023317516458 Li G, 2013, J ENVIRON SCI, V25, P144, DOI [10.1016/S1001-0742(12)60007-2, 10.1016/S1001-0742(14)60636-7] [梁坤伦 Liang Kunlun], 2016, [草业科学, Pratacultural Science], V33, P1054 Liu ZF, 2013, J INTEGR AGR, V12, P541, DOI [10.1016/s2095-3119(13)60255-5, 10.1016/S2095-3119(13)60255-5] Ma H. P., 2017, J PLATEAU AGR, V1, P27 Masuda H, 2009, RICE, V2, P155, DOI 10.1007/s12284-009-9031-1 Nguyen TP, 2020, ENVIRON GEOCHEM HLTH, V42, P191, DOI 10.1007/s10653-019-00333-3 Obrador A, 2007, GEODERMA, V137, P432, DOI 10.1016/j.geoderma.2006.10.001 Palumbo F, 2017, FOOD TECHNOL BIOTECH, V55, P29, DOI 10.17113/ftb.55.01.17.4858 Shah A, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10081209 Shen SG, 2013, ANAL METHODS-UK, V5, P6177, DOI 10.1039/c3ay40700d Varra MO, 2019, ITAL J FOOD SAF, V8, P21, DOI 10.4081/ijfs.2019.7872 Wang J, 2017, ENVIRON GEOCHEM HLTH, V39, P221, DOI 10.1007/s10653-016-9823-3 Wang ZhaoHui, 2017, Journal of Jilin Agricultural University, V39, P113 Wu YL, 2015, FOOD CHEM, V174, P553, DOI 10.1016/j.foodchem.2014.11.096 Xiao R, 2019, J FOOD MEAS CHARACT, V13, P238, DOI 10.1007/s11694-018-9937-7 Xie H, 2019, SPECTROSC SPECT ANAL, V39, P3267, DOI 10.3964/j.issn.1000-0593(2019)10-3267-06 Xie L., 2020, FOOD CHEM, V316, P7 Yu GY, 2016, FOOD CHEM, V194, P577, DOI 10.1016/j.foodchem.2015.08.058 Zaller JG, 2004, BIOL FERT SOILS, V40, P222, DOI 10.1007/s00374-004-0772-0 Zhang J, 2020, MED DOSIM, V45, P66, DOI 10.1016/j.meddos.2019.06.001 Zhao HY, 2013, J CEREAL SCI, V57, P391, DOI 10.1016/j.jcs.2013.01.008 Zhao HY, 2011, J AGR FOOD CHEM, V59, P4397, DOI 10.1021/jf200108d Zhao Y, 2016, MEAT SCI, V118, P103, DOI 10.1016/j.meatsci.2016.03.030 NR 39 TC 17 Z9 17 U1 29 U2 115 PD JUN 1 PY 2021 VL 346 AR 128928 DI 10.1016/j.foodchem.2020.128928 EA JAN 2021 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000614679900010 DA 2022-12-14 ER PT J AU Premanandh, J Bin Salem, S AF Premanandh, Jagadeesan Bin Salem, Samara TI Progress and challenges associated with halal authentication of consumer packaged goods SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Review DE halal; authentication; traceability; certification ID POLYMERASE-CHAIN-REACTION; REAL-TIME PCR; PORK ADULTERATION; MULTIPLEX PCR; FOOD; MEAT; PORCINE; BOVINE; PRODUCTS; GELATIN AB Abusive business practices are increasingly evident in consumer packaged goods. Although consumers have the right to protect themselves against such practices, rapid urbanization and industrialization result in greater distances between producers and consumers, raising serious concerns on the supply chain. The operational complexities surrounding halal authentication pose serious challenges on the integrity of consumer packaged goods. This article attempts to address the progress and challenges associated with halal authentication. Advancement and concerns on the application of new, rapid analytical methods for halal authentication are discussed. The significance of zero tolerance policy in consumer packaged foods and its impact on analytical testing are presented. The role of halal assurance systems and their challenges are also considered. In conclusion, consensus on the establishment of one standard approach coupled with a sound traceability system and constant monitoring would certainly improve and ensure halalness of consumer packaged goods. (c) 2017 Society of Chemical Industry C1 [Premanandh, Jagadeesan; Bin Salem, Samara] Abu Dhabi Qual & Conform Council, Abu Dhabi, U Arab Emirates. RP Premanandh, J (corresponding author), Abu Dhabi Qual & Conform Council, Abu Dhabi, U Arab Emirates. EM jpanandh@yahoo.com CR Ab Talib MS, 2014, J ISLAMIC MARK, V5, P322, DOI 10.1108/JIMA-03-2013-0018 Abdulmawjood A, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0100717 Aida AA, 2005, MEAT SCI, V69, P47, DOI 10.1016/j.meatsci.2004.06.020 Aida AA, 2007, J SCI FOOD AGR, V87, P569, DOI 10.1002/jsfa.2699 Ali ME, 2014, J EXP NANOSCI, V9, P152, DOI 10.1080/17458080.2011.640946 Ali ME, 2012, FOOD ANAL METHOD, V5, P935, DOI 10.1007/s12161-011-9357-3 Ali ME, 2012, FOOD ANAL METHOD, V5, P784, DOI 10.1007/s12161-011-9311-4 Ali ME, 2015, FOOD CHEM, V177, P214, DOI 10.1016/j.foodchem.2014.12.098 Arif S., 2015, ASIAN SOCIAL SCI, V11, DOI [10.5539/ass.v11n17p116, DOI 10.5539/ASS.V11N17P116] Armbruster David A, 2008, Clin Biochem Rev, V29 Suppl 1, pS49 Asensio L, 2008, FOOD ADDIT CONTAM A, V25, P677, DOI 10.1080/02652030701765731 Ballin NZ, 2009, MEAT SCI, V83, P165, DOI 10.1016/j.meatsci.2009.06.003 Batu A., 2015, LIT HIST TURKISH TUR, V10, P37, DOI [10.7827/TurkishStudies.8928, DOI 10.7827/TURKISHSTUDIES.8928] Calvo JH, 2001, J ANIM SCI, V79, P2108 Cebi N, 2016, FOOD CHEM, V190, P1109, DOI 10.1016/j.foodchem.2015.06.065 Ceranic S., 2009, Biotechnology in Animal Husbandry, V25, P261 Codex Alimentarius Commission, 1997, 241997 CACGL Dag H, 2013, J CHEM METROL, V7, P1 Dalmasso A, 2004, MOL CELL PROBE, V18, P81, DOI 10.1016/j.mcp.2003.09.006 Doosti A, 2014, J FOOD SCI TECH MYS, V51, P148, DOI 10.1007/s13197-011-0456-3 Erwanto Y, 2014, ASIAN AUSTRAL J ANIM, V27, P1487, DOI 10.5713/ajas.2014.14014 Fakruddin M, 2013, J PHARM BIOALLIED SC, V5, P245, DOI 10.4103/0975-7406.120066 Food Standards Agency Review of the Food Standards Agency's response to the adulteration of processed beef products with horse and pork meat and DNA [Online], 2013, REV FOOD STAND AG RE Freese L, 2015, CEREAL FOOD WORLD, V60, P9, DOI 10.1094/CFW-60-1-0009 Fuad F, 2012, ADV NATURAL APPL SCI, V6, P588 Gendel S, 2016, IAFP EUR S FOOD SAF, P1 HARTNETT DI, 1979, J AM OIL CHEM SOC, V56, P948, DOI 10.1007/BF02674140 Hashim DM, 2010, FOOD CHEM, V118, P856, DOI 10.1016/j.foodchem.2009.05.049 Hidaka S, 2003, J FOOD COMPOS ANAL, V16, P477, DOI 10.1016/S0889-1575(02)00174-6 Hossain MAM, 2017, FOOD CONTROL, V73, P175, DOI 10.1016/j.foodcont.2016.08.008 Hsieh YHP, 2016, FOOD SCI NUTR, V4, P588, DOI 10.1002/fsn3.322 Jahangir M, 2016, TRENDS FOOD SCI TECH, V47, P78, DOI 10.1016/j.tifs.2015.10.011 Chapela MJ, 2007, FOOD CONTROL, V18, P1211, DOI 10.1016/j.foodcont.2006.07.016 Kamaruddin R., 2012, 2012 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC 2012), P383, DOI 10.1109/BEIAC.2012.6226088 Kamruzzaman M, 2015, ANAL CHIM ACTA, V853, P19, DOI 10.1016/j.aca.2014.08.043 Kurth L, 2017, AGR HUM VALUES, V34, P103, DOI 10.1007/s10460-016-9698-z Kuswandi B, 2015, J FOOD SCI TECH MYS, V52, P7655, DOI 10.1007/s13197-015-1882-4 Lauri A, 2009, GENES NUTR, V4, P1, DOI 10.1007/s12263-008-0106-1 Lenstra J. A., 2003, Food authenticity and traceability, P34, DOI 10.1533/9781855737181.1.34 Liu LH, 2006, J FOOD SCI, V71, pM1, DOI 10.1111/j.1365-2621.2006.tb12393.x Lodhi A., 2009, UNDERSTANDING HALAL Marzuki SZS, 2012, J ISLAMIC MARK, V3, P47, DOI 10.1108/17590831211206581 Muhammad N.M., 2009, ASIAN SOCIAL SCI, V5, P44, DOI DOI 10.5539/ASS.V5N7P44 Mustafa Afifi A. H., 2012, World Applied Sciences Journal, V17, P6 Nhari RMHR, 2012, J FOOD SCI, V77, pR42, DOI 10.1111/j.1750-3841.2011.02514.x Notomi T, 2000, NUCLEIC ACIDS RES, V28, DOI 10.1093/nar/28.12.e63 Nurjuliana M, 2011, J AM OIL CHEM SOC, V88, P75, DOI 10.1007/s11746-010-1655-1 Ofori JA, 2016, J AGR FOOD CHEM, V64, P3705, DOI 10.1021/acs.jafc.5b06136 Premanandh J., 2011, Journal of Commercial Biotechnology, V17, P37, DOI 10.1057/jcb.2010.24 Ramon-Laca A, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0092043 Reid LM, 2006, TRENDS FOOD SCI TECH, V17, P344, DOI 10.1016/j.tifs.2006.01.006 Rohman A, 2011, CYTA-J FOOD, V9, P96, DOI 10.1080/19476331003774639 Ropodi AI, 2015, FOOD RES INT, V67, P12, DOI 10.1016/j.foodres.2014.10.032 Sahilah A. M., 2012, International Food Research Journal, V19, P371 Sawyer J, 2003, FOOD CONTROL, V14, P579, DOI 10.1016/S0956-7135(02)00148-2 Shabani H, 2015, FOOD CHEM, V184, P203, DOI 10.1016/j.foodchem.2015.02.140 Soares S, 2013, MEAT SCI, V94, P115, DOI 10.1016/j.meatsci.2012.12.012 Tasara T, 2005, J FOOD PROTECT, V68, P2420, DOI 10.4315/0362-028X-68.11.2420 Tieman M, 2011, J ISLAMIC MARK, V2, P186, DOI 10.1108/17590831111139893 Tsakanikas P, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0140122 von Bargen C, 2014, J AGR FOOD CHEM, V62, P9428, DOI 10.1021/jf503468t Wan Hassan W., 2007, HALAL J JUL, P38 World Health Organization Regional Office for the Eastern Mediterranean, 2001, 73 EDB WHO Wu D, 2013, INNOV FOOD SCI EMERG, V19, P15, DOI 10.1016/j.ifset.2013.04.016 Yang LX, 2014, BIOTECHNOL BIOTEC EQ, V28, P882, DOI 10.1080/13102818.2014.963789 Yanty N. A. M., 2014, International Food Research Journal, V21, P277 Yanty NAM, 2011, J OLEO SCI, V60, P333, DOI 10.5650/jos.60.333 Zach L, 2012, FOOD CONTROL, V27, P153, DOI 10.1016/j.foodcont.2012.03.013 Zhang GF, 2009, FOOD HYDROCOLLOID, V23, P2001, DOI 10.1016/j.foodhyd.2009.03.010 Zhang RY, 2012, J SCI FOOD AGR, V92, P2397, DOI 10.1002/jsfa.5702 NR 70 TC 12 Z9 12 U1 1 U2 36 PD NOV PY 2017 VL 97 IS 14 BP 4672 EP 4678 DI 10.1002/jsfa.8481 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000413156000002 DA 2022-12-14 ER PT J AU Dimauro, C Cellesi, M Steri, R Gaspa, G Sorbolini, S Stella, A Macciotta, NPP AF Dimauro, C. Cellesi, M. Steri, R. Gaspa, G. Sorbolini, S. Stella, A. Macciotta, N. P. P. TI Use of the canonical discriminant analysis to select SNP markers for bovine breed assignment and traceability purposes SO ANIMAL GENETICS DT Article DE allocation method; livestock products; multivariate analysis ID GENETIC DIVERSITY; IDENTIFICATION AB Several market research studies have shown that consumers are primarily concerned with the provenance of the food they eat. Among the available identification methods, only DNA-based techniques appear able to completely prevent frauds. In this study, a new method to discriminate among different bovine breeds and assign new individuals to groups was developed. Bulls of three cattle breeds farmed in Italy - Holstein, Brown, and Simmental - were genotyped using the 50K SNP Illumina BeadChip. Multivariate canonical discriminant analysis was used to discriminate among breeds, and discriminant analysis (DA) was used to assign new observations. This method was able to completely identify the three groups at chromosome level. Moreover, a genome-wide analysis developed using 340 linearly independent SNPs yielded a significant separation among groups. Using the reduced set of markers, the DA was able to assign 30 independent individuals to the proper breed. Finally, a set of 48 high discriminant SNPs was selected and used to develop a new run of the analysis. Again, the procedure was able to significantly identify the three breeds and to correctly assign new observations. These results suggest that an assay with the selected 48 SNP could be used to routinely track monobreed products. C1 [Dimauro, C.; Cellesi, M.; Steri, R.; Gaspa, G.; Sorbolini, S.; Macciotta, N. P. P.] Univ Sassari, Dipartimento Agr, I-07100 Sassari, Italy. [Stella, A.] CNR, Ist Biol & Biotecnol Agr, I-20133 Milan, Italy. C3 University of Sassari; Consiglio Nazionale delle Ricerche (CNR) RP Dimauro, C (corresponding author), Univ Sassari, Dipartimento Agr, Via De Nicola 9, I-07100 Sassari, Italy. EM dimauro@uniss.it CR BAUDOUIN L, 2000, ACTA HORTIC, V546, P81, DOI DOI 10.17660/ACTAH0RTIC.2001.546.5 Casellas J, 2004, J ANIM BREED GENET, V121, P101, DOI 10.1046/j.1439-0388.2003.00441.x Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 De Maesschalck R, 2000, CHEMOMETR INTELL LAB, V50, P1, DOI 10.1016/S0169-7439(99)00047-7 De Marchi M, 2006, ANIM GENET, V37, P101, DOI 10.1111/j.1365-2052.2005.01390.x Del Bol L, 2001, J ANIM BREED GENET, V118, P317, DOI 10.1046/j.1439-0388.2001.00306.x Dimauro C, 2011, J ANIM BREED GENET, V128, P440, DOI 10.1111/j.1439-0388.2011.00957.x Heaton MP, 2005, JAVMA-J AM VET MED A, V226, P1311, DOI 10.2460/javma.2005.226.1311 Macciotta NPP, 2010, J DAIRY SCI, V93, P2765, DOI 10.3168/jds.2009-3029 Manel S, 2005, TRENDS ECOL EVOL, V20, P136, DOI 10.1016/j.tree.2004.12.004 Mardia K. V., 2000, MULTIVARIATE ANAL Matukumalli LK, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0005350 Negrini R, 2007, ANIM GENET, V38, P147, DOI 10.1111/j.1365-2052.2007.01573.x Negrini R., 2008, ANIMAL GENETICS, V40, P18 Orru L, 2006, MEAT SCI, V72, P312, DOI 10.1016/j.meatsci.2005.07.018 Orru L, 2009, FOOD CONTROL, V20, P856, DOI 10.1016/j.foodcont.2008.10.015 PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Ramos AM, 2011, ANIM GENET, V42, P613, DOI 10.1111/j.1365-2052.2011.02198.x Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 Weller JI, 2006, ANIM GENET, V37, P387, DOI 10.1111/j.1365-2052.2006.01455.x NR 21 TC 28 Z9 29 U1 0 U2 25 PD AUG PY 2013 VL 44 IS 4 BP 377 EP 382 DI 10.1111/age.12021 WC Agriculture, Dairy & Animal Science; Genetics & Heredity SC Agriculture; Genetics & Heredity UT WOS:000329200100003 DA 2022-12-14 ER PT J AU Agrawal, AK AF Agrawal, AK TI Certified reference materials of trace elements in water SO BULLETIN OF MATERIALS SCIENCE DT Article; Proceedings Paper CT International Symposium on Ultrapure Materials CY NOV 22-23, 2004 CL Hyderabad, INDIA DE CRMs; traceability; accreditation; water AB Measurement of trace elements is playing a vital role in industries and various sectors of science and technology including semiconductors, food, health and environmental sectors. In most of the cases a small error in measurement can vitiate all the measures taken for quality control and management. Many decisions regarding the suitability of material/products are based on the analysis. To reduce or eliminate the rejection rate of the products, accurate and reliable measurements are needed which can be achieved by the use of certified reference materials (CRMs). Their use in calibration of analytical equipments and validation of test methods ensures high quality in measurements and it provides traceability to the measurement data with national/international measurement systems (SI unit) also. In the present scenario of globalization of economy, use of certified reference materials (CRMs) in measurements is essential for global acceptance of products and test reports. Their use fulfil a mandatory requirement of international quality systems (ISO 9000, ISO/IEC standard 17025) including our national accreditation body, National Accreditation Board for Testing and Calibration Laboratories (NABL), World Trade Organization (WTO) etc. International manufacturers of CRMs are meeting most of the requirement of CRMs of the country. To meet the demand of CRMs indigenously, the National Physical Laboratory, India initiated a national programme on preparation and dissemination of certified reference materials. C1 Natl Phys Lab, New Delhi 110012, India. C3 Council of Scientific & Industrial Research (CSIR) - India; CSIR - National Physical Laboratory (NPL) RP Agrawal, AK (corresponding author), Natl Phys Lab, Dr KS Krishnan Marg, New Delhi 110012, India. EM aka@mail.nplindia.ernet.in CR AGRAWAL AK, 2001, PREPARATION DISSEMIN, P393 AGRAWAL AK, 2003, ASSESSMENT QUALITY W, P3 Balaram V, 2000, INDIAN J CHEM A, V39, P567 EURACHEM/CITAC, 2000, EURACHEM CITAC GUID GUPTA PK, 1993, FRESEN J ANAL CHEM, V345, P278, DOI 10.1007/BF00322610 GUPTA PK, 2002, Patent No. 187019 GUPTA PK, 1989, P ISCRM 89, P82 *ISO IEC, 1999, 17025 ISOIEC MOODY JR, 1977, ANAL CHEM, V49, P2264, DOI 10.1021/ac50022a039 *NAT ACCR BOARD TE, 1996, GUID EST STAT OV UNC *NATA, 1997, NEW STAT NATAS PROF *PTAC, 1996, 204 PTAC NAT ASS TES [No title captured] [No title captured] NR 14 TC 1 Z9 2 U1 0 U2 6 PD JUL PY 2005 VL 28 IS 4 BP 373 EP 378 DI 10.1007/BF02704252 WC Materials Science, Multidisciplinary SC Materials Science UT WOS:000230802100014 DA 2022-12-14 ER PT J AU Giulia, T Vallauri, G Pavese, V Valentini, N Ruffa, P Botta, R Marinoni, DT AF Giulia, Talucci Vallauri, Giulia Pavese, Vera Valentini, Nadia Ruffa, Paola Botta, Roberto Torello Marinoni, Daniela TI Identification of the hazelnut cultivar in raw kernels and in semi-processed and processed products SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE Corylus avellana; Food matrices; SSR markers; Traceability ID DNA EXTRACTION METHODS; CORYLUS-AVELLANA L.; MICROSATELLITE MARKERS; TRACEABILITY; VARIETIES; ALLERGENS; ORIGIN; FRESH; MUSTS; PCR AB The request for an efficient traceability system able to identify hazelnut cultivars along the entire processing chain is becoming a critical point for avoiding fraudulent practices and safeguarding the interests of growers, food processors and consumers. In this study, DNA was extracted from different hazelnut matrices, including plant material (leaf, kernel and kernel episperm), and processed foods (paste, grain, flour and different types of snacks containing hazelnuts). The efficiency of Simple Sequence Repeat (SSR) markers was tested to identify the hazelnut cultivar 'Tonda Gentile' in all the supply chain. The analysis at 10 SSR loci was able to verify the presence/absence of the alleles of a declared cultivar contained in these matrices. The SSR analysis of DNA from raw episperm offers the possibility of identifying the mother cultivar and is suggested as an effective way to discover frauds since DNA analysis can be performed on individual kernels. For food matrices containing hazelnuts, the presence of the mother cultivar's DNA can be assessed based on the identification of its alleles in the sample, although the presence of multiple alleles from the pollenizers makes the interpretation of results more difficult. C1 [Giulia, Talucci; Vallauri, Giulia; Pavese, Vera; Valentini, Nadia; Ruffa, Paola; Botta, Roberto; Torello Marinoni, Daniela] Univ Turin, Dipartimento Sci Agr Forestali & Alimentari DISAF, Grugliasco, Turin, Italy. C3 University of Turin RP Pavese, V (corresponding author), Univ Turin, Dipartimento Sci Agr Forestali & Alimentari DISAF, Grugliasco, Turin, Italy. EM vera.pavese@unito.it CR Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Aoki N, 2003, PLANT CELL PHYSIOL, V44, P223, DOI 10.1093/pcp/pcg030 Bassil NV, 2005, J AM SOC HORTIC SCI, V130, P543, DOI 10.21273/JASHS.130.4.543 Ben Ayed R, 2019, BIOMED RES INT, V2019, DOI 10.1155/2019/8291341 Boccacci P, 2006, GENOME, V49, P598, DOI 10.1139/G06-017 Boccacci P, 2005, MOL ECOL NOTES, V5, P934, DOI 10.1111/j.1471-8286.2005.01121.x Boccacci P, 2012, EUR FOOD RES TECHNOL, V235, P439, DOI 10.1007/s00217-012-1770-3 Bojang KP, 2021, J FOOD SCI TECH MYS, V58, P3561, DOI 10.1007/s13197-021-05079-4 Botta R, 2019, ADVANCES IN PLANT BREEDING STRATEGIES, P157, DOI 10.1007/978-3-030-23112-5_6 Caramante M, 2011, FOOD CONTROL, V22, P549, DOI 10.1016/j.foodcont.2010.10.002 Costa J, 2015, FOOD CHEM, V187, P469, DOI 10.1016/j.foodchem.2015.04.073 Costa J, 2014, ANAL BIOANAL CHEM, V406, P2581, DOI 10.1007/s00216-014-7679-x Costa J, 2012, J AGR FOOD CHEM, V60, P8103, DOI 10.1021/jf302898z Fanelli V, 2021, FOODS, V10, DOI 10.3390/foods10071644 Faostat, 2022, US Faria MA, 2008, EUR FOOD RES TECHNOL, V227, P845, DOI 10.1007/s00217-007-0795-5 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Ganopoulos I, 2011, FOOD CONTROL, V22, P532, DOI 10.1016/j.foodcont.2010.09.040 Gryson N, 2010, ANAL BIOANAL CHEM, V396, P2003, DOI 10.1007/s00216-009-3343-2 Labra M, 2003, VITIS, V42, P137 Lang C, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108344 Liso G., 2017, SUPPL TERRA VITA, V5, P6 Lucchetti S, 2018, FOOD CHEM, V245, P812, DOI 10.1016/j.foodchem.2017.11.107 Mafra I, 2008, FOOD CONTROL, V19, P1183, DOI 10.1016/j.foodcont.2008.01.004 Melchiade D, 2007, FOOD BIOTECHNOL, V21, P33, DOI 10.1080/08905430701191114 Ogasawara T., 2003, JPN J FOOD CHEM, V10, P155 Piskata Z, 2017, INT J FOOD PROP, V20, pS430, DOI 10.1080/10942912.2017.1297953 Sardaro MLS, 2013, FOOD SCI NUTR, V1, P54, DOI 10.1002/fsn3.8 Schrader C, 2012, J APPL MICROBIOL, V113, P1014, DOI 10.1111/j.1365-2672.2012.05384.x Silvestri C, 2021, J SCI FOOD AGR, V101, P27, DOI 10.1002/jsfa.10557 Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 Yamamoto T, 2006, BREEDING SCI, V56, P165, DOI 10.1270/jsbbs.56.165 Zambianchi S, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107929 NR 33 TC 0 Z9 0 U1 4 U2 4 PD SEP PY 2022 VL 248 IS 9 BP 2431 EP 2440 DI 10.1007/s00217-022-04058-z EA JUN 2022 WC Food Science & Technology SC Food Science & Technology UT WOS:000815569900001 DA 2022-12-14 ER PT J AU Engelseth, P AF Engelseth, Per TI Multiplex Uses of Food-Product Standards SO INTERNATIONAL FOOD AND AGRIBUSINESS MANAGEMENT REVIEW DT Article DE seafood product standards; transvection; product traceability; multiplex resource use; food product value; supply networks ID SUPPLY CHAIN; TRACEABILITY; MANAGEMENT; INTEGRATION; INFORMATION AB Food-product traceability systems have been developed to achieve seamless electronic connectivity to assure food safety through the use of information technology. This is determined by legislation. While achieving customer value through quality, food supply is the core logistical purpose. Food-product traceability as such is seldom regarded as a core purpose. Food-product standards are a key resource in developing connectivity between information systems operated by different firms in a supply network using numerical product codes. This study couples the technical characteristics of a food-product standard with the organizational characteristics of a supply network. The common purpose is to achieve customer value in the supply network. Alderson's (1965) marketing-channels (transvection) model of product supply is applied to analyze potential multiple uses of the TraceFish product standard in its supply network. The case study of North Sea herring supply involves following raw material from in Norway to finished product in the Netherlands. Analysis of this empirical data exposed variation in TraceFish standard use, including coupling it with GTIN product codes. This facilitated seamless electronic information exchange between firms for a range of supply-network purposes, including tracing food. This perspective is possible when multiple functions and professions that are equally involved in operating and managing business processes are allowed to handle not only operation, but also develop information systems. C1 Molde Univ Coll, Dept Econ Informat & Social Sci, N-6402 Molde, More & Romsdal, Norway. C3 Molde University College RP Engelseth, P (corresponding author), Molde Univ Coll, Dept Econ Informat & Social Sci, POB 2110, N-6402 Molde, More & Romsdal, Norway. EM peen@himolde.no CR Alderson W., 1965, DYNAMIC MARKETING BE Bourlakis M, INT J LOGISTICS RES, V6, P211 Canavari M, 2010, COMPUT ELECTRON AGR, V70, P321, DOI 10.1016/j.compag.2009.08.014 Canavari M, 2010, BRIT FOOD J, V112, P171, DOI 10.1108/00070701011018851 EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P57, DOI 10.2307/258191 Ellram L.M., 1996, J BUSINESS LOGISTICS, V17, P93 Engelseth P., 2012, DECISION MAKING SUPP ENGELSETH P, 2007, SERIES DISSERTATIONS, V1 Engelseth P, 2006, P 7 INT C MAN AGRIFO Engelseth P, 2009, CRISIS FOOD BRANDS S Engelseth P, 2012, MODELLING VALUE Engelseth P, 2012, J BUS IND MARK, V27, P673, DOI 10.1108/08858621211273619 Engelseth P, 2009, J BUS IND MARK, V24, P421, DOI 10.1108/08858620910966291 Erlandson D. A., 1993, DOING NATURALISTIC I Fawcett SE, 2007, SUPPLY CHAIN MANAG, V12, P358, DOI 10.1108/13598540710776935 Florence D., 1993, LOGISTICS INFORM MAN, V6, P3 Folinas D, 2006, BRIT FOOD J, V108, P622, DOI 10.1108/00070700610682319 Fritz M, 2008, ACTA AGR SCANDINAV C, V4, P355 Frohlich MT, 2001, J OPER MANAG, V19, P185, DOI 10.1016/S0272-6963(00)00055-3 Hedaa L., 2002, MAKING TIME TIME MAN Heskett J.L., 1973, BUSINESS LOGISTICS P, Vsecond Hofstede GJ, 2010, BRIT FOOD J, V112, P671, DOI 10.1108/00070701011058226 Kees J.D, 2002, LOGISTICS INFORM MAN, V15, P24 Kovacs G., 2005, International Journal of Physical Distribution & Logistics Management, V35, P132, DOI 10.1108/09600030510590318 Lincoln YS., 1985, NAT, DOI 10.1016/0147-1767(85)90062-8 Meredith J, 1998, J OPER MANAG, V16, P441, DOI 10.1016/S0272-6963(98)00023-0 Rosenbloom B., 1995, COMPANION ENCY MARKE Senneset G, 2007, BRIT FOOD J, V109, P805, DOI 10.1108/00070700710821340 Stabell CB, 1998, STRATEGIC MANAGE J, V19, P413, DOI 10.1002/(SICI)1097-0266(199805)19:5<413::AID-SMJ946>3.0.CO;2-C Swaroop V.K, 2010, BRIT FOOD J, V112, P261 Thompson JD., 1967, ORG ACTION SOCIAL SC Toyryla I, 1999, ACTA POLYTECHNICA SC van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 VONHIPPEL E, 1994, MANAGE SCI, V40, P429, DOI 10.1287/mnsc.40.4.429 Vorst J.G.A.J.V.d., 2002, INT J LOGIST-RES APP, V5, P119, DOI 10.1080/13675560210148641 NR 35 TC 6 Z9 6 U1 0 U2 9 PY 2013 VL 16 IS 2 BP 75 EP 94 WC Agricultural Economics & Policy SC Agriculture UT WOS:000323066800006 DA 2022-12-14 ER PT J AU Palumbo, F Galla, G Barcaccia, G AF Palumbo, Fabio Galla, Giulio Barcaccia, Gianni TI Developing a Molecular Identification Assay of Old Landraces for the Genetic Authentication of Typical Agro-Food Products: The Case Study of the Barley 'Agordino' SO FOOD TECHNOLOGY AND BIOTECHNOLOGY DT Article DE microsatellites; genotyping; landraces; traceability; barley; food authentication ID MULTILOCUS GENOTYPE DATA; POPULATION; DIVERSITY; INFERENCE; NUMBER; LOCI AB The orzo Agordino is a very old local variety of domesticated barley (Hordeum vulgare ssp. distichum L.) that is native to the Agordo District, Province of Belluno, and is widespread in the Veneto Region, Italy. Seeds of this landrace are widely used for the preparation of very famous dishes of the dolomitic culinary tradition such as barley soup, bakery products and local beer. Understanding the genetic diversity and identity of the Agordino barley landrace is a key step to establish conservation and valorisation strategies of this local variety and also to provide molecular traceability tools useful to ascertain the authenticity of its derivatives. The gene pool of the Agordino barley landrace was reconstructed using 60 phenotypically representative individual plants and its genotypic relationships with commercial varieties were investigated using 21 pure lines widely cultivated in the Veneto Region. For genomic DNA analysis, following an initial screening of 14 mapped microsatellite (SSR) loci, seven discriminant markers were selected on the basis of their genomic position across linkage groups and polymorphic marker alleles per locus. The genetic identity of the local barley landrace was determined by analysing all SSR markers in a single multi-locus PCR assay. Extent of genotypic variation within the Agordino barley landrace and the genotypic differentiation between the landrace individuals and the commercial varieties was determined. Then, as few as four highly informative SSR loci were selected and used to develop a molecular traceability system exploitable to verify the genetic authenticity of food products deriving from the Agordino landrace. This genetic authentication assay was validated using both DNA pools from individual Agordino barley plants and DNA samples from Agordino barley food products. On the whole, our data support the usefulness and robustness of this DNA-based diagnostic tool for the orzo Agordino identification, which could be rapidly and efficiently exploited to guarantee the authenticity of local varieties and the typicality of food products. C1 [Palumbo, Fabio; Galla, Giulio; Barcaccia, Gianni] Univ Padua, Dept Agron Food Nat Resources Animals & Environm, Viale Univ 16, IT-35020 Padua, Italy. C3 University of Padua RP Barcaccia, G (corresponding author), Univ Padua, Dept Agron Food Nat Resources Animals & Environm, Viale Univ 16, IT-35020 Padua, Italy. EM gianni.barcaccia@unipd.it CR [Anonymous], 1974, GENETIC BASIS EVOLUT Applied Biosystems, 2006, PEAK SCANN SOFTW V 1 Barcaccia G, 2016, GENET RESOUR CROP EV, V63, P639, DOI 10.1007/s10722-015-0273-z Barcaccia G, 2002, GENET RESOUR CROP EV, V49, P415, DOI 10.1023/A:1020650804532 Bellucci E, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0083891 Chen ZW, 2012, GENET MOL RES, V11, P644, DOI 10.4238/2012.March.16.2 Doyle J. J., 1997, PHYTOCHEMISTRY B, V19, P11, DOI DOI 10.2307/4119796 Evanno G, 2005, MOL ECOL, V14, P2611, DOI 10.1111/j.1365-294X.2005.02553.x Falush D, 2003, GENETICS, V164, P1567 Frank R, 2012, TRADITIONAL PLANT CA Jilal A, 2008, GENET RESOUR CROP EV, V55, P1221, DOI 10.1007/s10722-008-9322-1 Khodayari H., 2012, INT J BIOSCI BIOCH B, V2, P287, DOI [10.7763/ijbbb.2012.v2.118, DOI 10.7763/IJBBB.2012.V2.118] KIMURA M, 1964, GENETICS, V49, P725 Maresio Bazolle A, 1986, LANDOWNER BELLUNO Nagy S, 2012, BIOCHEM GENET, V50, P670, DOI 10.1007/s10528-012-9509-1 NEI M, 1973, P NATL ACAD SCI USA, V70, P3321, DOI 10.1073/pnas.70.12.3321 Pritchard JK, 2000, GENETICS, V155, P945 Rohlf JF, 2015, NTSYS PC NUMERICAL T Scarano D., 2014, Diversity, V6, P579 Schuelke M, 2000, NAT BIOTECHNOL, V18, P233, DOI 10.1038/72708 Varshney RK, 2007, THEOR APPL GENET, V114, P1091, DOI 10.1007/s00122-007-0503-7 Villavecchia GV, 1975, NEW DICT MARKETABLE, V5 Yeh FC, 1997, POPGENE V 1 32 USER NR 23 TC 10 Z9 10 U1 1 U2 8 PD JAN-MAR PY 2017 VL 55 IS 1 BP 29 EP 39 DI 10.17113/ftb.55.01.17.4858 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000398138800004 DA 2022-12-14 ER PT J AU Ge, L AF Ge, Li TI To Buy or Not to Buy? A Research on the Relationship Between Traceable Food Extrinsic Cues and Consumers' Purchase Intention SO FRONTIERS IN PSYCHOLOGY DT Article DE traceable certification credibility; purchase intention; traceability knowledge; traceable information quality; peer influence ID WILLINGNESS-TO-PAY; PARTIAL LEAST-SQUARES; PERCEIVED VALUE; INFORMATION-SYSTEMS; ARGUMENT QUALITY; SOCIAL MEDIA; TRUST; PERCEPTIONS; ADOPTION; IMPACT AB With the prevalence of traceability technology in the turbulent Internet age, traceable food has become an important tool in addressing food safety issues. Under the combined effect of frequent food safety problems and sustainable development of traceability industry, the research on traceable food consumer behavior has become more extensive. However, it is still not fully understood how the multiple information brought by traceability affects consumers' purchase decision. This study proposes the effects of traceability knowledge, traceable information quality and traceable certification credibility on traceable food purchase intention via the mediation of perceived risk and perceived value, and integrates the moderating effect of peer influence in the context of Internet age into a research framework. The analytical results indicate that traceability knowledge, traceable information quality, and traceability certification credibility indirectly affect consumers' traceable food purchase intention through perceived risk and perceived value, while traceability knowledge, perceived risk, and perceived value directly affect "traceable food purchase intention." Furthermore, peer influence was found to be a significant moderator in the relationship between perceived risk (perceived value) and "traceable food purchase intention." Finally, based on the research results, traceability companies are suggested to focus on cultivating the traceable consumption habits. Meanwhile, although traceable food quality is the top priority, companies should also attach importance to the communication and interaction with consumer. C1 [Ge, Li] Weinan Normal Univ, Sch Comp Sci & Technol, Weinan, Peoples R China. C3 Weinan Normal University RP Ge, L (corresponding author), Weinan Normal Univ, Sch Comp Sci & Technol, Weinan, Peoples R China. EM joicege@163.com CR Abror A, 2022, J ISLAMIC MARK, V13, P2742, DOI 10.1108/JIMA-03-2021-0094 Alalwan AA, 2018, J RETAIL CONSUM SERV, V40, P125, DOI 10.1016/j.jretconser.2017.08.026 ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411 Anderson R.E., 2010, MULTIVARIATE DATA AN ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783 ARNOLD MB, 1969, ANN NY ACAD SCI, V159, P1041, DOI 10.1111/j.1749-6632.1969.tb12996.x Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Bagozzi RP, 2012, J ACAD MARKET SCI, V40, P8, DOI 10.1007/s11747-011-0278-x Bai JF, 2013, AGR ECON-BLACKWELL, V44, P537, DOI 10.1111/agec.12037 Barclay D., 1995, TECHNOL STUDIES, V2, P285, DOI DOI 10.1017/CBO9781107415324.004 Belz FM, 2010, BUS STRATEG ENVIRON, V19, P401, DOI 10.1002/bse.649 Bernues A, 2003, FOOD QUAL PREFER, V14, P265, DOI 10.1016/S0950-3293(02)00085-X Bontis N, 2007, MANAGE DECIS, V45, P1426, DOI 10.1108/00251740710828681 Brach S, 2018, EUR MANAG J, V36, P254, DOI 10.1016/j.emj.2017.03.005 Buaprommee N, 2016, ASIA PAC MANAG REV, V21, P161, DOI 10.1016/j.apmrv.2016.03.001 CAMPBELL EQ, 1965, AM J SOCIOL, V71, P284, DOI 10.1086/224087 Castro I, 2013, INT BUS REV, V22, P1034, DOI 10.1016/j.ibusrev.2013.02.004 Cheung CMK, 2008, INTERNET RES, V18, P229, DOI 10.1108/10662240810883290 Chin WW, 1998, QUANT METH SER, P295 Cox D. F., 1962, EMERG CONCEPTS MARK, V1962, P413 Cui L, 2019, DATA TECHNOL APPL, V53, P230, DOI 10.1108/DTA-05-2018-0046 Das G, 2015, J GLOB FASH MARK, V6, P180, DOI 10.1080/20932685.2015.1032316 Filieri R, 2015, J BUS RES, V68, P1261, DOI 10.1016/j.jbusres.2014.11.006 FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312 Giacomarra M, 2016, J CLEAN PROD, V112, P267, DOI 10.1016/j.jclepro.2015.09.026 Grunert KG, 2016, FOOD CONTROL, V59, P178, DOI 10.1016/j.foodcont.2015.05.021 Hair JF, 2012, LONG RANGE PLANN, V45, P312, DOI 10.1016/j.lrp.2012.09.011 HALLINAN MT, 1990, SOCIOL EDUC, V63, P122, DOI 10.2307/2112858 Hamlin RP, 2010, APPETITE, V55, P89, DOI 10.1016/j.appet.2010.04.007 Han M.C., 2016, J PROMOTION MANAGEME, V23, P24 Hobbs J. E., 2004, Agribusiness (New York), V20, P397, DOI 10.1002/agr.20020 Hoonsopon D, 2016, AUSTRALAS MARK J, V24, P157, DOI 10.1016/j.ausmj.2016.05.001 Hsu CL, 2012, INF SYST E-BUS MANAG, V10, P549, DOI 10.1007/s10257-011-0181-5 Isa SM, 2019, SOC RESPONSIB J, V16, P291, DOI 10.1108/SRJ-01-2018-0003 Jayashankar P, 2018, J BUS IND MARK, V33, P804, DOI 10.1108/JBIM-01-2018-0023 Jorgensen BL, 2010, FAM RELAT, V59, P465, DOI 10.1111/j.1741-3729.2010.00616.x KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185 Kandel D B, 1985, Adv Alcohol Subst Abuse, V4, P139 Kim JH, 2020, INT J HOSP MANAG, V90, DOI 10.1016/j.ijhm.2020.102617 Kim WG, 2009, INT J HOSP MANAG, V28, P144, DOI 10.1016/j.ijhm.2008.06.010 Klerck D, 2007, PSYCHOL MARKET, V24, P171, DOI 10.1002/mar.20157 Kohtamaki M, 2016, IND MARKET MANAG, V56, P4, DOI 10.1016/j.indmarman.2016.05.027 Konuk FA, 2018, J RETAIL CONSUM SERV, V43, P304, DOI 10.1016/j.jretconser.2018.04.011 Liang LJ, 2018, J TRAVEL TOUR MARK, V35, P73, DOI 10.1080/10548408.2016.1224750 Lim WM, 2014, J GLOB MARK, V27, P298 Lin JL, 2019, J STRATEG MARK, V27, P81, DOI 10.1080/0965254X.2017.1384044 Lin TTC, 2020, J COMPUT INFORM SYST, V60, P184, DOI 10.1080/08874417.2018.1432995 Liu A, 2017, FOOD CONTROL, V79, P185, DOI 10.1016/j.foodcont.2017.03.038 Liu C, 2019, ASIA PAC J MARKET LO, V31, P378, DOI 10.1108/APJML-05-2018-0170 Liu C, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0206793 McClure C, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101975 Menozzi D, 2015, FOOD CONTROL, V49, P40, DOI 10.1016/j.foodcont.2013.09.006 Mitchell VW., 1999, EUR J MARKETING, V33, P163 Molinillo S, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2020.102404 Nitzl C, 2016, IND MANAGE DATA SYST, V116, P1849, DOI 10.1108/IMDS-07-2015-0302 Olson J., 1972, P 3 ANN C ASS CONS R, P167 Paul J, 2016, J RETAIL CONSUM SERV, V29, P123, DOI 10.1016/j.jretconser.2015.11.006 Peng N, 2019, J SUSTAIN TOUR, V27, P1374, DOI 10.1080/09669582.2019.1622710 Petraviciute K, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126912 Petter S, 2007, MIS QUART, V31, P623 Ponte EB, 2015, TOURISM MANAGE, V47, P286, DOI 10.1016/j.tourman.2014.10.009 Riccioli F, 2020, FOOD CONTROL, V108, DOI 10.1016/j.foodcont.2019.106831 RICHARDSON PS, 1994, J MARKETING, V58, P28, DOI 10.2307/1251914 Rogers S, 1976, AGGRESSION, V14, P1 Samaraweera G. C, 2020, P 6 INT S MULTIDISCI SCHMIDT WE, 1975, PSYCHOL SCHOOLS, V12, P484, DOI 10.1002/1520-6807(197510)12:4<484::AID-PITS2310120419>3.0.CO;2-O Shao BJ, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102367 Sharaf MA, 2017, PERTANIKA J SOC SCI, V25, P239 SHEKO A., 2018, ACAD J INTERDISCIPLI, V7, P125 SJTU Food Safety Research Center,, 2019, OUTBR REL HARD BIOL Suki NM, 2016, BRIT FOOD J, V118, P2893, DOI 10.1108/BFJ-06-2016-0295 Sweeney JC, 1999, J RETAILING, V75, P77, DOI 10.1016/S0022-4359(99)80005-0 Tretyak OA, 2013, J BUS IND MARK, V28, P221, DOI 10.1108/08858621311302877 Tzavlopoulos I, 2019, INT J QUAL SERV SCI, V11, P576, DOI 10.1108/IJQSS-03-2019-0047 Ueland O, 2012, FOOD CHEM TOXICOL, V50, P67, DOI 10.1016/j.fct.2011.06.006 Tran VD, 2020, J ASIAN FINANC ECON, V7, P221, DOI 10.13106/jafeb.2020.vol7.no6.221 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 Vinzi V.E, 2010, HDB PARTIAL LEAST SQ, DOI DOI 10.1007/978-3-540-32827-8 Wang EST, 2019, FOOD QUAL PREFER, V78, DOI 10.1016/j.foodqual.2019.103723 Wang J, 2017, FOOD CONTROL, V79, P363, DOI 10.1016/j.foodcont.2017.04.013 Wang SY, 2018, TRANSPORT RES A-POL, V117, P58, DOI 10.1016/j.tra.2018.08.014 Wang X, 2012, J INTERACT MARK, V26, P198, DOI 10.1016/j.intmar.2011.11.004 Wang YC, 2016, INT J PROD ECON, V181, P460, DOI 10.1016/j.ijpe.2015.08.031 Wu LH, 2016, AGR ECON-BLACKWELL, V47, P71, DOI 10.1111/agec.12210 Wu LH, 2015, CHINA ECON REV, V35, P121, DOI 10.1016/j.chieco.2015.07.001 Yi MY, 2013, DECIS SUPPORT SYST, V55, P284, DOI 10.1016/j.dss.2013.01.029 Yoo CW, 2015, INFORM MANAGE-AMSTER, V52, P692, DOI 10.1016/j.im.2015.06.003 Yuan CL, 2020, IND MANAGE DATA SYST, V120, P810, DOI 10.1108/IMDS-09-2019-0469 Yue LQ, 2017, BRIT FOOD J, V119, P2724, DOI [10.1108/BFJ-09-2016-0421, 10.1108/bfj-09-2016-0421] ZEITHAML VA, 1988, J MARKETING, V52, P2, DOI 10.2307/1251446 Zheng CD, 2017, INFORM SYST FRONT, V19, P1261, DOI 10.1007/s10796-017-9766-y NR 92 TC 0 Z9 0 U1 9 U2 9 PD APR 25 PY 2022 VL 13 AR 873941 DI 10.3389/fpsyg.2022.873941 WC Psychology, Multidisciplinary SC Psychology UT WOS:000881788500001 DA 2022-12-14 ER PT J AU Allepuz, ET Bernal, PD Marti, AM AF Trave Allepuz, Esther del Fresno Bernal, Pablo Mauri Marti, Alfred TI Ontology-Mediated Historical Data Modeling: Theoretical and Practical Tools for an Integrated Construction of the Past SO INFORMATION DT Article DE unit of topography; unit of stratigraphy; information system; actor; history; archaeology; database ID THINGS AB Building upon the concepts of constructed past theory, this paper introduces the outcome of ontology-mediated data modeling developed by the authors within the last 15 years. Assuming that the past is something constructed through reflection of former times, one of our major concerns is guaranteeing the traceability of the construction process of an integrated historical discourse built from all available sources of information, regardless of their origin or nature. Therefore, by means of defining key concepts such as 'unit of topography' and 'actor', we created an information system for data gathering and exploitation and applied it to some experiences of construction of the past. When applied within the archaeological domain, the result is an archaeological information system interoperable with other sources of historical information. Its strength is that it ensures the traceability of the process from the beginning avoiding the introduction and repetition of errors within the system. Along with the main case example developed in this paper, we also summarize some other data modeling examples within the same conceptual framework. C1 [Trave Allepuz, Esther] Univ Barcelona, Dept Hist & Archaeol, Barcelona 08001, Spain. [del Fresno Bernal, Pablo] Sistemes Gestio Patrimoni SCCL, Barcelona 08004, Spain. [Mauri Marti, Alfred] Ctr Estudis Martorellencs, Martorell 08760, Spain. C3 University of Barcelona RP Allepuz, ET (corresponding author), Univ Barcelona, Dept Hist & Archaeol, Barcelona 08001, Spain. EM esther.trave@ub.edu; pdfsgp@gmail.com3; bnn@heraclit.net CR Amati V, 2019, J ARCHAEOL SCI, V104, P1, DOI 10.1016/j.jas.2019.01.006 [Anonymous], 2017, ANC1690T721 U BARC [Anonymous], 1997, HIST TIERRA MANUAL E Arostegui J., 1995, INVESTIGACION HISTOR Barcelo M., 1988, ARQUELOGIA MEDIEVAL Bolos J., 2010, CARACTERITZACIO PAIS Cosgrove Denis, 2004, GHI B, V35, P57 de Jong F, 2005, HUMANITIES, COMPUTERS AND CULTURAL HERITAGE, P161 Del Fresno P., 2017, 4 TROB EST FOIX, P144 Del Fresno P., 2016, THESIS Del Fresno P., 2018, P OP SCI HUM C BARC Diarte-Blasco P., 2018, ARCHAEOLOGICAL APPRO Ehrlinger L., 2016, SEMANTICS, V48, P2 Ganser G., 2018, INTERPARES TRUST PRO Gerritsen Anne, 2015, WRITING MAT CULTURE, P3 Gonzalez-Perez C., 2012, 6 INT C RES CHALL IN Graves M., 2017, E REA REV ELECT ETUD, V14 Harris E.C., 1989, PRINCIPLES ARCHAEOLO, V2nd Hicks Dan, 2010, MAT CULTURAL TURN, P25, DOI DOI 10.1093/OXFORDHB/9780199218714.001.0001/OXFORDHB-9780199218714-E-2. Hodder I., 1976, SPATIAL ANAL ARCHAEO Hodder I, 2014, NEW LITERARY HIST, V45, P19, DOI 10.1353/nlh.2014.0005 International Council on Archives, 2001, ISADG GEN INT STAND ISO, 2016, 154891 ISO ISO, 2008, 26122 ISOTR Ivanovs A., 2005, HUMANIT COMPUT CULT, V155, P155 Johnson T., 2018, OXFORD HDB LEGAL HIS, P1, DOI [10.1093/oxfordhb/9780198794356.013.27, DOI 10.1093/OXFORDHB/9780198794356.013.27] Krauwer S., 2019, P PARTH CEE WORKSH S Lopez-Poza S., HUMANIDADES DIGITALE Magalotti Lorenzo, 1933, VIAJE COSME MEDICIS Manrique R, 2019, SMART LEARN ENVIRON, V6, DOI 10.1186/s40561-019-0104-3 Martinon-Torres M., 2016, OXFORD HDB ARCHAEOLO Mauri A., 2006, THESIS Mauri A., 2018, P OP SCI HUM C BARC Mauri A., 2012, ARCHAEOLOGY NEW APPR, P41, DOI [10.5772/38934, DOI 10.5772/38934] Mauri A., 1997, THESIS Mauri A., 2013, 3 MONOGRAFIES FOIX, VIII, P150 Meghini C, 2017, ACM J COMPUT CULT HE, V10, DOI 10.1145/3064527 Mosca A., 2017, P JOINT ONT WORKSH 2 Nesmith T., 2006, ARCHIVARIA, V60, P259 Parthenos, GUID FAIR DAT MAN MA Puig P., 1995, MONESTIR SANT LLOREN Quiros J. A., 2007, TERRITORIO SOC PODER, V2, P65 Schouwenburg Hans, 2015, INT J HIST CULTURE M, V3, P59, DOI DOI 10.18352/hcm.476 Shanks M, 2007, WORLD ARCHAEOL, V39, P589, DOI 10.1080/00438240701679676 SigArq, SIST INF GEOESP ARQ Soler M., 2002, ACTA HIST ARCHEOL ME, V2002, P69 Thibodeau K, 2019, INFORMATION, V10, DOI 10.3390/info10110332 Torre A., 2008, ANN HIST SCI SOC, V2008, P1127 Trave E., 2020, ACT C MERC ESP EC SI Trave E., 2018, P OP SCI HUM C BARC Trave E., 2017, ACT 19 C AS CER OBR, P123 Trentmann F, 2009, J BRIT STUD, V48, P283, DOI 10.1086/596123 Vidal E., 1912, B CTR EXCURSION CATA, V206, P65 Vinyes J.M., 1998, GUARRO CASAS 300 ANY, P102 WHITE R, 2010, SPATIAL HIST LAB WOR Wickham H, 2014, J STAT SOFTW, V59, P1 Wilkinson MD, 2016, SCI DATA, V3, DOI 10.1038/sdata.2016.18 Witmore Christopher, 2012, BAR INT SERIES, V2363, P25 Yan JH, 2018, FRONT COMPUT SCI-CHI, V12, P55, DOI 10.1007/s11704-016-5228-9 Yeo G., 2012, ARCHIVARIA, V73, P43 ZADORA-RIO E., 1995, CAMPAGNES MEDIEVALES, P145 NR 61 TC 3 Z9 3 U1 0 U2 0 PD APR PY 2020 VL 11 IS 4 AR 182 DI 10.3390/info11040182 WC Computer Science, Information Systems SC Computer Science UT WOS:000533911600019 DA 2022-12-14 ER PT J AU Rao, MS Chakraborty, G Murthy, KS AF Rao, Monica Supriya Chakraborty, Geetanjali Murthy, K. Satya TI Market Drivers and Discovering Technologies in Meat Species Identification SO FOOD ANALYTICAL METHODS DT Article DE Food authenticity; Meat speciation; PCR; NGS; Blockchain traceability; Market challenges ID POLYMERASE-CHAIN-REACTION; REAL-TIME PCR; FRAGMENT LENGTH POLYMORPHISM; RIBOSOMAL-RNA GENE; TARGETING SPECIFIC SEQUENCES; CYTOCHROME-B GENE; D-LOOP; REACTION ASSAY; QUANTITATIVE DETECTION; FRAUD IDENTIFICATION AB Changing demographics and the need to know what we eat has fueled a drastic rise in consumer awareness and hence the demand for certification by government and regulatory bodies. The inceptions of regulations in emerging economies like India and China that have entered the global food trade are expected to increase the demand for testing services. Due to the sheer volume of the global meat market, there has been focus on technologies for meat speciation, like immunoassay-based techniques and electrophoretic techniques. However, by virtue of their intrinsic constraints, these technologies have been superseded by the recent molecular DNA-based methods. DNA-based technologies, mainly polymerase chain reaction (PCR) and real-time PCR (RTPCR), are in the spotlight because of their quantitative capabilities, better sensitivity, and rapidity. Next-generation sequencing (NGS), a high-throughput sequencing method, has revolutionized meat speciation studies in terms of speed, read length, and throughput, along with cost-reduction. Variants of chromatographic and spectroscopic techniques are an attractive option due to the speed of analysis and minimal sample preparation but they fail to quantify adulteration in cases such as cooked meat, which has more complex chromatographic patterns. Even with the advent of these methods, consumers need to be vigorously assured that players across the supply network are operating in the consumer's interest and not in their own financial gain. Under the Blockchain system of technology, traceability of the end product is feasible at every point of its processing or packaging and all the data can be retrieved in less than 2 min. Considering the intersection of the largest target segment, meat, and the fastest growing technology, PCR-based technology, this review intends to provide an updated overview, of market and research studies for authenticity, and a short summary of traceability systems to enhance trade transparency. C1 [Rao, Monica Supriya; Chakraborty, Geetanjali; Murthy, K. Satya] ITC Ltd, Agribusiness Div, Guntur, Andhra Pradesh, India. RP Rao, MS (corresponding author), ITC Ltd, Agribusiness Div, Guntur, Andhra Pradesh, India. EM monica.supriyarao@itc.in CR Aarts HJM, 2006, J AOAC INT, V89, P1443 Ali ME, 2011, J NANOMATER, V2011, DOI 10.1155/2011/781098 Ali ME, 2012, FOOD ANAL METHOD, V5, P613, DOI 10.1007/s12161-011-9290-5 Ali ME, 2014, FOOD ANAL METHOD, V7, P234, DOI 10.1007/s12161-013-9672-y Amaral JS, 2014, FOOD RES INT, V60, P140, DOI 10.1016/j.foodres.2013.11.003 [Anonymous], 2017, FOOD AUTHENTICITY TE Arana A, 2002, MEAT SCI, V61, P367, DOI 10.1016/S0309-1740(01)00206-6 Arslan A, 2006, MEAT SCI, V72, P326, DOI 10.1016/j.meatsci.2005.08.001 Arslan A, 2005, J MUSCLE FOODS, V16, P37, DOI 10.1111/j.1745-4573.2004.07504.x Barakat H, 2014, APPL MICROBIOL BIOT, V98, P9805, DOI 10.1007/s00253-014-6084-x Bertolini F, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0121701 Bocklandt S, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0014821 Bottero MT, 2003, INT DAIRY J, V13, P277, DOI 10.1016/S0958-6946(02)00170-X Buntjer JB, 1999, J SCI FOOD AGR, V79, P53, DOI 10.1002/(SICI)1097-0010(199901)79:1<53::AID-JSFA171>3.0.CO;2-E Buntjer JB, 1995, Z LEBENSM UNTERS FOR, V201, P577, DOI 10.1007/BF01201589 Calvo JH, 2002, J AGR FOOD CHEM, V50, P5262, DOI 10.1021/jf020051a Casellas J, 2004, J ANIM BREED GENET, V121, P101, DOI 10.1046/j.1439-0388.2003.00441.x Casiraghi M, 2010, BRIEF BIOINFORM, V11, P440, DOI 10.1093/bib/bbq003 Chen AL, 2015, INT J FOOD SCI TECH, V50, P834, DOI 10.1111/ijfs.12720 Chen SY, 2010, J GENET GENOMICS, V37, P763, DOI 10.1016/S1673-8527(09)60093-X CHIKUNI K, 1990, MEAT SCI, V27, P119, DOI 10.1016/0309-1740(90)90060-J CHIKUNI K, 1994, MEAT SCI, V37, P337, DOI 10.1016/0309-1740(94)90051-5 Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 CROUSE CA, 1995, J FORENSIC SCI, V40, P952 Dalmasso A, 2004, MOL CELL PROBE, V18, P81, DOI 10.1016/j.mcp.2003.09.006 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Di Domenico M, 2016, J DAIRY SCI, V100, P1 Dooley JJ, 2004, MEAT SCI, V68, P431, DOI 10.1016/j.meatsci.2004.04.010 Doosti A, 2014, J FOOD SCI TECH MYS, V51, P148, DOI 10.1007/s13197-011-0456-3 EBBEHOJ KF, 1991, MEAT SCI, V30, P359, DOI 10.1016/0309-1740(91)90044-Q EBBEHOJ KF, 1991, MEAT SCI, V30, P221, DOI 10.1016/0309-1740(91)90068-2 Espineira M, 2015, EUR FOOD RES TECHNOL, V241, P233, DOI 10.1007/s00217-015-2448-4 Fajardo V, 2007, MEAT SCI, V76, P234, DOI 10.1016/j.meatsci.2006.11.004 Fang X, 2016, FOOD CHEM, V192, P485, DOI 10.1016/j.foodchem.2015.07.020 Farag M. R., 2015, Advances in Animal and Veterinary Sciences, V3, P334, DOI 10.14737/journal.aavs/2015/3.6.334.346 Fernandez ME, 2013, GENET MOL BIOL, V36, P185, DOI 10.1590/S1415-47572013000200008 Ferri G, 2009, GENET TEST MOL BIOMA, V13, P421, DOI 10.1089/gtmb.2008.0144 Geiss GK, 2008, NAT BIOTECHNOL, V26, P317, DOI 10.1038/nbt1385 Ghovvati S, 2009, FOOD CONTROL, V20, P696, DOI 10.1016/j.foodcont.2008.09.002 Girish PS, 2007, VET RES COMMUN, V31, P447, DOI 10.1007/s11259-006-3390-5 Girish PS, 2005, MEAT SCI, V70, P107, DOI 10.1016/j.meatsci.2004.12.004 Girish PS, 2004, MEAT SCI, V66, P551, DOI 10.1016/S0309-1740(03)00158-X Giusti A, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0185586 Gowda CT, 2013, THESIS Gupta AR, 2008, MITOCHONDR DNA, V19, P394, DOI 10.1080/19401730802351251 HAA El-Jaafari, 2008, J ANIM SCI, DOI [10.5713/ajas.2008.60227, DOI 10.5713/AJAS.2008.60227] Haider N, 2012, MEAT SCI, V90, P490, DOI 10.1016/j.meatsci.2011.09.013 Han SH, 2017, J APPL ANIM RES, V45, P179, DOI 10.1080/09712119.2015.1124334 Heaton MP, 2002, MAMM GENOME, V13, P272, DOI 10.1007/s00335-001-2146-3 Hebert PDN, 2003, P ROY SOC B-BIOL SCI, V270, P313, DOI [10.1098/rspb.2002.2218, 10.1098/rsbl.2003.0025] Holmes BH, 2009, FISH RES, V95, P280, DOI 10.1016/j.fishres.2008.09.036 Huang MC, 2003, ASIAN AUSTRAL J ANIM, V16, P1406, DOI 10.5713/ajas.2003.1406 Hunt DJ, 1997, FOOD CHEM, V60, P437, DOI 10.1016/S0308-8146(96)00364-0 Hwang MT, 2018, ADV MATER, V30, DOI 10.1002/adma.201802440 Ilhak OI, 2007, TURK J VET ANIM SCI, V31, P159 Kappel K, 2017, FOOD CHEM, V234, P212, DOI 10.1016/j.foodchem.2017.04.178 Karabasanavar NS, 2014, FOOD CHEM, V145, P530, DOI 10.1016/j.foodchem.2013.08.084 Karabasanavar NS, 2011, SMALL RUMINANT RES, V100, P153, DOI 10.1016/j.smallrumres.2011.07.009 Kim M, 2016, FOOD CHEM, V210, P102, DOI 10.1016/j.foodchem.2016.04.084 Kitano T, 2007, INT J LEGAL MED, V121, P423, DOI 10.1007/s00414-006-0113-y Koveza O. V., 2005, Genetika, V41, P341 Kumar A, 2013, CRIT REV FOOD SCI NU Kumari R, 2015, BUFFALO BULL, V34, P124 La Neve F, 2008, MEAT SCI, V80, P216, DOI 10.1016/j.meatsci.2007.11.027 Latorra D, 1996, FORENSIC SCI INT, V83, P15, DOI 10.1016/0379-0738(96)02006-3 Li Bo, 2006, Journal of Forestry Research (Harbin), V17, P160, DOI 10.1007/s11676-006-0038-9 Mane BG, 2012, FOOD CONTROL, V28, P246, DOI 10.1016/j.foodcont.2012.05.031 Mane BG, 2013, J MEAT SCI TECHNOL, V1, P21 Manjunatha G, 2018, J FORENSIC SCI CRIM, V10 Martin I, 2009, WORLD RABBIT SCI, V17, P27 Martinez I, 2000, J SCI FOOD AGR, V80, P527, DOI 10.1002/(SICI)1097-0010(200003)80:4<527::AID-JSFA565>3.0.CO;2-7 Matsunaga T, 1999, MEAT SCI, V51, P143, DOI 10.1016/S0309-1740(98)00112-0 Mendoza-Romero L, 2004, J FOOD PROTECT, V67, P550, DOI 10.4315/0362-028X-67.3.550 Mohindra Vindhya, 2007, Acta Zoologica Sinica, V53, P725 Mousavi SM, 2015, J FOOD COMPOS ANAL, V40, P47, DOI 10.1016/j.jfca.2014.12.009 Murugaiah C, 2009, MEAT SCI, V83, P57, DOI 10.1016/j.meatsci.2009.03.015 Nijman IJ, 2003, HEREDITY, V90, P10, DOI 10.1038/sj.hdy.6800174 NOVICK GE, 1995, ELECTROPHORESIS, V16, P1596, DOI 10.1002/elps.11501601263 Ozpinar H, 2013, KAFKAS UNIV VET FAK, V19, P245, DOI 10.9775/kvfd.2012.7616 Pegels N, 2013, FOOD ADDIT CONTAM A, V30, P771, DOI 10.1080/19440049.2013.794978 Piorkowska K, 2016, APPL TARGETED NEXT G Popping B, 2010, MOL BIOL IMMUNOLOGIC, P2010 Rahman MM, 2016, FOOD ADDIT CONTAM A, V33, P10, DOI 10.1080/19440049.2015.1104558 Ramos AM, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006524 Rastogi G, 2007, MEAT SCI, V76, P666, DOI 10.1016/j.meatsci.2007.02.006 Ripp F, 2014, BMC GENOMICS, V15, DOI 10.1186/1471-2164-15-639 Rohrer GA, 2007, ANIM GENET, V38, P253, DOI 10.1111/j.1365-2052.2007.01593.x Rojas M, 2010, POULTRY SCI, V89, P1021, DOI 10.3382/ps.2009-00217 Safdar M, 2014, MEAT SCI, V98, P296, DOI 10.1016/j.meatsci.2014.06.006 Safdar M, 2016, FOOD CHEM, V192, P745, DOI 10.1016/j.foodchem.2015.07.082 Scarano D., 2014, Diversity, V6, P579 Shackell GH, 2005, MEAT SCI, V70, P337, DOI 10.1016/j.meatsci.2005.01.020 Singh A, 2004, FORENSIC SCI INT, V141, P143, DOI 10.1016/j.forsciint.2004.01.015 Singh VP, 2011, AM J FOOD TECHNOLOGY, V1, P1, DOI DOI 10.3923/ijmeat.2011.15.26 Soares S, 2010, MEAT SCI, V85, P531, DOI 10.1016/j.meatsci.2010.03.001 Srivastava GK, 2015, VET WORLD, V8, P532, DOI 10.14202/vetworld.2015.532-536 Stamoulis P, 2010, FOOD CONTROL, V21, P1061, DOI 10.1016/j.foodcont.2009.12.027 Tanabe S, 2007, BIOSCI BIOTECH BIOCH, V71, P3131, DOI 10.1271/bbb.70683 Tanabe S, 2007, BIOSCI BIOTECH BIOCH, V71, P1663, DOI 10.1271/bbb.70075 Tejedor M., 2006, Wildlife Biology in Practice, V2, P8 Tillmar AO, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0083761 Vazquez JF, 2004, J FOOD PROTECT, V67, P972, DOI 10.4315/0362-028X-67.5.972 Verkaar ELC, 2002, MEAT SCI, V60, P365, DOI 10.1016/S0309-1740(01)00144-9 Walker JA, 2003, ANAL BIOCHEM, V316, P259, DOI 10.1016/S0003-2697(03)00095-2 Walker JA, 2004, GENOMICS, V83, P518, DOI 10.1016/j.ygeno.2003.09.003 Wilkinson S, 2012, BMC GENOMICS, V13, DOI 10.1186/1471-2164-13-580 Wu XB, 2006, NEW ZEAL J ZOOL, V33, P65, DOI 10.1080/03014223.2006.9518431 Wu YJ, 2015, J AOAC INT, V98, P1640, DOI 10.5740/jaoacint.15-155 Yang Yaran, 2014, Genomics Proteomics & Bioinformatics, V12, P190, DOI 10.1016/j.gpb.2014.09.001 Yau FCF, 2002, J EXP ZOOL, V294, P382, DOI 10.1002/jez.10199 Zhang C, 2013, FOOD CONTROL, V31, P326, DOI 10.1016/j.foodcont.2012.11.002 Zhang GL, 1999, MEAT SCI, V51, P233, DOI 10.1016/S0309-1740(98)00116-8 NR 112 TC 11 Z9 11 U1 4 U2 32 PD NOV PY 2019 VL 12 IS 11 BP 2416 EP 2429 DI 10.1007/s12161-019-01591-8 WC Food Science & Technology SC Food Science & Technology UT WOS:000494049600002 DA 2022-12-14 ER PT J AU Sandak, A Sandak, J Janiszewska, D Hiziroglu, S Petrillo, M Grossi, P AF Sandak, Anna Sandak, Jakub Janiszewska, Dominika Hiziroglu, Salim Petrillo, Marta Grossi, Paolo TI Prototype of the Near-Infrared Spectroscopy Expert System for Particleboard Identification SO JOURNAL OF SPECTROSCOPY DT Article ID MELAMINE-UREA-FORMALDEHYDE; LIQUEFIED WOOD; SUBSTITUTE; RESIN; CORE; L. AB The overall goal of this work was to develop a prototype expert system assisting quality control and traceability of particleboard panels on the production floor. Four different types of particleboards manufactured at the laboratory scale and in industrial plants were evaluated. The material differed in terms of panel type, composition, and adhesive system. NIR spectroscopy was employed as a pioneer tool for the development of a two-level expert system suitable for classification and traceability of investigated samples. A portable, commercially available NIR spectrometer was used for nondestructive measurements of particleboard panels. Twenty-five batches of particleboards, each containing at least three independent replicas, was used for the original system development and assessment of its performance. Four alternative chemometric methods (PLS-DA, kNN, SIMCA, and SVM) were used for spectroscopic data classification. The models were developed for panel recognition at two levels differing in terms of their generality. In the first stage, four among twenty-four tested combinations resulted in 100% correct classification. Discrimination precision with PLS-DA and SVMC was high (>99%), even without any spectra preprocessing. SNV preprocessed spectra and SVMC algorithm were used at the second stage for panel batch classification. Panels manufactured by two producers were 100% correctly classified, industrial panels produced by different manufacturing plants were classified with 98.9% success, and the experimental panels manufactured in the laboratory were classified with 63.7% success. Implementation of NIR spectroscopy for wood-based product traceability and quality control may have a great impact due to the high versatility of the production and wide range of particleboards utilization. C1 [Sandak, Anna; Sandak, Jakub; Petrillo, Marta; Grossi, Paolo] CNR, IVALSA Trees & Timber Inst, I-38010 San Michele All Adige, Italy. [Sandak, Anna; Sandak, Jakub] InnoRenew CoE, Izola 6310, Slovenia. [Sandak, Jakub] Univ Primorska, Fac Math Nat Sci & Informat Technol, Koper 6000, Slovenia. [Janiszewska, Dominika] Wood Technol Inst, Composite Wood Prod Dept, Poznan, Poland. [Hiziroglu, Salim] Oklahoma State Univ, Nat Resource Ecol & Management, Stillwater, OK 74078 USA. C3 Consiglio Nazionale delle Ricerche (CNR); Istituto per la Valorizzazione del Legno e delle Specie Arboree (IVALSA-CNR); University of Primorska; Wood Technology Institute; Oklahoma State University System; Oklahoma State University - Stillwater RP Sandak, J (corresponding author), CNR, IVALSA Trees & Timber Inst, I-38010 San Michele All Adige, Italy.; Sandak, J (corresponding author), InnoRenew CoE, Izola 6310, Slovenia.; Sandak, J (corresponding author), Univ Primorska, Fac Math Nat Sci & Informat Technol, Koper 6000, Slovenia. EM jakub.sandak@innorenew.eu CR Adeniyi D. A., 2016, Applied Computing and Informatics, V12, P90, DOI 10.1016/j.aci.2014.10.001 [Anonymous], 2002, COMPILATION AIR POLL [Anonymous], 2018, BIOENERGY SYSTEM PLA Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Bevilacqua M, 2014, J AOAC INT, V97, P19, DOI 10.5740/jaoacint.SGEBevilacqua BRADLEY JH, 1995, EXPERT SYST APPL, V8, P157, DOI 10.1016/0957-4174(94)E0006-G Cuk N, 2011, MATER TEHNOL, V45, P241 Esteves B, 2015, MADERAS-CIENC TECNOL, V17, P277, DOI 10.4067/S0718-221X2015005000026 Fiorelli J, 2018, WASTE BIOMASS VALORI, V9, P1151, DOI 10.1007/s12649-017-9889-x Frackowiak I, 2008, DREW-WOOD, V51, P5 Hobballah MH, 2018, EXPERT SYST APPL, V92, P95, DOI 10.1016/j.eswa.2017.09.035 Hua LS, 2015, J OIL PALM RES, V27, P67 Janiszewska D., 2016, Annals of Warsaw University of Life Sciences - SGGW, Forestry and Wood Technology, P298 Janiszewska D, 2016, HOLZFORSCHUNG, V70, P1135, DOI 10.1515/hf-2016-0043 Janiszewska D, 2016, DREWNO, V59, P223, DOI 10.12841/wood.1644-3985.C37.01 Kalaycioglu H, 2006, IND CROP PROD, V24, P177, DOI 10.1016/j.indcrop.2006.03.011 Kunaver M, 2010, BIORESOURCE TECHNOL, V101, P1361, DOI 10.1016/j.biortech.2009.09.066 Lertsutthiwong P, 2008, BIORESOURCE TECHNOL, V99, P4841, DOI 10.1016/j.biortech.2007.09.051 Liu J, 2018, J SPECTROSC, V2018, DOI 10.1155/2018/4230681 Liukkonen M, 2011, EXPERT SYST APPL, V38, P8724, DOI 10.1016/j.eswa.2011.01.081 Campos ACM, 2009, BIORESOURCES, V4, P1058 Meder R, 2017, ANAL BIOANAL CHEM, V409, P763, DOI 10.1007/s00216-016-0098-4 Mo XQ, 2003, IND CROP PROD, V18, P47, DOI 10.1016/S0926-6690(03)00032-3 Nemli G, 2008, BIORESOURCE TECHNOL, V99, P6054, DOI 10.1016/j.biortech.2007.12.044 Ntalos GA, 2002, IND CROP PROD, V16, P59, DOI 10.1016/S0926-6690(02)00008-0 Paladini EP, 2000, EXPERT SYST APPL, V18, P133, DOI 10.1016/S0957-4174(99)00059-7 Qian Y, 2008, EXPERT SYST APPL, V35, P1252, DOI 10.1016/j.eswa.2007.07.061 Sandak J., 2015, P INT PAN PROD S, P27 Sandak J, 2016, J NEAR INFRARED SPEC, V24, P485, DOI 10.1255/jnirs.1255 Taramian A, 2007, WASTE MANAGE, V27, P1739, DOI 10.1016/j.wasman.2006.09.009 Taylor A, 2009, EUR J WOOD WOOD PROD, V67, P3, DOI 10.1007/s00107-008-0266-0 Tian Y, 2014, SPECTROCHIM ACTA B, V102, P52, DOI 10.1016/j.sab.2014.10.014 Ugovsek A., 2010, FUTURE FORESTS Yemele MCN, 2008, FOREST PROD J, V58, P48 NR 34 TC 6 Z9 6 U1 0 U2 7 PY 2018 VL 2018 AR 6025163 DI 10.1155/2018/6025163 WC Biochemical Research Methods; Spectroscopy SC Biochemistry & Molecular Biology; Spectroscopy UT WOS:000447543000001 DA 2022-12-14 ER PT J AU Chen, HL Chen, ZY Lin, FT Zhuang, PF AF Chen, Huilin Chen, Zheyi Lin, Feiting Zhuang, Peifen TI Effective Management for Blockchain-Based Agri-Food Supply Chains Using Deep Reinforcement Learning SO IEEE ACCESS DT Article DE Blockchain; Supply chains; Bitcoin; Safety; Reinforcement learning; Production facilities; Security; Agri-food supply chains; agri-food safety; product traceability; profit optimization; blockchain; deep reinforcement learning AB In agri-food supply chains (ASCs), consumers pay for agri-food products produced by farmers. During this process, consumers emphasize the importance of agri-food safety while farmers expect to increase their profits. Due to the complexity and dynamics of ASCs, the effective traceability and management for agri-food products face huge challenges. However, most of the existing solutions cannot well meet the requirements of traceability and management in ASCs. To address these challenges, we first design a blockchain-based ASC framework to provide product traceability, which guarantees decentralized security for the agri-food tracing data in ASCs. Next, a Deep Reinforcement learning based Supply Chain Management (DR-SCM) method is proposed to make effective decisions on the production and storage of agri-food products for profit optimization. The extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed blockchain-based framework and the DR-SCM method under different ASC environments. The results show that reliable product traceability is well guaranteed by using the proposed blockchain-based ASC framework. Moreover, the DR-SCM can achieve higher product profits than heuristic and Q-learning methods. C1 [Chen, Huilin; Lin, Feiting; Zhuang, Peifen] Fujian Agr & Forestry Univ, Coll Econ, Fuzhou 350002, Peoples R China. [Chen, Huilin] Fujian Jiangxia Univ, Coll Econ & Trade, Fuzhou 350108, Peoples R China. [Chen, Zheyi] Univ Exeter, Coll Engn Math & Phys Sci, Exeter EX4 4QF, Devon, England. [Lin, Feiting] Minjiang Univ, Sch Econ & Management, Fuzhou 350108, Peoples R China. C3 Fujian Agriculture & Forestry University; Fujian Jiangxia University; University of Exeter; Minjiang University RP Zhuang, PF (corresponding author), Fujian Agr & Forestry Univ, Coll Econ, Fuzhou 350002, Peoples R China. EM peifenzhuang@fafu.edu.cn CR Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265 Abeyratne S. A., 2016, INT J RES ENG TECHNO, V5, P1, DOI DOI 10.15623/IJRET.2016.0509001 Ableeva A.M., 2019, INT J SUPPLY CHAIN M, V8, P328 Agi MAN, 2021, INT J PROD RES, V59, P4736, DOI 10.1080/00207543.2020.1770893 Battini D, 2010, INT J PROD RES, V48, P477, DOI 10.1080/00207540903174981 Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Dong YH, 2020, IEEE ACCESS, V8, P161261, DOI 10.1109/ACCESS.2020.3019593 Dwivedi A., 2020, MODERN SUPPLY CHAIN, V2, P161, DOI 10.1108/MSCRA-04-2020-0007 Fan HL, 2019, IEEE ACCESS, V7, P71686, DOI 10.1109/ACCESS.2019.2919582 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Ganeshkumar C., 2017, INTELL INF MANAG, V9, P68, DOI [10.4236/iim.2017.92004, DOI 10.4236/IIM.2017.92004] Gilbert H, 2004, LECT NOTES COMPUT SC, V3006, P175 Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1 Habib A, 2016, INT CONF COMPUT INFO, P170, DOI 10.1109/ICCITECHN.2016.7860190 Halat K, 2019, COMPUT IND ENG, V128, P807, DOI 10.1016/j.cie.2019.01.009 Himmelstein J, 2017, INT J AGR SUSTAIN, V15, P1, DOI 10.1080/14735903.2016.1242332 Ivanov D, 2018, ANNU REV CONTROL, V46, P134, DOI 10.1016/j.arcontrol.2018.10.014 Kocaoglu Y., 2020, MATH PROB ENG, V2020, P1 Lin DY, 2019, IEEE ACCESS, V7, P150892, DOI 10.1109/ACCESS.2019.2946137 Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Mao DH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15081627 Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236 Nakamoto S., 2008, CISC VIS NETW IND GL, V21260, P1 National Bureau of Statistics of China, 2019, CHIN STAT YB O'Dwyer Karl J., 2014, 25th IET Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communications Technologies (ISSC 2014/CIICT 2014). Proceedings, P280 Castro JAO, 2017, J IND ENG MANAG-JIEM, V10, P687, DOI 10.3926/jiem.2147 Peng JQ, 2017, INT CONF ENTERP SYST, P52, DOI 10.1109/ES.2017.16 Raj A, 2018, J CLEAN PROD, V185, P275, DOI 10.1016/j.jclepro.2018.03.046 Read J., 2018, EUR WORKSH REINF LEA, V14 Samadi A, 2018, J IND PROD ENG, V35, P102, DOI 10.1080/21681015.2017.1422039 Shongwe MI, 2019, AIMS AGRIC FOOD, V4, P1, DOI 10.3934/agrfood.2019.1.1 Spiegler VLM, 2016, INT J PROD RES, V54, P265, DOI 10.1080/00207543.2015.1076945 Sutton RS, 2018, ADAPT COMPUT MACH LE, P1 Tian F, 2017, I C SERV SYST SERV M Toledo-Hernandez M, 2020, AGR ECOSYST ENVIRON, V304, DOI 10.1016/j.agee.2020.107160 Toyoda K, 2017, IEEE ACCESS, V5, P17465, DOI 10.1109/ACCESS.2017.2720760 Tse D, 2017, IN C IND ENG ENG MAN, P1357 Vasnani Neelesh N., 2019, International Journal of Applied Decision Sciences, V12, P56 Wu ZH, 2019, IEEE ACCESS, V7, P170703, DOI 10.1109/ACCESS.2019.2956287 Zhao WD, 2018, IEEE ACCESS, V6, P54215, DOI 10.1109/ACCESS.2018.2870856 NR 42 TC 26 Z9 26 U1 16 U2 69 PY 2021 VL 9 BP 36008 EP 36018 DI 10.1109/ACCESS.2021.3062410 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications UT WOS:000626508300001 DA 2022-12-14 ER PT J AU Lucchetti, S Pastore, G Leoni, G Arima, S Merendino, N Baima, S Ambra, R AF Lucchetti, Sabrina Pastore, Gianni Leoni, Guido Arima, Serena Merendino, Nicolo Baima, Simona Ambra, Roberto TI A simple microsatellite-based method for hazelnut oil DNA analysis SO FOOD CHEMISTRY DT Article DE Hazelnut oil; Genomic DNA extraction; Simple sequence repeats (SSRs); Capillary-electrophoresis (CE) ID CORYLUS-AVELLANA L.; REFINED OLIVE OIL; DIFFERENT CULTIVARS; MASS-SPECTROMETRY; IDENTIFICATION; ADULTERATION; TRACEABILITY; EXTRACTION; PCR; TRANSFERABILITY AB Molecular food traceability requires continuous updates to identify more robust, efficient and affordable methodologies to guarantee food quality and safety and especially consumers' health. Available commercial kits are often unsatisfactory and require modifications to successfully detect single components on complex and transformed food matrices. Here we report a simple method for molecular traceability of cold-pressed hazelnut oil based on microsatellite DNA markers. Different genomic extraction methodologies were tested and a total genome pre-amplification step was applied on PCR-negative samples. PCR-capillary electrophoresis using nine microsatellites demonstrates the accuracy of the fingerprint analysis even for filtered oil. C1 [Lucchetti, Sabrina; Pastore, Gianni; Baima, Simona; Ambra, Roberto] Council Agr Res & Econ, Food & Nutr Res Ctr, Via Ardeatina 546, I-00178 Rome, Italy. [Leoni, Guido] Nouscom SRL, Via Castel Romano 100, I-00128 Rome, Italy. [Arima, Serena] Sapienza Univ, Methods & Models Econ Terr & Finance, Piazzale Aldo Moro 5, I-00185 Rome, Italy. [Merendino, Nicolo] Tuscia Univ, Dept Ecol & Biol Sci, Via Santa Maria Gradi 4, I-01100 Viterbo, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Sapienza University Rome; Tuscia University RP Ambra, R (corresponding author), CREA AN, Via Ardeatina 546, I-00178 Rome, Italy. EM roberto.ambra@crea.gov.it CR Agiomyrgianaki A, 2010, TALANTA, V80, P2165, DOI 10.1016/j.talanta.2009.11.024 Alasalvar C, 2006, J AGR FOOD CHEM, V54, P10177, DOI 10.1021/jf061702w Alonso-Rebollo A, 2017, FOOD CHEM, V232, P827, DOI 10.1016/j.foodchem.2017.04.078 Bally MB, 1997, ADV DRUG DELIVER REV, V24, P275, DOI 10.1016/S0169-409X(96)00469-3 Bassil NV, 2005, J AM SOC HORTIC SCI, V130, P543, DOI 10.21273/JASHS.130.4.543 Boccacci P, 2006, GENOME, V49, P598, DOI 10.1139/G06-017 Boccacci P, 2005, MOL ECOL NOTES, V5, P934, DOI 10.1111/j.1471-8286.2005.01121.x Boccacci P, 2008, HORTSCIENCE, V43, P667, DOI 10.21273/HORTSCI.43.3.667 Botta R., 2005, ACTA HORTICULTURAE Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Calvano CD, 2005, RAPID COMMUN MASS SP, V19, P1315, DOI 10.1002/rcm.1933 Calvano CD, 2012, FOOD CHEM, V134, P1192, DOI 10.1016/j.foodchem.2012.02.154 Chen HL, 2011, FOOD CHEM, V125, P1423, DOI 10.1016/j.foodchem.2010.10.026 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 De Ceglie C, 2014, J AGR FOOD CHEM, V62, P9401, DOI 10.1021/jf504007d Field D, 1996, P ROY SOC B-BIOL SCI, V263, P209, DOI 10.1098/rspb.1996.0033 Flinterman AE, 2008, CURR OPIN ALLERGY CL, V8, P261, DOI 10.1097/ACI.0b013e3282ffb145 Garcia-Gonzalez DL, 2007, J AOAC INT, V90, P1346 Ghanbari A, 2005, ACTA HORTIC, P111, DOI 10.17660/ActaHortic.2005.686.14 Gokirmak T, 2009, GENET RESOUR CROP EV, V56, P147, DOI 10.1007/s10722-008-9352-8 Gokirmak T, 2005, ACTA HORTIC, P141 HAMADA H, 1982, P NATL ACAD SCI-BIOL, V79, P6465, DOI 10.1073/pnas.79.21.6465 Harvie P, 1998, BIOPHYS J, V75, P1040, DOI 10.1016/S0006-3495(98)77593-9 Iqbal S., 2017, REFERENCE MODULE LIF Maguire LS, 2004, INT J FOOD SCI NUTR, V55, P171, DOI 10.1080/09637480410001725175 Mehlenbacher SA, 2006, GENOME, V49, P122, DOI 10.1139/G05-091 Pasqualone A, 2004, J AGR FOOD CHEM, V52, P1068, DOI 10.1021/jf0348424 Pasqualone A, 2016, J SCI FOOD AGR, V96, P3642, DOI 10.1002/jsfa.7711 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Powell W, 1996, TRENDS PLANT SCI, V1, P215, DOI 10.1016/1360-1385(96)86898-1 Rallo P, 2003, THEOR APPL GENET, V107, P940, DOI 10.1007/s00122-003-1332-y Ramos-Gomez S, 2014, FOOD CHEM, V158, P374, DOI 10.1016/j.foodchem.2014.02.142 Ros E., 2016, ENCY FOOD HLTH, P111, DOI [DOI 10.1016/B978-0-12-384947-2.00496-7, 10.1016/b978-0-12-384947-2.00496-7] Sayago A, 2007, J AGR FOOD CHEM, V55, P2068, DOI 10.1021/jf061875l Spaniolas S, 2010, FOOD CHEM, V122, P850, DOI 10.1016/j.foodchem.2010.02.039 TAUTZ D, 1984, NUCLEIC ACIDS RES, V12, P4127, DOI 10.1093/nar/12.10.4127 Toth G, 2000, GENOME RES, V10, P967, DOI 10.1101/gr.10.7.967 Uncu AT, 2017, FOOD CHEM, V221, P1026, DOI 10.1016/j.foodchem.2016.11.059 Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Vlahov G, 2009, J AOAC INT, V92, P1747 NR 40 TC 7 Z9 7 U1 0 U2 69 PD APR 15 PY 2018 VL 245 BP 812 EP 819 DI 10.1016/j.foodchem.2017.11.107 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000418471100101 DA 2022-12-14 ER PT J AU Hernandez-Gomez, C Motoa, G Vallejo, M Blanco, VM Correa, A de la Cadena, E Villegas, MV AF Hernandez-Gomez, Cristhian Motoa, Gabriel Vallejo, Marta Blanco, Victor M. Correa, Adriana de la Cadena, Elsa Villegas, Maria Virginia TI Introduction of software tools for epidemiological surveillance in infection control in Colombia SO COLOMBIA MEDICA DT Article DE Cross infection; drug resistance; microbial; epidemiological monitoring; software; quality assurance; health care; Colombia ID CARE-ASSOCIATED INFECTIONS; ELECTRONIC SURVEILLANCE AB Introduction: Healthcare-Associated Infections (HAI) are a challenge for patient safety in the hospitals. Infection control committees (ICC) should follow CDC definitions when monitoring HAI. The handmade method of epidemiological surveillance (ES) may affect the sensitivity and specificity of the monitoring system, while electronic surveillance can improve the performance, quality and traceability of recorded information. Objective: To assess the implementation of a strategy for electronic surveillance of HAI, Bacterial Resistance and Antimicrobial Consumption by the ICC of 23 high-complexity clinics and hospitals in Colombia, during the period 2012-2013. Methods: An observational study evaluating the introduction of electronic tools in the ICC was performed; we evaluated the structure and operation of the ICC, the degree of incorporation of the software HAI Solutions and the adherence to record the required information. Results: Thirty-eight percent of hospitals (8/23) had active surveillance strategies with standard criteria of the CDC, and 87% of institutions adhered to the module of identification of cases using the HAI Solutions software. In contrast, compliance with the diligence of the risk factors for device-associated HAIs was 33%. Conclusions: The introduction of ES could achieve greater adherence to a model of active surveillance, standardized and prospective, helping to improve the validity and quality of the recorded information. C1 [Hernandez-Gomez, Cristhian; Motoa, Gabriel; Vallejo, Marta; Blanco, Victor M.; Correa, Adriana; de la Cadena, Elsa; Villegas, Maria Virginia] CIDEIM, Unidad Resistencia Bacteriana, Cali, Colombia. [Hernandez-Gomez, Cristhian; Motoa, Gabriel; Vallejo, Marta; Blanco, Victor M.; Correa, Adriana; de la Cadena, Elsa; Villegas, Maria Virginia] CIDEIM, Epidemiol Hosp, Cali, Colombia. [Vallejo, Marta] Univ Pontificia Bolivariana, Dept Invest, Medellin, Colombia. C3 Universidad Pontificia Bolivariana RP Hernandez-Gomez, C (corresponding author), Carrera 125, Cali 19225, Colombia. EM chernandez@cideim.org.co CR Acosta S, 2008, REV PANAM INFECTO S1, V10, pS112 Cartmill RS, 2012, INT J MED INFORM, V81, P782, DOI 10.1016/j.ijmedinf.2012.07.011 CDC, TRACK INF AC CAR HOS Chiou SF, 2011, AM J INFECT CONTROL, V39, P346, DOI 10.1016/j.ajic.2008.07.008 Edwards JR, 2008, AM J INFECT CONTROL, V36, pS21, DOI 10.1016/j.ajic.2007.07.007 El-Masri Maher M, 2012, Intensive Crit Care Nurs, V28, P26, DOI 10.1016/j.iccn.2011.10.003 El-Saed A, 2013, J INFECT PUBLIC HEAL, V6, P323, DOI 10.1016/j.jiph.2013.05.001 Feltovich F, 2010, AM J INFECT CONTROL, V38, P784, DOI 10.1016/j.ajic.2010.05.020 Freeman R, 2013, J HOSP INFECT, V84, P106, DOI 10.1016/j.jhin.2012.11.031 Graham D, 2009, AM J INFECT CONTROL, V37, P510, DOI 10.1016/j.ajic.2009.06.001 Grota PG, 2010, AM J INFECT CONTROL, V38, P509, DOI 10.1016/j.ajic.2009.10.007 HALEY RW, 1980, AM J EPIDEMIOL, V111, P472, DOI 10.1093/oxfordjournals.aje.a112928 Hebden JN, 2012, AM J INFECT CONTROL, V40, pS29, DOI 10.1016/j.ajic.2012.03.009 Instituto Nacional de Salud, 2012, 0000045 I NAC SAL Keller SC, 2013, INFECT CONT HOSP EP, V34, P678, DOI 10.1086/670999 Kramer A, 2013, GMS HYG INFECT CONTR, V8, DOI [10.3205/dgkh000211, 10.3205/dgkh000220] Alvarez MJL, 2012, REV ESP SALUD PUBLIC, V86, P627, DOI 10.4321/S1135-57272012000600008 Leal J, 2008, J HOSP INFECT, V69, P220, DOI 10.1016/j.jhin.2008.04.030 Lin MY, 2010, JAMA-J AM MED ASSOC, V304, P2035, DOI 10.1001/jama.2010.1637 Martin M, 2013, J HOSP INFECT, V83, P94, DOI 10.1016/j.jhin.2012.10.010 Mertens K, 2013, J HOSP INFECT, V84, P120, DOI 10.1016/j.jhin.2013.02.017 Organizacion Panamericana de Salud, 2011, GUIA EV RAP PROGR HO Villalobos AP, 2014, BIOMEDICA, V34, P67, DOI [10.7705/biomedica.v34i0.1698, 10.1590/S0120-41572014000500009] Perla RJ, 2009, AM J INFECT CONTROL, V37, P615, DOI 10.1016/j.ajic.2009.03.003 Shaban-Nejad A, 2012, PROCEDIA COMPUT SCI, V10, P1073, DOI 10.1016/j.procs.2012.06.151 Trick WE, 2008, AM J INFECT CONTROL, V36, pS75, DOI 10.1016/j.ajic.2007.07.004 Turner C, 2007, EXPERT SYST APPL, V32, P1059, DOI 10.1016/j.eswa.2006.02.018 Vozikis A, 2009, INT J INFORM MANAGE, V29, P15, DOI 10.1016/j.ijinfomgt.2008.04.012 Woeltje KF, 2013, J HOSP INFECT, V84, P103, DOI 10.1016/j.jhin.2013.03.005 World Health Organization, BURD HLTH CAR ASS IN Wright MO, 2012, AM J INFECT CONTROL, V40, P309, DOI 10.1016/j.ajic.2012.03.005 NR 31 TC 3 Z9 4 U1 0 U2 8 PD APR-JUN PY 2015 VL 46 IS 2 BP 60 EP 65 WC Medicine, General & Internal SC General & Internal Medicine UT WOS:000360140100003 DA 2022-12-14 ER PT J AU Wunsche, JF Fernqvist, F AF Wunsche, Julia Francesca Fernqvist, Fredrik TI The Potential of Blockchain Technology in the Transition towards Sustainable Food Systems SO SUSTAINABILITY DT Article DE blockchain; sustainability; food systems; food supply chains; transparency; agri-food; traceability ID SUPPLY CHAIN; AGRICULTURE AB Food systems are both contributing to and affected by environmental degradation and climate change. The transition towards resilient and sustainable food systems is essential to ensure food security and minimise negative environmental impacts. Innovative technologies can accelerate this transition. Blockchain technology (BCT) is attracting attention as it can deliver transparency to complex global food supply chains and has the potential to guide current food production towards better sustainability and efficiency. This case study investigated the opportunities that BCT can offer to food supply chains. Qualitative interviews with eight main BCT providers were conducted to evaluate the current state of BCT and put it into perspective by mapping out advantages, disadvantages, incentives, motives, and expectations connected to its implementation in global food systems. A thematic analysis showed that, while BCT was considered beneficial by all interviewees, uptake is slow due to high implementation costs and the lack of incentives for companies throughout the food chain from farms to food industry and retail. Results further revealed that the advantages of BCT go beyond communication of trustworthy information and development of closer producer-consumer relationships. In fact, it can provide the opportunity to decrease food waste, enhance working conditions throughout the supply chain, and promote sustainable consumption habits. As BCT may be increasingly used in the food supply chain, the results give a basis for future research that may leverage both qualitative and quantitative methods to examine actors' behaviours. Also, the importance of improving user experiences through functional applications and software to facilitate the adoption of the technology is stressed. C1 [Wunsche, Julia Francesca; Fernqvist, Fredrik] Swedish Univ Agr Sci, Dept People & Soc, POB 190, SE-23422 Lomma, Sweden. C3 Swedish University of Agricultural Sciences RP Wunsche, JF (corresponding author), Swedish Univ Agr Sci, Dept People & Soc, POB 190, SE-23422 Lomma, Sweden. EM juwu0001@stud.slu.se; fredrik.fernqvist@slu.se CR Abeyratne S. A., 2016, INT J RES ENG TECHNO, V5, P1, DOI DOI 10.15623/IJRET.2016.0509001 Adams R, 2018, WORLD SUSTAIN SER, P127, DOI 10.1007/978-3-319-67122-2_7 Aldrighetti A., 2021, International Journal on Food System Dynamics, V12, P6, DOI 10.18461/ijfsd.v12i1.72 Annosi MC, 2021, IND MARKET MANAG, V93, P208, DOI 10.1016/j.indmarman.2021.01.005 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Bennet M., PREDICTIONS 2020 DIS Boyatzis R.E., 1998, TRANSFORMING QUALITA, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA] Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Ciaian P., BLOCKCHAIN TECHNOLOG Clapp J, 2018, GLOBAL ENVIRON POLIT, V18, P12, DOI 10.1162/glep_a_00454 Clarke V, 2013, PSYCHOLOGIST, V26, P120 CRESWELL J. W., 2009, CONCISE INTRO MIXED Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Crosby M., 2016, APPL INNOVATION, V2, P6, DOI DOI 10.21626/innova/2016.1/01 Dooley L.M., 2002, ADV DEV HUM RESOUR, V4, P335, DOI DOI 10.1177/1523422302043007 Dudley Nigel, 2017, Biodiversity (Ottawa), V18, P45, DOI 10.1080/14888386.2017.1351892 Ericksen P.J., 2014, GLOBAL ENVIRON CHANG, P667 Ericksen PJ, 2008, GLOBAL ENVIRON CHANG, V18, P234, DOI 10.1016/j.gloenvcha.2007.09.002 European Commission, SUSTAINABLE FOOD SYS FAO, 2019, SUSTAINABLE FOOD AND AGRICULTURE: AN INTEGRATED APPROACH, P1, DOI 10.1016/C2016-0-01212-3 FAO, DEV SUSTAINABLE FOOD FAO, 2013, FOOD WASTAGE FOOTPRI Food and Agriculture Organization, 2020, The state of food and agriculture 2020: overcoming water challenges in agriculture, DOI 10.4060/cb1447en Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gardner TA, 2019, WORLD DEV, V121, P163, DOI 10.1016/j.worlddev.2018.05.025 Ge L., 2017, BLOCKCHAIN AGR FOOD Grunwald G, 2022, STRATEG CHANG, V31, P19, DOI 10.1002/jsc.2477 Holt-Gimenez E, 2013, AGROECOL SUST FOOD, V37, P90, DOI 10.1080/10440046.2012.716388 Howard P.H, 2008, J RURAL SOCIAL SCI, V24, P87 IFST, FOOD SYST FRAM FOC S Ingram J., 2016, SOLUT J, V7, P63 IPCC, 2021, CLIMATE CHANGE 2021 IPES-Food, NEW SCI SUST FOOD SY Jurgilevich A, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8010069 Kamble SS, 2020, INT J PROD ECON, V219, P179, DOI 10.1016/j.ijpe.2019.05.022 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Kouhizadeh M, 2020, PROD PLAN CONTROL, V31, P950, DOI 10.1080/09537287.2019.1695925 Mbow C., CLIMATE CHANGE LAND, P437 Motta GA, 2020, FRONT BLOCKCHAIN, V3, DOI 10.3389/Blockchain.2020.00006 Murphy S., 2008, Development (London), V51, P527, DOI 10.1057/dev.2008.57 Nakamoto S, BITCOIN PEER TO PEER OECD, IS THER ROL BLOCKCH Parung J., 2019, IOP Conference Series: Materials Science and Engineering, V703, DOI 10.1088/1757-899X/703/1/012001 Ratta P, 2021, J FOOD QUALITY, V2021, DOI 10.1155/2021/7608296 Rockstrom J, 2009, NATURE, V461, P472, DOI 10.1038/461472a Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Searchinger Tim, 2019, CREATING SUSTAINABLE, P1, DOI DOI 10.1038/S41586-018-0863-Y Seuring S, 2008, J CLEAN PROD, V16, P1545, DOI 10.1016/j.jclepro.2008.02.002 Tian F, 2017, I C SERV SYST SERV M UNFCCC, 2022, THE PARIS AGREEMENT van Hilten M, 2020, FRONT BLOCKCHAIN, V3, DOI 10.3389/fbloc.2020.567175 Visciano P, 2021, TRENDS FOOD SCI TECH, V114, P424, DOI 10.1016/j.tifs.2021.06.010 Wamba SF, 2020, PROD PLAN CONTROL, V31, P115, DOI 10.1080/09537287.2019.1631460 Ward T., BLOCKCHAIN COULD HEL WEF, 2018, WORLD EC FOR REP INN Westhoek H., 2016, FOOD SYSTEMS NATURAL Willett W, 2019, LANCET, V393, P447, DOI 10.1016/S0140-6736(18)31788-4 Xu Y, 2022, CRIT REV FOOD SCI, V62, P2800, DOI 10.1080/10408398.2020.1858752 Yiannas F., 2018, Innovations: Technology / Governance / Globalization, V12, P46, DOI 10.1162/inov_a_00266 Yin R., 2014, CASE STUDY RES DESIG, V5 NR 63 TC 1 Z9 1 U1 18 U2 18 PD JUL PY 2022 VL 14 IS 13 AR 7739 DI 10.3390/su14137739 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000822207000001 DA 2022-12-14 ER PT J AU Carrasco, L Vassileva, E AF Carrasco, Luis Vassileva, Emilia TI Determination of methylmercury in marine biota samples: Method validation SO TALANTA DT Article DE Methylmercury; Gas chromatography-pyrolysis-atomic fluorescence spectrometry; Marine biota; Sample preparation; Validation; Traceability; Uncertainty ID SOLID-PHASE MICROEXTRACTION; ATOMIC FLUORESCENCE SPECTROMETRY; MERCURY SPECIATION ANALYSIS; PLASMA-MASS SPECTROMETRY; ULTRASOUND-ASSISTED EXTRACTION; ROOM-TEMPERATURE PRECOLLECTION; ISOTOPE-DILUTION ANALYSIS; HPLC-ICP-MS; GAS-CHROMATOGRAPHY; FISH-TISSUES AB Regulatory authorities are expected to measure concentration of contaminants in foodstuffs, but the simple determination of total amount cannot be sufficient for fully judging its impact on the human health. In particular, the methylation of metals generally increases their toxicity; therefore validated analytical methods producing reliable results for the assessment of methylated species are highly needed. Nowadays, there is no legal limit for methylmercury (MeHg) in food matrices. Hence, no standardized method for the determination of MeHg exists within the international jurisdiction. Contemplating the possibility of a future legislative limit, a method for low level determination of MeHg in marine biota matrixes, based on aqueous-phase ethylation followed by purge and trap and gas chromatography (GC) coupled to pyrolysis-atomic fluorescence spectrometry (Py-AFS) detection, has been developed and validated. Five different extraction procedures, namely acid and alkaline leaching assisted by microwave and conventional oven heating, as well as enzymatic digestion, were evaluated in terms of their efficiency to extract MeHg from Scallop soft tissue IAEA-452 Certified Reference Material. Alkaline extraction with 25% (w/w) KOH in methanol, microwave-assisted extraction (MAE) with 5 M HCl and enzymatic digestion with protease XIV yielded the highest extraction recoveries. Standard addition or the introduction of a dilution step were successfully applied to overcome the matrix effects observed when microwave-assisted extraction using 25% (w/w) KOH in methanol or 25% (w/v) aqueous TMAH were used. ISO 17025 and Eurachem guidelines were followed to perform the validation of the methodology. Accordingly, blanks, selectivity, calibration curve, linearity (0.9995), working range (1-800 pg), recovery (97%), precision, traceability, limit of detection (0.45 pg), limit of quantification (0.85 pg) and expanded uncertainty (15.86%, k=2) were assessed with Fish protein Dorm-3 Certified Reference Material. The major contributions to the expanded uncertainty, i.e. 86.1%, arose from the uncertainty associated with recovery, followed by the contribution from fluorescence signal. Additional validation of the methodology developed was effectuated by the comparison with the values reported for MeHg in the IAEA-452 inter-laboratory comparison exercise. (C) 2014 Elsevier B.V. All rights reserved. C1 [Carrasco, Luis; Vassileva, Emilia] IAEA, Dept Nucl Sci & Applicat, Marine Environm Studies Labs, MC-98000 Monaco, Monaco. RP Vassileva, E (corresponding author), IAEA, Dept Nucl Sci & Applicat, Marine Environm Studies Labs, 4 Quai Antoine 1er, MC-98000 Monaco, Monaco. EM e.vasileva-veleva@iaea.org CR [Anonymous], 2012, 2002012EF JCGM Nevado JJB, 2011, J CHROMATOGR A, V1218, P4545, DOI 10.1016/j.chroma.2011.05.036 Berzas-Nevado J., 2005, Journal of Chromatography A, V1093, P21, DOI 10.1016/j.chroma.2005.07.054 BLOOM N, 1989, CAN J FISH AQUAT SCI, V46, P1131, DOI 10.1139/f89-147 Bravo-Sanchez LR, 2004, SPECTROCHIM ACTA B, V59, P59, DOI 10.1016/j.sab.2003.10.001 Cai Y, 1996, ANAL CHIM ACTA, V334, P251, DOI 10.1016/S0003-2670(96)00309-1 Carrasco L, 2007, J CHROMATOGR A, V1174, P2, DOI 10.1016/j.chroma.2007.09.051 Carrasco L, 2009, J CHROMATOGR A, V1216, P8828, DOI 10.1016/j.chroma.2009.10.043 Castillo A, 2010, ANAL CHEM, V82, P2773, DOI 10.1021/ac9027033 Chung SWC, 2011, J CHROMATOGR A, V1218, P1260, DOI 10.1016/j.chroma.2010.12.112 Clough R, 2005, J ANAL ATOM SPECTROM, V20, P1072, DOI 10.1039/b502670a Davis WC, 2004, J ANAL ATOM SPECTROM, V19, P1546, DOI 10.1039/b412668h Ebdon L, 2002, ANALYST, V127, P1108, DOI 10.1039/b202927h Esteban-Fernandez D, 2012, J AGR FOOD CHEM, V60, P8333, DOI 10.1021/jf302070y EURACHEM, 1998, FITN PURP AN METH European Union Comments-Codex committee on contaminants in food, 2013, DISC PAP REV GUID LE Fan ZF, 2008, J CHROMATOGR A, V1180, P187, DOI 10.1016/j.chroma.2007.12.010 *FAO WHO, 2006, 67 M JOINT FAO WHO E FISCHER R, 1993, ANAL CHEM, V65, P763, DOI 10.1021/ac00054a019 Franklin RL, 2012, QUIM NOVA, V35, P45, DOI 10.1590/S0100-40422012000100009 Grinberg P, 2003, J ANAL ATOM SPECTROM, V18, P902, DOI 10.1039/b212545e Grinberg P, 2003, SPECTROCHIM ACTA B, V58, P427, DOI 10.1016/S0584-8547(02)00272-0 GUM Workbench, D79639 GUM WORKB Hight SC, 2006, ANAL CHIM ACTA, V567, P160, DOI 10.1016/j.aca.2006.03.048 Hintelmann H, 2005, ANAL BIOANAL CHEM, V381, P360, DOI 10.1007/s00216-004-2878-5 HINTELMANN H, 1995, J ANAL ATOM SPECTROM, V10, P619, DOI 10.1039/ja9951000619 International Union of Pure and Applied Chemistry, 2002, HARM GUID SINGL LAB ISO, 2018, 170252018 UNI CEI EN Jagtap R, 2011, TALANTA, V85, P49, DOI 10.1016/j.talanta.2011.03.022 JCGM, 2008, EV MEAS DAT GUID EXP Jokai Z, 2005, J AGR FOOD CHEM, V53, P5499, DOI 10.1021/jf0501140 KRAGTEN J, 1994, ANALYST, V119, P2161, DOI 10.1039/an9941902161 Li YJ, 2007, SPECTROCHIM ACTA B, V62, P1153, DOI 10.1016/j.sab.2007.07.005 LIANG L, 1994, TALANTA, V41, P371, DOI 10.1016/0039-9140(94)80141-X LIANG L, 1994, CLIN CHEM, V40, P602 Lopez I, 2010, TALANTA, V82, P594, DOI 10.1016/j.talanta.2010.05.013 Mao YX, 2008, ANAL CHEM, V80, P7163, DOI 10.1021/ac800908b Martin-Doimeadios RCR, 2002, APPL ORGANOMET CHEM, V16, P610, DOI 10.1002/aoc.350 Mason R.P., 2003, ORGANOMETALLIC COMPO McNaught A.D., 1997, COMPENDIUM CHEM TERM Mergler D, 2007, AMBIO, V36, P3, DOI 10.1579/0044-7447(2007)36[3:MEAHEI]2.0.CO;2 Monperrus M, 2008, ANAL BIOANAL CHEM, V390, P655, DOI 10.1007/s00216-007-1598-z Ortiz AIC, 2002, J ANAL ATOM SPECTROM, V17, P1595, DOI 10.1039/b207334j Qiu J, 2013, NATURE, V493, P144, DOI 10.1038/493144a Rai R, 2002, J ANAL ATOM SPECTROM, V17, P1560, DOI 10.1039/b208041a Ramalhosa E, 2001, ANALYST, V126, P1583, DOI 10.1039/b104041n Reyes LH, 2009, ANAL CHIM ACTA, V631, P121, DOI 10.1016/j.aca.2008.10.044 Reyes LH, 2008, ANAL BIOANAL CHEM, V390, P2123, DOI 10.1007/s00216-008-1966-3 Rio-Segade S, 1999, J ANAL ATOM SPECTROM, V14, P263, DOI 10.1039/a806154h Rio-Segade S, 1999, TALANTA, V48, P477, DOI 10.1016/S0039-9140(98)00269-0 Rodil R, 2002, J CHROMATOGR A, V963, P313, DOI 10.1016/S0021-9673(02)00644-1 Rodriguez I, 1999, ANAL CHEM, V71, P4534, DOI 10.1021/ac990525d Sannac S, 2009, ACCREDIT QUAL ASSUR, V14, P263, DOI 10.1007/s00769-009-0509-8 Taylor VF, 2011, ANAL METHODS-UK, V3, P1143, DOI 10.1039/c0ay00528b U.S. Environmental Protection Agency, 1998, METH MERC WAT DIST A US EPA (U.S. Environmental Protection Agency Office of Science and Technology Office of Water), 2005, WAT QUAL CRIT PROT H Vasileva E, 2011, ACCREDIT QUAL ASSUR, V16, P439, DOI 10.1007/s00769-011-0793-y Wang M, 2007, TALANTA, V71, P2034, DOI 10.1016/j.talanta.2006.09.012 Yang DY, 2009, ANAL CHIM ACTA, V633, P157, DOI 10.1016/j.aca.2008.10.045 NR 59 TC 24 Z9 27 U1 2 U2 125 PD MAY PY 2014 VL 122 BP 106 EP 114 DI 10.1016/j.talanta.2014.01.027 WC Chemistry, Analytical SC Chemistry UT WOS:000335636700017 DA 2022-12-14 ER PT J AU Magdas, DA Marincas, O Cristea, G Feher, I Vedeanu, N AF Magdas, Dana Alina Marincas, Olivian Cristea, Gabriela Feher, Ioana Vedeanu, Nicoleta TI REEs - a possible tool for geographical origin assessment? SO ENVIRONMENTAL CHEMISTRY DT Review DE food authentication; traceability ID RARE-EARTH-ELEMENTS; HEALTH-RISK ASSESSMENT; TOMATO PLANTS; FRESH-WATER; GROWTH; LANTHANUM; CERIUM; RICE; AUTHENTICATION; IDENTIFICATION AB Environmental context Rare earth element profiles of foodstuffs reflect both the soil fingerprint and the specific agricultural practice for a certain location. This review describes the advantages and limitations of using rare earth elements as markers for geographical discrimination as a function of food matrix. The technique has great potential for establishing the geographical origin of foodstuffs. The present work aims to present the application of the content of rare earth elements (REEs) in the authentication of food and beverage studies, mainly regarding the geographical origin. Therefore, the potential, as well as the limitation, of these emerging markers are separately presented for different food matrices. It is observed that for most of the discussed matrices, the highest discrimination potential is provided by the LREEs (light REEs). It has also been suggested in the literature that the content of REEs is minimally affected by harvesting years, which enhances the potential to differentiate between samples from different origins. Reported studies have shown that the efficiency of the REEs profile is the most effective for the unprocessed food matrix (e.g. vegetables, fruits and meat) and has a low efficiency for commodities like wine, which suggests that the fractionation of REEs that occurs during the wine making process limits the use of these elements as geographical tracers. C1 [Magdas, Dana Alina; Marincas, Olivian; Cristea, Gabriela; Feher, Ioana] Natl Inst Res & Dev Isotop & Mol Technol, 67-103 Donat, Cluj Napoca 400293, Romania. [Vedeanu, Nicoleta] Iuliu Hatieganu Univ Med & Pharm, Dept Phys Biophys, FO-400023 Cluj Napoca, Romania. C3 National Institute for Research & Development of Isotopic & Molecular Technologies Cluj-Napoca; Iuliu Hatieganu University of Medicine & Pharmacy RP Magdas, DA (corresponding author), Natl Inst Res & Dev Isotop & Mol Technol, 67-103 Donat, Cluj Napoca 400293, Romania. EM alina.magdas@itim-cj.ro CR Abdelnour SA, 2019, SCI TOTAL ENVIRON, V672, P1021, DOI 10.1016/j.scitotenv.2019.02.270 Aceto M, 2018, BEVERAGES, V4, DOI 10.3390/beverages4010023 Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 Adeel M, 2019, ENVIRON INT, V127, P785, DOI 10.1016/j.envint.2019.03.022 Bandoniene D, 2018, J AGR FOOD CHEM, V66, P11729, DOI 10.1021/acs.jafc.8b03828 Bandoniene D, 2013, FOOD CHEM, V136, P1533, DOI 10.1016/j.foodchem.2012.06.040 Bettinelli M, 2005, ATOM SPECTROSC, V26, P41 Brown P.H., 1990, HDB PHYS CHEM RARE E, V13, ed., P423, DOI DOI 10.1016/S0168-1273(05)80135-7 Cai L, 2015, LIVEST SCI, V172, P43, DOI 10.1016/j.livsci.2014.11.013 Chang J., 1991, Plant Physiology Communications, P17 Charalampides G, 2015, PROC ECON FINANC, V24, P126, DOI 10.1016/S2212-5671(15)00630-9 Charitidis CA, 2014, MANUF REV, V1, DOI 10.1051/mfreview/2014009 d'Aquino L, 2009, CHEMOSPHERE, V75, P900, DOI 10.1016/j.chemosphere.2009.01.026 Danezis GP, 2019, MEAT SCI, V153, P45, DOI 10.1016/j.meatsci.2019.03.007 Danezis GP, 2017, ANAL CHIM ACTA, V991, P46, DOI 10.1016/j.aca.2017.09.013 Danezis G, 2019, MOLECULES, V24, DOI 10.3390/molecules24040670 de Nanclares MP, 2016, AQUAC RES, V47, P1885, DOI 10.1111/are.12647 Drivelos SA, 2016, FOOD CHEM, V213, P238, DOI 10.1016/j.foodchem.2016.06.088 Farmaki EG, 2012, ANAL LETT, V45, P920, DOI 10.1080/00032719.2012.655656 Gonzalez V, 2015, ENVIRON POLLUT, V199, P139, DOI 10.1016/j.envpol.2015.01.020 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 He R, 1998, GUANGXI AGR SCI, V5, P243 Hirano S, 1996, ENVIRON HEALTH PERSP, V104, P85, DOI 10.2307/3432699 Hollriegl V, 2010, J TRACE ELEM MED BIO, V24, P193, DOI 10.1016/j.jtemb.2010.03.001 HU QH, 1996, PLANT PHYSL COMMUNIC, V32, P296 Hu ZY, 2006, COMMUN SOIL SCI PLAN, V37, P1381, DOI 10.1080/00103620600628680 Hu ZY, 2004, J PLANT NUTR, V27, P183, DOI 10.1081/PLN-120027555 Jiang DG, 2012, BIOMED ENVIRON SCI, V25, P267, DOI 10.3967/0895-3988.2012.03.003 Joebstl D, 2010, FOOD CHEM, V123, P1303, DOI 10.1016/j.foodchem.2010.06.009 Kulaksiz S, 2013, EARTH PLANET SC LETT, V362, P43, DOI 10.1016/j.epsl.2012.11.033 Lagad RA, 2017, FOOD CHEM, V217, P254, DOI 10.1016/j.foodchem.2016.08.094 Li FK, 2019, MICROCHEM J, V147, P93, DOI 10.1016/j.microc.2019.02.060 Li XF, 2013, CHEMOSPHERE, V93, P1240, DOI 10.1016/j.chemosphere.2013.06.085 Liu D, 2013, PLANT SOIL ENVIRON, V59, P196, DOI 10.17221/760/2012-PSE Liu DW, 2012, J PLANT NUTR SOIL SC, V175, P907, DOI 10.1002/jpln.201200016 Magdas DA, 2019, FOOD CHEM, V277, P307, DOI 10.1016/j.foodchem.2018.10.103 Magdas DA, 2018, FOOD CHEM, V267, P231, DOI 10.1016/j.foodchem.2017.10.048 Mayfield DB, 2015, CHEMOSPHERE, V120, P68, DOI 10.1016/j.chemosphere.2014.06.010 NAIR RR, 1989, CURR SCI INDIA, V58, P696 Oddone M, 2009, J AGR FOOD CHEM, V57, P3404, DOI 10.1021/jf900312p Pagano G, 2019, ENVIRON RES, V171, P493, DOI 10.1016/j.envres.2019.02.004 Poniedziaek B, 2017, ENVIRON SCI POLLUT R, V24, P26148, DOI 10.1007/s11356-017-0359-6 Redling K, 2006, THESIS Rim KT, 2013, SAF HEALTH WORK, V4, P12, DOI 10.5491/SHAW.2013.4.1.12 Sadeghi M, 2013, J GEOCHEM EXPLOR, V133, P202, DOI 10.1016/j.gexplo.2012.12.007 Spalla S, 2009, RAPID COMMUN MASS SP, V23, P3285, DOI 10.1002/rcm.4244 Squadrone S, 2019, SCI TOTAL ENVIRON, V660, P1383, DOI 10.1016/j.scitotenv.2019.01.112 Suzuki H, 2003, THIN SOLID FILMS, V438, P288, DOI 10.1016/S0040-6090(03)00732-6 Swiss Federal Institute of Aquatic Science and Technology, 2013, EC RAR EARTH EL Thomas PJ, 2014, CHEMOSPHERE, V96, P57, DOI 10.1016/j.chemosphere.2013.07.020 Tyler G, 2004, PLANT SOIL, V267, P191, DOI 10.1007/s11104-005-4888-2 Van Gosen B.S., 2014, RARE EARTH ELEMENTS W Brade, 2008, LANDBAUFORSCHUNG VTI Wang MinQi, 2003, Chinese Journal of Veterinary Science, V23, P88 WEISS GB, 1975, J PHARMACOL EXP THER, V195, P557 Xie YL, 2016, REV ECON GEOL, V18, P115 Xun WenJuan, 2014, Italian Journal of Animal Science, V13, P357 YAMADA SB, 1982, J FISH BIOL, V20, P5, DOI 10.1111/j.1095-8649.1982.tb03889.x Yang LP, 2016, MAR POLLUT BULL, V107, P393, DOI 10.1016/j.marpolbul.2016.03.034 Yuan M, 2018, INT J PHYTOREMEDIAT, V20, P415, DOI 10.1080/15226514.2017.1365336 Zeng FL, 2003, BIOL TRACE ELEM RES, V93, P271, DOI 10.1385/BTER:93:1-3:271 Zereini Fathi, 2001, Journal of Soils and Sediments, V1, P188, DOI 10.1007/BF02986484 NR 62 TC 6 Z9 6 U1 4 U2 31 PY 2020 VL 17 IS 2 BP 148 EP 157 DI 10.1071/EN19163 WC Chemistry, Analytical; Environmental Sciences SC Chemistry; Environmental Sciences & Ecology UT WOS:000519929500009 DA 2022-12-14 ER PT J AU Lin, HC Chang, TY Kuo, SH AF Lin, Hung-Chou Chang, Te-Yung Kuo, Su-Hui TI Effects of Social Influence and System Characteristics on Traceable Agriculture Product Reuse Intention of Elderly People: Integrating Trust and Attitude Using the Technology Acceptance Model SO JOURNAL OF RESEARCH IN EDUCATION SCIENCES DT Article DE elderly people; health; social influence; system characteristics; traceable agricultural products ID INFORMATION-TECHNOLOGY; E-COMMERCE; AGE-DIFFERENCES; USER BEHAVIOR; SHOP ONLINE; EXTENSION; ADOPTION; SUCCESS; TAM; PERCEPTIONS AB Products such as vegetables and fruits in markets in Taiwan have small green labels denoting that they are traceable agricultural products. This study investigated the effect of social influence and system characteristics on perceived usefulness (PU) and perceived ease of use (PEOU) regarding the Taiwan Agricultural and Food Traceability (TAFT) system. This study also examined the effects of PU and PEOU on attitudes and reuse intention, and employed confirmatory factor analysis and structural equation modelling to test the hypotheses. Questionnaires were developed and the measurement items were based on an extensive review of related studies to ensure content validity in addition to items from an original scale. Four hundred questionnaires from older adults were collected through a quantitative survey. The respondents were all aged between 50 and 90 years, with an average age of 57.61 years. Almost one quarter (21.8%) of respondents had experiences of using traceability quick response (QR) codes in 2016. Regarding purchase experience, 59% of the respondents had experiences of purchasing agricultural products using traceability QR codes. The results revealed that whereas subjective norms, image, and visibility have a positive effect on PU, information quality and system quality affected the PEOU of the TAFT system. Moreover, trust has a positive influence on reuse intention. A healthy diet plays a crucial role in the health promotion activities of elderly people. The findings could help those involved in the TAFT system improve their understanding of the attendant factors of PU and PEOU and promote positive attitudes in elderly people about this system, thereby increasing their reuse intentions. C1 [Lin, Hung-Chou; Chang, Te-Yung] Natl Taiwan Normal Univ, Dept Adult & Continuing Educ, Taipei, Taiwan. [Kuo, Su-Hui] Corp Synergy Dev Ctr, Dept Enterprise Consulting, Taipei, Taiwan. C3 National Taiwan Normal University RP Kuo, SH (corresponding author), Corp Synergy Dev Ctr, Dept Enterprise Consulting, Taipei, Taiwan. EM ms330carol@yahoo.com.tw CR Aboelmaged MG, 2010, INT J QUAL RELIAB MA, V27, P268, DOI 10.1108/02656711011023294 Ahn T., 2004, ELECT COMM RES APPL, V3, P405, DOI [DOI 10.1016/J.ELERAP.2004.05.001, 10.1016/j.elerap.2004.05.001] Akudugu M. A., 2012, Journal of Biology, Agriculture and Healthcare, V2, P1 Anderson R.E., 2010, MULTIVARIATE DATA AN [Anonymous], 1986, EXCHANGE POWER SOC, DOI [10.4324/9780203792643., DOI 10.4324/9780203792643] Bigne-Alcaniz E, 2008, ONLINE INFORM REV, V32, P648, DOI 10.1108/14684520810914025 Blazun H, 2012, COMPUT HUM BEHAV, V28, P1202, DOI 10.1016/j.chb.2012.02.004 Byun H, 2018, INT J ASIAN BUS INF, V9, P52, DOI 10.4018/IJABIM.2018010105 Cao M, 2005, IND MANAGE DATA SYST, V105, P645, DOI 10.1108/02635570510600000 Chadwick-Dias A., 2002, SIGCAPH Newsletter, V73-74, P30, DOI 10.1145/960201.957212 Chen SYC, 2014, J RES EDUC SCI, V59, P1, DOI 10.6209/JORIES.2014.59(3).01 Chen YRR, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.4596 Cheong JH, 2005, INTERNET RES, V15, P125, DOI 10.1108/10662240510590324 Chung JE, 2010, COMPUT HUM BEHAV, V26, P1674, DOI 10.1016/j.chb.2010.06.016 Council of Agriculture Executive Yuan, 2016, TRAC AGR PROD STAT Q Council of Agriculture Executive Yuan, 2017, AGR FOOD TRAC TAFT W Council of Agriculture Executive Yuan, 2017, WHAT IS TRAC AGT PRO Crespo AH, 2008, INTERACT COMPUT, V20, P212, DOI 10.1016/j.intcom.2007.11.005 Dasgupta P., 1988, TRUST MAKING BREAKIN, P49 Davis F. D., 1985, THESIS MIT CAMBRIDGE DeLone WH, 2004, INT J ELECTRON COMM, V9, P31, DOI 10.1080/10864415.2004.11044317 DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748 DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60 Dillon A., 2001, ENCYCL HUM FACTORSER, V1, P1105 Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231 Flavian C, 2006, INFORM MANAGE-AMSTER, V43, P1, DOI 10.1016/j.im.2005.01.002 Fukuyama F., 1996, TRUST SOCIAL VIRTUES Gambetta D., 1988, TRUST MAKING BREAKIN, P213 Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519 Gefen D, 2003, IEEE T ENG MANAGE, V50, P307, DOI 10.1109/TEM.2003.817277 Gefen D, 2000, OMEGA-INT J MANAGE S, V28, P725, DOI 10.1016/S0305-0483(00)00021-9 Gonzalez GC, 2012, J INF SYST, V26, P51, DOI 10.2308/isys-50259 GULATI R, 1995, ACAD MANAGE J, V38, P85, DOI 10.2307/256729 Ha S, 2009, J BUS RES, V62, P565, DOI 10.1016/j.jbusres.2008.06.016 Herath T, 2009, EUR J INFORM SYST, V18, P106, DOI 10.1057/ejis.2009.6 Hsu CL, 2004, INFORM MANAGE-AMSTER, V41, P853, DOI 10.1016/j.im.2003.08.014 Huang JH, 2007, ELECTRON LIBR, V25, P585, DOI 10.1108/02640470710829569 Ilie V, 2005, INF RESOUR MANAG J, V18, P13, DOI 10.4018/irmj.2005070102 Jarvenpaa S. L., 1996, International Journal of Electronic Commerce, V1, P59 Jarvenpaa S.L., 1999, J COMPUT-MEDIAT COMM, V5, pJCMC526, DOI [10.1111/j.1083-6101.1999.tb00337.x, DOI 10.1111/J.1083-6101.1999.TB00337.X] Jones S., 2009, GENERATIONS ONLINE 2 Kim HB, 2009, TOURISM MANAGE, V30, P266, DOI 10.1016/j.tourman.2008.07.001 Kim J, 2005, J FASH MARK MANAG, V9, P106, DOI 10.1108/13612020510586433 Kim S, 2009, INFORM SYST FRONT, V11, P323, DOI 10.1007/s10796-008-9073-8 KUMAR N, 1995, J MARKETING RES, V32, P54, DOI 10.2307/3152110 Lanseng EJ, 2007, INT J SERV IND MANAG, V18, P394, DOI 10.1108/09564230710778155 Lederer AL, 2000, DECIS SUPPORT SYST, V29, P269, DOI 10.1016/S0167-9236(00)00076-2 Lee JYH, 2010, EUR J INFORM SYST, V19, P196, DOI 10.1057/ejis.2010.4 Lee MKO, 2011, INFORM MANAGE-AMSTER, V48, P185, DOI 10.1016/j.im.2010.08.005 Legris P, 2003, INFORM MANAGE-AMSTER, V40, P191, DOI 10.1016/S0378-7206(01)00143-4 Liaw SS, 2003, COMPUT HUM BEHAV, V19, P751, DOI 10.1016/S0747-5632(03)00009-8 Lin HF, 2007, ELECTRON COMMER R A, V6, P433, DOI 10.1016/j.elerap.2007.02.002 Lin HF, 2008, CYBERPSYCHOL BEHAV, V11, P138, DOI 10.1089/cpb.2007.0003 Liu C, 2000, INFORM MANAGE, V38, P23, DOI 10.1016/S0378-7206(00)00049-5 Luhmann N., 2018, ECOLOGICAL COMMUNICA McKechnie S, 2006, INT J RETAIL DISTRIB, V34, P388, DOI 10.1108/09590550610660297 Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192 MOORMAN C, 1992, J MARKETING RES, V29, P314, DOI 10.1177/002224379202900303 Morris MG, 2000, PERS PSYCHOL, V53, P375, DOI 10.1111/j.1744-6570.2000.tb00206.x Morris MG, 2005, IEEE T ENG MANAGE, V52, P69, DOI 10.1109/TEM.2004.839967 Nelson RR, 2005, J MANAGE INFORM SYST, V21, P199, DOI 10.1080/07421222.2005.11045823 Park SY, 2012, BRIT J EDUC TECHNOL, V43, P592, DOI 10.1111/j.1467-8535.2011.01229.x Pavlou PA, 2003, INT J ELECTRON COMM, V7, P101, DOI 10.1080/10864415.2003.11044275 Read W, 2011, AUSTRALAS MARK J, V19, P223, DOI 10.1016/j.ausmj.2011.07.004 Reichheld FF, 2000, HARVARD BUS REV, V78, P105 Roca JC, 2006, INT J HUM-COMPUT ST, V64, P683, DOI 10.1016/j.ijhcs.2006.01.003 SCHOFIELD JW, 1975, J PERS SOC PSYCHOL, V31, P1126, DOI 10.1037/h0076947 Shen J, 2012, J ELECTRON COMMER RE, V13, P198 Shin DH, 2008, CYBERPSYCHOL BEHAV, V11, P378, DOI 10.1089/cpb.2007.0117 Shin DH, 2010, ONLINE INFORM REV, V34, P473, DOI 10.1108/14684521011054080 Smith R, 2013, J BUS RES, V66, P328, DOI 10.1016/j.jbusres.2011.08.013 Sun HS, 2006, INT J HUM-COMPUT ST, V64, P53, DOI 10.1016/j.ijhcs.2005.04.013 TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144 Teo HH, 2003, INT J HUM-COMPUT ST, V59, P671, DOI 10.1016/S1071-5819(03)00087-9 Thong JYL, 2002, INT J HUM-COMPUT ST, V57, P215, DOI [10.1016/S1071-5819(02)91024-4, 10.1006/ijhc.1024] Tong X, 2010, INT J RETAIL DISTRIB, V38, P742, DOI 10.1108/09590551011076524 Tsai MT, 2011, TOTAL QUAL MANAG BUS, V22, P1091, DOI 10.1080/14783363.2011.614870 Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926 Wang EST, 2014, J ELECTRON COMMER RE, V15, P119 Williamson OE, 1985, EC I CAPITALISM Yang HD, 2009, J COMPUT INFORM SYST, V50, P25 Yi MY, 2006, INFORM MANAGE-AMSTER, V43, P350, DOI 10.1016/j.im.2005.08.006 Yousafzai SY, 2007, J MODEL MANAG, V2, P251, DOI 10.1108/17465660710834453 Yousafzai SY, 2010, J APPL SOC PSYCHOL, V40, P1172, DOI 10.1111/j.1559-1816.2010.00615.x NR 84 TC 10 Z9 10 U1 7 U2 28 PD SEP PY 2018 VL 63 IS 3 BP 291 EP 319 DI 10.6209/JORIES.201809_63(3).0010 WC Education & Educational Research SC Education & Educational Research UT WOS:000445779100011 DA 2022-12-14 ER PT J AU Lee, H Park, CJ Lee, G AF Lee, Hwashim Park, Chang Joon Lee, Gaeho TI Measurement of progesterone in human serum by isotope dilution liquid chromatography-tandem mass spectrometry and comparison with the commercial chemiluminescence immunoassay SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article DE Progesterone; ID LC-MS-MS; Traceability; Primary method AB Progesterone is one of the steroid hormones. The hormone is especially important in preparing the uterus for the implantation of the blastocyst and in maintaining pregnancy. Its concentration in serum is measured to determine ovarian function and to predict early pregnancy. The progesterone concentration is also important for in-vitro fertilization and embryo-transfer outcomes. We have established isotope dilution liquid chromatography-tandem mass spectrometry as a primary method for the measurement of progesterone in human serum. Progesterone and its isotopic analogue, progesterone-(13)C(2), in serum were monitored at mass transitions of m/z 315.2/109.2 and 317.2/111.2 respectively in multiple-reaction monitoring (MRM) mode with electrospray positive ionization. For validation of the method, progesterone in a National Institute of Standards and Technology standard reference material (NIST SRM) was measured, and the measured results were in good agreement with the reference values within the uncertainty. On the basis of the established method, progesterone certified reference material (CRM) was developed in this work. The certified value was (1.41 +/- 0.036) mu g kg(-1). The repeatability of 1.1% and reproducibility of 0.14% showed that ID LC-MS-MS is a reliable and reproducible method. The expanded uncertainty for the measurement of progesterone in the CRM was approximately 2.6% within 95% confidence limits. The detection limit of progesterone was approximately 0.6 mu g kg(-1). The progesterone CRMs were distributed to representative clinical laboratories in the Republic of Korea for comparison with the chemiluminescence immunoassay (CLIA), which is the most sensitive immunoassay method. The results from the comparison showed quite a large bias among the participating laboratories. This implies that the CRM is a very important material for establishment of traceability to its practical use. C1 [Lee, Hwashim] Korea Res Inst Stand & Sci, Ctr Bioanal, Div Metrol Qual Life, Taejon 305600, South Korea. [Park, Chang Joon] Korea Res Inst Stand & Sci, Ctr Adv Instrumentat, Div Ind Metrol, Taejon 305600, South Korea. [Lee, Gaeho] Chungnam Natl Univ, Dept Chem, Taejon 305764, South Korea. C3 Korea Research Institute of Standards & Science (KRISS); Korea Research Institute of Standards & Science (KRISS); Chungnam National University RP Lee, H (corresponding author), Korea Res Inst Stand & Sci, Ctr Bioanal, Div Metrol Qual Life, 209 Gajeong Ro, Taejon 305600, South Korea. EM eclhs@kriss.re.kr CR ALLEN WM, 1970, SOUTHERN MED J, V63, P1151, DOI 10.1097/00007611-197010000-00012 Boudou P, 2001, J STEROID BIOCHEM, V78, P97, DOI 10.1016/S0960-0760(01)00078-4 Bouma S., 1997, Clinical Chemistry, V43, pS171 DEMETRIOU J, 1987, PROGESTERONE FORD K, 1990, CLIN CHEM, V36, P1099 GRONOWSKI AM, 1999, REPROD ENDOCRINE FUN HOYLE R, 1991, CLIN CHEM, V37, P1020 LAGANA A, 1986, CLIN CHEM, V32, P508 Levesque A, 1997, CLIN CHEM, V43, P1601 Milton MJT, 2001, METROLOGIA, V38, P289, DOI 10.1088/0026-1394/38/4/1 RALPH HR, 1979, INT J CLIN LAB RES, V9, P85 Tai SSC, 2006, ANAL CHEM, V78, P6628, DOI 10.1021/ac060936b THIENPONT L, 1991, CLIN CHEM, V37, P540 Yanaihara K, 1977, Horumon To Rinsho, V25, P345 Zhuping W., 2000, ANALYST, V125, P2201, DOI [10.1039/B005631F, DOI 10.1039/B005631F] NR 15 TC 6 Z9 8 U1 0 U2 13 PD MAR PY 2010 VL 396 IS 5 BP 1713 EP 1719 DI 10.1007/s00216-009-3410-8 WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000274742900012 DA 2022-12-14 ER PT J AU Piarulli, L Savoia, MA Taranto, F D'Agostino, N Sardaro, R Girone, S Gadaleta, S Fucili, V De Giovanni, C Montemurro, C Pasqualone, A Fanelli, V AF Piarulli, Luciana Savoia, Michele Antonio Taranto, Francesca D'Agostino, Nunzio Sardaro, Ruggiero Girone, Stefania Gadaleta, Susanna Fucili, Vincenzo De Giovanni, Claudio Montemurro, Cinzia Pasqualone, Antonella Fanelli, Valentina TI A Robust DNA Isolation Protocol from Filtered Commercial Olive Oil for PCR-Based Fingerprinting SO FOODS DT Article DE DNA extraction protocol; traceability; authentication; genetic tagging; SSRs; SNPs ID OLEA-EUROPAEA L.; REAL-TIME PCR; TABLE OLIVES; IDENTIFICATION; TRACEABILITY; EXTRACTION; HRM; TOOL; MICROSATELLITES; ADULTERATION AB Extra virgin olive oil (EVOO) has elevated commercial value due to its health appeal, desirable characteristics and quantitatively limited production, and thus it has become an object of intentional adulteration. As EVOOs on the market might consist of a blend of olive varieties or sometimes even of a mixture of oils from different botanical species, an array of DNA-fingerprinting methods have been developed to check the varietal composition of the blend. Starting from a comparison between publicly available DNA extraction protocols, we set up a timely, low-cost, reproducible and effective DNA isolation protocol, which allows an adequate amount of DNA to be recovered even from commercial filtered EVOOs. Then, in order to verify the effectiveness of the DNA extraction protocol herein proposed, we applied PCR-based fingerprinting methods starting from the DNA extracted from three EVOO samples of unknown composition. In particular, genomic regions harboring nine simple sequence repeats (SSRs) and eight genotyping-by-sequencing-derived single nucleotide polymorphism (SNP) markers were amplified for authentication and traceability of the three EVOO samples. The whole investigation strategy herein described might favor producers in terms of higher revenues and consumers in terms of price transparency and food safety. C1 [Piarulli, Luciana; Savoia, Michele Antonio; Taranto, Francesca; Girone, Stefania; Montemurro, Cinzia] Univ Bari Aldo Moro, Spin Off, SINAGRI Srl, I-70126 Bari, Italy. [Taranto, Francesca] CREA Res Ctr Cereal & Ind Crops CREA CI, SS 673,Km 25-200, I-71122 Foggia, Italy. [D'Agostino, Nunzio] Univ Naples Federico II, Dept Agr Sci, Via Univ 100, I-80055 Naples, Italy. [Sardaro, Ruggiero; Fucili, Vincenzo] Univ Bari Aldo Moro, Dept Agr & Environm Sci, I-70126 Bari, Italy. [Gadaleta, Susanna; De Giovanni, Claudio; Montemurro, Cinzia; Pasqualone, Antonella; Fanelli, Valentina] Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, I-70126 Bari, Italy. C3 Universita degli Studi di Bari Aldo Moro; University of Naples Federico II; Universita degli Studi di Bari Aldo Moro; Universita degli Studi di Bari Aldo Moro RP Taranto, F; Montemurro, C (corresponding author), Univ Bari Aldo Moro, Spin Off, SINAGRI Srl, I-70126 Bari, Italy.; Taranto, F (corresponding author), CREA Res Ctr Cereal & Ind Crops CREA CI, SS 673,Km 25-200, I-71122 Foggia, Italy.; Montemurro, C (corresponding author), Univ Bari Aldo Moro, Dept Soil Plant & Food Sci, I-70126 Bari, Italy. EM luciana.piarulli@libero.it; micsav87@gmail.com; francesca.taranto@uniba.it; nunzio.dagostino@unina.it; ruggierosardaro@gmail.com; stefaniagirone@hotmail.com; sanna14@hotmail.it; vincenzo.fucilli@uniba.it; claudio.degiovanni@uniba.it; cinzia.montemurro@uniba.it; antonella.pasqualone@uniba.it; valentina.fanelli@uniba.it CR Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 Alba V, 2009, SCI HORTIC-AMSTERDAM, V123, P11, DOI 10.1016/j.scienta.2009.07.007 Baldoni L, 2009, MOL BREEDING, V24, P213, DOI 10.1007/s11032-009-9285-8 Bazakos C, 2016, PRODUCTS FROM OLIVE TREE, P115, DOI 10.5772/64494 Ben-Ayed R, 2012, EUR FOOD RES TECHNOL, V234, P263, DOI 10.1007/s00217-011-1631-5 Besnard G, 2018, ANN BOT-LONDON, V121, P385, DOI 10.1093/aob/mcx145 Bhandari M. P., 2018, PROCEEDINGS, V2, P1061 Bracci T, 2011, PLANT CELL REP, V30, P449, DOI 10.1007/s00299-010-0991-9 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Carriero F, 2002, THEOR APPL GENET, V104, P301, DOI 10.1007/s001220100691 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 D'Agostino N, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-34207-y De La Rosa R, 2002, MOL ECOL NOTES, V2, P265, DOI 10.1046/j.1471-8286.2002.00217.x de la Torre F, 2004, J FOOD AGRIC ENVIRON, V2, P84 di Rienzo V, 2018, PEERJ, V6, DOI 10.7717/peerj.5260 di Rienzo V, 2016, FOOD CONTROL, V60, P124, DOI 10.1016/j.foodcont.2015.07.015 Espineira M., 2016, ADV FOOD TRACEABILIT Ganopoulos I, 2013, J SCI FOOD AGR, V93, P2281, DOI 10.1002/jsfa.6040 Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Gomes S, 2018, J FOOD SCI, V83, P2415, DOI 10.1111/1750-3841.14333 Hrncirova Z, 2008, J FOOD NUTR RES-SLOV, V47, P23 Kalaitzis P., 2016, Lipid Technology, V28, P173, DOI 10.1002/lite.201600048 Likudis Z, 2016, PRODUCTS FROM OLIVE TREE, P175, DOI 10.5772/64909 Martelli GP, 2016, EUR J PLANT PATHOL, V144, P235, DOI 10.1007/s10658-015-0784-7 Martins-Lopes P, 2008, J AGR FOOD CHEM, V56, P11786, DOI 10.1021/jf801146z Meenu M, 2019, TRENDS FOOD SCI TECH, V91, P391, DOI 10.1016/j.tifs.2019.07.045 Montemurro C., 2018, AUTHENTICATION DETEC, P315 Montemurro C, 2015, J CHEM-NY, V2015, DOI 10.1155/2015/496986 Muzzalupo I, 2002, EUR FOOD RES TECHNOL, V214, P528, DOI 10.1007/s00217-001-0482-x Pasqualone A, 2007, J AGR FOOD CHEM, V55, P3312, DOI 10.1021/jf063383e Pasqualone A, 2016, J SCI FOOD AGR, V96, P3642, DOI 10.1002/jsfa.7711 Pasqualone A, 2015, EUR J LIPID SCI TECH, V117, P2044, DOI 10.1002/ejlt.201400654 Pasqualone A, 2013, J AGR FOOD CHEM, V61, P3068, DOI 10.1021/jf400014g Pasqualone A, 2010, EUR FOOD RES TECHNOL, V230, P723, DOI 10.1007/s00217-009-1210-1 Pereira L, 2018, FOOD RES INT, V103, P170, DOI 10.1016/j.foodres.2017.10.026 Perrier X, 2003, GENETIC DIVERSITY CU, P43, DOI DOI 10.1017/S0014479704252152 Raieta K, 2015, FOOD CHEM, V172, P596, DOI 10.1016/j.foodchem.2014.09.101 Ramos-Gomez S, 2014, FOOD CHEM, V158, P374, DOI 10.1016/j.foodchem.2014.02.142 Sardaro R, 2018, ACTA HORTIC, V1199, P183, DOI [10.17660/ActaHortic.2018.1199.30, 10.17660/actahortic.2018.1199.30] Schrader C, 2012, J APPL MICROBIOL, V113, P1014, DOI 10.1111/j.1365-2672.2012.05384.x Sefc KM, 2000, MOL ECOL, V9, P1171, DOI 10.1046/j.1365-294x.2000.00954.x Sion S, 2019, PLANTS-BASEL, V8, DOI 10.3390/plants8080268 Spadoni A, 2019, J AGR SCI TECH-IRAN, V21, P1215 Tanasijevic L, 2014, AGR WATER MANAGE, V144, P54, DOI 10.1016/j.agwat.2014.05.019 Uncu AT, 2015, J AGR FOOD CHEM, V63, P2284, DOI 10.1021/acs.jafc.5b00090 Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Xanthopoulou A, 2017, FUNCT FOOD SCI TECHN, P587 NR 48 TC 12 Z9 12 U1 0 U2 3 PD OCT PY 2019 VL 8 IS 10 AR 462 DI 10.3390/foods8100462 WC Food Science & Technology SC Food Science & Technology UT WOS:000494272000037 DA 2022-12-14 ER PT J AU Xiao, L Fang, X Zhou, Y Yu, ZF Ding, D AF Xiao, Ling Fang, Xi Zhou, Yang Yu, Zifang Ding, Ding TI Feedforward neural network-based chaos encryption method for polarization division multiplexing optical OFDM/OQAM system SO OPTICAL FIBER TECHNOLOGY DT Article DE PDM O-OFDM/OQAM; Lorenz mapping; Feedforward neural network; Secure communication ID PHYSICAL-LAYER SECURITY; OFDM-PON; SCHEME AB Optical orthogonal frequency division multiplexing offset quadrature amplitude modulation (O-OFDM/OQAM), as a technique that has a great potential to provide high spectral efficiency optical transmission in physical layer for future communication systems, faces challenges in data transmission security like malicious eavesdropping. In this letter, a novel twice encryption method based on chaotic mapping and feedforward neural network (FNN) is proposed to achieve the capability of anti-eavesdropping and security enhancement for polarization division multiplexing (PDM) O-OFDM/OQAM system. In the proposed method, the symbols in an OFDM/OQAM block are first used for symbol substitution by the chaotic sequences generated by Lorenz mapping and then permutated by chaotic scrambling vectors generated by FNN. The simulation results show that the FNN-based chaos encryption (FNNCE) method can strengthen the security of data transmission and realize the large key space simultaneously, and without apparent bit error rate (BER) deterioration in comparison with the original system. C1 [Xiao, Ling; Zhou, Yang; Yu, Zifang] Beijing Elect Sci & Technol Inst, Dept Cyber Sci & Engn, Beijing 100070, Peoples R China. [Fang, Xi; Ding, Ding] Beijing Elect Sci & Technol Inst, Dept Elect & Commun Engn, Beijing 100070, Peoples R China. [Fang, Xi] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Dept Elect Engn, Beijing 100084, Peoples R China. C3 Beijing Electronic Science & Technology Institute; Beijing Electronic Science & Technology Institute; Tsinghua University RP Fang, X (corresponding author), Beijing Elect Sci & Technol Inst, Dept Elect & Commun Engn, Beijing 100070, Peoples R China. EM xfang@besti.edu.cn CR Alvarez G, 2006, INT J BIFURCAT CHAOS, V16, P2129, DOI 10.1142/S0218127406015970 Bi MH, 2019, IEEE ACCESS, V7, P57129, DOI 10.1109/ACCESS.2019.2912535 Bi MH, 2017, IEEE PHOTONICS J, V9, DOI 10.1109/JPHOT.2017.2661581 Bodinier Q., 2016, IEICE T FUND ELECT C, P1 Cao P, 2014, IEEE PHOTONICS J, V6, DOI 10.1109/JPHOT.2014.2311451 Cui MW, 2021, IEEE ACCESS, V9, P18052, DOI 10.1109/ACCESS.2021.3054380 Fang X, 2017, J LIGHTWAVE TECHNOL, V35, P1837, DOI 10.1109/JLT.2017.2665464 Fang X, 2016, J LIGHTWAVE TECHNOL, V34, P891, DOI 10.1109/JLT.2015.2507605 Fok MP, 2011, IEEE T INF FOREN SEC, V6, P725, DOI 10.1109/TIFS.2011.2141990 Haykin S., 1999, NEURAL NETWORKS COMP, P897 He JL, 2016, OPT EXPRESS, V24, P13418, DOI 10.1364/OE.24.013418 Jun D.O.N.G., 1997, INF CONTROL, V26, P360 Liu ZJ, 2020, IEEE WIREL COMMUN LE, V9, P1840, DOI 10.1109/LWC.2020.3005656 Siohan P, 2002, IEEE T SIGNAL PROCES, V50, P1170, DOI 10.1109/78.995073 [汪彦 Wang Yan], 2017, [中南大学学报. 自然科学版, Journal of Central South University of Science and Technology], V48, P2678 Wang ZY, 2021, OPT EXPRESS, V29, P17890, DOI 10.1364/OE.424661 Wei HH, 2019, IEEE ACCESS, V7, P124452, DOI 10.1109/ACCESS.2019.2938910 Wu TW, 2020, IEEE ACCESS, V8, P75119, DOI 10.1109/ACCESS.2020.2989172 Wu TW, 2018, OPT EXPRESS, V26, P22857, DOI 10.1364/OE.26.022857 Wu W, 2011, NEURAL NETWORKS, V24, P91, DOI 10.1016/j.neunet.2010.09.007 Xiao L, 2020, I C COMM SOFTW NET, P39, DOI 10.1109/ICCSN49894.2020.9139052 Xiao YQ, 2021, OPT LETT, V46, P5583, DOI 10.1364/OL.436366 Xiao YQ, 2020, IEEE PHOTONICS J, V12, DOI 10.1109/JPHOT.2020.2987317 Xiao YQ, 2018, J OPT COMMUN NETW, V10, P46, DOI 10.1364/JOCN.10.000046 Xue CP, 2017, IEEE T COMMUN, V65, P312, DOI 10.1109/TCOMM.2016.2628060 Yuan YZ, 2022, DIGIT SIGNAL PROCESS, V126, DOI 10.1016/j.dsp.2022.103492 Zhang Z, 2022, J LIGHTWAVE TECHNOL, V40, P14, DOI 10.1109/JLT.2021.3119013 NR 27 TC 0 Z9 0 U1 3 U2 3 PD SEP PY 2022 VL 72 AR 102942 DI 10.1016/j.yofte.2022.102942 WC Engineering, Electrical & Electronic; Optics; Telecommunications SC Engineering; Optics; Telecommunications UT WOS:000833455400003 DA 2022-12-14 ER PT J AU Donno, D Beccaro, GL Carlen, C Ancay, A Cerutti, AK Mellano, MG Bounous, G AF Donno, Dario Beccaro, Gabriele L. Carlen, Christoph Ancay, Andre Cerutti, Alessandro K. Mellano, Maria Gabriella Bounous, Giancarlo TI Analytical fingerprint and chemometrics as phytochemical composition control tools in food supplement analysis: characterization of raspberry bud preparations of different cultivars SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE Rubus idaeus; bioactive compounds; traceability; chemometrics; HPLC ID CHEMICAL-COMPOSITION; MEDICINAL-PLANTS; QUALITY; HPLC; QUANTIFICATION; IDENTIFICATION; ANTHOCYANINS; SEPARATION; ACID AB BACKGROUND: The raspberry, Rubus idaeus L., provides several plant parts (as buds) used for food supplements. The aim of this researchwas to establish a technique for chemical composition control of R. idaeus herbal preparations, using chromatographic methods. These methods allowed us to identify and quantify the main phytochemicals, obtaining a specific phytochemical fingerprint (phytocomplex). Combinedwith two different chemometricmethods - clustering analysis and principal component analysis -the raspberry bud extracts of the different cultivars were efficiently characterized. RESULTS: Rubus idaeus buds were identified as a rich source of anti-inflammatory and antioxidant compounds: organic acids, vitamins andcatechinswere foundto be themostdiscriminating variablesby chemometric techniques todifferentiate raspberry cultivars. In particular, catechins (13.25%) and flavonols (8.71%) were the most important polyphenolic classes, followed by cinnamic and benzoic acids. CONCLUSION: This study developed a useful tool for R. idaeus extract phytochemical characterization that could be applied also for differentiation and composition control of other herbal preparations. (C) 2015 Society of Chemical Industry C1 [Donno, Dario; Beccaro, Gabriele L.; Cerutti, Alessandro K.; Mellano, Maria Gabriella; Bounous, Giancarlo] Univ Turin, Dipartimento Sci Agr Forestali & Alimentari, Largo Braccini 2, I-10095 Grugliasco, TO, Italy. [Carlen, Christoph; Ancay, Andre] Inst Plant Prod Sci, Agroscope, CH-1964 Conthey, Switzerland. C3 University of Turin; Swiss Federal Research Station Agroscope RP Donno, D (corresponding author), Univ Turin, Dipartimento Sci Agr Forestali & Alimentari, Largo Braccini 2, I-10095 Grugliasco, TO, Italy. EM dario.donno@unito.it CR Andrianjaka-Camps ZN, 2014, ACTA HORTIC, V1017, P63 Beccaro GL, 2012, SILVAE GENET, V61, P292, DOI 10.1515/sg-2012-0037 Betz JM, 2011, FITOTERAPIA, V82, P44, DOI 10.1016/j.fitote.2010.09.011 Bobinaite R, 2012, FOOD CHEM, V132, P1495, DOI 10.1016/j.foodchem.2011.11.137 Carlen C, 2011, ACTA HORTIC, V926, P381 Cerutti A.K., 2013, J CLEAN PROD, V73, P125 Chandra A, 2001, J AGR FOOD CHEM, V49, P3515, DOI 10.1021/jf010389p Cohen N, 2007, LIBR J, V132, P168 CROMBIE L, 1994, NATURE, V370, P428, DOI 10.1038/370428a0 Dillard CJ, 2000, J SCI FOOD AGR, V80, P1744, DOI [10.1002/1097-0010(20000915)80:12<1744::AID-JSFA725>3.0.CO;2-W, 10.1002/1097-0010(20000915)80:12<1744::AID-JSFA725>3.0.CO;2-W] Donno D, 2015, J FUNCT FOODS, V18, P1070, DOI 10.1016/j.jff.2014.05.020 Donno D, 2015, FOOD RES INT, V69, P179, DOI 10.1016/j.foodres.2014.12.020 Donno D, 2013, J APPL BOT FOOD QUAL, V86, P79, DOI 10.5073/JABFQ.2013.086.012 Donno D, 2013, J APPL BOT FOOD QUAL, V86, P11, DOI 10.5073/JABFQ.2013.086.002 Donno D, 2013, J APPL BOT FOOD QUAL, V86, P1, DOI 10.5073/JABFQ.2013.086.001 Donno D, 2013, PHARM BIOL, V51, P1282, DOI 10.3109/13880209.2013.786101 Donno D, 2012, VEGETOS, V25, P21 Donno D, 2014, J FOOD NUTR IN PRESS Donno D, 2015, PHYTOCHEMICALS Donno D, 2014, J SCI FOOD AGR, V94, P2863, DOI 10.1002/jsfa.6627 Durgo K, 2012, J MED FOOD, V15, P258, DOI 10.1089/jmf.2011.0087 Filzmoser P., 2009, MULTIVARIATE STAT AN, V66, P112 Gao Huimin, 2012, Zhongguo Zhong Yao Za Zhi, V37, P405 Gong F, 2006, ANAL CHIM ACTA, V572, P265, DOI 10.1016/j.aca.2006.05.032 Gonzalez-Molina E, 2008, J AGR FOOD CHEM, V56, P1669, DOI 10.1021/jf073282w Kelebek H, 2011, J SCI FOOD AGR, V91, P1855, DOI 10.1002/jsfa.4396 Kong WJ, 2008, J CHROMATOGR B, V871, P109, DOI 10.1016/j.jchromb.2008.06.053 Kong WJ, 2009, PHYTOMEDICINE, V16, P950, DOI 10.1016/j.phymed.2009.03.016 Liang YZ, 2004, J CHROMATOGR B, V812, P53, DOI 10.1016/j.jchromb.2004.08.041 Liu RH, 2003, AM J CLIN NUTR, V78, p517S, DOI 10.1093/ajcn/78.3.517S MOJENA R, 1977, COMPUT J, V20, P359, DOI 10.1093/comjnl/20.4.359 Mok DKW, 2006, CHEMOMETR INTELL LAB, V82, P210, DOI 10.1016/j.chemolab.2005.05.006 Ovalle-Magallanes B, 2014, PHARM BIOL, V52, P117, DOI 10.3109/13880209.2013.816972 Patel AV, 2004, CURR MED CHEM, V11, P1501, DOI 10.2174/0929867043365143 Pharmaciens ONd, 1965, PHARMACOPEE FRANCAIS Rivero-Cruz B, 2014, PHARM BIOL, V52, P1015, DOI 10.3109/13880209.2013.876054 Tistaert C, 2011, ANAL CHIM ACTA, V690, P148, DOI 10.1016/j.aca.2011.02.023 vanAcker SABE, 1996, CHEM RES TOXICOL, V9, P1305, DOI 10.1021/tx9600964 Zhao ZY, 2009, CHROMATOGRAPHIA, V69, P429, DOI 10.1365/s10337-008-0927-5 Zhou Y, 2008, CHROMATOGRAPHIA, V68, P781, DOI 10.1365/s10337-008-0786-0 NR 40 TC 16 Z9 17 U1 0 U2 33 PD JUL PY 2016 VL 96 IS 9 BP 3157 EP 3168 DI 10.1002/jsfa.7494 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000377203000028 DA 2022-12-14 ER PT J AU Chen, LP Zhu, HY Li, YF Zhang, Y Zhang, W Yang, LC Yin, H Dong, CY Wang, Y AF Chen, Li-ping Zhu, Hong-yu Li, Yun-fei Zhang, Ying Zhang, Wei Yang, Ling-chun Yin, Hong Dong, Chun-yan Wang, Ying TI Combining multielement analysis and chemometrics to trace the geographical origin of Thelephora ganbajun SO JOURNAL OF FOOD COMPOSITION AND ANALYSIS DT Article DE Wild edible mushroom; Mineral elements; Multivariate data analysis; Classification; Traceability ID EDIBLE MUSHROOMS; QUALITY AB Geographical traceability is essential to the highly valued wild edible mushroom Thelephora ganbajun (T. ganbajun). This work examined the possibility of using the combination of multielement together with multivariate statistics methods, to identify the origins of 40 T. ganbajun from four sites of Yunnan province, China. Multielement analysis of thirteen elements (Mg, K, Ca, Al, Cr, Zn, As, Se, Cd, Pb, Fe, Mn, and P) were investigated by inductively coupled plasma mass spectroscopy (ICP-MS). Pearson correlation analysis was performed to check for the potential relationship between elements. Concentrations were used as chemical indicators to determine the geographical origins of T. ganbajun samples by utilizing multivariate data analysis, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and linear discriminant analysis (LDA). Thirteen elements in T. ganbajun from different regions showed significant differences (p < 0.05), which proved that the elemental composition was an effective tool for distinguishing different origins of T. ganbajun. Classification of T. ganbajun using PCA gave satisfactory results, which permitted the reduction of 13 variables to three principal components explaining 92.84 % of the total variance. The HCA showed four clusters corresponding to the four origins of T. ganbajun. The LDA gave an overall correct classification rate of 100 % with an independent external sample set. These results revealed that multielement analysis combined with chemometrics is a useful tool for distinguishing T. ganbajun geographical origin. C1 [Chen, Li-ping; Zhu, Hong-yu; Li, Yun-fei; Zhang, Ying; Zhang, Wei; Yang, Ling-chun; Yin, Hong; Wang, Ying] Technol Ctr Kunming Customs, Xishan Dist Guangfu Rd 359, Kunming, Yunnan, Peoples R China. [Dong, Chun-yan] Yunnan Zhongjian Inspect Technol Co Ltd, Xishan Dist Guangfu Rd 359, Kunming, Yunnan, Peoples R China. RP Wang, Y (corresponding author), Xishan Dist Guangfu Rd 359, Kunming, Yunnan, Peoples R China. EM 594916964@qq.com CR Chudzinska M, 2010, FOOD CHEM TOXICOL, V48, P284, DOI 10.1016/j.fct.2009.10.011 Dimitrijevic MV, 2019, CHEM BIODIVERS, V16, DOI 10.1002/cbdv.201800492 El Sheikha AF, 2018, TRENDS FOOD SCI TECH, V78, P292, DOI 10.1016/j.tifs.2018.06.008 Fu ZQ, 2020, ENVIRON SCI POLLUT R, V27, P29218, DOI 10.1007/s11356-020-09242-w He J, 2011, ENVIRON MANAGE, V48, P98, DOI 10.1007/s00267-011-9691-7 Hua Rong, 2017, Journal of Agricultural Science (Toronto), V9, P158 Isiloglu M, 2001, FOOD CHEM, V73, P169, DOI 10.1016/S0308-8146(00)00257-0 Jedidi IK, 2017, J FOOD MEAS CHARACT, V11, P2069, DOI 10.1007/s11694-017-9590-6 Jiang L, 2018, SE ASIAN J TROP MED, V49, P509 Kalac P, 2000, FOOD CHEM, V69, P273, DOI 10.1016/S0308-8146(99)00264-2 Kaygusuz O, 2017, CYTOTECHNOLOGY, V69, P135, DOI 10.1007/s10616-016-0045-4 Keles A., 2017, J NAT PROD PLANT RES, V7, P37 Liu CW, 2003, SCI TOTAL ENVIRON, V313, P77, DOI 10.1016/S0048-9697(02)00683-6 Luo RJ, 2019, FOOD CHEM, V274, P1, DOI 10.1016/j.foodchem.2018.08.104 Ma XF, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-28558-9 Majewska E, 2019, FOOD SCI BIOTECHNOL, V28, P1307, DOI 10.1007/s10068-019-00598-5 Mleczek M, 2016, ENVIRON SCI POLLUT R, V23, P16280, DOI 10.1007/s11356-016-6760-8 Murata H, 2008, APPL ENVIRON MICROB, V74, P2023, DOI 10.1128/AEM.02411-07 Muszynska B, 2018, FOOD CHEM, V243, P373, DOI 10.1016/j.foodchem.2017.09.149 Rong H., 2018, MED PLANT, V9, DOI [10.19600/j.cnki.issn2152-3924.2018.03.001, DOI 10.19600/J.CNKI.ISSN2152-3924.2018.03.001] Saba M, 2016, ENVIRON SCI POLLUT R, V23, P2749, DOI 10.1007/s11356-015-5513-4 Sande D, 2019, FOOD RES INT, V125, DOI 10.1016/j.foodres.2019.108524 Tokalioglu S, 2019, LWT-FOOD SCI TECHNOL, V103, P301, DOI 10.1016/j.lwt.2019.01.015 Xu DP, 2016, INT J MOL SCI, V17, DOI 10.3390/ijms17101664 Yamac M, 2007, FOOD CHEM, V103, P263, DOI 10.1016/j.foodchem.2006.07.041 Yao S, 2018, J SCI FOOD AGR, V98, P2215, DOI 10.1002/jsfa.8707 Yildiz O, 2015, J FOOD BIOCHEM, V39, P148, DOI 10.1111/jfbc.12107 Yildiz S., 2017, J FOOD HLTH SCI, V3, P161, DOI [10.3153/JFHS17019, DOI 10.3153/JFHS17019] Yudthavorasit S, 2014, FOOD CHEM, V158, P101, DOI 10.1016/j.foodchem.2014.02.086 Zhang LM, 2019, POSTHARVEST BIOL TEC, V155, P47, DOI 10.1016/j.postharvbio.2019.05.013 Zhao YY, 2009, ULTRASON SONOCHEM, V16, P209, DOI 10.1016/j.ultsonch.2008.08.006 Zhu FK, 2011, ENVIRON MONIT ASSESS, V179, P191, DOI 10.1007/s10661-010-1728-5 NR 33 TC 5 Z9 5 U1 3 U2 19 PD MAR PY 2021 VL 96 AR 103699 DI 10.1016/j.jfca.2020.103699 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000607032400011 DA 2022-12-14 ER PT J AU Corbisier, P Broothaerts, W Gioria, S Schimmel, H Burns, M Baoutina, A Emslie, KR Furui, S Kurosawa, Y Holden, MJ Kim, HH Lee, Y Kawaharasaki, M Sin, D Wang, J AF Corbisier, Philippe Broothaerts, Wim Gioria, Sabrina Schimmel, Heinz Burns, Malcolm Baoutina, Anna Emslie, Kerry R. Furui, Satoshi Kurosawa, Yasunori Holden, Marcia J. Kim, Hyong-Ha Lee, Yun-mi Kawaharasaki, Mamoru Sin, Della Wang, Jing TI Toward metrological traceability for DNA fragment ratios in GM quantification. 1. Effect of DNA extraction methods on the quantitative determination of Bt176 corn by real-time PCR SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE feed; food; genetically modified organism; GM; measurement uncertainty; extraction method; DNA; PCR ID MAIZE; KERNEL AB An international CCQM-P60 pilot study involving eight national metrological institutes was organized to investigate if the quantification of genetically modified (GM) corn powder by real-time PCR was affected by the DNA extraction method applied. Four commonly used extraction methods were compared for the extraction of DNA from a GM Bt176 corn powder. The CTAB-based method yielded the highest DNA template quantity and quality. A difference in the 260 nm/230 nm absorbance ratio was observed among the different extraction methods. Real-time amplification of sequences specific for endogenous genes zein and hmg as well as transgenic sequences within the cryIA(b) gene and a fragment covering the junction between the transformed DNA and the plant genome were used to determine the GM percentage. The detection of the transgenic gene was affected by the quantity and quality of template used for the PCR reaction. The Bt176 percentages measured on diluted or purified templates were statistically different depending on the extraction method applied. C1 Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements, B-2440 Geel, Belgium. Lab Govt Chemist, Teddington TW11 0LY, Middx, England. Natl Measurement Inst, Pymble, NSW 2073, Australia. Natl Food Res Inst, GM Analyt Evaluat Team, Tsukuba, Ibaraki 3058642, Japan. NIST, DNA Measurements Grp, Gaithersburg, MD 20899 USA. Korea Res Inst Stand & Sci, Taejon 305340, South Korea. Natl Inst Adv Ind Sci & Technol, Inst Biol Resource & Funct, Tsukuba, Ibaraki 3058566, Japan. Hong Kong Government Lab, Homantin Govt Off, Kowloon, Hong Kong, Peoples R China. Natl Res Ctr Certified Reference Mat, Beijing 100013, Peoples R China. C3 European Commission Joint Research Centre; EC JRC Institute for Reference Materials & Measurements (IRMM); National Measurement Institute Australia - NMI; National Agriculture & Food Research Organization - Japan; National Food Research Institute - Japan; National Institute of Standards & Technology (NIST) - USA; Korea Research Institute of Standards & Science (KRISS); National Institute of Advanced Industrial Science & Technology (AIST) RP Corbisier, P (corresponding author), Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements, Retieseweg 111,2440 Geel, B-2440 Geel, Belgium. EM philippe.corbisier@ec.europa.eu CR [Anonymous], 215692005E ISO [Anonymous], 215712005E ISO [Anonymous], 2003, OFF J EUR UNION L, V268, P24 *AUSTR NZ FOOD AUT, 2003, REV LAB GEN MOD GM F BICKLEY J, ANAL MOL BIOL QUALIT, P81 Cankar K, 2006, BMC BIOTECHNOL, V6, DOI 10.1186/1472-6750-6-37 Corbisier P, 2005, ANAL BIOANAL CHEM, V383, P282, DOI 10.1007/s00216-005-0013-x Donald CE, 2005, BMC BIOTECHNOL, V5, DOI 10.1186/1472-6750-5-15 *EN TS, 21568 EN TS GANGULI PK, 1970, REV CAN BIOL EXPTL, V29, P339 Hernandez M, 2004, J AGR FOOD CHEM, V52, P4632, DOI 10.1021/jf049789d Holden MJ, 2003, J AGR FOOD CHEM, V51, P2468, DOI 10.1021/jf0211130 Holst-Jensen A, 2004, J AOAC INT, V87, P927 *ISO, 215702005E ISO *ISO, 242762006E ISO Moreano F, 2005, J AGR FOOD CHEM, V53, P9971, DOI 10.1021/jf051894f Nolan T, 2006, ANAL BIOCHEM, V351, P308, DOI 10.1016/j.ab.2006.01.051 OGUR M, 1950, ARCH BIOCHEM, V25, P262 Pandey RN, 1996, PLANT MOL BIOL REP, V14, P17, DOI 10.1007/BF02671898 Papazova N, 2006, SEED SCI TECHNOL, V34, P307, DOI 10.15258/sst.2006.34.2.06 Papazova N, 2005, SEED SCI TECHNOL, V33, P533, DOI 10.15258/sst.2005.33.3.01 Peano C, 2004, J AGR FOOD CHEM, V52, P6962, DOI 10.1021/jf040008i Pietsch K, 1997, DEUT LEBENSM-RUNDSCH, V93, P35 Smith DS, 2005, J AGR FOOD CHEM, V53, P9848, DOI 10.1021/jf051201v Taverniers I, 2005, J AGR FOOD CHEM, V53, P3041, DOI 10.1021/jf0483467 Terry CF, 2002, J AOAC INT, V85, P768 THOMPSON M, 1995, PURE APPL CHEM, V67, P649, DOI 10.1351/pac199567040649 TRAPMANN S, 2004, 20988 EUR IRMM OFF O Zimmermann A, 1998, Z LEBENSM UNTERS F A, V207, P81, DOI 10.1007/s002170050299 2003, OFF J EUR UNION L, V261, P1 NR 30 TC 44 Z9 46 U1 1 U2 14 PD MAY 2 PY 2007 VL 55 IS 9 BP 3249 EP 3257 DI 10.1021/jf062931l WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000245946300003 DA 2022-12-14 ER PT J AU Mace, T Iturrate-Garcia, M Pascale, C Niederhauser, B Vaslin-Reimann, S Sutour, C AF Mace, Tatiana Iturrate-Garcia, Maitane Pascale, Celine Niederhauser, Bernhard Vaslin-Reimann, Sophie Sutour, Christophe TI Air pollution monitoring: development of ammonia (NH3) dynamic reference gas mixtures at nanomoles per mole levels to improve the lack of traceability of measurements SO ATMOSPHERIC MEASUREMENT TECHNIQUES DT Article AB The measurement of ammonia (NH3) in ambient air is a sensitive and priority topic due to its impact on ecosystems. NH3 emissions have continuously increased over the last century in Europe because of intensive livestock practices and the enhanced use of nitrogen-based fertilizers. European air quality monitoring networks monitor atmospheric NH3 amount-of-substance fractions. However, the lack of stable reference gas mixtures (RGMs) of atmospheric amount-of-substance fractions of ammonia to calibrate NH3 analyzers is a common issue of the networks, which results in data that are not accurate, traceable, or, thus, geographically comparable. In order to cover this lack, LNE (Laboratoire National de Metrologie et d'Essais) developed, in close collaboration with the company 2M PROCESS, a gas reference generator to dynamically generate NH3 RGMs in air. The method is based on gas permeation and a further dynamic dilution to obtain an amount-of-substance fractions ranging between 1 and 400 nmol mol(-1) (also well known as ppb or parts per billion; 1 ppb (NH3) to approximate to 0.7 mu g m(-3)) to cover the amount-of-substance fractions of ammonia measured in ambient air (emissions) and the operating range of the NH3 analyzers used by the monitoring networks. The calibration of the elements of the generator against the LNE primary standards ensures the traceability of the RGMs to the international system of units. Furthermore, the highly accurate flow and oven temperature measurements of the reference generator, together with the associated calibration procedure de- fined by LNE, guarantee relative expanded uncertainties of the calibration of the NH3 analyzers that are lower than 2 % (coverage factor = 2). This result is very satisfactory, considering the low NH3 amount-of-substance fraction levels (1 to 400 nmol mol(-1)) and the phenomena of adsorption and desorption, especially in the presence of traces of water on contact surfaces. A bilateral comparison was organized between METAS (Swiss Federal Institute of Metrology) and LNE, which consisted of the calibration of a Picarro G2103 gas analyzer by both national metrology institutes (NMIs). The results highlighted the good agreement between the NH3 reference generators developed by the two institutes and allowed the validation of both LNE's reference generator and calibration procedure. Since the end of 2020, LNE has calibrated several NH3 analyzers from the French air quality monitoring networks (Associations Agreees de Surveillance de la Qualite de l'Air - AASQA) using the newly developed SI-traceable RGMs. The enhanced number of calibrations provided may increase the comparability, accuracy, and traceability of the NH3 measurements carried out on French territory. C1 [Mace, Tatiana; Vaslin-Reimann, Sophie; Sutour, Christophe] Lab Natl Metrol & Essais LNE, Dept Gas Metrol, F-75724 Paris 15, France. [Iturrate-Garcia, Maitane; Pascale, Celine; Niederhauser, Bernhard] Fed Inst Metrol METAS, Dept Chem & Biol Metrol, CH-3003 Bern, Switzerland. C3 Laboratoire National de Metrologie et d'Essais (LNE); Swiss Federal Institute for Metrology (METAS) RP Mace, T (corresponding author), Lab Natl Metrol & Essais LNE, Dept Gas Metrol, F-75724 Paris 15, France. EM tatiana.mace@lne.fr CR Aksoyoglu S, 2020, ATMOS CHEM PHYS, V20, P15665, DOI 10.5194/acp-20-15665-2020 [Anonymous], 2020, 17346 EN EN [Anonymous], 2012, 14211 EN EN Behera SN, 2013, ENVIRON SCI POLLUT R, V20, P8092, DOI 10.1007/s11356-013-2051-9 Bessagnet B, 2014, ENVIRON SCI POLICY, V44, P149, DOI 10.1016/j.envsci.2014.07.011 Braban C. F., LIT REV PERFORMANCE Cao JJ, 2009, AEROSOL AIR QUAL RES, V9, P277 Cape J. N., CRITICAL LEVELS AMMO Clappier A, 2021, ENVIRON INT, V156, DOI 10.1016/j.envint.2021.106699 Clarisse L, 2009, NAT GEOSCI, V2, P479, DOI 10.1038/ngeo551 EC Directive, 2016, OFFICIAL J EUROPEAN EEA, 2020, EUR UN EM INV REP 19 EMRP MetNH3 project, METR AMM AMB AIR Feng S., 1021, ENVIRON SCI TECH LET, V9, P10 Glaser M, 2009, REP PROG PHYS, V72, DOI 10.1088/0034-4885/72/12/126101 Gu MN, 2022, ENVIRON SCI TECHNOL, V56, P1578, DOI 10.1021/acs.est.1c05884 Holmes NS, 2007, ATMOS ENVIRON, V41, P2183, DOI 10.1016/j.atmosenv.2006.10.058 ISO, 2015, 61421 EN ISO ISO, 2002, 614510 EN ISO IUPAC, 2013, PURE APPL CHEM, V85 JCGM, 2008, 100 JCGM, V100 LUCERO DP, 1971, ANAL CHEM, V43, P1744, DOI 10.1021/ac60307a005 Martin NA, 2016, APPL PHYS B-LASERS O, V122, DOI 10.1007/s00340-016-6486-9 METAS, 2017, ENV55 FIN PUBL JRP R Mohr P. J., 2016, J. Phys. Chem. Ref. Data, V45, DOI 10.1063/1.4954402 Nair AA, 2020, ATMOSPHERE-BASEL, V11, DOI 10.3390/atmos11101092 OJEU, 2008, OFF J EUR UNION Paulot F, 2014, J GEOPHYS RES-ATMOS, V119, P4343, DOI 10.1002/2013JD021130 Pinder RW, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL033732 Pogny A., Meas. Sci. Technol, V27 Puchalski MA, 2011, J ENVIRON MONITOR, V13, P3156, DOI 10.1039/c1em10553a Schopp W, 2003, HYDROL EARTH SYST SC, V7, P436, DOI 10.5194/hess-7-436-2003 Shah SB, 2006, J AIR WASTE MANAGE, V56, P945, DOI 10.1080/10473289.2006.10464512 Tang Y., 2001, SCI WORLD J, V1 UNECE, 2012, DEC 2012 12 GUID ADJ United Nations, 1979, PROT 1979 CONV LONG Vaittinen O, 2018, APPL PHYS B-LASERS O, V124, DOI 10.1007/s00340-018-7054-2 Van Damme M, 2021, ENVIRON RES LETT, V16, DOI 10.1088/1748-9326/abd5e0 von Bobrutzki K, 2010, ATMOS MEAS TECH, V3, P91, DOI 10.5194/amt-3-91-2010 Warner JX, 2017, GEOPHYS RES LETT, V44, P2875, DOI 10.1002/2016GL072305 WHO, 2018, KEY FACTS AMB OUTD A Wu C, 2020, SCI TOTAL ENVIRON, V722, DOI 10.1016/j.scitotenv.2020.137756 Yardin C., 2013, 16 C INT MTROL 7 10 NR 43 TC 0 Z9 0 U1 1 U2 2 PD MAY 5 PY 2022 VL 15 IS 9 BP 2703 EP 2718 DI 10.5194/amt-15-2703-2022 WC Meteorology & Atmospheric Sciences SC Meteorology & Atmospheric Sciences UT WOS:000790730800001 DA 2022-12-14 ER PT J AU Bruel, JM Ebersold, S Galinier, F Naumchev, A Mazzara, M Meyer, B AF Bruel, Jean-Michel Ebersold, Sophie Galinier, Florian Naumchev, Alexandr Mazzara, Manuel Meyer, Bertrand TI The Role of Formalism in System Requirements SO ACM COMPUTING SURVEYS DT Article DE Formal; software; specification; requirement; seamless ID EVENT-B; SPECIFICATIONS; EDUCATION; CHORD AB A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, which is good for understandability but lacks precision. To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as "formal." Many exist, differing in their style, scope, and applicability. The present survey discusses some of the main formal approaches and compares them to informal methods. The analysis uses a set of nine complementary criteria, such as level of abstraction, tool availability, and traceability support. It classifies the approaches into five categories based on their principal style for specifying requirements: natural-language, semi-formal, automata/graphs, mathematical, and seamless (programming-language-based). It includes examples from all of these categories, altogether 21 different approaches, including for example SysML, Relax, Eiffel, Event-B, and Alloy. The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches. C1 [Bruel, Jean-Michel; Ebersold, Sophie; Galinier, Florian] Univ Toulouse, IRIT, Toulouse, France. [Naumchev, Alexandr; Mazzara, Manuel; Meyer, Bertrand] Innopolis Univ, Innopolis, Russia. [Meyer, Bertrand] Schaffhausen Inst Technol, Schaffhausen, Switzerland. C3 Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Innopolis University RP Bruel, JM (corresponding author), Univ Toulouse, IRIT, Toulouse, France. EM bruel@irit.fr; sophie.ebersold@irit.fr; florian.galinier@irit.fr; anaumchev@outlook.com; m.mazzara@innopolis.ru; Bertrand.Meyer@inf.ethz.ch CR Abrial J. R., 1980, On the construction of programs, P343 Abrial J.-R., 2010, MODELING EVENT B SYS Abualhaija S, 2019, INT REQUIR ENG CONF, P51, DOI 10.1109/RE.2019.00017 Aceituna D., 2011, 2011 First International Workshop on Empirical Requirements Engineering, P13, DOI 10.1109/EmpiRE.2011.6046248 Aceto L, 2009, LECT NOTES COMPUT SC, V5846, P158, DOI 10.1007/978-3-642-04912-5_11 Amyot D, 2003, COMPUT NETW, V42, P285, DOI 10.1016/S1389-1286(03)00244-5 [Anonymous], 1998, 8301998 IEEE, P1, DOI [DOI 10.1109/IEEESTD.1998.88286, 10.1109/IEEESTD.1998.88286] Arcaini P, 2017, INT J SOFTW TOOLS TE, V19, P247, DOI 10.1007/s10009-015-0394-x Arnoux P, 2010, INT J MATH EDUC SCI, V41, P229, DOI 10.1080/00207390903372429 Arora C, 2015, IEEE T SOFTWARE ENG, V41, P944, DOI 10.1109/TSE.2015.2428709 Bechhofer S, 2018, ENCY DATABASE SYSTEM, V2nd Berenbach B., 2012, 2012 IEEE 20th International Requirements Engineering Conference (RE 2012), P285, DOI 10.1109/RE.2012.6345816 Bjorner D., 1978, LECT NOTES COMPUTER, V61 Boniol F., 2014, ABZ 2014 LANDING GEA, V433, P1 Bourque P., 2014, GUIDE SOFTWARE ENG B Broy M, 2018, SOFTW SYST MODEL, V17, P365, DOI 10.1007/s10270-017-0619-4 Bruel J. M., 2021, ROLE FORMALISM SYSTE Carrillo de Gea J. M., 2011, IEEE Software, V28, P86, DOI 10.1109/MS.2011.81 Catano N, 2009, LECT NOTES COMPUT SC, V5846, P2, DOI 10.1007/978-3-642-04912-5_2 Cellier F.E., 1991, CONTINUOUS SYSTEM MO Chen P.P.-S., 2002, SOFTWARE PIONEERS, P311 Codd E. F., 1983, COMMUN ACM, V26, P64, DOI DOI 10.1145/357980.358007 Colaco J.-L., 2005, ACM INT C EMB SOFTW, P173 Cuccuru A, 2007, LECT NOTES COMPUT SC, V4735, P271 Dalpiaz F., 2015, ISTART CAISE CEUR WO, V1370, P1 DARDENNE A, 1993, SCI COMPUT PROGRAM, V20, P3, DOI 10.1016/0167-6423(93)90021-G Dassault Systems, 2016, CATIA REQ CATIA REQ Dean N., 1996, TEACHING LEARNING FO Dwyer M. B., 1998, Proceedings of FMSP'98. Second Workshop on Formal Methods in Software Practice, P7, DOI 10.1145/298595.298598 Eclipse foundation, 2015, PAP PAP Eysholdt M., 2010, P ACM INT C COMPANIO, P307, DOI DOI 10.1145/1869542.1869625 Ferrari A, 2018, EMPIR SOFTW ENG, V23, P3684, DOI 10.1007/s10664-018-9596-7 Ferrari A, 2017, IEEE SOFTWARE, V34, P28, DOI 10.1109/MS.2017.4121207 France RB, 2006, COMPUTER, V39, P59, DOI 10.1109/MC.2006.65 FRASER MD, 1991, IEEE T SOFTWARE ENG, V17, P454, DOI 10.1109/32.90448 Friedenthal Sanford, 2008, SYSML TUT SYSML TUT Gibbons J., 2009, LECT NOTES COMPUTER, V5846 Gibson J. P., 1998, IWFM WORKSH COMP BCS IWFM WORKSH COMP BCS Gleirscher M, 2020, ACM COMPUT SURV, V52, DOI 10.1145/3357231 Gleirscher Mario, 2018, ARXIV181208815 ARXIV181208815 Gmehlich R, 2013, FME WORKS FORM, P36, DOI 10.1109/FormaliSE.2013.6612275 Golra FR, 2018, LECT NOTES COMPUT SC, V10886, P54, DOI 10.1007/978-3-319-92970-5_4 Grinder M. T., 2002, SIGCSE Bulletin, V34, P63, DOI 10.1145/563517.563364 Hahnle R, 2002, LECT NOTES COMPUT SC, V2306, P233 Hainey T, 2011, COMPUT EDUC, V56, P21, DOI 10.1016/j.compedu.2010.09.008 HAREL D, 1987, SCI COMPUT PROGRAM, V8, P231, DOI 10.1016/0167-6423(87)90035-9 Hierons RM, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459354 Hoare C. A. R, 2002, SOFTWARE PIONEERS, P367 HOARE CAR, 1978, COMMUN ACM, V21, P666, DOI 10.1145/359576.359585 IBM, 2020, RAT DOORS RAT DOORS IBM, 2020, RAT RHAPS 8 1 5 RAT RHAPS 8 1 5 IEEE, 2011, 291482011E ISOIECIEE Ishikawa F, 2009, LECT NOTES COMPUT SC, V5846, P57, DOI 10.1007/978-3-642-04912-5_5 Jackson D, 2012, SOFTWARE ABSTRACTIONS: LOGIC, LANGUAGE, AND ANALYSIS, P1 Jackson M., 2001, PROBLEM FRAMES ANAL Jeannet B., 2015, EMB WORLD C Kassab M, 2014, INNOV SYST SOFTW ENG, V10, P235, DOI 10.1007/s11334-014-0232-4 Lamport L., 2002, SPECIFYING SYSTEMS T Laplante PA., 2017, REQUIREMENTS ENG SOF Larsen PG, 2009, FORM ASP COMPUT, V21, P245, DOI 10.1007/s00165-008-0068-5 Leavens G.T., 1998, FORMAL UNDERPINNINGS, P404 Li FL, 2015, LECT NOTES COMPUT SC, V9013, P164, DOI 10.1007/978-3-319-16101-3_11 Luckcuck M, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3342355 Lutz R.R., 1993, RE, P126 Magee J., 2006, CONCURRENCY STATE MO, V2nd Matoussi A, 2010, TRLACL20101 U PAR TRLACL20101 U PAR Mavin A, 2009, PROCEEDINGS OF THE 2009 17TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, P317, DOI 10.1109/RE.2009.9 Meyer B, 2003, LECT NOTES COMPUT SC, V2589, P108 MEYER B, 1985, IEEE SOFTWARE, V2, P6, DOI 10.1109/MS.1985.229776 Meyer B., 1997, OBJECTORIENTED SOFTW Meyer B., 2013, MULTIREQUIREMENTS MO Meyer B, 2009, COMPUTER, V42, P46, DOI 10.1109/MC.2009.296 Mich L, 2004, REQUIR ENG, V9, P40, DOI 10.1007/s00766-003-0179-8 Mich L., 1996, Natural Language Engineering, V2, P161, DOI 10.1017/S1351324996001337 Milner R., 1980, CALCULUS COMMUNICATI Minsky M.L., 1967, COMPUTATION FINITE I Moon SI, 2004, IEEE T SYST MAN CY B, V34, P1045, DOI 10.1109/TSMCB.2003.819485 Nakatani T, 2008, FRONT ARTIF INTEL AP, V180, P495, DOI 10.3233/978-1-58603-900-4-495 Naumchev Alexandr, 2019, 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Proceedings, P0743, DOI 10.1109/SIBIRCON48586.2019.8958211 Naumchev Alexandr, 2019, Software Technology: Methods and Tools. 51st International Conference, TOOLS 2019. Proceedings. Lecture Notes in Computer Science (LNCS 11771), P150, DOI 10.1007/978-3-030-29852-4_12 Naumchev A., 2019, THESIS U TOULOUSE U Naumchev A, 2019, J COMPUT LANG, V51, P131, DOI 10.1016/j.cola.2019.02.004 Naumchev A, 2016, LECT NOTES COMPUT SC, V9609, P233, DOI 10.1007/978-3-319-41579-6_18 Naumchev A, 2016, 2016 10TH INTERNATIONAL SYMPOSIUM ON THEORETICAL ASPECTS OF SOFTWARE ENGINEERING (TASE), P160, DOI 10.1109/TASE.2016.13 Nguyen T., 2015, ICSSEA Object Management Group (OMG), 2015, UML 2 5 UML 2 5 Omg, 2007, OMG SYST MOD LANG OM OMG, 2011, UNIFIED MODELING LAN Ouhbi S, 2015, REQUIR ENG, V20, P119, DOI 10.1007/s00766-013-0192-5 Paige R., 2002, J OBJECT TECHNOL, V1, P63 Parnas DL, 2011, FUTURE OF SOFTWARE ENGINEERING, P125, DOI 10.1007/978-3-642-15187-3_8 PETERSON JL, 1977, COMPUT SURV, V9, P223, DOI 10.1145/356698.356702 Pettersson P., 2000, B EUR ASS THEORETICA, V70, P40 Ranta A., 2011, GRAMMATICAL FRAMEWOR Raymond P, 2008, ELECTRON NOTES THEOR, V203, P19, DOI 10.1016/j.entcs.2008.05.008 Reed JN, 2004, LECT NOTES COMPUT SC, V3294, P32 Respect-it, 2011, OBJ V3 OBJ V3 Rodrigues P. L. da R., 2018, SBSI ACM, V53 Romanovsky Alexander., 2013, IND DEPLOYMENT SYSTE Roscoe A, 1997, THEORY PRACTICE CONC Schneider R. E., 2000, INCOSE SPRING S JUL INCOSE SPRING S JUL, V10, P352 Schneider S, 2014, FORM ASP COMPUT, V26, P251, DOI 10.1007/s00165-012-0265-0 Scott W., 2004, AWRE Shaoying Liu, 2009, SIGCSE Bulletin, V41, P17, DOI 10.1145/1595453.1595457 Slankas J, 2013, 2013 1ST INTERNATIONAL WORKSHOP ON NATURAL LANGUAGE ANALYSIS IN SOFTWARE ENGINEERING (NATURALISE), P9, DOI 10.1109/NAturaLiSE.2013.6611715 Soares MD, 2011, J SYST SOFTWARE, V84, P328, DOI 10.1016/j.jss.2010.10.020 Sparx Systems, 2017, ENTERPRISE ARCHITECT State Chart XML (SCXML), 2012, STAT CHART XML SCXML STAT CHART XML SCXML Stoica I, 2001, ACM SIGCOMM COMP COM, V31, P149, DOI 10.1145/964723.383071 Su W, 2017, INT J SOFTW TOOLS TE, V19, P141, DOI 10.1007/s10009-015-0400-3 Tarkan S, 2009, LECT NOTES COMPUT SC, V5846, P72, DOI 10.1007/978-3-642-04912-5_6 The Overture Project, 2017, OV TOOL FORM MOD VDM OV TOOL FORM MOD VDM Tilley T, 2005, LECT NOTES ARTIF INT, V3626, P250 Tillmann N, 2005, ESEC SIGSOFT FSE, P253 Tillmann N, 2008, LECT NOTES COMPUT SC, V4966, P134 Tillmann N, 2006, LECT NOTES COMPUT SC, V4260, P717 Tschannen Julian, 2015, Tools and Algorithms for the Construction and Analysis of Systems. 21st International Conference, TACAS 2015, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2015. Proceedings: LNCS 9035, P566, DOI 10.1007/978-3-662-46681-0_53 van Lamsweerde A, 2004, 12TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, PROCEEDINGS, P4 van Lamsweerde A., 2000, PROC FUTURE SOFTW EN, P147 von der Beeck M., 1994, Formal Techniques in Real-Time and Fault-Tolerant Systems. Third International Symposium Proceedings. ProCoS, P128 Whittle J, 2009, PROCEEDINGS OF THE 2009 17TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, P79, DOI 10.1109/RE.2009.36 Wiegers KE, 2013, SOFTWARE REQUIREMENT WING JM, 1988, IEEE SOFTWARE, V5, P66, DOI 10.1109/52.17803 Wong Cheng M. H. L, 1994, THESIS U BRIT COLUMB THESIS U BRIT COLUMB Woodcock J, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1592434.1592436 Yu Eric, 1998, P 4 INT WORKSHOP REQ, V15, P15 Yu ESK, 1997, RE '97 - PROCEEDINGS OF THE THIRD IEEE INTERNATIONAL SYMPOSIUM ON REQUIREMENTS ENGINEERING, P226, DOI 10.1109/ISRE.1997.566873 Zave P., 1997, ACM Transactions on Software Engineering and Methodology, V6, P1, DOI 10.1145/237432.237434 Zave P, 2017, IEEE T SOFTWARE ENG, V43, P1144, DOI 10.1109/TSE.2017.2655056 Zhao L., 2020, ABS200401099 CORR ABS200401099 CORR Zowghi D, 2003, 11TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, PROCEEDINGS, P233 NR 131 TC 6 Z9 6 U1 3 U2 5 PD JUN PY 2021 VL 54 IS 5 AR 93 DI 10.1145/3448975 WC Computer Science, Theory & Methods SC Computer Science UT WOS:000671787900002 DA 2022-12-14 ER PT J AU Al-Masri, MS Shakhashiro, A Amin, AY AF Al-Masri, MS Shakhashiro, A Amin, AY TI Method validation procedures for environmental radiochemical measurements at AECS SO ACCREDITATION AND QUALITY ASSURANCE DT Article DE method validation; radiochemical analysis; Eurachem; Syria AB The present paper describes the experience of the Atomic Energy Commission of Syria in relation to the application of Eurachem Guide on method validation for environmental radiochemical measurements. Methods validated include gamma and alpha spectrometry for natural and artificial radionuclides determination, and fluorometry determination for total uranium. Documents and records were first set to meet trackability and traceability requirements, where internal quality control mechanisms have been adopted. Methods stability was checked by means of Z-score control charts. Internal method validation parameters including method detection limits, repeatability limits, reproducibility limits, recovery coefficient and relative error were estimated. External method validation has been achieved by participating in international intercomparison exercises and proficiency tests organized by EML, IAEA and WU; some results of these activities are presented. Moreover, the application of both internal and external method validation gave the analysts in our laboratories more confidence in their skills, and it was of great assistance for our customers including regulatory authorities to evaluate the fitness of the method for their applications according to internationally agreed procedure. The steps followed here can be used for method validation in other laboratories with similar applications. C1 Atom Energy Commiss Syria, Damascus, Syria. RP Al-Masri, MS (corresponding author), Atom Energy Commiss Syria, POB 6091, Damascus, Syria. EM msmasri@aec.org.sy CR *BS EN ISO, 1997, 9004 BS EN ISO 1 CURRIE LA, 1968, ANAL CHEM, V40, P586, DOI 10.1021/ac60259a007 Debertin, 1988, GAMMA XRAY SPECTROME Deming WE., 1982, OUT CRISIS *EURACHEM, 1998, EURACHEM GUID FITN P GREEN MJ, 1996, ANAL CHEM, V1681, pA305 GREENLAW PD, 2002, SEMI ANN REPORT DEP *IAEA, 2001, IAEA320 *IAEA, 2001, 327 IAEA *ISE, 20024 ISE WU ENV SCI *ISO, 1980, ISO2602 *ISO, 1994, ISO8042 *ISO IEC, 1999, ISOIEC17025 *ISO IUPAC AOAC, 1994, HARM GUID INT QUAL C ISO5725, 1994, ISO5725 *LOS AL NAT LAB, 2000, ERSOP1507 LOS AL NAT PLATT SF, 1993, NAMAS GLP VAM B, P7 NR 17 TC 5 Z9 5 U1 0 U2 6 PD JUN PY 2004 VL 9 IS 6 BP 361 EP 368 DI 10.1007/s00769-003-0756-z WC Chemistry, Analytical; Instruments & Instrumentation SC Chemistry; Instruments & Instrumentation UT WOS:000221061600009 DA 2022-12-14 ER PT J AU Li, Y Zhang, JY Wang, YZ AF Li, Yun Zhang, Jin-Yu Wang, Yuan-Zhong TI FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article DE Panax notoginseng; Geographical traceability; Data fusion; Fourier transform mid-infrared spectroscopy; Near-infrared spectroscopy ID TRANSFORM INFRARED-SPECTROSCOPY; RAPID DISCRIMINATION; QUALITY ASSESSMENT; FOOD; UV; AUTHENTICATION; METABOLOMICS; SAPONINS; GINSENG; ORIGIN AB Three data fusion strategies (low-llevel, mid-llevel, and high-llevel) combined with a multivariate classification algorithm (random forest, RF) were applied to authenticate the geographical origins of Panax notoginseng collected from five regions of Yunnan province in China. In low-level fusion, the original data from two spectra (Fourier transform mid-IR spectrum and near-IR spectrum) were directly concatenated into a new matrix, which then was applied for the classification. Mid-level fusion was the strategy that inputted variables extracted from the spectral data into an RF classification model. The extracted variables were processed by iterate variable selection of the RF model and principal component analysis. The use of high-level fusion combined the decision making of each spectroscopic technique and resulted in an ensemble decision. The results showed that the mid-level and high-level data fusion take advantage of the information synergy from two spectroscopic techniques and had better classification performance than that of independent decision making. High-level data fusion is the most effective strategy since the classification results are better than those of the other fusion strategies: accuracy rates ranged between 93% and 96% for the low-level data fusion, between 95% and 98% for the mid-level data fusion, and between 98% and 100% for the high-level data fusion. In conclusion, the high-level data fusion strategy for Fourier transform mid-IR and near-IR spectra can be used as a reliable tool for correct geographical identification of P. notoginseng. C1 [Li, Yun; Zhang, Jin-Yu; Wang, Yuan-Zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences RP Zhang, JY; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM jyzhang2008@126.com; boletus@126.com CR Adewale P, 2014, VIB SPECTROSC, V72, P72, DOI 10.1016/j.vibspec.2014.02.016 Alamprese C, 2013, LWT-FOOD SCI TECHNOL, V53, P225, DOI 10.1016/j.lwt.2013.01.027 BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Biancolillo A, 2014, ANAL CHIM ACTA, V820, P23, DOI 10.1016/j.aca.2014.02.024 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324 Calvini R, 2016, ANAL BIOANAL CHEM, V408, P7351, DOI 10.1007/s00216-016-9713-7 Cao CL, 2013, INT J MED MUSHROOMS, V15, P57, DOI 10.1615/IntJMedMushr.v15.i1.70 Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199 Choong YK, 2011, VIB SPECTROSC, V57, P87, DOI 10.1016/j.vibspec.2011.05.008 de Vasconcelos FVC, 2012, ANAL CHIM ACTA, V716, P101, DOI 10.1016/j.aca.2011.12.027 Cubero-Leon E, 2014, FOOD RES INT, V60, P95, DOI 10.1016/j.foodres.2013.11.041 Dankowska A, 2017, EUR J LIPID SCI TECH, V119, DOI 10.1002/ejlt.201600268 de Rijke E, 2015, ANAL BIOANAL CHEM, V407, P5729, DOI 10.1007/s00216-015-8755-6 Dietterich TG, 2000, MACH LEARN, V40, P139, DOI 10.1023/A:1007607513941 Genuer R, 2010, PATTERN RECOGN LETT, V31, P2225, DOI 10.1016/j.patrec.2010.03.014 Geurts BP, 2016, CHEMOMETR INTELL LAB, V156, P231, DOI 10.1016/j.chemolab.2016.05.010 Guo HB, 2010, GENET RESOUR CROP EV, V57, P453, DOI 10.1007/s10722-010-9531-2 Guo LL, 2017, FOOD CONTROL, V80, P259, DOI 10.1016/j.foodcont.2017.05.007 Gutierrez S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0143197 Ho TK, 1998, IEEE T PATTERN ANAL, V20, P832, DOI 10.1109/34.709601 Huang CC, 2016, APPL SPECTROSC REV, V51, P1, DOI 10.1080/05704928.2015.1092154 Jiang MM, 2014, PHYTOCHEM ANALYSIS, V25, P50, DOI 10.1002/pca.2461 Kacurakova M, 2000, CARBOHYD POLYM, V43, P195, DOI 10.1016/S0144-8617(00)00151-X Klockmann S, 2017, J AGR FOOD CHEM, V65, P1456, DOI 10.1021/acs.jafc.6b05007 Kuligowski J, 2015, ANALYST, V140, P4521, DOI 10.1039/c5an00706b Kwon YK, 2014, J GINSENG RES, V38, P52, DOI 10.1016/j.jgr.2013.11.006 Li GF, 2017, ANAL METHODS-UK, V9, P1897, DOI 10.1039/c7ay00153c Li JR, 2014, SPECTROSC SPECT ANAL, V34, P634, DOI 10.3964/j.issn.1000-0593(2014)03-0634-04 Li L, 2005, J PHARMACEUT BIOMED, V38, P45, DOI 10.1016/j.jpba.2004.12.002 Li Y, 2017, SPECTROCHIM ACTA A, V177, P20, DOI 10.1016/j.saa.2017.01.029 Li Y, 2017, SPECTROSC SPECT ANAL, V37, P70, DOI 10.3964/j.issn.1000-0593(2017)01-0070-05 Lu GH, 2008, J MOL STRUCT, V883, P91, DOI 10.1016/j.molstruc.2007.12.008 Luo RC, 2012, IEEE T IND INFORM, V8, P49, DOI 10.1109/TII.2011.2173942 Ma F, 2016, J MOL STRUCT, V1124, P131, DOI 10.1016/j.molstruc.2016.02.087 Marquez C, 2016, TALANTA, V161, P80, DOI 10.1016/j.talanta.2016.08.003 Menze BH, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-213 Miao JC, 2016, ANAL METHODS-UK, V8, P1265, DOI [10.1039/C5AY03270A, 10.1039/c5ay03270a] Mitchell HB, 2007, MULTISENSOR DATA FUS, P3 Monakhova YB, 2014, ANAL CHIM ACTA, V833, P29, DOI 10.1016/j.aca.2014.05.005 Ng TB, 2006, J PHARM PHARMACOL, V58, P1007, DOI 10.1211/jpp.58.8.0001 Ning ZC, 2013, PLANTA MED, V79, P897, DOI 10.1055/s-0032-1328656 Obisesan KA, 2017, TALANTA, V170, P413, DOI 10.1016/j.talanta.2017.04.035 Okada T, 2010, CURR COMPUT-AID DRUG, V6, P179, DOI 10.2174/157340910791760055 Oliveri P, 2012, TRAC-TREND ANAL CHEM, V35, P74, DOI 10.1016/j.trac.2012.02.005 Roussel S, 2003, CHEMOMETR INTELL LAB, V65, P209, DOI 10.1016/S0169-7439(02)00111-9 Ruan JQ, 2010, J AGR FOOD CHEM, V58, P5770, DOI 10.1021/jf1005885 Saptoro A, 2012, CHEM PROD PROCESS MO, V7, DOI 10.1515/1934-2659.1645 Shen F, 2012, FOOD BIOPROCESS TECH, V5, P786, DOI 10.1007/s11947-010-0347-z Simonetti R, 2016, TALANTA, V149, P250, DOI 10.1016/j.talanta.2015.11.059 Spiteri M, 2016, ANAL BIOANAL CHEM, V408, P4389, DOI 10.1007/s00216-016-9538-4 Strobl C, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-25 Sun S, 2010, FOOD CHEM, V118, P307, DOI 10.1016/j.foodchem.2009.04.122 Sun WJ, 2017, SPECTROCHIM ACTA A, V171, P72, DOI 10.1016/j.saa.2016.07.039 Tjeerdsma BF, 2005, HOLZ ROH WERKST, V63, P102, DOI 10.1007/s00107-004-0532-8 Uzayisenga R, 2014, PHYTOTHER RES, V28, P510, DOI 10.1002/ptr.5026 Wang JR, 2014, J AGR FOOD CHEM, V62, P9024, DOI 10.1021/jf502214x Wang T, 2016, J ETHNOPHARMACOL, V188, P234, DOI 10.1016/j.jep.2016.05.005 Wong HY, 2016, ANAL CHIM ACTA, V938, P90, DOI 10.1016/j.aca.2016.07.028 Xie LJ, 2009, FOOD CHEM, V114, P1135, DOI 10.1016/j.foodchem.2008.10.076 Xu L, 2012, MEAT SCI, V92, P506, DOI 10.1016/j.meatsci.2012.05.019 Yang Y, 2017, SPECTROCHIM ACTA A, V171, P351, DOI 10.1016/j.saa.2016.08.033 Yang ZZ, 2017, J GINSENG RES Zagonel GF, 2004, TALANTA, V63, P1021, DOI 10.1016/j.talanta.2004.01.008 Zhang HZ, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0164384 Zhang Y, 2013, MOLECULES, V18, P10352, DOI 10.3390/molecules180910352 Zhao YL, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0089100 Zimmermann B, 2013, APPL SPECTROSC, V67, P892, DOI 10.1366/12-06723 NR 69 TC 82 Z9 88 U1 10 U2 98 PD JAN PY 2018 VL 410 IS 1 BP 91 EP 103 DI 10.1007/s00216-017-0692-0 WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000419117600012 DA 2022-12-14 ER PT J AU Yakubu, BM Latif, R Yakubu, A Khan, MI Magashi, AI AF Yakubu, Bello Musa Latif, Rabia Yakubu, Aisha Khan, Majid Iqbal Magashi, Auwal Ibrahim TI RiceChain: secure and traceable rice supply chain framework using blockchain technology SO PEERJ COMPUTER SCIENCE DT Article DE Agricultural supply chain; Ethereum blockchain; Food security and traceability; Rice production; Smart contract ID FOOD; CHALLENGES AB The increasing number of rice product safety issues and the potential for contamination have established an enormous need for an effective strategy for the traceability of the rice supply chain. Tracing the origins of a rice product from raw materials to end customers is very complex and costly. Existing food supply chain methods (for example, rice) do not provide a scalable and cost-effective means of agricultural food supply. Besides, consumers lack the capability and resources required to check or report on the quality of agricultural goods in terms of defects or contamination. Consequently, customers are forced to decide whether to utilize or discard the goods. However, blockchain is an innovative framework capable of offering a transformative solution for the traceability of agricultural products and food supply chains. The aim of this paper is to propose a framework capable of tracking and monitoring all interactions and transactions between all stakeholders in the rice chain ecosystem through smart contracts. The model incorporates a system for customer satisfaction feedback, which enables all stakeholders to get up-to-date information on product quality, enabling them to make more informed supply chain decisions. Each transaction is documented and stored in the public ledger of the blockchain. The proposed framework provides a safe, efficient, reliable, and effective way to monitor and track rice products safety and quality especially during product purchasing. The security and performance analysis results shows that the proposed framework outperform the benchmark techniques in terms of cost-effectiveness, security and scalability with low computational overhead. C1 [Yakubu, Bello Musa; Khan, Majid Iqbal] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad Campus, Islamabad, Pakistan. [Latif, Rabia] Prince Sultan Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia. [Yakubu, Aisha] Audu Bako Coll Agr, Dept Rem & Gen Studies, Kano, Nigeria. [Magashi, Auwal Ibrahim] Kano Univ Sci & Technol, Dept Crop Sci, Kano, Nigeria. C3 COMSATS University Islamabad (CUI); Prince Sultan University RP Yakubu, BM (corresponding author), COMSATS Univ Islamabad, Dept Comp Sci, Islamabad Campus, Islamabad, Pakistan. EM bellomyakubu.cui@gmail.com CR Aggarwal S, 2019, J NETW COMPUT APPL, V144, P13, DOI 10.1016/j.jnca.2019.06.018 Ali J, 2011, INT J INFORM MANAGE, V31, P149, DOI 10.1016/j.ijinfomgt.2010.07.008 Almadhoun R, 2018, I C COMP SYST APPLIC Atzei N, 2017, LECT NOTES COMPUT SC, V10204, P164, DOI 10.1007/978-3-662-54455-6_8 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Buterin V., 2014, CISC VIS NETW IND GL, P1, DOI DOI 10.5663/APS.V1I1.10138 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Conti M, 2018, IEEE COMMUN SURV TUT, V20, P3416, DOI 10.1109/COMST.2018.2842460 Curran B., 2020, WHAT IS MERKLE TREE, P14 Custodio MC, 2019, TRENDS FOOD SCI TECH, V92, P122, DOI 10.1016/j.tifs.2019.07.039 Deng A., 2017, WORLD J RES REV, V4, P29 Edwards N, 2017, BLOCKCHAIN MEETS SUP Ethereum, 2021, ETH GAS STAT Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fernandez-Carames TM, 2018, IEEE ACCESS, V6, P32979, DOI 10.1109/ACCESS.2018.2842685 Foth M, 2017, ACM INT C P SERIES Ge HT, 2015, INT J PROD ECON, V159, P208, DOI 10.1016/j.ijpe.2014.09.023 Hasan HR, 2018, IEEE ACCESS, V6, P65439, DOI 10.1109/ACCESS.2018.2876971 Hasan HR, 2018, IEEE ACCESS, V6, P46781, DOI 10.1109/ACCESS.2018.2866512 Hofmann E, 2018, SPRINGERBRIEF FINANC, P77, DOI 10.1007/978-3-319-62371-9_6 Hu Y.-C., 2018, P 1 WORKSH CRYPT BLO, P7 Hua AV, 2016, THESIS NORWEGIAN U S Infura, 2019, YPUR ACC ETH NETW Khan MA, 2018, FUTURE GENER COMP SY, V82, P395, DOI 10.1016/j.future.2017.11.022 Kim H. M., 2018, SSRN ELECT J, DOI [10.2139/ssrn.3097443, DOI 10.2139/SSRN.3097443] Kim HM, 2018, INTELL SYST ACCOUNT, V25, P18, DOI 10.1002/isaf.1424 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Lin WJ, 2020, IEEE ACCESS, V8, P143920, DOI 10.1109/ACCESS.2020.3014522 Lu QH, 2017, IEEE SOFTWARE, V34, P21, DOI 10.1109/MS.2017.4121227 Luu L, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P254, DOI 10.1145/2976749.2978309 Mao DH, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15081627 Metamask, 2019, BRINGS ETH YOUR BROW Nakamoto S, BITCOIN PEER TO PEER Nizamuddin N, 2018, LECT NOTES COMPUTER, V10974 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Praitheeshan P., 2019, ARXIV PREPRINT ARXIV Remix, 2019, REM IDE Reyna A, 2018, FUTURE GENER COMP SY, V88, P173, DOI 10.1016/j.future.2018.05.046 Rinkeby, 2019, T DET Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Tian F, 2017, I C SERV SYST SERV M Truffle, 2019, SWEET TOOLS AW COMP Waleed M, 2021, J THEOR APPL EL COMM, V16, P2405, DOI 10.3390/jtaer16060132 Wilson T., 1998, SUPPLY CHAIN MANAG I, V3, P127, DOI [10.1108/13598549810230831, DOI 10.1108/13598549810230831] Wood G., 2014, ETHEREUM SECURE DECE Xu Y, 2018, WIREL COMMUN MOB COM, DOI 10.1155/2018/2051693 Yakubu B. M., 2021, PROC 1 INT C MULTIDI, P1 Yakubu BM, 2021, COMPUTING, V103, P379, DOI 10.1007/s00607-020-00886-7 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhang YY, 2019, IEEE INTERNET THINGS, V6, P1594, DOI 10.1109/JIOT.2018.2847705 NR 50 TC 5 Z9 5 U1 14 U2 31 PD JAN 12 PY 2022 VL 8 AR e801 DI 10.7717/peerj-cs.801 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science UT WOS:000752564700001 DA 2022-12-14 ER PT J AU Perdigao, A Martins, CL Vieira, LC Sartori, MMP Niehues, MB Arrigoni, MD AF Perdigao, Alexandre Martins, Cyntia Ludovico Vieira Junior, Luiz Carlos Pereira Sartori, Maria Marcia Niehues, Maria Betania Arrigoni, Mario De Beni TI Identification of the production system of beef cattle by the stable isotope analysis SO PESQUISA AGROPECUARIA BRASILEIRA DT Article DE muscle tissue; Nelore; production system; traceability ID MUSCLE-FIBER CHARACTERISTICS; QUALITY; CARBON; MEAT AB The objective of this work was to evaluate the potential of the stable isotope technique to characterize beef cattle production systems in tropical conditions. For this, carbon and nitrogen stable isotopes were identified in non-defatted and defatted bovine muscles. A total of 45 cattle were evaluated in three production systems: pasture, conventional feedlot, and young beef bull feedlot (n = 15 per system). Samples from the Trapezius cervicis, Longissimus dorsi, and Semitendinosus muscles were collected to determine the isotopic composition of delta C-13 and delta N-15. The isotopic data of the delta C-13 and delta N-15 of non-defatted and defatted muscles were subjected to the principal component analysis (PCA) and to the discriminant analysis (DA). The PCA allowed separating the three production systems based on the results obtained for the non-defatted and defatted muscles. A correct global classification rate of 100% and a cross-validation rate of 100% were obtained with the DA. The carbon and nitrogen isotopic ratio of non-defatted and defatted muscles allows for the precise identification of beef cattle production systems in tropical conditions. C1 [Perdigao, Alexandre; Vieira Junior, Luiz Carlos; Niehues, Maria Betania; Arrigoni, Mario De Beni] Univ Estadual Paulista, Fac Med Vet & Zootecnia, Dept Melhoramento & Nutr Anim, Campus Botucatu, BR-18618681 Botucatu, SP, Brazil. [Martins, Cyntia Ludovico] Univ Estadual Paulista, Fac Med Vet & Zootecnia, Dept Prod Anim & Med, Campus Botucatu, BR-18618681 Botucatu, SP, Brazil. [Pereira Sartori, Maria Marcia] Univ Estadual Paulista, Fac Ciencias Agron, Dept Prod & Melhoramento Vegetal, Rua Jose Barbosa Barros 1-780,Caixa Postal 237, BR-18610307 Botucatu, SP, Brazil. C3 Universidade Estadual Paulista; Universidade Estadual Paulista; Universidade Estadual Paulista RP Niehues, MB (corresponding author), Univ Estadual Paulista, Fac Med Vet & Zootecnia, Dept Melhoramento & Nutr Anim, Campus Botucatu, BR-18618681 Botucatu, SP, Brazil. EM perdigaoper@gmail.com; cyntia.l.martins@unesp.br; vieira_zoo@hotmail.com; mmpsartori@fca.unesp.br; beh_niehues@hotmail.com; mario.arrigoni@unesp.br CR Associacao Brasileira de Importadores e Exportadores de Carne [ABIEC], 2020, BEEF REP PERF PEC BR Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Banks WJ., 1992, HISTOLOGIA VET APLIC Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Chen TJ, 2017, FOOD CONTROL, V72, P306, DOI 10.1016/j.foodcont.2015.08.034 Silva SDE, 2009, LIVEST SCI, V122, P290, DOI 10.1016/j.livsci.2008.09.013 De Smet S, 2004, RAPID COMMUN MASS SP, V18, P1227, DOI 10.1002/rcm.1471 DENIRO MJ, 1978, GEOCHIM COSMOCHIM AC, V42, P495, DOI 10.1016/0016-7037(78)90199-0 Gannes LZ, 1997, ECOLOGY, V78, P1271, DOI 10.2307/2265878 Gonzales E, 1994, FISIOLOGIA AVIARIA A Gonzalez-Martin I, 1999, MEAT SCI, V52, P437, DOI 10.1016/S0309-1740(99)00027-3 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 HANDLEY LL, 1992, PLANT CELL ENVIRON, V15, P965, DOI 10.1111/j.1365-3040.1992.tb01650.x Hwang YH, 2010, MEAT SCI, V86, P456, DOI 10.1016/j.meatsci.2010.05.034 Inacio CT, 2017, CRIT REV FOOD SCI, V57, P181, DOI 10.1080/10408398.2014.887056 Kirchofer KS, 2002, J ANIM SCI, V80, P2872 NRC, 1996, NUTR REQ BEEF CATTL, V7th, DOI DOI 10.17226/9791 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 Shibuya EK, 2006, FORENSIC SCI INT, V160, P35, DOI 10.1016/j.forsciint.2005.08.011 Therkildsen M, 1998, ACTA AGR SCAND A-AN, V48, P193, DOI 10.1080/09064709809362420 Vestergaard M, 2000, MEAT SCI, V54, P177, DOI 10.1016/S0309-1740(99)00097-2 Vinholis Marcela de Mello Brandão, 2016, Prod., V26, P540, DOI 10.1590/0103-6513.193615 Werner RA, 2002, PHYTOCHEMISTRY, V61, P465, DOI 10.1016/S0031-9422(02)00204-2 Yoneyama T., 1996, Mass spectrometry of soils., P205 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y NR 26 TC 0 Z9 0 U1 0 U2 2 PY 2020 VL 55 AR e01501 DI 10.1590/S1678-3921.pab2020.v55.01501 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000610957300001 DA 2022-12-14 ER PT J AU Liang, K Chen, XH He, RY Li, JW Okinda, C Han, DS Shen, MX AF Liang, Kun Chen, Xiaohe He, Ruiyin Li, Jiawei Okinda, Cedric Han, Dongsheng Shen, Mingxia TI Development and parameter optimization of automatic separation and identification equipment for grain tracing systems based on grain tracers with QR codes SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Grain tracing system; Grain tracers; QR code; Automatic separation and identification; Optimization ID FOOD-GRADE TRACERS; TRACEABILITY SYSTEM; SUPERVISION; FRAMEWORK; DEM AB It is difficult to trace grains to their original harvest because doing so requires unique and accurate grain labels, which are mixed when handled in massive quantities and derived from different origins. Food-grade grain tracers have been developed as an identification technology for tracing from original harvest to final destination. To implement online production operation for food-grade grain tracers by QR code scanning and information recording, automatic separation and identification equipment was designed in three parts: vibration screen separation, transmission and recognition assembly and host computer software for traceability. Improvements in the recognition rate, wear rate and screening rate of the equipment in the grain tracing system was achieved based on a Box Behnken design (BBD) using response surface methodology (RSM). Four factors that affect the working parameters (i.e., vibration frequency, screen angle, tracer density and conveyor speed) were selected, and their value ranges were confirmed. The following optimal working parameters were obtained: a vibration frequency of 40.13 Hz, a screen angle of 5.00 degrees, a tracer density of 5 tracers per 1.5 kg wheat and a conveyor speed of 0.15 m/s. Test results show that the automatic separation and identification equipment is practical for grain tracing systems. The experimental values of the recognition rate and screening rate under optimized conditions were similar to the predicted values, which confirmed the validity of the models. The advantage of this equipment is that it represents an online approach for separating and identifying tracers that would not require production interruptions or manual recognition. Therefore, this automatic separation and identification equipment holds a certain significance for the design and development of grain tracing systems, possibly providing a basis for the implementation and further research of other food traceability systems. C1 [Liang, Kun; Chen, Xiaohe; He, Ruiyin; Li, Jiawei; Okinda, Cedric; Han, Dongsheng; Shen, Mingxia] Nanjing Agr Univ, Jiangsu Prov Engn Lab Modern Facil Agr Technol &, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China. C3 Nanjing Agricultural University RP Liang, K (corresponding author), Nanjing Agr Univ, Jiangsu Prov Engn Lab Modern Facil Agr Technol &, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China. EM lkbb2006@126.com CR Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Dong H, 2016, WOODHEAD PUBL FOOD S, V301, P303, DOI 10.1016/B978-0-08-100310-7.00016-8 Dong KJ, 2009, MINER ENG, V22, P910, DOI 10.1016/j.mineng.2009.03.021 Hirai Y, 2006, APPL ENG AGRIC, V22, P747 Jafari A, 2016, POWDER TECHNOL, V297, P126, DOI 10.1016/j.powtec.2016.04.008 Kun L, 2017, INT J AGR BIOL ENG, V10, P221, DOI 10.25165/j.ijabe.20171006.3531 Kvarnstrom B, 2011, QUAL ENG, V23, P343, DOI 10.1080/08982112.2011.602278 Labuschagne MT, 2018, J CEREAL SCI, V84, P151, DOI 10.1016/j.jcs.2018.10.010 Lee KM, 2010, J AGR FOOD CHEM, V58, P10945, DOI 10.1021/jf101370k Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Liang K, 2012, BIOSYST ENG, V113, P395, DOI 10.1016/j.biosystemseng.2012.09.012 Monteiro D.M.S., 2004, EC IMPLEMENTING TRAC Myers R.H., 2008, RESPONSE SURFACE MET Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Sui R. X, 2007, ASABE ANN INT M Sun CH, 2014, FOOD CONTROL, V37, P126, DOI 10.1016/j.foodcont.2013.08.013 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Wilson T., 1998, SUPPLY CHAIN MANAG I, V3, P127, DOI [10.1108/13598549810230831, DOI 10.1108/13598549810230831] Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 Zhao LL, 2017, POWDER TECHNOL, V310, P307, DOI 10.1016/j.powtec.2017.01.049 NR 22 TC 6 Z9 7 U1 3 U2 24 PD JUL PY 2019 VL 162 BP 709 EP 718 DI 10.1016/j.compag.2019.04.039 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000473379500069 DA 2022-12-14 ER PT J AU Nicole, S Negrisolo, E Eccher, G Mantovani, R Patarnello, T Erickson, DL Kress, WJ Barcaccia, G AF Nicole, Silvia Negrisolo, Enrico Eccher, Giulia Mantovani, Roberto Patarnello, Tomaso Erickson, David L. Kress, W. John Barcaccia, Gianni TI DNA Barcoding as a Reliable Method for the Authentication of Commercial Seafood Products SO FOOD TECHNOLOGY AND BIOTECHNOLOGY DT Article DE DNA barcoding; genetic traceability; BOLD; seafood; mislabeling ID CYTOCHROME-B GENE; MITOCHONDRIAL-DNA; MEAT-PRODUCTS; PCR METHOD; IDENTIFICATION; FISH; SEQUENCE; FOOD; TUNA; AMPLIFICATION AB Animal DNA barcoding allows researchers to identify different species by analyzing a short nucleotide sequence, typically the mitochondrial gene cox1. In this paper, we use DNA barcoding to genetically identify seafood samples that were purchased from various locations throughout Italy. We adopted a multi-locus approach to analyze the cob, 16S-rDNA and cox1 genes, and compared our sequences to reference sequences in the BOLD and GenBank online databases. Our method is a rapid and robust technique that can be used to genetically identify crustaceans, mollusks and fishes. This approach could be applied in the future for conservation, particularly for monitoring illegal trade of protected and endangered species. Additionally, this method could be used for authentication in order to detect mislabeling of commercially processed seafood. C1 [Nicole, Silvia; Eccher, Giulia; Mantovani, Roberto; Barcaccia, Gianni] Univ Padua, Dept Agron Food Nat Resources Anim & Environm, Lab Genet & Genom, IT-35020 Legnaro, Italy. [Negrisolo, Enrico; Patarnello, Tomaso] Univ Padua, Dept Comparat Biomed & Food Sci, IT-35020 Legnaro, Italy. [Erickson, David L.; Kress, W. John] Smithsonian Inst, Natl Museum Amer Hist, Dept Bot, Washington, DC 20013 USA. [Erickson, David L.; Kress, W. John] Smithsonian Inst, Natl Museum Amer Hist, Labs Analyt Biol, Washington, DC 20013 USA. C3 University of Padua; University of Padua; Smithsonian Institution; Smithsonian National Museum of Natural History; Smithsonian Institution; Smithsonian National Museum of Natural History RP Barcaccia, G (corresponding author), Univ Padua, Dept Agron Food Nat Resources Anim & Environm, Lab Genet & Genom, Campus Agripolis,Via Univ 16, IT-35020 Legnaro, Italy. EM gianni.barcaccia@unipd.it CR ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999 Aquino LMG, 2011, MITOCHONDR DNA, V22, P143, DOI 10.3109/19401736.2011.624613 Arami S, 2011, FOOD HYG SAFE SCI, V52, P205, DOI 10.3358/shokueishi.52.205 Baker CS, 2000, P ROY SOC B-BIOL SCI, V267, P1191, DOI 10.1098/rspb.2000.1128 Balbontin F, 2004, NEW ZEAL J MAR FRESH, V38, P609, DOI 10.1080/00288330.2004.9517266 BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 Bremer JRA, 1997, J FISH BIOL, V50, P540, DOI 10.1111/j.1095-8649.1997.tb01948.x Carr SM, 1999, CAN J ZOOL, V77, P19, DOI 10.1139/cjz-77-1-19 Cawthorn DM, 2011, MOL ECOL RESOUR, V11, P979, DOI 10.1111/j.1755-0998.2011.03039.x Dawnay N, 2007, FORENSIC SCI INT, V173, P1, DOI 10.1016/j.forsciint.2006.09.013 Eddy SR, 1998, BIOINFORMATICS, V14, P755, DOI 10.1093/bioinformatics/14.9.755 Ekrem T, 2007, MOL PHYLOGENET EVOL, V43, P530, DOI 10.1016/j.ympev.2006.11.021 Folmer O., 1994, Molecular Marine Biology and Biotechnology, V3, P294 Forster R, 2003, ANN HUM GENET, V67, P2, DOI 10.1046/j.1469-1809.2003.00002.x Hajibabaei M, 2006, P NATL ACAD SCI USA, V103, P968, DOI 10.1073/pnas.0510466103 Hajibabaei M, 2006, MOL ECOL NOTES, V6, P959, DOI 10.1111/j.1471-8286.2006.01470.x Handy SM, 2011, J AOAC INT, V94, P201 Hebert PDN, 2004, P NATL ACAD SCI USA, V101, P14812, DOI 10.1073/pnas.0406166101 Hebert PDN, 2003, P ROY SOC B-BIOL SCI, V270, P313, DOI [10.1098/rspb.2002.2218, 10.1098/rsbl.2003.0025] Hebert PDN, 2004, PLOS BIOL, V2, P1657, DOI 10.1371/journal.pbio.0020312 Hogg ID, 2004, CAN J ZOOL, V82, P749, DOI 10.1139/Z04-041 Hsieh YHP, 1998, J FOOD QUALITY, V21, P1, DOI 10.1111/j.1745-4557.1998.tb00499.x Hsieh YW, 2002, J FOOD SCI, V67, P948, DOI 10.1111/j.1365-2621.2002.tb09433.x Hubert N, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0002490 Inada T, 1990, FAO FISHERIES SYNOPS, V125, P319 Ivanova NV, 2006, MOL ECOL NOTES, V6, P998, DOI 10.1111/j.1471-8286.2006.01428.x Chapela MJ, 2007, FOOD CONTROL, V18, P1211, DOI 10.1016/j.foodcont.2006.07.016 Kim S, 2010, MOL CELLS, V30, P507, DOI 10.1007/s10059-010-0148-2 KIMURA M, 1980, J MOL EVOL, V16, P111, DOI 10.1007/BF01731581 Kochzius M, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0012620 Kumar S, 2008, BRIEF BIOINFORM, V9, P299, DOI 10.1093/bib/bbn017 Lakra WS, 2011, MOL ECOL RESOUR, V11, P60, DOI 10.1111/j.1755-0998.2010.02894.x Lin WF, 2008, FOOD CHEM, V106, P390, DOI 10.1016/j.foodchem.2007.05.060 Lin WF, 2005, J FOOD DRUG ANAL, V13, P382 Liu J, 2011, MOL ECOL RESOUR, V11, P820, DOI 10.1111/j.1755-0998.2011.03025.x Logan CA, 2008, BIOL CONSERV, V141, P1591, DOI 10.1016/j.biocon.2008.04.007 Marko PB, 2004, NATURE, V430, P309, DOI 10.1038/430309b Martinez I, 2000, J SCI FOOD AGR, V80, P527, DOI 10.1002/(SICI)1097-0010(200003)80:4<527::AID-JSFA565>3.0.CO;2-7 Meyer CP, 2005, PLOS BIOL, V3, P2229, DOI 10.1371/journal.pbio.0030422 Moftah M, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0027001 Montiel-Sosa JF, 2000, J AGR FOOD CHEM, V48, P2829, DOI 10.1021/jf9907438 Naro-Maciel E, 2011, MAR BIOL, V158, P2027, DOI 10.1007/s00227-011-1710-y Olavarria C, 2006, NEW ZEAL J MAR FRESH, V40, P299, DOI 10.1080/00288330.2006.9517422 Palumbi SR, 1991, SIMPLE FOOLS GUIDE P Palumbi Stephen R., 1996, P205 Park MH, 2007, MOL CELLS, V23, P220 Pegg GG, 2006, SCI MAR, V70, P7, DOI 10.3989/scimar.2006.70s27 Pepe T, 2007, J AGR FOOD CHEM, V55, P3681, DOI 10.1021/jf063321o Rach J, 2008, P ROY SOC B-BIOL SCI, V275, P237, DOI 10.1098/rspb.2007.1290 Rasmussen RS, 2008, COMPR REV FOOD SCI F, V7, P280, DOI 10.1111/j.1541-4337.2008.00046.x Ratnasingham S, 2007, MOL ECOL NOTES, V7, P355, DOI 10.1111/j.1471-8286.2007.01678.x REHBEIN H, 1995, FOOD CHEM, V52, P193, DOI 10.1016/0308-8146(94)P4203-R Ronning SB, 2005, J AGR FOOD CHEM, V53, P8874, DOI 10.1021/jf0514569 Saccone C, 1999, GENE, V238, P195, DOI 10.1016/S0378-1119(99)00270-X SAITOU N, 1987, MOL BIOL EVOL, V4, P406, DOI 10.1093/oxfordjournals.molbev.a040454 Shivji M, 2002, CONSERV BIOL, V16, P1036, DOI 10.1046/j.1523-1739.2002.01188.x Simmons RB, 2001, MOL PHYLOGENET EVOL, V20, P196, DOI 10.1006/mpev.2001.0958 Tanabe S, 2007, BIOSCI BIOTECH BIOCH, V71, P1663, DOI 10.1271/bbb.70075 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 Trotta M, 2005, J AGR FOOD CHEM, V53, P2039, DOI 10.1021/jf048542d UNSELD M, 1995, PCR METH APPL, V4, P241 Vences M, 2005, PHILOS T R SOC B, V360, P1859, DOI 10.1098/rstb.2005.1717 Vinas J, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0007606 Ward RD, 2005, PHILOS T R SOC B, V360, P1847, DOI 10.1098/rstb.2005.1716 Willows-Munro S, 2005, MOL PHYLOGENET EVOL, V35, P624, DOI 10.1016/j.ympev.2005.01.018 Wong EHK, 2008, FOOD RES INT, V41, P828, DOI 10.1016/j.foodres.2008.07.005 Yoo HS, 2006, MOL CELLS, V22, P323 Zhang H, 2011, CHIN J OCEANOL LIMN, V29, P967, DOI 10.1007/s00343-011-0040-8 Zhang JB, 2011, EVID-BASED COMPL ALT, V2011, DOI 10.1155/2011/978253 NR 69 TC 41 Z9 43 U1 2 U2 76 PD OCT-DEC PY 2012 VL 50 IS 4 BP 387 EP 398 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000313544400001 DA 2022-12-14 ER PT J AU Cui, Y Idota, H Ota, M AF Cui, Yu Idota, Hiroki Ota, Masaharu TI Rebuilding the Food Supply Chain by Introducing a Decentralized Credit Mechanism SO REVIEW OF SOCIONETWORK STRATEGIES DT Article DE Food Supply Chain; Traceability; Blockchain; Credit mechanism ID BLOCKCHAIN TECHNOLOGY; SECURITY; SAFETY; SYSTEM AB Although a mechanism for trust in agricultural product transactions has systems and laws to endorse contracts, the credit relationship and security mechanism lack sufficient trust. Currently, an RFID traceability system is used to solve the problem that how to read and protect data, but the situation is such that the data can be tampered with, thus considerably lowering security. Blockchain technology, which is a distributed, shared, and encrypted database, however, can be used for such security. Through distributed accounting, a decentralized credit system can be established to enhance data security, which is a new mode to save time and costs. In this paper, the problems of the food supply chain system are systematically reanalyzed in the framework of traditional food supply chain systems. In addition, after analyzing the characteristics of blockchain technology, a decentralized credit mechanism for the food supply chain is proposed. The traceability of agricultural products based on the blockchain can realize fast and secure authentication permissions and achieve data security in which data cannot be tampered with or forged from anti-counterfeiting, and information privacy is protected. C1 [Cui, Yu] Otemon Gakuin Univ, Fac Management, 2-1-15 Nishiai, Ibaraki, Osaka 5678502, Japan. [Idota, Hiroki] Kindai Univ, Fac Econ, Osaka, Japan. [Ota, Masaharu] Osaka Gakuin Univ, Fac Business Adm, Osaka, Japan. C3 Kindai University (Kinki University) RP Cui, Y (corresponding author), Otemon Gakuin Univ, Fac Management, 2-1-15 Nishiai, Ibaraki, Osaka 5678502, Japan. EM yucui@otemon.ac.jp CR Abiyev RH, 2018, J FOOD QUALITY, DOI 10.1155/2018/2760907 Allen T, 2019, SOC INDIC RES, V141, P1307, DOI 10.1007/s11205-018-1865-8 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Chen X, 2015, AM J PUBLIC HEALTH, V105, P1734, DOI 10.2105/AJPH.2015.302792 Cruz F.Brazilian, 2016, J WIRELESS NETWORKS, V20, P2481 Drosatos G, 2019, COMPUT STRUCT BIOTEC, V17, P229, DOI 10.1016/j.csbj.2019.01.010 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Hew JJ, 2020, SUPPLY CHAIN MANAG, V25, P863, DOI 10.1108/SCM-01-2020-0044 Johnson-Hall TD, 2017, J MARK CHANNELS, V24, P115, DOI [10.1080/1046669X.2017.1393230, 10.1080/1046669x.2017.1393230] Kamble SS, 2020, INT J PROD ECON, V219, P179, DOI 10.1016/j.ijpe.2019.05.022 Kasten J, 2016, J SUPPLY CHAIN MANAG, V8, P45 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kim HM, 2018, INTELL SYST ACCOUNT, V25, P18, DOI 10.1002/isaf.1424 Kittipanya-ngam P, 2020, PROD PLAN CONTROL, V31, P158, DOI 10.1080/09537287.2019.1631462 Koufteros X, 2017, J MARK CHANNELS, V24, P111, DOI 10.1080/1046669X.2017.1393227 Kouhizadeh M, 2021, INT J PROD ECON, V231, DOI 10.1016/j.ijpe.2020.107831 Krzyzanowskiai K., 2019, Journal of Food Distribution Research, V50, P86 Lu GY, 2017, J MARK CHANNELS, V24, P190, DOI 10.1080/1046669X.2017.1393237 Muzammal M, 2019, FUTURE GENER COMP SY, V90, P105, DOI 10.1016/j.future.2018.07.042 Rejeb A, 2020, LOGISTICS-BASEL, V4, DOI 10.3390/logistics4040027 Rogerson M, 2020, SUPPLY CHAIN MANAG, V25, P601, DOI 10.1108/SCM-08-2019-0300 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 23 TC 1 Z9 1 U1 6 U2 15 PD JUN PY 2021 VL 15 IS 1 SI SI BP 239 EP 250 DI 10.1007/s12626-021-00079-4 EA APR 2021 WC Computer Science, Information Systems SC Computer Science UT WOS:000641219400002 DA 2022-12-14 ER PT J AU Kim, BJ Kim, BC Lee, KB Lee, JM Park, TS AF Kim, B. J. Kim, B. C. Lee, K. B. Lee, J. M. Park, T. S. TI Preparation of gaseous CRMs from the primary system for Rn-222 activity measurement SO APPLIED RADIATION AND ISOTOPES DT Article; Proceedings Paper CT 20th International Conference on Radionuclide Metrology and its Applications (ICRM) CY JUN 08-12, 2015 CL TU Wien, Vienna, AUSTRIA HO TU Wien DE Radon; Traceability; Gaseous radon CRM; Fitting function ID SPECTRA AB For disseminating the gaseous radon standard traceable to the KRISS primary system based on the defined solid angle counting method, two kinds of radon CRM (a glass ampule type and a stainless steel cylinder type) were developed. The activity of the CRM was certified by subtracting a residual activity from the measured activity by the primary system. After certification, the ampule CRM was used to calibrate a radon-monitoring instrument and the cylinder CRM to calibrate an HPGe system. We also improved the measurement procedure of the radon primary system. In a typical radon energy spectrum, the radon peak overlaps with the polonium peak. For more reliable and accurate measurement of radon activity, a fitting method was adopted for the evaluation of radon area in the alpha energy spectrum. The result of radon activity evaluated by using the fitting method is in good agreement with that by the previous integration method. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Kim, B. J.; Kim, B. C.; Lee, K. B.; Lee, J. M.; Park, T. S.] Korea Res Inst Stand & Sci, Daejeon 305340, South Korea. [Kim, B. J.; Lee, K. B.; Lee, J. M.] Univ Sci & Technol UST, Daejeon, South Korea. C3 Korea Research Institute of Standards & Science (KRISS); University of Science & Technology (UST) RP Lee, JM (corresponding author), Korea Res Inst Stand & Sci, Daejeon 305340, South Korea. EM jmlee@kriss.re.kr CR BORTELS G, 1987, APPL RADIAT ISOTOPES, V38, P831, DOI 10.1016/0883-2889(87)90180-8 Dersch R, 2004, APPL RADIAT ISOTOPES, V60, P387, DOI 10.1016/j.apradiso.2003.11.046 Kim BC, 2012, APPL RADIAT ISOTOPES, V70, P1934, DOI 10.1016/j.apradiso.2012.02.020 Lee JM, 2013, APPL RADIAT ISOTOPES, V81, P230, DOI 10.1016/j.apradiso.2013.03.086 Picolo JL, 1996, NUCL INSTRUM METH A, V369, P452, DOI 10.1016/S0168-9002(96)80029-5 ROOT, 2015, PROV 5 34 00 Sanchez AM, 1996, NUCL INSTRUM METH A, V369, P593, DOI 10.1016/S0168-9002(96)80058-1 Spring P, 2006, NUCL INSTRUM METH A, V568, P752, DOI 10.1016/j.nima.2006.07.055 NR 8 TC 3 Z9 3 U1 0 U2 4 PD MAR PY 2016 VL 109 BP 122 EP 125 DI 10.1016/j.apradiso.2015.12.057 WC Chemistry, Inorganic & Nuclear; Nuclear Science & Technology; Radiology, Nuclear Medicine & Medical Imaging SC Chemistry; Nuclear Science & Technology; Radiology, Nuclear Medicine & Medical Imaging UT WOS:000372676600026 DA 2022-12-14 ER PT J AU Grobner, J Reda, I Wacker, S Nyeki, S Behrens, K Gorman, J AF Groebner, J. Reda, I. Wacker, S. Nyeki, S. Behrens, K. Gorman, J. TI A new absolute reference for atmospheric longwave irradiance measurements with traceability to SI units SO JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES DT Article ID RADIATION MEASUREMENTS; WAVE-RADIATION; PYRGEOMETER; CALIBRATION; UNCERTAINTY; RADIOMETRY; ACCURACY; CAVITY AB Two independently designed and calibrated absolute radiometers measuring downwelling longwave irradiance were compared during two field campaigns in February and October 2013 at Physikalisch Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC). One absolute cavity pyrgeometer (ACP) developed by NREL and up to four Integrating Sphere Infrared Radiometers (IRIS) developed by PMOD/WRC took part in these intercomparisons. The internal consistency of the IRIS radiometers and the agreement with the ACP were within +/- 1 W m(-2), providing traceability of atmospheric longwave irradiance to the international system of units with unprecedented accuracy. Measurements performed during the two field campaigns and over the past 4 years have shown that the World Infrared Standard Group (WISG) of pyrgeometers is underestimating clear-sky atmospheric longwave irradiance by 2 to 6 W m(-2), depending on the amount of integrated water vapor (IWV). This behavior is an instrument-dependent feature and requires an individual sensitivity calibration of each pyrgeometer with respect to an absolute reference such as IRIS or ACP. For IWV larger than 10 mm, an average sensitivity correction of +6.5% should be applied to the WISG in order to be consistent with the longwave reference represented by the ACP and IRIS radiometers. A concerted effort at international level will need to be implemented in order to correct measurements of atmospheric downwelling longwave irradiance traceable to the WISG. C1 [Groebner, J.; Wacker, S.; Nyeki, S.] World Radiat Ctr, Phys Meteorol Observ Davos, Davos, Switzerland. [Reda, I.] Natl Renewable Energy Lab, Golden, CO USA. [Behrens, K.] Deutsch Wetterdienst, Meteorol Observ Lindenberg, Richard Assmann Observ, Lindenberg, Germany. [Gorman, J.] Bureau Meteorol, Melbourne, Vic, Australia. C3 United States Department of Energy (DOE); National Renewable Energy Laboratory - USA; Deutscher Wetterdienst; Bureau of Meteorology - Australia RP Grobner, J (corresponding author), World Radiat Ctr, Phys Meteorol Observ Davos, Davos, Switzerland. EM julian.groebner@pmodwrc.ch CR ALBRECHT B, 1977, J APPL METEOROL, V16, P188, DOI 10.1175/1520-0450(1977)016<0190:PFIPP>2.0.CO;2 FROHLICH C, 1991, METROLOGIA, V28, P111, DOI 10.1088/0026-1394/28/3/001 Grobner J, 2008, APPL OPTICS, V47, P4441, DOI 10.1364/AO.47.004441 Grobner J, 2007, APPL OPTICS, V46, P7419, DOI 10.1364/AO.46.007419 Grobner J, 2013, AIP CONF PROC, V1531, P488, DOI 10.1063/1.4804813 Grobner J, 2012, METROLOGIA, V49, pS105, DOI 10.1088/0026-1394/49/2/S105 Loeb NG, 2012, NAT GEOSCI, V5, P110, DOI [10.1038/ngeo1375, 10.1038/NGEO1375] Marty C, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002937 MISKOLCZI F, 1993, APPL OPTICS, V32, P3257, DOI 10.1364/AO.32.003257 Ohmura A, 1998, B AM METEOROL SOC, V79, P2115, DOI 10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2 PHILIPONA R, 1995, APPL OPTICS, V34, P1598, DOI 10.1364/AO.34.001598 Philipona R, 2001, APPL OPTICS, V40, P2376, DOI 10.1364/AO.40.002376 Philipona R., 2001, J GEOPHYS RES, V106 Reda I, 2002, J ATMOS SOL-TERR PHY, V64, P1623, DOI 10.1016/S1364-6826(02)00133-5 Reda I, 2012, J ATMOS SOL-TERR PHY, V77, P132, DOI 10.1016/j.jastp.2011.12.011 Wild M, 2013, CLIM DYNAM, V40, P3107, DOI 10.1007/s00382-012-1569-8 NR 16 TC 18 Z9 18 U1 0 U2 15 PD JUN 27 PY 2014 VL 119 IS 12 BP 7083 EP 7090 DI 10.1002/2014JD021630 WC Meteorology & Atmospheric Sciences SC Meteorology & Atmospheric Sciences UT WOS:000340247000005 DA 2022-12-14 ER PT J AU Li, CL Kang, XM Nie, J Li, A Farag, MA Liu, CL Rogers, KM Xiao, JB Yuan, YW AF Li, Chunlin Kang, Xuming Nie, Jing Li, An Farag, Mohamed A. Liu, Cuiling Rogers, Karyne M. Xiao, Jianbo Yuan, Yuwei TI Recent advances in Chinese food authentication and origin verification using isotope ratio mass spectrometry SO FOOD CHEMISTRY DT Review DE Authentication; Traceability; China; Food products; Organic; Stable isotopes ID CUCUMBER APOSTICHOPUS-JAPONICUS; NATURAL N-15 ABUNDANCE; ORYZA-SATIVA L.; GEOGRAPHICAL ORIGIN; STABLE-ISOTOPES; LIQUID-CHROMATOGRAPHY; ELEMENTAL ANALYZER; NITROGEN ISOTOPES; POTENTIAL TOOL; WATER AB Over the last decade, isotope ratio mass spectrometry (IRMS) using up to 5 light stable isotopes (C-13/C-12, H-2/H-1, N-15/N-14, O-18/O-16, S-34/S-32) has become more widely applied for food origin verification as well as food authentication in China. IRMS technology is increasingly used to authenticate a range of food products including organic foods, honey, beverages, tea, animal products, fruits, oils, cereals, spices and condiments that are frequently unique to a specific region of China. Compared to other food authenticity and traceability techniques, IRMS has been successfully used to characterize, classify and identify many Chinese food products, reducing fraud and food safety problems and improving consumer trust and confidence. IRMS techniques also provides scientific support to enhance China's strict government regulatory policies. Isotope testing verifies geographical origin labelling of domestic and imported foods, protects and verifies high value foods that are unique to China, and indicates environmentally friendly farming practices such as 'green' or 'organic' methods. This paper reviews recently published Chinese research to highlight the recent advances of IRMS as a regulatory and verification tool for Chinese food products. C1 [Li, Chunlin; Nie, Jing; Rogers, Karyne M.; Yuan, Yuwei] Zhejiang Acad Agr Sci, Inst Agroprod Safety & Nutr, State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou 310021, Peoples R China. [Kang, Xuming] Chinese Acad Fishery Sci, Yellow Sea Fisheries Res Inst, Qingdao 266071, Peoples R China. [Li, An] Beijing Res Ctr Agr Stand & Testing, Beijing 100097, Peoples R China. [Farag, Mohamed A.] Cairo Univ, Coll Pharm, Pharmacognosy Dept, Kasrel Aini St, Cairo 11562, Egypt. [Xiao, Jianbo] Univ Vigo, Fac Food Sci & Technol, Dept Analyt Chem & Food Sci, Vigo 36310, Spain. [Liu, Cuiling] Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China. [Rogers, Karyne M.] GNS Sci, Natl Isotope Ctr, Lower Hutt 5040, New Zealand. C3 Zhejiang Academy of Agricultural Sciences; Chinese Academy of Fishery Sciences; Yellow Sea Fisheries Research Institute, CAFS; Egyptian Knowledge Bank (EKB); Cairo University; Universidade de Vigo; Beijing Technology & Business University; GNS Science - New Zealand RP Yuan, YW (corresponding author), Zhejiang Acad Agr Sci, Inst Agroprod Safety & Nutr, State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou 310021, Peoples R China.; Xiao, JB (corresponding author), Univ Vigo, Fac Food Sci & Technol, Dept Analyt Chem & Food Sci, Vigo 36310, Spain. EM jianboxiao@uvigo.es; ywytea@163.com CR Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Cengiz MF, 2017, INT J FOOD PROP, V20, P3234, DOI 10.1080/10942912.2017.1283327 CFSY, 2020, CHINA FISHERIES STAT Chen CT, 2019, J FOOD DRUG ANAL, V27, P175, DOI 10.1016/j.jfda.2018.08.004 Chen TJ, 2017, FOOD CONTROL, V72, P306, DOI 10.1016/j.foodcont.2015.08.034 Chen TJ, 2016, FOOD CHEM, V209, P95, DOI 10.1016/j.foodchem.2016.04.029 Chen Y. Q., 2021, CHINA DAILY China Tea Marketing Association (CTMA), 2021, CHIN TEA PROD MARK R Chung IM, 2018, FOOD CHEM, V240, P840, DOI 10.1016/j.foodchem.2017.08.023 Costello C, 2020, NATURE, V588, P95, DOI 10.1038/s41586-020-2616-y Cunnif P., 1999, AOAC OFFICIAL METHOD, Vsixteenth, P27 Dasenaki ME, 2019, MOLECULES, V24, DOI 10.3390/molecules24061014 Deng XF, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106807 Di Giuseppe D, 2017, GEOSCIENCES, V7, DOI 10.3390/geosciences7030076 Dong H, 2019, J FOOD COMPOS ANAL, V79, P148, DOI 10.1016/j.jfca.2018.12.003 Dong H, 2018, FOOD CHEM, V240, P717, DOI 10.1016/j.foodchem.2017.08.008 Dong H, 2017, FOOD ANAL METHOD, V10, P2755, DOI 10.1007/s12161-017-0842-1 Dong H, 2016, J AGR FOOD CHEM, V64, P3258, DOI 10.1021/acs.jafc.6b00691 Dotsika E, 2010, J GEOCHEM EXPLOR, V107, P299, DOI 10.1016/j.gexplo.2010.07.002 EHLERINGER JR, 1992, PLANT CELL ENVIRON, V15, P1073, DOI 10.1111/j.1365-3040.1992.tb01657.x Elmarami H, 2017, ISOT ENVIRON HEALT S, V53, P184, DOI 10.1080/10256016.2016.1206095 European Union-People's Republic of China, 2021, AGR EUR UN GOV PEOPL Fan SX, 2018, J FOOD DRUG ANAL, V26, P1033, DOI 10.1016/j.jfda.2017.12.009 FAOSTAT, FOOD AGR ORG UN FAO FARQUHAR GD, 1989, ANNU REV PLANT PHYS, V40, P503, DOI 10.1146/annurev.pp.40.060189.002443 Ferreira RS, 2019, J VENOM ANIM TOXINS, V25, DOI [10.1590/1678-9199-JVATITD-1487-18, 10.1590/1678-9199-jvatitd-1487-18] General Administration of Quality Supervision, 2014, NAT FOOD SAF STAND P Gong Y, 2018, RAPID COMMUN MASS SP, V32, P583, DOI 10.1002/rcm.8071 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 Guo QJ, 2016, J GEOCHEM EXPLOR, V161, P112, DOI 10.1016/j.gexplo.2015.11.010 Guo R, 2019, WATER-SUI, V11, DOI 10.3390/w11051065 Han C, 2021, FOOD CHEM, V364, DOI 10.1016/j.foodchem.2021.130364 Hayashi N, 2011, J AGR FOOD CHEM, V59, P10317, DOI 10.1021/jf202215z Jin JY, 2020, EUR FOOD RES TECHNOL, V246, P955, DOI 10.1007/s00217-020-03469-0 Kang XM, 2022, J FOOD COMPOS ANAL, V111, DOI 10.1016/j.jfca.2022.104627 Kang XM, 2021, J FOOD COMPOS ANAL, V99, DOI 10.1016/j.jfca.2021.103852 Kang XM, 2020, FOOD CONTROL, V111, DOI 10.1016/j.foodcont.2019.107036 Lee SG, 2021, WATER-SUI, V13, DOI 10.3390/w13162191 Li A, 2021, FOOD CONTROL, V127, DOI 10.1016/j.foodcont.2021.108126 Li L, 2020, MAR FRESHWATER RES, V71, P1294, DOI 10.1071/MF19214 Li L, 2019, FOOD CONTROL, V95, P249, DOI 10.1016/j.foodcont.2018.08.015 Li L, 2018, AQUAC RES, V49, P1029, DOI 10.1111/are.13551 Li L, 2016, FOOD CHEM, V194, P1238, DOI 10.1016/j.foodchem.2015.08.123 Liang KH, 2022, J SCI FOOD AGR, V102, P673, DOI 10.1002/jsfa.11396 Liu HY, 2021, INT J FOOD SCI TECH, V56, P2604, DOI 10.1111/ijfs.14900 Liu HY, 2019, FOOD CHEM, V277, P448, DOI 10.1016/j.foodchem.2018.10.144 Liu HY, 2018, J FOOD COMPOS ANAL, V69, P149, DOI 10.1016/j.jfca.2018.01.009 Liu WW, 2021, FOOD CONTROL, V125, DOI 10.1016/j.foodcont.2021.107954 Liu XH, 2022, J FOOD COMPOS ANAL, V109, DOI 10.1016/j.jfca.2022.104424 Liu XH, 2021, J SCI FOOD AGR, V101, P3795, DOI 10.1002/jsfa.11012 Liu XL, 2013, FOOD CHEM, V140, P135, DOI 10.1016/j.foodchem.2013.02.020 Liu X, 2021, FOOD CHEM, V342, DOI 10.1016/j.foodchem.2020.128379 Liu Y, 2017, J SCI FOOD AGR, V97, P4912, DOI 10.1002/jsfa.8367 Liu Z, 2020, FOOD CHEM, V328, DOI 10.1016/j.foodchem.2020.127115 Liu Z, 2020, J AGR FOOD CHEM, V68, P1213, DOI 10.1021/acs.jafc.9b06847 Liu Z, 2019, FOOD CONTROL, V99, P1, DOI 10.1016/j.foodcont.2018.12.011 Liu Z, 2019, RAPID COMMUN MASS SP, V33, P778, DOI 10.1002/rcm.8405 Liu Z, 2019, RAPID COMMUN MASS SP, V33, P625, DOI 10.1002/rcm.8387 Lou YX, 2017, J ANAL METHODS CHEM, V2017, DOI 10.1155/2017/5454231 Luo DH, 2016, FOOD ANAL METHOD, V9, P437, DOI 10.1007/s12161-015-0204-9 Luo RJ, 2019, FOOD CHEM, V274, P1, DOI 10.1016/j.foodchem.2018.08.104 Lv J, 2017, FOOD ANAL METHOD, V10, P347, DOI 10.1007/s12161-016-0588-1 Lyu CG, 2021, FOOD CHEM, V343, DOI 10.1016/j.foodchem.2020.128506 Ma Y. H, 2021, ORGANIC FOOD DOMESTI Muccio Z, 2009, ANALYST, V134, P213, DOI 10.1039/b808232d National Bureau of Statistics, 2021, NAT DAT BANK Nie J, 2022, FOOD CHEM, V394, DOI 10.1016/j.foodchem.2022.133557 Nie J, 2021, J FOOD COMPOS ANAL, V99, DOI 10.1016/j.jfca.2021.103856 Nie J, 2020, MEAT SCI, V165, DOI 10.1016/j.meatsci.2020.108113 Pang GF, 2006, J SCI FOOD AGR, V86, P315, DOI 10.1002/jsfa.2328 Peng CY, 2019, J SCI FOOD AGR, V99, P2596, DOI 10.1002/jsfa.9475 Rangarajan R, 2011, RAPID COMMUN MASS SP, V25, P3323, DOI 10.1002/rcm.5229 Roden JS, 1999, PLANT PHYSIOL, V120, P1165, DOI 10.1104/pp.120.4.1165 Rogers KM, 2008, J AGR FOOD CHEM, V56, P4078, DOI 10.1021/jf800797w Rogers KM, 2022, APPL GEOCHEM, V143, DOI 10.1016/j.apgeochem.2022.105356 [邵圣枝 Shao Shengzhi], 2020, [核农学报, Journal of Nuclear Agricultural Sciences], V34, P78 Rogers KM, 2009, J AGR FOOD CHEM, V57, P4236, DOI 10.1021/jf803760s State Administration for Market Regulation, 2018, NAT STAND FOOD SAF D Sturm MB, 2017, ISOT ENVIRON HEALT S, V53, P157, DOI 10.1080/10256016.2016.1231184 Su YY, 2020, J FOOD PROTECT, V83, P1323, DOI 10.4315/JFP-19-499 Sun CJ, 2016, ISOT ENVIRON HEALT S, V52, P281, DOI 10.1080/10256016.2016.1125350 Sun SM, 2016, FOOD CHEM, V213, P675, DOI 10.1016/j.foodchem.2016.07.013 Sun YC, 2020, RAPID COMMUN MASS SP, V34, DOI 10.1002/rcm.8885 Tang Q, 2015, BIOSCI TRENDS, V9, P7, DOI 10.5582/bst.2015.01004 Wadood SA, 2019, J MASS SPECTROM, V54, P178, DOI 10.1002/jms.4312 Wang C, 2020, J FOOD COMPOS ANAL, V92, DOI 10.1016/j.jfca.2020.103577 Wang JS, 2020, FOOD CHEM, V313, DOI 10.1016/j.foodchem.2019.126093 Wang SJ, 2018, WATER RESOUR RES, V54, P9131, DOI 10.1029/2018WR023091 Wang T, 2019, FOOD CHEM, V293, P348, DOI 10.1016/j.foodchem.2019.04.109 Wang XR, 2019, J AGR FOOD CHEM, V67, P12144, DOI 10.1021/acs.jafc.9b04438 Wu H., 2020, FOOD SCI, V42, P304 Wu H, 2022, FOOD CHEM, V373, DOI 10.1016/j.foodchem.2021.131535 Wu H, 2021, FOOD CHEM, V339, DOI 10.1016/j.foodchem.2020.127760 Wu H, 2019, FOOD CHEM, V301, DOI 10.1016/j.foodchem.2019.125137 Wu H, 2015, CHINESE J ANAL CHEM, V43, P344 Wu YL, 2015, FOOD CHEM, V174, P553, DOI 10.1016/j.foodchem.2014.11.096 Wu ZB, 2015, J AGR FOOD CHEM, V63, P5388, DOI 10.1021/acs.jafc.5b01576 Xia W, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107907 Xiao CL, 2019, E3S WEB CONF, V98, DOI 10.1051/e3sconf/20199801052 Xiao J, 2020, EFOOD, V1, P399 Xiao W, 2016, ISOT ENVIRON HEALT S, V52, P443, DOI 10.1080/10256016.2016.1147442 Xie LN, 2020, FOOD CHEM, V316, DOI 10.1016/j.foodchem.2020.126332 Xing RR, 2019, FOOD CONTROL, V101, P173, DOI 10.1016/j.foodcont.2019.02.034 Xu YJ, 2022, J FOOD COMPOS ANAL, V105, DOI 10.1016/j.jfca.2021.104251 Yang XT, 2016, FOOD CONTROL, V66, P17, DOI 10.1016/j.foodcont.2016.01.032 Yin HM, 2020, ACTA GEOCHIM, V39, P326, DOI 10.1007/s11631-020-00407-5 Yuan YW, 2018, J AGR FOOD CHEM, V66, P2607, DOI 10.1021/acs.jafc.7b05422 Yuan YW, 2016, J AGR FOOD CHEM, V64, P5633, DOI 10.1021/acs.jafc.6b00453 Zhang XF, 2019, FOOD CHEM, V299, DOI 10.1016/j.foodchem.2019.125107 Zhang XF, 2019, FOOD CHEM, V298, DOI 10.1016/j.foodchem.2019.124966 Zhang XF, 2017, FOOD CHEM, V218, P269, DOI 10.1016/j.foodchem.2016.08.083 Zhang Y, 2020, FOOD CHEM, V308, DOI 10.1016/j.foodchem.2019.125584 Zhang Y, 2019, J AGR FOOD CHEM, V67, P12255, DOI 10.1021/acs.jafc.9b05190 Zhao RT, 2021, FOODS, V10, DOI 10.3390/foods10051119 Zhao SS, 2021, CRIT REV ANAL CHEM, V51, P742, DOI 10.1080/10408347.2020.1768359 Zhao SS, 2020, RAPID COMMUN MASS SP, V34, DOI 10.1002/rcm.8795 Zhao SS, 2020, FOOD CHEM, V310, DOI 10.1016/j.foodchem.2019.125826 Zhao XD, 2019, FOOD CONTROL, V102, P38, DOI 10.1016/j.foodcont.2019.03.016 Zhao XD, 2018, FOOD CONTROL, V91, P128, DOI 10.1016/j.foodcont.2018.03.041 Zhao Y, 2020, MEAT SCI, V165, DOI 10.1016/j.meatsci.2020.108129 Zhao Y, 2016, J SCI FOOD AGR, V96, P3950, DOI 10.1002/jsfa.7567 Zhao Y, 2016, CYTA-J FOOD, V14, P163, DOI 10.1080/19476337.2015.1057235 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y Zhou P, 2019, ANAL METHODS-UK, V11, P346, DOI [10.1039/c8ay02191k, 10.1039/C8AY02191K] Zhou W, 2013, PEDOSPHERE, V23, P835, DOI 10.1016/S1002-0160(13)60075-2 Zhou XW, 2021, J FOOD COMPOS ANAL, V101, DOI 10.1016/j.jfca.2021.103940 Zhou XW, 2021, FOOD CONTROL, V128, DOI 10.1016/j.foodcont.2021.108165 NR 127 TC 0 Z9 0 U1 29 U2 29 PD JAN 1 PY 2023 VL 398 AR 133896 DI 10.1016/j.foodchem.2022.133896 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000874865200007 DA 2022-12-14 ER PT J AU Huang, Y Wang, L Guan, HO Zuo, F Qian, LL AF Huang Yan Wang Lu Guan Hai-ou Zuo Feng Qian Li-li TI Nondestructive Detection Method of Mung Bean Origin Based on Optimized NIR Spectral Wavenumber SO SPECTROSCOPY AND SPECTRAL ANALYSIS DT Article DE Origin of mung bean; Spectral technology; Characteristic extraction; Nondestructive detecting; Traceability model AB Origin is an important environmental factor affecting crop production, and tracing the origin is of great significance for food safety. The chemical analysis method is generally used in traditional agricultural product origin detection, and its operation is cumbersome, destructive and time-consuming. In this study, northern cold mung beans were used as the research object. Near-infrared spectral data of mung bean in two states of seed and powder were obtained in the main origins of high-quality for Baicheng, Dumeng and Tailai. A new nondestructive detecting method for mung bean origins were established by optimizing the NIR characteristic spectrum wavenumbers. Firstly, in the range of 10 105.37 similar to 4 078.655 cm(-1) wavenumber with strong absorbance value, the raw spectral data of mung beans from different regions was preprocessed by using multivariate scattering correction (MSC) method to eliminate spectral interference information. Then competitive adaptive reweighted sampling(CARS) algorithm is applied to optimize the characteristic spectral wavenumbers of mung bean seed and powder states from different origins to reduce the feature vector dimension of the spectral curve. Finally, a feed-forward neural network (BP) adaptive inference mechanism was used to establish a non-linear mapping model between the origin of mung bean and its spectral characteristic wavenumber, and the encoding vector output by the network was parsed to the original name as the output result of the detection of the origin of the mung bean. The results show that; (1)Preprocessed with multiple scattering corrections in the raw spectral, the error of the spectral curve of mung bean powder is reduced from 12.87 to 3.20, and the error of the spectral curve of mung bean seed is reduced from 153.04 to 27.73, which provides effective and reliable spectral data. (2) Through the competitive adaptive reweighting sampling algorithm, the important characteristic wavenumbers of mung bean spectral curve are extracted. From the 2 114 original wavenumbers of seed and powder state, 61 and 107 characteristic wavenumbers are optimized respectively, and the total number of wavebands is reduced by 94.94%, which is taken as the characteristic index of mung bean origin recognition. (3) The MSC-CARS-BP mung bean origin detection model was put forward innovatively. Based on the optimized spectral characteristic wavenumber as the quantitative basis, the origin detection of mung bean seed and powder was carried out respectively. The accuracy of the prediction set was 92.59% and 98.63%, and the correlation coefficient was above 0.99. This method can use near-infrared spectrum processing technology to achieve the goal of non-destructive detecting of mung bean origin, and provide technical support and reference for automatic and rapid traceability of agricultural products origin. C1 [Huang Yan] Heilongjiang Bayi Agr Univ, Coll Engn, Daqing 163319, Peoples R China. [Wang Lu; Guan Hai-ou] Heilongjiang Bayi Agr Univ, Coll Informat & Elect Engn, Daqing 163319, Peoples R China. [Zuo Feng; Qian Li-li] Heilongjiang Bayi Agr Univ, Coll Food Sci, Daqing 163319, Peoples R China. [Zuo Feng; Qian Li-li] Natl Coarse Cereals Engn Res Ctr, Daqing 163319, Peoples R China. C3 Heilongjiang Bayi Agricultural University; Heilongjiang Bayi Agricultural University; Heilongjiang Bayi Agricultural University RP Guan, HO (corresponding author), Heilongjiang Bayi Agr Univ, Coll Informat & Elect Engn, Daqing 163319, Peoples R China. EM gho123@163.com NR 0 TC 0 Z9 1 U1 4 U2 8 PD APR PY 2021 VL 41 IS 4 BP 1188 EP 1193 DI 10.3964/j.issn.1000-0593(2021)04-1188-06 WC Spectroscopy SC Spectroscopy UT WOS:000646723300032 DA 2022-12-14 ER PT J AU Hu, SS Huang, S Qin, XH AF Hu, Sensen Huang, Shan Qin, Xinghong TI Exploring blockchain-supported authentication based on online and offline business in organic agricultural supply chain SO COMPUTERS & INDUSTRIAL ENGINEERING DT Article DE Blockchain; Organic agricultural products supply chain; Blockchain-based platform; Agricultural traceability; Offline-online business ID TECHNOLOGY AB The rise of blockchain technology has disrupted the traditional traceability method of organic agricultural products supply chain (OASC) by emerging authentication or certification. For an offline business scenario, a blockchain certification traceability model (BTM) is popular, in which consumers check the product information before purchasing by convenience scanning bar-code. Similarly, for an online business scenario, a blockchain-based e-commerce model (BEM) is popular, in which consumers verify and book the product while surfing the web. However, few studies have an insight into the potential OASCs business model and employ quantitative methods to confirm the benefits of the adoption of blockchain. According to a current real-world online and offline business, we first conceptualize two models in this paper. We employ the Stackelberg game to compare the supply chain profit and consumer surplus. Furthermore, we extend the analysis by using the case of Red Beauty orange, which proves that the blockchain e-commerce model is a good supply chain model under the condition of high shopping convenience and low operating cost of the blockchain platform. Our findings are probably generalizable to apply to other industrial products that require such services. C1 [Hu, Sensen; Huang, Shan; Qin, Xinghong] Chongqing Technol & Business Univ, Collaborat Innovat Ctr Chongqing Modern Trade Logi, Chongqing, Peoples R China. [Hu, Sensen; Qin, Xinghong] Univ Elect Sci & Technol, Sch Management & Econ, Chengdu, Peoples R China. C3 Chongqing Technology & Business University; University of Electronic Science & Technology of China RP Qin, XH (corresponding author), Chongqing Technol & Business Univ, Collaborat Innovat Ctr Chongqing Modern Trade Logi, Chongqing, Peoples R China. EM sensenhu@ctbu.edu.cn; 2019658005@email.ctbu.edu.cn; qinxinghong4515@sina.com CR Agbo M, 2015, J ECON BEHAV ORGAN, V109, P56, DOI 10.1016/j.jebo.2014.11.003 Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Biswas D, 2023, EUR J OPER RES, V305, P128, DOI 10.1016/j.ejor.2022.05.034 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Cao Y, 2022, TRANSPORT RES E-LOG, V163, DOI 10.1016/j.tre.2022.102731 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Casino F, 2019, IFAC PAPERSONLINE, V52, P2728, DOI 10.1016/j.ifacol.2019.11.620 Chen TB, 2020, FOOD CONTROL, V107, DOI 10.1016/j.foodcont.2019.106770 Chen YB, 2021, GAME ECON BEHAV, V126, P402, DOI 10.1016/j.geb.2021.01.009 Choi TM, 2019, TRANSPORT RES E-LOG, V128, P17, DOI 10.1016/j.tre.2019.05.011 Friedman N, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121403 Gomiero T, 2018, APPL SOIL ECOL, V123, P714, DOI 10.1016/j.apsoil.2017.10.014 Hong W, 2021, J CLEAN PROD, V303, DOI 10.1016/j.jclepro.2021.127044 Hu SS, 2021, COMPUT IND ENG, V153, DOI 10.1016/j.cie.2020.107079 Kamble S, 2019, INT J PROD RES, V57, P2009, DOI 10.1080/00207543.2018.1518610 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Lee JS, 2022, J INF SECUR APPL, V65, DOI 10.1016/j.jisa.2022.103117 Lezoche M, 2020, COMPUT IND, V117, DOI 10.1016/j.compind.2020.103187 Li JF, 2019, PROCEDIA MANUF, V30, P560, DOI 10.1016/j.promfg.2019.02.079 Liu YH, 2021, COMPUT IND ENG, V162, DOI 10.1016/j.cie.2021.107730 Liu ZY, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102059 Milford AB, 2021, J RURAL STUD, V88, P279, DOI 10.1016/j.jrurstud.2021.08.018 Patel H., 2021, ICT EXPRESS, DOI [10.1016/J.ICTE.2021.07.003, DOI 10.1016/J.ICTE.2021.07.003] Ren W, 2021, FUTURE GENER COMP SY, V117, P453, DOI 10.1016/j.future.2020.12.007 Ronaghi M. H., 2021, Information Processing in Agriculture, V8, P398, DOI 10.1016/j.inpa.2020.10.004 Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261 Sanka AI, 2021, J NETW COMPUT APPL, V195, DOI 10.1016/j.jnca.2021.103232 Schmidt CG, 2019, J PURCH SUPPLY MANAG, V25, DOI 10.1016/j.pursup.2019.100552 Shi XT, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.1953182 Souiden N, 2019, J RETAIL CONSUM SERV, V50, P286, DOI 10.1016/j.jretconser.2018.07.023 Stranieri S, 2021, FOOD CONTROL, V119, DOI 10.1016/j.foodcont.2020.107495 [孙梅 Sun Mei], 2020, [中国管理科学, Chinese Journal of Management Science], V28, P98 Treiblmaier H, 2021, ELECTRON COMMER R A, V48, DOI 10.1016/j.elerap.2021.101054 Wang ZY, 2021, PROD OPER MANAG, V30, P1965, DOI 10.1111/poms.13356 Wu XY, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.1894497 Xu Z., 2013, CHINA EC Q, V12, P1513 Yadav R, 2016, J RETAIL CONSUM SERV, V33, P92, DOI 10.1016/j.jretconser.2016.08.008 NR 39 TC 0 Z9 0 U1 13 U2 13 PD NOV PY 2022 VL 173 AR 108738 DI 10.1016/j.cie.2022.108738 WC Computer Science, Interdisciplinary Applications; Engineering, Industrial SC Computer Science; Engineering UT WOS:000878739200003 DA 2022-12-14 ER PT J AU Beckhoff, B AF Beckhoff, Burkhard TI Traceable Characterization of Nanomaterials by X-ray Spectrometry Using Calibrated Instrumentation SO NANOMATERIALS DT Review DE traceability; characterization; elemental analysis; speciation; nanostructures; nanoparticles; XRF; GIXRF; XAFS; XES ID FLUORESCENCE ANALYSIS; NANOPROBE BEAMLINE; METROLOGY; QUANTIFICATION; SPECTROSCOPY; SPECIATION; NANOLAYERS; SURFACES; LAYER AB Traceable characterization methods allow for the accurate correlation of the functionality or toxicity of nanomaterials with their underlaying chemical, structural or physical material properties. These correlations are required for the directed development of nanomaterials to reach target functionalities such as conversion efficiencies or selective sensitivities. The reliable characterization of nanomaterials requires techniques that often need to be adapted to the nano-scaled dimensions of the samples with respect to both the spatial dimensions of the probe and the instrumental or experimental discrimination capability. The traceability of analytical methods revealing information on chemical material properties relies on reference materials or qualified calibration samples, the spatial elemental distributions of which must be very similar to the nanomaterial of interest. At the nanoscale, however, only few well-known reference materials exist. An alternate route to establish the required traceability lays in the physical calibration of the analytical instrument's response behavior and efficiency in conjunction with a good knowledge of the various interaction probabilities. For the elemental analysis, speciation, and coordination of nanomaterials, such a physical traceability can be achieved with X-ray spectrometry. This requires the radiometric calibration of energy- and wavelength-dispersive X-ray spectrometers, as well as the reliable determination of atomic X-ray fundamental parameters using such instrumentation. In different operational configurations, the information depths, discrimination capability, and sensitivity of X-ray spectrometry can be considerably modified while preserving its traceability, allowing for the characterization of surface contamination as well as interfacial thin layer and nanoparticle chemical compositions. Furthermore, time-resolved and hybrid approaches provide access to analytical information under operando conditions or reveal dimensional information, such as elemental or species depth profiles of nanomaterials. The aim of this review is to demonstrate the absolute quantification capabilities of SI-traceable X-ray spectrometry based upon calibrated instrumentation and knowledge about X-ray interaction probabilities. C1 [Beckhoff, Burkhard] Phys Tech Bundesanstalt, Abbestr 2-12, D-10587 Berlin, Germany. C3 Physikalisch-Technische Bundesanstalt (PTB) RP Beckhoff, B (corresponding author), Phys Tech Bundesanstalt, Abbestr 2-12, D-10587 Berlin, Germany. EM burkhard.beckhoff@ptb.de CR Al Hassan A, 2018, PHY REV MATER, V2, DOI 10.1103/PhysRevMaterials.2.014604 Alonso-Mori R, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4737630 Andrle A, 2021, NANOMATERIALS-BASEL, V11, DOI 10.3390/nano11071647 Anklamm L, 2014, REV SCI INSTRUM, V85, DOI 10.1063/1.4875986 [Anonymous], 2017, INT INITIATIVE XRAY, V71 [Anonymous], NIST REFERENCE MAT 8 [Anonymous], 2020, GUM62020 JCGM BIPM S [Anonymous], NANOSCALED REFERENCE [Anonymous], 2008, UNCERTAINTY MEASUR 3 Baumann J, 2021, SPECTROCHIM ACTA B, V181, DOI 10.1016/j.sab.2021.106216 Baumann J, 2017, ANAL CHEM, V89, P1965, DOI 10.1021/acs.analchem.6b04449 Beckhoff B., 2006, HDB PRACTICAL XRAY F, DOI DOI 10.1007/978-3-540-36722-2 Beckhoff B, 2008, J ANAL ATOM SPECTROM, V23, P845, DOI 10.1039/b718355k Beckhoff B, 2007, ANAL CHEM, V79, P7873, DOI 10.1021/ac071236p Beckhoff B, 2009, PHYS STATUS SOLIDI B, V246, P1415, DOI 10.1002/pssb.200945162 Cagno S, 2017, ANAL CHEM, V89, P11435, DOI 10.1021/acs.analchem.7b02554 Dietrich PM, 2015, ANAL CHEM, V87, P10117, DOI 10.1021/acs.analchem.5b02846 Fehse M, 2021, PHYS CHEM CHEM PHYS, V23, P23445, DOI 10.1039/d1cp03263a Fischer T, 2015, ANAL CHEM, V87, P2685, DOI 10.1021/ac503850f Gerlach M, 2015, J APPL CRYSTALLOGR, V48, P1381, DOI 10.1107/S160057671501287X Giovannozzi AM, 2019, ANAL BIOANAL CHEM, V411, P217, DOI 10.1007/s00216-018-1431-x Grigorieva I, 2019, CONDENS MATTER, V4, DOI 10.3390/condmat4010018 Grotzsch D, 2017, REV SCI INSTRUM, V88, DOI 10.1063/1.5006122 Guerra M, 2018, PHYS REV A, V97, DOI 10.1103/PhysRevA.97.042501 Guerra M, 2015, PHYS REV A, V92, DOI 10.1103/PhysRevA.92.022507 Honicke P, 2022, SMALL, V18, DOI 10.1002/smll.202105776 Honicke P, 2020, SPECTROCHIM ACTA B, V174, DOI 10.1016/j.sab.2020.106009 Honicke P, 2020, NANOTECHNOLOGY, V31, DOI 10.1088/1361-6528/abb557 Honicke P, 2019, J VAC SCI TECHNOL A, V37, DOI 10.1116/1.5094891 Honicke P, 2014, PHYS REV LETT, V113, DOI 10.1103/PhysRevLett.113.163001 Honicke P, 2010, ANAL BIOANAL CHEM, V396, P2825, DOI 10.1007/s00216-009-3266-y Holfelder I, 2021, REV SCI INSTRUM, V92, DOI 10.1063/5.0061183 Jiang Z, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-16980-5 Johannes A, 2017, SCI ADV, V3, DOI 10.1126/sciadv.aao4044 Johansson U, 2021, J SYNCHROTRON RADIAT, V28, P1935, DOI 10.1107/S1600577521008213 Kayser Y, 2014, REV SCI INSTRUM, V85, DOI 10.1063/1.4869340 Kayser Y, 2013, SPECTROCHIM ACTA B, V88, P136, DOI 10.1016/j.sab.2013.06.011 Kayser Y, 2022, ANAL CHIM ACTA, V1192, DOI 10.1016/j.aca.2021.339367 Kayser Y, 2015, NANOSCALE, V7, P9320, DOI 10.1039/c5nr00791g Kayser Y, 2015, J ANAL ATOM SPECTROM, V30, P1086, DOI 10.1039/c4ja00461b King B, 1997, METROLOGIA, V34, P41, DOI 10.1088/0026-1394/34/1/7 Kolbe M, 2005, SPECTROCHIM ACTA B, V60, P505, DOI 10.1016/j.sab.2005.03.018 Kolbe M, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.042512 Kong DX, 2019, PROC SPIE, V10959, DOI 10.1117/12.2515257 Kuhn W, 2009, LECT NOTES COMPUT SC, V5892, P26, DOI 10.1007/978-3-642-10436-7_3 Lemelle L, 2017, TRAC-TREND ANAL CHEM, V91, P104, DOI 10.1016/j.trac.2017.03.008 Lubeck J., 2018, MICROSC MICROANAL, V24, P162, DOI [10.1017/S1431927618013181, DOI 10.1017/S1431927618013181] Luhl L, 2019, J SYNCHROTRON RADIAT, V26, P430, DOI 10.1107/S1600577518016879 Malzer W, 2018, REV SCI INSTRUM, V89, DOI 10.1063/1.5035171 Martinez-Criado G, 2016, J SYNCHROTRON RADIAT, V23, P344, DOI 10.1107/S1600577515019839 Menesguen Y, 2018, X-RAY SPECTROM, V47, P341, DOI 10.1002/xrs.2948 Menesguen Y, 2016, METROLOGIA, V53, P7, DOI 10.1088/0026-1394/53/1/7 Mino L, 2018, REV MOD PHYS, V90, DOI 10.1103/RevModPhys.90.025007 Muller M, 2009, PHYS REV A, V79, DOI 10.1103/PhysRevA.79.032503 Pollakowski B, 2015, ANAL CHEM, V87, P7705, DOI 10.1021/acs.analchem.5b01172 Pollakowski B, 2013, ANAL CHEM, V85, P193, DOI 10.1021/ac3024872 Pollakowski-Herrmann B, 2018, J PHARMACEUT BIOMED, V150, P308, DOI 10.1016/j.jpba.2017.12.007 Quinn PD, 2021, J SYNCHROTRON RADIAT, V28, P1006, DOI 10.1107/S1600577521002502 Schlesiger C, 2015, J ANAL ATOM SPECTROM, V30, P1080, DOI 10.1039/c4ja00303a Seeger S, 2021, ATMOSPHERE-BASEL, V12, DOI 10.3390/atmos12030309 Seim C., 2017, P ANALYTICAL TECHNIQ, V14, DOI [10.1002/pssc.201720017, DOI 10.1002/PSSC.201720017] Shin HJ, 2018, J SYNCHROTRON RADIAT, V25, P878, DOI 10.1107/S1600577518002564 Soltwisch V, 2018, NANOSCALE, V10, P6177, DOI [10.1039/C8NR00328A, 10.1039/c8nr00328a] Steinmann RG, 2020, J SYNCHROTRON RADIAT, V27, P1074, DOI 10.1107/S1600577520007110 Szczerba W, 2010, ANAL BIOANAL CHEM, V398, P1967, DOI 10.1007/s00216-010-4200-z Szlachetko J, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4756691 Troian A, 2018, NANO LETT, V18, P6461, DOI 10.1021/acs.nanolett.8b02957 Tsuji K, 1999, SPECTROCHIM ACTA B, V54, P1881, DOI 10.1016/S0584-8547(99)00143-3 Unterumsberger R, 2021, J ANAL ATOM SPECTROM, V36, P1933, DOI 10.1039/d1ja00103e Unterumsberger R, 2020, J ANAL ATOM SPECTROM, V35, P1022, DOI 10.1039/d0ja00049c Unterumsberger R, 2018, SPECTROCHIM ACTA B, V145, P71, DOI 10.1016/j.sab.2018.04.008 Unterumsberger R, 2018, J ANAL ATOM SPECTROM, V33, P1003, DOI 10.1039/c8ja00046h Unterumsberger R, 2011, ANAL CHEM, V83, P8623, DOI 10.1021/ac202074s Vaid A, 2011, PROC SPIE, V7971, DOI 10.1117/12.881632 Vinson J, 2019, PHYS REV B, V100, DOI [10.1103/PhysRevB.100.085143, 10.1103/physrevb.100.085143] Vinson J, 2017, PHYS REV B, V96, DOI 10.1103/PhysRevB.96.205116 Vinson J, 2016, PHYS REV B, V94, DOI 10.1103/PhysRevB.94.035163 Wahlisch A, 2020, J ANAL ATOM SPECTROM, V35, P1664, DOI 10.1039/d0ja00171f Wansleben M, 2020, J ANAL ATOM SPECTROM, V35, P2679, DOI 10.1039/d0ja00244e Wansleben M, 2019, X-RAY SPECTROM, V48, P105, DOI 10.1002/xrs.3000 Wansleben M, 2019, METROLOGIA, V56, DOI 10.1088/1681-7575/ab40d2 Witte K, 2016, J PHYS CHEM B, V120, P11619, DOI 10.1021/acs.jpcb.6b05791 Zastrau U, 2013, J INSTRUM, V8, DOI 10.1088/1748-0221/8/10/P10006 Zech C, 2021, J ANAL ATOM SPECTROM, V36, P2056, DOI 10.1039/d0ja00491j Zech C, 2021, J MATER CHEM A, V9, P10231, DOI 10.1039/d0ta12011a NR 85 TC 1 Z9 1 U1 9 U2 9 PD JUL PY 2022 VL 12 IS 13 AR 2255 DI 10.3390/nano12132255 WC Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Science & Technology - Other Topics; Materials Science; Physics UT WOS:000825546200001 DA 2022-12-14 ER PT J AU Vermeulen, P Nietner, T Haughey, SA Yang, ZL Tena, N Chmelarova, H van Ruth, S Tomaniova, M Boix, A Han, LJ Elliott, CT Baeten, V Fauhl-Hassek, C AF Vermeulen, Philippe Nietner, Thorben Haughey, Simon A. Yang, Zengling Tena, Noelia Chmelarova, Hana van Ruth, Saskia Tomaniova, Monika Boix, Ana Han, Lujia Elliott, Christopher T. Baeten, Vincent Fauhl-Hassek, Carsten TI Origin authentication of distillers' dried grains and solubles (DDGS)-application and comparison of different analytical strategies SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article DE DDGS; Feed; Authenticity; Traceability; Rapid spectroscopic method; Mass spectrometric method ID GEOGRAPHICAL ORIGIN; CALIBRATION TRANSFER; MASS-SPECTROMETRY; FATTY-ACID; FEED; TRANSFERABILITY; FOOD; STANDARDIZATION; CLASSIFICATION; CHROMATOGRAPHY AB In the context of products from certain regions or countries being banned because of an identified or non-identified hazard, proof of geographical origin is essential with regard to feed and food safety issues. Usually, the product labeling of an affected feed lot shows origin, and the paper documentation shows traceability. Incorrect product labeling is common in embargo situations, however, and alternative analytical strategies for controlling feed authenticity are therefore needed. In this study, distillers' dried grains and solubles (DDGS) were chosen as the product on which to base a comparison of analytical strategies aimed at identifying the most appropriate one. Various analytical techniques were investigated for their ability to authenticate DDGS, including spectroscopic and spectrometric techniques combined with multivariate data analysis, as well as proven techniques for authenticating food, such as DNA analysis and stable isotope ratio analysis. An external validation procedure (called the system challenge) was used to analyze sample sets blind and to compare analytical techniques. All the techniques were adapted so as to be applicable to the DDGS matrix. They produced positive results in determining the botanical origin of DDGS (corn vs. wheat), and several of them were able to determine the geographical origin of the DDGS in the sample set. The maintenance and extension of the databanks generated in this study through the analysis of new authentic samples from a single location are essential in order to monitor developments and processing that could affect authentication. C1 [Vermeulen, Philippe; Baeten, Vincent] Walloon Agr Res Ctr CRA W, Valorisat Agr Prod Dept, B-5030 Gembloux, Belgium. [Nietner, Thorben; Fauhl-Hassek, Carsten] BfR Fed Inst Risk Assessment, D-10589 Berlin, Germany. [Haughey, Simon A.; Elliott, Christopher T.] Queens Univ Belfast, Inst Global Food Secur, Belfast BT9 5AG, Antrim, North Ireland. [Yang, Zengling; Han, Lujia] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China. [Tena, Noelia; Boix, Ana] Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements EC JRC IRMM, B-2440 Geel, Belgium. [Chmelarova, Hana; Tomaniova, Monika] Univ Chem & Technol Prague UCT, Dept Food Anal & Nutr, Prague 16628 6, Czech Republic. [van Ruth, Saskia] Wageningen Univ & Res Ctr WUR, RIKILT Inst Food Safety, NL-6700 AE Wageningen, Netherlands. C3 Federal Institute for Risk Assessment; Queens University Belfast; China Agricultural University; European Commission Joint Research Centre; EC JRC Institute for Reference Materials & Measurements (IRMM); University of Chemistry & Technology, Prague; Wageningen University & Research RP Vermeulen, P (corresponding author), Walloon Agr Res Ctr CRA W, Valorisat Agr Prod Dept, Henseval Bldg,Chaussee Namur 24, B-5030 Gembloux, Belgium. EM p.vermeulen@cra.wallonie.be CR Abbas O., 2012, CHEM ANAL FOOD TECHN, P59, DOI DOI 10.1016/13978-0-12-384862-8.00003-0 [Anonymous], 2001, NEAR INFRARED TECHNO Araghipour N, 2008, FOOD CHEM, V108, P374, DOI 10.1016/j.foodchem.2007.10.056 Azizian H, 2012, J AM OIL CHEM SOC, V89, P2143, DOI 10.1007/s11746-012-2116-9 Baeten V., 2005, STRATEGIES METHODS D Baeten V, 2008, MODERN TECHNIQUES FO, P117 Baeten V, 2014, NEW FOOD, V17, P15 Boix A, 2012, FOOD ADDIT CONTAM A, V29, P1872, DOI 10.1080/19440049.2012.712551 BOUVERESSE E, 1994, ANAL CHIM ACTA, V297, P405, DOI 10.1016/0003-2670(94)00237-1 Cajka T, 2014, J SEP SCI, V37, P912, DOI 10.1002/jssc.201301292 Cajka T, 2011, METABOLOMICS, V7, P500, DOI 10.1007/s11306-010-0266-z Dardenne P, 1992, P 5 INT C NEAR INFR, P453 Debode F, 2012, J AM OIL CHEM SOC, V89, P1249, DOI 10.1007/s11746-012-2007-0 Esslinger S, 2014, FOOD RES INT, V60, P189, DOI 10.1016/j.foodres.2013.10.015 Fernandez Pierna JA, 2012, DETECTION IDENTIFICA, P81 Fernandez-Ahumada E, 2008, J AGR FOOD CHEM, V56, P10135, DOI 10.1021/jf801881n Fisher M, 2014, MIR 162 CHINESE FEED Hajslova J, 2011, TRAC-TREND ANAL CHEM, V30, P204, DOI 10.1016/j.trac.2010.11.001 Haughey SA, 2013, REC ADV FOOD AN RAFA ISO, 215712005 ISO Karoui R., 2008, MODERN TECHNIQUES FO, P27 Kellmann M, 2009, J AM SOC MASS SPECTR, V20, P1464, DOI 10.1016/j.jasms.2009.05.010 Mol HGJ, 2008, ANAL CHEM, V80, P9450, DOI 10.1021/ac801557f Murray I, 2004, P 11 INT C NEAR INFR, P291 Nietner T, 2014, FOOD RES INT, V60, P146, DOI 10.1016/j.foodres.2013.11.002 Nietner T, 2013, J AGR FOOD CHEM, V61, P7225, DOI 10.1021/jf401279w Novotna H, 2012, 1 EUR WORKSH AMB MAS Pedersen MB, 2014, ANIM FEED SCI TECH, V197, P130, DOI 10.1016/j.anifeedsci.2014.07.011 Pierna JAF, 2015, FOOD CHEM, V189, P2, DOI 10.1016/j.foodchem.2014.09.105 Pierna JAF, 2013, BIOTECHNOL AGRON SOC, V17, P547 Pierna JAF, 2010, APPL SPECTROSC, V64, P644, DOI 10.1366/000370210791414353 QSAFFE (Quality and SAfety of Feeds and Food for Europe), 2011, FEED MAT TRAC AUTH Tena N, 2012, 4 INT FEED SAF C MET Tres A, 2014, LWT-FOOD SCI TECHNOL, V59, P215, DOI 10.1016/j.lwt.2014.05.044 Vaclavik L, 2011, J AGR FOOD CHEM, V59, P5919, DOI 10.1021/jf200734x Vaclavik L, 2011, ANAL CHIM ACTA, V685, P45, DOI 10.1016/j.aca.2010.11.018 van der Lee MK, 2008, J CHROMATOGR A, V1186, P325, DOI 10.1016/j.chroma.2007.11.043 Vanden Avenne P, 2013, 26 FEFAC C 2013 EUR Vermeulen P., 2010, Applications of vibrational spectroscopy in food science: Volume II: Analysis of food, drink and related materials, P609 Vermeulen P, 2015, FOOD CHEM, V189, P19, DOI 10.1016/j.foodchem.2014.09.103 Vermeulen P, 2013, ANAL BIOANAL CHEM, V405, P7765, DOI 10.1007/s00216-013-6775-7 von Holst C, 2008, ANAL BIOANAL CHEM, V392, P313, DOI 10.1007/s00216-008-2232-4 Zhao HY, 2013, FOOD CHEM, V138, P1902, DOI 10.1016/j.foodchem.2012.11.037 Zhou XF, 2015, FOOD CHEM, V189, P13, DOI 10.1016/j.foodchem.2014.09.104 Ziggers D, 2014, ALLABOUTFEED NR 45 TC 3 Z9 3 U1 0 U2 27 PD AUG PY 2015 VL 407 IS 21 BP 6447 EP 6461 DI 10.1007/s00216-015-8807-y WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000358646400019 DA 2022-12-14 ER PT J AU Liu, J Zhao, J Deng, XA Yang, SM Xue, CF Wu, YQ Tai, RZ Hu, XK Dai, GL Li, TB Cheng, XB AF Liu, Jie Zhao, Jun Deng, Xiao Yang, Shumin Xue, Chaofan Wu, Yanqing Tai, Renzhong Hu, Xiukun Dai, Gaoliang Li, Tongbao Cheng, Xinbin TI Hybrid application of laser-focused atomic deposition and extreme ultraviolet interference lithography methods for manufacturing of self-traceable nanogratings SO NANOTECHNOLOGY DT Article DE laser-focused atomic deposition (LFAD); EUV interference lithography; grating; uniformity; pitch; traceability; metrological scanning probe microscopy (SPM) AB A novel hybrid method that combines the laser-focused atomic deposition (LFAD) and extreme ultraviolet (EUV) interference lithography has been introduced. The Cr grating manufactured by LFAD has advantages of excellent uniformity, low line edge roughness and its pitch value determined directly by nature constants (i.e. self-traceable). To further enhance the density of the Cr grating, the EUV interference lithography with 13.4 nm wavelength was employed, which replicated the master Cr grating onto a Si wafer with its pitch reduced to half. In order to verify the performance of the gratings manufactured by this novel method, both mask grating (Cr grating) and replicated grating (silicon grating) were calibrated by the metrological large range scanning probe microscope (Met.LR-SPM) at Physikalisch-Technische Bundesanstalt (PTB). The calibrated results show that both gratings have excellent short-term and long-term uniformity: (i) the calibrated position deviation (i.e. nonlinearity) of the grating is below 1 nm; (ii) the deviation of mean pitch values of 6 randomly selected measurement locations is below 0.003 nm. In addition, the mean pitch value of the Cr grating is calibrated as 212.781 0.008 nm (k = 2). It well agrees with its theoretical value of 212.7787 0.0049 nm, confirming the self-traceability of the manufactured grating by the LFAD. The mean pitch value of the Si grating is calibrated as 106.460 0.012 nm (k = 2). It corresponds to the shrinking factor of 0.500 33 of the applied EUV interference lithographic technique. This factor is very close to its theoretical value of 0.5. The uniform, self-traceable gratings fabricated using this novel approach can be well applied as reference materials in calibrating, e.g. the magnification and uniformity of almost all kinds of high resolution microscopes for nanotechnology. C1 [Liu, Jie; Deng, Xiao; Li, Tongbao; Cheng, Xinbin] Tongji Univ, Sch Phys Sci & Engn, Shanghai 200092, Peoples R China. [Zhao, Jun; Yang, Shumin; Xue, Chaofan; Wu, Yanqing; Tai, Renzhong] Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai Synchrotron Radiat Facil, Shanghai 201204, Peoples R China. [Hu, Xiukun; Dai, Gaoliang] Phys Tech Bundesanstalt, D-38116 Braunschweig, Germany. C3 Tongji University; Chinese Academy of Sciences; Shanghai Advanced Research Institute, CAS; Physikalisch-Technische Bundesanstalt (PTB) RP Deng, XA (corresponding author), Tongji Univ, Sch Phys Sci & Engn, Shanghai 200092, Peoples R China.; Dai, GL (corresponding author), Phys Tech Bundesanstalt, D-38116 Braunschweig, Germany. EM 18135@tongji.edu.cn CR Austin MD, 2005, NANOTECHNOLOGY, V16, P1058, DOI 10.1088/0957-4484/16/8/010 Auzelyte V, 2009, J MICRO-NANOLITH MEM, V8, DOI 10.1117/1.3116559 Bloomstein TM, 2006, OPT EXPRESS, V14, P6434, DOI 10.1364/OE.14.006434 Chen J, 2017, MEAS SCI REV, V17, P264, DOI 10.1515/msr-2017-0032 Chen YF, 2015, MICROELECTRON ENG, V135, P57, DOI 10.1016/j.mee.2015.02.042 Dai GL, 2018, MEAS SCI TECHNOL, V29, DOI 10.1088/1361-6501/aaaf8a Dai GL, 2015, MEAS SCI TECHNOL, V26, DOI 10.1088/0957-0233/26/9/095402 Dai GL, 2009, REV SCI INSTRUM, V80, DOI 10.1063/1.3109901 Dai GL, 2005, MEAS SCI TECHNOL, V16, P1241, DOI 10.1088/0957-0233/16/6/001 Dai GL, 2004, REV SCI INSTRUM, V75, P962, DOI 10.1063/1.1651638 Decker JE., 2009, METROLOGIA, V46, DOI [10.1088/0026-1394/46/1A/04001, DOI 10.1088/0026-1394/46/1A/04001] Deng X., 2020, ULTRAMICROSCOP UNPUB Fan D, 2016, SCI REP-UK, V6, DOI 10.1038/srep31301 Garcia R, 2014, NAT NANOTECHNOL, V9, P577, DOI [10.1038/NNANO.2014.157, 10.1038/nnano.2014.157] Golzhauser A, 2000, J VAC SCI TECHNOL B, V18, P3414, DOI 10.1116/1.1319711 GUPTA R, 1995, APPL PHYS LETT, V67, P1378, DOI 10.1063/1.115539 HEYDEMANN PLM, 1981, APPL OPTICS, V20, P3382, DOI 10.1364/AO.20.003382 Jang JW, 2007, SMALL, V3, P600, DOI 10.1002/smll.200600679 Khan M, 2001, J VAC SCI TECHNOL B, V19, P2423, DOI 10.1116/1.1418407 MCCLELLAND JJ, 1993, SCIENCE, V262, P877, DOI 10.1126/science.262.5135.877 McClelland JJ, 2003, J RES NATL INST STAN, V108, P99, DOI 10.6028/jres.108.0010 McGowan RW, 1995, OPT LETT, V20, P2535, DOI 10.1364/OL.20.002535 Misumi I, 2010, MEAS SCI TECHNOL, V21, DOI 10.1088/0957-0233/21/3/035105 MOHARAM MG, 1986, J OPT SOC AM A, V3, P1780, DOI 10.1364/JOSAA.3.001780 O'Sullivan G, 2012, J MOD OPTIC, V59, P855, DOI 10.1080/09500340.2012.678399 Otero R, 2020, NAT ELECTRON, V3, P584, DOI 10.1038/s41928-020-00490-9 Philp D, 1996, ANGEW CHEM INT EDIT, V35, P1154, DOI 10.1002/anie.199611541 Rissanen A, 2010, IEEE SENSOR, P767, DOI 10.1109/ICSENS.2010.5690769 Sanders DP, 2010, CHEM REV, V110, P321, DOI 10.1021/cr900244n Scholten RE, 1997, PHYS REV A, V55, P1331, DOI 10.1103/PhysRevA.55.1331 Sreenivasan SV, 2017, MICROSYST NANOENG, V3, DOI 10.1038/micronano.2017.75 Tarutani S, 2008, J PHOTOPOLYM SCI TEC, V21, P685, DOI 10.2494/photopolymer.21.685 te Sligte E, 2004, APPL PHYS LETT, V85, P4493, DOI 10.1063/1.1818347 TIMP G, 1992, PHYS REV LETT, V69, P1636, DOI 10.1103/PhysRevLett.69.1636 Tinazli A, 2007, NAT NANOTECHNOL, V2, P220, DOI 10.1038/nnano.2007.63 Tseng AA, 2005, J VAC SCI TECHNOL B, V23, P877, DOI 10.1116/1.1926293 WARLAUMONT J, 1989, J VAC SCI TECHNOL B, V7, P1634, DOI 10.1116/1.584505 Wu BQ, 2014, APPL PHYS REV, V1, DOI 10.1063/1.4863412 Xue CF, 2017, APPL SURF SCI, V425, P553, DOI 10.1016/j.apsusc.2017.07.010 Yang SM, 2015, NUCL SCI TECH, V26, P5, DOI 10.13538/j.1001-8042/nst.26.010101 Zharnikov M, 2002, J VAC SCI TECHNOL B, V20, P1793, DOI 10.1116/1.1514665 NR 41 TC 0 Z9 0 U1 7 U2 25 PD APR 23 PY 2021 VL 32 IS 17 AR 175301 DI 10.1088/1361-6528/abdcec WC Nanoscience & Nanotechnology; Materials Science, Multidisciplinary; Physics, Applied SC Science & Technology - Other Topics; Materials Science; Physics UT WOS:000620490000001 DA 2022-12-14 ER PT J AU Wu, XM Zhang, QZ Wang, YZ AF Wu, Xue-Mei Zhang, Qing-Zhi Wang, Yuan-Zhong TI Traceability the provenience of cultivated Paris polyphylla Smith var. yunnanensis using ATR-FTIR spectroscopy combined with chemometrics SO SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY DT Article DE Paris polyphylla Smith var. yunnanensis; Data fusion; ATR-FT1R; PIS-DA; Random forest; Biomass ID NEAR-INFRARED SPECTROSCOPY; DATA FUSION STRATEGY; GEOGRAPHICAL ORIGIN; LIQUID-CHROMATOGRAPHY; MIDINFRARED SPECTROSCOPY; RAPID DISCRIMINATION; QUALITY ASSESSMENT; WILD; OIL; AUTHENTICATION AB The conventional procedures, based on attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), have been developed for the origins traceability of cultivated Paris polyphylla Smith var. yunnanensis (PPY) samples with the help of partial least square discriminant analysis (PLS-DA) and random forest. In this study, a set of 219 batch cultivated PPY samples, containing the cultivation years of 5, 6 and 7, and covering the municipal districts of Chuxiong, Dali, Honghe, Lijiang and Yuxi in Yunnan Province, China, were used to build the discrimination models. Firstly, a visualized analysis was carried out by t-distributed stochastic neighbor embedding (t-SNE) to reduce each data point in a two-dimensional map and make a knowledge of the sample distribution tendency. Secondly, the single spectra data sets of Paridis rhizome and leaf tissues, and the combination of these two data sets with variable selection (mid-level data fusion strategy), were used to establish PLS-DA and random forest models, and parallelly compared the model performance. Results demonstrated that the discrimination ability of PLS-DA preceded the random forest model, and the classification performance was remarkably improved after mid-level data fusion. These results verified each other by 5-, 6- and 7-year old Paridis samples and indicated that the model performance established in the present study was reliable. Besides, five agronomic characters, including the plant height, dry weight of rhizome and leaf tissues, and the allocation of rhizome and leaf were determined and analyzed, results of which indicated that the dry weight and their allocation was significantly different among various origins and fluctuated with the cultivation years. This study was using a comprehensive and green analytical method to discriminate the cultivated Paridis according to their provenances, which was simultaneously benefited for the appropriate cultivation areas selection based on the dry weight of rhizome tissues. (C) 2019 Elsevier B.V. All rights reserved. C1 [Wu, Xue-Mei; Wang, Yuan-Zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. [Wu, Xue-Mei; Zhang, Qing-Zhi] Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University of Chinese Medicine RP Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM boletus@126.com CR Almeida MR, 2013, TALANTA, V117, P305, DOI 10.1016/j.talanta.2013.09.025 Bajoub A, 2017, FOOD CHEM, V215, P245, DOI 10.1016/j.foodchem.2016.07.140 Bhat A., 2017, J CHEM PHARM SCI, V10, P1202 Borras E, 2016, TALANTA, V155, P116, DOI 10.1016/j.talanta.2016.04.040 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Boughorbel S, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0177678 Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324 Chen C., 2017, J AGR SCI, V30, P1320 Chen JB, 2017, SPECTROCHIM ACTA A, V182, P81, DOI 10.1016/j.saa.2017.03.070 Chen X, 2012, GENOMICS, V99, P323, DOI 10.1016/j.ygeno.2012.04.003 Cunningham AB, 2018, J ETHNOPHARMACOL, V222, P208, DOI 10.1016/j.jep.2018.04.048 Dai SY, 2018, TALANTA, V189, P641, DOI 10.1016/j.talanta.2018.07.030 de Santana FB, 2018, FOOD ANAL METHOD, V11, P1927, DOI 10.1007/s12161-017-1142-5 [冯丽丽 Feng Lili], 2015, [中国药学杂志, Chinese Pharmaceutical Journal], V50, P664 Gallart-Mateu D, 2018, TALANTA, V189, P404, DOI 10.1016/j.talanta.2018.07.023 Gisbrecht A, 2015, NEUROCOMPUTING, V147, P71, DOI 10.1016/j.neucom.2013.11.045 Horn B, 2018, FOOD CHEM, V257, P112, DOI 10.1016/j.foodchem.2018.03.007 Hu LQ, 2018, MICROCHEM J, V137, P456, DOI 10.1016/j.microc.2017.12.012 Hu LQ, 2018, SPECTROCHIM ACTA A, V193, P87, DOI 10.1016/j.saa.2017.12.011 KAISER HF, 1960, EDUC PSYCHOL MEAS, V20, P141, DOI 10.1177/001316446002000116 Kang LP, 2017, J PHARMACEUT BIOMED, V142, P252, DOI 10.1016/j.jpba.2017.05.019 KIERS HAL, 1994, PSYCHOMETRIKA, V59, P81, DOI 10.1007/BF02294267 Lee LC, 2018, ANALYST, V143, P3526, DOI 10.1039/c8an00599k Li Heng, 2015, Journal of Hebei University of Technology, V44, P1, DOI 10.14081/j.cnki.hgdxb.2015.02.001 Li Y, 2016, SPECTROCHIM ACTA A, V165, P61, DOI 10.1016/j.saa.2016.04.012 Li YR, 2016, SPECTROCHIM ACTA A, V157, P186, DOI 10.1016/j.saa.2016.01.001 Li Y, 2018, MICROCHEM J, V140, P38, DOI 10.1016/j.microc.2018.04.001 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Liu T, 2017, PLANT DIVERSITY, V39, P60, DOI 10.1016/j.pld.2016.11.006 MATTHEWS BW, 1975, BIOCHIM BIOPHYS ACTA, V405, P442, DOI 10.1016/0005-2795(75)90109-9 Escamilla MN, 2013, TALANTA, V114, P304, DOI 10.1016/j.talanta.2013.05.046 Nunes KM, 2016, FOOD CHEM, V205, P14, DOI 10.1016/j.foodchem.2016.02.158 Obisesan KA, 2017, TALANTA, V170, P413, DOI 10.1016/j.talanta.2017.04.035 Pomerantsev AL, 2018, J CHEMOMETR, V32, DOI 10.1002/cem.3030 Poorter H, 2012, NEW PHYTOL, V193, P30, DOI 10.1111/j.1469-8137.2011.03952.x Qi LM, 2018, FOOD FUNCT, V9, P5903, DOI 10.1039/c8fo01376d Qin XJ, 2018, NAT PRODUCT BIOPROSP, V8, P265, DOI 10.1007/s13659-018-0179-5 Qin XJ, 2018, J ETHNOPHARMACOL, V224, P134, DOI 10.1016/j.jep.2018.05.028 Rajer-Kanduc K, 2003, CHEMOMETR INTELL LAB, V65, P221, DOI 10.1016/S0169-7439(02)00110-7 Rinnan A, 2009, TRAC-TREND ANAL CHEM, V28, P1201, DOI 10.1016/j.trac.2009.07.007 Rodionova OY, 2016, TRAC-TREND ANAL CHEM, V78, P17, DOI 10.1016/j.trac.2016.01.010 Saptoro A, 2012, CHEM PROD PROCESS MO, V7, DOI 10.1515/1934-2659.1645 SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 Sharma Angkita, 2015, [CELLMED, 셀메드], V5, P15, DOI 10.5667/tang.2015.0001 Strobl C, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-25 Sun CL, 2014, STEROIDS, V92, P90, DOI 10.1016/j.steroids.2014.09.008 Sun S.Q., 2011, INFRARED SPECTROSCOP SUN Su-gin, 2010, ANAL TRADITIONAL CHI Sun SQ, 2010, PLANTA MED, V76, P1987, DOI 10.1055/s-0030-1250520 Sun WJ, 2017, SPECTROCHIM ACTA A, V171, P72, DOI 10.1016/j.saa.2016.07.039 Turker-Kaya S, 2017, MOLECULES, V22, DOI 10.3390/molecules22010168 van der Maaten L, 2008, J MACH LEARN RES, V9, P2579 Wang JY, 2018, J INNOV OPT HEAL SCI, V11, DOI 10.1142/S1793545818500049 Wang JM, 2018, ANAL LETT, V51, P575, DOI 10.1080/00032719.2017.1340949 Wang YZ, 2018, PLANT GROWTH REGUL, V84, P373, DOI 10.1007/s10725-017-0348-2 Wu XM, 2018, SPECTROCHIM ACTA A, V205, P479, DOI 10.1016/j.saa.2018.07.067 Wu XM, 2018, VIB SPECTROSC, V96, P125, DOI 10.1016/j.vibspec.2018.04.001 Wu Z, 2017, J NAT MED-TOKYO, V71, P139, DOI 10.1007/s11418-016-1043-8 Yang H., 2014, ADV MAT RES, V926, P969, DOI DOI 10.4028/WWW.SCIENTIFIC.NET/AMR.926-930.969 Yang LF, 2018, J MOL STRUCT, V1165, P37, DOI 10.1016/j.molstruc.2018.03.061 Yang YG, 2017, BIOMED CHROMATOGR, V31, DOI 10.1002/bmc.3913 Yang YG, 2017, J NAT MED-TOKYO, V71, P148, DOI 10.1007/s11418-016-1044-7 Yang Y, 2018, SPECTROCHIM ACTA A, V191, P233, DOI 10.1016/j.saa.2017.10.019 [尹显梅 Yin Xianmei], 2017, [中草药, Chinese Traditional and Herbal Drugs], V48, P1199 Yu K, 2013, FUNCT PLANT BIOL, V40, P393, DOI 10.1071/FP12257 Zhang LL, 2018, J ETHNOPHARMACOL, V224, P119, DOI 10.1016/j.jep.2018.05.029 Zhang T, 2010, J PHARMACEUT BIOMED, V51, P114, DOI 10.1016/j.jpba.2009.08.020 Zhao YL, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0089100 Zhu Y., 2015, AM J ANAL CHEM, V6, P480, DOI DOI 10.4236/AJAC.2015.65047 Zhu Y, 2014, J MOL STRUCT, V1069, P272, DOI 10.1016/j.molstruc.2014.01.069 NR 70 TC 15 Z9 17 U1 2 U2 28 PD APR 5 PY 2019 VL 212 BP 132 EP 145 DI 10.1016/j.saa.2019.01.008 WC Spectroscopy SC Spectroscopy UT WOS:000471307100017 DA 2022-12-14 ER PT J AU Bustamante, V Meza, P Roman, JC Garcia, P AF Bustamante, Veronica Meza, Paulina Roman, Juan C. Garcia, Patricia TI Evaluation of an automated streaking system of urine samples for urine cultures SO REVISTA CHILENA DE INFECTOLOGIA DT Article DE Urine culture; streaking; automatization; Previ Isola; microbiology ID 1ST EVALUATION AB Introduction: Automated systems have simplified laboratory workflow, improved standardization, traceability and diminished human errors and workload. Although microbiology laboratories have little automation, in recent years new tools for automating pre analytical steps have appeared. Objectives: To assess the performance of an automated streaking machine for urine cultures and its agreement with the conventional manual plating method for semi quantitative colony counts. Materials and Methods: 495 urine samples for urinary culture were inoculated in CPS (R) agar using our standard protocol and the PREVI (TM) Isola. Rates of positivity, negativity, polymicrobial growth, bacterial species, colony counts and re-isolation requirements were compared. Results: Agreement was achieved in 98.97% of the positive/negative results, in 99.39% of the polymicrobial growth, 99.76% of bacterial species isolated and in 98.56 % of colony counts. The need for re-isolation of colonies decreased from 12.1% to 1.1% using the automated system. Discussion: PREVI (TM) Isola's performance was as expected, time saving and improving bacterial isolation. It represents a helpful tool for laboratory automation. C1 [Garcia, Patricia] Pontificia Univ Catolica Chile, Escuela Med, Dept Lab Clin, Santiago, Chile. [Bustamante, Veronica] Pontificia Univ Catolica Chile, Residente Lab Clin, Santiago, Chile. [Meza, Paulina; Roman, Juan C.] Red Salud UC CHRISTUS, Serv Lab Clin, Microbiol Lab, Santiago, Chile. C3 Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile RP Garcia, P (corresponding author), Pontificia Univ Catolica Chile, Escuela Med, Dept Lab Clin, Alameda 340, Santiago, Chile. EM pgarcia@med.puc.cl CR Bourbeau PP, 2009, J CLIN MICROBIOL, V47, P1101, DOI 10.1128/JCM.01963-08 Cerda J, 2008, REV CHIL PEDIATR-CHI, V79, P54 Chapin K C, 2012, 112 GEN M ASM SAN FR Dumitrescu O, 2011, CLIN MICROBIOL INFEC, V17, P649, DOI 10.1111/j.1469-0691.2011.03511.x Glasson JH, 2008, J CLIN MICROBIOL, V46, P1281, DOI 10.1128/JCM.01687-07 Greub G, 2011, CLIN MICROBIOL INFEC, V17, P655, DOI 10.1111/j.1469-0691.2011.03513.x McCarter YS, 2009, CUMITECH 2C LAB DIAG Mischnik A, 2012, J CLIN MICROBIOL, V50, P2732, DOI 10.1128/JCM.05501-11 Mulatero F, 2011, CLIN MICROBIOL INFEC, V17, P661, DOI 10.1111/j.1469-0691.2011.03520.x Talec R., 2010, IRBM NEWS, V31, P15 Verhoef-Verhage A P J, 2009, 19 ECCMID EUR C CLIN, P890 Viera AJ, 2005, FAM MED, V37, P360 Young DS, 2000, CLIN CHEM, V46, P740 NR 13 TC 0 Z9 0 U1 0 U2 8 PD DEC PY 2014 VL 31 IS 6 BP 670 EP 675 DI 10.4067/S0716-10182014000600005 WC Infectious Diseases SC Infectious Diseases UT WOS:000347596600005 DA 2022-12-14 ER PT J AU Liu, P Cui, XY Zhang, ZR Zhou, WW Long, Y AF Liu, Pan Cui, Xiaoyan Zhang, Ziran Zhou, Wenwen Long, Yue TI Pricing strategies of low-carbon enterprises in the Yellow River Basin considering demand information and traceability services SO KYBERNETES DT Article; Early Access DE Yellow River Basin; Big data; Blockchain; LWSC; Pricing ID SUPPLY CHAIN; COORDINATION; CONTRACTS; INTERNET; POLICIES; SYSTEM; YIELD; MODEL AB Purpose The purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level). Design/methodology/approach We choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed. Findings Results indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues. Originality/value This paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background. C1 [Liu, Pan; Cui, Xiaoyan; Zhang, Ziran] Henan Agr Univ, Zhengzhou, Peoples R China. [Zhou, Wenwen] Beijing Univ Technol, Beijing, Peoples R China. [Long, Yue] Chongqing Technol & Business Univ, Chongqing, Peoples R China. C3 Henan Agricultural University; Beijing University of Technology; Chongqing Technology & Business University RP Liu, P (corresponding author), Henan Agr Univ, Zhengzhou, Peoples R China. EM hnycliupan@163.com CR Ali MS, 2019, IEEE COMMUN SURV TUT, V21, P1676, DOI 10.1109/COMST.2018.2886932 Bhattacharyya M, 2019, RAIRO-OPER RES, V53, P1899, DOI 10.1051/ro/2018120 Cai H.J., 2019, THEORETICAL INVESTIG, P94 Cai L., 2020, J GUIZHOU U FINANCE, V1, P78 Cao H.P., 2020, REFORM, V321, P37 Chai D., 2020, COMPLEXITY VOL, V2020, P1 [陈化飞 Chen Huafei], 2019, [计算机工程与应用, Computer Engineering and Application], V55, P265 Chen K.B., 2012, CHINESE J MANAGEMENT, V20, P68 Chen S, 2021, WIREL COMMUN MOB COM, V2021, DOI 10.1155/2021/6626480 Choi TM, 2020, EUR J OPER RES, V284, P1031, DOI 10.1016/j.ejor.2020.01.049 [丁斌 Ding Bin], 2014, [系统工程, Systems Engineering], V32, P1 Du SF, 2017, ANN OPER RES, V255, P569, DOI 10.1007/s10479-015-1988-0 Fan ZP, 2022, ANN OPER RES, V309, P837, DOI 10.1007/s10479-020-03729-y Fei J., 2020, FRONTIERS EC MANAGEM, V1, P188 Ferrer G, 2006, MANAGE SCI, V52, P15, DOI 10.1287/mnsc.1050.0465 Gong W.F., 2021, YELLOW RIVER, V43, P1 Han H.Y., 2020, EC PROBLEMS, P1 Han SH, 2018, ANN OPER RES, V270, P155, DOI [10.1007/s10479-017-2696-8, 10.1007/s10479-016-2386-y] Hayrutdinov S, 2020, J ADV TRANSPORT, V2020, DOI 10.1155/2020/5635404 Hussain AA, 2021, T EMERG TELECOMMUN T, V32, DOI 10.1002/ett.4268 Idrees S.M., 2021, ELECT, V10, P1 Ji Wei, 2021, Research of Environmental Sciences, V34, P1700, DOI 10.13198/j.issn.1001-6929.2021.03.17 Jia X.Y., 2020, MERCANTILE THEORY, P49 Jiang W, 2016, DISCRETE DYN NAT SOC, V2016, DOI 10.1155/2016/9645087 [金凤君 Jin Fengjun], 2020, [资源科学, Resources Science], V42, P127 Jin X., 2020, ECOL ECON, V36, P50 Khan SN, 2021, PEER PEER NETW APPL, V14, P2901, DOI 10.1007/s12083-021-01127-0 Li B., 2021, INT J ENV RES PUB HE, V18, P1 Li G., 2017, ASIA PACIFIC J OPERA, V34, p1740003 Li HB, 2021, FRONT ENERGY RES, V9, DOI 10.3389/fenrg.2021.671133 [李剑 Li Jian], 2021, [中国管理科学, Chinese Journal of Management Science], V29, P131 [李少林 Li Shaolin], 2021, [中国环境科学, China Environmental Science], V41, P1455 Liang X., 2021, CHINESE J MANAGEMENT, V2021, P1 Lin Z.B., 2021, COMPUT INTEGR MANUF, V2021, P1 Liu ML, 2021, ENVIRON SCI POLLUT R, V28, P19969, DOI 10.1007/s11356-020-09608-0 Liu P., 2020, COAL EC RES, V40, P20 Liu P, 2020, J CLEAN PROD, V277, DOI 10.1016/j.jclepro.2020.123646 Liu P, 2019, J CLEAN PROD, V210, P343, DOI 10.1016/j.jclepro.2018.10.328 Liu PD, 2019, MATH PROBL ENG, V2019, DOI 10.1155/2019/3258018 [刘英 Liu Ying], 2021, [中国管理科学, Chinese Journal of Management Science], V29, P160 Ma DQ, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041685 Ma P., 2015, YUEJIANG ACAD J, V7, P51 Niu BZ, 2019, TRANSPORT RES E-LOG, V123, P29, DOI 10.1016/j.tre.2019.01.011 Pal B, 2018, INT J MANAG SCI ENG, V13, P33, DOI 10.1080/17509653.2017.1302829 Peng HJ, 2018, J CLEAN PROD, V205, P291, DOI 10.1016/j.jclepro.2018.09.038 Rabah K., 2017, MARA INT J SCI RES P, V1, P55 Ren B.P., 2021, YELLOW RIVER, V43, P1 Roy A, 2018, ANN OPER RES, V260, P481, DOI 10.1007/s10479-015-1996-0 Sana SS, 2022, ANN OPER RES, V315, P1997, DOI 10.1007/s10479-020-03895-z Sana SS, 2020, J RETAIL CONSUM SERV, V55 Shen B., 2020, TRANSPORTATION RES P, V142, P1 Tozanli O., 2020, SUSTAINABILITY VOL, V12, P1 Wang C, 2021, EURASIP J WIREL COMM, V2021, DOI 10.1186/s13638-021-01958-8 [闻卉 Wen Hui], 2020, [运筹与管理, Operations Research and Management Science], V29, P65 Xiang Z., 2020, COMPUTERS IND ENG VO, V145, P1 Xiang ZH, 2019, J CLEAN PROD, V220, P1180, DOI 10.1016/j.jclepro.2019.01.310 Xiao DY, 2021, INT J ENV RES PUB HE, V18, DOI [10.3390/ijerph18041844/, 10.3390/ijerph18041844] Xu FC, 2019, J CLEAN PROD, V209, P782, DOI 10.1016/j.jclepro.2018.10.240 Xu X., 2019, CHINA IND EC, P5, DOI [10.19581/j.cnki.ciejournal.2019.04.001, DOI 10.19581/J.CNKI.CIEJOURNAL.2019.04.001] Zhang T, 2017, ASIA PAC J OPER RES, V34, DOI 10.1142/S0217595917400188 Zhang X.H., 2018, CHINA BUSINESS MARKE, V32, P42 [张志 Zhang Zhi], 2021, [计算机应用研究, Application Research of Computers], V38, P1314 [周茂森 Zhou Maosen], 2018, [系统工程理论与实践, Systems Engineering-Theory & Practice], V38, P2993 NR 63 TC 0 Z9 0 U1 43 U2 65 DI 10.1108/K-06-2021-0529 EA NOV 2021 WC Computer Science, Cybernetics SC Computer Science UT WOS:000718167800001 DA 2022-12-14 ER PT J AU Myers, GL Kimberly, MM Waymack, PP Smith, SJ Cooper, GR Sampson, EJ AF Myers, GL Kimberly, MM Waymack, PP Smith, SJ Cooper, GR Sampson, EJ TI A reference method laboratory network for cholesterol: A model for standardization and improvement of clinical laboratory measurements SO CLINICAL CHEMISTRY DT Article ID NATIONAL REFERENCE SYSTEM; CANDIDATE REFERENCE METHOD; SERUM-CHOLESTEROL; MASS-SPECTROMETRY; DEFINITIVE METHOD; DISEASE; ACCURACY; PROGRAM; RISK AB Background: Accurate and precise measurement of blood cholesterol plays a central role in the National Cholesterol Education Program's strategy to reduce the morbidity and mortality attributable to coronary heart disease. Matrix effects hamper the ability of manufacturers to adequately calibrate and validate traceability to the National Reference System for Cholesterol (NRS/ CHOL). CDC created the Cholesterol Reference Method Laboratory Network (CRMLN) to improve cholesterol measurement by assisting manufacturers of in vitro diagnostic products with validation of the traceability of their assays to the NRS/CHOL, Methods: CRMLN laboratories established the CDC cholesterol reference method (modification of the Abell-Levy-Brodie-Kendall chemical method) and are standardized using CDC frozen serum reference materials. CRMLN laboratories use common quality-control materials and participate in monthly external performance evaluations conducted by CDC. The CRMLN performance criteria require member laboratories to agree with CDC within +/- 1.0% and maintain a CV less than or equal to2.0%. Results: From 1995 to 2000, the CRMLN laboratories met the accuracy criterion 97% of the time and the precision criterion 99% of the time. During this time period, the CRMLN maintained an average bias to CDC of 0.01% and an average collective CV of 0.33%, Conclusions: CDC established the CRMLN as the first international reference method laboratory network. The CRMLN assists manufacturers in the validation of the calibration of their diagnostic products so that clinical laboratories can measure blood cholesterol more reliably. The CRMLN can serve as a model for other clinical analytes where traceability to a hierarchy of methods is needed and matrix effects of the field methods with processed calibrators or reference materials are present. (C) 2000 American Association for Clinical Chemistry. C1 Ctr Dis Control & Prevent, Atlanta, GA 30341 USA. C3 Centers for Disease Control & Prevention - USA RP Myers, GL (corresponding author), Ctr Dis Control & Prevent, 4770 Buford Hwy NE,F25, Atlanta, GA 30341 USA. CR ABELL LL, 1952, J BIOL CHEM, V195, P357 BENNETT ST, 1992, CLIN CHEM, V38, P651 BERNERT JT, 1991, CLIN CHEM, V37, P2053 BOWERS GN, 1988, CLIN CHEM, V34, P192 CLEEMAN JI, 1988, ARCH INTERN MED, V148, P36, DOI 10.1001/archinte.148.1.36 COHEN A, 1980, CLIN CHEM, V26, P854 COOPER GR, 1986, CLIN CHEM, V32, P921 DUNCAN IW, 1988, PROCEDURE PROPOSED C ELLERBE P, 1990, CLIN CHEM, V36, P370 FASCE CF, 1973, CLIN CHEM, V19, P5 GRUNDY SM, 1993, JAMA-J AM MED ASSOC, V269, P3015, DOI 10.1001/jama.269.23.3015 GUNTER EW, 1996, VIIQ1220 CDCP *HLTH CAR FIN ADM, ONL SURV CERT REP SY Kimberly MM, 1999, CLIN CHEM, V45, P1803 KOCH DD, 1988, JAMA-J AM MED ASSOC, V260, P2252 *LAB STAND PAN, 1990, NIH PUBL *LAB STAND PAN NAT, 1988, CLIN CHEM, V34, P193 LENFANT C, 1986, CIRCULATION, V73, P855, DOI 10.1161/01.CIR.73.5.855 MILLER WG, 1997, HDB LIPOPROTEIN TEST, P199 MYERS GL, 1989, CLIN LAB MED, V9, P105, DOI 10.1016/S0272-2712(18)30645-0 NAITO HK, 1988, CLIN CHEM, V34, pB84 *NAT COMM CLIN LAB, 1995, EP9A NCCLS *NAT GLYC STAND PR, 1997, DIABETES S1, V46, pA151 National Committee for Clinical Laboratory Standards, 1999, C37A NCCLS RIFKIND BM, 1984, JAMA-J AM MED ASSOC, V251, P365 RIFKIND BM, 1984, JAMA-J AM MED ASSOC, V251, P351 Ross JW, 1998, ARCH PATHOL LAB MED, V122, P587 SOLBERG LA, 1983, ARTERIOSCLEROSIS, V3, P187, DOI 10.1161/01.ATV.3.3.187 STAMLER J, 1986, JAMA-J AM MED ASSOC, V256, P2823, DOI 10.1001/jama.256.20.2823 Steinberg D, 1999, JAMA-J AM MED ASSOC, V282, P2043, DOI 10.1001/jama.282.21.2043 Thienpont LM, 1996, CLIN CHEM, V42, P531 VANDERLINDE RE, 1989, CLIN LAB MED, V9, P89, DOI 10.1016/S0272-2712(18)30644-9 Yun DD, 1997, CARDIOLOGY, V88, P223, DOI 10.1159/000177335 NR 33 TC 119 Z9 131 U1 0 U2 9 PD NOV PY 2000 VL 46 IS 11 BP 1762 EP 1772 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000165228700007 DA 2022-12-14 ER PT J AU Brix, R Noguerol, TN Pina, B Balaam, J Nilsen, AJ Tollefsen, KE Levy, W Schramm, KW Barcelo, D AF Brix, Rikke Noguerol, Tania-Noelia Pina, Benjamin Balaam, Jan Nilsen, Anja Julie Tollefsen, Knut-Erik Levy, Walkiria Schramm, Karl-Werner Barcelo, Damia TI Evaluation of the suitability of recombinant yeast-based estrogenicity assays as a pre-screening tool in environmental samples SO ENVIRONMENT INTERNATIONAL DT Article DE Screening methods; Endocrine disruption; Bioassay; Wastewater ID TREATMENT PLANT EFFLUENTS; TANDEM MASS-SPECTROMETRY; WATER TREATMENT PLANTS; WASTE-WATER; NONYLPHENOL ETHOXYLATES; ENDOCRINE DISRUPTORS; DEGRADATION-PRODUCTS; CHEMICAL-ANALYSIS; SURFACE WATERS; NE SPAIN AB This paper presents a study evaluating the suitability of recombinant yeast-based estrogenicity assays as a pre-screening tool for monitoring of the chemical status of water bodies in support of the Water Framework Directive (WFD). Three different recombinant yeast-based assays were evaluated; the Yeast Estrogen Screen (YES), the Recombinant Yeast Assay (RYA) and the Rikilt Estrogen bioAssay (REA), of which the YES assay was employed by two different laboratories. No significant difference between the performance of neither the different laboratories, nor the different yeast-assays was observed. Six batches of eleven samples each were analysed one week apart by the four participating laboratories and the robustness, repeatability and reproducibility of the participating yeast-based assays were evaluated. The setup included a correlation between bioassay results and results from chemical target analysis, which gave valuable information in the evaluation of the assays' performance. A good agreement was found between chemical and bioassay results, showing that the yeast-based assays can give valuable information in WFD work. However, the low sensitivity of the assays towards alkylphenols needs to be significantly improved if they are to be used for monitoring of these compounds. The study further led to suggestions on ways to improve traceability and quality assurance of the yeast-based assays. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Brix, Rikke; Noguerol, Tania-Noelia; Pina, Benjamin; Barcelo, Damia] IDAEA, Dept Environm Chem, Barcelona 08034, Spain. [Balaam, Jan] Cefas, Lowestoft Lab, Lowestoft NR33 0HT, Suffolk, England. [Nilsen, Anja Julie; Tollefsen, Knut-Erik] Norwegian Inst Water Res NIVA, Sect Ecotoxicol & Risk Assessment, NO-0349 Oslo, Norway. [Levy, Walkiria; Schramm, Karl-Werner] GSF Natl Res Ctr Environm & Hlth, Inst Ecol Chem, D-85764 Neuherberg, Germany. [Schramm, Karl-Werner] Tech Univ Munich, Dept Biowissensch Grundlagen, D-85350 Freising Weihenstephan, Germany. [Barcelo, Damia] Univ Girona, ICRA, Girona, Spain. C3 Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Centro de Investigacion y Desarrollo Pascual Vila (CID-CSIC); CSIC - Instituto de Diagnostico Ambiental y Estudios del Agua (IDAEA); Centre for Environment Fisheries & Aquaculture Science; Norwegian Institute for Water Research (NIVA); Helmholtz Association; Helmholtz-Center Munich - German Research Center for Environmental Health; Technical University of Munich; Institut Catala de Recerca de l'Aigua (ICRA); Universitat de Girona RP Barcelo, D (corresponding author), IDAEA, Dept Environm Chem, C Jordi Girona 18-26, Barcelona 08034, Spain. EM dbcqam@cid.csic.es CR Andersen AG, 2000, HUM REPROD, V15, P366, DOI 10.1093/humrep/15.2.366 Andersen HR, 1999, ENVIRON HEALTH PERSP, V107, P89, DOI 10.2307/3434476 *AOAC, 1987, Z SCOR Bovee TFH, 2004, GENE, V325, P187, DOI 10.1016/j.gene.2003.10.015 BRIX R, 2009, RAPID CHEM BIOL TECH, P371 Cespedes R, 2004, ANAL BIOANAL CHEM, V378, P697, DOI 10.1007/s00216-003-2303-5 Cespedes R, 2005, CHEMOSPHERE, V61, P1710, DOI 10.1016/j.chemosphere.2005.03.082 Coldham NG, 1997, ENVIRON HEALTH PERSP, V105, P734, DOI 10.1289/ehp.97105734 de Alda MJL, 2002, ANALYST, V127, P1299, DOI 10.1039/b207658f DEAN RB, 1951, ANAL CHEM, V23, P636, DOI 10.1021/ac60052a025 Dhooge W, 2006, ANAL BIOANAL CHEM, V386, P1419, DOI 10.1007/s00216-006-0669-x Diaz A, 2002, ANAL CHEM, V74, P3869, DOI 10.1021/ac020124p Diaz-Cruz MS, 2003, J MASS SPECTROM, V38, P917, DOI 10.1002/jms.529 Ellison SLR, 2009, PRACTICAL STATISTICS FOR THE ANALYTICAL SCIENTIST: A BENCH GUIDE, SECOND EDITION, P1 Farre M, 2002, ANAL CHIM ACTA, V456, P19, DOI 10.1016/S0003-2670(01)00908-4 Farre M, 2007, J CHROMATOGR A, V1160, P166, DOI 10.1016/j.chroma.2007.05.032 Farre M, 2006, ANAL BIOANAL CHEM, V385, P1001, DOI 10.1007/s00216-006-0562-7 Garcia-Reyero N, 2004, ENVIRON TOXICOL CHEM, V23, P705, DOI 10.1897/03-141 Garcia-Reyero N, 2001, ENVIRON TOXICOL CHEM, V20, P1152, DOI 10.1002/etc.5620200603 Gonzalez S, 2004, J CHROMATOGR A, V1052, P111, DOI 10.1016/j.chroma.2004.08.047 GREEN S, 1991, OESTROGEN RECEPTOR P Kuster M, 2008, J HYDROL, V358, P112, DOI 10.1016/j.jhydrol.2008.05.030 Legler J, 2002, SCI TOTAL ENVIRON, V293, P69, DOI 10.1016/S0048-9697(01)01146-9 Murk AJ, 2002, ENVIRON TOXICOL CHEM, V21, P16, DOI 10.1002/etc.5620210103 Noguerol TN, 2006, TALANTA, V69, P351, DOI 10.1016/j.talanta.2005.09.044 Pawlowski S, 2004, TOXICOL IN VITRO, V18, P129, DOI 10.1016/j.tiv.2003.08.006 Pawlowski S, 2003, TOXICOL SCI, V75, P57, DOI 10.1093/toxsci/kfg162 Reddy S, 2005, ENVIRON TOXICOL CHEM, V24, P1041, DOI 10.1897/04-167R.1 Rodgers-Gray TP, 2001, ENVIRON SCI TECHNOL, V35, P462, DOI 10.1021/es001225c Rodriguez-Mozaz S, 2004, ANAL CHEM, V76, P6998, DOI 10.1021/ac049051v Routledge EJ, 1996, ENVIRON TOXICOL CHEM, V15, P241, DOI 10.1002/etc.5620150303 Rutishauser BV, 2004, ENVIRON TOXICOL CHEM, V23, P857, DOI 10.1897/03-286 Schmitt M, 2005, WATER RES, V39, P3211, DOI 10.1016/j.watres.2005.05.034 SCHNEIDER JC, 1991, METHOD ENZYMOL, V194, P373 Sole M, 2002, AQUAT TOXICOL, V60, P233, DOI 10.1016/S0166-445X(02)00009-7 Thomas KV, 2004, ENVIRON TOXICOL CHEM, V23, P471, DOI 10.1897/03-163 Tollefsen KE, 2007, MAR POLLUT BULL, V54, P277, DOI 10.1016/j.marpolbul.2006.07.012 NR 37 TC 24 Z9 25 U1 0 U2 17 PD MAY PY 2010 VL 36 IS 4 BP 361 EP 367 DI 10.1016/j.envint.2010.02.004 WC Environmental Sciences SC Environmental Sciences & Ecology UT WOS:000277526200008 DA 2022-12-14 ER PT J AU Dadousis, C Munoz, M Ovilo, C Fabbri, MC Araujo, JP Bovo, S Potokar, MC Charneca, R Crovetti, A Gallo, M Garcia-Casco, JM Karolyi, D Kusec, G Martins, JM Mercat, MJ Pugliese, C Quintanilla, R Radovic, C Razmaite, V Ribani, A Riquet, J Savic, R Schiavo, G Skrlep, M Tinarelli, S Usai, G Zimmer, C Fontanesi, L Bozzi, R AF Dadousis, Christos Munoz, Maria Ovilo, Cristina Fabbri, Maria Chiara Araujo, Jose Pedro Bovo, Samuele Potokar, Marjeta Candek Charneca, Rui Crovetti, Alessandro Gallo, Maurizio Garcia-Casco, Juan Maria Karolyi, Danijel Kusec, Goran Martins, Jose Manuel Mercat, Marie-Jose Pugliese, Carolina Quintanilla, Raquel Radovic, Cedomir Razmaite, Violeta Ribani, Anisa Riquet, Juliet Savic, Radomir Schiavo, Giuseppina Skrlep, Martin Tinarelli, Silvia Usai, Graziano Zimmer, Christoph Fontanesi, Luca Bozzi, Riccardo TI Admixture and breed traceability in European indigenous pig breeds and wild boar using genome-wide SNP data SO SCIENTIFIC REPORTS DT Article ID GENETIC DIVERSITY; ANCIENT DNA; MARKERS; ASSIGNMENT; SELECTION; DOMESTICATION; SIGNATURES; ANCESTRY AB Preserving diversity of indigenous pig (Sus scrofa) breeds is a key factor to (i) sustain the pork chain (both at local and global scales) including the production of high-quality branded products, (ii) enrich the animal biobanking and (iii) progress conservation policies. Single nucleotide polymorphism (SNP) chips offer the opportunity for whole-genome comparisons among individuals and breeds. Animals from twenty European local pigs breeds, reared in nine countries (Croatia: Black Slavonian, Turopolje; France: Basque, Gascon; Germany: Schwabisch-Hallisches Schwein; Italy: Apulo Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda; Lithuania: Indigenous Wattle, White Old Type; Portugal: Alentejana, Bisara; Serbia: Moravka, Swallow-Bellied Mangalitsa; Slovenia: Krskopolje pig; Spain: Iberian, Majorcan Black), and three commercial breeds (Duroc, Landrace and Large White) were sampled and genotyped with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip. A dataset of 51 Wild Boars from nine countries was also added, summing up to 1186 pigs (similar to 49 pigs/breed). The aim was to: (i) investigate individual admixture ancestries and (ii) assess breed traceability via discriminant analysis on principal components (DAPC). Albeit the mosaic of shared ancestries found for Nero Siciliano, Sarda and Moravka, admixture analysis indicated independent evolvement for the rest of the breeds. High prediction accuracy of DAPC mark SNP data as a reliable solution for the traceability of breed-specific pig products. C1 [Dadousis, Christos; Fabbri, Maria Chiara; Crovetti, Alessandro; Pugliese, Carolina; Bozzi, Riccardo] Univ Firenze, Dipartimento Sci & Tecnol Agr Alimentari Ambienta, I-50144 Florence, Italy. [Munoz, Maria; Ovilo, Cristina; Garcia-Casco, Juan Maria] Inst Nacl Invest & Tecnol Agr & Alimentaria INIA, Dept Mejora Genet Anim, Crta Coruna,Km 7,5, Madrid 28040, Spain. [Araujo, Jose Pedro] Inst Politecn Viana Do Castelo, Escola Super Agr, Ctr Invest Montanha CIMO, P-4990706 Ponte do Lima, Portugal. [Bovo, Samuele; Ribani, Anisa; Schiavo, Giuseppina; Fontanesi, Luca] Univ Bologna, Dept Agr & Food Sci, Div Anim Sci, Viale Fanin 46, I-40127 Bologna, Italy. [Potokar, Marjeta Candek; Skrlep, Martin] Agr Inst Slovenia, Hacquetova 17, Ljubljana 1000, Slovenia. [Charneca, Rui; Martins, Jose Manuel] Univ Evora, Med Mediterranean Inst Agr Environm & Dev, Polo Mitra, Ap 94, P-7006554 Evora, Portugal. [Charneca, Rui; Martins, Jose Manuel] Univ Evora, Escola Ciencias & Tecnol, Polo Mitra, Ap 94, P-7006554 Evora, Portugal. [Gallo, Maurizio; Tinarelli, Silvia] Assoc Nazl Allevatori Suini ANAS, Via Nizza 53, I-00198 Rome, Italy. [Karolyi, Danijel] Univ Zagreb, Fac Agr, Dept Anim Sci, Svetosimunska C 25, Zagreb 10000, Croatia. [Kusec, Goran] Josip Juraj Strossmayer Univ Osijek, Fac Agrobiotech Sci Osijek, Vladimira Preloga 1, Osijek 31000, Croatia. [Mercat, Marie-Jose] IFIP Inst Porc, BP 35104, F-35651 Le Rheu, France. [Quintanilla, Raquel] Inst Res & Technol Food & Agr IRTA, Programa Genet & Mejora Anim, Barcelona 08140, Spain. [Radovic, Cedomir] Inst Anim Husb, Dept Pig Breeding & Genet, Belgrade 11080, Serbia. [Razmaite, Violeta] Lithuanian Univ Hlth Sci, Anim Sci Inst, Baisogala, Lithuania. [Riquet, Juliet] Univ Toulouse, INRA, Genet Physiol & Syst Elevage GenPhySE, Chemin Borde Rouge 24, F-31326 Castanet Tolosan, France. [Savic, Radomir] Univ Belgrade, Fac Agr, Nemanjina 6, Belgrade 11080, Serbia. [Usai, Graziano] AGRIS SARDEGNA, I-07100 Sassari, Italy. [Zimmer, Christoph] Bauerl Erzeugergemeinschaft Schwabisch Hall, Schwabisch Hall, Germany. C3 University of Florence; Instituto Nacional Investigacion Tecnologia Agraria Alimentaria (INIA); Polytechnic Institute of Viana do Castelo; University of Bologna; Agricultural Institute Slovenia; University of Evora; University of Evora; University of Zagreb; University of Zagreb, School of Dental Medicine; University of JJ Strossmayer Osijek; IRTA; Lithuanian University of Health Sciences; INRAE; University of Belgrade RP Dadousis, C (corresponding author), Univ Firenze, Dipartimento Sci & Tecnol Agr Alimentari Ambienta, I-50144 Florence, Italy. EM christos.dadousis@unipr.it CR Abbott A, 2015, NATURE, V519, P397, DOI 10.1038/519397a Alexander D., ADMIXTURE VERSION 1 Alexander DH, 2009, GENOME RES, V19, P1655, DOI 10.1101/gr.094052.109 Alonso ME, 2020, ANIMALS-BASEL, V10, DOI 10.3390/ani10030385 [Anonymous], **DATA OBJECT** Blutke A, 2017, MOL METAB, V6, P931, DOI 10.1016/j.molmet.2017.06.004 Boitard S, 2010, ANIM GENET, V41, P608, DOI 10.1111/j.1365-2052.2010.02061.x Bovo S, 2020, GENET SEL EVOL, V52, DOI 10.1186/s12711-020-00553-7 Deperi SI, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0194398 Dimauro C, 2015, SMALL RUMINANT RES, V128, P27, DOI 10.1016/j.smallrumres.2015.05.001 Dimauro C, 2013, ANIM GENET, V44, P377, DOI 10.1111/age.12021 Ding LL, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-622 Edwards SA, 2005, LIVEST PROD SCI, V94, P5, DOI 10.1016/j.livprodsci.2004.11.028 Foulley JL, 2006, J HERED, V97, P244, DOI 10.1093/jhered/esj038 Frantz LAF, 2019, P NATL ACAD SCI USA, V116, P17231, DOI 10.1073/pnas.1901169116 Fries R, 2001, NAT BIOTECHNOL, V19, P508, DOI 10.1038/89213 Garcia-Gudino J, 2021, MEAT SCI, V172, DOI 10.1016/j.meatsci.2020.108317 Giuffra E, 2000, GENETICS, V154, P1785 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Gu ZG, 2016, BIOINFORMATICS, V32, P2847, DOI 10.1093/bioinformatics/btw313 Hlongwane NL, 2020, FRONT GENET, V11, DOI 10.3389/fgene.2020.00344 Jombart T, 2008, BIOINFORMATICS, V24, P1403, DOI 10.1093/bioinformatics/btn129 Jombart T, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-94 Jombart T, 2011, BIOINFORMATICS, V27, P3070, DOI 10.1093/bioinformatics/btr521 Larson G, 2005, SCIENCE, V307, P1618, DOI 10.1126/science.1106927 Larson G, 2007, P NATL ACAD SCI USA, V104, P15276, DOI 10.1073/pnas.0703411104 Laval G, 2000, GENET SEL EVOL, V32, P187, DOI 10.1051/gse:2000113 Lewis J, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018007 Lukic B, 2020, FRONT GENET, V11, DOI 10.3389/fgene.2020.00261 Moradi MH, 2021, ANN ANIM SCI, V21, P807, DOI 10.2478/aoas-2020-0097 Mu??oz M., 2019, SCI REP-UK, V9, P1 Mujibi FD, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0190080 Munoz M, 2020, MEAT SCI, V167, DOI 10.1016/j.meatsci.2020.108152 Munoz M, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0207475 Ollivier L, 2009, ANIMAL, V3, P915, DOI 10.1017/S1751731109004297 Ottoni C, 2013, MOL BIOL EVOL, V30, P824, DOI 10.1093/molbev/mss261 Paschou P, 2007, PLOS GENET, V3, P1672, DOI 10.1371/journal.pgen.0030160 Picardy JA, 2019, RENEW AGR FOOD SYST, V34, P7, DOI 10.1017/S1742170517000230 Qin M, 2020, FRONT GENET, V10, DOI 10.3389/fgene.2019.01351 R Core Team, 2020, R LANG ENV STAT COMP Ramos AM, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006524 SanCristobal M, 2006, ANIM GENET, V37, P232, DOI 10.1111/j.1365-2052.2006.01440.x SanCristobal M, 2006, ANIM GENET, V37, P189, DOI 10.1111/j.1365-2052.2005.01385.x Schachler K, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-79037-z Schiavo G, 2021, ANIM GENET, V52, P155, DOI 10.1111/age.13045 Schiavo G, 2020, LIVEST SCI, V240, DOI 10.1016/j.livsci.2020.104219 Wang J, 2020, GENES-BASEL, V11, DOI 10.3390/genes11030275 Wilkinson S, 2011, BMC GENET, V12, DOI 10.1186/1471-2156-12-45 Yang B, 2017, GENET SEL EVOL, V49, DOI 10.1186/s12711-017-0345-y NR 50 TC 0 Z9 0 U1 7 U2 11 PD MAY 5 PY 2022 VL 12 IS 1 AR 7346 DI 10.1038/s41598-022-10698-8 WC Multidisciplinary Sciences SC Science & Technology - Other Topics UT WOS:000791506100079 DA 2022-12-14 ER PT J AU Boccacci, P Akkak, A Marinoni, DT Gerbi, V Schneider, A AF Boccacci, Paolo Akkak, Aziz Marinoni, Daniela Torello Gerbi, Vincenzo Schneider, Anna TI Genetic traceability of Asti Spumante and Moscato d'Asti musts and wines using nuclear and chloroplast microsatellite markers SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE Grapevine; Vitis vinifera L.; Moscato bianco; DNA extraction; Simple sequence repeat (SSR) ID VITIS-VINIFERA; DNA ANALYSIS; CULTIVAR IDENTIFICATION; GRAPE MUSTS; PCR; POLYMORPHISMS; QUANTIFICATION; AUTHENTICATION; ORIGIN; DIFFERENTIATION AB The final characteristics of a wine are strongly influenced by must varietal composition. Further, wine quality and value can be heavily modified if grape varieties other than those expected/allowed are used, especially in the case of monovarietal wines. 'Moscato bianco', which is one of the main grape varieties grown in Piedmont (north-western Italy), is used for the production of two renowned monovarietal sparkling wines: Asti Spumante and Moscato d'Asti. Here, the genetic traceability of these wines was assessed using a simple sequence repeat (SSR or microsatellite) DNA-based method. Must and wine samples from two local wineries were collected at different winemaking steps: after grape crushing and pressing, without the skins (must sample 1, M1); after static clarification or flotation (M2); halfway through fermentation (M3); and finished wines. A DNA extraction protocol was developed, and samples were analysed using a set of 9 nuclear (nSSR) and 7 chloroplast (cpSSR) markers. The application of nSSR markers was successful for M1 and M2, but was inadequate for M3 and wines. CpSSR gave better results as amplifications were achieved using DNA extracted from M1, M2 and wines, despite the lack of amplification in M3. Furthermore, the amplified cpSSR loci showed high polymorphism, allowing the identification of 5 distinct chlorotypes among 7 muscat-flavoured and 2 non-aromatic grapevines. Altogether, these results suggest that this technique could be extended to wine quality and authenticity control, as well as origin protection. C1 [Boccacci, Paolo; Schneider, Anna] UOS Grugliasco, Plant Virol Inst, Natl Res Council IVV CNR, I-10095 Turin, Italy. [Akkak, Aziz] Univ Foggia, Dipartimento Sci Agroambientali Chim & Difesa Veg, I-71100 Foggia, Italy. [Marinoni, Daniela Torello] Univ Turin, Dipartimento Colture Arboree, I-10095 Turin, Italy. [Gerbi, Vincenzo] Univ Turin, Dipartimento Valorizzaz & Protez Risorse Agrofore, Sez Microbiol Agr & Tecnol Alimentari, I-10095 Turin, Italy. C3 University of Foggia; University of Turin; University of Turin RP Boccacci, P (corresponding author), UOS Grugliasco, Plant Virol Inst, Natl Res Council IVV CNR, Via Leonardo Da Vinci 44, I-10095 Turin, Italy. EM p.boccacci@ivv.cnr.it CR Arroyo-Garcia R, 2006, MOL ECOL, V15, P3707, DOI 10.1111/j.1365-294X.2006.03049.x Arroyo-Garcia R, 2002, GENOME, V45, P1142, DOI [10.1139/g02-087, 10.1139/G02-087] Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Borgo R, 1996, J FOOD SCI, V61, P1, DOI 10.1111/j.1365-2621.1996.tb14712.x Bowers JE, 1999, AM J ENOL VITICULT, V50, P243 Bowers JE, 1996, GENOME, V39, P628, DOI 10.1139/g96-080 Bryan GJ, 1999, THEOR APPL GENET, V99, P859, DOI 10.1007/s001220051306 Chung SM, 2003, THEOR APPL GENET, V107, P757, DOI 10.1007/s00122-003-1311-3 Crespan M, 2001, VITIS, V40, P23 DAY MP, 1994, AM J ENOL VITICULT, V45, P79 Drabek J, 2008, EUR FOOD RES TECHNOL, V226, P491, DOI 10.1007/s00217-007-0561-8 Faria MA, 2000, J AGR FOOD CHEM, V48, P1096, DOI 10.1021/jf990837h Faria MA, 2008, EUR FOOD RES TECHNOL, V227, P845, DOI 10.1007/s00217-007-0795-5 Garcia-Beneytez E, 2003, J AGR FOOD CHEM, V51, P5622, DOI 10.1021/jf0302207 Garcia-Beneytez E, 2002, J AGR FOOD CHEM, V50, P6090, DOI 10.1021/jf0202077 GONZALEZLARA R, 1989, FOOD CHEM, V34, P103, DOI 10.1016/0308-8146(89)90078-2 Imazio S, 2006, GENET RESOUR CROP EV, V53, P1003, DOI 10.1007/s10722-004-6896-0 Merdinoglu D, 2005, MOL BREEDING, V15, P349, DOI 10.1007/s11032-004-7651-0 Nakamura S, 2007, J AGR FOOD CHEM, V55, P10388, DOI 10.1021/jf072407u PIKAART MJ, 1993, BIOTECHNIQUES, V14, P24 PUEYO E, 1993, AM J ENOL VITICULT, V44, P255 Revilla E, 2001, J CHROMATOGR A, V915, P53, DOI 10.1016/S0021-9673(01)00635-5 Rodriguez-Plaza P, 2006, EUR FOOD RES TECHNOL, V223, P625, DOI 10.1007/s00217-005-0244-2 Salmaso M, 2010, AM J ENOL VITICULT, V61, P551, DOI 10.5344/ajev.2010.09111 Savazzini F, 2006, ANAL CHIM ACTA, V563, P274, DOI 10.1016/j.aca.2005.10.078 Sefc KM, 1999, GENOME, V42, P367, DOI 10.1139/gen-42-3-367 Sefc KM, 2009, GRAPEVINE MOL PHYSL Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Siret R, 2002, J AGR FOOD CHEM, V50, P3822, DOI 10.1021/jf011462e Spaniolas S, 2008, J AGR FOOD CHEM, V56, P7667, DOI 10.1021/jf801036f THOMAS MR, 1993, THEOR APPL GENET, V86, P173, DOI 10.1007/BF00222076 THOMAS MR, 1993, THEOR APPL GENET, V86, P985, DOI 10.1007/BF00211051 Weising K, 1999, GENOME, V42, P9, DOI 10.1139/gen-42-1-9 NR 33 TC 22 Z9 23 U1 0 U2 37 PD SEP PY 2012 VL 235 IS 3 BP 439 EP 446 DI 10.1007/s00217-012-1770-3 WC Food Science & Technology SC Food Science & Technology UT WOS:000307513600008 DA 2022-12-14 ER PT J AU Mazzei, P Piccolo, A AF Mazzei, Pierluigi Piccolo, Alessandro TI H-1 HRMAS-NMR metabolomic to assess quality and traceability of mozzarella cheese from Campania buffalo milk SO FOOD CHEMISTRY DT Article DE Metabolomic; Nuclear Magnetic Resonance; HRMAS; Mozzarella di Bufala Campana cheese; Multivariate statistical analysis ID RESOLUTION MAS NMR; GEOGRAPHICAL ORIGIN; PARMIGIANO-REGGIANO; SAMPLES; SPECTROSCOPY; H-1-NMR; WINES AB The production of Mozzarella di Bufala Campana (MBC) is relevant for the agro-food economy of the Campania Region of Italy and the mark of Protected Designation of Origin (PDO) has been assigned to MBC in relation to its geographical origin. Advanced analytical methods must be then employed to assess authenticity, traceability, and quality of MBC. H-1 HRMAS-NMR (High Resolution Magic Angle Spinning Nuclear Magnetic Resonance) spectroscopy was applied here to directly identify specific metabolites in MBC intact samples without time-consuming sample pre-treatments. Overcrowded conventional H-1 HRMAS-NMR spectra were selectively simplified with two NMR pulse sequences: eCPMG and eDiff, by modulating spin-spin relaxation times and diffusion of MBC molecular components, respectively. Signal elaboration of edited spectra was combined with multivariate analyses to enable significant metabolic differentiation between MBC samples from two different production sites in Campania. Principal Components Analysis (PCA) for eCPMG spectra explained 97.54% of total variance between the two MBC groups for four metabolites (beta-galactose, beta-lactose, acetic acid, and glycerol). Less efficient was groups distinction by PCA for eDiff spectra, although differences in polyunsaturated acids, such as linoleic and linolenic acids, were highlighted. Similarly, Discriminant Analysis (DA) provided MBC group classification with 100% success in validation tests for eCPMG spectra, while DA prediction ability was reduced to 94.12% for eDiff spectra. Hierarchical Cluster Analysis (HCA) gave a totally correct classification between the two MBC groups only for eCPMG spectra. eCPMG spectra were also used to identify metabolites during MBC aging. As compared to fresh samples, 2 days old MBC samples showed increasing signals for isobutylic alcohol, lactic acid, and acetic acid. This work shows that H-1 HRMAS-NMR spectroscopy can rapidly characterise the metabolic profile of intact MBC samples and statistically distinguish the geographical origin of buffalo milk mozzarella and its freshness. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Mazzei, Pierluigi; Piccolo, Alessandro] Univ Naples Federico II, Ctr Interdipartimentale Risonanza Magnet Nucl CER, I-80055 Portici, Italy. [Mazzei, Pierluigi] CNR, Ist Metodol Chim, I-00016 Monterotondo, Italy. C3 University of Naples Federico II; Consiglio Nazionale delle Ricerche (CNR); Istituto di Metodologie Chimiche (IMC-CNR) RP Piccolo, A (corresponding author), Univ Naples Federico II, Ctr Interdipartimentale Risonanza Magnet Nucl CER, Via Univ 100, I-80055 Portici, Italy. EM Alessandro.piccolo@unina.it CR Aponte M, 2010, J DAIRY SCI, V93, P2358, DOI 10.3168/jds.2009-2948 Arvanitoyannis I.S., 2005, NUTRITION, V45, P231 BLIGH EG, 1959, CAN J BIOCHEM PHYS, V37, P911 Brereton R.G., 2003, CHEMOMETRICS DATA AN, P184 Brescia MA, 2005, FOOD CHEM, V89, P139, DOI 10.1016/j.foodchem.2004.02.016 Brescia MA, 2004, J AM OIL CHEM SOC, V81, P431, DOI 10.1007/s11746-004-0918-3 Brescia MA, 2003, J AGR FOOD CHEM, V51, P21, DOI 10.1021/jf0206015 Cevallos-Cevallos JM, 2009, TRENDS FOOD SCI TECH, V20, P557, DOI 10.1016/j.tifs.2009.07.002 Cogan TM, 1997, J DAIRY RES, V64, P409, DOI 10.1017/S0022029997002185 Czerwenka C, 2010, FOOD CHEM, V122, P901, DOI 10.1016/j.foodchem.2010.03.034 DAGNELIE P, 1986, THEORIE METHODES STA, V2 Doty FD, 1998, CONCEPT MAGNETIC RES, V10, P239, DOI 10.1002/(SICI)1099-0534(1998)10:4<239::AID-CMR2>3.0.CO;2-Y Ercolini D, 2004, J APPL MICROBIOL, V96, P263, DOI 10.1046/j.1365-2672.2003.02146.x Gianferri R, 2007, INT DAIRY J, V17, P167, DOI 10.1016/j.idairyj.2006.02.006 Hazelwood LA, 2008, APPL ENVIRON MICROB, V74, P2259, DOI 10.1128/AEM.02625-07 Jacobsen N. E., 2007, SPIN ECHO ATTACHED P, P200 Kelleher BP, 2006, GEOCHIM COSMOCHIM AC, V70, P4080, DOI 10.1016/j.gca.2006.06.012 Kuo MI, 2003, J DAIRY SCI, V86, P2525, DOI 10.3168/jds.S0022-0302(03)73847-8 Lindon JC, 2007, MAMMALIAN SYSTEM, P1 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 Mazzei P, 2010, NMR BIOMED, V23, P1137, DOI 10.1002/nbm.1540 Mazzei P, 2010, ANAL CHIM ACTA, V673, P167, DOI 10.1016/j.aca.2010.06.003 Powers R, 2009, MAGN RESON CHEM, V47, pS2, DOI 10.1002/mrc.2461 Romano P, 2001, INT J FOOD MICROBIOL, V69, P45, DOI 10.1016/S0168-1605(01)00571-2 Sacco D, 2009, FOOD CHEM, V114, P1559, DOI 10.1016/j.foodchem.2008.11.056 Shintu L, 2005, J AGR FOOD CHEM, V53, P4026, DOI 10.1021/jf048141y Shintu L, 2004, MAGN RESON CHEM, V42, P396, DOI 10.1002/mrc.1359 Shintu L, 2006, J AGR FOOD CHEM, V54, P4148, DOI 10.1021/jf060532k Sitter B, 2009, PROG NUCL MAG RES SP, V54, P239, DOI 10.1016/j.pnmrs.2008.10.001 Tessem MB, 2008, MAGN RESON MED, V60, P510, DOI 10.1002/mrm.21694 Todeschini R., 1998, INTRO CHEMIOMETRIA, P37 NR 31 TC 78 Z9 82 U1 1 U2 119 PD JUN 1 PY 2012 VL 132 IS 3 BP 1620 EP 1627 DI 10.1016/j.foodchem.2011.11.142 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000301022400069 DA 2022-12-14 ER PT J AU Martinez-Esteso, MJ O'Connor, G Norgaard, J Breidbach, A Brohee, M Cubero-Leon, E Nitride, C Robouch, P Emons, H AF Martinez-Esteso, Maria Jose O'Connor, Gavin Norgaard, Jorgen Breidbach, Andreas Brohee, Marcel Cubero-Leon, Elena Nitride, Chiara Robouch, Piotr Emons, Hendrik TI A reference method for determining the total allergenic protein content in a processed food: the case of milk in cookies as proof of concept SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY DT Article DE Food allergen detection; Metrological traceability; Mass spectrometry; Peptide standards; Protein quantification; Processed food ID QUANTIFICATION; NITROGEN AB The establishment of a reference method for the determination of the allergen protein content in a processed food material has been explored. An analytical approach was developed to enable the comparability of food allergen measurement results expressed in a decision-relevant manner. A proof of concept is here presented, resulting in quantity values for the common measurand, namely 'mass of total allergen protein per mass of food'. The quantities are determined with SI traceability to enable the comparability of reported results. A method for the quantification of total milk protein content in an incurred baked food at a concentration level clinically relevant is presented. The strategy on how to obtain the final analytical result is outlined. Challenges associated with this method are discussed, in particular the optimal extraction of the marker proteins, the complete digestion and release of the peptides in an equimolar fashion, the use of conversion factors to translate the amount of measured proteins into total milk protein and the estimation of the uncertainty contributions as well as of the combined uncertainty of the final result. The implementation of such a reference method for the determination of the total allergen content in a processed food is an important step, which will provide comparable measurement data of relevance to risk assessors. C1 [Martinez-Esteso, Maria Jose; O'Connor, Gavin; Norgaard, Jorgen; Breidbach, Andreas; Brohee, Marcel; Cubero-Leon, Elena; Nitride, Chiara; Robouch, Piotr; Emons, Hendrik] European Commiss, Joint Res Ctr, Retieseweg 111, B-2440 Geel, Belgium. [Martinez-Esteso, Maria Jose] Univ Alicante, Dept Agrochim & Biochim, Carretera San Vicente Raspeig S-N, Alicante 03690, Spain. [O'Connor, Gavin] Phy Tech Bundesanstalt, Bundesallee 100, D-38116 Braunschweig, Germany. [Nitride, Chiara] Univ Naples Federico II, Dept Agr, I-80138 Naples, Italy. C3 Universitat d'Alacant; University of Naples Federico II RP Emons, H (corresponding author), European Commiss, Joint Res Ctr, Retieseweg 111, B-2440 Geel, Belgium. EM hendrik.emons@ec.europa.eu CR Agostoni C, 2014, EFSA J, V12, DOI 10.2903/j.efsa.2014.3894 Allergen Bureau, SUMM 2019 VITAL SCI Analyse-it, LEAD SOFTW STAT AN D [Anonymous], 2012, JCGM, V200, P108 Bar C, 2019, INT DAIRY J, V97, P167, DOI 10.1016/j.idairyj.2019.01.001 BOISEN S, 1987, ACTA AGR SCAND, V37, P299, DOI 10.1080/00015128709436560 Burkitt WI, 2008, ANAL BIOCHEM, V376, P242, DOI 10.1016/j.ab.2008.02.010 Cryar A, 2013, J AOAC INT, V96, P1350, DOI 10.5740/jaoacint.12-438 Ellison S.L.R., 2012, QUANTIFYING UNCERTAI, Vthird Farrell HM, 2004, J DAIRY SCI, V87, P1641, DOI 10.3168/jds.S0022-0302(04)73319-6 ISO, 1184322000 ISO ISO, 352017 ISO ISO/IEC, 9832008 ISOIEC Johnson PE, 2011, J AOAC INT, V94, P1026 KRAGTEN J, 1994, ANALYST, V119, P2161, DOI 10.1039/an9941902161 Mariotti F, 2008, CRIT REV FOOD SCI, V48, P177, DOI 10.1080/10408390701279749 Matricardi PM, 2016, PEDIAT ALLERG IMM-UK, V27, P1, DOI 10.1111/pai.12563 Munoz A, 2011, ANAL BIOCHEM, V408, P124, DOI 10.1016/j.ab.2010.08.037 Muraro A, 2014, ALLERGY, V69, P1008, DOI 10.1111/all.12429 Nitride C, 2019, ANAL BIOANAL CHEM, V411, P3463, DOI 10.1007/s00216-019-01816-z NVWA, 2017, ADV BURO PREL REF DO O'Connor GHM, 2017, JRC108259 Paez Vincent, 2016, J AOAC Int, V99, P1122, DOI 10.5740/jaoacint.SMPR2016.002 Parker CH, 2015, J AGR FOOD CHEM, V63, P10669, DOI 10.1021/acs.jafc.5b04287 Pritchard C, 2009, CLIN CHEM, V55, P1984, DOI 10.1373/clinchem.2009.124354 Remington BC, 2020, FOOD CHEM TOXICOL, V139, DOI 10.1016/j.fct.2020.111259 Taylor SL, 2014, FOOD CHEM TOXICOL, V63, P9, DOI 10.1016/j.fct.2013.10.032 Waiblinger HU, 2018, J AOAC INT, V101, P17, DOI 10.5740/jaoacint.17-0383 Walker MJ, 2016, ANALYST, V141, P24, DOI [10.1039/C5AN01457C, 10.1039/c5an01457c] NR 29 TC 9 Z9 9 U1 2 U2 15 PD DEC PY 2020 VL 412 IS 30 SI SI BP 8249 EP 8267 DI 10.1007/s00216-020-02959-0 EA OCT 2020 WC Biochemical Research Methods; Chemistry, Analytical SC Biochemistry & Molecular Biology; Chemistry UT WOS:000574714800001 DA 2022-12-14 ER PT J AU Ferrucci, M Leach, RK Giusca, C Carmignato, S Dewulf, W AF Ferrucci, Massimiliano Leach, Richard K. Giusca, Claudiu Carmignato, Simone Dewulf, Wim TI Towards geometrical calibration of x-ray computed tomography systems-a review SO MEASUREMENT SCIENCE AND TECHNOLOGY DT Review DE x-ray computed tomography; geometrical calibration; dimensional metrology ID CONE-BEAM CT; SELF-CALIBRATION; ANALYTIC METHOD; PARAMETERS; OPTIMIZATION; MISALIGNMENT; UNCERTAINTY; PROJECTION; METROLOGY; ALIGNMENT AB Industrial x-ray computed tomography (XCT) is seen as a potentially effective tool for the industrial inspection of complex parts. In particular, XCT is an attractive solution for the measurement of internal geometries, which are inaccessible by conventional coordinate measuring systems. While the technology is available and the benefits are recognized, methods to establish the measurement assurance of XCT systems are lacking. More specifically, the assessment of measurement uncertainty and the subsequent establishment of measurement traceability is a largely unknown process. This paper is a review of research that contributes to the development of a geometrical calibration procedure for XCT systems. A brief introduction to the geometry of cone-beam tomography systems is given, after which the geometrical influence factors are outlined. Mathematical measurement models play a significant role in understanding how geometrical offsets and misalignments contribute to error in measurements; therefore, the application of mathematical models in simulating geometrical errors is discussed and the corresponding literature is presented. Then, the various methods that have been developed to measure certain geometrical errors are reviewed. The findings from this review are discussed and suggestions are provided for future work towards the development of a comprehensive and practical geometrical calibration procedure. C1 [Ferrucci, Massimiliano; Leach, Richard K.; Giusca, Claudiu] Natl Phys Lab, Teddington TW11 0LW, Middx, England. [Ferrucci, Massimiliano; Dewulf, Wim] Katholieke Univ Leuven, Dept Mech Engn, Louvain, Belgium. [Carmignato, Simone] Univ Padua, Padua, Italy. C3 National Physical Laboratory - UK; KU Leuven; University of Padua RP Leach, RK (corresponding author), Univ Nottingham, Dept Mech Mat & Mfg Engn, Nottingham ND7 2RD, England. EM massimiliano.ferruci@npl.co.uk CR Abbe M, 2011, P ASPE 16 ANN M CRYS, P2 Angel J, 2014, CIRP ANN-MANUF TECHN, V63, P473, DOI 10.1016/j.cirp.2014.03.034 [Anonymous], 2011, 160164 EN EUR COMM S [Anonymous], 2005, B554 ANSIASME [Anonymous], 2001, 103603 ISO [Anonymous], 2010, 263012 VDIVDE SOC ME Arriba L, 1999, P 1 INT EUSPEN C BRE Balsamo A., 1996, ANN CIRP, V45, P479 Bartscher M, 2007, CIRP ANN-MANUF TECHN, V56, P495, DOI 10.1016/j.cirp.2007.05.118 Bartscher M, 2004, 8 INT S MEASUREMENT, P3 Ben Tekaya I, 2013, IEEE T NUCL SCI, V60, P3937, DOI 10.1109/TNS.2013.2279675 Beque D, 2004, IEEE NUCL SCI CONF R, P2507 Beque D, 2005, IEEE T MED IMAGING, V24, P180, DOI 10.1109/TMI.2004.839367 Beque D, 2003, IEEE T MED IMAGING, V22, P599, DOI 10.1109/TMI.2003.812258 Beque D, 2007, IEEE NUCL SCI CONF R, P2980, DOI 10.1109/NSSMIC.2007.4436760 BIPM IEC IFCC ILAC ISO IUPAC IUPA OIML, 2008, EV MEAS DAT GUID EXP Bronnikov AV, 2011, P 11 INT M FULL 3D I, P175 BUSCH K, 1985, PRECIS ENG, V7, P139, DOI 10.1016/0141-6359(85)90036-4 Buzug T. M., 2008, COMPUTED TOMOGRAPHY Carmignato S, 2009, MEAS SCI TECHNOL, V20, DOI 10.1088/0957-0233/20/8/084021 Carmignato S, 2012, P C IND COMP TOM ICT, P161 Cho YB, 2005, MED PHYS, V32, P968, DOI 10.1118/1.1869652 Clackdoyle R, 2011, PHYS MED BIOL, V56, P7371, DOI 10.1088/0031-9155/56/23/003 Clackdoyle R, 2011, IEEE NUCL SCI CONF R, P2763, DOI 10.1109/NSSMIC.2011.6153636 Cox M G, 1998, DETERMINING CMM BEHA Cox MG, 2006, METROLOGIA, V43, pS178, DOI 10.1088/0026-1394/43/4/S03 De Chiffre L, 2014, CIRP ANN-MANUF TECHN, V63, P655, DOI 10.1016/j.cirp.2014.05.011 Defrise M, 2008, IEEE T MED IMAGING, V27, P204, DOI 10.1109/TMI.2007.904687 Desbat L, 2006, IEEE NUCL SCI CONF R, P2859, DOI 10.1109/NSSMIC.2006.356473 Dewulf W, 2013, CIRP ANN-MANUF TECHN, V62, P535, DOI 10.1016/j.cirp.2013.03.017 Flack D., 2011, GOOD PRACTICE GUIDE Ford JC, 2011, MED PHYS, V38, P2829, DOI 10.1118/1.3589130 Froba T, 2011, 50 ANN C BRIT I NOND Furutani R, 2003, P 17 IMEKO WORLD C D, P1798 GULLBERG GT, 1987, PHYS MED BIOL, V32, P1581, DOI 10.1088/0031-9155/32/12/005 GULLBERG GT, 1990, MED PHYS, V17, P264, DOI 10.1118/1.596505 Hartley R., 2003, MULTIPLE VIEW GEOMET Hiller J, 2007, TM-TECH MESS, V74, P553, DOI 10.1524/teme.2007.74.11.553 Hiller J, 2012, MEAS SCI TECHNOL, V23, DOI 10.1088/0957-0233/23/8/085404 Holt Kevin M., 2007, Proceedings 2007 IEEE International Conference on Image Processing, ICIP 2007, P129 Holt KM., 2009, PROC SPI, V7258, P1 Hsieh J, 2019, COMPUT TOMOGR Hu CX, 2013, MEAS SCI TECHNOL, V24, DOI 10.1088/0957-0233/24/8/085007 ISO 230-7, 2006, 2307 ISO Johnston SM, 2008, MED PHYS, V35, P1820, DOI 10.1118/1.2900000 Kasperl S, 2009, MATER TEST, V51, P405, DOI 10.3139/120.110053 Kiekens K, 2011, MEAS SCI TECHNOL, V22, DOI 10.1088/0957-0233/22/11/115502 Kingston A, 2011, MED PHYS, V38, P4934, DOI 10.1118/1.3609096 Kruth JP, 2011, CIRP ANN-MANUF TECHN, V60, P821, DOI 10.1016/j.cirp.2011.05.006 Kumar J, 2011, MEAS SCI TECHNOL, V22, DOI 10.1088/0957-0233/22/3/035105 Kyriakou Y, 2008, PHYS MED BIOL, V53, P6267, DOI 10.1088/0031-9155/53/22/001 Lifton J.J., 2013, P SING INT NDT C EXH, P19 Meng YZ, 2013, IEEE T MED IMAGING, V32, P278, DOI 10.1109/TMI.2012.2224360 Mennessier C, 2009, PHYS MED BIOL, V54, P1633, DOI 10.1088/0031-9155/54/6/016 Mennessier C, 2008, IEEE NUCL SCI S MED, P5081, DOI DOI 10.1109/NSSMIC.2008.4774380 Mennessier C, 2005, IEEE NUCL SCI CONF R, P2743 Muders J, 2014, IEEE T NUCL SCI, V61, P202, DOI 10.1109/TNS.2013.2293969 Muller P, 2014, CIRP J MANUF SCI TEC, V7, P222, DOI 10.1016/j.cirpj.2014.04.002 Muller P., 2012, THESIS TU DENMARK Muralikrishnan B, 2009, J RES NATL INST STAN, V114, P21, DOI 10.6028/jres.114.003 Muralikrishnan B, 2013, P ASPE 28 ANN M Noo F, 2000, PHYS MED BIOL, V45, P3489, DOI 10.1088/0031-9155/45/11/327 Panetta D, 2008, PHYS MED BIOL, V53, P3841, DOI 10.1088/0031-9155/53/14/009 Park SR, 2010, J MECH SCI TECHNOL, V24, P175, DOI 10.1007/s12206-009-1139-0 Patel V, 2009, MED PHYS, V36, P48, DOI 10.1118/1.3026615 Quarteroni A., 2009, SIMULATION, V56, P10 ROUGEE A, 1993, COMPUT MED IMAG GRAP, V17, P295, DOI 10.1016/0895-6111(93)90020-N ROUGEE A, 1993, P SOC PHOTO-OPT INS, V1897, P161, DOI 10.1117/12.146963 Sartori S., 1995, CIRP ANN-MANUF TECHN, V44, P599 Schwenke H, 2008, CIRP ANN-MANUF TECHN, V57, P660, DOI 10.1016/j.cirp.2008.09.008 SIRE P, 1993, P SOC PHOTO-OPT INS, V2009, P229, DOI 10.1117/12.164741 Sun Y, 2007, IEEE T BIO-MED ENG, V54, P1461, DOI 10.1109/TBME.2007.891166 Sun Y, 2006, NDT&E INT, V39, P499, DOI 10.1016/j.ndteint.2006.03.002 Tan SQ, 2013, NDT&E INT, V58, P49, DOI 10.1016/j.ndteint.2013.04.011 TAUBIN G, 1991, IEEE T PATTERN ANAL, V13, P1115, DOI 10.1109/34.103273 Uhlman N., 2008, INT S NDT AER DEC FU, Vvol 1, P3 Vogeler F, 2011, P INT S DIG IND RAD von Smekal L, 2004, MED PHYS, V31, P3242, DOI 10.1118/1.1803792 Weckenmann A, 2010, KEY ENG MAT, V437, P73, DOI 10.4028/www.scientific.net/KEM.437.73 Wein W, 2011, 11 INT M FULLY 3 DIM, V2, P1 Weiss D, 2010, C IND COMP TOM, P227 Weiss D, 2012, 4 C IND COMP TOM ICT, P175 Weitkamp T, 2004, P SOC PHOTO-OPT INS, V5535, P623, DOI 10.1117/12.557094 Welkenhuyzen F, 2009, OPTICAL MEASUREMENT TECHNIQUES FOR STRUCTURES AND SYSTEMS, P401 Welkenhuyzen Frank, 2014, P 5 INT C IND COMP T, P217 Wenig P, 2006, 9 EUR C NOND TEST BE, P10 Xu JY, 2013, IEEE T MED IMAGING, V32, P1731, DOI 10.1109/TMI.2013.2266638 Xu JY, 2012, IEEE T MED IMAGING, V31, P825, DOI 10.1109/TMI.2012.2183003 Yang K, 2006, MED PHYS, V33, P1695, DOI 10.1118/1.2198187 Zhang F, 2014, OPTIK, V125, P2509, DOI 10.1016/j.ijleo.2013.10.090 [No title captured] NR 91 TC 68 Z9 70 U1 3 U2 47 PD SEP PY 2015 VL 26 IS 9 AR 092003 DI 10.1088/0957-0233/26/9/092003 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000364329200004 DA 2022-12-14 ER PT J AU Kang, YS Lee, K Lee, YH Chung, KY AF Kang, Yong-Shin Lee, Kyounghun Lee, Yong-Han Chung, Ku-Young TI RFID-based Supply Chain Process Mining for Imported Beef SO KOREAN JOURNAL FOR FOOD SCIENCE OF ANIMAL RESOURCES DT Article DE imported beef; supply chain; process mining; traceability system AB Through the development of efficient data collecting technologies like RFID, and inter-enterprise collaboration platforms such as web services, companies which participate in supply chains can acquire visibility over the whole supply chain, and can make decisions to optimize the overall supply chain networks and processes, based on the extracted knowledge from historical data collected by the visibility system. Although not currently active, the MeatWatch system has been developed, and is used in part for this purpose, in the imported beef distribution network in Korea. However, the imported beef distribution network is too complicated to analyze its various aspects using ordinary process analysis approaches. In this paper, we suggest a novel approach, called RFID-based supply chain process mining, to automatically discover and analyze the overall supply chain processes from the distributed RFID event data, without any prior knowledge. The proposed approach was implemented and validated, by using a case study of the imported beef distribution network in Korea. Specifically we demonstrated that the proposed approach can be successfully applied to discover supply chain networks from the distributed event data, to simplify the supply chain networks, and to analyze anomaly of the distribution networks. Such novel process mining functionalities can reinforce the capability of traceability services like MeatWatch in the future. C1 [Lee, Yong-Han] Dongguk Univ Seoul, Dept Ind & Syst Engn, Seoul 100715, South Korea. [Kang, Yong-Shin; Lee, Kyounghun] Dongguk Univ Seoul, Nano Informat Technol Acad, U SCM Res Ctr, Seoul 100272, South Korea. [Chung, Ku-Young] Sangji Univ, Dept Anim Resources Sci, Wonju 220702, South Korea. C3 Dongguk University; Dongguk University; Sangji University RP Lee, YH (corresponding author), Dongguk Univ Seoul, Dept Ind & Syst Engn, Seoul 100715, South Korea. EM yonghan@dgu.edu CR Agrawal R, 1998, LECT NOTES COMPUT SC, V1377, P469 Animal Plant and Fisheries Quarantine and Inspection Agency, 2010, SERV MAN TRAC IMP BE Bergenthum R, 2007, LECT NOTES COMPUT SC, V4714, P375 Cambridge University BT Research SAP Research, 2007, SER LEV INV TRACK MO Cantero JJ, 2008, IEEE INT C EMERG, P1332, DOI 10.1109/ETFA.2008.4638572 COOK JE, 1995, PROC INT CONF SOFTW, P73, DOI 10.1145/225014.225021 de Medeiros AKA, 2007, DATA MIN KNOWL DISC, V14, P245, DOI 10.1007/s10618-006-0061-7 *EPCGLOBAL INC, 2005, OBJ NAM SERV ONS VER *EPCGLOBAL INC, 2007, EPC INF SERV EPCIS V Gerke Kerstin, 2009, 2009 IEEE Conference on Commerce and Enterprise Computing, P285, DOI 10.1109/CEC.2009.72 Herbst J, 2000, LECT NOTES ARTIF INT, V1810, P183 Kang YS, 2013, COMPUT IND, V64, P609, DOI 10.1016/j.compind.2013.03.004 Muller J., 2010, P 43 HAW INT C SYST, P1 Rozinat A, 2008, INFORM SYST, V33, P64, DOI 10.1016/j.is.2007.07.001 Rozinat A, 2009, DATA KNOWL ENG, V68, P834, DOI 10.1016/j.datak.2009.02.014 Sole M, 2010, LECT NOTES COMPUT SC, V6128, P226, DOI 10.1007/978-3-642-13675-7_14 Song M, 2008, DECIS SUPPORT SYST, V46, P300, DOI 10.1016/j.dss.2008.07.002 Thiesse F, 2009, IEEE INTERNET COMPUT, V13, P36, DOI 10.1109/MIC.2009.46 van der Aalst W, 2004, IEEE T KNOWL DATA EN, V16, P1128, DOI 10.1109/TKDE.2004.47 van der Aalst WMP, 2007, INFORM SYST, V32, P713, DOI 10.1016/j.is.2006.05.003 van der Aalst WMP, 2011, INFORM SYST, V36, P450, DOI 10.1016/j.is.2010.09.001 van der Aalst WMP, 2011, LECT NOTES BUS INF P, V92, P1 van der Aalst WMP, 2004, COMPUT IND, V53, P231, DOI 10.1016/j.compind.2003.10.001 Van Dongen B.F., 2005, P CAISE 05 WORKSH EM, P309 van Dongen BF, 2005, LECT NOTES COMPUT SC, V3536, P444 VeriSign Inc, 2004, VERISIGN DISC SERV D Weijters A., 2006, WORKING PAPER SERIES NR 27 TC 0 Z9 0 U1 1 U2 16 PD AUG PY 2013 VL 33 IS 4 BP 463 EP 473 DI 10.5851/kosfa.2013.33.4.463 WC Food Science & Technology SC Food Science & Technology UT WOS:000335122700005 DA 2022-12-14 ER PT J AU Charels, D Broeders, S Corbisier, P Trapmann, S Schimmel, H Linsinger, T Emons, H AF Charels, Diana Broeders, Sylvia Corbisier, Philippe Trapmann, Stefanie Schimmel, Heinz Linsinger, Thomas Emons, Hendrik TI Toward metrological traceability for DNA fragment ratios in GM quantification. 2. Systematic study of parameters influencing the quantitative determination of MON 810 corn by real-time PCR SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE genetically modified organism; GMO; DNA; extraction; real-time PCR; PCR efficiency; DNA copy number ratio ID GENOMIC DNA; MAIZE; EXTRACTION; SIZE AB This paper is part of a set of three papers investigating metrological traceability of the quantification of DNA fragments as, for instance, used for quantification of genetic modifications. This paper evaluates the possible impact of several factors on results of real-time Polymerase Chain Reaction (PCR) measurements. It was found that the particle size of the powder samples does not have an influence, whereas the nature of the calibrant (plasmidic or genomic DNA) has a significant effect. Moreover, two real-time PCR detection methods (construct-specific and event-specific) for MON 810 corn were compared. The results obtained in a specifically designed interlaboratory study revealed a significant influence of the DNA extraction method on measurement results when the MON 810 construct-specific real-time PCR detection method was applied. Statistical analyses confirmed the importance of validating DNA extraction methods in conjunction with real-time PCR methods. C1 Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements, B-2440 Geel, Belgium. C3 European Commission Joint Research Centre; EC JRC Institute for Reference Materials & Measurements (IRMM) RP Charels, D (corresponding author), Commiss European Communities, Joint Res Ctr, Inst Reference Mat & Measurements, Retieseweg 111, B-2440 Geel, Belgium. EM diana.charels@ec.europa.eu CR Arumuganathan K, 1991, PLANT MOL BIOL REP, V1991, P211, DOI DOI 10.1007/BF02672016 EDWARDS K, 1991, NUCLEIC ACIDS RES, V19, P1349, DOI 10.1093/nar/19.6.1349 Harn C, 1998, PLANT MOL BIOL, V37, P639, DOI 10.1023/A:1006079009072 Krech AB, 1999, GENE, V234, P45, DOI 10.1016/S0378-1119(99)00187-0 Kuribara H, 2002, J AOAC INT, V85, P1077 Moreano F, 2005, J AGR FOOD CHEM, V53, P9971, DOI 10.1021/jf051894f Peano C, 2004, J AGR FOOD CHEM, V52, P6962, DOI 10.1021/jf040008i Poggio L, 1998, ANN BOT-LONDON, V82, P107, DOI 10.1006/anbo.1998.0757 Smith DS, 2005, J AGR FOOD CHEM, V53, P9848, DOI 10.1021/jf051201v Taverniers I, 2001, EUR FOOD RES TECHNOL, V213, P417, DOI 10.1007/s002170100405 Taverniers I, 2004, ANAL BIOANAL CHEM, V378, P1198, DOI 10.1007/s00216-003-2372-5 Trapmann S, 2005, ANAL BIOANAL CHEM, V381, P72, DOI 10.1007/s00216-004-2901-x NR 12 TC 19 Z9 21 U1 0 U2 8 PD MAY 2 PY 2007 VL 55 IS 9 BP 3258 EP 3267 DI 10.1021/jf062932d WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000245946300004 DA 2022-12-14 ER PT J AU Fallon, RJ AF Fallon, RJ TI The development and use of electronic ruminal boluses as a vehicle for bovine identification SO REVUE SCIENTIFIQUE ET TECHNIQUE DE L OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE boluses; cattle; electronic identification; readability; recovery; rumen; traceability; transponders ID CATTLE; SELENIUM; CALVES; TRANSPONDERS; RUMEN; COWS AB Traceability of meat to the farm of origin is becoming increasingly important to consumers and producers. Traceability systems would be greatly facilitated by electronic animal identification which, for example, would eliminate errors associated with the manual transcription of data. Additionally, the level of production supports provided to the bovine sectors within the European Union demands a high level of security in bovine identification within this region. An electronic rumen bolus provides a safe, tamper-proof method of electronic animal identification. The success of the electronic rumen bolus is facilitated by having an applicator for young calves which delivers the bolus directly to the rumen/reticulum, a bolus with a specific density of 3 g/cm(3) or greater and portable or static readers which are capable of reading the passive transponder in the boluses. In Europe, the different methods of electronic identification are compared in a trial organised by the Joint Research Centre (JRC) in Ispra, Italy. The JRC has developed a list of approved boluses used to electronically identify cattle with a unique number on the transponder which cannot be changed. The choice between a rumen bolus and ear tag as a method of electronic animal identification will depend on the degree of security required. If tampering with ear tags is thought to be possible, the rumen bolus would offer a secure alternative method of electronic animal identification. C1 TEAGASC, Beef Res Ctr, Agr & Food Dev Author, Dunsany, Meath, Ireland. C3 Teagasc; United States Department of Energy (DOE); Oak Ridge National Laboratory RP Fallon, RJ (corresponding author), TEAGASC, Beef Res Ctr, Agr & Food Dev Author, Dunsany, Meath, Ireland. CR Allen W. M., 1986, Proceedings of the Society for Veterinary Epidemiology and Preventive Medicine, 2-4 April 1986, P66 Allen W. M., 1983, Proceedings of the Fifth International Conference on Production Disease in Farm Animals: Uppsala, Sweden, August 10 to 12 1983., P334 *AMLC, 1995, AMLC01028 ANDERSON BC, 1983, CONT ED PRACG VET, V5, P431 Caja G., 1998, Journal of Dairy Science, V81, P260 Caja G, 1999, COMPUT ELECTRON AGR, V24, P45, DOI 10.1016/S0168-1699(99)00036-8 CAJA G, 1996, EAAP PUBLICATION, V87, P335 CAJA G, 1996, EAAP PUBLICATION, V87, P349 Caja G., 1997, OPTIONS MEDITERR, V33, P43 CAJA G, 1997, Patent No. 19980125 CAJA G, 1996, CAPSULA PARA IDENTIF CONILL C, 1996, PERFORMANCE RECORDIN, V87, P341 Eradus WJ, 1999, COMPUT ELECTRON AGR, V24, P91, DOI 10.1016/S0168-1699(99)00039-3 Fallon R. J., 1992, Irish Journal of Agricultural and Food Research, V31, P100 Fallon RJ, 1999, IRISH J AGR FOOD RES, V38, P189 FALLON RJ, 2001, IN PRESS J AGR FOOD, V40 GIVENS DI, 1988, J AGR SCI, V110, P199, DOI 10.1017/S0021859600079843 Hanton J. P., 1981, Proceedings of the United States Animal Health Association, V85, P342 HASKER PJS, 1992, AUST VET J, V69, P91, DOI 10.1111/j.1751-0813.1992.tb15560.x Hasker PJS, 1996, AUST J EXP AGR, V36, P19, DOI 10.1071/EA9960019 Hemingway RG, 1997, VET J, V153, P221, DOI 10.1016/S1090-0233(97)80043-3 Hemingway RG, 1999, VET RES COMMUN, V23, P481, DOI 10.1023/A:1006362422945 Henry P. R., 1995, Bioavailability of nutrients for animals: amino acids, minerals, and vitamins., P303, DOI 10.1016/B978-012056250-3/50041-X HIDIROGLOU M, 1987, J ANIM SCI, V65, P815, DOI 10.2527/jas1987.653815x HOVE K, 1993, SCI TOTAL ENVIRON, V137, P1 Klindtworth M, 1999, COMPUT ELECTRON AGR, V24, P65, DOI 10.1016/S0168-1699(99)00037-X KONERMANN H, 1991, 131987 COMM EUR, P37 LAMBOOIJ E, 1991, 14198 EUR, P21 LANGLANDS J P, 1975, Australian Journal of Experimental Agriculture and Animal Husbandry, V15, P5, DOI 10.1071/EA9750005 MAAS J, 1994, AM J VET RES, V55, P247 MANSTON R, 1985, J VET PHARMACOL THER, V8, P368, DOI 10.1111/j.1365-2885.1985.tb00969.x Merks J. W. M., 1990, Pig News and Information, V11, P35 MICOL BD, 1987, B TECH CRZV THEIX, V68, P25 MULLER KE, 1998, FEASIBILITY CONCEPT PARKINS JJ, 1994, BRIT VET J, V150, P547, DOI 10.1016/S0007-1935(94)80038-3 PIRKELMANN H, 1991, 31398 COMM EUR COMM, P53 RATNIKOV AN, 1998, SCI TOTAL ENVIRON, V223, P2 RIBO O, 1994, UAB0126 FEOGA EUR CO RINER JL, 1982, AM J VET RES, V43, P2028 RINER JL, 1981, J ECON ENTOMOL, V74, P359, DOI 10.1093/jee/74.3.359 TUDOR GD, 1980, AUST J EXP AGR, V20, P522, DOI 10.1071/EA9800522 WATSON M J, 1978, Proceedings of the Nutrition Society of Australia Annual Conference, V3, P86 WELCHMAN DD, 1987, VET REC, V121, P586 NR 43 TC 18 Z9 20 U1 0 U2 5 PD AUG PY 2001 VL 20 IS 2 BP 480 EP 490 DI 10.20506/rst.20.2.1285 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000170689800011 DA 2022-12-14 ER PT J AU Esteso, A Alemany, MME Ortiz, A AF Esteso, Ana Alemany, M. M. E. Ortiz, Angel TI Deterministic and Uncertain Methods and Models for Managing Agri-Food Supply Chain SO DIRECCION Y ORGANIZACION DT Article DE Agri-Food; Supply Chain; Mathematical Programming; Deterministic; Uncertainty ID PLANNING-MODEL; HOMOGENEITY; PRODUCTS AB The market for agricultural products has grown substantially. At the same time, social concern in food issues such as food safety, food quality, traceability and sustainability is constantly increasing. These reasons have pointed out the need of new models and tools to manage the agri-food supply chains while considering the characteristics that differentiate them from other industrial supply chains as well as the uncertainties present in the sector. Thus, the aim of this paper is to present the current status of a project which mains objectives are to describe the complexity faced by agri-food supply chain decision makers, and to develop new tools based on mathematical programming models to help the decision making process in agri-food supply chain planning. These models novelty will include the consideration of the inherent characteristics of agri-food supply chains and the sources of uncertainty present in the sector. The proposed models and tools will be applied to a real agri-food supply chain in order to prove their validity and applicability and to compare the results obtained by deterministic and uncertain tools. C1 [Esteso, Ana; Alemany, M. M. E.; Ortiz, Angel] Univ Politecn Valencia, Res Ctr Prod Management & Engn CIGIP, Camino Vera S-N, E-46022 Valencia, Spain. C3 Universitat Politecnica de Valencia RP Esteso, A (corresponding author), Univ Politecn Valencia, Res Ctr Prod Management & Engn CIGIP, Camino Vera S-N, E-46022 Valencia, Spain. EM aneslva@doctor.upv.es; mareva@omp.upv.es; aortiz@cigip.upv.es CR Ahumada O, 2012, AGR SYST, V112, P17, DOI 10.1016/j.agsy.2012.06.002 Ahumada O, 2011, INT J PROD ECON, V133, P677, DOI 10.1016/j.ijpe.2011.05.015 Ahumada O, 2009, EUR J OPER RES, V196, P1, DOI 10.1016/j.ejor.2008.02.014 Alemany MME, 2015, APPL MATH MODEL, V39, P4463, DOI 10.1016/j.apm.2014.12.057 Alemany MME, 2013, APPL MATH MODEL, V37, P3380, DOI 10.1016/j.apm.2012.07.022 Alemany MME, 2011, COMPUT IND, V62, P519, DOI 10.1016/j.compind.2011.02.002 Alemany MME, 2010, INT J PROD RES, V48, P5053, DOI 10.1080/00207540903055701 Amorim P, 2012, INT J PROD ECON, V138, P89, DOI 10.1016/j.ijpe.2012.03.005 [Anonymous], 2000, BRIT FOOD J, DOI DOI 10.1108/00070700010362176 Begen MA, 2003, INFOR, V41, P235 Bohle C, 2010, EUR J OPER RES, V200, P245, DOI 10.1016/j.ejor.2008.12.003 Boza A, 2014, PROD PLAN CONTROL, V25, P650, DOI 10.1080/09537287.2013.798085 Grillo H, 2016, COMPUT IND ENG, V91, P239, DOI 10.1016/j.cie.2015.11.013 Grillo H, 2016, IFIP ADV INF COMM TE, V480, P608, DOI 10.1007/978-3-319-45390-3_52 Hegeman J, 2014, STUD FUZZ SOFT COMP, V313, P317, DOI 10.1007/978-3-642-53939-8_14 Lowe T. J., 2004, Manufacturing & Service Operations Management, V6, P201, DOI 10.1287/msom.1040.0051 Miller WA, 1997, INT J PROD ECON, V53, P227, DOI 10.1016/S0925-5273(97)00110-2 Mundi I, 2013, STUD INFORM CONTROL, V22, P153 Mundi MI, 2016, FUZZY SET SYST, V293, P95, DOI 10.1016/j.fss.2015.06.009 Munhoz JR, 2014, COMPUT ELECTRON AGR, V107, P45, DOI 10.1016/j.compag.2014.05.016 Radulescu M, 2008, UKSIM INT CONF COMP, P549, DOI 10.1109/UKSIM.2008.40 Soto-Silva WE, 2016, EUR J OPER RES, V251, P345, DOI 10.1016/j.ejor.2015.08.046 Verdouw CN, 2010, TOWARDS EFFECTIVE FOOD CHAINS: MODELS AND APPLICATIONS, P225 NR 23 TC 2 Z9 2 U1 1 U2 7 PD JUL PY 2017 VL 62 BP 41 EP 46 WC Management SC Business & Economics UT WOS:000417992100004 DA 2022-12-14 ER PT J AU Liu, F AF Liu, F. TI RESEARCH ON THE TRACEABILITY SYSTEM OF AGRICULTURAL PRODUCTS IN HEALTH BASED ON CLOUD COMPUTING SO BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY DT Meeting Abstract C1 [Liu, F.] Yulin Univ, NetWork & Informat Ctr, Yulin, Peoples R China. C3 Yulin University NR 0 TC 0 Z9 0 U1 0 U2 4 PD MAR PY 2018 VL 122 SU 2 SI SI MA HHME17-Z83 BP 42 EP 42 WC Pharmacology & Pharmacy; Toxicology SC Pharmacology & Pharmacy; Toxicology UT WOS:000427562000151 DA 2022-12-14 ER PT J AU Ehsan, I Khalid, MI Ricci, L Iqbal, J Alabrah, A Ullah, SS Alfakih, TM AF Ehsan, Ibtisam Khalid, Muhammad Irfan Ricci, Laura Iqbal, Jawaid Alabrah, Amerah Ullah, Syed Sajid Alfakih, Taha M. TI A Conceptual Model for Blockchain-Based Agriculture Food Supply Chain System SO SCIENTIFIC PROGRAMMING DT Article ID TECHNOLOGY; IOT AB In agriculture supply chain management, traceability is a crucial aspect to ensure food safety for increasing customer loyalty and satisfaction. Lack of quality assurance in centralized data storage makes us move towards a new approach based on a decentralized system in which transparency and quality assurance is guaranteed throughout the supply chain from producer to consumer. The current supply chain model has some disadvantages like a communication gap between the entities of the supply chain and no information about the travel history and origin of the product. The use of technology improves the communication and relation between various farmers and stakeholders. Blockchain technology acquires transparency and traceability in the supply chain, provides transaction records traceability, and enhances security for the whole supply chain. In this paper, we present a blockchain-based, fully decentralized traceability model that ensures the integrity and transparency of the system. This new model eliminated most of the disadvantages of the traditional supply chain. For the coordination of all transactions in the supply chain, we proposed a decentralized supply chain model along with a smart contract. C1 [Ehsan, Ibtisam; Khalid, Muhammad Irfan] Univ Sialkot, Dept Informat Technol, Sialkot, Pakistan. [Ricci, Laura] Univ Pisa, Dept Comp Sci, Pisa, Italy. [Iqbal, Jawaid] Capital Univ Sci & Technol, Dept Software Engn, Islamabad, Pakistan. [Alabrah, Amerah] King Saud Univ, Coll Comp & Informat Sci, Dept Informat Syst, Riyadh 11543, Saudi Arabia. [Ullah, Syed Sajid] Univ Agder, Dept Informat & Commun Technol, N-4898 Grimstad, Norway. [Alfakih, Taha M.] Aljanad Univ Sci & Technol, Fac Engn & Informat Technically, Taizi, Yemen. C3 University of Pisa; Capital University of Science & Technology; King Saud University; University of Agder RP Ullah, SS (corresponding author), Univ Agder, Dept Informat & Commun Technol, N-4898 Grimstad, Norway.; Alfakih, TM (corresponding author), Aljanad Univ Sci & Technol, Fac Engn & Informat Technically, Taizi, Yemen. EM syed.s.ullah@uia.no; talfakih@just.edu.ye CR Akram SV, 2020, SECUR PRIVACY, V3, DOI 10.1002/spy2.109 Alfandi O, 2021, CLUSTER COMPUT, V24, P37, DOI 10.1007/s10586-020-03137-8 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Awan SH, 2020, INT J ADV COMPUT SC, V11, P420 Basnayake B. M. A. L., 2019, 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE). Proceedings, P103, DOI 10.23919/SCSE.2019.8842690 Bermeo-Almeida O, 2018, COMM COM INF SC, V883, P44, DOI 10.1007/978-3-030-00940-3_4 Bodkhe U, 2022, T EMERG TELECOMMUN T, V33, DOI 10.1002/ett.4059 Borah MD, 2020, SUPPLY CHAIN MANAGEM Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Choo KR., 2019, 2019 IEEE CAN C EL C Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Demestichas K, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124113 Devi M.S., 2019, DESIGN IOT BLOCKCHAI, V985 Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067 Ferrag MA, 2020, IEEE ACCESS, V8, P32031, DOI 10.1109/ACCESS.2020.2973178 Frameworks L.I, 2020, POAH NOVEL CONSENSUS Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Khan PW, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20102990 Kim H., 2018, SUSTAINABLE SOLUTION Kim M, 2018, 2018 IEEE 9TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P335, DOI 10.1109/IEMCON.2018.8615007 Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Madumidha S., 2019, 2019 TEQIP III Sponsored International Conference on Microwave Integrated Circuits, Photonics and Wireless Networks (IMICPW), P174, DOI 10.1109/IMICPW.2019.8933270 Mirabelli G, 2020, PROCEDIA MANUF, V42, P414, DOI 10.1016/j.promfg.2020.02.054 Patil P, 2021, WIRELESS PERS COMMUN, V117, P1815, DOI 10.1007/s11277-020-07947-2 Rathee G, 2021, J AMB INTEL HUM COMP, V12, P533, DOI 10.1007/s12652-020-02017-8 Ronaghi M. H., 2021, Information Processing in Agriculture, V8, P398, DOI 10.1016/j.inpa.2020.10.004 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Scuderi A, 2019, QUAL-ACCESS SUCCESS, V20, P580 Shahid A, 2020, IEEE ACCESS, V8, P69230, DOI 10.1109/ACCESS.2020.2986257 Taskinsoy J., 2019, SSRN ELECT J, P1 Tian F, 2017, I C SERV SYST SERV M Torky M, 2020, COMPUT ELECTRON AGR, V178, DOI 10.1016/j.compag.2020.105476 Umamaheswari S, 2019, INT CONF ADV COMPU, P324, DOI 10.1109/ICoAC48765.2019.246860 Vangala A, 2021, IEEE SENS J, V21, P17591, DOI 10.1109/JSEN.2020.3012294 Wamba SF, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102064 Xu J., 2020, ARTIF INTELL AGR, V4, P153, DOI [10.1016/j.aiia.2020.08.002, DOI 10.1016/J.AIIA.2020.08.002] Xu RH, 2020, SMART CITIES-BASEL, V3, P928, DOI 10.3390/smartcities3030047 Yadav V.S., 2019, P INT C IND ENG OPER, P973 Yadav VS, 2020, RESOUR CONSERV RECY, V161, DOI 10.1016/j.resconrec.2020.104877 Yadav VS, 2019, AIP CONF PROC, V2148, DOI 10.1063/1.5123972 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 46 TC 5 Z9 5 U1 12 U2 17 PD FEB 28 PY 2022 VL 2022 AR 7358354 DI 10.1155/2022/7358354 WC Computer Science, Software Engineering SC Computer Science UT WOS:000778692400011 DA 2022-12-14 ER PT J AU Pacifico, D Onofri, C Parisi, B Ostano, P Mandolino, G AF Pacifico, Daniela Onofri, Chiara Parisi, Bruno Ostano, Paola Mandolino, Giuseppe TI Influence of Organic Farming on the Potato Transcriptome SO SUSTAINABILITY DT Article DE traceability; POCI; conventional farming system; Solanum tuberosum; microarray ID ASCORBATE BIOSYNTHESIS; GENE; SYSTEMS; TUBERS; IDENTIFICATION; EXPRESSION; VEGETABLES; ACRYLAMIDE; PRODUCTS; YIELD AB Organic agriculture sparks a lively debate on its potential health and environmental benefits. Comparative studies often investigate the response of crops to organic farming through targeted approaches and within a limited experimental work. To clarify this issue, the transcriptomic profile of a cultivar of the potato grown for two years under organic and conventional farming was compared with the profile of an experimental clone grown in the same location of Southern Italy for one year. Transcriptomic raw data were obtained through Potato Oligo Chip Initiative (POCI) microarrays and were processed using unsupervised coupling multivariate statistical analysis and bioinformatics (MapMan software). One-hundred-forty-four genes showed the same expression in both years, and 113 showed the same expression in both genotypes. Their functional characterization revealed the strong involvement of the farming system in metabolism associated with the nutritional aspects of organic tubers (e.g., phenylpropanoid, flavonoid, glycoalcaloid, asparagine, ascorbic acid). Moreover, further investigation showed that eight of 42,034 features exhibited the same trend of expression irrespective of the year and genotype, making them possible candidates as markers of traceability. This paper raises the issue regarding the choice of genotype in organic management and the relevance of assessing seasonal conditions effects when studying the effects of organic cultivation on tuber metabolism. C1 [Pacifico, Daniela; Onofri, Chiara; Parisi, Bruno; Mandolino, Giuseppe] Res Ctr Cereal & Ind Crops CREA CI, Council Agr Res & Econ Anal, Via Corticella 133, I-40128 Bologna, Italy. [Ostano, Paola] Fdn Edo & Elvo Tempia Valenta, Canc Genom Lab, Via Malta 3, I-13900 Biella, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Pacifico, D (corresponding author), Res Ctr Cereal & Ind Crops CREA CI, Council Agr Res & Econ Anal, Via Corticella 133, I-40128 Bologna, Italy. EM daniela.pacifico@crea.gov.it; chiara.onofri@crea.gov.it; bruno.parisi@crea.gov.it; paola.ostano@gmail.com; giuseppe.mandolino@crea.gov.it CR BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x Brandt K, 2011, CRIT REV PLANT SCI, V30, P177, DOI 10.1080/07352689.2011.554417 Cui Y, 1996, PHYSIOL MOL PLANT P, V49, P187, DOI 10.1006/pmpp.1996.0048 Ducreux LJM, 2008, J EXP BOT, V59, P4219, DOI 10.1093/jxb/ern264 Gentleman RC, 2004, GENOME BIOL, V5, DOI 10.1186/gb-2004-5-10-r80 Helliwell CA, 1999, PLANT PHYSIOL, V119, P507, DOI 10.1104/pp.119.2.507 Hendriksen HV, 2009, J AGR FOOD CHEM, V57, P4168, DOI 10.1021/jf900174q Hoefkens C, 2009, BRIT FOOD J, V111, P1062, DOI 10.1108/00070700910992916 Kaminski KP, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0051248 Kloosterman B, 2008, FUNCT INTEGR GENOMIC, V8, P329, DOI 10.1007/s10142-008-0083-x Laing WA, 2007, P NATL ACAD SCI USA, V104, P9534, DOI 10.1073/pnas.0701625104 Lehesranta SJ, 2007, PROTEOMICS, V7, P597, DOI 10.1002/pmic.200600889 Lester GE, 2011, J AGR FOOD CHEM, V59, P10401, DOI 10.1021/jf202385x Livak KJ, 2001, METHODS, V25, P402, DOI 10.1006/meth.2001.1262 Lombardo S, 2013, RENEW AGR FOOD SYST, V28, P50, DOI 10.1017/S1742170511000640 Lorence A, 2004, PLANT PHYSIOL, V134, P1200, DOI 10.1104/pp.103.033936 Lu CG, 2005, P ROY SOC B-BIOL SCI, V272, P1901, DOI 10.1098/rspb.2005.3161 Maggio A, 2008, EUR J AGRON, V28, P343, DOI 10.1016/j.eja.2007.10.003 Merkl R, 2010, CZECH J FOOD SCI, V28, P275, DOI 10.17221/132/2010-CJFS Onofri C., 2012, MINERVA BIOTECNOL, V24, P11 Pacifico D, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8101054 Pacifico D, 2013, J AGR FOOD CHEM, V61, P11201, DOI 10.1021/jf402961m Perkins JR, 2014, MOL PAIN, V10, DOI 10.1186/1744-8069-10-7 Radonic A, 2004, BIOCHEM BIOPH RES CO, V313, P856, DOI 10.1016/j.bbrc.2003.11.177 Saeed AI, 2006, METHOD ENZYMOL, V411, P134, DOI 10.1016/S0076-6879(06)11009-5 Skrabule I, 2013, POTATO RES, V56, P259, DOI 10.1007/s11540-013-9242-0 Stushnoff C, 2010, J EXP BOT, V61, P1225, DOI 10.1093/jxb/erp394 Sundaresha Siddappa, 2014, Australian Journal of Crop Science, V8, P215 van Dijk JP, 2012, J AGR FOOD CHEM, V60, P2090, DOI 10.1021/jf204696w Weisshaar R, 2002, DEUT LEBENSM-RUNDSCH, V98, P397 NR 30 TC 7 Z9 7 U1 0 U2 13 PD MAY PY 2017 VL 9 IS 5 AR 779 DI 10.3390/su9050779 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000404127800104 DA 2022-12-14 ER PT J AU Sarmiento, C Detienne, P Heinz, C Molino, JF Grard, P Bonnet, P AF Sarmiento, Carolina Detienne, Pierre Heinz, Christine Molino, Jean-Francois Grard, Pierre Bonnet, Pierre TI PL@NTWOOD: A COMPUTER-ASSISTED IDENTIFICATION TOOL FOR 110 SPECIES OF AMAZON TREES BASED ON WOOD ANATOMICAL FEATURES SO IAWA JOURNAL DT Article DE Wood identification; computer-assisted identification; tropical tree species; Amazonian woods ID MICROCOMPUTER; TAXONOMY; CAPACITY; SYSTEM AB Sustainable management and conservation of tropical trees and forests require accurate identification of tree species. Reliable, user-friendly identification tools based on macroscopic morphological features have already been developed for various tree floras. Wood anatomical features provide also a considerable amount of information that can be used for timber traceability, certification and trade control. Yet, this information is still poorly used, and only a handful of experts are able to use it for plant species identification. Here, we present an interactive, user-friendly tool based on vector graphics, illustrating 99 states of 27 wood characters from 110 Amazonian tree species belonging to 34 families. Pl@ntWood is a graphical identification tool based on the IDAO system, a multimedia approach to plant identification. Wood anatomical characters were selected from the IAWA list of microscopic features for hardwood identification, which will enable us to easily extend this work to a larger number of species. A stand-alone application has been developed and an on-line version will be delivered in the near future. Besides allowing non-specialists to identify plants in a user-friendly interface, this system can be used with different purposes such as teaching, conservation, management, and self-training in the wood anatomy of tropical species. C1 [Sarmiento, Carolina; Grard, Pierre] CIRAD, UMR AMAP, F-34398 Montpellier 5, France. [Detienne, Pierre] CIRAD, Prod & Valorisat Bois Trop, F-34398 Montpellier 5, France. [Heinz, Christine] Univ Montpellier 2, UMR AMAP, F-34398 Montpellier 5, France. [Molino, Jean-Francois] IRD, UMR AMAP, F-34398 Montpellier 5, France. [Bonnet, Pierre] INRA, UMR AMAP, F-34398 Montpellier 5, France. C3 CIRAD; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; CIRAD; CIRAD; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; CIRAD; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; CIRAD; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); Universite de Montpellier; INRAE RP Sarmiento, C (corresponding author), Univ Los Andes, Dept Ciencias Biol, Calle 1 18A-12, Bogota, Colombia. EM carolinasar@gmail.com CR Barker J.A., 2005, THESIS ADELAIDE U Birnbaum P., 2009, P TDWG 2009 ANN C Caballe G, 1998, CAN J BOT, V76, P1703, DOI 10.1139/b98-127 Carlquist S., 2001, COMP WOOD ANATOMY CBIT (Centre for Biological Information Technology), 2010, LUC VERS 3 5 DALLWITZ MJ, 1980, TAXON, V29, P41, DOI 10.2307/1219595 DETiENNE P, 1983, ATLAS IDENTIFICATION Grard P., 2002, IDAO MULTIMEDIA APPR Hansen MC, 2008, P NATL ACAD SCI USA, V105, P9439, DOI 10.1073/pnas.0804042105 Heiss A.G., 2009, ANATOMY EUROPEAN N A Hubbell S.P., 2008, PNAS, V105, P1 ILIC J, 1993, IAWA J, V14, P333, DOI 10.1163/22941932-90000587 InsideWood, 2004, PUBL INT Jayeola AA, 2009, NOT BOT HORTI AGROBO, V37, P28 Koch G., 2007, P INT WORKSH FING ME Koch G, 2011, IAWA J, V32, P213, DOI 10.1163/22941932-90000052 KUMAR R, 1982, RESOUR POLICY, V8, P177, DOI 10.1016/0301-4207(92)90035-8 KURODA K, 1987, IAWA BULL, V8, P69, DOI 10.1163/22941932-90001030 LAPASHA CA, 1987, IAWA BULL, V8, P347, DOI 10.1163/22941932-90000454 Peres CA, 2010, BIOL CONSERV, V143, P2314, DOI 10.1016/j.biocon.2010.01.021 Richter H.G., 2005, CITES WOODID COMPUTE Safdari V, 2009, IAWA J, V30, P81, DOI 10.1163/22941932-90000205 Schalk PH, 1999, NATURE RESOUR, V35, P31 Schnitzer SA, 2005, AM NAT, V166, P262, DOI 10.1086/431250 Smith GF, 2009, TAXON, V58, P697, DOI 10.1002/tax.583001 SMITH N, 2004, FLOWERING PLANTS NEO Stern W.L., 1988, NATURE, V9, P203 Ung V, 2010, BIOINFORMATICS, V26, P703, DOI 10.1093/bioinformatics/btp715 Webb CO, 2010, BIODIVERS CONSERV, V19, P955, DOI 10.1007/s10531-010-9817-x Wheeler E.A., 1989, IAWA BULL, V10, P219, DOI 10.1163/22941932-90000496 Wheeler EA, 1998, IAWA J, V19, P241, DOI 10.1163/22941932-90001528 NR 31 TC 9 Z9 11 U1 0 U2 15 PY 2011 VL 32 IS 2 BP 221 EP 232 DI 10.1163/22941932-90000053 WC Forestry SC Forestry UT WOS:000291190000008 DA 2022-12-14 ER PT J AU Braga, F Frusciante, E Ferraro, S Panteghini, M AF Braga, Federica Frusciante, Erika Ferraro, Simona Panteghini, Mauro TI Trueness evaluation and verification of inter-assay agreement of serum folate measuring systems SO CLINICAL CHEMISTRY AND LABORATORY MEDICINE DT Article DE decision-making; standardization; total folate; trueness ID WORKING GROUP RECOMMENDATIONS; TANDEM MASS-SPECTROMETRY; INTERNATIONAL STANDARD; QUALITY; TRACEABILITY; PROTOCOL; HORMONE; BIAS AB Background: Definitive data to establish if the use of the WHO International Standard (IS) 03/178 as a common calibrator of commercial measuring systems (MSs) has improved the harmonization of serum total folate (tFOL) measurements to a clinically suitable level are lacking. Here, we report the results of an intercomparison study aimed to verify if the current inter-assay variability is acceptable for clinical application of tFOL testing. Methods: After confirming their commutability, the IS 03/178 and National Institute for Standards and Technology SRM 3949 L1 were used for evaluating the correctness of traceability implementation by manufacturers and the MSs trueness, respectively. The inter-assay agreement was verified using 20 patient pools. The measurement uncertainty (U) of tFOL measurements on clinical samples was also estimated. An outcome-based model for defining desirable performance specifications for bias and imprecision for serum tFOL measurements was applied. Results: The majority of evaluated MSs overestimated the WHO IS value of +5% or more with the risk to produce an unacceptably high number of false-negative results in clinical practice. The mean inter-assay CV on all pools and on those with tFOL values >3.0 mu g/L (n =15) was 12.5% and 7.1%, respectively. In neither case the goal of 3.0% was fulfilled. The residual bias resulted in an excessive U of tFOL measurement on clinical samples. Conclusions: The implementation of traceability of tFOL MSs to the WHO IS 03/178 is currently inadequate, resulting in an inter-assay variability that does not permit the use of a common threshold for detecting folate deficiency. C1 [Braga, Federica; Frusciante, Erika; Ferraro, Simona; Panteghini, Mauro] Univ Milan, Res Ctr Metrol Traceabil Lab Med CIRME, Milan, Italy. [Braga, Federica] ASST Fatebenfratelli Sacco, UOC Patol Clin, Via GB Grassi 74, I-20157 Milan, Italy. C3 University of Milan RP Braga, F (corresponding author), Univ Milan, Res Ctr Metrol Traceabil Lab Med CIRME, Milan, Italy.; Braga, F (corresponding author), ASST Fatebenfratelli Sacco, UOC Patol Clin, Via GB Grassi 74, I-20157 Milan, Italy. EM federica.braga@unimi.it CR [Anonymous], 2019, 209142019 ISOTS Blackmore S, 2005, CLIN CHIM ACTA, V355, pS459 Blackmore S, 2011, CLIN CHEM, V57, P986, DOI 10.1373/clinchem.2010.160135 Boulo S, 2013, CLIN CHEM, V59, P1074, DOI 10.1373/clinchem.2012.199489 Braga F, 2019, CLIN CHEM LAB MED, V57, P967, DOI 10.1515/cclm-2019-0154 Braga F, 2019, CLIN CHEM, V65, P473, DOI 10.1373/clinchem.2018.297655 Budd JR, 2018, CLIN CHEM, V64, P465, DOI 10.1373/clinchem.2017.277558 Ceriotti F, 2017, CLIN CHEM LAB MED, V55, P189, DOI 10.1515/cclm-2016-0091 CLSI, 1999, C37A CLSI CLSI, 2010, EP30A CLSI Danilenko U, 2020, CLIN CHEM LAB MED, V58, P368, DOI 10.1515/cclm-2019-0732 Ferraro S, 2019, AM J CLIN NUTR Ferraro S, 2020, CLIN CHEM LAB MED, V58, pE66, DOI 10.1515/cclm-2019-0695 Ferraro S, 2019, CLIN CHEM LAB MED, V57, P1112, DOI 10.1515/cclm-2019-0050 Ferraro S, 2017, CLIN CHEM LAB MED, V55, P1262, DOI 10.1515/cclm-2016-0804 Fraser CG, 1997, ANN CLIN BIOCHEM, V34, P8, DOI 10.1177/000456329703400103 Hannisdal R, 2009, CLIN CHEM, V55, P1147, DOI 10.1373/clinchem.2008.114389 Kristensen GBB, 2016, CLIN CHEM, V62, P1255, DOI 10.1373/clinchem.2016.258962 National Institute of Standards & Technology, 2018, CERT AN STAND REF MA Nilsson G, 2018, CLIN CHEM, V64, P455, DOI 10.1373/clinchem.2017.277541 Owen WE, 2003, AM J CLIN PATHOL, V120, P121, DOI 10.1309/L2U6HH5KAYG48L40 Panteghini Mauro, 2007, Clin Biochem Rev, V28, P97 Panteghini M, 2009, CLIN BIOCHEM, V42, P236, DOI 10.1016/j.clinbiochem.2008.09.098 Pasqualetti S, 2015, CLIN CHIM ACTA, V450, P125, DOI 10.1016/j.cca.2015.08.007 Pfeiffer CM, 2004, CLIN CHEM, V50, P423, DOI 10.1373/clinchem.2003.026955 Roberts NB, 2018, TIETZ TXB CLIN CHEM, V6, P639 Thorpe SJ, 2007, CLIN CHEM LAB MED, V45, P380, DOI 10.1515/CCLM.2007.072 WHO, 2015, GUID OPT SER RED BLO NR 28 TC 6 Z9 7 U1 4 U2 6 PD OCT PY 2020 VL 58 IS 10 BP 1697 EP 1705 DI 10.1515/cclm-2019-0928 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000582498800027 DA 2022-12-14 ER PT J AU Kouremeti, N Nevas, S Kazadzis, S Grobner, J Schneider, P Schwind, KM AF Kouremeti, Natalia Nevas, Saulius Kazadzis, Stelios Groebner, Julian Schneider, Philipp Schwind, Kerstin Maria TI SI-traceable solar irradiance measurements for aerosol optical depth retrieval SO METROLOGIA DT Article DE aerosol optical depth; solar irradiance; traceability ID SPECTRAL IRRADIANCE; CALIBRATION AB The overall aim of this study is to enable the traceable to International System of Units (SI) determination of column-integrated aerosol optical depth (AOD) retrieved from the passive remote sensing of the atmosphere using SI-traceable direct solar spectral irradiance measurements. A precision filter radiometer that measures direct solar spectral irradiance for the retrieval of AOD has been characterized and calibrated at the state-of-the-art calibration facilities of Physikalisch-Technische Bundesanstalt. The measured SI-traceable solar irradiances together with three state-of-the-art top-of-the-atmosphere (TOA) solar spectra have been used for retrieving AODs, which were validated against the reference AOD instruments of the World Aerosol Optical Depth Calibration Centre of the World Meteorological Organization (WMO). Calibration factors agreed within +/- 0.57% (3 sigma) using all three TOA spectra except for 368 nm (-1.1%) and 862 nm (1.8%) channels for one out of the three TOA spectra. Application of these results to the AOD retrieval showed AOD differences with the current reference methods/instruments well within the recommended WMO limits. The work provides a first step to opening a new era of AOD measurements traceability, providing a link to the SI through a laboratory-based approach, with the main advantages being the low uncertainty, the possibility of enhancing global AOD homogenization efforts and the chance to avoid calibration activities based on instrument relocations. C1 [Kouremeti, Natalia; Kazadzis, Stelios; Groebner, Julian] World Radiat Ctr, Phys Meteorol Observatorium Davos, Davos, Switzerland. [Nevas, Saulius; Schneider, Philipp; Schwind, Kerstin Maria] Phys Tech Bundesanstalt PTB, Braunschweig, Germany. [Nevas, Saulius; Schneider, Philipp; Schwind, Kerstin Maria] Phys Tech Bundesanstalt PTB, Berlin, Germany. C3 Physikalisch-Technische Bundesanstalt (PTB); Physikalisch-Technische Bundesanstalt (PTB) RP Kouremeti, N (corresponding author), World Radiat Ctr, Phys Meteorol Observatorium Davos, Davos, Switzerland. EM natalia.kouremeti@pmodwrc.ch CR [Anonymous], JCGM 100 SERIES 2008 Bais AF, 1997, APPL OPTICS, V36, P5199, DOI 10.1364/AO.36.005199 Bolsee D, 2014, SOL PHYS, V289, P2433, DOI 10.1007/s11207-014-0474-1 Chance K, 2010, J QUANT SPECTROSC RA, V111, P1289, DOI 10.1016/j.jqsrt.2010.01.036 Coddington OM, 2021, GEOPHYS RES LETT, V48, DOI 10.1029/2020GL091709 Grobner J, 2001, J GEOPHYS RES-ATMOS, V106, P7211, DOI 10.1029/2000JD900756 Grobner J, 2019, SOL ENERGY, V185, P199, DOI 10.1016/j.solener.2019.04.060 Grobner J, 2017, ATMOS MEAS TECH, V10, P3375, DOI 10.5194/amt-10-3375-2017 Harder JW, 2010, SOL PHYS, V263, P3, DOI 10.1007/s11207-010-9555-y Hilbig T, 2018, SOL PHYS, V293, DOI 10.1007/s11207-018-1339-9 Holben BN, 1998, REMOTE SENS ENVIRON, V66, P1, DOI 10.1016/S0034-4257(98)00031-5 Hulsen G, 2016, APPL OPTICS, V55, P7265, DOI 10.1364/AO.55.007265 IPCC (Intergovernmental Panel on Climate Change), 2013, CLIM CHANG 2013 PHYS Kazadzis S, 2018, ATMOS CHEM PHYS, V18, P3185, DOI 10.5194/acp-18-3185-2018 Kazadzis S, 2018, GEOSCI INSTRUM METH, V7, P39, DOI 10.5194/gi-7-39-2018 Kubarsepp T, 1998, APPL OPTICS, V37, P2716, DOI 10.1364/AO.37.002716 Levy RC, 2013, ATMOS MEAS TECH, V6, P2989, DOI 10.5194/amt-6-2989-2013 Nakajima T, 2020, ATMOS MEAS TECH, V13, P4195, DOI 10.5194/amt-13-4195-2020 Scholl M, 2016, J SPACE WEATHER SPAC, V6, DOI 10.1051/swsc/2016007 Schuster M, 2014, APPL OPTICS, V53, P2815, DOI 10.1364/AO.53.002815 Schuster M, 2012, APPL OPTICS, V51, P1950, DOI 10.1364/AO.51.001950 SHAW GE, 1983, B AM METEOROL SOC, V64, P4, DOI 10.1175/1520-0477(1983)064<0004:SP>2.0.CO;2 Souaidia N., 2003, COMP LASER BASED CON, V5155 Thuillier G, 2003, SOL PHYS, V214, P1, DOI 10.1023/A:1024048429145 Toledano C, 2018, ATMOS CHEM PHYS, V18, P14555, DOI 10.5194/acp-18-14555-2018 Wehrli C, 2000, METROLOGIA, V37, P419, DOI 10.1088/0026-1394/37/5/16 WMO, 2016, SITU MEAS AER RAD PR, V227 Xu QY, 2010, REV SCI INSTRUM, V81, DOI 10.1063/1.3331459 NR 28 TC 0 Z9 0 U1 0 U2 0 PD AUG 1 PY 2022 VL 59 IS 4 AR 044001 DI 10.1088/1681-7575/ac6cbb WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000809245400001 DA 2022-12-14 ER PT J AU Merlone, A Sanna, F Coppa, G Massano, L Musacchio, C AF Merlone, Andrea Sanna, Francesca Coppa, Graziano Massano, Laura Musacchio, Chiara TI Transportable system for on-site calibration of permafrost temperature sensors SO PERMAFROST AND PERIGLACIAL PROCESSES DT Article DE calibration uncertainty; cryosphere; environmental metrology; permafrost monitoring; sensor calibration; transportable system ID PROJECT AB Evaluating the degradation of permafrost is a major challenge in understanding global warming and its impact on the cryosphere. The Global Cryosphere Watch is promoting actions towards data quality and traceability, to achieve comparability of observations from different permafrost stations. In response to this, a transportable system for on-site calibrations of permafrost temperature sensors was studied, developed and tested in the field, within the project MeteoMet. The system, here described, allows users to establish metrological traceability to permafrost temperature profiles, by performing the calibration on-site, even in remote or high-elevation areas, in realistic conditions. A field campaign at 3,000 m elevation to test the system's performance and practical use is also reported. Overall calibration uncertainty in the field accounted for <0.05 degrees C, with contribution from reference sensors within 2 mK over the whole range; besides reducing uncertainties in each measuring point of a chain, the procedure also allows users to establish comparability among all the sensors within 0.03 degrees C. The self-heating effect of each sensor was also evaluated as 0.007 degrees C, and was thus considered a negligible component. The evolution of permafrost thawing can be more robustly evaluated, through documented data traceability together with improved comparability in space and time. C1 [Merlone, Andrea; Coppa, Graziano; Musacchio, Chiara] Ist Nazl Ric Metrolog INRiM, Turin, Italy. [Sanna, Francesca] Ist Macchine Agricole & Movimento Terra IMAMOTER, Turin, Italy. [Sanna, Francesca; Massano, Laura] Univ Torino, Turin, Italy. C3 Istituto Nazionale di Ricerca Metrologica (INRIM); University of Turin RP Sanna, F (corresponding author), Ist Macchine Agricole & Movimento Terra IMAMOTER, Turin, Italy. EM francesca.sanna@unito.it CR [Anonymous], 2013, CLIMATE CHANGE 2013, P1535, DOI [DOI 10.1017/CBO9781107415324, 10.1017/CBO9781107415324] Bertiglia F, 2015, INT J THERMOPHYS, V36, P589, DOI 10.1007/s10765-014-1806-y Biskaborn BK, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-018-08240-4 Colombo N, 2018, GLOBAL PLANET CHANGE, V162, P69, DOI 10.1016/j.gloplacha.2017.11.017 Everett K.R., 1989, ARCTIC ALPINE RES, V21, P213, DOI [10.2307/1551636, DOI 10.2307/1551636] French HM, 2017, CLIMATE WARMING PERM, V1, P544 Gruber S, 2004, PERMAFROST PERIGLAC, V15, P349, DOI 10.1002/ppp.503 Guglielmin M, 2018, CLIM PAST, V14, P709, DOI 10.5194/cp-14-709-2018 Harden JW, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051958 Harris C, 2001, PERMAFROST PERIGLAC, V12, P3, DOI 10.1002/ppp.377 Merlone A, 2018, MEAS SCI TECHNOL, V29, DOI 10.1088/1361-6501/aa99fc Merlone A, 2015, METEOROL APPL, V22, P820, DOI 10.1002/met.1528 Merlone A, 2015, METEOROL APPL, V22, P847, DOI 10.1002/met.1503 Musacchio C, 2016, REND LINCEI-SCI FIS, V27, P243, DOI 10.1007/s12210-016-0531-9 Paro L, 2011, NEVE VALANGHE, V80, P50 Paro L, 2019, PERMAFROST STATION S, P831 Pavlasek P, 2016, INT J CLIMATOL, V36, P1005, DOI 10.1002/joc.4404 Riseborough D, 2008, PERMAFROST PERIGLAC, V19, P137, DOI 10.1002/ppp.615 Sanna F, 2018, METEOROL APPL, V25, P228, DOI 10.1002/met.1685 Sanna F, 2014, ITAL J AGROMETEOROL, V19, P33 Schuur EAG, 2015, NATURE, V520, P171, DOI 10.1038/nature14338 Sharkhuu A, 2007, J GEOPHYS RES-EARTH, V112, DOI 10.1029/2006JF000543 Streletskiy D, 2017, STRATEGY IMPLEMENTAT Williams PJ, 1989, THE FROZEN EARTH, DOI [10.1017/CBO9780511564437, DOI 10.1017/CB09780511564437] WMO, 2018, GUID MET INSTR METH, VI & II WMO, 2018, GUID MET INSTR METH NR 26 TC 2 Z9 2 U1 0 U2 6 PD OCT PY 2020 VL 31 IS 4 BP 610 EP 620 DI 10.1002/ppp.2063 EA MAY 2020 WC Geography, Physical; Geology SC Physical Geography; Geology UT WOS:000533362500001 DA 2022-12-14 ER PT J AU El Sheikha, AF Montet, D AF El Sheikha, Aly Farag Montet, Didier TI How to Determine the Geographical Origin of Seafood? SO CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION DT Review DE Seafood; traceability; analytical techniques; microbial communities; PCR-DGGE; geographical origin ID GRADIENT GEL-ELECTROPHORESIS; 16S RIBOSOMAL-RNA; PROTEIN EXPRESSION SIGNATURES; FATTY-ACID-COMPOSITION; PCR-DGGE; PHYSALIS FRUITS; INTESTINAL MICROFLORA; MICROBIAL DIVERSITY; GENETIC DIFFERENTIATION; BACTERIAL COMMUNITIES AB Traceability of seafood is a much needed service for the seafood industry. Current ways of tracing seafood are minimal while tracing of shellfish is nearly nonexistent. Tracing fish and shellfish are necessary for indicating where the fish and shellfish were fished from, farmed and packed from. This study reviews history of traceability of aquaculture and analytical approaches to verify the origin of seafood. It then describes the new molecular technique of the traceability by using PCR-DGGE to discriminate the geographical origin of fish (cases studies of Pangasius fish from Viet Nam and Sea bass fish from France) by analysis the DNA fragments of microorganisms (bacteria) on fish. This method is based on the assumption that the microbial communities of food are specific to a geographic area. C1 [El Sheikha, Aly Farag] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Minufiya Govt, Shibin Al Kawm 32511, Egypt. [El Sheikha, Aly Farag] Al Baha Univ, Fac Sci, Dept Biol, Al Baha, Saudi Arabia. [Montet, Didier] UMR Qualisud, CIRAD, Ctr Cooperat Int Rech Agron Dev, Montpellier, France. C3 Egyptian Knowledge Bank (EKB); Menofia University; Al Baha University; CIRAD; Universite de Montpellier RP El Sheikha, AF (corresponding author), Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Minufiya Govt, Shibin Al Kawm 32511, Egypt. EM elsheikha_aly@yahoo.com CR ACOA, 2004, ATL FISH SEAF TRACK Al-Harbi AH, 2003, AQUAC RES, V34, P43, DOI 10.1046/j.1365-2109.2003.00791.x Ampe F, 1999, APPL ENVIRON MICROB, V65, P5464 AquaTT, 2004, TRAC AQ ARGYROPOULOU V, 1992, COMP BIOCHEM PHYS A, V101, P129, DOI 10.1016/0300-9629(92)90640-C Aursand M., 2004, 34 WEFTA C SEPT 12 1 BERGSTROM E, 1989, AQUACULTURE, V82, P205, DOI 10.1016/0044-8486(89)90409-2 CAMPBELL AC, 1983, J APPL BACTERIOL, V55, P215, DOI 10.1111/j.1365-2672.1983.tb01318.x de Sousa JA, 2001, BRAZ ARCH BIOL TECHN, V44, P373, DOI 10.1590/S1516-89132001000400007 Dorigo U, 2005, WATER RES, V39, P2207, DOI 10.1016/j.watres.2005.04.007 Drengstig A, 2000, AQUAT LIVING RESOUR, V13, P121, DOI 10.1016/S0990-7440(00)00142-X EL SHEIKHA A F, 2010, J LIFE SCI, V4, P9 El Sheikha A. F., 2010, THESIS El Sheikha A. F., 2011, DETERMINATION ORIGIN, P248 El Sheikha AF, 2012, FOOD CONTROL, V24, P57, DOI 10.1016/j.foodcont.2011.09.003 El Sheikha AF, 2011, FRUITS, V66, P79, DOI 10.1051/fruits/2011001 El Sheikha AF, 2011, QUAL ASSUR SAF CROP, V3, P40, DOI 10.1111/j.1757-837X.2010.00090.x El Sheikha AF, 2011, FOOD BIOTECHNOL, V25, P115, DOI 10.1080/08905436.2011.576556 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Ercolini D, 2004, J MICROBIOL METH, V56, P297, DOI 10.1016/j.mimet.2003.11.006 FAO/SOFIA, 2006, SIT MOND PECH AQ 200 Fevolden SE, 1997, J FISH BIOL, V51, P895, DOI 10.1006/jfbi.1997.0491 FISCHER SG, 1983, P NATL ACAD SCI-BIOL, V80, P1579, DOI 10.1073/pnas.80.6.1579 Food and Agriculture Organization, 2010, The State of World Fisheries and Aquaculture, 2010 Galand PE, 2000, SARSIA, V85, P183, DOI 10.1080/00364827.2000.10414570 Grigorakis K, 2002, INT J FOOD SCI TECH, V37, P477, DOI 10.1046/j.1365-2621.2002.00604.x Grisez L, 1997, AQUACULTURE, V155, P387, DOI 10.1016/S0044-8486(97)00113-0 HAARD NF, 1992, FOOD RES INT, V25, P289, DOI 10.1016/0963-9969(92)90126-P Harris C., 2008, GROWING DEMAND SEAFO Hastein T, 2001, REV SCI TECH OIE, V20, P564, DOI 10.20506/rst.20.2.1300 Hites RA, 2004, SCIENCE, V303, P226, DOI 10.1126/science.1091447 Hoelzel AR, 1992, MOL GENETIC ANAL POP HWANG DF, 1995, J FOOD DRUG ANAL, V3, P27 Le Nguyen D. D., 2008, THESIS, P233 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Leesing R., 2011, DYNAMIC BIOCH PROCES, V5, P83 Leesing R, 2005, THESIS U MONTPELLIER, P183 Lerman L. S., 1984, ANNU REV BIOPHYS BIO, V13, P393 Liu WT, 1997, APPL ENVIRON MICROB, V63, P4516, DOI 10.1128/AEM.63.11.4516-4522.1997 MARTINEZ I, 1990, FEBS LETT, V265, P23, DOI 10.1016/0014-5793(90)80874-I Martinez I, 1999, ICES J MAR SCI, V56, P640, DOI 10.1006/jmsc.1999.0511 MARTINEZ I, 1994, COMP BIOCHEM PHYS B, V107, P11, DOI 10.1016/0305-0491(94)90219-4 Martinez I., 1999, INT WHAL COMM 51 M 3 May Bernie, 1992, P1 McCracken VJ, 2001, J NUTR, V131, P1862, DOI 10.1093/jn/131.6.1862 Ministry of Fisheries Viet Nam, 2005, VIETN FISH AQ SECT S, P141 Moeseneder MM, 1999, APPL ENVIRON MICROB, V65, P3518 Montet D., 2004, SEM FOOD SAF INT TRA Montet D, 2004, TIRE RAPPORT AVIS CO, P24 Mooney B. D., 2002, 99331 FRDC CSIRO Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Mork J, 1999, SARSIA, V84, P157, DOI 10.1080/00364827.1999.10420442 Muyzer G, 1999, CURR OPIN MICROBIOL, V2, P317, DOI 10.1016/S1369-5274(99)80055-1 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Muyzer G., 1996, MOL MICROBIAL ECOLOG, P1 Nichols P. D., 2002, NUTR VALUE AUSTR SEA, P198 NIETO TP, 1984, AQUACULTURE, V42, P193, DOI 10.1016/0044-8486(84)90100-5 OKPOKWASILI G C, 1990, Journal of Aquaculture in the Tropics, V5, P87 Olsson B, 2004, HYDROBIOLOGIA, V514, P15, DOI 10.1023/B:hydr.0000018203.90350.8e Pastene L. A., 2003, SC55IA INT WHAL COMM Pogson GH, 2003, MOL ECOL, V12, P63, DOI 10.1046/j.1365-294X.2003.01713.x Ranjard L, 2000, RES MICROBIOL, V151, P167, DOI 10.1016/S0923-2508(00)00136-4 Renouf V., 2006, THESIS Rodriguez-Ortega M. J., 2003, THESIS Rodriguez-Ortega MJ, 2003, PROTEOMICS, V3, P1535, DOI 10.1002/pmic.200300491 Rosenlund G, 2001, AQUAC RES, V32, P323, DOI 10.1046/j.1355-557x.2001.00025.x Rueda FM, 1997, AQUACULT NUTR, V3, P161, DOI 10.1046/j.1365-2095.1997.00088.x SEAFOOD plus, 2005, 506359 SEAFOOD PLUS Serot T, 1998, AQUACULT INT, V6, P331, DOI 10.1023/A:1009284905854 SHEARER KD, 1994, AQUACULTURE, V119, P63, DOI 10.1016/0044-8486(94)90444-8 SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 Shepard JL, 2000, MAR ENVIRON RES, V50, P457, DOI 10.1016/S0141-1136(00)00119-7 Shepard JL, 2000, MAR ENVIRON RES, V50, P337, DOI 10.1016/S0141-1136(00)00065-9 Simpson JM, 2000, APPL ENVIRON MICROB, V66, P4705, DOI 10.1128/AEM.66.11.4705-4714.2000 Simpson JM, 1999, J MICROBIOL METH, V36, P167, DOI 10.1016/S0167-7012(99)00029-9 SODEKO OO, 1987, MICROBIOS, V51, P133 Spanggaard B, 2000, AQUACULTURE, V182, P1, DOI 10.1016/S0044-8486(99)00250-1 SUGITA H, 1985, B JPN SOC SCI FISH, V51, P1325 Temmerman R, 2003, APPL ENVIRON MICROB, V69, P220, DOI 10.1128/AEM.69.1.220-226.2003 Tiedje JM, 1999, APPL SOIL ECOL, V13, P109, DOI 10.1016/S0929-1393(99)00026-8 Turchini GM, 2003, AQUACULTURE, V225, P251, DOI 10.1016/S0044-8486(03)00294-1 TURCHINI GM, 2000, RIV ITALIANA ACQUACO, V35, P91 Vaughan EE, 1999, CURR OPIN BIOTECH, V10, P505, DOI 10.1016/S0958-1669(99)00018-X von Wintzingerode F, 1997, FEMS MICROBIOL REV, V21, P213 Waples RS, 1998, J HERED, V89, P438, DOI 10.1093/jhered/89.5.438 WARD DM, 1990, NATURE, V345, P63, DOI 10.1038/345063a0 WARD RD, 1994, J FISH BIOL, V44, P213, DOI 10.1111/j.1095-8649.1994.tb01200.x Wong HC, 1999, INT J FOOD MICROBIOL, V52, P181, DOI 10.1016/S0168-1605(99)00143-9 Yamashita M., 2008, FISHERIES GLOBAL WEL, P297 NR 89 TC 38 Z9 38 U1 5 U2 72 PY 2016 VL 56 IS 2 BP 306 EP 317 DI 10.1080/10408398.2012.745478 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000367552700007 DA 2022-12-14 ER PT J AU Yanagi, Y Hirooka, H Oishi, K Choumei, Y Hata, H Arai, M Kitagawa, M Gotoh, T Inada, S Kumagai, H AF Yanagi, Yuta Hirooka, Hiroyuki Oishi, Kazato Choumei, Yousuke Hata, Hiroshi Arai, Mamoru Kitagawa, Masayuki Gotoh, Takafumi Inada, Sunao Kumagai, Hajime TI Stable carbon and nitrogen isotope analysis as a tool for inferring beef cattle feeding systems in Japan SO FOOD CHEMISTRY DT Article DE Beef; Hair; Traceability; Stable isotope ratios; Carbon; Nitrogen ID GEOGRAPHICAL ORIGIN; RATIO ANALYSIS; DIET; HAIR; ANIMALS; DELTA-N-15; N-15; MILK; MEAT AB A pilot study was conducted to evaluate the suitability of stable isotope analysis for inferring the feeding histories of cattle fed known feeds. Stable isotope ratios of carbon and nitrogen (delta C-13 and delta N-15) were measured in meat and hair from cattle and in their feeds at five farms in different regions of Japan, and the correlations of the isotope ratios between meat and hair were analysed. The results showed that delta C-13 values in feed depend on the photosynthesis type: C-3 or C-4. The values of delta N-15 in feeds varied widely, indicating divergent feeds made from plant materials that have different nitrogen origins, such as soil, chemical fertilizer, manure and air. In both cattle meat and hair, the farms differed significantly in the values of delta C-13 and delta N-15. Both delta C-13 and delta N-15 were significantly higher in hair than in meat, and high correlations between meat and hair in both delta C-13 and delta N-15 were found. The results suggested that stable carbon and nitrogen isotope analysis for cattle meat and hair could be used to trace the feeding histories of cattle in Japan, and that hair samples would be used as an alternative to meat. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Yanagi, Yuta; Hirooka, Hiroyuki; Oishi, Kazato; Kitagawa, Masayuki; Kumagai, Hajime] Kyoto Univ, Grad Sch Agr, Sakyo Ku, Kyoto 6068502, Japan. [Choumei, Yousuke] Ryukoku Univ, Grad Sch Econ, Fushimi Ku, Kyoto 6128577, Japan. [Hata, Hiroshi] Hokkaido Univ, Agroecosyst Res Stn, Field Sci Ctr No Biosphere, Shizunai Livestock Farm, Shizunai, Hokkaido 0600811, Japan. [Arai, Mamoru] Saitama Prefectural Agr & Forestry Res Ctr, Kumagaya, Saitama 3680831, Japan. [Gotoh, Takafumi] Kyushu Univ, Kuju Agr Res Ctr, Kuju, Oita 8780201, Japan. [Inada, Sunao] Fukuoka Agr Res Ctr, Chikushino, Fukuoka 8188549, Japan. C3 Kyoto University; Ryukoku University; Hokkaido University; Kyushu University RP Kumagai, H (corresponding author), Kyoto Univ, Grad Sch Agr, Sakyo Ku, Kyoto 6068502, Japan. EM hkuma@kais.kyoto-u.ac.jp CR Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Bahar B, 2009, J ANIM SCI, V87, P905, DOI 10.2527/jas.2008-1360 Bateman AS, 2007, J AGR FOOD CHEM, V55, P2664, DOI 10.1021/jf0627726 Bong YS, 2010, RAPID COMMUN MASS SP, V24, P155, DOI 10.1002/rcm.4366 De Smet S, 2004, RAPID COMMUN MASS SP, V18, P1227, DOI 10.1002/rcm.1471 DENIRO MJ, 1978, GEOCHIM COSMOCHIM AC, V42, P495, DOI 10.1016/0016-7037(78)90199-0 DENIRO MJ, 1981, GEOCHIM COSMOCHIM AC, V45, P341, DOI 10.1016/0016-7037(81)90244-1 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Hogberg P, 1997, NEW PHYTOL, V137, P179, DOI 10.1046/j.1469-8137.1997.00808.x Horacek M, 2010, FOOD CHEM, V121, P517, DOI 10.1016/j.foodchem.2009.12.018 Lajtha K, 1994, STABLE ISOTOPES ECOL, P1 Manca G, 2006, J DAIRY SCI, V89, P831, DOI 10.3168/jds.S0022-0302(06)72146-4 MINAGAWA M, 1984, GEOCHIM COSMOCHIM AC, V48, P1135, DOI 10.1016/0016-7037(84)90204-7 Mitani Y, 2006, FISHERIES SCI, V72, P69, DOI 10.1111/j.1444-2906.2006.01118.x Mizukami RN, 2005, ISOT ENVIRON HEALT S, V41, P87, DOI 10.1080/10256010412331304211 Nakashita R, 2008, ANAL CHIM ACTA, V617, P148, DOI 10.1016/j.aca.2008.03.048 OLEARY MH, 1981, PHYTOCHEMISTRY, V20, P553, DOI 10.1016/0031-9422(81)85134-5 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Prache S, 2005, SMALL RUMINANT RES, V59, P157, DOI 10.1016/j.smallrumres.2005.05.004 *SAS I INC, 1998, SAS STAT US GUID Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 Schwertl M, 2005, AGR ECOSYST ENVIRON, V109, P153, DOI 10.1016/j.agee.2005.01.015 Sponheimer M, 2003, INT J OSTEOARCHAEOL, V13, P80, DOI 10.1002/oa.655 Zazzo A, 2007, CAN J ZOOL, V85, P1239, DOI 10.1139/Z07-110 NR 25 TC 27 Z9 28 U1 0 U2 60 PD SEP 1 PY 2012 VL 134 IS 1 BP 502 EP 506 DI 10.1016/j.foodchem.2012.02.107 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000304291400071 DA 2022-12-14 ER PT J AU Panattoni, A Rinaldelli, E Materazzi, A Bandinelli, R De Bellis, L Luvisi, A AF Panattoni, A. Rinaldelli, E. Materazzi, A. Bandinelli, R. De Bellis, L. Luvisi, A. TI Electronic identification systems for reducing diagnostic workloads after disease outbreak SO PLANT PATHOLOGY DT Article DE grapevine; MolU; radio frequency identification; virus ID MANAGEMENT-INFORMATION-SYSTEM; PCR TAQMAN(R) ASSAYS; RT-PCR; GRAPEVINE VIRUSES; PLANT PATHOLOGY; WOODY-PLANTS; TRACEABILITY; MICROCHIPS; EFFICIENCY; PATHOGENS AB Diagnostic tests for grapevine viruses subjected to phytosanitary rules involve a heavy workload for plant protection services and laboratories. Propagation schemes enable nurseries, where mother plants (MPs) are cultivated, to be linked to batches of certified plants (CPs). This approach entails post-production checks of MPs once infection occurs in CPs. However, this traceability system is not tight and follow ups are demanding. This study assessed radio frequency identification (RFID) tagging of plants in terms of its ability to reduce laboratory workloads for nursery health checks. RFID-tagged plants (RFID-CPs) were produced from individually tagged MPs (RFID-MPs) or row-tagged MPs (RFID-ROW, a less expensive approach). In a 10-year case study, the health status of CPs and RFID-CPs were assessed and the occurrence of infections then led to health checks in MPs, RFID-MPs or RFID-ROWs. Laboratory workloads were evaluated by considering two sampling methods (single or pool sampling). Using single sampling, the workload was reduced by 93-98% in RFID-ROW or RFID-MP checks compared to the conventional approach. Considerable reductions in workload due to the tagging system (93-96%) were also observed using pool sampling. Traceability of CPs and MPs using RFID reduces laboratory workloads, and supports emergency measures that can be taken to stop any unsafe sales of plants after a virus outbreak. C1 [Panattoni, A.; Materazzi, A.] Univ Pisa, Dept Agr Food & Environm, Pisa, Italy. [Rinaldelli, E.] Univ Florence, Dept Agrifood Prod & Environm Sci, Sesto Fiorentino, FI, Italy. [Bandinelli, R.] Assoc Toscana Costitutori Viticoli, San Piero A Grado, PI, Italy. [De Bellis, L.; Luvisi, A.] Univ Salento, Dept Biol & Environm Sci & Technol, Lecce, Italy. C3 University of Pisa; University of Florence; University of Salento RP Luvisi, A (corresponding author), Univ Salento, Dept Biol & Environm Sci & Technol, Lecce, Italy. EM andrea.luvisi@unisalento.it CR Ampatzidis Y, 2016, COMPUT ELECTRON AGR, V122, P161, DOI 10.1016/j.compag.2016.01.032 Ampatzidis YG, 2014, BIOSYST ENG, V120, P25, DOI 10.1016/j.biosystemseng.2013.07.011 Ampatzidis YG, 2013, PRECIS AGRIC, V14, P162, DOI 10.1007/s11119-012-9284-3 Anisi MH, 2015, PRECIS AGRIC, V16, P216, DOI 10.1007/s11119-014-9371-8 Bowman KD, 2005, HORTTECHNOLOGY, V15, P352, DOI 10.21273/HORTTECH.15.2.0352 Bowman KD, 2010, HORTSCIENCE, V45, P451, DOI 10.21273/HORTSCI.45.3.451 Cunha CR, 2010, COMPUT ELECTRON AGR, V73, P154, DOI 10.1016/j.compag.2010.05.007 Faggioli F., 2013, Advances in Horticultural Science, V27, P107 Fountas S, 2006, AGR SYST, V87, P192, DOI 10.1016/j.agsy.2004.12.003 Fountas S, 2015, COMPUT ELECTRON AGR, V115, P40, DOI 10.1016/j.compag.2015.05.011 Giraud G, 2003, SCI ALIMENT, V23, P40, DOI 10.3166/sda.23.40-46 Kaloxylos A, 2014, COMPUT ELECTRON AGR, V100, P168, DOI 10.1016/j.compag.2013.11.014 Kumagai MH, 2006, PLANT MOL BIOL, V61, P515, DOI 10.1007/s11103-006-0025-8 Luvisi A., 2012, Advances in Horticultural Science, V26, P39 Luvisi A, 2010, SCI HORTIC-AMSTERDAM, V124, P349, DOI 10.1016/j.scienta.2010.01.015 LUVISI A, 2017, SUSTAINABILITY-BASEL, V9, DOI DOI 10.3390/SU9040659 Luvisi A, 2016, AGRON SUSTAIN DEV, V36, DOI 10.1007/s13593-016-0352-3 Luvisi A, 2014, COMPUT ELECTRON AGR, V108, P130, DOI 10.1016/j.compag.2014.07.013 Luvisi A, 2012, CALIF AGR, V66, P97, DOI 10.3733/ca.v066n03p97 Luvisi A, 2012, COMPUT ELECTRON AGR, V84, P7, DOI 10.1016/j.compag.2012.02.008 MacKenzie DJ, 1997, PLANT DIS, V81, P222, DOI 10.1094/PDIS.1997.81.2.222 Marchi G, 2015, PHYTOPATHOL MEDITERR, V54, P504 Osman F, 2008, J VIROL METHODS, V154, P69, DOI 10.1016/j.jviromet.2008.09.005 Osman F, 2008, J VIROL METHODS, V149, P292, DOI 10.1016/j.jviromet.2008.01.012 Osman F, 2007, J VIROL METHODS, V141, P22, DOI 10.1016/j.jviromet.2006.11.035 Peets S, 2009, PRECIS AGRIC, V10, P382, DOI 10.1007/s11119-009-9106-4 Rizzo D., 2012, Advances in Horticultural Science, V26, P148 Rizzo D., 2015, J PLANT PATHOL, V972, P131 Sorensen CG, 2011, COMPUT ELECTRON AGR, V76, P266, DOI 10.1016/j.compag.2011.02.005 Stenhouse S, 2011, B ROYAL COLL PATHOLO, V155, P168 Thrane C, 2008, EUR J PLANT PATHOL, V121, P339, DOI 10.1007/s10658-007-9247-0 Vai N., 2005, ALBERI TERRITORIO, V2, P34 Wei T, 2012, AUSTRALAS PLANT PATH, V41, P93, DOI 10.1007/s13313-011-0095-1 NR 33 TC 1 Z9 1 U1 0 U2 5 PD APR PY 2018 VL 67 IS 3 BP 750 EP 756 DI 10.1111/ppa.12783 WC Agronomy; Plant Sciences SC Agriculture; Plant Sciences UT WOS:000426653800024 DA 2022-12-14 ER PT J AU Love, DC Lane, RM Kuehl, LM Hudson, B Harding, J Clancy, K Fry, JP AF Love, David C. Lane, Robert M. Kuehl, Lillian M. Hudson, Bobbi Harding, Jamie Clancy, Kate Fry, Jillian P. TI Performance and conduct of supply chains for United States farmed oysters SO AQUACULTURE DT Article DE Chesapeake bay; Oyster; Shellfish; Supply chain; Traceability; Washington ID SEAFOOD TRACEABILITY; FOOD; AQUACULTURE; SAFETY; NEEDS AB Farmed oysters are one of the most valuable aquacultured products in the United States (U.S.), are highly perishable, and increasingly shipped live year-round. Supply chain actors must work together to bring refrigerated oysters to market quickly, while maintaining product value, safety and traceability information. In light of these demands, this study assesses the performance and conduct of supply chains for U.S. farmed oysters (Crassostrea virginica, C. gigas). Over the two-year study period, we conducted interviews with 56 businesses and tracked 125 oyster shipments from two major growing regions in the U.S. through six different types of supply chains. We hypothesized that direct and intermediated supply chains would perform differently in terms of timeto-market, product temperature in cold chains, compliance with temperature regulations, and modeled risks from Vibrio parahaemolyticus. Intermediated supply chains, by their definition have more connections than direct supply chains, and we found this introduces a longer time-to-market and a higher incidence of time and temperature abuse. However, these factors did not lead to greater modeled V. parahaemolyticus risks. Participants in both direct and intermediated supply chains were aware of the importance of traceability and felt uniformly positive about their ability to perform recalls. A common concern was the speed of government-imposed recalls, which can be declared by regulators after the affected live oysters are consumed. Members of these supply chains play different roles in maintaining the cold chain, possess different levels of information related to traceability, and describe different levels of trust with other supply chain actors. This paper contributes to a growing body of knowledge on supply chains for seafood and their critical, and sometimes overlooked, role in larger food systems. C1 [Love, David C.; Kuehl, Lillian M.; Harding, Jamie; Clancy, Kate; Fry, Jillian P.] Johns Hopkins Univ, Johns Hopkins Ctr Livable Future, 111 Market Pl,Suite 840, Baltimore, MD 21202 USA. [Love, David C.; Harding, Jamie; Clancy, Kate; Fry, Jillian P.] Johns Hopkins Univ, Bloomberg Sch Publ Hlth, Dept Environm Hlth & Engn, Baltimore, MD 21202 USA. [Lane, Robert M.] Virginia Tech, Virginia Seafood Agr Res & Extens Ctr, Hampton, VA USA. [Kuehl, Lillian M.] Western Washington Univ, Dept Biol, Bellingham, WA 98225 USA. [Hudson, Bobbi] Pacific Shellfish Inst, Olympia, WA USA. [Fry, Jillian P.] Towson Univ, Sch Hlth Profess, Dept Hlth Sci, Towson, MD USA. C3 Johns Hopkins University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Virginia Polytechnic Institute & State University; Western Washington University; University System of Maryland; Towson University RP Love, DC (corresponding author), Johns Hopkins Univ, Johns Hopkins Ctr Livable Future, 111 Market Pl,Suite 840, Baltimore, MD 21202 USA. EM dlove8@jhu.edu CR [Anonymous], INT CERT SHELLF SHIP [Anonymous], FRAM ASS EFF FOOD SY [Anonymous], 2010, COMP STRUCTURE SIZE [Anonymous], EFFECT BRANDING GULF [Anonymous], FOLL FISH NEW ENGL C [Anonymous], NEW DIRECTIONS GLOBA [Anonymous], CENS AQ 2013 [Anonymous], INT SHELLF SAN C [Anonymous], FISH US 2016 [Anonymous], COASTS [Anonymous], INT J FOOD MICROBIOL [Anonymous], ACH1235 USDA [Anonymous], 201707 COLL WILL MAR [Anonymous], 2018, FISHERMENS DIRECT MA [Anonymous], VIRGINIA SHELLFISH A [Anonymous], GULF STATES MARINE F [Anonymous], EC IMP SHELLF AQ WAS Augusto K., 2015, SEA GRANT WOODS HOLE, P1 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bailey M, 2016, CURR OPIN ENV SUST, V18, P25, DOI 10.1016/j.cosust.2015.06.004 Baker-Austin C, 2017, TRENDS MICROBIOL, V25, P76, DOI 10.1016/j.tim.2016.09.008 Bjorndal T, 2015, AQUACULT ECON MANAG, V19, P148, DOI 10.1080/13657305.2015.994241 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Chase A., 2016, CONNECTING LOCAL SEA Cochet M, 2015, AQUAC RES, V46, P637, DOI 10.1111/are.12210 Gephart JA, 2019, P NATL ACAD SCI USA, V116, P9142, DOI 10.1073/pnas.1905650116 Gobel C, 2015, SUSTAINABILITY-BASEL, V7, P1429, DOI 10.3390/su7021429 Iles A, 2007, J CLEAN PROD, V15, P577, DOI 10.1016/j.jclepro.2006.06.001 Jespersen KS, 2014, FOOD POLICY, V49, P228, DOI 10.1016/j.foodpol.2014.08.004 Kecinski M, 2017, AGRIC RESOUR ECON RE, V46, P315, DOI 10.1017/age.2017.21 Kittinger JN, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0123856 Lawley M, 2016, J FOOD PROD MARK, V22, P792, DOI 10.1080/10454446.2015.1121430 Leal MC, 2015, TRENDS BIOTECHNOL, V33, P331, DOI 10.1016/j.tibtech.2015.03.003 Lewis SG, 2017, J FOOD SCI, V82, pA13, DOI 10.1111/1750-3841.13743 Love DC, 2019, J FOOD PROTECT, V82, P168, DOI 10.4315/0362-028X.JFP-18-044 McLaughlin JB, 2005, NEW ENGL J MED, V353, P1463, DOI 10.1056/NEJMoa051594 Newton A, 2012, CLIN INFECT DIS, V54, pS391, DOI 10.1093/cid/cis243 Olson J, 2014, MAR POLICY, V43, P104, DOI 10.1016/j.marpol.2013.05.001 Peake WO, 2014, FOOD POLICY, V49, P13, DOI 10.1016/j.foodpol.2014.06.006 Shapiro RL, 1998, J INFECT DIS, V178, P752, DOI 10.1086/515367 Sterling B, 2015, COMPR REV FOOD SCI F, V14, P205, DOI 10.1111/1541-4337.12130 Stevenson GW, 2011, J AGRIC FOOD SYST CO, V1, P27, DOI [10.5304/jafscd.2011.014.007, 10.5304/jalscd.2011.014.007] Stoll JS, 2015, ECOL SOC, V20, DOI 10.5751/ES-07686-200240 Taylor M, 2016, FOOD POLICY, V62, P56, DOI 10.1016/j.foodpol.2016.04.005 Wang JN, 2015, DISCRETE DYN NAT SOC, V2015, DOI 10.1155/2015/301245 NR 45 TC 6 Z9 6 U1 4 U2 27 PD JAN 15 PY 2020 VL 515 AR 734569 DI 10.1016/j.aquaculture.2019.734569 WC Fisheries; Marine & Freshwater Biology SC Fisheries; Marine & Freshwater Biology UT WOS:000496787000034 DA 2022-12-14 ER PT J AU Pereira, PRRX Barcellos, JOJ Grundling, RD Canozzi, MEA McManus, C Lopes, RB AF Ramos Xavier Pereira, Paulo Rodrigo Jardim Barcellos, Julio Otavio Gruendling, Roberta Dalla Porta Andrighetto Canozzi, Maria Eugenia McManus, Concepta Lopes, Rubia Branco TI Chilled boneless beef international trade: a cluster analysis SO REVISTA BRASILEIRA DE ZOOTECNIA-BRAZILIAN JOURNAL OF ANIMAL SCIENCE DT Article DE animal health; beef exports; Brazil; traceability ID BOVINE SPONGIFORM ENCEPHALOPATHY; COUNTRY-OF-ORIGIN; FOOD SAFETY; US; CATTLE; RISK; TRACEABILITY; PREFERENCES; QUALITY; CHOICE AB The objective of this study was to measure and classify the international beef trade. For this, data related to the international chilled boneless beef (CBB) trade, the major and most important market, were analyzed. Producing countries were classified into groups according to their trade relations, and the main factors that influenced one country to prefer to import CBB from a specific exporting country were analyzed. The results revealed four markets related to client demands with regard to the sanitation and traceability of beef products. Furthermore, extrinsic characteristics of the product are discussed, such as a productive system that aims to minimize environmental impacts and to value animal welfare and respect for social demands. The markets that pay highest prices require sanitary quality of suppliers, demanding traceable and process-certified products. Brazil does not access these markets because it does not meet these requirements. To change this scenario it is necessary to eradicate FMD across the Brazilian territory, acquiring a status of a zone with minimal BSE risk, aligning the intrinsic value of the CBB with expectations of consumers and implementing a traceability program that is both feasible and acceptable for clients. C1 [Ramos Xavier Pereira, Paulo Rodrigo; Jardim Barcellos, Julio Otavio; Gruendling, Roberta Dalla Porta] Programa Posgrad Agronegocios CEPAN UFRGS, Porto Alegre, RS, Brazil. [Jardim Barcellos, Julio Otavio; Andrighetto Canozzi, Maria Eugenia; McManus, Concepta] Programa Posgrad Zootecnia UFRGS, Porto Alegre, RS, Brazil. [Lopes, Rubia Branco] Acad Curso Agron UFRGS, Porto Alegre, RS, Brazil. RP Barcellos, JOJ (corresponding author), Programa Posgrad Agronegocios CEPAN UFRGS, Porto Alegre, RS, Brazil. EM julio.barcellos@ufrgs.br CR [Anonymous], 2007, BE J ECON ANAL POLI BARCELLOS M. D., 2007, THESIS U FEDERAL RIO, P329 Basdevant O, 2002, J POLICY MODEL, V24, P151, DOI 10.1016/S0161-8938(02)00103-5 Bernues A, 2003, FOOD QUAL PREFER, V14, P265, DOI 10.1016/S0950-3293(02)00085-X BUREAU J.C., 2005, DUBLIN 7 ANN C, P17 BUREAU OF LABOR STATISTICS-BLS, 2008, DAT TABL CALC SUBJ CENTRO DE ESTUDOS AVANCADOS EM ECONOMIA APLICADA - CEPEA, 2008, IND REG NAC EXP AGR EUROPEAN COMMISSION, 1999, TAX CUST UN INT COMM FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS STATISTICS - FAOSTAT, 2008, TRAD Hair J.F., 2006, MULTIVARIATE DATA AN Jin HJ, 2008, APPL ECON, V40, P357, DOI 10.1080/00036840500461824 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Mattson JW, 2007, REV AGR ECON, V29, P734, DOI 10.1111/j.1467-9353.2007.00384.x McCarthy M, 2007, FOOD QUAL PREFER, V18, P205, DOI 10.1016/j.foodqual.2005.10.002 McCarthy M, 2005, FOOD QUAL PREFER, V16, P435, DOI 10.1016/j.foodqual.2004.08.003 McCluskey JJ, 2005, AUST J AGR RESOUR EC, V49, P197, DOI 10.1111/j.1467-8489.2005.00282.x Mingoti SA, 2005, ANALISE DADOS ATRAVE Monte Edson Zambon, 2007, Nova econ., V17, P37, DOI 10.1590/S0103-63512007000100002 Polaquini LEM, 2006, REV BRAS ZOOTECN, V35, P321, DOI 10.1590/S1516-35982006000100040 Oliver MA, 2006, MEAT SCI, V74, P435, DOI 10.1016/j.meatsci.2006.03.010 Rich K. M., 2005, ANIMAL DIS COST COMP Saghaian S.H., 2007, INT FOOD AGRIBUS MAN, V10, P18 Sasaki Keisuke, 2004, Animal Science Journal, V75, P369, DOI 10.1111/j.1740-0929.2004.00199.x Schnettler B, 2008, FOOD QUAL PREFER, V19, P372, DOI 10.1016/j.foodqual.2007.11.005 Schnettler B, 2008, CHIL J AGR RES, V68, P80, DOI 10.4067/S0718-58392008000100008 Schwagele F, 2005, MEAT SCI, V71, P164, DOI 10.1016/j.meatsci.2005.03.002 SEGRILLO A., 2000, FIM URSS NOVA RUSSIA Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Sparling DH, 2006, REV AGR ECON, V28, P212, DOI 10.1111/j.1467-9353.2006.00282.x Sugiura K, 2008, J FOOD PROTECT, V71, P802, DOI 10.4315/0362-028X-71.4.802 Tonsor GT, 2005, J AGR RESOUR ECON, V30, P367 UNITED NATIONS COMMODITY TRADE STATISTICS DATABASE - DESA/UNSD, 2008, SHORTC QUER UNITED STATES DEPARTMENT OF AGRICULTURE-USDA, 2008, BRAZ LIV PROD ANN LI UNITED STATES DEPARTMENT OF AGRICULTURE - USDA, 2002, RUSS FED LIV PROD AN UNITED STATES DEPARTMENT OF AGRICULTURE - USDA, 2004, WORLD BEEF OV *USDA, 2007, LIV POULTR WORLD MAR WORLD BANK, 2008, COUNTR LEND GROUPS World Bank, 2008, DAT STAT COUNTR GROU WORLD ORGANIZATION FOR ANIMAL HEALTH-WHO, 2007, WORLD AN HLTH INF DA WORLD ORGANIZATION FOR ANIMAL HEALTH-WHO, 2008, TERR AN HLTH COD Zepeda C, 2005, PREV VET MED, V67, P125, DOI 10.1016/j.prevetmed.2004.11.005 NR 41 TC 5 Z9 5 U1 0 U2 19 PD MAR PY 2013 VL 42 IS 3 BP 220 EP 230 DI 10.1590/S1516-35982013000300010 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000316123700010 DA 2022-12-14 ER PT J AU Tsukazaki, H Fukuoka, H Song, YS Yamashita, KI Wako, T Kojima, A AF Tsukazaki, Hikaru Fukuoka, Hiroyuki Song, Yeon-Sang Yamashita, Ken-ichiro Wako, Tadayuki Kojima, Akio TI Considerable heterogeneity in commercial F1 varieties of bunching onion (Allium fistulosum) and proposal of breeding scheme for conferring variety traceability using SSR markers SO BREEDING SCIENCE DT Article DE Allium fistulosum; bunching onion; genetic homogeneity; simple sequence repeat; SSR-tagged breeding; traceability ID GENETIC PURITY ANALYSIS; CULTIVAR IDENTIFICATION; MICROSATELLITE DNA; HYBRID SEED; RAPD; TOMATO; POPULATIONS; SEQUENCE; AFLP; PCR AB DNA markers are powerful tools for verifying the varietal identity and genetic homogeneity of F-1 hybrid seeds. F-1 varieties are becoming increasingly prevalent in bunching onion (Allium fistulosum L.) production in Japan because of the high uniformity of agronomic traits. However, bunching onion is an allogamous crop and suffers from severe inbreeding depression when selfed. It is considered that not only open-pollinated varieties but also the parental lines of F-1 hybrids should maintain a certain degree of average heterozygosity and hence genetic heterogeneity. In the present study, the genetic homogeneity of eight bunching onion varieties, including six F-1 hybrids, was evaluated using 14 SSR markers. Two or more polymorphic alleles were detected at all of the SSR loci examined in each variety. The number of alleles detected in the eight varieties ranged from 3 to 7 among the 14 SSR loci, and the polymorphism information content from 0.41 to 0.76. All the varieties examined displayed very low degrees of uniformity at all of these polymorphic loci. Based on these results, it may be impossible to determine an appropriate genotypic identity for any of the existing bunching onion varieties. To facilitate and enhance the accuracy of variety identification, we proposed here an "SSR-tagged breeding" scheme in which the plants homozygous at a few SSR loci would be selected out of a foundation seed field. This scheme may enable to achieve efficient variety identification and purity determination of F-1 seeds not only in bunching onion but also in any allogamous crops exhibiting severe inbreeding depression. C1 Natl Agr & Food Res Org, Natl Inst Vegetable & Tea Sci, Tsu, Mie 5142392, Japan. C3 National Agriculture & Food Research Organization - Japan RP Tsukazaki, H (corresponding author), Natl Agr & Food Res Org, Natl Inst Vegetable & Tea Sci, 360 Ano Kusawa, Tsu, Mie 5142392, Japan. EM tsuka@affrc.go.jp CR ANDERSON JA, 1993, GENOME, V36, P181, DOI 10.1139/g93-024 Ballester J, 1998, EUPHYTICA, V103, P223, DOI 10.1023/A:1018372523343 Bredemeijer GMM, 1998, THEOR APPL GENET, V97, P584, DOI 10.1007/s001220050934 Crockett PA, 2000, GENOME, V43, P317, DOI 10.1139/gen-43-2-317 Crockett PA, 2002, AUST J AGR RES, V53, P51, DOI 10.1071/AR01022 Fischer D, 2000, THEOR APPL GENET, V101, P153, DOI 10.1007/s001220051464 Ford-Lloyd B.V., 1993, GENETIC IMPROVEMENT, P51, DOI [10.1016/B978-0-08-040826-2.50009-6, DOI 10.1016/B978-0-08-040826-2.50009-6] Friesen N, 1999, AM J BOT, V86, P554, DOI 10.2307/2656817 HAISHIMA M, 1993, JPN J BREED, V43, P537 HASHIZUME T, 1993, JPN J BREED, V43, P367 Jakse J, 2005, J AM SOC HORTIC SCI, V130, P912, DOI 10.21273/JASHS.130.6.912 Jones CJ, 1997, MOL BREEDING, V3, P381, DOI 10.1023/A:1009612517139 Klaas M., 2002, P159, DOI 10.1079/9780851995106.0159 Kuhl JC, 2004, PLANT CELL, V16, P114, DOI 10.1105/tpc.017202 Kumazawa, 1965, SOSAI ENGEI KAKURON, P280 LIVNEH O, 1990, SEED SCI TECHNOL, V18, P209 *MAFF, 2005, POCK NOUR SUIS TOUK Martin WJ, 2005, MOL GENET GENOMICS, V274, P197, DOI 10.1007/s00438-005-0007-6 McDonald MB, 1998, SEED SCI RES, V8, P265, DOI 10.1017/S0960258500004165 Meesang N, 2001, SEED SCI TECHNOL, V29, P637 NEI M, 1973, P NATL ACAD SCI USA, V70, P3321, DOI 10.1073/pnas.70.12.3321 Ohara T, 2005, EUPHYTICA, V144, P255, DOI 10.1007/s10681-005-6768-5 Rajora OP, 2003, THEOR APPL GENET, V106, P470, DOI 10.1007/s00122-002-1082-2 Rouamba A, 2001, THEOR APPL GENET, V103, P855, DOI 10.1007/s001220100631 Song YS, 2004, BREEDING SCI, V54, P361, DOI 10.1270/jsbbs.54.361 TANKSLEY SD, 1981, HORTSCIENCE, V16, P179 NR 26 TC 21 Z9 24 U1 0 U2 10 PD SEP PY 2006 VL 56 IS 3 BP 321 EP 326 DI 10.1270/jsbbs.56.321 WC Agronomy; Plant Sciences SC Agriculture; Plant Sciences UT WOS:000241978800013 DA 2022-12-14 ER PT J AU Azzini, E Maiani, G Turrini, A Intorre, F Lo Feudo, G Capone, R Bottalico, F El Bilali, H Polito, A AF Azzini, Elena Maiani, Giuseppe Turrini, Aida Intorre, Federica Lo Feudo, Gabriella Capone, Roberto Bottalico, Francesco El Bilali, Hamid Polito, Angela TI The health-nutrition dimension: a methodological approach to assess the nutritional sustainability of typical agro-food products and the Mediterranean diet SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE nutritional sustainability; quality agro-food products; safety; traceability; indicators; Mediterranean diet ID CORONARY-HEART-DISEASE; ALL-CAUSE MORTALITY; CARDIOVASCULAR-DISEASE; SERUM-CHOLESTEROL; OLIVE OIL; FISH CONSUMPTION; ANTIOXIDANT ACTIVITY; FATTY-ACIDS; COGNITIVE PERFORMANCE; PHYTOESTROGEN CONTENT AB BACKGROUNDThe aim of this paper is to provide a methodological approach to evaluate the nutritional sustainability of typical agro-food products, representing Mediterranean eating habits and included in the Mediterranean food pyramid. RESULTSFor each group of foods, suitable and easily measurable indicators were identified. Two macro-indicators were used to assess the nutritional sustainability of each product. The first macro-indicator, called business distinctiveness', takes into account the application of different regulations and standards regarding quality, safety and traceability as well as the origin of raw materials. The second macro-indicator, called nutritional quality', assesses product nutritional quality taking into account the contents of key compounds including micronutrients and bioactive phytochemicals. For each indicator a 0-10 scoring system was set up, with scores from 0 (unsustainable) to 10 (very sustainable), with 5 as a sustainability benchmark value. The benchmark value is the value from which a product can be considered sustainable. A simple formula was developed to produce a sustainability index. CONCLUSIONThe proposed sustainability index could be considered a useful tool to describe both the qualitative and quantitative value of micronutrients and bioactive phytochemical present in foodstuffs. This methodological approach can also be applied beyond the Mediterranean, to food products in other world regions. (c) 2018 Society of Chemical Industry C1 [Azzini, Elena; Maiani, Giuseppe; Turrini, Aida; Intorre, Federica; Polito, Angela] Res Ctr Food & Nutr, Council Agr Res & Econ CREA, Via Ardeatina 546, I-00178 Rome, Italy. [Lo Feudo, Gabriella] Res Ctr Olive Citrus & Tree Fruit, Council Agr Res & Econ CREA, Arcavacata Di Rende, CS, Italy. [Capone, Roberto; Bottalico, Francesco; El Bilali, Hamid] Int Ctr Adv Mediterranean Agron Studies Bari CIHE, Bari, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); CIHEAM; CIHEAM BARI RP Azzini, E (corresponding author), Res Ctr Food & Nutr, Council Agr Res & Econ CREA, Via Ardeatina 546, I-00178 Rome, Italy. EM elena.azzini@crea.gov.it CR Adebamowo CA, 2005, INT J CANCER, V114, P628, DOI 10.1002/ijc.20741 Alajaji SA, 2006, J FOOD COMPOS ANAL, V19, P806, DOI 10.1016/j.jfca.2006.03.015 Alekel DL, 2000, AM J CLIN NUTR, V72, P844 ALSAIKHAN MS, 1995, J FOOD SCI, V60, P341, DOI 10.1111/j.1365-2621.1995.tb05668.x Alvina M, 2004, EUR J CLIN NUTR, V58, P637, DOI 10.1038/sj.ejcn.1601859 Amarowicz R, 2003, J FOOD LIPIDS, V10, P1, DOI 10.1111/j.1745-4522.2003.tb00001.x Anderson JW, 2004, AM J CLIN NUTR, V80, P1459, DOI 10.1093/ajcn/80.6.1459 Anderson JW, 1999, AM J CLIN NUTR, V70, p464S, DOI 10.1093/ajcn/70.3.464s Andlauer W, 2003, INT J VITAM NUTR RES, V73, P152, DOI 10.1024/0300-9831.73.2.152 Andre CM, 2007, J AGR FOOD CHEM, V55, P1039 Martinez-Gonzalez MA, 2009, NUTR REV, V67, pS111, DOI 10.1111/j.1753-4887.2009.00172.x [Anonymous], 2012, SURV SYST OKK SAL 20 [Anonymous], 2010, EXP CONS FATS FATT A Aragon JM, 2001, IMPROLIVE 2000 PRESE, P29 Arion WJ, 1997, ARCH BIOCHEM BIOPHYS, V339, P315, DOI 10.1006/abbi.1996.9874 Arouca A, 2018, EUR J NUTR, V57, P1747, DOI 10.1007/s00394-017-1457-4 Aruoma OI, 2003, MUTAT RES-FUND MOL M, V523, P9, DOI 10.1016/S0027-5107(02)00317-2 Ascherio A, 1996, BMJ-BRIT MED J, V313, P84, DOI 10.1136/bmj.313.7049.84 Azzini E, 2011, J NUTR, V10, P25 Bach-Faig A, 2006, PUBLIC HEALTH NUTR, V9, P1110, DOI 10.1017/S1368980007668499 Bach-Faig A, 2011, PUBLIC HEALTH NUTR, V14, P2274, DOI 10.1017/S1368980011002515 Ballistreri G, 2013, FOOD CHEM, V140, P630, DOI 10.1016/j.foodchem.2012.11.024 Barbaro B, 2014, INT J MOL SCI, V15, P18508, DOI 10.3390/ijms151018508 Beavers KM, 2009, NUTR RES, V29, P616, DOI 10.1016/j.nutres.2009.09.002 Berry EM, 2011, PUBLIC HEALTH NUTR, V14, P2288, DOI 10.1017/S1368980011002539 Bes-Rastrollo M, 2006, LIPIDS, V41, P249, DOI 10.1007/s11745-006-5094-6 Bevilacqua A, 2015, INT J FOOD SCI NUTR, V66, P127, DOI 10.3109/09637486.2014.959901 Blasbalg TL, 2011, AM J CLIN NUTR, V93, P950, DOI 10.3945/ajcn.110.006643 Bockstaller C, 2008, AGRON SUSTAIN DEV, V28, P139, DOI 10.1051/agro:2007052 Boeing H, 2012, EUR J NUTR, V51, P637, DOI 10.1007/s00394-012-0380-y Borneo R, 2012, FOOD FUNCT, V3, P110, DOI 10.1039/c1fo10165j Bottalico F, 2016, J FOOD NUTR RES, V4, P258, DOI DOI 10.12691/JFNR-4-4-10 BRAND JC, 1991, DIABETES CARE, V14, P95, DOI 10.2337/diacare.14.2.95 Bricarello LP, 2004, NUTRITION, V20, P200, DOI 10.1016/j.nut.2003.10.005 Buckland G, 2010, AM J CLIN NUTR, V91, P381, DOI 10.3945/ajcn.2009.28209 Buckland G, 2009, AM J EPIDEMIOL, V170, P1518, DOI 10.1093/aje/kwp282 Burlingame B, 2011, PUBLIC HEALTH NUTR, V14, P2285, DOI 10.1017/S1368980011002527 Calder PC, 2015, BBA-MOL CELL BIOL L, V1851, P469, DOI 10.1016/j.bbalip.2014.08.010 Camire ME, 2009, CRIT REV FOOD SCI, V49, P823, DOI 10.1080/10408390903041996 Capone R., 2013, J FOOD NUTR RES, V1, P59, DOI [DOI 10.12691/JFNR-1-4-5, 10.12691/jfnr-1-4-5] Carbonaro M., 2015, Organic Agriculture, V5, P179, DOI 10.1007/s13165-014-0086-y Carnevali A, 2014, FOOD RES INT, V63, P218, DOI 10.1016/j.foodres.2014.01.065 Carnovale E, 2000, TABELLE COMPOSIZIONE Casas R, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0100084 CASTELLI WP, 1984, AM J MED, V76, P4, DOI 10.1016/0002-9343(84)90952-5 Champ MMJ, 2002, BRIT J NUTR, V88, pS307, DOI [10.1079/BJN2002721, 10.1079/BJN2002663] Chi D., 2004, Central European Journal of Public Health, V12, P84 Chowdhury R, 2012, BMJ-BRIT MED J, V345, DOI 10.1136/bmj.e6698 Curiel JA, 2015, INT J FOOD MICROBIOL, V196, P51, DOI 10.1016/j.ijfoodmicro.2014.11.032 D'Evoli L, 2009, P 7 INT S TRAC EL HU Dangour AD, 2009, AM J CLIN NUTR, V90, P680, DOI 10.3945/ajcn.2009.28041 De Smet S, 2016, MEAT SCI, V120, P145, DOI 10.1016/j.meatsci.2016.04.008 Del Rio D, 2013, ANTIOXID REDOX SIGN, V18, P1818, DOI 10.1089/ars.2012.4581 Dernini S, 2013, NEW MEDIT, V12, P28 desSouza RJ, 2015, BR MED J Di Lena G, 2016, J FOOD COMPOS ANAL, V45, P121, DOI 10.1016/j.jfca.2015.10.003 Djousse L, 2012, CLIN NUTR, V31, P846, DOI 10.1016/j.clnu.2012.05.010 DOLECEK TA, 1991, WORLD REV NUTR DIET, V66, P205 Donini LM, 2016, FRONT NUTR, V3, DOI 10.3389/fnut.2016.00037 Duane WC, 1997, J LIPID RES, V38, P1120 Dumas Y, 2003, J SCI FOOD AGR, V83, P369, DOI 10.1002/jsfa.1370 Durazzo A., 2014, Journal of Food Research, V3, P33 Durazzo A, 2014, INT J FOOD SCI NUTR, V65, P637, DOI 10.3109/09637486.2014.893283 Durazzo A., 2009, Tecnica Molitoria, V60, P150 Durazzo A, 2015, FOODS, V4, P391, DOI 10.3390/foods4030391 Durazzo A, 2013, FOODS, V2, P53, DOI 10.3390/foods2010053 Erdmann K, 2008, J NUTR BIOCHEM, V19, P643, DOI 10.1016/j.jnutbio.2007.11.010 Estruch R, 2013, ANN NUTR METAB S2, V62, P1 Estruch R, 2013, NEW ENGL J MED, V368, P1279, DOI 10.1056/NEJMoa1200303 Etemadi A, 2017, BMJ-BRIT MED J, V357, DOI 10.1136/bmj.j1957 EuroFiR (European Food Information Resouce Net work), 2015, EUROFIR EBASIS BIOAC European Food Safety Authority, 2010, EFSA J, V8 Farvid MS, 2016, BMJ-BRIT MED J, V353, DOI 10.1136/bmj.i2343 Feart C, 2009, JAMA-J AM MED ASSOC, V302, P638, DOI 10.1001/jama.2009.1146 FINOTTI E, 2009, FOOD, V3, P30 Fito M, 2000, LIPIDS, V35, P633, DOI 10.1007/s11745-000-0567-1 FOSTERPOWELL K, 1995, AM J CLIN NUTR, V62, p871S, DOI 10.1093/ajcn/62.4.871S Fratianni F, 2014, J FUNCT FOODS, V7, P551, DOI 10.1016/j.jff.2013.12.030 Friedman M, 2008, J AGR FOOD CHEM, V56, P6113, DOI 10.1021/jf0730486 Friedman M, 2009, ADVANCES IN POTATO CHEMISTRY AND TECHNOLOGY, P127, DOI 10.1016/B978-0-12-374349-7.00006-4 Fuller NR, 2015, NUTRIENTS, V7, P7399, DOI 10.3390/nu7095344 Fung TT, 2009, CIRCULATION, V119, P1093, DOI 10.1161/CIRCULATIONAHA.108.816736 Gao X, 2007, AM J CLIN NUTR, V86, P1486, DOI 10.1093/ajcn/86.5.1486 Garaulet M, 2011, INT J OBESITY, V35, P1308, DOI 10.1038/ijo.2011.149 German JB, 2009, EUR J NUTR, V48, P191, DOI 10.1007/s00394-009-0002-5 Gidding SS, 2009, CIRCULATION, V119, P1161, DOI 10.1161/CIRCULATIONAHA.109.191856 Givens DI, 2008, P NUTR SOC, V67, P273, DOI 10.1017/S0029665108007167 Gonzalez R, 2011, CRIT REV FOOD SCI, V51, P331, DOI 10.1080/10408390903584094 Graham I, 2007, EUR HEART J, V28, P2375, DOI 10.1093/eurheartj/ehm316 GUSSOW JD, 1986, J NUTR EDUC, V18, P1 Handelman GJ, 1999, AM J CLIN NUTR, V70, P247 Hashim YZHY, 2005, NUTR REV, V63, P374, DOI 10.1111/j.1753-4887.2005.tb00374.x Hernandez-Diaz S, 2002, EPIDEMIOLOGY, V13, P700, DOI 10.1097/00001648-200211000-00015 HERTOG MGL, 1992, J AGR FOOD CHEM, V40, P1591, DOI 10.1021/jf00021a023 Hooper L, 2006, BMJ-BRIT MED J, V332, P752, DOI 10.1136/bmj.38755.366331.2F Hu FB, 1997, NEW ENGL J MED, V337, P1491, DOI 10.1056/NEJM199711203372102 Hu FB, 2001, J AM COLL NUTR, V20, P5, DOI 10.1080/07315724.2001.10719008 Intorre F, 2013, INT J FOOD SCI NUTR, V64, P185, DOI 10.3109/09637486.2012.710893 Italian Society of Human Nutrition, 2014, REF INT LEV NUTR EN, V4th Kalogeropoulos N, 2010, FOOD CHEM, V121, P682, DOI 10.1016/j.foodchem.2010.01.005 KEYS A, 1986, AM J EPIDEMIOL, V124, P903, DOI 10.1093/oxfordjournals.aje.a114480 Keys A, 1997, Nutrition, V13, P250 Keys A, 1968, ACTA MED SCAND, V460, P1 Khosravi-Boroujeni H, 2013, ARCH IRAN MED, V16, P172, DOI 013163/AIM.009 KLAG MJ, 1993, NEW ENGL J MED, V328, P313, DOI 10.1056/NEJM199302043280504 Kliem KE, 2011, ANNU REV FOOD SCI T, V2, P21, DOI 10.1146/annurev-food-022510-133734 Knoops KTB, 2004, JAMA-J AM MED ASSOC, V292, P1433, DOI 10.1001/jama.292.12.1433 Kochar J, 2007, OBESITY, V15, P3039, DOI 10.1038/oby.2007.362 Kris-Etherton PM, 2002, CIRCULATION, V106, P2747, DOI 10.1161/01.CIR.0000038493.65177.94 KROMHOUT D, 1995, PREV MED, V24, P308, DOI 10.1006/pmed.1995.1049 KROMHOUT D, 1995, INT J EPIDEMIOL, V24, P340, DOI 10.1093/ije/24.2.340 KROMHOUT D, 1985, NEW ENGL J MED, V312, P1205, DOI 10.1056/NEJM198505093121901 Kuhnle GGC, 2009, NUTR CANCER, V61, P302, DOI 10.1080/01635580802567141 Kullisaar T, 2003, BRIT J NUTR, V90, P449, DOI 10.1079/BJN2003896 La Vecchia C, 2013, ANN NUTR METAB, V62, P1 La Vecchia C, 2009, NUTR REV, V67, pS126, DOI 10.1111/j.1753-4887.2009.00174.x Lacirignola C, 2015, P INT WORKSH ASS SUS Lacirignola C., 2012, DEV GUIDELINES IMPRO Lagiou P, 2006, BRIT J NUTR, V96, P384, DOI 10.1079/BJN20061824 Lazarov K, 1996, MED SCI RES, V24, P581 Leidy HJ, 2013, AM J CLIN NUTR, V97, P677, DOI 10.3945/ajcn.112.053116 Lombardi-Boccia G, 2002, J FOOD SCI, V67, P1738, DOI 10.1111/j.1365-2621.2002.tb08715.x Lombardi-Boccia G, 2005, J FOOD COMPOS ANAL, V18, P39, DOI 10.1016/j.jfca.2003.10.007 Longobardi F, 2012, FOOD CHEM, V133, P579, DOI 10.1016/j.foodchem.2012.01.059 Manzi P, 2013, FOODS, V2, P254, DOI 10.3390/foods2020254 MARTIN MJ, 1986, LANCET, V2, P933 Martin-Calvo N, 2016, INT J OBESITY, V40, P1103, DOI 10.1038/ijo.2016.59 Martinez-Gonzalez MA, 2011, NUTR METAB CARDIOVAS, V21, P237, DOI 10.1016/j.numecd.2009.10.005 Massiera F, 2010, J LIPID RES, V51, P2352, DOI 10.1194/jlr.M006866 Matthan NR, 2004, ARTERIOSCL THROM VAS, V24, P1092, DOI 10.1161/01.ATV.0000128410.23161.be McKevith B., 2004, Nutrition Bulletin, V29, P111, DOI 10.1111/j.1467-3010.2004.00418.x McKevith B, 2010, NUTR BULL, V35, P314, DOI 10.1111/j.1467-3010.2010.01856.x Menotti A, 2015, INT J CARDIOL, V201, P293, DOI 10.1016/j.ijcard.2015.08.050 Milder IEJ, 2005, BRIT J NUTR, V93, P393, DOI 10.1079/BJN20051371 Mitrou PN, 2007, ARCH INTERN MED, V167, P2461, DOI 10.1001/archinte.167.22.2461 Morales-Soto A, 2014, FOOD RES INT, V58, P35, DOI 10.1016/j.foodres.2014.01.050 Mozaffarian D, 2011, CIRCULATION, V123, P2870, DOI 10.1161/CIRCULATIONAHA.110.968735 Murniece I, 2011, J FOOD COMPOS ANAL, V24, P699, DOI 10.1016/j.jfca.2010.09.005 Musaiger AO, 2011, J OBES, V2011, DOI 10.1155/2011/407237 Nayak B, 2015, CRIT REV FOOD SCI, V55, P887, DOI 10.1080/10408398.2011.654142 NESTEL PJ, 2013, BR J NUTR, V12, P1 Nevigato T, 2012, LIPIDS, V47, P741, DOI 10.1007/s11745-012-3679-9 Nurk E, 2013, BRIT J NUTR, V109, P511, DOI 10.1017/S0007114512001249 Oh K, 2005, AM J EPIDEMIOL, V161, P672, DOI 10.1093/aje/kwi085 Oomah BD, 2011, FOOD RES INT, V44, P436, DOI 10.1016/j.foodres.2010.09.027 Opie RS, 2018, NUTR NEUROSCI, V21, P487, DOI [10.1080/1028415x.2017.1312841, 10.1080/1028415X.2017.1312841] Palupi E, 2012, J SCI FOOD AGR, V92, P2774, DOI 10.1002/jsfa.5639 Parodi PW, 2009, INT DAIRY J, V19, P345, DOI 10.1016/j.idairyj.2009.01.001 Perez-Jimenez F, 2005, EUR J CLIN INVEST, V35, P421, DOI 10.1111/j.1365-2362.2005.01516.x Peterson J, 2010, NUTR REV, V68, P571, DOI 10.1111/j.1753-4887.2010.00319.x Phelan M, 2011, FOOD FUNCT, V2, P153, DOI 10.1039/c1fo10017c Pizzoferrato L, 2007, J DAIRY SCI, V90, P4569, DOI 10.3168/jds.2007-0093 Poly C, 2011, AM J CLIN NUTR, V94, P1584, DOI 10.3945/ajcn.110.008938 Poppitt SD, 2002, EUR J CLIN NUTR, V56, P64, DOI 10.1038/sj.ejcn.1601282 Raffo A, 2006, J FOOD COMPOS ANAL, V19, P11, DOI 10.1016/j.jfca.2005.02.003 Ravaglia G, 2005, AM J CLIN NUTR, V82, P636, DOI 10.1093/ajcn/82.3.636 Reboredo-Rodriguez P, 2015, FOOD CHEM, V176, P493, DOI 10.1016/j.foodchem.2014.12.078 Reddivari L, 2007, CARCINOGENESIS, V28, P2227, DOI 10.1093/carcin/bgm117 Renna M, 2015, INT J GASTRON FOOD S, V2, P63, DOI 10.1016/j.ijgfs.2014.12.001 Ricci-Cabello I, 2012, NUTR REV, V70, P241, DOI 10.1111/j.1753-4887.2011.00448.x Rochfort S, 2007, J AGR FOOD CHEM, V55, P7981, DOI 10.1021/jf071704w ROHAN TE, 1993, CANCER CAUSE CONTROL, V4, P29, DOI 10.1007/BF00051711 RONG Y, 2013, BMJ-BRIT MED J, V346, P1 Ros E, 2015, BRIT J NUTR, V113, pS111, DOI 10.1017/S0007114514003924 Ryan L, 2011, J FOOD COMPOS ANAL, V24, P929, DOI 10.1016/j.jfca.2011.02.002 SAFA (Sustainability Assessment of Food and Agriculture systems)/FAO (Food and Agriculture Organization of the United Nations), 2013, GUID VERS 3 0, P45 SAFA (Sustainability Assessment of Food and Agriculture systems)/FAO (Food and Agriculture Organization of the United Nations), 2013, GUID CERS 3 0, P16 Sarmento A, 2015, J SCI FOOD AGR, V95, P179, DOI 10.1002/jsfa.6702 Scarmeas N, 2009, ARCH NEUROL-CHICAGO, V66, P216, DOI 10.1001/archneurol.2008.536 Scarmeas N, 2006, ANN NEUROL, V59, P912, DOI 10.1002/ana.20854 Schroder H, 2004, J NUTR, V134, P3355, DOI 10.1093/jn/134.12.3355 Schroder H, 2007, J NUTR BIOCHEM, V18, P149, DOI 10.1016/j.jnutbio.2006.05.006 Serra-Majem L, 2006, NUTR REV, V64, pS27, DOI 10.1301/nr.2006.feb.S27-S47 Shaw GM, 2004, AM J EPIDEMIOL, V160, P102, DOI 10.1093/aje/kwh187 SHEPHERD J, 1995, NEW ENGL J MED, V333, P1301, DOI 10.1056/NEJM199511163332001 SHIBATA A, 1992, BRIT J CANCER, V66, P673, DOI 10.1038/bjc.1992.336 Shin JY, 2013, AM J CLIN NUTR, V98, P146, DOI 10.3945/ajcn.112.051318 Simopoulos AP, 2003, WORLD REV NUTR DIET, V92, P57 Sistema di Sorveglianza PASSI (Progressi delle Aziende Sanitarie per la Salute in Italia), 2009, RAPP REG PUGL 2008 P Skeaff CM, 2009, ANN NUTR METAB, V55, P173, DOI 10.1159/000229002 Slavin J, 2004, NUTR RES REV, V17, P99, DOI 10.1079/NRR200374 Slavin J, 2013, NUTRIENTS, V5, P1417, DOI 10.3390/nu5041417 Slavin JL, 2012, ADV NUTR, V3, P506, DOI 10.3945/an.112.002154 Smith-Spangler C, 2012, ANN INTERN MED, V157, P348, DOI 10.7326/0003-4819-157-5-201209040-00007 Sofi F, 2010, AM J CLIN NUTR, V92, P1189, DOI 10.3945/ajcn.2010.29673 Sofi F, 2008, BMJ-BRIT MED J, V337, DOI 10.1136/bmj.a1344 Songisepp E, 2005, NUTR J, V4, DOI 10.1186/1475-2891-4-22 Stamler J, 2000, JAMA-J AM MED ASSOC, V284, P311, DOI 10.1001/jama.284.3.311 Stancliffe RA, 2011, AM J CLIN NUTR, V94, P422, DOI 10.3945/ajcn.111.013342 Stewart AJ, 2000, J AGR FOOD CHEM, V48, P2663, DOI 10.1021/jf000070p Sun Q, 2007, AM J CLIN NUTR, V86, P929, DOI 10.1093/ajcn/86.4.929 Takahashi R, 2005, J AGR FOOD CHEM, V53, P4578, DOI 10.1021/jf048062m Thompson LU, 2006, NUTR CANCER, V54, P184, DOI 10.1207/s15327914nc5402_5 TOMA RB, 1989, CEREAL FOOD WORLD, V34, P387 Trichopoulou A, 2003, NEW ENGL J MED, V348, P2599, DOI 10.1056/NEJMoa025039 Trichopoulou A, 2009, BMJ-BRIT MED J, V338, P2 Trinidad TP, 2010, BRIT J NUTR, V103, P569, DOI 10.1017/S0007114509992157 Tuck KL, 2002, J NUTR BIOCHEM, V13, P636, DOI 10.1016/S0955-2863(02)00229-2 ULBRICHT TLV, 1991, LANCET, V338, P985, DOI 10.1016/0140-6736(91)91846-M USDA ARS (United States Department of Agriculture Agricultural Research Service), 2014, USDA NUTR DAT STAND Van Cauwenbergh N, 2007, AGR ECOSYST ENVIRON, V120, P229, DOI 10.1016/j.agee.2006.09.006 Vander Wal JS, 2008, INT J OBESITY, V32, P1545, DOI 10.1038/ijo.2008.130 Verhoeven DTH, 1997, BRIT J CANCER, V75, P149, DOI 10.1038/bjc.1997.25 Vicente AR, 2009, FOOD SCI TECH-INT SE, P57, DOI 10.1016/B978-0-12-374112-7.00005-6 Visioli F, 2004, EUR J CANCER PREV, V13, P337, DOI 10.1097/01.cej.0000137513.71845.f6 Visioli F, 2002, CRIT REV FOOD SCI, V42, P209, DOI 10.1080/10408690290825529 Watson, 2015, MEDITERRANEAN DIET E, P249, DOI DOI 10.1016/B978-0-12-407849-9.00023-3 Wennersberg MH, 2009, AM J CLIN NUTR, V90, P960, DOI 10.3945/ajcn.2009.27664 Whittaker A, 2015, NUTRIENTS, V7, P3401, DOI 10.3390/nu7053401 Who J, 2003, WHO TECH REP SER, V916 Wijendran V, 2004, ANNU REV NUTR, V24, P597, DOI 10.1146/annurev.nutr.24.012003.132106 Williams P, 2007, NUTR DIET, V64, pS113, DOI 10.1111/j.1747-0080.2007.00197.x Williams PG, 2014, ADV NUTR, V5, p636S, DOI 10.3945/an.114.006247 WOLEVER TMS, 1992, DIABETIC MED, V9, P451, DOI 10.1111/j.1464-5491.1992.tb01816.x World Cancer Research Fund/American Institute for Cancer Research, 2007, FOOD NUTR PHYS ACT P Xu BJ, 2010, J AGR FOOD CHEM, V58, P1509, DOI 10.1021/jf903532y Zeisel SH, 2009, NUTR REV, V67, P615, DOI 10.1111/j.1753-4887.2009.00246.x Zemel MB, 2010, AM J CLIN NUTR, V91, P16, DOI 10.3945/ajcn.2009.28468 NR 218 TC 9 Z9 9 U1 1 U2 22 PD AUG 15 PY 2018 VL 98 IS 10 BP 3684 EP 3705 DI 10.1002/jsfa.8877 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000434976300010 DA 2022-12-14 ER PT J AU Zarringhabaie, GE Pirany, N Javanmard, A AF Zarringhabaie, Ghorban Elyasi Pirany, Nasrollah Javanmard, Arash TI Molecular traceability of the species origin of meats using multiplex PCR SO AFRICAN JOURNAL OF BIOTECHNOLOGY DT Article DE Food adulteration; meat origin species; Cytb; multiplex PCR ID MITOCHONDRIAL-DNA; IDENTIFICATION; PORK AB The objective of this study was the designing of a fast and reliable multiplex polymerase chain reaction (PCR) identification system for testing the pure and mixed species origin of meat samples. For conducting this research, different primers were designed for each species according to the conserved region of mitochondrial cytochrome b (Cytb) gene. The results revealed different specific amplified fragments of pure meat sources for buffalo, goat, cattle and sheep species. After mixing different portions of the mentioned meat sources, this method was able to trace less than 10% of the other species of meat in the mixture. Then, it can be concluded that this procedure is simple, cheap, rapid, and efficient, and so it can be used in the meat industry. C1 [Pirany, Nasrollah] Univ Shahrekord, Fac Agr, Dept Anim Sci, Shahrekord, Iran. [Zarringhabaie, Ghorban Elyasi] E Azerbaijan Agr & Nat Resources Res Ctr, Dept Anim Sci, Tabriz, Iran. [Javanmard, Arash] NW & W Agr Biotechnol Res Inst Iran ABRII, Dept Genom, Tabriz, Iran. C3 Shahrekord University RP Pirany, N (corresponding author), Univ Shahrekord, Fac Agr, Dept Anim Sci, Shahrekord, Iran. EM napirany@googlemail.com CR Borgo R, 1996, J FOOD SCI, V61, P1, DOI 10.1111/j.1365-2621.1996.tb14712.x Hopwood AJ, 1999, MEAT SCI, V53, P227, DOI 10.1016/S0309-1740(99)00060-1 Jorde LB, 1998, BIOESSAYS, V20, P126, DOI 10.1002/(SICI)1521-1878(199802)20:2<126::AID-BIES5>3.0.CO;2-R Leonard JV, 2000, LANCET, V355, P299, DOI 10.1016/S0140-6736(99)05225-3 MEYER R, 1994, J AOAC INT, V77, P617 Meyer R, 1995, J AOAC INT, V78, P1542 MORITZ C, 1987, ANNU REV ECOL SYST, V18, P269, DOI 10.1146/annurev.es.18.110187.001413 Partis L, 2000, MEAT SCI, V54, P369, DOI 10.1016/S0309-1740(99)00112-6 Piaggio AJ, 2001, MOL PHYLOGENET EVOL, V20, P335, DOI 10.1006/mpev.2001.0975 Rastogi A, 2004, CURRENT SCI, V87, P1278 SOUTHERN SO, 1988, J MOL EVOL, V28, P32, DOI 10.1007/BF02143495 Tanabe S, 2007, BIOSCI BIOTECH BIOCH, V71, P1663, DOI 10.1271/bbb.70075 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 WINTERO AK, 1990, MEAT SCI, V27, P75, DOI 10.1016/0309-1740(90)90030-A Yman I.M., 1998, VARFADA, V3, P6 NR 15 TC 8 Z9 8 U1 0 U2 12 PD NOV 21 PY 2011 VL 10 IS 73 BP 16461 EP 16465 DI 10.5897/AJB11.1250 WC Biotechnology & Applied Microbiology SC Biotechnology & Applied Microbiology UT WOS:000298558500009 DA 2022-12-14 ER PT J AU Mosca, A Paleari, R Carobene, A Weykamp, C Ceriotti, F AF Mosca, Andrea Paleari, Renata Carobene, Anna Weykamp, Cas Ceriotti, Ferruccio TI Performance of glycated hemoglobin (HbA(1c)) methods evaluated with EQAS studies using fresh blood samples: Still space for improvements SO CLINICA CHIMICA ACTA DT Article DE Standardization; Diabetes; Traceability; Accuracy ID GLYCOHEMOGLOBIN STANDARDIZATION; INTERNATIONAL STANDARDIZATION; BIOLOGICAL VARIATION; ANALYTICAL GOALS; LONG AB Background: The determination of glycated hemoglobin is a key indicator for the management of diabetic patients. A reference measurement system for its determination is available and IVD manufacturers should have aligned their assay to this system. Methods: Two fresh blood samples were distributed by courier to 206 Italian laboratories asking for the determination of their HbA(1c) concentration. Target HbA(1c) values were assigned by the IFCC reference measurement procedure. Results: From 193 laboratories using analytical systems from five manufacturers (Bio-Rad Laboratories, A. Menarini Diagnostics, Roche Diagnostics, Sebia and Tosoh), we obtained a global variability of 53% (in terms of CV) and of 3.8% at an HbA(1c), value of 37.4 mmol/mol (sample 1) and 62.0 mmol/mol (sample 2), respectively. With a goal for the allowable total error (TE) of 6.0%, 70% and 77% of the participants met this criterion for samples 1 and 2, respectively. Inter-laboratory CVs, were between 33 and 5.0% and between 2.2 and 3.7% for samples 1 and 2, respectively. Tosoh users registered the smallest inter-laboratory CV in sample 1, and Sebia's in sample 2. With regard to trueness, all methods had a mean bias of <= 2.8% with respect to the target values, with the exception of Tosoh (bias of +6.1 and + 5.8%, for samples 1 and 2, respectively). Conclusion: These results are in good agreement with those obtained by the CAP 2014 GH2-A survey, suggesting then that still there is an urgent need for improving a significant part of the methods currently used to measure HbA(1c). (C) 2015 Elsevier B.V. All rights reserved. C1 [Mosca, Andrea; Paleari, Renata] Univ Milan, Dip Fisiopatol Med Chirurg & Trapianti, Ctr Riferibilita Metrol Med Lab CIRME, Milan, Italy. [Carobene, Anna; Ceriotti, Ferruccio] Univ Milan, Osped San Raffaele, Serv Med Lab, Lab Standardizzaz, I-20127 Milan, Italy. [Weykamp, Cas] Locat Queen Beatrix Hosp, Dept Clin Chem, Winterswijk, Switzerland. [Weykamp, Cas] Locat Queen Beatrix Hosp, European Reference Lab, Winterswijk, Switzerland. C3 University of Milan; University of Milan; Vita-Salute San Raffaele University; IRCCS Ospedale San Raffaele RP Mosca, A (corresponding author), Dip Fisiopatol Med Chirurg & Trapianti, Via Fratelli Cervi 93, I-20090 Milan, Italy. EM andrea.mosca@unimi.it CR [Anonymous], 2015, DIABETES CARE, V38, pS33, DOI 10.2337/dc15-S009 Asberg A, 2015, CLIN CHEM LAB MED, V53, P1459, DOI 10.1515/cclm-2014-1125 Associazione Medici Diabetologi (AMD) - Societa Italiana di Diabetologia (SID), 2014, STAND IT CUR DIAB ME, P58 Braga F, 2013, CLIN CHEM LAB MED, V51, P1719, DOI 10.1515/cclm-2013-0060 Braga F, 2011, CLIN CHIM ACTA, V412, P1412, DOI 10.1016/j.cca.2011.04.014 Ceriotti F, 2014, CLIN CHIM ACTA, V432, P77, DOI 10.1016/j.cca.2013.12.032 Infusino I, 2011, BIOCHIM CLIN, V35, P377 Jeppsson JO, 2002, CLIN CHEM LAB MED, V40, P78, DOI 10.1515/CCLM.2002.016 Lapolla A, 2011, NUTR METAB CARDIOVAS, V21, P467, DOI 10.1016/j.numecd.2011.02.006 Little RR, 2013, CLIN CHIM ACTA, V418, P63, DOI 10.1016/j.cca.2012.12.026 Mosca A, 1997, EUR J CLIN CHEM CLIN, V35, P243 Mosca A, 1998, CLIN CHEM, V44, P632 Mosca A, 2009, BIOCHIM CLIN, V33, P258 Mosca A, 2014, CLIN CHEM LAB MED, V52, pE151, DOI 10.1515/cclm-2014-0084 Mosca A, 2011, BIOCHIM CLIN, V35, P36 Sacks DB, 2011, CLIN CHEM, V57, pE1, DOI 10.1373/clinchem.2010.161596 Weykamp C, 2008, CLIN CHEM, V54, P240, DOI 10.1373/clinchem.2007.097402 Weykamp C, 2015, CLIN CHEM, V61, P752, DOI 10.1373/clinchem.2014.235333 Weykamp CW, 2011, CLIN CHEM, V57, P1204, DOI 10.1373/clinchem.2011.162719 NR 19 TC 17 Z9 17 U1 0 U2 9 PD DEC 7 PY 2015 VL 451 BP 305 EP 309 DI 10.1016/j.cca.2015.10.014 PN B WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000366236900035 DA 2022-12-14 ER PT J AU Boella, G Capra, PP Cassiago, C Cerri, R Reedtz, GM Sosso, A AF Boella, G Capra, PP Cassiago, C Cerri, R Reedtz, GM Sosso, A TI Traceability of the 10-k Omega standard at IEN SO IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT DT Article DE digital voltmeters; measurement standards; resistance measurements; resistance scaling; uncertainty ID QUANTIZED HALL RESISTANCE AB The traditional scaling method used at the Istituto Elettrotecnico Nazionale (IEN) for the calibration of the 10-k Omega standard is compared with more straightforward techniques: one based on the linearity of a DVM and the other on a commercial current comparator bridge. For the three methods, the measurement results and the uncertainty budgets are reported. The agreement is better than 1 x 10(-7). C1 Ist Elettrotecn Nazl Galileo Ferraris, Turin, Italy. C3 Istituto Nazionale di Ricerca Metrologica (INRIM) RP Boella, G (corresponding author), Ist Elettrotecn Nazl Galileo Ferraris, Turin, Italy. CR BOELLA G, 1992, IEEE T INSTRUM MEAS, V41, P59, DOI 10.1109/19.126632 CAGE ME, 1991, IEEE T INSTRUM MEAS, V40, P262, DOI 10.1109/TIM.1990.1032933 Inglis AD, 1999, IEEE T INSTRUM MEAS, V48, P289, DOI 10.1109/19.769585 JAEGER KB, 1991, IEEE T INSTRUM MEAS, V40, P256, DOI 10.1109/TIM.1990.1032932 KINOSHITA J, 1991, IEEE T INSTRUM MEAS, V40, P249, DOI 10.1109/TIM.1990.1032930 REEDTZ GM, 1987, J RES NAT BUR STAND, V92, P303, DOI 10.6028/jres.092.030 NR 6 TC 8 Z9 8 U1 0 U2 1 PD APR PY 2001 VL 50 IS 2 BP 245 EP 248 DI 10.1109/19.918113 WC Engineering, Electrical & Electronic; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000168233200020 DA 2022-12-14 ER PT J AU Lachia, N Pichon, L Fourcaudot, R Tisseyre, B AF Lachia, N. Pichon, L. Fourcaudot, R. Tisseyre, B. TI Grazing livestock a sectorpoorly equipped with digital tools with specific needs SO FOURRAGES DT Article AB This article presents the results ofa study about the use ofdigital tools by grazing livestock farmers. The study was based on an online survey of 41 farmers, the results of which were analyzed and interpreted in the light ofadditional qualitative interviews. This article presents the digital tools used in the sector, the main types of use, as well as the obstacles to adopt such tools expressed by the different actors. The farmers interviewed are generally not very well equipped with digital tools. The main uses are smartphone applications and traceability software. Sensors are not widely used and are mainly used to control and monitor reproduction and feeding. Although pasture management is identified as an important need by the various actors in the sector, digital tools for this purpose are also little used. This paradox can be explained by adoption barriers common to many agricultural sectors, such as the cost and complexity of tools. In particular, the actors interviewed expressed the view that the tools are not adapted to field conditions and to the diversity of livestock farms. Although some tools are being tested or are in the research phase, farmers do not feel involved in their development, which, according to them, contributes to the marketing oftools that are not adapted to their needs. Some ofthe people interviewed also question the adequacy between the use ofdigital tools and the meaning ofthe job and the link to the animal. C1 [Lachia, N.; Pichon, L.; Fourcaudot, R.; Tisseyre, B.] INRAE, UMR Itap, Inst Agro, Montpellier SupAgro, Montpellier, France. C3 INRAE; Institut Agro; Montpellier SupAgro RP Lachia, N (corresponding author), INRAE, UMR Itap, Inst Agro, Montpellier SupAgro, Montpellier, France. CR Hostiou N, 2014, INRA PROD ANIM, V27, P113 IDELE, 2018, PRES PROJ CLOCH Kernecker M, 2020, PRECIS AGRIC, V21, P34, DOI 10.1007/s11119-019-09651-z Lachia Nina., 2019, PRECISION AGR 19 P 1, P851, DOI [DOI 10.3920/978-90-8686-888-9_105, 10.3920/978-90-8686-888-9_105] Lee C, 2018, FRONT VET SCI, V5, DOI 10.3389/fvets.2018.00187 Lescoat P., 2017, ELEVAGE PRECISION SO Lovarelli D, 2020, J CLEAN PROD, V262, DOI 10.1016/j.jclepro.2020.121409 Marie M., 2015, UNE TYPOLOGIE COMBIN, V14203 Meuret M., 2013, ELEVAGE PATURAGE PRE, V63, P13 Observatoire, 2018, US NUM EL BOV LAIT Pathak HS, 2019, PRECIS AGRIC, V20, P1292, DOI 10.1007/s11119-019-09653-x Reinermann S, 2020, REMOTE SENS-BASEL, V12, DOI 10.3390/rs12121949 SheepNet, 2018, EL OV PREC EUR DEB T Vayssade JA, 2019, COMPUT ELECTRON AGR, V162, P767, DOI 10.1016/j.compag.2019.05.021 NR 14 TC 0 Z9 0 U1 0 U2 0 PD SEP 30 PY 2021 IS 247 BP 97 EP 103 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000696092800014 DA 2022-12-14 ER PT J AU Pahlberg, T Hagman, O Thurley, M AF Pahlberg, Tobias Hagman, Olle Thurley, Matthew TI Recognition of boards using wood fingerprints based on a fusion of feature detection methods SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Wood fingerprint; Traceability; Feature detection; Biometrics; Hol-i-Wood Patching Robot ID TRACEABILITY; PERFORMANCE; TIMBER AB This paper investigates the possibility to automatically match and recognize individual Scots pine (Pinus sylvestris L.) boards using a fusion of two feature detection methods. The first method denoted Block matching method, detects corners and matches square regions around these corners using a normalized Sum of Squared Differences (SSD) measure. The second method denoted the SURF (Speeded-Up Robust Features) matching method, matches SURF features, between images (Bay et al., 2008). The fusion of the two feature detection methods improved the recognition rate of wooden floorboards substantially compared to the individual methods. Perfect matching accuracy was obtained for board pieces with more than 20 knots using high quality images. More than 90% matching accuracy was achieved for board pieces with more than 10 knots, using both high- and low quality images. (C) 2015 Elsevier B.V. All rights reserved. C1 [Pahlberg, Tobias; Hagman, Olle] Lulea Univ Technol, SE-93187 Skelleftea, Sweden. [Thurley, Matthew] Lulea Univ Technol, SE-97187 Lulea, Sweden. C3 Lulea University of Technology; Lulea University of Technology RP Pahlberg, T (corresponding author), Lulea Univ Technol, Campus Skelleftea,Forskargatan 1, SE-93187 Skelleftea, Sweden. EM tobias.pahlberg@ltu.se CR Alahi A., 2012, IEEE C COMP VIS PATT, VNew York Bay H, 2006, LECT NOTES COMPUT SC, V3951, P404, DOI 10.1007/11744023_32 Bjork A, 2011, COMPUT IND, V62, P830, DOI 10.1016/j.compind.2011.08.001 Bradski G., 2000, DOBBS J SOFTW TOOLS Broman N.O., 2008, P IUFRO WORK PART 5 Calonder M, 2010, LECT NOTES COMPUT SC, V6314, P778, DOI 10.1007/978-3-642-15561-1_56 Chiorescu S, 2004, SCAND J FOREST RES, V19, P374, DOI 10.1080/02827580410030118 Czajka A, 2013, INT CONF BIOMETR Dykstra D.P., 2002, ENV SOCIAL DEV E ASI FISCHLER MA, 1981, COMMUN ACM, V24, P381, DOI 10.1145/358669.358692 Flodin J., 2009, TECHNICAL REPORT Flodin J, 2008, FOREST PROD J, V58, P100 Forstner W., 1986, S INT SOC PHOT REM S, V26, P150 Gauglitz S, 2011, INT J COMPUT VISION, V94, P335, DOI 10.1007/s11263-011-0431-5 Gronlund A., 2008, 25 LTU Hakli J., 2013, RADIO FREQUENCY IDEN, P301 Harris C., 1988, P 4 ALV VIS C, P147 Hartley R., 2003, MULTIPLE VIEW GEOMET Jain A. K., 2012, 2 GENERATION BIOMETR, P49, DOI DOI 10.1007/978-94-007-3892-8_3 Jain AK, 1997, P IEEE, V85, P1365, DOI 10.1109/5.628674 Komogortsev O.V., 2013, INT C BIOMETRICS ICB, DOI [DOI 10.1109/ICB.2013.6612984, 10.1109/ICB.2013.6612984.] Leutenegger S, 2011, IEEE I CONF COMP VIS, P2548, DOI 10.1109/ICCV.2011.6126542 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 MathWorks MATLAB, 2013, COMP VIS SYST TOOLB Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188 Moravec H., 1980, CMURITR8003 NISTER D, 2004, PROC CVPR IEEE, P652, DOI DOI 10.1109/CVPR.2004.1315094 Nystrom J., 2008, P IUFRO WORK PART 5 Oja J., 2008, P IUFRO WORK PART 5 Oyallon E., 2013, ANAL IMPLEMENTATION Pahlberg T., 2012, WORLD C TIMB ENG, P724 Polder A, 2012, J VIBROENG, V14, P477 Rosten E, 2006, LECT NOTES COMPUT SC, V3951, P430, DOI 10.1007/11744023_34 Rublee E, 2011, IEEE I CONF COMP VIS, P2564, DOI 10.1109/ICCV.2011.6126544 Sandberg D, 2005, HOLZ ROH WERKST, V63, P11, DOI 10.1007/s00107-004-0546-2 Schmid C, 2000, INT J COMPUT VISION, V37, P151, DOI 10.1023/A:1008199403446 SHI JB, 1994, 1994 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, P593, DOI 10.1109/CVPR.1994.323794 Szeliski R, 2011, TEXTS COMPUT SCI, P1, DOI 10.1007/978-1-84882-935-0 Tola E, 2010, IEEE T PATTERN ANAL, V32, P815, DOI 10.1109/TPAMI.2009.77 Uusijarvi R., 2000, THESIS I PRODUKTIONS Viola P, 2001, PROC CVPR IEEE, P511, DOI 10.1109/cvpr.2001.990517 Yager N, 2004, PATTERN ANAL APPL, V7, P77, DOI 10.1007/s10044-004-0204-7 Yang JS, 2013, IEEE GLOB COMM CONF, P1, DOI 10.1109/GLOCOM.2013.6831038 NR 43 TC 9 Z9 11 U1 1 U2 8 PD FEB PY 2015 VL 111 BP 164 EP 173 DI 10.1016/j.compag.2014.12.014 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000350942800020 DA 2022-12-14 ER PT J AU Luvisi, A Triolo, E Rinaldelli, E Bandinelli, R Pagano, M Gini, B AF Luvisi, Andrea Triolo, Enrico Rinaldelli, Enrico Bandinelli, Roberto Pagano, Mario Gini, Barbara TI Radiofrequency applications in grapevine: From vineyard to web SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Traceability system; RFID chips; Grapevine marked plants ID IDENTIFICATION AB An experimental trial was commenced in January 2007 of a traceability system for grapevine plants produced in a nursery and for electronic management of vineyards. The main objective was producing grafted cuttings using common nursery procedures, but in which were internally installed Radio Frequency Identification chips. The trial used five common Tuscan grapevine clones. The modified plants were indistinguishable from unmarked plants, and will maintain this electronic feature throughout their life. The marked plants can be easily monitored, and will be able to supply various information, including identity, growth parameters, susceptibility to biotic stress factors, and productivity. All information is available by a website accessing a database, guaranteeing that users (e.g. nursery workers, grapevine growers, and plant pathologists) can use online access to retrieve information on every marked plant. (C) 2009 Elsevier B.V. All rights reserved. C1 [Luvisi, Andrea; Triolo, Enrico] Sez Patol Vegetale, Dipartimento Coltivaz & Difesa Specie Legnose G S, I-56124 Pisa, Italy. [Rinaldelli, Enrico; Bandinelli, Roberto; Pagano, Mario] Univ Florence, Dipartimento Ortoflorofrutticoltura, I-50019 Sesto Fiorentino, FI, Italy. [Gini, Barbara] Vivai New Plants, I-56040 Cenaia, PI, Italy. C3 University of Florence RP Luvisi, A (corresponding author), Sez Patol Vegetale, Dipartimento Coltivaz & Difesa Specie Legnose G S, Via Borghetto 80, I-56124 Pisa, Italy. EM aluvisi@agr.unipi.it; enrico.rinaldelli@unifi.it; ginib@inwind.it CR Bowman KD, 2005, HORTTECHNOLOGY, V15, P352, DOI 10.21273/HORTTECH.15.2.0352 Buguk C, 1998, J AM LEATHER CHEM AS, V93, P248 Caceci T, 1999, AQUACULTURE, V180, P41, DOI 10.1016/S0044-8486(99)00144-1 GRIECO PD, 2006, FRUTTICOLTURA, V10, P68 Hewett EW, 2006, ACTA HORTIC, P39, DOI 10.17660/ActaHortic.2006.712.2 Jones P, 2005, BRIT FOOD J, V107, P356, DOI 10.1108/00070700510602156 Kumagai MH, 2006, PLANT MOL BIOL, V61, P515, DOI 10.1007/s11103-006-0025-8 LUVISI A, 2007, P ID WORLD INT C 200 SORENSEN MA, 1995, J AM VET MED ASSOC, V207, P766 TRIOLO E, 2007, J PLANT PATHOL, V89, P46 NR 10 TC 20 Z9 22 U1 0 U2 7 PD JAN PY 2010 VL 70 IS 1 BP 256 EP 259 DI 10.1016/j.compag.2009.08.007 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000273933600029 DA 2022-12-14 ER PT J AU Santos, C Hirakawa, AR AF Santos Junior, C. Hirakawa, A. R. TI The Use of Agroxml Standard for Data Exchange Processes in the Cotton Culture SO IEEE LATIN AMERICA TRANSACTIONS DT Article DE metadata; traceability; xml AB The exchange of data among processes in computer systems is a challenge in heterogeneous environments, they are created with independent structures in terms of exchange, the use of consolidated standards for data exchange is key to an effective solution for communication between computer system heterogeneous. The integration of technologies used in different processes in the course of agricultural production is a challenge in terms of uniformity of information. The integration of various data processes in the production of cotton is an example of the difficulty of the structural point of view, the data format for the interchange between the various processes involved is critical to provide traceability of the product of logical drives in all its stages. The AgroXML provides the necessary structure for the exchange within the scope of the proposed approach in this work. The proposed scope of the research refers exclusively to trace the logical units in the productive chain of cotton using AgroXML as a format for standardization of data across heterogeneous systems. The use of AgroXML the proposed framework for standardization of data will be collected during the productive chain of cotton, is intended to present a model in high-level modules available the current version of AgroXML applied to existing processes in the production chain. C1 [Santos Junior, C.] Univ Fed Mato Grosso, Cuiaba, Mato Grosso, Brazil. [Hirakawa, A. R.] Univ Sao Paulo, Escola Politecn, Dept Engn Comp & Sistemas Digitais, BR-05508 Sao Paulo, Brazil. C3 Universidade Federal de Mato Grosso; Universidade de Sao Paulo RP Santos, C (corresponding author), Univ Fed Mato Grosso, Cuiaba, Mato Grosso, Brazil. CR AgroXML, 2010, LANDW SPRICHT AGROXM BOOTH M, 2006, TYPES METADATA INFOR Correa 2003, 2003, SAR TAV CORR EB PER IBM REDBOOKS, 2004, PATT SERV OR ARCH WE Kunisch Martin, 2009, STAND ENTWICKLUNG AG Lemos 2004, 2004, REV AN MAPA 2004 Ministerio da Agricultura Pecuaria a Abastecimento, 2010, AGR BRAS OP INV NR 7 TC 0 Z9 0 U1 0 U2 7 PD JAN PY 2012 VL 10 IS 1 BP 1425 EP 1427 DI 10.1109/TLA.2012.6142496 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering UT WOS:000300193600050 DA 2022-12-14 ER PT J AU Velez, JF Sanchez, A Sanchez, J Esteban, JL AF Velez, J. F. Sanchez, A. Sanchez, J. Esteban, J. L. TI Beef identification in industrial slaughterhouses using machine vision techniques SO SPANISH JOURNAL OF AGRICULTURAL RESEARCH DT Article DE animal identification; traceability; ear-tag detection; automatic digit recognition; threshold-based segmentation; mathematical morphology; image processing ID TRACEABILITY AB Accurate individual animal identification provides the producers with useful information to take management decisions about an individual animal or about the complete herd. This identification task is also important to ensure the integrity of the food chain. Consequently, many consumers are turning their attention to issues of quality in animal food production methods. This work describes an implemented solution for individual beef identification, taking in the time from cattle shipment arrival at the slaughterhouse until the animals are slaughtered and cut up. Our beef identification approach is image-based and the pursued goals are the correct automatic extraction and matching between some numeric information extracted from the beef ear-tag and the corresponding one from the Bovine Identification Document (BID). The achieved correct identification results by our method are near 90%, by considering the practical working conditions of slaughterhouses (i.e. problems with dirt and bad illumination conditions). Moreover, the presence of multiple machinery in industrial slaughterhouses make it difficult the use of Radio Frequency Identification (RFID) beef tags due to the high risks of interferences between RFID and the other technologies in the workplace. The solution presented is hardware/software since it includes a specialized hardware system that was also developed. Our approach considers the current EU legislation for beef traceability and it reduces the economic cost of individual beef identification with respect to RFID transponders. The system implemented has been in use satisfactorily for more than three years in one of the largest industrial slaughterhouses in Spain. C1 [Velez, J. F.; Sanchez, A.] Univ Rey Juan Carlos, ETS Ingn Informat, Dept Ciencias Computac, Mostoles 28933, Madrid, Spain. [Sanchez, J.; Esteban, J. L.] Invest & Programas SA, Madrid 28016, Spain. C3 Universidad Rey Juan Carlos; University of Sevilla RP Sanchez, A (corresponding author), Univ Rey Juan Carlos, ETS Ingn Informat, Dept Ciencias Computac, Campus Mostoles C Tulipan S-N, Mostoles 28933, Madrid, Spain. EM angel.sanchez@urjc.es CR Ahrendt P, 2011, COMPUT ELECTRON AGR, V76, P169, DOI 10.1016/j.compag.2011.01.011 Allen A, 2008, LIVEST SCI, V116, P42, DOI 10.1016/j.livsci.2007.08.018 Bowling M. B., 2008, Professional Animal Scientist, V24, P287 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Fowler M., 2003, UML DISTILLED BRIEF, V3rd Gonzalez Rafael, 2018, DIGITAL IMAGE PROCES ICAR, 2009, SYNTH ICAR GUID AN I IPSA, 2011, ATR SOFTW DOC CAPT M IPSA, 2003, TRAC CATTL US RAD FR Keilthy L, 2008, PARKING TREND INT Marchant J, 2002, SECURE ANIMAL IDENTI McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Rossing W, 1999, COMPUT ELECTRON AGR, V24, P1, DOI 10.1016/S0168-1699(99)00033-2 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Scottish Government, 2008, EFF AC MECH INT RAD Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Simeone P, 2011, P 16 C IM AN PROC RA, P118 Trier OD, 1996, PATTERN RECOGN, V29, P641, DOI 10.1016/0031-3203(95)00118-2 Tse D., 2005, FUNDAMENTALS WIRELES Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 NR 20 TC 9 Z9 9 U1 1 U2 17 PD DEC PY 2013 VL 11 IS 4 BP 945 EP 957 DI 10.5424/sjar/2013114-3924 WC Agriculture, Multidisciplinary; Soil Science SC Agriculture UT WOS:000328594100009 DA 2022-12-14 ER PT J AU Afzal, W Bruneliere, H Di Ruscio, D Sadovykh, A Mazzini, S Cariou, E Truscan, D Cabot, J Gomez, A Gorronogoitia, J Pomante, L Smrz, P AF Afzal, Wasif Bruneliere, Hugo Di Ruscio, Davide Sadovykh, Andrey Mazzini, Silvia Cariou, Eric Truscan, Dragos Cabot, Jordi Gomez, Abel Gorronogoitia, Jesus Pomante, Luigi Smrz, Pavel TI The MegaM@Rt2 ECSEL project: MegaModelling at Runtime - Scalable model-based framework for continuous development and runtime validation of complex systems SO MICROPROCESSORS AND MICROSYSTEMS DT Article; Proceedings Paper CT 20th Euromicro Conference on Digital System Design (DSD) / Session on European Projects in Digital Systems Design (EPESD) CY AUG 30-SEP 01, 2017 CL Vienna, AUSTRIA DE Model-driven engineering; Design time; Runtime; Megamodelling ID UML AB A major challenge for the European electronic industry is to enhance productivity by ensuring quality of development, integration and maintenance while reducing the associated costs. Model-Driven Engineering (MDE) principles and techniques have already shown promising capabilities, but they still need to scale up to support real-world scenarios implied by the full deployment and use of complex electronic components and systems. Moreover, maintaining efficient traceability, integration, and communication between two fundamental system life cycle phases (design time and runtime) is another challenge requiring the scalability of MDE. This paper presents an overview of the ECSEL-1 project entitled "MegaModelling at runtime Scalable model-based framework for continuous development and runtime validation of complex systems" (MegaM@Rt2), whose aim is to address the above mentioned challenges facing MDE. Driven by both large and small industrial enterprises, with the support of research partners and technology providers, MegaM@Rt2 aims to deliver a framework of tools and methods for: 1) system engineering/design and continuous development, 2) related runtime analysis and 3) global models and traceability management. Diverse industrial use cases (covering strategic domains such as aeronautics, railway, construction and telecommunications) will integrate and demonstrate the validity of the MegaM@Rt2 solution. This paper provides an overview of the MegaM@Rt2 project with respect to its approach, mission, objectives as well as to its implementation details. It further introduces the consortium as well as describes the work packages and few already produced deliverables. C1 [Afzal, Wasif] Malardalen Univ, Vasteras, Sweden. [Bruneliere, Hugo] IMT Atlantique, LS2N, CNRS, ARMINES, Brest, France. [Di Ruscio, Davide; Pomante, Luigi] Univ Aquila, DISIM, Ctr Excellence DEWS, Laquila, Italy. [Sadovykh, Andrey] Softeam, Paris, France. [Mazzini, Silvia] Intecs, Rome, Italy. [Cariou, Eric] Univ Pau & Pays Adour, LIUPPA, Pau, France. [Truscan, Dragos] Abo Akad Univ, Turku, Finland. [Cabot, Jordi] ICREA, Barcelona, Spain. [Cabot, Jordi; Gomez, Abel] UOC, Internet Interdisciplinary Inst IN3, Barcelona, Spain. [Gorronogoitia, Jesus] ATOS, Madrid, Spain. [Smrz, Pavel] Brno Univ Technol, Univ Oberta Catalunya (UDC), Brno, Czech Republic. C3 Malardalen University; Centre National de la Recherche Scientifique (CNRS); IMT - Institut Mines-Telecom; IMT Atlantique; University of L'Aquila; Universite de Pau et des Pays de l'Adour; Abo Akademi University; ICREA; UOC Universitat Oberta de Catalunya; Brno University of Technology RP Gomez, A (corresponding author), UOC, Internet Interdisciplinary Inst IN3, Barcelona, Spain. EM wasif.afzal@mdh.se; hugo.bruneliere@imt-atlantique.fr; davide.diruscio@univaq.it; andrey.sadovykh@softeam.fr; silvia.mazzini@intecs.it; eric.cariou@univ-pau.fr; dragos.truscan@abo.fi; jordi.cabot@icrea.cat; agomezlla@uoc.edu; jesus.gorronogoitia@atos.net; luigi.pomante@univaq.it; smrz@fit.vutbr.cz CR Afzal W., 2017, P 2017 EUR C DIG SYS Afzal W., 2008, P 20 INT C SOFTW ENG Afzal W, 2009, INFORM SOFTWARE TECH, V51, P957, DOI 10.1016/j.infsof.2008.12.005 Ahmed BS, 2017, IEEE ACCESS, V5, P25706, DOI 10.1109/ACCESS.2017.2771562 Aizenbud-Reshef N, 2006, IBM SYST J, V45, P515, DOI 10.1147/sj.453.0515 Anda B, 2006, EMPIR SOFTW ENG, V11, P555, DOI 10.1007/s10664-006-9020-6 Baker P., 2005, P 8 INT C MOD DRIV E Baresi L., 2010, P FSE SDP WORKSH FUT Brahneborg D., 2018, P 2018 ACM SPEC INT Brahneborg D., 2017, 2017 IEEE INT C SOFT Bruneliere Hugo, 2019, Software & Systems Modeling, V18, P1931, DOI 10.1007/s10270-017-0622-9 Cross SE, 2016, 2016 IEEE EUROPEAN TECHNOLOGY AND ENGINEERING MANAGEMENT SUMMIT (E-TEMS) Di Ruscio D, 2014, SCI COMPUT PROGRAM, V89, P69, DOI 10.1016/j.scico.2013.12.006 Favre JM, 2005, ELECTRON NOTES THEOR, V127, P59, DOI 10.1016/j.entcs.2004.08.034 Fitzgerald B, 2017, J SYST SOFTWARE, V123, P176, DOI 10.1016/j.jss.2015.06.063 Gorschek T, 2014, J SYST SOFTWARE, V95, P176, DOI 10.1016/j.jss.2014.03.082 Hebig R., 2012, ELECT COMMUN EASST, V42 Khaitan SK, 2015, IEEE SYST J, V9, P350, DOI 10.1109/JSYST.2014.2322503 Kleppe A., 2003, MDA EXPLAINED MODEL Moro A., 2015, 2015 12 INT WORKSH I Muttillo V, 2016, EURASIP J EMBED SYST, DOI 10.1186/s13639-016-0051-9 Petre M, 2014, SOFTW SYST MODEL, V13, P1225, DOI 10.1007/s10270-014-0430-4 Tomassetti F., 2012, 16 INT C EV ASS SOFT Valente G., 2016, 2016 24 EUR INT C PA Wallin P., 2009, 42 HAW INT C SYST SC NR 25 TC 18 Z9 18 U1 0 U2 5 PD SEP PY 2018 VL 61 BP 86 EP 95 DI 10.1016/j.micpro.2018.05.010 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering UT WOS:000441486700008 DA 2022-12-14 ER PT J AU Abbasi, T Hafeez, Y Asghar, S Hussain, S Yang, SK Ali, S AF Abbasi, Tehseen Hafeez, Yaser Asghar, Sohail Hussain, Shariq Yang, Shunkun Ali, Sadia TI Towards a component-based system model to improve the quality of highly configurable systems SO PEERJ COMPUTER SCIENCE DT Article DE Agile software development; Highly configurable systems; Quality and process improvement; Software product lines; Variability management AB Due to ever-evolving software developments processes, companies are motivated to develop desired quality products quickly and effectively. Industries are now focusing on the delivery of configurable systems to provide several services to a wide range of customers by making different configurations in a single largest system. Nowadays, component-based systems are highly demanded due to their capability of reusability and restructuring of existing components to develop new systems. Moreover, product line engineering is the major branch of the component-based system for developing a series of systems. Software product line engineering (SPLE) provides the ability to design several software modifications according to customer needs in a cost-effective manner. Researchers are trying to tailor the software product line (SPL) process that integrates agile development technologies to overcome the issues faced during the execution of the SPL process such as delay in product delivery, restriction to requirements change, and exhaustive initial planning. The selection of suitable components, the need for documentation, and tracing back the user requirements in the agile-integrated product line (APL) models still need to improve. Furthermore, configurable systems demand the selected features to be the least dependent. In this paper, a hybrid APL model, quality enhanced application product line engineering (QeAPLE) is proposed that provides support for highly configurable systems (HCS) by evaluating the dependency of features before making the final selection. It also has a documentation and requirement traceability function to ensure that the product meets the desired quality. Two-fold assessments are undertaken to validate the suggested model, with the proposed model being deployed on an active project. After that, we evaluated the proposed model performance and effectiveness using after implementing it in a real-world environment and compared the results with an existing method using statistical analysis. The results of the experimental study proofs that the proposed model is practically and statistically significant as compared to the existing method in terms of effectiveness and participants' performance. Hence, the statistical results of the comparative analysis show that the proposed model improved ease of understanding and adaptability, required effort, high-quality achievement, and version management are significant i.e., more the 50% as compared to the exiting method i.e., less than 50%. The proposed model offers to assist in the development of a highly configurable system that achieves the needed quality. Therefore, the proposed model manages the variation identification, versions control, components dependency for correct selection of components, and validation activities from domain engineering to application engineering. C1 [Abbasi, Tehseen; Hafeez, Yaser; Ali, Sadia] PMAS Arid Agr Univ, Univ Inst Informat Technol, Rawalpindi, Punjab, Pakistan. [Asghar, Sohail] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad, Pakistan. [Hussain, Shariq] Fdn Univ Islamabad, Dept Software Engn, Islamabad, Pakistan. [Yang, Shunkun] Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China. C3 Arid Agriculture University; COMSATS University Islamabad (CUI); Quaid I Azam University; Beihang University RP Yang, SK (corresponding author), Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China. EM ysk@buaa.edu.cn CR Abal I, 2018, ACM T SOFTW ENG METH, V26, DOI 10.1145/3149119 Aggarwal AK, 2019, INT CONF GLOBAL SOFT, P58, DOI 10.1109/ICGSE.2019.00023 Al-Hawari A, 2021, SOFTWARE QUAL J, V29, P667, DOI 10.1007/s11219-020-09529-8 Ali A, 2021, INF TECHNOL CONTROL, V50, P424, DOI 10.5755/j01.itc.50.3.27622 Ali S, 2021, EXPERT SYST, V38, DOI 10.1111/exsy.12770 Ali S, 2018, MEHRAN UNIV RES J EN, V37, P639, DOI 10.22581/muet1982.1803.17 Ardakani MRM, 2018, J ORGAN CHANGE MANAG, V31, P852, DOI 10.1108/JOCM-07-2016-0135 Bolander WJ, 2021, INSIGHT, V24, P42, DOI [10.1002/inst.12327, DOI 10.1002/INST.12327] Carbon R, 2008, SPLC 2008: 12TH INTERNATIONAL SOFTWARE PRODUCT LINE CONFERENCE, PROCEEDINGS, P180, DOI 10.1109/SPLC.2008.21 Cardozo N., 2012, P 2012 IEEE 17 INT C, P1 Carvalho L, 2020, SPLC'19: PROCEEDINGS OF THE 23RD INTERNATIONAL SYSTEMS AND SOFTWARE PRODUCT LINE CONFERENCE, VOL A, P26, DOI 10.1145/3336294.3336319 Camacho MC, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11156820 Chacon-Luna AE, 2019, 23RD INTERNATIONAL SYSTEMS AND SOFTWARE PRODUCT LINE CONFERENCE(SPLC 2019), VOL B, P82, DOI 10.1145/3307630.3342421 Clarke P, 2016, COMM COM INF SC, V609, P351, DOI 10.1007/978-3-319-38980-6_25 da Silva I, 2012, P 16 INT SOFTW PROD, V1 da Silva IF, 2014, J SYST SOFTWARE, V88, P189, DOI 10.1016/j.jss.2013.10.040 Dintzner N, 2018, EMPIR SOFTW ENG, V23, P905, DOI 10.1007/s10664-017-9557-6 Dove R, 2017, 2017 ANN IEEE INT SY, P1 Geogy M, 2016, PROC TECH, V25, P405, DOI 10.1016/j.protcy.2016.08.125 Ghanam Y, 2010, LECT NOTES BUS INF, V48, P43 Ghasemi A, 2012, INT J ENDOCRINOL MET, V10, P486, DOI 10.5812/ijem.3505 Giray G, 2021, J SYST SOFTWARE, V180, DOI 10.1016/j.jss.2021.111031 Haidar H, 2017, ICSOFT: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGIES, P275, DOI 10.5220/0006423902750285 Hanssen GK, 2008, J SYST SOFTWARE, V81, P843, DOI 10.1016/j.jss.2007.10.025 Hayashi K, 2018, SPLC'18: PROCEEDINGS OF THE 22ND INTERNATIONAL SYSTEMS AND SOFTWARE PRODUCT LINE CONFERENCE, VOL 1, P160, DOI 10.1145/3233027.3233048 Hayashi K, 2017, 21ST INTERNATIONAL SYSTEMS & SOFTWARE PRODUCT LINE CONFERENCE (SPLC 2017), VOL 1, P180, DOI 10.1145/3106195.3106221 Heck P, 2018, SOFTWARE QUAL J, V26, P127, DOI 10.1007/s11219-016-9336-4 Hohl Philipp, 2016, Product-Focused Software Process Improvement. 17th International Conference, PROFES 2016. Proceedings: LNCS 10027, P468, DOI 10.1007/978-3-319-49094-6_32 Hohl P, 2018, 2018 IEEE INT C ENG, P1, DOI DOI 10.1109/ICE.2018.8436277 Hohl P, 2017, ICSSP'17: PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON SOFTWARE AND SYSTEM PROCESS, P70, DOI 10.1145/3084100.3084109 Kasauli R, 2021, J SYST SOFTWARE, V172, DOI 10.1016/j.jss.2020.110851 Kiani AA, 2021, J SUPERCOMPUT, V77, P8391, DOI 10.1007/s11227-021-03627-5 Klunder J, 2018, PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON SOFTWARE AND SYSTEM PROCESS (ICSSP 2018), P1, DOI 10.1145/3202710.3203146 Klunder JAC, 2019, J SOFTW-EVOL PROC, V31, DOI 10.1002/smr.2168 Krueger Charles, 2019, INSIGHT, V22, P34, DOI 10.1002/inst.12244 Krueger C, 2018, SPLC'18: PROCEEDINGS OF THE 22ND INTERNATIONAL SYSTEMS AND SOFTWARE PRODUCT LINE CONFERENCE - VOL 2, P1, DOI 10.1145/3236405.3236409 Krueger C, 2017, 21ST INTERNATIONAL SYSTEMS & SOFTWARE PRODUCT LINE CONFERENCE (SPLC 2017), VOL 1, P227, DOI 10.1145/3106195.3106218 Lindohf R, 2021, EMPIR SOFTW ENG, V26, DOI 10.1007/s10664-020-09913-9 Meinicke J, 2016, IEEE INT CONF AUTOM, P483, DOI 10.1145/2970276.2970322 Mellado D, 2010, INFORM SOFTWARE TECH, V52, P1094, DOI 10.1016/j.infsof.2010.05.007 Mohan K, 2010, IEEE SOFTWARE, V27, P48, DOI 10.1109/MS.2010.31 NARASIMHAN S, 1986, AICHE J, V32, P1409, DOI 10.1002/aic.690320902 O'Leary P, 2012, J SOFTW-EVOL PROC, V24, P561, DOI 10.1002/smr.498 Oriol M, 2020, SOFTWARE QUAL J, V28, P931, DOI 10.1007/s11219-020-09509-y Parejo JA, 2016, J SYST SOFTWARE, V122, P287, DOI 10.1016/j.jss.2016.09.045 Qian Wu, 2021, Signal and Information Processing, Networking and Computers. Proceedings of the 7th International Conference on Signal and Information Processing, Networking and Computers (ICSINC). Lecture Notes in Electrical Engineering (LNEE 677), P199, DOI 10.1007/978-981-33-4102-9_25 Ro HJ, 2013, J QUAL ASSUR HOSP TO, V14, P295, DOI 10.1080/1528008X.2013.802624 ter Beek MH, 2020, IEEE T SOFTWARE ENG, V46, P321, DOI 10.1109/TSE.2018.2853726 Tian K, 2014, INT J KNOWL SYST SCI, V5, P17, DOI 10.4018/ijkss.2014100102 Uysal MP, 2021, J IND INF INTEGR, V22, DOI 10.1016/j.jii.2021.100202 Yoder JW, 2002, LECT NOTES COMPUT SC, V2319, P336 NR 51 TC 0 Z9 0 U1 2 U2 5 PD MAR 7 PY 2022 VL 8 AR e912 DI 10.7717/peerj-cs.912 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science UT WOS:000768601100003 DA 2022-12-14 ER PT J AU Wang, Y Zuo, ZT Wang, YZ AF Wang, Ye Zuo, Zhi-Tian Wang, Yuan-Zhong TI Pattern recognition: An effective tool for quality assessment of herbal medicine based on chemical information SO JOURNAL OF CHEMOMETRICS DT Review DE chemometrics; discrimination analysis; pattern recognition; quality control; traditional Chinese medicine ID NEAR-INFRARED SPECTROSCOPY; PERFORMANCE LIQUID-CHROMATOGRAPHY; TANDEM MASS-SPECTROMETRY; SQUARES DISCRIMINANT-ANALYSIS; POLYPHYLLA VAR. YUNNANENSIS; FLOS LONICERAE-JAPONICAE; DATA FUSION STRATEGY; FT-NIR SPECTROSCOPY; GEOGRAPHICAL ORIGIN; FINGERPRINT ANALYSIS AB Herbal medicine has obtained great attention for its effective efficacy using its crude materials and patent medicines. It is believed that the efficacy is a synergetic action by several chemical components. To present a detailed overview of the usage of multivariate statistical techniques developed for analytical chemistry in quality assessment and origins traceability, we provided an extensive pragmatic and practical overview of these techniques for the quality control of these crude medicines. Two pattern recognition methods, unsupervised and supervised approaches, were interpreted using practical instances. Overall, the review briefly summarized common applications for location, species, harvesting time, processed production, manufacture, botanical part, and authenticity. Besides, we focused on data pretreatment and fusion strategies listing recently published literature to provide a detailed reference to choose the most appropriate statistical method and fusion strategies. Actual applications have proved chemometrics as an effective and rapid tool for the quality control of herbal medicine. C1 [Wang, Ye; Zuo, Zhi-Tian; Wang, Yuan-Zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. [Wang, Ye] Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University of Chinese Medicine RP Zuo, ZT; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM yaaszztian@126.com; boletus@126.com CR BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Beebe Kenneth R, 1998, CHEMOMETRCIS A PRATI Bellon-Maurel V, 2010, TRAC-TREND ANAL CHEM, V29, P1073, DOI 10.1016/j.trac.2010.05.006 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Brereton RG, 2011, J CHEMOMETR, V25, P225, DOI 10.1002/cem.1397 Chan ECY, 2007, RAPID COMMUN MASS SP, V21, P519, DOI 10.1002/rcm.2864 Chen CY, 2007, J SEP SCI, V30, P3181, DOI 10.1002/jssc.200700204 Chen JB, 2017, SPECTROCHIM ACTA A, V182, P81, DOI 10.1016/j.saa.2017.03.070 Chen XJ, 2011, FOOD BIOPROCESS TECH, V4, P753, DOI 10.1007/s11947-009-0199-6 Chen XJ, 2008, ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 3, PROCEEDINGS, P107, DOI 10.1109/ICNC.2008.667 Chen Y, 2008, ANAL CHIM ACTA, V623, P146, DOI 10.1016/j.aca.2008.06.018 Chen Y, 2008, ANAL CHIM ACTA, V618, P121, DOI 10.1016/j.aca.2008.04.055 Cheng CG, 2010, APPL SPECTROSC REV, V45, P165, DOI 10.1080/05704920903574256 Cheng XP, 2013, ANAL METHODS-UK, V5, P6325, DOI 10.1039/c3ay41132j Cheung F, 2011, NATURE, V480, pS82, DOI 10.1038/480S82a Cui XB, 2015, BIOMED CHROMATOGR, V29, P1112, DOI 10.1002/bmc.3398 Diao JY, 2018, PLANTA MED, V84, P1280, DOI 10.1055/a-0639-5450 Ding GY, 2017, RSC ADV, V7, P22034, DOI 10.1039/c6ra28152d Dong XiaoLei, 2015, Journal of Henan Agricultural Sciences, V44, P113 Duan L, 2014, J CHROMATOGR A, V1339, P118, DOI 10.1016/j.chroma.2014.02.091 Eilers PHC, 2004, ANAL CHEM, V76, P404, DOI 10.1021/ac034800e Fu HY, 2017, SPECTROCHIM ACTA A, V182, P17, DOI 10.1016/j.saa.2017.03.074 Gad HA, 2013, PHYTOCHEM ANALYSIS, V24, P1, DOI 10.1002/pca.2378 Gendrin C, 2008, J PHARMACEUT BIOMED, V48, P533, DOI 10.1016/j.jpba.2008.08.014 Gishen M, 2005, AUST J GRAPE WINE R, V11, P296, DOI 10.1111/j.1755-0238.2005.tb00029.x Granato D, 2018, COMPR REV FOOD SCI F, V17, P663, DOI 10.1111/1541-4337.12341 Gredilla A, 2016, TRAC-TREND ANAL CHEM, V76, P30, DOI 10.1016/j.trac.2015.11.011 He SX, 2015, CHEMOMETR INTELL LAB, V146, P472, DOI 10.1016/j.chemolab.2015.07.002 Hu LQ, 2018, SPECTROCHIM ACTA A, V193, P87, DOI 10.1016/j.saa.2017.12.011 Huck Pezzei VA, 2011, CURR BIOACT COMPD, V77, P75, DOI DOI 10.2174/157140711796011188 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 Jiang MM, 2014, PHYTOCHEM ANALYSIS, V25, P50, DOI 10.1002/pca.2461 Jiang Y, 2012, PHYTOCHEM ANALYSIS, V23, P387, DOI 10.1002/pca.1369 Jiang Y, 2010, ANAL CHIM ACTA, V657, P9, DOI 10.1016/j.aca.2009.10.024 Jiang ZZ, 2016, J SEP SCI, V39, P2928, DOI 10.1002/jssc.201600246 Kong WJ, 2009, PHYTOMEDICINE, V16, P950, DOI 10.1016/j.phymed.2009.03.016 Kong WJ, 2010, NAT PROD RES, V24, P1616, DOI 10.1080/14786411003757937 KOWALSKI BR, 1975, J CHEM INF COMP SCI, V15, P201, DOI 10.1021/ci60004a002 Lavine B. K., 2005, CHEMOMETRICS CHEMOIN Li C, 2014, CHEMOMETR INTELL LAB, V136, P115, DOI 10.1016/j.chemolab.2014.05.008 Li FM, 2006, BIOMED CHROMATOGR, V20, P634, DOI 10.1002/bmc.678 Li GF, 2017, ANAL METHODS-UK, V9, P1897, DOI 10.1039/c7ay00153c Li J, 2017, AM J CHINESE MED, V45, P667, DOI 10.1142/S0192415X17500380 Li WL, 2016, J ZHEJIANG UNIV-SC B, V17, P382, DOI 10.1631/jzus.B1500186 Li WL, 2013, J PHARMACEUT BIOMED, V72, P33, DOI 10.1016/j.jpba.2012.09.012 Li YQ, 2018, REV BRAS FARMACOGN, V28, P533, DOI 10.1016/j.bjp.2018.06.007 Li Y, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-017-19131-x Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Liang JR, 2012, ANAL LETT, V45, P2109, DOI 10.1080/00032719.2012.686129 Liang YZ, 2011, CHIMIA, V65, P944, DOI 10.2533/chimia.2011.944 Liang YZ, 2008, SCI CHINA SER B, V51, P718, DOI 10.1007/s11426-008-0084-6 Liang YZ, 2010, J SEP SCI, V33, P410, DOI 10.1002/jssc.200900653 Liu P, 2019, SPECTROCHIM ACTA A, V206, P23, DOI 10.1016/j.saa.2018.07.094 Luo HL, 2015, J SEP SCI, V38, P1544, DOI 10.1002/jssc.201401430 Madsen R, 2010, ANAL CHIM ACTA, V659, P23, DOI 10.1016/j.aca.2009.11.042 MALMQUIST G, 1994, J CHROMATOGR A, V687, P71, DOI 10.1016/0021-9673(94)00726-8 Marquez C, 2016, TALANTA, V161, P80, DOI 10.1016/j.talanta.2016.08.003 Murugesan A, 2009, RENEW SUST ENERG REV, V13, P825, DOI 10.1016/j.rser.2008.02.003 Ni YN, 2012, ANAL METHODS-UK, V4, P4326, DOI 10.1039/c2ay25950h Ni YN, 2012, SPECTROCHIM ACTA A, V96, P252, DOI 10.1016/j.saa.2012.05.031 Ni YN, 2009, ANAL CHIM ACTA, V647, P149, DOI 10.1016/j.aca.2009.06.021 Nicotra AB, 2010, TRENDS PLANT SCI, V15, P684, DOI 10.1016/j.tplants.2010.09.008 Nielsen NPV, 1998, J CHROMATOGR A, V805, P17, DOI 10.1016/S0021-9673(98)00021-1 Oliver JD, 2014, ANAL CHIM ACTA, V809, P183, DOI 10.1016/j.aca.2013.12.001 OTTO M, 2017, CHEMOMETRICS STAT CO Ouyang Q, 2014, ANAL CHIM ACTA, V841, P68, DOI 10.1016/j.aca.2014.06.001 Pan Y, 2016, CHEM BIODIVERS, V13, P107, DOI 10.1002/cbdv.201500333 Pan Y, 2016, BIOMED CHROMATOGR, V30, P232, DOI 10.1002/bmc.3540 Pan Y, 2015, BMC BIOCHEM, V16, DOI 10.1186/s12858-015-0038-5 Pei YF, 2019, J MOL STRUCT, V1196, P478, DOI 10.1016/j.molstruc.2019.06.099 Pei YF, 2019, MOLECULES, V24, DOI 10.3390/molecules24142559 Pei YF, 2018, MOLECULES, V23, DOI 10.3390/molecules23123343 Pravdova V, 2002, ANAL CHIM ACTA, V456, P77, DOI 10.1016/S0003-2670(02)00008-9 Qin Jian-ping, 2015, Zhongguo Zhong Yao Za Zhi, V40, P1114 Ramos PM, 2007, ANAL CHIM ACTA, V584, P360, DOI 10.1016/j.aca.2006.11.051 Ren XF, 2012, ANAL LETT, V45, P1824, DOI 10.1080/00032719.2012.677971 Lucio-Gutierrez JR, 2011, FOOD RES INT, V44, P557, DOI 10.1016/j.foodres.2010.11.037 Roggo Y, 2007, J PHARMACEUT BIOMED, V44, P683, DOI 10.1016/j.jpba.2007.03.023 Sereshti H, 2018, FOOD CONTROL, V90, P48, DOI 10.1016/j.foodcont.2018.02.026 [沈涛 Shen Tao], 2015, [药物分析杂志, Chinese Journal of Pharmaceutical Analysis], V35, P979 [沈涛 Shen Tao], 2015, [中国药学杂志, Chinese Pharmaceutical Journal], V50, P579 Sun LiLi, 2017, China Journal of Traditional Chinese Medicine and Pharmacy, V32, P2194 Sun XM, 2014, CARBOHYD POLYM, V114, P432, DOI 10.1016/j.carbpol.2014.08.048 Sun YG, 2012, J SEP SCI, V35, P1796, DOI 10.1002/jssc.201200026 Tian RT, 2009, J CHROMATOGR A, V1216, P2150, DOI 10.1016/j.chroma.2008.10.127 Tistaert C, 2011, ANAL CHIM ACTA, V690, P148, DOI 10.1016/j.aca.2011.02.023 Toh DF, 2010, J PHARMACEUT BIOMED, V52, P43, DOI 10.1016/j.jpba.2009.12.005 Tu YY, 2011, NAT MED, V17, P1217, DOI 10.1038/nm.2471 Walczak B, 2005, CHEMOMETR INTELL LAB, V77, P173, DOI 10.1016/j.chemolab.2004.07.012 Wang F, 2017, J PHARMACEUT BIOMED, V138, P70, DOI 10.1016/j.jpba.2017.02.004 Wang JR, 2016, SPECTROSC SPECT ANAL, V36, P316, DOI 10.3964/j.issn.1000-0593(2016)02-0165-06 Wang LL, 2011, J ANAL APPL PYROL, V90, P13, DOI 10.1016/j.jaap.2010.09.010 Wang Pei, 2015, J Pharm Anal, V5, P277, DOI 10.1016/j.jpha.2015.04.001 Wang Y, 2020, ANAL LETT, V53, P1774, DOI 10.1080/00032719.2020.1719126 Wang Y, 2019, ROY SOC OPEN SCI, V6, DOI 10.1098/rsos.190399 Wang Y, 2018, ANAL LETT, V51, P2173, DOI 10.1080/00032719.2017.1416622 Wang YZ, 2018, PLANT GROWTH REGUL, V84, P373, DOI 10.1007/s10725-017-0348-2 Wang ZJ, 2014, J CHEM-NY, V2014, DOI 10.1155/2014/475389 Woody NA, 2004, ANAL CHEM, V76, P2595, DOI 10.1021/ac035382g Wu XM, 2018, SPECTROCHIM ACTA A, V205, P479, DOI 10.1016/j.saa.2018.07.067 Xie HP, 2006, ANAL SCI, V22, P1111, DOI 10.2116/analsci.22.1111 Xin N, 2012, SPECTROCHIM ACTA A, V89, P18, DOI 10.1016/j.saa.2011.12.006 Xu CJ, 2006, J CHROMATOGR A, V1134, P253, DOI 10.1016/j.chroma.2006.08.060 Xu L, 2016, FOOD ANAL METHOD, V9, P451, DOI 10.1007/s12161-015-0213-8 Xu L, 2015, J FOOD QUALITY, V38, P450, DOI 10.1111/jfq.12160 Xu QH, 2013, BMC COMPLEM ALTERN M, V13, DOI 10.1186/1472-6882-13-132 Xu X, 2020, ANAL CHEM, V92, P7646, DOI 10.1021/acs.analchem.0c00483 Yang SO, 2012, J PHARMACEUT BIOMED, V58, P19, DOI 10.1016/j.jpba.2011.09.016 Yang SL, 2015, ANAL METHODS-UK, V7, P943, DOI [10.1039/C4AY02230K, 10.1039/c4ay02230k] Yang XD, 2018, SPECTROCHIM ACTA A, V205, P457, DOI 10.1016/j.saa.2018.07.056 Yang YG, 2018, ANAL LETT, V51, P1730, DOI 10.1080/00032719.2017.1385618 Yang Y, 2018, SPECTROCHIM ACTA A, V191, P233, DOI 10.1016/j.saa.2017.10.019 Yang ZH, 2018, J GINSENG RES, V42, P334, DOI 10.1016/j.jgr.2017.04.005 Yu CH, 2014, J PHARMACEUT BIOMED, V99, P8, DOI 10.1016/j.jpba.2014.06.031 Yu K, 2006, CHINESE J ANAL CHEM, V34, P561, DOI 10.1016/S1872-2040(06)60029-7 Yu Y., 2016, MODERN FOOD SCI TECH, V32, P269 Yuan TJ, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-017-18458-9 Yue H, 2008, PHYTOCHEM ANALYSIS, V19, P141, DOI 10.1002/pca.1027 Zhao Y, 2015, J PHARMACEUT BIOMED, V107, P251, DOI 10.1016/j.jpba.2014.12.035 Zhao YL, 2015, J AOAC INT, V98, P22, DOI 10.5740/jaoacint.13-395 Zheng YY, 2018, MOLECULES, V23, DOI 10.3390/molecules23051235 Zhu GX, 2018, RSC ADV, V8, P22086, DOI 10.1039/c8ra03369b Zhu Y., 2015, AM J ANAL CHEM, V6, P480, DOI DOI 10.4236/AJAC.2015.65047 Zhu Y, 2016, SPECTROCHIM ACTA A, V159, P68, DOI 10.1016/j.saa.2016.01.018 NR 124 TC 2 Z9 2 U1 11 U2 53 PD MAR PY 2021 VL 35 IS 3 AR e3305 DI 10.1002/cem.3305 EA DEC 2020 WC Automation & Control Systems; Chemistry, Analytical; Computer Science, Artificial Intelligence; Instruments & Instrumentation; Mathematics, Interdisciplinary Applications; Statistics & Probability SC Automation & Control Systems; Chemistry; Computer Science; Instruments & Instrumentation; Mathematics UT WOS:000595852500001 DA 2022-12-14 ER PT J AU Di Prinzio, R deAlmeida, CE AF Di Prinzio, Renato deAlmeida, Carlos Eduardo TI Air kerma standard for calibration of well-type chambers in Brazil using Ir-192 HDR sources and its traceability SO MEDICAL PHYSICS DT Article DE brachytherapy; calibration; dosimetry; ionisation chambers; iridium; measurement uncertainty; radioactive sources ID FLUENCE NONUNIFORMITY CORRECTION; BRACHYTHERAPY SOURCES; IONIZATION CHAMBERS; MONTE-CARLO; STRENGTH; WATER AB In Brazil there are over 100 high dose rate (HDR) brachytherapy facilities using well-type chambers for the determination of the air kerma rate of Ir-192 sources. This paper presents the methodology developed and extensively tested by the Laboratorio de Ciencias Radiologicas (LCR) and presently in use to calibrate those types of chambers. The system was initially used to calibrate six well-type chambers of brachytherapy services, and the maximum deviation of only 1.0% was observed between the calibration coefficients obtained and the ones in the calibration certificate provided by the UWADCL. In addition to its traceability to the Brazilian National Standards, the whole system was taken to the University of Wisconsin Accredited Dosimetry Calibration Laboratory (UWADCL) for a direct comparison and the same formalism to calculate the air kerma was used. The comparison results between the two laboratories show an agreement of 0.9% for the calibration coefficients. Three Brazilian well-type chambers were calibrated at the UWADCL, and by LCR, in Brazil, using the developed system and a clinical HDR machine. The results of the calibration of three well chambers have shown an agreement better than 1.0%. Uncertainty analyses involving the measurements made both at the UWADCL and LCR laboratories are discussed. C1 [Di Prinzio, Renato; deAlmeida, Carlos Eduardo] Univ Estado Rio de Janeiro, LCR, BR-20550900 Rio De Janeiro, Brazil. [Di Prinzio, Renato] CNEN, IRD, BR-22780160 Rio De Janeiro, Brazil. C3 Universidade do Estado do Rio de Janeiro; Comissao Nacional de Energia Nuclear (CNEN); Instituto de Radioprotecao e Dosimetria RP Di Prinzio, R (corresponding author), Univ Estado Rio de Janeiro, LCR, R Sao Francisco Xavier 524,Pavilhao Haroldo Lisbo, BR-20550900 Rio De Janeiro, Brazil. EM rprinzio@cnen.gov.br; cea71@yahoo.com.br CR Austerlitz C, 2008, MED PHYS, V35, P5360, DOI 10.1118/1.2996178 BIELAJEW AF, 1990, PHYS MED BIOL, V35, P517, DOI 10.1088/0031-9155/35/4/004 Borg J, 1999, MED PHYS, V26, P2441, DOI 10.1118/1.598763 Borg J, 2000, MED PHYS, V27, P1804, DOI 10.1118/1.1287054 DEALMEIDA CE, 1999, PHYS MED BIOL, V44, P38 DIPRINZIO R, 2007, 12 BRAZ C MED UNPUB Douysset G, 2005, PHYS MED BIOL, V50, P1961, DOI 10.1088/0031-9155/50/9/003 Douysset G, 2008, PHYS MED BIOL, V53, pN85, DOI 10.1088/0031-9155/53/6/N02 GOETSCH SJ, 1991, MED PHYS, V18, P462, DOI 10.1118/1.596649 *IAEA TECDOC, 2002, 1274 IAEA TECDOC *IAEA TECDOC, 1999, 1079 IAEATECDOC *ICRU, 1995, 38 ICRU International Commission on Radiation Units and Measurement (ICRU), 1997, 58 ICRU KONDO S, 1960, RADIAT RES, V13, P37, DOI 10.2307/3570872 Mainegra-Hing E, 2006, MED PHYS, V33, P3340, DOI 10.1118/1.2239198 MARECHAL MH, 1996, 896 IAEA TECDOC MARECHAL MH, 1998, THESIS STATE U RIO D NATH R, 1995, MED PHYS, V22, P209, DOI 10.1118/1.597458 PODGORSAK MB, 1993, MED PHYS, V20, P1257, DOI 10.1118/1.596976 PODGORSAK MB, 1995, CONER INT, V29, P153 Rajan KNG, 2002, PHYS MED BIOL, V47, P1047, DOI 10.1088/0031-9155/47/7/304 Rodriguez ML, 2004, PHYS MED BIOL, V49, P1705, DOI 10.1088/0031-9155/49/9/008 Sander T, 2006, DQLRD004 NPL Sarfehnia A, 2007, MED PHYS, V34, P4957, DOI 10.1118/1.2815941 Selvam TP, 2001, PHYS MED BIOL, V46, P2299, DOI 10.1088/0031-9155/46/9/303 Stump KE, 2002, MED PHYS, V29, P1483, DOI 10.1118/1.1487860 Vianello EA, 2008, MED PHYS, V35, P3389, DOI 10.1118/1.2940160 NR 27 TC 3 Z9 3 U1 0 U2 2 PD MAR PY 2009 VL 36 IS 3 BP 953 EP 960 DI 10.1118/1.3056462 WC Radiology, Nuclear Medicine & Medical Imaging SC Radiology, Nuclear Medicine & Medical Imaging UT WOS:000263718100029 DA 2022-12-14 ER PT J AU Pendell, DL Tonsor, GT Dhuyvetter, KC Brester, GW Schroeder, TC AF Pendell, Dustin L. Tonsor, Glynn T. Dhuyvetter, Kevin C. Brester, Gary W. Schroeder, Ted C. TI Evolving beef export market access requirements for age and source verification SO FOOD POLICY DT Article DE Age and source verification; Beef trade; Producer surplus; Traceability ID ANIMAL IDENTIFICATION; TRACEABILITY SYSTEM; PREFERENCES; INDUSTRY; LESSONS AB We analyze the economic impacts of changes in age and source verification requirements and associated adjustments in international trade of US beef using an equilibrium displacement model. Because the United States lags behind many countries in adopting animal traceability systems, the United States risks losing export market access. The loss of an export market the size of South Korea's would cause a decline of US meat industry producer surplus of $1751 million over 10 years or 0.23% of its10-year cumulative discounted present value. Additionally, we find that only small increases in US beef exports would be necessary to offset direct costs associated with adoption of age and source verification. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Pendell, Dustin L.] Colorado State Univ, Dept Agr & Resource Econ, Ft Collins, CO 80523 USA. [Tonsor, Glynn T.; Dhuyvetter, Kevin C.; Schroeder, Ted C.] Kansas State Univ, Dept Agr Econ, Manhattan, KS 66506 USA. [Brester, Gary W.] Montana State Univ, Dept Agr Econ & Econ, Bozeman, MT USA. C3 Colorado State University; Kansas State University; Montana State University System; Montana State University Bozeman RP Pendell, DL (corresponding author), B315 Clark, Ft Collins, CO 80523 USA. EM dustin.pendell@colostate.edu CR [Anonymous], 2013, DAILY J US GOVT JAN [Anonymous], 2007, BE J ECON ANAL POLI Blank SC, 2009, CALIF AGR, V63, P225, DOI 10.3733/ca.v063n04p225 Boame A., 2004, MAD COW DIS BEEF TRA Brester G. W., 2011, EC ASSESSMENT EVOLVI Buhr B. L., 2003, 035 U MINN DEP APPL Cuthbertson B., 2007, CREDENCE EMERGING CO Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Gracia A., 2005, Journal of Food Distribution Research, V36, P45 Hobbs J. E., 1996, British Food Journal, V98, P16, DOI 10.1108/00070709610131339 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Lawrence J. D., 2002, Journal of Agribusiness, V20, P117 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 Livestock Marketing Information Center, 2011, MONTHL ALB CATTL PRI Livestock Marketing Information Center, 2010, BEEF CATTL PORK SWIN Livestock Marketing Information Center, 2012, EXP VAL DAT Lusk J.L., 2012, J AGR EC IN PRESS Murphy R. G. L., 2008, Professional Animal Scientist, V24, P277 Murphy RGL, 2009, INT FOOD AGRIBUS MAN, V12, P1 Pendell DL, 2010, AM J AGR ECON, V92, P927, DOI 10.1093/ajae/aaq037 Pouliot S, 2011, AM J AGR ECON, V93, P735, DOI 10.1093/ajae/aar019 Resende MA, 2008, AM J AGR ECON, V90, P1091, DOI 10.1111/j.1467-8276.2008.01150.x RTI International, 2007, GIPSA LIV MEAT MARK, V3 Schroeder TC, 2012, FOOD POLICY, V37, P31, DOI 10.1016/j.foodpol.2011.10.005 Schulz L. L., 2010, Journal of Agricultural and Applied Economics, V42, P659 Schulz LL, 2010, J AGR ECON, V61, P138, DOI 10.1111/j.1477-9552.2009.00226.x Schumacher T., 2012, Journal of Agricultural and Applied Economics, V44, P191 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Smith R., 2011, S KOREA INTRO PORK T Taylor M.R., 2012, AMGIT2012 K STAT DEP Tonsor GT, 2006, J INT FOOD AGRIBUS M, V18, P103, DOI 10.1300/J047v18n03_07 Tonsor GT, 2005, J AGR RESOUR ECON, V30, P367 US Department of Agriculture-Animal and Plant Health Inspection Service (USDA APHIS), 2006, NAT AN ID SYST NAIS US Department of Agriculture-Animal and Plant Health Inspection Service (USDA APHIS), 2005, NAT AN ID S IN PRESS US Department of Agriculture - Animal and Plant Health Inspection Service (USDA APHIS), 2009, BEN COST AN NAT AN I US Department of Agriculture-Food Safety Inspection Service (USDA FSIS), 2001, EXP REQ COUNTR APPR Zimmerman LC, 2012, J AGR RESOUR ECON, V37, P128 NR 37 TC 6 Z9 6 U1 1 U2 28 PD DEC PY 2013 VL 43 BP 332 EP 340 DI 10.1016/j.foodpol.2013.05.013 WC Agricultural Economics & Policy; Economics; Food Science & Technology; Nutrition & Dietetics SC Agriculture; Business & Economics; Food Science & Technology; Nutrition & Dietetics UT WOS:000329414100030 DA 2022-12-14 ER PT J AU Sanson, RL Dube, C Cork, SC Frederickson, R Morley, C AF Sanson, R. L. Dube, C. Cork, S. C. Frederickson, R. Morley, C. TI Simulation modelling of a hypothetical introduction of foot-and-mouth disease into Alberta SO PREVENTIVE VETERINARY MEDICINE DT Article DE Foot-and-mouth disease (FMD); Simulation modelling; Vaccination; Livestock traceability system ID EPIDEMIC; VACCINATION; SASKATCHEWAN; OUTBREAK; CANADA; SPREAD AB This study describes the use of simulation modelling to evaluate the predicted benefits of an effective livestock traceability system in responding to a hypothetical introduction of foot-and-mouth disease (FMD) in to the province of Alberta, Canada, and whether or not the implementation of emergency ring vaccination in addition to a standard stamping-out (SO) strategy would lead to smaller and shorter epidemics. Three introduction scenarios were defined, with the primary case in either an intensive beef feedlot operation, an extensive cow-calf operation or in a swine operation. Disease spread was simulated using, three levels of tracing effectiveness, five types of vaccination zone, three different vaccination start times, three lengths of vaccination campaigns, two levels of culling resource and using FMD strains with two different virulence levels. Using standard SO procedures (without vaccination), improving traceability effectiveness from a level whereby only 65% of movements were traced within 5-7 days, to a capability whereby all movements were traced within 1 day, led to a reduction in the number of infected premises (IPs) between 18.7 and 64.5%, an average saving of CAN$29,000,000 in livestock compensation costs alone, and a reduction in the length of epidemics ranging from 1 to 22 days. The implementation of emergency vaccination also led to a reduction in the number of IPs and a shortening of epidemics. The effects were more pronounced when the higher virulence settings were used, with a predicted reduction in IPs of 16.6-68.7% (mean= 48.6%) and epidemics shortened by up to 37 days. Multi-variable analyses showed these effects were highly significant, after accounting for the incursion location, virulence of virus and time of first detection. The results clearly demonstrated the benefits of having effective traceability systems with rapid query and reporting functionality. The results also supported the value of early vaccination as an adjunct to SO in reducing the number of IPs and shortening the length of the epidemics. The most effective vaccination strategy involved a 3 km or larger suppressive vaccination zone around all IPs, begun as soon as practicable after first detection, and which continued until the last IF was detected. (c) 2014 Elsevier B.V. All rights reserved. C1 [Sanson, R. L.] Asure Qual Ltd, Palmerston North 4440, New Zealand. [Dube, C.] Canadian Food Inspect Agcy, Ottawa, ON K1A 0Y9, Canada. [Cork, S. C.] Univ Calgary, Fac Vet Med, Calgary, AB T2N 4Z6, Canada. [Frederickson, R.; Morley, C.] Alberta Agr & Rural Dev, Edmonton, AB T6H 4P2, Canada. C3 Canadian Food Inspection Agency; University of Calgary RP Sanson, RL (corresponding author), Asure Qual Ltd, POB 585, Palmerston North 4440, New Zealand. EM robert.sanson@asurequality.com CR [Anonymous], 2012, WORLD ORG ANIMAL HLT [Anonymous], 2012, FOOT MOUTH DIS Backer JA, 2012, PREV VET MED, V107, P41, DOI 10.1016/j.prevetmed.2012.05.013 Backer JA, 2012, PREV VET MED, V107, P27, DOI 10.1016/j.prevetmed.2012.05.012 Canadian Food Inspection Agency, 2012, FOOT AND MOUTH DIS H Carpenter TE, 2011, J VET DIAGN INVEST, V23, P26, DOI 10.1177/104063871102300104 DAGGUPATY SM, 1990, CAN J VET RES, V54, P465 Diggle P.J., 1995, BERNOULLI, V1, DOI [10.2307/3318678, DOI 10.2307/3318678, 10.1002/sim.4780142106] Dube C, 2007, NEW ZEAL VET J, V55, P280, DOI 10.1080/00480169.2007.36782 Grubman MJ, 2004, CLIN MICROBIOL REV, V17, P465, DOI 10.1128/CMR.17.2.465-493.2004 Hagerman A.D., 2012, PREVENTIVE IN PRESS Kahn S, 2002, CAN VET J, V43, P349 Lamont A.G., 2010, UNPUBLISHED REPORT Lorenz R.J., 1986, EC EVALUATION FOOT M Mardones F.O., 2012, PREVENTIVE V IN PRES Mardones F, 2010, VET RES, V41, DOI 10.1051/vetres/2010017 Paarlberg PL, 2002, J AM VET MED ASSOC, V220, P988, DOI 10.2460/javma.2002.220.988 Park JH, 2013, EMERG INFECT DIS, V19, P655, DOI 10.3201/eid1904.121320 Pasick John, 2004, Animal Health Research Reviews, V5, P257, DOI 10.1079/AHR200479 R Core Team, 2012, R LANG ENV STAT COMP Reeves A., 2013, UNPUB Sanson RL, 2011, VET REC, V169, P336, DOI 10.1136/vr.d4401 Sanson R. L., 2006, P 11 INT S VET EP EC SELLERS RF, 1990, CAN J VET RES, V54, P457 Stevenson MA, 2013, PREV VET MED, V109, P10, DOI 10.1016/j.prevetmed.2012.08.015 Ward MP, 2009, PREV VET MED, V88, P286, DOI 10.1016/j.prevetmed.2008.12.006 NR 26 TC 10 Z9 11 U1 0 U2 22 PD JUN 1 PY 2014 VL 114 IS 3-4 BP 151 EP 163 DI 10.1016/j.prevetmed.2014.03.005 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000335633000002 DA 2022-12-14 ER PT J AU Maione, C Araujo, EM dos Santos-Araujo, SN Boim, AGF Barbosa, RM Alleoni, LRF AF Maione, Camila Araujo, Eloa Moura dos Santos-Araujo, Sabrina Novaes Friol Boim, Alexys Giorgia Barbosa, Rommel Melgaco Ferracciu Alleoni, Luis Reynaldo TI Determining the geographical origin of lettuce with data mining applied to micronutrients and soil properties SO SCIENTIA AGRICOLA DT Article DE ICP-OES; traceability; tropical soils; heavy metals; feature selection ID SUPPORT VECTOR MACHINE; PRINCIPAL COMPONENT ANALYSIS; FOOD AUTHENTICITY; FAULT-DETECTION; LEAST-SQUARES; DISCRIMINANT-ANALYSIS; PATTERN-RECOGNITION; BIOACTIVE COMPOUNDS; ENERGY-CONSUMPTION; LEARNING-METHODS AB Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in Sao Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called F-score, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables. C1 [Maione, Camila; Barbosa, Rommel Melgaco] Univ Fed Goias, Inst Informat, Campus Samambaia, BR-74690900 Goiania, Go, Brazil. [Araujo, Eloa Moura; dos Santos-Araujo, Sabrina Novaes; Friol Boim, Alexys Giorgia; Ferracciu Alleoni, Luis Reynaldo] Univ Sao Paulo, ESALQ, Dept Ciencia Solo, CP 09, BR-13418900 Piracicaba, SP, Brazil. C3 Universidade Federal de Goias; Universidade de Sao Paulo RP Barbosa, RM (corresponding author), Univ Fed Goias, Inst Informat, Campus Samambaia, BR-74690900 Goiania, Go, Brazil. EM rmbweb@gmail.com CR Abbas O, 2018, FOOD CHEM, V246, P6, DOI 10.1016/j.foodchem.2017.11.007 Akbarzadeh S, 2018, COMPUT ELECTRON AGR, V148, P250, DOI 10.1016/j.compag.2018.03.026 Alcazar A, 2012, FOOD CONTROL, V23, P258, DOI 10.1016/j.foodcont.2011.07.029 Ali SM, 2018, ALEX ENG J, V57, P491, DOI 10.1016/j.aej.2016.12.010 Anderson J.M., 1992, HDB METHODS Araujo GCL, 2002, SPECTROCHIM ACTA B, V57, P2121, DOI 10.1016/S0584-8547(02)00164-7 Baroni MV, 2015, J AGR FOOD CHEM, V63, P4638, DOI 10.1021/jf5060112 Battineni G., 2019, INFORM MED UNLOCKED, V16, DOI [10.1016/j.imu.2019.100200, DOI 10.1016/J.IMU.2019.100200] Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Bhavan A, 2019, KNOWL-BASED SYST, V184, DOI 10.1016/j.knosys.2019.104886 Biondi CM, 2011, REV BRAS CIENC SOLO, V35, P1057, DOI 10.1590/S0100-06832011000300039 Bommert A, 2020, COMPUT STAT DATA AN, V143, DOI 10.1016/j.csda.2019.106839 Braga D, 2019, ENG APPL ARTIF INTEL, V77, P148, DOI 10.1016/j.engappai.2018.09.018 Camargo WP, 2017, HORTIC BRAS, V35, P160, DOI [10.1590/S0102-053620170202, 10.1590/s0102-053620170202] Cambrai A, 2010, J AGR FOOD CHEM, V58, P1478, DOI 10.1021/jf903471e Carvalho K. L., 2013, Production, [s.l.], V24, P271, DOI 10.1590/s0103-65132013005000031 Cavanna D, 2018, TRENDS FOOD SCI TECH, V80, P223, DOI 10.1016/j.tifs.2018.08.007 Chawla NV, 2005, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, P853, DOI 10.1007/0-387-25465-X_40 Chen YW, 2006, STUD FUZZ SOFT COMP, V207, P315 Choubin B, 2019, J HYDROL, V577, DOI 10.1016/j.jhydrol.2019.123929 Choubin B, 2019, SCI TOTAL ENVIRON, V651, P2087, DOI 10.1016/j.scitotenv.2018.10.064 Choubin B, 2018, SCI TOTAL ENVIRON, V615, P272, DOI 10.1016/j.scitotenv.2017.09.293 Coetzee PP, 2005, J AGR FOOD CHEM, V53, P5060, DOI 10.1021/jf048268n CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Di Zonglin, 2019, Addict Behav Rep, V10, P100200, DOI 10.1016/j.abrep.2019.100200 dos Santos-Araujo SN, 2016, ENVIRON MONIT ASSESS, V188, DOI 10.1007/s10661-016-5100-2 Duda R. O, 2001, PATTERN CLASSIFICATI Esteki M, 2019, FOOD RES INT, V122, P303, DOI 10.1016/j.foodres.2019.04.025 Esteki M, 2018, FOOD CONTROL, V93, P165, DOI 10.1016/j.foodcont.2018.06.015 Esteki M, 2018, FOOD CONTROL, V91, P100, DOI 10.1016/j.foodcont.2018.03.031 Fan JL, 2018, ENERG CONVERS MANAGE, V164, P102, DOI 10.1016/j.enconman.2018.02.087 Fazai R, 2019, SOL ENERGY, V190, P405, DOI 10.1016/j.solener.2019.08.032 Feng PY, 2019, AGR SYST, V173, P303, DOI 10.1016/j.agsy.2019.03.015 Fernandes AM, 2019, COMPUT ELECTRON AGR, V163, DOI 10.1016/j.compag.2019.104855 Fink JR, 2014, ACTA SCI-AGRON, V36, P379, DOI 10.4025/actasciagron.v36i3.17937 Franca FCSS, 2017, FOOD CHEM, V215, P171, DOI 10.1016/j.foodchem.2016.07.168 Garcia-Nieto PJ, 2019, MATH COMPUT SIMULAT, V166, P461, DOI 10.1016/j.matcom.2019.07.011 Gee G.W., 2002, METHODS SOIL ANAL 4, P241 Ghalyani P, 2019, MEASUREMENT, V144, P214, DOI 10.1016/j.measurement.2019.05.036 Ceballos-Magana SG, 2012, FOOD ANAL METHOD, V5, P260, DOI 10.1007/s12161-011-9233-1 GOLDBERG S, 1989, COMMUN SOIL SCI PLAN, V20, P1181, DOI 10.1080/00103629009368144 Granato D, 2018, TRENDS FOOD SCI TECH, V72, P83, DOI 10.1016/j.tifs.2017.12.006 Griffel LM, 2018, COMPUT ELECTRON AGR, V153, P318, DOI 10.1016/j.compag.2018.08.027 HAIXIANG G, 2017, EXPERT SYST APPL, V73, P220, DOI DOI 10.1016/j.eswa.2016.12.035 Han H, 2019, APPL THERM ENG, V154, P540, DOI 10.1016/j.applthermaleng.2019.03.111 He HB, 2009, IEEE T KNOWL DATA EN, V21, P1263, DOI 10.1109/TKDE.2008.239 Huang G, 2017, SOIL DYN EARTHQ ENG, V102, P160, DOI 10.1016/j.soildyn.2017.09.002 Islam MMM, 2019, RELIAB ENG SYST SAFE, V184, P55, DOI 10.1016/j.ress.2018.02.012 Izenman AJ, 2008, SPRINGER TEXTS STAT, P1, DOI 10.1007/978-0-387-78189-1_1 Jain AK, 2010, PATTERN RECOGN LETT, V31, P651, DOI 10.1016/j.patrec.2009.09.011 Jimenez-Carvelo AM, 2019, FOOD RES INT, V122, P25, DOI 10.1016/j.foodres.2019.03.063 Jo T., 2004, ACM SIGKDD EXPLORATI, V6, P40, DOI [10.1145/1007730.1007737, DOI 10.1145/1007730.1007737] Jung H, 2018, J PETROL SCI ENG, V167, P396, DOI 10.1016/j.petrol.2018.04.017 Junior AVI, 2003, REV BRAS CIENC SOLO, V27, P1139, DOI 10.1590/S0100-06832003000600018 Karimi F, 2019, COMPUT ENVIRON URBAN, V75, P61, DOI 10.1016/j.compenvurbsys.2019.01.001 Kemsley EK, 2019, FOOD CONTROL, V105, P102, DOI 10.1016/j.foodcont.2019.05.021 Kim MJ, 2016, J FOOD COMPOS ANAL, V49, P19, DOI 10.1016/j.jfca.2016.03.004 Kisi O, 2019, HYDROLOG SCI J, V64, P1240, DOI 10.1080/02626667.2019.1632460 Klavinski R., 2013, 7 BENEFITS EATING LO Kotsiantis SB, 2006, ARTIF INTELL REV, V26, P159, DOI [10.1007/s10462-007-9052-3, 10.1007/S10462-007-9052-3] Kumar D, 2017, GEOMORPHOLOGY, V295, P115, DOI 10.1016/j.geomorph.2017.06.013 Kumpiene J, 2017, PEDOSPHERE, V27, P389, DOI 10.1016/S1002-0160(17)60337-0 Kundu S, 2017, GEOSCI FRONT, V8, P583, DOI 10.1016/j.gsf.2016.06.002 Leena N., 2019, Engineering in Agriculture, Environment and Food, V12, P126, DOI 10.1016/j.eaef.2018.11.002 Liu J, 2018, EXPERT SYST APPL, V102, P36, DOI 10.1016/j.eswa.2018.02.017 Liu JJ, 2019, EBIOMEDICINE, V43, P454, DOI 10.1016/j.ebiom.2019.04.040 Lopez V, 2013, INFORM SCIENCES, V250, P113, DOI 10.1016/j.ins.2013.07.007 Lukmanto RB, 2019, PROCEDIA COMPUT SCI, V157, P46, DOI 10.1016/j.procs.2019.08.140 Ma ZT, 2018, ENRGY PROCED, V152, P780, DOI 10.1016/j.egypro.2018.09.245 MAHVASH NM, 2018, J AFR EARTH SCI, V143, P301 Mainville DY, 2005, REV AGR ECON, V27, P130, DOI 10.1111/j.1467-9353.2005.00212.x Maione C, 2019, COMPUT ELECTRON AGR, V157, P436, DOI 10.1016/j.compag.2019.01.020 Maione C, 2019, CRIT REV FOOD SCI, V59, P1868, DOI 10.1080/10408398.2018.1431763 MCKEAGUE JA, 1966, CAN J SOIL SCI, V46, P13, DOI 10.4141/cjss66-003 Medina S, 2019, TRENDS FOOD SCI TECH, V85, P163, DOI 10.1016/j.tifs.2019.01.017 Medina S, 2019, FOOD CHEM, V278, P144, DOI 10.1016/j.foodchem.2018.11.046 MEHRA O. P., 1960, Proceedings 7th nat. Conf. Clays, V5, P317 Moreda-Pineiro A, 2003, J FOOD COMPOS ANAL, V16, P195, DOI 10.1016/S0889-1575(02)00163-1 Oliveira M, 2015, FOOD CHEM, V177, P330, DOI 10.1016/j.foodchem.2015.01.061 Oliveri P, 2017, ANAL CHIM ACTA, V982, P9, DOI 10.1016/j.aca.2017.05.013 Opatic AM, 2018, FOOD CONTROL, V89, P133, DOI 10.1016/j.foodcont.2017.11.013 Peris M, 2016, TRENDS FOOD SCI TECH, V58, P40, DOI 10.1016/j.tifs.2016.10.014 Callao MP, 2018, FOOD CONTROL, V86, P283, DOI [10.1016/J.foodcont.2017.11.034, 10.1016/j.foodcont.2017.11.034] Potorti AG, 2018, J FOOD COMPOS ANAL, V69, P122, DOI 10.1016/j.jfca.2018.03.001 Prati RC, 2004, LECT NOTES COMPUT SC, V2972, P312 Pu YY, 2019, INT J MIN SCI TECHNO, V29, P565, DOI 10.1016/j.ijmst.2019.06.009 Radhaknshnan S, 2018, PROCEDIA COMPUT SCI, V143, P493, DOI 10.1016/j.procs.2018.10.422 Rahmati O, 2019, SCI TOTAL ENVIRON, V688, P855, DOI 10.1016/j.scitotenv.2019.06.320 Rahmeni R, 2019, PROCEDIA COMPUT SCI, V159, P668, DOI 10.1016/j.procs.2019.09.222 Rodrigues SM, 2010, CHEMOSPHERE, V81, P1549, DOI 10.1016/j.chemosphere.2010.07.026 Ropodi AI, 2016, TRENDS FOOD SCI TECH, V50, P11, DOI 10.1016/j.tifs.2016.01.011 Saari J, 2019, MEASUREMENT, V137, P287, DOI 10.1016/j.measurement.2019.01.020 Sajedi-Hosseini F, 2018, SCI TOTAL ENVIRON, V644, P954, DOI 10.1016/j.scitotenv.2018.07.054 Serra F, 2005, RAPID COMMUN MASS SP, V19, P2111, DOI 10.1002/rcm.2034 Shichao Zhu, 2019, Advanced Industrial and Engineering Polymer Research, V2, P77, DOI 10.1016/j.aiepr.2019.04.001 Meza JKS, 2019, HELIYON, V5, DOI 10.1016/j.heliyon.2019.e02810 Sustainable Capitol Hill [SCH], 2019, TOP 10 BENEFITS EATI Tan PN, 2005, INTRO DATA MINING, V1st Tang JJ, 2019, PHYSICA A, V534, DOI 10.1016/j.physa.2019.03.007 Valdes A, 2018, TRENDS FOOD SCI TECH, V77, P120, DOI 10.1016/j.tifs.2018.05.014 Varma S, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-91 Vougas K, 2019, PHARMACOL THERAPEUT, V203, DOI 10.1016/j.pharmthera.2019.107395 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Wang J, 2019, SPEECH COMMUN, V110, P13, DOI 10.1016/j.specom.2019.04.002 Wang XY, 2018, ENERGY, V152, P539, DOI 10.1016/j.energy.2018.03.120 Xi PP, 2019, AEROSP SCI TECHNOL, V84, P56, DOI 10.1016/j.ast.2018.08.042 Xiao R, 2019, MEASUREMENT, V146, P479, DOI 10.1016/j.measurement.2019.06.050 Yu CJ, 2018, ENERG CONVERS MANAGE, V178, P137, DOI 10.1016/j.enconman.2018.10.008 Yu PS, 2017, J HYDROL, V552, P92, DOI 10.1016/j.jhydrol.2017.06.020 Zendehboudi A, 2018, J CLEAN PROD, V199, P272, DOI 10.1016/j.jclepro.2018.07.164 Zhou ZJ, 2019, COMPUT ELECTRON AGR, V162, P246, DOI 10.1016/j.compag.2019.03.038 NR 111 TC 1 Z9 1 U1 7 U2 30 PY 2022 VL 79 IS 1 AR e20200011 DI 10.1590/1678-992X-2020-0011 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000637763800001 DA 2022-12-14 ER PT J AU Pavlovic, B Kozmar, H Sunic, M AF Pavlovic, Berislav Kozmar, Hrvoje Sunic, Miljenko TI A NEW SYSTEM FOR THE CALIBRATION OF GAS FLOW METERS SO TRANSACTIONS OF FAMENA DT Article DE gas flow meter; liquid displacement method; measurement uncertainty AB A new system for the calibration of gas flow meters has been developed in Gradska Plinara Zagreb. It is designed to calibrate transfer gas flow standards and a bell prover for flow rates from 0.02 m(3/)h, to 1.4 m(3/)h. Several novel features have been designed, constructed and assembled in the system. The transfer gas flow standard ROMBACH NB2 has been calibrated in order to investigate characteristics of the system. The obtained results agree well with previous investigations, and the measurement uncertainty in single measurements was less than 0.09 %. The highest contributions to measurement uncertainty result from the temperature and humidity of the air in the tested gas flow meter and in the container. Total expanded measurement uncertainty was 0.27 %. Main advantages of this system are high stability and reliability, high accuracy of measurements, small measurement uncertainty, direct traceability with fundamental units and prevention of pressure and flow transients. C1 [Pavlovic, Berislav] Gradska Plinara Zagreb, HR-10000 Zagreb, Croatia. [Kozmar, Hrvoje] Univ Zagreb, Fac Mech Engn & Naval Architecture, HR-10000 Zagreb, Croatia. [Sunic, Miljenko] Univ Zagreb, Fac Min Geol & Petr Engn, HR-10000 Zagreb, Croatia. C3 University of Zagreb; University of Zagreb Faculty of Mechanical Engineering & Naval Architecture; University of Zagreb, School of Dental Medicine; University of Zagreb; University of Zagreb, School of Dental Medicine RP Pavlovic, B (corresponding author), Gradska Plinara Zagreb, Radnicka Cesta 1, HR-10000 Zagreb, Croatia. CR BAIR M, 1999, NCSL WORKSH S CHARL Bignell N, 2001, FLOW MEAS INSTRUM, V12, P245, DOI 10.1016/S0955-5986(01)00028-0 *BIPM IEC IFCC IUP, 1995, GUID EXPR UNC MEAS Cignolo G., 2005, Sensor Review, V25, P40, DOI 10.1108/02602280510577825 DAVIS RS, 1992, METROLOGIA, V29, P67, DOI 10.1088/0026-1394/29/1/008 DOPHEIDE D, 1994, METROLOGIA, V30, P453, DOI 10.1088/0026-1394/30/5/001 FANCEV M, 1982, MEHANIKA FLUIDA PART, V8 Figliola R.S., 1995, THEORY DESIGN MECH M, V20, DOI [10.1080/03043799508928292, DOI 10.1080/03043799508928292] GRINTEN JGM, 1993, FLOM 93 C SEOUL KOR JAROSCH B, 2006, 0701206 OFF LEG METR JAROSCH B, 2002, 0701402 OFF LEG METR Nakao S, 2006, FLOW MEAS INSTRUM, V17, P193, DOI 10.1016/j.flowmeasinst.2005.11.008 PAVLOVIC B, 2005, P 19 INT METR S OP C Sillanpaa S, 2006, MEASUREMENT, V39, P26, DOI 10.1016/j.measurement.2005.10.002 Tedeschi M, 2002, FLOW MEAS INSTRUM, V12, P397, DOI 10.1016/S0955-5986(01)00032-2 Wright J.D., 1998, NIST SPECIAL PUBLICA, V250-49 WRIGHT JD, 2001, LAB PRIMARY STANDARD, P731 NR 17 TC 2 Z9 2 U1 0 U2 2 PY 2009 VL 33 IS 1 BP 37 EP 46 WC Engineering, Mechanical; Materials Science, Multidisciplinary SC Engineering; Materials Science UT WOS:000265327100005 DA 2022-12-14 ER PT J AU Buttigieg, M Gianesella, M James, A AF Buttigieg, M. Gianesella, M. James, A. TI An appraisal of the Maltese national livestock database with regard to bovines SO REVUE SCIENTIFIQUE ET TECHNIQUE-OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE Bovines; European Union; Gozo; Identification; Malta; National livestock database; Registration; Traceability ID TRACEABILITY; MORTALITY; ANIMALS; CATTLE AB The creation of a centralised national livestock database for the islands of Malta and Gozo is of crucial importance for the identification and traceability of bovines. It is also important for compliance with the legal obligations that followed Malta's accession to the European Union in May 2004. This paper describes how the processes of identification, registration and traceability of bovines have changed since Malta's accession. The validation and integration of data originating from different departmental sections (such as the identification and registration section), the slaughterhouse and the National Veterinary Laboratory, ensures that any discrepancies are highlighted and can be investigated. Events recorded in the database enable the compliance and eligibility of bovine producers to be cross-checked when applications for European Union benefits are made. The main drawbacks and weak points of the system include financial costs for the government department, potentially late notification of the births and deaths of newborn calves, and insufficient uptake among bovine producers of the latest technology for notification of events such as births, deaths and movement of bovines. C1 [Buttigieg, M.; Gianesella, M.] Univ Padua, Dept Anim Med Prod & Hlth, Viale Univ 16, I-35020 Padua, Italy. [James, A.] Univ Reading, Sch Agr Policy & Dev, Vet Epidemiol & Econ Res Unit, Reading RG6 6AR, Berks, England. C3 University of Padua; University of Reading RP Gianesella, M (corresponding author), Univ Padua, Dept Anim Med Prod & Hlth, Viale Univ 16, I-35020 Padua, Italy. EM matteo.gianesella@unipd.it CR AGERHOLM JS, 1993, ACTA VET SCAND, V34, P371 Anderson D. P., 2010, Journal of Agricultural and Applied Economics, V42, P543 Blancou J, 2001, REV SCI TECH OIE, V20, P420 Brickell JS, 2009, ANIMAL, V3, P1175, DOI 10.1017/S175173110900456X British Cattle Movement Service, 2014, GUID KEEP CATTL BIS Carlberg J. G., 2010, Journal of Agricultural and Applied Economics, V42, P559 Council of the European Communities, 1992, OFF J EUR UNION L Department for Environment Food & Rural Affairs (Defra), 2013, CATTL ID REG MOV European Commission (EC), 1990, OFF J EUR UNION L, VL 224, P29 European Commission (EC), 1964, OJ P L, V121, P1977 European Commission (EC), 2008, OFF J EUR UNION L, V321, P1 European Commission (EC), 1989, OFF J EUR UNION L, V59, P33 European Commission (EC), 1992, OFF J EUR UNION L, VL 355, P1 European Commission (EC), 1991, OFFICIAL J EUROPEA L, V268, P56 European Commission (EC), 1988, OFF J EUR UNION L, V382, P36 Golan E., 2004, 830 USDA EC RES SERV, P1 James A, 2005, PREV VET MED, V67, P91, DOI 10.1016/j.prevetmed.2004.11.003 Ministry of Agriculture and Forestry Biosecurity New Zealand, 2009, REV SEL CATTL ID TRA *NAO, 2003, ID TRACK LIV ENGL National Statistics Office Malta, 2012, MALT FIG 2012 National Statistics Office Malta, 2013, CATTL SURV DEC 2012 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Sugiura Katsuaki, 2008, Vet Ital, V44, P519 United States Department of Agriculture Animal and Plant Health Inspection Service, 2005, NAT AN ID SYST NAIS Wismans WMG, 1999, COMPUT ELECTRON AGR, V24, P99, DOI 10.1016/S0168-1699(99)00040-X NR 25 TC 0 Z9 0 U1 0 U2 1 PD DEC PY 2015 VL 34 IS 3 BP 779 EP 793 DI 10.20506/rst.34.3.2395 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000371368300008 DA 2022-12-14 ER PT J AU Rock, L AF Rock, Luc TI The use of stable isotope techniques in egg authentication schemes: A review SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review ID MASS-SPECTROMETRY; LIQUID-CHROMATOGRAPHY; GEOGRAPHICAL ORIGIN; FREE RANGE; FOOD; CARBON; FRACTIONATION; TRACEABILITY; EGGSHELLS; PATTERNS AB The popularity of eggs is increasing worldwide, increased production is expected over the next few years, and it appears that protein production from eggs is more sustainable than other protein rich food production, such as beef. Recall of eggs has also occurred due to health scares. Hence, systems/tools are needed to ensure and guarantee egg traceability/authenticity. This review focuses on the current state of knowledge with regards to the use of stable isotope techniques for egg authentication schemes. A brief overview of egg production, usage, environmental benefits, traceability, and of alternative analytical methods for egg identification will also be provided. C1 [Rock, Luc] Queens Univ Belfast, EERC, Sch Planning Architecture & Civil Engn, Belfast BT9 5AG, Antrim, North Ireland. [Rock, Luc] Queens Univ Belfast, Ctr Food Assured Safe & Traceable Food ASSET, Inst Agri Food & Land Use, Sch Biol Sci, Belfast BT9 5AG, Antrim, North Ireland. C3 Queens University Belfast; Queens University Belfast RP Rock, L (corresponding author), Queens Univ Belfast, EERC, Sch Planning Architecture & Civil Engn, David Keir Bldg,Stranmillis Rd, Belfast BT9 5AG, Antrim, North Ireland. EM l.rock@qub.ac.uk CR AAFC, 2010, GOV CAN WORK EGG PRO [Anonymous], 2010, FARMERS GUARDIAN [Anonymous], 2000, OFFICIAL J EUROPEAN, VL 316, P16 [Anonymous], [No title captured] BFREPA, 2009, TRAC YELL RIV BLE, 2011, KNOWL GUID EGG SIZ Q Boner M., 2003, HERKUNFTSBESTIMMUNG Brand WA, 1996, J MASS SPECTROM, V31, P225, DOI 10.1002/(SICI)1096-9888(199603)31:3<225::AID-JMS319>3.0.CO;2-L Brenna JT, 1997, MASS SPECTROM REV, V16, P227, DOI 10.1002/(SICI)1098-2787(1997)16:5<227::AID-MAS1>3.0.CO;2-J CAIMI RJ, 1993, ANAL CHEM, V65, P3497, DOI 10.1021/ac00071a028 Carlsson-Kanyama A, 2009, AM J CLIN NUTR, V89, pS1704, DOI 10.3945/ajcn.2009.26736AA Carrijo A. S., 2000, Revista Brasileira de Ciencia Avicola, V2, P209, DOI 10.1590/S1516-635X2000000300003 Caspari C. C., 2010, IPBAGRIIC2009045 EUR Cherian G., 2006, HDB FOOD SCI TECHNOL, V4, P153 Coplen T. B., 2002, 014222 US GEOL SURV DARDNI, 2010, EGG PACK SURV 1999 2 DEFRA, 2011, UK EGG TRAD DAT Denadai JC, 2006, Rev. Bras. Cienc. Avic., V8, P251, DOI 10.1590/S1516-635X2006000400008 Denadai JC, 2008, BRAZ J POULTRY SCI, V10, P189, DOI 10.1590/S1516-635X2008000300010 Denadai JC, 2009, PESQUI AGROPECU BRAS, V44, P1, DOI 10.1590/S0100-204X2009000100001 Elflein L, 2008, APIDOLOGIE, V39, P574, DOI 10.1051/apido:2008042 Ellert BH, 2008, SOIL SAMPLING METHOD, P693 ERBEN HK, 1979, PALEOBIOLOGY, V5, P380, DOI 10.1017/S0094837300016900 EU, 2009, OFFICIAL J EUROPEA C, V199, P24 European Union, 2002, OFF J EUR COMMUNIT L, VL30, P44 FOLINSBEE RE, 1970, SCIENCE, V168, P1353, DOI 10.1126/science.168.3937.1353 Galyean R. D., 1995, ENG SCI TECHNOLOGY, P525 Giannenas I, 2009, FOOD CHEM, V114, P706, DOI 10.1016/j.foodchem.2008.09.079 Gregory NG, 2005, J SCI FOOD AGR, V85, P1421, DOI 10.1002/jsfa.2152 Hobson KA, 1997, AUK, V114, P467, DOI 10.2307/4089247 HOBSON KA, 1995, CONDOR, V97, P752, DOI 10.2307/1369183 Hobson KA, 1999, OECOLOGIA, V120, P314, DOI 10.1007/s004420050865 HOEFS J, 1979, NATURWISSENSCHAFTEN, V66, P313, DOI 10.1007/BF00441276 IEC, 2009, SPEC EC REP NAFTAS R Johnson BJ, 1998, GEOCHIM COSMOCHIM AC, V62, P2451, DOI 10.1016/S0016-7037(98)00175-6 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kelly S. D., 2003, Food authenticity and traceability, P156, DOI 10.1533/9781855737181.1.156 Kendall C., 1999, ISOTOPE TRACERS CATC, DOI [10.1016/C2009-0-10239-8, DOI 10.1016/C2009-0-10239-8] KENNEDY BV, 1990, CAN J PHYSIOL PHARM, V68, P960, DOI 10.1139/y90-146 Krummen M, 2004, RAPID COMMUN MASS SP, V18, P2260, DOI 10.1002/rcm.1620 Lob R., 1992, THESIS I TIERZUCHTWI Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 O'Faolain A., 2009, UK SEEKS EXTRADITION Oppel S, 2009, J ORNITHOL, V150, P109, DOI 10.1007/s10336-008-0325-7 Pascale M., 2009, WORLD POULTRY, V25 PROCTOR VA, 1988, CRC CR REV FOOD SCI, V26, P359, DOI 10.1080/10408398809527473 Reid LM, 2006, TRENDS FOOD SCI TECH, V17, P344, DOI 10.1016/j.tifs.2006.01.006 Rock L., 2012, FOOD CHEM Rogers KM, 2009, J AGR FOOD CHEM, V57, P4236, DOI 10.1021/jf803760s Rossi M, 2010, LWT-FOOD SCI TECHNOL, V43, P436, DOI 10.1016/j.lwt.2009.09.008 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Sakamoto N, 2002, J NUCL SCI TECHNOL, V39, P323, DOI 10.3327/jnst.39.323 SHARMA T, 1971, INDIAN J CHEM, V9, P456 Skulan J, 1997, GEOCHIM COSMOCHIM AC, V61, P2505, DOI 10.1016/S0016-7037(97)00047-1 van Ruth S, 2011, FOOD CHEM, V126, P1299, DOI 10.1016/j.foodchem.2010.11.081 VEC, 2011, WELC EGGSACTRACE Voerkelius S, 2010, FOOD CHEM, V118, P933, DOI 10.1016/j.foodchem.2009.04.125 Werner RA, 2001, RAPID COMMUN MASS SP, V15, P501, DOI 10.1002/rcm.258 WINKLER FJ, 1980, Z LEBENSM UNTERS FOR, V171, P85, DOI 10.1007/BF01140746 Zygmunt J., 2007, HIDDEN WATERS NR 60 TC 7 Z9 8 U1 3 U2 58 PD DEC PY 2012 VL 28 IS 2 BP 62 EP 68 DI 10.1016/j.tifs.2012.04.002 WC Food Science & Technology SC Food Science & Technology UT WOS:000317809500002 DA 2022-12-14 ER PT J AU Aguilera-Munoz, F Valenzuela-Munoz, V Gallardo-Escarate, C AF Aguilera-Munoz, Felipe Valenzuela-Munoz, Valentina Gallardo-Escarate, Cristian TI AUTHENTICATION OF COMMERCIAL CHILEAN MOLLUSKS USING RIBOSOMAL INTERNAL TRANSCRIBED SPACER (ITS) AS SPECIE-SPECIFIC DNA MARKER SO GAYANA DT Article DE Genetic traceability; ITS 1; ITS2; mollusk identification; specific divider design ID PCR-RFLP ANALYSIS; SEAFOOD PRODUCTS; IDENTIFICATION; TRACEABILITY; REGION; GENE; SEQUENCE; ANIMALS AB Contemporary world food safety standards have emphasized the implementation of efficient food traceability systems, including the correct labeling of products for the authentication of the commercialized species. In this context, Chile has reached an important development in agricultural exportations, reaching near US$ 3.82 billion in 2007. Since the identification based on morphologic and organoleptic characteristics is complex during the elaboration process of aquaculture products, molecular tools need to be developed in order to genetically trace aquaculture products. This study analyzed five species of Chilean commercial mollusks using molecular DNA markers. From the partial sequences of the ribosomal region ITS1-5.8SrDNA-ITS2, the specific primers for the internal transcribed spacers 1 and 2 (ITS1, ITS2) were designed. These primers in 8 fresh species of mollusks, 5 commercial presentations and 3 processed species, obtaining specie-specific band patterns in all cases for both ITS. Additionally, the primers are shown to be highly specific and of replicable amplification products, independently from the commercial presentation of the studied species. Genetically trace aquaculture products. The presence of fraud for species substitution between canned abalone (Haliotis) and loco (Concholepas) species is discussed. C1 [Aguilera-Munoz, Felipe; Valenzuela-Munoz, Valentina; Gallardo-Escarate, Cristian] Univ Concepcion, Dept Oceanog, Ctr Biotecnol, Lab Biotecnol Acuicola, Concepcion, Chile. C3 Universidad de Concepcion RP Aguilera-Munoz, F (corresponding author), Univ Concepcion, Dept Oceanog, Ctr Biotecnol, Lab Biotecnol Acuicola, Barrio Univ S-N,Casilla 160-C, Concepcion, Chile. EM cristian.gallardo@udec.cl CR Abdulmawjood A, 2001, J FOOD SCI, V66, P1287, DOI 10.1111/j.1365-2621.2001.tb15203.x AGUILERAMUNOZ F, 2008, PHYLOGENETICS RELATI Bossier P, 1999, J FOOD SCI, V64, P189, DOI 10.1111/j.1365-2621.1999.tb15862.x Carrera E, 1999, J SCI FOOD AGR, V79, P1654, DOI 10.1002/(SICI)1097-0010(199909)79:12<1654::AID-JSFA414>3.0.CO;2-S Chapela MJ, 2003, EUR FOOD RES TECHNOL, V217, P524, DOI 10.1007/s00217-003-0788-y Chapela MJ, 2002, J FOOD SCI, V67, P1672, DOI 10.1111/j.1365-2621.2002.tb08703.x CHIKUNI K, 1990, MEAT SCI, V27, P119, DOI 10.1016/0309-1740(90)90060-J Cocolin L, 2000, J FOOD SCI, V65, P1315, DOI 10.1111/j.1365-2621.2000.tb10604.x Coleman AW, 2002, J MOL EVOL, V54, P246, DOI 10.1007/s00239-001-0006-0 COLFMAN AW, 2003, GENETICS, V19, P370 Cunningham CO, 1997, J PARASITOL, V83, P215, DOI 10.2307/3284442 Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 DELRIOPORTILLA MA, 2008, J FOOD SCI IN PRESS Di Finizio A, 2007, EUR FOOD RES TECHNOL, V225, P337, DOI 10.1007/s00217-006-0420-z Fernandez A, 2001, J FOOD SCI, V66, P657, DOI 10.1111/j.1365-2621.2001.tb04617.x Flores-Aguilar RA, 2007, J SHELLFISH RES, V26, P705, DOI 10.2983/0730-8000(2007)26[705:DACSOA]2.0.CO;2 HANLIANG C, 2006, J SHELLFISH RES, V25, P833 Insua A, 2003, GENOME, V46, P595, DOI 10.1139/G03-045 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 Loftus R, 2005, REV SCI TECH OIE, V24, P231, DOI 10.20506/rst.24.1.1563 Lopez M., 2003, TECNOLOGIAS MOL TRAZ Lopez-Pinon MJ, 2002, MAR BIOTECHNOL, V4, P495, DOI 10.1007/s10126-002-0030-0 Odorico DM, 1997, MOL BIOL EVOL, V14, P465, DOI 10.1093/oxfordjournals.molbev.a025783 Quinteiro J, 1998, J AGR FOOD CHEM, V46, P1662, DOI 10.1021/jf970552+ Ram JL, 1996, J AGR FOOD CHEM, V44, P2460, DOI 10.1021/jf950822t Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Santaclara FJ, 2006, J AGR FOOD CHEM, V54, P8461, DOI 10.1021/jf061400u *SUBPESCA, 2007, INF SECT PESC AC Sweijd NA, 1998, J SHELLFISH RES, V17, P889 Tamura K, 2007, MOL BIOL EVOL, V24, P1596, DOI 10.1093/molbev/msm092 THOMPSON JD, 1994, NUCLEIC ACIDS RES, V22, P4673, DOI 10.1093/nar/22.22.4673 Thompson M, 2005, COMPR REV FOOD SCI F, V4, P1, DOI 10.1111/j.1541-4337.2005.tb00067.x UNSELD M, 1995, PCR METH APPL, V4, P241 NR 33 TC 10 Z9 11 U1 1 U2 11 PY 2008 VL 72 IS 2 BP 178 EP 187 WC Oceanography; Zoology SC Oceanography; Zoology UT WOS:000262269100007 DA 2022-12-14 ER PT J AU Ribarits, A Narendja, F Stepanek, W Hochegger, R AF Ribarits, Alexandra Narendja, Frank Stepanek, Walter Hochegger, Rupert TI Detection Methods Fit-for-Purpose in Enforcement Control of Genetically Modified Plants Produced with Novel Genomic Techniques (NGTs) SO AGRONOMY-BASEL DT Article DE GMO; genome editing; detection methods; regulation; enforcement AB The comprehensive EU regulatory framework regarding GMOs aims at preventing damage to human and animal health and the environment, and foresees labelling and traceability. Genome-edited plants and products fall under these EU GMO regulations, which have to be implemented in enforcement control activities. GMO detection methods currently used by enforcement laboratories are based on real-time PCR, where specificity and sensitivity are important performance parameters. Genome editing allows the targeted modification of nucleotide sequences in organisms, including plants, and often produces single nucleotide variants (SNVs), which are the most challenging class of genome edits to detect. The test method must therefore meet advanced requirements regarding specificity, which can be increased by modifying a PCR method. Digital PCR systems achieve a very high sensitivity and have advantages in quantitative measurement. Sequencing methods may also be used to detect DNA modifications caused by genome editing. Whereas most PCR methods can be carried out in an enforcement laboratory with existing technical equipment and staff, the processing of the sequencing data requires additional resources and the appropriate bioinformatic expertise. C1 [Ribarits, Alexandra; Stepanek, Walter; Hochegger, Rupert] Austrian Agcy Hlth & Food Safety, Spargelfeldstr 191, A-1220 Vienna, Austria. [Narendja, Frank] Environm Agcy Austria, Spittelauer Lande 5, A-1090 Vienna, Austria. RP Ribarits, A (corresponding author), Austrian Agcy Hlth & Food Safety, Spargelfeldstr 191, A-1220 Vienna, Austria. EM alexandra.ribarits@ages.at; frank.narendja@umweltbundesamt.at; walter.stepanek@ages.at; rupert.hochegger@ages.at CR Angers-Loustau A, 2014, BMC BIOINFORMATICS, V15, DOI 10.1186/s12859-014-0417-8 [Anonymous], 170252017 ISOIEC Ayalew H, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0217222 Basso MF, 2020, FRONT PLANT SCI, V11, DOI 10.3389/fpls.2020.00509 Bonfini L, 2012, J AOAC INT, V95, P1713, DOI 10.5740/jaoacint.12-050 Broccanello C, 2018, PLANT METHODS, V14, DOI 10.1186/s13007-018-0295-6 Broeders SRM, 2012, J BIOMED BIOTECHNOL, DOI 10.1155/2012/402418 Broothaerts W, 2020, FOOD CONTROL, V114, DOI 10.1016/j.foodcont.2020.107237 Bruge F, 2009, MUTAT RES-FUND MOL M, V669, P80, DOI 10.1016/j.mrfmmm.2009.05.007 Chhalliyil P, 2020, FOODS, V9, DOI 10.3390/foods9091245 Dobosy JR, 2011, BMC BIOTECHNOL, V11, DOI 10.1186/1472-6750-11-80 EUginius, EUR GMO IN UN DAT SY European Commission, EC STUD NEW GEN TECH European Commission, 2003, OFF J EUR UNION European Commission, 2003, OFF J EUR UNION L, VL268, P24 European Network of GMO Laboratories, ENGL DEF MIN PERF RE European Parliament and of the Council, 2009, OJ L, V125, P75 Findlay SD, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0153901 Grohmann L., 2016, GUIDELINES SINGLE LA, P17 Grohmann L, 2019, FRONT PLANT SCI, V10, DOI 10.3389/fpls.2019.00236 Holst-Jensen A, 2012, BIOTECHNOL ADV, V30, P1318, DOI 10.1016/j.biotechadv.2012.01.024 Johnson MP, 2004, NUCLEIC ACIDS RES, V32, DOI 10.1093/nar/gnh046 Joint Research Centre, JRC OV REC APPL DIG Miyaoka Y, 2014, NAT METHODS, V11, P291, DOI 10.1038/nmeth.2840 Mock U, 2016, NAT PROTOC, V11, P598, DOI 10.1038/nprot.2016.027 Mouritzen P, 2003, EXPERT REV MOL DIAGN, V3, P27, DOI 10.1586/14737159.3.1.27 Petrillo M, 2015, DATABASE-OXFORD, DOI 10.1093/database/bav101 Razzaq A, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20164045 Ugozzoli LA, 2004, ANAL BIOCHEM, V324, P143, DOI 10.1016/j.ab.2003.09.003 Van den Eede G, 2002, J AOAC INT, V85, P757 Verginelli D, 2020, J SCI FOOD AGR, V100, P2121, DOI 10.1002/jsfa.10235 Wang X., 2016, NEXT GENERATION SEQU, P258 Wenger AM, 2019, NAT BIOTECHNOL, V37, P1155, DOI 10.1038/s41587-019-0217-9 You Y, 2006, NUCLEIC ACIDS RES, V34, DOI 10.1093/nar/gkl175 NR 34 TC 5 Z9 5 U1 2 U2 9 PD JAN PY 2021 VL 11 IS 1 AR 61 DI 10.3390/agronomy11010061 WC Agronomy; Plant Sciences SC Agriculture; Plant Sciences UT WOS:000609686600001 DA 2022-12-14 ER PT J AU Liang, K Zhang, LL Chen, XH Shen, MX AF Liang, Kun Zhang, Lingling Chen, Xiaohe Shen, Mingxia TI Optimization of tracer coating parameters and their effects on the mechanical properties and quality of food-grade tracers for grain traceability SO INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING DT Article DE grain traceability; food-grade tracer; coating process; optimization; Box-Behnken design ID SYSTEM; TABLETS; CODE AB The purpose of this study was to optimize the coating process of food-grade tracers to manufacture tracers with good physical, mechanical and practical properties and an excellent appearance. The effects of the coating weight gain (1.00%-5.00%), coating solution spray rate (1.50-7.50 g/min) and tablet bed temperature (30 degrees C-40 degrees C) on the coating appearance quality, moisture absorption rate, friction coefficient, peak shear force, breaking rate, barcode recognition rate, transport wear rate and transport recognition rate were analysed using a Box-Behnken design (BBD) of response surface methodology (RSM). The experimental data were fitted to quadratic polynomial models by multiple regression analysis. The mathematical models of the barcode recognition rate, transport wear rate and transport recognition rate exhibited no statistically significant difference in these data. The optimum coating parameters were as follows: a 5.00% coating weight gain, spray rate of 5.47 g/min and tablet bed temperature of 35.42 degrees C. Under the optimized conditions, the tracers had a good appearance (coating appearance quality), moisture resistance (moisture absorption rate), and frictional (friction coefficient), compression (peak shear force), and impact characteristics (breaking rate). C1 [Liang, Kun; Zhang, Lingling; Chen, Xiaohe; Shen, Mingxia] Nanjing Agr Univ, Key Lab Intelligent Equipment Agr Jiangsu Prov, Coll Engn, Nanjing 210031, Jiangsu, Peoples R China. C3 Nanjing Agricultural University RP Shen, MX (corresponding author), 40 Dianjiangtai Rd, Nanjing 210031, Jiangsu, Peoples R China. EM lkbb2006@126.com; zhangllling@126.com; 1432167570@qq.com; mingxia@njau.edu.cn CR ASAE Standard, 2008, S3684 ASAE Cahyadi C, 2011, AAPS PHARMSCITECH, V12, P119, DOI 10.1208/s12249-010-9567-9 Christodoulou C, 2018, CHEM ENG SCI, V175, P40, DOI 10.1016/j.ces.2017.09.021 Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Dohi M, 2016, J PHARMACEUT BIOMED, V119, P104, DOI 10.1016/j.jpba.2015.11.046 Golan E. H., 2004, AGR EC REPORTS Heinamaki J, 1997, Pharm Dev Technol, V2, P357, DOI 10.3109/10837459709022634 Kun L, 2017, INT J AGR BIOL ENG, V10, P221, DOI 10.25165/j.ijabe.20171006.3531 Lee KM, 2011, FOOD CONTROL, V22, P1085, DOI 10.1016/j.foodcont.2010.12.016 Lee KM, 2010, J AGR FOOD CHEM, V58, P10945, DOI 10.1021/jf101370k Liang K, 2013, FOOD CONTROL, V33, P359, DOI 10.1016/j.foodcont.2013.03.029 Liang K, 2012, BIOSYST ENG, V113, P395, DOI 10.1016/j.biosystemseng.2012.09.012 Niblett D, 2017, INT J PHARMACEUT, V528, P180, DOI 10.1016/j.ijpharm.2017.05.060 Pandey P, 2014, AAPS PHARMSCITECH, V15, P296, DOI 10.1208/s12249-013-0060-0 Pearnchob N, 2003, DRUG DEV IND PHARM, V29, P925, DOI 10.1081/DDC-120024188 PRATER DA, 1981, J PHARM PHARMACOL, V33, P666, DOI 10.1111/j.2042-7158.1981.tb13896.x Rohera BD, 2002, PHARM DEV TECHNOL, V7, P407, DOI 10.1081/PDT-120015043 Sui R, 2007, ASABE ANN INT M MINN Sui R. X, 2007, ASABE ANN INT M Suzzi D, 2010, CHEM ENG SCI, V65, P5699, DOI 10.1016/j.ces.2010.07.007 Tabatabaekoloor R., 2013, INT J AGR FOOD SCI T, V4, P467 Teckoe J, 2013, AAPS PHARMSCITECH, V14, P531, DOI 10.1208/s12249-013-9935-3 Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Wang J, 2012, INT J PHARMACEUT, V427, P163, DOI 10.1016/j.ijpharm.2012.01.033 Yang ZuoMei, 2016, Transactions of the Chinese Society of Agricultural Engineering, V32, P258, DOI 10.11975/j.issn.1002-6819.2016.16.035 Yang ZuoMei, 2015, Transactions of the Chinese Society of Agricultural Engineering, V31, P253, DOI 10.11975/j.issn.1002-6819.2015.23.034 NR 26 TC 0 Z9 0 U1 0 U2 2 PD MAR PY 2019 VL 12 IS 2 BP 201 EP 209 DI 10.25165/j.ijabe.20191202.4180 WC Agricultural Engineering SC Agriculture UT WOS:000464947900025 DA 2022-12-14 ER PT J AU Ekawati, R Arkeman, Y Suprihatin Sunarti, TC AF Ekawati, Ratna Arkeman, Yandra Suprihatin Sunarti, Titi Candra TI Proposed Design of White Sugar Industrial Supply Chain System based on Blockchain Technology SO INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS DT Article DE Blockchain technology; supply chain; white crystal sugar ID TRACEABILITY; MODEL AB The white crystal sugar agro-industry is an industry with dynamic characteristics characterized by a sustainable relationship between actors ranging from farmers to consumers. An inefficient supply chain system will affect the flow of products, information, and finance because many actors are involved and have influence. Hence, complicating the system in the tracking process flow, product flow and creating problems that occur in business processes. The main objective of this research is to propose the design of an integrated white crystal sugar agro-industrial supply chain system based on blockchain technology so that it can increase competitiveness in realizing food security and resilience; by proposing a search for the problem of mismatches that occur along the supply chain from upstream to downstream. The variables that will be identified in the supply chain flow include quality, quantity, and price, with the suitability of transaction information data ranging from farmers, sugar factories, warehouses, distribution, retailers to the final consumer. It is hoped that consumers will feel happy to consume trusted local sugar with the best safety and quality, as well as ensure transparency of information between actors. Previous traditional methods, which were still centralized, would be transformed into decentralized information, to create trust among stakeholders. With a blockchain-based traceability architecture design, it is hoped that the proposed design can be implemented in the white crystal sugar agro-industry. C1 [Ekawati, Ratna] IPB, Cilegon, Banten, Indonesia. [Ekawati, Ratna] Ind Engn Untirta, Cilegon, Banten, Indonesia. [Arkeman, Yandra; Suprihatin; Sunarti, Titi Candra] IPB Univ, Agroind Engn, Bogor, West Java, Indonesia. C3 Bogor Agricultural University RP Ekawati, R (corresponding author), IPB, Cilegon, Banten, Indonesia.; Ekawati, R (corresponding author), Ind Engn Untirta, Cilegon, Banten, Indonesia. CR Abeyratne S. A., 2016, INT J RES ENG TECHNO, V5, P1, DOI DOI 10.15623/IJRET.2016.0509001 Arsyad AA, 2019, LECT NOTE DATA ENG, V23, P332, DOI 10.1007/978-3-319-98557-2_30 Asrol M., 2017, INT J SUPPLY CHAIN M, V6, P8 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Barilla D, 2020, QUAEST MATH, V43, P547, DOI 10.2989/16073606.2019.1583293 Casado-Vara R, 2018, PROCEDIA COMPUT SCI, V134, P393, DOI 10.1016/j.procs.2018.07.193 Charlebois S, 2017, J INT FOOD AGRIBUS M, V29, P260, DOI 10.1080/08974438.2017.1331149 Chen E., 2016, APPROACH IMPROVING T Cheraghalipour A, 2019, COMPUT ELECTRON AGR, V162, P651, DOI 10.1016/j.compag.2019.04.041 Chiadamrong N, 2008, COMPUT ELECTRON AGR, V64, P248, DOI 10.1016/j.compag.2008.05.018 Fadhilah A. F., 2017, JOFSA, V1, P60 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Fritz M, 2009, INT J PROD ECON, V117, P317, DOI 10.1016/j.ijpe.2008.10.015 Galvez J. F., 2018, TRENDS ANAL CHEM, P1 GARCIA DJ, 2015, COMPUT CHEM ENG, V81, P153, DOI DOI 10.1016/j.compchemeng.2015.03.015 Helo P., 2020, ROBOT COMPUT INTEGER, V63, P1 Helo P, 2019, COMPUT IND ENG, V136, P242, DOI 10.1016/j.cie.2019.07.023 Jonkman J, 2019, J CLEAN PROD, V210, P1065, DOI 10.1016/j.jclepro.2018.10.351 Khushk A. M., 2015, J AGR RES, V49, P137 Kumar M. V., 2018, ADV SCI TECHNOLOGY L, V146, P125 Kusumo W. A., 2013, DESIGN INFORM SYSTEM Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Panneerselvam R., 2014, INTELLIGENT INFORM M, V2014 Perboli G., 2018, IEEE ACCESS, VXX, P1 Ronaghi M. H., 2021, Information Processing in Agriculture, V8, P398, DOI 10.1016/j.inpa.2020.10.004 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Stutterheim P., 2006, INTEGRATED SUGARCANE Suliantoro H., 2015, SNST P, P17 Thiruchelvam V., 2018, J TELECOMMUNICATION, V10, P121 Van Der Vorst J., 2006, PERFORMANCE MEASUREM Zhang GB, 2011, COMPUT IND ENG, V60, P863, DOI 10.1016/j.cie.2011.02.002 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 33 TC 1 Z9 1 U1 2 U2 6 PD APR PY 2021 VL 12 IS 4 BP 459 EP 465 WC Computer Science, Theory & Methods SC Computer Science UT WOS:000648867700060 DA 2022-12-14 ER PT J AU Aguiar, ML Gaspar, PD Silva, PD Domingues, LC Silva, DM AF Aguiar, Martim L. Gaspar, Pedro D. Silva, Pedro D. Domingues, Luisa C. Silva, David M. TI Real-Time Temperature and Humidity Measurements during the Short-Range Distribution of Perishable Food Products as a Tool for Supply-Chain Energy Improvements SO PROCESSES DT Article DE intelligent packaging; temperature; humidity; supply chain; food waste; food quality; food preservation; transportation ID TRACEABILITY; QUALITY AB Food waste results in an increased need for production to compensate for losses. Increased production is directly related to an increase in the environmental impact of agriculture and in the energy needs associated with it. To reduce food waste, the supply chain should maintain ideal preservation conditions. In horticultural products, temperature, and relative humidity are two of the main parameters to be controlled. Monitoring these parameters can help decision-making in logistics and routes management, as well as to diagnose and timely prevent food losses. In the present work, eighteen wireless traceability devices with temperature and relative humidity sensors monitored crates with horticultural products along a short-range distribution route with five stops (4 h 30 m). Sensor data and a location tag were sent via GSM for real-time monitoring. The results showed fluctuations in temperature and relative humidity that reached up to 7.4 degrees C and 35.3%, respectively. These fluctuations happened mostly due to frequent door opening, operational procedures, and irregular refrigeration conditions. Furthermore, the results brought attention to a procedure that creates unnecessary temperature fluctuations and energy losses. This study highlights the importance of individual monitorization of goods, for quality control and optimization of energy efficiency along the supply chain. C1 [Aguiar, Martim L.; Gaspar, Pedro D.; Silva, Pedro D.; Domingues, Luisa C.; Silva, David M.] Univ Beira Interior, Dept Electromech Engn, Rua Marques Avila & Bolama, P-6201001 Covilha, Portugal. [Aguiar, Martim L.; Gaspar, Pedro D.; Silva, Pedro D.] Univ Beira Interior, Fac Engn, Ctr Mech & Aerosp Sci & Technol, C MAST, P-6201001 Covilha, Portugal. C3 Universidade da Beira Interior; Universidade da Beira Interior RP Gaspar, PD (corresponding author), Univ Beira Interior, Dept Electromech Engn, Rua Marques Avila & Bolama, P-6201001 Covilha, Portugal.; Gaspar, PD (corresponding author), Univ Beira Interior, Fac Engn, Ctr Mech & Aerosp Sci & Technol, C MAST, P-6201001 Covilha, Portugal. EM dinis@ubi.pt CR Aguiar M.L., 2020, P X IBERIAN C 8 IBER, P323 Ananias E, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10192394 Andrade L.P., 2019, P 25 IIR INT C REFR, DOI [10.18462/iir.icr.2019.0866, DOI 10.18462/IIR.ICR.2019.0866] Carneiro R, 2017, APPL THERM ENG, V113, P585, DOI 10.1016/j.applthermaleng.2016.11.046 Curto J., 2020, P 6 IIR INT C SUSTAI, P428 Curto J., 2021, P EIAETM C Curto JP, 2021, AIMS AGRIC FOOD, V6, P708, DOI 10.3934/agrfood.2021042 Curto JP, 2021, AIMS AGRIC FOOD, V6, P679, DOI 10.3934/agrfood.2021041 de Andrade LP, 2022, AGRONOMY-BASEL, V12, DOI 10.3390/agronomy12010188 Firouz MS, 2021, FOOD RES INT, V141, DOI 10.1016/j.foodres.2021.110113 Gaspar JP, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103530 Gaspar P. D., 2014, Applied Mechanics and Materials, V590, P878, DOI 10.4028/www.scientific.net/AMM.590.878 Gaspar P.D., 2008, P ASME 2008 HEAT TRA, VVolume 2, P63 Gaspar P.D, 2021, RES ANTHOLOGY FOOD W, P63, DOI [10.4018/978-1-7998-5354-1.ch004, DOI 10.4018/978-1-7998-5354-1.CH004] Gaspar PD, 2021, PROCESSES, V9, DOI 10.3390/pr9061065 Gaspar PD, 2012, MOD SIMUL ENG, V2012, DOI 10.1155/2012/867820 Gaspar PD, 2011, APPL THERM ENG, V31, P961, DOI 10.1016/j.applthermaleng.2010.11.020 Gustavsson J., 2011, Global food losses and food waste: extent, causes and prevention IPMA-Portuguese Institute for Sea and Atmosphere, 2022, B CLIM PORT CONT JUN Leitao F., 2021, P 15 INT C HEAT TRAN Leitao F., 2021, LECT NOTES ENG COMPU, P268 Leitao F., 2021, P 3 ENV INNOVATIONS Maciel V., 2021, COMPUTATIONAL MANAGE, P487, DOI [10.1007/978-3-030-72929-5_23, DOI 10.1007/978-3-030-72929-5_23] Madhan S.K., 2020, KNE ENG, V4, P232, DOI [10.18502/keg.v5i6.7037, DOI 10.18502/KEG.V5I6.7037] Madhan S.K., 2020, P 6 IIR INT C SUSTAI, P436 Magalhaes B, 2022, CLIMATE, V10, DOI 10.3390/cli10030029 Mendes Adriana, 2020, 2020 International Conference on Decision Aid Sciences and Application (DASA), P173, DOI 10.1109/DASA51403.2020.9317068 Morais D., 2019, P 25 IIR INT C REFRI, DOI [10.18462/iir.icr.2019.1294, DOI 10.18462/IIR.ICR.2019.1294] Morais D., 2019, P 25 IIR INT C REFR, DOI [10.18462/iir.icr.2019.0983, DOI 10.18462/IIR.ICR.2019.0983] Morais D., 2019, P INT C ENG ENGINEER Morais D., 2021, PROCEDIA ENV SCI ENG, V8, P195 Morais R, 1996, NINETEENTH CONVENTION OF ELECTRICAL AND ELECTRONICS ENGINEERS IN ISRAEL, P527, DOI 10.1109/EEIS.1996.567032 Nanga R., 2021, P 3 ENV INN ADV ENG Nunes J., 2014, WIT T ECOL ENV, V186, P763 Panoias P., 2019, P 25 IIR INT C REFRI, DOI [10.18462/iir.icr.2019.0989, DOI 10.18462/IIR.ICR.2019.0989] Pina M, 2021, APPL SYST INNOV, V4, DOI 10.3390/asi4040080 Rodrigues C, 2022, J FOOD PROCESS PRES, V46, DOI 10.1111/jfpp.14358 Silva PD, 2014, APPL MECH MATER, V675-677, P1880, DOI 10.4028/www.scientific.net/AMM.675-677.1880 Simoes M.P., 2021, GRUPOS OPERACIONAIS, P404 Simoes M.P., 2021, GRUPOS OPERACIONAIS Veloso A., 2021, REV 101 NC AGR R, V44, P82, DOI [10.19084/rca.21781, DOI 10.19084/RCA.21781] Zollier S, 2013, PROCEEDINGS OF THE 2013 38TH ANNUAL IEEE CONFERENCE ON LOCAL COMPUTER NETWORKS WORKSHOPS (LCN WORKSHOPS), P39, DOI 10.1109/LCNW.2013.6758496 NR 42 TC 0 Z9 0 U1 0 U2 0 PD NOV PY 2022 VL 10 IS 11 AR 2286 DI 10.3390/pr10112286 WC Engineering, Chemical SC Engineering UT WOS:000885810700001 DA 2022-12-14 ER PT J AU Innerebner, G Knapp, B Vasara, T Romantschuk, M Insam, H AF Innerebner, Gerd Knapp, Brigitte Vasara, Tuija Romantschuk, Martin Insam, Heribert TI Traceability of ammonia-oxidizing bacteria in compost-treated soils SO SOIL BIOLOGY & BIOCHEMISTRY DT Article DE sustainable agriculture; compost; soil quality; nitrification; ammonia-oxidizing bacteria (AOB); 16S rDNA; PCR-DGGE; real-time PCR ID 16S RIBOSOMAL-RNA; REAL-TIME PCR; GRADIENT GEL-ELECTROPHORESIS; CLASS PROTEOBACTERIA; BETA SUBDIVISION; ORGANIC-MATTER; ARABLE SOILS; GENES; COMMUNITIES; DIVERSITY AB Composts are increasingly used as environmentally safe biofertilizers in sustainable agriculture all over the world. Although it is well known that composts may contribute to soil vitality and sustainability, and in the enhancement of various soil microbiological processes, little is known about their direct or indirect effects on a microbial-community or population level. Ammonia oxidation by autotrophic ammonia-oxidizing bacteria (AOB) is a key process in agricultural and natural ecosystems and plays an important role in the global nitrogen cycle. Here, we studied the diversity and community composition of ammonia oxidizers in a long-term crop rotation field experiment (> 10 years) where four major types of compost (from organic waste, cattle manure, green waste and sewage sludge) had been applied annually. The methods used ranged from PCR-DGGE (denaturing gradient gel electrophoresis) and cloning of 16S rDNA fragments to quantitative real-time PCR. Cluster analysis of DGGE profiles differentiated between the microbial communities of composts, compost-treated soils and mineral-fertilized soils. The community composition of the composts was not reflected in the community composition of the compost-treated soils. Sequencing of screened clones revealed a characteristic AOB community structure for the representative soil sample and the four composts. All AOB-like sequences grouped within the Nitrosospira cluster 3 and 4 and within the Nitrosomonas cluster 6 and 7. The average AOB abundance in compost-treated soils was two times higher than in mineral-fertilized soils (4.3 X 10(7) and 1.9 X 10(7), respectively). Our data suggest that composts do not leave direct microbial imprints in soils after long-term amendment, but an indirect effect on the AOB community was evident. (c) 2005 Elsevier Ltd. All rights reserved. C1 Univ Innsbruck, Dept Microbiol, A-6020 Innsbruck, Austria. Univ Helsinki, Dept Ecol & Environm Sci, Lahti 15140, Finland. C3 University of Innsbruck; University of Helsinki RP Innerebner, G (corresponding author), Univ Innsbruck, Dept Microbiol, Technikerstr 25, A-6020 Innsbruck, Austria. EM gerd.innerebner@uibk.ac.at CR Aakra A, 1999, INT J SYST BACTERIOL, V49, P123, DOI 10.1099/00207713-49-1-123 AICHBERGER K, 2000, ALPENLANDISCHES EXPE, V6, P81 Alfreider A, 2002, COMPOST SCI UTIL, V10, P303 Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 Carney KM, 2004, ECOL LETT, V7, P684, DOI 10.1111/j.1461-0248.2004.00628.x Carnol M, 2002, SOIL BIOL BIOCHEM, V34, P1047, DOI 10.1016/S0038-0717(02)00039-1 Felsenstein J, 1993, PHYLIP PHYLOGENY INT Freitag TE, 2003, APPL ENVIRON MICROB, V69, P1359, DOI 10.1128/AEM.69.3.1359-1371.2003 GARCIA C, 1994, WASTE MANAGE RES, V12, P457, DOI 10.1177/0734242X9401200602 Hermansson A, 2001, APPL ENVIRON MICROB, V67, P972, DOI 10.1128/AEM.67.2.972-976.2001 Heuer H, 1999, APPL ENVIRON MICROB, V65, P1045 Heuer H, 1997, APPL ENVIRON MICROB, V63, P3233, DOI 10.1128/AEM.63.8.3233-3241.1997 Hooper AB, 1997, ANTON LEEUW INT J G, V71, P59, DOI 10.1023/A:1000133919203 JUKES T H, 1969, P21 KNAPP B, 2000, SOIL ORGANIC MATTER KOHLER W, 1996, BIOSTATISTIK KOSCHINSKY S, 2000, MICROBIAL BIOSYSTEMS Kowalchuk GA, 2001, ANNU REV MICROBIOL, V55, P485, DOI 10.1146/annurev.micro.55.1.485 Kowalchuk GA, 1999, APPL ENVIRON MICROB, V65, P396 Kowalchuk GA, 1997, APPL ENVIRON MICROB, V63, P1489, DOI 10.1128/AEM.63.4.1489-1497.1997 Kurola J, 2005, FEMS MICROBIOL ECOL, V53, P463, DOI 10.1016/j.femsec.2005.02.001 Marchesi JR, 1998, APPL ENVIRON MICROB, V64, P795 Mendum TA, 1999, APPL ENVIRON MICROB, V65, P4155 Mendum TA, 2002, SOIL BIOL BIOCHEM, V34, P1479, DOI 10.1016/S0038-0717(02)00092-5 Muyzer G, 1999, CURR OPIN MICROBIOL, V2, P317, DOI 10.1016/S1369-5274(99)80055-1 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Okano Y, 2004, APPL ENVIRON MICROB, V70, P1008, DOI 10.1128/AEM.70.2.1008-1016.2004 Pascual JA, 1997, BIOL FERT SOILS, V24, P429, DOI 10.1007/s003740050268 Pearson K, 1926, BIOMETRIKA, V18, P105, DOI 10.2307/2332498 Peters S, 2000, APPL ENVIRON MICROB, V66, P930, DOI 10.1128/AEM.66.3.930-936.2000 Phillips CJ, 2000, APPL ENVIRON MICROB, V66, P5410, DOI 10.1128/AEM.66.12.5410-5418.2000 Rivero C, 2004, GEODERMA, V123, P355, DOI 10.1016/j.geoderma.2004.03.002 Rowan AK, 2003, FEMS MICROBIOL ECOL, V43, P195, DOI 10.1111/j.1574-6941.2003.tb01059.x Ryckeboer J, 2003, ANN MICROBIOL, V53, P349 SAISON C, 2005, IN PRESS ALTERATION, DOI DOI 10.1111/J.1462-2G20.2005.008G2.X SAITOU N, 1987, MOL BIOL EVOL, V4, P406, DOI 10.1093/oxfordjournals.molbev.a040454 Shannon C.E., 1963, MATH THEORY COMMUNIC Stephen JR, 1996, APPL ENVIRON MICROB, V62, P4147, DOI 10.1128/AEM.62.11.4147-4154.1996 Stubner S, 2004, J MICROBIOL METH, V57, P219, DOI 10.1016/j.mimet.2004.01.008 Suzuki MT, 1996, APPL ENVIRON MICROB, V62, P625, DOI 10.1128/AEM.62.2.625-630.1996 WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967 NR 41 TC 91 Z9 110 U1 4 U2 46 PD MAY PY 2006 VL 38 IS 5 BP 1092 EP 1100 DI 10.1016/j.soilbio.2005.09.008 WC Soil Science SC Agriculture UT WOS:000237545900026 DA 2022-12-14 ER PT J AU Asorey, CM Jilberto, F Haase, I Schubbert, R Larrain, MA Araneda, C AF Asorey, Cynthia M. Jilberto, Felipe Haase, Ilka Schubbert, Rainer Angelica Larrain, Maria Araneda, Cristian TI Comparison of two commercial methods for smooth-shelled mussels (Mytilus spp.) species identification SO FOOD CHEMISTRY: MOLECULAR SCIENCES DT Article DE Seafood traceability; H1C gene; HRM; PAPM; FINS; DNA sequence analysis ID DNA; EDULIS; GALLOPROVINCIALIS; HYBRIDIZATION; AQUACULTURE; ORIGIN AB Seafood international trade has increased the labeling requirements in standards and regulations to include product information that enable traders and consumers to make informed choices. The European Union (EU) Regulation No. 1379/2013 imposes the declaration of an official commercial designation and scientific names for all the fishery and aquaculture products to be offered for sale to the final consumers. DNA analyses are used to enforce this regulation and to test authenticity in processed foods. We compared the performance of two mono-locus approaches for species identification (SI) in 61 Mytilus mussels: the high-resolution melting analysis of the polyphenolic adhesive protein gene and the partial sequencing of the histone H1C gene. The H1C sequences were analyzed with five different methods. Both approaches show discrepancies in the identification of putative hy-brids (0.0 < kappa < 0.687 and 0.0 < MCC < 0.724). Excluding putative hybrids, methods show substantial to perfect agreement (0.772 < kappa < 1.0 and 0.783 < MCC < 1.0). This study highlights the need to use standardized mo-lecular tools, as well as to use multi-locus methods for SI of Mytilus mussels in testing laboratories. C1 [Asorey, Cynthia M.; Jilberto, Felipe; Angelica Larrain, Maria; Araneda, Cristian] Univ Chile, Food Qual Res Ctr, Santiago, Chile. [Asorey, Cynthia M.] Univ Catolica Norte, Fac Ciencias Mar, Sala Colecc Biol, Dept Biol Marina, Larrondo 1281, Coquimbo, Chile. [Jilberto, Felipe; Araneda, Cristian] Univ Chile, Fac Ciencias Agron, Dept Prod Anim, Ave Santa Rosa 11315, Santiago, Chile. [Jilberto, Felipe; Angelica Larrain, Maria] Univ Chile, Fac Ciencias Quim & Farmaceut, Dept Ciencia Alimentos & Tecnol Quim, Santiago, Chile. [Haase, Ilka; Schubbert, Rainer] Eurofins Genom, Anzinger Str 7a, D-85560 Ebersberg, Germany. C3 Universidad de Chile; Universidad Catolica del Norte; Universidad de Chile; Universidad de Chile RP Araneda, C (corresponding author), Univ Chile, Fac Ciencias Agron, Dept Prod Anim, Ave Santa Rosa 11315, Santiago, Chile. EM craraned@uchile.cl CR Larrain MA, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-55855-8 Larrain MA, 2012, LAT AM J AQUAT RES, V40, P1077, DOI 10.3856/vol40-issue4-fulltext-23 [Anonymous], OFFICIAL J EUROPEAN Armani A, 2015, FOOD CONTROL, V50, P589, DOI 10.1016/j.foodcont.2014.09.025 Barbuto M, 2010, FOOD RES INT, V43, P376, DOI 10.1016/j.foodres.2009.10.009 BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 Codex Alimentarius, 2020, GEN PRINC FOOD HYG C, P35 COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104 Coleman PL, 2010, ENSURING GLOBAL FOOD SAFETY: EXPLORING GLOBAL HARMONIZATION, P99, DOI 10.1016/B978-0-12-374845-4.00005-9 Collins RA, 2013, MOL ECOL RESOUR, V13, P969, DOI 10.1111/1755-0998.12046 COUSTAU C, 1991, MAR BIOL, V111, P87, DOI 10.1007/BF01986350 D'Amico P, 2016, MAR POLICY, V71, P147, DOI 10.1016/j.marpol.2016.05.026 Drabent B, 1999, J MOL EVOL, V49, P645, DOI 10.1007/PL00006585 Eirin-Lopez JM, 2002, J MOL EVOL, V55, P272, DOI 10.1007/s00239-002-2325-1 EU, 2013, OFFCIAL J EUROPEAN U EU-European Union, 2013, OFFICIAL J EUROPEAN FAO, 2019, FAO FISHERIES AQUACU Fernandez-Tajes J, 2011, EUR FOOD RES TECHNOL, V233, P791, DOI 10.1007/s00217-011-1574-x Garcia LV, 2004, OIKOS, V105, P657, DOI 10.1111/j.0030-1299.2004.13046.x Giusti A, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107379 Giusti A, 2022, FOOD CONTROL, V134, DOI 10.1016/j.foodcont.2021.108692 Hebert PDN, 2003, P ROY SOC B-BIOL SCI, V270, P313, DOI [10.1098/rspb.2002.2218, 10.1098/rsbl.2003.0025] Inoue K, 1995, BIOL BULL, V189, P370, DOI 10.2307/1542155 Jilberto F, 2017, FOOD CHEM, V229, P716, DOI 10.1016/j.foodchem.2017.02.109 Katoh K, 2013, MOL BIOL EVOL, V30, P772, DOI 10.1093/molbev/mst010 Larrain MA, 2018, EVOL APPL, V11, P298, DOI 10.1111/eva.12553 Leray M, 2019, P NATL ACAD SCI USA, V116, P22651, DOI 10.1073/pnas.1911714116 MATTHEWS BW, 1975, BIOCHIM BIOPHYS ACTA, V405, P442, DOI 10.1016/0005-2795(75)90109-9 Meier R, 2006, SYST BIOL, V55, P715, DOI 10.1080/10635150600969864 Michalek K, 2016, MAR GENOM, V27, P3, DOI 10.1016/j.margen.2016.04.008 Nakasawa M., 2018, FUNCTIONS MED STAT B Perez-Garcia C, 2014, BMC GENET, V15, DOI 10.1186/1471-2156-15-84 Puillandre N, 2012, MOL ECOL, V21, P1864, DOI 10.1111/j.1365-294X.2011.05239.x Quintrel M, 2021, FOODS, V10, DOI 10.3390/foods10081684 Rotondi M.A., 2018, SAMPLE SIZE ESTIMATI Santaclara FJ, 2006, J AGR FOOD CHEM, V54, P8461, DOI 10.1021/jf061400u Terol J, 2002, J AGR FOOD CHEM, V50, P963, DOI 10.1021/jf011032o Tinacci L, 2019, FOOD CONTROL, V96, P68, DOI 10.1016/j.foodcont.2018.09.002 Tinacci L, 2018, ITAL J FOOD SAF, V7, P83, DOI 10.4081/ijfs.2018.6894 Vainola R, 2011, MAR BIOL, V158, P817, DOI 10.1007/s00227-010-1609-z Verrez-Bagnis V, 2018, BIOACTIVE MOL FOOD R, P1, DOI DOI 10.1007/978-3-319-54528-8_69-1 Wenne R, 2022, AQUACULTURE, V554, DOI 10.1016/j.aquaculture.2022.738135 NR 42 TC 0 Z9 0 U1 2 U2 2 PD DEC 30 PY 2022 VL 5 AR 100121 DI 10.1016/j.fochms.2022.100121 WC Food Science & Technology SC Food Science & Technology UT WOS:000830084000001 DA 2022-12-14 ER PT J AU Spitzer, P Pratt, KW AF Spitzer, Petra Pratt, Kenneth W. TI The history and development of a rigorous metrological basis for pH measurements SO JOURNAL OF SOLID STATE ELECTROCHEMISTRY DT Review DE pH; Potentiometry; Metrology; Traceability; Reference standard; Primary measurement; Differential cell ID STANDARDIZATION; CHLORIDE; RECOMMENDATIONS; ELECTROLYTES; DEFINITION; ELECTRODES; ACCURATE; VALUES; POINT; ACID AB This paper discusses the basis and historical development of the traceability chain for pH. The quantity pH, first introduced in 1909, is among the most frequently measured analytical quantities. The practical measurement of the pH value of a sample is inexpensive, easy to perform, and yields a rapid result. However, the problems posed by the traceability of pH are not easy to solve. Most pH measurements are performed by potentiometry, using a glass electrode as the pH sensor. Such pH electrodes must be calibrated at regular intervals. Confidence in the reliability of pH measurements requires establishment of a metrological hierarchy including an uncertainty budget for calibration that links the pH measured in the sample to an internationally agreed and stated reference. For pH, this reference is the primary measurement of pH. A traceability chain can be established that links field measurements of pH to primary buffer solutions that are certified using this primary method. This allows the user in the field to estimate the measurement uncertainty of the measured pH data. As the realization of the primary measurement is sophisticated and time-consuming, primary standards are generally realized at national metrology institutes. A number of potentiometric methods are suitable for the determination of the pH of reference buffer solutions by comparison with the primary standard buffers. The choice between the methods should be made according to the uncertainty required for the application. For reference buffer solutions that have the same nominal composition as the primary standard, the differential potentiometric cell, often called the Baucke cell, is recommended. C1 [Spitzer, Petra] PTB, D-38116 Braunschweig, Germany. [Pratt, Kenneth W.] NIST, Gaithersburg, MD 20899 USA. C3 Physikalisch-Technische Bundesanstalt (PTB); National Institute of Standards & Technology (NIST) - USA RP Spitzer, P (corresponding author), PTB, Bundesallee 100, D-38116 Braunschweig, Germany. EM petra.spitzer@ptb.de CR Arrhenius S., 1887, Z PHYS CHEM, V1U, P631, DOI [DOI 10.1515/ZPCH-1887-0164, 10.1515/zpch-1887-0164] Bates R., 1960, PURE APPL CHEM, V1, P163, DOI [10.1351/pac196001010163, DOI 10.1351/PAC196001010163, 10.1351/PAC196001010163] Bates R.G., 1973, J ELECTROCHEM SOC, V120, p263C, DOI [10.1149/1.2403829, DOI 10.1149/1.2403829] Bates RG, 1943, J RES NAT BUR STAND, V30, P129, DOI 10.6028/jres.030.012 BATES RG, 1948, CHEM REV, V42, P1, DOI 10.1021/cr60131a001 BATES RG, 1980, J SOLUTION CHEM, V9, P455 BAUCKE FGK, 1994, ANAL CHEM, V66, P4519, DOI 10.1021/ac00096a019 Baucke FGK, 1998, ANAL CHEM, V70, p226A, DOI 10.1021/ac9817826 Baucke FGK, 2002, ANAL BIOANAL CHEM, V374, P772, DOI 10.1007/s00216-002-1523-4 BAUCKE FGK, 1979, ELECTROCHIM ACTA, V24, P95, DOI 10.1016/0013-4686(79)80048-1 BAUCKE FGK, 1993, ANAL CHEM, V65, P3244, DOI 10.1021/ac00070a013 BAUCKE FGK, 1977, CHEM-ING-TECH, V49, P739, DOI 10.1002/cite.330490909 BAUCKE FGK, 1994, J ELECTROANAL CHEM, V68, P67 BAUCKE FGK, 1997, W68 PTB, P10 BREITENBACH M, 1980, MITTEILUNGSBLATT CHE, V27, P209 Brown RJC, 2007, CHEM SOC REV, V36, P904, DOI 10.1039/b507452p Buck RP, 2002, PURE APPL CHEM, V74, P2169, DOI 10.1351/pac200274112169 COVINGTON AK, 1985, PURE APPL CHEM, V57, P531, DOI 10.1351/pac198557030531 COVINGTON AK, 1981, ANAL CHIM ACTA, V127, P1, DOI 10.1016/S0003-2670(01)83957-X DEBIEVRE P, 2008, IUPAC RECOM IN PRESS Debye P, 1923, PHYS Z, V24, P185 *DIN, 2000, 19266 DIN *DIN, 2007, 19268200705 DIN *EURAMET, 843 EURAMET Friedenthal H, 1904, Z ELKTROCHEM ANGEW P, V10, P113, DOI 10.1002/bbpc.19040100805 GALSTER H, 1991, PH MEASUREMENT, P47 Haber F, 1909, Z PHYS CHEM-STOCH VE, V67, P385 Hamed H.S., 1932, J AM CHEM SOC, V54, P1350 Hamer WJ, 1939, J RES NAT BUR STAND, V23, P647, DOI 10.6028/jres.023.044 Hamer WJ, 1944, J RES NAT BUR STAND, V32, P215, DOI 10.6028/jres.032.011 Harned HS, 1928, J AM CHEM SOC, V50, P3157, DOI 10.1021/ja01399a004 ISO, 2005, 170252005 ISO IEC ISO, 2008, 9832008 ISOIEC *ISO, 2000, 26149 ISODIS [ISO] International Organization for Standardization, 2008, 105232008 ISO *IUPAC PROJ, 2005, IUPAC PROJ COMP PH M Lewis G, 2020, THERMODYNAMICS Mariassy M, 2009, METROLOGIA, V46, P199, DOI 10.1088/0026-1394/46/3/007 Meinrath G, 2000, MIKROCHIM ACTA, V135, P155, DOI 10.1007/s006040070005 Michaelis L., 1914, WASSERSTOFFIONENKONZ *OIML, 1980, PH SCAL AQ SOL Sorensen S., 1924, CR TRAV LAB CARLSB, V15, P1 Sorensen SPL, 1909, BIOCHEM Z, V21, P131 Spitzer P, 2002, ANAL BIOANAL CHEM, V374, P787, DOI 10.1007/s00216-002-1453-1 Spitzer P, 2001, ACCREDIT QUAL ASSUR, V6, P55, DOI 10.1007/PL00010440 Spitzer P, 1996, FRESEN J ANAL CHEM, V356, P178 WHIFFEN DH, 1979, PURE APPL CHEM, V51, P30 CMCS PH BIPM DATA BA NR 48 TC 26 Z9 26 U1 2 U2 56 PD JAN PY 2011 VL 15 IS 1 BP 69 EP 76 DI 10.1007/s10008-010-1106-9 WC Electrochemistry SC Electrochemistry UT WOS:000287457300007 DA 2022-12-14 ER PT J AU Violino, S Ortenzi, L Antonucci, F Pallottino, F Benincasa, C Figorilli, S Costa, C AF Violino, Simona Ortenzi, Luciano Antonucci, Francesca Pallottino, Federico Benincasa, Cinzia Figorilli, Simone Costa, Corrado TI An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer SO FOODS DT Article DE VIS-NIR; ANN; made in Italy; minor components; pigments; antioxidants; non-destructive techniques; ready-to-use; spectral signature; artificial intelligence AI ID VIRGIN OLIVE OILS; NEAR-INFRARED SPECTROSCOPY; LIQUID-CHROMATOGRAPHY; DEGRADATION PRODUCTS; GEOGRAPHICAL ORIGIN; PHENOLIC-COMPOUNDS; STABLE-ISOTOPE; FOOD; CHLOROPHYLLS; AUTHENTICITY AB Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p< 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308-373 nm and 594-605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples' origin and thus could help to preserve the "Made in Italy" from fraud and sophistication related to its commerce. C1 [Violino, Simona; Ortenzi, Luciano; Antonucci, Francesca; Pallottino, Federico; Figorilli, Simone; Costa, Corrado] Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn & Trasformaz Agroalimentari, Via Pascolare 16, I-00015 Rome, Italy. [Benincasa, Cinzia] Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Olivicoltura Frutticoltura & Agrumicoltur, Contrada Rocchi Vermicelli 83, I-87036 Arcavacata Di Rende, CS, Italy. C3 Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Pallottino, F (corresponding author), Consiglio Ric Agr & Anal Econ Agr CREA, Ctr Ric Ingn & Trasformaz Agroalimentari, Via Pascolare 16, I-00015 Rome, Italy. EM simonaviolino@hotmail.com; luciano.ortenzi@crea.gov.it; francesca.antonucci@crea.gov.it; federico.pallottino@crea.gov.it; cinzia.benincasa@crea.gov.it; simone.figorilli@crea.gov.it; corrado.costa@crea.gov.it CR AITZETMULLER K, 1989, FETT WISS TECHNOL, V91, P99, DOI 10.1002/lipi.19890910304 [Anonymous], 2002, OFFICIAL J EUROPEA L, V31, P1 Antonucci F, 2019, J SCI FOOD AGR, V99, P6129, DOI 10.1002/jsfa.9912 Aparicio R., 2013, HDB OLIVE OIL, P431 Aprile A, 2019, ANTIOXIDANTS-BASEL, V8, DOI 10.3390/antiox8050138 Azizian H, 2015, LIPIDS, V50, P705, DOI 10.1007/s11745-015-4038-4 Banko M, 2001, 39TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P26, DOI 10.3115/1073012.1073017 Beghi R, 2017, REV ANAL CHEM, V36, DOI 10.1515/revac-2016-0016 Ben Mohamed M, 2018, BIOCHEM SYST ECOL, V78, P84, DOI 10.1016/j.bse.2018.04.005 Benincasa C., 2018, MASS SPECTROM PURIF, V4, P1, DOI [10.4172/2469-9861.1000124, DOI 10.4172/2469-9861.1000124, 10.4172/2469-9861.1000124.] Benincasa C, 2007, ANAL CHIM ACTA, V585, P366, DOI 10.1016/j.aca.2006.12.040 Benito M, 2010, FOOD SCI TECHNOL INT, V16, P523, DOI 10.1177/1082013210367542 Bevilacqua M, 2012, ANAL CHIM ACTA, V717, P39, DOI 10.1016/j.aca.2011.12.035 Bucci R, 2002, J AGR FOOD CHEM, V50, P413, DOI 10.1021/jf010696v Cappelli L, 2019, MANAG MARK, V14, P31, DOI 10.2478/mmcks-2019-0003 Cayuela JA, 2017, J FOOD ENG, V202, P79, DOI 10.1016/j.jfoodeng.2017.01.015 Chiavaro E, 2011, EUR J LIPID SCI TECH, V113, P1509, DOI 10.1002/ejlt.201100174 Cosio MS, 2006, ANAL CHIM ACTA, V567, P202, DOI 10.1016/j.aca.2006.03.035 Pereira AFC, 2008, FOOD RES INT, V41, P341, DOI 10.1016/j.foodres.2007.12.013 Criado MN, 2007, FOOD CHEM, V100, P748, DOI 10.1016/j.foodchem.2005.10.035 de Torres A, 2018, LWT-FOOD SCI TECHNOL, V90, P22, DOI 10.1016/j.lwt.2017.12.003 Ramirez-Anaya JD, 2015, FOOD CHEM, V188, P430, DOI 10.1016/j.foodchem.2015.04.124 Domenici V, 2014, J AGR FOOD CHEM, V62, P9317, DOI 10.1021/jf503818k Meras ID, 2018, TALANTA, V178, P751, DOI 10.1016/j.talanta.2017.09.095 El-Sheikh H. M., 2016, AM J FOOD TECHNOL, V11, P1, DOI [DOI 10.3923/ajft.2016.1.11, DOI 10.3923/AJFT.2016.1.11] Espineira M, 2016, WOODHEAD PUBL FOOD S, V301, P3, DOI 10.1016/B978-0-08-100310-7.00001-6 Ferronato M., 2016, THESIS Foresee FD, 1997, 1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, P1930, DOI 10.1109/ICNN.1997.614194 Fuentes E, 2012, FOOD ANAL METHOD, V5, P1311, DOI 10.1007/s12161-012-9379-5 Galtier O, 2007, ANAL CHIM ACTA, V595, P136, DOI 10.1016/j.aca.2007.02.033 Gertz C, 2006, EUR J LIPID SCI TECH, V108, P1062, DOI 10.1002/ejlt.200600164 Girelli CR, 2018, METABOLITES, V8, DOI 10.3390/metabo8040060 Giuffrida D, 2011, FOOD CHEM, V124, P1119, DOI 10.1016/j.foodchem.2010.07.012 Giuliani A, 2011, CRIT REV FOOD SCI, V51, P678, DOI 10.1080/10408391003768199 Harwood J., 2000, HDB OLIVE OIL ANAL P Infantino A, 2016, J PLANT PATHOL, V98, P541, DOI 10.4454/JPP.V98I3.008 Jaswir I, 2011, J MED PLANTS RES, V5, P7119, DOI 10.5897/JMPRx11.011 Jimenez-Carvelo AM, 2017, LWT-FOOD SCI TECHNOL, V86, P174, DOI 10.1016/j.lwt.2017.07.050 Karunathilaka SR, 2016, J FOOD SCI, V81, pC2390, DOI 10.1111/1750-3841.13432 KENNARD RW, 1969, TECHNOMETRICS, V11, P137, DOI 10.2307/1266770 Knolhoff AM, 2016, J CHROMATOGR A, V1428, P86, DOI 10.1016/j.chroma.2015.08.059 Kumar S, 2011, FOOD CHEM, V127, P1335, DOI 10.1016/j.foodchem.2011.01.094 Langsrud O, 2002, J ROY STAT SOC D-STA, V51, P305, DOI 10.1111/1467-9884.00320 MACKAY DJC, 1992, NEURAL COMPUT, V4, P415, DOI 10.1162/neco.1992.4.3.448 MINGUEZMOSQUERA MI, 1990, J AM OIL CHEM SOC, V67, P192, DOI 10.1007/BF02539624 Mossoba MM, 2017, LIPIDS, V52, P443, DOI 10.1007/s11745-017-4250-5 Moyano MJ, 2008, FOOD RES INT, V41, P505, DOI 10.1016/j.foodres.2008.03.007 Murphy KJ, 2019, NUTR RES, V61, P64, DOI 10.1016/j.nutres.2018.10.006 Nenadis N, 2017, EUR J LIPID SCI TECH, V119, DOI 10.1002/ejlt.201600148 Ou GZ, 2015, ANAL METHODS-UK, V7, P5731, DOI 10.1039/c5ay00048c Oussama, 2015, SKY J FOOD SCI, V4, P60 Perri E, 2012, OLIVE GERMPLASM - THE OLIVE CULTIVATION, TABLE OLIVE AND OLIVE OIL INDUSTRY IN ITALY, P265, DOI 10.5772/51796 Portarena S, 2017, FOOD CHEM, V215, P1, DOI 10.1016/j.foodchem.2016.07.135 Portarena S, 2015, FOOD CONTROL, V57, P129, DOI 10.1016/j.foodcont.2015.03.052 Psomiadou E, 2001, J SCI FOOD AGR, V81, P640, DOI 10.1002/jsfa.859 Qiao YQ, 2018, ARCH MED SCI, V14, P617, DOI 10.5114/aoms.2016.59871 Roca M, 2001, J AM OIL CHEM SOC, V78, P133, DOI 10.1007/s11746-001-0233-z Rotondo A, 2019, FOOD ANAL METHOD, V12, P1238, DOI 10.1007/s12161-019-01460-4 SCHWARTZ SJ, 1981, J AGR FOOD CHEM, V29, P533, DOI 10.1021/jf00105a025 Tengstrand E, 2013, ANAL BIOANAL CHEM, V405, P1237, DOI 10.1007/s00216-012-6506-5 Torabfam M., 2019, ENVIRON ENG, V6, P7, DOI [10.37023/EE.6.1.2, DOI 10.37023/EE.6.1.2, 10.37023/ee.6.1.2] Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Violino S, 2019, FOODS, V8, DOI 10.3390/foods8110529 Violino S, 2019, EUR FOOD RES TECHNOL, V245, P2089, DOI 10.1007/s00217-019-03321-0 Vlek C, 2007, J SOC ISSUES, V63, P1, DOI 10.1111/j.1540-4560.2007.00493.x NR 65 TC 19 Z9 19 U1 4 U2 8 PD JUN PY 2020 VL 9 IS 6 AR 834 DI 10.3390/foods9060834 WC Food Science & Technology SC Food Science & Technology UT WOS:000551526800001 DA 2022-12-14 ER PT J AU Kim, TG Heo, SW Min, WJ Han, TH Yim, YH Yu, H Kim, KJ AF Kim, Tae Gun Heo, Sung Woo Min, Won Ja Han, Tae-Hun Yim, Yong-Hyeon Yu, Hyunung Kim, Kyung Joong TI Traceable quantitative analysis of Ag x Cu1-x alloy films by ID ICP-MS, RBS and MEIS SO METROLOGIA DT Article DE traceability; quantitative analysis; ID ICP-MS; MEIS; RBS ID MASS-SPECTROMETRY; SURFACE-ANALYSIS; THIN-FILMS; SIMS; ACCURATE; MULTILAYER; SCATTERING; XPS AB The measurement traceability of Rutherford backscattering spectroscopy (RBS), medium energy ion scattering spectroscopy (MEIS) and isotope dilution inductively coupled plasma mass spectrometry (ID ICP-MS) was compared for the quantitative analysis of alloy thin films. A set of thin Ag x Cu1-x alloy films were selected as a model alloy system for the quantitative analysis of MEIS and ID ICP-MS. Two sets of five Ag x Cu1-x alloy films with different mole fractions were grown on Si (100) wafers by ion beam sputter deposition. The mole fractions of thick Ag x Cu1-x alloy films (100 nm) measured by RBS and ID ICP-MS showed a great agreement within 0.4% difference. The mole fractions of thin Ag x Cu1-x alloy films (10 nm) measured with MEIS and ID ICP-MS also showed a small difference of about 1.0%. As a result, ID ICP-MS, RBS and MEIS can be used to certify the mole fractions of thin alloy reference films. ID ICP-MS is an absolute method for the mole fraction analysis of thin Ag x Cu1-x alloy films. Although the contribution of sample homogeneity was included, the uncertainties of ID ICP-MS results were much smaller than those of RBS and MEIS. C1 [Kim, Tae Gun; Yu, Hyunung; Kim, Kyung Joong] KRISS, Surface Anal Team, 267 Gajeong Ro, Daejeon 34113, South Korea. [Heo, Sung Woo; Han, Tae-Hun; Yim, Yong-Hyeon] KRISS, Inorgan Metrol Grp, 267 Gajeong Ro, Daejeon 34113, South Korea. [Min, Won Ja] HB Solut, 77-26 Yeonamyulgeum Ro, Asan, Chungcheongnam, South Korea. [Han, Tae-Hun] Kyungpook Natl Univ, Dept Chem, 80 Daehak Ro, Daegu 41566, South Korea. C3 Korea Research Institute of Standards & Science (KRISS); Korea Research Institute of Standards & Science (KRISS); Kyungpook National University RP Kim, KJ (corresponding author), KRISS, Surface Anal Team, 267 Gajeong Ro, Daejeon 34113, South Korea. EM kjkim@kriss.re.kr CR ANDERSEN HH, 1980, PHYS REV A, V21, P1891, DOI 10.1103/PhysRevA.21.1891 [Anonymous], 2004, 18118 ISO [Anonymous], 2008, 9832008 ISOIEC Betru TG, 2020, MASS SPECTROM LETT, V11, P108, DOI 10.5478/MSL.2020.11.4.108 Choi J, 2017, B KOREAN CHEM SOC, V38, P211, DOI 10.1002/bkcs.11066 Chu W., 1978, BACKSCATTERING SPECT, DOI 10.1016/B978-0-12-173850-1.50008-9 FASSETT JD, 1989, ANAL CHEM, V61, pA643, DOI 10.1021/ac00185a715 Hsieh J, 2016, MATERIALS, V9, DOI 10.3390/ma9110914 Jang JS, 2012, METROLOGIA, V49, P522, DOI 10.1088/0026-1394/49/4/522 Jeynes C, 2012, ANAL CHEM, V84, P6061, DOI 10.1021/ac300904c Jeynes C, 2016, ANALYST, V141, P5944, DOI 10.1039/c6an01167e Kim KJ, 2007, SURF INTERFACE ANAL, V39, P665, DOI 10.1002/sia.2575 Kim KJ, 2012, SURF INTERFACE ANAL, V44, P192, DOI 10.1002/sia.3795 Kim KJ, 1998, SURF INTERFACE ANAL, V26, P9, DOI 10.1002/(SICI)1096-9918(199801)26:1<9::AID-SIA341>3.0.CO;2-I Kim KJ, 2007, APPL SURF SCI, V253, P6000, DOI 10.1016/j.apsusc.2006.12.116 Kim KJ, 2016, METROLOGIA, V53, DOI 10.1088/0026-1394/53/1A/08011 Kim KJ, 2010, METROLOGIA, V47, DOI 10.1088/0026-1394/47/1A/08011 Kim KJ, 2010, METROLOGIA, V47, P253, DOI 10.1088/0026-1394/47/3/016 Kim SH, 2016, TRAC-TREND ANAL CHEM, V85, P98, DOI 10.1016/j.trac.2016.09.004 Kim SH, 2016, ANAL METHODS-UK, V8, P796, DOI 10.1039/c5ay02040a LECUYER J, 1979, NUCL INSTRUM METHODS, V160, P337, DOI 10.1016/0029-554X(79)90612-8 Lee HS, 2015, METROLOGIA, V52, P619, DOI 10.1088/0026-1394/52/5/619 Linnarsson MK, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4750195 M SARGENT, 2002, GUIDELINES ACHIEVING, P1, DOI DOI 10.1039/9781847559302-00001 Min WJ, 2019, SURF INTERFACE ANAL, V51, P712, DOI 10.1002/sia.6642 Oh WJ, 2018, APPL SURF SCI, V432, P72, DOI 10.1016/j.apsusc.2017.08.136 Seah M.P., 2003, SURFACE ANAL AUGER X Ziegler JF., 1985, TREATISE HEAVY ION S, V1 NR 28 TC 1 Z9 1 U1 3 U2 4 PD DEC PY 2021 VL 58 IS 6 AR 065004 DI 10.1088/1681-7575/ac28e2 WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000711172000001 DA 2022-12-14 ER PT J AU van Klinken, RD Fiedler, K Kingham, L Barbour, D AF van Klinken, Rieks D. Fiedler, Kathryn Kingham, Lloyd Barbour, Darryl TI The importance of distinguishing between demonstrating the efficacy and implementation of phytosanitary systems approaches SO CROP PROTECTION DT Article DE Fruit fly; IPPC; Market access; Pest risk assessment; Phytosanitary systems approaches; Trade; WTO AB A risk framework including four risk reduction objectives was developed to guide the selection of the most effective, least trade restrictive measures for use in phytosanitary systems approaches. Here we discuss its role in relation to control points, verification and traceability systems. Development of systems approaches is typically a two-step process, identifying and assessing measures to reduce pest risk to set a phytosanitary import requirement for a regulated article, and then agreeing on how those measures are to be implemented within a protocol or work plan. The risk framework was explicitly designed to address the first step, by classifying proposed measures according to how they reduce risk. We argue that control points, verification and traceability systems are most relevant to the implementation of protocols. Control points focus on where and when specific measures can be applied to mitigate risk, and should not be used as the basis for determining how the measures manage risk as measures applied at the one control point can manage risk in very different ways and times. Continued effort is required to develop, test and harmonise concepts that underpin phytosanitary systems approaches. C1 [van Klinken, Rieks D.; Fiedler, Kathryn] CSIRO, GPOB 2583, Brisbane, Qld 4001, Australia. [Kingham, Lloyd] Wagga Wagga Agr Inst, New South Wales Dept Primary Ind, PMB Pine Gully Rd, Wagga Wagga, NSW 2800, Australia. [Barbour, Darryl] Plant Hlth Australia, Level 1 Phipps Close, Canberra, ACT, Australia. C3 Commonwealth Scientific & Industrial Research Organisation (CSIRO); NSW Department of Primary Industries RP van Klinken, RD (corresponding author), CSIRO, GPOB 2583, Brisbane, Qld 4001, Australia. EM rieks.vanklinken@csiro.au CR Allen E, 2017, BIOL INVASIONS, V19, P3365, DOI 10.1007/s10530-017-1515-0 [Anonymous], 2016, COMPLIANCE PRODUCTIO Clarke A.R, 2019, BIOL MANAGEMENT BACT Codex, 1997, HAZ AN CRIT CONTR PO Ekramirad N., 2015, INNOV FOOD RES, V2, P6 FAO, 2017, 11 FAO ISPM, P37 FAO, 2017, 14 FAO IPPC, P16 FAO, 2011, 7 FAO IPPC, P10 Groefsema H, 2020, COMPUT IND, V115, DOI 10.1016/j.compind.2019.103181 Holt J, 2018, RISK ANAL, V38, P297, DOI 10.1111/risa.12852 IAEA, 2010, FAO IAEA GUID IMPL S Jamieson L. E., 2016, New Zealand Plant Protection, V69, P186 Moore SD, 2016, J ECON ENTOMOL, V109, P1564, DOI 10.1093/jee/tow139 Quinlan M.M., 2020, CROP PROTECT Quinlan M.M., 2009, PRATIQUE PD NO 4 2 R, P69 Rahman T, 2016, CROP PROT, V90, P170, DOI 10.1016/j.cropro.2016.09.001 van Klinken R.D., 2020, CROP PROT, V129, P10499, DOI [10.1016/j.cro-pro.2019.104994,104994, DOI 10.1016/J.CR0-PR0.2019.104994] NR 17 TC 0 Z9 0 U1 1 U2 2 PD JAN PY 2021 VL 139 AR 105287 DI 10.1016/j.cropro.2020.105287 WC Agronomy SC Agriculture UT WOS:000582806100003 DA 2022-12-14 ER PT J AU Ohtsuki, T Matsuoka, K Fuji, Y Nishizaki, Y Masumoto, N Sugimoto, N Sato, K Matsufuji, H AF Ohtsuki, Takashi Matsuoka, Kiyoaki Fuji, Yushiro Nishizaki, Yuzo Masumoto, Naoko Sugimoto, Naoki Sato, Kyoko Matsufuji, Hiroshi TI Development of an HPLC method with relative molar sensitivity based on H-1-qNMR to determine acteoside and pedaliin in dried sesame leaf powders and processed foods SO PLOS ONE DT Article ID POLYPHENOLS; STANDARD AB A high-performance liquid chromatography (HPLC) method with relative molar sensitivity (RMS) based on H-1 quantitative NMR spectroscopy (H-1-qNMR) has been developed for food ingredients such as acteoside (verbascoside) and pedaliin (pedalitin-6-O-glucoside) without requiring authentic and identical standards as the reliable analytical methods. This method is used methyl 4-hydroxybenzoate (MHB) as an alternative reference standard. Each RMS is also calculated from the ratio of each analyte's molar absorption coefficient to that of MHB after correcting the purities of the analytes and reference standard by H-1-qNMR. Therefore, this method can quantify several analytes with metrological traceability to the International System of Units (SI) using the RMS and one alternative reference standard. In this study, the content of acteoside and pedaliin in several samples, such as dried sesame leaf powders and commercially processed foods, can be determined by the proposed RMS method and demonstrated in good agreement that obtained by a conventional method. Moreover, the proposed method yields analytical data with SI-traceability without the need for an authentic and identical analyte standard. Thus, the proposed RMS method is a useful and practical tool for determining acteoside and pedaliin in terms of the accuracy of quantitative values, the routine analysis, and the cost of reagents. C1 [Ohtsuki, Takashi; Matsuoka, Kiyoaki; Fuji, Yushiro; Matsufuji, Hiroshi] Nihon Univ, Coll Bioresource Sci, Dept Food Biosci & Biotechnol, Fujisawa, Kanagawa, Japan. [Nishizaki, Yuzo; Masumoto, Naoko; Sugimoto, Naoki; Sato, Kyoko] Natl Inst Hlth Sci, Div Food Additives, Kawasaki Ku, Kawasaki, Kanagawa, Japan. C3 Nihon University; National Institute of Health Sciences - Japan RP Ohtsuki, T (corresponding author), Nihon Univ, Coll Bioresource Sci, Dept Food Biosci & Biotechnol, Fujisawa, Kanagawa, Japan. EM ohtsuki.takashi@nihon-u.ac.jp CR Fernandez L, 2005, PHARM BIOL, V43, P226, DOI 10.1080/13880200590928799 Fuji Y, 2018, ACS OMEGA, V3, P17287, DOI 10.1021/acsomega.8b02798 Fuji Y, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0194449 Kitamaki Y, 2017, ANAL CHEM, V89, P6963, DOI 10.1021/acs.analchem.6b05074 Korshavn KJ, 2015, SCI REP-UK, V5, DOI 10.1038/srep17842 Liao X, 2020, J PHARM ANAL Masumoto N, 2019, J NAT MED-TOKYO, V73, P566, DOI 10.1007/s11418-019-01306-7 Matsufuji H, 2011, J JPN SOC FOOD SCI, V58, P88, DOI 10.3136/nskkk.58.88 Nishizaki Y, 2019, FOOD ADDIT CONTAM A, V36, P203, DOI 10.1080/19440049.2018.1556817 Nishizaki Y, 2018, FOOD ADDIT CONTAM A, V35, P838, DOI 10.1080/19440049.2018.1440642 Nishizaki Y, 2015, FOOD HYG SAFE SCI, V56, P185, DOI 10.3358/shokueishi.56.185 Ohtsuki T, 2015, TALANTA, V131, P712, DOI 10.1016/j.talanta.2014.08.002 Quinn TJ, 1997, METROLOGIA, V34, P61, DOI 10.1088/0026-1394/34/1/9 Rehecho S, 2011, LWT-FOOD SCI TECHNOL, V44, P875, DOI 10.1016/j.lwt.2010.11.035 Seo ES, 2013, MOL CELLS, V35, P348, DOI 10.1007/s10059-013-0021-1 Shiao YJ, 2017, INT J MOL SCI, V18, DOI 10.3390/ijms18040895 Tahara M, 2014, ENV SCI, V27, P142 Takahashi M, 2018, SEP SCI PLUS, V1, P498, DOI 10.1002/sscp.201800081 Takahashi M, 2018, J CHROMATOGR A, V1555, P45, DOI 10.1016/j.chroma.2018.04.029 Wang J, 2015, TOXICOL APPL PHARM, V285, P128, DOI 10.1016/j.taap.2015.04.004 Wang SQ, 2010, BIOORG MED CHEM LETT, V20, P6411, DOI 10.1016/j.bmcl.2010.09.086 Wells RJ, 2004, ACCREDIT QUAL ASSUR, V9, P450, DOI 10.1007/s00769-004-0779-0 Wu YT, 2007, J CHROMATOGR B, V853, P281, DOI 10.1016/j.jchromb.2007.03.033 Wu YT, 2006, J CHROMATOGR B, V844, P89, DOI 10.1016/j.jchromb.2006.07.011 Xiong QB, 1996, BIOL PHARM BULL, V19, P1580, DOI 10.1248/bpb.19.1580 Yuan JW, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0162696 NR 26 TC 7 Z9 7 U1 0 U2 9 PD DEC 3 PY 2020 VL 15 IS 12 AR e0243175 DI 10.1371/journal.pone.0243175 WC Multidisciplinary Sciences SC Science & Technology - Other Topics UT WOS:000597149100160 DA 2022-12-14 ER PT J AU Fernandez-Ibanez, V Fearn, T Soldado, A de la Roza-Delgado, B AF Fernandez-Ibanez, V. Fearn, T. Soldado, A. de la Roza-Delgado, B. TI Development and validation of near infrared microscopy spectral libraries of ingredients in animal feed as a first step to adopting traceability and authenticity as guarantors of food safety SO FOOD CHEMISTRY DT Article DE Near infrared (NIR) microscopy; Spectral libraries; Discriminant analysis; KNN; Safety; Ingredients; Animal feeds ID BANNED MEAT; BONE MEAL AB Traceability of animal products has become a priority for governments of the developed countries as a guarantee of food safety. Near infrared microscopy (NIRM) has been proposed as an alternative technology to detect and quantify banned ingredients in feedstuffs. The great advantage of this technique is its objectivity, whilst retaining the sensitivity of classic microscopy. The aim of this work was to build an NIRM reference spectral library on animal feed, consisting of samples of animal feed ingredients and possible contaminants, and to assess its ability to discriminate between ingredients using an internal cross-validation. A total of 48,899 spectra were measured on 229 samples representing 30 different ingredients. The method chosen for classification was K-nearest-neighbours (KNN) using first derivative spectra. Although the results showed an overall classification error of 35.88%, there was good discrimination between ingredients of animal and vegetable origin. There was some confusion between similar vegetable ingredients but this is unimportant. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Fernandez-Ibanez, V.; Soldado, A.; de la Roza-Delgado, B.] SERIDA, Reg Inst Res & Agrofood Dev, Dept Anim Nutr Grasslands & Forages, Villaviciosa 33300, Spain. [Fearn, T.] UCL, Dept Stat Sci, London WC1E 6BT, England. C3 Servicio Regional Investigacion Desarrollo Agroalimentario - SERIDA; University of London; University College London RP de la Roza-Delgado, B (corresponding author), SERIDA, Reg Inst Res & Agrofood Dev, Dept Anim Nutr Grasslands & Forages, POB 13, Villaviciosa 33300, Spain. EM broza@serida.org CR Baeten V, 2005, ANAL BIOANAL CHEM, V382, P149, DOI 10.1007/s00216-005-3193-5 BAETEN V, 2001, P 6 INT S FOOD AUTH, P1 Baeten V., 2001, NIRS NEWS, V12, P12 Barnes R., 1993, J NEAR INFRARED SPEC, V1, P185, DOI DOI 10.1255/JNIRS.21 BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Clarke FC, 2002, APPL SPECTROSC, V56, P1475, DOI 10.1366/00037020260377797 De la Haba MJ, 2007, J NEAR INFRARED SPEC, V15, P81, DOI 10.1255/jnirs.688 de la Roza-Delgado B, 2007, FOOD CHEM, V105, P1164, DOI 10.1016/j.foodchem.2007.02.041 DEBLAS C, 1999, FUNDACION ESPANOLA D Fernandez-Ibanez MDV, 2008, J NEAR INFRARED SPEC, V16, P243, DOI 10.1255/jnirs.783 DELAROZADELGADO B, 2007, NEAR INFRARED SPECTR, P140 GIZZI G, 2002, RENDERING PROCESSES MARK H, 2001, NEAR INFRARED TECHNO, P233 NAES T, 2002, MULTIVARIATE CALIBRA, P221 *PERK ELM INSTR LL, 2002, IR SPECTR SOFTW US G PIRAUX F, 2000, NEAR INFRARED SPECTR, P535 Shenk J.S., 2008, HDB NEAR INFRARED AN, VVolume 13, P347 von Holst C, 2008, ANAL BIOANAL CHEM, V392, P313, DOI 10.1007/s00216-008-2232-4 [No title captured] NR 19 TC 9 Z9 10 U1 0 U2 25 PD AUG 1 PY 2010 VL 121 IS 3 BP 871 EP 877 DI 10.1016/j.foodchem.2009.10.072 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000276292700035 DA 2022-12-14 ER PT J AU Li, L Zuo, ZT Wang, YZ AF Li, Lian Zuo, Zhi-Tian Wang, Yuan-Zhong TI Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR SO PHYTOCHEMICAL ANALYSIS DT Article DE FT-NIR; geographical traceability; PLS-DA; ResNet; Wolfiporia cocos ID INFRARED-SPECTROSCOPY; PORIA-COCOS; SUPPRESSION AB Introduction Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology. Objective This research aimed to identify the geographical traceability for different parts of W. cocos. Methods The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm(-1), 6300-5150 cm(-1) and 4450-4050 cm(-1) of original spectra and the second-order derivative (SD) spectra preprocessed. Results From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified. Conclusion Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation. C1 [Li, Lian; Zuo, Zhi-Tian; Wang, Yuan-Zhong] Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. [Li, Lian] Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming, Yunnan, Peoples R China. C3 Yunnan Academy of Agricultural Sciences; Yunnan University of Chinese Medicine RP Zuo, ZT; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China. EM zzhitian0331@126.com; boletus@126.com CR Alamprese C, 2013, LWT-FOOD SCI TECHNOL, V53, P225, DOI 10.1016/j.lwt.2013.01.027 Basati Z, 2018, SPECTROCHIM ACTA A, V203, P308, DOI 10.1016/j.saa.2018.05.123 Chen JB, 2018, J MOL STRUCT, V1163, P327, DOI 10.1016/j.molstruc.2018.02.061 Cheng WW, 2019, J FOOD ENG, V246, P200, DOI 10.1016/j.jfoodeng.2018.10.029 Commission CP., 2020, PHARMACOPOEIA PEOPLE, P251 Dong JE, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108132 Gao YQ, 2016, PHARM BIOL, V54, P2528, DOI 10.3109/13880209.2016.1168853 Hao X, 2016, INT J SEMANT COMPUT, V10, P417, DOI 10.1142/S1793351X16500045 Huang YJ, 2020, J ETHNOPHARMACOL, V258, DOI 10.1016/j.jep.2020.112566 Jiang Y, 2020, J ETHNOPHARMACOL, V257, DOI 10.1016/j.jep.2020.112851 KANEMATS.A, 1970, YAKUGA ZASSHI, V90, P475, DOI 10.1248/yakushi1947.90.4_475 LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539 Li JR, 2014, J MOL STRUCT, V1069, P229, DOI 10.1016/j.molstruc.2014.03.067 Lu ZY, 2020, PATTERN RECOGN LETT, V133, P173, DOI 10.1016/j.patrec.2020.03.007 Mellit A, 2008, PROG ENERG COMBUST, V34, P574, DOI 10.1016/j.pecs.2008.01.001 Nie AZ, 2020, FRONT PHARMACOL, V11, DOI 10.3389/fphar.2020.505249 NODA I, 1993, APPL SPECTROSC, V47, P1329, DOI 10.1366/0003702934067694 NODA I, 1990, APPL SPECTROSC, V44, P550, DOI 10.1366/0003702904087398 Noda I, 2014, J MOL STRUCT, V1069, P3, DOI 10.1016/j.molstruc.2014.01.025 Rinnan A, 2009, TRAC-TREND ANAL CHEM, V28, P1201, DOI 10.1016/j.trac.2009.07.007 Sahu RK, 2016, APPL SPECTROSC REV, V51, P484, DOI 10.1080/05704928.2016.1157809 van der Maaten L, 2008, J MACH LEARN RES, V9, P2579 Walkowiak A, 2019, SPECTROCHIM ACTA A, V208, P222, DOI 10.1016/j.saa.2018.10.008 Wang Y, 1998, ANALUSIS, V26, pM64, DOI 10.1051/analusis:199826040064 Wu ZL, 2014, J ETHNOPHARMACOL, V155, P563, DOI 10.1016/j.jep.2014.05.054 Wu ZF, 2019, PATTERN RECOGN, V90, P119, DOI 10.1016/j.patcog.2019.01.006 Xu H, 2019, J AGR FOOD CHEM, V67, P10871, DOI 10.1021/acs.jafc.9b04613 Yi Y, 2020, SPECTROCHIM ACTA A, V240, DOI 10.1016/j.saa.2020.118623 Yue JQ, 2021, MICROCHEM J, V160, DOI 10.1016/j.microc.2020.105731 Zagoruyko S., 2016, ARXIV161203928, DOI DOI 10.5244/C.30.87 Zhao YL, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0089100 Zhou YP, 2006, J CHEMOMETR, V20, P13, DOI 10.1002/cem.974 NR 32 TC 2 Z9 2 U1 11 U2 14 PD JUL PY 2022 VL 33 IS 5 BP 792 EP 808 DI 10.1002/pca.3130 EA MAY 2022 WC Biochemical Research Methods; Plant Sciences; Chemistry, Analytical SC Biochemistry & Molecular Biology; Plant Sciences; Chemistry UT WOS:000789220000001 DA 2022-12-14 ER PT J AU Campos-Climent, V Apetrei, A Chaves-Avila, R AF Campos-Climent, Vanessa Apetrei, Andreea Chaves-Avila, Rafael TI Delphi method applied to horticultural cooperatives SO MANAGEMENT DECISION DT Article DE Delphi method; Strategic analysis; Strategy design and evaluation; Horticultural cooperatives; Horticulture AB Purpose - Agricultural cooperatives have been able to become a strong and consolidated organizational form, although the new challenges of globalization and trade liberalization require changes in the strategic approach. The requirements of the distribution companies, consumers and government about the concentration of demand, traceability, food safety and respect for the environment had led to a thorough reorganization of agricultural food systems. So it is necessary to undertake a strategic review of horticultural cooperatives in order to conduct a strategic assessment and hence identify the strategic actions to be followed in the coming years. This paper seeks to address these issues. Design/methodology/approach - An empirical study has been carried out during the first half of 2011 consisting in the application of the Delphi method and sending a questionnaire to experts whose purpose was to gain a view of the strategic situation of horticultural cooperatives in Spain. The Delphi method is a projection technique of the qualitative and subjective type which is appropriate for studies where there is little information on the subject to be analysed, and also for exploratory studies, as it is the case study of examining the role of agricultural cooperatives in coming out of the crisis of Mediterranean agriculture. Findings - The performed Delphi analysis revealed that Mediterranean agriculture suffers from a severe crisis for which the solutions are hard to find, although the existence of the agricultural cooperatives and certain specific forms of performance and financing can partly improve the described situation. The application of the SWOT analysis based on the opinions of the experts provided sufficient detailed insights of the actual situation of the cooperatives. Thus, from the Delphi SWOT applied to Mediterranean agriculture and agricultural cooperatives, the authors can make some important assessments which are included in their paper. Originality/value - It is a forward-looking analysis that tries to give measures to the sector, but measures that come from the sector, in order to face the Mediterranean agriculture crisis. C1 [Campos-Climent, Vanessa] Univ Valencia, Dept Management, Valencia, Spain. [Apetrei, Andreea] Alexandru Ioan Cuza Univ, Iasi, Romania. [Chaves-Avila, Rafael] Univ Valencia, Appl Econ Dept, Valencia, Spain. C3 University of Valencia; Alexandru Ioan Cuza University; University of Valencia RP Campos-Climent, V (corresponding author), Univ Valencia, Dept Management, Valencia, Spain. EM vanessa.campos@uv.es CR Camison C., 2008, REV EUROPEA DIRECCIO, V17, P79 Campos V., 2001, PAPERS TURISME, P94 Castella M., 2007, FORUM CALIDAD, P17 CORBETTA P., 2003, METODOLOGIA TECNICAS Dalkey N.C., 1971, RAND CORPORATION REP, V189, P1 Dios-Palomares R, 2010, REV ESTUD EMPRESARIA, P54 Fernandez-Zamudio M.A., 2006, CIRIEC ESPAN REV EC, P193 FILDES R, 1978, LONG RANGE PLANN, V11, P29, DOI 10.1016/0024-6301(78)90005-5 Filippi M., 2006, Economie Rurale, P20 Gallego J.R., 2008, CIRIEC ESPAN REV EC, P7 Gupta UG, 1996, TECHNOL FORECAST SOC, V53, P185, DOI 10.1016/S0040-1625(96)00094-7 Julia J.F., 2003, CIRIEC ESPAN REV EC, P231 JuliaIgual J. F., 2002, Revista de Economia Publica, Social y Cooperativa, P25 Landeta J., 1999, METODO DELPHI TECNIC Landeta J, 2008, TECHNOL FORECAST SOC, V75, P32, DOI 10.1016/j.techfore.2007.01.005 Landeta J, 2006, TECHNOL FORECAST SOC, V73, P467, DOI 10.1016/j.techfore.2005.09.002 Martin M.M., 2004, ALTA DIRECCION, P77 Mateos Ronco A., 2009, Revista Espanola de Estudios Agrosociales y Pesqueros, P77 MELIA E, 2008, ESTUDIOS EC APLICADA, V26, P57 Melian A., 2011, EMPRENDIMIENTO EC SO, P33 Monzon Jose Luis, 2003, CIRIEC ESPANA REV EC, V44, P9 Okoli C, 2004, INFORM MANAGE-AMSTER, V42, P15, DOI 10.1016/j.im.2003.11.002 Pashiardis P., 1993, INT J EDUC MANAG, V7, P8 Rikkonen P, 2005, AGR FOOD SCI, V14, P205, DOI 10.2137/145960605775013227 Rodriguez-Alvarez J.A., 2008, APPROACHES TRENDS CU, P209 Rojas J.L., 2007, B EC ICE INFORM COME, P57 Ruiz Jimenez M. C., 2006, Revista de Economia Publica, Social y Cooperativa, P65 SANCHEZ Maria Paloma, 1999, EKONOMIAZ REV VASCA, P188 Schmid O., 2007, Revista Espanola de Estudios Agrosociales y Pesqueros, P15 Segui E., 2010, CIRIEC ESPANA REV EC, P35 Segui E., 2007, THESIS U POLITECNICA Segui-Mas E, 2010, REVESCO-REV ESTUD CO, P107 Segui-Mas E, 2009, INTERCIENCIA, V34, P718 Stake R.E., 1998, INVESTIGACION ESTUDI UTECO-Valencia, 2004, EST SECT COOP AGR RI Valentinov V., 2015, ORG I EC WHY ARE COO, V2007, P55, DOI [10.1017/S1744137406000555, DOI 10.1017/S1744137406000555] NR 36 TC 37 Z9 38 U1 1 U2 24 PY 2012 VL 50 IS 7-8 BP 1266 EP 1284 DI 10.1108/00251741211247003 WC Business; Management SC Business & Economics UT WOS:000310184500007 DA 2022-12-14 ER PT J AU El Sheikha, AF Bouvet, JM Montet, D AF El Sheikha, Aly F. Bouvet, Jean-Marc Montet, Didier TI Biological bar code for determining the geographical origin of fruits using 285 rDNA fingerprinting of fungal communities by PCR-DGGE: an application to Shea tree fruits SO QUALITY ASSURANCE AND SAFETY OF CROPS & FOODS DT Article DE geographical origin; PCR-DGGE; Shea tree fruits; traceability; 285 rDNA fingerprinting. ID GRADIENT GEL-ELECTROPHORESIS; BACTERIAL; DYNAMICS AB Objectives Shea tree is a multi-purpose tree daily used by rural African communities. Economic importance of Shea tree fruits has been rising and achieving a great success in African, American and European markets. Shea butter is used mainly in chocolate industry, cosmetic or pharmacological products. Traceability is now one of the great concerns of the customers and the lawyers. In view of the difficulties of installing these documentary systems in developing country particularly the countries of sub-Saharan Africa, the new strategies of traceability emerge. Methods Molecular technique using 28S rDNA profiles generated by polymerase chain reaction denaturing gradient gel electrophoresis was used to detect the variation in fungal community structures of Shea tree fruit from Senegal, Mali and Cameroon. Results 28S rDNA profiles were analysed by multivariate analysis, distinct microbial communities were detected. Band profiles of Shea tree fruit fungi from different countries were specific for each location and could be used as a bar code to discriminate the origin of fruits. Conclusion We propose the polymerase chain reaction denaturing gradient gel electrophoresis method as the fingerprinting of Shea tree fruits using 28S rDNA of fungi that provides the fruits with a unique bar code and make it possible to trace back the Shea tree fruit to their original locations. C1 [El Sheikha, Aly F.] Menoufia Univ, Fac Agr, Minufiya Govt, Dept Food Sci & Technol, Shibin Al Kawm 32511, Egypt. [El Sheikha, Aly F.; Montet, Didier] CIRAD, Ctr Cooperat Int Rech Agron Dev, UMR Qualisud, Montpellier, France. [Bouvet, Jean-Marc] CIRAD, Ctr Cooperat Int Rech Agron Dev, UPR Genet Forestiere 39, Montpellier, France. C3 Egyptian Knowledge Bank (EKB); Menofia University; CIRAD; Universite de Montpellier; CIRAD RP El Sheikha, AF (corresponding author), Menoufia Univ, Fac Agr, Minufiya Govt, Dept Food Sci & Technol, Shibin Al Kawm 32511, Egypt. EM elsheikha_aly@yahoo.com CR Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 Ampe F, 2001, INT J FOOD MICROBIOL, V65, P45, DOI 10.1016/S0168-1605(00)00502-X Anaissie EJ, 2001, CLIN INFECT DIS, V33, P1871, DOI 10.1086/324501 ben Omar N, 2000, APPL ENVIRON MICROB, V66, P3664, DOI 10.1128/AEM.66.9.3664-3673.2000 Diarrassouba N, 2007, PLANT GENETIC RESOUR, V152, P65 EL SHEIKHA A F, 2010, J LIFE SCI, V4, P9 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 ELSHEIKHA AF, 2010, FOOD BIOTEC IN PRESS Florez AB, 2006, INT J FOOD MICROBIOL, V110, P165, DOI 10.1016/j.ijfoodmicro.2006.04.016 *FSA, 2007, WHAT CONS WANT LIT R Ghidini S., 2006, Annali della Facolta di Medicina Veterinaria, Universita di Parma, V26, P193 GUIGNARD JL, 1986, ABREGE BOT Heyndrickx M, 1996, J MICROBIOL METH, V26, P247, DOI 10.1016/0167-7012(96)00916-5 *ISO, 2007, QUAL MAN SYST TRAC F Karakousis A, 2006, J MICROBIOL METH, V65, P38, DOI 10.1016/j.mimet.2005.06.008 Kowalchuk GA, 1997, APPL ENVIRON MICROB, V63, P1489, DOI 10.1128/AEM.63.4.1489-1497.1997 Le Nguyen DD, 2008, FRUITS, V63, P75, DOI 10.1051/fruits:2007049 LEROY JF, 1982, PRECIS BOT ANGIOSPER, V2, P201 Li XY, 2008, J ENVIRON SCI-CHINA, V20, P619, DOI 10.1016/S1001-0742(08)62103-8 Montet D., 2008, Aspects of Applied Biology, P11 Montet D., 2004, SEM FOOD SAF INT TRA Montet D, 2010, BIOFUTUR, P36 MUYZER G, 1995, ARCH MICROBIOL, V164, P165, DOI 10.1007/BF02529967 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Roling WFM, 2001, APPL ENVIRON MICROB, V67, P1995, DOI 10.1128/AEM.67.5.1995-2003.2001 SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 Smith CJ., 2005, MOL MICROBIAL ECOLOG, P72 SODEKO OO, 1987, MICROBIOS, V51, P133 Suzuki R., 2004, 15 INT C GEN INF PAC, pP034 *UN FAOSTAT, 2007, SHEA TREE FRUITS PRO *UNCTAD, 2006, 2885 UNCTAD van Hannen EJ, 1999, APPL ENVIRON MICROB, V65, P795 Wu ZH, 2002, J ENVIRON MONITOR, V4, P377, DOI 10.1039/b200490a NR 33 TC 21 Z9 21 U1 0 U2 14 PD MAR PY 2011 VL 3 IS 1 BP 40 EP 47 DI 10.1111/j.1757-837X.2010.00090.x WC Food Science & Technology SC Food Science & Technology UT WOS:000287887200006 DA 2022-12-14 ER PT J AU Vassileva, E Han, E Levy, I AF Vassileva, Emilia Han, Eunmi Levy, Isabelle TI Determination of low-level plutonium in seawater by sector field inductively coupled plasma mass spectrometry: method validation SO ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH DT Article; Proceedings Paper CT Euroanalysis Conference CY 2015 CL Bordeaux, FRANCE DE Seawater; Plutonium; Isotopic ratio; SF ICP-MS; Method validation; Uncertainty; Traceability ID ENVIRONMENTAL-SAMPLES; INTRODUCTION SYSTEM; ICP-MS; SEPARATION; PU; PERSPECTIVE; ISOTOPES; PU-240; RATIO AB Sources of plutonium isotopes to the marine environment are well defined, both spatially and temporally which makes plutonium (Pu) a potential tracer for oceanic processes. This paper presents the optimisation and validation of an analytical procedure for ultra-trace determination of Pu isotopes (Pu-239 and Pu-240) in seawater based on the external calibration and sector field inductively coupled plasma mass spectrometry (SF ICP-MS) determination. Additionally, method for Pu isotope ratio (Pu-240/Pu-239) in marine samples is also discussed. A combination of two-step anion exchange (AG1-X8) and one-step extraction chromatography (TEVA) was very efficient resulting in uranium (U) decontamination factor of 5 x 10(6)-1 x 10(8). A full validation approach in line with ISO 17025 standard and Eurachem guidelines was followed. With this in mind, blanks, recovery (87 +/- 8 %, k = 2), within-laboratory repeatability (5.6 %), limits of detection (0.12 and 0.08 fg mL(-1) for Pu-239 and Pu-240, respectively) and expanded uncertainty (13 %, k = 2) were systematically assessed. The procedure was applied for the determination of Pu-239 and Pu in seawater sample coming from Mediterranean Sea. Obtained results were in good agreement with results obtained with alpha spectrometry, applied on the same seawater sample. Pu/Pu-239 atom ratio in seawater sample from the Mediterranean Sea was also determined. The precision and accuracy of Pu-240/Pu-239 isotopic ratio analysis were carefully examined using NBS-947 isotopic standard. Pu-240/Pu-239 ratio was found to be 0.187 +/- 0.006 and is in agreement with accepted ratios for the global fallout of Pu. C1 [Vassileva, Emilia; Han, Eunmi; Levy, Isabelle] IAEA, Environm Labs, 4 Quai Antoine 1er, MC-98000 Monaco, Monaco. RP Vassileva, E (corresponding author), IAEA, Environm Labs, 4 Quai Antoine 1er, MC-98000 Monaco, Monaco. EM e.vasileva-veleva@iaea.org CR BIPM, 2008, BIPM MON, P1162 Bu WT, 2014, J CHROMATOGR A, V1337, P171, DOI 10.1016/j.chroma.2014.02.066 Chamizo E, 2010, NUCL INSTRUM METH B, V268, P1273, DOI 10.1016/j.nimb.2009.10.151 Dong W, 2010, J ENVIRON RADIOACTIV, V101, P622, DOI 10.1016/j.jenvrad.2010.03.011 Epov VN, 2007, J ANAL ATOM SPECTROM, V22, P1131, DOI 10.1039/b704901c GUM Workbench, 2001, SOFTW TOOL EXPR UNC Hernandez-Mendoza H, 2011, J ANAL ATOM SPECTROM, V26, P1509, DOI 10.1039/c0ja00093k Hirose K., 2006, RADIOACT ENV, P67 Hirose K., 2009, J NUCL RADIOCHEM SCI, V10, pR7 HORWITZ EP, 1995, ANAL CHIM ACTA, V310, P63, DOI 10.1016/0003-2670(95)00144-O ISO/IEC 17025, 2017, 17025 ISOIEC Jakopic R, 2007, APPL RADIAT ISOTOPES, V65, P504, DOI 10.1016/j.apradiso.2006.12.005 Jakopic R, 2010, J ANAL ATOM SPECTROM, V25, P815, DOI 10.1039/b925918j JCGM, 2008, EV MEAS DAT GUID EXP KEENEYKENNICUTT WL, 1985, GEOCHIM COSMOCHIM AC, V49, P2577, DOI 10.1016/0016-7037(85)90127-9 Ketterer ME, 2008, SPECTROCHIM ACTA B, V63, P719, DOI 10.1016/j.sab.2008.04.018 Kim CS, 2007, J ANAL ATOM SPECTROM, V22, P827, DOI 10.1039/b617568f KRAGTEN J, 1994, ANALYST, V119, P2161, DOI 10.1039/an9941902161 Krey P., 1976, TRANSURANIUM NUCLIDE Lindahl P, 2010, ANAL CHIM ACTA, V671, P61, DOI 10.1016/j.aca.2010.05.012 Lindahl P, 2010, MAR ENVIRON RES, V69, P73, DOI 10.1016/j.marenvres.2009.08.002 Livingston HD, 2002, HEALTH PHYS, V82, P656, DOI 10.1097/00004032-200205000-00012 Muramatsu Y, 1999, J ANAL ATOM SPECTROM, V14, P859, DOI 10.1039/a900071b Nakano M, 2003, J ENVIRON RADIOACTIV, V69, P85, DOI 10.1016/S0265-931X(03)00088-2 Nelms SM, 2001, J ANAL ATOM SPECTROM, V16, P333, DOI 10.1039/b007913h Nygren U, 2005, J ANAL ATOM SPECTROM, V20, P529, DOI 10.1039/b503160e Nygren U, 2003, J ANAL ATOM SPECTROM, V18, P1426, DOI 10.1039/b306357g Pointurier F, 2011, SPECTROCHIM ACTA B, V66, P261, DOI 10.1016/j.sab.2011.03.003 Qiao JX, 2010, J ANAL ATOM SPECTROM, V25, P1769, DOI 10.1039/c003222k Qiao JX, 2009, ANAL CHIM ACTA, V652, P66, DOI 10.1016/j.aca.2009.03.010 Quetel C, 2001, PLASMA SOURCE MASS S, P270 Thompson M, 2002, PURE APPL CHEM, V74, P835, DOI 10.1351/pac200274050835 Varga Z, 2007, MICROCHEM J, V85, P39, DOI 10.1016/j.microc.2006.02.006 Vassileva E, 2003, SPECTROCHIM ACTA B, V58, P1553, DOI 10.1016/S0584-8547(03)00100-9 Vintro LL, 2004, RADIOACTIV ENVIRONM, V6, P79 Yamada M, 2012, SCI TOTAL ENVIRON, V430, P20, DOI 10.1016/j.scitotenv.2012.04.065 Zheng J, 2006, TALANTA, V69, P1246, DOI 10.1016/j.talanta.2005.12.047 NR 37 TC 4 Z9 4 U1 1 U2 14 PD MAR PY 2017 VL 24 IS 9 BP 7898 EP 7910 DI 10.1007/s11356-016-6633-1 WC Environmental Sciences SC Environmental Sciences & Ecology UT WOS:000399162900006 DA 2022-12-14 ER PT J AU Cruz-Correia, R Boldt, I Lapao, L Santos-Pereira, C Rodrigues, PP Ferreira, AM Freitas, A AF Cruz-Correia, Ricardo Boldt, Isabel Lapao, Luis Santos-Pereira, Catia Rodrigues, Pedro Pereira Ferreira, Ana Margarida Freitas, Alberto TI Analysis of the quality of hospital information systems audit trails SO BMC MEDICAL INFORMATICS AND DECISION MAKING DT Article AB Background: Audit Trails (AT) are fundamental to information security in order to guarantee access traceability but can also be used to improve Health information System's (HIS) quality namely to assess how they are used or misused. This paper aims at analysing the existence and quality of AT, describing scenarios in hospitals and making some recommendations to improve the quality of information. Methods: The responsibles of HIS for eight Portuguese hospitals were contacted in order to arrange an interview about the importance of AT and to collect audit trail data from their HIS. Five institutions agreed to participate in this study; four of them accepted to be interviewed, and four sent AT data. The interviews were performed in 2011 and audit trail data sent in 2011 and 2012. Each AT was evaluated and compared in relation to data quality standards, namely for completeness, comprehensibility, traceability among others. Only one of the AT had enough information for us to apply a consistency evaluation by modelling user behaviour. Results: The interviewees in these hospitals only knew a few AT (average of 1 AT per hospital in an estimate of 21 existing HIS), although they all recognize some advantages of analysing AT. Four hospitals sent a total of 7 AT - 2 from Radiology Information System (RIS), 2 from Picture Archiving and Communication System (PACS), 3 from Patient Records. Three of the AT were understandable and three of the AT were complete. The AT from the patient records are better structured and more complete than the RIS/PACS. Conclusions: Existing AT do not have enough quality to guarantee traceability or be used in HIS improvement. Its quality reflects the importance given to them by the CIO of healthcare institutions. Existing standards (e.g. ASTM: E2147, ISO/TS 18308: 2004, ISO/IEC 27001: 2006) are still not broadly used in Portugal. C1 [Cruz-Correia, Ricardo; Boldt, Isabel; Lapao, Luis; Santos-Pereira, Catia; Rodrigues, Pedro Pereira; Ferreira, Ana Margarida; Freitas, Alberto] Univ Porto, Fac Med, CINTESIS Ctr Res Hlth Technol & Informat Syst, P-4100 Oporto, Portugal. [Lapao, Luis] Univ Nova Lisboa, Inst Higiene & Med Trop, Lisbon, Portugal. [Lapao, Luis] Univ Nova Lisboa, Inst Higiene & Med Trop, WHO Collaborating Ctr Hlth Workforce Policy & Pla, Lisbon, Portugal. [Cruz-Correia, Ricardo; Rodrigues, Pedro Pereira; Ferreira, Ana Margarida; Freitas, Alberto] Univ Porto, Fac Med, Dpt Hlth Informat & Decis Sci, P-4100 Oporto, Portugal. C3 Universidade do Porto; Universidade Nova de Lisboa; Institute of Hygiene & Tropical Medicine - UNL; Universidade Nova de Lisboa; Institute of Hygiene & Tropical Medicine - UNL; Universidade do Porto RP Cruz-Correia, R (corresponding author), Univ Porto, Fac Med, CINTESIS Ctr Res Hlth Technol & Informat Syst, Rua Campo Alegre 823, P-4100 Oporto, Portugal. EM ricardo.jc.correia@gmail.com CR Aalst W. M. P, 2011, PROCESS MINING DISCO [Anonymous], 2009, E214701 ASTM Bakker AR, 2007, INT J MED INFORM, V76, P438, DOI 10.1016/j.ijmedinf.2006.09.009 BARNETT O, 1990, JAMA-J AM MED ASSOC, V263, P2631, DOI 10.1001/jama.263.19.2631 Bose RPJC, 2013, 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), P127, DOI 10.1109/CIDM.2013.6597227 Bouarfa L, 2012, J BIOMED INFORM, V45, P1185, DOI 10.1016/j.jbi.2012.08.003 Chen DQ, 2009, CLIN TRIALS, V6, P378, DOI 10.1177/1740774509338228 Chen XM, 2005, P ANN INT IEEE EMBS, P562 Cruz-Correia RJ, 2010, BMC MED INFORM DECIS, V10, DOI 10.1186/1472-6947-10-15 De Weerdt J, 2012, INFORM SYST, V37, P654, DOI 10.1016/j.is.2012.02.004 European Institute for Health Records, 2013, EUROREC Feied CF, 2004, ACAD EMERG MED, V11, P1162, DOI 10.1197/j.aem.2004.08.010 Freitas A, 2013, PROCESS MINING ANAL Gschwandtner Theresia, 2012, Multidisciplinary Research and Practice for Information Systems. International Cross-Domain Conference and Workshop on Availability, Reliability and Security (CD-ARES 2012). Proceedings, P58, DOI 10.1007/978-3-642-32498-7_5 Guttman B, 1995, SPECIAL PUBLICATION Halamka JD, 1997, J AM MED INFORM ASSN, V4, P458, DOI 10.1136/jamia.1997.0040458 Hripcsak G, 2007, J AM MED INFORM ASSN, V14, P235, DOI 10.1197/jamia.M2206 IHE - Integrating the Healthcare Enterprise, 2005, IT TECHN FRAM PROF International Standard Organisation, 2004, 8601 ISO ISO, 2008, 250122008 ISOIEC ISO, 2006, HLTH INF HL7 VERS 3 Kim W, 2003, DATA MIN KNOWL DISC, V7, P81, DOI 10.1023/A:1021564703268 Kuhn KA, 2007, METHOD INFORM MED, V46, P500, DOI 10.1160/ME9058 Lang M, 2008, STUD HEALTH TECHNOL, V136, P229 Lapao L., 2011, ELECT J INFORM SYSTE, V14, P37 Mack EH, 2009, PEDIATR CRIT CARE ME, V10, P23, DOI 10.1097/PCC.0b013e3181936b23 Malin B, 2011, AMIA ANN S P AM MED Mans Ronny S., 2013, Process Support and Knowledge Representation in Health Care. BPM 2012 Joint Workshop. ProHealth 2012/KR4HC 2012. Revised Selected Papers, P140, DOI 10.1007/978-3-642-36438-9_10 Marshall G, 2004, 3881 RFC INT SOC, VRFC 3881, P1 Mills D., 2002, COMMUNICATIONS IEEE, V39, P1482 Mitchell Tom M., 1997, MACHINE LEARNING Muller H., 2003, HUBIB164 Nunn Sandra, 2009, J AHIMA, V80, P44 Oliveira P, 2005, INT C INF QUAL 2005 Rahm E., 2000, IEEE DATA ENG B, V23, P3 Reynolds R, 2009, FUNDAMENTALS LAW HLT Ribeiro L, 2010, HEALTHINF 2010: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON HEALTH INFORMATICS, P337 Rostad L, 2006, 22 ANN COMP SEC APPL Shapiro JS, 2006, ANN EMERG MED, V48, P426, DOI 10.1016/j.annemergmed.2006.03.032 Sousa L., 2011, THESIS U PORTO van der Aalst W, 2004, IEEE T KNOWL DATA EN, V16, P1128, DOI 10.1109/TKDE.2004.47 Walter M, 2011, BPM WORKSH 1, P433 Weiskopf NG, 2013, J AM MED INFORM ASSN, V20, P144, DOI 10.1136/amiajnl-2011-000681 NR 43 TC 17 Z9 17 U1 0 U2 30 PD AUG 6 PY 2013 VL 13 AR 84 DI 10.1186/1472-6947-13-84 WC Medical Informatics SC Medical Informatics UT WOS:000323788500001 DA 2022-12-14 ER PT J AU Liang, KH Liang, S Lu, LG Zhu, DZ Cheng, L AF Liang, Kehong Liang, Shan Lu, Lingang Zhu, Dazhou Cheng, Lei TI Geographical origin traceability of foxtail millet based on the combination of multi-element and chemical composition analysis SO INTERNATIONAL JOURNAL OF FOOD PROPERTIES DT Article DE Foxtail millet; mineral element; chemical composition; geographic origin ID PLASMA-MASS SPECTROMETRY; SETARIA-ITALICA; FINGER MILLET; FLOUR FUNCTIONALITY; RICE; QUALITY; ELEMENT; CHINA; WHEAT; GRAIN AB The potential approach of classifying foxtail millet according to geographical origin was investigated using mineral element and chemical composition analysis of samples from various provinces in China. Total 16 mineral elements and five chemical compositions of foxtail millets were analyzed. There were significant differences in 12 elements of millets from different regions. Notable differences were also observed for chemical composition, with Hebei samples showing higher protein content, Henan samples showing higher fat and ash contents and Shandong samples showed higher dietary fiber and amylose contents. Based on the combination of both methods, discriminant analysis provided optimal discrimination among the various geographical origins with a 95.2% classification rate. Our study provides an effective tool to trace the foxtail millet geographic origin through a combination of multi-element and chemical composition analysis. C1 [Liang, Kehong; Lu, Lingang; Zhu, Dazhou] Minist Agr, Inst Food & Nutr Dev, Beijing, Peoples R China. [Liang, Shan; Cheng, Lei] Beijing Technol & Business Univ, Beijing Adv Innovat Ctr Food Nutr & Human Hlth, Beijing, Peoples R China. [Liang, Shan; Cheng, Lei] Beijing Technol & Business Univ, Beijing Engn & Technol Res Ctr Food Addit, Beijing, Peoples R China. C3 Ministry of Agriculture & Rural Affairs; Beijing Technology & Business University; Beijing Technology & Business University RP Liang, KH (corresponding author), Minist Agr, Inst Food & Nutr Dev, Lab Qual & Nutr Funct Risk Assessment Agroprod Be, Beijing 100081, Peoples R China. EM liangkehong@caas.cn CR *AACC INT, 2000, 1450 AACC INT [Anonymous], TRACE ELEM SCI Cheajesadagul P, 2013, FOOD CHEM, V141, P3504, DOI 10.1016/j.foodchem.2013.06.060 Cheng QL, 2014, CHEM SPEC BIOAVAILAB, V26, P184, DOI 10.3184/095422914X14042081874564 Devi PB, 2014, J FOOD SCI TECH MYS, V51, P1021, DOI 10.1007/s13197-011-0584-9 Devisetti R, 2014, LWT-FOOD SCI TECHNOL, V59, P889, DOI 10.1016/j.lwt.2014.07.003 Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 Englyst HN, 1996, FOOD CHEM, V57, P15, DOI 10.1016/0308-8146(96)00056-8 Fujita S, 1996, FOOD CHEM, V55, P209, DOI 10.1016/0308-8146(95)00107-7 Gonzalez-Martin MI, 2014, FOOD CHEM, V145, P802, DOI 10.1016/j.foodchem.2013.08.103 Jiang SL, 2007, J AGR FOOD CHEM, V55, P9608, DOI 10.1021/jf071785w Jones MK, 2009, SCIENCE, V324, P730, DOI 10.1126/science.1172082 Kalinova J, 2006, PLANT FOOD HUM NUTR, V61, P45, DOI 10.1007/s11130-006-0013-9 Karabagias IK, 2017, INT J FOOD PROP, V20, pS520, DOI 10.1080/10942912.2017.1300811 Karabagias IK, 2017, EUR FOOD RES TECHNOL, V243, P889, DOI 10.1007/s00217-016-2803-0 Kitta K, 2005, J FOOD COMPOS ANAL, V18, P269, DOI 10.1016/j.jfca.2004.10.001 Kumar KVP, 2016, LWT-FOOD SCI TECHNOL, V73, P274, DOI 10.1016/j.lwt.2016.06.028 Liu Hang-sheng, 2008, Huanjing Kexue, V29, P1699 Liu MX, 2016, J INTEGR AGR, V15, P1449, DOI 10.1016/S2095-3119(15)61160-1 Liu RH, 2007, J CEREAL SCI, V46, P207, DOI 10.1016/j.jcs.2007.06.010 Liu W., 1995, J SHANXI AGR U NATUR, V3, P244 Luo DH, 2015, FOOD CHEM, V174, P197, DOI 10.1016/j.foodchem.2014.11.006 Maione C, 2016, COMPUT ELECTRON AGR, V121, P101, DOI 10.1016/j.compag.2015.11.009 Obilana AB, 2002, PSEUDOCEREALS AND LESS COMMON CEREALS, P177 Pawar VS, 1997, J FOOD SCI TECH MYS, V34, P416 Shobana S, 2007, J FOOD ENG, V79, P529, DOI 10.1016/j.jfoodeng.2006.01.076 Siwela M, 2010, FOOD CHEM, V121, P443, DOI 10.1016/j.foodchem.2009.12.062 SOWBHAGYA CM, 1971, STARKE, V23, P53, DOI 10.1002/star.19710230206 Sreenivasulu N, 2004, J PLANT PHYSIOL, V161, P467, DOI 10.1078/0176-1617-01112 [孙治军 SUN Zhijun], 2007, [水土保持学报, Journal of Soil and Water Conservation], V21, P57 Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Upadhyaya HD, 2006, GENET RESOUR CROP EV, V53, P679, DOI 10.1007/s10722-004-3228-3 Verma S, 2015, J FOOD SCI TECH MYS, V52, P5147, DOI 10.1007/s13197-014-1617-y Wang J, 2009, J AGR FOOD CHEM, V57, P10081, DOI 10.1021/jf902286p Wei F. S., 1991, ENV SCI, V04, P12, DOI [DOI 10.13227/J.HJKX.1991.04.005), 10.13227/j.hjkx.1991.04.005, DOI 10.13227/J.HJKX.1991.04.005] Wen Y, 2014, FOOD SCI BIOTECHNOL, V23, P1371, DOI 10.1007/s10068-014-0188-z Wu YL, 2015, FOOD CHEM, V174, P553, DOI 10.1016/j.foodchem.2014.11.096 Yasui A, 2000, BUNSEKI KAGAKU, V49, P405, DOI 10.2116/bunsekikagaku.49.405 Zhang GY, 2012, NAT BIOTECHNOL, V30, P549, DOI 10.1038/nbt.2195 Zhang HX, 2012, J SAUDI CHEM SOC, V16, P31, DOI 10.1016/j.jscs.2010.10.014 Zhao HY, 2011, J AGR FOOD CHEM, V59, P4397, DOI 10.1021/jf200108d NR 41 TC 6 Z9 7 U1 5 U2 16 PY 2018 VL 21 IS 1 BP 1769 EP 1777 DI 10.1080/10942912.2018.1506479 WC Food Science & Technology SC Food Science & Technology UT WOS:000441054300005 DA 2022-12-14 ER PT J AU Negrini, R Nicoloso, L Crepaldi, P Milanesi, E Colli, L Chegdani, F Pariset, L Dunner, S Leveziel, H Williams, JL Marsan, PA AF Negrini, R. Nicoloso, L. Crepaldi, P. Milanesi, E. Colli, L. Chegdani, F. Pariset, L. Dunner, S. Leveziel, H. Williams, J. L. Marsan, P. Ajmone TI Assessing SNP markers for assigning individuals to cattle populations SO ANIMAL GENETICS DT Article DE allocation method; cattle; SNPs; traceability ID GENETIC DIVERSITY; BREED ASSIGNMENT; INFERENCE; PATERNITY; TESTS AB The effectiveness of single nucleotide polymorphisms (SNPs) for the assignment of cattle to their source breeds was investigated by analysing a panel of 90 SNPs assayed on 24 European breeds. Breed assignment was performed by comparing the Bayesian and frequentist methods implemented in the STRUCTURE 2.2 and GENECLASS 2 software programs. The use of SNPs for the reallocation of known individuals to their breeds of origin and the assignment of unknown individuals was tested. In the reallocation tests, the methods implemented in STRUCTURE 2.2 performed better than those in GENECLASS 2, with 96% vs. 85% correct assignments respectively. In contrast, the methods implemented in GENECLASS 2 showed a greater correct assignment rate in allocating animals treated as unknowns to a reference dataset (62% vs. 51% and 80% vs. 65% in field tests 1 and 2 respectively). These results demonstrate that SNPs are suitable for the assignment of individuals to reference breeds. The results also indicate that STRUCTURE 2.2 and GENECLASS 2 can be complementary tools to assess breed integrity and assignment. Our findings also stress the importance of a high-quality reference dataset in allocation studies. C1 [Negrini, R.; Colli, L.; Chegdani, F.; Marsan, P. Ajmone] Univ Cattolica Sacro Cuore, Ist Zootecn, Piacenza, Italy. [Nicoloso, L.; Crepaldi, P.; Milanesi, E.] Univ Milan, Dipartimento Sci Anim, Sez Zootecn Agr, Milan, Italy. [Pariset, L.] Univ Tuscia, Dipartimento Prod Anim, Viterbo, Italy. [Dunner, S.] Univ Complutense Madrid, Fac Vet, Dept Anim Prod, Madrid, Spain. [Leveziel, H.] Univ Limoges, Fac Sci & Tech, INRA, UMR 1061,Unite Genet Mol Anim, Limoges, France. [Williams, J. L.] Polo Univ, Lodi, Italy. C3 Catholic University of the Sacred Heart; University of Milan; Tuscia University; Complutense University of Madrid; INRAE; Universite de Limoges RP Negrini, R (corresponding author), Univ Cattolica Sacro Cuore, Ist Zootecn, Piacenza, Italy. EM riccardo.negrini@unicatt.it CR [Anonymous], 1996, GENETIC DATA ANAL Ayres KL, 2005, FORENSIC SCI INT, V154, P167, DOI 10.1016/j.forsciint.2004.10.004 Baudouin L, 2004, J HERED, V95, P217, DOI 10.1093/jhered/esh035 Baudouin L, 2001, ACTA HORTIC, P81, DOI 10.17660/ActaHortic.2001.546.5 Casellas J, 2004, J ANIM BREED GENET, V121, P101, DOI 10.1046/j.1439-0388.2003.00441.x Castric V, 2004, MOL ECOL, V13, P1299, DOI 10.1111/j.1365-294X.2004.02129.x Cegelski CC, 2003, MOL ECOL, V12, P2907, DOI 10.1046/j.1365-294X.2003.01969.x Ciampolini R, 2006, J ANIM SCI, V84, P11, DOI 10.2527/2006.84111x Corander J, 2003, GENETICS, V163, P367 Dalvit C, 2008, MEAT SCI, V80, P389, DOI 10.1016/j.meatsci.2008.01.001 Dalvit C, 2008, FOOD RES INT, V41, P301, DOI 10.1016/j.foodres.2007.12.010 Diez-Tascon C, 2000, ANIM GENET, V31, P243, DOI 10.1046/j.1365-2052.2000.00636.x Falush D, 2007, MOL ECOL NOTES, V7, P574, DOI 10.1111/j.1471-8286.2007.01758.x Fries R, 2001, NAT BIOTECHNOL, V19, P508, DOI 10.1038/89213 GUO SW, 1992, BIOMETRICS, V48, P361, DOI 10.2307/2532296 Heaton MP, 2005, JAVMA-J AM VET MED A, V226, P1311, DOI 10.2460/javma.2005.226.1311 Khatkar MS, 2007, GENETICS, V176, P763, DOI 10.1534/genetics.106.069369 Koskinen MT, 2003, ANIM GENET, V34, P297, DOI 10.1046/j.1365-2052.2003.01005.x Krawczak M, 1999, ELECTROPHORESIS, V20, P1676, DOI 10.1002/(SICI)1522-2683(19990101)20:8<1676::AID-ELPS1676>3.3.CO;2-4 Lecis R, 2006, MOL ECOL, V15, P119, DOI 10.1111/j.1365-294X.2005.02812.x LEWONTIN RC, 1988, GENETICS, V120, P849 Lindblad-Toh K, 2000, NAT GENET, V24, P381, DOI 10.1038/74215 Linz B, 2007, NATURE, V445, P915, DOI 10.1038/nature05562 Liron JP, 2004, J FORENSIC SCI, V49, P96 MacHugh DE, 1998, ANIM GENET, V29, P333, DOI 10.1046/j.1365-2052.1998.295330.x Maudet C, 2002, J ANIM SCI, V80, P942 Negrini R, 2007, ANIM GENET, V38, P147, DOI 10.1111/j.1365-2052.2007.01573.x PAETKAU D, 1995, MOL ECOL, V4, P347, DOI 10.1111/j.1365-294X.1995.tb00227.x Peter C, 2007, ANIM GENET, V38, P37, DOI 10.1111/j.1365-2052.2007.01561.x Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Pritchard JK, 2000, GENETICS, V155, P945 Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 REYNOLDS J, 1983, GENETICS, V105, P767 Ruzzante DE, 2001, MOL ECOL, V10, P2107, DOI 10.1046/j.1365-294X.2001.01352.x SHACKELL GH, 2001, P ASS ADVMT ANIM BRE, V14, P533 SMOUSE PE, 1988, J HERED, V89, P143 Werner FAO, 2004, ANIM GENET, V35, P44, DOI 10.1046/j.1365-2052.2003.01071.x WRIGHT S, 1965, EVOLUTION, V19, P395, DOI 10.2307/2406450 NR 38 TC 42 Z9 42 U1 0 U2 15 PD FEB PY 2009 VL 40 IS 1 BP 18 EP 26 DI 10.1111/j.1365-2052.2008.01800.x WC Agriculture, Dairy & Animal Science; Genetics & Heredity SC Agriculture; Genetics & Heredity UT WOS:000262515400003 DA 2022-12-14 ER PT J AU Zhao, Y Zhang, B Guo, B Wang, DH Yang, SM AF Zhao, Yan Zhang, Bin Guo, Bin Wang, Donghua Yang, Shuming TI Combination of multi-element and stable isotope analysis improved the traceability of chicken from four provinces of China SO CYTA-JOURNAL OF FOOD DT Article DE stable isotope; multi-element; chicken; geographic origin ID GEOGRAPHIC ORIGIN; POULTRY MEAT; DRIED BEEF; RATIO ANALYSIS; TRACE-ELEMENT; LAMB MEAT; CARBON; OXYGEN; FOOD; AUTHENTICATION AB The establishment of appropriate analytical methods to authenticate the geographic origin of poultry meat independent of paper record becomes especially important nowadays. In this study, data were analyzed in terms of both the contents of two stable isotopes (C and N) and 12 mineral elements of chicken samples from four provinces to trace their geographical origin. The results showed that classification of chicken was improved by combining both types of data, as compared to that using the mineral element data alone. The present study demonstrates that it is possible to indicate the poultry origin by applying stable isotope and multi-element analysis, which is an effective tool to trace the geographic origin of poultry. C1 [Zhao, Yan; Wang, Donghua; Yang, Shuming] Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China. [Zhao, Yan; Wang, Donghua; Yang, Shuming] Minist Agr, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Zhang, Bin] Henan Univ Sci & Technol, Coll Food & Bioengn, Luoyang 471023, Peoples R China. [Guo, Bin] NW Univ Xian, Minist Educ, Key Lab Resource Biol & Biotechnol Western China, Xian 710069, Peoples R China. [Guo, Bin] NW Univ Xian, Sch Life Sci, Xian 710069, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs; Henan University of Science & Technology; Northwest University Xi'an; Northwest University Xi'an RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Key Lab Agroprod Qual & Safety, Inst Qual Standard & Testing Technol Agroprod, Beijing 100081, Peoples R China.; Zhao, Y (corresponding author), Minist Agr, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR Angerosa F, 1999, J AGR FOOD CHEM, V47, P1013, DOI 10.1021/jf9809129 Bahar B, 2005, RAPID COMMUN MASS SP, V19, P1937, DOI 10.1002/rcm.2007 Bahar B, 2009, J ANIM SCI, V87, P905, DOI 10.2527/jas.2008-1360 Brunner M, 2010, EUR FOOD RES TECHNOL, V231, P623, DOI 10.1007/s00217-010-1314-7 Chesson LA, 2008, J AGR FOOD CHEM, V56, P4084, DOI 10.1021/jf0733618 Chesson LA, 2010, J AGR FOOD CHEM, V58, P2358, DOI 10.1021/jf904151c Crittenden RG, 2007, INT DAIRY J, V17, P421, DOI 10.1016/j.idairyj.2006.05.012 Durek J, 2014, INNOV FOOD SCI EMERG, V26, P397, DOI 10.1016/j.ifset.2014.05.001 Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 Franke BM, 2008, MEAT SCI, V80, P944, DOI 10.1016/j.meatsci.2008.03.018 Franke BM, 2008, EUR FOOD RES TECHNOL, V226, P761, DOI 10.1007/s00217-007-0588-x Franke BM, 2007, EUR FOOD RES TECHNOL, V225, P501, DOI 10.1007/s00217-006-0446-2 Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Furia E, 2011, J AGR FOOD CHEM, V59, P8450, DOI 10.1021/jf201556e Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Knobbe N, 2006, ANAL BIOANAL CHEM, V386, P104, DOI 10.1007/s00216-006-0644-6 Li K, 2015, CYTA-J FOOD, V13, P213, DOI 10.1080/19476337.2014.941411 Millet S, 2005, MEAT SCI, V69, P335, DOI 10.1016/j.meatsci.2004.08.003 Moreno-Rojas JM, 2008, RAPID COMMUN MASS SP, V22, P3701, DOI 10.1002/rcm.3773 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3285, DOI 10.1021/jf1040433 Osorio MT, 2011, J AGR FOOD CHEM, V59, P3295, DOI 10.1021/jf1040959 Perini M, 2009, RAPID COMMUN MASS SP, V23, P2573, DOI 10.1002/rcm.4140 Pilgrim TS, 2010, FOOD CHEM, V118, P921, DOI 10.1016/j.foodchem.2008.08.077 Pisani A, 2008, FOOD CHEM, V107, P1553, DOI 10.1016/j.foodchem.2007.09.029 Reykdal O, 2011, J FOOD COMPOS ANAL, V24, P980, DOI 10.1016/j.jfca.2011.03.002 Richter EK, 2010, J AGR FOOD CHEM, V58, P8048, DOI 10.1021/jf101128f Sacco D, 2005, MEAT SCI, V71, P542, DOI 10.1016/j.meatsci.2005.04.038 Sakamoto N, 2002, J NUCL SCI TECHNOL, V39, P323, DOI 10.3327/jnst.39.323 Santato A, 2012, J MASS SPECTROM, V47, P1132, DOI 10.1002/jms.3018 [孙丰梅 SUN Fengmei], 2008, [分析测试学报, Journal of Instrumental Analysis], V27, P925 Wei F. S., 1991, ENV SCI, V04, P12, DOI [DOI 10.13227/J.HJKX.1991.04.005), 10.13227/j.hjkx.1991.04.005, DOI 10.13227/J.HJKX.1991.04.005] Yanagi Y, 2012, FOOD CHEM, V134, P502, DOI 10.1016/j.foodchem.2012.02.107 Zhao Y, 2013, J AGR FOOD CHEM, V61, P7055, DOI 10.1021/jf400947y NR 34 TC 20 Z9 20 U1 4 U2 50 PD APR 2 PY 2016 VL 14 IS 2 BP 163 EP 168 DI 10.1080/19476337.2015.1057235 WC Food Science & Technology SC Food Science & Technology UT WOS:000372132500001 DA 2022-12-14 ER PT J AU Heuillet, M Lalere, B Peignaux, M De Graeve, J Vaslin-Reimann, S De Barros, JPP Gambert, P Duvillard, L Delatour, V AF Heuillet, Maud Lalere, Beatrice Peignaux, Maryline De Graeve, Jacques Vaslin-Reimann, Sophie De Barros, Jean-Paul Pais Gambert, Philippe Duvillard, Laurence Delatour, Vincent TI Validation of a reference method for total cholesterol measurement in human serum and assignation of reference values to proficiency testing samples SO CLINICAL BIOCHEMISTRY DT Article DE Metrological traceability; Cholesterol assay; Reference method; Isotope dilution mass spectrometry; External Quality Assessment Schemes; Clinical laboratories ID EXTERNAL QUALITY ASSESSMENT; DILUTION MASS-SPECTROMETRY; GAS-CHROMATOGRAPHY; TRACEABILITY; GLUCOSE AB Objectives: Our objective was to develop a reference method to measure total cholesterol in human serum, in order to assign values and assess the accuracy of field methods in French clinical laboratories. Design and methods: A reference method based on gas chromatography coupled with mass spectrometry and isotope dilution (GC-IDMS) was developed and validated. It was then used to assign reference values to five frozen serum samples from voluntary proficiency testing schemes gathering 170 French clinical laboratories. Three peer groups were defined and bias against the reference method target value was calculated. Results: Accuracy of the reference method was assessed against NIST SRM 1951b. Bias of the reference method was less than 0.5% and imprecision was less than 1.0%. Our study indicated that field methods tended to overestimate total cholesterol concentration, mean bias being + 5.02% +/- 1.02%. The most popular methods (phenolic chromogen with spectrophotometric detection, 80% of participants) exhibited the highest bias (peer group mean bias: +5.51 +/- 1.24%). Neither these methods nor those using a non-phenolic chromogen with reflectometric detection (10% of participants, peer group mean bias: +4.20 +/- 1.44%) met NCEP recommendations according to which bias should be less than 3%. Only the methods using a non phenolic chromogen with a spectrophotometric detection met these recommendations (10% of participants, peer group mean bias: +1.39 +/- 2.75%). Conclusions: As all three peer groups provided positively biased results, the consensus mean usually used to assess the trueness of routine methods is biased as well, which results in an erroneous estimation of method bias. Therefore, this study highlights the value added by reference method target values to assess trueness of field methods and monitor performance of clinical laboratories. (C) 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. C1 [Heuillet, Maud; Lalere, Beatrice; Peignaux, Maryline; Vaslin-Reimann, Sophie; Delatour, Vincent] Dept Biomed & Organ Chem, Lab Natl Metrol & Essais LNE, F-75015 Paris, France. [De Graeve, Jacques] Univ Hosp Rangueil Larrey, Biochim Lab, Toulouse, France. [De Barros, Jean-Paul Pais; Gambert, Philippe; Duvillard, Laurence] Univ Bourgogne, Fac Med, INSERM, U866, Dijon, France. C3 Laboratoire National de Metrologie et d'Essais (LNE); CHU de Toulouse; Institut Agro; AgroSup Dijon; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bourgogne RP Delatour, V (corresponding author), Dept Biomed & Organ Chem, Lab Natl Metrol & Essais LNE, 1 Rue Gaston Boissier, F-75015 Paris, France. EM Vincent.delatour@lne.fr CR ANDERSON KM, 1987, JAMA-J AM MED ASSOC, V257, P2176, DOI 10.1001/jama.257.16.2176 [Anonymous], 2010, C53A CLSI [Anonymous], 1999, C37A NCCLS [Anonymous], 2005, 17025 ISO Briche CSJW, 2002, RAPID COMMUN MASS SP, V16, P848, DOI 10.1002/rcm.646 COOPER GR, 1986, CLIN CHEM, V32, P921 Edwards SH, 2011, CLIN CHEM, V57, P614, DOI 10.1373/clinchem.2010.158766 ELLERBE P, 1989, ANAL CHEM, V61, P1718, DOI 10.1021/ac00190a025 IFCC ring trials for reference laboratories, IFCC RING TRIALS REF *ISO, 2003, 17511 ISO ISO-International Organization for Stardardization, 2008, ISO IEC GUID 98 2008 ISO-International Organization for Stardardization, 2004, 15195 ISO ISO-International Organization for Stardardization, 2012, ISO IEC GUID 99 INT Koch M, 2012, ACCREDIT QUAL ASSUR, P1 NCEP, 1988, CLIN CHEM, V34, P193 Panteghini Mauro, 2007, Clin Biochem Rev, V28, P97 Panteghini M, 2009, CLIN BIOCHEM, V42, P236, DOI 10.1016/j.clinbiochem.2008.09.098 Urquiza MP, 2009, ACCREDIT QUAL ASSUR, V14, P269, DOI 10.1007/s00769-009-0493-z Ricos C, 1999, CLIN CHIM ACTA, V280, P135, DOI 10.1016/S0009-8981(98)00187-9 RIFKIND BM, 1984, JAMA-J AM MED ASSOC, V251, P351 SIEKMANN L, 1976, FRESEN Z ANAL CHEM, V279, P145, DOI 10.1007/BF00440813 Thienpont LM, 2003, CLIN CHEM LAB MED, V41, P183, DOI 10.1515/CCLM.2003.030 THIENPONT LM, 1993, CLIN CHEM, V39, P1001 White GH, 2011, ANN CLIN BIOCHEM, V48, P393, DOI 10.1258/acb.2011.011079 World Health Organization, 2011, CARD DIS 2001, JAMA, V285, P2486 NR 26 TC 10 Z9 10 U1 0 U2 20 PD MAR PY 2013 VL 46 IS 4-5 BP 359 EP 364 DI 10.1016/j.clinbiochem.2012.11.026 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000315661700015 DA 2022-12-14 ER PT J AU Marinoni, DT Ruffa, P Pavese, V Giuggioli, NR AF Marinoni, Daniela Torello Ruffa, Paola Pavese, Vera Giuggioli, Nicole Roberta TI Apple juice evaluation: Qualitative analysis and microsatellite traceability SO AIMS AGRICULTURE AND FOOD DT Article DE origin; DNA; cultivar; polyphenols; panelist ID POLYPHENOLIC COMPOSITION; DNA; PRODUCTS; MARKERS; FRUITS; PCR AB Qualitative and DNA analysis can be performed by taking a multidisciplinary approach to evaluate apple juices, the relevant values of which are a function of the origin, processing method and cultivar used. In detail, the aims of this study were to characterize apple juices through physiochemical analysis, sensory analysis and DNA analysis to evaluate the efficiency of simple sequence repeat (SSR) markers for cultivar identification. Six apple juices made with cv Golden Delicious, cv Granny Smith and a mix of these cultivars from an e-commerce platform (Samples A and B), DISAFA (Samples C and D) and a local farm (Piedmont, Italy) (Samples E and F) were considered. Apple juices A, B, E and F (clarified and pasteurized) can be considered as being of high quality, while Samples C and D were unclarified, unpasteurized and made with apples purchased from a local store. Considering the qualitative analysis, it was observed that the cultivar of apple affected the parameters assessed. In the case of total phenolic compounds, the highest values were observed for juices made only with cv Granny Smith, suggesting how this cultivar contributes to maintaining these nutraceutical compounds more than cv Golden Delicious. Regarding DNA analysis, a limited number of markers, i.e., 4 and 3, respectively, for the apple juices originating from e-commerce and a local farm could successfully produce reproducible amplified fragments. These results can be related to the different procedures used in processing apple juices of different origins. C1 [Marinoni, Daniela Torello; Ruffa, Paola; Pavese, Vera; Giuggioli, Nicole Roberta] Univ Torino, Dept Agr Forest & Food Sci, Largo Paolo Braccini 2, I-10095 Turin, Italy. C3 University of Turin RP Giuggioli, NR (corresponding author), Univ Torino, Dept Agr Forest & Food Sci, Largo Paolo Braccini 2, I-10095 Turin, Italy. EM nicole.giuggioli@unito.it CR Alonso-Salces RM, 2004, J CHROMATOGR A, V1046, P89, DOI 10.1016/j.chroma.2004.06.077 Boccacci P, 2012, EUR FOOD RES TECHNOL, V235, P439, DOI 10.1007/s00217-012-1770-3 Catalano V, 2016, J AGR FOOD CHEM, V64, P6969, DOI 10.1021/acs.jafc.6b02560 Cavanna M, 2008, J HORTIC SCI BIOTECH, V83, P549, DOI 10.1080/14620316.2008.11512421 Corrado G, 2016, TRENDS FOOD SCI TECH, V52, P80, DOI 10.1016/j.tifs.2016.04.003 Dasenaki ME, 2019, MOLECULES, V24, DOI 10.3390/molecules24061014 De Paepe D, 2015, FOOD CHEM, V173, P986, DOI 10.1016/j.foodchem.2014.10.019 Doyle J. J., 1997, PHYTOCHEMISTRY B, V19, P11, DOI DOI 10.2307/4119796 Fanelli V, 2021, FOODS, V10, DOI 10.3390/foods10071644 Galimberti A, 2014, ADV AGR, V2014, P831875, DOI [10.1155/2014/831875, DOI 10.1155/2014/831875, 10.1155/2014/831875.] Golebiewska E, 2022, MATERIALS, V15, DOI 10.3390/ma15051788 Guyot S, 2003, J AGR FOOD CHEM, V51, P6240, DOI 10.1021/jf0301798 Han JX, 2012, FOOD CONTROL, V25, P696, DOI 10.1016/j.foodcont.2011.12.001 Hu YX, 2020, ACS SENSORS, V5, P2168, DOI 10.1021/acssensors.0c00786 Ibrahim GE, 2011, FOOD HYDROCOLLOID, V25, P91, DOI 10.1016/j.foodhyd.2010.05.009 Karadeniz F, 2002, EUR FOOD RES TECHNOL, V215, P145, DOI 10.1007/s00217-002-0505-2 Korir NK, 2013, CRIT REV BIOTECHNOL, V33, P111, DOI 10.3109/07388551.2012.675314 Kumar P, 2009, PLANT OMICS, V2, P141 Liebhard R, 2002, MOL BREEDING, V10, P217, DOI 10.1023/A:1020525906332 Melchiade D, 2007, FOOD BIOTECHNOL, V21, P33, DOI 10.1080/08905430701191114 Niu S, 2010, INNOV FOOD SCI EMERG, V11, P91, DOI 10.1016/j.ifset.2009.09.002 Okayasu H, 2001, J FOOD SCI, V66, P1025, DOI 10.1111/j.1365-2621.2001.tb08229.x Piarulli L, 2019, FOODS, V8, DOI 10.3390/foods8100462 Pompei C, 2005, EDAGRICOLE EDIZIONE, P41 Priyadarshini A, 2018, FRUIT JUICES, P15, DOI 10.1016/B978-0-12-802230-6.00002-3 Renard CMGC, 2011, FOOD CHEM, V124, P117, DOI 10.1016/j.foodchem.2010.05.113 Rydzak L, 2020, PROCESSES, V8, DOI 10.3390/pr8111457 Saadat S, 2022, FORENSIC SCI INT, V333, DOI 10.1016/j.forsciint.2022.111243 Sabetta W, 2017, RIV ITAL SOSTANZE GR, V94, P37 Sato K, 1991, ENCY FRUIT HORTICULT Scarano D., 2014, Diversity, V6, P579 Schrader C, 2012, J APPL MICROBIOL, V113, P1014, DOI 10.1111/j.1365-2672.2012.05384.x SLINKARD K, 1977, AM J ENOL VITICULT, V28, P49 Stagnati L, 2020, FOOD CONTROL, V118, DOI 10.1016/j.foodcont.2020.107392 Talucci G, 2022, EUR FOOD RES TECHNOL, V22, P532 Tsuji S, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0176608 van der Sluis AA, 2005, J AGR FOOD CHEM, V53, P1073, DOI 10.1021/jf040270r Wu YJ, 2018, J FOOD SCI, V83, P1494, DOI 10.1111/1750-3841.14177 Yamamoto T, 2006, BREEDING SCI, V56, P165, DOI 10.1270/jsbbs.56.165 Zambianchi S, 2021, FOOD CONTROL, V124, DOI 10.1016/j.foodcont.2021.107929 NR 40 TC 0 Z9 0 U1 1 U2 1 PY 2022 VL 7 IS 4 BP 819 EP 830 DI 10.3934/agrfood.2022050 WC Agriculture, Multidisciplinary; Agronomy; Food Science & Technology SC Agriculture; Food Science & Technology UT WOS:000875705900001 DA 2022-12-14 ER PT J AU Panprommin, D Manosri, R AF Panprommin, Dutrudi Manosri, Rittikai TI DNA barcoding as an approach for species traceability and labeling accuracy of fish fillet products in Thailand SO FOOD CONTROL DT Article DE DNA barcoding; Fish fillet products; Species identification; Mislabeled; Species substitution ID MARKET SUBSTITUTION; IDENTIFICATION; REVEALS; RATES AB The lack of regulations and serious enforcement in the traceability of fishery products in some countries is one cause of food fraud, especially species substitution. Thailand is one of those countries where regulations are not fully enforced. Therefore, the labeling situation needs to be investigated in fish fillet products. Fifty-four products were collected from several supermarkets in Thailand. The labeling and molecular identity of each product were assessed by the application of a DNA barcoding technique on a cytochrome c oxidase I (COI) gene fragment of approximately 650 bp. All samples were successfully identified at the species level with 98-100% similarity in both the GenBank and BOLD databases. Only one sample could be identified at the genus level because the highest COI sequence match in the database was reported only at the genus level. Among all products, there were 25 species and 18 genera. Only one species, Pangasianodon hypophthalmus, was assessed as an endangered species according to IUCN status. FishBase was utilized to investigate the scientific names from common or market names. Two criteria were used to determine mislabeling, including i) mismatches between the scientific name identified by DNA barcoding and product labels and ii) the absence of common or market names in FishBase. Eighteen samples (33.33%) were mislabeled, which included 16 samples (88.89%) and 2 samples (11.11%) for criteria 1 and 2, respectively. Only one (11.11%) of nine products labeled with scientific names was mislabeled. Therefore, labeling products with scientific names may be a tool to reduce mislabeling to protect consumer rights. C1 [Panprommin, Dutrudi; Manosri, Rittikai] Univ Phayao, Sch Agr & Nat Resources, Phayao 56000, Thailand. [Panprommin, Dutrudi] Ctr Excellence Agr Biotechnol Ag BIO PERDO CHE, Bangkok 10900, Thailand. C3 University of Phayao RP Panprommin, D (corresponding author), Univ Phayao, Sch Agr & Nat Resources, Phayao 56000, Thailand. EM dutrudeep@yahoo.com CR ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999 Pardo MA, 2020, J FOOD COMPOS ANAL, V91, DOI 10.1016/j.jfca.2020.103521 Armani A, 2017, FOOD CONTROL, V79, P126, DOI 10.1016/j.foodcont.2017.03.030 Cawthorn DM, 2018, CONSERV LETT, V11, DOI 10.1111/conl.12573 Cawthorn DM, 2012, FOOD RES INT, V46, P30, DOI 10.1016/j.foodres.2011.11.011 Christiansen H, 2018, FOOD CONTROL, V85, P66, DOI 10.1016/j.foodcont.2017.09.005 Cline E, 2012, FOOD RES INT, V45, P388, DOI 10.1016/j.foodres.2011.10.043 Department of Fisheries, 2021, FISH STAT THAIL 2019 Di Pinto A, 2015, FISH RES, V170, P9, DOI 10.1016/j.fishres.2015.05.006 FAO, 2020, STATE WORLD FISHERIE, DOI [10.4060/ca9229en, 10.4060/ca9229-n, DOI 10.4060/CA9229-N, DOI 10.4060/CA9229EN] Fox M, 2018, FOOD SECUR, V10, P939, DOI 10.1007/s12571-018-0826-z Hanner R, 2011, MITOCHONDR DNA, V22, P106, DOI 10.3109/19401736.2011.588217 Hebert PDN, 2003, P ROY SOC B-BIOL SCI, V270, P313, DOI [10.1098/rspb.2002.2218, 10.1098/rsbl.2003.0025] Helgoe J, 2020, FISH RES, V222, DOI 10.1016/j.fishres.2019.105400 Holmes BH, 2009, FISH RES, V95, P280, DOI 10.1016/j.fishres.2008.09.036 Hu YX, 2018, FOOD CONTROL, V94, P38, DOI 10.1016/j.foodcont.2018.06.023 KIMURA M, 1980, J MOL EVOL, V16, P111, DOI 10.1007/BF01731581 Kumar S, 2016, MOL BIOL EVOL, V33, P1870, DOI [10.1093/molbev/msv279, 10.1093/molbev/msw054] Meusnier I, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-214 Minoudi S, 2020, FOOD CONTROL, V113, DOI 10.1016/j.foodcont.2020.107213 Mojekwu TO, 2021, J FISH BIOL, V98, P498, DOI 10.1111/jfb.14594 Nelson J. S., 2016, FISHES WORLD Ng T., 2011, INT J ENV SCI, V1, P2048 Piskata Z., 2016, Potravinarstvo, V10, P308, DOI 10.5219/612 Ratnasingham S, 2007, MOL ECOL NOTES, V7, P355, DOI 10.1111/j.1471-8286.2007.01678.x ROBERTS TR, 1991, P ACAD NAT SCI PHILA, V143, P97 Sambrook J., 2001, MOL CLONING LAB MANU Sampantamit T, 2021, FOODS, V10, DOI 10.3390/foods10040880 Shehata HR, 2018, FOOD CONTROL, V92, P147, DOI 10.1016/j.foodcont.2018.04.045 Smith PJ, 2008, J FISH BIOL, V72, P464, DOI 10.1111/j.1095-8649.2007.01745.x Tinacci L, 2019, FOOD CONTROL, V96, P68, DOI 10.1016/j.foodcont.2018.09.002 Tinacci L, 2018, FOOD CONTROL, V90, P180, DOI 10.1016/j.foodcont.2018.03.007 USDA, 2018, THAIL SEAF REP Vidthayanon C., 1993, 150 DEP FISH Vinas J, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0007606 Ward RD, 2005, PHILOS T R SOC B, V360, P1847, DOI 10.1098/rstb.2005.1716 Wong EHK, 2008, FOOD RES INT, V41, P828, DOI 10.1016/j.foodres.2008.07.005 Xing BP, 2020, FOOD CONTROL, V112, DOI 10.1016/j.foodcont.2020.107143 Xiong X, 2018, FOOD CONTROL, V88, P123, DOI 10.1016/j.foodcont.2017.12.035 NR 39 TC 2 Z9 2 U1 2 U2 2 PD JUN PY 2022 VL 136 AR 108895 DI 10.1016/j.foodcont.2022.108895 WC Food Science & Technology SC Food Science & Technology UT WOS:000790969500003 DA 2022-12-14 ER PT J AU Lavin, AD Diaz-Arce, N Larrain, MA Araneda, C Rodriguez-Ezpeleta, N Jimenez, E Pardo, MA AF del Rio Lavin, Ane Diaz-Arce, Natalia Larrain, Maria Angelica Araneda, Cristian Rodriguez-Ezpeleta, Naiara Jimenez, Elisa Pardo, Miguel Angel TI Population structure and geographic origin assignment of Mytilus galloprovincialis mussels using SNPs SO AQUACULTURE DT Article DE Mytilus galloprovincialis; RAD-seq; SNPs; Origin assignment; Aquaculture; Traceability ID CHILENSIS HUPE 1854; GENETIC DIVERSITY; R PACKAGE; TOOL SET; IDENTIFICATION; DISCONTINUITY; DISCOVERY; MARKERS; BAY AB Seafood traceability represents a major goal for regulators and fishing industries worldwide who seek to prevent commercial fraud, protect marine resources and ensure consumer safety. Genetic approaches can be used to trace the provenance of seafood based on the ability of DNA markers to assign samples back to their population of origin. Here, we have used thousands of genome-wide Single Nucleotide Polymorphism markers to provide a detailed genetic structure of the highly farmed Mediterranean mussel Mytilus galloprovincialis in part of its native (Atlantic and Mediterranean areas) and introduced ranges (South-eastern Pacific area). Also, we have assessed the power of the newly developed markers to assign samples to their geographic origin. Results showed a clear differentiation between the Atlantic and the Mediterranean M. galloprovincialis populations, with significant differences also observed between the Mediterranean and South-eastern Pacific individuals. In addition, we found 90-100% of individuals could be correctly assigned to the Atlantic or Mediterranean/South-eastern Pacific populations when using only 10 to 25 SNPs. Our results support the possibility of the development of an accurate and cost-effective origin assignment tool with global uses in aquaculture management, seafood traceability and food safety. C1 [del Rio Lavin, Ane; Jimenez, Elisa; Pardo, Miguel Angel] AZTI, Basque Res & Technol Alliance BRTA, Food Res, Parque Tecnol Bizkaia, Edificio 609, Derio 48160, Spain. [del Rio Lavin, Ane; Diaz-Arce, Natalia; Larrain, Maria Angelica; Rodriguez-Ezpeleta, Naiara] AZTI, Marine Res, Basque Res & Technol Alliance BRTA, Txatxarramendi Ugartea Z-G, Sukarrieta 48395, Spain. [Larrain, Maria Angelica] Univ Chile, Fac Ciencias Quim & Farm, Dept Ciencias Alimentos & Tecnol Quim, Santiago 1007, Chile. [Diaz-Arce, Natalia] Fac Ciencias Agron, Dept Producc Anim, Avda Santa Rosa, Santiago 11315, Chile. C3 AZTI; AZTI; Universidad de Chile RP Lavin, AD (corresponding author), AZTI, Basque Res & Technol Alliance BRTA, Food Res, Parque Tecnol Bizkaia, Edificio 609, Derio 48160, Spain. EM adelrio@azti.es CR Alexander DH, 2009, GENOME RES, V19, P1655, DOI 10.1101/gr.094052.109 Anderson EC, 2010, MOL ECOL RESOUR, V10, P701, DOI 10.1111/j.1755-0998.2010.02846.x Araneda C, 2016, ECOL EVOL, V6, P3632, DOI 10.1002/ece3.2110 Astorga MP, 2015, J SHELLFISH RES, V34, P919, DOI 10.2983/035.034.0322 BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x Bronner I.F, 2014, CURR PROTOC HUM GENE, V80, P1 Catchen J, 2013, MOL ECOL, V22, P3124, DOI 10.1111/mec.12354 del Rio-Lavin A, 2021, FOOD CONTROL, V130, DOI 10.1016/j.foodcont.2021.108257 Dias PJ, 2008, J EXP MAR BIOL ECOL, V367, P253, DOI 10.1016/j.jembe.2008.10.011 Diz AP, 2008, MAR BIOL, V154, P277, DOI 10.1007/s00227-008-0921-3 Etter PD, 2011, METHODS MOL BIOL, V772, P157, DOI 10.1007/978-1-61779-228-1_9 EUMOFA, 2019, EU FISH MAR 2019 ED FAO, 2020, FAO Yearbook of Fishery Statistics, DOI 10.4060/cb1213t Fraisse C, 2016, MOL ECOL, V25, P269, DOI 10.1111/mec.13299 Gardner JPA, 2016, GLOBAL CHANGE BIOL, V22, P3182, DOI 10.1111/gcb.13332 Helyar SJ, 2011, MOL ECOL RESOUR, V11, P123, DOI 10.1111/j.1755-0998.2010.02943.x Jombart T, 2011, BIOINFORMATICS, V27, P3070, DOI 10.1093/bioinformatics/btr521 Keenan K, 2013, METHODS ECOL EVOL, V4, P782, DOI 10.1111/2041-210X.12067 Kijewski T, 2011, J SEA RES, V65, P224, DOI 10.1016/j.seares.2010.10.004 Larrain MA, 2018, EVOL APPL, V11, P298, DOI 10.1111/eva.12553 Lins DM, 2021, AQUACULTURE, V540, DOI 10.1016/j.aquaculture.2021.736753 Lischer HEL, 2012, BIOINFORMATICS, V28, P298, DOI 10.1093/bioinformatics/btr642 Luu K, 2017, MOL ECOL RESOUR, V17, P67, DOI 10.1111/1755-0998.12592 Martinsohn JT, 2009, FORENS SCI INT-GEN S, V2, P294, DOI 10.1016/j.fsigss.2009.08.108 Montes I, 2017, J AGR FOOD CHEM, V65, P4351, DOI 10.1021/acs.jafc.7b00619 Morin PA, 2004, TRENDS ECOL EVOL, V19, P208, DOI 10.1016/j.tree.2004.01.009 Nielsen EE, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1845 Ogden R, 2015, FORENSIC SCI INT-GEN, V18, P152, DOI 10.1016/j.fsigen.2015.02.008 Paris JR, 2017, METHODS ECOL EVOL, V8, P1360, DOI 10.1111/2041-210X.12775 Paterno M, 2019, FRONT MAR SCI, V6, DOI 10.3389/fmars.2019.00566 Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Popovic I, 2020, EVOL APPL, V13, DOI 10.1111/eva.12857 Purcell S, 2007, AM J HUM GENET, V81, P559, DOI 10.1086/519795 QUESADA H, 1995, MAR ECOL PROG SER, V116, P99, DOI 10.3354/meps116099 QUESADA H, 1995, MOL BIOL EVOL, V12, P521 Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Rousset F, 2008, MOL ECOL RESOUR, V8, P103, DOI 10.1111/j.1471-8286.2007.01931.x Scarano D., 2014, Diversity, V6, P579 Tarifeno E, 2012, GAYANA, V76, P167, DOI 10.4067/S0717-65382012000300010 Toro JE, 2005, J SHELLFISH RES, V24, P1117 Wenne R, 2018, AQUAT LIVING RESOUR, V31, DOI 10.1051/alr/2017043 Westfall KM, 2010, BIOL J LINN SOC, V101, P898, DOI 10.1111/j.1095-8312.2010.01549.x Zardi GI, 2018, MAR FRESHWATER RES, V69, P607, DOI 10.1071/MF17132 Zbawicka M, 2019, FRONT ZOOL, V16, DOI 10.1186/s12983-019-0332-y Zbawicka M, 2018, GENET SEL EVOL, V50, DOI 10.1186/s12711-018-0376-z Zbawicka M, 2012, MAR BIOL, V159, P1347, DOI 10.1007/s00227-012-1915-8 NR 47 TC 2 Z9 2 U1 6 U2 10 PD MAR 15 PY 2022 VL 550 AR 737836 DI 10.1016/j.aquaculture.2021.737836 WC Fisheries; Marine & Freshwater Biology SC Fisheries; Marine & Freshwater Biology UT WOS:000765399500002 DA 2022-12-14 ER PT J AU Theodoridis, G Pechlivanis, A Thomaidis, NS Spyros, A Georgiou, CA Albanis, T Skoufos, I Kalogiannis, S Tsangaris, GT Stasinakis, AS Konstantinou, I Triantafyllidis, A Gkagkavouzis, K Kritikou, AS Dasenaki, ME Gika, H Virgiliou, C Kodra, D Nenadis, N Sampsonidis, I Arsenos, G Halabalaki, M Mikros, E AF Theodoridis, Georgios Pechlivanis, Alexandros Thomaidis, Nikolaos S. Spyros, Apostolos Georgiou, Constantinos A. Albanis, Triantafyllos Skoufos, Ioannis Kalogiannis, Stavros Tsangaris, George Th. Stasinakis, Athanasios S. Konstantinou, Ioannis Triantafyllidis, Alexander Gkagkavouzis, Konstantinos Kritikou, Anastasia S. Dasenaki, Marilena E. Gika, Helen Virgiliou, Christina Kodra, Dritan Nenadis, Nikolaos Sampsonidis, Ioannis Arsenos, Georgios Halabalaki, Maria Mikros, Emmanuel CA FoodOmicsGR Ri Consortium TI FoodOmicsGR_RI: A Consortium for Comprehensive Molecular Characterisation of Food Products SO METABOLITES DT Review DE metabolomics; genomics; authenticity; traceability; nutritional value; food composition ID MASS-SPECTROMETRY METHOD; QUALITY-CONTROL; CHROMATOGRAPHY; VALIDATION AB The national infrastructure FoodOmicsGR_RI coordinates research efforts from eight Greek Universities and Research Centers in a network aiming to support research and development (R&D) in the agri-food sector. The goals of FoodOmicsGR_RI are the comprehensive in-depth characterization of foods using cutting-edge omics technologies and the support of dietary/nutrition studies. The network combines strong omics expertise with expert field/application scientists (food/nutrition sciences, plant protection/plant growth, animal husbandry, apiculture and 10 other fields). Human resources involve more than 60 staff scientists and more than 30 recruits. State-of-the-art technologies and instrumentation is available for the comprehensive mapping of the food composition and available genetic resources, the assessment of the distinct value of foods, and the effect of nutritional intervention on the metabolic profile of biological samples of consumers and animal models. The consortium has the know-how and expertise that covers the breadth of the Greek agri-food sector. Metabolomics teams have developed and implemented a variety of methods for profiling and quantitative analysis. The implementation plan includes the following research axes: development of a detailed database of Greek food constituents; exploitation of "omics" technologies to assess domestic agricultural biodiversity aiding authenticity-traceability control/certification of geographical/genetic origin; highlighting unique characteristics of Greek products with an emphasis on quality, sustainability and food safety; assessment of diet's effect on health and well-being; creating added value from agri-food waste. FoodOmicsGR_RI develops new tools to evaluate the nutritional value of Greek foods, study the role of traditional foods and Greek functional foods in the prevention of chronic diseases and support health claims of Greek traditional products. FoodOmicsGR_RI provides access to state-of-the-art facilities, unique, well-characterised sample sets, obtained from precision/experimental farming/breeding (milk, honey, meat, olive oil and so forth) along with more than 20 complementary scientific disciplines. FoodOmicsGR_RI is open for collaboration with national and international stakeholders. C1 [Theodoridis, Georgios; Pechlivanis, Alexandros; Virgiliou, Christina; Kodra, Dritan] Aristotle Univ Thessaloniki, Dept Chem, Lab Analyt Chem, Thessaloniki 54124, Greece. [Theodoridis, Georgios; Pechlivanis, Alexandros; Triantafyllidis, Alexander; Gkagkavouzis, Konstantinos; Virgiliou, Christina; Kodra, Dritan] Ctr Interdisciplinary Res & Innovat CIRI AUTH, Biom Auth Bioanal & Om Lab, B1-4,10th Km Thessaloniki Thermi Rd,POB 8318, Thessaloniki 57001, Greece. [Thomaidis, Nikolaos S.; Kritikou, Anastasia S.; Dasenaki, Marilena E.] Natl & Kapodistrian Univ Athens, Dept Chem, Lab Analyt Chem, Athens 15771, Greece. [Spyros, Apostolos] Univ Crete, Dept Chem, Voutes Campus, Iraklion 71003, Greece. [Georgiou, Constantinos A.] Agr Univ Athens, Dept Food Sci & Human Nutr, Chem Lab, 75 Iera Odos, Athens 11855, Greece. [Albanis, Triantafyllos; Konstantinou, Ioannis] Univ Ioannina, Dept Chem, Ioannina 45110, Greece. [Skoufos, Ioannis] Univ Ioannina, Dept Agr, Lab Anim Hlth Food Hyg & Qual, Arta 47100, Greece. [Kalogiannis, Stavros; Sampsonidis, Ioannis] Int Hellenic Univ, Dept Nutr Sci & Dietet, Sindos Campus, Thessaloniki 57400, Greece. [Tsangaris, George Th.] Acad Athens, Biomed Res Fdn, Prote Res Unit, Athens 11527, Greece. [Stasinakis, Athanasios S.] Univ Aegean, Dept Environm, Mitilini 81100, Greece. [Triantafyllidis, Alexander; Gkagkavouzis, Konstantinos] Aristotle Univ Thessaloniki, Dept Genet Dev & Mol Biol, Thessaloniki 54124, Greece. [Gika, Helen] Aristotle Univ Thessaloniki, Dept Med, Lab Forens Med & Toxicol, Thessaloniki 54124, Greece. [Nenadis, Nikolaos] Aristotle Univ Thessaloniki, Sch Chem, Lab Food Chem & Technol, Thessaloniki 54124, Greece. [Arsenos, Georgios] Aristotle Univ Thessaloniki, Sch Hlth Sci, Dept Vet Med, Thessaloniki 54124, Greece. [Halabalaki, Maria; Mikros, Emmanuel] Natl & Kapodistrian Univ Athens, Dept Pharm, Athens 15771, Greece. C3 Aristotle University of Thessaloniki; National & Kapodistrian University of Athens; University of Crete; Agricultural University of Athens; University of Ioannina; University of Ioannina; Academy of Athens; University of Aegean; Aristotle University of Thessaloniki; Aristotle University of Thessaloniki; Aristotle University of Thessaloniki; Aristotle University of Thessaloniki; National & Kapodistrian University of Athens RP Theodoridis, G (corresponding author), Aristotle Univ Thessaloniki, Dept Chem, Lab Analyt Chem, Thessaloniki 54124, Greece.; Theodoridis, G (corresponding author), Ctr Interdisciplinary Res & Innovat CIRI AUTH, Biom Auth Bioanal & Om Lab, B1-4,10th Km Thessaloniki Thermi Rd,POB 8318, Thessaloniki 57001, Greece. EM gtheodor@chem.auth.gr; al_pechliv@hotmail.com; ntho@chem.uoa.gr; aspyros@uoc.gr; cag@aua.gr; talbanis@uoi.gr; jskoufos@uoi.gr; kalogian@nutr.teithe.gr; gthtsangaris@bioacademy.gr; astas@env.aegean.gr; iokonst@uoi.gr; atriant@bio.auth.gr; gagavou@bio.auth.gr; ankritik@chem.uoa.gr; mdasenaki@chem.uoa.gr; gkikae@auth.gr; cr_virgi@hotmail.com; drity.kodra@gmail.com; niknen@chem.auth.gr; isampsonides@gmail.com; arsenosg@vet.auth.gr; mariahal@pharm.uoa.gr; mikros@pharm.uoa.gr CR Aalizadeh R., 2017, P 15 INT C ENV SCI T Aalizadeh R, 2019, J HAZARD MATER, V363, P277, DOI 10.1016/j.jhazmat.2018.09.047 Aalizadeh R, 2016, J CHEM INF MODEL, V56, P1384, DOI 10.1021/acs.jcim.5b00752 Agalias A, 2007, J AGR FOOD CHEM, V55, P2671, DOI 10.1021/jf063091d Alispahic M, 2010, J MED MICROBIOL, V59, P295, DOI 10.1099/jmm.0.016576-0 Alygizakis NA, 2019, J HAZARD MATER, V361, P19, DOI 10.1016/j.jhazmat.2018.08.073 Amargianitaki M, 2017, CHEM BIOL TECHNOL AG, V4, DOI 10.1186/s40538-017-0092-x Anagnostopoulos A.K., 2017, P 16 HUM PROT ORG WO, P127 Anagnostopoulos AK, 2018, DATA BRIEF, V19, P2037, DOI 10.1016/j.dib.2018.06.084 Anagnostopoulos AK, 2017, J CHROMATOGR B, V1047, P92, DOI 10.1016/j.jchromb.2016.08.031 Anagnostopoulos AK, 2016, J PROTEOMICS, V147, P76, DOI 10.1016/j.jprot.2016.04.008 Anastasiadi M, 2010, FOOD RES INT, V43, P805, DOI 10.1016/j.foodres.2009.11.017 Anastasiadi M, 2009, J AGR FOOD CHEM, V57, P11067, DOI 10.1021/jf902137e Andjelkovic U, 2017, FOOD TECHNOL BIOTECH, V55, P290, DOI 10.17113/ftb.55.03.17.5044 Andreadou I, 2006, J NUTR, V136, P2213, DOI 10.1093/jn/136.8.2213 Andreadou I, 2015, PLANTA MED, V81, P655, DOI 10.1055/s-0034-1383306 Andreadou I, 2014, J MOL CELL CARDIOL, V69, P4, DOI 10.1016/j.yjmcc.2014.01.007 Andreadou I, 2011, J CARDIOVASC PHARM, V58, P609, DOI 10.1097/FJC.0b013e31822fc783 Andreadou I, 2009, NMR BIOMED, V22, P585, DOI 10.1002/nbm.1370 Angelis A, 2018, PHYTOCHEM LETT, V26, P190, DOI 10.1016/j.phytol.2018.06.020 Angelis A, 2011, J SEP SCI, V34, P2528, DOI 10.1002/jssc.201100192 Aprea E, 2011, J CHROMATOGR A, V1218, P4517, DOI 10.1016/j.chroma.2011.05.019 Arapitsas P, 2016, FOOD CHEM, V197, P1331, DOI 10.1016/j.foodchem.2015.09.084 Axiotis E, 2020, MOLECULES, V25, DOI 10.3390/molecules25092022 Aznar-Alemany O, 2018, SCI TOTAL ENVIRON, V612, P492, DOI 10.1016/j.scitotenv.2017.08.199 Bazoti FN, 2006, ANAL CHIM ACTA, V573, P258, DOI 10.1016/j.aca.2006.03.075 Begou O, 2017, ANALYST, V142, P3079, DOI 10.1039/c7an00812k Begou O, 2019, J CHROMATOGR B, V1114, P76, DOI 10.1016/j.jchromb.2019.03.028 Begou O, 2018, METHODS MOL BIOL, V1738, P15, DOI 10.1007/978-1-4939-7643-0_2 Beteinakis S, 2020, MOLECULES, V25, DOI 10.3390/molecules25153339 Boti VI, 2009, J CHROMATOGR A, V1216, P1296, DOI 10.1016/j.chroma.2008.12.070 Brieudes V, 2016, PLANTA MED, V82, P1070, DOI 10.1055/s-0042-107472 Cifuentes A., 2012, ISRN ANAL CHEM, P1, DOI [10.5402/2012/801607, DOI 10.5402/2012/801607] Cunningham EP, 2001, REV SCI TECH OIE, V20, P491, DOI 10.20506/rst.20.2.1284 Danezis GP, 2020, INT DAIRY J, V104, DOI 10.1016/j.idairyj.2019.104599 Danezis GP, 2017, ANAL CHIM ACTA, V991, P46, DOI 10.1016/j.aca.2017.09.013 Danezis G.P., 2020, COMPREHENSIVE FOODOM Danezis G, 2019, MOLECULES, V24, DOI 10.3390/molecules24040670 Dasenaki M.E., 2020, CHROMATOGRAPHIC RELA, VB Dasenaki ME, 2019, FOODS, V8, DOI 10.3390/foods8060212 Dasenaki ME, 2019, FOOD CHEM, V275, P668, DOI 10.1016/j.foodchem.2018.09.138 Dasenaki ME, 2016, J CHROMATOGR A, V1452, P67, DOI 10.1016/j.chroma.2016.05.031 Dasenaki ME, 2015, ANAL CHIM ACTA, V880, P103, DOI 10.1016/j.aca.2015.04.013 Dasenaki ME, 2015, J AGR FOOD CHEM, V63, P4493, DOI 10.1021/acs.jafc.5b00962 Dasenaki ME, 2010, ANAL CHIM ACTA, V672, P93, DOI 10.1016/j.aca.2010.04.034 Deda O, 2017, J CHROMATOGR B, V1047, P115, DOI 10.1016/j.jchromb.2016.06.047 Deda O, 2015, J PHARMACEUT BIOMED, V113, P137, DOI 10.1016/j.jpba.2015.02.006 Diamantidou D, 2019, J CHROMATOGR A, V1603, P165, DOI 10.1016/j.chroma.2019.06.034 Diamantidou D, 2018, METABOLOMICS, V14, DOI 10.1007/s11306-018-1458-1 Drivelos SA, 2021, FOOD CHEM, V338, DOI 10.1016/j.foodchem.2020.127936 Drivelos SA, 2016, FOOD CHEM, V213, P238, DOI 10.1016/j.foodchem.2016.06.088 Drivelos SA, 2014, FOOD CHEM, V165, P316, DOI 10.1016/j.foodchem.2014.03.083 Efentakis P, 2015, PLANTA MED, V81, P648, DOI 10.1055/s-0035-1546017 Farmaki EG, 2012, ANAL LETT, V45, P920, DOI 10.1080/00032719.2012.655656 Foddai ACG, 2020, APPL MICROBIOL BIOT, V104, P4281, DOI 10.1007/s00253-020-10542-x Fotou K, 2011, ANAEROBE, V17, P315, DOI 10.1016/j.anaerobe.2011.05.002 Fragaki G, 2005, J AGR FOOD CHEM, V53, P2810, DOI 10.1021/jf040279t Fronimaki P, 2002, J AGR FOOD CHEM, V50, P2207, DOI 10.1021/jf011380q Garbi A, 2010, TALANTA, V82, P1286, DOI 10.1016/j.talanta.2010.06.046 Gika H, 2019, J CHROMATOGR B, V1117, P136, DOI 10.1016/j.jchromb.2019.04.009 Gika H, 2012, ANAL BIOANAL CHEM, V404, P701, DOI 10.1007/s00216-012-6015-6 Gika H, 2011, BIOANALYSIS, V3, P1647, DOI [10.4155/BIO.11.122, 10.4155/bio.11.122] Gika HG, 2008, J CHROMATOGR B, V871, P299, DOI 10.1016/j.jchromb.2008.05.048 Gika HG, 2008, J SEP SCI, V31, P1598, DOI 10.1002/jssc.200700644 Gika HG, 2007, J PROTEOME RES, V6, P3291, DOI 10.1021/pr070183p Gika HG, 2016, J CHROMATOGR B, V1008, P15, DOI 10.1016/j.jchromb.2015.10.045 Gika HG, 2014, J CHROMATOGR B, V966, P1, DOI 10.1016/j.jchromb.2014.01.054 Gika HG, 2012, BIOANALYSIS, V4, P2239, DOI [10.4155/BIO.12.212, 10.4155/bio.12.212] Gika HG, 2012, J CHROMATOGR A, V1259, P128, DOI 10.1016/j.chroma.2012.02.053 Gika HG, 2012, J CHROMATOGR A, V1259, P121, DOI 10.1016/j.chroma.2012.02.010 Gkagkavouzis K., 2016, P INT S GEN AQ ATH G, P54 Halabalaki M, 2014, CURR OPIN BIOTECH, V25, P1, DOI 10.1016/j.copbio.2013.08.005 Haroutinian S., 2004, GR Patent, Patent No. [GR1005157, 1005157] Imsiridou A., 2019, J NUTR FOOD LIPID SC, V1, P54, DOI DOI 10.33513/NFLS/1901-08 Kalogiouri NP, 2020, ANAL CHIM ACTA, V1134, P150, DOI 10.1016/j.aca.2020.07.029 Kalogiouri NP, 2020, MOLECULES, V25, DOI 10.3390/molecules25122919 Kalogiouri NP, 2018, FOOD CHEM, V256, P53, DOI 10.1016/j.foodchem.2018.02.101 Kalogiouri NP, 2017, ANAL BIOANAL CHEM, V409, P5413, DOI 10.1007/s00216-017-0395-6 Kalogiouri NP, 2016, ANAL BIOANAL CHEM, V408, P7955, DOI 10.1007/s00216-016-9891-3 Karkoula E, 2018, J SEP SCI, V41, DOI 10.1002/jssc.201800516 Kazalaki A, 2015, ANAL METHODS-UK, V7, P5962, DOI [10.1039/C5AY01243K, 10.1039/c5ay01243k] Kotoula D, 2020, J CLEAN PROD, V271, DOI 10.1016/j.jclepro.2020.122704 Lai L, 2010, MOL BIOSYST, V6, P108, DOI 10.1039/b910482h Lambropoulou DA, 2007, ANAL BIOANAL CHEM, V389, P1663, DOI 10.1007/s00216-007-1348-2 Lioupi A, 2020, J CHROMATOGR B, V1150, DOI 10.1016/j.jchromb.2020.122161 Loftus N, 2011, J PROTEOME RES, V10, P705, DOI 10.1021/pr100885w Mackie IM, 1999, TRENDS FOOD SCI TECH, V10, P9, DOI 10.1016/S0924-2244(99)00013-8 Malissiova E, 2015, DAIRY SCI TECHNOL, V95, P437, DOI 10.1007/s13594-015-0224-7 Malliou F, 2018, J NUTR BIOCHEM, V59, P17, DOI 10.1016/j.jnutbio.2018.05.013 Maragou NC, 2008, FOOD ADDIT CONTAM A, V25, P373, DOI 10.1080/02652030701509998 Mariani P., 2005, P 4 WORLD IT BEEF CA, P297 Marinou KA, 2010, LIPIDS HEALTH DIS, V9, DOI 10.1186/1476-511X-9-73 Michailidis D, 2019, FRONT PHARMACOL, V10, DOI 10.3389/fphar.2019.00723 Michailidou S, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0226179 Michailidou S, 2018, MOL GENET GENOMICS, V293, P753, DOI 10.1007/s00438-018-1421-x Michopoulos F, 2009, J PROTEOME RES, V8, P2114, DOI 10.1021/pr801045q Mikropoulou EV, 2018, MOLECULES, V23, DOI 10.3390/molecules23071541 Minoudi S, 2020, FOOD CONTROL, V113, DOI 10.1016/j.foodcont.2020.107213 Moros G, 2017, BIOANALYSIS, V9, P53, DOI 10.4155/bio-2016-0224 Ndoye B, 2011, DAIRY SCI TECHNOL, V91, P495, DOI 10.1007/s13594-011-0031-8 Nika M.C., 2017, P 15 INT C ENV SCI T Nikou T, 2020, FRONT PUBLIC HEALTH, V8, DOI 10.3389/fpubh.2020.558226 Orfanidis A, 2021, J ANAL TOXICOL, V45, P28, DOI 10.1093/jat/bkaa032 Papachristodoulou A., 2018, J CANC RES TREAT, V4, P61, DOI [10.12691/jcrt-4-4-2, DOI 10.12691/JCRT-4-4-2] Papadopoulou A, 2017, OXID MED CELL LONGEV, V2017, DOI 10.1155/2017/8273160 Papaspyrou SD, 2007, APPL ORGANOMET CHEM, V21, P412, DOI 10.1002/aoc.1235 Pappas AC, 2019, ANTIOXIDANTS-BASEL, V8, DOI 10.3390/antiox8090361 Paraschos S, 2016, FITOTERAPIA, V114, P12, DOI 10.1016/j.fitote.2016.08.003 Peroulis N, 2019, EUR J NUTR, V58, P2545, DOI 10.1007/s00394-018-1807-x Petrakis PV, 2008, J AGR FOOD CHEM, V56, P3200, DOI 10.1021/jf072957s Petropoulos G, 2018, FOOD CHEM, V267, P313, DOI 10.1016/j.foodchem.2017.06.041 Pina A, 2018, J CHROMATOGR A, V1531, P53, DOI 10.1016/j.chroma.2017.11.019 Putignani L, 2016, J PROTEOMICS, V147, P3, DOI 10.1016/j.jprot.2016.04.033 Ralli E, 2018, METHODS MOL BIOL, V1738, P203, DOI 10.1007/978-1-4939-7643-0_14 Raptopoulou KG, 2014, FOOD CHEM TOXICOL, V69, P25, DOI 10.1016/j.fct.2014.03.023 Rozos G, 2018, FRONT MICROBIOL, V9, DOI 10.3389/fmicb.2018.00517 Sarafian MH, 2015, ANAL CHEM, V87, P9662, DOI 10.1021/acs.analchem.5b01556 Siametis A., 2016, P 16 HELL C ICHTH KA, P453 Sklavos S, 2015, J ENVIRON MANAGE, V162, P46, DOI 10.1016/j.jenvman.2015.07.034 Skoufos I., 2014, P 65 ANN M EUR FED A, P244 Skoufos I., 2015, P 32 WORLD VET C WVC, P357 Skoufos I, 2017, INT J DAIRY TECHNOL, V70, P345, DOI 10.1111/1471-0307.12349 Spagou K, 2011, J CHROMATOGR B, V879, P1467, DOI 10.1016/j.jchromb.2011.01.028 Spyros A, 2013, RSC FOOD ANAL MONOGR, P1, DOI 10.1039/9781849735339 Spyros A, 2000, J AGR FOOD CHEM, V48, P802, DOI 10.1021/jf9910990 Spyros A., 2017, MODERN MAGNETIC RESO, P1, DOI [10.1007/978-3-319-28275-6_7-1, DOI 10.1007/978-3-319-28275-6_7-1] Spyros A, 2016, NUC MAGN RESON, V45, P269, DOI 10.1039/9781782624103-00269 Synaridou MES, 2014, J CHROMATOGR A, V1348, P71, DOI 10.1016/j.chroma.2014.04.092 Tang FF, 2019, ANNU REP NMR SPECTRO, V98, P239, DOI 10.1016/bs.arnmr.2019.04.005 Termentzi A., 2015, OLIVE OLIVE OIL BIOA, P147 Theodoridis G, 2012, METABOLOMICS, V8, P175, DOI 10.1007/s11306-011-0298-z Tomazou M, 2019, PROTEOMES, V7, DOI 10.3390/proteomes7040032 Tsakelidou E, 2017, METABOLITES, V7, DOI 10.3390/metabo7020013 Tsangaris G.T, 2018, P 12 ANN C IT PROT A, P18 Tsartsianidou V., 2020, P 71 ANN M EUR FED A, P572 Tsochatzis ED, 2020, ANAL CHIM ACTA, V1130, P49, DOI 10.1016/j.aca.2020.07.018 Tsochatzis ED, 2017, J CHROMATOGR B, V1047, P197, DOI 10.1016/j.jchromb.2016.05.018 Tsoutsi CS, 2008, FOOD ADDIT CONTAM A, V25, P1225, DOI 10.1080/02652030802130025 Tuenter E, 2021, FOOD CHEM, V343, DOI 10.1016/j.foodchem.2020.128446 Tzora A., 2020, EFFECT DIETARY POLYU Tzora A., 2020, 34 INT EFFOST 2020 I Valdes A, 2017, TRAC-TREND ANAL CHEM, V96, P2, DOI 10.1016/j.trac.2017.06.004 Vigli G, 2003, J AGR FOOD CHEM, V51, P5715, DOI 10.1021/jf030100z Virgiliou C, 2020, J CHROMATOGR A, V1616, DOI 10.1016/j.chroma.2019.460783 Virgiliou C, 2015, ELECTROPHORESIS, V36, P2215, DOI 10.1002/elps.201500208 Voidarou C, 2011, ANAEROBE, V17, P354, DOI 10.1016/j.anaerobe.2011.07.004 Voidarou C, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10207309 Vougogiannopoulou K, 2015, PLANTA MED, V81, P1205, DOI 10.1055/s-0035-1546243 Want EJ, 2013, NAT PROTOC, V8, P17, DOI 10.1038/nprot.2012.135 Want EJ, 2010, NAT PROTOC, V5, P1005, DOI 10.1038/nprot.2010.50 Xynos N, 2018, J SUPERCRIT FLUID, V133, P349, DOI 10.1016/j.supflu.2017.10.011 Xynos N, 2015, PLANTA MED, V81, P1621, DOI 10.1055/s-0035-1558111 Zdolec N, 2018, BIOMED RES INT, V2018, DOI 10.1155/2018/3902698 Zhang P, 2018, BRIEF BIOINFORM, V19, P524, DOI 10.1093/bib/bbw131 Zioris IV, 2009, FOOD ADDIT CONTAM A, V26, P1256, DOI 10.1080/02652030903045122 NR 157 TC 9 Z9 9 U1 2 U2 14 PD FEB PY 2021 VL 11 IS 2 AR 74 DI 10.3390/metabo11020074 WC Biochemistry & Molecular Biology SC Biochemistry & Molecular Biology UT WOS:000622777700001 DA 2022-12-14 ER PT J AU Kim, H Kumar, KS Shin, KH AF Kim, Heejoong Kumar, K. Suresh Shin, Kyung-Hoon TI Applicability of stable C and N isotope analysis in inferring the geographical origin and authentication of commercial fish (Mackerel, Yellow Croaker and Pollock) SO FOOD CHEMISTRY DT Article DE Fish; delta C-13; delta N-15; Origin; Traceability ID ORGANIC-MATTER; CARBON; NITROGEN; FOOD; BEEF; TRACEABILITY; DELTA-N-15; ALASKAN; ECOLOGY; RATIOS AB Globalisation of seafood and aquaculture products and their convenient marketing worldwide, increases the possibility for the distribution of mislabelled products; thereby, underlining the need to identify their origin. Stable isotope analysis is a promising approach to identify the authenticity and traceability of seafood and aquaculture products. In this investigation, we measured carbon and nitrogen stable isotope ratios (delta C-13 and delta N-15) of three commercial fish, viz. Mackerel, Yellow Croaker and Pollock, originating from various countries. Apart from the species-dependent variation in the isotopic values, marked differences in the delta C-13 and delta N-15 ratios were also observed with respect to the country of origin. This suggests that C and N isotopic signatures could be reliable tools to identify and trace the origin of commercial fish. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Kim, Heejoong; Kumar, K. Suresh; Shin, Kyung-Hoon] Hanyang Univ, Dept Marine Sci & Convergent Technol, Ansan 425791, South Korea. C3 Hanyang University RP Shin, KH (corresponding author), Hanyang Univ, Dept Marine Sci & Convergent Technol, Ansan 425791, South Korea. EM shinkh@hanyang.ac.kr CR Abend AG, 1997, ICES J MAR SCI, V54, P500, DOI 10.1006/jmsc.1996.0192 Al-Habsi SH, 2008, MAR ECOL PROG SER, V353, P55, DOI 10.3354/meps07167 Arcagni M, 2013, LIMNOLOGICA, V43, P131, DOI 10.1016/j.limno.2012.08.009 Brodie JE, 2012, MAR POLLUT BULL, V65, P81, DOI 10.1016/j.marpolbul.2011.12.012 Chen GQG, 2012, ACTA MATH SCI, V32, P1 Dehn LA, 2007, POLAR BIOL, V30, P167, DOI 10.1007/s00300-006-0171-0 DENIRO MJ, 1978, GEOCHIM COSMOCHIM AC, V42, P495, DOI 10.1016/0016-7037(78)90199-0 European Union (EU), 2002, OFFICIAL J L, V31 FRANCE RL, 1995, MAR ECOL PROG SER, V124, P307, DOI 10.3354/meps124307 Gomez-Requeni P, 2004, AQUACULTURE, V232, P493, DOI 10.1016/S0044-8486(03)00532-5 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 Guo BL, 2010, FOOD CHEM, V118, P915, DOI 10.1016/j.foodchem.2008.09.062 Hobson KA, 2008, TERR ECOL SER, V2, P1 Hobson KA, 1997, MAR MAMMAL SCI, V13, P114, DOI 10.1111/j.1748-7692.1997.tb00615.x Ji WW, 2011, CHIN J OCEANOL LIMN, V29, P1033, DOI 10.1007/s00343-011-0188-2 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kurle CM, 2011, MAR BIOL, V158, P2389, DOI 10.1007/s00227-011-1741-4 Layman CA, 2012, BIOL REV, V87, P545, DOI 10.1111/j.1469-185X.2011.00208.x Lee Y., 2014, MARINE POLLUTION B Libes S.M., 1992, INTRO MARINE BIOGEOC Liu XL, 2013, FOOD CHEM, V140, P135, DOI 10.1016/j.foodchem.2013.02.020 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 MacKenzie KM, 2011, SCI REP-UK, V1, DOI 10.1038/srep00021 Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Osorio MT, 2013, FOOD CHEM, V141, P2795, DOI 10.1016/j.foodchem.2013.05.118 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 SCHELL DM, 1989, MAR BIOL, V103, P433, DOI 10.1007/BF00399575 Schmidt O, 2005, FOOD CHEM, V91, P545, DOI 10.1016/j.foodchem.2004.08.036 Schukat A, 2014, J SEA RES, V85, P186, DOI 10.1016/j.seares.2013.04.018 Suh YJ, 2013, ESTUAR COAST SHELF S, V135, P94, DOI 10.1016/j.ecss.2013.06.029 Tacon AGJ, 2013, REV FISH SCI, V21, P22, DOI 10.1080/10641262.2012.753405 Tamelander T, 2008, DEEP-SEA RES PT II, V55, P2330, DOI 10.1016/j.dsr2.2008.05.019 Tanaka H, 2010, FISH RES, V102, P217, DOI 10.1016/j.fishres.2009.11.002 Turchini GM, 2009, J AGR FOOD CHEM, V57, P274, DOI 10.1021/jf801962h Wu Y, 2003, BIOGEOCHEMISTRY, V65, P31, DOI 10.1023/A:1026044324643 Yanagi Y, 2012, FOOD CHEM, V134, P502, DOI 10.1016/j.foodchem.2012.02.107 NR 36 TC 43 Z9 48 U1 3 U2 164 PD APR 1 PY 2015 VL 172 BP 523 EP 527 DI 10.1016/j.foodchem.2014.09.058 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000345207200072 DA 2022-12-14 ER PT J AU Bueno-Solano, A Lagarda-Leyva, EA Miranda-Ackerman, MA Velarde-Cantu, JM Perez, KG AF Bueno-Solano, Alfredo Lagarda-Leyva, Ernesto A. Miranda-Ackerman, Marco A. Velarde-Cantu, Jose M. Gabriela Perez, Karla TI Conceptual fluidity model for resilient agroindustry supply chains SO PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL DT Article DE Supply chain flow; agroindustry fluidity model; agro-logistics; efficient and resilient systems ID FOOD; TRACEABILITY; TRANSPARENCY; LOGISTICS; SAFETY AB Fluidity models in the supply chain privilege the sustainable integration of capabilities and collaboration among its members in order to guarantee an efficient and safe flow of resources throughout all its processes. This research proposes a fluidity model for the agroindustry supply chain as a solution with regard to the sector's needs of supply chain processes, and opportunities to collaborate within the field of innovation and sustainability through of traceability and proactive risk management as a tool for creating resilient systems. The model is based on a holistic vision that will allow it to adapt to an ever more complex and continuously transformed global environment that demands solutions to assess the global impact of local decision-making in the supply chain over a period of time, considering its implications and contributions to the agroindustry and agro-logistics sector. Finally, pertinent research areas are identified in the integration of agroindustry supply chain echelons. C1 [Bueno-Solano, Alfredo; Lagarda-Leyva, Ernesto A.; Velarde-Cantu, Jose M.; Gabriela Perez, Karla] Inst Tecnol Sonora, Inudstrial Engn Dept, Obregon, Mexico. [Miranda-Ackerman, Marco A.] Univ Autonoma Baja California, Fac Ciencias Quim & Ingn, Tijuana, Mexico. C3 Universidad Autonoma de Baja California RP Lagarda-Leyva, EA (corresponding author), Inst Tecnol Sonora, Obregon, Mexico. EM elagarda@itson.edu.mx CR Accorsi R, 2017, PROCEDIA MANUF, V11, P889, DOI 10.1016/j.promfg.2017.07.192 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Beulens AJM, 2005, FOOD CONTROL, V16, P481, DOI 10.1016/j.foodcont.2003.10.010 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Solano AB, 2016, INT J COMB OPTIM PRO, V7, P3 Bueno-Solano A., 2021, MODELO DINAMICO CONT Bueno-Solano A, 2014, TRANSPORT RES E-LOG, V61, P1, DOI 10.1016/j.tre.2013.09.005 CAC, 2003, 11969 CAC CACRCP Cedillo-Campos M.C., 2017, JULIO FORBES MEXICO Chopra Sunil, 2008, ADM CADENA SUMINISTR, VTercera Christopher M., 2004, INT J LOGIST MANAG, V15, P1, DOI [10.1108/09574090410700275, DOI 10.1108/09574090410700275] Eggert A, 2019, IND MARKET MANAG, V79, P13, DOI 10.1016/j.indmarman.2019.03.004 Eisele W., 2015, ADV FREIGHT FLUIDITY, V1, P98, DOI [10.17226/23623, DOI 10.17226/23623] Flynn K, 2019, TRENDS FOOD SCI TECH, V84, P1, DOI 10.1016/j.tifs.2018.09.012 Fredriksson A, 2015, INT J LOGIST-RES APP, V18, P16, DOI 10.1080/13675567.2014.944887 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Cedillo-Campos MG, 2015, SAFETY SCI, V79, P358, DOI 10.1016/j.ssci.2015.06.009 Handayati Yuanita, 2015, Logistics Research, V8, DOI 10.1007/s12159-015-0125-4 Hintsa J, 2010, J TRANSP SECUR, V3, P105, DOI 10.1007/s12198-010-0042-3 I-95 Corridor Coalition, 2016, CISC VIS NETW IND GL Kelepouris T, 2007, IND MANAGE DATA SYST, V107, P183, DOI 10.1108/02635570710723804 Kommerskollegium, 2008, SUPPL CHAIN SEC IN T Lagarda-Leyva E.A., 2018, INT J EC MANAGEMENT, V12, DOI [10.5281/zenodo.1340404, DOI 10.5281/ZENODO.1340404] Lazarides HN, 2011, PROC FOOD SCI, V1, P1918, DOI 10.1016/j.profoo.2011.09.282 Liddiard R, 2017, ENRGY PROCED, V123, P196, DOI 10.1016/j.egypro.2017.07.238 Liu R, 2007, DECIS SUPPORT SYST, V43, P761, DOI 10.1016/j.dss.2006.12.009 Melkonyan A, 2017, ENRGY PROCED, V123, P131, DOI 10.1016/j.egypro.2017.07.236 Morales-Gaytan R., 2019, THESIS I TECNOLOGICO Narsimhalu U, 2015, PROCD SOC BEHV, V189, P17, DOI 10.1016/j.sbspro.2015.03.188 Navarro J.C., 2017, GESTI N CADENA SUMIN, P272 Opara LU, 2003, J FOOD AGRIC ENVIRON, V1, P101 Perez-Escamilla R, 2017, GLOB FOOD SECUR-AGR, V14, P96, DOI 10.1016/j.gfs.2017.06.003 Pfohl H.C., 2010, LOGISTICS RES, V2, P33, DOI DOI 10.1007/S12159-010-0023-8 Sgarbossa F, 2017, INT J PROD ECON, V183, P596, DOI 10.1016/j.ijpe.2016.07.022 Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 Wu T, 2007, INT J PROD RES, V45, P1665, DOI 10.1080/00207540500362138 Zondag MM, 2017, SCAND J MANAG, V33, P199, DOI 10.1016/j.scaman.2017.10.002 NR 37 TC 1 Z9 1 U1 8 U2 8 PD DEC 31 PY 2022 VL 10 IS 1 BP 281 EP 293 DI 10.1080/21693277.2022.2075947 WC Engineering, Industrial SC Engineering UT WOS:000804001500001 DA 2022-12-14 ER PT J AU Barcaccia, G Lucchin, M Cassandro, M AF Barcaccia, Gianni Lucchin, Margherita Cassandro, Martino TI DNA Barcoding as a Molecular Tool to Track Down Mislabeling and Food Piracy SO DIVERSITY-BASEL DT Review DE cpDNA barcoding; mtDNA barcoding; genetic traceability; foodstuffs ID BAR-HRM ANALYSIS; CATTLE BREEDS; SNP MARKERS; OLIVE OIL; TRACEABILITY; IDENTIFICATION; PRODUCTS; GENE; AUTHENTICATION; ASSIGNMENT AB DNA barcoding is a molecular technology that allows the identification of any biological species by amplifying, sequencing and querying the information from genic and/or intergenic standardized target regions belonging to the extranuclear genomes. Although these sequences represent a small fraction of the total DNA of a cell, both chloroplast and mitochondrial barcodes chosen for identifying plant and animal species, respectively, have shown sufficient nucleotide diversity to assess the taxonomic identity of the vast majority of organisms used in agriculture. Consequently, cpDNA and mtDNA barcoding protocols are being used more and more in the food industry and food supply chains for food labeling, not only to support food safety but also to uncover food piracy in freshly commercialized and technologically processed products. Since the extranuclear genomes are present in many copies within each cell, this technology is being more easily exploited to recover information even in degraded samples or transformed materials deriving from crop varieties and livestock species. The strong standardization that characterizes protocols used worldwide for DNA barcoding makes this technology particularly suitable for routine analyses required by agencies to safeguard food safety and quality. Here we conduct a critical review of the potentials of DNA barcoding for food labeling along with the main findings in the area of food piracy, with particular reference to agrifood and livestock foodstuffs. C1 [Barcaccia, Gianni; Lucchin, Margherita; Cassandro, Martino] Univ Padua, DAFNAE, Lab Genom, LabGEN, Via Univ 16, I-35020 Legnaro, Italy. C3 University of Padua RP Barcaccia, G (corresponding author), Univ Padua, DAFNAE, Lab Genom, LabGEN, Via Univ 16, I-35020 Legnaro, Italy. EM gianni.barcaccia@unipd.it; margherita.lucchin@unipd.it; martino.cassandro@unipd.it CR AIJN, 2014, LID FRUIT MARK REP Ajmone-Marsan P., 2004, P 7 WORLD C BROWN SW, P101 Barcos LO, 2001, REV SCI TECH OIE, V20, P640, DOI 10.20506/rst.20.2.1294 Bruni I, 2015, FOOD CHEM, V170, P308, DOI 10.1016/j.foodchem.2014.08.060 Casiraghi M., 2010, BRIEF BIOINFORM Chase M.W., 2005, TRENDS ECOL EVOL, V18, P273 Chase MW, 2007, TAXON, V56, P295, DOI 10.1002/tax.562004 Ciampolini R, 2000, MEAT SCI, V54, P35, DOI 10.1016/S0309-1740(99)00061-3 Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Cozzi G., 2003, AGR CONSPECTUS SCI, V68, P1331 De Mattia F, 2011, FOOD RES INT, V44, P693, DOI 10.1016/j.foodres.2010.12.032 Decaens T., 2013, BARCODING ADN OUTIL Di Pinto A, 2016, FOOD CHEM, V194, P279, DOI 10.1016/j.foodchem.2015.07.135 Di Pinto A, 2015, FISH RES, V170, P9, DOI 10.1016/j.fishres.2015.05.006 Dimauro C, 2015, SMALL RUMINANT RES, V128, P27, DOI 10.1016/j.smallrumres.2015.05.001 EC-European Commission, 2002, J EUROP COMM, VL031, P1 Enan M., 2012, American Journal of Plant Sciences, V3, P1304 European Commission, 2000, WORK REL NECK UPP LI, P1 European Parliament and European council, 2000, OFFICIAL J EUROPEA L, V204, P1 Everstine K, 2013, J FOOD PROTECT, V76, P723, DOI 10.4315/0362-028X.JFP-12-399 Faria MA, 2013, FOOD CONTROL, V33, P136, DOI 10.1016/j.foodcont.2013.02.020 Galimberti A, 2014, ADV AGR, V2014, P831875, DOI [10.1155/2014/831875, DOI 10.1155/2014/831875, 10.1155/2014/831875.] Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Ganopoulos I, 2013, J SCI FOOD AGR, V93, P2281, DOI 10.1002/jsfa.6040 Ganopoulos I, 2012, FOOD CHEM, V133, P505, DOI 10.1016/j.foodchem.2012.01.015 GELLYNCK X, 2005, P 92 SEM QUAL MAN QU Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 He J, 2013, FOOD CONTROL, V31, P71, DOI 10.1016/j.foodcont.2012.07.001 Hilu KW, 1997, AM J BOT, V84, P830, DOI 10.2307/2445819 Hollingsworth PM, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0019254 Kress WJ, 2007, PLOS ONE, V2, DOI 10.1371/journal.pone.0000508 Madesis P, 2013, FOOD RES INT, V50, P351, DOI 10.1016/j.foodres.2012.10.038 Marchant J, 2002, SECURE ANIMAL IDENTI Mateus JC, 2015, FOOD CONTROL, V47, P487, DOI 10.1016/j.foodcont.2014.07.038 Maudet C, 2002, J DAIRY SCI, V85, P707, DOI 10.3168/jds.S0022-0302(02)74127-1 Maudet C, 2002, J ANIM SCI, V80, P942 Meuwissen M. P. M., 2003, Journal of Agribusiness, V21, P167 Meyer CP, 2005, PLOS BIOL, V3, P2229, DOI 10.1371/journal.pbio.0030422 Ng J, 2014, J FOOD SCI TECH MYS, V51, P4060, DOI 10.1007/s13197-012-0893-7 Nicole Silvia, 2013, BMC Res Notes, V6, P502, DOI 10.1186/1756-0500-6-502 Nicole S, 2012, FOOD TECHNOL BIOTECH, V50, P387 Nicole S, 2011, GENOME, V54, P529, DOI [10.1139/G11-018, 10.1139/g11-018] OECD, 2007, GUID DOC VAL QUANT S, DOI DOI 10.1787/9789264085442EN Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Rea S, 2001, J DAIRY RES, V68, P689, DOI 10.1017/S0022029901005106 Sardina MT, 2015, FOOD RES INT, V74, P115, DOI 10.1016/j.foodres.2015.04.038 Shaw J, 2007, AM J BOT, V94, P275, DOI 10.3732/ajb.94.3.275 Shokralla S, 2015, SCI REP-UK, V5, DOI 10.1038/srep15894 Smith GC, 2005, MEAT SCI, V71, P174, DOI 10.1016/j.meatsci.2005.04.002 Spaniolas S, 2008, EUR FOOD RES TECHNOL, V227, P175, DOI 10.1007/s00217-007-0707-8 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 The European Cosmetic and Perfumeryn Association, 1997, GUID PERC ABS PEN CO, P1 UNSELD M, 1995, PCR METH APPL, V4, P241 Valentini A., 2010, Diversity, V2, P610 Ward RD, 2005, PHILOS T R SOC B, V360, P1847, DOI 10.1098/rstb.2005.1716 Wiemers M, 2007, FRONT ZOOL, V4, DOI 10.1186/1742-9994-4-8 NR 56 TC 45 Z9 49 U1 3 U2 18 PD MAR PY 2016 VL 8 IS 1 AR 2 DI 10.3390/d8010002 WC Biodiversity Conservation; Ecology SC Biodiversity & Conservation; Environmental Sciences & Ecology UT WOS:000374049300002 DA 2022-12-14 ER PT J AU Ren, GX Wang, SP Ning, JM Xu, RR Wang, YX Xing, ZQ Wan, XC Zhang, ZZ AF Ren, Guangxin Wang, Shengpeng Ning, Jingming Xu, Rongrong Wang, Yuxia Xing, Zhiqiang Wan, Xiaochun Zhang, Zhengzhu TI Quantitative analysis and geographical traceability of black tea using Fourier transform near-infrared spectroscopy (FT-NIRS) SO FOOD RESEARCH INTERNATIONAL DT Article DE Camellia sinensis; Geographic origin identification; Caffeine; Polyphenols; Free amino acids ID ATOMIC EMISSION-SPECTROMETRY; PARTIAL LEAST-SQUARES; PATTERN-RECOGNITION; REFLECTANCE SPECTROSCOPY; CAPILLARY-ELECTROPHORESIS; GASOLINE CLASSIFICATION; AMINO-ACIDS; QUALITY; OPTIMIZATION; PREDICTION AB Near-infrared spectroscopy (NIRS) combined with chemometric tools was utilized as a rapid analysis method to assess quality and to differentiate geographical origins of black tea. A partial least squares (PLS) algorithm was employed for the calibration of models predicting the levels of caffeine, water extract, total polyphenols, and free amino acids, while a factorization method was proposed to trace black tea from different geographical origins. In the calibration set, the root mean squared error of cross validation (%) and the correlation coefficient (R) for caffeine, water extracts, total polyphenols and free amino acids were 0.102%, 0.654%, 0.552%, and 0.248% and 0.983, 0.977, 0.975, and 0.943, respectively. In the prediction set, the root mean squared error of prediction and R for the corresponding constituents were 0.160%, 0.685%, 0.594%, and 0273% and 0.955, 0.962, 0.954, and 0.927, respectively. The identification accuracy for black tea from different geographical origins reached 94.3%. This study demonstrated that NIR spectroscopy can be successfully applied to rapidly determine the main chemical compositions and geographical origins of black tea. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Ren, Guangxin; Wang, Shengpeng; Ning, Jingming; Xu, Rongrong; Wang, Yuxia; Xing, Zhiqiang; Wan, Xiaochun; Zhang, Zhengzhu] Anhui Agr Univ, Minist Agr, Key Lab Tea & Med Plants & Prod Safety, Hefei 230036, Peoples R China. [Ren, Guangxin] All China Federat Supply & Mkt Cooperat, Hangzhou Tea Res Inst, Hangzhou 310016, Zhejiang, Peoples R China. C3 Anhui Agricultural University; Ministry of Agriculture & Rural Affairs; Chinese Academy of Agricultural Sciences; Tea Research Institute, CAAS RP Zhang, ZZ (corresponding author), Anhui Agr Univ, Minist Agr, Key Lab Tea & Med Plants & Prod Safety, Hefei 230036, Peoples R China. EM zzz@ahau.edu.cn CR Balabin RM, 2008, FUEL, V87, P1096, DOI 10.1016/j.fuel.2007.07.018 Balabin RM, 2010, ANAL CHIM ACTA, V671, P27, DOI 10.1016/j.aca.2010.05.013 Baptistao M, 2011, J MOL STRUCT, V1002, P167, DOI 10.1016/j.molstruc.2011.07.019 Beelders T, 2012, J CHROMATOGR A, V1219, P128, DOI 10.1016/j.chroma.2011.11.012 Chen L, 2009, J FOOD COMPOS ANAL, V22, P137, DOI 10.1016/j.jfca.2008.08.007 Chen QS, 2010, J FOOD COMPOS ANAL, V23, P353, DOI 10.1016/j.jfca.2009.12.010 Cleve E, 2000, ANAL CHIM ACTA, V420, P163, DOI 10.1016/S0003-2670(00)00888-6 Cozzolino D, 2011, FOOD RES INT, V44, P1888, DOI 10.1016/j.foodres.2011.01.041 Cozzolino D, 2004, ANAL CHIM ACTA, V513, P73, DOI 10.1016/j.aca.2003.08.066 Durand A, 2007, ANAL CHIM ACTA, V595, P72, DOI 10.1016/j.aca.2007.03.024 HALL MN, 1988, FOOD CHEM, V27, P61, DOI 10.1016/0308-8146(88)90036-2 Herrador MA, 2001, TALANTA, V53, P1249, DOI 10.1016/S0039-9140(00)00619-6 Hsieh MM, 2007, TALANTA, V73, P326, DOI 10.1016/j.talanta.2007.03.049 Kim K, 2005, CHEMOMETR INTELL LAB, V79, P22, DOI 10.1016/j.chemolab.2005.03.003 Kurz C, 2010, FOOD CHEM, V119, P806, DOI 10.1016/j.foodchem.2009.07.028 Luo WQ, 2011, FOOD CHEM, V128, P555, DOI 10.1016/j.foodchem.2011.03.065 Mierzwa J, 1998, TALANTA, V47, P1263, DOI 10.1016/S0039-9140(98)00214-8 Moros J, 2007, ANAL CHIM ACTA, V584, P215, DOI 10.1016/j.aca.2006.11.020 OSBORNE BG, 1988, FOOD CHEM, V29, P233, DOI 10.1016/0308-8146(88)90136-7 Said MM, 2011, INT J PHARMACEUT, V415, P102, DOI 10.1016/j.ijpharm.2011.05.057 Sciarrone D, 2010, J CHROMATOGR A, V1217, P6422, DOI 10.1016/j.chroma.2010.08.019 Seetohul LN, 2006, J SCI FOOD AGR, V86, P2092, DOI 10.1002/jsfa.2578 Togari N, 1995, FOOD RES INT, V28, P495, DOI 10.1016/0963-9969(95)00029-1 Tran H, 2010, J DAIRY SCI, V93, P4961, DOI 10.3168/jds.2008-1893 Wang L, 2010, FOOD CHEM, V123, P1259, DOI 10.1016/j.foodchem.2010.05.063 Wright LP, 2001, J CHROMATOGR A, V919, P205, DOI 10.1016/S0021-9673(01)00762-2 NR 26 TC 76 Z9 93 U1 5 U2 132 PD OCT PY 2013 VL 53 IS 2 SI SI BP 822 EP 826 DI 10.1016/j.foodres.2012.10.032 WC Food Science & Technology SC Food Science & Technology UT WOS:000324511400031 DA 2022-12-14 ER PT J AU Kim, H Hwang, E Park, J Heo, SW Yim, YH Lim, Y Lim, MC Lee, JW Lee, KS AF Kim, Hwijin Hwang, Euijin Park, Jwahaeng Heo, Sung Woo Yim, Yong-Hyeon Lim, Youngran Lim, Myung Chul Lee, Jong Wha Lee, Kyoung-Seok TI Proficiency testing for total mercury in oyster with a metrologically traceable reference value from isotope dilution mass spectrometry: implications on laboratory practices using mercury analyzers SO ACCREDITATION AND QUALITY ASSURANCE DT Article DE Proficiency testing; Certified reference value; Metrological traceability; Mercury; Mercury analyzer; Isotope dilution mass spectrometry ID ATOMIC-ABSORPTION-SPECTROMETRY; THERMAL-DECOMPOSITION; UNCERTAINTY EVALUATION; ENVIRONMENTAL-SAMPLES; MULTIPLE MEASUREMENTS; METHOD VALIDATION; ELEMENT CONTENTS; FISH; WATER; CERTIFICATION AB Mercury is a toxic element of particular concern for the environment and human health, but its accurate analysis is challenging due to its unique physical and chemical properties. In order to enhance the quality and traceability of measurements, a proficiency testing (PT) program (MFDS-PT-101FM-2017) for mercury in oyster tissue has been conducted by the Ministry of Food and Drug Safety (MFDS) of Korea. A majority of the participating laboratories have used commercial mercury analyzers based on thermal decomposition amalgamation atomic absorption spectrometry (TDA-AAS). Good agreement between the participants' results and the reference value (RV) was observed, where the RV is traceable to the International System of Units and verified for international equivalence. The results of this PT program support the participating laboratories in demonstrating their competence in quantitative analysis, and show the potential of commercial TDA-AAS systems for accurate mercury analysis. C1 [Kim, Hwijin; Hwang, Euijin; Heo, Sung Woo; Yim, Yong-Hyeon; Lim, Youngran; Lim, Myung Chul; Lee, Jong Wha; Lee, Kyoung-Seok] KRISS, Div Chem & Med Metrol, Ctr Analyt Chem, Gajeong Ro 267, Daejeon, South Korea. [Kim, Hwijin; Yim, Yong-Hyeon] UST, Dept Bioanalyt Sci, Gajeong Ro 217, Daejeon, South Korea. [Park, Jwahaeng] Consumer Risk Prevent Bur, Minist Food & Drug Safety, Cheongju, Chungcheongbuk, South Korea. C3 Korea Research Institute of Standards & Science (KRISS); University of Science & Technology (UST); Ministry of Food & Drug Safety (MFDS), Republic of Korea RP Lee, JW; Lee, KS (corresponding author), KRISS, Div Chem & Med Metrol, Ctr Analyt Chem, Gajeong Ro 267, Daejeon, South Korea. EM jongwhalee@kriss.re.kr; kslee@kriss.re.kr CR ANDERSON DH, 1971, ANAL CHEM, V43, P1511, DOI 10.1021/ac60305a040 [Anonymous], 2006, IAEA436 [Anonymous], INT EQ MEAS CIPM MRA [Anonymous], 2015, D7622102015 ASTM [Anonymous], 2017, REF MAT GUID CHAR AS [Anonymous], 2015, D7623102015 ASTM [Anonymous], 2015, 135282015 ISO, V2nd [Anonymous], 2007, 7473SW846 US EPA [Anonymous], 2008, JCGMWG1100 ISOIEC 3 [Anonymous], 2011, D672211 ASTM [Anonymous], 2016, EURL HM 22 PROF TEST [Anonymous], CAL MEAS CAP CMCS Araujo P, 2006, ANAL CHIM ACTA, V555, P348, DOI 10.1016/j.aca.2005.09.024 Baer I, 2011, FOOD CHEM, V126, P1498, DOI 10.1016/j.foodchem.2010.12.042 Butala SJM, 2007, J FOOD PROTECT, V70, P2422, DOI 10.4315/0362-028X-70.10.2422 Butala SJM, 2006, J FOOD PROTECT, V69, P2720, DOI 10.4315/0362-028X-69.11.2720 Choi J, 2003, ACCREDIT QUAL ASSUR, V8, P13, DOI 10.1007/s00769-002-0520-9 Choi J, 2017, B KOREAN CHEM SOC, V38, P211, DOI 10.1002/bkcs.11066 Cizdziel JV, 2004, TALANTA, V64, P918, DOI 10.1016/j.talanta.2004.04.013 Clarkson TW, 2006, CRIT REV TOXICOL, V36, P609, DOI 10.1080/10408440600845619 Cohen E. R., 2008, QUANTITIES UNITS SYM Costley CT, 2000, ANAL CHIM ACTA, V405, P179, DOI 10.1016/S0003-2670(99)00742-4 COYNE RV, 1972, ANAL CHEM, V44, P1093, DOI 10.1021/ac60314a055 FELDMAN C, 1974, ANAL CHEM, V46, P99, DOI 10.1021/ac60337a002 Giang A, 2015, ENVIRON SCI TECHNOL, V49, P5326, DOI 10.1021/acs.est.5b00074 Guntinas MBD, 2009, TRAC-TREND ANAL CHEM, V28, P454, DOI 10.1016/j.trac.2009.02.005 Gustin MS, 2013, ENVIRON SCI TECHNOL, V47, P7295, DOI 10.1021/es3039104 Has-Schon E, 2008, ARCH ENVIRON CON TOX, V54, P75, DOI 10.1007/s00244-007-9008-2 Haynes S, 2006, WATER AIR SOIL POLL, V172, P359, DOI 10.1007/s11270-006-9101-6 HINGLE DN, 1967, ANALYST, V92, P759, DOI 10.1039/an9679200759 Horwitz W, 2006, J AOAC INT, V89, P1095 ISO, 2010, 170432010 ISOIEC Jaffe DA, 2014, ENVIRON SCI TECHNOL, V48, P7204, DOI 10.1021/es5026432 Juillerat JI, 2012, ENVIRON TOXICOL CHEM, V31, P1720, DOI 10.1002/etc.1896 Kim B, 2010, B KOREAN CHEM SOC, V31, P3139, DOI 10.5012/bkcs.2010.31.11.3139 Kim IJ, 2014, B KOREAN CHEM SOC, V35, P1057, DOI 10.5012/bkcs.2014.35.4.1057 Kim SH, 2016, TRAC-TREND ANAL CHEM, V85, P98, DOI 10.1016/j.trac.2016.09.004 Kim SH, 2016, ANAL METHODS-UK, V8, P796, DOI 10.1039/c5ay02040a KUCERA J, 1990, FRESEN J ANAL CHEM, V338, P66, DOI 10.1007/BF00322788 KUCERA J, 1995, FRESEN J ANAL CHEM, V352, P66, DOI 10.1007/BF00322299 Kwaansa-Ansah EE, 2016, B ENVIRON CONTAM TOX, V97, P677, DOI 10.1007/s00128-016-1920-6 Lasrado JA, 2005, J FOOD PROTECT, V68, P879, DOI 10.4315/0362-028X-68.4.879 Lee HS, 2015, METROLOGIA, V52, P619, DOI 10.1088/0026-1394/52/5/619 Leopold K, 2010, ANAL CHIM ACTA, V663, P127, DOI 10.1016/j.aca.2010.01.048 Li YF, 2006, J ANAL ATOM SPECTROM, V21, P94, DOI 10.1039/b511367a LO JM, 1975, ANAL CHEM, V47, P1869, DOI 10.1021/ac60361a003 Maggi C, 2012, TRAC-TREND ANAL CHEM, V36, P82, DOI 10.1016/j.trac.2012.01.006 Melendez-Perez JJ, 2013, J BRAZIL CHEM SOC, V24, P1880, DOI 10.5935/0103-5053.20130235 Mergler D, 2007, AMBIO, V36, P3, DOI 10.1579/0044-7447(2007)36[3:MEAHEI]2.0.CO;2 Myors RB, 2005, J ANAL ATOM SPECTROM, V20, P1051, DOI 10.1039/b504521e Ouedraogo O, 2013, SCI TOTAL ENVIRON, V444, P243, DOI 10.1016/j.scitotenv.2012.11.095 Palkovicova L, 2008, J EXPO SCI ENV EPID, V18, P326, DOI 10.1038/sj.jes.7500606 Parker JL, 2005, SCI TOTAL ENVIRON, V337, P253, DOI 10.1016/j.scitotenv.2004.07.006 Pereira E, 2008, TRAC-TREND ANAL CHEM, V27, P959, DOI 10.1016/j.trac.2008.09.001 Reis AT, 2015, TRAC-TREND ANAL CHEM, V64, P136, DOI 10.1016/j.trac.2014.08.015 Roy NK, 2008, GEOSTAND GEOANAL RES, V32, P331, DOI 10.1111/j.1751-908X.2008.00851.x Selin NE, 2009, ANNU REV ENV RESOUR, V34, P43, DOI 10.1146/annurev.environ.051308.084314 Szakova I, 2004, CHEM PAP-CHEM ZVESTI, V58, P311 Taverniers I, 2004, TRAC-TREND ANAL CHEM, V23, P535, DOI 10.1016/j.trac.2004.04.001 Thompson M, 2006, PURE APPL CHEM, V78, P145, DOI 10.1351/pac200678010145 Torres DP, 2012, FOOD ADDIT CONTAM A, V29, P625, DOI 10.1080/19440049.2011.642310 Torres DP, 2015, J ENVIRON SCI HEAL B, V50, P622, DOI 10.1080/03601234.2015.1028861 Torres DP, 2015, J ENVIRON SCI HEAL B, V50, P514, DOI 10.1080/03601234.2015.1018764 UNEP & WHO, 2008, GUID ID POP RISK MER United Nations Environment Programme, 2018, UNEPMCCOP2INF8 Vogl J, 2010, MAPAN-J METROL SOC I, V25, P135, DOI 10.1007/s12647-010-0017-7 Watters RL, 1997, METROLOGIA, V34, P87, DOI 10.1088/0026-1394/34/1/13 Yarita T, 2015, TALANTA, V132, P269, DOI 10.1016/j.talanta.2014.09.001 Zahir F, 2005, ENVIRON TOXICOL PHAR, V20, P351, DOI 10.1016/j.etap.2005.03.007 NR 69 TC 2 Z9 2 U1 1 U2 14 PD AUG PY 2019 VL 24 IS 4 BP 253 EP 261 DI 10.1007/s00769-019-01379-7 WC Chemistry, Analytical; Instruments & Instrumentation SC Chemistry; Instruments & Instrumentation UT WOS:000474346100001 DA 2022-12-14 ER PT J AU Bode, P Greenberg, RR Fernandesc, EAD AF Bode, Peter Greenberg, Robert R. De Nadai Fernandesc, Elisabete A. TI Neutron Activation Analysis: A Primary (Ratio) Method to Determine SI-Traceable Values of Element Content in Complex Samples SO CHIMIA DT Article DE Metrology; Neutron activation analysis; SI traceability; Uncertainty ID INAA AB The metrological principles of neutron activation analysis are discussed. It has been demonstrated that this method can provide elemental amount of substance with values fully traceable to the SI. The method has been used by several laboratories worldwide in a number of CCQM key comparisons - interlaboratory comparison tests at the highest metrological level - supplying results equivalent to values from other methods for elemental or isotopic analysis in complex samples without the need to perform chemical destruction and dissolution of these samples. The CCOM accepted therefore in April 2007 the claim that neutron activation analysis should have the similar status as the methods originally listed by the CCOM as 'primary methods of measurement'. Analytical characteristics and scope of application are given. C1 [Bode, Peter] Delft Univ Technol, Fac Sci Appl, Dept Radiat Radionuclides & Reactors, NL-2629 JB Delft, Netherlands. [Greenberg, Robert R.] NIST, Div Analyt Chem, Gaithersburg, MD 20899 USA. [De Nadai Fernandesc, Elisabete A.] Univ Sao Paulo, Ctr Energia Nucl Agr, Lab Radioisotopos, Piracicaba, SP, Brazil. C3 Delft University of Technology; National Institute of Standards & Technology (NIST) - USA; Universidade de Sao Paulo RP Bode, P (corresponding author), Delft Univ Technol, Fac Sci Appl, Dept Radiat Radionuclides & Reactors, Mekelweg 15, NL-2629 JB Delft, Netherlands. EM p.bode@tudelft.nl CR AREGBE Y, 2002, GERIM3702 IRMM Bode P, 2000, J RADIOANAL NUCL CH, V245, P109, DOI 10.1023/A:1006752509734 BODE P, 1998, ACTIVATUN ANAL ENCY *CCQM, 1996, 2 M CCQM *CCQM, 2007, 13 M CCQM 19 20 APRS *CCQM, 2008, 14 M CCQM 3 4 APRS *CCQM, 1998, 5 M FEB 1998 CCQM FE Greenberg RR, 2008, J RADIOANAL NUCL CH, V278, P231, DOI 10.1007/s10967-008-9101-7 Greenberg RR, 2000, J RADIOANAL NUCL CH, V245, P57, DOI 10.1023/A:1006703131599 MSTON MJT, 2001, METROLOGIA, V38, P289 NOACK S, 2008, CCQMK42 BAM Tian WZ, 2001, ACCREDIT QUAL ASSUR, V6, P488 NR 12 TC 10 Z9 11 U1 0 U2 3 PY 2009 VL 63 IS 10 BP 678 EP 680 DI 10.2533/chimia.2009.678 WC Chemistry, Multidisciplinary SC Chemistry UT WOS:000271524800018 DA 2022-12-14 ER PT J AU Ioka, H AF Ioka, Hisashi TI Promotion of making local marine products to brand by use of traceability system SO NIPPON SUISAN GAKKAISHI DT News Item C1 Shimane Prefectural Govt, Fisheries Management Div, Dept Agr Forestry & Fisheries, Matsue, Shimane, Japan. RP Ioka, H (corresponding author), Shimane Prefectural Govt, Fisheries Management Div, Dept Agr Forestry & Fisheries, Matsue, Shimane, Japan. NR 0 TC 0 Z9 0 U1 0 U2 0 PD SEP PY 2006 VL 72 IS 5 BP 972 EP 973 WC Fisheries SC Fisheries UT WOS:000241993300047 DA 2022-12-14 ER PT J AU Suprem, A Mahalik, N Kim, K AF Suprem, Abhijit Mahalik, Nitaigour Kim, Kiseon TI A review on application of technology systems, standards and interfaces for agriculture and food sector SO COMPUTER STANDARDS & INTERFACES DT Review DE Technology for food and agriculture; Sensor network; RFID ID NETWORK AB Application of technology systems is seen in many sectors including agriculture and food. Traditionally, agricultural industry has been solely dependent on human labor with limited application of mechanical equipment and machines. The applications of advanced technology such as embedded computing, robotics, wireless technology, GPS/GIS (Geographical Positioning System/Geographical Information System) and DBMS (Database Management System) software are seen to be recent developments. This paper reviews the applications of technology systems in agriculture and food. Because knowledge on standardization and interfacing plays a key role in using the technology systems in any application domain, this paper aims to highlight the important attributes of such an emerging research area. In particular, the paper describes soil sampling methods and technology applications; field and yield mapping with GPS and GIS; harvesters and future research in robotic-based harvesters; food processing and packaging technology such as traceability and status of RFID networking research; application of sensor network; data management and execution systems; and the automation and control standards such as fieldbus systems and OMAC guidelines. (C) 2012 Elsevier B.V. All rights reserved. C1 [Suprem, Abhijit] Calif State Univ Fresno, Lyles Coll Engn, Fresno, CA 93740 USA. [Mahalik, Nitaigour] Calif State Univ Fresno, Jordan Coll Agr Sci & Technol, Fresno, CA 93740 USA. [Kim, Kiseon] Gwangju Inst Sci & Technol, Sch Commun & Sensor Networks, Kwangju, South Korea. C3 California State University System; California State University Fresno; California State University System; California State University Fresno; Gwangju Institute of Science & Technology (GIST) RP Suprem, A (corresponding author), Calif State Univ Fresno, Lyles Coll Engn, Fresno, CA 93740 USA. EM asuprem@mail.fresnostate.edu CR Agricultural Engineering and Technology Working Group, 2006, 7 FRAM PROGR RES EUR Alsop Sean Michael, 2008, 29 ANN CENT IN PRESS, P111 [Anonymous], 2005, SPATIAL DATA INFRAST, V2 [Anonymous], 1999, AGR EQ TECHN C FEBR, P1 Asha Devi D., 2011, INT J INNOVATIVE TEC, V1 Baeten J., 2007, 6 INT C FIELD SERV R Balasankari P. K., 1999, AMA, Agricultural Mechanization in Asia, Africa and Latin America, V30, P14 Beddoes JC, 2007, ANAL ENERGY PRODUCTI, P1 Blackmore B., 2001, 5 EUR C PREC AGR WAG, P621 Blackmore S., 2007, PREC AGR C, P23 Blackmore Simon, 2009, HGCA C PREV AR FARM Bouma J, 2001, AGR SYST, V70, P355, DOI 10.1016/S0308-521X(01)00051-8 Buettner M, 2008, SENSYS'08: PROCEEDINGS OF THE 6TH ACM CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS, P393 Cheng Fan-Tien, 2006, ARS PIV, P908 Cugnasca CE, 2003, FIELDBUS TECHNOLOGY: INDUSTRIAL NETWORK STANDARDS FOR REAL-TIME DISTRIBUTED CONTROL, P435 Franzen D., 2008, SF11762 NDSU EXT SER Garcia JG, 2007, 2007 IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, PROCEEDINGS, VOLS 1-8, P2004, DOI 10.1109/ISIE.2007.4374915 GOODENOUGH DG, 2008, IEEE INT GEOSC REM S, V2, P129 Guda Ravinder, 2010, MS PROJECT REPORT ST Guda Ravinder, 2010, 31 CENTR CA RES S AP Hirakawacho Chiyoda-ku, 2006, REPORT ASIAN PRODUCT Hsu CH, 2009, PERS UBIQUIT COMPUT, V13, P489, DOI 10.1007/s00779-009-0224-9 Jiang S., 2011, P 2011 INT C EL INF, P1328, DOI [10.1109/ICEICE.2011.5777293, DOI 10.1109/ICEICE.2011.5777293] Langendoen K., 2006, 20 IEEE INT PAR DIST LAV-Landtechnik-Vereinigung, 1998, LBS DOC V 2 0 08 198 Lian FL, 2001, IEEE CONTR SYST MAG, V21, P66, DOI 10.1109/37.898793 Luo Zongwei, 2005, P IEEE INT C E BUS E MAGISON EC, 1978, IEEE T IND APPL, V14, P87, DOI 10.1109/TIA.1978.4503495 Mahalik Nitaigour P., 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P12, DOI 10.1007/s11694-009-9076-2 Mahalik NP, 2010, TRENDS FOOD SCI TECH, V21, P115, DOI 10.1016/j.tifs.2010.01.003 Mahalik N.P., 2011, INTERNAL REPORT FEAS Mahalik N.P., 2003, FIELDBUS TECHNOLOGY Monta M., 1998, J ARTIFICIAL INTELLI, V12 Murdock L., 1997, SOIL SCI NEWS VIEWS, V18 Nambiar A.N., 2010, P 4 INT S I IN PRESS Neethirajan S., 2005, BIOSENSORS EMERGING, P1 Nolte K. D., 2011, Journal of Soil Science and Environmental Management, V2, P159 Nowatzki J., 2008, GPS APPL CROP PRODUC Oetomo D, 2010, INTEL SERV ROBOT, V3, P207, DOI 10.1007/s11370-010-0079-y Oztekin A, 2010, DECIS SUPPORT SYST, V49, P100, DOI 10.1016/j.dss.2010.01.007 Qu RT, 2003, 2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS, P396 Ranasinghe DC, 2005, Proceedings of the 2005 Intelligent Sensors, Sensor Networks & Information Processing Conference, P7 Roberti Flavio, 2006, OPEN SOFTWARE STRUCT, P498 Sarac A, 2010, INT J PROD ECON, V128, P77, DOI 10.1016/j.ijpe.2010.07.039 Sethuramasamyraja B., 2010, INT J ENG SCI TECHNO, V2, P6058 Shannon K., 1998, PRECISION AGR GLOBAL Sistler F, 2003, IEEE ROBOTICS AUTOMA, V3, P3 Sumi M, 2019, P 2 INT C SEC INF NE, V9, P111, DOI DOI 10.1145/1626195.1626225 Testin RF, 1995, FOOD DRUG LAW J, V50, P575 Till RD, 1995, PROC NAECON IEEE NAT, P169, DOI 10.1109/NAECON.1995.521931 Torii T, 2000, COMPUT ELECTRON AGR, V25, P133, DOI 10.1016/S0168-1699(99)00060-5 Uneyyz Murat, 2007, GRAPHICAL MODEL BASE van Henten EJ, 2002, AUTON ROBOT, V13, P241, DOI 10.1023/A:1020568125418 Verhappen I., 2006, MANUFACTURING AUTOMA, P1 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 Wang W., 2005, 17 EUR C REAL TIM SY Wark T., 2007, PERVASIVE COMPUTING, P50 Xinzhong Wang, 2011, INT C ZIB CHIN MAY 2 Yuksel Mehmet Erkan, 2011, J EC SOCIAL STUDIES, V1 Zhang P, 2010, INT GEOSCI REMOTE SE, P1815, DOI 10.1109/IGARSS.2010.5650394 NR 60 TC 67 Z9 68 U1 3 U2 164 PD JUN PY 2013 VL 35 IS 4 BP 355 EP 364 DI 10.1016/j.csi.2012.09.002 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering SC Computer Science UT WOS:000317029700001 DA 2022-12-14 ER PT J AU Lin, HC Kuo, SH AF Lin, Hung-Chou Kuo, Su-Hui TI How does health consciousness influence attitudes of elderly people towards traceable agricultural products? Perspectives of the technology acceptance model SO AGEING & SOCIETY DT Article DE health consciousness; traceable agricultural product; Taiwan agricultural and food traceability; technology acceptance model ID USER BEHAVIOR; ORGANIC FOOD; EXTENSION; CHOICE; CARE; COMMUNITY; COMMERCE; ADOPTION; ROLES; TRUST AB Recently, internet usage among elderly adults has been increasing and becoming more mainstream; with the ageing population in Taiwan, concerns over health are on the rise, and this is directly related to the products that people eat. The main objectives of this study were to develop an integrated extensibility model incorporating the technology acceptance model and to investigate the impact of health consciousness on elderly adults' acceptance of technology in relation to traceability information websites in Taiwan. This study used structural equation modelling to analyse the data. The results revealed that elderly people with high health consciousness and high perceived usefulness had more positive attitudes towards products than those with low health consciousness and low perceived usefulness, and those with high health consciousness and high perceived ease of use had more positive attitudes than those with low health consciousness and low perceived ease of use in relation to the agricultural product traceability system. C1 [Lin, Hung-Chou] Natl Taiwan Normal Univ, Dept Adult & Continuing Educ, Taipei, Taiwan. [Kuo, Su-Hui] Corp Synergy Dev Ctr, Dept Enterprise Consulting, Taipei, Taiwan. C3 National Taiwan Normal University RP Lin, HC (corresponding author), Natl Taiwan Normal Univ, Dept Adult & Continuing Educ, Taipei, Taiwan. EM kevinathk@gmail.com CR Aboelmaged MG, 2010, IND MANAGE DATA SYST, V110, P392, DOI 10.1108/02635571011030042 Aiken L. S., 1991, MULTIPLE REGRESSION [Anonymous], 2009, GENERATIONS ONLINE 2 BECKER MH, 1977, J HEALTH SOC BEHAV, V18, P348, DOI 10.2307/2955344 Bigne-Alcaniz E, 2008, ONLINE INFORM REV, V32, P648, DOI 10.1108/14684520810914025 Chung JE, 2010, COMPUT HUM BEHAV, V26, P1674, DOI 10.1016/j.chb.2010.06.016 Council of Agriculture, 2017, WHAT IS TRAC AGR PRO DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008 Dawson JF, 2006, J APPL PSYCHOL, V91, P917, DOI 10.1037/0021-9010.91.4.917 Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519 Gefen D, 2000, OMEGA-INT J MANAGE S, V28, P725, DOI 10.1016/S0305-0483(00)00021-9 GOULD SJ, 1988, J CONSUM AFF, V22, P96, DOI 10.1111/j.1745-6606.1988.tb00215.x Grankvist G, 2001, J ENVIRON PSYCHOL, V21, P405, DOI 10.1006/jevp.2001.0234 Hair J. F., 2009, CLUSTER ANAL MULTIVA Huang JH, 2007, ELECTRON LIBR, V25, P585, DOI 10.1108/02640470710829569 Jarvenpaa S.L., 1999, J COMPUT-MEDIAT COMM, V5, pJCMC526, DOI [10.1111/j.1083-6101.1999.tb00337.x, DOI 10.1111/J.1083-6101.1999.TB00337.X] Jayanti RK, 1998, J ACAD MARKET SCI, V26, P6, DOI 10.1177/0092070398261002 Kim HB, 2009, TOURISM MANAGE, V30, P266, DOI 10.1016/j.tourman.2008.07.001 Kim J, 2005, J FASH MARK MANAG, V9, P106, DOI 10.1108/13612020510586433 Kim S, 2009, INFORM SYST FRONT, V11, P323, DOI 10.1007/s10796-008-9073-8 Kraft F B, 1993, J Health Care Mark, V13, P18 Lanseng EJ, 2007, INT J SERV IND MANAG, V18, P394, DOI 10.1108/09564230710778155 Lockie S, 2004, APPETITE, V43, P135, DOI 10.1016/j.appet.2004.02.004 Magnusson M. K., 2001, British Food Journal, V103, P209, DOI 10.1108/00070700110386755 Magnusson MK, 2003, APPETITE, V40, P109, DOI 10.1016/S0195-6663(03)00002-3 McKechnie S, 2006, INT J RETAIL DISTRIB, V34, P388, DOI 10.1108/09590550610660297 Michaelidou N, 2008, INT J CONSUM STUD, V32, P163, DOI 10.1111/j.1470-6431.2007.00619.x Newsom JT, 2005, SOC SCI MED, V60, P433, DOI 10.1016/j.socscimed.2004.05.015 Pavlou PA, 2003, INT J ELECTRON COMM, V7, P101, DOI 10.1080/10864415.2003.11044275 PLANK RE, 1990, HLTH MARKETING Q, V7, P65, DOI DOI 10.1300/J026V07N03_06 Read W, 2011, AUSTRALAS MARK J, V19, P223, DOI 10.1016/j.ausmj.2011.07.004 Shin DH, 2008, CYBERPSYCHOL BEHAV, V11, P378, DOI 10.1089/cpb.2007.0117 Shin DH, 2010, ONLINE INFORM REV, V34, P473, DOI 10.1108/14684521011054080 Sun HS, 2006, INT J HUM-COMPUT ST, V64, P53, DOI 10.1016/j.ijhcs.2005.04.013 Tarkiainen A, 2009, PSYCHOL MARKET, V26, P844, DOI 10.1002/mar.20302 Tong X, 2010, INT J RETAIL DISTRIB, V38, P742, DOI 10.1108/09590551011076524 Tsai MT, 2011, TOTAL QUAL MANAG BUS, V22, P1091, DOI 10.1080/14783363.2011.614870 Walls HL, 2009, AM J PUBLIC HEALTH, V99, P590, DOI 10.2105/AJPH.2008.156232 Wang EST, 2014, J ELECTRON COMMER RE, V15, P119 Yousafzai SY, 2007, J MODEL MANAG, V2, P251, DOI 10.1108/17465660710834453 Yousafzai SY, 2007, J MODEL MANAG, V2, P281, DOI 10.1108/17465660710834462 Yousafzai SY, 2010, J APPL SOC PSYCHOL, V40, P1172, DOI 10.1111/j.1559-1816.2010.00615.x NR 42 TC 2 Z9 2 U1 2 U2 24 PD AUG PY 2020 VL 40 IS 8 BP 1808 EP 1821 AR PII S0144686X19000308 DI 10.1017/S0144686X19000308 WC Gerontology SC Geriatrics & Gerontology UT WOS:000547264100010 DA 2022-12-14 ER PT J AU Armitage, WJ Ashford, P Crow, B Dahl, P DeMatteo, J Distler, P Gopinathan, U Madden, PW Mannis, MJ Moffatt, SL Ponzin, D Tan, D AF Armitage, W. John Ashford, Paul Crow, Barbara Dahl, Patricia DeMatteo, Jennifer Distler, Pat Gopinathan, Usha Madden, Peter W. Mannis, Mark J. Moffatt, S. Louise Ponzin, Diego Tan, Donald TI Standard Terminology and Labeling of Ocular Tissue for Transplantation SO CORNEA DT Article DE eye bank standard terminology; global harmonization; traceability; information management; coding; labeling; ISBT 128 AB Purpose: To develop an internationally agreed terminology for describing ocular tissue grafts to improve the accuracy and reliability of information transfer, to enhance tissue traceability, and to facilitate the gathering of comparative global activity data, including denominator data for use in biovigilance analyses. Methods: ICCBBA, the international standards organization for terminology, coding, and labeling of blood, cells, and tissues, approached the major Eye Bank Associations to form an expert advisory group. The group met by regular conference calls to develop a standard terminology, which was released for public consultation and amended accordingly. Results: The terminology uses broad definitions (Classes) with modifying characteristics (Attributes) to define each ocular tissue product. The terminology may be used within the ISBT 128 system to label tissue products with standardized bar codes enabling the electronic capture of critical data in the collection, processing, and distribution of tissues. Guidance on coding and labeling has also been developed. Conclusions: The development of a standard terminology for ocular tissue marks an important step for improving traceability and reducing the risk of mistakes due to transcription errors. ISBT 128 computer codes have been assigned and may now be used to label ocular tissues. Eye banks are encouraged to adopt this standard terminology and move toward full implementation of ISBT 128 nomenclature, coding, and labeling. C1 [Armitage, W. John] Univ Bristol, Sch Clin Sci, Bristol, Avon, England. [Ashford, Paul] ICCBBA, Chatham, Kent, England. [Crow, Barbara] Lions VisionGift, Portland, OR USA. [Dahl, Patricia] Eye Bank Sight Restorat Inc, New York, NY USA. [DeMatteo, Jennifer] Eye Bank Assoc Amer, Washington, DC USA. [Distler, Pat] ICCBBA, Redlands, CA USA. [Gopinathan, Usha] LV Prasad Eye Inst, Ramayama Int Eye Bank, Hyderabad, Andhra Pradesh, India. [Madden, Peter W.] Queensland Eye Inst, Brisbane, Qld, Australia. [Mannis, Mark J.] Univ Calif Davis, Ctr Eye, Dept Ophthalmol & Vis Sci, Davis, CA 95616 USA. [Moffatt, S. Louise] New Zealand Natl Eye Bank, Auckland, New Zealand. [Ponzin, Diego] Veneto Eye Bank Fdn, Venice, Italy. [Tan, Donald] Singapore Natl Eye Ctr, Singapore, Singapore. C3 University of Bristol; L. V. Prasad Eye Institute; Queensland Eye Institute; University of California System; University of California Davis; Singapore National Eye Center RP Distler, P (corresponding author), ICCBBA, POB 11309, San Bernardino, CA 92423 USA. EM pat.distler@iccbba.org CR [Anonymous], 160222006 ISOIEC Cabana E, 2011, ISBT 128 STAND STAND Distler P, 2011, ISBT 128 STAND TECH International Organization for Standardization, 86012004 ISO Shinozaki N, 2008, TRANSPLANTATION, V86, P181, DOI 10.1097/TP.0b013e31817c0ef4 NR 5 TC 3 Z9 3 U1 1 U2 8 PD JUN PY 2013 VL 32 IS 6 BP 725 EP 728 DI 10.1097/ICO.0b013e3182873405 WC Ophthalmology SC Ophthalmology UT WOS:000318967800004 DA 2022-12-14 ER PT J AU Laca, EA AF Laca, Emilio A. TI Precision livestock production: tools and concepts SO REVISTA BRASILEIRA DE ZOOTECNIA-BRAZILIAN JOURNAL OF ANIMAL SCIENCE DT Article DE animal position; animal behavior; plant-animal interaction; precision grazing ID FLAVORED WHEAT-STRAW; GOATS CAPRA-HIRCUS; CONDITIONED FOOD AVERSIONS; DAIRY-COWS; INTRARUMINAL INFUSIONS; DWARF GOATS; SECONDARY REINFORCEMENT; POSTINGESTIVE FEEDBACK; VISUAL-DISCRIMINATION; FORAGING BEHAVIOR AB Precision livestock production (PLP) is the augmentation of precision agriculture (PA) concepts to include all components of agroecosystems, particularly animals and plant-animal interactions. Soil, plants and soil-plant interactions are the subjects of PA or site-specific farming, where the main principle is to exploit natural spatial heterogeneity to increase efficiency and reduce environmental impacts. For the most part, PA has been studied and developed for intensive cropping systems with little attention devoted to pastoral and agropastoral systems. PLP focuses on the animal component and exploits heterogeneity in space and among individual animals towards more efficient and environmentally friendly production. Within PLP, precision grazing consists of the integration of information and communication technologies with knowledge about animal behavior and physiology to improve production of meat, milk and wool in grazing conditions. Two main goals are to minimize overgrazing of sensitive areas and to maximize the quality of the product through enhanced traceability. An integrated precision grazing system is outlined with its components: sensors of animal position, behavior and physiological status, real-time transmission of information to a decision support system, and feed-back through a series of actuators. Control of animal movement and diets is based on knowledge about species specific responses to various stimuli within the paradigms of flavor aversions and operant conditioning. Recent advances in the technologies and instrumentation available are reviewed briefly and linked to current livestock identification systems. The precision grazing vision is presented in full and the areas that need further research and development are discussed. C1 Univ Calif Davis, Dept Plant Sci, Davis, CA 95616 USA. C3 University of California System; University of California Davis RP Laca, EA (corresponding author), Univ Calif Davis, Dept Plant Sci, Davis, CA 95616 USA. EM ealaca@ucdavis.edu CR Agouridis CT, 2004, T ASAE, V47, P1321, DOI 10.13031/2013.16566 Anderson SW, 2007, J CLIN EXP NEUROPSYC, V29, P1, DOI 10.1080/13803390590954182 Baymann U, 2007, BERL MUNCH TIERARZTL, V120, P89, DOI 10.2376/0005-9366-120-89 Bewley JM, 2008, J DAIRY SCI, V91, P4661, DOI 10.3168/jds.2007-0835 Bishop-Hurley GJ, 2007, COMPUT ELECTRON AGR, V56, P14, DOI 10.1016/j.compag.2006.12.003 Brosh A, 2006, J ANIM SCI, V84, P1951, DOI 10.2527/jas.2005-315 Buerkert A, 2009, COMPUT ELECTRON AGR, V65, P85, DOI 10.1016/j.compag.2008.07.010 Burritt EA, 1997, APPL ANIM BEHAV SCI, V54, P317, DOI 10.1016/S0168-1591(97)00005-1 Butler Z, 2006, INT J ROBOT RES, V25, P485, DOI 10.1177/0278364906065375 *CAN FOOD INSP AG, 2008, CAN CATTL ID PROGR Champion RA, 1997, APPL ANIM BEHAV SCI, V54, P291, DOI 10.1016/S0168-1591(96)01210-5 Charmley E, 2006, AUST J EXP AGR, V46, P831, DOI 10.1071/EA05314 Clark PE, 2006, RANGELAND ECOL MANAG, V59, P334, DOI 10.2111/05-162R.1 DISTEL RA, 1991, J CHEM ECOL, V17, P431, DOI 10.1007/BF00994343 DUTOIT JT, 1991, APPL ANIM BEHAV SCI, V30, P35, DOI 10.1016/0168-1591(91)90083-A Edwards GR, 1996, APPL ANIM BEHAV SCI, V50, P147, DOI 10.1016/0168-1591(96)01077-5 Eigenberg RA, 2008, COMPUT ELECTRON AGR, V62, P41, DOI 10.1016/j.compag.2007.08.011 Felmer R, 2006, ARCH MED VET, V38, P197, DOI 10.4067/S0301-732X2006000300002 Firk R, 2002, LIVEST PROD SCI, V75, P219, DOI 10.1016/S0301-6226(01)00323-2 Frost AR, 1997, COMPUT ELECTRON AGR, V17, P139, DOI 10.1016/S0168-1699(96)01301-4 Galli JR, 2006, ANIM FEED SCI TECH, V128, P14, DOI 10.1016/j.anifeedsci.2005.09.013 Ginane C, 2006, BEHAV PROCESS, V73, P178, DOI 10.1016/j.beproc.2006.05.006 Ginnett TF, 1997, OECOLOGIA, V110, P291, DOI 10.1007/s004420050162 Gonzalez LA, 2008, J DAIRY SCI, V91, P1017, DOI 10.3168/jds.2007-0530 Halachmi I, 1998, COMPUT ELECTRON AGR, V20, P131, DOI 10.1016/S0168-1699(98)00013-1 Hewitson L, 2005, ANIM BEHAV, V69, P1069, DOI 10.1016/j.anbehav.2004.09.004 Kimball BA, 2002, APPL ANIM BEHAV SCI, V76, P249, DOI 10.1016/S0168-1591(02)00007-2 Laca EA, 2000, GRASS FORAGE SCI, V55, P97, DOI 10.1046/j.1365-2494.2000.00203.x Laca EA, 1998, J RANGE MANAGE, V51, P370, DOI 10.2307/4003320 LACA EA, 1994, APPL ANIM BEHAV SCI, V39, P3, DOI 10.1016/0168-1591(94)90011-6 Langbein J, 2007, J COMP PSYCHOL, V121, P447, DOI 10.1037/0735-7036.121.4.447 Langbein J, 2007, APPL ANIM BEHAV SCI, V103, P35, DOI 10.1016/j.applanim.2006.04.019 Langbein J, 2006, J COMP PSYCHOL, V120, P58, DOI 10.1037/0735-7036.120.1.58 Launchbaugh KL, 1997, APPL ANIM BEHAV SCI, V54, P327, DOI 10.1016/S0168-1591(96)01194-X Launchbaugh KL, 2001, J RANGE MANAGE, V54, P431, DOI 10.2307/4003114 McCall CA, 2002, APPL ANIM BEHAV SCI, V78, P253, DOI 10.1016/S0168-1591(02)00109-0 Milone DH, 2009, COMPUT ELECTRON AGR, V65, P228, DOI 10.1016/j.compag.2008.10.004 Mottram T, 1997, LIVEST PROD SCI, V48, P209, DOI 10.1016/S0301-6226(97)00029-8 Myers KP, 2007, APPETITE, V48, P123, DOI 10.1016/j.appet.2006.07.077 Ortega I.M., 1995, P 1 INT RANG C SALT, V5, P129 Pastell M, 2008, COMPUT ELECTRON AGR, V64, P34, DOI 10.1016/j.compag.2008.05.007 Pastell M, 2008, COMPUT ELECTRON AGR, V62, P48, DOI 10.1016/j.compag.2007.09.003 PEIPER UM, 1993, J AGR ENG RES, V56, P13, DOI 10.1006/jaer.1993.1057 Provenza FD, 2007, CROP SCI, V47, P382, DOI 10.2135/cropsci2006.02.0083 PROVENZA FD, 1994, PHYSIOL BEHAV, V55, P429, DOI 10.1016/0031-9384(94)90096-5 Provenza FD, 1996, J ANIM SCI, V74, P2010 PROVENZA FD, 1995, J RANGE MANAGE, V48, P2, DOI 10.2307/4002498 Putfarken D, 2008, APPL ANIM BEHAV SCI, V111, P54, DOI 10.1016/j.applanim.2007.05.012 RALPHS MH, 1995, J ANIM SCI, V73, P1651 Rutter SM, 1997, APPL ANIM BEHAV SCI, V54, P185, DOI 10.1016/S0168-1591(96)01191-4 Schellberg J, 2008, EUR J AGRON, V29, P59, DOI 10.1016/j.eja.2008.05.005 Schwager M, 2007, COMPUT ELECTRON AGR, V56, P46, DOI 10.1016/j.compag.2007.01.002 SKINNER BF, 1958, J EXP ANAL BEHAV, V1, P67, DOI 10.1901/jeab.1958.1-67 Staddon J. E. R., 1989, LEARNING INTRO PRINC Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 Steinfeld H, 2006, LIVESTOCKS LONG SHAD, DOI [10.1890/1540-9295(2007)5[4:D]2.0.CO;2, DOI 10.1890/1540-9295(2007)5[4:D]2.0.CO;2] THORHALLSDOTTIR AG, 1990, APPL ANIM BEHAV SCI, V25, P45, DOI 10.1016/0168-1591(90)90068-O UETAKE K, 1994, APPL ANIM BEHAV SCI, V42, P1, DOI 10.1016/0168-1591(94)90002-7 Ungar ED, 2005, RANGELAND ECOL MANAG, V58, P256, DOI 10.2111/1551-5028(2005)58[256:IOAAFG]2.0.CO;2 Ungar ED, 2006, APPL ANIM BEHAV SCI, V98, P11, DOI 10.1016/j.applanim.2005.08.011 Villalba JJ, 1999, J ANIM SCI, V77, P3185 Villalba JJ, 2000, APPL ANIM BEHAV SCI, V66, P87, DOI 10.1016/S0168-1591(99)00066-0 Villalba JJ, 1997, J ANIM SCI, V75, P2905 Villalba JJ, 1996, J ANIM SCI, V74, P2362 Villalba JJ, 1997, BRIT J NUTR, V77, P287, DOI 10.1079/BJN19970030 Villalba JJ, 1999, J ANIM SCI, V77, P378 Villalba JJ, 2000, J ANIM SCI, V78, P3060 VILLALBA JJ, 2009, RANGELAND E IN PRESS Villalba JJ, 2006, ANIM BEHAV, V71, P1131, DOI 10.1016/j.anbehav.2005.09.012 Williams JL, 2004, APPL ANIM BEHAV SCI, V88, P331, DOI 10.1016/j.applanim.2004.03.008 Wredle E, 2006, APPL ANIM BEHAV SCI, V101, P27, DOI 10.1016/j.applanim.2006.01.004 NR 71 TC 30 Z9 36 U1 1 U2 42 PD JUL PY 2009 VL 38 BP 123 EP 132 DI 10.1590/S1516-35982009001300014 WC Agriculture, Dairy & Animal Science; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000271908200014 DA 2022-12-14 ER PT J AU Gebresenbet, G Bosona, T Olsson, SO Garcia, D AF Gebresenbet, Girma Bosona, Techane Olsson, Sven-Olof Garcia, Daniel TI Smart System for the Optimization of Logistics Performance of the Pruning Biomass Value Chain SO APPLIED SCIENCES-BASEL DT Article DE smart logistics system; smart box; information platform; renewable biomass energy; agricultural pruning ID TECHNOLOGY ACCEPTANCE MODEL; QUANTIFICATION; MANAGEMENT AB Agricultural pruning biomass is one of the important resources in Europe for generating renewable energy. However, utilization of the agricultural residues requires development of efficient and effective logistics systems. The objective of this study was to develop smart logistics system (SLS) appropriate for the management of the pruning biomass supply chain. The paper describes the users' requirement of SLS, defines the technical and functional requirements and specifications for the development of SLS, and determines relevant information/data to be documented and managed by the SLS. This SLS has four major components: (a) Smart box, a sensor unit that enables measurement of data such as relative humidity, temperature, geographic positions; (b) On-board control unit, a unit that performs route planning and monitors the recordings by the smart box; (c) Information platform, a centralized platform for data storing and sharing, and management of pruning supply chain and traceability; and (d) Central control unit, an interface linking the Information platform and On-board control unit that serves as a point of administration for the whole pruning biomass supply chain from harvesting to end user. The SLS enables the improvement of performance of pruning biomass supply chain management and product traceability leading to a reduction of product loss, increased coordination of resources utilisation and quality of solid biofuel supply, increased pruning marketing opportunity, and reduction of logistics cost. This SLS was designed for pruning biomass, but could also be adapted for any type of biomass-to-energy initiatives. C1 [Gebresenbet, Girma; Bosona, Techane] Swedish Univ Agr Sci, Dept Energy & Technol, POB 75651, Uppsala, Sweden. [Olsson, Sven-Olof] Mobitron AB, POB 56146, Huskvarna, Sweden. [Garcia, Daniel] Res Ctr Energy Resources & Consumpt CIRCE, Mariano Esquillor Gomez 15, Zaragoza 50018, Spain. C3 Swedish University of Agricultural Sciences RP Bosona, T (corresponding author), Swedish Univ Agr Sci, Dept Energy & Technol, POB 75651, Uppsala, Sweden. EM girma.gebresenbet@slu.se; techane.bosona@slu.se; soo@mobitron.se; daniel.garcia@fcirce.es CR [Anonymous], IMPL EL TRAD FIN MAR Bosona T, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10020258 Esteban LS, 2011, BIOMASS BIOENERG, V35, pS21, DOI 10.1016/j.biombioe.2011.03.045 Hendershott T., 2003, IT Professional, V5, P10, DOI 10.1109/MITP.2003.1216227 Iakovou E, 2010, WASTE MANAGE, V30, P1860, DOI 10.1016/j.wasman.2010.02.030 Legris P, 2003, INFORM MANAGE-AMSTER, V40, P191, DOI 10.1016/S0378-7206(01)00143-4 Oliveira RR, 2015, EXPERT SYST APPL, V42, P6082, DOI 10.1016/j.eswa.2015.04.001 Santa J, 2012, COMPUT ELECTRON AGR, V80, P31, DOI 10.1016/j.compag.2011.10.010 Velazquez-Marti B, 2011, BIOMASS BIOENERG, V35, P3453, DOI 10.1016/j.biombioe.2011.04.009 Velazquez-Marti B, 2011, BIOMASS BIOENERG, V35, P3208, DOI 10.1016/j.biombioe.2011.04.042 Xiao XQ, 2015, APPL SCI-BASEL, V5, P747, DOI 10.3390/app5040747 Yi MY, 2003, INT J HUM-COMPUT ST, V59, P431, DOI 10.1016/S1071-5819(03)00114-9 Yu WT, 2018, TRANSPORT RES E-LOG, V114, P371, DOI 10.1016/j.tre.2017.04.002 NR 13 TC 11 Z9 11 U1 2 U2 10 PD JUL PY 2018 VL 8 IS 7 AR 1162 DI 10.3390/app8071162 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000441814300152 DA 2022-12-14 ER PT J AU Fernandes, CHD Silva, LCE Guarnieri, P Vieira, BD AF de Araujo Fernandes, Ciro Henrique Camara e Silva, Lucio Guarnieri, Patricia Vieira, Barbara de Oliveira TI Multicriteria Model Proposition to Support the Management of Systems of E-Waste Collection SO LOGISTICS-BASEL DT Article DE e-waste; collection systems; multicriteria approach; reverse logistics; solid waste; WEEE ID REVERSE LOGISTICS; ENVIRONMENTAL IMPACTS; WEEE; NETWORK; ELICITATION; INTEGRATION; BENEFITS; DECISION; WEIGHTS; SCIENCE AB Background: Considering the global concern in balancing economic growth with environmental sustainability, the study proposes a model to support multicriteria decision-making. From the systematic literature review and bibliometric analysis, there was an increasing trend in studies on electronic waste due to governments, stakeholders, and the population to better address the management of this waste; Methods: We propose a decision model considering some aspects and phases that help from collecting information to support decision making, based on the FITradeoff ordering method, to support policy decisions for managing Waste from Electrical and Electronic Equipment (WEEE) collection systems.; Results: After applying the proposed model, validated based on the perception of a decision-maker working in a federal public agency, we obtained the final classification with ten positions of alternatives; Conclusions: This outcome can assist in decision making and management of the collection of WEEE. In addition, we made recommendations to manufacturers have more responsibility in the design and traceability of the product to guarantee its recovery after disposal effectively. C1 [de Araujo Fernandes, Ciro Henrique; Camara e Silva, Lucio] Univ Fed Pernambuco, Postgrad Program Prod Engn, BR-55014900 Caruaru, Brazil. [Guarnieri, Patricia] Univ Brasilia, Business Dept, Postgrad Program Agribusiness, BR-70910900 Brasilia, DF, Brazil. [Guarnieri, Patricia; Vieira, Barbara de Oliveira] Univ Brasilia, Postgrad Program Management, BR-70910900 Brasilia, DF, Brazil. C3 Universidade Federal de Pernambuco; Universidade de Brasilia; Universidade de Brasilia RP Vieira, BD (corresponding author), Univ Brasilia, Postgrad Program Management, BR-70910900 Brasilia, DF, Brazil. EM ciro.fernandes@aol.com; lucio.silva@ufpe.br; patriciaguarnieris@gmail.com; vieiraa.barbara@gmail.com CR ABINEE, 2017, IND EL EL IMP EC VER ABRELPE, 2014, PAN RES SOL BRAZ 201 Agrawal R., 2002, P INT WAST MAN BIENN Al Razi KMH, 2016, RESOUR CONSERV RECY, V110, P30, DOI 10.1016/j.resconrec.2016.03.017 Alves M. A., 2017, REV SISTEMAS INFORM, V20, P2 Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007 Athawale V.M., 2010, INT C IND ENG OP MAN, P9 Balde C.P., 2017, GLOBAL WASTE MONITOR Barbieri JC, 2011, GESTAO AMBIENTAL EMP Behzadian M, 2010, EUR J OPER RES, V200, P198, DOI 10.1016/j.ejor.2009.01.021 Brazilian Industrial Development Agency, 2013, REV LOG EL EQ TECHN Caiado N, 2017, RESOUR CONSERV RECY, V118, P47, DOI 10.1016/j.resconrec.2016.11.021 Calderoni S., 2015, BILHOES PERDIDOS LIX, V6 Chatterjee P, 2011, MATER DESIGN, V32, P851, DOI 10.1016/j.matdes.2010.07.010 Chen WC, 2008, IEEE T ELECTRON PACK, V31, P326, DOI 10.1109/TEPM.2008.2004572 Cid-Lopez A, 2015, APPL SOFT COMPUT, V37, P897, DOI 10.1016/j.asoc.2015.09.019 Cucchiella F, 2015, RENEW SUST ENERG REV, V51, P263, DOI 10.1016/j.rser.2015.06.010 Danielson M, 2017, GROUP DECIS NEGOT, V26, P677, DOI 10.1007/s10726-016-9494-6 De Almeida A.T., 2013, DECISION MAKING PROC de Almeida AT, 2016, EUR J OPER RES, V250, P179, DOI 10.1016/j.ejor.2015.08.058 de Almeida AT, 2017, ANN OPER RES, V259, P65, DOI 10.1007/s10479-017-2519-y De Araujo MVF, 2015, PROCEDIA COMPUT SCI, V55, P688, DOI 10.1016/j.procs.2015.07.075 de Souza C.D.R., 2015, REV GESTAO SUSTENTAB, V3, P29, DOI [10.19177/rgsa.v3e2201429-44, DOI 10.19177/RGSA.V3E2201429-44] Demajorovic J., 2013, GESTAO REG GESTAO REG, V29, P64 Echegaray F, 2017, J CLEAN PROD, V142, P180, DOI 10.1016/j.jclepro.2016.05.064 Elkington J.B., 1997, CANNIBALS FORKS TRIP Figueiredo G.J.P., 2005, CURSO INTERDISCIPLIN Frej EA, 2017, MATH PROBL ENG, V2017, DOI 10.1155/2017/4541914 Garcia C.O., 2011, P C INT SUST AMB IND Ghisolfi V, 2017, WASTE MANAGE, V60, P14, DOI 10.1016/j.wasman.2016.12.018 Gouveia N, 2012, CIENC SAUDE COLETIVA, V17, P1503, DOI 10.1590/S1413-81232012000600014 Helmann K.S., 2007, REV GESTAO IND, V3, P123, DOI [10.3895/S1808-04482007000100011, DOI 10.3895/S1808-04482007000100011] Hsu CW, 2009, J CLEAN PROD, V17, P255, DOI 10.1016/j.jclepro.2008.05.004 Ikhlayel M, 2017, WASTE MANAGE, V68, P458, DOI 10.1016/j.wasman.2017.06.038 Instituto Brasileiro de Geografia e Estatistica, 2013, PESQUISA NACL AMOSTR Jacso P, 2005, CURR SCI INDIA, V89, P1537 Jayaraman V, 2007, ACAD MANAGE PERSPECT, V21, P56, DOI 10.5465/AMP.2007.25356512 Keeney R.L, 1976, DECISION MAKING MULT Khetriwal DS, 2009, J ENVIRON MANAGE, V90, P153, DOI 10.1016/j.jenvman.2007.08.019 Kiddee P, 2013, WASTE MANAGE, V33, P1237, DOI 10.1016/j.wasman.2013.01.006 Kilic HS, 2015, RESOUR CONSERV RECY, V95, P120, DOI 10.1016/j.resconrec.2014.12.010 Kim M, 2013, J ENVIRON MANAGE, V128, P941, DOI 10.1016/j.jenvman.2013.06.049 Kumar A, 2017, RESOUR CONSERV RECY, V122, P32, DOI 10.1016/j.resconrec.2017.01.018 Kuo RJ, 2010, J CLEAN PROD, V18, P1161, DOI 10.1016/j.jclepro.2010.03.020 Leite P.R., 2003, REVERSE LOGISTICS EN Lima Junior F., 2018, REV PRODUCAO ONLINE, V18, P713, DOI [10.14488/1676-1901.v18i2.2958, DOI 10.14488/1676-1901.V18I2.2958] Lindhqvist T., 2000, EXTENDED PRODUCER RE Luo CL, 2011, J HAZARD MATER, V186, P481, DOI 10.1016/j.jhazmat.2010.11.024 Manomaivibool P, 2014, RESOUR CONSERV RECY, V83, P202, DOI 10.1016/j.resconrec.2013.10.011 Merino E.A.D., 2013, P AN SEM INT RES EQ Miguez E.C., 2010, LOGISTICA REVERSA CO, V1 Morris A, 2016, J ENVIRON MANAGE, V181, P218, DOI 10.1016/j.jenvman.2016.06.013 Murad W, 2007, WASTE MANAGE RES, V25, P3, DOI 10.1177/0734242X07070766 Nicholas J.C, 1995, ELEMENTOS EC GERENCI Oliveira M.C.B., 2012, GESTAO RESIDUOS PLAS Pope J, 2004, ENVIRON IMPACT ASSES, V24, P595, DOI 10.1016/j.eiar.2004.03.001 Prajapati H, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118219 Prakash C, 2015, J MANUF SYST, V37, P599, DOI 10.1016/j.jmsy.2015.03.001 Rangel Luís Alberto Duncan, 2011, Pesqui. Oper., V31, P235, DOI 10.1590/S0101-74382011000200003 Rousis K, 2008, WASTE MANAGE, V28, P1941, DOI 10.1016/j.wasman.2007.12.001 Roy B., 1996, MULTICRITERIA METHOD Shih HS, 2012, COMPUT MATH APPL, V64, P1545, DOI 10.1016/j.camwa.2011.12.082 Silva E., 2018, P ENC NAC ENG PROD E Silva M.E., 2011, REV GESTAO SOC AMBIE, V5, P18, DOI [10.24857/rgsa.v5i2.312, DOI 10.24857/RGSA.V5I2.312] Tansel B, 2017, ENVIRON INT, V98, P35, DOI 10.1016/j.envint.2016.10.002 Tasca Jorge Eduardo, 2010, Journal of European Industrial Training, V34, P631, DOI 10.1108/03090591011070761 Tibben-Lembke R.S., 1999, GOING BACKWARDS REVE Ushizima M.M., 2014, P 11 S EXC GEST TECN Vieira BD, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12104337 Vieira BO, 2020, LOGISTICS-BASEL, V4, DOI 10.3390/logistics4020011 Williams E, 2005, INT ACTIVITIES E WAS, P1 Xavier L.H., 2014, GESTAO RESIDUOS ELET, V1 Yla-Mella J, 2015, WASTE MANAGE, V45, P374, DOI 10.1016/j.wasman.2015.02.031 Yla-Mella J, 2014, RESOUR CONSERV RECY, V86, P38, DOI 10.1016/j.resconrec.2014.02.001 Zafeirakopoulos IB, 2015, ENVIRON IMPACT ASSES, V54, P101, DOI 10.1016/j.eiar.2015.05.002 Zlamparet GI, 2017, J CLEAN PROD, V149, P126, DOI 10.1016/j.jclepro.2017.02.004 NR 76 TC 1 Z9 1 U1 3 U2 9 PD SEP PY 2021 VL 5 IS 3 AR 60 DI 10.3390/logistics5030060 WC Management; Operations Research & Management Science SC Business & Economics; Operations Research & Management Science UT WOS:000702842000001 DA 2022-12-14 ER PT J AU Ordiales, E Fernandez, M Benito, MJ Hernandez, A Martin, A Cordoba, MG AF Ordiales, Elena Fernandez, Margarita Benito, Maria J. Hernandez, Alejandro Martin, Alberto Cordoba, Maria G. TI Differentiation of Wild Cardoon Quality Used in the Elaboration of Traditional Cheeses by DNA Typing Analytical Methods SO FOOD ANALYTICAL METHODS DT Article DE Cheese; Cynara cardunculus L; RAPD; SSR; Texture; Quality ID CYNARA-CARDUNCULUS L.; FRAGMENT LENGTH POLYMORPHISM; GENETIC DIVERSITY ASSESSMENT; CAPSICUM-ANNUUM L.; GLOBE ARTICHOKE; MICROSATELLITE MARKERS; VAR. SCOLYMUS; RAPD; POPULATIONS; LANDRACE AB The purpose of this work was to develop a PCR method for the identification of Cynara cardunculus used in the elaboration of "Torta del Casar" cheese. One hundred five specimens were collected from different parts of the Extremadura region (Spain). Morphological characterisation was performed in situ at the time of sampling. Comparison of the morphological data was by genotypic characterisation. Different genetic profiles were obtained using seven random primers with the random amplified polymorphic DNA (RAPD) technique obtaining better results with the primer OPAE10. For the simple sequence repeat (SSR) technique, ten microsatellites were used, the best genetic profiles being obtained with the microsatellite CMAFLP-24. All the specimens were analysed with both primers, showing that the method provided a fast and accurate characterisation of C. cardunculus that could be used for the Protected Designation of Origin "Torta del Casar" as a tool for traceability and control in the process of elaboration of the cheese. Thistles with different genetic profiles were used in cheese-making finding that this is a good tool to monitorisation for quality control of cheese texture characteristics elaborated with these rennets. C1 [Ordiales, Elena] Ctr Tecnol Agroalimentario Extremadura CTAEX, Badajoz 06195, Spain. [Fernandez, Margarita; Benito, Maria J.; Hernandez, Alejandro; Martin, Alberto; Cordoba, Maria G.] Univ Extremadura, Escuela Ingn Agr, E-06071 Badajoz, Spain. C3 Universidad de Extremadura RP Benito, MJ (corresponding author), Ctr Tecnol Agroalimentario Extremadura CTAEX, Ctra Villafranco Balboa Km 1-2, Badajoz 06195, Spain. EM mjbenito@unex.es CR Acquadro A, 2003, MOL ECOL NOTES, V3, P37, DOI 10.1046/j.1471-8286.2003.00343.x Acquadro A, 2005, MOL ECOL NOTES, V5, P272, DOI 10.1111/j.1471-8286.2005.00897.x Acquadro A, 2005, GENOME, V48, P217, DOI [10.1139/G04-111, 10.1139/g04-111] Barbosa M., 1976, Lait, V56, P1, DOI 10.1051/lait:1976551-5521 Cordeiro M, 1992, MILCHWISSENSCHAFT, V47, P638 FERNANDEZ-SALGUERO J, 1991, Journal of Food Composition and Analysis, V4, P262, DOI 10.1016/0889-1575(91)90038-8 Fernandez-Salguero J., 1997, ESTUDIO QUESOS TRADI Hernandez A, 2010, J AGR FOOD CHEM, V58, P11688, DOI 10.1021/jf102414q Lanteri S, 2003, GENET RESOUR CROP EV, V50, P723, DOI 10.1023/A:1025075118200 Lanteri S, 2004, THEOR APPL GENET, V108, P1534, DOI 10.1007/s00122-003-1576-6 Lanteri S, 2004, J HORTIC SCI BIOTECH, V79, P863, DOI 10.1080/14620316.2004.11511858 Lanteri S, 2001, PLANT BREEDING, V120, P243, DOI 10.1046/j.1439-0523.2001.00605.x MACEDO AC, 1993, J DAIRY SCI, V76, P1725, DOI 10.3168/jds.S0022-0302(93)77505-0 Ordiales E, 2013, J DAIRY RES, V80, P429, DOI 10.1017/S0022029913000411 Pagnotta MA, 2004, ACTA HORTIC, P99, DOI 10.17660/ActaHortic.2004.660.11 Pires E, 1994, QUIMICA, V54, P66 Porebski S, 1997, PLANT MOL BIOL REP, V15, P8, DOI 10.1007/BF02772108 Portis E, 2004, GENET RESOUR CROP EV, V51, P581, DOI 10.1023/B:GRES.0000024648.48164.c3 Portis E, 2005, PLANT SCI, V168, P1591, DOI 10.1016/j.plantsci.2005.02.009 Portis E, 2005, PLANT BREEDING, V124, P299, DOI 10.1111/j.1439-0523.2005.01098.x Portis E, 2005, PLANT SCI, V169, P199, DOI 10.1016/j.plantsci.2005.03.014 Raccuia SA, 2004, PLANT BREEDING, V123, P280, DOI 10.1111/j.1439-0523.2004.00983.x Sonnante G., 2003, Plant Genetic Resources: Characterization and Utilization, V1, P125, DOI 10.1079/PGR200319 Sonnante G, 2002, GENET RESOUR CROP EV, V49, P247, DOI 10.1023/A:1015574627621 Sonnante G, 2004, ISHS ACTA HORTIC, V660, P61 Sonnante G, 2008, GENET RESOUR CROP EV, V55, P1029, DOI 10.1007/s10722-008-9310-5 Trionfetti Nisini P, 2007, ISHS ACTA HORTICULT, V730, P101 Valdes B., 1987, FLORA VASCULAR ANDAL VIEIRADESA F, 1972, J DAIRY RES, V39, P335, DOI 10.1017/S0022029900014163 Vioque M, 2000, J AGR FOOD CHEM, V48, P451, DOI 10.1021/jf990326v NR 30 TC 1 Z9 1 U1 0 U2 13 PD JAN PY 2015 VL 8 IS 1 BP 7 EP 17 DI 10.1007/s12161-014-9889-4 WC Food Science & Technology SC Food Science & Technology UT WOS:000347250800002 DA 2022-12-14 ER PT J AU Kulkarni, DB Joseph, L Anuradha, R Kulkarni, MS Tomar, BS AF Kulkarni, D. B. Joseph, Leena Anuradha, R. Kulkarni, M. S. Tomar, B. S. TI Standardization of Ge-68-Ga-68 using 4 pi beta(LS)-gamma coincidence counting system for activity measurements SO APPLIED RADIATION AND ISOTOPES DT Article DE 4 pi beta(LS)-gamma coincidence counting system; Ge-68-Ga-68; CIEMAT/NIST; Monte-Carlo method AB Ga-68 has great scope for use in future for positron emission tomography (PET) imaging due to its very fast blood clearance and fast target localization, even though at present F-18 is widely used. Ge-68 in equilibrium with Ga-68 (Ge-68-Ga-68) can also be used as a surrogate for F-18 calibration, as F-18 source standardization can be done at national metrology institute (NMI) but, these standards cannot be sent to nuclear medicine centers (NMCs) across India for calibration of isotope calibrators, due to the short half-life of F-18 (110 min). Providing Ge-68-Ga-68 standards to NMCs requires that first standardization must be carried out at NMI (BARC in India) to provide traceability to the measurements carried out at NMCs. In the present work, standardization of Ge-68-Ga-68 was carried out using 4 pi beta(LS)-gamma coincidence counting system and CIEMAT/NIST efficiency tracing technique. The decay scheme correction factors for two gamma windows were calculated by Monte Carlo technique using general purpose code FLUKA. The activity concentration values were normalized by the activity concentration obtained by 4 pi beta(LS)-gamma coincidence counting system using window-1. The final result reported to BIPM for 4 pi beta(LS)-gamma coincidence counting was calculated by taking arithmetic mean of activity concentrations obtained for two gamma windows. The normalized activity concentration obtained by 4 pi beta(LS)-gamma coincidence counting was 0.998 +/- 0.005 and that obtained using CIEMAT/NIST efficiency tracing was 1.002 +/- 0.007 which are in excellent agreement within uncertainty limits. C1 [Kulkarni, D. B.; Joseph, Leena; Anuradha, R.; Kulkarni, M. S.] Bhabha Atom Res Ctr, Radiat Safety Syst Div, Bombay 400085, Maharashtra, India. [Tomar, B. S.] Bhabha Atom Res Ctr, Radioanalyt Chem Div, Bombay 400085, Maharashtra, India. C3 Bhabha Atomic Research Center (BARC); Bhabha Atomic Research Center (BARC) RP Kulkarni, DB (corresponding author), Bhabha Atom Res Ctr, Radiat Safety Syst Div, Bombay 400085, Maharashtra, India. EM devak@barc.gov.in CR ANGER HO, 1963, J NUCL MED, V4, P326 Asti M, 2008, NUCL MED BIOL, V35, P721, DOI 10.1016/j.nucmedbio.2008.04.006 BAERG AP, 1973, NUCL INSTRUM METHODS, V112, P143, DOI 10.1016/0029-554X(73)90788-X BAERG AP, 1966, METROLOGIA, V2, P23 Banerjee SR, 2013, APPL RADIAT ISOTOPES, V76, P2, DOI 10.1016/j.apradiso.2013.01.039 BE MM, 2004, NUCLEIDE TABLE RADIO Bohlen TT, 2014, NUCL DATA SHEETS, V120, P211, DOI 10.1016/j.nds.2014.07.049 Campion P.J., 1959, INT J APPL RAD ISOT, V4, P232 Fani M, 2008, CONTRAST MEDIA MOL I, V3, P53, DOI 10.1002/cmmi.232 Ferrari A, 2005, CERN YELLOW REPORTS Garcia-Torano E., 1991, LIQUID SCINTILLATION, P307 Grigorescu EL, 2004, APPL RADIAT ISOTOPES, V60, P429, DOI 10.1016/j.apradiso.2003.11.054 Gunther E, 2002, APPL RADIAT ISOTOPES, V56, P357, DOI 10.1016/S0969-8043(01)00214-7 IDO T, 1978, J LABELLED COMPD RAD, V14, P175, DOI 10.1002/jlcr.2580140204 Joseph L., 1998, 12 NAT S RAD PHYS, P28 Kulkarni DB, 2013, APPL RADIAT ISOTOPES, V72, P68, DOI 10.1016/j.apradiso.2012.09.024 MALONDA AG, 1982, INT J APPL RADIAT IS, V33, P249, DOI 10.1016/0020-708X(82)90022-9 Prata MIM, 2012, CURR RADIOPHARM, V5, P142, DOI 10.2174/1874471011205020142 SCHONFELD E, 1994, APPL RADIAT ISOTOPES, V45, P955, DOI 10.1016/0969-8043(94)90235-6 Zimmerman BE, 2008, J RES NATL INST STAN, V113, P265, DOI 10.6028/jres.113.020 NR 20 TC 7 Z9 7 U1 0 U2 7 PD MAY PY 2017 VL 123 BP 6 EP 10 DI 10.1016/j.apradiso.2017.01.045 WC Chemistry, Inorganic & Nuclear; Nuclear Science & Technology; Radiology, Nuclear Medicine & Medical Imaging SC Chemistry; Nuclear Science & Technology; Radiology, Nuclear Medicine & Medical Imaging UT WOS:000399854900002 DA 2022-12-14 ER PT J AU Girelli, CR Del Coco, L Fanizzi, FP AF Girelli, Chiara Roberta Del Coco, Laura Fanizzi, Francesco Paolo TI Tunisian Extra Virgin Olive Oil Traceability in the EEC Market: Tunisian/Italian (Coratina) EVOOs Blend as a Case Study SO SUSTAINABILITY DT Article DE extra virgin olive oil; commercial purpose; H-1-NMR spectroscopy; EVOOs origin assessment; multivariate statistical analysis; PLSR; projections to latent structure by means of partial least squares regression ID MULTIVARIATE-ANALYSIS; H-1-NMR SPECTROSCOPY; GEOGRAPHICAL ORIGIN; NMR-SPECTROSCOPY; EDIBLE OILS; CHEMOMETRICS; TOOL; CLASSIFICATION; METABOLOMICS; QUALITY AB In order to check the reliability of an NMR-based metabolomic approach to evaluating blend composition (and declaration), a series of 81 Italian/Tunisian blends samples at different percentage composition (from 10/90 to 90/10% Coratina/Tunisian oil by 10% increase step) were prepared starting from five Coratina (Apulia) and five Tunisian extra virgin olive oil (EVOO) batches. Moreover, a series of nine binary mixtures blend oils were obtained, starting from the two batches' oil sums. The models built showed the linear relationship between the NMR signals and the percentage composition of the blends. In particular, a high correlation with the percentage composition of blends was obtained from the partial least squares (PLS) regression model, when the two batches oil sums were used for the binary mixtures of blend samples. These proposed methods suggest that a multivariate analysis (MVA)-based NMR approachin particular PLS regression (PLSR)could be a very useful tool (including for trading purposes) to assess quantitative blend composition. This is important for the sustainability of the goods' free movement, especially in the agrifood sector. This cornerstone policy of current common markets is also clearly linked to the availability of methods for certifying the origin of the foodstuffs and their use in the assembly of final product for the consumer. C1 [Girelli, Chiara Roberta; Del Coco, Laura; Fanizzi, Francesco Paolo] Univ Salento, Dept Biol & Environm Sci & Technol Di S Te BA, Via Provle Lecce Monteroni, I-73100 Lecce, Italy. C3 University of Salento RP Fanizzi, FP (corresponding author), Univ Salento, Dept Biol & Environm Sci & Technol Di S Te BA, Via Provle Lecce Monteroni, I-73100 Lecce, Italy. EM chiara.girelli@unisalento.it; laura.delcoco@unisalento.it; fp.fanizzi@unisalento.it CR Alonso-Salces RM, 2011, FOOD CONTROL, V22, P2041, DOI 10.1016/j.foodcont.2011.05.026 Alonso-Salces RM, 2010, J AGR FOOD CHEM, V58, P5586, DOI 10.1021/jf903989b [Anonymous], CONTR OL OL REL COMM Barbarisi C, 2014, CURR NUTR FOOD SCI, V10, P234, DOI 10.2174/157340131003140828122901 Barison A, 2010, MAGN RESON CHEM, V48, P642, DOI 10.1002/mrc.2629 Binetti G, 2017, FOOD CHEM, V219, P131, DOI 10.1016/j.foodchem.2016.09.041 Boccard J, 2013, ANAL CHIM ACTA, V769, P30, DOI 10.1016/j.aca.2013.01.022 Camin F, 2016, FOOD CHEM, V196, P98, DOI 10.1016/j.foodchem.2015.08.132 Corsaro C, 2015, J ANAL METHODS CHEM, V2015, DOI 10.1155/2015/175696 Dabbou S, 2015, CHEM BIODIVERS, V12, P397, DOI 10.1002/cbdv.201400142 Dais P, 2013, ANAL CHIM ACTA, V765, P1, DOI 10.1016/j.aca.2012.12.003 Del Coco L, 2014, FOODS, V3, P238, DOI 10.3390/foods3020238 Del Coco L, 2016, J AM OIL CHEM SOC, V93, P373, DOI 10.1007/s11746-015-2778-1 Del Coco L, 2013, EUR J LIPID SCI TECH, V115, P1043, DOI 10.1002/ejlt.201300160 Forbes The Olive Oil Scam, OL OIL SCAM 80 IS FA Girelli CR, 2016, PEERJ, V4, DOI 10.7717/peerj.2740 Girelli CR, 2016, EUR J LIPID SCI TECH, V118, P1380, DOI 10.1002/ejlt.201500401 Gomez-Caravaca AM, 2016, ANAL CHIM ACTA, V913, P1, DOI 10.1016/j.aca.2016.01.025 Holmes E, 2008, NATURE, V453, P396, DOI 10.1038/nature06882 Laincer F, 2016, FOOD RES INT, V89, P1123, DOI 10.1016/j.foodres.2016.04.024 Lindon J. C., 2006, HDB METABONOMICS MET, DOI [10.1016/b978-044452841-4/50002-3, DOI 10.1016/B978-044452841-4/50002-3] Longobardi F, 2012, FOOD CHEM, V130, P177, DOI 10.1016/j.foodchem.2011.06.045 Mannina L, 2011, MAGN RESON CHEM, V49, pS3, DOI 10.1002/mrc.2856 Piccinonna S, 2016, FOOD CHEM, V199, P675, DOI 10.1016/j.foodchem.2015.12.064 Popescu R, 2015, FOOD CONTROL, V48, P84, DOI 10.1016/j.foodcont.2014.04.046 Sun XD, 2015, ANAL METHODS-UK, V7, P3939, DOI 10.1039/c5ay00472a Sundekilde UK, 2013, METABOLITES, V3, P204, DOI 10.3390/metabo3020204 The Commission of the European Communities. Commission Regulation (EC), 2009, OFF J EUR UNION, V63, P6 Trygg J, 2002, J CHEMOMETR, V16, P119, DOI 10.1002/cem.695 Wold S, 2001, CHEMOMETR INTELL LAB, V58, P109, DOI 10.1016/S0169-7439(01)00155-1 NR 30 TC 15 Z9 15 U1 0 U2 12 PD AUG PY 2017 VL 9 IS 8 AR 1471 DI 10.3390/su9081471 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000408861800185 DA 2022-12-14 ER PT J AU Zhao, HG Li, C Hargrove, JS Bowen, BR Thongda, W Zhang, DD Mohammed, H Beck, BH Austin, JD Peatman, E AF Zhao, Honggang Li, Chao Hargrove, John S. Bowen, Bryant R. Thongda, Wilawan Zhang, Dongdong Mohammed, Haitham Beck, Benjamin H. Austin, James D. Peatman, Eric TI SNP marker panels for parentage assignment and traceability in the Florida bass (Micropterus floridanus) SO AQUACULTURE DT Article DE GBS; Parentage analysis; Florida bass; SNP; Microsatellite ID LARGEMOUTH BASS; ASSESSING HYBRIDIZATION; ONCORHYNCHUS-MYKISS; GENOTYPING ERRORS; GENETIC DIVERSITY; COMPUTER-PROGRAM; HIGH-THROUGHPUT; SALMOIDES; RAINBOW; WILD AB The Florida bass (Micropterus floridanus) is a species endemic to peninsular Florida that is held in high esteem by bass anglers for its tendency to attain a larger maximum size and aggressiveness relative to that of its sister taxon, the Northern largemouth bass, Micropterus salmoides. Hatchery rearing and stocking of Florida bass outside of their native range are commonplace, particularly in the southern United States. In many cases, however, there has been minimal assessment of the persistence and success of these fish. Genetic markers are an important tool for tagging and tracing the contributions of particular lines and crosses of fish. Single nucleotide polymorophism (SNP) markers, in particular, can provide rapid and affordable genotyping of large numbers of fish. In the present study, we generated 58,450 genome-wide SNPs and population-level genotypes for Florida bass using a cost-effective genotyping-by-sequencing method. A total of 58 SNPs were shown to assign parents to offspring with 100% accuracy, irrespective of sex and with the presence of full-sib relationships. Depending on the population, sex information, and genetic relationships between parents, we also demonstrated that smaller SNP subsets may be sufficient for parentage assignment. The accuracy and assignment power of the SNP panels were found to compare favorably to those of 10 microsatellites genotyped on the same parents and progeny. This study demonstrated the utility of simple and low-cost GBS techniques for SNP discovery and the relatively small number of variable SNPs needed for accurate parentage assignment in Florida bass. The SNP resources created in this study should facilitate parentage-based research and breeding, genetic tagging, and conservation of Florida bass. C1 [Zhao, Honggang; Thongda, Wilawan; Zhang, Dongdong; Mohammed, Haitham; Peatman, Eric] Auburn Univ, Sch Fisheries Aquaculture & Aquat Sci, Auburn, AL 36849 USA. [Li, Chao] Qingdao Agr Univ, Marine Sci & Engn Coll, Qingdao 266109, Peoples R China. [Hargrove, John S.] Tennessee Technol Univ, Dept Biol, Cookeville, TN 38505 USA. [Bowen, Bryant R.] Georgia Dept Nat Resources Wildlife Resources Div, Social Circle, GA 30025 USA. [Mohammed, Haitham] Assiut Univ, Dept Aquat Anim Med & Management, Fac Vet Med, Assiut 71526, Egypt. [Beck, Benjamin H.] ARS, USDA, Aquat Anim Hlth Res Unit, Auburn, AL 36832 USA. [Austin, James D.] Univ Florida, Dept Wildlife Ecol & Conservat, Gainesville, FL 32611 USA. C3 Auburn University System; Auburn University; Qingdao Agricultural University; Tennessee Technological University; Egyptian Knowledge Bank (EKB); Assiut University; United States Department of Agriculture (USDA); State University System of Florida; University of Florida RP Peatman, E (corresponding author), Auburn Univ, Sch Fisheries Aquaculture & Aquat Sci, Auburn, AL 36849 USA. EM peatmer@auburn.edu CR Allendorf FW, 1987, POPULATION GENETICS, P141, DOI DOI 10.1007/978-1-4020-6148-6_8 Anderson EC, 2006, GENETICS, V172, P2567, DOI 10.1534/genetics.105.048074 Anderson EC, 2010, COMPUTATIONAL ALGORI Austin JD, 2012, AQUAC RES, V43, P628, DOI 10.1111/j.1365-2109.2011.02873.x Ball AD, 2010, BMC GENOMICS, V11, DOI 10.1186/1471-2164-11-218 Barthel BL, 2010, T AM FISH SOC, V139, P1615, DOI 10.1577/T09-185.1 BERGMAN PK, 1992, FISHERIES, V17, P20, DOI 10.1577/1548-8446(1992)017<0020:PODUAM>2.0.CO;2 Bert TM, 2007, REV-METHODS TECHNOL, V6, P123 Bielenberg DG, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0139406 Buynak Gerard L., 1999, North American Journal of Fisheries Management, V19, P1017, DOI 10.1577/1548-8675(1999)019<1017:SSLBTM>2.0.CO;2 Buynak Gerard L., 1999, North American Journal of Fisheries Management, V19, P494, DOI 10.1577/1548-8675(1999)019<0494:COSAFL>2.0.CO;2 Danecek P, 2011, BIOINFORMATICS, V27, P2156, DOI 10.1093/bioinformatics/btr330 De Donato M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0062137 Elshire RJ, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0019379 Estoup A, 1998, CAN J FISH AQUAT SCI, V55, P715, DOI 10.1139/cjfas-55-3-715 Glaubitz JC, 2003, MOL ECOL, V12, P1039, DOI 10.1046/j.1365-294X.2003.01790.x Glaubitz JC, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0090346 Hargrove JS, 2017, BIOL INVASIONS, V19, P2261, DOI 10.1007/s10530-017-1437-x Hargrove JS, 2017, AQUAC RES, V48, P3272, DOI 10.1111/are.13051 Hargrove JS, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0130056 Hauser L, 2011, MOL ECOL RESOUR, V11, P150, DOI 10.1111/j.1755-0998.2010.02961.x HEIDINGER R C, 1976, FAO (Food and Agriculture Organization of the United Nations) Fisheries Synopsis, V115, P1 Herbinger CM, 1995, AQUACULTURE, V137, P245, DOI 10.1016/0044-8486(95)01109-9 Hess JE, 2015, MOL ECOL RESOUR, V15, P187, DOI 10.1111/1755-0998.12283 Hohenlohe PA, 2011, MOL ECOL RESOUR, V11, P117, DOI 10.1111/j.1755-0998.2010.02967.x Isaac J, 1998, PROG FISH CULT, V60, P59, DOI 10.1577/1548-8640(1998)060<0059:SBOFLB>2.0.CO;2 Jepsen N., 2015, ANIM BIOTELEM, V3, P1, DOI DOI 10.1186/S40317-015-0086-Z Jin YL, 2014, GENES GENOM, V36, P129, DOI 10.1007/s13258-013-0150-0 Johnson JL, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0127013 Johnson RL, 1999, ECOL FRESHW FISH, V8, P35, DOI 10.1111/j.1600-0633.1999.tb00050.x Kaiser SA, 2017, MOL ECOL RESOUR, V17, P183, DOI 10.1111/1755-0998.12589 Kalinowski ST, 2006, MOL ECOL NOTES, V6, P576, DOI 10.1111/j.1471-8286.2006.01256.x Kalinowski ST, 2007, MOL ECOL, V16, P1099, DOI 10.1111/j.1365-294X.2007.03089.x Kim C, 2016, PLANT SCI, V242, P14, DOI 10.1016/j.plantsci.2015.04.016 Li C, 2015, MOL ECOL RESOUR, V15, P395, DOI 10.1111/1755-0998.12308 Li C, 2014, MOL ECOL RESOUR, V14, P1261, DOI 10.1111/1755-0998.12272 Li H, 2009, BIOINFORMATICS, V25, P1754, DOI 10.1093/bioinformatics/btp324 Liu H, 2014, BMC GENOMICS, V15, DOI 10.1186/1471-2164-15-104 Liu SX, 2016, AQUACULTURE, V452, P178, DOI 10.1016/j.aquaculture.2015.11.001 MACEINA MJ, 1988, T AM FISH SOC, V117, P221, DOI 10.1577/1548-8659(1988)117<0221:FRFLBS>2.3.CO;2 MACEINA MJ, 1992, T AM FISH SOC, V121, P686, DOI 10.1577/1548-8659-121.5.686 Marshall TC, 1998, MOL ECOL, V7, P639, DOI 10.1046/j.1365-294x.1998.00374.x Near TJ, 2003, EVOLUTION, V57, P1610 NEDBAL MA, 1994, T AM FISH SOC, V123, P460, DOI 10.1577/1548-8659(1994)123<0460:DOMDIL>2.3.CO;2 Oeth P, 2005, SEQUENOME APPL NOTE, P8876 Olsen JB, 2001, J HERED, V92, P243, DOI 10.1093/jhered/92.3.243 Peakall R, 2006, MOL ECOL NOTES, V6, P288, DOI 10.1111/j.1471-8286.2005.01155.x Peakall R, 2012, BIOINFORMATICS, V28, P2537, DOI 10.1093/bioinformatics/bts460 Peterson G. W., 2014, Diversity, V6, P665, DOI 10.3390/d6040665 PHILIPP DP, 1983, T AM FISH SOC, V112, P1, DOI 10.1577/1548-8659(1983)112<1:ABGEOT>2.0.CO;2 Pompanon F, 2005, NAT REV GENET, V6, P847, DOI 10.1038/nrg1707 Pritchard VL, 2012, MOL ECOL RESOUR, V12, P918, DOI 10.1111/j.1755-0998.2012.03149.x RAYMOND M, 1995, J HERED, V86, P248, DOI 10.1093/oxfordjournals.jhered.a111573 Rice AM, 2011, ECOL LETT, V14, P9, DOI 10.1111/j.1461-0248.2010.01546.x Ryan J. F., 2013, ESTIMATE GENOME SIZE Sekino M, 2005, AQUACULTURE, V244, P49, DOI 10.1016/j.aquaculture.2004.11.006 Seyoum S, 2013, CONSERV GENET RESOUR, V5, P697, DOI 10.1007/s12686-013-9885-9 Slate J, 2009, GENETICA, V136, P97, DOI 10.1007/s10709-008-9317-z Stadele V, 2016, ECOL EVOL, V6, P6107, DOI 10.1002/ece3.2346 Steele CA, 2013, CAN J FISH AQUAT SCI, V70, P1046, DOI 10.1139/cjfas-2012-0451 Uncu AO, 2016, MOL BREEDING, V36, DOI 10.1007/s11032-016-0604-6 Van Oosterhout C, 2004, MOL ECOL NOTES, V4, P535, DOI 10.1111/j.1471-8286.2004.00684.x Vandeputte M, 2014, FRONT GENET, V5, DOI 10.3389/fgene.2014.00432 Walling CA, 2010, MOL ECOL, V19, P1914, DOI 10.1111/j.1365-294X.2010.04604.x Waples R.S., 2004, STOCK ENHANCEMENT SE, P160, DOI DOI 10.1002/9780470751329.CH22 Yue GH, 2014, J WORLD AQUACULT SOC, V45, P89, DOI 10.1111/jwas.12107 Zbawicka M, 2014, AQUAT BIOL, V21, P25, DOI 10.3354/ab00566 Zimin AV, 2013, BIOINFORMATICS, V29, P2669, DOI 10.1093/bioinformatics/btt476 NR 68 TC 20 Z9 20 U1 8 U2 62 PD FEB 2 PY 2018 VL 485 BP 30 EP 38 DI 10.1016/j.aquaculture.2017.11.014 WC Fisheries; Marine & Freshwater Biology SC Fisheries; Marine & Freshwater Biology UT WOS:000417627900005 DA 2022-12-14 ER PT J AU Palade, LM Popa, ME AF Palade, Laurentiu Mihai Popa, Mona Elena TI Polyphenol Fingerprinting Approaches in Wine Traceability and Authenticity: Assessment and Implications of Red Wines SO BEVERAGES DT Review DE red wine; authenticity; polyphenols; markers; fingerprinting ID PRE-FERMENTATIVE MACERATION; CABERNET-SAUVIGNON WINES; LIQUID-CHROMATOGRAPHY; PHENOLIC-COMPOUNDS; MASS-SPECTROMETRY; GRAPE VARIETIES; ANTIOXIDANT ACTIVITY; ANTHOCYANIN PROFILE; GEOGRAPHICAL ORIGIN; ARGENTINEAN WINES AB Like any other food/feed matrix, regardless of the employed analytical method, wine requires authentication strategies; a suitable qualitative and quantitative analysis represents the fingerprint which defines its identity. Until recently, fingerprinting approaches using liquid chromatography applications have been regarded as an effective tool for the assessment of wines employing polyphenol profiles. These profiles are of considerable importance for grapes and wines as they influence greatly the color, sensory, and nutritional quality of the final product. The authenticity and typicity characters are fundamental characteristics, which may be evaluated by the use of polyphenol fingerprinting techniques. Under these conditions, the evolution of polyphenols during the red wine elaboration and maturation processes shows a high importance at the level of the obtained fingerprints. Moreover, the environment factors (vintage, the area of origin, and variety) and the technological conditions significantly influence wine authenticity through the use of polyphenol profiles. Taking into account the complexity of the matter at hand, this review outlines the latest trends in the polyphenol fingerprinting of red wines in association with the transformations that occur during winemaking and storage. C1 [Palade, Laurentiu Mihai] Natl Inst Res & Dev Anim Biol & Nutr, Calea Bucuresti St 1, Balotesti 077015, Ilfov, Romania. [Palade, Laurentiu Mihai; Popa, Mona Elena] Univ Agron Sci & Vet Med Bucharest, Fac Biotechnol, 59 Marasti Blvd, Bucharest 011464, Romania. C3 University of Agronomic Science & Veterinary Medicine - Bucharest RP Palade, LM (corresponding author), Natl Inst Res & Dev Anim Biol & Nutr, Calea Bucuresti St 1, Balotesti 077015, Ilfov, Romania.; Palade, LM (corresponding author), Univ Agron Sci & Vet Med Bucharest, Fac Biotechnol, 59 Marasti Blvd, Bucharest 011464, Romania. EM palade_laurentiu_mihai@yahoo.com; pandry2002@yahoo.com CR Alvarez I, 2006, ANAL CHIM ACTA, V563, P109, DOI 10.1016/j.aca.2005.10.068 Amargianitaki M, 2017, CHEM BIOL TECHNOL AG, V4, DOI 10.1186/s40538-017-0092-x Anesi A, 2015, BMC PLANT BIOL, V15, DOI 10.1186/s12870-015-0584-4 Bakker J, 2012, WINE FLAVOUR CHEMISTRY, 2ND EDITION, P1 Bautista-Ortin AB, 2014, FOOD CHEM, V152, P558, DOI 10.1016/j.foodchem.2013.12.009 Bellomarino SA, 2009, TALANTA, V80, P833, DOI 10.1016/j.talanta.2009.08.001 Bertacchini L, 2013, DATA HANDL SCI TECHN, V28, P371, DOI 10.1016/B978-0-444-59528-7.00010-7 Bosona T, 2013, FOOD CONTROL, V33, P32, DOI 10.1016/j.foodcont.2013.02.004 Bosso A, 2008, EUR FOOD RES TECHNOL, V227, P911, DOI 10.1007/s00217-007-0805-7 Cadot Y, 2012, ANAL CHIM ACTA, V732, P91, DOI 10.1016/j.aca.2012.02.013 Castellarin SD, 2007, PLANTA, V227, P101, DOI 10.1007/s00425-007-0598-8 Castillo-Munoz N, 2009, J AGR FOOD CHEM, V57, P209, DOI 10.1021/jf802863g Chorti E, 2016, NOT BOT HORTI AGROBO, V44, P133, DOI 10.15835/nbha44110254 Cohen SD, 2008, ANAL CHIM ACTA, V621, P57, DOI 10.1016/j.aca.2007.11.029 Constantin C, 2009, ANN UNIV DUNAREA JOS, V32, P50 Costa E, 2014, J INT SCI VIGNE VIN, V48, P51 Croitoru C., 2012, OENOLOGIE INOVARI SI Croitoru C, 2009, TRATAT STIINTA SI IN Czibulya Z, 2012, J FOOD SCI, V77, pC880, DOI 10.1111/j.1750-3841.2012.02826.x Daniel C, 2015, MOLECULES, V20, P726, DOI 10.3390/molecules20010726 de Orduna RM, 2010, FOOD RES INT, V43, P1844, DOI 10.1016/j.foodres.2010.05.001 de Villiers A, 2004, J CHROMATOGR A, V1054, P195, DOI [10.1016/j.chroma.2004.07.087, 10.1016/S0021-9673(04)01291-9] Degu A, 2014, BMC PLANT BIOL, V14, DOI 10.1186/s12870-014-0188-4 DeLaPresaOwens C, 1995, AM J ENOL VITICULT, V46, P529 Deloire A, 2005, J INT SCI VIGNE VIN, V39, P149 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Diaz R, 2016, J CHROMATOGR A, V1433, P90, DOI 10.1016/j.chroma.2016.01.010 Donno D, 2016, J FOOD SCI TECH MYS, V53, P1071, DOI 10.1007/s13197-015-2115-6 Downey MO, 2006, AM J ENOL VITICULT, V57, P257 Dumitriu GD, 2016, EUR FOOD RES TECHNOL, V242, P2171, DOI 10.1007/s00217-016-2714-0 Figueiredo-Gonzalez M, 2012, FOOD CHEM, V130, P9, DOI 10.1016/j.foodchem.2011.06.006 Flamini R, 2016, PHYSIOL MOL PLANT P, V93, P112, DOI 10.1016/j.pmpp.2016.01.011 Flamini R, 2013, INT J MOL SCI, V14, P19651, DOI 10.3390/ijms141019651 Francesca N, 2014, INT J FOOD MICROBIOL, V171, P84, DOI 10.1016/j.ijfoodmicro.2013.11.008 Gad HA, 2013, PHYTOCHEM ANALYSIS, V24, P1, DOI 10.1002/pca.2378 Gambuti A, 2013, J AGR FOOD CHEM, V61, P1618, DOI 10.1021/jf302822b Garcia-Marino M, 2011, TALANTA, V85, P2060, DOI 10.1016/j.talanta.2011.07.039 Gatto P, 2008, J AGR FOOD CHEM, V56, P11773, DOI 10.1021/jf8017707 Geana EI, 2016, FOOD CONTROL, V62, P1, DOI 10.1016/j.foodcont.2015.10.003 Geana EI, 2016, FOOD CHEM, V192, P1015, DOI 10.1016/j.foodchem.2015.07.112 Georgiev V, 2014, NUTRIENTS, V6, P391, DOI 10.3390/nu6010391 Gomez-Ariza JL, 2006, ANAL CHIM ACTA, V570, P101, DOI 10.1016/j.aca.2006.04.004 Gomez-Miguez M, 2007, J FOOD ENG, V79, P271, DOI 10.1016/j.jfoodeng.2006.01.054 Gomez-Plaza E, 2008, EUR FOOD RES TECHNOL, V227, P479, DOI 10.1007/s00217-007-0744-3 Gonzalez-Neves G, 2016, INT J FOOD SCI TECH, V51, P260, DOI 10.1111/ijfs.12958 Gonzalez-Neves G, 2015, J FOOD SCI TECH MYS, V52, P3449, DOI 10.1007/s13197-014-1410-y Guerrero RF, 2009, FOOD CHEM, V112, P949, DOI 10.1016/j.foodchem.2008.07.014 Hakimzadeh N, 2014, J CHROMATOGR A, V1326, P63, DOI 10.1016/j.chroma.2013.12.045 He F, 2012, MOLECULES, V17, P1571, DOI 10.3390/molecules17021571 He F, 2010, MOLECULES, V15, P9057, DOI 10.3390/molecules15129057 Heras-Roger J, 2016, FOOD CHEM, V196, P1224, DOI 10.1016/j.foodchem.2015.10.085 Heredia FJ, 2010, FOOD CHEM, V118, P377, DOI 10.1016/j.foodchem.2009.04.132 Hernandez-Jimenez A, 2009, J AGR FOOD CHEM, V57, P10798, DOI 10.1021/jf903465p Ivanova V, 2012, J FOOD SCI TECH MYS, V49, P161, DOI 10.1007/s13197-011-0279-2 Ivanova V, 2011, FOOD CHEM, V124, P316, DOI 10.1016/j.foodchem.2010.06.039 Ivanova-Petropulos V, 2015, FOOD CHEM, V171, P412, DOI 10.1016/j.foodchem.2014.09.014 Jaakola L, 2013, TRENDS PLANT SCI, V18, P477, DOI 10.1016/j.tplants.2013.06.003 Jackson R, 2014, WINE SCI, V4th ed., P69 Jackson R.S., 2014, WINE SCI, P347, DOI DOI 10.1016/B978-0-12-381468-5.00006-3 Jackson R. S., 2014, WINE SCI, P143, DOI [10.1016/B978-0-12-381468-5.00004-X, DOI 10.1016/B978-0-12-381468-5.00004-X] Jackson RS, 2014, WINE SCI, P307, DOI DOI 10.1016/B978-0-12-381468-5.00005-1 Jackson RS., 2014, WINE SCI, V4th ed, P535 Jaitz L, 2010, FOOD CHEM, V122, P366, DOI 10.1016/j.foodchem.2010.02.053 JURD L, 1967, TETRAHEDRON, V23, P1057, DOI 10.1016/0040-4020(67)85056-7 Kallithraka S, 2006, FOOD CHEM, V99, P784, DOI 10.1016/j.foodchem.2005.07.059 Kallithraka S, 2007, J AGR FOOD CHEM, V55, P3233, DOI 10.1021/jf070114v Knaggs AR, 2001, NAT PROD REP, V18, P334, DOI 10.1039/b001717p Kougan GB, 2013, ELSEV INSIGHT, P225, DOI 10.1016/B978-0-12-405927-6.00006-0 Leicach S.R., 2014, STUD NAT PROD CHEM, V42, P267, DOI [10.1016/B978-0-444-63281-4.00009-4, DOI 10.1016/B978-0-444-63281-4.00009-4] Lorrain B, 2013, MOLECULES, V18, P1076, DOI 10.3390/molecules18011076 Ma Y, 2016, J AGR FOOD CHEM, V64, P505, DOI 10.1021/acs.jafc.5b04890 Makris DP, 2006, TALANTA, V70, P1143, DOI 10.1016/j.talanta.2006.03.024 Malacarne M, 2016, FOOD CHEM, V206, P274, DOI 10.1016/j.foodchem.2016.03.038 Mardones C, 2005, J CHROMATOGR A, V1085, P285, DOI 10.1016/j.chroma.2005.06.022 McCallum J., 2009, FRUIT VEGETABLE PHYT, P131, DOI 10.1002/9780813809397.ch5 Moe T, 1998, TRENDS FOOD SCI TECH, V9, P211, DOI 10.1016/S0924-2244(98)00037-5 Moreno J, 2012, ENOLOGICAL CHEMISTRY, P389, DOI 10.1016/B978-0-12-388438-1.00022-4 Moreno J, 2012, ENOLOGICAL CHEMISTRY, P289, DOI 10.1016/B978-0-12-388438-1.00017-0 Moreno J, 2012, ENOLOGICAL CHEMISTRY, P137, DOI 10.1016/B978-0-12-388438-1.00010-8 Moreno J, 2012, ENOLOGICAL CHEMISTRY, P53, DOI 10.1016/B978-0-12-388438-1.00005-4 Muccillo L, 2014, FOOD CHEM, V143, P506, DOI 10.1016/j.foodchem.2013.07.133 Nicholas KA, 2011, AGR FOREST METEOROL, V151, P1556, DOI 10.1016/j.agrformet.2011.06.010 Niculescu V.-C., 2018, GRAPES WINES ADV PRO, DOI [10.5772/intechopen.72800, DOI 10.5772/INTECHOPEN.72800] Nikfardjam MSP, 2006, FOOD CHEM, V98, P453, DOI 10.1016/j.foodchem.2005.06.014 Olle D, 2011, AUST J GRAPE WINE R, V17, P90, DOI 10.1111/j.1755-0238.2010.00121.x Ortega-Heras M, 2012, LWT-FOOD SCI TECHNOL, V48, P1, DOI 10.1016/j.lwt.2012.03.012 Palade M., 2014, SCI B F, VXVIII, p[226, 233] Papouskova B, 2011, J CHROMATOGR A, V1218, P7581, DOI 10.1016/j.chroma.2011.07.027 Pavlousek P, 2013, CZECH J FOOD SCI, V31, P474, DOI 10.17221/40/2013-CJFS Pisano PL, 2015, FOOD CHEM, V175, P174, DOI 10.1016/j.foodchem.2014.11.124 Quaglieri C, 2017, MOLECULES, V22, DOI 10.3390/molecules22020192 Radovanovic A, 2016, J FOOD COMPOS ANAL, V49, P42, DOI 10.1016/j.jfca.2016.04.001 Radovanovic BC, 2010, J SCI FOOD AGR, V90, P2455, DOI 10.1002/jsfa.4106 Rastija V, 2009, FOOD CHEM, V115, P54, DOI 10.1016/j.foodchem.2008.11.071 Regattieri A, 2007, J FOOD ENG, V81, P347, DOI 10.1016/j.jfoodeng.2006.10.032 Revilla E, 2009, AUST J GRAPE WINE R, V15, P70, DOI 10.1111/j.1755-0238.2008.00037.x Ribereau-Gayon P, 2006, HDB ENOLOGY, V2 Riccardo F., 2006, MASS SPECTROM REV, V25, p[741, 774], DOI [10.1002/mas.20087, DOI 10.1002/MAS.20087] Rodriguez-Delgado MA, 2002, FOOD CHEM, V78, P523, DOI 10.1016/S0308-8146(02)00206-6 Romisch U, 2009, EUR FOOD RES TECHNOL, V230, P31, DOI 10.1007/s00217-009-1141-x Rubert J, 2014, ANAL BIOANAL CHEM, V406, P6791, DOI 10.1007/s00216-014-7864-y Ruiz-Garcia Y, 2013, AGRICULTURE-BASEL, V3, P33, DOI 10.3390/agriculture3010033 Sacchi KL, 2005, AM J ENOL VITICULT, V56, P197 Salvatore E, 2013, ANAL CHIM ACTA, V761, P34, DOI 10.1016/j.aca.2012.11.015 Saurina J, 2010, TRAC-TREND ANAL CHEM, V29, P234, DOI 10.1016/j.trac.2009.11.008 Schlesier K, 2009, EUR FOOD RES TECHNOL, V230, P1, DOI 10.1007/s00217-009-1140-y Sen I, 2014, FOOD CONTROL, V46, P446, DOI 10.1016/j.foodcont.2014.06.015 Serrano-Lourido D, 2012, FOOD CHEM, V135, P1425, DOI 10.1016/j.foodchem.2012.06.010 Siracusa L, 2014, POLYPHENOLS IN PLANTS: ISOLATION, PURIFICATION AND EXTRACT PREPARATION, P15, DOI 10.1016/B978-0-12-397934-6.00002-4 Smeyers-Verbeke J, 2009, EUR FOOD RES TECHNOL, V230, P15, DOI 10.1007/s00217-009-1142-9 SOMERS TC, 1971, PHYTOCHEMISTRY, V10, P2175, DOI 10.1016/S0031-9422(00)97215-7 Sun BS, 2011, J AGR FOOD CHEM, V59, P6550, DOI 10.1021/jf201383e Tarara JM, 2008, AM J ENOL VITICULT, V59, P235 Teixeira A, 2013, INT J MOL SCI, V14, P18711, DOI 10.3390/ijms140918711 Temsamani H., 2015, NUTR AGING, V3, P49 Terrier Nancy, 2009, P463, DOI 10.1007/978-0-387-74118-5_18 Tsao R, 2010, NUTRIENTS, V2, P1231, DOI 10.3390/nu2121231 Vaclavik L, 2011, ANAL CHIM ACTA, V685, P45, DOI 10.1016/j.aca.2010.11.018 Van Leeuwen Cornelis, 2006, Journal of Wine Research, V17, P1, DOI 10.1080/09571260600633135 Verries C, 2008, J AGR FOOD CHEM, V56, P5896, DOI 10.1021/jf800028k Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Vilanova M, 2015, FOOD CHEM, V169, P187, DOI 10.1016/j.foodchem.2014.08.015 Villano C, 2017, FOOD CONTROL, V80, P1, DOI 10.1016/j.foodcont.2017.04.020 Vitrac X, 2001, J AGR FOOD CHEM, V49, P5934, DOI 10.1021/jf010522d Vivas N., 2003, Bulletin de l'OIV, V76, P281 Vivas N., 2014, REV OENOL, V41, p[29, 30] von Baer D, 2008, ANAL CHIM ACTA, V621, P52, DOI 10.1016/j.aca.2007.11.034 WAGENER GWW, 1981, AM J ENOL VITICULT, V32, P179 Xia EQ, 2010, INT J MOL SCI, V11, P622, DOI 10.3390/ijms11020622 Zhu L, 2012, INT J MOL SCI, V13, P3492, DOI 10.3390/ijms13033492 Zsofi Z, 2009, AUST J GRAPE WINE R, V15, P36, DOI 10.1111/j.1755-0238.2008.00036.x NR 133 TC 10 Z9 10 U1 7 U2 19 PD DEC PY 2018 VL 4 IS 4 AR 75 DI 10.3390/beverages4040075 WC Food Science & Technology SC Food Science & Technology UT WOS:000455154000005 DA 2022-12-14 ER PT J AU Stevenson, MA Sanson, RL Miranda, AO Lawrence, KA Morris, RS AF Stevenson, M. A. Sanson, R. L. Miranda, A. O. Lawrence, K. A. Morris, R. S. TI Decision support systems for monitoring and maintaining health in food animal populations SO NEW ZEALAND VETERINARY JOURNAL DT Review DE decision support systems; food safety; animal disease surveillance; traceability; epidemiology ID BOVINE SPONGIFORM ENCEPHALOPATHY; MOUTH-DISEASE; SPATIAL-ANALYSIS; GREAT-BRITAIN; NEW-ZEALAND; EPIDEMIC; CATTLE; OUTBREAK; MANAGEMENT; ARGENTINA AB To mitigate the effects of risks to food safety and infectious disease outbreaks in farmed animals, animal health authorities need to have systems in place to identify and trace the source of identified problems in a timely manner. In the event of emergencies, these systems will allow infected or contaminated premises (and/or animals) to be identified and contained, and will allow the extent of problems to be communicated to consumers and trading partners in a clear and unambiguous manner. The key to achieving these goals is the presence of an effective animal health decision support system that will provide the facilities to record and store detailed information about cases and the population at risk, allowing information to be reported back to decision makers when it is required. Described here are the components of an animal health decision support system, and the ways these components can be used to enhance food safety, responses to infectious disease incursions, and animal health and productivity. Examples are provided to illustrate the benefit these systems can return, using data derived from countries that have such systems (or parts of systems) in place. Emphasis is placed on the features that make particular system components effective, and strategies to ensure that these are kept up to date. C1 [Stevenson, M. A.; Lawrence, K. A.; Morris, R. S.] Massey Univ, Inst Vet Anim & Biomed Sci, Palmerston North, New Zealand. [Sanson, R. L.] AsureQual Ltd, Palmerston North, New Zealand. [Miranda, A. O.] Inst Nacl Tecnol Agropecuria, La Pampa, Argentina. C3 Massey University; Instituto Nacional de Tecnologia Agropecuaria (INTA) RP Stevenson, MA (corresponding author), Massey Univ, Inst Vet Anim & Biomed Sci, Private Bag 11222, Palmerston North, New Zealand. EM m.stevenson@massey.ac.nz CR Alvarez J, 2006, COMPUT ELECTRON AGR, V50, P48, DOI 10.1016/j.compag.2005.08.013 Anderson, 2002, FOOT AND MOUTH DIS 2 Anderson AC, 1996, BRIT J SPORT MED, V30, P347, DOI 10.1136/bjsm.30.4.347 [Anonymous], [No title captured] [Anonymous], 2002, COMMUNICATION CRISIS Barlow ND, 1998, PREV VET MED, V36, P25, DOI 10.1016/S0167-5877(98)00075-0 Campbell S., 2007, ANAL POLICY EVIDENCE Christley R. M., 2005, Society for Veterinary Epidemiology and Preventive Medicine. Proceedings of a meeting held at Nairn, Inverness, Scotland, 30th March-1st April 2005, P234 Clements ACA, 2006, TROP MED INT HEALTH, V11, P490, DOI 10.1111/j.1365-3156.2006.01594.x Cleveland R.B., 1990, J OFF STAT, V6, P3, DOI DOI 10.1016/J.SOILBIO.2009.09.001 Diggle P J, 1995, Stat Methods Med Res, V4, P124, DOI 10.1177/096228029500400203 Diggle P.J., 1995, BERNOULLI, V1, DOI [10.2307/3318678, DOI 10.2307/3318678, 10.1002/sim.4780142106] Eddy RG, 1995, VET REC, V137, P648 Elbers ARW, 2001, REV SCI TECH OIE, V20, P614, DOI 10.20506/rst.20.2.1296 Glik DC, 2007, ANNU REV PUBL HEALTH, V28, P33, DOI 10.1146/annurev.publhealth.28.021406.144123 Golan E.H., 2004, TRACEABILITY US FOOD HAYES D, 1997, THESIS MASSEY U PALM Kao RR, 2002, TRENDS MICROBIOL, V10, P279, DOI 10.1016/S0966-842X(02)02371-5 Kulldorff M, 1997, COMMUN STAT-THEOR M, V26, P1481, DOI 10.1080/03610929708831995 Kusiluka LJM, 2003, PREV VET MED, V59, P113, DOI 10.1016/S0167-5877(03)00087-4 LAWRENCE KA, 2005, LIVESTOCK TRACEABILI Lawson AB, 2000, STAT MED, V19, P2451, DOI 10.1002/1097-0258(20000915/30)19:17/18<2451::AID-SIM581>3.0.CO;2-W Mandl KD, 2004, J AM MED INFORM ASSN, V11, P141, DOI 10.1197/jamia.M1356 Mansley LM, 2003, VET REC, V153, P43, DOI 10.1136/vr.153.2.43 Mattion N, 2004, VACCINE, V22, P4149, DOI 10.1016/j.vaccine.2004.06.040 McIntyre L. H., 2006, P 11 S INT SOC VET E, P942 McIntyre L.H., 2003, P 10 S INT SOC VET E, P334 MORRIS R, 1997, P 8 INT S VET EP EC, P1 Murray G, 1999, REV SCI TECH OIE, V18, P15, DOI 10.20506/rst.18.1.1147 NELSON LS, 1984, J QUAL TECHNOL, V16, P237, DOI 10.1080/00224065.1984.11978921 Perez AM, 2004, PREV VET MED, V65, P217, DOI 10.1016/j.prevetmed.2004.08.002 Rangel JM, 2005, EMERG INFECT DIS, V11, P603, DOI 10.3201/eid1104.040739 RENEAU JK, 2001, HERD HLTH, P107 Rothman KJ., 2008, MODERN EPIDEMIOLOGY, V3 Sanson R, 1997, P 8 INT S VET EP EC, P11 Sheahan M, 2002, IRISH VET J, V55, P394 Shephard RW, 2006, THESIS U SYDNEY SYDN SPATH E, 2003, P 10 S INT SOC VET E, P527 STARK K, 2006, BMC HEALTH SERV RES, V6, P164 Stevenson M., 2003, THESIS MASSEY U PALM Stevenson MA, 2005, PREV VET MED, V71, P241, DOI 10.1016/j.prevetmed.2005.07.007 Stevenson MA, 2005, PREV VET MED, V69, P129, DOI 10.1016/j.prevetmed.2005.01.016 Stevenson MA, 2000, VET REC, V147, P349, DOI 10.1136/vr.147.13.349 Stevenson MA, 2000, VET REC, V147, P379, DOI 10.1136/vr.147.14.379 Suarez V. H., 2006, RIA, Revista de Investigaciones Agropecuarias, V35, P43 Taguchi M, 2005, JPN J INFECT DIS, V58, P55 Thompson D, 2002, REV SCI TECH OIE, V21, P675, DOI 10.20506/rst.21.3.1353 THRELFALL EJ, 1994, VET REC, V134, P577, DOI 10.1136/vr.134.22.577 THRUSFIELD M, 2007, VET EPIDEMIOLOGY, P63 Turban E, 1995, DECISION SUPPORT EXP Vourc'h G, 2006, EMERG INFECT DIS, V12, P204, DOI 10.3201/eid1202.050498 WILESMITH JW, 1996, BOVINE SPONGIFORM EN, P45 2003, DATA INTERCHANGE FOR 2006, HOME PAGE ANIMAL HLT 1996, DAIRY 96 1 2000, PHILLIPS INQUIRY BSE 2006, BRAZILIAN SYSTEM IDE 2007, FOODBORNE PATHOGENS, V4, P2 2003, HELSTJENSTEN STORFE 2007, MEDIA ADVISORY MEDIA 2007, NATL LIVESTOCK IDENT 2007, SANITARY PHYTOSANITA [No title captured] 2006, SYSTEM IDENTIFICATIO 2007, EXERCISE COWCATCHER, V2 2006, LIVESTOCK RANCH OFFI 2002, ANN REPORT MINISTRY, P147 2005, VET RECORD, V157, P5 NR 68 TC 12 Z9 12 U1 0 U2 11 PY 2007 VL 55 IS 6 BP 264 EP 272 DI 10.1080/00480169.2007.36780 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000253439000003 DA 2022-12-14 ER PT J AU Meland, PH Nesheim, DA Bernsmed, K Sindre, G AF Meland, Per Hakon Nesheim, Dag Atle Bernsmed, Karin Sindre, Guttorm TI Assessing cyber threats for storyless systems SO JOURNAL OF INFORMATION SECURITY AND APPLICATIONS DT Article DE Cyber threats; Decision-making; Estimation; Empirical evaluation; Case study; Maritime communication ID RISK-MANAGEMENT; CYBERSECURITY; OPPORTUNITY; FRAMEWORK; CHOICE; MODEL AB A proper assessment of potential cyber threats is vital for security decision-making. This becomes an even more challenging task when dealing with new system designs and industry sectors where there is little or no historical data about past security incidents. We have developed a threat likelihood estimation approach that supports risk management under such circumstances. Quantifiable conditions are determined from the environment in which the system will reside and operate, that is the availability of potential threat actors, their opportunities of performing attacks, the required means that are needed for the attack to succeed, and motivation factors. Our research method follows the principles of practice research where both researchers and practitioners have played central roles in a real-life development project for a maritime communication system. We used a qualitative case study for feature-based evaluation of the approach and associated tool template, and to gather evidence on practical aspects such as suitability for purpose, efficiency and drawbacks from five user groups. The results show that representative participants from the cyber security and maritime community gave positive and consistent scores on the features, and regarded time usage, traceability of the threat assessment and the ability to indicate underlying uncertainty to be very appropriate. The approach has been proven useful for this domain and should be applicable to others as well, but the template requires up-front investments in gathering knowledge that is relevant and reusable in additional context situations. C1 [Meland, Per Hakon; Bernsmed, Karin] SINTEF Digital, Strindvegen 4, N-7465 Trondheim, Norway. [Nesheim, Dag Atle] SINTEF Ocean, Postboks 4762 Torgard, N-7465 Trondheim, Norway. [Meland, Per Hakon; Sindre, Guttorm] Norwegian Univ Sci & Technol, Hogskoleringen 1, N-7491 Trondheim, Norway. C3 SINTEF; SINTEF; Norwegian University of Science & Technology (NTNU) RP Meland, PH (corresponding author), SINTEF Digital, Strindvegen 4, N-7465 Trondheim, Norway. EM per.h.meland@sintef.no; dag.atle.nesheim@sintef.no; karin.bernsmed@sintef.no; guttorm.sindre@ntnu.no CR Ahrend JM, 2017, HDB ANTICIPATION THE, P1, DOI [10.1007/978-3-319-31737-3_26-1, DOI 10.1007/978-3-319-31737-3_26-1] Al-Hadhrami N, 2021, LECT NOTES COMPUT SC, V12528, P201, DOI 10.1007/978-3-030-68887-5_12 Almukaynizi M, 2020, DATA SCI CYBERSECURI, P13, DOI [10.1007/978-3-030-38788-4_2, DOI 10.1007/978-3-030-38788-4_2] [Anonymous], 2018, 270052018 ISOIEC [Anonymous], 2020, SEAFARERS PROFESSION [Anonymous], 2020, INT SHIP PORT FACILI [Anonymous], 2020, MARINE SURVEYOR [Anonymous], 2018, ISO31000 [Anonymous], 2020, WHAT IS DAT CONTR DA Aust J, 2020, AEROSPACE-BASEL, V7, DOI 10.3390/aerospace7070086 Bagnato Alessandra, 2012, International Journal of Secure Software Engineering, V3, P1, DOI 10.4018/jsse.2012040101 Bernsmed K, 2018, LECT NOTES COMPUT SC, V10744, P38, DOI 10.1007/978-3-319-74860-3_3 Bohme R., 2019, VARIANCE, V12, P161 Box G. E. P., 1987, EMPIRICAL MODEL BUIL Braiterman Z., 2020, THREAT MODELING MANI Brantly AF, 2021, J CYBERSECURITY, V7, DOI 10.1093/cybsec/tyab001 Buldas A, 2006, LECT NOTES COMPUT SC, V4347, P235 Buldas A, 2020, COMPUT SECUR, V88, DOI 10.1016/j.cose.2019.101630 Burt T, 2020, MICROSOFT DIGITAL DE Calleja A, 2016, LECT NOTES COMPUT SC, V9854, P325, DOI 10.1007/978-3-319-45719-2_15 Caprolu M, 2020, IEEE COMMUN MAG, V58, P90, DOI 10.1109/MCOM.001.1900632 Casey T, 2015, UNDERSTANDING CYBER Casey T, 2007, THREAT AGENT LIB HEL, P2 Chang C, 2019, P INT ASS MAR U IAMU Cimpean D, 2011, AN CYB SEC ASP MAR S Cockshott JE, 2005, PROCESS SAF ENVIRON, V83, P307, DOI 10.1205/psep.04380 Cruzes D.S., 2017, EMPIRICAL RES SOFTWA, P295 CySiMS, 2021, CYB SEC MERCH SHIPP DHS, 2013, NAT MAR DOM AW PLAN Dubay D, 2019, WHY WE WILL NEVER SE ENISA, 2020, ENISA THREAT LANDSC Farnsworth V, 2016, BRIT J EDUC STUD, V64, P139, DOI 10.1080/00071005.2015.1133799 Franco EG, 2021, GLOBAL RISK REPORT 2, V16th Goldkuhl G., 2011, INT J COMMUNICATION, V5, P7 Haga Kristian, 2020, Graphical Models for Security. 7th International Workshop, GraMSec 2020. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12419), P111, DOI 10.1007/978-3-030-62230-5_6 Holm H, 2014, EXPERT SYST, V31, P299, DOI 10.1111/exsy.12039 Hong JB, 2017, COMPUT SCI REV, V26, P1, DOI 10.1016/j.cosrev.2017.09.001 Hubbard DW, 2016, MEASURE ANYTHING CYB Hutchins EM, 2021, CYBER KILL CHAIN Hutchins EM, 2010, LEADING ISSUES INFOR IALA, 2020, VDES VHF DAT EXCH SY IEC, 2018, 611624502018 IEC ISO, 2018, 270002018 ISOIEC Jacq O, 2018, 2018 2ND CYBER SECURITY IN NETWORKING CONFERENCE (CSNET) Jalali MS, 2019, J STRATEGIC INF SYST, V28, P66, DOI 10.1016/j.jsis.2018.09.003 Kessler GC, 2018, TRANSNAV, V12, P429, DOI 10.12716/1001.12.03.01 Kissoon T., J INF SECUR, V12, P137, DOI [10.4236/jis.2021.121007, DOI 10.4236/JIS.2021.121007] Kitchenham B, 1996, METHOD EVALUATING SO Kitchenham B. A., 1996, SIGSOFT Software Engineering Notes, V21, P11, DOI 10.1145/381790.381795 Knez C, 2016, AEROSP CONF PROC Kontovas CA, 2009, MAR TECHNOL SNAME N, V46, P45 Llanso T, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P5968 Marshall C., 2016, THESIS U KEELE McKendall MA, 1997, ORGAN SCI, V8, P624, DOI 10.1287/orsc.8.6.624 McNeil M, 2018, P 5 ANN S BOOTC HOT, P1, DOI [10.1145/3190619.3190644, DOI 10.1145/3190619.3190644] Meland Per Hakon, 2019, Information and Computer Security, V27, P536, DOI 10.1108/ICS-11-2018-0132 Meland PH, 2019, 2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019), P54, DOI 10.1109/CSCI49370.2019.00016 Meland PH, 2021, CYSIMS THREAT LIKELI, DOI [10.5281/zenodo.4899525, DOI 10.5281/ZENODO.4899525] Meland PH, 2021, P 14 INT C MAR NAV S Mrakovic I, 2019, TRANS MARIT SCI-TOMS, V8, P132, DOI 10.7225/toms.v08.n01.013 NIST, 2014, CYB FRAM VERS 1 0 Pate-Cornell ME, 2018, RISK ANAL, V38, P226, DOI 10.1111/risa.12844 Pendse SG, 2012, J BUS ETHICS, V107, P265, DOI 10.1007/s10551-011-1037-0 Pols P., 2017, UNIFIED KILL CHAIN D Santini P, 2019, SECUR COMMUN NETW, V2019, DOI 10.1155/2019/6716918 Schneier B, 1999, DR DOBBS J, V24, P21 Schneier B, 2000, E PRIVACY, P214, DOI [10.1007/978-3-322-89183-9_20, DOI 10.1007/978-3-322-89183-9_20] Shinder DL, 2008, SCENE CYBERCRIME, DOI [10.1016/B978-1-59749-276-8.X0001-5, DOI 10.1016/B978-1-59749-276-8.X0001-5] Stoneburner G, 2002, NIST SPEC PUBL, V800 Svilicic B, 2019, J NAVIGATION, V72, P1108, DOI 10.1017/S0373463318001157 Tam K, 2019, WMU J MARIT AFF, V18, P129, DOI 10.1007/s13437-019-00162-2 ter Beek MH, 2021, COMPUT SECUR, V109, DOI 10.1016/j.cose.2021.102381 Figueira PT, 2020, COMPUT SECUR, V88, DOI 10.1016/j.cose.2019.101609 Van Ruitenbeek E, 2010, 40 ANN IEEE IF INT C, P17 Warikoo A, 2014, INF SECUR J, V23, P172, DOI 10.1080/19393555.2014.931491 Webster DA, 2019, IS THER DIFF INT MOT Williams Jeff., 2020, OWASP RISK RATING ME You B, 2017, 30 ANN C INT CHIN TR, P19 Zelkowitz MV, 1998, SOFTW QUAL PRACT, V1 NR 79 TC 6 Z9 6 U1 2 U2 8 PD FEB PY 2022 VL 64 AR 103050 DI 10.1016/j.jisa.2021.103050 EA NOV 2021 WC Computer Science, Information Systems SC Computer Science UT WOS:000719238100001 DA 2022-12-14 ER PT J AU Stranieri, S Riccardi, F Meuwissen, MPM Soregaroli, C AF Stranieri, Stefanella Riccardi, Federica Meuwissen, Miranda P. M. Soregaroli, Claudio TI Exploring the impact of blockchain on the performance of agri-food supply chains SO FOOD CONTROL DT Article DE Blockchain; Traceability; Economic performances; Food supply chains ID TRACEABILITY SYSTEM; STANDARDS; BENEFITS; QUALITY; COST; TRANSPARENCY; MANAGEMENT; FRAUD AB The implementation of the blockchain technology in the agri-food supply chains is in its introductory phase. Lead companies, often retailers, introduce this technology for specific objectives, such as assuring traceability or improving sales and reputation. At the same time, the technology could impact much more broadly the per-formances of food chains. Little is known about this impact as the evidence provided in the literature is scarce and mostly focused on specific indicators. This paper addresses this gap assessing the impact of the blockchain technology on food supply chains from an explorative perspective. An integrated conceptual framework is proposed which includes a broad set of performance dimensions discussed in the literature: efficiency, flexibility, responsiveness, food quality, and transparency of supply chains. These dimensions are assessed using a case study, consisting of three supply chains where a large European retailer has promoted the blockchain adoption. Data was collected through semi-structured interviews with key managers at different stages of the three supply chains and were systematically analysed through a thematic analysis. Results reveal that blockchain technology impacts positively on the profit and/or return on investment of supply chains, it leads to an increase of extrinsic food quality attributes and it fosters a better information management along the food chains due to an improved information accessibility, availability and sharing. The current analysis also suggests an improved management of behavioural uncertainty among the agents of the supply chains and an increase of firm's knowledge as well as supply chain management competencies. While the study remains of explorative nature, it offers a basis for the selection of theoretical approaches and the formulation of new hypotheses for future blockchain studies. C1 [Stranieri, Stefanella] Univ Milan, Dept Environm Sci & Policy, Milan, Italy. [Riccardi, Federica; Soregaroli, Claudio] Univ Cattolica Sacro Cuore, Dept Agr & Food Econ, Piacenza, Italy. [Riccardi, Federica; Meuwissen, Miranda P. M.] Wageningen Univ & Res, Business Econ, Wageningen, Netherlands. C3 University of Milan; Catholic University of the Sacred Heart; Wageningen University & Research RP Soregaroli, C (corresponding author), Univ Cattolica Sacro Cuore, Dept Agr & Food Econ, Piacenza, Italy. EM stefanella.stranieri@unimi.it; miranda.meuwissen@wur.nl; claudio.soregaroli@unicatt.it CR Aramyan LH, 2007, SUPPLY CHAIN MANAG, V12, P304, DOI 10.1108/13598540710759826 Asioli D, 2014, FOOD CONTROL, V46, P10, DOI 10.1016/j.foodcont.2014.04.048 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Azfar KRW, 2014, PROCD SOC BEHV, V150, P803, DOI 10.1016/j.sbspro.2014.09.089 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Banterle A, 2013, SUSTAINABILITY-BASEL, V5, P5272, DOI 10.3390/su5125272 BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108 Beamon BM, 1999, INT J OPER PROD MAN, V19, P275, DOI 10.1108/01443579910249714 Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025 Biswas A, 2017, 2017 8TH ANNUAL INDUSTRIAL AUTOMATION AND ELECTROMECHANICAL ENGINEERING CONFERENCE (IEMECON), P56, DOI 10.1109/IEMECON.2017.8079561 Caro MP, 2018, IOT VERT TOP SUMM AG, P1 Chatfield DC, 2004, PROD OPER MANAG, V13, P340, DOI 10.1111/j.1937-5956.2004.tb00222.x Chavez-Dreyfuss Gertrude, 2018, GERTRUDE CHAVEZ DREY Chrysochou P, 2009, APPETITE, V53, P322, DOI 10.1016/j.appet.2009.07.011 Creydt M, 2019, FOOD CONTROL, V105, P45, DOI 10.1016/j.foodcont.2019.05.019 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 de Wildt, 2017, 2017112 WAG EC RES DeGroote SE, 2013, INT J INFORM MANAGE, V33, P909, DOI 10.1016/j.ijinfomgt.2013.09.001 Deimel M., 2008, Journal on Chain and Network Science, V8, P21, DOI 10.3920/JCNS2008.x086 Deloitte U., 2017, US BLOCKCH DRIV SUPP Donnelly KAM, 2013, FOOD CONTROL, V33, P25, DOI 10.1016/j.foodcont.2013.01.021 Exposito I, 2013, IEEE ANTENN PROPAG M, V55, P255, DOI 10.1109/MAP.2013.6529365 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Field A. M., 2017, J COMMERCE Forslund H, 2009, INT J OPER PROD MAN, V29, P77, DOI 10.1108/01443570910925370 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Ghozzi H, 2016, SUPPLY CHAIN MANAG, V21, P743, DOI 10.1108/SCM-03-2016-0089 Grunert KG, 1997, FOOD QUAL PREFER, V8, P157, DOI 10.1016/S0950-3293(96)00038-9 Gunasekera D, 2020, ECON PAP, V39, P152, DOI 10.1111/1759-3441.12274 Kamath R., 2018, J BRIT BLOCKCHAIN AS, V1, P3712 Kamilaris A, 2019, TRENDS FOOD SCI TECH, V91, P640, DOI 10.1016/j.tifs.2019.07.034 Kendall H, 2019, TRENDS FOOD SCI TECH, V94, P79, DOI 10.1016/j.tifs.2019.10.005 Kumar R., 2019, RES METHODOLOGY STEP Lai KH, 2002, TRANSPORT RES E-LOG, V38, P439, DOI 10.1016/S1366-5545(02)00019-4 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Lao G, 2008, INT C SERV SYST SERV, P1 Li Z, 2017, IND MANAGE DATA SYST, V117, P1906, DOI 10.1108/IMDS-11-2016-0489 Lucena P., 2018, P S FDN APPL BLOCKCH Mai N, 2010, BRIT FOOD J, V112, P976, DOI 10.1108/00070701011074354 Manning L, 2016, CURR OPIN FOOD SCI, V10, P16, DOI 10.1016/j.cofs.2016.07.001 Nowell LS, 2017, INT J QUAL METH, V16, DOI 10.1177/1609406917733847 Olson J.C., 1972, P 3 ANN C ASS CONS R, P167, DOI DOI 10.1108/EB026082 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Passuello F, 2015, BRIT FOOD J, V117, P2564, DOI 10.1108/BFJ-11-2014-0380 Pazaitis A, 2017, TECHNOL FORECAST SOC, V125, P105, DOI 10.1016/j.techfore.2017.05.025 Persson F, 2002, INT J PROD ECON, V77, P231, DOI 10.1016/S0925-5273(00)00088-8 Prashar D, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083497 Saltini R, 2013, FOOD CONTROL, V29, P167, DOI 10.1016/j.foodcont.2012.05.054 Scuderi A, 2019, QUAL-ACCESS SUCCESS, V20, P580 Simatupang T, 2001, ORSNZ C U CANT NZ Stranieri S, 2017, FOOD CONTROL, V80, P187, DOI 10.1016/j.foodcont.2017.04.047 Swan M., 2015, BLOCKCHAIN BLUEPRINT Trienekens JH, 2012, ADV ENG INFORM, V26, P55, DOI 10.1016/j.aei.2011.07.007 Varacca A., 2014, AgBioForum, V17, P123 Verhoeven P, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2030020 Wang X, 2017, COMPUT ELECTRON AGR, V135, P195, DOI 10.1016/j.compag.2016.12.019 Williamson OE, 1996, MECH GOVERNANCE Yan B, 2016, IND MANAGE DATA SYST, V116, P1397, DOI 10.1108/IMDS-12-2015-0512 Yin R.K., 2011, QUALITATIVE RES STAR Zelbst PJ, 2010, J BUS IND MARK, V25, P582, DOI 10.1108/08858621011088310 NR 61 TC 54 Z9 54 U1 41 U2 182 PD JAN PY 2021 VL 119 AR 107495 DI 10.1016/j.foodcont.2020.107495 WC Food Science & Technology SC Food Science & Technology UT WOS:000584380000001 HC Y HP N DA 2022-12-14 ER PT J AU Zhang, CP Bai, JF Wahl, TI AF Zhang, Caiping Bai, Junfei Wahl, Thomas I. TI Consumers' willingness to pay for traceable pork, milk, and cooking oil in Nanjing, China SO FOOD CONTROL DT Article DE Food safety; Food traceability; Information effects; Willingness-to-pay ID COUNTRY-OF-ORIGIN; FOOD SAFETY; CHEAP TALK; QUALITY; BEEF; PREFERENCES; DEMAND; DESIGN; MARKET; SYSTEM AB We analyzed consumers' willingness to pay (WTP) for traceable pork, milk and cooking oil, and its determinants using data from Nanjing, China, with particular focus on the effects of consumer knowledge. The major findings suggest that Nanjing consumers are willing to pay a significant positive price premium for food traceability despite variations across products. Meanwhile, consumers' WTP for food traceability was positively affected by consumer knowledge about food traceability and awareness of food quality- and safety-related certifications. A number of demographics such as income and age also have statistically significant impacts on the WTP. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Bai, Junfei] Chinese Acad Sci, Inst Geog Sci & Natl Resources Res, Ctr Chinese Agr Policy, Beijing 100101, Peoples R China. [Zhang, Caiping] Cent Univ Finance & Econ, Sch Econ, Beijing 100081, Peoples R China. [Wahl, Thomas I.] N Dakota State Univ, Dept Agribusiness & Appl Econ, Fargo, ND 58108 USA. C3 Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Central University of Finance & Economics; North Dakota State University Fargo RP Bai, JF (corresponding author), Chinese Acad Sci, Inst Geog Sci & Natl Resources Res, Ctr Chinese Agr Policy, Jia 11,Anwai Datun Rd, Beijing 100101, Peoples R China. EM cpzhang77@gmail.com; jfbai.ccap@igsnrr.ac.cn; tom.wahl@ndsu.edu CR AKERLOF GA, 1970, Q J ECON, V84, P488, DOI 10.2307/1879431 Blamey RK, 1999, LAND ECON, V75, P126, DOI 10.2307/3146997 Calvin L., 2006, Amber Waves, V4, P16 Charlier C, 2008, EUR J LAW ECON, V25, P1, DOI 10.1007/s10657-007-9038-2 Cummings RG, 1999, AM ECON REV, V89, P649, DOI 10.1257/aer.89.3.649 Dickinson D. L., 2005, J AGRIC APPL ECON, V37, P537, DOI DOI 10.1017/S1074070800027061 Dickinson DL, 2002, J AGR RESOUR ECON, V27, P348 Ehmke MD, 2008, AGR ECON-BLACKWELL, V38, P277, DOI 10.1111/j.1574-0862.2008.00299.x Golan E.H., 2004, TRACEABILITY US FOOD Greene W. H., 2003, ECONOMETRIC ANAL, P227 HANEMANN M, 1991, AM J AGR ECON, V73, P1255, DOI 10.2307/1242453 Hobbs J. E., 2003, CURRENT AGR FOOD RES, V4, P36 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Huang KS, 2009, CHINA AGRIC ECON REV, V1, P395, DOI 10.1108/17561370910992307 KANNINEN BJ, 1993, LAND ECON, V69, P138, DOI 10.2307/3146514 Lee JY, 2011, AUST J AGR RESOUR EC, V55, P360, DOI 10.1111/j.1467-8489.2011.00553.x List JA, 2001, ENVIRON RESOUR ECON, V20, P241, DOI 10.1023/A:1012791822804 Loureiro ML, 2007, FOOD POLICY, V32, P496, DOI 10.1016/j.foodpol.2006.11.006 Lusk JL, 2003, AM J AGR ECON, V85, P840, DOI 10.1111/1467-8276.00492 McCluskey JJ, 2005, AUST J AGR RESOUR EC, V49, P197, DOI 10.1111/j.1467-8489.2005.00282.x Mittelhammer Ron C, 2000, ECONOMETRIC FDN PACK NEILL HR, 1994, LAND ECON, V70, P145, DOI 10.2307/3146318 Ortega DL, 2011, FOOD POLICY, V36, P318, DOI 10.1016/j.foodpol.2010.11.030 Shogren JF, 1999, AM J AGR ECON, V81, P1192, DOI 10.2307/1244106 Song M, 2008, J FAC AGR KYUSHU U, V53, P569 Tonsor GT, 2011, AM J AGR ECON, V93, P1015, DOI 10.1093/ajae/aar036 Ubilava D, 2009, FOOD POLICY, V34, P305, DOI 10.1016/j.foodpol.2009.02.002 Verbeke W, 2002, FOOD POLICY, V27, P339, DOI 10.1016/S0306-9192(02)00037-4 Wang F, 2009, FOOD CONTROL, V20, P918, DOI 10.1016/j.foodcont.2009.01.008 Yang B., 2009, RURAL EC, V8, P57 Yu XH, 2009, AM J AGR ECON, V91, P57, DOI 10.1111/j.1467-8276.2008.01159.x Zhang L., 2009, CHINAS DAIRY IND DEV NR 32 TC 100 Z9 108 U1 7 U2 95 PD SEP PY 2012 VL 27 IS 1 BP 21 EP 28 DI 10.1016/j.foodcont.2012.03.001 WC Food Science & Technology SC Food Science & Technology UT WOS:000304790800004 DA 2022-12-14 ER PT J AU de Rijke, E Schoorl, JC Cerli, C Vonhof, HB Verdegaal, SJA Vivo-Truyols, G Lopatka, M Dekter, R Bakker, D Sjerps, MJ Ebskamp, M de Koster, CG AF de Rijke, E. Schoorl, J. C. Cerli, C. Vonhof, H. B. Verdegaal, S. J. A. Vivo-Truyols, G. Lopatka, M. Dekter, R. Bakker, D. Sjerps, M. J. Ebskamp, M. de Koster, C. G. TI The use of delta H-2 and delta O-18 isotopic analyses combined with chemometrics as a traceability tool for the geographical origin of bell peppers SO FOOD CHEMISTRY DT Article DE Isotope ratio mass spectrometry; Gas chromatography; Geographic origin; Food products authenticity; Linear discriminant analysis; Likelihood ratio; Bell peppers; Capsicum annuum ID FOOD ANALYSIS; D VALUES; MULTIELEMENT; CAPSICUM; RATIOS; PLANTS; FRUIT; WINE AB Two approaches were investigated to discriminate between bell peppers of different geographic origins. Firstly, delta O-18 fruit water and corresponding source water were analyzed and correlated to the regional GNIP (Global Network of Isotopes in Precipitation) values. The water and GNIP data showed good correlation with the pepper data, with constant isotope fractionation of about -4. Secondly, compound-specific stable hydrogen isotope data was used for classification. Using n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based classification, using the kernel-density smoothed data, were developed to discriminate between peppers from different origins. Both methods were evaluated using the delta H-2 values and n-alkanes relative composition as variables. Misclassification rates were calculated using a Monte-Carlo 5-fold cross-validation procedure. Comparable overall classification performance was achieved, however, the two methods showed sensitivity to different samples. The combined values of delta H-2 IRMS, and complimentary information regarding the relative abundance of four main alkanes in bell pepper fruit water, has proven effective for geographic origin discrimination. Evaluation of the rarity of observing particular ranges for these characteristics could be used to make quantitative assertions regarding geographic origin of bell peppers and, therefore, have a role in verifying compliance with labeling of geographical origin. (C) 2016 Elsevier Ltd. All rights reserved. C1 [de Rijke, E.; de Koster, C. G.] Univ Amsterdam, Swammerdam Inst Life Sci, Mass Spectrometry Biomacromol, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands. [Schoorl, J. C.; Cerli, C.] Univ Amsterdam, Inst Biodivers & Ecosyst Dynam, Earth Surface Sci, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands. [Vonhof, H. B.; Verdegaal, S. J. A.] Vrije Univ Amsterdam, Fac Earth & Life Sci, De Boelelaan 1085, NL-1081 HV Amsterdam, Netherlands. [Vivo-Truyols, G.] Univ Amsterdam, vant Hoff Inst Mol Sci, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands. [Lopatka, M.; Sjerps, M. J.] Univ Amsterdam, Korteweg de Vries Inst, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands. [Lopatka, M.; Sjerps, M. J.] Netherlands Forens Inst, POB 24044, NL-2490 AA The Hague, Netherlands. [Dekter, R.; Bakker, D.; Ebskamp, M.] Naktuinbouw Labs, Sotaweg 25,POB 40, NL-2370 AA Roelofarendsveen, Netherlands. C3 University of Amsterdam; University of Amsterdam; Vrije Universiteit Amsterdam; University of Amsterdam; University of Amsterdam RP de Rijke, E (corresponding author), Univ Amsterdam, Swammerdam Inst Life Sci, Mass Spectrometry Biomacromol, Sci Pk 904, NL-1090 GE Amsterdam, Netherlands. EM e.derijke@uva.nl; J.C.Schoorl@uva.nl; c.cerli@uva.nl; h.b.vonhof@vu.nl; s.j.a.verdegaal-warmer-dam@vu.nl; g.vivotruyols@uva.nl; m.lopatka@u-va.nl; r.dekter@naktuinbouw.nl; d.bakker@naktuinbouw.nl; m.j.sjerps@uva.nl; m.ebskamp@naktuinbouw.nl; c.g.dekoster@uva.nl CR Bauer S, 2005, EUR FOOD RES TECHNOL, V220, P5, DOI 10.1007/s00217-004-1046-7 Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Calderone G, 2008, FOOD CHEM, V106, P1399, DOI 10.1016/j.foodchem.2007.01.080 Christoph N., 2009, ACS SYM SER, V952, P166 de Rijke E, 2015, ANAL BIOANAL CHEM, V407, P5729, DOI 10.1007/s00216-015-8755-6 EPSTEIN S, 1953, GEOCHIM COSMOCHIM AC, V4, P213, DOI 10.1016/0016-7037(53)90051-9 Hastie R., 2009, SPRINGER, V2, P1, DOI DOI 10.1007/978-0-387-21606-5 Kahmen A, 2013, GEOCHIM COSMOCHIM AC, V111, P39, DOI 10.1016/j.gca.2012.09.003 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kjeldahl K, 2010, J CHEMOMETR, V24, P558, DOI 10.1002/cem.1346 Laursen KH, 2014, TRAC-TREND ANAL CHEM, V59, P73, DOI 10.1016/j.trac.2014.04.008 Martyna A, 2014, FOOD CHEM, V150, P287, DOI 10.1016/j.foodchem.2013.10.111 McInerney FA, 2011, GEOCHIM COSMOCHIM AC, V75, P541, DOI 10.1016/j.gca.2010.10.022 Oulhote Y, 2011, TRAC-TREND ANAL CHEM, V30, P302, DOI 10.1016/j.trac.2010.10.015 Parsons EP, 2013, PHYSIOL PLANTARUM, V149, P160, DOI 10.1111/ppl.12035 Portielje D.A., 1844, HANDEL NEDERLAND 184 Raco B, 2015, FOOD CHEM, V168, P588, DOI 10.1016/j.foodchem.2014.07.043 Rossmann A, 2001, FOOD REV INT, V17, P347, DOI 10.1081/FRI-100104704 Sachse D, 2010, GEOCHIM COSMOCHIM AC, V74, P6741, DOI 10.1016/j.gca.2010.08.033 NR 19 TC 23 Z9 23 U1 3 U2 88 PD AUG 1 PY 2016 VL 204 BP 122 EP 128 DI 10.1016/j.foodchem.2016.01.134 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000371882700017 DA 2022-12-14 ER PT J AU Lali, N Jungbauer, A Satzer, P AF Lali, Narges Jungbauer, Alois Satzer, Peter TI Traceability of products and guide for batch definition in integrated continuous biomanufacturing SO JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY DT Article DE residence time distribution; RTD; batch definition; continuous ID PLATFORM AB BACKGROUND A major improvement in biomanufacturing will arise with the transition from batch processing to continuous processing. Two important challenges to address in this change are batch definition and the ability to trace raw material through the process. RESULTS We used an established simulation of a process train to compare the conventional batch definition based on a fixed time to a new batch definition method based on the greatest common divisor (GCD) of the time period of the unit operations. We successfully demonstrated that, by using the new concept based on GCD, we will have a constant periodic concentration of product. With this basis, we can define batches in a continuous process, which will lead to higher control over the process, and we will be able to trace the material through the process. CONCLUSION We achieved better control over the process using the batch definition based on the GCD method. In comparison to collecting the outlet products over arbitrary hours or days, collecting the product based on a section using the GCD method meets the criteria for knowing the residence-time distribution of the process, as advised by regulatory authorities. This method can be used in a continuous process or a hybrid process in which there are only a few continuous unit operations along with batch process operations. (c) 2021 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI). C1 [Lali, Narges; Jungbauer, Alois] Univ Nat Resources & Life Sci, Dept Biotechnol, Vienna, Austria. [Lali, Narges; Satzer, Peter] ACIB Austrian Ctr Ind Biotechnol, Graz, Austria. C3 University of Natural Resources & Life Sciences, Vienna; Austrian Centre of Industrial Biotechnology RP Jungbauer, A (corresponding author), Univ Nat Resources & Life Sci, Dept Biotechnol, Vienna, Austria. EM alois.jungbauer@boku.ac.at CR [Anonymous], 2017, ICH QUALITY GUIDELIN, P509 [Anonymous], 2019, CTR DRUG EVALUATION Cataldo AL, 2020, J BIOTECHNOL, V308, P87, DOI 10.1016/j.jbiotec.2019.12.001 Desai SG, 2015, J BIOTECHNOL, V213, P20, DOI 10.1016/j.jbiotec.2015.02.021 El-Sabbahy H, 2018, BIOTECHNOL PROGR, V34, P1380, DOI 10.1002/btpr.2709 Engisch WE, 2015, J PHARM INNOV, V10, P56, DOI 10.1007/s12247-014-9206-1 Godawat R, 2012, BIOTECHNOL J, V7, P1496, DOI 10.1002/biot.201200068 Jungbauer A, 2013, TRENDS BIOTECHNOL, V31, P479, DOI 10.1016/j.tibtech.2013.05.011 Konstantinov KB, 2015, J PHARM SCI-US, V104, P813, DOI 10.1002/jps.24268 Kruisz J, 2017, INT J PHARMACEUT, V528, P334, DOI 10.1016/j.ijpharm.2017.06.001 Low D, 2007, J CHROMATOGR B, V848, P48, DOI 10.1016/j.jchromb.2006.10.033 Martins DL, 2020, BIOTECHNOL BIOENG, V117, P1406, DOI 10.1002/bit.27292 Martins DL, 2019, BIOTECHNOL J, V14, DOI 10.1002/biot.201800646 Pollock J, 2017, BIOTECHNOL PROGR, V33, P854, DOI 10.1002/btpr.2492 Satzer P, 2022, J CHEM TECHNOL BIOT, V97, P2393, DOI 10.1002/jctb.6648 Scott Folger, 2016, ELEMENTS CHEM REACTI Sencar J, 2020, BIOTECHNOL J, V15, DOI 10.1002/biot.202000008 Sencar J, 2020, NEW BIOTECHNOL, V55, P98, DOI 10.1016/j.nbt.2019.10.006 Shi C, 2020, J CHROMATOGR A, V1619, DOI 10.1016/j.chroma.2020.460936 Shukla AA, 2007, J CHROMATOGR B, V848, P28, DOI 10.1016/j.jchromb.2006.09.026 Walther J, 2015, J BIOTECHNOL, V213, P3, DOI 10.1016/j.jbiotec.2015.05.010 NR 21 TC 2 Z9 2 U1 2 U2 8 PD SEP PY 2022 VL 97 IS 9 BP 2386 EP 2392 DI 10.1002/jctb.6953 EA OCT 2021 WC Biotechnology & Applied Microbiology; Chemistry, Multidisciplinary; Engineering, Environmental; Engineering, Chemical SC Biotechnology & Applied Microbiology; Chemistry; Engineering UT WOS:000710438000001 DA 2022-12-14 ER PT J AU Verbeke, W Frewer, LJ Scholderer, J De Brabander, HF AF Verbeke, Wim Frewer, Lynn J. Scholderer, Joachim De Brabander, Hubert F. TI Why consumers behave as they do with respect to food safety and risk information SO ANALYTICA CHIMICA ACTA DT Article; Proceedings Paper CT 5th International Symposium on Hormone and Veterinary Drug Residue Analysis CY MAY 16-19, 2006 CL Antwerp, BELGIUM DE consumer; meat; perception; risk; traceability ID FRESH MEAT; PERCEPTIONS; ATTITUDES; TRACEABILITY; KNOWLEDGE; QUALITY; SYSTEM; TRUST AB In recent years, it seems that consumers are generally uncertain about the safety and quality of their food and their risk perception differs substantially from that of experts. Hormone and veterinary drug residues in meat persist to occupy a high position in European consumers' food concern rankings. The aim of this contribution is to provide a better understanding to food risk analysts of why consumers behave as they do with respect to food safety and risk information. This paper presents some cases of seemingly irrational and inconsistent consumer behaviour with respect to food safety and risk information and provides explanations for these behaviours based on the nature of the risk and individual psychological processes. Potential solutions for rebuilding consumer confidence in food safety and bridging between lay and expert opinions towards food risks are reviewed. These include traceability and labelling, segmented communication approaches and public involvement in risk management decision-making. (c) 2006 Elsevier B.V. All rights reserved. C1 Univ Ghent, Dept Agr Econ, B-9000 Ghent, Belgium. Univ Wageningen & Res Ctr, Mkt & Consumer Behav Grp, NL-6706 KN Wageningen, Netherlands. Aarhus Sch Business, MAPP, DK-8210 Aarhus V, Denmark. Univ Ghent, Dept Vet Publ Hlth & Food Safety, B-9820 Merelbeke, Belgium. C3 Ghent University; Wageningen University & Research; Aarhus University; Ghent University RP Verbeke, W (corresponding author), Univ Ghent, Dept Agr Econ, Coupure Links 653, B-9000 Ghent, Belgium. EM wim.verbeke@UGent.be CR Becker T., 2000, British Food Journal, V102, P158, DOI 10.1108/00070700010371707 Bennett P., 1999, RISK COMMUNICATION P Boenke A, 2002, ANAL CHIM ACTA, V473, P83, DOI 10.1016/S0003-2670(02)00771-7 Brambilla G, 2005, ANAL CHIM ACTA, V529, P7, DOI 10.1016/j.aca.2004.07.067 Bredahl L., 2001, J CONSUMER POLICY, V24, P23, DOI [10.1023/A:1010950406128, DOI 10.1023/A:1010950406128] Einsiedel EF, 2001, PUBLIC UNDERST SCI, V10, P83 *EUR COMM, 2006, 238 EUR COMM FAO and WHO, 1999, CACGL30 FAO WHO Fife-Schaw C., 2000, J RISK RES, V3, P167, DOI DOI 10.1080/136698700376653 FISCHER A, 2006, IN PRESS RISK ANAL, V26 FISCHHOFF B, 1978, POLICY SCI, V9, P127, DOI 10.1007/BF00143739 Frewer L, 2004, FOOD CHEM TOXICOL, V42, P1181, DOI 10.1016/j.fct.2004.02.002 Frewer LJ, 1996, RISK ANAL, V16, P473, DOI 10.1111/j.1539-6924.1996.tb01094.x FREWER LJ, 1994, J FOOD SAFETY, V14, P19, DOI 10.1111/j.1745-4565.1994.tb00581.x Frewer LJ, 1997, SCI TECHNOL HUM VAL, V22, P98, DOI 10.1177/016224399702200105 Frewer L, 2005, INNOVATION IN AGRI-FOOD SYSTEMS: PRODUCT QUALITY AND CONSUMER ACCEPTANCE, P125 Grunert KG, 2005, EUR REV AGRIC ECON, V32, P369, DOI 10.1093/eurrag/jbi011 Grunert KG, 2001, FOOD QUAL PREFER, V12, P527, DOI 10.1016/S0950-3293(01)00049-0 HAGEMANN K, 2004, 062004 MAPP Hansen J, 2003, APPETITE, V41, P111, DOI 10.1016/S0195-6663(03)00079-5 HILGARTNER S, 1990, SOC STUD SCI, V20, P519, DOI 10.1177/030631290020003006 Hobbs JE, 2005, CAN J AGR ECON, V53, P47, DOI 10.1111/j.1744-7976.2005.00412.x Jonge J. de, 2004, British Food Journal, V106, P837, DOI 10.1108/00070700410561423 KAHNEMAN D, 1991, J ECON PERSPECT, V5, P193, DOI 10.1257/jep.5.1.193 KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185 KASPERSON RE, 1988, RISK ANAL, V8, P177, DOI 10.1111/j.1539-6924.1988.tb01168.x KORNELIS M, IN PRESS RISK ANAL KORTHALS M, IN PRESS UNDERSTANDI Loewenstein GF, 2001, PSYCHOL BULL, V127, P267, DOI 10.1037//0033-2909.127.2.267 McCarthy M, 2002, PARADOXES IN FOOD CHAINS AND NETWORKS, P176 McCluskey J. J., 2004, American Journal of Agricultural Economics, V86, P1230, DOI 10.1111/j.0002-9092.2004.00670.x MIDDEN C, 2002, BIOTECHNOLOGY MAKING, P203 Miles S., 2004, British Food Journal, V106, P9, DOI 10.1108/00070700410515172 Miles S, 2003, NUTR RES REV, V16, P3, DOI 10.1079/NRR200249 Miles S, 2001, FOOD QUAL PREFER, V12, P47, DOI 10.1016/S0950-3293(00)00029-X Petty R. E., 1996, COMMUNICATION PERSUA Saba A, 2002, FOOD QUAL PREFER, V13, P13, DOI 10.1016/S0950-3293(01)00052-0 SCHOLDERER J, 2000, MARKET COMMUN, P129 Scholderer J, 1999, EUROPEAN ADV CONSUME, V4, P123 SCHUTZ H, 1999, GENTECHNIK OFFENTLIC, P133 Siegrist M, 2000, RISK ANAL, V20, P195, DOI 10.1111/0272-4332.202020 Swinnen JFM, 2005, AGR ECON-BLACKWELL, V32, P175, DOI 10.1111/j.0169-5150.2004.00022.x van Kleef E, 2006, APPETITE, V47, P46, DOI 10.1016/j.appet.2006.02.002 Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017 Verbeke W, 2002, FOOD POLICY, V27, P339, DOI 10.1016/S0306-9192(02)00037-4 Verbeke W, 2002, J HEALTH COMMUN, V7, P455, DOI 10.1080/10810730290001819 Verbeke W, 2001, AGR ECON-BLACKWELL, V25, P359, DOI 10.1111/j.1574-0862.2001.tb00215.x Verbeke W, 2001, FOOD QUAL PREFER, V12, P489, DOI 10.1016/S0950-3293(01)00042-8 Verbeke W, 2006, FOOD QUAL PREFER, V17, P453, DOI 10.1016/j.foodqual.2005.05.010 WEINSTEIN ND, 1980, J PERS SOC PSYCHOL, V39, P806, DOI 10.1037/0022-3514.39.5.806 WIERENGA B, 1983, J FOOD QUALITY, V48, P119 NR 51 TC 211 Z9 218 U1 8 U2 89 PD MAR 14 PY 2007 VL 586 IS 1-2 BP 2 EP 7 DI 10.1016/j.aca.2006.07.065 WC Chemistry, Analytical SC Chemistry UT WOS:000244823500002 DA 2022-12-14 ER PT J AU Pegels, N Gonzalez, I Garcia, T Martin, R AF Pegels, Nicolette Gonzalez, Isabel Garcia, Teresa Martin, Rosario TI Avian-specific real-time PCR assay for authenticity control in farm animal feeds and pet foods SO FOOD CHEMISTRY DT Article DE TaqMan real-time PCR; 12S rRNA gene; Avian; Farm animal feeds; Pet feeds; Traceability ID POLYMERASE-CHAIN-REACTION; BONE MEAL; IDENTIFICATION; DNA; COMPONENTS; PRODUCTS; TISSUES; DOG AB A highly sensitive TaqMan real-time PCR assay targeting the mitochondrial 12S rRNA gene was developed for detection of an avian-specific DNA fragment (68 bp) in farm animal and pet feeds. The specificity of the assay was verified against a wide representation of animal and plant species. Applicability assessment of the avian real-time PCR was conducted through representative analysis of two types of compound feeds: industrial farm animal feeds (n = 60) subjected to extreme temperatures, and commercial dog and cat feeds (n = 210). Results obtained demonstrated the suitability of the real-time PCR assay to detect the presence of low percentages of highly processed avian material in the feed samples analysed. Although quantification results were well reproducible under the experimental conditions tested, an accurate estimation of the target content in feeds is impossible in practice. Nevertheless, the method may be useful as an alternative tool for traceability purposes within the framework of feed control. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Pegels, Nicolette; Gonzalez, Isabel; Garcia, Teresa; Martin, Rosario] Univ Complutense Madrid, Fac Vet, Dept Nutr Bromatol & Tecnol Alimentos, E-28040 Madrid, Spain. C3 Complutense University of Madrid RP Gonzalez, I (corresponding author), Univ Complutense Madrid, Fac Vet, Dept Nutr Bromatol & Tecnol Alimentos, E-28040 Madrid, Spain. EM npegels@vet.ucm.es; gonzalzi@vet.ucm.es; tgarcia@vet.ucm.es; rmartins@vet.ucm.es CR Andreoletti O, 2011, EFSA J, V9, DOI 10.2903/j.efsa.2011.1947 [Anonymous], 2004, OFFICIAL J EUROPEA L, VL139, P55 Ballin NZ, 2009, MEAT SCI, V83, P165, DOI 10.1016/j.meatsci.2009.06.003 Bottero MT, 2011, VET J, V190, P34, DOI 10.1016/j.tvjl.2010.09.024 Casazza AP, 2011, FOOD CHEM, V124, P685, DOI 10.1016/j.foodchem.2010.06.073 Chiappini B, 2005, J AOAC INT, V88, P1399 Collins MJ, 2002, ARCHAEOMETRY, V44, P383, DOI 10.1111/1475-4754.t01-1-00071 Dalmasso A, 2004, MOL CELL PROBE, V18, P81, DOI 10.1016/j.mcp.2003.09.006 Dalmasso A, 2011, FOOD CHEM, V124, P362, DOI 10.1016/j.foodchem.2010.06.017 EC, 2009, OFF J EU L 200, P1 European Commission, 2001, OFFICIAL J EUROPEAN, VL147, P1 Frezza D, 2003, J FOOD PROTECT, V66, P103, DOI 10.4315/0362-028X-66.1.103 Frezza D, 2008, INNOV FOOD SCI EMERG, V9, P18, DOI 10.1016/j.ifset.2007.04.008 Fumiere O, 2010, FOOD ADDIT CONTAM A, V27, P1118, DOI 10.1080/19440049.2010.481639 Fumiere O, 2006, ANAL BIOANAL CHEM, V385, P1045, DOI 10.1007/s00216-006-0533-z Fumiere O, 2009, BIOTECHNOL AGRON SOC, V13, P59 Gizzi G, 2003, REV SCI TECH OIE, V22, P311, DOI 10.20506/rst.22.1.1399 Goffaux F, 2005, FORENSIC SCI INT, V151, P239, DOI 10.1016/j.forsciint.2005.02.013 Gotherstrom A, 2002, ARCHAEOMETRY, V44, P395, DOI 10.1111/1475-4754.00072 Hefle SL, 2001, J FOOD PROTECT, V64, P1812, DOI 10.4315/0362-028X-64.11.1812 Kang JH, 2002, RES VET SCI, V73, P177, DOI 10.1016/S0034-5288(02)00102-9 Kusama T, 2004, J FOOD PROTECT, V67, P1289, DOI 10.4315/0362-028X-67.6.1289 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 Martin I, 2008, J FOOD PROTECT, V71, P564, DOI 10.4315/0362-028X-71.3.564 Momcilovic D, 2000, J FOOD PROTECT, V63, P1602, DOI 10.4315/0362-028X-63.11.1602 Myers MJ, 2004, AM J VET RES, V65, P99, DOI 10.2460/ajvr.2004.65.99 Pascoal A, 2011, FOOD CHEM, V125, P1457, DOI 10.1016/j.foodchem.2010.10.053 Pegels N, 2011, FOOD CONTROL, V22, P1189, DOI 10.1016/j.foodcont.2011.01.015 Pereira Filipe, 2008, Recent Pat DNA Gene Seq, V2, P187, DOI 10.2174/187221508786241738 Prado M, 2007, J AGR FOOD CHEM, V55, P7495, DOI 10.1021/jf0707583 Tanabe S, 2007, BIOSCI BIOTECH BIOCH, V71, P3131, DOI 10.1271/bbb.70683 Wang HC, 2004, J VET MED SCI, V66, P855, DOI 10.1292/jvms.66.855 Yancy HF, 2009, J FOOD PROTECT, V72, P2368, DOI 10.4315/0362-028X-72.11.2368 NR 33 TC 20 Z9 21 U1 0 U2 105 PD JAN 1 PY 2014 VL 142 BP 39 EP 47 DI 10.1016/j.foodchem.2013.07.031 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000326359400005 DA 2022-12-14 ER PT J AU Klauke, R Kytzia, HJ Weber, F Grote-Koska, D Brand, K Schumann, G AF Klauke, Rainer Kytzia, Hans-Joachim Weber, Friederike Grote-Koska, Denis Brand, Korbinian Schumann, Gerhard TI Reference measurement procedure for total bilirubin in serum re-evaluated and measurement uncertainty determined SO CLINICA CHIMICA ACTA DT Article DE Bilirubin; Reference measurement procedure; Uncertainty of measurement; Metrological traceability; Molar absorption coefficient; Reference material ID CANDIDATE REFERENCE METHOD; VARIABILITY AB Background: For the determination of total bilirubin in serum the candidate reference method developed by Doumas et al. has international recognition. The primary standard SRM 916a (NIST) was recommended for use as the primary reference material for calibration. Nowadays, no primary standard is anymore commercially available. Further, a description of uncertainty components was missing. Methods: Two reference laboratories have re-investigated the candidate reference measurement procedure. Beside minor modifications, mainly the use of a molar absorption coefficient instead of calibration by use of bilirubin standard solutions has facilitated the operating, and improved the analytical performance. All relevant sources of measurement uncertainty were investigated. Results: A measurement range of 5-525 mu mol/L and a CV of 0.5% to 1.4% (long term imprecision) were determined. Excellent agreement was obtained comparing to Doumas procedure (r = 0.9999) and during a two laboratory comparison participating at IFCC RELA ring trials (mean deviation: 0.6%). The combined expanded measurement uncertainty (probability 95%) for bilirubin concentrations > 30 mu mol/L was estimated as 2.2%. Conclusion: A reference system for total bilirubin based on the described reference procedure shall enable metrological traceability and optimized standardization of the values obtained in clinical routine laboratories. C1 [Klauke, Rainer; Grote-Koska, Denis; Brand, Korbinian; Schumann, Gerhard] Hannover Med Sch, Inst Clin Chem, Carl Neuberg Str 1, D-30625 Hannover, Germany. [Kytzia, Hans-Joachim; Weber, Friederike] Roche Diagnost, Nonnenwald 2, D-82377 Penzberg, Germany. C3 Hannover Medical School; Roche Holding RP Grote-Koska, D (corresponding author), Hannover Med Sch, Inst Clin Chem, Carl Neuberg Str 1, D-30625 Hannover, Germany. EM rainer.klauke@mh-hannover.de; friederike.weber@roche.com; grote-koska.denis@mh-hannover.de; brand.korbinian@mh-hannover.de; gerhard.schumann@geord.net CR [Anonymous], 2008, ISO GUID 98 3 EVALUA [Anonymous], 2005, 17025 ISO [Anonymous], 2003, 175112003 ISO [Anonymous], 2012, JCGM 200 Apperloo JJ, 2005, CLIN CHEM LAB MED, V43, P531, DOI 10.1515/CCLM.2005.092 Cobbaert C, 2010, CLIN CHEM, V56, P872, DOI 10.1373/clinchem.2009.142059 DOUMAS BT, 1985, CLIN CHEM, V31, P1779 DOUMAS BT, 1990, CLIN CHEM, V36, P1698 Doumas BT, 1996, CLIN CHEM, V42, P845 International Organization for Standardization, 2003, 151952003 ISO Jendrassik L, 1938, BIOCHEM Z, V297, P81 Lin JP, 2010, CLIN CHEM, V56, P1535, DOI 10.1373/clinchem.2010.151043 Lo SF, 2009, CLIN BIOCHEM, V42, P1328, DOI 10.1016/j.clinbiochem.2009.05.007 MCMASTER D, 1992, AM J CLIN NUTR, V56, P440, DOI 10.1093/ajcn/56.2.440 Panteghini Mauro, 2007, Clin Biochem Rev, V28, P97 PASSING H, 1983, J CLIN CHEM CLIN BIO, V21, P709 PERRY BW, 1983, CLIN CHEM, V29, P297 RELA, IFCC EXT QUAL ASS SC SCHREINER RL, 1982, PEDIATRICS, V69, P277 Vreman HJ, 1996, CLIN CHEM, V42, P869 Wise S. A., 2016, 916A NAT I STAND TEC NR 21 TC 11 Z9 11 U1 0 U2 5 PD JUN PY 2018 VL 481 BP 115 EP 120 DI 10.1016/j.cca.2018.02.037 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000431936800017 DA 2022-12-14 ER PT S AU Fonayet, JV Loupit, G Richard, T AF Fonayet, Josep Valls Loupit, Gregoire Richard, Tristan BE Petriacq, P Bouchereau, A TI MS- and NMR-metabolomic tools for the discrimination of wines: Applications for authenticity SO PLANT METABOLOMICS IN FULL SWING SE Advances in Botanical Research DT Review; Book Chapter ID PINOT-NOIR WINES; RED WINES; WHITE WINES; H-1-NMR SPECTROSCOPY; GEOGRAPHICAL ORIGIN; FOOD AUTHENTICITY; MASS-SPECTROMETRY; GAS-CHROMATOGRAPHY; SAUVIGNON BLANC; GRAPE VARIETY AB Wine is an extremely sophisticated chemical matrix in which the harmonious equilibria between many components (alcohols, acids, aromas, polyphenols, etc.) determine its final quality and value. The study of wine composition by metabolomic tools, referred as Wineomics, has emerged as a revolutionary approach to unravel the chemical space of wines and to associate wine composition with important viticultural and enological traits including grape variety, geographical origin, terroir or typicity. One of the most promising applications of metabolomic tools for wine analysis is the study of wine traceability and authenticity, a major issue for the wine industry and consumers. These studies are extremely complex, because counterfeits are becoming more sophisticated and they require powerful techniques capable to detect subtle differences between wines. In this chapter, we describe the targeted and untargeted approaches by Mass and NMR spectrometry-based metabolomics as well as some recent examples of their abilities in wine authenticity studies. C1 [Fonayet, Josep Valls; Richard, Tristan] Univ Bordeaux, Bordeaux Metabolome Facil, Unite Rech Oenol, Axe Mol Interet Biol,INRAE,ISVV,EA 4577,USC 1366, Villenave Dornon, France. [Loupit, Gregoire] Univ Bordeaux, Bordeaux Sci Agro, EGFV, INRAE,ISVV, Villenave Dornon, France. C3 INRAE; UDICE-French Research Universities; Universite de Bordeaux; INRAE; UDICE-French Research Universities; Universite de Bordeaux RP Fonayet, JV (corresponding author), Univ Bordeaux, Bordeaux Metabolome Facil, Unite Rech Oenol, Axe Mol Interet Biol,INRAE,ISVV,EA 4577,USC 1366, Villenave Dornon, France. EM josep.valls-fonayet@u-bordeaux.fr CR Alanon ME, 2015, TRAC-TREND ANAL CHEM, V74, P1, DOI 10.1016/j.trac.2015.05.006 Ali K, 2011, J BIOMOL NMR, V49, P255, DOI 10.1007/s10858-011-9487-3 Allamy L, 2018, FOOD CHEM, V266, P245, DOI 10.1016/j.foodchem.2018.06.022 Alves EG, 2019, FOOD CHEM, V289, P558, DOI 10.1016/j.foodchem.2019.03.103 Amargianitaki M, 2017, CHEM BIOL TECHNOL AG, V4, DOI 10.1186/s40538-017-0092-x Anastasiadi M, 2009, J AGR FOOD CHEM, V57, P11067, DOI 10.1021/jf902137e Arapitsas P, 2020, J AGR FOOD CHEM, V68, P13353, DOI 10.1021/acs.jafc.0c00879 Arapitsas P, 2016, J CHROMATOGR A, V1429, P155, DOI 10.1016/j.chroma.2015.12.010 Arapitsas P, 2016, FOOD CHEM, V197, P1331, DOI 10.1016/j.foodchem.2015.09.084 Arapitsas P, 2015, FOOD RES INT, V69, P21, DOI 10.1016/j.foodres.2014.12.002 Arapitsas P, 2012, J AGR FOOD CHEM, V60, P10461, DOI 10.1021/jf302617e Arapitsas P, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0037783 Arbulu M, 2015, ANAL CHIM ACTA, V858, P32, DOI 10.1016/j.aca.2014.12.028 Arias I, 2019, OENO ONE, V53, P695, DOI 10.20870/oeno-one.2019.53.4.2381 Arvanitoyannis IS, 2010, WOODHEAD PUBL FOOD S, P218, DOI 10.1533/9781845699284.2.218 Arvanitoyannis IS, 1999, TRENDS FOOD SCI TECH, V10, P321, DOI 10.1016/S0924-2244(99)00053-9 Azcarate SM, 2015, FOOD CONTROL, V57, P268, DOI 10.1016/j.foodcont.2015.04.025 Basalekou M, 2020, BEVERAGES, V6, DOI 10.3390/beverages6020030 Baxter MJ, 1997, FOOD CHEM, V60, P443, DOI 10.1016/S0308-8146(96)00365-2 Berna AZ, 2009, ANAL CHIM ACTA, V648, P146, DOI 10.1016/j.aca.2009.06.056 Budic-Leto I, 2017, J FOOD COMPOS ANAL, V62, P211, DOI 10.1016/j.jfca.2017.05.015 Cabrita MJ, 2018, FOOD CONTROL, V92, P80, DOI 10.1016/j.foodcont.2018.04.041 Cajka T, 2016, WOODHEAD PUBL FOOD S, P171, DOI 10.1016/B978-0-08-100220-9.00007-2 Canizo BV, 2019, QUALITY CONTROL IN THE BEVERAGE INDUSTRY, VOL 17: THE SCIENCE OF BEVERAGES, P335, DOI 10.1016/B978-0-12-816681-9.00010-2 Carlin S, 2016, FOOD CHEM, V208, P68, DOI 10.1016/j.foodchem.2016.03.112 Carpentieri A, 2019, HELIYON, V5, DOI 10.1016/j.heliyon.2019.e02287 Cassino C, 2019, FOOD RES INT, V116, P566, DOI 10.1016/j.foodres.2018.08.075 Cassino Claudio, 2017, Journal of Wine Research, V28, P259, DOI 10.1080/09571264.2017.1388225 Castro CC, 2014, FOOD CHEM, V143, P384, DOI 10.1016/j.foodchem.2013.07.138 Causon TJ, 2019, ANAL CHIM ACTA, V1052, P179, DOI 10.1016/j.aca.2018.11.040 Chapman J, 2019, CURR OPIN FOOD SCI, V28, P67, DOI 10.1016/j.cofs.2019.09.001 Colombo C, 2015, J AGR FOOD CHEM, V63, P8551, DOI 10.1021/acs.jafc.5b03641 Cuadros-Inostroza A, 2020, METABOLITES, V10, DOI 10.3390/metabo10060220 Cuadros-Inostroza A, 2010, ANAL CHEM, V82, P3573, DOI 10.1021/ac902678t Cynkar W, 2010, ANAL CHIM ACTA, V660, P227, DOI 10.1016/j.aca.2009.09.030 da Costa NL, 2018, MEASUREMENT, V120, P92, DOI 10.1016/j.measurement.2018.01.052 Dall'Asta C, 2011, J CHROMATOGR A, V1218, P7557, DOI 10.1016/j.chroma.2011.08.042 de Pascali SA, 2014, FOOD CHEM, V161, P112, DOI 10.1016/j.foodchem.2014.03.128 de Simon BF, 2008, J AGR FOOD CHEM, V56, P9046, DOI 10.1021/jf8014602 Dey G., 2017, FOOD TRACEABILITY AU, V1, P90, DOI [10.1201/9781351228435-5, DOI 10.1201/9781351228435-5] Diaz R, 2016, J CHROMATOGR A, V1433, P90, DOI 10.1016/j.chroma.2016.01.010 Duarte-Mermoud M., 2009, INT J WINE RES, V1, P209, DOI DOI 10.2147/IJWR.S4609 Duchowicz PR, 2013, FOOD CHEM, V140, P210, DOI 10.1016/j.foodchem.2013.02.064 Fan SX, 2018, FOOD CONTROL, V88, P113, DOI 10.1016/j.foodcont.2017.11.002 Ferreira V, 2019, BIOMOLECULES, V9, DOI 10.3390/biom9120818 Ferri E, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/365794 Forleo T, 2020, EUR FOOD RES TECHNOL, V246, P1805, DOI 10.1007/s00217-020-03534-8 Fotakis C, 2016, FOOD CHEM, V196, P760, DOI 10.1016/j.foodchem.2015.10.002 Fraige K, 2014, FOOD CHEM, V145, P395, DOI 10.1016/j.foodchem.2013.08.066 Geana EI, 2016, FOOD CHEM, V192, P1015, DOI 10.1016/j.foodchem.2015.07.112 Geana EI, 2019, MOLECULES, V24, DOI 10.3390/molecules24224166 Gil M, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-58193-2 Giraudeau P, 2015, METABOLOMICS, V11, P1041, DOI 10.1007/s11306-015-0794-7 Godelmann R, 2013, J AGR FOOD CHEM, V61, P5610, DOI 10.1021/jf400800d Gonzalez-Barreiro C, 2015, CRIT REV FOOD SCI, V55, P202, DOI 10.1080/10408398.2011.650336 Gougeon L, 2019, FOOD CHEM, V301, DOI 10.1016/j.foodchem.2019.125257 Gougeon L, 2019, FOOD ANAL METHOD, V12, P956, DOI 10.1007/s12161-018-01425-z Gougeon L, 2018, FOOD ANAL METHOD, V11, P3425, DOI 10.1007/s12161-018-1310-2 Green JA, 2011, FOOD RES INT, V44, P2788, DOI 10.1016/j.foodres.2011.06.005 Guerrero RF, 2020, FOOD CONTROL, V108, DOI 10.1016/j.foodcont.2019.106821 Haggerty J, 2016, AUST J GRAPE WINE R, V22, P3, DOI 10.1111/ajgw.12167 Herbert-Pucheta JE, 2019, BIO WEB CONF, V15, DOI 10.1051/bioconf/20191502016 Herbert-Pucheta JE, 2019, BIO WEB CONF, V12, DOI 10.1051/bioconf/20191202029 Hernandez-Orte P, 2014, FOOD RES INT, V57, P234, DOI 10.1016/j.foodres.2014.01.044 Holmberg L., 2010, INT J WINE RES, V2, P105, DOI [10.2147/IJWR.S14102, DOI 10.2147/IJWR.S14102, 10.2147/ijwr.s14102] Hu BR, 2019, AMB EXPRESS, V9, DOI 10.1186/s13568-019-0861-y Hu BR, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0142840 International Organisation of Vine and Wine, 2019, 2019 STAT REP WORLD Isaac-Lam MF, 2016, INT J SPECTROSC, DOI 10.1155/2016/2526946 Jimenez-Carvelo AM, 2019, FOOD RES INT, V122, P25, DOI 10.1016/j.foodres.2019.03.063 Kamiloglu S, 2019, FOOD CHEM, V277, P12, DOI 10.1016/j.foodchem.2018.10.091 Kemsley EK, 2019, FOOD CONTROL, V105, P102, DOI 10.1016/j.foodcont.2019.05.021 Kumsta M, 2014, FOOD TECHNOL BIOTECH, V52, P383, DOI 10.17113/ftb.52.04.14.3650 Kustos M, 2020, FOOD RES INT, V130, DOI 10.1016/j.foodres.2019.108903 Laghi L., 2014, Food and Nutrition Sciences, V5, P52, DOI 10.4236/fns.2014.51007 Lambert M, 2015, MOLECULES, V20, P7890, DOI 10.3390/molecules20057890 Lecat B, 2017, BRIT FOOD J, V119, P84, DOI [10.1108/BFJ-09-2016-0398, 10.1108/bfj-09-2016-0398] Liu ZQ, 2020, METABOLITES, V10, DOI 10.3390/metabo10070294 Lopez-Rituerto E, 2012, J AGR FOOD CHEM, V60, P3452, DOI 10.1021/jf204361d Lukic I, 2019, FOOD CHEM, V300, DOI 10.1016/j.foodchem.2019.125251 Ma Y, 2016, J AGR FOOD CHEM, V64, P505, DOI 10.1021/acs.jafc.5b04890 Magdas DA, 2019, LWT-FOOD SCI TECHNOL, V109, P422, DOI 10.1016/j.lwt.2019.04.054 Magdas DA, 2018, FOOD CONTROL, V85, P385, DOI 10.1016/j.foodcont.2017.10.024 Makris DP, 2006, TALANTA, V70, P1143, DOI 10.1016/j.talanta.2006.03.024 Mannu A, 2020, FOODS, V9, DOI 10.3390/foods9081040 Mascellani A, 2021, FOOD CHEM, V339, DOI 10.1016/j.foodchem.2020.127852 Mayr CM, 2018, BEVERAGES, V4, DOI 10.3390/beverages4040074 Mazzei P, 2013, J AGR FOOD CHEM, V61, P10816, DOI 10.1021/jf403567x Medina S, 2019, TRENDS FOOD SCI TECH, V85, P163, DOI 10.1016/j.tifs.2019.01.017 Medina S, 2019, FOOD CHEM, V278, P144, DOI 10.1016/j.foodchem.2018.11.046 Monakhova YB, 2011, MAGN RESON CHEM, V49, P734, DOI 10.1002/mrc.2823 Moyano L, 2019, TALANTA, V192, P301, DOI 10.1016/j.talanta.2018.09.032 Muhammad A, 2019, AUST J AGR RESOUR EC, V63, P742, DOI 10.1111/1467-8489.12333 Pasvanka K, 2019, QUALITY CONTROL IN THE BEVERAGE INDUSTRY, VOL 17: THE SCIENCE OF BEVERAGES, P289 Perutka Z., 2020, REFERENCE MODULE FOO, DOI [10.1016/B978-0-08-100596-5.22835-7, DOI 10.1016/B978-0-08-100596-5.22835-7] Picard M, 2018, ANAL CHIM ACTA, V1001, P168, DOI 10.1016/j.aca.2017.11.074 Picone G, 2016, FOOD CHEM, V213, P187, DOI 10.1016/j.foodchem.2016.06.077 Pinto J, 2018, FOOD CHEM, V257, P120, DOI 10.1016/j.foodchem.2018.02.156 Pinu FR, 2018, FERMENTATION-BASEL, V4, DOI 10.3390/fermentation4040092 Pisano PL, 2015, FOOD CHEM, V175, P174, DOI 10.1016/j.foodchem.2014.11.124 Poitou X, 2017, J AGR FOOD CHEM, V65, P383, DOI 10.1021/acs.jafc.6b03042 Pons A, 2008, J AGR FOOD CHEM, V56, P5285, DOI 10.1021/jf073513z Pons A, 2008, J AGR FOOD CHEM, V56, P1606, DOI 10.1021/jf072337r Pons A, 2010, J AGR FOOD CHEM, V58, P7273, DOI 10.1021/jf100150q Ragone R, 2015, FOOD SCI BIOTECHNOL, V24, P817, DOI 10.1007/s10068-015-0106-z Robinson AL, 2011, J CHROMATOGR A, V1218, P504, DOI 10.1016/j.chroma.2010.11.008 Robles A, 2019, TRAC-TREND ANAL CHEM, V120, DOI 10.1016/j.trac.2019.115630 Rocchetti G, 2018, J FOOD COMPOS ANAL, V71, P87, DOI 10.1016/j.jfca.2018.05.010 Rochfort S, 2010, FOOD CHEM, V121, P1296, DOI 10.1016/j.foodchem.2010.01.067 Romanini E, 2019, FOOD CHEM, V288, P78, DOI 10.1016/j.foodchem.2019.02.073 Roullier-Gall C, 2015, TETRAHEDRON, V71, P2983, DOI 10.1016/j.tet.2015.02.054 Roullier-Gall C, 2017, FOOD CHEM, V237, P106, DOI 10.1016/j.foodchem.2017.05.039 Roullier-Gall C, 2014, FRONT CHEM, V2, DOI 10.3389/fchem.2014.00102 Rubert J, 2015, FOOD ADDIT CONTAM A, V32, P1685, DOI 10.1080/19440049.2015.1084539 Saenz C, 2010, J AOAC INT, V93, P1916 Schueuermann C, 2017, J MASS SPECTROM, V52, P625, DOI 10.1002/jms.3956 Schueuermann C, 2016, J AGR FOOD CHEM, V64, P2342, DOI 10.1021/acs.jafc.5b05861 Schwarzinger S., 2018, Modern Magnetic Resonance, P1795, DOI 10.1007/978-3-319-28388-3_91 Sen I, 2014, FOOD CONTROL, V46, P446, DOI 10.1016/j.foodcont.2014.06.015 de Andrade RHS, 2013, MICROCHEM J, V110, P256, DOI 10.1016/j.microc.2013.04.003 Sobolev AP, 2016, WOODHEAD PUBL FOOD S, P147, DOI 10.1016/B978-0-08-100220-9.00006-0 Sobolev AP, 2019, TRENDS FOOD SCI TECH, V91, P347, DOI 10.1016/j.tifs.2019.07.035 Son HS, 2008, J AGR FOOD CHEM, V56, P8007, DOI 10.1021/jf801424u Springer AE, 2014, J AGR FOOD CHEM, V62, P6844, DOI 10.1021/jf502042c Stoj A, 2017, S AFR J ENOL VITIC, V38, P245, DOI 10.21548/38-2-2079 Stoj A, 2020, MOLECULES, V25, DOI 10.3390/molecules25061342 Stoj A, 2017, INT J FOOD PROP, V20, pS830, DOI 10.1080/10942912.2017.1315590 Stupak M, 2018, ANAL CHIM ACTA, V1042, P60, DOI 10.1016/j.aca.2018.09.017 Thibon C, 2015, J CHROMATOGR A, V1415, P123, DOI 10.1016/j.chroma.2015.08.027 Tominaga T, 1998, J AGR FOOD CHEM, V46, P5215, DOI 10.1021/jf980481u Tufariello M, 2014, LWT-FOOD SCI TECHNOL, V58, P35, DOI 10.1016/j.lwt.2014.03.016 Ubeda C, 2017, AM J ENOL VITICULT, V68, P390, DOI 10.5344/ajev.2017.16086 Uttl L, 2019, CZECH J FOOD SCI, V37, P239, DOI 10.17221/82/2019-CJFS Vaclavik L, 2011, ANAL CHIM ACTA, V685, P45, DOI 10.1016/j.aca.2010.11.018 Valls J, 2017, EUR FOOD RES TECHNOL, V243, P2211, DOI 10.1007/s00217-017-2923-1 Valls J, 2009, J CHROMATOGR A, V1216, P7143, DOI 10.1016/j.chroma.2009.07.030 Versari A, 2014, FOOD RES INT, V60, P2, DOI 10.1016/j.foodres.2014.02.007 Villano C, 2017, FOOD CONTROL, V80, P1, DOI 10.1016/j.foodcont.2017.04.020 Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 Weldegergis BT, 2011, FOOD CHEM, V128, P1100, DOI 10.1016/j.foodchem.2010.09.100 Welke JE, 2013, FOOD CHEM, V141, P3897, DOI 10.1016/j.foodchem.2013.06.100 Wu H, 2019, FOOD CHEM, V301, DOI 10.1016/j.foodchem.2019.125137 Zanuttin F, 2019, TALANTA, V203, P99, DOI 10.1016/j.talanta.2019.05.024 Zhang J, 2010, ANAL CHIM ACTA, V662, P137, DOI 10.1016/j.aca.2009.12.043 Zhang XK, 2020, FOOD RES INT, V134, DOI 10.1016/j.foodres.2020.109226 Ziolkowska A, 2016, FOOD CHEM, V213, P714, DOI 10.1016/j.foodchem.2016.06.120 NR 146 TC 3 Z9 3 U1 8 U2 19 PY 2021 VL 98 BP 297 EP 357 DI 10.1016/bs.abr.2020.11.003 WC Biochemistry & Molecular Biology; Plant Sciences SC Biochemistry & Molecular Biology; Plant Sciences UT WOS:000748724300011 DA 2022-12-14 ER PT J AU Tena, N Aparicio, R Baeten, V Garcia-Gonzalez, DL Fernandez-Pierna, JA AF Tena, Noelia Aparicio, Ramon Baeten, Vincent Luis Garcia-Gonzalez, Diego Antonio Fernandez-Pierna, Juan TI Assessment of Vibrational Spectroscopy Performance in Geographical Identification of Virgin Olive Oils: A World Level Study SO EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY DT Article DE chemometric analysis; geographical authenticity; Raman spectroscopy; traceability; virgin olive oils ID QUANTITATIVE-ANALYSIS; RAMAN-SPECTROSCOPY; TOTAL UNSATURATION; FATTY-ACIDS; FT-RAMAN; ORIGIN; CLASSIFICATION; SPECTRA AB In order to guarantee food integrity, testing methods should also include the tools needed to verify the geographical origin described on the label. In the particular case of virgin olive oil (VOO), regulatory bodies have yet to establish a standard for geographical identification. This manuscript describes a procedure based on Raman spectroscopy to identify the provenance of these oils based on a classification criterion (European vs non-European VOOs). The sample collection is considered a relevant step in which multiple factors are taken into account (cultivar distribution, complexity of production in each location, new agricultural practices, and new/old cultivars). A total of 78 virgin olive oils collected around the world allow checking the classification accuracy of the model for identifying sample origin. The PLS-model built around the Raman spectroscopic data is validated with a blind set of samples, and the results are evaluated in terms of false negative and false positive quality parameters. This study provides an analytical application to detect mislabeling and fraudulent practices related to the geographical provenance of virgin olive oils declared on the labels. Practical Applications: The results of this study provide a robust mathematical model to assess the origin of virgin olive oils (EU vs non-EU) based on spectroscopic data. Nowadays, the extensive knowledge of olive oil chemical composition has proven that this composition varies according to pedoclimatic conditions, while non-targeted methods can be proposed in geographical traceability for their ability to analyze the chemical profile of samples. However, it was deemed necessary to conduct a study through a collaborative work initiative. Thus, this study provides a strict evaluation of Raman spectroscopy through a defined strategy for evaluating non-targeted methods for geographical identification. C1 [Tena, Noelia; Aparicio, Ramon; Luis Garcia-Gonzalez, Diego] CSIC, Inst Grasa, Ctra Utrera,Km 1,Campus Univ Pablo Olavide, Seville 41013, Spain. [Baeten, Vincent; Antonio Fernandez-Pierna, Juan] Walloon Agr Res Ctr CRA, Food & Feed Unit, Valorisat Agr Prod Dept, Henseval Bldg,Chaussee Namur 24, B-5030 Gembloux 5030, Belgium. C3 Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de la Grasa (IG); Universidad Pablo de Olavide RP Garcia-Gonzalez, DL (corresponding author), CSIC, Inst Grasa, Ctra Utrera,Km 1,Campus Univ Pablo Olavide, Seville 41013, Spain. EM dlgarcia@ig.csic.es CR Abbas O, 2009, J MOL STRUCT, V924, P294, DOI 10.1016/j.molstruc.2009.01.027 APARICIO R, 1994, PROG LIPID RES, V33, P29, DOI 10.1016/0163-7827(94)90006-X Aparicio R., 2013, HDB OLIVE OIL ANAL P Aparicio R., 2013, HDB OLIVE OIL ANAL P Araghipour N, 2008, FOOD CHEM, V108, P374, DOI 10.1016/j.foodchem.2007.10.056 Baeten V, 1998, J AGR FOOD CHEM, V46, P2638, DOI 10.1021/jf9707851 Baeten V, 1996, J AGR FOOD CHEM, V44, P2225, DOI 10.1021/jf9600115 Baeten V, 2005, J AGR FOOD CHEM, V53, P6201, DOI 10.1021/jf050595n Baeten V., 2015, HDB FOOD ANAL, V2 Bajoub A, 2016, EUR J LIPID SCI TECH, V118, P1223, DOI 10.1002/ejlt.201500251 Barthus RC, 2001, VIB SPECTROSC, V26, P99, DOI 10.1016/S0924-2031(01)00107-2 Bontempo L, 2019, FOOD CHEM, V276, P782, DOI 10.1016/j.foodchem.2018.10.077 Dais P., 2013, HDB OLIVE OIL ANAL P De Gelder J, 2007, J RAMAN SPECTROSC, V38, P1133, DOI 10.1002/jrs.1734 Duda RO., 1973, PATTERN CLASSIFICATI, V3 Emmanouilides C, 2014, SPAN J AGRIC RES, V12, P3, DOI 10.5424/sjar/2014121-4606 Gao F, 2017, INT J AGR BIOL ENG, V10, P255, DOI 10.3965/j.ijabe.20171003.3144 GARCIA MVA, 1993, GRASAS ACEITES, V44, P18 Garcia-Gonzalez DL, 2017, GRASAS ACEITES, V68, DOI 10.3989/gya.0446171 Garcia-Gonzalez DL, 2012, GRASAS ACEITES, V63, P26, DOI 10.3989/gya.071011 Garcia-Gonzalez D. L., 2013, HDB OLIVE OIL ANAL P Garcia-Gonzalez D. L., 2013, HDB OLIVE OIL ANAL P Garcia-Gonzalez DL, 2009, EUR J LIPID SCI TECH, V111, P1003, DOI 10.1002/ejlt.200900015 GELADI P, 1985, APPL SPECTROSC, V39, P491, DOI 10.1366/0003702854248656 Giles JH, 1999, J RAMAN SPECTROSC, V30, P767, DOI 10.1002/(SICI)1097-4555(199909)30:9<767::AID-JRS447>3.0.CO;2-J Guzman E, 2012, TALANTA, V93, P94, DOI 10.1016/j.talanta.2012.01.053 Larbi A, 2011, SPAN J AGRIC RES, V9, P1279, DOI [10.5424/sjar/20110904-062-11, 10.5424/http://dx.doi.org/10.5424/sjar/20110904-062-11] Lerma-Garcia MJ, 2008, FOOD CHEM, V108, P1142, DOI 10.1016/j.foodchem.2007.11.065 Martens H., 1989, MULTIVARIATE CALIBRA Massart D.L., 1988, CHEMOMETRICS TXB Olsen EF, 2007, MEAT SCI, V76, P628, DOI 10.1016/j.meatsci.2007.02.004 Poiana M, 2004, GRASAS ACEITES, V55, P282 Romero N, 2016, J SCI FOOD AGR, V96, P583, DOI 10.1002/jsfa.7127 Sacco A, 2000, J AM OIL CHEM SOC, V77, P619, DOI 10.1007/s11746-000-0100-y SADEGHIJORABCHI H, 1991, SPECTROCHIM ACTA A, V47, P1449, DOI 10.1016/0584-8539(91)80236-C SADEGHIJORABCHI H, 1990, J AM OIL CHEM SOC, V67, P483, DOI 10.1007/BF02540752 Sanchez-Pena CM, 2005, MEAT SCI, V69, P635, DOI 10.1016/j.meatsci.2004.10.015 Sass-Kiss A., 2008, MODERN TECHNIQUES FO Tena N., 2017, FOOD TRACEABILITY AU Torres M, 2017, FRONT PLANT SCI, V8, DOI 10.3389/fpls.2017.01830 TSIMIDOU M, 1993, J SCI FOOD AGR, V62, P253, DOI 10.1002/jsfa.2740620308 Valcarcel M., 2002, 20605 EUR EN BCR INF, P166 Vermeulen P., 2017, FOOD TRACEABILITY B Vossen P., 2013, HDB OLIVE OIL ANAL P NR 44 TC 5 Z9 5 U1 4 U2 19 PD DEC PY 2019 VL 121 IS 12 SI SI AR 1900035 DI 10.1002/ejlt.201900035 EA OCT 2019 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000493158500001 DA 2022-12-14 ER PT J AU Part, F Zaba, C Bixner, O Zafiu, C Hann, S Sinner, EK Huber-Humer, M AF Part, Florian Zaba, Christoph Bixner, Oliver Zafiu, Christian Hann, Stephan Sinner, Eva-Kathrin Huber-Humer, Marion TI Traceability of fluorescent engineered nanomaterials and their fate in complex liquid waste matrices SO ENVIRONMENTAL POLLUTION DT Article DE Nanomaterials; Nanowaste; Quantum dots; Tracer; Environmental monitoring ID DISSOLVED ORGANIC-MATTER; CAPPED CDTE NANOCRYSTALS; QUANTUM DOTS; ENVIRONMENTAL CONCENTRATIONS; NANOPARTICLES; WATER; CDSE; LANDFILL; AGGREGATION; EMISSIONS AB The number of products containing engineered nanomaterials (ENMs) has increased due to their high industrial relevance as well as their use in diverse consumer products. At the end of their life cycle ENMs might be released to the environment and therefore concerns arise regarding their environmental impact. In order to track their fate upon disposal, it is crucial to establish methods to trace ENMs in complex environmental samples and to differentiate them from naturally-occurring nanoparticles. The goal of this study was to distinctively trace ENMs by (non-invasive) detection methods. For this, fluorescent ENMs, namely quantum dots (QDs), were distinctively traced in complex aqueous matrices, and were still detectable after a period of two months using fluorescence spectroscopy. In particular, two water-dispersible QD-species, namely CdTe/CdS QDs with N-acetyl-L-cysteine as capping agent (NAC-QDs) and surfactant-stabilized CdSe/ZnS QDs (Brij (R) 58-QDs), were synthesized to examine their environmental fate during disposal as well as their potential interaction with naturally-occurring substances present in landfill leachates. When QDs were spiked into a leachate from an old landfill site, alteration processes, such as sorption, aggregation, agglomeration, and interactions with dissolved organic carbon (DOC), led to modifications of the optical properties of QDs. The spectral signatures of NAC-QDs deteriorated depending on residence time and storage temperature, while Bij (R) 58-QDs retained their photoluminescence fingerprints, indicating their high colloidal stability. The observed change in photoluminescence intensity was mainly caused by DOC-interaction and association with complexing agents, such as fulvic or humic acids, typically present in mature landfill leachates. For both QD-species, the results also indicated that pH of the leachate had no significant impact on their optical properties. As a result, the unique spectroscopic fingerprints of QDs, specifically surfactant-stabilized QDs, allowed distinctive tracing in complex aqueous waste matrices in order to study their long-term behavior and ultimate fate. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Part, Florian; Huber-Humer, Marion] Univ Nat Resources & Life Sci, Inst Waste Management, Dept Water Atmosphere Environm, Muthgasse 107, A-1190 Vienna, Austria. [Part, Florian; Zaba, Christoph; Bixner, Oliver; Sinner, Eva-Kathrin] Univ Nat Resources & Life Sci, Inst Synthet Bioarchitectures, Dept Nanobiotechnol, Muthgasse 11, A-1190 Vienna, Austria. [Zafiu, Christian] Forschungszentrum Julich, ICS Struct Biochem 6, Wilhelm Johnen Str, D-52425 Julich, Germany. [Hann, Stephan] Univ Nat Resources & Life Sci, Div Analyt Chem, Dept Chem, Muthgasse 18, A-1190 Vienna, Austria. C3 University of Natural Resources & Life Sciences, Vienna; University of Natural Resources & Life Sciences, Vienna; Helmholtz Association; Research Center Julich; University of Natural Resources & Life Sciences, Vienna RP Sinner, EK (corresponding author), Univ Nat Resources & Life Sci, Inst Synthet Bioarchitectures, Dept Nanobiotechnol, Muthgasse 11, A-1190 Vienna, Austria. EM eva.sinner@boku.ac.at CR Anikeeva PO, 2009, NANO LETT, V9, P2532, DOI 10.1021/nl9002969 [Anonymous], 18992 OENORM EN AUST [Anonymous], 2008, 105232008 ISO [Anonymous], 1997, 13395 OENORM EN ISO [Anonymous], 1991, M6265 OENORM AUSTR S [Anonymous], 1484 OENORM EN Baker A, 2004, WATER RES, V38, P2605, DOI 10.1016/j.watres.2004.02.027 Baker A, 2001, ENVIRON SCI TECHNOL, V35, P948, DOI 10.1021/es000177t Bloemen M, 2014, RSC ADV, V4, P10208, DOI 10.1039/c3ra47844k Boldrin A, 2014, J NANOPART RES, V16, DOI 10.1007/s11051-014-2394-2 Caballero-Guzman A, 2015, WASTE MANAGE, V36, P33, DOI 10.1016/j.wasman.2014.11.006 Chai XL, 2012, WASTE MANAGE, V32, P438, DOI 10.1016/j.wasman.2011.10.011 Coble PG, 1996, MAR CHEM, V51, P325, DOI 10.1016/0304-4203(95)00062-3 Coble PG, 1998, DEEP-SEA RES PT II, V45, P2195, DOI 10.1016/S0967-0645(98)00068-X Dabbousi BO, 1997, J PHYS CHEM B, V101, P9463, DOI 10.1021/jp971091y Debruyne D, 2015, NANOTECHNOLOGY, V26, DOI 10.1088/0957-4484/26/25/255703 Gottschalk F, 2013, ENVIRON POLLUT, V181, P287, DOI 10.1016/j.envpol.2013.06.003 Guo J, 2005, J PHYS CHEM B, V109, P17467, DOI 10.1021/jp044770z Hansen SF, 2016, ENVIRON SCI-NANO, V3, P169, DOI 10.1039/c5en00182j Hennebert P, 2013, WASTE MANAGE, V33, P1870, DOI 10.1016/j.wasman.2013.04.014 Howard AG, 2010, J ENVIRON MONITOR, V12, P135, DOI [10.1039/013681a, 10.1039/b913681a, 10.1039/B913681A] Hrad M, 2013, WASTE MANAGE, V33, P2061, DOI 10.1016/j.wasman.2013.01.027 Hudson N, 2007, RIVER RES APPL, V23, P631, DOI 10.1002/rra.1005 Kamat PV, 2013, J PHYS CHEM LETT, V4, P908, DOI 10.1021/jz400052e Kang T, 2016, SENSOR ACTUAT B-CHEM, V222, P871, DOI 10.1016/j.snb.2015.09.036 Keller AA, 2014, ENVIRON SCI TECH LET, V1, P65, DOI 10.1021/ez400106t Kjeldsen P, 2002, CRIT REV ENV SCI TEC, V32, P297, DOI 10.1080/10643380290813462 Lou YB, 2014, J MATER CHEM C, V2, P595, DOI 10.1039/c3tc31937g Lu F, 2009, CHEMOSPHERE, V74, P575, DOI 10.1016/j.chemosphere.2008.09.060 Moore DE, 2001, LANGMUIR, V17, P2541, DOI 10.1021/la001416t Mueller NC, 2013, ENVIRON SCI-PROC IMP, V15, P251, DOI 10.1039/c2em30761h Navarro DA, 2011, ENVIRON SCI TECHNOL, V45, P6343, DOI 10.1021/es201010f Noh M, 2010, COLLOID SURFACE A, V359, P39, DOI 10.1016/j.colsurfa.2010.01.059 Noipa T, 2011, J FLUORESC, V21, P1941, DOI 10.1007/s10895-011-0893-4 Nowack B, 2015, ENVIRON SCI-NANO, V2, P421, DOI 10.1039/c5en00100e Part F, 2015, WASTE MANAGE, V43, P407, DOI 10.1016/j.wasman.2015.05.035 Peijnenburg WJGM, 2015, CRIT REV ENV SCI TEC, V45, P2084, DOI 10.1080/10643389.2015.1010430 PEN, 2016, CONS PROD INV NAN BA Petryayeva E, 2013, APPL SPECTROSC, V67, P215, DOI 10.1366/12-06948 Pong BK, 2008, LANGMUIR, V24, P5270, DOI 10.1021/la703431j Pons T, 2006, J PHYS CHEM B, V110, P20308, DOI 10.1021/jp065041h Porres L, 2006, J FLUORESC, V16, P267, DOI 10.1007/s10895-005-0054-8 Reinhart DR, 2010, WASTE MANAGE, V30, P2020, DOI 10.1016/j.wasman.2010.08.004 Reiss P, 2002, NANO LETT, V2, P781, DOI 10.1021/nl025596y Rogach AL, 2007, J PHYS CHEM C, V111, P14628, DOI 10.1021/jp072463y Sperling RA, 2007, J PHYS CHEM C, V111, P11552, DOI 10.1021/jp070999d Stedmon CA, 2003, MAR CHEM, V82, P239, DOI 10.1016/S0304-4203(03)00072-0 Sun TY, 2014, ENVIRON POLLUT, V185, P69, DOI 10.1016/j.envpol.2013.10.004 Ulrich A, 2012, J ANAL ATOM SPECTROM, V27, P1120, DOI 10.1039/c2ja30024a Vance ME, 2015, BEILSTEIN J NANOTECH, V6, P1769, DOI 10.3762/bjnano.6.181 Vejerano EP, 2014, ENVIRON SCI-NANO, V1, P133, DOI 10.1039/c3en00080j von der Kammer F, 2012, ENVIRON TOXICOL CHEM, V31, P32, DOI 10.1002/etc.723 Wagner S, 2014, ANGEW CHEM INT EDIT, V53, P12398, DOI 10.1002/anie.201405050 Walser T, 2012, NAT NANOTECHNOL, V7, P520, DOI [10.1038/NNANO.2012.64, 10.1038/nnano.2012.64] Wurth C, 2013, NAT PROTOC, V8, P1535, DOI 10.1038/nprot.2013.087 Xiao Q, 2012, NANOTECHNOLOGY, V23, DOI 10.1088/0957-4484/23/49/495717 Yu WW, 2003, CHEM MATER, V15, P2854, DOI 10.1021/cm034081k NR 57 TC 11 Z9 12 U1 2 U2 38 PD JUL PY 2016 VL 214 BP 795 EP 805 DI 10.1016/j.envpol.2016.04.032 WC Environmental Sciences SC Environmental Sciences & Ecology UT WOS:000378448600089 DA 2022-12-14 ER PT J AU Brinkmann, A Raza, M Melanson, JE AF Brinkmann, Andreas Raza, Mohammad Melanson, Jeremy E. TI Metrologically traceable quantification of trifluoroacetic acid content in peptide reference materials by F-19 solid-state NMR SO METROLOGIA DT Article DE quantitative solid-state NMR; F-19 NMR; qNMR; traceability; TFA; peptides ID NUCLEAR-MAGNETIC-RESONANCE; CONTACT CROSS-POLARIZATION; C-13 NMR; QUANTITATIVE NMR; CELLULOSE I; ACTIVE PRINCIPLES; SIMPLE TOOL; SIGNAL; SPECTRA; ASSIGNMENT AB Although solution-state NMR is frequently used in metrologically-traceable quantification studies, this is not the case for solid-state NMR. However, solid-state NMR allows quantification of substances without the need of dissolution, providing a truly non-destructive approach, and extending metrologically-traceable quantitative NMR to sample classes that are difficult to characterize in solution. In this contribution we present a thorough and rigorous protocol for( 19)F quantitative solid-state NMR employing a certified reference material as external calibrant to provide metrological traceability to absolutely quantify the content of trifluoroacetic acid (TFA) in a peptide sample, typically the major impurity in synthetic peptides. The protocol includes determining the quantitative volume of the solid-state NMR sample holder (rotor), the ERETIC (electronic reference to access in vivo concentrations) method (Akoka et a1 1999 Anal. Chem. 71 2554) to compensate for variations in the sensitivity of the radio frequency resonant circuit when an external calibrant is used, and the EASY (elimination of artefacts in NMR spectroscopy) method (Jaeger and Hemmann 2014 Solid State Nucl. Magn. Reson. 57-58 22) to effectively suppress the F-19 NMR background signal from the probe head. We applied the protocol to quantify the amount of TFA in a candidate NRC certified reference material of the peptide angiotensin II. The results obtained by( 19)F quantitative solid-state NMR are in excellent agreement with those obtained by quantitative NMR in solution employing an internal calibrant. C1 [Brinkmann, Andreas; Raza, Mohammad; Melanson, Jeremy E.] Natl Res Council Canada, Metrol, 1200 Montreal Rd, Ottawa, ON K1A 0R6, Canada. [Raza, Mohammad] McMaster Univ, Dept Chem & Chem Biol, 1280 Main St West, Hamilton, ON L8S 4M1, Canada. [Raza, Mohammad] McMaster Univ, Brockhouse Inst Mat Res, 1280 Main St West, Hamilton, ON L8S 4M1, Canada. C3 National Research Council Canada; McMaster University; McMaster University RP Brinkmann, A (corresponding author), Natl Res Council Canada, Metrol, 1200 Montreal Rd, Ottawa, ON K1A 0R6, Canada. EM Andreas.Brinkmann@nrc-cnrc.gc.ca CR Akoka S, 1999, ANAL CHEM, V71, P2554, DOI 10.1021/ac981422i Akoka S, 2002, INSTRUM SCI TECHNOL, V30, P21, DOI 10.1081/CI-100108768 ANDREW ER, 1959, NATURE, V183, P1802, DOI 10.1038/1831802a0 Arsene CG, 2018, CLIN CHIM ACTA, V487, P318, DOI 10.1016/j.cca.2018.10.024 ATALLA RH, 1984, SCIENCE, V223, P283, DOI 10.1126/science.223.4633.283 ATALLA RH, 1980, J AM CHEM SOC, V102, P3249, DOI 10.1021/ja00529a063 Atalla RH, 1999, SOLID STATE NUCL MAG, V15, P1, DOI 10.1016/S0926-2040(99)00042-9 Avadhut YS, 2009, J MAGN RESON, V201, P1, DOI 10.1016/j.jmr.2009.07.019 Barantin L, 1997, MAGN RESON MED, V38, P179, DOI 10.1002/mrm.1910380203 Bernardinelli OD, 2015, BIOTECHNOL BIOFUELS, V8, DOI 10.1186/s13068-015-0292-1 Brinkmann A, 2016, LANGMUIR, V32, P6105, DOI 10.1021/acs.langmuir.6b01376 Budarin VL, 2007, PHYS CHEM CHEM PHYS, V9, P2274, DOI 10.1039/b701023k Budarin VL, 2004, CHEM COMMUN, P524, DOI 10.1039/b315005d Chunilall V, 2010, HOLZFORSCHUNG, V64, P693, DOI 10.1515/HF.2010.097 Duncan T. M., 1997, PRINCIPAL COMPONENTS EARL WL, 1980, J AM CHEM SOC, V102, P3251, DOI 10.1021/ja00529a064 EFRON B, 1991, SCIENCE, V253, P390, DOI 10.1126/science.253.5018.390 Efron B., 1986, STAT SCI, V1, P54 Fry RA, 2003, J AM CHEM SOC, V125, P2378, DOI 10.1021/ja0275717 FRYE JS, 1982, J MAGN RESON, V48, P125, DOI 10.1016/0022-2364(82)90243-8 Fu L, 2015, FUEL, V141, P39, DOI 10.1016/j.fuel.2014.10.039 Gerstein B. C., 1985, TRANSIENT TECHNIQUES Ghassemzadeh L, 2013, J AM CHEM SOC, V135, P8181, DOI 10.1021/ja4037466 Gunne JSAD, 2012, ANGEW CHEM INT EDIT, V51, P7847, DOI 10.1002/anie.201203515 Harris RK, 2005, J PHARMACEUT BIOMED, V38, P858, DOI 10.1016/j.jpba.2005.01.052 Harris RK, 2008, PURE APPL CHEM, V80, P59, DOI 10.1351/pac200880010059 Harris RK, 2007, J PHARM PHARMACOL, V59, P225, DOI 10.1211/jpp.59.2.0009 Hennig A, 2015, ANALYST, V140, P1804, DOI 10.1039/c4an02248c Holzgrabe U, 2010, PROG NUCL MAG RES SP, V57, P229, DOI 10.1016/j.pnmrs.2010.05.001 Hoofnagle AN, 2016, CLIN CHEM, V62, P48, DOI 10.1373/clinchem.2015.250563 HOULT DI, 1976, J MAGN RESON, V24, P71, DOI 10.1016/0022-2364(76)90233-X Huber A, 2012, ANAL CHEM, V84, P3654, DOI 10.1021/ac3000682 Jaeger C, 2014, SOLID STATE NUCL MAG, V63-64, P13, DOI 10.1016/j.ssnmr.2014.08.001 Jaeger C, 2014, SOLID STATE NUCL MAG, V57-58, P22, DOI 10.1016/j.ssnmr.2013.11.002 JESCHKE G, 1993, J MAGN RESON SER A, V103, P323, DOI 10.1006/jmra.1993.1173 Johnson RL, 2014, J MAGN RESON, V239, P44, DOI 10.1016/j.jmr.2013.11.009 Josephs RD, 2018, TRAC-TREND ANAL CHEM, V101, P108, DOI 10.1016/j.trac.2017.09.026 JOSEPHS RD, 2017, METROLOGIA S, V54, DOI DOI 10.1088/0026-1394/54/1A/08011 JOSEPHS RD, 2017, METROLOGIA S, V54, DOI DOI 10.1088/0026-1394/54/1A/08007 King C, 2017, ACS SUSTAIN CHEM ENG, V5, P8011, DOI 10.1021/acssuschemeng.7b01589 Larsson PT, 2013, CELLULOSE, V20, P623, DOI 10.1007/s10570-012-9850-x Larsson PT, 1997, CARBOHYD RES, V302, P19, DOI 10.1016/S0008-6215(97)00130-4 LENNHOLM H, 1994, CARBOHYD RES, V261, P119, DOI 10.1016/0008-6215(94)80011-1 Li CS, 2019, J PHARMACEUT BIOMED, V166, P105, DOI 10.1016/j.jpba.2018.12.028 LOWE IJ, 1959, PHYS REV LETT, V2, P285, DOI 10.1103/PhysRevLett.2.285 Malz F, 2008, NMR SPECTROSCOPY IN PHARMACEUTICAL ANALYSIS, P43, DOI 10.1016/B978-0-444-53173-5.00002-0 MARICQ MM, 1979, J CHEM PHYS, V70, P3300, DOI 10.1063/1.437915 MARKLEY JL, 1971, J CHEM PHYS, V55, P3604, DOI 10.1063/1.1676626 Melanson JE, 2018, ANAL BIOANAL CHEM, V410, P6719, DOI 10.1007/s00216-018-1272-7 Nevzorov AA, 2011, J MAGN RESON, V209, P161, DOI 10.1016/j.jmr.2011.01.006 Newman RH, 1998, HOLZFORSCHUNG, V52, P157, DOI 10.1515/hfsg.1998.52.2.157 NEWMAN RH, 1995, CELLULOSE, V2, P95, DOI 10.1007/BF00816383 Newman RH, 1999, SOLID STATE NUCL MAG, V15, P21, DOI 10.1016/S0926-2040(99)00043-0 Pan JH, 2010, MACROMOLECULES, V43, P3851, DOI 10.1021/ma902383k Park S, 2010, BIOTECHNOL BIOFUELS, V3, DOI 10.1186/1754-6834-3-10 Park S, 2009, CELLULOSE, V16, P641, DOI 10.1007/s10570-009-9321-1 Pritchard C, 2014, ANAL CHEM, V86, P6525, DOI 10.1021/ac501032q Raya J, 2013, J MAGN RESON, V227, P93, DOI 10.1016/j.jmr.2012.12.006 Remaud GS, 2005, ACCREDIT QUAL ASSUR, V10, P415, DOI 10.1007/s00769-005-0044-1 RIPMEESTER JA, 1980, CHEM PHYS LETT, V74, P536, DOI 10.1016/0009-2614(80)85269-9 Sacui IA, 2014, ACS APPL MATER INTER, V6, P6127, DOI 10.1021/am500359f Saidi F, 2016, J PHARM SCI-US, V105, P2397, DOI 10.1016/j.xphs.2016.05.025 Sanchez S, 2008, J PHARMACEUT BIOMED, V47, P683, DOI 10.1016/j.jpba.2008.03.030 Sanders RL, 2010, J PHYS CHEM C, V114, P5491, DOI 10.1021/jp906132k SCHAEFER J, 1976, J AM CHEM SOC, V98, P1031, DOI 10.1021/ja00420a036 STEJSKAL EO, 1977, J MAGN RESON, V28, P105, DOI 10.1016/0022-2364(77)90260-8 Stocks BB, 2018, ANAL BIOANAL CHEM, V410, P6963, DOI 10.1007/s00216-018-1302-5 Sun Y, 2019, QUANTIFICATION UNPUB The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH), 1996, VAL AN PROC TEXT MET Thevis M, 2011, FORENSIC SCI INT, V213, P35, DOI 10.1016/j.forsciint.2011.06.015 Tyburn J-M, 2016, TOPSPIN ERETIC 2 USE VANDERHART DL, 1984, MACROMOLECULES, V17, P1465, DOI 10.1021/ma00138a009 Vitzthum F, 2007, PROTEOM CLIN APPL, V1, P1016, DOI 10.1002/prca.200700223 Vogt FG, 2015, EMAGRES, V4, P255, DOI 10.1002/9780470034590.emrstm1393 Vogt FG, 2010, FUTURE MED CHEM, V2, P915, DOI [10.4155/fmc.10.200, 10.4155/FMC.10.200] Wawer I, 2008, NMR SPECTROSCOPY IN PHARMACEUTICAL ANALYSIS, P63, DOI 10.1016/B978-0-444-53173-5.00003-2 Wawer I, 2008, NMR SPECTROSCOPY IN PHARMACEUTICAL ANALYSIS, P201, DOI 10.1016/B978-0-444-53173-5.00009-3 Wickholm K, 1998, CARBOHYD RES, V312, P123, DOI 10.1016/S0008-6215(98)00236-5 Wider G, 2006, J AM CHEM SOC, V128, P2571, DOI 10.1021/ja055336t Wormald P, 1996, CELLULOSE, V3, P141, DOI 10.1007/BF02228797 ZHANG SM, 1990, CHEM PHYS LETT, V166, P92, DOI 10.1016/0009-2614(90)87056-W Ziarelli F, 2006, SOLID STATE NUCL MAG, V29, P214, DOI 10.1016/j.ssnmr.2005.08.013 Ziarelli F, 2007, J MAGN RESON, V188, P260, DOI 10.1016/j.jmr.2007.07.006 NR 83 TC 5 Z9 5 U1 2 U2 22 PD APR PY 2019 VL 56 IS 2 AR 024002 DI 10.1088/1681-7575/ab04e3 WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000459557200001 DA 2022-12-14 ER PT J AU Liang, KH Zhu, H Zhao, SS Liu, HJ Zhao, Y AF Liang, Kehong Zhu, Hong Zhao, Shanshan Liu, Haijin Zhao, Yan TI Determining the geographical origin of flaxseed based on stable isotopes, fatty acids and antioxidant capacity SO JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE DT Article DE flaxseed; traceability; stable isotopes; fatty acids; antioxidant activity ID ORYZA-SATIVA L.; PHENOLIC-COMPOUNDS; RATIOS; RICE; CHINA; DIFFERENTIATION; CLASSIFICATION; SPECTROSCOPY; FLAVONOIDS; CULTIVARS AB BACKGROUND Flaxseed is an economically important oilseed crop whose geographic origin is of significant interest to producers and consumers because every region may exhibit particular quality characteristics. The lipid/fatty acid method of determining the geographic origin of flaxseed has not been found to be adequate. RESULTS To improve the discrimination rate and the geographical traceability of this crop, the chemical profiles of the flaxseed samples were characterized via lipids/fatty acids, stable isotopes, and antioxidant capacity. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were also performed. A satisfactory discrimination rate of 98.6% was obtained after combining fatty acids, stable isotopes, and antioxidant capacity to trace the origin of flaxseed from five regions in northern China. CONCLUSION This study provides an effective method for distinguishing the geographic origin of flaxseed. (c) 2021 Society of Chemical Industry. C1 [Zhao, Shanshan; Zhao, Yan] Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. [Liang, Kehong; Zhu, Hong] Minist Agr, Inst Food & Nutr Dev, Beijing, Peoples R China. [Liu, Haijin] Tibet Autonomous Reg Agr & Livestock Prod Qual &, Lhasa, Peoples R China. C3 Chinese Academy of Agricultural Sciences; Institute of Quality Standards & Testing Technology for Agro-Products, CAAS; Ministry of Agriculture & Rural Affairs RP Zhao, Y (corresponding author), Chinese Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Key Lab Agroprod Qual & Safety, Beijing 100081, Peoples R China. EM zhaoyan01@caas.cn CR Akamatsu F, 2020, FOOD CHEM, V315, DOI 10.1016/j.foodchem.2020.126239 ALTABET MA, 1995, NATURE, V373, P506, DOI 10.1038/373506a0 Alu'datt MH, 2013, FOOD CHEM, V139, P93, DOI 10.1016/j.foodchem.2012.12.061 Ariyama K, 2012, J AGR FOOD CHEM, V60, P1628, DOI 10.1021/jf204296p Babova O, 2016, PHYTOCHEMISTRY, V123, P33, DOI 10.1016/j.phytochem.2016.01.016 Barthet VJ, 2014, CAN J PLANT SCI, V94, P593, DOI [10.4141/CJPS2013-018, 10.4141/cjps2013-018] Bateman AS, 2007, J AGR FOOD CHEM, V55, P2664, DOI 10.1021/jf0627726 Benzie IFF, 1996, ANAL BIOCHEM, V239, P70, DOI 10.1006/abio.1996.0292 Bontempo L, 2019, FOOD CHEM, V276, P782, DOI 10.1016/j.foodchem.2018.10.077 Boschin G, 2011, FOOD CHEM, V127, P1199, DOI 10.1016/j.foodchem.2011.01.124 Choi SH, 2020, FOOD CONTROL, V111, DOI 10.1016/j.foodcont.2019.107064 Chung IM, 2016, J SCI FOOD AGR, V96, P2433, DOI 10.1002/jsfa.7363 Chung IM, 2015, J CEREAL SCI, V65, P252, DOI 10.1016/j.jcs.2015.08.001 Colovic D, 2016, ZEMDIRBYSTE, V103, P175, DOI 10.13080/z-a.2016.103.023 Deng QC, 2018, INT J FOOD PROP, V20, pS2708, DOI 10.1080/10942912.2017.1402029 Dittgen CL, 2019, FOOD CHEM, V288, P297, DOI 10.1016/j.foodchem.2019.03.006 Esteki M, 2017, CHEMOMETR INTELL LAB, V171, P251, DOI 10.1016/j.chemolab.2017.10.014 HATANO T, 1988, CHEM PHARM BULL, V36, P2090 Alvarez LVH, 2017, LWT-FOOD SCI TECHNOL, V84, P385, DOI 10.1016/j.lwt.2017.05.078 Kajla P, 2015, J FOOD SCI TECH MYS, V52, P1857, DOI 10.1007/s13197-014-1293-y Kharbach M, 2019, FOOD CONTROL, V95, P95, DOI 10.1016/j.foodcont.2018.07.046 Laza A., 2012, Research Journal of Agricultural Science, V44, P96 Liu HL, 2020, J FOOD COMPOS ANAL, V91, DOI 10.1016/j.jfca.2020.103513 Oomah BD, 2001, J SCI FOOD AGR, V81, P889, DOI 10.1002/jsfa.898 Ricardo F, 2017, FOOD CONTROL, V81, P173, DOI 10.1016/j.foodcont.2017.06.005 RiceEvans CA, 1996, FREE RADICAL BIO MED, V20, P933, DOI 10.1016/0891-5849(95)02227-9 Rutkowska J, 2015, FOOD CHEM, V178, P26, DOI 10.1016/j.foodchem.2015.01.036 Sediqi M., 2012, THESIS COLORADO STAT Suzuki Y, 2008, FOOD CHEM, V109, P470, DOI 10.1016/j.foodchem.2007.12.063 Villeneuve S, 2013, FOOD BIOPROD PROCESS, V91, P183, DOI 10.1016/j.fbp.2012.09.002 Walder F, 2013, SOIL BIOL BIOCHEM, V58, P341, DOI 10.1016/j.soilbio.2012.12.008 Wang C, 2020, J FOOD COMPOS ANAL, V92, DOI 10.1016/j.jfca.2020.103577 Wang GA, 2013, QUATERNARY SCI REV, V63, P83, DOI 10.1016/j.quascirev.2012.12.004 Wang H, 2017, FOOD CHEM, V214, P227, DOI 10.1016/j.foodchem.2016.07.075 Wu XM, 2019, SPECTROCHIM ACTA A, V212, P132, DOI 10.1016/j.saa.2019.01.008 Xie LN, 2020, FOOD CHEM, V316, DOI 10.1016/j.foodchem.2020.126332 Xing L, 2014, SPECTROSC SPECT ANAL, V34, P2538, DOI 10.3964/j.issn.1000-0593(2014)09-2538-06 Zhang B, 2014, FOOD CHEM, V161, P296, DOI 10.1016/j.foodchem.2014.04.014 Zhang Q, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-08808-y Zhang XiaoXia, 2017, China Oils and Fats, V42, P142 Zhao SS, 2020, FOOD CHEM, V310, DOI 10.1016/j.foodchem.2019.125826 NR 41 TC 1 Z9 1 U1 11 U2 27 PD JAN 30 PY 2022 VL 102 IS 2 BP 673 EP 679 DI 10.1002/jsfa.11396 EA JUL 2021 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000673278900001 DA 2022-12-14 ER PT J AU Elgadi, S Ouhammou, A Taous, F Zine, H Papazoglou, EG Elghali, T Amenzou, N El Allali, H Aitlhaj, A El Antari, A AF Elgadi, Sara Ouhammou, Ahmed Taous, Fouad Zine, Hamza Papazoglou, Eleni G. Elghali, Tibari Amenzou, Noureddine El Allali, Hassan Aitlhaj, Abderrahmane El Antari, Abderraouf TI Combination of Stable Isotopes and Fatty Acid Composition for Geographical Origin Discrimination of One Argan Oil Vintage SO FOODS DT Article DE argan oil; traceability; isotope and elemental techniques; fatty acids; environmental conditions; Morocco ID FOOD; TRACEABILITY; OLIVE AB Quality control and traceability of Argan oil requires precise chemical characterization considering different provenances. The fatty acid profile is an essential parameter that certifies the quality and purity of Argan oil. In addition, stable isotopes were recently shown to be accurate as an indicator for geographical origin. In this study, fatty acid composition by gas chromatography (GC) and stable isotope ratio by isotope ratio mass spectrometry (IRMS) were investigated for classifying Argan oil according to its geographical origin. Forty-one Argan oil samples, belonging to six geographical origins of Moroccan natural Argan population (Safi, Essaouira, Agadir Ida Outanane, Taroudant, Tiznit and Sidi Ifni) were collected and extracted under the same conditions. The results show that the isotope delta C-13, palmitic acid (C16:0), linoleic acid (C18:2) and unsaturated fatty acids (UFA) were strongly influenced by ecological parameters. Linear discriminant analysis (LDA) was performed to discriminate the six studied provenances. Discriminant models predicted the origin of Argan oil with 92.70% success. Samples from Safi, Essaouira and Agadir Ida Outanane presented the highest classification rate (100%). In contrast, the lowest rate was reported for samples from Tiznit (85.70%). The findings obtained for fatty acids and isotope combination might be considered as an accurate tool for determining the geographical origins of Argan oil. Moreover, they can potentially be used as specific markers for oils labeled with Protected Geographical Indication (PGI). C1 [Elgadi, Sara; Ouhammou, Ahmed; Zine, Hamza] Cadi Ayyad Univ, Fac Sci Semlalia, Lab Microbial Biotechnol Agrosci & Environm, Marrakech 40000, Morocco. [Taous, Fouad; Elghali, Tibari; Amenzou, Noureddine] Ctr Natl Energie Sci & Tech Nucl, Rabat 10001, Morocco. [Papazoglou, Eleni G.] Agr Univ Athens, Dept Crop Sci, Lab Systemat Bot, Athens 11855, Greece. [El Allali, Hassan] Interprofess Federat Argan Sector, Agadir 80000, Morocco. [Aitlhaj, Abderrahmane] Natl Agcy Dev Oasis & Argan Zones, Agadir 80000, Morocco. [El Antari, Abderraouf] Natl Inst Agron Res INRA, Reg Ctr Agron Res Marrakech, Lab Agro Food Technol & Qual, Marrakech 40000, Morocco. C3 Cadi Ayyad University of Marrakech; Agricultural University of Athens RP Elgadi, S (corresponding author), Cadi Ayyad Univ, Fac Sci Semlalia, Lab Microbial Biotechnol Agrosci & Environm, Marrakech 40000, Morocco. EM sara.elgadi@ced.uca.ma; ouhammou@uca.ac.ma; taous@cnesten.org.ma; hamza.zine@edu.uca.ac.ma; elpapazo@aua.gr; elghali@cnesten.org.ma; amenzou@cnesten.org.ma; elallhas@gmail.com; aitlhaj.abderrahmane@gmail.com; a_elantari@yahoo.fr CR Aabd NA, 2013, MEDITERR J NUTR META, V6, P217, DOI 10.3233/s12349-013-0134-2 Aithammou R, 2019, FOOD CHEM, V297, DOI 10.1016/j.foodchem.2019.05.024 [Anonymous], 2020, ARGAN OIL MARKET SIZ Brand WA, 2014, PURE APPL CHEM, V86, P425, DOI 10.1515/pac-2013-1023 Chalouan A., 2007, GEOLOGIE MAROC NOUVE Charrouf Z, 2008, NAT PROD COMMUN, V3, P283 Charrouf Z, 2014, EUR J LIPID SCI TECH, V116, P1316, DOI 10.1002/ejlt.201400261 Consonni R, 2010, ADV FOOD NUTR RES, V59, P87, DOI 10.1016/S1043-4526(10)59004-1 Elgadi S, 2021, J FOOD QUALITY, V2021, DOI 10.1155/2021/8869060 Fick SE, 2017, INT J CLIMATOL, V37, P4302, DOI 10.1002/joc.5086 Gharby S, 2013, NAT PROD COMMUN, V8, P25 Gharby S, 2012, MEDITERR J NUTR META, V5, P31, DOI 10.1007/s12349-011-0076-5 Gharby S, 2011, LWT-FOOD SCI TECHNOL, V44, P1, DOI 10.1016/j.lwt.2010.07.003 Harhar H, 2014, IND CROP PROD, V56, P156, DOI 10.1016/j.indcrop.2014.01.046 Harhar H, 2010, NAT PROD COMMUN, V5, P1799 Hilali M, 2005, J AGR FOOD CHEM, V53, P2081, DOI 10.1021/jf040290t Hilali M, 2007, J AM OIL CHEM SOC, V84, P761, DOI 10.1007/s11746-007-1084-y Jimenez-Morillo NT, 2020, FOODS, V9, DOI 10.3390/foods9121855 Jordan M, 2012, J BIOMED NANOTECHNOL, V8, P944, DOI 10.1166/jbn.2012.1460 Karmaoui A., 2016, ADV RES, V8, P1, DOI [10.9734/AIR/2016/21353, DOI 10.9734/AIR/2016/21353] Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kharbach M, 2020, J PHARMACEUT BIOMED, V177, DOI 10.1016/j.jpba.2019.112849 Kharbach M, 2018, FOOD CHEM, V263, P8, DOI 10.1016/j.foodchem.2018.04.059 Kharbach M, 2017, CHEMOMETR INTELL LAB, V162, P182, DOI 10.1016/j.chemolab.2017.02.003 Li YN, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3442347 Miklavcic MB, 2020, MOLECULES, V25, DOI 10.3390/molecules25184080 Msanda Fouad, 2005, Cahiers Agricultures, V14, P357 Msanda F, 2021, ENVIRON SCI POLLUT R, V28, P64156, DOI 10.1007/s11356-020-11936-0 Portarena S, 2014, FOOD CHEM, V164, P12, DOI 10.1016/j.foodchem.2014.04.115 Romagny B, 2009, MAGHREB-MACHREK, V4, P85, DOI [10.3917/machr.202.0085, DOI 10.3917/MACHR.202.0085] SNIMA 08.5.090 Service de Normalisation Industrielle Marocaine (SNIMA) Huiles d'argane, 2003, 085090 SNIMA NM Taous F, 2020, FORENSIC CHEM, V17, DOI 10.1016/j.forc.2019.100198 Tharwat A, 2017, AI COMMUN, V30, P169, DOI 10.3233/AIC-170729 NR 33 TC 5 Z9 5 U1 4 U2 15 PD JUN PY 2021 VL 10 IS 6 AR 1274 DI 10.3390/foods10061274 WC Food Science & Technology SC Food Science & Technology UT WOS:000665926000001 DA 2022-12-14 ER PT J AU Giraldo, PA Cogan, NOI Spangenberg, GC Smith, KF Shinozuka, H AF Giraldo, Paula A. Cogan, Noel O., I Spangenberg, German C. Smith, Kevin F. Shinozuka, Hiroshi TI Development and Application of Droplet Digital PCR Tools for the Detection of Transgenes in Pastures and Pasture-Based Products SO FRONTIERS IN PLANT SCIENCE DT Article DE genetically modified (GM); forage; real-time PCR (qPRC); droplet digital PCR (ddPCR); TaqMan-probe; SYBR Green I ID TRIFOLIUM-REPENS L.; REAL-TIME PCR; DNA; QUANTIFICATION; EXPRESSION; TISSUES; PROTEIN; TRAITS; POLLEN; SILAGE AB Implementation of molecular biotechnology, such as transgenic technologies, in forage species can improve agricultural profitability through achievement of higher productivity, better use of resources such as soil nutrients, water, or light, and reduced environmental impact. Development of detection and quantification techniques for genetically modified plants are necessary to comply with traceability and labeling requirements prior to regulatory approval for release. Real-time PCR has been the standard method used for detection and quantification of genetically modified events, and droplet digital PCR is a recent alternative technology that offers a higher accuracy. Evaluation of both technologies was performed using a transgenic high-energy forage grass as a case study. Two methods for detection and quantification of the transgenic cassette, containing modified fructan biosynthesis genes, and a selectable marker gene, hygromycin B phosphotransferase used for transformation, were developed. Real-time PCR was assessed using two detection techniques, SYBR Green I and fluorescent probe-based methods. A range of different agricultural commodities were tested including fresh leaves, tillers, seeds, pollen, silage and hay, simulating a broad range of processed agricultural commodities that are relevant in the commercial use of genetically modified pastures. The real-time and droplet digital PCR methods were able to detect both exogenous constructs in all agricultural products. However, a higher sensitivity and repeatability in transgene detection was observed with the droplet digital PCR technology. Taking these results more broadly, it can be concluded that the droplet digital PCR technology provides the necessary resolution for quantitative analysis and detection, allowing absolute quantification of the target sequence at the required limits of detection across all jurisdictions globally. The information presented here provides guidance and resources for pasture-based biotechnology applications that are required to comply with traceability requirements. C1 [Giraldo, Paula A.; Smith, Kevin F.] Univ Melbourne, Fac Vet & Agr Sci, Parkville, Vic, Australia. [Giraldo, Paula A.; Cogan, Noel O., I; Spangenberg, German C.; Shinozuka, Hiroshi] Ctr AgriBiosci, AgriBio, Agr Victoria, Bundoora, Vic, Australia. [Cogan, Noel O., I; Spangenberg, German C.] La Trobe Univ, Sch Appl Syst Biol, Bundoora, Vic, Australia. [Spangenberg, German C.; Smith, Kevin F.] Agr Victoria, Hamilton, Vic, Australia. C3 University of Melbourne; La Trobe University; La Trobe University RP Smith, KF (corresponding author), Univ Melbourne, Fac Vet & Agr Sci, Parkville, Vic, Australia.; Smith, KF (corresponding author), Agr Victoria, Hamilton, Vic, Australia. EM kfsmith@unimelb.edu.au CR [Anonymous], 2011, STATE WORLDS LAND WA Ardizzone M., 2018, EFSA Supporting Publications, V15, p1366E, DOI 10.2903/sp.efsa.2018.en-1366 Badenhorst PE, 2018, MOL BREEDING, V38, DOI 10.1007/s11032-018-0776-3 Becker JD, 2003, PLANT PHYSIOL, V133, P713, DOI 10.1104/pp.103.028241 BLOCHLINGER K, 1984, MOL CELL BIOL, V4, P2929, DOI 10.1128/MCB.4.12.2929 Broeders S, 2015, EUR FOOD RES TECHNOL, V241, P275, DOI 10.1007/s00217-015-2454-6 Cankar K, 2006, BMC BIOTECHNOL, V6, DOI 10.1186/1472-6750-6-37 Collier R, 2017, PLANT J, V90, P1014, DOI 10.1111/tpj.13517 Corbisier P, 2015, ANAL BIOANAL CHEM, V407, P1831, DOI 10.1007/s00216-015-8458-z Damira FU, 2016, ANAL CHEM, V88, P812, DOI 10.1021/acs.analchem.5b03238 Day CD, 2000, GENE DEV, V14, P2869, DOI 10.1101/gad.849600 Demeke Tigst, 2018, Biomol Detect Quantif, V15, P24, DOI 10.1016/j.bdq.2018.03.002 Demeke T, 2016, FOOD CONTROL, V68, P105, DOI 10.1016/j.foodcont.2016.03.007 Fearing PL, 1997, MOL BREEDING, V3, P169, DOI 10.1023/A:1009611613475 Flachowsky G, 2017, ANIM FRONT, V7, P15, DOI 10.2527/af.2017.0114 Flachowsky G, 2012, J VERBRAUCH LEBENSM, V7, P179, DOI 10.1007/s00003-012-0777-9 Gao HW, 2016, BMC BIOTECHNOL, V16, DOI 10.1186/s12896-016-0303-8 Grelewska-Nowotko K, 2018, APPL BIOCHEM BIOTECH, V185, P207, DOI 10.1007/s12010-017-2634-x Hupfer C, 1999, EUR FOOD RES TECHNOL, V209, P301, DOI 10.1007/s002170050498 Iwobi A, 2016, FOOD CONTROL, V69, P205, DOI 10.1016/j.foodcont.2016.04.048 Koppel R, 2015, EUR FOOD RES TECHNOL, V241, P521, DOI 10.1007/s00217-015-2481-3 Lalhmangaihi Ralte, 2014, J Biomol Tech, V25, P92, DOI 10.7171/jbt.14-2504-001 Lievens A, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0153317 Mamedov TG, 2008, COMPUT BIOL CHEM, V32, P452, DOI 10.1016/j.compbiolchem.2008.07.021 Marin I, 2009, BMC EVOL BIOL, V9, DOI 10.1186/1471-2148-9-267 Marmiroli N, 2008, ANAL BIOANAL CHEM, V392, P369, DOI 10.1007/s00216-008-2303-6 Nadal A, 2018, FOOD CHEM TOXICOL, V117, P13, DOI 10.1016/j.fct.2017.08.032 Panter S, 2015, CROP PASTURE SCI, V66, P474, DOI 10.1071/CP14075 Panter S, 2012, TRANSGENIC RES, V21, P619, DOI 10.1007/s11248-011-9557-z Panter S, 2017, AGRONOMY-BASEL, V7, DOI 10.3390/agronomy7020036 Putnam DH, 2016, CROP FORAGE TURF MAN, V2, DOI 10.2134/cftm2015.0164 Racki N, 2014, PLANT METHODS, V10, DOI 10.1186/s13007-014-0042-6 Ren R, 2018, MOL PLANT, V11, P414, DOI 10.1016/j.molp.2018.01.002 Smith KF, 2007, AUST J EXP AGR, V47, P1032, DOI 10.1071/EA06065 SMITH KF, 2016, AGRONOMY-BASEL, V6, DOI DOI 10.3390/AGRONOMY6040059 Tremblay GF, 2008, CAN J ANIM SCI, V88, P85, DOI 10.4141/CJAS07068 Wan JinRong, 2016, Advances in Bioscience and Biotechnology, V7, P403 Wang ZY, 2012, ANN BOT-LONDON, V110, P1317, DOI 10.1093/aob/mcs027 Xu XL, 2016, TRANSGENIC RES, V25, P855, DOI 10.1007/s11248-016-9982-0 Xue BT, 2014, INT J MOL SCI, V15, P8846, DOI 10.3390/ijms15058846 NR 40 TC 7 Z9 7 U1 5 U2 10 PD JAN 8 PY 2019 VL 9 AR 1923 DI 10.3389/fpls.2018.01923 WC Plant Sciences SC Plant Sciences UT WOS:000455108100001 DA 2022-12-14 ER PT J AU Cristina, Z Miguel, S AF Cristina, Zurbriggen Miguel, Sierra TI Collaborative Innovation: the Case of the National Livestock Information System SO AGROCIENCIA-URUGUAY DT Article DE innovation; public policy; traceability; livestock information system; meat industry ID PERSPECTIVE; GOVERNANCE; IMPACTS AB The aim of this paper is to contribute to the discussion on new forms of governance to achieve sustainable development that can deal with complex, uncertain issues and with multiple actors involved with conflicting values and interests. In this scenario, the article aims to provide evidence of experimentation in innovation in public policy based on a case study: the National Livestock Information System. Specifically, it focuses on analyzing how the process of innovation through collaborative knowledge networks contributes to a better dialogue between science and politics, based on a pragmatic, reflective culture that fosters change, sustainability and the creation of public value. The key finding of the study is that the interaction between policy makers, farmers, scientists, information technology companies and other stakeholders, was driven by a pragmatic way of co-production of knowledge that improved the understanding of the problem, design, and decision-making processes. C1 [Cristina, Zurbriggen] Univ Republica, Fac Ciencias Sociales, Constituyente 1502, Montevideo, Uruguay. [Miguel, Sierra] INIA Direcc Nacl, Colonia, Uruguay. C3 Universidad de la Republica, Uruguay; Instituto Nacional de Investigacion Agropecuaria Uruguay (INIA) RP Cristina, Z (corresponding author), Univ Republica, Fac Ciencias Sociales, Constituyente 1502, Montevideo, Uruguay. CR Ahlqvist T, 2015, FUTURES, V71, P91, DOI 10.1016/j.futures.2015.07.012 Bammer G, 2013, DISCIPLINING INTERDISCIPLINARITY: INTEGRATION AND IMPLEMENTATION SCIENCES FOR RESEARCHING COMPLEX REAL-WORLD PROBLEMS, P1 Bason C, 2010, LEADING PUBLIC SECTOR INNOVATION: CO-CREATING FOR A BETTER SOCIETY, P1, DOI 10.1332/policypress/9781847426345.001.0001 Bijker W., 1995, BICYCLES BAKELITES B Bonache J, 1999, CUAD ECON DIR EMPRES, V3, P123 Bozeman B., 2003, KNOWLEDGE FLOWS KNOW, V2, P3 Carpenter SR, 2004, ECOL SOC, V9 Dewey J., 1940, PUBLIC ITS PROBLEMS Errea E., 2011, TRANSFORMACIONES AGR Folke C, 2005, ANNU REV ENV RESOUR, V30, P441, DOI 10.1146/annurev.energy.30.050504.144511 Folke C, 2006, GLOBAL ENVIRON CHANG, V16, P253, DOI 10.1016/j.gloenvcha.2006.04.002 Geels FW, 2002, RES POLICY, V31, P1257, DOI 10.1016/S0048-7333(02)00062-8 Hadorn G.H., 2010, OXFORD HDB INTERDISC, P431 HIRSCHMAN AO, 1984, GETTING AHEAD COLLEC Holling CS, 2001, ECOSYSTEMS, V4, P390, DOI 10.1007/s10021-001-0101-5 Klijn E. H., 2008, HDB INTERORGANIZATIO, P118 Klijn EH, 2010, ADMIN SOC, V42, P193, DOI 10.1177/0095399710362716 Kooiman J., 2003, GOVERNING GOVERNANCE Lechner N., 1996, ESTUDIOS SOCIALES, V11, P9 Lubell M, 2013, INT J WATER GOV, V1, P177, DOI 10.7564/13-IJWG14 Lubell M, 2015, CURR OPIN ENV SUST, V12, P41, DOI 10.1016/j.cosust.2014.08.011 Meadows Donella H., 2008, THINKING SYSTEMS PRI MGAP, 2004, MEM AN 2004 Moore M. H., 1995, CREATING PUBLIC VALU Navajas E. A., 2014, Archivos Latinoamericanos de Produccion Animal, V22, P13 Osorio G., 2009, EXPERIENCIA URUGUAY, P33 Ostrom E, 1996, WORLD DEV, V24, P1073, DOI 10.1016/0305-750X(96)00023-X Ostrom E, 1998, NEW HOR ENV ECO, P68 OSTROM E., 1990, POLITICAL EC I DECIS Ostrom E, 2009, SCIENCE, V325, P419, DOI 10.1126/science.1172133 Paolino C., 2010, ANUARIO OPYPA2010, P7 Pierre J, 2005, GOVERNING COMPLEX SOCIETIES: TRAJECTORIES AND SCENARIOS, P1, DOI 10.1057/9780230512641 Pittaluga L., 2015, LESS DEV COUNTRIES P Poteete AR, 2010, WORKING TOGETHER: COLLECTIVE ACTION, THE COMMONS, AND MULTIPLE METHODS IN PRACTICE, P1 Putnam R. D., 1994, MAKING DEMOCRACY WOR, DOI DOI 10.2307/206637 RITTEL HWJ, 1973, POLICY SCI, V4, P155, DOI 10.1007/BF01405730 Schon D., 1983, REFLECTIVE PRACTITIO Schon D., 1994, FRAME REFLECTION RES SIRA, 2006, DESCR ESQ OP MGAP Terra M. M., 2009, CUAL IMPORTANCIA REA Thomas H., 2001, 9 SEM LAT IB GEST TE, P1 Toro G, 2009, NODO COOPERACION EXP Voss JP, 2011, ECOL SOC, V16 Walker B. H., 2004, ECOL SOC, V9 YIN R, 1984, CASE STUDY RES DESIN Zurbriggen C., 2015, REDES INNOVACION TRA Zurbriggen C, 2014, REV GEST PUBLICA, V3, P329 NR 47 TC 3 Z9 3 U1 0 U2 12 PD JUN PY 2017 VL 21 IS 1 BP 140 EP 152 WC Agronomy SC Agriculture UT WOS:000409993200016 DA 2022-12-14 ER PT J AU Brouziotis, AA Giarra, A Marano, A Di Nunzio, A Lombardo, F Guida, M Libralato, G Trifuoggi, M AF Brouziotis, Antonios Apostolos Giarra, Antonella Marano, Alessandra Di Nunzio, Aldo Lombardo, Francesco Guida, Marco Libralato, Giovanni Trifuoggi, Marco TI Comparative study of two methods for rare earth elements analysis in human urine samples using inductively coupled plasma-mass spectrometry SO FRONTIERS IN ENVIRONMENTAL SCIENCE DT Article DE rare earth elements; human urine; ICP-mass spectrometry; analytical method; method validation ID TRACE-ELEMENTS; HUMAN HEALTH; ICP-MS; KNOWLEDGE; EXPOSURE; WORKERS AB The application of rare earth elements (REEs) in several areas, including high-tech technology, agriculture, medicine, and fuels, has made them an essential component of our everyday life. This extensive use of REEs in several technologies is expected to potentially impact human health. Even if several studies investigated the levels of REEs in human matrices, until now no standard method has been established for analyzing these elements in human matrices. The sample analysis should be of high quality, and the methods should be validated properly to ensure the quality of the procedure and traceability of the analytical data. In this research, we compared the validation and effectiveness of two different methods of sample preparation for human urine samples: a simple dilution of the sample (DIL) was compared with microwave assisted-acid decomposition (MIN) for tracing REE levels in human urine samples. The analysis was carried out by inductively coupled plasma mass spectrometry (ICP-MS). The working conditions have been set in high-sensitivity mode. Accuracy of the proposed method was evaluated by spiking the sample matrix with known concentrations of analyte standards. Both methods showed adequate precision of repeatability and intra-laboratory reproducibility, with the DIL method showing better precision of both repeatability and reproducibility than the MIN method. The CVr% values of repeatability range from 1.5 to 12% for the DIL and from 8.4 to 16% for the MIN method. The CVr% values of reproducibility range from 6.2-23% for the DIL and from 8.6 to 24% for the MIN method. REE recoveries for both methods were very close to 100%. Both methods proved to be effective for the determination of REE levels in human urine matrices. C1 [Brouziotis, Antonios Apostolos; Guida, Marco; Libralato, Giovanni; Trifuoggi, Marco] Univ Naples Federico II, Dept Biol, Naples, Italy. [Brouziotis, Antonios Apostolos; Giarra, Antonella; Marano, Alessandra; Di Nunzio, Aldo; Lombardo, Francesco] Univ Naples Federico II, Dept Chem Sci, Analyt Chem Environm, Naples, Italy. [Guida, Marco; Libralato, Giovanni; Trifuoggi, Marco] Univ Naples Federico II, CeSMA Adv Metrol & Technol Serv Ctr, Naples, Italy. C3 University of Naples Federico II; University of Naples Federico II; University of Naples Federico II RP Brouziotis, AA; Trifuoggi, M (corresponding author), Univ Naples Federico II, Dept Biol, Naples, Italy.; Brouziotis, AA (corresponding author), Univ Naples Federico II, Dept Chem Sci, Analyt Chem Environm, Naples, Italy.; Trifuoggi, M (corresponding author), Univ Naples Federico II, CeSMA Adv Metrol & Technol Serv Ctr, Naples, Italy. EM antonis.brouziotis@outlook.com; marco.trifuoggi@unina.it CR Alimonti A., 2015, 1530 ISTISAN I SUP S ALLAIN P, 1990, CLIN CHEM, V36, P2011 [Anonymous], 2018, 17025 UNI CEI EN ISO Asante KA, 2012, SCI TOTAL ENVIRON, V424, P63, DOI 10.1016/j.scitotenv.2012.02.072 Batista BL, 2009, J BRAZIL CHEM SOC, V20, P1406, DOI 10.1590/S0103-50532009000800004 Bettinelli M, 2002, RAPID COMMUN MASS SP, V16, P579, DOI 10.1002/rcm.609 Bocca B, 2019, ENVIRON RES, V177, DOI 10.1016/j.envres.2019.108599 Choe KY, 2016, ANAL METHODS-UK, V8, P6754, DOI 10.1039/c6ay01877g Cirtiu CM, 2022, CHEMOSPHERE, V289, DOI 10.1016/j.chemosphere.2021.133142 Cui YJ, 2005, ENVIRON INT, V31, P784, DOI 10.1016/j.envint.2005.05.025 de Matos AR, 2022, J TRACE ELEM MED BIO, V70, DOI 10.1016/j.jtemb.2021.126920 Hao Z, 2015, CHEMOSPHERE, V128, P161, DOI 10.1016/j.chemosphere.2015.01.057 Li Y, 2017, BIOL TRACE ELEM RES, V175, P298, DOI 10.1007/s12011-016-0795-z Li Y, 2016, INT J ENV RES PUB HE, V13, DOI 10.3390/ijerph13030350 Liang QQ, 2018, MEDICINE, V97, DOI 10.1097/MD.0000000000012717 Liu YY, 2019, ENVIRON INT, V133, DOI 10.1016/j.envint.2019.105222 Pham LH, 2017, ENVIRON GEOCHEM HLTH, V39, P517, DOI 10.1007/s10653-016-9831-3 Magnusson B., 2014, EURACHEM GUIDE FITNE, V2nd Meryem B, 2016, J RARE EARTH, V34, P1156, DOI 10.1016/S1002-0721(16)60148-5 Moore RET, 2018, RSC ADV, V8, P38022, DOI 10.1039/c8ra06794e Pagano G, 2017, RARE EARTH ELEMENTS IN HUMAN AND ENVIRONMENTAL HEALTH: AT THE CROSSROADS BETWEEN TOXICITY AND SAFETY, P1, DOI 10.1201/9781315364735 Pagano G, 2019, ENVIRON RES, V171, P493, DOI 10.1016/j.envres.2019.02.004 Pagano G, 2015, ENVIRON RES, V142, P215, DOI 10.1016/j.envres.2015.06.039 Pagano G, 2015, ECOTOX ENVIRON SAFE, V115, P40, DOI 10.1016/j.ecoenv.2015.01.030 Rim Kyung-Taek, 2016, Toxicology and Environmental Health Sciences, V8, P189, DOI 10.1007/s13530-016-0276-y Schmied A, 2021, CHEMOSPHERE, V285, DOI 10.1016/j.chemosphere.2021.131425 Takyi SA, 2021, CHEMOSPHERE, V280, DOI 10.1016/j.chemosphere.2021.130677 Tong SM, 2022, ENVIRON SCI POLLUT R, V29, P26498, DOI 10.1007/s11356-021-17418-1 World Medical Association, 2013, JAMA-J AM MED ASSOC Zhang L, 2013, J SEP SCI, V36, P2158, DOI 10.1002/jssc.201300100 NR 30 TC 0 Z9 0 U1 0 U2 0 PD AUG 26 PY 2022 VL 10 AR 942441 DI 10.3389/fenvs.2022.942441 WC Environmental Sciences SC Environmental Sciences & Ecology UT WOS:000892148900001 DA 2022-12-14 ER PT J AU Wielgosz, RI Kaarls, R AF Wielgosz, Robert I. Kaarls, Robert TI International Activities in Metrology in Chemistry SO CHIMIA DT Article DE BIPM; Calibration; Metrology; Traceability; Uncertainty AB In today's economy goods and information are being exchanged globally, international travel is commonplace as is the cross-boarder transport of livestock and agricultural products, and this trend is set to continue. Just as important are issues that impinge on our quality of life, such as health care, the environment and food quality. A strong international measurement and standards infrastructure is critical to assure equity in trade and a high quality of life, by ensuring that products and services meet their specifications. In the field of chemical measurements, certified reference materials (CRMs), measurement standards and reference measurement results provide stated references upon which analytical laboratories can anchor their measurement results. The traceability of measurement results to internationally accepted stated references, together with their stated measurement uncertainties, as described in ISO/IEC 17025, provides the basis for their comparability and global acceptance. Recent global activities which are succeeding in developing a system for the international acceptance of chemical measurements are described, notably: activities of the National Metrology Institutes and the BIPM; the Mutual Recognition Arrangement of the International Committee of Weights and Measures (CIPM MRA) for National Calibration Certificates and Measurement Capabilities; the Inter-Laboratory Comparisons organised through the working groups of the Consultative Committee for Amount of Substance - Metrology in Chemistry (CCQM), and the activities of the Joint Committee for Traceability in Laboratory Medicine (JCTLM). C1 [Wielgosz, Robert I.] Bur Int Poids & Mesures, F-92312 Sevres, France. RP Wielgosz, RI (corresponding author), Bur Int Poids & Mesures, Pavillon Breteuil, F-92312 Sevres, France. EM rwielgosz@bipm.org CR [Anonymous], 2008, EVALUATION MEASUREME Ellison S. L. R., 2000, EURACHEM CITAC GUID Greenberg RR, 2008, METROLOGIA, V45, DOI 10.1088/0026-1394/45/1A/08016 *ISO, ISO175112003 Kaarls R, 1997, METROLOGIA, V34, P1, DOI 10.1088/0026-1394/34/1/1 Viallon J, 2006, METROLOGIA, V43, P441, DOI 10.1088/0026-1394/43/5/016 Viallon J., 2006, METROLOGIA S, V43, DOI DOI 10.1088/0026-1394/43/1A/08010 Viallon J, 2009, METROLOGIA, V46, DOI 10.1088/0026-1394/46/1A/08015 Wielgosz RI, 2008, METROLOGIA, V45, DOI 10.1088/0026-1394/45/1A/08002 NR 9 TC 1 Z9 1 U1 0 U2 7 PY 2009 VL 63 IS 10 BP 606 EP 612 DI 10.2533/chimia.2009.606 WC Chemistry, Multidisciplinary SC Chemistry UT WOS:000271524800002 DA 2022-12-14 ER PT J AU Onache, AP Badulescu, A Dumitru, AM Sumedrea, DI Popescu, CF AF Onache, Anca P. Badulescu, Adriana Dumitru, Anamaria M. Sumedrea, Dorin I. Popescu, Carmen F. TI Comparison of some DNA extraction methods from monovarietal must and wines SO NOTULAE BOTANICAE HORTI AGROBOTANICI CLUJ-NAPOCA DT Article DE DNA quality; grapevine; spectrophotometry; SSR markers; traceability ID VINIFERA L. DNA; VITIS-VINIFERA; COMMERCIAL WINES; AUTHENTICATION; IDENTIFICATION; PROTOCOLS; NUCLEAR; PCR AB The methods applied for DNA extraction from must and wine samples with monovarietal origin are presented and discussed aiming to prove the quality of extracted DNA and its good properties for further use in molecular tests. In the present research were compared four different DNA extraction methods from must and wine samples obtained from eleven V. vinifera varieties (five grapevine varieties for white wines and six grapevine varieties for red wines, respectively). Taking into consideration the amounts of obtained DNA, the concentrations and purities of the final DNA extracts, were stood out two modified methods. For all must samples, very efficient was the second method, which allowed obtaining a mean value of 87.9 ng mu l-1 for the DNA concentration with 1.55 purity. Among the tested procedures, for monovarietal wine samples, the fourth method proved to be the most efficient which brought a mean value of 64.7 ng mu l-1 for DNA concentration with 1.66 purity. This method adequate for wine samples involves two CTAB solution treatments and the RNase treatment applied before DNA resuspension. The DNA from must and wine extracts and the DNA from leaves of the corresponding grapevine varieties were amplified with five specific microsatellite primers (VVS2, VVMD27, VVMD32, VrZAG79 and VrZAG62). The aspects of pattern profiles were analysed in parallel and proved that the extracted DNA was suitable for amplification with these specific V. vinifera primers. The two selected extraction procedures are considered good for research purposes and ensure obtaining of good-quality extracted DNA from musts and one-year old wines. C1 [Onache, Anca P.; Badulescu, Adriana; Dumitru, Anamaria M.; Sumedrea, Dorin I.; Popescu, Carmen F.] Natl Res & Dev Inst Biotechnol Hort, Stefanesti,Calea Bucuresti,Pitesti,37,Stefanesti, Arges, Romania. C3 National Research & Development Institute for Biotechnology in Horticulture Stefanesti-Arges RP Popescu, CF (corresponding author), Natl Res & Dev Inst Biotechnol Hort, Stefanesti,Calea Bucuresti,Pitesti,37,Stefanesti, Arges, Romania. EM anca27il@yahoo.com; badulescuadriana18@yahoo.com; anamaria.ilina@yahoo.com; dsumedrea@yahoo.com; carm3n_popescu@yahoo.com CR Agrimonti C, 2018, EUR FOOD RES TECHNOL, V244, P2127, DOI 10.1007/s00217-018-3121-5 Akkurt M, 2012, GENET MOL RES, V11, P2343, DOI 10.4238/2012.August.13.8 Arroyo-Garcia R, 2002, GENOME, V45, P1142, DOI [10.1139/g02-087, 10.1139/G02-087] Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Bigliazzi J, 2012, AM J ENOL VITICULT, V63, P568, DOI 10.5344/ajev.2012.12014 Briciu D, 2010, B UNIV AGRIC SCI VET, V67, P1843, DOI [10.15835/buasvmcn-asb, DOI 10.15835/BUASVMCN-ASB] Cabezas JA, 2011, BMC PLANT BIOL, V11, DOI 10.1186/1471-2229-11-153 Catalano V, 2016, J AGR FOOD CHEM, V64, P6969, DOI 10.1021/acs.jafc.6b02560 Drabek J, 2008, EUR FOOD RES TECHNOL, V226, P491, DOI 10.1007/s00217-007-0561-8 Faria MA, 2000, J AGR FOOD CHEM, V48, P1096, DOI 10.1021/jf990837h Garcia-Beneytez E, 2002, J AGR FOOD CHEM, V50, P6090, DOI 10.1021/jf0202077 Gheorghe RN, 2010, ROM BIOTECH LETT, V15, P26 Gilmanov KKh, 2020, FOOD SYSTEMS, V3, P11 Harta M., 2011, Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Horticulture, V68, P143 Isci B, 2014, J I BREWING, V120, P238, DOI 10.1002/jib.129 Lee A, 2017, S CHINA MORNING POST Lodhi M. A., 1994, Plant Molecular Biology Reporter, V12, P6, DOI 10.1007/BF02668658 Nakamura S, 2007, J AGR FOOD CHEM, V55, P10388, DOI 10.1021/jf072407u Nazhad Nafiseh Rigi, 2008, Pak J Biol Sci, V11, P1436, DOI 10.3923/pjbs.2008.1436.1442 Oganesyants LA, 2018, FOOD RAW MATER, V6, P438, DOI 10.21603/2308-4057-2018-2-438-448 Pereira L, 2017, FOOD CHEM, V216, P80, DOI 10.1016/j.foodchem.2016.07.185 Pereira L, 2011, AM J ENOL VITICULT, V62, P547, DOI 10.5344/ajev.2011.10022 Savazzini F, 2006, ANAL CHIM ACTA, V563, P274, DOI 10.1016/j.aca.2005.10.078 Scali M., 2014, Advances in Bioscience and Biotechnology, V5, P142, DOI 10.4236/abb.2014.52018 Siret R, 2000, J AGR FOOD CHEM, V48, P5035, DOI 10.1021/jf991168a Siret R, 2002, J AGR FOOD CHEM, V50, P3822, DOI 10.1021/jf011462e Vignani R, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0211962 NR 27 TC 0 Z9 0 U1 0 U2 4 PY 2021 VL 49 IS 2 AR 12349 DI 10.15835/nbha49212349 WC Plant Sciences SC Plant Sciences UT WOS:000668514700001 DA 2022-12-14 ER PT J AU Miguel, ALR Moreira, RPL de Oliveira, AF AF Miguel, Anna Luisa Ribeiro Moreira, Renata Pereira Lopes de Oliveira, Andre Fernando TI ISO/IEC 17025: HISTORY AND INTRODUCTION OF CONCEPTS SO QUIMICA NOVA DT Article DE quality management system; quality assurance; laboratory accreditation; traceability; reliable results ID ACCREDITATION AB Quality is an increasingly present concept nowadays, so meeting the customers' needs, who buy and use products and hire services, becomes essential. For laboratories, the concept is applied to the reliability and traceability of the results produced and presents itself not only to meet the customer's needs, but also to allow the signing of agreements in the international trade. The concept of Quality in a laboratory can be carried out from the elaboration of a Quality Management System (QMS). To this end, the normative document ISO/IEC 17025, internationally accepted, has been changing over the years aimed at instructing the elaboration of a management system which proves the technical capacity of testing and calibration laboratories and guides the generation of reliable results. The document is in its third version as a standard, the most current one published in 2017, and it presents requirements to achieve the proposed objective and the quality assurance. In the face of the importance of these concepts and the unquestionable need of laboratories to provide reliable and traceable results, this article presents the norm history and its most recent changes. Its intent is support laboratories whose objective is to implement a QMS according to this normative reference. C1 [Miguel, Anna Luisa Ribeiro; Moreira, Renata Pereira Lopes; de Oliveira, Andre Fernando] Univ Fed Vicosa, Dept Quim, BR-36570000 Vicosa, MG, Brazil. C3 Universidade Federal de Vicosa RP Miguel, ALR (corresponding author), Univ Fed Vicosa, Dept Quim, BR-36570000 Vicosa, MG, Brazil. EM annaluisarmiguel@gmail.com CR Berhe L., 2016, INTELLIGENT INFORM M, V8, P143 Bodnar M, 2013, TRAC-TREND ANAL CHEM, V51, P117, DOI 10.1016/j.trac.2013.06.011 Christelsohn M, 1997, ACCREDIT QUAL ASSUR, V2, P82 Dizadji F., 2004, Accreditation and Quality Assurance, V9, P317, DOI 10.1007/s00769-003-0659-z Dos santos L. L., 2010, AN 6 C NAC EXC GEST Gomes P. J. P., 2004, CADERNOS BAD, V2, P6 Masson P, 2007, J CHROMATOGR A, V1158, P168, DOI 10.1016/j.chroma.2007.03.003 NATA, 2018, 170252017 NATA ISOIE Olivares I. R. B., 2009, GESTAO QUALIDADE LAB, V2nd RMMG, 2018, 170252017 RMMG ISOIE Rozet E, 2011, TRAC-TREND ANAL CHEM, V30, P797, DOI 10.1016/j.trac.2010.12.009 Squirrell A, 2008, ACCREDIT QUAL ASSUR, V13, P543, DOI 10.1007/s00769-008-0418-2 STAATS G, 1993, FRESEN J ANAL CHEM, V345, P739, DOI 10.1007/BF00323001 UNIDO, 2009, 17025 UNIDO ISO United Nations Industrial Development Organisation, 2017, IMPR BUS ENV Van de Leemput P. J. H. A. M., 2000, ACCREDIT QUAL ASSUR, V5, P394 Wong SK, 2017, ACCREDIT QUAL ASSUR, V22, P103, DOI 10.1007/s00769-017-1256-x NR 17 TC 0 Z9 0 U1 0 U2 7 PY 2021 VL 44 IS 6 BP 792 EP 796 DI 10.21577/0100-4042.20170726 WC Chemistry, Multidisciplinary SC Chemistry UT WOS:000684862600014 DA 2022-12-14 ER PT J AU Duarte, B Melo, J Mamede, R Carreiras, J Figueiredo, A Fonseca, VF de Sousa, ML Silva, AB AF Duarte, Bernardo Melo, Juliana Mamede, Renato Carreiras, Joao Figueiredo, Andreia Fonseca, Vanessa F. de Sousa, Miguel Leao Silva, Anabela B. TI In the trail of ?Macade Alcobaca? protected geographical indication (PGI): Multielement chemometrics as a security and anti-fraud tool to depict clones, cultivars and geographical origins and nutritional value SO JOURNAL OF FOOD COMPOSITION AND ANALYSIS DT Article DE Elemental fingerprints; Chemometrics; Traceability; Authenticity; “ Macade Alcobaca” certification ID CLASSIFICATION; TRACEABILITY; FOOD; XRF AB Food fraud associated with the intentional mislabelling of non-Protected Geographical Indication (PGI) is a concern for consumers. "Maca similar to de Alcobaca" (Alcobaca apple) is one of the oldest Portuguese PGI products, characteristic of the main apple-growing regions in the country, being of utmost importance to develop trace-ability and authenticity tools to depict the PGI certification status of these products. Pulp multielement signa-tures were able to discriminate with moderate accuracy (65.7 %) different Royal Gala clones, grown within the same cultivation area. Moreover, Variable Importance in Projection Partial Least-Squares Discriminant Analysis (VIP-PLS-DA) allowed the discrimination of the Royal Gala samples from different PGI producers with 70.0 % accuracy. Apple PGI cultivars were also discriminated accurately (82.0 %). Expanding the approach to non-PGI production areas, several cultivars could be distinguished, according to their provenance with high accuracy, namely Starking (100.0 % accuracy), Granny Smith (100.0 % accuracy), Fuji (100.0 % accuracy), Royal Gala (86.7 % accuracy) and Reineta (90.3 % accuracy). The PGI fruit's microelement nutritional traits highlighted their higher nutritional value, an important trait for food fraud reduction, informing the consumer of the product authenticity, and providing insights on the nutritional value of these high-value market products. C1 [Duarte, Bernardo; Mamede, Renato; Carreiras, Joao; Fonseca, Vanessa F.] Univ Lisbon, Fac Ciencias, MARE Marine & Environm Sci Ctr, P-1749016 Campo Grande, Lisbon, Portugal. [Duarte, Bernardo; Mamede, Renato; Carreiras, Joao; Fonseca, Vanessa F.] Univ Lisbon, Fac Ciencias, Aquat Res Network Associated Lab, ARNET, P-1749016 Campo Grande, Lisbon, Portugal. [Duarte, Bernardo; Silva, Anabela B.] Univ Lisbon, Fac Ciencias, Dept Biol Vegetal, P-1749016 Campo Grande, Lisbon, Portugal. [Melo, Juliana] Univ Lisbon, Fac Ciencias, Ctr Ecol Evolut & Environm Changes, cE3c, Edificio C2,Piso 5, P-1749016 Campo Grande, Lisboa, Portugal. [Figueiredo, Andreia] Univ Lisbon, Biosyst & Integrat Sci Inst, Fac Ciencias, Dept Biol Vegetal, P-1749016 Campo Grande, Lisboa, Portugal. [Fonseca, Vanessa F.] Univ Lisbon, Fac Ciencias, Dept Biol Anim, P-1749016 Campo Grande, Lisbon, Portugal. [de Sousa, Miguel Leao] Inst Nacl Invest Agr & Vet, Estacao Nacl Fruticultura Vieira Natividade INIAV, P-2460059 Oeiras, Portugal. C3 Universidade de Lisboa; Universidade de Lisboa; Universidade de Lisboa; Universidade de Lisboa; Universidade de Lisboa; Universidade de Lisboa RP Duarte, B (corresponding author), Univ Lisbon, Fac Ciencias, MARE Marine & Environm Sci Ctr, P-1749016 Campo Grande, Lisbon, Portugal.; Duarte, B (corresponding author), Univ Lisbon, Fac Ciencias, Aquat Res Network Associated Lab, ARNET, P-1749016 Campo Grande, Lisbon, Portugal. EM baduarte@fc.ul.pt CR Albergamo A, 2018, J FOOD SCI, V83, P2933, DOI 10.1111/1750-3841.14382 Albuquerque R, 2016, SCI REP-UK, V6, DOI 10.1038/srep27787 Almeida DPF, 2017, INT J FOOD PROP, V20, P2206, DOI 10.1080/10942912.2016.1233431 Andreasen R, 2021, FRONT ECOL EVOL, V8, DOI 10.3389/fevo.2020.588422 APMA, 2014, ASS PROD MAC ALC CAD, P13 Arbuckle NSM, 2014, HYDROBIOLOGIA, V725, P115, DOI 10.1007/s10750-013-1608-4 Banerjee P, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0080940 Barendse J, 2019, CURR BIOL, V29, pR198, DOI 10.1016/j.cub.2019.02.014 Bat KB, 2016, FOOD CHEM, V203, P86, DOI 10.1016/j.foodchem.2016.02.039 Bennion M, 2021, FOOD CONTROL, V121, DOI 10.1016/j.foodcont.2020.107515 Bua GD, 2017, FOOD ANAL METHOD, V10, P1181, DOI 10.1007/s12161-016-0680-6 Campbell LM, 2005, CAN J FISH AQUAT SCI, V62, P1161, DOI 10.1139/F05-027 Coelho I, 2019, J FOOD COMPOS ANAL, V77, P1, DOI 10.1016/j.jfca.2018.12.005 Costas-Rodriguez M, 2010, ANAL CHIM ACTA, V664, P121, DOI 10.1016/j.aca.2010.03.003 DGADR, 2016, DIR GER AGR DES RUR, P68 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Duarte B, 2022, J FOOD COMPOS ANAL, V109, DOI 10.1016/j.jfca.2022.104479 Duarte B, 2022, MOLECULES, V27, DOI 10.3390/molecules27041298 Duarte B, 2022, FOOD CONTROL, V133, DOI 10.1016/j.foodcont.2021.108592 European Commission, 2015, COMMISSION IMPLEMENT European Commission, 2002, OFFICIAL J EUROPEAN, P8, DOI DOI 10.1017/CBO9781107415324.004 European Food and Safety Authority (EFSA), 2019, DIET REF VAL EU European Union, 2022, EAMBROSIA EU GEOGR I Feng X, 2021, CRIT REV FOOD SCI, V61, P2340, DOI 10.1080/10408398.2020.1776677 Fonseca VF, 2022, FOOD CONTROL, V134, DOI 10.1016/j.foodcont.2021.108735 Ghidini S, 2019, MOLECULES, V24, DOI 10.3390/molecules24091812 Ghidini S, 2019, FOOD CHEM, V280, P321, DOI 10.1016/j.foodchem.2018.12.075 Gonzalvez A, 2009, TRAC-TREND ANAL CHEM, V28, P1295, DOI 10.1016/j.trac.2009.08.001 Harmankaya M, 2012, ENVIRON MONIT ASSESS, V184, P5415, DOI 10.1007/s10661-011-2349-3 Inacio M, 2008, J GEOCHEM EXPLOR, V98, P22, DOI 10.1016/j.gexplo.2007.10.004 Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Kuzin A, 2021, PLANTS-BASEL, V10, DOI 10.3390/plants10122624 Leal MC, 2015, TRENDS BIOTECHNOL, V33, P331, DOI 10.1016/j.tibtech.2015.03.003 Lim CM, 2021, CURR RES FOOD SCI, V4, P45, DOI 10.1016/j.crfs.2021.02.002 Liu XH, 2022, FOOD CHEM, V391, DOI 10.1016/j.foodchem.2022.133269 Marschner H., 1995, MINERAL NUTR HIGHER, Vsecond Molinaro AM, 2005, BIOINFORMATICS, V21, P3301, DOI 10.1093/bioinformatics/bti499 Mottese AF, 2018, J FOOD COMPOS ANAL, V72, P66, DOI 10.1016/j.jfca.2018.05.009 Mottese AF, 2020, FOOD CONTROL, V110, DOI 10.1016/j.foodcont.2019.107004 R Core Team, 2020, R LANG ENV STAT COMP Rajapaksha D, 2017, X-RAY SPECTROM, V46, P220, DOI 10.1002/xrs.2748 Ricardo F, 2021, ECOL INDIC, V129, DOI 10.1016/j.ecolind.2021.108017 Rohart F, 2017, PLOS COMPUT BIOL, V13, DOI 10.1371/journal.pcbi.1005752 Sanchez G, 2013, PACKAGE DISCRIMINER SPARR M C, 1970, Communications in Soil Science and Plant Analysis, V1, P241, DOI 10.1080/00103627009366265 Treder W, 2022, SCI HORTIC-AMSTERDAM, V297, DOI 10.1016/j.scienta.2022.110975 USEPA, 1996, 3052 USEPA Varra MO, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107778 Varra MO, 2019, ITAL J FOOD SAF, V8, P21, DOI 10.4081/ijfs.2019.7872 World Health Organization, 2014, EURRC6414 WHO Wyszkowski M, 2020, AGRICULTURE-BASEL, V10, DOI 10.3390/agriculture10090398 Zhang ZY, 2021, GEOCHIM COSMOCHIM AC, V313, P99, DOI 10.1016/j.gca.2021.08.010 NR 52 TC 0 Z9 0 U1 0 U2 0 PD JAN PY 2023 VL 115 AR 104976 DI 10.1016/j.jfca.2022.104976 WC Chemistry, Applied; Food Science & Technology SC Chemistry; Food Science & Technology UT WOS:000878627900001 DA 2022-12-14 ER PT J AU Acciaro, M Decandia, M Sitzia, M Manca, C Giovanetti, V Cabiddu, A Addis, M Rassu, SPG Molle, G Dimauro, C AF Acciaro, Marco Decandia, Mauro Sitzia, Maria Manca, Carla Giovanetti, Valeria Cabiddu, Andrea Addis, Margherita Rassu, S. Piegiacomo G. Molle, Giovanni Dimauro, Corrado TI Discriminant analysis as a tool to identify bovine and ovine meat produced from pasture or stall-fed animals SO ITALIAN JOURNAL OF ANIMAL SCIENCE DT Article DE Multivariate statistical analysis; sheep; cattle; meat; traceability ID FATTY-ACID-COMPOSITION; RUMEN; AUTHENTICATION; PROFILE; MUSCLE; MILK; CHEMOMETRICS; SPECTROSCOPY; COMMUNITIES; METABOLISM AB This work evaluated the reliability of the multivariate statistical analysis to discriminate the feeding system and the species of ruminants using their intramuscular fatty acids (FA) profile. FA composition of 53 meat samples (longissimus dorsi muscle) from animals of different species (sheep and cattle) raised with different feeding systems (pasture and stall-fed) (4 groups overall) was determined and expressed as % fatty acid methyl ester (FAME). A stepwise discriminant analysis (SDA) was applied to the full set of FA to select the variables that best discriminated between feeding systems and animal species. The selected variables were then submitted to a canonical discriminant analysis (CDA) to test the ability of those variables in discriminating against the four groups. Discriminant analysis (DA) was then exploited to classify meat samples. From the 62 initial variables detected in the FA profile, 24 were retained in the SDA. The subsequent CDA developed by using the selected variables, significantly discriminated the four groups (Hotelling's testp < 0.0001) by extracting three canonical functions. Heptadecenoic acid C17:1 c10, seemed to play a pivotal role both in discriminating species and feeding system while some 18:1 isomers (C18:1 c12, C18:1 c13 C18:1 t13/t14) together with CLA c9, t11 and omega-3 were important in discriminating feeding systems. Multivariate statistical analysis of FA was able to track both the species and the feeding system of source animals with good accuracy. C1 [Acciaro, Marco; Decandia, Mauro; Sitzia, Maria; Manca, Carla; Giovanetti, Valeria; Cabiddu, Andrea; Addis, Margherita; Molle, Giovanni] AGRIS Sardegna, Olmedo, Italy. [Rassu, S. Piegiacomo G.; Dimauro, Corrado] Univ Sassari, Dipartimento Agr, Sassari, Italy. C3 University of Sassari RP Acciaro, M (corresponding author), AGRIS Sardegna, Olmedo, Italy. EM macciaro@agrisricerca.it CR Addis M, 2013, SMALL RUMINANT RES, V115, P51, DOI 10.1016/j.smallrumres.2013.08.002 Alves SP, 2006, J DAIRY SCI, V89, P170, DOI 10.3168/jds.S0022-0302(06)72081-1 Arsalane A, 2019, J FOOD MEAS CHARACT, V13, P1730, DOI 10.1007/s11694-019-00090-y Buccioni A, 2012, ANIM FEED SCI TECH, V174, P1, DOI 10.1016/j.anifeedsci.2012.02.009 Cabiddu A, 2014, GRASS FORAGE SCI, V69, P678, DOI 10.1111/gfs.12082 Daley CA, 2010, NUTR J, V9, DOI 10.1186/1475-2891-9-10 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 De Maesschalck R, 2000, CHEMOMETR INTELL LAB, V50, P1, DOI 10.1016/S0169-7439(99)00047-7 Doreau M, 2011, ANIM PROD SCI, V51, P19, DOI 10.1071/AN10043 Duncan Alan J., 2008, V195, P89, DOI 10.1007/978-3-540-72422-3_4 Enjalbert F, 2017, J APPL MICROBIOL, V123, P782, DOI 10.1111/jam.13501 Esteves C, 2019, ARQ BRAS MED VET ZOO, V71, P303, DOI 10.1590/1678-4162-9376 Fruet APB, 2018, MEAT SCI, V140, P112, DOI 10.1016/j.meatsci.2018.03.008 Granato D, 2018, COMPR REV FOOD SCI F, V17, P663, DOI 10.1111/1541-4337.12341 Griinari JM, 1999, ADVANCES IN CONJUGATED LINOLEIC ACID RESEARCH, VOL 1, P180 Huang Y, 2015, MEAT SCI, V101, P5, DOI 10.1016/j.meatsci.2014.10.024 Jami E, 2012, ANAEROBE, V18, P338, DOI 10.1016/j.anaerobe.2012.04.003 Kong YH, 2010, FEMS MICROBIOL ECOL, V74, P612, DOI 10.1111/j.1574-6941.2010.00977.x Laverroux S, 2011, LIPIDS, V46, P843, DOI 10.1007/s11745-011-3584-7 Lee HJ, 2012, APPL ENVIRON MICROB, V78, P5983, DOI 10.1128/AEM.00104-12 Lisitsyn A. B., 2013, Scientific Journal of Animal Science, V2, P124 Lopez-Pedrouso M, 2020, FOODS, V9, DOI 10.3390/foods9020176 Lourenco M, 2008, ANIM FEED SCI TECH, V145, P418, DOI 10.1016/j.anifeedsci.2007.05.043 Maia MRG, 2010, BMC MICROBIOL, V10, DOI 10.1186/1471-2180-10-52 Mardia KV, 2000, BIOMETRIKA, V87, P285, DOI 10.1093/biomet/87.2.285 Moloney AP, 2018, IRISH J AGR FOOD RES, V57, P84, DOI 10.1515/ijafr-2018-0009 Monahan FJ, 2018, MEAT SCI, V144, P2, DOI 10.1016/j.meatsci.2018.05.008 Moon YH, 2010, ANIM SCI J, V81, P642, DOI 10.1111/j.1740-0929.2010.00782.x Nudda A, 2008, LIVEST SCI, V118, P195, DOI 10.1016/j.livsci.2008.01.020 Osorio MT, 2013, FOOD CHEM, V141, P2795, DOI 10.1016/j.foodchem.2013.05.118 Piasentier E, 2003, MEAT SCI, V64, P239, DOI 10.1016/S0309-1740(02)00183-3 Prache S, 2020, ANIMAL, V14, P854, DOI 10.1017/S1751731119002568 Rohman A, 2019, J ADV VET ANIM RES, V6, P9, DOI 10.5455/javar.2019.f306 Santos-Silva J, 2019, MEAT SCI, V147, P28, DOI 10.1016/j.meatsci.2018.08.015 Scollan N, 2006, MEAT SCI, V74, P17, DOI 10.1016/j.meatsci.2006.05.002 Sinanoglou VJ, 2013, SMALL RUMINANT RES, V113, P1, DOI 10.1016/j.smallrumres.2013.01.008 SMITH SB, 1986, J NUTR, V116, P1279, DOI 10.1093/jn/116.7.1279 Toral PG, 2016, J DAIRY SCI, V99, P301, DOI 10.3168/jds.2015-10292 Troegeler-Meynadier A, 2003, J DAIRY SCI, V86, P4054, DOI 10.3168/jds.S0022-0302(03)74017-X ULBRICHT TLV, 1991, LANCET, V338, P985, DOI 10.1016/0140-6736(91)91846-M Vasta V, 2011, MEAT SCI, V87, P282, DOI 10.1016/j.meatsci.2010.11.003 Vlaeminck B, 2005, J DAIRY SCI, V88, P1031, DOI 10.3168/jds.S0022-0302(05)72771-5 Wu DQ, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0156835 Xu L, 2011, J AUTOM METHOD MANAG, DOI 10.1155/2011/323196 Zhou M, 2009, APPL ENVIRON MICROB, V75, P6524, DOI 10.1128/AEM.02815-08 NR 45 TC 1 Z9 1 U1 1 U2 10 PD DEC 14 PY 2020 VL 19 IS 1 BP 1065 EP 1070 DI 10.1080/1828051X.2020.1816507 WC Agriculture, Dairy & Animal Science; Agriculture, Multidisciplinary; Veterinary Sciences SC Agriculture; Veterinary Sciences UT WOS:000570073000001 DA 2022-12-14 ER PT J AU Chen, LJ Yang, ZL Han, LJ AF Chen, Longjian Yang, Zengling Han, Lujia TI A Review on the Use of Near-Infrared Spectroscopy for Analyzing Feed Protein Materials SO APPLIED SPECTROSCOPY REVIEWS DT Review DE Feed; protein materials; near-infrared; traceability; chemical content ID AMINO-ACID-COMPOSITION; ANIMAL BY-PRODUCTS; FISH-MEAL; REFLECTANCE SPECTROSCOPY; BONE MEAL; CHEMICAL-COMPOSITION; COMPOUND FEEDINGSTUFFS; INGREDIENT COMPOSITION; DISCRIMINANT-ANALYSIS; CALIBRATION TRANSFER AB Due to the strong link between feed and food, the quality and safety of feed protein materials are of public concern. Firstly, this article summarizes the recent advances in near-infrared reflectance spectroscopy (NIRS) techniques applied to the chemical content and traceability analyses of feed protein materials. The results show the potential of NIRS as an efficient first-line screening tool for monitoring the quality and safety of feed protein materials. Finally, future prospects and the need to increase the feasibility of industrial applications and improve the limit of detection of NIRS techniques for feed protein materials are discussed. C1 [Chen, Longjian; Yang, Zengling; Han, Lujia] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China. C3 China Agricultural University RP Han, LJ (corresponding author), China Agr Univ, Coll Engn, East Campus,17 Qing Hua Dong Lu,POB 191, Beijing 100083, Peoples R China. EM hanlj@cau.edu.cn CR Abbas O, 2010, ANAL BIOANAL CHEM, V397, P1965, DOI 10.1007/s00216-010-3706-8 [Anonymous], THESIS Baeten V, 2005, ANAL BIOANAL CHEM, V382, P149, DOI 10.1007/s00216-005-3193-5 Baeten V, 2002, GRASAS ACEITES, V53, P45 Baeten V., 2004, PROCEEDINGS OF THE 1 Bakalli RI, 2000, J APPL POULTRY RES, V9, P204, DOI 10.1093/japr/9.2.204 Bellorini S, 2005, ANAL BIOANAL CHEM, V382, P1073, DOI 10.1007/s00216-005-3213-5 Burns D.A., 2008, ANAL BIOANAL CHEM Campagnoli A., 2004, BIOTECHNOL AGRON SOC, V8, P235 Chen GL, 2011, ANIM FEED SCI TECH, V165, P111, DOI 10.1016/j.anifeedsci.2011.02.004 Cozzolino D, 2002, AQUACULT NUTR, V8, P149, DOI 10.1046/j.1365-2095.2002.00206.x Cozzolino D, 2009, CIENC INVESTIG AGRAR, V36, P209 Daszykowski M, 2008, ANALYST, V133, P1523, DOI 10.1039/b803687j De la Haba MJ, 2007, J NEAR INFRARED SPEC, V15, P81, DOI 10.1255/jnirs.688 European Commission, 2009, OFF J EUR COMM L, VL54, P103 European Commission, 2003, OFF J EUR UNION L, VL339, P78 European Commission (EC), 2001, OFFICIAL J EUROPEA L, V147, P1 Fernandez-Ahumada E, 2008, J AGR FOOD CHEM, V56, P10135, DOI 10.1021/jf801881n Fernandez-Cabanas VM, 2008, APPL SPECTROSC, V62, P51, DOI 10.1366/000370208783412663 Fontaine J, 2001, J AGR FOOD CHEM, V49, P57, DOI 10.1021/jf000946s Fumiere O, 2006, ANAL BIOANAL CHEM, V385, P1045, DOI 10.1007/s00216-006-0533-z Garrido-Varo A., 1998, WORKSHOP ON IDENTIFI Garrido-Varo A, 2008, J NEAR INFRARED SPEC, V16, P281, DOI 10.1255/jnirs.788 Garrido-Varo Ana, 2005, Biotechnologie Agronomie Societe et Environnement, V9, P3 Gonzalez-Martin I, 2006, TALANTA, V69, P706, DOI 10.1016/j.talanta.2005.11.015 Graham SF, 2012, FOOD CHEM, V132, P1614, DOI 10.1016/j.foodchem.2011.11.136 Grahn H.F., 2007, TECHNIQUES APPL HYPE, P289, DOI [10.1002/9780470010884.ch12, DOI 10.1002/9780470010884.CH12, DOI 10.1002/9780470010884] Kawasaki M, 2008, COMPUT ELECTRON AGR, V63, P22, DOI 10.1016/j.compag.2008.01.006 Kryeziu A., 2007, World Poultry Science Association, Proceedings of the 16th European Symposium on Poultry Nutrition, Strasbourg, France, 26-30 August, 2007, P561 Meador M. M., 2011, CHI1008 CHINA PEOPLE Ministry of Agriculture of the People's Republic of China, 2004, MOA NO 40 THE SAFETY Murray I, 2001, J NEAR INFRARED SPEC, V9, P297, DOI 10.1255/jnirs.315 Niu Z. Y., 2005, THESIS Marin DCP, 2008, J FOOD QUALITY, V31, P96, DOI 10.1111/j.1745-4557.2007.00186.x Perez-Marin D, 2008, APPL SPECTROSC, V62, P536, DOI 10.1366/000370208784344389 Perez-Marin DC, 2004, ANIM FEED SCI TECH, V116, P333, DOI 10.1016/j.anifeedsci.2004.05.002 Perez-Marin D, 2009, TALANTA, V80, P48, DOI 10.1016/j.talanta.2009.06.026 Pierna JAF, 2011, ANAL CHIM ACTA, V705, P30, DOI 10.1016/j.aca.2011.03.023 Pierna JAF, 2004, J CHEMOMETR, V18, P341, DOI 10.1002/cem.877 Piraux F., 2000, Biotechnologie, Agronomie, Societe et Environnement, V4, P226 Prince MJ, 2003, REV SCI TECH OIE, V22, P37, DOI 10.20506/rst.22.1.1389 Riccioli C, 2011, APPL SPECTROSC, V65, P771, DOI 10.1366/10-06177 Shi G.-L., 2010, FOSSIL PLANTS OLIGOC, P257 Shi GT, 2010, J NEAR INFRARED SPEC, V18, P217, DOI 10.1255/jnirs.878 Soldado A, 2011, SPAN J AGRIC RES, V9, P41 Thiry Etienne, 2004, Biotechnologie Agronomie Societe et Environnement, V8, P221 Tlustos C., 2009, ORGAN COMP, V71, P1169 van Kempen T, 1998, ANIM FEED SCI TECH, V76, P139, DOI 10.1016/S0377-8401(98)00207-7 van Raamsdonk LWD, 2007, ANIM FEED SCI TECH, V133, P63, DOI 10.1016/j.anifeedsci.2006.08.004 Xiccato G, 2003, ANIM FEED SCI TECH, V104, P153, DOI 10.1016/S0377-8401(02)00294-8 Yang Z, 2007, J ANIM FEED SCI, V16, P442, DOI 10.22358/jafs/74576/2007 Yang ZL, 2008, ANIM FEED SCI TECH, V147, P357, DOI 10.1016/j.anifeedsci.2008.02.005 NR 52 TC 20 Z9 28 U1 3 U2 159 PD OCT 1 PY 2013 VL 48 IS 7 BP 509 EP 522 DI 10.1080/05704928.2012.756403 WC Instruments & Instrumentation; Spectroscopy SC Instruments & Instrumentation; Spectroscopy UT WOS:000316779700001 DA 2022-12-14 ER PT J AU Knels, R Monig, HJ Wittmann, G von Versen, R Pruss, A AF Knels, Ralf Moenig, Hans-Joachim Wittmann, Georg von Versen, Ruediger Pruss, Axel TI "Eurocode International Blood Labeling System" enables unique identification of all biological products from human origin in accordance with the European Directive 2004/23/EC SO CELL AND TISSUE BANKING DT Article DE Eurocode; Tissue; Cells; Coding; Traceability; European Directive 2006/86/EC AB Due to their limited availability and compatibility, biological products must be exchanged between medical institutions. In addition to a number of national systems and agreements which strive to implement a unique identification and classification of blood products, the ISBT 128 was developed in 1994, followed by the Eurocode in 1998. In contrast to other coding systems, these both make use of primary identifiers as stipulated by the document ISO/IEC 15418 of the International Organization for Standardization (ISO), and thus provide a unique international code. Due to their flexible data structures, which make use of secondary identifiers, both systems are able to integrate additional biological products and their producers. Tissue and cells also constitute a comparable risk to the recipient as that of blood products in terms of false labeling and the danger of infection. However, in contrast to blood products, the exchange of tissue and cells is much more intensively pursued at the international level. This fact is recognised by Directives 2004/23/EC and 2006/86/EC of the European Union (EU), which demand a standardized coding system for cells and tissue throughout the EU. The 2008 workshop agreement of the European Committee for Standardization (CEN) was unique identification by means of a Key Code consisting of country code corresponding to ISO 3166-1, as well as competent authority and tissue establishment. As agreed at the meeting of the Working Group on the European Coding System for Human Tissues and Cells of the Health and Consumers Directorate-General of the European Commission (DG SANCO) held on 19 May 2010 in Brussels, this Key Code could also be used with existing coding systems to provide unique identification and allow EU traceability of all materials from one donation event. Today Eurocode already uses country codes according to ISO 3166-1, and thus the proposed Key Code can be integrated into the current Eurocode data structure and does not need to be introduced separately. The Eurocode product classification for all products is based on its own unique coding system, which can be accessed over the internet by all users who are not themselves members of Eurocode. In summary, it can be said that the standardized single coding system for tissues and cells requires only unique sections in the data structure such the Key Code to fulfil the requirements of the EU Directive. Thus, various systems currently in place in different EU member states can continue to operate if the Key Code as suggested by the EU is integrated into them. The classification and description of each product characteristic is currently being discussed by the DG SANCO Working Group on the European Coding System for Human Tissues and Cells. Following intensive scrutiny in light of the stipulations laid out in EU Directives 2004/23/EC and 2006/86/EC as well as the CEN/ISSS workshop agreements, the Germany Federal Ministry for Health and organisations representing German tissue establishments under the responsibility of the German Society of Transfusion Medicine and Immunohematology, Working Party "Tissue preparations" proposed in 2009 that Eurocode be adopted for the donor identification and product coding of tissue and cells in Germany. The technical details for implementation have already been completed and are presented in the current article. C1 [Knels, Ralf] Eurocode Int Blood Labeling Syst eV, D-01277 Dresden, Germany. [Moenig, Hans-Joachim] German Inst Cell & Tissue Replacement, DIZG, D-12555 Berlin, Germany. [Wittmann, Georg] Klinikum LMU Munchen Grosshadern, Dept Transfus Med & Hemostaseol, D-81377 Munich, Germany. [von Versen, Ruediger] VVC von Versen Int Consultants, D-16348 Wandlitz, Germany. [Pruss, Axel] Charite, Inst Transfus Med, Tissue Bank, D-10117 Berlin, Germany. C3 University of Munich; Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin RP Knels, R (corresponding author), Eurocode Int Blood Labeling Syst eV, Oehmestr 5, D-01277 Dresden, Germany. EM knels@eurocode.org CR [Anonymous], 2006, EUR UN STAND TRAIN I [Anonymous], 2005, EUR REG ORG TISS CEL [Anonymous], 2008, CEN ISSS WORKSH AGR ASHFORD P, 2009, 128 ISBT ICCBBA *EUR, 2004, EUR GUID DAT STRUCT *JOINT TECHN COMM, 2009, 1 ISOIECJTC Knels R, 2002, INFUS THER TRANSFUS, V29, P226 NR 7 TC 2 Z9 2 U1 0 U2 3 PD NOV PY 2010 VL 11 IS 4 SI SI BP 345 EP 352 DI 10.1007/s10561-010-9186-4 WC Cell Biology; Engineering, Biomedical SC Cell Biology; Engineering UT WOS:000288436100005 DA 2022-12-14 ER PT J AU Fu, XP Ying, YB AF Fu, Xiaping Ying, Yibin TI Food Safety Evaluation Based on Near Infrared Spectroscopy and Imaging: A Review SO CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION DT Review DE Chemical hazards; microbiological hazards; physical hazards; transgenic food; food traceability; NIR ID VIRGIN OLIVE OILS; DIFFUSE-REFLECTANCE SPECTROSCOPY; AEROBIC-BACTERIA COUNT; NIR SPECTROSCOPY; RAPID DETECTION; CHEMICAL-COMPOSITION; FECAL CONTAMINATION; PESTICIDE-RESIDUES; QUALITY EVALUATION; MELAMINE DETECTION AB In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food hazards, food authentication, and traceability. Near infrared (NIR) spectroscopy and imaging techniques have gained wide acceptance in many fields because of their advantages over other analytical techniques. Following a brief introduction of NIR spectroscopy and imaging basics, this review mainly focuses on recent NIR spectroscopy and imaging applications for food safety evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; (3) physical hazards detection; (4) new technology-induced food safety concerns; and (5) food traceability. The review shows NIR spectroscopy and imaging to be effective tools that will play indispensable roles for food safety evaluation. In addition, on-line/real-time applications of these techniques promise to be a huge growth field in the near future. C1 [Fu, Xiaping; Ying, Yibin] Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou, Zhejiang, Peoples R China. [Fu, Xiaping; Ying, Yibin] Minist Agr, Key Lab Equipment & Informatizat Environm Control, Hangzhou, Zhejiang, Peoples R China. C3 Zhejiang University; Ministry of Agriculture & Rural Affairs RP Ying, YB (corresponding author), Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou, Zhejiang, Peoples R China. EM yingyb@zju.edu.cn CR Alishahi A, 2010, SPECTROCHIM ACTA A, V75, P1, DOI 10.1016/j.saa.2009.10.001 [Anonymous], GB223882008 CHIN NAT [Anonymous], 13722007 NYT CHIN MI Arana I, 2005, J NEAR INFRARED SPEC, V13, P349, DOI 10.1255/jnirs.566 Ariana DP, 2008, T ASABE, V51, P705, DOI 10.13031/2013.24367 Baker JE, 1999, BIOL CONTROL, V16, P88, DOI 10.1006/bcon.1999.0733 Balabin RM, 2011, TALANTA, V85, P562, DOI 10.1016/j.talanta.2011.04.026 Berardo N, 2005, J AGR FOOD CHEM, V53, P8128, DOI 10.1021/jf0512297 Bertran E, 2000, J NEAR INFRARED SPEC, V8, P45, DOI 10.1255/jnirs.263 Bevilacqua M, 2012, ANAL CHIM ACTA, V717, P39, DOI 10.1016/j.aca.2011.12.035 Blanco M, 2002, TRAC-TREND ANAL CHEM, V21, P240, DOI 10.1016/S0165-9936(02)00404-1 Bosco GL, 2010, TRAC-TREND ANAL CHEM, V29, P197, DOI 10.1016/j.trac.2010.01.003 Buckley M., 2010, Global food safety: keeping food safe from farm to table Casale M, 2008, J NEAR INFRARED SPEC, V16, P39, DOI 10.1255/jnirs.759 Casale M, 2010, TALANTA, V80, P1832, DOI 10.1016/j.talanta.2009.10.030 Casale M, 2010, FOOD CHEM, V118, P163, DOI 10.1016/j.foodchem.2009.04.091 Chao K, 2008, APPL ENG AGRIC, V24, P49 Chen LZ, 2012, FOOD CHEM, V135, P338, DOI 10.1016/j.foodchem.2012.02.156 Chu XL, 2004, PROG CHEM, V16, P528 Cozzolino D, 2003, J AGR FOOD CHEM, V51, P7703, DOI 10.1021/jf034959s Cozzolino D, 2011, FOOD CHEM, V126, P673, DOI 10.1016/j.foodchem.2010.11.005 Cozzolino D., 2009, INT J WINE RES, V1, P123, DOI [DOI 10.2147/IJWR.S4585, 10.2147/ijwr.s4585] Dong YW, 2009, SPECTROSC SPECT ANAL, V29, P2934, DOI 10.3964/j.issn.1000-0593(2009)11-2934-05 Dowell FE, 1999, J ECON ENTOMOL, V92, P165, DOI 10.1093/jee/92.1.165 Dowell FE, 1998, J ECON ENTOMOL, V91, P899, DOI 10.1093/jee/91.4.899 Elizabeth B. M., 2002, 023067 ASAE Elmasry G, 2012, CRIT REV FOOD SCI, V52, P999, DOI 10.1080/10408398.2010.543495 Filigenzi MS, 2008, J AGR FOOD CHEM, V56, P7593, DOI 10.1021/jf801008s Fu XP, 2007, ANAL CHIM ACTA, V598, P27, DOI 10.1016/j.aca.2007.07.032 Galtier O, 2007, ANAL CHIM ACTA, V595, P136, DOI 10.1016/j.aca.2007.02.033 Galtier O, 2011, VIB SPECTROSC, V55, P132, DOI 10.1016/j.vibspec.2010.09.012 Haff RP, 2006, T ASABE, V49, P1105, DOI 10.13031/2013.21716 Hennessy S, 2008, APPL SPECTROSC, V62, P1115, DOI 10.1366/000370208786049033 Hennessy S, 2009, J AGR FOOD CHEM, V57, P1735, DOI 10.1021/jf803714g Huang HB, 2008, J FOOD ENG, V87, P303, DOI 10.1016/j.jfoodeng.2007.12.022 Hurburgh C. R., 2000, P PITTC NEW ORL LA Kim Moon S., 2011, Sensing and Instrumentation for Food Quality and Safety, V5, P155, DOI 10.1007/s11694-012-9122-3 Kim M. S., 2000, SPIE P, V4206, P174 Kim MS, 2004, KEY ENG MATER, V270-273, P1055, DOI 10.4028/www.scientific.net/KEM.270-273.1055 Kim MS, 2002, T ASAE, V45, P2027 Kim SS, 2003, CEREAL CHEM, V80, P346, DOI 10.1094/CCHEM.2003.80.3.346 Knezevic Z, 2009, FOOD CONTROL, V20, P419, DOI 10.1016/j.foodcont.2008.07.014 Kuligowski J, 2012, FOOD CHEM, V131, P353, DOI 10.1016/j.foodchem.2011.07.139 Landau S, 2006, SMALL RUMINANT RES, V61, P1, DOI 10.1016/j.smallrumres.2004.12.012 Lin M, 2004, LETT APPL MICROBIOL, V39, P148, DOI 10.1111/j.1472-765X.2004.01546.x Liu L, 2008, FOOD CHEM, V106, P781, DOI 10.1016/j.foodchem.2007.06.015 Liu L, 2006, J AGR FOOD CHEM, V54, P6754, DOI 10.1021/jf061528b Liu SL, 2010, J FOOD DRUG ANAL, V18, P34 Liu YL, 2007, J FOOD ENG, V81, P412, DOI 10.1016/j.jfoodeng.2006.11.018 Lu CH, 2009, J NEAR INFRARED SPEC, V17, P59, DOI 10.1255/jnirs.829 Luypaert J, 2007, TALANTA, V72, P865, DOI 10.1016/j.talanta.2006.12.023 Mauer LJ, 2009, J AGR FOOD CHEM, V57, P3974, DOI 10.1021/jf900587m McGraw L. C., 1998, AGR RES, V14 Munck L, 2004, J CEREAL SCI, V40, P213, DOI 10.1016/j.jcs.2004.07.006 Naes T., 2002, USER FRIENDLY GUIDE Nakariyakul S, 2007, PROC SPIE, V6565, DOI 10.1117/12.718162 Neethirajan S, 2007, FOOD CONTROL, V18, P157, DOI 10.1016/j.foodcont.2005.09.008 Nicolai BM, 2007, POSTHARVEST BIOL TEC, V46, P99, DOI 10.1016/j.postharvbio.2007.06.024 Oliveri P, 2011, FOOD CHEM, V125, P1450, DOI 10.1016/j.foodchem.2010.10.047 Ortelli D, 2004, ANAL CHIM ACTA, V520, P33, DOI 10.1016/j.aca.2004.03.037 Perez-Mendoza J, 2005, J ECON ENTOMOL, V98, P2282, DOI 10.1603/0022-0493-98.6.2282 Prevolnik M, 2004, CZECH J ANIM SCI, V49, P500, DOI 10.17221/4337-CJAS Prieto N, 2009, MEAT SCI, V83, P175, DOI 10.1016/j.meatsci.2009.04.016 Reich G, 2005, ADV DRUG DELIVER REV, V57, P1109, DOI 10.1016/j.addr.2005.01.020 Rinnan A, 2009, TRAC-TREND ANAL CHEM, V28, P1201, DOI 10.1016/j.trac.2009.07.007 Riovanto R, 2011, J AGR FOOD CHEM, V59, P10356, DOI 10.1021/jf202578f Roberts C.A., 2001, FOOD SAFETY INFORM H Rodriguez-Saona LE, 2001, J AGR FOOD CHEM, V49, P574, DOI 10.1021/jf000776j Roggo Y, 2007, J PHARMACEUT BIOMED, V44, P683, DOI 10.1016/j.jpba.2007.03.023 Roussel SA, 2001, APPL SPECTROSC, V55, P1425, DOI 10.1366/0003702011953586 Rui YK, 2005, SPECTROSC SPECT ANAL, V25, P1581 Sanchez MT, 2010, PEST MANAG SCI, V66, P580, DOI 10.1002/ps.1910 Saranwong S, 2001, J NEAR INFRARED SPEC, V9, P287, DOI 10.1255/jnirs.314 Saranwong S, 2008, J NEAR INFRARED SPEC, V16, P497, DOI 10.1255/jnirs.817 Saranwong S, 2008, J NEAR INFRARED SPEC, V16, P389, DOI 10.1255/jnirs.807 Shen F, 2012, FOOD BIOPROCESS TECH, V5, P786, DOI 10.1007/s11947-010-0347-z Shen F, 2009, SPECTROSC SPECT ANAL, V29, P2421, DOI 10.3964/j.issn.1000-0593(2009)09-2421-04 Siesler HW, 2007, NEAR INFRARED SPECTR Sivakesava S, 2004, T ASAE, V47, P951, DOI 10.13031/2013.16074 Sun SM, 2011, SPECTROSC SPECT ANAL, V31, P937, DOI 10.3964/j.issn.1000-0593(2011)04-0937-05 Suthiluk P, 2008, INT J FOOD SCI TECH, V43, P160, DOI 10.1111/j.1365-2621.2006.01416.x Tateo F, 2006, FOOD ADDIT CONTAM, V23, P1030, DOI 10.1080/02652030600847077 Tewari JC, 2008, SPECTROCHIM ACTA A, V71, P1119, DOI 10.1016/j.saa.2008.03.005 Tsenkova R, 2006, J NEAR INFRARED SPEC, V14, P363, DOI 10.1255/jnirs.661 Tsuchikawa S, 2007, APPL SPECTROSC REV, V42, P43, DOI 10.1080/05704920601036707 Tyan YC, 2009, ANAL BIOANAL CHEM, V395, P729, DOI 10.1007/s00216-009-3009-0 U.S. Department of Food and Drug Administration Health and Human Services Food and Drug Administration Center for Food Safety and Applied Nutrition (CFSAN), 1998, GUID MIN MICR FOOD S UNITED STATES DEPARTMENT OF AGRICULTURE, 1994, FED REGISTER, V59, P35659 Veleva-Doneva P., 2010, IFAC P VOLUMES, V43, P225, DOI [10.3182/20101206-3-JP-3009.00039, DOI 10.3182/20101206-3-JP-3009.00039] Wang SH, 2012, ENVIRON CHEM LETT, V10, P383, DOI 10.1007/s10311-012-0363-5 WILLIAMS PC, 1982, CEREAL CHEM, V59, P473 Windham WR, 2002, PROC SPIE, V4816, P317, DOI 10.1117/12.451653 Windham WR, 2003, T ASAE, V46, P747, DOI 10.13031/2013.13569 Windham WR, 2003, T ASAE, V46, P1733, DOI 10.13031/2013.15629 Woodcock T, 2008, J AGR FOOD CHEM, V56, P11520, DOI 10.1021/jf802792d World Health Organization (WHO), 2002, WHO GLOB STRAT FOOD Xiccato G, 2004, FOOD CHEM, V86, P275, DOI 10.1016/j.foodchem.2003.09.026 Xie LJ, 2010, T ASABE, V53, P313 Xie LJ, 2007, J FOOD ENG, V82, P395, DOI 10.1016/j.jfoodeng.2007.02.062 Xie LJ, 2007, ANAL CHIM ACTA, V584, P379, DOI 10.1016/j.aca.2006.11.071 Xie LJ, 2009, J FOOD ENG, V94, P34, DOI 10.1016/j.jfoodeng.2009.02.023 Xu XM, 2009, ANAL CHIM ACTA, V650, P39, DOI 10.1016/j.aca.2009.04.026 Yan Y, 2005, ANAL BASICS APPL NEA Yang R., 2012, FOOD SCI Yu HY, 2007, EUR FOOD RES TECHNOL, V225, P313, DOI 10.1007/s00217-006-0416-8 Zhou YJ, 2009, ANAL LETT, V42, P1518, DOI 10.1080/00032710902961032 Zhou Z., 1994, AGR MAT NR 107 TC 67 Z9 74 U1 13 U2 189 PY 2016 VL 56 IS 11 BP 1913 EP 1924 DI 10.1080/10408398.2013.807418 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000379554500011 DA 2022-12-14 ER PT J AU Nguyen, THN Yeh, QJ Huang, CY AF Nguyen, Thi Hong Nhung Yeh, Quey-Jen Huang, Ching-Ying TI Understanding consumer' switching intention toward traceable agricultural products: Push-pull-mooring perspective SO INTERNATIONAL JOURNAL OF CONSUMER STUDIES DT Article DE consumer trust; food consumption; food traceability systems; push-pull-mooring model; switching intention; traceable agricultural products ID WILLINGNESS-TO-PAY; INFORMATION ASYMMETRY; PURCHASE INTENTION; SERVICE PROVIDERS; PERCEIVED RISK; FOOD QUALITY; PERCEPTIONS; PRICE; ATTITUDES; BEHAVIOR AB Food traceability systems (FTSs) have been increasingly implemented in the food supply chain as a feasible solution to categorize the safety and quality of food. Governments across the world are striving to build public trust and to shift demand to traceable agricultural products (TAPs). This article stresses an important subject by focusing on consumer' switching intention toward TAPs as product switching has a great impact on food producers and sellers. A total of 478 valid samples were collected from Taiwanese consumers via an online survey. By applying the Push-Pull-Mooring (PPM) model, the empirical results of PLS-SEM analyses identified critical factors and their impacts on consumer switching intention: perceived risk uncertainty associated with non-traceable food; perceived quality of TAP-labeled food, willingness to pay a premium price, and health consciousness. Theoretically, this study sheds new light on the literature of migration studies, by examining consumer behavior toward FTSs, it is posited that the PPM theoretical model can predict switching decisions in the food sector. Practically, the findings suggested that FTS is a viable risk-relieving strategy that should be utilized more heavily to boost consumer retention related to credible food options. C1 [Nguyen, Thi Hong Nhung; Yeh, Quey-Jen; Huang, Ching-Ying] Natl Cheng Kung Univ, Coll Management, Dept Business Adm, 1 Univ Rd, Tainan, Taiwan. C3 National Cheng Kung University RP Nguyen, THN (corresponding author), Natl Cheng Kung Univ, Coll Management, Dept Business Adm, 1 Univ Rd, Tainan, Taiwan. EM r48077012@mail.ncku.edu.tw CR Aiello G, 2015, EUR J OPER RES, V244, P176, DOI 10.1016/j.ejor.2015.01.028 Athanassopoulos AD, 2000, J BUS RES, V47, P191, DOI 10.1016/S0148-2963(98)00060-5 Augusto M, 2018, J RETAIL CONSUM SERV, V42, P1, DOI 10.1016/j.jretconser.2018.01.005 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Bansal H.S., 2015, P 1997 AC MARK SCI A, P304 Bansal HS, 2005, J ACAD MARKET SCI, V33, P96, DOI 10.1177/0092070304267928 Bauer R.A., 1960, DYNAMIC MARKETING CH, P389, DOI DOI 10.4018/978-1-4666-7357-1.CH101 Berry, 1986, FIELD METHODS CROSS, P137, DOI DOI 10.1177/017084068800900318 Birch D, 2018, J RETAIL CONSUM SERV, V40, P221, DOI 10.1016/j.jretconser.2017.10.013 Boumphrey S., 2017, MEGATREND ANAL PUTTI Buaprommee N, 2016, ASIA PAC MANAG REV, V21, P161, DOI 10.1016/j.apmrv.2016.03.001 Chen MF, 2013, FOOD CONTROL, V33, P313, DOI 10.1016/j.foodcont.2013.03.022 Cheng S, 2019, COMPUT HUM BEHAV, V92, P198, DOI 10.1016/j.chb.2018.10.035 Chin WW, 2003, INFORM SYST RES, V14, P189, DOI 10.1287/isre.14.2.189.16018 Chiu HC, 2005, J BUS RES, V58, P1681, DOI 10.1016/j.jbusres.2004.11.005 Choe YC, 2009, INFORM SYST FRONT, V11, P167, DOI 10.1007/s10796-008-9134-z Connelly BL, 2011, J MANAGE, V37, P39, DOI 10.1177/0149206310388419 Cunningham M.S., 1967, RISK TAKING INFORM H Diallo MF, 2021, J BUS ETHICS, V169, P241, DOI 10.1007/s10551-020-04486-5 DOWLING GR, 1994, J CONSUM RES, V21, P119, DOI 10.1086/209386 Eger L, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102542 Ergonul B, 2013, FOOD CONTROL, V32, P461, DOI 10.1016/j.foodcont.2013.01.018 Everard A, 2005, J MANAGE INFORM SYST, V22, P55, DOI 10.2753/MIS0742-1222220303 Feng HH, 2020, J CLEAN PROD, V260, DOI 10.1016/j.jclepro.2020.121031 GABOR A, 1979, MANAGE DECIS, V17, P569, DOI 10.1108/eb001212 Galanakis CM, 2021, TRENDS FOOD SCI TECH, V110, P193, DOI 10.1016/j.tifs.2021.02.002 Ganesh J, 2000, J MARKETING, V64, P65, DOI 10.1509/jmkg.64.3.65.18028 Gordon-Wilson S, 2022, INT J CONSUM STUD, V46, P575, DOI 10.1111/ijcs.12701 GOULD SJ, 1988, J CONSUM AFF, V22, P96, DOI 10.1111/j.1745-6606.1988.tb00215.x Han H, 2011, INT J HOSP MANAG, V30, P619, DOI 10.1016/j.ijhm.2010.11.006 Hansstein F, 2017, INT J CONSUM STUD, V41, P754, DOI 10.1111/ijcs.12388 Harman HH, 1976, MODERN FACTOR ANAL Hou B, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11051464 Hsieh JK, 2012, COMPUT HUM BEHAV, V28, P1912, DOI 10.1016/j.chb.2012.05.010 Hsu JSC, 2014, DECIS SUPPORT SYST, V59, P152, DOI 10.1016/j.dss.2013.11.003 Islam S, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107848 Jung J, 2017, TOURISM MANAGE, V59, P139, DOI 10.1016/j.tourman.2016.07.018 Kasilingam D, 2022, INT J CONSUM STUD, V46, P102, DOI 10.1111/ijcs.12648 Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002 Kiatkawsin K, 2019, INT J HOSP MANAG, V82, P209, DOI 10.1016/j.ijhm.2019.04.024 Kirmani A, 2000, J MARKETING, V64, P66, DOI 10.1509/jmkg.64.2.66.18000 Kitz R, 2022, INT J CONSUM STUD, V46, P434, DOI 10.1111/ijcs.12691 Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101 Kshetri N, 2018, INT J INFORM MANAGE, V39, P80, DOI 10.1016/j.ijinfomgt.2017.12.005 Lacombe A, 2021, J FOOD SAFETY, V41, DOI 10.1111/jfs.12878 LEE ES, 1966, DEMOGRAPHY, V3, P47, DOI 10.2307/2060063 Liao PA, 2011, FOOD POLICY, V36, P686, DOI 10.1016/j.foodpol.2011.06.010 LICHTENSTEIN DR, 1988, J CONSUM RES, V15, P243, DOI 10.1086/209161 Lin HC, 2020, AGEING SOC, V40, P1808, DOI 10.1017/S0144686X19000308 MACINNIS DJ, 1991, J MARKETING, V55, P32, DOI 10.2307/1251955 Mattevi M, 2016, FOOD CONTROL, V64, P120, DOI 10.1016/j.foodcont.2015.12.014 Matzembacher DE, 2018, FOOD CONTROL, V92, P420, DOI 10.1016/j.foodcont.2018.05.014 Mehrolia S, 2021, INT J CONSUM STUD, V45, P396, DOI 10.1111/ijcs.12630 Miles S, 2003, J RISK RES, V6, P267, DOI 10.1080/1366987032000088883 Mishra DP, 1998, J MARKETING RES, V35, P277, DOI 10.2307/3152028 Mitchell V.W., 1999, EUROPEAN J MARKETING, V33, P163 MONROE KB, 1973, J MARKETING RES, V10, P70, DOI 10.2307/3149411 Moon B, 1995, PROG HUM GEOG, V19, P504, DOI 10.1177/030913259501900404 Moussa S, 2008, INT J CONSUM STUD, V32, P526, DOI 10.1111/j.1470-6431.2008.00713.x Myae AC, 2012, INT J CONSUM STUD, V36, P192, DOI 10.1111/j.1470-6431.2011.01084.x Nocella G, 2014, INT J CONSUM STUD, V38, P153, DOI 10.1111/ijcs.12080 Okojie PW, 2019, INT J CONSUM STUD, V43, P528, DOI 10.1111/ijcs.12538 Olsen P, 2013, TRENDS FOOD SCI TECH, V29, P142, DOI 10.1016/j.tifs.2012.10.003 Pappa IC, 2018, J RURAL STUD, V58, P123, DOI 10.1016/j.jrurstud.2018.01.001 Paul J, 2018, ASIA PAC BUS REV, V24, P90, DOI 10.1080/13602381.2017.1357316 Pavlou PA, 2004, INFORM SYST RES, V15, P37, DOI 10.1287/isre.1040.0015 Pavlou PA, 2007, MIS QUART, V31, P105 Pelaez A, 2019, J COMPUT INFORM SYST, V59, P73, DOI 10.1080/08874417.2017.1300514 Pires G., 2004, J CONSUMER BEHAVIOUR, V4, P118 Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879 Prentice C, 2022, INT J CONSUM STUD, V46, P132, DOI 10.1111/ijcs.12649 Rao AR, 1999, J MARKETING RES, V36, P258, DOI 10.2307/3152097 Rao AR, 1996, J BUS, V69, P511, DOI 10.1086/209703 Ringle CM, 2020, INT J HUM RESOUR MAN, V31, P1617, DOI 10.1080/09585192.2017.1416655 Ahangarkolaee SS, 2021, INT J CONSUM STUD, V45, P273, DOI 10.1111/ijcs.12619 Shin YH, 2017, J HOSP TOUR MANAG, V33, P113, DOI 10.1016/j.jhtm.2017.10.010 Stranieri S, 2017, FOOD CONTROL, V80, P187, DOI 10.1016/j.foodcont.2017.04.047 Sun SN, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9060999 van Rijswijk W, 2008, BRIT FOOD J, V110, P1034, DOI 10.1108/00070700810906642 van Rijswijk W, 2008, FOOD QUAL PREFER, V19, P452, DOI 10.1016/j.foodqual.2008.02.001 Van Rijswijk W, 2012, INT J CONSUM STUD, V36, P282, DOI 10.1111/j.1470-6431.2011.01001.x Wells JD, 2011, MIS QUART, V35, P373 Wieringa JE, 2007, J SERV RES-US, V10, P174, DOI 10.1177/1094670507306686 Wilson L, 2020, FOOD POLICY, V97, DOI 10.1016/j.foodpol.2020.101938 Yin SJ, 2017, BRIT FOOD J, V119, P1276, DOI [10.1108/BFJ-11-2016-0555, 10.1108/bfj-11-2016-0555] Yoo CW, 2015, INFORM MANAGE-AMSTER, V52, P692, DOI 10.1016/j.im.2015.06.003 Zhang AR, 2020, J CONSUM PROT FOOD S, V15, P99, DOI 10.1007/s00003-020-01277-y Zhang B, 2018, J CLEAN PROD, V197, P1498, DOI 10.1016/j.jclepro.2018.06.273 NR 88 TC 4 Z9 3 U1 15 U2 47 PD MAY PY 2022 VL 46 IS 3 BP 870 EP 888 DI 10.1111/ijcs.12733 EA JUL 2021 WC Business SC Business & Economics UT WOS:000674151100001 DA 2022-12-14 ER PT J AU Heryani, H Legowo, AC Yanti, NR Marimin Raharja, S Machfud Djatna, T Martini, S Baidawi, T Afrianto, I AF Heryani, Hesty Legowo, Agung Cahyo Yanti, Noor Ridha Marimin Raharja, Sapta Machfud Djatna, Taufik Martini, Sri Baidawi, Taufik Afrianto, Irawan TI Institutional Development in the Supply Chain System of Oil Palm Agroindustry in South Kalimantan SO INTERNATIONAL JOURNAL OF TECHNOLOGY DT Article DE Analytical Network Process (ANP); Effectiveness; Efficiency; Strategic Assumption Surfacing and Testing (SAST); Supply Chain Operation Reference (SCOR) ID MANAGEMENT AB Developing oil palm agroindustry in South Kalimantan involves internal and external factors for its sustainability. The long distance of each stakeholder requires a digitally connected system. The research aimed at identifying criteria, sub-criteria, and alternatives in the developed network model, determine the sensitivity to determine the relevance of actor's role, determine the effectiveness and efficiency before and after implementation of the system, and analyse the level of certainty and importance of assumptions developed. The methods used were surveys, focus group discussions, and questionnaires. Experts in the Analytical Network Process (ANP) method were from academia, business, society and government. Supply Chain Operation Reference (SCOR) method to analyse supply chain performance involved 178 respondents. The level of certainty and importance of assumptions was with Strategic Assumption Surfacing and Testing (SAST). The results of the analysis show there was an interdependence between the sub-criteria on the specified criteria and alternatives applied to achieve the goals. Pairwise comparisons showed the highest sub-criteria cluster was the replanting program with a weight of 0.662, for alternative cluster the highest priority weight in the system involving all actors playing a role was 0.391, with relevant sensitivity test results. The results of the analysis of effectiveness and efficiency before and after implementation of the system showed an increase in SCOR value for all actors. The results of SAST analysis were in Quadrant I with the highest level of importance and certainty of 7.6 meaning very important and certain. The implications of the research results can be seen in fostering the use of technology in realizing transparency, especially regarding the price, quality, and traceability of fresh fruit bunches, the realization of a monitoring system for policymakers and capital assistance for independent oil palm smallholders. C1 [Heryani, Hesty; Legowo, Agung Cahyo] Univ Lambung Mangkurat, Dept Agroind Technol, Fac Agr, Banjarbaru 70714, Indonesia. [Yanti, Noor Ridha] Univ Cahaya Bangsa, Fac Sci & Technol, Dept Publ Hlth, Banjar 70122, Indonesia. [Marimin; Raharja, Sapta; Machfud; Djatna, Taufik; Martini, Sri; Baidawi, Taufik; Afrianto, Irawan] Bogor Agr Univ, Fac Agr Technol, Dept Agroind Technol, Bogor 16680, Indonesia. C3 Universitas Lambung Mangkurat; Bogor Agricultural University RP Heryani, H (corresponding author), Univ Lambung Mangkurat, Dept Agroind Technol, Fac Agr, Banjarbaru 70714, Indonesia. EM hheryani@ulm.ac.id CR Abdulameer SS, 2020, INT J TECHNOL, V11, P677, DOI 10.14716/ijtech.v11i4.3496 Alwarritzi W, 2015, PROCEDIA ENVIRON SCI, V28, P630, DOI 10.1016/j.proenv.2015.07.074 APICS, 2017, SUPPL CHAIN OP REF M Aritonang K, 2020, INT J TECHNOL, V11, P642, DOI 10.14716/ijtech.v11i3.3750 Ashari H, 2018, INT J TECHNOL, V9, P1651, DOI 10.14716/ijtech.v9i8.2749 BPS (Statistics Indonesia), 2019, STAT FOR PROD 2019 Dano UL, 2019, WATER-SUI, V11, DOI 10.3390/w11030615 Ervural BC, 2018, RENEW SUST ENERG REV, V82, P1538, DOI 10.1016/j.rser.2017.06.095 Etikan I., 2016, AM J THEORETICAL APP, V5, P1, DOI DOI 10.11648/J.AJTAS.20160501.11 Godar J, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/3/035015 Hasibuan S, 2018, IOP CONF SER-MAT SCI, V453, DOI 10.1088/1757-899X/453/1/012006 Heryani H., 2020, IOP C SERIES EARTH E, V499, P1 Hidayati J, 2018, INT J ADV SCI ENG IN, V8, P588, DOI 10.18517/ijaseit.8.2.4148 Hidayati J, 2018, IOP C SERIES MAT SCI, V309, P1 Jeon J, 2017, TECHNOL ANAL STRATEG, V29, P790, DOI 10.1080/09537325.2016.1241873 Karlsson E., 2017, CRITICAL SUCCESS FAC, P1 Kauppi K, 2013, INT J OPER PROD MAN, V33, P1318, DOI 10.1108/IJOPM-10-2011-0364 Kholil K., 2017, SNAPP P SCI TECHNOLO, V7, P384 Lee JSH, 2014, AGRON SUSTAIN DEV, V34, P501, DOI 10.1007/s13593-013-0159-4 Marimin, 2020, IOP C SER EARTH ENV, V443, DOI 10.1088/1755-1315/443/1/012056 Matondang N., 2020, IOP C SERIES MAT SCI, V1003, P1 McCarthy JF, 2012, WORLD DEV, V40, P555, DOI 10.1016/j.worlddev.2011.07.012 Palma-Mendoza JA, 2014, INT J INFORM MANAGE, V34, P634, DOI 10.1016/j.ijinfomgt.2014.06.002 Peters NJ, 2011, INT J LOGIST MANAG, V22, P52, DOI 10.1108/09574091111127552 Rachmayanti D., 2015, STRATEGIC ASSUMPTION, V24, P255 Rahmanda P.O., 2017, SCI J INFORMS, V4, P199 Saaty R. W., 2016, DECISION MAKING COMP, P1 Sjahza A, 2019, MANAG ENVIRON QUAL, V30, P1256, DOI 10.1108/MEQ-02-2018-0036 Statistics Indonesia (BPS), 2020, KAL SEL PROV FIG 202 Supply Chain Council, 2012, SUPPL CHAIN OP REF M Yadav S, 2021, J ENTERP INF MANAG, V34, P292, DOI 10.1108/JEIM-11-2019-0369 NR 31 TC 3 Z9 3 U1 3 U2 3 PD JUL 1 PY 2022 VL 13 IS 3 BP 643 EP 654 DI 10.14716/ijtech.v13i3.4754 WC Engineering, Multidisciplinary SC Engineering UT WOS:000822706700018 DA 2022-12-14 ER PT J AU Bibi, F Guillaume, C Gontard, N Sorli, B AF Bibi, Fabien Guillaume, Carole Gontard, Nathalie Sorli, Brice TI A review: RFID technology having sensing aptitudes for food industry and their contribution to tracking and monitoring of food products SO TRENDS IN FOOD SCIENCE & TECHNOLOGY DT Review DE RFID technology; Traceability; Food monitoring for safety; Sensors; RFID tags ID RADIO-FREQUENCY IDENTIFICATION; WIRELESS SMART TAG; CAPACITIVE HUMIDITY SENSOR; TRACEABILITY SYSTEM; OXYGEN INDICATOR; CHAIN; FRESHNESS; DESIGN; SAFETY; CO2 AB RFID (Radio Frequency Identification) technology has considerably grown in the past few years and is nowadays sought to be implemented for the identification of products and for traceability in the agrifood sector, ensuring food safety and quality. RFID is now considered as the worthy successor of the barcode with a foreseen expansion not only in the agrifood sector, but also in industrial sectors for environmental monitoring (temperature, relative humidity and luminosity) through namely WSN (Wireless Sensor Network) and WST (Wireless Sensor Technology). Research studies are being progressively performed in the objective of coupling sensors to the RFID technology. This interfacing would lead to a better monitoring of packaging headspace by means of the development of different sensors, as well as their coupling to RFID tags through the microchip or directly to the RFID antenna. The present work gives an overview of the basics of the RFID technology, the existing sensors and the ones being developed to be interfaced with the technology, as well as the existing RFID sensor tags. The presented literature studies, mainly in the agrifood sector, demonstrate how RFID may meet our needs for a better monitoring of food quality by coupling radio frequency communication and traceability. The implementation of sensors which is a very new technology being studied and concomitantly developed may lead to a better detection of food degradation markers and thus to a reduction in food loss which is one of the world's major issue. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Bibi, Fabien; Guillaume, Carole; Gontard, Nathalie] CIRAD, Joint Res Unit Agropolymers Engn & Emerging Techn, UMII, UMR 1208,INRA,SupAgroM, 2 Pl Pierre Viala, F-34060 Montpellier, France. [Sorli, Brice] Univ Montpellier 2, Inst Elect & Syst, 860 Rue St Priest, F-34090 Montpellier, France. C3 CIRAD; INRAE; Institut Agro; Montpellier SupAgro; Universite de Montpellier; Universite de Montpellier RP Bibi, F (corresponding author), CIRAD, Joint Res Unit Agropolymers Engn & Emerging Techn, UMII, UMR 1208,INRA,SupAgroM, 2 Pl Pierre Viala, F-34060 Montpellier, France. EM bibi.fabien@hotmail.com CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 Aguzzi J, 2011, SENSORS-BASEL, V11, P9532, DOI 10.3390/s111009532 Amador Cecilia, 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P26, DOI 10.1007/s11694-009-9072-6 [Anonymous], 2011, GLOBAL FOOD LOSSES F Atsushi O, 2006, NEC TECH J, V1, P82 Bernardi P, 2008, ECCSC 08: 4TH EUROPEAN CONFERENCE ON CIRCUITS AND SYSTEMS FOR COMMUNICATIONS, P227, DOI 10.1109/ECCSC.2008.4611682 Bhadra S, 2015, SENSOR ACTUAT B-CHEM, V209, P803, DOI 10.1016/j.snb.2014.12.021 Bhattacharyya Rahul, 2010, 2010 IEEE International Conference on RFID (IEEE RFID 2010), P126, DOI 10.1109/RFID.2010.5467235 Bhattacharyya R, 2009, IEEE RFID: 2009 IEEE INTERNATIONAL CONFERENCE ON RFID, P95, DOI 10.1109/RFID.2009.4911195 Castro-Giraldez M, 2010, J FOOD ENG, V97, P484, DOI 10.1016/j.jfoodeng.2009.11.005 Castro-Giraldez M, 2010, INNOV FOOD SCI EMERG, V11, P376, DOI 10.1016/j.ifset.2010.01.011 Vu CHT, 2013, FOOD CHEM, V140, P52, DOI 10.1016/j.foodchem.2013.02.056 Chen Z, 2005, SENS LETT, V3, P274, DOI 10.1166/sl.2005.045 Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7 Duncan TV, 2011, J COLLOID INTERF SCI, V363, P1, DOI 10.1016/j.jcis.2011.07.017 Endres HE, 1999, SENSOR ACTUAT B-CHEM, V57, P83, DOI 10.1016/S0925-4005(99)00060-X Espinosa E, 2010, SENSOR ACTUAT B-CHEM, V144, P462, DOI 10.1016/j.snb.2009.07.005 Feng JY, 2013, FOOD CONTROL, V31, P314, DOI 10.1016/j.foodcont.2012.10.016 Fiddes LK, 2013, SENSOR ACTUAT B-CHEM, V186, P817, DOI 10.1016/j.snb.2013.05.008 Finkenzeller Klaus, 2010, RFID HDB FUNDAMENTAL, V3rd Galagan Y, 2008, FOOD RES INT, V41, P653, DOI 10.1016/j.foodres.2008.04.012 Gandino F, 2007, PROCEEDINGS OF THE 1ST RFID EURASIA CONFERENCE, P143 Grabacki ST, 2008, UASGCP REP, V2008, P101 Hong SI, 2000, J FOOD ENG, V46, P67, DOI 10.1016/S0308-8146(00)00141-2 Hu JY, 2013, FOOD CONTROL, V30, P341, DOI 10.1016/j.foodcont.2012.06.037 Jang NY, 2014, INT J FOOD SCI TECH, V49, P650, DOI 10.1111/ijfs.12310 Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Kassal P, 2013, SENSOR ACTUAT B-CHEM, V184, P254, DOI 10.1016/j.snb.2013.04.049 Kim Y, 2009, SENSOR ACTUAT B-CHEM, V141, P441, DOI 10.1016/j.snb.2009.07.007 Lalauze R., 2012, CHEM SENSORS BIOSENS Laniel M, 2011, TRANSPORT RES C-EMER, V19, P1071, DOI 10.1016/j.trc.2011.06.008 Martinez-Olmos A, 2013, ANAL CHEM, V85, P11098, DOI 10.1021/ac4028802 Mascheroni E, 2011, FOOD CONTROL, V22, P1582, DOI 10.1016/j.foodcont.2011.03.014 Metzger C, 2007, LECT NOTES COMPUT SC, V4793, P255 Nikitin PV, 2006, IEEE ANTENN PROPAG M, V48, P212, DOI 10.1109/MAP.2006.323323 Oprea Alexandru, 2007, TRANSDUCERS '07 & Eurosensors XXI. 2007 14th International Conference on Solid-State Sensors, Actuators and Microsystems, P2039, DOI 10.1109/SENSOR.2007.4300564 Papetti P, 2012, FOOD CONTROL, V27, P234, DOI 10.1016/j.foodcont.2012.03.025 Philipose M, 2005, IEEE PERVAS COMPUT, V4, P37, DOI 10.1109/MPRV.2005.7 Potyrailo RA, 2013, SENSOR ACTUAT B-CHEM, V185, P587, DOI 10.1016/j.snb.2013.04.107 Potyrallo RA, 2007, ANAL CHEM, V79, P45, DOI 10.1021/ac061748o Realini CE, 2014, MEAT SCI, V98, P404, DOI 10.1016/j.meatsci.2014.06.031 Rivadeneyra A, 2014, SENSOR ACTUAT B-CHEM, V195, P123, DOI 10.1016/j.snb.2013.12.117 Roberts CM, 2006, COMPUT SECUR, V25, P18, DOI 10.1016/j.cose.2005.12.003 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Rukchon C, 2014, TALANTA, V130, P547, DOI 10.1016/j.talanta.2014.07.048 Salmeron JF, 2014, SENSOR ACTUAT A-PHYS, V220, P281, DOI 10.1016/j.sna.2014.10.023 Sarac A, 2010, INT J PROD ECON, V128, P77, DOI 10.1016/j.ijpe.2010.07.039 Schmidt A, 2000, FOURTH INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS, DIGEST OF PAPERS, P193, DOI 10.1109/ISWC.2000.888497 SHELLHAMMER TH, 1991, J FOOD SCI, V56, P402, DOI 10.1111/j.1365-2621.1991.tb05290.x Stegmeier S, 2009, PROCEDIA CHEM, V1, P236, DOI 10.1016/j.proche.2009.07.059 Steinberg IM, 2009, SENSOR ACTUAT B-CHEM, V138, P120, DOI 10.1016/j.snb.2009.02.040 Steinberg MD, 2014, SENSOR ACTUAT B-CHEM, V196, P208, DOI 10.1016/j.snb.2014.02.012 Steinberg MD, 2014, TALANTA, V118, P375, DOI 10.1016/j.talanta.2013.10.033 STOCKMAN H, 1948, P IRE, V36, P1196, DOI 10.1109/JRPROC.1948.226245 SWEDBERG C, 2010, RFID J, P1 Tajima May, 2007, Journal of Purchasing and Supply Management, V13, P261, DOI 10.1016/j.pursup.2007.11.001 Taoukis P, 2011, FOOD REFRIGERATION I TAOUKIS PS, 1989, J FOOD SCI, V54, P783, DOI 10.1111/j.1365-2621.1989.tb07882.x Thakur M, 2009, J FOOD ENG, V95, P617, DOI 10.1016/j.jfoodeng.2009.06.028 Vanderroost M, 2014, TRENDS FOOD SCI TECH, V39, P47, DOI 10.1016/j.tifs.2014.06.009 Vergara A, 2007, SENSOR ACTUAT B-CHEM, V127, P143, DOI 10.1016/j.snb.2007.07.107 Wang LX, 2010, J FOOD ENG, V101, P120, DOI 10.1016/j.jfoodeng.2010.06.020 Wang TM, 2010, AFR J BIOTECHNOL, V9, P6146 Wu XL, 2010, TRENDS FOOD SCI TECH, V21, P44, DOI 10.1016/j.tifs.2009.10.010 Xiao Y, 2007, WIREL COMMUN MOB COM, V7, P457, DOI 10.1002/wcm.365 Xiong BH, 2007, NEW ZEAL J AGR RES, V50, P725 Yam KL, 2005, J FOOD SCI, V70, pR1, DOI 10.1111/j.1365-2621.2005.tb09052.x Zampolli S, 2008, MICROSYST TECHNOL, V14, P581, DOI 10.1007/s00542-007-0444-8 [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] [No title captured] NR 80 TC 127 Z9 131 U1 11 U2 172 PD APR PY 2017 VL 62 BP 91 EP 103 DI 10.1016/j.tifs.2017.01.013 WC Food Science & Technology SC Food Science & Technology UT WOS:000399510000009 DA 2022-12-14 ER PT J AU Scarafoni, A Ronchi, A Duranti, M AF Scarafoni, A. Ronchi, A. Duranti, M. TI A real-time PCR method for the detection and quantification of lupin flour in wheat flour-based matrices SO FOOD CHEMISTRY DT Article DE Food traceability; Detection methods; Real-time PCR; Food ingredients; Allergenicity ID IGE-BINDING; DNA; PROTEIN; FOOD; IDENTIFICATION; CONTAMINATION; EXTRACTION; EXPRESSION; ALLERGY; PEANUT AB Lupin flour is growingly being used in bakery products, mainly as a soybean protein substitute. The aim of the present work was to detect and quantify the presence of lupin flour in wheat-based foods using a newly set up qPCR system based on SYBR green. Although DNA sequence information for lupin is scarce, it has been possible to design a primer pair highly specific for the target gene and devoid of any primer-dimers amplification capacity. Lupin flour revealed to be a difficult matrix, since large amounts of compounds tend to co-purify with DNA, even adopting well established extraction protocols. Nonetheless, the primers used allowed to reach high PCR efficiencies and did not show any cross-reactivity with DNAs extracted from various plant and animal foods. The sensitivity achieved was 7 pg of lupin DNA, corresponding to a percentage of less than 0.1% of lupin flour in the foods. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Scarafoni, A.; Ronchi, A.; Duranti, M.] Univ Milan, Dipartimento Sci Mol Agroaliment, I-20133 Milan, Italy. C3 University of Milan RP Scarafoni, A (corresponding author), Univ Milan, Dipartimento Sci Mol Agroaliment, Via G Celoria 2, I-20133 Milan, Italy. EM alessio.scarafoni@unimi.it CR [Anonymous], ANAL MOL BIOL QUALIT BEZ J, 2005, P FIN C HLTH PROF EU, P51 Cankar K, 2006, BMC BIOTECHNOL, V6, DOI 10.1186/1472-6750-6-37 Capraro J, 2008, LWT-FOOD SCI TECHNOL, V41, P1011, DOI 10.1016/j.lwt.2007.07.011 Dahinden I, 2001, EUR FOOD RES TECHNOL, V212, P228, DOI 10.1007/s002170000252 Demmel A, 2008, J AGR FOOD CHEM, V56, P4328, DOI 10.1021/jf800216h Dervas G, 1999, FOOD CHEM, V66, P67, DOI 10.1016/S0308-8146(98)00234-9 Doxastakis G, 2002, FOOD CHEM, V77, P219, DOI 10.1016/S0308-8146(01)00362-4 EGANA JI, 1992, J NUTR, V122, P2341, DOI 10.1093/jn/122.12.2341 Engel K. H., 2003, Genetically engineered food: methods and detection, P205 Engel KH, 2006, TRENDS FOOD SCI TECH, V17, P490, DOI 10.1016/j.tifs.2006.04.008 Herman L, 2004, POULTRY SCI, V83, P2083, DOI 10.1093/ps/83.12.2083 Herman L, 2001, J DAIRY RES, V68, P429, DOI 10.1017/S0022029901004940 HIRANO H, 1992, PHYTOCHEMISTRY, V31, P731, DOI 10.1016/0031-9422(92)80003-W Holden L, 2008, INT ARCH ALLERGY IMM, V146, P267, DOI 10.1159/000121461 Holden L, 2007, J AGR FOOD CHEM, V55, P2536, DOI 10.1021/jf063320w HOSENEY RC, 1986, PRINCIPLE CEREAL SCI, P36 LABARCA C, 1980, ANAL BIOCHEM, V102, P344, DOI 10.1016/0003-2697(80)90165-7 Leduc V, 2006, ALLERGY, V61, P349, DOI 10.1111/j.1398-9995.2006.01013.x Magni C, 2005, J AGR FOOD CHEM, V53, P4567, DOI 10.1021/jf0500785 MOE D, 1994, J BIOCHEM BIOPH METH, V28, P263, DOI 10.1016/0165-022X(94)90002-7 Moneret-Vautrin DA, 1999, J ALLERGY CLIN IMMUN, V104, P883, DOI 10.1016/S0091-6749(99)70303-9 Olexova L, 2004, EUR FOOD RES TECHNOL, V218, P390, DOI 10.1007/s00217-004-0872-y Parisot L, 2001, ALLERGY, V56, P918, DOI 10.1034/j.1398-9995.2001.00254.x PFAFFL MW, 2001, NUCLEIC ACIDS RES, V29, P1 Pollard NJ, 2002, CEREAL CHEM, V79, P662, DOI 10.1094/CCHEM.2002.79.5.662 POMPEI C, 1985, SCI ALIMENT, V5, P665 Qin Q, 2003, PLANT J, V34, P327, DOI 10.1046/j.1365-313X.2003.01726.x ROGERS SO, 1985, PLANT MOL BIOL, V5, P69, DOI 10.1007/BF00020088 Rutledge RG, 2003, NUCLEIC ACIDS RES, V31, DOI 10.1093/nar/gng093 Scarafoni A, 2001, BBA-GENE STRUCT EXPR, V1519, P147, DOI 10.1016/S0167-4781(01)00225-1 Scarafoni A, 2007, TRENDS FOOD SCI TECH, V18, P454, DOI 10.1016/j.tifs.2007.04.002 Sironi E, 2005, EUR FOOD RES TECHNOL, V221, P145, DOI 10.1007/s00217-005-1151-2 TABERLET P, 2007, NUCLEIC ACIDS RES, V35, P2 Terry CF, 2002, J AOAC INT, V85, P768 Terzi V, 2003, J CEREAL SCI, V38, P87, DOI 10.1016/S0733-5210(02)00138-8 VanGuilder HD, 2008, BIOTECHNIQUES, V44, P619, DOI 10.2144/000112776 WILKIE SE, 1993, THEOR APPL GENET, V86, P497, DOI 10.1007/BF00838566 NR 38 TC 32 Z9 32 U1 0 U2 13 PD AUG 1 PY 2009 VL 115 IS 3 BP 1088 EP 1093 DI 10.1016/j.foodchem.2008.12.087 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000265348100049 DA 2022-12-14 ER PT J AU Portarena, S Leonardi, L Scartazza, A Lauteri, M Baldacchini, C Farinelli, D Famiani, F Ciolfi, M Brugnoli, E AF Portarena, Silvia Leonardi, Luca Scartazza, Andrea Lauteri, Marco Baldacchini, Chiara Farinelli, Daniela Famiani, Franco Ciolfi, Marco Brugnoli, Enrico TI Combining analysis of fatty acid composition and delta C-13 in extra-virgin olive oils as affected by harvest period and cultivar: Possible use in traceability studies SO FOOD CONTROL DT Article DE Olive oil; Cultivar; Harvest date; Environmental variation; Fatty acids; Stable isotopes; ASCA ID STABLE-ISOTOPE; VEGETABLE-OILS; GEOGRAPHICAL ORIGIN; CARBON; DISCRIMINATION; AUTHENTICITY; QUALITY; PROFILE; BULK; TOLERANCE AB The variability in carbon isotope composition (delta C-13) of the main olive oil fatty acids, together with their relative contents, have been measured in 60 monovarietal olive oils, produced from plants grown in the same orchard and harvested at five ripening stages. The fatty acid content was mainly influenced by the cultivar, without significant changes during ripening. On the contrary, the fatty acid delta C-13 value varied from October to December reflecting physiological responses to environmental variations. ANOVA-Simultaneous Component Analysis (ASCA) was applied to explore the sample variability among factors and interactions. In particular harvest date resulted the main factor affecting our multivariate dataset and OA delta C-13 resulted the most discriminating parameter related to this factor. On the other side, LA relative content displayed the highest sensitivity to the range of cultivar variability, suggesting that this parameter may be used as a useful tool for studying inter-varietal differences in olive oils. The results find possible application in olive oil authentication and traceability studies. C1 [Portarena, Silvia; Leonardi, Luca; Lauteri, Marco; Baldacchini, Chiara; Ciolfi, Marco; Brugnoli, Enrico] CNR, Natl Res Council, Res Inst Terr Ecosyst IRET, I-05010 Porano, TR, Italy. [Scartazza, Andrea] CNR, Natl Res Council, Res Inst Terr Ecosyst IRET, Via Moruzzi 1, I-56124 Pisa, Italy. [Baldacchini, Chiara] Univ Tuscia, DEB, Biophys & Nanosci Ctr, I-01100 Viterbo, Italy. [Farinelli, Daniela; Famiani, Franco] Univ Perugia, Dept Agr Food & Environm Sci, I-06121 Perugia, Italy. C3 Consiglio Nazionale delle Ricerche (CNR); Istituto di Ricerca sugli Ecosistemi Terrestri (IRET); Consiglio Nazionale delle Ricerche (CNR); Istituto di Ricerca sugli Ecosistemi Terrestri (IRET); Tuscia University; University of Perugia RP Portarena, S (corresponding author), CNR, IRET, Via G Marconi 2, I-05010 Porano, TR, Italy. EM silvia.portarena@iret.cnr.it CR Anastasopoulos E, 2011, INT J FOOD SCI TECH, V46, P170, DOI 10.1111/j.1365-2621.2010.02485.x Aparicio R, 2013, FOOD RES INT, V54, P2025, DOI 10.1016/j.foodres.2013.07.039 Bontempo L, 2019, FOOD CHEM, V276, P782, DOI 10.1016/j.foodchem.2018.10.077 Brugnoli Enrico, 2000, VVolume 9, P399 Camin F, 2017, FOOD FORENSICS STABL Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Camin F, 2016, FOOD CHEM, V196, P98, DOI 10.1016/j.foodchem.2015.08.132 Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Checa A, 2015, ANAL CHIM ACTA, V885, P1, DOI 10.1016/j.aca.2015.02.068 Chiocchini F, 2016, FOOD CHEM, V202, P291, DOI 10.1016/j.foodchem.2016.01.146 Cimato A., 1990, OLIVAE, V31, P20 CMA e Research unit for Climatology and Meteorology applied to Agriculture ex Agricultural Ecology Central Department, 2012, IT AGR DAT BAS Diefendorf AF, 2015, GEOCHIM COSMOCHIM AC, V170, P145, DOI 10.1016/j.gca.2015.08.018 Faberi A, 2014, J MASS SPECTROM, V49, P840, DOI 10.1002/jms.3399 Farinelli D, 2002, ACTA HORTIC, P607, DOI 10.17660/ActaHortic.2002.586.128 Farinelli D, 2015, SCI HORTIC-AMSTERDAM, V192, P97, DOI 10.1016/j.scienta.2015.04.035 FARQUHAR GD, 1989, ANNU REV PLANT PHYS, V40, P503, DOI 10.1146/annurev.pp.40.060189.002443 Giuffrida D, 2011, FOOD CHEM, V124, P1119, DOI 10.1016/j.foodchem.2010.07.012 GLEIXNER G, 1993, PLANT PHYSIOL, V102, P1287, DOI 10.1104/pp.102.4.1287 Guarrasi V, 2010, J CHROMATOGR SCI, V48, P663, DOI 10.1093/chromsci/48.8.663 Harwood J., 2000, HDB OLIVE OIL ANAL P Inglese P, 2011, HORTICUL RE, V38, P83 Kumar Arun, 2016, Indian Journal of Agricultural Research, V50, P440, DOI 10.18805/ijare.v0iOF.10776 Lavine BK, 2005, ACS SYM SER, V894, P1 Leon-Camacho M., 2013, HDB OLIVE OIL ANAL P, P163, DOI DOI 10.1007/978-1-4614-7777-8 MONSON KD, 1982, J BIOL CHEM, V257, P5568 MONSON KD, 1982, GEOCHIM COSMOCHIM AC, V46, P139, DOI 10.1016/0016-7037(82)90241-1 Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Pannelli G, 2002, ACTA HORTIC, P247, DOI 10.17660/ActaHortic.2002.586.46 PANNELLI G, 1990, ACTA HORTIC, V286, P477, DOI DOI 10.17660/ActaHortic.1990.286.97 Poiana M, 2004, GRASAS ACEITES, V55, P282 Portarena S, 2017, FOOD CHEM, V215, P1, DOI 10.1016/j.foodchem.2016.07.135 Portarena S, 2015, FOOD CONTROL, V57, P129, DOI 10.1016/j.foodcont.2015.03.052 Portarena S, 2014, FOOD CHEM, V164, P12, DOI 10.1016/j.foodchem.2014.04.115 Portarena S, 2019, FOOD CONTROL, V96, P137, DOI 10.1016/j.foodcont.2018.09.011 Rondanini DP, 2014, EUR J AGRON, V52, P237, DOI 10.1016/j.eja.2013.09.002 Royer A, 1999, J AM OIL CHEM SOC, V76, P357, DOI 10.1007/s11746-999-0243-8 Smilde AK, 2005, BIOINFORMATICS, V21, P3043, DOI 10.1093/bioinformatics/bti476 Spangenberg JE, 1998, J AGR FOOD CHEM, V46, P4179 Spangenberg JE, 2001, J AGR FOOD CHEM, V49, P1534, DOI 10.1021/jf001291y Spangenberg JE, 2016, RAPID COMMUN MASS SP, V30, P2447, DOI 10.1002/rcm.7734 Tognetti R, 2007, ANN APPL BIOL, V150, P169, DOI 10.1111/j.1744-7348.2007.00117.x van Leeuwen KA, 2014, COMPR REV FOOD SCI F, V13, P814, DOI 10.1111/1541-4337.12096 Woodbury SE, 1998, J AM OIL CHEM SOC, V75, P371, DOI 10.1007/s11746-998-0055-2 NR 44 TC 7 Z9 8 U1 1 U2 26 PD NOV PY 2019 VL 105 BP 151 EP 158 DI 10.1016/j.foodcont.2019.05.029 WC Food Science & Technology SC Food Science & Technology UT WOS:000477691300020 DA 2022-12-14 ER PT J AU Carrasco, S Panea, B Ripoll, G Sanz, A Joy, M AF Carrasco, S. Panea, B. Ripoll, G. Sanz, A. Joy, M. TI Influence of feeding systems on cortisol levels, fat colour and instrumental meat quality in light lambs SO MEAT SCIENCE DT Article DE Grazing lambs; Indoor lambs; Colour; Texture; Traceability ID SLAUGHTER WEIGHT; ORGANOLEPTIC PROPERTIES; LONGISSIMUS-DORSI; CARCASS; CONCENTRATE; GRASS; TRANSPORT; SHEEP; BREED; PERFORMANCE AB Forty-eight lambs were fed as follows: GR, lambs and dams grazed perennial pasture: GR+S, the same as GR except that lambs had access to concentrate; DRL-GRE, lambs in drylot and dams in rationed grazing; DRL, lambs with dams were stall-fed. DRL-GRE and DRL lambs were weaned at 45 days of age. Lambs were slaughtered when they reached 22-24 kg of live weight. Plasma cortisol concentration was determined three times before slaughter. Subcutaneous fat and meat colour, and texture were analysed. The different levels of cortisol did not affect meat quality. Both grazing systems gave yellower subcutaneous fat and redder muscles than drylot lambs. Differences between systems relating to colour and texture of the meat disappeared with ageing time, which supports the idea that grazing systems are a good alternative in order to offer similar meat to that coming from drylot systems to which consumers are accustomed. Subcutaneous fat colour was a suitable method to discriminate between grazing and drylot systems, but not within them. (C) 2009 Published by Elsevier Ltd. C1 [Carrasco, S.; Panea, B.; Ripoll, G.; Sanz, A.; Joy, M.] Ctr Invest & Tecnol Agroalimentaria Aragon, Zaragoza 50059, Spain. RP Joy, M (corresponding author), Ctr Invest & Tecnol Agroalimentaria Aragon, Ave Montanana 930, Zaragoza 50059, Spain. EM mjoy@aragon.es CR AALHUS JL, 1991, MEAT SCI, V29, P57, DOI 10.1016/0309-1740(91)90023-J Alvarez-Rodriguez J, 2007, LIVEST SCI, V107, P152, DOI 10.1016/j.livsci.2006.09.011 Angood KM, 2008, MEAT SCI, V78, P176, DOI 10.1016/j.meatsci.2007.06.002 APPLE JK, 1995, J ANIM SCI, V73, P2295 Bianchi G., 2004, Agrociencia (Montevideo), V8, P89 CARPENTER ZL, 1966, J ANIM SCI, V25, P1232, DOI 10.2527/jas1966.2541232x Carrasco S, 2009, LIVEST SCI, V121, P56, DOI 10.1016/j.livsci.2008.05.017 CARRASCO S, RUMIND092012 CIE, 1986, COL Cockram MS, 2000, VET J, V159, P139, DOI 10.1053/tvjl.1999.0411 Colomer-Rocher F, 1988, CUADERNOS INIA, V17, P19 Diaz MT, 2002, SMALL RUMINANT RES, V43, P257, DOI 10.1016/S0921-4488(02)00016-0 Dunne PG, 2006, MEAT SCI, V74, P231, DOI 10.1016/j.meatsci.2006.02.003 Goddard PJ, 2000, APPL ANIM BEHAV SCI, V66, P305, DOI 10.1016/S0168-1591(99)00091-X Joy M, 2008, SMALL RUMINANT RES, V75, P24, DOI 10.1016/j.smallrumres.2007.07.005 KEMP JD, 1980, J ANIM SCI, V51, P321, DOI 10.2527/jas1980.512321x KEMP JD, 1976, J ANIM SCI, V42, P575, DOI 10.2527/jas1976.423575x Kerth CR, 2007, MEAT SCI, V75, P324, DOI 10.1016/j.meatsci.2006.07.019 Lanza M., 2001, P ASPA 16 C 12 15 JU LEPETIT J, 1994, MEAT SCI, V36, P203, DOI 10.1016/0309-1740(94)90042-6 Linares MB, 2008, LIVEST SCI, V115, P53, DOI 10.1016/j.livsci.2007.06.010 Lynch A, 2000, MEAT SCI, V56, P211, DOI 10.1016/S0309-1740(00)00033-4 Martinez-Cerezo S, 2005, MEAT SCI, V69, P325, DOI 10.1016/j.meatsci.2004.08.002 McInerney John., 2004, ANIMAL WELFARE EC PO Napolitano F, 2008, APPL ANIM BEHAV SCI, V110, P58, DOI 10.1016/j.applanim.2007.03.020 NOTTER DR, 1991, J ANIM SCI, V69, P3523, DOI 10.2527/1991.6993523x Perez G. Chacon, 2004, Spanish Journal of Agricultural Research, V2, P45 Priolo A, 2002, MEAT SCI, V62, P179, DOI 10.1016/S0309-1740(01)00244-3 Priolo A, 2001, ANIM RES, V50, P185, DOI 10.1051/animres:2001125 Ripoll G, 2008, MEAT SCI, V80, P239, DOI 10.1016/j.meatsci.2007.11.025 Ripoll G., 2005, Proceedings of the British Society of Animal Science annual conference 2005, York, UK. 4th-6th April, 2005, P147 Ripoll G., 2007, XXXII Jornadas Cientificas y XI Jornadas Internacionales de Ovinotecnia y Caprinotecnia, Mallorca, Spain, 19-21 de septiembre de 2007, P85 RIVERO L, 2007, THESIS I AGRONOMICO, P193 Roussel S, 2006, APPL ANIM BEHAV SCI, V97, P172, DOI 10.1016/j.applanim.2005.07.001 Sante-Lhoutellier V, 2008, FOOD CHEM, V109, P573, DOI 10.1016/j.foodchem.2007.11.081 Sanudo C, 1998, MEAT SCI, V49, pS29, DOI 10.1016/S0309-1740(98)00073-4 Sanudo C, 2007, MEAT SCI, V75, P610, DOI 10.1016/j.meatsci.2006.09.009 Sanudo C, 1997, MEAT SCI, V46, P357, DOI 10.1016/S0309-1740(97)00030-2 Sanudo C, 2000, MEAT SCI, V56, P89, DOI 10.1016/S0309-1740(00)00026-7 SILVA JS, 2002, LIVEST SCI, V76, P7 Vergara H, 1999, MEAT SCI, V53, P211, DOI 10.1016/S0309-1740(99)00059-5 Vestergaard M, 2000, MEAT SCI, V54, P177, DOI 10.1016/S0309-1740(99)00097-2 Walsh K, 2008, LIVEST SCI, V116, P223, DOI 10.1016/j.livsci.2007.10.010 WARRISS PD, 1990, J SCI FOOD AGR, V51, P517, DOI 10.1002/jsfa.2740510408 Wyszecki G., 2000, COLOR SCI CONCEPTS M Zervas G, 1999, LIVEST PROD SCI, V61, P245, DOI 10.1016/S0301-6226(99)00073-1 NR 46 TC 61 Z9 64 U1 2 U2 7 PD SEP PY 2009 VL 83 IS 1 BP 50 EP 56 DI 10.1016/j.meatsci.2009.03.014 WC Food Science & Technology SC Food Science & Technology UT WOS:000267510100008 DA 2022-12-14 ER PT J AU Espel, P Poletaeff, A Ndilimabaka, H AF Espel, P. Poletaeff, A. Ndilimabaka, H. TI Traceability of Voltage Measurements for Non-Sinusoidal Waveforms. SO MEASUREMENT SCIENCE REVIEW DT Article DE distorted voltage waveforms; thermal converters; sampling techniques; analog to digital converters ID STANDARDS AB This paper describes the result of work performed at the Laboratoire National de Metrologie et d'Essais (LNE) aiming at developing a standard system to measure RMS value and harmonic contents of distorted voltage waveforms by means of a sampling voltmeter. Thermal converters are used to trace the RMS value to the SI units. The error of the DVM has been generally found less than 10 mu V/V up to 2 kHz but can reach about 50 mu V/V at 2.5 kHz for RMS voltage measurements for sine waves. For distorted waveforms, deviations within 15 mu V/V have been obtained whatever the total harmonic distortion of the waveforms. C1 [Espel, P.; Poletaeff, A.; Ndilimabaka, H.] Lab Natl Metrol & Essais, F-78197 Trappes, France. C3 Laboratoire National de Metrologie et d'Essais (LNE) RP Espel, P (corresponding author), Lab Natl Metrol & Essais, 29 Ave Roger Hennequin, F-78197 Trappes, France. EM patrick.espel@lne.fr CR Espel P., 2009, FUND APPL METR 19 IM Espel P, 2009, METROLOGIA, V46, P578, DOI 10.1088/0026-1394/46/5/023 ILHENFELD WGK, 2001, E75 PTB INGLIS BD, 1992, METROLOGIA, V29, P191, DOI 10.1088/0026-1394/29/2/007 Klonz M, 1997, IEEE T INSTRUM MEAS, V46, P342, DOI 10.1109/19.571852 NR 5 TC 6 Z9 6 U1 0 U2 3 PY 2010 VL 10 IS 6 BP 200 EP 204 DI 10.2478/v10048-010-0034-2 WC Instruments & Instrumentation SC Instruments & Instrumentation UT WOS:000286458300004 DA 2022-12-14 ER PT J AU Dey, S Saha, S Singh, AK McDonald-Maier, K AF Dey, Somdip Saha, Suman Singh, Amit Kumar McDonald-Maier, Klaus TI FoodSQRBlock: Digitizing Food Production and the Supply Chain with Blockchain and QR Code in the Cloud SO SUSTAINABILITY DT Article DE food production; supply chain; blockchain; QR code; cloud computing; food safety; barcode; traceability system; agri-food; agriculture ID TECHNOLOGY AB Food safety is an important issue in today's world. The traditional agri-food production system does not offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and the origin of the outbreak. In recent years, the blockchain based food production system has resolved this challenge; however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework that digitises the food production information and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large-scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, as well as give an experimental evaluation to prove this. C1 [Dey, Somdip; Singh, Amit Kumar; McDonald-Maier, Klaus] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England. [Dey, Somdip; Saha, Suman] Nosh Technol, Colchester CO4 3SL, Essex, England. [Dey, Somdip] Univ Essex, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England. C3 University of Essex; University of Essex RP Dey, S (corresponding author), Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England.; Dey, S (corresponding author), Nosh Technol, Colchester CO4 3SL, Essex, England.; Dey, S (corresponding author), Univ Essex, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England. EM somdip.dey@essex.ac.uk; suman.saha@nosh.tech; a.k.singh@essex.ac.uk; kdm@essex.ac.uk CR Ali AQ, 2019, IEEE ACCESS, V7, P88196, DOI 10.1109/ACCESS.2019.2925499 Astill J, 2019, TRENDS FOOD SCI TECH, V91, P240, DOI 10.1016/j.tifs.2019.07.024 Baralla G., P 2019 IEEE ACM 2 IN Bohn R. B., 2011, Proceedings of the 2011 IEEE World Congress on Services (SERVICES 2011), P594, DOI 10.1109/SERVICES.2011.105 De Donno M, 2019, IEEE ACCESS, V7, P150936, DOI 10.1109/ACCESS.2019.2947652 Dey S., 2012, INT J MOD ED COMPUT, V4, P59, DOI [10.5815/ijmecs.2012.06.08, DOI 10.5815/IJMECS.2012.06.08] Dey S., P 2013 INT MULT AUT, P313 Dey S., ARXIV12054829 Dey S, 2018, COMPUT SCI ELECTR, P7, DOI 10.1109/CEEC.2018.8674185 Dey S, 2013, INT CONF COMM SYST, P512, DOI 10.1109/CSNT.2013.112 Drobnik O., 2015, BARCODES IOS BRINGIN Foroglou G., P 12 STUD C MAN SCI, P1 Huang PC, 2020, IEEE ACCESS, V8, P86706, DOI 10.1109/ACCESS.2020.2992694 Irving Greg, 2016, F1000Res, V5, P222, DOI 10.12688/f1000research.8114.3 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Krishnan S.P.T., 2015, BUILDING YOUR NEXT B Kumar Randhir, 2019, 2019 11th International Conference on Communication Systems & Networks (COMSNETS), P568, DOI 10.1109/COMSNETS.2019.8711418 Li D, 2006, INT J ADV MANUF TECH, V30, P938, DOI 10.1007/s00170-005-0066-1 Lin PY, 2016, IEEE T IND INFORM, V12, P384, DOI 10.1109/TII.2015.2514097 Meeuw A., 2016, P 6 INT C INT THINGS, P177 Nath C.A., P INT C INT COMP ICO, P1 Qi QL, 2019, IEEE ACCESS, V7, P86769, DOI 10.1109/ACCESS.2019.2923610 Tian F., P 2016 13 INT C SERV, P1 Yiannas F., 2018, Innovations: Technology / Governance / Globalization, V12, P46, DOI 10.1162/inov_a_00266 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 25 TC 25 Z9 25 U1 13 U2 47 PD MAR PY 2021 VL 13 IS 6 AR 3486 DI 10.3390/su13063486 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000645806600001 DA 2022-12-14 ER PT J AU Xiao, XQ He, QL Li, ZG Antoce, AO Zhang, XS AF Xiao, Xinqing He, Qile Li, Zhigang Antoce, Arina Oana Zhang, Xiaoshuan TI Improving traceability and transparency of table grapes cold chain logistics by integrating WSN and correlation analysis SO FOOD CONTROL DT Article DE Quality parameters; Table grapes; Wireless sensor network; Correlation analysis; Cold chain logistics ID FOOD-SUPPLY CHAIN; VITIS-VINIFERA; CORRELATION-COEFFICIENT; CRIMSON SEEDLESS; QUALITY; TEMPERATURE; SAFETY; MANAGEMENT; STORAGE; SYSTEM AB Effective and efficient measurement and determination of critical quality parameter(s) is the key to improve the traceability and transparency of the table grapes quality as well as the sustainability performance of the table grapes cold chain logistics, and ensure the table grapes quality and safety. This paper is to determine the critical quality parameter(s) in the cold chain logistics through the real time monitoring of the temperature fluctuation implemented with the Wireless Sensor Network (WSN), and the correlation analysis among the various quality parameters. The assessment was conducted through three experiments. Experiment I indicated that the temperature have a large fluctuation from 0 degrees C to 30 degrees C, and the critical temperatures could be determined as 0 degrees C, 5 degrees C, 10 degrees C, 15 degrees C, 20 degrees C, 25 degrees C and 30 degrees C. Experiment II described that the firmness and moisture loss rate, whose Pearson correlation coefficient with the sensory evaluation. were all greater than 0.9 at the critical temperatures determined in Experiment I, could be the critical quality parameters. Experiment III illustrated that the critical quality parameters, firmness and moisture loss rate, could be reliable indicators of table grapes quality by the Arrhenius kinetic equation, and results showed that the evaluation model based on the firmness is better to predict the shelf life than that based on the moisture loss rate. The best quality table grapes could be provided for the consumers via the easily and directly tracing and controlling the critical quality parameters in real time in actual cold chain logistics. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Xiao, Xinqing; Zhang, Xiaoshuan] China Agr Univ, Beijing 100083, Peoples R China. [Xiao, Xinqing; Zhang, Xiaoshuan] Beijing Lab Food Qual & Safety, Beijing 100083, Peoples R China. [He, Qile] Coventry Univ, Coventry CV1 5FB, W Midlands, England. [Li, Zhigang] Shihezi Univ, Shihezi 832003, Peoples R China. [Antoce, Arina Oana] Univ Agron Sci & Vet Med Bucharest, Bucharest 011464, Romania. C3 China Agricultural University; Coventry University; Shihezi University; University of Agronomic Science & Veterinary Medicine - Bucharest RP Zhang, XS (corresponding author), China Agr Univ, Beijing 100083, Peoples R China. EM zhxshuan@cau.edu.cn CR Adler J, 2010, CYTOM PART A, V77A, P733, DOI 10.1002/cyto.a.20896 Ahlgren P, 2003, J AM SOC INF SCI TEC, V54, P550, DOI 10.1002/asi.10242 Akaberi M, 2016, PHYTOTHER RES, V30, P540, DOI 10.1002/ptr.5570 Aung MM, 2014, FOOD CONTROL, V40, P198, DOI 10.1016/j.foodcont.2013.11.016 Aung MM, 2014, FOOD CONTROL, V39, P172, DOI 10.1016/j.foodcont.2013.11.007 Balic I, 2014, POSTHARVEST BIOL TEC, V93, P15, DOI 10.1016/j.postharvbio.2014.02.001 Brummell DA, 2006, FUNCT PLANT BIOL, V33, P103, DOI 10.1071/FP05234 Cal H., 2014, J AGR FOOD CHEM, V62, P10118 Carreno I, 2015, TREE GENET GENOMES, V11, DOI 10.1007/s11295-014-0818-x Champa WAH, 2015, J FOOD SCI TECH MYS, V52, P3607, DOI 10.1007/s13197-014-1422-7 Correa EC, 2014, FOOD BIOPROCESS TECH, V7, P3166, DOI 10.1007/s11947-014-1328-4 Coulomb D, 2008, TRENDS FOOD SCI TECH, V19, P413, DOI 10.1016/j.tifs.2008.03.006 Feng JY, 2014, BRIT FOOD J, V116, P611, DOI 10.1108/BFJ-04-2012-0101 Fortea MI, 2009, FOOD CHEM, V113, P1008, DOI 10.1016/j.foodchem.2008.08.053 Freitas PM, 2015, POSTHARVEST BIOL TEC, V105, P51, DOI 10.1016/j.postharvbio.2015.03.011 Fu ZeTian, 2013, Journal of China Agricultural University, V18, P186 Giacosa S, 2015, AUST J GRAPE WINE R, V21, P213, DOI 10.1111/ajgw.12126 Giacosa S, 2015, FOOD CHEM, V174, P8, DOI 10.1016/j.foodchem.2014.10.155 Gwanpua SG, 2015, J FOOD ENG, V148, P2, DOI 10.1016/j.jfoodeng.2014.06.021 Jang MS, 1997, SCIENCE, V275, P218, DOI 10.1126/science.275.5297.218 Jiao WH, 2012, BRIT FOOD J, V114, P978, DOI 10.1108/00070701211241572 Kantsadi AL, 2014, FOOD CHEM TOXICOL, V67, P35, DOI 10.1016/j.fct.2014.01.055 Kim H, 2014, SCI WORLD J, DOI 10.1155/2014/683045 Kim WR, 2015, FOOD CONTROL, V47, P510, DOI 10.1016/j.foodcont.2014.07.051 Lim TP, 2016, FOOD CONTROL, V60, P241, DOI 10.1016/j.foodcont.2015.07.042 Ma CY, 2016, J FOOD SCI TECH MYS, V53, P1363, DOI 10.1007/s13197-016-2177-0 Mirdehghan SH, 2016, FOOD CHEM, V196, P1040, DOI 10.1016/j.foodchem.2015.10.038 Ngcobo MEK, 2013, POSTHARVEST BIOL TEC, V86, P201, DOI 10.1016/j.postharvbio.2013.06.037 Ngcobo MEK, 2013, BIOSYST ENG, V115, P346, DOI 10.1016/j.biosystemseng.2013.03.013 Parreno-Marchante A, 2014, J FOOD ENG, V122, P99, DOI 10.1016/j.jfoodeng.2013.09.007 Porep JU, 2014, FOOD CONTROL, V37, P77, DOI 10.1016/j.foodcont.2013.09.012 Puth MT, 2014, ANIM BEHAV, V93, P183, DOI 10.1016/j.anbehav.2014.05.003 Qi L, 2014, FOOD CONTROL, V38, P19, DOI 10.1016/j.foodcont.2013.09.023 Raposo A, 2015, FOOD CONTROL, V56, P177, DOI 10.1016/j.foodcont.2015.01.052 Sato A, 1997, VITIS, V36, P7 Solyom K, 2013, J FOOD ENG, V119, P33, DOI 10.1016/j.jfoodeng.2013.05.005 Sweetman C, 2014, J EXP BOT, V65, P5975, DOI 10.1093/jxb/eru343 Tzamalis PG, 2016, FOOD CONTROL, V63, P179, DOI 10.1016/j.foodcont.2015.11.011 Wiedermann W, 2016, COMMUN STAT-THEOR M, V45, P6263, DOI 10.1080/03610926.2014.960582 Xiao XQ, 2015, APPL SCI-BASEL, V5, P747, DOI 10.3390/app5040747 Xiao XinQing, 2013, Transactions of the Chinese Society of Agricultural Engineering, V29, P259 Xu GB, 2014, SENSORS-BASEL, V14, P16932, DOI 10.3390/s140916932 Ye MQ, 2014, FOOD BIOPROCESS TECH, V7, P3055, DOI 10.1007/s11947-014-1385-8 NR 43 TC 37 Z9 39 U1 10 U2 110 PD MAR PY 2017 VL 73 BP 1556 EP 1563 DI 10.1016/j.foodcont.2016.11.019 PN B WC Food Science & Technology SC Food Science & Technology UT WOS:000405537400095 DA 2022-12-14 ER PT J AU Kirtaev, RV Kuzin, AY Maslov, VG Mityukhlyaev, VB Todua, PA Filippov, MN AF Kirtaev, R. V. Kuzin, A. Yu. Maslov, V. G. Mityukhlyaev, V. B. Todua, P. A. Filippov, M. N. TI Calibration of Scanning Electron Microscopes over a Wide Range of Magnifications SO MEASUREMENT TECHNIQUES DT Article DE calibration of SEM; end gauge; step structure; traceability of calibration results AB A new method is proposed for calibrating scanning electron microscopes (SEM) with magnification in the 10-50000(x) range using an end gauge and an auxiliary structure. Use of a certified end gauge ensures traceability of the calibration results to the primary standard for the meter. The relative expanded uncertainty of measurements on an S-4800 SEM at magnifications of 1000, 10000, and 50000(x) is less than 0.14%. C1 [Kirtaev, R. V.; Maslov, V. G.; Mityukhlyaev, V. B.; Todua, P. A.; Filippov, M. N.] Properties Surfaces & Vacuum NITsPV, Res Ctr, Moscow, Russia. [Kuzin, A. Yu.] All Russia Res Inst Metrol Serv VNIIMS, Moscow, Russia. [Kirtaev, R. V.; Todua, P. A.; Filippov, M. N.] State Univ, Moscow Inst Phys & Technol, Dolgoprudnyi, Moscow Region, Russia. [Filippov, M. N.] IGIC RAS, Inst Gen & Inorgan Chem, Moscow, Russia. C3 Moscow Institute of Physics & Technology; Russian Academy of Sciences; Kurnakov Institute of General & Inorganic Chemistry of the Russian Academy of Sciences RP Kirtaev, RV (corresponding author), Properties Surfaces & Vacuum NITsPV, Res Ctr, Moscow, Russia. EM fgupnicpv@mail.ru CR [Anonymous], 2008, E76698 ASTM Gogolinskii K., 2012, Nanoindustry, P48 Goldstein J. I., 1981, SCANNING ELECT MICRO KANAYA K, 1972, J PHYS D APPL PHYS, V5, P43, DOI 10.1088/0022-3727/5/1/308 Yu A, 2006, T IOFAN, V62, P36 NR 5 TC 1 Z9 1 U1 0 U2 9 PD MAY PY 2017 VL 59 IS 12 BP 1245 EP 1249 DI 10.1007/s11018-017-1123-5 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000400941400002 DA 2022-12-14 ER PT J AU Plavsic, B Nedic, D Micovic, Z Tesic, M Stanojevic, S Rizica, A Krnjaic, D Nada, T Milanovic, S AF Plavsic, B. Nedic, D. Micovic, Z. Tesic, M. Stanojevic, S. Rizica, Asanin Krnjaic, D. Nada, Tajdic Milanovic, S. TI VETERINARY INFORMATION MANAGEMENT SYSTEM (VIMS) IN THE PROCESS OF NOTIFICATION AND MANAGEMENT OF ANIMAL DISEASES SO ACTA VETERINARIA-BEOGRAD DT Article DE animal disease notification system; animal health control; food safety; risk analysis; veterinary information management system ID FOOD SAFETY; ECONOMIC-IMPLICATIONS; INFECTIOUS-DISEASES; EPIDEMIOLOGY; HEALTH; LABORATORIES; PRODUCTS; BSE; LIVESTOCK; CRISIS AB A prerequisite to the development of an efficient animal health, food safety and traceability management system in the animal food production chain is the implementation of an integrated veterinary informational management system (VIMS) capable for the capture, storage, analysis and retrieval of data and providing the opportunity for the cumulative gathering of the knowledge and capability for its competent interpretation. Such a system will enable collecting appropriate data, including quality management and inspection controls, from all establishments and commodities in the "from farm to fork" food production chain (farms, holdings, slaughterhouses, laboratories, traders etc.) in a structured, predefined format, and facilitate competent analyses and reporting of such data, as well as the improvement of the existing programs and strategies. The role of information system in animal disease diagnosis, surveillance and notification, control of national and international trade of commodities, food safety management, investigation of diseases, predictive microbiology and quantitative risk assessment is of great importance for the quality of veterinary service. Integral part of the VIMS is animal disease notification system designed according to and in compliance with international requirements, standards and recommendation and able to exchange relevant information with similar information systems. The aim of this contribution is to describe national animal disease notification system which is in place in Serbia as a part of VIMS. C1 [Plavsic, B.] Minist Agr Forestry & Water Management, Vet Directorate, Anim Hlth & Welf Dept, Belgrade 11070, Serbia. [Nedic, D.] Pan European Univ Apeiron, Banja Luka, Bosnia & Herceg. [Tesic, M.; Rizica, Asanin; Krnjaic, D.; Nada, Tajdic] Univ Belgrade, Fac Vet Med, Belgrade 11001, Serbia. [Milanovic, S.] Inst Med Res, Belgrade, Serbia. C3 University of Belgrade; University of Belgrade RP Plavsic, B (corresponding author), Minist Agr Forestry & Water Management, Vet Directorate, Anim Hlth & Welf Dept, Omladinskih Brigada 1, Belgrade 11070, Serbia. EM b.plavsic@minpolj.sr.gov.yu CR Anderson RM, 1996, NATURE, V382, P779, DOI 10.1038/382779a0 Bellini S, 2000, REV SCI TECH OIE, V19, P841, DOI 10.20506/rst.19.3.1255 Blaha T, 1999, PREV VET MED, V39, P81, DOI 10.1016/S0167-5877(98)00150-0 BLAHA TH, 1996, P AD LEMAN C 21 24 S, V23, P136 BOEHLJE M, 1996, P AD LEMAN C 21 24 S, V23, P1 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Claus A, 2008, J CEREAL SCI, V47, P118, DOI 10.1016/j.jcs.2007.06.016 Dijkhuizen A. A., 1997, ANIMAL HLTH EC PRINC, P306 Donaldson AI, 2002, REV SCI TECH OIE, V21, P569, DOI 10.20506/rst.21.3.1362 Edwards S, 1998, REV SCI TECH OIE, V17, P418, DOI 10.20506/rst.17.2.1107 Ferguson NM, 1997, P ROY SOC B-BIOL SCI, V264, P1445, DOI 10.1098/rspb.1997.0201 Gosser HS, 1998, REV SCI TECH OIE, V17, P444, DOI 10.20506/rst.17.2.1112 Holden S, 1999, REV SCI TECH OIE, V18, P425, DOI 10.20506/rst.18.2.1166 HORST HS, 1999, EC EVALUATION CONT C, V18, P440 HUGHJONES N, 1975, ANIMAL DIS MONITORIN, P220 James A, 2005, PREV VET MED, V67, P91, DOI 10.1016/j.prevetmed.2004.11.003 Leslie J, 1999, REV SCI TECH OIE, V18, P440, DOI 10.20506/rst.18.2.1165 Lubroth J, 2006, REV SCI TECH OIE, V25, P361, DOI 10.20506/rst.25.1.1672 Marsh W, 1999, REV SCI TECH OIE, V18, P357, DOI 10.20506/rst.18.2.1170 McMeekin TA, 2006, INT J FOOD MICROBIOL, V112, P181, DOI 10.1016/j.ijfoodmicro.2006.04.048 Morris RS, 1999, REV SCI TECH OIE, V18, P305, DOI 10.20506/rst.18.2.1173 Morrison R. J., 1992, Proceedings of the 2nd KEK Topical Conference on e+e- Collision Physics (KEK Proceedings 92-9), P1 O'Brien SJ, 2006, WOODHEAD PUBL FOOD S, P50, DOI 10.1533/9781845691394.1.50 *OIE WORLD ORG ANI, 2004, HDB IMP RISK AN AN A, V1 Petersen B, 2002, LIVEST PROD SCI, V76, P207, DOI 10.1016/S0301-6226(02)00120-3 PFEIFFER D, 1998, VET EPIDEMIOLOGY INT PLAVSIC B, 2005, THESIS U BELGRADE BE Prusiner SB, 1997, SCIENCE, V278, P245, DOI 10.1126/science.278.5336.245 Ramsay GC, 1999, REV SCI TECH OIE, V18, P343, DOI 10.20506/rst.18.2.1171 REILLY J, 1999, MESSAGE RECEIVED GLA, P128 Rushton J, 1999, REV SCI TECH OIE, V18, P315, DOI 10.20506/rst.18.2.1172 Smith A. P., 2007, EUR J MARKETING, V33, P1107, DOI [10.1108/03090569910292294, DOI 10.1016/J.BIOMBIOE.2009.03.010] Tesic M, 2005, ACTA VET-BEOGRAD, V55, P335, DOI 10.2298/AVB0504335T TESIC M, 2004, PROS INT C SUST AGR, P572 TESIC M, 2003, CONT AGR, V52, P491 THRUSFIELD M, 1995, VET EPIDEMIOLOGY, P479 Truszczynski MJ, 1998, REV SCI TECH OIE, V17, P405, DOI 10.20506/rst.17.2.1109 Verbeke W, 2001, FOOD QUAL PREFER, V12, P489, DOI 10.1016/S0950-3293(01)00042-8 Zepeda C, 2001, PREV VET MED, V48, P261, DOI 10.1016/S0167-5877(00)00200-2 NR 39 TC 5 Z9 7 U1 1 U2 9 PY 2009 VL 59 IS 1 BP 99 EP 108 DI 10.2298/AVB0901099P WC Veterinary Sciences SC Veterinary Sciences UT WOS:000264662100010 DA 2022-12-14 ER PT J AU Krajnc, B Bontempo, L Araus, JL Giovanetti, M Alegria, C Lauteri, M Augusti, A Atti, N Smeti, S Taous, F Amenzou, NE Podgornik, M Camin, F Reis, P Maguas, C Miklavcic, MB Ogrinc, N AF Krajnc, Bor Bontempo, Luana Luis Araus, Jose Giovanetti, Manuela Alegria, Carla Lauteri, Marco Augusti, Angela Atti, Naziha Smeti, Samir Taous, Fouad Amenzou, Nour Eddine Podgornik, Maja Camin, Federica Reis, Pedro Maguas, Cristina Bucar Miklavcic, Milena Ogrinc, Nives TI Selective Methods to Investigate Authenticity and Geographical Origin of Mediterranean Food Products SO FOOD REVIEWS INTERNATIONAL DT Review DE Geographical origin; stable isotope ratios; elemental profiles; molecular characterization; authenticity; traceability ID ARGAN OIL ADULTERATION; VOLATILE ORGANIC-COMPOUNDS; TRACE-ELEMENT COMPOSITION; TRUFFLE-FLAVORED OILS; TUBER-MAGNATUM; IBERIAN PIG; MEAT QUALITY; MASS-SPECTROMETRY; MOLECULAR CHARACTERIZATION; CARCASS COMPOSITION AB The Mediterranean diet is promoted as one of the healthiest and closely linked to socioecological practices, knowledge and traditions, promoting sustainable food production, and linking geographical origin with food quality and ecosystem services. Consumer adherence to this dietary pattern drives increased consumption of authentic "premium" foods, such as Iberian pig meat and dry-cured ham from Portugal and Spain, argan oil from Morocco, "Djebel" lamb from Tunisia and truffles from Italy and Slovenia, i.e., food products that respond to current ethical, environmental and socially sustainable demands. Geographical indication and appellation of origin can increase traditional food products competitiveness, but the high-value recognition of these products can also lead to economically motivated product adulteration. It is therefore imperative to protect the high added value of these unique food products by ensuring their quality, authenticity, provenance and sustainable production systems. In this review, we provide a critical evaluation of the analytical methods that are currently used for the determination of provenance and authenticity of these Mediterranean products as well as possible strategies for improving the throughput and affordability of the methods discussed. C1 [Krajnc, Bor; Ogrinc, Nives] Jozef Stefan Inst, Dept Environm Sci, Ljubljana, Slovenia. [Bontempo, Luana; Camin, Federica] Fdn Edmund Mach, Dept Food Qual & Nutr, Res & Innovat Ctr, San Michele All Adige, Italy. [Luis Araus, Jose] Univ Barcelona, Sect Plant Physiol, AGROTECNIO, Lleida, Spain. [Giovanetti, Manuela; Alegria, Carla; Maguas, Cristina] Univ Lisbon, Fac Ciencias, Ctr Ecol Evolut & Environm Changes, Lisbon, Portugal. [Lauteri, Marco; Augusti, Angela] CNR, Ist Ric Ecosistemi Terr, Porano, Italy. [Atti, Naziha; Smeti, Samir] Univ Carthage, Inst Natl Rech Agron Tunisie, Lab Prod Anim & Fourragere, Tunis, Tunisia. [Taous, Fouad; Amenzou, Nour Eddine] Ctr Natl Energie Sci & Tech Nucl, Rabat, Morocco. [Podgornik, Maja; Bucar Miklavcic, Milena] Sci & Res Ctr Koper, Inst Oliveculture, Koper, Slovenia. [Reis, Pedro] Inst Nacl Invest Agr & Vet, Sistemas Agr & Florestais & Sanidade Vegetal, Oeiras, Portugal. C3 Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; Fondazione Edmund Mach; University of Barcelona; Universidade de Lisboa; Consiglio Nazionale delle Ricerche (CNR); Universite de Carthage RP Ogrinc, N (corresponding author), Jozef Stefan Inst, Dept Environm Sci, Ljubljana, Slovenia. EM nives.ogrinc@ijs.si CR Aabd NA, 2013, MEDITERR J NUTR META, V6, P217, DOI 10.3233/s12349-013-0134-2 Adnoy T, 2005, LIVEST PROD SCI, V94, P25, DOI 10.1016/j.livprodsci.2004.11.026 Agrimonti C, 2019, EUR J LIPID SCI TECH, V121, DOI 10.1002/ejlt.201800132 Alves E, 2003, ANIM GENET, V34, P319, DOI 10.1046/j.1365-2052.2003.01010.x Alves E, 2002, MEAT SCI, V61, P157, DOI 10.1016/S0309-1740(01)00179-6 Alves E, 2009, ANIMAL, V3, P1216, DOI 10.1017/S1751731109004819 Amicucci A, 2000, FEMS MICROBIOL LETT, V189, P265, DOI 10.1111/j.1574-6968.2000.tb09241.x Amicucci A, 2002, J SCI FOOD AGR, V82, P1391, DOI 10.1002/jsfa.1196 Angelini P., 2016, PLANT SOIL MICROBES, P225 [Anonymous], 1951, PORTANT ATTRIBUTION [Anonymous], 1938, CODIRECTORIAL ORDER [Anonymous], 1998, URADNI LIST REPUBLIK [Anonymous], 1925, PROTECTION DELIMITAT [Anonymous], 2011, URADNI LIST REPUBLIK Aprea E, 2007, RAPID COMMUN MASS SP, V21, P2564, DOI 10.1002/rcm.3118 Lopez-Bascon MA, 2015, J AGR FOOD CHEM, V63, P692, DOI 10.1021/jf505189x Atti N, 2011, TROP ANIM HEALTH PRO, V43, P1371, DOI 10.1007/s11250-011-9865-6 Ayad A., 1989, RABAT MORS, V13-17, P9 Belo Carlos Carmona, 2014, Rev. de Ciências Agrárias, V37, P122 Berch S. M., 2013, INT S FOR MUSHR KOR Bergant J., 2013, V41145 CRP KMET I SL Bergant J., 2014, GEOGR INF SIST V SLO, P95 Bertini L, 2006, MICROBIOL RES, V161, P59, DOI 10.1016/j.micres.2005.06.003 Bonito G, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0052765 Bonito GM, 2010, MOL ECOL, V19, P4994, DOI 10.1111/j.1365-294X.2010.04855.x Bougrini M, 2014, J SENSORS, V2014, DOI 10.1155/2014/245831 Bragato G, 2010, PLANT SOIL, V329, P51, DOI 10.1007/s11104-009-0133-8 Bratek Z., 2004, P 1 HYP MUSHR C RAB Bryla P, 2018, QUAL ASSUR SAF CROP, V10, P155, DOI 10.3920/QAS2017.1189 Bryla P, 2015, APPETITE, V91, P302, DOI 10.1016/j.appet.2015.04.056 Buntgen U, 2012, NAT CLIM CHANGE, V2, P827, DOI 10.1038/nclimate1733 Camin F, 2017, TRENDS FOOD SCI TECH, V61, P176, DOI 10.1016/j.tifs.2016.12.007 Caporarello N, 2017, INT J MOL MED, V40, P1277, DOI 10.3892/ijmm.2017.3104 Castellano R, 2013, SPAN J AGRIC RES, V11, P417, DOI 10.5424/sjar/2013112-3415 Chakhchar A, 2017, FRONT PLANT SCI, V8, P1, DOI 10.3389/fpls.2017.00276 Chalh A., 2007, Journal of Biological Sciences, V7, P1347 Charrouf Z, 1999, J ETHNOPHARMACOL, V67, P7, DOI 10.1016/S0378-8741(98)00228-1 Charrouf Z, 2018, OCL OILS FAT CROP LI, V25, DOI 10.1051/ocl/2018006 Charrouf Zoubida, 2002, Phytochemistry Reviews, V1, P345, DOI 10.1023/A:1026030100167 Chaussod Remi, 2005, Cahiers Agricultures, V14, P351 Chevalier G., 2002, TRUFFE BOURGOGNE TUB Chung IM, 2018, FOOD CHEM, V264, P92, DOI 10.1016/j.foodchem.2018.04.138 CIHEAM/FAO, 2015, MED FOOD CONS PATT D Ciolfi M., 2015, 10 C NAZ SISEF SOST, P15 Clop A, 2004, GENET SEL EVOL, V36, P97, DOI 10.1051/gse:2003053 Cox RB, 2006, J FOOD SCI, V71, pS542, DOI 10.1111/j.1750-3841.2006.00124.x Cullere L, 2013, FOOD CHEM, V141, P105, DOI 10.1016/j.foodchem.2013.03.027 Cullere L, 2010, FOOD CHEM, V122, P300, DOI 10.1016/j.foodchem.2010.02.024 D'Auria M, 2014, NAT PROD RES, V28, P1709, DOI 10.1080/14786419.2014.940942 D'Auria M, 2012, J CHROMATOGR SCI, V50, P775, DOI 10.1093/chromsci/bms060 Danezis GP, 2016, TRAC-TREND ANAL CHEM, V85, P123, DOI 10.1016/j.trac.2016.02.026 del Moral FG, 2009, J FOOD ENG, V90, P540, DOI 10.1016/j.jfoodeng.2008.07.027 Delgado-Chavero CL, 2013, GRASAS ACEITES, V64, P157, DOI 10.3989/gya.130412 Diaz MT, 2005, MEAT SCI, V71, P256, DOI 10.1016/j.meatsci.2005.03.020 Diaz P, 2003, J CHROMATOGR A, V1017, P207, DOI 10.1016/j.chroma.2003.08.016 Elloumi M., 2006, AFR CONT, V219, P63, DOI [10.3917/AFCO.219.0063, DOI 10.3917/AFCO.219.0063] Falasconi M, 2005, SENSOR ACTUAT B-CHEM, V106, P88, DOI 10.1016/j.snb.2004.05.041 Fantinic J., 2014, THESIS Federico V, 2015, SCI REP-UK, V5, DOI 10.1038/srep12629 Fernandez A, 2004, J SCI FOOD AGR, V84, P1855, DOI 10.1002/jsfa.1829 Frizzi G, 2001, MYCOL RES, V105, P365, DOI 10.1017/S0953756201003513 Furlani A., 2015, THESIS Galian M, 2007, ANIM SCI J, V78, P659, DOI 10.1111/j.1740-0929.2007.00487.x Casco JMG, 2013, GRASAS ACEITES, V64, P191, DOI 10.3989/gya.130812 Garcia D, 2006, MEAT SCI, V72, P560, DOI 10.1016/j.meatsci.2005.09.005 Giannaccini G, 2012, ENVIRON MONIT ASSESS, V184, P7579, DOI 10.1007/s10661-012-2520-5 Giomaro G, 2002, FEMS MICROBIOL LETT, V216, P9, DOI 10.1111/j.1574-6968.2002.tb11407.x Gliszczynska-Swiglo A, 2017, FOOD ANAL METHOD, V10, P1800, DOI 10.1007/s12161-016-0739-4 Gonzalez-Martin I, 2002, ANAL CHIM ACTA, V468, P293, DOI 10.1016/S0003-2670(02)00657-8 Gonzalez-Martin I, 2001, MEAT SCI, V58, P25, DOI 10.1016/S0309-1740(00)00126-1 Gonzalvez A, 2010, FOOD SCI TECHNOL INT, V16, P65, DOI 10.1177/1082013209353343 Gonzalvez A, 2010, FOOD CHEM, V121, P878, DOI 10.1016/j.foodchem.2009.11.091 Grebenc T., 2013, Gozdarski Vestnik, V71, P365 Grebenc T., 2007, CULTURE TRUFFE MONDE Guillen A, 2010, NEURAL COMPUT APPL, V19, P465, DOI 10.1007/s00521-009-0327-2 Haddi Z, 2011, PROCEDIA ENGINEER, V25, DOI 10.1016/j.proeng.2011.12.280 Hajji H, 2019, ANIMAL, V13, P2669, DOI 10.1017/S1751731119000909 Hajji H, 2016, J FOOD COMPOS ANAL, V48, P102, DOI 10.1016/j.jfca.2016.02.011 Hall IR, 1998, ECON BOT, V52, P192, DOI 10.1007/BF02861209 Henn MR, 2000, APPL ENVIRON MICROB, V66, P4180, DOI 10.1128/AEM.66.10.4180-4186.2000 Hilali M, 2007, J AM OIL CHEM SOC, V84, P761, DOI 10.1007/s11746-007-1084-y Hobbie EA, 2001, NEW PHYTOL, V150, P601, DOI 10.1046/j.1469-8137.2001.00134.x Islam MT, 2013, J PROTEOME RES, V12, P5349, DOI 10.1021/pr400650c Jeandroz S, 2008, J BIOGEOGR, V35, P815, DOI 10.1111/j.1365-2699.2007.01851.x Kelly S, 2005, TRENDS FOOD SCI TECH, V16, P555, DOI 10.1016/j.tifs.2005.08.008 Khallouki F, 2003, EUR J CANCER PREV, V12, P67, DOI 10.1097/00008469-200302000-00011 Kharbach M, 2018, FOOD CHEM, V263, P8, DOI 10.1016/j.foodchem.2018.04.059 Khemiri I., 2017, Journal of New Sciences, V41, P2260 Krebs R.E., 2003, PRINCIPAL DOMESTICAT LANFRANCO L, 1993, FEMS MICROBIOL LETT, V114, P245, DOI 10.1111/j.1574-6968.1993.tb06581.x Lopez-Bote CJ, 1998, MEAT SCI, V49, pS17, DOI 10.1016/S0309-1740(98)00072-2 Luykx DMAM, 2008, FOOD CHEM, V107, P897, DOI 10.1016/j.foodchem.2007.09.038 March RE, 2006, INT J MASS SPECTROM, V249, P60, DOI 10.1016/j.ijms.2005.12.038 Matthaus B, 2010, FOOD CHEM, V120, P426, DOI 10.1016/j.foodchem.2009.10.023 Mekki I, 2019, J ANIM FEED SCI, V28, P22, DOI 10.22358/jafs/102757/2019 Mekki I, 2016, J FOOD COMPOS ANAL, V53, P40, DOI 10.1016/j.jfca.2016.09.002 Mello A, 2010, PLANT BIOSYST, V144, P323, DOI 10.1080/11263500903374724 Mello A, 2002, ENVIRON MICROBIOL, V4, P584, DOI 10.1046/j.1462-2920.2002.00343.x Merkle S., 2015, CHROMATOGRAPHY, V2, P293, DOI [10.3390/chromatography2030293, DOI 10.3390/CHROMATOGRAPHY2030293] Mohammed FAE, 2013, FOOD CHEM, V136, P105, DOI 10.1016/j.foodchem.2012.07.098 Montossi F, 2013, MEAT SCI, V95, P772, DOI 10.1016/j.meatsci.2013.04.048 Murat-Furminieux C., 2004, THESIS Nikkarinen M, 2004, J FOOD COMPOS ANAL, V17, P301, DOI 10.1016/j.jfca.2004.03.013 Ourrach I, 2012, GRASAS ACEITES, V63, P355, DOI 10.3989/gya.047212 Oussama A, 2012, SPECTROSC LETT, V45, P458, DOI 10.1080/00387010.2011.639121 Ovilo C, 2000, ANIM GENET, V31, P117, DOI 10.1046/j.1365-2052.2000.00603.x Pacioni Giovanni, 2018, Italian Journal of Mycology, V47, P1, DOI 10.6092/issn.2531-7342/7748 Pacioni G, 2014, FOOD CHEM, V146, P30, DOI 10.1016/j.foodchem.2013.09.016 Pagliuca G, 2018, J PHARMACEUT BIOMED, V150, P121, DOI 10.1016/j.jpba.2017.11.059 Pakhrou O., 2016, Australian Journal of Crop Science, V10, P990, DOI 10.21475/ajcs.2016.10.07.p7680 Paolocci F, 1999, FEMS MICROBIOL ECOL, V28, P23, DOI 10.1111/j.1574-6941.1999.tb00557.x Paolocci F, 2004, FEMS MICROBIOL LETT, V235, P109, DOI [10.1111/j.1574-6968.2004.tb09574.x, 10.1016/femsle.2004.04.029] Patel S, 2017, TRENDS FOOD SCI TECH, V70, P1, DOI 10.1016/j.tifs.2017.09.009 Pico V., 1788, THESIS Piltaver A., 2006, Gozdarski Vestnik, V64, P303 Puscas RH, 2019, ANAL LETT, V52, P102, DOI 10.1080/00032719.2017.1376218 Ramirez R, 2007, MEAT SCI, V75, P388, DOI 10.1016/j.meatsci.2006.08.003 Recio C, 2013, GRASAS ACEITES, V64, P181, DOI 10.3989/gya.130712 Recio C., 2007, Patente Espanola, DOI 10.1094/PDIS-91-4-0467B, Patent No. [P20070210, 20070210] Riousset L, 2001, TRUFFES EUROPE CHINE Rizzello R, 2012, FOOD RES INT, V48, P792, DOI 10.1016/j.foodres.2012.06.019 Rubini A, 1998, FEMS MICROBIOL LETT, V164, P7, DOI 10.1016/S0378-1097(98)00183-9 Rubini A, 2005, APPL ENVIRON MICROB, V71, P6584, DOI 10.1128/AEM.71.11.6584-6589.2005 Rueda A, 2016, J AOAC INT, V99, P489, DOI 10.5740/jaoacint.15-0121 Rupp R., TROUBLE TRUFFLES Salghi R, 2014, FOOD CHEM, V153, P387, DOI 10.1016/j.foodchem.2013.12.084 SAWAYA WN, 1985, J FOOD SCI, V50, P450, DOI 10.1111/j.1365-2621.1985.tb13425.x Schmidberger PC, 2017, J AGR FOOD CHEM, V65, P9287, DOI 10.1021/acs.jafc.7b04073 Sciarrone D, 2018, ANAL CHEM, V90, P6610, DOI 10.1021/acs.analchem.8b00386 Scopoli G. A., 1772, BIBLIOPOLAE VINDOBON Segneanu AE, 2012, DIG J NANOMATER BIOS, V7, P199 Sejalon-Delmas N, 2000, J AGR FOOD CHEM, V48, P2608, DOI 10.1021/jf9910382 Slimeni O., 2013, THESIS Smeti S, 2014, J APPL ANIM RES, V42, P297, DOI 10.1080/09712119.2013.845102 Sourzat P., 2000, TRUFFICULTURE RESULT TALOU T, 1987, J AGR FOOD CHEM, V35, P774, DOI 10.1021/jf00077a031 Tang Y, 2012, FOOD CHEM, V132, P1207, DOI 10.1016/j.foodchem.2011.11.077 Toro M, 2002, CONSERV GENET, V3, P309, DOI 10.1023/A:1019921131171 Toro M, 1999, GENET SEL EVOL, V31, P255, DOI 10.1051/gse:19990305 Torregiani E, 2017, FOOD ANAL METHOD, V10, P1857, DOI 10.1007/s12161-016-0749-2 UNESCO, STRENGTH ARG BIOSPH Ursoniu S, 2018, PHYTOTHER RES, V32, P377, DOI 10.1002/ptr.5959 van Asch B, 2012, ANIM GENET, V43, P35, DOI 10.1111/j.1365-2052.2011.02222.x Venegas C, 2011, J AGR FOOD CHEM, V59, P12102, DOI 10.1021/jf203428t Vietina M, 2013, FOOD CHEM, V141, P3820, DOI 10.1016/j.foodchem.2013.06.075 Vraj B., 2015, NOVI IZZIVI AGRONOMI, P19 Wang SA, 2011, FOOD RES INT, V44, P2567, DOI 10.1016/j.foodres.2011.06.008 Wernig F, 2018, FOOD CONTROL, V87, P9, DOI 10.1016/j.foodcont.2017.11.045 Zahar M., 2007, 234 AM CHEM SOC M BO Zamora-Rojas E, 2012, MEAT SCI, V90, P636, DOI 10.1016/j.meatsci.2011.10.006 Zampioglou D., 2013, 2013 IEEE 7 INT C IN Zougagh M, 2011, ANAL BIOANAL CHEM, V399, P2395, DOI 10.1007/s00216-010-4628-1 NR 152 TC 12 Z9 12 U1 11 U2 66 PD AUG 18 PY 2021 VL 37 IS 6 BP 656 EP 682 DI 10.1080/87559129.2020.1717521 EA FEB 2020 WC Food Science & Technology; Nutrition & Dietetics SC Food Science & Technology; Nutrition & Dietetics UT WOS:000515433500001 DA 2022-12-14 ER PT J AU Lama-Munoz, A Contreras, MD AF Lama-Munoz, Antonio Del Mar Contreras, Maria TI Extraction Systems and Analytical Techniques for Food Phenolic Compounds: A Review SO FOODS DT Review DE analysis; extraction; green technologies; mass spectrometry; phenolic compounds ID SOLID-PHASE EXTRACTION; PERFORMANCE LIQUID-CHROMATOGRAPHY; FLIGHT MASS-SPECTROMETRY; VIRGIN OLIVE OILS; ULTRASOUND-ASSISTED EXTRACTION; SUPERCRITICAL-FLUID EXTRACTION; AQUEOUS 2-PHASE SYSTEM; ESI-QTOF-MS; BIOACTIVE COMPOUNDS; ANTIOXIDANT ACTIVITY AB Phenolic compounds are highly valuable food components due to their potential utilisation as natural bioactive and antioxidant molecules for the food, cosmetic, chemical, and pharmaceutical industries. For this purpose, the development and optimisation of efficient extraction methods is crucial to obtain phenolic-rich extracts and, for some applications, free of interfering compounds. It should be accompanied with robust analytical tools that enable the standardisation of phenolic-rich extracts for industrial applications. New methodologies based on both novel extraction and/or analysis are also implemented to characterise and elucidate novel chemical structures and to face safety, pharmacology, and toxicity issues related to phenolic compounds at the molecular level. Moreover, in combination with multivariate analysis, the extraction and analysis of phenolic compounds offer tools for plant chemotyping, food traceability and marker selection in omics studies. Therefore, this study reviews extraction techniques applied to recover phenolic compounds from foods and agri-food by-products, including liquid-liquid extraction, solid-liquid extraction assisted by intensification technologies, solid-phase extraction, and combined methods. It also provides an overview of the characterisation techniques, including UV-Vis, infra-red, nuclear magnetic resonance, mass spectrometry and others used in minor applications such as Raman spectroscopy and ion mobility spectrometry, coupled or not to chromatography. Overall, a wide range of methodologies are now available, which can be applied individually and combined to provide complementary results in the roadmap around the study of phenolic compounds. C1 [Lama-Munoz, Antonio] Univ Seville, Dept Cristalog Mineral & Quim Agr, C Prof Garcia Gonzalez 1, Seville 41012, Spain. [Del Mar Contreras, Maria] Univ Jaen, Dept Chem Environm & Mat Engn, Ctr Adv Studies Earth Sci Energy & Environm CEACT, Campus Lagunillas S-N, Jaen 23071, Spain. C3 University of Sevilla; Universidad de Jaen RP Contreras, MD (corresponding author), Univ Jaen, Dept Chem Environm & Mat Engn, Ctr Adv Studies Earth Sci Energy & Environm CEACT, Campus Lagunillas S-N, Jaen 23071, Spain. EM mcgamez@ujaen.es CR Abu-Reidah IM, 2013, J CHROMATOGR A, V1313, P212, DOI 10.1016/j.chroma.2013.07.020 Abu-Reidah IM, 2014, ELECTROPHORESIS, V35, P1571, DOI 10.1002/elps.201300646 Agati G, 2005, J AGR FOOD CHEM, V53, P1354, DOI 10.1021/jf048381d Alanon ME, 2020, ARAB J CHEM, V13, P1685, DOI 10.1016/j.arabjc.2018.01.003 Flores MIA, 2012, FOOD CHEM, V134, P2465, DOI 10.1016/j.foodchem.2012.04.058 Algieri F, 2016, J ETHNOPHARMACOL, V190, P142, DOI 10.1016/j.jep.2016.05.063 Alirezalu K, 2020, TRENDS FOOD SCI TECH, V100, P292, DOI 10.1016/j.tifs.2020.04.010 Ammar S, 2015, FOOD FUNCT, V6, P3663, DOI [10.1039/C5FO00837A, 10.1039/c5fo00837a] Martin MA, 2017, CURR OPIN FOOD SCI, V14, P20, DOI 10.1016/j.cofs.2016.12.002 Antonie P, 2019, J FOOD ENG, V245, P131, DOI 10.1016/j.jfoodeng.2018.10.012 Aouidi F, 2012, IND CROP PROD, V37, P292, DOI 10.1016/j.indcrop.2011.12.024 Avila-Roman J, 2021, TRENDS FOOD SCI TECH, V113, P77, DOI 10.1016/j.tifs.2021.04.050 Bakhytkyzy I, 2018, J PHARMACEUT BIOMED, V156, P206, DOI 10.1016/j.jpba.2018.04.031 Barba-Orellana S, 2020, AGRI-FOOD INDUSTRY STRATEGIES FOR HEALTHY DIETS AND SUSTAINABILITY: NEW CHALLENGES IN NUTRITION AND PUBLIC HEALTH, P3, DOI 10.1016/B978-0-12-817226-1.00001-1 Barbieri JB, 2020, IND CROP PROD, V144, DOI 10.1016/j.indcrop.2019.112049 Lima LGB, 2020, MOLECULES, V25, DOI 10.3390/molecules25020342 Lasta HFB, 2019, BIOCATAL AGR BIOTECH, V21, DOI 10.1016/j.bcab.2019.101353 Bellincontro A, 2012, J AGR FOOD CHEM, V60, P2665, DOI 10.1021/jf203925a Benito-Roman O, 2015, FOOD RES INT, V75, P252, DOI 10.1016/j.foodres.2015.06.006 Beteinakis S, 2020, MOLECULES, V25, DOI 10.3390/molecules25153339 Bhuyan DJ, 2015, IND CROP PROD, V69, P290, DOI 10.1016/j.indcrop.2015.02.044 Bi PY, 2010, J CHROMATOGR A, V1217, P2716, DOI 10.1016/j.chroma.2009.11.020 Bianchi S, 2019, HOLZFORSCHUNG, V73, P353, DOI 10.1515/hf-2018-0105 Boudet AM, 2007, PHYTOCHEMISTRY, V68, P2722, DOI 10.1016/j.phytochem.2007.06.012 Boudiar T, 2019, NAT PROD RES, V33, P2208, DOI 10.1080/14786419.2018.1495635 Brahmi F, 2020, J PHARMACEUT BIOMED, V189, DOI 10.1016/j.jpba.2020.113430 Bruins AP, 1998, J CHROMATOGR A, V794, P345, DOI 10.1016/S0021-9673(97)01110-2 Cadiz-Gurrea ML, 2014, J FUNCT FOODS, V10, P485, DOI 10.1016/j.jff.2014.07.016 Camel V, 2000, TRAC-TREND ANAL CHEM, V19, P229, DOI 10.1016/S0165-9936(99)00185-5 Cassol L, 2019, IND CROP PROD, V133, P168, DOI 10.1016/j.indcrop.2019.03.023 Chemat F, 2004, ULTRASON SONOCHEM, V11, P281, DOI 10.1016/j.ultsonch.2003.07.004 Chen MS, 2015, FOOD CHEM, V172, P543, DOI 10.1016/j.foodchem.2014.09.110 Chia SR, 2020, SEP PURIF TECHNOL, V242, DOI 10.1016/j.seppur.2020.116831 Chmelova D, 2020, J BIOTECHNOL, V314, P25, DOI 10.1016/j.jbiotec.2020.04.003 Contreras MD, 2021, FOODS, V10, DOI 10.3390/foods10010111 Contreras MD, 2018, SENSOR ACTUAT B-CHEM, V273, P1413, DOI 10.1016/j.snb.2018.07.031 da Silva ACP, 2019, J PHOTOCH PHOTOBIO B, V193, P162, DOI 10.1016/j.jphotobiol.2019.03.003 da Silva LC, 2020, FOOD CHEM, V318, DOI 10.1016/j.foodchem.2020.126450 Daniel C, 2015, MOLECULES, V20, P726, DOI 10.3390/molecules20010726 Fernandez MD, 2018, FOOD CHEM, V239, P671, DOI 10.1016/j.foodchem.2017.06.150 de Malezieu ML, 2019, BIOMOLECULES, V9, DOI 10.3390/biom9120802 de Melo MMR, 2014, J SUPERCRIT FLUID, V92, P115, DOI 10.1016/j.supflu.2014.04.007 Contreras MD, 2020, FOOD CHEM, V314, DOI 10.1016/j.foodchem.2020.126218 Contreras MD, 2019, FOOD CHEM, V288, P315, DOI 10.1016/j.foodchem.2019.02.104 Contreras MD, 2018, ELECTROPHORESIS, V39, P1284, DOI 10.1002/elps.201700393 Contreras MD, 2017, INT J ANAL CHEM, V2017, DOI 10.1155/2017/5178729 Del Villegas-Aguilar M.C., 2022, BIOACTIVE FOOD COMPO, P27 Diaz-de-Cerio E, 2018, ANAL BIOANAL CHEM, V410, P3607, DOI 10.1007/s00216-018-1051-5 Dopico-Garcia MS, 2007, TALANTA, V74, P20, DOI 10.1016/j.talanta.2007.05.022 Farre M, 2019, ANAL METHODS-UK, V11, P472, DOI [10.1039/C8AY01865K, 10.1039/c8ay01865k] Feng YC, 2015, SEP SCI TECHNOL, V50, P1785, DOI 10.1080/01496395.2015.1014054 Ferarsa S, 2018, FOOD BIOPROD PROCESS, V109, P19, DOI 10.1016/j.fbp.2018.02.006 Ferro DM, 2019, J SUPERCRIT FLUID, V149, P10, DOI 10.1016/j.supflu.2019.03.013 Figueroa JG, 2018, ELECTROPHORESIS, V39, P1908, DOI 10.1002/elps.201700379 Forino M, 2016, FOOD CHEM, V194, P1254, DOI 10.1016/j.foodchem.2015.08.129 Gai QY, 2021, FOOD CHEM, V335, DOI 10.1016/j.foodchem.2020.127602 Garcia A, 2016, FOOD CHEM, V197, P554, DOI 10.1016/j.foodchem.2015.10.131 Garcia-Mendoza MD, 2017, J SUPERCRIT FLUID, V119, P9, DOI 10.1016/j.supflu.2016.08.014 Gatt L, 2021, J OLEO SCI, V70, P145, DOI 10.5650/jos.ess20130 Goldsmith CD, 2018, LWT-FOOD SCI TECHNOL, V89, P284, DOI 10.1016/j.lwt.2017.10.065 Gomez-Caravaca A.M., 2015, OLIVE OLIVE OIL BIOA, P261 Gomez-Cruz I, 2022, FOODS, V11, DOI 10.3390/foods11142002 Gomez-Cruz I, 2021, ANTIOXIDANTS-BASEL, V10, DOI 10.3390/antiox10111781 Gomez-Cruz I, 2021, J IND ENG CHEM, V96, P356, DOI 10.1016/j.jiec.2021.01.042 Gomez-Urios C, 2022, FOODS, V11, DOI 10.3390/foods11162457 Gopal K, 2020, MICROCHEM J, V157, DOI 10.1016/j.microc.2020.105110 Gracia A, 2011, GRASAS ACEITES, V62, P268, DOI 10.3989/gya.089610 Grimalt S, 2010, J MASS SPECTROM, V45, P421, DOI 10.1002/jms.1728 Gullon B, 2018, SCI TOTAL ENVIRON, V645, P533, DOI 10.1016/j.scitotenv.2018.07.155 Guo T, 2015, MOLECULES, V20, P15273, DOI 10.3390/molecules200815273 Hajslova J, 2011, TRAC-TREND ANAL CHEM, V30, P204, DOI 10.1016/j.trac.2010.11.001 Mekky RH, 2019, FOODS, V8, DOI 10.3390/foods8100432 Mekky RH, 2015, RSC ADV, V5, P17751, DOI 10.1039/c4ra13155j Herrera MC, 2005, J CHROMATOGR A, V1100, P1, DOI 10.1016/j.chroma.2005.09.021 Iqbal M, 2016, BIOL PROCED ONLINE, V18, DOI 10.1186/s12575-016-0048-8 Irakli M, 2018, IND CROP PROD, V124, P382, DOI 10.1016/j.indcrop.2018.07.070 Iswaldi I, 2012, J PHARMACEUT BIOMED, V58, P34, DOI 10.1016/j.jpba.2011.09.027 Jerman T, 2010, FOOD CHEM, V123, P175, DOI 10.1016/j.foodchem.2010.04.006 Karkoula E, 2014, J AGR FOOD CHEM, V62, P600, DOI 10.1021/jf404421p Kazan A, 2014, J SUPERCRIT FLUID, V92, P55, DOI 10.1016/j.supflu.2014.05.006 Klejdus B, 2009, J CHROMATOGR A, V1216, P763, DOI 10.1016/j.chroma.2008.11.096 Klikarova J, 2019, FOOD ANAL METHOD, V12, P1759, DOI 10.1007/s12161-019-01508-5 Kotsiou K, 2016, FOOD CHEM, V200, P255, DOI 10.1016/j.foodchem.2015.12.090 Koutsoukos S, 2019, J CLEAN PROD, V241, DOI 10.1016/j.jclepro.2019.118384 Lama-Munoz A, 2019, ENERGIES, V12, DOI 10.3390/en12132486 Lama-Munoz A, 2019, FOOD CHEM, V293, P161, DOI 10.1016/j.foodchem.2019.04.075 Lang HH, 2019, LWT-FOOD SCI TECHNOL, V107, P221, DOI 10.1016/j.lwt.2019.03.018 Lanucara F, 2014, NAT CHEM, V6, P281, DOI [10.1038/nchem.1889, 10.1038/NCHEM.1889] Lia F, 2020, FOODS, V9, DOI 10.3390/foods9060689 Liang ZJ, 2021, TRENDS FOOD SCI TECH, V116, P130, DOI 10.1016/j.tifs.2021.07.020 Liberatore L, 2001, FOOD CHEM, V73, P119, DOI 10.1016/S0308-8146(00)00322-8 Lin LZ, 2012, J AGR FOOD CHEM, V60, P5832, DOI 10.1021/jf3006905 Lin LZ, 2012, J AGR FOOD CHEM, V60, P544, DOI 10.1021/jf204612t Liu B, 2020, CARBOHYD POLYM, V247, DOI 10.1016/j.carbpol.2020.116667 Lizcano SC, 2019, SCI BEVERAGES, V1, P151, DOI 10.1016/B978-0-12-815260-7.00005-5 Llano T, 2015, WASTE BIOMASS VALORI, V6, P1149, DOI 10.1007/s12649-015-9425-9 Longo E, 2017, FOOD CHEM, V237, P91, DOI 10.1016/j.foodchem.2017.05.099 Lopez-Cobo A, 2016, LWT-FOOD SCI TECHNOL, V73, P505, DOI 10.1016/j.lwt.2016.06.049 Lozano-Sanchez J, 2013, FOOD CONTROL, V30, P606, DOI 10.1016/j.foodcont.2012.06.036 Lucarini M, 2020, FOODS, V9, DOI 10.3390/foods9010010 M'Hiri N, 2014, FOOD REV INT, V30, P265, DOI 10.1080/87559129.2014.924139 Ma HF, 2018, ELECTROPHORESIS, V39, P260, DOI 10.1002/elps.201700239 Santana CM, 2009, MOLECULES, V14, P298, DOI 10.3390/molecules14010298 Manach C, 2004, AM J CLIN NUTR, V79, P727, DOI 10.1093/ajcn/79.5.727 Marques LLM, 2016, FOOD CHEM, V212, P703, DOI 10.1016/j.foodchem.2016.06.028 Meneses NGT, 2013, SEP PURIF TECHNOL, V108, P152, DOI 10.1016/j.seppur.2013.02.015 Mocan A, 2018, FOOD CHEM TOXICOL, V119, P189, DOI 10.1016/j.fct.2018.04.045 Monasterio RP, 2016, INT J MOL SCI, V17, DOI 10.3390/ijms17101627 Monroy YM, 2016, J SUPERCRIT FLUID, V116, P10, DOI 10.1016/j.supflu.2016.04.011 Mustafa A, 2011, ANAL CHIM ACTA, V703, P8, DOI 10.1016/j.aca.2011.07.018 Nagy MM, 2022, TRENDS FOOD SCI TECH, V123, P290, DOI 10.1016/j.tifs.2022.03.005 Nathia-Neves G, 2017, FOOD RES INT, V102, P595, DOI 10.1016/j.foodres.2017.09.041 Ohara S, 2003, HOLZFORSCHUNG, V57, P145, DOI 10.1515/HF.2003.023 Olmo-Garcia L, 2018, MOLECULES, V23, DOI 10.3390/molecules23102419 Olmo-Garcia L, 2018, FOOD CHEM, V261, P184, DOI 10.1016/j.foodchem.2018.04.006 Olubiyi O.I., 2015, IMAGE GUIDED NEUROSU, P407, DOI [10.1016/B978-0-12-800870-6.00017-0, DOI 10.1016/B978-0-12-800870-6.00017-0] Oussaid S, 2017, LWT-FOOD SCI TECHNOL, V86, P635, DOI 10.1016/j.lwt.2017.08.064 Palma M, 2002, J CHROMATOGR A, V968, P1, DOI 10.1016/S0021-9673(02)00823-3 Pimentel-Moral S, 2019, J SUPERCRIT FLUID, V147, P213, DOI 10.1016/j.supflu.2018.11.005 Pinto D, 2020, J CO2 UTIL, V40, DOI 10.1016/j.jcou.2020.101194 Porcari AM, 2016, ANALYST, V141, P1172, DOI 10.1039/c5an01415h Proestos C., 2018, ISOLATION CHARACTERI, V57 Proestos C, 2013, FOODS, V2, P90, DOI 10.3390/foods2010090 Ricci A, 2017, J FOOD COMPOS ANAL, V59, P95, DOI 10.1016/j.jfca.2017.01.014 Rocchetti G, 2022, COMPR REV FOOD SCI F, V21, P811, DOI 10.1111/1541-4337.12921 Rodriguez-Perez C, 2015, FOOD CHEM, V174, P392, DOI 10.1016/j.foodchem.2014.11.061 Routray W, 2014, IND CROP PROD, V58, P36, DOI 10.1016/j.indcrop.2014.03.038 Rubio-Senent F, 2012, J AGR FOOD CHEM, V60, P1175, DOI 10.1021/jf204223w Ryan D, 1999, J CHROMATOGR A, V832, P87, DOI 10.1016/S0021-9673(98)00838-3 Saifullah M, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e03666 Salerno TMG, 2022, ANAL BIOANAL CHEM, V414, P703, DOI 10.1007/s00216-021-03693-x Sanchez-Rangel JC, 2016, J CHEM TECHNOL BIOT, V91, P144, DOI 10.1002/jctb.4553 Santos DT, 2012, J FOOD ENG, V108, P444, DOI 10.1016/j.jfoodeng.2011.08.022 Sas OG, 2019, J CHEM THERMODYN, V131, P159, DOI 10.1016/j.jct.2018.11.002 Senes CER, 2020, FOOD ANAL METHOD, V13, P155, DOI 10.1007/s12161-019-01566-9 Setyaningsih W, 2016, FOOD CHEM, V192, P452, DOI 10.1016/j.foodchem.2015.06.102 Sharma A, 2019, MOLECULES, V24, DOI 10.3390/molecules24132452 Zlabur JS, 2021, MOLECULES, V26, DOI 10.3390/molecules26071866 Sikorska E, 2012, OLIVE OIL - CONSTITUENTS, QUALITY, HEALTH PROPERTIES AND BIOCONVERSIONS, P63 Silva EK, 2020, FOOD BIOPROD PROCESS, V122, P245, DOI 10.1016/j.fbp.2020.05.012 Sim YY, 2019, IND CROP PROD, V140, DOI 10.1016/j.indcrop.2019.111708 Stalikas CD, 2007, J SEP SCI, V30, P3268, DOI 10.1002/jssc.200700261 Suciu RC, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-54697-8 Sumere BR, 2018, ULTRASON SONOCHEM, V48, P151, DOI 10.1016/j.ultsonch.2018.05.028 Tahir H.E., 2019, EFOOD, V1, P173, DOI [10.2991/efood.k.191018.001, DOI 10.2991/EFOOD.K.191018.001] Talhaoui N, 2015, FOOD RES INT, V77, P92, DOI 10.1016/j.foodres.2015.09.011 Tena N, 2009, J AGR FOOD CHEM, V57, P10505, DOI 10.1021/jf902009b Teo CC, 2010, J CHROMATOGR A, V1217, P2484, DOI 10.1016/j.chroma.2009.12.050 Tsimogiannis D, 2007, MOLECULES, V12, P593, DOI 10.3390/12030593 Tsujita T, 2014, FOOD CHEM, V151, P15, DOI 10.1016/j.foodchem.2013.11.072 Tyskiewicz K, 2018, MOLECULES, V23, DOI 10.3390/molecules23102625 Venter P, 2018, ANAL CHEM, V90, P11643, DOI 10.1021/acs.analchem.8b03234 Verardo V, 2015, J AGR FOOD CHEM, V63, P4130, DOI 10.1021/acs.jafc.5b01425 Vieira V, 2017, IND CROP PROD, V107, P341, DOI 10.1016/j.indcrop.2017.06.012 Vigano J, 2020, ULTRASON SONOCHEM, V64, DOI 10.1016/j.ultsonch.2020.104999 Wang XQ, 2020, LWT-FOOD SCI TECHNOL, V129, DOI 10.1016/j.lwt.2020.109389 Wang XQ, 2020, FOODS, V9, DOI 10.3390/foods9060770 Wang XQ, 2020, J FOOD SCI, V85, P1450, DOI 10.1111/1750-3841.15019 Wang XQ, 2017, FOOD RES INT, V102, P184, DOI 10.1016/j.foodres.2017.09.089 Wen C, 2018, J PHARMACEUT BIOMED, V150, P144, DOI 10.1016/j.jpba.2017.11.061 Worton DR, 2015, ENVIRON SCI TECHNOL, V49, P13130, DOI 10.1021/acs.est.5b03472 Wu LF, 2020, SEP PURIF TECHNOL, V247, DOI 10.1016/j.seppur.2020.117014 Xavier L, 2015, MADERAS-CIENC TECNOL, V17, P345, DOI 10.4067/S0718-221X2015005000032 Xie PJ, 2021, FOODS, V10, DOI 10.3390/foods10112823 Yang ZC, 2020, ANAL BIOANAL CHEM, V412, P8361, DOI 10.1007/s00216-020-02972-3 Zhang C, 2022, CHINESE J CHEM ENG, V42, P245, DOI 10.1016/j.cjche.2021.05.031 Zheng B, 2016, SCI REP-UK, V6, DOI 10.1038/srep32264 Zhou DD, 2018, SEP SCI PLUS, V1, P676, DOI 10.1002/sscp.201800108 NR 168 TC 0 Z9 0 U1 0 U2 0 PD NOV PY 2022 VL 11 IS 22 AR 3671 DI 10.3390/foods11223671 WC Food Science & Technology SC Food Science & Technology UT WOS:000887270700001 DA 2022-12-14 ER PT J AU Knels, R Stupmann, K Pruss, A Klerke, J Kardoeus, J Hiller, J AF Knels, Ralf Stuepmann, Kirstin Pruss, Axel Klerke, Jan Kardoeus, Joachim Hiller, Jens TI Coding of Tissue and Cell Preparations Using Eurocode SO TRANSFUSION MEDICINE AND HEMOTHERAPY DT Review DE SEC; Eurocode; Tissue; Cell preparations; Coding systems; Traceability; European Directives; 2006/86/EC; 2015/565/EC AB Traceability of products requires their unique identification. In Germany blood products have been encoded by Eurocode since 1998. EU Directives 2004/23/EC, 2006/86/EC and 2015/565/EC demanded unique identification and safe traceability procedure also for tissues and cells. Eurocode IBLS e.V. and the German Society of Transfusion Medicine and Immunohematology (DGTI) working parties 'Tissue Preparations' and 'Automation and Data Processing' supplemented the already available Eurocode nomenclature for blood products with further data structures for tissue preparations. Based on an agreement with the European Commission, Eurocode Product Codes can be used as 'product number' within the Single European Code (SEC). Several data elements of Eurocode can be used to create the complete SEC data structure, except the tissue establishment number. This can be found on the EU Coding Platform in the internet. Consequently, existing software and labeling solutions in Eurocode format could be easily upgraded with SEC. (C) 2017 S. Karger GmbH, Freiburg C1 [Knels, Ralf] Eurocode Int Blood Labeling Syst eV, Oehmestr 5, D-01277 Dresden, Germany. [Stuepmann, Kirstin] German Red Cross Blood Donat Serv Mecklenburg Vor, Rostock, Germany. [Pruss, Axel] Charite Univ Med Berlin, Inst Transfus Med, Univ Tissue Bank, Berlin, Germany. [Klerke, Jan] German Soc Tissue Transplantat DGFG, Hannover, Germany. [Kardoeus, Joachim] Univ Munster, Dept Med, Munster, Germany. [Hiller, Jens] Univ Hosp Hamburg Eppendorf, Inst Transfus Med, Hamburg, Germany. C3 Free University of Berlin; Humboldt University of Berlin; Charite Universitatsmedizin Berlin; University of Munster; University of Hamburg; University Medical Center Hamburg-Eppendorf RP Knels, R (corresponding author), Eurocode Int Blood Labeling Syst eV, Oehmestr 5, D-01277 Dresden, Germany. EM knels@eurocode.org CR [Anonymous], 2016, 154182016 ISOIEC [Anonymous], 2013, 316612013 ISO, P72 European Commission, 2015, OFF J EUR UNION, VL93, P43 European Commission, 2004, OFFICIAL J EUROPEA L, VL102, P48 European Commission, EU COD PLATF REF COM European Union, 2006, OFF J EUROP UNION, V294, P32 EuropeanCommission, 2002, OFFICIAL J EUROPEA L, V45, P30 ICCBBA, 2017, 128 ICCBBAISBT NR 8 TC 3 Z9 3 U1 1 U2 1 PY 2017 VL 44 IS 6 BP 401 EP 405 DI 10.1159/000484416 WC Hematology; Immunology SC Hematology; Immunology UT WOS:000416870300005 DA 2022-12-14 ER PT J AU Xiang, BK Cheng, CH Xia, J Tang, L Mu, JR Bi, YM AF Xiang, Boka Cheng, Changhe Xia, Jun Tang, Liang Mu, Jirui Bi, Yiming TI Simultaneous identification of geographical origin and grade of flue-cured tobacco using NIR spectroscopy SO VIBRATIONAL SPECTROSCOPY DT Article DE Multidimensional profiling; Chemometrics; Near-infrared spectroscopy; Geographical origin; Origin traceability ID NEAR-INFRARED SPECTROSCOPY; PARTIAL LEAST-SQUARES; VIRGIN OLIVE OILS; LIQUID-CHROMATOGRAPHY; CHEMICAL-CONSTITUENTS; NICOTINE; CLASSIFICATION; PERFORMANCE; COTININE; SAMPLES AB The accuracy of classification models using spectral data decreases significantly with the increase numbers of categories. In order to overcome this problem, a middle layer is built based on the main factors of the sample. Herein, ten indicators, including chemical components, position and aroma styles, were selected to determine the identification of geographical origin and grade of flue-cured tobacco. Chemometrical algorithms were used to build quantitative prediction models based on labeled data. A voting algorithm was performed to determine the most likely geographical origin and grade of unknown samples. Experimental results show that the proposed method provides outstanding results for the independent test samples compared with traditional classify methods such as SIMCA and PLS-DA. The proposed method can be useful for origin traceability or adulteration detection of various agricultural products. C1 [Xiang, Boka; Cheng, Changhe; Xia, Jun; Mu, Jirui; Bi, Yiming] China Tobacco Zhejiang Ind Co Ltd, Technol Ctr, Hangzhou 310008, Zhejiang, Peoples R China. [Tang, Liang] Aieden Intelligent Technol Wuxi Co Ltd, Wuxi 214024, Jiangsu, Peoples R China. [Mu, Jirui] Tian Ze Tobacco Co PVT Ltd, Harare 00263, Zimbabwe. C3 China National Tobacco Corporation RP Bi, YM (corresponding author), China Tobacco Zhejiang Ind Co Ltd, Technol Ctr, Hangzhou 310008, Zhejiang, Peoples R China. EM yimbi@163.com CR [Anonymous], 2016, WHO FRAM CONV TOB CO Barker M, 2003, J CHEMOMETR, V17, P166, DOI 10.1002/cem.785 BARNES RJ, 1989, APPL SPECTROSC, V43, P772, DOI 10.1366/0003702894202201 Bevilacqua M, 2012, ANAL CHIM ACTA, V717, P39, DOI 10.1016/j.aca.2011.12.035 Bi YM, 2019, SPECTROCHIM ACTA A, V215, P398, DOI 10.1016/j.saa.2019.01.094 Bi YM, 2012, J CHEMOMETR, V26, P565, DOI 10.1002/cem.2479 Bin J, 2016, RSC ADV, V6, P30353, DOI 10.1039/c5ra25052h Casale M, 2010, FOOD CHEM, V118, P163, DOI 10.1016/j.foodchem.2009.04.091 Du R.H., 2007, CHINA MEASUR TECHNOL, V33, P76, DOI [10.3969/j.issn.1674-5124.2007.03.026, DOI 10.3969/J.ISSN.1674-5124.2007.03.026] Duan J, 2012, IND CROP PROD, V40, P21, DOI 10.1016/j.indcrop.2012.02.040 Forina M, 2008, ANAL CHIM ACTA, V622, P85, DOI 10.1016/j.aca.2008.05.065 Galtier O, 2007, ANAL CHIM ACTA, V595, P136, DOI 10.1016/j.aca.2007.02.033 Hana M., 1997, J NEAR INFRARED SPEC, V5, P19, DOI [10.1255/jnirs.96, DOI 10.1255/jnirs.96] Huang LF, 2006, ANAL CHIM ACTA, V575, P236, DOI 10.1016/j.aca.2006.05.079 Karoui R, 2006, INT DAIRY J, V16, P1211, DOI 10.1016/j.idairyj.2005.10.002 Kulick J, 2016, INT J LAW CRIME JUST, V46, P69, DOI 10.1016/j.ijlcj.2016.03.002 Liu X, 2007, SPECTROSC SPECT ANAL, V27, P2460 Mahoney GN, 2001, J CHROMATOGR B, V753, P179, DOI 10.1016/S0378-4347(00)00540-5 Ni LJ, 2009, ANAL CHIM ACTA, V633, P43, DOI 10.1016/j.aca.2008.11.044 Omar J, 2019, FORENSIC SCI INT, V294, P15, DOI 10.1016/j.forsciint.2018.10.016 Page-Sharp M, 2003, J CHROMATOGR B, V796, P173, DOI 10.1016/j.jchromb.2003.08.020 SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 Shao YN, 2007, EUR FOOD RES TECHNOL, V224, P591, DOI 10.1007/s00217-006-0342-9 Shin HS, 2002, J CHROMATOGR B, V769, P177, DOI 10.1016/S1570-0232(02)00007-7 Sun SM, 2011, SPECTROSC SPECT ANAL, V31, P937, DOI 10.3964/j.issn.1000-0593(2011)04-0937-05 Tan C, 2007, ANAL BIOANAL CHEM, V389, P667, DOI 10.1007/s00216-007-1461-2 Tan C, 2009, VIB SPECTROSC, V51, P276, DOI 10.1016/j.vibspec.2009.07.004 [唐远驹 TANG Yuanju], 2011, [中国烟草科学, Chinese Tobacco Science], V32, P1 Vitale R, 2013, CHEMOMETR INTELL LAB, V121, P90, DOI 10.1016/j.chemolab.2012.11.019 WOLD S, 1984, SIAM J SCI STAT COMP, V5, P735, DOI 10.1137/0905052 Wold S., 1977, CHEMOMETRICS THEORY, V52, P243, DOI [DOI 10.1021/BK-1977-0052.CH012, 10.1021/bk-1977-0052.ch012] Woodcock T, 2008, J AGR FOOD CHEM, V56, P11520, DOI 10.1021/jf802792d Woodcock T, 2007, J AGR FOOD CHEM, V55, P9128, DOI 10.1021/jf072010q Xu WH, 2005, CRIT REV ANAL CHEM, V35, P237, DOI 10.1080/10408340500323362 Zhang JQ, 2018, ANAL LETT, V51, P1029, DOI 10.1080/00032719.2017.1365882 Zhang Y, 2008, SPECTROCHIM ACTA A, V71, P1408, DOI 10.1016/j.saa.2008.04.020 NR 36 TC 4 Z9 4 U1 6 U2 27 PD NOV PY 2020 VL 111 AR 103182 DI 10.1016/j.vibspec.2020.103182 WC Chemistry, Analytical; Chemistry, Physical; Spectroscopy SC Chemistry; Spectroscopy UT WOS:000599677100005 DA 2022-12-14 ER PT J AU Chen, BJ Ho, CP Huang, NY AF Chen, Bao-Ji Ho, Chung-Ping Huang, Nai-Yun TI Threats from farm animals to food and human security SO ASIA PACIFIC JOURNAL OF CLINICAL NUTRITION DT Review DE food security; government organization; traceability; GAP; import control AB This paper discussed the threats from farm animals to food and human security. In response to these threats, a radical reform plan was adapted by several countries and the plan includes restructure of the organization of governing agencies, implementation of a traceability system from the farm sector to end users, application of hazard control measures, as well as tightening the food import control system. C1 [Chen, Bao-Ji] Natl Taiwan Univ, Coll BioResources & Agr, Taipei 107, Taiwan. [Ho, Chung-Ping; Huang, Nai-Yun] Food Safety Inst Int, Taipei, Taiwan. C3 National Taiwan University RP Chen, BJ (corresponding author), Natl Taiwan Univ, Coll BioResources & Agr, 1,Sect 4,Roosevelt Rd, Taipei 107, Taiwan. EM bjchen@ntu.edu.tw CR BISSERA K, 2009, ANIMAL HLTH FOOD SEC CHEN BJ, 2005, 94AS1351BQB13 CHEN BJ, 2005, DOH93TDF1130372 EX Y *EUR COMM, 2000, WHIT PAP FOOD SAF, P29 *FAO, 2009, ASIA URG NEED TACKL Ho CP, 2004, J FOOD PROTECT, V67, P2809, DOI 10.4315/0362-028X-67.12.2809 HSIH F, 2004, TAIWAN AGR FOOD TRAC HUANG NY, 2008, COMP HYGIENE CONTROL, P129 HUANG NY, 2007, RES SEAFOOD QUALITY, P3 *USDA, 2009, LIV POULTR WORLD MAR *WHO, 2009, CONF HUM CAS AV INFL, P12380 NR 11 TC 4 Z9 4 U1 0 U2 1 PY 2009 VL 18 IS 4 BP 549 EP 552 WC Nutrition & Dietetics SC Nutrition & Dietetics UT WOS:000273103500014 DA 2022-12-14 ER PT J AU Bardakci, B Masoero, G AF Bardakci, Belgin Masoero, Giorgio TI An IR spectroscopic investigation of tarhana SO AGRO FOOD INDUSTRY HI-TECH DT Article DE tarhana; IR spectroscopy; discrimination; traceability; quality; commercial; home-made ID FLOUR-YOGURT MIXTURE; L-ASCORBIC-ACID; VITAMIN-C; VIBRATIONAL CHARACTERISTICS; FUNCTIONAL-PROPERTIES; DRYING METHODS; CEREAL FOOD; FT-RAMAN; FERMENTATION; SPECTRA AB Tarhana is a popular Turkish fermented wheat-yogurt mixture for soup making. This traditional food is widely consumed in Turkey. In the last decades, tarhana has been produced commercially in factory. Ingredients of tarhana both homemade and fabricated are display variations. Quality control of tarhana is becoming a problem. The IR spectra of 12 commercial (COM) or home-made (HM) samples were investigated. The T% values were constantly inferior in the HM (-15%) sign of a richer content. Along the spectra from 4000 to 400 cm-1 the F fisher's test of single wavelength differentiated maximally the two types at 402 cm-1(P=0.23), but a wavelength selection process clusterized 6 bands (2476, 1596, 1364, 608, 596, 402 cm-1). A multiple regression was developed on the six bands in a discriminative equation provided by a R2 0.92 (R2adj 0.83). After full cross-validation the R2 values descended to 0.65, but only 1 sample of COM type was missed as HM. Some hypothesis concerned the implicated bands in regards to a different vitamin B3 (Niacine) contents. IR spectroscopy is a useful tool to identify a sample due to its unique results. FTIR Spectra results show that vibrational frequencies can be specific data to confirm the quality and traceability of tarhana. C1 [Bardakci, Belgin] Mehmet Akit Ersoy Univ, Dept Phys, Fac Arts & Sci, TR-15030 Burdur, Turkey. [Masoero, Giorgio] Accademia Agr Torino, I-10100 Turin, Italy. RP Bardakci, B (corresponding author), Mehmet Akit Ersoy Univ, Dept Phys, Fac Arts & Sci, TR-15030 Burdur, Turkey. CR [Anonymous], 2009, INFRARED SPECTROSCOP Bilgicli N, 2009, LWT-FOOD SCI TECHNOL, V42, P514, DOI 10.1016/j.lwt.2008.09.006 Bilgicli N, 2007, J FOOD ENG, V78, P681, DOI 10.1016/j.jfoodeng.2005.11.012 Bunghez IR, 2011, DIG J NANOMATER BIOS, V6, P1349 Carpino S, 2010, DAIRY SCI TECHNOL, V90, P715, DOI 10.1051/dst/2010027 Daglioglu O, 2000, NAHRUNG, V44, P85, DOI [10.1002/(SICI)1521-3803(20000301)44:2<85::AID-FOOD85>3.0.CO;2-H, 10.1002/(SICI)1521-3803(20000301)44:2<85::AID-FOOD85>3.0.CO;2-H] Daglioglu O, 2002, EUR FOOD RES TECHNOL, V215, P515, DOI 10.1007/s00217-002-0584-0 Daimay L.-V., 1991, HDB INFRARED RAMAN C Dalgic AC, 2008, INT J FOOD SCI TECH, V43, P1352, DOI 10.1111/j.1365-2621.2007.01619.x Dimitrova Y, 2006, SPECTROCHIM ACTA A, V63, P427, DOI 10.1016/j.saa.2005.03.037 Ekinci R, 2005, FOOD CHEM, V90, P127, DOI 10.1016/j.foodchem.2004.03.036 Erbas M, 2005, INT J FOOD SCI NUTR, V56, P349, DOI 10.1080/09637480500194937 Erbas M, 2005, LWT-FOOD SCI TECHNOL, V38, P409, DOI 10.1016/j.lwt.2004.06.009 Hassan I, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0036273 Hayta M, 2002, J FOOD SCI, V67, P740, DOI 10.1111/j.1365-2621.2002.tb10669.x Ibanoglu S, 2004, J FOOD ENG, V64, P243, DOI 10.1016/j.jfoodeng.2003.10.004 Kabak B, 2011, CRIT REV FOOD SCI, V51, P248, DOI 10.1080/10408390903569640 Kose E, 2002, INT J FOOD SCI TECH, V37, P219, DOI 10.1046/j.1365-2621.2002.00559.x Maskan M, 2002, EUR FOOD RES TECHNOL, V215, P413, DOI 10.1007/s00217-002-0572-4 Paasch S, 2004, ANAL BIOANAL CHEM, V380, P734, DOI 10.1007/s00216-004-2820-x Panicker CY, 2006, SPECTROCHIM ACTA A, V65, P802, DOI 10.1016/j.saa.2005.12.044 Refat MS, 2010, J MOL STRUCT, V969, P163, DOI 10.1016/j.molstruc.2010.01.064 Rodriguez-Saona L.E., 2011, FOOD SCI TECHNOLOGY, V2, P467 Singh P, 2010, J CHEM PHARM RES, V2, P656 Yadav RA, 2011, SPECTROCHIM ACTA A, V84, P6, DOI 10.1016/j.saa.2011.07.043 Yang H, 2002, J PHARM PHARMACOL, V54, P1247, DOI 10.1211/002235702320402099 NR 26 TC 2 Z9 2 U1 0 U2 12 PD NOV-DEC PY 2013 VL 24 IS 6 BP 10 EP 12 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000330226700004 DA 2022-12-14 ER PT J AU Dube, G AF Dube, G TI Metrology in chemistry - a public task SO ACCREDITATION AND QUALITY ASSURANCE DT Article; Proceedings Paper CT Analytical Conference 2000 CY APR 11-14, 2000 CL MUNICH, GERMANY DE reliability; uncertainty; traceability; National Metrology Institutes; CIPM MRA AB The importance of analytical chemistry is increasing in many public fields, and the demand for reliable measurement results is growing accordingly. A measurement result will be reliable only if its uncertainty has been quantified. This can be achieved only by tracing the result back to a standard realizing the unit in which the measurement result is expressed. The National Metrology Institutes (NMIs) can contribute to the reliability of the measurement results by developing measuring methods, and by providing reference materials and standard measuring devices. In fields in which the comparability of measurement results is of particular importance, they establish traceability structures. Responding to the globalization of trade and industry the International Committee for Weights and Measures (CIPM) agreed on an arrangement on the mutual recognition of calibration certificates (CIPM MRA) issued by the NMIs. C1 Phys Tech Bundesanstalt, D-38116 Braunschweig, Germany. C3 Physikalisch-Technische Bundesanstalt (PTB) RP Dube, G (corresponding author), Phys Tech Bundesanstalt, Bundesallee 100, D-38116 Braunschweig, Germany. EM gunther.dube@ptb.de CR *BIPM, 1999, 5 M FEB 1999 BUR INT *BUND, 1988, DTSCH ARZTEBL, V85, P699 *BUND, 1994, DT ARZTEBLATT, V91, P211 *BUND BILD FORSCH, 2000, GES BUND *BUND WIRTSCH TECH, 1999, ENTW EINF NAT BUND H DOERFFEL K, 1987, PREFACE STAT ANALYTI *DTSCH I NORM, 1994, INT WORT METR National Institute of Metrology, 2019, GBW13654 NAT I METR Quinn TJ, 1997, METROLOGIA, V34, P61, DOI 10.1088/0026-1394/34/1/9 Richter W, 1997, ACCREDIT QUAL ASSUR, V2, P354, DOI 10.1007/s007690050165 Richter W, 1999, FRESEN J ANAL CHEM, V365, P569, DOI 10.1007/s002160051524 Semerjian HG, 1998, PTB TEXT, V9, P99 Spitzer P, 1997, METROLOGIA, V34, P375 Spitzer P, 1996, FRESEN J ANAL CHEM, V356, P178 NR 14 TC 9 Z9 9 U1 0 U2 5 PD JAN PY 2001 VL 6 IS 1 BP 3 EP 7 DI 10.1007/PL00010431 WC Chemistry, Analytical; Instruments & Instrumentation SC Chemistry; Instruments & Instrumentation UT WOS:000166418700003 DA 2022-12-14 ER PT J AU Xiao, L Fang, X Zhou, Y Yu, ZF Ding, D AF Xiao, Ling Fang, Xi Zhou, Yang Yu, Zifang Ding, Ding TI Feedforward neural network-based chaos encryption method for polarization division multiplexing optical OFDM/OQAM system SO OPTICAL FIBER TECHNOLOGY DT Article DE PDM O-OFDM; OQAM; Lorenz mapping; Feedforward neural network; Secure communication ID PHYSICAL-LAYER SECURITY; OFDM-PON; SCHEME AB Optical orthogonal frequency division multiplexing offset quadrature amplitude modulation (O-OFDM/OQAM), as a technique that has a great potential to provide high spectral efficiency optical transmission in physical layer for future communication systems, faces challenges in data transmission security like malicious eavesdropping. In this letter, a novel twice encryption method based on chaotic mapping and feedforward neural network (FNN) is proposed to achieve the capability of anti-eavesdropping and security enhancement for polarization division multiplexing (PDM) O-OFDM/OQAM system. In the proposed method, the symbols in an OFDM/OQAM block are first used for symbol substitution by the chaotic sequences generated by Lorenz mapping and then permutated by chaotic scrambling vectors generated by FNN. The simulation results show that the FNN-based chaos encryption (FNNCE) method can strengthen the security of data transmission and realize the large key space simultaneously, and without apparent bit error rate (BER) deterioration in comparison with the original system. C1 [Xiao, Ling; Zhou, Yang; Yu, Zifang] Beijing Elect Sci & Technol Inst, Dept Cyber Sci & Engn, Beijing 100070, Peoples R China. [Fang, Xi; Ding, Ding] Beijing Elect Sci & Technol Inst, Dept Elect & Commun Engn, Beijing 100070, Peoples R China. [Fang, Xi] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol BNRist, Dept Elect Engn, Beijing 100084, Peoples R China. C3 Beijing Electronic Science & Technology Institute; Beijing Electronic Science & Technology Institute; Tsinghua University RP Fang, X (corresponding author), Beijing Elect Sci & Technol Inst, Dept Elect & Commun Engn, Beijing 100070, Peoples R China. EM xfang@besti.edu.cn CR Alvarez G, 2006, INT J BIFURCAT CHAOS, V16, P2129, DOI 10.1142/S0218127406015970 Bi MH, 2019, IEEE ACCESS, V7, P57129, DOI 10.1109/ACCESS.2019.2912535 Bi MH, 2017, IEEE PHOTONICS J, V9, DOI 10.1109/JPHOT.2017.2661581 Bodinier Q., 2016, IEICE T FUND ELECT C, P1 Cao P, 2014, IEEE PHOTONICS J, V6, DOI 10.1109/JPHOT.2014.2311451 Cui MW, 2021, IEEE ACCESS, V9, P18052, DOI 10.1109/ACCESS.2021.3054380 Fang X, 2017, J LIGHTWAVE TECHNOL, V35, P1837, DOI 10.1109/JLT.2017.2665464 Fang X, 2016, J LIGHTWAVE TECHNOL, V34, P891, DOI 10.1109/JLT.2015.2507605 Fok MP, 2011, IEEE T INF FOREN SEC, V6, P725, DOI 10.1109/TIFS.2011.2141990 Haykin S., 1998, NEURAL NETWORKS COMP He JL, 2016, OPT EXPRESS, V24, P13418, DOI 10.1364/OE.24.013418 Jun D.O.N.G., 1997, INF CONTROL, V26, P360 Liu ZJ, 2020, IEEE WIREL COMMUN LE, V9, P1840, DOI 10.1109/LWC.2020.3005656 Siohan P, 2002, IEEE T SIGNAL PROCES, V50, P1170, DOI 10.1109/78.995073 [汪彦 Wang Yan], 2017, [中南大学学报. 自然科学版, Journal of Central South University of Science and Technology], V48, P2678 Wang ZY, 2021, OPT EXPRESS, V29, P17890, DOI 10.1364/OE.424661 Wei HH, 2019, IEEE ACCESS, V7, P124452, DOI 10.1109/ACCESS.2019.2938910 Wu TW, 2020, IEEE ACCESS, V8, P75119, DOI 10.1109/ACCESS.2020.2989172 Wu TW, 2018, OPT EXPRESS, V26, P22857, DOI 10.1364/OE.26.022857 Wu W, 2011, NEURAL NETWORKS, V24, P91, DOI 10.1016/j.neunet.2010.09.007 Xiao L, 2020, I C COMM SOFTW NET, P39, DOI 10.1109/ICCSN49894.2020.9139052 Xiao YQ, 2021, OPT LETT, V46, P5583, DOI 10.1364/OL.436366 Xiao YQ, 2020, IEEE PHOTONICS J, V12, DOI 10.1109/JPHOT.2020.2987317 Xiao YQ, 2018, J OPT COMMUN NETW, V10, P46, DOI 10.1364/JOCN.10.000046 Xue CP, 2017, IEEE T COMMUN, V65, P312, DOI 10.1109/TCOMM.2016.2628060 Yuan YZ, 2022, DIGIT SIGNAL PROCESS, V126, DOI 10.1016/j.dsp.2022.103492 Zhang Z, 2022, J LIGHTWAVE TECHNOL, V40, P14, DOI 10.1109/JLT.2021.3119013 NR 27 TC 0 Z9 0 U1 1 U2 1 PD SEP PY 2022 VL 72 AR 102942 DI 10.1016/j.yofte.2022.102942 WC Engineering, Electrical & Electronic; Optics; Telecommunications SC Engineering; Optics; Telecommunications UT WOS:000886271200003 DA 2022-12-14 ER PT J AU Bravo, E Calzolari, A De Castro, P Mabile, L Napolitani, F Rossi, AM Cambon-Thomsen, A AF Bravo, Elena Calzolari, Alessia De Castro, Paola Mabile, Laurence Napolitani, Federica Rossi, Anna Maria Cambon-Thomsen, Anne TI Developing a guideline to standardize the citation of bioresources in journal articles (CoBRA) SO BMC MEDICINE DT Article DE Biobanks; Bioresource; Bioresource Research Impact Factor; CoBRA; Data sharing; Guideline; Open policies; Repository; Standardized citation; Traceability ID CLINICAL-TRIAL DATA; OPEN SCIENCE AB Background: Many biomedical publications refer to data obtained from collections of biosamples. Sharing such bioresources (biological samples, data, and databases) is paramount for the present governance of research. Recognition of the effort involved in generating, maintaining, and sharing high quality bioresources is poorly organized, which does not encourage sharing. At publication level, the recognition of such resources is often neglected and/or highly heterogeneous. This is a true handicap for the traceability of bioresource use. The aim of this article is to propose, for the first time, a guideline for reporting bioresource use in research articles, named CoBRA: Citation of BioResources in journal Articles. Methods: As standards for citing bioresources are still lacking, the members of the journal editors subgroup of the Bioresource Research Impact Factor (BRIF) initiative developed a standardized and appropriate citation scheme for such resources by informing stakeholders about the subject and raising awareness among scientists and in science editors' networks, mapping this topic among other relevant initiatives, promoting actions addressed to stakeholders, launching surveys, and organizing focused workshops. Results: The European Association of Science Editors has adopted BRIF's suggestion to incorporate statements on biobanks in the Methods section of their guidelines. The BRIF subgroup agreed upon a proposed citation system: each individual bioresource that is used to perform a study and that is mentioned in the Methods section should be cited as an individual "reference [BIORESOURCE]" according to a delineated format. The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network mentioned the proposed reporting guideline in their "guidelines under development" section. Conclusions: Evaluating bioresources' use and impact requires that publications accurately cite such resources. Adopting the standard citation scheme described here will improve the quality of bioresource reporting and will allow their traceability in scientific publications, thus increasing the recognition of bioresources' value and relevance to research. C1 [Bravo, Elena; Calzolari, Alessia; De Castro, Paola; Napolitani, Federica; Rossi, Anna Maria] Ist Super Sanita, Dept Hematol Oncol & Mol Med, I-00161 Rome, Italy. [Mabile, Laurence; Cambon-Thomsen, Anne] Univ Toulouse 3, INSERM, UMR U 1027, F-31000 Toulouse, France. C3 Istituto Superiore di Sanita (ISS); Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Toulouse; Universite Toulouse III - Paul Sabatier RP Bravo, E (corresponding author), Ist Super Sanita, Dept Hematol Oncol & Mol Med, Viale Regina Elena 299, I-00161 Rome, Italy. EM elena.bravo@iss.it CR Bravo E, 2013, EUROPEAN SCI EDITING, V39, P36 BRIF Editorial Subgroup, ROM M Calzolari A, 2014, ANN I SUPER SANITA, V50, P178, DOI 10.4415/ANN_14_02_12 Cambon-Thomsen A, 2013, CHIC US INT C PEER R Cambon-Thomsen A, 2012, BELGR SERB 5 BELGR I Cambon-Thomsen A, 2011, NAT GENET, V43, P503, DOI 10.1038/ng.831 De Castro P, 2012, 11 EASE GEN ASS C De Castro Paola, 2013, Acta Inform Med, V21, P291, DOI 10.5455/aim.2013.21.291-292 European Commission, 2009, OFF J EUROP UNION L, V206 European Commission, HOR 2020 EU FRAM PRO He S, 2013, STUD HEALTH TECHNOL, V192, P1201, DOI 10.3233/978-1-61499-289-9-1201 Hrynaszkiewicz I, 2009, TRIALS, V10, DOI 10.1186/1745-6215-10-17 Kauffmann F, 2008, JAMA-J AM MED ASSOC, V299, P2316, DOI 10.1001/jama.299.19.2316 Lin J, 2014, PLOS BIOL, V12, DOI 10.1371/journal.pbio.1001975 Mabile L, 2013, GIGASCIENCE, V2, DOI 10.1186/2047-217X-2-7 Meyer GS, 2012, BMJ QUAL SAF, V21, P964, DOI 10.1136/bmjqs-2012-001081 Moher D, 2010, PLOS MED, V7, DOI 10.1371/journal.pmed.1000217 Moore HM, 2011, J PROTEOME RES, V10, P3429, DOI 10.1021/pr200021n Public Population Project in Genomics and Society, BRIF BIOSHARE PIL ST Ross JS, 2013, JAMA-J AM MED ASSOC, V309, P1355, DOI 10.1001/jama.2013.1299 Ross JS, 2012, CIRC-CARDIOVASC QUAL, V5, P238, DOI 10.1161/CIRCOUTCOMES.112.965798 Ross JS, 2009, PLOS MED, V6, DOI 10.1371/journal.pmed.1000144 Simeon-Dubach D, 2011, NATURE, V475, P454, DOI 10.1038/475454d Wellcome Trust, SHAR RES DAT IMPR PU NR 24 TC 37 Z9 37 U1 1 U2 16 PD FEB 17 PY 2015 VL 13 AR 33 DI 10.1186/s12916-015-0266-y WC Medicine, General & Internal SC General & Internal Medicine UT WOS:000349417800001 DA 2022-12-14 ER PT J AU Ampatzidis, Y Vougioukas, S Bochtis, D Tsatsarelis, C AF Ampatzidis, Y. G. Vougioukas, S. G. Bochtis, D. D. Tsatsarelis, C. A. TI A yield mapping system for hand-harvested fruits based on RFID and GPS location technologies: field testing SO PRECISION AGRICULTURE DT Article DE Yield mapping; RFID technology; Hand-harvested fruits; Traceability; Labelling AB It is proposed that radio frequency identification (RFID) technology be used to overcome the limitations of existing yield mapping systems for manual fresh fruit harvesting. Two methods are proposed for matching bins-containing harvested fruits-with corresponding pairs of trees. In the first method, a long-range RFID reader and a DGPS are mounted on an orchard tractor and passive low-cost RFID tags are attached to the bins. In the second method, the DGPS is not used and RFID tags are attached to individual trees as well as bins. An experimental evaluation of the accuracy and reliability of both methods was performed in an orchard. The first method failed in half of the trials because the tree canopies interfered with the GPS signal. The RFID reader miss ratio for the detection of the bins was 0.32% for both methods. However, the attachment of RFID tags on suitable tree branches (to achieve 100% detection), in the second method, is not a well-defined procedure; some trial is demanded to determine the best positions and orientations of the tree tags in order for the RFID reader to successfully detect them. The first method seems more promising if robust tractor location under foliage can be achieved. C1 [Ampatzidis, Y. G.; Vougioukas, S. G.; Bochtis, D. D.; Tsatsarelis, C. A.] Aristotle Univ Thessaloniki, Dept Agr Engn, Thessaloniki 54124, Greece. C3 Aristotle University of Thessaloniki RP Ampatzidis, Y (corresponding author), Aristotle Univ Thessaloniki, Dept Agr Engn, Thessaloniki 54124, Greece. EM iampatzi@agro.auth.gr CR ANNAMALAI P, 2003, CITRUS YIELD MAPPING HEIDMAN BC, 2005, INTEGRATION SENSOR D HETZRONI A, 2005, P EFITA WCCA 2005 JO, P922 Neff E., 1997, CITRUS IND, V78, P20 SALEHI F, 2000, AUTOMATIC TRIGGERING Schueller JK, 1999, COMPUT ELECTRON AGR, V23, P145, DOI 10.1016/S0168-1699(99)00028-9 SLAUGHTER DC, 1989, T ASAE, V32, P757 TSATSARELIS CA, 2003, MECH HARVESTING AGR Whitney JD, 2001, APPL ENG AGRIC, V17, P115 NR 9 TC 40 Z9 42 U1 2 U2 11 PD FEB PY 2009 VL 10 IS 1 BP 63 EP 72 DI 10.1007/s11119-008-9095-8 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000262484600005 DA 2022-12-14 ER PT J AU Hoshino, Y Mukojima, K Minami, N Imai, H AF Hoshino, Y. Mukojima, K. Minami, N. Imai, H. TI TRACEABILITY SYSTEM FOR AN INDIVIDUAL FROZEN SEMEN STRAW BY A TINY RADIO FREQUENCY IDENTIFICATION CHIP SO REPRODUCTION FERTILITY AND DEVELOPMENT DT Meeting Abstract C1 [Hoshino, Y.; Mukojima, K.] Gifu Prefectural Livestock Res Inst, Takayama, Gifu, Japan. [Minami, N.; Imai, H.] Kyoto Univ, Grad Sch Agr, Kyoto, Japan. C3 Kyoto University NR 0 TC 0 Z9 0 U1 0 U2 1 PY 2012 VL 24 IS 1 MA 10 BP 116 EP 116 DI 10.1071/RDv24n1Ab10 WC Developmental Biology; Reproductive Biology; Zoology SC Developmental Biology; Reproductive Biology; Zoology UT WOS:000297647800023 DA 2022-12-14 ER PT J AU Mandrile, L Zeppa, G Giovannozzi, AM Rossi, AM AF Mandrile, Luisa Zeppa, Giuseppe Giovannozzi, Andrea Mario Rossi, Andrea Mario TI Controlling protected designation of origin of wine by Raman spectroscopy SO FOOD CHEMISTRY DT Article DE Wine; Raman spectroscopy; Food traceability; Chemometrics; Fingerprint ID VISIBLE SPECTROSCOPY; RED WINES; DIFFERENTIATION; CLASSIFICATION; PHENOLICS; SPECTRA; ETHANOL; FTIR; NMR; NIR AB In this paper, a Fourier Transform Raman spectroscopy method, to authenticate the provenience of wine, for food traceability applications was developed. In particular, due to the specific chemical fingerprint of the Raman spectrum, it was possible to discriminate different wines produced in the Piedmont area (North West Italy) in accordance with i) grape varieties, ii) production area and iii) ageing time. In order to create a consistent training set, more than 300 samples from tens of different producers were analyzed, and a chemometric treatment of raw spectra was applied. A discriminant analysis method was employed in the classification procedures, providing a classification capability (percentage of correct answers) of 90% for validation of grape analysis and geographical area provenance, and a classification capability of 84% for ageing time classification. The present methodology was applied successfully to raw materials without any preliminary treatment of the sample, providing a response in a very short time. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Mandrile, Luisa] Univ Turin, Dept Drug Sci & Technol, Via Giuria 9, I-10125 Turin, Italy. [Zeppa, Giuseppe] Dipartimento Sci Agr Forestali & Alimentari DISAF, Microbiol Agr & Tecnol Alimentari, Largo Paolo Braccini 2, I-10095 Grugliasco, TO, Italy. [Mandrile, Luisa; Giovannozzi, Andrea Mario; Rossi, Andrea Mario] Ist Nazl Ric Metrol, Qual Life Div, Food Metrol Program, Str Cacce 91, I-10135 Turin, Italy. C3 University of Turin; Istituto Nazionale di Ricerca Metrologica (INRIM) RP Rossi, AM (corresponding author), Ist Nazl Ric Metrol, Qual Life Div, Food Metrol Program, Str Cacce 91, I-10135 Turin, Italy. EM l.mandrile@inrim.it; giuseppe.zeppa@unito.it; a.giovannozzi@inrim.it; a.rossi@inrim.it CR Acevedo FJ, 2007, J AGR FOOD CHEM, V55, P6842, DOI 10.1021/jf070634q Adam L., 1995, QUALITATSKONTROLLE A Pardo MA, 2015, FOOD CHEM, V172, P377, DOI 10.1016/j.foodchem.2014.09.096 Bauer R, 2008, ANAL CHEM, V80, P1371, DOI 10.1021/ac086051c Beelman R. B., 1979, Advances in Food Research, V25, P1, DOI 10.1016/S0065-2628(08)60234-7 Bernuy B, 2008, J AGR FOOD CHEM, V56, P1159, DOI 10.1021/jf703712w BREAS O, 1994, RAPID COMMUN MASS SP, V8, P967, DOI 10.1002/rcm.1290081212 Cozzolino D, 2012, FOOD CONTROL, V26, P81, DOI 10.1016/j.foodcont.2012.01.003 Cozzolino D., 2006, J NEAR INFRARED SPEC Cozzolino D., 2014, J SCI FOOD AGR De Maesschalck R, 2000, CHEMOMETR INTELL LAB, V50, P1, DOI 10.1016/S0169-7439(99)00047-7 Dordevic N, 2012, ANAL CHIM ACTA, V757, P19, DOI 10.1016/j.aca.2012.10.046 Downey G., 2009, IDENTITY CONFIRMATIO Durante C, 2015, FOOD CHEM, V173, P557, DOI 10.1016/j.foodchem.2014.10.086 Finley T., 2005, P INT C MACH LEARN I Fronza G, 1998, J AGR FOOD CHEM, V46, P477, DOI 10.1021/jf9706179 Gallego AL, 2011, IEEE T INSTRUM MEAS, V60, P507, DOI 10.1109/TIM.2010.2051611 GARCIAJARES C, 1995, ANALYST, V120, P1891, DOI 10.1039/an9952001891 Godelmann Rolf, 2014, J AGR FOOD CHEM Harbertson JF, 2006, AM J ENOL VITICULT, V57, P280 Li-Chan E., 2010, APPL VIBRATIONAL SPE Li -Chan E., 1994, PROTEIN STRUCTURE FU, P163, DOI [DOI 10.1007/978-1-4615-2670-4_8, 10.1007/978-1-4615-2670-4_8] Mahalanobis P.C., 1936, P NATL I SCI INDIA, V2, P49, DOI DOI 10.1145/1390156.1390302 MAMMONE JF, 1980, J PHYS CHEM-US, V84, P3130, DOI 10.1021/j100460a032 Martin C, 2015, FOOD CHEM, V181, P235, DOI 10.1016/j.foodchem.2015.02.076 MATHLOUTHI M, 1986, ADV CARBOHYD CHEM BI, V44, P7 Mazzei P, 2010, ANAL CHIM ACTA, V673, P167, DOI 10.1016/j.aca.2010.06.003 Meneghini C, 2008, IEEE SENS J, V8, P1250, DOI 10.1109/JSEN.2008.926172 Misselhorn K., 1990, Branntweinwirtschaft, V130, P70 Monakhova YB, 2014, ANAL CHIM ACTA, V833, P29, DOI 10.1016/j.aca.2014.05.005 Nordon A, 2005, ANAL CHIM ACTA, V548, P148, DOI 10.1016/j.aca.2005.05.067 Ozbalci B, 2013, FOOD CHEM, V136, P1444, DOI 10.1016/j.foodchem.2012.09.064 Paradkar MM, 2002, APPL ENG AGRIC, V18, P379 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 Petronis A, 2010, NATURE, V465, P721, DOI 10.1038/nature09230 Rossmann A., 2001, FOOD REV INT SAVITZKY A, 1964, ANAL CHEM, V36, P1627, DOI 10.1021/ac60214a047 Schulz H, 2007, VIB SPECTROSC, V43, P13, DOI 10.1016/j.vibspec.2006.06.001 Socrates G., 2004, INFRARED RAMAN CHARA, DOI 10.1002/jrs.1238.2001Price135.J.RamanSpectrosc.905-905 Thomas O., 2003, CARBOHYDRATE RES Thygesen L. G., 2003, TRENDS FOOD SCI TECH Urbano M, 2006, FOOD CHEM, V97, P166, DOI 10.1016/j.foodchem.2005.05.001 Yang H, 2005, FOOD CHEM, V93, P25, DOI 10.1016/j.foodchem.2004.08.039 Yang H, 2001, J AM OIL CHEM SOC, V78, P889, DOI 10.1007/s11746-001-0360-6 NR 44 TC 52 Z9 54 U1 6 U2 115 PD NOV 15 PY 2016 VL 211 BP 260 EP 267 DI 10.1016/j.foodchem.2016.05.011 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000377543500031 DA 2022-12-14 ER PT J AU Liu, JT Li, ZZ Li, L Dong, JL Jiao, BN Su, XS AF Liu, Jintao Li, Zhenzhu Li, Ling Dong, Jiali Jiao, Bining Su, Xuesu TI Determination and uncertainty estimation of tangeretin purity certified reference material SO MICROCHEMICAL JOURNAL DT Article DE Tangeretin; Uncertainty estimation; Mass balance method; Differential scanning calorimetry; Expanded uncertainty ID NOBILETIN; CANCER; ACID AB Certified reference materials (CRMs) with high accuracy and traceability are essential tools for validation of analytical methods and calibration of equipment. They are widely used in various fields and have an important role in analytical science. In this study, tangeretin purity certified reference material was first developed in compliance with the principles of ISO guides. The investigation included preparation, characterization, homogeneity test, stability study and uncertainty estimation. In the characterization, mass balance method (MB) and differential scanning calorimetry (DSC) were simultaneously utilized, which would make up for the deficiency of a single method and cross-check the determination results. The mass balance method involved measurement of main component, as well as moisture, volatile impurities and inorganic impurities. Homogeneity, together with short-term and long-term stability, was investigated and the uncertainty estimation was performed. The results showed that tangeretin CRM was sufficiently homogeneous and stable for 12 months at 4 degrees C. The certified value is 99.8%, and the expanded uncertainty is 0.1% (k = 2). C1 [Liu, Jintao; Li, Zhenzhu; Li, Ling; Dong, Jiali; Su, Xuesu] Southwest Univ, Coll Chem & Chem Engn, Chongqing, Peoples R China. [Jiao, Bining] Southwest Univ, Citrus Res Inst, Lab Qual & Safety Risk Assessment Citrus Prod, Minist Agr & Rural Affairs,Chinese Acad Agr Sci, Chongqing, Peoples R China. C3 Southwest University - China; Chinese Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs; Southwest University - China RP Su, XS (corresponding author), Southwest Univ, Coll Chem & Chem Engn, Chongqing, Peoples R China.; Jiao, BN (corresponding author), Southwest Univ, Citrus Res Inst, Lab Qual & Safety Risk Assessment Citrus Prod, Minist Agr & Rural Affairs,Chinese Acad Agr Sci, Chongqing, Peoples R China. EM jiaobining@cric.cn; suxuesu@163.com CR Arivazhagan L, 2014, J NUTR BIOCHEM, V25, P1140, DOI 10.1016/j.jnutbio.2014.06.007 Gong H, 2012, TALANTA, V101, P96, DOI 10.1016/j.talanta.2012.09.012 Gong NB, 2012, ANAL METHODS-UK, V4, P3443, DOI 10.1039/c2ay25566a Huang T, 2014, TALANTA, V125, P94, DOI 10.1016/j.talanta.2014.02.059 International Organization for Standardization (ISO), 2017, ISO GUID 35 REF MAT International Standard Organization (ISO), 2018, ISO GUID 30 TERMS DE ISO, 2016, 17034 CASCO ISO Jang SE, 2013, INT IMMUNOPHARMACOL, V17, P502, DOI 10.1016/j.intimp.2013.07.012 Kou GN, 2019, J FUNCT FOODS, V54, P249, DOI 10.1016/j.jff.2019.01.018 Ma K, 2009, ANAL CHIM ACTA, V650, P227, DOI 10.1016/j.aca.2009.07.046 Morley KL, 2007, CANCER LETT, V251, P168, DOI 10.1016/j.canlet.2006.11.016 Nogueira R, 2013, EUR J PHARM SCI, V48, P502, DOI 10.1016/j.ejps.2012.11.005 Ogawa K, 2013, J JPN SOC FOOD SCI, V60, P603, DOI 10.3136/nskkk.60.603 Quan C, 2014, FOOD CHEM, V153, P378, DOI 10.1016/j.foodchem.2013.12.086 Sundaram R, 2014, PHYTOMEDICINE, V21, P793, DOI 10.1016/j.phymed.2014.01.007 Ting YW, 2015, J FUNCT FOODS, V15, P264, DOI 10.1016/j.jff.2015.03.034 Yang DZ, 2016, ELECTROANAL, V28, P1539, DOI 10.1002/elan.201501123 Yang MR, 2016, ACCREDIT QUAL ASSUR, V21, P341, DOI 10.1007/s00769-016-1221-0 Yip YC, 2011, TRAC-TREND ANAL CHEM, V30, P628, DOI 10.1016/j.trac.2010.12.003 Yoon JH, 2011, J AGR FOOD CHEM, V59, P222, DOI 10.1021/jf103204x NR 20 TC 3 Z9 3 U1 0 U2 11 PD NOV PY 2020 VL 158 AR 105205 DI 10.1016/j.microc.2020.105205 WC Chemistry, Analytical SC Chemistry UT WOS:000573292500012 DA 2022-12-14 ER PT J AU Namin, SM Yeasmin, F Choi, HW Jung, C AF Namin, Saeed Mohamadzade Yeasmin, Fatema Choi, Hyong Woo Jung, Chuleui TI DNA-Based Method for Traceability and Authentication of Apis cerana and A. dorsata Honey (Hymenoptera: Apidae), Using the NADH dehydrogenase 2 Gene SO FOODS DT Article DE honey; entomological origin; mitochondrial DNA; NADH dehydrogenase 2; PCR ID COMPLETE MITOCHONDRIAL GENOME; ENTOMOLOGICAL ORIGIN; BEE; IDENTIFICATION; ADULTERATION; EVOLUTION; PRODUCTS; GROWTH; MEAT AB Honey is a widely used natural product and the price of honey from Apis cerana (ACH) and A. dorsata (ADH) is several times more expensive than the one from A. mellifera (AMH), thus there are increasing fraud issues reported in the market by mislabeling or mixing honeys with different entomological origins. In this study, three species-specific primers, targeting the NADH dehydrogenase 2 (ND2) region of honeybee mitochondrial DNA, were designed and tested to distinguish the entomological origin of ACH, ADH, and AMH. Molecular analysis showed that each primer set can specifically detect the ND2 region from the targeted honeybee DNA, but not from the others. The amplicon size for A. cerana, A. dorsata and A. mellifera were 224, 302, and 377 bp, respectively. Importantly, each primer set also specifically produced amplicons with expected size from the DNA prepared from honey samples with different entomological origins. The PCR adulteration test allowed detection of 1% of AMH in the mixture with either ACH or ADH. Furthermore, real-time PCR and melting curve analysis indicated the possible discrimination of origin of honey samples. Therefore, we provide the newly developed PCR-based method that can be used to determine the entomological origin of the three kinds of honey. C1 [Namin, Saeed Mohamadzade; Jung, Chuleui] Andong Natl Univ, Agr Sci & Technol Inst, Andong 36729, South Korea. [Namin, Saeed Mohamadzade] Islamic Azad Univ, Fac Agr, Dept Plant Protect, Varamin Pishva Branch, Varamin 3381774895, Iran. [Yeasmin, Fatema; Choi, Hyong Woo; Jung, Chuleui] Andong Natl Univ, Dept Plant Med, Andong 36729, South Korea. C3 Andong National University; Islamic Azad University; Andong National University RP Jung, C (corresponding author), Andong Natl Univ, Agr Sci & Technol Inst, Andong 36729, South Korea.; Jung, C (corresponding author), Andong Natl Univ, Dept Plant Med, Andong 36729, South Korea. EM saeedmn2005@gmail.com; fatema.setudu@gmail.com; hwchoi@anu.ac.kr; cjung@andong.ac.kr CR Ajibola A., 2013, Scientific Research and Essays, V8, P543 Amaral J, 2016, WOODHEAD PUBL FOOD S, P369, DOI 10.1016/B978-0-08-100220-9.00014-X [Anonymous], BIOGEOGRAPHY TAXONOM, DOI DOI 10.1007/978-3-642-72649-1 Bogdanov S., 2004, APIDOLOGIE, V35, pS4, DOI [10.1051/apido:2004047, DOI 10.1051/APIDO:2004047] Bottero MT, 2011, VET J, V190, P34, DOI 10.1016/j.tvjl.2010.09.024 Bovo S, 2020, SCI REP-UK, V10, DOI 10.1038/s41598-020-66127-1 Brudzynski K, 2012, FOOD CHEM, V133, P329, DOI 10.1016/j.foodchem.2012.01.035 Chen LZ, 2011, FOOD CHEM, V128, P1110, DOI 10.1016/j.foodchem.2010.10.027 Corlett RT, 2011, HONEYBEES OF ASIA, P215, DOI 10.1007/978-3-642-16422-4_10 Deug-Chan L, 1998, KOREAN J FOOD SCI, V30, P1 Eimanifar A, 2017, MITOCHONDRIAL DNA B, V2, P589, DOI 10.1080/23802359.2017.1372722 Eimanifar A, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-19759-3 Eimanifar A, 2017, MITOCHONDRIAL DNA B, V2, P270, DOI 10.1080/23802359.2017.1325343 Eimanifar A, 2017, MITOCHONDRIAL DNA B, V2, P268, DOI 10.1080/23802359.2017.1325342 Eimanifar A, 2016, MITOCHONDRIAL DNA B, V1, P817, DOI 10.1080/23802359.2016.1241682 Fuller ZL, 2015, BMC GENOMICS, V16, DOI 10.1186/s12864-015-1712-0 Gibson JD, 2016, MITOCHONDRIAL DNA A, V27, P561, DOI 10.3109/19401736.2014.905858 Guler A, 2007, FOOD CHEM, V105, P1119, DOI 10.1016/j.foodchem.2007.02.024 Haddad NJ, 2016, MITOCHONDRIAL DNA A, V27, P4067, DOI 10.3109/19401736.2014.1003846 Hall TA., 1999, NUCL ACIDS S SER, V41, P95, DOI DOI 10.1021/BK-1999-0734.CH008 He XJ, 2013, APIDOLOGIE, V44, P38, DOI 10.1007/s13592-012-0156-7 Hu P, 2016, MITOCHONDRIAL DNA A, V27, P1791, DOI 10.3109/19401736.2014.963815 Ilyasov RA, 2018, J APIC SCI, V62, P189, DOI 10.2478/JAS-2018-0018 Jaafar MB, 2020, INT J INTEGR ENG, V12, P125 Jain SA, 2013, FOOD SCI TECH-BRAZIL, V33, P753, DOI 10.1590/S0101-20612013000400022 Jung C., 2016, ASIAN BEEKEEPING 21, P175 Kek SP, 2017, FOOD CONTROL, V78, P150, DOI 10.1016/j.foodcont.2017.02.025 Kim CK, 2017, KOREAN J FOOD SCI AN, V37, P599, DOI 10.5851/kosfa.2017.37.4.599 Kumar A, 2015, CRIT REV FOOD SCI, V55, P1340, DOI 10.1080/10408398.2012.693978 Moniruzzaman M, 2013, BMC COMPLEM ALTERN M, V13, DOI 10.1186/1472-6882-13-43 Moore JC, 2012, J FOOD SCI, V77, pR118, DOI 10.1111/j.1750-3841.2012.02657.x MORITZ C, 1987, ANNU REV ECOL SYST, V18, P269, DOI 10.1146/annurev.es.18.110187.001413 Nakagawa I, 2018, MITOCHONDRIAL DNA B, V3, P66, DOI 10.1080/23802359.2017.1422401 Okuyama H, 2018, MITOCHONDRIAL DNA B, V3, P338, DOI 10.1080/23802359.2018.1450660 Okuyama H, 2017, MITOCHONDRIAL DNA B, V2, P475, DOI 10.1080/23802359.2017.1361344 Okuyama H, 2017, CONSERV GENET RESOUR, V9, P557, DOI 10.1007/s12686-017-0721-5 Oldroyd BP, 2006, ASIAN HONEY BEES BIO Partap U, 2011, HONEYBEES OF ASIA, P227, DOI 10.1007/978-3-642-16422-4_11 R Core Team, 2018, R LANGUAGE ENV STAT R LANGUAGE ENV STAT Sahinler N, 2004, J APICULT RES, V43, P53, DOI 10.1080/00218839.2004.11101110 Schnell IB, 2010, ARTHROPOD-PLANT INTE, V4, P107, DOI 10.1007/s11829-010-9089-0 Soares S, 2018, FOOD RES INT, V105, P686, DOI 10.1016/j.foodres.2017.11.081 Soares S, 2017, COMPR REV FOOD SCI F, V16, P1072, DOI 10.1111/1541-4337.12278 Song SW, 2005, P NATL ACAD SCI USA, V102, P4990, DOI 10.1073/pnas.0500253102 Takahashi J, 2018, CONSERV GENET RESOUR, V10, P833, DOI 10.1007/s12686-017-0942-7 Takahashi J, 2016, MITOCHONDRIAL DNA B, V1, P156, DOI 10.1080/23802359.2016.1144108 Tan HW, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0023008 Utzeri VJ, 2018, FOOD CONTROL, V91, P294, DOI 10.1016/j.foodcont.2018.04.010 Wang AR, 2018, J APICULT RES, V57, P484, DOI 10.1080/00218839.2018.1494885 Willette DA, 2017, CONSERV BIOL, V31, P1076, DOI 10.1111/cobi.12888 Won SR, 2008, FOOD RES INT, V41, P952, DOI 10.1016/j.foodres.2008.07.014 Wongsiri S., 2000, P 7 INT C TROP BEES, P9 Yang J, 2019, MITOCHONDRIAL DNA B, V4, P231, DOI 10.1080/23802359.2018.1544869 Yong P.L., 2007, EC VALUE HONEY BEES Zhang YZ, 2019, FOOD RES INT, V116, P362, DOI 10.1016/j.foodres.2018.08.049 Zhang YZ, 2019, MOLECULES, V24, DOI 10.3390/molecules24020289 Zink RM, 2008, MOL ECOL, V17, P2107, DOI 10.1111/j.1365-294X.2008.03737.x NR 57 TC 1 Z9 1 U1 3 U2 7 PD APR PY 2022 VL 11 IS 7 AR 928 DI 10.3390/foods11070928 WC Food Science & Technology SC Food Science & Technology UT WOS:000780499000001 DA 2022-12-14 ER PT J AU Rodgers, J Campbell, J Cui, XL Thibodeaux, D AF Rodgers, James Campbell, Jacqueline Cui, Xiaoliang Thibodeaux, Devron TI Feasibility of Traceable Color Standards for Cotton Color SO AATCC REVIEW DT Article DE CIELAB; Color; Cotton; HVI; Spectrophotometer; Traceability AB Cotton color is measured on the Uster High Volume Instrument (HVI) for Rd (diffuse reflectance) and +b (yellowness): cotton-specific color parameters not as well known as other globally recognized color systems (e.g., CIELAB). Standards used for HVI color are ceramic tiles and cotton batts, but there is no "National Institute of Standards and Technology (NIST)-like" traceability or means to verify/certify these standards. A comparative program investigated new standard procedures and protocols, evaluated globally recognized color systems, and developed traceable or verified/certified HVI color standards. Good to excellent color unit agreement was observed for the color tiles and Agricultural Marketing Service (AMS) tiles when glass was not used in the measurement, but only fair agreement was observed for all units when glass was placed between the sample and the spectrophotometer port. C1 [Rodgers, James] ARS, Res Unit, SRRC, USDA, New Orleans, LA 70124 USA. C3 United States Department of Agriculture (USDA) RP Rodgers, J (corresponding author), ARS, Res Unit, SRRC, USDA, 1100 Robert E Lee Blvd, New Orleans, LA 70124 USA. EM James.Rodgers@ars.usda.gov CR Berger-Schunn A., 1994, PRACTICAL COLOR MEAS BILLMEYER F, 1996, ASTM MANUAL, V17 Billmeyer F. W., 2000, PRINCIPLES COLOR TEC Hunter R., 1975, MEASUREMENT APPEARAN Judd DB., 1975, COLOR BUSINESS SCI I, V3 Nassau K., 2001, PHYS CHEM COLOR, V2nd NICKERSON D, 1950, J OPT SOC AM, V40, P446, DOI 10.1364/JOSA.40.000446 Nickerson D, 1931, J OPT SOC AM, V21, P640, DOI 10.1364/JOSA.21.000640 NICKERSON D, 1951, TEXT RES J, V21, P33, DOI 10.1177/004051755102100108 OHNO Y, 2000, CIE FUNDAMENTALS COL THIBODEAUX D, 2005, DEV NIST TRACEABLE H Thibodeaux D, 2008, AATCC REV, V8, P44 NR 12 TC 3 Z9 3 U1 0 U2 3 PD JAN PY 2009 VL 9 IS 1 BP 42 EP 47 WC Chemistry, Applied; Engineering, Chemical; Materials Science, Textiles SC Chemistry; Engineering; Materials Science UT WOS:000262734200006 DA 2022-12-14 ER PT J AU Cvijanovic, V Saric, B Dramicanin, A Kodranov, I Manojlovic, D Momirovic, N Momirovic, N Milojkovic-Opsenica, D AF Cvijanovic, Vojin Saric, Beka Dramicanin, Aleksandra Kodranov, Igor Manojlovic, Dragan Momirovic, Nevena Momirovic, Nebojsa Milojkovic-Opsenica, Dusanka TI Content and Distribution of Macroelements, Microelements, and Rare-Earth Elements in Different Tomato Varieties as a Promising Tool for Monitoring the Distinction between the Integral and Organic Systems of Production in Zeleni hit-Official Enza and Vitalis Trial and Breeding Station SO AGRICULTURE-BASEL DT Article DE tomato; ICP-OES; ICP-MS; rare-earth elements; macroelements; microelements and potentially toxic elements; organic and integral type of production ID METALS; HEALTH; ACCUMULATION; MAGNESIUM; CALCIUM AB The identification of agricultural food production systems has gained importance in order to protect both human health and the environment. The importance of organic production system of agriculture which involves the application of natural processes and substances, and limits or completely eliminates the use of synthesized means is emphasized. Knowledge of the mineral composition in tomato samples can be used as a potent tool in the identification of chemical markers as potential indicators of the farming system. A set of tomato samples taken from two factorial randomized trials were comprehended eight different varieties, belonging to four tomato types: large-BEEF and CLUSTER, and mini and midi-CHERRY and PLUM tomatoes, cultivated under two different farming systems: integral (IPM) and organic (O) were characterized based on the composition of the minerals. A total of 44 elements were quantified. To establish criteria for the classification of the samples and confirm a unique set of parameters of variation among the types of production, sophisticated chemometric techniques were used. The results indicate that the accumulation of elements varies between 8 tomato varieties and 2 different growing systems. The contents of Al, Mn, As, Pb, and some of the rare-earth elements (REEs) are able to distinguish between production types. Examination of different hybrids, which belong to different types in two production systems: organic and integral within Zeleni hit (official Enza and Vitalis trial and breeding station), was done with the aim of reaching a methodology of diversification, ie complete traceability of organic production, and to contribute to distinguishing types of agricultural systems and enhancing the possibility of acquiring a valuable authenticity factor about the type of agricultural production system employed for the cultivation of tomatoes. C1 [Cvijanovic, Vojin] Inst Applicat Sci Agr, Blv Despota Stefana 68b, Belgrade 11000, Serbia. [Saric, Beka; Dramicanin, Aleksandra; Kodranov, Igor; Milojkovic-Opsenica, Dusanka] Univ Belgrade, Fac Chem, Ctr Excellence Mol Food Sci, Studentski Trg 12-16, Belgrade 11158, Serbia. [Saric, Beka; Dramicanin, Aleksandra; Kodranov, Igor; Milojkovic-Opsenica, Dusanka] Univ Belgrade, Fac Chem, Dept Analyt Chem, Studentski Trg 12-16, Belgrade 11158, Serbia. [Manojlovic, Dragan] South Ural State Univ, Dept Ecol & Chem Technol, Chelyabinsk 454080, Russia. [Momirovic, Nevena; Momirovic, Nebojsa] Univ Belgrade, Fac Agr, Nemanjina 6, Zemun 11080, Serbia. C3 University of Belgrade; University of Belgrade; South Ural State University; University of Belgrade RP Milojkovic-Opsenica, D (corresponding author), Univ Belgrade, Fac Chem, Ctr Excellence Mol Food Sci, Studentski Trg 12-16, Belgrade 11158, Serbia.; Milojkovic-Opsenica, D (corresponding author), Univ Belgrade, Fac Chem, Dept Analyt Chem, Studentski Trg 12-16, Belgrade 11158, Serbia. EM cvija91@yahoo.com; beka@chem.bg.ac.rs; akosovic@chem.bg.ac.rs; ikodranov@chem.bg.ac.rs; manojlo@chem.bg.ac.rs; nevena.momirovic@zelenihit.rs; emomirov@agrif.bg.ac.rs; dusankam@chem.bg.ac.rs CR Abeshu M., 2016, J NUTR SCI, V6, P2 Benbrook C, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11071266 Bhattacharya PT, 2016, SCIENTIFICA, V2016, DOI 10.1155/2016/5464373 Bressy FC, 2013, MICROCHEM J, V109, P145, DOI 10.1016/j.microc.2012.03.010 Burlo F, 1999, J AGR FOOD CHEM, V47, P1247, DOI 10.1021/jf9806560 Silva MLD, 2014, APPL RES AGROTECHNOL, V7, P47, DOI 10.5935/PAeT.V7.N1.05 Dorais Martine, 2008, Phytochemistry Reviews, V7, P231, DOI 10.1007/s11101-007-9085-x Dramicanin A, 2021, FOOD CHEM, V354, DOI 10.1016/j.foodchem.2021.129507 Duma M., 2015, CHEM TECHNOL, V66, P24, DOI [10.5755/j01.ct.66.1.12053, DOI 10.5755/J01.CT.66.1.12053] Fatima T., 2009, COMPENDIUM TRANSGENI, VVolume 6, P1 Fenech M, 2001, MUTAT RES-FUND MOL M, V475, P1, DOI 10.1016/S0027-5107(01)00069-0 Fernandez Sanchez M.L., 2019, ENCY ANAL SCI, V10, P169 Gad N., 2013, World Applied Sciences Journal, V22, P1527 Giovanelli G, 2002, J FOOD ENG, V52, P135, DOI 10.1016/S0260-8774(01)00095-4 Gvozden G.M., 2016, THESIS U BELGRADE ZE Hajslova J, 2005, FOOD ADDIT CONTAM A, V22, P514, DOI 10.1080/02652030500137827 Han F, 2005, NEW PHYTOL, V165, P481, DOI 10.1111/j.1469-8137.2004.01256.x Hao XM, 2004, HORTSCIENCE, V39, P512, DOI 10.21273/HORTSCI.39.3.512 Ilic ZS, 2014, POL J ENVIRON STUD, V23, P2027 Jorhem L, 2000, J SCI FOOD AGR, V80, P43, DOI [10.1002/(SICI)1097-0010(20000101)80:1<43::AID-JSFA482>3.0.CO;2-Y, 10.1002/(SICI)1097-0010(20000101)80:1<43::AID-JSFA482>3.0.CO;2-Y] Kaiser BN, 2005, ANN BOT-LONDON, V96, P745, DOI 10.1093/aob/mci226 Krgovic R, 2014, SCI WORLD J, DOI 10.1155/2014/212506 Miteva E., 2001, Bulgarian Journal of Agricultural Science, V7, P435 Muhammad A., 2021, AGRINULA JURNAL AGRO, V4, P13, DOI [10.36490/agri.v4i1.109, DOI 10.36490/AGRI.V4I1.109] Paskovic I, 2021, FOOD CHEM, V359, DOI 10.1016/j.foodchem.2021.129961 Ramaraj R, 2015, INT J SUSTAINABLE GR, V4, P33, DOI [10.11648/j.ijrse.s.2015040101.15, DOI 10.11648/J.IJRSE.S.2015040101.15] Roosta HR, 2011, SCI HORTIC-AMSTERDAM, V129, P396, DOI 10.1016/j.scienta.2011.04.006 Rossi F, 2008, EUR J NUTR, V47, P266, DOI 10.1007/s00394-008-0721-z Santos BM, 2007, PROC FL STATE HORTIC, V120, P189 Shan XQ, 2003, PLANT SCI, V165, P1343, DOI 10.1016/S0168-9452(03)00361-3 Shekar C.C., 2011, INT J PHARM BIO SCI, V2, pB358, DOI DOI 10.1007/s11356-018-1498-0 White PJ, 2010, ANN BOT-LONDON, V105, P1073, DOI 10.1093/aob/mcq085 White PJ, 2009, NEW PHYTOL, V182, P49, DOI 10.1111/j.1469-8137.2008.02738.x NR 33 TC 2 Z9 2 U1 2 U2 4 PD OCT PY 2021 VL 11 IS 10 AR 1009 DI 10.3390/agriculture11101009 WC Agronomy SC Agriculture UT WOS:000716181600001 DA 2022-12-14 ER PT J AU Gonzalez, CC Pena-Vinces, J AF Chamorro Gonzalez, Candy Pena-Vinces, Jesus TI A framework for a green accounting system-exploratory study in a developing country context, Colombia SO ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY DT Article; Early Access DE Green accounting; Accounting system; Environmental accounting; Developing economies; Colombia ID MARINE PROTECTED AREAS; ECO-EFFICIENCY; SUSTAINABLE DEVELOPMENT; REFLECTIONS; INFORMATION; GOVERNANCE; PROPOSAL; POLICY AB Current accounting systems assume a purely financial approach, without including environmental information, such as environmental costs and companies' expenses. On the one hand, this study proposes a framework that considers the environmental impact of firms within their accounting system, the Green Accounting System (GAS). On the other hand, and in the context of developing countries, Colombia carried out an exploratory study. With a sample of 150 Colombian industrial and commercial companies, this research revealed that 100% of them had not yet implemented environmental practices within the accounting system. Therefore, this research would be useful not only for academia, but also for practitioners and governments. As GAS would contribute to traceability in the quantification of environmental accounting, it would simultaneously generate a movement toward cleaner production that would increase environmental quality. C1 [Chamorro Gonzalez, Candy] Univ Catolica Luis Amigo, Medellin, Colombia. [Pena-Vinces, Jesus] Univ Seville, Fac Ciencias Econ & Empresariales, Dept Adm Empresas & Mkt, Av Ramon Y Cajal 1, Seville 41018, Spain. C3 University of Sevilla RP Pena-Vinces, J (corresponding author), Univ Seville, Fac Ciencias Econ & Empresariales, Dept Adm Empresas & Mkt, Av Ramon Y Cajal 1, Seville 41018, Spain. EM candiilorena@gmail.com; jesuspvinces@us.es CR Abbott BP, 2016, PHYS REV LETT, V116, DOI 10.1103/PhysRevLett.116.241102 Alba Marisleidy, 2018, Cuad. Contab., V19, P117, DOI 10.11144/javeriana.cc19-47.apfi Altamirano S, 2020, PROSPECTIVAS UTC REV, V3, P186 Angell LC, 1999, J OPER MANAG, V17, P575, DOI 10.1016/S0272-6963(99)00006-6 [Anonymous], 1997, ACCOUNT AUDIT ACCOUN, DOI DOI 10.1108/09513579710166703 [Anonymous], 2019, THESIS U BUENAVENTUR [Anonymous], 2012, THEORY METHODOLOGY P Araujo J, 1995, CONTABILIDAD SOCIAL Aronsson T., 1997, WELFARE MEASUREMENT Aznar J., 2015, VALORACIN ACTIVOS AM, V2nd Banguat U., 2009, DOCUMENTO 26, V24 Bennett M., 2017, GREEN BOTTOM LINE EN, DOI [10.4324/9781351283328, DOI 10.4324/9781351283328] Burritt RL, 2006, J CLEAN PROD, V14, P1262, DOI 10.1016/j.jclepro.2005.08.012 Cabello J, 2016, IJMSOR, V1, P4, DOI [10.17981/ijmsor.01.01.01, DOI 10.17981/IJMSOR.01.01.01] Cairns RD, 2009, ENERG J, V30, P113 Capusneanu S., 2008, THEORETICAL APPL EC, V1, P57 Carvalho V., 2019, DERECHO CAMBIO SOCIA, V56, P483 Cavalletti B, 2020, ECOL ECON, V173, DOI 10.1016/j.ecolecon.2020.106623 Ceballos Sandoval J., 2020, RESIDUOS SLIDOS ALTE Chamorro C., 2016, THESIS U COSTA COLOM Chamorro C., 2015, REV RED INVESTIGACI, V5, P164 Gonzalez CC, 2021, ENVIRON DEV SUSTAIN, V23, P6453, DOI 10.1007/s10668-020-00880-1 Chvez M., 2020, HIST MODERNA CONTEMP Cortes O, 2016, IJMSOR, V1, P54, DOI [10.17981/ijmsor.01.01.08, DOI 10.17981/IJMSOR.01.01.08] Craig PP, 1994, ASSIGNING ECONOMIC VALUE TO NATURAL RESOURCES, P67 Crissien-Borreroa T., 2016, PROENVIRONMENTAL ASS Deegan C, 2013, CRIT PERSPECT ACCOUN, V24, P448, DOI 10.1016/j.cpa.2013.04.004 Doria D., 2020, EC MICAS, DOI [10.17981/econcuc.41.1.2020.Org.2, DOI 10.17981/ECONCUC.41.1.2020.ORG.2] Duvic-Paoli L., 2018, PREVENTION PRINCIPLE, P67 El Sefary S, 2000, CONTABILIDAD VERDE S Ellingson M., 2015, EPISODE 24 FUTURE GE ElSerafy S, 1997, ECOL ECON, V21, P217, DOI 10.1016/S0921-8009(96)00107-3 Evangelinos K, 2015, CORP SOC RESP ENV MA, V22, P257, DOI 10.1002/csr.1342 Figge F, 2013, MANAGE ACCOUNT RES, V24, P387, DOI 10.1016/j.mar.2013.06.009 Finsterwalder J, 2020, J SERV MANAGE, V31, P1107, DOI 10.1108/JOSM-06-2020-0201 Fleischman R.K., 2006, J ACCOUNTING ED, V24, P35, DOI [DOI 10.1016/J.JACCEDU.2006.04.001, https://doi.org/10.1016/j.jaccedu.2006.04.001] Fogarassy C., 2018, ADMIN MANAGE, V31, P52, DOI 10.24818/amp/2018.31-04 FOWLER E, 2008, CUESTIONES CONTABLES Galvis M., 2019, LIBRE EMPRESA, V16, P97, DOI [10.18041/1657-2815/libreempresa.2019v16n2.6620, DOI 10.18041/1657-2815/LIBREEMPRESA.2019V16N2.6620] Garcia-Sanchez IM, 2015, ECOL INDIC, V48, P171, DOI 10.1016/j.ecolind.2014.08.004 Geba NB, 2010, ACTUAL CONTAB FACES, V13, P49 Gomez M., 2004, REV INT LEGIS CONTAB, V18, P87 Gonzalez C., 2019, REV ACTIVOS, V17, P177 Gonzalez C., 2015, SABER CIENCIA LIBERT, V10, P53, DOI [10.18041/2382-3240/saber.2015v10n2.782, DOI 10.18041/2382-3240/SABER.2015V10N2.782] Gonzlez J, 2017, CONTABILIDAD GUBERNA Gray R, 2012, ACCOUNT AUDIT ACCOUN, V25, P228, DOI 10.1108/09513571211198755 Greenham Tony, 2010, International Journal of Green Economics, V4, P333, DOI 10.1504/IJGE.2010.037655 Haque F, 2018, BUS STRATEG ENVIRON, V27, P415, DOI 10.1002/bse.2007 Hens L, 2018, J CLEAN PROD, V172, P3323, DOI 10.1016/j.jclepro.2017.11.082 Hernandez M., 2017, REV CIENCIA UNEMI, V10, P90, DOI [10.29076/issn.2528-7737vol10iss23.2017pp90-103p, DOI 10.29076/ISSN.2528-7737VOL10ISS23.2017PP90-103P] Hernndez D., 2012, THESIS U NORTE BARRA Higuera E., 2015, THESIS U MILITAR NUE Homan H., 2016, S E ASIA J CONT BUSI, V11, P9 Ignat G., 2016, AGRONOMY SERIES SCI, V59, P245 Ikram M, 2019, J CLEAN PROD, V226, P628, DOI 10.1016/j.jclepro.2019.03.265 Islam M, 2019, RESOUR CONSERV RECY, V145, P126, DOI 10.1016/j.resconrec.2019.02.027 Jara A., 2017, ECUADOR REV PUBLICAN, V4, P213 Kitchen H, 2019, LOCAL PUBLIC FINANCE AND ECONOMICS: AN INTERNATIONAL PERSPECTIVE, P331, DOI 10.1007/978-3-030-21986-4_10 Lako A., 2018, CONCEPTUAL FRAMEWORK, P60 Lee W.E., 2017, SOCIAL ENV ACCOUNTAB, V37, P81, DOI [https://doi.org/10.1080/0969160X.2016.1270225, DOI 10.1080/0969160X.2016.1270225] Lehman G., 1995, CRIT PERSPECT, V6, P393, DOI DOI 10.1006/CPAC.1995.1037 Lieder M, 2016, J CLEAN PROD, V115, P36, DOI 10.1016/j.jclepro.2015.12.042 Malca O, 2020, SMALL BUS ECON, V55, P831, DOI 10.1007/s11187-019-00185-2 Martínez Galvis María Rita, 2019, Rev.fac.cienc.econ., V27, P87, DOI 10.18359/rfce.3196 Mason C, 2014, J BUS ETHICS, V119, P77, DOI 10.1007/s10551-012-1615-9 Masud MAK, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101717 Maunders K. T., 1991, ACCOUNT AUDIT ACCOUN, V4, P9 Medina V., 2019, CONTABILIDAD VERDE D, P107 Soto EM, 2016, REICE-REV ELECTRON I, V4, P74 Moller A, 2005, J IND ECOL, V9, P73, DOI 10.1162/108819805775247927 Montagnini F, 2015, BIOCENOSIS COSTA RIC, V2, P5 Montemayor E, 2019, RESOUR CONSERV RECY, V146, P395, DOI 10.1016/j.resconrec.2019.03.013 Moreno I., 2019, FACE REV FACULTAD CI, DOI DOI 10.24054/01204211.V2.N2.2018.3040 Mylonakis J, 2006, INT J ENERG RES, V30, P915, DOI 10.1002/er.1194 Nakasone GT, 2015, CONTAB NEG, V10, P5 Nilsson A, 2017, ENVIRON EDUC RES, V23, P573, DOI 10.1080/13504622.2016.1250148 Novillo M., 2014, THESIS U POLITECNICA Opdam P, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10010220 Passetti E, 2016, J CLEAN PROD, V122, P228, DOI 10.1016/j.jclepro.2016.02.035 Pecl GT, 2017, SCIENCE, V355, DOI 10.1126/science.aai9214 Pena-Vinces J, 2021, J TECHNOL TRANSFER, V46, P1734, DOI 10.1007/s10961-020-09807-4 Pena-Vinces J, 2019, TECHNOL ECON DEV ECO, V25, P300, DOI 10.3846/tede.2019.8073 Pena-Vinces JC, 2013, INT ENTREP MANAG J, V9, P603, DOI 10.1007/s11365-013-0265-4 Alvarado ESQ, 2016, REV PUBLICANDO, V3, P156 Raouf M. A, 2002, 9 ANN C EC RES FORUM Rodriguez D, 2015, PERFIL COYUNTURA EC, V26, P115, DOI [10.17533/udea.pece.n26a04, DOI 10.17533/UDEA.PECE.N26A04] Rossi M, 2016, J CLEAN PROD, V129, P361, DOI 10.1016/j.jclepro.2016.04.051 Russell S, 2017, ACCOUNT AUDIT ACCOUN, V30, P1426, DOI 10.1108/AAAJ-07-2017-3010 Saleh M. M. A., 2020, INT J ENERGY EC POLI, V10, P417, DOI [10.32479/ijeep.8608, DOI 10.32479/IJEEP.8608] Samaraweera M., 2021, REV MARKETING CONSUM, V38, P305, DOI [10.1108/JCM-04-2020-3771, DOI 10.1108/JCM-04-2020-3771] Scarpellini S, 2020, SUSTAIN ACCOUNT MANA, V11, P1129, DOI 10.1108/SAMPJ-04-2019-0150 Schaltegger S., 2017, CONT ENV ACCOUNTING Schoennel W, 2017, THESIS U MILITAR NUE Silva J, 2017, REV CIENCIA TECNOLOG, V12, P25 Slawinski N, 2015, ORGAN SCI, V26, P531, DOI 10.1287/orsc.2014.0960 Taleb M, 2015, INT J ACCOUNTING FIN, V5, P36, DOI 10.5296/ijafr.v5i1.6726 Tiwari K, 2020, J CLEAN PROD, V258, DOI 10.1016/j.jclepro.2020.120783 Vassallo P, 2017, ECOL MODEL, V355, P12, DOI 10.1016/j.ecolmodel.2017.03.013 Vlez B., 2007, THESIS U ANTIOQUIA Wojcik D, 2015, ENVIRON PLANN C, V33, P1173, DOI 10.1177/0263774X15612338 World Health Organization, 2012, SIGARCH COMPUT ARCHI, P1, DOI [DOI 10.1145/1186736.1186737, 10.1016/j.jcyt.2020.03.255, 10.1145/1186736.1186737] Yang W., 2018, ADV MANAGEMENT APPL, V8, P33 Yepes H, 2008, BAMBOO EMPRESARIAL Zandi G., 2019, INT J ENERGY EC POLI, V9, P342, DOI [10.32479/ijeep.8369, DOI 10.32479/IJEEP.8369] Zou T, 2019, J CLEAN PROD, V228, P619, DOI 10.1016/j.jclepro.2019.04.309 NR 105 TC 0 Z9 0 U1 21 U2 21 DI 10.1007/s10668-022-02445-w EA JUN 2022 WC Green & Sustainable Science & Technology; Environmental Sciences SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000805910600002 DA 2022-12-14 ER PT J AU Fernandez, A Butz, P Corrales, M Picouet, P Tauscher, B AF Fernandez, Avelina Butz, Peter Corrales, Margarita Picouet, Pierre Tauscher, Bernhard TI Colour-based ultra-high-pressure indicators for monitoring process traceability SO HIGH PRESSURE RESEARCH DT Article; Proceedings Paper CT 46th Annual Meeting of the European-High-Pressure-Research-Group Meeting (EHPRG 46) CY SEP 07-12, 2008 CL Valencia, SPAIN DE guaiacol; indicator; integrator; peroxidase; potassium sorbate; ubiquinone ID INACTIVATION AB Intensity-related indicators are useful devices to monitor process uniformity and quality-related aspects. In this work, two systems were proposed for monitoring the best practices in ultra-high-pressure processing based on irreversible changes of colour. First, a system where moderate pressures above 500MPa unexpectedly induce a mechanism-based inactivation of peroxidase, a quality-related food enzyme, at ambient temperature was particularly suitable for current commercial high-pressure applications. Second, the reaction of a diene with an appropriate dienophile via Diels-Alder, showed a pressure-dependent discolouration; this system was effective at pressure/heat levels necessary to achieve high pressure-assisted-sterilisations. C1 [Butz, Peter; Corrales, Margarita; Tauscher, Bernhard] Max Rubner Inst, Dept Safety & Qual Fruits & Vegetables, Karlsruhe, Germany. [Picouet, Pierre] Inst Recerca & Tecnol Agroalimentaries, Girona, Spain. [Fernandez, Avelina] CSIC, Inst Agroquim & Tecnol Alimentos, Burjassot, Spain. C3 IRTA; Consejo Superior de Investigaciones Cientificas (CSIC); Instituto de Agroquimica y Tecnologia de los Alimentos (IATA) RP Fernandez, A (corresponding author), CSIC, Inst Agroquim & Tecnol Alimentos, Burjassot, Spain. EM avelina.fernandez@iata.csic.es CR Butz P, 2004, J FOOD SCI, V69, pS117 Garcia AF, 2009, J FOOD ENG, V92, P410, DOI 10.1016/j.jfoodeng.2008.12.033 FernandezGarcia A, 2003, J AGR FOOD CHEM, V51, P8093, DOI 10.1021/jf0348471 Garcia AF, 2002, BIOTECHNOL PROGR, V18, P1076, DOI 10.1021/bp025529+ Guiavarc'h Y, 2005, J FOOD PROTECT, V68, P384, DOI 10.4315/0362-028X-68.2.384 Khandelwal GD, 1997, FOOD CHEM, V60, P237, DOI 10.1016/S0308-8146(96)00326-3 Ludikhuyze L, 2003, CRIT REV FOOD SCI, V43, P527, DOI 10.1080/10408690390246350 Matser AA, 2004, TRENDS FOOD SCI TECH, V15, P79, DOI 10.1016/j.tifs.2003.08.005 Pfister MKH, 2001, DEUT LEBENSM-RUNDSCH, V97, P257 Rajan S, 2006, J FOOD PROTECT, V69, P853, DOI 10.4315/0362-028X-69.4.853 Rastogi NK, 2007, CRIT REV FOOD SCI, V47, P69, DOI 10.1080/10408390600626420 Van der Plancken I, 2008, TRENDS FOOD SCI TECH, V19, P337, DOI 10.1016/j.tifs.2007.10.004 NR 12 TC 3 Z9 3 U1 1 U2 11 PY 2009 VL 29 IS 1 BP 8 EP 13 AR PII 909546625 DI 10.1080/08957950802417784 WC Physics, Multidisciplinary SC Physics UT WOS:000264375400003 DA 2022-12-14 ER PT J AU Mihailova, A Pedentchouk, N Kelly, SD AF Mihailova, A. Pedentchouk, N. Kelly, S. D. TI Stable isotope analysis of plant-derived nitrate - Novel method for discrimination between organically and conventionally grown vegetables SO FOOD CHEMISTRY DT Article DE Organic food; Authentication; Stable nitrogen isotopes; Stable oxygen isotopes; Denitrifier method; Nitrate; Fertilisers; Organic agriculture; Food traceability ID NATURAL N-15 ABUNDANCE; NITROGEN ISOTOPES; FERTILIZER; OXYGEN; INDICATOR; FRACTIONATION; SEAWATER; TOOL AB The lack of reliable markers for the discrimination between organic and conventional products makes the organic food market susceptible to attempted fraud. Robust analytical methodologies for organic food authentication are urgently needed. In this study a new approach, compound-specific nitrogen and oxygen isotope analysis of plant-derived nitrate, has been applied alongside bulk nitrogen isotope analysis for discrimination between organically and conventionally greenhouse-grown lettuce and retail potatoes and tomatoes. The method revealed significant differences between conventional and organic fertilisation. An intra-plant isotopic variation as well as significant impact of the fertiliser application rate on the nitrogen and oxygen isotope values of plant-derived nitrate has been observed. Nitrogen and oxygen isotope analysis of nitrate has a potential for differentiation between organic and conventional crops. Further analysis is needed to improve our understanding of the scope of application and robustness of this compound-specific approach. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Mihailova, A.; Pedentchouk, N.; Kelly, S. D.] Univ E Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England. C3 University of East Anglia RP Mihailova, A (corresponding author), Univ E Anglia, Sch Environm Sci, Norwich Res Pk, Norwich NR4 7TJ, Norfolk, England. EM a.mihailova@uea.ac.uk CR AMBERGER A, 1987, GEOCHIM COSMOCHIM AC, V51, P2699, DOI 10.1016/0016-7037(87)90150-5 ANDERSSON KK, 1983, FEBS LETT, V164, P236, DOI 10.1016/0014-5793(83)80292-0 [Anonymous], 2009, WORLD ORGANIC AGR ST Bateman AS, 2007, ISOT ENVIRON HEALT S, V43, P237, DOI 10.1080/10256010701550732 Bateman AS, 2007, J AGR FOOD CHEM, V55, P2664, DOI 10.1021/jf0627726 Bateman AS, 2005, J AGR FOOD CHEM, V53, P5760, DOI 10.1021/jf050374h Bohlke JK, 2003, RAPID COMMUN MASS SP, V17, P1835, DOI 10.1002/rcm.1123 Bowen G.J., 2012, ONLINE ISOTOPES PREC Casciotti KL, 2002, ANAL CHEM, V74, P4905, DOI 10.1021/ac020113w Choi W. J., 2003, World Intellectual Property Organization, VWO 03/087813 A1, Patent No. 03087813 Evans RD, 2001, TRENDS PLANT SCI, V6, P121, DOI 10.1016/S1360-1385(01)01889-1 EVANS RD, 1996, PLANT CELL ENVIRON, V19, P121 HANDLEY LL, 1992, PLANT CELL ENVIRON, V15, P965, DOI 10.1111/j.1365-3040.1992.tb01650.x Hufton CA, 1996, NEW PHYTOL, V133, P495, DOI 10.1111/j.1469-8137.1996.tb01917.x Kaiser J, 2007, ANAL CHEM, V79, P599, DOI 10.1021/ac061022s Kendall C, 1998, ISOTOPE TRACERS IN CATCHMENT HYDROLOGY, P519 KROOPNICK P, 1972, SCIENCE, V175, P54, DOI 10.1126/science.175.4017.54 Laursen KH, 2013, FOOD CHEM, V141, P2812, DOI 10.1016/j.foodchem.2013.05.068 MARIOTTI A, 1983, NATURE, V303, P685, DOI 10.1038/303685a0 MARIOTTI A, 1982, PLANT PHYSIOL, V69, P880, DOI 10.1104/pp.69.4.880 Rogers KM, 2008, J AGR FOOD CHEM, V56, P4078, DOI 10.1021/jf800797w SHEARER G, 1975, SOIL SCI SOC AM J, V39, P896, DOI 10.2136/sssaj1975.03615995003900050030x Sigman DM, 2001, ANAL CHEM, V73, P4145, DOI 10.1021/ac010088e Sturm M, 2011, ISOT ENVIRON HEALT S, V47, P214, DOI 10.1080/10256016.2011.570865 Sturm M, 2011, J SCI FOOD AGR, V91, P262, DOI 10.1002/jsfa.4179 Vitoria L, 2004, ENVIRON SCI TECHNOL, V38, P3254, DOI 10.1021/es0348187 YONEYAMA T, 1993, PLANT CELL PHYSIOL, V34, P489 YONEYAMA T, 1989, PLANT CELL PHYSIOL, V30, P957 Zhou W, 2012, J FOOD AGRIC ENVIRON, V10, P287 NR 29 TC 25 Z9 29 U1 6 U2 103 PD JUL 1 PY 2014 VL 154 BP 238 EP 245 DI 10.1016/j.foodchem.2014.01.020 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000332430800032 DA 2022-12-14 ER PT J AU Fang, WP Meinhardt, LW Mischke, S Bellato, CM Motilal, L Zhang, DP AF Fang, Wanping Meinhardt, Lyndel W. Mischke, Sue Bellato, Claudia M. Motilal, Lambert Zhang, Dapeng TI Accurate Determination of Genetic Identity for a Single Cacao Bean, Using Molecular Markers with a Nanofluidic System, Ensures Cocoa Authentication SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE authentication; chocolate; conservation; fair trade; fluidigm; food forensics; food adulteration; gourmet food; germplasm; molecular markers; Theobroma cacao; traceability; tropical tree ID THEOBROMA-CACAO; FOOD; DIVERSITY; TRACEABILITY; POPULATIONS; VARIETIES; GENOTYPES; L. AB Cacao (Theobroma cacao L.), the source of cocoa, is an economically important tropical crop. One problem with the premium cacao market is contamination with off-types adulterating raw premium material. Accurate determination of the genetic identity of single cacao beans is essential for ensuring cocoa authentication. Using nanofluidic single nucleotide polymorphism (SNP) genotyping with 48 SNP markers, we generated SNP fingerprints for small quantities of DNA extracted from the seed coat of single cacao beans. On the basis of the SNP profiles, we identified an assumed adulterant variety, which was unambiguously distinguished from the authentic beans by multilocus matching. Assignment tests based on both Bayesian clustering analysis and allele frequency clearly separated all 30 authentic samples from the non-authentic samples. Distance-based principle coordinate analysis further supported these results. The nanofluidic SNP protocol, together with forensic statistical tools, is sufficiently robust to establish authentication and to verify gourmet cacao varieties. This method shows significant potential for practical application. C1 [Fang, Wanping; Meinhardt, Lyndel W.; Mischke, Sue; Bellato, Claudia M.; Zhang, Dapeng] ARS, SPCL, BARC, USDA,BARC W, Beltsville, MD 20705 USA. [Fang, Wanping] Nanjing Agr Univ, Coll Hort, Nanjing 210095, Jiangsu, Peoples R China. [Motilal, Lambert] Univ W Indies, Cocoa Res Ctr, St Augustine, Trinidad Tobago. C3 United States Department of Agriculture (USDA); Nanjing Agricultural University; University West Indies Mona Jamaica; University West Indies Saint Augustine RP Zhang, DP (corresponding author), ARS, SPCL, BARC, USDA,BARC W, 10300 Baltimore Ave,Bldg 001,Room 223, Beltsville, MD 20705 USA. EM dapeng.zhang@ars.usda.gov CR Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 Argout X, 2011, NAT GENET, V43, P101, DOI 10.1038/ng.736 Argout X, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-512 Bartley BGD, 2005, GENETIC DIVERSITY OF CACAO AND ITS UTILIZATION: BOTANY, PRODUCTION AND USES, P1, DOI 10.1079/9780851996196.0000 BERGMANN JF, 1969, ANN ASSOC AM GEOGR, V59, P85, DOI 10.1111/j.1467-8306.1969.tb00659.x Buckler ES, 2002, CURR OPIN PLANT BIOL, V5, P107, DOI 10.1016/S1369-5266(02)00238-8 Cheesman E. E., 1944, TROP AGRIC [TRINIDAD], V21, P144 Chuang HY, 2011, BOT STUD, V52, P393 Cornuet JM, 1999, GENETICS, V153, P1989 Dias L.A.D.S., 2001, GENETIC IMPROVEMENT Evett IW., 1998, INTERPRETING DNA EVI, P23 Fluidigm, 2011, FLUID SNP GEN US GUI Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Gomez-Pompa A., 1990, LAT AM ANTIQ, V1, P247, DOI [DOI 10.2307/972163, 10.2307/972163] Guiltinan Mark J., 2008, V1, P145 Henderson JS, 2007, P NATL ACAD SCI USA, V104, P18937, DOI 10.1073/pnas.0708815104 Ji K, 2013, GENET RESOUR CROP EV, V60, P441, DOI 10.1007/s10722-012-9847-1 Lanaud C, 2006, P 15 INT COC RES C S, P185 Livingstone DS, 2012, MOL BREEDING, V30, P33, DOI 10.1007/s11032-011-9596-4 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Martins-Lopes P, 2013, FOOD TECHNOL BIOTECH, V51, P198 Ministerio de Agricultura, 2003, CAR ZON PROD CAC PER, P209 Montealegre C, 2010, J AGR FOOD CHEM, V58, P28, DOI 10.1021/jf902619z Motilal LA, 2010, PLANT GENET RESOUR-C, V8, P106, DOI 10.1017/S1479262109990232 Palmieri L, 2009, NUTRIENTS, V1, P316, DOI 10.3390/nu1020316 Peakall R, 2006, MOL ECOL NOTES, V6, P288, DOI 10.1111/j.1471-8286.2005.01155.x Peakall R, 2012, BIOINFORMATICS, V28, P2537, DOI 10.1093/bioinformatics/bts460 Pound F. J., 1945, Report and Proceedings of the Cocoa Research Conference held at the Colonial Office, London, May-June 1945., P131 Prins TW, 2010, FOOD CHEM, V118, P966, DOI 10.1016/j.foodchem.2008.10.085 Qin J, 2008, NUCLEIC ACIDS RES, V36, DOI 10.1093/nar/gkn518 Rannala B, 1997, P NATL ACAD SCI USA, V94, P9197, DOI 10.1073/pnas.94.17.9197 Stoeckle MY, 2011, SCI REP-UK, V1, DOI 10.1038/srep00042 Takrama J., 2012, African Crop Science Journal, V20, P67 Tornincasa P, 2010, TOOLS IDENTIFYING BI, P307 Waits LP, 2001, MOL ECOL, V10, P249, DOI 10.1046/j.1365-294X.2001.01185.x Wang J, 2009, BMC GENOMICS, V10, DOI 10.1186/1471-2164-10-561 Woolfe M, 2004, TRENDS BIOTECHNOL, V22, P222, DOI 10.1016/j.tibtech.2004.03.010 Young A. M., 2007, CHOCOLATE TREE NATUR, p[92, 93, 219] Zhang D., 2011, WEALTH CROP RELATIVE, P277, DOI DOI 10.1007/978-3-642-21201-7_13 Zhang DP, 2006, CROP SCI, V46, P2084, DOI 10.2135/cropsci2006.01.0004 Zhang DP, 2009, TREE GENET GENOMES, V5, P1, DOI 10.1007/s11295-008-0163-z NR 41 TC 32 Z9 34 U1 0 U2 51 PD JAN 15 PY 2014 VL 62 IS 2 BP 481 EP 487 DI 10.1021/jf404402v WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000330018100019 DA 2022-12-14 ER PT J AU Steinmeier, U Neudecker, M Witt, A von Horsten, D Schroter, M AF Steinmeier, U. Neudecker, M. Witt, A. von Hoersten, D. Schroeter, M. TI SEGREGATION OF SIMULATED RFID MARKERS DURING HANDLING AND TRANSPORT OF WHEAT SO TRANSACTIONS OF THE ASABE DT Article DE Bulk good; Computed tomography; Marking; Shaker; Silo; Traceability ID VIBRATION; SYSTEM AB The ability to mark bulk goods from different origins with RFID markers is of industrial interest, as such a method would improve the traceability of, e. g., cereals. However, due to a number of open technical questions, this method has not been utilized on a larger scale yet. This article studies the amount of segregation occurring between RFID markers, which are simulated as grain-sized plastic capsules, and marked wheat using two different setups. This segregation could occur during handling and transport due to the slightly different physical properties of the markers and the grains; it would then lead to erroneous results during subsequent quantitative analysis. In the first experiment, two samples of wheat, one marked with RFID dummies, were discharged in several steps from a test silo. A comparison of the marker concentration in the samples with the amount of associated wheat showed no discernible segregation. An additional statistical analysis allowed us to establish a relationship between the marker concentration and the error margin. In the second experiment, a mixture of wheat and markers was vertically shaken in a container, mimicking transport of wheat in large vessels. The position of the markers inside the container was determined by three-dimensional scans using x-ray tomography. We found that shaking induced some segregation due to sidewall-driven convection rolls, which indicated that the simulated markers were not optimally matched to the wheat grains. C1 [Steinmeier, U.; von Hoersten, D.] Univ Gottingen, Dept Crop Sci, D-37075 Gottingen, Germany. [Neudecker, M.; Witt, A.; Schroeter, M.] Max Planck Inst Dynam & Self Org, Gottingen, Germany. C3 University of Gottingen; Max Planck Society RP Steinmeier, U (corresponding author), Univ Gottingen, Dept Crop Sci, Gutenbergstr 33, D-37075 Gottingen, Germany. EM ulrichsteinmeier@yahoo.de CR Barchi GL, 2002, BIOSYST ENG, V82, P305, DOI 10.1006/bioe.2002.0067 Beplate-Haarstrich L., 2008, THESIS G AUGUST U GO Danzer K, 2001, CHEMOMETRIK Eddy SR, 2004, NAT BIOTECHNOL, V22, P1177, DOI 10.1038/nbt0904-1177 Gelman A., 1995, BAYESIAN DATA ANAL Gutierrez G, 2004, EUROPHYS LETT, V67, P369, DOI 10.1209/epl/i2003-10300-3 HINSCH RT, 1993, T ASAE, V36, P1039, DOI 10.13031/2013.28431 Hirai Y, 2006, APPL ENG AGRIC, V22, P747 Hornbacker R., 2011, RFID AGR PRODUCT FOO Kersten J., 2004, MISCHFUTTERHERSTELLU Kruse M., 2008, HDB SAATGUTAUFBEREIT Mitschke M., 2004, DYNAMIK KRAFTFAHRZEU, V4 Nelson S. O., 2001, 016137 ASAE Nienow A.W., 1997, MIXING PROCESS IND Ottino JM, 2000, ANNU REV FLUID MECH, V32, P55, DOI 10.1146/annurev.fluid.32.1.55 Schneider CA, 2012, NAT METHODS, V9, P671, DOI 10.1038/nmeth.2089 Schroter M, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.011307 SCHULZE D, 2006, PULVER SCHUTTGUTER Schwedes J., 2006, VDI BERICHTE 1918 SC, P103 Stiess M., 2009, MECH VERFAHRENSTECHN, V1 Sui R., 2007, 076032 ASABE Trienekens J, 2006, SAFETY IN THE AGRI-FOOD CHAIN, P439 Tscheuschner H., 1996, GRUNDZUGE LEBENSMITT USDA, 2011, 0211 USDA FG NR 24 TC 0 Z9 0 U1 0 U2 4 PY 2014 VL 57 IS 2 BP 555 EP 563 WC Agricultural Engineering SC Agriculture UT WOS:000335896600017 DA 2022-12-14 ER PT J AU Kim, MY Her, JY Kim, MK Lee, KG AF Kim, Min Yeop Her, Jae-Young Kim, Mina K. Lee, Kwang-Geun TI Formation and reduction of furan in a soy sauce model system SO FOOD CHEMISTRY DT Article; Proceedings Paper CT 2nd Food Integrity and Traceability Conference CY APR 08-10, 2014 CL Queen's Univ, Belfast, NORTH IRELAND HO Queen's Univ DE Furan; Soy sauce; Model system; Fermentation; Food additives ID SPECTROMETRY METHOD; HEADSPACE METHOD; FOOD; FERMENTATION; METHYLFURAN; STRATEGIES; FLAVOR; ACID AB The formation and reduction of furan using a soy sauce model system were investigated in the present study. The concentration of furan fermented up to 30 days increased by 211% after sterilization compared to without sterilization. Regarding fermentation temperature, furan level after 30 days' fermentation was the highest at 30 C (86.21 ng/mL). The furan levels in the soy sauce fermentation at 20 degrees C and 40 degrees C were reduced by 45% and 88%, respectively compared to 30 degrees C fermentation. Five metal ions (iron sulfate, zinc sulfate, manganese sulfate, magnesium sulfate, and calcium sulfate), sodium sulfite, ascorbic acid, dibutyl hydroxyl toluene (BHT), and butylated hydroxyanisole (BHA) were added in a soy sauce model system. The addition of metal ions such as magnesium sulfate and calcium sulfate reduced the furan concentration significantly by 36-90% and 27-91%, respectively in comparison to furan level in the control sample (p < 0.05). Iron sulfate and ascorbic acid increased the furan level at 30 days' fermentation in the soy sauce model system by 278% and 87%, respectively. In the case of the BHT and BHA, furan formation generally was reduced in the soy sauce model system by 84%, 56%, respectively. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Kim, Min Yeop; Her, Jae-Young; Kim, Mina K.; Lee, Kwang-Geun] Dongguk Univ Seoul, Dept Food Sci & Biotechnol, Seoul 100715, South Korea. C3 Dongguk University RP Lee, KG (corresponding author), Dongguk Univ Seoul, Dept Food Sci & Biotechnol, 30,Pildong Ro 1 Gil, Seoul 100715, South Korea. EM kwglee@dongguk.edu CR Anese M, 2013, J AGR FOOD CHEM, V61, P10209, DOI 10.1021/jf305085r Anese M, 2013, FOOD RES INT, V51, P257, DOI 10.1016/j.foodres.2012.12.024 Becalski A, 2005, J AOAC INT, V88, P102 Bolger MP, 2009, PROCESS INDUCED FOOD, P117 Contreras-Calderon J, 2009, FOOD CHEM, V114, P1265, DOI 10.1016/j.foodchem.2008.11.004 Crews C, 2007, TRENDS FOOD SCI TECH, V18, P365, DOI 10.1016/j.tifs.2007.03.006 FDA, 2009, EXPL DAT FUR FOOD Feng YZ, 2013, INT J FOOD SCI TECH, V48, P609, DOI 10.1111/ijfs.12006 International Agency for Research on Cancer, 1995, IARC MON EV CARC RIS, V63, P393 Kim JS, 2008, EUR FOOD RES TECHNOL, V227, P933, DOI 10.1007/s00217-007-0808-4 Kim TK, 2010, FOOD CHEM, V123, P1328, DOI 10.1016/j.foodchem.2010.06.015 Kim TK, 2009, J TOXICOL ENV HEAL A, V72, P1304, DOI 10.1080/15287390903212378 Limacher A, 2008, J AGR FOOD CHEM, V56, P3639, DOI 10.1021/jf800268t Locas CP, 2004, J AGR FOOD CHEM, V52, P6830, DOI 10.1021/jf0490403 Mariotti M, 2012, J AGR FOOD CHEM, V60, P10162, DOI 10.1021/jf3022699 Mark J, 2006, J AGR FOOD CHEM, V54, P2786, DOI 10.1021/jf052937v MFDS, 2014, FOOD ADD COD MFDS-b (Ministry of Food and Drug Safety), 2014, KOR FOOD STAND COD Nie S., 2013, FOOD SCI HUM WELL, V2, P87, DOI DOI 10.1016/J.FSHW.2013.05.001 Owczarek-Fendor A, 2011, J AGR FOOD CHEM, V59, P2368, DOI 10.1021/jf103168s Rufian-Henares JA, 2009, FOOD CHEM, V114, P93, DOI 10.1016/j.foodchem.2008.09.021 van Boekel MAJS, 2006, BIOTECHNOL ADV, V24, P230, DOI 10.1016/j.biotechadv.2005.11.004 Van Lancker F, 2011, J AGR FOOD CHEM, V59, P229, DOI 10.1021/jf102929u Vishwanatha KS, 2010, J IND MICROBIOL BIOT, V37, P129, DOI 10.1007/s10295-009-0654-4 Vranova J, 2007, J FOOD NUTR RES, V46, P123 Wu SJ, 2014, FOOD CHEM TOXICOL, V64, P34, DOI 10.1016/j.fct.2013.11.012 Yaylayan V. A., 2006, Journal fur Verbraucherschutz und Lebensmittelsicherheit, V1, P5, DOI 10.1007/s00003-006-0003-8 Zhang YF, 2009, AFR J BIOTECHNOL, V8, P673 Zoller O, 2007, FOOD ADDIT CONTAM A, V24, P91, DOI 10.1080/02652030701447389 NR 29 TC 12 Z9 12 U1 1 U2 87 PD DEC 15 PY 2015 VL 189 SI SI BP 114 EP 119 DI 10.1016/j.foodchem.2015.02.015 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000358460600017 DA 2022-12-14 ER PT J AU El Sheikha, AF Metayer, I Montet, D AF El Sheikha, Aly Farag Metayer, Isabelle Montet, Didier TI A Biological Bar Code for Determining the Geographical Origin of Fruit by Using 28S rDNA Fingerprinting of Fungal Communities by PCR-DGGE: An Application to Physalis Fruits from Egypt SO FOOD BIOTECHNOLOGY DT Article DE traceability; PCR-DGGE; Physalis; Egypt; fungal communities; origin ID GRADIENT GEL-ELECTROPHORESIS; BACTERIAL; DYNAMICS AB Traceability is now one of the great concerns of the customers and the food regulators. We proposed a new analytical molecular method based on the analysis in a universal way of the microbial communities of food that we linked statistically to its geographical origin. 28S rDNA profiles generated by PCR-DGGE were used to detect the variation in fungal communities of three species of Physalis fruit (Physalis ixocarpa Brot, Physalis pubescens L, and Physalis pruinosa L) from four Egyptian regions (Qalyoubia, Minufiya, Beheira, and Alexandria). The fungal profiles of Physalis from different regions were specific for each location and could be used as a unique biological bar code to discriminate the origin of fruits. To the best of our knowledge, this is the first paper describing a molecular method of fungal ecology to determine the fruit origin by using 28S rDNA fingerprinting of fungi. C1 [El Sheikha, Aly Farag] Menoufia Univ, Fac Agr, Dept Food Sci & Technol, Shibin Al Kawm, Minufiya Govern, Egypt. [El Sheikha, Aly Farag; Metayer, Isabelle; Montet, Didier] Ctr Cooperat Int Rech Agr Dev CIRAD, Montpellier 5, France. C3 Egyptian Knowledge Bank (EKB); Menofia University; CIRAD RP El Sheikha, AF (corresponding author), Menoufia Univ, Fac Agr, Dept Food Sci & Technol, 32511 Shibin El Kom, Shibin Al Kawm, Minufiya Govern, Egypt. EM elsheikha_aly@yahoo.com CR Ampe F, 2001, INT J FOOD MICROBIOL, V65, P45, DOI 10.1016/S0168-1605(00)00502-X [Anonymous], 2007, TRAC FEED FOOD CHAIN *BAYER CROP SCI, 2006, BAYER CROP SCI MAG M ben Omar N, 2000, APPL ENVIRON MICROB, V66, P3664, DOI 10.1128/AEM.66.9.3664-3673.2000 El Sheikha AF, 2009, YEAST, V26, P567, DOI 10.1002/yea.1707 Ercolini D, 2004, J MICROBIOL METH, V56, P297, DOI 10.1016/j.mimet.2003.11.006 Florez AB, 2006, INT J FOOD MICROBIOL, V110, P165, DOI 10.1016/j.ijfoodmicro.2006.04.016 *FOOD STAND AG, 2007, WHAT CONS WANT LIT R Ghidini S., 2006, Annali della Facolta di Medicina Veterinaria, Universita di Parma, V26, P193 Heyndrickx M, 1996, J MICROBIOL METH, V26, P247, DOI 10.1016/0167-7012(96)00916-5 Karakousis A, 2006, J MICROBIOL METH, V65, P38, DOI 10.1016/j.mimet.2005.06.008 Kowalchuk GA, 1997, APPL ENVIRON MICROB, V63, P1489, DOI 10.1128/AEM.63.4.1489-1497.1997 Le Nguyen DD, 2008, FRUITS, V63, P75, DOI 10.1051/fruits:2007049 Li XY, 2008, J ENVIRON SCI-CHINA, V20, P619, DOI 10.1016/S1001-0742(08)62103-8 Montet D., 2008, Aspects of Applied Biology, P11 Montet D., 2004, SEM FOOD SAF INT TRA Montet D, 2010, BIOFUTUR, P36 MUYZER G, 1995, ARCH MICROBIOL, V164, P165, DOI 10.1007/BF02529967 MUYZER G, 1993, APPL ENVIRON MICROB, V59, P695, DOI 10.1128/AEM.59.3.695-700.1993 Olsen M, 2008, WORLD MYCOTOXIN J, V1, P123, DOI 10.3920/WMJ2008.1032 Peres B, 2007, FOOD CONTROL, V18, P228, DOI 10.1016/j.foodcont.2005.09.018 RANDALL RP, 2001, PLANT DATABASE COMPR Roling WFM, 2001, APPL ENVIRON MICROB, V67, P1995, DOI 10.1128/AEM.67.5.1995-2003.2001 SHEFFIELD VC, 1989, P NATL ACAD SCI USA, V86, P232, DOI 10.1073/pnas.86.1.232 Smith CJ., 2005, MOL MICROBIAL ECOLOG, P72 SODEKO OO, 1987, MICROBIOS, V51, P133 van Hannen EJ, 1999, APPL ENVIRON MICROB, V65, P795 Wu ZH, 2002, J ENVIRON MONITOR, V4, P377, DOI 10.1039/b200490a NR 28 TC 22 Z9 22 U1 0 U2 22 PY 2011 VL 25 IS 2 BP 115 EP 129 AR PII 937754400 DI 10.1080/08905436.2011.576556 WC Biotechnology & Applied Microbiology; Food Science & Technology SC Biotechnology & Applied Microbiology; Food Science & Technology UT WOS:000290984500002 DA 2022-12-14 ER PT J AU Dupuy, AM Almeras, M Badiou, S Bargnoux, AS Cristol, JP AF Dupuy, Anne Marie Almeras, Marion Badiou, Stephanie Bargnoux, Anne Sophie Cristol, Jean Paul TI Evaluation of Immunoturbidimetric Albumin Reagent from Diagam on c502/Cobas8000 (R) Analyzer: Comparison with Immunonephelometry and Colorimetric Methods SO CLINICAL LABORATORY DT Article DE albumin; turbidimetric method; consolidation ID SERUM-ALBUMIN; CALCIUM; ASSAY AB Background: Until now, specific proteins have traditionally been analyzed by immunonephelometry requiring specific equipment. Due to laboratory consolidation, turbidimetry is commonly used for the determination of specific proteins. Biochemistry analyzers have a wide range of turbidimetric assays available to a single workstation, which makes them particularly efficient. For our laboratory consolidation, we installed two distinct Cobas8000 (R) modular analyzer series. However, the turbidimetric albumin from Roche did not give satisfactory results in regard to the traceability to the reference measurement material. Methods: Analytical assays of turbidimetric albumin from Diagam (Liege, Belgique) on c502/Cobas8000 (R), including imprecision studies, accuracy using the ERM (R)-DA470k/IFCC, and correlation with nephelometry and colorimetry, were performed. Results: Total precision for the immunoturbidimetric assays was consistently better than 2.3% CV and linear throughout the dynamic range of the assays. Correlation of serum albumin by turbidimetry is in good agreement with nephelometry. The mean +/- SD of the reference material ERM-DA470k was 37.6 +/- 0.25 g/L, with a bias of 1.07%. Conclusions: Turbidimetric albumin from Diagam meets the requirements of accuracy and precision for optimal clinical use and could be an alternative without the associated cost of a dedicated instrument in the context of laboratory consolidation. C1 [Dupuy, Anne Marie; Almeras, Marion; Badiou, Stephanie; Bargnoux, Anne Sophie; Cristol, Jean Paul] CHRU Montpellier, Lapeyronie Hosp, Biochem Lab, Montpellier, France. C3 Universite de Montpellier; CHU de Montpellier RP Cristol, JP (corresponding author), Lapeyronie Hosp, Biochem Lab, 191 Ave Doyen Gaston Giraud, F-34295 Montpellier 5, France. EM jp-cristol@chu-montpellier.fr CR Bland JM, 1999, STAT METHODS MED RES, V8, P135, DOI 10.1177/096228029900800204 Child III CG, 1964, LIVER PORTAL HYPERTE, P90 Clase CM, 2000, NEPHROL DIAL TRANSPL, V15, P1841, DOI 10.1093/ndt/15.11.1841 Clinical and Laboratory Standards Institute (CLSI), 2004, EVALUATION PRECISION Infusino I, 2013, CLIN CHIM ACTA, V419, P15, DOI 10.1016/j.cca.2013.01.005 Kato A, 2011, THER APHER DIAL, V15, P540, DOI 10.1111/j.1744-9987.2011.00997.x Kidney Disease: Improving Global Outcomes (KDIGO) CKD-MBD Work Group, 2009, Kidney Int Suppl, pS1, DOI 10.1038/ki.2009.188 Labriola L, 2009, NEPHROL DIAL TRANSPL, V24, P1834, DOI 10.1093/ndt/gfn747 MABUCHI H, 1987, CLIN CHIM ACTA, V167, P89, DOI 10.1016/0009-8981(87)90089-1 PAYNE RB, 1973, BMJ-BRIT MED J, V4, P636 SPEICHER CE, 1978, AM J CLIN PATHOL, V69, P347 NR 11 TC 0 Z9 0 U1 1 U2 4 PY 2014 VL 60 IS 10 BP 1769 EP 1773 DI 10.7754/Clin.Lab.2014.131120 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000350097600024 DA 2022-12-14 ER PT J AU Watanabe, T Kato, K Maeda, T AF Watanabe, Takuro Kato, Kenji Maeda, Tsuneaki TI Novel Method for the Determination of SI-Traceable Characteristic Values of Organic Standard Materials Using Reaction Gas Chromatography SO BUNSEKI KAGAKU DT Article DE reaction gas chromatography; determination; traceable; characteristic value; organic standard material ID ON-COLUMN TRANSESTERIFICATION; DETECTOR RESPONSE FACTORS; GENERATION; IDENTIFICATION; PESTICIDES; COMPOUND; MIXTURES; METHANOL; SYSTEM AB In this article, novel methods based on reaction gas chromatography are proposed to give SI-traceable characteristic values to compounds of interest in an organic standard material. In order to the assure reliability of an analytical value, its traceability is indispensable. Usually, in that case, standard materials whose characteristic values have their own uncertainties are needed to prepare the calibration curve. It is, however, difficult to obtain such standard materials. To prepare such a standard material, it costs a lot of money, and is time consuming. Therefore, a limited number of standard materials are available, and they are expensive. A study was carried out in order to prepare SI-traceable standard materials more simply. The systems that connected one or two parts using a technique for chemical conversion by reactions between a column and a detector were developed. Especially, the system that connected an oxidizing part and a reducing part in series between the column and the flame ionization detector (FID) showed excellent results. In this system, components to be evaluated were separated by the column, changed to carbon dioxide by oxidation, then reduced to methane, and detected with FID as methane. Both reaction efficiencies in the two reaction parts, oxidation to carbon dioxide and reduction to methane, were proved to be 100 %. The proposed method was validated using SI-traceable standard materials. Verification of the reaction efficiencies of the two reaction parts were established using SI-traceable standard materials, too. In this verification, the relation between the atomic concentration of carbon measured as methane and the chromatographic peak area, namely the linear regression was, obtained. At first the system was calibrated using SI-traceable standard materials that did not contain ethane; then, an ethane standard material whose characteristic value was determined by gravimetry was measured. The obtained value was SI-traceable. This value and the characteristic value were in agreement with each other. The proposed method can be applied to organic compounds that consist of three elements: carbon, hydrogen, and oxygen. SI-traceable values of the compounds of interest in the organic standard materials can be obtained without each target compounds' standard materials by this method. C1 [Watanabe, Takuro; Kato, Kenji] Natl Inst Adv Ind Sci & Technol, NMIJ, Organ Analyt Chem Div, Gas Stand Sect, Tsukuba, Ibaraki 3058563, Japan. [Maeda, Tsuneaki] Natl Inst Adv Ind Sci & Technol, NMIJ, Metrol Management Ctr, Reference Mat Off, Tsukuba, Ibaraki 3058563, Japan. C3 National Institute of Advanced Industrial Science & Technology (AIST); National Metrology Institute of Japan; National Institute of Advanced Industrial Science & Technology (AIST); National Metrology Institute of Japan RP Watanabe, T (corresponding author), Natl Inst Adv Ind Sci & Technol, NMIJ, Organ Analyt Chem Div, Gas Stand Sect, AIST Tsukuba Cent 3,1-1-1 Umezono, Tsukuba, Ibaraki 3058563, Japan. EM watanabe-takuro@aist.go.jp CR ACKMAN RG, 1964, J GAS CHROMATOGR, V2, P173, DOI 10.1093/chromsci/2.6.173 [Anonymous], 1997, ISO GUID 43 1 PROF T [Anonymous], 2003, BUNSEKI KAGAKU, V52, P265 [Anonymous], 1996, INT CT JUST Y, V51, P1 [Anonymous], 2001, 61432001 ISO [Anonymous], 2006, ISO GUID 35 REF MAT ASAI RI, 1971, J AGR FOOD CHEM, V19, P396 BEROZA M, 1962, ANAL CHEM, V34, P1801, DOI 10.1021/ac60193a041 COOKE M, 1980, J CHROMATOGR, V193, P437, DOI 10.1016/S0021-9673(00)87745-6 Deming W.E., 1943, STAT ADJUSTMENT DATA DRAWERT F, 1960, ANGEW CHEM INT EDIT, V72, P555, DOI 10.1002/ange.19600721603 Everaert K, 2004, J HAZARD MATER, V109, P113, DOI 10.1016/j.jhazmat.2004.03.019 FREED DJ, 1977, ANAL CHEM, V49, P1544, DOI 10.1021/ac50019a020 FREED DJ, 1977, ANAL CHEM, V49, P139, DOI 10.1021/ac50009a043 Halket JM, 2006, EUR J MASS SPECTROM, V12, P1, DOI 10.1255/ejms.785 HOBO T, 1985, J CHROMATOGR, V330, P131, DOI 10.1016/S0021-9673(01)81969-5 ISHIKAWA K, 1984, J CHROMATOGR, V295, P445, DOI 10.1016/S0021-9673(01)87646-9 ISHIKAWA K, 1983, BUNSEKI KAGAKU, V32, pE321 ISO/IEC 17043, 2010, 17043 ISOIEC JORGENSEN AD, 1990, ANAL CHEM, V62, P683, DOI 10.1021/ac00206a007 Matsumoto N, 2004, METROLOGIA, V41, P178, DOI 10.1088/0026-1394/41/3/011 Milton MJT, 2001, METROLOGIA, V38, P289, DOI 10.1088/0026-1394/38/4/1 MOYE HA, 1973, J AGR FOOD CHEM, V21, P621, DOI 10.1021/jf60188a047 MOYE HA, 1971, J AGR FOOD CHEM, V19, P452, DOI 10.1021/jf60175a022 PERKINS G, 1963, ANAL CHEM, V35, P360, DOI 10.1021/ac60196a028 Perkins G., 1962, GAS CHROMATOGRAPHY, P269 Saito T, 2009, ACCREDIT QUAL ASSUR, V14, P79, DOI 10.1007/s00769-008-0461-z SCANION JT, 1985, J CHROMATOGR SCI, V23, P333, DOI 10.1093/chromsci/23.8.333 SPIVEY JJ, 1987, IND ENG CHEM RES, V26, P2165, DOI 10.1021/ie00071a001 Sternberg J.C., 1962, GAS CHROMATOGRAPHY, P231 THOMPSON CJ, 1962, ANAL CHEM, V34, P154, DOI 10.1021/ac60181a047 TONG HY, 1984, ANAL CHEM, V56, P2124, DOI 10.1021/ac00276a033 TSANG W, 1977, ANAL CHEM, V49, P13, DOI 10.1021/ac50009a011 Watanabe T., 2006, CHROMATOGRAPHY, V27, P49 Watanabe T, 2008, ANAL CHIM ACTA, V619, P26, DOI 10.1016/j.aca.2008.03.059 Watanabe T, 2007, TALANTA, V72, P1655, DOI 10.1016/j.talanta.2007.03.032 NR 36 TC 3 Z9 3 U1 0 U2 6 PD MAR PY 2013 VL 62 IS 3 BP 183 EP 198 DI 10.2116/bunsekikagaku.62.183 WC Chemistry, Analytical SC Chemistry UT WOS:000316829600001 DA 2022-12-14 ER PT J AU Chen, JB Cao, XL Fu, HC Lam, A AF Chen Jinbo Cao Xiangliang Fu Han-Chi Lam, Anthony TI Agricultural product monitoring system supported by cloud computing SO CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS DT Article DE Modern agriculture; Internet of things; Cloud computing; Big data; Internet of things cloud platform ID BIG-DATA; INTERNET; THINGS AB In order to fully use Internet of things to solve the agricultural fine production, fertilizer, fine and precise control, full traceability and other bottlenecks, and to solve the quality safety of agricultural products from the source and agriculture environmental pollution, a networking application system for modern agriculture is constructed, and networking intelligent gateway based on open source hardware is designed and developed, which realies the video monitoring function based on motion detection. In addition, basic cloud platform system for modern agriculture network monitoring system is designed and achieved. Based on the RESTful interface service system provided by cloud platform, ExtJs client technology and We Chat re applied in the development and realization of the Demo system of an application layer. As a result, it shows part of application assumption of agriculture network monitoring system, and designs the big data processing and analysis module. What's more, the Hadoop platform is used to achieve massive data processing produced by applications of Internet of things, and combined with machine learning technology, the corresponding model is established. It is concluded that the best solution is given such as crop variety selection, production and cultivation management and time to market. C1 [Chen Jinbo; Cao Xiangliang; Fu Han-Chi] Hubei Univ Econ, Res Ctr Hubei Logist Dev, Wuhan, Hubei, Peoples R China. [Lam, Anthony] Katholieke Univ Leuven, Fac Econ & Business, Leuven, Belgium. C3 Hubei University of Economics; KU Leuven RP Fu, HC (corresponding author), Hubei Univ Econ, Res Ctr Hubei Logist Dev, Wuhan, Hubei, Peoples R China. EM chenjinbo@hbue.edu.cn; ccdrea@163.com; frankfu0318@126.com; anthonyvlam@gmail.com CR Botta A, 2016, FUTURE GENER COMP SY, V56, P684, DOI 10.1016/j.future.2015.09.021 Carolan M, 2017, SOCIOL RURALIS, V57, P135, DOI 10.1111/soru.12120 Fyhn K, 2011, IEEE T SIGNAL PROCES, V59, P4225, DOI 10.1109/TSP.2011.2159499 Gill SS, 2017, J ORGAN END USER COM, V29, P1, DOI 10.4018/JOEUC.2017100101 Ibrahim SS, 2016, PASTORALISM, V6, DOI 10.1186/s13570-016-0055-z Nobre GC, 2017, SCIENTOMETRICS, V111, P463, DOI 10.1007/s11192-017-2281-6 Olinde L, 2015, WATER RESOUR RES, V51, P7572, DOI 10.1002/2014WR016120 Puthal D, 2016, IEEE CLOUD COMPUT, V3, P64, DOI 10.1109/MCC.2016.63 Ranjan R, 2016, IEEE T EMERG TOP COM, V4, P262, DOI 10.1109/TETC.2016.2524219 Rose DP, 2015, IEEE T BIO-MED ENG, V62, P1457, DOI 10.1109/TBME.2014.2369991 Shi P, 2016, SPRINGERPLUS, V5, DOI 10.1186/s40064-016-3708-x Yan B, 2015, INFORM TECHNOL MANAG, V16, P67, DOI 10.1007/s10799-014-0196-y Zhang DH, 2014, INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND ENGINEERING (ACSE 2014), P1, DOI 10.1109/JSYST.2014.2346625 NR 13 TC 9 Z9 10 U1 6 U2 42 PD JUL PY 2019 VL 22 SU 4 BP S8929 EP S8938 DI 10.1007/s10586-018-2022-5 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science UT WOS:000502007000121 DA 2022-12-14 ER PT J AU van Lint, CL van der Boog, PJM Romijn, FPHTM Schenk, PW van Dijk, S Rovekamp, TJM Kessler, A Siekmann, L Rabelink, TJ Cobbaert, CM AF van Lint, Celine L. van der Boog, Paul J. M. Romijn, Fred P. H. T. M. Schenk, Paul W. van Dijk, Sandra Rovekamp, Ton J. M. Kessler, Anja Siekmann, Lothar Rabelink, Ton J. Cobbaert, Christa M. TI Application of a point of care creatinine device for trend monitoring in kidney transplant patients: fit for purpose? SO CLINICAL CHEMISTRY AND LABORATORY MEDICINE DT Article DE creatinine; kidney transplantation; metrological traceability; reference standardization; self-monitoring; test purpose ID RANDOMIZED CONTROLLED-TRIAL; COST-EFFECTIVENESS ANALYSIS; SELF-MANAGEMENT; ORAL ANTICOAGULATION; HYPERTENSION TASMINH2; ECONOMIC-EVALUATION; WARFARIN THERAPY; EXPERT-SYSTEM; METAANALYSIS; ANALYZER AB Background: The StatSensor (R) Xpress-i (TM), a point-of-care system for blood creatinine measurement, offers patients the possibility of self-monitoring creatinine. In this study, the analytical performance of the StatSensor (R) for both detecting current renal function and monitoring renal (dys) function in kidney transplant patients was examined. Methods: Accuracy of the StatSensor (R) with capillary and venous whole blood was evaluated and compared to an isotopic dilution mass spectrometry (IDMS)-traceable enzymatic creatinine test in venous serum (n = 138). Twenty Li-heparin samples were compared to the IDMS reference method performed by a Joint Committee for Traceability in Laboratory Medicine (JCTLM)-listed reference laboratory (RfB, Bonn, Germany). To evaluate StatSensor (R)'s suitability to monitor kidney function, both venous and capillary samples were obtained in 20 hospitalized transplantation patients. Venous samples were analyzed with an IDMS-traceable enzymatic test, capillary samples were measured using the StatSensor (R). For all 2-day intervals, percentage change in creatinine was compared between both methods. Results: The StatSensor (R) did not meet total allowable error criterion of 6.9%. Average overall CV a for the StatSensor (R) was 10.4% and 5.2% for capillary and venous whole blood results, respectively. Overall CV a for the central laboratory serum creatinine method was <1.5%. For monitoring renal (dys) function, total agreement of the StatSensor (R) with an IDMS-traceable enzymatic test was 68% using a 10%. change. No significant differences were found between the changes observed by both methods. Conclusions: Capillary blood testing with the StatSensor (R) is not advisable for determining current renal function with a single creatinine measurement in kidney transplant patients, mainly due to excessive analytical imprecision. However, our results suggest that capillary blood testing with the StatSensor (R) can be used for daily trend monitoring of kidney function after renal transplantation. C1 [Romijn, Fred P. H. T. M.; Schenk, Paul W.; Cobbaert, Christa M.] Leiden Univ, Med Ctr, Dept Clin Chem & Lab Med, NL-2300 RC Leiden, Netherlands. [van Lint, Celine L.; van der Boog, Paul J. M.; van Dijk, Sandra; Rabelink, Ton J.] Leiden Univ, Med Ctr, Dept Nephrol, NL-2300 RC Leiden, Netherlands. [Rovekamp, Ton J. M.] Dutch Org Appl Sci Res TNO, Dept Technol Healthcare Prevent & Hlth, Leiden, Netherlands. [Kessler, Anja; Siekmann, Lothar] Univ Hosp Bonn, Inst Clin Biochem & Pharmacol, Bonn, Germany. C3 Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; University of Bonn RP Cobbaert, CM (corresponding author), Leiden Univ, Med Ctr, Dept Clin Chem & Lab Med, POB 9600 Zone E2-P, NL-2300 RC Leiden, Netherlands. EM C.M.Cobbaert@lumc.nl CR Agarwal R, 2011, HYPERTENSION, V57, P29, DOI 10.1161/HYPERTENSIONAHA.110.160911 Amuzie R, 2012, BRIT J GEN PRACT, V62, P68, DOI [10.3399/bjgp12X625049, 10.3399/bjgp12X625201] Aumatell A, 2010, POINT CARE, V9, P25, DOI 10.1097/POC.0b013e3181d2d8a5 Barnard Katharine D, 2010, BMC Res Notes, V3, P318, DOI 10.1186/1756-0500-3-318 Bloomfield HE, 2011, ANN INTERN MED, V154, DOI 10.7326/0003-4819-154-7-201104050-00005 Braun S, 2009, ANAL BIOANAL CHEM, V393, P1463, DOI 10.1007/s00216-008-2225-3 Craig Joyce A, 2014, J Med Econ, V17, P184, DOI 10.3111/13696998.2013.877468 Cromheecke ME, 2000, LANCET, V356, P97, DOI 10.1016/S0140-6736(00)02470-3 Fraser CG., 2002, BIOL VARIATION PRINC Gadisseur APA, 2004, J THROMB HAEMOST, V2, P584, DOI 10.1111/j.1538-7836.2004.00659.x Gardiner C, 2009, J CLIN PATHOL, V62, P168, DOI 10.1136/jcp.2008.059634 Gerkens S, 2012, J THROMB THROMBOLYS, V34, P300, DOI 10.1007/s11239-012-0715-9 Glynn LG, 2010, BRIT J GEN PRACT, V60, DOI 10.3399/bjgp10X544113 Green BB, 2008, JAMA-J AM MED ASSOC, V299, P2857, DOI 10.1001/jama.299.24.2857 Haneder S, 2012, WORLD J RADIOL, V4, P328, DOI 10.4329/wjr.v4.i7.328 Heneghan C, 2012, LANCET, V379, P322, DOI 10.1016/S0140-6736(11)61294-4 Hood L, 2013, GENOME MED, V5, DOI 10.1186/gm514 Horvath AR, 2014, CLIN CHIM ACTA, V427, P49, DOI 10.1016/j.cca.2013.09.018 Jaana M, 2007, J EVAL CLIN PRACT, V13, P242, DOI 10.1111/j.1365-2753.2006.00686.x Kaambwa B, 2014, EUR J PREV CARDIOL, V21, P1517, DOI 10.1177/2047487313501886 Korpi-Steiner NL, 2009, AM J CLIN PATHOL, V132, P920, DOI 10.1309/AJCPTE5FEY0VCGOZ Lafata JE, 2000, J THROMB THROMBOLYS, V9, pS13, DOI 10.1023/A:1018704318655 McManus R, 2010, LANCET, V376, P163, DOI 10.1016/S0140-6736(10)60964-6 Medical Advisory Secretariat, 2009, Ont Health Technol Assess Ser, V9, P1 O'Shea SI, 2008, J THROMB THROMBOLYS, V26, P14, DOI 10.1007/s11239-007-0068-y Ryan F, 2009, J THROMB HAEMOST, V7, P1284, DOI 10.1111/j.1538-7836.2009.03497.x Sawicki PT, 2003, J INTERN MED, V254, P515, DOI 10.1046/j.1365-2796.2003.01215.x Schaefer HM, 2012, BLOOD PURIFICAT, V33, P205, DOI 10.1159/000334158 Shephard M, 2010, CLIN CHEM LAB MED, V48, P1113, DOI 10.1515/CCLM.2010.238 Sicotte C, 2011, TELEMED E-HEALTH, V17, P95, DOI 10.1089/tmj.2010.0142 Straseski JA, 2011, CLIN CHEM, V57, P1566, DOI 10.1373/clinchem.2011.165480 van der Meer V, 2010, RESP RES, V11, DOI 10.1186/1465-9921-11-74 Van Gaalen JL, 2012, CURR OPIN ALLERGY CL, V12, P235, DOI 10.1097/ACI.0b013e3283533700 Yeung MY., OVERVIEW CARE ADULT NR 34 TC 16 Z9 16 U1 0 U2 9 PD SEP PY 2015 VL 53 IS 10 BP 1547 EP 1556 DI 10.1515/cclm-2014-0932 WC Medical Laboratory Technology SC Medical Laboratory Technology UT WOS:000360851500020 DA 2022-12-14 ER PT J AU Amodio, ML Chaudhry, MMA Colelli, G AF Amodio, Maria Luisa Chaudhry, Muhammad Mudassir Arif Colelli, Giancarlo TI Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops SO AGRONOMY-BASEL DT Review DE origin; variety; time of harvest; production system; discriminant analysis ID NEAR-INFRARED SPECTROSCOPY; NIR SPECTROSCOPY; CLASSIFICATION; DISCRIMINATION; VARIETIES; WINES; AUTHENTICATION; IDENTIFICATION; PREDICT; GRAPES AB Nowadays, consumer awareness of the impact of site of origin and method of production on the quality and safety of foods, and particularly of fresh produce, is driving the research towards developing various techniques to assist present certifications, traceability, and audit procedures. With regard to horticultural produce, consumer preferences have shifted to fruit and vegetables, which are healthy and ecologically produced, and toward processed foods having sustainable or social certifications and with sites of origin clearly reported on the label. Some recent studies demonstrate the potentiality of near infrared (NIR) technology (including hyperspectral imaging) for discriminating fresh and processed horticultural products based on their composition, quality attributes, and origin. These studies principally mention that each biological tissue possesses a fingerprint NIR spectrum, which consists of a unique and characteristic pattern of radiation, distinguishing a particular biological tissue from physically and/or chemically different samples. Particularly, recent studies discriminated apples, wine, wheat kernels, and derived flours based on their geographical origins. Spectral information allowed discrimination among growing methods (organic and conventional) for asparagus and strawberry fruits, and among harvest dates for fennels, table grapes, and artichokes. Moreover, information about freshness and storage days after minimal processing can be obtained. Recent literature and original results will be discussed. From our perspective, present results suggest that these techniques may have a potentiality to increase information about product history, but if and only if the variability captured by the classification models is vast in terms of diverse samples belonging to various cultivars, varieties, harvest times, cultural practices, geographical origins, storage conditions, and maturity stages, while being used as a complementary method to the conventional ones-either to make an initial screening of critical features, or to add to the amount of available information. Lacking the inclusion of these parameters could result in good classification results, but the reliability of the classification in this case would be dubious in terms of assessment of the factor contributing towards correct classification. C1 [Amodio, Maria Luisa; Chaudhry, Muhammad Mudassir Arif; Colelli, Giancarlo] Univ Foggia, Dept Agr Food & Environm, Via Napoli 25, I-71122 Foggia, Italy. C3 University of Foggia RP Amodio, ML (corresponding author), Univ Foggia, Dept Agr Food & Environm, Via Napoli 25, I-71122 Foggia, Italy. EM marialuisa.amodio@unifg.it; mudassir.chaudhry@unifg.it; giancarlo.colelli@unifg.it CR Amodio ML, 2017, COMPUT ELECTRON AGR, V134, P1, DOI 10.1016/j.compag.2017.01.005 Amodio ML, 2017, POSTHARVEST BIOL TEC, V125, P112, DOI 10.1016/j.postharvbio.2016.11.013 Berardinelli A, 2010, J FOOD SCI, V75, pE462, DOI 10.1111/j.1750-3841.2010.01741.x Bona E, 2017, LWT-FOOD SCI TECHNOL, V76, P330, DOI 10.1016/j.lwt.2016.04.048 Camps C, 2009, LWT-FOOD SCI TECHNOL, V42, P1125, DOI 10.1016/j.lwt.2009.01.015 Consonni R, 2010, J AGR FOOD CHEM, V58, P7520, DOI 10.1021/jf100949k Cozzolino D, 2009, FOOD CHEM, V116, P761, DOI 10.1016/j.foodchem.2009.03.022 Devos O, 2014, FOOD CHEM, V148, P124, DOI 10.1016/j.foodchem.2013.10.020 ElMasry G., 2010, HYPERSPECTRAL IMAGIN, P175, DOI DOI 10.1016/B978-0-12-374753-2.10006-1 ElMasry G, 2009, POSTHARVEST BIOL TEC, V52, P1, DOI 10.1016/j.postharvbio.2008.11.008 Fernandez-Novales J, 2009, INT J FOOD SCI NUTR, V60, P265, DOI 10.1080/09637480903093116 Fu X, 2007, T ASABE, V50, P1355, DOI 10.13031/2013.23613 Garcia DD, 1998, VITIS, V37, P181 Guo Z., 2013, SENSING AGR FOOD QUA He Y, 2006, SPECTROSC SPECT ANAL, V26, P850 Gonzalez-Martin MI, 2014, FOOD CHEM, V145, P802, DOI 10.1016/j.foodchem.2013.08.103 Jaren C, 2006, INT J INFRARED MILLI, V27, P391, DOI 10.1007/s10762-006-9076-9 Khanmohammadi M, 2014, J FOOD ENG, V142, P17, DOI 10.1016/j.jfoodeng.2014.06.003 Laroussi-Mezghani S, 2016, EUR FOOD RES TECHNOL, V242, P1087, DOI 10.1007/s00217-015-2613-9 Li XL, 2007, J FOOD ENG, V81, P357, DOI 10.1016/j.jfoodeng.2006.10.033 Lorente D, 2012, FOOD BIOPROCESS TECH, V5, P1121, DOI 10.1007/s11947-011-0725-1 Luo WQ, 2011, FOOD CHEM, V128, P555, DOI 10.1016/j.foodchem.2011.03.065 Marais J., 2017, S AFR J ENOL VITIC, V2, P45, DOI [10.21548/2-2-2395, DOI 10.21548/2-2-2395] Azcarate SM, 2013, J FOOD SCI, V78, pC432, DOI 10.1111/1750-3841.12060 Marquetti I, 2016, COMPUT ELECTRON AGR, V121, P313, DOI 10.1016/j.compag.2015.12.018 Oliveri P, 2011, FOOD CHEM, V125, P1450, DOI 10.1016/j.foodchem.2010.10.047 Perez-Marin D, 2010, J FOOD ENG, V99, P294, DOI 10.1016/j.jfoodeng.2010.03.002 Piazzolla F., 2013, Journal of Agricultural Engineering, V44, pe7 Piazzolla F, 2017, J AGRIC ENG, V48, P109, DOI 10.4081/jae.2017.639 Reid LM, 2006, TRENDS FOOD SCI TECH, V17, P344, DOI 10.1016/j.tifs.2006.01.006 Roberts BT, 2004, CELL CYCLE, V3, P100 Sanchez MT, 2013, POSTHARVEST BIOL TEC, V85, P116, DOI 10.1016/j.postharvbio.2013.05.008 Sanchez MT, 2012, J FOOD ENG, V110, P102, DOI 10.1016/j.jfoodeng.2011.12.003 Shrestha S, 2015, SENSORS-BASEL, V15, P4496, DOI 10.3390/s150204496 Sorensen KM, 2016, CURR OPIN FOOD SCI, V10, P45, DOI 10.1016/j.cofs.2016.08.001 Stella E, 2013, J AGRIC ENG, V44, P274, DOI 10.4081/jae.2013.s2.e55 Su WH, 2016, COMPUT ELECTRON AGR, V125, P113, DOI 10.1016/j.compag.2016.04.034 Sun MJ, 2017, FOOD CHEM, V218, P413, DOI 10.1016/j.foodchem.2016.09.023 Suphamitmongkol W, 2013, COMPUT ELECTRON AGR, V91, P87, DOI 10.1016/j.compag.2012.11.014 Tian Y, 2017, SPECTROCHIM ACTA B, V135, P91, DOI 10.1016/j.sab.2017.07.003 Trienekens J, 2008, INT J PROD ECON, V113, P107, DOI 10.1016/j.ijpe.2007.02.050 Yang KS, 2010, ADV MATER RES-SWITZ, V108-111, P262, DOI 10.4028/www.scientific.net/AMR.108-111.262 Yoon SC, 2011, COMPUT ELECTRON AGR, V79, P159, DOI 10.1016/j.compag.2011.09.008 Zhao HY, 2013, FOOD CHEM, V138, P1902, DOI 10.1016/j.foodchem.2012.11.037 NR 44 TC 6 Z9 6 U1 9 U2 24 PD JAN PY 2020 VL 10 IS 1 AR 7 DI 10.3390/agronomy10010007 WC Agronomy; Plant Sciences SC Agriculture; Plant Sciences UT WOS:000513232600007 DA 2022-12-14 ER PT J AU Murato, Y Hayama, Y Shimizu, Y Sawai, K Yamaguchi, E Yamamoto, T AF Murato, Yoshinori Hayama, Yoko Shimizu, Yumiko Sawai, Kotaro Yamaguchi, Emi Yamamoto, Takehisa TI Region-wise analysis of dairy cow movements in Japan SO BMC VETERINARY RESEARCH DT Article DE Animal infectious diseases; Cattle movement; Japan; Traceability system ID CATTLE MOVEMENTS; BRITAIN AB Background Animal movement is considered the most significant factor in the transmission of infectious diseases in livestock. A better understanding of its effects would help provide a more reliable estimation of the disease spread and help develop effective control measures. If the movement pattern is heterogeneous, its characteristics should be considered in epidemiological analyses, such as when using simulation models to obtain reliable outputs. In Japan, following the bovine spongiform encephalopathy epidemic, a traceability system for cattle was established in 2003, and the registration of all cattle movements in the national database began. This study is the first to analyze cattle movements in Japan. We examined regional and seasonal heterogeneity in dairy cow movements, which accounted for most Japanese breeding cattle. Results In the 14 years from April 2005 to March 2018, 4,577,709 between-farm movements of dairy cows were recorded, and the number of movements was counted by month and age for both inter- and intra-regional movements. As a result, two characteristic round-trip movements were observed: one was non-seasonal and inter-regional movements related to cattle-breeding ranches in Hokkaido (the northern region of Japan), which consists of the movement of cows around ages 6 to 8 and 21 to 23 months old. In addition, the seasonal movement of heifers for summer grazing within Hokkaido occurred in May and October at the peak ages of 13 to 14 and 19 to 20 months old, respectively. The observed heterogeneity seemed to reflect the suitability of raising the Holstein breed in Hokkaido and the shortage of supply of replacement heifers and available farming areas outside Hokkaido. Conclusions Understanding the patterns of dairy cow movements will help develop reliable infectious disease models and be beneficial for developing effective control measures against these diseases. C1 [Murato, Yoshinori; Hayama, Yoko; Shimizu, Yumiko; Sawai, Kotaro; Yamaguchi, Emi; Yamamoto, Takehisa] Natl Agr & Food Res Org, Epidemiol Unit, Natl Inst Anim Hlth, Tsukuba, Ibaraki, Japan. C3 National Agriculture & Food Research Organization - Japan; National Institute of Animal Health - Japan RP Yamamoto, T (corresponding author), Natl Agr & Food Res Org, Epidemiol Unit, Natl Inst Anim Hlth, Tsukuba, Ibaraki, Japan. EM mtbook@affrc.go.jp CR Akagami M, 2020, RES VET SCI, V129, P187, DOI 10.1016/j.rvsc.2020.02.001 Artificial Insemination Association of Japan, 2020, SIT INS JAP BLACK SE Aznar MN, 2011, PREV VET MED, V98, P119, DOI 10.1016/j.prevetmed.2010.11.004 Baptista Filipa Matos, 2007, Vet Ital, V43, P611 BERMAN A, 1985, J DAIRY SCI, V68, P1488, DOI 10.3168/jds.S0022-0302(85)80987-5 Cleveland R.B., 1990, J OFF STAT, V6, P3, DOI DOI 10.1016/J.SOILBIO.2009.09.001 Gibbens JC, 2001, VET REC, V149, P729, DOI 10.1136/vr.149.24.729 Gilbert M, 2005, NATURE, V435, P491, DOI 10.1038/nature03548 HOKUREN Federation of Agricultural Cooperatives, 2021, LIV MARK INF HOKUR Hyndman RJ, 2008, J STAT SOFTW, V27, P1, DOI 10.18637/jss.v027.i03 Iglesias RM, 2015, AUST VET J, V93, P394, DOI 10.1111/avj.12377 Livestock Improvement Association of Japan, 2020, SUMM DAIRY PERF TEST Ministry of Agriculture Forestry and Fisheries., 2020, SIT REG PUBL RANCH G Ministry of Agriculture Forestry and Fisheries., 2019, STAT CULT LAND PLANT Mitchell A, 2005, ANIM SCI, V80, P265, DOI 10.1079/ASC50020265 Palisson A, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0152578 Presi P, 2011, PREV VET MED, V99, P112, DOI 10.1016/j.prevetmed.2011.01.012 R Core Team, 2021, R R PROJECT STAT COM, DOI DOI 10.1007/978-3-540-74686-7 Roach C, 2021, J FORECASTING, V40, P1118, DOI 10.1002/for.2750 Sakatani M., 2015, Japanese Journal of Large Animal Clinics, V5, P238 Sugiura Katsuaki, 2008, Vet Ital, V44, P519 Vidondo B, 2018, BMC VET RES, V14, DOI 10.1186/s12917-018-1406-3 NR 22 TC 0 Z9 0 U1 0 U2 1 PD SEP 9 PY 2021 VL 17 IS 1 AR 305 DI 10.1186/s12917-021-03008-3 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000694887600002 DA 2022-12-14 ER PT J AU Martin, NA Ferracci, V Cassidy, N Hook, J Battersby, RM di Meane, EA Tang, YS Stephens, ACM Leeson, SR Jones, MR Braban, CF Gates, L Hangartner, M Stoll, JM Sacco, P Pagani, D Hoffnagle, JA Seitler, E AF Martin, Nicholas A. Ferracci, Valerio Cassidy, Nathan Hook, Josh Battersby, Ross M. di Meane, Elena Amico Tang, Yuk S. Stephens, Amy C. M. Leeson, Sarah R. Jones, Matthew R. Braban, Christine F. Gates, Linda Hangartner, Markus Stoll, Jean-Marc Sacco, Paolo Pagani, Diego Hoffnagle, John A. Seitler, Eva TI Validation of ammonia diffusive and pumped samplers in a controlled atmosphere test facility using traceable Primary Standard Gas Mixtures SO ATMOSPHERIC ENVIRONMENT DT Article DE Diffusive sampling rates; Denuders; Ammonia; Metrological traceability ID NITROGEN DEPOSITION; MITIGATION; TRENDS; AIR AB We report the determination of ammonia (NH3) diffusive sampling rates for six different designs of commercial diffusive samplers (CEH ALPHA sampler, Gradko diffusion tube, Gradko DIFRAM-400, Passam ammonia sampler, and ICS Maugeri Radiello radial sampler (blue and white turbulence barriers)), together with the validation test results for a pumped sampler (CEH DELTA denuder). The devices were all exposed in the UK's National Physical Laboratory's (NPL) controlled atmosphere test facility (CATFAC). For each of the seven diffusive sampler exposure tests there were traceable concentrations of ammonia (in the range 3-25 mu g m(-3)) generated under well-defined conditions of temperature, relative humidity and wind speed, which are applicable to a variety of ambient monitoring environments. The sampler exposure time at each concentration was 28 days, except for the radial devices, which were exposed for 14 days. The work relied on the dilution of newly developed stable Primary Standard Gas Mixtures (PSMs) prepared by gravimetry in passivated gas cylinders as a method of improving the metrological traceability of ammonia measurements. The exposed diffusive samplers were sent blind to the participants for analysis and the reported NH3 concentrations were then compared against the known reference concentration. From the results for each sampler type a diffusive sampling rate was calculated and compared against the rate used routinely by the participants. Some measurement results were in good agreement with the known traceable reference concentration (particularly for one diffusive sampler design (ALPHA)), while other devices exhibited over-reading and under-reading (each with a clear bias). The new diffusive sampling rates determined in the laboratory study were then applied to measurements in a field comparison campaign, and this was found to deliver an improvement in agreement between the different devices deployed. C1 [Martin, Nicholas A.; Ferracci, Valerio; Cassidy, Nathan; Hook, Josh; Battersby, Ross M.; di Meane, Elena Amico] Natl Phys Lab, Chem Med & Environm Sci Dept, Hampton Rd, Teddington TW11 0LW, Middx, England. [Tang, Yuk S.; Stephens, Amy C. M.; Leeson, Sarah R.; Jones, Matthew R.; Braban, Christine F.] Ctr Ecol & Hydrol, Bush Estate, Penicuik EH26 0QB, Midlothian, Scotland. [Gates, Linda] Gradko Int Ltd, 77 Wales St, Winchester SO23 0RH, Hants, England. [Hangartner, Markus; Stoll, Jean-Marc] Passam AG, Schellenstr 44, CH-8708 Mannedorf, Switzerland. [Sacco, Paolo; Pagani, Diego] ICSM, Via Atene 9, I-35010 Vigonza, Italy. [Hoffnagle, John A.] Picarro Inc, 3015 Patrick Henry Dr, Santa Clara, CA 95054 USA. [Seitler, Eva] FUB Res Grp Environm Monitoring AG, Alte Jonastr 83, CH-8640 Rapperswil, Switzerland. [Ferracci, Valerio] Cranfield Univ, Ctr Environm & Agr Informat, Coll Rd, Cranfield MK43 0AL, Beds, England. C3 National Physical Laboratory - UK; UK Centre for Ecology & Hydrology (UKCEH); Cranfield University RP Martin, NA (corresponding author), Natl Phys Lab, Chem Med & Environm Sci Dept, Hampton Rd, Teddington TW11 0LW, Middx, England. EM nick.martin@npl.co.uk CR Air Liquide/Scott, 2012, AC REG NR 4104439 [Anonymous], ISO GUID 98 3 EV MEA [Anonymous], 2010, 61457 ISO [Anonymous], 16339201311 EN [Anonymous], 2010, METROLOGIA S, DOI DOI 10.1088/0026-1394/47/1A/08023 Bessagnet B, 2014, ENVIRON SCI POLICY, V44, P149, DOI 10.1016/j.envsci.2014.07.011 BOC plc, 2010, SPECTR SEAL REG NR 3 Braban C, 2018, LIT REV PERFORMANCE Cape JN, 2009, ATMOSPHERIC AMMONIA, P375, DOI 10.1007/978-1-4020-9121-6_22 Erisman JW, 2008, NAT GEOSCI, V1, P636, DOI 10.1038/ngeo325 European Environment Agency, 2017, AIR POLL AGR AMM EXC FERM M, 1979, ATMOS ENVIRON, V13, P1385, DOI 10.1016/0004-6981(79)90107-0 Ferracci V, 2015, J CHROMATOGR A, V1383, P144, DOI 10.1016/j.chroma.2015.01.030 G. International Organisation for Standardization, 2001, 6142 ISO Hornung M, 1995, UNECE WORKSH GRANG O, P207 Leith I., 2004, WATER AIR SOIL POLL, V4, P207, DOI DOI 10.1007/S11267-004-3031-3 Martin NA, 2003, ATMOS ENVIRON, V37, P871, DOI 10.1016/S1352-2310(02)01000-2 Martin NA, 2016, APPL PHYS B-LASERS O, V122, DOI 10.1007/s00340-016-6486-9 Martin NA, 2014, ATMOS ENVIRON, V94, P529, DOI 10.1016/j.atmosenv.2014.05.064 Massman WJ, 1998, ATMOS ENVIRON, V32, P1111, DOI 10.1016/S1352-2310(97)00391-9 Pinho P, 2012, BIOGEOSCIENCES, V9, P1205, DOI 10.5194/bg-9-1205-2012 Pitcairn CER, 1998, ENVIRON POLLUT, V102, P41, DOI 10.1016/S0269-7491(98)80013-4 Pogany A, 2015, METROLOGY AMMONIA AM, P1, DOI [10.1051/metrology/201507003, DOI 10.1051/METROLOGY/201507003] Pogany A, 2016, MEAS SCI TECHNOL, V27, DOI 10.1088/0957-0233/27/11/115012 SilcoTek Inc. SilcoNertT, 2000, US Patent, Patent No. [6,511,760, 6511760] SilcoTek Inc. SilcoNertT, 2000, US Patent, Patent No. [6,444,326, 6444326] Smith I., 2010, MS11 NPL Stephens Amy, 2017, C05204 NERC CEH Sutton M. A., 2001, WATER AIR SOIL POLL, V1, P145, DOI DOI 10.1023/A:1013138601753 TA Luft, 2002, ERST ALLG VERW ZUM B Tang Y S, 2001, ScientificWorldJournal, V1, P513 Tang YS, 2018, ATMOS CHEM PHYS, V18, P705, DOI 10.5194/acp-18-705-2018 US EPA, 2001, INT RISK INF SYST van Zanten MC, 2017, ATMOS ENVIRON, V148, P352, DOI 10.1016/j.atmosenv.2016.11.007 VDI (Verein Deutscher Ingeniure), 2012, MEAS AMM AMB AIR S 4 Vieno M, 2016, ATMOS CHEM PHYS, V16, P265, DOI 10.5194/acp-16-265-2016 Wintergerst E, 1930, ANN PHYS, V396, P323, DOI [10.1002/andp.19303960304, DOI 10.1002/ANDP.19303960304] WordNet, 2016, NON TRADITIONAL REF WYERS GP, 1993, ATMOS ENVIRON A-GEN, V27, P2085, DOI 10.1016/0960-1686(93)90280-C NR 39 TC 9 Z9 9 U1 5 U2 24 PD FEB 15 PY 2019 VL 199 BP 453 EP 462 DI 10.1016/j.atmosenv.2018.11.038 WC Environmental Sciences; Meteorology & Atmospheric Sciences SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences UT WOS:000456639900042 DA 2022-12-14 ER PT J AU Dadousis, C Cecchi, F Ablondi, M Fabbri, MC Stella, A Bozzi, R AF Dadousis, Christos Cecchi, Francesca Ablondi, Michela Fabbri, Maria Chiara Stella, Alessandra Bozzi, Riccardo TI Keep Garfagnina alive. An integrated study on patterns of homozygosity, genomic inbreeding, admixture and breed traceability of the Italian Garfagnina goat breed SO PLOS ONE DT Article ID GENETIC-RESOURCES; POPULATIONS; DOMESTICATION; SIGNATURES; MUTATIONS; SELECTION; RUNS; TOOL AB The objective of this study was to investigate the genetic diversity of the Garfagnina (GRF) goat, a breed that currently risks extinction. For this purpose, 48 goats were genotyped with the Illumina CaprineSNP50 BeadChip and analyzed together with 214 goats belonging to 9 other Italian breeds (similar to 25 goats/breed), whose genotypes were available from the AdaptMap project [Argentata (ARG), Bionda dell'Adamello (BIO), Ciociara Grigia (CCG), Di Teramo (DIT), Garganica (GAR), Girgentana (GGT), Orobica (ORO), Valdostana (VAL) and Valpassiria (VSS)]. Comparative analyses were conducted on i) runs of homozygosity (ROH), ii) admixture ancestries and iii) the accuracy of breed traceability via discriminant analysis on principal components (DAPC) based on cross-validation. ROH analyses was used to assess the genetic diversity of GRF, while admixture and DAPC to evaluate its relationship to the other breeds. For GRF, common ROH (more than 45% in GRF samples) was detected on CHR 12 at, roughly 50.25-50.94Mbp (ARS1 assembly), which spans the CENPJ (centromere protein) and IL17D (interleukin 17D) genes. The same area of common ROH was also present in DIT, while a broader region (similar to 49.25-51.94Mbp) was shared among the ARG, CCG, and GGT. Admixture analysis revealed a small region of common ancestry from GRF shared by BIO, VSS, ARG and CCG breeds. The DAPC model yielded 100% assignment success for GRF. Overall, our results support the identification of GRF as a distinct native Italian goat breed. This work can contribute to planning conservation programmes to save GRF from extinction and will improve the understanding of the socio-agro-economic factors related with the farming of GRF. C1 [Dadousis, Christos; Fabbri, Maria Chiara; Bozzi, Riccardo] Univ Firenze, Dipartimento Sci & Tecnol Agr Alimentari Ambienta, Florence, Italy. [Cecchi, Francesca] Univ Pisa, Dipartimento Sci Vet, Pisa, Italy. [Ablondi, Michela] Univ Parma, Dipartimento Sci Med Vet, Parma, Italy. [Stella, Alessandra] CNR, Ist Biol & Biotecnol Agr, Milan, Italy. C3 University of Florence; University of Pisa; University of Parma; Consiglio Nazionale delle Ricerche (CNR) RP Dadousis, C (corresponding author), Univ Firenze, Dipartimento Sci & Tecnol Agr Alimentari Ambienta, Florence, Italy. EM christos.dadousis@unipr.it CR Ablondi M, 2020, ANIMALS-BASEL, V10, DOI 10.3390/ani10061005 Alexander D., ADMIXTURE VERSION 1 Alexander DH, 2009, GENOME RES, V19, P1655, DOI 10.1101/gr.094052.109 Bertolini F, 2018, GENET SEL EVOL, V50, DOI 10.1186/s12711-018-0424-8 Bertolini F, 2016, MAMM GENOME, V27, P144, DOI 10.1007/s00335-016-9623-1 Bond J, 2005, NAT GENET, V37, P353, DOI 10.1038/ng1539 Cecchi F, 2019, TROP ANIM HEALTH PRO, V51, P729, DOI 10.1007/s11250-018-1728-y Cecchi F, 2018, J APPL ANIM RES, V46, P879, DOI 10.1080/09712119.2017.1417129 Cecchi F, 2017, TROP ANIM HEALTH PRO, V49, P1135, DOI 10.1007/s11250-017-1306-8 Curone G, 2018, RES VET SCI, V116, P88, DOI 10.1016/j.rvsc.2017.11.020 Ding WY, 2019, J NEUROSCI, V39, P1994, DOI 10.1523/JNEUROSCI.1849-18.2018 Domestic Animal Diversity Information System (DAD-IS), 2019, DOM AN DIV INF SYST Duerinckx S, 2018, SEMIN CELL DEV BIOL, V76, P76, DOI 10.1016/j.semcdb.2017.09.015 Faheem M, 2015, BMC MED GENOMICS, V8, DOI 10.1186/1755-8794-8-S1-S4 Feng YP, 2009, ACTA BIOCH BIOPH SIN, V41, P285, DOI 10.1093/abbs/gmp012 Food and Agriculture Organization of the United Nations, GLOB PLAN ACT AN GEN Food and Agriculture Organization of the United Nations, FAOSTAT INT Gomez-Raya L, 2015, GENET SEL EVOL, V47, DOI 10.1186/s12711-015-0153-1 Jombart T, 2008, BIOINFORMATICS, V24, P1403, DOI 10.1093/bioinformatics/btn129 Jombart T, 2010, BMC GENET, V11, DOI 10.1186/1471-2156-11-94 Jombart T, 2011, BIOINFORMATICS, V27, P3070, DOI 10.1093/bioinformatics/btr521 Kim ES, 2016, HEREDITY, V116, P255, DOI 10.1038/hdy.2015.94 Lamartine J, 2000, NAT GENET, V26, P142, DOI 10.1038/79851 Lovreglio R, 2014, IFOREST, V7, P260, DOI 10.3832/ifor1112-007 Marras G, 2015, ANIM GENET, V46, P110, DOI 10.1111/age.12259 Martini M, 2010, J DAIRY SCI, V93, P4659, DOI 10.3168/jds.2010-3207 Mastrangelo S, 2016, ANIMAL, V10, P746, DOI 10.1017/S1751731115002943 McIntyre RE, 2012, PLOS GENET, V8, DOI 10.1371/journal.pgen.1003022 Naderi S, 2008, P NATL ACAD SCI USA, V105, P17659, DOI 10.1073/pnas.0804782105 Naveed M, 2018, GENET RES, V100, DOI 10.1017/S0016672318000046 Paiva SR, 2016, LIVEST SCI, V193, P32, DOI 10.1016/j.livsci.2016.09.010 Pandya A, 2003, GENET MED, V5, P295, DOI 10.1097/01.GIM.0000078026.01140.68 Purcell S, 2007, AM J HUM GENET, V81, P559, DOI 10.1086/519795 R Core Team, 2020, R LANG ENV STAT COMP Ribeiro NL, 2016, SMALL RUMINANT RES, V144, P236, DOI 10.1016/j.smallrumres.2016.10.001 Salari F, 2016, ITAL J ANIM SCI, V15, P568, DOI 10.1080/1828051X.2016.1247658 Samori P, 2019, ADV MATER TECHNOL-US, V4, DOI 10.1002/admt.201900303 Schiavo G, 2020, ANIMAL, V14, P910, DOI 10.1017/S175173111900332X Silversides FG, 2012, BRIT POULTRY SCI, V53, P599, DOI 10.1080/00071668.2012.727383 Sponenberg DP, 2019, DIVERSITY-BASEL, V11, DOI 10.3390/d11100177 Stella A, 2018, GENET SEL EVOL, V50, DOI 10.1186/s12711-018-0427-5 Tosser-Klopp G, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0086227 Villanueva B, 2004, BSAS PUBL, V30, P113, DOI 10.1017/S0263967X00041987 Wimmer V, 2012, BIOINFORMATICS, V28, P2086, DOI 10.1093/bioinformatics/bts335 Windig JJ, 2008, ANIMAL, V2, P1733, DOI 10.1017/S1751731108003029 Woolliams JA, 2015, J ANIM BREED GENET, V132, P89, DOI 10.1111/jbg.12148 Woolliams J, 2007, UTILISATION AND CONSERVATION OF FARM ANIMAL GENETIC RESOURCES, P55 Zeder MA, 2000, SCIENCE, V287, P2254, DOI 10.1126/science.287.5461.2254 Zomerdijk F, 2020, BIOPRESERV BIOBANK, V18, P244, DOI 10.1089/bio.2019.0128 NR 50 TC 8 Z9 8 U1 0 U2 6 PD JAN 15 PY 2021 VL 16 IS 1 AR e0232436 DI 10.1371/journal.pone.0232436 WC Multidisciplinary Sciences SC Science & Technology - Other Topics UT WOS:000609991600042 DA 2022-12-14 ER PT J AU Mazarakioti, EC Zotos, A Thomatou, AA Kontogeorgos, A Patakas, A Ladavos, A AF Mazarakioti, Eleni C. Zotos, Anastasios Thomatou, Anna-Akrivi Kontogeorgos, Achilleas Patakas, Angelos Ladavos, Athanasios TI Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin SO FOODS DT Review DE ICP-MS; geographical origin; authenticity; traceability; agricultural products; foods; beverages ID RARE-EARTH-ELEMENTS; VIRGIN OLIVE OILS; SR-87/SR-86 ISOTOPE RATIO; GEOGRAPHICAL ORIGIN; MULTIELEMENT ANALYSIS; TRACE-ELEMENT; STABLE-ISOTOPE; MULTIVARIATE-ANALYSIS; PNEUMATIC NEBULIZERS; CHEMOMETRIC ANALYSIS AB Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed. C1 [Mazarakioti, Eleni C.; Thomatou, Anna-Akrivi; Patakas, Angelos; Ladavos, Athanasios] Univ Patras, Dept Food Sci & Technol, Agrinion 30100, Greece. [Zotos, Anastasios] Univ Patras, Dept Sustainable Agr, Agrinion 30100, Greece. [Kontogeorgos, Achilleas] Int Hellen Univ, Dept Agr, Thessaloniki 57001, Greece. C3 University of Patras; University of Patras; International Hellenic University RP Mazarakioti, EC; Ladavos, A (corresponding author), Univ Patras, Dept Food Sci & Technol, Agrinion 30100, Greece. EM e.mazarakioti@upnet.gr; alantavo@upatras.gr CR Aceto M., 2015, COMPR ANAL CHEM, VVolume 68, P441, DOI [10.1016/B978-0-444-63340-8.00009-1, DOI 10.1016/B978-0-444-63340-8.00009-1] Aceto M, 2019, FOOD CHEM, V298, DOI 10.1016/j.foodchem.2019.125047 Aceto M, 2018, BEVERAGES, V4, DOI 10.3390/beverages4010023 Aceto M, 2017, J AGR FOOD CHEM, V65, P4200, DOI [10.1021/acs.jafc.7b00916, 10.1021/acs.jafc.7b009] Aceto M, 2013, FOOD CHEM, V138, P1914, DOI 10.1016/j.foodchem.2012.11.019 Acierno V, 2020, FOOD RES INT, V133, DOI 10.1016/j.foodres.2020.109212 Ahmad R, 2020, BIOMED CHROMATOGR, V34, DOI 10.1002/bmc.4772 Albals D, 2021, SCI PROGRESS-UK, V104, DOI 10.1177/00368504211026162 Alexa L, 2018, J MICROB BIOTEC FOOD, V7, P432, DOI 10.15414/jmbfs.2018.7.4.432-436 Penanes PA, 2022, J ANAL ATOM SPECTROM, V37, P701, DOI 10.1039/d2ja00018k Ameeta Sharma, 2017, International Journal for Research in Applied Science and Engineering Technology, V5, P686 Amit, 2022, CURR RES FOOD SCI, V5, P545, DOI 10.1016/j.crfs.2022.03.003 Angus NS, 2006, AUST J GRAPE WINE R, V12, P170, DOI 10.1111/j.1755-0238.2006.tb00057.x [Anonymous], INFLATION IS CHANGIN [Anonymous], 2021, HERBS SPICES [Anonymous], FOOD SAFETY [Anonymous], DETECTION ADULTERATI [Anonymous], FORMATION DOUBLY CHA Arif M, 2021, FOOD CONTROL, V123, DOI 10.1016/j.foodcont.2020.107827 Ariyama K, 2007, J AGR FOOD CHEM, V55, P347, DOI 10.1021/jf062613m Ariyama K, 2012, J AGR FOOD CHEM, V60, P1628, DOI 10.1021/jf204296p Astolfi ML, 2021, FRONT CHEM, V9, DOI 10.3389/fchem.2021.769620 Azcarate SM, 2015, FOOD CONTROL, V57, P268, DOI 10.1016/j.foodcont.2015.04.025 Bandoniene D, 2020, FOOD RES INT, V132, DOI 10.1016/j.foodres.2020.109106 Bandoniene D, 2018, J AGR FOOD CHEM, V66, P11729, DOI 10.1021/acs.jafc.8b03828 Barbaste M, 2001, TALANTA, V54, P307, DOI 10.1016/S0039-9140(00)00651-2 Batista BL, 2012, FOOD RES INT, V49, P209, DOI 10.1016/j.foodres.2012.07.015 Baxter MJ, 1997, FOOD CHEM, V60, P443, DOI 10.1016/S0308-8146(96)00365-2 Beltran M, 2015, FOOD CHEM, V169, P350, DOI 10.1016/j.foodchem.2014.07.104 Benabdelkamel H, 2012, J AGR FOOD CHEM, V60, P3717, DOI 10.1021/jf2050075 Benincasa C, 2008, FOOD CHEM, V110, P257, DOI 10.1016/j.foodchem.2008.01.049 Benincasa C, 2007, ANAL CHIM ACTA, V585, P366, DOI 10.1016/j.aca.2006.12.040 Bertoldi D, 2016, FOOD CONTROL, V65, P46, DOI 10.1016/j.foodcont.2016.01.013 Bettinelli M, 2005, ATOM SPECTROSC, V26, P41 Bong YS, 2012, FOOD CHEM, V135, P2666, DOI 10.1016/j.foodchem.2012.07.045 Bontempo L, 2011, RAPID COMMUN MASS SP, V25, P899, DOI 10.1002/rcm.4935 Bora FD, 2018, NOT BOT HORTI AGROBO, V46, P223, DOI 10.15835/nbha46110853 Borges EM, 2015, FOOD RES INT, V77, P299, DOI 10.1016/j.foodres.2015.06.008 Branch S, 2003, J ANAL ATOM SPECTROM, V18, P17, DOI 10.1039/b207055n Bronzi B, 2020, FOOD CHEM, V315, DOI 10.1016/j.foodchem.2020.126248 Brunner M, 2010, EUR FOOD RES TECHNOL, V231, P623, DOI 10.1007/s00217-010-1314-7 Bua GD, 2017, FOOD ANAL METHOD, V10, P1181, DOI 10.1007/s12161-016-0680-6 Bulska E, 2016, PHILOS T R SOC A, V374, DOI 10.1098/rsta.2015.0369 Camin F, 2015, RAPID COMMUN MASS SP, V29, P415, DOI 10.1002/rcm.7117 Camin F, 2010, J AGR FOOD CHEM, V58, P570, DOI 10.1021/jf902814s Camin F, 2010, FOOD CHEM, V118, P901, DOI 10.1016/j.foodchem.2008.04.059 Carter JA, 2018, FRONT CHEM, V6, DOI 10.3389/fchem.2018.00504 Cellier R, 2022, OENO ONE, V56, P29, DOI 10.20870/oeno-one.2022.56.1.4520 Cheajesadagul P, 2013, FOOD CHEM, V141, P3504, DOI 10.1016/j.foodchem.2013.06.060 Chen H, 2014, J AGR FOOD CHEM, V62, P2443, DOI 10.1021/jf405045q Chen L, 2022, RSC ADV, V12, P16790, DOI 10.1039/d2ra02148j Choi YH, 2017, FOOD SCI BIOTECHNOL, V26, P375, DOI 10.1007/s10068-017-0051-0 Chudzinska M, 2011, FOOD CHEM TOXICOL, V49, P2741, DOI 10.1016/j.fct.2011.08.014 Coelho I, 2019, J FOOD COMPOS ANAL, V77, P1, DOI 10.1016/j.jfca.2018.12.005 Costas-Rodriguez M, 2010, ANAL CHIM ACTA, V664, P121, DOI 10.1016/j.aca.2010.03.003 Crandall PG, 2017, J FOOD PROTECT, V80, P1613, DOI 10.4315/0362-028X.JFP-16-481 Cubadda F, 2006, ANAL BIOANAL CHEM, V384, P887, DOI 10.1007/s00216-005-0256-6 D'Archivio AA, 2019, FOOD ANAL METHOD, V12, P2572, DOI 10.1007/s12161-019-01610-8 da Costa NL, 2020, EUR FOOD RES TECHNOL, V246, P1193, DOI 10.1007/s00217-020-03480-5 da Silva IJS, 2020, FOOD CHEM, V319, DOI 10.1016/j.foodchem.2020.126435 Damak F, 2021, EURO-MEDITERR J ENVI, V6, DOI 10.1007/s41207-021-00241-y Damak F, 2019, FOOD CHEM, V283, P656, DOI 10.1016/j.foodchem.2019.01.082 Danezis GP, 2019, MEAT SCI, V153, P45, DOI 10.1016/j.meatsci.2019.03.007 Danezis GP, 2017, ANAL CHIM ACTA, V991, P46, DOI 10.1016/j.aca.2017.09.013 de Gois JS, 2018, ANAL METHODS-UK, V10, P1656, DOI [10.1039/c8ay00331a, 10.1039/C8AY00331A] de Souza R.M., 2022, J TRACE ELEM MINER, V1, DOI [10.1016/j.jtemin.2022.100003, DOI 10.1016/J.JTEMIN.2022.100003] Debbarma N, 2021, FOOD CHEM, V339, DOI 10.1016/j.foodchem.2020.127896 Di Bella G, 2015, J FOOD COMPOS ANAL, V44, P25, DOI 10.1016/j.jfca.2015.05.003 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Dimitrakopoulou ME, 2021, FOOD REV INT, DOI 10.1080/87559129.2021.1923028 Drivelos SA, 2021, FOOD CHEM, V338, DOI 10.1016/j.foodchem.2020.127936 Drivelos SA, 2016, FOOD CHEM, V213, P238, DOI 10.1016/j.foodchem.2016.06.088 Drivelos SA, 2014, FOOD CHEM, V165, P316, DOI 10.1016/j.foodchem.2014.03.083 Drivelos SA, 2012, TRAC-TREND ANAL CHEM, V40, P38, DOI 10.1016/j.trac.2012.08.003 Du MJ, 2018, INT J FOOD SCI TECH, V53, P2088, DOI 10.1111/ijfs.13795 Eickhorst T, 2004, J CHROMATOGR A, V1050, P103, DOI 10.1016/j.chroma.2004.04.084 Epova EN, 2018, FOOD CHEM, V246, P313, DOI 10.1016/j.foodchem.2017.10.143 Farmaki EG, 2012, ANAL LETT, V45, P920, DOI 10.1080/00032719.2012.655656 Feher I, 2019, J FOOD SCI TECH MYS, V56, P5225, DOI 10.1007/s13197-019-03991-4 Flem B, 2017, FISH RES, V190, P183, DOI 10.1016/j.fishres.2017.02.010 Fortunato G, 2004, J ANAL ATOM SPECTROM, V19, P227, DOI 10.1039/b307068a Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Furia E, 2011, J AGR FOOD CHEM, V59, P8450, DOI 10.1021/jf201556e Gajek M, 2022, FOODS, V11, DOI 10.3390/foods11111616 Gajek M, 2021, MOLECULES, V26, DOI 10.3390/molecules26082186 Gajek M, 2021, MOLECULES, V26, DOI 10.3390/molecules26010214 Galvin-King P, 2018, FOOD CONTROL, V88, P85, DOI 10.1016/j.foodcont.2017.12.031 Garcia-Ruiz S, 2007, ANAL CHIM ACTA, V590, P55, DOI 10.1016/j.aca.2007.03.016 Geana EI, 2017, FOOD ANAL METHOD, V10, P63, DOI 10.1007/s12161-016-0550-2 Geana I, 2013, FOOD CHEM, V138, P1125, DOI 10.1016/j.foodchem.2012.11.104 Grainger C, 2021, FOOD CHEM, V334, DOI 10.1016/j.foodchem.2020.127386 Granell B, 2022, BEVERAGES, V8, DOI 10.3390/beverages8010003 Greenough J. D., 1997, Australian Journal of Grape and Wine Research, V3, P75, DOI 10.1111/j.1755-0238.1997.tb00118.x Gumus ZP, 2017, EUR FOOD RES TECHNOL, V243, P1719, DOI 10.1007/s00217-017-2876-4 Guo LP, 2013, J FOOD SCI, V78, pC1852, DOI 10.1111/1750-3841.12302 Haldimann M, 1998, CLIN CHEM, V44, P817 Hao LZ, 2019, KAFKAS UNIV VET FAK, V25, P93, DOI 10.9775/kvfd.2018.20366 Hao XY, 2021, FOODS, V10, DOI 10.3390/foods10123108 Heaton K, 2008, FOOD CHEM, V107, P506, DOI 10.1016/j.foodchem.2007.08.010 Hopfer H, 2017, BEVERAGES, V3, DOI 10.3390/beverages3010008 Huang CY, 2003, J ANAL ATOM SPECTROM, V18, P951, DOI 10.1039/b303355d Hwang IM, 2019, ANAL LETT, V52, P932, DOI 10.1080/00032719.2018.1508293 Kalogiouri NP, 2021, FOODS, V10, DOI 10.3390/foods10020349 Kalpage M, 2022, FOODS, V11, DOI 10.3390/foods11030275 Kang XM, 2018, FOOD CONTROL, V94, P361, DOI 10.1016/j.foodcont.2018.07.019 Karasinski J, 2018, ANAL LETT, V51, P2643, DOI 10.1080/00032719.2018.1442472 Katerinopoulou K, 2020, FOODS, V9, DOI 10.3390/foods9040489 Kelly S, 2002, EUR FOOD RES TECHNOL, V214, P72, DOI 10.1007/s002170100400 Keyes WR, 2002, J ANAL ATOM SPECTROM, V17, P1153, DOI 10.1039/b202250h Khazaei H, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10040511 Kim JS, 2017, MEAT SCI, V123, P13, DOI 10.1016/j.meatsci.2016.08.011 Kokot S, 1999, ANALYST, V124, P561, DOI 10.1039/a808799g Kongsri S, 2021, FOODS, V10, DOI 10.3390/foods10102349 Kruzlicova D, 2013, FOOD RES INT, V54, P621, DOI 10.1016/j.foodres.2013.07.053 Lafontaine S, 2022, FOOD CHEM, V395, DOI 10.1016/j.foodchem.2022.133543 Laursen KH, 2011, J AGR FOOD CHEM, V59, P4385, DOI 10.1021/jf104928r Laursen KH, 2009, J ANAL ATOM SPECTROM, V24, P1198, DOI 10.1039/b901960j Li Q, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-03275-x Lima MMM, 2021, FOOD RES INT, V141, DOI 10.1016/j.foodres.2020.110045 Liu HL, 2020, FOOD RES INT, V136, DOI 10.1016/j.foodres.2020.109483 Liu HL, 2020, J SCI FOOD AGR, V100, P3507, DOI 10.1002/jsfa.10392 Liu HL, 2020, J FOOD DRUG ANAL, V28, P54, DOI 10.38212/2224-6614.1059 Liu HL, 2020, J FOOD COMPOS ANAL, V91, DOI 10.1016/j.jfca.2020.103513 Liu HY, 2017, INT J FOOD SCI TECH, V52, P1018, DOI 10.1111/ijfs.13366 Liu XF, 2012, FOOD CONTROL, V23, P522, DOI 10.1016/j.foodcont.2011.08.025 Liu Z, 2019, FOOD CONTROL, V99, P1, DOI 10.1016/j.foodcont.2018.12.011 Liu Z, 2019, RAPID COMMUN MASS SP, V33, P778, DOI 10.1002/rcm.8405 Llorent-Martinez EJ, 2011, FOOD CHEM, V127, P1257, DOI 10.1016/j.foodchem.2011.01.064 Llorent-Martinez EJ, 2014, J AM OIL CHEM SOC, V91, P1823, DOI 10.1007/s11746-014-2511-5 Lo Feudo G, 2010, J AGR FOOD CHEM, V58, P3801, DOI 10.1021/jf903868j Lou YX, 2017, J ANAL METHODS CHEM, V2017, DOI 10.1155/2017/5454231 Luo RJ, 2019, FOOD CHEM, V274, P1, DOI 10.1016/j.foodchem.2018.08.104 Mahmood N, 2012, ANAL BIOANAL CHEM, V402, P861, DOI 10.1007/s00216-011-5452-y Maione C, 2019, CRIT REV FOOD SCI, V59, P1868, DOI 10.1080/10408398.2018.1431763 Mara A, 2022, MOLECULES, V27, DOI 10.3390/molecules27062009 May TW, 1998, ATOM SPECTROSC, V19, P150 McGrath TF, 2021, J AOAC INT, V104, P16, DOI 10.1093/jaoacint/qsaa109 Mehari B, 2016, ANAL LETT, V49, P2474, DOI 10.1080/00032719.2016.1151023 MILLER PE, 1986, J CHEM EDUC, V63, P617, DOI 10.1021/ed063p617 Bui MQ, 2022, FOOD ADDIT CONTAM B, V15, P177, DOI 10.1080/19393210.2022.2070932 Munoz-Redondo JM, 2022, FOOD CONTROL, V137, DOI 10.1016/j.foodcont.2022.108975 Munoz-Redondo JM, 2021, FOOD CONTROL, V125, DOI 10.1016/j.foodcont.2021.107961 Naccarato A, 2016, FOOD CHEM, V206, P217, DOI 10.1016/j.foodchem.2016.03.072 Nasr EG, 2022, MOLECULES, V27, DOI 10.3390/molecules27062014 Nasr EG, 2022, FOODS, V11, DOI 10.3390/foods11010082 Trung NQ, 2021, J ANAL METHODS CHEM, V2021, DOI 10.1155/2021/5583860 Ni ZL, 2017, J BRAZIL CHEM SOC, V28, P1960, DOI 10.21577/0103-5053.20170039 Oddone M, 2009, J AGR FOOD CHEM, V57, P3404, DOI 10.1021/jf900312p Ordog A, 2018, J ELEMENTOL, V23, P521, DOI 10.5601/jelem.2017.22.4.1497 Oroian M, 2016, INT J FOOD PROP, V19, P1825, DOI 10.1080/10942912.2015.1107578 Ortea I, 2015, FOOD CHEM, V170, P145, DOI 10.1016/j.foodchem.2014.08.049 Padovan GJ, 2007, EURASIAN J ANAL CHEM, V2, P134, DOI 10.12973/ejac.2007.00017a Palmer CD, 2006, SPECTROCHIM ACTA B, V61, P980, DOI 10.1016/j.sab.2006.09.001 Pasvanka K, 2021, SEPARATIONS, V8, DOI 10.3390/separations8080119 Pasvanka K, 2019, ANAL LETT, V52, P2741, DOI 10.1080/00032719.2019.1596118 Patriarca M, 2022, J ANAL ATOM SPECTROM, V37, P410, DOI [10.1039/D2JA90005J, 10.1039/d2ja90005j] Pawlaczyk A, 2019, MOLECULES, V24, DOI 10.3390/molecules24071193 Pepi S, 2019, ENVIRON GEOCHEM HLTH, V41, P1559, DOI 10.1007/s10653-018-0232-7 Perez M, 2022, CRIT REV FOOD SCI, V62, P475, DOI 10.1080/10408398.2020.1819769 Perez-Alvarez EP, 2019, FOOD CHEM, V270, P273, DOI 10.1016/j.foodchem.2018.07.087 Perez-Rodriguez M, 2019, FOOD CONTROL, V95, P232, DOI 10.1016/j.foodcont.2018.08.001 Perini M, 2020, J MASS SPECTROM, V55, DOI 10.1002/jms.4595 Phuong TD, 1999, ANALYST, V124, P553, DOI 10.1039/a808796b Pillonel L, 2003, LEBENSM-WISS TECHNOL, V36, P615, DOI 10.1016/S0023-6438(03)00081-1 Pipan B, 2021, J ELEMENTOL, V26, P613, DOI 10.5601/jelem.2021.26.2.2143 Plotka-Wasylka J, 2018, MOLECULES, V23, DOI 10.3390/molecules23112886 Podio NS, 2013, J AGR FOOD CHEM, V61, P3763, DOI 10.1021/jf305258r Potorti AG, 2021, LWT-FOOD SCI TECHNOL, V138, DOI 10.1016/j.lwt.2020.110643 Potorti AG, 2020, FOOD CHEM, V313, DOI 10.1016/j.foodchem.2019.126094 Potorti AG, 2018, J FOOD COMPOS ANAL, V69, P122, DOI 10.1016/j.jfca.2018.03.001 Praamsma ML, 2011, J ANAL ATOM SPECTROM, V26, P1224, DOI 10.1039/c1ja10039d Pucci E, 2022, FOODS, V11, DOI 10.3390/foods11081085 Qian LL, 2019, FOOD SCI TECHNOL RES, V25, P619, DOI 10.3136/fstr.25.619 Qian LL, 2019, J FOOD COMPOS ANAL, V83, DOI 10.1016/j.jfca.2019.103276 Quinn B, 2022, FOOD CHEM, V386, DOI 10.1016/j.foodchem.2022.132738 Ranaweera RKR, 2021, FOOD CHEM, V335, DOI 10.1016/j.foodchem.2020.127592 Rocha S, 2019, FOOD CONTROL, V103, P27, DOI 10.1016/j.foodcont.2019.03.034 Rodrigues C, 2013, COMP ANAL C, V60, P77, DOI 10.1016/B978-0-444-59562-1.00004-9 Rodrigues C, 2011, J AGR FOOD CHEM, V59, P10239, DOI 10.1021/jf200788p Rodrigues SM, 2011, J FOOD COMPOS ANAL, V24, P548, DOI 10.1016/j.jfca.2010.12.003 Rodushkin I, 2007, ANAL CHIM ACTA, V583, P310, DOI 10.1016/j.aca.2006.10.038 Rodushkin I, 2013, ANAL BIOANAL CHEM, V405, P2785, DOI 10.1007/s00216-012-6457-x Santato A, 2012, J MASS SPECTROM, V47, P1132, DOI 10.1002/jms.3018 Segelke T, 2020, FOODS, V9, DOI 10.3390/foods9111708 Segelke T, 2020, J AGR FOOD CHEM, V68, P14374, DOI 10.1021/acs.jafc.0c02334 SHARP BL, 1988, J ANAL ATOM SPECTROM, V3, P613, DOI 10.1039/ja9880300613 SHARP BL, 1988, J ANAL ATOM SPECTROM, V3, P939, DOI 10.1039/ja9880300939 Shen SG, 2013, ANAL METHODS-UK, V5, P6177, DOI 10.1039/c3ay40700d Shuai MY, 2022, FOOD CHEM, V374, DOI 10.1016/j.foodchem.2021.131713 Silva B, 2021, J FOOD COMPOS ANAL, V96, DOI 10.1016/j.jfca.2020.103727 Smith RG, 2005, J AGR FOOD CHEM, V53, P4041, DOI 10.1021/jf040166+ Squadrone S, 2020, J TRACE ELEM MED BIO, V62, DOI 10.1016/j.jtemb.2020.126646 Sun SM, 2011, FOOD CHEM, V124, P1151, DOI 10.1016/j.foodchem.2010.07.027 Tanabe CK, 2020, MOLECULES, V25, DOI 10.3390/molecules25112552 Taylor VF, 2003, J AGR FOOD CHEM, V51, P856, DOI 10.1021/jf025761v Tedesco R, 2021, FOOD CONTROL, V121, DOI [10.1016/j.foodcont.2020.107595, 10.1016/j.foodcount.2020.107595] Tokalioglu S, 2019, LWT-FOOD SCI TECHNOL, V103, P301, DOI 10.1016/j.lwt.2019.01.015 Tsagkaris AS, 2021, RSC ADV, V11, P11273, DOI 10.1039/d1ra00069a Vadala R, 2016, FOODS, V5, DOI 10.3390/foods5010020 Vanderschueren R, 2019, J FOOD COMPOS ANAL, V83, DOI 10.1016/j.jfca.2019.103277 Varra MO, 2021, FOOD CHEM, V360, DOI 10.1016/j.foodchem.2021.129790 Varra MO, 2021, FOODS, V10, DOI 10.3390/foods10020270 Varra MO, 2019, ITAL J FOOD SAF, V8, P21, DOI 10.4081/ijfs.2019.7872 Voica C, 2016, ANAL LETT, V49, P2627, DOI 10.1080/00032719.2015.1116003 Voica C, 2021, MOLECULES, V26, DOI 10.3390/molecules26237081 Voica C, 2020, J MASS SPECTROM, V55, DOI 10.1002/jms.4512 Voica C, 2017, ANAL LETT, V50, P2755, DOI 10.1080/00032719.2016.1261880 von Wuthenau K, 2022, FOOD CONTROL, V134, DOI 10.1016/j.foodcont.2021.108689 Wali A, 2021, EUR FOOD RES TECHNOL, V247, P1401, DOI 10.1007/s00217-021-03717-x Wang F, 2020, J SCI FOOD AGR, V100, P1294, DOI 10.1002/jsfa.10144 Wilschefski Scott C, 2019, Clin Biochem Rev, V40, P115, DOI 10.33176/AACB-19-00024 Xiong Q, 2021, APPL SPECTROSC REV, V56, P115, DOI 10.1080/05704928.2020.1742729 Xu F, 2021, NPJ SCI FOOD, V5, DOI 10.1038/s41538-021-00100-8 Xu L, 2019, J ANAL METHODS CHEM, V2019, DOI 10.1155/2019/2796502 Zhang J, 2020, J SCI FOOD AGR, V100, P3046, DOI 10.1002/jsfa.10335 Zhang J, 2019, FOOD SCI BIOTECHNOL, V28, P1627, DOI 10.1007/s10068-019-00619-3 Zhang J, 2018, MOLECULES, V23, DOI 10.3390/molecules23113013 Zhao HY, 2016, FOOD CONTROL, V66, P62, DOI 10.1016/j.foodcont.2016.01.045 Zhao HY, 2013, J CEREAL SCI, V57, P391, DOI 10.1016/j.jcs.2013.01.008 Zhao HY, 2011, J AGR FOOD CHEM, V59, P4397, DOI 10.1021/jf200108d Zhou XT, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-32764-w Zhu LL, 2021, IND CROP PROD, V172, DOI 10.1016/j.indcrop.2021.114078 NR 222 TC 0 Z9 0 U1 0 U2 0 PD NOV PY 2022 VL 11 IS 22 AR 3705 DI 10.3390/foods11223705 WC Food Science & Technology SC Food Science & Technology UT WOS:000887563400001 DA 2022-12-14 ER PT J AU Barry, B Corkery, G Gonzales-Barron, U Mc Donnell, K Butler, F Ward, S AF Barry, B. Corkery, G. Gonzales-Barron, U. Mc Donnell, K. Butler, F. Ward, S. TI A longitudinal study of the effect of time on the matching performance of a retinal recognition system for lambs SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Sheep; Lamb; Retina; Identification; Biometrics; Traceability ID EAR TAGS; SHEEP; IDENTIFICATION; TRACEABILITY; TECHNOLOGY; CATTLE AB While there is strong evidence supporting retinal vascular pattern as a distinctive marker for sheep, it would be advantageous to get an insight into its robustness; in other words, to determine whether retinal recognition of young animals (lambs) can reach as good a matching performance as the one demonstrated for adult sheep. To this aim, a longitudinal study was devised to observe the evolution of matching scores (ms) of lamb retinal images (n = 38) acquired from 1 to 22 weeks after birth. It was observed that four lamb retinas (out of 38) underwent slight curving of one or two secondary arteries, which ceased by the time they were 6-8 weeks old. However, this slight artery curving did not affect matching performance. A random effects statistical model demonstrated that lamb age had an effect (P < 0.01) on the matching scores produced using this commercially available retinal recognition system. As lambs grew older (larger eyes) and they became easier to handle, retinal images of progressively better quality could be obtained in a more consistent way; and thus matching scores increased from an average of 86 at the first week of life, up to an average of 96 by week 8. Dunnett simultaneous tests of means indicated that no further improvement in matching score took place once lambs were at least 6-8 weeks old, meaning that the retinal image quality became by then optimal and consistent. Although the variable retinal image quality of younger lambs (1-4 weeks old) caused a reduction in matching score, they did not lead to false non-matches in any case (considering a cut-off matching score of 70 for acceptance of a positive match). Therefore, the results of these trials have shown that, with the available technology, retinal images can be used as a robust biometric marker of lambs from 1 week of age. (C) 2008 Elsevier B.V. All rights reserved. C1 [Barry, B.; Corkery, G.; Gonzales-Barron, U.; Mc Donnell, K.; Butler, F.; Ward, S.] Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Dublin 2, Ireland. C3 University College Dublin RP Gonzales-Barron, U (corresponding author), Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Dublin 2, Ireland. EM ursula.gonzalesbarron@ucd.ie CR Barry B, 2007, T ASABE, V50, P1073, DOI 10.13031/2013.23121 Corkery GP, 2007, T ASABE, V50, P313, DOI 10.13031/2013.22395 Der G, 2002, HDB STAT ANAL USING Dziuk P, 2003, ANIM REPROD SCI, V79, P319, DOI 10.1016/S0378-4320(03)00170-2 Edwards DS, 1999, VET REC, V144, P603, DOI 10.1136/vr.144.22.603 Edwards DS, 2001, ANIM WELFARE, V10, P141 Gonzales-Barron U, 2008, COMPUT ELECTRON AGR, V60, P156 Jimenez-Gamero I, 2006, SMALL RUMINANT RES, V65, P266, DOI 10.1016/j.smallrumres.2005.07.019 KRZANOWSKI WJ, 1995, MULTIVARIATE ANAL 2, P2 Loftus R, 2005, REV SCI TECH OIE, V24, P231, DOI 10.20506/rst.24.1.1563 Musgrave C, 2002, uS Patent, Patent No. [6424727, 6,424,727] Opara LU, 2001, OUTLOOK AGR, V30, P239, DOI 10.5367/000000001101293724 Raschke A, 2006, FOOD CONTROL, V17, P65, DOI 10.1016/j.foodcont.2004.09.004 RUSK CP, 2006, J EXTENSION, V44 SHADDUCK JA, 2002, P ID INFO EXP 2002 N Springer AD, 2004, VISUAL NEUROSCI, V21, P775, DOI 10.1017/S0952523804215115 SUZAKI M, 2001, Patent No. 6229905 VERKAIK J, 2001, PUBLICATIE PRAKTIJKO, V152, P35 WHITTIER JC, 2003, P 2003 ASAS W SECT M 2005, NATL SHEEP GOAT CENS NR 20 TC 8 Z9 8 U1 1 U2 4 PD DEC PY 2008 VL 64 IS 2 BP 202 EP 211 DI 10.1016/j.compag.2008.05.011 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000261074500013 DA 2022-12-14 ER PT J AU Fiorino, GM Garino, C Arlorio, M Logrieco, AF Losito, I Monaci, L AF Fiorino, Giuseppina M. Garino, Cristiano Arlorio, Marco Logrieco, Antonio F. Losito, Ilario Monaci, Linda TI Overview on Untargeted Methods to Combat Food Frauds: A Focus on Fishery Products SO JOURNAL OF FOOD QUALITY DT Review ID ATR-FTIR SPECTROSCOPY; PCR-RFLP ANALYSIS; IONIZATION-MASS-SPECTROMETRY; RESOLUTION MELTING ANALYSIS; NEAR-INFRARED SPECTROSCOPY; COD GADUS-MORHUA; SPECIES IDENTIFICATION; LIQUID-CHROMATOGRAPHY; ATLANTIC SALMON; 2-DIMENSIONAL ELECTROPHORESIS AB Authenticity and traceability of food products are of primary importance at all levels of the production process, from raw materials to finished products. Authentication is also a key aspect for accurate labeling of food, which is required to help consumers in selecting appropriate types of food products. With the aim of guaranteeing the authenticity of foods, various methodological approaches have been devised over the past years, mainly based on either targeted or untargeted analyses. In this review, a brief overview of current analytical methods tailored to authenticity studies, with special regard to fishery products, is provided. Focus is placed on untargeted methods that are attracting the interest of the analytical community thanks to their rapidity and high throughput; such methods enable a fast collection of "fingerprinting signals" referred to each authentic food, subsequently stored into large database for the construction of specific information repositories. In the present case, methods capable of detecting fish adulteration/substitution and involving sensory, physicochemical, DNA-based, chromatographic, and spectroscopic measurements, combined with chemometric tools, are illustrated and commented on. C1 [Fiorino, Giuseppina M.; Logrieco, Antonio F.; Losito, Ilario; Monaci, Linda] CNR, ISPA, Inst Sci Food Prod, Bari, Italy. [Garino, Cristiano; Arlorio, Marco] Univ Piemonte Orientale Amedeo Avogadro UNIUPO, Novara, Italy. [Losito, Ilario] Univ Bari Aldo Moro, Dept Chem, Via Orabona 4, I-70126 Bari, Italy. [Losito, Ilario] Univ Bari Aldo Moro, SMART Interdept Res Ctr, Via Orabona 4, I-70126 Bari, Italy. C3 Consiglio Nazionale delle Ricerche (CNR); Istituto Scienze delle Produzioni Alimentari (ISPA-CNR); University of Eastern Piedmont Amedeo Avogadro; Universita degli Studi di Bari Aldo Moro; Universita degli Studi di Bari Aldo Moro RP Monaci, L (corresponding author), CNR, ISPA, Inst Sci Food Prod, Bari, Italy. EM linda.monaci@ispa.cnr.it CR Akasaki T, 2006, J FOOD SCI, V71, pC190, DOI 10.1111/j.1750-3841.2006.00009.x Alamprese C, 2015, LWT-FOOD SCI TECHNOL, V63, P720, DOI 10.1016/j.lwt.2015.03.021 Alamprese C, 2013, LWT-FOOD SCI TECHNOL, V53, P225, DOI 10.1016/j.lwt.2013.01.027 Anderson KA, 2010, J AGR FOOD CHEM, V58, P11768, DOI 10.1021/jf102046b [Anonymous], 2012, J ASSOC PUBLIC ANAL Armani A, 2015, FOOD CONTROL, V50, P589, DOI 10.1016/j.foodcont.2014.09.025 Asensio L, 2008, FOOD ADDIT CONTAM A, V25, P677, DOI 10.1080/02652030701765731 Aursand M, 2009, J AGR FOOD CHEM, V57, P3444, DOI 10.1021/jf8039268 Avula B, 2015, FOOD ADDIT CONTAM A, V32, P1, DOI 10.1080/19440049.2014.981763 Axelson DE, 2009, J AGR FOOD CHEM, V57, P7634, DOI 10.1021/jf9013235 BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 Berge JP, 2005, ADV BIOCHEM ENG BIOT, V96, P49, DOI 10.1007/b135782 Berrini A, 2006, FOOD CHEM, V96, P163, DOI 10.1016/j.foodchem.2005.04.007 Black C, 2017, METABOLOMICS, V13, DOI 10.1007/s11306-017-1291-y Bonne K, 2008, AGR HUM VALUES, V25, P35, DOI 10.1007/s10460-007-9076-y Broadaway BJ, 2012, J SHELLFISH RES, V31, P671, DOI 10.2983/035.031.0310 Cajka T, 2009, J CHROMATOGR A, V1216, P1458, DOI 10.1016/j.chroma.2008.12.066 Cajka T, 2013, METABOLOMICS, V9, P545, DOI 10.1007/s11306-013-0495-z Cajka T, 2011, METABOLOMICS, V7, P500, DOI 10.1007/s11306-010-0266-z Calves I, 2013, MAR ECOL PROG SER, V472, P257, DOI 10.3354/meps09797 Carrera E, 2014, FOOD AGR IMMUNOL, V25, P569, DOI 10.1080/09540105.2013.858310 Carson HS, 2013, MAR ECOL PROG SER, V473, P133, DOI 10.3354/meps10078 Carvalho DC, 2015, FOOD CONTROL, V50, P784, DOI 10.1016/j.foodcont.2014.10.025 Charlebois S, 2014, COMPR REV FOOD SCI F, V13, P1104, DOI 10.1111/1541-4337.12101 Chen RK, 2004, RAPID COMMUN MASS SP, V18, P1167, DOI 10.1002/rcm.1460 Chen TY, 2002, J FOOD SCI, V67, P936, DOI 10.1111/j.1365-2621.2002.tb09431.x Civera T, 2003, VET RES COMMUN, V27, P481, DOI 10.1023/B:VERC.0000014205.87859.ab Comesana AS, 2003, J SCI FOOD AGR, V83, P752, DOI 10.1002/jsfa.1368 Coscia I, 2013, CONSERV GENET, V14, P451, DOI 10.1007/s10592-012-0404-4 D'Avignon G, 2013, FISH RES, V147, P1, DOI 10.1016/j.fishres.2013.04.006 De Battisti C, 2014, J AGR FOOD CHEM, V62, P198, DOI 10.1021/jf403545m Ribeiroa MVDM, 2017, J FOOD COMPOS ANAL, V57, P24, DOI 10.1016/j.jfca.2016.12.004 Dempson JB, 2004, ECOL FRESHW FISH, V13, P176, DOI 10.1111/j.1600-0633.2004.00057.x DERDE MP, 1986, ANAL CHIM ACTA, V184, P33, DOI 10.1016/S0003-2670(00)86468-5 Di Pinto A, 2013, FOOD CHEM, V141, P1757, DOI 10.1016/j.foodchem.2013.05.093 Di Stefano V, 2012, J CHROMATOGR A, V1259, P74, DOI 10.1016/j.chroma.2012.04.023 Dooley JJ, 2005, FOOD CONTROL, V16, P601, DOI 10.1016/j.foodcont.2004.06.022 El Darra N, 2017, FOOD CONTROL, V78, P132, DOI 10.1016/j.foodcont.2017.02.043 Esslinger S, 2014, FOOD RES INT, V60, P189, DOI 10.1016/j.foodres.2013.10.015 European Commission, 2005, QUEST ANSW SUD DYES Forina M, 2008, CHEMOMETR INTELL LAB, V93, P132, DOI 10.1016/j.chemolab.2008.05.003 FORINA M, 1991, J CHEMOMETR, V5, P435, DOI 10.1002/cem.1180050504 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Ghosh Prabal K., 2009, Sensing and Instrumentation for Food Quality and Safety, V3, P3, DOI 10.1007/s11694-008-9068-7 Grahl-Nielsen O, 2010, BIOCHEM SYST ECOL, V38, P478, DOI 10.1016/j.bse.2010.04.010 Grassi S, 2018, FOOD CHEM, V243, P382, DOI 10.1016/j.foodchem.2017.09.145 Guardone L, 2017, FOOD CONTROL, V80, P204, DOI 10.1016/j.foodcont.2017.03.056 Guelpa A, 2017, FOOD CONTROL, V73, P1388, DOI 10.1016/j.foodcont.2016.11.002 Hall JM, 2002, BIOL BULL, V202, P201, DOI 10.2307/1543469 Hellberg RS, 2017, FOOD CONTROL, V80, P23, DOI 10.1016/j.foodcont.2017.04.025 Heres L, 2010, FOOD ADDIT CONTAM A, V27, P1733, DOI 10.1080/19440049.2010.522598 Herrero AM, 2008, FOOD CHEM, V107, P1642, DOI 10.1016/j.foodchem.2007.10.014 Hold GL, 2001, EUR FOOD RES TECHNOL, V212, P385, DOI 10.1007/s002170000237 Hong E, 2017, J SCI FOOD AGR, V97, P3877, DOI 10.1002/jsfa.8364 Hovda MB, 2007, FOOD MICROBIOL, V24, P362, DOI 10.1016/j.fm.2006.07.018 Hrbek V, 2014, FOOD CONTROL, V36, P138, DOI 10.1016/j.foodcont.2013.08.003 Hsieh HS, 2007, FOOD CONTROL, V18, P369, DOI 10.1016/j.foodcont.2005.11.002 Lopez MI, 2014, FOOD CHEM, V147, P177, DOI 10.1016/j.foodchem.2013.09.139 Jerome M, 2003, J AGR FOOD CHEM, V51, P7326, DOI 10.1021/jf034652t Kamruzzaman, 2016, FOOD SAFETY, P127, DOI DOI 10.1007/978-3-319-39253-0_7 Kapper K, 2016, FOOD CONTROL, V59, P478, DOI 10.1016/j.foodcont.2015.06.024 Karoui R, 2007, FOOD CHEM, V102, P621, DOI 10.1016/j.foodchem.2006.05.042 Kasprzyk I, 2018, FOOD CONTROL, V84, P33, DOI 10.1016/j.foodcont.2017.07.015 Kelly JR, 2012, MAR ECOL PROG SER, V446, P1, DOI 10.3354/meps09559 Kleter GA, 2009, FOOD CHEM TOXICOL, V47, P932, DOI 10.1016/j.fct.2007.12.022 Klinbunga S, 2003, MAR BIOTECHNOL, V5, P505, DOI 10.1007/s10126-002-0108-8 Kochzius M, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0012620 Kokkonen M, 2005, FOOD ADDIT CONTAM A, V22, P449, DOI 10.1080/02652030500089861 Kuballa T, 2018, FOOD CHEM, V245, P112, DOI 10.1016/j.foodchem.2017.10.065 Kuuliala L, 2018, FOOD CONTROL, V84, P49, DOI 10.1016/j.foodcont.2017.07.018 Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 Lenstra J.A., 2013, FOOD AUTHENTICITY TR, P34 Lin WF, 2007, FOOD CONTROL, V18, P1050, DOI 10.1016/j.foodcont.2006.07.001 Lin YS, 2002, ZOOL STUD, V41, P421 Liu D, 2013, APPL SPECTROSC REV, V48, P609, DOI 10.1080/05704928.2013.775579 Liu Y, 2018, FOOD CHEM, V242, P62, DOI 10.1016/j.foodchem.2017.09.040 Liu ZJ, 2004, AQUACULTURE, V238, P1, DOI 10.1016/j.aquaculture.2004.05.027 Maralit BA, 2013, FOOD CONTROL, V33, P119, DOI 10.1016/j.foodcont.2013.02.018 Martinez I, 2004, PROTEOMICS, V4, P347, DOI 10.1002/pmic.200300569 Martinez I, 2007, FOOD CHEM, V102, P504, DOI 10.1016/j.foodchem.2006.03.037 Masoum S, 2007, ANAL BIOANAL CHEM, V387, P1499, DOI 10.1007/s00216-006-1025-x Mazzeo MF, 2008, J AGR FOOD CHEM, V56, P11071, DOI 10.1021/jf8021783 Montet D., 2008, Aspects of Applied Biology, P11 Moore JC, 2012, J FOOD SCI, V77, pR118, DOI 10.1111/j.1750-3841.2012.02657.x Moretti VM, 2003, VET RES COMMUN, V27, P497, DOI 10.1023/B:VERC.0000014207.01900.5c Muller L, 2008, ELECTROPHORESIS, V29, P2088, DOI 10.1002/elps.200700794 Nardiello D, 2018, FOOD CHEM, V244, P317, DOI 10.1016/j.foodchem.2017.10.052 Neufeld M, 2018, INT J DRUG POLICY, V51, P1, DOI 10.1016/j.drugpo.2017.09.006 Newmaster SG, 2013, BMC MED, V11, DOI 10.1186/1741-7015-11-222 Nunes CA, 2014, FOOD RES INT, V60, P255, DOI 10.1016/j.foodres.2013.08.041 Nunes KM, 2016, FOOD CHEM, V205, P14, DOI 10.1016/j.foodchem.2016.02.158 O'Brien N, 2013, J NEAR INFRARED SPEC, V21, P299, DOI 10.1255/jnirs.1063 O'Mahony PJ, 2013, QJM-INT J MED, V106, P595, DOI 10.1093/qjmed/hct087 Olafsdottir G, 1997, TRENDS FOOD SCI TECH, V8, P258, DOI 10.1016/S0924-2244(97)01049-2 Oliveri P, 2012, TRAC-TREND ANAL CHEM, V35, P74, DOI 10.1016/j.trac.2012.02.005 Olsen BR, 2009, BIOCHEM SYST ECOL, V37, P662, DOI 10.1016/j.bse.2009.10.003 Ottavian M, 2012, J AGR FOOD CHEM, V60, P639, DOI 10.1021/jf203385e Petrakis EA, 2017, TALANTA, V162, P558, DOI 10.1016/j.talanta.2016.10.072 Callao MP, 2018, FOOD CONTROL, V86, P283, DOI [10.1016/J.foodcont.2017.11.034, 10.1016/j.foodcont.2017.11.034] Rego I, 2002, J AGR FOOD CHEM, V50, P1780, DOI 10.1021/jf0110957 Rehbein H, 1997, J SCI FOOD AGR, V74, P35, DOI 10.1002/(SICI)1097-0010(199705)74:1<35::AID-JSFA765>3.0.CO;2-2 Rehbein H, 1999, FOOD CHEM, V64, P263, DOI 10.1016/S0308-8146(98)00125-3 Reis-Santos P, 2012, ESTUAR COAST SHELF S, V112, P216, DOI 10.1016/j.ecss.2012.07.027 Riedl J, 2015, ANAL CHIM ACTA, V885, P17, DOI 10.1016/j.aca.2015.06.003 Rosa R, 2010, MAR ECOL PROG SER, V410, P205, DOI 10.3354/meps08635 Rossier JS, 2014, CHIMIA, V68, P696, DOI 10.2533/chimia.2014.696 Sanjuan A, 2002, J FOOD PROTECT, V65, P1016, DOI 10.4315/0362-028X-65.6.1016 Santaclara FJ, 2006, EUR FOOD RES TECHNOL, V223, P609, DOI 10.1007/s00217-005-0241-5 Smirnov S. V., 2011, P 2011 3 INT WORKSH, V2, P19 Smulevich G, 2007, FOOD CHEM, V101, P1071, DOI 10.1016/j.foodchem.2006.03.006 Soggiu A., 2018, Proteomics in Domestic Animals: from Farm to Systems Biology, P169, DOI 10.1007/978-3-319-69682-9_9 Sorte CJB, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0080868 SOTELO CG, 1993, TRENDS FOOD SCI TECH, V4, P395, DOI 10.1016/0924-2244(93)90043-A Spielmann G., 2017, EUROPEAN FOOD RES TE, P1 Spink J., 2011, BACKGROUNDER DEFININ, V1 Staffen CF, 2017, PEERJ, V5, DOI 10.7717/peerj.4006 Stahl A, 2017, J AGR FOOD CHEM, V65, P7519, DOI 10.1021/acs.jafc.7b02826 Taboada L, 2014, J AGR FOOD CHEM, V62, P5699, DOI 10.1021/jf500173j Tatsadjieu NL, 2010, FOOD CONTROL, V21, P673, DOI 10.1016/j.foodcont.2009.10.006 Terova G, 2014, FISH PHYSIOL BIOCHEM, V40, P311, DOI 10.1007/s10695-013-9855-x Toci AT, 2016, CRIT REV ANAL CHEM, V46, P83, DOI 10.1080/10408347.2014.966185 Tomas C, 2017, FOOD CONTROL, V71, P255, DOI 10.1016/j.foodcont.2016.07.004 Ha TTT, 2018, TURK J FISH AQUAT SC, V18, P457, DOI 10.4194/1303-2712-v18_3_11 U.S. Pharmacopeial Convention, 2016, GUID DEV VAL NONT ME Uddin M, 2005, J FOOD SCI, V70, pC506, DOI 10.1111/j.1365-2621.2005.tb11509.x Ulrich S, 2017, FOOD CONTROL, V80, P281, DOI 10.1016/j.foodcont.2017.05.005 Vaclavik L, 2011, J AGR FOOD CHEM, V59, P5919, DOI 10.1021/jf200734x Vaclavik L, 2009, ANAL CHIM ACTA, V645, P56, DOI 10.1016/j.aca.2009.04.043 Wang L, 2017, CRIT REV FOOD SCI, V57, P1524, DOI 10.1080/10408398.2015.1115954 Miaw CSW, 2018, FOOD CHEM, V254, P272, DOI 10.1016/j.foodchem.2018.02.015 Wold S., 1977, CHEMOMETRICS THEORY, V52, P243, DOI [DOI 10.1021/BK-1977-0052.CH012, 10.1021/bk-1977-0052.ch012] Wulff T, 2013, J PROTEOME RES, V12, P5253, DOI 10.1021/pr4006525 Xiu CB, 2010, FOOD POLICY, V35, P463, DOI 10.1016/j.foodpol.2010.05.001 Yan S, 2016, FOOD CHEM, V202, P116, DOI 10.1016/j.foodchem.2016.01.133 Zhang JB, 2006, EUR FOOD RES TECHNOL, V223, P413, DOI 10.1007/s00217-005-0221-9 Zotte AD, 2014, FOOD RES INT, V60, P180, DOI 10.1016/j.foodres.2013.10.033 NR 136 TC 18 Z9 19 U1 3 U2 36 PY 2018 AR 1581746 DI 10.1155/2018/1581746 WC Food Science & Technology SC Food Science & Technology UT WOS:000430620700001 DA 2022-12-14 ER PT J AU Yacoot, A Klapetek, P Valtr, M Grolich, P Dongmo, H Lazzerini, GM Bridges, A AF Yacoot, Andrew Klapetek, Petr Valtr, Miroslav Grolich, Petr Dongmo, Herve Lazzerini, Giovanni M. Bridges, Angus TI Design and performance of a test rig for evaluation of nanopositioning stages SO MEASUREMENT SCIENCE AND TECHNOLOGY DT Article DE multi-axis positioning stages; traceability; nanopositioning; dimensional metrology ID MICROSCOPE AB Nanopositioning stages are used in many areas of nanotechnology and advanced materials analysis, often being integrated into analytical devices such as scanning probe and optical microscopes and manufacturing devices (e.g. lithographic systems). We present a metrological instrument, together with software, designed for traceable evaluation of stage performance. The system capabilities and performance are illustrated by measurement of stages of different levels of accuracy, including a low cost custom built stage manufactured by 3D printing. The traceability of the system is described and main uncertainty sources are discussed. Guidelines are given for the specification of stage performance. C1 [Yacoot, Andrew; Dongmo, Herve; Lazzerini, Giovanni M.; Bridges, Angus] Natl Phys Lab, Hampton Rd, Teddington TW11 0LW, Middx, England. [Klapetek, Petr; Valtr, Miroslav; Grolich, Petr] Czech Metrol Inst, Okruzni 31, Brno 63800, Czech Republic. [Klapetek, Petr] Brno Univ Technol, CEITEC, Purkynova 123, Brno 61200, Czech Republic. C3 National Physical Laboratory - UK; Czech Metrology Institute; Brno University of Technology RP Yacoot, A (corresponding author), Natl Phys Lab, Hampton Rd, Teddington TW11 0LW, Middx, England. EM andrew.yacoot@npl.co.uk CR [Anonymous], 9392007 BS BIRCH KP, 1990, PRECIS ENG, V12, P195, DOI 10.1016/0141-6359(90)90060-C BIRCH KP, 1993, METROLOGIA, V30, P155, DOI 10.1088/0026-1394/30/3/004 BIRCH KP, 1994, METROLOGIA, V31, P315, DOI 10.1088/0026-1394/31/4/006 Corbett AD, 2018, OPT EXPRESS, V26, P21887, DOI 10.1364/OE.26.021887 Gillmer SR, 2014, MEAS SCI TECHNOL, V25, DOI 10.1088/0957-0233/25/7/075205 HEYDEMANN PLM, 1981, APPL OPTICS, V20, P3382, DOI 10.1364/AO.20.003382 ISO, 12016 ISO Klapetek P, 2011, MEAS SCI TECHNOL, V22, DOI 10.1088/0957-0233/22/2/025501 Lazar J, 2009, MEAS SCI TECHNOL, V20, DOI 10.1088/0957-0233/20/8/084007 Leach RK, 2011, NANOTECHNOLOGY, V22, DOI 10.1088/0957-4484/22/6/062001 Lee C, 2014, REV SCI INSTRUM, V85, DOI 10.1063/1.4895912 Liu CH, 2010, PRECIS ENG, V34, P497, DOI 10.1016/j.precisioneng.2010.01.003 Manske E, 2007, MEAS SCI TECHNOL, V18, P520, DOI 10.1088/0957-0233/18/2/S27 Manske E, 2012, MEAS SCI TECHNOL, V23, DOI 10.1088/0957-0233/23/7/074001 Necas D, 2012, CENT EUR J PHYS, V10, P181, DOI 10.2478/s11534-011-0096-2 Taniguchi N., 1983, ANN CIRP, V32, P573, DOI [10.1016/S0007-8506(07)60185-1, DOI 10.1016/S0007-8506(07)60185-1] Torralba M, 2016, MEASUREMENT, V89, P55, DOI 10.1016/j.measurement.2016.03.075 Xu M, 2008, MEAS SCI TECHNOL, V19, DOI 10.1088/0957-0233/19/2/025105 Yacoot A, 2000, MEAS SCI TECHNOL, V11, P1126, DOI 10.1088/0957-0233/11/8/305 Yacoot A, 2007, MEAS SCI TECHNOL, V18, P350, DOI 10.1088/0957-0233/18/2/S05 NR 21 TC 4 Z9 4 U1 0 U2 6 PD MAR PY 2019 VL 30 IS 3 AR 035002 DI 10.1088/1361-6501/aafd03 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000458209200002 DA 2022-12-14 ER PT J AU Bosona, T Gebresenbet, G Olsson, SO Garcia, D Germer, S AF Bosona, Techane Gebresenbet, Girma Olsson, Sven-Olof Garcia, Daniel Germer, Sonja TI Evaluation of a Smart System for the Optimization of Logistics Performance of a Pruning Biomass Value Chain SO APPLIED SCIENCES-BASEL DT Article DE smart logistics system; information platform; pruning biomass; performance evaluation; product quality model; product usability testing ID MANAGEMENT AB The paper presents a report on the performance evaluation of a newly developed smart logistics system (SLS). Field tests were conducted in Spain, Germany, and Sweden. The evaluation focused on the performance of a smart box tool (used to capture information during biomass transport) and a web-based information platform (used to monitor the flow of agricultural pruning from farms to end users and associated information flow). The tests were performed following a product usability testing approach, considering both qualitative and quantitative parameters. The detailed performance evaluation included the following: systematic analysis of 41 recordable parameters (stored in a spreadsheet database), analysis of feedback and problems encountered during the tests, and overall quality analysis applying the product quality model adapted from ISO/IEC FDIS 9126-1 standard. The data recording and storage and the capability to support product traceability and supply chain management were found to be very satisfactory, while assembly of smart box components (mainly the associated cables), data transferring intervals, and manageability could be improved. From the data retrieved during test activities, in more than 95% of the parameters within 41 columns, the expected values were displayed correctly. Some errors were observed, which might have been caused mainly by barriers that could hinder proper data recording and transfer from the smart box to the central database. These problems can be counteracted and the performance of the SLS can be improved so that it can be upgraded to be a marketable tool that can promote sustainable biomass-to-energy value chains. C1 [Bosona, Techane; Gebresenbet, Girma] Swedish Univ Agr Sci, Dept Energy & Technol, POB 75651, Uppsala, Sweden. [Olsson, Sven-Olof] Mobitron AB, S-56146 Huskvarna, Sweden. [Garcia, Daniel] CIRCE, Fdn Circe Ctr Invest Recursos & Consumos Energet, Mariano Esquillor Gomez 15, Zaragoza 1550018, Spain. [Germer, Sonja] Leibniz Inst Agrartech & Biookon eV ATB, Max Eyth Allee 100, D-14469 Potsdam, Germany. C3 Swedish University of Agricultural Sciences RP Bosona, T (corresponding author), Swedish Univ Agr Sci, Dept Energy & Technol, POB 75651, Uppsala, Sweden. EM techane.bosona@slu.se; girma.gebresenbet@slu.se; soo@mobitron.se; daniel.garcia@fcirce.es; sgermer@atb-potsdam.de CR Bosona T, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10020258 CIRCE, 2014, DESCR BIOM SPEC VAL Dumas JS, 2009, HUM FACTORS ERGON, P231 Gebresenbet G, 2018, APPL SCI-BASEL, V8, DOI 10.3390/app8071162 Hendershott T., 2003, IT Professional, V5, P10, DOI 10.1109/MITP.2003.1216227 Iakovou E, 2010, WASTE MANAGE, V30, P1860, DOI 10.1016/j.wasman.2010.02.030 ISO/IEC, 2000, 91261 ISOIEC FDIS ISO/IEC, 250102011 ISOIEC Santa J, 2012, COMPUT ELECTRON AGR, V80, P31, DOI 10.1016/j.compag.2011.10.010 Seuring S, 2008, J CLEAN PROD, V16, P1699, DOI 10.1016/j.jclepro.2008.04.020 Shashi, 2018, INT J LOGIST MANAG, V29, P792, DOI 10.1108/IJLM-01-2017-0007 NR 11 TC 3 Z9 3 U1 1 U2 17 PD OCT PY 2018 VL 8 IS 10 AR 1987 DI 10.3390/app8101987 WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied SC Chemistry; Engineering; Materials Science; Physics UT WOS:000448653700282 DA 2022-12-14 ER PT J AU Zappi, A Melucci, D Scaramagli, S Zelano, A Marcazzan, GL AF Zappi, Alessandro Melucci, Dora Scaramagli, Sonia Zelano, Antonia Marcazzan, Gian Luigi TI Botanical traceability of unifloral honeys by chemometrics based on head-space gas chromatography SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE Flash GC; Honey; Botanical origin; LDA; Multivariate analysis; Untargeted analysis ID ELECTRONIC NOSE; DISCRIMINATION; MELISSOPALYNOLOGY; SPECTROSCOPY; ORIGIN; SPME AB The botanical origin of honey is subjected to severe controls by Food Control Institutions, both for health protection and for frauds prevention. The complexity of honey makes it very difficult to verify the botanical origin. Among the available validated methods, sensory analysis and melissopalynology are the most widely employed. These methods require a long time and deep consolidated expertise. To shorten analysis time while simplifying the analytical procedure, head-space flash gas chromatography was applied in the present study. Chromatographic peak areas were processed by chemometrics (in particular principal components analysis and linear discriminant analysis). Three hundred and thirty-nine honey samples from twelve categories of unifloral honey were analyzed: acacia, citrus, chestnut, thistle, tree heath, eucalyptus, sunflower, rhododendron, lime, French honeysuckle, fir honeydew, and wood honeydew. Each sample was a priori classified by sensory analysis. The multivariate models were validated by cross validation and test-set validation, with predictive abilities always higher than 80%: good results were obtained both in calibration and in prediction mode, showing a good agreement between this new approach and the traditional one for the determination of the botanical origin of honey. C1 [Zappi, Alessandro; Melucci, Dora] Univ Bologna, Dept Chem G Ciamician, Via Selmi 2, I-40126 Bologna, Italy. [Scaramagli, Sonia; Zelano, Antonia] COOP ITALIA Soc Cooperat, Via Lavoro 6-8, I-40033 Bologna, Italy. [Marcazzan, Gian Luigi] Council Agr Res & Econ, CREA, Res Ctr Agr & Environm, Via Corticella 133, I-40128 Bologna, Italy. C3 University of Bologna; Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA) RP Zappi, A (corresponding author), Univ Bologna, Dept Chem G Ciamician, Via Selmi 2, I-40126 Bologna, Italy. EM alessandro.zappi4@unibo.it CR Alimentarius C., 1987, CODEX STAN 12 1981 S, V11, P7 Alissandrakis E, 2007, J AGR FOOD CHEM, V55, P8152, DOI 10.1021/jf071442y Ampuero S, 2004, EUR FOOD RES TECHNOL, V218, P198, DOI 10.1007/s00217-003-0834-9 [Anonymous], 2002, OFFICIAL J EUROPEAN, P10 Bogdanov S, 1997, HARMONIZED METHODS I Corvucci F, 2015, FOOD CHEM, V169, P297, DOI 10.1016/j.foodchem.2014.07.122 Davies AMC, 2002, J NEAR INFRARED SPEC, V10, P121, DOI 10.1255/jnirs.329 Escriche I, 2012, J FOOD ENG, V109, P449, DOI 10.1016/j.jfoodeng.2011.10.036 Kafadar K., 1999, AM STAT, V53, DOI 10.2307/2685660 Karabagias IK, 2014, FOOD CHEM, V165, P181, DOI 10.1016/j.foodchem.2014.05.033 Kumar N, 2014, TALANTA, V123, P186, DOI 10.1016/j.talanta.2014.02.003 Martin P, 2005, BEE WORLD, V86, P75, DOI 10.1080/0005772X.2005.11417317 McLachlan Geoffrey J., 2004, DISCRIMINANT ANAL ST, V544 Melucci D, 2016, FOOD CHEM, V204, P263, DOI 10.1016/j.foodchem.2016.02.131 Molan P, 1998, BEE WORLD, V79, P59, DOI 10.1080/0005772X.1998.11099381 Moore JC, 2012, J FOOD SCI, V77, pR118, DOI 10.1111/j.1750-3841.2012.02657.x Peng Q, 2015, FOOD CHEM, V178, P301, DOI 10.1016/j.foodchem.2015.01.023 Piana M, 2004, APIDOLOGIE, V37, P275, DOI [10.1051/apido, DOI 10.1051/APIDO] Piasenzotto L, 2003, J SCI FOOD AGR, V83, P1037, DOI 10.1002/jsfa.1502 Radovic BS, 2001, FOOD CHEM, V72, P511, DOI 10.1016/S0308-8146(00)00263-6 Revell LE, 2014, J FOOD MEAS CHARACT, V8, P81, DOI 10.1007/s11694-013-9167-y Riedl J, 2015, ANAL CHIM ACTA, V885, P17, DOI 10.1016/j.aca.2015.06.003 Smith Gordon C S, 2004, J Int Assoc Physicians AIDS Care (Chic), V3, P108, DOI 10.1177/154510970400300401 STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x Todeschini R, 1998, INTRO CHEMIOMETRIA Van Aelst S, 2013, J STAT SOFTW, V53, P1 Verzera A, 2012, COMPREHENSIVE SAMPLI, V4, P87 NR 27 TC 3 Z9 3 U1 2 U2 33 PD DEC PY 2018 VL 244 IS 12 BP 2149 EP 2157 DI 10.1007/s00217-018-3123-3 WC Food Science & Technology SC Food Science & Technology UT WOS:000446540600008 DA 2022-12-14 ER PT J AU Fang, X Suo, ZF Zhang, L Zhang, QC Zhang, F AF Fang, Xi Suo, Zhufeng Zhang, Lei Zhang, Qingchuan Zhang, Fan TI Combined phase offset channel estimation method for optical OFDM/OQAM SO OPTICAL FIBER TECHNOLOGY DT Article DE OFDM/OQAM; Channel estimation; Time-frequency localization property; IMI AB On the basis of coherent optical orthogonal frequency-division-multiplexing (CO-OFDM), CO-OFDM/offset quadrature amplitude modulation (CO-OFDM/OQAM) improves the spectrum efficiency by utilizing specially designed filter banks to eliminate the cyclic prefix (CP) inserted between consecutive OFDM blocks and decreases the demanding of guard band between two channels, being a promising candidate for the next generation optical transmission link. Due to the relaxation of the orthogonal condition from the complex field to the real field, chromatic Dispersion (CD) and polarization mode dispersion (PMD) bring serious intrinsic imaginary interference (IMI) to an optical OFDM/OQAM system. Channel estimation schemes based on the interference approximation method (IAM) have been proved to be effective in suppressing the IMI with low complexity. For the IAMs, increasing the power of the pseudo pilot could decrease the effects of amplified spontaneous emission (ASE) noise and other frequency domain residual error on the channel estimation accuracy. In most of the previous reported IAM works, pilot blocks were designed according to the structure of the center pilot block loaded, while the two side pilots nulled for simplicity. However, the power of the pseudo pilot under this method did not reach the maximum, resulting in non-optimal channel estimation accuracy. In this paper, channel estimation method based on the combined phase offset (CMPO) is proposed and systematically discussed. For the proposed method, three consecutive full loaded blocks are treated as the pilot blocks and the optimal pilot structure have been obtained by utilizing the maximum likelihood algorithm. As validated by numerous Montel Carlo simulations results, system robustness against IMI, phase noised induced interference, and nonlinear effect have been improved significantly, thanks to CMPO. C1 [Fang, Xi; Suo, Zhufeng; Zhang, Lei] Beijing Elect Sci & Technol Inst, Beijing 100070, Peoples R China. [Suo, Zhufeng] Beijing Univ Posts & Telecommun, Sch Cyberspace Secur, Beijing 100876, Peoples R China. [Zhang, Lei; Zhang, Qingchuan] Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing 100048, Peoples R China. [Zhang, Fan] Peking Univ, State Key Lab Adv Opt Commun Syst & Networks, Frontiers Sci Ctr Nanooptoelect, Dept Elect, Beijing 100871, Peoples R China. C3 Beijing Electronic Science & Technology Institute; Beijing University of Posts & Telecommunications; Beijing Technology & Business University; Peking University RP Fang, X (corresponding author), Beijing Elect Sci & Technol Inst, Beijing 100070, Peoples R China. EM xfang@besti.edu.cn CR Aoki Y., P OFC, P1 Bi MH, 2017, OPT COMMUN, V394, P129, DOI 10.1016/j.optcom.2017.03.017 Bodinier Q., 2016, IEICE T FUND ELECT C, P1 CHAO L, 2015, OPT LETT, V40, P1185, DOI DOI 10.1364/OL.40.001185 Cheng G B, 2013, Science China Information Sciences, V56, P1 DOBROSLAV T, 2014, IEEE PHOTONIC TECH L, V26, P637, DOI DOI 10.1109/LPT.2013.2297621 Fang, 2019, 18 INT C OPT COMM NE Fang, 2018, 2018 13 APCA INT C C, P120 Fang X, 2019, J LIGHTWAVE TECHNOL, V37, P5392, DOI 10.1109/JLT.2019.2936032 Fang X, 2019, IEEE PHOTONIC TECH L, V31, P1281, DOI 10.1109/LPT.2019.2925662 Fang X, 2017, J LIGHTWAVE TECHNOL, V35, P1837, DOI 10.1109/JLT.2017.2665464 Fang X, 2016, J LIGHTWAVE TECHNOL, V34, P891, DOI 10.1109/JLT.2015.2507605 Fang X, 2015, J LIGHTWAVE TECHNOL, V33, P2743, DOI 10.1109/JLT.2015.2410281 Fang X, 2014, IEEE PHOTONIC TECH L, V26, P376, DOI 10.1109/LPT.2013.2293515 Fang X, 2013, IEEE PHOTONIC TECH L, V25, P619, DOI 10.1109/LPT.2013.2245887 He JL, 2016, OPT EXPRESS, V24, P13418, DOI 10.1364/OE.24.013418 Javaudin JP, 2003, IEEE VTS VEH TECHNOL, P1581 Jiang Z., 2013, NETW OP MAN S APNOMS, P1 Katselis D., 2011, P EUSIPCO 11 BARC SP Kofidis E., 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA 2011), P579, DOI 10.1109/ICSIPA.2011.6144161 Lacroix-Penther, 2002, P 7 INT OFDM WORKSH, V19 lcskei H. B?, 2003, ADV GABOR ANAL Lele C, 2008, EUR T TELECOMMUN, V19, P741, DOI 10.1002/ett.1332 Lele C, 2008, IEEE ICC, P1302, DOI 10.1109/ICC.2008.253 Li A, 2012, J LIGHTWAVE TECHNOL, V30, P3931, DOI 10.1109/JLT.2012.2206369 Mandal GC, 2019, CHIN OPT LETT, V17, DOI 10.3788/COL201917.060602 Mao TQ, 2017, IEEE COMMUN LETT, V21, P761, DOI 10.1109/LCOMM.2016.2635634 Meihua, 2018, IEEE PHOTONICS J, V10, P1 Nedic S, 2000, IEEE T COMMUN, V48, P1077, DOI 10.1109/26.855512 Schaich, 2010, 2010 EUR WIR C EW LU, P1051, DOI DOI 10.1109/EW.2010.5483518 Schaich F, 2014, 2014 6TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS, CONTROL AND SIGNAL PROCESSING (ISCCSP), P457, DOI 10.1109/ISCCSP.2014.6877912 Schmidl TM, 1997, IEEE T COMMUN, V45, P1613, DOI 10.1109/26.650240 Signell S., 2008, P ICC 09 DRESD GERM Wang, 2017, COMM TECHN ICCT 2017 Ying CL, 2014, IEEE PHOTONICS J, V6, DOI 10.1109/JPHOT.2014.2366124 Zhang QW, 2017, OPT COMMUN, V387, P12, DOI 10.1016/j.optcom.2016.11.032 Zhao J, 2016, PHOTONIC NETW COMMUN, V31, P294, DOI 10.1007/s11107-015-0545-8 Zhao J, 2015, OPT EXPRESS, V23, P17638, DOI 10.1364/OE.23.017638 Zhao J, 2014, OPT EXPRESS, V22, P25661, DOI 10.1364/OE.22.025651 Zhou Z, 2017, IEEE J SEL AREA COMM, V35, P1524, DOI 10.1109/JSAC.2017.2699338 NR 40 TC 3 Z9 3 U1 1 U2 9 PD JAN PY 2021 VL 61 AR 102390 DI 10.1016/j.yofte.2020.102390 WC Engineering, Electrical & Electronic; Optics; Telecommunications SC Engineering; Optics; Telecommunications UT WOS:000621212200010 DA 2022-12-14 ER PT J AU Hidalgo, MJ Fechner, DC Ballabio, D Marchevsky, EJ Pellerano, RG AF Hidalgo, Melisa J. Fechner, Diana C. Ballabio, Davide Marchevsky, Eduardo J. Pellerano, Roberto G. TI Traceability of soybeans produced in Argentina based on their trace element profiles SO JOURNAL OF CHEMOMETRICS DT Article DE class-modeling techniques; geographical origin; soybean grains ID FOOD; IDENTIFICATION; VERIFICATION; RECOGNITION AB Soybean (Glycine max(L.) Merril) is a popular foodstuff and crop plant, used in human and animal food. In this work, multielement analysis of soybean grains samples in combination with chemometric tools was used to classify the geographical origins. For this purpose, 120 samples from three provinces of Argentina were analyzed for a panel of 20 trace elements by inductively coupled plasma mass spectrometry. First, we used principal component analysis for exploratory analysis. Then, supervised classification techniques such as support vector machine (SMV) discriminant analysis (SVM-DA), random forest,k-nearest neighbors, and class-modeling techniques such as soft independent modeling of class analogy (SIMCA), potential functions, and one-class SVM were applied as tools to establish a model of origin of samples. The performance of the techniques was compared using global indexes. Among all the models tested, SVM and SIMCA showed the highest percentages in terms of prediction ability in cross-validation with average values of 99.3% for SVM-DA and a median value of balanced accuracy of 96.0%, 91.7%, and 88.3% for the three origins using SIMCA. Results suggested that the developed methodology by chemometric techniques is robust and reliable for the geographical classification of soybean samples from Argentina. C1 [Hidalgo, Melisa J.; Fechner, Diana C.; Pellerano, Roberto G.] IUNNE, CONICET, Inst Quim Basica & Aplicada Nordeste Argentino IQ, Fac Ciencias Exactas & Nat & Agrimensura, Ave Libertad 5400, RA-3400 Corrientes, Argentina. [Ballabio, Davide] Univ Milano Bicocca, Milano Chemometr & QSAR Res Grp, Dept Ciencias Ambientales, Piazza Sci 1, I-20126 Milan, Italy. [Marchevsky, Eduardo J.] UNSL, CONICET, Inst Quim San Luis INQUISAL, Fac Quim Bioquim & Farm, Ave Ejercito Andes 950, RA-5700 San Luis, Argentina. C3 Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); University of Milano-Bicocca; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); Universidad Nacional de San Luis RP Hidalgo, MJ (corresponding author), UNNE, Nstituto Quim Basica & Aplicada Nordeste Argentin, CONICET, Fac Ciencias Exactas & Nat Agrimensura, Ave Libertad 5400, RA-3400 Corrientes, Argentina. EM hidalgo.melisa@conicet.gov.ar CR Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Ballabio D, 2013, ANAL METHODS-UK, V5, P3790, DOI 10.1039/c3ay40582f Barbosa RM, 2016, J FOOD COMPOS ANAL, V45, P95, DOI 10.1016/j.jfca.2015.09.010 Berrueta LA, 2007, J CHROMATOGR A, V1158, P196, DOI 10.1016/j.chroma.2007.05.024 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Bro R, 2014, ANAL METHODS-UK, V6, P2812, DOI 10.1039/c3ay41907j Gemperline P., 2006, PRACTICAL GUIDE CHEM Granato D, 2018, COMPR REV FOOD SCI F, V17, P663, DOI 10.1111/1541-4337.12341 Guerbai Y, 2015, PATTERN RECOGN, V48, P103, DOI 10.1016/j.patcog.2014.07.016 Kemsley EK, 2019, FOOD CONTROL, V105, P102, DOI 10.1016/j.foodcont.2019.05.021 Marini F, 2013, DATA HANDL SCI TECHN, V28, P1 Mataveli LRV, 2010, METALLOMICS, V2, P800, DOI 10.1039/c0mt00040j Mees C, 2018, TALANTA, V177, P4, DOI 10.1016/j.talanta.2017.09.056 Oliveri P, 2017, ANAL CHIM ACTA, V982, P9, DOI 10.1016/j.aca.2017.05.013 Oliveri P, 2012, TRAC-TREND ANAL CHEM, V35, P74, DOI 10.1016/j.trac.2012.02.005 Otaka A, 2014, FOOD CHEM, V147, P318, DOI 10.1016/j.foodchem.2013.09.142 Team RC, 2015, R LANG ENV STAT COMP Tibshirani, 2014, INTRO STAT LEARNING Varmuza K., 2009, INTRO MULTIVARIATE S, DOI [10.1201/9781420059496.First, DOI 10.1201/9781420059496.FIRST] Wadood SA, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104295 NR 20 TC 2 Z9 2 U1 1 U2 17 PD DEC PY 2020 VL 34 IS 12 SI SI AR e3252 DI 10.1002/cem.3252 EA JUN 2020 WC Automation & Control Systems; Chemistry, Analytical; Computer Science, Artificial Intelligence; Instruments & Instrumentation; Mathematics, Interdisciplinary Applications; Statistics & Probability SC Automation & Control Systems; Chemistry; Computer Science; Instruments & Instrumentation; Mathematics UT WOS:000540900600001 DA 2022-12-14 ER PT J AU Pilipuk, AV Gusakov, GV Chaikouski, AL Rastorgouev, PV Karpovich, NV Makrak, SV Pochtovaya, IG AF Pilipuk, Andrey, V Gusakov, Gordei, V Chaikouski, Andrey L. Rastorgouev, Petr, V Karpovich, Natallya, V Makrak, Svetlana, V Pochtovaya, Irina G. TI SYSTEM OF MEASURES FOR THE DEVELOPMENT OF THE VEGETABLE SEED MARKET IN THE REPUBLIC OF BELARUS SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF BELARUS-AGRARIAN SERIES DT Article DE system of measures; selection; seed production; vegetable growing; vegetable crops; vegetable market; regulation; self-sufficiency; import substitution AB Original and elite seeds of agricultural plants are the basis for production of reproductive seeds and contribute to an increase in the production of vegetable products, providing raw materials to processing companies of the republic. Greenhouse varieties and hybrids of vegetable crops have a high potential, have a complex of economically valuable features and can occupy a certain niche in professional and especially amateur vegetable growing. However, implementation of the potential of the created varieties and hybrids of vegetable crops is largely determined by the conditions of seed production. It has been determined, that the situation in the country is when over 85 % of the seeds of vegetable crops used for the production of vegetables are imported. At the same time, the production of the necessary volumes of seeds, which will ensure the standard production of vegetable products for consumption by the population and the manufacturing industry, is not only a factor in the development of the vegetable market, but also a condition for helping to guarantee the country's food security. In this regard, it is necessary to intensify domestic selection and seed production in the vegetable sector. The article substantiates a practice-oriented system of measures for the development of the market of seeds of vegetable crops in the Republic of Belarus, including the following blocks of regulatory action: stimulating the development of the sphere of reproduction of vegetable seeds; improving the accounting of the movement of commodity flows in the market of vegetable seeds; integration of the country into the world system of commodity seed production of vegetable crops; ensuring traceability of seed turnover based on digitalization; development of an integrated technical and economic approach to the implementation of production activities; monitoring of the market of seeds of vegetable crops. The development contains a set of systematized tools, the use of which allows justifying effective management decisions in the field of agro-industrial complex for the effective formation of a favorable organizational and economic environment for the production and sale of competitive seeds of vegetable crops of domestic selection. C1 [Pilipuk, Andrey, V; Rastorgouev, Petr, V; Karpovich, Natallya, V; Makrak, Svetlana, V; Pochtovaya, Irina G.] Natl Acad Sci Belarus, Inst Syst Res Agroind Complex, Minsk, BELARUS. [Gusakov, Gordei, V] Natl Acad Sci Belarus, Inst Meat & Dairy Ind, Minsk, BELARUS. [Chaikouski, Andrey L.] Natl Acad Sci Belarus, Inst Vegetable Growing, Agrotown Samokhvalovichi, BELARUS. C3 National Academy of Sciences of Belarus (NASB); Institute of Systems Researches in Agroindustrial Complex of the National Academy of Sciences of Belarus; National Academy of Sciences of Belarus (NASB); Institute for the Meat & Dairy Industry of the National Academy of Sciences of Belarus; National Academy of Sciences of Belarus (NASB); Institute of Vegetable Growing of the National Academy of Sciences of Belarus RP Pilipuk, AV (corresponding author), Natl Acad Sci Belarus, Inst Syst Res Agroind Complex, Minsk, BELARUS. EM pilipuk@list.ru; gordei.v.gusakov@gmail.com; director@belniio.by; rastorgouev-pv@rambler.ru; karpovich_nv@list.ru; makraksv@inbox.ru; pochira@rambler.ru CR Assosementi, ASS IT SEM belstat, EXPORT IMPORT GOODS Chaikovsky A, 2020, NAUKA INNOVATSII, P79 eurasiancommission, SINGLE REQUIREMENTS FAOSTAT, DATA fsvps, ROSSELKHOZNADZOR Gusakov G., 2019, NAUKA INNOVATSII, P68 Gusakov G., 2020, NAUKA INNOVATSII, P24 Gusakov G. V., 2018, INTEGRATED FOOD SECU Karpovich N. V., AGRARNAYA EKONOMIKA, V2021, P53, DOI [10.29235/1818-9806-2021-9-53-63, DOI 10.29235/1818-9806-2021-9-53-63] Klimenko N. N, 2022, KARTOFELOVOSHCHI, P4 Makrak S. V., AGRARNAYA EKONOMIKA, V2022, P32, DOI [10.29235/1818-9806-2022-4-32-46, DOI 10.29235/1818-9806-2022-4-32-46] Makutsenya E. P., 2021, EKONOMICHESKIE VOPRO, P257, DOI [10.47612/0132-3555-2021-49, DOI 10.47612/0132-3555-2021-49] National Statistical Committee of the Republic of Belarus, 2021, AGR REP BEL STAT BOO Pilipuk A. V., 2018, COMPETITIVENESS ENTE Rastorguev P. V., 2021, EKONOMICHESKIE VOPRO, P305 semena, FEDERAL STATE INFORM Syngenta, RUSSIA vatzum, STATE PLANT SERVICE NR 19 TC 0 Z9 0 U1 1 U2 1 PY 2022 VL 60 IS 3 BP 263 EP 278 DI 10.29235/1817-7204-2022-60-3-263-278 WC Agriculture, Multidisciplinary SC Agriculture UT WOS:000840724100001 DA 2022-12-14 ER PT J AU Tnah, LH Lee, SL Ng, KKS Tani, N Bhassu, S Othman, RY AF Tnah, Lee Hong Lee, Soon Leong Ng, Kevin K. S. Tani, Naoki Bhassu, Subha Othman, Rofina Yasmin TI Geographical traceability of an important tropical timber (Neobalanocarpus heimii) inferred from chloroplast DNA SO FOREST ECOLOGY AND MANAGEMENT DT Article DE Timber tracking; Illegal logging; Forest certification; Chain of custody certification; Dipterocarpaceae ID NONCODING REGIONS; UNIVERSAL PRIMERS; PCR PRIMERS; OAK WOOD; PHYLOGEOGRAPHY; PLANTS; POLYMORPHISMS; AMPLIFICATION; CERTIFICATION; HISTORY AB The inbuilt unique properties of DNA within the timber could serve as tracking and monitoring tools to verify the legality of a suspected timber in the context of illegal logging, forest certification and chain of custody certification. By using Neobalanocarpus heimii (Dipterocarpaceae) as an example, a population identification database and haplotype distribution map in Peninsular Malaysia were generated for authenticity testing based on four chloroplast DNA markers (trnL intron, trnG intron, trnK intron and psbK-trnS spacer) Twenty one haplotypes were identified from 10 significant intraspecific variable sites The results clearly revealed that only northern and southern regions of Peninsular Malaysia were distinguishable. Thus, this database could only be used to determine the wood lot of unknown origin at the regional level. Statistical procedure based on the composition of wood lot was used to test whether a suspected timber conforms to a given regional origin Overall, the observed type I and II errors of the database showed good concordance with the predicted 5% threshold, which might indicate that the database is useful to reveal provenance and establish conformity of wood lot from northern and southern regions of Peninsular Malaysia. Applications of this database for timber tracking are discussed. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved C1 [Lee, Soon Leong] FRIM, Genet Lab, Kepong 52109, Selangor Darul, Malaysia. [Tani, Naoki] Japan Int Res Ctr Agr Sci, Div Forestry, Tsukuba, Ibaraki 3058686, Japan. [Bhassu, Subha; Othman, Rofina Yasmin] Univ Malaya, Kuala Lumpur 50603, Malaysia. C3 Institute Penyelidikan Perhutanan Malaysia; Japan International Research Center for Agricultural Sciences; Universiti Malaya RP Lee, SL (corresponding author), FRIM, Genet Lab, Kepong 52109, Selangor Darul, Malaysia. CR *AS FOR PARTN, 2005, DEV MIN STAND LEG TI Avise J. C., 2000, PHYLOGEOGRAPHY HIST Carr M., 2007, CONSERVATION MAGAZIN, V8, P37 Cavers S, 2003, MOL ECOL, V12, P1451, DOI 10.1046/j.1365-294X.2003.01810.x Cheng YP, 2005, MOL ECOL, V14, P2075, DOI 10.1111/j.1365-294X.2005.02567.x Chihambakwe M., 1997, Commonwealth Forestry Review, V76, P191 CHUA LSL, 1998, IUCN 2008 2008 IUCN Deguilloux MF, 2003, MOL ECOL, V12, P1629, DOI 10.1046/j.1365-294X.2003.01836.x Deguilloux MF, 2002, P ROY SOC B-BIOL SCI, V269, P1039, DOI 10.1098/rspb.2002.1982 DEMESURE B, 1995, MOL ECOL, V4, P129, DOI 10.1111/j.1365-294X.1995.tb00201.x Durand SR, 1999, J ARCHAEOL SCI, V26, P185, DOI 10.1006/jasc.1998.0315 English NB, 2001, P NATL ACAD SCI USA, V98, P11891, DOI 10.1073/pnas.211305498 European Commission, 2003, COM20030449 EUR COMM Fjellheim S, 2006, J BIOGEOGR, V33, P1470, DOI 10.1111/j.1365-2699.2006.01521.x *FSC, 2005, BEC FOR PEOPL MATT F *FSC, 2005, LEAD OUR WORLD TOW R *GLOB EC NETW, 2008, WHAT IS EC Grivet D, 2001, MOL ECOL NOTES, V1, P345, DOI 10.1046/j.1471-8278.2001.00107.x Heinze B, 2007, PLANT METHODS, V3, DOI 10.1186/1746-4811-3-4 HOFFMANN E, 1994, FRESEN J ANAL CHEM, V350, P253, DOI 10.1007/BF00322478 Huang SF, 2004, J BIOGEOGR, V31, P1251, DOI 10.1111/j.1365-2699.2004.01082.x Lyke J, 1996, J FOREST, V94, P16 MURRAY MG, 1980, NUCLEIC ACIDS RES, V8, P4321, DOI 10.1093/nar/8.19.4321 Newsom, 2004, GOVERNING MARKETS FO PerezCoello MS, 1997, J CHROMATOGR A, V778, P427, DOI 10.1016/S0021-9673(97)00286-0 PETIT RJ, 1993, HEREDITY, V71, P630, DOI 10.1038/hdy.1993.188 PETIT RJ, 1993, THEOR APPL GENET, V87, P122, DOI 10.1007/BF00223755 RICHERT W, 2003, ONDERZOEK BELEIDSMAT Shepherd LD, 2007, MOL ECOL, V16, P4536, DOI 10.1111/j.1365-294X.2007.03451.x Symington C. F., 1943, MALAYAN FOREST RECOR, V16 TABERLET P, 1991, PLANT MOL BIOL, V17, P1105, DOI 10.1007/BF00037152 Thomas A. V., 1953, Malayan Forester, V16, P103 TNAH LH, 2007, THESIS U PUTRA MALAY Towey JP, 1996, AM J ENOL VITICULT, V47, P17 Visseren-Hamakers IJ, 2007, GLOBAL ENVIRON CHANG, V17, P408, DOI 10.1016/j.gloenvcha.2006.11.003 Weising K, 1999, GENOME, V42, P9, DOI 10.1139/gen-42-1-9 NR 36 TC 22 Z9 22 U1 0 U2 20 PD OCT 10 PY 2009 VL 258 IS 9 BP 1918 EP 1923 DI 10.1016/j.foreco.2009.07.029 WC Forestry SC Forestry UT WOS:000271093200013 DA 2022-12-14 ER PT J AU Voulodimos, AS Patrikakis, CZ Sideridis, AB Ntafis, VA Xylouri, EM AF Voulodimos, Athanasios S. Patrikakis, Charalampos Z. Sideridis, Alexander B. Ntafis, Vasileios A. Xylouri, Eftychia M. TI A complete farm management system based on animal identification using RFID technology SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE RFID; Animal identification; Agriculture; Database; Traceability; Livestock; Monitoring ID ELECTRONIC IDENTIFICATION; TRACEABILITY; REGISTRATION; SHEEP; GOAT AB In this paper, a platform for livestock management based on RFID-enabled mobile devices is described. The platform is the outcome of a research project named FARMA, and is based on the deployment of mobile computing, combined with RFID technology and wireless and mobile networking. The platform apart from using a data repository through which the RFID tag numbers are associated with animal data records. it introduces the use of rewritable tags, for the storage of information that can be used to identify the animal in case it gets lost, or even recognize some basic information about it (e.g. behavior against other animals) without the need of contacting the related database. An implementation in the context of the FARMA project is also given, together with the corresponding details, while the results of the evaluation that took place in the context of the project are discussed. (C) 2009 Elsevier B.V. All rights reserved. C1 [Voulodimos, Athanasios S.; Patrikakis, Charalampos Z.] Natl Tech Univ Athens, Sch Elect & Comp Engn, GR-15773 Athens, Greece. [Sideridis, Alexander B.; Ntafis, Vasileios A.; Xylouri, Eftychia M.] Agr Univ Athens, Athens 11855, Greece. C3 National Technical University of Athens; Agricultural University of Athens RP Voulodimos, AS (corresponding author), Natl Tech Univ Athens, Sch Elect & Comp Engn, 9 Heroon Polytechneiou Str, GR-15773 Athens, Greece. EM thanos@telecom.ntua.gr CR BARBARI M, 2008, SPATIAL IDENTIFICATI, V8 Bass P. D., 2008, Professional Animal Scientist, V24, P302 Bowling MB, 2008, PROFESSIONAL ANIMAL Caja G, 2005, J ANIM SCI, V83, P2215 Caja G, 1999, COMPUT ELECTRON AGR, V24, P45, DOI 10.1016/S0168-1699(99)00036-8 Conill C, 2002, J ANIM SCI, V80, P919 Conill C, 2000, J ANIM SCI, V78, P3001 Eradus WJ, 1999, COMPUT ELECTRON AGR, V24, P91, DOI 10.1016/S0168-1699(99)00039-3 *FARMA, 2008, PROJ FIN REP P UNPUB Garin D, 2003, J ANIM SCI, V81, P879 Johnson RT, 1998, NEW ENGL J MED, V339, P1994, DOI 10.1056/NEJM199812313392707 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Ng ML, 2005, IEEE 2005 International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications Proceedings, Vols 1 and 2, P67 Ntafis V, 2008, WIREL NETW MOB COMMU, P165 *RFID, TECHN NEWS INS Ribo O, 2001, REV SCI TECH OIE, V20, P426 Saa C, 2005, J ANIM SCI, V83, P1215 Shanahan C, 2009, COMPUT ELECTRON AGR, V66, P62, DOI 10.1016/j.compag.2008.12.002 Silva Késia O. da, 2006, Eng. Agríc., V26, P11, DOI 10.1590/S0100-69162006000100002 Stanford K, 2001, REV SCI TECH OIE, V20, P510, DOI 10.20506/rst.20.2.1291 TREVHARTEN A, 2008, 7 INT C MOB BUS BARC *USDA, 2008, PUSH PLAN MOV NAIS F Wismans WMG, 1999, COMPUT ELECTRON AGR, V24, P99, DOI 10.1016/S0168-1699(99)00040-X ELECTROCOM ELECTROCO ASP TECHNOLOGY FEATU NR 25 TC 122 Z9 143 U1 2 U2 69 PD MAR PY 2010 VL 70 IS 2 SI SI BP 380 EP 388 DI 10.1016/j.compag.2009.07.009 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000274829400013 DA 2022-12-14 ER PT J AU Chis, LM Vodnar, DC AF Chis, Lavinia-Maria Vodnar, Dan-Cristian TI Identification of Molecular Markers Specific to the Bovine Species Used in the Production of Food Products of Animal Origin SO BULLETIN OF UNIVERSITY OF AGRICULTURAL SCIENCES AND VETERINARY MEDICINE CLUJ-NAPOCA-FOOD SCIENCE AND TECHNOLOGY DT Article DE DNA markers; genotyping; traceability; PCR Multiplex; bovine ID DNA-SEQUENCES; RETROPOSONS; PSEUDOGENES; VIABILITY AB Molecular markers are a helpful tool for species detection of meat origin, in order to determine with accuracy the possible fraud of animal-based food products. In this study, blood, milk and cheese samples were taken from the Romanian "Baltata" bovine species. In the blood extraction stage, the commercial Wizard Genomic DNA Extraction Kit was used, and for the milk and cheese extraction, the SureFood (R) PREP Advanced kit was used. Target DNA amplification in all the three matrices was performed with the StockMarks for Cattle kit. It was found that the analyzed loci had the size of the fragments within the reference range given by the kit used, which concludes that the blood, milk and the cheese have the same origin, from the same animal of the bovine species. Therefore, through the genotyping technique, traceability of food products can be achieved and the species of origin can be identified. C1 [Chis, Lavinia-Maria; Vodnar, Dan-Cristian] Univ Agr Sci & Vet Med, Life Sci Inst, Dept Food Sci, 3-5 Calea Manastur St, Cluj Napoca 400372, Romania. C3 University of Agricultural Sciences & Veterinary Medicine Cluj Napoca RP Vodnar, DC (corresponding author), Univ Agr Sci & Vet Med, Life Sci Inst, Dept Food Sci, 3-5 Calea Manastur St, Cluj Napoca 400372, Romania. EM dan.vodnar@usamvcluj.ro CR BARENDSE W, 1994, NAT GENET, V6, P227, DOI 10.1038/ng0394-227 Calinoiu LF, 2020, BIOMOLECULES, V10, DOI 10.3390/biom10010021 Calinoiu LF, 2019, ANTIOXIDANTS-BASEL, V8, DOI 10.3390/antiox8090372 Calinoiu LF, 2018, NUTRIENTS, V10, DOI 10.3390/nu10111615 Calinoiu LF, 2019, COATINGS, V9, DOI 10.3390/coatings9030194 Calinoiu LF, 2016, B U A SCI VET MED CL, V73, P55, DOI 10.15835/buasvmcn-fst:12448 DYER MR, 1989, BIOCHEM J, V260, P249, DOI 10.1042/bj2600249 HWU HR, 1986, P NATL ACAD SCI USA, V83, P3875, DOI 10.1073/pnas.83.11.3875 KAUKINEN J, 1992, NUCLEIC ACIDS RES, V20, P2955, DOI 10.1093/nar/20.12.2955 Kelemen CD, 2018, SCI PAP-SER B-HORTIC, V62, P629 Martau GA, 2019, POLYMERS-BASEL, V11, DOI 10.3390/polym11111837 Mitrea L, 2017, B U A SCI VET MED CL, V74, P43, DOI 10.15835/buasvmcn-fst:0014 Mocan A, 2018, FOOD CHEM TOXICOL, V115, P414, DOI 10.1016/j.fct.2018.01.054 Okada N., 1995, IMPACT SHORT INTERSP, P61 Pop R, 2018, NOT BOT HORTI AGROBO, V46, P90, DOI 10.15835/nbha46110884 Popescu RA, 2016, BIOMATER SCI-UK, V4, P1252, DOI 10.1039/c6bm00270f Putnova L., 2011, IRANINAN J APPL ANIM ROGERS JH, 1985, INT REV CYTOL, V93, P187, DOI 10.1016/S0074-7696(08)61375-3 SCOTT A F, 1987, Genomics, V1, P113, DOI 10.1016/0888-7543(87)90003-6 Stana A, 2016, MOLECULES, V21, DOI 10.3390/molecules21111595 Szabo K, 2019, ANTIOXIDANTS-BASEL, V8, DOI 10.3390/antiox8080292 TAUTZ D, 1984, NUCLEIC ACIDS RES, V12, P4127, DOI 10.1093/nar/12.10.4127 Teleky BE, 2019, POLYMERS-BASEL, V11, DOI 10.3390/polym11061035 Vaiman D, 1997, MAMM GENOME, V8, P153, DOI 10.1007/s003359900378 van de Goor LHP, 2011, INT J LEGAL MED, V125, P111, DOI 10.1007/s00414-009-0353-8 Vasile C, 2017, EXPRESS POLYM LETT, V11 Vodnar DC, 2010, INT J FOOD SCI TECH, V45, P2345, DOI 10.1111/j.1365-2621.2010.02406.x Vodnar DC, 2014, LWT-FOOD SCI TECHNOL, V57, P406, DOI 10.1016/j.lwt.2013.12.043 WEINER AM, 1986, ANNU REV BIOCHEM, V55, P631, DOI 10.1146/annurev.bi.55.070186.003215 Xu LY, 2017, GENOME BIOL EVOL, V9, P20, DOI 10.1093/gbe/evw256 Zaulet M, 2008, IMPLEMENTAREA SISTEM NR 32 TC 0 Z9 0 U1 0 U2 9 PY 2020 VL 77 IS 1 BP 62 EP 73 DI 10.15835/buasvmcn-fst:2019.0035 WC Food Science & Technology SC Food Science & Technology UT WOS:000536725500007 DA 2022-12-14 ER PT J AU van der Berg, JP Kleter, GA Battaglia, E Groenen, MAM Kok, EJ AF van der Berg, Jan Pieter Kleter, Gijs A. Battaglia, Evy Groenen, Martien A. M. Kok, Esther J. TI Developments in genetic modification of cattle and implications for regulation, safety and traceability SO FRONTIERS OF AGRICULTURAL SCIENCE AND ENGINEERING DT Review DE cattle; food safety; gene editing; genetic modification; GMO detection; regulation ID TRANSGENIC CATTLE; DAIRY-CATTLE; NUCLEAR TRANSFER; MILK; MICROINJECTION; EXPRESSION; GENERATION; SELECTION; PRODUCE; FUTURE AB Genetic modification techniques, in particular novel gene editing technologies, hold the yet unfulfilled promise of altering genetic traits in farm animals more efficiently than by crossbreeding, allowing for a more rapid development of new cattle breeds with distinct traits. Gene editing technologies allow for the directed alteration of specific traits and thereby have the potential to enhance, for instance, disease resilience, production yield and the production of desired substances in milk. The potential implications of these technological advancements, which are often combined with animal cloning methods, are discussed both for animal health and for consumer safety, also with consideration of available methods for the detection and identification of the related products in the food supply chain. Finally, an overview is provided of current regulatory approaches in the European Union (EU) and major countries exporting beef to the EU, for products from animals bred through established practices as well as modern biotechnologies. C1 [van der Berg, Jan Pieter; Kleter, Gijs A.; Battaglia, Evy; Kok, Esther J.] Wageningen Univ & Res, Wageningen Food Safety Res, NL-6700 AE Wageningen, Netherlands. [Groenen, Martien A. M.] Wageningen Univ & Res, Anim Breeding & Genom, NL-6700 AH Wageningen, Netherlands. C3 Wageningen University & Research; Wageningen University & Research RP van der Berg, JP (corresponding author), Wageningen Univ & Res, Wageningen Food Safety Res, NL-6700 AE Wageningen, Netherlands. EM janpieter.vanderberg@wur.nl CR Alimentarius C., 2008, GUIDELINE CONDUCT FO Andersson HC, 2012, EFSA J, V10, DOI 10.2903/j.efsa.2012.2501 Barlow S, 2008, EFSA J, V6, DOI 10.2903/j.efsa.2008.767 Bellini J., 2018, THIS GENE EDITED CAL Brophy B, 2003, NAT BIOTECHNOL, V21, P157, DOI 10.1038/nbt783 Canada Food Inspection Agency (CFIA), 2019, AN AN PROD DER MOD B Carlson DF, 2016, NAT BIOTECHNOL, V34, P479, DOI 10.1038/nbt.3560 Chan AWS, 1998, P NATL ACAD SCI USA, V95, P14028, DOI 10.1073/pnas.95.24.14028 Chen SH, 2002, BIOL REPROD, V67, P1488, DOI 10.1095/biolreprod.102.006981 Cibelli JB, 1998, SCIENCE, V280, P1256, DOI 10.1126/science.280.5367.1256 Dikmen S, 2014, J DAIRY SCI, V97, P5508, DOI 10.3168/jds.2014-8087 Drost M, 2007, THERIOGENOLOGY, V68, P487, DOI 10.1016/j.theriogenology.2007.04.023 EGE, 2008, ETH ASP AN CLON FOOD Eurofins Gene Scan Technologies GmbH, 2019, NEW KIT DET GEN MOD European Commission (EC), 2015, STUD LAB PROD CLON A, DOI 10.2762/028232 European Commission (EC), EU MEAT MARK OBS BEE Eyestone WH, 1999, THERIOGENOLOGY, V51, P509, DOI 10.1016/S0093-691X(98)00244-1 EYESTONE WH, 1994, REPROD FERT DEVELOP, V6, P647, DOI 10.1071/RD9940647 FAO, 2016, DEV INT MULT AN REC, V19 GAGNE M, 1995, MOL REPROD DEV, V41, P184, DOI 10.1002/mrd.1080410209 Galli C, 2012, REPROD DOMEST ANIM, V47, P2, DOI 10.1111/j.1439-0531.2012.02045.x Gao YP, 2017, GENOME BIOL, V18, DOI 10.1186/s13059-016-1144-4 Government of Argentina, 2019, ARG PRES WTO DECL GE Government of Zimbabwe, 2019, CUST EXC SUSP AM REG Grosse-Hovest L, 2004, P NATL ACAD SCI USA, V101, P6858, DOI 10.1073/pnas.0308487101 Haires W., 2019, WHAT IS EBV CAN IT H Hayes BJ, 2013, TRENDS GENET, V29, P206, DOI 10.1016/j.tig.2012.11.009 Health Canada, 2003, FOOD DIR INT POL FOO Hodges Craig A, 2003, Reprod Biol Endocrinol, V1, P81, DOI 10.1186/1477-7827-1-81 Hofmann A, 2004, BIOL REPROD, V71, P405, DOI 10.1095/biolreprod.104.028472 Huffman J., 2019, SPECIAL SKRETTING FE Huson HJ, 2014, FRONT GENET, V5, DOI 10.3389/fgene.2014.00101 IETS (International Embryo Transfer Society), 2008, HLTH ASS CAR AN INV KRIMPENFORT P, 1991, BIO-TECHNOL, V9, P844, DOI 10.1038/nbt0991-844 KRISHER RL, 1994, TRANSGENIC RES, V3, P226, DOI 10.1007/BF02336775 Kuroiwa Y, 2002, NAT BIOTECHNOL, V20, P889, DOI 10.1038/nbt727 Mallapaty Smriti, 2019, Nature, DOI 10.1038/d41586-019-01282-8 Miao DQ, 2019, BIOL REPROD, V101, P177, DOI 10.1093/biolre/ioz075 Miglior F, 2017, J DAIRY SCI, V100, P10251, DOI 10.3168/jds.2017-12968 Mikkola M., 2017, THESIS Moore SG, 2017, J DAIRY SCI, V100, P10314, DOI 10.3168/jds.2017-13138 Mrode R, 2019, FRONT GENET, V9, DOI 10.3389/fgene.2018.00694 Norris A L, 2019, BIORXIV Office of the Gene Technology Regulator (OGTR), 2019, TECHN REV GEN TECHN Proudfoot C, 2015, TRANSGENIC RES, V24, P147, DOI 10.1007/s11248-014-9832-x Richt JA, 2007, NAT BIOTECHNOL, V25, P132, DOI 10.1038/nbt1271 Ryu J, 2018, J ANIM SCI BIOTECHNO, V9, DOI 10.1186/s40104-017-0228-7 Schurmann A, 2006, REPRODUCTION, V132, P839, DOI 10.1530/REP-06-0054 Seidel G E, 1991, 77 FAO UN Shanthalingam S, 2016, P NATL ACAD SCI USA, V113, P13186, DOI 10.1073/pnas.1613428113 Su XH, 2018, THERIOGENOLOGY, V119, P282, DOI 10.1016/j.theriogenology.2018.07.011 Tait-Burkard C, 2018, GENOME BIOL, V19, DOI 10.1186/s13059-018-1583-1 Tavares KCS, 2016, GENET MOL RES, V15, DOI 10.4238/gmr.15017476 U.S. Food and Drug Administration (FDA), GUID IND US AN CLON U.S. Food and Drug Administration (FDA), 2019, GUID IND 187 REG INT U.S. Food and Drug Administration (FDA), 2019, AQUADVANTAGE SALM AP U.S. Food and Drug Administration (FDA), 2019, AN CLON RISK MAN PLA United States Department of Agriculture Foreign Agricultural Service (USDA FAS), 2018, BRAZ AGR BIOT ANN BR United States Department of Agriculture Foreign Agricultural Service (USDA FAS), 2018, AUSTR AGR BIOT ANN A United States Department of Agriculture Foreign Agricultural Service (USDA FAS), 2012, UR ANN BIOT REP 2012 United States Department of Agriculture Foreign Agricultural Service (USDA FAS), 2018, EU 28 AGR BIOT ANN F United States Department of Agriculture Foreign Agricultural Service (USDA FAS), 2019, ARG AGR BIOT ANN van der Berg JP, 2019, THERIOGENOLOGY, V135, P85, DOI 10.1016/j.theriogenology.2019.06.001 Wall RJ, 2005, NAT BIOTECHNOL, V23, P445, DOI 10.1038/nbt1078 Wall RJ, 1997, J DAIRY SCI, V80, P2213, DOI 10.3168/jds.S0022-0302(97)76170-8 Wang J, 2008, J DAIRY SCI, V91, P4466, DOI 10.3168/jds.2008-1189 Wang M, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-28502-x World Trade Organization (WTO), 2019, MIN DECL SO AGR COUN Wu HB, 2015, P NATL ACAD SCI USA, V112, pE1530, DOI 10.1073/pnas.1421587112 Wu XJ, 2012, TRANSGENIC RES, V21, P1359, DOI 10.1007/s11248-012-9612-4 Yang B, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0017593 Young AE, 2020, NAT BIOTECHNOL, V38, P245, DOI 10.1038/s41587-020-0423-5 Yu SL, 2011, CELL RES, V21, P1638, DOI 10.1038/cr.2011.153 Yu Y, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0062457 Yum SY, 2016, SCI REP-UK, V6, DOI 10.1038/srep27185 NR 75 TC 2 Z9 2 U1 8 U2 32 PD JUN PY 2020 VL 7 IS 2 BP 136 EP 147 DI 10.15302/J-FASE-2019306 WC Agronomy SC Agriculture UT WOS:000539036900004 DA 2022-12-14 ER PT J AU Siracusa, L Patane, C Avola, G Ruberto, G AF Siracusa, Laura Patane, Cristina Avola, Giovanni Ruberto, Giuseppe TI Polyphenols as Chemotaxonomic Markers in Italian "Long-Storage" Tomato Genotypes SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE long-storage tomato; polyphenols; traceability ID ANTIOXIDANT CAPACITY; NUTRITIONAL QUALITY; STRESS; FLAVONOIDS; PARAMETERS; PHENOLICS; LYCOPENE; FRUITS; ACIDS; L. AB "Long-storage" tomato (Solanum lycopersicum L.) is a niche product typical of the Mediterranean area, traditionally cultivated under no water supply, the fruits of which combine a good taste with excellent nutritional properties. High-performance liquid chromatography coupled with diode array detection and electron spray-mass spectrometry (HPLC/DAD/ESI-MS) was used to identify the phenolic profile in 10 landraces of long-storage tomato, grown under a typical semiarid climate, as compared to a processing tomato hybrid cultivated in the same environment, under both well-irrigated and unirrigated conditions. Sixteen different secondary metabolites, belonging to the classes of cinnamoylquinic acids and flavonoids, were identified. Quantitative analyses were also performed to monitor the changes in the phenolic content along the batch. The results highlighted that landraces originating from the same area exhibit different fruit morphologies but own a similar biochemical profile. Moreover, the two controls (well irrigated and unirrigated) are placed into the same cluster, suggesting that these secondary metabolites in tomato fruits may be more genetics-dependent than environment-dependent. Given the analysis of phenols nowadays represents a useful tool to assess the genetic variability in tomato, these compounds could be adopted as chemotaxonomic markers in the traceability of this niche product. C1 [Patane, Cristina; Avola, Giovanni] Ist CNR & Sistemi Agr & Forestali Mediterraneo, I-95121 Catania, Italy. [Siracusa, Laura; Ruberto, Giuseppe] Ist CNR Chim Biomol, I-95126 Catania, Italy. C3 Consiglio Nazionale delle Ricerche (CNR); Istituto di Chimica Biomolecolare (ICB-CNR) RP Patane, C (corresponding author), Ist CNR & Sistemi Agr & Forestali Mediterraneo, Strle V Lancia,Blocco Palma 1, I-95121 Catania, Italy. EM cristinamaria.patane@cnr.it; giuseppe.ruberto@icb.cnr.it CR Ahmed L, 2011, FOOD CHEM, V124, P1451, DOI 10.1016/j.foodchem.2010.07.106 Atkinson NJ, 2011, J AGR FOOD CHEM, V59, P9673, DOI 10.1021/jf202081t Barbagallo R. N., 2006, Italus Hortus, V13, P622 Bor M, 2003, PLANT SCI, V164, P77, DOI 10.1016/S0168-9452(02)00338-2 Boros B, 2010, J CHROMATOGR A, V1217, P7972, DOI 10.1016/j.chroma.2010.07.042 Capanoglu E, 2010, TRENDS FOOD SCI TECH, V21, P399, DOI 10.1016/j.tifs.2010.05.001 Clifford MN, 2006, PHYTOCHEM ANALYSIS, V17, P384, DOI 10.1002/pca.935 Clifford MN, 2003, J AGR FOOD CHEM, V51, P2900, DOI 10.1021/jf026187q DIXON RA, 1995, PLANT CELL, V7, P1085, DOI 10.1105/tpc.7.7.1085 EULER H, 1931, HELV CHIM ACTA, V14, P154 Frusciante L, 2007, MOL NUTR FOOD RES, V51, P609, DOI 10.1002/mnfr.200600158 Hernandez M, 2007, J AGR FOOD CHEM, V55, P8604, DOI 10.1021/jf071069u Ilahy R, 2011, SCI HORTIC-AMSTERDAM, V127, P255, DOI 10.1016/j.scienta.2010.10.001 Incerti A, 2009, J SCI FOOD AGR, V89, P1326, DOI 10.1002/jsfa.3589 Lavelli V, 2000, J AGR FOOD CHEM, V48, P1442, DOI 10.1021/jf990782j Lenucci MS, 2006, J AGR FOOD CHEM, V54, P2606, DOI 10.1021/jf052920c Li JH, 2010, PHYTOCHEMISTRY, V71, P1342, DOI 10.1016/j.phytochem.2010.05.002 Parvu M, 2010, NAT PROD RES, V24, P1318, DOI 10.1080/14786410903309484 Patane C., 2008, INF AGRAR, V35, P29 Pernice R, 2010, SCI HORTIC-AMSTERDAM, V126, P156, DOI 10.1016/j.scienta.2010.06.021 Reyes LF, 2003, J AGR FOOD CHEM, V51, P5296, DOI 10.1021/jf034213u Riggi E., 2006, Italus Hortus, V13, P779 Riggi E, 2008, RESOUR CONSERV RECY, V53, P96, DOI 10.1016/j.resconrec.2008.09.005 Robles C, 2003, J ENVIRON QUAL, V32, P2265, DOI 10.2134/jeq2003.2265 Shen YC, 2007, J AGR FOOD CHEM, V55, P6475, DOI 10.1021/jf070799z Slimestad R, 2008, J AGR FOOD CHEM, V56, P2436, DOI 10.1021/jf073434n Slimestad R, 2009, J SCI FOOD AGR, V89, P1255, DOI 10.1002/jsfa.3605 Stewart AJ, 2000, J AGR FOOD CHEM, V48, P2663, DOI 10.1021/jf000070p Vallverdu-Queralt A, 2011, J AGR FOOD CHEM, V59, P3994, DOI 10.1021/jf104400g Vallverdu-Queralt A, 2010, RAPID COMMUN MASS SP, V24, P2986, DOI 10.1002/rcm.4731 NR 30 TC 31 Z9 31 U1 0 U2 29 PD JAN 11 PY 2012 VL 60 IS 1 BP 309 EP 314 DI 10.1021/jf203858y WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000298943000044 DA 2022-12-14 ER PT J AU Seo, DW Hoque, MR Choi, NR Sultana, H Park, HB Heo, KN Kang, BS Lim, HT Lee, SH Jo, C Lee, JH AF Seo, D. W. Hoque, M. R. Choi, N. R. Sultana, H. Park, H. B. Heo, K. N. Kang, B. S. Lim, H. T. Lee, S. H. Jo, C. Lee, J. H. TI Discrimination of Korean Native Chicken Lines Using Fifteen Selected Microsatellite Markers SO ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES DT Article DE Discrimination; Diversity; Microsatellite; Korean Native Chicken; Traceability ID GENETIC DIVERSITY; POPULATION-STRUCTURE; LINKAGE MAP; BREEDS; POLYMORPHISMS; INFERENCE; DNA AB In order to evaluate the genetic diversity and discrimination among five Korean native chicken lines, a total of 86 individuals were genotyped using 150 microsatellite (MS) markers, and 15 highly polymorphic MS markers were selected. Based on the highest value of the number of alleles, the expected heterozygosity (He) and polymorphic information content (PIC) for the selected markers ranged from 6 to 12, 0.466 to 0.852, 0.709 to 0.882 and 0.648 to 0.865, respectively. Using these markers, the calculated genetic distance (Fst), the heterozygote deficit among chicken lines (Fit) and the heterozygote deficit within chicken line (Fis) values ranged from 0.0309 to 0.2473, 0.0013 to 0.4513 and -0.1002 to 0.271, respectively. The expected probability of identity values in random individuals (PI), random half-sib (PIhalf-sibs) and random sibs (PIsibs) were estimated at 7.98x10(-29), 2.88x10(-20) and 1.25x10(-08), respectively, indicating that these markers can be used for traceability systems in Korean native chickens. The unrooted phylogenetic neighbor-joining (NJ) tree was constructed using 15 MS markers that clearly differentiated among the five native chicken lines. Also, the structure was estimated by the individual clustering with the K value of 5. The selected 15 MS markers were found to be useful for the conservation, breeding plan, and traceability system in Korean native chickens. C1 [Seo, D. W.; Hoque, M. R.; Choi, N. R.; Sultana, H.; Jo, C.; Lee, J. H.] Chungnam Natl Univ, Dept Anim Sci & Biotechnol, Taejon 305764, South Korea. [Park, H. B.; Lim, H. T.] Gyeongsang Natl Univ, Dept Anim Sci, Jinju 660701, South Korea. [Park, H. B.; Lim, H. T.] Gyeongsang Natl Univ, Inst Agr & Life Sci, Jinju 660701, South Korea. [Heo, K. N.; Kang, B. S.] RDA, Natl Inst Anim Sci, Poultry Sci Div, Cheonan 331801, South Korea. [Lee, S. H.] RDA, Natl Inst Anim Sci, Hanwoo Expt Stn, Pyeongchang 232956, South Korea. C3 Chungnam National University; Gyeongsang National University; Gyeongsang National University; National Institute of Animal Science, Republic of Korea; Rural Development Administration (RDA), Republic of Korea; National Institute of Animal Science, Republic of Korea; Rural Development Administration (RDA), Republic of Korea RP Lee, JH (corresponding author), Chungnam Natl Univ, Dept Anim Sci & Biotechnol, Taejon 305764, South Korea. EM junheon@cnu.ac.kr CR Almasy L, 2009, GENETICA, V136, P333, DOI 10.1007/s10709-008-9305-3 Berthouly C, 2008, ANIM GENET, V39, P121, DOI 10.1111/j.1365-2052.2008.01703.x Blott SC, 1999, HEREDITY, V82, P613, DOI 10.1046/j.1365-2540.1999.00521.x Bodzsar N, 2009, ANIM GENET, V40, P516, DOI 10.1111/j.1365-2052.2009.01876.x BOTSTEIN D, 1980, AM J HUM GENET, V32, P314 CHENG HH, 1994, POULTRY SCI, V73, P539, DOI 10.3382/ps.0730539 Dalvit C, 2007, MEAT SCI, V77, P437, DOI 10.1016/j.meatsci.2007.05.027 Ding FX, 2010, ASIAN AUSTRAL J ANIM, V23, P154 Hillel J, 2003, GENET SEL EVOL, V35, P533, DOI [10.1186/1297-9686-35-6-533, 10.1051/gse:2003038] Hoque MR, 2011, ASIAN AUSTRAL J ANIM, V24, P1637, DOI 10.5713/ajas.2011.11144 Hoque M. R., 2009, Korean Journal of Poultry Science, V36, P323 Jacobsson L, 2004, POULTRY SCI, V83, P1825, DOI 10.1093/ps/83.11.1825 Kaya M, 2008, BIOCHEM GENET, V46, P480, DOI 10.1007/s10528-008-9164-8 Kong HS, 2006, ASIAN AUSTRAL J ANIM, V19, P1546, DOI 10.5713/ajas.2006.1546 Liu KJ, 2005, BIOINFORMATICS, V21, P2128, DOI 10.1093/bioinformatics/bti282 Marshall TC, 1998, MOL ECOL, V7, P639, DOI 10.1046/j.1365-294x.1998.00374.x MIFAFF, 2009, PRIM STAT FOOD AGR F MILLER SA, 1988, NUCLEIC ACIDS RES, V16, P1215, DOI 10.1093/nar/16.3.1215 Muchadeyi FC, 2007, ANIM GENET, V38, P332, DOI 10.1111/j.1365-2052.2007.01606.x Mwacharo JM, 2007, ANIM GENET, V38, P485, DOI 10.1111/j.1365-2052.2007.01641.x NEI M, 1983, J MOL EVOL, V19, P153, DOI 10.1007/BF02300753 Nei M., 1987, MOL EVOLUTIONARY GEN, DOI 10.7312/nei-92038 Pritchard JK, 2000, GENETICS, V155, P945 Rischkowsky B., 2007, STATE WORLDS ANIMAL, P23 Tadano R, 2008, ANIM GENET, V39, P71, DOI 10.1111/j.1365-2052.2007.01690.x Tadano R, 2007, POULTRY SCI, V86, P2301, DOI 10.3382/ps.2007-00233 Tadano R, 2007, POULTRY SCI, V86, P460, DOI 10.1093/ps/86.3.460 WRIGHT S, 1965, EVOLUTION, V19, P395, DOI 10.2307/2406450 NR 28 TC 15 Z9 18 U1 0 U2 7 PD MAR PY 2013 VL 26 IS 3 BP 316 EP 322 DI 10.5713/ajas.2012.12469 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000319093600003 DA 2022-12-14 ER PT J AU Barge, P Biglia, A Comba, L Aimonino, DR Tortia, C Gay, P AF Barge, Paolo Biglia, Alessandro Comba, Lorenzo Aimonino, Davide Ricauda Tortia, Cristina Gay, Paolo TI Radio Frequency IDentification for Meat Supply-Chain Digitalisation SO SENSORS DT Article DE UHF and HF RFID; NFC; food industry; meat traceability; blockchain; supply-chain ID RADIOFREQUENCY IDENTIFICATION; TRACEABILITY; UHF; MANAGEMENT; PRODUCTS; TAGS AB Digitalised supply-chain traceability systems can offer wide prospects both for improving safety as well as enhancing perceived quality. However, the coupling between physical goods and information is often difficult for agri-food items. A solution could be the use of RFID (Radio Frequency IDentification) systems. Due to its wide reading range, Ultra-High Frequency (UHF) technology is already widely used in logistics and warehousing, mostly for the identification of batches of items. A growing interest is also emerging in Near Field Communication (NFC), as several smartphones embed an integrated NFC antenna. This paper deals with the automatic identification of meat products at item level, proposing and evaluating the adoption of different RFID technologies. Different UHF and NFC solutions are proposed, which benchmark tag performances in different configurations, including four meat types (fatty beef, lean beef, chicken and pork), by using a specifically designed test bench. As avoiding the application of two different tags could be advantageous, dual frequency devices (UHF and NFC) are also considered. Significant differences in tag performances, which also depend on meat type and packaging, are highlighted. The paper highlights that tag positioning should consider the geometry of the packaging and the relative positioning of tag, meat and reader antenna. C1 [Barge, Paolo; Biglia, Alessandro; Comba, Lorenzo; Aimonino, Davide Ricauda; Tortia, Cristina; Gay, Paolo] Univ Torino, Dept Agr Forest & Food Sci DiSAFA, Largo Paolo Braccini 2, I-10095 Grugliasco, TO, Italy. [Comba, Lorenzo] CNR IEIIT Politecn Torino, Corso Duca Abruzzi 24, I-10129 Turin, Italy. C3 University of Turin; Polytechnic University of Turin RP Tortia, C (corresponding author), Univ Torino, Dept Agr Forest & Food Sci DiSAFA, Largo Paolo Braccini 2, I-10095 Grugliasco, TO, Italy. EM paolo.barge@unito.it; alessandro.biglia@unito.it; lorenzo.comba@unito.it; davide.ricauda@unito.it; cristina.tortia@unito.it; paolo.gay@unito.it CR [Anonymous], 2011, J VASC SURG, V54, pe32, DOI DOI 10.3000/19770677.L_2011.304.ENG AOAC International, 2000, AOAC OFF METH AN, V17th Azzi R, 2019, COMPUT IND ENG, V135, P582, DOI 10.1016/j.cie.2019.06.042 Badia-Melis R, 2015, FOOD CONTROL, V57, P393, DOI 10.1016/j.foodcont.2015.05.005 Barge P, 2014, J FOOD ENG, V125, P119, DOI 10.1016/j.jfoodeng.2013.10.019 Barge P, 2013, CAN J ANIM SCI, V93, P23, DOI [10.4141/CJAS2012-029, 10.4141/cjas2012-029] Barge P., 2017, CHEM ENG T, V58, P169, DOI [10.3303/CET1758029, DOI 10.3303/CET1758029] Barge P., 2013, J AGR ENG, V44, DOI [10.4081/jae.2013.301, DOI 10.4081/JAE.2013.301] Barge P., 2015, P 36 CIOSTA CIGR 5 C, P48 Barge P., 2013, P 35 CIOSTA CIGR C E Barge P, 2019, J FOOD ENG, V246, P242, DOI 10.1016/j.jfoodeng.2018.11.014 Bolic M., 2010, RFID SYSTEMS RES TRE Cena F., 2019, P 5 IT C ICT SMART C Comba L, 2013, PACKAG TECHNOL SCI, V26, P339, DOI 10.1002/pts.1984 Comba L, 2013, BIOSYST ENG, V116, P51, DOI [10.1016/j.biosystemseng.2013.06.006, 10.1016/j.biosystem] Dabbene F, 2016, WOODHEAD PUBL FOOD S, V301, P67, DOI 10.1016/B978-0-08-100310-7.00005-3 Dabbene F, 2016, SUPPLY CHAIN MANAGEMENT FOR SUSTAINABLE FOOD NETWORKS, P159 Dabbene F, 2014, BIOSYST ENG, V120, P65, DOI 10.1016/j.biosystemseng.2013.09.006 Fan BL, 2019, FOOD CONTROL, V98, P449, DOI 10.1016/j.foodcont.2018.12.002 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Hammer N, 2016, LIVEST SCI, V187, P125, DOI 10.1016/j.livsci.2016.03.007 International Organization for Standardization, 1996, 117841996 ISO International Organization for Standardization, 1996, 117851996 ISO Jedermann R., 2008, P 3 INT WORKSH BONN Karlsen KM, 2011, J FOOD ENG, V102, P1, DOI 10.1016/j.jfoodeng.2010.06.022 Kerry JP, 2006, MEAT SCI, V74, P113, DOI 10.1016/j.meatsci.2006.04.024 Laniel M, 2011, TRANSPORT RES C-EMER, V19, P1071, DOI 10.1016/j.trc.2011.06.008 Lanko A, 2018, MATEC WEB CONF, V170, DOI 10.1051/matecconf/201817003032 Liang WJ, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0139558 Mainetti L., 2013, INT J ANTENN PROPAG, V2013, DOI [DOI 10.1155/2013/531364, 10.1155/2013/531364] Mayer L.W., 2008, P 68 IEEE VEH TECHN, DOI [10.1109/VETECF.2008.27, DOI 10.1109/VETECF.2008.27] Mc Carthy U., 2011, AGR ENG INT CIGR J, DOI [10.1007/s40496-018-0186-y, DOI 10.1007/S40496-018-0186-Y] Mc Carthy U, 2010, PACKAG TECHNOL SCI, V23, P339, DOI 10.1002/pts.903 Mc Carthy UM, 2009, AGR ENG INT, VXI, P1280 McMillin KW, 2017, MEAT SCI, V132, P153, DOI 10.1016/j.meatsci.2017.04.015 Pigini D, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9101910 Rizzi A, 2019, INT J RF TECHNOL-RES, V10, P39, DOI 10.3233/RFT-180106 Trebar M, 2013, INT J ANTENN PROPAG, V2013, DOI 10.1155/2013/875973 Umstatter C., 2014, P INT C AGR ENG ZUR, P6 Volpi A, 2019, LECT NOTES ELECTR EN, V525, P151, DOI 10.1007/978-3-319-98038-6_12 Westerkamp M, 2020, DIGIT COMMUN NETW, V6, P167, DOI 10.1016/j.dcan.2019.01.007 Wisanmongkol J., 2009, P INT S ANT PROP ISA, P879 NR 42 TC 5 Z9 5 U1 8 U2 47 PD SEP PY 2020 VL 20 IS 17 AR 4957 DI 10.3390/s20174957 WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation SC Chemistry; Engineering; Instruments & Instrumentation UT WOS:000571647700001 DA 2022-12-14 ER PT J AU Papadimitriou, N Li, S Henriksson, HB AF Papadimitriou, Nikolaos Li, Susann Henriksson, Helena Barreto TI Iron Sucrose-Labeled Human Mesenchymal Stem Cells: In Vitro Multilineage Capability and In Vivo Traceability in a Lapine Xenotransplantation Model SO STEM CELLS AND DEVELOPMENT DT Article ID THERAPY POSITION STATEMENT; INTERNATIONAL-SOCIETY; CLINICAL-APPLICATIONS; PROGENITOR CELLS; STROMAL CELLS; TRACKING; BONE; DIFFERENTIATION; CHONDROGENESIS; OSTEOGENESIS AB For evaluation of cell therapy applications, it is of interest to be able to trace and observe cellular distribution of the transplanted cells. The aim with the study was to examine viability, traceability, and multilineage capability of iron sucrose-labeled mesenchymal stem cells (MSCs) after transplantation into lapine intervertebral discs (IVDs). MSCs were collected from three human donors, age 31-50 years, and IVDs from 12 rabbits, age 3 months. MSCs were isolated from the bone marrow and cultured using standard protocols. Iron sucrose labeling of MSCs was performed in Dulbecco's Modified Eagle's Medium-low glucose with Venofer((R)). The iron sucrose-labeled MSCs were differentiated into the adipogenic, osteogenic, and chondrogenic lineages. Results were evaluated using Oil red, von Kossa, Alcian blue, and collagen II (immunohistochemistry). For the animal experiments, iron sucrose-labeled MSCs and nonlabeled MSCs were injected into lapine IVDs (LI-LIV level). After transplantation, at the time points of 1 and 3 months, IVDs were collected and cells were analyzed for cell viability (fluorescence-activated cell sorting). The lapine IVDs were collected and examined for presence of cells positive for iron deposits using Berliner blue staining. Differentiation of the iron sucrose-labeled MSCs into adipogenic (lipid droplets), osteogenic (calcium deposits), and chondrogenic lineage (proteoglycan/collagen II accumulation) (3/3 donors) was observed in vitro. After transplantation, the mean cell viability for iron-labeled MSCs/IVD cells was 99%, for nonlabeled MSCs/IVD cells was 95%, and for control IVD cells was 99% at a time point of 1 month. At a time point of 3 months, mean cell viability was 73% for iron sucrose-labeled MSCs/IVD cells, for nonlabeled MSCs/IVD cells was 77%, and for control IVD cells was 98%. At the time point of 1 month, cells positive for iron deposits were detected sparsely distributed in IVDs (tissue sections) in 4/4 animals and at the time point of 3 months in 4/4 animals. The results indicate that iron sucrose can be used as a cell tracer with a stable detection potential in tissues (histologies). This may be an important evaluation tool for understanding stem cell distribution/function after transplantation into degenerated cartilaginous tissues. C1 [Papadimitriou, Nikolaos; Henriksson, Helena Barreto] Gothenburg Univ, Sahlgrenska Acad, Inst Clin Sci, Dept Orthoped, Gothenburg, Sweden. [Papadimitriou, Nikolaos; Henriksson, Helena Barreto] Sahlgrens Univ Hosp, Dept Orthoped, Gothenburg, Sweden. [Li, Susann] Gothenburg Univ, Sahlgrenska Univ Hosp, Inst Biomed, Dept Clin Chem & Transfus Med, Gothenburg, Sweden. C3 University of Gothenburg; Sahlgrenska University Hospital; Sahlgrenska University Hospital; University of Gothenburg RP Henriksson, HB (corresponding author), Sahlgrens Univ Hosp, Dept Orthoped, SE-41345 Gothenburg, Sweden. EM helena.barreto.henriksson@gu.se CR Abdallah BM, 2008, GENE THER, V15, P109, DOI 10.1038/sj.gt.3303067 Barot BS, 2014, PHARM DEV TECHNOL, V19, P513, DOI 10.3109/10837450.2013.795171 Barry Frank P., 2003, Birth Defects Research, V69, P250, DOI 10.1002/bdrc.10021 Bobis S, 2006, FOLIA HISTOCHEM CYTO, V44, P215 Delorme Bruno, 2007, V140, P67 Dominici M, 2006, CYTOTHERAPY, V8, P315, DOI 10.1080/14653240600855905 El Haj AJ, 2015, J TISSUE ENG REGEN M, V9, P724, DOI 10.1002/term.1636 Farrell E, 2008, BIOCHEM BIOPH RES CO, V369, P1076, DOI 10.1016/j.bbrc.2008.02.159 FOWLER SD, 1985, J HISTOCHEM CYTOCHEM, V33, P833, DOI 10.1177/33.8.4020099 GOLDSTEIN DJ, 1974, HISTOCHEM J, V6, P175, DOI 10.1007/BF01011805 Grootendorst DJ, 2013, CONTRAST MEDIA MOL I, V8, P83, DOI 10.1002/cmmi.1498 Handley C, 2015, WORLD J STEM CELLS, V7, P65, DOI 10.4252/wjsc.v7.i1.65 HANSEN HA, 1959, ACTA MED SCAND, V165, P333 Henriksson HB, 2013, STEM CELL RES THER, V4, DOI 10.1186/scrt315 Heymer A, 2008, BIOMATERIALS, V29, P1473, DOI 10.1016/j.biomaterials.2007.12.003 Horwitz EM, 2005, CYTOTHERAPY, V7, P393, DOI 10.1080/14653240500319234 Horwitz EM, 2002, P NATL ACAD SCI USA, V99, P8932, DOI 10.1073/pnas.132252399 Jing XH, 2008, JOINT BONE SPINE, V75, P432, DOI 10.1016/j.jbspin.2007.09.013 Johnstone B, 1998, EXP CELL RES, V238, P265, DOI 10.1006/excr.1997.3858 Karlsson C, 2007, J ORTHOP RES, V25, P152, DOI 10.1002/jor.20287 Knoblich JA, 2008, CELL, V132, P583, DOI 10.1016/j.cell.2008.02.007 Koelling S, 2009, CELL STEM CELL, V4, P324, DOI 10.1016/j.stem.2009.01.015 Kosch M, 2001, NEPHROL DIAL TRANSPL, V16, P1239, DOI 10.1093/ndt/16.6.1239 Kostura L, 2004, NMR BIOMED, V17, P513, DOI 10.1002/nbm.925 Kuroda Y, 2014, ANAT REC, V297, P98, DOI 10.1002/ar.22798 Lawen A, 2013, ANTIOXID REDOX SIGN, V18, P2473, DOI 10.1089/ars.2011.4271 Liu W, 2009, EUR J RADIOL, V70, P258, DOI 10.1016/j.ejrad.2008.09.021 Markides H, 2013, STEM CELL RES THER, V4, DOI 10.1186/scrt337 Moewis G, 1978, HISTOPATOLOGISK TEKN, P201 Mukherjee S, 2015, STEM CELLS DEV, V24, P405, DOI 10.1089/scd.2014.0442 Papadimitriou N, 2014, STEM CELLS DEV, V23, P2568, DOI 10.1089/scd.2014.0153 Qi YY, 2013, MOL BIOL REP, V40, P2733, DOI 10.1007/s11033-012-2364-7 SCOTT J. E., 1964, HISTOCHEMIE, V4, P73, DOI 10.1007/BF00306149 Singh H, 2006, CLIN J AM SOC NEPHRO, V1, P475, DOI 10.2215/CJN.01541005 Stigen O, 2007, ACTA VET SCAND, V49, DOI 10.1186/1751-0147-49-39 Svanvik T, 2010, CELLS TISSUES ORGANS, V191, P2, DOI 10.1159/000223236 Svenberg P, 2004, BIOL BLOOD MARROW TR, V10, P877, DOI 10.1016/j.bbmt.2004.08.002 Thu MS, 2012, NAT MED, V18, P463, DOI 10.1038/nm.2666 Wagner W, 2008, TRANSFUS MED HEMOTH, V35, P185, DOI 10.1159/000128956 Wimpenny I, 2012, STEM CELL RES THER, V3, DOI 10.1186/scrt104 NR 40 TC 7 Z9 7 U1 0 U2 17 PD OCT 15 PY 2015 VL 24 IS 20 BP 2403 EP 2412 DI 10.1089/scd.2015.0140 WC Cell & Tissue Engineering; Hematology; Medicine, Research & Experimental; Transplantation SC Cell Biology; Hematology; Research & Experimental Medicine; Transplantation UT WOS:000362269100006 DA 2022-12-14 ER PT J AU Nadal, A De Giacomo, M Einspanier, R Kleter, G Kok, E McFarland, S Onori, R Paris, A Toldra, M van Dijk, J Wal, JM Pla, M AF Nadal, Anna De Giacomo, Marzia Einspanier, Ralf Kleter, Gijs Kok, Esther McFarland, Sarah Onori, Roberta Paris, Alain Toldra, Monica van Dijk, Jeroen Wal, Jean-Michel Pla, Maria TI Exposure of livestock to GM feeds: Detectability and measurement SO FOOD AND CHEMICAL TOXICOLOGY DT Article DE Animal tissue; DNA and protein transfer; Exposure assessment; Genetically modified organism (GMO); Livestock; Traceability ID GENETICALLY-MODIFIED MAIZE; ENDOGENOUS PLANT DNA; INGESTED FOREIGN DNA; REAL-TIME PCR; TRANSGENIC DEOXYRIBONUCLEIC-ACID; ROUNDUP READY; MODIFIED ORGANISMS; GASTROINTESTINAL-TRACT; SOYBEAN-MEAL; DIETARY DNA AB This review explores the possibilities to determine livestock consumption of genetically modified (GM) feeds ingredients including detection of genetically modified organism (GMO)-related DNA or proteins in animal samples, and the documentary system that is in place for GM feeds under EU legislation. The presence and level of GMO-related DNA and proteins can generally be readily measured in feeds, using established analytical methods such as polymerase chain reaction and immuno-assays, respectively. Various technical challenges remain, such as the simultaneous detection of multiple GMOs and the identification of unauthorized GMOs for which incomplete data on the inserted DNA may exist. Given that transfer of specific GMO-related DNA or protein from consumed feed to the animal had seldom been observed, this cannot serve as an indicator of the individual animal's prior exposure to GM feeds. To explore whether common practices, information exchange and the specific GM feed traceability system in the EU would allow to record GM feed consumption, the dairy chain in Catalonia, where GM maize is widely grown, was taken as an example. It was thus found that this system would neither enable determination of an animal's consumption of specific GM crops, nor would it allow for quantitation of the exposure. (C) 2017 Elsevier Ltd. All rights reserved. C1 [Nadal, Anna; Toldra, Monica; Pla, Maria] Univ Girona, Inst Food & Agr Technol INTEA, Campus Montilivi,EPS 1, Girona 17003, Spain. [De Giacomo, Marzia; Onori, Roberta] Italian Natl Inst Hlth, GMO & Mycotoxins Unit, Dept Vet Publ Hlth & Food Safety, Viale Regina Elena 299, I-00161 Rome, Italy. [Einspanier, Ralf; McFarland, Sarah] Free Univ Berlin, Inst Vet Biochem, Oertzenweg 19b, D-14163 Berlin, Germany. [Kleter, Gijs; Kok, Esther; van Dijk, Jeroen] Wageningen Univ & Res, RIKILT, Akkermaalsbos 2, NL-6708 WB Wageningen, Netherlands. [Paris, Alain] Sorbonne Univ, Museum Natl Hist Nat, CNRS, MCAM UMR7245, Paris, France. [Wal, Jean-Michel] INRA, AgroParisTech, Paris, France. C3 Universitat de Girona; Istituto Superiore di Sanita (ISS); Free University of Berlin; Wageningen University & Research; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Museum National d'Histoire Naturelle (MNHN); UDICE-French Research Universities; Sorbonne Universite; Universite Paris Cite; AgroParisTech; INRAE RP Nadal, A (corresponding author), Univ Girona, Inst Food & Agr Technol INTEA, Campus Montilivi,EPS 1, Girona 17003, Spain. EM anna.nadal@udg.edu CR Aeschbacher K, 2005, POULTRY SCI, V84, P385, DOI 10.1093/ps/84.3.385 Agodi A, 2006, INT J HYG ENVIR HEAL, V209, P81, DOI 10.1016/j.ijheh.2005.08.005 Alderborn A, 2010, FOOD CHEM TOXICOL, V48, P453, DOI 10.1016/j.fct.2009.10.049 Alexander TW, 2006, BRIT J NUTR, V96, P997, DOI 10.1017/BJN20061935 Alexander TW, 2007, ANIM FEED SCI TECH, V133, P31, DOI 10.1016/j.anifeedsci.2006.08.003 Allen RC, 2006, J IMMUNOL METHODS, V308, P109, DOI 10.1016/j.jim.2005.10.006 Andersson HC, 2011, EFSA J, V9, DOI 10.2903/j.efsa.2011.2150 Angers-Loustau A, 2014, BMC BIOINFORMATICS, V15, DOI 10.1186/s12859-014-0417-8 Antunovic B, 2011, EFSA J, V9, DOI 10.2903/j.efsa.2011.2438 Arulandhu AJ, 2016, ANAL BIOANAL CHEM, V408, P4575, DOI 10.1007/s00216-016-9513-0 Babekova R, 2009, EUR FOOD RES TECHNOL, V228, P707, DOI 10.1007/s00217-008-0981-0 Baker M, 2012, NAT METHODS, V9, P541, DOI 10.1038/nmeth.2027 Baranowski Antoni, 2006, Animal Science Papers and Reports, V24, P129 Beagle JM, 2006, J ANIM SCI, V84, P597, DOI 10.2527/2006.843597x Beever DE, 2003, ASIAN AUSTRAL J ANIM, V16, P764, DOI 10.5713/ajas.2003.764 Berben G., 2014, 27021 EUR EN Bertheau Y, 2009, J AGR FOOD CHEM, V57, P509, DOI 10.1021/jf802262c Blair R., 2015, GENETIC MODIFICATION Block A, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-256 Bonfini L, 2012, J AOAC INT, V95, P1713, DOI 10.5740/jaoacint.12-050 Brod FCA, 2014, ANAL BIOANAL CHEM, V406, P1397, DOI 10.1007/s00216-013-7562-1 Broll H, 2005, J ANIM FEED SCI, V14, P337 Buzoianu SG, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0047851 Calsamiglia S, 2007, J DAIRY SCI, V90, P4718, DOI 10.3168/jds.2007-0286 Calsamiglia S., 2003, Journal of Dairy Science, V86, P62 Cankar K, 2006, BMC BIOTECHNOL, V6, DOI 10.1186/1472-6750-6-37 Castillo AR, 2004, J DAIRY SCI, V87, P1778, DOI 10.3168/jds.S0022-0302(04)73333-0 Catalan Parliament, 2013, DIARI OFICIAL GEN CA Chainark P, 2008, FISHERIES SCI, V74, P380, DOI 10.1111/j.1444-2906.2008.01535.x Chainark P, 2006, FISHERIES SCI, V72, P1072, DOI 10.1111/j.1444-2906.2006.01258.x Chaouachi M, 2008, J AGR FOOD CHEM, V56, P11596, DOI 10.1021/jf801482r Chen MJ, 2016, CROP J, V4, P177, DOI 10.1016/j.cj.2016.03.004 Chowdhury EH, 2004, J FOOD PROTECT, V67, P365, DOI 10.4315/0362-028X-67.2.365 Chowdhury EH, 2003, VET HUM TOXICOL, V45, P95 Chowdhury EH, 2003, J ANIM SCI, V81, P2546 De Giacomo M, 2016, J ANIM FEED SCI, V25, P109, DOI 10.22358/jafs/65570/2016 de Luis R, 2009, EUR FOOD RES TECHNOL, V229, P15, DOI 10.1007/s00217-009-1021-4 Deaville ER, 2005, J AGR FOOD CHEM, V53, P10268, DOI 10.1021/jf051652f Demeke T, 2010, ANAL BIOANAL CHEM, V396, P1977, DOI 10.1007/s00216-009-3150-9 Dinon AZ, 2011, ANAL BIOANAL CHEM, V400, P1433, DOI 10.1007/s00216-011-4875-9 Dobnik D, 2015, ANAL CHEM, V87, P8218, DOI 10.1021/acs.analchem.5b01208 Dong W, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-260 Duggan PS, 2003, BRIT J NUTR, V89, P159, DOI 10.1079/BJN2002764 Einspanier R, 2004, EUR FOOD RES TECHNOL, V218, P269, DOI 10.1007/s00217-003-0842-9 Einspanier R, 2001, EUR FOOD RES TECHNOL, V212, P129, DOI 10.1007/s002170000248 Einspanier R., 2013, CABI BIOTECHNOLOGY S Einspanier R., 2009, ENCY ANIMAL SCI El Sayed RM, 2006, J FOOD BIOCHEM, V30, P556, DOI 10.1111/j.1745-4514.2006.00082.x EU (European Union), 2017, EU REG AUTH GMOS Flachowsky G, 2005, ARCH ANIM NUTR, V59, P449, DOI 10.1080/17450390500353549 Fraiture MA, 2015, BIOMED RES INT, V2015, DOI 10.1155/2015/392872 Fraiture MA, 2014, FOOD CHEM, V147, P60, DOI 10.1016/j.foodchem.2013.09.112 Furgal-Dieriuk I, 2015, J ANIM FEED SCI, V24, P19, DOI 10.22358/jafs/65649/2015 Garcia-Canas V, 2011, MASS SPECTROM REV, V30, P396, DOI 10.1002/mas.20286 Gerdes L, 2012, FOOD ANAL METHOD, V5, P1368, DOI 10.1007/s12161-012-9378-6 GM090plus, 2015, GMOS SUBCHR TOX 3 6 GMOChips, 2002, EUR COMM RES PROJ Gryson N, 2010, ANAL BIOANAL CHEM, V396, P2003, DOI 10.1007/s00216-009-3343-2 Guertler P, 2008, EUR J WILDLIFE RES, V54, P36, DOI 10.1007/s10344-007-0104-4 Guertler P., 2009, J VERBRAUCHERSCHUTZ, V3, P26, DOI DOI 10.1007/S00003-009-0400-X Guertler P, 2010, LIVEST SCI, V131, P250, DOI 10.1016/j.livsci.2010.04.010 Guo F, 2013, APPL MICROBIOL BIOT, V97, P4607, DOI 10.1007/s00253-012-4244-4 Hamels S, 2009, EUR FOOD RES TECHNOL, V228, P531, DOI 10.1007/s00217-008-0960-5 Harrigan GG, 2016, METABOLOMICS, V12, DOI 10.1007/s11306-016-1017-6 Harrigan GG, 2015, J AGR FOOD CHEM, V63, P4690, DOI 10.1021/acs.jafc.5b01069 Heinemann JA, 2011, ENVIRON INT, V37, P1285, DOI 10.1016/j.envint.2011.05.006 Hohlweg U, 2001, MOL GENET GENOMICS, V265, P225, DOI 10.1007/s004380100450 Holst-Jensen A, 2016, ANAL BIOANAL CHEM, V408, P4595, DOI 10.1007/s00216-016-9549-1 Holst-Jensen A, 2012, BIOTECHNOL ADV, V30, P1318, DOI 10.1016/j.biotechadv.2012.01.024 Holst-Jensen A, 2009, BIOTECHNOL ADV, V27, P1071, DOI 10.1016/j.biotechadv.2009.05.025 ILSI, 2003, BEST PRACT COND AN S ISO (International Organization for Standardization), 2005, 25712005 ISO Jang HJ, 2011, SENSOR ACTUAT B-CHEM, V155, P598, DOI 10.1016/j.snb.2011.01.016 Jennings JC, 2003, BULLETIN OF THE INTERNATIONAL DAIRY FEDERATION NO 383/2003, P41 Jennings JC, 2003, J ANIM SCI, V81, P1447 Jennings JC, 2003, POULTRY SCI, V82, P371, DOI 10.1093/ps/82.3.371 Jonas DA, 2001, ANN NUTR METAB, V45, P235, DOI 10.1159/000046734 JRC (Joint Research Centre-European Comission), 2017, GMOMETHODS EU DAT RE Klaften M, 2004, ACS SYM SER, V866, P83 Klotz A, 2002, EUR FOOD RES TECHNOL, V214, P271, DOI 10.1007/s00217-001-0444-3 KLOTZ A, 1998, MAIS, V3, P109 Korwin-Kossakowska A, 2013, ARCH TIERZUCHT, V56, DOI 10.7482/0003-9438-56-060 Kovalic D, 2012, PLANT GENOME-US, V5, P149, DOI 10.3835/plantgenome2012.10.0026 Kusano M, 2015, METABOLOMICS, V11, P261, DOI 10.1007/s11306-014-0702-6 Leimanis S, 2008, EUR FOOD RES TECHNOL, V227, P1621, DOI 10.1007/s00217-008-0886-y Liang CJ, 2014, ANAL BIOANAL CHEM, V406, P2603, DOI 10.1007/s00216-014-7667-1 Lipton CR, 2000, FOOD AGR IMMUNOL, V12, P153, DOI 10.1080/095401000404094 Ma QG, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0061138 Mano J, 2009, J AGR FOOD CHEM, V57, P26, DOI 10.1021/jf802551h Margarit E, 2006, FOOD RES INT, V39, P250, DOI 10.1016/j.foodres.2005.07.013 Marzok M. A. T. A., 2004, THESIS Mazza R, 2005, TRANSGENIC RES, V14, P775, DOI 10.1007/s11248-005-0009-5 Mohanta RK, 2010, TROP ANIM HEALTH PRO, V42, P431, DOI 10.1007/s11250-009-9439-z Monsanto, 2016, ANN MON REP CULT MON Morera P, 2016, J ANIM SCI, V94, P1287, DOI 10.2527/jas.2015-0025 Morisset D, 2008, EUR FOOD RES TECHNOL, V227, P1287, DOI 10.1007/s00217-008-0850-x Morisset D, 2014, BMC BIOINFORMATICS, V15, DOI 10.1186/1471-2105-15-258 Morrison T, 2006, NUCLEIC ACIDS RES, V34, DOI 10.1093/nar/gkl639 Nemeth A, 2004, J AGR FOOD CHEM, V52, P6129, DOI 10.1021/jf049567f Netherwood T, 2004, NAT BIOTECHNOL, V22, P204, DOI 10.1038/nbt934 Nielsen C, 2005, EUR FOOD RES TECHNOL, V221, P1, DOI 10.1007/s00217-005-1160-1 Palka-Santini M, 2003, MOL GENET GENOMICS, V270, P201, DOI 10.1007/s00438-003-0907-2 Paris A., 2006, ORGANISMES GENETIQUE, P43 Paris A., 2004, CHARACTERIZATION ANA Paul V, 2008, ANAL CHIM ACTA, V607, P106, DOI 10.1016/j.aca.2007.11.022 Paul V, 2010, TRANSGENIC RES, V19, P683, DOI 10.1007/s11248-009-9339-z Petrillo M, 2015, DATABASE-OXFORD, DOI 10.1093/database/bav101 Phipps RH, 2005, J DAIRY SCI, V88, P2870, DOI 10.3168/jds.S0022-0302(05)72968-4 Phipps RH, 2003, J DAIRY SCI, V86, P4070, DOI 10.3168/jds.S0022-0302(03)74019-3 Phipps RH, 2002, LIVEST PROD SCI, V74, P269, DOI 10.1016/S0301-6226(02)00038-6 Pla M., 2012, GENETICALLY MODIFIED, P333, DOI DOI 10.1002/9781118373781.CH19 Poms RE, 2003, J FOOD PROTECT, V66, P304, DOI 10.4315/0362-028X-66.2.304 Prins TW, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-584 Querci M, 2009, FOOD ANAL METHOD, V2, P325, DOI 10.1007/s12161-009-9093-0 Rehout V, 2008, Journal of Agrobiology, V25, P141 Rehout V, 2008, Journal of Agrobiology, V25, P145 Reuter T, 2003, EUR FOOD RES TECHNOL, V216, P185, DOI 10.1007/s00217-002-0642-7 Ricroch AE, 2013, NEW BIOTECHNOL, V30, P349, DOI 10.1016/j.nbt.2012.12.001 Ricroch AE, 2011, PLANT PHYSIOL, V155, P1752, DOI 10.1104/pp.111.173609 Rizzi A, 2008, EUR FOOD RES TECHNOL, V227, P1699, DOI 10.1007/s00217-008-0896-9 Rizzi A, 2012, CRIT REV FOOD SCI, V52, P142, DOI 10.1080/10408398.2010.499480 Rossi F, 2005, POULTRY SCI, V84, P1022, DOI 10.1093/ps/84.7.1022 Sanden M, 2004, AQUACULTURE, V237, P391, DOI 10.1016/j.aquaculture.2004.04.004 Sanden M, 2011, AQUACULT NUTR, V17, pE750, DOI 10.1111/j.1365-2095.2010.00842.x Santiago-Felipe S, 2014, ANAL CHIM ACTA, V811, P81, DOI 10.1016/j.aca.2013.12.017 Scheideler SE, 2008, POULTRY SCI, V87, P1089, DOI 10.3382/ps.2007-00429 Scholtens I, 2013, J AGR FOOD CHEM, V61, P9097, DOI 10.1021/jf4018146 Schubbert R, 1998, MOL GEN GENET, V259, P569, DOI 10.1007/s004380050850 SCHUBBERT R, 1994, MOL GEN GENET, V242, P495, DOI 10.1007/BF00285273 Schubbert R, 1997, P NATL ACAD SCI USA, V94, P961, DOI 10.1073/pnas.94.3.961 Sharma R, 2006, J AGR FOOD CHEM, V54, P1699, DOI 10.1021/jf052459o Sieradzki Z, 2013, POL J VET SCI, V16, P435, DOI 10.2478/pjvs-2013-0061 Singhal KK, 2011, ANIMAL, V5, P1769, DOI 10.1017/S1751731111000899 Sissener NH, 2010, BRIT J NUTR, V103, P3, DOI 10.1017/S0007114509991401 Snell C, 2012, FOOD CHEM TOXICOL, V50, P1134, DOI 10.1016/j.fct.2011.11.048 Spurgeon SL, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0001662 Swiatkiewicz M, 2013, B VET I PULAWY, V57, P413, DOI 10.2478/bvip-2013-0071 Swiatkiewicz M, 2011, B VET I PULAWY, V55, P121 Swiatkiewicz S, 2014, ANIM FEED SCI TECH, V198, P1, DOI 10.1016/j.anifeedsci.2014.09.009 Swiatkiewicz S, 2010, B VET I PULAWY, V54, P237 Tang WJ, 2017, J AGR FOOD CHEM, V65, P5215, DOI 10.1021/acs.jafc.7b00456 Tengs T, 2009, BMC BIOTECHNOL, V9, DOI 10.1186/1472-6750-9-87 Tisch D. A., 2006, ANIMAL FEEDS FEEDING Tony MA, 2003, ARCH ANIM NUTR, V57, P235, DOI 10.1080/00039420310001594397 Trabalza-Marinucci M, 2008, LIVEST SCI, V113, P178, DOI 10.1016/j.livsci.2007.03.009 Tudisco R, 2010, WORLD RABBIT SCI, V18, P83, DOI 10.4995/wrs.2010.18.11 Ujhelyi G, 2012, BMC BIOTECHNOL, V12, DOI 10.1186/1472-6750-12-4 Valencia CA, 2012, J MOL DIAGN, V14, P233, DOI 10.1016/j.jmoldx.2012.01.009 Van den Bulcke M, 2010, ANAL BIOANAL CHEM, V396, P2113, DOI 10.1007/s00216-009-3286-7 Volpicella Mariateresa, 2012, Biology (Basel), V1, P495, DOI 10.3390/biology1030495 Wahler D, 2013, FOOD ANAL METHOD, V6, P1718, DOI 10.1007/s12161-013-9673-x Waiblinger HU, 2010, ANAL BIOANAL CHEM, V396, P2065, DOI 10.1007/s00216-009-3173-2 Walsh MC, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0036141 Walsh MC, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0027177 Weber TE, 2001, J ANIM SCI, V79, P2542 Wiedemann S, 2009, MAMM BIOL, V74, P193, DOI 10.1016/j.mambio.2008.07.002 Yang LT, 2013, SCI REP-UK, V3, DOI 10.1038/srep02839 Yonemochi C., 2002, Animal Science Journal, V73, P221, DOI 10.1046/j.1344-3941.2002.00031.x Yonemochi Chisato, 2003, Animal Science Journal, V74, P81, DOI 10.1046/j.1344-3941.2003.00090.x Yonemochi C, 2010, ANIM SCI J, V81, P94, DOI 10.1111/j.1740-0929.2009.00718.x Zhang DB, 2011, J INTEGR PLANT BIOL, V53, P539, DOI 10.1111/j.1744-7909.2011.01060.x Zhu YZ, 2004, ARCH ANIM NUTR, V58, P295, DOI 10.1080/00039420412331273277 NR 162 TC 10 Z9 10 U1 3 U2 34 PD JUL PY 2018 VL 117 BP 13 EP 35 DI 10.1016/j.fct.2017.08.032 WC Food Science & Technology; Toxicology SC Food Science & Technology; Toxicology UT WOS:000437058700003 DA 2022-12-14 ER PT J AU Toth, A Gaal, KK Turcsan, Z Asvanyi-Molnar, N Asvanyi, B Szigeti, J Febel, H AF Toth, Agnes Gaal, Katalin Kovacsne Turcsan, Zsolt Asvanyi-Molnar, Noemi Asvanyi, Balazs Szigeti, Jeno Febel, Hedvig TI Tracking possibilities in the poultry sector - a review SO ARCHIV FUR TIERZUCHT-ARCHIVES OF ANIMAL BREEDING DT Article DE traceability; means of bird marking; RFID; radio-frequency identification ID PATAGIAL TAGS; INDIVIDUAL IDENTIFICATION; NASAL MARKER; NECK BANDS; TRACEABILITY; TRANSPONDERS; BEHAVIOR; SIZE; WILD AB In accordance with the regulations, the Cross Compliance law in Hungary is estimated to be put into practice from 2009, with which the tracking, registration and marking of animals, furthermore the food safety will receive a more elaborated role. That is why we provide a full-scale review of traceability opportunities of the poultry sector. The Poultry Information System (PIS) could create a more perspicuous poultry sector, however, its extremely high need of administration is an obligation hard to put up with for the breeders. The means of bird marking, such as the use of leg bands, wing tags, nasal markers, and the individual marking with transponders, based on radio-frequency identification were developed in order to overcome the growing need of complying the rules of food safety. The most up to date mean of marking in the poultry sector is the RFID (radio-frequency identification) technology. C1 [Toth, Agnes; Gaal, Katalin Kovacsne; Asvanyi-Molnar, Noemi; Asvanyi, Balazs; Szigeti, Jeno] Univ W Hungary, Inst Food Sci, Fac Agr & Food Sci, H-9200 Mosonmagyarovar, Hungary. [Turcsan, Zsolt] PSS Plus Innovat Ltd, Mezokovacshaza, Hungary. [Febel, Hedvig] Res Inst Anim Breeding & Nutr, Herceghalom, Hungary. C3 University of West Hungary RP Toth, A (corresponding author), Univ W Hungary, Inst Food Sci, Fac Agr & Food Sci, H-9200 Mosonmagyarovar, Hungary. EM totha@mtk.nyme.hu CR Anderson A., 1963, Journal of Wildlife Management, V27, P284, DOI 10.2307/3798408 ANTAL A, 2007, IDENTIFICATION REGIS Applegate Roger D., 2000, Transactions of the Kansas Academy of Science, V103, P150, DOI 10.2307/3628263 BARTELT GA, 1980, J WILDLIFE MANAGE, V44, P236, DOI 10.2307/3808377 Becker PH, 1997, CONDOR, V99, P534, DOI 10.2307/1369963 BENCSIK A, 2007, THESIS U DEBRECENIEN, P56 BENCSIK A, 2007, OPPORTUNITIES RFID U Broderick AC, 1999, ANIM BEHAV, V58, P587, DOI 10.1006/anbe.1999.1183 Brua RB, 1998, J FIELD ORNITHOL, V69, P530 BURLEY N, 1981, SCIENCE, V211, P721, DOI 10.1126/science.211.4483.721 BURLEY N, 1988, ANIM BEHAV, V36, P1235, DOI 10.1016/S0003-3472(88)80085-X BUSTNES JO, 1990, J WILDLIFE MANAGE, V54, P216, DOI 10.2307/3809032 Caporale V, 2001, REV SCI TECH OIE, V20, P372, DOI 10.20506/rst.20.2.1279 Carver AV, 1999, J WILDLIFE MANAGE, V63, P162, DOI 10.2307/3802497 Dennis RL, 2008, POULTRY SCI, V87, P1052, DOI 10.3382/ps.2007-00240 DENNIS RL, 2004, THESIS U MARYLAND CO ELBIN SB, 1994, WILDLIFE SOC B, V22, P677 Estevez I, 2003, APPL ANIM BEHAV SCI, V84, P213, DOI 10.1016/j.applanim.2003.08.006 Evrard JO, 1996, WILDLIFE SOC B, V24, P717 Fallon M, 2001, REV SCI TECH OIE, V20, P538, DOI 10.20506/rst.20.2.1289 FUZESI I, 2005, ACTA AGRARIA DEBRECE, V16, P341 GUHL A. M., 1953, CONDOR, V55, P287, DOI 10.2307/1365008 GUHL AM, 1953, TECH B KANS AGR EXP, V73, P1 HANNON SJ, 1990, WILDLIFE SOC B, V18, P116 Jackson Dewaine H., 1993, Journal of the Iowa Academy of Science, V100, P60 Jamison BE, 2000, POULTRY SCI, V79, P946, DOI 10.1093/ps/79.7.946 KECSKES K, 2004, MEAT, V4, P243 Kort WJ, 1998, LAB ANIM-UK, V32, P260, DOI 10.1258/002367798780559329 KOSA Z, 2007, RADIO FREQUENCY IDEN, P65 KOSSACK CW, 1950, AM MIDL NAT, V43, P627, DOI 10.2307/2421856 LOKEMOEN JT, 1985, WILDLIFE SOC B, V13, P53 Low M, 2005, EMU, V105, P33, DOI 10.1071/MU04060 MARLOK P, 2008, POULTRY, V9, P4 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 Mellor D.J., 2004, MARKING AMPHIBIANS R, P1 *NAT RES COUNC, 1985, MEAT POULTR INSP SCI, P209 Pelayo JT, 2000, J FIELD ORNITHOL, V71, P484, DOI 10.1648/0273-8570-71.3.484 SAHIN E, 2002, P IEEE INT C SYST MA, V3, P210 SAMUEL MD, 1990, J WILDLIFE MANAGE, V54, P45, DOI 10.2307/3808899 Simchi-Levi D., 2003, DESIGNING MANAGING S, P354 SOLYMOSI VK, 2007, HUNG J ANIM PROD, V56, P171 SZABO S, 2007, HUNG GRAPHICS, V2, P22 Vitiello DJ, 2001, REV SCI TECH OIE, V20, P598, DOI 10.20506/rst.20.2.1298 WADKINS LA, 1948, J WILDLIFE MANAGE, V12, P388, DOI 10.2307/3795927 WEAVER P, 1981, BIRDWATCHERS DICT, P156 NR 45 TC 1 Z9 1 U1 0 U2 15 PY 2010 VL 53 IS 3 BP 328 EP 336 DI 10.5194/aab-53-328-2010 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000280493400009 DA 2022-12-14 ER PT J AU Ampatzidis, Y Tan, L Haley, R Whiting, MD AF Ampatzidis, Yiannis Tan, Li Haley, Ronald Whiting, Matthew D. TI Cloud-based harvest management information system for hand-harvested specialty crops SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE Labor management; Cloud-based software; RFID; Embedded systems; Arduino ID FUTURE; AGRICULTURE; EFFICIENCY; MODEL AB The harvest process for specialty crops is generally one of intensive activity because many people are required for harvest and packing, and the harvest window is brief due to the high perishability of the produce. Herein we present a cloud-based Harvest Management Information System (HMIS) that combines a novel real-time Portable Labor Monitoring System (PLMS) with a cloud-based harvest management software. The PLMS comprised of three key elements (1) a self-leveling scale, (2) electronic control box, and (3) a frame that supports all hardware. The electronic control box includes: (i) a RFID reader, (ii) a LCD display, (iii) a thermal printer, (iv) a GPS module, and (v) a communication system. RFID tags, containing unique ID numbers, embedded within rubber wrist bands, are worn by pickers. This system can read a picker's ID (RFID bracelet), measure the weight of fruit, and record the time and location (optional) of every, fruit 'transaction' (i.e., every time a picker brings a bucket of fruit to the collection bin). The collected data can be transmitted wirelessly to the server in real-time. The cloud-based software receives and processes the PLMS data on labor activities, visualizes the collected data, and can extract the data necessary for management information and automated filling of documents (e.g. payroll, yield maps). The HMIS is unique in its ability to: (1) accurately credit pickers for the fruit they have harvested in the field without impeding or altering the harvest process, (2) streamline data entry to payroll, (3) provide real-time tracking of harvest, yield mapping, and traceability, and, (4) generate precise and reliable harvest efficiency data. This integrated system was evaluated in sweet cherry, blueberry and apple orchards in Washington, USA. The weight of harvested fruit, time and location of every fruit drop were calculated accurately; all the data were transmitted wirelessly to the server and no errors were recorded. (C) 2016 Elsevier B.V. All rights reserved. C1 [Ampatzidis, Yiannis] Calif State Univ, Dept Phys & Engn, Bakersfield, CA 93311 USA. [Ampatzidis, Yiannis; Tan, Li; Whiting, Matthew D.] Washington State Univ, Ctr Precis & Automated Agr Syst, Prosser, WA 99350 USA. [Whiting, Matthew D.] Washington State Univ, Dept Hort, Prosser, WA 99350 USA. [Tan, Li; Haley, Ronald] Washington State Univ, Sch Elect Engn & Comp Sci, Richland, WA 99354 USA. C3 California State University System; California State University Bakersfield; Washington State University; Washington State University; Washington State University RP Ampatzidis, Y (corresponding author), Calif State Univ, Dept Phys & Engn, Bakersfield, CA 93311 USA. EM yampatzidis@csub.edu; litan@wsu.edu; ronald_haley@wsu.edu; mdwhiting@wsu.edu CR Ampatzidis G.Y., 2012, 121338439 ASABE Ampatzidis Y., 2015, P ASABE 2015 ANN INT Ampatzidis YG, 2012, ACTA HORTIC, V965, P195 Ampatzidis YG, 2011, COMPUT ELECTRON AGR, V78, P222, DOI 10.1016/j.compag.2011.07.011 Ampatzidis YG, 2009, COMPUT ELECTRON AGR, V66, P166, DOI 10.1016/j.compag.2009.01.008 Ampatzidis Y.G., 2012, PRECIS AGRIC, V14, P162 Ampatzidis Y.G., 2013, P 12 INT C PREC AGR Ampatzidis YG, 2014, BIOSYST ENG, V120, P25, DOI 10.1016/j.biosystemseng.2013.07.011 Ampatzidis YG, 2013, HORTSCIENCE, V48, P547, DOI 10.21273/HORTSCI.48.5.547 Ampatzidis YG, 2012, COMPUT ELECTRON AGR, V88, P85, DOI 10.1016/j.compag.2012.06.009 [Anonymous], 2007, WALL STREET J, pA12 Chauvin MA, 2009, HORTTECHNOLOGY, V19, P748, DOI 10.21273/HORTSCI.19.4.748 Cunha CR, 2010, COMPUT ELECTRON AGR, V73, P154, DOI 10.1016/j.compag.2010.05.007 Fountas S, 2006, AGR SYST, V87, P192, DOI 10.1016/j.agsy.2004.12.003 Fountas S, 2015, COMPUT ELECTRON AGR, V115, P40, DOI 10.1016/j.compag.2015.05.011 Growing Produce, 2013, PAYING FRUIT PICK PE Kaloxylos A, 2014, COMPUT ELECTRON AGR, V100, P168, DOI 10.1016/j.compag.2013.11.014 Luvisi A, 2014, COMPUT ELECTRON AGR, V108, P130, DOI 10.1016/j.compag.2014.07.013 Luvisi A, 2012, COMPUT ELECTRON AGR, V84, P7, DOI 10.1016/j.compag.2012.02.008 Mell P., 2011, NIST DEFINITION CLOU Qiu Z., 2010, GUANGDONG AGR SCI, V4, P246 Ruiz-Garcia L, 2011, COMPUT ELECTRON AGR, V79, P42, DOI 10.1016/j.compag.2011.08.010 Schueller JK, 1999, COMPUT ELECTRON AGR, V23, P145, DOI 10.1016/S0168-1699(99)00028-9 Sorensen CG, 2011, COMPUT ELECTRON AGR, V76, P266, DOI 10.1016/j.compag.2011.02.005 Tan L., 2013, P 14 IEEE INT C INF Tan L., 2015, US Patent, Patent No. 8959594 Tan L, 2015, P IEEE 4 S NETW CLOU Tan L, 2014, P 15 IEEE INT C INF Teng C.C., 2012, P SYST C SYSCON 2012 Vougioukas S, 2013, BIOSYST ENG, V114, P454, DOI 10.1016/j.biosystemseng.2012.08.011 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 Whiting M.D., 2009, UPRIGHT FRUITING OFF Zhang M., 2011, AGR ENG, V1, P26 NR 33 TC 27 Z9 30 U1 3 U2 36 PD MAR PY 2016 VL 122 BP 161 EP 167 DI 10.1016/j.compag.2016.01.032 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000371944900019 DA 2022-12-14 ER PT J AU Franke, BM Haldimann, M Gremaud, G Bosset, JO Hadorn, R Kreuzer, M AF Franke, Bettina M. Haldimann, Max Gremaud, Gerard Bosset, Jacques-Olivier Hadorn, Ruedi Kreuzer, Michael TI Element signature analysis: its validation as a tool for geographic authentication of the origin of dried beef and poultry meat SO EUROPEAN FOOD RESEARCH AND TECHNOLOGY DT Article DE beef; broiler; meat; trace elements; authentication; traceability ID SELENIUM; THALLIUM; CADMIUM; TISSUES; GUIZHOU; AREAS; CHINA; SOILS AB Element concentrations of 56 poultry meat and 53 dried beef samples were determined and statistically analyzed using analysis of variance and linear discriminant analysis (LDA) to identify the single or combination of elements with the highest potential to determine the geographic origin. In order to validate the applicability of this technique, the results were additionally combined with data from an earlier assessment including 25 poultry meat and 23 dried beef samples. Validation was performed by estimating the origin of the first samples based on the data of the second, larger, dataset. Elements significantly discriminating among countries were As, Na, Rb, Se, Sr, and Tl for poultry meat and As, B, Ba, Ca, Cd, Cu, Dy, Er, Fe, Li, Mn, Pd, Rb, Se, Sr, Te, Tl, U, and V for dried beef out of about 50 elements each. The LDA gave mean correct classification rates of 77 and 79% for poultry meat and dried beef, respectively. Validation allowed identifying some, but not all, origins. For a higher discriminative power, this method should be combined with other ways of authentication. C1 [Franke, Bettina M.; Kreuzer, Michael] Swiss Fed Inst Technol, Inst Anim Sci, CH-8092 Zurich, Switzerland. [Haldimann, Max; Gremaud, Gerard] Swiss Fed Off Publ Hlth, CH-3003 Bern, Switzerland. [Bosset, Jacques-Olivier; Hadorn, Ruedi] Agroscope Liebefeld Posieux Res Stn ALP, CH-3003 Bern, Switzerland. C3 Swiss Federal Institutes of Technology Domain; ETH Zurich; Swiss Federal Research Station Agroscope RP Kreuzer, M (corresponding author), Swiss Fed Inst Technol, Inst Anim Sci, Univ Str 2, CH-8092 Zurich, Switzerland. EM michael.kreuzer@inw.agrl.ethz.ch CR ANKE M, 1995, FRESEN J ANAL CHEM, V352, P236, DOI 10.1007/BF00322334 [Anonymous], 2001, ARSENIC DRINKING WAT AUSSCHUSS F, 1999, ERNHRUNGSPHYSIOLOGIE Baldini M, 2000, FOOD ADDIT CONTAM, V17, P679, DOI 10.1080/02652030050083204 Bruce SL, 2003, TOXICOL LETT, V137, P23, DOI 10.1016/S0378-4274(02)00378-8 CHESSA G, 2000, TRACE ELEMENTS THEIR, P497 ESCHNAUER H, 1984, Z LEBENSM UNTERS FOR, V178, P453, DOI 10.1007/BF02157308 EUROLA M, 2005, SELENIUM SUPPLEMENTE Franke BM, 2007, EUR FOOD RES TECHNOL, V225, P501, DOI 10.1007/s00217-006-0446-2 Franke BM, 2005, EUR FOOD RES TECHNOL, V221, P493, DOI 10.1007/s00217-005-1158-8 Gremaud Gerard, 2002, Mitteilungen aus Lebensmitteluntersuchung und Hygiene, V93, P481 Haldimann Max, 1999, Mitteilungen aus Lebensmitteluntersuchung und Hygiene, V90, P241 Hintze KJ, 2002, J AGR FOOD CHEM, V50, P3938, DOI 10.1021/jf011200c Hintze KJ, 2001, J AGR FOOD CHEM, V49, P1062, DOI 10.1021/jf000699s Horvat M, 2003, SCI TOTAL ENVIRON, V304, P231, DOI 10.1016/S0048-9697(02)00572-7 Jevsnik M, 2003, J FOOD PROTECT, V66, P686, DOI 10.4315/0362-028X-66.4.686 KIM KW, 1993, ENVIRON GEOCHEM HLTH, V15, P119, DOI 10.1007/BF02627830 LEIBER F, 2005, THESIS ETH ZURICH ZU National Research Council, 1994, NUTR REQUIREMENTS PO, V9th National Research Council, 2000, NUTR REQ BEEF CATTL RUZ M, 1995, J TRACE ELEM MED BIO, V9, P156, DOI 10.1016/S0946-672X(11)80040-4 Sager M., 2005, Ernahrung, V29, P199 Tremel A, 1997, ENVIRON POLLUT, V95, P293, DOI 10.1016/S0269-7491(96)00145-5 *WHO, 2005, ARS CONT GROUND WAT Xiao TF, 2004, GEOCHEM-EXPLOR ENV A, V4, P243, DOI 10.1144/1467-7873/04-204 2005, ENV HLTH CRITERIA, V18 2005, MINERALFUTTER RINDER NR 27 TC 27 Z9 36 U1 0 U2 17 PD JUL PY 2008 VL 227 IS 3 BP 701 EP 708 DI 10.1007/s00217-007-0776-8 WC Food Science & Technology SC Food Science & Technology UT WOS:000256529400007 DA 2022-12-14 ER PT J AU Avramidou, EV Doulis, AG Petrakis, PV AF Avramidou, Evangelia V. Doulis, Andreas G. Petrakis, Panos V. TI Chemometrical and molecular methods in olive oil analysis: A review SO JOURNAL OF FOOD PROCESSING AND PRESERVATION DT Article ID OLEA-EUROPAEA L.; PARTIAL LEAST-SQUARES; PRINCIPAL COMPONENT ANALYSIS; GENETIC DIVERSITY; HIGH-RESOLUTION; DISCRIMINANT-ANALYSIS; NMR-SPECTROSCOPY; HAZELNUT OIL; GEOGRAPHICAL CLASSIFICATION; POPULATION-STRUCTURE AB Foods based on Olea europaea are of substantial importance for Mediterranean people. The association between olive products with great civilizations emphasizes the role that played the olive tree products and especially the olive oil. This generated the need for authentication, quality control and traceability of olive products. The physicochemical and sensory analysis methods, both classical (discrimination, ordination, classification) and novel (artificial neural networks, fuzzy logic, expert systems, decision trees, support vector machines) supplemented with molecular marker techniques (SSR, AFLP, RAPD), generated the need for effective multivariate analytical methods. These mathematically complex methods are greatly needed in the detection of adulterations of oil products and particularly for olive oil. The lack of mathematical symbolism and jargon is believed to make the text attractive to all potential workers in the field and help them improve the already low level of adulterations and augment the quality control databases for Mediterranean olive products. C1 [Avramidou, Evangelia V.] Inst Mediterranean Forest Ecosyst, Lab Genet, Athens, Greece. [Doulis, Andreas G.] Hellen Agr Org DEMETER, Inst Olive Tree Subtrop Crops & Viticulture, Lab Plant Biotechnol & Genom Resources, Iraklion, Greece. [Petrakis, Panos V.] Inst Mediterranean Forest Ecosyst, Lab Entomol, Athens 11528, Greece. RP Petrakis, PV (corresponding author), Inst Mediterranean Forest Ecosyst, Lab Entomol, Athens 11528, Greece. EM pvpetrakis@fria.gr CR Agiomyrgianaki A, 2012, FOOD CHEM, V135, P2561, DOI 10.1016/j.foodchem.2012.07.050 Agiomyrgianaki A, 2010, TALANTA, V80, P2165, DOI 10.1016/j.talanta.2009.11.024 Aksehirli-Pakyurek M, 2017, PLANT MOL BIOL REP, V35, P575, DOI 10.1007/s11105-017-1046-y Alba V, 2009, SCI HORTIC-AMSTERDAM, V123, P11, DOI 10.1016/j.scienta.2009.07.007 Alba V, 2009, EUR FOOD RES TECHNOL, V229, P375, DOI 10.1007/s00217-009-1062-8 Albertini E, 2011, MOL BREEDING, V27, P533, DOI 10.1007/s11032-010-9452-y Anderson MJ, 2003, ECOLOGY, V84, P511, DOI 10.1890/0012-9658(2003)084[0511:CAOPCA]2.0.CO;2 ANDERSON TW, 1963, ANN MATH STAT, V34, P122, DOI 10.1214/aoms/1177704248 Angiolillo A, 1999, THEOR APPL GENET, V98, P411, DOI 10.1007/s001220051087 [Anonymous], FUZZY LINGUISTIC SEN [Anonymous], 1984, INTERPRETATION ECOLO Aparicio R, 2002, EUR J LIPID SCI TECH, V104, P614, DOI 10.1002/1438-9312(200210)104:9/10<614::AID-EJLT614>3.0.CO;2-L APARICIO R, 1994, GRASAS ACEITES, V45, P241, DOI 10.3989/gya.1994.v45.i4.1003 Aparicio R, 1996, J SCI FOOD AGR, V72, P435, DOI 10.1002/(SICI)1097-0010(199612)72:4<435::AID-JSFA677>3.0.CO;2-L Baldoni L, 2006, ANN BOT-LONDON, V98, P935, DOI 10.1093/aob/mcl178 Baldoni L, 2009, MOL BREEDING, V24, P213, DOI 10.1007/s11032-009-9285-8 Balestre M, 2008, GENET MOL RES, V7, P695, DOI 10.4238/vol7-3gmr458 Barker M, 2003, J CHEMOMETR, V17, P166, DOI 10.1002/cem.785 Beaton D., 2015, DISTATISR DISTATIS 3 Beiki O, 2012, BREAST CANCER RES, V14, DOI 10.1186/bcr3086 Belaj A, 2002, THEOR APPL GENET, V105, P638, DOI 10.1007/s00122-002-0981-6 Belaj A, 2012, TREE GENET GENOMES, V8, P365, DOI 10.1007/s11295-011-0447-6 Ben-Ayed R, 2012, EUR FOOD RES TECHNOL, V234, P263, DOI 10.1007/s00217-011-1631-5 Benzecri J. P., 1973, ANAL DONNEES LECONS, V2 Besnard G, 2001, THEOR APPL GENET, V102, P251, DOI 10.1007/s001220051642 Besnard G, 2001, GENET RESOUR CROP EV, V48, P165, DOI 10.1023/A:1011239308132 Bonastre A, 2004, ANAL CHIM ACTA, V506, P189, DOI 10.1016/j.aca.2003.11.039 BOWCOCK AM, 1994, NATURE, V368, P455, DOI 10.1038/368455a0 Bracci T, 2011, PLANT CELL REP, V30, P449, DOI 10.1007/s00299-010-0991-9 Breiman L., 1984, CLASSIFICATION REGRE, DOI 10.1201/9781315139470 Brereton RG, 2014, J CHEMOMETR, V28, P213, DOI 10.1002/cem.2609 Bro R, 2014, ANAL METHODS-UK, V6, P2812, DOI 10.1039/c3ay41907j Caetano S, 2007, J CHEMOMETR, V21, P324, DOI 10.1002/cem.1077 CALVENTE JJ, 1995, ANAL CHIM ACTA, V312, P281, DOI 10.1016/0003-2670(95)00224-N CAPASSO R, 1992, PHYTOCHEM ANALYSIS, V3, P270, DOI 10.1002/pca.2800030607 Cercaci L, 2003, J CHROMATOGR A, V985, P211, DOI 10.1016/S0021-9673(02)01397-3 Cerretani L, 2011, FOOD CHEM, V127, P1899, DOI 10.1016/j.foodchem.2011.02.041 Chalak L, 2015, GENET RESOUR CROP EV, V62, P621, DOI 10.1007/s10722-014-0187-1 CHASE B, 1987, MIKROCHIM ACTA, V3, P81, DOI 10.1007/BF01201684 Christy AA, 2004, ANAL SCI, V20, P935, DOI 10.2116/analsci.20.935 Dais P, 2013, ANAL CHIM ACTA, V765, P1, DOI 10.1016/j.aca.2012.12.003 DEJONG S, 1993, CHEMOMETR INTELL LAB, V18, P251, DOI 10.1016/0169-7439(93)85002-X Dervishi A., 2015, THESIS DIGBY PGN, 2012, MULTIVARIATE ANAL EC Dominguez-Garcia MC, 2012, SCI HORTIC-AMSTERDAM, V136, P50, DOI 10.1016/j.scienta.2011.12.017 Dourtoglou VG, 2003, J AM OIL CHEM SOC, V80, P203, DOI 10.1007/s11746-003-0677-1 Downey G, 2002, J AGR FOOD CHEM, V50, P5520, DOI 10.1021/jf0257188 Dryden I, 2012, SHAPES PACKAGE Espejel J., 2007, MANAG SERV QUAL, V17, P681, DOI [10.1108/09604520710835000., DOI 10.1108/09604520710835000] Evanno G, 2005, MOL ECOL, V14, P2611, DOI 10.1111/j.1365-294X.2005.02553.x EXCOFFIER L, 1992, GENETICS, V131, P479 FABBRI A, 1995, J AM SOC HORTIC SCI, V120, P538, DOI 10.21273/JASHS.120.3.538 Farina A., 2008, PRINCIPLES METHODS L Fauhl C, 2000, MAGN RESON CHEM, V38, P436, DOI 10.1002/1097-458X(200006)38:6<436::AID-MRC672>3.3.CO;2-O Fragaki G, 2005, J AGR FOOD CHEM, V53, P2810, DOI 10.1021/jf040279t Ganopoulos I, 2013, FOOD CHEM, V141, P835, DOI 10.1016/j.foodchem.2013.02.130 GELADI P, 1986, ANAL CHIM ACTA, V185, P1, DOI 10.1016/0003-2670(86)80028-9 Gemas VJV, 2004, GENET RESOUR CROP EV, V51, P501, DOI 10.1023/B:GRES.0000024152.16021.40 Giordani P., 2015, R PACKAGE FCLUST TIT Gittins R., 1979, Statistical Ecology Series, V7, P309 Giubileo G., 2015, OLIVE OIL ADULTERATI Gliszczynska-Swiglo A, 2017, FOOD ANAL METHOD, V10, P1800, DOI 10.1007/s12161-016-0739-4 GOODALL D. W., 1954, AUSTRALIAN JOUR BOT, V2, P304, DOI 10.1071/BT9540304 GOWER JC, 1975, PSYCHOMETRIKA, V40, P33, DOI 10.1007/BF02291478 Grati-Kamoun N, 2006, GENET RESOUR CROP EV, V53, P265, DOI 10.1007/s10722-004-6130-0 Guimet F, 2004, J AGR FOOD CHEM, V52, P6673, DOI 10.1021/jf040169m HATHAWAY RJ, 1994, PATTERN RECOGN, V27, P429, DOI 10.1016/0031-3203(94)90119-8 HILL MO, 1980, VEGETATIO, V42, P47, DOI 10.1007/BF00048870 Husson F., 2009, FACTOMINER MULTIVARI Jabeur H, 2016, FOOD ANAL METHOD, V9, P712, DOI 10.1007/s12161-015-0249-9 Jacques J, 2010, J CHEMOMETR, V24, P719, DOI 10.1002/cem.1355 Johnson R.A., 1999, APPL MULTIVARIATE AN Kalogianni DP, 2015, J AGR FOOD CHEM, V63, P3121, DOI 10.1021/jf5054657 KETTENRING JR, 1971, BIOMETRIKA, V58, P433, DOI 10.1093/biomet/58.3.433 Kiritsakis AK, 1998, J AM OIL CHEM SOC, V75, P673, DOI 10.1007/s11746-998-0205-6 Kohavi R., 1999, C5 1 3 DECISION TREE Kruskal J. B., 1978, MULTIDIMENSIONAL SCA, V11 Le S, 2008, J STAT SOFTW, V25, P1, DOI 10.18637/jss.v025.i01 Lee SH, 2008, J BIOTECHNOL, V133, P486, DOI 10.1016/j.jbiotec.2007.11.001 Li XH, 2016, FOOD ANAL METHOD, V9, P1713, DOI 10.1007/s12161-015-0355-8 Liao SH, 2005, EXPERT SYST APPL, V28, P93, DOI 10.1016/j.eswa.2004.08.003 Lorenzo IM, 2002, ANAL BIOANAL CHEM, V374, P1205, DOI 10.1007/s00216-002-1607-1 Mannina L, 1999, ITAL J FOOD SCI, V11, P139 MANTEL N, 1967, CANCER RES, V27, P209 Mekuria GT, 1999, J HORTIC SCI BIOTECH, V74, P309, DOI 10.1080/14620316.1999.11511114 Messai H, 2016, FOODS, V5, DOI 10.3390/foods5040077 Mevik B. H., 2016, PACKAGE PLS Monteleone E, 2014, OLIVE OIL SENSORY SCIENCE, P109 Montemurro C, 2015, J CHEM-NY, V2015, DOI 10.1155/2015/496986 MORALES MT, 1995, J AGR FOOD CHEM, V43, P2925, DOI 10.1021/jf00059a029 Moreno-Garcia J, 2010, IEEE INT CONF FUZZY Muleo R, 2009, GENOME, V52, P252, DOI 10.1139/G09-002 Nikoloudakis N, 2003, J AM SOC HORTIC SCI, V128, P741, DOI 10.21273/JASHS.128.5.0741 Oksanen J., 2015, MULTIVARIATE ANAL EC Oliveros MCC, 2002, ANAL CHIM ACTA, V459, P219, DOI 10.1016/S0003-2670(02)00119-8 Owen CA, 2005, THEOR APPL GENET, V110, P1169, DOI 10.1007/s00122-004-1861-z Pag`es J., 2010, APPL MATH DEP Pagliuca MM, 2014, PROC ECON FINANC, V17, P221, DOI 10.1016/S2212-5671(14)00897-1 Pasqualone A, 2016, J SCI FOOD AGR, V96, P3642, DOI 10.1002/jsfa.7711 Pena F, 2005, J CHROMATOGR A, V1074, P215, DOI 10.1016/j.chroma.2005.03.081 Perez-Jimenez M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070507 Petrakis PV, 2008, J AGR FOOD CHEM, V56, P3200, DOI 10.1021/jf072957s Piggott J. R., 1992, J SENS STUD, V7, P133, DOI DOI 10.1111/j.1745-459X.1992.tb00529.x Poulli KI, 2005, ANAL CHIM ACTA, V542, P151, DOI 10.1016/j.aca.2005.03.061 Pritchard JK, 2000, GENETICS, V155, P945 Rallo P, 2000, THEOR APPL GENET, V101, P984, DOI 10.1007/s001220051571 Ramensky L. G., 1930, BEITR BIOL PFLANZ, V18, P29 Ramos-Gomez S, 2016, FOOD CHEM, V194, P447, DOI 10.1016/j.foodchem.2015.08.036 Rezzi S, 2005, ANAL CHIM ACTA, V552, P13, DOI 10.1016/j.aca.2005.07.057 Rohlf F. J., 1992, NTSYS PC NUMERICAL T Rohlf F.J., 2005, NTSYS PC NUMERICAL T Rohman A, 2010, FOOD RES INT, V43, P886, DOI 10.1016/j.foodres.2009.12.006 SACCHI R, 1992, Italian Journal of Food Science, V4, P117 Santosa M, 2010, FOOD QUAL PREFER, V21, P881, DOI 10.1016/j.foodqual.2010.05.011 Sanz-Cortes F, 2003, PLANT BREEDING, V122, P173, DOI 10.1046/j.1439-0523.2003.00808.x Sebastiani L, 2017, PLANT CELL REP, V36, P1345, DOI 10.1007/s00299-017-2145-9 Segre A, 2003, SPECTROSC EUR, V15, P6 Shirzad H, 2017, J AOAC INT, V100, P1804, DOI 10.5740/jaoacint.17-0122 Sikorska E., 2012, ANAL OLIVE OILS FLUO Sohn KA, 2007, BIOINFORMATICS, V23, pI479, DOI 10.1093/bioinformatics/btm171 Song XH, 1996, ANAL CHIM ACTA, V334, P57, DOI 10.1016/S0003-2670(96)00315-7 Spaniolas S, 2010, FOOD CHEM, V122, P850, DOI 10.1016/j.foodchem.2010.02.039 Taamalli W, 2006, ELECTRON J BIOTECHN, V9, P467, DOI 10.2225/vol9-issue5-fulltext-12 Tellaroli P, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0152333 Terouzi W, 2011, VIB SPECTROSC, V56, P123, DOI 10.1016/j.vibspec.2011.01.004 Theodosiou M, 2011, INT J FORECASTING, V27, P1178, DOI 10.1016/j.ijforecast.2010.11.002 Tominaga M, 1999, DIABETES CARE, V22, P920, DOI 10.2337/diacare.22.6.920 Torkzaban B, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0143465 TSIMIDOU M, 1993, J SCI FOOD AGR, V62, P253, DOI 10.1002/jsfa.2740620308 Uncu AT, 2017, FOOD CHEM, V221, P1026, DOI 10.1016/j.foodchem.2016.11.059 Wiesman Z, 1998, J AM SOC HORTIC SCI, V123, P837, DOI 10.21273/JASHS.123.5.837 Witten I., 2005, DATA MINING PRACTICA WOLD S, 1987, CHEMOMETR INTELL LAB, V2, P37, DOI 10.1016/0169-7439(87)80084-9 Xanthopoulou A, 2014, PLANT GENET RESOUR-C, V12, P273, DOI 10.1017/S147926211400001X Xie Y. F., 2016, DISINFECTION BYPRODU Yang H, 2001, J AM OIL CHEM SOC, V78, P889, DOI 10.1007/s11746-001-0360-6 Yogi K.S., 2015, PAC SCI REV B HUMANI, V1, P57, DOI DOI 10.1016/J.PSRB.2016.02.001 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X Zupan J, 1997, CHEMOMETR INTELL LAB, V38, P1, DOI 10.1016/S0169-7439(97)00030-0 NR 140 TC 7 Z9 7 U1 0 U2 42 PD NOV PY 2018 VL 42 IS 11 AR e13770 DI 10.1111/jfpp.13770 WC Food Science & Technology SC Food Science & Technology UT WOS:000450155300018 DA 2022-12-14 ER PT J AU Perez, M Presa, P AF Perez, Montse Presa, Pablo TI Validation of a tRNA-Glu-cytochrome b Key for the Molecular Identification of 12 Hake Species (Merluccius spp.) and Atlantic Cod (Gadus morhua) Using PCR-RFLPs, FINS, and BLAST SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE Merluccius spp.; hakes; molecular traceability; identification key; PCR-RFLPs; cytochrome b; FINS; BLAST ID MITOCHONDRIAL-DNA; COMMERCIAL HAKE; CONTROL REGION; PHYLOGENIES AB The goal of this study was to develop a diagnostic key for hake meat to solve the limitations of previous identification methodologies, mainly related to the high degradation of the DNA recovered from processed foods. We describe the development of two molecular tools based on polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphisms of the cytochrome b gene, respectively, to identify DNA from 12 hake species in commercial products. The first assay is an exclusion test consisting of the PCR amplification of a 122 bp fragment using nested primers interspecifically conserved in Merluccius spp. and in Gadus morhua. This 122 bp amplicon, being the shortest one so far designed for hake DNA, is a useful traceability tool for highly degraded samples because its sequence contains enough interspecific diagnostic variation to identify 10 hake species and cod and has been successfully amplified from most commercial products so far tested. The second identification key follows a positive outcome of the exclusion test and consists of the PCR amplification of a 464-465 bp fragment and its. digestion with three restriction enzymes whose targets map at interspecifically nonconserved sites of the cytochrome b. The key presented here has passed through a rigorous methodological calibration including its testing for genus specificity, its validation on a large number of authenticated sample types from each species range, and its implementation with a maximum likelihood method for the assignment of unknown samples. Together, these two procedures constitute the most complete molecular key so far developed for Merluccius spp., which is optimal for routine identification of hakes in large commercial samples at a reasonable cost-time ratio. C1 [Perez, Montse; Presa, Pablo] Univ Vigo, Fac Marine Sci ECIMAT, Dept Biochem Genet & Immunol, Vigo 36310, Spain. C3 Universidade de Vigo; CIM UVIGO RP Perez, M (corresponding author), Univ Vigo, Fac Marine Sci ECIMAT, Dept Biochem Genet & Immunol, Vigo 36310, Spain. EM mon@uvigo.es CR Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 Baker C. Scott, 1996, P10 BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 BROWN JR, 1993, MOL BIOL EVOL, V10, P326 Carrera M, 2006, PROTEOMICS, V6, P5278, DOI 10.1002/pmic.200500899 Edelman I., 1996, PROGRAM MANUAL WISCO Etienne M, 2000, J AGR FOOD CHEM, V48, P2653, DOI 10.1021/jf990907k FELSENSTEIN J, 1985, EVOLUTION, V39, P783, DOI 10.1111/j.1558-5646.1985.tb00420.x Grant WS, 1998, CAN J FISH AQUAT SCI, V55, P2539, DOI 10.1139/cjfas-55-12-2539 Heiman M, 1997, WEBCUTTER 2 0 Hold GL, 2001, J AGR FOOD CHEM, V49, P1175, DOI 10.1021/jf001149x Inada T, 1981, B FAR SEAS FISH RES, V18, P1 LLORIS D, 2003, MERLUZAS MUNDO CATAL, V2, P1 MACKIE IM, 2003, AUTHENTICITY SPECIES, P9 Maddison Wayne Paul, 1996, P47 Matallanas J, 2006, J MAR BIOL ASSOC UK, V86, P193, DOI 10.1017/S0025315406013038 Meyer Axel, 1993, Biochemistry and Molecular Biology of Fishes, V2, P1 MILLER SA, 1988, NUCLEIC ACIDS RES, V16, P1215, DOI 10.1093/nar/16.3.1215 Perez M., 2004, Journal of Food Product Technology, V13, P59, DOI 10.1300/J030v13n02_06 Perez M., 2004, Journal of Food Product Technology, V13, P49, DOI 10.1300/J030v13n02_05 Perez M, 2005, J AGR FOOD CHEM, V53, P5239, DOI 10.1021/jf048012h Pineiro C, 2001, ELECTROPHORESIS, V22, P1545, DOI 10.1002/1522-2683(200105)22:8<1545::AID-ELPS1545>3.0.CO;2-5 Quinteiro J, 2001, J AGR FOOD CHEM, V49, P5108, DOI 10.1021/jf010421f Roberts RJ, 2001, NUCLEIC ACIDS RES, V29, P268, DOI 10.1093/nar/29.1.268 Roldan MI, 2001, SCI MAR, V65, P81, DOI 10.3989/scimar.2001.65n181 RYCHLIK W, 1989, NUCLEIC ACIDS RES, V17, P8543, DOI 10.1093/nar/17.21.8543 SAITOU N, 1987, MOL BIOL EVOL, V4, P406, DOI 10.1093/oxfordjournals.molbev.a040454 SAMBROOK J, 1998, MOL CLONING LAB MANU, pE3 Tamura K, 2004, P NATL ACAD SCI USA, V101, P11030, DOI 10.1073/pnas.0404206101 Tamura K, 2007, MOL BIOL EVOL, V24, P1596, DOI 10.1093/molbev/msm092 Wolf C, 2000, LEBENSM-WISS TECHNOL, V33, P144, DOI 10.1006/fstl.2000.0630 NR 31 TC 30 Z9 30 U1 0 U2 5 PD NOV 26 PY 2008 VL 56 IS 22 BP 10865 EP 10871 DI 10.1021/jf801700x WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000261056700064 DA 2022-12-14 ER PT J AU Tortorici, L Di Gerlando, R Tolone, M Mastrangelo, S Sardina, MT AF Tortorici, Lina Di Gerlando, Rosalia Tolone, Marco Mastrangelo, Salvatore Sardina, Maria Teresa TI 12S rRNA mitochondrial gene as marker to trace Sicilian mono-species dairy products SO LIVESTOCK SCIENCE DT Article DE Mitochondrial DNA; Molecular traceability; Dairy products; Autochthonous Sicilian breeds ID POLYMERASE-CHAIN-REACTION; REAL-TIME PCR; GOATS MILK; COWS MILK; QUANTITATIVE DETECTION; MOZZARELLA CHEESE; SHEEPS MILK; ITALIAN MOZZARELLA; BUFFALO MILK; DNA AB For a rapid, specific and sensitive identification of cows', ewes' and goats' milk in mono-species Sicilian dairy products, species-specific duplex-PCR protocol was applied. DNA samples from blood and experimental cheeses of Sicilian autochthonous breeds were extracted to amplify the 12S rRNA (and part of 16S rRNA in case of Ovis aries) mitochondrial species-specific gene fragment. The use of species-specific primers for Bos taurus, Capra hircus and Ovis aries species, after electrophoresis on agarose gel, yielded fragments of 256 bp, 326 bp and 172 bp, respectively. Amplification by duplex-PCR of DNA pools from two species showed detection thresholds of 0.1% of "contaminant" DNA in each mixture. Finally, duplex-PCR assay was applied to experimental cheeses in order to detect the minimum threshold of DNA belonging to one species in cheese made with milk of two species. The results showed a sensitive threshold of 0.1% of ewes' milk in cows' and goats' cheeses, 0.1% of cows' milk in ewes' and goats' cheeses, and finally 0.1% of goats' milk in cows' and ewes' cheeses. The proposed assay represents a rapid and straightforward method of species traceability for the detections of adulteration in Sicilian mono-species dairy products. C1 [Tortorici, Lina; Di Gerlando, Rosalia; Tolone, Marco; Mastrangelo, Salvatore; Sardina, Maria Teresa] Univ Palermo, Dipartimento Sci Agr & Forestali, Viale Sci, I-90128 Palermo, Italy. C3 University of Palermo RP Sardina, MT (corresponding author), Univ Palermo, Dipartimento Sci Agr & Forestali, Viale Sci, I-90128 Palermo, Italy. EM mariateresa.sardina@unipa.it CR [Anonymous], OFF J EUR COMM L, V88, P1 Banati D, 2011, TRENDS FOOD SCI TECH, V22, P56, DOI 10.1016/j.tifs.2010.12.007 Bottero MT, 2002, J FOOD PROTECT, V65, P362, DOI 10.4315/0362-028X-65.2.362 Bottero MT, 2003, INT DAIRY J, V13, P277, DOI 10.1016/S0958-6946(02)00170-X Branciari R, 2000, J FOOD PROTECT, V63, P408, DOI 10.4315/0362-028X-63.3.408 CHIANESE L, 1990, SCI TECNICA LATTIERO, V41, P315 Dalmasso A, 2011, FOOD CHEM, V124, P362, DOI 10.1016/j.foodchem.2010.06.017 De la Fuente MA, 2005, CRIT REV FOOD SCI, V45, P563, DOI 10.1080/10408690490478127 Enne G, 2005, J CHROMATOGR A, V1094, P169, DOI 10.1016/j.chroma.2005.09.004 Feligini M, 2005, FOOD TECHNOL BIOTECH, V43, P91 Fernandez A, 2004, J SCI FOOD AGR, V84, P1855, DOI 10.1002/jsfa.1829 Galimberti A, 2013, FOOD RES INT, V50, P55, DOI 10.1016/j.foodres.2012.09.036 Guerreiro JS, 2012, INT DAIRY J, V25, P42, DOI 10.1016/j.idairyj.2012.01.002 Hurley IP, 2004, J DAIRY SCI, V87, P543, DOI 10.3168/jds.S0022-0302(04)73195-1 LIPKIN E, 1993, J DAIRY SCI, V76, P2025, DOI 10.3168/jds.S0022-0302(93)77536-0 Lopez-Calleja I, 2007, FOOD CONTROL, V18, P1466, DOI 10.1016/j.foodcont.2006.11.006 Lopez-Calleja I, 2007, INT DAIRY J, V17, P729, DOI 10.1016/j.idairyj.2006.09.005 Lopez-Calleja I, 2005, INT DAIRY J, V15, P1122, DOI 10.1016/j.idairyj.2004.12.003 Lopez-Calleja I, 2005, J DAIRY SCI, V88, P3115, DOI 10.3168/jds.S0022-0302(05)72993-3 Lopez-Calleja I, 2004, J DAIRY SCI, V87, P2839, DOI 10.3168/jds.S0022-0302(04)73412-8 Lopez-Calleja IM, 2007, INT DAIRY J, V17, P87, DOI 10.1016/j.idairyj.2006.01.006 Mafra I, 2004, J AGR FOOD CHEM, V52, P4943, DOI 10.1021/jf049635y Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Mafra I, 2007, INT DAIRY J, V17, P1132, DOI 10.1016/j.idairyj.2007.01.009 Maudet C, 2001, J DAIRY RES, V68, P229, DOI 10.1017/S0022029901004794 Mayer HK, 2005, INT DAIRY J, V15, P595, DOI 10.1016/j.idairyj.2004.10.012 McKean JD, 2001, REV SCI TECH OIE, V20, P363, DOI 10.20506/rst.20.2.1280 MILLER SA, 1988, NUCLEIC ACIDS RES, V16, P1215, DOI 10.1093/nar/16.3.1215 Molina E, 1999, INT DAIRY J, V9, P99, DOI 10.1016/S0958-6946(99)00028-X Nicolaou N, 2011, ANAL BIOANAL CHEM, V399, P3491, DOI 10.1007/s00216-011-4728-6 Noni I. de, 1996, Scienza e Tecnica Lattiero-Casearia, V47, P7 Plath A, 1997, Z LEBENSM UNTERS F A, V205, P437, DOI 10.1007/s002170050195 [薛海燕 Xue Haiyan], 2010, [食品科学, Food Science], V31, P370 Zelenakova L, 2008, MILCHWISSENSCHAFT, V63, P137 NR 34 TC 7 Z9 7 U1 1 U2 5 PD NOV PY 2016 VL 193 BP 39 EP 44 DI 10.1016/j.livsci.2016.09.015 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000388776900006 DA 2022-12-14 ER PT J AU Zava, A Sebastiao, PJ Catarino, S AF Zava, Andrea Sebastiao, Pedro J. Catarino, Sofia TI WINE TRACEABILITY AND AUTHENTICITY: APPROACHES FOR GEOGRAPHICAL ORIGIN, VARIETY AND VINTAGE ASSESSMENT SO CIENCIA E TECNICA VITIVINICOLA DT Article DE Wine; authenticity; isotopes; NMR; biotic/abiotic fractionation ID NUCLEAR-MAGNETIC-RESONANCE; H-1-NMR SPECTROSCOPY; MALOLACTIC FERMENTATION; WINEMAKING TECHNIQUES; DISCRIMINANT-ANALYSIS; PHENOLIC EXTRACTION; MASS-SPECTROMETRY; ROOTSTOCK CONTROL; ISOTOPE RATIO; SNIF-NMR AB The aim of this work is to identify and discuss physicochemical wine characteristics, to provide to some extent a link to the vintage, variety, and/or geographical origin. Bibliographic datasets were attempted to provide the main information for topic comprehension, identifying the sources of wine compositional variability and how these can be expressed in terms of the belonging categories. Since all the environmental and technological conditions which vineyard and wine are subjected are rarely known, different sources were inspected. Great importance was given to the study of isotopic composition because of its importance in food frauds detection history. The interaction of the plant genotype with the environmental conditions of the vintage is the main responsible for the wines organic and inorganic fraction variability in terms of both total and relative content. This phenotypical expression, together with human and abiotic variability sources, has been examined since it contains to some extent the information for the discrimination of wines according to their category. Recently, new proton nuclear magnetic resonance CH NMR) spectroscopy techniques have been under study and, used concurrently to chemometric data management procedures, showed to be an interesting and promising tool for wine characterization according to both vintage and variety. C1 [Zava, Andrea; Catarino, Sofia] Univ Lisbon, Inst Super Agron, LEAF Linking Landscape Environm Agr & Food, P-1349017 Lisbon, Portugal. [Sebastiao, Pedro J.; Catarino, Sofia] Univ Lisbon, Inst Super Tecn, CEFEMA Ctr Phys & Engn Adv Mat, Av Rovisco Pais 1, P-1049001 Lisbon, Portugal. [Sebastiao, Pedro J.] Univ Lisbon, Inst Super Tecn, Dept Phys, Av Rovisco Pais, P-1049001 Lisbon, Portugal. C3 Universidade de Lisboa; Universidade de Lisboa; Instituto Superior Tecnico; Universidade de Lisboa; Instituto Superior Tecnico RP Catarino, S (corresponding author), Univ Lisbon, Inst Super Agron, LEAF Linking Landscape Environm Agr & Food, P-1349017 Lisbon, Portugal.; Catarino, S (corresponding author), Univ Lisbon, Inst Super Tecn, CEFEMA Ctr Phys & Engn Adv Mat, Av Rovisco Pais 1, P-1049001 Lisbon, Portugal. EM sofiacatarino@isa.ulisboa.pt CR Anastasiadi M, 2009, J AGR FOOD CHEM, V57, P11067, DOI 10.1021/jf902137e Aru V, 2018, MOLECULES, V23, DOI 10.3390/molecules23010160 Asimov E., 2009, NEW YORK TIMES 0812 Bautista-Ortin AB, 2016, J INT SCI VIGNE VIN, V50, P91, DOI 10.20870/oeno-one.2016.50.2.781 Bavaresco L, 2007, VITIS, V46, P57 BENDER MM, 1971, PHYTOCHEMISTRY, V10, P1239, DOI 10.1016/S0031-9422(00)84324-1 Bora FD, 2018, NOT BOT HORTI AGROBO, V46, P223, DOI 10.15835/nbha46110853 Bosso A, 2009, AM J ENOL VITICULT, V60, P379 BREAS O, 1994, RAPID COMMUN MASS SP, V8, P967, DOI 10.1002/rcm.1290081212 Brescia MA, 2003, J AGR FOOD CHEM, V51, P21, DOI 10.1021/jf0206015 Canals R, 2005, J AGR FOOD CHEM, V53, P4019, DOI 10.1021/jf047872v Cassino C, 2019, FOOD RES INT, V116, P566, DOI 10.1016/j.foodres.2018.08.075 Castellarin SD, 2007, PLANT CELL ENVIRON, V30, P1381, DOI 10.1111/j.1365-3040.2007.01716.x Catarino S, 2008, CIENC TEC VITIVINIC, V23, P3 Catarino S., 2011, Bulletin de l'OIV, V84, P333 Catarino S, 2008, J AGR FOOD CHEM, V56, P158, DOI 10.1021/jf0720180 Catarino S, 2018, BEVERAGES, V4, DOI 10.3390/beverages4040085 Cheng JF, 2012, J FOOD PROCESS PRES, V36, P262, DOI 10.1111/j.1745-4549.2011.00586.x Christoph N., 2015, BIO WEB C, V5 Coetzee PP, 2005, ANAL BIOANAL CHEM, V383, P977, DOI 10.1007/s00216-005-0093-7 CRAIG H, 1961, SCIENCE, V133, P1702, DOI 10.1126/science.133.3465.1702 Danezis GP, 2016, CURR OPIN FOOD SCI, V10, P22, DOI 10.1016/j.cofs.2016.07.003 Diamond JM, 2005, COLLAPSE SOC CHOOSE Dinca OR, 2015, UNIV POLITEH BUCHAR, V77, P177 DONGMANN G, 1974, RADIAT ENVIRON BIOPH, V11, P41, DOI 10.1007/BF01323099 DUNBAR J, 1982, Z LEBENSM UNTERS FOR, V174, P355, DOI 10.1007/BF01459952 English NB, 2001, P NATL ACAD SCI USA, V98, P11891, DOI 10.1073/pnas.211305498 Esteki M, 2018, FOOD CONTROL, V91, P100, DOI 10.1016/j.foodcont.2018.03.031 Everstine K, 2013, J FOOD PROTECT, V76, P723, DOI 10.4315/0362-028X.JFP-12-399 Fan SX, 2018, FOOD CONTROL, V88, P113, DOI 10.1016/j.foodcont.2017.11.002 FARQUHAR GD, 1982, AUST J PLANT PHYSIOL, V9, P121, DOI 10.1071/PP9820121 Farrar T. C., 1971, PULSE FOURIER TRANSF, P1 Faure G., 1972, STRONTIUM ISOTOPE GE, P1, DOI [DOI 10.1007/978-3-642-65367-4_1, 10.1007/978-3-642-65367-4_1, DOI 10.1007/978-3-642-65367-4] Ferrandino A, 2012, J AGR FOOD CHEM, V60, P4931, DOI 10.1021/jf2045608 Ferreira RB, 2001, TRENDS FOOD SCI TECH, V12, P230, DOI 10.1016/S0924-2244(01)00080-2 Ferreira RB, 2000, AM J ENOL VITICULT, V51, P22 Formisyn P, 1997, AM J ENOL VITICULT, V48, P345 Geana EI, 2017, FOOD ANAL METHOD, V10, P63, DOI 10.1007/s12161-016-0550-2 Gill RB, 2007, ANCIENT MESOAM, V18, P283, DOI 10.1017/S0956536107000193 Godelmann R, 2016, J AOAC INT, V99, P1295, DOI 10.5740/jaoacint.15-0318 Godelmann R, 2013, J AGR FOOD CHEM, V61, P5610, DOI 10.1021/jf400800d Haynes M., 2017, CRC HDB CHEM PHYS, V15 Holmberg L., 2010, INT J WINE RES, V2, P105, DOI [10.2147/IJWR.S14102, DOI 10.2147/IJWR.S14102, 10.2147/ijwr.s14102] Jin ZM, 2009, MOLECULES, V14, P4922, DOI 10.3390/molecules14124922 Kaya AD, 2017, J AGR FOOD CHEM, V65, P4766, DOI 10.1021/acs.jafc.7b01510 Keeler James, 2002, UNDERSTANDING NMR SP, P1 Kosir IJ, 2002, ANAL CHIM ACTA, V458, P77, DOI 10.1016/S0003-2670(01)01549-5 Lerno L, 2015, AM J ENOL VITICULT, V66, P444, DOI 10.5344/ajev.2015.14129 Loira I, 2018, FERMENTATION-BASEL, V4, DOI 10.3390/fermentation4030070 Maicas S, 2001, APPL MICROBIOL BIOT, V56, P35, DOI 10.1007/s002530100662 Marguerit E, 2012, NEW PHYTOL, V194, P416, DOI 10.1111/j.1469-8137.2012.04059.x MARTIN GJ, 1982, ANAL CHEM, V54, P2380, DOI 10.1021/ac00250a057 MARTIN GJ, 1983, J AGR FOOD CHEM, V31, P311, DOI 10.1021/jf00116a032 MARTIN GJ, 1991, J SCI FOOD AGR, V56, P419, DOI 10.1002/jsfa.2740560403 Martins P, 2014, J INT SCI VIGNE VIN, V48, P21 Mazzei P, 2013, J AGR FOOD CHEM, V61, P10816, DOI 10.1021/jf403567x Mazzei P, 2010, ANAL CHIM ACTA, V673, P167, DOI 10.1016/j.aca.2010.06.003 Medina B., 2010, FOOD ADDIT CONTAM, V17, P435 Medina S, 2019, TRENDS FOOD SCI TECH, V85, P163, DOI 10.1016/j.tifs.2019.01.017 Meija J, 2016, PURE APPL CHEM, V88, P293, DOI 10.1515/pac-2015-0503 Meloni G, 2013, J WINE ECON, V8, P244, DOI 10.1017/jwe.2013.33 Monakhova YB, 2014, ANAL CHIM ACTA, V833, P29, DOI 10.1016/j.aca.2014.05.005 Moreira C, 2017, S AFR J ENOL VITIC, V38, P82 Naes T, 2001, J CHEMOMETR, V15, P413, DOI 10.1002/cem.676 Ogrinc N, 2001, J AGR FOOD CHEM, V49, P1432, DOI 10.1021/jf000911s OIV, 2019, COMP INT METH WIN MU, V1 OIV, 2019, COMP INT METH WIN MU, V2 OLeary M.H., 1978, TRANSITION STATES BI, P285 Paris W., 2002, OBSERVER Pereira GE, 2006, J AGR FOOD CHEM, V54, P6765, DOI 10.1021/jf061013k Petrini R, 2015, FOOD CHEM, V170, P138, DOI 10.1016/j.foodchem.2014.08.051 Pocock KF, 2000, J AGR FOOD CHEM, V48, P1637, DOI 10.1021/jf9905626 Redan BW, 2019, J AGR FOOD CHEM, V67, P2670, DOI 10.1021/acs.jafc.8b06062 Rib?reau-Gayon P., 2006, HDB ENOLOGY CHEM WIN, P109, DOI DOI 10.1002/0470010398 Ribereau-Gayon P., 2006, HDB ENOLOGY, V2, P3 Ribereau-Gayon P., 2006, HDB ENOLOGY, V2, P141, DOI DOI 10.1002/0470010398.CH6 Rodionova OY, 2016, TRAC-TREND ANAL CHEM, V78, P17, DOI 10.1016/j.trac.2016.01.010 ROMANO P, 1994, FEMS MICROBIOL LETT, V118, P213, DOI 10.1016/0378-1097(94)90506-1 Rossano EC, 2007, J AGR FOOD CHEM, V55, P311, DOI 10.1021/jf061828t Sacchi KL, 2005, AM J ENOL VITICULT, V56, P197 Schmidt HL, 2003, NATURWISSENSCHAFTEN, V90, P537, DOI 10.1007/s00114-003-0485-5 Sebastiao PJ, 2011, J PHYS CHEM B, V115, P14348, DOI 10.1021/jp206429j Shriver D., 2014, INORG CHEM, P3 Smith PA, 2015, AUST J GRAPE WINE R, V21, P601, DOI 10.1111/ajgw.12188 Son HS, 2008, J AGR FOOD CHEM, V56, P8007, DOI 10.1021/jf801424u Sousa MJ, 1991, J WINE RES, V2, P115 Suklje K, 2012, J AGR FOOD CHEM, V60, P9454, DOI 10.1021/jf3020766 Tcherkez G, 2011, TRENDS PLANT SCI, V16, P499, DOI 10.1016/j.tplants.2011.05.010 Tramontini S, 2013, ENVIRON EXP BOT, V93, P20, DOI 10.1016/j.envexpbot.2013.04.001 Tyagi S., 2010, INT J PHARM SCI REV, V3, P83 U.S. Food and Drug Administration, 2010, AD FOOD, VIV, P342 Vorster C, 2010, S AFR J CHEM-S-AFR T, V63, P207 Waters EJ, 2005, AUST J GRAPE WINE R, V11, P215, DOI 10.1111/j.1755-0238.2005.tb00289.x NR 93 TC 3 Z9 3 U1 4 U2 12 PY 2020 VL 35 IS 2 BP 133 EP 147 DI 10.1051/ctv/20203502133 WC Food Science & Technology SC Food Science & Technology UT WOS:000581718800002 DA 2022-12-14 ER PT J AU Espineira, M Gonzalez-Lavin, N Vieites, JM Santaclara, FJ AF Espineira, Montserrat Gonzalez-Lavin, Nerea Vieites, Juan M. Santaclara, Francisco J. TI Development of a method for the genetic identification of flatfish species on the basis of mitochondrial DNA sequences SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE flatfish; pleuronectiformes; genetic identification; cytochrome b; cytochrome oxidase subunit I; sequencing; FINS; BLAST ID SOLE SOLEA-SOLEA; CYTOCHROME-B; PCR-RFLP; FRAGMENT; GENOME AB In the present study a method for genetic identification of flatfish species was developed. The technique is based on DNA sequencing of amplified DNA by PCR and subsequent phylogenetic analysis (FINS). A phylogenetic tree using the cytochrome oxidase subunit I (COI) was constructed and the bootstrap values calculated. The mentioned technique allows the genetic identification of more than 50 flatfish species in fresh, frozen, and precooked products. This analytical system was validated and subsequently applied to 30 commercial samples, obtaining 13 that were incorrectly labeled (43%). Four of the mislabeled samples were whole fish (31%), and nine were fillets (69%). The species with the higher rate of incorrect labeling were Pleuronectes platessa (117%) and Solea solea (10%). Other species incorrectly labeled were Hipoglossus hipoglossus (7%), Reinharditus hippoglossoides, Limanda ferruginea, and Microstomus kitt (3% each species). Therefore, this molecular tool is appropriate to clarify questions related with the correct labeling of commercial products, the traceability of raw materials, and the control of imported flatfish, and also can be applied to questions linked to the control of fisheries. C1 [Espineira, Montserrat; Gonzalez-Lavin, Nerea; Vieites, Juan M.; Santaclara, Francisco J.] ANFACO CECOPESCA, Area Mol Biol & Biotechnol, Vigo 36310, Pontevedra, Spain. RP Santaclara, FJ (corresponding author), ANFACO CECOPESCA, Area Mol Biol & Biotechnol, Vigo 36310, Pontevedra, Spain. EM fran-santa@hotmail.com CR Altschul S, 1998, FASEB J, V12, pA1326 BARTLETT SE, 1992, BIOTECHNIQUES, V12, P408 Burgener M, 1997, THESIS U BERN SWITZE Cas R., 1987, PLANT MOL BIOL MAN A, DOI [DOI 10.1007/978-94-017-5294-7_, 10.1007/978-94-009-3167-1] Cespedes A, 1998, J FOOD PROTECT, V61, P1684, DOI 10.4315/0362-028X-61.12.1684 Cespedes A, 2000, J SCI FOOD AGR, V80, P29, DOI [10.1002/(SICI)1097-0010(20000101)80:1<29::AID-JSFA470>3.0.CO;2-4, 10.1002/(SICI)1097-0010(20000101)80:1<29::AID-JSFA470>3.0.CO;2-4] Cespedes A, 1999, J FOOD PROTECT, V62, P1178, DOI 10.4315/0362-028X-62.10.1178 Hall TA., 1999, NUCL ACIDS S SER, V41, P95, DOI DOI 10.1021/BK-1999-0734.CH008 Kumar S, 2004, BRIEF BIOINFORM, V5, P150, DOI 10.1093/bib/5.2.150 Lavoue S, 2007, MOL PHYLOGENET EVOL, V43, P1096, DOI 10.1016/j.ympev.2006.09.018 LINDBERG G, 1993, OSTEICHTHHYS ACTINOP, V31, P272 Mc Carthy C, 1996, CHROMAS VERSION 1 45 Mikkelsen PM, 2006, ZOOL J LINN SOC-LOND, V148, P439, DOI 10.1111/j.1096-3642.2006.00262.x Nakabo T., 2002, FISHES JAPAN PICTORI Nelson J.S., 1994, FISHES WORLD, V3, P624 Oh DJ, 2007, DNA SEQUENCE, V18, P295, DOI 10.1080/10425170701248525 Pardo BG, 2005, SCI MAR, V69, P531, DOI 10.3989/scimar.2005.69n4531 Quinteiro J, 1998, J AGR FOOD CHEM, V46, P1662, DOI 10.1021/jf970552+ Rozas J, 2003, BIOINFORMATICS, V19, P2496, DOI 10.1093/bioinformatics/btg359 Santaclara FJ, 2007, J AGR FOOD CHEM, V55, P9913, DOI 10.1021/jf0707177 Santaclara FJ, 2007, J AGR FOOD CHEM, V55, P305, DOI 10.1021/jf061840l Sevilla RG, 2007, MOL ECOL NOTES, V7, P730, DOI 10.1111/j.1471-8286.2007.01863.x Sotelo CG, 2001, J AGR FOOD CHEM, V49, P4562, DOI 10.1021/jf010452a Wang XZ, 2007, GENE, V399, P11, DOI 10.1016/j.gene.2007.04.019 Winfrey MR, 1997, UNRAVELING DNA MOL B Yamanoue Y, 2007, MOL PHYLOGENET EVOL, V45, P89, DOI 10.1016/j.ympev.2007.03.008 1996, WORLD BIODIVERSITY D NR 27 TC 53 Z9 56 U1 1 U2 18 PD OCT 8 PY 2008 VL 56 IS 19 BP 8954 EP 8961 DI 10.1021/jf800570r WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000259675300026 DA 2022-12-14 ER PT J AU Scollo, F Egea, LA Gentile, A La Malfa, S Dorado, G Hernandez, P AF Scollo, Francesco Egea, Leticia A. Gentile, Alessandra La Malfa, Stefano Dorado, Gabriel Hernandez, Pilar TI Absolute quantification of olive oil DNA by droplet digital-PCR (ddPCR): Comparison of isolation and amplification methodologies SO FOOD CHEMISTRY DT Article DE Olea europaea; Olive oil; Traceability; Molecular markers; qRT-PCR ID REAL-TIME PCR; POLYMERASE-CHAIN-REACTION; MOLECULAR MARKERS; OLEA-EUROPAEA; SCAR MARKERS; IDENTIFICATION; STABILIZATION; QUANTITATION; EXTRACTION; STABILITY AB Olive oil is considered a premium product for its nutritional value and health benefits, and the ability to define its origin and varietal composition is a key step towards ensuring the traceability of the product. However, isolating the DNA from such a matrix is a difficult task. In this study, the quality and quantity of olive oil DNA, isolated using four different DNA isolation protocols, was evaluated using the qRT-PCR and ddPCR techniques. The results indicate that CTAB-based extraction methods were the best for unfiltered oil, while Nucleo Spin-based extraction protocols showed greater overall reproducibility. The use of both qRT-PCR and ddPCR led to the absolute quantification of the DNA copy number. The results clearly demonstrate the importance of the choice of DNA-isolation protocol, which should take into consideration the qualitative aspects of DNA and the evaluation of the amplified DNA copy number. (C) 2016 Elsevier Ltd. All rights reserved. C1 [Scollo, Francesco; Egea, Leticia A.; Hernandez, Pilar] CSIC, Inst Agr Sostenible IAS CSIC, Alameda Obispo S-N, Cordoba 14080, Spain. [Scollo, Francesco; Gentile, Alessandra; La Malfa, Stefano] Univ Catania, Dipartimento Agr Alimentaz & Ambiente Di3A, Via Valdisavoia 5, I-95123 Catania, Italy. [Egea, Leticia A.; Dorado, Gabriel] Univ Cordoba, Dept Bioquim & Biol Mol, Campus Rabanales C6-1-E17, E-14071 Cordoba, Spain. [Gentile, Alessandra] Hunan Agr Univ, Hort & Landscape Coll, Changsha, Hunan, Peoples R China. C3 Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Agricultura Sostenible (IAS); University of Catania; Universidad de Cordoba; Hunan Agricultural University RP Scollo, F (corresponding author), Univ Catania, Dipartimento Agr Alimentaz & Ambiente Di3A, Via Valdisavoia 5, I-95123 Catania, Italy. EM fscollo@unict.it; z32ayegl@uco.es; gentilea@unict.it; slamalfa@unict.it; bb1dopeg@uco.es; phernandez@ias.csic.es CR Agrimonti C, 2011, TRENDS FOOD SCI TECH, V22, P237, DOI 10.1016/j.tifs.2011.02.002 [Anonymous], CURR BIOTECHNOL Ben Ayed R, 2009, EUR FOOD RES TECHNOL, V229, P757, DOI 10.1007/s00217-009-1111-3 Besnard G, 2011, BMC PLANT BIOL, V11, DOI 10.1186/1471-2229-11-80 Bogani P, 2009, FOOD CHEM, V113, P658, DOI 10.1016/j.foodchem.2008.07.056 Breton C, 2004, J AGR FOOD CHEM, V52, P531, DOI 10.1021/jf034588f Busconi M, 2003, FOOD CHEM, V83, P127, DOI 10.1016/S0308-8146(03)00218-8 Bustin SA, 2000, J MOL ENDOCRINOL, V25, P169, DOI 10.1677/jme.0.0250169 Consolandi C, 2008, EUR FOOD RES TECHNOL, V227, P1429, DOI 10.1007/s00217-008-0863-5 Costa J, 2012, TRENDS FOOD SCI TECH, V26, P43, DOI 10.1016/j.tifs.2012.01.009 Costa J, 2010, EUR FOOD RES TECHNOL, V230, P915, DOI 10.1007/s00217-010-1238-2 Demeke T, 2010, ANAL BIOANAL CHEM, V396, P1977, DOI 10.1007/s00216-009-3150-9 Doveri S, 2006, J AGR FOOD CHEM, V54, P9221, DOI 10.1021/jf061564a Gimenez MJ, 2010, FOOD CHEM, V118, P482, DOI 10.1016/j.foodchem.2009.05.012 Hayden RT, 2013, J CLIN MICROBIOL, V51, P540, DOI 10.1128/JCM.02620-12 Hellebrand M, 1998, Z LEBENSM UNTERS F A, V206, P237, DOI 10.1007/s002170050250 Hernandez P, 2002, GENOME, V45, P198, DOI 10.1139/g01-087 Hernandez P, 2001, THEOR APPL GENET, V102, P1082, DOI 10.1007/s001220000515 Hernandez P, 2001, THEOR APPL GENET, V103, P788, DOI 10.1007/s001220100603 Hindson BJ, 2011, ANAL CHEM, V83, P8604, DOI 10.1021/ac202028g Huggett JF, 2013, CLIN CHEM, V59, P892, DOI 10.1373/clinchem.2013.206375 Kim TG, 2014, APPL MICROBIOL BIOT, V98, P1417, DOI 10.1007/s00253-013-5057-9 Li M, 2006, NAT METHODS, V3, P95, DOI 10.1038/NMETH850 Marmiroli N., 2009, ADV OLIVE RESOURCES, P157 Montemurro C, 2008, EUR FOOD RES TECHNOL, V226, P1439, DOI 10.1007/s00217-007-0675-z Morisset D, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0062583 MURRAY MG, 1980, NUCLEIC ACIDS RES, V8, P4321, DOI 10.1093/nar/8.19.4321 Nakano M, 2014, J PHYS CHEM B, V118, P379, DOI 10.1021/jp406647b Pafundo S, 2007, J AGR FOOD CHEM, V55, P6052, DOI 10.1021/jf0701638 Paolacci AR, 2009, BMC MOL BIOL, V10, DOI 10.1186/1471-2199-10-11 Perez-Jimenez M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070507 Pinheiro LB, 2012, ANAL CHEM, V84, P1003, DOI 10.1021/ac202578x R Core Team, 2019, R LANG ENV STAT COMP Ramos-Gomez S, 2014, FOOD CHEM, V158, P374, DOI 10.1016/j.foodchem.2014.02.142 Ronning SB, 2006, J AGR FOOD CHEM, V54, P682, DOI 10.1021/jf052328n Spiess AN, 2004, CLIN CHEM, V50, P1256, DOI 10.1373/clinchem.2004.031336 SYKES PJ, 1992, BIOTECHNIQUES, V13, P444 Tateishi-Karimata H, 2014, NUCLEIC ACIDS RES, V42, P8831, DOI 10.1093/nar/gku499 Testolin R, 2005, J FOOD SCI, V70, pC108, DOI 10.1111/j.1365-2621.2005.tb09011.x Velasco J, 2002, EUR J LIPID SCI TECH, V104, P661, DOI 10.1002/1438-9312(200210)104:9/10<661::AID-EJLT661>3.0.CO;2-D Vietina M, 2011, J SCI FOOD AGR, V91, P1381, DOI 10.1002/jsfa.4317 Wahrburg U, 2002, EUR J LIPID SCI TECH, V104, P698, DOI 10.1002/1438-9312(200210)104:9/10<698::AID-EJLT698>3.0.CO;2-A Wu YJ, 2008, EUR FOOD RES TECHNOL, V227, P1117, DOI 10.1007/s00217-008-0827-9 NR 43 TC 23 Z9 29 U1 0 U2 223 PD DEC 15 PY 2016 VL 213 BP 388 EP 394 DI 10.1016/j.foodchem.2016.06.086 WC Chemistry, Applied; Food Science & Technology; Nutrition & Dietetics SC Chemistry; Food Science & Technology; Nutrition & Dietetics UT WOS:000380289900048 DA 2022-12-14 ER PT J AU Israel, W Tiemann, I Metz, G Yamaryo, Y Maeda, F Shimomura, T AF Israel, W Tiemann, I Metz, G Yamaryo, Y Maeda, F Shimomura, T TI An international length comparison at an industrial level using a photoelectric incremental encoder as transfer standard SO PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY DT Article DE length comparison; direct traceability; linear encoder; laser interferometry; measurement uncertainty AB A Japanese-German interlaboratory comparison of length measurements was conducted. A photoelectric incremental encoder with a measurement length of 270 nun was used as transfer standard. An agreement of better than 27 nm over the entire length was ascertained, and the "short-range" deviations within a length interval of approximately 10 mm could be characterized with a standard deviation of sigma = 0.8 run. The results attained are considered as consistent with the estimated uncertainties of measurement. Since the measurements performed are directly traceable to the SI unit of the "metre", the comparison supports ideas currently being discussed by some National Metrology Institutes and dealing with the question of how foundations can be laid for a generally accepted application of this method of traceability. (C) 2002 Elsevier Science Inc. All rights reserved. C1 DR JOHANNES HEIDENHAIN GmbH, D-83301 Traunreut, Germany. MITUTOYO Corp, Takatsu Ku, Kawasaki, Kanagawa 2130012, Japan. RP Israel, W (corresponding author), DR JOHANNES HEIDENHAIN GmbH, Dr Johannes Heidenhain Str 5, D-83301 Traunreut, Germany. CR Bonsch G, 1998, METROLOGIA, V35, P133, DOI 10.1088/0026-1394/35/2/8 BOSSE H, 2001, P 2 EUSP INT C, V1, P302 FLUGGE J, 1999, P 1 EUSP INT C, P446 HAMON J, 1987, METROLOGIA, V24, P187, DOI 10.1088/0026-1394/24/4/006 International Organization for Standardization, 1993, GUID EXPR UNC MEAS KUNZMANN H, 2001, SPIE P, V4401, P253 *LNE, 1994, LIN SCAL MEAS Quinn TJ, 1999, METROLOGIA, V36, P211, DOI 10.1088/0026-1394/36/3/7 SAWABE M, 1988, P SOC PHOTO-OPT INS, V954, P448 Thiel J., 1999, P 1 INT EUSP C, V2, P419 WOGER W, 1999, 109 PTB YOSHIIKE K, 1990, STD008 MITUTOYO NR 12 TC 11 Z9 11 U1 0 U2 4 PD APR PY 2003 VL 27 IS 2 BP 151 EP 156 AR PII S0141-6359(02)00192-7 DI 10.1016/S0141-6359(02)00192-7 WC Engineering, Multidisciplinary; Engineering, Manufacturing; Nanoscience & Nanotechnology; Instruments & Instrumentation SC Engineering; Science & Technology - Other Topics; Instruments & Instrumentation UT WOS:000182335000005 DA 2022-12-14 ER PT J AU Gardiner, TD Coleman, MD Innocenti, F Tompkins, J Connor, A Garnsworthy, PC Moorby, JM Reynolds, CK Waterhouse, A Wills, D AF Gardiner, T. D. Coleman, M. D. Innocenti, F. Tompkins, J. Connor, A. Garnsworthy, P. C. Moorby, J. M. Reynolds, C. K. Waterhouse, A. Wills, D. TI Determination of the absolute accuracy of UK chamber facilities used in measuring methane emissions from livestock SO MEASUREMENT DT Article DE Respiration chambers; Methane emissions; Livestock emissions; Calibration; Traceability ID TECHNICAL-NOTE AB Respiration chambers are one of the primary sources of data on methane emissions from livestock. This paper describes the results from a coordinated set of chamber validation experiments which establishes the absolute accuracy of the methane emission rates measured by the chambers, and for the first time provides metrological traceability to international standards, assesses the impact of both analyser and chamber response times on measurement uncertainty and establishes direct comparability between measurements made across different facilities with a wide range of chamber designs. As a result of the validation exercise the estimated combined uncertainty associated with the overall capability across all facilities reduced from 25.7% (k = 2, 95% confidence) before the validation to 2.1% (k = 2, 95% confidence) when the validation results are applied to the facilities' data. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved. C1 [Gardiner, T. D.; Coleman, M. D.; Innocenti, F.; Tompkins, J.; Connor, A.] Natl Phys Lab, Teddington TW11 0LW, Middx, England. [Garnsworthy, P. C.] Univ Nottingham, Sch Biosci, Loughborough LE12 5RD, Leics, England. [Moorby, J. M.] Aberystwyth Univ, Inst Biol Environm & Rural Sci, Aberystwyth SY23 4AD, Dyfed, Wales. [Reynolds, C. K.] Univ Reading, Sch Agr Policy & Dev, Ctr Dairy Res, Reading RG6 6AR, Berks, England. [Waterhouse, A.] Scotlands Rural Coll, Future Farming Syst, Edinburgh EH9 3JG, Midlothian, Scotland. [Wills, D.] AFBI Hillsborough, Agrifood & Biosci Inst, Hillsborough BT26 6DR, Down, North Ireland. C3 National Physical Laboratory - UK; University of Nottingham; Aberystwyth University; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Institute of Biological, Environmental, Rural & Sciences (IBERS); University of Reading; Scotland's Rural College; Agri-Food & Biosciences Institute RP Gardiner, TD (corresponding author), Natl Phys Lab, Hampton Rd, Teddington TW11 0LW, Middx, England. EM tom.gardiner@npl.co.uk CR [Anonymous], 152673 EN Armsby H.P., 1908, PRINCIPLES ANIMAL NU Cammell S. B., 1981, Laboratory Practice, V30, P115 Church J., 2013, CLIMATE CHANGE 2013, DOI [10.1017/ CBO9781107415324, 10.1017/CBO9781107415324] Derno M, 2009, J DAIRY SCI, V92, P2804, DOI 10.3168/jds.2008-1839 Eggleston H. S., 2006, 2006 IPCC GUIDELINES, DOI DOI 10.35-10.49 European Environment Agency, 2013, 102013 EEA Grainger C, 2007, J DAIRY SCI, V90, P2755, DOI 10.3168/jds.2006-697 Hellwing ALF, 2012, J DAIRY SCI, V95, P6077, DOI 10.3168/jds.2012-5505 Klein L, 2006, AUST J EXP AGR, V46, P1257, DOI 10.1071/EA05340 McLean JA, 1987, ANIMAL HUMAN CALORIM Murray PJ, 1999, J AGR SCI, V133, P439, DOI 10.1017/S0021859699007182 Pinares C., 2012, TECHNICAL MANUAL RES NR 13 TC 34 Z9 37 U1 0 U2 21 PD APR PY 2015 VL 66 BP 272 EP 279 DI 10.1016/j.measurement.2015.02.029 WC Engineering, Multidisciplinary; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation UT WOS:000350825500031 DA 2022-12-14 ER PT J AU Lee, HS Kim, SH Jeong, JS Lee, YM Yim, YH AF Lee, Hyun-Seok Kim, Sook Heun Jeong, Ji-Seon Lee, Yong-Moon Yim, Yong-Hyeon TI Sulfur-based absolute quantification of proteins using isotope dilution inductively coupled plasma mass spectrometry SO METROLOGIA DT Article DE isotope dilution ICP-MS; sulfur-based protein quantification; SI-traceability; size-exclusion chromatography ICP-MS; protein standard ID AMINO-ACID-ANALYSIS; HUMAN GROWTH-HORMONE; DNA; OPTIMIZATION; QUANTITATION; PHOSPHORUS; STRATEGIES; ACCURACY AB An element-based reductive approach provides an effective means of realizing International System of Units (SI) traceability for high-purity biological standards. Here, we develop an absolute protein quantification method using double isotope dilution (ID) inductively coupled plasma mass spectrometry (ICP-MS) combined with microwave-assisted acid digestion for the first time. We validated the method and applied it to certify the candidate protein certified reference material (CRM) of human growth hormone (hGH). The concentration of hGH was determined by analysing the total amount of sulfur in hGH. Next, the size-exclusion chromatography method was used with ICP-MS to characterize and quantify sulfur-containing impurities. By subtracting the contribution of sulfur-containing impurities from the total sulfur content in the hGH CRM, we obtained a SI-traceable certification value. The quantification result obtained with the present method based on sulfur analysis was in excellent agreement with the result determined via a well-established protein quantification method based on amino acid analysis using conventional acid hydrolysis combined with an ID liquid chromatography-tandem mass spectrometry. The element-based protein quantification method developed here can be generally used for SI-traceable absolute quantification of proteins, especially pure-protein standards. C1 [Lee, Hyun-Seok; Kim, Sook Heun; Jeong, Ji-Seon; Yim, Yong-Hyeon] Korea Res Inst Stand & Sci, Ctr Inorgan Anal, Daejeon 305340, South Korea. [Lee, Hyun-Seok; Lee, Yong-Moon] Chungbuk Natl Univ, Coll Pharm, Cheongju, South Korea. [Jeong, Ji-Seon; Yim, Yong-Hyeon] Univ Sci & Technol, Dept Analyt Sci Biol, Daejeon, South Korea. C3 Korea Research Institute of Standards & Science (KRISS); Chungbuk National University; University of Science & Technology (UST) RP Lee, HS (corresponding author), Korea Res Inst Stand & Sci, Ctr Inorgan Anal, Gajeong Ro 267, Daejeon 305340, South Korea. EM yhyim@kriss.re.kr CR [Anonymous], 2012, GUANT UNC AN MEAS Arsene CG, 2008, ANAL CHEM, V80, P4154, DOI 10.1021/ac7024738 Barr JR, 1996, CLIN CHEM, V42, P1676 BIPM IEC IFCC ILAC ISO IUPAC IUPAP OIML, 2008, GUID EXPR UNC MEAS J Boulo S, 2013, CLIN CHEM, V59, P1074, DOI 10.1373/clinchem.2012.199489 Camp CL, 2012, ANAL BIOANAL CHEM, V402, P367, DOI 10.1007/s00216-011-5347-y Carter PJ, 2011, EXP CELL RES, V317, P1261, DOI 10.1016/j.yexcr.2011.02.013 Choi J, 2003, ACCREDIT QUAL ASSUR, V8, P13, DOI 10.1007/s00769-002-0520-9 Eisenacher M, 2011, PROTEOMICS, V11, P1031, DOI 10.1002/pmic.201000441 FASSETT JD, 1989, ANAL CHEM, V61, pA643, DOI 10.1021/ac00185a715 Holden MJ, 2007, ANAL CHEM, V79, P1536, DOI 10.1021/ac061463b Jaquinod M, 2012, PROTEOMICS, V12, P1217, DOI 10.1002/pmic.201100538 Jeong JS, 2011, J CHROMATOGR A, V1218, P6596, DOI 10.1016/j.chroma.2011.07.053 Kato M, 2015, ANAL BIOANAL CHEM, V407, P3137, DOI 10.1007/s00216-014-8190-0 Kim SH, 2015, B KOREAN CHEM SOC, V36, P936, DOI 10.1002/bkcs.10175 Krastins B, 2013, CLIN BIOCHEM, V46, P399, DOI 10.1016/j.clinbiochem.2012.12.019 Kulasingam V, 2008, NAT CLIN PRACT ONCOL, V5, P588, DOI 10.1038/ncponc1187 Leader B, 2008, NAT REV DRUG DISCOV, V7, P21, DOI 10.1038/nrd2399 Munoz A, 2011, ANAL BIOCHEM, V408, P124, DOI 10.1016/j.ab.2010.08.037 Nakanishi K, 2001, J BIOSCI BIOENG, V91, P233, DOI 10.1016/S1389-1723(01)80127-4 Quack M, 2007, QUANTITIES UNITS SYM Raynal B, 2014, MICROB CELL FACT, V13, DOI 10.1186/s12934-014-0180-6 Sargent M., 2002, GUIDELINES ACHIEVING Wang M, 2010, MASS SPECTROM REV, V29, P326, DOI 10.1002/mas.20241 Weiss M, 1998, J CHROMATOGR A, V795, P263, DOI 10.1016/S0021-9673(97)00983-7 Yang I, 2004, ANAL BIOCHEM, V335, P150, DOI 10.1016/j.ab.2004.08.038 Yim JH, 2014, ANAL BIOANAL CHEM, V406, P4401, DOI 10.1007/s00216-014-7838-0 NR 27 TC 20 Z9 20 U1 2 U2 30 PD OCT PY 2015 VL 52 IS 5 BP 619 EP 627 DI 10.1088/0026-1394/52/5/619 WC Instruments & Instrumentation; Physics, Applied SC Instruments & Instrumentation; Physics UT WOS:000362491400019 DA 2022-12-14 ER PT J AU Montes, I Laconcha, U Iriondo, M Manzano, C Arrizabalaga, H Estonba, A AF Montes, Iratxe Laconcha, Urtzi Iriondo, Mikel Manzano, Carmen Arrizabalaga, Haritz Estonba, Andone TI Reduced Single Nucleotide Polymorphism Panels for Assigning Atlantic Albacore and Bay of Biscay Anchovy Individuals to Their Geographic Origin: Toward Sustainable Fishery Management SO JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY DT Article DE traceability; illegal fishing; fraud; food safety; false eco-certification; origin protection; Bay of Biscay ID GENE-ASSOCIATED MARKERS; POPULATION; IDENTIFICATION; DIVERSITY; MARINE; DIFFERENTIATION; ECOSYSTEM; AUTHENTICATION; INFERENCE; DOMINANT AB There is an increasing trend upon adding a detailed description of the origin of seafood products driven by a general interest in the implementation of sustainable fishery management plans for the conservation of marine ecosystems. North Atlantic albacore ("Bonito del Norte con Eusko Label") and Bay of Biscay anchovy ("Anchoa Cantabrico") are two commercially important fish populations with high economical value and vulnerable to commercial fraud. This fact, together with the overexploited situation of these two populations, makes it necessary to develop a tool to identify individual origin and to detect Commercial fraud. In the present study, we have developed and validated a traceability tool consisting, of reduced panels of gene-associated single nucleotide polymorphisms (SNPs) suitable for assigning individuals of two species to their origin with unprecedented accuracy levels. Only 48 SNPs are necessary to assign 81.1% albacore and 93.4% anchovy individuals with 100% accuracy to their geographic origin. The total accuracy of the results demonstrates how gene-associated SNPs can revolutionize food traceability. Gene-associated SNP panels are not of mere commercial interest, but they also can result in a positive impact on sustainability of marine ecosystems through conservation of fish populations through establishing more effective and sustainable fishery management framework and contributing to the prevention of falsified labeling. C1 [Montes, Iratxe; Laconcha, Urtzi; Iriondo, Mikel; Manzano, Carmen; Estonba, Andone] Univ Basque Country, UPV EHU, Dept Genet Phys Anthropol & Anim Physiol, Leioa 48940, Spain. [Laconcha, Urtzi; Arrizabalaga, Haritz] AZTI, Marine Res Div, Pasaia 20110, Spain. C3 University of Basque Country; AZTI RP Montes, I (corresponding author), Univ Basque Country, UPV EHU, Dept Genet Phys Anthropol & Anim Physiol, Leioa 48940, Spain. EM iratxe.montes@ehu.eus CR Abreu AG, 2012, MOL ECOL RESOUR, V12, P374, DOI 10.1111/j.1755-0998.2011.03109.x Albaina A, 2013, ANIM GENET, V44, P678, DOI 10.1111/age.12051 Behrmann K, 2015, J AGR FOOD CHEM, V63, P802, DOI 10.1021/jf506201m Bekkevold D, 2015, ICES J MAR SCI, V72, P1790, DOI 10.1093/icesjms/fsu247 BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x BLAXTER JHS, 1982, ADV MAR BIOL, V20, P3 Chairi H, 2014, J AGR FOOD CHEM, V62, P2803, DOI 10.1021/jf405680g Collette BB, 2011, SCIENCE, V333, P291, DOI 10.1126/science.1208730 De Battisti C, 2014, J AGR FOOD CHEM, V62, P198, DOI 10.1021/jf403545m Espineira M, 2009, J AGR FOOD CHEM, V57, P495, DOI 10.1021/jf802787d Espineira M, 2008, J AGR FOOD CHEM, V56, P8954, DOI 10.1021/jf800570r EU, 2013, OFF J EUR UNION L, V354, P1 EU, 2011, OFF J EUR UNION, V54, P1 Falush D, 2007, MOL ECOL NOTES, V7, P574, DOI 10.1111/j.1471-8286.2007.01758.x Fernandez-Tajes J, 2007, J AGR FOOD CHEM, V55, P7278, DOI 10.1021/jf0709855 Foll M, 2008, GENETICS, V180, P977, DOI 10.1534/genetics.108.092221 Freamo H, 2011, MOL ECOL RESOUR, V11, P254, DOI 10.1111/j.1755-0998.2010.02952.x Garcia-Vazquez E, 2011, J AGR FOOD CHEM, V59, P475, DOI 10.1021/jf103754r Gonzalez M. A. Pardo, 2013, Patent No. [2 392 293 Al, 2392293] Gutierrez NL, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0043765 Hauser L, 2002, P NATL ACAD SCI USA, V99, P11742, DOI 10.1073/pnas.172242899 Herrero B, 2010, J AGR FOOD CHEM, V58, P4794, DOI 10.1021/jf904018h Hughes AR, 2004, P NATL ACAD SCI USA, V101, P8998, DOI 10.1073/pnas.0402642101 International Council for the Exploration of the Sea (ICES), 2009, WORKSH AG READ EUR A, P122 International Council for the Exploration of the Sea (ICES), 2010, 2010ACOM16 ICES CM, P290 ISSF, 2015, 201503 ISSF Jerome M, 2008, J AGR FOOD CHEM, V56, P3460, DOI 10.1021/jf703704m Kenchington E, 2003, ICES J MAR SCI, V60, P1172, DOI 10.1016/S1054-3139(03)00136-X Knutsen H, 2011, MOL ECOL, V20, P768, DOI 10.1111/j.1365-294X.2010.04979.x Laconcha U, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0128247 Lago FC, 2011, J AGR FOOD CHEM, V59, P2223, DOI 10.1021/jf104505q Langella O, 2000, POPULATIONS 1 2 31 Mace GM, 2008, MOL ECOL, V17, P9, DOI 10.1111/j.1365-294X.2007.03455.x Machado-Schiaffino G, 2008, J AGR FOOD CHEM, V56, P5091, DOI 10.1021/jf800207t Montes I, 2016, MAR BIOL, V163, DOI 10.1007/s00227-016-2979-7 Montes I, 2016, MAR BIOL, V163, DOI 10.1007/s00227-016-2866-2 Montes I, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070051 Mulazzani L, 2013, MAR POLICY, V38, P407, DOI 10.1016/j.marpol.2012.06.020 Nielsen EE, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1845 Petit E, 2001, EVOLUTION, V55, P635, DOI 10.1554/0014-3820(2001)055[0635:SBDIAM]2.0.CO;2 Piry S, 2004, J HERED, V95, P536, DOI 10.1093/jhered/esh074 Pritchard JK, 2000, GENETICS, V155, P945 Reiss H, 2009, FISH FISH, V10, P361, DOI 10.1111/j.1467-2979.2008.00324.x Rekalde A. Estonba, 2013, Patent No. [2 392 610 Al, 2392610] Reusch TBH, 2005, P NATL ACAD SCI USA, V102, P2826, DOI 10.1073/pnas.0500008102 Ruzzante DE, 2000, ECOL APPL, V10, P1090, DOI 10.1890/1051-0761(2000)010[1090:MSAOAC]2.0.CO;2 Syvanen AC, 2001, NAT REV GENET, V2, P930, DOI 10.1038/35103535 Velasco A, 2016, HELIYON, V2, DOI 10.1016/j.heliyon.2016.e00124 Waples RS, 2008, FISH FISH, V9, P423, DOI 10.1111/j.1467-2979.2008.00303.x Waples RS, 2009, FISH FISH SER, V31, P427, DOI 10.1007/978-1-4020-9210-7_23 Waples RS, 1998, J HERED, V89, P438, DOI 10.1093/jhered/89.5.438 Waples RS, 2006, MOL ECOL, V15, P1419, DOI 10.1111/j.1365-294X.2006.02890.x WARD RD, 1994, J FISH BIOL, V44, P213, DOI 10.1111/j.1095-8649.1994.tb01200.x Ward RD, 2000, HYDROBIOLOGIA, V420, P191, DOI 10.1023/A:1003928327503 WEIR BS, 1984, EVOLUTION, V38, P1358, DOI [10.2307/2408641, 10.1111/j.1558-5646.1984.tb05657.x] Worm B, 2006, SCIENCE, V314, P787, DOI 10.1126/science.1132294 Zarraonaindia I, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0042201 NR 57 TC 5 Z9 5 U1 0 U2 15 PD MAY 31 PY 2017 VL 65 IS 21 BP 4351 EP 4358 DI 10.1021/acs.jafc.7b00619 WC Agriculture, Multidisciplinary; Chemistry, Applied; Food Science & Technology SC Agriculture; Chemistry; Food Science & Technology UT WOS:000402691900014 DA 2022-12-14 ER PT J AU McMeekin, TA Baranyi, J Bowman, J Dalgaard, P Kirk, M Ross, T Schmid, S Zwietering, MH AF McMeekin, T. A. Baranyi, J. Bowman, J. Dalgaard, P. Kirk, M. Ross, T. Schmid, S. Zwietering, M. H. TI Information systems in food safety management SO INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY DT Article; Proceedings Paper CT 19th International ICFMH Symposium on Food Micro CY 2004 CL Portoroz, SLOVENIA DE information systems; databases; applied systematics; applications software; knowledge management; RFID technology ID QUANTITATIVE RISK-ASSESSMENT; DECISION-SUPPORT-SYSTEM; LISTERIA-MONOCYTOGENES; PREDICTIVE MICROBIOLOGY; OUTBREAK DETECTION; EXPERT-SYSTEMS; GROWTH; HEALTH; SURVEILLANCE; TEMPERATURE AB Information systems are concerned with data capture, storage, analysis and retrieval. In the context of food safety management they are vital to assist decision making in a short time frame, potentially allowing decisions to be made and practices to be actioned in real time. Databases with information on microorganisms pertinent to the identification of foodborne pathogens, response of microbial populations to the environment and characteristics of foods and processing conditions are the cornerstone of food safety management systems. Such databases find application in: Identifying pathogens in food at the genus or species level using applied systematics in automated ways. Identifying pathogens below the species level by molecular subtyping, an approach successfully applied in epidemiological investigations of foodborne disease and the basis for national surveillance programs. Predictive modelling software, such as the Pathogen Modeling Program and Growth Predictor (that took over the main functions of Food Micromodel) the raw data of which were combined as the genesis of an international web based searchable database (ComBase). Expert systems combining databases on microbial characteristics, food composition and processing information with the resulting "pattern match" indicating problems that may arise from changes in product formulation or processing conditions. Computer software packages to aid the practical application of HACCP and risk assessment and decision trees to bring logical sequences to establishing and modifying food safety management practices. In addition there are many other uses of information systems that benefit food safety more globally, including: Rapid dissemination of information on foodborne disease outbreaks via websites or list servers carrying commentary from many sources, including the press and interest groups, on the reasons for and consequences of foodbome disease incidents. Active surveillance networks allowing rapid dissemination of molecular subtyping information between public health agencies to detect foodbome outbreaks and limit the spread of human disease. Traceability of individual animals or crops from (or before) conception or germination to the consumer as an integral part of food supply chain management. Provision of high quality, online educational packages to food industry personnel otherwise precluded from access to such courses. (c) 2006 Elsevier B.V. All rights reserved. C1 Univ Tasmania, Australian Food Safety Ctr Excellence, Hobart, Tas 7001, Australia. AFRC, Inst Food Res, Norwich NR4 7UA, Norfolk, England. Tech Univ Denmark, Danish Inst Fisheries Res, Dept Seafood Res, Minist Food Agr & Fisheries, DK-2800 Lyngby, Denmark. OzFoodNet, Dept Hlth & Ageing, Canberra, ACT 2601, Australia. EAN Australia, Mt Waverley, Vic 3149, Australia. Food Microbiol Lab, NL-6700 EV Wageningen, Netherlands. C3 University of Tasmania; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); John Innes Center; Quadram Institute; University of East Anglia; Technical University of Denmark RP McMeekin, TA (corresponding author), Univ Tasmania, Australian Food Safety Ctr Excellence, Private Bag 54, Hobart, Tas 7001, Australia. EM Tom.McMeekin@utas.edu.au CR ADAIR C, 1993, J IND MICROBIOL, V12, P263, DOI 10.1007/BF01584200 [Anonymous], 2002, OFF J EUR COMMUNITIE, P1 *AUSTR FOOD SAF CT, 2004, FOOD SAF TOOLS RISK Backer HD, 2001, PUBLIC HEALTH REP, V116, P257, DOI 10.1016/S0033-3549(04)50041-9 Baranyi J, 2004, J FOOD PROTECT, V67, P1967, DOI 10.4315/0362-028X-67.9.1967 Barker GC, 2002, INT BIODETER BIODEGR, V50, P167, DOI 10.1016/S0964-8305(02)00083-5 Bean NH, 2001, EMERG INFECT DIS, V7, P773 Bigelow WD, 1921, J INFECT DIS, V29, P528, DOI 10.1093/infdis/29.5.528 BLACK JFP, 2002, THESIS MONASH U VICT Bridson EY, 2000, LETT APPL MICROBIOL, V30, P95, DOI 10.1046/j.1472-765x.2000.00673.x BROCKLEHURST T, 2003, MODELING MICROBIAL R, P233 Brown MH, 1998, J FOOD PROTECT, V61, P1446, DOI 10.4315/0362-028X-61.11.1446 Buche P, 2002, INT J FOOD MICROBIOL, V73, P171, DOI 10.1016/S0168-1605(01)00647-X *CAC, 2001, FOOD HYG BAS TEXTS Cassin MH, 1998, INT J FOOD MICROBIOL, V41, P21, DOI 10.1016/S0168-1605(98)00028-2 [Anonymous], 2001, MMWR Recomm Rep, V50, P1 Chick H, 1910, J Hyg (Lond), V10, P237 Denton W, 2003, QUALITY OF FISH FROM CATCH TO CONSUMER: LABELLING, MONITORING AND TRACEABILITY, P75 Doumith M, 2004, INFECT IMMUN, V72, P1072, DOI 10.1128/IAI.72.2.1072-1083.2004 Effler P, 1999, JAMA-J AM MED ASSOC, V282, P1845, DOI 10.1001/jama.282.19.1845 Esty JR, 1922, J INFECT DIS, V31, P650, DOI 10.1093/infdis/31.6.650 Euzeby JP, 1997, INT J SYST BACTERIOL, V47, P590, DOI 10.1099/00207713-47-2-590 *EXP, 2004, RPOB RISK ASS US FAR Fazil AM, 2002, INT J FOOD MICROBIOL, V73, P315, DOI 10.1016/S0168-1605(01)00667-5 Frederiksen M., 2002, Journal of Aquatic Food Product Technology, V11, P13, DOI 10.1300/J030v11n02_03 Furness A., 2003, Food authenticity and traceability, P473, DOI 10.1533/9781855737181.3.473 Gimenez B, 2004, J APPL MICROBIOL, V96, P96, DOI 10.1046/j.1365-2672.2003.02137.x GRAHAM A, 1993, LETT APPL MICROBIOL, V16, P158, DOI 10.1111/j.1472-765X.1993.tb01383.x Gyllenberg M, 2002, SYST APPL MICROBIOL, V25, P403, DOI 10.1078/0723-2020-00109 HEDBERG CW, 2003, WHITE PAPER APPL EPI Heymann DL, 2004, EMERG INFECT DIS, V10, P173, DOI 10.3201/eid1002.031038 *HHS USDA, 2003, QUANT ASS REL RISK P Hickey RM, 2003, MICROBIOL-SGM, V149, P655, DOI 10.1099/mic.0.25949-0 HIGHTOWER AW, 1995, EMERG INFECT DIS, V1, P156, DOI 10.3201/eid0104.950414 Hurd S., 2000, Morbidity and Mortality Weekly Report, V49, P1129 *ILSI, 1997, SIMPL GUID UND APPL Jarvis RM, 2004, ANAL CHEM, V76, P40, DOI 10.1021/ac034689c Kirk MD, 2004, EPIDEMIOL INFECT, V132, P571, DOI 10.1017/S095026880400216X Koutsoumanis KP, 2004, FOOD MICROBIOL, V21, P415, DOI 10.1016/j.fm.2003.11.003 Larsen E., 2003, Food authenticity and traceability, P507, DOI 10.1533/9781855737181.3.507 Lay JO, 2001, MASS SPECTROM REV, V20, P172, DOI 10.1002/mas.10003.abs Le Marc Y, 2005, INT J FOOD MICROBIOL, V100, P3, DOI 10.1016/j.ijfoodmicro.2004.10.003 Liao SH, 2003, EXPERT SYST APPL, V25, P155, DOI 10.1016/S0957-4174(03)00043-5 Lindsay EA, 2002, EMERG INFECT DIS, V8, P732, DOI 10.3201/eid0807.010414 Linko S, 1998, TRENDS FOOD SCI TECH, V9, P3, DOI 10.1016/S0924-2244(97)00002-2 Lopman B, 2004, LANCET, V363, P682, DOI 10.1016/S0140-6736(04)15641-9 McMeekin T. A., 2003, Detecting pathogens in food McMeekin TA, 2002, INT J FOOD MICROBIOL, V78, P133, DOI 10.1016/S0168-1605(02)00231-3 Mead PS, 1999, EMERG INFECT DIS, V5, P607, DOI 10.3201/eid0505.990502 Mermelstein NH, 2000, FOOD TECHNOL-CHICAGO, V54, P56 MONOD J, 1949, ANNU REV MICROBIOL, V3, P371, DOI 10.1146/annurev.mi.03.100149.002103 *NACMCF, 1997, J FOOD PROTECT, V61, P762 Nagamine K, 2001, CLIN CHEM, V47, P1742 NAUTA M, 2002, INT J FOOD MICROBIOL, V38, P45 Neumeyer K, 1997, AUST J DAIRY TECHNOL, V52, P120 Oscar TP, 2004, INT J FOOD MICROBIOL, V93, P231, DOI 10.1016/j.ijfoodmicro.2003.12.002 Panackal AA, 2002, EMERG INFECT DIS, V8, P685, DOI 10.3201/eid0807.010493 Ratkowsky DA, 1996, J APPL BACTERIOL, V80, P131, DOI 10.1111/j.1365-2672.1996.tb03200.x RATKOWSKY DA, 2004, MODELING MICROBIAL R, P233 Raupach Jane C A, 2003, Commun Dis Intell Q Rep, V27, P380 Ravichandran V, 2004, ELECTROPHORESIS, V25, P297, DOI 10.1002/elps.200305748 Ross T., 2004, Modeling microbial responses in food, P63 Ross T, 2003, RISK ANAL, V23, P179, DOI 10.1111/1539-6924.00299 Ross T, 2003, INT J FOOD MICROBIOL, V82, P33, DOI 10.1016/S0168-1605(02)00252-0 Ross T, 2002, INT J FOOD MICROBIOL, V77, P39, DOI 10.1016/S0168-1605(02)00061-2 ROSS T, 1995, HAZARD ANAL CRITICAL, P330 Ross T., 2000, ENCY FOOD MICROBIOLO, P1699 ROWAN C, 2002, FOOD ENG INGRED 0214 SANDERSON K, 2003, DETECTING PATHOGENS, P259 SCHELLEKENS M, 1994, INT J FOOD MICROBIOL, V24, P1, DOI 10.1016/0168-1605(94)90102-3 Sobel J, 2002, PUBLIC HEALTH REP, V117, P8, DOI 10.1093/phr/117.1.8 Stern L, 1999, EPIDEMIOL INFECT, V122, P103, DOI 10.1017/S0950268898001939 Swaminathan B, 2001, EMERG INFECT DIS, V7, P382 Tamplin M., 2004, Modeling microbial responses in food, P233 Tuominen P, 2003, FOOD CONTROL, V14, P573, DOI 10.1016/S0956-7135(02)00147-0 Uyttendaele M., 2003, Detecting pathogens in food, P332, DOI 10.1533/9781855737044.2.332 van Gerwen SJC, 1997, INT J FOOD MICROBIOL, V38, P1, DOI 10.1016/S0168-1605(97)00077-9 van Gerwen SJC, 2000, J APPL MICROBIOL, V88, P938, DOI 10.1046/j.1365-2672.2000.01059.x Ventura M, 2003, APPL ENVIRON MICROB, V69, P6908, DOI 10.1128/AEM.69.11.6908-6922.2003 VOYER R, 1993, J IND MICROBIOL, V12, P256, DOI 10.1007/BF01584199 WHITING RC, 1993, J IND MICROBIOL, V12, P240, DOI 10.1007/BF01584196 Widdowson MA, 2003, EMERG INFECT DIS, V9, P1046 Wijtzes T, 1998, INT J FOOD MICROBIOL, V42, P79, DOI 10.1016/S0168-1605(98)00068-3 Williams TL, 2003, J AM SOC MASS SPECTR, V14, P342, DOI 10.1016/S1044-0305(03)00065-5 Zeigler DR, 2003, INT J SYST EVOL MICR, V53, P1893, DOI 10.1099/ijs.0.02713-0 ZWIETERING MH, 1992, J FOOD PROTECT, V55, P973, DOI 10.4315/0362-028X-55.12.973 Zwietering MH, 2002, INT DAIRY J, V12, P263, DOI 10.1016/S0958-6946(01)00156-X ZWIETERING MH, 1997, TRANSICHEME, V75, P159 ZWIETERING MH, 1997, T IND CHEM ENG, V75, P168 NR 89 TC 133 Z9 149 U1 2 U2 134 PD DEC 1 PY 2006 VL 112 IS 3 BP 181 EP 194 DI 10.1016/j.ijfoodmicro.2006.04.048 WC Food Science & Technology; Microbiology SC Food Science & Technology; Microbiology UT WOS:000242639400001 DA 2022-12-14 ER PT J AU Galvan, D Andrade, JCD Effting, L Lelis, CA Melquiades, FL Bona, E Conte-Junior, CA AF Galvan, Diego Andrade, Jelmir Craveiro de Effting, Luciane Lelis, Carini Aparecida Melquiades, Fabio Luiz Bona, Evandro Conte-Junior, Carlos Adam TI Energy-dispersive X-ray fluorescence combined with chemometric tools applied to tomato and sweet pepper classification SO FOOD CONTROL DT Article DE EDXRF; Multivariate analysis; Multi -block analysis; Organic products; Organic farming; Elemental composition; Food authentication; Food traceability ID INFRARED-SPECTROSCOPY; PESTICIDE-RESIDUES; ORIGIN; FRUIT; QUANTIFICATION; SPECTROMETRY; VALIDATION; PRODUCTS AB Consumers' interest in organic foods is increasing, and robust analytical tools are necessary to authenticate them. This study investigated the potential of Energy-Dispersive X-Ray Fluorescence (EDXRF) under two measurement conditions combined with chemometric tools to classify tomato and sweet pepper samples according to the agronomic production mode and geographical area cultivation. Initially, the Common Dimension Analysis (ComDim) was applied to 15 kV (Na-Sc) and 50 kV (Ti-U) EDXRF spectra to extract the common information generated by both spectral measurement conditions. According to the ComDim analysis, it was possible to verify that the data generated by both instrumental conditions are complementary. In contrast, it was impossible to cluster tomato or sweet pepper samples effectively according to the agronomic production mode or geographical origin. On the other hand, the junction between EDXRF data and Partial Least Squares with Discriminant Analysis (PLS-DA) proved to be a perfect match, reaching optimal prediction accuracy values for the classifi-cations between 98.8 and 100.0%. These results suggest that EDXRF associated with chemometrics can be an excellent technique to verify the authenticity of tomato and sweet pepper. Besides, it does not require complex sample preparation steps and is fast and relatively inexpensive compared to some chromatographic or spectro-metric analytical techniques. C1 [Galvan, Diego; Andrade, Jelmir Craveiro de; Lelis, Carini Aparecida; Conte-Junior, Carlos Adam] Fed Univ Rio De Janeiro UFRJ, Inst Chem IQ, Analyt & Mol Lab Ctr CLAn, Cidade Univ, Rio De Janeiro 21941909, RJ, Brazil. [Galvan, Diego; Andrade, Jelmir Craveiro de; Lelis, Carini Aparecida; Conte-Junior, Carlos Adam] Fed Univ Rio De Janeiro UFRJ, Ctr Food Anal NAL, Technol Dev Support Lab LADETEC, Cidade Univ, Rio De Janeiro 21941598, RJ, Brazil. [Galvan, Diego; Andrade, Jelmir Craveiro de; Lelis, Carini Aparecida; Conte-Junior, Carlos Adam] Fed Univ Rio De Janeiro UFRJ, Dept Biochem, Lab Adv Anal Biochem & Mol Biol LAABBM, Cidade Univ, Rio De Janeiro 21941909, RJ, Brazil. [Galvan, Diego; Andrade, Jelmir Craveiro de; Lelis, Carini Aparecida; Conte-Junior, Carlos Adam] Fed Univ Rio De Janeiro UFRJ, Inst Chem IQ, Grad Program Chem PGQu, Cidade Univ, Rio De Janeiro 21941909, RJ, Brazil. [Effting, Luciane] State Univ Londrina UEL, Chem Dept, Londrina 86057970, PR, Brazil. [Melquiades, Fabio Luiz] State Univ Londrina UEL, Appl Nucl Phys Lab, Londrina 86057970, PR, Brazil. [Bona, Evandro] Fed Univ Technol Parana UTFPR, Postgrad Program Food Technol PPGTA, Campus Campo Moura, Campo Moura 87301899, PR, Brazil. [Galvan, Diego] Univ Fed Rio de Janeiro, Chem Inst, Rio De Janeiro, Brazil. C3 Universidade Estadual de Londrina; Universidade Estadual de Londrina; Universidade Tecnologica Federal do Parana; Universidade Federal do Rio de Janeiro RP Galvan, D (corresponding author), Univ Fed Rio de Janeiro, Chem Inst, Rio De Janeiro, Brazil. EM diegogalvann@gmail.com CR Anju T, 2022, FOOD CONTROL, V141, DOI 10.1016/j.foodcont.2022.109161 Ballabio D, 2013, ANAL METHODS-UK, V5, P3790, DOI 10.1039/c3ay40582f Bishop C.M., 2006, PATTERN RECOGN, DOI [10.1007/978-0-387-45528-0, DOI 10.1007/978-0-387-45528-0, DOI 10.1016/C2009-0-22409-3] Bona E, 2017, LWT-FOOD SCI TECHNOL, V76, P330, DOI 10.1016/j.lwt.2016.04.048 Camara-Martos F, 2021, FOOD CHEM, V339, DOI 10.1016/j.foodchem.2020.127860 Carbonaro M, 2002, J AGR FOOD CHEM, V50, P5458, DOI 10.1021/jf0202584 Castrignano A, 2019, FOOD ANAL METHOD, V12, P1497, DOI 10.1007/s12161-019-01475-x de Aguiar LM, 2022, TALANTA, V236, DOI 10.1016/j.talanta.2021.122838 de Rijke E, 2016, FOOD CHEM, V204, P122, DOI 10.1016/j.foodchem.2016.01.134 DEJONG S, 1993, CHEMOMETR INTELL LAB, V18, P251, DOI 10.1016/0169-7439(93)85002-X Demir K, 2010, SCI HORTIC-AMSTERDAM, V127, P16, DOI 10.1016/j.scienta.2010.08.009 Ding XX, 2015, CHEMOMETR INTELL LAB, V144, P17, DOI 10.1016/j.chemolab.2015.03.004 dos Santos FR, 2021, SPECTROCHIM ACTA B, V175, DOI 10.1016/j.sab.2020.106016 dos Santos FR, 2020, MICROCHEM J, V152, DOI 10.1016/j.microc.2019.104275 Eurostat, 2022, GLOSS ORG FARM STAT Farres M, 2015, J CHEMOMETR, V29, P528, DOI 10.1002/cem.2736 Fathabad AE, 2018, FOOD CHEM TOXICOL, V115, P436, DOI 10.1016/j.fct.2018.03.044 Fernandez-Gonzalez A, 2014, COMPUT ELECTRON AGR, V108, P166, DOI 10.1016/j.compag.2014.07.009 Makimori GYF, 2019, FOOD ANAL METHOD, V12, P1067, DOI 10.1007/s12161-019-01443-5 Galvan D, 2021, FOOD CHEM, V365, DOI 10.1016/j.foodchem.2021.130476 Galvan D, 2022, CRIT REV FOOD SCI, V62, P6605, DOI 10.1080/10408398.2021.1903384 Galvan D, 2020, ANAL CHEM, V92, P12809, DOI 10.1021/acs.analchem.0c00902 Gamela RR, 2021, FOOD ANAL METHOD, V14, P545, DOI 10.1007/s12161-020-01904-2 Ghidotti M, 2021, FOOD CHEM, V342, DOI 10.1016/j.foodchem.2020.128350 Gomes HD, 2021, FOOD CHEM, V345, DOI 10.1016/j.foodchem.2020.128768 Guilherme R, 2020, MICROCHEM J, V157, DOI 10.1016/j.microc.2020.105034 Hohmann M, 2015, J AGR FOOD CHEM, V63, P9666, DOI 10.1021/acs.jafc.5b03853 Hohmann M, 2014, J AGR FOOD CHEM, V62, P8530, DOI 10.1021/jf502113r Jenkins R., 1999, XRAY FLUORESCENCE SP, DOI [10.1002/9781118521014, DOI 10.1002/9781118521014, 10.1002/9781118521014.ch11, DOI 10.1002/9781118521014.CH11] Kurz MHS, 2019, FOOD ANAL METHOD, V12, P282, DOI 10.1007/s12161-018-1359-y Lelis CA, 2022, J FOOD COMPOS ANAL, V109, DOI 10.1016/j.jfca.2022.104515 MAPA P. L., 2009, NO 10831 2 INSTR NOR Margui E, 2009, TRAC-TREND ANAL CHEM, V28, P362, DOI 10.1016/j.trac.2008.11.011 Marquetti I, 2016, COMPUT ELECTRON AGR, V121, P313, DOI 10.1016/j.compag.2015.12.018 Peinado FM, 2010, GEODERMA, V159, P76, DOI 10.1016/j.geoderma.2010.06.019 Novotna H, 2012, FOOD ADDIT CONTAM A, V29, P1335, DOI 10.1080/19440049.2012.690348 Panebianco S, 2022, FOOD CHEM, V383, DOI 10.1016/j.foodchem.2022.132364 Parsons C, 2013, J HAZARD MATER, V262, P1213, DOI 10.1016/j.jhazmat.2012.07.001 Pashkova GV, 2018, TRAC-TREND ANAL CHEM, V106, P183, DOI 10.1016/j.trac.2018.06.014 Perez-Lopez AJ, 2007, J AGR FOOD CHEM, V55, P8158, DOI 10.1021/jf071534n Qannari E, 2000, FOOD QUAL PREFER, V11, P151, DOI 10.1016/S0950-3293(99)00069-5 Ramesh K. V., 2021, Plant Physiology Reports, V26, DOI 10.1007/s40502-020-00546-0 Flores IR, 2021, FOOD CHEM, V344, DOI 10.1016/j.foodchem.2020.128608 Reale S, 2021, MOLECULES, V26, DOI 10.3390/molecules26206177 Rubio C, 2002, EUR FOOD RES TECHNOL, V214, P501, DOI 10.1007/s00217-002-0534-x Ruggiero L, 2022, FOOD CHEM, V375, DOI 10.1016/j.foodchem.2021.131822 SANTOS H.G. dos, 2018, SISTEMA BRASILEIRO C, V5 Scatigno C, 2021, MICROCHEM J, V171, DOI 10.1016/j.microc.2021.106863 Shi T, 2021, FOOD CHEM, V352, DOI 10.1016/j.foodchem.2021.129422 Silva RD, 2019, FOOD CHEM, V297, DOI 10.1016/j.foodchem.2019.06.001 Sousa ES, 2017, MICROCHEM J, V134, P131, DOI 10.1016/j.microc.2017.05.017 Soylak M, 2015, ANAL LETT, V48, P464, DOI 10.1080/00032719.2014.949732 Speranca MA, 2021, FOOD CHEM, V362, DOI 10.1016/j.foodchem.2021.130172 Spoladore S. F., 2021, Applied Food Research, V1, P100019, DOI 10.1016/j.afres.2021.100019 Tordin D, 2021, STUDY CHARACTERIZES Vallverdu-Queralt A, 2011, J AGR FOOD CHEM, V59, P11703, DOI 10.1021/jf202822s Van Grieken R. E., 2001, HDB XRAY SPECTROMETR Vats S, 2022, CRIT REV FOOD SCI, V62, P1003, DOI 10.1080/10408398.2020.1832954 Westad F, 2015, ANAL CHIM ACTA, V893, P14, DOI 10.1016/j.aca.2015.06.056 NR 59 TC 0 Z9 0 U1 6 U2 6 PD JAN PY 2023 VL 143 AR 109326 DI 10.1016/j.foodcont.2022.109326 WC Food Science & Technology SC Food Science & Technology UT WOS:000862886700004 DA 2022-12-14 ER PT J AU Metras, R Magalhaes, RJS Dinh, QH Fournie, G Gilbert, J Do Huu, D Roland-Holst, D Otte, J Pfeiffer, DU AF Metras, R. Magalhaes, R. J. Soares Dinh, Q. Hoang Fournie, G. Gilbert, J. Do Huu, D. Roland-Holst, D. Otte, J. Pfeiffer, D. U. TI An assessment of the feasibility of a poultry tracing scheme for smallholders in Vietnam SO REVUE SCIENTIFIQUE ET TECHNIQUE-OFFICE INTERNATIONAL DES EPIZOOTIES DT Article DE Animal movement; Avian influenza; Longitudinal study; Marketing practice; Poultry market; Poultry supply chain; Poultry trader; Traceability; Vietnam ID AVIAN INFLUENZA-VIRUSES; HONG-KONG; H5N1; NETWORK; MARKETS AB Tracing movements could assist the implementation of bio-containment measures during a disease outbreak. To evaluate the potential for implementing a tracing system for a poultry supply chain in northern Vietnam, a four-month longitudinal study was conducted to identify marketing practices associated with poultry traceability. Poultry sold in batches were traced between farms and markets, and their traceability was assessed upon market arrival. A total of 315 batches were released from the farms; 37% arrived at a market, from which 57.3% were 'traceable'. The results of the multivariable analysis showed that traceability was associated with farms operating through no more than two traders (Odds ratio [OR] = 5.97, 95% CI 1.15-30.92) and batches brought to the market on the day of purchase (OR = 4.05, 95% CI 1.23-13.27). No specific incentives were provided to farmers or traders. Results suggest that there is potential for implementing a poultry traceability scheme, although the tracing methodology should be refined. C1 [Metras, R.; Fournie, G.; Pfeiffer, D. U.] Univ London Royal Vet Coll, Vet Epidemiol & Publ Hlth Grp, Dept Vet Clin Sci, Hatfield, Herts, England. [Magalhaes, R. J. Soares] Univ Queensland, Sch Populat Hlth, Herston, Qld 4006, Australia. [Dinh, Q. Hoang; Gilbert, J.] Food & Agr Org United Nations FAO, Hanoi, Vietnam. [Do Huu, D.] Dept Anim Hlth, Hanoi, Vietnam. [Roland-Holst, D.] Univ Calif Berkeley, Dept Agr & Resource Econ, Berkeley, CA 94720 USA. [Otte, J.] Food & Agr Org United Nations FAO, I-00153 Rome, Italy. [Gilbert, J.] CIAT Asia, Int Livestock Res Inst, Viangchan, Laos. C3 University of London; University of London Royal Veterinary College; University of Queensland; Food & Agriculture Organization of the United Nations (FAO); University of California System; University of California Berkeley; Food & Agriculture Organization of the United Nations (FAO) RP Metras, R (corresponding author), Univ London Royal Vet Coll, Vet Epidemiol & Publ Hlth Grp, Dept Vet Clin Sci, Hatfield, Herts, England. CR Agrifoocl Consulting International, 2007, EC IMP HIGHL PATH AV, P74 Alexander DJ, 2007, AVIAN DIS, V51, P161, DOI 10.1637/7602-041306R.1 Alexander DJ, 2003, AVIAN DIS, V47, P792, DOI 10.1637/0005-2086-47.s3.792 Bates D., 2008, LME4 LINEAR MIXED EF Bowman J.E., 2009, DAI NEWSLETTER SUM, P9 Brown H, 2006, APPL MIXED MODELS ME, P107 Choi YK, 2005, VIROLOGY, V332, P529, DOI 10.1016/j.virol.2004.12.002 Claas ECJ, 1998, LANCET, V351, P472, DOI 10.1016/S0140-6736(97)11212-0 Cristalli A, 2007, AVIAN DIS, V51, P461, DOI 10.1637/7564-033106R.1 D'Andlau G., 2004, MISSION APPUI GRIPPE Delquiny T., 2004, EVOLUTION IMPACT AVI, P119 FAO-Food and Agriculture Organization of the United Nations, 2004, FAO REC PREV CONTR E [Food and Agriculture Organization of the United Nations (FAO) World Health Organization (WHO)], 2008, COD AL COMM PROC MAN Kiss IZ, 2006, J R SOC INTERFACE, V3, P669, DOI 10.1098/rsif.2006.0129 Kung NY, 2007, EMERG INFECT DIS, V13, P412, DOI 10.3201/eid1303.060365 Magalhaes RJS, 2010, BMC VET RES, V6, DOI 10.1186/1746-6148-6-10 MCLEOD A, 2005, EC SOCIAL IMPACT AVI Meyers LA, 2005, J THEOR BIOL, V232, P71, DOI 10.1016/j.jtbi.2004.07.026 Minh PQ, 2009, PREV VET MED, V89, P16, DOI 10.1016/j.prevetmed.2009.01.004 Nguyen DC, 2005, J VIROL, V79, P4201, DOI 10.1128/JVI.79.7.4201-4212.2005 Nguyen TD, 2008, EMERG INFECT DIS, V14, P632, DOI 10.3201/eid1404.071343 Ortiz-Pelaez A, 2006, PREV VET MED, V76, P40, DOI 10.1016/j.prevetmed.2006.04.007 Otte J., 2008, CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, V3, P1, DOI 10.1079/PAVSNNR20083080 Pfeiffer DU, 2007, VET J, V174, P302, DOI 10.1016/j.tvjl.2007.05.010 Phan Huy L., 1997, COUNTRY LIFE RED RIV R Core Development Team, 2021, R LANG ENV STAT COMP Rivas A.L., 2009, EPIDEMIOL INFECT, P1 Roland-Holst D., 2006, PILOT PROGRAMME CERT Sarkar D., 2008, LATTICE LATTICE GRAP Sims LD, 2003, AVIAN DIS, V47, P832, DOI 10.1637/0005-2086-47.s3.832 Soares Magalhaes R., 2007, FARM GATE TRADE PATT Trovan, 2009, TROVAN RAD FREQ ID R Tung P. D., 2004, VIETNAM HOUSEHOLD LI Venables W.N., 1999, GEN LINEAR MODELS MO, P211 WHO, 2010, CUM NUMB CONF HUM CA NR 35 TC 4 Z9 4 U1 0 U2 8 PD DEC PY 2011 VL 30 IS 3 BP 703 EP 714 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000300462200005 DA 2022-12-14 ER PT J AU Whittier, JC Scanga, J Umberger, W Cunningham, W Heckendorf, C Heller, J AF Whittier, J. C. Scanga, J. Umberger, W. Cunningham, W. Heckendorf, C. Heller, J. TI Tri-National national animal identification system (NAIS) project synopsis SO JOURNAL OF ANIMAL SCIENCE DT Meeting Abstract DE animal identification; traceability C1 [Whittier, J. C.; Scanga, J.; Umberger, W.] Colorado State Univ, Ft Collins, CO 80523 USA. [Cunningham, W.; Heckendorf, C.] Colorado Dept Agr, Lakewood, CO USA. [Heller, J.] Res Management Syst Inc, Ft Collins, CO USA. C3 Colorado State University NR 0 TC 0 Z9 0 U1 0 U2 0 PY 2005 VL 83 SU 2 BP 119 EP 119 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000203407900469 DA 2022-12-14 ER PT J AU Kempter, J Kielpinski, M Panicz, R Keszka, S AF Kempter, Jolanta Kielpinski, Maciej Panicz, Remigiusz Keszka, Slawomir TI MICROSATELLITE DNA-BASED GENETIC TRACEABILITY OF TWO POPULATIONS OF SPLENDID ALFONSINO, BERYX SPLENDENS (ACTINOPTERYGII: BERYCIFORMES: BERYCIDAE)-PROJECT CELFISH-PART 2 SO ACTA ICHTHYOLOGICA ET PISCATORIA DT Article DE conservation genetics; marine resources; seafood authentication; seafood counterfeiting ID FISH AB Background. The study is a contribution to Project CELFISH which involves genetic identification of populations of fish species presenting a particular economic importance or having a potential to be used in the so-called commercial substitutions. The EU fish trade has been showing a distinct trend of more and more fish species previously unknown to consumers being placed on the market. Molecular assays have become the only way with which to verify the reliability of exporters. This paper is aimed at pinpointing genetic markers with which to label and differentiate between two populations of splendid alfonsino, Beryx splendens Lowe, 1834, a species highly attractive to consumers in Asia and Oceania due to the meat taste and low fat content. Material and methods. DNA was isolated from fragments of fins collected at local markets in Japan (MJ) (n = 10) and New Zealand (MNZ) (n = 18). The rhodopsin gene (RH1) fragment and 16 microsatellite DNA fragments (SSR) were analysed in all the individuals. The sequences obtained were processed using the BioEdit and BLAST software, whereas SSR data were processed with the GeAlEX analysis package. Results. The BioEdit software-aided comparison of MJ and MNZ nucleotide sequences of the rhodopsin gene fragments were identical and showed 100% agreement with the alfonsino sequence deposited under access number DQ197832. The preliminary analysis of SSR markers showed all the loci analysed in both populations to be polymorphic, and when randomly selected specimens were assigned to the original populations. The affinity test correctly identified the provenance of all those specimens. Conclusion. The results obtained constitute a tool for molecular differentiation between alfonsino populations collected in the FAO 81 (New Zealand) and FAO 71 (Japan) areas for the purpose of catch quota control and for checking the agreement between the label declaration and the actual product. C1 [Kempter, Jolanta; Kielpinski, Maciej; Panicz, Remigiusz; Keszka, Slawomir] West Pomeranian Univ Technol, Aquaculture Div, Kazimierza Krolewicza 4, PL-71550 Szczecin, Poland. C3 West Pomeranian University of Technology RP Kempter, J (corresponding author), Zachodniopomorski Uniwersytet Technol Szczecinie, Zaklad Akwakultury, Wydzial Nauk Zywnosci & Rybactwa, Ul Kazimierza Krolewicza 4, PL-71550 Szczecin, Poland. EM jkempter@zut.edu.pl; mkielpinski@zut.edu.pl; rpanicz@zut.edu.pl; skeszka@zut.edu.pl CR Adachi K, 2000, FISHERIES SCI, V66, P232, DOI 10.1046/j.1444-2906.2000.00039.x ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999 [Anonymous], 2013, FISHSTAT PLUS UNIVER Bensch A., 2009, 522 FAO Cheung WWL, 2005, BIOL CONSERV, V124, P97, DOI 10.1016/j.biocon.2005.01.017 Dubochkin A. S., 1989, J ICHTHYOL+, V29, P1 Faith DP, 2004, CONSERV BIOL, V18, P255, DOI 10.1111/j.1523-1739.2004.00330.x Froese R., 2016, FISHBASE Gotoh RO, 2013, MOL ECOL RESOUR, V13, P461, DOI 10.1111/1755-0998.12070 Hall T.A., 1999, NUCL ACIDS S SER, V41, P95, DOI DOI 10.1021/BK-1999-0734.CH008 Hoarau G, 2000, CR ACAD SCI III-VIE, V323, P315, DOI 10.1016/S0764-4469(00)90124-0 JOHNSON GD, 1993, B MAR SCI, V52, P554 JOHNSON R, 2014, 75700 CRS, P7 Kotlar A. N., 1996, BERIKSOOBRAZNYE RYBY Lehodey P, 1997, MAR BIOL, V128, P17, DOI 10.1007/s002270050064 Levy-Hartmann L, 2011, GENETICA, V139, P1349, DOI 10.1007/s10709-012-9628-y Niklitschek E., 2007, 200609 FIP U AUSTR C OJIMA Y, 1986, P JPN ACAD B-PHYS, V62, P317, DOI 10.2183/pjab.62.317 Paxton J. R., 1999, FAO SPECIES IDENTIFI, V4, P2218 Peakall R, 2012, BIOINFORMATICS, V28, P2537, DOI 10.1093/bioinformatics/bts460 Sevilla RG, 2007, MOL ECOL NOTES, V7, P730, DOI 10.1111/j.1471-8286.2007.01863.x NR 21 TC 0 Z9 0 U1 1 U2 7 PY 2016 VL 46 IS 4 BP 287 EP 291 DI 10.3750/AIP2016.46.4.02 WC Fisheries; Zoology SC Fisheries; Zoology UT WOS:000392312300002 DA 2022-12-14 ER PT J AU Teixeira, RJS Gomes, S Malheiro, V Pereira, L Fernandes, JR Mendes-Ferreira, A Gomes, MEP Martins-Lopes, P AF Teixeira, Rui J. S. Gomes, Sonia Malheiro, Vitorino Pereira, Leonor Fernandes, Jose R. Mendes-Ferreira, Alexandra Gomes, Maria E. P. Martins-Lopes, Paula TI A Multidisciplinary Fingerprinting Approach for Authenticity and Geographical Traceability of Portuguese Wines SO FOODS DT Article DE Vitis vinifera L; wine authenticity; high-resolution melting; geographical provenance; Sr and Pb isotopic data; Alvarinho; Douro ID SR-87/SR-86 ISOTOPIC-RATIOS; VINEYARD SOILS; TRACE-ELEMENTS; RED WINES; LEAD; STRONTIUM; IDENTIFICATION; GRANITOIDS; SIGNATURES; VARIETIES AB The interest in developing reliable wine authenticity schemes is a hot-topic, especially for wines with recognized added-value. In order to accomplish this goal, two dimensions need to be considered: the grapevine variety determination and the geographical provenance. The aim of this study was to develop a multidisciplinary approach applicable to wines from the sub region Melgaco and Moncao of the demarcated Vinho Verde region and from the demarcated Douro region. The proposed scheme consists on the use of DNA-based assays to detect Single Nucleotide Polymorphisms (SNPs) on three genes of the anthocyanin pathway (UFGT, F3H and LDOX) coupled with High-resolution melting (HRM) analysis aiming the varietal identification. The Alvarinho wines revealed to have the same haplotype using this marker set, demonstrating its applicability for genetic identification. In addition, to assess their geographical provenance, a multi-elemental approach using Sr and Pb isotopic ratios of wine, soil and bedrock samples was used. The isotopic data suggest a relation between Sr and Pb uptake by vine roots and soil's texture and clay content, rather than with the whole rock's isotopic ratios, but also highlights the potential of a discriminating method based on the combination of selected isotopic signatures. C1 [Teixeira, Rui J. S.; Gomes, Sonia; Malheiro, Vitorino; Pereira, Leonor; Fernandes, Jose R.; Mendes-Ferreira, Alexandra; Gomes, Maria E. P.; Martins-Lopes, Paula] Univ Tras Os Montes & Alto Douro, UTAD, Sch Life Sci & Environm, P-5000801 Vila Real, Portugal. [Teixeira, Rui J. S.; Gomes, Maria E. P.] Univ Tras Os Montes & Alto Douro, Dept Geol, P-5000801 Vila Real, Portugal. [Teixeira, Rui J. S.; Gomes, Maria E. P.] Univ Tras Os Montes & Alto Douro, Pole Geosci Ctr CGeo, P-5000801 Vila Real, Portugal. [Gomes, Sonia; Pereira, Leonor; Mendes-Ferreira, Alexandra; Martins-Lopes, Paula] Univ Lisbon, BioISI Biosyst & Integrat Sci Inst, Fac Sci, P-1749016 Lisbon, Portugal. [Fernandes, Jose R.] Univ Tras Os Montes & Alto Douro, CQVR, P-5000801 Vila Real, Portugal. [Fernandes, Jose R.] Univ Tras Os Montes & Alto Douro, Dept Phys, P-5000801 Vila Real, Portugal. [Mendes-Ferreira, Alexandra] Univ Tras Os Montes & Alto Douro, Dept Biol & Environm, WM&B Lab Wine Microbiol & Biotechnol, P-5000801 Vila Real, Portugal. C3 University of Tras-os-Montes & Alto Douro; University of Tras-os-Montes & Alto Douro; University of Tras-os-Montes & Alto Douro; BIOISI; Universidade de Lisboa; University of Tras-os-Montes & Alto Douro; University of Tras-os-Montes & Alto Douro; University of Tras-os-Montes & Alto Douro RP Martins-Lopes, P (corresponding author), Univ Tras Os Montes & Alto Douro, UTAD, Sch Life Sci & Environm, P-5000801 Vila Real, Portugal.; Martins-Lopes, P (corresponding author), Univ Lisbon, BioISI Biosyst & Integrat Sci Inst, Fac Sci, P-1749016 Lisbon, Portugal. EM rteixeir@utad.pt; sgomes@utad.pt; vitorinoltmalheiro@hotmail.com; leopereira@utad.pt; jraf@utad.pt; anamf@utad.pt; mgomes@utad.pt; plopes@utad.pt CR Aires C.M.A., 2018, THESIS U PORTO PORT Almeida CMR, 2003, J AGR FOOD CHEM, V51, P3012, DOI 10.1021/jf0259664 Almeida CMR, 1999, ANAL CHIM ACTA, V396, P45 Baleiras-Couto MM, 2006, ANAL CHIM ACTA, V563, P283, DOI 10.1016/j.aca.2005.09.076 Barrias S, 2019, FOOD CHEM, V270, P299, DOI 10.1016/j.foodchem.2018.07.058 Belda I, 2017, FRONT MICROBIOL, V8, DOI [10.3389/fmicb.2017.00821, 10.3389/fmicb.2017.01065] Boccacci P, 2012, EUR FOOD RES TECHNOL, V235, P439, DOI 10.1007/s00217-012-1770-3 Burger A, 2019, SCI TOTAL ENVIRON, V653, P1458, DOI 10.1016/j.scitotenv.2018.10.312 Cabezas JA, 2011, BMC PLANT BIOL, V11, DOI 10.1186/1471-2229-11-153 Cardoso AS, 2019, APPL GEOGR, V107, P51, DOI 10.1016/j.apgeog.2019.03.011 Cardoso JC, 1973, AGRON LUSITANA, V33, P481 Catalano V, 2016, J AGR FOOD CHEM, V64, P6969, DOI 10.1021/acs.jafc.6b02560 Catarino S, 2019, BIO WEB CONF, V12, DOI 10.1051/bioconf/20191202031 Christoph Norbert, 2004, Mitteilungen Klosterneuburg, V54, P144 Comerford NB, 2005, ECOL STU AN, V181, P1 Commission Regulation (EC), 2006, OFF J EUR UNION, VL369, P1 Costa MM, 2014, LITHOS, V196, P83, DOI 10.1016/j.lithos.2014.02.023 Council Regulation (EC), 2006, **NON-TRADITIONAL**, VL93, P12 Council Regulation (EC), 2006, OFF J EUR UNION, VL93, P1 Deniel C, 2001, ANAL CHIM ACTA, V426, P95, DOI 10.1016/S0003-2670(00)01185-5 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Dias G, 1998, LITHOS, V45, P349, DOI 10.1016/S0024-4937(98)00039-5 Dias R., 2013, EVOLUCAO ESTRUTURAL, VI, P73 Doyle J.J., 1990, PHYTOCHEMISTRY B, V12, P13 Durante C, 2016, FOOD CHEM, V210, P648, DOI 10.1016/j.foodchem.2016.04.108 Epova EN, 2020, FOOD CHEM, V303, DOI 10.1016/j.foodchem.2019.125277 Epova EN, 2019, FOOD CHEM, V294, P35, DOI 10.1016/j.foodchem.2019.04.068 Faure G., 2005, ISOTOPES PRINCIPLES, VThird Geana EI, 2017, FOOD ANAL METHOD, V10, P63, DOI 10.1007/s12161-016-0550-2 Gomes M.E.P, 2014, COMUN GEOL, V101, P115 Gomes S, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-24158-9 Gonzalez-Neves G, 2016, INT J FOOD SCI TECH, V51, P260, DOI 10.1111/ijfs.12958 International Organization of Vine and Wine-OIV, 2020, COMP INT METH AN WIN, V2 Katoh M, 2004, SOIL SCI PLANT NUTR, V50, P721, DOI 10.1080/00380768.2004.10408528 Kawasaki A, 2002, SOIL SCI PLANT NUTR, V48, P635, DOI 10.1080/00380768.2002.10409251 Larcher R, 2003, J AGR FOOD CHEM, V51, P5956, DOI 10.1021/jf021064r Lim MP, 2015, J HAZARD MATER, V299, P656, DOI 10.1016/j.jhazmat.2015.07.082 Ma YJ, 2001, SCI CHINA SER D, V44, P726, DOI 10.1007/BF02907202 Malheiro V.L.T., 2008, THESIS U TRAS OS MON Marchionni S, 2016, FOOD CHEM, V190, P777, DOI 10.1016/j.foodchem.2015.06.026 Marchionni S, 2013, J AGR FOOD CHEM, V61, P6822, DOI 10.1021/jf4012592 Margui E, 2006, SCI TOTAL ENVIRON, V367, P988, DOI 10.1016/j.scitotenv.2006.03.036 MIDDELBURG JJ, 1988, CHEM GEOL, V68, P253, DOI 10.1016/0009-2541(88)90025-3 Mihaljevic M, 2006, J GEOCHEM EXPLOR, V88, P130, DOI 10.1016/j.gexplo.2005.08.025 Ministerio da Agricultura e do Mar, 2016, PORT 333 2016 DIAR R, P4782 Ministerio da Agricultura e do Mar, 2015, PORT 152 2015 DIAR R, P3136 Moreira C, 2017, S AFR J ENOL VITIC, V38, P82 Peng B, 2014, APPL GEOCHEM, V51, P191, DOI 10.1016/j.apgeochem.2014.09.007 Pereira L, 2017, FOOD CHEM, V216, P80, DOI 10.1016/j.foodchem.2016.07.185 Pereira L, 2015, J AGR FOOD CHEM, V63, P9165, DOI 10.1021/acs.jafc.5b03463 Pereira L, 2011, AM J ENOL VITICULT, V62, P547, DOI 10.5344/ajev.2011.10022 Petrini R, 2015, FOOD CHEM, V170, P138, DOI 10.1016/j.foodchem.2014.08.051 Pii Y, 2017, FRONT PLANT SCI, V8, DOI 10.3389/fpls.2017.00640 Rieuwerts JS, 1998, CHEM SPEC BIOAVAILAB, V10, P61, DOI 10.3184/095422998782775835 Santos S, 2014, ELECTROPHORESIS, V35, P3201, DOI 10.1002/elps.201400107 Sousa M. B., 1982, THESIS MUS LAB MIN G Teixeira RJS, 2021, J IBER GEOL, V47, P281, DOI 10.1007/s41513-020-00160-x Teixeira RJS, 2012, LITHOS, V153, P177, DOI 10.1016/j.lithos.2012.04.024 Teixeira RJS, 2011, LITHOS, V125, P321, DOI 10.1016/j.lithos.2011.02.015 Tescione I, 2018, FOOD CHEM, V258, P374, DOI 10.1016/j.foodchem.2018.03.083 Thirlwall MF, 2002, CHEM GEOL, V184, P255, DOI 10.1016/S0009-2541(01)00365-5 Victor V, 2015, PROCED EARTH PLAN SC, V13, P252, DOI 10.1016/j.proeps.2015.07.059 Vignani R, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0211962 Villano C, 2017, FOOD CONTROL, V80, P1, DOI 10.1016/j.foodcont.2017.04.020 Vinciguerra V, 2016, FOOD CHEM, V210, P121, DOI 10.1016/j.foodchem.2016.04.017 NR 65 TC 1 Z9 1 U1 5 U2 18 PD MAY PY 2021 VL 10 IS 5 AR 1044 DI 10.3390/foods10051044 WC Food Science & Technology SC Food Science & Technology UT WOS:000653870400001 DA 2022-12-14 ER PT J AU Rohrle, FT Moloney, AP Osorio, MT Luciano, G Priolo, A Caplan, P Monahan, FJ AF Roehrle, F. T. Moloney, A. P. Osorio, M. T. Luciano, G. Priolo, A. Caplan, P. Monahan, F. J. TI Carotenoid, colour and reflectance measurements in bovine adipose tissue to discriminate between beef from different feeding systems SO MEAT SCIENCE DT Article DE beta-carotene; Lutein; Colour; Traceability; Beef ID BETA-CAROTENE; VITAMIN-E; CARCASS CHARACTERISTICS; FAT COLOR; PASTURE; GRASS; MEAT; TRACEABILITY; SHEEP; PIGMENTS AB Our hypothesis was that carotenoids in bovine subcutaneous adipose tissue (SAT) together with colour and reflectance spectra could be used to differentiate between beef production systems based on grass, concentrates or combinations thereof. SAT was sampled from the carcasses of heifers fed pasture (P), a barley-based concentrate (C), silage followed by pasture (Sip) or silage followed by pasture with concentrate (SiPC). beta-carotene in the SAT from the C group (0.09 mu g g(-1)) was lower (P<0.05) than that from the P (0.54 mu g g(-1)), SiP (0.49 mu g g(-1)) and SiPC (0.49 mu g g(-1)) groups. Lutein in the SAT differed (P<0.05) between all groups with 0.13, 0.10, 0.08 and 0.04 mu g g(-1) the P. SiP, SiPC and C groups, respectively. Principal component analysis of the carotenoid data, SAT colour variables ['L', 'a', 'b', 'C', 'H'] and the reflectance data made it possible to distinguish between the animals fed a barley-based concentrate diet and the animals fed pasture-based diets, but not between different pasture-based groups. (c) 2011 Elsevier Ltd. All rights reserved. C1 [Roehrle, F. T.; Osorio, M. T.; Caplan, P.; Monahan, F. J.] Univ Coll Dublin, UCD Sch Agr Food Sci & Vet Med, Dublin 4, Ireland. [Moloney, A. P.] Anim & Grassland Res & Innovat Ctr, Dunsany, Meath, Ireland. [Luciano, G.; Priolo, A.] Univ Catania, DISPA Sez Sci Prod Animali, I-95123 Catania, Italy. C3 University College Dublin; Teagasc; University of Catania RP Monahan, FJ (corresponding author), Univ Coll Dublin, UCD Sch Agr Food Sci & Vet Med, Dublin 4, Ireland. EM frank.monahan@ucd.ie CR AFRC, 1993, ENERGY PROTEIN REQUI Alvarez JB, 1999, PLANT BREEDING, V118, P187, DOI 10.1046/j.1439-0523.1999.118002187.x Andersen HJ, 2005, LIVEST PROD SCI, V94, P105, DOI 10.1016/j.livprodsci.2004.11.027 Ballet N, 2000, FORAGE EVALUATION IN RUMINANT NUTRITION, P399, DOI 10.1079/9780851993447.0399 Bauernfeind J. C., 1981, CAROTENOIDS COLORANT, P564 Coultate T. P., 1996, FOOD CHEM ITS COMPON, P178 Descalzo AM, 2005, MEAT SCI, V70, P35, DOI 10.1016/j.meatsci.2004.11.018 Dian PHM, 2007, J ANIM SCI, V85, P3054, DOI 10.2527/jas.2006-477 Dunne PG, 2009, MEAT SCI, V81, P28, DOI 10.1016/j.meatsci.2008.06.013 Dunne PG, 2006, MEAT SCI, V74, P231, DOI 10.1016/j.meatsci.2006.02.003 HART DJ, 1995, FOOD CHEM, V54, P101, DOI 10.1016/0308-8146(95)92669-B KALAC P, 1981, J SCI FOOD AGR, V32, P767, DOI 10.1002/jsfa.2740320804 Knight TW, 1996, NEW ZEAL J AGR RES, V39, P281, DOI 10.1080/00288233.1996.9513187 Knight TW, 2001, AUST J AGR RES, V52, P1023, DOI 10.1071/AR01017 Kumar Rajender, 1995, Crop Research (Hisar), V10, P51 Liu Q, 1996, J ANIM SCI, V74, P106 Mahal G. S., 1998, Crop Improvement, V25, P197 Maynard L. A., 1979, ANIMAL NUTR, P283 McDowell LR, 2000, VITAMINS ANIMAL HUMA, P15 Mercier Y, 2004, MEAT SCI, V66, P467, DOI 10.1016/S0309-1740(03)00135-9 Mir PS, 2004, AM J CLIN NUTR, V79, p1207S, DOI 10.1093/ajcn/79.6.1207S Muller CE, 2007, ANIM FEED SCI TECH, V137, P182, DOI 10.1016/j.anifeedsci.2006.10.007 Noziere P, 2006, ANIM FEED SCI TECH, V131, P418, DOI 10.1016/j.anifeedsci.2006.06.018 Parodi P. W., 2003, Advances in conjugated linoleic acid research, volume 2, P101 Prache S, 2007, J AGR SCI-CAMBRIDGE, V145, P435, DOI 10.1017/S0021859607007174 Prache S, 2005, SMALL RUMINANT RES, V59, P157, DOI 10.1016/j.smallrumres.2005.05.004 Prache S, 2003, J ANIM SCI, V81, P360 Prache S, 1999, ANIM SCI, V69, P29, DOI 10.1017/S1357729800051067 Prache S, 2009, ANIMAL, V3, P598, DOI 10.1017/S1751731108003881 Priolo A, 2002, J ANIM SCI, V80, P886 Realini CE, 2004, MEAT SCI, V66, P567, DOI 10.1016/S0309-1740(03)00160-8 Reynoso CR, 2004, ANIM FEED SCI TECH, V113, P183, DOI 10.1016/j.anifeedsci.2003.11.007 Serrano E, 2006, ANIM SCI, V82, P909, DOI 10.1017/ASC200698 Simonne AH, 1996, J FOOD SCI, V61, P1254, DOI 10.1111/j.1365-2621.1996.tb10973.x STRACHAN DB, 1993, AUST J EXP AGR, V33, P269, DOI 10.1071/EA9930269 Wahle KWJ, 2004, PROG LIPID RES, V43, P553, DOI 10.1016/j.plipres.2004.08.002 Watanabe A., 2002, TOHOKU AGR RES, P137 YANG A, 1992, AUST J AGR RES, V43, P1809, DOI 10.1071/AR9921809 Yang A, 2002, MEAT SCI, V60, P35, DOI 10.1016/S0309-1740(01)00102-4 NR 39 TC 35 Z9 39 U1 1 U2 28 PD JUL PY 2011 VL 88 IS 3 BP 347 EP 353 DI 10.1016/j.meatsci.2011.01.005 WC Food Science & Technology SC Food Science & Technology UT WOS:000289822800004 DA 2022-12-14 ER PT J AU Munoz, M Garcia-Casco, JM Alves, E Benitez, R Barragan, C Caraballo, C Fernandez, AI Garcia, F Nunez, Y Ovilo, C Fernandez, A Rodriguez, C Silio, L AF Munoz, M. Garcia-Casco, J. M. Alves, E. Benitez, R. Barragan, C. Caraballo, C. Fernandez, A., I Garcia, F. Nunez, Y. Ovilo, C. Fernandez, A. Rodriguez, C. Silio, L. TI Development of a 64 SNV panel for breed authentication in Iberian pigs and their derived meat products SO MEAT SCIENCE DT Article DE Traceability; Porcine products; Iberian breed; Duroc breed; Genetic tool ID QUALITY AB Spanish legislation regulates the labelling of Iberian pig meat and dry-cured products, which are labelled as "Iberico" or "100% Iberico" when they come from Duroc x Iberian crossbred or Iberian purebred pigs. Although the analytical authentication of breed origin is not mandatory, a genetic diagnostic tool is demanded by producers and consumers. We have designed a 64 Single Nucleotide Variant genotyping panel displaying extreme allelic frequencies between Duroc and Iberian purebred samples. Average proportions of Iberian alleles of 0.99, 0.01, 0.77 and 0.48 were estimated by admixture clustering analysis of known origin samples, for Iberian and Duroc purebred, 75% Iberian and 50% Iberian classes, respectively. A supervised analysis with 1419 samples showed some overlapping between contiguous classes, but the calculated degrees of separability ranged from 0.800 to 0.996, exceeding the threshold value (0.70) for considering suitable for prediction. Therefore, this panel is a useful genetic tool to infer purebred or crossbred Iberian origin of live animals, meat and dry-cured products. C1 [Munoz, M.; Garcia-Casco, J. M.; Caraballo, C.] INIA Zafra, Ctr I D Cerdo Iberico, Badajoz 06300, Spain. [Munoz, M.; Garcia-Casco, J. M.; Alves, E.; Benitez, R.; Barragan, C.; Fernandez, A., I; Garcia, F.; Nunez, Y.; Ovilo, C.; Fernandez, A.; Rodriguez, C.; Silio, L.] INIA, Dept Mejora Genet Anim, Madrid 28040, Spain. RP Munoz, M (corresponding author), INIA, Dept Mejora Genet Anim, Madrid 28040, Spain. EM mariamm@inia.es CR Altshuler D, 2000, NATURE, V407, P513, DOI 10.1038/35035083 Alves E, 2006, SPAN J AGRIC RES, V4, P37, DOI 10.5424/sjar/2006041-176 Alves E, 2009, ANIMAL, V3, P1216, DOI 10.1017/S1751731109004819 Alves E., 2012, OPTIONS MEDITERRAN A Barragan C., 2015, 7 DRY CUR HAM WORLD Barragan C., 2007, 6 INT S MED PIG CAP Brockman W, 2008, GENOME RES, V18, P763, DOI 10.1101/gr.070227.107 Burgos-Paz W, 2013, HEREDITY, V110, P321, DOI 10.1038/hdy.2012.109 Cao KAL, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-253 Corander J., 2013, BAPS BAYESIAN ANAL P, P6 Fabuel E, 2004, HEREDITY, V93, P104, DOI 10.1038/sj.hdy.6800488 Fernandez A, 2004, J SCI FOOD AGR, V84, P1855, DOI 10.1002/jsfa.1829 Gama LT, 2013, GENET SEL EVOL, V45, DOI 10.1186/1297-9686-45-18 Garcia D, 2006, MEAT SCI, V72, P560, DOI 10.1016/j.meatsci.2005.09.005 Garcia-Casco J. M., 2015, P 4 INT C NEW PERSP, P288 Hulsegge I, 2019, LIVEST SCI, V223, P60, DOI 10.1016/j.livsci.2019.03.002 Li H, 2009, BIOINFORMATICS, V25, P1754, DOI 10.1093/bioinformatics/btp324 McKenna A, 2010, GENOME RES, V20, P1297, DOI 10.1101/gr.107524.110 Munoz M., 2013, 7 C MUND JAM OUR Nicoloso Letizia, 2013, Recent Pat Food Nutr Agric, V5, P9 Ovilo C, 2014, BMC GENOMICS, V15, DOI [10.1186/1471-2164-15-413, 10.1186/s12863-014-0148-x] Ramos AM, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006524 Rodriguez-Ramilo ST, 2010, SPAN J AGRIC RES, V8, P347, DOI 10.5424/sjar/2010082-1188 Rodriguez-Valdovinos C, 2019, SPAN J AGRIC RES, V17, DOI 10.5424/sjar/2019171-13618 Rogberg-Munoz A, 2016, MEAT SCI, V111, P47, DOI 10.1016/j.meatsci.2015.08.014 Rohart F, 2017, PLOS COMPUT BIOL, V13, DOI 10.1371/journal.pcbi.1005752 Rubin CJ, 2012, P NATL ACAD SCI USA, V109, P19529, DOI 10.1073/pnas.1217149109 Schiavo G, 2020, ANIMAL, V14, P223, DOI 10.1017/S1751731119002167 Serrano MP, 2008, MEAT SCI, V78, P420, DOI 10.1016/j.meatsci.2007.07.006 Shriver MD, 1997, AM J HUM GENET, V60, P957 Ventanas S, 2006, MEAT SCI, V73, P651, DOI 10.1016/j.meatsci.2006.03.009 Wilkinson S, 2012, BMC GENOMICS, V13, DOI 10.1186/1471-2164-13-580 NR 32 TC 7 Z9 7 U1 1 U2 7 PD SEP PY 2020 VL 167 AR 108152 DI 10.1016/j.meatsci.2020.108152 WC Food Science & Technology SC Food Science & Technology UT WOS:000540657800005 DA 2022-12-14 ER PT J AU Kinds, A Sys, K Schotte, L Mondelaers, K Polet, H AF Kinds, Arne Sys, Kim Schotte, Laura Mondelaers, Koen Polet, Hans TI VALDUVIS: An innovative approach to assess the sustainability of fishing activities SO FISHERIES RESEARCH DT Article DE Indicators; Integrated sustainability assessment; Electronic logbook; Monitoring tool; Stakeholder participation AB The Belgian fishing sector is under pressure to demonstrate the sustainability of its fishing methods. First, the beam trawl (which accounts for 80% of the landings) is contested due to its low selectivity and significant disturbance of the sea bed. Second, the Belgian retail market has committed to sourcing sustainable seafood. However, converting to sustainable methods is costly and may not be feasible for the majority of fishers who have suffered economic losses in the wake of the 2008 fuel crisis. Instead of a full-scale transition to sustainable fishing, fishers have developed modifications to the beam trawl that reduce environmental impact and save fuel. We propose an indicator-based sustainability assessment tool (VALDUVIS) that recognizes these efforts and offers incentives for fishers to adopt more sustainable fishing practices. In this article, we describe the development of the tool and its potential applications. Integrated Sustainability Assessment (ISA) was used as a framework to develop the tool and to initiate the transition towards sustainability in the Belgian fishery. VALDUVIS offers a promising new method to assess sustainability in fisheries. The approach is innovative in several ways. First, indicator scores are calculated using official data flows (e.g., the electronic logbook), which enhances traceability and provides the possibility of communicating sustainability data soon after landing the fish. Second, indicators are scored on a fine scale (e.g., per fishing trip). Third, stakeholder participation was essential in the development of the tool. This enhanced the support of the wider fishing sector and assured the relevance of the indicators and the users' understanding of the tool. Fourth, the delivered tool is multi-purpose and can be easily adapted to the needs of a range of end users (wholesalers, retailers, authorities, researchers, etc.). The VALDUVIS tool offers a cost-effective alternative to known certification schemes that could be applied to any type of fishery. The Belgian fishing sector considers VALDUVIS to be suitable for monitoring the progress towards sustainability as well as for providing incentives for fishers to adopt better practices. (C) 2015 Elsevier B.V. All rights reserved. C1 [Kinds, Arne; Sys, Kim; Polet, Hans] Inst Agr & Fisheries Res ILVO, Anim Sci Unit, Fisheries & Aquat Prod, Ankerstr 1, B-8400 Oostende, Belgium. [Schotte, Laura; Mondelaers, Koen] Inst Agr & Fisheries Res ILVO, Social Sci Unit, Burg van Gansberghelaan 115 Bus 2, B-9820 Merelbeke, Belgium. C3 Institute For Agricultural & Fisheries Research; Institute For Agricultural & Fisheries Research RP Kinds, A (corresponding author), Inst Agr & Fisheries Res ILVO, Anim Sci Unit, Fisheries & Aquat Prod, Ankerstr 1, B-8400 Oostende, Belgium. EM arne.kinds@ilvo.vlaanderen.be CR Accenture, 2009, ASS STUD ON PACK WIL Anderson JL, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0122809 Bohunovsky L, 2011, REG ENVIRON CHANGE, V11, P271, DOI 10.1007/s10113-010-0143-3 BOSSEL H., 1999, INDICATORS SUSTAINAB, V1st Brommelstroet MT, 2010, ENVIRON PLANN B, V37, P3, DOI 10.1068/b35019 Caddis E.J.B., 2010, ENVIRON MODELL SOFTW, V25, P1428, DOI DOI 10.1016/J.ENVS0FT.2009.06.004 De Snijder N., 2015, VISTRAJECT DUURZAAMH De Vos B., 2010, ECOLABELS VISSERIJ V Depestele J., 2007, 2007M6 ICES CM Eayrs S., 2014, ICES J MAR SCI FAO, 1995, COD COND RESP FISH FAO, 1999, FAO TECH GUIDELINES, V8, P68 FAO, 2009, 97 FAO Fletcher WJ, 2005, FISH RES, V71, P175, DOI 10.1016/j.fishres.2004.08.030 Garcia SM, 2000, OCEAN COAST MANAGE, V43, P537, DOI 10.1016/S0964-5691(00)00045-4 Jacquet J, 2010, ORYX, V44, P45, DOI 10.1017/S0030605309990470 Jacquet JL, 2007, MAR POLICY, V31, P308, DOI 10.1016/j.marpol.2006.09.003 James Sullivan Consulting, 2012, SMART FISH IN COMP W Kinds A., 2015, VALDUVIS INSTRUMENT Lindeboom N., 1998, C00398 RIVODLO McHarry J., 2002, MULTISTAKEHOLDER PRO Meul M, 2008, AGRON SUSTAIN DEV, V28, P321, DOI 10.1051/agro:2008001 Parkes G, 2010, REV FISH SCI, V18, P344, DOI 10.1080/10641262.2010.516374 Polet H, 2010, IMPACT ASSESSMENT EF Poos JJ, 2013, ICES J MAR SCI, V70, P675, DOI 10.1093/icesjms/fss196 Rindorf A., 2013, FRAMEWORK MULTISPECI, P550 Rogge E, 2009, THESIS Sainsbury K., 2010, 533 FAO Suuronen P, 2012, FISH RES, V119, P135, DOI 10.1016/j.fishres.2011.12.009 Tessens E., 2013, BELGISCHE ZEEVISSERI UNFCCC, 2015, COMP METH TOOLS EV I Van Meensel J, 2012, DECIS SUPPORT SYST, V54, P164, DOI 10.1016/j.dss.2012.05.002 Voinov A, 2010, ENVIRON MODELL SOFTW, V25, P1268, DOI 10.1016/j.envsoft.2010.03.007 Vonk G, 2005, ENVIRON PLANN A, V37, P909, DOI 10.1068/a3712 Ward TJ, 2008, FISH FISH, V9, P169, DOI 10.1111/j.1467-2979.2008.00277.x Washington S., 2008, ECOLABELS MARINE CAP Weaver PM, 2006, INT J INNOV SUSTAIN, V1, P284, DOI 10.1504/IJISD.2006.013732 Wessels C.R., 2001, FAO FISHERIES TECHNI [No title captured] NR 39 TC 10 Z9 10 U1 0 U2 23 PD OCT PY 2016 VL 182 SI SI BP 158 EP 171 DI 10.1016/j.fishres.2015.10.027 WC Fisheries SC Fisheries UT WOS:000379557100017 DA 2022-12-14 ER PT J AU Losio, MN Ferrando, ML Daminelli, P Chegdani, F AF Losio, MN Ferrando, ML Daminelli, P Chegdani, F TI Setting up a PCR based method to trace animal species in processed meat products SO VETERINARY RESEARCH COMMUNICATIONS DT Article; Proceedings Paper CT 57th Annual Meeting of the Italian-Society-for-Veterinary-Sciences (SISVET) CY 2003 CL Ischia, ITALY DE meat traceability; protein analysis; PCR ID MIXTURES; CHICKEN; TURKEY C1 Ist Zooprofilatt Sperimentale Lombardia & Emilia, I-25214 Brescia, Italy. Univ Cattolica Sacro Cuore, Fac Agr, Ist Zootecn, Piacenza, Italy. C3 IZS Lombardia e Emilia; Catholic University of the Sacred Heart RP Losio, MN (corresponding author), Ist Zooprofilatt Sperimentale Lombardia & Emilia, Via Bianchi 7-9, I-25214 Brescia, Italy. EM nlosio@bs.izs.it CR ASHOOR SH, 1988, J ASSOC OFF ANA CHEM, V71, P403 JONES SJ, 1985, MEAT SCI, V15, P1, DOI 10.1016/0309-1740(85)90070-1 KIM H, 1986, J FOOD SCI, V51, P731, DOI 10.1111/j.1365-2621.1986.tb13922.x NR 3 TC 1 Z9 1 U1 0 U2 2 PD AUG PY 2004 VL 28 IS 1 SU 1 SI SI BP 253 EP 255 DI 10.1023/B:VERC.0000045419.02969.c7 WC Veterinary Sciences SC Veterinary Sciences UT WOS:000223397900048 DA 2022-12-14 ER PT J AU Dunchenko, NI Voloshina, ES Kuptsova, SV Cherkasova, EI Sychev, RV Keener, K AF Dunchenko, Nina, I Voloshina, Elena S. Kuptsova, Svetlana, V Cherkasova, El'mira, I Sychev, Roman, V Keener, Kevin TI COMPLEX ESTIMATION OF EFFECTIVENESS OF QUALITY SYSTEM PROCESSES AT FOOD INDUSTRY ENTERPRISES SO FOODS AND RAW MATERIALS DT Article DE Quality management system; effectiveness; food enterprise; process approach ID COMPANIES AB The modern trends in the field of food quality management place emphasis on ensuring the traceability and systemic control of the parameters of the life cycle of products. ISO 9000 international standards recommend a process approach for these purposes. Since the standards do not give direct recommendations on the procedure for estimating the effectiveness of the quality management system (QMS), the development of approaches is an extremely urgent task for developers and has a wide application value. The given paper proposes a mathematical model for the complex estimation of the effectiveness of the QMS of a food enterprise. At the first stage, IDEF0 functional modeling methods were used to identify the processes of the life cycle of food products. Then, using the qualimetric approach, 27 unique indices were generated and coefficients were determined for each of them using Fishburn's weight coefficients. To derive a mathematical model for the complex estimation of the effectiveness of QMS processes of a food enterprise, all data were summarized from four levels of the hierarchy. The proposed mathematical model includes the quantitative and qualitative estimation of enterprise processes. The estimation indicators form a treelike hierarchy in which the factors of each sublevel have their own weight coefficients and are in preference or indifference relation to each other. The application of a mathematical model for the complex estimation of the effectiveness of QMS of a food or processing enterprise allows full compliance with the requirements of international standards, but does not require significant financial costs for implementation. C1 [Dunchenko, Nina, I; Voloshina, Elena S.; Kuptsova, Svetlana, V; Cherkasova, El'mira, I; Sychev, Roman, V] Russian State Agrarian Univ, Moscow Timiryazev Agr Acad, Timiryazevskaya Str 49, Moscow 127550, Russia. [Keener, Kevin] Iowa State Univ, Ames, IA 50011 USA. C3 Russian State Agrarian University - Moscow Timiryazev Agricultural Academy; Iowa State University RP Voloshina, ES (corresponding author), Russian State Agrarian Univ, Moscow Timiryazev Agr Acad, Timiryazevskaya Str 49, Moscow 127550, Russia. EM yudakovaes@gmail.com CR [Anonymous], 2013, ISO 9001 2015 QUAL M [Anonymous], 2015, 90002015 ISO Beckmerhagen IA, 2004, THE TQM MAGAZINE, V16, P14, DOI 10.1108/09544780410511443 Chen H.R., 2012, TQM J, V24, P418, DOI [10.1108/175427, DOI 10.1108/175427] Dunchenko N. I., 2016, KVALIMETRIYA Goryachev V. V., 2012, METHODS QUALITY MANA, P15 Leonov O. A., 2016, RAZRABOTKA SISTEMY M, DOI [10.18413/2408-9346-2017-3-2-42-50, DOI 10.18413/2408-9346-2017-3-2-42-50] Miljkovic B, 2017, FILOMAT, V31, P2991, DOI 10.2298/FIL1710991M Psomas EL, 2013, MANAG SERV QUAL, V23, P149, DOI 10.1108/09604521311303426 Repin V. V., 2013, PROTSESSNYYPODKHOD K Salimova T. A., 2015, STANDARDS QUALITY, P90 Shilkina A. T., 2011, B VOLGA U NAMED VN T, P91 Sumaedi S, 2015, PROC FOOD SCI, V3, P436, DOI 10.1016/j.profoo.2015.01.048 Sundqvist E, 2014, PROCD SOC BEHV, V119, P278, DOI 10.1016/j.sbspro.2014.03.032 Usakov A. A., 2015, SOFTWARE PRODUCTS SY, P34, DOI [10.15827/0236-235X.109.034-037, DOI 10.15827/0236-235X.109.034-037] Voloshina E S, 2017, THEORY PRACTICE MEAT, V2, P21, DOI 10.21323/2414-438X-2017-2-3-21-30 Zanni MA, 2014, INT J ENERGY SECT MA, V8, P562, DOI 10.1108/IJESM-04-2014-0005 Zelenskaya A. S., 2011, COMPETENCE, P37 Zimovets O. A., 2011, BELGOROD STATE U SCI, V20, P127 NR 19 TC 1 Z9 2 U1 0 U2 5 PY 2018 VL 6 IS 1 BP 182 EP 190 DI 10.21603/2308-4057-2018-1-182-190 WC Food Science & Technology SC Food Science & Technology UT WOS:000435448500021 DA 2022-12-14 ER PT J AU Boquete, L Cambralla, R Rodriguez-Ascariz, JM Miguel-Jimenez, JM Cantos-Frontela, JJ Dongil, J AF Boquete, Luciano Cambralla, Rafael Rodriguez-Ascariz, J. M. Miguel-Jimenez, J. M. Cantos-Frontela, J. J. Dongil, J. TI Portable system for temperature monitoring in all phases of wine production SO ISA TRANSACTIONS DT Article DE Wine monitoring; Wireless; Temperature; ZigBee; Traceability ID WIRELESS SENSORS; TECHNOLOGY; SACCHAROMYCES; AGRICULTURE; INDUSTRY; QUALITY AB This paper presents a low-cost and highly versatile temperature-monitoring system applicable to all phases of wine production, from grape cultivation through to delivery of bottled wine to the end customer. Monitoring is performed by a purpose-built electronic system comprising a digital memory that stores temperature data and a ZigBee communication system that transmits it to a Control Centre for processing and display. The system has been tested under laboratory conditions and in real-world operational applications. One of the system's advantages is that it can be applied to every phase of wine production. Moreover, with minimum modification, other variables of interest (pH, humidity, etc.) could also be monitored and the system could be applied to other similar sectors, such as olive-oil production. (C) 2010 ISA. Published by Elsevier Ltd. All rights reserved. C1 [Boquete, Luciano; Rodriguez-Ascariz, J. M.; Miguel-Jimenez, J. M.; Cantos-Frontela, J. J.; Dongil, J.] Univ Alcala de Henares, Dept Elect, Alcala De Henares 28801, Spain. [Cambralla, Rafael] Univ Alcala de Henares, Dept Teoria Senal & Comunicac, Alcala De Henares 28801, Spain. [Boquete, Luciano; Dongil, J.] Univ Alcala de Henares, CATECHOM, High Technol & Homologat Ctr, Alcala De Henares 28801, Spain. C3 Universidad de Alcala; Universidad de Alcala; Universidad de Alcala RP Boquete, L (corresponding author), Univ Alcala de Henares, Dept Elect, Alcala De Henares 28801, Spain. EM luciano@depeca.uah.es CR Beckwith R, 2004, IEEE SENSOR, P561, DOI 10.1109/ICSENS.2004.1426227 BLOUIN J, 2006, MAITRISE TEMPERATURE BURINI A, 2006, P IEEE INT C GEOSC R, DOI DOI 10.1109/IGARSS.2006.133 Burrell J, 2004, IEEE PERVAS COMPUT, V3, P38, DOI 10.1109/MPRV.2004.1269130 Camilli A, 2007, COMPUT ELECTRON AGR, V58, P25, DOI 10.1016/j.compag.2007.01.019 Egea-Lopez E, 2005, COMPUT IND, V56, P29, DOI 10.1016/j.compind.2004.10.001 FLANZY C, 2005, OENOLOGIE FONDEMENTS GALMES S, 2006, IEEE INT C MOB ADH S, P542 HIRAFUJI M, 2005, WIRELESS SENSOR NETW Jones GV, 2000, AM J ENOL VITICULT, V51, P249 KLIEWER WM, 1972, AM J ENOL VITICULT, V23, P71 LI X, 2006, IMACS MULT COMP ENG Lozano J, 2006, IEEE SENS J, V6, P173, DOI 10.1109/JSEN.2005.854598 Makra L, 2009, AM J ENOL VITICULT, V60, P312 Mattoli V, 2009, PROCEDIA CHEM, V1, P1215, DOI 10.1016/j.proche.2009.07.303 Mendoza LM, 2009, J IND MICROBIOL BIOT, V36, P229, DOI 10.1007/s10295-008-0489-4 Morais R, 2008, COMPUT ELECTRON AGR, V62, P94, DOI 10.1016/j.compag.2007.12.004 RANKINE BC, 1971, AM J ENOL VITICULT, V22, P6 Rodriguez-Mendez ML, 2004, IEEE SENS J, V4, P348, DOI 10.1109/JSEN.2004.824236 THEODORE D, 2004, MASK WINE QUALITY MA, P53 Tiusanen J, 2009, PRECIS AGRIC, V10, P372, DOI 10.1007/s11119-008-9096-7 Torija MJ, 2003, INT J FOOD MICROBIOL, V80, P47, DOI 10.1016/S0168-1605(02)00144-7 Tseng CL, 2006, COMPUT ELECTRON AGR, V53, P45, DOI 10.1016/j.compag.2006.03.005 Vall-Ilossera M, 2005, IEEE T GEOSCI REMOTE, V43, P973, DOI 10.1109/TGRS.2005.844102 Wang N, 2006, COMPUT ELECTRON AGR, V50, P1, DOI 10.1016/j.compag.2005.09.003 Wark T, 2007, IEEE PERVAS COMPUT, V6, P50, DOI 10.1109/MPRV.2007.47 Willig A, 2005, P IEEE, V93, P1130, DOI 10.1109/JPROC.2005.849717 Zhu YW, 2006, J PHYS CONF SER, V48, P1195, DOI 10.1088/1742-6596/48/1/223 Zinnai Angela, 2006, 2006 First International Symposium on Environment Identities and Mediterranean Area, P522, DOI 10.1109/ISEIMA.2006.345010 NR 29 TC 21 Z9 24 U1 0 U2 21 PD JUL PY 2010 VL 49 IS 3 BP 270 EP 276 DI 10.1016/j.isatra.2010.03.001 WC Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation SC Automation & Control Systems; Engineering; Instruments & Instrumentation UT WOS:000279414400004 DA 2022-12-14 ER PT J AU Bajzik, P Golian, J Zidek, R Krall, M Walczycka, M Tkaczewska, J AF Bajzik, Pavol Golian, Josef Zidek, Radoslav Krall, Martin Walczycka, Maria Tkaczewska, Joanna TI IDENTIFICATION OF THE COMMON CARP (CYPRINUS CARPIO) SPECIES USING REAL-TIME PCR METHODS SO ZYWNOSC-NAUKA TECHNOLOGIA JAKOSC DT Article DE species identification; Common Carp (Cyprinus carpio); Real-Time PCR method ID MITOCHONDRIAL-DNA SEQUENCE; RFLP ANALYSIS; FISH; QUANTIFICATION; AUTHENTICATION; EVOLUTION; TUNA AB Before being put out onto the market many fish species sold around the world need to be processed, which may result in the subsequent removal of characteristics used for their classification (head, fins, internal organs). The biochemical characterization of fish species could be achieved using proteins or DNA sequences as species-specific markers. However, since different fish products undergo different processes, the method of analysis has to be chosen according to the modifications undergone by fish constituents during processing. As DNA molecules are more resistant than proteins to various processes (including thermal treatment), DNA analysis appears to be a promising method for fish species identification. For the species identification of the Common Carp (Cyprinus carpio) among 15 different freshwater fish species a specific pre-designed molecular - genetic marker of Common Carp (Cyprinus carpio) was used, which comes from the mtDNA control D - loop area. Next we analyzed the presence of mtDNA in DNA isolates of the 15 kinds of freshwater fish and compared them with the Common Carp markers by using the following two PCR identification methods. The isolates were diluted to 10 % concentration, using the TaqMan Real-Time PCR method and the SYBR (R) Green Real-Time PCR method. The results of using the optimized the SYBR (R) Green Real-Time PCR method for species identification of the Common Carp (C. carpio) pointed to its suitability. We were able to create an analysis of the monitored standard curve which represented the PCR positive control (C. carpio), containing the characteristic melting peak (up to the melting point 80.72 degrees C). A single peak indicated a single product (C. carpio) which can be verified upon characterization of the PCR product by agarose gel electrophoresis. The TaqMan Real-Time PCR method with a TaqMan probe is a very sensitive and reliable method of authentication used on food of animal origin. The suitability of this method, which we used for species identification of the Common Carp (C. carpio), was confirmed. Thanks to using this method, already in the 17th cycle of the PCR amplification procedure, the presence of the Common Carp gene (C. carpio) was detected in the positive control and not detected in the rest of the fish samples. C1 [Bajzik, Pavol; Golian, Josef; Zidek, Radoslav; Krall, Martin] Slovak Univ Agr, Dept Food Hyg & Safety, Fac Biotechnol & Food Sci, Nitra, Slovakia. [Walczycka, Maria; Tkaczewska, Joanna] Agr Univ Krakow, Katedra Przetworstwa Prod Zwierzecych, Wydz Technol Zywnosci, PL-30149 Krakow, Poland. C3 Slovak University of Agriculture Nitra; Agricultural University Krakow RP Bajzik, P (corresponding author), Slovak Univ Agr, Dept Food Hyg & Safety, Fac Biotechnol & Food Sci, Nitra, Slovakia. CR Aranishi F, 2005, J FOOD SCI, V70, pC235, DOI 10.1111/j.1365-2621.2005.tb07165.x Bromham L, 2003, TRENDS ECOL EVOL, V18, P2, DOI 10.1016/S0169-5347(02)00009-5 Gil LA, 2007, TRENDS FOOD SCI TECH, V18, P558, DOI 10.1016/j.tifs.2007.04.016 Hird HJ, 2005, EUR FOOD RES TECHNOL, V220, P633, DOI 10.1007/s00217-004-1050-y Hoelzel AR, 2001, CONSERV GENET, V2, P69, DOI 10.1023/A:1011590517389 Ishiguro N, 2001, FISHERIES SCI, V67, P474, DOI 10.1046/j.1444-2906.2001.00283.x Jerome M, 2003, J AGR FOOD CHEM, V51, P7326, DOI 10.1021/jf034652t Klossa-Kilia E, 2002, FOOD CONTROL, V13, P169, DOI 10.1016/S0956-7135(01)00097-4 Kochzius M, 2008, MAR BIOTECHNOL, V10, P207, DOI 10.1007/s10126-007-9068-3 Liu ZJ, 2004, AQUACULTURE, V238, P1, DOI 10.1016/j.aquaculture.2004.05.027 Lockley AK, 2000, TRENDS FOOD SCI TECH, V11, P67, DOI 10.1016/S0924-2244(00)00049-2 Lopez I, 2005, J AGR FOOD CHEM, V53, P4554, DOI 10.1021/jf0500841 Mabuchi K, 2007, BMC EVOL BIOL, V7, DOI 10.1186/1471-2148-7-10 Mafra I, 2008, EUR FOOD RES TECHNOL, V227, P649, DOI 10.1007/s00217-007-0782-x Manchado M, 2004, FISHERIES SCI, V70, P68, DOI 10.1111/j.1444-2906.2003.00772.x Mandavilli BS, 2002, MUTAT RES-FUND MOL M, V509, P127, DOI 10.1016/S0027-5107(02)00220-8 Marko PB, 2004, NATURE, V430, P309, DOI 10.1038/430309b Mitsutoshi N., 2010, FOOD SCI TECHNOL RES, V16, P403 Pepe T, 2007, J AGR FOOD CHEM, V55, P3681, DOI 10.1021/jf063321o Rasmussen RS, 2008, COMPR REV FOOD SCI F, V7, P280, DOI 10.1111/j.1541-4337.2008.00046.x Rehbein H, 1999, FOOD CHEM, V64, P263, DOI 10.1016/S0308-8146(98)00125-3 Rokas A, 2003, TRENDS ECOL EVOL, V18, P411, DOI 10.1016/S0169-5347(03)00125-3 Sanjuan A, 2002, J FOOD SCI, V67, P2644, DOI 10.1111/j.1365-2621.2002.tb08792.x Sotelo C. G., 2003, 1 JOINT T ATL FISH T, P195 Stoneking M, 1996, CURR OPIN GENET DEV, V6, P731, DOI 10.1016/S0959-437X(96)80028-1 Teletchea F, 2005, TRENDS BIOTECHNOL, V23, P359, DOI 10.1016/j.tibtech.2005.05.006 Teletchea F, 2009, REV FISH BIOL FISHER, V19, P265, DOI 10.1007/s11160-009-9107-4 Trotta M, 2005, J AGR FOOD CHEM, V53, P2039, DOI 10.1021/jf048542d Yanagimoto T, 2004, FISHERIES SCI, V70, P885, DOI 10.1111/j.1444-2906.2004.00883.x Zidek R, 2008, PROT SBORN PRISP 5 R, P235 NR 30 TC 0 Z9 0 U1 0 U2 10 PY 2012 VL 19 IS 5 BP 166 EP 176 WC Food Science & Technology SC Food Science & Technology UT WOS:000314374700015 DA 2022-12-14 ER PT J AU Ruiz-Garcia, L Lunadei, L AF Ruiz-Garcia, Luis Lunadei, Loredana TI The role of RFID in agriculture: Applications, limitations and challenges SO COMPUTERS AND ELECTRONICS IN AGRICULTURE DT Article DE RFID; Precision agriculture; Animal identification; Food traceability; Cold chain ID RADIO-FREQUENCY IDENTIFICATION; TECHNICAL-NOTE; CHAIN; SYSTEM; TECHNOLOGIES; TEMPERATURE; IMPACT; SENSORS AB The recent advances in RFID offer vast opportunities for research, development and innovation in agriculture. The aim of this paper is to give readers a comprehensive view of current applications and new possibilities, but also explain the limitations and challenges of this technology. RFID has been used for years in animal identification and tracking, being a common practice in many farms. Also it has been used in the food chain for traceability control. The implementation of sensors in tags, make possible to monitor the cold chain of perishable food products and the development of new applications in fields like environmental monitoring, irrigation, specialty crops and farm machinery. However, it is not all advantages. There are also challenges and limitations that should be faced in the next years. The operation in harsh environments, with dirt, extreme temperatures; the huge volume of data that are difficult to manage; the need of longer reading ranges, due to the reduction of signal strength due to propagation in crop canopy; the behavior of the different frequencies, understanding what is the right one for each application; the diversity of the standards and the level of granularity are some of them. (C) 2011 Elsevier B.V. All rights reserved. C1 [Ruiz-Garcia, Luis; Lunadei, Loredana] Univ Politecn Madrid, ETSI Agronomos, Lab Propiedades Fis & Tecnol Avanzadas Agroalimen, E-28040 Madrid, Spain. C3 Universidad Politecnica de Madrid RP Ruiz-Garcia, L (corresponding author), Univ Politecn Madrid, ETSI Agronomos, Lab Propiedades Fis & Tecnol Avanzadas Agroalimen, Edificio Motores,Avda Complutense S-N, E-28040 Madrid, Spain. EM luis.ruiz@upm.es CR Abad E, 2009, J FOOD ENG, V93, P394, DOI 10.1016/j.jfoodeng.2009.02.004 AMADOR C, 2009, SENSING INSTRUMENTAT, P26 AMADOR C, 2008, FOOD PROC AUT C PROV AMADOR C, 2010, 17 WORLD C INT COMM Ampatzidis YG, 2009, COMPUT ELECTRON AGR, V66, P166, DOI 10.1016/j.compag.2009.01.008 ANDRADESANCHEZ P, 2007, 2007 ASABE ANN INT M ANDRECHAK G, 2008, HITACHI MU CHIP RFID Angeles R, 2005, INFORM SYST MANAGE, V22, P51, DOI 10.1201/1078/44912.22.1.20051201/85739.7 Atsushi O, 2006, NEC TECH J, V1, P82 Attaran M, 2007, SUPPLY CHAIN MANAG, V12, P249, DOI 10.1108/13598540710759763 BARGE P, 2010, INT C RAG SHWA2010 R Bowman KD, 2010, HORTSCIENCE, V45, P451, DOI 10.21273/HORTSCI.45.3.451 Brown-Brandl TM, 2003, APPL ENG AGRIC, V19, P583 Chang K, 2007, ELECTRON LETT, V43, P259, DOI 10.1049/el:20073739 CHANSUD W, 2008, EL ENG EL COMP TEL I Cho N, 2005, PROC EUR SOLID-STATE, P279, DOI 10.1109/ESSCIR.2005.1541614 Curkendall L. D., 2002, METHOD APPARATUS LIV *ECPGLOBAL, 2008, EPC RAD FREQ ID PROT Emond JP, 2006, COOL CHAIN ASS WORKS Engels D. W., 2005, STANDARDIZATION REQU Finkenzeller K., 2004, RFID HDB RADIO FREQU FLETCHER R, 2005, ANT PROP SOC INT S 2 HAAPALA HES, 2008, LIVESTOCK ENV, V8 Hamrita TK, 2005, APPL ENG AGRIC, V21, P139 Hanton J.P., 1981, U.S. Patent, Patent No. 4262632 Hostettor J., 2003, US TODAY *ISO, 2004, 11784 ISO *ISO, 1996, 11785 ISO *ISO, 2003, 14223 ISO Jedermann R, 2006, SENSOR ACTUAT A-PHYS, V132, P370, DOI 10.1016/j.sna.2006.02.008 Jedermann R, 2009, COMPUT ELECTRON AGR, V65, P145, DOI 10.1016/j.compag.2008.08.006 Jones P, 2004, INT J RETAIL DISTRIB, DOI DOI 10.1108/09590550410524957 Kononoff PJ, 2002, J DAIRY SCI, V85, P1801, DOI 10.3168/jds.S0022-0302(02)74254-9 Koutsoumanis K, 2005, INT J FOOD MICROBIOL, V100, P253, DOI 10.1016/j.ijfoodmicro.2004.10.024 LANIEL M, 2008, FOOD PROC AUT C PROV LANIEL M, 2010, 17 WORLD C INT COMM LENG NM, 2005, MICR ANT PROP EMC TE Luvisi A, 2010, SCI HORTIC-AMSTERDAM, V124, P349, DOI 10.1016/j.scienta.2010.01.015 Luvisi A, 2011, BIOSYST ENG, V109, P167, DOI 10.1016/j.biosystemseng.2011.03.001 Luvisi A, 2010, COMPUT ELECTRON AGR, V70, P256, DOI 10.1016/j.compag.2009.08.007 MALLISON H, 2005, OVERVIEW LOCATION ID Marsh J. R., 2008, ASABE ANN INT M 2008 MCMEEKIN T, 2006, 2 INT WORKSH COLD CH Meyer GG, 2009, COMPUT IND, V60, P137, DOI 10.1016/j.compind.2008.12.005 MORRISON MJ, 2001, APPARATUS METHOD REA Moureh J, 2002, COMPUT ELECTRON AGR, V34, P89, DOI 10.1016/S0168-1699(01)00181-8 MUNAK A, 2006, CIGR HDB AGR ENG Ngai E, 2008, INT J PROD ECON, V112, P507, DOI 10.1016/j.ijpe.2007.05.003 Ngai EWT, 2007, DECIS SUPPORT SYST, V43, P1, DOI 10.1016/j.dss.2005.05.002 Opasjumruskit K, 2006, IEEE PERVAS COMPUT, V5, P54, DOI 10.1109/MPRV.2006.15 Peets S, 2009, PRECIS AGRIC, V10, P382, DOI 10.1007/s11119-009-9106-4 Raab V., 2008, Journal on Chain and Network Science, V8, P59, DOI 10.3920/JCNS2008.x089 Reiners K, 2009, COMPUT ELECTRON AGR, V68, P178, DOI 10.1016/j.compag.2009.05.010 Roberti M, 2003, RFID J ROBERTI M, 2005, RFID J Roberts CM, 2006, COMPUT SECUR, V25, P18, DOI 10.1016/j.cose.2005.12.003 Ruiz-Garcia, 2010, DEV MONITORING SYSTE Ruiz-Garcia L, 2010, FOOD CONTROL, V21, P112, DOI 10.1016/j.foodcont.2008.12.003 RUIZGARCIA L, 2008, DEV MONITORING APPL Sarac A, 2010, INT J PROD ECON, V128, P77, DOI 10.1016/j.ijpe.2010.07.039 Scheer FP, 2006, WOODHEAD PUBL FOOD S, P52, DOI 10.1533/9781845691233.1.52 Schirmann K, 2009, J DAIRY SCI, V92, P6052, DOI 10.3168/jds.2009-2361 Sjolander AJ, 2011, COMPUT ELECTRON AGR, V75, P34, DOI 10.1016/j.compag.2010.09.015 Steinberg IM, 2009, SENSOR ACTUAT B-CHEM, V138, P120, DOI 10.1016/j.snb.2009.02.040 SUGAHARA K, 2009, COMPUTER COMPUTING T, V3 TATE RF, 2008, 2008 ASABE ANN INT M *TECHN S, 2008, SEL RIGHT ACT FREQ THOMAS VM, 2008, EL ENV 2008 ISEE 200 Todd B, 2009, IEEE SENS J, V9, P464, DOI 10.1109/JSEN.2009.2014410 Trevarthen A., 2007, Journal of Theoretical and Applied Electronic Commerce Research, V2 Twist DC, 2005, J FACIL MANAG, V3, P226, DOI 10.1108/14725960510808491 *USC, PREC AGR 2006 Vellidis G, 2008, COMPUT ELECTRON AGR, V61, P44, DOI 10.1016/j.compag.2007.05.009 VERGARA A, 2006, EUROSENSORS 2006 Voulodimos AS, 2010, COMPUT ELECTRON AGR, V70, P380, DOI 10.1016/j.compag.2009.07.009 Want R, 2004, COMPUTER, V37, P84, DOI 10.1109/MC.2004.1297315 Watts A., 2002, Pesticide Outlook, V13, P254, DOI 10.1039/b211207h WENTWORTH SM, 2003, 2003 IFT I FOOD ENG Wisanmongkol J., 2009, 2009 INT S ANT PROP Wise KD, 2007, SENSOR ACTUAT A-PHYS, V136, P39, DOI 10.1016/j.sna.2007.02.013 Yam KL, 2005, J FOOD SCI, V70, pR1, DOI 10.1111/j.1365-2621.2005.tb09052.x YANG IC, 2008, 2008 ASABE ANN INT M [No title captured] [No title captured] NR 84 TC 178 Z9 192 U1 9 U2 94 PD OCT PY 2011 VL 79 IS 1 BP 42 EP 50 DI 10.1016/j.compag.2011.08.010 WC Agriculture, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Agriculture; Computer Science UT WOS:000296308000006 DA 2022-12-14 ER PT J AU Ruamkuson, D Tongpim, S Ketudat-Cairns, M AF Ruamkuson, Darawan Tongpim, Saowanit Ketudat-Cairns, Mariena TI A model to develop biological probes from microflora to assure traceability of tilapia SO FOOD CONTROL DT Article DE Tilapia microflora; 16S rDNA-DGGE; GC clamp ID GRADIENT GEL-ELECTROPHORESIS; OREOCHROMIS-NILOTICUS; BACTERIAL-FLORA; RIBOSOMAL-RNA; PCR-DGGE; BACTERIOPLANKTON; COMMUNITIES; FRAGMENTS; ORIGIN; FISH AB The bacterial community of Suranaree University of Technology (SUT) tilapia was studied with the aim to develop a model for traceable biological markers. Five fish per time were collected from SUT farm and other sources. Total viable counts (TVC) of bacteria from SUT farm were quite similar in all seasons. Seventy three percent of the bacteria were Gram-negative. Total DNA extracted from the fish gills and intestines were used as template to amplify bacterial 16S rDNA V3 region using GC clamp primer to identify specific bacteria of fish origin by PCR-DGGE technique. The results showed 3 DNA bands on DGGE gel that were specific to only bacterial DNA of SUT tilapia when compared to the other sources. The 3 DNA bands were sequenced and identified as uncultured bacteria of different species. Primers were designed from the 3 specific sequences and used to amplify DNA samples from four sources and some pure cultured bacteria. The results indicated that, only one primer pair can amplify DNA samples from SUT farm but not other samples. This primer pair can be used to identify and trace samples from SUT farm. This method can be used as a model to develop bacterial primer specific location for other food products. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Ketudat-Cairns, Mariena] Suranaree Univ Technol, Embryo Technol & Stem Cell Res Ctr, Inst Agr Technol, Nakhon Ratchasima 30000, Thailand. [Ruamkuson, Darawan; Ketudat-Cairns, Mariena] Suranaree Univ Technol, Sch Biotechnol, Inst Agr Technol, Nakhon Ratchasima 30000, Thailand. [Tongpim, Saowanit] Khon Kaen Univ, Dept Microbiol, Fac Sci, Khon Kaen 40002, Thailand. C3 Suranaree University of Technology; Suranaree University of Technology; Khon Kaen University RP Ketudat-Cairns, M (corresponding author), Suranaree Univ Technol, Embryo Technol & Stem Cell Res Ctr, Inst Agr Technol, 111 Univ Ave, Nakhon Ratchasima 30000, Thailand. EM ketudat@sut.ac.th CR Al-Harbi AH, 2004, AQUACULTURE, V229, P37, DOI 10.1016/S0044-8486(03)00388-0 Ampe F, 1999, APPL ENVIRON MICROB, V65, P5464 [Anonymous], 2002, OFFICIAL J EUROPEAN COLWELL RR, 1962, J APPL BACTERIOL, V25, P147, DOI 10.1111/j.1365-2672.1962.tb01131.x HARISH R, 2003, ASIAN FISHERIES SCI, V16, P185 Hatha A.A.M., 2000, Fishery Technology, V37, P59 Heyndrickx M, 1996, J MICROBIOL METH, V26, P247, DOI 10.1016/0167-7012(96)00916-5 Huber I, 2004, J APPL MICROBIOL, V96, P117, DOI 10.1046/j.1365-2672.2003.02109.x Le Nguyen DD, 2008, FOOD CONTROL, V19, P454, DOI 10.1016/j.foodcont.2007.05.006 LIMSUWAN T, 1981, J NUTR, V111, P2125, DOI 10.1093/jn/111.12.2125 Moeseneder MM, 1999, APPL ENVIRON MICROB, V65, P3518 Ovreas L, 1997, APPL ENVIRON MICROB, V63, P3367 Riemann L, 1999, DEEP-SEA RES PT II, V46, P1791, DOI 10.1016/S0967-0645(99)00044-2 Rollins DM, 2003, MICROBIAL PATHOGENES Ruamkuson D, 2009, SURANAREE J SCI TECH, V16, P311 Tatsadjieu NL, 2010, FOOD CONTROL, V21, P673, DOI 10.1016/j.foodcont.2009.10.006 *UN FAO, 2008, FAO FISHERIES CIRCUL, V1033, P88 NR 18 TC 1 Z9 5 U1 0 U2 11 PD NOV PY 2011 VL 22 IS 11 BP 1742 EP 1747 DI 10.1016/j.foodcont.2011.04.008 WC Food Science & Technology SC Food Science & Technology UT WOS:000292710900007 DA 2022-12-14 ER PT J AU Hughes, RC Lightfoot, HA McComb, TR AF Hughes, RC Lightfoot, HA McComb, TR TI Assuring the quality of impulse voltage calibration results issued by a calibration laboratory SO IEEE TRANSACTIONS ON POWER DELIVERY DT Article DE high voltage impulse measurement; traceability; calibration; quality assurance AB This paper describes high voltage impulse measurements made under the controlled conditions of a calibration or national measurement laboratory to establish the best agreement that could be achieved in comparison measurements on small reference dividers. This comparison to establish mutual international traceability between NRC and National Grid has shown that the peak voltage of a smooth lightning impulse was measured by both laboratories to within 0.2% with an estimated uncertainty of 0.2% at a level of confidence not less than 95%. The authors make recommendations on how calibrations of reference measuring systems should be performed. C1 Natl Grid Co, Leatherhead KT22 7ST, Surrey, England. Natl Res Council Canada, Ottawa, ON K1A 0R6, Canada. C3 National Research Council Canada RP Hughes, RC (corresponding author), Natl Grid Co, Leatherhead KT22 7ST, Surrey, England. CR CREED FC, 1963, AIEE T COMMUNICATION, V69, P621 HUGHES RC, 3390 CIGRE HUGHES RC, 3387 CIGRE McComb T. R., 1995, Electra, P104 MCCOMB TR, 1999, P 11 INT S HIGH VOLT MCCOMB TR, 1988, IEEE T POWER DELIVER, V3, P906 MCCOMB TR, 1995, P 9 INT S HIGH VOLT MINER DF, 1941, IMPULSE BREAKDOWN OI, P199 1994, HIGH VOLTAGE TECHN 2 1999, GEN REQUIREMENTS COM NR 10 TC 1 Z9 1 U1 0 U2 1 PD JUL PY 2003 VL 18 IS 3 BP 701 EP 704 DI 10.1109/TPWRD.2003.813810 WC Engineering, Electrical & Electronic SC Engineering UT WOS:000183823000005 DA 2022-12-14 ER PT J AU Pakseresht, A Kaliji, SA Xhakollari, V AF Pakseresht, Ashkan Kaliji, Sina Ahmadi Xhakollari, Vilma TI How Blockchain Facilitates the Transition toward Circular Economy in the Food Chain? SO SUSTAINABILITY DT Review DE circularity; distributed ledger; eco-efficiency; green technology; supply chain; sustainability; traceability ID SUPPLY CHAIN; TRACEABILITY SYSTEM; CHALLENGES; WASTE; ARCHITECTURE; AGRICULTURE; INFORMATION; RESOURCE; BARRIERS; REVIEWS AB Food loss and waste are two of the many problems that modern society is facing. To date, among many solutions, the circular economy is the one prevailing. A successful transition toward a circular economy (CE) requires the food sector to overcome the challenges of today's complex food supply chains such as information asymmetry, poor cooperation among stakeholders, and concerns about food safety. Blockchain, a form of distributed ledger technology, has been progressively gaining traction in supply chains in areas like data management, certifying product provenance and tracking products. Despite its importance, knowledge around the potential of the blockchain technology in facilitating the transition towards a circular economy in the agri-food sector is fragmented. This review provides evidence-based insights into the blockchain implementations in the food supply chains and the implications for CE. Our findings indicated four major areas that blockchain could accelerate CE in the agri-food sector: improving data utility; supply chain management efficacy; enhanced eco-efficiency; and superior traceability. C1 [Pakseresht, Ashkan] Novia Univ Appl Sci, Dept Bioecon, Tammisaari 10600, Finland. [Kaliji, Sina Ahmadi; Xhakollari, Vilma] Alma Mater Studiorum Univ Bologna, Dept Agr & Food Sci, I-40127 Bologna, Italy. C3 Novia University of Applied Sciences; University of Bologna RP Pakseresht, A (corresponding author), Novia Univ Appl Sci, Dept Bioecon, Tammisaari 10600, Finland. EM ashkan.pakseresht@novia.if CR Abeyratne S. A., 2016, INT J RES ENG TECHNO, V5, P1, DOI DOI 10.15623/IJRET.2016.0509001 Acai A, 2018, PERSPECT MED EDUC, V7, P147, DOI 10.1007/s40037-018-0434-9 Ada N, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126812 Adams D, 2021, SUSTAIN PROD CONSUMP, V28, P1491, DOI 10.1016/j.spc.2021.08.019 Alamri MS, 2021, SAUDI J BIOL SCI, V28, P4490, DOI 10.1016/j.sjbs.2021.04.047 Allard MW, 2018, CURR OPIN BIOTECH, V49, P224, DOI 10.1016/j.copbio.2017.11.002 Alonso RS, 2020, AD HOC NETW, V98, DOI 10.1016/j.adhoc.2019.102047 Altarawneh A, 2020, 2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), P727, DOI 10.1109/CCWC47524.2020.9031204 Altay A, 2022, TRENDS BIOTECHNOL, V40, P141, DOI 10.1016/j.tibtech.2021.04.011 Altera O., 2017, FRAN VARDEKEDJA VARD, P22 Amin M.R., 2020, J ENG APPL SCI, V15, P99, DOI [10.36478/jeasci.2020.99.106, DOI 10.36478/JEASCI.2020.99.106] Antle JM, 2001, HANDB ECON, V18, P1083 Arancon RAD, 2013, ENERGY SCI ENG, V1, P53, DOI 10.1002/ese3.9 Aschemann-Witzel J, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120749 Avraamidou S, 2020, COMPUT CHEM ENG, V133, DOI 10.1016/j.compchemeng.2019.106629 Baker JD, 2016, ASSOC OPER ROOM NURS, V103, P265, DOI 10.1016/j.aorn.2016.01.016 Banasik A, 2017, ANN OPER RES, V250, P341, DOI 10.1007/s10479-016-2199-z Berchicci L., 2005, BUSINESS STRATEGY EN, V14, P272, DOI [DOI 10.1002/BSE.488, 10.1002/bse.488] Bettin-Diaz R, 2018, LECT NOTES COMPUT SC, V10961, P19, DOI 10.1007/978-3-319-95165-2_2 Bhat SA, 2022, AGRICULTURE-BASEL, V12, DOI 10.3390/agriculture12010040 Bierbaum R, 2020, ENVIRON SCI POLICY, V105, P134, DOI 10.1016/j.envsci.2019.11.002 Bogetoft P., 2004, DESIGN PRODUCTION CO, P208 Brandon-Jones E, 2014, J SUPPLY CHAIN MANAG, V50, P55, DOI 10.1111/jscm.12050 Braungart M, 2007, J CLEAN PROD, V15, P1337, DOI 10.1016/j.jclepro.2006.08.003 Bridgens B, 2018, J CLEAN PROD, V189, P145, DOI 10.1016/j.jclepro.2018.03.317 Brown ME, 2015, PROCEDIA ENVIRON SCI, V29, P307, DOI 10.1016/j.proenv.2015.07.278 Bumblauskas D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.09.004 Cao SF, 2021, COMPUT ELECTRON AGR, V180, DOI 10.1016/j.compag.2020.105886 Caro M. P., 2018, 2018 IOT VERTICAL TO, P1, DOI 10.1109/IOT-TUSCANY.2018.8373021 Casino F, 2021, INT J PROD RES, V59, P5758, DOI 10.1080/00207543.2020.1789238 Chen HS, 2020, ACM COMPUT SURV, V53, DOI 10.1145/3391195 Chen YC, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11070149 Chen YY, 2020, J CLEAN PROD, V268, DOI 10.1016/j.jclepro.2020.122071 Chopra M., 2022, SUSTAIN TECHNOL ENTR, V1, DOI DOI 10.1016/J.STAE.2022.100012 Cillo V, 2019, CORP SOC RESP ENV MA, V26, P1012, DOI 10.1002/csr.1783 Co HC, 2009, INT J OPER PROD MAN, V29, P591, DOI 10.1108/01443570910957573 Corona B, 2019, RESOUR CONSERV RECY, V151, DOI 10.1016/j.resconrec.2019.104498 de Oliveira MM, 2021, J CLEAN PROD, V294, DOI 10.1016/j.jclepro.2021.126284 Despoudi S., 2020, Food Science & Technology, V34, P48, DOI 10.1002/fsat.3401_13.x Dharmapalan V, 2021, FRONT BUILT ENVIRON, V7, DOI 10.3389/fbuil.2021.651294 Dobrovnik M, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2030018 Duan J, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17051784 Duarte LO, 2019, SN APPL SCI, V1, DOI 10.1007/s42452-019-0937-y Eijk F.V., 2015, BARRIERS DRIVERS CIR EMF, CIRC EC ACC SCAL GLO Esmaeilian B, 2020, RESOUR CONSERV RECY, V163, DOI 10.1016/j.resconrec.2020.105064 FAO, 2019, STAT FOOD AGR MOV FO, DOI DOI 10.4324/9781315764788 FAO (Food and Agriculture Organization of the United Nations), CIRC EC WAST RES COV Farooque M, 2019, SUPPLY CHAIN MANAG, V24, P677, DOI 10.1108/SCM-10-2018-0345 Feng HH, 2020, IEEE ACCESS, V8, P54361, DOI 10.1109/ACCESS.2020.2977723 Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424 Ferdousi T, 2020, IEEE ACCESS, V8, P154833, DOI 10.1109/ACCESS.2020.3019000 Foundation E.M., 2019, CIRC EC EC BUS RAT A, P21 Francisco K, 2018, LOGISTICS-BASEL, V2, DOI 10.3390/logistics2010002 Friedman N, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121403 Galvez JF, 2018, TRAC-TREND ANAL CHEM, V107, P222, DOI 10.1016/j.trac.2018.08.011 Gang Liu, 2018, Procedia Computer Science, V131, P860, DOI 10.1016/j.procs.2018.04.286 Garaus M, 2021, FOOD CONTROL, V129, DOI 10.1016/j.foodcont.2021.108082 Garrard R, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101298 George RV, 2019, J CLEAN PROD, V240, DOI 10.1016/j.jclepro.2019.118021 Geueke B, 2018, J CLEAN PROD, V193, P491, DOI 10.1016/j.jclepro.2018.05.005 Gramoli V., 2016, P WORKSH DISTR CRYPT Grant MJ, 2009, HEALTH INFO LIBR J, V26, P91, DOI 10.1111/j.1471-1842.2009.00848.x Grant S., 2017, J SUSTAIN ED, V14, P1 Grecuccio J, 2020, ENERGIES, V13, DOI 10.3390/en13153820 Green BN, 2006, J CHIROPR MED, V5, P101, DOI 10.1016/S0899-3467(07)60142-6 Gu B., 2022, ARTIFICIAL INTELLIGE, V6, P10, DOI [DOI 10.1016/J.AIIA.2022.01.001, 10.1016/j.aiia.2022.01.001] Gubeladze D., 2020, INT J INNOV TECHNOL, V5, P1, DOI [10.31435/rsglobal_ijite/30122020/7286, DOI 10.31435/RSGLOBAL_IJITE/30122020/7286, 10.31435/RSGLOBAL_IJITE/30122020/7286] Guido R, 2020, INT J IND ENG MANAGE, V11, P50, DOI 10.24867/IJIEM-2020-1-252 Gupta S, 2019, TECHNOL FORECAST SOC, V144, P466, DOI 10.1016/j.techfore.2018.06.030 Hang L, 2020, COMPUT ELECTRON AGR, V170, DOI 10.1016/j.compag.2020.105251 Hewa T, 2021, J NETW COMPUT APPL, V177, DOI 10.1016/j.jnca.2020.102857 Hobson K, 2016, PROG HUM GEOG, V40, P88, DOI 10.1177/0309132514566342 Hua J, 2018, IEEE INT VEH SYM, P97 Iqbal R, 2020, CLUSTER COMPUT, V23, P2139, DOI 10.1007/s10586-020-03092-4 Jaeger B, 2020, J ENTERP INF MANAG, V33, P729, DOI 10.1108/JEIM-02-2019-0047 Clemente-Suarez VJ, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14137726 Johnston DA, 2004, J OPER MANAG, V22, P23, DOI 10.1016/j.jom.2003.12.001 Joo J, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su131910980 Kamath R, 2018, J BRIT BLOCKCHAIN AS, V1, P47, DOI 10.31585/jbba-1-1-(10)2018 Kamble S, 2019, INT J PROD RES, V57, P2009, DOI 10.1080/00207543.2018.1518610 Kamble SS, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.023 Karame GO, 2016, CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1861, DOI 10.1145/2976749.2976756 Kaske D, 2018, J AGRIC FOOD INF, V19, P284, DOI 10.1080/10496505.2017.1371023 Kirchherr J, 2018, ECOL ECON, V150, P264, DOI 10.1016/j.ecolecon.2018.04.028 Kohler S, 2020, J CLEAN PROD, V269, DOI 10.1016/j.jclepro.2020.122193 Kumar K.D., 2020, INT J SCI TECHNOL RE, V9, P3446 LaCanne CE, 2018, PEERJ, V6, DOI 10.7717/peerj.4428 Lansiti M, 2017, HARVARD BUS REV, V95, P119 Leng KJ, 2018, FUTURE GENER COMP SY, V86, P641, DOI 10.1016/j.future.2018.04.061 Li KP, 2021, INT J PROD RES, DOI 10.1080/00207543.2021.1970849 Li XH, 2020, J CLEAN PROD, V271, DOI 10.1016/j.jclepro.2020.122503 Li XQ, 2020, FUTURE GENER COMP SY, V107, P841, DOI 10.1016/j.future.2017.08.020 Li Y, 2020, INT J PROD ECON, V229, DOI 10.1016/j.ijpe.2020.107777 Lin CSK, 2013, ENERG ENVIRON SCI, V6, P426, DOI 10.1039/c2ee23440h Lin J, 2018, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON CROWD SCIENCE AND ENGINEERING (ICCSE 2018), DOI 10.1145/3265689.3265692 Lin YP, 2017, ENVIRONMENTS, V4, DOI 10.3390/environments4030050 Liu P, 2020, J CLEAN PROD, V277, DOI 10.1016/j.jclepro.2020.123646 Liwei Ouyang, 2019, 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). Proceedings, P76, DOI 10.1109/SOLI48380.2019.8955075 Longo F, 2020, INT J FOOD ENG, V16, DOI 10.1515/ijfe-2019-0109 Luque R., 2013, SUSTAIN CHEM PROCESS, V1, P10, DOI [10.1186/2043-7129-1-10, DOI 10.1186/2043-7129-1-10] Maass O, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10041125 Malarvizhi P., 2019, INT J RECENT TECHNOL, V8, P1485, DOI DOI 10.35940/IJRTE.B1087.0882S819 Mao DH, 2019, IEEE ACCESS, V7, P73131, DOI 10.1109/ACCESS.2019.2920776 Mao DH, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093149 Modgil S, 2021, TECHNOL FORECAST SOC, V166, DOI 10.1016/j.techfore.2021.120607 Mondal S, 2019, IEEE INTERNET THINGS, V6, P5803, DOI 10.1109/JIOT.2019.2907658 Monostori J, 2018, PROC CIRP, V67, P110, DOI 10.1016/j.procir.2017.12.185 Monostori L, 2016, CIRP ANN-MANUF TECHN, V65, P621, DOI 10.1016/j.cirp.2016.06.005 Moraga G, 2019, RESOUR CONSERV RECY, V146, P452, DOI 10.1016/j.resconrec.2019.03.045 Vu N, 2021, PROD PLAN CONTROL, DOI 10.1080/09537287.2021.1939902 Nandi S, 2021, SUSTAIN PROD CONSUMP, V27, P10, DOI 10.1016/j.spc.2020.10.019 Newton P, 2020, FRONT SUSTAIN FOOD S, V4, DOI 10.3389/fsufs.2020.577723 Nizetic S, 2020, J CLEAN PROD, V274, DOI 10.1016/j.jclepro.2020.122877 Ojha S, 2020, WASTE MANAGE, V118, P600, DOI 10.1016/j.wasman.2020.09.010 Pandey M., 2022, BLOCKCHAIN TECHNOLOG, P207 Pandey V, 2022, TECHNOL SOC, V69, DOI 10.1016/j.techsoc.2022.101954 Park A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13041726 Patil A.S., 2018, FRAMEWORK BLOCKCHAIN, P1162 Qlikchain, ROL BLOCKCH PREV INF Queiroz MM, 2019, INT J INFORM MANAGE, V46, P70, DOI 10.1016/j.ijinfomgt.2018.11.021 Rajput SPS, 2020, MATER TODAY-PROC, V26, P2515, DOI 10.1016/j.matpr.2020.02.535 Khan SAR, 2022, INT J LOGIST-RES APP, V25, P605, DOI 10.1080/13675567.2021.1872512 Rejeb A, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14010083 Rejeb A, 2020, LOGISTICS-BASEL, V4, DOI 10.3390/logistics4040027 Rogerson M, 2020, SUPPLY CHAIN MANAG, V25, P601, DOI 10.1108/SCM-08-2019-0300 Ronaghi M. H., 2021, Information Processing in Agriculture, V8, P398, DOI 10.1016/j.inpa.2020.10.004 Salah K, 2019, IEEE ACCESS, V7, P73295, DOI 10.1109/ACCESS.2019.2918000 Sanka AI, 2021, J NETW COMPUT APPL, V195, DOI 10.1016/j.jnca.2021.103232 Scott V. N., 2009, Food Protection Trends, V29, P342 Singh A, 2020, J NETW COMPUT APPL, V149, DOI 10.1016/j.jnca.2019.102471 Sokolowska B, 2020, SAFETY ISSUES IN BEVERAGE PRODUCTION, VOL 18: THE SCIENCE OF BEVERAGES, P79, DOI 10.1016/B978-0-12-816679-6.00003-6 Song JM, 2019, PROCEDIA COMPUT SCI, V162, P119, DOI 10.1016/j.procs.2019.11.266 Starbird S. A., 2007, Journal of Agricultural & Food Industrial Organization, V5, P2, DOI 10.2202/1542-0485.1141 Surasak T, 2019, INT J ADV COMPUT SC, V10, P578 Syromyatnikov D., 2020, INT J SUPPLY CHAIN M, V9, P377 Tan A., 2020, SUSTAINABLE FUTURES, V2, P100034 Tan A, 2022, INT J LOGIST-RES APP, V25, P947, DOI 10.1080/13675567.2020.1825653 Tian F, 2017, I C SERV SYST SERV M Tonnissen S, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.009 Toma L, 2020, EUROCHOICES, V19, P24, DOI 10.1111/1746-692X.12254 Uddin M.A., 2021, BLOCKCHAIN RES APPL, V2, DOI 10.1016/j.bcra.2021.100006 Valoppi F, 2021, FRONT SUSTAIN FOOD S, V5, DOI 10.3389/fsufs.2021.626227 van Wassenaer L, 2021, FRONT BLOCKCHAIN, V4, DOI 10.3389/fbloc.2021.653128 Vinnari M, 2009, FUTURES, V41, P269, DOI 10.1016/j.futures.2008.11.014 Violino S, 2020, FOODS, V9, DOI 10.3390/foods9050624 Wagner S. M., 2006, Journal of Purchasing and Supply Management, V12, P301, DOI 10.1016/j.pursup.2007.01.004 Wamba SF, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102064 Wang YJ, 2020, J PHYS CONF SER, V1634, DOI 10.1088/1742-6596/1634/1/012025 Wu J., 2022, FOOD ENG INNOVATIONS, P61 Wu ML, 2019, IEEE INTERNET THINGS, V6, P8114, DOI 10.1109/JIOT.2019.2922538 Xiong H., 2020, APPL RATION, V3, DOI [10.3389/fbloc.2020.00007, DOI 10.3389/FBLOC.2020.00007] Xu J., 2019, CONTROL SALMONELLA L Xu J., 2020, ARTIF INTELL AGR, V4, P153, DOI [10.1016/j.aiia.2020.08.002, DOI 10.1016/J.AIIA.2020.08.002] Yang XT, 2021, IEEE ACCESS, V9, P36282, DOI 10.1109/ACCESS.2021.3062845 Yang Y, 2019, FOOD CONTROL, V95, P308, DOI 10.1016/j.foodcont.2018.08.019 Yetis H, 2022, ENG SCI TECHNOL, V36, DOI 10.1016/j.jestch.2022.101151 Yousefi M. R., 2015, International Journal of Advanced Biological and Biomedical Research, V3, P7 Yu B, 2020, IEEE ACCESS, V8, P12479, DOI 10.1109/ACCESS.2020.2966020 Zeng AZ, 2005, APPL OPTIMIZAT, V92, P141, DOI 10.1007/0-387-23392-X_5 Zhang Q, 2020, COMPUT ELECTRON AGR, V173, DOI 10.1016/j.compag.2020.105395 Zhang X, 2020, IEEE ACCESS, V8, P36398, DOI 10.1109/ACCESS.2020.2975415 Zhang YJ, 2021, J FOOD PROCESS ENG, V44, DOI 10.1111/jfpe.13669 Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002 NR 164 TC 0 Z9 0 U1 11 U2 11 PD SEP PY 2022 VL 14 IS 18 AR 11754 DI 10.3390/su141811754 WC Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies SC Science & Technology - Other Topics; Environmental Sciences & Ecology UT WOS:000856801800001 DA 2022-12-14 ER PT J AU Wu, XM Zuo, ZT Zhang, QZ Wang, YZ AF Wu, Xue-Mei Zuo, Zhi-Tian Zhang, Qing-Zhi Wang, Yuan-Zhong TI FT-MIR and UV-vis data fusion strategy for origins discrimination of wild Paris Polyphylla Smith var. yunnanensis SO VIBRATIONAL SPECTROSCOPY DT Article DE Paris Polyphylla Smith var. yunnanensis; Data fusion; Traceability; SVM-GS; PLS-DA ID TRANSFORM INFRARED-SPECTROSCOPY; ANALYSIS PLS-DA; GEOGRAPHICAL TRACEABILITY; MIDINFRARED SPECTROSCOPY; LIQUID-CHROMATOGRAPHY; QUALITY ASSESSMENT; CLASSIFICATION; IDENTIFICATION; MEDICINES; RAMAN AB Paris Polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz has multiple therapeutic properties and the origins may affect clinical efficacy. Tracing the geographical origin is important to the authentication and quality assessment of this species. 177 wild samples collected from central, southeast and northwest Yunnan Province, China, were analyzed by single analytical method and data fusion strategies (low- and mid-levels) using Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV-vis) spectroscopies combined with chemometrics (partial least squares discrimination analysis (PLS-DA) and support vector machines grid search (SVM-GS)), for categorizing samples from different geographic origins. According to the results, mid-level data fusion strategy presented a better generalization performance and accuracy rates based on latent variables selected by PLS-DA than single analytical method and low-level data fusion strategy. Accuracy rates were almost 100% when both of the PLS-DA and SVM-GS were employed for classifying samples picked from southeast and northwest districts based on mid-level dataset. For samples collected from central of Yunnan where was divided into seven categories in this paper, the accuracy rates of training set and test set of PLS-DA and SVM-GS were preferable (>87%). Based on the mid-level data set, both of the classification results of PLS-DA and SVM-GS presented satisfying accuracy for 177 samples. Additionally, as small as possible parameters showed in mid-level data set, it suggested that this method was robust and generalized. Therefore, the comprehensive method was established for the origin traceability of wild P. Polyphylla Smith var. yunnanensis, which is meaningful for the quality control of herbal medicines. (C) 2018 Elsevier B.V. All rights reserved. C1 [Wu, Xue-Mei; Zhang, Qing-Zhi] Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China. [Wu, Xue-Mei; Zuo, Zhi-Tian; Wang, Yuan-Zhong] Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. C3 Yunnan University of Chinese Medicine; Yunnan Academy of Agricultural Sciences RP Zhang, QZ (corresponding author), Yunnan Univ Tradit Chinese Med, Coll Tradit Chinese Med, Kunming 650500, Yunnan, Peoples R China.; Wang, YZ (corresponding author), Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Yunnan, Peoples R China. EM ynkzqz@126.com; boletus@126.com CR Anjos O, 2015, FOOD CHEM, V169, P218, DOI 10.1016/j.foodchem.2014.07.138 Awonyemi I.O., 2015, J PHYTOMED, V1, P1 Bevilacqua M., 2013, NIR NEWS, V24, P12, DOI DOI 10.1255/NIRN.1355 Biancolillo A, 2014, ANAL CHIM ACTA, V820, P23, DOI 10.1016/j.aca.2014.02.024 Borras E, 2016, FOOD CHEM, V203, P314, DOI 10.1016/j.foodchem.2016.02.038 Borras E, 2015, ANAL CHIM ACTA, V891, P1, DOI 10.1016/j.aca.2015.04.042 Chen JB, 2014, SPECTROCHIM ACTA A, V128, P629, DOI 10.1016/j.saa.2014.03.010 Chen JB, 2017, SPECTROCHIM ACTA A, V182, P81, DOI 10.1016/j.saa.2017.03.070 De Bleye C, 2012, J PHARMACEUT BIOMED, V69, P125, DOI 10.1016/j.jpba.2012.02.003 Devos O, 2014, FOOD CHEM, V148, P124, DOI 10.1016/j.foodchem.2013.10.020 Ge LL, 2015, BIOMED REP, V3, P430, DOI 10.3892/br.2015.443 Hall DL, 1997, P IEEE, V85, P6, DOI 10.1109/5.554205 Huang, 2017, J LIGHT SCATT, V29, P79, DOI [DOI 10.13883/J.ISSN1004-5929.201701016, 10.13883/j.issn1004-5929.201701016] Huang CL, 2006, EXPERT SYST APPL, V31, P231, DOI 10.1016/j.eswa.2005.09.024 Jing SS, 2017, NAT PROD RES, V31, P660, DOI 10.1080/14786419.2016.1219861 Kang LP, 2017, J PHARMACEUT BIOMED, V142, P252, DOI 10.1016/j.jpba.2017.05.019 Karoui R, 2010, CHEM REV, V110, P6144, DOI 10.1021/cr100090k Li C, 2016, SPECTROCHIM ACTA A, V152, P391, DOI 10.1016/j.saa.2015.07.086 Li M., 2010, J AGROMETEOROL, V31, P442, DOI [10.3969/j.issn.1000-6362.2010.03.022, DOI 10.3969/J.ISSN.1000-6362.2010.03.022] Li Y, 2016, SPECTROCHIM ACTA A, V165, P61, DOI 10.1016/j.saa.2016.04.012 Li YR, 2016, SPECTROCHIM ACTA A, V157, P186, DOI 10.1016/j.saa.2016.01.001 Li Y, 2018, ANAL BIOANAL CHEM, V410, P91, DOI 10.1007/s00216-017-0692-0 Li Y, 2017, SPECTROCHIM ACTA A, V177, P20, DOI 10.1016/j.saa.2017.01.029 Man SL, 2010, J CHROMATOGR B, V878, P2943, DOI 10.1016/j.jchromb.2010.08.033 Marquez C, 2016, TALANTA, V161, P80, DOI 10.1016/j.talanta.2016.08.003 Perez-Enciso M, 2003, HUM GENET, V112, P581, DOI 10.1007/s00439-003-0921-9 Rajer-Kanduc K, 2003, CHEMOMETR INTELL LAB, V65, P221, DOI 10.1016/S0169-7439(02)00110-7 Saptoro A, 2012, CHEM PROD PROCESS MO, V7, DOI 10.1515/1934-2659.1645 SEKKAL M, 1995, J MOL STRUCT, V349, P349, DOI 10.1016/0022-2860(95)08781-P Shen S, 2018, INT J BIOL MACROMOL, V107, P1613, DOI 10.1016/j.ijbiomac.2017.10.026 Silvestri M, 2014, CHEMOMETR INTELL LAB, V137, P181, DOI 10.1016/j.chemolab.2014.06.012 Song Y, 2017, MITOCHONDRIAL DNA A, V28, P159, DOI 10.3109/19401736.2015.1115489 Sun S., 2003, 2 DIMENSIONAL CORREL, P26 Sun SQ, 2010, PLANTA MED, V76, P1987, DOI 10.1055/s-0030-1250520 Sun WJ, 2017, SPECTROCHIM ACTA A, V171, P72, DOI 10.1016/j.saa.2016.07.039 Tao Yun, 2013, Journal of Yunnan University - Natural Sciences Edition, V35, P652 The State Pharmacopoeia Commission, 2015, CHINESE PHARMACOPOEI, P260 Vera L, 2010, ANAL BIOANAL CHEM, V397, P3043, DOI 10.1007/s00216-010-3852-z Verma A., 2013, AM J ADV DRUG DELIV, V1, P770 Wang CW, 2016, MOLECULES, V21, DOI 10.3390/molecules21060727 Wang J, 2007, ANAL CHIM ACTA, V601, P156, DOI 10.1016/j.aca.2007.08.040 Wang Pei, 2015, J Pharm Anal, V5, P277, DOI 10.1016/j.jpha.2015.04.001 Wang P, 2015, SPECTROCHIM ACTA A, V137, P1403, DOI 10.1016/j.saa.2014.09.002 Wang X, 2015, SPECTROCHIM ACTA A, V141, P94, DOI 10.1016/j.saa.2015.01.053 Wang YZ, 2017, J PHARMACEUT BIOMED, V140, P20, DOI 10.1016/j.jpba.2017.03.024 Warren FJ, 2013, ANAL CHEM, V85, P3999, DOI 10.1021/ac303552s Wu X, 2013, CARBOHYD RES, V368, P1, DOI 10.1016/j.carres.2012.11.027 Wu Zhe, 2017, Zhongguo Zhong Yao Za Zhi, V42, P3403, DOI 10.19540/j.cnki.cjcmm.20170728.009 Wu Z, 2017, MOLECULES, V22, DOI 10.3390/molecules22071238 Wu Z, 2017, J NAT MED-TOKYO, V71, P139, DOI 10.1007/s11418-016-1043-8 Yan Q, 2013, J LIGHT SCATT, V25, P85 Yang H., 2014, ADV MAT RES, V926, P969, DOI DOI 10.4028/WWW.SCIENTIFIC.NET/AMR.926-930.969 Yang YG, 2018, ANAL LETT, V51, P1730, DOI 10.1080/00032719.2017.1385618 Yang YG, 2017, J NAT MED-TOKYO, V71, P148, DOI 10.1007/s11418-016-1044-7 Yao N, 2017, AM J CHINESE MED, V45, P575, DOI [10.1142/S0192415X17500343, 10.1142/s0192415x17500343] Yao S, 2018, J SCI FOOD AGR, V98, P2215, DOI 10.1002/jsfa.8707 Zhang JY, 2011, J ASIAN NAT PROD RES, V13, P670, DOI 10.1080/10286020.2011.578247 Zhang T, 2010, J PHARMACEUT BIOMED, V51, P114, DOI 10.1016/j.jpba.2009.08.020 Zhao J., 2012, CHINA J TRADIT CHIN, V8 Zhao YL, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0089100 Zimmermann B, 2013, APPL SPECTROSC, V67, P892, DOI 10.1366/12-06723 NR 61 TC 18 Z9 18 U1 6 U2 28 PD MAY PY 2018 VL 96 BP 125 EP 136 DI 10.1016/j.vibspec.2018.04.001 WC Chemistry, Analytical; Chemistry, Physical; Spectroscopy SC Chemistry; Spectroscopy UT WOS:000434749100017 DA 2022-12-14 ER PT J AU Liang, XP Shetty, S Tosh, DK Zhao, J Li, DY Liu, JH AF Liang, Xueping Shetty, Sachin Tosh, Deepak K. Zhao, Juan Li, Danyi Liu, Jihong TI A Reliable Data Provenance and Privacy Preservation Architecture for Business-Driven Cyber-Physical Systems Using Blockchain SO INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY DT Article DE Blockchain; Cyber Physical Systems; Privacy Preservation; Reliability; Survivability AB Cyber-physical systems (CPS) including power systems, transportation, industrial control systems, etc. support both advanced control and communications among system components. Frequent data operations could introduce random failures and malicious attacks or even bring down the whole system. The dependency on a central authority increases the risk of single point of failure. To establish an immutable data provenance scheme for CPS, the authors adopt blockchain and propose a decentralized architecture to assure data integrity. In business-driven CPS, end users are required to share their personal information with multiple third parties. To prevent data leakage and preserve user privacy, the authors isolate and feed different information retrieval requests using tokens specifically generated for each type of request. Providing both traceability of data operations, and unlinkability of end user activities, a robust blockchain-based CPS is prototyped. Evaluation indicates the architecture is capable of assured data provenance validation and user privacy preservation at a low overhead. C1 [Liang, Xueping; Li, Danyi; Liu, Jihong] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China. [Liang, Xueping] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China. [Liang, Xueping] Old Dominion Univ, Norfolk, VA 23529 USA. [Shetty, Sachin] Old Dominion Univ, Virginia Modeling Anal & Simulat Ctr, Norfolk, VA 23529 USA. [Shetty, Sachin] Old Dominion Univ, Ctr Cyber Secur Educ & Res, Dept Modeling Simulat & Visualizat Engn, Norfolk, VA 23529 USA. [Tosh, Deepak K.] Univ Texas El Paso, Dept Comp Sci, El Paso, TX 79968 USA. [Zhao, Juan] Tennessee State Univ, Nashville, TN 37203 USA. C3 Chinese Academy of Sciences; Institute of Information Engineering, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Old Dominion University; Old Dominion University; Old Dominion University; University of Texas System; University of Texas El Paso; Tennessee State University RP Liang, XP (corresponding author), Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.; Liang, XP (corresponding author), Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China.; Liang, XP (corresponding author), Old Dominion Univ, Norfolk, VA 23529 USA. CR [Anonymous], 2014, 151 ETH [Anonymous], 2007, DONT SWEAT YOUR PRIV Bestavros A, 2017, COMMUN ACM, V60, P37, DOI 10.1145/3029603 Brewster C, 2015, SEMANTIC BLOCKCHAINS Buldas A., 2014, IACR CRYPTOLOGY EPRI, V2014, P689 Buldas A, 2013, LECT NOTES COMPUT SC, V8208, P313, DOI 10.1007/978-3-642-41488-6_21 Cachin C., 2016, P WORKSH DISTR CRYPT Cramer R., 2000, ACM Transactions on Information and Systems Security, V3, P161, DOI 10.1145/357830.357847 Douligeris C, 2004, COMPUT NETW, V44, P643, DOI 10.1016/j.comnet.2003.10.003 English S.M., 2017, ARXIV170304206 ARXIV170304206 FIAT A, 1987, LECT NOTES COMPUT SC, V263, P186, DOI 10.1007/3-540-47721-7_12 Fotiou Nikos, 2016, 2016 IEEE Conference on Computer Communications: Workshops (INFOCOM WKSHPS), P415, DOI 10.1109/INFCOMW.2016.7562112 GOLDREICH O, 1994, J CRYPTOL, V7, P1, DOI 10.1007/BF00195207 Grid N. S., 2010, GUID Griffor E. R., 2017, NIST SP, V2 Kaku E, 2017, THESIS Kalogridis G., 2011, International Journal of Security and Networks, V6, P14, DOI 10.1504/IJSN.2011.039630 Khaitan SK, 2013, IEEE POW ENER SOC GE Liang XP, 2017, IEEE ACM INT SYMP, P468, DOI 10.1109/CCGRID.2017.8 Liu J, 2012, IEEE COMMUN SURV TUT, V14, P981, DOI 10.1109/SURV.2011.122111.00145 Nakamoto S., 2008, DECENTRALIZED BUS RE, P21260 Nicoletti B., 2018, AGILE PROCUREMENT, P189, DOI [10.1007/978-3-319-61085-6_8, DOI 10.1007/978-3-319-61085-6] Ramakrishna M. V., 1997, Database Systems for Advanced Applications '97. Proceedings of the Fifth International Conference, P215, DOI 10.1142/9789812819536_0023 Shirey RW, 2007, INTERNET SECURITY GL Szydlo M, 2004, LECT NOTES COMPUT SC, V3027, P541 Vaughan W., 2017, CHAINPOINT STANDARD Vaughan W., 2016, CHAINPOINT STANDARD Vaughan-Nichols, 2012, OWNCLOUD BUILD YOUR Yin S.-y., 2017, M2M SECURITY TECHNOL Yu S., 2010, P 5 ACM S INF COMP C, P261, DOI DOI 10.1145/1755688.1755720 Zyskind G., 2015, ARXIV150603471 NR 31 TC 3 Z9 3 U1 0 U2 25 PD OCT-DEC PY 2018 VL 12 IS 4 BP 68 EP 81 DI 10.4018/IJISP.2018100105 WC Computer Science, Software Engineering SC Computer Science UT WOS:000456123400005 DA 2022-12-14 ER PT J AU Markus, SB Aalhus, JL Janz, JAM Larsen, IL AF Markus, S. B. Aalhus, J. L. Janz, J. A. M. Larsen, I. L. TI A survey comparing meat quality attributes of beef from credence attribute-based production systems SO CANADIAN JOURNAL OF ANIMAL SCIENCE DT Article DE Beef; tenderness; credence attributes; branded beef; meat quality; production systems ID VITAMIN-D-3 SUPPLEMENTATION; UNITED-STATES; CONSUMER; PALATABILITY; CATTLE; STEERS; TENDERNESS; CARCASS; COLOR; PREFERENCES AB Markus, S. B., Aalhus, J. L., Janz, J. A. M. and Larsen, I. L. 2011. A survey comparing meat quality attributes of beef from credence attribute-based production systems. Can. J. Anim. Sci. 91: 283-294. Two branded beef programs based on producer-defined production systems differentiated by intangible credence attributes (Organic and Natural) were compared with Commodity beef to determine meat quality and assess consumer acceptability. In each of four slaughter seasons (winter, spring, summer and fall) longissimus lumborum muscle samples were collected from two industry slaughter plants; Organic n=30, 30, 27 and 31; Natural n = 30, 27, 29 and 25; Commodity 1 n = 12 and 18 for spring and summer, respectively; Commodity 2 a = 14 and 12 for spring and fall, respectively. Samples were vacuum packaged and aged for 16 +/- 2 d at 2 degrees C. Seasonal effects (P < 0.01) were evident for mean shear force, composition, drip loss, colour and pH. While all mean shear values were classified as being tender (< 5.6 kg), a smaller proportion of steaks were classified as tender in the Organic beef compared with the Natural and Commodity beef (55.9 vs. 70.3 and 78.6%; P < 0.01), indicating that even after industry normal ageing times there was higher tenderness variability in the Organic beef. Fat content (SEM = 0.23; P < 0.01) was lowest for the Organic line (3.98%) with Natural (5.34%) and Commodity being intermediate (5.73%). Some statistically significant differences (P < 0.05) in mean scores for aroma, juiciness, flavour, tenderness and overall acceptability of cooked beef steaks were observed amongst the three production systems when samples were not matched on the basis of intramuscular fat (IMF). Clearly there are measureable differences in quality between "credence" based production systems and commodity beef with an overall better quality in Commodity beef. However, if the consumer is willing to pay for credence-based attributes then there is an opportunity for these production systems to improve the quality of their product, specifically in respect to age at slaughter and content of IMF. C1 [Markus, S. B.] Alberta Agr & Rural Dev, Div Agr Res, Stettler, AB T0C 2L0, Canada. [Aalhus, J. L.; Larsen, I. L.] Agr & Agri Food Canada, Lacombe Res Ctr, Lacombe, AB T4L 1W1, Canada. [Janz, J. A. M.] Alberta Agr & Rural Dev, Div Food Proc, Leduc, AB T9E 7C5, Canada. C3 Agriculture & Agri Food Canada RP Markus, SB (corresponding author), Alberta Agr & Rural Dev, Div Agr Res, 4705-49 Ave, Stettler, AB T0C 2L0, Canada. EM susan.markus@gov.ab.ca CR Aalhus J., 2009, P 55 INT C MEAT SCI, P1058 Aalhus JL, 2004, CAN J ANIM SCI, V84, P631, DOI 10.4141/A03-106 ABERLE ED, 1981, J ANIM SCI, V52, P757, DOI 10.2527/jas1981.524757x *ASS OFF AN CHEM, 1995, 96039 OAOC ASS OFF A Association of Official Analytical Chemists, 1995, 99215 OAOC ASS OFF A, V39, P6 Becker T., 2000, British Food Journal, V102, P158, DOI 10.1108/00070700010371707 Bouton P E, 1978, Meat Sci, V2, P301, DOI 10.1016/0309-1740(78)90031-1 Brooks JC, 2000, J ANIM SCI, V78, P1852 *CAN FOOD INSP AG, 2009, ORG PROD Canadian Food Inspection Agency, 2009, GUID FOOD LAB ADV Carpenter CE, 2001, MEAT SCI, V57, P359, DOI 10.1016/S0309-1740(00)00111-X Chambaz A, 2003, MEAT SCI, V63, P491, DOI 10.1016/S0309-1740(02)00109-2 Ferguson DM, 2001, AUST J EXP AGR, V41, P879, DOI 10.1071/EA00022 FISHELL VK, 1985, J ANIM SCI, V61, P151, DOI 10.2527/jas1985.611151x Fox JT, 2008, FOODBORNE PATHOG DIS, V5, P559, DOI 10.1089/fpd.2008.0094 Glitsch K., 2000, British Food Journal, V102, P177, DOI 10.1108/00070700010332278 GRUNERT KG, 1995, FOOD QUAL PREFER, V6, P171, DOI 10.1016/0950-3293(95)00011-W HIDIROGLOU M, 1979, J DAIRY SCI, V62, P1076, DOI 10.3168/jds.S0022-0302(79)83377-9 HILL F, 1966, J FOOD SCI, V31, P161, DOI 10.1111/j.1365-2621.1966.tb00472.x Honeyman MS, 2006, J ANIM SCI, V84, P2269, DOI 10.2527/jas.2005-680 Jeremiah LE, 2003, MEAT SCI, V65, P985, DOI 10.1016/S0309-1740(02)00308-X Killinger KM, 2004, J ANIM SCI, V82, P3288 King DA, 2010, J ANIM SCI, V88, P1160, DOI 10.2527/jas.2009-2544 Koohmaraie M, 2006, MEAT SCI, V74, P34, DOI 10.1016/j.meatsci.2006.04.025 KOOHMARAIE M, 1994, MEAT SCI, V36, P93, DOI 10.1016/0309-1740(94)90036-1 LAWRIE RA, 1998, EATING QUALITY MEAT, P219 Mach N, 2008, MEAT SCI, V78, P232, DOI 10.1016/j.meatsci.2007.06.021 McKenna DR, 2002, J ANIM SCI, V80, P1212 Monson F, 2005, MEAT SCI, V71, P471, DOI 10.1016/j.meatsci.2005.04.026 Montgomery JL, 2002, J ANIM SCI, V80, P971 Muir PD, 1998, NEW ZEAL J AGR RES, V41, P623, DOI 10.1080/00288233.1998.9513346 NEWELL GJ, 1987, J FOOD SCI, V52, P1721, DOI 10.1111/j.1365-2621.1987.tb05913.x Okumura T, 2007, J ANIM SCI, V85, P1902, DOI 10.2527/jas.2006-752 Oliver MA, 2006, MEAT SCI, V74, P435, DOI 10.1016/j.meatsci.2006.03.010 Platter WJ, 2003, J ANIM SCI, V81, P984 Polkinghorne RJ, 2006, MEAT SCI, V74, P180, DOI 10.1016/j.meatsci.2006.05.001 Quail A., 1990, J MUSCLE FOODS, V1, P129, DOI DOI 10.1111/J.1745-4573.1990.TB00360.X Resurreccion AVA, 2004, MEAT SCI, V66, P11, DOI 10.1016/S0309-1740(03)00021-4 *SAS I INC, 2001, SAS US GUID STAT SAS Swanek SS, 1999, J ANIM SCI, V77, P874 Thompson J, 2002, MEAT SCI, V62, P295, DOI 10.1016/S0309-1740(02)00126-2 TROXEL TR, 2008, NATURAL ORGANIC BEEF Verbeke W, 2005, J FOOD PROD MARK, V11, P27, DOI 10.1300/J038v11n03_03 Verbeke W, 2010, MEAT SCI, V84, P284, DOI 10.1016/j.meatsci.2009.05.001 Wang YH, 2009, J ANIM SCI, V87, P119, DOI 10.2527/jas.2008-1082 Warner RD, 2010, MEAT SCI, V86, P171, DOI 10.1016/j.meatsci.2010.04.042 1992, CANADA GAZETTE 1125 NR 47 TC 9 Z9 9 U1 0 U2 14 PD JUN PY 2011 VL 91 IS 2 BP 283 EP 294 DI 10.4141/CJAS10082 WC Agriculture, Dairy & Animal Science SC Agriculture UT WOS:000291780200011 DA 2022-12-14 ER PT J AU Hou, DJ Guo, SM Huang, JW Wu, C Wu, JJ AF Hou, Dongjie Guo, Siming Huang, Jianwei Wu, Chong Wu, Jinjie TI Monte Carlo simulation of HPGe gamma-spectrometry systems SO JOURNAL OF ENGINEERING-JOE DT Article DE calibration; gamma-ray detection; gamma-ray spectroscopy; Monte Carlo methods; radioactive sources; radioisotopes; germanium radiation detectors; source distribution; Monte Carlo simulation; HPGe gamma-spectrometry systems; detection efficiency; high purity germanium gamma-spectrometry system; radioactive source; maximum absolute efficiency locates; radionuclides; dead layer; distance 15; 0 cm; distance 20; 0 cm; distance 25; 0 cm; distance 50; 0 cm ID ENERGY PEAK EFFICIENCY; DEAD-LAYER THICKNESS; DETECTOR; EDGE; CODE AB In order to obtain the relationship between energy and channel, the suitable radionuclides are chosen to calibrate. The result shows a goodness of fitting. The Monte Carlo simulation about the detection efficiency of the high purity germanium (HPGe) gamma-spectrometry system was performed. The distance between radioactive source and the end cap is set 15, 20, 25 and 50cm, respectively. The maximum absolute efficiency locates at around 80keV. It can be concluded that the absolute efficiency is declining accompany with the increasing of distance. In the end, at the distance of 25cm, the authors changed thickness of the dead layer and the source distribution to calculate. The maximum absolute efficiency is 8.56E-03, and the relative deviation is 0.0011. Then it was compared with the experimental result provided with the manufacturer. The result of simulation shows a certain agreement with the experimental results. This work will be helpful for calculating the efficiency of HPGe gamma-spectrometry. C1 [Hou, Dongjie; Wu, Chong] China Univ Petr, Beijing, Peoples R China. [Guo, Siming; Huang, Jianwei; Wu, Jinjie] Natl Inst Metrol, Beijing, Peoples R China. C3 China University of Petroleum; National Institute of Metrology China RP Wu, JJ (corresponding author), Natl Inst Metrol, Beijing, Peoples R China. EM wujj@nim.ac.cn CR Abbas MI, 2007, NUCL INSTRUM METH B, V256, P554, DOI 10.1016/j.nimb.2006.12.056 Abbas MI, 2006, J PHYS D APPL PHYS, V39, P3952, DOI 10.1088/0022-3727/39/18/005 BALSLEV I, 1966, PHYS REV, V143, P636, DOI 10.1103/PhysRev.143.636 Briesmeister J, 2003, MCNP GEN MONTE CARLO Brun R, 1997, NUCL INSTRUM METH A, V389, P81, DOI 10.1016/S0168-9002(97)00048-X Chham E, 2015, APPL RADIAT ISOTOPES, V95, P30, DOI 10.1016/j.apradiso.2014.09.007 Elanique A, 2012, APPL RADIAT ISOTOPES, V70, P538, DOI 10.1016/j.apradiso.2011.11.014 Ewa IOB, 2001, APPL RADIAT ISOTOPES, V55, P103, DOI 10.1016/S0969-8043(00)00366-3 Li CX, 1999, PHYS REV B, V59, P1571, DOI 10.1103/PhysRevB.59.1571 Maleka PP, 2005, NUCL INSTRUM METH A, V538, P631, DOI 10.1016/j.nima.2004.09.012 Huy NQ, 2011, NUCL INSTRUM METH A, V641, P101, DOI 10.1016/j.nima.2011.02.097 Ngo QH, 2010, NUCL INSTRUM METH A, V621, P390, DOI 10.1016/j.nima.2010.05.007 Querol A, 2015, RADIAT PHYS CHEM, V116, P219, DOI 10.1016/j.radphyschem.2015.01.027 Rodenas J, 2000, NUCL INSTRUM METH A, V450, P88, DOI 10.1016/S0168-9002(00)00253-9 Rutledge A.R., 1980, AECL6692 Yu-Ming D.I., 2008, AT ENERGY SCI TECHNO, V23, P806 NR 16 TC 0 Z9 0 U1 0 U2 2 PD DEC PY 2019 VL 2019 IS 23 BP 9064 EP 9068 DI 10.1049/joe.2018.9185 WC Engineering, Multidisciplinary SC Engineering UT WOS:000519744000122 DA 2022-12-14 ER PT J AU Bora, FD Donici, A Rusu, T Bunea, A Popescu, D Bunea, CI AF Bora, Florin D. Donici, Alina Rusu, Teodor Bunea, Andrea Popescu, Daniela Bunea, Claudiu I. TI Elemental Profile and Pb-207/Pb-206, Pb-208/Pb-206, Pb-204/Pb-206, Sr-87/Sr-86 Isotope Ratio as Fingerprints for Geographical Traceability of Romanian Wines SO NOTULAE BOTANICAE HORTI AGROBOTANICI CLUJ-NAPOCA DT Article DE geographic origin; isotope ratio; metal composition; wine fingerprint ID PATTERN-RECOGNITION TECHNIQUES; SOUTH-AFRICAN WINES; ICP-MS; MULTIELEMENT ANALYSIS; LEAD CONTAMINATION; VINEYARD SOILS; CLASSIFICATION; ORIGIN; DIFFERENTIATION; SPECTROMETRY AB Geographical wine traceability is an important topic in the context of wine authentification. Therefore, many researchers have addressed this subject by developing different methodologies based on multivariate analysis of organic and inorganic parameters and also by isotopic signature. The goal of this research was to assess the potential of elemental composition and isotopic signature of lead (Pb-207/Pb-206, Pb-208/(206)Pband Pb-204/Pb-206) and strontium (Sr-87/Sr-86)of wines from three Romanian vineyards, in order to highlight reliable markers for wine geographical origin. The ICP-MS method was used for the concentration determination for 30 elements (Ag, Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, V, U, Zn, Hg). In this study 10 wines (2 red and 8 white) obtained from 'Merlot', 'Feteasca neagra', 'Feteasca alba', 'Feteasca regala', 'Babeasca gri', '.arba', 'Aligote', 'Sauvignon blanc', 'Muscat Ottonel', 'Italian Riesling' cultivars were investigated. The wine samples were obtained from micro-wine production under conditions of 2014, 2015, 2016 from Dealu Bujorului, Murfatlar and.tefane.ti-Arge. vineyards. The high level of K (148.66 +/- 5.41-633.74 +/- 4.13 mg/L), Mg (88.23 +/- 0.84-131.66 +/- 3.42 mg/L), Ca (49.84 +/- 1.22-89.18 +/- 2.34) and Fe were observed in the wine samples analysed. Heavy metals like Hg, Pb, As and Cd (10.2-315 mu g/L) were found below acceptable limits. Concentration of Na (1 mg/L), Cu (1 mg/L), As (0.2 mg/L), Cd (0.01 mg/L), Zn (5 mg/L) and Pb (0.15 mg/L)metals in analysed wine samples were under Maximum Permissible Limits (MPL), respectively as published by the Organization of Vine and Wine. The variation of the Pb-207/Pb-206, Pb-208/Pb-206, Pb-204/Pb-206 and Sr-87/Sr-86 ratio and K/Rb, Ca/Sr of the investigated wine clearly demonstrated that these variables are suitable traces for wine geographical origin determination. The proposed methodology allowed a 100% successful classification of wines according to the region of provenance. C1 [Bora, Florin D.; Donici, Alina] Res Stn Viticulture & Enol Targu Bujor, Targu Bujor 805200, Romania. [Rusu, Teodor; Bunea, Andrea; Bunea, Claudiu I.] Univ Agr Sci & Vet Med, 3-5 Manastur Str, Cluj Napoca 400372, Romania. [Popescu, Daniela] SC Jidvei SRL, Dept Res & Dev, 45 Garii St, Jidvei 517385, Alba, Romania. C3 Research Institute for Viticulture & Oenology; University of Agricultural Sciences & Veterinary Medicine Cluj Napoca RP Bunea, CI (corresponding author), Univ Agr Sci & Vet Med, 3-5 Manastur Str, Cluj Napoca 400372, Romania. EM boraflorindumitru@gmail.com; donicialina79@gmail.com; rusuteodor23@yahoo.com; andrea_bunea@yahoo.com; hodordaniela@yahoo.com; claus_bunea@yahoo.com CR Alkis IM, 2014, J FOOD COMPOS ANAL, V33, P105, DOI 10.1016/j.jfca.2013.11.006 Almeida CMS, 2016, J BRAZIL CHEM SOC, V27, P1026, DOI 10.5935/0103-5053.20150358 Almeida CMR, 2003, J AGR FOOD CHEM, V51, P4788, DOI 10.1021/jf034145b Almeida CMR, 1999, J ANAL ATOM SPECTROM, V14, P1815, DOI 10.1039/a905426j Alvarez M, 2012, FOOD CHEM, V135, P309, DOI 10.1016/j.foodchem.2012.04.113 Alvarez-Iglesias P, 2012, SCI TOTAL ENVIRON, V437, P22, DOI 10.1016/j.scitotenv.2012.07.063 AULT WU, 1970, ENVIRON SCI TECHNOL, V4, P305, DOI 10.1021/es60039a001 Avram V, 2014, REV ROUM CHIM, V59, P1009 Barbaste M, 2002, J ANAL ATOM SPECTROM, V17, P135, DOI 10.1039/b109559p Barbaste M, 2001, TALANTA, V54, P307, DOI 10.1016/S0039-9140(00)00651-2 Barbaste M, 2001, THESIS Berglund M, 2011, PURE APPL CHEM, V83, P397, DOI 10.1351/PAC-REP-10-06-02 Bora FD, 2016, NOT BOT HORTI AGROBO, V44, P593, DOI 10.15835/nbha44210434 Capo RC, 1998, GEODERMA, V82, P197, DOI 10.1016/S0016-7061(97)00102-X Catarino S., 2014, C 37 WORLD C VIN WIN Catarino S, 2016, C 39 WORLD C VIN WIN Cheng HF, 2010, ENVIRON POLLUT, V158, P1134, DOI 10.1016/j.envpol.2009.12.028 Coetzee PP, 2014, FOOD CHEM, V164, P485, DOI 10.1016/j.foodchem.2014.05.027 Coetzee PP, 2005, J AGR FOOD CHEM, V53, P5060, DOI 10.1021/jf048268n Dehelean A, 2012, ROM J PHYS, V57, P1194 Di Paola-Naranjo RD, 2011, J AGR FOOD CHEM, V59, P7854, DOI 10.1021/jf2007419 Dordevic N, 2012, ANAL CHIM ACTA, V757, P19, DOI 10.1016/j.aca.2012.10.046 Dreyfus S, 2007, J ANAL ATOM SPECTROM, V22, P351, DOI 10.1039/b610803b Durante C, 2013, FOOD CHEM, V141, P2779, DOI 10.1016/j.foodchem.2013.05.108 Durdic S, 2017, RSC ADV, V7, P2151, DOI 10.1039/c6ra25105f Dutra SV, 2011, ANAL BIOANAL CHEM, V401, P1571, DOI 10.1007/s00216-011-5181-2 ELBAZPOULICHET F, 1984, NATURE, V308, P409, DOI 10.1038/308409a0 Ettler V, 2004, ANAL BIOANAL CHEM, V378, P311, DOI 10.1007/s00216-003-2229-y Fabani MP, 2010, FOOD CHEM, V119, P372, DOI 10.1016/j.foodchem.2009.05.085 Faure G., 1986, PRINCIPLES ISOTOPE G Fiket Z, 2011, FOOD CHEM, V126, P941, DOI 10.1016/j.foodchem.2010.11.091 Flegal AR, 1995, REV ENVIRON CONTA