https://doi.org/10.4081/jae.2022.1328 Artificial intelligence to boost traceability systems for fraud prevention in the meat industry
Published:30 December 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Traceability was introduced about twenty years ago to face the worldwide spread of food safety crises. Traceability data flow associated with each lot of food products during any production and/or delivery phases can also be used to guarantee product authenticity. For this purpose, it is necessary to protect the data from cyber intrusions and, at the same time, to guarantee the integrity of the bond between the physical product and the data. Price grading related to quality perceivable or credence attributes attracts criminals to attempt item substitution fraud. Improved track and trace technologies supported by artificial intelligence (AI) could highly enhance systems’ capability to detect authenticity violations by product substitution. This paper proposes an innovative method based on AI, to reinforce traceability systems in detecting possible counterfeiting by product substitution. It is an item-based mass balance method that analyses the congruity of the traceability data flows not by using explicit (even stochastic) rules but by exploiting the learning capabilities of a neural network. The system can then detect suspect information in a traceability data flow, alerting a possible profit-driven crime. The AI-based method was applied to a pork slaughtering and meat cutting chain case study.
Al-Hashedi K.G., Magalingam P. 2021. Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Comput. Sci. Rev. 40:100402. DOI: https://doi.org/10.1016/j.cosrev.2021.100402
Barge P., Biglia A., Comba L., Gay P., Ricauda Aimonino D., Tortia C. 2017. Temperature and position effect on readability of passive UHF RFID labels for beverage packaging. Chem. Engine. Trans. 58:169-74.
Barge P., Biglia A., Comba L., Gay P., Ricauda Aimonino D., Tortia C. 2019. The influence of food composition and tag orientation on UHF Rf IDentification. J. Food Eng. 246:242-52. DOI: https://doi.org/10.1016/j.jfoodeng.2018.11.014
Barge P., Biglia A., Comba L., Ricauda Aimonino D., Tortia C., Gay P. 2020. Radio Frequency IDentification for meat supply chain digitalisation. Sensors 20:4957. DOI: https://doi.org/10.3390/s20174957
Böhme K., Calo-Mata P., Barros-Velázquez J., Ortea I. 2019. Review of recent DNA-based methods for main food-authentication topics. J. Agric. Food Chem. 67:3854-64. DOI: https://doi.org/10.1021/acs.jafc.8b07016
Comba L., Belforte G., Gay P. 2011. Modeling techniques for the control of thermal exchanges in mixed continuous-discontinuous flow food plants. J. Food Eng. 106:177-87. DOI: https://doi.org/10.1016/j.jfoodeng.2011.04.015
Comba L., Belforte G., Dabbene F., Gay P. 2013. Methods for traceability in food production processes involving bulk products. Biosyst. Eng. 116:51-63. DOI: https://doi.org/10.1016/j.biosystemseng.2013.06.006
Dabbene F., Gay P., Tortia C. 2014. Traceability issues in food supply chain management: a review. Biosyst. Eng. 120:65-80. DOI: https://doi.org/10.1016/j.biosystemseng.2013.09.006
Dey S., Saha S., Singh A.K., McDonald-Maier K. 2021. FoodSQRBlock: Digitizing food production and the supply chain with blockchain and QR code in the cloud. Sustainability 13:3486. DOI: https://doi.org/10.3390/su13063486
Dupuy C., Botta-Genoulaz V., Guinet A. 2005. Batch dispersion model to optimize traceability in food industry. J. Food Eng. 70:333-9. DOI: https://doi.org/10.1016/j.jfoodeng.2004.05.074
El Sheikha A.F. 2021. 1 - Food authentication: Introduction, techniques, and prospects. Food Authent. Trace. 1-34. DOI: https://doi.org/10.1016/B978-0-12-821104-5.00006-4
Esteki M., Cardador M.J., Jurado-Campos N., Martín-Gómez A., Arce L., Simal-Gandara J. 2021. Chapter 8 - Innovations in analytical methods for food authenticity. Innov. Food Analysis 181-248. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00008-X
European Commission. 1996. Regulation (EC) No 1107/96 of 12 June 1996 on the registration of geographical indications and designations of origin under the procedure laid down in Article 17 of Council Regulation (EEC) No 2081/92. Official Journal L 148, 21/06/1996 P. 0001-0010.
European Commission. 2009. Regulation 1239/2009 of 15 December 2009 entering a name in the register of protected designations of origin and protected geographical indications Crudo di Cuneo (PDO). GU L 332, 17.12.2009, 50-51.
European Commission. 2014. Commission implementation decision of 24 January 2014 authorising methods for grading pig carcases in Italy, Official Journal of the European Union 28/1/2014, 57, 35-40.
European Union, December 2013. Regulation (EU) No 1308/2013 of the European Parliament and of the Council establishing a common organisation of the markets in agricultural products and repealing Council Regulations (EEC) No 922/72, (EEC) No 234/79, (EC) No 1037/2001 and (EC) No 1234/2007.
Galimberti A., De Mattia F., Losa A., Bruni I., Federici S., Casiraghi M., et al. 2013. DNA barcoding as a new tool for food traceability. Food Res. Int. 50:55-63. DOI: https://doi.org/10.1016/j.foodres.2012.09.036
Hellberg R., Everstine K., Sklare S. 2021. A Global threat with public health and economic consequences. Food Fraud. doi:10.1016/B978-0-12-817242-1.00021-X. DOI: https://doi.org/10.1016/B978-0-12-817242-1.00021-X
Jahanbakhshi A., Abbaspour-Gilandeh Y., Heidarbeigi K., Momeny M. 2021. A novel method based on machine vision system and deep learning to detect fraud in turmeric powder. Comput. Biol. Med. 136:104728. DOI: https://doi.org/10.1016/j.compbiomed.2021.104728
Karlsen K.M., Donnelly K.A.M., Olsen P. 2011. Granularity and its importance for traceability in a farmed salmon supply chain. J. Food Eng. 102:1-8. DOI: https://doi.org/10.1016/j.jfoodeng.2010.06.022
Kemény Zs., Ilie-Zudor E. 2016. 4 - Alphanumerical and optical coding systems for food traceability. Adv. Food Trace. Techn. Technol. 49-65. DOI: https://doi.org/10.1016/B978-0-08-100310-7.00004-1
Kendall H., Clark B., Rhymer C., Kuznesof S., Hajslova J., Tomaniova M. 2019. A systematic review of consumer perceptions of food fraud and authenticity: A European perspective. Trends Food Sci. Tech. 94:79-90. DOI: https://doi.org/10.1016/j.tifs.2019.10.005
MathWorks© 2021. Available from: https://it.mathworks.com/products/matlab.html
M.I.P.A.A.F. - Ministero delle Politiche Agricole e Alimentari della Repubblica Italiana 2010. Provvedimento 1 marzo 2010 Modifica del disciplinare di produzione della denominazione «Prosciutto di Parma» registrata in qualità di denominazione di origine protetta in forza al regolamento (CE) 1107 della Commissione del 12 giugno 1996. (10A02925). Gazzetta Ufficiale Serie Generale, 66 del 20-03-2010, 1-80.
Mol A.P.J., Oosterveer P. 2015. Certification of markets, markets of certificates: Tracing sustainability in global agro-food value chains. Sustainability 7:12258-78. DOI: https://doi.org/10.3390/su70912258
Niknejad N., Ismail W., Bahari M., Hendradi R., Salleh A.Z. 2021. Mapping the research trends on blockchain technology in food and agriculture industry: A bibliometric analysis. Environ. Technol. Innov. 21:101272. DOI: https://doi.org/10.1016/j.eti.2020.101272
Robson K., Dean M., Haughey S., Elliot C. 2021. A comprehensive review of food fraud terminologies and food fraud mitigation guides. Food Control 120:107516. DOI: https://doi.org/10.1016/j.foodcont.2020.107516
Soon J.M., Manning L. 2019. Developing anti-counterfeiting measures: The role of smart packaging. Food Res. Int. 123:135-43. DOI: https://doi.org/10.1016/j.foodres.2019.04.049
Spink J.W., Ortega D.L., Chen C., Wu F. 2017. Food fraud prevention shifts the food risk focus to vulnerability. Trends Food Sci. Tech. 62:215-20. DOI: https://doi.org/10.1016/j.tifs.2017.02.012
Spink J.W. 2019. The current state of food fraud prevention: overview and requirements to address ‘How to Start?’ and ‘How Much is Enough?’. Curr. Opin. Food Sci. 27:130-8. DOI: https://doi.org/10.1016/j.cofs.2019.06.001
Spink J.W., Chen W., Zhang G., Speier-Pero C. 2019. Introducing the Food Fraud Prevention Cycle (FFPC): A dynamic information management and strategic roadmap. Food Control 105:233-41. DOI: https://doi.org/10.1016/j.foodcont.2019.06.002
Vo S.A., Scanlan J., Turner P. 2020. An application of convolutional neural network to lobster grading in the southern rock lobster supply chain. Food Control 113:107184. DOI: https://doi.org/10.1016/j.foodcont.2020.107184
Wadood S.A., Boli G., Xiaowen Z., Hussain I., Yimin W. 2020. Recent development in the application of analytical techniques for the traceability and authenticity of food of plant origin. Microchem. J. 152:104295. DOI: https://doi.org/10.1016/j.microc.2019.104295
Wang J., Yue H., Zhou Z. 2017. An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control 79:363-70. DOI: https://doi.org/10.1016/j.foodcont.2017.04.013
Wilkinson S., Archibald A.L., Haley C.S., Megens H.J., Crooijmans R.P.M.A., Groenen M.A.M., 2012. Development of a genetic tool for product regulation in the diverse British pig breed market. BMC Genomics 13:580. DOI: https://doi.org/10.1186/1471-2164-13-580
Xu J.., Guo S, Xie D., Yan Y. 2020. Blockchain: A new safeguard for agri-foods. Artif. Intell. Agric. 4:153-61. DOI: https://doi.org/10.1016/j.aiia.2020.08.002
Young M., Himmelreich J., Honcharov D., Soundarajan S. 2021. The right tool for the job? Assessing the use of artificial intelligence for identifying administrative errors. DG.O 2021: The 22nd Annu. Int. Conf. Digital Govern. Res. 15-26. DOI: https://doi.org/10.1145/3463677.3463714
How to Cite
Biglia, A. (2022) “Artificial intelligence to boost traceability systems for fraud prevention in the meat industry”, Journal of Agricultural Engineering, 53(4). doi: 10.4081/jae.2022.1328.
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