Artificial intelligence (AI) in food safety and quality New approach, advantages, and disadvantages
Main Article Content
Keywords
Abstract
The adoption of artificial intelligence (AI) within the framework of fourth industrial revolution (Industry 4.0) is reshaping contemporary food safety management by enabling a transition from predominantly reactive prac-tices to proactive and predictive approaches. This study explores how AI-based technologies, including machine learning, computer vision, and Internet of Things (IoT) technologies, contribute to improved food safety control, quality monitoring, and supply chain traceability. The analysis indicates that AI-driven solutions outperform traditional manual methods by delivering faster and more accurate detection of contaminants, improved identification of foodborne pathogens, and more reliable shelf-life prediction. The integration of AI with blockchain further strengthens traceability mechanisms, allowing rapid identification and containment of contamination events. Nevertheless, several limitations remain, notably the limited interpretability of complex deep-learning models, substantial implementation costs, and persistent challenges related to data quality and standardization. In addition, ethical issues, such as data protection and potential algorithmic bias, highlight the importance of transparent governance frameworks. The findings suggest that optimal outcomes are achieved when AI systems operate within a human-in-the-loop model, supported by interdisciplinary expertise and harmonized global datasets. Collectively, these advancements indicate that AI has strong potential to enhance the resilience, efficiency, and transparency of the global food supply chain, supporting progress toward a zero-contamination objective.
References
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Sharman, N., Wallace, C.A. and Jespersen, L. 2020. Terminology and the understanding of culture, climate, and behavioural change—impact of organisational and human factors on food safety management. Trends Food Sci Technol. 96:13–20. https://doi.org/10.1016/j.tifs.2019.12.005
Singh, D. 2025. Harnessing artificial intelligence to safeguard food quality and safety. J Food Prot. 88:1-15, Article 100621. https://doi.org/10.1016/j.jfp.2025.100621
Tajkarimi, M. 2020. Food safety and quality data management using artificial intelligence. Food Prot Trends. 40:464–467.
Theodore Armand, T.P., Nfor, K.A., Kim, J.I. and Kim, H.C. 2024. Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review. Nutrients. 16(7):1-24, 1073. https://doi.org/10.3390/nu16071073
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Alexander, C.S., Smith, A. and Ivanek, R. 2023. Safer not to know? Shaping liability law and policy to incentivize adoption of predictive AI technologies in the food system. Front AI. 6:1-8, Article 1298604. https://doi.org/10.3389/frai.2023.1298604
Ambikalekshmi, A.K., Pushparaj, P., Cherian, E., Rajan, R. and Mohan, L. 2025. Advancing food safety through artificial intelligence: a detailed review of monitoring and detection technologies. Food Safety Health. 4(1):82-90. https://doi.org/10.1002/fsh3.70050
Arslan, S. 2025. Artificial intelligence in food safety and nutrition practices: opportunities and risks. Acad Nutrit Dietet. 2(3):1-11. https://doi.org/10.20935/AcadNutr7904
Balta, I., Lemon, J., Pet, I., McCleery, D., Douglas, A., Stef, L., Iancu, T., Popescu, C.A. and Corcionivoschi, N. 2025. Food safety—the transition to artificial intelligence (AI) modus operandi. Trends Food Sci Technol. 165:1-13, Article 105278. https://doi.org/10.1016/j.tifs.2025.105278
Bedoya, M.G., Montoya, D.R., Tabilo-Munizaga, G., Pérez-Won, M. and Lemus-Mondaca, R. 2022. Promising perspectives on novel protein food sources combining artificial intelligence and 3D food printing for food industry. Trends Food Sci Technol. 128:38–52. https://doi.org/10.1016/j.tifs.2022.05.013
Bronzwaer, S., Kass, G., Robinson, T., Tarazona, J., Verhagen, H., Verloo, D., Vrbos, D. and Hugas, M. 2019. Food safety regulatory research needs 2030. EFSA J. 17(7):e170622.
De Baerdemaeker, J., Hemming, S., Polder, G., Chauhan, A., Petropoulou, A., Rovira-Más, F., Moshou, D., Wyseure, G., Norton, T. Nicolai, B., Hennig-Possenti, F., Hostens, I. 2023. Artificial Intelligence in the Agri-Food Sector: Applications, Risks and Impacts. EPRS study PE 734.711. Panel for the Future of Science and Technology (STOA). European Parliamentary Research Service (EPRS). ISBN: 978-92-848-0190-9. https://doi.org/10.2861/516636 | QA-09-23-064-EN-N
Demiral, Y.M., Karahan, S., Uca Karacabey, S. and Özkul, E. 2025. AI-powered digital solutions in food safety: the GastroBlue Example. Int J Food Syst Dynam. 17(1):1-13. https://doi.org/10.1163/18696945-bja00024
Dhal, S.B. and Kar, D. 2025. Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review. Discov Appl Sci. 7(75):1-46. https://doi.org/10.1007/s42452-025-06472-w
Dimitrakopoulou, M.E. and Garre, A. 2025. AI’s intelligence for improving food safety: only as strong as the data that feeds it. Curr Food Sci Technol Rep. 3:1-13, Article 15. https://doi.org/10.1007/s43555-025-00060-0
Ding, H., Tian, J., Yu, W., Wilson, D.I., Young, B.R., Cui, X., Xin, X., Wang, Z. and Li, W. 2023. The application of artificial intelligence and big data in the food industry. Foods. 12:1-29, Article 4511. https://doi.org/10.3390/foods12244511
Dlamini, B.C. and Adetunji, A.I. 2023. Hazard Analysis and Critical Control Point (HACCP) and Food Safety Management Systems. In: Ijabadeniyi, O.A., Folake Olagunju, O. (Eds.) Food Safety and Toxicology: Present and Future Perspectives. De Gruyter, Berlin, Germany, Chap. 11, pp 243–262.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P. and Vayena, E. 2018. AI4 people—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Machines. 28(4):689–707. https://doi.org/10.1007/s11023-018-9482-5
Friedlander, A. and Zoellner, C. 2020. Artificial intelligence opportunities to improve food safety at retail. Food Prot Trends. 40:272–278.
Grylls, B. 2024. Global food recall trends 2024. https://www.food manufacture.co.uk/Article/2024/08/08/Global-food-recall-trends-2024. (Accessed: 12 November 2024).
Gong, W., Yao, H.B., Chen, T., Xu, Y., Fang, Y., Zhang, H.Y., Li, B.W. and Hu, J.N. 2023. Smartphone platform based on gelatin methacryloyl (GelMA) combined with deep learning models for real-time monitoring of food freshness. Talanta. 253:1-9,124057. https://doi.org/10.1016/j.talanta.2022.124057
Ijaiya, H. and Odumuwagun, O.O. 2024. Advancing artificial intelligence and safeguarding data privacy: a comparative study of EU and US regulatory frameworks amid emerging cyber threats. Int J Res Pub Rev. 5:3357–3375.
Ikram, A., Mehmood, H., Arshad, M.T., Rasheed, A., Noreen, S. and Gnedeka, K.T. 2024. Applications of artificial intelligence (AI) in managing food quality and ensuring global food security. CyTA J Food. 22(1):1-15, Article 2393287. https://doi.org/10.1080/19476337.2024.2393287
Kahneman, D., Sibony, O. and Sunstein, C.R. 2021. Noise: A Flaw in Human Judgment. Little, Brown and Company, New York, NY.
Kakani, V., Nguyen, V.H., Kumar, B.P., Kim, H. and Pasupuleti, V.R. 2020. A critical review on computer vision and artificial intelligence in food industry. J Agri Food Res. 2:1-12, Article 100033. https://doi.org/10.1016/j.jafr.2020.100033
Kamath, R. 2018. Food Traceability on blockchain: Walmart’s pork and mango pilots with IBM. J Br Blockchain Ass. 1:47-53, Article 3712. https://doi.org/10.31585/jbba-1-1-(10)2018
Kowalski, T. 2025. Integration of IoT and artificial intelligence for automated food hazard monitoring. J Biotechnol Food Sci. 2(1):27–32. https://doi.org/ https://doi.org/10.61784/jbfs3006
Kudashkina, K., Corradini, M.G., Thirunathan, P., Yada, R.Y. and Fraser, E.D.G. 2022. Artificial intelligence technology in food safety: a behavioral approach. Trends Food Sci Technol. 123:376–381. https://doi.org/10.1016/j.tifs.2022.03.021
Leligou, H.C., Lakka, A., Karkazis, P.A., Costa, J.P., Tordera, E.M., Santos, H.M.D. and Romero, A.A. 2024. Cybersecurity in supply chain systems: the farm-to-fork use case. Electronics. 13(1):1-15, 215. https://doi.org/10.3390/electronics13010215
Maharana, A., Cai, K., Hellerstein, J., Hswen, Y., Munsell, M., Staneva, V., Verma, M., Vint, C., Wijaya, D. and Nsoesie, E.O. 2019. Detecting reports of unsafe foods in consumer product reviews. JAMIA Open. 2:330–338. https://doi.org/10.1093/jamiaopen/ooz030
Mavani, N.R., Ali, J.M., Othman, S., Hussain, M.A., Hashim, H. and Rahman, N.A. 2022. Application of artificial intelligence in food industry—a guideline. Food Eng Rev. 14:134–175. https://doi.org/10.1007/s12393-021-09290-z
Nazir, A., Ochani, S., Nazir, A., Fatima, B., Ochani, K., Hasibuzzaman, M.A., Ullah, K. 2023. Rising trends of foodborne illnesses in the US: short communication. Ann Med Surg. 85(5):2280–2281. https://doi.org/10.1097/MS9.0000000000000630
Nikzadfar, M., Rashvand, M., Zhang, H., Shenfield, A., Genovese, F., Altieri, G., Matera, A., Tornese, I., Laveglia, S., Paterna, G., Lovallo, C. and Di Renzo, G.C. 2024. Hyperspectral imaging aiding artificial intelligence: a reliable approach for food qualification and safety. Appl Sci. 14(21):1-33, 9821.
Palakurti, N.R. 2022. AI applications in food safety and quality control. ESP J Eng Technol Adv. 2:48–61. https://doi.org/10.56472/25832646/JETA-V2I3P111
Qian, C., Murphy, S.I., Orsi, R.H. and Wiedmann, M. 2023. How can AI help improve food safety? Ann Rev Food Sci Technol. 14:517–538. https://doi.org/10.1146/annurev-food-060721-013815
Onyeaka, H., Akinsemolu, A., Miri, T., Nnaji, N.D., Emeka, C., Tamasiga, P., Pang, G. and Al-sharify, Z. 2024. Advancing food security: the role of machine learning in pathogen detection. Appl Food Res. 4(2):1-13, 100532. https://doi.org/10.1016/j.afres.2024.100532
Rashvand, M., Ren, Y., Sun, D.W., Senge, J., Krupitzer, C., Fadiji, T., Miró, M.S., Shenfield, A., Watson, N.J. and Zhang, H. 2025. Artificial intelligence for prediction of shelf-life of various food products: recent advances and ongoing challenges. Trends Food Sci Technol. 159:1-44, Article 104989. https://doi.org/10.1016/j.tifs.2025.104989
Santoni de Sio, F. and van den Hoven, J. 2018. Meaningful human control over autonomous systems: a philosophical account. Front Robot AI. 5(15):1-14. https://doi.org/10.3389/frobt.2018.00015
Sari, O.F., Amer, E., Bader-El-Den, M., Ince, V. and Leadley, C. 2025. AI-driven risk assessment in food safety using EU RASFF database. Food Bioproc Technol. 18:6282–6303 https://doi.org/10.1007/s11947-025-03819-4
Sartoni, M., Semercioz Oduncuoglu, A.S., Guidi, A., Annosi, M.C. and Luning, P.A. 2025. Towards digitalisation of food safety management systems—enablers and constraints. Food Control. 168:1-13, Article 110952. https://doi.org/10.1016/j.foodcont.2024.110952
Sharman, N., Wallace, C.A. and Jespersen, L. 2020. Terminology and the understanding of culture, climate, and behavioural change—impact of organisational and human factors on food safety management. Trends Food Sci Technol. 96:13–20. https://doi.org/10.1016/j.tifs.2019.12.005
Singh, D. 2025. Harnessing artificial intelligence to safeguard food quality and safety. J Food Prot. 88:1-15, Article 100621. https://doi.org/10.1016/j.jfp.2025.100621
Tajkarimi, M. 2020. Food safety and quality data management using artificial intelligence. Food Prot Trends. 40:464–467.
Theodore Armand, T.P., Nfor, K.A., Kim, J.I. and Kim, H.C. 2024. Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review. Nutrients. 16(7):1-24, 1073. https://doi.org/10.3390/nu16071073
Tripathi, A.D., Mishra, R., Maurya, K.K., Singh, R.B. and Wilson, D.W. 2019. Estimates for world population and global food availability for global health. In: Reddy, P.G. (Ed.), The Role of Functional Food Security in Global Health. Academic Press, Amsterdam, the Netherlands, pp. 3–24. https://doi.org/10.1016/B978-0-12-813148-0.00001-3
van Meer, F., van der Velden, B. and Takeuchi, M. 2025. Artificial Intelligence for Food Safety – A Literature Synthesis, Real-World Applications and Regulatory Frameworks. FAO, Rome, Italy. https://doi.org/10.4060/cd7242en
Wang, K., Mirosa, M., Hou, Y. and Bremer, P. 2025. Advancing food safety behavior with AI: innovations and opportunities in the food manufacturing sector. Trends Food Sci Technol. 161:1-12, Article 105050. https://doi.org/10.1016/j.tifs.2025.105050
World Health Organization (WHO). 2015. WHO Estimates of the Global Burden of Foodborne Diseases. WHO, Geneva, Switzerland.
Yin, B., Tan, G., Muhammad, R., Liu, J. and Bi, J. 2025. AI‐powered innovations in food safety from farm to fork. Foods. 14:1-38, Article 1973. https://doi.org/10.3390/foods14111973
Zhang, Q., Lu, Z., Liu, Z., Li, J., Chang, M. and Zuo, M. 2025. Application of machine learning in food safety risk assessment. Foods. 14:1-31, Article 4005. https://doi.org/10.3390/foods14234005
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Apr 18, 2026
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Uzunov, R., & Angeleska, A. (2026). Artificial intelligence (AI) in food safety and quality: New approach, advantages, and disadvantages. Italian Journal of Food Science, 38(2), 148–158. https://doi.org/10.15586/ijfs.v38i2.3525
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Uzunov, R., & Angeleska, A. (2026). Artificial intelligence (AI) in food safety and quality: New approach, advantages, and disadvantages. Italian Journal of Food Science, 38(2), 148–158. https://doi.org/10.15586/ijfs.v38i2.3525