Traceability in food processing: problems, methods, and performance evaluations-a review

• Published in: Critical reviews in food science and nutrition Vol. 62; no. 3; pp. 679 - 692
• Main Authors: Qian, Jianping, Dai, Bingye, Wang, Baogang, Zha, Yan, Song, Qian
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 2022; Taylor & Francis Ltd
• Subjects: Artificial Intelligence; Artificial intelligence (AI); batch mixing; Big Data; Blockchain; Cryptography; Deoxyribonucleic acid; DNA; Food; Food chains; Food consumption; Food Handling; Food processing; Food Supply; Humans; Mathematical models; Nutrition; Optimization; Performance evaluation; Processed foods; Raw materials; resource transformation; Statistical analysis; Supply chains; traceability; resource transformation; batch mixing; traceability; Artificial intelligence (AI); food processing
• ISSN: 1040-8398; 1549-7852
• DOI: 10.1080/10408398.2020.1825925

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.