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

文献类型： 外文期刊

作者： Qian, Jianping 1 ; Dai, Bingye 2 ; Wang, Baogang 3 ; Zha, Yan 1 ; Song, Qian 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing AGRIRS, Beijing 100081, Peoples R China

2.Beijing Technol & Business Univ, Beijing, Peoples R China

3.Beijing Acad Forestry & Pomol Sci, Beijing, Peoples R China

关键词： Artificial intelligence (AI); batch mixing; food processing; resource transformation; traceability

期刊名称：CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION （ 影响因子：11.176； 五年影响因子：11.193 ）

ISSN： 1040-8398

年卷期：

页码：

收录情况： SCI

摘要： 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.

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