Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China.
Beijing Technology and Business University, Beijing, China.
Crit Rev Food Sci Nutr. 2022;62(3):679-692. doi: 10.1080/10408398.2020.1825925. Epub 2020 Oct 4.
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.
加工食品已成为人类食物链中不可或缺的一部分。它为人类健康提供了丰富的营养，并满足了各种其他食品消费需求。然而，与初级农产品相比，加工食品的可追溯系统建立面临着不同的挑战，因为其原材料种类繁多、批次混合以及资源转化。本文综述了加工食品可追溯性的研究进展。通过对食品供应链和加工阶段的分析，发现食品加工中的可追溯性问题源于资源经历的转化。文中总结了在食品加工中实施可追溯性的方法，包括不同批次之间的物理分离、定义和关联批次、同位素分析和 DNA 追踪、统计数据模型、内部可追溯性系统开发、人工智能 (AI) 和基于区块链的方法。通过召回效果、可追溯资源单位 (TRUs) 和综合粒度来评估可追溯性。不同方法各有优缺点。在考虑到食品加工的具体应用场景的情况下，应结合应用不同方法以提高粒度。另一方面，还提出了一些新的技术，包括使用人工智能优化批次混合、利用大数据进行质量预测以及使用区块链进行可信追溯，以提高食品加工中的可追溯性性能。
