文章基本信息

标题：Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk
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作者：Enguang Zuo ; Xusheng Du ; Alimjan Aysa 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2022
卷号：11
期号：14
DOI：10.3390/foods11142076
语种：English
出版社：MDPI Publishing
摘要：Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.
关键词：food safety risk early warning;anomaly detection;machine learning;detection data;auto-encoder