深度学习在食品安全检测与风险预警中的应用  

Application of Deep Learning in Food Safety Detection and Risk Early Warning

作　　者：丁浩晗 王龙 侯浩钶 谢祯奇 韩瑜 崔晓晖 DING Haohan;WANG Long;HOU Haoke;XIE Zhenqi;HAN Yu;CUI Xiaohui(Science Center for Future Foods,Jiangnan University,Wuxi 214122,China;School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China;School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China)

机构地区：[1]江南大学未来食品科学中心,江苏无锡214122 [2]江南大学人工智能与计算机学院,江苏无锡214122 [3]武汉大学国家网络安全学院,湖北武汉430072

出　　处：《食品科学》2025年第6期295-308,共14页Food Science

基　　金：“十四五”国家重点研发计划重点专项(2024YFE0199500,2022YFF1101100);中央高校基本科研业务费专项(JUSRP123053)。

摘　　要：深度学习技术在食品安全检测与风险预警中的应用日益广泛,为提升食品安全、质量控制和真实性鉴别提供了新的机遇。本文首先介绍深度学习的基本概念及其在食品安全领域的发展现状,探讨卷积神经网络、递归神经网络、Transformer架构与图神经网络等技术在食品安全检测与风险预测中的应用。尽管深度学习在提升食品安全检测效率和准确性方面表现出色,但其实际应用仍面临数据质量差、隐私保护能力弱和模型缺乏可解释性等挑战。针对这些问题,本文分析其可能带来的风险,并提出解决思路,如推动数据标准化、加强隐私保护、推动人工智能相关政策的制定等。未来,深度学习与物联网和区块链技术的结合、生成式人工智能的进一步发展,这些都将推动食品行业的数字化转型,实现全程可追溯的食品安全监控。The application of deep learning in food safety detection and risk early warning is becoming more and more extensive,thus providing new opportunities for improving food safety,quality control and authenticity identification.This paper first introduces the basic concept of deep learning and its current development in the field of food safety,and discusses the application of convolutional neural network(CNN),recursive neural network(RNN),transformer architecture and graph neural network(GNN)in food safety detection and risk prediction.Although deep learning performs well in improving the efficiency and accuracy of food safety detection,its practical application still faces challenges such as poor data quality,weak privacy protection capacity and lack of model interpretability.Next,this paper analyzes potential risks that could be brought about by these problems and proposes possible solutions such as promoting data standardization,strengthening privacy protection,and promoting the formulation of policies regarding artificial intelligence.In the future,the combination of deep learning with the Internet of Things(IoT)and blockchain technology and further development of generative artificial intelligence will promote the digital transformation of the food industry and enable the whole-process traceability of food safety monitoring.

关 键 词：食品安全 深度学习 食品检测 风险预警 

分 类 号：TS207.3[轻工技术与工程—食品科学]