食品危害因子检测与安全风险预测研究进展  

Research Progress on the Detection of Food Hazard Factors and Safety Risk Prediction

作　　者：蒋华伟[1] 蒋谨优 张书龙 杨震 赵丽科 李冰奇 JIANG Huawei;JIANG Jinyou;ZHANG Shulong;YANG Zhen;ZHAO Like;LI Bingqi(College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China)

机构地区：[1]河南工业大学信息科学与工程学院,河南郑州450001

出　　处：《食品科学》2024年第15期360-373,共14页Food Science

基　　金：河南省科技研发计划联合基金项目(222103810078);河南省科技攻关项目(232102211010);河南省高等学校重点科研项目(22A520003);河南工业大学自然科学创新基金项目(2021ZKCJ18)。

摘　　要：食品危害因子检测与安全风险预测作为食品质量管理的重要环节,一直是学者高度关注的热点问题。为了今后更好地开展相关工作,本文首先回顾了常规食品危害因子检测方法的研究进展;同时介绍了如纳米酶、成簇的间隔短回文重复序列(clustered regularly interspaced short palindromic repeats,CRISPR)及其相关蛋白(CRISPRassociated protein,Cas)组成的CRISPR/Cas系统传感器、核磁共振等新型食品危害因子检测方法以及磁性共价有机骨架等新型材料在该领域的应用;然后对主观赋权评价、机器学习及深度学习等方法在食品安全风险预测中的应用进行了阐述;最后对各方法的优缺点作了对比分析,并探讨了未来的研究方向。As an important part of food quality management,the detection of food hazard factors and safety risk prediction have always been a research hotspot.In order to better carry out related work in the future,this article reviews research progress on conventional methods for the detection of food hazard factors,and introduces new methods for the detection of food hazard factors such as nanozymes,clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins(CRISPR/Cas) sensors and nuclear magnetic resonance(NMR) spectroscopy and the application of new materials such as magnetic covalent organic frameworks in this field.Then,it summarizes the application of subjective weighting,machine learning and deep learning in food safety risk prediction.Finally,the advantages and disadvantages of each method are analyzed,and future research directions are discussed.

关 键 词：食品安全 食品检测 纳米酶 核磁共振 风险预测 深度学习 

分 类 号：TS201.6[轻工技术与工程—食品科学]