摘要
肉品营养丰富,极易受到致病菌、腐败菌等有害微生物的污染,其食用安全性备受关注。近年来,机器学习方法在食品安全领域内得到了充分应用。本文分别从肉品中有害微生物的检测和预测建模2个角度对机器学习方法的应用进行综述,分析了现阶段机器学习方法的不足,并对其在肉类微生物安全中的发展前景进行展望。
Meat is nutritious and highly susceptible to contamination by foodborne pathogens,spoilage bacteria and other harmful microorganisms,so that its food safety is of great concern.In recent years,machine learning methods have been extensively employed in the field of food safety.In this paper,we review the application of machine learning methods from two perspectives:detection and predictive modeling of harmful microorganisms in meat.This review also analyzes the limitations of machine learning methods at the present stage,and provides an outlook on their prospects in meat microbial safety.
作者
林梓杰
董庆利
LIN Zijie;DONG Qingli(School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《肉类研究》
2022年第11期36-41,I0015,共7页
Meat Research
基金
国家重点研发计划政府间国际科技创新合作重点专项(2019YFE0103800)。
关键词
肉品
微生物检测
食源性致病菌
预测微生物学
机器学习
meat
microorganism detection
foodborne pathogen
predictive microbiology
machine learning
