{"title":"How Can AI Help Improve Food Safety?","authors":"C Qian, S I Murphy, R H Orsi, M Wiedmann","doi":"10.1146/annurev-food-060721-013815","DOIUrl":null,"url":null,"abstract":"<p><p>With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning models, natural language processing, and computer vision to improve food safety. Possible AI applications are broad and include, but are not limited to, (<i>a</i>) food safety risk prediction and monitoring as well as food safety optimization throughout the supply chain, (<i>b</i>) improved public health systems (e.g., by providing early warning of outbreaks and source attribution), and (<i>c</i>) detection, identification, and characterization of foodborne pathogens. However, AI technologies in food safety lag behind in commercial development because of obstacles such as limited data sharing and limited collaborative research and development efforts. Future actions should be directed toward applying data privacy protection methods, improving data standardization, and developing a collaborative ecosystem to drive innovations in AI applications to food safety.</p>","PeriodicalId":8187,"journal":{"name":"Annual review of food science and technology","volume":"14 ","pages":"517-538"},"PeriodicalIF":10.6000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual review of food science and technology","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1146/annurev-food-060721-013815","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 5
Abstract
With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning models, natural language processing, and computer vision to improve food safety. Possible AI applications are broad and include, but are not limited to, (a) food safety risk prediction and monitoring as well as food safety optimization throughout the supply chain, (b) improved public health systems (e.g., by providing early warning of outbreaks and source attribution), and (c) detection, identification, and characterization of foodborne pathogens. However, AI technologies in food safety lag behind in commercial development because of obstacles such as limited data sharing and limited collaborative research and development efforts. Future actions should be directed toward applying data privacy protection methods, improving data standardization, and developing a collaborative ecosystem to drive innovations in AI applications to food safety.
期刊介绍:
Since 2010, the Annual Review of Food Science and Technology has been a key source for current developments in the multidisciplinary field. The covered topics span food microbiology, food-borne pathogens, and fermentation; food engineering, chemistry, biochemistry, rheology, and sensory properties; novel ingredients and nutrigenomics; emerging technologies in food processing and preservation; and applications of biotechnology and nanomaterials in food systems.