Center for Food Safety, University of Georgia, Griffin, Georgia 30223, USA; email:
Department of Mathematics and Statistics, Washington University, St. Louis, Missouri 63105, USA; email:
Annu Rev Food Sci Technol. 2021 Mar 25;12:513-538. doi: 10.1146/annurev-food-071720-024112. Epub 2021 Jan 20.
Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment. In this article, we provide a gentle introduction to machine learning in the context of food safety and an overview of recent developments and applications. With many of these applications still in their nascence, general and domain-specific pitfalls and challenges associated with machine learning have begun to be recognized and addressed, which are critical to prospective use and future deployment of large data sets and their associated machine learning models for food safety applications.
食品安全持续威胁公众健康。机器学习在利用大型新兴数据集提高食品安全和减轻食品安全事件影响方面具有潜力。食源性病原体基因组和新型数据流，包括文本、交易和贸易数据，已经通过机器学习方法实现了新的应用，例如抗生素耐药性预测、病原体来源归因以及食源性疾病爆发检测和风险评估。在本文中，我们将在食品安全的背景下对机器学习进行简要介绍，并概述其最新发展和应用。由于这些应用中的许多仍处于起步阶段，与机器学习相关的一般和特定于领域的陷阱和挑战已经开始被认识到并得到解决，这对于未来大规模数据集及其相关机器学习模型在食品安全应用中的预期使用和未来部署至关重要。
