Emerging Applications of Machine Learning in Food Safety

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 domainspecific 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.

Automated summary

Food safety remains a threat to public health, but machine learning shows potential in leveraging emerging data sets to enhance safety of the food supply, such as predicting antibiotic resistance, identifying pathogen sources, and detecting foodborne outbreaks. Although many of these applications are still in their infancy, both general and domain-specific pitfalls and challenges related to machine learning are being recognized and addressed.