We see more and more that large volumes of data associated with and throughout the farm-to-table continuum can be used to inform food safety and public health.¹ From pathogen genomes to consumer reviews, innovative applications that use machine leaning to analyze these data are on the horizon. This article is meant to accompany a recent scholarly review on the topic² by providing a primer and synopsis for the general food safety audience.
Whether machine learning, or the more encompassing subject of artificial intelligence, may turn into a major boon or a disappointing bust for food safety is already a much-discussed topic, including here at Food Safety Magazine. In our effort to review emerging applications of machine learning in food safety, we tried to examine both potentials and pitfalls, hoping to contribute to constructive and cautious practices of these still-new approaches in food safety analytics.