Systematic review maps rapid growth of AI research in food safety, from pathogen detection to fraud screening
By
Hugo Francisco de Souza
Crisp on the outside, thoughtful on the inside. A keeper.
Summary
A systematic review of 161 peer-reviewed publications published in npj Science of Food maps the rapid growth of AI research in food safety. The research shows AI applications expanding from one study in 2012 to 46 in 2023, covering pathogen detection (e.g., Salmonella), chemical contamination prediction, food fraud screening, outbreak surveillance, and regulatory decision-making. Machine learning and deep learning are being studied to help food systems detect threats faster and more predictively. However, major barriers remain including data gaps, class imbalance, privacy limitations, and model interpretability challenges.
Key quotes
· 3 pulledFrom detecting Salmonella to flagging risky food suppliers, a new review shows how AI is moving food safety research toward faster, more predictive monitoring
The review analyzed how machine learning (ML) and deep learning are being studied for pathogen detection, chemical contamination prediction, food fraud screening, outbreak surveillance, and regulatory decision-making
Data gaps, class imbalance, privacy limits, and model interpretability remain major barriers
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