Multimodal AI for Real‐Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation

Real-time assurance of food safety and quality requires decisions at line speed, from farm to retail, using signals that span vision, spectroscopy, volatiles, biosensing, and process telemetry. This review investigates and summarizes evidence on multimodal artificial intelligence that fuses such heterogeneous data to detect hazards, verify authenticity, and predict freshness within seconds. We outline sensing coverage along the chain, typical response times, and reported limits of detection, then detail data engineering practices that make disparate streams analysis-ready, including time synchronization, co-registration to ground truth, and robust sampling for multisite and multiseason generalization. We…