AI, complexity and research-driven innovation in food safety

In the twenty-first century, food safety is a challenge requiring increasingly capable systems for detecting hazards that often remain invisible to sight, including microbial contamination, oxidative deterioration, small changes in composition, adulterations hidden by globalized and fragmented supply chains. Ensuring the quality and integrity of food today requires both regulatory compliance and real-time monitoring. In this complex environment, Artificial Intelligence (AI) can support us effectively through controls based on past problems and errors and by predicting risks in advance. Methodologically, this article relies on a narrative review of peer-reviewed scientific works, synthesizing broad international research as well as key studies conducted at the Signals and Images Laboratory of the National Research Council (CNR-ISTI). The analysis covers advancements in precision agriculture related to wheat quality, image classification and segmentation, visible and hyperspectral assessment of milk quality, and the potential of extending these methodologies to rice bran oil authenticity. The results describe five main points: 1) the significant contribution of AI to prediction, anomaly detection, and multimodal information interpretation; 2) how modern methodologies use computer vision with hyperspectral imaging, spectroscopy, and machine learning methods; 3) concrete cases demonstrating that computational systems can detect information invisible to sight and unaided human perception, though they remain dependent on domain-specific data and expert validation; 4) AI allows for the analysis of huge amounts of data that a human could not handle quickly, however, it should not be used as an autonomous solution, but as a decision support tool that only works when integrated into a well-organized system of laboratory tests, sensors, and clear rules; 5) the final decision about using AI for new problems, must be made by expert researchers and scientists who have to study it to ensure it is seeing and learning information correctly, and that are essential to validate the integrity of automatic results.