Analysis of food safety based on machine learning: A comprehensive review and future prospects

Food safety challenges escalate with global population growth and complex supply chains. Traditional analytical methods, though precise, face limitations in speed and adaptability. Machine learning (ML) offers data-driven solutions, excelling in contaminant detection (pesticides, heavy metals, microbes), quality assessment via image/sensory analysis, and supply-chain traceability. Despite its potential, ML confronts challenges like data quality inconsistencies, model interpretability gaps, and a lack of standardized protocols. Future advancements hinge on optimizing algorithms for accuracy, enhancing transparency through explainable AI, integrating Internet of Things for real-time monitoring, and establishing regulatory frameworks. These innovations promise to transform food safety analysis, ensuring safer and traceable food globally.