The Chemistry of Food Adulteration in the Age of Artificial Intelligence: A Comprehensive Review

The review presents principles behind food adulteration, the emergence of AI to revolutionize the detection systems, and the multi-level problems concerning the implementation of these technologies. We consider over 30 of the most common food adulterants, such as melamine in milk, Sudan dyes in spices, and diluted honey, describing their chemical properties and health risks. Detection methods using AI, such as spectroscopy with a machine-learning approach and blockchain-based traceability supported by IoT, have surpassed old chromatographic methods with detection accuracies of above 95%. But massive implementation costs along with data security and regulatory shortfalls have held back the deployment of these technologies. We postulate a multi-tier regulatory framework in which AI can be integrated into food safety practice and call for global coordination as an indispensable feature in combating food fraud.