Integrating AI in food contaminant analysis: Enhancing quality and environmental protection

• AI enhances food contaminant detection, improving accuracy and reducing human error. • Machine learning predicts contamination patterns, enabling proactive risk mitigation. • AI's real-time monitoring swiftly identifies contaminants, boosting consumer safety. • AI integration raises food safety, efficiency, and sustainability in the supply chain. This paper marks a groundbreaking step toward ensuring food safety by applying artificial intelligence (AI) in the detection of food contaminants. It argues that AI offers a significant advantage over traditional methods, addressing both food safety and environmental risk issues. We aim to make rapid, precise online analysis of chemical contaminants a reality. While traditional methods work well, they struggle with the demands for simplicity, large datasets and speed. In contrast, AI excels with its data manipulation and predictive analytics. This paper explores AI's applications and future perspectives in detecting, quantifying and reducing food contaminants, showcasing examples like machine learning, neural networks, and data mining techniques for identifying pests, heavy metals and mycotoxins. Additionally, AI-driven sensor technologies and spectroscopic methods are discussed for improving detection accuracy. AI's real-time detection capabilities can help prevent health crises and economic loss, while its predictive power supports sustainable agriculture by reducing the use of harmful chemicals.