Trends and Perspectives of AI-based technology in food safety and toxicity prediction

AI, or artificial intelligence, is a rapidly growing field in computer science, involving machine learning and deep learning. It has evolved through stages of knowledge-based rules, pattern design, and automation using deep learning. In the first stage, human experts define rules, while in the second stage, ML classifies and recognizes patterns. Artificial intelligence (AI) is transforming the food industry by enhancing microbial metabolic engineering, food safety, and toxicity, thereby reducing human intelligence requirements for tasks typically performed by humans. AI tools are increasingly crucial in food biotechnology sectors, including food microbiology, microbial fermentation, food safety and toxicity and understanding the relationship between food and the gut microbiome. This paper emphasizes the growing significance of AI in food safety and toxicity and emphasizes the use of AI and machine learning (ML) in food quality management, highlighting their numerous applications in assessing food toxicity. The detection of toxic compounds of both chemical and biological origin has been significantly enhanced in terms of speed and cost-effectiveness. AI tools enable rapid detection and classification of toxic compounds in large datasets, addressing food toxicity risks through chemical migration from package to food. AI-based food sorting and packaging like TOMRA and TensorFlow increase productivity by 90% through laser technology, IR spectroscopy, X-ray systems, defect detection, and natural language processing. The food industry uses optical and ultrasonic sensors to monitor food material removal, reducing consumer health risks and integrating data from sensors and IoT devices for real-time food safety monitoring.