Definition and strategy of intelligent foodomics for diagnosis and identification of hazards and pathogens in food-borne diseases

Abstract

Background

Foodborne diseases are defined as those caused by the ingestion of various toxins, hazardous substances and pathogens that enter the human body, usually with symptoms of infection or poisoning. Systematic screening of hazardous substances, toxins, pathogens is a prerequisite and foundation for preventing and controlling food risks and protecting consumers from foodborne illness. The introduction of Artificial Intelligence (AI) technology has facilitated the development of foodomics and provided new solutions for intelligent screening and analysis of hazards and pathogens in food and diagnosis of foodborne diseases. AI-driven foodomics is an emerging and promising strategy for food analysis that can greatly accelerate the screening of toxic and hazardous substances in food, especially the analysis and non-targeted screening of unknown or new toxicants.

Scope and methods

In this review, we have firstly defined the concept of intelligent foodomics from the perspective of artificial intelligence, proposed the feasible workflow of intelligent foodomics, and summarized the recent progress of intelligent foodomics in food hazards and pathogens screening studies (2014–2024) from different perspectives, and evaluate its advantages and disadvantages. Finally, we discussed the prospects and opportunities for the development of intelligent foodomics.

Key findings and conclusions

As a practicable paradigm, intelligent foodomics demonstrates a great application potential for screening and analysis of hazardous substances, toxins and pathogens in food, and for diagnosis of foodborne diseases. Intelligent foodomics promote to analyze toxic and hazardous substances, discover new unknown toxins, innovate high-throughput screening and risk assessment of food pathogens or hazardous substances, and diagnose and identify of foodborne diseases. In the future, the deep integration of intelligent foodomics with other methods will undoubtedly advance food safety analysis and foodborne disease diagnosis. This review provides useful reference for AI-driven foodomics strategies to further bridge the gap from laboratory-scale studies to translational food industry safety assessment and clinical diagnosis of foodborne diseases.