AI-driven methods have been applied across mycotoxin research in detection, prediction, and control, offering high laboratory accuracy ( > 90%), robust forecasting (75-99%), and emerging mitigation strategies (80-86%). Integrating AI with multi-omics data, mixture toxicity modeling, and standardized protocols promises to enhance food safety, streamline regulatory decision-making, and reduce animal testing.
Leveraging artificial intelligence for mycotoxin management in food systems / F. Caloni, T.H.. - In: NPJ SCIENCE OF FOOD. - ISSN 2396-8370. - 10:1(2026 Jul 30), pp. 291.1-291.5. [10.1038/s41538-026-01005-0]
Leveraging artificial intelligence for mycotoxin management in food systems
F. CaloniPrimo
;
2026
Abstract
AI-driven methods have been applied across mycotoxin research in detection, prediction, and control, offering high laboratory accuracy ( > 90%), robust forecasting (75-99%), and emerging mitigation strategies (80-86%). Integrating AI with multi-omics data, mixture toxicity modeling, and standardized protocols promises to enhance food safety, streamline regulatory decision-making, and reduce animal testing.
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