2025/08/08 by M. Focker, C. Liu, X. Wang +1 · 18 citations
paper · doi:10.1007/s12550-025-00602-4
published in Mycotoxin Research 41(4), 529-540 (Springer Science and Business Media LLC)
crossref issued 2025/08/08 · crossref published 2025/08/08 · crossref published-online 2025/08/08 · crossref created 2025/08/08 · crossref published-print 2025/11/01 · crossref deposited 2025/11/12 · crossref indexed 2026/08/05
Abstract The management of mycotoxin contamination in the supply chain is continuously evolving in response to growing knowledge about mycotoxins, shifting factors that influence mycotoxin occurrence, and ongoing technological developments. One of the technological developments is the potential for using artificial intelligence (AI) in mycotoxin management. AI can be used in various fields of mycotoxin management, including for predictive modelling of mycotoxins and for analytical detection and analyses. This review aimed to investigate the state-of-the-art of the use of AI for mycotoxin management. This review focuses on (1) predictive models for the presence of mycotoxins in commodities at both pre-harvest and post-harvest levels and (2) the detection of mycotoxins in samples by processing large datasets resulting from imaging data or chemical analyses of the sample. A systematic review was conducted, resulting in a total of 70 relevant references, including 15 references focusing on mycotoxin prediction models and 54 references focusing on mycotoxin detection, ranging from imaging to chemical analysis, and including relevant reviews. The AI applications and the most popular AI algorithms are presented. As shown by this review, AI is able to improve mycotoxin prediction models both at pre- and post-harvest levels and makes the emergence of non-invasive and fast detection methods such as imaging detection or electronic noses possible. A major challenge remains in the applicability and scalability of AI models to practical settings.