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. 2025 Nov;41(4):529-540.
doi: 10.1007/s12550-025-00602-4. Epub 2025 Aug 8.

The use of artificial intelligence to improve mycotoxin management: a review

M Focker  1 , C Liu  1 , X Wang  1 , H J van der Fels-Klerx  2
Affiliations

The use of artificial intelligence to improve mycotoxin management: a review

M Focker et al. Mycotoxin Res. 2025 Nov.

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.

Keywords: Chemical analysis; Contaminant; Detection; Food safety; Machine learning; Predictive modelling.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram showing the selection process for this review
Fig. 2
Fig. 2
Number of papers retrieved during the time period 2009–2025 (n = 70)
See this image and copyright information in PMC

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