Focker M, Liu C, Wang X, van der Fels-Klerx H J
Wageningen Food Safety Research, Akkermaalsbos 2, 6708WB, Wageningen, the Netherlands.
Mycotoxin Res. 2025 Aug 8. doi: 10.1007/s12550-025-00602-4.
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.
随着人们对霉菌毒素的认识不断加深、影响霉菌毒素产生的因素不断变化以及技术的持续发展，供应链中霉菌毒素污染的管理也在不断演进。技术发展之一是在霉菌毒素管理中使用人工智能（AI）的潜力。AI可用于霉菌毒素管理的各个领域，包括霉菌毒素的预测建模以及分析检测和分析。本综述旨在研究AI在霉菌毒素管理中的应用现状。本综述重点关注：（1）收获前和收获后阶段商品中霉菌毒素存在情况的预测模型；（2）通过处理由样品的成像数据或化学分析产生的大型数据集来检测样品中的霉菌毒素。进行了系统综述，共得到70篇相关参考文献，其中15篇聚焦于霉菌毒素预测模型，54篇聚焦于霉菌毒素检测，涵盖从成像到化学分析等方面，并包括相关综述。文中介绍了AI的应用以及最流行的AI算法。如本综述所示，AI能够在收获前和收获后阶段改进霉菌毒素预测模型，并使成像检测或电子鼻等非侵入性快速检测方法的出现成为可能。AI模型在实际应用中的适用性和可扩展性仍然是一个重大挑战。
