Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general, due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies as well as a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.

翻译：真菌毒素是由特定真菌产生的有毒次级代谢产物，对全球食品安全和公共健康构成严重威胁。这些化合物可污染多种农作物，导致经济损失并对人类和动物健康造成风险。传统的真菌毒素检测实验室分析方法耗时较长，且未必适用于大规模筛查。然而，近年来，机器学习方法因其准确、及时的预测能力，在真菌毒素检测及食品安全领域的应用日益广泛。本文对近年来机器学习在多种食品原料中检测/预测真菌毒素存在情况的应用进行了系统性综述，重点分析了其优势、挑战及未来发展的潜力。我们强调通过开放数据和代码提高机器学习研究的可重复性与透明度。研究发现，多数研究在超参数报告方面存在明显不足，且缺乏开源代码，这引发了人们对所使用机器学习模型的可重复性和优化性的担忧。结果显示，尽管大多数研究主要采用神经网络进行真菌毒素检测，但所用神经网络架构类型存在显著差异，其中卷积神经网络最为常用。