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.

从事机器学习方法在食品安全行业中，尤其是检测霉菌毒素和其他食品成分的存在方面的研究，探讨了近年来机器学习应用的优势、挑战和未来发展潜力，以及对数据和代码的公开获取有关的疑虑。研究结果显示，尽管多数研究主要利用神经网络进行霉菌毒素的检测，但在所采用的神经网络架构中存在着显著的多样性，其中卷积神经网络最为普遍。