  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.
