Machine Learning Applied to the Detection of Mycotoxin in Food: A Review

TL;DR

This review discusses recent machine learning techniques, especially neural networks, used for detecting mycotoxins in food, emphasizing their advantages, challenges, and the importance of reproducibility and transparency in research.

Contribution

It provides a systematic overview of ML applications in mycotoxin detection, highlighting current practices, gaps in reporting, and future research directions.

Findings

Neural networks are the most commonly used ML models for mycotoxin detection.

Convolutional neural networks are the most popular neural network architecture.

Many studies lack detailed hyperparameter reporting and open source code, affecting reproducibility.

Abstract

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.