Machine Learning Techniques for Detecting Mycotoxins in Food: A Comprehensive Review

Grunnleggende konsepter

Machine learning techniques, including neural networks, random forests, gradient boosting, and support vector machines, have emerged as powerful tools for the efficient and accurate detection of mycotoxins in various food products.

Sammendrag

This comprehensive review examines the application of machine learning (ML) techniques to the detection and prediction of mycotoxin contamination in food. The authors provide a systematic overview of the literature, focusing on the most commonly used ML algorithms, including neural networks (NNs), random forests (RFs), gradient boosting (GB), and support vector machines (SVMs).

The review begins by introducing the problem of mycotoxin contamination in food and the limitations of traditional detection methods. It then delves into the ML process, explaining key concepts such as training, validation, and test data, as well as common performance metrics used to evaluate the models.

The main body of the review is dedicated to discussing the application of each ML algorithm to different types of mycotoxin data, including spatio-temporal, spectral, and treatment-related data. For NNs, the authors highlight their use in analyzing hyperspectral imaging data and electronic nose data for mycotoxin detection. RFs are shown to be effective in classifying mycotoxin contamination levels using spectral data and predicting the impact of treatments on fungal growth and toxin production. GB models demonstrate strong predictive capabilities in forecasting aflatoxin and fumonisin contamination in crops based on spatio-temporal data.

The review also identifies critical areas that warrant further attention, such as the need for more detailed reporting on hyperparameter selection and the importance of ensuring reproducibility through open access to data and code. Overall, the review provides a comprehensive understanding of the current state of ML applications in mycotoxin detection and highlights the significant potential of these techniques to enhance food safety and quality control.

Statistikk

"An estimated 60–80% of the world's crop supply is contaminated by mycotoxins, and an estimated 20% of those crops surpass the legally mandated food safety thresholds set by the European Union (EU)."
"It is estimated that, between 2010 and 2019, approximately 75 million tonnes of wheat in Europe, which constitutes 5% of the wheat intended for human consumption, surpassed the maximum threshold for DON contamination, resulting in an economic loss of around €3 billion."
"Between 2010 and 2020, aflatoxins were responsible for the demotion of 4.2% of wheat intended for food, which potentially represented an additional economic loss of €2.5 billion."

Sitater

"Machine Learning (ML) approaches for both detection and prediction of the presence of mycotoxins have seen a rise in recent years as an alternative to traditional detection methods."
"ML methods can alleviate some of the current burdens of mycotoxin detection by providing an efficient and low-cost solution."
"With the impact of climate change, the need for these models to provide reliable predictions at the farm level is increasingly crucial, especially in terms of food safety and health."

Viktige innsikter hentet fra

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

by Alan Inglis,... klokken arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15387.pdf

Dypere Spørsmål

How can machine learning models be further improved to provide real-time, on-site mycotoxin detection capabilities for farmers and food producers?

Machine learning models can be enhanced to offer real-time, on-site mycotoxin detection by incorporating sensor technologies and IoT devices. By integrating these technologies, data can be collected continuously from the field, allowing for immediate analysis and detection of mycotoxin contamination. Additionally, the development of portable and user-friendly devices equipped with machine learning algorithms can enable farmers and food producers to perform on-site testing quickly and accurately. These devices can be designed to provide instant feedback on mycotoxin levels, enabling timely interventions to mitigate contamination risks. Furthermore, the implementation of cloud-based systems can facilitate the sharing of data and insights across different stakeholders in the food supply chain, enhancing collaboration and decision-making processes.

What are the potential ethical and privacy concerns associated with the widespread use of machine learning in food safety monitoring, and how can they be addressed?

The widespread use of machine learning in food safety monitoring raises several ethical and privacy concerns. One major issue is the protection of sensitive data, such as personal information and proprietary algorithms, from unauthorized access or misuse. To address these concerns, robust data encryption techniques and access controls should be implemented to safeguard data integrity and confidentiality. Additionally, transparency in the use of machine learning models is essential to ensure accountability and trust among stakeholders. Clear guidelines on data collection, storage, and sharing practices should be established to uphold ethical standards and compliance with regulations. Moreover, continuous monitoring and auditing of machine learning systems can help identify and mitigate potential biases or discriminatory outcomes, promoting fairness and equity in food safety monitoring processes.

Given the complex interactions between environmental factors, crop genetics, and mycotoxin production, how can machine learning be leveraged to gain a deeper understanding of the underlying biological mechanisms involved?

Machine learning can be leveraged to gain a deeper understanding of the complex interactions between environmental factors, crop genetics, and mycotoxin production by analyzing large and diverse datasets to identify patterns and correlations that may not be apparent through traditional methods. By applying machine learning algorithms such as neural networks and gradient boosting, researchers can uncover hidden relationships and predictive models that elucidate the underlying biological mechanisms driving mycotoxin contamination. Furthermore, the integration of multi-omics data, including genomics, transcriptomics, and metabolomics, can provide a comprehensive view of the molecular processes involved in mycotoxin production. Machine learning techniques can help integrate and analyze these complex datasets to identify key biomarkers, pathways, and regulatory networks associated with mycotoxin biosynthesis, offering valuable insights for targeted interventions and crop management strategies.