EU project: early warning of mycotoxins in grains using machine learning
Wageningen University Food Safety Research has embarked on a four-year project to create an early warning system for detecting mycotoxins in European cereal grains.
The project, named "Mycotoxin Early Alert in the European Grain Supply Chain through Machine Learning and Big Data," establishes a Public-Private Partnership by uniting scientific and nonprofit groups, government bodies, and prominent industry figures.
Mycotoxins, toxic compounds generated by plant fungi, pose a considerable risk to human and animal health when ingested via tainted crops. Studies have shown a growing unease regarding mycotoxins in European agriculture, prompting the need for immediate action to protect the grain supply chain's integrity.
The project's ambitious goal is to exploit big data, machine learning, and existing predictive models to create a pioneering early warning instrument. By focusing on cereals produced in Europe, the initiative aims to foresee and control mycotoxins formation in cereal crops during the early stages of the production process.
Stakeholders across the supply chain stand to benefit from this undertaking. The system's capacity to predict mycotoxin presence during harvest will be advantageous to traders, food and feed manufacturers, government agencies, and farmers alike. By alerting stakeholders about high mycotoxin levels, the system will encourage proactive measures, including additional testing or quarantine of contaminated lots.
Esteemed partners from both public and private sectors have joined forces in this project.Wageningen Food Safety Research is working alongside with industry leaders (including SGS, Cargill, Alltech, GMP+ International, and the Royal Dutch Grain and Feed Trade Association) to develop the tool. This collaborative effort will ensure a well-rounded strategy to address the mycotoxin issue, drawing on expertise from various fields.
The early warning system under development will merge big data analytics and machine learning methods, enhancing existing mycotoxin prediction models. By utilizing these advanced technologies, the project aims to modernize the grain sector's capacity to effectively identify and manage mycotoxin hazards.
The early warning system holds tremendous potential to safeguard the health and well-being of consumers, secure agricultural investments, and strengthen the overall resilience of the European grain supply chain.