Early warning of mycotoxins in European cereal crops

GMP+ International is working together with Wageningen Food Safety Research, SGS, Cargill, Alltech and the Committee of Grain Traders to develop an early warning system for early detection of the presence of mycotoxins (natural toxins) in cereal grains.

Big data and machine learning

The project is entitled ‘Early warning of mycotoxins in European grain supply chain using machine learning and big data’ and focuses on cereals of European origin. The aim is the early prediction of – and control of – mycotoxin formation in cereal crops. Big data, machine learning and existing prediction models are being used to develop the early warning tool, and new prediction models for mycotoxins are also being developed. The system is intended for various stakeholders in the supply chain, such as traders, food and feed producers, government agencies, and farmers.

Cereals susceptible to contamination

Mycotoxins are produced by fungi that grow naturally in cereals. The cereal crops grown in many regions of Europe are susceptible to contamination by those fungi, and also to infection with mycotoxins. Consuming these cereals can cause health problems in both humans and animals. Because it is difficult to remove mycotoxins during food and feed processing, it is important to detect such fungal growth in time.

Early warning

The system under development allows for the early prediction of the presence of mycotoxins during the harvest. The system warns of the presence of high levels of mycotoxins and also offers recommendations for mycotoxin control, such as additional mycotoxin tests or isolating contaminated batches.

As such, the early warning tool can also contribute to reducing waste and unexpected costs.


The 4-year project started on April 1, 2023. In this Public Private Partnership (PPP), Wageningen Food Safety Research works together with SGS, Cargill, Alltech, GMP+ International and the Committee of Grain Traders. Wageningen Food Safety Research is the driving force in terms of content.