How Data Can Help Us Anticipate Food Safety Risks

Imagine being able to explore how changing climate conditions could affect a food safety hazard in your region. That is the kind of question predictive research is working to address, with the aim of giving decision makers better evidence for planning.

Connecting information that is usually separate

Climate observations, crop information and laboratory results each describe part of a food safety problem. Bringing relevant datasets together can help researchers investigate how environmental conditions relate to the occurrence of particular hazards.

Predictive models use evidence and assumptions to estimate what may happen under specified conditions. Some use established biological relationships; others apply machine learning to identify patterns in data. EFSA's AI strategy identifies data integration and predictive modelling as areas with potential to support future food safety assessment.

A useful prediction needs a clear purpose. A food business may want evidence to help prioritise monitoring. A researcher may want to compare scenarios. A public authority may need to understand how uncertainty affects a longer term assessment. These questions require different outputs and different levels of detail.

Reliable tools need reliable evidence

Information collected in different places must be described consistently before it can be compared meaningfully. Knowing what was sampled, where and when it was collected, and how it was analysed matters. EFSA's work on data standardisation illustrates why agreed structures are important for sharing food safety information.

Model performance also needs to be assessed in the conditions where the tool will be used. EFSA's approach to AI emphasises transparency and human oversight. For users, this means being able to understand a model's purpose and limitations, rather than treating its output as a guarantee.

What AMBROSIA is working towards

AMBROSIA aims to develop a digital platform that integrates climate and food hazard data with predictive models and AI services. Its research includes selected mycotoxins in cereals and enteric pathogens in fresh produce, examined across four European biogeographical regions.

The intended value is to support more geographically relevant assessments and help stakeholders explore adaptation and mitigation options. These are project objectives under development, and their practical value will depend on validation and use in the relevant setting.

Stakeholders can contribute to the conversation by explaining which decisions need support. What information would change an action? How much advance notice would help? Which uncertainty needs to be made clear? The AMBROSIA Innovation Hub is designed to connect these practical perspectives with research and knowledge exchange.