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There are a large number of both commercial and in-house-written digital tools that attempt to classify and predict food safety risks based upon historic records in the EU Rapid Alert Service in Food and Feeds (RASFF) database.  With all such tools, it is important to remember that RASFF records are not a representative sample of either tests or results, and were never intended as a source of trends; the purpose of RASFF is rather to share specific individual alerts which may require regulatory action  on a cross-border basis.  Official tests are highly targeted, and often informed by previous RASFF alerts, so more alerts about a specific issue drives more official tests which drives more alerts (i.e. a feedback mechanism).  Also, RASFF only records the “positive” results, so there is no denominator; no indication of the number of “negative” results or the % incidence of an issue.  And finally, RASFF only records issues with a food safety concern so most food authenticity test results are excluded.

Despite these caveats, RASFF is still one of the most extensive and systematic public databases of food safety incidents and is likely to form the basis of many AI risk-prediction systems for years to come.

This paper (purchase required) evaluated the effectiveness of the Machine Learning models that sit behind such systems. The authors report that transformer-based models significantly outperform traditional machine learning methods, with RoBERTa achieving the highest classification accuracy. SHAP analysis highlights key hazards salmonella, aflatoxins, listeria and sulphites as primary factors in serious risk classification, while procedural attributes like certification status and temperature control are less impactful.

They conclude that despite improvements in accuracy, computational efficiency and scalability remain challenges for real-world deployment of AI risk-scoring and prediction systems.

Photo by Clarisse Croset on Unsplash

The European Commission has published its 2023 report on the Alert and Cooperation Network, which facilitates cooperation and information exchange between Member States on official controls in the agri-food chain. The report reveals a significant increase in notifications compared to 2022 – a sign of the growth in cooperation between Member States in this area.

The Alert and Cooperation Network is composed of four sub-networks, each with an individual focus.

The Rapid Alert System for Food and Feed (RASFF) facilitates the rapid exchange information between food safety authorities on health risks related to food, feed or food contact materials. In 2023, there was an 8% rise in RASFF notifications, with a total of 4695 notifications. As in previous years, the most reported issue in RASFF concerned pesticide residues, followed closely by pathogenic micro-organisms. The top notifying countries continued to be Germany, the Netherlands, and Belgium.

The Administrative Assistance and Cooperation component (AAC) allows Member States to notify violations of EU food safety legislation which do not constitute a health risk. In 2023, there was a 24% increase in AAC notifications, with 3166 notifications.

The majority of AAC notifications in 2023 were linked to non-compliant fruits and vegetables, again mainly due to pesticide residues, followed by cases of mislabelling, such as unauthorised health claims for food supplements.

The Agri-Food Fraud Network (FFN) registered a 26% rise in notifications, with 758 fraud suspicions. The illegal trade of cats and dogs remained a major issue, with 414 notifications. Other suspicions related to meat substitution, honey adulteration, and mislabelled olive oil. Additionally, 1075 AAC notifications and 1625 RASFF notifications were flagged as potential fraud, prompting deeper inspections or investigations by Member States.

In its first operational year, the Plant Health Network (PHN) generated 128 notifications, as Member States shared details about non-compliant consignments of plants, plant products, and other items (such as seeds, fruits, vegetables, wood, and flowers) and other plant health concerns.