In the European Union, official food safety monitoring data collected by member states are submitted to the European Food Safety Authority (EFSA) and published on Zenodo. This data includes 392 million analytical results derived from over 15.2 million samples covering more than 4,000 different types of food products, offering great opportunities for artificial intelligence to analyze trends, predict hazards, and support early warning systems. However, the current format with data distributed across approximately 1000 files totaling several hundred gigabytes hinders accessibility and analysis. To address this, we introduce the CompreHensive European Food Safety (CHEFS) database, which consolidates EFSA monitoring data on pesticide residues, veterinary medicinal product residues, and chemical contaminants into a unified and structured dataset. We describe the creation and structure of the CHEFS database and demonstrate its potential by analyzing trends in European food safety monitoring data from 2000 to 2024. Our analyses explore changes in monitoring activities, the most frequently tested products, which products were most often non-compliant and which contaminants were most often found, and differences across countries. These findings highlight the CHEFS database as both a centralized data source and a strategic tool for guiding food safety policy, research, and regulation.

Food fraud is a serious problem that may compromise the safety of the food products being sold on the market. Previous studies have shown that food fraud is associated with a large variety of food products and the fraud type may vary from deliberate changing of the food product (i.e. substitution, tampering, dilution etc.) to the manipulation of documents. It is therefore important that all actors within the food supply chain (food producers, authorities), have methodologies and tools available to detect fraudulent products at an early stage so that preventative measures can be taken. Several of such systems exist (i.e. iRASFF, EMA, HorizonScan, AAC-FF, MedISys-FF), but currently only MedISys-FF is publicly online available. In this study, we analyzed food fraud cases collected by MedISys-FF over a 6-year period (2015–2020) and show global trends and developments in food fraud activities. In the period investigated, the system has collected 4375 articles on food fraud incidents from 164 countries in 41 different languages. Fraud with meat and meat products were most frequently reported (27.7%), followed by milk and milk products (10.5%), cereal and bakery products (8.3%), and fish and fish products (7.7%). Most of the fraud was related to expiration date (58.3%) followed by tampering (22.2%) and mislabeling of country of origin (11.4%). Network analysis showed that the focus of the articles was on food products being frauded. The validity of MedISys-FF as an early warning system was demonstrated with COVID-19. The system has collected articles discussing potential food fraud risks due to the COVID-19 crisis. We therefore conclude that MedISys-FF is a very useful tool to detect early trends in food fraud and may be used by all actors in the food system to ensure safe, healthy, and authentic food.

The massive rise of Big Data generated from smartphones, social media, Internet of Things (IoT), and multimedia, has produced an overwhelming flow of data in either structured or unstructured format. Big Data technologies are being developed and implemented in the food supply chain that gather and analyse these data. Such technologies demand new approaches in data collection, storage, processing and knowledge extraction. In this article, an overview of the recent developments in Big Data applications in food safety are presented. This review shows that the use of Big Data in food safety remains in its infancy but it is influencing the entire food supply chain. Big Data analysis is used to provide predictive insights in several steps in the food supply chain, support supply chain actors in taking real time decisions, and design the monitoring and sampling strategies. Lastly, the main research challenges that require research efforts are introduced.