Scientific Documentation
The Pathogens in Foods (PIF) Database is an open-access scientific resource designed to support food safety surveillance, evidence synthesis, and quantitative microbial risk assessment. The publications below describe the scientific foundations of the database, including its FAIR-compliant data architecture, systematic review methodology, harmonized data model, artificial intelligence capabilities, and strategic roadmap for future development.
Together, these publications provide a comprehensive reference for researchers, food safety authorities, risk assessors, and other stakeholders interested in understanding how PIF is developed, maintained, and applied to support evidence-based decision making in food safety.
Pathogens-in-Foods (PIF): An Open-Access European Database of Occurrence Data of Biological Hazards in Foods
This publication introduces the scientific foundations of the Pathogens in Foods (PIF) Database. It describes the complete data lifecycle, from protocol-driven systematic reviews and standardized data extraction to quality assurance and publication under the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). The paper also presents the PIF system architecture, harmonized data model, controlled vocabularies, and the novel CCC data quality framework (Consistency, Conformity, and Completeness) developed to ensure reliable and reusable occurrence data for food safety research.
Journal: Microbial Risk Analysis
A Retrieval-Augmented Natural Language Interface for Data Description and Meta-Analysis in the Pathogens-in-Foods (PIF) Database
This article presents the next generation of the PIF platform by integrating large language models with retrieval-augmented generation (RAG) and deterministic statistical analysis. The proposed interface enables users to query the database using natural language, automatically generate evidence summaries, and perform reproducible meta-analyses without requiring programming expertise. The study evaluates several open-source and proprietary language models, demonstrating reliable tool selection, accurate data retrieval, and grounded AI-assisted analytical workflows for food safety research.
Journal: Journal of Food Protection
Feasibility Study on the Pathogens-in-Foods Database
This European Food Safety Authority (EFSA) scientific report evaluates the long-term sustainability, adoption potential, and future evolution of the PIF Database. The study assesses technical, organizational, legal, and ethical aspects of the platform and proposes a strategic roadmap that includes the expansion of pathogen catalogues, artificial intelligence for data analytics, antimicrobial resistance (AMR) data integration, and the application of large language models to support systematic reviews and data verification.
Publisher: European Food Safety Authority (EFSA)
