Towards a Unique Data Model for Chemical and Microbiological Food Information

• Autor(es): Machado, Claudia
• Data: 2011
• Identificador Persistente: http://hdl.handle.net/10400.18/442
• Origem: Repositório Científico do Instituto Nacional de Saúde
• Assunto(s): Segurança Alimentar; Modelo de Dados de Informação Alimentar Química e Microbiológica
• Tipo de Documento: Documento de conferência
• Idioma: Inglês

Descrição

Introduction: The integration of food data from research, monitoring, control, epidemiology and other sources is crucial to improve food safety and public health. Consequently, INSA launched the Portuguese Food Information Resource Programme (PortFIR) in a partnership with GS1 Portugal CODIPOR to create national food expert networks and sustainable databases on food composition, consumption and chemical and microbiological contamination. Presently, the PortFIR data model is being developed. Existing data models on food information usually refer to either chemical substances or microorganisms. However, for food safety, particularly for risk-benefit evaluation, a unique data model to compile chemical and microbiological food information (CMFI) would be a huge step forward, regarding data standardization and optimization of resources. Purpose: The aim of this work is to explore the possibility of creating a unique data model for the compilation, management and use of CMFI compatible with EFSA’s chemical and microbiological calls for data and for nutrition applications, namely EuroFIR European Food Composition Data Bank. Method: The work was developed in two steps: 1) identification of existing relevant Data Models; 2) comparison and listing of all required attributes. Results: The data models identified as relevant were EFSA’s Standard Sample Description for Food and Feed, for chemical contaminants, Zoonoses Data Collection for microbiological contaminants and foodborne outbreaks and the CEN/TC 387 prEN_16104 Food Data – Data structure. The reasons to choose these references were the need to report data to EFSA and to update national data in the EuroFIR Food Composition Databank whose structure formed the basis of CEN/TC 387 work. All attributes in each model were listed and correspondence among models was cross-referenced. Conclusions: This work was the first stage in the development of a unique data model for CMFI. The biggest advantage of such a data model is the ability to store all the information needed in a single database whose compatibility allows to exchange and to share information with national and international partners, contributing to improve food safety at a global level.