Designing a Food Matrix Ontology for Supporting a Predictive Microbiology Database

Cubero, S.; Perez-Rodriguez, F.; Carrasco, E.; Valero, A.; Filter, M.

Introduction: Predictive microbiology models are intended to represent microorganism behavior in food matrices and provide accurate mathematical predictions. In this field, food matrices are usually reported in unstandardized fashion, thus limiting its application and/or validation when models are required for specific food products. Purpose: The main objective was to design an ontology for food matrix to be applied to predictive microbiology. Methods: Food matrices from the Open Food Safety Model Repository (Open FSMR) were used in this project. An ontology prototype was generated considering ontologies available at Bioportal through Ontomaton. Then, the prototype was reviewed and refined using the software Protègè. Food matrices were categorized according to logical criteria based on its origin and composition, and in the case of foods containing multiple ingredients, allocating it in multiple categories. Results: More than 700 food matrices were mapped (i.e., cross-referenced with other ontologies at Bioportal) and categorized. Few matches were found when compared with existing ontologies at Bioportal. The main reason was due to differences in the type of food matrix used in biomedical science (Bioportal). Therefore, in this work, several categories had to be created from scratch. As an example, salads were categorized as “salad” within “group of vegetables”, provided that salads have a vegetable base in the recipe. Moreover, depending on the rest of the ingredients, it could be included at the same time into other food categories (i.e., multiple categorization). Significance: Further steps in this project will consist of integrating this ontology into PMMLab program so as to provide with a more accurate re-implementation and application of predictive microbiology models.


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Cubero, S. / Perez-Rodriguez, F. / Carrasco, E. / et al: Designing a Food Matrix Ontology for Supporting a Predictive Microbiology Database. 2016.


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