FSO: Food Safety Monitoring Ontology

Food safety is more than ever dependent on data collected about food samples by means of both laboratory analysis and, more recently, non-destructive sensors. The latter emerging technologies for food safety monitoring show great promise for ensuring the quality and suitability of food. Being non-destructive and quantitative as well as rapid and automated, they generate large amounts of data. However, because this is a relatively young field and given the large variety of measurement methods, types of sensors and devices available, so far there exist no data standards to enable data sharing and interoperability between data collections. Therefore, much of this data remains inaccessible in separate laboratories and research institutions limiting the community's ability to develop comprehensive data driven predictive analysis and monitoring models. In the context of the Horizon 2020 DiTECT project, we have developed semantically enabled software systems for standardized data management to enable data sharing and data analytics by stakeholders in the food safety sector. In this paper, we present DiTECT food safety ontology for covering food safety analysis technologies and methods for noninvasive and rapid assessment of safety and authenticity of food products. The ontology maximally reuses well-known concepts from widely used reference ontologies related to the domains of measurement, agriculture, food and biochemical analysis. Our expectation is that our ontology will become a key, publicly accessible resource for enabling the use of data science in food safety sector.