Data analytics for food chains and consumer-oriented research

Agrifood research is rapidly adopting digitalized, data-driven methods. This requires for end-users to have access to data and further to be supported for decision making. This project aims to demonstrate how data sharing infrastructures developed using semantic technologies following FAIR principles can support decision making. There will be a use case to demonstrate how making information on food attributes and food safety available and accessible can help consumer’s decision making and how consumer’s acceptance can be used accordingly.

Over the past four years, in Project 1 of DDHT programme, a lot of knowledge have been developed by WFBR, WecR and WFSR. More specifically, WFBR developed on a linked data model using mainly NEVO products and make this data available through web-based APIs, called Personalized Dietary Advice (PDA) services. The linked data model consists information on nutrient composition, product category, taste, meal moment, sustainability, etc. developed a demonstrator which provides personalized dietary advice and feedback based on consumer’s daily intake, consumer profile and food product attributes into account. WEcR developed a Consumer Data Platform to easily create survey designs based on harmonised components. In the background this module has been set up in such a way it matches the structure of the WEcR data management solution. And WFSR, developed a knowledge graph model to store food fraud issues on which a dashboard is developed as a food fraud early warning system. The goal of this project for institutes individually is to develop further on their previous work and to work on a collective use case to demonstrate how these three infrastructures developed by each institute can be used to support decision making of consumers.