Consumers are confronted with an increasingly complex decision-making process when it comes to food choices. The current trend is to provide consumers with more and more data, via printed labels or in digital form. This however only leads to more confusion, uncertainty and indifference. Instead, consumers need personalized and contextualized advice that is attractive and motivating. Validated knowledge should translate individual and contextual data into an advice that indicates which action is preferable. This advice must be given automatically and be based on scientific evidence.
Solution / approach
Although it would be possible to create a separate software solution for each application, we believe that a generic platform can be developed that provides a flexible and modular framework for
all kinds of similar applications. This allows us to adapt to changing
requirements and new insights in nutrition science. Scientifically, the
challenge is to set up a flexible platform that comprises two types of
reasoning. The first uses logics to infer a conclusion from some initial data.
It applies predicate logics for expressing for example food-health or
food-sustainability relations, but also other context relations. It reasons by
adding ‘triples’ in a knowledge base using modular algorithms. Triples are
subject-predicate-object expressions, such as ‘apple – is_a – fruit’, or
‘amount_of_fibre – has_value – 20_gr’, each element of the triple having its own URI (hyperlink). They express data and knowledge in the most flexible way as a connected graph, which is machine readable. The second approach provides estimations for missing or uncertain data and knowledge. We apply Bayesian Belief Networks for dealing with incomplete or inaccurate data, for example to estimate one’s individual food intake from the typical diet of a consumer segment.
The generic and flexible platform forms the basis for the Food Advice Demonstrator. This engine uses proven scientific knowledge about food-health relations (which most current food apps lack), consumer preference data, and food intake data to create personal advice. The first version of the Demonstrator has been realized. The system is now being applied and evaluated within the Personalized Nutrition and Health research program on the use case ‘stimulating a fibre-rich diet’.