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A methodological framework for the detection and prevention of fraudulent activities in the food chain

T he WATSON framework will improve sustainability of food chains by increasing food safety and reducing food fraud through systemic innovations that:
a) increase transparency in food supply chains b) equip authorities and policy makers with data, knowledge and insights, and c) raise consumers awareness on food safety and value.

WATSON relies upon emerging technologies (AI, IoT, DLT, etc.) enabling transparency within supply chains through the development of a rigorous, traceability regime, and novel tools for rapid, non-invasive, on-the-spot analysis of food products.

The results will be demonstrated in 6 pilots: a) prevention of counterfeit alcoholic beverages, b) preservation of the authenticity of PGI honey, c) on-site authenticity check and traceability of olive oil, d) the identification of possible manipulations at all stages of the meat chain, e) the improved traceability of high-value products in cereal and dairy chain, f) combat of salmon counterfeiting.

Funding Program:
Horizon Europe
GA No.:
EC Funding:
€ 9,744,008.25

A holistic framework with Anticounterfeit and intelligence-based technologies that will assist food chain stakeholders in rapidly identifying and preventing the spread of fraudulent practices

Our role in the project

Aiming to tackle the increasing number of food fraud cases, FSH leads the development of pilot demonstrators and validation campaigns towards the detection and mitigation of fraudulent practices.

In this context, FSH will design the process for the implementation of the pilot demonstrators which will test, evaluate, and verify the WATSON framework and its systemic innovations. Validation campaigns will be organised to involve stakeholders in the validation process. FSH will gather and analyse the outcomes and feedback received from the campaigns aiming at improving the initial methodology and to set and update relevant criteria for the WATSON solutions.

WATSON by numbers