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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
Call:
HORIZON-CL6-2022-FARM2FORK-01-11
GA No.:
101084265
Duration:
2023-2026
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

FSH being the Leader of WP5 Dissemination/Communication activities, is responsible for the development of the Dissemination, Exploitation and Communication Plan as well as the standardized communication material and the deployment of relevant actions for approaching the existing network of actors. Moreover, FSH will develop 5 business model blueprints, 1 for each of the 5 themes selected by the project (food and agriculture, forestry and natural habitats, aquatic systems, bioenergy and biomaterials) and a sustainability plan for the project and it’s result (e.g. rural bioconomy network and BioRural toolkit).

WATSON by numbers

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