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.

Projektinformationen

WATSON

ID Finanzhilfevereinbarung: 101084265

DOI

10.3030/101084265

Projekt abgeschlossen

EK-Unterschriftsdatum 6 Oktober 2022

Startdatum 1 März 2023

Enddatum 28 Februar 2026

Finanziert unter

• Food, Bioeconomy Natural Resources, Agriculture and Environment

Gesamtkosten

€ 9 744 008,25

EU-Beitrag

€ 9 744 008,25

Koordiniert durch

UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Ireland

Periodic Reporting for period 1 - WATSON (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.)

Berichtszeitraum: 2023-03-01 bis 2024-08-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

The rise in food fraud incidents in recent years highlights a critical vulnerability in the world’s food supply chain that puts consumer trust, public health and business ethics at risk. An overall framework is required along with anticounterfeit and intelligence-based technologies to assist food chain stakeholders in rapidly identifying and preventing the spread of fraudulent practices. Watson is a large-scale 3-year project with 47 partners (40 Beneficiaries, 2 Affiliated Entities and 5 Associated Partners) from 20 countries of a distinctly multidisciplinary mix. It takes a full-chain systems-based approach, from producer to consumer. The overall objective of Watson is to provide a methodological framework combined with a set of tools and systems that can detect and prevent fraudulent activities throughout the whole food chain thus accelerating the deployment of transparency solutions in the EU food system. The project takes a holistic approach to understanding the underlying reasons for and drivers of food fraud, and how to contribute to its identification, prediction and prevention. The framework is tested in 6 pilots including the (1) prevention of counterfeit wine; (2) preservation of the authenticity of PGI honey; (3) on-site authenticity check and traceability of extra virgin olive oil; (4) identification of possible manipulations at all stages of the meat chain; (5) the improved traceability of high-value products in cereal and dairy chain and (6) combat of white fish counterfeiting. Watson aspires to improve sustainability of food chains by increasing food safety and reducing food fraud through systemic innovations that increase supply chain transparency by improved track-and-trace mechanisms, equip food safety authorities and policy makers with data, knowledge and tools, and raise consumers’ awareness on food safety.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

Kicked-off in March 2023, the 3-year Watson project has made solid progress towards establishing a framework that will provide the food chain stakeholders, regulatory authorities and research organisations with accurate information, intelligence-based risk calculation approaches and the provision of tools to detect and counter food fraud. The first 18 months focused on the identification of operational challenges faced by food chain stakeholders in relation to data availability, traceability, and transparency designing and developing the framework. The project has also analysed digital technology (i.e. Blockchain based platform, digital food product passport, early warning system, mobile applications, smart sensors, spectroscopy-based tools and scanning devices) that are currently used to detect and prevent food fraud. The project consolidated the causes and factors that contribute to food fraud, and connected each pilot food chain with the risk of fraud. This included the definition of fraud per chain, the investigation of food chain stakeholders’ attitudes and behaviours towards adulterated food, the identification of the fraud motivations and the quantification of the economic dimension of the food fraud problem. This formed the basis of the vulnerability assessment per chain. Feedback from stakeholders along the supply chain has been collected, identifying the main gaps in food chains and processes, which was used in the development of the Watson reference architecture, the methods and the tools to tackle fraud. Overall, the developed Watson architecture followed a layered as well as modular approach and is organised into three tiers including trustworthy data sources, the intelligence and application layer, and the user interface. The next phase of the project will focus on the implementation and demonstration of the Watson digital technology solutions through the 6 use cases.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Watson builds on state-of-the-art technologies and systems, and develops the Watson framework, a reference architecture for the implementation of viable food fraud detection and prevention systems. Th framework is intended to provide data for traceability or fraud detection, but also mechanisms to enable the flexible and interoperable communication of data input modules and Internet of Things (IoT) platforms using different technologies. Specifically, Watson integrated digital technology solution includes: a blockchain-based platform for trusted transactions in the food chain through information being shared among relevant stakeholders; an extended Electronic Product Code Information Services data model to facilitate interoperability among traceability systems and an IoT middleware platform to support end-to-end food traceability. A suite of food scanning tools being investigated and developed are diverse in scope, such as colour analysis, Portal Near-infrared Spectroscopy, Portal Hyperspectral imaging, Mass spectrometry imaging, and DNA analysis. Furthermore, the project develops an AI-supported early warning system for food safety authorities based on the processing of various data with connection to food fraud related databases; an on-site DNA based system for authentication and traceability of food products; smart tags integrated on packages embedding intelligence and monitoring capabilities; and robust multi-sensor food product scanning devices for food safety and authenticity based on AI-combined spectroscopy. The project further developed a food vulnerability assessment system covering several food chains, which identified different vulnerabilities and risks per chain, involving food fraud drivers, opportunities, enablers and overall investigating chain stakeholders’ attitudes towards food fraud.

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