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.

Watson提供了一个方法论框架，结合了一套工具和系统，可以在整个食物链中检测和防止欺诈活动，从而加快欧盟食品系统透明度解决方案的部署。拟议的框架将通过以下系统性创新提高食物链的可持续性：(A)通过改进的跟踪和追踪机制提高食品供应链的透明度，这些机制在整个过程中包含关于食品的准确、与时间相关和不可篡改的信息；(B)使当局和政策制定者掌握数据、知识和见解，以便对食物链的情况有全面的了解；(C)提高消费者对食品安全和价值的认识，从而采用更健康的生活方式和开发可持续的食品生态系统。Watson实施了一种基于情报的风险计算方法，以全面解决食品欺诈现象。该项目包括三个不同的支柱，即：(A)查明食物链中的数据差距；(B)提供发现和打击食品欺诈的方法、程序和工具；(C)通过准确和可信的信息共享，有效地与公共当局进行跨界协作。Watson将依赖新兴技术(AI、IoT、DLT等)。通过开发严格的可追溯性制度和用于快速、非侵入性、现场分析食品的新工具，实现供应链内的透明度。结果将在6个使用案例中得到验证：a)防止假冒酒精饮料，b)保存PGI蜂蜜的真实性，c)现场真实性检查和橄榄油的可追溯性，d)识别肉类链所有阶段可能的操纵，e)改善谷物和乳制品链中高价值产品的可追溯性，f)打击三文鱼造假

WATSON provides a methodological framework combined with a set of tools and systemsthat can detect and prevent fraudulent activities throughout the whole food chain thus accelerating the deployment of transparency solutions in the EU food systems. The proposed 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 through improved track-and-trace mechanisms containing accurate, time-relevant and untampered information for the food product throughout its whole journey, b) equip authorities and policy makers with data, knowledge and insights in order to have the complete situational awareness of the food chain and c) raise the consumer awareness on food safety and value, leading to the adoption of healthier lifestyles and the development of sustainable food ecosystems. WATSON implements an intelligence-based risk calculation approach to address the phenomenon of food fraud in a holistic way. The project includes three distinct pillars, namely, a) the identification of data gaps in the food chain, b) the provision of methods, processes and tools to detect and counter food fraud and c) the effective cross border collaboration of public authorities through accurate and trustworthy information sharing. WATSON will rely 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 use cases: 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