The Universitat Autònoma de Barcelona (UAB) has initiated a specialized laboratory aimed at analyzing wine composition using spectrometry. This development is part of the TRACEWINDU project, a European initiative led by UAB to enhance wine traceability through blockchain technology. By assigning a unique smart label to each bottle, the system ensures transparent tracking of the wine’s journey from vineyard to consumer.
Leveraging Technology for Transparency and Fraud Prevention
Over the past four years, UAB has collaborated with an international consortium to establish a reliable traceability system in the wine industry. By employing blockchain-backed smart labels, the initiative offers detailed tracking of each bottle throughout the production and distribution chain. This technological approach aims to combat fraudulent activities that result in significant financial losses for the industry.
Consumers will benefit from transparent information on the wine’s origin and production process. Artificial intelligence (AI) is used to analyze data, thereby enhancing food safety and strengthening trust between producers and buyers. Through this digital registry, the wine industry is expected to experience a notable reduction in counterfeiting incidents.
Advanced Spectrometry for Precise Analysis
In March, UAB’s Chemistry Separation Techniques Group (GTS) installed a state-of-the-art spectrometer, inspired by technology used by ADERA-UT2A, a French project partner. This system allows researchers to conduct detailed compositional analyses of wines. By correlating sensory data, they generate a digital passport for each product. The corresponding information is accessible via a QR code embedded in the smart label, providing a transparent view of the wine’s lifecycle.
A newly developed analytical method enables a more cost-effective determination of wine composition. Unlike conventional methods that rely on expensive mass spectrometers for isotope analysis of elements like strontium and lead, the TRACEWINDU system uses routine mass spectrometry. By determining isotopes of up to 23 elements, the methodology delivers more robust and accurate results.
Building a Global Wine Database
A comprehensive database containing wine sample data from Europe and Argentina is under construction. This resource will form the foundation for blockchain-based traceability, benefiting both consumers and producers. Additionally, the smart label system grants producers insights into the geographical areas where their wines are consumed. Such data is invaluable for refining marketing strategies, optimizing production, and detecting fraudulent activities.
Promoting Sustainable Wine Production
The project also explores sustainable agricultural practices through the use of plant biostimulants. These substances aid vineyards in managing environmental stress caused by factors such as water scarcity, fungal attacks, or adverse weather conditions. Trials conducted in Europe, particularly in Montenegro, and in Argentina’s Mendoza region demonstrated that biostimulants improved grape yields without altering the wine’s sensory qualities.
Collaborative Efforts for Industry Advancement
TRACEWINDU, identified by reference number GA101007979, receives funding from the European Union under the Horizon 2020 Programme through Marie Skłodowska-Curie actions. Alongside UAB, the consortium comprises ten institutions across six countries, including Fundació Parc Tecnològic del Vi (Spain), INNOLABS SRL (Italy), Universita di Pisa (Italy), Université de Pau et des Pays de l’Adour (France), DNET LABS Doo Novi Sad (Serbia), Univerzitet Donja Gorica Podgorica (Montenegro), 13 Jul Plantaze ad Podgorica (Montenegro), ADERA SAS-UT2A (France), Instituto Nacional de Tecnología Industrial (Argentina), and Instituto Nacional de Tecnología Agropecuaria (Argentina).
This collaborative initiative aims to set a new benchmark in wine industry transparency and sustainability, ensuring that consumers receive authentic and verified products while providing producers with actionable insights and fraud detection capabilities.
