Application of Hyper Spectral Imaging for early detection of rachis browning in table grapes

It is often said that wine is a complex matrix and the chemical analysis of wine with the thousands of compounds detected and often measured is proof. New technologies can assist not only in separating and identifying wine compounds, but also in providing information about the sample as a whole. Information-rich techniques can offer a fingerprint of a sample (untargeted analysis), a comprehensive view of its chemical composition. Applying statistical analysis directly to the raw data can significantly reduce the number of compounds to be identified to the ones relevant to a particular scientific question. More data can equal more information, but also more noise for the subsequent statistical handling.

Must oxidation is a complex process involving multiple enzymatic transformations, including the oxidation of phenolics containing an ortho-diphenol function. The latter process has a primary influence on wine aroma characteristics and stability, due to the central role of ortho-diphenols in the non-enzymatic oxidative reactions taking place during winemaking and in finished wine. Although oxidation of must is traditionally avoided, in recent years its contribution to wine quality has been revisited, and in some cases improvements to wine aroma have been observed with the application of controlled must oxidation. Nowadays there is a great interest in the wine industry towards the identification of specific markers or patterns to characterize and classify the response of grape must to oxidation.

Introduction increasing sustainability is essential and a societal challenge, requiring fundamental changes in behaviour and attitudes. This applies to both producers and consumers. For the wine industry in particular, such a change is a major challenge. An eip-agri research project is evaluating the introduction of a returnable glass system in the german wine industry as a key solution for increasing sustainability. Given the need for change associated with a returnable system, the project is theoretically grounded in systems innovation, as this approach provides solutions for complex, transformative change.

Overarching surveys of QTL (Quantitative Trait Loci) studies in both model plants and staple crops have facilitated the access to information and boosted the impact of existing data on plant improvement activities. Today, the grapevine community is ready to take up the challenge of making the wealth of QTL information F.A.I.R.. To ensure that all valuable published data can be used more effectively, the myriad of identified QTLs have to be captured, standardised and stored in a dedicated public database.
As an outcome of the GRAPEDIA initiative, QTL-dedicated experts from around the world have gathered to compile the grapevine QTLome: the complete information (e.g., map positions, associated phenotypes) describing all experimentally supported QTLs for a specific trait.

In 2023–2024, the Republic of Moldova launched its first AI-powered wine pilot, integrating artificial intelligence into the vitivinicultural value chain.