Identification and biological properties of new resveratrol derivatives formed in red wine

Polyphenols are present in a wide variety of plants and foods such as tea, cacao and grape1. An important sub-class of these compounds is the flavanols present in grapes and wines as monomers (e.g (+)-catechin or (-)-epicatechin), or polymers also called condensed tannins or proanthocyanidins. They have important antioxidant properties2 but their biosynthesis remains partly unknown. Some recent studies have focused on the role of glycosylated intermediates that are involved in the transport of the monomers and may serve as precursors in the polymerization mechanism3, 4. The global objective of this work is to identify flavanol glycosides in grapes or wines, describe their structure and determine their abundance during grape development and in wine.

Protected Designation of Origin “Jumilla” (PDO Jumilla) is located in the Spanish provinces of Albacete and Murcia, in the South-eastern part of the Iberian Peninsula, where most of the models predict a severe impact of climate change in next decades. PDO Jumilla covers an area of 247,054 hectares, of which more than 22,000 hectares

Produced since 1998, the De Martino Single Vineyard Carmenère is the first Carmenère Icon wine of Chile. The grapes are coming form a plot of 11 ha in Isla de Maipo, where the technicians of the winery have developed knowledge of their work, resulting in 3 levels of quality of the grapes.

The growing demand for renewable energy and sustainable agricultural practices has highlighted the potential of agrivoltaics (Agri-PV) as a promising solution, particularly in the context of German viticulture.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.