AIM To determine the effect of both ripeness and enzyme maceration on the astringency and bitterness perception of Cabernet Sauvignon winesRecent work has contributed to a more detailed understanding of the grape cell wall deconstruction process from ripening through crushing and fermentation, providing a better understanding of what role polysaccharides play in post-harvest fermentation of grapes(1,2). Current research on glycomics in red wine making suggest polysaccharides are important sensory impact molecules (3–6). METHODSOur experimental system harvests Cabernet Sauvignon grapes at three different ripeness levels and makes wine both with and without enzyme treatment.

Depuis le début des années 90, les vignerons des Caves Coopératives de l’Appellation “Côtes du Rhône” se sont penchés, au sein des structures de réflexion du Syndicat Général, sur deux axes de réflexion:
– Détermination des critères de sélection et de rémunération différenciée des apports de vendange;
– Organisation des sélections de vendanges sur la base des terroirs afin d’augmenter quantitativement le potentiel de sélection au sein de l’unité de production.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

The recovery of bioactive compounds from grape and wine by-products is currently an important objective for revaluation and sustainability. Grape pomace is one of the main by-products and is a rich source of some bioactive compounds. The aim of this study was to evaluate the polysaccharide (PS) composition of extracts obtained from pomaces of different white and red grape varieties of Castilla y León. Grape pomaces were obtained after the pressing in the winemaking process.