Chemical and sensory influences of the UV-C light of 254 nm in combination with the antioxidant substances in wine

The aim of this study was to evaluate the volatile and phenolic profiles of three red and one rosé wines stored in bottles for 30 months. Four wines were provided by a winery located in South Tyrol

La diffusion récente et “explosive” du Chardonnay dans pratiquement toutes les zones de culture viticole du monde a fait penser, à tort, que cette variété s’adapte facilement à toutes les conditions pédo-climatiques ou presque. Cette thèse a été confirmée par la grande faculté d’adaptation dont a fait preuve le vignoble et par la popularité dont jouit le vin auprès des consommateur du monde entier.

AIM: Volatile compounds joint to aromatic precursors form the aroma of grape must that will provide a characteristic aroma to the wine.

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.

Proteic haze is a problem which may occur in all fruit-based beverages and fermented juices (beer, cider, wine). When it occurs, the economic loss is important.