The effect of ultrasounds in syrah wine quality is not dependent on the ripening or sanitary status of the grapes

Aromatic characterization is a key issue to enhance wines knowledge. While several studies argue the importance of wine expertise in the ability of performing odor-related sensory tasks, there is still little attention paid to the influence of expertise on the semantic representation of wine odors.

Nowadays, the rapid growth of vineyards with organic practices and the use of copper as the only fun-gicide against downy mildew raises again the question of the effect of copper on varietal thiols in wine, especially 3-sulfanylhexan-1-ol (3SH) and its acetate (3SHA). A few decades ago, several works indicated that the use of copper in the vineyard had a negative effect on the content of varietal thiols in Sauvignon blanc wines [1, 2]. However, these studies only considered the concentration of the reduced form (RSH) of varietal thiols, without quantifying the oxidised ones. For this purpose, we proposed to monitor both reduced and oxidised forms of varietal thiols in wine under copper stress during alcoholic fermentation to have a more complete picture of the biological and chemical mechanisms.

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 aim of this work was the identification and quantification of polyphenols present in natural precipitate of a Sauvignon wine. Phenol analysis in wine precipitate was based on acid hydrolysis, CG- MS after derivatization, and LC-MS.

Grapevine is one of the most extensively cultivated fruit crops, playing a crucial role in the economies of many grape-growing regions around the world.