L’effetto paesaggio sul sistema delle preferenze: i vini veneti tra evocazioni di consumo e determinanti di scelta

When talking about terroir, scientists and lay wine tasters, very much including wine journalists and wine growers, too often talk past one another.

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 aromatic profile of a wine is the result of volatile molecules present in grapes (varietal or primary aromas) and those produced during the winemaking process of fermentation (secondary aromas) and during wine aging (tertiary aromas).

Among the Moscato grapes, Moscato Giallo is a winegrape variety characterized by a high content of free and glycosylated monoterpenoids, which gives very aromatic wines. The aromatic bouquet of Moscato Giallo is strongly influenced by the high concentration of linalool, geraniol, linalool oxides, limonene, α-terpineol, citronellol, HO-trienol, HO-diols, 8-Hydroxylinalool, geranic acid and β-myrcene, that give citrus, rose, and peach notes.

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.