Effect of power ultrasound treatment on free and glycosidically-bound volatile compounds and the sensorial profile of red wines

Vineyard water management in Australia is often associated with irrigation in warm and hot climates, but in cooler regions the larger share of the seasonal water demand is met by rainfall.

In mid to late ripening, sugar and potassium (K+) accumulation into the berry slows and is eventually completed1. K+ is the most abundant cation in the berry, undertaking important physiological roles.

[English version below]

A tre anni dal suo inizio, nel 1997 si è conclusa la prima fase della ricerca “Caratterizzazione della produzione DOC Soave”. Lo studio ha basato il suo percorso sperimentale su alcuni punti fondamentali tra i quali:
• Recupero di tutte le informazioni storico-colturali sul vino Soave e sul suo territorio di produzione.

In the global economic context, an Appellation d’Origine Contrôlée must now more than ever control the typicity and originality of the wines it produces. It is in this spirit that the Côtes du Rhône have decided to acquire the means necessary for this control.

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.