A microwave digestion ICP-MS method for grapevine bark elemental profiling

Vine water status is one of the most influential factors in grape vigor, yield, and quality (Ojeda et al., 2002; Guilpart et al., 2014). Severe water deficits during the first stage of crop development (bud break to fruit set) impact yield in the current year and the following year. While during grape ripening, water availability impacts berry size, grape composition, and health status. Therefore, a correct assessment of plant water status allows for proper water management with an impact on grape yield and composition (McClymont et al, 2012; Pereyra et al., 2022).

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.

Favoring the formation of volatile compounds and their precursors in must and wine represent one of the principal goals during winemaking technology. In recent years, most attention has been placed on using glycosidases to enlarge the aroma profile of white wines. The effect of enzymes makes odorless glycosidically-bound precursors be converted into aromatic compounds. This paper focuses to study the influence of enzymes (pectolytic and β-glycosides) administered before alcoholic fermentation, even if most studies analyze their use in different winemaking stages. Two semi-aromatic varieties such as Fetească regală and Sauvignon blanc were chosen.

In this work, an innovative analytical method has been proposed for fast and reliable in-line analysis of tannins in wines; the method is fast, does not require sample preparation and is based on the selective reactivity of tannins in a mixture containing proteinaceous matter (i.e. gelatin), under pH 3.5, resulting in the formation of white cloudiness.

Il lavoro si prefigge di esaminare la propensione alla modernizzazione della viticoltura del Veneto Occidentale, letto attraverso la diffusione di forme di allevamento a sviluppo contenuto.