The inhibition of hydrogen sulfide and methanethiol accumulation in wine by Cu(II): The influence of temperature on the duration of protection

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.

Spatial variability of vine productivity in winegrapes is important to characterise as both yield and quality are relevant for the production of different wine styles and products. The objectives were to understand how patterns of variability of Cabernet Sauvignon fruit composition changed over time and space, how these patterns could be characterised with indirect measurements, and how spatial patterns of the variation in fruit compositional attributes can aid in improving management. Prior to the 2017 vintage, 125 data vines were distributed across each of four vineyards in the Lodi American Viticultural Area (AVA) of California. Each data vine was sampled at commercial harvest in 2017, 2018, and 2019. Yield components and fruit composition were measured at harvest for each data vine, and maps of yield and fruit composition were produced for eight ‘objective measures of fruit quality’: total anthocyanins, polymeric tannins, quercetin glycosides, malic acid, yeast assimilable nitrogen, β-damascenone, C6 alcohols and aldehydes, and 3-isobutyl-2-methoxypyrazine. Patterns of variation in anthocyanins and phenolic compounds were found to be most stable over time. Given this relative stability, management decisions focused on fruit quality could be based on zonal descriptions of anthocyanins or phenolics to increase profitability in some vineyards. In each vineyard, dormant season pruning weights and soil cores were collected at each location, elevation and soil apparent electrical conductivity surveys were completed, and remotely sensed imagery was captured by fixed wing aircraft and two satellite platforms at major phenological stages. The data collected were used to develop relationships among biophysical data, soil, imagery, and fruit composition. The standardised and aggregated samples from four vineyards over three seasons were included in the estimation of ‘common variograms’ to assess how this technique could aid growers in producing geostatistically rigorous maps of fruit composition variability without cumbersome, single season sampling efforts.

Harvesting grapes at adequate maturity is key to the production of high-quality red wines. Enologists and wine makers define several types of maturity, including technical maturity, phenolic maturity and aromatic maturity. Technical maturity and phenolic maturity are relatively well documented in the scientific literature, while articles on aromatic maturity are scarcer. This is surprising, because aromatic maturity is, without a doubt, the most important of the three in determining wine quality and typicity (including terroir expression). Optimal terroir expression can be obtained when the different types of maturity are reached at the same time, or within a short time frame. This is more likely to occur when the ripening takes place under mild temperatures, neither too cool, nor too hot. Aromatic expression in wine can be driven, from low to high maturity, by green, herbal, fresh fruit, ripe fruit, jammy fruit, candied fruit or cooked fruit aromas. Green and cooked fruit aromas are not desirable in red wines, while the levels of other aromatic compounds contribute to the typicity of the wine in relation to its origin. Wines produced in cool climates, or on cool soils in temperate climates, are likely to express herbal or fresh fruit aromas; while wines produced under warm climates, or on warm soils in temperate climates, may express ripe fruit, jammy fruit or candied fruit aromas. Growers can optimize terroir expression through their choice of grapevine variety. Early ripening varieties perform better in cool climates and late ripening varieties in warm climates. Additionally, maturity can be advanced or delayed by different canopy management practices or training systems.

Vegetative growth and yield are reported to affect grape and wine quality. They can be controlled through different techniques linked to vine management. The objective of this research was to determine the effect of vine vigour and number of clusters per vine on physicochemical composition and phenolic profile of red wines. The experiment was carried out during two vegetative cycles, with cv. Cabernet Sauvignon grafted onto Paulsen 1103. Three vine vigour were defined, according to shoot weight at previous harvests, being low, medium and high. Five treatments of number of clusters were used for each vigour, with 15, 22, 29, 36, and 45 clusters per vine. Grapes from all treatments were harvested in the same day from Brix and total acidity criteria. Thirty days after bottling, classical analyzes and phenolic compounds were performed. As results, different responses were obtained from each vintage. In 2020, a dry season from veraison to harvest, grapes and wines obtained from low vigour treatment and 45 clusters per vine was the highest in sugar and alcohol content respectively, while grapes and wines from high vigour and 15 clusters presented the lowest sugar and alcohol content. Total anthocyanins were higher in treatment with low vigour and 15 clusters, while the lowest amounts were found in low vigour with 45 clusters, as well as medium and high vigour with 36 clusters per vine. Total tannins were higher in high vigour with 22 clusters and medium vigour with 29 clusters, while were lower in low vigour with 36 clusters. In 2021, a wet season at harvest, responses were different, and great variations were observed between treatments. As conclusions, yield and vine vigour had strong influence on grape and wine quality, promoting different enological potentials on which can be indicated/used for aging strategies of red and even rosé wines.

Besides location and microclimatic conditions, soil plays an important role in the quality of grapes and wine. Soil properties influence…