Optimization of a tool to determine the oxygen avidity of a wine through the kinetics of consumption by its phenolic and aromatic fractions (PAFs)

Yeast species S. cerevisiae, S. uvarum, S. kudriavzevii and their hybrids present clear metabolic differences, even when we compared S. cerevisiae wine versus wild strain. These species and hybrids produced significantly higher amounts of glycerol, organic acids, 2,3-butanediol, and 2-phenyl ethanol and a reduction of the ethanol yield, properties very interesting in the sector to deal with climate change effects. To understand the existing differences, we have used several omics techniques to analyze the dynamics of the (intra- and extracellular) metabolomes and/or transcriptomes of representative strains of S. cerevisiae, S. uvarum, S. kudriavzevii, and hybrids.

Riesling wines are appreciated for their diverse aromas, ranging from the fruity fresh characters in young vintages to the fragrant empyreumatic notes developed with aging. Wine tasters often refer to Riesling wines as prime examples showcasing terroir, with their typical aroma profiles reflecting the geographical provenance of the wine. However, the molecular basis of the distinctive aromas of these varietal wines from major Riesling producing regions in Europe have not been fully elucidated. In this study, new lights were shed on the chemical characterization and the sensory contribution of volatile thiols to Riesling wines from Rheingau, Mosel, and Alsace. First, Riesling wines (n = 46) from the three regions were collected and assessed for their aroma typicality by an expert panel.

Acetaldehyde is a volatile carbonyl compound synthetized by yeast during alcoholic fermentation, but it can also be formed by oxidation of ethanol during wine aging [1]. At low concentration, it enhances the fruity aroma, however, at higher levels, it can generate the appearance of notes of bruised and rotten apple [2]. From a chemical point of view, acetaldehyde is a reactive low-

Advanced knowledge on grapevine row orientation is required to improve establishment, management and outcomes of vineyards on terroirs with different environmental conditions (climate, soil, topography) and in view of a future change to more extreme climatic conditions. The purpose of this study was to determine the combined effect of row orientation, plant water status and ripeness level on the physiological and viticultural reaction of Shiraz/101-14 Mgt.

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.