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

Grapevine powdery mildew resistance is a key target for grape breeders and grape growers worldwide. The driver of the USDA-NIFA-SCRI VitisGen3 project is completing the pipeline from germplasm identification to QTL to candidate gene characterization to new cultivars to vineyards to consumers. This is a common thread across such projects internationally. We will discuss how our objectives and approaches leverage big data to advance this initiative, starting with genomics and computer vision phenotyping for gene discovery and genetic improvement. To manage and maintain resistances for long-term sustainability, growers will be trained through our nation-wide extension and outreach plan.

Fermentation derived sulfidic off-odors due to hydrogen sulfide (H2S) and low molecular weight thiols are commonly encountered in wine production and removed by Cu(II) fining. However, the mechanism underlying Cu(II) fining remains poorly understood, and generally results in increased Cu concentration that lead to deleterious reactions in finished wine. The present study describes a mechanistic investigation of the iron and copper mediated reaction of H2S, cysteine, 3-sulfanylhexan-1-ol, and 6-sulfanylhexan-1-ol with oxygen. The concentrations of H2S, thiols, oxygen, and acetaldehyde were monitored over time. It was found that Cu(II) was rapidly reduced by both H2S and thiols to Cu(I).

Over the last decade, much attention has been paid on the oxidative susceptibility of white wines, given its key role in determining their ageing potential.

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.

Characterization of phenolics and VOCs in wines obtained from Malbec vineyards of the Uco Valley submitted to high-altitude solar UV-B and water restriction