Altered lignans accumulation in a somatic variant of Tempranillo with increased extractability of polyphenols during winemaking

The Tannat grape, native to the foothills of the Pyrenees in France, is known for producing wines with intense colour, exceptional tannic structure, and remarkable aging potential. These distinctive characteristics are attributed to its unique genome, making Tannat one of the grape varieties with the highest tannins concentration.

In the past, the contribution of non-Saccharomyces yeasts in winemaking has always been considered negative for their limited enological attitude if compared with Saccharomyces cerevisiae. In recent decades there has been a reevaluation of the role of non-Saccharomyces wine yeasts especially when used in combination and in support with S. cerevisiae (mixed fermentation). In this regard, selected non-Saccharomyces yeasts could be profitable used to give distinctive features, to enhance flavor and aroma complexity and to reduce the ethanol content of wines. Further emerging trends in the use of these yeasts are related to their role as bioprotectants and producers of health promoters compounds.

Grape stalks are a byproduct of the winemaking process and represent a valuable and inexpensive source of bioactive compounds. While their direct use in whole bunch fermentation is known, the majority of grape stalks are discarded, posing environmental and economic challenges.

We must state that two conditions above are only prevalent in the case when the processed grapes are full or over ripened, besides following the Tokaj wines preparation criterions (grape variety, soil, microclimate, vintage, etc.). These two conditions mentioned before were followed up only sensory based analysis up to now, altough the study of chemical idetified compounds which confirms these are obviuos.

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.