Recherche de relations entre terroir et caractéristiques sensorielles des eaux-de-vie de Cognac

In the challenge of transforming waste into useful products that can be re-used in a circular economy perspective, winery by-products can be considered as a source of potentially bioactive molecules such as polyphenols. The wine industry generates each year 20 million tons of by-products. Kombucha fermentation is an ancestral process which allow to increase the biological properties of tea by the action of a microbial consortium formed by yeasts and bacteria called SCOBY. It belongs to the field of healthy food for which the interest of consumers is growing. The objective of this work was to propose a new functional beverage made from winemaking by-products fermented by a Kombucha SCOBY.

Red wines can sometimes exhibit undesirable green, herbaceous, and vegetative aromas, negatively impacting their sensory profile and consumer acceptance.

In recent years, Uruguay has embraced the Albariño grape variety (Vitis vinifera L.) as one of the most promising for commercial growth. Originally cultivated in Galicia and northern Portugal, Albariño has risen to prominence in the global wine market, driving strong demand and significantly increasing grape prices [1].

Studies on wineomics (wine’s metabolome) have increased considerably over the last two decades. Wine results from many environmental, human and biological factors leading to a specific metabolome for each terroir. NMR metabolomics is a particularly effective tool for studying the metabolome since it allows the rapid and simultaneous detection of major compounds from several chemical families.1 Quantitative NMR has already proven its effectiveness in monitoring the authenticity of still wines.

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.