Évolutions qualitative et quantitative des flores microbiennes de moûts de pommes à cidre au cours de la fermentation: relations avec le terroir et la composition physico-chimique des fruits

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.

The precise molecular mechanisms underlying the domestication of grapevine (Vitis vinifera L.) Are still not fully understood. In the recent years, next-generation sequencing (NGS) of plastid genomes has emerged as a powerful and increasingly effective tool for plant phylogenetics and evolution. To uncover the biological profile of the grapevine domestication process comprehensively, an investigation should encompass both the cultivated varieties (V. vinifera subsp. Vinifera) and their wild ancestors V. vinifera subsp. Sylvestris) across all potential sites of their distribution and domestication.

Bentonite fining is widely used to prevent protein haze in white wines. Most wineries use laboratory-scale fining trials to define the appropriate amount of bentonite to be used in the cellar. Those pre-tests need to mimic as much as possible the industrial scale fining procedure to determine the exact amount of bentonite necessary for protein stability. Nevertheless it is frequent that, after fining with the recommended amount of bentonite, wines appear still unstable and need an additional fining treatment. It remains a major challenge to understand why the same wine, fined with the same dosage of the same bentonite, achieves stability in the lab, but not in the cellar.

According to OIV reports, annual world wine consumption fluctuated around 240-245 mln hL over the past decade. The general market globalization has led to the situation when almost half of the consumed wine is exported to other countries. Of this volume, more than 60 mln hL are bottled still and sparkling wines.

Fruity aromas, characterized by red and black fruit descriptors, are central to the identity of Bordeaux red wines [1,2]. Despite extensive research focused on identifying and quantifying volatile compounds that contribute to fruity aromas in wine, the mechanisms underlying their interactions and sensory perception remain poorly understood [3].