Méthode et outils de valorisation des terroirs en cave coopérative

In Enology the conventional processes of filtration for clarification and stabilization are giving place to alternative membrane processes, including nanofiltration (NF). Furthermore, the increased alcohol content in wines recorded in recent years became an important issue for all the main wine producing countries. Among techniques available to the wine industry to reduce the ethanol content, NF is certainly one of the newest. This study is focused on the evaluation of NF influence on wine physical-chemical composition, including mineral content, which in accordance to our best knowledge is a novelty.

In Valtellina zone (north Italy), the winemaking of ‘Nebbiolo’ grapes leads to the production of two main wine types: classic red wines from fresh grapes, usually classified as Valtellina Superiore DOCG (mandatory oak aging) or Rosso di Valtellina DOC, and the Sforzato di Valtellina DOCG, which is produced using withered grapes according to traditional product specification and subjected to mandatory oak aging process. The withering process influences grape chemical composition and, in turn, the wine sensory profile, which is strongly linked to the wine quality and typicity perceived by consumers.

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.

Nowadays there are many challenges facing both winegrowers and workers, in other agricultural practices, related to the growing demand for food products, the safety and quality of these products, and the preservation of the environment…

Selected strains of non-Saccharomyces yeasts showed a positive effect on sensory characteristics and aromatic complexity of wine. A sequential microbial culture of non-Saccharomyces and S. cerevisiae species is usually inoculated due to poorer fermentability of non-Saccharomyces species. The aim of the study was to investigate the role of non-Saccharomyces yeasts in the production of white wines. We evaluated how individual combinations of sequential inoculations of non-Saccharomyces and S. cerevisiae species affect the aromatic compounds (volatile thiols and esters) and sensory characteristics of the wines.