Prefermentative CO₂ saturation of grape must to obtaining white wines with low SO₂ content

Oenococcus oeni is the main Lactic Acid Bacteria responsible for malolactic fermentation, converting malic acid into lactic acid and carbon dioxide in wines. Following the alcoholic fermentation, this second fermentation ensures a deacidification and remains essential for the release of aromatic notes and the improvement of microbial stability in many wines. Nevertheless, wine is a harsh environment for microbial growth, especially because of its low pH (between 2.9 and 3.6 depending on the type of wine) and nutrient deficiency. In order to maintain homeostasis and ensure viability, O. oeni possesses different cellular mechanisms including organic acid metabolisms which represent also the major pathway to synthetize energy in wine.

Environment features and management operations on shoot and leaves modify the canopy during the vegetative season, changing the grapevine microclimate and the ratio between photo synthetic sources (the canopy) and productive sinks (the grapes).

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, the use of food preservatives is controversial, SO2 being no exception. Microbial communities have been particularly studied during the prefermentary and fermentation stages in a context of without added SO2. However, microbial risks associated with SO2 reduction or absence, particularly during the wine aging process, have so far been little studied. The microbiological control of wine aging is a key issue for winemakers wishing to produce wines without added SO2. The aim of the present study is to evaluate the impact of different wine aging strategies according to the addition or not of SO2 on the microbiological population levels and diversity.

Among the factors that influence the sensory quality, style, safety, sustainability, and sense of place of a wine, the contributions of microbial biodiversity are widely becoming more recognized. Throughout winemaking, multiple biochemical reactions are performed by a myriad of different microorganisms interacting in many ways.