Adaptation of Lactobacilli towards low ph and SO₂ to develop MLF in base musts for sparkling wines

Barrel fermentation and barrel-ageing of wine are commonly utilised practices in premium wine production. The wine aroma compounds related to barrel contact are varied and can enhance a range of wine aromas and flavours, such as ‘struck flint’, ‘caramel’, ‘red berry’, ‘toasty’ and ‘nutty’, as well as conventional oaky characters such as ‘vanilla’, ‘spice’, ‘smoky’ and ‘coconut’. A survey of commercially produced premium Shiraz, Cabernet Sauvignon, Pinot Noir and Chardonnay wines was conducted, assessing the prevalence of compounds that have been proposed as barrel-ageing markers¹ including oak lactones, volatile phenols, furanones, aldehydes, thiazoles2,3, phenylmethanethiol⁴ and 2-furylmethanethiol.⁵

It is well known that free varietal thiols, in particular 3-mercaptohexanol (3MH) and 3-mercaptohexyl ace-tate (3MHA), are important constituents to the aroma of New Zealand Sauvignon blanc 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.

A relevant trend in winemaking is to reduce the use of chemical compounds in both the vineyard and winery.

Aromatic composition contributes mainly to the quality aroma of white wine. A natural and gradual evolution of the aroma in the bottle occurs over storage with a very low oxygen content.