A browser application for comprehensive 3-dimensional LC × LC × IM – MS data analysis to study grape and wine polyphenols

There is a high demand in the wine industry for analytical methods able to provide useful information to support the decision-making process in the vineyard and in the winery. Ideally these methods should be rapid (e.g.

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.

Climate change is affecting wine production and inducing significant variability in wine composition between vintages.

Aim: The aim of this work is educating people about soil variability, vine rootzone depth and readily available water holding capacity. The concept of terroir is readily discussed in the wine industry but many people involved are unable to describe a soil profile and interpret its limitations that impact on vine growth, fruit quality and wine produced. This paper discusses soil physical characteristics important to vine root growth and readily available water holding capacity (RAW).

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.