Oenotannins addition in wine: can be the modulation of redox potential predictable?

Water tolerance in plants is often associated with the accumulation of osmotic protectants, which are secondary metabolites that can help the plant to cope with water stress. One of the key osmotic protectants is a sugar called Raffinose, which is synthesized by a family of enzymes called Raffinose synthases. In this work, we focused on one of these enzymes, VviRAF2, which is a gene that shows different expression levels and genetic variants (SNPs) among different grapevine cultivars, ranging from tolerant to susceptible to water stress, and the transcription factors that may regulate the expression of this gene family.

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.

Temperature is a key environmental factor affecting grape primary and secondary metabolites. Even if several mesoscale studies have already been conducted on temperature
especially within a Protected Designation of Origin area, few data are available at an intra-block scale. The present study aimed at i) assessing the variability in bunch zone air temperature within a single vineyard block and the temporal stability of temperature spatial patterns, ii) understanding temperature drivers and
iii) identifying the impact of temperature on grape berry attributes.

AIM: The aim of this study was to evaluate the impact of the organic, biodynamic and conventional production processes on the typicality of the Chianti DOCG wine and the relation with the environmental impact in terms of CO2 production

The objective of these work was to demonstrate that toasted fragments of pruning vine-shoots added to the wines after fermentation provide them with differentiated aromatic notes and improve their quality.