LC-MS based metabolomics discriminates premium from varietal chilean Cabernet Sauvignon cv. Wines

The decision for grape berries to ripen involves a complex interplay of genetic regulation and environmental cues. This review explores the molecular mechanisms underlying the transition from vegetative growth to ripening, focusing on transcriptomic studies and the role of the NAC gene family. Transcriptomic analyses reveal a significant rearrangement of gene expression patterns during this transition, with up-regulation of ripening-related genes and down-regulation of those associated with vegetative growth. A molecular phenology scale providing a high-precision map of berry transcriptomic development, indicates that key molecular changes occur well before the onset of ripening.

Rootstocks play a key role in the grapevine’s adaptation to the increasing soil water scarcity related to climate change. A pot experiment carried out in 2022 aimed at assessing the physiological responses of seven ungrafted rootstocks to a progressive soil water deficit and a subsequent recovery to field capacity.

Tartrate precipitation affects the properties of wines, due to the formation of crystals that cause turbidity, even after being bottled. The forced tartaric stabilization is carried out frequently for young wines, through various physicochemical procedures. The traditional treatment for tartaric stabilization is refrigeration, but it can have a negative effect on wine’s sensory properties, and particularly on the color of red wines. The aim of this study was to evaluate the effect of different tartaric stabilization options on the color and phenolic composition of Tannat red wines from Uruguay.

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.

AIM: This study aimed to use simultaneous measurements of absorbance, transmittance, and fluorescence excitation-emission matrix (A-TEEM) combined with chemometrics as a rapid method to authenticate wines from three vintages within a single geographical indication (GI) according to their subregional variations