Exploring multisensory interactions through the study of astringency diversity of mono-varietal Italian red wines

From fruit set to ripening, the grape berry mesocarp experiences a wide range of dynamic physical, physiological, and biochemical changes, such as mesocarp cell death (MCD) and hydraulic isolation. The premature occurrence of such events is a characteristic of the Niagara Rosada (NR) variety, utilised as table grapes and winemaking. In our opinion, the onset of ripening would not cause MCD, but a down-regulation of respiratory enzymes during the early loss of cell viability, while maintaining membrane integrity. For this, we investigated three distinct developmental stages (green (E-L33), veraison (E-L35), and ripe (E-L39)) of NR berries by label-free proteomics, enzymatic respiratory activity and outer mesocarp imaging. Cell wall-modifying proteins were found to accumulate differently throughout ripening, while cytoplasmic membranes continue intact.

The effects of different oxygen and sulphur dioxide additions to South African ‘Sauvignon blanc’ musts were investigated. Oxygen addition without SO2 protection led to lower levels of certain volatile thiols in the wines, with a corresponding decrease in certain phenols and glutathione concentrations.

Context and purpose of the study. Anthocyanins are essential phenolic compounds in red wine, contributing significantly to colour intensity, stability, and sensory quality.

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.

Context and purpose of the study. Epidermal Patterning Factors are a family of small peptides that are highly conserved in the plant kingdom and are involved in several physiological and developmental processes.