Evaluation of mannoprotein formation by different yeast strains by enzymatic analysis of mannose and tribological estimation of astringency

The assessment of physiological parameters in vineyards can be done by direct measurements or by remote, indirect methods. The latter option frequently yields useful data, and development of methods and techniques that make them possible is worthwhile. One of the parameters most looked for to define the quality status of a vineyard is the degree Brix of its grapes, a quantity usually determined by direct measurement.

Does a white wine smell like red wine if you color it with red food coloring? A study by Morrot, Brochet, and Dubourdieu (2001, Brain and Language) suggests so. Subjects perceived red wine odors when tasting white wine that had been colored red. The perceived odor profile of the colored white wine became similar to that of a red wine. However, the forced-choice procedure used by Morrot et al. has some methodological shortcomings. Here, we used an alternative method (a rating procedure) to evaluate the presented 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.

At CREA – Centro di Ricerca di Viticoltura ed Enologia, based in Velletri (RM), an experimental vineyard including 10 downy mildew resistent/tolerant grape varieties and two susceptible varieties was set up with the principal goal to evaluate the behavoir of these varieties in term of resistance to downy mildew (Plasmopara viticola). This evaluation, together to oenological studies, are necessary to register them also in Regional Register (in Lazio region). Monitoring of behavior towards Plasmopara viticulture of resistant vines were done in 2020 and 2021 at different times (phenological stages) and until harvesting, according to an international standard code BBCH a centesimal phenological scale, based on coding system.

Besides location and microclimatic conditions, soil plays an important role in the quality of grapes and wine. Soil properties influence…