Impact of aging on dimethyl sulfide (DMS) in Corvina and Corvinone wines

The economic importance of grapevine as a crop plant makes Vitis vinífera a good model system to study the improvement of the nutraceutical properties of food products (Vezulli et al. 2007). Stilbenes in general, and trans-resveratrol in particular, have been reported to be responsible for various beneficial effects. Resveratrol´s biological properties include antibacteria and antifungal effects, as well as cardioprotective, neuroprotective and anticâncer actions (Guerrero et al. 2010 ). Stilbenes can be induced by biotic and abiotic elicitors since they are phytoalexins (Bavaresco et al. 2001).

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.

Air temperature is arguably one of the most decisive factors for winegrape varieties developmental cycle, ripening potential and yield.

The climate is the environmental factor that contributes with greater weight in the variability of the yield and the composition of the grape, therefore, it is key in the determination of the typicity of the product. Of the environmental factors, the evolution of water availability conditions, among other things, the biochemical evolution of the compounds of the grape and the type of wine to be elaborated. An integrating parameter of the hydric state of the plant is the carbon isotopic composition (δ13C). This indicator is a useful parameter to characterize the water status during the maturation period and estimate the transpiration efficiency or water use efficiency (EUA) in the vine.

The monitoring of the number of micro-orgranisms in wine is crucial for the wine producer. Traditional counting methods include microscopic enumeration and plating on selective media, which measures the culturability of the cells. The use of epifluorescence microscopy is, however, a method, which can measure both culturability and