NMR profiling of grape musts from some italian regions

Grape downy mildew, caused by plasmopara viticola, is one of the main diseases affecting viticulture worldwide and its control usually relies on frequent sprays with agrochemicals. Grapevine varieties resistant to p. Viticola represent an effective solution to control downy mildew and reduce the environmental impact of viticulture. Loci of resistance to p. Viticola (Rpv) have been introgressed from wild vitis species and some of them, like Rpv1, Rpv3.1 and Rpv10, are currently the most utilized genetic resources in grape breeding.

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.

The increasing demand for low-alcohol and non-alcoholic wines has led to the development of advanced dealcoholisation techniques aimed at preserving wine quality while reducing ethanol content. Reverse osmosis is one of the most widely used membrane-based processes for the selective removal of ethanol [1].

Climate is one of the main components that defines the development and behavior of the plant, conditioning the health status and the final quality of the grapes. In temperate coastal climates such as in Uruguay (latitude 35° S, longitude 55° O), precipitations during the growing season present high interannual variability, with a average of 100 mm per month. This variability means that plants must adapt to conditions from one year to the next.

The fresh mushroom off-flavour (FMOff) has been appearing in wines since the 2000s. Some C8 compounds such as 1-octen-3-one, 1-octen-3-ol, 1-hydroxyoctan-3-one, 3-octanol and others are involved in this specific off-flavour [1-3]. At the same time, glycosidic precursors of some FMOff compounds have been identified in musts contaminated by Crustomyces subabruptus [4], highlighting the role of aroma precursors in this specific taint. However, the fate of these volatile molecules and glycosidic fractions during fermentation is not well known.