Varietal volatile patterns of Italian white wines

Marsannay est l’appellation la plus septentrionale de la Côte de Nuits, à la limite Sud de Dijon. Elle regroupe trois villages, Chenôve, Marsannay-la-Côte et Couchey, et constitue l’appellation la plus vaste de la Côte de Nuits avec 315 hectares en A.O.C. Elle possède uniquement des niveaux d’appellations régionales et communales depuis 1987.

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.

Vitis vinifera is one of the most diffused grapevines over the word and it is the raw material for high quality wines production. The availability of more resistant interspecific hybrid vine varieties, developed from crosses between Vitis vinifera and other Vitis species, has generating much interest, also due to the low environmental effect of production. However, hybrid grape wine composition and varietal differences between interspecific hybrids are not well defined. Different studies revealed that wine consumption has health effects due to its high content of antioxidants, as phenolic compounds. In particular, simple phenols are appreciated not only for their physiological health benefits, including antioxidant, anti-inflammatory and cardioprotective effects, but also because they affect wines organoleptic profile and have a significant role in defining their nutritional characteristics.

Aromatic composition contributes mainly to the quality aroma of white wine. A natural and gradual evolution of the aroma in the bottle occurs over storage with a very low oxygen content.

One of the key factors of the economical development of viticulture and wine industry in specific limited areas is the exploitation of ancient, local grape varieties. Therefore, in recent years the growing interest to rediscover minor varieties, previously cultivated, has promoted many studies. With this regard, the focus of this study was the Vitis vinifera L. cultivar Moscato nero d’Acqui, nowadays found only in old vineyards in the Acqui zone (North-West Italy). In particular, the aims of this work were: i) to investigate secondary metabolites profile of the grapes, and ii) to evaluate the attitude to the production of special wines.