Evolution of the crown procyanidins during wine making and aging in bottle

Many factors influence aging of bottled wine, oxygen transfer through the closure is included. The maximum uptake of wine before oxidation begins varies from 60 mg.L-¹ to 180 mg.L-1 for white and red wines respectively [1].
The process of bottling may lead to considerable amounts of oxygen. The actual contribution of the transfer through the closure system becomes relevant at the bottle storage, but the amounts are small compared to prepacking operations [2] and to the total oxygen attained during filling.

Wine tourism has transformed from a leisure activity into a crucial part of the tourist experience, significantly contributing to rural tourism’s expansion in italy. It has witnessed a notable surge in popularity in recent years, evolving as a key motivator for travel (antonioli corigliano, 2002; brunori & rossi, 2000; città del vino & censis servizi, 2011; garibaldi, 2018; 2019a; 2020; montanari, 2009; romano & natilli, 2009). The allure of wine tourism, driven by sensory experiences and cultural immersion, continues to attract a diverse group of tourists. The economic impact is substantial, with events and festivals contributing approximately €2.5 billion annually.

Oxygen transfer rate (OTR) is a technical property of closure, and it modulates the oxygen supply to the wine during its bottle aging. It’s an important parameter to take into account in the analysis of wine aroma evolution. OTR distribution is well documented for new closures, but little research has been published on its determination for aged closures. Initial oxygen release after bottling impacts the composition of wines during the first years of storage), but the link between OTR, sensory perception and aroma composition after many years of aging has not yet been clearly studied. 

Climate change is increasing the frequency and severity of environmental hazards (e.g., prolonged drought), and even non-extreme climate events (e.g., a period of slightly warmer temperatures) can lead to extreme impacts when they occur simultaneously with other (non-extreme) events.

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.