“Terroir” and climate change in Franconia / Germany

The aromatic complexity of a wine results from the perception of the association of volatile molecules and each aroma can be categorized into different families. The “green” aromas family in red wines has retained our attention by its close link with the fruity perception. In that study, the “green” olfactory concept of red wines was considered through a strategy combining both sensory analysis and hyphenated chromatographic techniques including HPLC and MDGC (Multidimensional Gas Chromatography). The aromatic space of this concept was specified by lexical generation through a free association task on 22 selected wines by a panel of wine experts. Then, 70 French red wines were scored on the basis of the intensity of their “green” and “fruity” attributes.

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.

We compared 68 aroma compounds in wines from 5 vineyards in order to see similarities among the wine aroma and the scent of some of the main native plants from the respective vineyards.

Yield estimation is very important for the wine industry since provides useful information for vineyard and winery management. The early yield estimation of the grapevine provides information to winegrowers in making management decisions to achieve a better quantity and quality of grapes. In general, yield forecasts are based on destructive sampling of bunches and manual counting of berries per bunch and bunches per vine.

loss and color browning which lead to wine unacceptance by consumers. These changes are mainly driven by the consumption of oxygen by polyphenols leading to the production of quinones which are oxidant compounds. Quinones can react with numerous nucleophilic compounds notably aromatic thiols, decreasing the aromatic bouquet of the wine.