Grapevine performances in five areas of ‘Chianti Classico’ Comportement de la vigne en cinq zones des « Chianti Classico »

Valpolicella is a famous Italian wine-producing region. One of its main characteristic is the intensive use of grapes that are submitted to post-harvest withering. This is rather unique in the context of red wine, especially for the production of a dry red wine such as Amarone. Amarone wines produced in Valpolicella different geographic origin are anecdotally believed to be aromatically different, although there is no systematic study addressing the chemical bases of such diversity. Aroma is the product of a biochemical and technological series of steps, resulting from the contribution of different volatile molecules deriving from grapes, fermentations, and reactions linked to aging, as well as one of the most important features in the expression of the geographic identity and sensory uniqueness of a wine.

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.

Global warming due to greenhouse gases (GHG) has become a serious worldwide concern.

Nel biennio 2008-2009, nell’ambito di un progetto multidisciplinare coordinato e finanziato dal Consorzio Tuscania, 4 vigneti in differenti zone della Toscana sono stati monitorati con strumenti di proximal sensing al fine di valutare la variabilità riscontrabile e ottenere delle indicazioni sulle risposte vegetative delle piante e quanti-qualitative delle produzioni.

The Matrix Assisted Laser Desorption/Ionization–Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) technology is commonly used in food and medical sector to identify yeast or bacteria species isolated from a nutritive culture media. Since a decade, brewery and oenology industries have been attracted to this method which combines fast analysis times, reliability and low cost of analysis. Briefly, this method is based on the comparison of the MALDI-TOF/MS protein spectra of an isolated colony of yeast or bacteria with those contain in a manufacturer’s reference protein spectra database. Initiated in 2015, the creation of the first oenological mass spectra database has proved to be essential for increase quality of species identification.