Improvement of the red wine AOC Grignolino d’Asti typicality using some technological innovations

It is not easy at first sight to give an exhaustive definition of the notion of terroir as it can be simplified or complicated at will. Thus the vagueness that surrounds this concept leaves the door open to various interpretations of the terroir. These tend towards a questionable level of objectivity because the fields they explore are not sufficient to explain the notion on their own, constituting only part of a whole.

The implementation of food safety assurance systems in wineries involves ensuring that the wines produced do not pose a risk to consumer health and are therefore free from harmful substances, such as those that may be incorporated during the production process (pesticides, additives, etc.), allergens or mycotoxins.

The wine industry widely recognizes that early-harvested grapes or those with uneven ripeness at harvest can produce wines with an “unripe fruit” mouthfeel [1,2]. Despite this, it is still unknown which compounds cause these sensory flaws or the most effective winemaking techniques to address them.

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.

Atmospheric CO2 levels have been rising continuously since the industrial revolution, affecting crop physiology, yield and quality of harvest products, and grapevine is no exception [1]. Most of previously reported studies used potted plants in controlled environments, and explored grapevine response to relatively high CO2 levels, 700 ppm or more. The vineyardFACE, established in Geisenheim in 2012, uses a free air carbon dioxide enrichment (FACE) system to simulate a moderate (ambient +20%) increase in atmospheric CO2 in a vineyard planted with cvs. Cabernet-Sauvignon and Riesling grafted on rootstock 161-49 Couderc and SO4, respectively.