The Hungarian system of geographical indications and the preparation of product specifications

The selection of ancient grapevine varieties aims to achieve genetic gains in several important traits that can make the variety more interesting for the objectives of the producers. Traditionally, yield and quality traits of the must have been considered for selection, but many others can be taken into account. Linear mixed models are fitted to the data to predict the empirical best linear unbiased predictors (EBLUPs) of genotypic effects for each evaluated trait, which will be the basis for selection.

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.

Lipids are crucial in alcoholic fermentation, influencing yeast metabolism by providing nutrients and modulating membrane composition [1]. They also serve as precursors to aromatic compounds shaping wine sensory profiles [2].

The traditional methods of grape extraction of enochemical compounds use very often mechanical energy by pistons such as the pigeage or mechanical energy produced by must (delestage, pumping over). Recent trend by winemaker is trying to introduce in the fermentation tank, whole berry grape to avoid even minimal oxidation. Unfortunately, the use of the traditional mechanical techniques aforementioned, very often do not guarantee the optimal extraction with residual sugars in the marc. Use of resonance waves (airmixingtm) and a slight overpressure by CO2 (adcftm) permit to work on whole berry guaranteeing the perfect extraction.

In a previous work1, it was suggested that the different contents in delphinidin and catechin of the grapes were determinant on the O2 consumption and Strecker aldehyde (SAs) accumulation rates. Higher delphinidin seemed to be related to a faster O2 consumption and a smaller SAs accumulation rate, and the opposite was observed regarding catechin.
In the present paper, these observations were fully corroborated by adding synthetic delphinidin to a wine model containing polyphenolic fractions (PFs) extracted from garnacha and synthetic catechin to a wine model containing PF extracted from tempranillo: The delphinin-containing garnacha model consumed O₂ significantly faster and accumulated significantly smaller amounts of SAs than the original garnacha model, and the catechin-containing tempranillo model, consumed O2 significantly slower and accumulated significantly higher amounts of SAs than the original tempranillo model.