Effect of ozone application for low-input postharvest dehydration of wine grapes 

Nitrogen composition of grape berries plays a key role in determining wine quality, affecting the development of alcoholic fermentation and the formation of volatile compounds. Grape nitrogen composition is influenced by several factors such as viticultural practices, soil management, timing or rate of fertilization and use of rootstock, among others.In this study a proximal, non-destructive tool based on NIR spectroscopy is presented to track the accumulation of a wide range of amino acids in intact grape berries during the ripening process.

In a recent study several genes controlling the acidification properties of the wine yeast Saccharomyces cerevisiae have been identified by a QTL approach [1]. Many of these genes showed allelic variations that affect the metabolism of malic acid and the pH homeostasis during the alcoholic fermentation. Such alleles have been used for driving genetic selection of new S. cerevisiae starters that may conversely acidify or deacidify the wine by producing or consuming large amount of malic acid [2]. This particular feature drastically modulates the final pH of wine with difference of 0.5 units between the two groups.

The historical Tokaj region in northeast Hungary is a UNESCO World Heritage region since 2002 owning 5.500 ha vineyards. Produced from „noble rot” grapes, Tokaji Aszú is known as one of the oldest botrytized wines all over the world. Special microclimatic conditions (due to Bodrog and Tisza rivers, Indian summer), soil circumstances (clay, loess on volcanic bedrock) and grape-varieties (Furmint, Hárslevelű) of Tokaj-region offer favourable parameters to the formation of noble rot caused by Botrytis cinerea. The special metabolic activity of Botrytis results in noble rot grapes called “aszú” berries. The grapes undergo complex chemical modifications as the joint result of the enzymatic activity of Botrytis and the physical process of concentration.

Light is responsible for adverse reactions in wine including the formation of unpleasant flavors, loss of vitamins or photodegradation of anthocyanins. Among them, the riboflavin degradation leads to the formation of undesirable volatile compounds, known as light-struck taste. These photo-chemical reactions could be avoided by simply using opaque packaging. However, most rosé wines are kept in transparent bottles due to different commercial reasons. Some agri-food waste extracts have been studied for their photoprotective action which turn to be highly correlated with phenolic content [1].

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.