Variety “Rebula” (Vitis vinifera L.) determines the terroir Goriška brda “Collio” in Slovenia

Grape pomace (GP) is a winemaking by-product particularly rich in (poly)phenols and dietary fiber, which are the main active compounds responsible for its health-promoting effects. GP-derived products have been proposed to manage cardiovascular risk factors, including endothelial dysfunction, inflammation, hypertension, hyperglycemia, and obesity. Studies on the potential impact of GP on gut health are much more recent. However, it is suggested that, to some extent, this activity of GP as a cardiometabolic health-promoting ingredient would begin in the gastrointestinal tract as GP components (i.e., (poly)phenols and fiber) undergo extensive catabolism, mainly by the action of the intestinal microbiota, that gives rise to low-molecular-weight bioactive compounds that can be absorbed and utilized by the body.

What influence do these bacteria have on wines? What new bacteria are being studied to carry out this fermentation? Find below articles about malolactic fermentation published in our 3 media (OENO One, IVES Technical Reviews and IVES Conference Series). OENO One...

Red grape pomace, a waste from wine production, can be valorised by extracting polyphenols, high-added value compounds used in cosmetics or oenology. For use at an industrial level, using green extraction techniques, pomace need to be stored before being processed. The aim of this study is to test various storage conditions in order to maintain high level of polyphenols over 180 days, while keeping storage cost economically interesting. In a first step, different storage conditions (ambient temperature or cooled (4°C) temperature, anaerobic (saturation with N2) or aerobic conditions, and addition of sulphur dioxide (SO2)) were compared on small samples (1 kg) packed in plastic pockets. The quality of storage was assessed by following the optical density of the pomace extract at 280 nm (DO 280 expressed as mg/l eq gallic acid), which is an indication of the amount of remaining extractable polyphenols.

Over the last years, due to climate change, several red wines, such as the Sangiovese wines, have been often subjected to loss of clarity due to the formation of deposits of fine needle-shaped crystals. This phenomenon turned out to be due to an excess of quercetin (Q) and its glycosides (Q-Gs) in wines. These compounds are synthesized to a large extent when grapes are excessively exposed to UVB radiations in vineyards[1]. Unfortunately, it is not easy to predict the degree of Q precipitation because its solubility strongly depends on the wine and matrix composition[2].

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.