Evaluating analytical methods for quantification of glutathione in grape juice and wine

One of the main aspects of Climate Change is the increase of temperatures during summer and grape maturity period. Physiological processes are influenced by these high temperatures and result in grapes with higher sugar concentration, less acidity and less anthocyanin content among other quality changes. One strategy to deal with the climate change effects is the implementation of late winter pruning to alter the effect of high temperatures during key periods by delays in maturity time.

Dans le cadre de TerclimPro 2025, Markus Rienth a présenté un article IVES Technical Reviews. Retrouvez la présentation ci-dessous ainsi que l’article associé : https://ives-technicalreviews.eu/article/view/8396

Copper and iron are known to substantially impact wine stability through oxidative, reductive or colloidal phenomena. However, the binding of metal ions to different wine components under wine conditions, and the impact of this binding on the ability of the metal ions to induce spoilage processes, is not well understood. This study surveyed a range of red and white wines for an understanding of the variability of broad metal categories within the wines. The techniques utilized included an electrochemical constant current stripping potentiometry technique (ccSP), and solid phase extraction (SPE) fractionation of wine with subsequent analysis of the metal content of each fraction by inductively coupled plasma – optical emission spectroscopy (ICP-OES).

Une étude de la physiologie de la vigne (cv. Merlot) et de la qualité des vins a été réalisée au Tessin de 2006 à 2008. La méthodologie utilisée pour cette étude intégrait tous les paramètres qui définissent les terroirs: facteurs naturels (géologie, pédologie et climat), facteurs physiologiques de la vigne et qualité des vins qui sont les révélateurs de la valeur d’un terroir.

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.