Influence of pre-fermentative steps on varietal thiol precursors

Despite the extensive research performed during the last decades, the multifactorial mechanism responsible for the white wine protein haze formation is not fully characterized. Herein, a new model is proposed, which is based on the experimental identification of sulfur dioxide as a major modulating factor inducing wine protein haze upon heating. As opposed to other reducing agents, such as 2-mercaptoethanol, dithiothreitol and tris(2-carboxyethyl)phosphine hydrochloride (TCEP), the addition of SO2 to must/wine upon heating cleaves intraprotein disulfide bonds, hinders thiol-disulfide exchange during protein interactions and can lead to the formation of novel inter/intraprotein disulfide bonds. Those are eventually responsible for wine protein aggregation which follows a nucleation-growth kinetic model as shown by dynamic light scattering [1].

Le “terroir” est dans tous les discours, les articles, les étiquettes et les publicités. Le voca­ble est en situation d’utilisation euphorique. Indiscutablement l’emploi historique est agri­cole, puis viticole, mais il n’est jamais juridique.

Planting grape varieties with several resistance loci towards powdery and downy mildew reduces the use of fungicides significantly. These fungus resistant or PIWI varieties (acronym of German Pilzwiderstandsfähig) contribute significantly to the 50% pesticide reduction goal, set by the European Green Deal for 2030. However, wine growers hesitate to plant PIWIs as they lack experience in vinification and are uncertain, how consumer accept and buy wines from these yet mostly unknown varieties. Grapes from four white and three red PIWI varieties were vinified in three vintages to obtain four diffe-rent white and red wine styles, respectively plus one rosé.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65

Consumer demands for environmentally friendly products, including wine, are constantly increasing.