Despite their significant impact on wine quality and stability, colloids in red wine remain relatively under-researched. A series of studies, developed in the context of the d-wines project, aimed to provide a comprehensive understanding of the structure, composition, and formation mechanisms of red wine colloids by studying monovarietal wines from 10 of the most significant Italian red grape varieties. Starting from the idea that proteins, polysaccharides, and tannins should be involved in colloid formation, 110 monovarietal red wines were analysed for these components, revealing high inter- and intra-varietal diversity [1].

The effects of temperature on cv Piedirosso, indigenous of the Campania region (South of Italy), were tested in order to study its possible influence on grapevine and to discover how to optimize the qualitative expression

Sensory analysis of grape berries is a common tool to evaluate the degree of grape maturation and to make sound picking decisions.

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

Nitrogen is an important nutrient of yeast and its low content in grape must is a major cause for sluggish fermentations. To prevent problems during fermentation, a supplementation of the must with ammonium salts or more complex nitrogen mixtures is practiced in the cellar. However this correction seems to improve only partially the quality of wine [1]. In fact, yeast is using nitrogen in many of its metabolic pathways and depending of the sort of the nitrogen source (ammonium or amino acids) it produces different flavor active compounds. A limitation in amino acids can lead to a change in the metabolic pathways of yeast and consequently alter wine quality.