Recognition of terroir in american viticultural areas

Sotolon (3-hydroxy-4,5-dimethyl-2(5H)-furanone) is a naturally occurring odorant compound with a strong caramel/spice-like scent, present in many foodstuffs. Its positive contribution for the aroma of different fortified wines such as Madeira, Port and Sherry is recognized. In contrast, it is also known to be responsible for the off-flavor character of prematurely aged dry white wines. The formation mechanisms of sotolon in wine are still not well elucidated, particularly in Madeira wines, which are submitted to thermal processing during its traditional ageing. The sotolon formation in these wines has been related to sugar degradation mechanisms, particularly from fructose [1].

Cultivar and rootstock selection are two well-known strategies for adapting vine production in challenging environments. Despite the vast diversity of rootstocks and cultivars, their effective contribution to grapevine sustainable development and acclimation to changing growing conditions remains an open question. The use of robust and prompt monitoring tools can allow a powerful screening of the water status of the vineyard before considering a further detailed characterization. This study leveraged new tools to monitor the stomatal conductance (gs), transpiration rate (E), and quantum efficiency of photosystem II (ᶲPSII) throughout a season, from pre-veraison to after-harvest.

Gum arabic is composed of a polysaccharide rich in galactose and arabinose along with a small protein fraction [1, 2], which gives its stabilizing power with respect to the coloring substances or tartaric precipitation of bottled wine. It is a gummy exudation from Acacia trees; the products used in enology have two possible botanical origins, i.e. Acacia seyal and Acacia senegal, with different chemical-physical features and consequently different technological effects on wines. The aim of this work is to evaluate the feasibility of discrimination of commercial gums Arabic between their two different sources, on the basis of the absorption of the Fourier Transform Infrared (FT-IR) spectra of their aqueous solutions, in order to propose an extremely rapid and cost-saving method for quality control laboratories.

When someone watches a hilly landscape, the image beauty creates emotions and frames of mind not easily forgettable, but sometimes man’s intervention by means of soil movement and reduction of the natural biodiversity can significantly modify the landscape and consequently the above-mentioned emotions. One speculates if sensory appreciation of a wine may be strongly affected by psychological factor: landscape beauty.

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