Revisión de estudios sobre suelos vitícolas de las tierras del Jerez

Sterols are a fraction of the eukaryotic lipidome that is essential for the maintenance of the cell membrane integrity and their good functionality. During alcoholic fermentation, they ensure yeast growth, metabolism and viability, as well as resistance to osmotic stress and ethanol inhibition. Two sterol sources can support yeasts to adapt to fermentation stress conditions: ergosterol, produced by yeast in aerobic conditions, and phytosterols, plant sterols found in grape musts imported by yeasts in anaerobiosis. Little is known about the physiological impact of the assimilation of phytosterols in comparison to ergosterol and the influence of sterol type on fermentation kinetics parameters.

Context and Purpose of the Study. PIWI grape varieties (Pilzwiderstandsfähig, fungus-resistant) offer innovative solutions for sustainable viticulture by addressing environmental challenges faced by traditional Vitis vinifera.

The knowledge of the grape aromatic composition during ripening provides very important information for winegrowers, who may carry out different viticultural practices, or determine the harvest date more accurately. However, there are currently no tools that allow this measurement to be carried out in a non-invasive and rapid way. For this reason, the aim of this work was to design a non-invasive methodology, based on hyperspectral imaging to estimate the aromatic composition and total soluble solids (TSS) of Tempranillo Blanco berries during ripening.

The assessment of wine authenticity is of great importance for consumers, producers and regulatory agencies to guarantee the geographical origin of wines and grape variety as well. Since mid-infrared (MIR) spectroscopy with chemometrics represent a suitable tool to ascertain the wine composition, including features associated with the polyphenolic compounds, the aim of this study was to generate MIR spectra of red wines to be exploited for classification of red wines based on the relationship between grape variety and wine composition. Several multivariate data analyses were used, including Principal Component Analysis (PCA), Discriminant Analysis (DA), Support Vector Machine (SVM), and Soft Intelligent Modelling of Class Analogy (SIMCA).

Climate change is affecting wine production and inducing significant variability in wine composition between vintages.