Effect of foliar application of urea and nano-urea on the cell wall of Monastrell grape skins

The plant rhizosphere microbial communities are an essential component of plant microbiota, which is crucial for sustaining the production of healthy crops. The main drivers of the composition of such communities are the growing environment and the planted genotype. Recent viticulture studies focus on understanding the effects of these factors on soil microbial composition since microbial biodiversity is an important determinant of plant phenotype, and of wine’s organoleptic properties. Microbial biodiversity of different wine regions, for instance, is an important determinant of wine terroir.

The presence of undesirable compounds in wines, such as OTA, biogenic amines and pesticides residues, affects wine quality and can cause health problems for the consumer. The main tool that a winemaker has to reduce their content in the wine is fining. However, some of the fining agents commonly used in the winery can cause allergies or even increase the protein content in the wine, increasing the turbidity. To avoid these problems, the use of plant fibers may be an alternative, such as those from grape pomace[1] or other plant origins.

Wine is the result of the alcoholic fermentation (AF) of grape must. Besides AF, wine can also undergo the malolactic fermentation (MLF) driven out by lactic acid bacteria (LAB). Among LAB, Oenococcus oeni and Lactiplantibacillus plantarum are the dominant species in wine. Even if O. oeni is the most common LAB undergoing MLF in wine, due to its high tolerance to wine conditions, L. plantarum can be used to undergo MLF in must. The moderate tolerance of L. plantarum to low pH and ethanol, may compromise the fermentative process in harsh wines.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

Reducing sugar accumulation in grape (Vitis vinifera L.) berries may be a way to mitigate the effect of climate change. Managing canopy and crop load is an effective way to do so, however, reducing canopy size has been demonstrated to induce undesirable effects on anthocyanins. The aim of this study was to test if an application of exogenous ABA on the grape berries of defoliated vines (⅔ of the leaves removed) can result in slower sugar accumulation while maintaining grape and wine quality. An experiment with defoliation and exogenous ABA application on directly on clusters (factorial design 2×2) was performed with ‘Tempranillo’ fruit-bearing cuttings.