Dynamics of Saccharomyces cerevisiae population in spontaneous fermentations from Granxa D’Outeiro terroir (DOP Ribeiro, NW Spain)

This essay was aimed to describe the clonal diversity of Oenococcus oeni in the malolactic fermentation of the carbonic maceration (CM) winemaking. The free and the pressed liquids from CM were sampled and compared to the wine from a standard winemaking with previous destemming and crushing (DC) of grapes [1]. O. oeni strain typification was performed by PFGE as González-Arenzana et al. described (2014) [2]. Results showed that 13 genotypes, referred as to letters, were distinguished from the 49 isolated strains, meaning the genotype “a” the 27%, the “b” the 14%, the “c” the 12%, the “d and e” the 10 % each other, and the remaining ones less than the 8% each one.

The foliar application of urea has been shown to be able to satisfy the specific nutritional needs of the vine as well as to increase the nitrogen composition of the must. On the other hand, the use of nanotechnology could be of great interest in viticulture as it would help to slow down the release of urea and protect it against possible degradation. Several studies indicate that cell wall synthesis and remodeling are affected by nitrogen availability.

Global warming is accelerating the technological ripening of the grape, with a loss of acidity, which requires that vineyard management can delay ripening to avoid it. The source-sink relation is essential for grape ripening, since it affects the distribution of photosynthates and substances derived from plant metabolism. A work is proposed to know the response of the vineyard to the drastic reduction of the foliar surface by trim down the shoots in cv.

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).

The wine industry produces an enormous amount of waste every year. A wider inclusion of disregarded by-products in the human diet or its use as a source of bioactive compounds is a good strategy for reducing waste. It will not only introduce an added value to a waste product but also come upon the European Union and United Nations’ demands towards more sustainable agricultural approaches and circular economy.