Use of UHPH to improve the implantation of non-Saccharomyces yeasts

Several studies have tried to develop different methods to study the photodegradation of wine in an accelerated way, trying to elucidate the effect of light on the wine compounds[1]. In a previous study, our team developed a chamber that speeds up the photodegradation of rosé wine[2]. In the present work we have tried to establish a correlation between irradiation times in accelerated conditions and the natural exposure to the cycles of light that usually exist in markets or at home.

Grapevine cultivars can be unequivocally typed by both physical differences (ampelography) and genetic tests. However due to their very similar characteristics, the identification of clones within a cultivar relies on the accurate tracing of supply records to the point of origin. Such records are not always available or reliable, particularly for older accessions. Whole genome sequencing (WGS) provides the most highly detailed methodology for defining grapevine cultivars and more importantly, this can be extended to differentiating clones within those cultivars.

The recovery of bioactive compounds from grape and wine by-products is currently an important objective for revaluation and sustainability. Grape pomace is one of the main by-products and is a rich source of some bioactive compounds. The aim of this study was to evaluate the polysaccharide (PS) composition of extracts obtained from pomaces of different white and red grape varieties of Castilla y León. Grape pomaces were obtained after the pressing in the winemaking process.

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 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).