Study of Spanish wine sensory analysis data over a 3-year period

Pomace is one of the main residues generated by the wine industry and represents an environmental problem. Currently, there is a growing interest in the revaluation of these products because different bioactive compounds can be obtained from them, such as polyphenols, grape seed oils and polysaccharides. Red grape pomace can be an important source of polysaccharides, but they are currently little studied and even less with viable and environmental extraction processes (green extraction), such as flash extraction. The residual amount of the fraction rich in pectin (residual pulp) and component rich in hemicellulose in the pomace and the strength of association of the pectin with the cellulose-xyloglucan network depend on the degree of extractability of the polysaccharides in red winemaking and on the winemaking conditions.

Yeast species S. cerevisiae, S. uvarum, S. kudriavzevii and their hybrids present clear metabolic differences, even when we compared S. cerevisiae wine versus wild strain. These species and hybrids produced significantly higher amounts of glycerol, organic acids, 2,3-butanediol, and 2-phenyl ethanol and a reduction of the ethanol yield, properties very interesting in the sector to deal with climate change effects. To understand the existing differences, we have used several omics techniques to analyze the dynamics of the (intra- and extracellular) metabolomes and/or transcriptomes of representative strains of S. cerevisiae, S. uvarum, S. kudriavzevii, and hybrids.

Grapes are one of the world’s primary fruit crops, and pruning activities generate high amounts of annual wood wastes [1]. These pruning shoots contain valuable phenolic compounds and could have numerous potential applications [1,2]. Consequently, the aim of this work was to evaluate the physico-chemical properties of vine pruning residues with potential as enological additives. For this purpose, grapevine shoots from 12 varieties grown in Chile were collected during the winter of 2021.

In the production of sparkling wines, during the second fermentation, mannoproteins are released by yeast autolysis, which affect the quality of the wines. The effect of mannoproteins has been extensively studied, and may affect aroma and foam quality. However, there are no studies on the effect of other polysaccharides such as those from grapes. Considering the large production of waste from the wine industry, it was proposed to obtain polysaccharide-rich extracts from some of these by-products[1].

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).