LC-MS based metabolomics discriminates premium from varietal chilean Cabernet Sauvignon cv. Wines

Phenolic composition is essential to wine quality (Cleary et al., 2015; Bindon et al., 2020; Niimi et al., 2020) and its assessment is a strong industrial need to quality management.

The wine industry widely recognizes that early-harvested grapes or those with uneven ripeness at harvest can produce wines with an “unripe fruit” mouthfeel [1,2]. Despite this, it is still unknown which compounds cause these sensory flaws or the most effective winemaking techniques to address them.

Reductive wine fault is characterized by the presence of odors such as rotten eggs or spoiled camembert cheese, originating from hydrogen sulfide (H2S) and methanethiol (MeSH) [1]. These compounds stabilize in polysulfide forms, creating a complex pool of precursors that will revert to both molecules when the environment becomes anoxic [2].

Measuring the effect of oxygen consumption on the color of wines as the level of dissolved oxygen decreases over time is very useful to know how much oxygen a wine can consume without significantly altering its color. The changes produced in wine after being exposed to high oxygen concentrations have been studied by different authors, but in all cases the wine has been analyzed once the oxygen consumption process has been completed. This work presents the results obtained with the use of an equipment designed and made to measure simultaneously the level of dissolved oxygen and the spectrum of the wine, during the oxygen consumption process from saturation levels with air to very low levels, which indicate the total consumption of the dosed oxygen[1,2].

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.