Ripening of cv. Cabernet Sauvignon grapes: polysaccharides fractions evolution and phenolic extractability

Among red wines ethyl esters, those from short hydroxylated and branched-chain aliphatic acids constitute a family with a particular behavior and sensory importance. They have been previously discussed in the literature [1] and recent studies have established that some of them were strongly involved in of red wines’ fruity aroma [2]. As some among them have an asymmetrical carbon atom, it seemed important to separate their different enantiomers to obtain an accurate assessment of their organoleptic impact. Three chiral esters have been identified, presenting alkyl and/or hydroxyle substituants: ethyl 2-hydroxy-4-methylpentanoate, ethyl 2-methylbutanoate, and ethyl 3-hydroxybutanoate.

1,1,6-trimethyl-1,2-dihydronaphtelene (TDN) evokes the odor of “petrol” in wine, especially in the variety Riesling. Increasing UV-radiation due to climate change intensifies formation of carotenoids in the berry skins and an increase of TDN-precursors1. Exploring new viticultural and oenological strategies to limit TDN formation in the future requires precise knowledge of TDN thresholds in different matrices. Thresholds reported in the literature vary substantially between 2 µg/L up to 20 µg/L2,3,4 due to the use of different methods. As Riesling grapes are used for very different wine styles such as dry, sweet or sparkling wines, it is essential to study the impact of varying ethanol and carbonation levels.

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can consistently stimulate (MLF+ phenotype) or inhibit (MLF- phenotype) malolactic bacteria and the MLF process as a function of numerous winemaking practices, but the molecular evidence behind still remains a mystery. In this study, such evidence was elucidated by the direct comparison of extracellular metabolic profiles of MLF+ and MLF- yeast phenotypes. Untargeted metabolomics combining ultrahigh-resolution FT-ICR-MS analysis, powerful machine learning methods and a comprehensive wine metabolite database, discovered around 800 putative biomarkers and 2500 unknown masses involved in phenotypic distinction.

Sorting Chenin blanc is one of the most important white wine cultivars in South Africa. It has received a lot of attention and accolades in the past years and more research than ever is dedicated to this versatile cultivar. According to the Chenin blanc association of South Africa, there are three recognized dry wine styles, Fresh and Fruity (FF), Rich and Ripe Unwooded
(RRU), and Rich and Ripe Wooded (RRW). They are traditionally established with the aid of expert sensory evaluation, but the cost and the (subjective) human factor are aspects to be taken into account. A more objective and possibly robust way of assessing and attributing these styles can be the use of chemical analysis.

Yeast cells possess a cell wall comprising primarily glycoproteins, mannans, and glucan polymers. Several yeast phenotypes relevant for fermentation, wine processing, and wine quality are correlated with cell wall properties. To investigate the effect of wine fermentation on cell wall composition, a study was performed using mid-infrared (MIR) spectroscopy coupled with multivariate methods (i.e., PCA and OPLS-DA). A total of 40 yeast strains were evaluated, including Saccharomyces strains (laboratory and industrial) and non-Saccharomyces species. Cells were fermented in both synthetic MS300 and Chardonnay grape must to stationery phase, processed, and scanned in the MIR spectrum.