Reaction Mechanisms of Copper and Iron with Hydrogen Sulfide and Thiols in Model Wine

Malolactic fermentation (MLF) is a secondary step in the vinification process and it follows alcoholic fermentation (AF) which is predominantly carried out by Saccharomyces cerevisiae. These two processes result in the degradation of metabolites to produce secondary metabolites which also contribute to the final wine flavour and quality. AF results in the production of ethanol and carbon dioxide from sugars and MLF stems from the degradation of L-malic acid (a dicarboxylic acid) to L-lactic acid (a monocarboxylic acid). The latter process results in a smoother texture as the acidity of the wine is reduced by the process, it also adds to the flavour complexity of the wine.

With climate change, it is progressively more often to obtain grapes with an acceptable content in sugars or acids but with immature tannins described as green, aggressive or hard (noted as GAH onwards). During winemaking, the oenologist has to make decisions related to the elaboration of such grapes based mainly on empirical experience, given the lack of objective criteria to this concern. An increase in the chemical and sensory knowledge of immature tannins would allow managing this GAH character of grapes with the maximum possible efficiency during winemaking processes. The present work aims at isolating and identifying the group of compounds responsible for the GAH character present in wines.

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.

Bases on oxoluminescence, we have developed an innovative device for measuring dissolved oxygen in wines in barrels without opening the bung. This system is directly inserted into the wood during the barrel elaboration and can be positioned at different locations of the barrel (the head, the hull …). During two successive vintages we have used this device notably to follow the oxygen dissolved of whites wines elaborated in barrels. This allowed us initially to monitor the oxygen levels of the harvest to bottling the whole elaboration process in barrels of white wines without using techniques of measurement suitable to modify the real values in wines (opening the bung to plunge an oximeter).

A method of suspect screening analysis to study grape metabolomics, was developed [1]. By performing ultra-high performance liquid chromatography (UHPLC) – high-resolution mass spectrometry (HRMS) analysis of the grape extract, averaging 320-450 putative grape compounds are identified which include mainly polyphenols. Identification of metabolites is performed by a new HRMS-database of putative grape and wine compounds expressly constructed (GrapeMetabolomics) which currently includes around 1,100 entries.