Correlations between N,S,O-heterocycle levels and age of Champagne base wines

Saccharomyces cerevisiae (SC) is the main driver of alcoholic fermentation however, in wine, non-Saccharomyces species can have a powerful effect on aroma and flavor formation. This study aimed to compare untargeted volatile compound profiles from SPME-GC×GC-TOF-MS of Sauvignon blanc and Shiraz wine inoculated with six different non-Saccharomyces yeasts followed by SC. Torulaspora delbrueckii (TD), Lachancea thermotolerans (LT), Pichia kluyveri (PK) and Metschnikowia pulcherrima (MP) were commercial starter strains, while Candida zemplinina (CZ) and Kazachstania aerobia (KA), were isolated from wine grape environments. Each fermentation produced a distinct chemical profile that was unique for both grape musts. The SC-monoculture and CZ-SC sequential fermentations were the most distinctly different in the Sauvignon blanc while the LT-SC sequential fermentations were the most different from the control in the Shiraz fermentations.

Different separation techniques are frequently used during vinification process. Nowadays, clarification and microbiological stabilization of wine or beer can be done using precoat filters or crossflow filters to remove yeast and bacteria. Kieselguhr powders are the most used filter aids for precoat filtration. Their crystalline structure and their pulverulent nature induce ecotoxicological risks when used. Moreover, regeneration and reuse of these filter aids is not efficient and the filtration waste requires cost effective retreatment.

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.

Filtration is a critical step in ensuring the clarity and microbial stability of wine prior to bottling. However the process of filtering potentially reduces red wine quality by removing some of the macromolecules that contribute to the texture of the wine. Commercial red wines, Cabernet Sauvignon (CAS) and Shiraz (SHZ), of two vintages and two grades (premium grade wines from the older vintage: CAS13 and SHZ13; and standard grade wines from a younger vintage: CAS14 and SHZ14) were filtered through industrial-scale commercial filtration units prior to bottling. Samples were taken before and after cross-flow filtration, lenticular filters, 0.65 µm and 0.45 µm pore size nylon membrane filters. The concentration and composition of macromolecules, including tannins and polysaccharides, were measured in all samples as well as particle size distribution and wine colour.

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.