South Africa’s top 10 Sauvignon blanc wines. How do the chemical and sensory profiles compare?

The Ability of PVPP (Polyvinylpolypyrrolidone), PVP-DEGMA-TAIC (copolimerization of N-vinyl-2-pyrrolidinone with ethylene glycol dimethacrylate and triallyl isocyanurate) and PAEGDMA
(poly(acrylamide-co-ethylene glycol dimethacrylate)) polymers was tested as removal agents for Fumonisin B1 (FB1) and Fumonisin B2 (FB2) from model solutions and red wine. The polymers removal capacity was checked at three different resident times (2, 8 and 24 hours of contact time between the polymer and the sample), showing no differences in the percentage of FB1 and FB2 removal. Then, different polymer concentrations (1, 5 and 10 mg mL-1) were tested in model solution with and without phenolics (i.e. gallic acid and 4-methylcatechol).

Fermentative aromas (especially esters and higher alcohols) highly impact the organoleptic profile of young and white wines. The production of these volatile compounds depends mainly on temperature and Yeast Available Nitrogen (YAN) content in the must. Available dynamic models predict the main reaction
(bioconversion of sugar into ethanol and CO2 production) but none of them considers the production kinetics of fermentative aroma compounds during the process of fermentation. We determined the production kinetics of the main esters and higher alcohols for different values of initial YAN content and temperature, using an innovative online monitoring Gas Chromatography device.

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.

Phenolic compounds are very important in crop plants and have been the subject of a large number of studies. Three main reasons can be cited for optimizing the level of phenolic compounds in crop plants: their physiological role in plants, their technological significance for food processing, and their nutritional characteristics1 Indeed, an enormous diversity of phenolic antioxidants is found in fruits and vegetables, and their presence and roles can be affected or modified by several pre- and postharvest cultural practices and/or food processing technologies (Ruiz-García et al. 2012, Goldman et al. 1999, Tudela et al. 2002). In winegrapes, the technological importance of phenolic compounds, mainly flavonoids, is well-known.

The use of next-generation sequencing (NGS) has helped understand microbial genetics in oenology. Current studies mainly focus on barcoded amplicon NGS but not shotgun sequencing, which is useful for functional analyses. Since the high percentage of grapevine DNA conceals the microbial DNA in must, the majority of sequencing data is wasted in bioinformatic analyses. Here we present capture depletion of grapevine whole genome DNA.