Characterization of various groups of pyranoanthocyanins in Merlot red wine

The above-ground parts of plants, which constitute the phyllosphere, have long been considered devoid of bacteria and fungi, at least in their internal tissues and microbial presence there was long considered a sign of disease. However, recent studies have shown that plants harbour complex bacterial communities, the so-called “microbiome”[1]. We are only beginning to unravel the origin of these bacterial plant inhabitants, their community structure and their roles, which in analogy to the gut microbiome, are likely to be of essential nature. Among their multifaceted metabolic possibilities, bacteria have been recently demonstrated to emit a wide range of volatile organic compounds (VOCs), which can greatly impact the growth and development of both the plant and its disease-causing agents.

Botrytis cinerea is a fungus that causes common infection in grapes and other fruits. In winemaking, its presence can be both considered desirable in the case of noble rot infection or undesirable when grey rot is developed. This fungus produces an extracellular enzyme known as laccase which is able to cause oxidation of phenolic compounds present in must and wine, causing most of the times a decrease in its quality and problems during the winemaking process [1]. Material and methods: Three B. cinerea strains (B0510, VA612 and RM344) were selected and grown in a liquid medium adapted from one previously described [2]. The enzyme was isolated by tangential ultrafiltration of the culture medium using a QuixStand system equipped with a 30 KDa filtration membrane.

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.

The aim of this work was to reduce the alcohol content of Tempranillo wine. Tempranillo wines were produced by grapes harvested at different ripening dates (August 11 which was 21 oBrix and September 28 with 25 oBrix). At the second date, the Tempranillo wines were elaborated as follows: grapes were destemmed, crushed and collected into 50 L stainless-steel vats. Before preferementative maceration in cold, 50 % (M1) and 70 % (M2) of the must have been replaced by the same percentage of must from the first harvest. In addition, a control wine (C) was performed with only grapes from the second harvest.

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.