Application of high power ultrasounds during red wine vinification

A part, at least, of the fruity aroma of red wines is the consequence of perceptive interactions between various aromatic compounds, particularly ethyl esters and acetates, which may contribute to the perception of fruity aromas, specifically thanks to synergistic effects.1,2 The question of the indirect impact of non-fruity compounds on this particular aromatic expression has not yet been widely investigated. Among these compounds higher alcohols (HA) represent the main group, from a quantitative standpoint, of volatiles in many alcoholic beverages. Moreover, some bibliographic data suggested their contribution to the aromatic complexity by either increasing or masking flavors of wine, depending of their concentrations.

Grape phenolic compounds are very important constituents of red wine because, in addition to their antioxidant properties, they contribute to color, astringency and bitterness, oxidation reactions, interactions with proteins and ageing behavior of wines. The aim of our study was to assess the structural characteristics of grape and wine proanthocyanidins of Agiorgitiko variety and to evaluate the influence of the vintage year. Twelve vineyard locations were designated in the Nemea wine region. For three consecutive years (2012-2014), the grapes were harvested at technological maturity and the method of phloroglucinolysis was employed to determine the mean degree of polymerization (mDP) and subunit composition of the samples.

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.

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.

All techniques usually used to assay proteins was not reliable in vegetable extract due to interferences with the components included in extracts like polyphenols, tanins, pectines, aromatics compounds. Absorbance at 280nm, Kjeldhal assay, Biuret and Lowry methods, Acid Bicinchonique technique and Bradford assay give the results depending on the composition of extract, on the presence or not of detergent and on the raw material (Marchal, 1995). Another difficulty in these extracts for the quantification of proteins comes from the large amount of water included in vegetable and the low concentration of proteins. Thus in red wines, proteins are usually not taken into account due to their low concentration (typically below 10 mgL-1) and to the presence of anthocyanis and polyphenols.