Effect of topography on vine evapotranspiration and water status in hillside vineyards

The aim of this study is to provide an overview of the terroir of Saint-Romain, Burgundy, based on three main information sources: official data relating to vines (CVI), soil cartography and a survey of winegrowers’ practices.

This essay was aimed to describe the clonal diversity of Oenococcus oeni in the malolactic fermentation of the carbonic maceration (CM) winemaking. The free and the pressed liquids from CM were sampled and compared to the wine from a standard winemaking with previous destemming and crushing (DC) of grapes [1]. O. oeni strain typification was performed by PFGE as González-Arenzana et al. described (2014) [2]. Results showed that 13 genotypes, referred as to letters, were distinguished from the 49 isolated strains, meaning the genotype “a” the 27%, the “b” the 14%, the “c” the 12%, the “d and e” the 10 % each other, and the remaining ones less than the 8% each one.

In recent years, “no-low” products seem to become a new worldwide trend. It appears to be a possible answer to the well-known context of climate change, the decline in wine consumption, and the wellness/health trend (“free from” claims, vegan, and so on…) That consumers are looking for. The aim of this study is to provide an overview of the “no-low” products sold in the french market (but not only french products), focusing on the labelling, packaging, and sales presentation of these products.

The aim of this study was to evaluate the effects of different soil types on the chemical composition of Mediterranean red wines, through untargeted and targeted 1H-NMR metabolomics. One milliliter of raw wine was analyzed by means of a Bruker Avance II 400 spectrometer operating at 400.15 MHz. The spectra were recorded by applying the NOESYGPPS1D pulse sequency, to achieve water and ethanol signals suppression. No modification of the pH was performed to avoid any chemical alteration of the matrix. The generation of input variables for untargeted analysis was done via bucketing the spectra. The resulting dataset was preprocessed prior to perform unsupervised PCA, by means of MetaboAnalyst web-based tool suite. The identification of compounds for the targeted analysis was performed by comparison to pure compounds spectra by means of SMA plug-in of MNova 14.2.3 software. The dataset containing the concentrations (%) of identified compounds was subjected to one-way analysis of variance (ANOVA) to highlight significant differences among the wines. The untargeted analysis, carried out through the PCA, revealed a clear differentiation among the wines. The fragments of the spectra contributing mostly to the separation were attributed to flavonoids, aroma compounds and amino acids. The targeted analysis leaded to the identification of 68 compounds, whose concentrations were significant different among the wines. The results were related to soils physical-chemical analysis and showed that: 1) high concentrations of flavan-3-ols and flavonols are correlated with high clay content in soils; 2) high concentrations of anthocyanins, amino acids, and aroma compounds are correlated with neutral and moderately alkaline soil pH; 3) low concentrations of flavonoids and aroma compounds are correlated with high soil organic matter content and acidic pH. The 1H-NMR metabolomic analysis proved to be an excellent tool to discriminate between wines originating from grapes grown on different soil types and revealed that soils in the Mediterranean area exert a strong impact on the chemical composition of the 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.