Use of ultrasounds to accelerate aging on lees of red wines

Aims: The aims of this work were (1) to assess the changes in some of the main bioclimatic indices used for climate viticultural zoning within the Rioja Appellation area in the north of Spain between 1950-2014 (60 years), and (2) to carry out a comprehensive sociological evaluation among grapegrowers and winemakers of this region, to better understand the impact of climate change on their activity, their degree of concern about it and the potential adaptation measures they would be willing to adopt to cope with it in future years.

In the last decades, climate change required already adaptation of vineyard management. Increase in temperature and unexpected weather events cause changes in all phenological stages requiring new management tools. For example, defoliation can be a useful tool to reduce the sugar content in the berries creating differences in the wine profiles. In a ten-year field experiment using Riesling (Vitis vinifera L, planted 1986, Geisenheim, Germany), various mechanical defoliation strategies and different intensities were trialed until 2016 before the vineyard was uprooted. Wood was sampled from the plant compartments root, trunk, cordon and shoot for analyses of physicochemical properties (e.g. lignin and element content, pH, diameter), nonstructural carbohydrates and the microbial communities. The aim of the study was to investigate the influence of reduced canopy leaf area on the sink-source allocation into different compartments and potential changes of the fungal and prokaryotic wood-inhabiting community using a metabarcoding approach. Severe summer pruning (SSP) of the canopy and mechanical defoliation (MDC) above the bunch zone decreased the leaf area by 50% compared to control (C). SSP reduced the photosynthetic capacity, which resulted in an altered source-sink allocation and carbohydrate storage. With lower leaf area, less carbohydrates are allocated. This for example resulted in a decreased trunk diameter. Further, it affected the composition of the grapevine wood microbiota. SSP and MDC management changed significantly the prokaryotic community composition in wood of the root samples, but had no effect in other compartments. In general, this study found strong compartment and less management effects of the microbial community composition and associated physicochemical properties. The highest microbial diversities were identified in the wood of the trunk, and several species were recorded the first time in grapevine.

The phenolic component of red wine is responsible for important elements of flavor and mouthfeel, and thus quality of the finished wine. Additionally, many of these phenolics have been associated with health benefits such as reduction of the risk of developing cardiovascular disease, cancer, osteoporosis and preventing Alzheimer’s disease. While the origins, concentrations, and chemistries of the phenolics in a finished red wine are well known, the fundamental mechanisms and kinetics of extraction of these phenolics from grape skins and seeds during red wine fermentation are poorly understood. This lack of knowledge regarding the extraction mechanisms of phenolics during red wine fermentation makes informed manipulations of the finished wine’s phenolic composition difficult.

Grapevine downy mildew, caused by the oomycete Plasmopara viticola, is responsible for significant economic losses each year and for a large proportion of the fungicides used in viticulture.

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.