Effects of mesoclimate on the yield, quality and phenolic maturity of Grenache

Xinomavro is a red grape variety from Northern Greece (Protected Designation of Origin), known for the nice acidities, perfectly appropriate for sparkling wine production (Rosé and Blanc de Noir). The elabo- ration of sparkling wine requires technical as well as scientific skills. Although the impact of the yeast strains and their metabolites on the final product quality is well documented, the action of bacteria still remains unknown. The present work focuses (i) on the population diversity of lactic acid bacteria isolated from sparkling wines and (ii) on the technological effect of the species during sparkling wine elaboration.

Saccharomyces cerevisiae, as the workhorse of alcoholic fermentation, is a major actor of winemaking. In this context, this yeast species uses alcoholic fermentation to convert sugars from the grape must into ethanol and CO2 with an outstanding efficiency: it reaches on average 92% of the maximum theoretical yield of conversion. Moreover, S. cerevisiae is also known for its great genetic diversity and plasticity that is directly related to its living environment, natural or technological and therefore to domestication. This leads to a great phenotypic diversity of metabolites production.

Context and purpose of the study. Sparkling wine production is majorly impacted by climate change as sugar accumulation and aromatic development in grapes are often decoupled.

Aims: Soils are an intrinsic feature of the landscape and have influenced culturally and economically important terroir delineation in many wine-producing regions of the world. Soil physiochemical properties govern a wide array of ecosystem services, and can therefore affect grapevine health and fruit development. These physiochemical properties can reflect a combination of factors,

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.