Focus on terroir studies in the eger wine region of Hungary

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.

The use of cover crops in vineyards has been encouraged by positive effects on wine grape yield and sensory attributes, and improved soil function. This study examined the efficacy of three alleyway and three undervine cover crop treatments in an organic vineyard in the semiarid Okanagan Valley, Canada in 2021.

Carbonic Maceration (CM) winemaking is typically used in different European regions. But It is paradoxical that being a traditional processing system and widely used in many wineries, some of the phenomena that take place and the parameters that characterize them are barely known. In this vinification system the intact grape clusters are placed in a carbon dioxide (CO2) enriched medium, and they immediately change from a respiratory metabolism to an anaerobic fermentative metabolism called intracellular fermentation, which is carried out by grape enzymes. But some grapes located in the lower zone of the tank are crushed by the weight of the ones above and release must, which is fermented by yeasts.

In response to changes in their environment, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soil-plant-atmosphere continuum. Expressed as bulk stomatal conductance at the canopy scale, it varies diurnally in response to changes in vapor pressure deficit and net radiation, and over the season to changes in soil water deficits and hydraulic conductivity of both soil and plant. It is necessary to characterize the response of conductance to these variables to better model how vine transpiration also responds to these variables. Furthermore, to be relevant for vineyard-scale modeling, conductance is best characterized using data collected in a vineyard setting. Applying a crop canopy energy flux model developed by Shuttleworth and Wallace, bulk stomatal conductance was estimated using measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. These measurements were taken on several vines in a non-irrigated vineyard in Bordeaux France, using equipment that did not interfere with ongoing vineyard operations. An inverted Penman-Monteith equation was then used to calculate bulk stomatal conductance on 15-minute intervals from July to mid-September 2020. Time-series plots show significant diurnal variation and seasonal decreases in conductance, with overall values similar to those in the literature. Global sensitivity analysis using non-parametric regression found transpiration flux and vapor pressure deficit to be the most important input variables to the calculation of bulk stomatal conductance, with absorbed net radiation and bulk boundary layer conductance being much less important. Conversely, bulk stomatal conductance was one of the most important inputs when calculating vine transpiration, further emphasizing the need for characterizing its response to environmental changes for use in vineyard water use modeling.

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.