Immobilization of S. cerevisiae and O. œni for the control of wine fermentation steps

In sparkling winemaking several yeasts can be used to perform the primary alcoholic fermentation that leads to the elaboration of the base wine. However, only a few Saccharomyces cerevisiae yeast strains are regularly used for the secondary in-bottle alcoholic fermentation 1. Recently, advances in yeast development programs have resulted in new breeds of interspecific wine yeast hybrids that ferment efficiently while producing novel flavours and aromas 2. In this work, sparkling wines produced using interspecific yeast hybrids for the secondary in-bottle alcoholic fermentation have been chemically and sensorially characterized.METHODS: Three commercial English base wines have been prepared for secondary in-bottle alcoholic fermentation with different yeast strains, including two commercial and several novel interspecific hybrids derived from Saccharomyces species not traditionally used in sparkling winemaking. After 12 months of lees ageing, the 14 wines produced were analysed for their chemical and macromolecular composition 3,4, phenolic profile 5, foaming and viscosity properties [6]. The analytical data were supplemented with a sensory analysis.

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.

The chemical composition of grape berries at harvest is one of the most important factors that should be considered to produce high quality wines. Among the different chemical classes which characterize the grape juice, the polyphenolic compound, such as flavonoids, contribute to the final taste and color of wines. Recently, an innovative non-destructive method, based on chlorophyll fluorescence, was developed to estimate the phenolic maturity of red grape varieties through the evaluation of anthocyanins accumulated in the berry skin. To date, only few data are available about the application of this method on white grape varieties.

Specific inactivated yeast derivatives (SYDs) from S. cerevisiae are obtained through thermal, mechanical, and enzymatic processes and are used to enhance wine quality.

By definition, Regenerative Agricultural Systems seek to promote soil and plant health by using photosynthesis for the removal and retention of atmospheric carbon dioxide into stable soil carbon.