Validation of the viticulture zoning methodology applied to determine the homogenous soil units present on D.O. Ribera de Duero region

Obtaining high-quality RNA from grape tissues, including berry pulp, berry skins, stems, rachis, or roots, is challenging due to their composition, which includes polysaccharides, phenolic compounds, sugars, and organic acids that can negatively affect RNA extraction. For instance, polyphenols and other secondary metabolites can bind to RNA, making it difficult to extract a pure sample. Additionally, RNA can co-precipitate with polysaccharides, leading to lower extraction yield. Also, sugars and organic acids can interfere with the pH and ionic properties of the extraction buffer. To address these challenges, we optimized a protocol for RNA isolation from grape tissues.

Phenolic compounds are among the most important quality factors of wines. They contribute to the organoleptic characteristics of wine such as colour, astringency, and bitterness. Although tannins found in wine can come from microbial and oak sources, the main sources of polyphenols are skin and seed from grapes. Yet, the link between grape seed phenolic content and wine composition, or even the link between seed maturity stage and wine composition are poorly studied. This work describes and explains the seed tannins kinetics release in wine, but also the impact of seed maturity stage on seed tannins extractability.

Plants have been shown to redistribute water between root sections and soil layers along a gradient of decreasing water availability. One benefit of this hydraulic redistribution is that water can be transported from roots in wet soil to others in dry soil, delaying the onset of water stress and increasing root longevity in dry environments. Grapevines are thought to redistribute water laterally across the trunk from wet to dry portions of the root system. However, it is unknown whether the phloem contributes to such water redistribution.

La brise de mer et les facteurs climatiques qu’elle entraîne (accélération de la vitesse du vent au cours de l’après midi, augmentation de l’humidité et baisse de la temperature) sont d’un intérêt particulier pour la viticulture.

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.