Genome wide association mapping of phenology related traits in Vitis vinifera L

Context and purpose of the study. Conventional viticulture relies heavily on synthetic inputs (fertilizers, pesticides), as well as mechanization to manage pests, weeds, and diseases and maximize yields.

Nowadays, winemaking is dealing with great challenges, notably climate change, disease resistance and low pesticide inputs, desire for more sustainable agricultural productions and permanent changing of consumer preference.

For a long time environment was known as one of the most important factors to characterize the quality of wines but at the same time it appears very difficult to distinguish inside the “terroir” the role of the single factor. These remarks partially explain why methods for viticultural evaluation are often quite different (Amerine et al., 1944; Antoniazzi et al., 1986; Asselin et al., 1987; Astruc et al., 1980; Bonfils, 1977; Boselli, 1991; Colugnati, 1990; Costantinescu, 1967; Costantini et al., 1987; Dutt et al., 1981; Falcetti et al., 1992; Fregoni et al., 1992; Hidalgo, 1980; Intrieri et al., 1988; Laville, 1990; Morlat et al., 1991; Scienza et al., 1990; Shubert et al., 1987; Turri et al., 1991).

Marselan wines have an unusual high proportion of seed derived tannins from grapes having high proportions of skins, which are rich in tannins. But the causes behind this characteristic have not yet been identified. In vintage 2023 wines were made at experimental scale (9 kg by experimental unit) from Arinarnoa, Marselan and Tannat Vitis vinifera grape cultivars by traditional maceration, and by techniques aimed to increase the wine content in skin derived tannin: addition of extraction enzymes, addition at vatting of grape-skin enological tannins, or by extended maceration, known to increase the seed derived tannin contents of wines.

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.