Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.

Aged red wines possess phenolic composition very different from young ones due to the transformations among native grape phenolics and the formation of new polymeric polyphenols during aging process.

Grapevine (Vitis vinifera L.) is a globally cultivated crop and economically significant, particularly in the wine industry (Varela et al., 2024). Climate change is already affecting vineyards and is expected to worsen (Averbeck et al., 2019; Dupuis and Knoepfel, 2011).

Every year, viticulture is facing several outbreaks caused by established diseases, such as downy mildew and grey mould, which possess different life cycles and modes of infection. To cope with these different aggressors, grapevine must recognize them and arm itself with an arsenal of defense strategies.
The regulation of secondary metabolites is one of the first reactions of plants upon pathogen challenge. Their rapid biosynthesis can highly contribute to strengthen the defense mechanisms allowing the plant to adapt, defend and survive.

Astringency is a crucial sensory attribute typically described as the drying and/or puckering sensation occurring after the consumption of tannin-rich foods and beverages. In this study, thirty-seven red wines from different varieties, origins and styles were evaluated, analyzing both chemical and sensory features. Principal Component Analysis was used for dimensionality-reduction and for correlating selected chemical parameters against astringency. The results showed that tannin content was the most important chemical parameter influencing overall astringency but more clearly the dryness sub-quality, followed by pH, titratable acidity and alcohol content.