Port wine region settling

Wine aroma quality is a complex interplay of factors like terroir, vinification techniques, that modulate aroma compound composition.

The definition of the term “quality” in sensory evaluation of food products does not seem to be consensual. Descriptive or liking methods are generally used to differentiate between wines (Lawless et al., 1997). Nevertheless, quality evaluation of a product such as wine can also relate to emotional aspects. As exposed by Costell (2002), product quality is defined as an integrated impression, like acceptability, pleasure, or emotional experiences during tasting. According to the ‘modality appropriateness’ hypothesis which predicts that wine tasters weigh the most suitable sensory inputs for a specific assess- ment (Freides, 1974; Welch & Warren, 1980), the nature of the quality definitions may modulate sensory influences.

In general, there is a well-established lexicon related to wine aroma and taste properties; however mouth-feel-related vocabulary usually includes heterogeneous, multimodal and personalized terms. Gawel et al.
(2000) published a wheel related to mouthfeel properties of red wine. However, its use in scientific publications has been limited. The authors accepted that the approach had certain limitations as it included redundant and terms with hedonic tone and some others were absent. It is of high interest to generate a mouth-feel lexicon and finding the chemical compound or group of compounds responsible for such properties in red wine. In the present work a chemical fractionation method has been developed.

Depuis la création des outils d’amélioration et de suivi de la qualité, le CREDO développe et réalise des zonages de potentialités viticoles.

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.