Implementation of hyperspectral image analysis for evaluating table grape quality on bunch and berry level

Anthocyanins and phenolic compounds play a crucial role in winemaking, contributing to the profile, flavor, color, texture, and stability of wine. Grape clusters, specifically Vitis vinifera L. cv. Grenache, were handpicked from a commercial vineyard sited in Tudelilla, La Rioja, Spain (42°18′ 52.26″, Long. -2°7′ 59.15″, Alt. 582 m) on five distinct dates from veraison to harvest during the 2015 season. Non-contact spectral measurements were conducted on intact grape berries using a VIS-NIR spectrometer operating in the 570 – 1000 nm spectral range under controlled laboratory conditions, positioned at a distance of 25 cm from the berries. The quantification of 16 anthocyanins and phenols in 120 grape clusters was performed using HPLC, established as the reference method for validating the spectral tool.

Grape postharvest dehydration is a widely used technique for the special wines production, where genetic features, ripeness degree and environmental factors strongly influence the metabolic processes [1].

La recherche, conduite en 2002, a envisagé l’estimation, a priori, de l’effet du changement mesoclimatique sur le potentiel qualitatif de la région viticole de la Serra Gaúcha (Vallée du Rio das Antas) – Brésil, en fonction de la construction de 3 usines hydroélectriques.

In Spain, the wide diversity of red grapevine varieties represents an advantage when choosing the most suitable one for cultivation based on different climatic conditions, without implying a loss of their enological potential.

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.