Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.

Context and purpose of the study. Climate change is affecting wine production and induces a large variability in wine composition between vintages.

According to the vintage, it may be difficult for vine growers to make a decision regarding the type of wine in relation with the soils.

Aim: Sauvignon blanc displays a range of styles that can include prominent tropical and passionfruit aromas. Both sensory evaluation and chemical analysis have confirmed the above-average presence of ‘varietal thiols’ in the Sauvignon blanc wines from Marlborough, New Zealand.

One of the major challenges for food security and sovereignty is to produce stress-tolerant plants without introducing foreign DNA, because the legislative process, that bans transgenics, challenges us to find new solutions for producing plants that can survive the drought. To achieve this goal, we need to identify genes that can be modified to improve stress tolerance in plants. In this work, we present an online tool for exploring the transcriptome of grapevines under water stress, which is one of the most important abiotic stresses affecting viticulture. The tool is based on a comprehensive collection of rna-seq data from 997 experiments, covering four different tissues (leaf, root, berry, and shoot), various levels of water stress, and diverse genetic backgrounds (cultivars and rootstocks) with different levels of tolerance to water stress.