Grape pomace (GP) is a winemaking by-product particularly rich in (poly)phenols and dietary fiber, which are the main active compounds responsible for its health-promoting effects. GP-derived products have been proposed to manage cardiovascular risk factors, including endothelial dysfunction, inflammation, hypertension, hyperglycemia, and obesity. Studies on the potential impact of GP on gut health are much more recent. However, it is suggested that, to some extent, this activity of GP as a cardiometabolic health-promoting ingredient would begin in the gastrointestinal tract as GP components (i.e., (poly)phenols and fiber) undergo extensive catabolism, mainly by the action of the intestinal microbiota, that gives rise to low-molecular-weight bioactive compounds that can be absorbed and utilized by the body.

Full understanding of grapevine responses to variable soil resources requires
assessing the grapevine root system. Grapevine root systems are expansive and examining deep roots (i.e., >40 cm)
is particularly important in conditions where grapevines increase reliance on deep soil resources, such as drought
or plant competition. Traditional methods of assessing roots rely on morphological traits associated specific
functions (e.g., root color, diameter, length), while recent methodological advances allow for estimating root
function more directly (e.g., omics). Yet, the potential of applying refined methods remains underexplored for roots
at deep depths.

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.

The growing demand for non-alcoholic wines, along with issues related to waste disposal and environmental pollution amid military conflicts, natural disasters, and industrial emissions, necessitates the implementation of environmentally sustainable technologies in the winemaking industry.

Grapevine (Vitis vinifera L.) is one of the most important economic crops in the world. Because of this importance, one finds widespread molecular genetic research on this species, an important element of which is high quality RNA.