Which potential for Near Infrared Spectroscopy to characterize rootstock effects on grapevines?

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.

Volatile phenols namely 4-ethylphenol and 4-ethylguaiacol are formed by enzymatic decarboxylation of hydroxycinnamic acids by Brettanomyces yeasts to give vinylphenols and subsequent reduction of the vinyl group to form the correspondent ethylphenols. The presence of these compounds in wine affects negatively its aromatic quality, conferring unpleasant animal and phenolic odor when present in quantities above the olfactory detection threshold [1]. Several methods have been described to remove these undesirable compounds from wines, including the use laccase enzymes [2, 3]. Due to this, the aim of this work was to evaluate the effect of several natural redox mediators on the activity of Botrytis cinerea laccase against these volatile phenols.

Climate change is increasing water needs in most of the wine growing regions while reducing the availability and quality of water resources for irrigation. In this context, the sustainability of Mediterranean viticulture depends on grapevine responses to the combinations of water and salt stress. With this aim, this work studies the effects of deficit irrigation and salinity on the physiology of the Tempranillo cultivar (Vitis vinifera L.) grafted onto a drought and salinity tolerant rootstock (1103 Paulsen).

This study examined the evolution of wine consumption trends from 2019 to 2024, analyzing market dynamics, sustainability preferences, and generational shifts in consumer behavior.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).