Optimization of the acquisition of NIR spectrum in grape must and wine 

Grapevine (Vitis vinifera L.) exhibits a high level of genetic and phenotypic diversity among the approximately 6000 cultivars recorded. This perennial crop is highly vulnerable to numerous fungal diseases, including esca, which is a complex vascular pathology that poses a significant threat to the wine sector, as there is currently no cost-efficient curative method[1]. In this context, an effective approach to mitigate the impact of such diseases is by leveraging the crop’s genetic diversity. Indeed, susceptibility to esca disease appears to vary between cultivars, under artificial or natural infection. However, the mechanisms and varietal characteristics underlying cultivar susceptibility to esca are still unknown.

Grapes are one of the world’s primary fruit crops, and pruning activities generate high amounts of annual wood wastes [1]. These pruning shoots contain valuable phenolic compounds and could have numerous potential applications [1,2]. Consequently, the aim of this work was to evaluate the physico-chemical properties of vine pruning residues with potential as enological additives. For this purpose, grapevine shoots from 12 varieties grown in Chile were collected during the winter of 2021.

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).

Wine acidity is a parameter of great importance that influences different quality factors of the product such as biological stability or organoleptic characteristics. In the current context of climate change, which gives rise to wines with higher levels of ethanol and lower acidity, the biological acidification with yeast species such as Lachancea thermotolerans could be a solution.
In this work, the effect of the inoculation of different L. thermotolerans on the acidity of wine was studied.

In the context of climate change with increasing evaporative demand, understanding the water use behavior of different grapevine cultivars is of critical importance. Carbon isotope discrimination (δ13C) measurements in wine provide a precise and integrated assessment of the water status of the vines during the sugar accumulation period in grape berries. When collected over multiple vintages on different cultivars, δ13C measurements can also provide insights into the effects of genotype on water use efficiency.