Accumulation of deleterious mutations in grapevine and its relationship with traits of interest for wine production and resilience

Grapevine cultivars can be unequivocally typed by both physical differences (ampelography) and genetic tests. However due to their very similar characteristics, the identification of clones within a cultivar relies on the accurate tracing of supply records to the point of origin. Such records are not always available or reliable, particularly for older accessions. Whole genome sequencing (WGS) provides the most highly detailed methodology for defining grapevine cultivars and more importantly, this can be extended to differentiating clones within those cultivars.

The odors of wines are diverse, complex and dynamic and much research has been devoted to the understanding of their chemical bases. However, while the “basic” chemical part of the problem, namely the identity of the chemicals responsible for the different odor nuances, was satisfactorily solved years ago, there are some relevant questions precluding a clear understanding. These questions are related to the physicochemical interactions determining the effective volatilities of the odorants and, particularly, to the perceptual interactions between different odor molecules affecting in different ways to the final sensory outputs.

Glutathione plays a key role in preventing some oxidative processes during winemaking. This molecule limits the must enzymatic oxidation, reacts with caffeic acid and generates a colourless compound that prevents subsequent browning. It also has a protective effect on wine aroma, preventing the oxidation of the volatile compounds with a high sensory impact.

The phenolic profile of a wine plays an essential role in its oxidative capacity and in both white and red wines it defines its shelf life[1]. The study of minority varieties to produce wines with peculiar characteristics necessarily includes the phenolic and oxidative characterization of the wines produced. This paper presents the study of wines made from 24 minority and majority white and red grape varieties, focusing on phenolic characteristics (total phenols, slightly polymerized phenols, highly polymerized phenols, anthocyanins…), color, as well as parameters related to the oxidability of the wines and their capacity to consume oxygen [2].

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).