Symbiotic microorganisms application in vineyards: impacts on grapevine performance and microbiome

Wine is one of the most representative components of Mediterranean diet. Moderate wine intake together with food, has been positively correlated with reduced risk of many chronic diseases. This beneficial effect seems to be ascribed to elevated polyphenolic content of wine [1]. Traditional approaches for the identification of wine biomarkers consumption include targeted metabolomics that focuses on the quantification of well-defined metabolites, losing a valuable information about a massive number of compounds. On the other hand, untargeted metabolomics can disclose a large quantity of signals corresponding to potential biomarkers in a single analysis with high sensitivity and resolution.

Grapevine trunk diseases (GTD) occur wherever grapes are grown and are considered the main biotic factor reducing yields and shortening vineyards’ lifespan. Currently, no product is available to eradicate GTD once grapevines are infected. Therefore, prophylactic strategies based on pruning wound protection and ‘remedial surgery’, the only eradication method based on the elimination of infected wood and renewal of the vine by means of new canes or suckers, are the only effective strategies available. The Canadian grape and wine industry focusses on a sustainable production and thus, looking for alternatives to chemicals for disease management is a top priority.

Atypical aging (ATA) is a white wine fault characterized by the appearance of notes of wet rag, acacia blossoms and naphthalene, along with the vanishing of varietal aromas. 2-aminoacetophenone (AAP) – a degradation compound of indole-3-acetic acid (IAA) – is regarded as the main sensorial and chemical marker responsible for this defect. About the origin of ATA, a stress reaction occurring in the vineyard has been looked as the leading cause of this defect. Agronomic, climatic and pedological factors are the main triggers and among them, drought stress seems to play a crucial role.[1]

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).

It is common to find tanks in the winery with wine below their capacity due to wine transfers between tanks of different capacities or the interruption of operations for periods of a few days. This situation implies the existence of an ullage space in the tank with prolonged contact with the wine causing its absorption/oxidation. Oxygen uptake from the air headspace over the wine due to differences in the partial pressure of O2 can be rapid, up to 1.5 mL of O2 per liter of wine in one hour and 100 cm2 of surface area1 and up to saturation after 4 hours.