Macrowine 2021
IVES 9 IVES Conference Series 9 Winemaking processes discrimination by using qNMR metabolomics

Winemaking processes discrimination by using qNMR metabolomics

Abstract

AIM: Metabolomics in food science has been increasingly used over the last twenty years. Among the tools used for wine, qNMR has emerged as a powerful tool to discern wines based on environmental factors such as geographical origin, grape variety and vintage (Gougeon et al., 2019a). Since human factors are less studied while they also contribute a lot to the wine making, we wondered if this technique could also dissociate physical or chemical processes used in oenology. The goal of this work is to allow a better understanding of the interactions between the oenological processes and wine by finding metabolites that are responsible of winemaking processes’s differentiations through 1H‑NMR metabolomics targeted and untargeted (fingerprinting) approaches combined with advanced chemiometrics.

METHODS: Wine analyses were realized by qNMR approaches. Targeted (based on nearly fifty wine constituents) and untargeted analyses were carried out on wines having undergone several physical and chemical processes. Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and similarity score (S-score) (Gougeon et al., 2019b) were performed out for the analytical discrimination of winemaking processes.

RESULTS: qNMR analyses associated with chemometrics allow discriminating not only the physical processed such as the filtration but also chemical processes like the maceration temperature, enzyme treatment and fining agent effects. Furthermore, the impacted metabolites were highlighted providing valuable data on the winemaking processes investigated.

CONCLUSIONS:

qNMR metabolomics offers a fast and reliable method to study the effects of winemaking practices on wine quality.

DOI:

Publication date: September 10, 2021

Issue: Macrowine 2021

Type: Article

Authors

Inès Le Mao

University of Bordeaux, Œnology EA 4577, USC 1366 INRA, INP, ISVV, 210 chemin de Leysotte, 33882 Villenave d’Ornon, France,Gregory Da Costa, Jean Martin, Wiame El Batoul, Charlyne Bautista, Soizic Lacampagne, Tristan Richard University of Bordeaux, Œnology EA 4577, USC 1366 INRA, INP, ISVV, 210 chemin de Leysotte, 33882 Villenave d’Ornon, France

Contact the author

Keywords

metabolomics, qnmr, winemaking processes, quality

Citation

Related articles…

Analysis of Cabernet Sauvignon and Aglianico winegrape (V. vinifera L.) responses to different pedo-climatic environments in southern Italy

Water deficit is one of the most important effects of climate change able to affect agricultural sectors. In general, it determines a reduction in biomass production, and for some plants, as in the case of grapevine, it can endorse fruit quality. The monitoring and management of plant water stress in the vineyard

Comparative studies on the dynamics of fermentation of selected wine yeasts

Alcoholic fermentation is an anaerobic biochemical process of oxidation-reduction in which carbohydrates are metabolized by the action of yeast enzymes in major products

Natural variability and vine-growers behaviour

Le vigneron est confronté à une variabilité naturelle omniprésente, liée au millésime et aux facteurs pédoclimatiques. Depuis 10 ans, en Champagne, la relation qu’entretient le vigneron avec l’espace a évolué. Les exemples d’entreprises collectives à vocation territoriale se sont multipliés : gestion de l’hydraulique viticole, maillages de groupements de conseil viticole (Magister), sites en confusion sexuelle, réseau maturation, analyses de sols par secteur, …

Where the sky is no limit — The transformation of wine marketing through text-to-video generation AI model

The introduction of ai-driven tools in digital content creation represents a significant shift in the landscape of marketing, particularly for industries reliant on rich visual storytelling such as the wine sector. The development of ai models like openai’s sora, runway’s gen-2 or google’s lumiere, which can generate realistic video content from textual descriptions, offers promising new avenues for enhancing brand narrative and consumer engagement. This research explores the potential of text-to-video (t2v) ai models to revolutionize wine marketing by creating dynamic, engaging content that captures the essence of vineyards and their products without the need for traditional video production processes.

Data mining approaches for time series data analysis in viticulture. Potential of the bliss (Bayesian functional linear regression with sparse step functions) method to identify temperature effects on yield potential

Context and purpose of the study – Vine development, and hence management, depends on dynamic factors (climate, soil moisture, cultural practices etc.) whose impact can vary depending upon their temporal modalities.