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

Phenolic compounds are important quality indicators in red wine. A large number of polyphenols play an important role in wine development, contributing to the colour and the sensory perception of the wines. Anthocyanins are the pigments responsible for the colour in young red wines while tannins are the principal contributors to the bitterness and the astringency of the wines. Wine polyphenols are considered more complex molecules than grape phenolics, due to the enormous number of chemical reactions which take place during the entire winemaking process and storage, forming more stable compounds.

Wine is a widely consumed alcoholic beverage with a high commercial value. More specifically, the worldwide consumption of rosé wine has increased by 20% since 2002[1]. But because of its high commercial value, it can become a subject of fraud, and authenticity control is necessarily required. More than one hundred polyphenols have been recently quantified in various rosé wines [2]. They are key components defining color, taste and quality of wines. Their amount and composition depend on many different factors such as grape variety, winemaking and age of the wine. In this study, the influence of geographic origin of some rosé French wines was investigated. An original and very fast UPLC-QTOF-MS method was developed and used to predict the geographic origin authenticity of rosé wines.

Global grape production amounts to approximately 70 million tons per year, with Europe contributing 61% of the world’s wine output, primarily from Italy, France, and Spain.

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can consistently stimulate (MLF+ phenotype) or inhibit (MLF- phenotype) malolactic bacteria and the MLF process as a function of numerous winemaking practices, but the molecular evidence behind still remains a mystery. In this study, such evidence was elucidated by the direct comparison of extracellular metabolic profiles of MLF+ and MLF- yeast phenotypes. Untargeted metabolomics combining ultrahigh-resolution FT-ICR-MS analysis, powerful machine learning methods and a comprehensive wine metabolite database, discovered around 800 putative biomarkers and 2500 unknown masses involved in phenotypic distinction.

One cultivar could produce distinct wines with typical properties and qualities different depending on its cultivated and its mesoclimatic conditions.