Merging fast sensory profiling with non-targeted GC-MS analysis for multifactorial experimental wine making

Fermentative aromas (especially esters and higher alcohols) highly impact the organoleptic profile of young and white wines. The production of these volatile compounds depends mainly on temperature and Yeast Available Nitrogen (YAN) content in the must. Available dynamic models predict the main reaction
(bioconversion of sugar into ethanol and CO2 production) but none of them considers the production kinetics of fermentative aroma compounds during the process of fermentation. We determined the production kinetics of the main esters and higher alcohols for different values of initial YAN content and temperature, using an innovative online monitoring Gas Chromatography device.

1,1,6-trimethyl-1,2-dihydronaphtelene (TDN) evokes the odor of “petrol” in wine, especially in the variety Riesling. Increasing UV-radiation due to climate change intensifies formation of carotenoids in the berry skins and an increase of TDN-precursors1. Exploring new viticultural and oenological strategies to limit TDN formation in the future requires precise knowledge of TDN thresholds in different matrices. Thresholds reported in the literature vary substantially between 2 µg/L up to 20 µg/L2,3,4 due to the use of different methods. As Riesling grapes are used for very different wine styles such as dry, sweet or sparkling wines, it is essential to study the impact of varying ethanol and carbonation levels.

Initiatives have been ongoing in recent years to safeguard biodiversity in the oenological sector via a process of enhancement of ancient varieties, under a pressure of a market strongly oriented towards production deriving from native vines of specific geographical zones. In that sense, Aosta Valley
(Italy) has raised the need to preserve and characterize its minority vine varieties which have the potentiality to give varietal wines. Fumin represents the 7% of the production of the region with 16 hectares of vineyards and 753 hectolitres of derived wine. Due to its large phenolic potential, strong astringency and deep colour, it has long been, and is still today, assembled or blended with other varieties as occurs, for example, for the Torrette.

The use of multivariate statistics to correlate chemical data to spectral information seems as a valid alternative for the quantification of red wine phenolics. The advantages of these techniques include simplicity and cost effectiveness together with the limited time of analysis required. Although many
publications on this subject are nowadays available in the literature most of them only reported feasibility
studies. In this study 400 samples from thirteen fermentations including five different cultivars plus 150
wine samples from a varying number of vintages were submitted to spectrophotometric and chromatographic phenolic analysis.

In general, there is a well-established lexicon related to wine aroma and taste properties; however mouth-feel-related vocabulary usually includes heterogeneous, multimodal and personalized terms. Gawel et al.
(2000) published a wheel related to mouthfeel properties of red wine. However, its use in scientific publications has been limited. The authors accepted that the approach had certain limitations as it included redundant and terms with hedonic tone and some others were absent. It is of high interest to generate a mouth-feel lexicon and finding the chemical compound or group of compounds responsible for such properties in red wine. In the present work a chemical fractionation method has been developed.