Sensory definition of green aroma concept in red French wines. Evidence for the contribution of novel volatile markers

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.

The occurrence of faults and taints in wine, such as those caused by microbial spoilage or various taints, have resulted in significant financial losses to wine producers. The wine industry commits significant financial resources towards fining and taint removal processes each year. Fining involves the addition of one or more adsorptive substrates to juice or wine to bind certain components, thus reducing their concentration [1]. However, these processes are often not selective and can also remove desirable flavour and aroma compounds.

Grapevine berries produce thousands of secondary metabolites of diverse chemical nature that have been largely detailed in the past due to their importance for defining wine quality. The wide Vitis vinifera diversity, resulting in thousands of different varieties well detailed in many studies regarding berries, is still not investigated in vegetative organs, leaves in particular. Deepening knowledge related to this aspect could be of great interest for many reasons (for example the possibility of using leaf extract for pharmaceutical, cosmetic and nutrition purposes) but, above all, for understanding the susceptibility of different grapevine varieties to pathogens.

“The Human being has an excellent ability to detect and discriminate odors but typically has great difficulty in identifying specific odorants”(1). Furthermore, “from a cognitive point of view the mechanism used to judge wines is closer to pattern recognition than descriptive analysis.” Therefore, when one wants to reveal the volatile “wine-like feature” pattern recognition techniques are required. Sensomics is one of the most recent “omics”, i.e. a holistic perspective of a complex system, which deals with the description of substances originated from microorganism metabolism that are “active” to human senses (2). Depicting the relevant volatile fraction in wines has been an ongoing task in recent decades to which several research groups have allocated important resources. The most common strategy has been the “target approach” in order to identify the “key odorants” for a given wine varietal.

Wine aroma is influenced by several viticultural and oenological factors. In this study we used experimental wine making in a full factorial design to determine the impact of grapevine age, must turbidity, and yeast strain on the aroma of Vitis vinifera L. cv. Riesling wines. A recently developed, non-targeted SPME-GC-MS fingerprinting approach for wine volatiles was used. This approach includes the segmentation and mathematical transformation of chromatograms in combination with Parallel Factor Analysis (PARAFAC) and subsequent deconvolution of important chromatogram segments.