Chenin blanc Wine Volatiles and the Intensity of a Guava-like Flavour

Background and Aims:Wine aroma is influenced by complex interactions between various wine constituents. This study investigated the sensorial interactive effects of Sauvignon Blanc impact compounds, 3-mercaptohexan-1-ol, 3-isobutyl-2-methoxypyrazine with aldehydes typically originating from oxidation of white wines, methional and phenylacetaldehyde. Methods and Results: Spiked model wines were subjected to sensory descriptive analysis using a trained panel. The concentration of each compound varied from below aroma threshold value to high values reported in wine. Combinations of the four compounds were also evaluated sensorially. Depending on the concentration of the compounds, methional had a strong suppressive effect on the intensity of attributes contributed by 3-mercaptohexan-1-ol, such as grapefruit and guava, whereas methional and 3-isobutyl-2-methoxypyrazine enhanced the intensity of certain attributes, which included cooked beans and cooked potato. Conclusions: Complex sensory interactions may occur between Sauvignon Blanc impact compounds and compounds related to oxidation depending on concentration of the compounds. Oxidation-derived compounds such as methional can suppress pleasant aroma attributes linked to volatile thiols, while contributing negative attributes especially in the presence of 3-isobutyl-2-methoxypyrazine. Significance of the Study: This study highlights the importance of preserving fresh and fruity flavours while preventing the formation of unwanted aldehydes due to interactions that can influence the overall aroma profile of the wine. This study may also contribute to the sensory characterisation of oxidised Sauvignon Blanc wine.

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“…The use of the guava descriptor has rarely been reported in literature (Van Rooyen et al. , Swiegers et al. ).…”

Section: Resultsmentioning

confidence: 99%

Sensory interaction between 3-mercaptohexan-1-ol, 3-isobutyl-2-methoxypyrazine and oxidation-related compounds

Coetzee

Brand

Emerton

et al. 2015

Australian Journal of Grape and Wine Research

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“…Most esters found in alcoholic bever ages are produced by yeasts during fermentation as secondary products of sugar metabolism and constitute one of the largest and most important groups of compounds affecting flavour (Engan, 1974;Peddie, 1990;Fujii et al, 1994). However, a par ticular aroma property can only rarely be associated with a spe cific ester (Van Rooyen et al, 1982). The concentration of esters usually found in wine is generally well above their sensory threshold levels and they make up numerically the largest group of aroma compounds in alcoholic beverages (Salo, 1970a, b;De Wet, 1978;Simpson & Miller, 1984).…”

Section: Estersmentioning

confidence: 99%

Yeast and its Importance to Wine Aroma - A Review

Lambrechts¹,

Pretorius²

2019

SAJEV

325

330

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The most mysterious aspect of wine is the endless variety of flavours that stem from a complex, completely non-lin ear system of interactions among many hundreds of compounds. In its widest sense, wine flavour refers to the over all impression of both aroma and taste components. Aroma is usually associated with odorous, volatile compounds; the bouquet of wine refers to the more complex flavour compounds which evolve as a result of fermentation, ele vage and ageing. With the exception of terpenes in the aromatic grape varieties and alkoxypyrazines in the herba ceous cultivars, perceived flavour is the result of absolute amounts and specific ratios of many of these interactive compounds, rather than being attributable to a single "impact" compound. Without underestimating the complex ity of these interactive effects or negating the definitive role played by the accumulated secondary grape metabo lites in the varietal character of wine, this review will focus mainly on the contribution of yeast fermentation to the sensorial quality of the final product. Yeast and fermentation conditions are claimed to be the most important fac tors influencing the flavours in wine. Both spontaneous and inoculated wine fermentations are affected by the diversity of yeasts associated with the vineyard and winery. During the primary alcoholic fermentation of sugar, the wine yeast, Saccharomyces cerevisiae, together with other indigenous non-Saccharomyces species, produce ethanol, carbon dioxide and a number of by-products. Of these yeast-derived metabolites, the alcohols, acetates and C4-C8 1tfatty acid ethyl esters are found in the highest concentration in wine. While the volatile metabolites contribute to the fermentation bouquet ubiquitous to all young wines, the production levels of these by-products are variable and yeast strain specific. Therefore, this article also highlights the importance of untapping the hidden wealth of indige nous yeast species present on grapes, and the selection and genetic development of yeast starter culture strains with improved flavour profiles. In the future, some winemakers may prefer to use mixtures of indigenous yeast species and tailored S. cerevisiae strains as starter cultures to reflect the biodiversity and stylistic distinctiveness of a given region. This will help winemakers to fullfil the consumer's demand for individual wines with intact local character and to ensure the survival of wine's most enthralling aspect -its endless variety.

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“…Preprocessing was done by employing the programmes TUNE (to generate feature ratios), AUTOSCALE (to normalise the data) and SELECT as set out by Van Rooyen et al, 1982. Variable reduction procedure: The programme SELECT, operating on a principal component principle, was used to select 10 final features by using correlation to property weighting, the latter being either overall or aroma quality, depending on the analysis. The application of principal component analysis (PCA) further reduced the actual number of variables to three or four eigenvectors with factor loadings for each of the IO selected features, using the programmes KAPRIN, KATRAN and KA VARI as explained by Van Rooyen et al, (1982). The programme KA VARI applied the Varimax rotation to PCA vectors to elucidate important features (Preston-Whyte, 1974).…”

Section: Methodsmentioning

confidence: 99%