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  • Universite de Bordeaux  (2)
  • 1
    In: OENO One, Universite de Bordeaux, Vol. 55, No. 1 ( 2021-03-03), p. 223-234
    Abstract: Polyfunctional thiols have previously been shown to be key aroma compounds in Sauvignon blanc and more recently in Chardonnay wines. Their role in other wine varieties such as those made from three popular indigenous Cypriot grape varieties has remained unexplored. As an extension of a previous project that profiled the sensory and chemical characteristics of Cypriot wines and their comparison to Australian wines, this study aimed to investigate five potent thiols in Xynisteri, Maratheftiko, Giannoudhi, Pinot gris, Chardonnay and Shiraz wines. Wines were analysed utilising Stable Isotope Dilution Assay (SIDA) with derivatisation and High-Performance Liquid Chromatography–Tandem Mass Spectrometry (HPLC-MS/MS). The varietal thiols measured were 4-methyl-4-sulfanylpentan-2-one (4MSP) that has an aroma of “boxwood” and “cat urine” at high concentration, 3-sulfanylhexan-1-ol (3SH) which has been described as having a “grapefruit/tropical fruit” aroma, and 3-sulfanylhexyl acetate (3SHA) that has also been described as having an aroma of “passionfruit”. Additionally, two other potent thiols were measured including benzyl mercaptan (BM) that has an aroma of “smoke and meat” and furfuryl thiol (FFT) that has been described as having a “roasted coffee” like aroma. The reason these thiols are known as potent thiols are due to their very low aroma detection thresholds in the low ng/L (ppt) range. Of the thiols that were measured, 3SH was the only varietal thiol detected in the red wine samples. All of the white wine samples contained 3SH, BM and 3SHA, whereas 4MSP was only detected in Pinot gris and three Xynisteri wines. The potent thiol, FFT, was detected only in the Chardonnay and four of the Xynisteri wines. Interestingly the thiols that were present in the samples were found at concentrations above their aroma detection thresholds (determined in hydroalcoholic solutions), especially 3SH which was found in an order of magnitude above its aroma detection threshold. These findings provide early knowledge of the presence of these thiols in Cypriot wines, compared with Australian wines and establish any relationships between this chemical data with previous wine sensory profile data.
    Type of Medium: Online Resource
    ISSN: 2494-1271
    Language: Unknown
    Publisher: Universite de Bordeaux
    Publication Date: 2021
    detail.hit.zdb_id: 2878923-4
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  • 2
    Online Resource
    Online Resource
    Universite de Bordeaux ; 2021
    In:  OENO One Vol. 55, No. 4 ( 2021-10-08), p. 19-33
    In: OENO One, Universite de Bordeaux, Vol. 55, No. 4 ( 2021-10-08), p. 19-33
    Abstract: Understanding how wine compositional traits can be related to sensory profiles is an important and ongoing challenge. Enhancing knowledge in this area could assist producers to select practices that deliver wines of the desired style and sensory specifications. This work reports the use of spectrofluorometry in conjunction with chemometrics for prediction, correlation, and classification based on sensory descriptors obtained using a rate-all-that-apply sensory assessment of Cabernet-Sauvignon wines (n = 26). Sensory results were first subjected to agglomerative hierarchical cluster analysis, which separated the wines into five clusters represented by different sensory profiles. The clusters were modelled in conjunction with excitation-emission matrix (EEM) data from fluorescence measurements using extreme gradient boosting discriminant analysis. This machine learning technique was able to classify the wines into the pre-defined sensory clusters with 100 % accuracy. Parallel factor analysis of the EEMs identified four main fluorophore components that were tentatively assigned as catechins, phenolic aldehydes, anthocyanins, and resveratrol (C1, C2, C3, and C4, respectively). Association of these four components with different sensory descriptors was possible through multiple factor analysis, with C1 relating to ‘dark fruits’ and ‘savoury’, C2 with ‘barnyard’, C3 with ‘cooked vegetables’ and ‘vanilla/chocolate’, and C4 with ‘barnyard’ and a lack of C1 descriptors. Partial least squares regression modelling was undertaken with EEM data and sensory results, with a model for perceived astringency being able to predict the panel scores with 68.1 % accuracy. These encouraging outcomes pave the way for further studies that relate sensory traits to fluorescence data and move research closer to the ultimate goal of predicting wine sensory expression from a small number of compositional factors.
    Type of Medium: Online Resource
    ISSN: 2494-1271
    Language: Unknown
    Publisher: Universite de Bordeaux
    Publication Date: 2021
    detail.hit.zdb_id: 2878923-4
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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