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Untangling the wine metabolome by combining untargeted SPME–GCxGC-TOF-MS and sensory analysis to profile Sauvignon blanc co-fermented with seven different yeasts

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Abstract

Saccharomyces cerevisiae (SC) is the main driver of alcoholic fermentation, however for aroma and flavor formation in wine, non-Saccharomyces species can have a powerful effect. This study aimed to compare untargeted volatile compound profiles from SPME–GCxGC-TOF-MS and sensory analysis data of Sauvignon blanc wine inoculated with six different non-Saccharomyces yeasts followed by SC. Torulaspora delbrueckii (TD), Lachancea thermotolerans (LT), Pichia kluyveri (PK) and Metschnikowia pulcherrima (MP) where commercial starter strains, while Candida zemplinina (CZ) and Kazachstania aerobia (KA), were isolated from wine grape environments. Each wine showed a distinct profile both sensorially and chemically. SC and CZ wines were the most distinct in both of these cases. SC wine had guava, grapefruit, banana, and pineapple aromas while CZ wine was driven by fermented apple, dried peach/apricot, and stewed fruit as well as sour flavor. Chemically over 300 unique features were identified as significantly different across the fermentations. SC wine had the highest number of esters in the highest relative concentration but all the yeasts had distinct ester profiles. CZ wine displayed the highest number of terpenes in high concentration but also produced a large amount of acetic acid. KA wine was high in ethyl acetate. TD wine had fewer esters but three distinctly higher thiol compounds. LT wine showed a relatively high number of increased acetate esters and certain terpenes. PK wine had some off odor compounds while the MP wine had high levels of methyl butyl-, methyl propyl-, and phenethyl esters. Overall, this study gives a more detailed profile of these yeasts contribution to Sauvignon blanc wine than previously reported.

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Acknowledgments

This work was supported by funds from the GMPF program and Fondazione Edmund Mach (FEM) as well as research Grant VKR023371 from VILLUMFONDEN. We would like to thank Lallemand and Chr. Hansen for donating the commercial yeast strains used in this study. Also, the South African National Research Foundation (www.nrf.ac.za) and Human Resources Programme (THRIP) and Winetech for financial assistance. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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  1. Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), via E. Mach 1, 38010, San Michele all’Adige, TN, Italy

    Margaret E. Beckner Whitener, Jan Stanstrup, Silvia Carlin & Urska Vrhovsek

  2. Department of Viticulture and Oenology, Institute for Wine Biotechnology, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, 7602, South Africa

    Margaret E. Beckner Whitener, Benoit Divol & Maret Du Toit

  3. Institute for Grape and Wine Sciences, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, 7602, South Africa

    Valeria Panzeri

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11306_2016_962_MOESM2_ESM.xlsx

Table 1S is provided in its native Excel format for ease of use. Filters are in place for each header so that data may be easily sorted for review. The cluster values provided indicate the hierarchical clustering order for each extraction time (10 s, 5 m, 30 m). The date presented are the average peak areas after unit variance scaling for each yeast responsible for the start of fermentation. SC represents S. cerevisiae, TD represents T. delbrueckii, CZ represents C. zemplinina, KA represents K. aerobia, LT represents L. thermotolerans, PK represents P. kluyveri, and MP represents M. pulcherrima. The data is color coded such that high values are shown in red, low values in blue and all other values fall on a gradient between the two colors. Supplementary material 2 (XLSX 110 kb)

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Beckner Whitener, M.E., Stanstrup, J., Panzeri, V. et al. Untangling the wine metabolome by combining untargeted SPME–GCxGC-TOF-MS and sensory analysis to profile Sauvignon blanc co-fermented with seven different yeasts. Metabolomics 12, 53 (2016). https://doi.org/10.1007/s11306-016-0962-4

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