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Metabolomic Characterization of Malolactic Fermentation and Fermentative Behaviors of Wine Yeasts in Grape Wine

Authors
Son, Hong-SeokHwang, Geum-SookPark, Won-MokHong, Young-ShickLee, Cherl-Ho
Issue Date
10-Jun-2009
Publisher
AMER CHEMICAL SOC
Keywords
Wine; NMR; PCA; metabolomics; metabolites; malolactic fermentation (MLF); yeast
Citation
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, v.57, no.11, pp.4801 - 4809
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
Volume
57
Number
11
Start Page
4801
End Page
4809
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/119832
DOI
10.1021/jf9005017
ISSN
0021-8561
Abstract
Wine contains a number of metabolites that are produced during alcoholic and malolactic fermentations (MLF) or aging, which are important compounds for determining wine quality. This study investigated changes in metabolites in wines to characterize malolactic fermentation (MLF) and to assess fermentative behaviors of wine yeast strains using H-1 nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistics. Principal component analysis (PCA) showed clear differentiation between non- and induced-malolactic fermented wines by wine lactic acid bacteria (LAB) and between wines fermented with various wine yeast strains. Metabolites such as glycerol, lactate, 2,3-butanediol, succinate, leucine, isoleucine, alanine, valine, proline, choline, gamma-aminobutyric acid (GABA), and polyphenols contributed to the differentiations. Decreased levels of malate and citrate along with increased levels of lactate were the metabolites most responsible for the differentiation of induced-MLF wines from non-MLF wines. In particular, high succinate levels provided evidence of an inhibitory effect of Saccharomyces bayanus against spontaneous MLF. Furthermore, dependence of metabolites on wine yeast strains was observed, demonstrating their different fermentative behaviors. This study demonstrates that wine fermentation by yeast and LAB can be characterized through global and multivariate statistical analysis of H-1 NMR spectral data.
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