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Characterization of Fermentative Behaviors of Lactic Acid Bacteria in Grape Wines through H-1 NMR- and GC-Based Metabolic Profiling

Authors
Lee, Jang-EunHong, Young-ShickLee, Cherl-Ho
Issue Date
10-6월-2009
Publisher
AMER CHEMICAL SOC
Keywords
Wine; NMR; GC; metabolomics; volatile compound; metabolites
Citation
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, v.57, no.11, pp.4810 - 4817
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
Volume
57
Number
11
Start Page
4810
End Page
4817
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/119830
DOI
10.1021/jf900502a
ISSN
0021-8561
Abstract
The effects of five commercial Oenococcus oeni strains (MCW, Enoferm alpha, Wyeast, Vinibacti111, and Vinibacti222) on fermentative behaviors, and variations of metabolites in Meoru (Vitis coigneties) wines during malolactic fermentation (MLF) were investigated by metabolomic analysis of H-1 NMR and GC data sets. In the development of MLF with various O. oeni strains, the fastest conversions of malic acid to lactic acid occurred in wines fermented with Enoferm a and Vinibacti111 strains. Seventeen primary metabolites and 65 secondary metabolites of volatile compounds in the wines were identified by H-1 NMR spectroscopy and GC-MS, respectively. In pattern recognition models of principal component analysis (PCA) and orthogonal projection to latent structures cliscriminant analysis (OPLS-DA), significant differentiations between wines with O. oeni strains were identified by the secondary metabolites rather than by the primary metabolites, showing the effects of O. oeni strains only on the secondary metabolites. Twelve volatile compounds, 2-phenylethanol, isoamyl alcohol, 2-butanol, ethyl octanoate, ethyl hexanoate, hexadecanoic acid, diethyl succinate, butyl butyrate, octanoic acid, 9-hexadecanoic acid, isobutyric acid, and 2-ethyl-1-hexanol, contributed to the differentiation of wines according to O. oeni strain, including spontaneous MLF This study demonstrates that O. oeni strains affect the secondary metabolites, which are easily identified through multivariate statistical analysis of GC-MS data set.
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