Taxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis

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dc.contributor.authorSuk Weon Kim-
dc.contributor.authorS H Ban-
dc.contributor.authorChi-Yong Ahn-
dc.contributor.authorHee-Mock Oh-
dc.contributor.authorH Chung-
dc.contributor.authorS H Cho-
dc.contributor.authorY M Park-
dc.contributor.authorJang Ryol Liu-
dc.date.accessioned2017-04-19T09:05:23Z-
dc.date.available2017-04-19T09:05:23Z-
dc.date.issued2006-
dc.identifier.issnI000-0094-
dc.identifier.uri10.1007/BF03031154ko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/7624-
dc.description.abstractWhen whole-cell extracts are analyzed, proton nuclear magnetic resonance (1H NMR) spectroscopy provides biochemical profiles that contain overlapping signals of the majority of the compounds. To determine whether cyanobacteria could be taxonomically discriminated on the basis of metabolic fingerprinting, we subjected whole-cell extracts of the cyanobacteria to 1H NMR. The 1H NMR spectra revealed a predominance of signals in the aliphatic region. Principal component analysis (PCA) of the data then enabled discrimination of the cyanobacteria. The hierarchical dendrogram, based on PCA of the aliphatic region data, showed that six cyanobacterial taxa were discriminated from two eukaryotic microalgal species, and that the six taxa could be subsequently divided into three groups. This agrees with the current taxonomy of cyanobacteria. Therefore, our overall results indicate that metabolic fingerprinting using 1H NMR spectra and multivariate statistical analysis provide a simple, rapid method for the taxonomical discrimination of cyanobacteria.-
dc.publisherSpringer-
dc.titleTaxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis-
dc.title.alternativeTaxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis-
dc.typeArticle-
dc.citation.titleJournal of Plant Biology-
dc.citation.number4-
dc.citation.endPage275-
dc.citation.startPage271-
dc.citation.volume49-
dc.contributor.affiliatedAuthorSuk Weon Kim-
dc.contributor.affiliatedAuthorChi-Yong Ahn-
dc.contributor.affiliatedAuthorHee-Mock Oh-
dc.contributor.affiliatedAuthorJang Ryol Liu-
dc.contributor.alternativeName김석원-
dc.contributor.alternativeName반성희-
dc.contributor.alternativeName안치용-
dc.contributor.alternativeName오희목-
dc.contributor.alternativeName정회일-
dc.contributor.alternativeName조수화-
dc.contributor.alternativeName박영목-
dc.contributor.alternativeName유장렬-
dc.identifier.bibliographicCitationJournal of Plant Biology, vol. 49, no. 4, pp. 271-275-
dc.identifier.doi10.1007/BF03031154-
dc.subject.keywordcyanobacteria-
dc.subject.keyworddendrogram-
dc.subject.keywordpattern recognition-
dc.subject.keywordprincipal component analysis-
dc.subject.keywordtaxonomy-
dc.subject.localcyanobacteria-
dc.subject.localcyanobacterium-
dc.subject.localDendrogram-
dc.subject.localdendrogram-
dc.subject.localPattern recognition-
dc.subject.localpattern recognition-
dc.subject.localprincipal component analysis (PCA)-
dc.subject.localPrincipal Component Analysis-
dc.subject.localPrincipal component analysis (PCA)-
dc.subject.localprincipal component analysis-
dc.subject.localprincipal components analysis-
dc.subject.localPrincipal component analysis-
dc.subject.localTaxonomy-
dc.subject.localtaxonomy-
dc.description.journalClassY-
Appears in Collections:
Jeonbuk Branch Institute > Biological Resource Center > 1. Journal Articles
Synthetic Biology and Bioengineering Research Institute > Cell Factory Research Center > 1. Journal Articles
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