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

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dc.contributor.authorKim, Suk Weon-
dc.contributor.authorBan, Sung Hee-
dc.contributor.authorAhn, Chi Yong-
dc.contributor.authorOh, Hee Mock-
dc.contributor.authorChung, Hoeil-
dc.contributor.authorCho, Soo Hwa-
dc.contributor.authorPark, Young Mok-
dc.contributor.authorLiu, Jang Ryol-
dc.date.accessioned2022-12-21T10:50:13Z-
dc.date.available2022-12-21T10:50:13Z-
dc.date.issued2006-08-
dc.identifier.issn1226-9239-
dc.identifier.issn1867-0725-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/181187-
dc.description.abstractWhen whole-cell extracts are analyzed, proton nuclear magnetic resonance (H-1 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 H-1 NMR. The H-1 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.format.extent5-
dc.language영어-
dc.language.isoENG-
dc.publisher한국식물학회-
dc.titleTaxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.1007/BF03031154-
dc.identifier.scopusid2-s2.0-33748698640-
dc.identifier.wosid000240394900002-
dc.identifier.bibliographicCitationJournal of Plant Biology(한국식물학회지), v.49, no.4, pp 271 - 275-
dc.citation.titleJournal of Plant Biology(한국식물학회지)-
dc.citation.volume49-
dc.citation.number4-
dc.citation.startPage271-
dc.citation.endPage275-
dc.type.docTypeArticle-
dc.identifier.kciidART001028019-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaPlant Sciences-
dc.relation.journalWebOfScienceCategoryPlant Sciences-
dc.subject.keywordPlusTRANSFORM INFRARED-SPECTROSCOPY-
dc.subject.keywordPlusARTIFICIAL NEURAL-NETWORKS-
dc.subject.keywordPlusRAPID IDENTIFICATION-
dc.subject.keywordPlusDISTINCT TYPES-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusMETABONOMICS-
dc.subject.keywordPlusBACTERIA-
dc.subject.keywordPlusNMR-
dc.subject.keywordAuthorcyanobacteria-
dc.subject.keywordAuthordendrogram-
dc.subject.keywordAuthorpattern recognition-
dc.subject.keywordAuthorprincipal component analysis-
dc.subject.keywordAuthortaxonomy-
dc.identifier.urlhttps://link.springer.com/article/10.1007/BF03031154-
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