Taxonomic discrimination of higher plants by pyrolysis mass spectrometry

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dc.contributor.authorKim, SWko
dc.contributor.authorBan, SHko
dc.contributor.authorChung, HJko
dc.contributor.authorChoi, DWko
dc.contributor.authorChoi, PSko
dc.contributor.authorYoo, Ook-Joonko
dc.contributor.authorLiu, JRko
dc.date.accessioned2013-03-06T05:08:08Z-
dc.date.available2013-03-06T05:08:08Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2004-02-
dc.identifier.citationPLANT CELL REPORTS, v.22, no.7, pp.519 - 522-
dc.identifier.issn0721-7714-
dc.identifier.urihttp://hdl.handle.net/10203/85903-
dc.description.abstractPyrolysis mass spectrometry (PyMS) is a rapid, simple, high-resolution analytical method based on thermal degradation of complex material in a vacuum and has been widely applied to the discrimination of closely related microbial strains. Leaf samples of six species and one variety of higher plants (Rosa multiflora, R. multiflora var. platyphylla, Sedum kamtschaticum, S. takesimense, S. sarmentosum, Hepatica insularis, and H. asiatica) were subjected to PyMS for spectral fingerprinting. Principal component analysis of PyMS data was not able to discriminate these plants in discrete clusters. However, canonical variate analysis of PyMS data separated these plants from one another. A hierarchical dendrogram based on canonical variate analysis was in agreement with the known taxonomy of the plants at the variety level. These results indicate that PyMS is able to discriminate higher plants based on taxonomic classification at the family, genus, species, and variety level.-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.subjectARTIFICIAL NEURAL NETWORKS-
dc.subjectRAPID IDENTIFICATION-
dc.titleTaxonomic discrimination of higher plants by pyrolysis mass spectrometry-
dc.typeArticle-
dc.identifier.wosid000188457400013-
dc.identifier.scopusid2-s2.0-1242276612-
dc.type.rimsART-
dc.citation.volume22-
dc.citation.issue7-
dc.citation.beginningpage519-
dc.citation.endingpage522-
dc.citation.publicationnamePLANT CELL REPORTS-
dc.identifier.doi10.1007/s00299-003-0714-6-
dc.contributor.localauthorYoo, Ook-Joon-
dc.contributor.nonIdAuthorKim, SW-
dc.contributor.nonIdAuthorBan, SH-
dc.contributor.nonIdAuthorChung, HJ-
dc.contributor.nonIdAuthorChoi, DW-
dc.contributor.nonIdAuthorChoi, PS-
dc.contributor.nonIdAuthorLiu, JR-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorcanonical variate analysis-
dc.subject.keywordAuthorchemotaxonomy-
dc.subject.keywordAuthordendrogram-
dc.subject.keywordAuthorprincipal component analysis-
dc.subject.keywordAuthortaxonomy-
dc.subject.keywordPlusARTIFICIAL NEURAL NETWORKS-
dc.subject.keywordPlusRAPID IDENTIFICATION-
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