Neural networks in chemosystematic studies of asteraceae: A classification based on a dichotomic approach

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dc.contributor.authorFerreira, MJP
dc.contributor.authorBrant, AJC
dc.contributor.authorAlvarenga, SAV
dc.contributor.authorEmerenciano, V. P.
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T15:24:25Z
dc.date.available2014-05-20T15:24:25Z
dc.date.issued2005-01-01
dc.description.abstractThis paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R > 0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied.en
dc.description.affiliationUniv São Paulo, Inst Quim, BR-05513970 São Paulo, Brazil
dc.description.affiliationUniv Estadual Paulista, Fac Engn Guaratingueta, BR-12516410 Guaratingueta, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Fac Engn Guaratingueta, BR-12516410 Guaratingueta, SP, Brazil
dc.format.extent633-644
dc.identifierhttp://dx.doi.org/10.1002/cbdv.200590040
dc.identifier.citationChemistry & Biodiversity. Zurich: Verlag Helvetica Chimica Acta Ag, v. 2, n. 5, p. 633-644, 2005.
dc.identifier.doi10.1002/cbdv.200590040
dc.identifier.issn1612-1872
dc.identifier.urihttp://hdl.handle.net/11449/35036
dc.identifier.wosWOS:000229553300003
dc.language.isoeng
dc.publisherVerlag Helvetica Chimica Acta Ag
dc.relation.ispartofChemistry & Biodiversity
dc.relation.ispartofjcr1.617
dc.relation.ispartofsjr0,531
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.titleNeural networks in chemosystematic studies of asteraceae: A classification based on a dichotomic approachen
dc.typeArtigo
dcterms.licensehttp://olabout.wiley.com/WileyCDA/Section/id-406071.html
dcterms.rightsHolderVerlag Helvetica Chimica Acta Ag

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