GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T14:20:27Z
dc.date.available2014-05-20T14:20:27Z
dc.date.created2014-05-20T14:20:27Z
dc.date.issued2009-10-01
dc.identifierChromatographia. Wiesbaden: Vieweg, v. 70, n. 7-8, p. 1135-1142, 2009.
dc.identifier0009-5893
dc.identifierhttp://hdl.handle.net/11449/26154
dc.identifier10.1365/s10337-009-1277-7
dc.identifierWOS:000271069400016
dc.description.abstractASTM D6729 gas chromatographic fingerprinting coupled to pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality. SIMCA, was performed on gas chromatographic fingerprints to classify the quality of representative commercial gasoline samples selected by hierarchical cluster analysis and collected over a 5 month period from gas stations in So Paulo State, Brazil. Following an optimized ASTM D6729 gas chromatographic-SIMCA algorithm, it was possible to correctly classify the majority of commercial gasoline samples. The method could be employed for rapid monitoring to discourage adulteration.
dc.languageeng
dc.publisherVieweg
dc.relationChromatographia
dc.relation1.401
dc.relation0,514
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectGas chromatography
dc.subjectASTM D6729
dc.subjectPattern-recognition multivariate SIMCA
dc.subjectBrazilian gasoline
dc.titleGC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies
dc.typeArtículos de revistas


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