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dc.creatorMalenović, Anđelija
dc.creatorIvanović, Darko
dc.creatorJančić-Stojanović, Biljana
dc.creatorMedenica, Mirjana
dc.date.accessioned2019-09-02T11:18:03Z
dc.date.available2019-09-02T11:18:03Z
dc.date.issued2009
dc.identifier.issn1082-6076
dc.identifier.urihttps://farfar.pharmacy.bg.ac.rs/handle/123456789/1248
dc.description.abstractLiquid chromatography employing the microemulsion as eluent was applied for the analysis of simvastatin and its six impurities. Previously, the optimized and validated method was tested to prove the method's capability to perform during the small changes of important parameters. In this paper, influence of parameters tipical for microemulsion liquid chromatography (MELC) on the method's robustness was presented. For the results evaluation, and presentation, two statistical methods were applied. In order to designate the most convinient one, multiple regression (MR) and artificial neural networks (ANN) were applied and compared. A set of experiments was defined by the central composite design (CCD). Indepedent variables (SDS, n-buthanol and diisopropyl ether content) and dependent variables (retention factors of investigated substances) were the framework for both, MR and ANN. This study demonstrated that MR and radial basis function ANN are useful tools in understanding the effects of the investigated factors on the chromatographic system and definition of the robustness limits.en
dc.publisherTaylor & Francis Inc, Philadelphia
dc.relationinfo:eu-repo/grantAgreement/MESTD/MPN2006-2010/142077/RS//
dc.rightsrestrictedAccess
dc.sourceJournal of Liquid Chromatography & Related Technologies
dc.subjectArtificial neural networksen
dc.subjectMELCen
dc.subjectMultiple regressionen
dc.subjectRobustness testingen
dc.subjectSimvastatinen
dc.titleRobustness Testing of Microemulsion Liquid Chromatographic Separation of Simvastatin and its Impuritiesen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractМеденица, Мирјана; Маленовић, Aнђелија; Ивановић, Дарко; Јанчић-Стојановић, Биљана;
dc.citation.volume32
dc.citation.issue6
dc.citation.spage874
dc.citation.epage889
dc.citation.other32(6): 874-889
dc.citation.rankM23
dc.identifier.wos000263646300009
dc.identifier.doi10.1080/10826070902768161
dc.identifier.scopus2-s2.0-61649084961
dc.type.versionpublishedVersion


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