Show simple item record

dc.creatorJančić-Stojanović, Biljana
dc.creatorIvanović, D.
dc.creatorMalenović, Anđelija
dc.creatorMedenica, Mirjana
dc.date.accessioned2019-09-02T11:17:59Z
dc.date.available2019-09-02T11:17:59Z
dc.date.issued2009
dc.identifier.issn0039-9140
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/1247
dc.description.abstractArtificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2(4) was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.en
dc.publisherElsevier Science BV, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/MPN2006-2010/142077/RS//
dc.rightsrestrictedAccess
dc.sourceTalanta
dc.subjectExperimental designen
dc.subjectArtificial neural networksen
dc.subjectLiquid chromatographyen
dc.subjectIndinaviren
dc.subjectDegradation productsen
dc.titleArtificial neural networks in analysis of indinavir and its degradation products retentionen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractЈанчић-Стојановић, Биљана; Меденица, Мирјана; Маленовић, Aнђелија; Ивановић, Д.;
dc.citation.volume78
dc.citation.issue1
dc.citation.spage107
dc.citation.epage112
dc.citation.other78(1): 107-112
dc.citation.rankM21
dc.identifier.wos000263634700016
dc.identifier.doi10.1016/j.talanta.2008.10.066
dc.identifier.pmid19174211
dc.identifier.scopus2-s2.0-58649105768
dc.identifier.rcubconv_2116
dc.type.versionpublishedVersion


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record