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dc.creatorJančić, Biljana
dc.creatorMedenica, Mirjana
dc.creatorIvanović, D.
dc.creatorJanković, Saša
dc.creatorMalenović, Anđelija
dc.date.accessioned2019-09-02T11:13:21Z
dc.date.available2019-09-02T11:13:21Z
dc.date.issued2008
dc.identifier.issn0021-9673
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/1068
dc.description.abstractIn this paper, the mass spectrometry (MS) detection has been applied for screening of fosinopril sodium impurities which arise during forced stress study. Before MS analysis, liquid chromatographic method with suitable mobile phase composition was developed. The separation was done on SunFire 100 turn x 4.6 mm 3.5 mu m particle size column. The mobile phases which consisted of methanol-ammonium acetate buffer-acetic acid, in different ratios, were used in a preliminary study. Flow rate was 0.3 mL min(-1). Under these conditions, percent of methanol, concentration of ammonium acetate buffer and acetic acid content were tested simultaneously applying central composite design (CCD) and artificial neural network (ANN). The combinations of experimental design (ED) and ANN present powerful technique in method optimization. Input and output variables from CCD were used for network training, verification and testing. Multiple layer perceptron (MLP) with back propagation (BP) algorithm was chosen for network training. When the optimal neural topology was selected, network was trained by adjusting strength of connections between neurons in order to adapt the outputs of whole network to be closer to the desired outputs, or to minimize the sum of the squared errors. From the method optimization the following mobile phase composition was selected as appropriate: methanol-10 mM ammonium acetate buffer-acidic acid (80:19.5:0.5 v/v/v). This mobile phase was used as inlet for MS. According to molecular structure and literature data, electrospray positive ion mode was applied for analysis of fosinopril sodium and its impurities. The proposed method could be used for screening of fosinopril sodium impurities in bulk and pharmaceuticals, as well as for tracking the degradation under stress conditions.en
dc.publisherElsevier Science BV, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/MPN2006-2010/142077/RS//
dc.rightsrestrictedAccess
dc.sourceJournal of Chromatography A
dc.subjectfosinopril sodiumen
dc.subjectimpurity profilingen
dc.subjectartificial neural networksen
dc.subjectMS detectionen
dc.subjectforced degradation studiesen
dc.titleMonitoring of fosinopril sodium impurities by liquid chromatography-mass spectrometry including the neural networks in method evaluationen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractМеденица, Мирјана; Јанковић, Саша; Маленовић, Aнђелија; Ивановић, Д.; Јанчић, Биљана;
dc.citation.volume1189
dc.citation.issue1-2
dc.citation.spage366
dc.citation.epage373
dc.citation.other1189(1-2): 366-373
dc.citation.rankaM21
dc.identifier.wos000255698000036
dc.identifier.doi10.1016/j.chroma.2007.11.076
dc.identifier.pmid18154978
dc.identifier.scopus2-s2.0-41949101118
dc.identifier.rcubconv_1992
dc.type.versionpublishedVersion


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