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dc.creatorSremac, Snežana
dc.creatorPopović, Aleksandar R.
dc.creatorTodorović, Žaklina
dc.creatorČokeša, Đuro
dc.creatorOnjia, Antonije
dc.date.accessioned2021-03-10T10:59:56Z
dc.date.available2021-03-10T10:59:56Z
dc.date.issued2008
dc.identifier.issn0039-9140
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/1347
dc.description.abstractAn interpretative strategy (factorial design experimentation + total resolution analysis + chromatogram simulation) was employed to optimize the separation of 16 polycyclic aromatic hydrocarbons (PAHs) (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(k)fluoranthene, benzo(b)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene) in temperature-programmed gas chromatography (GC). Also, the retention behavior of PAHs in the same GC system was studied by a feed-forward artificial neural network (ANN). GC separation was investigated as a function of one (linear temperature ramp) or two (linear temperature ramp+the final hold temperature) variables. The applied interpretative approach resulted in rather good agreement between the measured and the predicted retention times for PAHs in both one and two variable modeling. The ANN model, strongly affected by the number of input experiments, was shown to be less effective for one variable used, but quite successful when two input variables were used. All PAHs, including difficult to separate peak pairs (benzo(k)fluoranthene/benzo(b)fluoranthene and indeno(1,2,3-c,d)pyrene/dibenzo(a,h)anthracene), were separated in a standard (5% phenyl-95% climethylpolysiloxane) capillary column at an optimum temperature ramp of 8.0 degrees C/min and final hold temperature in the range of 260-320 degrees C.en
dc.publisherElsevier, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/MPN2006-2010/142039/RS//
dc.rightsrestrictedAccess
dc.sourceTalanta
dc.subjectPAHsen
dc.subjectfactorial designen
dc.subjectANNen
dc.subjectGCen
dc.subjectresolution producten
dc.titleInterpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbonsen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage71
dc.citation.issue1
dc.citation.other76(1): 66-71
dc.citation.rankM21
dc.citation.spage66
dc.citation.volume76
dc.identifier.doi10.1016/j.talanta.2008.02.004
dc.identifier.pmid18585242
dc.identifier.rcubconv_3026
dc.identifier.scopus2-s2.0-43649100022
dc.identifier.wos000256934200012
dc.type.versionpublishedVersion


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