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dc.creatorVasiljević, Tatjana
dc.creatorOnjia, Antonije
dc.creatorČokeša, Đuro
dc.creatorLaušević, Mila
dc.date.accessioned2021-03-10T10:18:06Z
dc.date.available2021-03-10T10:18:06Z
dc.date.issued2004
dc.identifier.issn0039-9140
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/702
dc.description.abstractAn artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK(a) and log K-ow values.en
dc.publisherElsevier, Amsterdam
dc.rightsrestrictedAccess
dc.sourceTalanta
dc.subjectHPLCen
dc.subjectphenolsen
dc.subjectexperimental designen
dc.subjectANNen
dc.subjectback-propagationen
dc.titleOptimization of artificial neural network for retention modeling in high-performance liquid chromatographyen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage790
dc.citation.issue3
dc.citation.other64(3): 785-790
dc.citation.rankM21
dc.citation.spage785
dc.citation.volume64
dc.identifier.doi10.1016/j.talanta.2004.03.032
dc.identifier.pmid18969673
dc.identifier.rcubconv_2458
dc.identifier.scopus2-s2.0-4544266488
dc.identifier.wos000224285300031
dc.type.versionpublishedVersion


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