Optimisation of HPLC gradient separations using artificial neural networks (ANNs): Application to benzodiazepines in post-mortem samples

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dc.contributor.author Webb, R
dc.contributor.author Doble, P
dc.contributor.author Dawson, M
dc.date.accessioned 2010-05-28T09:56:20Z
dc.date.issued 2009-03-01
dc.identifier.citation Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 2009, 877 (7), pp. 615 - 620
dc.identifier.issn 1570-0232
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/10432
dc.description.abstract Artificial neural networks (ANNs) were used in conjunction with an experimental design to optimise a gradient HPLC separation of nine benzodiazepines. Using the best performing ANN, the optimum conditions predicted were 25 mM formate buffer (pH 2.8), 10% MeOH, acetonitrile (ACN) gradient 0-15 min, 6.5-48.5%. The error associated with the prediction of retention times and peak widths under these conditions was less than 5% for six of the nine analytes. The optimised method, with limits of detection (LODs) in the range of 0.0057-0.023 μg/mL and recoveries between 58% and 92%, was successfully applied to authentic post-mortem samples. This method represents a more flexible and convenient means for optimising gradient elution separations using ANNs than has been previously reported. © 2009 Elsevier B.V. All rights reserved.
dc.language eng
dc.relation.isbasedon 10.1016/j.jchromb.2009.01.012
dc.title Optimisation of HPLC gradient separations using artificial neural networks (ANNs): Application to benzodiazepines in post-mortem samples
dc.type Journal Article
dc.parent Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
dc.journal.volume 7
dc.journal.volume 877
dc.journal.number 7 en_US
dc.publocation Amsterdam en_US
dc.identifier.startpage 615 en_US
dc.identifier.endpage 620 en_US
dc.cauo.name SCI.Faculty of Science en_US
dc.conference Verified OK en_US
dc.for 0399 Other Chemical Sciences
dc.personcode 910324
dc.personcode 010494
dc.percentage 100 en_US
dc.classification.name Other Chemical Sciences en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity ISI:000264225200006 en_US
dc.description.keywords Artificial neural networks (ANNs)
dc.description.keywords Benzodiazepines
dc.description.keywords Gradient elution
dc.description.keywords HPLC
dc.description.keywords Optimisation
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
pubs.organisational-group /University of Technology Sydney/Faculty of Science/School of Chemistry and Forensic Science
pubs.organisational-group /University of Technology Sydney/Strength - Forensic Science
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)
utslib.collection.history School of Chemistry and Forensic Science (ID: 339)


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