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

Webb, R; Doble, P; Dawson, M

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

Webb, R Doble, P

Dawson, M

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Publication Type:: Journal Article
Citation:: Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 2009, 877 (7), pp. 615 - 620
Issue Date:: 2009-03-01

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Field	Value	Language
dc.contributor.author	Webb, R	en_US
dc.contributor.author	Doble, P https://orcid.org/0000-0002-8472-1301	en_US
dc.contributor.author	Dawson, M	en_US
dc.date.available	2009-01-15	en_US
dc.date.issued	2009-03-01	en_US
dc.identifier.citation	Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 2009, 877 (7), pp. 615 - 620	en_US
dc.identifier.issn	1570-0232	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.	en_US
dc.relation.ispartof	Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences	en_US
dc.relation.isbasedon	10.1016/j.jchromb.2009.01.012	en_US
dc.subject.mesh	Animals	en_US
dc.subject.mesh	Sheep	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Benzodiazepines	en_US
dc.subject.mesh	Autopsy	en_US
dc.subject.mesh	Chromatography, High Pressure Liquid	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Optimisation of HPLC gradient separations using artificial neural networks (ANNs): Application to benzodiazepines in post-mortem samples	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	877	en_US
utslib.for	0399 Other Chemical Sciences	en_US
utslib.for	0301 Analytical Chemistry	en_US
utslib.for	0601 Biochemistry and Cell Biology	en_US
utslib.for	1115 Pharmacology and Pharmaceutical Sciences	en_US
dc.location.activity	ISI:000264225200006	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	877	en_US

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/10432