Optimisation of HPLC gradient separations using artificial neural networks (ANNs): application to benzodiazepines in post-mortem samples.
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 microg/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.
Webb
Rebecca
R
Centre for Forensic Science, Department of Chemistry and Forensic Science, University of Technology Sydney, PO Box 123, Broadway, NSW, 2007, Australia.
Doble
Philip
P
Dawson
Michael
M
D000818:Animals; D001344:Autopsy; D001569:Benzodiazepines / Q000097:blood*; D002851:Chromatography, High Pressure Liquid / Q000379:methods* / Q000592:standards*; D006801:Humans; D016571:Neural Networks, Computer; D012756:Sheep
D001569:Benzodiazepines