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 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.