Central Composite Design with/without Artificial Neural Networks in Microemulsion Liquid Chromatography Separation Robustness Testing

Abstract

In past few years, for overcoming some analytical problems in liquid chromatography, the microemulsion as eluent was employed. Due to the strict regulatory requirements, robustness testing became important especially when proposing completely new method such as microemulsion liquid chromatography (MELC). In this paper robustness testing of MELC method, proposed for carbamazepine and its impurities (iminostilben and iminodibenzyl) separation, was done using two different approaches both based on experiments defined using central composite design (CCD). Input and output data from CCD were either handled as second order polynomials and tested with Analysis of variance (ANOVA), or as variables in Artificial Neural Networks (ANN). From both approaches appropriate conclusions about system robustness were distinguished, e. g. that the influence of surfactant content on chromatographic retention was the largest for all analytes, meaning that small changes in its concentration will strongly influenced on chromatographic retention. On the other hand influence of the pH of the mobile phase proved to be negligible, meaning that the substances are mainly distributed in the interfacial layer. ANN gave better results and proved to be better tool for explanation and understanding of investigated factors effects on the chromatographic system and for definition of the robustness limits.