Biljana Jančić-Stojanović,1,* Andjelija Malenović,1 Darko Ivanović1 and Mirjana Medenica2
1 Faculty of Pharmacy, Institute of Drug Analysis, Vojvode Stepe 450, Belgrade,
Serbia
2 Faculty of Pharmacy, Institute of Physical Chemistry, Vojvode Stepe 450,
Belgrade, Serbia
* Corresponding author: E-mail:
jancic.stojanovic@pharmacy.bg.ac.yu
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.
Keywords: Robustness, experimental design, artificial neural networks, microemulsion liquid chromatography