Predicting and evaluating separation quality of micellar electrokinetic capillary chromatography by artificial neural networks.
Computer-aided optimization of micellar electrokinetic capillary chromatography (MEKC) separations was demonstrated by artificial neural networks (ANNs) using a Levenberg-Marquardt algorithm and an orthogonal experimental design. A novel criterion, named Q, for evaluating the separation quality of MEKC was firstly presented, which considered both separation selectivity and analysis time. MEKC separation conditions of seven plant hormones were then simulated and optimized using ANNs based on this novel criterion. The result was further compared to that obtained using ANNs based on a traditionally used criterion of overall normalization resolution (named r). Finally, the separation under optimum conditions predicted by ANNs using the criterion Q was compared to, and proved to be better than that obtained by empirical step-by-step optimization procedures. This method may also be adapted to other separation methods due to its generality.
Liu
Bi-Feng
BF
Key Lab of MOE for Plant Developmental Biology, Wuhan Center for Developmental Biology, Wuhan University, Wuhan, P. R. China.
Zhang
Jian-Feng
JF
Lu
Ying-Tang
YT
D000465:Algorithms; D020374:Chromatography, Micellar Electrokinetic Capillary / Q000592:standards*; D003198:Computer Simulation; D016571:Neural Networks, Computer; D010937:Plant Growth Regulators / Q000302:isolation & purification*
D010937:Plant Growth Regulators