Quantitative structure migration relationship modeling of migration factor for some benzene derivatives in micellar electrokinetic chromatography.
Multiple linear regression (MLR) and artificial neural network (ANN) were used to predict the migration factors of benzene derivatives in MEKC. Some topological and electronic descriptors were calculated for each solute in the data set, and then the stepwise MLR method was used to select more significant descriptors and MLR model development. The selected descriptors are: Kier & Hall index (order1), relative negative charge surface area, HA dependent HDSA-2/TMSA, C component of moment of inertia, Y component of dipole moment and SDS to decanol ratio in mobile phase. In the next step these descriptors were used as input of an ANN. After optimization and training of ANN it was used to predict the migration factors of external test set as well as internal and training sets. The root mean square errors for ANN predicted migration factors of training, internal and external test set were 0.110, 0.231 and 0.228, respectively, while these values are 0.200, 0.240 and 0.247 for the MLR model, respectively. Comparison between these values and other statistical parameters for these two models revealed that there were not any significant differences between ANN and MLR in prediction of solute migration factors in MEKC.
Fatemi
Mohammad Hossein
MH
Department of Chemistry, University of Mazandran, Babolsar, Iran. mhfatemi@umz.ac.ir
Shamseddin
Hoda
H
Malekzadeh
Hanieh
H
D001555:Benzene Derivatives / Q000032:analysis* / Q000737:chemistry*; D020374:Chromatography, Micellar Electrokinetic Capillary; D016208:Databases, Factual; D016014:Linear Models; D015394:Molecular Structure; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013499:Surface Properties
D001555:Benzene Derivatives