Submit Manuscript  

Article Details


Prediction of Electrophoretic Mobility of Analytes Using Abraham Solvation Parameters by Different Chemometric Methods

[ Vol. 13 , Issue. 4 ]

Author(s):

Samin Hamidi, Ali Shayanfar, Hossein Hamidi, Elnaz Mehdizadeh Aghdam and Abolghasem Jouyban   Pages 325 - 339 ( 15 )

Abstract:


Background: Quantitative structure-mobility relationships are proposed to estimate the electrophoretic mobility of diverse sets of analytes in capillary zone electrophoresis using Abraham solvation parameters of analytes, namely the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. Multiple linear regression (MLR) as a linear model, adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The applicability of the Abraham solvation parameters to the mobility prediction of analytes was studied employing various datasets consisting of organic acids, benzoate derivatives, pyridines, and ammoniums.

Methods: To evaluate the simulation ability of the proposed models, datasets were subdivided into training and test sets in the ratio of 3:1. To evaluate the goodness of fit of the models, squared correlation coefficients (R2) between experimental and calculated mobilities were calculated.

Results: R2 values were better than 0.78 for all datasets except for organic acids, in which the ANFIS model showed better ability to predict their mobility than that of MLR and ANN. In addition, the accuracy of the models is calculated using mean percentage deviation (MPD) and the overall MPD values for test sets were better than 15% for all models.

Conclusion: The results showed the ability of the developed models to predict the electrophoretic mobility of analytes in capillary zone electrophoresis.

Keywords:

Electrophoretic mobility, capillary zone electrophoresis, quantitative structure-mobility relationship, Abraham solvation parameters, multiple linear regression, artificial neural network, adaptive neuro-fuzzy inference system.

Affiliation:

Food and Drug Safety Research Center, Tabriz University of Medical Science, Tabriz 51664, Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Department of Control Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Pharmaceutical Analysis Research Center and Faculty of Pharmacy University, Tabriz University of Medical Sciences, P.O. Box: 51664, Tabriz

Graphical Abstract:



Read Full-Text article