Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks.
Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.
Jalali-Heravi
M
M
Department of Chemistry, Sharif University of Technology, Tehran, Iran. jalali@sina.sharif.ac.ir
Garkani-Nejad
Z
Z
D019075:Electrophoresis, Capillary / Q000379:methods*; D006863:Hydrogen-Ion Concentration; D016571:Neural Networks, Computer; D012044:Regression Analysis; D013449:Sulfonamides / Q000737:chemistry*
D013449:Sulfonamides