dc.contributor.author | Hamidi, H | |
dc.contributor.author | Hamidi, S | |
dc.contributor.author | Vaez, H | |
dc.date.accessioned | 2018-08-26T08:32:42Z | |
dc.date.available | 2018-08-26T08:32:42Z | |
dc.date.issued | 2017 | |
dc.identifier.uri | http://dspace.tbzmed.ac.ir:8080/xmlui/handle/123456789/52326 | |
dc.description.abstract | A quantitative structure-mobility relationship (QSMR) is proposed to estimate the electrophoretic mobility of diverse sets of analyses in capillary zone electrophoresis using Abraham solvation parameters of analyses, such as the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. QSMR was developed for prediction the electrophoretic mobility of 231 organic acids using the solvation parameters calculated by Abraham. Multiple linear regression (MLR) as a linear model and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The prediction results are obtained by nonlinear model, ANN, seem to be superior over MLR and were in good agreement with experimental data. In the proposed ANN-QSMR model, the overall mean percentage deviation values were 5.6, 5.4, and 5.3% and the coefficients of determinations (R2) were 0.84, 0.84, and 0.84 for training, test, and verification set, respectively. To investigate the robustness of the model, cross-validation methods have been established, i.e., leave-one-out and leave-N-out (N = 5 and 10) and model is showed good predictive ability against data variation in cross-validation process. This model is not only able to accurately predict the migration order of a diverse set of organic acids but also model finds that solvation parameters are responsible in separation mechanism. é 2017 Taylor & Francis. | |
dc.language.iso | English | |
dc.relation.ispartof | Journal of Liquid Chromatography and Related Technologies | |
dc.subject | Capillary electrophoresis | |
dc.subject | Electrophoresis | |
dc.subject | Forecasting | |
dc.subject | Hydrogen bonds | |
dc.subject | Linear regression | |
dc.subject | Neural networks | |
dc.subject | Organic acids | |
dc.subject | Solvation | |
dc.subject | Abraham parameters | |
dc.subject | Abraham solvation parameters | |
dc.subject | Capillary zone electrophoresis | |
dc.subject | Cross-validation methods | |
dc.subject | Mobility relationships | |
dc.subject | Multiple linear regressions | |
dc.subject | Quantitative structure - property relationships | |
dc.subject | Quantitative structures | |
dc.subject | Electrophoretic mobility | |
dc.subject | carboxylic acid | |
dc.subject | algorithm | |
dc.subject | Article | |
dc.subject | artificial neural network | |
dc.subject | controlled study | |
dc.subject | electrophoretic mobility | |
dc.subject | hydrodynamics | |
dc.subject | multiple linear regression analysis | |
dc.subject | prediction | |
dc.subject | predictive value | |
dc.subject | quantitative structure mobility relationship | |
dc.subject | regression analysis | |
dc.subject | solvation | |
dc.title | A quantitative structure-mobility relationship of organic acids using solvation parameters | |
dc.type | Short Survey | |
dc.citation.volume | 40 | |
dc.citation.issue | 19 | |
dc.citation.spage | 967 | |
dc.citation.epage | 977 | |
dc.citation.index | Scopus | |
dc.identifier.DOI | https://doi.org/10.1080/10826076.2017.1398171 | |