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An improved structure models to explain retention behavior of atmospheric nanoparticles | ||
| Iranian chemical communication | ||
| مقاله 7، دوره 2، Issue 1, pp. 1-81, Serial No. 2، فروردین 2014، صفحه 56-71 اصل مقاله (282.04 K) | ||
| نوع مقاله: Original Research Article | ||
| نویسندگان | ||
| Sharmin Esmaeilpoor1؛ Zahra Shirzadi2؛ Hadi noorizadeh* 1 | ||
| 1Department of Chemistry, Payame Noor University of ilam, Ilam, Iran | ||
| 2Department of chemistry, Islamic Azad University, Shahreza Branch, Isfahan, Iran | ||
| چکیده | ||
| The quantitative structure-retention relationship (QSRR) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multivariate regressions [e.g. the partial least squares (PLS)] as well as the nonlinear regressions [e.g. the kernel PLS (KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN)] were utilized to construct the linear and nonlinear QSRR models. The correlation coefficient cross validation (Q2) and relative error for test set L-M ANN model are 0.939 and 4.89, respectively. The resulting data indicated that L-M ANN could be used as a powerful modeling tool for the QSPR studies. | ||
| کلیدواژه‌ها | ||
| Atmospheric nanoparticles؛ QSRR؛ GA-KPLS؛ Levenberg -Marquardt artificial neural network | ||
| اصل مقاله | ||
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