An improved structure models to explain retention behavior of atmospheric nanoparticles

Esmaeilpoor, Sharmin; Shirzadi, Zahra; Noorizadeh, Hadi

Document Type : Original Research Article

Authors

¹ Department of Chemistry, Payame Noor University of ilam, Ilam, Iran

² Department of chemistry, Islamic Azad University, Shahreza Branch, Isfahan, Iran

Abstract

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.

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Keywords

Main Subjects

Nanochemistry

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Iranian chemical communication

An improved structure models to explain retention behavior of atmospheric nanoparticles

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Volume 2, Issue 1, pp. 1-81, Serial No. 2
January 2014
Pages 56-71

An improved structure models to explain retention behavior of atmospheric nanoparticles

Full Text

Full Text

References

References

Volume 2, Issue 1, pp. 1-81, Serial No. 2January 2014Pages 56-71

Volume 2, Issue 1, pp. 1-81, Serial No. 2
January 2014
Pages 56-71