An improved structure models to explain retention behavior of atmospheric nanoparticles

سال انتشار: 1392
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 528

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شناسه ملی سند علمی:

JR_ICC-2-1_006

تاریخ نمایه سازی: 18 تیر 1394

چکیده مقاله:

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 (Q^2) 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.

کلیدواژه ها:

Atmospheric nanoparticles ، QSRR ، GA-KPLS ، Levenberg -Marquardt artificial neural network

نویسندگان

Sharmin Esmaeilpoor

Department of Chemistry, Payame Noor University, P.O. BOX ۱۹۳۹۵-۴۶۹۷ , Tehran, Iran

Zahra Shirzadi

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

Hadi Noorizadeh

Department of Chemistry, Payame Noor University, P.O. BOX ۱۹۳۹۵-۴۶۹۷ , Tehran, Iran