PAHs Component Recognition Based on Nonnegative Matrix Factorization

Abstract

Abstract:

It is difficult to extract each component from overlapping three-dimensional fluorescence spectra of mixture. Considering intrinsic nonnegativity constraints on spectra, three-dimensional fluorescence spectra data of polycyclic aromatic hydrocarbons (PAHs) mixtures of phenanthrene, pyrene and anthracene is analyzed by using projected gradient and alternating least square algorithms based on nonnegative matrix factorization (NMF) by taking the results of K-means clusting as initial values. The negative data of separated spectra is eradicated. Three-dimensional fluorescence spectra of each component is extracted, and the similarity coefficients between computed spectra and its corresponding standard spectra are computed, which is greater than 0.970. Results demonstrate that three components are recognized accurately by NMF, which could overcome the interference caused by overlapping spectra and extract spectral components effectively. Alternating least square algorithms based on NMF is more suitable for online real-time monitoring.

Key words: spectroscopy, component recognition, nonnegative matrix factorization, three-dimensional fluorescence spectra, polycyclic aromatic hydrocarbons

YANG Rui-Fang, ZHAO Nan-Jing, XIAO Xue, YU Shao-Hui, YU Xiao-Ya, LIU Jian-Guo, LIU Wen-Qing. PAHs Component Recognition Based on Nonnegative Matrix Factorization[J]. Journal of Atmospheric and Environmental Optics, 2015, 10(5): 386-391.

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