基于非负矩阵分解的多环芳烃成份识别

PAHs Component Recognition Based on Nonnegative Matrix Factorization

从重叠比较严重的混合物三维荧光光谱中恢复单一光谱信号,是光谱解析的难点。考虑到光谱内在的非负性,采用非负矩阵分解的投影梯度和交替最小二乘两种算法,并结合K均值初始化方法,来解析菲、芘、蒽3种芳烃混合物的三维荧光光谱数据,有效避免出现负数的分解结果,提取3种成份的三维荧光光谱,得到计算光谱与对应参考光谱的相似系数均大于0.970。计算结果表明,非负矩阵分解能够克服光谱重叠带来的干扰,有效提取光谱成份,从而实现对菲、芘、蒽的成份识别。其中,交替最小二乘的NMF算法更适合实时在线监测。

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 coeffi- cients 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.