基于三维荧光光谱的Krawtchouk图像矩算法在多环芳烃定量分析中的应用

The study objects of this paper were PAHs fluorene and acenaphthene. A method combining three-dimensional (3D) fluorescence spectroscopy with Krawtchouk moment and generalized regression neural network was proposed for quantitative analysis of PAHs. By using the 3D fluorescence spectra data of samples measured directly, the corresponding grayscale images of 3D spectra could be obtained. The Krawtchouk moments were directly calculated based on the grayscale images of 3D spectra, and the quantitative models for the PAHs were established on the mean impact value and the generalized regression neural network. The average relative errors of the 8 groups mixed samples of fluorene and acenaphthene were predicted to be 0.98% and 2.15%, respectively. The results showed that the proposed method can extract the characteristic information of the spectra effectively and predict the concentration of PAHs simply and accurately.