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  

Krawtchouk Moment Method for the Quantitative Analysis of Polycyclic Aromatic Hydrocarbons Based on Fluorescence Three-Dimensional Spectra

作　　者：潘钊[1] 崔耀耀 吴希军[1] 苑媛媛 刘婷婷 PAN Zhao;CUI Yao-yao;WU Xi-jun;YUAN Yuan-yuan;LIU Ting-ting(Key Lab of Measurement Technology and Instrumentation of Hebei Province,Qinhuangdao 066004,China;School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China)

机构地区：[1]燕山大学河北省测试计量技术及仪器重点实验室,河北秦皇岛066004 [2]燕山大学信息科学与工程学院,河北秦皇岛066004

出　　处：《光谱学与光谱分析》2018年第12期3785-3789,共5页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61471312);河北省自然科学基金项目(F2015203072);河北省高等学校科学技术研究项目(QN2018071);燕山大学基础研究专项课题(16LGA008)资助

摘　　要：以多环芳烃中的芴和苊为研究对象,提出一种将三维荧光光谱技术与Krawtchouk图像矩、广义回归神经网络相结合的定量分析的方法。利用FS920荧光光谱仪获取样品的三维荧光光谱数据,得到对应的三维光谱灰度图。直接计算三维光谱灰度图的Krawtchouk矩,将得到的Krawtchouk矩经平均影响值筛选后作为广义回归神经网络的输入,建立多环芳烃(PAHs)的定量模型。预测8组混合溶液的测试样本,芴和苊的平均相对误差分别为0. 98%和2. 15%。研究结果表明,Krawtchouk矩经过筛选后预测结果更为准确,该方法能够有效提取光谱的特征信息,简单、准确的预测PAHs的浓度。The study objects of this paper were PAHs fluore ne and acenaphthene.A method combining three-dimensional(3D)fluorescence spe ctroscopy 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 wer e established on the mean impact value and the generalized regression neural net work.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.

关 键 词：三维荧光光谱 KRAWTCHOUK矩 平均影响值 广义回归神经网络 

分 类 号：X830.2[环境科学与工程—环境工程]