光谱学与光谱分析, 2013, 33 (1): 111, 网络出版: 2013-02-04  

微分谱结合独立成分分析对三维荧光重叠光谱的解析

Analysis of Three-Dimensional Fluorescence Overlapping Spectra Using Differential Spectra and Independent Component Analysis
作者单位
1 中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥230031
2 合肥师范学院数学系, 安徽 合肥230061
摘要
多组分三维荧光重叠光谱是三维荧光光谱的数据解析中的难点之一。 本文基于二维微分谱的计算原理, 充分利用三维荧光光谱具有激发光谱和发射光谱的特点, 获得了三维荧光光谱展开后的激发微分谱和发射微分谱. 之后利用独立成分分析对激发光谱或发射光谱的多组分混合微分谱分别进行解析, 得到了单一组分的激发微分谱和发射微分谱。 其中三次样条插值有效的弥补了实测激发波长数据点少的缺点, 而粗糙惩罚平滑技术的引入则很大程度上减少了发射光谱的噪声, 为微分谱的计算提供了有利的条件。 单一组分的标准谱与解析谱的相似性系数的计算表明, 利用独立成分分析对微分谱进行解析更有利于多组分混合三维荧光光谱所含成分的识别。
Abstract
The analysis of multi-component three-dimensional fluorescence overlapping spectra is always very difficult. In view of the advantage of differential spectra and based on the calculation principle of two-dimensional differential spectra, the three-dimensional fluorescence spectra with both excitation and emission spectra is fully utilized. Firstly, the excitation differential spectra and emission differential spectra are respectively computed after unfolding the three-dimensional fluorescence spectra. Then the excitation differential spectra and emission differential spectra of the single component are obtained by analyzing the multi-component differential spectra using independent component analysis. In this process, the use of cubic spline increases the data points of excitation spectra, and the roughness penalty smoothing reduces the noise of emission spectra which is beneficial for the computation of differential spectra. The similarity indices between the standard spectra and recovered spectra show that independent component analysis based on differential spectra is more suitable for the component recognition of three-dimensional fluorescence overlapping spectra.

于绍慧, 张玉钧, 赵南京, 肖雪, 王欢博, 殷高方. 微分谱结合独立成分分析对三维荧光重叠光谱的解析[J]. 光谱学与光谱分析, 2013, 33(1): 111. YU Shao-hui, ZHANG Yu-jun, ZHAO Nan-jing, XIAO Xue, WANG Huan-bo, YIN Gao-fang. Analysis of Three-Dimensional Fluorescence Overlapping Spectra Using Differential Spectra and Independent Component Analysis[J]. Spectroscopy and Spectral Analysis, 2013, 33(1): 111.

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