Spectral analysis and optimization of time-resolved oil fluorescence based on PCA

Jie LI; Xiao-long LI; Qiu-hua TANG; Chao-fang ZHAO; Jiong-jiong WANG

doi:10.3788/OPE.20172504.0884

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Spectral analysis and optimization of time-resolved oil fluorescence based on PCA

Modern Applied Optics | 更新时间：2020-07-07

- Spectral analysis and optimization of time-resolved oil fluorescence based on PCA
- Optics and Precision Engineering Vol. 25, Issue 4, Pages: 884-890(2017)
- 作者机构：
  
  1.中国海洋大学信息科学与工程学院, 山东青岛 266100
  2.国家海洋局第一海洋研究所, 山东青岛 266061
  3.中国科学院海洋研究所, 山东青岛 266071
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172504.0884
  CLC： O433.4;TE58
- Received：22 August 2016，
  
  Accepted：22 September 2016，
  
  Published：25 April 2017
- 稿件说明：
移动端阅览
Jie LI, Xiao-long LI, Qiu-hua TANG, et al. Spectral analysis and optimization of time-resolved oil fluorescence based on PCA[J]. Optics and precision engineering, 2017, 25(4): 884-890.
DOI：

Jie LI, Xiao-long LI, Qiu-hua TANG, et al. Spectral analysis and optimization of time-resolved oil fluorescence based on PCA[J]. Optics and precision engineering, 2017, 25(4): 884-890. DOI： 10.3788/OPE.20172504.0884.

摘要

激光诱导荧光技术可广泛应用于油污染的监测中，然而普通的油荧光光谱技术只能实现油污染监测的粗分类，无法区分原油与燃料油的荧光特征。本文基于主成分分析方法（PCA）的时间分辨油荧光分类方法，实验测量了20种油样本的时间分辨荧光光谱特征，给出了对应的荧光寿命和时间分辨油荧光光谱的时序特征。在此基础上，利用前三个主成分构成的三维特征矢量空间，通过分析不同采集时刻下油样本矢量间相关距离的变化，对油样本的时间分辨荧光光谱进行聚类分析。为了体现油荧光变化的时序性，引入矢量距离的离散度参量，提出基于PCA进行时间分辨油荧光光谱分析的优化方法。实验结果表明，基于时间分辨油荧光光谱识别可实现原油与燃料油的光谱时序特征区分，具备良好的油荧光分类效果。

Abstract

Laser-induced Fluorescence (LIF) technique can be widely used in oil pollution monitoring. However

ordinary oil fluorescence spectra can only achieve cursory oil classification

which was disabled to distinguish crude and fuel oils. Herein

time-resolved fluorescence spectra classification method based on Principle Component Analysis (PCA) was investigated and employed to analyze the spectral features of 20 kinds of oils

of which the fluorescence lifetimes and the spectral timing characteristics were obtained. Then referring to fluorescence lifetimes of oils (less than 10 ns commonly)

three-dimensional spectra of samples within this time range were used for obtaining a vector space which was composed of first three principal components and was considered as a three-dimensional coordinate system. In this coordinate system

correlation distances of position vectors at difference delay time of fluorescence acquisition were analyzed for spectral clustering of time-resolved oil fluorescence. To reflect timing characteristics of correlation distances

dispersion parameters were introduced into the PCA optimization method. The experimental result indicates that the method based on time-resolved fluorescence spectroscopy can discriminate between crude oils and fuel oils with a higher recognition rate.

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Keywords

references

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