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。

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.