[Identification of spill oil species based on low concentration synchronous fluorescence spectra and RBF neural network].
In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.
Optics & Optoelectronics Laboratory, Ocean University of China, Qingdao 266100, China. liuqianqian9119@163.com
Qian-qian
QQ
Liu
Chun-yan
CY
Wang
Xiao-feng
XF
Shi
Wen-dong
WD
Li
Xiao-ning
XN
Luan
Shi-lin
SL
Hou
Jin-liang
JL
Zhang
Rong-er
RE
Zheng