| 光谱学与光谱分析 |
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| Research on the 3D Fluorescence Spectra Differentiation of Phytoplankton by Coiflet2 Wavelet |
| LIU Bao1,4, SU Rong-guo1*, SONG Zhi-jie2, ZHANG Fang3，WANG Xiu-lin1 |
1. Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China
2. College of Information Science & Engineering, Ocean University of China, Qingdao 266100, China
3. Polar Research Institute of China，Shanghai 200136，China
4. Shandong Supervision and Inspection Institute for Product Quality, Ji’nan 250103, China |
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Abstract In the present paper, the authors utilize the wavelet base function coiflet2 (coif2) to analyze the 3D fluorescence spectra of 37 phytoplankton species belonging to 30 genera of 7 divisions, and these phytoplankton species include common species frequently causing harmful algal blooms and most predominant algal species in the inshore area of China Sea. After the Rayleigh and Raman scattering peaks were removed by the Delaunay triangulation interpolation, the fluorescence spectra of those phytoplankton species were transformed with the coiflet2 wavelet, and the scale vectors and the wavelet vectors were candidate for the feature spectra. Based on the testing results by Bayesian analysis, the 3rd scale vectors were the best feature segments at the division level and picked out as the fluorescence division feature spectra of those phytoplankton species, and the group of the 3rd scale vectors, the 2nd and 3rd wavelet vectors were the best feature segments at the genus level and chosen as the fluorescent genus feature spectra of those phytoplankton species. The reference spectra of those phytoplankton species at the division level and that at the genus level were obtained from these feature spectra by cluster analysis, respectively. The reference spectra base for 37 phytoplankton species was composed of 107 reference spectra at the division level and 155 ones at the genus level. Based on this reference spectra base, a fluorometric discriminating method for phytoplankton populations was established by multiple linear regression resolved by the nonnegative least squares. For 1 776 samples of single phytoplankton species, a correct discriminating rate of 97.0% at genus level and 98.1% at division level can be obtained; The correct discriminating rates are more than 92.7% at the genus level and more than 94.8% at the division level for 384 mixed samples from two phytoplankton species.
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Received: 2008-11-06
Accepted: 2009-02-08
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Corresponding Authors:
SU Rong-guo
E-mail: surongguo@mail.ouc.edu.cn
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