从荧光分光光度计所测浮游植物三维荧光光谱中, 选取12个激发波长点(400, 430, 450, 460, 470, 490, 500, 510, 525, 550, 570, 590 nm)组成离散三维荧光光谱, 采用db7小波技术及Bayes判别法对光谱进行特征提取, 并利用多元线性回归辅以非负最小二乘建立识别技术, 实现了浮游植物群落组成门、 属水平上的荧光识别测定。 所测样品中, 32种赤潮藻: 模拟混合样品(优势种比例分别为70%, 80%, 90%)及单种藻, 属水平平均识别正确率分别为67.5%, 75.8%, 81.4%, 79.4%。 43种浮游植物: 模拟混合样品(优势门比例分别为50%, 75%, 100%)门水平平均识别率分别为95.2%, 99.7%, 91.9%; 平均定量测定百分比分别为38.1%, 63.2%, 90.5%。

The present research was targeted to develop a fluorescence analyser for phytoplankton population which uses a series of LEDs as the light source. So the 3D discrete fluorescence spectra with 12 excitation wavelengths (400, 430, 450, 460, 470, 490, 500, 510, 525, 550, 570 and 590 nm) were determined by fluorescence spectrophotometer for 43 phytoplankton species. Then, the wavelet, Daubechies-7 (Db7), and Bayes Classifier were applied to extract the characteristics for each classes from the 3D discrete fluorescence spectra. Lastly, the fluorescence differentiation method for phytoplankton populations was established by multivariate linear regression and non-negative least squares, which could differentiate phytoplankton populations at the levels of both divisions and genus. This method was tested: for simulatively mixed samples(the dominant species accounted for 70%, 80%, 90% and 100% of the gross biomass, respectively) from 32 red tide algal species, and the correct discrimination rates at the level of genus were 67.5%, 75.8%, 81.4% and 79.4%, respectively. For simulatively mixed samples (the dominant divisions algae accounted for 50%, 75% and 100% of the gross biomass, respectively) from 43 algal species, the discrimination rates at the level of division were 95.2%, 99.7% and 91.9% with average relative content of 38.1%, 63.2% and 90.5%, respectively.