为了区分和识别不同门和属的浮游植物,以coiflet2小波函数(coif2)为基函数对4个门类9个属的12种浮游植物的三维荧光光谱进行分解,选取第三层尺度分量作为浮游植物识别特征谱。不同门类和属(种)的浮游植物的特征谱具有明显的特征差异。Bayes判别分析结果表明,此类特征谱对浮游植物在门类层次上的总分类正确率可达99.0%,属层次上的总分类正确率可达97.4%。以聚类分析法确立浮游植物特征谱的标准谱库,以此为基础,利用线性回归法(非负最小二乘法解析)建立浮游植物荧光识别测定技术。该技术对单种浮游植物样品在门类及属层次上的识别正确率均大于98.0%,当加入10%或20%的随机噪声时,在门类及属的层次上的识别正确率分别大于98.0%和85.0%; 对浮游植物混合样品中的优势种在门及属的层次上的识别正确率均为100%。

In order to discriminate and identify phytoplankton of different divisions and genuses, coiflet2 (coif2) wavelet function was utilized to extract the characteristics of the three-dimensional (3D) fluorescence spectra of 12 phytoplankton species belonging to 9 genuses of 4 divisions. The third scale vectors selected as the discriminating characteristic spectra, obviously express the distinguish characteristics of different genuses and divisions. The results of Bayes discriminant analysis showed that these characteristic spectra had average discriminating rates of 99.0% and 97.4% at the division and the genus level, respectively. Reference spectra were obtained from these characteristic spectra by cluster analysis. A fluormetric method was established by multiple linear regression resolved by the nonnegative least squares. These reference spectra identified the single species added with 10% and 20% ratios of random noise with the rates of more than 98.0% and 85.0%, respectively, at the division and the genus level. All the dominant species of the phytoplankton mixtures could be identified 100% at both the division and the genus level.