基于小波高频分量的浮游植物活体荧光识别技术研究

提出了一种基于小波高频分量的浮游植物活体荧光识别技术。通过测量近海常见52种浮游植物的三维荧光光谱,利用小波函数将光谱分解6层后得到系列正交高频分量cd1～cd6,通过标准偏差选择稳定而特异性强的分量特征点及其组合作为浮游植物荧光识别特征谱,并对其稳定性和判别能力进行贝叶斯判别分析,以判别正确率为基准选择分量组合cd3～cd6作为最佳荧光识别特征谱构建浮游植物荧光标准特征谱库,结合非负最小二乘法实现了浮游植物群落组成门、属水平上的识别测定:单种浮游植物在门、属水平上的识别正确率分别为95.5%和85.7%;浮游植物混合样品(混合比例分别为100%,75%,25%)在门水平上的识别正确率分别为100%,90.9%,53.3%,平均识别相对含量分别为79.7%,68.3%,17.5%;优势藻(单种优势度达75%)在属水平上的识别正确率为81.2%。将该技术用于围隔实验和现场调查采集水样,有效实现了浮游植物在门水平上的定性定量识别测定。

Differentiation of Phytoplankton Populations by in vivo Fluorescence Based on High-Frequency Component of Wavelet

A fluorescence spectroscopy method of classification for phytoplankton populations is developed based on the high-frequency component of wavelet transform. Three-dimensional (3D) fluorescence spectra of 52 species are projected onto the wavelet function and a series of high-frequency components (cd1~cd6) are obtained. The characteristic points are chosen by the standard deviation and used to form new feature vectors. These feature vectors are analyzed by Bayesian discrimination and cd3~cd6 components are selected as the optimal feature vector for differentiation with the discriminant accuracy rate as a standard, based on which, nonnegative least squares (NNLS) method is introduced to establish the discrimination technique. The technique is used to identify algal species at both the division and the genus level and the correct discrimination rates (CDRs) are 95.5% and 85.7%, respectively. For the actual mixture samples (the mixed proportions are 100%, 75%, 25%), the CDRs are 100%, 90.9%, 53.3% with the relative contents of 79.7%, 68.3%, 17.5%, respectively at the division level and the CDRs of the dominant species (75%) is 81.2% at the genus level. For the water samples from mesocosm experiment and the Jiaozhou Bay, the method can be used to realize the identification of phytoplankton population and estimate the relative abundance of different classes at the division level effectively.