利用三维荧光光谱(EEMs)技术测定了实验室培养的分属5个门13个属的17种中国近海常见赤潮藻的荧光性质， 通过db7二维小波变换提取赤潮藻三维荧光光谱的特征参量， 采用系统聚类法构建荧光特征标准谱库， 并利用多元线性回归辅以粒子群算法进行参数估计建立识别技术， 实现了赤潮藻在门、 属水平上的识别测定。 测定样品中： 赤潮藻纯种培养体在门、 属水平上的平均识别正确率分别为96.1%和73.6%； 对于优势度分别为60%， 75%， 80%， 90%的赤潮藻模拟混合样品， 优势藻在门水平上的平均识别正确率分别为86.7%， 96.9%， 98.7%， 99.4%， 识别相对含量分别为62.6%， 72.7%， 76.0%， 81.6%； 优势藻在属水平上的平均识别正确率分别为51.0%， 68.9%， 72.0%， 78.8%； 对于优势度达75%的赤潮藻实际混合样品， 优势藻在门、 属水平上的平均识别正确率分别为99.4%和75.9%。 将所建技术用于围隔实验水样和现场调查采集水样进行分析， 有效实现了浮游藻群落组成的识别测定， 尤其是硅藻和甲藻的分类识别。

Three-dimensional fluorescence of 17 red tide algae species that belong to 13 genera of five divisions was measured by fluorescence excitation-emission matrix spectroscopy. And 2-D wavelet db7 was selected to decompose the spectra at different levels to choose the alternative characteristic spectra. Based on the norm reference spectra constructed by cluster analysis, the linear regression model was solved by particle swarm optimization (PSO) algorithm and the discrimination method was established at the division and genus level. Some samples were tested: for single algal samples, and the correct discrimination ratios (CDRs) were 96.1% and 73.6%, respectively; For simulative mixed algal samples, when the dominance were 60%, 75%, 80% and 90% of the total biomass, the CDRs of the dominant algae were 86.7%, 96.9%, 98.7% and 99.4% with the average relative contents of 62.6%, 72.7%, 76.0% and 81.6%, respectively at the division level. And the CDRs were 51.0%, 68.9%, 72.0%, and 78.8% at the genus level, respectively. For 364 actual mixture samples, the CDRs of the dominant species (75%) were 99.4% at the division level and 75.9% at the genus level . For the particular field samples from mesocosm experiment and corrected from Jiaozhou Bay, results showed that the method can be used to realize the identification of red tide algae population and estimate the relative abundance of different classes, especially between diatoms and dinoflagellates.