基于浮游藻色素萃取液三维荧光光谱(EEM)，利用交替三线性分解(ATLD)和非负最小二乘(NNLS)发展浮游藻群落组成荧光分析技术。将ATLD 模型应用于48种浮游藻色素萃取液的EEM，通过残差分析确定浮游藻色素萃取液EEM 由14个荧光成分组成。Bayesian判别分析表明浮游藻色素萃取液荧光成分组成具有显著的门类特征性，利用聚类分析将浮游藻色素萃取液荧光成分组成按属水平聚类获得116 条特征光谱，使用NNLS 方法识别浮游植物群落组成。结果表明：该技术对512 个单种藻样品的平均识别正确率为99.8%。对256 个实验室混合样品，优势和次优势藻的平均识别正确率分别为93.5%和71.1%。将此技术应用于长江口18个浮游藻膜样品，分析结果与HPLC(高效液相色谱)-CHEMTAX 结果基本一致。

A fluorescence-excitation-emission-matrix (EEM) method for the determination of algae community composition is developed by alternating trilinear decomposition (ATLD) analysis and nonnegative least squares (NNLS). ATLD model is applied to fluorescence EEMs of algal pigment extracts, and 14 fluorescent components are identified according to the residual sum of squares. Bayesian discriminant analysis is used to test the discriminatory capability of 14 fluorescent components. All composition spectra of the fluorescent components for the 48 phytoplankton species are spectrographically clustered into 116 reference spectra by hierarchical cluster analysis. The fluorometric method is developed by NNLS and applied to algal samples. For 512 samples of singlespecies, the average correctly discriminated rate (CDR) of the five phytoplankton groups is 99.8%. The CDR for the 256 mixture samples is above 93.5% for the dominant algae species and above 71.1% for the subdominant algae species. This method is also tested by the 18 samples from the Yangtze River estuary, the results of the dominant algae groups are in good agreement with HPLC (high performance liquid chromatography)-CHEMTAX.