基于三维荧光光谱-平行因子技术联用的湖泊浮游藻化学分类学研究

浮游藻荧光分析技术因其能够实现现场、快速、低成本测定而受到广泛研究和应用.本研究以浮游藻活体三维荧光光谱(EEM)为基础,利用平行因子(PARAFAC)和CHEMTAX发展了浮游藻群落组成荧光分析技术.首先,将PARAFAC模型应用于23种浮游藻的EEM,通过残差分析、荧光成分谱形分析等方法确定浮游藻EEM由12个荧光成分组成;然后,利用Bayesian判别分析表明浮游藻12个荧光成分的组成具有明显的门类特征性;最后,以获得的12个荧光成分构建浮游藻"荧光成分比值矩阵",结合CHEMTAX建立浮游藻荧光识别分析技术.通过测试表明,该技术对531个单种藻样品的平均识别正确率是99.1%,其中,绿藻的识别正确率为97.5%,其余藻的识别正确率为100%.对于95个实验室混合样品,优势藻和次优势藻的平均识别正确率分别为98.5%和90.5%;测定的平均相对含量分别为69.7%和26.4%.结果表明,本研究所建立的EEM-PARAFAC-CHEMTAX方法能够实现浮游藻群落组成的快速定性定量测定.

Lake Algae Chemotaxonomy Technology Based on Fluorescence Excitation Emission Matrix and Parallel Factor Analysis

An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor (PARAFAC) analysis and CHEMTAX. The PARAFAC model was applied to fluorescence excitation-emission matrix (EEM) of 23 algae species and 12 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. Based on the 12 fluorescent components, the algae species at different growth stages were correctly classified at the division level using Bayesian discriminant analysis (BDA). Then the reference fluorescent component ratio matrix was constructed for CHEMTAX, and the EEM-PARAFAC-CHEMTAX method was developed to differentiate taxonomic groups of algae. When the fluorometric method was used for 531 single-species samples, the average correct discrimination ratio (CDR) was 99. 1% and the correct discrimination ratios (CDRs) were 100% at the division level except Chlorophyta, the CDR of which was 97.5%. The CDRs for 95 mixtures were above 98.5% for the dominant algae species and above 90.5% for the subdominant algae species, with average relative contents of 69.7% and 26. 4%, respectively. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch.