Assessment of phytoplankton class abundance using fluorescence excitation-emission matrix by parallel factor analysis and nonnegative least squares

• Published in: Chinese journal of oceanology and limnology Vol. 33; no. 4; pp. 878 - 889
• Main Author: 苏荣国 陈小娜 吴珍珍 姚鹏 石晓勇
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.07.2015; Springer Nature B.V
• Subjects: Algae; Analysis; Bacillariophyceae; Bacillariophyta; Bayesian analysis; Biology; Chlorophycota; Chlorophyta; Cluster analysis; Components; Cryptophyceae; Cryptophycophyta; Cryptophyta; Differentiation; Dinophyta; Discriminant analysis; Dominant species; Earth and Environmental Science; Earth Sciences; Emission analysis; Emissions; Estuaries; Estuarine dynamics; Excitation; Factor analysis; Fluorescence; fluorometry; Genera; High-performance liquid chromatography; HPLC; Least squares; Liquid chromatography; Marine biology; mixtures; Oceanography; Phytoplankton; Plankton; Probability theory; rivers; sampling; Spectra; Taxonomy; 光谱数据库; 因子分析法; 平行; 最小二乘法; 植物类; 浮游植物; 荧光光谱; 荧光激发; INW, China, People's Rep., Changjiang Estuary; phytoplankton; fluorescence components; fluorescence excitation-emission matrix; nonnegative least squares; parallel factor analysis
• ISSN: 0254-4059; 2096-5508; 1993-5005; 2523-3521
• DOI: 10.1007/s00343-015-4179-6

The feasibility of using fluorescence excitation-emission matrix (EEM) along with parallel factor analysis (PARAFAC) and nonnegative least squares (NNLS) method for the differentiation of phytoplankton taxonomic groups was investigated. Forty-one phytoplankton species belonging to 28 genera of five divisions were studied. First, the PARAFAC model was applied to EEMs, and 15 fluorescence components were generated. Second, 15 fluorescence components were found to have a strong discriminating capability based on Bayesian discriminant analysis (BDA). Third, all spectra of the fluorescence component compositions for the 41 phytoplankton species were spectrographically sorted into 61 reference spectra using hierarchical cluster analysis (HCA), and then, the reference spectra were used to establish a database. Finally, the phytoplankton taxonomic groups was differentiated by the reference spectra database using the NNLS method. The five phytoplankton groups were differentiated with the correct discrimination ratios (CDRs) of 100% for single-species samples at the division level. The CDRs for the mixtures were above 91% for the dominant phytoplankton species and above 73% for the subdominant phytoplankton species. Sixteen of the 85 field samples collected from the Changjiang River estuary were analyzed by both HPLC-CHEMTAX and the fluorometric technique developed. The results of both methods reveal that Bacillariophyta was the dominant algal group in these 16 samples and that the subdominant algal groups comprised Dinophyta, Chlorophyta and Cryptophyta. The differentiation results by the fluorometric technique were in good agreement with those from HPLC-CHEMTAX. The results indicate that the fluorometric technique could differentiate algal taxonomic groups accurately at the division level.