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SU Rongguo, CHEN Xiaona, WU Zhenzhen, YAO Peng, SHI Xiaoyong. Assessment of phytoplankton class abundance using fluorescence excitation-emission matrix by parallel factor analysis and nonnegative least squares[J]. Journal of Oceanology and Limnology, 2015, 33(4): 878-889

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

SU Rongguo1, CHEN Xiaona2, WU Zhenzhen1, YAO Peng1, SHI Xiaoyong1
1 College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China;
2 Tianjin University Center for Marine Environmental Ecology, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
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.
Key words:    fluorescence excitation-emission matrix|parallel factor analysis|nonnegative least squares|phytoplankton|fluorescence components   
Received: 2014-07-31   Revised: 2014-12-05
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Alexander R, Gikuma-Njuru P, Imberger J. 2012. Identifying spatial structure in phytoplankton communities using multi-wavelength fluorescence spectral data and principal component analysis. Limnology and Oceanography : Methods, 10 (6): 402-415.
Alves J C L, Poppi R J. 2009. Simultaneous determination of acetylsalicylic acid, paracetamol and caffeine using solidphase molecular fluorescence and parallel factor analysis. Analytica Chimica Acta, 642 (1-2): 212-216.
Andersen C M, Bro R. 2003. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. Journal of Chemometrics, 17 (4): 200-215.
Bains S, Norris R D, Corfield R M, Faul K L. 2000. Termination of global warmth at the Palaeocene/Eocene boundary through productivity feedback. Nature, 407 (6801): 171- 174.
Beutler M, Wiltshire K H, Meyer B, Moldaenke C, Lüring C, Meyerhöfer M, Hansen U-P, Dau H. 2002. A fluorometric method for the differentiation of algal populations in vivo and in situ. Photosynthesis Research, 72 (1): 39-53.
Bona M T, Andrés J M. 2007. Coal analysis by diffuse reflectance near-infrared spectroscopy: hierarchical cluster and linear discriminant analysis. Talanta, 72 (4): 1 423-1 431.
Bosco M V, Larrechi M S. 2007. PARAFAC and MCR-ALS applied to the quantitative monitoring of the photodegradation process of polycyclic aromatic hydrocarbons using three-dimensional excitation emission fluorescent spectra: comparative results with HPLC. Talanta, 71 (4): 1 703-1 709.
Bro R. 1999. Exploratory study of sugar production using fluorescence spectroscopy and multi-way analysis. Chemometrics and Intelligent Laboratory Systems, 46 (2): 133-147.
Christensen J H, Hansen A B, Mortensen J, Andersen O. 2005. Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis. Anal ytical Chem istry, 77 (7): 2 210-2 217.
Clarke S E, Stuart J, Sanders-Loehr J. 1987. Induction of siderophore activity in Anabaena spp. and its moderation of copper toxicity. Appl ied and Environ mental Microbiol ogy, 53 (5): 917-922.
Divya O, Mishra A K. 2007. Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study. Analytica Chimica Acta, 592 (1): 82-90.
Drinovec L, Flander-Putrle V, Knez M, Beran A, Berden-Zrimec M. 2011. Discrimination of marine algal taxonomic groups using delayed fluorescence spectroscopy. Environmental and Experimental Botany, 73 : 42-48.
Falkowski P G. 1994. The role of phytoplankton photosynthesis in global biogeochemical cycles. Photosynthesis Research, 39 (3): 235-258.
Gieskes W W C, Kraay G W. 1983. Dominance of Cryptophyceae during the phytoplankton spring bloom in the central North Sea detected by HPLC analysis of pigments. Marine Biology, 75 (2-3): 179-185.
Goldman E A, Smith E M, Richardson T L. 2013. Estimation of chromophoric dissolved organic matter (CDOM) and photosynthetic activity of estuarine phytoplankton using a multiple-fixed-wavelength spectral fluorometer. Water Research, 47 (4): 1 616-1 630.
Harshman R A. 1970. Foundations of the PARAFAC procedure: models and conditions for an “explanatory” multi-modal factor analysis. UCLA Working Papers in Phonetics, 16 (1): 1-84.
Jeffrey S W, Hallegraeff G M. 1980. Studies of phytoplankton species and photosynthetic pigments in a warm core eddy of the East Australian Current. I. Summer populations. Marine Ecology Progress Series, 3 : 285-294.
Kong F Z. 2012. Size-fraction Structure, Species Component and Pigments Analyses of Phytoplankton in the Bloom Zone Near Changjiang Estuary. PhD dissertation. Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China. (in Chinese)
Li Y M, Anderson-Sprecher R. 2006. Facies identification from well logs: a comparison of discriminant analysis and naïve Bayes classifier. Journal of Petroleum Science and Engineering, 53 (3-4): 149-157.
Mackey M D, Mackey D J, Higgins H W, Wright S W. 1996. CHEMTAX- a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Marine Ecology Progress Series, 144 : 265-283.
Moberg L, Robertsson G, Karlberg B. 2001. Spectrofluorimetric determination of chlorophylls and pheopigments using parallel factor analysis. Talanta, 54 (1): 161-170.
Proctor C W, Roesler C S. 2010. New insights on obtaining phytoplankton concentration and composition from in situ multispectral Chlorophyll fluorescence. Limnology and Oceanography : Methods, 8 (12): 695-708.
Richardson T L, Lawrenz E, Pinckney J L, Guajardo R C, Walker E A, Paerl H W, MacIntyre H L. 2010. Spectral fluorometric characterization of phytoplankton community composition using the Algae Online Analyser. Water Research, 44 (8): 2 461-2 472.
Seppälä J, Olli K. 2008. Multivariate analysis of phytoplankton spectral in vivo fluorescence: estimation of phytoplankton biomass during a mesocosm study in the Baltic Sea. Marine Ecology Progress Series, 370 : 69-85.
Sharma A, Schulman S G. 1999. Introduction to Fluorescence Spectroscopy. Wiley, New York.
Simis S G H, Huot Y, Babin M, Seppälä J, Metsamaa L. 2012. Optimization of variable fluorescence measurements of phytoplankton communities with cyanobacteria. Photosynthesis Research, 112 (1): 13-30.
Søndergaard M, Jeppesen E. 2007. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. Journal of Applied Ecology, 44 (6): 1 089-1 094.
Stedmon C A, Bro R. 2008. Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography : Methods, 6 (11): 572-579.
Stedmon C A, Markager S, Bro R. 2003. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Marine Chemistry, 82 (3-4): 239-254.
Stedmon C A, Markager S. 2005. Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis. Limnology and Oceanography, 50 (2): 686-697.
Wright S W, Jeffrey S W. 1997. High resolution HPLC system for chlorophylls and carotenoids of marine plankton. In : Jeffrey S W, Mantoura R F C, Wright S W eds. Phytoplankton Pigments in Oceanography: Guidelines to Modern Methods. UNESCO Public, Paris, ISBN: 92-3- 103275-5, p.327-341.
Zapata M, Rodríguez F, Garrido J L. 2000. Separation of chlorophylls and carotenoids from marine phytoplankton: a new HPLC method using a reversed phase C8 column and pyridine-containing mobile phases. Marine Ecology Progress Series, 195 : 29-45.
Zepp R G, Sheldon W M, Moran M A. 2004. Dissolved organic fluorophores in southeastern US coastal waters: correction method for eliminating Rayleigh and Raman scattering peaks in excitation-emission matrices. Marine Chemistry, 89 (1-4): 15-36.
Zhang F, Su R G, He J F, Cai M H, Luo W, Wang X L. 2010. Identifying phytoplankton in seawater based on discrete excitation-emission fluorescence spectra. J ournal of Phycol ogy, 46 (2): 403-411.
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