Skip to main content

Advertisement

Log in

Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX

  • Published:
Acta Oceanologica Sinica Aims and scope Submit manuscript

Abstract

An in vivo three-dimensional fluorescence method for the determination of algae community structure was developed by parallel factor analysis (PARAFAC) and CHEMTAX. The PARAFAC model was applied to fluorescence excitation-emission matrix (EEM) of 60 algae species belonging to five divisions and 11 fluorescent components were identified according to the residual sum of squares and specificity of the composition profiles of fluorescent. By the 11 fluorescent components, the algae species at different growth stages were classified correctly 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 algae taxonomic groups. The correct discrimination ratios (CDRs) when the fluorometric method was used for single-species samples were 100% at the division level, except for Bacillariophyta with a CDR of 95.6%. The CDRs for the mixtures were above 94.0% for the dominant algae species and above 87.0% for the subdominant algae species. However, the CDRs of the subdominant algae species were too low to be unreliable when the relative abundance estimated was less than 15.0%. The fluorometric method was tested using the samples from the Jiaozhou Bay and the mesocosm experiments in the Xiaomai Island Bay in August 2007. The discrimination results of the dominant algae groups agreed with microscopy cell counts, as well as the subdominant algae groups of which the estimated relative abundance was above 15.0%. This technique would be of great aid when low-cost and rapid analysis is needed for samples in a large batch. The fluorometric technique has the ability to correctly identify dominant species with proper abundance both in vivo and in situ.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Andersen C M, Bro R. 2003. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. J Chemom, 17(4): 200–215

    Article  Google Scholar 

  • Arrigo K R. 2005. Marine microorganisms and global nutrient cycles. Nature, 437(7057): 349–355

    Article  Google Scholar 

  • Barber C B, Dobkin D P, Huhdanpaa H. 1996. The Quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 22(4): 469–483

    Article  Google Scholar 

  • Beutler M, Wiltshire K H, Arp M, et al. 2003. A reduced model of the fluorescence from the cyanobacterial photosynthetic apparatus designed for the in situ detection of cyanobacteria. Biochimica et Biophysica Acta (BBA)-Bioenergetics, 1604(1): 33–46

    Article  Google Scholar 

  • 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): 1703–1709

    Article  Google Scholar 

  • Bro R. 1997. PARAFAC tutorial and applications. Chemometrics and Intelligent Laboratory Systems, 38(2): 149–171

    Article  Google Scholar 

  • Bro R. 1999. Exploratory study of sugar production using fluorescence spectroscopy and multi-way analysis. Chemometrics and Intelligent Laboratory Systems, 46(2): 133–147

    Article  Google Scholar 

  • Chen J Q, Guo R X. 2012. Access the toxic effect of the antibiotic cefradine and its UV light degradation products on two freshwater algae. Journal of Hazardous Materials, 209–210: 520–523

    Article  Google Scholar 

  • Drinovec L, Flander-Putrle V, Knez M, et al. 2011. Discrimination of marine algal taxonomic groups using delayed fluorescence spectroscopy. Environmental and Experimental Botany, 73: 42–48

    Article  Google Scholar 

  • Fellman J B, Miller M P, Cory R M, et al. 2009. Characterizing dissolved organic matter using PARAFAC modeling of fluorescence spectroscopy: A comparison of two models. Environmental Science & Technology, 43(16): 6228–6234

    Article  Google Scholar 

  • Gameiro C, Cartaxana P, Brotas V. 2007. Environmental drivers of phytoplankton distribution and composition in Tagus Estuary, Portugal. Estuarine, Coastal and Shelf Science, 75(1–2): 21–34

    Article  Google Scholar 

  • Guillard R R L. 1975. Culture of phytoplankton for feeding marine invertebrates. In: Culture of Marine Invertebrate Animals. New York: Springer, 29–60

    Chapter  Google Scholar 

  • Harshman R A. 1970. Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis. UCLA Working Papers in Phonetics, 16: 1–84

    Google Scholar 

  • Havskum H, Schlüter L, Scharek R, et al. 2004. Routine quantification of phytoplankton groups-microscopy or pigment analyses. Marine Ecology Progress Series, 273: 31–42

    Article  Google Scholar 

  • Harwati T U, Willke T, Vorlop K D. 2012. Characterization of the lipid accumulation in a tropical freshwater microalgae Chlorococcum sp. Bioresource Technology, 121: 54–60

    Article  Google Scholar 

  • Hu Yuxi, Li Xibing. 2012. Bayes discriminant analysis method to identify risky of complicated goaf in mines and its application. Transactions of Nonferrous Metals Society of China, 22(2): 425–431

    Article  Google Scholar 

  • Jeffrey S W, Hallegraeff G M. 1980. Studies of phytoplankton species and photosynthetic pigments in a warm core eddy of East Australian Current: I. Summer populations. Marine Ecology Progress Series, 3: 285–294

    Article  Google Scholar 

  • Khullar S, Michael A, Correa N, et al. 2011. Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics. Neuroimage, 54(4): 2867–2884

    Article  Google Scholar 

  • Latasa M. 2007. Improving estimations of phytoplankton class abundances using CHEMTAX. Marine Ecology Progress Series, 329: 13–21

    Article  Google Scholar 

  • Lee T, Tsuzuki M, Takeuchi T, et al. 1995. Quantitative determination of cyanobacteria in mixed phytoplankton assemblages by an in vivo fluorimetric method. Analytica Chimica Acta, 302(1): 81–87

    Article  Google Scholar 

  • Li Yumei, Anderson-Sprecher R. 2006. Facies identification from well logs: A comparison of discriminant analysis and naïe Bayes classifier. Journal of Petroleum Science and Engineering, 53(3–4): 149–157

    Article  Google Scholar 

  • Liu Xianli, Tao Shu, Deng Nansheng. 2005. Synchronous-scan fluorescence spectra of Chlorella vulgaris solution. Chemosphere, 60(11): 1550–1554

    Article  Google Scholar 

  • Mackey M D, Mackey D J, Higgins H W, et al. 1996. CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Marine Ecology Progress Series, 144: 265–283

    Article  Google Scholar 

  • Nie Jinfang, Wu Hailong, Zhang Shurong, et al. 2010. Self-weighted alternating normalized residue fitting algorithm with application to quantitative analysis of excitation-emission matrix fluorescence data. Analytical Methods, 2: 1918–1926

    Article  Google Scholar 

  • Oldham P B, Zillioux E J, Walker I M. 1985. Spectral “fingerprinting” of phytoplankton populations by two-dimensional fluorescence and Fourier-transform-based pattern recognition. Journal of Marine Research, 43:893–906

    Article  Google Scholar 

  • Paerl H W, Huisman J. 2009. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environmental Microbiology Reports, 1(1): 27–37

    Article  Google Scholar 

  • Rodriguez F, Varela M, Zapata M. 2002. Phytoplankton assemblages in the Gerlache and Bransfield Straits (Antarctic Peninsula) determined by light microscopy and CHEMTAX analysis of HPLC pigment data. Deep-Sea Research Part II: Topical Studies in Oceanography, 49(4–5): 723–747

    Article  Google Scholar 

  • Ruivo M, Amorim A, Cartaxana P. 2011. Effects of growth phase and irradiance on phytoplankton pigment ratios: implications for chemotaxonomy in coastal waters. Journal of Plankton Research, 33(7): 1012–1022

    Article  Google Scholar 

  • Schlüter L, Lauridsen T L, Krogh G, et al. 2006. Identification and quantification of phytoplankton groups in lakes using new pigment ratios-a comparison between pigment analysis by HPLC and microscopy. Freshwater Biology, 51(8): 1474–1485

    Article  Google Scholar 

  • Schlüter L, Møhlenberg F, Havskum H, et al. 2000. The use of phytoplankton pigments for identifying and quantifying phytoplankton groups in coastal areas: testing the influence of light and nutrients on pigment/chlorophyll a ratios. Marine Ecology Progress Series, 192: 49–63

    Article  Google Scholar 

  • Sheng Guoping, Yu Hanqing. 2006. Characterization of extracellular polymeric substances of aerobic and anaerobic sludge using three-dimensional excitation and emission matrix fluorescence spectroscopy. Water Research, 40(6): 1233–1239

    Article  Google Scholar 

  • Stedmon C A, Bro R. 2008. Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnology and Oceanography: Methods, 6: 572–579

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wang Zhiwei, Wu Zhichao, Tang Shujuan. 2009. Characterization of dissolved organic matter in a submerged membrane bioreactor by using three-dimensional excitation and emission matrix fluorescence spectroscopy. Water Research, 43(6): 1533–1540

    Article  Google Scholar 

  • Wright S W, Thomas D P, Marchant H J, et al. 1996. Analysis of phytoplankton of the Australian sector of the Southern Ocean: comparisons of microscopy and size frequency data with interpretations of pigment HPLC data using the ‘CHEMTAX’ matrix factorisation program. Marine Ecology Progress Series, 144: 285–298

    Article  Google Scholar 

  • Wright S W, van den Enden R L, Pearce I, et al. 2010. Phytoplankton community structure and stocks in the Southern Ocean (30–801E) determined by CHEMTAX analysis of HPLC pigment signatures. Deep-Sea Research Part II, 57: 758–778

    Article  Google Scholar 

  • Zelen M, Severo N C. 1970. Probability functions. In: Abramowitz M, Stegun I A, eds. Handbook of Mathematical Functions. New York: Dover Publications, 925–995

    Google Scholar 

  • 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 excitationemission matrices. Marine Chemistry, 89(1–4): 15–36

    Article  Google Scholar 

  • Zhang Fang, Su Rongguo, He Jianfeng, et al. 2010. Identifying phytoplankton in seawater based on discrete excitation-emission fluorescence spectra. Journal of Phycology, 46(2): 403–411

    Article  Google Scholar 

  • Zhang Fang, Su Rongguo, Wang Xiulin, et al. 2009. A fluorometric method for the discrimination of harmful algal bloom species developed by wavelet analysis. Journal of Experimental Marine Biology and Ecology, 368(1): 37–43

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rongguo Su.

Additional information

Foundation item: The National Natural Science Foundation of China under contract Nos 41376106 and 41276069.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Su, R., Bai, Y. et al. Discrimination of marine algal taxonomic groups based on fluorescence excitation emission matrix, parallel factor analysis and CHEMTAX. Acta Oceanol. Sin. 33, 192–205 (2014). https://doi.org/10.1007/s13131-014-0576-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13131-014-0576-5

Key words

Navigation