Abstract
The discrete excitation-emission-matrix fluorescence spectra (EEMS) at 12 excitation wavelengths (400, 430, 450, 460, 470, 490, 500, 510, 525, 550, 570, and 590 nm) and emission wavelengths ranging from 600–750 nm were determined for 43 phytoplankton species. A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed. For laboratory simulatively mixed samples, the samples mixed from 43 algal species (the algae of one division accounted for 25%, 50%, 75%, 85%, and 100% of the gross biomass, respectively), the average discrimination rates at the level of division were 65.0%, 87.5%, 98.6%, 99.0%, and 99.1%, with average relative contents of 18.9%, 44.5%, 68.9%, 73.4%, and 82.9%, respectively; the samples mixed from 32 red tide algal species (the dominant species accounted for 60%, 70%, 80%, 90%, and 100% of the gross biomass, respectively), the average correct discrimination rates of the dominant species at the level of genus were 63.3%, 74.2%, 78.8%, 83.4%, and 79.4%, respectively. For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass (chlorophyll), the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus, respectively. For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007, the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level; for the 12 samples obtained from Jiaozhou Bay in August 2007, the dominant species of all the 12 samples were recognized at the division level. The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for phytoplankton population.
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References
Beutler, M., Wiltshire, K. H., Arp, M., Kruse, J., Reineke, C., Moldaenke, C., and Hansen, U. P., 2003. A reduced model of the fluorescence from the cyanobacterial photosynthetic apparatus designed for the in situ detection of cyanobacteria. Biochimica et Biophysica Acta, 1604(1): 33–46.
Boddy, L., Morris, C. W., Wilkins, M. F., Al-Haddad, L., Tarran, G. A., Jonker, R. R., and Burkill, P. H., 2008. Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data. Marine Ecology Progress Series, 195: 47–59. DOI: 10.3354/meps195047.
David, I. C., Kells, Joe D. J., O’Neil, and Theo, H., 1984. A method for eliminating Rayleigh scattering from fluorescence spectra. Analytical Biochemisty, 139(2): 316–318. DOI: 10.1016/0003-2697(84)90010-1.
Gerhardt, V., and Bodemer, U., 1998. Delayed fluorescence excitation spectroscopy: a method for automatic determination of phytoplankton composition of freshwaters and sediments (interstitial) and of algal composition of benthos. Limnologica. 28(3): 313–322.
Hallegraeff, G. M., 1993. A review of harmful algal blooms and their apparent global increase. Phycologia, 32(2): 79–99. DOI: 10.2216/i0031-8884-32-2-79.1.
Hu, X. P., Su, R. G., Zhang, C. S., and Wang, X. L., 2008. Fluorescence discrimination technology for the red tide algae by spectra similarity index. Chinese Journal of Lasers, 35(1): 115–119 (in Chinese with English abstract). DOI: CNKI: SUN:JJZZ. 0. 2008-01-025.
Ji, J. R. D., and Booksh, K. S., 2000. Mitigation of Rayleigh and Raman spectral interferences in multiway calibration of excitation — emission matrix fluorescence spectra. Analytical Chemistry, 72(4): 718–725.
Lee, T. Y., Tsuzuki, M., Takeuchi, T., Yokoyama, K., and Karube, I., 1995. Quantitative determination of cyanobacteria in mixed phytoplankton assemblages by an in vivo fluorimetric method. Analytica Chimica Acta, 302(1): 81–87. DOI: 10.1016/j.na.2006.07.046.
Lewitus, A. J., White, D. L., Tymowski, R. G., Geesey, M. E., Hymel, S. N., and Noble, P. A., 2005. Adapting the CHEM-TAX method for assessing phytoplankton taxonomic composition in southeastern U.S. estuaries. Estuaries, 28(1): 160–172. DOI: 10.1007/BF02732761
Liu, X. L., Tao, S., and Deng, N. S. 2005. Synchronous-scan fluorescence spectra of Chlorella vulgaris solution. Chemosphere, 60(11): 1550–1554 (in Chinese with English abstract).
Lu, L., Su, R. G., Hu, X. P., Wang, W. G., Wang, X. L., and Zhu, C. J., 2007. Research on phytoplankton chlorophyll fluorescence excitation spectra by Gaussian decomposition. Chinese Journal of Lasers, 34(8): 1115–1119 (in Chinese with English abstract).
Mackey, M. D., Mackey, D. J., Higgins, H. W., and 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(1–3): 265–283. DOI: 10.3354/meps144265.
Millie, D. F., Dionigi, C. P., Schofield, O., Kirkpatrick, G. J., and Tester, P. A., 1999. The importance of understanding the molecular, cellular, and ecophysiological bases of harmful algal blooms. Journal of Phycology, 35(6): 1353–1355. DOI: 10. 1046/j.1529-8817.1999.3561353.x.
Seppala, J., and Balode, M., 1998. The use of spectral fluorescence methods to detect changes in the phytoplankton community. Hydrobiologia, 363(1–3): 207–217. DOI: 10.1023/A:1003129906730.
Wong, C. K., and Wong, C. K., 2003. HPLC pigment analysis of marine phytoplankton during a red tide occurrence in Tolo Harbour, Hong Kong. Chemosphere 52(9): 1633–1640. DOI: 10.1016/S0045-6535(03)00503-4.
Yentsch, C. S., and Yentsch, C. M., 1979. Fluorescence spectral signatures: The characterization of phytoplankton population by the use of excitation and emission spectra. Journal of Marine Research, 37: 471–483.
Yentsch, C. S., and Phinney, D. A., 1985. Spectral fluorescence: an ataxonomic tool for studying the structure of phytoplankton populations. Journal of Plankton Research, 7(5): 617–632. DOI: 10.1093/plankt/7.5.617.
Zepp, R. G., Sheldon, W. N., and 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 Chemisty, 89(1–4): 15–36. DOI: 10.1016/j.marchem.2004.02.006.
Zhang, F., Su, R. G., Wang, X. L., Wang, L., and He, J. F., 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. DOI: 10.1016/j.jembe.2008.10.004.
Zhang, Q. Q., Lei, S. H., Wang, X. L., Wang, L., and Zhu, C. J., 2006. Discrimination of phytoplankton classes using characteristic spectra of 3D fluorescence spectra. Spectrochimica Acta Part A, 63(2): 3 61–369. DOI:10.1521/aeap.17.1.79.58690.
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Zhang, S., Su, R., Duan, Y. et al. Fluorometric discrimination technique of phytoplankton population based on wavelet analysis. J. Ocean Univ. China 11, 339–346 (2012). https://doi.org/10.1007/s11802-012-1890-1
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DOI: https://doi.org/10.1007/s11802-012-1890-1