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JPR Advance Access published online on February 7, 2008
Journal of Plankton Research, doi:10.1093/plankt/fbn024
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© The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org

Use of fluorescence information for automated phytoplankton investigation by image analysis

Burkhard A. Hense1,*, Peter Gais2, Uta Jütting1, Hagen Scherb1 and Karsten Rodenacker1

1 Institute of Biomathematics and Biometry, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg/Munich, Germany 2 Institute of Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg/Munich, Germany

* Corresponding author: burkhard.hense{at}helmholtz-muenchen.de

Received on January 11, 2008; revised on February 4, 2008; accepted on February 5, 2008


  Abstract

Automated identification and quantification of algae in microscopic images is a tool that allows high taxonomic resolution with reasonable technical efforts. However, in samples containing various non-algal objects this is still not a satisfactorily solved problem. We show that autofluorescence information improves discrimination of algae from non-algal objects as well as phycoerythrin containing algae from others. We analyze the stability of the autofluorescence to estimate its constraints. Cold and dark storage of glutaraldehyde fixed samples maintains autofluorescence sufficiently for three weeks. Under repeated excitations, chlorophyll a or phycoerythrin autofluorescence show an exponential decrease followed by an intermediate maximum. A peak also occurs in emission wavelength ranges without chlorophyll and phycoerythrin fluorescence. The unspecific autofluorescence causing the peaks is at least partly identical with the blue-green fluorescence (BGF) in plant cells. BGF interferes with identification of algae, thus correction of pigment autofluorescence with such unspecific fluorescence allows a more reliable algal discrimination procedure. A classification scheme for discrimination of chlorophyll a and phycoerythrin containing algae shows a high performance in a test with natural samples. Integration of fluorescence and bright-field image information provides a powerful tool for phytoplankton analysis in complex samples.

Communicating Editor: Dr Roger Harris


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