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Applied and Environmental Microbiology, October 1999, p. 4404-4410, Vol. 65, No. 10
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.

Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks

M. F. Wilkins,1 Lynne Boddy,1,* C. W. Morris,2 and R. R. Jonker3,dagger

Cardiff School of Biosciences, University of Cardiff, Cardiff CF1 3TL,1 and School of Computing, University of Glamorgan, Pontypridd CF37 1DL,2 United Kingdom, and Department of Aquatic Ecology, Universiteit van Amsterdam, 1098 SM Amsterdam, The Netherlands3

Received 12 April 1999/Accepted 20 July 1999

We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91.5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from "novel" species (species not present in the training data) were analyzed.

* Corresponding author. Mailing address: Cardiff School of Biosciences, Cardiff University, P.O. Box 915, Cardiff CF1 3TL, United Kingdom. Phone: 44-1222-874776. Fax: 44-1222-874305. E-mail: BoddyL{at}cf.ac.uk.

dagger Present address: AquaSense Lab, 1090 HC Amsterdam, The Netherlands.

Applied and Environmental Microbiology, October 1999, p. 4404-4410, Vol. 65, No. 10
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.


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