Fast phytoplankton classification from fluorescence spectra: comparison between PSVM and SOM

Evaluation of phytoplankton communities is an important task to characterize marine environments. Fluorescence spectroscopy is a powerful technique usually used for this goal. This study presents a comparison between two different techniques for fast phytoplankton discrimination: Self-Organizing Maps (SOM) and Potential Support Vector Machines (P-SVM), evaluating its capability to achieve phytoplankton classification from its fluorescence spectra. Several cultures representing different algae groups were grown under the same conditions and their emission fluorescence spectra were measured every day. Finally, the classification results obtained from both techniques, SOM and P-SVM, are presented. In the case of using emission fluorescence spectra, the results show that we are able to reduce the acquisition time required for some of the existing techniques, obtaining encouraging classification performance.