Potential support vector machines and Self-Organizing Maps for phytoplankton discrimination

Fluorescence spectroscopy is a powerful technique usually used to evaluate phytoplankton marine environments. In this study, a kernel method (Potential Support Vector Machine, P-SVM) is presented, evaluating its capability to achieve phytoplankton classification from its fluorescence spectra. Different phytoplankton species were studied, and their fluorescence spectra were acquired in laboratory. In a previous study working with Self-Organizing Maps (SOM), it was proved with experimental data from laboratory that excitation spectra were more discriminative than emission spectra. It was also shown that using some preprocessing techniques, such as derivative analysis, the classification performance from emission fluorescence data can be improved. The classification results were encouraging to keep working with emission fluorescence, and herein we present a comparison between P-SVM and SOM for this goal.