Authors: Aymerich, Ismael F.¹; Piera, Jaume²; Soria-Frisch, Aureli³; Cros, Lluïsa⁴

Source: Applied Spectroscopy, Volume 63, Issue 6, Pages 136A-156A and 591-726 (June 2009) , pp. 716-726(11)

Publisher: Society for Applied Spectroscopy

Abstract:

Fluorescence spectroscopy has been demonstrated to be a powerful tool for characterizing phytoplankton communities in marine environments. Using different fluorescence spectra techniques, it is now possible to discriminate the major phytoplankton groups. However, most of the current techniques are based on fluorescence excitation measurements, which require stimulation at different wavelengths and thus considerable time to obtain the complete spectral profile. This requirement may be an important constraint for several mobile oceanographic platforms, such as vertical profilers or autonomous underwater vehicles, which require rapid-acquisition instruments. This paper presents a novel technique for classifying fluorescence spectra based on self-organizing maps (SOMs), one of the most popular artificial neural network (ANN) methods. The method is able to achieve phytoplankton discrimination using only fluorescence emission spectra (single wavelength excitation), thus reducing the acquisition time. The discrimination capabilities of SOM using excitation and emission spectra are compared. The analysis shows that the SOM has a good performance using excitation spectra, whereas data preprocessing is required in order to obtain similar discrimination capabilities using emission spectra. The final results obtained using emission spectra indicate that the discrimination is properly achieved even between algal groups, such as diatoms and dinoflagellates, which cannot be discriminated with previous methods. We finally point out that although techniques based on excitation spectra can achieve a better taxonomic accuracy, there are some applications that require faster acquisition processes. Acquiring emission spectra is almost instantaneous, and techniques such as SOM can achieve good classification performance using appropriately preprocessed data.

Keywords: SELF-ORGANIZING MAPS; SOMS; PHYTOPLANKTON DISCRIMINATION; CLASSIFICATION; FLUORESCENCE SPECTRA; DERIVATIVE ANALYSIS

Document Type: Research article

DOI: http://dx.doi.org/10.1366/000370209788559683

Affiliations: 1: Unidad de Tecnología Marina (UTM-CSIC), Pg. Marítim de la Barceloneta 37-49, E-08003 Barcelona, Spain; Technical University of Catalonia (UPC), Barcelona, Spain 2: Unidad de Tecnología Marina (UTM-CSIC), Pg. Marítim de la Barceloneta 37-49, E-08003 Barcelona, Spain 3: Starlab S.L., C. de l'Observatori Fabra s/n, E-08035 Barcelona, Spain 4: Institut de Ciències del Mar (ICM-CSIC), Pg. Marítim de la Barceloneta 37-49, E-08003 Barcelona, Spain

Publication date: 2009-06-01

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