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05:54
Autofluorescence Imaging to Evaluate Red Algae Physiology
Published on: February 17, 2023
A rapid technique for classifying phytoplankton fluorescence spectra based on self-organizing maps
Ismael F Aymerich1, Jaume Piera, Aureli Soria-Frisch
1Unidad de Tecnología Marina (UTM-CSIC), Pg. Marítim de la Barceloneta 37-49, E-08003 Barcelona, Spain. ismaelf@utm.csic.es
Applied Spectroscopy
|June 18, 2009
Summary
This study introduces a new method using artificial neural networks (ANNs) to quickly identify phytoplankton groups in the ocean. The technique classifies fluorescence emission spectra, reducing measurement time for faster marine ecosystem monitoring.
Area of Science:
- Marine biology
- Oceanography
- Spectroscopy
Background:
- Fluorescence spectroscopy is vital for marine phytoplankton analysis.
- Current excitation-based methods are time-consuming, limiting use on mobile platforms.
- Rapid phytoplankton identification is crucial for oceanographic research.
Purpose of the Study:
- To develop a faster method for phytoplankton classification using fluorescence spectra.
- To evaluate the effectiveness of self-organizing maps (SOMs) for this purpose.
- To compare the performance of emission versus excitation spectra analysis.
Main Methods:
- Utilized self-organizing maps (SOMs), a type of artificial neural network (ANN).
- Classified phytoplankton using fluorescence emission spectra with single wavelength excitation.
- Compared SOM performance with both excitation and emission spectral data.
Main Results:
- SOMs effectively classify phytoplankton using emission spectra, significantly reducing acquisition time.
- Preprocessing emission spectra data is necessary to match the discrimination capabilities of excitation spectra.
- The method successfully distinguishes between closely related groups like diatoms and dinoflagellates.
Conclusions:
- The novel SOM-based technique offers rapid phytoplankton classification via emission spectra.
- This approach is suitable for time-sensitive applications on autonomous oceanographic platforms.
- While excitation spectra offer higher taxonomic accuracy, emission spectra provide a faster alternative for specific applications.
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