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Spectroscopic classification of 14 different microalga species: first steps towards spectroscopic measurement of phytoplankton biodiversity

Background: Phytoplankton species composition and abundance may reflect the nutritional status of aquatic environments. Species-specific spectral signatures and spectral features dependent on the nutritional status of cells can be derived by Fourier Transform InfraRed (FTIR) spectroscopy. Aims: This study uses analytical methods to objectively and automatically identify microalga species and their environmental conditions based on FTIR data. Methods: Fourteen species and two growth regimes were used for this study. Each of these species–nutrient combinations constituted a ‘class’. The assignment of a sample to a class was based on the similarity of its FTIR spectrum to known class members, derived from correlation coefficients. To assess to what extent classes could be discriminated and how reliable this discrimination was, each spectrum was correlated with all others. The unknown was assigned to all classes with whose members it had a high similarity. Results: Correct classifications were achieved in >90% cases. Subsequently, an unambiguous classification was performed by assigning an unknown to the class with which it had the highest similarity. Use of derivative spectra for baseline shifts removal increased the success percentage to 94. Conclusions: Chemometric methods applied to FTIR spectra of microalgae allow to discriminate species and nutritional conditions to which the cells had been exposed.