[Identification of Microalgae Species Using Visible/Near Infrared Transmission Spectroscopy]

Area of Science:

• Marine Biology
• Spectroscopy
• Biotechnology

Background:

• Microalgae identification and biochemical analysis are crucial in marine biology.
• Accurate classification of microalgae species is essential for research and applications.

Purpose of the Study:

• To explore the feasibility of using Vis/NIR transmission spectroscopy for rapid, in situ identification of four microalgae species.
• To develop and compare chemometric models for microalgae classification.

Main Methods:

• Collected Vis/NIR transmission spectral data from Chlorella vulgaris, Chlorella pyrenoidosa, Nannochloropsis oculata, and Chlamydomonas reinhardtii.
• Applied spectral pre-processing (Savitzky-Golay smoothing) and successive projections algorithm (SPA) for effective wavelength selection.
• Developed and compared Partial Least Squares (PLS), Least Square Support Vector Machines (LS-SVM), and Extreme Learning Machine (ELM) models.

Main Results:

• Savitzky-Golay smoothing proved superior for spectral pre-processing.
• Six effective wavelengths selected by SPA correlated with carotenoid and chlorophyll content.
• The SPA-LS-SVM and SPA-ELM models achieved average prediction accuracies of 80% and 85%, respectively.

Conclusions:

• Vis/NIR spectroscopy with a portable optic probe offers a rapid, non-destructive, and precise method for microalgae identification and biochemical detection.
• This methodology provides a novel approach for microalgae classification applicable to diverse species.