Laboratory-based hyperspectral reflectance analysis for phytoplankton species identification

Monitoring phytoplankton communities is essential for assessing ecosystem health and detecting harmful algal blooms (HABs). Hyperspectral imaging has emerged as a promising tool to discriminate among microalgal species based on their unique reflectance signatures. This study presents a laboratory spectral analysis of five phytoplankton species, including bloom-forming and toxin-producing taxa common in coastal waters. Reflectance spectra were measured at multiple cell concentrations and analyzed using two normalization approaches, second- and fourth-derivative transformations, and dimensionality reduction techniques including principal component analysis (PCA) and linear discriminant analysis (LDA). Our results demonstrate that specific spectral features, particularly in the 470–500 nm and 620–680 nm ranges, enable species-level discrimination. PCA and LDA effectively enhanced separability by reducing spectral redundancy and emphasizing class features. We further applied linear spectral unmixing (LSU) to estimate fractional species abundances in synthetic mixtures. LSU performed well in simple mixtures but revealed limitations in complex communities, where nonlinear effects and spectral similarity reduced accuracy. Beyond classification, LSU enables quantitative assessment of species contributions, providing a valuable complement to PCA and LDA for ecological interpretation and bloom dynamics investigation. This integrated approach lays the foundation for future development of operational tools that combine spectral unmixing and machine learning for automated HAB detection. The combined use of hyperspectral reflectance data and computational methods supports scalable, real-time monitoring of phytoplankton diversity and abundance, with strong potential for deployment in early-warning systems and coastal observatories.