Pigment analysis based on a line-scanning fluorescence hyperspectral imaging microscope combined with multivariate...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Microscopy

Background:

• Harmful algae blooms pose significant threats to water quality.
• Accurate and rapid detection of harmful algae is crucial for environmental management.
• Previous methods struggled with overlapping spectral signatures of algae pigments.

Purpose of the Study:

• To develop an optimized multivariate curve resolution (MCR) method for analyzing hyperspectral fluorescence imaging (HSI) data.
• To improve the accuracy of pigment analysis in microalgae detection.
• To enable precise mapping of microalgae pigment distribution and concentration.

Main Methods:

• Utilized hyperspectral fluorescence imaging microscopy to capture spectral signatures.
• Applied an optimized multivariate curve resolution (MCR) technique for spectral unmixing.
• Reconstructed images to visualize pigment location and concentration.

Main Results:

• The optimized MCR method successfully resolved overlapping spectra.
• Generated images accurately depicted cyanobacterial pigment distribution.
• Quantified relative pigment content, aiding in harmful algae identification.

Conclusions:

• Hyperspectral imaging (HSI) combined with MCR provides a powerful tool for analyzing microalgae.
• This approach efficiently integrates spectral and spatial information for accurate harmful algae detection.
• The developed system offers a rapid and cost-effective solution for water quality monitoring.