Resolving mixed algal species in hyperspectral images

Area of Science:

• Algal Ecology
• Optical Remote Sensing
• Biomass Quantification

Background:

• Hyperspectral imaging (HSI) offers advanced spectral resolution for analyzing biological samples.
• Accurate characterization of algal cultures is crucial for environmental monitoring and resource management.
• Understanding HSI system performance with varying algal compositions is essential for reliable data interpretation.

Purpose of the Study:

• To evaluate a lab-based hyperspectral imaging system's performance in characterizing pure and mixed algal cultures.
• To determine the system's accuracy in quantifying known algal types and volumetric ratios.
• To assess the relationship between optical properties and algal volumes using Beer-Lambert's law.

Main Methods:

• Investigated spectral responses of single and mixed algal cultures with known volumetric ratios.
• Applied constrained linear spectral unmixing to determine algal abundances.
• Calculated percent prediction error and root mean square error (RMSE) to assess accuracy.
• Utilized Beer-Lambert's law to correlate transmittance with algal suspension volumes.

Main Results:

• Achieved best prediction errors of 0.4%, 0.4%, and 6.3% for mixed algal spectra across three experiments.
• Reported worst prediction errors ranging from 5.4% to 13.4% for the same experiments.
• Demonstrated linear logarithmic trends between transmittance and algal volumes, validating Beer-Lambert's law.

Conclusions:

• The hyperspectral imaging system shows high accuracy in quantifying algal mixtures, with low prediction errors.
• Spectral unmixing is an effective method for determining algal composition from hyperspectral data.
• Optical measurements, guided by Beer-Lambert's law, provide insights into algal biomass and optical properties.