Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices.
Pauliina SalmiMatti A EskelinenMatti T LeppänenIlkka PölönenPublished in: Plants (Basel, Switzerland) (2021)
Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A - B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.
Keyphrases
- energy transfer
- high resolution
- escherichia coli
- anaerobic digestion
- optical coherence tomography
- water soluble
- climate change
- wastewater treatment
- single molecule
- computed tomography
- stem cells
- magnetic resonance
- antibiotic resistance genes
- mesenchymal stem cells
- machine learning
- deep learning
- social media
- sensitive detection
- contrast enhanced
- photodynamic therapy