We assess the measurement of hyperspectral reflectance for the outdoor monitoring of green algae and cyanobacteria cultures with a multi-channel, fiber-coupled spectroradiometer. Reflectance data acquired over a four-week period are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed as a quadratic function of the single backscattering albedo, dependent on the absorption and backscatter coefficients. The absorption coefficient is treated as the sum of component spectra consisting of the cultured species (green algae or cyanobacteria), dissolved organic matter, and water (including the temperature dependence of the water absorption spectrum). The backscatter coefficient is approximated as the scaled Hilbert transform of the culture absorption spectrum with a wavelength-independent vertical offset. Additional terms in the reflectance model account for the pigment fluorescence features and the water surface reflection of sunlight and skylight. For both the green algae and cyanobacteria, the wavelength-independent vertical offset of the backscatter coefficient is found to scale linearly with daily dry weight measurements, providing the capability for a non-sampling measurement of biomass in outdoor ponds. Other fitting parameters in the reflectance model are compared to auxiliary measurements and physics-based calculations. The magnitudes of the sunlight and skylight water-surface contributions derived from the reflectance model compare favorably with Fresnel reflectance calculations, while the reflectance-derived quantum efficiency of Chl-a fluorescence is found to be in agreement with literature values. To conlclude, the water temperature derived from the reflectance model exhibits excellent agreement with thermocouple measurements during the morning hours and highlights significantly elevated temperatures in the afternoon hours.
Microalgae have been identified as a promising renewable feedstock for production of lipids for feeds and fuels. Current methods for identifying algae strains and growth conditions that support high lipid production require a variety of fluorescent chemical indicators, such as Nile Red and more recently, Bodipy. Despite notable successes using these approaches, chemical indicators exhibit several drawbacks, including non-uniform staining, low lipid specificity, cellular toxicity, and variable permeability based on cell-type, limiting their applicability for high-throughput bioprospecting. In this work, we used in vivo hyperspectral confocal fluorescence microscopy of a variety of potential microalgae production strains (Nannochloropsis sp., Dunaliella salina, Neochloris oleoabundans, and Chlamydomonas reinhardtii) to identify a label-free method for localizing lipid bodies and quantifying the lipid yield on a single-cell basis. By analyzing endogenous fluorescence from chlorophyll and resonance Raman emission from lipid-solubilized carotenoids we deconvolved pure component emission spectra and generated diffraction limited projections of the lipid bodies and chloroplast organelles, respectively. Applying this imaging method to nutrient depletion time-courses from lab-scale and outdoor cultivation systems revealed an additional autofluorescence spectral component that became more prominent over time, and varied inversely with the chlorophyll intensity, indicative of physiological compromise of the algal cell. This signal could result in false-positives for conventional measurements of lipid accumulation (via spectral overlap with Nile Red), however, the additional spectral feature was found to be useful for classification of lipid enrichment and culture crash conditions in the outdoor cultivation system. Under nutrient deprivation, increases in the lipid fraction of the cellular volume of ~. 500% were observed, as well as a correlated decrease in the chloroplast fraction of the total cellular volume. The results suggest that a membrane recycling mechanism dominates for nutrient deprivation-based lipid accumulation in the microalgae tested.