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Quantification of cell concentration in biofuel-important microalgae using hyperspectral reflectance and hyperspectral extinction coefficient

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Monitoring of microalgae cell concentration during their growing phase is imperative to ensure efficiency in biomass production and to study the cell division kinetics since it impacts the penetration depth of the active light radiation and thus determines the thickness of the active growing layer. Conventionally, the cell concentration (number of cells per unit volume) of microalgal solutions is estimated by microscopic enumeration method that is laborious and time consuming and can be performed only in the laboratory. In this study, we developed algorithms that relate cell concentration to hyperspectral reflectance and extinction coefficient (EC) for quick estimates of cell concentration. A multi-layer radiative transfer model was developed to correct the effect of the bottom of the microalgal solution container and table surface to obtain the hyperspectral reflectance and EC of only microalgal solution. Regression results show that the reflectance-based Band Ratio (BR) algorithm and the EC-based Spectral Shape (SS) index in the near-infrared (NIR) band gave the best results for all three microalgae species with the coefficient of determination [Inline formula], mean relative errors MRE < 5% and root mean squared error RMSE < 5%, and especially for A. cylindrica and N. gaditana, with [Inline formula], MRE < 2% and RMSE < 1%, respectively. These relationships can be used to quickly estimate microalgal cell concentration from hyperspectral measurements that can be carried out quickly and easily either in laboratory or in field.
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Document Type: Research Article

Affiliations: 1: Department of Geophysical Engineering, Montana Technological University, Butte, MT, USA 2: Department of Biological Sciences, Montana Technological University, Butte, MT, USA 3: Center for Advanced Mineral and Metallurgical Processing, Montana Technological University, Butte, MT, USA 4: Energy Research Institute, Montana State University, Bozeman, MT, USA

Publication date: December 2, 2019

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