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NIR Reflectance and MIR Attenuated Total Reflectance Spectroscopy for Characterizing Algal Biomass Composition

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 59(2): 435-442. (doi: 10.13031/trans.59.11396) @2016
Authors:   Yufeng Ge, J. Alex Thomasson
Keywords:   Algae, MIR, NIR, Partial least squares regression, Renewable energy, Vibrational spectroscopy.

Abstract. Algae have long been investigated as a potential feedstock for renewable energy production. There is growing interest in rapid and cost-effective techniques for characterizing algal biomass composition relevant to biofuel production. The objective of this study is to investigate the usefulness of near-infrared (NIR) and mid-infrared (MIR) spectroscopy in determining neutral lipids, crude protein, gross calorific value (GCV), and ash content in lyophilized green algae samples ( and ). NIR spectra were acquired in diffuse reflectance mode, and MIR spectra were acquired in attenuated total reflectance mode. Partial least squares regression models were calibrated and validated to correlate laboratory chemical data with absorption spectra in the NIR and MIR regions. The results show that, for both spectral regions, crude protein can be predicted most accurately, with validation R2 higher than 0.85, followed by neutral lipids (R2 > 0.70). Validation accuracy for GCV and ash is somewhat lower (R2 from 0.55 to 0.70). Large ash content, with its diverse chemical composition, was determined to negatively impact the prediction accuracy of the NIR and MIR models. It is concluded that both NIR and MIR have the potential to characterize algal biomass composition and to support the future algae-based biofuel and bioproducts industry.

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