TY - JOUR
T1 - NIR reflectance and MIR attenuated total reflectance spectroscopy for characterizing algal biomass composition
AU - Ge, Y.
AU - Thomasson, J. A.
N1 - Funding Information:
We would like to acknowledge funding of this work by the U.S. Department of Energy under Contract No. DE-EE0003046 awarded to the National Alliance for Advanced Biofuels and Bioproducts. We would also like to acknowledge Mr. Lou Brown and Ms. Yola Brown at Texas A&M AgriLife Research Center for providing the lyophilized algae samples and associated lipid profile data.
Publisher Copyright:
© 2016 American Society of Agricultural and Biological Engineers.
PY - 2016
Y1 - 2016
N2 - 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 (Nannochloropsis and Nannochloris). 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.
AB - 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 (Nannochloropsis and Nannochloris). 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.
KW - Algae
KW - MIR
KW - NIR
KW - Partial least squares regression
KW - Renewable energy
KW - Vibrational spectroscopy
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U2 - 10.13031/trans.59.11396
DO - 10.13031/trans.59.11396
M3 - Article
AN - SCOPUS:84964713760
SN - 2151-0032
VL - 59
SP - 435
EP - 442
JO - Transactions of the ASABE
JF - Transactions of the ASABE
IS - 2
ER -