Shih-Fang Chen1, Mary-Grace C Danao, Vijay Singh, Patrick J Brown. 1. Energy Biosciences Institute, University of Illinois, 1206 West Gregory Drive, Urbana, IL, 61801, USA; Department of Agricultural and Biological Engineering, University of Illinois, 1304 West Pennsylvania Avenue, Urbana, IL, 61801, USA.
Abstract
BACKGROUND: Sorghum is an advanced biomass feedstock from which grain, sugar and stover can be used for biofuel production. Determinations of specific sugar contents in sorghum stalks help to make strategic decisions during plant breeding, processing, storage and optimization of fermentation conditions. In this study, Fourier transform near infrared (FT-NIR) spectroscopy was used as a relatively fast, low-cost, high-throughput assay to predict sucrose and glucose levels in stalks of 40 dwarf grain sorghum inbreds. RESULTS: The diffuse reflection spectra were pretreated with multiplicative scatter correction (MSC) and first-derivative Savitzy-Golay (SG-1). Calibrated models were developed by partial least squares regression (PLSR) analysis. Martens' uncertainty test was used to determine the most effective spectral region. The PLSR model for stalk sucrose content was built on 380 significant wavenumbers in the 4000-6999 cm(-1) range. The model was based on four factors and had RPD = 2.40, RMSEP = 1.77 and R(2) = 0.81. Similarly, the model for stalk glucose was built using 4000-9000 cm(-1) and six factors, with RPD = 2.45, RMSEP = 0.73 and R(2) = 0.81. CONCLUSION: PLSR models were developed based on FT-NIR spectra coupled with multivariate data analysis to provide a quick and low-cost estimate of specific sugar contents in grain sorghum stalks. This sugar information helps decision making for sorghum-based biomass processing and storage strategies.
BACKGROUND:Sorghum is an advanced biomass feedstock from which grain, sugar and stover can be used for biofuel production. Determinations of specific sugar contents in sorghum stalks help to make strategic decisions during plant breeding, processing, storage and optimization of fermentation conditions. In this study, Fourier transform near infrared (FT-NIR) spectroscopy was used as a relatively fast, low-cost, high-throughput assay to predict sucrose and glucose levels in stalks of 40 dwarf grain sorghum inbreds. RESULTS: The diffuse reflection spectra were pretreated with multiplicative scatter correction (MSC) and first-derivative Savitzy-Golay (SG-1). Calibrated models were developed by partial least squares regression (PLSR) analysis. Martens' uncertainty test was used to determine the most effective spectral region. The PLSR model for stalk sucrose content was built on 380 significant wavenumbers in the 4000-6999 cm(-1) range. The model was based on four factors and had RPD = 2.40, RMSEP = 1.77 and R(2) = 0.81. Similarly, the model for stalk glucose was built using 4000-9000 cm(-1) and six factors, with RPD = 2.45, RMSEP = 0.73 and R(2) = 0.81. CONCLUSION: PLSR models were developed based on FT-NIR spectra coupled with multivariate data analysis to provide a quick and low-cost estimate of specific sugar contents in grain sorghum stalks. This sugar information helps decision making for sorghum-based biomass processing and storage strategies.