A rapid quantification method was developed and validated for non-destructive measurement of starch content, theoretical ethanol yield and actual ethanol yield of 48 cultivars of sorghum grain using Fourier transform near infrared (FT-NIR) spectroscopy in diffuse reflectance mode. Multiplicative scatter correction, Savitzky-Golay derivative smoothing and mean centring were used for processing the spectra of ground sorghum grain. The processed spectra were correlated with starch content, theoretical ethanol yield and ethanol produced through simultaneous saccharification and fermentation using partial least-squares regression (PLSR). The spectral range and number of factors were optimised for the low number of factors, high coefficients of determination for calibration (R2) and validation (r2), low root mean square error of prediction (RMSEP), high ratio of performance to deviation (RPD) and high ratio of the standard error of prediction to the range (RER). The best PLSR model for starch content utilised the 4000-6000 cm-1 wavebands and had the following values: R2 = r2 = 0.97, RMSEP = 5.5 g kg-1 grain, RPD = 5.9 and RER = 15. Likewise, the model for theoretical ethanol yield utilised the 4000-8000 cm-1 wavebands and had R2 and r2 values of >0.90, RMSEP = 4.9 g kg-1 grain, RPD = 4.47 and RER = 12.8. It was more difficult to predict actual ethanol yield using FT-NIR spectroscopy given the small data set, and spectra were collected prior to the fermentation step. Resulting PLSR models had R2 and r2 values of <0.60, RMSEP = 11.2-21.4 g kg-1, RPD < 3 and RER < 6. These results demonstrated that FT-NIR spectroscopy may be a practical method for rough screening of sorghum cultivars for desirable starch content and theoretical ethanol yield. The models may be improved by including more cultivars in the model and additional compositional information, such as tannin and free amino nitrogen contents, in the chemometric analysis and using FT-NIR scans of the fermentation products to predict actual ethanol yields.
- Fourier transform near-infrared spectroscopy
- Partial least-squares regression
ASJC Scopus subject areas