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.

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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 (R 2 ) and validation (r 2 ), 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: R 2 = r 2 = 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 R 2 and r 2 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 R 2 and r 2 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.

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The efficacy of using a handheld near infrared spectrometer to predict metanil yellow (MY) adulteration levels (0-30% w/w) in dried turmeric powder was tested against a benchtop near infrared spectrometer using partial least squares regression models. The differences between near infrared instruments were resolution (i.e., 1 nm (handheld) vs. 0.5 nm (benchtop)) and sample container during scanning (plastic pouch (handheld) vs. quartz glass cup (benchtop)). Prediction performance of the calibration models developed was evaluated using number of model factors ([Formula: see text]), coefficients of determination of calibration and validation ([Formula: see text] and[Formula: see text], respectively), root-mean-square errors of calibration, cross-validation, and validation (RMSEC, RMSECV, and RMSEP), ratio of prediction error to standard deviation (RPD), and limits of detection (LOD) and quantification (LOQ). The best benchtop calibration models were based on spectral data preprocessed with Savitzky–Golay first derivative algorithm for the benchtop near infrared and standard normal variate for the handheld near infrared, yielding low[Formula: see text], high [Formula: see text] and[Formula: see text], low RMSEC, RMSECV, RMSEP, and high RPD. The LOD and LOQ for both spectrometers were 0.33 and 1.10%, respectively, and no significant difference was found between the predicted [Formula: see text] values by the benchtop and handheld near infrared spectrometers. The models were, in general, not sensitive to sample source and size of the validation set. When spectra from the benchtop near infrared were standardized using a reverse standardization strategy, calibrated against[Formula: see text], and transferred to the handheld near infrared, prediction performance dropped, from [Formula: see text] of 0.99 to 0.98, RMSEP increased from 0.96% to 1.53%, and [Formula: see text] decreased from 10.1 to 6.3. Despite the reduced prediction performance, the handheld near infrared with a transferred calibration model from the benchtop near infrared was still useful for screening, quality control, and process control applications.

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