A comparative study of Fourier transform Raman and NIR spectroscopic methods for assessment of protein and apparent amylose in rice

Fourier-transform Raman (FT-Raman) spectroscopy and near-infrared (NIR) reflectance spectroscopy were used to compare calibration models for determining rice cooking quality parameters such as apparent amylose and protein. Samples from two seasons were used in each calibration set. The laboratory values ranged from 4.89 to 12.48% for protein and from 0.2 to 25.7% for amylose. The data for both FT-Raman and NIR were preprocessed with orthogonal signal correction (OSC) for standardization. For both spectroscopic methods, five models were optimized by partial least squares regression (PLSR) and by Martens' uncertainty regression (MUR), including no processing, smoothing, normalization, first derivative (D1), and second derivative (D2). Based solely on standard error of cross-validation (SECV), the FT-Raman method was superior to the NIR method for protein. For amylose, the FT-Raman and NIR methods resulted in similar calibration statistics with a high precision, with the, FT-Raman requiring fewer factors. The best FT-Raman models were generated from OSC preprocessing with MUR for protein (SECV 0.15%, five factors) and from OSC without MUR for amylose (SECV 0.70%, seven factors). The best NIR models were obtained with D2 transform of OSC spectra for protein (SECV 0.22%, four factors) and with OSC spectra for amylose (SECV 0.57%, 11 factors).