Necessity of Log(1/R) and Kubelka-Munk transformation in chemometrics analysis to predict white rice flour adulteration in brown rice flour using visible-near-infrared spectroscopy

This study compared the calibration model performance of reflectance to absorbance transformation spectra combined with pre-processing spectra to find the best model to predict white rice flour adulteration in brown rice flour using the visible and near-infrared spectrometer. Partial least squares regression (PLSR) and principal component regression (PCR) were compared using reflectance, Kubelka-Munk (KM), and Log(1/R) spectra. Area normalization (AN) and Savitsky-smoothing Golay's (SGS) were pre-processing methods. The sample was white rice flour mixed with brown rice flour at 0%, 5%, 10%, 15%, 20%, and 25%. Reflectance spectra outperformed KM and log (1/R) spectra in this study. Reflectance spectra provided the best model for PLSR and PCR. Pre-processed SGS spectra were best for PLSR, while raw reflectance spectra were best for PCR. PLSR and PCR both had an R2 of prediction of 0.96, while the overall average R2 of prediction favors PLSR over PCR. The present study led to the discovery of a simple novel method for developing adulteration flour and showed that a visible near-infrared spectrometer combined with PLSR, or PCR, could predict white rice flour adulteration in brown rice flour.