Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy

Abstract

A rapid predictive method based on near-infrared spectroscopy (NIR), was developed to measure rice starch quality parameters. A calibration set of 100 samples and validation set of 62 samples of rice flour of Chinese genotypes was used. Results of partial least squares modeling indicated that NIR was reasonably accurate in predicting apparent amylose content (AAC) (standard error of prediction [SEP]=1.39 percentage units, coefficient of determination [R2]=0.91); pasting parameters of setback (SB) (SEP=13.6 RVU, R2=0.92), and breakdown (BD) (SEP=10.2 RVU, R2=0.88); and gelatinization peak temperature (Tp) (SEP=1.33°C, R2=0.89). Gel consistency (GC), cool paste viscosity (CPV), gelatinization onset temperature (To), and textural properties of chewiness, hardness and gumminess, were modeled less well with R2 between 0.75 and 0.86. NIR analysis is sufficiently accurate for routine screening of large numbers of samples in early generation selection in rice breeding programs.