Combination of near-infrared spectroscopy and key wavelength-based screening algorithm for rapid determination of rice protein content

Rice taste quality is one of the most important factors influencing rice marketing and distribution, and rice with a high taste quality is more popular with consumers and has a higher price. Accurate and rapid determination of rice protein content helps to assess the rice taste quality and aids in marketing. In this study, NIR spectra of 84 rice samples combined with partial least squares regression (PLSR) were used to model protein content, and different selection algorithms based on key wavelengths (competitive adaptive reweighted sampling, CARS; Monte-Carlo uninformative wavelength elimination, MC-UVE; random frog, RF) were used to understand the accuracy of NIR spectra in predicting the rice protein content. Our results showed that the R2P and RPD of the original full-spectrum PLSR model were 0.83 and 1.95, respectively. After the second-order derivative pre-processing, the R2P and RPD of the full spectrum were improved to 0.95 and 4.14. Both CARS and MC-UVE increased the R2P of the PLSR model to 0.97 and the RPD to 5.57 and 5.65, respectively. R2C and R2CV in the PLSR model based on CARS algorithm were 0.93 and 0.91, respectively. The CARS algorithm had excellent results in predicting the rice protein content.

Automated summary

The taste quality of rice is a crucial factor affecting its marketing and distribution, as rice with high taste quality is more favored by consumers and commands a higher price. Accurate and rapid determination of rice protein content plays a vital role in assessing taste quality and facilitating marketing. In this study, NIR spectra combined with partial least squares regression (PLSR) were utilized to model protein content in 84 rice samples, while different selection algorithms (CARS, MC-UVE, RF) based on key wavelengths were employed to evaluate the accuracy of NIR spectra in predicting rice protein content. The results demonstrated that the CARS algorithm achieved excellent performance in predicting rice protein content.