Quantitative detection of crude protein in brown rice by near-infrared spectroscopy based on hybrid feature selection

Abstract

It is important for rice breeding and quality evaluation to predict the protein content of brown rice rapidly and easily. In this study, near-infrared spectroscopy (NIRS) was utilized to establish a model for detecting crude protein content in brown rice based on 349 samples prepared from three kinds of brown rice, and the performance of the model was evaluated. Improved interval partial least squares (iPLS) was used to divide and screen different feature intervals after spectral preprocessing in this research. On this basis, competitive adaptive reweighted sampling (CARS) optimized the selected feature intervals, and finally 14 effective spectral features concentrated in 1160 nm–1338 nm were selected from 1050 features. The above hybrid feature selection has more advantages than the single selection. Support vector regression (SVR) calibration model was established, and partial least squares regression (PLSR) model commonly used in similar studies was selected as a comparison. The optimal spectral preprocessing method was selected according to the model prediction effect. The coefficient of determination (R2), R2 of cross-validation set (Rcv2), root mean square error of prediction (RMSEP), and relative percent difference (RPD) evaluated for the prediction model reached 0.9185, 0.8876, 0.2040% and 3.5194, respectively. The results showed that the designed method can be used for the rapid determination of the crude protein content of brown rice, providing a convenient, efficient and non-destructive method for related detection.