Combination of near infrared spectroscopy with characteristic interval selection for rapid detection of rice protein content

Protein level significantly influences the nutritional quality of rice. For this reason, this study introduced a method to rapidly measure the rice protein content through a combination of near infrared spectroscopy (NIRS) with characteristic spectral interval (CSI) selection. Using the interval partial least squares (iPLS) concept as a basis, this study integrated genetic simulated annealing algorithm (GSA) with partial least squares (PLS) and support vector machine (SVM) to develop two CSI selection algorithms, namely GSA-iPLS and GSA-iSVM, respectively. The CSI selected by the above algorithms were compared with synergy iPLS and backward iPLS, and quantitative calibration models were established for PLS and SVM, respectively. The study revealed that the PLS calibration model for rice protein content, developed using CSI selected by GSA-iPLS, exhibited the highest regression accuracy. The optimal model achieved determination coefficients of 0.945 and 0.964, relative root mean square errors of 2.598 % and 2.796 %, and residual predictive deviations of 4.265 and 5.023 for the validation and the external test sets, respectively, which met practical detection requirements. The results indicate that the combination NIRS with GSA CSI intelligent search is a reliable approach for the rapid and accurate detection of rice protein content.