Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy

The precise and prompt determination of quality control indicators such as moisture, stilbene glycosides, and anthraquinone glycosides is crucial in assessing the quality of Polygoni Multiflori Radix. Near-infrared spectroscopy is a nondestructive analytical technique that offers a more desirable approach than traditional methods for assessing content levels. In this study, various spectral preprocessing techniques were used to preprocess the raw spectral data. The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. The data suggest that the first-order deconvolution derivative (1^st Dev.) processing of the spectral data is superior to other methods in all three model evaluation metrics. The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R²_C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSE_CV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R²_P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSE_P) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. After optimizing the model using CARS, R²_C increased by 0.15%, 0.41%, and 0.34%, RMSE_CV decreased by 0.53%, 0.32%, and 0.24%, R²_P increased by 0.21%, 0.63%, and 0.35%, RMSE_P decreased by 0.36%, 0.41%, and 0.31%, and RPD increased by 1.1, 0.9, and 0.6, significantly improving the predictive capacity of the model. This research provides a feasible method for rapid compliance testing of Polygoni Multiflori Radix. To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.