Using Near-Infrared Hyperspectral Imaging Combined with Machine Learning to Predict the Components and the Origin of Radix Paeoniae Rubra

The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content prediction models and origin identification models to predict the components and origin of Radix Paeoniae Rubra (RPR). These models are quick, non-destructive, and accurate for assessing both component content and origin. Spectral data were preprocessed using multiple scattering correction (MSC), Savitzky-Golay smoothing (S-G), and standard normal variate (SNV). Content prediction models for paeoniflorin were developed using principal component regression (PCR), partial least squares regression (PLSR), and ridge regression (RR). Classification models for origin identification utilized support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF). The SNV-RR model achieved a determination coefficient of 0.8943, while the SNV-SVM model achieved an accuracy of 0.9790. Meanwhile, two feature selection methods were used to further simplify the prediction model while ensuring accuracy, in order to improve the detection efficiency in practical applications. This research demonstrates the feasibility of combining NIR-HSI with machine learning for quality analysis of RPR, providing a theoretical basis for promoting hyperspectral imaging technology in the food and pharmaceutical sectors.