Application of three-dimensional fluorescence spectral characterization and chemometrics in the analysis of traceability of Paeoniae Radix Rubra

Natural products are treasure troves of resources that the environment has given upon humans and are directly linked to human health and well-being. Extracting natural products from medicinal plants is the material basis for treating various diseases but the natural product content of the same medicinal plant can vary due to environmental conditions, which may exert an influence on the therapeutic outcome. Since the existing identification methods for the origin of medicinal plants are cumbersome, it is necessary to find a easy, quick, and accurate way to trace the origins of medicinal plants and ensures the quality of natural products. This experiment uses chemometric techniques in conjunction with three-dimensional fluorescence technology to classify Paeoniae Radix Rubra (PRR) from various geographical sources, taking the natural products of PRR as the research object. Three-dimensional fluorescence technology can be used to identify the origin of PRR based on the presence of different endogenous luminous chemicals. In this experiment, the principal component analysis (PCA) algorithm was used to examine the overall distribution and grouping of the samples after initial characterizing the 3D fluorescence spectrum of PRR using the alternating trilinear decomposition (ATLD) algorithm. In order to predict the origin traceability of PRR samples, we combined the 3D fluorescence spectral features with four pattern recognition techniques: random forest (RF), partial least squares-discriminant analysis (PLS-DA), and k-nearest neighbor (kNN) method. The findings demonstrated that, following ATLD factorization, the sample data could successfully identify, using various models, the PRR’s production areas (Heilongjiang, Greater Khingan Mountains, Inner Mongolia, Liaoning, Hebei, Gansu, Sichuan), with 100% correct recognition rates for both the cross-validation and external validation sets. This technique offers a fresh and quick fix for PRR grading and origin tracing. Besides, this method also provides a new research idea for the origin traceability and quality evaluation of other Medicinal Plants.