Yuexing Li

Ziziphi Spinosae Semen (ZSS) is a traditional Chinese medicinal material used for both medicinal and dietary purposes, but substitution and adulteration can compromise its authenticity and quality. Ten batches of authentic ZSS and one source batch of each of five adulterant materials were used to prepare 325 laboratory samples. This study aimed to develop a rapid method for ZSS identification and adulteration quantification using Fourier transform near-infrared spectroscopy (FT-NIR). Spectral distributions of six sample classes were analyzed using PCA-X, PLS-DA, and OPLS-DA, and the classification performance of SVM, DA, ANN, and KNN was compared. DD-SIMCA was used for one-class authentication, while PLSR was applied to quantify five adulterants using 15 spectral processing approaches. In the internal test set, SVM achieved 98.0% accuracy and 98.4% balanced accuracy. Within the laboratory samples studied, DD-SIMCA achieved 100% accuracy, sensitivity, and specificity. Across the five selected PLSR models, prediction-set R² ranged from 0.9272 to 0.9940 and RMSEP from 1.36 to 4.33 percentage points of adulterant mass fraction. These results demonstrate the feasibility of combining FT-NIR with chemometric methods for rapid ZSS classification, authenticity assessment, and adulteration-level prediction. Independent validation with additional source batches is required before practical application.