Objective To address the issues of strong subjectivity and low efficiency in traditional Chinese medicine origin tracing, we explore the method of Chinese medicine origin tracing based on mid-infrared spectroscopy (MIRS) combined with machine learning algorithm modeling, and provide new technical support for traditional Chinese medicine origin tracing. Methods Based on the mid-infrared spectral dataset, this study performed outlier handling, missing value imputation, first-order difference, high-variance feature selection, distance calculation and clustering analysis, and locally linear embedding (LLE) for dimensionality reduction. Subsequently, we constructed six machine learning models including Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and artificial neural network (ANN) models. We then employed the Ivy Algorithm (IVYA) to optimize model parameters. A multidimensional evaluation framework was established, incorporating the area under the receiver operating characteristic (ROC) curve (AUC) alongside accuracy, recall, precision, and F1 score (F1). This framework was used to identify the optimal model for Chinese herbal medicine origin tracing. Results SVM (macro-average AUC = 0.998, F1 = 0.949, accuracy = 0.949, recall = 0.949, precision = 0.956) and ANN (macro-average AUC = 0.999, F1 = 0.940, accuracy = 0.939, recall = 0.939, precision = 0.944) demonstrated optimal performance in identifying the origins of Chinese herbal medicines. They exhibited high consistency in predicting the origins of unknown samples, with core evaluation metrics significantly outperforming RF, LightGBM, KNN, and XGBoost. The SVM model can accurately capture the functional group vibration differences in the medium chemical components of medicinal materials from different origins. ANN models simulate the multifactorial nonlinear coupling effects formed by Chinese herbal medicine origins. Both models not only achieve efficient differentiation among 11 origins but also exhibit complementary strengths: SVM’s globally optimal classification hyperplane excels at processing origins with markedly distinct spectral features, while ANN’s distributed representation capability better suits complex scenarios with high feature overlap. The MIRS method, integrating SVM and ANN, offers rapid, non-destructive, and high-throughput advantages for Chinese herbal medicine traceability. This provides an objective tool for large-scale origin tracing of authentic medicinal materials and delivers a practical technical pathway for the modernization of the Chinese herbal medicine industry. Conclusion MIRS combined with SVM, RF, LightGBM, KNN, XGBoost, and ANN demonstrates effectiveness for Chinese herbal medicine origin tracing. Among these, SVM and ANN emerge as preferred models for this application, offering an interdisciplinary solution. Future integration of multimodal data and deep learning techniques holds promise for enhancing model performance.

traditional Chinese medicine  /  origin traceability  /  machine learning  /  support vector machine  /  artificial neural network  /  Ivy algorithm  /  mid-infrared spectroscopic technique