Identifying the geographical origin and processing technology of Moyao(Myrrh) on the basis of near-infrared spectroscopy combined with chemometrics

作     者：XU Ningning YAN Ganming XU Fengjie DENG Linfeng QIAO Xinjiang LU Changzheng CHENG Shaomin 

作者机构：TCM Diagnosis InstituteCollege of Traditional Chinese MedicineJiangxi University of Chinese Medicine TCM Processing InstitutePharmaceutical CollegeJiangxi University of Chinese medicine School of Electronic Information and Artificial IntelligenceShaanxi University of Science and Technology Jiangzhong Pharmaceutical Co.Ltd. Jiangxi Guhan Refined Chinese Herbal Pieces Co.Ltd. 

出 版 物：《Journal of Traditional Chinese Medicine》 (中医杂志(英文版))

年 卷 期：2024年第44卷第3期

页      面：505-514页

核心收录：

学科分类：1006[医学-中西医结合] 1002[医学-临床医学] 10[医学] 100602[医学-中西医结合临床] 

基　　金：Jiangxi Provincial Administration of Traditional Chinese Medicine Key Research Laboratory on the Fundamentals of Chinese Medicine Evidence (Gan TCM Science and Education Word No. 8-4) Jiangxi University of Chinese Medicine Science and Technology Innovation Team Development Program:Traditional Chinese Medicine Constitution-State Identification Health Management Research Team (No. CXTD22016) 

主　　题：Moyao () geographical origin near-infrared spectroscopy processing technology 

摘      要：OBJECTIVE: To evaluate the quality of Moyao(Myrrh) in the identification of the geographical origin and processing of the products. METHODS: Raw Moyao(Myrrh) and two kinds of Moyao(Myrrh) processed with vinegar from three countries were identified using near-infrared(NIR) spectroscopy combined with chemometric techniques. Principal component analysis(PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm(PLS-DA) and two machine learning algorithms [K-nearest neighbor(KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao(Myrrh) samples, and their discriminative performance was evaluated. RESULTS: Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao(Myrrh) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively. CONCLUSIONS: NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao(Myrrh) and can also provide a reference for evaluations of its quality and the clinical use.