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化学计量学辅助的中药真伪鉴别技术聚类分析

The cluster analysis of traditional Chinese medicine authenticity identification technique assisted by chemometrics.

作者信息

Bai Yunxia, Zhang Huiwen

机构信息

College of Computer Science and Technology, Baotou Medical College, Baotou, 014040, China.

College of Pharmacy, Inner Mongolia Medical University, Hohhot, 010110, China.

出版信息

Heliyon. 2024 Sep 5;10(18):e37479. doi: 10.1016/j.heliyon.2024.e37479. eCollection 2024 Sep 30.

Abstract

This study explore the authenticity identification technique of traditional Chinese medicine (TCM) using chemometrics in conjunction with cluster analysis. A clustering Gaussian mixture model was constructed and applied for the data clustering analysis of four types of TCM. Chemical measurements combined with discrete wavelet transform (DWT), Fourier transform infrared spectroscopy (FTIR), and Fourier self-deconvolution (FSD) were utilized for the detailed differentiation of Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium. Differences in the attenuated total reflection-FTIR (ATR-FTIR) spectra among the four TCMs were observed. Utilizing clustering algorithms, the one-dimensional DWT of the infrared spectra of samples was employed for the authentication of Chinese herbal medicines. The model demonstrates optimal performance throughout 2000 rounds of network training. The accuracy (88.6 %), sensitivity (86.5 %), and specificity (82.7 %) of the model constructed in this study significantly surpassed those of the CNN model: accuracy (67.7 %), sensitivity (70.4 %), and specificity (68.5 %) ( < 0.05). By setting the cluster size  = 5 and the number of Gaussian mixture model components to 5, the model effectively fits the actual number of categories within the dataset. Infrared spectroscopy analysis revealed distinct carbon-oxygen stretching vibration absorption peaks between 1025 and 1200 cm for Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium, indicating strong absorption peaks of carbohydrates. A comprehensive structural information analysis revealed a similarity of above 0.982 among the four types of TCM. Combined with chemometrics and intelligent algorithm-based cluster analysis, successful and accurate authentication of TCM authenticity was achieved, providing an effective methodology for quality control in TCM.

摘要

本研究探索了结合化学计量学与聚类分析的中药真伪鉴别技术。构建了聚类高斯混合模型并将其应用于四种中药的数据聚类分析。采用结合离散小波变换(DWT)、傅里叶变换红外光谱(FTIR)和傅里叶自去卷积(FSD)的化学测量方法,对狭叶柴胡、银州柴胡、竹叶柴胡和小叶黑柴胡进行详细鉴别。观察到四种中药的衰减全反射 - 傅里叶变换红外光谱(ATR - FTIR)存在差异。利用聚类算法,将样品红外光谱的一维DWT用于中药材的鉴别。该模型在2000轮网络训练中均表现出最优性能。本研究构建模型的准确率(88.6%)、灵敏度(86.5%)和特异性(82.7%)显著高于CNN模型:准确率(67.7%)、灵敏度(70.4%)和特异性(68.5%)(P < 0.05)。通过将聚类大小设置为5且高斯混合模型的组件数量设置为5,该模型有效地拟合了数据集中实际的类别数量。红外光谱分析显示,狭叶柴胡、银州柴胡、竹叶柴胡和小叶黑柴胡在1025至1200 cm之间有明显的碳 - 氧伸缩振动吸收峰,表明碳水化合物有较强吸收峰。综合结构信息分析表明,四种中药之间的相似度高于0.982。结合化学计量学和基于智能算法的聚类分析,成功且准确地实现了中药真伪鉴别,为中药质量控制提供了一种有效方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/635f/11416282/395174505f42/gr1.jpg

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