基于多元统计分析和模式识别技术建立浙贝母和川贝母的品种鉴别体系

doi:10.5246/jcps.2023.05.034

中国药学(英文版) ›› 2023, Vol. 32 ›› Issue (5): 406-416.DOI: 10.5246/jcps.2023.05.034

基于多元统计分析和模式识别技术建立浙贝母和川贝母的品种鉴别体系

苏静华¹, 张超¹^,^*(), 孙磊²^,^*(), 马双成², 邢以文¹

1. 苏州市药品检验检测研究中心, 江苏苏州 215104
2. 广州大学化学化工学院化学基础实验室, 广东广州 510006

收稿日期:2022-10-27 修回日期:2022-11-13 接受日期:2023-12-24 出版日期:2023-06-02 发布日期:2023-06-02
通讯作者: 张超, 孙磊
作者简介:
+ Tel.: +86-512-68226135, E-mail: colinspecial@163.com
+ E-mail: dasunlei@163.com
基金资助:
Pharmaceutical Science Foundation of Jiangsu Medical Products Administration (Grant No. 202128).

Establishment of a variety identification system for Fritillaria Thunbergii Bulbus and Fritillaria Cirrhosae Bulbus based on nucleosides and nucleobases with multivariate analysis and pattern recognition

Jinghua Su¹, Chao Zhang¹^,^*(), Lei Sun²^,^*(), Shuangcheng Ma², Yiwen Xing¹

1 Suzhou Institute for Drug Control, Suzhou 215104, Jiangsu, China
2 Chemical Basic Laboratory, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China

Received:2022-10-27 Revised:2022-11-13 Accepted:2023-12-24 Online:2023-06-02 Published:2023-06-02
Contact: Chao Zhang, Lei Sun

摘要/Abstract

摘要：

浙贝母和川贝母因其具有特色药理活性, 作为中医药已经在临床上使用上千年。本文利用高效液相色谱法, 使用多元统计学分析手段和模式识别技术对浙贝母和川贝母的鉴别进行研究。共收集49份样品, 10种核苷类成分被用于特征组分进行分析测定, 采用聚类分析、主成分分析、最小二乘回归分析等多元分析方法评价品种和特征组分的相关性。通过建模, 利用不同的算法如k-近邻法、偏最小二乘判别、簇类独立软模式法和支持向量机法对未知样本进行品种预测。结果表明, 基于此建立的模式识别模型, 具有较好的预测准确率。结合多元统计分析方法和模式识别技术可以提高在特殊情况下浙贝母和川贝母的品种预测准确率, 有利于质量控制和产品监督。

关键词: 川贝母, 浙贝母, 多元分析, 模式识别, 核苷, 碱基

Abstract:

Fritillaria Thunbergii Bulbus and Fritillaria Cirrhosae Bulbus have been used as traditional Chinese medicine for thousands of years due to their pharmacological activities. In the present study, multivariate analysis and pattern recognition were applied for the identification of Fritillaria Thunbergii Bulbus and Fritillaria Cirrhosae Bulbus by HPLC. A total of 49 samples from two varieties were collected, and 10 nucleosides and nucleobases were chosen for analysis. Multivariate analyses, such as hierarchical cluster analysis, principal component analysis, and partial least square regression analysis, were used to reveal the correlation between its components and varieties. Moreover, different algorithms in pattern recognition, such as k-nearest neighbor, partial least squares discrimination, soft independent modeling of class analogies, and support vector machine, were applied to identify the species of unknown samples. Results by pattern recognition suggested that the prediction accuracy was satisfactory. A combination of multivariate analysis and pattern recognition was more reasonable, improving the analytical accuracy of variety identification.

Key words: Fritillaria Thunbergii Bulbus, Fritillaria Cirrhosae Bulbus, Multivariate analysis, Pattern recognition, Nucleosides, Nucleobases

Supporting:

苏静华, 张超, 孙磊, 马双成, 邢以文. 基于多元统计分析和模式识别技术建立浙贝母和川贝母的品种鉴别体系[J]. 中国药学(英文版), 2023, 32(5): 406-416.

Jinghua Su, Chao Zhang, Lei Sun, Shuangcheng Ma, Yiwen Xing. Establishment of a variety identification system for Fritillaria Thunbergii Bulbus and Fritillaria Cirrhosae Bulbus based on nucleosides and nucleobases with multivariate analysis and pattern recognition[J]. Journal of Chinese Pharmaceutical Sciences, 2023, 32(5): 406-416.

图/表 8

Figure 1. HPLC chromatograms and common pattern of FTB and FCB for nucleosides and nucleobases. (A) FTB; (B) FCB; (C) Common pattern. 1-Uracial, 2-Cytidine, 3-Hypoxanthine, 4-Uridine, 5-Thymine, 6-Adenine, 7-Inosine, 8-Guanosine, 9-Thymidine, 10-Adenosine.

Figure 2. PCA results of FTB and FCB for nucleosides and nucleobases. (A) Results of AVE for PCs; (B) PCA result on the 2D plot; (C) Loading plot.

Figure 3. PLS and HCA heatmap results of FTB and FCB for nucleosides and nucleobases. (A1) Results for R2X; (A2) Results for R2Y; (A3) Results for Q2X; (A4) Results for Q2Y; (B) PLS result on the 3D plot; (C) HCA heatmap result.

Figure 4. Results of residual analysis and ANOVA. (A) Results of residual analysis; (B) Results of ANOVA.

Table 1. Results of analysis by different algorithms in pattern recognition.

Figure 5. Results of pattern recognition by different mathematical methods. (A) Normal distribution curves for the probability distribution of prediction by PLS-DA; (B) Statistical map of SVW classier; (C) Results of σ and penalty parameter C determined by grid search and cross-validation in SVM.

Figure 6. Analytical results of SIMCA. (A) Scatter plot maps (log Q and log T2) of PCA submodel for FTB; (B) PCA submodel result for FTB; (C) Scatter plot maps (log Q and log T2) of PCA submodel for FCB; (D) PCA submodel result for FCB.

Figure 7. The flowchart of the recent inspection system and variety identification system.

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基于多元统计分析和模式识别技术建立浙贝母和川贝母的品种鉴别体系

Establishment of a variety identification system for Fritillaria Thunbergii Bulbus and Fritillaria Cirrhosae Bulbus based on nucleosides and nucleobases with multivariate analysis and pattern recognition

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