今日药学

2019, v.29(06) 383-386

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基于HPLC图谱和化学计量学方法对不同产地与种源陈皮的鉴别研究
Discrimination of Pericarpium Citri Reticulatae from Different Species and Habitats Based on HPLC and Chemometric Methods

胡继藤;刘基华;陈富钦;刘韬;蒋林;
HU Jiteng;LIU Jihua;CHEN Fuqin;LIU Tao;JIANG Lin;School of Pharmaceutical Sciences,Sun Yat-sen University;State Key Laboratory of Oncology in South China,Collaborative Innovation Center for Cancer Medicine,Sun Yat-sen University Cancer Center;

摘要(Abstract):

目的采用高效液相色谱(HPLC)法指纹图谱技术结合化学计量学方法鉴别陈皮药材的产地与种源。方法通过HPLC及中药色谱指纹图谱相似度评价系统获得来自5个不同产地分别从属于3种不同植物种源的25批次陈皮药材的6个共有成分峰,应用聚类分析、主成分分析及人工神经网络对共有峰面积数据进行分析鉴别。结果聚类分析及主成分分析能够对新会陈皮样品与其他陈皮样品进行准确区分,不同种源陈皮样品亦可各自区分;在对新会陈皮与非新会陈皮的识别训练与预测中,人工神经网络的识别准确率均达到了100%,在对不同品种陈皮样本的识别训练中准确率达到100%,识别预测准确率为90%。结论 HPLC与化学计量学方法相结合对不同产地不同种源陈皮药材进行鉴别的结果理想,对完善新会陈皮的质量控制具有参考意义。
OBJECTIVE To identify different species and habitats of Pecicarpium Citri Reticulatae using HPLC and chemometric methods. METHODS 25 batches of sample of three different species from five different habitats were analyzed by HPLC. And six peaks were identified as common fingerprint peaks using the similarity evaluation system for TCM. Their areas were analyzed by cluster analysis,principal component analysis and artificial neural network. RESULTS CA and PCA could recognize the sample which was Citri Reticulatae from Xinhui District from other sample. And the sample from different species could be distinguished with each other respectively. ANN recognition accuracy for data from training group was 100%. And the accuracy for forecasting sample from Xinhui District and other places was 100%. The accuracy for forecasting the species of different Pericarpium Citri Reticulatae was 90%.CONCLUSION HPLC combined with chemometrics methods can be used as a means of identification of Pericarpium Citri Reticulatae from different habitats and species. And will improve the quality control of Pericarpium Citri Reticulatae from Xinhui District.

关键词(KeyWords): 新会陈皮;聚类分析;主成分分析;人工神经网络;高效液相色谱法
Pericarpium Citri Reticulatae;cluster analysis;principal component analysis;artificial neural network;HPLC

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作者(Author): 胡继藤;刘基华;陈富钦;刘韬;蒋林;
HU Jiteng;LIU Jihua;CHEN Fuqin;LIU Tao;JIANG Lin;School of Pharmaceutical Sciences,Sun Yat-sen University;State Key Laboratory of Oncology in South China,Collaborative Innovation Center for Cancer Medicine,Sun Yat-sen University Cancer Center;

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