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
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摘要
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.
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.
引文
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[13]郑国栋,蒋林,杨雪,等.不同贮藏年限广陈皮黄酮类成分的变化规律研究[J].中成药,2010,32(6):977-980.
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[15]邱新华,唐铁鑫,刘燕,等.几种模式识别方法用于中药挥发油红外光谱法鉴别的比较研究[J].中国药房,2015,26(21):2986-2988.
[2]孙继燕,张鑫,杨全,等.高良姜-陈皮组方不同配伍比例对胃寒证模型大鼠的影响[J].广东药科大学学报,2018,34(2):210-212.
[3]成树森,王武静,陈超志,等.基于ITS2序列广陈皮基原植物及药材的DNA分子鉴定[J].广东药科大学学报,2017,33(6):719-722.
[4]易伦朝,谢培山,梁逸曾,等.液相色谱特定(指纹)图谱分析用于陈皮的质量考察[J].药物分析杂志,2006,26(7):918-922.
[5]申明金,贾飞云,柴震.化学计量学在中药研究中的应用[J].山西医药杂志,2010,39(17):795-798.
[6]宫海燕,欧依塔,热娜·卡斯木.不同产地牛至挥发油的主成分分析[J].中国现代应用药学,2018,35(2):239-243.
[7]Roggo Y,Chalus P,Maurer L,et al. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies[J]. Journal of pharmaceutical and biomedical analysis,2007,44(3):683-700.
[8]刘沭华,张学工,周群,等.模式识别和红外光谱法相结合鉴别中药材产地[J].光谱学与光谱分析,2005,25(6):878-881.
[9]张耀齐,潘阳,王天山,等.术类中药及其相关成药质量的主成分分析[J].南京中医药大学学报,1997,13(3):22-23.
[10]Markou M T,Kassomenos P. Cluster analysis of five years of back trajectories arriving in Athens,Greece[J]. Atmospheric Research,2010,98(2-4):438-457.
[11]张学工.模式识别[M].北京:清华大学出版社,2010:3-20.
[12]胡继藤,唐铁鑫,刘韬,等. GC-MS结合化学计量学方法鉴别不同产地与种源陈皮的研究[J].时珍国医国药,2017,28(7):1665-1667.
[13]郑国栋,蒋林,杨雪,等.不同贮藏年限广陈皮黄酮类成分的变化规律研究[J].中成药,2010,32(6):977-980.
[14]郑国栋,蒋林,杨得坡,等. HPLC法同时测定不同产地广陈皮中5种活性黄酮成分[J].中草药,2010,41(4):652-655.
[15]邱新华,唐铁鑫,刘燕,等.几种模式识别方法用于中药挥发油红外光谱法鉴别的比较研究[J].中国药房,2015,26(21):2986-2988.