基于主成分分析法识别不同产地防风和黄芩
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摘要
在中草药现代化的进程中,识别不同产地的中草药对于中医药的发展十分重要。利用傅里叶变换红外光谱仪测量不同产地黄芩和防风的红外光谱,选取指纹特征区域400～3 500 cm-1的红外光谱,经过预处理、归一化建立基于主成分分析的识别模型。结果表明,防风中三个主成分能代表99.99%的指纹区变量信息,黄芩中三个主成分能代表99.70%的指纹区变量信息。样本在主成分及主成分得分值空间中的分布情况,不仅实现了对不同产地防风、黄芩的识别,还可以通过样本的分布疏密度反映样本的亲缘关系。基于主成分分析原理建立的数学模型可以高效地识别不同产地防风和黄芩,为中草药鉴别提供了有效的参考。
In the modernization process of Chinese herbal medicine,it is very important to recognize the Chinese herbal medicine from different habitats.The infrared spectra of Divaricate Saposhnikovia Root and Scutellaria Baicalensis Georgi in different habitats were measured by Fourier Transform Infrared spectrometer,and the infrared spectra of 400 ～3 500 cm-1 in the fingerprint characteristic region were selected.The recognition model based on PCA was established through pretreatment and normalization.The results showed that three main components in Divaricate Saposhnikovia Root could represent 99.99% fingerprint region variable information,and three main components in Scutellaria Baicalensis Georgi could represent 99.7% fingerprint region variable information.The samples were aggregated into different categories in principal component and principal component score space,and the identification of Divaricate Saposhnikovia Root and Scutellaria Baicalensis Georgi in different habitats was realized.The sample sparsity and density of distribution could reflect their relationship.Mathematic model established by PCA could quickly and non-destructively identify Divaricate Saposhnikovia Root and Scutellaria Baicalensis Georgi in different habitats,which would provide an effective reference for the identification of Chinese herbal medicine.
In the modernization process of Chinese herbal medicine,it is very important to recognize the Chinese herbal medicine from different habitats.The infrared spectra of Divaricate Saposhnikovia Root and Scutellaria Baicalensis Georgi in different habitats were measured by Fourier Transform Infrared spectrometer,and the infrared spectra of 400 ～3 500 cm-1 in the fingerprint characteristic region were selected.The recognition model based on PCA was established through pretreatment and normalization.The results showed that three main components in Divaricate Saposhnikovia Root could represent 99.99% fingerprint region variable information,and three main components in Scutellaria Baicalensis Georgi could represent 99.7% fingerprint region variable information.The samples were aggregated into different categories in principal component and principal component score space,and the identification of Divaricate Saposhnikovia Root and Scutellaria Baicalensis Georgi in different habitats was realized.The sample sparsity and density of distribution could reflect their relationship.Mathematic model established by PCA could quickly and non-destructively identify Divaricate Saposhnikovia Root and Scutellaria Baicalensis Georgi in different habitats,which would provide an effective reference for the identification of Chinese herbal medicine.
引文
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[15]吴习宇,祝诗平,黄华,等.近红外光谱技术鉴别花椒产地[J].光谱学与光谱分析,2018(1):68-72.
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[17] Kim H J,Baek W S,Jang Y P.Identification of ambiguous cubeb fruit by DART-MS-based fingerprinting combined with principal component analysis[J].Food Chemistry,2011,129(3):1305-1310.
[18]王韵篪,鞠玉琳,姚琳,等.3种黄芩黄酮物质体外抗弓形虫效果比较试验[J].延边大学农学学报,2012(1):75-78.
[19]范钊,陈遇英.中药不同提取方法对两种病原菌的抑制研究[J].黑龙江八一农垦大学学报,2017(3):29-34.
[20]唐学彬.恩施州县市林业生产情况的主成分分析研究[J].湖北民族学院学报:自然科学版,2016(3):341-343.
[21]周祖文.影响中药微量元素的相关因素研究概况[J].微量元素与健康研究,2002,19(1):66-68.
[22]刘敬,邓仙梅,赵斌,等.MIR指纹结合主成分分析法鉴别布渣叶和鸡蛋花[J].安徽农业科学,2018(6):162-163.
[2]姚森,张霁,刘鸿高,等.红外光谱在食用菌研究中的应用[J].食品科学,2018,39(1):305-312.
[3]张鹏.基于主成分分析的综合评价研究[D].南京:南京理工大学,2004.
[4] J Edward Jackson. A User's Guide To Principal Components[M].New York:Awiley-Interscience blication,1992.
[5]林海明.如何用SPSS软件一步算出主成分得分值[J].统计与信息论坛,2007,22(5):15-17.
[6]何晓群.现代统计分析方法与应用[M].北京:中国人民大学出版社,2007.
[7]任雪松,于秀林.多元统计分析.2版[M].北京:中国统计出版社,2011.
[8] Richard A. Johnson,Dean W. Wichern.Applied Multivariate Statistical Analysis[M].陆漩译.北京:清华大学出版社,2001.
[9]秦寿康.综合评价原理与应用[M].北京:电子工业出版社,2003.
[10]彭邦远,张洪礼,孙小静,等.热处理刺梨汁香气物质的SPME-GC-MS检测与主成分分析[J].食品科学,2018,39(6):230-236.
[11]黄丽娟,刘晓慧,李海朝.基于主成分分析的中药微量元素含量研究[J].山西大同大学学报:自然科学版,2017,33(5):38-41.
[12]达拉胡,辛颖.黄芩属植物的内部质量控制研究进展[J].内蒙古民族大学学报:自然科学版,2016(4):333-341.
[13]林源,陈敏.小儿柴桂退热颗粒的UPLC指纹图谱及聚类、主成分分析[J].中国药房,2018(4):45-50.
[14]贺敬霞,牟倩倩,张建琪,等.UPLC结合化学计量法对不同产地黄芪的快速鉴别[J].中草药,2017,481(1):179-184.
[15]吴习宇,祝诗平,黄华,等.近红外光谱技术鉴别花椒产地[J].光谱学与光谱分析,2018(1):68-72.
[16]基于主成分分析的粳稻品质评价及聚类分析[J].河南科技学院学报:自然科学版,2016,44(5):1-6.
[17] Kim H J,Baek W S,Jang Y P.Identification of ambiguous cubeb fruit by DART-MS-based fingerprinting combined with principal component analysis[J].Food Chemistry,2011,129(3):1305-1310.
[18]王韵篪,鞠玉琳,姚琳,等.3种黄芩黄酮物质体外抗弓形虫效果比较试验[J].延边大学农学学报,2012(1):75-78.
[19]范钊,陈遇英.中药不同提取方法对两种病原菌的抑制研究[J].黑龙江八一农垦大学学报,2017(3):29-34.
[20]唐学彬.恩施州县市林业生产情况的主成分分析研究[J].湖北民族学院学报:自然科学版,2016(3):341-343.
[21]周祖文.影响中药微量元素的相关因素研究概况[J].微量元素与健康研究,2002,19(1):66-68.
[22]刘敬,邓仙梅,赵斌,等.MIR指纹结合主成分分析法鉴别布渣叶和鸡蛋花[J].安徽农业科学,2018(6):162-163.