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基于形态和红外光谱分析的云南重楼及近似种的快速鉴别
引用本文:赵飞亚,陶爱恩,黎氏文梅,段宝忠,张海珠,夏从龙.基于形态和红外光谱分析的云南重楼及近似种的快速鉴别[J].中草药,2019,50(3):702-709.
作者姓名:赵飞亚  陶爱恩  黎氏文梅  段宝忠  张海珠  夏从龙
作者单位:大理大学药学院, 云南 大理 671000,大理大学药学院, 云南 大理 671000,大理大学药学院, 云南 大理 671000,大理大学药学院, 云南 大理 671000,大理大学药学院, 云南 大理 671000,大理大学药学院, 云南 大理 671000
基金项目:国家自然科学基金资助项目(31860080);云南省地方本科高校(部分)基础研究联合专项(2017FH-001-097);大理州区域创新能力提升专项(2018CA011-01)
摘    要:目的基于形态和红外光谱法建立云南重楼及近似种的鉴别方法,并进一步分析其亲缘关系,为资源开发利用提供依据。方法对云南重楼及其近似种进行了原植物形态比较,并对光谱数据进行自动基线校正、自动平滑、纵坐标归一化、二阶求导等预处理,采用主成分分析(principalcomponentanalysis,PCA)、偏最小二乘判别分析(partialleastsquares discriminantanalysis,PLS-DA)及系统聚类分析(hierarchicalclusteranalysis,HCA)分析光谱数据。结果云南重楼与近似种在植株大小、叶片、叶脉、萼片、花瓣、花亭和雄蕊等形态特征上存在典型的鉴别特征,可为其提供形态鉴别依据。6种重楼的二阶导数在3 000~2 000 cm-1和指纹区1 800~500 cm-1波段内差异明显,HCA和PLS-DA均能较好地区分云南重楼及近似种,可为其提供红外光谱鉴别证据。HCA结果表明云南重楼、七叶一枝花、狭叶重楼、矮重楼和多叶重楼亲缘关系较近。结论傅里叶变换红外光谱(FTIR)结合原植物形态鉴定能够快速鉴别云南重楼及其近似种,为指导重楼栽培、临床应用和资源开发提供科学依据。

关 键 词:云南重楼  七叶一枝花  狭叶重楼  多叶重楼  矮重楼  傅里叶红外光谱  形态学特征  多元统计分析  主成分分析
收稿时间:2018/9/5 0:00:00

Rapid identification of Paris polyphylla var. yunnanensis and its closely relative species by infrared spectroscopy combined with morphological characteristics
ZHAO Fei-y,TAO Ai-en,LISHI Wen-mei,DUAN Bao-zhong,ZHANG Hai-zhu and XIA Cong-long.Rapid identification of Paris polyphylla var. yunnanensis and its closely relative species by infrared spectroscopy combined with morphological characteristics[J].Chinese Traditional and Herbal Drugs,2019,50(3):702-709.
Authors:ZHAO Fei-y  TAO Ai-en  LISHI Wen-mei  DUAN Bao-zhong  ZHANG Hai-zhu and XIA Cong-long
Institution:School of Pharmacy, Dali University, Dali 671000, China,School of Pharmacy, Dali University, Dali 671000, China,School of Pharmacy, Dali University, Dali 671000, China,School of Pharmacy, Dali University, Dali 671000, China,School of Pharmacy, Dali University, Dali 671000, China and School of Pharmacy, Dali University, Dali 671000, China
Abstract:Objective To explore morphology and infrared spectrum identification evidence of Paris polyphylla var. yunnanensis and its closely relative species, and further analyze their genetic relationship to provide basis for the development and utilization of medicinal plant resources of genus Paris. Methods The morphology and infrared spectrum of P. polyphylla var. yunnanensis and its closely relative species were studied systematically and compared with each other. The original infrared spectra data were pretreated by automatic baseline correction, automatic smoothing, ordinate normalization, and second derivative, and analyzed by principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and hierarchical cluster analysis (HCA). Results There were typical characteristics of P. polyphylla var. yunnanensis and its closely relative species, such as plant size, leaves, veins, sepals, petals, flower pavilions and stamens, which could provide morphological identification evidence. There were obviously differences of the second derivative in 3 000-2 000 cm-1 and the fingerprint in 1 800-500 cm-1 in the six types of genus Paris. Both HCA and PLS-DA could better distinguish Paris polyphylla var. yunnanensis and its closely relative species, which could provide an infrared spectral identified evidence. The results of HCA showed that P. polyphylla var. yunnanensis, P. polyphylla var. chinensis, P. polyphylla, P. polyphylla var. stenophylla and P. polyphylla var. nana were relatively close. Conclusion Fourier transform infrared spectroscopy (FTIR) combined with original plant morphological identification can quickly identify P. polyphylla var. yunnanensis and its closely relative species, which will provide a scientific basis for the cultivation, clinical application, and resource development of genus Paris.
Keywords:Paris polyphylla Smith var  yunnanensis (Franch  ) Hand  -Mazz    Paris polyphylla Smith var  chinensis (Franch  ) Hara  P  polyphylla Smith var  stenophylla Franch    P  polyphylla Smith  P  polyphylla Smith var  nana Franch    fourier transform infrared spectroscopy  morphological characteristics  multivariate statistical analysis  principal component analysis
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