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GC-MS指纹图谱结合化学模式识别评价不同种类苍术的质量 被引量:1

Quality evaluation of different kinds of Atractylodis Rhizoma by GC-MS fingerprinting with chemical pattern recognition
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摘要 目的建立不同种类苍术的气相色谱-质谱联用(gas chromatography-mass spectrometry,GC-MS)指纹图谱,并结合化学计量学方法评价苍术药材的质量。方法色谱条件为HP-5MS毛细管色谱柱(30 m×0.25 mm,0.25μm),采用程序升温,质谱条件为电子轰击电离(electron impact ionization,EI)离子源,电子能量为70 eV,在GC-MS图谱的基础上采用中药色谱指纹图谱相似度评价系统及聚类分析、主成分分析进行化学模式识别方法分析。结果通过指纹图谱相似度发现,43批苍术药材的GC-MS指纹图谱中发现17个共有峰及5个差异峰,不同品种苍术组内相似度均达到0.823。指纹图谱可以区分不同品种苍术,聚类分析与主成分分析的结果一致,且共有模式也能区分茅苍术、北苍术和关苍术。结论建立的苍术药材质量评价方法操作简便,重复性好,结果可靠,可用于苍术药材的质量控制和评价。 Objective To establish gas chromatography-mass spectrometry(GC-MS)fingerprints of various types of Atractylodis Rhizoma,and to utilized chemometrics for quality evaluation.Methods GC-MS analysis was conducted using an HP-5MS column(30 m×0.25 mm,0.25μm)with an electron impact ionization(EI)ion source and a 70 eV electron multiplier alongside the similarity evaluation system of Traditional Chinese Medicine Chromatographic Fingerprints,cluster analysis,and principal component analysis software to analyze the chemical pattern recognition methods.Results By calculating the similarity of the fingerprints,17 common peaks and 5 uncommon peaks were identified across 43 sample batches,achieving a similarity score of 0.823.The fingerprints effectively discriminated between different varieties of Atractylodis Rhizoma.Consistent results were obtained from both principal component analysis and cluster analysis,enabling the clear distinction among Atractylodes lancea,Atractylodes chinensis,and Atractylodes japonica.Conclusion The proposed method is straightforward,reproducible,and reliable,making it suitable for the quality control and evaluation of various types of Atractylodis Rhizoma.
作者 陈文婷 CHEN Wenting(The First People’s Hospital of Neijiang,Neijiang 641000,China)
出处 《西北药学杂志》 2025年第2期22-29,共8页 Northwest Pharmaceutical Journal
关键词 苍术 气相色谱-质谱联用 指纹图谱 化学计量学 质量评价 化学模式识别 Atractylodis Rhizoma gas chromatography-mass spectrometry fingerprint chemometrics quality evaluation chemical pattern recognition
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