目的: 建立准确、有效的广陈皮和陈皮鉴别模式,为岭南道地药材广陈皮等具有地理标志中药产品的保护提供新手段。方法: 分别采用顶空-气相-离子迁移色谱(HS-GC-IMS)与顶空-气相色谱(HS-GC)技术对广陈皮和陈皮样品中挥发性成分进行研究,并通过ChemPattern软件进行主成分分析(PCA)、偏最小二乘判别分析(PLS-DA),以建立广陈皮与陈皮的区分模式。结果: HS-GC-IMS共检出广陈皮、陈皮样品中109个特征信号,HS-GC共检出24个色谱峰,2种技术所建立的PLS-DA模型均能区别广陈皮和陈皮。其中HS-GC-IMS技术能更全面地反映广陈皮、陈皮样品中挥发性成分的组成,建立的模型能更好地体现广陈皮和陈皮间的差异。通过动态PCA筛选出18个差异性较大的特征信号,其中属广陈皮的有15个,可作为监控广陈皮道地性信号。通过数据降维,以差异性信号所建立的区分模型能有效识别广陈皮和陈皮。结论: HS-GC-IMS结合化学计量学方法建立的广陈皮和陈皮识别模式快速、准确、高效,可为中药近缘品种的区分提供新方法。
Objective: To establish an accurate and effective identification model for dried pericarps of Citrus reticulate ‘Chachi’(named guangchenpi) and dried pericarps from other Citrus reticulata Blanco varieties(named chenpi), so as to provide a new way for protecting the geographical indications of Traditional Chinese Medicine(TCM). Methods: The volatile components in guangchenpi and chenpi were analyzed by headspace-gas chromatography-ion mobility spectrometry(HS-GC-IMS) and headspace-gas chromatography(HS-GC). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed by chempattern software to establish the distinguishing model for guangchenpi and chenpi. Results: 109 signals were detected in guangchenpi and chenpi by HS-GC-IMS, and 24 chromatographic peaks were detected by HS-GC. The PLS-DA results showed that these two techniques could distinguish guangchenpi and chenpi. Among these two methods, HS-GC-IMS reflected the volatile components more comprehensively and presented preferably for the differences between guangchenpi and chenpi. 18 potential differential signals were screened by dynamic PCA, among which 15 signals were the main components of guangchenpi. Furthermore, 13 components were discerned by qualitative software to provide data basis for genuine monitoring and clinical application. In our study, the analysis with dimensionality reduction could identify guangchenpi and chenpi effectively. Conclusion: The identification model of guangchenpi and chenpi established by HS-GC-IMS combined with chemometrics is rapid, accurate and efficient, which provids a new way for discriminating the related varieties of TCM with volatile components.
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