基于GC指纹图谱结合化学计量方法的大活络丸挥发性成分质量控制

    Quality Control of Volatile Components in Dahuoluo Pills Based on GC Fingerprint and Stoichiometric Model

    • 摘要:
      目的 利用气相色谱法(GC)建立大活络丸挥发性成分指纹图谱,通过多模式化学计量学识别方法探索不同厂家及不同制剂工艺对大活络丸质量的影响。
      方法 建立挥发性成分指纹图谱,标注其共有峰,并建立大活络丸GC多指标成分测定方法,利用SPSS25.0、SIMCA14.0数据分析软件对不同厂家、不同批次大活络丸进行聚类分析、主成分分析、偏最小二乘法判别分析和相关分析。
      结果 建立的GC方法可以很好地实现大活络丸中多种挥发性成分的分离与测定,指纹图谱结果显示全部样品中能够识别的共有峰共25个,并标注了其中的15个已知峰。聚类分析结果显示全部样品被分为5类,其中小蜜丸单独为一类,7种不同厂家大蜜丸被分为4类,可能是由于厂家之间制剂工艺、药材品质较为相似而导致。主成分分析及正交偏最小二乘法判别分析结果显示25种共有峰峰面积能很好地将不同剂型、不同厂家大活络丸进行区分,其中对分类结果影响程度较大的9个共有峰分别是1号峰、10号峰(苯甲酸)、7号峰、5号峰(桉油精)、14号峰、15号峰(香草醛)、4号峰(柠檬烯)、12号峰(5-羟甲基糠醛)、20号峰。
      结论 大活络丸中挥发性成分的含量多少受不同剂型及制剂工艺影响较大。本研究为建立大活络丸质量控制体系提供可参考的依据。

       

      Abstract:
      OBJECTIVE To establish the fingerprint of volatile components of Dahuoluo pills by gas chromatography(GC), and to explore the impact of different manufacturers and different preparation processes on the quality of Dahuoluo pills through multi-mode chemometric identification methods.
      METHODS Volatile component fingerprints were established to identify common peaks and a GC multi-index component determination method was developed for Dahuoluo pills. SPSS25.0 and SIMCA14.0 data analysis software were used to perform cluster analysis, principal component analysis, partial least squares discriminant analysis, and correlation analysis.
      RESULTS The established GC method effectively separated and determined the different volatile components in Dahuoluo pills. The fingerprint analysis revealed the presence of 25 common peaks in all samples, with 15 of them being known peaks. Clustering analysis results indicated that all samples could be classified into 5 categories. Specifically, the small honeyed pills belonged to a single category, while the large honeyed pills from 7 different manufacturers were divided into four categories. This different maight be due to the preparation process and medicinal materials between manufacturers. The results of principal component analysis and orthogonal partial least squares discriminant analysis demonstrated that the peak areas of 25 common peaks effectively differentiate between different dosage forms and manufacturers of Dahuoluo pills. Among these peaks, the 9 most influential ones for classification were peak No.1, peak No.10(benzoic acid), peak No.7, peak No.5(eucalyptol), peak No.14, peak No.15(vanillin), peak No.4(limonene), peak No.12(5-hydroxymethylfurfural), and peak No.20.
      CONCLUSION The content of volatile components in Dahuoluo pills is greatly affected by different dosage forms and preparation processes, which provides a reference basis for establishing a quality control system for Dahuoluo pills.

       

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