Journal of Chemistry (Jan 2023)

Quality Evaluation of Bioactive Ingredients in Lianhua Qingwen Capsule Based on Quantitative Analysis of Multicomponent by the Single Marker Method and the Chemical Recognition Patterns Method

  • Qianqian Zhou,
  • Lin Yang,
  • Yiwu Wang,
  • Jiajia Zou,
  • Shuya Li,
  • Lei Dai,
  • Yan Li,
  • Dan He

DOI
https://doi.org/10.1155/2023/2694284
Journal volume & issue
Vol. 2023

Abstract

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Based on high performance liquid chromatography with the diode array detector (HPLC-DAD), a new strategy for simultaneous determination of ten bioactive ingredients in Lianhua Qingwen capsule (LHQW) was developed for comprehensive quality assessment of LHQW. In this work, with rhein regarded as the internal reference substance (IRS), the relative correction factors (RCFs) of neochlorogenic acid, amygdalin, chlorogenic acid, forsythoside A, quercitrin, phillyin, glycyrrhizic acid, isoforsythiaside, and (+) pinoresinol-β-D-glucoside were calculated for simultaneous determination of ten bioactive ingredients. More importantly, compared to previous work, the simultaneous determination of the content of ten pharmacologically important active ingredients at one detection wavelength with only one reference substance has been achieved. Based on the contents of ten bioactive ingredients, the quality of the 20 batches of LHQW samples was further analyzed by chemical recognition patterns method. Ten bioactive ingredients showed a good linear relationship in their respective concentration ranges (r ≥ 0.999). The relative standard deviations (RSDs) of precision (≤4.62%), stability (≤4.04%), repeatability (≤3.87%), and the average recovery of ten bioactive components (99.8%∼104.1%) demonstrated the QAMS developed for LHQW which had good durability. The correlation coefficient (P>0.05) showed that no significant difference existed in the results of QAMS and external standard method (ESM). Hierarchical clustering analysis (HCA) divided samples into three main groups. Radar plot analysis and principal component analysis (PCA) found some quality differences existed between the three groups of samples. Orthogonal partial least-squares discrimination analysis (OPLS-DA) showed that forsythoside A could be used as the primary marker responsible for the quality differences. In conclusion, the established QAMS method combined with chemometric analysis can simultaneously determine the content of 10 active components and comprehensively evaluate the quality of different batches of LHQW. It can provide scientific basis and reference of quality consistency evaluation for the formulation manufacturers and drug regulatory authorities.