Objective: To establish the HPLC fingerprint of Balanophora involucrata from different habitats, and evaluate the quality through chemical pattern recognition. Methods: Samples were separated by Shim-pack GIS C18 column(250 mm×4.6 mm, 5 μm) with acetonitrile -0.2% acetic acid solution as gradient mobile phase at a flow rate of 1.0 mL·min-1. The detection wavelength was set at 320 nm. The HPLC fingerprint and similarity calculation were performedby traditional Chinese medicine chromatographic fingerprint similarity evaluation system. The cluster analysis, principal component analysis, and orthogonal partial least square discriminate analysis models were established by SPSS20.0 and SIMCA-P14.1 software. Results: A total of 20 common peaks were identified and the similarities of 11 batches of Balanophora involucrata from different habitats were good with the level above 0.90. Five principal components with the characteristic root cumulative contribution rate reaching 88.509% were screened out by PCA results. The comprehensive scores of the 11 batches of samples were between -1.52 and -1.46, indicating that the difference in the quality of each batch of medicinal materials was relatively small. The quality of Balanophora involucrata from twelve ridgesin Badong County was the best with a highest comprehensive score. Conclusion: The HPLC fingerprintcan be used as the identification basis for Balanophora involucrata, and can effectively evaluate the quality difference of Balanophora involucrata from different habitats.
ZHENG Lu, LI Hong, ZHANG Yu, TU Xing, ZHAO Fang-yu
. HPLC fingerprint and chemical pattern recognition of Balanophora involucrata from different habitats*[J]. Chinese Journal of Pharmaceutical Analysis, 2022
, 42(5)
: 896
-903
.
DOI: 10.16155/j.0254-1793.2022.05.19
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