[关键词]
[摘要]
目的 基于成分分析探讨黑顺片和白附片的差异。方法 基于傅里叶红外光谱(fourier transforms infrared,FTIR)和HPLC,结合化学计量学方法,对黑顺片和白附片的整体化学成分进行研究,并通过独立样本t检验对其有效和毒性成分含量进行比较分析。结果 FTIR结合正交偏最小二乘法判别分析(orthogonal partial least squares-discrimination analysis,OPLS-DA)结果显示,白附片和黑顺片整体化学成分差异明显,饮片类型的差异高于产地差异;HPLC的聚类分析(hierarchical clustering analysis,HCA)、主成分分析(principal component analysis,PCA)、OPLS-DA和相似度分析(similarity analysis,SA)均能将黑顺片、白附片区分为2类,表明2种饮片的成分差异较大;含量测定结果显示,黑顺片的单酯型生物碱量显著高于白附片,具有统计学意义(P<0.01),而双酯型生物碱含量差异不显著,无统计学意义。结论 白附片和黑顺片化学成分差异较大,临床上应区分使用,古代本草"白缓黑急"理论具有一定的合理性。
[Key word]
[Abstract]
Objective To explore the rationality for clinical application of Heishunpian and Baifupian based on chemical composition. Methods Fourier transforms infrared (FTIR), high-performance liquid chromatographic (HPLC), and combined with pattern recognition methods were used to investigate the difference in comprehensive chemical composition of Heishunpian and Baifupian. Furthermore, the content of active and toxic components of Heishunpian and Baifupian was comparative analyzed using Student's t-test. Results The FTIR combined with orthogonal partial least squares discrimination analysis (OPLS-DA) analysis revealed a significant difference in the chemical ingredients of Heishunpian and Baifupian, which was more influenced by the decoction type than the region. While the Heishunpian was distinguishable from Baifupian by hierarchical cluster analysis (HCA), principal component analysis (PCA), OPLS-DA, and similarity analysis (SA) of HPLC, there was a significant difference in composition. Compared with Baifupian, Heishunpian showed a significant increase in monoester diterpenoid alkaloids (P < 0.01), but no significant difference in diester diterpenoid alkaloids according to the content determination results. Conclusion The chemical composition of Heishunpian and Baifupian exhibited apparent discrepancies, which are used for different diseases in clinics. The theory of "Baihuanheiji" in ancient materia medica is reasonable.
[中图分类号]
R286.2
[基金项目]
云南省专家工作站(202105AF150053);云南省重大科技专项(202002AA100007);云南省万人“青年拔尖人才”计划(YNWR-QNBJ-2020251)