指纹图谱及多成分定量结合化学模式识别法评价不同产地青钱柳质量

目的基于UPLC指纹图谱、多成分定量与化学模式识别相结合的方法,评价不同产地青钱柳质量,为其进一步开发利用提供依据。方法建立5个省20批不同产地青钱柳UPLC指纹图谱,确定共有峰,通过对照品比对指认3种化学成分并测定样品中含量;使用SPSS22.2、SIMCA软件进行聚类分析和主成分分析。结果选取了16个色谱峰作为指纹图谱的共有峰,通过聚类分析可将20批青钱柳分为6类,主成分分析与聚类分析结果基本一致;经主成分分析,5个主成分因子的累积方差贡献率为86.765%,通过主成分分析推测出产于湖北恩施的S14、S15药材样品的质量最好,产于江西修水的S4、S5、S8、S16次之;被指认的异槲皮苷、槲皮苷、阿福豆苷在20批样品中的质量分数分别为0.360%~0.884%、0.263%~1.097%、0.092%~0.403%,其中,S4、S8、S14、S15的主成分分析综合得分及3个成分含量总和均处在前4位,而S18、S20则均处在倒数前2位。结论指纹图谱、结合聚类分析及主成分分析可以更全面地评价青钱柳质量,异槲皮苷、槲皮苷、阿福豆苷可以作为青钱柳质量控制的指标性成分。

Quality evaluation of Cyclocarya paliurus from different habitats by fingerprint and multi-component quantification combined with chemical pattern recognition

Objective Based on the method of UPLC fingerprint, multi-component quantification and chemical pattern recognition, the quality of Cyclocarya paliurus from different producing areas was evaluated to provide basis for further development and utilization. Methods The UPLC fingerprints of 20 batches of C. paliurus from different habitats in five provinces were established to determine the common peaks. Three chemical components were identified and the content of the samples was determined by comparison with the reference materials. Cluster analysis and principal component analysis were carried out by SPSS 22.2 and SIMCA software. Results A total of 16 chromatographic peaks were selected as the common peaks of the fingerprint, 20 batches of C. paliurus could be divided into six categories by cluster analysis, and the results of principal component analysis and cluster analysis were basically the same; By principal component analysis, the cumulative variance contribution rate of the five principal component factors was 86.765%. The quality of S14 and S15 samples from Enshi, Hubei Province was the best, and S4, S5, S8, and S16 of Xiushui, Jiangxi Province were the second. The content of isoquercetin, quercitrin and afzelin were 0.360%-0.884%, 0.263%-1.097%, and 0.092%-0.403%, respectively, among which the total content of S4, S8, S14, and S15 were the top 4, and that of S18 and S20 was in the last two places. Conclusion Combined with fingerprint, cluster analysis and principal component analysis, we can evaluate the quality of C. paliurus more comprehensively. Isoquercetin, quercitrin and afzelin can be used as the index components for C. paliurus quality control.