基于HPLC指纹图谱与多成分定量结合化学模式识别法评价不同产地白芍的质量

Quality Evaluation of Baishao (Paeoniae Radix Alba) from Different Habitats Based on HPLC Fingerprint and Multi-component Quantification Combined with Chemical Pattern Recognition Method

目的:基于HPLC指纹图谱与多成分含量测定,并结合化学模式识别法,评价引种白芍与主产区白芍质量,为其进一步开发利用提供依据。方法:采用HPLC法,Shim-pack GIST-HP C18色谱柱(250 mm×4.6 mm,5.0μm),乙腈(A)-0.1%磷酸溶液(B)为流动相,梯度洗脱,流速为1 mL/min,柱温为30℃,检测波长为232 nm,采集不同产地50批次白芍的HPLC指纹图谱,确定共有峰,通过对照品比对指认6种指标成分,同时测定样品中含量;通过SPSS 22.0、SIMCA14.1软件进行聚类分析和主成分分析。结果:建立了白芍指纹图谱,确定了9个共有峰,并指认了其中6个峰(1.没食子酸、2.儿茶素、3.芍药苷内酯、4.芍药苷、7.1,2,3,4,6-五没食子酰基葡萄糖、8.苯甲酰芍药苷),其峰面积占共有峰面积98%,50批白芍指纹图谱的相似度大于0.996;同时测定了6个指标成分的含量;通过聚类分析可将50批白芍分为3类;主成分分析与聚类分析结果一致;经主成分分析,2个主成分因子的累积方差贡献率为76.8%,综合得分结果显示,四川、浙江产白芍质量最好,甘肃地区引种白芍质量与安徽、山东产白芍质量相近;50批饮片主成分综合得分与含量测定结果存在相关性(r=0.860,P<0.01)。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白芍质量,没食子酸、儿茶素、芍药苷内酯、芍药苷、1,2,3,4,6-五没食子酰基葡萄糖、苯甲酰芍药苷可以作为白芍质量控制的指标性成分。

Objective: To evaluate the quality of introduced Baishao(Paeoniae Radix Alba) and Baishao(Paeoniae Radix Alba) in main producing areas based on HPLC fingerprint and multi-component content determination,combined with chemical pattern recognition method, provide a basis for its further development and utilization.Methods: HPLC was used with Shim-pack GIST-HP C18 column(250 mm×4.6 mm, 5 μm), acetonitrile(A)-0.1%phosphoric acid solution(B) as mobile phase, gradient elution, flow rate of 1 mL/min, column temperature of 30 ℃,detection wavelength of 232 nm. To collection the HPLC fingerprints of 50 batches of Baishao(Paeoniae Radix Alba) from different habitats and determine the common peaks. The 6 index components were identified by comparison of the reference substance, and the content in the sample was determined at the same time. Cluster analysis and principal component analysis were performed by SPSS 22.0 and SIMCA14.1 software. Results: The fingerprint of Baishao(Paeoniae Radix Alba) was established, 9 common peaks were identified, and 6 peaks were identified(1. Gallic acid, 2. Catechin, 3. Paeoniflorin lactone, 4. Paeoniflorin, 7.1.2,3,4,6-pentagalloyl glucose,8. benzoylpaeoniflorin). The peaks area accounts for 98% of the common peak area, and the similarity of fingerprints of 50 batches of Baishao(Paeoniae Radix Alba) is greater than 0.996. The content of 6 index components was determined at the sam e time. 50 batches of Baishao(Paeoniae Radix Alba) can be divided into 3 categories through cluster analysis. The results of principal component analysis and cluster analysis are consistent. After principal component analysis, the cumulative variance contribution rate of the two principal component factors is76.8%. The results showed that the quality of Baishao(Paeoniae Radix Alba) from Sichuan and Zhejiang was the best, and the quality of introduced Baishao(Paeoniae Radix Alba) in Gansu was similar to that in Anhui and Shandong. There was a correlation between the comprehensive scores of the principal components of 50 batches of decoction pieces and the content determination results(r=0.860, P<0.01). Conclusion: Fingerprint combined with cluster analysis and principal component analysis can more comprehensively evaluate the quality of Baishao(Paeoniae Radix Alba). Gallic acid, catechin, paeoniflorin lactone, paeoniflorin, 1,2,3,4,6-pentagalloyl glucose,benzoylpaeoniflorin can be used as an index components for quality control of Baishao(Paeoniae Radix Alba).