目的: 建立多腺悬钩子的超高效液相色谱(UPLC)指纹图谱及含量测定的方法,结合化学模式识别分析,为其质量评价提供依据。方法: 采用菲罗门Titank C18色谱柱(100 mm×2.1 mm,1.8 μm),以0.1%磷酸-乙腈为流动相,梯度洗脱,流速0.4 mL·min-1,柱温40 ℃,检测波长254 nm,进样量3 μL,建立多腺悬钩子UPLC方法,测定其中咖啡酸、鞣花酸、异槲皮苷的含量;基于指纹图谱共有峰峰面积结果,采用二维聚类分析、主成分分析、正交偏最小二乘判别分析、统计学分析、模式识别的化学计量学方法评价多腺悬钩子整体质量。结果: 多腺悬钩子UPLC指纹图谱共确定16个共有峰;3个指标性成分在各自浓度范围内线性关系良好(r≥0.999 5),19批多腺悬钩子中咖啡酸、鞣花酸、异槲皮苷的含量分别为0.014%~0.118%,0.013%~0.120%和0.012%~0.374%。33批悬钩子聚为4类,可区分不同的悬钩子;主成分分析得到4个影响悬钩子分类的主要因子;正交偏最小二乘判别分析显示13号色谱峰的化合物、鞣花酸、咖啡酸、异槲皮苷、5号色谱峰的化合物、15号色谱峰的化合物可作为鉴别和区分多腺悬钩子与其他悬钩子的主要差异性标志物;统计学分析显示咖啡酸与异槲皮苷的含量差异是多腺悬钩子与粉枝莓的重要区别点;模式识别研究中所建模型可准确识别多腺悬钩子,预测结果较为理想。结论: 所建立的方法稳定、可靠,能较全面地反映并评价多腺悬钩子的整体质量,为多腺悬钩子的质量控制提供参考。
Objective: To establish a method for the UPLC fingerprint and content determination of Rubus phoenicolasius Ramulus, and combine with chemical pattern recognition analysis to provide basis for its quality evaluation. Methods:Phenomenex Titank C18 (100 mm×2.1 mm, 1.8 μm) chromatographic column was used, the mobile phase was acetonitrile-0.1% phosphoric acid with gradient elution at the flow rate of 0.4 mL·min-1, the detection wavelength was 254 nm, the column temperature was 40 ℃, and the injection volume was 3 μL. UPLC fingerprints of Rubus phoenicolasius Ramulus was established, and the contents of caffeic acid, ellagic acid and isoquercitrin were determined at the same time. Based on the results of the common peak areas of the fingerprint, the quality of Rubus phoenicolasius Ramulus was evaluated by using two dimensional clustering analysis, principal component analysis, orthogonal partial least squares discriminant analysis, statistical analysis and pattern recognition. Results: The fingerprints identified 16 common peaks. Three compounds had good linearity within their ranges of mass concentration (r≥0.999 5). The contents of caffeic acid, ellagic acid and isoquercitrin were 0.014%-0.118%, 0.013%-0.120% and 0.012%-0.374% in 19 sample batches of Rubus phoenicolasius Ramulus. 33 batches of Rubus were divided into 4 categories, which could distinguish different rubus herbs. Principal component analysis showed four main factors affecting the classification of Rubus. Orthogonal partial least squares discriminant analysis showed that the compound of chromatographic peak No.13, ellagic acid, caffeic acid, isoquercitrin, compound of chromatographic peak No.5 and compound of chromatographic peak No.15 could be used as the main markers of difference between Rubus phoenicolasius Ramulus and other Rubus. Statistical analysis showed that the difference of caffeic acid and isoquercetin content was the important difference between Rubus phoenicolasius Ramulus and Rubus biflorus Ramulus. The model established in the pattern recognition study could accurately identify Rubus phoenicolasius Ramulus, and the prediction result was satisfactory. Conclusion:The method is stable and reliable, which can be used for the quality control of Rubus phoenicolasius Ramulus.
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