开心散基准样品多指标成分的薄层色谱鉴定OA
Thin-Layer Chromatography Identification of Multi-Index Components in Kaixinsan Reference Samples
目的:建立一种高效鉴别经典名方"开心散"基准样品中多指标成分的薄层色谱方法.方法:该研究系统地考察了诸如供试品制备、展开剂组成、展开环境(温度和湿度)、不同厂家薄层色谱板等主要因素对开心散基准样品中人参皂苷Rb1、人参皂苷Re、细叶远志皂苷、β-细辛醚、去氢土莫酸和茯苓酸 6 个指标成分薄层色谱行为的影响,并对其进行了样品的实测验证.结果:所建立的薄层色谱方法实现了开心散中 6 个指标成分的有效分离和鉴别,6 种指标成分的斑点显色清晰、分离效能高、重现性强、阴性无干扰、耐用性能良好.结论:该研究所建立的开心散基准样品多种标性成分同时检测的薄层色谱鉴别方法专属性强、适用面广,为后续开心散质量标准的研究制定提供了重要的实验依据.
Objective:To establish a thin layer chromatography(TLC)method for the efficient identification of multiple index com-ponents in the reference samples of the classic prescription"Kaixinsan".Methods:This study systematically investigated the effects of major factors such as the preparation of the test sample,the composition of the developing agent,the developing environment(tempera-ture and humidity),and thin-layer chromatography plates from different manufacturers on the thin-layer chromatography behavior of six indicator components,namely ginsenoside Rb1,ginsenoside Re,tenuifolin,β-asarone,dehydrotumulosic acid and pachymic acid,in the Kaixinsan reference sample.And it was verified through actual measurement of samples.Results:The established thin-layer chromatogra-phy method effectively isolated and identified six indicator components in Kaixinsan.The spots of the six indicator components were clearly colored,with good separation,high reproducibility,negative without interference,and good durability.Conclusion:The thin-layer chromatography identification method for simultaneous detection of multiple standard components in the Kaixinsan reference sample es-tablished by this research institute has strong specificity and applicability,providing necessary experimental basis for the subsequent re-search and formulation of Kaixinsan quality standards.
陶稳稳;李大伟;张彩云;金凤华;汝天乐;年立忠;黄顺旺
安徽中医药大学药学院,安徽 合肥 230013安徽中医药大学药学院,安徽 合肥 230013安徽中医药大学药学院,安徽 合肥 230013合肥市食品药品安全检验检测中心,安徽 合肥 230061蚌埠丰原涂山制药有限公司,安徽 蚌埠 233050蚌埠丰原涂山制药有限公司,安徽 蚌埠 233050合肥创新医药技术有限公司,安徽 合肥 230088
医药卫生
开心散基准样品薄层鉴别质量标准指标性成分
KaixinsanReference sampleThin layer chromatographicQuality standardIndicative components
《中药材》 2025 (9)
2194-2200,7
安徽省科技重大专项项目(202203a07020031)安徽省自然科学基金项目(2408085MH229)
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