摘要
目的分析胃肠安丸(木香、檀香、沉香等)挥发性成分,并建立其GC-MS指纹图谱。方法乙酸乙酯提取挥发性成分,GC-MS结合保留指数进行鉴定,聚类分析、主成分分析、正交偏最小二乘法判别分析进行化学模式识别。结果鉴定了102种挥发性成分,其中和厚朴酚、厚朴酚,三乙酸甘油酯,单硬脂酸甘油酯,2-单棕榈酸甘油相对含量高于5%。20批样品GC-MS指纹图谱中有35个共有峰,相似度均大于0.90。各批样品按生产日期被聚为2类,并筛选出12种主要差异成分。结论该方法稳定可靠,可用于胃肠安丸的质量控制。
AIM To analysis volatile components in Weichang’an Pills(Aucklandiae Radix,Santali albi Lignum,Aquilariae lignum Resinatum,etc.)and to establish their GC-MS fingerprints.METHODS The volatile components were extracted by ethyl acetate,after which the identification was performed by GC-MS combined with retention index,and cluster analysis,principal component analysis and orthogonal partial least square discriminant analysis were adopted in the chemical pattern recognition.RESULTS One hundred and two volatile components were identified,among which honokiol,magnolol,glycerol triacetate,glyceryl monostearate and 2-monopalmitin demonstrated the relative contents of more than 5%.There were thirty-five common peaks in the fingerprints of twenty batches of samples with the similarities of more than 0.90.Various batches of samples were clustered into two types according to production date,and twelve main differential constituents were screened.CONCLUSION This stable and reliable method can be used for the quality control of Weichang’an Pills.
作者
尚非
柴丽娟
王怡
张晗
王琳
王玉晶
孙英杰
张鹏
SHANG Fei;CHAI Li-juan;WANG Yi;ZHANG Han;WANG Lin;WANG Yu-jing;SUN Ying-jie;ZHANG Peng(Research Institute of Traditional Chinese Medicine,State Key Laboratory for Component-based Chinese Medicine Co-founded by Province and MOST,Tianjin University of Traditional Chinese Medicine,Tianjin 301617,China;Lerentang Pharmaceutical Factory,Tianjin Zhongxin Pharmaceutical Group Co.,Ltd.,Tianjin 300019,China)
出处
《中成药》
CAS
CSCD
北大核心
2022年第5期1420-1426,共7页
Chinese Traditional Patent Medicine
基金
国家"重大新药创制"科技重大专项(2018ZX09735-002)
国家自然科学基金青年项目(81803691)
天津市自然科学基金项目(19JCYBJC28500)。
关键词
胃肠安丸
挥发性成分
GC-MS指纹图谱
保留指数
聚类分析
主成分分析
正交偏最小二乘法判别分析
Weichang’an Pills
volatile components
GC-MS fingerprints
retention index
cluster analysis
principal component analysis
orthogonal partial least square discriminant analysis