返回 
加入收藏
12种成分含量测定结合多元统计分析的扎冲十三味丸质量评价
x
请在关注微信后，向客服人员索取文件
| 篇名： | 12种成分含量测定结合多元统计分析的扎冲十三味丸质量评价 |
| TITLE： | Quality evaluation of Zhachong-13 pills based on the content determination of 12 components combined with multivariate statistical analysis |
| 摘要： | 目的 评价扎冲十三味丸的质量。方法采用高效液相色谱串联三重四极杆质谱（HPLC-MS/MS）法测定18批样品中沉香四醇、木香烃内酯、次乌头碱、去氢木香内酯、新乌头碱、乌头碱、甘草素、甘草酸、鞣花酸、甘草苷、柯里拉京、没食子酸的含量；采用微生信在线分析平台进行聚类分析（CA），采用SIMCA14.1软件进行主成分分析（PCA）和正交偏最小二乘法-判别分析（OPLS-DA）；采用SPSSAU在线分析平台构建随机森林、极端随机树、极端梯度提升机器学习模型；采用熵权-逼近理想解排序（TOPSIS）法评价样品的综合质量。结果上述12种成分的含量分别为104.331～694.382、652.217～1085.075、6.051～130.980、612.174～1417.179、1.018～32.239、0.953～24.967、65.552～652.239、2019.412～5385.807、52.867～4990.450、156.307～883.180、10.142～475.722、1102.863～4826.396μg/g。CA、PCA结果均显示，18批样品分为两类，S1～S6、S10～S15为一类，S7～S9、S16～S18为一类；OPLS-DA结果显示，新乌头碱、甘草酸、甘草苷、鞣花酸、次乌头碱、甘草素、沉香四醇、没食子酸、去氢木香内酯的变量重要性投影值＞1。3种机器学习模型结果均显示，鞣花酸、次乌头碱的差异贡献度较大。熵权-TOPSIS法结果显示，B厂家样品整体质量优于其他4个厂家。结论所建含量测定方法稳定、可行，结合多元统计分析可用于评价扎冲十三味丸的质量；鞣花酸、次乌头碱是影响该制剂质量的差异标志物。 |
| ABSTRACT： | OBJECTIVE To evaluate the quality of Zhachong-13 pills.METHODS The contents of agarotetrol， costunolide， hypaconitine， dehydrocostus lactone， mesaconitine， aconitine， liquiritigenin， glycyrrhizic acid， ellagic acid， liquiritin， corilagin， and gallic acid in 18 batches of samples were determined by high-performance liquid chromatography-tandem triple quadrupole mass spectrometry （HPLC-MS/MS） method. Cluster analysis （CA） was performed using the Microbioinformatics online analysis platform. Principal component analysis （PCA） and orthogonal partial least squares discriminant analysis （OPLS-DA） were performed using SIMCA 14.1 software. Random forest， extremely randomized trees， and extreme gradient boosting machine learning models were constructed using the SPSSAU online analysis platform. The comprehensive quality of the samples was evaluated by the entropy weight-technique for order preference by similarity to ideal solution （TOPSIS） method.RESULTS The contents of the above 12 components were 104.331-694.382， 652.217-1 085.075， 6.051-130.980， 612.174-1 417.179， 1.018-32.239， 0.953-24.967， 65.552-652.239， 2 019.412-5 385.807， 52.867-4 990.450， 156.307-883.180， 10.142-475.722， and 1 102.863-4 826.396 μg/g， respectively. The results of CA and PCA showed that the 18 batches of samples were divided into two categories： S1-S6， S10-S15 were grouped into one category， while S7-S9 and S16-S18 were grouped into another category. The OPLS-DA results showed that the variable importance in projection values of mesaconitine， glycyrrhizic acid， liquiritin， ellagic acid， hypaconitine， liquiritigenin， agarotetrol， gallic acid， and dehydrocostus lactone were greater than 1. The results of the three machine learning models showed that ellagic acid and hypaconitine had relatively large differential contributions. The entropy weight-TOPSIS results showed that the overall quality of samples from manufacturer B was superior to that of the other four manufacturers.CONCLUSIONS The established method is stable and feasible， and can be used to evaluate the quality of Zhachong-13 pills， in combination with multivariate statistical analysis. Ellagic acid and hypaconitine are differential markers that affect its quality. |
| 期刊： | 2026年第37卷第19期 |
| 作者： | 张微；张谦；白图雅；李鑫奎；胡玉霞；张梦迪；刘静；王芳；李君 |
| AUTHORS： | ZHANG Wei，ZHANG Qian，BAI Tuya，LI Xinkui，HU Yuxia，ZHANG Mengdi，LIU Jing，WANG Fang，LI Jun |
| 关键字： | 扎冲十三味丸;含量测定;HPLC-MS/MS法;化学模式识别分析;熵权-TOPSIS分析;机器学习;质量评价 |
| KEYWORDS： | Zhachong-13 pills;Content determination;HPLC-MS/MS method;chemical pattern recognition analysis;entropy weight-TOPSIS analysis;machine learning;Quality evaluation |
| 阅读数： | 1 次 |
| 本月下载数： | 0 次 |
* 注：未经本站明确许可，任何网站不得非法盗链资源下载连接及抄袭本站原创内容资源!在此感谢您的支持与合作！
