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在硅胶薄层板上用氯仿 -乙酸乙酯 -甲醇 -水 (体积比为 15∶ 4 0∶ 2 2∶ 10 )作展开剂 ,测定了常见人参皂甙的Rf值。为了研究它们的结构与保留值之间的关系 ,对它们的 17种结构参数进行了计算。除了拓扑指数和理化参数外 ,引入了代表构成人参皂甙的母体化合物种类并反映它们分子极性特征的新参数“E”。通过相关分析优化选出范德华分子表面积 AW、拓扑指数0 B和参数 E,建立了多参数线性回归方程 ,较好地描述了在正相薄层色谱中常见人参皂甙结构与保留值之间的关系 ,并与人工神经网络方法进行了比较。结果表明 ,非线性方法更适合于该研究 ,为进一步采用人工神经网络方法研究正相薄层色谱中人参皂甙结构与保留之间的关系打下了良好基础
The Rf value of common ginsenosides was determined on a silica gel plate using chloroform-ethyl acetate-methanol-water (15: 40: 2: 10 by volume) as a developing solvent. In order to study the relationship between their structure and retention, their 17 structural parameters were calculated. In addition to the topological indices and physico-chemical parameters, a new parameter “E”, which represents the type of parent compound that makes up the ginsenosides and reflects the polarity of their molecules, has been introduced. Through the correlation analysis, the van der Waals molecular surface area AW, topological index 0 B and parameter E were optimized to establish a multi-parameter linear regression equation, which better described the relationship between the structure and retention of common ginsenosides in normal-phase thin layer chromatography , And compared with the artificial neural network method. The results show that the nonlinear method is more suitable for this study, which lays a good foundation for the further study on the relationship between structure and retention of ginsenosides in normal-phase thin layer chromatography by artificial neural network