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作 者:张雅雄[1] 景琳 ZHANG Yaxiong;JING Lin(Key Laboratory of Magnetic Molecules & Magnetic Information Materials, Ministry of Education, Shanxi Normal University, Linfen 041004, Shanxi, China;School of Chemistry and Material Science, Shanxi Normal University, Linfen 041004, Shanxi, China)
机构地区:[1]山西师范大学磁性分子与磁性信息材料教育部重点实验室,山西省临汾市041004 [2]山西师范大学化学与材料科学学院,山西省临汾市041004
出 处:《计算机与应用化学》2018年第3期189-197,共9页Computers and Applied Chemistry
基 金:山西省留学回国人员项目(2014-045);山西省自然科学基金项目(2010011013-2);山西师大教改项目(SD2013JGXM-54)
摘 要:本文收集了环烷烃类、环烯烃类、酮类、胺类、醚类、酯类等有机物在固定相角鲨烷和SE-30上的气相色谱保留指数,并采用基于Monte Carlo采样的模型集群分析(Monte Carlo sampling model population analysis,MCS MPA)方法进行了定量结构-色谱保留指数相关关系建模方法的比较研究。对于两种固定相上的有机化合物,分别采用不同的分子描述符予以表征,分子描述符的选择基于统计学与遗传算法。采用的建模方法包括多元线性回归(multivariate linear regression,MLR)、支持向量机回归(support vector machine,SVM)、径向基函数人工神经网络方法(radial basis function artificial neural networks,RBF ANN),通过所建模型预测了独立外部测试样本的气相色谱保留指数。研究结果表明,对于本文所研究的数据,SVM回归方法的建模效果优于MLR与RBF ANN方法。The authors performed a comparative study of QSRR modeling methods based on Monte Carlo sampling model population analysis(MCS MPA). The varieties of GC analytes include naphthenic hydrocarbons, cycloolefins, ethers, amines, ketones, and esters, etc. The retention data of the analytes was collected from two kinds of stationary phases, i.e., squalane and SE-30. Moreover, different molecular descriptors were applied to describe the molecular structures of the analytes on each stationary phase. The molecular descriptors were selected based on statistical methods and genetic algorithm. The modeling tools include multivariate linear regression(MLR), support vector machine(SVM), and radial basis function artificial neural networks(RBF ANN). The retention indices of independent external test samples were predicted by the models. The research shows that the modeling results of SVM were better than those of MLR and RBF ANN.
关 键 词:气相色谱保留指数 多元线性回归 支持向量机 径向基函数人工神经网络 MONTE Carlo采样模型集群分析
分 类 号:TQ015.9[化学工程] TP391.9[自动化与计算机技术—计算机应用技术]
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