Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes.
Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes.
复制标题
非环和单环萜烯的 Kováts 保留指数的人工神经网络建模。
DOI：
10.1016/s0021-9673(00)01274-7
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Mohammad Hossein Fatemi
中科院分区：
文献类型：
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作者：
M. Jalali;Mohammad Hossein Fatemi
采用多元线性回归（MLR）和人工神经网络（ANN）技术，研究了几种萜烯在极性固定相Carbowax 20 M上的保留行为。选取53个非环萜烯和单环萜烯作为数据集，随机分为两组，训练集和预测集分别由41个分子和12个分子组成。MLR模型共有六个描述符，包括一个电子描述符、两个几何描述符、两个拓扑描述符和一个物理化学描述符。除几何参数外，其余描述符对萜烯的保留行为有显著影响。使用MLR模型中出现的6个描述符作为输入，生成6-5-1人工神经网络。预测集Kováts保留指数的人工神经网络计算值与实验值的相对误差均值为1.88%。这与实验得到的相对误差一致。
A quantitative structure–property relationship study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques was carried out to investigate the retention behavior of some terpenes on the polar stationary phase (Carbowax 20 M). A collection of 53 noncyclic and monocyclic terpenes was chosen as data set that was randomly divided into two groups, a training set and a prediction set consist of 41 and 12 molecules, respectively. A total of six descriptors appearing in the MLR model consist of one electronic, two geometric, two topological and one physicochemical descriptors. Except for the geometric parameters the remaining descriptors have a pronounced effect on the retention behavior of the terpenes. A 6-5-1 ANN was generated by using the six descriptors appearing in the MLR model as inputs. The mean of relative errors between the ANN calculated and the experimental values of the Kováts retention indexs for the prediction set was 1.88%. This is in aggreement with the relative error obtained by experiment.