Goudarzi Nasser
Faculty of Chemistry, University of Shahrood, P.O. Box 316, Shahrood, Iran.
Spectrochim Acta A Mol Biomol Spectrosc. 2016 Apr 5;158:60-4. doi: 10.1016/j.saa.2016.01.023. Epub 2016 Jan 19.
In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the (19)F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the (19)F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.
在本研究中，两种新的强大化学计量学方法被应用于一些含氟有机化合物的（19）F化学位移值的建模和预测。采用径向基函数-偏最小二乘法（RBF-PLS）和随机森林（RF）构建模型来预测（19）F化学位移。在本研究中，我们未使用任何变量选择方法，且RF方法可作为变量选择和建模技术。研究了影响RF预测能力的重要参数的影响，如树的数量（nt）和每个节点随机选择用于分割的变量数量（m）。RBF-PLS和RF模型训练集和预测集的预测均方根误差（RMSEP）分别为44.70、23.86、29.77和23.69。此外，RBF-PLS和RF模型预测集的相关系数分别为0.8684和0.9313。所得结果表明，RF模型可作为定量结构-性质关系（QSPR）研究的强大化学计量学工具。
