Prediction of gas-phase reduced ion mobility constants (K(0)) based on the multiple linear regression and projection pursuit regression.
Multiple linear regression and projection pursuit regression were used to develop the linear and nonlinear models for predicting the gas-phase reduced ion mobility constant (K(0)) of 159 diverse compounds. The six descriptors selected by heuristic method were used as the inputs of the linear and nonlinear models. The linear and nonlinear models gave very satisfactory results; the square of correlation coefficient was 0.9082 and 0.9379, the squared standard error was 0.0043 and 0.0030, respectively for the whole data set. The proposed models can identify and provide some insight into what structural features are related to the K(0) of compounds. They can also help to understand the separation mechanism in ion mobility spectrometry. Additionally, this paper provided two simple, practical and effective methods for analytical chemists to predict the K(0) of compounds in ion mobility spectrometry.
Liu
Huanxiang
H
Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
Yao
Xiaojun
X
Liu
Mancang
M
Hu
Zhide
Z
Fan
Botao
B