Optimal modeling pattern of variables selection on analog complex using UVE-PLS regression

Abstract

This study aimed to determine the composition of chemical complex by partial least square (PLS) regression models combined with uninformative variable elimination (UVE). The near-infrared (NIR) spectra of the forty samples were determined and then UVE was used to compress full NIR spectra from 12011 redundant variables to dozens of variables. Finally, 54, 16, 27, 31 and 42 variables were selected by UVE for 2,2,4-Trimethylpentane, Heptane, Cyclohexane, Ethyl formate and Butyl acetate respectively. Selected variables were used as the inputs of PLS model for quantitative analysis which made the prediction of the model more robust and accurate compared with the conventional PLS.