A Progressive Combined Variable Selection Method for Near-Infrared Spectral Analysis Based on Three-Step Hybrid Strategy

Abstract

A specific variable selection method was proposed based on a three-step hybrid strategy for near-infrared spectral analysis. By analyzing functions of each step and characteristics of various variable selection methods, synergy interval partial least squares, iterative variable subset optimization, and bootstrapping soft shrinkage were chosen for three steps. To test the effect of the three-step hybrid method, it was applied to corn and soil spectral data and compared to other common methods. Results for oil content in corn data showed that the three-step hybrid variable selection method selected 1% variables of full spectrum, calibration determination coefficient, and prediction determination coefficient reached 0.998 and 0.993 where the explained variance was increased by 27.30%. It could effectively extract variables related to the tested substance and provide a new variable selection method for near-infrared spectral analysis.

Quantitative Inversion Model Design of Mine Water Characteristic Ions Based on Hyperspectral Technology

“…ree hundred and ninety-nine standard solution spectral data were selected, the largest seven principal components were selected, and the weights were set equally [47][48][49][50][51]. CV prediction detection, cross-validation, and the principal component analysis model were established when the proportion of the validation set and the modeling set was 0.70. e first three principal components can represent more than 90% of the content information.…”