Rough and Fine Selection Strategy Binary Gray Wolf Optimization Algorithm for Infrared Spectral Feature Selection

Due to the seriously overlapped infrared spectral peaks of each component in hydrocarbon gas mixtures, which is caused by the high similarity of molecular structures, it has always been a difficult problem in stoichiometry to precisely monitor the concentration. A rough and fine selection strategy binary gray wolf optimization (RSBGWO) algorithm is proposed to optimize infrared spectral features and establish a high-precision quantitative analysis model to address this challenge. It takes the mean value of root mean square error (RMSECV) of the spectral quantitative analysis model based on cross-validation as the fitness function. In the rough selection stage, the first global iteration is carried out to update the location information of the selected characteristic variables for alpha wolf, beta wolf and delta wolf. In the fine selection stage, combining the characteristic variables for alpha wolf, the characteristic variables for beta wolf and delta wolf after eliminating the corresponding characteristic variables in which position are not selected for alpha wolf, are used to update the location information of wolves, in order to reduce the RMSECV value gradually and make sure that the extracted characteristic wavelength is globally optimal. In addition, a nonlinear convergence factor is introduced to accelerate the convergence speed. The algorithm is tested on the infrared spectral data set of 359 mixed alkane gas samples, and the effect of the proposed algorithm is verified. Compared with bGWO and bPSO feature extraction algorithms, the MLR model based ontheRSBGWO algorithm proposed in this paper reduces the number of the selected feature by more than 96% and increases the relative prediction deviation (RPD) by more than 15. The root mean square error of prediction (RMSEP) is lower than the instrument error of gas distribution system used for data acquisition when analyzing the concentrations of methane, ethane, propane and carbon dioxide. Compared with the MLR model and PLS model of full spectrum modeling, the prediction accuracy of the MLR model and PLS model based on the RSBGWO algorithm proposed in this paper is significantly improved, and the dependence of prediction effect on the quantitative analysis model is reduced. The experimental results show that the method proposed in this paper can significantly improve the analysis effect of the quantitative analysis model of infrared spectroscopy. The method can promote the application of spectral detection technology in biopharmaceuticals, the food chemical industry, oil and gas exploration, etc., especially in the application occasions containing homologous organic compounds.