A near infrared wavelength selection method based on the variable stability and population analysis
Received:December 19, 2019  Revised:May 12, 2020  download
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Author NameAffiliationE-mail
ZHANG Feng Xi’an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an 710049, China 774149296@qq.com 
TANG Xiao-Jun Xi’an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an 710049, China xiaojun_tang@mail.xjtu.edu.cn 
TONG Ang-Xin Xi’an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an 710049, China  
WANG Bin Xi’an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an 710049, China  
WANG Jing-Wei Xi’an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an 710049, China  
Abstract:In order to improve the efficiency and performance of the analysis model, a wavelength selection method based on variable stability and population analysis (VSPA) is proposed. Firstly, the variables are divided into sample space and variable space, and the stability of variables is calculated in the sample space. According to the stability value, the variables are divided into useful variables and useless variables by weighted bootstrap sampling technology. Then, in the variable space, the frequency of each variable is calculated, and the exponential decline function is used to remove the variables with lower frequency from the useless variables. Finally, the proposed algorithm is applied to corn NIR data set to predict the starch content. The predicted root root-mean-square (RMSEP) and predicted correlation coefficient (RP) is 0.0409 and 0.9974, respectively. The variables after selection are only 2.7% of the original spectral data. It shows that the proposed variable selection method can improve the operational efficiency and prediction accuracy of the model, and is proved to be an effective variable selection method.
keywords:Wavelength selection  weighted bootstrap sampling  near infrared spectral  partial least squares
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Copyright:《Journal of Infrared And Millimeter Waves》