Variable Selection and Optimization in Rapid Detection of Soybean Straw Biomass Based on CARS

【摘要】：Data is the basis of analysis and modeling.It is essential and necessary for performance optimization to extract useful data or characteristic variables from large amounts of data.In this paper,variable selection and trusted computing in rapid detection of soybean straw biomass is studied.CARS is a variable selection method which simulate the "survival of the fittest" principle of Darwin's evolution theory.CARS is applied into soybean straw NIRS analysis data and compared to other variable selection methods,such as iPLS,siPLs,biPLS and SPA.Built PLS model using the optimization data based on above five methods,and contrasted the RMSEP of the PLS model and the difference between RMSECV and RMSEP.The experimental results show that the iPLS and SPA methods are not suitable for analyzing soybean straw NIRS uaia.Also,the CARS method is found to be more effective in extracting character variables than siPLS and biPLS.Performance of prediction model is improved by variable selection.In contrast to the global PLS model,the RMSECV is reduced to 0.6572 from 0.8082,the RMSECP is reduced to 0.5710 from 0.6250,and correlation coefficient of the calibration model is increased to 0.8162 from 0.7087 for the hemicellulose.The difference between RMSECV and RMSEP is small,so the model will be stable.Similar results appear in the cellulose and lignin analyzing process.It is concluded that CARS method is effective in variables selection and optimization for soybean straw NIRS data.

【作者单位】：College of Electrical and Information,Northeast Agricultural University Department of Computer Science,Harbin Institute of Technology Department of Computer Engineering,National Institute of Technology Department of Media Software,Sungkyul University Harbin University of Finance
【分类号】：S216.2