A new spectral variable selection pattern using competitive adaptive reweighted sampling combined with successive projections algorithm
The competitive adaptive reweighted sampling-successive projections algorithm(CARS-SPA)method was proposed as a novel variable selection approach to process multivariate calibration.The CARS was first used to select informative variables,and then SPA to refine the variables with minimum redundant information.The proposed method was applied to near-infrared(NIR)reflectance data of nicotine in tobacco lamina and NIR transmission data of active ingredient in pesticide formulation.As a result,fewer but more informative variables were selected by CARS-SPA than by direct CARS.In the system of pesticide formulation,amultiple linear regression(MLR)model using variables selected by CARS-SPA provided a better prediction than the full-range partial least-squares(PLS)model,successive projections algorithm(SPA)model and uninformative variables elimination-successive projections algorithm(UVE-SPA)processed model.The variable subsets selected by CARS-SPA included the spectral ranges with sufficient chemical information,whereas the uninformative variables were hardly selected.
Guo Tang Yue Huang Kuangda Tian Xiangzhong Song Hong Yan Jing Hu Yanmei Xiong Shungeng Min
College of Science,China Agricultural University,Beijing 100193,P.R.China College of Science,China Agricultural University,Beijing 100193,P.R.China;Beijing Third-class Superv
国内会议
北京
英文
4894-4902
2015-12-01（万方平台首次上网日期，不代表论文的发表时间）