An ensemble of Monte Carlo uninformative variable elimination for wavelength selection

被引：180

作者：

Han, Qing-Juan ^{[1
]}

Wu, Hai-Long ^{[1
]}

Cai, Chen-Bo ^{[1
]}

Xu, Lu ^{[1
]}

Yu, Ru-Qin ^{[1
]}

机构：

[1] Hunan Univ, Coll Chem & Chem Engn, State Key Lab Chemo Biosensing & Chemometr, Changsha 410082, Hunan, Peoples R China

来源：

ANALYTICA CHIMICA ACTA | 2008年 / 612卷 / 02期

基金：

中国国家自然科学基金;

关键词：

wavelength selection; multivariate calibration; Monte Carlo; uninformative variable elimination; partial least squares;

D O I：

10.1016/j.aca.2008.02.032

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

An improved method based on an ensemble of Monte Carlo uninformative variable elimination (EMCUVE) is presented for wavelength selection in multivariate calibration of spectral data. The proposed algorithm introduces Monte Carlo (MC) strategy to uninformative variable elimination-PLS (UVE-PLS) instead of leave-one-out strategy for estimating the contributions of each wavelength variable in the PLS model. In EMCUVE wavelength variables are evaluated by different Monte Carlo uninformative variable elimination (MCUVE) models. Moreover, a fusion of MCUVE and the vote rule can obtain an improvement over the original uninformative variable elimination method. Results obtained from simulated data and real data sets demonstrate that EMCUVE can properly carry out wavelength selection in the course of data analysis and improve predictive ability for multivariate calibration model. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：121 / 125

页数：5

共 23 条

[1] A review of the main factors influencing the FT-IR-PLS abilities exemplified with petrochemical qualimetric applications [J].