title: Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra.
abstract: Near-infrared (NIR) spectrometry will present a more promising tool for quantitative measurement if the robustness and predictive ability of the partial least square (PLS) model are improved. In order to achieve the purpose, we present a new algorithm for simultaneous wavelength selection and outlier detection; at the same time, the problems of background and noise in multivariate calibration are also solved. The strategy is a combination of continuous wavelet transform (CWT) and modified iterative predictors and objects weighting PLS (mIPOW-PLS). CWT is performed as a pretreatment tool for eliminating background and noise synchronously; then, mIPOW-PLS is proposed to remove both the useless wavelengths and the multiple outliers in CWT domain. After pretreatment with CWT-mIPOW-PLS, a PLS model is built finally for prediction. The results indicate that the combination of CWT and mIPOW-PLS produces robust and parsimonious regression models with very few wavelengths.
authors.affiliation: Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, People's Republic of China.
authors.forename: Da
authors.initials: D
authors.lastname: Chen
authors.forename: Xueguang
authors.initials: X
authors.lastname: Shao
authors.forename: Bin
authors.initials: B
authors.lastname: Hu
authors.forename: Qingde
authors.initials: Q
authors.lastname: Su