Simultaneous Wavelength Selection and Outlier Detection in Multivariate Regression of Near-Infrared Spectra
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Accession number;05A0184735
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| Title;Simultaneous Wavelength Selection and Outlier Detection in Multivariate Regression of Near-Infrared Spectra |
| Author;
CHEN D
(Univ. Sci. And Technol. Of China, Anhui, Chn)
SHAO X
(Univ. Sci. And Technol. Of China, Anhui, Chn)
HU B
(Univ. Sci. And Technol. Of China, Anhui, Chn)
SU Q
(Univ. Sci. And Technol. Of China, Anhui, Chn)
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Journal Title;Anal Sci
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Journal Code:G0673B
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ISSN:0910-6340
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VOL.21;NO.2;PAGE.161-166(2005)
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| Figure&Table&Reference;FIG.6, TBL.2, REF.21 |
| Pub. Country;Japan |
| Language;English |
| 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. (author abst.) |
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