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• W4286515923 abstract "In the spectral quantitative analysis of complex solutions, the broad width of the spectral peak comprises the multi-component spectra, which mix up with neighboring characteristic bands and cause collinearity of spectra and seriously affecting the accuracy of the analysis. A two-dimensional variable selection method is proposed to solve this problem by reforming the component spectrum in this paper. The useful data is selected from the spectral dataset with two dimensions of wavelength and pathlength which together represent the chemical and physical properties of the component. The new spectra of multiple components are formed with these data, and they are different from each other to eliminate the collinearity. The method's effectiveness is studied through the quantitative analysis of four components in human blood. According to one-dimensional and two-dimensional variable selection methods, interval partial least squares (iPLS), genetic algorithm PLS (GA-PLS), and ant colony optimization PLS (ACO-PLS) were used to select the effective datasets of four components, respectively. The experimental results show that the model's prediction accuracy based on the two-dimensional variable selection method is much better than that based on one-dimensional variables." @default.
• W4286515923 created "2022-07-22" @default.
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• W4286515923 date "2022-12-01" @default.
• W4286515923 modified "2023-09-26" @default.
• W4286515923 title "Reducing collinearity by reforming spectral lines with two-dimensional variable selection method" @default.
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• W4286515923 doi "https://doi.org/10.1016/j.molstruc.2022.133743" @default.
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