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Principal component analysis or kernel principal component analysis based joint spectral subspace method for calibration transfer

Shan, Peng ; Zhao, Yuhui ; Wang, Qiaoyun ; Ying, Yao ; Peng, Silong

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy, 2020-02, Vol.227, p.117653, Article 117653 [Periódico revisado por pares]

England: Elsevier B.V

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  • Título:
    Principal component analysis or kernel principal component analysis based joint spectral subspace method for calibration transfer
  • Autor: Shan, Peng ; Zhao, Yuhui ; Wang, Qiaoyun ; Ying, Yao ; Peng, Silong
  • Assuntos: Calibration transfer ; Joint spectral subspace ; Kernel principal component analysis (KPCA) ; Multivariate calibration ; Partial least squares (PLS) ; Principal component analysis (PCA)
  • É parte de: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy, 2020-02, Vol.227, p.117653, Article 117653
  • Descrição: To transfer a calibration model in the case where only the master and slave spectra of standardization samples are available, principal component analysis (PCA) and kernel principal component analysis (KPCA) based joint spectral space (termed as JPCA or JKPCA) methods are proposed. As a feature subspace shared by master and slave spectra, the joint spectral subspace in JPCA and JKPCA are the projection of the joint spectral matrix comprising all the spectra of standardization by utilizing PCA and KPCA, respectively. The two corresponding low-dimensional feature matrices for master and slave spectra are extracted from the joint spectral subspace, and then a transfer matrix is estimated based on the least square criterion. In JKPCA, a partial least squares (PLS) model, named the primary model, is constructed using the low-dimensional feature matrix of master calibration spectra, and the model is then used to predict the transferred low-dimensional feature matrix of slave test spectra. Different from JKPCA, JPCA firstly reconstructs master calibration spectra and transferred slave test spectra, respectively. Then the primary model built on the reconstructed version of master calibration spectra is applied to predict the reconstructed version of transferred slave test spectra. A comparative study of the two proposed methods, multiplicative scatter correction (MSC), orthogonal signal correction (OSC), piecewise direct standardization (PDS), canonical correlation analysis based calibration transfer (CCACT), generalized least squares (GLS), slope and bias correction (SBC) and spectral space transformation (SST) is conducted on two datasets. All the statistical results together exhibit that the transfer ability of JKPCA is the best. Except JKPCA, JPCA performs at least comparable with the GLS or SST, and frequently better than the other methods. [Display omitted] •Two calibration transfer methods are developed based on the joint spectral subspace.•Suitable for calibration transfer with only the master and slave spectral of standardization samples available.•A comparative study of the two proposed methods and 7 classical calibration transfer methods.•Comprehensive Statistical evaluation on the transfer ability of each calibration transfer method.•Calibration results are greatly improved by the two new calibration transfer methods.
  • Editor: England: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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