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A Nonlinear Calibration Transfer Method Based on Joint Kernel Subspace by Peng Shan; Yuhui Zhao; Qiaoyun Wang; Shuyu Wang; Yao Ying; Silong Peng is a Chemistry article available to read on EtoBox.

What is A Nonlinear Calibration Transfer Method Based on Joint Kernel Subspace about?

A nonlinear strategy is proposed to develop calibration transfer method with the available joint spectral data composed of the master and slave spectra of standard samples. Its core idea is to minimize the instrument-induced spectral variation in the reproducing kernel Hilbert space (RKHS) where the joint spectral data is implicitly mapped by proper kernel function. The nonlinear feature representation of master or slave spectra are reconstructed by singular value decomposition (SVD) in the RKHS. Then the transferred feature of slave spectra approaching the reconstructed feature of master spectra in the RKHS can be acquired by the procedure that (1) calculates the transfer matrix to match the maser and slave kernel features in the joint kernel subspace and (2) utilizes the transferred slave kernel feature to reconstruct the nonlinear feature representation of slave spectra. As a better feature representation suited for multivariate calibration, both the reconstructed feature of master calibration spectra and the transferred feature of slave test spectra are derived in the RKHS. A kernel partial least squares (KPLS) model built on the former is applied to the latter. The KPLS master

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Author

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

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Chemistry (Physical Sciences)