Premium
Calibration transfer of near‐infrared spectra for extraction of informative components from spectra with canonical correlation analysis
Author(s) -
Zheng Kaiyi,
Zhang Xuan,
Iqbal Jibran,
Fan Wei,
Wu Ting,
Du Yiping,
Liang Yizeng
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2637
Subject(s) - calibration , canonical correlation , spectral line , piecewise , partial least squares regression , component analysis , mathematics , noise (video) , chemometrics , analytical chemistry (journal) , pattern recognition (psychology) , biological system , statistics , computer science , chemistry , artificial intelligence , chromatography , physics , mathematical analysis , astronomy , image (mathematics) , biology
A new calibration transfer method that applies canonical correlation analysis (CCA) to transfer the informative components extracted from a spectral dataset is proposed to reduce the interference of noise, background and non‐predicted properties. This method employs the partial least squares method to extract the informative components related to the predicted properties from the raw spectra and then corrects the informative components based on CCA. The performance of this algorithm was tested using three pairs of spectra batches: two pairs of corn spectra and one pair of tri‐component solvent spectra. The results showed that this method can significantly reduce prediction errors compared with CCA and piecewise direct standardization. Copyright © 2014 John Wiley & Sons, Ltd.