Publication: Kernel analysis of partial least squares (PLS) regression models
Issued Date
2011-05-01
Resource Type
ISSN
00037028
DOI
Other identifier(s)
2-s2.0-79958724637
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Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Applied Spectroscopy. Vol.65, No.5 (2011), 549-556
Suggested Citation
Hideyuki Shinzawa, Pitiporn Ritthiruangdej, Yukihiro Ozaki Kernel analysis of partial least squares (PLS) regression models. Applied Spectroscopy. Vol.65, No.5 (2011), 549-556. doi:10.1366/10-06187 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/11718
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Title
Kernel analysis of partial least squares (PLS) regression models
Abstract
An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid. © 2011 Society for Applied Spectroscopy.