30 May 1995 Principal component and canonical correlation analysis of near-infrared spectra
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Abstract
Principal component analysis (PCA) is used to analyze the components of variable concentration (chromophores) from near IR spectroscopy measurement. Up until now, this method was only used in some special cases when the singular vectors of the measurement data matrix clearly resemble the component spectra or some simple combinations (e.g. sum or difference) of the spectra. In this paper, we find and derive a generally applicable theoretical relationship between the singular vectors and the component spectra. With this relationship, PCA becomes a powerful tool to detect how many and also which chromophores contribute to the observed attenuation changes measured by NIRS. Based on this relationship, a method is proposed to estimate the components of variable concentration from the data matrix by means of a principal component analysis and canonical correlation analysis (CCA). This method, which detects the chromophores of variable concentrations by analyzing numerically the canonical correlations, is more accurate and more generally applicable. Some results of the ananlysis are given.
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Jie Liang, Sabine Van Huffel, Paul Casaer, "Principal component and canonical correlation analysis of near-infrared spectra", Proc. SPIE 2389, Optical Tomography, Photon Migration, and Spectroscopy of Tissue and Model Media: Theory, Human Studies, and Instrumentation, (30 May 1995); doi: 10.1117/12.210014; https://doi.org/10.1117/12.210014
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