Principal component analysis based two-dimensional correlation spectroscopy for noise filtering effect

This paper demonstrates the potential of principal component analysis based two-dimensional (PCA-2D) correlation analysis for noise filtering effect. A substantial amount of artificial noise was added to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation to demonstrate the practical noise-suppressing benefit of PCA-2D technique, 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. The PCA-reconstruction of data matrix from the significant scores and loading vectors of PCA yields improved 2D correlation spectra. which is highly noise suppressed but faithfully retain fine spectral features. (C) 2004 Elsevier B.V. All rights reserved.