Modelling phase shifts, peak shifts and peak width variations in spectral data sets: its value in multivariate data analysis

A mathematical procedure is presented for the fast determination and remova l of phase shifts, peak shifts, and peak width variations in spectral data sets. The method is applicable to any spectrum type (NMR, Raman, infrared, etc.) that contains spectrally isolated peaks. The procedure is illustrated on a series of simulated spectra and on a series of low signal-to-noise NM R spectra containing a single disturbed peak. Its value in multivariate cal ibration is demonstrated on a series of Raman spectra of the solution co-po lymerisation of styrene and butyl acrylate in dioxane. The prediction of st yrene and butyl acrylate conversions from the Raman spectra using partial l east squares regression appears to be sensitive to peak shifts and, to a le sser extent, to peak width variations. It is shown that after removal of pe ak shifts and peak width variations the ability of partial least squares to predict styrene and butyl acrylate conversions is considerably improved. ( C) 2001 Elsevier Science B.V. All rights reserved.