Application of new least-squares methods for the quantitative infrared analysis of multicomponent samples

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Courtesy of CIC Photonics, Inc.

Improvements have been in previous least-squares regression analyses of infrared spectra for the quantitative estimaiton of conecntratons of multicomponent mixtures. Spectral baselines are fitted by least-squares methods, and overlaping spectral features are accouted for in the fitting procedure. Selection peaks above a threshold value reduces computation time and data storage requirements. Four weighted least-squares methods incorporating different baseline assumptions were investigated using FT-IR spectra of the three pure xylene ismers and their mixtures. By fitting only regions of the spectra that follow Beer's Law, accurate resulta can be obtained using three of the fitting methods even when baselines are not corected to zero. Accurate results can also be obtained using one of the fits even in the presence of Beer's Law deviations. This is a consequence of pooling the weighted results for each spectral peak such that the greatest weighting is automatically given to those peaks that adhere to Beer's Law. It has been shown with the xylene spectra that semiquantitative results can be obtained even when all the major components are not known or when expected components are not present. This improvements over previous methods greatly expands the utility of quantitative least-squares analyses.

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