Application of New Least-squares Methods for the Quantitative Infrared Analysis of Multicomponent Samples

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