Theoretical Comparison of Classical (K-Matrix) and Inverse (P-Matrix) Least-Squares Methods for Quantitative Infrared Spectroscopy

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Abstract
Both classical and inverse least-squares methods have been applied to the quantitative IR spectral analysis of multicomponent samples. The two methods are presented and compared in this paper. The classical method is capable of higher precision and accuracy since it can use all of the data in a spectrum. Its ability to obtain full-spectrum residuals provides methods for identifying problems in the spectral data. The inverse method has the potential advantage that it can be applied even if only one component in the sample is known.

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