Infrared Spectral Search for Mixtures in Medium-Size Libraries

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Abstract
A new algorithm is presented for searching medium-size infrared spectral libraries for the components in spectra of mixtures. The algorithm treats the spectra in the library as an m-component quantitative analysis problem in which each of the library spectra represents a standard mixture having a concentration of 1.0 for that component. Principal component regression (PCR) is used to reduce the dimensionality of the problem and to provide the regression coefficients for determining pseudo-concentrations or composition indices (CI) in mixtures. The PCR analysis is followed by the application of an adaptive filter to remove all similarity of the first target component from the mixture and from a selected subgroup of the library. This is followed by a second PCR analysis on the modified spectral data to identify the next target compound. If the correct target components are selected with successive applications of the adaptive filter, the residuals will approach zero. All components in five two- and three-component mixtures were correctly identified by this new Mix-Match algorithm, whereas only two of the five mixtures were completely identified by a typical dot-product search routine.