RESOLUTION OF MIDINFRARED SPECTRA BY FACTOR-ANALYSIS USING SPHERICAL PROJECTIONS - INFLUENCE OF NOISE, SPECTRAL SIMILARITY, WAVELENGTH RESOLUTION AND MIXTURE COMPOSITION ON SUCCESS OF THE METHOD

A new method for the resolution and recovery of mid-infrared spectra by factor analysis is described. The key to the method is to determine a few 'composition-one' points in a set of mixture spectra, where one component uniquely absorbs. The method involves filtering the data using Savitzky-Golay filters, performing principal components analysis, elimination of composition-zero (noise) points, normalization of scores (projection onto the surface of a hypersphere), determining the best N composition-one points for each compound, and finally factor rotation/recovery of spectra. The method is evaluated using two criteria of success namely, the number of true composition-one points recovered and the correlation between true and recovered spectra. The influence of spectral similarity, spectral resolution, component concentration, noise levels, and cut-off threshold is investigated on two separate simulated datasets. Finally, the method is shown to work on a real dataset.