Abstract
A new approach for factor analysis of MIR spectra is proposed and applied to 15 mixtures of three potential workplace pollutants. PCA is performed on small regions of the overall experimental data matrix consisting of ω spectral frequencies and 15 spectra. The window is moved across the spectral data set, and the number of significant components in each window is computed. Regions where only one component absorbs (‘composition one’) are then identified. Correlations between the abstract concentration profiles for these points are used to sort the composition one regions into three groups. Each group represents a single component. Elimination of one of these groups permits new orthogonal spectra to be computed consisting of two components. The process is further repeated to yield one pure spectrum. Choosing and eliminating different groups allows the other pure spectra to be revealed. For correct choice of window size, the recovery of the pure spectra is good. It is shown that combination of orthogonal loadings spectra is mathematically equivalent to rotation as used in evolutionary factor analysis in HPLC.
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