Blind decomposition of infrared spectra using flexible component analysis

The paper presents flexible component analysis-based blind decomposition of the mixtures of Fourier transform of infrared spectral (FT-IR) data into pure components, wherein the number of mixtures is less than number of pure components. The novelty of the proposed approach to blind FT-IR spectra decomposition is in use of hierarchical or local alternating least square nonnegative matrix factorization (HALS NMF) method with smoothness and sparseness constraints simultaneously imposed on the pure components. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm in the wavelet domain. The HALS NMF method is compared favorably against three sparse component analysis algorithms on experimental data with the known pure component spectra. Proposed methodology can be implemented as a part of software packages used for the analysis of FT-IR spectra and identification of chemical compounds.