Computational methods are proposed and tested that enhance the spatial resolution of infrared microspectroscopic data collected from multilayer polymeric materials film structures. The data collected from such a structure with the use of an infrared microspectroscopic system are diffraction limited at approximately 10 μm (however, diffraction limits are wavelength dependent); therefore, layers of thickness less than approximately 10 μm give rise to spectra that are mixtures of spectra from surrounding layers. Some authors have even pointed out that this could be the case for areas sampled that were much greater than 10 μm. Factor analysis of the data matrix can reveal the number of spectrally different layers that are present, and the eigenvectors will give an abstract representation of the positional and wavelength information. An algorithm has been devised that uses layer boundary positions, aperture width, and aperture step size to model the positional information from such an experiment. The boundary layer positions may be used as adjustable parameters in a nonlinear optimization problem that fits the positional model to the abstract factor analysis positional data. This algorithm is applied to simulated and real data. Simulation results indicate superior performance in comparison with spectral matching to the raw data, and analysis of real data indicates consistent results as well as the ability to resolve unique spectral features when compared with results from more painstaking data collection experiments.
R. J. Pell, M. L. McKelvy, and M. A. Harthcock, "Effective Resolution Enhancement of Infrared Microspectroscopic Data by Multiresponse Nonlinear Optimization," Appl. Spectrosc. 47, 634-642 (1993)