EFFECTIVE RESOLUTION ENHANCEMENT OF INFRARED MICROSPECTROSCOPIC DATA BY MULTIRESPONSE NONLINEAR OPTIMIZATION

Computational methods are proposed and tested that enhance the spatial resolution of infrared microspectroscopic data collected from multila yer 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 mum (however, diffraction limi ts are wavelength dependent); therefore, layers of thickness less than approximately 10 mum give rise to spectra that are mixtures of spectr a from surrounding layers. Some authors have even pointed out that thi s could be the case for areas sampled that were much greater than 10 m um. Factor analysis of the data matrix can reveal the number of spectr ally different layers that are present, and the eigenvectors will give an abstract representation of the positional and wavelength informati on. An algorithm has been devised that uses layer boundary positions, aperture width, and aperture step size to model the positional informa tion from such an experiment. The boundary layer positions may be used as adjustable parameters in a nonlinear optimization problem that fit s the positional model to the abstract factor analysis positional data . This algorithm is applied to simulated and real data. Simulation res ults indicate superior performance in comparison with spectral matchin g to the raw data, and analysis of real data indicates consistent resu lts as well as the ability to resolve unique spectral features when co mpared with results from more painstaking data collection experiments.