Two-dimensional correlation spectroscopy as a tool for analyzing vibrational images

A new aspect of two-dimensional correlation spectroscopy is illustrated through its application to image analysis. Sample-sample 2D correlation spectra are used as a clustering tool to select the most distinctive spectra in the imaging set, which are subsequently used to develop so-called multivariate images. Only simple and easily understandable equations and ideas are used to discern the spectra that are representative of the species present in the imaged sample. The technique is first applied to a spectral model resembling a real image of a blend of two polymers, a multicomponent system, and to the real reflection infrared/near infrared (IR/NIR) image of a tablet. A critical review of advantages and shortcomings of the approach is given.