Quantitative Image Analysis of Broadband CARS Hyperspectral Images of Polymer Blends

Abstract

We demonstrate that broadband coherent anti-Stokes Raman scattering (CARS) microscopy can be very useful for fast acquisition of quantitative chemical images of multilayer polymer blends. This is challenging because the raw CARS signal results from the coherent interference of resonant Raman and nonresonant background and its intensity is not linearly proportional to the concentration of molecules of interest. Here we have developed a sequence of data-processing steps to retrieve background-free and noise-reduced Raman spectra over the whole frequency range including both the fingerprint and C-H regions. Using a classical least-squares approach, we are able to decompose a Raman hyperspectral image of a tertiary polymer blend into quantitative chemical images of individual components. We use this method to acquire 3-D sectioned quantitative chemical images of a multilayer polymer blend of polystyrene, styrene-ethylene/propylene copolymer, and polypropylene that have overlapping spectral peaks.