Material-agnostic characterization of spatially offset Raman spectroscopy in turbid media via Monte Carlo simulations

Abstract

Spatially offset Raman spectroscopy (SORS) is a transformative method for probing subsurface chemical compositions in turbid media. This systematic study of Monte Carlo simulations provides closed-form characterizations of key SORS parameters, such as the distribution of spatial origins of collected Raman photons and optimal SORS geometry to selectively interrogate a subsurface region of interest. These results are unified across an extensive range of material properties by multiplying spatial dimensions by the medium's effective attenuation coefficient, which can be calculated when the absorption and reduced scattering coefficients are known from the literature or experimentation. This method of spatial nondimensionalization is validated via goodness-of-fit analysis on the aggregate models and by training a subsurface sample localization model on a heterogeneous population of materials. The findings reported here advance the understanding of SORS phenomena while providing a quantitative and widely applicable foundation for designing and interpreting SORS experiments, facilitating its application in disciplines such as biomedical, materials science, and cultural heritage fields.