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.

中文翻译：

---

通过 Monte Carlo 模拟对浑浊介质中的空间偏移拉曼光谱进行材料无关表征


空间偏移拉曼光谱 （SORS） 是一种用于探测浑浊介质中地下化学成分的变革性方法。这项对蒙特卡洛模拟的系统研究提供了关键 SORS 参数的闭式表征，例如收集的拉曼光子的空间原点分布和最佳 SORS 几何形状，以选择性地询问感兴趣的地下区域。通过将空间尺寸乘以介质的有效衰减系数，这些结果在广泛的材料特性中是统一的，当从文献或实验中知道吸收和减少的散射系数时，可以计算出该系数。这种空间无量纲化方法通过对聚集模型的拟合优度分析和在异质材料群体上训练地下样本定位模型来验证。这里报道的发现促进了对 SORS 现象的理解，同时为设计和解释 SORS 实验提供了定量和广泛应用的基础，促进了其在生物医学、材料科学和文化遗产领域等学科中的应用。