The effectiveness of a scatter correction approach based on decoupling absorption and scattering effects through the use of the radiative transfer theory to invert a suitable set of measurements is studied by considering a model multicomponent suspension. The method was used in conjunction with partial least-squares regression to build calibration models for estimating the concentration of two types of analytes: an absorbing (nonscattering) species and a particulate (absorbing and scattering) species. The performances of the models built by this approach were compared with those obtained by applying empirical scatter correction approaches to diffuse reflectance, diffuse transmittance, and collimated transmittance measurements. It was found that the method provided appreciable improvement in model performance for the prediction of both types of analytes. The study indicates that, as long as the bulk absorption spectra are accurately extracted, no further empirical preprocessing to remove light scattering effects is required.