To simultaneously and fully deterministically quantify the chemical composition, phase content, and morphology of paticles in pharmaceutical dispersions (samples) their optical properties have to be obtained by a certain measuring setup. We will apply experimental measuring setups based on fiber probes and hyperspectral imaging systems and use their models (parameters) in Monte Carlo (MC) simulations. As the MC simulation results heavily depend on the accuracy of the model, we will first concentrate on accurate modeling of measurement setups. For this purpose, we will develop computationally efficient parametric forward light propagation models that can incorporate all the relevant details of the experimental setup such as materials forming the probe tip or optical components of the imaging system and produce trustworthy simulations of the measured quantities (e.g. reflectance, transmittance) for the target samples. The developed light propagation models will be used to optimize the layout and numerical aperture of the multimode optical fibers and optical components of the imaging systems with the aim to attain maximum sensitivity to the morphology and chemical composition of the samples and to develop computationally efficient inverse models that take the measured quantities and produce geometrical and optical properties of the sample from which the morphology, phase content and chemical composition can be inferred. Finally, we will devise procedures for accurate calibration and validation of such measurement systems using optical phantoms with well-defined optical properties and apply the methodology to selected pharmaceutical processes. The methodology that will be developed, tested, and applied, could substantially simplify if not revolutionize the use of spectroscopy in pharmaceutical manufacturing, enable new applications and research opportunities, and serve as a foundation for development of next generation process analytical technologies. The results will be also highly relevant to many other prioritized research fields, such as biophotonics, wearable health monitoring systems, remote sensing, particulate matter pollution, and modelling of light transport in the atmosphere that may improve global climate prediction models.