Presented works describe a novel approach to assess mixing in a stirred vessel using light-based tomography. The study is driven by key research questions: What are the obstacles in three-dimensional dynamic tracer distribution reconstruction? How should the experimental equipment be constructed to obtain the best possible data from three cameras recording the back-lit stirred tank? How can raw images be processed to isolate the ray-dye interaction? And, how can relevant mixing information be obtained from projections and reconstructed volumetric data? The research begins with a short introduction of traditional mixing measurement techniques, establishing the context and relevance of the work. The theoretical background of the study is then presented, including the principles of tomographic reconstruction and the main algorithms used in the process. The methodology involves the use of synthetic data obtained from LES for the framework and baseline creation followed by the acquisition of experimental data. A significant part of the methodology is dedicated to image pre-processing, which incorporates as main steps the inverted grayscale transformation, brightness normalization, background removal using image similarity metrics and object removal with the use of a neural network. The use of the simplistic forward model based on the Lambert-Beer law is described, followed by the implementation of the projection matrix-free Simultaneous Algebraic Reconstruction Technique. The outcomes of both synthetic and experimental data reconstruction are presented and despite the shortcomings of the used experimental setup the 2D and 3D mixing maps were created, supported by the local Coefficient of Variance calculation to gain further insight into the process. The conclusions highlight the potential of light-based tomography for evaluating mixing while acknowledging the need for significant refinement and validation of the methodology. Recommendations include the improvement of imaging, equipment modifications, and reconstruction implementation. ii