Articular cartilage possesses unique material properties due to a complex depth-dependent composition of sub-components. Raman spectroscopy has proven valuable in quantifying this composition through cartilage cross-sections. However, cross-sectioning requires tissue destruction and is not practical in-situ. In this thesis, Raman spectroscopy-based multivariate curve resolution was employed in porcine cartilage samples (n = 12) to measure collagen II, glycosaminoglycan, and water distributions through-the-surface and in cross-sections. These data were then used to create depth-dependent material property finite element models of cartilage, optimized to match experimental results. Through-the-surface Raman measurements could predict composition distributions up to a depth of approximately 0.5 mm. Depth-dependent FE models averaged an 18% reduction in error for predicted reaction force compared to simplified homogeneous distribution models. Use of a fructose-based optical clearing agent was found ineffective in homogenizing scattering. This measurement technique could be applicable for non-destructively modeling the evolution of joint diseases such as osteoarthritis.