When light interacts with a scattering medium, the spectrum of the incident light undergoes changes that are dependent on the size of the scatterers in the medium. Spectroscopic Optical Coherence Tomography (S-OCT) is a method that can be used to ascertain the resulting spatially-dependent spectral information. In fact, S-OCT is sensitive to structures that are below the spatial resolution of the system, making S-OCT a promising tool for diagnosing many diseases and biological processes that change tissue structure, like cancer. The most important signal processing steps for S-OCT are the depth-resolved spectral analysis and the calculation of a spectroscopic metric. While the former calculates the spectra from the raw OCT data, the latter analyzes the information content of the processed depth-resolved spectra. We combine the Dual Window spectral analysis with different spectroscopic metrics, which are used as an input to colorize intensity based images. These metrics include the spectral center of mass method, principal component (PCA) and phasor analysis. To compare the performance of the metrics in a quantitative manner, we use a cluster algorithm to calculate efficiencies for all methods. For this purpose we use phantom samples which contain areas of microspheres of different sizes. Our results demonstrate that PCA and phasor analysis have the highest efficiencies, and can clearly separate these areas. Finally we will present data from cartilage tissue under static load in vitro. These preliminary results show that S-OCT can generate additional contrast in biological tissue in comparison to the pure intensity based images.