Spectral variation contributed from the absorbance of unwanted correlated signals, such as blood at variable pathlengths between an in vivo catheter optic probe and a coronary vessel wall is an obstacle in the detection of vulnerable plaque. Preprocessing methods are described to reduce the impact of blood upon the spectral signal, based on the principles of Orthogonal Subspace Projection (OSP) and Generalized Least Square (GLS). The multivariate discrimination models used on the processed spectral information reduce the number of independent factors that include contributions from blood. The disclosed chemometric processing including preprocessing methods provide for in vivo spectral detection of medical analytes within the human body and in particular within the coronary vessel wall. A demonstration of how the preprocessing methods impact a discrimination modeling technique is provided, how the blood filters were developed and optimized, and finally how the OSP and GLS blood filters correct the spectral signal and improve the discrimination results of the models.