Magnetic resonance spectroscopic imaging (MRSI) is utilized clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability due to partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity and measurement noise. This study investigates spectral separation as a novel quantification tool, addressing these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. Present results on 20 clinical cases of brain tumors show reduced cross-subject variability. This reduced variability leads to improved discrimination between high and low-grade gliomas, confirming the physiological relevance of the extracted spectra. Further validation on phantom data demonstrates the accuracy of the estimated abundances. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool.