MRS quantification tools such as LCModel require the use of prior knowledge and can be time-consuming. Therefore, we propose an untargeted metabolomics approach to in vivo 1H-MRS using pattern recognition and machine learning to analyze spectral features. The ability of our approach to measure changes in brain glucose while blood glucose levels were increased was studied using high quality spectra and reliable data acquisition methods. Results showed similar time-course glucose signals and sensitivity to changes in glucose concentrations for both LCModel and our pattern recognition analysis. Thus, demonstrating that untargeted metabolomics techniques can be used for in vivo MRS quantification.