For the analysis of metabolite systems, nuclear magnetic resonance (NMR) spectroscopy has become an important quantitative monitoring technology. Automated quantitation methods are highly desired and mainly characterized by the tasks of model selection and parameter approximation. This paper proposes a promising automated two stage approach in the frequency-domain, in which signaling peaks are first identified and filtered from noise based on curvature properties of the spectrum, and then proportionally approximated based on the analytical solution of a Lorentz-function. Remarkably, in opposition to common least-squares approaches, the proposed approximation scheme does not rely on partial derivatives, and furthermore, the runtime is independent to the number of spectral datapoints. Simulations provide promising empirical evidence for successful peak selection and parameter approximation, with the results for the latter highly outperforming the Levenberg-Marquardt algorithm in terms of error minimization and robustness.