The perfect 1D NMR signal is a sum of exponentially decaying oscillations, and its spectrum is a sum of Lorentzian "peaks" whose positions in the frequency domain and integrals are related to the molecular structure. To analyze the kinetics of a process monitored using serial NMR measurement, one has to find peaks in each of the collected spectra and integrate them. This procedure, known as peak-picking, is more complex than it may seem as the 1 H NMR spectra, although sensitive and fast to measure, often suffer from severe peak overlap. The peak-picking programs must employ the most advanced methods, such as machine-learning, to decompose an NMR spectrum into a set of peaks and determine their parameters. Alternative approaches, such as CRAFT, can determine the parameters directly from a time-domain signal. Notably, these approaches are only effective if the right model of a peak shape is assumed (e.g., Lorentzian or Gaussian function). However, various measurement imperfections can distort the shape of the spectral lines and make them far from ideal.