Using machine learning to improve the hard modeling of NMR time series.

Modeling time series of NMR spectra is a useful method to accurately extract information such as temporal concentration profiles from complex processes, e.g. reactions.

Model-based signal tracking in the quantitative analysis of time series of NMR spectra.

Hard modeling of NMR spectra by Gauss-Lorentz peak models is an effective way for dimensionality reduction. In this manner high-dimensional measured data are reduced to low-dimensional information as peak centers, amplitudes or peak widths. For time series of spectra these parameters can be assumed to be smooth functions in time.