Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification
This work addresses chemical quantification in NMR spectroscopy, offering incremental improvements in accuracy for applications in chemistry and materials science.
The authors tackled the problem of chemical quantification from NMR spectroscopy signals by proposing a probabilistic generative model, which demonstrated improved robustness to low signal-to-noise ratios and overlapping peaks, achieving 1% sensitivity compared to 5% with conventional methods.
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic properties of atomic nuclei to discover the structure, reaction state and chemical environment of molecules. We propose a probabilistic generative model and inference procedures for NMR spectroscopy. Specifically, we use a weighted sum of trigonometric functions undergoing exponential decay to model free induction decay (FID) signals. We discuss the challenges in estimating the components of this general model -- amplitudes, phase shifts, frequencies, decay rates, and noise variances -- and offer practical solutions. We compare with conventional Fourier transform spectroscopy for estimating the relative concentrations of chemicals in a mixture, using synthetic and experimentally acquired FID signals. We find the proposed model is particularly robust to low signal to noise ratios (SNR), and overlapping peaks in the Fourier transform of the FID, enabling accurate predictions (e.g., 1% sensitivity at low SNR) which are not possible with conventional spectroscopy (5% sensitivity).