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. We suggest to model these time-dependent parameter functions by cubic spline functions, which makes a stable quantitative analysis of NMR series possible even for crossing, highly overlapping peaks. Applications are presented for the batch distillation of methanol and diethylamine, and the reaction of acetic anhydride with 2-propanol.

A comparison of non-uniform sampling and model-based analysis of NMR spectra for reaction monitoring.

Nuclear magnetic resonance (NMR) spectroscopy is widely used for applications in the field of reaction and process monitoring. When complex reaction mixtures are studied, NMR spectra often suffer from low resolution and overlapping peaks, which places high demands on the method used to acquire or to analyse the NMR spectra. This work presents two NMR methods that help overcome these challenges: 2D non-uniform sampling (NUS) and a recently proposed model-based fitting approach for the analysis of 1D NMR spectra. We use the reaction of glycerol with acetic acid as it produces five reaction products that are all chemically similar and, hence, challenging to distinguish. The reaction was measured on a high-field 400 MHz NMR spectrometer with a 2D NUS-heteronuclear single quantum coherence (HSQC) and a conventional 1D 1 H NMR sequence. We show that comparable results can be obtained using both 2D and 1D methods, if the 2D volume integrals of the 2D NUS-HSQC NMR spectra are calibrated. Further, we monitor the same reaction on a low-field 43 MHz benchtop NMR spectrometer and analyse the acquired 1D 1 H NMR spectra with the model-based approach and with partial least-squares regression (PLS-R), both trained using a single, calibrated data set. Both methods achieve results that are in good quantitative agreement with the high-field data. However, the model-based method was found to be less sensitive to the training data set used than PLS-R and, hence, was more robust when the reaction conditions differed from that of the training data.

Prediction of flow effects in quantitative NMR measurements.

A method for the prediction of the magnetization in flow NMR experiments is presented, which can be applied to mixtures. It enables a quantitative evaluation of NMR spectra of flowing liquid samples even in cases in which the magnetization is limited by the flow. A transport model of the nuclei's magnetization, which is based on the Bloch-equations, is introduced into a computational fluid dynamics (CFD) code. This code predicts the velocity field and relative magnetization of different nuclei for any chosen flow cell geometry, fluid and flow rate. The prediction of relative magnetization is used to correct the observed reduction of signal intensity caused by incomplete premagnetization in fast flowing liquids. By means of the model, quantitative NMR measurements at high flow rates are possible. The method is predictive and enables calculating correction factors for any flow cell design and operating condition based on simple static T1 time measurements. This makes time-consuming calibration measurements for assessing the influence of flow effects obsolete, which otherwise would have to be carried out for each studied condition. The new method is especially interesting for flow measurements with compact medium field NMR spectrometers, which have small premagnetization volumes. In the present work, experiments with three different flow cells in a medium field NMR spectrometer were carried out. Acetonitrile, water, and mixtures of these components were used as model fluids. The experimental results for the magnetization were compared to the predictions from the CFD model and good agreement was observed.