Improving the Performance of High-Precision qNMR Measurements by a Double Integration Procedure in Practical Cases

Abstract

Quantitative (1)H NMR (qNMR) is a widely applied technique for compound concentration and purity determinations. The NMR spectrum will display signals from all species in the sample, and this is generally a strength of the method. The key spectral determination is the full and accurate determination of one or more signal areas. Accurate peak integration can be an issue when unrelated peaks resonate in an important integral region. We describe a "hybrid" approach to signal integration that provides an accurate estimation of signal area, removing the component(s) that may arise from unrelated peaks. This is achieved by using the most accurate integration method for the region and removing unwanted contributions. The key to this performing well, and in almost all cases, is the use of areas from deconvolved peaks. We describe this process and show that it can be very successfully applied to cases where the highest precision is required and for more common cases of NMR-based quantitation.

Quantitative NMR Studies of Multiple Compound Mixtures

“…Line broadening theoretically does not affect the accuracy of quantification presuming proper integration is carried out. But, in practice, even for wellresolved resonances, numerical integration may still substantially underestimate the area because of truncation of the long tails of the Lorentzian lineshape [29]. For peaks with inconsistent shapes or with different peak widths, integration range should be adjusted on the basis of the lineshape and peak width in order to capture comparable percentage of the peak areas, otherwise, the relative concentration calculated from the integral ratio could be biased.…”

“…Two important aspects of spectral deconvolution [29][30][31][32][33] are estimation of the number of signals and proper model function to describe the lineshape. There is overfitting risk to allow a deconvolution algorithm to determine the number of peaks and carry out the fit because increasing the number of peaks will always appear to perfectly fit any spectrum.…”

“…There is overfitting risk to allow a deconvolution algorithm to determine the number of peaks and carry out the fit because increasing the number of peaks will always appear to perfectly fit any spectrum. In such a case, the experimental data may be mathematically well fit but are physically meaningless [29]. Global spectral deconvolution (GSD) is a fast and automatic deconvolution algorithm for the detection and quantification of all spectral peaks present in a spectrum.…”

A discussion about the potentials and pitfalls of quantitative nuclear magnetic resonance (qNMR) spectroscopy in food science and beyond

“…So if your S/N is less than 100 then you can also use the line fitting algorithm. We have also published one combination, one hybrid approach of integration and line fitting algorithm 8 , which is the best I would say if you want to be really, really precise and you want to calculate out the impurity signals in there. But okay, the simplest way of course is the integration, but sometimes you are forced to do some other evaluation and when your matrix is more complex, you have to use some kind of chemometric evaluation.…”

“…The participating experts were: ■ Torsten Schönberger (TS) who works for the Bundeskriminalamt (BKA) in Wiesbaden, Germany, at the Forensic Science Institute. His main interests with qNMR are not only the analysis of drugs including illicit drugs but also pharmaceuticals, doping, plastics, and explosives, among other forensic materials [5][6][7][8] . …”