Abstract
Low spectral resolution and extensive peak overlap are the common challenges that preclude quantitative analysis of nuclear magnetic resonance (NMR) data with the established peak integration method. While numerous model-based approaches overcome these obstacles and enable quantification, they intrinsically rely on rigid assumptions about functional forms for peaks, which are often insufficient to account for all unforeseen imperfections in experimental data. Indeed, even in spectra with well-separated peaks whose integration is possible, model-based methods often achieve suboptimal results, which in turn raises the question of their validity for more challenging datasets. We address this problem with a simple model adjustment procedure, which draws its inspiration directly from the peak integration approach that is almost invariant to lineshape deviations. Specifically, we assume that the number of mixture components along with their ideal spectral responses are known; we then aim to recover all useful signals left in the residual after model fitting and use it to adjust the intensity estimates of modelled peaks. We propose an alternative objective function, which we found particularly effective for correcting imperfect phasing of the data - a critical step in the processing pipeline. Application of our method to the analysis of experimental data shows the accuracy improvement of 20 %-40 % compared to the simple least-squares model fitting.
Highlights
Proposed 30 years ago (Miller and Greene, 1989; Bretthorst, 1990; Chylla and Markley, 1995), model-based approaches for quantitative nuclear magnetic resonance (NMR) data analysis are getting wider acceptance as an effective alternative to the established peak integration (Kriesten et al, 2008; Krishnamurthy et al, 2017; Kern et al, 2018)
Since the true ratios of peak intensities are known and fixed, by estimating them separately – as if the peaks belonged to several chemical species in unknown concentrations – we can unequivocally compare different qNMR methods in terms of their accuracy
We proposed an effective and computationally simple mechanism to improve the accuracy of model-based quantification in NMR data analysis
Summary
Proposed 30 years ago (Miller and Greene, 1989; Bretthorst, 1990; Chylla and Markley, 1995), model-based approaches for quantitative nuclear magnetic resonance (NMR) data analysis (qNMR) are getting wider acceptance as an effective alternative to the established peak integration (Kriesten et al, 2008; Krishnamurthy et al, 2017; Kern et al, 2018). Based on the idea that an experimental spectrum can be represented as a collection of parametric lineshapes, e.g. Lorentzians with certain positions, widths, and heights, they offer a principled mechanism to resolve overlapping peaks and are less susceptible to noise (Matviychuk et al, 2017). Quantum mechanical formulations minimize the number of free model parameters and are inherently invariant with respect to the spectrometer field strength (Kuprov et al, 2007; Tiainen et al, 2014; Dashti et al, 2017); they enable the analysis of highly complex low-resolution spectra acquired on medium-field benchtop instruments and are found to be successful in modern practical applications (Matviychuk et al, 2019).
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