Improved peak-parameter estimation in overlapping 1D NMR spectra through a joint-refinement extension of Global Spectral Deconvolution
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1SciY - Mestrelab, Santiago de Compostela, Spain; Centro de Investigación Mestrelab (CIM), Av. de Barcelona 7, 15706, Santiago de Compostela, Spain.
Background:
Overlapping one-dimensional nuclear magnetic resonance (NMR) signals can leave coupled errors when peak parameters are refined sequentially, as in Global Spectral Deconvolution (GSD). These errors affect the allocation of signal among neighbouring components and their use in quantitative analysis.
Results:
We introduce quantitative Global Spectral Deconvolution Plus (qGSD+), a post-GSD algorithm that jointly refines interacting generalized-Lorentzian components while preserving the detected peak list. Constrained variable projection and analytic derivatives support coordinated parameter updates, with overlapping windows extending the refinement to large interacting groups. For simulated generalized-Lorentzian signals with complete GSD peak lists, qGSD+ substantially reduces errors in peak centres, widths, heights, shapes and areas compared with GSD alone. Experimental spectra from diverse samples show improved fits and reduced structured residuals. An offline Monte Carlo study assesses parameter uncertainty and shows that interval reliability is sensitive to line-shape mismatch.
Significance:
qGSD+ improves the quantitative interpretation of crowded NMR spectra through more accurate estimates of the components already detected by GSD. The benefit depends on an adequate peak model.
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