Extended automated quantification algorithm (AQuA) for targeted H NMR metabolomics of highly complex samples: application to plant root exudates.

Introduction: The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.

Objectives: To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation.

Complete H and C NMR spectral assignment of d-glucofuranose.

Although d-glucose is the most common sugar in nature, only a few NMR studies have focused on its minor furanose forms, and they have been limited to the anomeric position. Here, complete H and C NMR spectral analysis of α- and β-d-glucofuranose was performed, including signal assignment, chemical shifts, and coupling constants. Selective and non-selective 1D and 2D NMR experiments were used for the analysis, complemented by spin simulations and iterative spectral analysis.

Band-selective NMR experiments for suppression of unwanted signals in complex mixtures.

Band-selective NMR experiments are presented that allow selective suppression of unwanted signals (SUN) from the spectra of complex metabolite mixtures. As a result, spectral overlap and dynamic range problems are substantially reduced and low-intensity signals normally covered by dominant signals can be observed. The usefulness of the experiments is exemplified with selective suppression of sugar signals from the NMR spectra of fruit juice and a plant sample.