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Lookup NU author(s): Professor Mark Walker
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Optimizing NMIR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the total biochemical information obtainable from H-1 NMR data, which includes concentration and molecular dynamics information. Here we have applied two different types of mathematical transformation (calculation of the first derivative of the NMIR spectrum and Gaussian shaping of the free-induction decay) to attenuate broad spectral features from macromolecules and enhance the signals of small molecules. By application of chemometric methods such as principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (O-PLS-DA) and statistical spectroscopic tools such as statistical total correlation spectroscopy (STOCSY), we show that these methods successfully identify the same potential biomarkers as spin-echo H-1 NMR spectra in which broad lines are suppressed via T-2 relaxation editing. Finally, we applied these methods for identification of the metabolic phenotype of patients with type 2 diabetes. This "virtual" relaxation-edited spectroscopy (RESY) approach can be particularly useful for high-throughput screening of complex mixtures such as human plasma and may be useful for extraction of latent biochemical information from legacy or archived NMR data sets for which only standard 1D data sets exist.
Author(s): Maher AD, Crockford D, Toft H, Malmodin D, Faber JH, McCarthy MI, Barrett A, Allen M, Walker M, Holmes E, Lindon JC, Nicholson JK
Publication type: Article
Publication status: Published
Journal: Analytical Chemistry
ISSN (print): 0003-2700
ISSN (electronic): 1520-6882
Publisher: American Chemical Society
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