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Metabolomic analysis using optimized NMR and statistical methods. | LitMetric

AI Article Synopsis

  • NMR-based metabolomics relies on automated methods and the accuracy of its data is heavily affected by how consistently data is collected and processed.
  • Key factors for effective analysis include properly suppressing water signals and maintaining consistent spectral phasing and baseline traces across samples.
  • The study evaluates common solvent suppression techniques and introduces modifications to improve data quality, concluding that combining water presaturation with excitation sculpting results in the most reliable NMR spectra for analyzing biofluids.

Article Abstract

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.

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Source
http://dx.doi.org/10.1016/j.ab.2006.04.014DOI Listing

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