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A scoring metric for multivariate data for reproducibility analysis using chemometric methods.

David A Sheen1, Werickson Fortunato de Carvalho Rocha2, Katrice A Lippa1

  • 1Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

Chemometrics and Intelligent Laboratory Systems : an International Journal Sponsored by the Chemometrics Society
|July 12, 2017
PubMed
Summary
This summary is machine-generated.

Binned spectral data from nuclear magnetic resonance (NMR) spectroscopy offers a faster quality control method for metabolomics interlaboratory comparisons. This approach aids in assessing laboratory performance and identifying process issues without full metabolite quantification.

Keywords:
Nuclear magnetic resonanceinterlaboratory comparisonmetabolomics

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Spectroscopy

Background:

  • Interlaboratory comparison testing is crucial for quality control in emerging fields like metabolomics.
  • Typically, metabolite concentrations derived from spectral data are compared between laboratories.

Purpose of the Study:

  • To demonstrate the utility of binned spectral data for interlaboratory comparison and composition analysis in metabolomics.
  • To evaluate binned nuclear magnetic resonance (NMR) spectra as a direct data quality assessment tool.

Main Methods:

  • Utilized binned NMR spectra from synthetic and biological metabolite mixtures.
  • Applied cluster analysis with various distance and entropy metrics for spectral comparison.
  • Developed a laboratory-level scoring metric based on individual measurement clustering.

Main Results:

  • Binned spectra preserve information on trace constituents and process difficulties.
  • Cluster analysis effectively compared spectral data from different laboratories.
  • A novel scoring metric was developed to assess individual laboratory performance.

Conclusions:

  • Binned NMR spectra provide an efficient alternative for quality control in metabolomics.
  • This method enhances interlaboratory comparisons by directly analyzing spectral data.
  • The developed scoring metric offers a robust evaluation of laboratory performance.