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PUBLICATIONS

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

Published

Author(s)

David A. Sheen, Werickson Fortunato de Carvalho Rocha, Katrice A. Lippa, Dan Bearden

Abstract

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Use of the binned spectra has some advantages, including preserving information about trace constituents and enabling identification of process difficulties. In this paper, we demonstrate the use of binned NMR spectra to conduct a detailed interlaboratory comparison and composition analysis. Spectra of synthetic and biologically- obtained metabolite mixtures, taken from a previous interlaboratory study, are compared with cluster analysis using a variety of distance and entropy metrics. The individual measurements are then evaluated based on where they fall within their clusters, and a laboratory-level scoring metric is developed, which provides an assessment of each laboratory’s individual performance.
Citation
Chemometrics and Intelligent Laboratory Systems
Volume
162
Pub Type
Journals

Download Paper

https://doi.org/10.1016/j.chemolab.2016.12.010

Keywords

Nuclear magnetic resonance, metabolomics, interlaboratory comparison
Analytical chemistry, Chemical engineering and processing, Uncertainty quantification, Metrology and Spectroscopy

Citation

Sheen, D. , Fortunato, W. , Lippa, K. and Bearden, D. (2016), A scoring metric for multivariate data for reproducibility analysis using chemometric methods, Chemometrics and Intelligent Laboratory Systems, [online], https://doi.org/10.1016/j.chemolab.2016.12.010 (Accessed April 19, 2024)

Created December 23, 2016, Updated November 10, 2018