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Themis: Batch Preprocessing for Ultrahigh-Resolution Mass Spectra of Complex Mixtures

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§ Molecular Analytical Sciences Centre for Doctoral Training, Department of Statistics, and §Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
Department of Economics & Business, Universitat Pompeu Fabra, Barcelona 08005, Spain
*(R.G.) E-mail [email protected]
*(M.P.B.) E-mail [email protected]; phone +44 (0) 24 76151013.
Cite this: Anal. Chem. 2017, 89, 21, 11383–11390
Publication Date (Web):October 6, 2017
Copyright © 2017 American Chemical Society

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    Abstract Image

    Fourier transform ion cyclotron resonance mass spectrometry affords the resolving power to determine an unprecedented number of components in complex mixtures, such as petroleum. The software tools required to also analyze these data struggle to keep pace with advancing instrument capabilities and increasing quantities of data, particularly in terms of combining information efficiently across multiple replicates. Improved confidence in data and the use of replicates is particularly important where strategic decisions will be based upon the analysis. We present a new algorithm named Themis, developed using R, to jointly preprocess replicate measurements of a sample with the aim of improving consistency as a preliminary step to assigning peaks to chemical compositions. The main features of the algorithm are quality control criteria to detect failed runs, ensuring comparable magnitudes across replicates, peak alignment, and the use of an adaptive mixture model-based strategy to help distinguish true peaks from noise. The algorithm outputs a list of peaks reliably observed across replicates and facilitates data handling by preprocessing all replicates in a single step. The processed data produced by our algorithm can subsequently be analyzed by use of relevant specialized software. While Themis has been demonstrated with petroleum as an example of a complex mixture, its basic framework will be useful for complex samples arising from a variety of other applications.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.7b02345.

    • Eight figures showing quantile–quantile plots before and after quantile normalization, automated detection of outliers, enlargement of low S/N region for NIST light sour crude oil, stacked bar plots of carbon number and DBE distributions, histograms showing mass errors associated with assignments, and MDS 2D plot before and after Themis (PDF)

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