Peak picking as a pre-processing technique for imaging time of flight secondary ion mass spectrometry

Authors

  • Jimmy D. Moore,

    1. Manchester Institute of Biotechnology, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
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  • Alex Henderson,

    Corresponding author
    • Manchester Institute of Biotechnology, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
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  • John S. Fletcher,

    1. Manchester Institute of Biotechnology, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
    2. Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
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  • Nicholas P. Lockyer,

    1. Manchester Institute of Biotechnology, School of Chemistry, The University of Manchester, Manchester, UK
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  • John C. Vickerman

    1. Manchester Institute of Biotechnology, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
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Alex Henderson, Manchester Institute of Biotechnology, School of Chemical Engineering and Analytical Science, The University of Manchester, 131 Princess St., Manchester, M1 7DN, UK.

E-mail: alex.henderson@manchester.ac.uk

Abstract

High surface sensitivity and lateral resolution imaging make time-of-flight secondary ion mass spectrometry (ToF-SIMS) a unique and powerful tool for biological analysis. However, with the leaps forward made in the capabilities of the ToF-SIMS instrumentation, the data being recorded from these instruments has dramatically increased. Unfortunately, with these large, often complex, datasets, a bottleneck appears in their processing and interpretation. Here, an application of peak picking is described and applied to ToF-SIMS images allowing for large compression of data, noise removal and improved contrast, while retaining a high percentage of the original signal. Peak picking is performed to locate peaks within ToF-SIMS data. By using this information, signal arising from the same distribution can be summed and overlapping signals separated. As a result, the data size and complexity can be dramatically reduced. This method also acts as an effective noise filter, discarding unwanted noise from the data set. Peak picking and separation are evaluated against the conventional methods of mass binning and manually selecting regions of a peak to image on a model data set. Copyright © 2012 John Wiley & Sons, Ltd.

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