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Data Analysis Methods for Synthetic Polymer Mass Spectrometry: Autocorrelation

Published

Author(s)

William E. Wallace, Charles M. Guttman

Abstract

Autocorrelation is shown to be useful in describing the periodic patterns found in high-resolution mass spectra of synthetic polymers. Examples of this usefulness are described for a simple linear homopolymer to demonstrate the method fundamentals, a condensation polymer to demonstrate its utility in understanding complex spectra with multiple repeating patterns on different mass scales, and a condensation copolymer to demonstrate how it can elegantly and efficiently reveal unexpected phenomena. It is shown that using autocorrelation to determine where the signal devolves into noise can be useful in determining molecular mass distributions of synthetic polymers, a primary focus of the NIST synthetic polymer mass spectrometry effort. The appendices describe some of the effects of transformation from time to mass space when time?of?flight mass separation is used, as well as the effects of non-trivial baselines on the autocorrelation function.
Citation
Journal of Research (NIST JRES) -
Volume
107 No. 1

Keywords

autocorrelation, correlation function, data analysis methods, informatics, mass spectrometry, polymer, time series

Citation

Wallace, W. and Guttman, C. (2002), Data Analysis Methods for Synthetic Polymer Mass Spectrometry: Autocorrelation, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=851939 (Accessed July 20, 2024)

Issues

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Created February 1, 2002, Updated February 19, 2017