Incorporating measurement variability when comparing sets of high-resolution mass spectra

Published

May 5, 2022

Author(s)

Matthew Roberts, Arun Moorthy, Edward Sisco, Anthony J. Kearsley

Abstract

Mass spectra are an important signature by which compounds can be identifi ed. We recently formulated a mathematical approach for incorporating measurement variability when comparing sets of high-resolution mass spectra. Leveraging replicate mass spectra, we construct high-dimensional consensus mass spectra representing each of the compared analytes -- and compute the similarity between these data structures. In this paper, we present this approach and discuss its applications and limitations when trying to discriminate methamphetamine and phentermine using in-source collision induced dissociation mass spectra collected with direct analysis in real time mass spectrometry.

Citation

Analytica Chimica ACTA

Pub Type

Journals

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https://doi.org/10.1016/j.aca.2022.340247

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Keywords

sample discrimination, high-dimensional consensus (HDC) mass spectra, high-resolution mass spectrometry, mass spectral similarity, mass spectrometry, probability distributions.

Citation

Roberts, M. , Moorthy, A. , Sisco, E. and Kearsley, A. (2022), Incorporating measurement variability when comparing sets of high-resolution mass spectra, Analytica Chimica ACTA, [online], https://doi.org/10.1016/j.aca.2022.340247, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934820 (Accessed December 9, 2024)