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Rhapso : automatic stitching of mass segments from fourier transform ion cyclotron resonance mass spectra

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Gavard, Remy, Palacio Lozano, Diana Catalina, Guzman, Alexander, Rossell, David, Spencer, Simon E. F. and Barrow, Mark P. (2019) Rhapso : automatic stitching of mass segments from fourier transform ion cyclotron resonance mass spectra. Analytical Chemistry, 91 (23). pp. 15130-15137. doi:10.1021/acs.analchem.9b03846

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Official URL: http://doi.org/10.1021/acs.analchem.9b03846

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Abstract

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) provides the resolution and mass accuracy needed to analyze complex mixtures such as crude oil. When mixtures contain many different components, a competitive effect within the ICR cell takes place that hampers the detection of a potentially large fraction of the components. Recently, a new data collection technique, which consists of acquiring several spectra of small mass ranges and assembling a complete spectrum afterward, enabled the observation of a record number of peaks with greater accuracy compared to broadband methods. There is a need for statistical methods to combine and preprocess segmented acquisition data. A particular challenge of quadrupole isolation is that near the window edges there is a drop in intensity, hampering the stitching of consecutive windows. We developed an algorithm called Rhapso to stitch peak lists corresponding to multiple different m/z regions from crude oil samples. Rhapso corrects potential edge effects to enable the use of smaller windows and reduce the required overlap between windows, corrects mass shifts between windows, and generates a single peak list for the full spectrum. Relative to a stitching performed manually, Rhapso increased the data processing speed and avoided potential human errors, simplifying the subsequent chemical analysis of the sample. Relative to a broadband spectrum, the stitched output showed an over 2-fold increase in assigned peaks and reduced mass error by a factor of 2. Rhapso is expected to enable routine use of this spectral stitching method for ultracomplex samples, giving a more detailed characterization of existing samples and enabling the characterization of samples that were previously too complex to analyze.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
Q Science > QC Physics
Q Science > QD Chemistry
Divisions: Faculty of Science > Chemistry
Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Fourier transformations , Fourier transform spectroscopy , Ion cyclotron resonance spectrometry, Mass spectrometry
Journal or Publication Title: Analytical Chemistry
Publisher: American Chemical Society
ISSN: 0003-2700
Official Date: 3 December 2019
Dates:
DateEvent
3 December 2019Published
30 October 2019Available
30 October 2019Accepted
Volume: 91
Number: 23
Page Range: pp. 15130-15137
DOI: 10.1021/acs.analchem.9b03846
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: “This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [insert ACS Articles on Request author-directed link to Published Work, see http://pubs.acs.org/page/policy/articlesonrequest/index.html].”
Access rights to Published version: Restricted or Subscription Access
Grant number: EP/L015307/1
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/L015307/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
75910721Newton Fundhttp://dx.doi.org/10.13039/100010897
5211770Universidad Industrial de Santanderhttp://dx.doi.org/10.13039/501100009087
5211770Instituto Colombiano de Petróleoshttp://viaf.org/viaf/140698210
P44842-039-201Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)http://dx.doi.org/10.13039/100007637
RYC-2015-1854Ministeri d'Economia i Competitivitat d'Espanyahttp://viaf.org/viaf/255657103
Ayudas investigación cientifica BigDataFundación BBVAUNSPECIFIED
Programa Estatal I+D+iEspaña‏. Gobiernohttp://viaf.org/viaf/141321160
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