Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Extracting homologous series from mass spectrometry data by projection on predefined vectors
LuleƄ University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-6216-6132
University of Bergen.
University of Bergen.
Statoil Research Centre, Trondheim.
2012 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 114, 36-43 p.Article in journal (Refereed) Published
Abstract [en]

Multivariate statistical methods, such as Principal Component Analysis (PCA), have been used extensively over the past decades as tools for extracting significant information from complex data sets. As such they are very powerful and in combination with an understanding of underlying chemical principles, they have enabled researchers to develop useful models. A drawback with the methods is that they do not have the ability to incorporate any physical / chemical model of the system being studied during the statistical analysis. In this paper we present a method that can be used as a complement to traditional chemometric tools in finding patterns in mass spectrometry data. The method uses a pre-defined set of equally spaced sequences that are assumed to be present in the data. Allowing for some uncertainty in the peak locations due to the uncertainties for the measurement instrumentation, the measured spectra are then projected onto this set. It is shown that the resulting scores can be used to identify homologous series in measured mass spectra that differ significantly between different measured samples. As opposed to PCA, the loading vectors, in this case the pre-defined homologous series, are readily interpretable.

Place, publisher, year, edition, pages
2012. Vol. 114, 36-43 p.
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:ltu:diva-3144DOI: 10.1016/j.chemolab.2012.02.007Local ID: 0ee4112b-a0b8-466c-b229-d82ea3d2a4e0OAI: oai:DiVA.org:ltu-3144DiVA: diva2:976000
Note
Validerad; 2012; 20120320 (ysko)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

Open Access in DiVA

fulltext(663 kB)16 downloads
File information
File name FULLTEXT01.pdfFile size 663 kBChecksum SHA-512
09e111ab64a0ca8de05c2d7feced22be1385a323f2090f69ba5f6cdbd9be7c2815bf1ac55953936e2b840d28dbeb9b4f76c4ef9b3a6cfb5e26b284619a34e21a
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Carlson, Johan E.
By organisation
Signals and Systems
In the same journal
Chemometrics and Intelligent Laboratory Systems
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 16 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 27 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf