umu.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Multivariate curve resolution provides a high-throughput data processing pipeline for pyrolysis-gas chromatography/mass spectrometry
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. (Computational Life Science Cluster (CLiC))
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. (Computational Life Science Cluster (CLiC))
Vise andre og tillknytning
2012 (engelsk)Inngår i: Journal of Analytical and Applied Pyrolysis, ISSN 0165-2370, E-ISSN 1873-250X, Vol. 95, s. 95-100Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We present a data processing pipeline for Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) data that is suitable for high-throughput analysis of lignocellulosic samples. The aproach applies multivariate curve resolution by alternate regression (MCR-AR) and automated peak assignment. MCR-AR employs parallel processing of multiple chromatograms, as opposed to sequential processing used in prevailing applications. Parallel processing provides a global peak list that is consistent for all chromatograms, and therefore does not require tedious manual curation. We evaluated this approach on wood samples from aspen and Norway spruce, and found that parallel processing results in an overall higher precision of peak area from integrated peaks. To further increase the speed of data processing we evaluated automated peak assignment solely based on basepeak mass. This approach gave estimates of the proportion of lignin (as syringyl-, guaiacyl and p-hydroxyphenyl-type lignin) and carbohydrate polymers in the wood samples that were in high agreement with those where peak assignments were based on full spectra. This method establishes Py-GC/MS as a sensitive, robust and versatile high-throughput screening platform well suited to a non-specialist operator.

sted, utgiver, år, opplag, sider
2012. Vol. 95, s. 95-100
Emneord [en]
Py-GC/MS, High-throughput, Multivariate analysis, Data processing, Lignocellulose, Wood
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-55867DOI: 10.1016/j.jaap.2012.01.011ISI: 000303549400014OAI: oai:DiVA.org:umu-55867DiVA, id: diva2:531370
Tilgjengelig fra: 2012-06-07 Laget: 2012-06-07 Sist oppdatert: 2018-06-08bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Eliasson, MattiasTrygg, Johan

Søk i DiVA

Av forfatter/redaktør
Eliasson, MattiasTrygg, Johan
Av organisasjonen
I samme tidsskrift
Journal of Analytical and Applied Pyrolysis

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 247 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf