• norsk
    • English
  • norsk 
    • norsk
    • English
  • Logg inn
Vis innførsel 
  •   Hjem
  • Faculty of Mathematics and Natural Sciences
  • Department of Chemistry
  • Department of Chemistry
  • Vis innførsel
  •   Hjem
  • Faculty of Mathematics and Natural Sciences
  • Department of Chemistry
  • Department of Chemistry
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Toxicological evaluation of complex mixtures by pattern recognition: Correlating chemical fingerprints to mutagenicity

Ingvar, Eide; Neverdal, Gunhild; Thorvaldsen, Bodil; Grung, Bjørn; Kvalheim, Olav Martin
Journal article
Published version
Thumbnail
Åpne
PDF (159.0Kb)
Permanent lenke
https://hdl.handle.net/1956/12168
Utgivelsesdato
2002-12
Metadata
Vis full innførsel
Samlinger
  • Department of Chemistry [267]
Sammendrag
We describe the use of pattern recognition and multivariate regression in the assessment of complex mixtures by correlating chemical fingerprints to the mutagenicity of the mixtures. Mixtures were 20 organic extracts of exhaust particles, each containing 102-170 individual compounds such as polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, oxy-PAHs, and saturated hydrocarbons. Mixtures were characterized by full-scan GC-MS (gas chromatography-mass spectrometry). Data were resolved into peaks and spectra for individual compounds by an automated curve resolution procedure. Resolved chromatograms were integrated, resulting in a predictor matrix that was used as input to a principal component analysis to evaluate similarities between mixtures (i.e., classification). Furthermore, partial least-squares projections to latent structures were used to correlate the GC-MS data to mutagenicity, as measured in the Ames Salmonella assay (i.e., calibration). The best model (high r2 and Q2) identifies the variables that co-vary with the observed mutagenicity. These variables may subsequently be identified in more detail. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts. We emphasize that both chemical fingerprints as well as detailed data on composition can be used in pattern recognition.
Utgiver
The National Institute of Environmental Health Sciences

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit
 

 

Bla i

Hele arkivetDelarkiv og samlingerUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifterDenne samlingenUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifter

Min side

Logg inn

Statistikk

Besøksstatistikk

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit