Solving GC-MS problems with PARAFAC2

José Manuel Amigo, Thomas Skov, Rasmus Bro, Jordi Coello, Santiago Maspoch

Research output: Contribution to journalArticleResearchpeer-review

104 Citations (Scopus)


Gas chromatography combined with mass spectrometry (GC-MS) is an important technique for identification and quantification of analytes in multifactor systems. Nevertheless, the experimental sources of variability related to GC-MS (e.g., column and flow-meter ageing, changes in certain characteristics or properties of the stationary phase, and changes in temperature, experimental conditions or preparation of standards or chemicals) may cause variations (e.g., elution time, baseline drifts, unexpected overlapping of peaks, or non-Gaussian peaks). Several approaches have been proposed to handle these problems, with the standardization of peak areas using internal standards being one of the most efficient techniques. However, such a solution is not sufficiently versatile when deviations from the ideal are more pronounced. Since a mass spectrum can be obtained at each elution time during chromatographic separation, GC-MS data of several samples can be considered a three-way structure. PARAllel FACtor analysis 2 (PARAFAC2) is a model capable of handling three-way data and, unlike the PARAFAC model, does not assume that the elution profiles of each factor are invariant across samples. This, coupled with its uniqueness property, allows PARAFAC2 to solve several problems derived from experimental conditions in GC-MS datasets. In this article, we aim to show the potential of PARAFAC2 for solving common GC-MS problems, using GC-MS data from wine samples to illustrate the solutions. © 2008 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)714-725
JournalTrAC - Trends in Analytical Chemistry
Issue number8
Publication statusPublished - 1 Sep 2008


  • Baseline drift
  • Elution-time shift
  • GC-MS
  • Hyphenated chromatography
  • Low-intensity peak
  • Overlapping peaks
  • Peak-shape change
  • Three-way data
  • Wine sample


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