Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics

Anal Chem. 2007 Jan 15;79(2):464-76. doi: 10.1021/ac061443+.


Metabolomics seeks to measure potentially all the metabolites in a biological sample, and consequently, we need to develop and optimize methods to increase significantly the number of metabolites we can detect. We extended the closed-loop (iterative, automated) optimization system that we had previously developed for one-dimensional GC-TOF-MS (O'Hagan, S.; Dunn, W. B.; Brown, M.; Knowles, J. D.; Kell, D. B. Anal. Chem. 2005, 77, 290-303) to comprehensive two-dimensional (GCxGC) chromatography. The heuristic approach used was a multiobjective version of the efficient global optimization algorithm. In just 300 automated runs, we improved the number of metabolites observable relative to those in 1D GC by some 3-fold. The optimized conditions allowed for the detection of over 4000 raw peaks, of which some 1800 were considered to be real metabolite peaks and not impurities or peaks with a signal/noise ratio of less than 5. A variety of computational methods served to explain the basis for the improvement. This closed-loop optimization strategy is a generic and powerful approach for the optimization of any analytical instrumentation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers / blood*
  • Biomarkers / metabolism
  • Gas Chromatography-Mass Spectrometry / methods*
  • Gas Chromatography-Mass Spectrometry / standards*
  • Humans


  • Biomarkers