MCMC-based peak template matching for GCxGC

Mingtian Ni, Qingping Tao, S. E. Reichenbach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Comprehensive two-dimensional gas chromatography (GCxGC) is a new technology for chemical separation. Peak template matching is a technique for automatic chemical identification in GCxGC analysis. Peak template matching can be formulated as a largest common point set problem (LCP). Minimizing Hausdorff distances is one of the many techniques proposed for solving the LCP problem. This paper proposes two novel strategies to search the transformation space based on Markov chain Monte Carlo (MCMC) methods. Experiments on seven real data sets indicate that the transformations found by the new algorithms are effective and searching with two Markov chains is much faster than searching with one Markov chain.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Pages514-517
Number of pages4
ISBN (Electronic)0780379977
DOIs
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2003-January

Other

OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
CountryUnited States
CitySt. Louis
Period9/28/0310/1/03

Keywords

  • Chemical analysis
  • Chemical engineering
  • Chemical technology
  • Computer science
  • Gas chromatography
  • Monte Carlo methods
  • Pixel
  • Shape
  • Space technology
  • Visualization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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  • Cite this

    Ni, M., Tao, Q., & Reichenbach, S. E. (2003). MCMC-based peak template matching for GCxGC. In Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003 (pp. 514-517). [1289460] (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2003-January). IEEE Computer Society. https://doi.org/10.1109/SSP.2003.1289460