MCMC-based peak template matching for GCxGC

Mingtian Ni, Qingping Tao, Stephen E Reichenbach

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

4 Citations (Scopus)

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 publicationIEEE Workshop on Statistical Signal Processing Proceedings
PublisherIEEE Computer Society
Pages514-517
Number of pages4
Volume2003-January
ISBN (Print)0780379977
DOIs
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Other

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

Fingerprint

Template matching
Template Matching
Markov Chain Monte Carlo
Markov processes
Markov chain
Gas Chromatography
Hausdorff Distance
Markov Chain Monte Carlo Methods
Point Sets
Gas chromatography
Monte Carlo methods
Experiment
Experiments
Strategy

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

Cite this

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

MCMC-based peak template matching for GCxGC. / Ni, Mingtian; Tao, Qingping; Reichenbach, Stephen E.

IEEE Workshop on Statistical Signal Processing Proceedings. Vol. 2003-January IEEE Computer Society, 2003. p. 514-517 1289460.

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

Ni, M, Tao, Q & Reichenbach, SE 2003, MCMC-based peak template matching for GCxGC. in IEEE Workshop on Statistical Signal Processing Proceedings. vol. 2003-January, 1289460, IEEE Computer Society, pp. 514-517, IEEE Workshop on Statistical Signal Processing, SSP 2003, St. Louis, United States, 9/28/03. https://doi.org/10.1109/SSP.2003.1289460
Ni M, Tao Q, Reichenbach SE. MCMC-based peak template matching for GCxGC. In IEEE Workshop on Statistical Signal Processing Proceedings. Vol. 2003-January. IEEE Computer Society. 2003. p. 514-517. 1289460 https://doi.org/10.1109/SSP.2003.1289460
Ni, Mingtian ; Tao, Qingping ; Reichenbach, Stephen E. / MCMC-based peak template matching for GCxGC. IEEE Workshop on Statistical Signal Processing Proceedings. Vol. 2003-January IEEE Computer Society, 2003. pp. 514-517
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