A statistics-guided progressive RAST algorithm for peak template matching in GCxGC

Mingtian Ni, S. E. Reichenbach

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

4 Scopus citations

Abstract

Comprehensive two-dimensional gas chromatography (GCxGC) is an emerging technology for chemical separation. Chemical identification is one of the critical tasks in GCxGC analysis. Peak template matching is a technique for automatic chemical identification. Peak template matching can be formulated as a point pattern matching problem. This paper proposes a progressive RAST algorithm to solve the problem. Search space pruning techniques based on peak location distributions and transformation distributions are also investigated for guided search. Experiments on seven real data sets indicate that the new techniques are effective.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Pages383-386
Number of pages4
ISBN (Electronic)0780379977
DOIs
Publication statusPublished - Jan 1 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

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Keywords

  • Chemical analysis
  • Chemical engineering
  • Chemical processes
  • Chemical technology
  • Computer science
  • Data visualization
  • Gas chromatography
  • Image analysis
  • Pattern matching
  • Space technology

ASJC Scopus subject areas

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

Cite this

Ni, M., & Reichenbach, S. E. (2003). A statistics-guided progressive RAST algorithm for peak template matching in GCxGC. In Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003 (pp. 383-386). [1289425] (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2003-January). IEEE Computer Society. https://doi.org/10.1109/SSP.2003.1289425