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 Citations (Scopus)

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
StatePublished - 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

Fingerprint

Template matching
Template Matching
Statistics
Pattern matching
Gas chromatography
Gas Chromatography
Pattern Matching
Matching Problem
Pruning
Search Space
Experiments
Experiment

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

A statistics-guided progressive RAST algorithm for peak template matching in GCxGC. / Ni, Mingtian; Reichenbach, S. E.

Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003. IEEE Computer Society, 2003. p. 383-386 1289425 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2003-January).

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

Ni, M & Reichenbach, SE 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., 1289425, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2003-January, IEEE Computer Society, pp. 383-386, IEEE Workshop on Statistical Signal Processing, SSP 2003, St. Louis, United States, 9/28/03. https://doi.org/10.1109/SSP.2003.1289425
Ni M, Reichenbach SE. 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. IEEE Computer Society. 2003. p. 383-386. 1289425. (IEEE Workshop on Statistical Signal Processing Proceedings). https://doi.org/10.1109/SSP.2003.1289425
Ni, Mingtian ; Reichenbach, S. E. / A statistics-guided progressive RAST algorithm for peak template matching in GCxGC. Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003. IEEE Computer Society, 2003. pp. 383-386 (IEEE Workshop on Statistical Signal Processing Proceedings).
@inproceedings{b6c3b622969a4f66a93a7ab8a1865d00,
title = "A statistics-guided progressive RAST algorithm for peak template matching in GCxGC",
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.",
keywords = "Chemical analysis, Chemical engineering, Chemical processes, Chemical technology, Computer science, Data visualization, Gas chromatography, Image analysis, Pattern matching, Space technology",
author = "Mingtian Ni and Reichenbach, {S. E.}",
year = "2003",
month = "1",
day = "1",
doi = "10.1109/SSP.2003.1289425",
language = "English (US)",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
pages = "383--386",
booktitle = "Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003",

}

TY - GEN

T1 - A statistics-guided progressive RAST algorithm for peak template matching in GCxGC

AU - Ni, Mingtian

AU - Reichenbach, S. E.

PY - 2003/1/1

Y1 - 2003/1/1

N2 - 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.

AB - 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.

KW - Chemical analysis

KW - Chemical engineering

KW - Chemical processes

KW - Chemical technology

KW - Computer science

KW - Data visualization

KW - Gas chromatography

KW - Image analysis

KW - Pattern matching

KW - Space technology

UR - http://www.scopus.com/inward/record.url?scp=84948685209&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84948685209&partnerID=8YFLogxK

U2 - 10.1109/SSP.2003.1289425

DO - 10.1109/SSP.2003.1289425

M3 - Conference contribution

AN - SCOPUS:84948685209

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 383

EP - 386

BT - Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003

PB - IEEE Computer Society

ER -