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

Author

Ni, Mingtian ; Reichenbach, Stephen E.

Author_Institution

Dept. of Comput. Sci. & Eng., Nebraska Univ., Lincoln, NE, USA

fYear

2003

fDate

28 Sept.-1 Oct. 2003

Firstpage

383

Lastpage

386

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 engineering computing; chromatography; pattern matching; statistical analysis; automatic chemical identification; chemical separation; comprehensive two-dimensional gas chromatography; peak location distributions; peak template matching; point pattern matching problem; search space pruning techniques; statistics-guided progressive RAST algorithm; transformation distributions; Chemical analysis; Chemical engineering; Chemical processes; Chemical technology; Computer science; Data visualization; Gas chromatography; Image analysis; Pattern matching; Space technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2003 IEEE Workshop on

Print_ISBN

0-7803-7997-7

Type

conf

DOI

10.1109/SSP.2003.1289425

Filename

1289425