Title :
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
fDate :
28 Sept.-1 Oct. 2003
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;
Conference_Titel :
Statistical Signal Processing, 2003 IEEE Workshop on
Print_ISBN :
0-7803-7997-7
DOI :
10.1109/SSP.2003.1289425
