Comprehensive two-dimensional gas chromatography (GCxGC) is a new technology for chemical separation. In GCxGC analysis, chemical identification is a critical task that can be performed by peak pattern matching. Peak pattern matching tries to identify the chemicals by establishing correspondences from the known peaks in a peak template to the unknown peaks in a target peak pattern. After the correspondences are established, information carried by known peaks are copied into the unknown peaks. The peaks in the target peak
pattern are then identified. Using peak locations as the matching features, peak patterns can be represented as point patterns and the peak pattern matching problem becomes a point pattern matching problem. In GCxGC, the chemical separation process imposes an ordering constraint on peak retention time (peak location). Based on the ordering constraint, the matching technique proposed in this paper forms directed edge patterns from point patterns and then matches the point patterns by matching the edge patterns. Preliminary
experiments on GCxGC peak patterns suggest that matching the edge patterns is much more efficient than matching the corresponding point patterns.
Pattern matching is one of the well-known pattern recognition techniques. When
using points as matching features, a pattern matching problem becomes a point
pattern matching problem. This paper proposes a novel point pattern matching
algorithm that searches transformation space by transformation sampling. The
algorithm defines a constraint set (a polygonal region in transformation space)
for each possible pairing of a template point and a target point. Under
constrained polynomial transformations that have no more than two parameters on
each coordinate, the constraint sets and the transformation space can be
represented as Cartesian products of 2D polygonal regions. The algorithm then
rasterizes the transformation space into a discrete canvas and calculates the
optimal matching at each sampled transformation efficiently by scan-converting
polygons. Preliminary experiments on randomly generated point patterns show
that the algorithm is effective and efficient. In addition, the running time of
the algorithm is stable with respect to missing points.
Comprehensive two-dimensional gas chromatography (GCxGC) is an emerging technology for chemical separation that provides an order-of-magnitude increase in separation capacity over traditional gas chromatography. GCxGC separates chemical species with two capillary columns interfaced by two-stage thermal desorption. Because GCxGC is comprehensive and has high separation capacity, it can perform multiple traditional analytical methods with a single analysis. GCxGC has great potential for a wide variety of environmental sensing applications, including detection of chemical warfare agents (CWA) and other harmful chemicals. This paper demonstrates separation of nerve agents sarin and soman from a matrix of gasoline and diesel fuel. Using a combination of an initial column separating on the basis of boiling point and a second column separating on the basis of polarity, GCxGC clearly separates the nerve agents from the thousands of other chemicals in the sample. The GCxGC data is visualized, processed, and analyzed as a two-dimensional digital image using a software system for GCxGC image processing developed at the University of Nebraska - Lincoln.