Mingtian Ni

Image background removal in comprehensive two-dimensional gas chromatography

This paper describes a new technique for removing the background level from digital images produced in comprehensive two-dimensional gas chromatography (GCxGC). Background removal is an important first step in the larger problem of quantitative analysis. The approach estimates the background level across the chromatographic image based on structural and statistical properties of GCxGC data. Then, the background level is subtracted from the image, producing a chromatogram in which the peaks rise above a near-zero mean background. After the background level is removed, further analysis is required to determine the quantitative relationship between the peaks and chemicals in the sample. The algorithm is demonstrated experimentally to be effective at determining and removing the background level from GCxGC images. The algorithm has several parametric controls and is incorporated into an interactive program with graphical interface for rapid and accurate detection of GCxGC peaks.

Chemical warfare agent detection in complex environments with comprehensive two-dimensional gas chromatography

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.

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

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.

Pattern Matching by Scan-Converting Polygons

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.

MCMC-based peak template matching for GCxGC

Comprehensive two-dimensional gas chromatography (GCxGC) is a new technology for chemical separation. Peak template matching is a technique for automatic chemical identification in GCxGC analysis. Peak template matching can be formulated as a largest common point set problem (LCP). Minimizing Hausdorff distances is one of the many techniques proposed for solving the LCP problem. This paper proposes two novel strategies to search the transformation space based on Markov chain Monte Carlo (MCMC) methods. Experiments on seven real data sets indicate that the transformations found by the new algorithms are effective and searching with two Markov chains is much faster than searching with one Markov chain.

Using edge pattern matching for automatic chemical identification in GCXGC

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 by sequential subdivision of transformation space

Pattern matching is a well-known pattern recognition technique. This paper proposes a novel pattern matching algorithm that searches transformation space by sequential subdivision. The algorithm subdivides the transformation space in depth-first manner by conducting Boolean operations on the constraint sets that are defined by pairs of template points and target points. For constrained polynomial transformations that have no more than two parameters on each coordinate, a constraint set can be represented as a 2D polygon or a Cartesian product of 2D polygons. Then, the Boolean operations can be computed through generic polygon clipping algorithms. Preliminary experiments on randomly generated point patterns show that the algorithm is effective and efficient under practical conditions.