Source identification of underground fuel spills by solid-phase microextraction/high-resolution gas chromatography/genetic algorithms

Solid-phase microextraction (SPME), capillary column gas chromatography, an d pattern recognition methods were used to develop a potential method for t yping jet fuels so a spill sample in the environment can be traced to its s ource. The test data consisted of gas chromatograms from 180 neat jet fuel samples representing common aviation turbine fuels found in the United Stat es (JP-4, Jet-A, JP-7, JPTS, JP-5, JP-8). SPME sampling of the fuel's heads pace afforded well-resolved reproducible profiles, which were standardized using special peak-matching software. The peak-matching procedure yielded 8 4 standardized retention time windows, though not all peaks were present in all gas chromatograms. A genetic algorithm (GA) was employed to identify f eatures (in the standardized chromatograms of the neat jet fuels) suitable for pattern recognition analysis. The GA selected peaks, whose two largest principal components showed clustering of the chromatograms on the basis of fuel type. The principal component analysis routine in the fitness functio n of the GA acted as an information filter, significantly reducing the size of the search space, since it restricted the search to feature subsets who se variance is primarily about differences between the various fuel types i n the training set. In addition, the GA focused on those classes and/or sam ples that were difficult to classify as it trained using a form of boosting . Samples that consistently classify correctly were not as heavily weighted as samples that were difficult to classify. Over time, the GA learned its optimal parameters in a manner similar to a perceptron. The pattern recogni tion GA integrated aspects of strong and weak learning to yield a "smart" o ne-pass procedure for feature selection.