Source Identification of Underground Fuel Spills by Solid-Phase Microextraction/High-Resolution Gas Chromatography/Genetic Algorithms

Abstract

Solid-phase microextraction (SPME), capillary column gas chromatography, and pattern recognition methods were used to develop a potential method for typing jet fuels so a spill sample in the environment can be traced to its source. The test data consisted of gas chromatograms from 180 neat jet fuel samples representing common aviation turbine fuels found in the United States (JP-4, Jet-A, JP-7, JPTS, JP-5, JP-8). SPME sampling of the fuel's headspace afforded well-resolved reproducible profiles, which were standardized using special peak-matching software. The peak-matching procedure yielded 84 standardized retention time windows, though not all peaks were present in all gas chromatograms. A genetic algorithm (GA) was employed to identify features (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 function of the GA acted as an information filter, significantly reducing the size of the search space, since it restricted the search to feature subsets whose variance is primarily about differences between the various fuel types in the training set. In addition, the GA focused on those classes and/or samples 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 recognition GA integrated aspects of strong and weak learning to yield a "smart" one-pass procedure for feature selection.

Selecting wavelet transform scales for multivariate classification

“…Two datasets are used to demonstrate the application of the wavelet transform to classification problems: a set of gas chromatograms of jet fuels [20] and a set of FTIR spectra of various bacteria [21]. The jet fuel dataset is a seven-class (jet fuel types) problem defined by 199 gas chromatograms sampled at 85 time points.…”

“…The most recent chemical applications of the DWT have included applications such as variable selection of the coefficients [9], removal of selected scales [5], and fusion of all scales [1,20]. The DWT is particularly attractive as a preprocessing method for classification problems because the transform process itself makes no use of class label information in the wavelet decomposition of the data.…”

Hybrid Bayesian networks: making the hybrid Bayesian classifier robust to missing training data

“…This dataset, first reported by Lavine et al [17], is gas chromatographic data taken on several different types of jet fuel. The dataset contains 85 variables and six classes.…”

Application of uniform design and genetic algorithm in optimization of reversed-phase chromatographic separation

“…However, up to now, there are a few reports on the application of genetic algorithm in optimization of chromatography [10 -12]. Genetic algorithm has been applied in source identification of underground fuel spills by Lavine et al [10] and in prediction for chromatographic retention by Zhang et al [11]. Recently, Nikitas et al [12] used genetic algorithm in response surface modeling in high-performance liquid chromatography.…”