Pattern recognition of jet fuels: comprehensive GC×GC with ANOVA-based feature selection and principal component analysis

Two-dimensional comprehensive gas chromatography (GC×GC) is applied to a pattern recognition problem involving classification of jet fuel mixtures. Analysis of variance (ANOVA)-based feature selection is initially used to identify and select chromatographic features relevant to a given classification in two studies. Then, principal component analysis (PCA) was used for pattern recognition classification. In the first study, a 1% volumetric composition change in mixtures of JP-5 and JP-7 jet fuel is readily distinguished. In this first study, the effective combination of GC×GC, ANOVA-based feature selection and PCA is developed and evaluated as a chemical analysis tool. The second study involved the analysis of three samples each of three different jet fuel types, JP-5, JP-8, and JP-TS, as well as blends incorporating two or three jet fuels. Each of the nine jet fuel samples originated from various geographic locations within the United States. These samples were analyzed in order to determine if a classification based on fuel type is possible in the presence of sample variability (due to geographic origin) with GC×GC/pattern recognition analysis. Chromatographic features that are adept at classification of jet fuel type and are not sensitive to geographic origin of the sample were generated for the sample set consisting of the original fuel types as well as mixtures of the three different, original jet fuels. The combination of GC×GC with ANOVA-based feature selection was found to be a useful tool to enhance the chemical selectivity, and thus the classification power of the analytical procedure, when coupled with PCA.