Fuel Identification by Neural Network Analysis of the Response of Vapor-Sensitive Sensor Arrays

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Abstract
Neural network analysis of the response of an array of vapor-sensitive detectors has been used to identify six different types of aviation fuel. The data set included 96 samples of JP-4, JP-5, JP-7, JP-8, JetA, and aviation gasoline (AvGas). A sample of each neat fuel was injected into a continuous stream of breathing air through an injection port from a gas chromatograph. The aspirated sample was then swept from the injection port to the chamber without separation. In the chamber, the sample was exposed to an array of eight vapor-sensitive detectors. The analog output of each detector was digitized and stored while the sample was swept into and through the chamber. The response of each detector was then averaged and stored as the final response or pattern of each sample. It was clear from a visual inspection of each of the radar plots that there was a characteristic pattern in the response of the array to five of the six different fuel types. This was confirmed using neural network analysis to study the entire data set. A two-step procedure was developed to separate the patterns of all six fuel types into their respective classes. In the first step, fuels were separated into one of five groups: JP-4, JP-5, JP-7, AvGas, or a combined JP-8/JetA group. In the second step, the fuels in the combined group were separated into either JP-8 or JetA groups.