Detection of Nitro-aromatic Explosives with Parametric Regression Modeling Approach

Abstract

Nitrobenzene and Nitrotoluene are potential explosives and pose a threat to mankind. A data driven modeling approach is developed for detection of nitro-aromatic explosives in this paper. An Arduino based system is developed consisting of four gas sensors along with a temperature and humidity sensor. Data is transferred serially to a computer for model development. As the aroma of nitro-aromatic explosives contains ammonia from few parts per billion (ppb) to hundred parts per million (ppm), a parametric regression model for each sensor is developed for varying concentrations of ammonia from 200 ppb to 200 ppm. The parameters of Area, Slope and Relative Response derived from the response of each sensor are used to develop this model. Using the parametric regression model, a signature print of the sensor array is developed by subjecting it to varying concentrations of Nitrobenzene and Nitrotoluene. A multivariate linear regression model is developed using these signature prints to determine the presence of the explosives. The performance of the model is validated experimentally for mixtures of Nitrobenzene/ nitrotoluene with air. It is observed that the model identified the presence of nitroaromatic explosive correctly in 92 % cases.