Cumulative sum and neural network approach to the detection and identification of hazardous chemical agents from ion mobility spectra

Abstract

The detection and identification of hazardous chemical agents are important problems in the fields of security and defense. Although the diverse environmental conditions and varying concentrations of the chemical agents make the problem challenging, the identification system should be able to give early warnings, identify the gas reliably, and operate with low false alarm rate. We have researched detection and identification of chemical agents with a swept-field aspiration condenser type ion mobility spectrometry prototype. This paper introduces an identification system, which consists of a cumulative sum algorithm (CUSUM) -based change detector and a neural network classifier. As a novelty, the use of CUSUM algorithm allows the gas identification task to be accomplished using carefully selected measurements. For the identification of hazardous agents we, as a further novelty, utilize the principal component analysis to transform the swept-field ion mobility spectra into a more compact and appropriate form. Neural networks have been found to be a reliable method for spectra categorization in the context of swept-field technology. However, the proposed spectra reduction raises the accuracy of the neural network classifier and decreases the number of neurons. Finally, we present comparison to the earlier neural network solution and demonstrate that the percentage of correctly classified sweeps can be considerably raised by using the CUSUM-based change detector.