Hybrid Pattern Recognition for Rapid Explosive Sensing With Comprehensive Analysis
Vijay Palaparthy, Shambhulingayya N. Doddapujar, Sandeep G. Surya, Saurabh A. Chandorkar, Soumyo Mukherji, Maryam Shojaei Baghini, V. Ramgopal Rao
Abstract
This paper presents a hybrid pattern recognition with temperature compensation (HPR-TC) used within an E-Nose system. HPR-TC with E-nose has the novelty, amongst MEMS sensor platforms, of having two modes of operation i.e., rapid mode of detection to be used in time-critical conditions and comprehensive analysis mode for improved detection accuracy. Two modes of operations in HPR-TC are possible because of the implementation of hybrid PR featuring a combination of two different data analysis techniques for explosive sensing. The first part of the hybrid PR is the binary PR based on threshold-based detection and the second one is the analog PR based on PCA and K-mean. The E-Nose system with proposed HPR-TC is validated with two different highly sensitive MEMS sensor types, i.e., SU8 and Si3Nx piezo-resistive cantilever. These MEMS sensors are coated with surface receptors, 4-MBA, 6-MNA and 4-ATP, to improve the selectivity. The E-Nose system can detect explosive compounds such as TNT, RDX, and PETN, in a controlled environment at a concentration as low as 16ppb of TNT, 56ppb of RDX and 134ppb of PETN. Furthermore, measurements show that E-Nose with temperature compensated binary PR can detect the explosives with a detection accuracy higher than 74% as true positives and higher than 79% as true negatives in a short time, within initial 17 seconds of the experiment. However, the temperature compensated analog PR gives a detailed classification of explosives with a higher detection accuracy of 80% as true positives and 86% as true negatives after approximately 95 seconds.