A High Performance, Low Cost, Compact, and Wireless E-nose System for Explosives Detection

Detection of non-buried explosives is of great significance to both military and homeland security. Although many existing sniffing devices may be used for explosives detection, the size, cost, weight, and portability are primary concern in the development of a field deployable sniffing devices. Herein we propose to build high performance, low cost, compact, and wireless e-nose devices for explosives detection. The system has three key features. First, we have identified an e-nose chip family, developed by Cyrano Sciences, Inc. In addition to small size and low cost, the chip has a wireless capability, which makes the wireless data retrieval possible. Second, we propose to use Support Vector Machines (SVM) to improve the accuracy of explosives classification and nonlinear unmixing for accurate explosive concentration estimation in a mixture. SVM has several attractive advantages and better classification performance than most other classifiers. Most existing unmixing methods are linear in nature. However, our proposed unmixing is a nonlinear approach, which addresses the nonlinear relationship between e-nose responses and different explosive compounds. Finally, we will use a PDA to wirelessly collect the e-nose sensor reading and analyze the data. We believe that our proposed system can find many applications.