Electronic nose based on multiple electrospinning nanofibers sensor array and application in gas classification

Abstract

To mimic the human olfactory system, an electronic nose (E-nose, also known as artificial olfactory) has been proposed based on a multiple gas sensor array and a pattern recognition algorithm. Detection of volatile organic components (VOCs) has many potential applications in breath analysis, food quality estimation, and indoor and outdoor air quality monitoring, etc. In this study, a facile single-needle electrospinning technology was applied to develop the four different semiconductor metal oxide (MOS) nanofibers sensor arrays (SnO2, CuO, In2O3 and ZnO, respectively). The array shows a smooth surface and constant diameter of nanofiber (average of 150 nm) resulting in high sensitivity to multiple target analyte gases. Five human health related VOCs gases were measured by fabricated E-nose and different response patterns were obtained from four MOS nanofibers sensors. Combined with feature extraction from the response curves, a principal component analysis (PCA) algorithm was applied to reduce the dimension of feature matrix, Thus, the fabricated E-nose system successfully discriminated five different VOCs gases. Real-time and non-invasive gas monitoring by E-nose is very promising for application in human health monitoring, food monitoring, and other fields.