Quartz crystal microbalance based electronic nose system implemented on Field Programmable Gate Array

Nowadays, an electronic nose becomes an important tool for detecting gas. The electronic nose consists of gas sensor array combined with neural networks to recognize patterns of the sensor array. Currently, the implementation of the neural network on the electronic nose systems still use personal computer so that less flexible or not portable. This paper discusses the electronic nose system implemented in a Field Programmable Gate Array (FPGA). The sensor array consists of eight Quartz Crystal Microbalance (QCM) coated with chemical materials. The eight channel-frequency counter is used to measure the frequency change of the sensor due to the presence of gas adsorbed to the surfaces. The bipolar sigmoid activation function used in the neuron model is approximated by a second order equation. The experimental result showed that the electronic nose system could recognize all the types of gas with 92% success rate