ENOSE Performance in Transient Time and Steady State Area of Gas Sensor Response for Ammonia Gas: Comparison and Study

Abstract

This paper proposed an electronic nose system that utilized a SnO2 semiconductor sensor array to detect volatile ammonia gas in farmland. All sensors were controlled by the Arduino development board. The system could collect data during both the steady-state and transient phases of sensor operation. The collected data was analyzed using PCA (principal component analysis) and MLP (Multi-layer perceptron) neural networks. The experiment was divided into two parts: The first part analyzed four concentrations of ammonia (100ppm, 200ppm, 400ppm, and Air) using PCA and MLP, which successfully distinguished the concentrations with an identification rate of over 95%. In the second part, four gases (air mixed with ammonia, pure ammonia gas, air mixed with ethanol, and pure ethanol) were analyzed using PCA and MLP, with the electronic nose system successfully distinguishing between the four types of gases. The system could read and process data during the transient phase of the sensor, and the constructed sensor array electronic nose system and acquisition method has significant potential for ammonia detection in agricultural environments.