Gas Prediction Method Based on Dynamic Response Analysis of Metal Oxide Sensors under Temperature Modulation

In this study, we employed temperature modulation to address the low selectivity issue of MOX sensors towards gases. We utilized low-frequency square wave and triangle wave signals as heating voltages to classify and predict the concentrations of ethanol, methanol, MEK, and ethyl ethanoate. The results demonstrated that the proposed approach achieved a classification accuracy of 100% under both heating voltages, while the root mean square error (RMSE) for concentration prediction was 0.6928% and 0.9198%, respectively.