Inkjet-printed ZnO-based MEMS sensor array combined with feature selection algorithm for VOCs gas analysis

An electronic nose system can enhance the selectivity of semiconductor gas-sensor systems but increase system power consumption and signal data processing complexity. Herein, we developed a gas sensor array composed of six ZnO-based micro-electro-mechanical systems (MEMS) sensors prepared by inkjet printing sensing materials on a micro-hotplate. Its power consumption decreased to 36 mW. Furthermore, the particle swarm optimization (PSO) algorithm was used to select the optimal features, decreasing the total of 72 features to 5. Finally, support vector machine and artificial neural network models based on the optimal 5 features were performed to recognize and quantify volatile organic compounds (VOCs), including formaldehyde, ethanol, toluene, and xylene. The identification accuracy and R2 reach 97.9 % and 0.975, respectively. The result demonstrates that the MEMS sensor array, combined with PSO and pattern recognition algorithms, is a promising approach to accurately identifying and quantifying VOCs.