A miniaturized electronic nose with artificial neural network for anti-interference detection of mixed indoor hazardous gases

• Published in: Sensors and actuators. B, Chemical Chemical, 2021-01, Vol.326, p.128822, Article 128822
• Main Authors: Zhang, Junyu, Xue, Yingying, Sun, Qiyong, Zhang, Tao, Chen, Yuantao, Yu, Weijie, Xiong, Yizhou, Wei, Xinwei, Yu, Guitao, Wan, Hao, Wang, Ping
• Format: Article
• Language: English
• Subjects: Air quality; Artificial neural networks; Back-propagation artificial neural network (BP-ANN); Carbon monoxide; Combustible gases; Concentration recognition; Electronic nose (e-nose); Electronic noses; Flammability; Gas sensors; Indoor air pollution; Indoor air quality; Interference; Interference environment; Methane; Model accuracy; Sensor arrays
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2020.128822

•A miniaturized electronic nose was developed for anti-interference detection of CO and CH4.•A combination strategy of temperature, humidity and three signal features of each gas sensor was proposed for model training.•The BP-ANN models show the highest 10-fold cross validation accuracy for CO and CH4 with interference.•The study provides feasible reference for E-nose design in gas sensing with multiple interference. Indoor air quality attracted great attention for its significant threats to human health and safety, especially the potential hazardous gases in kitchens. To meet the requirements of the anti-interference detection of multiple combustible gases, in this paper, a miniaturized electronic nose was developed using MOS sensor array for semi-quantitative and anti-interference detection of carbon monoxide and methane with the interference of hydrogen and formaldehyde. The sensor array was constructed using 6 MOS sensors and cross-reaction to target and interference gases. To implement the anti-interference capability, different models were utilized and evaluated including PCA, LDA and BP-ANN. The 10-fold cross validation results indicate that BP-ANN models have the best performance than other models with the accuracy of 93.35 % for CO and 93.22 % for CH4 without interference. With the interference of H2 and CH2O, the BP-ANN model shows the accuracies of 78.92 % for CO and 89.75 % for CH4. Adding interfering samples of H2 has a more significant impact on BP-ANN models than adding that of CH2O. The results demonstrate that the proposed e-nose with the BP-ANN model can realize semi-quantitative, simultaneous and anti-interference detection of CO and CH4 in the interference environment, which provides a promising platform for gas sensing with multiple interference.