Classification and concentration estimation of CO and NO2 mixtures under humidity using neural network-assisted pattern recognition analysis

This study addresses the concerns regarding the cross-sensitivity of metal oxide sensors by building an array of sensors and subsequently utilizing machine earning techniques to analyze the data from the sensor arrays. Sensors were built using In2O3, Au-ZnO, Au-SnO2, and Pt-SnO2 and they were operated simultaneously in the presence of 25 different concentrations of nitrogen dioxide (NO2), carbon monoxide (CO), and their mixtures. To investigate the effects of humidity, experiments were conducted to detect 13 distinct CO and NO2 gas combinations in atmospheres with 40% and 90% relative humidity. Principal component analysis was performed for the normalized resistance variation collected for a particular gas atmosphere over a certain period, and the results were used to train deep neural network-based models. The dynamic curves produced by the sensor array were treated as pixelated images and a convolutional neural network was adopted for classification. An accuracy of 100% was achieved using both models during cross-validation and testing. The results indicate that this novel approach can eliminate the time-consuming feature extraction process.

Automated summary

This study addresses the cross-sensitivity issue of metal oxide sensors by using a sensor array and machine learning techniques. The sensors built with In2O3, Au-ZnO, Au-SnO2, and Pt-SnO2 were operated simultaneously with different concentrations of NO2, CO, and their mixtures. By conducting experiments at different humidity levels, the researchers trained deep neural network-based models using principal component analysis and achieved an accuracy of 100% in classification using a convolutional neural network. This approach eliminates the time-consuming feature extraction process.