Rapid gas detection for electronic noses using optimal measurement time search and multi-sensor energy change based feature extraction

Rapid detection of gases using electronic noses is essential and challenging owing to the associated requirements of high accuracy recognition and rapid measurement. In this study, we propose a strategy based on performing feature extraction followed by searching for the appropriate measurement time to improve the measurement speed. To implement our strategy and further improve classification accuracy, we propose a novel feature extraction algorithm based on the energy change of multi-sensor. The superiority of the proposed approach was verified by six classification models on 15 electronic nose datasets. The experimental results show that it outperforms other typical algorithms in terms of classification accuracy. Moreover, the proposed approach achieved reliable estimates using only the first 23.68% of the measurement data on average. The findings indicate the effectiveness of the proposed method in improving the accuracy and measurement speed, which is expected to contribute to the future design of rapid detection systems.