Aiming at the defects of response baseline instability and cross-sensitivity of semiconductor gas sensors

a real-time gas detection system is designed.A high-precision analog-to-digital converter chip is employed to collect electric signals output from sensor array.A response feature extraction algorithm is designed to extract the response signals in the electric signals sequence

which is combined with neural networks to achieve real-time gas detection.The system has high scalability and is suitable for multiple practical application scenarios.Experimental results show that the system can accurately extract the sensor response signals

and it has excellent anti-drift and anti-interference performance

thereby realizing the classification and detection of five types of volatile organic compounds (VOCs).This system can achieve real-time VOCs gas detection and effectively promote the practical development of this field.