A Phase-Based Feature for Gas Detection Under Unstable Preheating Condition for E-Noses

Electronic noses (e-noses) utilizing metal oxide semiconductor (MOS) gas sensors are widely used; however, they typically require several days of preheating after power-on, whether during initial startup or following a power interruption, to reach a stable operation. Such prolonged preheating significantly limits immediate deployment, particularly in portable systems, and has rarely been systematically addressed in the existing literature. To tackle this challenge, we propose a phase-based feature extraction method to capture stable signal patterns under temperature modulation to address baseline drift during the unstable preheating stage. Building on this feature, we develop a detection method that markedly outperforms conventional magnitude-based techniques, which typically exhibit very low detection probabilities during early preheating. The proposed method achieves high-precision gas detection within just 14 hours, and reaches even better detection probability in 2 hours that conventional methods require 154 hours to match when most commercial sensors remain in the pre-conditioning stage. By incorporating the phase-based feature extraction strategy, the required stabilization time is substantially shortened, enabling rapid and reliable gas detection, and facilitating real-time deployment of e-nose systems in critical applications such as emergency response and industrial safety.