Response Transient-Aware Artificial Olfaction for Nitrogenous Biomarkers Recognition and Asthma Diagnosis

Noninvasive disease diagnosis through the exhaled nitric oxide (NO) biomarker is of great significance for the real-time screening of asthma patients. Herein, a high-performance artificial olfactory system (E-nose) based on porous In 2 O 3 nanorods functionalized with Pt, Pd, and PtPd was designed and prepared for the precise recognition of the asthma biomarker NO and the noninvasive diagnosis of asthma. The sensor array demonstrates a limit of detection (LOD) of approximately 1.33 ppb (PtPd/In 2 O 3 ) for NO and maintains high selectivity. To overcome the prolonged response/recovery times of room-temperature gas sensors and the indistinguishable response values for structurally similar molecules, a novel response transient-aware prototypical network model is proposed. By dynamically extracting transient features from early response stages, the model achieves a high classification accuracy of 94.44% for structurally similar nitrogen oxides (NO, NO 2 , and N 2 O). On a clinical dataset comprising 61 participants (42 healthy volunteers and 19 asthma patients), the E-nose system achieved a high accuracy of 93.75% based on the full time-series response stage. Notably, the accuracy of the early-response stage reached 92.85%, validating the feasibility of rapid diagnosis by using early-response segments. These results demonstrate that the artificial olfactory system based on metal-functionalized porous In 2 O 3 nanorods has great potential in the precise and non-invasive diagnosis of asthma. It also offers useful guidance for rapid detection, even when sensors with slow response and recovery times are employed.