Electronic nose platforms based on metal-oxide (MOX) sensors offer potential for low-power gas classification under dynamic operating conditions. This study evaluates a BME688-based digital nose configured with a temperature-modulated heater profile (HP-354) and reduced duty cycle (RDC-5-10) for binary ethylene presence classification in fruit headspace. Seven climacteric fruit types were sealed in bags to allow natural ethylene accumulation and were sampled across multiple sessions over a two-week period. A structured alternating protocol between fruit headspace (Class A) and neutral air (Class B) generated 21 ethylene sessions and 23 neutral-air sessions, comprising 38,882 individual thermal scan cycles (~10 s per cycle). Each full heater cycle was treated as a training instance within BME AI-Studio. A supervised neural-network classifier trained on 70% of cycle-level data achieved 92.9% overall accuracy with a macro F1 score of 91.9% on validation data. Results demonstrate that temperature-modulated MOX signatures enable robust discrimination of biologically generated ethylene from baseline air under realistic headspace variability. This study demonstrated classification feasibility under naturally accumulated fruit emissions while highlighting the need for future concentration-resolved calibration studies.
Palmer et al. (2026) studied this question.