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Machine learning-enhanced multi-sensor e-nose to quantify and classify low-level ammonia under dynamic environmental condition
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DOI：10.1016/j.snr.2026.100465.png)
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Abstract
En 中文
• A custom Enose system was developed using a multichannel MOX gas sensor array (GM102B, GM302B, GM502B, GM702B) with integrated temperature and humidity sensors. • Ammonia gas detection was successfully performed across a broad concentration range (2.5 to 100 PPM), covering low, medium, and high exposure levels. • XGBoost consistently outperformed SVM and LightGBM in terms of accuracy, precision, recall, and F1-score across all experimental conditions. • LightGBM was introduced for the first time in Enose-based ammonia classification, offering novel insights into its strengths and limitations in gas detection. • Permutation feature importance analysis revealed “Max” and “Min” as the most critical features, significantly influencing classification accuracy and model interpretability. • XGBoost was identified as the most robust and scalable model, well-suited for practical and cost-effective deployment of real-time Enose systems. • Environmental variations were thoroughly investigated across three structured cases involving controlled changes in temperature and humidity: Case 1: 23 °C at 50% RH; Case 2: 30–60 °C at fixed 50% RH; Case 3: 40%–60% RH at fixed 23 °C; Sensor data were collected under all these conditions to assess MOX sensor sensitivity and system robustness in real-world variability. • Three machine learning models—SVM, XGBoost, and LightGBM—were evaluated using key time-domain features such as RMS, Skewness, Max, and Min. • A six-level classification evaluation framework was employed to analyze model performance across individual sensors, combined cases, and full sensor configurations. • Least influential features such as ZC and RES were identified, aiding in future optimization of feature selection and system design.
Keywords:
Electronic nose (enose)
Metal oxide (MOX) gas sensors
Ammonia detection
Environmental compensation
Machine learning
Support vector machine (SVM)
XGBoost
LightGBM
Gas classification
Real-time monitoring
Sensor performance evaluation
Temperature and humidity variation
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