Machine learning-enhanced multi-sensor e-nose to quantify and classify low-level ammonia under dynamic environmental condition

A B S T R A C T

This study presents an advanced Enose system equipped with a multichannel MOX sensor array (GM102B, GM302B, GM502B, GM702B) and environmental sensors for temperature and humidity compensation, with the aim of detecting ammonia across low to high concentrations (2.5-100 PPM) under varying environmental conditions. A custom sampling setup was developed to evaluate the system across three distinct cases: Case 1: Fixed temperature and humidity (baseline condition), Case 2: Variable humidity (40%, 50%, 60%) with fixed temperature, Case 3: Variable temperature (30 • C, 40 • C, 50 • C, 60 • C) with fixed humidity. These scenarios simulate real-world environmental fluctuations to test system robustness. Classification performance was rigorously evaluated using SVM, XGBoost, and LightGBM across individual sensors, all sensors per case, and all cases per sensor. XGBoost consistently delivered superior results (up to 1.00 accuracy), particularly with the 702B sensor and in data-rich environments, outperforming both SVM and LightGBM. While SVM showed solid, consistent results with lower computational complexity, LightGBM underperformed in most configurations, except with the 102B sensor. Compared to earlier studies using MOX sensors and classifiers like LDA, MLP, and BP-NN (typically 93%-97% accuracy), this approach achieves significantly higher accuracy, improved reliability, and greater adaptability to environmental changes. By thoroughly analyzing environmental impacts across structured scenarios, this research sets a new benchmark for MOX-based ammonia detection and supports the development of scalable, real-time multi-gas monitoring platforms.