Development of WT-ANN Model in thick film SnO2 Gas Sensor for Precise Detection of Volatile Organic Compounds in Exhaled Breath
Keywords:
Gas sensor, Artificial Neural Network (ANN), Wavelet transform, Volatile Organic Compounds (VOC), Tin oxide, ConcentrationAbstract
Breath analysis for early-stage detection and monitoring of chronic illnesses, aiming to reduce medical costs and improve patient quality of life. Electronic sensors, functioning as diagnostic tools, can analyze body odors and detect pathological gases. This study focuses on tin oxide (SnO2) thick film gas sensors for detecting VOCs exhaled in breath, including acetone, ethanol, and benzene, which are indicators of diseases like diabetes, lung cancer, and fatty liver disease. A custom gas chamber equipped with a sensor array was constructed, and the sensors' responses to different gas concentrations were recorded. Using artificial neural networks (ANNs), specifically the Wavelet-Transformed ANN (WT-ANN) model, and the study demonstrated the precise detection of VOC concentrations. The WT-ANN employs B-spline wavelet transfer functions for enhanced nonlinearity, allowing for accurate correlation of complex data. Initial results showed that the system could closely estimate acetone concentrations, with minimal error. The findings suggest that the WT-ANN model, combined with semiconductor-based gas sensors, might assist as a non-invasive instrument for diagnosis diseases like diabetes, lung cancer, and fatty liver disease by identifying specific VOC patterns in exhaled breath. The study underscores the potential of ANN-based breath analysis systems in medical diagnostics and highlights the need for continued research to refine this innovative approach.
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