Synergistic Integration of Machine Learning with Microstructure/Composition-Designed SnO 2 and WO 3 Breath Sensors

• Published in: ACS sensors 2024-01, Vol.9 (1), p.182-194
• Main Authors: Nam, Yoonmi, Kim, Ki-Beom, Kim, Sang Hun, Park, Ki-Hong, Lee, Myeong-Ill, Cho, Jeong Won, Lim, Jongtae, Hwang, In-Sung, Kang, Yun Chan, Hwang, Jin-Ha
• Format: Article
• Language: English
• ISSN: 2379-3694; 2379-3694
• DOI: 10.1021/acssensors.3c01814

A high-performance semiconductor metal oxide gas sensing strategy is proposed for efficient sensor-based disease prediction by integrating a machine learning methodology with complementary sensor arrays composed of SnO - and WO -based sensors. The six sensors, including SnO - and WO -based sensors and neural network algorithms, were used to measure gas mixtures. The six constituent sensors were subjected to acetone and hydrogen environments to monitor the effect of diet and/or irritable bowel syndrome (IBS) under the interference of ethanol. The SnO - and WO -based sensors suffer from poor discrimination ability if sensors (a single sensor or multiple sensors) within the same group (SnO - or WO -based) are separately applied, even when deep learning is applied to enhance the sensing operation. However, hybrid integration is proven to be effective in discerning acetone from hydrogen even in a two-sensor configuration through the synergistic contribution of supervised learning, i.e., neural network approaches involving deep neural networks (DNNs) and convolutional neural networks (CNNs). DNN-based numeric data and CNN-based image data can be exploited for discriminating acetone and hydrogen, with the aim of predicting the status of an exercise-driven diet and IBS. The ramifications of the proposed hybrid sensor combinations and machine learning for the high-performance breath sensor domain are discussed.