Synergistic Integration of Machine Learning with Microstructure/Composition-Designed SnO2 and WO3 Breath Sensors

Yoonmi Nam, Ki Beom Kim, Sang Hun Kim, Ki Hong Park, Myeong Ill Lee, Jeong Won Cho, Jongtae Lim, In Sung Hwang, Yun Chan Kang, Jin Ha Hwang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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 SnO2- and WO3-based sensors. The six sensors, including SnO2- and WO3-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 SnO2- and WO3-based sensors suffer from poor discrimination ability if sensors (a single sensor or multiple sensors) within the same group (SnO2- or WO3-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.

Original languageEnglish
Pages (from-to)182-194
Number of pages13
JournalACS Sensors
Issue number1
Publication statusPublished - 2024 Jan 26

Bibliographical note

Publisher Copyright:
© 2024 American Chemical Society.


  • Breath sensors
  • Deep learning
  • Gas sensing
  • Image
  • Numbers
  • SnO sensors
  • WO sensors

ASJC Scopus subject areas

  • Bioengineering
  • Instrumentation
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes


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