AI-Assisted Sensor System for the Acetone and Ethanol Detection Using Commercial Metal Oxide-Based Sensor Arrays and Convolutional Neural Network

In recent decades, the traditional landscape of volatile organic compound (VOC) sensing has adopted a new perspective in enhancing the detection of useful VOCs using data intelligence to extract constructive insights of the sensor behaviour towards multiple gases. In the domain of gas sensing, VOCs such as acetone and ethanol have been widely used in sensor testing due to their closely related chemical properties, which poses a challenge in discrimination. Therefore, this study aims to discriminate acetone from ethanol with the use of readily available commercial metal oxide (MOx) sensors through the implementation of Deep Learning (DL) techniques. The data set obtained after exposing a sensing array comprising various MOx sensors to acetone and ethanol was converted to a time-frequency representation known as a scalogram to train and test a multi-input convolutional neural network (CNN). The results show that training the CNN model on the sensor array data set yields better results than with an individual sensor data set. The findings of this research substantiated the ability of DL models to better capture the dynamic interaction of the sensors with acetone and ethanol, leading to the implication of the DL classifier having the capacity to reject sensor inconsistencies and variations in the responses. This research holds promise for advancing health monitoring and disease detection, as the combination of MOx sensors and DL techniques is expected to make significant future contributions in these areas.