A Fast and Cost-Effective Electronic Nose Model for Methanol Detection Using Ensemble Learning

Abstract

Methanol, commonly used to cut costs in the production of counterfeit alcohol, is extremely harmful to human health, potentially leading to severe outcomes, including death. In this study, an electronic nose system was designed using 11 inexpensive gas sensors to detect the proportion of methanol in an alcohol mixture. A total of 168 odor samples were taken and analyzed from eight types of ethanol–methanol mixtures prepared at different concentrations. Only 4 features out of 264 were selected using the feature selection method based on feature importance. These four features were extracted from the data of MQ-3, MQ-4, and MQ-137 sensors, and the classification process was carried out using the data of these sensors. A Voting Classifier, an ensemble model, was used with Linear Discriminant Analysis, Support Vector Machines, and Extra Trees algorithms. The Voting Classifier achieved 85.88% classification accuracy before and 81.85% after feature selection. With its cost effectiveness, fast processing time, and practicality, the recommended system shows great potential for detecting methanol, which threatens human health in counterfeit drink production.