Metal Oxide Semiconductor Based Electronic Nose as Classification and Prediction Instrument for Nicotine Concentration in Unflavoured Electronic Juice

Abstract

This study aims to apply electronic nose as an instrument to measure the concentration of dissolved nicotine in unflavored e-juice. The electronic nose used in this study consisted of six metal oxide semiconductor (MOS) gas sensors. E-nose response data were analyzed using statistical methods to create predictive models. The classification algorithm, Linear Discriminant Analysis (LDA), and the regression algorithm, Partial Least Square (PLS), show that MOS based electronics noses can be applied to classify and predict the concentration of dissolved nicotine in e-juice.