Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study. we aim to counter the drift effect in more challenging situations in which the category information (labels) of ... https://www.ealisboa.com/