The long-term drift is well known as one of the most challenging issues regarding gas sensing and applications involving electronic noses, undermining the reliability and accuracy of these systems over time, as the pattern recognition models lose their capability of performing gas discrimination. In this work, we adopt a probabilistic view of the drift problem. This perspective yields a novel univariate scheme for drift compensation, which is grounded on probabilistic modelling. The potential usefulness of the proposed approach is highlighted through experimental results on the gas classification task, which reveal a superior and more stable performance when compared to traditional component correction multivariate methods. Besides its stable performance, the proposed method also presents several desired attributes, such as computational simplicity, continuous adaptability and suitability for online applications. The experiments were conducted using a dataset lasting 7 months, comprising measurements from three different gases at different concentration levels in an array containing 17 polymeric sensors.