Using context to make gas classifiers robust to sensor drift

The interaction of a gas particle with a metal-oxide based gas sensor changes the sensor irreversibly. The compounded changes, referred to as sensor drift, are unstable, but adaptive algorithms can sustain the accuracy of odor sensor systems. Here we focus on extending the lifetime of sensor systems without additional data acquisition by transfering knowledge from one time window to a subsequent one after drift has occurred. To support generalization across sensor states, we introduce a context-based neural network model which forms a latent representation of sensor state. We tested our models to classify samples taken from unseen subsequent time windows and discovered favorable accuracy compared to drift-naive and ensemble methods on a gas sensor array drift dataset. By reducing the effect that sensor drift has on classification accuracy, context-based models may extend the effective lifetime of gas identification systems in practical settings.