Statistical hypothesis testing for chemical detection in changing environments

This paper addresses the problem of adaptive chemical detection, using the Receptor Density Algorithm (RDA), an immune inspired anomaly detection algorithm. Our approach is to first detect when and if something has changed in the environment and then adapt the RDA to this change. Statistical hypothesis testing is used to determine whether there has been concept drift in consecutive time windows of the data. Five different statistical methods are tested on mass spectrometry data, enhanced with artificial events that signify a changing environment. The results show that, while no one method is universally best, statistical hypothesis testing performs reasonably well on the context of chemical sensing and it can differentiate between anomalies and concept drift.