A comparison of online methods for change point detection in ion-mobility spectrometry data

Abstract

When on-site classification of volatile organic compounds (VOCs) is required, a portable ion mobility spectrometer (IMS) is a suitable choice. However, the IMS readings often show transient phases before they stabilize. Even so the importance of transient phase and features extracted from it has been highlighted in the literature, it has not, to our knowledge, been used for IMS-based classification so far. This paper analyzes whether change point detection algorithms with low computational complexity can separate transient and stable phases in IMS readings. The algorithms were tested on IMS data from different types of mushrooms. All algorithms successfully detected switches from transient to stable phase. The most accurate results were provided by the previously proposed multivariate max-CUSUM algorithm and the matrix form CUSUM algorithm, which is developed in this paper. • A dataset of IMS measurements from 2 types of mushrooms is presented and shared. • A modification of the CUSUM change point detection algorithm is proposed. • The proposed algorithm is compared to existing change point detection algorithms.