Analysis of biomarker features from a differential mobility spectrometer for the detection of tuberculosis

In this article, we present an investigation of an approach to extract discriminating features from differential mobility spectrometer (DMS) signals generated from two sets of in vitro samples of headspace that contain volatile organic compounds. The two classes of signals we analyze are a strain of tuberculosis grown in media and the media alone. Our approach first preprocesses the DMS signals to recover a baselined signal and then applies a wavelet transform to obtain localized measures of chemical activity in the detector output. The approach then ranks the wavelet coefficients using a common measure of class separability to identify distinguishing wavelet coefficients. Our analysis indicates that the subsequent ranking can often identify areas of signal devoid of chemical structures and that when discriminating chemical features are identified, the constraints of the wavelet transform as a decompositional tool can result in mismatches between the main lobe of the wavelet basis function and the chemical peak. Techniques to mitigate these effects are also discussed, and considerations are made for how to track features across multiple experiments.