  Coupling a multi-capillary column (MCC) with an ion mobility (IM)
spectrometer (IMS) opened a multitude of new application areas for gas
analysis, especially in a medical context, as volatile organic compounds (VOCs)
in exhaled breath can hint at a person's state of health. To obtain a potential
diagnosis from a raw MCC/IMS measurement, several computational steps are
necessary, which so far have required manual interaction, e.g., human
evaluation of discovered peaks. We have recently proposed an automated pipeline
for this task that does not require human intervention during the analysis.
Nevertheless, there is a need for improved methods for each computational step.
In comparison to gas chromatography / mass spectrometry (GC/MS) data, MCC/IMS
data is easier and less expensive to obtain, but peaks are more diffuse and
there is a higher noise level. MCC/IMS measurements can be described as samples
of mixture models (i.e., of convex combinations) of two-dimensional probability
distributions. So we use the expectation-maximization (EM) algorithm to
deconvolute mixtures in order to develop methods that improve data processing
in three computational steps: denoising, baseline correction and peak
clustering. A common theme of these methods is that mixture components within
one model are not homogeneous (e.g., all Gaussian), but of different types.
Evaluation shows that the novel methods outperform the existing ones. We
provide Python software implementing all three methods and make our evaluation
data available at http://www.rahmannlab.de/research/ims.
