Software tool for coupling chromatographic total ion current dependencies of GC/MSD and MCC/IMS

Detection of axillary perspiration metabolites using ion mobility spectrometry coupled to rapid gas chromatography

The composition of human sweat-and as a consequence the composition of volatiles released from human skin-strongly depends on genetic preconditions, diet, stress, personal hygiene but also on health status and medication. Accordingly, the composition is a carrier of information on the physical and mental states of a person. Therefore, rapid on-site analysis of the relevant substances may be used for medical diagnosis and medication control or even for psychological characterisation. Ion mobility spectrometry coupled to rapid gas chromatography (GC-IMS) was applied to the analysis of human axillary sweat as a sensitive, selective, rapid, and non-invasive method in a feasibility study. For this purpose, a sampling chamber was designed and manufactured. The design and the experimental setup were validated successfully. At least 179 human metabolites could be detected by GC-IMS from the skin of 7 volunteers. Fifteen metabolites were available in all samples from all volunteers and therefore can be characterised as basic sweat compounds which might enable the localisation of hidden persons. Furthermore, in a preliminary feasibility study, the potential of GC-IMS for differentiating the composition of sweat after physical exercises and in a stressful situation-even gender specific-could be demonstrated. Thus, with GC-IMS, a rapid and mobile analytical tool for the analysis of skin volatiles is available for a broad range of applications, e.g. with regard to axillary odour, human health, nutrition, consumption of remedies or drugs of abuse, the localisation of trapped or hidden persons, or even the characterisation of the reaction on stressful situations. Graphical abstract.

Review on ion mobility spectrometry. Part 2: hyphenated methods and effects of experimental parameters

Ion Mobility Spectrometry (IMS) is a widely used and 'well-known' technique of ion separation in the gaseous phase based on the differences of ion mobilities under an electric field. This technique has received increased interest over the last several decades as evidenced by the pace and advances of new IMS devices available. In this review we explore the hyphenated techniques that are used with IMS, specifically mass spectrometry as an identification approach and a multi-capillary column as a pre-separation approach. Also, we will pay special attention to the key figures of merit of the ion mobility spectrum and how data sets are treated, and the influences of the experimental parameters on both conventional drift time IMS (DTIMS) and miniaturized IMS also known as high Field Asymmetric IMS (FAIMS) in the planar configuration. The present review article is preceded by a companion review article which details the current instrumentation and contains the sections that configure both conventional DTIMS and FAIMS devices. These reviews will give the reader an insightful view of the main characteristics and aspects of the IMS technique.

Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.