GC/IMS and GC/MS analysis of pre-concentrated medical and biological samples

Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance.

Analysis of drugs including illicit and new psychoactive substances in oral fluids by gas chromatography-drift tube ion mobility spectrometry

In this study, a gas chromatograph (GC) has been coupled to a drift tube ion mobility spectrometer (IMS) in order to develop an analytical procedure for the determination of psychoactive substances in oral fluids. Working parameters, including the GC-IMS interface ones, were adjusted in order to obtain sensitive and robust signals. A volume of 500 μL of oral fluid was extracted with 250 μL chloroform and, after centrifugation, were injected into the GC-IMS system. Amphetamine, methylone, α-PVP, ketamine, lidocaine, MPHP, cocaine, THJ-2201, and 5F-ADB were employed as model compounds, providing limits of detection from 6 to 15 μg L^-1 and recoveries from 70 to 115% for field oral fluids spiked with target analytes at 250, 500, and 600 μg L^-1. Moreover, two oral fluid certified reference materials were analysed by the proposed GC-IMS based methodology with obtained relative percentage errors lower than 8.4%, being the proposed GC-IMS procedure a reliable, selective, and sensitive technique for the determination of psychoactive substances in oral fluids.

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.