Alignment of retention time obtained from multicapillary column gas chromatography used for VOC analysis with ion mobility spectrometry

Multicapillary column (MCC) ion mobility spectrometers (IMS) are increasingly in demand for medical diagnosis, biological applications and process control. In a MCC-IMS, volatile compounds are differentiated by specific retention time and ion mobility when rapid preseparation techniques are applied, e.g. for the analysis of complex and humid samples. Therefore, high accuracy in the determination of both parameters is required for reliable identification of the signals. The retention time in the MCC is the subject of the present investigation because, for such columns, small deviations in temperature and flow velocity may cause significant changes in retention time. Therefore, a universal correction procedure would be a helpful tool to increase the accuracy of the data obtained from a gas-chromatographic preseparation. Although the effect of the carrier gas flow velocity and temperature on retention time is not linear, it could be demonstrated that a linear alignment can compensate for the changes in retention time due to common minor deviations of both the carrier gas flow velocity and the column temperature around the MCC-IMS standard operation conditions. Therefore, an effective linear alignment procedure for the correction of those deviations has been developed from the analyses of defined gas mixtures under various experimental conditions. This procedure was then applied to data sets generated from real breath analyses obtained in clinical studies using different instruments at different measuring sites for validation. The variation in the retention time of known signals, especially for compounds with higher retention times, was significantly improved. The alignment of the retention time—an indispensable procedure to achieve a more precise identification of analytes—using the proposed method reduces the random error caused by small accidental deviations in column temperature and flow velocity significantly

Multi-capillary column-ion mobility spectrometry (MCC-IMS) as a new method for the quantification of occupational exposure to sevoflurane in anaesthesia workplaces: an observational feasibility study

BACKGROUND: Occupational exposure to sevoflurane has the potential to cause health damage in hospital personnel. Workplace contamination with the substance mostly is assessed by using photoacoustic infrared spectrometry with detection limits of 10 ppbv. Multi-capillary column-ion mobility spectrometry (MCC-IMS) could be an alternative technology for the quantification of sevoflurane in the room air and could be even more accurate because of potentially lower detection limits. The aim of this study was to test the hypothesis that MCC-IMS is able to detect and monitor very low concentrations of sevoflurane (<10 ppbv) and to evaluate the exposure of hospital personnel to sevoflurane during paediatric anaesthesia and in the post anaesthesia care unit (PACU). METHODS: A MCC-IMS device was calibrated to several concentrations of sevoflurane and limits of detection (LOD) and quantification (LOQ) were calculated. Sevoflurane exposure of hospital personnel was measured at two anaesthesia workplaces and time-weighted average (TWA) values were calculated. RESULTS: The LOD was 0.0068 ppbv and the LOQ was 0.0189 ppbv. During paediatric anaesthesia the mean sevoflurane concentration was 46.9 ppbv (8.0 - 314.7 ppbv) with TWA values between 5.8 and 45.7 ppbv. In the PACU the mean sevoflurane concentration was 27.9 ppbv (8.0 – 170.2 ppbv) and TWA values reached from 8.3 to 45.1 ppbv. CONCLUSIONS: MCC-IMS shows a significantly lower LOD and LOQ than comparable methods. It is a reliable technology for monitoring sevoflurane concentrations at anaesthesia workplaces and has a particular strength in quantifying low-level contaminations of sevoflurane. The exposure of the personnel working in these areas did not exceed recommended limits and therefore adverse health effects are unlikely

Towards a multisensor station for automated biodiversity monitoring

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution. (C) 2022 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie

Web2.0 paves new ways for collaborative and exploratory analysis of Chemical Compounds in Spectrometry Data

In nowadays life science projects, sharing data and data interpretation is becoming increasingly important. This considerably calls for novel information technology approaches, which enable the integration of expert knowledge from different disciplines in combination with advanced data analysis facilities in a collaborative manner. Since the recent development of web technologies offers scientific communities new ways for cooperation and communication, we propose a fully web-based software approach for the collaborative analysis of bioimage data and demonstrate the applicability of Web2.0 techniques to ion mobility spectrometry image data. Our approach allows collaborating experts to easily share, explore and discuss complex image data without any installation of software packages. Scientists only need a username and a password to get access to our system and can directly start exploring and analyzing their data