Determination of benzene, toluene and xylene concentration in humid air using differential ion mobility spectrometry and partial least squares regression

Dual ionization sources high-field asymmetric waveform ion mobility spectrometry with combined ultraviolet lamp source and corona discharge source

Dual ionization sources combining an ultraviolet (UV) lamp source and a corona discharge (CD) source were first used as ionization sources for high-field asymmetric waveform ion mobility spectrometry (FAIMS) to switch ionization modes without changing hardware. The CD source was manufactured using a tungsten needle and a copper ring and its usability was verified. The UV source and the CD source were combined into the FAIMS sensor and multiple ionization modes were attempted by controlling their on-off status. The optimal flow rate, quantitative and qualitative capabilities of the system were investigated, and the experiments on the impact of humidity and interferent on the system were conducted. The experimental results indicated that the flow rate of 3-4 L/min could ensure sufficient sensitivity and resolution of the system. The dual ionization sources configuration provided richer qualitative and quantitative information for FAIMS. This platform could operate in three ionization modes and had the potential to expand the analyzable compounds of UV-FAIMS. In summary, the research demonstrates that the dual ionization sources could be used as ionization sources for FAIMS, and the combined system provided convenience for chemical analysis.

Long-term in situ air quality assessment in closed environments: A gas chromatography-ion mobility spectrometry applicability study

Contemporary life is mostly spent in indoor spaces like private houses, workplaces, vehicles and public facilities. Nonetheless, the air quality in these closed environments is often poor which leads to people being exposed to a vast range of toxic and hazardous compounds. Volatile organic compounds (VOCs) are among the main factors responsible for the lack of air quality in closed spaces and, in addition, some of them are particularly hazardous to the human organism. Considering this fact, we conducted daily in situ air analyses over 1 year using a gas chromatography-ion mobility spectrometry (GC-IMS) device in an indoor location. The obtained results show that 10 VOCs were consistently present in the indoor air throughout the entire year, making them particularly important for controlling air quality. All of these compounds were successfully identified, namely acetic acid, acetone, benzene, butanol, ethanol, isobutanol, propanoic acid, propanol, 2-propanol and tert-butyl methyl ether. The behaviour of the total VOCs (tVOCs) intensity during the period of analysis and the relative variation between consecutive months were studied. It was observed that the overall trend of tVOCs closely mirrored the variation of air temperature throughout the year suggesting their strong correlation. The results obtained from this study demonstrate the high quality and relevance of the data, highlighting the suitability of GC-IMS for in situ long-term air quality assessment in indoor environments and, consequently, for identifying potential health risks for the human organism in both short-term and long-term exposure scenarios.

Analytical techniques for monitoring of toluene and xylene exposure biomarkers hippuric acid and methylhippuric acid in human urine samples

Quantification of hippuric acid and methylhippuric acid in human urine matrices provides information on the toluene and xylene exposure conditions. High performance liquid chromatography coupled with UV detection is the preferable technique for hippuric acid and methylhippuric acid detection in human urine. This study was conducted to present analytical techniques developed for monitoring of hippuric acid and methylhippuric acid in human urine matrices during 2016-2021.

Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning

High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems.

Detection of Triacetone Triperoxide (TATP) and Hexamethylene Triperoxide Diamine (HMTD) from the Gas Phase with Differential Ion Mobility Spectrometry (DMS)

One of the significant problems in the modern world is the detection of improvised explosives made of materials synthesized at home. Such compounds include triacetone triperoxide (TATP) and hexamethylene triperoxide diamine (HMTD). An attempt was made to construct an instrument allowing for the simultaneous detection of both compounds despite the large difference of vapor pressure: very high for TATP and very low for HMTD. The developed system uses differential ion mobility spectrometry (DMS) in combination with a specially designed gas sample injection system. The created system of detectors allowed for the detection of a high concentration of TATP and a very low concentration of HMTD. TATP detection was possible despite the presence of impurities—acetone remaining from the technological process and formed as a coproduct of diacetone diperoxide (DADP) synthesis. Ammonia added to the carrier gas improved the possibility of detecting the abovementioned explosives, reducing the intensity of the acetone signal. The obtained results were then compared with the detection capabilities of drift tube ion mobility spectrometer (DT-IMS), which has not made possible such detection as DMS.

Real-time continuous measurement of intraoperative trace exhaled propofol by planar differential mobility spectrometry

In order to study anesthetic pharmacokinetics and adequately adjust the anaesthesia depth of patients, real-time measurement of the intraoperative exhaled propofol concentration is of significant importance for anaesthetists. Although a series of analytical techniques and methods have been developed for the detection of exhaled propofol, differential mobility spectrometry (DMS) with the advantages of a much smaller instrument, faster response time and cheaper cost shows great potential for the point of care in the operating room. In this paper, a planar DMS was constructed for real-time continuous measurement of trace propofol in exhaled air. The effects of DMS parameters, such as the radio frequency voltage, the drift gas flow rate and the sampling flow rate of exhaled air on the propofol measurement under high humidity conditions were carefully investigated and discussed. Under the optimum experimental conditions, the limit of detection (LOD) for propofol was achieved in ppbv with a linear range of 0.5 to 25 ppbv, both of which meet clinical requirements. Finally, the planar DMS was performed on a patient undergoing thyroidectomy surgery to real-time monitor the intraoperative exhaled propofol, which demonstrated the capability of DMS for sensitive and breath-by-breath continuous measurement of intraoperative trace exhaled propofol.

Predicting lecithin concentration from differential mobility spectrometry measurements with linear regression models and neural networks

Differential mobility spectrometry (DMS) analysis of electrosurgical smoke can be used to distinguish cancerous and healthy tissues. Mass spectrometry studies of surgical smoke have revealed phospholipids as the key compounds enabling this discrimination. Lecithin is a mixture of phospholipids encountered in tissues. We hypothesized that DMS is capable of detecting and quantifying lecithin from water solution in headspace chamber, paving way for analysis of surgical smoke. We measured different lecithin concentrations in a biologically relevant range considering healthy and cancerous tissues with DMS and trained regression models to predict the analyte concentration. The models were internally cross-validated and externally validated. The best cross-validation results were obtained with convolutional neural networks, with root mean square error (RMSE) = 0.38 mg/ml. This is the first demonstration of estimation of analyte concentration from DMS measurements with neural networks. The best external validation results were acquired with sparse linear regression methods, with RMSE varying from 0.40 mg/ml to 0.41 mg/ml. The results demonstrate that DMS is sufficiently sensitive to detect biologically relevant changes in phospholipid concentration, potentially explaining its ability to detect cancerous tissue. In the future, we aim to reproduce the results by using surgical smoke as the medium. In this scenario, the complex background of surgical smoke will be the main challenge to overcome. Predicting concentration with neural networks also lays the foundation for wider analytical usage of DMS.

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

This is the first comprehensive review on chemometric techniques used in ion mobility spectrometry data analysis.