Highly sensitive splitless programmed temperature vaporizing (PTV)-large volume injection (LVI)-GC-MS-negative chemical ionization (NCI) method was developed and validated for the trace detection of explosives and related compounds from environmental matrices.

The human respiratory system is a highly complex matrix that exhales many volatile organic compounds (VOCs). Breath‐exhaled VOCs are often “unknowns” and possess low concentrations, which make their analysis, peak digging and data processing challenging. We report a new methodology, applied in a proof‐of‐concept experiment, for the detection of VOCs in breath. For this purpose, we developed and compared four complementary analysis methods based on solid‐phase microextraction and thermal desorption (TD) tubes with two GC–mass spectrometer (MS) methods. Using eight model compounds, we obtained an LOD range of 0.02–20 ng/ml. We found that in breath analysis, sampling the exhausted air from Tedlar bags is better when TD tubes are used, not only because of the preconcentration but also due to the stability of analytes in the TD tubes. Data processing (peak picking) was based on two data retrieval approaches with an in‐house script written for comparison and differentiation between two populations: sick and healthy. We found it best to use “raw” AMDIS deconvolution data (.ELU) rather than its NIST (.FIN) identification data for comparison between samples. A successful demonstration of this method was conducted in a pilot study (n = 21) that took place in a closed hospital ward (Covid‐19 ward) with the discovery of four potential markers. These preliminary findings, at the molecular level, demonstrate the capabilities of our method and can be applied in larger and more comprehensive experiments in the omics world.

The measurement of a temperature‐dependent volatilization rate of the nerve agent GB (sarin) from various common matrices, using a laboratory‐designed wind tunnel (model system), is described. Small GB droplets are dispersed on the matrices surfaces, and samples are collected from the model system and analyzed utilizing a solid phase extraction/gas chromatography analytical method. Profiles demonstrating the vapor concentrations as a function of time are calculated. The results indicate that asphalt blocks relatively conserve GB, exhibiting a slow‐release mode of action over at least 2 weeks. Nevertheless, using standard decontamination procedures such as super tropical bleach may prevent this secondary evaporation risk. The concentration profile of commercial sidewalk bricks show a moderate decay, probably due to the existence of basic sites in the cement‐containing bricks. Fast clearance of GB is measured from smooth surface tiles, as these porous tiles both adsorb and/or degrade GB droplets very quickly.

We developed and optimized surface-enhanced Raman spectrometry (SERS) methods for trace analysis of explosive vapour and particles using a hand-held Raman spectrometer in the field.

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Complex mixtures, characterized by high density of compounds, challenge trace detection and identification. This is further exacerbated in nontargeted analysis, where a compound of interest may be well hidden under thousands of matrix compounds. We studied the effect of matrix complexity on nontargeted detection (peak picking) by LC−MS/MS (Orbitrap) analysis. A series of ∼20 drugs, V-type chemical warfare agents and pesticides, simulating toxic unknowns, were spiked at various concentrations in several complex matrices including urine, rosemary leaves, and soil extracts. Orbitrap "TraceFinder" software was used to explore their peak intensities in relation to the matrix (peak location in an intensity-sorted list). Average practical detection limits of nontargets were determined. While detection among the first 10,000 peaks was achieved at 0.3−1 ng/mL levels in the extract, for the more realistic "top 1000" list, much higher concentrations were required, approaching 10−30 ng/mL. A negative power law functional dependence between the peak location in an intensity-sorted suspect list and the nontarget concentration is proposed. Controlled complexity was explored with a series of urine dilutions, resulting in an excellent correlation between the power law coefficient and dilution factor. The intensity distribution of matrix peaks was found to spread (unevenly) on a broad range, fitting well the Weibull distribution function with all matrices and extracts. The quantitative approach demonstrated here gives a measure of the actual capabilities and limitations of LC−MS in the analysis of nontargets in complex matrices. It may be used to estimate and compare the complexity of matrices and predict the typical detection limits of unknowns.

Volatile metabolites in exhaled air have promising potential as diagnostic biomarkers. However, the combination of low mass, similar chemical composition, and low concentrations introduces the challenge of sorting the data to identify markers of value. In this paper, we report the development of pyAIR, a software tool for searching for volatile organic compounds (VOCs) markers in multi-group datasets, tailored for Thermal-Desorption Gas-Chromatography High Resolution Mass-Spectrometry (TD-GC-HRMS) output. pyAIR aligns the compounds between samples by spectral similarity coupled with retention times (RT), and statistically compares the groups for compounds that differ by intensity. This workflow was successfully tested and evaluated on gaseous samples spiked with 27 model VOCs at six concentrations, divided into three groups, down to 0.3 nL/L. All analytes were correctly detected and aligned. More than 80% were found to be significant markers with a p-value < 0.05; several were classified as possibly significant markers (p-value < 0.1), while a few were removed due to background level. In all group comparisons, low rates of false markers were found. These results showed the potential of pyAIR in the field of trace-level breathomics, with the capability to differentially examine several groups, such as stages of illness.