Aroma Characterization of Roasted Meat and Meat Substitutes Using Gas Chromatography-Mass Spectrometry with Simultaneous Selective Detection and a Dedicated Software Tool, AromaMS.

The development of healthier and more sustainable food products, such as plant-based meat substitutes (PBMSs), have received significant interest in recent years. A thorough understanding of the aroma composition can support efforts to improve the sensory properties of PBMS products and promote their consumer acceptability. Here, we developed an integrated hardware and software approach for aroma analysis of roasted food based on simultaneous analysis with three complementary detectors. Following the standard procedure of aroma headspace sampling and separation using solid-phase microextraction-gas chromatography, the column flow was split into three channels for the following detectors for the selective detection of nitrogen and sulfur (N/S)-containing compounds: an electron ionization-mass spectrometry for identification through a library search, a nitrogen-phosphorous detector, and a flame-photometric detector (FPD)/pulsed-FPD. Integration of results from the different types of detectors was achieved using a software tool, called AromaMS, developed in-house for data processing. As stipulated by the user, AromaMS performed either non-targeted screening for all volatile organic compounds (VOCs) or selective screening for N/S-containing VOCs that play a major role in the aroma experience. User-defined parameters for library matching and the retention index were applied to further eliminate false identifications. This new approach was successfully applied for comparative analysis of roasted meat and PBMS samples.

Evaluation of Matrix Complexity in Nontargeted Analysis of Small-Molecule Toxicants by Liquid Chromatography-High-Resolution Mass Spectrometry.

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.

pyAIR-A New Software Tool for Breathomics Applications-Searching for Markers in TD-GC-HRMS Analysis.

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.

A multiple-method comparative study using GC-MS, AMDIS and in-house-built software for the detection and identification of "unknown" volatile organic compounds in breath.

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.

Specificity enhancement by electrospray ionization multistage mass spectrometry--a valuable tool for differentiation and identification of 'V'-type chemical warfare agents.

The use of chemical warfare agents has become an issue of emerging concern. One of the challenges in analytical monitoring of the extremely toxic 'V'-type chemical weapons [O-alkyl S-(2-dialkylamino)ethyl alkylphosphonothiolates] is to distinguish and identify compounds of similar structure. MS analysis of these compounds reveals mostly fragment/product ions representing the amine-containing residue. Hence, isomers or derivatives with the same amine residue exhibit similar mass spectral patterns in both classical EI/MS and electrospray ionization-MS, leading to unavoidable ambiguity in the identification of the phosphonate moiety. A set of five 'V'-type agents, including O-ethyl S-(2-diisopropylamino)ethyl methylphosphonothiolate (VX), O-isobutyl S-(2-diethylamino)ethyl methylphosphonothiolate (RVX) and O-ethyl S-(2-diethylamino)ethyl methylphosphonothiolate (VM) were studied by liquid chromatography/electrospray ionization/MS, utilizing a QTRAP mass detector. MS/MS enhanced product ion scans and multistage MS(3) experiments were carried out. Based on the results, possible fragmentation pathways were proposed, and a method for the differentiation and identification of structural isomers and derivatives of 'V'-type chemical warfare agents was obtained. MS/MS enhanced product ion scans at various collision energies provided information-rich spectra, although many of the product ions obtained were at low abundance. Employing MS(3) experiments enhanced the selectivity for those low abundance product ions and provided spectra indicative of the different phosphonate groups. Study of the fragmentation pathways, revealing some less expected structures, was carried out and allowed the formulation of mechanistic rules and the determination of sets of ions typical of specific groups, for example, methylphosphonothiolates versus ethylphosphonothiolates. The new group-specific ions elucidated in this work are also useful for screening unknown 'V'-type agents and related compounds, utilizing precursor ion scan experiments.