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

Abstract

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.

Expert considerations and consensus for using dogs to detect human SARS-CoV-2-infections

“…Prior studies have reported quantifiable differences in about two dozen VOCs between individuals with COVID-19 versus healthy individuals as well as individuals with other respiratory diseases. Particularly striking here are COVID-19-associated elevated concentrations of certain alcohols such as butanol and propanol or derivatives ( 33 , 35 , 37 , 38 ), aldehydes such as heptanal, octanal, and nonanal ( 33 , 34 , 36 ), as well as ketones such as acetone and butanone or derivatives ( 33 , 38 ). Other substances with reported increased concentrations are various alkanes, alkenes, further aldehydes, aromatic substances, and their derivatives ( 33 , 34 , 36 – 38 ).…”

“…Particularly striking here are COVID-19-associated elevated concentrations of certain alcohols such as butanol and propanol or derivatives (33,35,37,38), aldehydes such as heptanal, octanal, and nonanal (33,34,36), as well as ketones such as acetone and butanone or derivatives (33,38). Other substances with reported increased concentrations are various alkanes, alkenes, further aldehydes, aromatic substances, and their derivatives (33,34,(36)(37)(38). Decreased VOC-concentrations in COVID-19-breath were shown for methanol (33) and -in contrast to Ruszkiewicz et al (33) -acetone (35).…”

“…Various studies exist, which give striking insights into SARS-CoV-2-VOC-profiles with differing identifiable VOCs mainly via gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS), time-of-flight-mass spectrometry (TOF-MS) or related techniques (33)(34)(35)(36)(37)(38). In principle, spectrometric techniques enable the identification and quantification of VOCs in breath samples, preceded by gas chromatographic separation if needed.…”

“…Small ions, molecules or molecular fragments cannot be easily detected and make it difficult to interpret and draw conclusions about originally contained compounds. For example, small hydrocarbon-based molecules occur abundantly in exhaled breath, making their detection complicated due to overlap with molecules of similar spectra ( 38 ). In addition, certain measurable VOCs are non-specifically altered across diseases making disease discrimination prone to errors.…”

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

“…The peaks obtained by GC-MS can be identified by matching measured spectra with spectra in a reference library, which takes into consideration not only the mass and intensities of the ions obtained but also the chromatographic retention index (RI) of the compound of interest [ 14 ]. Hence, the use of GC-MS for targeted and non-targeted metabolic studies, especially for breathomics, is highly suitable and powerful [ 15 ]. In recent years, GC-HRMS has gained popularity [ 16 ].…”