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Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

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Diving and Submarine Medical Center, Royal Netherlands Navy, Rijkszee en Marinehaven, 1780 CA Den Helder, The Netherlands
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Department of Anesthesiology, Amsterdam University Medical Center, location AMC, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
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Department of Pulmonology, Amsterdam University Medical Center, location AMC, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
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Breathomix, Pascalstraat 13H, 2811 EL Reeuwijk, the Netherlands
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Department of Surgery, Alrijne Hospital, Simon Smitweg 1, 2353 GA Leiderdorp, The Netherlands
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Defense Healthcare Organisation, Ministry of Defence, Herculeslaan 1, 3584 AB Utrecht, The Netherlands
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Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
*
Author to whom correspondence should be addressed.
Metabolites 2019, 9(12), 286; https://doi.org/10.3390/metabo9120286
Received: 8 October 2019 / Revised: 4 November 2019 / Accepted: 20 November 2019 / Published: 22 November 2019
(This article belongs to the Special Issue Volatile Metabolites’ New Frontier for Metabolomics)
Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS. View Full-Text
Keywords: oxygen diving; two-way orthogonal partial least square regression; O2PLS; mixomics oxygen diving; two-way orthogonal partial least square regression; O2PLS; mixomics
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Wingelaar, T.T.; Brinkman, P.; de Vries, R.; van Ooij, P.-J.A.; Hoencamp, R.; Maitland-van der Zee, A.-H.; Hollmann, M.W.; van Hulst, R.A. Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data. Metabolites 2019, 9, 286.

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