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

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–h...

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Autores principales: Wingelaar, Thijs T., Brinkman, Paul, de Vries, Rianne, van Ooij, Pieter-Jan A.M., Hoencamp, Rigo, Maitland-van der Zee, Anke-Hilse, Hollmann, Markus W., van Hulst, Rob A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950559/
https://www.ncbi.nlm.nih.gov/pubmed/31766640
http://dx.doi.org/10.3390/metabo9120286
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author Wingelaar, Thijs T.
Brinkman, Paul
de Vries, Rianne
van Ooij, Pieter-Jan A.M.
Hoencamp, Rigo
Maitland-van der Zee, Anke-Hilse
Hollmann, Markus W.
van Hulst, Rob A.
author_facet Wingelaar, Thijs T.
Brinkman, Paul
de Vries, Rianne
van Ooij, Pieter-Jan A.M.
Hoencamp, Rigo
Maitland-van der Zee, Anke-Hilse
Hollmann, Markus W.
van Hulst, Rob A.
author_sort Wingelaar, Thijs T.
collection PubMed
description 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.
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spelling pubmed-69505592020-01-16 Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data Wingelaar, Thijs T. Brinkman, Paul de Vries, Rianne van Ooij, Pieter-Jan A.M. Hoencamp, Rigo Maitland-van der Zee, Anke-Hilse Hollmann, Markus W. van Hulst, Rob A. Metabolites Article 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. MDPI 2019-11-22 /pmc/articles/PMC6950559/ /pubmed/31766640 http://dx.doi.org/10.3390/metabo9120286 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wingelaar, Thijs T.
Brinkman, Paul
de Vries, Rianne
van Ooij, Pieter-Jan A.M.
Hoencamp, Rigo
Maitland-van der Zee, Anke-Hilse
Hollmann, Markus W.
van Hulst, Rob A.
Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
title Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
title_full Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
title_fullStr Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
title_full_unstemmed Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
title_short Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data
title_sort detecting pulmonary oxygen toxicity using enose technology and associations between electronic nose and gas chromatography–mass spectrometry data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950559/
https://www.ncbi.nlm.nih.gov/pubmed/31766640
http://dx.doi.org/10.3390/metabo9120286
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