Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis
[Display omitted] •Breath samples belonging to Bronchial infections patients in Bronchiectasis were analysed by electronic nose and gas chromatography-mass spectrometry (GC-MS)•After proper experimental design to control for the identified confounding factors, predictive models based on electronic n...
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| Veröffentlicht in: | Clinica chimica acta 2022-02, Vol.526, p.6-13 |
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| container_title | Clinica chimica acta |
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| creator | Oliveira, Luciana Fontes de Mallafré-Muro, Celia Giner, Jordi Perea, Lidia Sibila, Oriol Pardo, Antonio Marco, Santiago |
| description | [Display omitted]
•Breath samples belonging to Bronchial infections patients in Bronchiectasis were analysed by electronic nose and gas chromatography-mass spectrometry (GC-MS)•After proper experimental design to control for the identified confounding factors, predictive models based on electronic nose data provided excellent results in blind samples.•Instead, the more complex GC-MS analysis protocol provided inconsistent results that did not pass the permutation test.•The potential reasons underlying the better performance of electronic noses compared with GC-MS are described.
In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.
A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.
Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI95%: 84–100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.
Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples. |
| doi_str_mv | 10.1016/j.cca.2021.12.019 |
| format | Article |
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•Breath samples belonging to Bronchial infections patients in Bronchiectasis were analysed by electronic nose and gas chromatography-mass spectrometry (GC-MS)•After proper experimental design to control for the identified confounding factors, predictive models based on electronic nose data provided excellent results in blind samples.•Instead, the more complex GC-MS analysis protocol provided inconsistent results that did not pass the permutation test.•The potential reasons underlying the better performance of electronic noses compared with GC-MS are described.
In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.
A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.
Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI95%: 84–100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.
Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.</description><identifier>ISSN: 0009-8981</identifier><identifier>EISSN: 1873-3492</identifier><identifier>DOI: 10.1016/j.cca.2021.12.019</identifier><identifier>PMID: 34953821</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Breath analysis ; Breath Tests ; Bronchiectasis ; Bronchiectasis - diagnosis ; E-nose ; Electronic Nose ; Gas Chromatography-Mass Spectrometry ; GC-MS ; Humans ; Pilot Projects ; Signal processing ; Volatile Organic Compounds</subject><ispartof>Clinica chimica acta, 2022-02, Vol.526, p.6-13</ispartof><rights>2021 The Author(s)</rights><rights>Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-a289a94cefebf443589289ab61cc2a373ae06da040d7e93a1365acff640dd9d73</citedby><cites>FETCH-LOGICAL-c396t-a289a94cefebf443589289ab61cc2a373ae06da040d7e93a1365acff640dd9d73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0009898121004484$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34953821$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Oliveira, Luciana Fontes de</creatorcontrib><creatorcontrib>Mallafré-Muro, Celia</creatorcontrib><creatorcontrib>Giner, Jordi</creatorcontrib><creatorcontrib>Perea, Lidia</creatorcontrib><creatorcontrib>Sibila, Oriol</creatorcontrib><creatorcontrib>Pardo, Antonio</creatorcontrib><creatorcontrib>Marco, Santiago</creatorcontrib><title>Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis</title><title>Clinica chimica acta</title><addtitle>Clin Chim Acta</addtitle><description>[Display omitted]
•Breath samples belonging to Bronchial infections patients in Bronchiectasis were analysed by electronic nose and gas chromatography-mass spectrometry (GC-MS)•After proper experimental design to control for the identified confounding factors, predictive models based on electronic nose data provided excellent results in blind samples.•Instead, the more complex GC-MS analysis protocol provided inconsistent results that did not pass the permutation test.•The potential reasons underlying the better performance of electronic noses compared with GC-MS are described.
In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.
A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.
Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI95%: 84–100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.
Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.</description><subject>Breath analysis</subject><subject>Breath Tests</subject><subject>Bronchiectasis</subject><subject>Bronchiectasis - diagnosis</subject><subject>E-nose</subject><subject>Electronic Nose</subject><subject>Gas Chromatography-Mass Spectrometry</subject><subject>GC-MS</subject><subject>Humans</subject><subject>Pilot Projects</subject><subject>Signal processing</subject><subject>Volatile Organic Compounds</subject><issn>0009-8981</issn><issn>1873-3492</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMuO1DAQRS0EYpqBD2CDvGST4EdehtXMiJc0EhtYW9V2pdutJA6uBClfwG_jpmdYsirXrVvXqsPYaylKKWTz7lQ6B6USSpZSlUKaJ2wnu1YXujLqKdsJIUzRmU5esRdEp9xWopHP2VWe17pTcsd-3yaE5chhgmGjQHylMB04DuiWFKfg-BQJ89jzAxB3xxRHWOIhwXzcihGIOM1_vSMuaXvPb_gchrhwWla_8TjxfY5xxwADD1OfnSFOlJ-PelYg__uSPethIHz1UK_Zj08fv999Ke6_ff56d3NfOG2apQDVGTCVwx73fVXpujNnZd9I5xToVgOKxkO-07doNEjd1OD6vsmCN77V1-ztJXdO8eeKtNgxkMNhgAnjSlY1smrrrqpFtsqL1aVIlLC3cwojpM1KYc_87clm_vbM30plM_-88-Yhft2P6P9tPALPhg8XA-YjfwVMllzAyaEPKaOwPob_xP8B5tCZYA</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Oliveira, Luciana Fontes de</creator><creator>Mallafré-Muro, Celia</creator><creator>Giner, Jordi</creator><creator>Perea, Lidia</creator><creator>Sibila, Oriol</creator><creator>Pardo, Antonio</creator><creator>Marco, Santiago</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20220201</creationdate><title>Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis</title><author>Oliveira, Luciana Fontes de ; Mallafré-Muro, Celia ; Giner, Jordi ; Perea, Lidia ; Sibila, Oriol ; Pardo, Antonio ; Marco, Santiago</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-a289a94cefebf443589289ab61cc2a373ae06da040d7e93a1365acff640dd9d73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Breath analysis</topic><topic>Breath Tests</topic><topic>Bronchiectasis</topic><topic>Bronchiectasis - diagnosis</topic><topic>E-nose</topic><topic>Electronic Nose</topic><topic>Gas Chromatography-Mass Spectrometry</topic><topic>GC-MS</topic><topic>Humans</topic><topic>Pilot Projects</topic><topic>Signal processing</topic><topic>Volatile Organic Compounds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oliveira, Luciana Fontes de</creatorcontrib><creatorcontrib>Mallafré-Muro, Celia</creatorcontrib><creatorcontrib>Giner, Jordi</creatorcontrib><creatorcontrib>Perea, Lidia</creatorcontrib><creatorcontrib>Sibila, Oriol</creatorcontrib><creatorcontrib>Pardo, Antonio</creatorcontrib><creatorcontrib>Marco, Santiago</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Clinica chimica acta</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oliveira, Luciana Fontes de</au><au>Mallafré-Muro, Celia</au><au>Giner, Jordi</au><au>Perea, Lidia</au><au>Sibila, Oriol</au><au>Pardo, Antonio</au><au>Marco, Santiago</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis</atitle><jtitle>Clinica chimica acta</jtitle><addtitle>Clin Chim Acta</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>526</volume><spage>6</spage><epage>13</epage><pages>6-13</pages><issn>0009-8981</issn><eissn>1873-3492</eissn><abstract>[Display omitted]
•Breath samples belonging to Bronchial infections patients in Bronchiectasis were analysed by electronic nose and gas chromatography-mass spectrometry (GC-MS)•After proper experimental design to control for the identified confounding factors, predictive models based on electronic nose data provided excellent results in blind samples.•Instead, the more complex GC-MS analysis protocol provided inconsistent results that did not pass the permutation test.•The potential reasons underlying the better performance of electronic noses compared with GC-MS are described.
In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.
A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.
Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI95%: 84–100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.
Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>34953821</pmid><doi>10.1016/j.cca.2021.12.019</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Breath analysis Breath Tests Bronchiectasis Bronchiectasis - diagnosis E-nose Electronic Nose Gas Chromatography-Mass Spectrometry GC-MS Humans Pilot Projects Signal processing Volatile Organic Compounds |
| title | Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis |
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