2026-04-17T11:33:36Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/4713942025-07-29T22:13:53Zcom_2072_98col_2072_378192

Breath analysis using electronic nose and gas chromatography-mass spectrometry : A pilot study on bronchial infections in bronchiectasis Fontes de Oliveira, Luciana Mallafré-Muro, Celia Giner, Jordi Perea, Lidia Sibila, Oriol Pardo, Antonio Marco, Santiago Breath analysis Bronchiectasis Signal processing E-nose GC-MS Altres ajuts: Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya; the European Social Fund (ESF); Institut de Bioenginyeria de Catalunya (IBEC); Sociedad Española de Neumología y Cirugía Torácica (SEPAR); Societat Catalana de Pneumologia (SOCAP); Fundació Catalana de Pneumologia (FUCAP). Background and aims: 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. Materials and methods: 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. Results: 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% (CI: 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. Conclusions: Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples. 2022 Article European Commission 712754 Ministerio de Economía y Competitividad SEV-2014-0425 Ministerio de Economía y Competitividad RTI2018-098577-B-C22 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1721 Instituto de Salud Carlos III PI18/00311 Clinica Chimica Acta ; Vol. 526 (january 2022), p. 6-13 open access Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. https://creativecommons.org/licenses/by-nc-nd/4.0/