Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

Luciana Fontes de Oliveira; Celia Mallafré-Muro; Jordi Giner; Lidia Perea; Oriol Sibila; Antonio Pardo; Santiago Marco

doi:10.1016/j.cca.2021.12.019

Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

Clin Chim Acta. 2022 Feb 1:526:6-13. doi: 10.1016/j.cca.2021.12.019. Epub 2021 Dec 23.

Authors

Luciana Fontes de Oliveira¹, Celia Mallafré-Muro², Jordi Giner³, Lidia Perea⁴, Oriol Sibila⁴, Antonio Pardo⁵, Santiago Marco⁶

Affiliations

¹ Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028, Barcelona, Spain.
² Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028, Barcelona, Spain; Department of Electronics and Biomedical Engineering, University of Barcelona, Marti I Franqués 1, 08028 Barcelona, Spain.
³ Department of Pneumology and Allergy. Hospital de la Sta. Creu I Sant Pau. Barcelona, Spain.
⁴ Respiratory Department, Hospital Clinic, IDIBAPS, Barcelona, Spain.
⁵ Department of Electronics and Biomedical Engineering, University of Barcelona, Marti I Franqués 1, 08028 Barcelona, Spain.
⁶ Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028, Barcelona, Spain; Department of Electronics and Biomedical Engineering, University of Barcelona, Marti I Franqués 1, 08028 Barcelona, Spain. Electronic address: smarco@ibecbarcelona.eu.

PMID: 34953821
DOI: 10.1016/j.cca.2021.12.019

Abstract

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_95%: 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.

Keywords: Breath analysis; Bronchiectasis; E-nose; GC-MS; Signal processing.

MeSH terms

Breath Tests
Bronchiectasis* / diagnosis
Electronic Nose
Gas Chromatography-Mass Spectrometry
Humans
Pilot Projects
Volatile Organic Compounds*

Substances

Volatile Organic Compounds