Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 14;16(4):463.
doi: 10.3390/mi16040463.

Design of an Electronic Nose System with Automatic End-Tidal Breath Gas Collection for Enhanced Breath Detection Performance

Dongfu Xu  1 , Pu Liu  1 , Xiangming Meng  1 , Yizhou Chen  2 , Lei Du  1 , Yan Zhang  1 , Lixin Qiao  1 , Wei Zhang  1 , Jiale Kuang  1 , Jingjing Liu  1
Affiliations

Design of an Electronic Nose System with Automatic End-Tidal Breath Gas Collection for Enhanced Breath Detection Performance

Dongfu Xu et al. Micromachines (Basel). .

Abstract

End-tidal breath gases originate deep within the lungs, and their composition is an especially accurate reflection of the body's metabolism and health status. Therefore, accurate collection of end-tidal breath gases is crucial to enhance electronic noses' performance in breath detection. Regarding this issue, this study proposes a novel electronic nose system and employs a threshold control method based on exhaled gas flow characteristics to design a gas collection module. The module monitors real-time gas flow with a flow meter and integrates solenoid valves to regulate the gas path, enabling automatic collection of end-tidal breath gas. In this way, the design reduces dead space gas contamination and the impact of individual breathing pattern differences. The sensor array is designed to detect the collected gas, and the response chamber is optimized to improve the detection stability. At the same time, the control module realizes automation of the experiment process, including control of the gas path state, signal transmission, and data storage. Finally, the system is used for breath detection. We employ classical machine learning algorithms to classify breath samples from different health conditions with a classification accuracy of more than 90%, which is better than the accuracy achieved in other studies of this type. This is due to the improved quality of the gas we extracted, demonstrating the superiority of our proposed electronic nose system.

Keywords: electronic nose; end-tidal breath gases; exhaled breath analysis.

PubMed Disclaimer

Conflict of interest statement

Author Xiangming Meng was employed by the company Chongqing Onespace Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure A1
Figure A1
Results of PCA dimensionality reduction.
Figure 1
Figure 1
Structural diagram of electronic nose system.
Figure 2
Figure 2
Schematic diagram of end-tidal breath gas collection state.
Figure 3
Figure 3
System control flow chart.
Figure 4
Figure 4
(a) Sensor peripheral circuit; (b) reference power supply circuit; (c) 5 V switching power supply circuit; (d) adjustable switching power supply circuit.
Figure 5
Figure 5
Schematic diagram of the expiratory flow–time curve.
Figure 6
Figure 6
Gas distribution at time t1.
Figure 7
Figure 7
(a) Sensor 1–5 response data box plot. (b) Sensor 6–10 response data box plot.
Figure 8
Figure 8
Results of LDA dimensionality reduction.
Figure 9
Figure 9
The graph illustrating the optimization of SVM parameters using GA.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Li Y., Wei X., Zhou Y., Wang J., You R. Research progress of electronic nose technology in exhaled breath disease analysis. Microsyst. Nanoeng. 2023;9:129. doi: 10.1038/s41378-023-00594-0. - DOI - PMC - PubMed
    1. Shanmugasundaram A., Manorama S.V., Kim D.-S., Jeong Y.-J., Weon Lee D. Toward Point-of-Care chronic disease Management: Biomarker detection in exhaled breath using an E-Nose sensor based on rGO/SnO2 superstructures. Chem. Eng. J. 2022;448:137736. doi: 10.1016/j.cej.2022.137736. - DOI
    1. Moon H.G., Jung Y., Han S.D., Shim Y.-S., Shin B., Lee T., Kim J.-S., Lee S., Jun S.C., Park H.-H., et al. Chemiresistive Electronic Nose toward Detection of Biomarkers in Exhaled Breath. ACS Appl. Mater. Interfaces. 2016;8:20969–20976. doi: 10.1021/acsami.6b03256. - DOI - PubMed
    1. Wilder-Smith C.H., Olesen S.S., Materna A., Drewes A.M. Breath methane concentrations and markers of obesity in patients with functional gastrointestinal disorders. United Eur. Gastroenterol. J. 2017;6:595–603. doi: 10.1177/2050640617744457. - DOI - PMC - PubMed
    1. Scharte M., von Ostrowski T.A., Daudel F., Freise H., Van Aken H., Bone H.G. Endogenous carbon monoxide production correlates weakly with severity of acute illness. Eur. J. Anaesthesiol. 2006;23:117–122. doi: 10.1017/S0265021505002012. - DOI - PubMed

LinkOut - more resources