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

**Authors:** Dongfu Xu, Pu Liu, Xiangming Meng, Yizhou Chen, Lei Du, Yan Zhang, Lixin Qiao, Wei Zhang, Jiale Kuang, Jingjing Liu

PMC · DOI: 10.3390/mi16040463 · Micromachines · 2025-04-14

## TL;DR

This paper introduces an electronic nose system that automatically collects end-tidal breath gases to improve breath detection accuracy for health monitoring.

## Contribution

A novel electronic nose system with automatic end-tidal breath gas collection and threshold control method is proposed.

## Key findings

- The system achieves breath detection with over 90% classification accuracy using machine learning.
- Automatic gas collection reduces contamination and breathing pattern variability effects.
- Improved gas quality leads to better performance compared to previous studies.

## 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.

## Full-text entities

- **Diseases:** injury to (MESH:D014947), respiratory diseases (MESH:D012140), diabetes (MESH:D003920), lung and (MESH:D008171), weakness (MESH:D018908), cancer (MESH:D009369), lung cancer (MESH:D008175), mouth eruptions (MESH:D009059), diarrhea (MESH:D003967), airway diseases (MESH:D029424), gastrointestinal distress (MESH:D012128), metabolic disorders (MESH:D008659), toothache (MESH:D014098), gastrointestinal disorders (MESH:D005767), nasal congestion (MESH:D009668), Digestive system disorders (MESH:D004066), coughing (MESH:D003371), renal disorders (MESH:D007674), panic (MESH:D016584)
- **Chemicals:** PTFE (MESH:D011138), methane (MESH:D008697), carbon monoxide (MESH:D002248), carbon (MESH:D002244), water (MESH:D014867), VOCs (MESH:D055549), ethanol (MESH:D000431), hydrogen sulfide (MESH:D006862), MP-135 (-), acetone (MESH:D000096), gases (MESH:D005740), CO2 (MESH:D002245), propane (MESH:D011407), hydrogen (MESH:D006859), Gas (MESH:D005708)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12029757/full.md

## References

51 references — full list in the complete paper: https://tomesphere.com/paper/PMC12029757/full.md

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Source: https://tomesphere.com/paper/PMC12029757