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Purpose <p>Electronic noses (eNose) and gas chromatography mass spectrometry (GC–MS) are two important breath analysis approaches for differentiating between respiratory diseases. We evaluated the performance of a novel electronic nose for different respiratory diseases, and exhaled breath samples from patients were analyzed by GC–MS.</p> Materials and Methods <p>Patients with lung cancer, pneumonia, structural lung diseases, and healthy controls were recruited (May 2019–July 2022). Exhaled breath samples were collected for eNose and GC–MS analysis. Breathprint features from eNose were analyzed using support vector machine model and leave-one-out cross-validation was performed.</p> Results <p>A total of 263 participants (including 95 lung cancer, 59 pneumonia, 71 structural lung disease, and 38 healthy participants) were included. Three-dimensional linear discriminant analysis (LDA) showed a clear distribution of breathprints. The overall accuracy of eNose for four groups was 0.738 (194/263). The accuracy was 0.86 (61/71), 0.81 (77/95), 0.53 (31/59), and 0.66 (25/38) for structural lung disease, lung cancer, pneumonia, and control groups respectively. Pair-wise diagnostic performance comparison revealed excellent discriminant power (AUC: 1–0.813) among four groups. The best performance was between structural lung disease and healthy controls (AUC: 1), followed by lung cancer and structural lung disease (AUC: 0.958). Volatile organic compounds revealed a high individual occurrence rate of cyclohexanone and N,N-dimethylacetamide in pneumonic patients, ethyl acetate in structural lung disease, and 2,3,4-trimethylhexane in lung cancer patients.</p> Conclusions <p>Our study showed that the novel eNose effectively distinguishes respiratory diseases and holds potential as a point-of-care diagnostic tool, with GC–MS identifying candidate VOC biomarkers.</p>

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Exhaled Breath Analysis Using a Novel Electronic Nose for Different Respiratory Disease Entities

  • 期刊论文

  • 发表日期：

    2025年1月3日

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  • Kai-Lun Yu,
  • Han-Ching Yang,
  • Chien-Feng Lee,
  • Shang-Yu Wu,
  • Zhong-Kai Ye,
  • Sujeet Kumar Rai,
  • Meng-Rui Lee,
  • Kea-Tiong Tang,
  • Jann-Yuan Wang

摘要

Purpose

Electronic noses (eNose) and gas chromatography mass spectrometry (GC–MS) are two important breath analysis approaches for differentiating between respiratory diseases. We evaluated the performance of a novel electronic nose for different respiratory diseases, and exhaled breath samples from patients were analyzed by GC–MS.

Materials and Methods

Patients with lung cancer, pneumonia, structural lung diseases, and healthy controls were recruited (May 2019–July 2022). Exhaled breath samples were collected for eNose and GC–MS analysis. Breathprint features from eNose were analyzed using support vector machine model and leave-one-out cross-validation was performed.

Results

A total of 263 participants (including 95 lung cancer, 59 pneumonia, 71 structural lung disease, and 38 healthy participants) were included. Three-dimensional linear discriminant analysis (LDA) showed a clear distribution of breathprints. The overall accuracy of eNose for four groups was 0.738 (194/263). The accuracy was 0.86 (61/71), 0.81 (77/95), 0.53 (31/59), and 0.66 (25/38) for structural lung disease, lung cancer, pneumonia, and control groups respectively. Pair-wise diagnostic performance comparison revealed excellent discriminant power (AUC: 1–0.813) among four groups. The best performance was between structural lung disease and healthy controls (AUC: 1), followed by lung cancer and structural lung disease (AUC: 0.958). Volatile organic compounds revealed a high individual occurrence rate of cyclohexanone and N,N-dimethylacetamide in pneumonic patients, ethyl acetate in structural lung disease, and 2,3,4-trimethylhexane in lung cancer patients.

Conclusions

Our study showed that the novel eNose effectively distinguishes respiratory diseases and holds potential as a point-of-care diagnostic tool, with GC–MS identifying candidate VOC biomarkers.