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Exhaled breath analysis using electronic noses (e-noses) is a promising non-invasive diagnostic tool. However, a lack of standardized protocols limits clinical implementation. This study evaluates the consistency of breathprints in healthy subjects using the Cyranose 320 e-nose to support standardization efforts. Breath samples from 139 healthy non-smoking subjects (age range 18-65 years) were collected using a standardized protocol. Participants exhaled into a Tedlar bag for immediate analysis with the Cyranose 320. Principal Component Analysis (PCA) was used to reduce data dimensionality, and K-means clustering grouped subjects based on breathprints. PCA identified four principal components explaining 97.15% of variance. K-means clustering revealed two clusters: 1 outlier and 138 subjects with highly similar breathprints. The median distance from the cluster center was 0.21 (IQR: 0.18-0.24), indicating low variability. Box plots confirmed breathprint consistency across subjects. The high consistency of breathprints in healthy subjects supports the feasibility of standardizing e-nose protocols. These findings highlight the potential of e-noses for clinical diagnostics, warranting further research in diverse populations and disease cohorts.
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http://dx.doi.org/10.3390/s25082610 | DOI Listing |
Sensors (Basel)
April 2025
Respiratory Diseases, University of Bari "Aldo Moro", 70121 Bari, Italy.
Exhaled breath analysis using electronic noses (e-noses) is a promising non-invasive diagnostic tool. However, a lack of standardized protocols limits clinical implementation. This study evaluates the consistency of breathprints in healthy subjects using the Cyranose 320 e-nose to support standardization efforts.
View Article and Find Full Text PDFACS Sens
April 2025
Department of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, PR China.
Early screening of individuals with high-risk lung nodules can significantly improve the prognosis of lung cancer patients, and accurate identification of lung nodule subtypes can provide guidance for medical treatment. Exhaled breath (EB) analysis via eNoses offers a quick and noninvasive approach, but current eNose technology lacks quality control and solid validation in large population studies. Herein, an eNose platform integrated with a metal ion-decorated graphene sensor array and a breath sampling accessory was established.
View Article and Find Full Text PDFLung
January 2025
Department of Internal Medicine, National Taiwan University Hospital, No.7, Chung Shan S. Rd., Zhongzheng District, Taipei City, 100225, Taiwan.
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).
Sci Rep
July 2024
Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, V6T 1Z3, Canada.
Non-human primates remain the most useful and reliable pre-clinical model for many human diseases. Primate breath profiles have previously distinguished healthy animals from diseased, including non-human primates. Breath collection is relatively non-invasive, so this motivated us to define a healthy baseline breath profile that could be used in studies evaluating disease, therapies, and vaccines in non-human primates.
View Article and Find Full Text PDFRespir Res
May 2024
Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Background: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed.
Methods: Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site.