The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Advances in Electronic-Nose Technologies for the Detection of Volatile Biomarker Metabolites in the Human Breath
Open accessAbstract
Recent advancements in the use of electronic-nose (e-nose) devices to analyze human breath profiles for the presence of specific volatile metabolites, known as biomarkers or chemical bio-indicators of specific human diseases, metabolic disorders and the overall health status of individuals, are providing the potential for new noninvasive tools and techniques useful to point-of-care clinical disease diagnoses. This exciting new area of electronic disease detection and diagnosis promises to yield much faster and earlier detection of human diseases and disorders, allowing earlier, more effective treatments, resulting in more rapid patient recovery from various afflictions. E-nose devices are particularly suited for the field of disease diagnostics, because they are sensitive to a wide range of volatile organic compounds (VOCs) and can effectively distinguish between different complex gaseous mixtures via analysis of electronic aroma sensor-array output profiles of volatile metabolites present in the human breath. This review provides a summary of some recent developments of electronic-nose technologies, particularly involving breath analysis, with the potential for providing many new diagnostic applications for the detection of specific human diseases associated with different organs in the body, detectable from e-nose analyses of aberrant disease-associated VOCs present in air expired from the lungs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Volatile Organic Compounds of Lung Cancer and Possible Biochemical Pathways
Hakim, M., Broza, Y. Y., Barash, O., Peled, N., Phillips, M., Amann, A., & Haick, H. (2012) · Chemical Reviews · doi:10.1021/cr300174a · Cited by 953
Turner, C., Španěl, P., & Smith, D. (2006) · Physiological Measurement · doi:10.1088/0967-3334/27/4/001 · Cited by 379
Cited by
Recent advances in wearable sensors for animal health management
Neethirajan, S. (2017) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2016.11.004 · Cited by 502
Non-Invasive Blood Glucose Monitoring Technology: A Review
Tang, L., Chang, S. J., Chen, C.-J., & Liu, J.-T. (2020) · Sensors · doi:10.3390/s20236925 · Cited by 481
Breathomics for the clinician: the use of volatile organic compounds in respiratory diseases
Ibrahim, W., Carr, L., Cordell, R., Wilde, M. J., Salman, D., Monks, P. S., Thomas, P., Brightling, C. E., Siddiqui, S., & Greening, N. J. (2021) · Thorax · doi:10.1136/thoraxjnl-2020-215667 · Cited by 115
Breath VOC analysis and machine learning approaches for disease screening: a review
P, H., Rangarajan, M., & Pandya, H. J. (2023) · Journal of Breath Research · doi:10.1088/1752-7163/acb283 · Cited by 37
Metadata
- Authors
- Wilson, A.
- Venue
- Metabolites
- Publisher
- MDPI AG
- Published
- 2015-03-02
- Volume
- 5
- Issue
- 1
- Pages
- 140-163
- ISSN
- 2218-1989 (ISSN-L)
- OpenAlex
- W2036953109
- Cited by
- 242
- References
- 2
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Advances in Electronic-Nose Technologies for the Detection of Volatile Biomarker Metabolites in the Human Breath. https://app.cochat.ai/papers/10.3390/metabo5010140
- The paper
- Wilson, A. (2015). Advances in Electronic-Nose Technologies for the Detection of Volatile Biomarker Metabolites in the Human Breath. Metabolites. https://doi.org/10.3390/metabo5010140
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.