The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring
Open accessAbstract
The review describes the technologies used in the field of breath analysis to diagnose and monitor diabetes mellitus. Currently the diagnosis and monitoring of blood glucose and ketone bodies that are used in clinical studies involve the use of blood tests. This method entails pricking fingers for a drop of blood and placing a drop on a sensitive area of a strip which is pre-inserted into an electronic reading instrument. Furthermore, it is painful, invasive and expensive, and can be unsafe if proper handling is not undertaken. Human breath analysis offers a non-invasive and rapid method for detecting various volatile organic compounds thatare indicators for different diseases. In patients with diabetes mellitus, the body produces excess amounts of ketones such as acetoacetate, beta-hydroxybutyrate and acetone. Acetone is exhaled during respiration. The production of acetone is a result of the body metabolising fats instead of glucose to produce energy. There are various techniques that are used to analyse exhaled breath including Gas Chromatography Mass Spectrometry (GC–MS), Proton Transfer Reaction Mass Spectrometry (PTR–MS), Selected Ion Flow Tube-Mass Spectrometry (SIFT–MS), laser photoacoustic spectrometry and so on. All these techniques are not portable, therefore this review places emphasis on how nanotechnology, through semiconductor sensing nanomaterials, has the potential to help individuals living with diabetes mellitus monitor their disease with cheap and portable devices.
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
Faist, J., Capasso, F., Sivco, D. L., Sirtori, C., Hutchinson, A. L., & Cho, A. Y. (1994) · Science · doi:10.1126/science.264.5158.553 · Cited by 4,552
Metal Oxide Gas Sensors: Sensitivity and Influencing Factors
Wang, C., Yin, L., Zhang, L., Xiang, D., & Gao, R. (2010) · Sensors · doi:10.3390/s100302088 · Cited by 2,928
Classification and Diagnosis of Diabetes
Fajans, S. S. (1990) · Medical Entomology and Zoology · Cited by 2,356
(2) Classification and diagnosis of diabetes
American Diabetes Association (2015) · Diabetes Care · doi:10.2337/dc15-s005 · Cited by 1,841
Electronic Nose: Current Status and Future Trends
Röck, F., Barsan, N., & Weimar, U. (2008) · Chemical Reviews · doi:10.1021/cr068121q · Cited by 1,437
Metal Oxide Nanostructures and Their Gas Sensing Properties: A Review
Sun, Y.-F., Liu, S.-B., Meng, F.-L., Liu, J.-Y., Jin, Z., Kong, L.-T., & Liu, J.-H. (2012) · Sensors · doi:10.3390/s120302610 · Cited by 1,218
Diagnostic potential of breath analysis—focus on volatile organic compounds
Miekisch, W., Schubert, J. K., & Noeldge-Schomburg, G. F. E. (2004) · Clinica Chimica Acta · doi:10.1016/j.cccn.2004.04.023 · Cited by 1,096
Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study
Phillips, M., Gleeson, K., Hughes, J. M. B., Greenberg, J., Cataneo, R. N., Baker, L., & McVay, W. P. (1999) · The Lancet · doi:10.1016/s0140-6736(98)07552-7 · Cited by 918
Ceramic sensors for humidity detection: the state-of-the-art and future developments
Traversa, E. (1995) · Sensors and Actuators B: Chemical · doi:10.1016/0925-4005(94)01268-m · Cited by 848
Amann, A., Costello, B. D. L., Miekisch, W., Schubert, J., Buszewski, B., Pleil, J., Ratcliffe, N., & Risby, T. (2014) · Journal of Breath Research · doi:10.1088/1752-7155/8/3/034001 · Cited by 804
Cited by
Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone
Shokrekhodaei, M., & Quinones, S. (2020) · Sensors · doi:10.3390/s20051251 · Cited by 221
Metadata
- Authors
- Saasa, V., Malwela, T., Beukes, M., Mokgotho, M., Liu, C.-P., & Mwakikunga, B.
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2018-01-31
- Volume
- 8
- Issue
- 1
- Pages
- 12
- ISSN
- 2075-4418 (ISSN-L)
- OpenAlex
- W2792831187
- Cited by
- 287
- References
- 96
- FWCI
- 8.21
- 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: Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring. https://app.cochat.ai/papers/10.3390/diagnostics8010012
- The paper
- Saasa, V., Malwela, T., Beukes, M., Mokgotho, M., Liu, C.-P., & Mwakikunga, B. (2018). Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring. Diagnostics. https://doi.org/10.3390/diagnostics8010012
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.