Cited by 179Advanced Chemical Sensor TechnologiesSpectroscopy and Laser ApplicationsArtificial Intelligence in Healthcare

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Electronic nose: a non-invasive technology for breath analysis of diabetes and lung cancer patients

Behera, Bhagaban, Joshi, Rathin, Anil Vishnu, G K, Bhalerao, Sanjay, Pandya, Hardik J·2019·Journal of Breath Research

DOI 10.1088/1752-7163/aafc77

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Abstract

In human exhaled breath, more than 3000 volatile organic compounds (VOCs) are found, which are directly or indirectly related to internal biochemical processes in the body. Electronic noses (E-noses) could play a potential role in screening/analyzing various respiratory and systemic diseases by studying breath signatures. An E-nose integrates a sensor array and an artificial neural network that responds to specific patterns of VOCs, and thus can act as a non-invasive technology for disease monitoring. The gold standard blood glucose monitoring test for diabetes diagnostics is invasive and highly uncomfortable. This contributes to the massive need for technologies which are non-invasive and can be used as an alternative to blood measurements for glucose detection. While lung cancer is one of the deadliest cancers with the highest death rate and an extremely high yearly global burden, the conventional diagnosis means, such as sputum cytology, chest radiography, or computed tomography, do not support wide-range population screening. A few standard non-invasive techniques, such as mass spectrometry and gas chromatography, are expensive, non-portable, and require skilled personnel for operation and are again not suitable for large-scale screening. Breath contains markers for both diabetes and lung cancer along with markers for several diseases and thus, a non-invasive technique such as the E-nose would greatly improve analysis procedures over existing invasive methods. This review shows the state-of-the-art technologies for VOC detection and machine learning approaches for two clinical models: diabetes and lung cancer detection.

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Authors: Behera, B., Joshi, R., Anil Vishnu, G. K., Bhalerao, S., & Pandya, H. J.
Affiliations: Indian Institute of Science Bangalore (IN)
Corresponding author: Pandya, Hardik J
ORCID: Behera, Bhagaban
Joshi, Rathin
Pandya, Hardik J
Venue: Journal of Breath Research
Publisher: IOP Publishing
Published: 2019-03-06
Volume: 13
Issue: 2
Pages: 024001
ISSN: 1752-7155 (ISSN-L)
1752-7163
DOI: 10.1088/1752-7163/aafc77
OpenAlex: W2910501941
Cited by: 179
References: 170
Citations by year: 2026: 12 · 2025: 21 · 2024: 28 · 2023: 25 · 2022: 35 · 2021: 24 · 2020: 25 · 2019: 9
FWCI: 1.88
Type: journal article
Language: en
Open access: closed
Licence: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor)
iopscience.iop.org/info/page/text-and-data-mining (tdm)
Locations: Journal of Breath Research (publishedVersion)
PubMed (publishedVersion)
NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
Physical Sciences › Chemistry › Spectroscopy › Spectroscopy and Laser Applications
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: electronic nose, breath analysis, volatile organic compounds, diabetes diagnosis, lung cancer detection, non-invasive diagnosis, blood glucose monitoring, VOC detection, machine learning classification, disease screening
MeSH terms: Machine Learning, Breath Tests, Diabetes Mellitus, Humans, Lung Neoplasms, Nanoparticles, Electronic Nose

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Electronic nose: a non-invasive technology for breath analysis of diabetes and lung cancer patients

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