ISSN: 2222-6990
Open access
Breath analysis offers a non-invasive, rapid, and cost-effective method for diagnosing respiratory diseases and monitoring various health conditions. However, non-invasive breath analysis techniques often suffer from lower sensitivity and specificity compared to invasive methods, primarily due to the lack of standardized protocols. While invasive methods provide greater accuracy, they are associated with increased discomfort and risk. This study focuses on enhancing the precision and reliability of non-invasive breath sensors by developing a breath sensor using the commercially available MH-Z14A CO2 sensor. Breath samples were collected from smokers and non-smokers using Tedlar bags and analysed to detect CO2 levels. The sensor’s output was processed through an ESP32, providing real-time CO2 concentration readings. Results revealed that smokers exhibited longer response and recovery times, attributed to elevated CO2 levels, while sensitivity analysis demonstrated the sensor's ability to detect minute variations in CO2 concentrations. These findings underscore the potential of this sensor for non-invasive respiratory monitoring and early detection of respiratory conditions.
WHO EMRO | Chronic obstructive pulmonary disease (COPD) | Health topics. Accessed: Jan. 10, 2024. [Online]. Available: https://www.emro.who.int/health-topics/chronic-obstructive-pulmonary-disease-copd/index.html
Hashoul, D., & Haick, H. (2019). Sensors for detecting pulmonary diseases from exhaled breath. European Respiratory Review, vol. 28, no. 152. https://doi.10.1183/16000617.0011
Saidi, T., Zaim, O., Moufid, M., El Bari, N., Ionescu, R., & Bouchikhi, B. (2018). Exhaled breath analysis using electronic nose and gas chromatography–mass spectrometry for non-invasive diagnosis of chronic kidney disease, diabetes mellitus and healthy subjects. Sens Actuators B Chem, vol. 257, pp. 178–188. https://doi.10.1016/J.SNB.2017.10.178
Lutz, É. & Coradi, P.C. (2022). Applications of new technologies for monitoring and predicting grains quality stored: Sensors, Internet of Things, and Artificial Intelligence. Measurement, vol. 188, p. 110609. https://doi. 10.1016/J.MEASUREMENT.2021.110609
Smith, J. & Jones, A. (2021). Breath analysis using commercial sensors: Benefits in terms of cost-effectiveness and usability, but potential drawbacks in sensitivity, selectivity, stability, and reproducibility. vol. 10, no. 3, pp. 123–145
Amal, H. & Haick, H. (2020). Point of care breath analysis systems. Advanced Nanomaterials for Inexpensive Gas Microsensors: Synthesis, Integration and Applications, pp. 315–334. https://doi.10.1016/B978-0-12-814827-3.00014-1
Ramanathan, S., Malarvili, M. B. & Gopinath, S. C. B. (2023). Assessing respiratory complications by carbon dioxide sensing platforms: Advancements in infrared radiation technology and IoT integration. Arabian Journal of Chemistry, vol. 16, no. 2, p. 104478, Feb. https://doi.10.1016/J.ARABJC.2022.104478
Hong, C. S., Ghani, A. S. A. & Khairuddin, I. M. (2018). Development of an Electronic Kit for detecting asthma in Human Respiratory System. IOP Conf Ser Mater Sci Eng, vol. 319, no. 1, p. 012040. https://doi.10.1088/1757-899X/319/1/012040
Fuadi, R. M., Yulianto, E., Irianto, B. G. & Mishra, A. (2023). Design of Carbon Dioxide Levels Measurement in Human Expiration Using EtCO2 Capnography Method. Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 5, no. 1. https://doi.10.35882/IJEEEMI.V5I1.266
Kaloumenou, M., Skotadis, E., Lagopati, N., Efstathopoulos, E. & Tsoukalas, D. (2022). Breath Analysis: A Promising Tool for Disease Diagnosis—The Role of Sensors. Sensors (Basel), vol. 22, no. 3. https://doi.10.3390/S22031238
Salama, R., Al-Turjman, F., Chaudhary, P. & Yadav, S. P. (2023). Benefits of Internet of Things (IoT) Applications in Health care - An Overview. 2023 International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023, pp. 778–784. https://doi.10.1109/CICTN57981.2023.10141452
Laniado-Laborin, R. (2009). Smoking and Chronic Obstructive Pulmonary Disease (COPD). Parallel Epidemics of the 21st Century. Int J Environ Res Public Health, vol. 6, no. 1, p. 209. https://doi. 10.3390/IJERPH6010209
Azuma, K., Kagi, N., Yanagi, U. & Osawa, H. (2018). Effects of low-level inhalation exposure to carbon dioxide in indoor environments: A short review on human health and psychomotor performance. Environ Int, vol. 121, no. Pt 1, pp. 51–56. https://doi.10.1016/J.ENVINT.2018.08.059
Loeb, E. (2024). Association between occupational exposure and chronic obstructive pulmonary disease and respiratory symptoms in the Spanish population. Arch Bronconeumol, vol. 60, no. 1, pp. 16–22. https://doi.10.1016/J.ARBRES.2023.10.014
Hernandez-Miranda, L. R. & Birchmeier, C. (2015). CO2 in the spotlight. Elife, vol. 4. https://doi.10.7554/ELIFE.08086
Dayekh, M. Lo., Hussain, S. A., & Hussain, S. A. (2022). Gas Sensor and Sensitivity. Metal-Oxide Gas Sensors. https://doi.10.5772/INTECHOPEN.108040
Nadargi, D. Y. (2023). Gas sensors and factors influencing sensing mechanism with a special focus on MOS sensors. Journal of Materials Science 2022 58:2, vol. 58, no. 2, pp. 559–582. https://doi.10.1007/S10853-022-08072-0
Yunus, S. A. M. J. @, Annuar, M. A. K., Chachuli, S. A. M., Said, M. M., Sulaiman, H. M. @, & Shamsudin, N. H. (2025). Development of a Non-Invasive Co2 Breath Sensor with Iot Integration. International Journal of Academic Research in Business and Social Sciences, 15(1), 732–744.
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