Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 8 , ISSUE 2 (June 2015) > List of articles


Anand Thati * / Arunangshu Biswas / Shubhajit Roy Chowdhury / Tapan Kumar Sau

Keywords : acetone sensor, exhaled breath, glucose, artificial neural network

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,244-1,260, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 01-April-2015 / Accepted: 25-April-2015 / Published Online: 01-June-2015



There has been a constant demand for the development of non-invasive, sensitive glucose sensor system that offers fast and real-time electronic readout of blood glucose levels. In this article, we propose a new system for detecting blood glucose levels by estimating the concentration of acetone in the exhaled breath. A TGS822 tin oxide (SnO2) sensor has been used to detect the concentration of acetone in the exhaled air. Acetone in exhaled breath showed a correlation with the blood glucose levels. Effects of pressure, temperature and humidity have been considered. Artificial Neural Network (ANN) has been used to extract features from the output waveform of the sensors. The system has been trained and tested with patient data in the blood glucose ranges from 80 mg/dl to 180 mg/dl. Using the proposed system, the blood glucose concentration has been estimated within an error limit of ±7.5 mg/dl.

Content not available PDF Share



[1] Chetan Sharma, Sachin Kumar, Anshul Bhargava, Shubhajit Roy Chowdhury. "Field
programmable gate array based embedded system for non-invasive estimation of hemoglobin in
blood using photoplethysmography." International Journal on Smart Sensing and Intelligent
Systems, vol. 6, no. 3, pp. 1267- 1282, 2013.
[2] Lieschnegg, M., M. Zacherl, B. Lechner, C. Weger, and A. Fuchs. "Non-invasive
characterization of total hip arthroplasty by means of passive acceleration
measurement." International Journal on Smart Sensing and Intelligent Systems, vol. 3, no. 1, pp.
75-87, 2010.
[3] Gavin, J.R. “The Importance of Monitoring Blood Glucose”. In US Endocrine Disease; Touch
Briefings: Atlanta, GA, USA, pp. 1–3, 2007.
[4] C. Turner, C. Walton, S. Hoashi, and M. Evans, “Breath acetone concentration decreases with
blood glucose concentration in type I diabetes mellitus patients during hypoglycaemic clamps,”
Journal of breath research, vol. 3, no. 4, pp. 1-9, 2009.
[5] Md. Mahbubur Rahman, A. J. Saleh Ahammad, Joon-Hyung Jin, Sang Jung Ahn and Jae-Joon
Lee, “A Comprehensive Review of Glucose Biosensors Based on Nanostructured Metal-Oxides:”
Sensors, vol. 10, pp. 4855-4886, 2010.
[6] Tong Wang, Yanan Yu, Huifeng Tian and Jingbo Hu, “A Novel Non-Enzymatic Glucose
Sensor Based on Cobalt Nanoparticles Implantation-Modified Indium Tin Oxide Electrode,”
Electroanalysis, vol. 26, pp. 2693–2700, 2014.
[7] Saei, A. A., Dolatabadi, J. E. N., Najafi-Marandi, P., Abhari, A., De la Guardia, M.:
“Electrochemical biosensors for glucose based on metal nanoparticles”. TrAC Trends in
Analytical Chemistry, vol. 42, pp. 216–227, 2013.
[8] Dongyuan Zhai, Borui Liu, Yi Shi, Lijia Pan, Yaqun Wang, Wenbo Li, Rong Zhang, and
Guihua Yu, “Highly Sensitive Glucose Sensor Based on Pt Nanoparticle/Polyaniline Hydrogel
Heterostructures,” ACS Nano, vol. 7, pp. 3540–3546, 2013.
[9] S. Updike, G. Hicks, “The enzyme electrode”, nature publishing group, vol. 214, pp. 986–
988, 1967.
[10] Luaibi, Ahmed Y., Ahmed J. Al-Ghusain, Asif Rahman, Mohammad H. Al-Sayah, and
Hasan Al-Nashash. "Noninvasive blood glucose level measurement using nuclear magnetic
resonance." Proceeding of 8th IEEE GCC Conference and Exhibition (GCCCE), Muscat, pp. 1-4,
[11] Ramasahayam, Swathi, Sri Haindavi Koppuravuri, Lavanya Arora, and Shubhajit Roy
Chowdhury. "Noninvasive Blood Glucose Sensing Using Near Infra-Red Spectroscopy and
Artificial Neural Networks Based on Inverse Delayed Function Model of Neuron." Journal of
medical systems, vol. 39, no. 1, pp. 1-15, 2015.
[12] U. M¨uller, B. Mertes, C. Fischbacher, K. Jageman, and K. Danzer, “Non-invasive blood
glucose monitoring by means of near infrared spectroscopy: methods for improving the reliability
of the calibration models.” The International journal of artificial organs, vol. 20, no. 5, pp. 285–
290, 1997.
[13] G. Dongmin, D. Zhang, L. Zhanga, L. Guangming “Non-invasive blood glucose monitoring
for diabetics by means of breath signal analysis”, Sensors and Actuators B: Chemical, volume
173, pp. 106–113, October 2012.
[14] S. Vashist, “Non-invasive glucose monitoring technology in diabetes management: A
review”, Analytica Chimica Acta, vol. 750, pp. 16–27, 2012.
[15] Mohammad Goodarzi, Sandeep Sharma, Herman Ramon, Wouter Saeys, “Multivariate
calibration of NIR spectroscopic sensors for continuous glucose monitoring,” TrAC Trends in
Analytical Chemistry, vol. 67, pp. 147–158, 2015.
[16] Marco Righettoni, Anton Amann and Sotiris E. Pratsinis, “Breath analysis by nanostructured
metal oxides as chemo-resistive gas sensors,” Materials Today, vol. 18, pp. 163-171, 2015.
[17] Paweł Mochalski, Karl Unterkofler, Gerald Teschl, Anton Amann, “Potential of volatile
organic compounds as markers of entrapped humans for use in urban search-and-rescue
operations,” TrAC Trends in Analytical Chemistry, vol. 68, pp. 88–106, 2015.
[18] Q. Zhang, P. Wang, J. Li, and X. Gao, “Diagnosis of diabetes by image detection of breath
using gas-sensitive laps,” Biosensors and Bioelectronics, vol. 15, no. 5, pp. 249–256, 2000.
[19] C. Deng, J. Zhang, X. Yu, W. Zhang, and X. Zhang, “Determination of acetone in human
breath by gas chromatography–mass spectrometry and solid-phase microextraction with on-fiber
derivatization,” Journal of Chromatography B, vol. 810, no. 2, pp. 269–275, 2004.
[20] S. Chakraborty, I. Ray, A. Sen, and D. Banerjee, “Detection of biomarker in breath: A step
towards noninvasive diabetes monitoring,” Current Science, vol. 94, no. 2, pp. 237–242, 2008.
[21] Nasution, Tulus Ikhsan, Irwana Nainggolan, Sabar Derita Hutagalung, Khairel Rafezi
Ahmad, and Zainal Arifin Ahmad. "The sensing mechanism and detection of low concentration
acetone using chitosan-based sensors." Sensors and Actuators B: Chemical, vol. 177, pp. 522-
528, 2013.
[22] Hill, Darryl, and Russell Binions. "Breath analysis for medical diagnosis." International
Journal on Smart Sensing and Intelligent Systems, vol. 5, no. 2, pp. 401-440, 2012.
[23] Elosua, Cesar, Ignacio R. Matias, Candido Bariain, and Francisco J. Arregui. "Detection of
volatile organic compounds based on optical fibre using nanostructured films." International
Journal on Smart Sensing and Intelligent Systems, vol. 1, no. 1, pp. 123-136, 2008.
[24] Yu-Feng Sun, Shao-Bo Liu, Fan-Li Meng, Jin-Yun Liu, Zhen Jin, Ling-Tao Kong and Jin-
Huai Liu, “Metal Oxide Nanostructures and Their Gas Sensing Properties: A Review,” Sensors,
vol. 12, pp. 2610-2631, 2012.
[25] V.K. Khanna, “Nanoparticle-based Sensors,” Defence Science Journal, vol. 58, pp. 608-616,
[26] C.-C. Wang, Y.-C. Weng, and T.-C. Chou, “Acetone sensor using lead foil as working
electrode,” Sensors and Actuators B: Chemical, vol. 122, no. 2, pp. 591–595, 2007.
[27] J. Li, Y. Lu, Q. Ye, M. Cinke, J. Han, and M. Meyyappan, “Carbon nanotube sensors for gas
and organic vapor detection,” Nano Letters, vol. 3, no. 7, pp. 929–933, 2003.
[28] D. Zohir and B. Hakim, “Enhancement of the neural network modeling accuracy using a
submodeling decomposition-based technique, application in gas sensor,” Neural Computing and
Applications, vol. 21, no. 8, pp. 1981–1986, 2012.
[29] Dmitriev, S. "Nanosensors engineering: I. structurally modulated sno2
nanowires." International Journal on Smart Sensing and Intelligent Systems, vol. 3, no. 4, pp.
643- 654, 2010.
[30] Zvyagin, A. A., A. V. Shaposhnik, S. V. Ryabtsev, D. A. Shaposhnik, A. A. Vasil’ev, and I.
N. Nazarenko. "Determination of acetone and ethanol vapors using semiconductor sensors."
Journal of Analytical Chemistry, vol. 65, no. 1, pp. 94-98, 2010.
[31] N. K. Suryadevara, S. C. Mukhopadhyay and L. Barrack, Towards a Smart Non-Invasive
Fluid Loss Measurement System, Journal of Medical Systems, Springer (NON-INVASIVE
DIAGNOSTIC SYSTEMS), February 2015, 39:38, 10 pages, DOI 10.1007/s10916-015-0206-6.
[32] A. Reungchaiwat, T. Wongchanapiboon, S. Liawruangrath, and S. Phanichphant, “Homemade
detection device for a mixture of ethanol and acetone,” Sensors, vol. 7, no. 2, pp. 202–213,
[33] Yadav, Lokendra. "Noninavsive biosensor for diabetes monitoring." Asian Journal of
Pharmaceutical and Clinical Research, vol. 7, no. 3, pp. 206-211, 2014.