Publication: Self-screening for diabetes by sniffing urine samples based on a hand-held electronic nose
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Issued Date
2017-02-21
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2-s2.0-85015956808
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Mahidol University
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SCOPUS
Bibliographic Citation
BMEiCON 2016 - 9th Biomedical Engineering International Conference. (2017)
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Thara Seesaard, Chutintorn Sriphrapradang, Taya Kitiyakara, Teerakiat Kerdcharoen Self-screening for diabetes by sniffing urine samples based on a hand-held electronic nose. BMEiCON 2016 - 9th Biomedical Engineering International Conference. (2017). doi:10.1109/BMEiCON.2016.7859586 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/42591
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Title
Self-screening for diabetes by sniffing urine samples based on a hand-held electronic nose
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Abstract
© 2016 IEEE. Biomedical equipment for early health screening and classification of diseases are useful to assist in the medical diagnosis and maintaining health. Diabetes is a common and increasing disease worldwide. Thus, the development of a new screening method using a urine odor detection device for identifying type 2 diabetes mellitus should be very beneficial. In this study, we report a self-monitoring system to detect specific sweet-smelling urine odor made from four polymer/functionalized-SWCNTs nanocomposites gas sensors, also known as a hand-held e-nose device. The sensitivity and specificity of gas sensing units were characterized and evaluated in the static chamber contains nitrogen gas at room temperature (25°C). Six volatile organic compounds (VOCs) such as ammonia, ethyl methyl ketone, butyric acid, acetic acid, acetone and water were used as biomarkers to represent the many types of urinary volatile compounds found in diabetes mellitus and were used to examine the performance of our sensors. Preliminary evaluation of urine odor sensors with a hand held e-nose device showed that these sensors have high response to ammonia, ethyl methyl ketone and acetone, respectively. Furthermore, the hand-held e-nose was able to discriminate between urinary odors from four diabetic patients and three healthy volunteers. The individual's specific urine odor (urine's odor print) from seven volunteers was confirmed by cluster analysis (CA) method and principal component analysis (PCA) which successfully classified 99.5%. Therefore, this personal diagnostic screening device is likely to be useful for real-Time self-monitoring of urine odor in patients with diabetes mellitus and those who are at high risk of developing diabetes disease. In addition, this screening method is painlessness, non-invasive, safe and convenient for integrating health tracking into the future smart home.