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Exhaled Breath Sensors

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Smart Sensors for Health and Environment Monitoring

Part of the book series: KAIST Research Series ((KAISTRS))

Abstract

This chapter reports a comprehensive review of the state-of-the-art in research on daily health monitoring and early diagnosis of specific diseases via the analysis of exhaled breath biomarkers. Different types of breath analyzing techniques including gas chromatography/mass spectroscopy (GC/MS), selected-ion flow-tube mass spectroscopy (SIFT-MS), and proton transfer reaction-mass spectrometry (PTR-MS) are compared to evaluate the unique strengths of each method. Recently, as an emerging breathsensing technique, we highlight chemiresistive-type gas sensors with characteristics of portability, cost effectiveness, and real-time analysis. Among various diseases, we focused on studies related to the diagnosis of diabetes and lung cancer. A number of studies have demonstrated a strong correlation between exhaled breath components and specific diseases, thus offering strong potential for clinical diagnostic application using exhaled breath sensors. In addition, we also summarized recent progress on daily healthcare such as fat-burning and halitosis through breath analysis. Finally, future perspectives on clinical applications using breath analyzing techniques are discussed.

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Acknowledgement

This work is supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as the Global Frontier Project.

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  1. Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, People’s Republic of Korea

    Il-Doo Kim, Seon-Jin Choi, Sang-Joon Kim & Ji-Su Jang

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Correspondence to Il-Doo Kim .

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  1. Electrical Engineering Center for Integrated Smart Sensors, KAIST, Daejoen, Korea, Republic of (South Korea)

    Chong-Min Kyung

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Kim, ID., Choi, SJ., Kim, SJ., Jang, JS. (2015). Exhaled Breath Sensors. In: Kyung, CM. (eds) Smart Sensors for Health and Environment Monitoring. KAIST Research Series. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9981-2_2

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