Detection of acetone as a potential non-invasive diagnosis tool for diabetes patients

This work investigated the demonstration of acetone detection device as a potential tool to diagnose diabetes patients. It offers simple and low cost approach based on glass substrate platform. The glass substrate was coated with agarose gel as a sensitive material to increase the sensing response....

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Main Authors: Jali, Mohd Hafiz, Zamzuri, Ahmad Nur Azeim Ahmad, Rahim, Hazli Rafis Abdul, Johari, Md. Ashadi Md, Ahmad, Aminah, Baharom, Mohamad Faizal, Zain, Huda Adnan, Harun, Sulaiman Wadi
Format: Article
Published: Przeglad Elektrotechniczny 2022
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Online Access:http://eprints.um.edu.my/43523/
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Summary:This work investigated the demonstration of acetone detection device as a potential tool to diagnose diabetes patients. It offers simple and low cost approach based on glass substrate platform. The glass substrate was coated with agarose gel as a sensitive material to increase the sensing response. It has superiority in term of high porosity and capable to absorb molecule around it. The sensing mechanism is based on the change in refractive index (RI) of the agarose gel coating layer when exposed to variation acetone concentration level. This is due to the intensity of the light weakening by absorption and scattering when light propagated through the sensing material. The proposed sensor produces a significant response towards acetone concentrations with the output voltage reduced linearly from 1.6V to 1.2V. The sensitivity and resolution of the agarose coated glass substrate improves by a factor of 1.08 and 1.14 respectively as compared to uncoated glass substrate. It also performed better in term of linearity, stability, response time and hysteresis. The non-involvement of costly laser source based instruments make the proposed sensor become more practical for large production while maintaining a good sensing performances. Based on the experiment results, the proposed acetone sensor has a persuasive potential as an early biomarker for diabetes diagnosis tool. © 2022 Wydawnictwo SIGMA-NOT. All rights reserved.