Non-invasive blood glucose monitoring using acetone sensor
Diabetes is currently the leading cause of death in the world, and is caused by a disordered metabolism that raises blood glucose levels. This cause makes it imperative that researchers in the field of medicine find ways to monitor, prevent, and diagnose people without the need for intrusive, and pa...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_physics/4 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1009&context=etdb_physics |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Diabetes is currently the leading cause of death in the world, and is caused by a disordered metabolism that raises blood glucose levels. This cause makes it imperative that researchers in the field of medicine find ways to monitor, prevent, and diagnose people without the need for intrusive, and painful procedures. Volatile organic compounds or VOCs have been studied to be great indicators of metabolic diseases thus allowing a better alternative to the traditional finger pricking method. Acetone, usually found in the blood and breath, has been investigated to be a significant ketone biomarker, which has been found to be an excellent indicator of diabetes mellitus. This study aims to create an Arduino-based biosensor using a TGS822 sensor that would allow the measurement of acetone concentrations in the breath of diabetic and non-diabetic participants and to correlate these findings with their blood glucose levels taken before and after meals. In addition, to validate the correlation, a calibration curve must be created. The study made use of experimental methods, which included six participants, excluding the researchers, who were evenly divided into diabetic and non-diabetic individuals. The researchers also managed to test themselves to generate data to be compared against data taken from the calibration curve. Results show a strong positive correlation between the variables, with an r value of 0.802 and an r squared value of 0.643 for both diabetics and non-diabetics. Additionally, a low error margin was found taken from the calibration curve, thus indicating that acetone could be used to monitor glucose levels in the body, thus leading to the conclusion that breath acetone may be a promising biomarker for diabetes detection. |
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