Non-invasive blood glucose monitoring using near-infrared LED spectrophotometry
Diabetes is an incurable disease that was recorded to affect 9% of the world population in 2019. Research shows that the numbers have been continuously growing and are projected to increase by 45% in 2045 [20]. Part of the treatment for diabetes is self-administering medication which is dependent on...
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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/2 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1015&context=etdb_physics |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Diabetes is an incurable disease that was recorded to affect 9% of the world population in 2019. Research shows that the numbers have been continuously growing and are projected to increase by 45% in 2045 [20]. Part of the treatment for diabetes is self-administering medication which is dependent on the monitoring of one’s blood glucose levels. Most blood glucose monitoring devices require the pricking of one’s finger, which is inconvenient and painful given that this must be done several times a day. This study tackles a non-invasive approach to monitoring blood glucose using Near-Infrared Light Emitting Diodes (NIR LED) spectrophotometry. A device was made using two NIR LEDs as transmitter and receiver. Blood glucose control solutions in glass cuvettes that acted as phantom fingers were tested to determine glucose concentration. The receiver LED’s voltage was collected and used to compute the absorbance of the samples via Beer-Lambert Law. The aim was to correlate the voltage response with glucose levels. Using simple linear regression analysis and pearson correlation, a very strong negative linear relationship was found between the voltage of the receiver LED and the absorbance of the samples. A calibration curve equation for the expected glucose concentration of the samples was derived from the best fit line of the scatter plot of the voltage versus the reference glucose concentration. A t-test comparing the computed expected glucose concentration and the reference values measured using a glucose meter revealed that there is a significant difference between the two variables. To determine the clinical accuracy of the device, a Clarke Error Grid Analysis was also used which revealed that only 50 of the 90 data points from the experiment were found to be within the zone of acceptance. |
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