48863251460500-1428752286000VIETNAM one is blood – drawing finger –

48863251460500-1428752286000VIETNAM NATIONAL UNIVERSITY, HCMC
Lecturer: Dr. Nguyen Thao Trang
Name & ID: Phan Vi?t Hà – BTBCIU15052
Date submission: 04/06/2018
Overview of non – invasive glucose blood test
Diabetes is a condition in which the amount of insulin is made insufficiently or the insulin made not be used in human body. This disease impacts on the glucose metabolism which is the main sources of fuel for body so that it can be defined as a metabolic disorder1. People with diabetes must regularly check their blood glucose levels to know how much medication to use, to keep track of fluctuating levels: 4 – 5times per day. Traditionally, glucose levels are measured by using invasive devices and the most common one is blood – drawing finger – prick test; however, it brings several cons to individuals such as causing painful, expensive, long-term analyzing, finger soreness, etc. Therefore, non – invasive becomes the prospect of detecting glucose technique which is convenient, less painful, inexpensive and easy reading data2.
In non – invasive detection, the concentration of glucose in the tissues is estimated by measuring the variations in the light intensity due to transmittance and reflection followed the Beer’s law. Near Infrared (NIR) Spectroscopic is used to determine glucose levels in earlobe and it is the method used the IR radiation in NIR region of the electromagnetic spectrum (750 – 2500nm). NIR is chosen because of it has several advantages for detecting the glucose level in blood which are high signal-to-noise ratio, skin penetrate 1 – 100mm in depth, photoconductive detector sensitive and low cost3.

Principle of operation in the device
center50355500In this experiment, near-infrared (NIR) spectroscopy is used to detect blood glucose non – invasively in earlobe since it is thin. Beer Lambert’s law is applied for calculating the amount of light absorbed by glucose and water. Additionally, light intensity ratio declares the absorbance:
Then, intensive light is converted into electric specifically in voltage (V) by using Indium Gallium Arsenide (InGaAs) photodiode detector. When light achieves to the photodiode, it emits a current which will transform into voltage. Ip represents the current from the photodiode and Rf is the feedback capacitor which minimizes the noise gain and keeps SNR big. Additionally, op-amp is also known as transimpedance amplifier (TIA) circuit which connects with photodiode to decrease high-frequency noise. Photodiode voltage directly proportional to an intensive light of near infrared which relates to the concentration of blood glucose4.

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The reason why the absorbance is converted to voltage is solutions transmit more than 90% or less than 10% incident light will deviate from Beer’s law and give unreliable results. Voltage is logarithmically related to the absorbance: A = logVo – logV5.

Penetrated light is measured in various concentration of glucose solution with 3 distinct wavelengths 1300nm, 1450nm, and 1550nm in this experiment. For a couple of crucial reasons, why these wavelengths are selected to detect the blood glucose concentration.

In fact, the maximum absorbance of glucose appears in the UV region (10 – 400nm) but UV is harmful to human body. Thus, NIR is chosen because of its wavelength is optimize for soft tissue (600 – 1300nm) in which the respond of glucose is quite high6.

The table below indicates some possible stretching, vibration of glucose molecules and the absorbance range used to collect the optimal wavelength at which glucose levels are highly response and the other components in blood such as DNA, RNA, hemoglobin and protein that absorb less light.
Regarding this article, 1300nm, 1450nm and 1550nm are considered as potential wavelength. As can be seen from the first graph, the spectrum of glucose and water are recorded from 1500 to 2500nm. The absorption of glucose is higher than pure water in the range between 1500nm and 1800nm, and the normalize absorption of water is nearly zero at 1660nm especially. Therefore, this range is used to apply in clinical tests. The other figure illustrates the intensity of concentrated glucose solution and water are recorded. At 1330nm, the absorption of concentrated glucose is less than the absorption of pure water because of the intensity of glucose is lower than water. According to the Beer’s law equation, the absorbance can be measured by logarithms between the experimental values of intensity light and the intensity light after passing
through the solution. Researchers claim that the signal-to-noise ratio of glucose at 1550nm wavelength is higher compared to others wavelength7.

Besides, another researcher collects the spectrum of glucose from 200 to 3600nm and a new wavelength is analyzed based on the peaks in this spectrum. It can be seen from the graph that the noise appears much in 200nm – 700nm and above 2500nm. The smoothest part is from 1450nm to 1950 and it reach a peak at approximately 1550nm8.

Method and data analysis
Before testing two experiments on people, doing glucose solution testing and earlobe testing first to choose the best wavelength for detecting glucose. In testing glucose solution, the first wavelength selected 1550nm, inversely relationship between voltage and concentration which means more light absorbed and less light detected. Moreover, the glucose concentration examined is belong to the normal range of this in human (50 – 250 mg/dl)9.

For two others wavelength selected which are 1450nm and 1330nm respectively, there is a positive proportional between voltage and glucose concentration that determines water is absorbed more instead of glucose9.
In the second testing – earlobe testing, in the same concentration of glucose, voltage detected in hundred mV of 1550nm which suitable for human body compared to two remaining wavelengths with 3V – higher electrical current. To sum up, the best wavelength should be chosen applied on human body is 1550nm9.

Stating with the first experiment which just applied the linear approach method by using calibration curve to indicate the relationship between blood glucose concentration and voltage which based on Beer Lambert’s law. In this experiment, 10 random people includes 5 men and 5 women with various ages from 20 to 22 years old are tested. Especially, the relationship of two factors is reversed to reduce the errors percentage in this method from nearly 30% to a half but it still not valid enough because variable influences such as skin structure, body temperature, pigment, etc. Therefore, another method is needed for generalizing these effects9.
564832587122000Since using the first method causes large errors, so that two approaches are used to get glucose concentration which are linear correlation and two – point equation. This apply in only one person to remove all various factors that influence on glucose concentration. Five data is recorded from each day that are: fasting moment, 15′, 30′, 60′ and 120′ after break fasting. Similar to the first experiment, doing only with linear method 30% error still occurred. Besides, two – point method is obtained by inserting the highest and the lowest points which illustrate by:
Then, the concentration of blood glucose is calculated by subtracting the actual to approximate data. However, this estimate still approximately with 28% errors9.
Evaluate of the factors affect the results
The amount of near infrared light passing through the earlobe depends on the amount of blood glucose in that region. The earlobe was chosen due to the absence of bone tissues and also because of its relatively small thickness. Near Infrared (NIR) light is applied onto one side of the ear lobe, while a receiver on the other side receives the attenuated light. There are two main types of factors that affect the accuracy of the measurement: the physical parameters and the environmental variations.

The major factor affects to the results in using NIR to measure the blood glucose concentration is physical restriction. The first parameter relates to earlobe tissue thickness which determines the ‘pathlength’ of NIR, so that a greater pathlength would result in lower transmittance. It is measured by using green light, which has high skin-based attenuation. The tissue thickness is computed by approximating the exponential Beer Lambert’s Law by a linear small signal model.
Another physical parameter is the amount of blood in body. Apart from the level of glucose in blood, the transmittance of NIR light also depends on the amount of blood in the path of the light. For the same glucose level, a large amount of blood will result in lower transmittance, whereas less blood will result in a larger transmittance. The glucose value need to be scaled according to the amount of blood residing inside the earlobe at a time of measurement. Similarly, any medical condition that inhibits blood flow to the earlobe will result in erroneous readings. The amount of blood can be estimated by measuring the blood oxygen levels by Pulse Oximetry10. Pulse Oximetry is used to measure blood oxygen and it uses red and Infrared (IR) light to distinguish between Hemoglobin and Oxy-Hemoglobin in the blood, on which further processing is applied to get the oxygen saturation. After measuring the blood oxygen level following this table, the amount of blood glucose level will be scaled.

Beside the inside body factors, surrounding environmental variations also factors that interfere with measurement of blood glucose. They are changes in temperature, humidity, skin hydration, carbon dioxide and atmospheric pressure11.

1 Tamar Lin. (31 July 2017). Current Trends in Biomedical Engineering & Biosciences. Non – Invasive Glucose Monitoring: A Review of Challenges and Recent Advances.

2 Full text View – ClinicalTrials.gov. Comparison of Noninvasive Blood Glucose Concentrations Relative to Finger Capillary Blood Glucose References.

Retrieved from: https://clinicaltrials.gov/ct2/show/NCT01415544
3 Asmat Nawaz, Review: Non-Invasive Continuous Blood Glucose Measurement Techniques, Journal of Bioinformatics and Diabetes, 2016, 3, 1, 1-27.

4 Delgado, J., I.A. Quintero-Ortega, and A. Vega-Gonzalez, From Voltage to Absorbance and Chemical Kinetics Using a Homemade Colorimeter. Journal of Chemical Education, 2014. 91(12): p. 2158-2162.

5 Retrieved from: https://www.coursehero.com/file/p16hcup/A-plot-of-A-absorbance-vs-C-concentration-will-yield-a-straight-line-with-a/6 Kaijanen, L., Paakkunainen, M., Pietarinen, S., Jernström, E., & Reinikainen, S. P. (2015). Ultraviolet Detection of Monosaccharides: Multiple Wavelength Strategy to Evaluate Results after Capillary Zone Electrophoretic Separation. Int. J. Electrochem. Sci, 10, 2950-2961.

7 Sia, D. Y., Saba Mohtashami and Fangfang Zhang. (April 2010). Design of a Near-Infrared Device for the Study of Glucose Concentration Measurements.
8 Non-invasive blood glucose monitoring using near infrared spectroscopy. Accessed 8 October 2014.

Retrieved from: http://edn.com/design/medical/4422840/9 Rolamjaya Hotmartua-Pujianto Pangestu-Hasballah Zakaria-Yoke Irawan. (August 2015). International Conference on Electrical Engineering and Informatics (ICEEI). Noninvasive blood glucose detection using near infrared sensor.

10 Masab Ahmad, A. K. A. K. (2013). Non-invasive blood glucose monitoring using near-infrared spectroscopy.

11 Yadav, J., et al. (2015). “Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy.” Biomedical Signal Processing and Control 18: 214-227.



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