Feasibility in the use of the UV-Visible region for the characterization of glucose in deionized water using Arduino 1

Authors

DOI:

https://doi.org/10.17488/RMIB.44.2.3

Keywords:

Deionized water, glucose concentration, UV-visible, Arduino, K-Nearest Neighbor

Abstract

With an estimated approximately 2 million deaths per year, diabetes is one of the top 5 deadliest noncommunicable diseases globally. Although this disease is not fatal, the degradation of the patient's health due to a bad plan to control their glucose levels can have a fatal outcome. In order to lay the foundations for the development of a device that allows estimating glucose levels in some body fluid, we present the results obtained not only for the estimation of glucose in deionized water, but also describe the development and configuration of the created device. After analyzing 50 signals obtained from 5 different glucose concentrations, the feasibility of using the developed device for the analysis is evident, since, considering the K-Nearest Neighbors (KNN) algorithm, all the signals were associated correctly to the glucose group to which they belong.

Downloads

Download data is not yet available.

References

World Health Organization (WHO), Non communicable diseases (2022). Available: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (accessed Oct. 03, 2022).

World Health Organization (WHO), Diabetes (2022). Available: https://www.who.int/es/news-room/fact-sheets/detail/diabetes (accessed Oct. 03, 2022).

American Diabetes Association (ADA), Diabetes Symptoms, Causes, & Treatment (2023). Available: https://diabetes.org/diabetes (accessed Oct. 03, 2022).

M. Erbach, G. Freckmann, R. Hinzmann, B. Kulzer, R. Ziegler, L. Heinemann and O. Schnell, “Interferences and Limitations in Blood Glucose Self-Testing: An Overview of the Current Knowledge,” J. Diabetes Sci. Technol., vol. 10, no. 5, pp. 1161–1168, Sep. 2016, doi: https://doi.org/10.1177/1932296816641433

M. Franciosi, G. Lucisano, F. Pellegrini, A. Cantarello, A. Consoli, L. Cucco, R. Ghidelli, G. Sartore, L. Sciangula and A. Nicolucci, “ROSES: role of self-monitoring of blood glucose and intensive education in patients with Type 2 diabetes not receiving insulin. A pilot randomized clinical trial,” Diabet. Med., vol. 28, no. 7, pp. 789–796, 2011, doi: https://doi.org/10.1111/j.1464-5491.2011.03268.x

O. Schnell, H. Alawi, T. Battelino, A. Ceriello, P. Diem, A.-M. Felton, W. Grzeszczak, K. Harno, P. Kempler, I. Satman, B. Vergès, “Self-Monitoring of Blood Glucose in Type 2 Diabetes: Recent Studies,” J. Diabetes Sci. Technol., vol. 7, no. 2, pp. 478–488, Mar. 2013, doi: https://doi.org/10.1177/193229681300700225

A. S. Bolla and R. Priefer, “Blood glucose monitoring- an overview of current and future non-invasive devices,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 5, pp. 739–751, Sep. 2020, doi: https://doi.org/10.1016/j.dsx.2020.05.016

C. Srichan, W. Srichan, P. Danvirutai, C. Ritsongmuang, A. Sharma, and S. Anutrakulchai, “Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features,” Sci. Rep., vol. 12, no. 1, art. 1769, Feb. 2022, doi: https://doi.org/10.1038/s41598-022-05570-8

X. Yang, T. Fang, Y Li, L. Guo, F. Li, F. Huang, and L. Li, “Pre-diabetes diagnosis based on ATR-FTIR spectroscopy combined with CART and XGBoots,” Optik, vol. 180, pp. 189–198, Feb. 2019, doi: https://doi.org/10.1016/j.ijleo.2018.11.059

E. Guevara, J. C. Torres-Galván, M. G. Ramírez-Elías, C. Luevano-Contreras, and F. J. González, “Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools,” Biomed. Opt. Express, vol. 9, no. 10, pp. 4998–5010, Oct. 2018, doi: https://doi.org/10.1364/BOE.9.004998

B. M. Chege, Z. Birech, P. W. Mwangi, and F. O. Bukachi, “Utility of Raman spectroscopy in diabetes detection based on biomarker Raman bands and in antidiabetic efficacy studies of herbal extract Rotheca myricoides Hochst,” J. Raman Spectrosc., vol. 50, no. 10, pp. 1358–1366, 2019, doi: https://doi.org/10.1002/jrs.5619

S. K. Benson, K. M. Boyce, R. M. Bunker, N. B. Collins, K. J. Daily, A. S. Esway, G. T. Gilmore, C. W. Hartzler, G. P. Howard, N. A. Kasmar, K. J. Kennedy, B. L. King, T. N. Kordahi, T. A. Mattioli, D. M. Pugh, L. A. Ray, S. L. Ross, M. H. Torcasio, D. P. Webber, D. L. Morris, and T. C. Leeper, “Multinuclear NMR and UV–Vis spectroscopy of site directed mutants of the diabetes drug target protein mitoNEET suggest that folding is intimately coupled to iron–sulfur cluster formation,” Inorg. Chem. Commun., vol. 63, pp. 86–92, Jan. 2016, doi: https://doi.org/10.1016/j.inoche.2015.11.022

J. Torres-Gamez, J. A. Rodriguez, M. E. Paez-Hernandez, and C. A. Galan-Vidal, “Application of Multivariate Statistical Analysis to Simultaneous Spectrophotometric Enzymatic Determination of Glucose and Cholesterol in Serum Samples,” Int. J. Anal. Chem., vol. 2019, art. 7532687, Jan. 2019, doi: https://doi.org/10.1155/2019/7532687

S. Shokouhi, M. R. Sohrabi, and S. Mofavvaz, “Comparison between UV/Vis spectrophotometry based on intelligent systems and HPLC methods for simultaneous determination of anti-diabetic drugs in binary mixture,” Optik, vol. 206, art. 164304, Mar. 2020, doi: https://doi.org/10.1016/j.ijleo.2020.164304

N. D. Nguyen, T. V. Nguyen, A. D. Chu, H. V. Tran, L. T. Tran, and C. D. Huynh, “A label-free colorimetric sensor based on silver nanoparticles directed to hydrogen peroxide and glucose,” Arab. J. Chem., vol. 11, no. 7, pp. 1134–1143, Nov. 2018, doi: https://doi.org/10.1016/j.arabjc.2017.12.035

X. Luo, J. Xia, X. Jiang, M. Yang, and S. Liu, “Cellulose-Based Strips Designed Based on a Sensitive Enzyme Colorimetric Assay for the Low Concentration of Glucose Detection,” Anal. Chem., vol. 91, no. 24, pp. 15461–15468, Dec. 2019, doi: https://doi.org/10.1021/acs.analchem.9b03180

S. Jiang, Y. Zhang, Y. Yang, Y. Huang, G. Ma, Y. Luo, P. Huang, and J. Lin, “Glucose Oxidase-Instructed Fluorescence Amplification Strategy for Intracellular Glucose Detection,” ACS Appl. Mater. Interfaces, vol. 11, no. 11, pp. 10554–10558, Mar. 2019, doi: https://doi.org/10.1021/acsami.9b00010

M. Bartosiak, J. Giersz, and K. Jankowski, “Analytical monitoring of selenium nanoparticles green synthesis using photochemical vapor generation coupled with MIP-OES and UV–Vis spectrophotometry,” Microchem. J., vol. 145, pp. 1169–1175, Mar. 2019, doi: https://doi.org/10.1016/j.microc.2018.12.024

Digi-Key Electronics. “UV5TZ-400-15”. Digi-Key Electronics. Available: https://www.digikey.com/es/products/detail/bivar-inc/UV5TZ-400-15/3095679 (accessed Oct. 15, 2022).

Everlight. “PT1302B-C2 Datasheet - 5mm Phototransistor”. Everlight. Available: http://www.datasheet.es/PDF/796537/PT1302B-C2-pdf.html (accessed Dec. 18, 2022).

L. Liberti, and C. Cavor, Euclidean Distance Geometry: An Introduction, Durham, USA: Springer, 2010, pp. 133. [Online]. Available: https://doi.org/10.1007/978-3-319-60792-4 (accessed Nov. 21, 2022).

A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Canada: O’Reilly Media, Inc., 2019, pp. 856.

Q. Chen, H. Lin, and J. Zhao, Advanced Nondestructive Detection Technologies in Food, Singapure: Springer, 2021, pp. 333. [Online]. Available: https://doi.org/10.1007/978-981-16-3360-7 (accessed Nov. 21, 2022).

J. Workman, The Concise Handbook of Analytical Spectroscopy, USA: World Scientific, 2016, pp. 1828. [Online]. Available: https://doi.org/10.1142/8800 (accessed Nov. 21, 2022).

R. White, Chromatography/Fourier Transform Infrared Spectroscopy and Its Applications, Boca Raton, USA: CRP Press, 1989, pp. 344. [Online]. Available: https://doi.org/10.1201/9781003066323 (accessed Nov. 21, 2022).

E. B. Mode, Elementos de probabilidad y estadística, Barcelona, Spain: Editorial Reverté, 2021, pp. 380.

dispositivo

Published

2023-06-28

How to Cite

Sanchez-Monroy, V., Barros-Martinez, L. E., Hidalgo-Pedraza, A., Mendoza-Munguia, B. A., & Sanchez-Brito, M. (2023). Feasibility in the use of the UV-Visible region for the characterization of glucose in deionized water using Arduino 1. Revista Mexicana De Ingenieria Biomedica, 44(2), 27–37. https://doi.org/10.17488/RMIB.44.2.3

Issue

Section

Research Articles

Share on:

Dimensions Citation

Most read articles by the same author(s)