Non-invasive optical techniques for determination of blood glucose levels: a review article
TLDR
This article reviews the development of non-invasive optical techniques for determination of blood glucose concentrations in diabetic patients and describes the major optical technologies for noninvasive glucose monitoring and compares their advantages and disadvantages.Abstract:
This article reviews the development of non-invasive optical techniques for determination of blood glucose concentrations in diabetic patients. Early diagnosis and daily management are essential for ensuring the healthy life of diabetic patients. The determination of blood glucose concentration with common devices involves the chemical analysis of blood samples, which are obtained by pricking the finger or extracting blood from the forearm. Pain, discomfort, and inconvenience, associated with current invasive methods, have necessitated the investigation of non-invasive measurement techniques. Non-invasive monitoring of blood glucose level offers several advantages, including absence of pain and biohazard materials, non- exposure to sharp objects, increased testing frequency and consequently, tighter control of glucose concentration. Considering these potential advantages commercialization of non-invasive glucose monitoring devices has become a subject of increasing interest. Several optical technologies have the potential to provide viable noninvasive measuring devices. this review study aimed to describe the major optical technologies for noninvasive glucose monitoring and compare their advantages and disadvantages. second scenario) better than other methods in presence of a typically low false positive rate equal to 3%.read more
Citations
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Two-dimensional metal chalcogenides analogous NiSe2 nanosheets and its efficient electrocatalytic performance towards glucose sensing.
TL;DR: Electrochemical Impedance Spectroscopy (EIS) data indicate that electron transfer is facile at the NiSe2-NS modified glassy carbon electrode (GCE), and the selectivity data exhibited excellent anti-interference property of NiSe 2-NS/GCE towards glucose in the presence of possible interfering agents.
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Fiber Optic Sensors: A Review for Glucose Measurement.
José Luis Cano Perez,Jaime Gutiérrez-Gutiérrez,Christian Perezcampos Mayoral,Eduardo Pérez-Campos,María del Socorro Pina Canseco,Lorenzo Tepech Carrillo,Laura Pérez-Campos Mayoral,Marciano Vargas Treviño,Edmundo López Apreza,Roberto Rojas Laguna +9 more
TL;DR: In this paper, the authors compare different types of fiber optic sensors made with different experimental techniques applied to biomedicine, especially for glucose sensing, and make observations on the way of elaboration, as well as the advantages and disadvantages that each one could have in real applications.
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On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks.
TL;DR: An analysis of the performance of Raman spectroscopy, combined with feed-forward neural networks (FFNN), for the estimation of concentration percentages of glucose, sucrose, and fructose in water solutions shows that both the classifier and the fitting systems performed better than a Support Vector Machine (SVM), a linear Discriminant Analysis (LDA), a Linear Regression (LR), and interval Partial Least Squares (iPLS).
Journal ArticleDOI
Implementation of an integrating sphere for the enhancement of noninvasive glucose detection using quantum cascade laser spectroscopy
Alexandra Werth,Sabbir Liakat,Anqi Dong,Callie Marie Woods,Callie Marie Woods,Claire F. Gmachl +5 more
TL;DR: In this paper, an integrating sphere is used to enhance the collection of backscattered light in a noninvasive glucose sensor based on quantum cascade laser spectroscopy, which enhances signal stability by roughly an order of magnitude, allowing a thermoelectrically cooled detector while maintaining comparable glucose prediction accuracy levels.
Journal ArticleDOI
Noninvasive blood glucose monitoring system based on near-infrared method
TL;DR: A method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor and it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG).
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