|3/2016 - 6|
ABC Algorithm based Fuzzy Modeling of Optical Glucose DetectionSARACOGLU, O. G. , BAGIS, A. , KONAR, M. , TABARU, T. E.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,221 KB) | Citation | Downloads: 396 | Views: 1,278|
fuzzy systems, heuristic algorithms, evolutionary computation, optical sensors, computational modeling
fuzzy(21), glucose(13), systems(12), biosensors(9), algorithm(9), measurement(8), modeling(7), control(7), vivo(6), system(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 37 - 42
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03006
Web of Science Accession Number: 000384750000006
SCOPUS ID: 84991111440
This paper presents a modeling approach based on the use of fuzzy reasoning mechanism to define a measured data set obtained from an optical sensing circuit. For this purpose, we implemented a simple but effective an in vitro optical sensor to measure glucose content of an aqueous solution. Measured data contain analog voltages representing the absorbance values of three wavelengths measured from an RGB LED in different glucose concentrations. To achieve a desired model performance, the parameters of the fuzzy models are optimized by using the artificial bee colony (ABC) algorithm. The modeling results presented in this paper indicate that the fuzzy model optimized by the algorithm provide a successful modeling performance having the minimum mean squared error (MSE) of 0.0013 which are in clearly good agreement with the measurements.
|References|||||Cited By «-- Click to see who has cited this paper|
| G. A. Bray, "Energy and fructose from beverages sweetened with sugar or high-fructose corn syrup pose a health risk for some people", Advances in Nutrition: An International Review Journal, 4(2), pp.220-225, 2013. |
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 69]
 L. D. Mello, L. T. Kubota, "Review of the use of biosensors as analytical tools in the food and drink industries", Food Chemistry, 77(2), pp.237-256, 2002.
[CrossRef] [Web of Science Times Cited 256] [SCOPUS Times Cited 277]
 S. F. Clarke, J. R. Foster, "A history of blood glucose meters and their role in self-monitoring of diabetes mellitus", British Journal of Biomedical Science, 69(2), pp.83-93, 2012. [PubMed]
 A. Arnoldi (Ed.). "Functional Foods, Cardiovascular Disease and Diabetes", pp.19-55, Elsevier, 2004.
 D. A. Stuart, J. M. Yuen, N. Shah, O. Lyandres, C. R. Yonzon, M. R. Glucksberg, J. T. Walsh, R. P. Van Duyne, "In Vivo Glucose Measurement by Surface-Enhanced Raman Spectroscopy", Analytical Chemistry, 78(20), pp.7211-7215, 2006.
[CrossRef] [Web of Science Times Cited 217] [SCOPUS Times Cited 243]
 D. Chen, C. Wang, W. Chen, Y. Chen, J.X. Zhang, "PVDF-Nafion Nanomembranes Coated Microneedles for in Vivo Transcutaneous Implantable Glucose Sensing", Biosensors and Bioelectronics, 74, pp. 1047-1052, 2015.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6]
 L. B. Mohammadi, T. Klotzbuecher, S. Sigloch, K. Welzel, M. Goeddel, T.R. Pieber, L. Schaupp, L., "Clinical Performance of A Low Cost Near Infrared Sensor for Continuous Glucose Monitoring Applied with Subcutaneous Microdialysis", Biomedical Microdevices, 17(4), pp.1-10, 2015.
[CrossRef] [Web of Science Record] [SCOPUS Times Cited 2]
 D. Li, Y. Sun, S. Yu, C. Sun, H. Yu, K. Xu, "A Single-Loop Fiber Attenuated Total Reflection Sensor Enhanced by Silver Nanoparticles for Continuous Glucose Monitoring". Sensors and Actuators B: Chemical, 220, pp.1033-1042, 2015.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 5]
 P. U. Abel, T. von Woedtke, "Biosensors for In Vivo Glucose Measurement: Can We Cross The Experimental Stage", Biosensors and Bioelectronics, 17(11), pp. 1059-1070, 2002.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 69]
 G. S. Wilson, R. Gifford, "Biosensors for Real-Time In Vivo Measurements", Biosensors and Bioelectronics, 20(12), pp. 2388-2403, 2005.
[CrossRef] [Web of Science Times Cited 380] [SCOPUS Times Cited 431]
 H. E. Koschwanez, W.M. Reichert, "In Vitro, In Vivo and Post Explantation Testing of Glucose-Detecting Biosensors: Current Methods and Recommendations", Biomaterials, 28(25), pp.3687-3703, 2007.
[CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 96]
 S. Yu, D. Li, H. Chong, C. Sun, H. Yu, K. Xu, "In Vitro Glucose Measurement Using Tunable Mid-Infrared Laser Spectroscopy Combined with Fiber-Optic Sensor", Biomedical Optics Express, 5(1), pp.275-286, 2014.
[CrossRef] [SCOPUS Times Cited 18]
 J. C. Pickup, F. Hussain, N. D. Evans, N. Sachedina, "In Vivo Glucose Monitoring: The Clinical Reality and The Promise", Biosensors and Bioelectronics, 20(10), pp. 1897-1902, 2005.
[CrossRef] [Web of Science Times Cited 122] [SCOPUS Times Cited 153]
 D. Li, J. Wu, P. Wu, Y. Lin, Y. Sun, R. Zhu, J. Yang, K. Xu, "Affinity based Glucose Measurement using Fiber Optic Surface Plasmon Resonance Sensor with Surface Modification by Borate Polymer", Sensors and Actuators B: Chemical, 213, pp. 295-304, 2015.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 10]
 S. Singh, B.D. Gupta, "Fabrication and Characterization of A Surface Plasmon Resonance based Fiber Optic Sensor using Gel Entrapment Technique for The Detection of Low Glucose Concentration", Sensors and Actuators B: Chemical, 177, pp.589-595, 2013.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 32]
 B. Nacht, C. Larndorfer, S. Sax, S.M. Borisov, M. Hajnsek, F. Sinner, E.J.W. List-Kratochvil, I. Klimant, "Integrated Catheter System for Continuous Glucose Measurement and Simultaneous Insulin Infusion", Biosensors and Bioelectronics, 64, pp.102-110, 2015.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]
 P. Squara, M. Imhoff, M. Cecconi, "Review Article: Metrology in Medicine: From Measurements to Decision, with Specific Reference to Anesthesia and Intensive Care", Anesthesia and Analgesia, 120(1), pp.66-75, 2015.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]
 T. J. Ross, "Fuzzy Logic with Engineering Applications", pp.476-536, McGrawHill, 1995.
 H. T. Nguyen, M. Sugeno, "Fuzzy Systems: Modeling and Control", pp.63-90, Kluwer Academic Publishers, 1998.
 A. Bagis, "Fuzzy Rule Base Design using Tabu Search Algorithm for Nonlinear System Modeling", ISA Transactions, 47(1), pp.32-44, 2008.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 29]
 A. Bagis, M. Konar, "Comparison of Sugeno and Mamdani Fuzzy Models Optimized by Artificial Bee Colony Algorithm for Nonlinear System Modelling", Transactions of the Institute of Measurement and Control,38(5), pp.579-592, 2016.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]
 M. Konar, A. Bagis, "Performance Comparison of Particle Optimization, Differential Evolution and Artificial Bee Colony Algorithms for Fuzzy Modelling of Nonlinear Systems (Accepted for publication)", Elektronika IR Elektrotechnika, 2016, to be published.
 H. Du, N. Zhang, "Application of Evolving Takagi-Sugeno Fuzzy Model to Nonlinear System Identification", Applied Soft Computing, vol.8, pp.676-686, 2008.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 83]
 A. Evsukoff, A. C. S. Branco, S. Galichet, "Structure Identification and Parameter Optimization for Non-Linear Fuzzy Modeling", Fuzzy Sets and Systems, vol.132, pp.173-188, 2002.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 46]
 W. A. Farag, V. H. Quintana, G. L. Torres, "A Genetic based Neuro-Fuzzy Approach for Modeling and Control of Dynamical Systems", IEEE Trans. Neural Netw., 9(5), pp.756-767, 1998.
[CrossRef] [Web of Science Times Cited 129] [SCOPUS Times Cited 173]
 K. Guney, N. Sarikaya, "Comparison of Mamdani and Sugeno Fuzzy Inference System Models for Resonant Frequency Calculation of Rectangular Microstrip Antennas", Progress In Electromagnetics Research B, vol.12, pp.81-104, 2009.
 S.-J. Kang, C.-H. Woo, H.-S. Hwang, K. B. Woo, "Evolutionary Design of Fuzzy Rule Base for Nonlinear Systems Modeling and Control", IEEE Transactions on Fuzzy Systems, (8)1, pp.37-45, 2000.
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 90]
 T. Takagi, M. Sugeno, "Fuzzy Identification of Systems and Its Applications to Modeling and Control", IEEE Transactions on Systems, Man, and Cybernetics, vol.15, pp.116-132, 1985.
[CrossRef] [SCOPUS Times Cited 11526]
 R. M. Tong, "The Evaluation of Fuzzy Models Derived from Experimental Data", Fuzzy Sets and Systems, vol.4, pp.1-12, 1980.
[CrossRef] [Web of Science Times Cited 129] [SCOPUS Times Cited 183]
 L. Wang, R. Langari, "Complex Systems Modeling via Fuzzy Logic", IEEE Trans. Syst. Man Cybern-Part B: Cybern, 26(1), pp.100-106, 1996.
[CrossRef] [Web of Science Times Cited 111] [SCOPUS Times Cited 132]
 J. R. Jang , C. Sun, E. Mizutani, "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence", pp.333-368, Prentice-Hall, Inc., 1997.
 F. Kulic, D. Matic, B. Dumnic, V. Vasic, "Optimal Fuzzy Controller Tuned by TV-PSO for Induction Motor Speed Control", Advances in Electrical and Computer Engineering, 11(1), pp.49-54, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 9] [SCOPUS Times Cited 9]
 R. E. Precup, R.C. David, E.M. Petriu, S. Preitl, M.B. Radac, "Fuzzy Logic based Adaptive Gravitational Search Algorithm for Optimal Tuning of Fuzzy-Controlled Servo Systems", IET Control Theory Appl., 7(1), pp.99-107, 2013.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 43]
 M. J. Gacto, M. Galende, R. Alcala, F. Herrera, "METSK-HDe: A Multiobjective Evolutionary Algorithm to Learn Accurate TSK-Fuzzy Systems in High-Dimensional and Large-Scale Regression Problems, Information Sciences, 276, pp. 63-79, 2014.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 25]
 D. Karaboga, "An idea based on honey bee swarm for numerical optimization", Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
 D. Karaboga, B. Akay, "A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm", Journal of Global Optimization, vol.39, pp.459-471, 2007.
[CrossRef] [Web of Science Times Cited 1495] [SCOPUS Times Cited 2069]
 D. Karaboga, B. Akay, "A Comparative Study of Artificial Bee Colony Algorithm", Applied Mathematics and Computation, vol.214, pp.108-132, 2009.
[CrossRef] [Web of Science Times Cited 913] [SCOPUS Times Cited 1295]
 D. Karaboga, C. Ozturk, "A Novel Clustering Approach: Artificial Bee Colony (ABC) Algorithm", Applied Soft Computing, 11(1), pp.652-657, 2011.
[CrossRef] [Web of Science Times Cited 326] [SCOPUS Times Cited 455]
 A. Kulanthaisamy, R. Vairamani, N.K. Karunamurthi, C. Koodalsamy, "A Multi-Objective PMU Placement Method Considering Observability and Measurement Redundancy using ABC Algorithm", Advances in Electrical and Computer Engineering, 14(2), pp.117-128, 2014.
[CrossRef] [Full Text] [Web of Science Times Cited 3] [SCOPUS Times Cited 5]
 T. E. Tabaru, O. G. Saracoglu, E. Aslan, "Optical absorbance measurement of glucose in aqueous solution by using an RGB based simple spectrophotometer", Cankaya University 7th Engineering and Technology Symposium, May 15-16, 2014, pp. 219-223, Ankara, Turkey (in Turkish).
Web of Science® Citations for all references: 4,575 TCR
SCOPUS® Citations for all references: 17,589 TCR
Web of Science® Average Citations per reference: 112 ACR
SCOPUS® Average Citations per reference: 429 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2017-06-21 15:50 in 214 seconds.
Note1: Web of Science® is a registered trademark of Thomson Reuters.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.