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Article

Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

1
Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico
2
Computer Science Department, Universitat Politecnica de Catalunya, Barcelona 08034, Spain
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 13th Mexican International Conference on Artificial Intelligence (MICAI), Tuxtla Gutierrez, Mexico, 16–22 November 2014.
Academic Editors: Miguel González-Mendoza, Ma. Lourdes Martínez-Villaseñor and Hiram Ponce
Sensors 2016, 16(11), 1483; https://doi.org/10.3390/s16111483
Received: 30 May 2016 / Revised: 29 July 2016 / Accepted: 9 August 2016 / Published: 26 October 2016
Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization. View Full-Text
Keywords: machine learning; biosensors; glucose-oxidase; neural networks; support vector machines; PLS; multivariate polynomial regression; optimization machine learning; biosensors; glucose-oxidase; neural networks; support vector machines; PLS; multivariate polynomial regression; optimization
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MDPI and ACS Style

Gonzalez-Navarro, F.F.; Stilianova-Stoytcheva, M.; Renteria-Gutierrez, L.; Belanche-Muñoz, L.A.; Flores-Rios, B.L.; Ibarra-Esquer, J.E. Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods. Sensors 2016, 16, 1483. https://doi.org/10.3390/s16111483

AMA Style

Gonzalez-Navarro FF, Stilianova-Stoytcheva M, Renteria-Gutierrez L, Belanche-Muñoz LA, Flores-Rios BL, Ibarra-Esquer JE. Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods. Sensors. 2016; 16(11):1483. https://doi.org/10.3390/s16111483

Chicago/Turabian Style

Gonzalez-Navarro, Felix F.; Stilianova-Stoytcheva, Margarita; Renteria-Gutierrez, Livier; Belanche-Muñoz, Lluís A.; Flores-Rios, Brenda L.; Ibarra-Esquer, Jorge E. 2016. "Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods" Sensors 16, no. 11: 1483. https://doi.org/10.3390/s16111483

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