Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods
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 capabi...
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MDPI AG
2016-10-01
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Online Access: | http://www.mdpi.com/1424-8220/16/11/1483 |
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author | Felix F. Gonzalez-Navarro Margarita Stilianova-Stoytcheva Livier Renteria-Gutierrez Lluís A. Belanche-Muñoz Brenda L. Flores-Rios Jorge E. Ibarra-Esquer |
author_facet | Felix F. Gonzalez-Navarro Margarita Stilianova-Stoytcheva Livier Renteria-Gutierrez Lluís A. Belanche-Muñoz Brenda L. Flores-Rios Jorge E. Ibarra-Esquer |
author_sort | Felix F. Gonzalez-Navarro |
collection | DOAJ |
description | 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. |
first_indexed | 2024-04-11T13:21:06Z |
format | Article |
id | doaj.art-25812c8d4668408a97e6e52c9449a74c |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-04-11T13:21:06Z |
publishDate | 2016-10-01 |
publisher | MDPI AG |
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series | Sensors |
spelling | doaj.art-25812c8d4668408a97e6e52c9449a74c2022-12-22T04:22:12ZengMDPI AGSensors1424-82202016-10-011611148310.3390/s16111483s16111483Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning MethodsFelix F. Gonzalez-Navarro0Margarita Stilianova-Stoytcheva1Livier Renteria-Gutierrez2Lluís A. Belanche-Muñoz3Brenda L. Flores-Rios4Jorge E. Ibarra-Esquer5Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, MexicoInstituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, MexicoInstituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, MexicoComputer Science Department, Universitat Politecnica de Catalunya, Barcelona 08034, SpainInstituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, MexicoInstituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, MexicoBiosensors 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.http://www.mdpi.com/1424-8220/16/11/1483machine learningbiosensorsglucose-oxidaseneural networkssupport vector machinesPLSmultivariate polynomial regressionoptimization |
spellingShingle | Felix F. Gonzalez-Navarro Margarita Stilianova-Stoytcheva Livier Renteria-Gutierrez Lluís A. Belanche-Muñoz Brenda L. Flores-Rios Jorge E. Ibarra-Esquer Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods Sensors machine learning biosensors glucose-oxidase neural networks support vector machines PLS multivariate polynomial regression optimization |
title | Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods |
title_full | Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods |
title_fullStr | Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods |
title_full_unstemmed | Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods |
title_short | Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods |
title_sort | glucose oxidase biosensor modeling and predictors optimization by machine learning methods |
topic | machine learning biosensors glucose-oxidase neural networks support vector machines PLS multivariate polynomial regression optimization |
url | http://www.mdpi.com/1424-8220/16/11/1483 |
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