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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Main Authors: Felix F. Gonzalez-Navarro, Margarita Stilianova-Stoytcheva, Livier Renteria-Gutierrez, Lluís A. Belanche-Muñoz, Brenda L. Flores-Rios, Jorge E. Ibarra-Esquer
Format: Article
Language:English
Published: MDPI AG 2016-10-01
Series:Sensors
Subjects:
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.
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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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AT livierrenteriagutierrez glucoseoxidasebiosensormodelingandpredictorsoptimizationbymachinelearningmethods
AT lluisabelanchemunoz glucoseoxidasebiosensormodelingandpredictorsoptimizationbymachinelearningmethods
AT brendalfloresrios glucoseoxidasebiosensormodelingandpredictorsoptimizationbymachinelearningmethods
AT jorgeeibarraesquer glucoseoxidasebiosensormodelingandpredictorsoptimizationbymachinelearningmethods