Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods†
AbstractBiosensors 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
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Description: Biosensor data set as described in section 3.1 are available from the authors at http://nova.mxl.uabc.mx/~fernando/sensors16_data/
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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.
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.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.
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