title: Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods.
abstract: 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.
authors: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico. fernando.gonzalez@uabc.edu.mx.
authors: Felix F
authors: FF
authors: Gonzalez-Navarro
authors: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico. margarita.stoytcheva@uabc.edu.mx.
authors: Margarita
authors: M
authors: Stilianova-Stoytcheva
authors: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico. livier.renteria@uabc.edu.mx.
authors: Livier
authors: L
authors: Renteria-Gutierrez
authors: Computer Science Department, Universitat Politecnica de Catalunya, Barcelona 08034, Spain. belanche@lsi.upc.edu.
authors: Lluís A
authors: LA
authors: Belanche-Muñoz
authors: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico. brenda.flores@uabc.edu.mx.
authors: Brenda L
authors: BL
authors: Flores-Rios
authors: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico. jorge.ibarra@uabc.edu.mx.
authors: Jorge E
authors: JE
authors: Ibarra-Esquer
mesh_terms: D016227:Benzoquinones / Q000737:chemistry / Q000378:metabolism; D015374:Biosensing Techniques / Q000379:methods*; D005947:Glucose / Q000032:analysis*; D005949:Glucose Oxidase / Q000378:metabolism*; D006863:Hydrogen-Ion Concentration; D016018:Least-Squares Analysis; D000069550:Machine Learning; D013696:Temperature
chemical_list: D016227:Benzoquinones; D005949:Glucose Oxidase; D005947:Glucose
keywords: PLS; biosensors; glucose-oxidase; machine learning; multivariate polynomial regression; neural networks; optimization; support vector machines