Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 11; p. 1483
• Main Authors: Gonzalez-Navarro, Felix F, Stilianova-Stoytcheva, Margarita, Renteria-Gutierrez, Livier, Belanche-Muñoz, Lluís A, Flores-Rios, Brenda L, Ibarra-Esquer, Jorge E
• Format: Journal Article Publication
• Language: English
• Published: Switzerland MDPI AG 01.11.2016; MDPI
• Subjects: Aprenentatge automàtic; Artificial intelligence; Benzoquinones - chemistry; Benzoquinones - metabolism; Biosensing Techniques - methods; Biosensors; Computer simulation; Dependent variables; Electrical measurement; Genetic algorithms; Glucose; Glucose - analysis; Glucose oxidase; Glucose Oxidase - metabolism; Hydrogen-Ion Concentration; Informàtica; Intel·ligència artificial; Least-Squares Analysis; Machine Learning; Modelling; Multivariate polynomial regression; Neural networks; Optimization; Organic chemistry; PLS; Quinones; Sensitivity; Simulated annealing; Statistical analysis; Statistical methods; Support vector machines; Temperature; Àrees temàtiques de la UPC; multivariate polynomial regression; support vector machines; optimization; PLS; neural networks; glucose-oxidase; biosensors; machine learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16111483

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.