Sensors 2016, 16(11), 1483; https://doi.org/10.3390/s16111483
Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods†
Felix F. Gonzalez-Navarro 1,*
, Margarita Stilianova-Stoytcheva 1, Livier Renteria-Gutierrez 1, Lluís A. Belanche-Muñoz 2
, Brenda L. Flores-Rios 1 and Jorge E. Ibarra-Esquer 1
, Brenda L. Flores-Rios 1 and Jorge E. Ibarra-Esquer 11
Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico
2
Computer Science Department, Universitat Politecnica de Catalunya, Barcelona 08034, Spain
†
This paper is an extended version of our paper published in the 13th Mexican International Conference on Artificial Intelligence (MICAI), Tuxtla Gutierrez, Mexico, 16–22 November 2014.
*
Author to whom correspondence should be addressed.
Academic Editors: Miguel González-Mendoza, Ma. Lourdes Martínez-Villaseñor and Hiram Ponce
Received: 30 May 2016 / Revised: 29 July 2016 / Accepted: 9 August 2016 / Published: 26 October 2016
(This article belongs to the Special Issue Advances in Artificial Intelligence: Selected Papers from MICAI 2013, 2014 and 2015—12th, 13th and 14th Mexican International Conferences on Artificial Intelligence)
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. View Full-TextKeywords:
machine learning; biosensors; glucose-oxidase; neural networks; support vector machines; PLS; multivariate polynomial regression; optimization
▼
Figures
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Supplementary material
-
Externally hosted supplementary file 1
Link: http://nova.mxl.uabc.mx/~fernando/sensors16_data/
Description: Biosensor data set as described in section 3.1 are available from the authors at http://nova.mxl.uabc.mx/~fernando/sensors16_data/
Share & Cite This Article
MDPI and ACS Style
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.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.