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Glucose oxidase biosensor modeling and predictors optimization by machine learning methods

Autor
González, F.F.; Stilianova-Stoytcheva , M.; Rentería, L.; Belanche, Ll.; Flores, B.; Ibarra, J.
Tipus d'activitat
Article en revista
Revista
Sensors
Data de publicació
2016-11-01
Volum
16
Número
11
Pàgina inicial
Article 1483
DOI
https://doi.org/10.3390/s16111483 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/100496 Obrir en finestra nova
URL
http://www.mdpi.com/1424-8220/16/11/1483 Obrir en finestra nova
Resum
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 st...
Citació
González, F.F., Stilianova-Stoytcheva, M., Rentería, L., Belanche, Ll., Flores, B., Ibarra, J. Glucose oxidase biosensor modeling and predictors optimization by machine learning methods. "Sensors", 1 Novembre 2016, vol. 16, núm. 11.
Paraules clau
Glucose-oxidase, Multivariate polynomial regression, Neural networks, Optimization, PLS, Support vector machines
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants

  • González Navarro, Félix Fernando  (autor)
  • Stilianova-Stoytcheva, Margarita  (autor)
  • Rentería Gutiérrez, Livier  (autor)
  • Belanche Muñoz, Luis Antonio  (autor)
  • Flores Ríos, Brenda L.  (autor)
  • Ibarra Esquer, Jorge E.  (autor)

Arxius