TY - CHAP
T1 - High dimensionality voltammetric biosensor data processed with artificial neural networks
AU - González-Calabuig, Andreu
AU - Faura, Georgina
AU - Del Valle, Manel
N1 - Funding Information:
Financial support for this work was provided by the Spanish Ministry of Economy and Innovation, MINECO (Madrid) through project CTQ2013-41577-P. Andreu González-Calabuig thanks Universitat Autònoma de Barcelona for the PIF fellowship. Manel del Valle thanks the support from program ICREA Academia.
Publisher Copyright:
© ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.
PY - 2017
Y1 - 2017
N2 - This work report the coupling of an array of voltammetric sensors with artificial neural networks (ANN), usually named Electronic Tongue, for the simultaneous quantification of tryptophan, tyrosine and cysteine aminoacids. The obtained signals were compressed using fast Fourier transform (FFT) and then the ANN model was constructed from a set of low-frequency components. An ANN predictive model was obtained by back-propagation, which had 160 input neurons, one hidden layer with 7 neurons and used purelin and satlins functions in the hidden and output layer respectively, trained with a factorial design scheme . The model attained a total normalized root mean square error of 0.032 for an independent test set of data (n=15).
AB - This work report the coupling of an array of voltammetric sensors with artificial neural networks (ANN), usually named Electronic Tongue, for the simultaneous quantification of tryptophan, tyrosine and cysteine aminoacids. The obtained signals were compressed using fast Fourier transform (FFT) and then the ANN model was constructed from a set of low-frequency components. An ANN predictive model was obtained by back-propagation, which had 160 input neurons, one hidden layer with 7 neurons and used purelin and satlins functions in the hidden and output layer respectively, trained with a factorial design scheme . The model attained a total normalized root mean square error of 0.032 for an independent test set of data (n=15).
UR - http://www.scopus.com/inward/record.url?scp=85069493670&partnerID=8YFLogxK
M3 - Chapter
AN - SCOPUS:85069493670
T3 - ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
SP - 245
EP - 250
BT - ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
ER -