High dimensionality voltammetric biosensor data processed with artificial neural networks

Andreu González-Calabuig, Georgina Faura, Manel Del Valle*

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review

1 Citation (Scopus)

Abstract

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).

Original languageEnglish
Title of host publicationESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages245-250
Number of pages6
ISBN (Electronic)9782875870391
Publication statusPublished - 2017

Publication series

NameESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

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