Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/176679
Title: Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
Author: Roselló, Adam
Serrano i Plana, Núria
Díaz Cruz, José Manuel
Ariño Blasco, Cristina
Keywords: Voltametria
Cervesa
Xarxes neuronals (Informàtica)
Voltammetry
Beer
Neural networks (Computer science)
Issue Date: 9-Apr-2021
Publisher: Wiley-VCH
Abstract: A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.
Note: Versió postprint del document publicat a: https://doi.org/10.1002/elan.202060515
It is part of: Electroanalysis, 2021, vol. 33, num. 4, p. 864 -872
URI: http://hdl.handle.net/2445/176679
Related resource: https://doi.org/10.1002/elan.202060515
ISSN: 1040-0397
Appears in Collections:Articles publicats en revistes (Enginyeria Química i Química Analítica)

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