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Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode |
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| Authors: | Adam Roselló Núria Serrano José Manuel Díaz-Cruz Cristina Ariño |
| Affiliation: | 1. Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1–11, 08028 Barcelona, Spain.;2. Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1–11, 08028 Barcelona, Spain. Institut de Recerca de l'Aigua (IdRA), University of Barcelona, 08028 Barcelona, Spain. |
| 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. |
| Keywords: | Beer discrimination Cyclic voltammetry Screen-printed carbon electrode Partial least squares Partial least squares discriminant analysis Support vector machine discriminant analysis Artificial neural networks |