Discrimination of beers by cyclic voltammetry using a single carbon screen-printed electrode
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 subm...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2021 |
| País: | España |
| Recursos: | Universidad de Barcelona |
| Repositório: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/176679 |
| Acesso em linha: | https://hdl.handle.net/2445/176679 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Voltametria Cervesa Xarxes neuronals (Informàtica) Voltammetry Beer Neural networks (Computer science) |
| Resumo: | 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. |
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