Beer classification by means of a potentiometric electronic tongue
In this work, an Electronic Tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) is presented for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:154800 |
| Acesso em linha: | https://ddd.uab.cat/record/154800 https://dx.doi.org/urn:doi:10.1016/j.foodchem.2013.05.091 |
| Access Level: | acceso abierto |
| Palavra-chave: | Electronic Tongue Linear Discriminant Analysis Potentiometric sensors Classification Beer Alcohol by volume |
| Resumo: | In this work, an Electronic Tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) is presented for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, Principal Component Analysis (PCA) and Linear Discriminant Analysis(LDA) in order to achieve the correct recognition of samples variety. In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. Finally, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show a quantitative application, alcohol content was predicted from the array data employing an Artificial Neural Network model. |
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