Details

Autor(en) / Beteiligte

• Cetó, Xavier
• Gutiérrez-Capitán, Manuel
• Calvo, Daniel
• del Valle, Manel

Titel

Beer classification by means of a potentiometric electronic tongue

Ist Teil von

• Food chemistry, 2013-12, Vol.141 (3), p.2533-2540

Ort / Verlag

Kidlington: Elsevier Ltd

Erscheinungsjahr

2013

Quelle

ScienceDirect

Beschreibungen/Notizen

• •A new methodology based on multisensory system for the analysis of beer.•Electronic tongue using an array of potentiometric ion selective electrodes (ISEs).•Qualitative and quantitative examples of electronic tongue applications.•Discrimination of different beer classes by means of an LDA model.•Prediction of alcohol by volume (abv) content by means of an ANN model. In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) 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), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). 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. In this respect, 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 an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).