# Application of electronic nose to beer recognition using supervised artificial neural networks

Abstract : Employment of electronic nose is drawing many attentions in brewery because of its unique capability in assessing multi-component analytes, which is largely feasible for traditional single-sensor devises. This study was aimed to recognize between alcoholic and non alcoholic beers by use of a MOS-based electronic nose system coupled with artificial neural networks (ANN) to evaluate the capability of the system for a binary discrimination. The PCA score plot of the two first principal components accounted for 78$\%$ of variance and clearly discrimination was observed. This observation was confirmed by ANN in such as way radial basis function (RBF) and Backpropagation (BP) showed satisfactory results to binary discrimination between two types of beer as 100 $\%$ of classification accuracy for both training and testing data sets. This result confirms the ability of the electronic nose to be used in future for other applications to beer evaluation in our project.
Type de document :
Communication dans un congrès
International Conference on Control, Decision and information technologies (CoDIT’14), Nov 2014, Metz, France. International Conference on Control, Decision and information technologies (CoDIT’14)
Domaine :

https://hal.univ-lorraine.fr/hal-01372315
Contributeur : Lcoms Ul <>
Soumis le : mardi 27 septembre 2016 - 10:25:38
Dernière modification le : mercredi 22 novembre 2017 - 10:56:27

### Identifiants

• HAL Id : hal-01372315, version 1

### Citation

Maryam Siadat, Mahdi Ghasemi-Varnamkhasti, Seyed Saeid Mohtasebi, Etienne Losson. Application of electronic nose to beer recognition using supervised artificial neural networks. International Conference on Control, Decision and information technologies (CoDIT’14), Nov 2014, Metz, France. International Conference on Control, Decision and information technologies (CoDIT’14). 〈hal-01372315〉

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