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APPLYING OF NEURAL NETWORK ON THE WINE SENSORIAL ANALYSIS FROM BARBADOS CHERRY

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Abstract:

ABSTRACT

This paper presents an approach of artificial neural networks to predict the sensorial qualities of wines. A Kohonen network has been used as a software tool in order to increase the human skills in this kind of application. An initial prototype was implemented using the artificial neural networks technology, together with the Visual Basic software from Microsoft, for evaluation of sensorial qualities of seven samples of Barbados cherry (Malpighia glabra L.) wine. Fifty consumers chosen, perhaps, had been used to obtain the sensorial data using a hedonic scale of 1–9 times. Sensorial values of flavor, aroma and appearance obtained of the hedonic dating were compared. The characteristics of wine, such as flavor and color, were similar to the Barbados cherry fresh fruit. The consumers evaluated the wines as very good; all sensorial qualities were more than 5 in hedonic scale. Results showed that Kohonen network classified the Barbados cherry wines in a distinct group, for frequency among their sensorial responses. Kohonen network results were similar or better than statistical classification; this shows that the use of Kohonen algorithm in the sensorial analysis of wines is valid. Kohonenalgorithm is very good in clustering of Barbados cherry wine samples, and its uses in sensorial analyses of wines is promising. PRACTICAL APPLICATIONS

The results of this work have practical uses in fruit wine production of Barbados cherry and in sample selections by sensorial analysis. Thus, it shows the optimal conditions to produce the Barbados cherry wine, and the effects of fruit pulp mass and sugar content on the sensorial qualities of wines had been evaluated. A visual basic program had been elaborated, in this work, by Kohonen neural network algorithm, which will be a powerful tool for taking of decision in sensorial analyses of beverages, foods and too much products that use the sensorial methods to make the comparison between them.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1745-4530.2009.00521.x

Affiliations: 1: Department of Industrial Engineering (DEP) of Nine of July University (UNINOVE)Av. Francisco Matarazo, 612Água Branca, São Paulo 05001-100, Brazil 2: Department of Chemical Engineering (DEQ)Federal University of Sergipe (UFS)São Cristóvão, Sergipe, Brazil 3: Department of Engineering of Chemical Systems (DESQ)School of Chemical Engineering (FEQ)State University of Campinas (UNICAMP)Barão Geraldo 6066, Campinas 13083-970, São Paulo, Brazil

Publication date: February 1, 2010

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