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dc.contributor.authorRodríguez Méndez, María Luz 
dc.contributor.authorGhasemi-Varnamkhasti, Mahdi
dc.contributor.authorS. Mohtasebi, S.
dc.contributor.authorLozano, J.
dc.contributor.authorH. Razavi, S.
dc.date.accessioned2018-07-17T09:43:22Z
dc.date.issued2012
dc.identifier.citationExpert systems with applications vol.39 p. 4315-4327es
dc.identifier.issn0957-4174es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/30765
dc.descriptionProducción Científicaes
dc.description.abstractSensory evaluation is the application of knowledge and skills derived from several different scientific and technical disciplines, physiology, chemistry, mathematics and statistics, human behavior, and knowledge about product preparation practices. This research was aimed to evaluate aftertaste sensory attributes of commercial non-alcoholic beer brands (P1, P2, P3, P4, P5, P6, P7) by several chemometric tools. These attributes were bitter, sour, sweet, fruity, liquorice, artificial, body, intensity and duration. The results showed that the data are in a good consistency. Therefore, the brands were statistically classified in several categories. Linear techniques as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed over the data that revealed all types of beer are well separated except a partial overlapping between zones corresponding to P4, P6 and P7. In this research, for the confirmation of the groups observed in PCA and in order to calculate the errors in calibration and in validation, PLS-DA technique was used. Based on the quantitative data of PLS-DA, the classification accuracy values were ranked within 49-86%. Moreover, it was found that the classification accuracy of LDA was much better than PCA. It shows that this trained sensory panel can discriminate among the samples except an overlapping between two types of beer. Also, two types of artificial networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Back Propagation (BP) learning method. The highest classification success rate (correct predicted number over total number of measurements) of about 97% was obtained for RBF followed by 94% for BP. The results obtained in this study could be used as a reference for electronic nose and electronic tongue in beer quality control.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleClassification of non alcoholic beer based on aftertaste sensory evaluation by chemometric toolses
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.1016/j.eswa.2011.09.101es
dc.peerreviewedSIes
dc.description.embargo2022-07-6es
dc.description.lift2022-07-06
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International


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