2024-03-28T09:18:20Zhttp://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/307652022-07-06T21:15:19Zcom_10324_1186com_10324_931com_10324_894col_10324_1404
Classification of non alcoholic beer based on aftertaste sensory evaluation by chemometric tools
Rodríguez Méndez, María Luz
Ghasemi-Varnamkhasti, Mahdi
Mohtasebi, S.
Lozano, J.
Razavi, S.H.
Producción Científica
Sensory 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.
2018-07-17T09:43:22Z
2012
info:eu-repo/semantics/article
Expert systems with applications vol.39 p. 4315-4327
0957-4174
http://uvadoc.uva.es/handle/10324/30765
10.1016/j.eswa.2011.09.101
eng
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 International
SI