Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools

Authors:
Mahdi Ghasemi-Varnamkhasti;Seyed Saeid Mohtasebi;Maria Luz Rodriguez-Mendez;Jesus Lozano;Seyed Hadi Razavi;Hojat Ahmadi;Constantin Apetrei
Affiliations:
Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University of Tehran, Karaj, Iran and Department of Inorganic Chemistry, E.T.S. Ingenieros Indus ...;Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University of Tehran, Karaj, Iran;Department of Inorganic Chemistry, E.T.S. Ingenieros Industriales, University of Valladolid, P del Cauce s/n, 47011 Valladolid, Spain;Grupo de Investigación en Sistemas Sensoriales, Universidad de Extremadura, Av. Elvas s/n, 06006 Badajoz, Spain;Department of Food Science and Engineering, Faculty of Agricultural Engineering & Technology, University of Tehran, Karaj, Iran;Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, University of Tehran, Karaj, Iran;Department of Chemistry, Physics and Environment, Faculty of Sciences and Environment, "Dunrea de Jos" University of Galai, Romania
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 5
Cited 2

Adaptive image interpolation using probabilistic neural network

Expert Systems with Applications: An International Journal
Adaptive kernel principal component analysis

Signal Processing
Applying moving back-propagation neural network and moving fuzzy neuron network to predict the requirement of critical spare parts

Expert Systems with Applications: An International Journal
Probabilistic neural network approach to the classification of demonstrative pronouns for indirect anaphora in Hindi

Expert Systems with Applications: An International Journal
Short communication: The use of radial basis function networks (RBFN) to predict critical water parameters in desalination plants

Expert Systems with Applications: An International Journal

Differential electronic nose and support vector machine for fast recognition of tobacco

Expert Systems with Applications: An International Journal
NIR spectroscopy coupled with multivariate computational tools for qualitative characterization of the aging of beer

Computers and Electronics in Agriculture

Quantified Score

Hi-index	12.05

Visualization

Abstract

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.