Volume 25, Issue 5 p. 275-286
Special Issue Article

Comparison of novel sensory panel performance evaluation techniques with e-nose analysis integration

László Sipos

László Sipos

Sensory Laboratory, Sensory Laboratory, Corvinus University of Budapest (CUB), Hungary

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Zoltán Kovács

Zoltán Kovács

Department of Physics and Control, Corvinus University of Budapest (CUB), Hungary

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Dániel Szöllősi

Dániel Szöllősi

Department of Physics and Control, Corvinus University of Budapest (CUB), Hungary

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Zoltán Kókai

Corresponding Author

Zoltán Kókai

Sensory Laboratory, Corvinus University of Budapest (CUB), Hungary

Sensory Laboratory, Corvinus University of Budapest (CUB), Hungary.Search for more papers by this author
István Dalmadi

István Dalmadi

Department of Refrigeration and Livestock Technology, Corvinus University of Budapest (CUB), Hungary

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András Fekete

András Fekete

Department of Physics and Control, Corvinus University of Budapest (CUB), Hungary

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First published: 30 March 2011
Citations: 21

Abstract

Reliability and validity of sensory data is an important issue in scientific researches. If sensory analysis is performed in an analytical approach, the resulting data will show a similar structure to the chemical analyses. In the present paper the authors have used a complex approach to evaluate the performance of a sensory panel. The tested samples were black tea batches from different plantations of Sri Lanka. Profile analysis was applied to identify the odor profiles of the samples. Sensory profile data was submitted to two novel techniques of panel performance evaluation. GCAP (Gravity Center Area/Perimeter) is based on the profile polygons of the individual assessors. If the area/perimeter ratio of two panelists' profiles is similar and the gravity center is located near to each other, the panelists performed the tests consistently. CRRN (Compare Ranks with Random Numbers) is applicable not only to sensory data but also to other field of chemometrics. The essence of CRRN method is based on the evaluation of an ‘average’ vector, corresponding to the coordinate-averages of the measured points, and on a produced random vector series of the same dimension as the measured points. Sensory and e-nose data were evaluated with principal component analysis, cluster analysis and linear discriminant analysis. Partial least square regression and support vector machine regression were used to predict sensory data with electronic nose results. Prediction by support vector machine gave close correlation between the results of electronic nose measurement and odor attributes. Copyright © 2011 John Wiley & Sons, Ltd.