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

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  • Published: Jun 30, 2011
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Comparison of novel sensory panel performance evaluation techniques with e-nose analysis integration

Comparison of novel sensory panel performance evaluation techniques with e-nose analysis integration
Journal of Chemometrics 2011, 25, 275-286
László Sipos, Zoltán Kovács, Dániel Szöllõsi, Zoltán Kókai, István Dalmadi and András Fekete

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.

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A complex approach was used to evaluate the performance of a sensory panel with two novel techniques: GCAP (Gravity Center Area/Perimeter) and CRRN (Compare Ranks with Random Numbers). Profile analysis was performed on Sri Lanka black tea batches from different plantations; for the prediction of sensory data from electronic nose results partial least square regression and support vector machine regression were used. Prediction by support vector machine gave close correlation between the results of electronic nose measurement and odor attributes.

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