Reading: Use of Mandel’s bundle of lines model to improve agreement of a panel of tea tasters

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Use of Mandel’s bundle of lines model to improve agreement of a panel of tea tasters

Authors:

T. Ursla S. Peiris ,

LK
About T. Ursla S.
Biostatistics Unit, Faculty of Livestock Fisheries and Nutrition, Wayamba University of Sri Lanka, Makandura, Gonawila
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Chamila K. Walgampaya,

LK
About Chamila K.
Department of Engineering Mathematics, Faculty of Engineering, University of Peradeniya, Peradeniya
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David L. Banks,

US
About David L.
Department of Statistical Science, Duke University, Durham, North Carolina, USA
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R.O. Thattil,

LK
About R.O.
Department of Crop Science, Faculty of Agriculture, University of Peradeniya, Peradeniya
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I. Sarath B. Abeysinghe

LK
About I. Sarath B.
Tea Research Institute of Sri Lanka, Talawakelle
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Abstract

Sensory evaluation is of prime importance in the tea industry. The price of tea is predominantly determined by the tea taster’s judgement. Hence, statisticians and sensory analysts have significant interest in improving the reliability of sensory panel evaluations. This study suggests a statistical de-biasing approach to improve the agreement of panels of tea tasters by using the Mandel’s bundle of lines model. This paper analyses data from a panel of eight experts who evaluated 72 black tea samples collected from 13 different geographical regions of Sri Lanka over a one year (January 2010 ‒ December 2010) period at monthly intervals. The results showed that in only 20 instances out of 72, the intra-class correlation coefficients were greater than 0.5, but after bias correction, 64 instances out of 72 have intra-class correlations greater than 0.5. Therefore, the Mandel’s bundle of lines approach improves consensus among the experts by enforcing better statistical calibration.

How to Cite: Peiris, T.U.S. et al., (2018). Use of Mandel’s bundle of lines model to improve agreement of a panel of tea tasters. Journal of the National Science Foundation of Sri Lanka. 46(1), pp.95–102. DOI: http://doi.org/10.4038/jnsfsr.v46i1.8269
Published on 28 Mar 2018.
Peer Reviewed

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