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

Peiris, T.U.S.; Walgampaya, Chamila; Banks, David; Thattil, R.O.; Abeysinghe, I. Sarath B.

doi:10.4038/jnsfsr.v46i1.8269

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…This CLV3W method was applied to the secondary data of Sri Lankan tea tasters used in the study Peiris et al (2018). Data set contained scores given by 8 well-experienced tea tasters for samples from 13 tea growing regions of Sri Lanka on 6 sensory attributes.…”

Section: Application Of Clv3wmentioning

confidence: 99%

Clustering around latent variables approach to detect panel disagreement in three–way tea sensory evaluation

Fernando¹,

Samita²,

Peiris³

2020

IJSRP

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Sensory evaluation plays a remarkable role in maintaining the quality standards of beverages such as tea. The quality, which determines the price of tea, is evaluated by professional tea tasters. Uncertainty and vagueness of sensory evaluation has been a serious issue in selection of good quality tea. An issue existing when analyzing sensory data to detect panel disagreement is that data of three dimensional (three-way) or higher are often reduced to two-way data. Present study aimed to investigate the possibility of using Clustering around Latent Variables for three-way data (CLV3W) method to detect panel disagreement in sensory data of Sri Lankan tea. A three-way data set, 8 tea tasters× 13 tea growing regions×6 attributes, for each month with four replicates (4 factories per region), for a period of one calendar year were used for the study. When CLV3W analysis was performed separately for each month data, it was found two-clusters (two-latent components) exist for the data. Attribute loadings of colour and strength indicated that they are represented by the first latent component, and those loadings for brightness, flavour, aroma, and quality indicated that they are represented by the second component. Region scores for the two components revealed grouping of regions indicating some regions have similar tea quality attributes. Assessor weights varied across latent components and months. However, by examining assessor weights it was possible to identify assessors those who were in agreement and those who were in disagreement for each component. This finding demonstrated the fact that certain assessors are more sensitive to certain attributes and thus in order to detect tea with certain attributes, appropriate assessors can be employed. Therefore, CLV3W method is a useful method to detect disagreement between assessors, especially in 3-way data, and it can effectively be used when selecting assessors for sensory evaluation.

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Section: Application Of Clv3wmentioning

confidence: 99%