Are assessors levels of a split-plot factor in the analysis of variance of sensory profile experiments?

Steinsholt, K.

doi:10.1016/s0950-3293(96)00042-0

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…These are termed random effects. However, it is possible to regard assessors as fixed effects, on the grounds that they are not randomly chosen representatives of a population, but are a selected and trained (calibrated) measuring device (Steinsholt, 1998). It is quite natural to regard the products as being fixed effects, so, if the assessors are regarded as random effects, a mixed model results.…”

Section: Statistical Aspectsmentioning

confidence: 99%

“…Quite different conclusions may result when data are tested according to a mixed as opposed to a fixed model, and care must be taken in specifying exactly what is being tested (Naes & Langsrud, 1998). Where the full experiment cannot be replicated, it may be more appropriate to regard the assessors as levels in a split-plot design (Steinsholt, 1998). Whatever model is used for analysis, it is instructive to remember that a slight change in the initial assumptions or in the construction of the model can lead to a change in the conclusion, and therefore any conclusions should be regarded with caution.…”

Section: Statistical Aspectsmentioning

confidence: 99%

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Sensory analysis

Piggott

Simpson

Williams

1998

Int J of Food Sci Tech

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Sensory methods can be loosely separated into two groups: discriminant methods and descriptive methods. Simple models of difference tests rest on a number of assumptions, and not only are they not very good at showing that samples are the same, they are not good at detecting small differences. Quantitative Descriptive Analysis was developed from the Flavor Profile Method, and used an interval scale with emphasis on statistical evaluation of results. A variation of descriptive analysis is Free‐Choice Profiling, where data are normally examined by generalized Procrustes analysis. Initial suspicion of the results has been overcome by more rigorous testing of their reliability. Time‐intensity measurement is a special case of descriptive analysis, where a single characteristic is tracked as it changes over a period of time. Time‐intensity has only relatively recently achieved wide application, and there have been rather few methodological studies.

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Section: Statistical Aspectsmentioning

confidence: 99%

Section: Statistical Aspectsmentioning

confidence: 99%

Sensory analysis

Piggott

Simpson

Williams

1998

Int J of Food Sci Tech

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show abstract

“…3,4 Members of a well-trained laboratory panel are supposed to give small variation in their analytical evaluations. 5 The variations between assessors should be minimised or removed as much as possible before testing, during the selection of panel members or in the training period, but it is very dif®cult to eliminate the differences completely. 6 When analysing sensory pro®ling data, several problems occur.…”

Section: Introductionmentioning

confidence: 99%

“…Analysis of variance (ANOVA) is commonly used to analyse sensory data, but in many research papers a description of the model of the analysis of variance is often ignored or only very brie¯y mentioned. 5 There are instead two main models of ANOVA: the ®xed model, in which the assessors are considered as ®xed effects, and the mixed model, in which the assessors are random effects. 12±16 Whether subjects are to be regarded as random or ®xed effects depends on whether or not the conclusions need to be extended beyond the panellists tested to the population from which the assessors were drawn.…”

Section: Introductionmentioning

confidence: 99%

Statistical validation of sensory data: a study on wine

Carlucci

Monteleone

2001

J Sci Food Agric

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A methodological procedure involving an appropriate statistical validation of sensory data was de®ned in order to describe the typical sensory pro®le of a young red wine (Aglianico) destined to aging. A trained panel of eight assessors rated the intensity of nine attributes on 16 products. Sensory data were submitted to statistical validation using a procedure organised in three main steps, namely ®xed and mixed models of analysis of variance (ANOVA) and data standardisation. Results of the ®xed ANOVA model computed on raw data showed signi®cant differences between the products for all the attributes except cherry aroma and pungent mouthfeel. However, the results showed inconsistency between assessors. For many attributes, interaction effects were found. When individual differences between assessors were minimised or eliminated using scaled data, results of the ®xed and mixed models showed no signi®cant differences (p 0.05) amongst the products for any of the sensory attributes. Therefore the differences amongst the products before data handling and before applying the mixed model of ANOVA were due to the assessors' variability. The results after statistical validation showed that the wines are very similar and that all the attributes used to describe the sensory characteristics of the products can be considered, at a qualitative and a quantitative level, as typical descriptors of the Aglianico wine destined to aging.

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Sensory and Consumer Data

Bower

2013

Statistical Methods for Food Science

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Are assessors levels of a split-plot factor in the analysis of variance of sensory profile experiments?

Cited by 8 publications

References 2 publications

Sensory analysis

Sensory analysis

Statistical validation of sensory data: a study on wine

Sensory and Consumer Data

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