Product Characterisation

Product Characterisation

Purpose

The product characterisation in the suite of sensory analysis can help identify the most and least characterizing attributes for each product, or group of products and demonstrates their relative discriminating ability.

Data Format

Note that for EyeOpenR to read your dataset, the first five columns must be: Assessor, Product, Session, Replica and Sequence. For more information on data format, see the sensory demo data sets.

Background

The tool uses analysis of variance to check for significant differences above and below the mean value of each attribute. For each attribute an ANOVA model is applied to check if the scores given by the assessors are significantly different for the product in question. The character of the product, displayed in terms of attributes, is then visualised on a plot, with more discriminating attributes highlighted.

Options

The options are very simple to setup and relate to output display only:

  1. Significance level (5%, 10%, 20%) – the user can choose what level of statistical significance (5%, 10% or 20%) the software marks as ‘significant’ using colours and for the extra V-test charts.
  2. No. of decimals for Values – number of decimal places displayed in the adjusted means and v-test outputs
  3. No. of decimals for P-values– number of decimal places displayed for any p-values in the output

Results and Interpretation

  1. Adjusted Means  - the first tab reports the values of the adjusted means for each sample and attribute (derived from the ANOVA model fitted) both in a table and then below as a plot. In the table, for each product, attributes with a score statistically significantly higher than the mean for that attribute are coloured blue, whereas those coloured red are statistically significantly lower. Cells that are white are indicating products that are not statistically significant different from the mean attribute score for the attribute.  Below the table is a line chart showing the same scores with one line per product across each of the different attributes.
  2. V-test Plot – the V-test plots are displayed on one tab for each product. The plot shows the size of the V-test score for all the attributes and ties in with the previous table of adjusted means. Where a product has a lower adjusted mean score for an attribute when comparing to the attribute mean, the v-test score will be negative and the plot shows this in red (to the left). Where a product has a higher adjusted mean score for an attribute when comparing to the attribute mean, the v-test score will be positive and the plot shows this in yellow (to the right). The user can quickly see which attributes are scoring higher and lower than average for the product. The V-test statistics are shown in the table below the V-test plot, with the associated mean scores and p-values for all the attributes for that product. The V-test score can be understood to be similar to the normal Z-statistic, as a quantile of the normal distribution and is a transformation of the p-value, i.e. when the absolute value of the V-test statistic is > 1.96 then the p-value will be statistically significant at p=0.05. The V-test statistic helps by showing whether the difference is positive or negative.
  3. V-test Plot (only signif.) – This plot and table are the same as the previous, however this time only those attributes that are statistically significantly different at the selected significance level are shown on the plot and in the table.

Technical Information

  1. R packages used – SensoMineR, FactoMineR

References

  1. Husson F. , Lê S. and Pagès J. (2009). SensoMineR dans Evaluation sensorielle - Manuel méthodologique. Lavoisier, SSHA, 3ème édition.
  2. Lê S. and Husson F. (2008). SensoMineR: a package for sensory data analysis. Journal of Sensory Studies. 23(1). 14-25.

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