Assessing the performance of a sensory panel–panellist monitoring and tracking

Sensory science uses the human senses as instruments of measurement. This study presents univariate and multivariate data analysis methods to assess individual and group performances in a sensory panel. Green peas were evaluated by a trained panel of 10 assessors for six attributes over two replicates. A consonance analysis with principal component analysis (PCA) is run to get an overview of the panel agreement and detect major individual errors. The origin of the panellist errors is identified by a series of tests based on analysis of variance, i.e. sensitivity, reproducibility, crossover and panel agreement, complemented with an eggshell correlation test. One assessor is identified with further need for training in attributes pea flavour, sweetness, fruity and off‐flavour, showing errors in sensitivity, reproducibility and crossover. Another assessor shows poor performance for attribute mealiness and to some extent also fruity flavour. Only one panellist performs well to very well in all attributes. The specificity and complementarity of the series of univariate tests are explored and verified with the use of a PCA model. Copyright © 2005 John Wiley & Sons, Ltd.