Sensory profiling data studied by partial least squares regression

The statistical analysis of a descriptive sensory profiling data set distributed at the sensometrics meeting is presented. The data set is analysed with focus on the sensory differences between products (cooked potatoes). The data analytical strategy involves a descriptive statistical analysis to obtain an overview of the distribution and standard deviations of the scores for each sensory attribute. Subsequently, three-way analysis of variance (AVOVA) of the data gives a statistical measure of the reliability of the sensory attributes supplemented by principal component analysis, which visualise the main tendencies of systematic variation. Discriminant and ANOVA partial least squares regressions are used to relate the sensory structure to product design structure and vice versa. Statistical reliability and predictive validity of the product differences are obtained by ANOVA and cross-validation. Similar data structures are observed in the various multivariate models. Texture, taste and flavour attributes differentiated the potato samples, with the texture attributes being most reliable. It is emphasised that an appropriate interpretation of the profiling data should also include knowledge of the experimental background.