Statistical validation of sensory data: a study on wine

Abstract

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.

Flavor release and sensory characteristics of o/w emulsions. Influence of composition, microstructure and rheological behavior

“…Individual differences between assessors were analyzed by a fixed model, considering assessors as fixed factor. When a significant interaction between assessors and sample was observed for a descriptor, a mixed model ANOVA, considering assessors as random effect, was performed (Carlucci & Monteleone, 2001). F sample values were then recalculated taking the average square of the interaction as denominator.…”

Preference Mapping of Commercial Whole Wheat Breads

“…For attributes with a significant sample × panelist interaction (P < 0.05), the F value for sample in the ANOVA model and LSD values were recalculated with the mean square of the sample × panelist interaction term in place of the mean square error to confirm significance among samples (Carlucci and Monteleone 2001). For the descriptive panel, three-way ANOVA models with interactions were constructed for each descriptor.…”

Impact of sensory differences on consumer acceptability of yoghurt and yoghurt-like products

“…This was not surprising since it is well known that, in spite of the selection and training of judges, some variability always remains. Variation among assessors can be due to individual differences in the use of scales or to individual differences in sensitivity or motivation and it is very difficult to eliminate completely (Carlucci & Monteleone, 2001;Tomic, Nilsen, Martens, & Naes, 2007). However, it is important to know whether assessor variability may influence the estimation of sample differences.…”