Descriptive analysis utilizes human perceptions functioning as instrumental measures to quantify the sensory parameters of products. These perceptions reflect how the panelists respond to the products, including all sensations related to appearance, auditory, smell, touch, feeling, etc4,7
. Sensations are complex processes affected by many different factors. As a result, individual panelists can perceive and interpret the same stimulus differently5
. This may result from panelists assigning different meanings to the same stimulus1
, differences in threshold intensity, ability/inability to detect small intensity differences8
and different scoring behaviors9
. Individual scoring effects from scale and ranges are quite common in descriptive analysis. For example, if one panelist scores the products at a high intensity levels, but another one evaluates the same products at low intensity levels, a scale effect is created. In the range effect, some panelists use a greater range of intensities on the scale to rate the products than others2
. As a result, one or more panelists may commit magnitude or crossover errors; some may fail to discriminate the products.
In practice, all these effects or variations from panelists should be removed or minimized. Generally, carefully training is considered to improve panelists’ awareness and understanding of terms and definitions, increase reliability, discrimination and panel agreement3
. Panel performance assessment and monitoring are important practices in descriptive profiling.
The performance panel and individual panelist can be evaluated by their reliability, reproducibility and discrimination in sensory descriptive tests.
- Reliability is the ability to provide the same attribute scores to the same product; also referred to as “repeatability”6
- Reproducibility indicates how an individual agrees, on average, with the panel as a whole; for the panel; shows the homogeneity of the team6;
- Discrimination refers to the ability of a panel or panelist to differentiate between the products based on their attributes.
Among these requirements, discrimination among the products is critical since this is often the main goal of descriptive analysis.
1 Dijksterhuis G. 1995b. Multivariate data analysis in sensory and consumer science: an overview of development. Trends Food Sci. Techno 6: 206-11.
2 Dijksterhuis G. 1996. Procrustes analysis in sensory research. In Næs, T and Risvik, E, editors. Multivariate analysis of data in sensory science. Elsevier Science. Pp. 185-219.
3 Labbe D., Rytz A., Hugi A. 2004. Training is a critical step to obtain reliable product profiles in a real food industry context. Food Quality and Preference 15: 341-48.
4 Meilgaard MC, Civille GV, and Carr BT (2007). Sensory Evaluations Techniques, CRC Press, Boca Raton, FL
5 Næs T. 1990. Handling individual differences between assessors in sensory profiling. Food Quality and Preference 2: 187-99.
6 Rossi F. 2001. Assessing sensory panelist performance using repeatability and reproducibility measures. Food Quality and Preference 12:467-79.
7 Stone H., Sidel JL. 2004. Sensory Evaluation Practices. San Diego, CA: Elsevier Academic Press.
8 Schiffman S., Lockhead GR. 1983. Individual difference scaling of taste and smell. In: Martens, H and H.
9 Wilkinson C., Yuksel D. 1997. Modeling difference between panelists in use of measurement scales. J.Sensory Stud 12: 55-68.