Trained sensory panellists’ response to product alcohol content in the projective mapping task: Observations on alcohol content, product complexity and prior knowledge

Abstract

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tProjective mapping has been validated as a practical tool for the rapid sensory profiling of brandy products, although repeatability concerns necessitate repeated measurements in larger sample sets. The reason for poor repeatability could be linked to the complexity of the product type, as well as the physical and possibly psychological factors associated with its high alcohol content. To date no information has been published that tested the effect of these specific factors on panellist performance in projective mapping tasks. This study tested the effect of sample complexity and alcohol content on sensory panel repeatability and accuracy in projective mapping, using six types of commercial alcoholic beverages. In a second objective, the study also tested the effect of prior knowledge of alcohol content of a given product set on panellist performance in projective mapping. The results showed that complexity had the biggest impact on panel performance, while alcohol content had a secondary but decisive influence, largely due to its chemosensory fatiguing nature. Knowledge of the product alcohol content appeared to affect individuals differently, and also had an effect on the terminology used by the panellists to describe the products. The study also introduces the Relative Performance Indicator (RPI) as a new panel performance monitoring tool for projective mapping.

Panelists' Performances and Strategies in Paper‐Based and Computer‐Based Projective Mapping

“…The study setup was deliberately selected to explore the relative performance of paper‐ and computer‐based projective mapping in challenging conditions relevant to routine data acquisition conditions: consumers, rather than a trained panel, were used and a complex product (beer) was selected as this has been shown to impact on results (Louw et al . ).…”

“…In a food context, asking panelists to "think aloud" as they perform a task to understand their working has been insightfully used elsewhere not only to investigate emotion reporting (Jaeger et al 2013) but also to obtain an insight into participants' cognitive strategies when presented with different recall aids to estimate portion sizes (Chambers et al 2000). The study setup was deliberately selected to explore the relative performance of paper-and computer-based projective mapping in challenging conditions relevant to routine data acquisition conditions: consumers, rather than a trained panel, were used and a complex product (beer) was selected as this has been shown to impact on results (Louw et al 2014).…”

“…) and high alcohol beverages (Louw et al . ) to cite only the most recent examples. In this context of fast growth, it is not surprising that all the major sensory data acquisition software packages have now developed interfaces to collect projective mapping data directly on screen, by‐passing thus the elaboration of a map using physical products placed on a large flat surface.…”

“…Despite these well-documented limitations, projective mapping has been applied and validated with an ever growing range of food products such as fresh strawberries (Vicente et al 2014), mortadellas (Santos et al 2013), potato purees (Jimenez et al 2013) and high alcohol beverages (Louw et al 2014) to cite only the most recent examples. In this context of fast growth, it is not surprising that all the major sensory data acquisition software packages have now developed interfaces to collect projective mapping data directly on screen, by-passing thus the elaboration of a map using physical products placed on a large flat surface.…”

A comparison of generalised procrustes analysis and multiple factor analysis for projective mapping data

“…Below we also consider the standard deviation of the SI k,c values which reflects how it varies between the individuals. Note the close resemblance of the SI k,c index with the index proposed in Louw et al (2014).…”