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2669414 
Journal Article 
A summary of projective mapping observations - The effect of replicates and shape, and individual performance measurements 
Hopfer, H; Heymann, H 
2013 
Yes 
Food Quality and Preference
ISSN: 0950-3293 
28 
1 
164-181 
WOS:000315557500021 
Projective mapping (PM) or Napping (R) are fast alternatives to traditional descriptive analysis (DA), and are becoming more popular among sensory scientists to obtain a quick overview of (dis)similarities among a certain sample set. Ideally, PM should be able to deliver similar results as a DA, and this aspect has been studied extensively in the last years, also in comparison to other fast alternative descriptive methods. Other aspects of research include the effect of replication and how to analyze the data.



Besides the two previously mentioned aspects (the effect of replicates and the comparability with DA), we focused in this study on the effect of the provided PM space, and compared a square to a rectangular space, and whether the obtained results would differ. In two consecutive studies, we compared a square configuration to a horizontal and a vertical rectangle configuration. We found that the judges did position their samples in a different way when confronted with a differently shaped space. These results suggest that the PM product representation depends on the provided space.



A last aspect of this study was dealing with individual performance, how this could be measured, and how large the effect of the individual judges on the overall solution is. Generally, judges that did not use the usual Cartesian coordinate system to position their samples did not affect the consensus PM solution. This can be most likely attributed to the large number of panelists in our studies. Additionally, we propose a people performance index (PPI) to measure the ability of an individual to place blind duplicated samples close to each other. (C) 2012 Elsevier Ltd. All rights reserved. 
Projective mapping/Napping (R); Shape; Descriptive analysis; Comparison; Red wine blends; Multi-factor analysis