Comparison of Preference Mapping Techniques for the Optimization of Strawberry Yogurt

Abstract

Many product optimization methods, known in general terms as preference mapping, have been described in the literature. However, a direct comparison of some of the different approaches (e.g., internal versus external mapping) has not been undertaken. This was attempted here by formulating strawberry yogurt prototypes according to an experimental design including three factors, investigating four approaches of optimization, comparing the sensory profiles of the ideal solutions and formulating the ideal solutions for validation with consumers. Results of this study show that the optimization methods did not yield identical ideal solutions but tended to agree on the ideal level for two of the three sensory dimensions investigated. These results warrant further investigation and comparison of the preference mapping methods available today.

Sensory and physicochemical evaluation of acerola nectar sweetened with sucrose and different sweeteners

“…It also allows identification of the samples that were most accepted by the majority of the panelists and, considering the individuality of each tester, it characterizes groups with different preferences and consumption patterns. The Internal Preference Map is constructed with acceptance data generated from affective tests from a team of consumers (OLIVEIRA et al, 2004;LOVELY;MEULLENET,2009). …”

Assessment of the consistency of ideal profiles according to non-ideal data for IPM

“…Van Trijp, Punter, Mickartz, and Kruithof (2007) gave positive arguments concerning the stability of the description of the ideal product obtained by different methodologies (PrefMap, JAR and IPM [presented as the variant method]). Lovely and Meullenet (2009) have shown that yoghurts synthesized on the bases of ideals obtained by JAR or estimated through PrefMap, EDIPM or LSA are similar, especially on attributes that drive liking. Moreover, the synthesized products were not significantly differently liked.…”

Optimization of blended wine quality through maximization of consumer liking

“…They also allow for greater insight into product perception and understanding without conducting extensive or inefficient testing, and may be constructed to account for composition and/ or cost requirements. Ideally, the optimized blends would be liked significantly more than the non-optimized wines, as found by Lovely and Meullenet (2009). Though scores did not indicate that type of result, other information was obtained through this design and analysis.…”