Two-step rating-based ‘double-faced applicability’ test for sensory analysis of spread products as an alternative to descriptive analysis with trained panel

Abstract

Descriptive analysis with a trained sensory panel has thus far been the most well defined methodology to characterize various products. However, in practical terms, intensive training in descriptive analysis has been recognized as a serious defect. To overcome this limitation, various novel rapid sensory profiling methodologies have been suggested in the literature. Among these, attribute-based methodologies such as check-all-that-apply (CATA) questions showed results comparable to those of conventional sensory descriptive analysis. Kim, Hopkinson, van Hout, and Lee (2017a, 2017b) have proposed a novel attribute-based methodology termed the two-step rating-based ‘double-faced applicability’ test with a novel output measure of applicability magnitude (d'A) for measuring consumers' product usage experience throughout various product usage stages. In this paper, the potential of the two-step rating-based ‘double-faced applicability’ test with d'A was investigated as an alternative to conventional sensory descriptive analysis in terms of sensory characterization and product discrimination. Twelve commercial spread products were evaluated using both conventional sensory descriptive analysis with a trained sensory panel and two-step rating-based ‘double-faced applicability’ test with an untrained sensory panel. The results demonstrated that the ‘double-faced applicability’ test can be used to provide a direct measure of the applicability magnitude of sensory attributes of the samples tested in terms of d'A for sensory characterization of individual samples and multiple sample comparisons. This suggests that when the appropriate list of attributes to be used in the questionnaire is already available, the two-step rating-based ‘double-faced applicability’ test with d'A can be used as a more efficient alternative to conventional descriptive analysis, without requiring any intensive training process. © 2017 Elsevier Ltd