Interpreting consumer preferences: Physicohedonic and psychohedonic models yield different information in a coffee-flavored dairy beverage

Abstract

Designed experiments provide product developers feedback on the relationship between formulation and consumer acceptability. While actionable, this approach typically assumes a simple psychophysical relationship between ingredient concentration and perceived intensity. This assumption may not be valid, especially in cases where perceptual interactions occur. Additional information can be gained by considering the liking-intensity function, as single ingredients can influence more than one perceptual attribute. Here, 20 coffee-flavored dairy beverages were formulated using a fractional mixture design that varied the amount of coffee extract, fluid milk, sucrose, and water. Overall liking (liking) was assessed by 388 consumers using an incomplete block design (4 out of 20 prototypes) to limit fatigue; all participants also rated the samples for intensity of coffee flavor (coffee), milk flavor (milk), sweetness (sweetness) and thickness (thickness). Across product means, the concentration variables explained 52% of the variance in liking in main effects multiple regression. The amount of sucrose (β = 0.46) and milk (β = 0.46) contributed significantly to the model (p’s <0.02) while coffee extract (β = −0.17; p = 0.35) did not. A comparable model based on the perceived intensity explained 63% of the variance in mean liking; sweetness (β = 0.53) and milk (β = 0.69) contributed significantly to the model (p’s <0.04), while the influence of coffee flavor (β = 0.48) was positive but marginally (p = 0.09). Since a strong linear relationship existed between coffee extract concentration and coffee flavor, this discrepancy between the two models was unexpected, and probably indicates that adding more coffee extract also adds a negative attribute, e.g. too much bitterness. In summary, modeling liking as a function of both perceived intensity and physical concentration provides a richer interpretation of consumer data.

Just-about-right and ideal scaling provide similar insights into the influence of sensory attributes on liking

“…Bitterness is generally regarded as a negative factor to consumer liking, and it is possible that dumping of their ratings of bitterness into coffee flavor caused the decrease in liking above the ideal coffee flavor level. This result is compatible with our previous findings on psychohedonic and physicohedonic models (Li, Hayes & Ziegler, 2014). …”

“…Coffee extract is a complex ingredient. Adding more coffee extract into a coffee-flavored beverage would also inevitably produce some negative attribute, like bitterness, which would negatively impact liking (Li et al, 2014). Therefore, the level of coffee extract for a coffee-flavored dairy beverage should be well designed to balance positive and negative sensory perceptions.…”

Comparison of sensory product profiles generated by trained assessors and consumers using CATA questions: Four case studies with complex and/or similar samples

“…This result provides additional evidence to support the use of CATA questions to indirectly measure consumers' perceived intensity of sensory attributes, particularly when dealing with simple sensory attributes and/or sets of samples where differences are ''medium' ' (or greater). This is in agreement with the fact that naïve consumers have been reported to be able to measure the intensity of simple sensory attributes (Ares et al, 2011;Hayes, Sullivan, & Duffy, 2010;Husson, Le Dien, & Pagès, 2001;Li, Hayes, & Ziegler, 2014).…”

Maximizing overall liking results in a superior product to minimizing deviations from ideal ratings: An optimization case study with coffee-flavored milk

“…In study I, mean liking was not significantly influenced by research method (Li et al, 2014a), so liking data were aggregated and mean liking ( Overall_Liking ) for each sample (n = 20) was calculated across all methods and panelists. Overall_Liking was regressed on formulation variables (coffee, milk, sucrose, and water) to yield an optimal formulation using eChip® software (Wilmington, DE).…”

“…Coffee flavor can be a positive factor for consumer acceptance of a coffee beverage (Li, Hayes, & Ziegler, 2014a). However, increasing coffee flavor by adding more coffee extract also increases bitterness, and excessive bitterness typically reduces consumer acceptance (Harwood, Ziegler, & Hayes, 2012; Hayes, Sullivan & Duffy, 2010; Lanier, Hayes & Duffy, 2005; Moskowitz & Gofman, 2007).…”