Exploration of product optimization using consumer-based tools in a coffee-flavored dairy beverage

Open Access
Author:
Li, Bangde
Graduate Program:
Food Science
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
January 17, 2014
Committee Members:
  • John E Hayes, Thesis Advisor
Keywords:
  • Optimization
  • consumer insight
  • dissatisfaction
  • JAR scaling
  • ideal scaling
  • coffee extract
  • milk
  • coffee
  • formulation
Abstract:
Consumer insight plays a critical role in product development. A product can be optimized based on its formulations or sensory properties by maximizing consumer acceptability (i.e., liking). Both psychohedonic (sensation-liking) and physicohedonic (formulation-liking) models provide their own unique insights into consumer preferences. JAR scaling and ideal scaling have become quite popular to meet the demand of rapid optimization. In both these methods, a product attribute is measured for its dysfunctionality (delta) relative to one’s ideal. Attribute delta (i.e., “Too Little” or “Too Much”) estimates a subject’s dissatisfaction (disliking) level with an attribute quality. Moreover, these methods differ in defining ideal levels on the scale. Dissatisfaction and liking may be two distinct constructs of consumer acceptability. We hypothesized that minimizing dissatisfaction and maximizing liking may yield different optimal formulations. The purpose of this study was to: 1) interpret consumer preference using physicohedonic and psychohedonic models; 2) investigate the difference between ideal scaling and JAR scaling for diagnosing attribute performance; 3) compare attribute delta (Ideal_Delta and JAR_Delta) models against liking models for product optimization of a coffee-flavored dairy beverage. Coffee-flavored dairy beverages (n=20) were formulated using a fractional mixture design that constrained coffee extract, fluid milk (2% fat), sugar, and water. Participants (n=388) were randomly assigned into one of 3 research conditions that differed in ballot formats. Each participant tasted only 4 samples out of 20 using an incomplete block design. Samples were rated for liking and intensities for four attributes--sweetness, milk flavor, coffee flavor, and thickness. Data were processed and treated differently to investigate specific research questions. Details are presented in the corresponding chapters. The results show that: 1) both psychohedonic and physicohedonic models provide useful insights for product development; 2) ideal scaling and JAR scaling are very similar in estimating the attribute “Too Little” and “Too Much,” and these attribute deltas showed similar impacts on liking; 3) attribute delta and liking models yield different product optimization. That is what participants say they like differs from what they actually like.