Identifying ideal product composition of chocolate-flavored milk using preference mapping

Area of Science:

• Food Science
• Sensory Science
• Consumer Behavior

Background:

• Growing concern over added sugar intake from flavored milk, aligning with World Health Organization recommendations.
• Nutritional components like fat, protein, and salt significantly influence sensory attributes (color, flavor, mouthfeel) of flavored milk.
• Understanding these relationships is crucial for effective sugar reduction strategies in processed beverages.

Purpose of the Study:

• To determine the ideal product composition for chocolate flavored milk.
• To link variations in product composition to consumer liking data using External Preference Mapping (EPM).
• To guide reformulation efforts for reduced-sugar, high-acceptability chocolate milk.

Main Methods:

• Analysis of 34 commercial chocolate milk samples using Principal Component Analysis (PCA).
• Consumer evaluation (n=235) of 10 selected samples based on overall liking (9-point hedonic scale).
• Application of Hierarchical Cluster Analysis (HCA) and External Preference Mapping (EPM) to link product composition and consumer preferences.

Main Results:

• Significant differences in consumer liking (p < 0.0001) were observed across samples.
• Four distinct consumer clusters emerged, each with unique product preferences.
• Optimal chocolate milk formulations identified with 10.6%-10.9% sugar, 3.4%-3.5% fat, 3.5%-3.6% protein, and 0.05% salt, achieving 70%-80% consumer satisfaction.

Conclusions:

• Product composition, particularly sugar concentration, is critical for sensory perception and consumer acceptance.
• Specific ingredient profiles can be established as targets for sugar reduction in chocolate milk.
• Findings provide a benchmark for developing appealing, lower-sugar chocolate milk products.