Sensory optimization of Peruvian lucuma fruit ice cream using I-optimal mixture design
Year: 2025
Type: article
Abstract: The inherent variability in sensory and consumer data poses a challenge when optimizing product formulations for consumer liking. This study employed an I-optimal mixture design to determine the optimal formula for a novel lucuma ice cream for the US market, maximizing information gained from consumer responses. Eleven ice cream prototypes, varying in concentrations of Lucuma Fruit Powder (LFP), milk fat, and sucrose, with additional ingredients serving as the filler were evaluated by 104 consumers using a completely balanced randomized design. Consumer acceptance scores were modeled using Scheffé’s linear mixture model, and desirability function analysis was applied to identify the optimal formulation. Correspondence analysis of CATA data revealed distinct flavor profiles across prototypes, with higher LFP concentrations associated with stronger, potentially less desirable flavors. The optimal formulation, maximizing consumer acceptance, contained 1.5% LFP, 13% sucrose, and 6% milk fat, achieving high scores for overall liking (7.3), texture liking (7.9), and mouthfeel liking (7.8) with a desirability score of 1. Results indicate that reducing sucrose and increasing LFP in the ice cream formulation led to a drop in consumer acceptance. The variations in fat levels did not significantly impact consumer liking, suggesting that LFP could serve as a potential fat replacer in ice creams without compromising acceptance. Incorporating LFP at optimal levels could enhance the texture of reduced-fat ice cream products.
Source: Applied Food Research
Authors: Sidharth Babu, Jeff Garza, Rajesh Kumar, Martin J. Talavera
Institution: Kansas State University
Language: English
Subfield: Food Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,050
OpenAlex ID: W4408122990