Texture Independently Drives Liking in AI-Generated Alternative Protein Burgers

Texture shapes how we perceive and like food, yet clear links between mechanical measurements and sensory perception of texture remain elusive. Here we combine sensory data from a blind tasting involving 101 participants with mechanical texture profile analysis across six burgers to identify the textural features that drive consumer perception and liking. We compare five burgers-generated via artificial intelligence-with animal-based, plant-based, mushroom-based, and hybrid animal-mushroom patties, and the classical Big Mac^®. Three main findings emerge: First, animal-based burgers occupy a distinctive and coherent sensory-mechanical region associated with attributes such as firm, fatty, and holds together. Second, mushroom- and plant-based burgers deviate from this region in protein-dependent ways: mushroom-based burgers are associated with springy and gummy textures, while plant-based burgers are associated with dry, brittle, and crumbly textures. Hybrid animal-mushroom burgers, however, maintain sensory profiles comparable to fully animal-based burgers. Third, resilience emerges as the strongest mechanical correlate of perceived meatiness and sensory texture, while stiffness and hardness show no statistically significant association with consumer perception. Texture independently predicts overall liking alongside flavor: increasing texture liking by one point increases overall liking by 0.28. Among all sensory attributes, meatiness is the dominant predictor of texture liking. These findings suggest that resilience may be a promising target for texture engineering and establish texture as a critical design objective for sustainable alternative proteins.