品酒
感觉系统
签名(拓扑)
压缩(物理)
品味
食品科学
纹理(宇宙学)
感知
计算机科学
人工智能
模式识别(心理学)
生物
数学
生物系统
物理
神经科学
几何学
葡萄酒
图像(数学)
热力学
作者
Skyler R. St. Pierre,Ethan C. Darwin,Divya Adil,Magaly C. Aviles,Abhijit A. Date,Reese A. Dunne,Yanav Lall,María Parra Vallecillo,Valerie A Pérez Medina,Kevin Linka,Marc E. Levenston,Ellen Kuhl
标识
DOI:10.1038/s41538-024-00330-6
摘要
Abstract Eating less meat is associated with a healthier body and planet. Yet, we remain reluctant to switch to a plant-based diet, largely due to the sensory experience of plant-based meat. Food scientists characterize meat using a double compression test, which only probes one-dimensional behavior. Here we use tension, compression, and shear tests–combined with constitutive neural networks–to automatically discover the behavior of eight plant-based and animal meats across the entire three-dimensional spectrum. We find that plant-based sausage and hotdog, with stiffnesses of 95.9 ± 14.1 kPa and 38.7 ± 3.0 kPa, successfully mimic their animal counterparts, with 63.5 ± 45.7 kPa and 44.3 ± 13.2 kPa, while tofurky is twice as stiff, and tofu is twice as soft. Strikingly, a complementary food tasting survey produces in nearly identical stiffness rankings for all eight products ( ρ = 0.833, p = 0.015). Probing the fully three-dimensional signature of meats is critical to understand subtle differences in texture that may result in a different perception of taste. Our data and code are freely available at https://github.com/LivingMatterLab/CANN
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