Automated Model Discovery and Mechanical Analysis of Fungi-Based Meats

  • Boyle, Lucas (Stanford University (Living Matter Lab))
  • Goodson, Marie (Stanford University (Living Matter Lab))
  • Palomares, Manuel (Stanford University (Living Matter Lab))
  • Zhang, Nancy (Stanford University (Living Matter Lab))
  • St. Pierre, Skyler (Stanford University (Living Matter Lab))
  • Kuhl, Ellen (Stanford University (Living Matter Lab))

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Plant-based meat products have substantially lower environmental impacts than conventional meat; yet their global dietary adoption remains low [1, 2]. Despite the growing market for these alternative products, the sensory experience, particularly taste and texture, is among the primary reasons consumers continue to prefer animal meat [2]. To date, mycelium products --- created from the roots of mushrooms --- seem to be the most promising alternative in terms of sensory and mechanical characteristics [3]; however, there is limited research on the mechanical behavior of products derived from the mushroom fruiting body rather than mycelium. Here, we sought to understand the fundamental mechanical properties of fungi-based products derived from the fruiting body. We mechanically tested OMNI Lion’s Mane Mushroom Steak in tension, compression, and shear to 10% strain at 0.2%/s. We also performed a texture-profile analysis [3], compressing samples to 50% strain at 25%/s to probe rate dependence. We used a 16-term transversely-isotropic, incompressible constitutive artificial neural network composed of invariants I1, I2, I4, and I5 raised to the first or second power and activated by the identity or exponential functions to discover the best constitutive model for the product. Using L0 regularization, we probed all combinations and collected the mean-squared error of the simultaneous fit to the tension, compression, and shear data. The best model largely included the I1 and exp(I1) terms, with smaller contributions from higher-order and anisotropic terms. Our results also allow us to derive sensory-relevant comparisons from the observed rate dependence, showing that stiffness nearly doubles at higher deformation rates. Understanding the mechanical identity of fungi-based meats can guide the design of improved products to better mimic the mechanical signature of conventional animal meat. [1] Smetana S. et al. Resources, Conservation and Recycling. 2023; 190:106831. [2] Szenderák J., Fróna D., Rákos M. Foods. 2022; 11:1274. [3] Vervenne T. et al. Acta Biomaterialia. 2025; 202:341-351.