Switching from meat to plant-based alternatives can be challenging, but a team of Stanford engineers is working to make the transition more appetizing. By focusing on food texture, they’re helping plant-based meats better replicate the feel of real meat—a game-changer for those hesitant to make the switch.
Using mechanical testing combined with machine learning, the team has developed a method that mimics how human taste testers evaluate texture. According to Ellen Kuhl, the senior author of the study, their findings show that plant-based products can match the full range of textures found in animal meats. “We were surprised to find that today’s plant-based products can reproduce the whole texture spectrum of animal meats,” Kuhl explained.
The team’s experiments began with student researchers testing materials like tofu and hot dogs to study stress and load properties. Over time, their work expanded to include eight products, both plant-based and animal-based, such as hot dogs, sausages, and turkey. A machine simulated chewing to analyze how the foods reacted under various forces, while survey participants rated the textures on factors like chewiness, softness, and meat-likeness. Remarkably, the mechanical tests closely matched human rankings, which Kuhl noted could lead to standardized, reproducible testing.
The implications of this research extend beyond the lab. With open-source data sharing, the team aims to encourage collaboration among researchers and companies to accelerate innovation in plant-based meat development. Their database could even pave the way for generative AI to create new recipes tailored to specific textures.
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