Stanford Engineers Harness AI to Perfect Plant-Based Meat Textures

Scientists are perfecting plant-based meats with AI - Earth.com

Meat lovers can attest that the move to plant-based alternatives isn't always a simple transition. However, thanks to a team of innovative Stanford engineers, life is becoming a little tastier for those wishing to embrace greener diets. Working from a unique mechanical engineering perspective, they've developed an intriguing approach to food texture evaluation. This could potentially lead to plant-based steaks that even the staunchest meat enthusiasts will find impossible to resist. The team from Stanford University recently showcased how mechanical testing, combined with machine learning, can essentially mimic the palate of human taste testers. Their ingenious method could potentially accelerate the evolution of superior plant-based meats. Interestingly, they discovered that several plant-based products are already successfully emulating the texture of the meats they strive to replicate. According to Ellen Kuhl, the senior author of the recent study, it is possible to close the gap between animal meat texture and that of plant-based meats. "We were surprised to find that today's plant-based products can reproduce the whole texture spectrum of animal meats," noted Kuhl. This is phenomenal progress since the days when tofu was the only meat substitute available. Industrial animal agriculture's environmental toll is heavy; it leads to climate change, pollution, habitat loss, and antibiotic resistance. An effective way to lighten this is by shifting from animal proteins to plant proteins. Previous studies have shown that plant-based meats cause, on average, 50% less environmental impact than animal meats. However, most meat eaters are hesitant to make this change. For instance, in one survey, only a third of Americans stated their likelihood of purchasing plant-based alternatives as "very likely' or 'extremely likely." "People love meat. If we want to convince the hardcore meat eaters that alternatives are worth trying, the closer we can mimic animal meat with plant-based products, the more likely people might be open to trying something new," explained Skyler St. Pierre, the lead author of the paper. Unfortunately, conventional food testing methods are neither standardized nor transparent, making it tougher for scientists to collaborate on creating new recipes for alternatives. This novel and futuristic food texture test that makes use of AI came to life as part of a class project by St. Pierre. He was looking for affordable materials to use in engineering tests on stress, load and stretching. He turned to tofu and hot dogs as possible study materials. Through the summer of 2023, student researchers experimented with the texture and mechanics of these and other foods, and then trialed their findings on eight products including animal and plant-based hot dogs, sausages, turkey, and firm and extra firm tofu. A machine was used to simulate the act of chewing, and to test how the materials reacted when pulled, pushed, and sheared. This data was then processed using machine learning to design a new type of neural network, and resulted in equations defining the properties of the different meats. A survey was later conducted to test if these equations could truly replicate the sensation of texture. The survey respondents, after sampling the eight products, rated them in 12 categories including softness, hardness, brittleness, chewiness, gumminess, viscosity, springiness, stickiness, fibrousness, fattiness, moistness, and meat-likeness. The results were thrilling. For instance, plant-based hot dogs and sausages were very similar to their animal counterparts in terms of their stiffness. Human testers ranked the stiffness of the hot dogs and sausages very similarly to the mechanical tests. "What's really cool is that the ranking of the people was almost identical to the ranking of the machine. That's great because now we can use the machine to have a quantitative, very reproducible test," said Kuhl. Such discoveries suggest that data-driven methodologies may accelerate the development of delectable plant-based products. The team even suggests using generative AI for creating plant-based meat recipes that have specific desired properties. In an effort to push this field forward, the team has opted for open-source data, allowing other researchers to view, add to, and learn from their work. "Historically, some researchers, and especially companies, don't share their data and that's a really big barrier to innovation," Kuhl added. The team continues in their food testing endeavors as they look to establish a public database of their findings. They're now testing deli slices, both vegetable and meat-based. They also plan to test engineered fungi that has been developed by a new addition to their team. Kuhl has generously extended an invitation to the wider community to contribute to the ongoing study. "If anybody has a plant-based meat they want to test, we're so happy to test it to see how it stacks up," said Kuhl. In their quest to save the planet one bite at a time, these Stanford engineers are transforming our perception of plant-based diets. Their commitment to open-source information and collaboration is setting the stage for a more sustainable future. Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.

Can AI improve plant-based meats?

Cutting back on animal protein in our diets can save on resources and greenhouse gas emissions. But convincing meat-loving consumers to switch up their menu is a challenge. Looking at this problem from a mechanical engineering angle, Stanford engineers are pioneering a new approach to food texture testing that could pave the way for faux filets that fool even committed carnivores. In a new paper in Science of Food, the team demonstrated that a combination of mechanical testing and machine learning can describe food texture with striking similarity to human taste testers. Such a method could speed up the development of new and better plant-based meats. The team also found that some plant-based products are already nailing the texture of the meats they're mimicking. "We were surprised to find that today's plant-based products can reproduce the whole texture spectrum of animal meats," said Ellen Kuhl, professor of mechanical engineering and senior author of the study. Meat substitutes have come a long way from when tofu was the only option, she added. Industrial animal agriculture contributes to climate change, pollution, habitat loss, and antibiotic resistance. That burden on the planet can be eased by swapping animal proteins for plant proteins in diets. One study estimated that plant-based meats, on average, have half the environmental impact as animal meat. But many meat eaters are reluctant to change; only about a third of Americans in one survey indicated they were "very likely" or "extremely likely" to buy plant-based alternatives. "People love meat," said Skyler St. Pierre, a PhD student in mechanical engineering and lead author of the paper. "If we want to convince the hardcore meat eaters that alternatives are worth trying, the closer we can mimic animal meat with plant-based products, the more likely people might be open to trying something new." To successfully mimic animal meat, food scientists analyze the texture of plant-based meat products. Unfortunately, traditional food testing methods are not standardized and the results are rarely made available to science and to the public, said St. Pierre. This makes it harder for scientists to collaborate and create new recipes for alternatives. The research grew out of a class project by St. Pierre. Looking for affordable materials to use in mechanical tests, he turned to hot dogs and tofu. Over the summer of 2023, undergraduate researchers joined in to test the foods and learn how engineers depict material responses to stress, loading, and stretching. Realizing how this work could aid the development of plant-based meats, the Stanford team debuted a three-dimensional food test. They put eight products to the test: animal and plant-based hot dog, animal and plant-based sausage, animal and plant-based turkey, and extra firm and firm tofu. They mounted bits of meat into a machine that pulled, pushed, and sheared on the samples. "These three loading modes represent what you do when you chew," said Kuhl, who is also the Catherine Holman Johnson Director of Stanford Bio-X and the Walter B. Reinhold Professor in the School of Engineering. Then, they used machine learning to process the data from these tests: They designed a new type of neural network that takes the raw data from the tests and produces equations that explain the properties of the meats. To see if these equations can explain the perception of texture, the team carried out a test survey. The testers - who first completed surveys on their openness to new foods and their attachment to meat - ate samples of the eight products and rated them on 5-point scale for 12 categories: soft, hard, brittle, chewy, gummy, viscous, springy, sticky, fibrous, fatty, moist, and meat-like. In the mechanical testing, the plant-based hot dog and sausage behaved very similarly in the pulling, pushing, and shear tests to their animal counterparts, and showed similar stiffnesses. Meanwhile, the plant-based turkey was twice as stiff as animal turkey, and the tofu was much softer than the meat products. Strikingly, the human testers also ranked the stiffness of the hot dogs and sausages very similarly to the mechanical tests. "What's really cool is that the ranking of the people was almost identical to the ranking of the machine," said Kuhl. "That's great because now we can use the machine to have a quantitative, very reproducible test." The findings suggest that new, data-driven methods hold promise for speeding up the process of developing tasty plant-based products. "Instead of using a trial-and-error approach to improve the texture of plant-based meat, we could envision using generative artificial intelligence to scientifically generate recipes for plant-based meat products with precisely desired properties," the authors wrote in the paper. But artificial intelligence recipe development, like other AIs, needs lots of data. That's why the team is sharing their data online, making it open for other researchers to view and add to. "Historically, some researchers, and especially companies, don't share their data and that's a really big barrier to innovation," said St. Pierre. Without sharing information and working together, he added, "how are we going to come up with a steak mimic together?" The team is continuing to test foods and build a public database. This summer, St. Pierre oversaw undergraduates testing veggie and meat deli slices. The researchers also plan to test engineered fungi developed by Vayu Hill-Maini, who recently joined Stanford as an assistant professor of bioengineering. "If anybody has an artificial or a plant-based meat they want to test," said Kuhl, "we're so happy to test it to see how it stacks up."

Can AI improve plant-based meats? Using mechanical testing and machine learning to mimic the sensory experience

Cutting back on animal protein in our diets can save on resources and greenhouse gas emissions. But convincing meat-loving consumers to switch up their menu is a challenge. Looking at this problem from a mechanical engineering angle, Stanford engineers are pioneering a new approach to food texture testing that could pave the way for faux filets that fool even committed carnivores. In a new paper published in npj Science of Food, the team demonstrated that a combination of mechanical testing and machine learning can describe food texture with striking similarity to human taste testers. Such a method could speed up the development of new and better plant-based meats. The team also found that some plant-based products are already nailing the texture of the meats they're mimicking. "We were surprised to find that today's plant-based products can reproduce the whole texture spectrum of animal meats," said Ellen Kuhl, professor of mechanical engineering and senior author of the study. Meat substitutes have come a long way from when tofu was the only option, she added. Industrial animal agriculture contributes to climate change, pollution, habitat loss, and antibiotic resistance. That burden on the planet can be eased by swapping animal proteins for plant proteins in diets. One study estimated that plant-based meats, on average, have half the environmental impact as animal meat. But many meat eaters are reluctant to change; only about a third of Americans in one survey indicated they were "very likely" or "extremely likely" to buy plant-based alternatives. "People love meat," said Skyler St. Pierre, a Ph.D. student in mechanical engineering and lead author of the paper. "If we want to convince the hardcore meat eaters that alternatives are worth trying, the closer we can mimic animal meat with plant-based products, the more likely people might be open to trying something new." To successfully mimic animal meat, food scientists analyze the texture of plant-based meat products. Unfortunately, traditional food testing methods are not standardized and the results are rarely made available to science and to the public, said St. Pierre. This makes it harder for scientists to collaborate and create new recipes for alternatives. New food texture tests The research grew out of a class project by St. Pierre. Looking for affordable materials to use in mechanical tests, he turned to hot dogs and tofu. Over the summer of 2023, undergraduate researchers joined in to test the foods and learn how engineers depict material responses to stress, loading, and stretching. Realizing how this work could aid the development of plant-based meats, the Stanford team debuted a three-dimensional food test. They put eight products to the test: animal and plant-based hot dog, animal and plant-based sausage, animal and plant-based turkey, and extra firm and firm tofu. They mounted bits of meat into a machine that pulled, pushed, and sheared on the samples. "These three loading modes represent what you do when you chew," said Kuhl, who is also the Catherine Holman Johnson Director of Stanford Bio-X and the Walter B. Reinhold Professor in the School of Engineering. Then, they used machine learning to process the data from these tests: They designed a new type of neural network that takes the raw data from the tests and produces equations that explain the properties of the meats. To see if these equations can explain the perception of texture, the team carried out a test survey. The testers -- who first completed surveys on their openness to new foods and their attachment to meat -- ate samples of the eight products and rated them on 5-point scale for 12 categories: soft, hard, brittle, chewy, gummy, viscous, springy, sticky, fibrous, fatty, moist, and meat-like. Impressive hot dogs and sausages In the mechanical testing, the plant-based hot dog and sausage behaved very similarly in the pulling, pushing, and shear tests to their animal counterparts, and showed similar stiffnesses. Meanwhile, the plant-based turkey was twice as stiff as animal turkey, and the tofu was much softer than the meat products. Strikingly, the human testers also ranked the stiffness of the hot dogs and sausages very similarly to the mechanical tests. "What's really cool is that the ranking of the people was almost identical to the ranking of the machine," said Kuhl. "That's great because now we can use the machine to have a quantitative, very reproducible test." The findings suggest that new, data-driven methods hold promise for speeding up the process of developing tasty plant-based products. "Instead of using a trial-and-error approach to improve the texture of plant-based meat, we could envision using generative artificial intelligence to scientifically generate recipes for plant-based meat products with precisely desired properties," the authors wrote in the paper. But artificial intelligence recipe development, like other AIs, needs lots of data. That's why the team is sharing their data online, making it open for other researchers to view and add to. "Historically, some researchers, and especially companies, don't share their data and that's a really big barrier to innovation," said St. Pierre. Without sharing information and working together, he added, "how are we going to come up with a steak mimic together?" The team is continuing to test foods and build a public database. This summer, St. Pierre oversaw undergraduates testing veggie and meat deli slices. The researchers also plan to test engineered fungi developed by Vayu Hill-Maini, who recently joined Stanford as an assistant professor of bioengineering. "If anybody has an artificial or a plant-based meat they want to test," said Kuhl, "we're so happy to test it to see how it stacks up."