Can AI improve plant-based meats?

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su-flex-wrap"><h2 class="su-text-18 su-font-semibold su-mb-0 su-font-sans su-leading-[2.25rem]" id="tray-group-label">Along with Stanford news and stories, show me:</h2><ul class="su-flex su-list-none su-pl-0 su-gap-18 su-w-full" aria-labelledby="tray-group-label"><li class="su-mb-0 su-w-1/2"><span tabindex="0" data-tp-to="close-prefs"></span><button type="button" id="preference-student" aria-pressed="false" class="report-header__pref-toggle hover:su-text-white su-w-full su-h-full aria-pressed:su-text-white group su-bg-white dark:aria-pressed:su-text-black-true dark:aria-pressed:su-bg-transparent dark:hover:su-bg-transparent aria-pressed:su-bg-transparent hover:su-bg-transparent dark:su-bg-black-true &[aria-pressed=“true”]:dark:su-bg-transparent before:su-rounded-inherit after:su-rounded-inherit before:su-bg-gradient-light-red after:su-transition-none su-transition before:su-transition after:su-bg-gradient-light-red dark:after:su-rotate-180 before:su-absolute before:-su-z-10 before:-su-m-2 after:su-absolute after:-su-z-10 before:su-top-0 before:su-bottom-0 before:su-right-0 before:su-left-0 after:su-top-0 after:su-bottom-0 after:su-right-0 after:su-left-0 su-relative su-bg-transparent su-text-center" data-location="student-info"><span class="icon-add su-block su-mx-auto su-relative su-w-44 su-h-44 su-text-digital-red dark:su-text-dark-mode-red"><svg class="su-absolute su-top-1/2 su-left-1/2 -su-translate-x-1/2 -su-translate-y-1/2 su-w-44 su-h-44 su-fill-none" xmlns="http://www.w3.org/2000/svg" width="44" height="44" viewBox="0 0 44 44" fill="none"><path fill="transparent" d="M22 16.5V22M22 22V27.5M22 22H27.5M22 22H16.5M38.5 22C38.5 31.1127 31.1127 38.5 22 38.5C12.8873 38.5 5.5 31.1127 5.5 22C5.5 12.8873 12.8873 5.5 22 5.5C31.1127 5.5 38.5 12.8873 38.5 22Z" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></span><span class="icon-remove su-block su-mx-auto su-relative su-w-44 su-h-44"><svg class="su-absolute su-top-1/2 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data-hydration-props='{"storyType":"Basic","bannerType":"normal","contentType":"Research","titleAlignment":"left","orientation":"vertical","pubDate":"2024-11-15 00:00:00","pubDateFormatted":"November 15th, 2024","mediaType":"image","topic":{"asset_assetid":"28406","asset_name":"Science & Engineering","asset_url":"https://news.stanford.edu/research-and-scholarship/topic/science-and-engineering","topicPath":"/research-and-scholarship/topic/science-and-engineering","target_id":"141681","group":"127851","type":"content_topic","sectionSummary":""},"media":{"type":"standard","featureImage":{"id":"161863","url":"https://news.stanford.edu/__data/assets/image/0025/161863/20241108_edible_foods16.jpg","alt":"Skyler St. Pierre, Marc Levenston, Ellen Kuhl, Reese Dunne, Ethan Darwin, Valerie Perez Medina, and Divya Adil pose with the meat and plant-based meat they analyzed.","height":"1000","width":"1500"},"featureVideo":{"id":""},"carousel":null,"caption":"Is it possible to close the texture gap between plant-based meat and animal meat? Ellen Kuhl and her lab are trying. From left: Skyler St. Pierre, Marc Levenston, Ellen Kuhl, Reese Dunne, Ethan Darwin, Valerie Perez Medina, and Divya Adil pose with the meat and plant-based meat they analyzed.","credit":"Kurt Hickman","captions":[{"caption":"","credit":""},{"caption":"","credit":""},{"caption":"","credit":""},{"caption":"","credit":""},{"caption":"","credit":""},{"caption":"","credit":""}]},"title":"Can AI improve plant-based meats?","summary":"<p>Stanford engineers are testing foods using an approach that combines a three-dimensional mechanical technique and AI to mimic our sensory experience.</p>"}'></div> </div> <article data-main-topic="Science & Engineering"> <div class="su-page-wrap"> <div class="su-sidebar-content-wrap"> <div class="su-page-content"> <p>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.</p><p>In a <span class="underline"><a href="https://www.nature.com/articles/s41538-024-00330-6" target="_self">new paper</a></span> in <em>Science of Food</em>, 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.</p><p>“We were surprised to find that today’s plant-based products can reproduce the whole texture spectrum of animal meats,” said <span class="underline"><a href="https://profiles.stanford.edu/ellen-kuhl" target="_self">Ellen Kuhl</a></span>, 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.</p><p>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 <span class="underline"><a href="https://www.sciencedirect.com/science/article/pii/S0921344922006632" target="_self">half the environmental impact</a></span> as animal meat. But many meat eaters are reluctant to change; <span class="underline"><a href="https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2019.00011/full" target="_self">only about a third of Americans</a></span> in one survey indicated they were “very likely” or “extremely likely” to buy plant-based alternatives.</p><p>“People love meat,” said <span class="underline"><a href="https://livingmatter.stanford.edu/people/skyler-st-pierre" target="_self">Skyler St. Pierre</a></span>, 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.”</p><p>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.</p><h2>New food texture tests</h2><p>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.</p><p>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 <span class="underline"><a href="http://biox.stanford.edu/" target="_self">Stanford Bio-X</a></span> and the Walter B. Reinhold Professor in the <span class="underline"><a href="https://engineering.stanford.edu/" target="_self">School of Engineering</a></span>.</p><div data-component="single-image-video" data-hydration-component="single-image-video" data-hydration-props="{"image":"matrix-asset://StanfordNews/161864","width":"Wide","credit":"Kurt Hickman","caption":"A mechanical test of plant-based meat can help quantify food texture.","imageData":{"id":"161864","type":"image","type_name":"Image","version":"0.0.1","name":"20241108_edible_foods2.jpg","short_name":"vegan sausage 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Brodhead\nbrodhead@stanford.edu\nwww.andrewbrodhead.com","ImageHeight":1000,"ImageWidth":1500,"Make":"Canon","Model":"Canon EOS R5","ModifyDate":"2024:11:11 15:59:03","ResolutionUnit":0,"Software":"Adobe Photoshop Lightroom Classic 13.5.1 (Macintosh)","XResolution":72,"YResolution":72},"thumbnail":{"Compression":0,"ThumbnailLength":12378,"ThumbnailOffset":1022},"exif":{"ApertureValue":2,"ColorSpace":0,"CreateDate":"2024:11:08 12:08:48","DateTimeOriginal":"2024:11:08 12:08:48","ExposureCompensation":0,"ExposureMode":0,"ExposureProgram":0,"ExposureTime":1,"Flash":0,"FNumber":2,"FocalLength":35,"FocalPlaneResolutionUnit":0,"FocalPlaneXResolution":5773,"FocalPlaneYResolution":5769,"ISO":800,"LensMake":"RF15-35mm F2.8 L IS USM","MaxApertureValue":2,"MeteringMode":0,"SceneCaptureType":0,"ShutterSpeedValue":1,"WhiteBalance":0},"makernote":{"Sharpness":25}}}}}"><div class="su-mx-auto su-component-container su-container-wide su-container-px"><section class="su-flex su-flex-col su-items-center su-gap-8 su-gap-15"><div class="su-relative su-w-full su-aspect-[16/9]"><img src="https://news.stanford.edu/__data/assets/image/0026/161864/20241108_edible_foods2.jpg" alt="Plant-based meat undergoes a mechanical test to help quantify food texture." class="su-w-full su-object-cover"/></div><div class="su-flex su-gap-8 md:su-gap-22 su-w-full su-relative su-flex-col su-items-center lg:su-flex-row lg:su-items-start"><div class="su-mx-auto su-flex su-justify-center su-w-full"><p class="dark:su-text-white su-m-0 su-text-14 su-max-w-[633px] su-leading-[16.72px] su-font-normal su-text-black-70 md:su-text-16 su-leading-[19.11px] md:su-text-left">A mechanical test of plant-based meat can help quantify food texture. | Kurt Hickman</p></div></div></section></div></div><p>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.</p><p>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.</p><h2>Impressive hot dogs and sausages</h2><p>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.”</p><p>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.</p><p>But artificial intelligence recipe development, like other AIs, needs lots of data. That’s why the team is <span class="underline"><a href="https://github.com/LivingMatterLab/CANN" target="_self">sharing their data</a></span> 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?”</p><p>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 <span class="underline"><a href="https://profiles.stanford.edu/349659" target="_self">Vayu Hill-Maini</a></span>, 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.” </p><div data-component="acknowledgement" data-hydration-component="acknowledgement" data-hydration-props="{"title":"For more information","content":"<p>Additional Stanford co-authors of the paper include <span class=\"underline\"><a href=\"https://profiles.stanford.edu/marc-levenston\" target=\"_self\">Marc Levenston</a></span>, associate professor of mechanical engineering; postdoctoral researcher Kevin Linka; graduate students Ethan C. Darwin, Divya Adil, Reese A. Dunne, and Valerie A. Perez Medina; visiting researcher María Parra Vallecillo; and undergraduate researchers Magaly C. Aviles, Archer Date, and Yanav Lall.</p><p>Kuhl is also a professor, by courtesy, of bioengineering in the schools of Engineering and <span class=\"underline\"><a href=\"https://med.stanford.edu/\" target=\"_self\">Medicine</a></span>, and a member of the <span class=\"underline\"><a href=\"http://cvi.stanford.edu/\" target=\"_self\">Cardiovascular Institute</a></span>, the <span class=\"underline\"><a href=\"https://humanperformance.stanford.edu/\" target=\"_self\">Wu Tsai Human Performance Alliance</a></span>, the <span class=\"underline\"><a href=\"https://icme.stanford.edu/\" target=\"_self\">Institute for Computational and Mathematical Engineering</a></span>, and the <span class=\"underline\"><a href=\"https://neuroinstitute.stanford.edu/\" target=\"_self\">Wu Tsai Neurosciences Institute</a></span>. Levenston is also a member of Bio-X, the Cardiovascular Institute, the Wu Tsai Human Performance Alliance and the <span class=\"underline\"><a href=\"https://med.stanford.edu/mchri.html\" target=\"_self\">Maternal & Child Health Research Institute</a></span>.</p><p>This work was funded by the National Science Foundation, the Emmy Noether Programme, and the European Research Council.</p>"}"><div class="su-mx-auto su-component-container su-container-narrow su-container-px"><hr aria-hidden="true" class="su-mb-36 su-border-none su-w-100 su-h-6 lg:su-h-9 su-bg-gradient-to-r su-from-digital-red su-to-digital-red-dark dark:su-from-palo-verde dark:su-to-olive"/><h2 class="sr-only">For more information</h2><div data-test="acknowledgement" class="su-wysiwyg-content *:su-basefont-19 su-mb-36 [&>*:last-child]:su-mb-0"><p>Additional Stanford co-authors of the paper include <span class="underline"><a href="https://profiles.stanford.edu/marc-levenston" target="_self">Marc Levenston</a></span>, associate professor of mechanical engineering; postdoctoral researcher Kevin Linka; graduate students Ethan C. Darwin, Divya Adil, Reese A. Dunne, and Valerie A. Perez Medina; visiting researcher María Parra Vallecillo; and undergraduate researchers Magaly C. Aviles, Archer Date, and Yanav Lall.</p><p>Kuhl is also a professor, by courtesy, of bioengineering in the schools of Engineering and <span class="underline"><a href="https://med.stanford.edu/" target="_self">Medicine</a></span>, and a member of the <span class="underline"><a href="http://cvi.stanford.edu/" target="_self">Cardiovascular Institute</a></span>, the <span class="underline"><a href="https://humanperformance.stanford.edu/" target="_self">Wu Tsai Human Performance Alliance</a></span>, the <span class="underline"><a href="https://icme.stanford.edu/" target="_self">Institute for Computational and Mathematical Engineering</a></span>, and the <span class="underline"><a href="https://neuroinstitute.stanford.edu/" target="_self">Wu Tsai Neurosciences Institute</a></span>. Levenston is also a member of Bio-X, the Cardiovascular Institute, the Wu Tsai Human Performance Alliance and the <span class="underline"><a href="https://med.stanford.edu/mchri.html" target="_self">Maternal & Child Health Research Institute</a></span>.</p><p>This work was funded by the National Science Foundation, the Emmy Noether Programme, and the European Research Council.</p></div></div></div> <div class="su-border-t border-t-black-20"> <div class="su-flex su-flex-col su-gap-27 su-pt-32 su-pb-22 md:su-pt-45 md:su-pt-36 su-justify-left mediaContacts"> <p><strong>Media contacts</strong></p><p>Jill Wu, School of Engineering: <a href="mailto:jillwu@stanford.edu">jillwu@stanford.edu</a></p> </div> </div> <div class="su-lower-content" id="component_130052"> <div data-component="metadata-fields" data-hydration-component="metadata-fields" data-hydration-props='{"type":"Basic","authors":[],"producers":[],"writers":[{"asset_name":"Ula 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