Artificial Intelligence in “Artificial” Meat Burger- Precision Foods

Imagine you’re sitting in Mc-donald restaurant and about to bite into the burger for protein rich meal. You are drooling to take a quick juicy bite and smelling the aroma of the meat. It’s perfect but your best friend says it is the veggie burger he has ordered for you as quick treat.

The new disruptive food companies such as impossible foods and beyond meat are using meat analogues. The meatless burger is designed to “bleed” like a conventional burger. The possible use of AI and computers, is advancing research efforts in plant identification and formulation that makes for delicious and highly nutritious foods. The terms ‘cultured burger’ and ‘cultured meat’ were foregrounded in the 2013 event, in place of the scientific term ‘in vitro’ meat.

They claim to make vegan burgers that would satisfy even a sworn carnivore. They also aim to save the planet by reducing the environmental impact of cattle farming. And, they’re becoming big business.

“You’re not going to make anything that appeals to a hardcore meat lover by mushing together a bunch of vegetables,” Pat Brown, founder and CEO of the plant-based food startup Impossible Foods, told in California last month. “So we had to do a deep, molecular investigation into what it is that accounts for the desirable properties — texture, juiciness, the aromas, how it cooks.”

•               Scientists have used AI to find right ingredients to build right textured meat.
•             Hampton Creek CEO Josh Tetrick hired a Google’s top data scientist to build world’s biggest plant database to find right  ingredient to disrupt the food industry.

Impossible foods started by using the heme-containing protein from the roots of soy plants. It’s called soy leghemoglobin. We took the DNA from soy plants and inserted it into a genetically engineered yeast. And we ferment this yeast—very similar to the way Belgian beer is made. But instead of producing alcohol, our yeast multiply and produce a lot of heme.

“The way we eat today is, mostly, crazy,” says Josh Tetrick, the founder and CEO of food start-up Hampton Creek, who are among those using AI to develop new foods. “Six billion people are just eating really bad food.” Despite being a strict vegan who would prefer a kale salad rather than a muffin, Tetrick is convinced that today a “healthy and sustainable food only works for a tiny slice of the population”. He imagines a future where choosing to be vegetarian or vegan is not something only open to the better off in society. He wants to reach those who don’t get to choose. His quest started in a very unsophisticated fashion – he just scouted for plant-based food, adding them to a basic database. “I had no idea of what machine learning was,” he says. “I had no idea of what computational biology was.”

              At The Nordic Food Lab in the department of Food science at University of Copenhagen, Denmark, culinary entrepreneur, professor and co-founder of one of the world’s best restaurants, Claus Meyers explores the use of science to achieve its goal of eating well.

Now the next stage would be personalized food designed as per body type. Though the idea looks futuristic but advancements in AI, data sciences and personalized omics technologies would help us to find right food for your body type. The robotics and automation would make these ideas feasible for masses in near future. There is a good possibility to develop a precise food formula for individuals with right nutrients to combat diseases such as obesity and diabetes. So next time, it is possible that seemingly unhealthy French fries which you have ordered at fast food restaurant would be loaded with power of proteins, vitamins and antioxidants with that right ‘crispy’, ‘texture’ and taste.  

Note: The author was assisted by AI based algorithms to write this article in minimal time frame & mining several million scientific and news texts pertaining to health. The applications and details of these algorithms were published in two research papers listed below.

A)   Jagannadham J, Jaiswal HK, Agrawal S, Rawal K (2016) Comprehensive Map of Molecules Implicated in Obesity. PLoS ONE 11(2): e0146759. https://doi.org/10.1371/journal.pone.0146759

B)   Rawal K, Khurana T, Sharma H, Verma S, Gupta S, Kubba C, Strych U, Hotez PJ, Bottazzi ME. 2019. An extensive survey of molecular docking tools and their applications using text mining and deep curation strategies. PeerJ Preprints7:e27538v1 https://doi.org/10.7287/peerj.preprints.27538v1