Researchers are looking to engineer chocolate with less oil, which could reduce some of its detriments to health.
Creamy milk with velvety texture. Dark with sprinkles of sea salt. Crunchy hazelnut-studded chunks. Chocolate is a treat that appeals to billions of people worldwide, no matter the age. And it’s not only the taste, but the feel of a chocolate morsel slowly melting in our mouths—the smoothness and slipperiness—that’s part of the overwhelming satisfaction. Why is it so enjoyable?
That’s what an interdisciplinary research team of chocolate lovers from the University of Leeds School of Food Science and Nutrition and School of Mechanical Engineering in the U.K. resolved to study in 2021. They wanted to know, “What is making chocolate that desirable?” says Siavash Soltanahmadi, one of the lead authors of a new study about chocolates hedonistic quality.
Besides addressing the researchers’ general curiosity, their answers might help chocolate manufacturers make the delicacy even more enjoyable and potentially healthier. After all, chocolate is a billion-dollar industry. Revenue from chocolate sales, whether milk or dark, is forecasted to grow 13 percent by 2027 in the U.K. In the U.S., chocolate and candy sales increased by 11 percent from 2020 to 2021, on track to reach $44.9 billion by 2026. Figuring out how chocolate affects the human palate could up the ante even more.
Building a 3D tongue
The team began by building a 3D tongue to analyze the physical process by which chocolate breaks down inside the mouth.
As part of the effort, reported earlier this year in the scientific journal ACS Applied Materials and Interfaces, the team studied a large variety of human tongues with the intention to build an “average” 3D model, says Soltanahmadi, a lubrication scientist. When it comes to edible substances, lubrication science looks at how food feels in the mouth and can help design foods that taste better and have more satisfying texture or health benefits.
There are variations in how people enjoy chocolate; some chew it while others “lick it” inside their mouths.
Tongue impressions from human participants studied using optical imaging helped the team build a tongue with key characteristics. “Our tongue is not a smooth muscle, it’s got some texture, it has got some roughness,” Soltanahmadi says. From those images, the team came up with a digital design of an average tongue and, using 3D printed molds, built a “mimic tongue.” They also added elastomers—such as silicone or polyurethane—to mimic the roughness, the texture and the mechanical properties of a real tongue. “Wettability" was another key component of the 3D tongue, Soltanahmadi says, referring to whether a surface mixes with water (hydrophilic) or, in the case of oil, resists it (hydrophobic).
Notably, the resulting artificial 3D-tongues looked nothing like the human version, but they were good mimics. The scientists also created “testing kits” that produced data on various physical parameters. One such parameter was viscosity, the measure of how gooey a food or liquid is — honey is more viscous compared to water, for example. Another was tribology, which defines how slippery something is — high fat yogurt is more slippery than low fat yogurt; milk can be more slippery than water. The researchers then mixed chocolate with artificial saliva and spread it on the 3D tongue to measure the tribology and the viscosity. From there they were able to study what happens inside the mouth when we eat chocolate.
The team focused on the stages of lubrication and the location of the fat in the chocolate, a process that has rarely been researched.
The artificial 3D-tongues look nothing like human tongues, but they function well enough to do the job.
Courtesy Anwesha Sarkar and University of Leeds
The oral processing of chocolate
We process food in our mouths in several stages, Soltanahmadi says. And there is variation in these stages depending on the type of food. So, the oral processing of a piece of meat would be different from, say, the processing of jelly or popcorn.
There are variations with chocolate, in particular; some people chew it while others use their tongues to explore it (within their mouths), Soltanahmadi explains. “Usually, from a consumer perspective, what we find is that if you have a luxury kind of a chocolate, then people tend to start with licking the chocolate rather than chewing it.” The researchers used a luxury brand of dark chocolate and focused on the process of licking rather than chewing.
As solid cocoa particles and fat are released, the emulsion envelops the tongue and coats the palette creating a smooth feeling of chocolate all over the mouth. That tactile sensation is part of the chocolate’s hedonistic appeal we crave.
Understanding the make-up of the chocolate was also an important step in the study. “Chocolate is a composite material. So, it has cocoa butter, which is oil, it has some particles in it, which is cocoa solid, and it has sugars," Soltanahmadi says. "Dark chocolate has less oil, for example, and less sugar in it, most of the time."
The researchers determined that the oral processing of chocolate begins as soon as it enters a person’s mouth; it starts melting upon exposure to one’s body temperature, even before the tongue starts moving, Soltanahmadi says. Then, lubrication begins. “[Saliva] mixes with the oily chocolate and it makes an emulsion." An emulsion is a fluid with a watery (or aqueous) phase and an oily phase. As chocolate breaks down in the mouth, that solid piece turns into a smooth emulsion with a fatty film. “The oil from the chocolate becomes droplets in a continuous aqueous phase,” says Soltanahmadi. In other words, as solid cocoa particles and fat are released, the emulsion envelops the tongue and coats the palette, creating a smooth feeling of chocolate all over the mouth. That tactile sensation is part of the chocolate’s hedonistic appeal we crave, says Soltanahmadi.
Finding the sweet spot
After determining how chocolate is orally processed, the research team wanted to find the exact sweet spot of the breakdown of solid cocoa particles and fat as they are released into the mouth. They determined that the epicurean pleasure comes only from the chocolate's outer layer of fat; the secondary fatty layers inside the chocolate don’t add to the sensation. It was this final discovery that helped the team determine that it might be possible to produce healthier chocolate that would contain less oil, says Soltanahmadi. And therefore, less fat.
Rongjia Tao, a physicist at Temple University in Philadelphia, thinks the Leeds study and the concept behind it is “very interesting.” Tao, himself, did a study in 2016 and found he could reduce fat in milk chocolate by 20 percent. He believes that the Leeds researchers’ discovery about the first layer of fat being more important for taste than the other layer can inform future chocolate manufacturing. “As a scientist I consider this significant and an important starting point,” he says.
Chocolate is rich in polyphenols, naturally occurring compounds also found in fruits and vegetables, such as grapes, apples and berries. Research found that plant polyphenols can protect against cancer, diabetes and osteoporosis as well as cardiovascular ad neurodegenerative diseases.
Not everyone thinks it’s a good idea, such as chef Michael Antonorsi, founder and owner of Chuao Chocolatier, one of the leading chocolate makers in the U.S. First, he says, “cacao fat is definitely a good fat.” Second, he’s not thrilled that science is trying to interfere with nature. “Every time we've tried to intervene and change nature, we get things out of balance,” says Antonorsi. “There’s a reason cacao is botanically known as food of the gods. The botanical name is the Theobroma cacao: Theobroma in ancient Greek, Theo is God and Brahma is food. So it's a food of the gods,” Antonorsi explains. He’s doubtful that a chocolate made only with a top layer of fat will produce the same epicurean satisfaction. “You're not going to achieve the same sensation because that surface fat is going to dissipate and there is no fat from behind coming to take over,” he says.
Without layers of fat, Antonorsi fears the deeply satisfying experiential part of savoring chocolate will be lost. The University of Leeds team, however, thinks that it may be possible to make chocolate healthier - when consumed in limited amounts - without sacrificing its taste. They believe the concept of less fatty but no less slick chocolate will resonate with at least some chocolate-makers and consumers, too.
Chocolate already contains some healthful compounds. Its cocoa particles have “loads of health benefits,” says Soltanahmadi. Dark chocolate usually has more cocoa than milk chocolate. Some experts recommend that dark chocolate should contain at least 70 percent cocoa in order for it to offer some health benefit. Research has shown that the cocoa in chocolate is rich in polyphenols, naturally occurring compounds also found in fruits and vegetables, such as grapes, apples and berries. Research has shown that consuming plant polyphenols can be protective against cancer, diabetes and osteoporosis as well as cardiovascular and neurodegenerative diseases.
“So keeping the healthy part of it and reducing the oily part of it, which is not healthy, but is giving you that indulgence of it … that was the final aim,” Soltanahmadi says. He adds that the team has been approached by individuals in the chocolate industry about their research. “Everyone wants to have a healthy chocolate, which at the same time tastes brilliant and gives you that self-indulging experience.”
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”