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Look at taste testers, there’s a new competitor in the world of taste evaluation – and it’s made up of circuits and sensors.
And ultimately, scientists say the technology could be used to find contaminants in food that could make consumers sick, or to detect when something is no longer safe to eat.
Researchers at Penn State in Pennsylvania have developed an electronic tongue that identifies differences in similar liquids, such as milk with varying water content, and products such as types of soda and coffee blends. It can even distinguish between Pepsi and Coke with a high degree of accuracy.
The research, published Wednesday in Naturediscovered that the AI tongue is able to detect, classify and assess the quality and freshness of various substances. This means that if milk is contaminated, the tongue may be able to detect it.
“We’re trying to create an artificial tongue, but the process of how we experience different foods involves more than just the tongue,” says corresponding author Saptarshi Das, professor of engineering science and mechanics at Penn State.
“We have the tongue itself, made up of taste receptors that interact with food types and send their information to the gustatory cortex – a biological neural network.”
The electronic tongue consists of a graphene-based ion-sensitive field-effect transistor, or a conductive device that can detect chemical ions, coupled with an artificial neural network trained on various data sets. This is located at the top right of the device.
Saptarshi Das Lab/Penn State
The gustatory cortex, located in the brain, perceives and interprets flavors beyond the basic categories of sweet, sour, bitter, salty and savory detected by taste receptors, the researchers explained. As the brain becomes more familiar with these flavors, it becomes better at detecting the subtle nuances between different favors.
The researchers attempted to replicate the function of the gustatory cortex by developing a machine learning algorithm designed to mimic it.
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The AI tongue is made up of graphene and chemical sensors. The chemical sensors detect and measure the chemical composition of fluids (such as carbohydrates, proteins, lipids, acids and vitamins) and convert the information into electrical signals, the researchers said.
The neural network was trained on different data sets and given twenty specific tasks to learn. These tasks were about how fluids affect the electrical properties of the sensors.
The AI tongue accurately detected samples including watered down milk, different types of soda (Diet Coke, Pepsi, Coke Zero Sugar), coffee blends (espresso, breakfast, Italian), and multiple fruit juices with different freshness levels. The researchers reported that it could accurately decipher the contents in about a minute, with an accuracy rate of more than 80 percent.
For the fruit juice, the AI tongue achieved accuracy values of approximately 98 percent for identifying the fruit type and 99 percent for determining its age.
“After achieving reasonable accuracy with human-selected parameters, we decided to let the neural network define its own merits by feeding it the raw sensor data,” said co-author Andrew Pannone, a doctoral student in engineering at Penn. Stands.
“We found that the neural network achieved near-ideal inference accuracy of more than 95 percent when using machine-derived merit metrics instead of human ones,” he said in the press release.
By using this AI tongue to accurately detect subtle differences in liquids that are potentially beyond human perception, the sensors could be applied to identify harmful contaminants in food, such as PFAS (a toxic chemical), and to measure freshness of food, such as tracking juice quality over food. several days, the researchers argued.
“While corrective actions exist for food adulteration and contamination incidents, monitoring food freshness is more challenging due to the time-varying and complex chemical compositions in food. Spoiled food is dangerous to consume and has reduced nutritional value,” the study said.
The authors emphasize that the timely detection of harmful contaminants in food production and distribution remains an ongoing challenge. For example, hazardous PFAS are widely used in industrial processes and are present in consumer products, with the potential to accumulate in the environment, including drinking water.
PFAS, known as “forever chemicals,” are a group of thousands of sustainable, man-made chemicals used in textiles, cosmetics, furniture, paints, firefighting foam, food packaging and other commonly used consumer products.
Exposure to certain PFAS is associated with reproductive, developmental, endocrine, hepatic, renal and immunological effects. according to Health Canada.
However, the AI tongue shows promise for accurately detecting PFAS in water and offers a potential solution to this problem.
“We believe that miniaturized graphene-based technology, enhanced by a range of machine learning methodologies, can serve as a cost-effective platform for a wide range of chemical detection systems.
applications in the food supply chain and beyond,” the authors say.
The researchers note that the AI tongue’s capabilities are limited only by the data it is trained on. Although this study focused on food assessment, its potential applications could also extend to areas such as medical diagnostics.
“These results highlight that machine learning-assisted graphene ISFETs (ion sensitive field effect transistors) can be applied to address a broad spectrum of challenges in the food industry,” the researchers said.
— With files from Saba Aziz of Global News
Katie Dangerfield
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