Scientists in China have developed the first artificial tongue capable of detecting and identifying taste entirely in liquids, closely replicating the way human taste buds function.
The breakthrough, published July 15 in the journal PNAS, could lead to automated systems for food safety, early disease detection, and advanced chemical analysis.
Researchers say the technology also represents progress toward neuromorphic computing — artificial intelligence designed to mimic the brain’s learning processes.
Graphene oxide membranes slow ion movement
The device is built from graphene oxide membranes, ultra-thin carbon sheets that act as molecular filters for ionic taste forms
Instead of separating large particles, the membranes slow the movement of ions, allowing the system to detect and retain information about flavors.
By reducing ion flow to 500 times slower than normal, the device can store a taste for up to 140 seconds, far longer than the millisecond range in most systems.
Device shows high accuracy in taste detection
The artificial tongue identified four basic tastes in tests — sweet, sour, salty, and bitter — with an accuracy rate of 72.5% to 87.5%. It achieved 96% accuracy for complex drinks, such as coffee and Coca-Cola, a result attributed to the distinctive electrical composition of these mixtures.
Researchers say it is the first time sensing and information processing have been combined in a single liquid-based system.
Liquid-based processing replaces external computers
Earlier tasting devices relied on external computers to process all data. This new system performs both sensing and much of its processing directly in liquid, enabling flavors to be analyzed in their natural ionic state. That approach avoids the loss of information that occurs when samples are converted for processing in dry systems.
Overcoming a major electronics challenge
Traditional electronic components fail in liquid environments, which forces researchers to separate sensing from processing. The use of graphene oxide membranes overcomes the limitation, enabling both functions in a single device.
System learns flavors over time
The artificial tongue works by dissolving chemical compounds into ions, which pass through channels thousands of times thinner than a human hair. As they move through, the ions create distinct patterns linked to specific flavors. Over time, the system learns these patterns, improving its ability to identify tastes, even complex blends.
The device implements a hybrid method of reservoir computing and a basic neural network, with part of the process handled by the physical hardware itself.
Yong Yan, a chemistry professor at the National Center for Nanoscience and Technology and co-author of the study, said the device can match the performance of more advanced neural networks in distinguishing complex flavors.
Potential uses span multiple industries
Potential applications range from medical diagnostics to quality control in food and beverage production, and environmental monitoring of water supplies.
Yan acknowledged that the system is still too large for practical use; its sensitivity needs improvement, and its power consumption is high.
However, he said advances in production scaling, power efficiency, multi-sensor integration, and neuromorphic hardware could make the technology a transformative tool in healthcare, robotics, and environmental science within the next decade.
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