Researchers Develop AI Tongue That Can Differentiate Between Different Tastes

In Education

Researchers at Penn State University are developing an “electronic tongue” that can mimic human preferences for specific foods. This development is part of a broader effort to incorporate emotional intelligence, such as taste-based likes and dislikes, into artificial intelligence (AI). Despite AI’s advancements, it has traditionally overlooked human psychology, including emotional intelligence nuances.

Researches leverage AI to create an electronic tongue

Saptarshi Das, the corresponding author and an associate professor at Penn State, said that the primary objective of our research was to incorporate emotional intelligence into AI. Das highlights the challenge of bridging the emotional aspect of human intelligence with artificial intelligence. While psychology researchers explore emotions extensively, computer engineers rely on mathematical models and diverse data sets for design. Human behavior is challenging to quantify, hindering the replication of emotional intelligence in robots. Currently, there is no practical method to achieve this goal.

Eating habits exemplify emotional intelligence, where hunger drives the need to eat, but food choices are influenced by taste preferences, known as gustation. Das points out that even when not hungry, human’s psychological desires can lead us to choose a sweet treat over something less appealing, such as a piece of meat. For those with a wide range of food options, they tend to select their favorite foods rather than something bitter, typically opting for sweeter options.

Graphene used to produce chemitransistors

Penn State researchers studied the human tongue’s role in translating chemical data into signals for taste perception. They created an electronic “tongue” using graphene-based chemitransistors and molybdenum disulfide memtransistors to mimic the process. This electronic system, resembling the brain’s gustatory cortex, links neurons related to hunger, appetite, and feeding.

The study’s co-author, Andrew Pannone, explained that two different materials were employed in the research. Graphene, known for its effectiveness as a chemical sensor, was utilized alongside molybdenum disulfide, a semiconductor. This combination of nanomaterials allowed the creation of a circuit resembling the gustatory system, as it harnessed the strengths of both materials.

This technology has a broad range of applications as it can recognize all primary taste profiles, including sweet, salty, and umami.

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