Deep Neural Network Capable Of Tracking Traits Of Beverages

By Supriya Mangalpalli

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University of Maine researchers developed a device that can track beverage type, volume, and sugar content using electrical impedance spectroscopy

(Credit: Multisensory Interactive Media Lab, University of Maine)

Strict diets have become essential for healthy living. Senior citizens and people with serious ailments such as cancer, heart and kidney diseases, diabetes, and other conditions manually record their food intake details to help preserve their health. To avoid this time-consuming process which can also lead to a miscalculation in some cases, Ranasinghe and Amarasinghe introduced a device, trained to recognize traits of beverages such as beverage type, volume, and sugar content once submerged in liquid. Deep learning algorithms are used to recognize these traits, and the electrical impedance measurements over a range of frequencies of drink are applied as data to these algorithms. “Through electrical impedance, we can recognize different beverage types and traits based on different signatures; for example, a cup of Pepsi versus a cup of Coca-Cola,” Amarasinghe says.

Electrical impedance is the resistance offered to the flowing current by an object. A given set of measurements of an electrical impedance traces particular beverage traits such as whether it’s tea, coffee, or soda. SipBit device applies a known electric pulse thousand of frequencies and then measures the impedance through the drink, this process is called electrical impedance spectroscopy. Hence, using this measurement the SipBit can analyze the physical and chemical properties of a beverage.

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“We’re starting from this foundation,” Ranasinghe says. “Our next step is to explore different applications and create a smart cup or tumbler where people can automatically record their calorie intake in detail and real-time”. “We can provide new opportunities for human-food interactions,” Ranasinghe says. “One drive I have is figuring out how we can add these unexplored senses—smell and taste—into our technologies. SipBit is part of a bigger puzzle of future human-food digital interactions because if you want to incorporate new senses, you have to invent technology that can simulate them, sense them and collect data on them.”

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