  This paper presents a novel beverage intake monitoring system that can
accurately recognize beverage kinds and freshness. By mounting carbon
electrodes on the commercial cup, the system measures the electrochemical
impedance spectrum of the fluid in the cup. We studied the frequency
sensitivity of the electrochemical impedance spectrum regarding distinct
beverages and the importance of features like amplitude, phase, and real and
imaginary components for beverage classification. The results show that
features from a low-frequency domain (100 Hz to 1000 Hz) provide more
meaningful information for beverage classification than the higher frequency
domain. Twenty beverages, including carbonated drinks and juices, were
classified with nearly perfect accuracy using a supervised machine learning
approach. The same performance was also observed in the freshness recognition,
where four different kinds of milk and fruit juice were studied.
