title: Smart chemical taste sensor for determination and prediction of taste qualities based on a two-phase optimized radial basis function network.
abstract: A novel, smart, chemical taste sensor that realistically mimics the behavior of the human gustatory system is described. The taste sensor consists of an array of electrochemical sensors that represent the gustatory receptors on the human tongue, and a two-phase optimized radial basis function network (RBFN) to represent the human brain, which comprehensively analyzes the gustatory stimulation and judges the overall taste. In the illustrated model, eight electrodes were fabricated to determine the eight major taste-causing substances, Na+, K+, Cl-, H+, sucrose, glucose, glutamate, and caffeine. The detected signals were fed to a two-phase RBFN optimized by the implementation of a basis optimization algorithm and weight decay term for appropriate data processing. The first phase of the two-phase RBFN quantifies the amount of taste-causing substances in food samples from the responses of the electrodes. These results are then fed to the second phase, which correlates the amount of substances with the overall taste. The final output is scored on a scale of 1-5 for each of the five basic tastes sensed by the human gustatory system, which are saltiness, sourness, sweetness, bitterness, and umami. The constructed network estimated the intensity of the basic tastes of 30 drink varieties with an average relative error of 7.0% compared to the human scores. The network could also estimate the variance in the human sensory perception. Moreover, the sensor successfully predicted the interactions of tastes such as suppression of bitterness by sweetness and enhancement of umami by saltiness, which are illusions sensed by the human gustatory system. With these abilities, the novel taste sensor can be considered as a quantitative yet humanlike sensor with a great potential for practical applications.
authors.affiliation: Department of Applied Chemistry and of Information and Computer Science, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.
authors.forename: Saeko
authors.initials: S
authors.lastname: Ishihara
authors.forename: Atsushi
authors.initials: A
authors.lastname: Ikeda
authors.forename: Daniel
authors.initials: D
authors.lastname: Citterio
authors.forename: Kenichi
authors.initials: K
authors.lastname: Maruyama
authors.forename: Masafumi
authors.initials: M
authors.lastname: Hagiwara
authors.forename: Koji
authors.initials: K
authors.lastname: Suzuki
mesh_terms: D000465:Algorithms; D015374:Biosensing Techniques / Q000295:instrumentation*; D002110:Caffeine / Q000032:analysis; D002712:Chlorides / Q000032:analysis; D004566:Electrodes; D005947:Glucose / Q000032:analysis; D018698:Glutamic Acid / Q000032:analysis; D006863:Hydrogen-Ion Concentration; D016571:Neural Networks, Computer; D011188:Potassium / Q000032:analysis; D012964:Sodium / Q000032:analysis; D013395:Sucrose / Q000032:analysis; D013649:Taste
chemical_list: D002712:Chlorides; D002110:Caffeine; D018698:Glutamic Acid; D013395:Sucrose; D012964:Sodium; D005947:Glucose; D011188:Potassium