Method of measuring taste using two phase radial basis...

Data processing: artificial intelligence – Neural network – Learning task

Reexamination Certificate

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Reexamination Certificate

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07899765

ABSTRACT:
A method for measuring tastes, which can better simulate the human gustation than known methods, as well as a taste sensor, computer program and an apparatus for measuring tastes, is disclosed. In this method, data processing is carried out by a two-phase radial basis function neural network. That is, by sensors, each of which sensors can quantify at least one component representing, individually or cooperatively, the taste of saltiness, sourness, sweetness, umami or bitterness, to obtain a response value from each sensor, and each of the obtained response values is input to a first phase radial basis function neural network to calculate the concentration of each component from each response value. Then, the concentration of each component is fed into a second phase radial basis function neural network, which correlates the concentration of each component with the intensities of saltiness, sourness, sweetness, umami and bitterness sensed by humans, to calculate the intensities of saltiness, sourness, sweetness, umami and bitterness sensed by humans.

REFERENCES:
patent: 6850252 (2005-02-01), Hoffberg
patent: 2002/0186875 (2002-12-01), Burmer et al.
patent: 2007/0053513 (2007-03-01), Hoffberg
patent: 63-241354 (1988-10-01), None
patent: 6-174689 (1994-06-01), None
Legin et al., “Evaluation of Italian wine by the electronic tongue: recognition, quantitative analysis and correlation with human sensory perception,” Analytica Chimica acta, vol. 484, pp. 33-44, 2003.
Albrecht et al., “Generalized radical basis function networks for classification and novelty detection: self-organization of optimal Bayesian decision,” Neural Networks, vol. 13, pp. 1075-1093, 2000.

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