An Inference of the Human Tastes Evaluation by an Artificial Neural Network

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概要

• 論文の詳細を見る

• Applications of sensory information processing in neural networks have made significant and varied advances with regard to senses such as vision and hearing. Although similar research has been conducted on gustatory sensation, progress in these area lags behind due to the complexity of the mechanisms involved. If the complex gustatory mechanisms could be elucidated from an engineering standpoint, however, it could help in the medical field, for example, in the diagnosis of gustatory disturbances and treatment planning, while in the food industry it could hold promise for the production and processing of better tasting, healthier foods. Our group has therefore attempted an engineering analysis of the mechanisms of gustatory sensation. The present study examines a model for the evaluation of human taste; that is, how does our sense of taste determine "good" and "bad." In our experiments we used tofu as a model, since there are relatively strong taste preferences toward this food. The sensory evaluations were enforced by professional tasters, who have a developed sense of taste. Using a neural network with these evaluation results as the master data, the possibility of evaluating human taste evaluation from sensory information was evaluated. The results indicated that, using the present method and evaluation is indeed possible[1][2][3][4][5].

• バイオメディカル・ファジィ・システム学会の論文

著者

• IWATA Akira

  Department of Computer Science & Engineering, Nagoya Institute of Technology

• Iwata A

  Department Of Electrical & Computer Engineering Nagoya Institute Of Technology

• OGURI Koji

  Faculty of Information Science and Technology, Aichi Prefectural University

• SUGIMOTO Toshitaka

  Department of Electrical & Computer Engineering, Nagoya Institute of Technology

• Oguri K

  Faculty Of Information Science And Technology Aichi Prefectural University

• Oguri Koji

  Faculty Of Information Science And Technology Aichi Prefectural University

• Sugimoto Toshitaka

  Department Of Electrical & Computer Engineering Nagoya Institute Of Technology

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