Sensory Modeling of Coffee with a Fuzzy Neural Network

Authors

  • O. Tominaga,

    1. Authors Tominaga and Ito are with the Central Research Laboratories, Ajinomoto General Foods Inc., 6410 Minamitamagaki-Cho, Suzuka, Mie Pref., Japan. Authors Hanai, Honda, and Kobayashi are in the Dept. of Biotechnology, Nagoya Univ, Furocho, Chikusaku, Nagoya, Japan. Direct inquiries to Osamu Tominaga (E-mail: Osamu_Tominaga@notesgw.agf.co.jp)
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  • F. Ito,

    1. Authors Tominaga and Ito are with the Central Research Laboratories, Ajinomoto General Foods Inc., 6410 Minamitamagaki-Cho, Suzuka, Mie Pref., Japan. Authors Hanai, Honda, and Kobayashi are in the Dept. of Biotechnology, Nagoya Univ, Furocho, Chikusaku, Nagoya, Japan. Direct inquiries to Osamu Tominaga (E-mail: Osamu_Tominaga@notesgw.agf.co.jp)
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  • T. Hanai,

    1. Authors Tominaga and Ito are with the Central Research Laboratories, Ajinomoto General Foods Inc., 6410 Minamitamagaki-Cho, Suzuka, Mie Pref., Japan. Authors Hanai, Honda, and Kobayashi are in the Dept. of Biotechnology, Nagoya Univ, Furocho, Chikusaku, Nagoya, Japan. Direct inquiries to Osamu Tominaga (E-mail: Osamu_Tominaga@notesgw.agf.co.jp)
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  • H. Honda,

    1. Authors Tominaga and Ito are with the Central Research Laboratories, Ajinomoto General Foods Inc., 6410 Minamitamagaki-Cho, Suzuka, Mie Pref., Japan. Authors Hanai, Honda, and Kobayashi are in the Dept. of Biotechnology, Nagoya Univ, Furocho, Chikusaku, Nagoya, Japan. Direct inquiries to Osamu Tominaga (E-mail: Osamu_Tominaga@notesgw.agf.co.jp)
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  • T. Kobayashi

    1. Authors Tominaga and Ito are with the Central Research Laboratories, Ajinomoto General Foods Inc., 6410 Minamitamagaki-Cho, Suzuka, Mie Pref., Japan. Authors Hanai, Honda, and Kobayashi are in the Dept. of Biotechnology, Nagoya Univ, Furocho, Chikusaku, Nagoya, Japan. Direct inquiries to Osamu Tominaga (E-mail: Osamu_Tominaga@notesgw.agf.co.jp)
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  • This study was partially supported by Grant-in Aid for Scientific Research (No.11832013) from the Ministry of Education, Science, Sports, and Culture of Japan.

Abstract

ABSTRACT: Models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty-two blended coffees were prepared from 3 representative beans and were evaluated with respect to 10 sensory attributes by 5 coffee cup-tasters and by models constructed using the response surface method (RSM), multiple regression analysis (MRA), and a fuzzy neural network (FNN). The RSM and MRA models showed good correlations for some sensory attributes, but lacked sufficient overall accuracy. The FNN model exhibited high correlations for all attributes, clearly demonstrated the relationships between blending ratio and flavor characteristics, and was accurate enough for practical use. FNN, thus, constitutes a powerful tool for accelerating product development.

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