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清酒の成分に対する味覚センサデータの非線形重回帰モデル

清酒の成分に対する味覚センサデータの非線形重回帰モデル

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カテゴリ: 論文誌(論文単位)

グループ名: 【E】センサ・マイクロマシン部門

発行日: 2019/03/01

タイトル(英語): A Nonlinear Multiple Regression Model of Taste Sensor Data for Components in Sake

著者名: 佐藤 雅子(情報ノ宮蕗の下工房),高尾 佳史(菊正宗酒造(株)総合研究所),佐藤 仁樹(公立はこだて未来大学 システム情報科学部)

著者名(英語): Masako Satoh (Johonomiya Fukinoshita Studio), Yoshifumi Takao (General research laboratory, Kiku-masamune sake brewing Co. Ltd.), Hideki Satoh (School of Systems Information Science, Future University Hakodate)

キーワード: 味覚センサ,化学分析,関数近似,遺伝的アルゴリズム,主成分分析  taste sensor,chemical analysis,function approximation,genetic algorithm,principal component analysis

要約(英語): A nonliner function that expresses the relationship between taste sensor data and components in sake was approximated using a polynomial of Legendre functions. First, the number of components in sake was reduced using principal component analysis. Second, the number of Legendre functions of the polynomial and their degrees were selected using a genetic algorithm. Third, the coefficients of the polynomial were calculated using multiple regression analysis. The approximation error was estimated using cross-validation, and the number of Legendre functions and their degrees were optimized so as to maximize the generalization of the polynomial. As a result, sufficiently small approximation errors were obtained, and the explicit relationship between taste sensor data and components in sake was clarified using the polynomial. Furthermore, it was possible not only to confirm the taste sensor response but also to improve manufacturing processes of sake using the estimates of the variations in the taste sensor data.

本誌: 電気学会論文誌E(センサ・マイクロマシン部門誌) Vol.139 No.3 (2019)

本誌掲載ページ: 45-53 p

原稿種別: 論文/日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejsmas/139/3/139_45/_article/-char/ja/

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