Machine Learning in Analyses of the Relationship between Japanese Sake Physicochemical Features and Comprehensive Evaluations
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- SHIMOFUJI Satoru
- Kochi Prefecture Industrial Technology Center Department of Food Sciences and Nutritional Health, Graduate School of Kyoto Prefectural University
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- MATSUI Motoko
- Department of Food Sciences and Nutritional Health, Graduate School of Kyoto Prefectural University
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- MURAMOTO Yukari
- Kyoto Prefectural University
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- MORIYAMA Hironori
- Kochi Prefecture Industrial Technology Center
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- KATO Reina
- Kochi Prefecture Industrial Technology Center
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- HOKI Yoshiro
- Kochi Prefecture Industrial Technology Center
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- UEHIGASHI Haruhiko
- Kochi Prefecture Industrial Technology Center
Bibliographic Information
- Other Title
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- 日本酒の総合評価と物理化学的特徴との関係性の解析における機械学習の適用
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Description
<p>We investigated the contributions of physicochemical features to a comprehensive evaluation of the Japanese sake known as ‘Junmai Ginjo’ by applying machine learning. We used 173 samples of the commercial Japanese sake. The sensory evaluation was conducted by 35 panelists. The panel conducted the evaluation of each sample using five statements for the comprehensive evaluation of the sample. General analysis, substance-related nucleic acid, volatile components and simplified analyses were measured as physicochemical analyses. We performed regression analyses using a multiple regression analysis (MRA), partial least squares regression (PLS) and machine learning employing a support vector machine (SVM), an artificial neural network (ANN), and random forest (RF). The results of these five analysis methods have demonstrated that machine learning (especially RF) provides comparable or higher prediction accuracy and better fitting than MRA. We also discuss the contribution of each physicochemical feature to the evaluation scores based on the regression coefficients obtained by MRA and the features’ importance obtained in RF. The analysis of the individual scores indicated that ethyl caproate and isoamyl acetate make large contributions to influence the sake evaluation.</p>
Journal
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- Japan Journal of Food Engineering
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Japan Journal of Food Engineering 21 (1), 37-50, 2020-03-15
Japan Society for Food Engineering
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Keywords
Details 詳細情報について
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- CRID
- 1390002184887758336
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- NII Article ID
- 130007818501
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- NII Book ID
- AA12076107
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- ISSN
- 18845924
- 13457942
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- NDL BIB ID
- 030333490
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- Text Lang
- en
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- Data Source
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- JaLC
- IRDB
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed