Machine Learning in Analyses of the Relationship between Japanese Sake Physicochemical Features and Comprehensive Evaluations

Shimofuji, Satoru; Matsui, Motoko; Muramoto, Yukari; Moriyama, Hironori; Kato, Reina; Hoki, Yoshiro; Uehigashi, Haruhiko

doi:10.11301/jsfe.19560

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…However, other potential key variables could be overlooked due to the limited explanatory variables. & Many available explanatory variables are introduced, and necessary explanatory variables are extracted by variable selection methods [14,15]. The extracted explanatory variables are called determining variables.…”

Section: Explanatory Variablesmentioning

confidence: 99%

Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

et al. 2020

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In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

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Section: Explanatory Variablesmentioning

confidence: 99%

Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

et al. 2020

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“…Researches had been conducted to clarify the sensory characteristics of sake by using these analytical values [1][2]. We have applied machine learning to predict a comprehensive evaluation from physicochemical features that characterize sake [3], and our study showed that the prediction accuracy was low-especially for the samples evaluated as having defects. We speculated that it is necessary to add the data of a comprehensive analysis to the explanatory variables in order to predict comprehensive evaluations more accurately.…”

Section: Introductionmentioning

confidence: 97%

Prediction of Sake Component Values Using E-nose and E-tongue Data by Machine Learning

et al. 2021

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We estimated the quality component values of the commercial Japanese sake Junmai Ginjo by using electronic (e)-nose and e-tongue data. Regression analysis methods were applied to predict the components. Characteristic features of Junmai Ginjo such as acidity, amino acid content, glucose and nine volatile components were used as objective variables. Explanatory variables were the 99 peak data obtained by an e-nose and seven sensor data obtained by an e-tongue. The prediction accuracy by the partial least squares regression method using e-nose and e-tongue data was 7.57 average error% (the ratio of the mean absolute error to the component value range). With the application of other regression analyses (multiple regression analysis, support-vector machine, random forest, gradient boosting), the prediction accuracy was improved for all components except the acidity and amino acid content. With the application of other regression analyses and the addition of the data of seven simplified analyses (Brix, pH, electrical conductivity, OD260, OD280, simplified alcohol content, simplified glucose content), the prediction accuracy was improved for all components. (average error%: 5.04) The analysis conditions (i.e., the regression analysis and the dataset of explanator y variables) for the best score dif fered depending on the component. Thus, when predicting components by a regression analysis, it is necessary to prepare a plurality of analysis conditions and challenges.

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Recognition of Japanese Sake Quality Using Machine Learning Based Analysis of Physicochemical Properties

Saville

Kazuoka

Shimoguchi

et al. 2021

Journal of the American Society of Brewing Chemists

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Machine Learning in Analyses of the Relationship between Japanese Sake Physicochemical Features and Comprehensive Evaluations

Cited by 5 publications

References 20 publications

Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

Prediction of Sake Component Values Using E-nose and E-tongue Data by Machine Learning

Recognition of Japanese Sake Quality Using Machine Learning Based Analysis of Physicochemical Properties

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