Prediction of sake component values using e-nose and e-tongue data by machine learning

Abstract

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 explanatory 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.