Machine learning in analyses of the relationship between japanese sake physicochemical features and comprehensive evaluations

Abstract

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.