Correlation between Sensory Evaluation Scores of Japanese <i>Sake</i> and Metabolome Profiles

The aim of this study was to explore the association between taste and metabolite profiles of Japanese refined sake. Nontarget metabolome analysis was conducted using capillary electrophoresis mass spectrometry. Zatsumi, an unpleasant not clear flavor, and sweetness, bitterness, and sourness were graded by four experienced panelists. Regression models based on support vector regression (SVR) were used to estimate the relationships among sensory evaluation scores and quantified metabolites and visualized as a nonlinear relationship between sensory scores and metabolite components. The SVR model was highly accurate and versatile: the correlation coefficients for whole training data, cross-validation, and separated validation data were 0.86, 0.73, and 0.73, respectively, for zatsumi. Other sensory scores were also analyzed and modeled by SVR. The methodology demonstrated here carries great potential for predicting the relevant parameters and quantitative relationships between charged metabolites and sensory evaluation in Japanese refined sake.本研究旨在探究日本精制清酒（Japanese refined sake）的风味与代谢物谱（metabolite profiles）之间的关联。本研究采用毛细管电泳质谱法（capillary electrophoresis mass spectrometry）开展非靶向代谢组学分析。由4名经验丰富的感官评定员对杂味（Zatsumi）——一种不清爽的不愉快风味——以及甜味、苦味和酸味进行等级评定。本研究采用基于支持向量回归（support vector regression, SVR）的回归模型，对感官评定得分与定量代谢物之间的关联进行拟合，并将感官得分与代谢物组分间的非线性关联进行可视化呈现。该支持向量回归模型具备优异的准确性与泛化能力：针对杂味，其在全部训练数据集、交叉验证集及独立验证集上的相关系数分别为0.86、0.73和0.73。其余感官评定指标亦通过支持向量回归完成建模与分析。本研究所采用的方法，在预测日本精制清酒带电代谢物与感官评定间的相关参数及定量关联方面具备巨大应用潜力。