Abstract
Background/Objectives: The components of food and beverages are important elements that determine their palatability. Although the components of sake, a traditional Japanese alcoholic beverage, have been studied for many years, their correlation with sensory characteristics is unclear. Methods: We investigate the correlation with the sake metabolome analysis method developed by our group using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. We constructed orthogonal projections to latent structure models to predict sensory evaluation data obtained through the quantitative descriptive analysis method from the sake metabolome data. Results: For two years of study, 8 sensory evaluation models of the 2016 brewing year and 11 sensory evaluation models of the 2017 brewing year, including color, ethyl hexanoate, Hine-ka, Nama hine-ka, ethyl acetate, grainy/sweet aroma, sweetness, sourness, body, astringency, harsh taste/acrid taste, aftertaste, and overall quality, demonstrated a predictive performance with Q(2) > 0.5. Liquid chromatography-based analytical data indicated that it is possible to predict not only taste but also aroma. Additionally, the generalization performance of the prediction models for sensory evaluation attributes common to both years was verified. Conclusions: These results provide a new option for explaining the sensory characteristics of sake from its components and contribute to a deeper understanding of them.