Binary classification of sake acidity using excitation-emission matrix fluorescence spectroscopy combined with...

Acidity is a key sensory and quality attribute of Japanese sake; however, its conventional determination by titration is destructive and time-consuming. This study investigated excitation-emission matrix (EEM) fluorescence spectroscopy combined with chemometric modeling as a rapid, non-destructive alternative for binary classification of sake according to acidity level. A total of 100 commercial sake samples were classified into lower-acidity (≤ 1.5; n = 43) and higher-acidity (> 1.5; n = 57) groups based on industry standards. Six classification pipelines were compared using stratified nested cross-validation: Partial Least Squares Discriminant Analysis (PLS-DA) and Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) applied to the full EEM dataset, and Support Vector Machine (linear kernel) and Random Forest classifiers applied to features extracted by Parallel Factor Analysis (PARAFAC) and Principal Component Analysis (PCA). EEM-PLS-DA achieved the best overall performance (Accuracy 0.880, Macro-F1 0.866, ROC-AUC 0.901) and ranked highest in the Friedman test (p < 0.05). Nemenyi post-hoc comparisons (CD = 2.384) confirmed a significant difference only between EEM-PLS-DA and the worst-performing model in each metric. Variable Importance in Projection (VIP) and regression coefficient analyses identified five fluorescence regions associated with tyrosine-like fluorophores, aromatic amino acids, polyphenols, Maillard reaction products, and riboflavin. These results indicate that acidity-related information is distributed across multiple fluorophore systems. Overall, these results support EEM fluorescence spectroscopy combined with PLS-DA as a promising proof-of-concept for rapid, reagent-free screening, indicating potential for sake quality control pending validation on independent samples.