Manuscript received November 14, 2025; accepted December 1, 2025; published June 29, 2026
Abstract—Sake acidity is a critical quality parameter that significantly influences taste perception and consumer preference. Conventional titrimetric methods, while accurate, are labor-intensive, time-consuming, and destructive, limiting their suitability for rapid quality assessment. This study demonstrates the feasibility of Excitation–Emission Matrix (EEM) fluorescence spectroscopy combined with chemometric modeling for rapid, non-destructive classification of sake acidity. A total of 100 commercial sake samples were analyzed and classified into low- and high-acidity groups using Partial Least Squares Discriminant Analysis (PLS-DA) and principal component analysis–support vector machine (PCA-SVM). The optimized PLS-DA model with seven latent variables achieved superior classification performance (81% accuracy, F1-scores >80%), substantially outperforming PCA-SVM variants (58–75% accuracy). VARIABLE Importance in Projection (VIP) analysis revealed that aromatic amino acid fluorescence (Peak A, Ex/Em 265–305/285–420 nm) and Maillard reaction products (Peak B, Ex/Em 315–350/390–445 nm) served as the primary discriminators, with riboflavin regions (Peak C, Ex/Em 300–355/470–580 nm) contributing secondarily. High-acidity sake exhibited enhanced Maillard-derived fluorescence, whereas low-acidity sake retained stronger aromatic amino acid signals. This fluorescence-based approach provides a rapid, reagent-free alternative to conventional titration, offering practical potential for real-time brewery quality control and production monitoring without sample consumption.
Keywords—Chemometrics, EEM fluorescence spectroscopy, PLS-DA, Sake acidity
Cite: Pornpimol Moolkaew, Yoshito Saito, Takumi Murai, Yu Obata, and Hideo Hasegawa, "Non-Destructive Classification of Sake Acidity Using Excitation–Emission Matrix Combined with Chemometrics," International Journal of Engineering and Technology, vol. 18, no. 2, pp. 84-91, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (
CC BY 4.0). 
