Establishment of original liquor discrimination using fluorescence spectroscopy combined with chemometric analysis and its application in Chinese mild-flavor liquors

Abstract

Original liquor quality control and grade identification are important for the quality improvement of finished liquor. For the purpose of this study, a meticulous collection of 209 samples of original Chinese mild-flavored liquors was conducted. The grading of these samples was determined through sensory tasting, which is the prevailing method used in major liquor factories. These samples were scanned by three-dimensional fluorescence spectroscopy to obtain the respective excitation-emission matrices (EEM). The suitability of EEM was evaluated for the classification of these samples using principal component analysis (PCA), Discrete Cosine Transform (DCT), Fast Independent Component Analysis (Fast-ICA) combined with linear discriminant analysis (LDA) and support vector machine (SVM). In general, it was found that a fast and labor-saving model for quality identification of original liquor was obtained, PCA was a more suitable algorithm for reducing the number of variables. The classification accuracy of the original liquor obtained by the SVM method after PCA dimensionality reduction was the highest, the training set accuracy was 89.81%. Therefore, the PCA-SVM model was selected, and after testing, its prediction accuracy was 84.62%. The model has undergone five-fold cross-validation (CV), and the CV accuracy is 81.53%, which proves that the model is sufficiently stable.