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An overview of alcoholic beverages discrimination plus the author’s experimental research work on identification of bland Chinese liquors is presented in this article. First, a summary was given on analytical techniques combined with multivariate statistical analysis for discrimination problems related to alcoholic beverages; and then, our experimental research was presented on discrimination of different aroma types of Chinese liquors by  1 H-Nuclear Magnetic Resonance (NMR) and  13 C-NMR combined with multivariate statistical analysis. Principal Component Analysis (PCA) was used to highlight the most significant features from high-dimensional data to acquire visual inspections of NMR spectra in the first step. The representative new score values were used as the input variables for Support Vector Machine (SVM), and the performance of this model was evaluated by Leave-One-Out Cross Validation method (LOOCV). Compared with  1 H-NMR, the scores of PCA and predications of the SVM model from  13 C-NMR spectra showed more significant separation and better prediction performance.  13 C-NMR for multivariate analysis might be a more effective tool for identification of these Chinese liquors because of its complementary component information as well as less spectral overlapping than that in  1 H-NMR.

An overview of alcoholic beverages discrimination and a study on identification of bland Chinese liquors by 13 C-NMR and 1 H-NMR spectra