Classification of Chinese wine varieties using 1 H NMR spectroscopy combined with multivariate statistical analysis

Abstract In this study, the feasibility of discriminating grape varieties of Chinese red and white wines was investigated using 1 H NMR spectroscopy in combination with a multivariate statistical procedure consisting of two steps: principal component analysis (PCA) plus linear discriminant analysis (LDA). Three grape varieties of red wines (Cabernet Sauvignon, Rose Honey, Cabernet Gernischt) and white wines (Ugni Blanc, Long Yan, Chardonnay) were examined, respectively. A segment-wise peak alignment was employed to handle peak misalignments of recorded 1 H NMR spectra. Binning of the aligned 1 H NMR spectra was performed for data reduction. The resulting bins were employed as input variables for the subsequent PCA and LDA analyses. The combination of PCA and LDA yielded in a sufficient discrimination of the examined grape varieties. The validity of the PCA/LDA model was confirmed by internal leave-one-out cross validation (LOOCV) as well as by external repeated double random cross validation (RDRCV). LOOCV and RDRCV led to average correct classification rates of 82% and 83% for red wine varieties, respectively, and 94% and 90% for white wine varieties, respectively. The results demonstrate that 1 H NMR spectroscopy combined with multivariate analysis is an effective tool for verifying the authenticity of Chinese wines.