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A holistic and comparative quality assessment of vinegars from different countries is needed with international trade of vinegar become frequent. In this study, compounds characterization and comparison of commercial-grade Chinese cereal and European grape vinegars were performed using 1H NMR spectroscopy coupled with principal component analysis (PCA) and orthogonal projection to latent structures discriminate analysis (OPLS-DA). The results showed that Balsamic vinegars of Modena were clearly discriminated by higher amount of fructose and glucose, while Chinese aromatic vinegar and aged vinegars were characterized by higher amount of amino acids, volatile compounds, succinate and betaine. On the other hand, flavoring compounds in Chinese rice vinegar and European wine vinegars are less than the others. These characteristic components are associated with the special raw materials and producing process of each types of vinegar and endow them special flavor. The results obtained in this study provide a global insight into vinegar through a 1H NMR based compounds analysis that allows a holistic quality assessment and comparison of vinegars from different manufacture origins. [ABSTRACT FROM AUTHOR]

[Display omitted] • The method is the combination of peak-picking and binning for NMR based untargeted metabonomics studies. • The method takes peak top as bin center. • The method improves the following PCA and OPLS-DA analysis. In NMR-based untargeted metabolomic studies, 1H NMR spectra are usually divided into equal bins/buckets to diminish the effects of peak shift caused by sample status or instrument instability, and to reduce the number of variables used as input for the multivariate statistical analysis. It was noticed that the peaks near bin boundaries may cause significant changes in integral values of adjacent bins, and the weaker peak may be obscured if it is allocated in the same bin with intense peaks. Several efforts have been taken to improve the performance of binning. Here we propose an alternative method, named P-Bin, based on the combination of the classic peak-picking and binning procedures. The location of each peak defined by peak-picking is used as the center of the individual bin. P-Bin is expected to keep all spectral information associated with the peaks and significantly reduce the data size as the spectral regions without peaks are not considered. In addition, both peak-picking and binning are routine procedures, making P-Bin easy to be implemented. To verify the performance, two sets of experimental data from human plasma and Ganoderma lucidum (G. lucidum) extracts were processed using the conventional binning method and the proposed method, before the principal component analysis (PCA) and the orthogonal projection to latent structures discriminant analysis (OPLS-DA). The results indicate that the proposed method has improved both the clustering performance of PCA score plots and the interpretability of OPLS-DA loading plots, and P-Bin could be an improved version of data preparation for metabonomic study. [ABSTRACT FROM AUTHOR]