1H nuclear magnetic resonance K-nearest neighbor Soft independent modeling of class analogies Partial least squares e discriminant analysis a b s t r a c tThe potential of NMR spectroscopy to differentiate honeys concerning to the nectar employed in its production was evaluated. The application of chemometric methods to 1 H NMR spectra has allowed to discriminate the honeys produced in the state of São Paulo, being identified the signals of responsible substances for the discrimination. Application of PCA and HCA methods to 1 H NMR data have resulted in the natural clustering of the samples. Wildflower honeys were characterized by higher concentration of phenylalanine and tyrosine. Citrus honeys showed higher amounts of sucrose than other compounds, while eucalyptus honeys had higher amount of lactic acid than the others. Assa-peixe honeys showed spectra similar to eucalyptus and citrus. Sugar-cane honeys showed some signals similar to eucalyptus and citrus honeys, but also showed the tyrosine and phenylalanine signals. Adulterated honeys showed 5-hydroxymethylfurfural, citric acid and ethanol signals. KNN, SIMCA and PLS-DA methods were used to build predictive models for honey classification. In the commercial honeys prediction KNN, SIMCA and PLS-DA models correctly classified 66.7; 22.2 and 72.2% of the samples, respectively.

beta-Glucans of Agaricus brasiliensis fruiting bodies in different stages of maturity were isolated and characterized by FTIR and NMR. These fractions had greater amount of (1-->6)-beta-glucan and the (1-->3)-beta-glucan increased with fruiting bodies maturation. Yields of beta-glucans increased from 42 mg beta-glucans g(-1) fruiting bodies (dry wt) in immature stage to 43 mg g(-1) in mature stage with immature spores, and decreased to 40 mg g(-1) in mature stage with spore maturation. Mature fruiting bodies, which included these glucans, have potential therapeutical benefits for use in nutraceutical products.

1 H NMR SIMCA KNN PLS-DA a b s t r a c tThis work describes using 1 H NMR data and pattern recognition analysis to classify vinegars. Vinegar authenticity is linked to raw ingredient source and manufacturing conditions. Application of PCA and HCA methods resulted in the natural clustering of the samples according to the raw material used. Wine vinegars were characterized by a high concentration of ethyl acetate, glycerol, methanol and tartaric acid, while glycerol and ethyl acetate signals were not visible in alcohol/agrin vinegars. Apple vinegars showed to be richer in alanine. The KNN, SIMCA and PLS-DA methods were used to build predictive models for classification of vinegar type wine, apple and alcohol/agrin (27 samples -22 as training set). The models were tested using an independent set (5 samples), no samples were wrongly classified. Validated models were used to predict the class of 21 commercial samples, which, as expected, were correctly classified. Eight commercial vinegars (honey, orange, pineapple and rice) were discriminated from these samples using PCA method. Honey vinegars did not present ethanol signals and pineapple vinegars presented the largest amount of tartaric acid. Rice and orange vinegars are richer in lactic acid and did not present the methanol signal. Alanine signals were not visible in orange vinegars.

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the use of hydrogen nuclear magnetic resonance (1H NMR) with a statistical approach for identifying gasoline adulteration by organic solvents is described. Both principal component analysis (PCA) and hierarchical cluster analysis (HCA) from NMR data of 47 commercial samples allowed the distinction between conform and nonconform samples. The 1H NMR−PCA and 1H NMR−HCA models were evaluated through the analyses of 21 intentionally adulterated samples, which showed a tendency to meet in the nonconform group with the increase of the solvent concentration.