Synthesis and Total 1H- and 13C-NMR Assignment of Cephem Derivatives for Use in ADEPT Approaches

Blau, Lorena; Menegon, Renato Farina; Ferreira, Elizabeth Igne; Ferreira, Antônio Gilberto; Boffo, Elisangela Fabiana; Tavares, Leila Aley; Heleno, Vladimir Constantino Gomes; Chin, Chung Man

doi:10.3390/molecules13040841

Cited by 6 publications

(1 citation statement)

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“…NMR is a powerful technique used to obtain structural information (Blau et al, 2008;Valente et al, 2008), and therefore it can help to understand the structure of components in complex systems such as food (Cazor, Deborde, Moing, Rolin, & This, 2006). The 1 H NMR spectroscopy can also be considered a fingerprinting technique (Bertram et al, 2005).…”

Section: Introductionmentioning

confidence: 98%

Identification of components of Brazilian honey by 1H NMR and classification of its botanical origin by chemometric methods

Boffo

Tavares

Tobias

et al. 2012

LWT

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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.

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Section: Introductionmentioning

confidence: 98%