ABSTRACT
Chemical study of three medicinal plants: from leaves of Piper renitens (Miq.) Yunck, Piperaceae, and Siparuna guianensis Aubl., Siparunaceae, and from flowers of Alternanthera brasiliana (L.) Kuntze, Amaranthaceae, resulted in isolation of nine compounds: three steroids, β-sitosterol, stigmasterol from P. renitens and sitosterol-3-O-β-D-glucopyranoside from A. brasiliana, the diterpene kaurane ent-kauran-16α,17-diol from P. renitens, two derivatives kaempferol-methylether, kumatakenine (kaempferol-3,7-dimethylether) and kaempferol-3,7,3'-trimethylether from S. guianensis and three flavones, crysoeriol (5,7,4'-trihydroxy-3'-methoxyflavone), tricin (5,7,4'-trihydroxy-3',5'-dimethoxyflavone) and 7-O-β-D-glucopyranoside-5,4'-dihydroxy-3'-methoxyflavone from A. brasiliana. Compounds structures were determinate using 1D and 2D ¹H NMR and 13C spectral data, mass and IR spectra, comparing with literature data.
ABSTRACT
In this article a simple method for automated pretreatment of chromatograms is presented. The resulting data matrix can be used as input for multivariate statistical analysis. Application of this method to high-performance anion-exchange chromatography-pulsed amperometric detection (HPAEC-PAD) chromatograms of honeys before canonical discriminant analysis results in very good performance in the data reduction with regard to the preservation of the original information content of the data. This pretreatment of the chromatogram allows for the use of all the peaks corresponding to the sugars present in the sample. This results in a high-quality discrimination between honeys of various types. A versatile program has been developed to apply this method. This serves as a starting point for software suited for food characterization and adulteration detection by semi-automatic pattern recognition applied to chromatographic analysis.
ABSTRACT
An improved COFRAC (COmité FRançais d'ACréditation) method for the analysis and evaluation of the quality of honey by high-performance anion-exchange chromatography of sugar profiles is proposed. With this method, both minor and major sugars are simultaneously analyzed and the technique is integrated in a new chemometric approach, which uses the entire chromatographic sugars profile of each analyzed sample to characterize honey floral species. Sixty-eight authentic honey samples (6 varieties) were analyzed by high-performance anion-exchange chromatography-pulsed amperometric detection. A new algorithm was developed to create automatically the corresponding normalized data matrix, ready-to-use in various chemometric procedures. This algorithm transforms the analytical profiles to produce the corresponding calibrated table of the surfaces or intensities according to retention times of peaks. The possibility of taking into account unknown peaks (those for which no standards are available) allows the maximum chemical information provided by the chromatograms to be retained. The parallel application of principal component analysis (PCA)/linear discriminant analysis (LDA) and artificial neural networks (ANN) shows a high capability in the classification of the analyzed samples (LDA, 93%; ANN, 100%) and a very good discrimination of honey groups. This work is the starting point of the elaboration of a new system designed for the automatic pattern recognition of food samples (first application on honey samples) from chromatographic analyses for food characterization and adulteration detection.
