Honey Characterization and Adulteration Detection by Pattern Recognition Applied on HPAEC-PAD Profiles. 1. Honey Floral Species Characterization

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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.

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