4.7 Article

Fast determination of cations in honey by capillary electrophoresis: A possible method for geographic origin discrimination

Journal

TALANTA
Volume 99, Issue -, Pages 450-456

Publisher

ELSEVIER
DOI: 10.1016/j.talanta.2012.06.009

Keywords

Capillary electrophoresis; Cation determination; Honey; Peakmaster (R) software; Principal component analysis

Funding

  1. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)
  2. Empresa de Pesquisa Agropecuaria e Extensao Rural de Santa Catarina (EPA-GRI)
  3. Fundacao de Apoio a Pesquisa Cientifica do Estado de Santa Catarina (FAPESC)

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This study reports the development and validation of a fast capillary electrophoresis method for cation determination in honey samples and the classification of honey by geographical origin using Principal Components Analysis (PCA). The background electrolyte (BGE) was optimized using the Peakmaster (R) software, which evaluates the tendency of the analytes to undergo electromigration dispersion and the BGE buffer capacity and conductivity. The final BGE composition was defined as 30 mmol L-1 imidazole, 300 mmol L-1 acetic acid and 140 mmol L-1 Lactic acid, at pH 3,0, and the separation of K+, Na+, Ca2+, Mg2+ and Mn2+ using Ba2+ as the internal standard was achieved in less than 2 min. The method showed satisfactory results in terms of linearity (R-2 > 0.999), the detection limits ranged from 0.27-3.17 mg L-1 and the quantification limits ranged from 0.91-10.55 mg L-1. Precision measurements within 0.55 and 4.64%RSD were achieved and recovery values for the analytes in the honey samples ranged from 93.6%-108.6%. Forty honey samples were analyzed to test the proposed method. These samples were dissolved in deionized water and filtered before injection. The CE-UV reliability in the cation analysis in the real sample was compared statistically with ICP-MS methodology. No significant differences were found, with a 95% confidence interval between the methodologies. The PCA showed that the cumulative variance for the first two principal components explain more than 85% of the variability of the data. The analytical data suggest a significant influence of the geographical origin on the mineral composition. (C) 2012 Elsevier B.V. All rights reserved.

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