Geographical characterisation of honeys according to their mineral content and antioxidant activity using a chemometric approach

Benedetta Pasquini, Mohammad Goodarzi, Serena Orlandini, Giangiacomo Beretta, Sandra Furlanetto, Bieke Dejaegher

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

In this article, discrimination models are presented, relating the origin of honey samples to several variables, being the concentrations of different cations and anions in the honey samples measured by ion chromatography, and parameters that measure/reflect the antioxidant activity of the honey samples. The unsupervised method, principal component analysis, and supervised discrimination methods, such as linear and quadratic discriminant analysis, and classification and regression trees (CART), were applied to evaluate the existence of data patterns and the relationship between geographical origin and the measured parameters. The model with the best predictive ability (%CCRTEST = 66.67%), the best overall % specificity (80%) and the best overall % sensitivity (67%) was found to be CART. It was proven that the mineral content and parameters analysed can provide enough information for the geographical characterisation and discrimination of honey.

Original languageEnglish (US)
Pages (from-to)1351-1359
Number of pages9
JournalInternational Journal of Food Science and Technology
Volume49
Issue number5
DOIs
StatePublished - May 2014

Keywords

  • Antioxidant activity
  • Classification and regression trees
  • Honey
  • Linear discriminant analysis
  • Mineral content
  • Principal component analysis
  • Quadratic discriminant analysis

ASJC Scopus subject areas

  • Food Science
  • Industrial and Manufacturing Engineering

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