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Geographical characterisation of honeys according to their mineral content and antioxidant activity using a chemometric approach
Author(s) -
Pasquini Benedetta,
Goodarzi Mohammad,
Orlandini Serena,
Beretta Giangiacomo,
Furlanetto Sandra,
Dejaegher Bieke
Publication year - 2014
Publication title -
international journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.831
H-Index - 96
eISSN - 1365-2621
pISSN - 0950-5423
DOI - 10.1111/ijfs.12436
Subject(s) - principal component analysis , linear discriminant analysis , quadratic classifier , mathematics , statistics , pattern recognition (psychology) , artificial intelligence , linear regression , regression analysis , computer science , support vector machine
Summary 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 (%CCR TEST = 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.