Classification of organic olives based on chemometric analysis of elemental data
Fecha
2018Autor
Hidalgo, Melisa Jazmín
Pozzi, María T.
Furlong, Octavio J.
Marchevsky, Eduardo Jorge
Pellerano, Roberto Gerardo
Metadatos
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The aim of this study was to discriminate organic from conventional olive samples based on the levels of macro
and trace elements, combined with chemometric techniques. Ten elements (Na, K, Ca, Fe, Mg, Cu, Zn, Se, S and
P) were determined in organic (n=30) and conventional (n=30) olive samples by inductively coupled plasma
optical emission spectrometry analysis (ICP-OES). The classification of samples was performed by using a wellknown
chemometric techniques, linear discriminant analysis (LDA), partial least square-discriminant analysis
(PLS-DA), support vector machine-discriminant analysis (SVM-DA), k-nearest neighbors (k-NN) and random
forest (RF). The k-NN technique showed the best performance in discriminating organic from conventional
samples (Accuracy: 94%) using all chemical variables. After variable reduction, an accuracy of 83% was found
by using only the elements K and P. The use of a fingerprint based on multielemental levels associated with
classification chemometric techniques may be used as a simple method to authenticate organic olive samples.
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