Journal
MICROCHEMICAL JOURNAL
Volume 142, Issue -, Pages 30-35Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.microc.2018.06.002
Keywords
Olive; Multivariate classification; ICP-OES; Chemometrics
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Funding
- Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) [PIF013/2017]
- Secretaria General de Ciencia y Tecnica of UNNE, Argentina
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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 well-known 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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