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Classification of organic olives based on chemometric analysis of elemental data
dc.contributor.author | Hidalgo, Melisa Jazmín | |
dc.contributor.author | Pozzi, María T. | |
dc.contributor.author | Furlong, Octavio J. | |
dc.contributor.author | Marchevsky, Eduardo Jorge | |
dc.contributor.author | Pellerano, Roberto Gerardo | |
dc.date.accessioned | 2021-05-20T17:28:35Z | |
dc.date.available | 2021-05-20T17:28:35Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Hidalgo, Melisa Jazmin., et al., 2018. Classification of organic olives based on chemometric analysis of elemental data. Microchemical Journal. Amsterdam: Elsevier, vol. 142, p. 30-35. ISSN 0026-265X. | es |
dc.identifier.issn | 0026-265X | es |
dc.identifier.uri | http://repositorio.unne.edu.ar/handle/123456789/27961 | |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.rights | openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ | es |
dc.source | Microchemical Journal, 2018, vol. 142, p. 30-35. | es |
dc.subject | Olive | es |
dc.subject | Multivariate classification | es |
dc.subject | ICP-OES | es |
dc.subject | Chemometrics | es |
dc.title | Classification of organic olives based on chemometric analysis of elemental data | es |
dc.type | Artículo | es |
unne.affiliation | Fil: Hidalgo, Melisa Jazmín. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. | es |
unne.affiliation | Fil: Hidalgo, Melisa Jazmín. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina. | es |
unne.affiliation | Fil: Pozzi, María T. Universidad Nacional del Catamarca. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. | es |
unne.affiliation | Fil: Furlong, Octavio J. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Aplicada; Argentina. | es |
unne.affiliation | Fil: Marchevsky, Eduardo J. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química San Luis; Argentina. | es |
unne.affiliation | Fil: Pellerano, Roberto Gerardo. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentina. | es |
unne.affiliation | Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina. | es |
unne.journal.pais | Países Bajos | es |
unne.journal.ciudad | Amsterdam | es |
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