Classification of organic olives based on chemometric analysis of elemental data

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...

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Autores principales: Hidalgo, Melisa Jazmín, Pozzi, María T., Furlong, Octavio Javier, Marchevsky, Eduardo Jorge, Pellerano, Roberto Gerardo
Formato: Artículo
Lenguaje:Inglés
Publicado: Elsevier 2021
Materias:
Olive
Multivariate classification
ICP-OES
Chemometrics
Acceso en línea:http://repositorio.unne.edu.ar/handle/123456789/27961
Aporte de:
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste
id I48-R184-123456789-27961
record_format dspace
spelling I48-R184-123456789-279612025-03-06T11:08:30Z Classification of organic olives based on chemometric analysis of elemental data Hidalgo, Melisa Jazmín Pozzi, María T. Furlong, Octavio Javier Marchevsky, Eduardo Jorge Pellerano, Roberto Gerardo Olive Multivariate classification ICP-OES Chemometrics 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. 2021-05-20T17:28:35Z 2021-05-20T17:28:35Z 2018 Artículo 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. 0026-265X http://repositorio.unne.edu.ar/handle/123456789/27961 eng openAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf application/pdf Elsevier Microchemical Journal, 2018, vol. 142, p. 30-35.
institution Universidad Nacional del Nordeste
institution_str I-48
repository_str R-184
collection RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
language Inglés
topic Olive
Multivariate classification
ICP-OES
Chemometrics
spellingShingle Olive
Multivariate classification
ICP-OES
Chemometrics
Hidalgo, Melisa Jazmín
Pozzi, María T.
Furlong, Octavio Javier
Marchevsky, Eduardo Jorge
Pellerano, Roberto Gerardo
Classification of organic olives based on chemometric analysis of elemental data
topic_facet Olive
Multivariate classification
ICP-OES
Chemometrics
description 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.
format Artículo
author Hidalgo, Melisa Jazmín
Pozzi, María T.
Furlong, Octavio Javier
Marchevsky, Eduardo Jorge
Pellerano, Roberto Gerardo
author_facet Hidalgo, Melisa Jazmín
Pozzi, María T.
Furlong, Octavio Javier
Marchevsky, Eduardo Jorge
Pellerano, Roberto Gerardo
author_sort Hidalgo, Melisa Jazmín
title Classification of organic olives based on chemometric analysis of elemental data
title_short Classification of organic olives based on chemometric analysis of elemental data
title_full Classification of organic olives based on chemometric analysis of elemental data
title_fullStr Classification of organic olives based on chemometric analysis of elemental data
title_full_unstemmed Classification of organic olives based on chemometric analysis of elemental data
title_sort classification of organic olives based on chemometric analysis of elemental data
publisher Elsevier
publishDate 2021
url http://repositorio.unne.edu.ar/handle/123456789/27961
work_keys_str_mv AT hidalgomelisajazmin classificationoforganicolivesbasedonchemometricanalysisofelementaldata
AT pozzimariat classificationoforganicolivesbasedonchemometricanalysisofelementaldata
AT furlongoctaviojavier classificationoforganicolivesbasedonchemometricanalysisofelementaldata
AT marchevskyeduardojorge classificationoforganicolivesbasedonchemometricanalysisofelementaldata
AT pelleranorobertogerardo classificationoforganicolivesbasedonchemometricanalysisofelementaldata
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