Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laserinduced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work...

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Autores principales:	Pérez Rodríguez, Michael, Dirchwolf, Pamela Maia, Silva, Tiago Varão, Villafañe, Roxana Noelia, Gómez Neto, José Anchieta, Pellerano, Roberto Gerardo, Ferreira, Edilene Cristina
Formato:	Artículo
Lenguaje:	Inglés
Publicado:	Elsevier 2021
Materias:	Food authenticity Pdo Brown rice Sd-Libs Pattern recognition
Acceso en línea:	http://repositorio.unne.edu.ar/handle/123456789/27982
Aporte de:	RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste

id	I48-R184-123456789-27982
record_format	dspace
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	Food authenticity Pdo Brown rice Sd-Libs Pattern recognition
spellingShingle	Food authenticity Pdo Brown rice Sd-Libs Pattern recognition Pérez Rodríguez, Michael Dirchwolf, Pamela Maia Silva, Tiago Varão Villafañe, Roxana Noelia Gómez Neto, José Anchieta Pellerano, Roberto Gerardo Ferreira, Edilene Cristina Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
topic_facet	Food authenticity Pdo Brown rice Sd-Libs Pattern recognition
description	Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laserinduced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.
format	Artículo
author	Pérez Rodríguez, Michael Dirchwolf, Pamela Maia Silva, Tiago Varão Villafañe, Roxana Noelia Gómez Neto, José Anchieta Pellerano, Roberto Gerardo Ferreira, Edilene Cristina
author_facet	Pérez Rodríguez, Michael Dirchwolf, Pamela Maia Silva, Tiago Varão Villafañe, Roxana Noelia Gómez Neto, José Anchieta Pellerano, Roberto Gerardo Ferreira, Edilene Cristina
author_sort	Pérez Rodríguez, Michael
title	Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
title_short	Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
title_full	Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
title_fullStr	Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
title_full_unstemmed	Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
title_sort	brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy
publisher	Elsevier
publishDate	2021
url	http://repositorio.unne.edu.ar/handle/123456789/27982
work_keys_str_mv	AT perezrodriguezmichael brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy AT dirchwolfpamelamaia brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy AT silvatiagovarao brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy AT villafaneroxananoelia brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy AT gomeznetojoseanchieta brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy AT pelleranorobertogerardo brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy AT ferreiraedilenecristina brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy
bdutipo_str	Repositorios
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Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy

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