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
spelling I48-R184-123456789-279822025-03-06T10:57:05Z Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy Pérez Rodríguez, Michael Dirchwolf, Pamela Maia Silva, Tiago Varão Villafañe, Roxana Noelia Gómez Neto, José Anchieta Pellerano, Roberto Gerardo Ferreira, Edilene Cristina Food authenticity Pdo Brown rice Sd-Libs Pattern recognition 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. 2021-05-26T22:10:29Z 2021-05-26T22:10:29Z 2019-06-08 Artículo Pérez Rodríguez, Michael, et. al., 2019. Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy. Food Chemistry. Países Bajos, Ámsterdam: Elsevier, vol. 297, p. 1-6. ISSN 0308-8146. 0308-8146 http://repositorio.unne.edu.ar/handle/123456789/27982 eng openAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf application/pdf Elsevier Food Chemistry, 2019, vol. 297, p. 1-6.
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
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AT gomeznetojoseanchieta brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy
AT pelleranorobertogerardo brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy
AT ferreiraedilenecristina brownriceauthenticityevaluationbysparkdischargelaserinducedbreakdownspectroscopy
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