Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination

A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were...

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Autores principales: Pérez Rodríguez, Michael, Dirchwolf, Pamela Maia, Varão Silva, Tiago, Lima Vieira, Alan, Gomes Neto, José Anchieta, Pellerano, Roberto Gerardo, Ferreira, Edilene Cristina
Formato: Artículo
Lenguaje:Inglés
Publicado: Elsevier 2025
Materias:
Rice
Botanical origin
SD-LIBS
Support vector machine
Acceso en línea:http://repositorio.unne.edu.ar/handle/123456789/59135
Aporte de:
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste
id I48-R184-123456789-59135
record_format dspace
spelling I48-R184-123456789-591352025-12-09T18:19:54Z Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination Pérez Rodríguez, Michael Dirchwolf, Pamela Maia Varão Silva, Tiago Lima Vieira, Alan Gomes Neto, José Anchieta Pellerano, Roberto Gerardo Ferreira, Edilene Cristina Rice Botanical origin SD-LIBS Support vector machine A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92–100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste. 2025-12-05T11:11:38Z 2025-12-05T11:11:38Z 2020-11-30 Artículo Pérez Rodríguez, Michael, et al., 2020. Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination. Food Chemistry. Ámsterdam: Países Bajos, vol. 331, p. 1-5. E-ISSN 2772-753X. DOI https://doi.org/10.1016/j.foodchem.2020.127051 http://repositorio.unne.edu.ar/handle/123456789/59135 en https://doi.org/10.1016/j.foodchem.2020.127051 restrictedAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf p. 1-5 application/pdf Elsevier Food Chemistry, 2020, vol. 331, p. 1-5.
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 Rice
Botanical origin
SD-LIBS
Support vector machine
spellingShingle Rice
Botanical origin
SD-LIBS
Support vector machine
Pérez Rodríguez, Michael
Dirchwolf, Pamela Maia
Varão Silva, Tiago
Lima Vieira, Alan
Gomes Neto, José Anchieta
Pellerano, Roberto Gerardo
Ferreira, Edilene Cristina
Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination
topic_facet Rice
Botanical origin
SD-LIBS
Support vector machine
description A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92–100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.
format Artículo
author Pérez Rodríguez, Michael
Dirchwolf, Pamela Maia
Varão Silva, Tiago
Lima Vieira, Alan
Gomes Neto, José Anchieta
Pellerano, Roberto Gerardo
Ferreira, Edilene Cristina
author_facet Pérez Rodríguez, Michael
Dirchwolf, Pamela Maia
Varão Silva, Tiago
Lima Vieira, Alan
Gomes Neto, José Anchieta
Pellerano, Roberto Gerardo
Ferreira, Edilene Cristina
author_sort Pérez Rodríguez, Michael
title Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination
title_short Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination
title_full Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination
title_fullStr Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination
title_full_unstemmed Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination
title_sort fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination
publisher Elsevier
publishDate 2025
url http://repositorio.unne.edu.ar/handle/123456789/59135
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AT ferreiraedilenecristina fastsparkdischargelaserinducedbreakdownspectroscopymethodforricebotanicorigindetermination
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