Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy

Autores
Pérez Rodríguez, Michael; Mendoza, Alberto; González, Lucy T.; Lima Vieira, Alan; Pellerano, Roberto Gerardo; Gomes Neto, José Anchieta; Ferreira, Edilene Cristina
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice’s value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample’s elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92–100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.
Fil: Pérez Rodríguez, Michael. Instituto Tecnologico de Monterrey.; México. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Mendoza, Alberto. Instituto Tecnologico de Monterrey.; México
Fil: González, Lucy T.. Instituto Tecnologico de Monterrey.; México
Fil: Lima Vieira, Alan. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil
Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Gomes Neto, José Anchieta. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil
Fil: Ferreira, Edilene Cristina. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil
Materia
RICE
GRAIN QUALITY
LIBS
SPECTRAL PROCESSING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/214011
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown SpectroscopyPérez Rodríguez, MichaelMendoza, AlbertoGonzález, Lucy T.Lima Vieira, AlanPellerano, Roberto GerardoGomes Neto, José AnchietaFerreira, Edilene CristinaRICEGRAIN QUALITYLIBSSPECTRAL PROCESSINGhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice’s value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample’s elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92–100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.Fil: Pérez Rodríguez, Michael. Instituto Tecnologico de Monterrey.; México. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Mendoza, Alberto. Instituto Tecnologico de Monterrey.; MéxicoFil: González, Lucy T.. Instituto Tecnologico de Monterrey.; MéxicoFil: Lima Vieira, Alan. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Gomes Neto, José Anchieta. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Ferreira, Edilene Cristina. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilMDPI2023-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/214011Pérez Rodríguez, Michael; Mendoza, Alberto; González, Lucy T.; Lima Vieira, Alan; Pellerano, Roberto Gerardo; et al.; Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy; MDPI; Foods; 12; 2; 1-2023; 365-3732304-8158CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2304-8158/12/2/365info:eu-repo/semantics/altIdentifier/doi/10.3390/foods12020365info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:14:08Zoai:ri.conicet.gov.ar:11336/214011instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:14:08.85CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
title Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
spellingShingle Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
Pérez Rodríguez, Michael
RICE
GRAIN QUALITY
LIBS
SPECTRAL PROCESSING
title_short Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
title_full Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
title_fullStr Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
title_full_unstemmed Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
title_sort Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy
dc.creator.none.fl_str_mv Pérez Rodríguez, Michael
Mendoza, Alberto
González, Lucy T.
Lima Vieira, Alan
Pellerano, Roberto Gerardo
Gomes Neto, José Anchieta
Ferreira, Edilene Cristina
author Pérez Rodríguez, Michael
author_facet Pérez Rodríguez, Michael
Mendoza, Alberto
González, Lucy T.
Lima Vieira, Alan
Pellerano, Roberto Gerardo
Gomes Neto, José Anchieta
Ferreira, Edilene Cristina
author_role author
author2 Mendoza, Alberto
González, Lucy T.
Lima Vieira, Alan
Pellerano, Roberto Gerardo
Gomes Neto, José Anchieta
Ferreira, Edilene Cristina
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv RICE
GRAIN QUALITY
LIBS
SPECTRAL PROCESSING
topic RICE
GRAIN QUALITY
LIBS
SPECTRAL PROCESSING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice’s value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample’s elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92–100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.
Fil: Pérez Rodríguez, Michael. Instituto Tecnologico de Monterrey.; México. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Mendoza, Alberto. Instituto Tecnologico de Monterrey.; México
Fil: González, Lucy T.. Instituto Tecnologico de Monterrey.; México
Fil: Lima Vieira, Alan. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil
Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Gomes Neto, José Anchieta. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil
Fil: Ferreira, Edilene Cristina. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil
description Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice’s value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample’s elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92–100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.
publishDate 2023
dc.date.none.fl_str_mv 2023-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/214011
Pérez Rodríguez, Michael; Mendoza, Alberto; González, Lucy T.; Lima Vieira, Alan; Pellerano, Roberto Gerardo; et al.; Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy; MDPI; Foods; 12; 2; 1-2023; 365-373
2304-8158
CONICET Digital
CONICET
url http://hdl.handle.net/11336/214011
identifier_str_mv Pérez Rodríguez, Michael; Mendoza, Alberto; González, Lucy T.; Lima Vieira, Alan; Pellerano, Roberto Gerardo; et al.; Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy; MDPI; Foods; 12; 2; 1-2023; 365-373
2304-8158
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2304-8158/12/2/365
info:eu-repo/semantics/altIdentifier/doi/10.3390/foods12020365
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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