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

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorTecnol Monterrey-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniv Nacl Nordeste-
Autor(es): dc.creatorPerez-Rodriguez, Michael-
Autor(es): dc.creatorMendoza, Alberto-
Autor(es): dc.creatorGonzalez, Lucy T.-
Autor(es): dc.creatorLima Vieira, Alan-
Autor(es): dc.creatorPellerano, Roberto Gerardo-
Autor(es): dc.creatorGomes Neto, Jose Anchieta-
Autor(es): dc.creatorFerreira, Edilene Cristina-
Data de aceite: dc.date.accessioned2025-08-21T16:40:57Z-
Data de disponibilização: dc.date.available2025-08-21T16:40:57Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/foods12020365-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/245796-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/245796-
Descrição: dc.descriptionRice 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Cient�fico e Tecnol�gico (CNPq)-
Descrição: dc.descriptionTecnologico de Monterrey-
Descrição: dc.descriptionTecnol Monterrey, Escuela Ingn & Ciencias, Ave Eugenio Garza Sada 2501, Monterrey 64849, NL, Mexico-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Chem, R Prof Francisco Degni 55, BR-14800900 Araraquara, SP, Brazil-
Descrição: dc.descriptionUniv Nacl Nordeste, Fac Ciencias Exactas & Nat & Agrimensura, Consejo Nacl Invest Cient & Tecn CONICET, Inst Quim Bas & Aplicada Nordeste Argentino IQUIBA, Ave Libertad 5470, RA-3400 Corrientes, Argentina-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Chem, R Prof Francisco Degni 55, BR-14800900 Araraquara, SP, Brazil-
Descrição: dc.descriptionCNPq: 304026/2021-2-
Formato: dc.format9-
Idioma: dc.languageen-
Publicador: dc.publisherMdpi-
Relação: dc.relationFoods-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectrice-
Palavras-chave: dc.subjectgrain quality features-
Palavras-chave: dc.subjectLIBS-
Palavras-chave: dc.subjectspectral processing-
Palavras-chave: dc.subjectk-nearest neighbors-
Título: dc.titleRice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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