Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil

Detalhes bibliográficos
Autor(a) principal: Batista, Milana Aboboreira Simões
Data de Publicação: 2020
Outros Autores: Santos, Luana Novaes, Chagas, Bruna Cirineu, Lôbo, Ivon Pinheiro, Novaes, Cleber Galvão, Guedes, Wesley Nascimento [UNESP], De Jesus, Raildo Mota, Amorim, Fábio Alan Carqueija, Pacheco, Clissiane Soares Viana, Moreira, Luana Santos, Da Silva, Erik Galvão Paranhos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1039/d0ay00799d
http://hdl.handle.net/11449/199273
Resumo: Fish are important sources of protein, making them very significant in the human diet. Although the consumption of this food is beneficial for health, it is essential that the product does not contain inorganic components above the limits recommended by the current legislation. Therefore, a method for determination of elements in fish (Mugil cephalus) samples was optimized. A simplex centroid mixture design with restriction was applied for optimization of the acid digestion of samples in an open system under reflux in order to evaluate the best ratio between the reagents HNO3, H2O2 and H2O. The results indicated that more intense analyte signals were obtained when a mixture containing 3.6 mL of HNO3 (65% v/v), 0.4 mL of H2O2 (30% v/v) and 6.0 mL of H2O was used. The accuracy of the method was assessed with a CRM of oyster tissue (NIST 1566b). The method presented relative standard deviations (RSDs) of 3.54%; 3.82%; 4.81% and 3.50% for Zn, Fe, Cu and S, respectively. The detection limits were 0.002 mg kg-1 for Cu and Zn and 0.02 mg kg-1 for Fe and S. The proposed method was applied for the determination of Zn, Fe, Cu and S in fish samples. A Kohonen Self-Organizing Map (KSOM) with K-means implementation was applied to better delimit the boundary between groups and the spatial and temporal influence on how concentrations of the chemical elements were perceived. To verify the separation, the Davies-Bouldin and Silhouette indices were used, obtaining 0.5374 and 0.8541, respectively, indicating satisfactory separation.
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spelling Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, BrazilFish are important sources of protein, making them very significant in the human diet. Although the consumption of this food is beneficial for health, it is essential that the product does not contain inorganic components above the limits recommended by the current legislation. Therefore, a method for determination of elements in fish (Mugil cephalus) samples was optimized. A simplex centroid mixture design with restriction was applied for optimization of the acid digestion of samples in an open system under reflux in order to evaluate the best ratio between the reagents HNO3, H2O2 and H2O. The results indicated that more intense analyte signals were obtained when a mixture containing 3.6 mL of HNO3 (65% v/v), 0.4 mL of H2O2 (30% v/v) and 6.0 mL of H2O was used. The accuracy of the method was assessed with a CRM of oyster tissue (NIST 1566b). The method presented relative standard deviations (RSDs) of 3.54%; 3.82%; 4.81% and 3.50% for Zn, Fe, Cu and S, respectively. The detection limits were 0.002 mg kg-1 for Cu and Zn and 0.02 mg kg-1 for Fe and S. The proposed method was applied for the determination of Zn, Fe, Cu and S in fish samples. A Kohonen Self-Organizing Map (KSOM) with K-means implementation was applied to better delimit the boundary between groups and the spatial and temporal influence on how concentrations of the chemical elements were perceived. To verify the separation, the Davies-Bouldin and Silhouette indices were used, obtaining 0.5374 and 0.8541, respectively, indicating satisfactory separation.Departamento de Ciências Exatas e Tecnológicas Universidade Estadual de Santa Cruz Campus Soane Nazaré de Andrade, Km 16 BR-415Instituto de Química Universidade Federal da Bahia Campus Universitário de OndinaDepartamento de Química e Exatas Universidade Estadual Do Sudoeste da Bahia Campus Universitário de Jequié-BA Avenida José Moreira Sobrinho, 677-JequiezinhoInstitute of Chemistry São Paulo State University (UNESP)Institute of Chemistry São Paulo State University (UNESP)Universidade Estadual de Santa CruzUniversidade Federal da Bahia (UFBA)Universidade Estadual Do Sudoeste da BahiaUniversidade Estadual Paulista (Unesp)Batista, Milana Aboboreira SimõesSantos, Luana NovaesChagas, Bruna CirineuLôbo, Ivon PinheiroNovaes, Cleber GalvãoGuedes, Wesley Nascimento [UNESP]De Jesus, Raildo MotaAmorim, Fábio Alan CarqueijaPacheco, Clissiane Soares VianaMoreira, Luana SantosDa Silva, Erik Galvão Paranhos2020-12-12T01:35:19Z2020-12-12T01:35:19Z2020-08-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article3713-3721http://dx.doi.org/10.1039/d0ay00799dAnalytical Methods, v. 12, n. 29, p. 3713-3721, 2020.1759-96791759-9660http://hdl.handle.net/11449/19927310.1039/d0ay00799d2-s2.0-85089545331Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnalytical Methodsinfo:eu-repo/semantics/openAccess2021-10-23T06:37:33Zoai:repositorio.unesp.br:11449/199273Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T00:10:15.116352Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
title Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
spellingShingle Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
Batista, Milana Aboboreira Simões
title_short Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
title_full Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
title_fullStr Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
title_full_unstemmed Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
title_sort Artificial neural network employment for element determination in: Mugil cephalus by ICP OES in Pontal Bay, Brazil
author Batista, Milana Aboboreira Simões
author_facet Batista, Milana Aboboreira Simões
Santos, Luana Novaes
Chagas, Bruna Cirineu
Lôbo, Ivon Pinheiro
Novaes, Cleber Galvão
Guedes, Wesley Nascimento [UNESP]
De Jesus, Raildo Mota
Amorim, Fábio Alan Carqueija
Pacheco, Clissiane Soares Viana
Moreira, Luana Santos
Da Silva, Erik Galvão Paranhos
author_role author
author2 Santos, Luana Novaes
Chagas, Bruna Cirineu
Lôbo, Ivon Pinheiro
Novaes, Cleber Galvão
Guedes, Wesley Nascimento [UNESP]
De Jesus, Raildo Mota
Amorim, Fábio Alan Carqueija
Pacheco, Clissiane Soares Viana
Moreira, Luana Santos
Da Silva, Erik Galvão Paranhos
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual de Santa Cruz
Universidade Federal da Bahia (UFBA)
Universidade Estadual Do Sudoeste da Bahia
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Batista, Milana Aboboreira Simões
Santos, Luana Novaes
Chagas, Bruna Cirineu
Lôbo, Ivon Pinheiro
Novaes, Cleber Galvão
Guedes, Wesley Nascimento [UNESP]
De Jesus, Raildo Mota
Amorim, Fábio Alan Carqueija
Pacheco, Clissiane Soares Viana
Moreira, Luana Santos
Da Silva, Erik Galvão Paranhos
description Fish are important sources of protein, making them very significant in the human diet. Although the consumption of this food is beneficial for health, it is essential that the product does not contain inorganic components above the limits recommended by the current legislation. Therefore, a method for determination of elements in fish (Mugil cephalus) samples was optimized. A simplex centroid mixture design with restriction was applied for optimization of the acid digestion of samples in an open system under reflux in order to evaluate the best ratio between the reagents HNO3, H2O2 and H2O. The results indicated that more intense analyte signals were obtained when a mixture containing 3.6 mL of HNO3 (65% v/v), 0.4 mL of H2O2 (30% v/v) and 6.0 mL of H2O was used. The accuracy of the method was assessed with a CRM of oyster tissue (NIST 1566b). The method presented relative standard deviations (RSDs) of 3.54%; 3.82%; 4.81% and 3.50% for Zn, Fe, Cu and S, respectively. The detection limits were 0.002 mg kg-1 for Cu and Zn and 0.02 mg kg-1 for Fe and S. The proposed method was applied for the determination of Zn, Fe, Cu and S in fish samples. A Kohonen Self-Organizing Map (KSOM) with K-means implementation was applied to better delimit the boundary between groups and the spatial and temporal influence on how concentrations of the chemical elements were perceived. To verify the separation, the Davies-Bouldin and Silhouette indices were used, obtaining 0.5374 and 0.8541, respectively, indicating satisfactory separation.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T01:35:19Z
2020-12-12T01:35:19Z
2020-08-07
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1039/d0ay00799d
Analytical Methods, v. 12, n. 29, p. 3713-3721, 2020.
1759-9679
1759-9660
http://hdl.handle.net/11449/199273
10.1039/d0ay00799d
2-s2.0-85089545331
url http://dx.doi.org/10.1039/d0ay00799d
http://hdl.handle.net/11449/199273
identifier_str_mv Analytical Methods, v. 12, n. 29, p. 3713-3721, 2020.
1759-9679
1759-9660
10.1039/d0ay00799d
2-s2.0-85089545331
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Analytical Methods
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 3713-3721
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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