Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)

Autores
Gonçalves Dias Diniz, Paulo Henrique; Pistonesi, Marcelo Fabian; Álvarez, Mónica Beatriz; Fernández Band, Beatriz Susana; Ugulino de Araújo, Mário César
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this study, several possible approaches for simultaneous discrimination of teas based on a linear discriminant analysis with variables selected by the successive projections algorithm (SPA-LDA), featuring selection from the chemical composition according to variety (black or green tea) and geographical origin (Argentina or Sri Lanka), are explored. Chemical composition (moisture, ash, caffeine, fluoride, polyphenols, and 15 elements from both tea leaves and infusions) was used as input data for identification of the differentiating characteristics of tea samples. Thus, a strategy that allows tea discrimination using a reduced number of chemical parameters was developed. SIMCA (softindependent modeling of class analogy) and PLS-DA (partial least squares-discriminant analysis) were used along with SPA-LDA for comparison. The elemental fingerprint (chemical signature) can be used for identifying the variety and origin of the tea, and SPA-LDA provided the most successful result (100% correct classification), despite having selected just three chemical parameters (namely K, Al, and Mg). The result is extremely positive from the viewpoint of chemical analyses, because quantifications made using fewer elements naturally provide simpler, faster and less expensive methods.
Fil: Gonçalves Dias Diniz, Paulo Henrique. Universidade Estadual Da Paraí­ba; Brasil
Fil: Pistonesi, Marcelo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Álvarez, Mónica Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Fernández Band, Beatriz Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Ugulino de Araújo, Mário César. Universidade Federal Da Paraíba; Brasil
Materia
Camellia Sinensis
Tea Leaves
Tea Infusions
Food Composition
Food Analysis
Elemental Fingerprint
Classification
Spa-Lda Feature Selection
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:11336/40474
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/40474
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)Gonçalves Dias Diniz, Paulo HenriquePistonesi, Marcelo FabianÁlvarez, Mónica BeatrizFernández Band, Beatriz SusanaUgulino de Araújo, Mário CésarCamellia SinensisTea LeavesTea InfusionsFood CompositionFood AnalysisElemental FingerprintClassificationSpa-Lda Feature Selectionhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In this study, several possible approaches for simultaneous discrimination of teas based on a linear discriminant analysis with variables selected by the successive projections algorithm (SPA-LDA), featuring selection from the chemical composition according to variety (black or green tea) and geographical origin (Argentina or Sri Lanka), are explored. Chemical composition (moisture, ash, caffeine, fluoride, polyphenols, and 15 elements from both tea leaves and infusions) was used as input data for identification of the differentiating characteristics of tea samples. Thus, a strategy that allows tea discrimination using a reduced number of chemical parameters was developed. SIMCA (softindependent modeling of class analogy) and PLS-DA (partial least squares-discriminant analysis) were used along with SPA-LDA for comparison. The elemental fingerprint (chemical signature) can be used for identifying the variety and origin of the tea, and SPA-LDA provided the most successful result (100% correct classification), despite having selected just three chemical parameters (namely K, Al, and Mg). The result is extremely positive from the viewpoint of chemical analyses, because quantifications made using fewer elements naturally provide simpler, faster and less expensive methods.Fil: Gonçalves Dias Diniz, Paulo Henrique. Universidade Estadual Da Paraí­ba; BrasilFil: Pistonesi, Marcelo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Álvarez, Mónica Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Fernández Band, Beatriz Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Ugulino de Araújo, Mário César. Universidade Federal Da Paraíba; BrasilAcademic Press Inc Elsevier Science2015-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/40474Gonçalves Dias Diniz, Paulo Henrique; Pistonesi, Marcelo Fabian; Álvarez, Mónica Beatriz; Fernández Band, Beatriz Susana; Ugulino de Araújo, Mário César; Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA); Academic Press Inc Elsevier Science; Journal Of Food Composition And Analysis; 39; 3-2015; 103-1100889-1575CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jfca.2014.11.012info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0889157514002130info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:15:11Zoai:ri.conicet.gov.ar:11336/40474instacron: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:34982025-10-15 15:15:11.627CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
title Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
spellingShingle Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
Gonçalves Dias Diniz, Paulo Henrique
Camellia Sinensis
Tea Leaves
Tea Infusions
Food Composition
Food Analysis
Elemental Fingerprint
Classification
Spa-Lda Feature Selection
title_short Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
title_full Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
title_fullStr Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
title_full_unstemmed Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
title_sort Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)
dc.creator.none.fl_str_mv Gonçalves Dias Diniz, Paulo Henrique
Pistonesi, Marcelo Fabian
Álvarez, Mónica Beatriz
Fernández Band, Beatriz Susana
Ugulino de Araújo, Mário César
author Gonçalves Dias Diniz, Paulo Henrique
author_facet Gonçalves Dias Diniz, Paulo Henrique
Pistonesi, Marcelo Fabian
Álvarez, Mónica Beatriz
Fernández Band, Beatriz Susana
Ugulino de Araújo, Mário César
author_role author
author2 Pistonesi, Marcelo Fabian
Álvarez, Mónica Beatriz
Fernández Band, Beatriz Susana
Ugulino de Araújo, Mário César
author2_role author
author
author
author
dc.subject.none.fl_str_mv Camellia Sinensis
Tea Leaves
Tea Infusions
Food Composition
Food Analysis
Elemental Fingerprint
Classification
Spa-Lda Feature Selection
topic Camellia Sinensis
Tea Leaves
Tea Infusions
Food Composition
Food Analysis
Elemental Fingerprint
Classification
Spa-Lda Feature Selection
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this study, several possible approaches for simultaneous discrimination of teas based on a linear discriminant analysis with variables selected by the successive projections algorithm (SPA-LDA), featuring selection from the chemical composition according to variety (black or green tea) and geographical origin (Argentina or Sri Lanka), are explored. Chemical composition (moisture, ash, caffeine, fluoride, polyphenols, and 15 elements from both tea leaves and infusions) was used as input data for identification of the differentiating characteristics of tea samples. Thus, a strategy that allows tea discrimination using a reduced number of chemical parameters was developed. SIMCA (softindependent modeling of class analogy) and PLS-DA (partial least squares-discriminant analysis) were used along with SPA-LDA for comparison. The elemental fingerprint (chemical signature) can be used for identifying the variety and origin of the tea, and SPA-LDA provided the most successful result (100% correct classification), despite having selected just three chemical parameters (namely K, Al, and Mg). The result is extremely positive from the viewpoint of chemical analyses, because quantifications made using fewer elements naturally provide simpler, faster and less expensive methods.
Fil: Gonçalves Dias Diniz, Paulo Henrique. Universidade Estadual Da Paraí­ba; Brasil
Fil: Pistonesi, Marcelo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Álvarez, Mónica Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Fernández Band, Beatriz Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Ugulino de Araújo, Mário César. Universidade Federal Da Paraíba; Brasil
description In this study, several possible approaches for simultaneous discrimination of teas based on a linear discriminant analysis with variables selected by the successive projections algorithm (SPA-LDA), featuring selection from the chemical composition according to variety (black or green tea) and geographical origin (Argentina or Sri Lanka), are explored. Chemical composition (moisture, ash, caffeine, fluoride, polyphenols, and 15 elements from both tea leaves and infusions) was used as input data for identification of the differentiating characteristics of tea samples. Thus, a strategy that allows tea discrimination using a reduced number of chemical parameters was developed. SIMCA (softindependent modeling of class analogy) and PLS-DA (partial least squares-discriminant analysis) were used along with SPA-LDA for comparison. The elemental fingerprint (chemical signature) can be used for identifying the variety and origin of the tea, and SPA-LDA provided the most successful result (100% correct classification), despite having selected just three chemical parameters (namely K, Al, and Mg). The result is extremely positive from the viewpoint of chemical analyses, because quantifications made using fewer elements naturally provide simpler, faster and less expensive methods.
publishDate 2015
dc.date.none.fl_str_mv 2015-03
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/11336/40474
Gonçalves Dias Diniz, Paulo Henrique; Pistonesi, Marcelo Fabian; Álvarez, Mónica Beatriz; Fernández Band, Beatriz Susana; Ugulino de Araújo, Mário César; Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA); Academic Press Inc Elsevier Science; Journal Of Food Composition And Analysis; 39; 3-2015; 103-110
0889-1575
CONICET Digital
CONICET
url http://hdl.handle.net/11336/40474
identifier_str_mv Gonçalves Dias Diniz, Paulo Henrique; Pistonesi, Marcelo Fabian; Álvarez, Mónica Beatriz; Fernández Band, Beatriz Susana; Ugulino de Araújo, Mário César; Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA); Academic Press Inc Elsevier Science; Journal Of Food Composition And Analysis; 39; 3-2015; 103-110
0889-1575
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jfca.2014.11.012
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0889157514002130
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
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
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 13.22299

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