Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study

Autores
de Girolamo, Annalisa; Arroyo, Marcia Carolina; Cervellieri, Salvatore; Cortese, Marina; Pascale, Michelangelo; Logrieco, Antonio Francesco; Lippolis, Vincenzo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fourier transform (FT) infrared spectroscopy, in combination with Partial-Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA), was used to discriminate commercial durum wheat pasta from Italy and Argentina for common wheat adulteration. Samples were analyzed by both near- and mid-infrared spectroscopy (FT-NIR, FT-MIR) and the performance results were compared. Classification models were devel oped and validated using Argentinean and Italian durum wheat pasta samples containing common wheat at levels up to 28% and lower than 0.5%, respectively (as determined by ELISA method). The first LDA and PLS-DA models grouped samples into three-classes, i.e. common wheat ≤1%, from 1 to ≤5% and > 5%; while the second LDA and PLS-DA models grouped samples into two-classes using a cut-off of 2% common wheat. The accuracy of the validated models were between 80 and 95% for the three-classes approach and between 91 and 97% for the two-classes approach. In general, the three-classes approach provided better results in the FT-NIR range while the two-classes approach provided comparable results in both spectral ranges. Results indicate that FT-NIR and FT-MIR spectroscopy, in combination with chemometric models, represent a promising, inexpensive and easy-to-use screening tool to rapidly analyze durum wheat pasta samples for monitoring common wheat adulteration.
Fil: de Girolamo, Annalisa. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Arroyo, Marcia Carolina. 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: Cervellieri, Salvatore. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Cortese, Marina. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Pascale, Michelangelo. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Logrieco, Antonio Francesco. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Lippolis, Vincenzo. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Materia
FT-NIR/MIR SPECTROSCOPY
DURUM WHEATPASTA ADULTERATION
RAPID METHOD
LDA
PLS-DA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/141850
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/141850
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case studyde Girolamo, AnnalisaArroyo, Marcia CarolinaCervellieri, SalvatoreCortese, MarinaPascale, MichelangeloLogrieco, Antonio FrancescoLippolis, VincenzoFT-NIR/MIR SPECTROSCOPYDURUM WHEATPASTA ADULTERATIONRAPID METHODLDAPLS-DAhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Fourier transform (FT) infrared spectroscopy, in combination with Partial-Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA), was used to discriminate commercial durum wheat pasta from Italy and Argentina for common wheat adulteration. Samples were analyzed by both near- and mid-infrared spectroscopy (FT-NIR, FT-MIR) and the performance results were compared. Classification models were devel oped and validated using Argentinean and Italian durum wheat pasta samples containing common wheat at levels up to 28% and lower than 0.5%, respectively (as determined by ELISA method). The first LDA and PLS-DA models grouped samples into three-classes, i.e. common wheat ≤1%, from 1 to ≤5% and > 5%; while the second LDA and PLS-DA models grouped samples into two-classes using a cut-off of 2% common wheat. The accuracy of the validated models were between 80 and 95% for the three-classes approach and between 91 and 97% for the two-classes approach. In general, the three-classes approach provided better results in the FT-NIR range while the two-classes approach provided comparable results in both spectral ranges. Results indicate that FT-NIR and FT-MIR spectroscopy, in combination with chemometric models, represent a promising, inexpensive and easy-to-use screening tool to rapidly analyze durum wheat pasta samples for monitoring common wheat adulteration.Fil: de Girolamo, Annalisa. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; ItaliaFil: Arroyo, Marcia Carolina. 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: Cervellieri, Salvatore. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; ItaliaFil: Cortese, Marina. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; ItaliaFil: Pascale, Michelangelo. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; ItaliaFil: Logrieco, Antonio Francesco. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; ItaliaFil: Lippolis, Vincenzo. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; ItaliaElsevier Science2020-04-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/141850de Girolamo, Annalisa; Arroyo, Marcia Carolina; Cervellieri, Salvatore; Cortese, Marina; Pascale, Michelangelo; et al.; Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study; Elsevier Science; LWT - Food Science and Technology; 127; 9-4-2020; 1-8; 1093680023-6438CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0023643820303571info:eu-repo/semantics/altIdentifier/doi/10.1016/j.lwt.2020.109368info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:02:48Zoai:ri.conicet.gov.ar:11336/141850instacron: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:34982025-09-03 10:02:48.84CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
title Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
spellingShingle Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
de Girolamo, Annalisa
FT-NIR/MIR SPECTROSCOPY
DURUM WHEATPASTA ADULTERATION
RAPID METHOD
LDA
PLS-DA
title_short Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
title_full Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
title_fullStr Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
title_full_unstemmed Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
title_sort Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study
dc.creator.none.fl_str_mv de Girolamo, Annalisa
Arroyo, Marcia Carolina
Cervellieri, Salvatore
Cortese, Marina
Pascale, Michelangelo
Logrieco, Antonio Francesco
Lippolis, Vincenzo
author de Girolamo, Annalisa
author_facet de Girolamo, Annalisa
Arroyo, Marcia Carolina
Cervellieri, Salvatore
Cortese, Marina
Pascale, Michelangelo
Logrieco, Antonio Francesco
Lippolis, Vincenzo
author_role author
author2 Arroyo, Marcia Carolina
Cervellieri, Salvatore
Cortese, Marina
Pascale, Michelangelo
Logrieco, Antonio Francesco
Lippolis, Vincenzo
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv FT-NIR/MIR SPECTROSCOPY
DURUM WHEATPASTA ADULTERATION
RAPID METHOD
LDA
PLS-DA
topic FT-NIR/MIR SPECTROSCOPY
DURUM WHEATPASTA ADULTERATION
RAPID METHOD
LDA
PLS-DA
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Fourier transform (FT) infrared spectroscopy, in combination with Partial-Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA), was used to discriminate commercial durum wheat pasta from Italy and Argentina for common wheat adulteration. Samples were analyzed by both near- and mid-infrared spectroscopy (FT-NIR, FT-MIR) and the performance results were compared. Classification models were devel oped and validated using Argentinean and Italian durum wheat pasta samples containing common wheat at levels up to 28% and lower than 0.5%, respectively (as determined by ELISA method). The first LDA and PLS-DA models grouped samples into three-classes, i.e. common wheat ≤1%, from 1 to ≤5% and > 5%; while the second LDA and PLS-DA models grouped samples into two-classes using a cut-off of 2% common wheat. The accuracy of the validated models were between 80 and 95% for the three-classes approach and between 91 and 97% for the two-classes approach. In general, the three-classes approach provided better results in the FT-NIR range while the two-classes approach provided comparable results in both spectral ranges. Results indicate that FT-NIR and FT-MIR spectroscopy, in combination with chemometric models, represent a promising, inexpensive and easy-to-use screening tool to rapidly analyze durum wheat pasta samples for monitoring common wheat adulteration.
Fil: de Girolamo, Annalisa. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Arroyo, Marcia Carolina. 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: Cervellieri, Salvatore. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Cortese, Marina. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Pascale, Michelangelo. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Logrieco, Antonio Francesco. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
Fil: Lippolis, Vincenzo. Consiglio Nazionale delle Ricerche. Istituto di Scienze delle Produzioni Alimentari; Italia
description Fourier transform (FT) infrared spectroscopy, in combination with Partial-Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA), was used to discriminate commercial durum wheat pasta from Italy and Argentina for common wheat adulteration. Samples were analyzed by both near- and mid-infrared spectroscopy (FT-NIR, FT-MIR) and the performance results were compared. Classification models were devel oped and validated using Argentinean and Italian durum wheat pasta samples containing common wheat at levels up to 28% and lower than 0.5%, respectively (as determined by ELISA method). The first LDA and PLS-DA models grouped samples into three-classes, i.e. common wheat ≤1%, from 1 to ≤5% and > 5%; while the second LDA and PLS-DA models grouped samples into two-classes using a cut-off of 2% common wheat. The accuracy of the validated models were between 80 and 95% for the three-classes approach and between 91 and 97% for the two-classes approach. In general, the three-classes approach provided better results in the FT-NIR range while the two-classes approach provided comparable results in both spectral ranges. Results indicate that FT-NIR and FT-MIR spectroscopy, in combination with chemometric models, represent a promising, inexpensive and easy-to-use screening tool to rapidly analyze durum wheat pasta samples for monitoring common wheat adulteration.
publishDate 2020
dc.date.none.fl_str_mv 2020-04-09
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/11336/141850
de Girolamo, Annalisa; Arroyo, Marcia Carolina; Cervellieri, Salvatore; Cortese, Marina; Pascale, Michelangelo; et al.; Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study; Elsevier Science; LWT - Food Science and Technology; 127; 9-4-2020; 1-8; 109368
0023-6438
CONICET Digital
CONICET
url http://hdl.handle.net/11336/141850
identifier_str_mv de Girolamo, Annalisa; Arroyo, Marcia Carolina; Cervellieri, Salvatore; Cortese, Marina; Pascale, Michelangelo; et al.; Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study; Elsevier Science; LWT - Food Science and Technology; 127; 9-4-2020; 1-8; 109368
0023-6438
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://linkinghub.elsevier.com/retrieve/pii/S0023643820303571
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.lwt.2020.109368
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv 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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