Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy

Detalhes bibliográficos
Autor(a) principal: Encina-Zelada, Christian René
Data de Publicação: 2017
Outros Autores: Cadavez, Vasco, Pereda, Jorge, Gómez-Pando, Luz, Salvá-Ruíz, Bettit, Teixeira, J. A., Ibañez, Martha, Liland, Kristian H., Gonzales-Barron, Ursula
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/44666
Resumo: The aim of this study was to develop robust chemometric models for the routine determination of dietary constituents of quinoa (Chenopodium quinoa Willd.) using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa grains of 77 cultivars were acquired while dietary constituents were determined by reference methods. Spectra were subjected to multiplicative scatter correction (MSC) or extended multiplicative signal correction (EMSC), and were (or not) treated by Savitzky-Golay (SG) filters. Latent variables were extracted by partial least squares regression (PLSR) or canonical powered partial least squares (CPPLS) algorithms, and the accuracy and predictability of all modelling strategies were compared. Smoothing the spectra improved the accuracy of the models for fat (root mean square error of cross-validation, RMSECV: 0.3190.327%), ashes (RMSECV: 0.2240.230%), and particularly for protein (RMSECV: 0.5180.564%) and carbohydrates (RMSECV: 0.5420.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.2480.335%) and ashes (RMSEP: 0.1370.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.3760.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6510.901]), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6500.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.4780.654]) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.6580.833]).
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spelling Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopyCanonicalPartial least squaresChemometricsScatter correctionSavitzy-GolayScience & TechnologyThe aim of this study was to develop robust chemometric models for the routine determination of dietary constituents of quinoa (Chenopodium quinoa Willd.) using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa grains of 77 cultivars were acquired while dietary constituents were determined by reference methods. Spectra were subjected to multiplicative scatter correction (MSC) or extended multiplicative signal correction (EMSC), and were (or not) treated by Savitzky-Golay (SG) filters. Latent variables were extracted by partial least squares regression (PLSR) or canonical powered partial least squares (CPPLS) algorithms, and the accuracy and predictability of all modelling strategies were compared. Smoothing the spectra improved the accuracy of the models for fat (root mean square error of cross-validation, RMSECV: 0.3190.327%), ashes (RMSECV: 0.2240.230%), and particularly for protein (RMSECV: 0.5180.564%) and carbohydrates (RMSECV: 0.5420.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.2480.335%) and ashes (RMSEP: 0.1370.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.3760.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6510.901]), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6500.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.4780.654]) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.6580.833]).Mr. Encina-Zelada acknowledges the financial aid provided by the Peruvian National Programme of Scholarships and Student Loans (PRONABEC) in the mode of PhD grants (Presidente de La República Grant Number 183308). Dr. Gonzales-Barron wishes to acknowledge the financial support provided by the Portuguese Foundation for Science and Technology (FCT) through the award of a five-year Investigator Fellowship (IF) in the mode of Development Grants (IF/00570).ElsevierUniversidade do MinhoEncina-Zelada, Christian RenéCadavez, VascoPereda, JorgeGómez-Pando, LuzSalvá-Ruíz, BettitTeixeira, J. A.Ibañez, MarthaLiland, Kristian H.Gonzales-Barron, Ursula2017-062017-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/44666engEncina-Zelada, Christian; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Teixeira, J. A.; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, Ursula, Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy. LWT - Food Science and Technology, 79, 126-134, 20170023-64380023-643810.1016/j.lwt.2017.01.026http://www.journals.elsevier.com/lwt-food-science-and-technology/info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:45:28Zoai:repositorium.sdum.uminho.pt:1822/44666Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:43:19.937739Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
title Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
spellingShingle Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
Encina-Zelada, Christian René
Canonical
Partial least squares
Chemometrics
Scatter correction
Savitzy-Golay
Science & Technology
title_short Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
title_full Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
title_fullStr Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
title_full_unstemmed Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
title_sort Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
author Encina-Zelada, Christian René
author_facet Encina-Zelada, Christian René
Cadavez, Vasco
Pereda, Jorge
Gómez-Pando, Luz
Salvá-Ruíz, Bettit
Teixeira, J. A.
Ibañez, Martha
Liland, Kristian H.
Gonzales-Barron, Ursula
author_role author
author2 Cadavez, Vasco
Pereda, Jorge
Gómez-Pando, Luz
Salvá-Ruíz, Bettit
Teixeira, J. A.
Ibañez, Martha
Liland, Kristian H.
Gonzales-Barron, Ursula
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Encina-Zelada, Christian René
Cadavez, Vasco
Pereda, Jorge
Gómez-Pando, Luz
Salvá-Ruíz, Bettit
Teixeira, J. A.
Ibañez, Martha
Liland, Kristian H.
Gonzales-Barron, Ursula
dc.subject.por.fl_str_mv Canonical
Partial least squares
Chemometrics
Scatter correction
Savitzy-Golay
Science & Technology
topic Canonical
Partial least squares
Chemometrics
Scatter correction
Savitzy-Golay
Science & Technology
description The aim of this study was to develop robust chemometric models for the routine determination of dietary constituents of quinoa (Chenopodium quinoa Willd.) using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa grains of 77 cultivars were acquired while dietary constituents were determined by reference methods. Spectra were subjected to multiplicative scatter correction (MSC) or extended multiplicative signal correction (EMSC), and were (or not) treated by Savitzky-Golay (SG) filters. Latent variables were extracted by partial least squares regression (PLSR) or canonical powered partial least squares (CPPLS) algorithms, and the accuracy and predictability of all modelling strategies were compared. Smoothing the spectra improved the accuracy of the models for fat (root mean square error of cross-validation, RMSECV: 0.3190.327%), ashes (RMSECV: 0.2240.230%), and particularly for protein (RMSECV: 0.5180.564%) and carbohydrates (RMSECV: 0.5420.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.2480.335%) and ashes (RMSEP: 0.1370.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.3760.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6510.901]), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6500.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.4780.654]) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.6580.833]).
publishDate 2017
dc.date.none.fl_str_mv 2017-06
2017-06-01T00:00:00Z
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://hdl.handle.net/1822/44666
url http://hdl.handle.net/1822/44666
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Encina-Zelada, Christian; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Teixeira, J. A.; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, Ursula, Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy. LWT - Food Science and Technology, 79, 126-134, 2017
0023-6438
0023-6438
10.1016/j.lwt.2017.01.026
http://www.journals.elsevier.com/lwt-food-science-and-technology/
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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