Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/15293 |
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: 0.319–0.327%), ashes (RMSECV: 0.224–0.230%), and particularly for protein (RMSECV: 0.518–0.564%) and carbohydrates (RMSECV: 0.542–0.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.248–0.335%) and ashes (RMSEP: 0.137–0.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.376–0.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.651–0.901] ), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.650–0.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.478–0.654] ) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.658–0.833]). |
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Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopyCanonicalChemometricsPartial least squaresSavitzy-GolayScatter correctionThe 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: 0.319–0.327%), ashes (RMSECV: 0.224–0.230%), and particularly for protein (RMSECV: 0.518–0.564%) and carbohydrates (RMSECV: 0.542–0.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.248–0.335%) and ashes (RMSEP: 0.137–0.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.376–0.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.651–0.901] ), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.650–0.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.478–0.654] ) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.658–0.833]).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).Biblioteca Digital do IPBEncina-Zelada, ChristianCadavez, VascoPereda, JorgeGómez-Pando, LuzSalvá-Ruíz, BettitTeixeira, JoséIbañez, MarthaLiland, Kristian H.Gonzales-Barron, Ursula2018-01-25T10:00:00Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/15293engEncina-Zelada, Christian; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Teixeira, José A.; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, Ursula (2017). Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy. LWT - Food Science and Technology. ISSN 0023-6438. 79, p. 126-1340023-643810.1016/j.lwt.2017.01.026info: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:RCAAP2023-11-21T10:35:46Zoai:bibliotecadigital.ipb.pt:10198/15293Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:04:55.214695Repositório 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 Canonical Chemometrics Partial least squares Savitzy-Golay Scatter correction |
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 |
author_facet |
Encina-Zelada, Christian Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, José Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
author_role |
author |
author2 |
Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, José Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Encina-Zelada, Christian Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, José Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
dc.subject.por.fl_str_mv |
Canonical Chemometrics Partial least squares Savitzy-Golay Scatter correction |
topic |
Canonical Chemometrics Partial least squares Savitzy-Golay Scatter correction |
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: 0.319–0.327%), ashes (RMSECV: 0.224–0.230%), and particularly for protein (RMSECV: 0.518–0.564%) and carbohydrates (RMSECV: 0.542–0.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.248–0.335%) and ashes (RMSEP: 0.137–0.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.376–0.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.651–0.901] ), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.650–0.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.478–0.654] ) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.658–0.833]). |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z 2018-01-25T10:00:00Z |
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/10198/15293 |
url |
http://hdl.handle.net/10198/15293 |
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, José A.; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, Ursula (2017). Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy. LWT - Food Science and Technology. ISSN 0023-6438. 79, p. 126-134 0023-6438 10.1016/j.lwt.2017.01.026 |
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.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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