Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy

Bibliographic Details
Main Author: Encina-Zelada, Christian René
Publication Date: 2018
Other Authors: Cadavez, Vasco, Pereda, Jorge, Gómez-Pando, Luz, Salvá-Ruíz, Bettit, Ibañez, Martha, Teixeira, J. A., Gonzales-Barron, Ursula
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/56434
Summary: The aim of this study was to develop chemometric models for protein, fat, ashes and carbohydrates contents of quinoa flour using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour obtained from grains of 70 different cultivars were scanned while dietary constituents were determined by reference AOAC methods. As a pre-treatment, spectra were subjected to extended multiplicative signal correction (EMSC) with polynomial degree 0, 1 or 2. Next, the Canonical Powered Partial Least Squares (CPPLS) algorithm was applied, and models were compared in terms of accuracy and predictability. For all models, root mean square errors of cross-validation (RMSECV), root meat square errors of prediction (RMSEP) and coefficient of correlation of cross-validation (RCV) were computed. Robust models were obtained when quinoa spectra were pre-processed using EMSC of polynomial degree 2 for both fat (RMSECV: 0.268% and RMSEP: 0.256%) and carbohydrates (RMSECV: 0.641% and RMSEP: 0.643%) following extraction of five CPPLS latent variables. Good coefficients of correlation of prediction (RP: 0.6900.821) were found for all constituents when models were validated on a test data set consisting of 13 quinoa flour spectra. Thus, good predictions of the dietary constituents of quinoa flour could be achieved by using NIT technology, as implied by the low coefficient of variation of prediction (CVP): 5.64% for protein, 3.88% for fat 7.32% for ashes and 0.80% for carbohydrates contents.
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spelling Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission SpectroscopyQuinoa FlourCalibrationChemometricsBootstrapScience & TechnologyThe aim of this study was to develop chemometric models for protein, fat, ashes and carbohydrates contents of quinoa flour using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour obtained from grains of 70 different cultivars were scanned while dietary constituents were determined by reference AOAC methods. As a pre-treatment, spectra were subjected to extended multiplicative signal correction (EMSC) with polynomial degree 0, 1 or 2. Next, the Canonical Powered Partial Least Squares (CPPLS) algorithm was applied, and models were compared in terms of accuracy and predictability. For all models, root mean square errors of cross-validation (RMSECV), root meat square errors of prediction (RMSEP) and coefficient of correlation of cross-validation (RCV) were computed. Robust models were obtained when quinoa spectra were pre-processed using EMSC of polynomial degree 2 for both fat (RMSECV: 0.268% and RMSEP: 0.256%) and carbohydrates (RMSECV: 0.641% and RMSEP: 0.643%) following extraction of five CPPLS latent variables. Good coefficients of correlation of prediction (RP: 0.6900.821) were found for all constituents when models were validated on a test data set consisting of 13 quinoa flour spectra. Thus, good predictions of the dietary constituents of quinoa flour could be achieved by using NIT technology, as implied by the low coefficient of variation of prediction (CVP): 5.64% for protein, 3.88% for fat 7.32% for ashes and 0.80% for carbohydrates contents.info:eu-repo/semantics/publishedVersionSpringer International Publishing AGUniversidade do MinhoEncina-Zelada, Christian RenéCadavez, VascoPereda, JorgeGómez-Pando, LuzSalvá-Ruíz, BettitIbañez, MarthaTeixeira, J. A.Gonzales-Barron, Ursula20182018-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/56434engEncina-Zelada, C.; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Ibañez, Martha; Teixeira, José A.; Gonzales-Barron, Ursula, Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy. INCREaSE 2017 - Proceedings of the 1st International Congress on Engineering and Sustainability in the XXI Century. Faro, Portugal, Oct 11-13, 2017, Springer International Publishing, 227-235, 2018. ISBN: 978-3-319-70272-8978-3-319-70272-810.1007/978-3-319-70272-8_18https://link.springer.com/book/10.1007/978-3-319-70272-8info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T06:41:57Zoai:repositorium.sdum.uminho.pt:1822/56434Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:01:45.485425Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy
title Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy
spellingShingle Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy
Encina-Zelada, Christian René
Quinoa Flour
Calibration
Chemometrics
Bootstrap
Science & Technology
title_short Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy
title_full Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy
title_fullStr Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy
title_full_unstemmed Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy
title_sort Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour 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
Ibañez, Martha
Teixeira, J. A.
Gonzales-Barron, Ursula
author_role author
author2 Cadavez, Vasco
Pereda, Jorge
Gómez-Pando, Luz
Salvá-Ruíz, Bettit
Ibañez, Martha
Teixeira, J. A.
Gonzales-Barron, Ursula
author2_role 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
Ibañez, Martha
Teixeira, J. A.
Gonzales-Barron, Ursula
dc.subject.por.fl_str_mv Quinoa Flour
Calibration
Chemometrics
Bootstrap
Science & Technology
topic Quinoa Flour
Calibration
Chemometrics
Bootstrap
Science & Technology
description The aim of this study was to develop chemometric models for protein, fat, ashes and carbohydrates contents of quinoa flour using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour obtained from grains of 70 different cultivars were scanned while dietary constituents were determined by reference AOAC methods. As a pre-treatment, spectra were subjected to extended multiplicative signal correction (EMSC) with polynomial degree 0, 1 or 2. Next, the Canonical Powered Partial Least Squares (CPPLS) algorithm was applied, and models were compared in terms of accuracy and predictability. For all models, root mean square errors of cross-validation (RMSECV), root meat square errors of prediction (RMSEP) and coefficient of correlation of cross-validation (RCV) were computed. Robust models were obtained when quinoa spectra were pre-processed using EMSC of polynomial degree 2 for both fat (RMSECV: 0.268% and RMSEP: 0.256%) and carbohydrates (RMSECV: 0.641% and RMSEP: 0.643%) following extraction of five CPPLS latent variables. Good coefficients of correlation of prediction (RP: 0.6900.821) were found for all constituents when models were validated on a test data set consisting of 13 quinoa flour spectra. Thus, good predictions of the dietary constituents of quinoa flour could be achieved by using NIT technology, as implied by the low coefficient of variation of prediction (CVP): 5.64% for protein, 3.88% for fat 7.32% for ashes and 0.80% for carbohydrates contents.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/56434
url http://hdl.handle.net/1822/56434
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Encina-Zelada, C.; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Ibañez, Martha; Teixeira, José A.; Gonzales-Barron, Ursula, Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy. INCREaSE 2017 - Proceedings of the 1st International Congress on Engineering and Sustainability in the XXI Century. Faro, Portugal, Oct 11-13, 2017, Springer International Publishing, 227-235, 2018. ISBN: 978-3-319-70272-8
978-3-319-70272-8
10.1007/978-3-319-70272-8_18
https://link.springer.com/book/10.1007/978-3-319-70272-8
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 Springer International Publishing AG
publisher.none.fl_str_mv Springer International Publishing AG
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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