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

2017Journal article Open access
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dc.contributor.authorEncina-Zelada, Christian
dc.contributor.authorCadavez, Vasco
dc.contributor.authorPereda, Jorge
dc.contributor.authorGómez-Pando, Luz
dc.contributor.authorSalvá-Ruíz, Bettit
dc.contributor.authorTeixeira, José
dc.contributor.authorIbañez, Martha
dc.contributor.authorLiland, Kristian H.
dc.contributor.authorGonzales-Barron, Ursula
dc.date.accessioned2018-01-25T10:00:00Z
dc.date.accessioned2018-01-30T15:00:24Z
dc.date.available2018-01-25T10:00:00Z
dc.date.available2018-01-30T15:00:24Z
dc.date.issued2017
dc.description.abstractThe 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]).pt_PT
dc.description.sponsorshipMr. 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).
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEncina-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. 126-134en_EN
dc.identifier.doi10.1016/j.lwt.2017.01.026pt_PT
dc.identifier.issn0023-6438
dc.identifier.urihttp://hdl.handle.net/10198/15293
dc.language.isoeng
dc.peerreviewedyespt_PT
dc.subjectCanonicalen_EN
dc.subjectChemometricsen_EN
dc.subjectPartial least squaresen_EN
dc.subjectSavitzy-Golayen_EN
dc.subjectScatter correctionen_EN
dc.titleEstimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopyen_EN
dc.typejournal article
dspace.entity.typePublication
person.familyNameCadavez
person.familyNameGonzales-Barron
person.givenNameVasco
person.givenNameUrsula
person.identifierR-000-HDG
person.identifier.ciencia-id441B-01AB-A12E
person.identifier.ciencia-id0813-C319-B62A
person.identifier.orcid0000-0002-3077-7414
person.identifier.orcid0000-0002-8462-9775
person.identifier.ridA-3958-2010
person.identifier.scopus-author-id9039121900
person.identifier.scopus-author-id9435483700
rcaap.rightsopenAccessen_EN
rcaap.typearticlept_PT
relation.isAuthorOfPublication57b410e9-f6b7-42ff-ab3d-b526278715eb
relation.isAuthorOfPublication17c6b98f-4fb5-41d3-839a-6f77ec70021a
relation.isAuthorOfPublication.latestForDiscovery57b410e9-f6b7-42ff-ab3d-b526278715eb

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