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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 A.
dc.contributor.authorIbáñez, Maria
dc.contributor.authorLiland, Kristian Hovde
dc.contributor.authorGonzales-Barron, Ursula
dc.date.accessioned2018-04-18T08:43:15Z
dc.date.available2018-04-18T08:43:15Z
dc.date.created2017-06-27T13:45:14Z
dc.date.issued2017
dc.identifier.citationLebensmittel-Wissenschaft + Technologie. 2017, 79 126-134.
dc.identifier.issn0023-6438
dc.identifier.urihttp://hdl.handle.net/11250/2494622
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]).
dc.language.isoeng
dc.titleEstimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionacceptedVersion
dc.source.pagenumber126-134
dc.source.volume79
dc.source.journalLebensmittel-Wissenschaft + Technologie
dc.identifier.doi10.1016/j.lwt.2017.01.026
dc.identifier.cristin1479219
dc.relation.projectNorges forskningsråd: 262308
dc.relation.projectNofima AS: 201702
cristin.unitcode7543,3,2,0
cristin.unitnameRåvare og prosess
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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