Repository logo
 
Publication

Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models

2017Conference object Open access
Simple item page
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.accessioned2018-03-23T17:30:49Z
dc.date.available2018-03-23T17:30:49Z
dc.date.issued2017
dc.description.abstractThe aim of this study was to develop chemometric models for protein, fat, moisture, ashes and carbohydrates contents of quinoa flour using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour originated from grains of 77 different cultivars were scanned while dietary constituents were determined in duplicate by reference AOAC methods. As a pre-treatment, spectra were subjected to extended multiplicative signal correction (EMSC) with polynomial degree 0, 1 or 2. The performance of two algorithms, partial least squares regression (PLSR) and Canonical Powered Partial Least Squares (CPPLS), was compared in terms ofaccuracy and predictability. For all dietary constituents,as opposed to PLSR, the CPPLS regression produced lower root meat square errors of cross-validation (RMSECV), lower root meat square errors of prediction (RMSEP) and higher coefficient of correlation of cross-validation (RCV) while retaining fewer number of components. More robust models were obtained when quinoa flour spectra were pre-processed using EMSC of polynomial degree 2 for moisture (RMSECV: 0.564 and RMSEP: 0.648), fat (RMSECV: 0.268 and RMSEP: 0.256) and carbohydrates (RMSECV: 0.641 and RMSEP: 0.643) following extraction of five CPPLS latent variables. High coefficients of correlation of prediction (RP: 0.7-0.8) were found when models were validated on a test data set consisting of 15 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): 6.1% for moisture, 5.6% for protein, 3.9% for fat 7.4% for ashes and 0.8% for carbohydrates contents.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.isbn978-960-9510-23-3
dc.identifier.urihttp://hdl.handle.net/10198/16510
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectQuinoapt_PT
dc.subjectSpectrapt_PT
dc.subjectPLSpt_PT
dc.subjectCalibrationpt_PT
dc.subjectChemometricspt_PT
dc.titleEstimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy modelspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceRhodes Island, Greecept_PT
oaire.citation.startPage414pt_PT
oaire.citation.titleProceedings of the FABE 2017 Food and Biosystems Engineering Conferencept_PT
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.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication57b410e9-f6b7-42ff-ab3d-b526278715eb
relation.isAuthorOfPublication17c6b98f-4fb5-41d3-839a-6f77ec70021a
relation.isAuthorOfPublication.latestForDiscovery17c6b98f-4fb5-41d3-839a-6f77ec70021a

Files

Collections

CIMO - Resumos em Proceedings Não Indexados à WoS/Scopus