Publicação
Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models
| Resumo: | The 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. |
|---|---|
| Autores principais: | Encina-Zelada, Christian René |
| Outros Autores: | Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Teixeira, J. A.; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, Ursula |
| Ano: | 2017 |
| País: | Portugal |
| Tipo de documento: | outro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1867438950473269248 |
|---|---|
| author | Encina-Zelada, Christian René |
| author2 | Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, J. A. Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
| author2_role | author author author author author author author author |
| author_facet | Encina-Zelada, Christian René Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, J. A. Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
| author_role | author |
| contributor_name_str_mv | RepositóriUM - Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Encina-Zelada, Christian René\"},{\"Person.name\":\"Cadavez, Vasco\"},{\"Person.name\":\"Pereda, Jorge\"},{\"Person.name\":\"Gómez-Pando, Luz\"},{\"Person.name\":\"Salvá-Ruíz, Bettit\"},{\"Person.name\":\"Teixeira, J. A.\"},{\"Person.name\":\"Ibañez, Martha\"},{\"Person.name\":\"Liland, Kristian H.\"},{\"Person.name\":\"Gonzales-Barron, Ursula\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RepositóriUM - Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Encina-Zelada, Christian René Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, J. A. Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
| datacite.date.Accepted.fl_str_mv | 2017-06-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2020-04-15T10:40:01Z |
| datacite.date.embargoed.fl_str_mv | 2020-04-15T10:40:01Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.titles.title.fl_str_mv | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| dc.contributor.none.fl_str_mv | RepositóriUM - Universidade do Minho |
| dc.creator.none.fl_str_mv | Encina-Zelada, Christian René Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, J. A. Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
| dc.date.Accepted.fl_str_mv | 2017-06-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2020-04-15T10:40:01Z |
| dc.date.embargoed.fl_str_mv | 2020-04-15T10:40:01Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/64863 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.title.fl_str_mv | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_1843 |
| description | The 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | other |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/abb22a69-410b-476b-89c6-d8e32a71a40b/download |
| id | rum_d5dbbb847653e5a3e4b06caefd93d111 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/64863 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/64863 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Encina-Zelada, Christian René Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Teixeira, J. A. Ibañez, Martha Liland, Kristian H. Gonzales-Barron, Ursula |
| publishDate | 2017 |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engporThe 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.application/pdfporEstimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy modelsEncina-Zelada, Christian RenéCadavez, VascoPereda, JorgeGómez-Pando, LuzSalvá-Ruíz, BettitTeixeira, J. A.Ibañez, MarthaLiland, Kristian H.Gonzales-Barron, UrsulaHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptCITATIONEncina-Zelada, C.; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Teixeira, José A.; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, Ursula, Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models. FABE 2017 - 3rd International Conference of Food and Biosystems Engineering (Proceedings). Rhodes Island, Greece, June 1-4, 414-415, 2017. ISBN: 978-960-9510-23-3ISBNIsPartOf978-960-9510-23-32020-04-15T10:40:01Z2017-062020-04-12T12:04:26Z2017-06-01T00:00:00ZHandlehttps://hdl.handle.net/1822/64863http://purl.org/coar/access_right/c_abf2open access728415 bytesother research producthttp://purl.org/coar/resource_type/c_1843otherhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/abb22a69-410b-476b-89c6-d8e32a71a40b/download |
| spellingShingle | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models Encina-Zelada, Christian René |
| status | SINGLETON |
| title | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| title_full | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| title_fullStr | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| title_full_unstemmed | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| title_short | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| title_sort | Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy models |
| url | https://hdl.handle.net/1822/64863 |
| visible | 1 |