Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression

Detalhes bibliográficos
Autor(a) principal: Costa, Maria Cristina A.; et. al.
Data de Publicação: 2019
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório do Instituto de Tecnologia de Alimentos
Texto Completo: http://repositorio.ital.sp.gov.br/jspui/handle/123456789/209
Resumo: Bee pollen consumption has increased in the last years, mainly due to its nutritional value and therapeutic applications. The quantification of mineral constituents is of great importance in order to evaluate both, the toxicity and the beneficial effect of essential elements. The purpose of this work was to quantify the essential elements, Ca, Mg, Zn, P and K, by diffuse reflectance spectra in the near infrared region (NIR) combined with partial least squares regression (PLS), which is a clean and fast method. Reference method used was ICP OES. The determination coefficients for calibration models (R2) were above 0.87 and the mean percent calibration error varied from 5 to 10%. For external validation R2 values were higher than 0.76. The results indicated that NIR spectroscopy can be useful for an approximate quantification of these minerals in bee pollen samples and can be used as a faster alternative to the standard methodologies.
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spelling Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regressionChemometricsDiffuse reflectanceMineralsPLSNIR spectroscopyBee pollen consumption has increased in the last years, mainly due to its nutritional value and therapeutic applications. The quantification of mineral constituents is of great importance in order to evaluate both, the toxicity and the beneficial effect of essential elements. The purpose of this work was to quantify the essential elements, Ca, Mg, Zn, P and K, by diffuse reflectance spectra in the near infrared region (NIR) combined with partial least squares regression (PLS), which is a clean and fast method. Reference method used was ICP OES. The determination coefficients for calibration models (R2) were above 0.87 and the mean percent calibration error varied from 5 to 10%. For external validation R2 values were higher than 0.76. The results indicated that NIR spectroscopy can be useful for an approximate quantification of these minerals in bee pollen samples and can be used as a faster alternative to the standard methodologies.CNPq / FAPESPElsevier Ltd.Costa, Maria Cristina A.; et. al.2021-12-09T19:01:12Z2021-12-09T19:01:12Z2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfFood Chemistry, v. 273, p. 85-90, 2019. https://doi.org/10.1016/j.foodchem.2018.02.0170308-8146http://repositorio.ital.sp.gov.br/jspui/handle/123456789/209reponame:Repositório do Instituto de Tecnologia de Alimentosinstname:Instituto de Tecnologia de Alimentos (ITAL)instacron:ITALenginfo:eu-repo/semantics/openAccess2022-05-20T16:13:40Zoai:http://repositorio.ital.sp.gov.br:123456789/209Repositório InstitucionalPUBhttp://repositorio.ital.sp.gov.br/oai/requestbjftsec@ital.sp.gov.br || bjftsec@ital.sp.gov.bropendoar:2022-05-20T16:13:40Repositório do Instituto de Tecnologia de Alimentos - Instituto de Tecnologia de Alimentos (ITAL)false
dc.title.none.fl_str_mv Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
title Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
spellingShingle Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
Costa, Maria Cristina A.; et. al.
Chemometrics
Diffuse reflectance
Minerals
PLS
NIR spectroscopy
title_short Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
title_full Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
title_fullStr Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
title_full_unstemmed Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
title_sort Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
author Costa, Maria Cristina A.; et. al.
author_facet Costa, Maria Cristina A.; et. al.
author_role author
dc.contributor.none.fl_str_mv







dc.contributor.author.fl_str_mv Costa, Maria Cristina A.; et. al.
dc.subject.none.fl_str_mv

dc.subject.por.fl_str_mv Chemometrics
Diffuse reflectance
Minerals
PLS
NIR spectroscopy
topic Chemometrics
Diffuse reflectance
Minerals
PLS
NIR spectroscopy
description Bee pollen consumption has increased in the last years, mainly due to its nutritional value and therapeutic applications. The quantification of mineral constituents is of great importance in order to evaluate both, the toxicity and the beneficial effect of essential elements. The purpose of this work was to quantify the essential elements, Ca, Mg, Zn, P and K, by diffuse reflectance spectra in the near infrared region (NIR) combined with partial least squares regression (PLS), which is a clean and fast method. Reference method used was ICP OES. The determination coefficients for calibration models (R2) were above 0.87 and the mean percent calibration error varied from 5 to 10%. For external validation R2 values were higher than 0.76. The results indicated that NIR spectroscopy can be useful for an approximate quantification of these minerals in bee pollen samples and can be used as a faster alternative to the standard methodologies.
publishDate 2019
dc.date.none.fl_str_mv




2019
2021-12-09T19:01:12Z
2021-12-09T19:01:12Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv

dc.identifier.uri.fl_str_mv Food Chemistry, v. 273, p. 85-90, 2019. https://doi.org/10.1016/j.foodchem.2018.02.017
0308-8146
http://repositorio.ital.sp.gov.br/jspui/handle/123456789/209
identifier_str_mv
Food Chemistry, v. 273, p. 85-90, 2019. https://doi.org/10.1016/j.foodchem.2018.02.017
0308-8146
url http://repositorio.ital.sp.gov.br/jspui/handle/123456789/209
dc.language.none.fl_str_mv
dc.language.iso.fl_str_mv eng
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language eng
dc.rights.none.fl_str_mv

dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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application/pdf
dc.publisher.none.fl_str_mv

Elsevier Ltd.
publisher.none.fl_str_mv

Elsevier Ltd.
dc.source.none.fl_str_mv
reponame:Repositório do Instituto de Tecnologia de Alimentos
instname:Instituto de Tecnologia de Alimentos (ITAL)
instacron:ITAL
instname_str Instituto de Tecnologia de Alimentos (ITAL)
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reponame_str Repositório do Instituto de Tecnologia de Alimentos
collection Repositório do Instituto de Tecnologia de Alimentos
repository.name.fl_str_mv Repositório do Instituto de Tecnologia de Alimentos - Instituto de Tecnologia de Alimentos (ITAL)
repository.mail.fl_str_mv bjftsec@ital.sp.gov.br || bjftsec@ital.sp.gov.br
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