Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression
Autor(a) principal: | |
---|---|
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. |
id |
ITAL-2_0edd9ca9154ee106dcfa155d6c5474d6 |
---|---|
oai_identifier_str |
oai:http://repositorio.ital.sp.gov.br:123456789/209 |
network_acronym_str |
ITAL-2 |
network_name_str |
Repositório do Instituto de Tecnologia de Alimentos |
repository_id_str |
|
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 |
language_invalid_str_mv |
|
language |
eng |
dc.rights.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
|
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
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) |
instacron_str |
ITAL |
institution |
ITAL |
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 |
_version_ |
1813095546110345216 |