Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
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/123456789/181 |
Resumo: | In the present work partial least square regression (PLS) models were built for quantification of the majorcomponents of 154 Brazilian bee pollen samples. Bee pollen has nutritive and therapeutic properties thatmake it attractive for human health. However, studies on the nutrient and bioactive compoundcomposition of this product are needed, as well as the verification of the presence of contaminants thatare harmful to health. The conventional analysis methods are costly and time-consuming, while nearinfrared spectroscopy (NIR) associated to PLS regression allows a fast and non-costly quantification of thebee pollen components without samples pre-treatment.The calibration models exhibited the determination coefficients,R2>0.94. The mean percent cali-bration error varied from 1.49 to 5.58%. For external validation,R2ranged from 0.89 to 0.98 among thesix. The results indicated that some models are good for quantification, while others are qualified forscreening calibration |
id |
ITAL-2_627c1da7a9627d99ac7073ec2b649825 |
---|---|
oai_identifier_str |
oai:http://repositorio.ital.sp.gov.br:123456789/181 |
network_acronym_str |
ITAL-2 |
network_name_str |
Repositório do Instituto de Tecnologia de Alimentos |
repository_id_str |
|
spelling |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regressionIn the present work partial least square regression (PLS) models were built for quantification of the majorcomponents of 154 Brazilian bee pollen samples. Bee pollen has nutritive and therapeutic properties thatmake it attractive for human health. However, studies on the nutrient and bioactive compoundcomposition of this product are needed, as well as the verification of the presence of contaminants thatare harmful to health. The conventional analysis methods are costly and time-consuming, while nearinfrared spectroscopy (NIR) associated to PLS regression allows a fast and non-costly quantification of thebee pollen components without samples pre-treatment.The calibration models exhibited the determination coefficients,R2>0.94. The mean percent cali-bration error varied from 1.49 to 5.58%. For external validation,R2ranged from 0.89 to 0.98 among thesix. The results indicated that some models are good for quantification, while others are qualified forscreening calibrationCNPq / FAPESPCosta, Maria Cristina A.; et. al.2021-09-30T15:33:25Z2021-09-30T15:33:25Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfLWT - Food Science and Technology, Elsevier, v. 80, p. 76-83, jul. 2017.http://repositorio.ital.sp.gov.br/jspui/handle/123456789/181reponame:Repositório do Instituto de Tecnologia de Alimentosinstname:Instituto de Tecnologia de Alimentos (ITAL)instacron:ITALenginfo:eu-repo/semantics/openAccess2022-05-20T16:13:23Zoai:http://repositorio.ital.sp.gov.br:123456789/181Repositório InstitucionalPUBhttp://repositorio.ital.sp.gov.br/oai/requestbjftsec@ital.sp.gov.br || bjftsec@ital.sp.gov.bropendoar:2022-05-20T16:13:23Repositório do Instituto de Tecnologia de Alimentos - Instituto de Tecnologia de Alimentos (ITAL)false |
dc.title.none.fl_str_mv |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression |
title |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression |
spellingShingle |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression Costa, Maria Cristina A.; et. al. |
title_short |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression |
title_full |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression |
title_fullStr |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression |
title_full_unstemmed |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression |
title_sort |
Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR 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 |
|
description |
In the present work partial least square regression (PLS) models were built for quantification of the majorcomponents of 154 Brazilian bee pollen samples. Bee pollen has nutritive and therapeutic properties thatmake it attractive for human health. However, studies on the nutrient and bioactive compoundcomposition of this product are needed, as well as the verification of the presence of contaminants thatare harmful to health. The conventional analysis methods are costly and time-consuming, while nearinfrared spectroscopy (NIR) associated to PLS regression allows a fast and non-costly quantification of thebee pollen components without samples pre-treatment.The calibration models exhibited the determination coefficients,R2>0.94. The mean percent cali-bration error varied from 1.49 to 5.58%. For external validation,R2ranged from 0.89 to 0.98 among thesix. The results indicated that some models are good for quantification, while others are qualified forscreening calibration |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2021-09-30T15:33:25Z 2021-09-30T15:33:25Z |
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 |
LWT - Food Science and Technology, Elsevier, v. 80, p. 76-83, jul. 2017. http://repositorio.ital.sp.gov.br/jspui/handle/123456789/181 |
identifier_str_mv |
LWT - Food Science and Technology, Elsevier, v. 80, p. 76-83, jul. 2017. |
url |
http://repositorio.ital.sp.gov.br/jspui/handle/123456789/181 |
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 |
|
publisher.none.fl_str_mv |
|
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_ |
1798311817353625600 |