Analysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regression

Detalhes bibliográficos
Autor(a) principal: Costa, Maria Cristina A.; et. al.
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
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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
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dc.language.iso.fl_str_mv eng
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language eng
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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
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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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