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dc.contributor.authorCosta, Maria Cristina A.; et. al.-
dc.contributor.otherPT_Br
dc.date.accessioned-
dc.date.accessioned2021-09-30T15:33:25Z-
dc.date.availablePT_Br
dc.date.available2021-09-30T15:33:25Z-
dc.date.copyright-
dc.date.issued2017-
dc.identifierPT_Br
dc.identifier.citationLWT - Food Science and Technology, Elsevier, v. 80, p. 76-83, jul. 2017.pt_BR
dc.identifier.urihttp://repositorio.ital.sp.gov.br/jspui/handle/123456789/181-
dc.description.abstractIn 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 calibrationpt_BR
dc.description.sponsorshipCNPq / FAPESPpt_BR
dc.formatPT_Br
dc.languagePT_Br
dc.language.isoenpt_BR
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dc.sourcePT_Br
dc.titleAnalysis of bee pollen constituents from different Brazilian regions:Quantification by NIR spectroscopy and PLS regressionpt_BR
dc.typeArticlept_BR
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dc.subject.cnpqPT_Br
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