Monitoring vitamin C extraction using multivariate calibration models by NIR

Detalhes bibliográficos
Autor(a) principal: Silva,Luciana Maria Herculano da
Data de Publicação: 2021
Outros Autores: Ribeiro,Lívia Paulia Dias, Costa,Bianca Carvalho, Silva,Ebenezer Oliveira, Miranda,Maria Raquel Alcântara de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista ciência agronômica (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000100408
Resumo: ABSTRACT Due to high of vitamin C content, acerola is exploited as source of this vitamin for the enrichment of industrial products. This work aimed to develop a method for monitoring vitamin C content using near infrared (NIR) during extraction procedure from acerola, thereby different processing steps were evaluated. The calibration and validation models were obtained by partial least squares regression with correlation between values ​​by the reference method, spectrophotometry at visible 525 nm, and absorption data by near infrared spectroscopy, 800 to 2500 nm. The most robust quantification model was determined using coefficient of determination (R2), root mean square error of calibration (RMSECV) and root mean square error of prediction (RMSEP). Vitamin C content ranged from 1,188.39 to 9,959.74 mg. 100 g-1, throughout extraction procedure. The obtained RMSEP, 166.27 mg 100 g-1, indicates NIR spectroscopy as a promising tool for quantification of vitamin C during extraction from acerola, with the possibility of verifying the content in intermediate stages of production line and moreover, enabling adjustments for correction.
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spelling Monitoring vitamin C extraction using multivariate calibration models by NIRNIRAscorbic acidExtractAcerolaABSTRACT Due to high of vitamin C content, acerola is exploited as source of this vitamin for the enrichment of industrial products. This work aimed to develop a method for monitoring vitamin C content using near infrared (NIR) during extraction procedure from acerola, thereby different processing steps were evaluated. The calibration and validation models were obtained by partial least squares regression with correlation between values ​​by the reference method, spectrophotometry at visible 525 nm, and absorption data by near infrared spectroscopy, 800 to 2500 nm. The most robust quantification model was determined using coefficient of determination (R2), root mean square error of calibration (RMSECV) and root mean square error of prediction (RMSEP). Vitamin C content ranged from 1,188.39 to 9,959.74 mg. 100 g-1, throughout extraction procedure. The obtained RMSEP, 166.27 mg 100 g-1, indicates NIR spectroscopy as a promising tool for quantification of vitamin C during extraction from acerola, with the possibility of verifying the content in intermediate stages of production line and moreover, enabling adjustments for correction.Universidade Federal do Ceará2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000100408Revista Ciência Agronômica v.52 n.1 2021reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20210008info:eu-repo/semantics/openAccessSilva,Luciana Maria Herculano daRibeiro,Lívia Paulia DiasCosta,Bianca CarvalhoSilva,Ebenezer OliveiraMiranda,Maria Raquel Alcântara deeng2021-06-09T00:00:00Zoai:scielo:S1806-66902021000100408Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2021-06-09T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false
dc.title.none.fl_str_mv Monitoring vitamin C extraction using multivariate calibration models by NIR
title Monitoring vitamin C extraction using multivariate calibration models by NIR
spellingShingle Monitoring vitamin C extraction using multivariate calibration models by NIR
Silva,Luciana Maria Herculano da
NIR
Ascorbic acid
Extract
Acerola
title_short Monitoring vitamin C extraction using multivariate calibration models by NIR
title_full Monitoring vitamin C extraction using multivariate calibration models by NIR
title_fullStr Monitoring vitamin C extraction using multivariate calibration models by NIR
title_full_unstemmed Monitoring vitamin C extraction using multivariate calibration models by NIR
title_sort Monitoring vitamin C extraction using multivariate calibration models by NIR
author Silva,Luciana Maria Herculano da
author_facet Silva,Luciana Maria Herculano da
Ribeiro,Lívia Paulia Dias
Costa,Bianca Carvalho
Silva,Ebenezer Oliveira
Miranda,Maria Raquel Alcântara de
author_role author
author2 Ribeiro,Lívia Paulia Dias
Costa,Bianca Carvalho
Silva,Ebenezer Oliveira
Miranda,Maria Raquel Alcântara de
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Silva,Luciana Maria Herculano da
Ribeiro,Lívia Paulia Dias
Costa,Bianca Carvalho
Silva,Ebenezer Oliveira
Miranda,Maria Raquel Alcântara de
dc.subject.por.fl_str_mv NIR
Ascorbic acid
Extract
Acerola
topic NIR
Ascorbic acid
Extract
Acerola
description ABSTRACT Due to high of vitamin C content, acerola is exploited as source of this vitamin for the enrichment of industrial products. This work aimed to develop a method for monitoring vitamin C content using near infrared (NIR) during extraction procedure from acerola, thereby different processing steps were evaluated. The calibration and validation models were obtained by partial least squares regression with correlation between values ​​by the reference method, spectrophotometry at visible 525 nm, and absorption data by near infrared spectroscopy, 800 to 2500 nm. The most robust quantification model was determined using coefficient of determination (R2), root mean square error of calibration (RMSECV) and root mean square error of prediction (RMSEP). Vitamin C content ranged from 1,188.39 to 9,959.74 mg. 100 g-1, throughout extraction procedure. The obtained RMSEP, 166.27 mg 100 g-1, indicates NIR spectroscopy as a promising tool for quantification of vitamin C during extraction from acerola, with the possibility of verifying the content in intermediate stages of production line and moreover, enabling adjustments for correction.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000100408
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902021000100408
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5935/1806-6690.20210008
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal do Ceará
publisher.none.fl_str_mv Universidade Federal do Ceará
dc.source.none.fl_str_mv Revista Ciência Agronômica v.52 n.1 2021
reponame:Revista ciência agronômica (Online)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Revista ciência agronômica (Online)
collection Revista ciência agronômica (Online)
repository.name.fl_str_mv Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv ||alekdutra@ufc.br|| ccarev@ufc.br
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