Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Food Science and Technology (Campinas) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101379 |
Resumo: | Abstract Tea polyphenols are one of the most important ingredients in Qingzhuan tea. Usually, a chemical method is used to determine tea polyphenols content, but it was time-consuming and laborious. This paper attempted to use near infrared spectroscopy (NIRS) technology combined with three partial least squares methods to predict tea polyphenols content quickly and nondestructively. The partial least squares (PLS), synergy interval PLS (siPLS) and genetic algorithm based PLS (gaPLS) were used to establish prediction models, the performance of the final model was showed by root mean square error of prediction (RMSEP) and determination coefficient (Rp2) in prediction set. The best spectral preprocessing method was multivariate scattering correction (MSC); the RMSEP and Rp2 of PLS model were 0.145% and 0.8974, respectively; the siPLS model was established with four spectral regions (4377.6 cm-1-4751.7 cm-1, 4755.6 cm-1-5129.7 cm-1, 6262.7 cm-1-6633.9 cm-1 and 7386 cm-1-7756.3 cm-1), whose RMSEP and Rp2 were 0.0652% and 0.9235, respectively; the gaPLS model was established with 36 spectra dada points and showed the best performance (RMSEP=0.0624%, Rp2=0.9769) compared with the PLS and si-PLS models. Therefore, the application of near infrared technology combined with the gaPLS method could predict tea polyphenols content in Qingzhuan tea more accurately and rapidly. |
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Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithmsQingzhuan teatea polyphenolsnear infrared spectroscopypartial least squaresgenetic algorithmAbstract Tea polyphenols are one of the most important ingredients in Qingzhuan tea. Usually, a chemical method is used to determine tea polyphenols content, but it was time-consuming and laborious. This paper attempted to use near infrared spectroscopy (NIRS) technology combined with three partial least squares methods to predict tea polyphenols content quickly and nondestructively. The partial least squares (PLS), synergy interval PLS (siPLS) and genetic algorithm based PLS (gaPLS) were used to establish prediction models, the performance of the final model was showed by root mean square error of prediction (RMSEP) and determination coefficient (Rp2) in prediction set. The best spectral preprocessing method was multivariate scattering correction (MSC); the RMSEP and Rp2 of PLS model were 0.145% and 0.8974, respectively; the siPLS model was established with four spectral regions (4377.6 cm-1-4751.7 cm-1, 4755.6 cm-1-5129.7 cm-1, 6262.7 cm-1-6633.9 cm-1 and 7386 cm-1-7756.3 cm-1), whose RMSEP and Rp2 were 0.0652% and 0.9235, respectively; the gaPLS model was established with 36 spectra dada points and showed the best performance (RMSEP=0.0624%, Rp2=0.9769) compared with the PLS and si-PLS models. Therefore, the application of near infrared technology combined with the gaPLS method could predict tea polyphenols content in Qingzhuan tea more accurately and rapidly.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101379Food Science and Technology v.42 2022reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/fst.94322info:eu-repo/semantics/openAccessWANG,ShengpengLIU,PanpanFENG,LinTENG,JingYE,FeiGUI,AnhuiWANG,XuepingZHENG,LinGAO,ShiweiZHENG,Pengchengeng2022-10-10T00:00:00Zoai:scielo:S0101-20612022000101379Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2022-10-10T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms |
title |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms |
spellingShingle |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms WANG,Shengpeng Qingzhuan tea tea polyphenols near infrared spectroscopy partial least squares genetic algorithm |
title_short |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms |
title_full |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms |
title_fullStr |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms |
title_full_unstemmed |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms |
title_sort |
Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms |
author |
WANG,Shengpeng |
author_facet |
WANG,Shengpeng LIU,Panpan FENG,Lin TENG,Jing YE,Fei GUI,Anhui WANG,Xueping ZHENG,Lin GAO,Shiwei ZHENG,Pengcheng |
author_role |
author |
author2 |
LIU,Panpan FENG,Lin TENG,Jing YE,Fei GUI,Anhui WANG,Xueping ZHENG,Lin GAO,Shiwei ZHENG,Pengcheng |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
WANG,Shengpeng LIU,Panpan FENG,Lin TENG,Jing YE,Fei GUI,Anhui WANG,Xueping ZHENG,Lin GAO,Shiwei ZHENG,Pengcheng |
dc.subject.por.fl_str_mv |
Qingzhuan tea tea polyphenols near infrared spectroscopy partial least squares genetic algorithm |
topic |
Qingzhuan tea tea polyphenols near infrared spectroscopy partial least squares genetic algorithm |
description |
Abstract Tea polyphenols are one of the most important ingredients in Qingzhuan tea. Usually, a chemical method is used to determine tea polyphenols content, but it was time-consuming and laborious. This paper attempted to use near infrared spectroscopy (NIRS) technology combined with three partial least squares methods to predict tea polyphenols content quickly and nondestructively. The partial least squares (PLS), synergy interval PLS (siPLS) and genetic algorithm based PLS (gaPLS) were used to establish prediction models, the performance of the final model was showed by root mean square error of prediction (RMSEP) and determination coefficient (Rp2) in prediction set. The best spectral preprocessing method was multivariate scattering correction (MSC); the RMSEP and Rp2 of PLS model were 0.145% and 0.8974, respectively; the siPLS model was established with four spectral regions (4377.6 cm-1-4751.7 cm-1, 4755.6 cm-1-5129.7 cm-1, 6262.7 cm-1-6633.9 cm-1 and 7386 cm-1-7756.3 cm-1), whose RMSEP and Rp2 were 0.0652% and 0.9235, respectively; the gaPLS model was established with 36 spectra dada points and showed the best performance (RMSEP=0.0624%, Rp2=0.9769) compared with the PLS and si-PLS models. Therefore, the application of near infrared technology combined with the gaPLS method could predict tea polyphenols content in Qingzhuan tea more accurately and rapidly. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
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=S0101-20612022000101379 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101379 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/fst.94322 |
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 |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
dc.source.none.fl_str_mv |
Food Science and Technology v.42 2022 reponame:Food Science and Technology (Campinas) instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) instacron:SBCTA |
instname_str |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
instacron_str |
SBCTA |
institution |
SBCTA |
reponame_str |
Food Science and Technology (Campinas) |
collection |
Food Science and Technology (Campinas) |
repository.name.fl_str_mv |
Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
repository.mail.fl_str_mv |
||revista@sbcta.org.br |
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1752126335504875520 |