Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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=S0101-20612019000600563 |
Resumo: | Abstract Freshness, a very important criterion for pork quality control, is normally assessed by the index of K value. In this paper, Terahertz (THz) spectroscopy was employed to predict K value of pork nondestructively. The THz spectra (0.2~2.0THz) of 80 pork samples with different freshness in the attenuated total reflectance (ATR) mode were acquired. Simultaneously, their K values were determined by high performance liquid chromatography (HPLC). A back propagation artificial neural network (BP-ANN) prediction model of K value was established. The precision of BP-ANN was further improved after optimization by the algorithm of Adaptive boosting (AdaBoost), whose root mean square error of prediction (RMSEP) and correlation coefficient (RP) were 9.89% and 0.84 respectively in the prediction set, indicating that the non-linear models (BP-ANN and BP-AdaBoost) were superior to the linear principal component regression (PCR) model. The topological neural network architecture was much more suitable for analyzing complicated regression relationship between K value and THz spectra. It can be concluded that the THz spectral coupled with BP-AdaBoost algorithm is capable of predicting the pork K value. |
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Food Science and Technology (Campinas) |
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Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructivelyporkK valueTHz spectroscopychemometryBP-ANN adaptive boostingnon-destructionAbstract Freshness, a very important criterion for pork quality control, is normally assessed by the index of K value. In this paper, Terahertz (THz) spectroscopy was employed to predict K value of pork nondestructively. The THz spectra (0.2~2.0THz) of 80 pork samples with different freshness in the attenuated total reflectance (ATR) mode were acquired. Simultaneously, their K values were determined by high performance liquid chromatography (HPLC). A back propagation artificial neural network (BP-ANN) prediction model of K value was established. The precision of BP-ANN was further improved after optimization by the algorithm of Adaptive boosting (AdaBoost), whose root mean square error of prediction (RMSEP) and correlation coefficient (RP) were 9.89% and 0.84 respectively in the prediction set, indicating that the non-linear models (BP-ANN and BP-AdaBoost) were superior to the linear principal component regression (PCR) model. The topological neural network architecture was much more suitable for analyzing complicated regression relationship between K value and THz spectra. It can be concluded that the THz spectral coupled with BP-AdaBoost algorithm is capable of predicting the pork K value.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2019-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612019000600563Food Science and Technology v.39 suppl.2 2019reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/fst.25718info:eu-repo/semantics/openAccessLiang,QIMaocheng,ZHAOJie,ZHAOYuweiyi,TANGeng2019-12-02T00:00:00Zoai:scielo:S0101-20612019000600563Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2019-12-02T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively |
title |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively |
spellingShingle |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively Liang,QI pork K value THz spectroscopy chemometry BP-ANN adaptive boosting non-destruction |
title_short |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively |
title_full |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively |
title_fullStr |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively |
title_full_unstemmed |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively |
title_sort |
Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively |
author |
Liang,QI |
author_facet |
Liang,QI Maocheng,ZHAO Jie,ZHAO Yuweiyi,TANG |
author_role |
author |
author2 |
Maocheng,ZHAO Jie,ZHAO Yuweiyi,TANG |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Liang,QI Maocheng,ZHAO Jie,ZHAO Yuweiyi,TANG |
dc.subject.por.fl_str_mv |
pork K value THz spectroscopy chemometry BP-ANN adaptive boosting non-destruction |
topic |
pork K value THz spectroscopy chemometry BP-ANN adaptive boosting non-destruction |
description |
Abstract Freshness, a very important criterion for pork quality control, is normally assessed by the index of K value. In this paper, Terahertz (THz) spectroscopy was employed to predict K value of pork nondestructively. The THz spectra (0.2~2.0THz) of 80 pork samples with different freshness in the attenuated total reflectance (ATR) mode were acquired. Simultaneously, their K values were determined by high performance liquid chromatography (HPLC). A back propagation artificial neural network (BP-ANN) prediction model of K value was established. The precision of BP-ANN was further improved after optimization by the algorithm of Adaptive boosting (AdaBoost), whose root mean square error of prediction (RMSEP) and correlation coefficient (RP) were 9.89% and 0.84 respectively in the prediction set, indicating that the non-linear models (BP-ANN and BP-AdaBoost) were superior to the linear principal component regression (PCR) model. The topological neural network architecture was much more suitable for analyzing complicated regression relationship between K value and THz spectra. It can be concluded that the THz spectral coupled with BP-AdaBoost algorithm is capable of predicting the pork K value. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-01 |
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=S0101-20612019000600563 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612019000600563 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/fst.25718 |
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.39 suppl.2 2019 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 |
_version_ |
1752126324664696832 |