Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of the Brazilian Chemical Society (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532018000701480 |
Resumo: | A cigarette brand automatic classification method using near-infrared (NIR) spectroscopy and sparse representation classification (SRC) algorithm is put forward by the paper. Comparing with the traditional methods, it is more robust to redundancy because it uses non-negative least squares (NNLS) sparse coding instead of principal component analysis (PCA) for dimensionality reduction of the spectral data. The effectiveness of SRC algorithm is compared with PCA-linear discriminant analysis (LDA) and PCA-particle swarm optimization-support vector machine (PSO-SVM) algorithms. The results show that the classification accuracy of the proposed method is higher and is much more efficient. |
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Journal of the Brazilian Chemical Society (Online) |
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Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithmnear-infrared spectroscopydeep learningsparse representation classificationnon-negative least squaresA cigarette brand automatic classification method using near-infrared (NIR) spectroscopy and sparse representation classification (SRC) algorithm is put forward by the paper. Comparing with the traditional methods, it is more robust to redundancy because it uses non-negative least squares (NNLS) sparse coding instead of principal component analysis (PCA) for dimensionality reduction of the spectral data. The effectiveness of SRC algorithm is compared with PCA-linear discriminant analysis (LDA) and PCA-particle swarm optimization-support vector machine (PSO-SVM) algorithms. The results show that the classification accuracy of the proposed method is higher and is much more efficient.Sociedade Brasileira de Química2018-07-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532018000701480Journal of the Brazilian Chemical Society v.29 n.7 2018reponame:Journal of the Brazilian Chemical Society (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.21577/0103-5053.20180019info:eu-repo/semantics/openAccessShuangyan,YangYing,HouLingchun,YangJianqiang,ZhangWeijuan,LiuChanggui,QiuMing,LiYanmei,Yangeng2018-06-20T00:00:00Zoai:scielo:S0103-50532018000701480Revistahttp://jbcs.sbq.org.brONGhttps://old.scielo.br/oai/scielo-oai.php||office@jbcs.sbq.org.br1678-47900103-5053opendoar:2018-06-20T00:00Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ)false |
dc.title.none.fl_str_mv |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm |
title |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm |
spellingShingle |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm Shuangyan,Yang near-infrared spectroscopy deep learning sparse representation classification non-negative least squares |
title_short |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm |
title_full |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm |
title_fullStr |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm |
title_full_unstemmed |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm |
title_sort |
Automatic Identification of Cigarette Brand Using Near-Infrared Spectroscopy and Sparse Representation Classification Algorithm |
author |
Shuangyan,Yang |
author_facet |
Shuangyan,Yang Ying,Hou Lingchun,Yang Jianqiang,Zhang Weijuan,Liu Changgui,Qiu Ming,Li Yanmei,Yang |
author_role |
author |
author2 |
Ying,Hou Lingchun,Yang Jianqiang,Zhang Weijuan,Liu Changgui,Qiu Ming,Li Yanmei,Yang |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Shuangyan,Yang Ying,Hou Lingchun,Yang Jianqiang,Zhang Weijuan,Liu Changgui,Qiu Ming,Li Yanmei,Yang |
dc.subject.por.fl_str_mv |
near-infrared spectroscopy deep learning sparse representation classification non-negative least squares |
topic |
near-infrared spectroscopy deep learning sparse representation classification non-negative least squares |
description |
A cigarette brand automatic classification method using near-infrared (NIR) spectroscopy and sparse representation classification (SRC) algorithm is put forward by the paper. Comparing with the traditional methods, it is more robust to redundancy because it uses non-negative least squares (NNLS) sparse coding instead of principal component analysis (PCA) for dimensionality reduction of the spectral data. The effectiveness of SRC algorithm is compared with PCA-linear discriminant analysis (LDA) and PCA-particle swarm optimization-support vector machine (PSO-SVM) algorithms. The results show that the classification accuracy of the proposed method is higher and is much more efficient. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-07-01 |
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=S0103-50532018000701480 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532018000701480 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.21577/0103-5053.20180019 |
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 Química |
publisher.none.fl_str_mv |
Sociedade Brasileira de Química |
dc.source.none.fl_str_mv |
Journal of the Brazilian Chemical Society v.29 n.7 2018 reponame:Journal of the Brazilian Chemical Society (Online) instname:Sociedade Brasileira de Química (SBQ) instacron:SBQ |
instname_str |
Sociedade Brasileira de Química (SBQ) |
instacron_str |
SBQ |
institution |
SBQ |
reponame_str |
Journal of the Brazilian Chemical Society (Online) |
collection |
Journal of the Brazilian Chemical Society (Online) |
repository.name.fl_str_mv |
Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ) |
repository.mail.fl_str_mv |
||office@jbcs.sbq.org.br |
_version_ |
1750318180846272512 |