Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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=S0103-50532022000300251 |
Resumo: | A novel recognition method was put forward to identify the producing areas of the flue-cured tobacco leaves rapidly and non-destructively by using a near-infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. In contrast to traditional linear discriminant analysis (LDA) and extreme learning machine (ELM) algorithms, the accuracy, sensitivity and specificity were the highest for the proposed ML-ELM algorithm. The ML-ELM models for different producing areas of Yunnan tobacco leaves had the best generalization ability and prediction results. Besides, the above three algorithms were also identified by using the chemical index data. The experimental results indicated that the NIR spectroscopy technology together with ML-ELM algorithm achieved the best prediction performance both using the NIR spectral data and chemical index data. It indicates that the combination of NIR and ML-ELM can recognize different producing areas of Yunnan tobacco leaves rapidly, accurately, and non-destructively. |
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Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine AlgorithmNIR spectroscopyML-ELM algorithmtobacco leavesproducing area identificationA novel recognition method was put forward to identify the producing areas of the flue-cured tobacco leaves rapidly and non-destructively by using a near-infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. In contrast to traditional linear discriminant analysis (LDA) and extreme learning machine (ELM) algorithms, the accuracy, sensitivity and specificity were the highest for the proposed ML-ELM algorithm. The ML-ELM models for different producing areas of Yunnan tobacco leaves had the best generalization ability and prediction results. Besides, the above three algorithms were also identified by using the chemical index data. The experimental results indicated that the NIR spectroscopy technology together with ML-ELM algorithm achieved the best prediction performance both using the NIR spectral data and chemical index data. It indicates that the combination of NIR and ML-ELM can recognize different producing areas of Yunnan tobacco leaves rapidly, accurately, and non-destructively.Sociedade Brasileira de Química2022-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532022000300251Journal of the Brazilian Chemical Society v.33 n.3 2022reponame:Journal of the Brazilian Chemical Society (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.21577/0103-5053.20210143info:eu-repo/semantics/openAccessLi,RuidongHuang,WenyongShang,GuanlanZhang,XiaobingWang,XinLiu,JianguoWang,YongQiao,JunfengFan,XingWu,KaiZi,Wenhuaeng2022-02-18T00:00:00Zoai:scielo:S0103-50532022000300251Revistahttp://jbcs.sbq.org.brONGhttps://old.scielo.br/oai/scielo-oai.php||office@jbcs.sbq.org.br1678-47900103-5053opendoar:2022-02-18T00:00Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ)false |
dc.title.none.fl_str_mv |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm |
title |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm |
spellingShingle |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm Li,Ruidong NIR spectroscopy ML-ELM algorithm tobacco leaves producing area identification |
title_short |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm |
title_full |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm |
title_fullStr |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm |
title_full_unstemmed |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm |
title_sort |
Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm |
author |
Li,Ruidong |
author_facet |
Li,Ruidong Huang,Wenyong Shang,Guanlan Zhang,Xiaobing Wang,Xin Liu,Jianguo Wang,Yong Qiao,Junfeng Fan,Xing Wu,Kai Zi,Wenhua |
author_role |
author |
author2 |
Huang,Wenyong Shang,Guanlan Zhang,Xiaobing Wang,Xin Liu,Jianguo Wang,Yong Qiao,Junfeng Fan,Xing Wu,Kai Zi,Wenhua |
author2_role |
author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Li,Ruidong Huang,Wenyong Shang,Guanlan Zhang,Xiaobing Wang,Xin Liu,Jianguo Wang,Yong Qiao,Junfeng Fan,Xing Wu,Kai Zi,Wenhua |
dc.subject.por.fl_str_mv |
NIR spectroscopy ML-ELM algorithm tobacco leaves producing area identification |
topic |
NIR spectroscopy ML-ELM algorithm tobacco leaves producing area identification |
description |
A novel recognition method was put forward to identify the producing areas of the flue-cured tobacco leaves rapidly and non-destructively by using a near-infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. In contrast to traditional linear discriminant analysis (LDA) and extreme learning machine (ELM) algorithms, the accuracy, sensitivity and specificity were the highest for the proposed ML-ELM algorithm. The ML-ELM models for different producing areas of Yunnan tobacco leaves had the best generalization ability and prediction results. Besides, the above three algorithms were also identified by using the chemical index data. The experimental results indicated that the NIR spectroscopy technology together with ML-ELM algorithm achieved the best prediction performance both using the NIR spectral data and chemical index data. It indicates that the combination of NIR and ML-ELM can recognize different producing areas of Yunnan tobacco leaves rapidly, accurately, and non-destructively. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-01 |
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=S0103-50532022000300251 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532022000300251 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.21577/0103-5053.20210143 |
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.33 n.3 2022 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 |
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1750318184806744064 |