Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples
Autor(a) principal: | |
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Data de Publicação: | 2010 |
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=S0103-50532010000400024 |
Resumo: | This short report proposes a sequential regression implementation for the successive projections algorithm (SPA), which is a variable selection technique for multiple linear regression. An example involving the near-infrared determination of protein in wheat is presented for illustration. The resulting model predictions exhibited a correlation coefficient of 0.989 and an RMSEP (root-mean-square error of prediction) value of 0.2% m/m in the range 10.2-16.2% m/m. The proposed implementation provided computational gains of up to five-fold. |
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Journal of the Brazilian Chemical Society (Online) |
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Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samplessuccessive projections algorithmmultivariate calibrationsequential regressionscomputational efficiencynear-infrared spectrometrywheatThis short report proposes a sequential regression implementation for the successive projections algorithm (SPA), which is a variable selection technique for multiple linear regression. An example involving the near-infrared determination of protein in wheat is presented for illustration. The resulting model predictions exhibited a correlation coefficient of 0.989 and an RMSEP (root-mean-square error of prediction) value of 0.2% m/m in the range 10.2-16.2% m/m. The proposed implementation provided computational gains of up to five-fold.Sociedade Brasileira de Química2010-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532010000400024Journal of the Brazilian Chemical Society v.21 n.4 2010reponame:Journal of the Brazilian Chemical Society (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.1590/S0103-50532010000400024info:eu-repo/semantics/openAccessSoares,Anderson S.Galvão Filho,Arlindo R.Galvão,Roberto K. H.Araújo,Mário César U.eng2010-05-21T00:00:00Zoai:scielo:S0103-50532010000400024Revistahttp://jbcs.sbq.org.brONGhttps://old.scielo.br/oai/scielo-oai.php||office@jbcs.sbq.org.br1678-47900103-5053opendoar:2010-05-21T00:00Journal of the Brazilian Chemical Society (Online) - Sociedade Brasileira de Química (SBQ)false |
dc.title.none.fl_str_mv |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples |
title |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples |
spellingShingle |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples Soares,Anderson S. successive projections algorithm multivariate calibration sequential regressions computational efficiency near-infrared spectrometry wheat |
title_short |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples |
title_full |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples |
title_fullStr |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples |
title_full_unstemmed |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples |
title_sort |
Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples |
author |
Soares,Anderson S. |
author_facet |
Soares,Anderson S. Galvão Filho,Arlindo R. Galvão,Roberto K. H. Araújo,Mário César U. |
author_role |
author |
author2 |
Galvão Filho,Arlindo R. Galvão,Roberto K. H. Araújo,Mário César U. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Soares,Anderson S. Galvão Filho,Arlindo R. Galvão,Roberto K. H. Araújo,Mário César U. |
dc.subject.por.fl_str_mv |
successive projections algorithm multivariate calibration sequential regressions computational efficiency near-infrared spectrometry wheat |
topic |
successive projections algorithm multivariate calibration sequential regressions computational efficiency near-infrared spectrometry wheat |
description |
This short report proposes a sequential regression implementation for the successive projections algorithm (SPA), which is a variable selection technique for multiple linear regression. An example involving the near-infrared determination of protein in wheat is presented for illustration. The resulting model predictions exhibited a correlation coefficient of 0.989 and an RMSEP (root-mean-square error of prediction) value of 0.2% m/m in the range 10.2-16.2% m/m. The proposed implementation provided computational gains of up to five-fold. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-01-01 |
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=S0103-50532010000400024 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-50532010000400024 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-50532010000400024 |
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.21 n.4 2010 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_ |
1750318170719125504 |