Local partial least squares based on global PLS scores
A local‐based method for near‐infrared spectroscopy predictions, the local partial least squares regression on global PLS scores (LPLS‐S), is proposed in this work and compared with the usual local PLS (LPLS) regression approach. LPLS‐S is based on the idea of replacing the original spectra with a global PLS score matrix before using the usual LPLS. This is done with the aim of increasing the speed of the calculations, which can be an important parameter for online applications in particular, especially when implemented on large databases. In this study, the performance of the two local approaches was compared in terms of efficiency and speed. It could be concluded that the root‐mean‐square error of prediction of LPLS and LPLS‐S were 1.1962 and 1.1602, respectively, but the calculation speed for LPLS‐S was more than 20 times faster than for the LPLS algorithm.
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Subjects: | Q01 - Sciences et technologies alimentaires - Considérations générales, U30 - Méthodes de recherche, spectroscopie infrarouge, technologie alimentaire, technique analytique, qualité des aliments, manioc, Manihot esculenta, http://aims.fao.org/aos/agrovoc/c_28568, http://aims.fao.org/aos/agrovoc/c_3030, http://aims.fao.org/aos/agrovoc/c_1513, http://aims.fao.org/aos/agrovoc/c_10965, http://aims.fao.org/aos/agrovoc/c_9649, http://aims.fao.org/aos/agrovoc/c_4579, http://aims.fao.org/aos/agrovoc/c_1767, |
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dig-cirad-fr-5924062024-01-29T01:56:57Z http://agritrop.cirad.fr/592406/ http://agritrop.cirad.fr/592406/ Local partial least squares based on global PLS scores. Shen Guanghui, Lesnoff Matthieu, Baeten Vincent, Dardenne Pierre, Davrieux Fabrice, Ceballos Hernan, Belalcazar John, Dufour Dominique, Yang Zengling, Han Lujia, Fernandez Pierna Juan Antonio. 2019. Journal of Chemometrics, 33 (5):e3117, 12 p.https://doi.org/10.1002/cem.3117 <https://doi.org/10.1002/cem.3117> Local partial least squares based on global PLS scores Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernan Belalcazar, John Dufour, Dominique Yang, Zengling Han, Lujia Fernandez Pierna, Juan Antonio eng 2019 Journal of Chemometrics Q01 - Sciences et technologies alimentaires - Considérations générales U30 - Méthodes de recherche spectroscopie infrarouge technologie alimentaire technique analytique qualité des aliments manioc Manihot esculenta http://aims.fao.org/aos/agrovoc/c_28568 http://aims.fao.org/aos/agrovoc/c_3030 http://aims.fao.org/aos/agrovoc/c_1513 http://aims.fao.org/aos/agrovoc/c_10965 http://aims.fao.org/aos/agrovoc/c_9649 http://aims.fao.org/aos/agrovoc/c_4579 Colombie http://aims.fao.org/aos/agrovoc/c_1767 A local‐based method for near‐infrared spectroscopy predictions, the local partial least squares regression on global PLS scores (LPLS‐S), is proposed in this work and compared with the usual local PLS (LPLS) regression approach. LPLS‐S is based on the idea of replacing the original spectra with a global PLS score matrix before using the usual LPLS. This is done with the aim of increasing the speed of the calculations, which can be an important parameter for online applications in particular, especially when implemented on large databases. In this study, the performance of the two local approaches was compared in terms of efficiency and speed. It could be concluded that the root‐mean‐square error of prediction of LPLS and LPLS‐S were 1.1962 and 1.1602, respectively, but the calculation speed for LPLS‐S was more than 20 times faster than for the LPLS algorithm. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/592406/7/Shen_et_al-2019-Journal_of_Chemometrics.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1002/cem.3117 10.1002/cem.3117 info:eu-repo/semantics/altIdentifier/doi/10.1002/cem.3117 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1002/cem.3117 |
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Q01 - Sciences et technologies alimentaires - Considérations générales U30 - Méthodes de recherche spectroscopie infrarouge technologie alimentaire technique analytique qualité des aliments manioc Manihot esculenta http://aims.fao.org/aos/agrovoc/c_28568 http://aims.fao.org/aos/agrovoc/c_3030 http://aims.fao.org/aos/agrovoc/c_1513 http://aims.fao.org/aos/agrovoc/c_10965 http://aims.fao.org/aos/agrovoc/c_9649 http://aims.fao.org/aos/agrovoc/c_4579 http://aims.fao.org/aos/agrovoc/c_1767 Q01 - Sciences et technologies alimentaires - Considérations générales U30 - Méthodes de recherche spectroscopie infrarouge technologie alimentaire technique analytique qualité des aliments manioc Manihot esculenta http://aims.fao.org/aos/agrovoc/c_28568 http://aims.fao.org/aos/agrovoc/c_3030 http://aims.fao.org/aos/agrovoc/c_1513 http://aims.fao.org/aos/agrovoc/c_10965 http://aims.fao.org/aos/agrovoc/c_9649 http://aims.fao.org/aos/agrovoc/c_4579 http://aims.fao.org/aos/agrovoc/c_1767 |
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Q01 - Sciences et technologies alimentaires - Considérations générales U30 - Méthodes de recherche spectroscopie infrarouge technologie alimentaire technique analytique qualité des aliments manioc Manihot esculenta http://aims.fao.org/aos/agrovoc/c_28568 http://aims.fao.org/aos/agrovoc/c_3030 http://aims.fao.org/aos/agrovoc/c_1513 http://aims.fao.org/aos/agrovoc/c_10965 http://aims.fao.org/aos/agrovoc/c_9649 http://aims.fao.org/aos/agrovoc/c_4579 http://aims.fao.org/aos/agrovoc/c_1767 Q01 - Sciences et technologies alimentaires - Considérations générales U30 - Méthodes de recherche spectroscopie infrarouge technologie alimentaire technique analytique qualité des aliments manioc Manihot esculenta http://aims.fao.org/aos/agrovoc/c_28568 http://aims.fao.org/aos/agrovoc/c_3030 http://aims.fao.org/aos/agrovoc/c_1513 http://aims.fao.org/aos/agrovoc/c_10965 http://aims.fao.org/aos/agrovoc/c_9649 http://aims.fao.org/aos/agrovoc/c_4579 http://aims.fao.org/aos/agrovoc/c_1767 Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernan Belalcazar, John Dufour, Dominique Yang, Zengling Han, Lujia Fernandez Pierna, Juan Antonio Local partial least squares based on global PLS scores |
description |
A local‐based method for near‐infrared spectroscopy predictions, the local partial least squares regression on global PLS scores (LPLS‐S), is proposed in this work and compared with the usual local PLS (LPLS) regression approach. LPLS‐S is based on the idea of replacing the original spectra with a global PLS score matrix before using the usual LPLS. This is done with the aim of increasing the speed of the calculations, which can be an important parameter for online applications in particular, especially when implemented on large databases. In this study, the performance of the two local approaches was compared in terms of efficiency and speed. It could be concluded that the root‐mean‐square error of prediction of LPLS and LPLS‐S were 1.1962 and 1.1602, respectively, but the calculation speed for LPLS‐S was more than 20 times faster than for the LPLS algorithm. |
format |
article |
topic_facet |
Q01 - Sciences et technologies alimentaires - Considérations générales U30 - Méthodes de recherche spectroscopie infrarouge technologie alimentaire technique analytique qualité des aliments manioc Manihot esculenta http://aims.fao.org/aos/agrovoc/c_28568 http://aims.fao.org/aos/agrovoc/c_3030 http://aims.fao.org/aos/agrovoc/c_1513 http://aims.fao.org/aos/agrovoc/c_10965 http://aims.fao.org/aos/agrovoc/c_9649 http://aims.fao.org/aos/agrovoc/c_4579 http://aims.fao.org/aos/agrovoc/c_1767 |
author |
Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernan Belalcazar, John Dufour, Dominique Yang, Zengling Han, Lujia Fernandez Pierna, Juan Antonio |
author_facet |
Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernan Belalcazar, John Dufour, Dominique Yang, Zengling Han, Lujia Fernandez Pierna, Juan Antonio |
author_sort |
Shen, Guanghui |
title |
Local partial least squares based on global PLS scores |
title_short |
Local partial least squares based on global PLS scores |
title_full |
Local partial least squares based on global PLS scores |
title_fullStr |
Local partial least squares based on global PLS scores |
title_full_unstemmed |
Local partial least squares based on global PLS scores |
title_sort |
local partial least squares based on global pls scores |
url |
http://agritrop.cirad.fr/592406/ http://agritrop.cirad.fr/592406/7/Shen_et_al-2019-Journal_of_Chemometrics.pdf |
work_keys_str_mv |
AT shenguanghui localpartialleastsquaresbasedonglobalplsscores AT lesnoffmatthieu localpartialleastsquaresbasedonglobalplsscores AT baetenvincent localpartialleastsquaresbasedonglobalplsscores AT dardennepierre localpartialleastsquaresbasedonglobalplsscores AT davrieuxfabrice localpartialleastsquaresbasedonglobalplsscores AT ceballoshernan localpartialleastsquaresbasedonglobalplsscores AT belalcazarjohn localpartialleastsquaresbasedonglobalplsscores AT dufourdominique localpartialleastsquaresbasedonglobalplsscores AT yangzengling localpartialleastsquaresbasedonglobalplsscores AT hanlujia localpartialleastsquaresbasedonglobalplsscores AT fernandezpiernajuanantonio localpartialleastsquaresbasedonglobalplsscores |
_version_ |
1792499731346227200 |