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.
Main Authors: | , , , , , , , , , , |
---|---|
Format: | Journal Article biblioteca |
Language: | English |
Published: |
Wiley
2019-05
|
Online Access: | https://hdl.handle.net/10568/100718 https://doi.org/10.1002/cem.3117 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-cgspace-10568-100718 |
---|---|
record_format |
koha |
spelling |
dig-cgspace-10568-1007182023-09-12T07:04:35Z Local partial least squares based on global PLS scores Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernán Belalcázar, John Eiver Dufour, Dominique Yang, Zengling Han, Lujia Fernández Pierna, Juan Antonio 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. 2019-05 2019-04-09T14:33:12Z 2019-04-09T14:33:12Z Journal Article Shen, Guanghui; Lesnoff, Matthieu; Baeten, Vincent; Dardenne, Pierre; Davrieux, Fabrice; Ceballos, Hernan; Belalcazar, John; Dufour, Dominique; Yang, Zengling; Han, Lujia & Fernández Pierna, Juan Antonio (2019). Local partial least squares based on global PLS scores. Journal of Chemometrics, 1-12 P. 0886-9383 https://hdl.handle.net/10568/100718 https://doi.org/10.1002/cem.3117 en Copyrighted; all rights reserved Open Access 1-12 p. Wiley Journal of Chemometrics |
institution |
CGIAR |
collection |
DSpace |
country |
Francia |
countrycode |
FR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-cgspace |
tag |
biblioteca |
region |
Europa del Oeste |
libraryname |
Biblioteca del CGIAR |
language |
English |
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 |
Journal Article |
author |
Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernán Belalcázar, John Eiver Dufour, Dominique Yang, Zengling Han, Lujia Fernández Pierna, Juan Antonio |
spellingShingle |
Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernán Belalcázar, John Eiver Dufour, Dominique Yang, Zengling Han, Lujia Fernández Pierna, Juan Antonio Local partial least squares based on global PLS scores |
author_facet |
Shen, Guanghui Lesnoff, Matthieu Baeten, Vincent Dardenne, Pierre Davrieux, Fabrice Ceballos, Hernán Belalcázar, John Eiver Dufour, Dominique Yang, Zengling Han, Lujia Fernández 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 |
publisher |
Wiley |
publishDate |
2019-05 |
url |
https://hdl.handle.net/10568/100718 https://doi.org/10.1002/cem.3117 |
work_keys_str_mv |
AT shenguanghui localpartialleastsquaresbasedonglobalplsscores AT lesnoffmatthieu localpartialleastsquaresbasedonglobalplsscores AT baetenvincent localpartialleastsquaresbasedonglobalplsscores AT dardennepierre localpartialleastsquaresbasedonglobalplsscores AT davrieuxfabrice localpartialleastsquaresbasedonglobalplsscores AT ceballoshernan localpartialleastsquaresbasedonglobalplsscores AT belalcazarjohneiver localpartialleastsquaresbasedonglobalplsscores AT dufourdominique localpartialleastsquaresbasedonglobalplsscores AT yangzengling localpartialleastsquaresbasedonglobalplsscores AT hanlujia localpartialleastsquaresbasedonglobalplsscores AT fernandezpiernajuanantonio localpartialleastsquaresbasedonglobalplsscores |
_version_ |
1779061060969431040 |