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 g...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2019
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| Online Access: | https://hdl.handle.net/10568/100718 |
| _version_ | 1855535653036490752 |
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| 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 |
| author_browse | Baeten, Vincent Belalcázar, John Eiver Ceballos, Hernán Dardenne, Pierre Davrieux, Fabrice Dufour, Dominique Fernández Pierna, Juan Antonio Han, Lujia Lesnoff, Matthieu Shen, Guanghui Yang, Zengling |
| 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 |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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 |
| id | CGSpace100718 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1007182025-03-13T09:45:01Z 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 https://hdl.handle.net/10568/100718 en Open Access Wiley 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. |
| 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 |
| title | 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_short | Local partial least squares based on global PLS scores |
| title_sort | local partial least squares based on global pls scores |
| url | https://hdl.handle.net/10568/100718 |
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