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Local partial least squares based on global PLS scores
Author(s) -
Shen Guanghui,
Lesnoff Matthieu,
Baeten Vincent,
Dardenne Pierre,
Davrieux Fabrice,
Ceballos Hernan,
Belalcazar John,
Dufour Dominique,
Yang Zengling,
Han Lujia,
Fernández Pierna Juan Antonio
Publication year - 2019
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3117
Subject(s) - partial least squares regression , mathematics , statistics , linear regression
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.