Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy

Wavelength selection is a critical step in multivariate calibration. Variable selection methods are used to find the most relevant variables, leading to improved prediction accuracy, while simplifying both the built models and their interpretation. In addition, different spectrophotometer designs an...

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Detalles Bibliográficos
Publicado:	2019
Materias:	FCAM-PLS Near-Infrared spectroscopy Orthogonalization Replacement Method ROWS-MLR
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0026265X_v145_n_p872_Goodarzi http://hdl.handle.net/20.500.12110/paper_0026265X_v145_n_p872_Goodarzi
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_0026265X_v145_n_p872_Goodarzi
record_format	dspace
spelling	paper:paper_0026265X_v145_n_p872_Goodarzi2023-06-08T14:53:48Z Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy FCAM-PLS Near-Infrared spectroscopy Orthogonalization Replacement Method ROWS-MLR Wavelength selection is a critical step in multivariate calibration. Variable selection methods are used to find the most relevant variables, leading to improved prediction accuracy, while simplifying both the built models and their interpretation. In addition, different spectrophotometer designs and measurement principles result in non-destructive technologies applied in many fields, such as agriculture, food chemistry and pharmaceutics. However, an on-chip or portable device does not allow acquiring data from a large number of wavelengths. Therefore, the most informative combination of a limited number of variables should be selected. The Replacement Orthogonal Wavelengths Selection (ROWS) method is described here as a new method. This algorithm aims at selecting as few wavelengths as possible, while keeping or improving the prediction performance of the model, compared to when no variable selection is applied. The ROWS is applied to several near infrared spectroscopic data sets leading to improved analytical figures of merits upon wavelength selection in comparison to a built PLS model using entire spectral range. The performance of the ROWS-MLR method was compared to the FCAM-PLS method. The resulting models are not significantly different from those of FCAM-PLS; however, it involves a significantly smaller amount of variables. © 2018 2019 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0026265X_v145_n_p872_Goodarzi http://hdl.handle.net/20.500.12110/paper_0026265X_v145_n_p872_Goodarzi
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	FCAM-PLS Near-Infrared spectroscopy Orthogonalization Replacement Method ROWS-MLR
spellingShingle	FCAM-PLS Near-Infrared spectroscopy Orthogonalization Replacement Method ROWS-MLR Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
topic_facet	FCAM-PLS Near-Infrared spectroscopy Orthogonalization Replacement Method ROWS-MLR
description	Wavelength selection is a critical step in multivariate calibration. Variable selection methods are used to find the most relevant variables, leading to improved prediction accuracy, while simplifying both the built models and their interpretation. In addition, different spectrophotometer designs and measurement principles result in non-destructive technologies applied in many fields, such as agriculture, food chemistry and pharmaceutics. However, an on-chip or portable device does not allow acquiring data from a large number of wavelengths. Therefore, the most informative combination of a limited number of variables should be selected. The Replacement Orthogonal Wavelengths Selection (ROWS) method is described here as a new method. This algorithm aims at selecting as few wavelengths as possible, while keeping or improving the prediction performance of the model, compared to when no variable selection is applied. The ROWS is applied to several near infrared spectroscopic data sets leading to improved analytical figures of merits upon wavelength selection in comparison to a built PLS model using entire spectral range. The performance of the ROWS-MLR method was compared to the FCAM-PLS method. The resulting models are not significantly different from those of FCAM-PLS; however, it involves a significantly smaller amount of variables. © 2018
title	Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
title_short	Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
title_full	Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
title_fullStr	Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
title_full_unstemmed	Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
title_sort	replacement orthogonal wavelengths selection as a new method for multivariate calibration in spectroscopy
publishDate	2019
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0026265X_v145_n_p872_Goodarzi http://hdl.handle.net/20.500.12110/paper_0026265X_v145_n_p872_Goodarzi
_version_	1768545590420963328

Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy

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