An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration

A new optimization strategy for multivariate partial-least-squares (PLS) regression analysis is described. It was achieved by integrating three efficient strategies to improve PLS calibration models: (1) variable selection based on ant colony optimization, (2) mathematical pre-processing selectio...

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Autores principales: Allegrini, Franco, Olivieri, Alejandro César
Lenguaje:Inglés
Publicado: Elsevier 2018
Materias:
Partial least-squares
Multivariate Calibration
Variable Selection
Pre-processing Selection
Sample Selection
Outlier Detection
Acceso en línea:http://hdl.handle.net/2133/10470
http://hdl.handle.net/2133/10470
Aporte de:
Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR) de Universidad Nacional de Rosario
id I15-R121-2133-10470
record_format dspace
institution Universidad Nacional de Rosario
institution_str I-15
repository_str R-121
collection Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR)
language Inglés
orig_language_str_mv eng
topic Partial least-squares
Multivariate Calibration
Variable Selection
Pre-processing Selection
Sample Selection
Outlier Detection
spellingShingle Partial least-squares
Multivariate Calibration
Variable Selection
Pre-processing Selection
Sample Selection
Outlier Detection
Allegrini, Franco
Olivieri, Alejandro César
An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
topic_facet Partial least-squares
Multivariate Calibration
Variable Selection
Pre-processing Selection
Sample Selection
Outlier Detection
description A new optimization strategy for multivariate partial-least-squares (PLS) regression analysis is described. It was achieved by integrating three efficient strategies to improve PLS calibration models: (1) variable selection based on ant colony optimization, (2) mathematical pre-processing selection by a genetic algorithm, and (3) sample selection through a distance-based procedure. Outlier detection has also been included as part of the model optimization. All the above procedures have been combined into a single algorithm, whose aim is to find the best PLS calibration model within a Monte Carlo-type philosophy. Simulated and experimental examples are employed to illustrate the success of the proposed approach.
author Allegrini, Franco
Olivieri, Alejandro César
author_facet Allegrini, Franco
Olivieri, Alejandro César
author_sort Allegrini, Franco
title An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
title_short An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
title_full An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
title_fullStr An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
title_full_unstemmed An integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
title_sort integrated approach to the simultaneous selection of variables, mathematical pre-processing and calibration samples in partial least-squares multivariate calibration
publisher Elsevier
publishDate 2018
url http://hdl.handle.net/2133/10470
http://hdl.handle.net/2133/10470
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