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: | , |
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| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2018
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/2133/10470 http://hdl.handle.net/2133/10470 |
| Aporte de: |
| Sumario: | 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. |
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