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Enhanced predictions of wood properties using hybrid models of PCR and PLS withhigh-dimensional NIR spectral data

Enhanced predictions of wood properties using hybrid models of PCR and PLS withhigh-dimensional... Near infrared (NIR) spectroscopy is a rapid, non-destructive technology to predict a variety of wood properties and provides great opportunities to optimize manufacturing processes through the realization of in-line assessment of forest products. In this paper, a novel multivariate regression procedure, the hybrid model of principal component regression (PCR) and partial least squares (PLS), is proposed to develop more accurate prediction models for high-dimensional NIR spectral data. To integrate the merits of PCR and PLS, both principal components defined in PCR and latent variables in PLS are utilized in hybrid models by a common iterative procedure under the constraint that they should keep orthogonal to each other. In addition, we propose the modified sequential forward floating search method, originated in feature selection for classification problems, in order to overcome difficulties of searching the vast number of possible hybrid models. The effectiveness and efficiency of hybrid models are substantiated by experiments with three real-life datasets of forest products. The proposed hybrid approach can be applied in a wide range of applications with high-dimensional spectral data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Operations Research Springer Journals

Enhanced predictions of wood properties using hybrid models of PCR and PLS withhigh-dimensional NIR spectral data

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References (22)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Business and Management; Operation Research/Decision Theory; Combinatorics; Theory of Computation
ISSN
0254-5330
eISSN
1572-9338
DOI
10.1007/s10479-009-0554-z
Publisher site
See Article on Publisher Site

Abstract

Near infrared (NIR) spectroscopy is a rapid, non-destructive technology to predict a variety of wood properties and provides great opportunities to optimize manufacturing processes through the realization of in-line assessment of forest products. In this paper, a novel multivariate regression procedure, the hybrid model of principal component regression (PCR) and partial least squares (PLS), is proposed to develop more accurate prediction models for high-dimensional NIR spectral data. To integrate the merits of PCR and PLS, both principal components defined in PCR and latent variables in PLS are utilized in hybrid models by a common iterative procedure under the constraint that they should keep orthogonal to each other. In addition, we propose the modified sequential forward floating search method, originated in feature selection for classification problems, in order to overcome difficulties of searching the vast number of possible hybrid models. The effectiveness and efficiency of hybrid models are substantiated by experiments with three real-life datasets of forest products. The proposed hybrid approach can be applied in a wide range of applications with high-dimensional spectral data.

Journal

Annals of Operations Research – Springer Journals

Published: Jun 4, 2009

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