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

Yi Fang, Jong I. Park, Young Seon Jeong, Myong K. Jeong, Seung H. Baek, Hyun Woo Cho

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3-15
Number of pages13
JournalAnnals of Operations Research
Issue number1
StatePublished - Oct 2011

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Management Science and Operations Research


  • Floating search
  • Hybrid models
  • Latent variables
  • Multivariate regression
  • NIR spectroscopy
  • Principal component analysis


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