IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v190y2011i1p3-1510.1007-s10479-009-0554-z.html
   My bibliography  Save this article

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

Author

Listed:
  • Yi Fang
  • Jong Park
  • Young-Seon Jeong
  • Myong Jeong
  • Seung Baek
  • Hyun Cho

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. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Yi Fang & Jong Park & Young-Seon Jeong & Myong Jeong & Seung Baek & Hyun Cho, 2011. "Enhanced predictions of wood properties using hybrid models of PCR and PLS with high-dimensional NIR spectral data," Annals of Operations Research, Springer, vol. 190(1), pages 3-15, October.
    • Handle: RePEc:spr:annopr:v:190:y:2011:i:1:p:3-15:10.1007/s10479-009-0554-z
    DOI: 10.1007/s10479-009-0554-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-009-0554-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-009-0554-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ling Tang & Shuai Wang & Kaijian He & Shouyang Wang, 2015. "A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, Springer, vol. 234(1), pages 111-132, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:190:y:2011:i:1:p:3-15:10.1007/s10479-009-0554-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.