Paper Title:
Use of BP Neural Network in Near-Infrared Spectroscopy Calibrations for Predicting of Wood Density
  Abstract

Application of BP neural network and NIRS for larch wood density prediction was investigated in this paper. The original spectra were collected and pretreated with the first derivative and 9 point smoothing. Eleven typical wave lengths were selected as BP network inputs to establish prediction model for wood density. Model was validated using cross-validation approach. The prediction correlation coefficient (R) is 0.916 while the root mean square error of prediction (RMSEP) is 0.0221. The results showed that using BP neural network in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

  Info
Periodical
Advanced Materials Research (Volumes 129-131)
Edited by
Xie Yi and Li Mi
Pages
306-311
DOI
10.4028/www.scientific.net/AMR.129-131.306
Citation
P. Li, H. F. Zhang, Y. X. Li, Y. Z. Zhang, H. J. Zhang, "Use of BP Neural Network in Near-Infrared Spectroscopy Calibrations for Predicting of Wood Density", Advanced Materials Research, Vols. 129-131, pp. 306-311, 2010
Online since
August 2010
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