Use of BP Neural Network in Near-Infrared Spectroscopy Calibrations for Predicting of Wood Density

Abstract:

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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 et al., "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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Price:

$35.00

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