NIR-Based Wood Water Content Prediction with an Integration of ANN and PCA

Abstract:

In this paper, an integration of BP neural network and PCA for modeling wood water content of larch combined with NIRS was investigated. The original spectra were collected and pretreated with 9 point smoothing and multiplicative scatter correction (MSC). Five typical principal components were extracted from PCA with the application of establishing prediction model. Full cross-validation approach was applied to achieve desirable modeling performance. The prediction correlation coefficient (R) was 0.952 while the mean square error of prediction (MSEP) was 38.27. This study indicated that NIR is a useful tool for rapid and accurate prediction of wood water content.

• Periodical: Advanced Materials Research (Volume 502)
• Pages: 253-257
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.502.253
• Online since: April 2012
• Authors: Pai Li, Yao Xiang Li
• Keywords: BP Neural Network (BPNN), Near Infrared Spectroscopy (NIRS), Principal Component Analysis (PCA), Wood Water Content

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