Modeling Wood Crystallinity with Multiple Linear Regression

Yao Xiang Li; Li Chun Jiang

doi:10.4028/www.scientific.net/KEM.480-481.550

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 480-481Modeling Wood Crystallinity with Multiple Linear...

Modeling Wood Crystallinity with Multiple Linear Regression

Abstract:

The crystallinity of wood has an important effect on the physical, mechanical and chemical properties of cellulose fibers. Crystallinity of larch plantation wood was investigated with near infrared spectroscopy and multiple linear regression. Five typical wave lengths were selected to establish prediction model for wood crystallinity. Full-cross validation was applied to the model development. The model performance is satisfied with prediction correlation coefficient of 0.896 and bias of 0.0004. The results indicated that prediction of wood crystallinity with near infrared spectroscopy and multiple linear regression is feasible, which provides a fast and nondestructive method for wood crystallinity prediction.

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Key Engineering Materials (Volumes 480-481)

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550-555

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.480-481.550

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Online since:

June 2011

Authors:

Yao Xiang Li, Li Chun Jiang

Keywords:

Multiple Linear Regression, Near Infrared Spectroscopy (NIRS), Wood Crystallinity

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