Model for predicting wood density of Larch was established using near-infrared spectroscopy (NIR) combined with support vector machine (SVM). A hundred and seventeen Larch samples were used in the study. Wood density of samples was measured according to standard test methods for physical and mechanical properties of wood. Support vector machines for regression (SVR) was used for model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting wood density. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8504 and 0.6460×10-3, while the R2 and MSE was 0.8520 and 0.4451×10-3, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.
[1]
Xiao-jun Tang,et al.
[Quantitative analysis of multi-component gas mixture based on KPCA and SVR].
,
2008,
Guang pu xue yu guang pu fen xi = Guang pu.
[2]
An-min Huang,et al.
[Determination of holocellulose and lignin content in Chinese fir by near infrared spectroscopy].
,
2007,
Guang pu xue yu guang pu fen xi = Guang pu.
[3]
Gao Hong.
Combination predication model based on SVR and its application
,
2009
.
[4]
Chih-Jen Lin,et al.
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
,
2003,
Neural Computation.
[5]
Corinna Cortes,et al.
Support-Vector Networks
,
1995,
Machine Learning.