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Via infrared spectroscopy (IR) combined with support vector machine (SVM), the study on timber species identification was carried on. Ten kinds of precious timber were used as experimental materials; each timber picked three sets of samples. The corresponding spectrum was recorded by infrared spectrometer. The spectral data was pretreated by baseline correction and dimensionality reduction. Radial basis function ( RBF )was selected as kernel function , and RBF coefficient (γ) was 0.01.As for cross- validation ,the model of timber species identification was respectively established by the adjustment of the training set and test set, the discriminant accuracy rate of three models were 70%, 80%, and 100 %. The optimal model was compared with the model of Cluster analysis and Bayes discriminant, which indicated that the SVM- infrared spectroscopy technology has better prediction results and certain research value for the development of the timber species identification. p j i j i b a x x x x K        ,

Study on Support Vector Machine Combined with Infrared Spectroscopy for Timber Species Identification