[Study on artificial neural network combined with near infrared spectroscopy for wood species identification]

In the present article, near infrared spectra of 89 wood samples of different geographical provenances and species were measured, and back propagation artificial neural networks(BPANN) and generalized regression neural network (GRNN) were used for modeling of wood species NIRS identifying. Parameters for two neural networks were chosen via analysis of variance, respectively; and networks were trained with optimum parameters. Considering the difference between spectra, spectra with different levels of white noise and different levels of bias were simulated and predicted by using the models built. It was found that both the two models had satisfactory prediction results, identification correct rates obtained by BPANN model applied to spectra with bias level no higher than 2% and noise level no higher than 4% were above 97%; correct rates obtained by GRNN model applied to spectra with bias level no higher than 2% and noise level no higher than 4% were above 99%.