Abstract

In this study, the rapid Fourier transform infrared (FTIR) spectroscopic method was used to indirectly measure the wood density of Pinus brutia Ten. samples. A model was constructed to relate FTIR data to wood density determined by laboratory analysis, through the application of artificial neural network (ANN) modelling approach to a set of calibration observations. The proposed model with two hidden neurons performed very good to estimate the wood density with high correlation R² values of 0.9833 for training and 0.9814 for testing, respectively, and with a low prediction error of 0.71% in the validation. This analysis showed that ANN coupled with FTIR spectroscopy could be used to accurately predict the density of wood samples.

Key words: Wood density, Fourier transform infrared (FTIR) spectroscopy, Artificial neural network (ANN), Pinus brutia.