| dc.creator |
Yasar, Samim |
|
| dc.creator |
Guller, Bilgin |
|
| dc.date |
2010-07-03T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T10:33:08Z |
|
| dc.date.available |
2020-10-06T10:33:08Z |
|
| dc.identifier |
7a06c27f-cdd2-4640-92c4-2746764eb5fc |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/7a06c27f-cdd2-4640-92c4-2746764eb5fc/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/64066 |
|
| dc.description |
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 R(2) 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. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Estimation of Pinus brutia Ten. wood density from Fourier Transform Infrared (FTIR) spectroscopic bands by Artificial Neural Network (ANN) |
|
| dc.type |
info:eu-repo/semantics/article |
|