| dc.creator |
Yasar, Samim |
|
| dc.date |
2014-08-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:29:45Z |
|
| dc.date.available |
2020-10-06T09:29:45Z |
|
| dc.identifier |
1dc199ce-ec29-416e-947c-386f9e645b19 |
|
| dc.identifier |
10.14233/ajchem.2014.16721 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/1dc199ce-ec29-416e-947c-386f9e645b19/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/54857 |
|
| dc.description |
Spectrophotometric data were used to estimate monosaccharide content in Pinta brutia Ten. (brutian pine) wood using artificial neural network (ANN) modelling. The monosaccharide conaposition. of R brutia Ten samples ranged for glucose from 4233 to 54.67 %, for mannose from 8.55 to 11.95 %, for xylose from 7.15 to 9.83 %; for galactose from 1.12 to 249% and for arabinose from 1.19 to 1.65 %,. based on. extractive free dry Wood. Three layered artificial neural network model with six hidden neurons gave in,general better results With correlation le values between 0.9987 and 09916 in training and between 0.9984 and 0.9902 in testing. In validation, this model was scored with a small average relative error (12 %) fairly good. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Spectrophotometric Determination of Monosaccharide Composition of Wood (Pinus brutia Ten.) Using Artificial Neural Network Modelling |
|
| dc.type |
info:eu-repo/semantics/article |
|