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Vol. 26 No. 18 (2014): Vol 26 Issue 18

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Spectrophotometric Determination of Monosaccharide Composition of Wood (Pinus brutia Ten.) Using Artificial Neural Network Modelling

https://doi.org/10.14233/ajchem.2014.16721
Samim Yasar
Department of Forest Products Engineering, Faculty of Forestry, Suleyman Demirel University, 32260-Isparta, Turkey

Corresponding Author(s) : Samim Yasar

samimyasar@sdu.edu.tr

Asian Journal of Chemistry, Vol. 26 No. 18 (2014): Vol 26 Issue 18

Abstract

Spectrophotometric data were used to estimate monosaccharide content in Pinus brutia Ten. (brutian pine) wood using artificial neural network (ANN) modelling. The monosaccharide composition of P. brutia Ten. samples ranged for glucose from 42.33 to 54.67 %, for mannose from 8.55 to 11.95 %, for xylose from 7.15 to 9.83 %, for galactose from 1.72 to 2.49 % 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 R2 values between 0.9987 and 0.9916 in training and between 0.9984 and 0.9902 in testing. In validation, this model was scored with a small average relative error (1.2 %) fairly good.

Keywords

Pinus brutia Ten. Monosaccharide composition Spectrophotometric data Artificial neural network
Yasar, S. (2014). Spectrophotometric Determination of Monosaccharide Composition of Wood (Pinus brutia Ten.) Using Artificial Neural Network Modelling. Asian Journal of Chemistry, 26(18), 6084–6088. https://doi.org/10.14233/ajchem.2014.16721
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