Prediction of Lignin Content of Manchurian Walnut by BP Neural Network and Near-Infrared Spectroscopy

Zhi Hua Qu; Li Hai Wang

doi:10.4028/www.scientific.net/AMR.267.991

Paper Titles

Fast Restoration Algorithm for Rotational Motion Blurred Images
p.969

Index Tracking Method Based on the Neural Networks and its Empirical Study
p.974

Research on the Evolution of Software Development Ideas and Development Method
p.979

An Artificial Neural Network Approach for Short-Term Electric Prices Forecasting
p.985

Prediction of Lignin Content of Manchurian Walnut by BP Neural Network and Near-Infrared Spectroscopy
p.991

Hill Climbing Algorithm for License Plate Recognition
p.995

The Design of Electronic Code Lock
p.1001

Study on Piezoelectric Stacked Generator
p.1005

Study on Image Search Engine Based on Color Feature Algorithm
p.1010

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 267Prediction of Lignin Content of Manchurian Walnut...

Prediction of Lignin Content of Manchurian Walnut by BP Neural Network and Near-Infrared Spectroscopy

Abstract:

The lignin as a main component of wood, its content is an important chemical property of wood materials, it has an great effect on the other properties of wood and wood processing and utilization property. In paper making industry, the lignin content is a basis for developing pulp cooking and bleaching process. With the advantages of simple structure, plasticity and obviously superiority in nonlinear data processing, BP neural network and NIR for Manchurian Walnut wood lignin content prediction was investigated in this paper. The original spectra were collected and pretreated with the first derivative. Thriteen typical wave lengths were selected as BP network inputs to establish prediction model for wood lignin content. Model was validated using cross-validation approach. The prediction correlation coefficient (R) is 0.9233 while the root mean square error of prediction (RMSEP) is 0.0179. The results showed that using BP neural network in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood lignin content.