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Journal of Spectroscopy
Volume 2014 (2014), Article ID 901310, 5 pages
http://dx.doi.org/10.1155/2014/901310
Research Article

Analysis of the Oil Content of Rapeseed Using Artificial Neural Networks Based on Near Infrared Spectral Data

1Key Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, China
2 College of Life Science and Technology, Dalian University of Technology, Dalian 116021, China
3College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China

Received 9 May 2014; Accepted 2 June 2014; Published 23 June 2014

Academic Editor: Qingrui Zhang

Copyright © 2014 Dazuo Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The oil content of rapeseed is a crucial property in practical applications. In this paper, instead of traditional analytical approaches, an artificial neural network (ANN) method was used to analyze the oil content of 29 rapeseed samples based on near infrared spectral data with different wavelengths. Results show that multilayer feed-forward neural networks with 8 nodes (MLFN-8) are the most suitable and reasonable mathematical model to use, with an RMS error of 0.59. This study indicates that using a nonlinear method is a quick and easy approach to analyze the rapeseed oil’s content based on near infrared spectral data.