Analysis of the Oil Content of Rapeseed Using Artificial Neural Networks Based on Near Infrared Spectral Data. (22nd June 2014)

• Record Type: Journal Article
• Authors: Yang, Dazuo; Li, Hao; Cao, Chenchen; Chen, Fudi; Zhou, Yibing; Xiu, Zhilong
• Other Names: Zhang Qingrui Academic Editor.
• Is Part Of: Journal of spectroscopy. Volume 2014(2014)
• Publication Date: 2014-06-22
• Subjects: Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1155/2014/901310
• Languages: English
• ISSNs: 2314-4920

Abstract

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.