title: A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples.
abstract: Artificial neural networks have gained much attention in recent years as fast and flexible methods for quality control in traditional medicine. Near-infrared (NIR) spectroscopy has become an accepted method for the qualitative and quantitative analyses of traditional Chinese medicine since it is simple, rapid, and non-destructive. The present paper describes a method by which to discriminate official and unofficial rhubarb samples using three layer perceptron neural networks applied to NIR data. Multilayer perceptron neural networks were trained with back propagation, delta-bar-delta and quick propagation algorithms. Results obtained using these methods were all satisfactory, but the best outcomes were obtained with the delta-bar-delta algorithm.
authors: Department of Chemistry, MOE Key Lab for 3-D Information Acquisition and Applications, Capital Normal University, Beijing 100037, People's Republic of China. gusto2008@vip.sina.com
authors: Zhuoyong
authors: Z
authors: Zhang
authors: Yamin
authors: Y
authors: Wang
authors: Guoqiang
authors: G
authors: Fan
authors: Peter de B
authors: Pde B
authors: Harrington
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012250:Rheum / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared