A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples.
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.
Zhang
Zhuoyong
Z
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
Wang
Yamin
Y
Fan
Guoqiang
G
Harrington
Peter de B
Pde B
D000465:Algorithms; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012250:Rheum / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared