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A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples
Author(s) -
Zhang Zhuoyong,
Wang Yamin,
Fan Guoqiang,
Harrington Peter De B.
Publication year - 2006
Publication title -
phytochemical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 72
eISSN - 1099-1565
pISSN - 0958-0344
DOI - 10.1002/pca.957
Subject(s) - artificial neural network , perceptron , bar (unit) , multilayer perceptron , artificial intelligence , pattern recognition (psychology) , identification (biology) , backpropagation , chemistry , simple (philosophy) , machine learning , data mining , computer science , geography , botany , meteorology , biology , philosophy , epistemology
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. Copyright © 2006 John Wiley & Sons, Ltd.