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The application of an artificial neural network in the identification of medicinal rhubarbs by near‐infrared spectroscopy
Author(s) -
Xiang Lan,
Fan Guoqiang,
Li Junhui,
Kang Hui,
Yan Yanlu,
Zheng Junhua,
Guo Dean
Publication year - 2002
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.654
Subject(s) - test set , artificial neural network , set (abstract data type) , artificial intelligence , pattern recognition (psychology) , training set , chemistry , backpropagation , node (physics) , momentum (technical analysis) , identification (biology) , machine learning , biological system , computer science , engineering , botany , structural engineering , finance , economics , biology , programming language
This paper describes a method to combine near‐infrared spectroscopy and a three layer back‐propagation artificial neural network in order to identify official and unofficial rhubarbs. Thirty‐three samples were taken as the training set, and 62 samples as the test set. The effects of input node number, learning rate and momentum on the final error and recognition accuracy for the training set, and on prediction accuracy for the test set were determined. A neural network with eight input nodes, a 0.5 learning rate, and a momentum of 0.3 can achieve a recognition accuracy of 100% for the training set and a prediction accuracy of 96.8% for the test set. The method described offers a quick and efficient means of identifying rhubarbs. Copyright © 2002 John Wiley & Sons, Ltd.