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Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

Published online by Cambridge University Press:  03 August 2012

Q. Lu ,
Y. Fan ,
Z. Peng ,
H. Ding  and
H. Gao
Q. Lu
Affiliation:
State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 3888 Dongnanhu Road, Changchun, Jilin 130033, P.R. China
Y. Fan*
Affiliation:
State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 3888 Dongnanhu Road, Changchun, Jilin 130033, P.R. China Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
Z. Peng
Affiliation:
State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 3888 Dongnanhu Road, Changchun, Jilin 130033, P.R. China
H. Ding
Affiliation:
State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 3888 Dongnanhu Road, Changchun, Jilin 130033, P.R. China
H. Gao
Affiliation:
State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, 3888 Dongnanhu Road, Changchun, Jilin 130033, P.R. China
*
ae-mail: fsmfyc@gmail.com
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Abstract

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample’ truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.

Type
Research Article
Information
The European Physical Journal - Applied Physics , Volume 59 , Issue 1 , July 2012 , 10701
Copyright
© EDP Sciences, 2012

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