| 光谱学与光谱分析 |
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| Drug Discrimination by Near Infrared Spectroscopy Based on Summation Wavelet Extreme Learning Machine |
| LIU Zhen-bing1, JIANG Shu-jie1*, YANG Hui-hua1, ZHANG Xue-bo2 |
1. College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
2. National Institute for the Control of Pharmaceutical and Biological Products, Beijing 100050, China |
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Abstract As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM’s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near infrared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper. Experiments for binary classification and multi-label classification were conducted, and the conclusion proved that the proposed method has more stable performance, higher classification accuracy and lower sensitivity to training samples than the existing ones, such as the BP neural network, ELM and ELM by particle swarm optimization.
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Received: 2014-05-28
Accepted: 2014-07-31
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Corresponding Authors:
JIANG Shu-jie
E-mail: 462832784@qq.com
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[1] Li Y, Shao X, Cai W. Talanta, 2007, 72(1): 217.
[2] Dou Y, Mi H, Zhao L, et al. Analytical Biochemistry, 2006, 351(2): 174.
[3] Freitas M P, Sabadin A, Silva L M, et al. Journal of Pharmaceutical and Biomedical Analysis, 2005, 39(1): 17.
[4] Deconinck E, Sacré P Y, Coomans D, et al. Journal of Pharmaceutical and Biomedical Analysis, 2012, 57(5): 68.
[5] Xin N, Gu X F, Wu H, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2012, 89(4): 18.
[6] Storme-Paris I, Rebiere H, Matoga M, et al. Analytica Chimica Acta, 2010, 658(2): 163.
[7] Huang G B, Zhou H, Ding X, et al. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2012, 42(2): 513.
[8] GAO Guang-yong, JIANG Guo-ping(高光勇, 蒋国平). Acta Physica Sinica(物理学报), 2012, 61(4): 0405.
[9] Zhu Q Y, Qin A K, Suganthan P N, et al. Pattern recognition, 2005, 38(10): 1759.
[10] LU Hui-juan, AN Chun-lin, MA Xiao-ping, et al(陆慧娟, 安春霖, 马小平, 等). Chinese Journal of Computers(计算机学报), 2013, 36(2): 341.
[11] Han F, Yao H F, Ling Q H. Neurocomputing, 2013, 116(20): 87.
[12] Javed K, Gouriveau R, Zerhouni N. Neurocomputing, 2014, 123(10): 299.
[13] Yang X S, Deb S. International Journal of Mathematical Modelling and Numerical Optimisation, 2010, 1(4): 330. |
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