Sparse Denoising Autoencoder Application in Identification of Counterfeit Pharmaceutical
YANG Hui-hua1, 2, LUO Zhi-chao1, JIANG Shu-jie1, ZHANG Xue-bo3, YIN Li-hui3
1. College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 2. College of Automation, Beijing University of Posts & Telecommunications, Beijing 100876, China 3. National Institute for Food and Drug Control, Beijing 100050, China
Abstract：Near-infrared(NIR)As a fast and non-destructive testing technology, spectroscopy techniques is very suitable for pharmaceutical discrimination. Autoencoder network, as a hot research topic, has drawn widespread attention in machine learning research in recent years. Compared with traditional surface learning algorithm models, Autoencoder network has more powerful modeling capability as a typical deep networks model. Based on the unsupervised greedy layer-wise pre-training, autoencoder trains the network layer by layer while minimizing the error in reconstructing. Each layer is pre-trained with an unsupervised learning algorithm, learning a nonlinear transformation of the input of each layer which is the output of the previous layer. Pre-whitening process could get the inner structural features of the data more effectively. The supervised fine-tuning is followed with the unsupervised pre-training which sets the stage for a final training phase. The deep architecture is fine-tuned with respect to a supervised training criterion with gradient-based optimization. In this paper, firstly, the preprocessing step and pre-whitening transformation were used to treat near-infrared spectroscopy data of erythromycin ethylsuccinate, The pre-whitening transformation would reduce the correlation of the features, which gave each feature the same variance. Experimental results showed that the pre-whitening process had improved the classification accuracy of Sparse Denoising Autoencoder (SDAE) effectively. The SDAE with two hidden layers combined with pre-whitening was used to build the classification model for the identification of counterfeit pharmaceutical. The BP neural networks was compared with SVM algorithm for the classification accuracy and mean absolute difference (MAD). SDAE algorithm had higher classification accuracy than BP neural networks which had the same network structure with the SDAE networks, and SDAE algorithm also performed better than the SVM algorithm when the train datasets achieved a certain amount. As to the generalization performances, SDAE algorithm had less mean absolute difference of classification accuracy than SVM and BP Neural Networks. This result showed that SDAE algorithm could be effectively used to discriminate the counterfeit pharmaceutical.
[1] CHU Xiao-li, LU Wan-zhen(褚小立, 陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(10): 2595. [2] Storme-Paris I, Rebiere H, Matoga M, et al. Analytica Chimica Acta, 2010, 658(2): 163. [3] Deconinck E, Sacré P, Coomans D, et al. Journal of Pharmaceutical and Biomedical Analysis, 2012, 57: 68. [4] Anzanello M J, Ortiz R S, Limberger R, et al. Forensic Science International, 2014, 235: 1. [5] De Peinder P, Vredenbregt M J, Visser T, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 47(4): 688. [6] Dowell F E, Maghirang E B, Fernandez F M, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 48(3): 1011. [7] Feng Y C, Yang X L, Yang Z H, et al. Journal of Chinese Pharmaceutical Sciences, 2011, 20(3): 290. [8] YU Ke, CHENG Yi-yu(虞 科，程翼宇). Chinese Journal of Analytical Chemistry(分析化学), 2006, 34(4): 561. [9] Hinton G E, Salakhutdinov R R. Science, 2006, 313(5786): 504. [10] Vincent P, Larochelle H, Bengio Y, et al. Proceedings of the 25th International Conference on Machine Learning, 2008: 1096. [11] Lee H, Ekanadham C, Ng A Y. Proc. Conference on Advances in Neural Information Processing Systems (NIPS), 2008. 873.
