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Citation: ZHANG Wei-Dong, LI Ling-Qiao, HU Jin-Quan, FENG Yan-Chun, YIN Li-Hui, HU Chang-Qin, YANG Hui-Hua. Drug Discrimination by Near Infrared Spectroscopy Based on Stacked Sparse Auto-encoders Combined with Kernel Extreme Learning Machine[J]. Chinese Journal of Analytical Chemistry, ;2018, 46(9): 1446-1454. doi: 10.11895/j.issn.0253-3820.171343 shu

Drug Discrimination by Near Infrared Spectroscopy Based on Stacked Sparse Auto-encoders Combined with Kernel Extreme Learning Machine

1.
College of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China;
2.
College of Automation, Beijing University of Posts & Telecommunications, Beijing 100876, China;
3.
National Institutes for Food and Drug Control, Beijing 100050, China

Received Date: 16 October 2017
Revised Date: 5 June 2018

Fund Project: This work was supported by the National Natural Science Foundation of China (Nos. 21365008, 61562013).

A method for drug discrimination with near infrared spectroscopy based on stacked sparse auto-encoders combined with kernel extreme learning machine (SSAE-KELM) was developed. By introducing the KELM instead of the SSAE's Softmax classification and BP fine-tuning stage, the training steps, training parameters and training time of the SSAE were reduced, and the practical application of the deep learning network was improved, as well the classification ability of the model was improved by introduction of kernel function. Among which SSAE was used to initialize the entire network model and learn useful features from the input data and KELM was used to perform the classification. To identify binary-classification and multi-classification of drugs, the predictability, stability and training time of SSAE-KELM model for the same package (Aluminum-plastic or non-Aluminum-plastic) drug by different manufactures were investigated. At the same time, SSAE-KELM was compared with ELM, SSAE, SVM, BP and Dropout-DBN, and it was found that SSAE-KELM not only reduced the training time but had higher classification accuracy and stability in binary and multi-class classification. Therefore, SSAE-KELM is an effective spectral classification modeling tool.
- Stacked sparse auto-encoders,
- Kernel extreme learning machine,
- Kernel function,
- Near infrared spectroscopy,
- Drug identification
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