Affiliation:
1. School of Automation, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Beijing 100876, P. R. China
2. School of Computer Science and Information Security, Guilin University of Electronic Technology, 1 Jinji Road, Guilin 541004, P. R. China
3. National Institutes for Food and Drug Control, 10 Tiantanxili Road, Beijing 100050, P. R. China
Abstract
Near infrared (NIR) spectrum analysis technology has outstanding advantages such as rapid, nondestructive, pollution-free, and is widely used in food, pharmaceutical, petrochemical, agricultural products production and testing industries. Convolutional neural network (CNN) is one of the most successful methods in big data analysis because of its powerful feature extraction and abstraction ability, and it is especially suitable for solving multi-classification problems. CNN-based transfer learning is a machine learning technique, which migrates parameters of trained model to the new one to improve the performance. The transfer learning strategy can speed up the learning efficiency of the model instead of learning from scratch. In view of the difficulty in acquisition of drug NIR spectral data and high labeling cost, this paper proposes three simple but very effective transfer learning methods for multi-manufacturer identification of drugs based on one-dimensional CNN. Compared with the original CNN, the transfer learning method can achieve better classification performance with fewer NIR spectral data, which greatly reduces the dependence on labeled NIR spectral data. At the same time, this paper also compares and discusses three different transfer learning methods, and selects the most suitable transfer learning model for drug NIR spectral data analysis. Compared with the current popular methods, such as SVM, BP, AE and ELM, the proposed method achieves higher classification accuracy and scalability in multi-variety and multi-manufacturer NIR spectrum classification experiments.
Funder
Young Scientists Fund
Key Technologies Research and Development Program
Publisher
World Scientific Pub Co Pte Lt
Subject
Biomedical Engineering,Atomic and Molecular Physics, and Optics,Medicine (miscellaneous),Electronic, Optical and Magnetic Materials
Cited by
23 articles.
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