Multi-manufacturer drug identification based on near infrared spectroscopy and deep transfer learning

Author:

Li Lingqiao12,Pan Xipeng2,Chen Wenli2,Wei Manman2,Feng Yanchun3,Yin Lihui3,Hu Changqin3,Yang Huihua12ORCID

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. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3