Home Journals Articles Updates About
CN
image
收藏切换
Based on Near Infrared Spectroscopy Combined with Data Enhancement CNN Algorithm Origin Traceability Method of Angelica Dahurica
收藏切换
PDF
Zhaohua GUO1, Shizhao WEN2, Sifan LI3, Qi WANG3, Yingxin WANG4, Xinguo WANG4, Liying NIU4, Yawei LI5, *, Wei FENG4, *
Chinese Pharmaceutical Journal | 2024, 59(21) : 2022 - 2029
Less
收藏切换
Chinese Pharmaceutical Journal | 2024, 59(21): 2022-2029
Based on Near Infrared Spectroscopy Combined with Data Enhancement CNN Algorithm Origin Traceability Method of Angelica Dahurica
Full
Zhaohua GUO1, Shizhao WEN2, Sifan LI3, Qi WANG3, Yingxin WANG4, Xinguo WANG4, Liying NIU4, Yawei LI5, *, Wei FENG4, *
Affiliations
  • 1 China Electronics Technology Group Corporation Network Communication Research Institute, Shijiazhuang 050050, China
  • 2 School of Statistics and Data Science, Nankai University, Tianjin 300192, China
  • 3 Northeastern University, Shenyang 110167, China
  • 4 Quality Evaluation & Standardization Hebei Province Engineering Research Center of Traditional Chinese Medicine, School of Pharmaceutical Sciences, Hebei University of Chinese Medicine, Shijiazhuang 050091, China
  • 5 Liaoning Academy of Analytical Sciences, Liaoning Inspection, Examination and Certification Centrer, Shenyang 110032, China
Published: 2024-11-08 doi: 10.11669/cpj.2024.21.005
Outline
收藏切换

OBJECTIVE To establish an origin classification model of Angelica dahurica with unbalanced sample size based on near-infrared spectroscopy combined with data-enhanced convolutional neural network(CNN) algorithm. METHODS In this study, 95 samples of Angelica dahurica were collected, and near-infrared spectroscopy was performed on different samples within the wavelength range of 12 500 to 4 000 cm-1. The near-infrared spectroscopy dataset of Angelica dahurica used in this study faces issues such as small sample size and uneven distribution of sample origins. To enhance the generalizability of the model, three data augmentation algorithms were proposed, including spectral shifting, spectral noise addition, and spectral combination. Additionally, to address the problem of sample imbalance, Focal Loss was used as the loss function for training the CNN model. RESULTS The three data enhancement algorithms were applied to the SVM model. Adding Gaussian noise with a signal-to-noise ratio of 20 to the spectral data had the best effect, which could increase the accuracy of the model to 84.2%. Aiming at the problem of sample imbalance, Focal Loss is used as the loss function to train the CNN model, and the accuracy rate can reach 94.7%. CONCLUSION The infrared spectroscopy combined with data-enhanced CNN algorithm provides a rapid and non-destructive detection method and reliable data analysis method for the origin traceability of Radix Angelicae Dahuricae, and provides a new method reference for the origin traceability of Chinese medicinal materials.

near infrared spectroscopy  /  Angelica dahurica  /  origin traceability  /  data enhancement  /  convolutional neural network
Zhaohua GUO, Shizhao WEN, Sifan LI, Qi WANG, Yingxin WANG, Xinguo WANG, Liying NIU, Yawei LI, Wei FENG. Based on Near Infrared Spectroscopy Combined with Data Enhancement CNN Algorithm Origin Traceability Method of Angelica Dahurica[J]. Chinese Pharmaceutical Journal, 2024 , 59 (21) : 2022 -2029 . DOI: 10.11669/cpj.2024.21.005
Appendix
Less
Year 2024 volume 59 Issue 21
PDF
100
52
Cite this Article
BibTeX
Article Info
doi: 10.11669/cpj.2024.21.005
  • Receive Date：2024-01-20
  • Online Date：2025-11-16
  • Published：2024-11-08
Article Data
Affiliations
History
  • Received：2024-01-20
Funding
Affiliations
    1 China Electronics Technology Group Corporation Network Communication Research Institute, Shijiazhuang 050050, China
    2 School of Statistics and Data Science, Nankai University, Tianjin 300192, China
    3 Northeastern University, Shenyang 110167, China
    4 Quality Evaluation & Standardization Hebei Province Engineering Research Center of Traditional Chinese Medicine, School of Pharmaceutical Sciences, Hebei University of Chinese Medicine, Shijiazhuang 050091, China
    5 Liaoning Academy of Analytical Sciences, Liaoning Inspection, Examination and Certification Centrer, Shenyang 110032, China
References
Share
https://castjournals.cast.org.cn/joweb/zgyxzz/EN/10.11669/cpj.2024.21.005
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House