Yu, Shixiang1,3; Li, Hanfei2,3; Li, Xin1,3; Fu, Yu Vincent2; Liu, Fanghua1,4,5
发表期刊SCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
2020-07-15
卷号726页码:9
关键词ClassificationGenerative adversarial networkPathogensRaman spectroscopy
DOI10.1016/j.scitotenv.2020.138477
通讯作者Fu, Yu Vincent(fuyu@im.ac.cn); Liu, Fanghua(fhliu@yic.ac.cn)
英文摘要Rapid identification of marine pathogens is very important in marine ecology. Artificial intelligence combined with Raman spectroscopy is a promising choice for identifying marine pathogens due to its rapidity and efficiency. However, considering the cost of sample collection and the challenging nature of the experimental environment, only limited spectra are typically available to build a classification model, which hinders qualitative analysis. In this paper, we propose a novel method to classify marine pathogens by means of Raman spectroscopy combined with generative adversarial networks (GANs). Three marine strains, namely, Staphylococcus hominis, Vibrio alginolyticus, and Bacillus licheniformis, were cultured. Using Raman spectroscopy, we acquired 100 spectra of each strain, and we fitted them into GAN models for training. After 30,000 training iterations, the spectra generated by G were similar to the actual spectra, and D was used to test the accuracy of the spectra. Our results demonstrate that our method not only improves the accuracy of machine learning classification but also solves the problem of requiring a large amount of training data. Moreover, we have attempted to find potential identifying regions in the Raman spectra that can be used for reference in subsequent related work in this field. Therefore, this method has tremendous potential to be developed as a tool for pathogen identification. (C) 2020 Elsevier B.V. All rights reserved.
资助机构Chinese Academy of Sciences; Training Program of the Major Research Plan of the National Natural Science Foundation of China; Young Taishan Scholars Program of Shandong Province; GDAS' Project of Science and Technology Development; Guangdong Foundation for Program of Science and Technology Research
收录类别SCI
语种英语
关键词[WOS]CONVOLUTIONAL NEURAL-NETWORKS; BACTERIA; IDENTIFICATION; BLOOD
研究领域[WOS]Environmental Sciences & Ecology
WOS记录号WOS:000537422600002
引用统计被引频次：8[WOS][WOS记录][WOS相关记录]
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/28737
专题海岸带生物学与生物资源利用重点实验室
海岸带生物学与生物资源利用重点实验室_海岸带生物学与生物资源保护实验室
通讯作者Fu, Yu Vincent; Liu, Fanghua作者单位1.Chinese Acad Sci, Key Lab Coastal Biol & Biol Resources Utilizat, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
2.Chinese Acad Sci, Inst Microbiol, State Key Lab Microbial Resources, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Guangdong Acad Sci, Guangdong Inst Ecoenvironm Sci & Technol, Guangdong Key Lab Integrated Agroenvironm Pollut, Natl Reg Joint Engn Res Ctr Soil Pollut Control &, Guangzhou 510650, Peoples R China
5.Chinese Acad Sci, Guangzhou Inst Geochem, Guangdong Hong Kong Macao Joint Lab Environm Poll, Guangzhou 510640, Peoples R China
推荐引用方式
GB/T 7714Yu, Shixiang,Li, Hanfei,Li, Xin,et al. Classification of pathogens by Raman spectroscopy combined with generative adversarial networks[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2020,726:9.
APAYu, Shixiang,Li, Hanfei,Li, Xin,Fu, Yu Vincent,&Liu, Fanghua.(2020).Classification of pathogens by Raman spectroscopy combined with generative adversarial networks.SCIENCE OF THE TOTAL ENVIRONMENT,726,9.
MLAYu, Shixiang,et al."Classification of pathogens by Raman spectroscopy combined with generative adversarial networks".SCIENCE OF THE TOTAL ENVIRONMENT 726(2020):9.
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