Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens
文献类型:期刊论文
作者 | Yu, Shixiang3,4; Li, Xin3,4; Lu, Weilai2,3; Li, Hanfei2,3; Fu, Yu Vincent2,3; Liu, Fanghua1,4 |
刊名 | ANALYTICAL CHEMISTRY |
出版日期 | 2021-08-17 |
卷号 | 93期号:32页码:11089-11098 |
ISSN号 | 0003-2700 |
DOI | 10.1021/acs.analchem.1c00431 |
通讯作者 | Fu, Yu Vincent(fuyu@im.ac.cn) ; Liu, Fanghua(fhliu@yic.ac.cn) |
英文摘要 | The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data. |
WOS关键词 | CONVOLUTIONAL NEURAL-NETWORKS ; MICROSCOPY IMAGES ; SPECTROSCOPY ; CARCINOMA ; BACTERIA |
资助项目 | National Key Research and Development Program of China[2020YFA0907300] ; National Natural Science Foundation of China[U20A20109] ; National Natural Science Foundation of China[91751103] ; Guangdong Foundation for Program of Sci ence and Technology Research[2020B1111530002] ; Guangdong Foundation for Program of Sci ence and Technology Research[2020B1212060048] ; GDAS' Project of Science and Technology Development[2019GDA-SYL-0102003] ; GDAS' Project of Science and Technology Development[2019GDASYL-0102005] ; Pearl River Talent Recruitment Program of Guangdong Province[2019QN01L735] ; Chinese Academy of Sciences[GQRC-19-18] ; Senior User Project of RV KEXUE[KEXUE2019GZ05] |
WOS研究方向 | Chemistry |
语种 | 英语 |
WOS记录号 | WOS:000687058400005 |
资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Guangdong Foundation for Program of Sci ence and Technology Research ; GDAS' Project of Science and Technology Development ; Pearl River Talent Recruitment Program of Guangdong Province ; Chinese Academy of Sciences ; Senior User Project of RV KEXUE |
源URL | [http://ir.yic.ac.cn/handle/133337/29700] |
专题 | 烟台海岸带研究所_海岸带信息集成与综合管理实验室 烟台海岸带研究所_海岸带生物学与生物资源利用所重点实验室 |
通讯作者 | Fu, Yu Vincent; Liu, Fanghua |
作者单位 | 1.Guangdong Acad Sci, Natl Reg Joint Engn Res Ctr Soil Pollut Control &, Inst Ecoenvironm & Soil Sci, Guangdong Key Lab Integrated Agroenvironm Pollut, Guangzhou 510650, 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.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Biol & Biol Resources Utilizat, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China |
推荐引用方式 GB/T 7714 | Yu, Shixiang,Li, Xin,Lu, Weilai,et al. Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens[J]. ANALYTICAL CHEMISTRY,2021,93(32):11089-11098. |
APA | Yu, Shixiang,Li, Xin,Lu, Weilai,Li, Hanfei,Fu, Yu Vincent,&Liu, Fanghua.(2021).Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens.ANALYTICAL CHEMISTRY,93(32),11089-11098. |
MLA | Yu, Shixiang,et al."Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens".ANALYTICAL CHEMISTRY 93.32(2021):11089-11098. |
入库方式: OAI收割
来源:烟台海岸带研究所
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