Yu, Shixiang1,2; Li, Xin1,2; Lu, Weilai2,3; Li, Hanfei2,3; Fu, Yu Vincent2,3; Liu, Fanghua1,4
发表期刊ANALYTICAL CHEMISTRY
ISSN0003-2700
2021-08-17
卷号93期号:32页码:11089-11098
DOI10.1021/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.
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
收录类别SCI
语种英语
关键词[WOS]CONVOLUTIONAL NEURAL-NETWORKS; MICROSCOPY IMAGES; SPECTROSCOPY; CARCINOMA; BACTERIA
研究领域[WOS]Chemistry
WOS记录号WOS:000687058400005
引用统计被引频次：1[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/133337/29701
专题中科院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
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, 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
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Microbiol, State Key Lab Microbial Resources, Beijing 100101, Peoples R China
4.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
推荐引用方式
GB/T 7714Yu, 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.
APAYu, 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.
MLAYu, 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.
PDF全文下载地址:
点我下载PDF
