中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria

文献类型：期刊论文

作者Feng, Chun1,2; Zhao, Nanjing1,3; Yin, Gaofang1,3; Gan, Tingting1,3; Yang, Ruifang1,3; Chen, Xiaowei1,2; Chen, Min1,2; Duan, Jingbo1,3
刊名SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
出版日期2021-04-15
卷号251
ISSN号1386-1425
关键词Bacterial species Identification Artificial neural networks Multi-wavelength transmission spectrum Generalized regression neural network Back propagation neural network
DOI10.1016/j.saa.2020.119423
通讯作者Zhao, Nanjing(njzhao@aiofm.ac.cn) ; Yin, Gaofang(gfyin@aiofm.ac.cn)
英文摘要Present research is focused on the rapid and accurate identification of bacterial species based on artificial neural networks combined with spectral data processing technology. The spectra of different bacterial species in the logarithmic growth phase were obtained. Model input features were extracted from the raw spectra using signal processing techniques, including normalization, principal component analysis (PCA) and area-based feature value extraction. The identification models based on artificial neural network of back propagation neural networks (BPNN), generalized regression neural networks (GRNN) and probabilistic neural networks (PNN) were developed using the extracted features in order to ascertain whether the different species of bacteria could be differentiated. The performance of developed models and its corresponding signal processing techniques is tested by the recognition accuracy of validation set and test set, and model error. The maximum recognition accuracy of normalized spectrum combined with BPNN was 95.5% (error: 10%, test accuracy: 100%). The total recognition accuracy of PCA-reduced features (200-400 nm) combined with GRNN resulted in 96.3%-96.8% (error: 3.3%-6.7%, test accuracy: 97.5%-100%). While the overall recognition accuracy of area-based features combined with GRNN reached 97.3% with test accuracy of 100% (model error: 5.0%). Choosing of model and signal processing techniques has a positive influence on improving classification accuracy, so as to make it possible to realize the rapid detection and online monitoring of waterborne microbial contamination. (c) 2020 Elsevier B.V. All rights reserved.
资助项目Key Research and Development Plan of Anhui Province[1804a0802192] ; Natural Science Foundation of China[61875254] ; Natural Science Foundation of China[61705237] ; Natural Science Foundation of China[61805254]
WOS研究方向Spectroscopy
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000621417000003
资助机构Key Research and Development Plan of Anhui Province ; Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/120985]  
专题中国科学院合肥物质科学研究院
通讯作者Zhao, Nanjing; Yin, Gaofang
作者单位1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China
2.Univ Sci & Technol China, Hefei 230026, Peoples R China
3.Key Lab Opt Monitoring Technol Environm, Hefei 230031, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Feng, Chun,Zhao, Nanjing,Yin, Gaofang,et al. Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria[J]. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,2021,251.
APA Feng, Chun.,Zhao, Nanjing.,Yin, Gaofang.,Gan, Tingting.,Yang, Ruifang.,...&Duan, Jingbo.(2021).Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria.SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,251.
MLA Feng, Chun,et al."Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria".SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 251(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。