Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria

doi:10.1016/j.saa.2020.119423

HFCAS OpenIR

	Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria
	Feng, Chun 1,2; Zhao, Nanjing1,3 ; Yin, Gaofang 1,3; Gan, Tingting1,3 ; Yang, Ruifang 1,3; Chen, Xiaowei 1,2; Chen, Min 1,2; Duan, Jingbo 1,3
	2021-04-15
发表期刊	SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
ISSN	1386-1425
通讯作者	Zhao, Nanjing(njzhao@aiofm.ac.cn) ; Yin, Gaofang(gfyin@aiofm.ac.cn)
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.
关键词	Bacterial species Identification Artificial neural networks Multi-wavelength transmission spectrum Generalized regression neural network Back propagation neural network
DOI	10.1016/j.saa.2020.119423
收录类别	SCI
语种	英语
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]
项目资助者	Key Research and Development Plan of Anhui Province ; Natural Science Foundation of China
WOS研究方向	Spectroscopy
WOS类目	Spectroscopy
WOS记录号	WOS:000621417000003
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/120986
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
第一作者单位	中科院安徽光学精密机械研究所
通讯作者单位	中科院安徽光学精密机械研究所
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).