Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning

作者全名："Liu, Bo; Liu, Kunxiang; Sun, Jide; Shang, Lindong; Yang, Qingxiang; Chen, Xueping; Li, Bei"

作者地址："[Liu, Bo; Liu, Kunxiang; Shang, Lindong; Li, Bei] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, State Key Lab Appl Opt, Changchun, Peoples R China; [Liu, Bo; Liu, Kunxiang; Shang, Lindong; Li, Bei] Univ Chinese Acad Sci, Beijing, Peoples R China; [Sun, Jide] Chongqing Med Univ, Affiliated Hosp 1, Lab Med, Chongqing, Peoples R China; [Yang, Qingxiang] Third Mil Med Univ, Army Med Univ, Southwest Hosp, Dept Lab Med, Chongqing, Peoples R China; [Chen, Xueping] Chongqing Med Univ, Affiliated Hosp 1, Ctr Clin Mol Med Detect, Chongqing, Peoples R China; [Chen, Xueping] Chongqing Med Univ, Affiliated Hosp 1, Ctr Clin Mol Med Detect, Chongqing 400016, Peoples R China; [Li, Bei] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, State Key Lab Appl Opt, Changchun 130033, Peoples R China"

通信作者："Chen, XP (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Ctr Clin Mol Med Detect, Chongqing 400016, Peoples R China.; Li, B (通讯作者)，Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, State Key Lab Appl Opt, Changchun 130033, Peoples R China."

来源：JOURNAL OF BIOPHOTONICS

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:000908940700001

JCR分区：Q3

影响因子：2

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：classification; long short-term memory network; pathogenic bacteria; Raman spectroscopy; variational auto-encoder

摘要："Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the ""fingerprint "" of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified."

基金机构：Strategic Priority Research Program of the Chinese Academy of Sciences; Chongqing Postdoctoral Science Foundation; [XDA22020403]; [2022NSCQ-BHX1305]

基金资助正文："ACKNOWLEDGMENTS This research was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA22020403), Chongqing Postdoctoral Science Foundation (2022NSCQ-BHX1305)."