Rapid Detection of Three Common Bacteria Based on Fluorescence Spectroscopy

被引:25
|
作者
Du, Ranran [1 ,2 ]
Yang, Dingtian [1 ,3 ]
Yin, Xiaoqing [1 ,2 ]
机构
[1] South China Sea Inst Oceanol, Chinese Acad Sci, Guangdong Key Lab Ocean Remote Sensing, State Key Lab Trop Oceanog, Guangzhou 510301, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China
基金
国家重点研发计划;
关键词
laser-induced fluorescence (LIF); disease-causing bacteria; fluorescence spectrum analysis; fluorescence intensity ratio (FIR); EXCITATION;
D O I
10.3390/s22031168
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
As an important part of environmental water quality monitoring, efficient bacterial detection has attracted widespread attention. Among them, LIF (laser-induced fluorescence) technology has the characteristics of high efficiency and sensitivity for bacterial detection. To simplify the experimental process of bacterial detection, fluorescence emission spectra of E. coli (Escherichia coli) and its deactivated controls, K. pneumoniae (Klebsiella pneumoniae) and S. aureus (Staphylococcus aureus), were analyzed with fluorescence excitation by a 266 nm laser. By analyzing the results, it was found that the dominant fluorescence peaks of bacterial solutions at 335~350 nm were contributed by tryptophan, and the subfluorescence peaks at 515.9 nm were contributed by flavin; besides, K. pneumoniae and S. aureus had their own fluoresces characteristics, such as tyrosine contributing to sub-fluorescence peaks at 300 nm. The three species of bacteria can be differentiated with whole fluorescence spectrum by statistically analysis (p < 0.05), for various concentrations of aromatic amino acids and flavin in different bacteria. The experimental results also proved that the inactivation operation did not alter the spectral properties of E. coli. The indexes of fluorescence intensity and FIR (fluorescence intensity ratio, I-335~350/I-515.9) can be used to retrieve the bacteria concentration as well as for bacteria differentiation using the index of slopes. The detection limit of bacteria is less than ~10(5) cell/mL using laser induced fluorescence methods in the paper. The study demonstrated the rapid detection capability of the LIF bacterial detection system and its great potential for rapid quantitative analysis of bacteria. This may bring new insight into the detection of common bacteria in water in situ.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Femtosecond laser-induced fluorescence spectroscopy for the rapid detection of pathogenic bacteria
    Ezzat, Sarah
    Samad, Fatma Abdel
    El-Gendy, Ahmed O.
    Mohamed, Tarek
    OPTICAL AND QUANTUM ELECTRONICS, 2024, 56 (06)
  • [2] Detection of common ocular pathogens using fluorescence spectroscopy
    Will, DV
    Rosen, RB
    Shah, M
    Katz, A
    Savage, H
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2002, 43 : U356 - U356
  • [3] Fluorescence Spectroscopy for Rapid Detection and Classification of Bacterial Pathogens
    Sohn, Miryeong
    Himmelsbach, David S.
    Barton, Franklin E., II
    Fedorka-Cray, Paula J.
    APPLIED SPECTROSCOPY, 2009, 63 (11) : 1251 - 1255
  • [4] Characterization of bacterial fluorescence: insight into rapid detection of bacteria in water
    Mao, Yu
    Chen, Xiao-Wen
    Chen, Zhuo
    Chen, Gen-Qiang
    Lu, Yun
    Wu, Yin-Hu
    Hu, Hong-Ying
    WATER REUSE, 2021, 11 (04) : 621 - 631
  • [5] RAPID DETECTION OF BACTERIA AND PARASITES USING ACRIDINE ORANGE FLUORESCENCE
    LEWIN, PK
    HUBER, J
    CANADIAN JOURNAL OF PUBLIC HEALTH-REVUE CANADIENNE DE SANTE PUBLIQUE, 1975, 66 (01): : 47 - 47
  • [6] Rapid bacteria detection methods - Comparison of three methods
    Schmidt, M
    Hourfar, KM
    Wahl, A
    Montag, T
    Nicol, SB
    Spengler, H
    Roth, W
    Seifried, E
    TRANSFUSION, 2005, 45 (03) : 26A - 26A
  • [7] Research Advances and Trends of Rapid Detection Technologies for Pathogenic Bacteria Based on Fingerprint Spectroscopy
    Liao Wen-long
    Liu Kun-ping
    Hu Jian-ping
    Gan Ya
    Lin Qing-yu
    Duan Yi-xiang
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41 (08) : 2372 - 2377
  • [8] Using three-dimensional fluorescence spectroscopy and machine learning for rapid detection of adulteration in camellia oil
    Hu, Yating
    Wei, Chaojie
    Wang, Xiaorong
    Wang, Wei
    Jiao, Yanna
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2025, 329
  • [9] Rapid Detection of Bacteria Using Raman Spectroscopy and Deep Learning
    Kukula, Kaitlyn
    Farmer, Denzel
    Duran, Jesse
    Majid, Nishatul
    Chatterley, Christie
    Jessing, Jeff
    Li, Yiyan
    2021 IEEE 11TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2021, : 796 - 799
  • [10] Rapid detection of Clostridium botulinum among three clostridia based on confocal Raman spectroscopy
    Zhang, Jin
    Jiang, Hong
    Xu, Xuefang
    Chinese Journal of Analysis Laboratory, 2022, 41 (02) : 158 - 162