A new method for detecting mixed bacteria based on multi-wavelength transmission spectroscopy technology.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Published Date: Jan 5, 2022

Abstract

Previously, we successfully realized the identification of a single species of bacteria based on the multi-wavelength transmission spectrum of bacteria. The current research is focused on realizing the spectral analysis of mixed bacteria. Principal component analysis-Monte Carlo (PCA-MC) model was developed for the implementation of spectral separation of mixed bacteria by obtaining the ratio of components. And, the separated spectrum was regarded as the model input of the neural network concentration inversion model to obtain the concentration of each bacteria in the mix. Mean relative errors in component analysis of mixing S.aureus with K.pneumoniae, mixing S.aureus with S.typhimurium twice, mixing K.pneumoniae with S.typhimurium are 3%, 2%, 3.9% and 6.1%, respectively. The coefficient of determination (R) of validation set and test set are 0.9947 and 0.9954 in concentration inversion model. The results show that this method can quickly and accurately determine the component ratio and concentration information in the mixed bacteria. A new method was proposed to separate the spectrum of mixed bacteria effectively and measure its concentration quickly, which makes a big step forward in the detection and online monitoring of waterborne microbial contamination based on multi-wavelength transmission spectroscopy.

Authors

  • Chun Feng
  • Nanjing Zhao
    Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.
  • Gaofang Yin
    Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Key Laboratory of Optical Monitoring Technology for Environment, Anhui Province, Hefei 230031, China. Electronic address: gfyin@aiofm.ac.cn.
  • Tingting Gan
    Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Key Laboratory of Optical Monitoring Technology for Environment, Anhui Province, Hefei 230031, China.
  • Ruifang Yang
    Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.
  • Min Chen
    School of Computer Science and TechnologyHuazhong University of Science and Technology Wuhan 430074 China.
  • Jingbo Duan
    Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Key Laboratory of Optical Monitoring Technology for Environment, Anhui Province, Hefei 230031, China.
  • Yuxia Hu

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