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Study on the classification and identification of microorganisms in municipal sludge

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Abstract

To solve the problem that the traditional microbial classification and identification methods cannot output the corresponding results in time when the number of microorganisms to be identified changes, this paper studies the classification and identification methods of microorganisms in urban domestic sludge. The microorganisms in municipal sewage sludge are collected by sedimentation, and the classification standard of microorganisms in municipal sewage sludge is established. Extract the DNA sequence of microorganisms and analyze the information in the DNA sequence by PCR. Collect protozoan images in the sludge, and extract image features after pre-processing. Using DNA information characteristics and protozoan image characteristics as the basis for classification and identification, and using support vector machines to complete the classification and identification of microorganisms in urban domestic sludge, the design of methods for classification and identification of microorganisms was realized. Through comparison experiments with traditional classification and identification methods, it is proved that the studied microbial classification and identification methods can quickly respond when the number of microorganisms in the identification target changes.

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Acknowledgements

The study was supported by National Natural Science Foundation of China (The function of transporter TwPDR1 in the efflux transportation of triptolide and its regulation mechanism by phosphorylation (No. 31800256).

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Authors and Affiliations

  1. School of Biological Engineering, Huainan Normal University, Huainan, 232001, China

    Kegui Zhang, **aomei Zhang & Qianqian Yang

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  1. Kegui Zhang
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  2. **aomei Zhang
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  3. Qianqian Yang
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Correspondence to Kegui Zhang.

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Zhang, K., Zhang, X. & Yang, Q. Study on the classification and identification of microorganisms in municipal sludge. Appl Nanosci 13, 2115–2128 (2023). https://doi.org/10.1007/s13204-021-02078-x

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