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Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis

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Abstract

Bacterial species within the Acinetobacter baumannii-calcoaceticus (Acb) complex are very similar and are difficult to discriminate. Misidentification of these species in human infection may lead to severe consequences in clinical settings. Therefore, it is important to accurately discriminate these pathogens within the Acb complex. Raman spectroscopy is a simple method that has been widely studied for bacterial identification with high similarities. In this study, we combined surfaced-enhanced Raman spectroscopy (SERS) with a set of machine learning algorithms for identifying species within the Acb complex. According to the results, the support vector machine (SVM) model achieved the best prediction accuracy at 98.33% with a fivefold cross-validation rate of 96.73%. Taken together, this study confirms that the SERS-SVM method provides a convenient way to discriminate between A. baumannii, Acinetobacter pittii, and Acinetobacter nosocomialis in the Acb complex, which shows an application potential for species identification of Acinetobacter baumannii-calcoaceticus complex in clinical settings in near future.

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Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. No datasets were generated or analysed during the current study.

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Acknowledgements

We thank the anonymous reviewers for their thoughtful comments that greatly improve the quality of the manuscript.

Funding

This study was supported by Guangdong Basic and Applied Basic Research Foundation [Grant No. 2022A1515220023], Talent Start-Up Funding of Guangdong Provincial People’s Hospital [Grant No. KY012023293], and Research Funding for Guangdong Provincial Medical Technology [Grant No. B2021010].

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Author notes

  1. Xue-Song Xiong, Lin-Fei Yao, and Yan-Fei Luo have contributed equally to the study.

Authors and Affiliations

  1. Laboratory Medicine, The Fifth People’s Hospital of Huai’an, Huai’an, Jiangsu Province, China

    Xue-Song Xiong

  2. The Affiliated Huai’an Hospital of Yangzhou University, Huai’an, Jiangsu Province, China

    Xue-Song Xiong

  3. School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China

    Lin-Fei Yao, Quan Yuan & Xin-Ru Wen

  4. Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China

    Yan-Fei Luo, Jie Chen, Jia-Wei Tang, Su-Ling Liu & Liang Wang

  5. Laboratory Medicine, Medical Technology School of Xuzhou Medical University, Xuzhou, Jiangsu Province, China

    Yu-Ting Si

Authors
  1. Xue-Song Xiong
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Contributions

L.W., S.L.L. and J.W.T. conceived and designed the experiments. L.W. provided platform and resources, and contributed to project administration and student supervision. X.S.X., L.F.Y., and Y.F.L. carried out the experimental and computational investigations. Q.Y., Y.T.S., J.C., and X.R.W. contributed to the data analysis and visualization. All the authors wrote and revised the manuscript. All the authors approved the submitted version of the manuscript.

Corresponding authors

Correspondence to Jia-Wei Tang, Su-Ling Liu or Liang Wang.

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Xiong, XS., Yao, LF., Luo, YF. et al. Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis. World J Microbiol Biotechnol 40, 146 (2024). https://doi.org/10.1007/s11274-024-03948-6

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