Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy : a comparative machine learning analysis
© 2024. The Author(s), under exclusive licence to Springer Nature B.V..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:40 |
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| Enthalten in: |
World journal of microbiology & biotechnology - 40(2024), 5 vom: 28. März, Seite 146 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xiong, Xue-Song [VerfasserIn] |
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| Links: |
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| Themen: |
Acinetobacter baumannii |
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| Anmerkungen: |
Date Completed 29.03.2024 Date Revised 26.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11274-024-03948-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM37028464X |
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| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Nature B.V. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Acinetobacter baumannii | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a SERS spectra | |
| 650 | 4 | |a Support vector machine | |
| 650 | 4 | |a Surface-enhanced Raman spectroscopy | |
| 700 | 1 | |a Yao, Lin-Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Yan-Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Quan |e verfasserin |4 aut | |
| 700 | 1 | |a Si, Yu-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Xin-Ru |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Jia-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Su-Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Liang |e verfasserin |4 aut | |
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