Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis.
World J Microbiol Biotechnol
; 40(5): 146, 2024 Mar 28.
Article
en En
| MEDLINE
| ID: mdl-38538920
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Acinetobacter
/
Infecciones por Acinetobacter
/
Acinetobacter baumannii
Límite:
Humans
Idioma:
En
Revista:
World J Microbiol Biotechnol
Año:
2024
Tipo del documento:
Article
País de afiliación:
China