Species-level identification of Nocardia spp. from clinical samples via intelligent analysis of Raman spectroscopic fingerprints
Author Identifier (ORCID)
Abstract
Background: Nocardia spp. are clinically opportunistic pathogens that are frequently underdiagnosed. They often lead to severe clinical consequences. These infections are often invasive, involving the lungs, nervous system, skin, and soft tissues. Different Nocardia spp. show significant differences in virulence and antimicrobial susceptibility. However, clinical manifestations are highly diverse, and species-level identification remains technically difficult. The precise diagnosis of Nocardia spp. is challenging. Therefore, rapid identification of Nocardia spp. is essential for guiding clinical treatment.
Methods: In this study, an intelligent analytical model integrating machine learning (ML) with surface-enhanced Raman spectroscopy (SERS) was developed for the rapid identification of seven clinically common Nocardia spp. We isolated and cultured 46 Nocardia strains, including seven Nocardia spp. from clinical samples. For each strain, a total of 64 SERS spectra were generated to enhance data reproducibility. We performed principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to evaluate spectral differences among Nocardia spp. Subsequently, we developed and optimized nine machine learning models. We quantitatively assessed model performance using Accuracy, Precision, Recall, F1-score, fivefold cross-validation, and further evaluated classification performance using confusion matrices and receiver operating characteristic (ROC) curves. We applied the SHapley Additive exPlanations (SHAP) method to interpret and analyze the optimal model.
Results: Among all models, the support vector machine (SVM) achieved the highest identification accuracy of 99.47%, demonstrating superior classification performance.
Conclusion: SERS-SVM is an accurate and effective analytical technique for the rapid identification of seven clinically common Nocardia spp.
Keywords
Nocardia spp., machine learning, rapid diagnosis, SERS, SVM
Document Type
Journal Article
Date of Publication
9-8-2026
E-ISSN
15730972
Volume
42
Issue
9
PubMed ID
42706448
Publication Title
World Journal of Microbiology and Biotechnology
Publisher
Springer
School
School of Medical and Health Sciences
Funding Information
This work was supported by the Research Foundation for Advanced Talents of Guangdong Provincial People's Hospital [Grant No. KY012023293] and Dongying Municipal Natural Science Foundation and Health Development Joint Fund [Grant No. 2025ZRWS012].
Copyright
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Recommended Citation
Chen, J., Pan, S., Zhou, Z., Xie, Y., Zhang, L., Gao, C., Wang, L., & Chen, H. (2026). Species-level identification of Nocardia spp. from clinical samples via intelligent analysis of Raman spectroscopic fingerprints. World Journal of Microbiology and Biotechnology, 42, Article 525. https://doi.org/10.1007/s11274-026-05255-8