Abstract
The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, and medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical and structural information, has gained significant traction in these fields, especially with the adoption of various excitation wavelengths and tailored optical setups. The choice of wavelength and setup in Raman spectroscopy is influenced by factors such as applicability, cost, and whether bulk or single-cell analysis is performed, each impacting sensitivity and specificity in bacterial detection. In this study, we investigate the potential of different excitation wavelengths for bacterial identification, utilizing a mock culture composed of six bacterial species: three Gram-positive (S. warneri, S. cohnii, and E. malodoratus) and three Gram-negative (P. stutzeri, K. terrigena, and E. coli). To improve bacterial classification, we applied machine learning models to analyze and extract unique spectral features from Raman data. The results indicate that the choice of excitation wavelength significantly influences the bacterial spectra obtained, thereby impacting the accuracy and effectiveness of the subsequent classification results.
Published Version
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