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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.

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Abstract The hospital environment is a field with unique microbiological characteristics. Pathogens evolve and spread in different areas of the hospital affecting patients and staff. In addition, a constant circulation of pathogens between the hospital and the outer environment is ongoing. In this context, an extensive management is required in order to minimize the harmful effect of hospital flora on humans as well as the natural environment. Raman spectroscopy has been shown to be an effective tool for this purpose since it is applicable in a variety of biological samples ranging from the patient samples to the hospital's wastewater. It enables the detection of infection, bacterial species identification, antimicrobial resistance determination, epidemiological typing as well as infection control, mandatory for hospital management. The biggest advantages of this analytical method are the limited time and minimal resources required in its workflow. In the current review the Raman‐based analytical methods that have been developed over years in the field of microbiology are presented and their applicability in the different areas of the hospital environment is discussed.