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.

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.

Thanks to its noninvasive, nondestructive, and label‐free features, Raman spectroscopy has gained intensive attention in biomedical applications over the last two decades. In combination with machine learning techniques, the rich molecular fingerprints contained in Raman spectra bring huge possibilities to early diagnosis and deeper understanding of biomedical processes. To bring it to real‐world applications, however, remains a challenge mainly because spectral variations of interest are easily overwhelmed by replicate‐to‐replicate, patient‐to‐patient, and device‐to‐device variations. Machine learning models that are robust to these variations are in definite need. To fill this gap, we developed an approach based on graph neural networks (GNNs), which represent Raman spectra with graphs and achieve classification at the graph level. The method was tested on four Raman datasets, three from single‐cell bacteria of four species measured on three devices, and one from oil of eight types. The GNN demonstrated robustness to unwanted variations and achieved promising across‐device transferability, with significantly improved balanced accuracy compared to both linear discriminant analysis (LDA) and RamanNet. In addition, graph representation not only brings intrinsic advantage of model interpretation but also provides a new dimension that supports better spectral distinguishment. All the results and features endorsed the proposed method to highly benefit Raman‐based biomedical applications.

Wastewater represents a structurally and chemically heterogeneous environment in which suspended particulates and polymeric organic matter influence microbial recovery and measurable phenotype. Here, we systematically evaluate how matrix architecture and sample handling shape single-cell Raman phenotyping. Using defined synthetic wastewater supplemented with either agarose (structurally stable) or tapioca (dynamic, particulate-rich) matrices, we demonstrate that matrix structure alters bacterial recoverability and redistributes Raman-detectable biochemical phenotypes across five clinically relevant species (Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, Enterococcus faecalis, and Enterococcus faecium). We further establish that filtration-based isolation constitutes a pre-analytical bias factor, systematically altering spectral profiles and selectively depleting subpopulations. Despite these influences, PCA–LDA maintained high intra- and inter- species-level classification accuracy, showing that Raman spectroscopy captures not only taxonomic identity but also matrix- and workflow-driven restructuring of the observable phenotype. Together, these findings position matrix-aware standardization as a prerequisite for translating Raman-based surveillance from controlled settings to real-world wastewater monitoring.