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.

Raman spectroscopy differentiates aerobic and anaerobic metabolism, vegetative cells from endospores, and enables precise genus- and species-level ID of Clostridium and Bacillus species, offering a reliable, and robust tool for diverse applications.

In the clinical environment, the identification of phylogenetic closely related genera and species like Clostridium and Bacillus spp. is challenging. Both genera contain representatives of pathogenic and nonpathogenic species that need to be distinguished for a proper diagnostic read-out. Therefore, reliable and accurate detection methods must be employed for the correct identification of these genera and species. Despite their high pathogenicity, clostridial infections and food contaminations present significant challenges due to their unique cultivation conditions and developmental needs. Therefore, in many diagnostic protocols, the toxins are used for microbiological documentation. However, the applied laboratory methods suffer in accuracy, sometimes require large bacterial loads to provide reliable results, and cannot differentiate pathogenic from nonpathogenic strains. Here, Raman spectroscopy was employed to create an extensive Raman database consisting of pathogenic and nonpathogenic Bacillus and Clostridium species. These genera, as well as representatives of Paraclostridium and Clostridioides were specifically selected for their phylogenetic relation, cultivation conditions, and metabolic activity. A chemometric evaluation of the Raman spectra of single vegetative cells revealed a high discriminating power at the genus level. However, bacilli are considerably easier to classify at the species level than clostridia. The discrimination between the genera and species was based on their phylogeny and not their aerobic and anaerobic cultivation conditions. These encouraging results demonstrated that Raman spectroscopy coupled with chemometrics is a robust and helpful method for differentiating Clostridium species from Bacillus, Clostridioides, and Paraclostridium species. This approach has the potential to be a valuable tool in clinical diagnostics.

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.

Clostridioides difficile is known to produce p-cresol, a phenolic compound with selective antimicrobial properties, which may contribute to its competitive advantage within the gut microbiome. In this study, we investigated the interaction between Clostrioides difficile and Escherichia coli in coculture to assess the role of p-cresol in modulating interspecies dynamics. Raman spectroscopy was employed as a label-free, nondestructive analytical technique to profile the molecular signatures of both species in mono- and coculture. Excitation wavelengths at 244 and 532 nm were used to enhance complementary vibrational features, including those associated with aromatic compounds like p-cresol. Our results demonstrate distinct spectral changes in coculture conditions suggesting the involvement of p-cresol and its impact on the biochemical composition of E. coli. This dual-wavelength Raman approach offers a powerful means of characterizing microbial interactions and identifying metabolic markers that may drive microbial competition and survival.