Fiber array-based large spot confocal Raman system for rapid in situ detection of pathogenic bacterial colonies

Pathogenic bacteria infections are a major public health problem in current society. Rapid and reliable identification of these pathogens can help avoid the misuse of antibiotics and enable precision therapy. In this study, we present a large-spot confocal Raman system based on fiber array (LSCR-FA) for the in situ detection of microbial colonies on agar plates. This method can alleviate the problem of spatial heterogeneity of colonies to a certain extent and is fast and high-throughput. Additionally, we also applied machine learning algorithms with 5-fold cross-validation to analyze colony Raman spectral data and classify seven different pathogenic bacteria. Among them, the Support Vector Machine (SVM) achieved a high accuracy of 98.74 %. The results of the study demonstrate that the mentioned LSCR-FA system combined with machine learning algorithms provides a new, fast, and effective strategy for the identification of pathogenic bacteria and precise clinical treatment. [Display omitted] •A new method for accurate identification of microorganisms at the colony level.•The method helps to address the spatial heterogeneity of microbial colonies.•The proposed new system is characterized by both high throughput and resolution.•Potential for in situ detection of pathogenic bacteria and drug resistance testing.