LAUSR

Biofilms are the preferred habitat of microorganisms on living and artificial surfaces. Biofilm-related infections, such as infections of medical implants, are difficult to treat, and due to a reduced cultivability of the included bacteria, difficult to diagnose. Therefore, it is highly important to rapidly identify and investigate biofilms on implant surfaces, e.g., during surgery. In this study, we present fiber-probe-based Raman spectroscopy with an excitation wavelength of 785 nm, which was applied to investigate six different pathogen species involved in biofilm-related infections. Biofilms were cultivated in a drip flow reactor, which can model a biofilm growth environment. The signals collected from a fiber probe allowed us to collect Raman spectra not only from the embedded bacterial and yeast cells but also the surrounding extracellular polymeric substance matrix. This information was used in a classification model. The model consists of a principal component analysis in combination with linear discriminant analysis and was examined by applying a leave-one-batch-out cross-validation. This model achieved a classification accuracy of 93.8%. In addition, the identification accuracy increased up to 97.5% when clinical strains were used for identification. A fiber-probe-based Raman spectroscopy method combined with a chemometric analysis might therefore serve as a fast, accurate, and portable strategy for the species identification of biofilm-related infections, e.g., during surgical procedures.